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Liang Jingyuan, Ge Yahang, Ke Xizheng
2024,38(12):1-11,
Abstract:
A complete communication system must encompass a full two-way communication link, which includes both uplink and downlink. The uplink has always been a challenge for two-way communication systems. In recent years, the rapid development of visible light communication technology, with its advantages of no electromagnetic radiation, large communication capacity, and environmental friendliness, can serve as a supplement to traditional uplink solutions. The article first introduces the application scenarios and system composition of visible light communication, and then provides a review of the current research status of visible light uplink at home and abroad in recent years. In addition, it presents various schemes for visible light uplink, such as visible light with radio frequency, visible light with visible light, visible light with power line carrier, and single-source reverse modulation technology. Finally, it summarizes the current issues faced by the visible light communication uplink and summarizes the advantages and disadvantages of various schemes, as well as prospects for future development trends.
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Deng Peng, Tang Wentao, Luo Jing
2024,38(12):12-25,
Abstract:
The research on pre-trained large models has made remarkable achievements in recent years, this paper reviews the application of pre-trained large model in robotics. Traditional deep learning models in robots were trained on small datasets customized for specific tasks, which limits their adaptability in different applications. In contrast, large models pre-trained on internet-scale data appear to have superior generalization capabilities and in some cases show an exploratory ability to find one-shot solutions where they are not present in the training data. The underlying model has the potential to enhance the various components of a robot’s autonomous task, from perception to decision making and control. This paper examines recent papers that use or build large models to solve robotics problems, exploring how large models can help improve robots’ capabilities in the areas of perception, decision making, and control, thereby promoting the application of large robot models in more fields. Meanwhile, the challenges that hinder the application of large models in robotic autonomous systems were discussed in this paper, such as data scarcity in robotic applications, the variability of robots themselves, the limitations of multimodal representations, and real-time performance, and provides opportunities and potential approaches for future improvements.
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Gan Zihao, Hong Huajie, Liu Zhaoyang, Lyu Jianming, Zhang Meng
2024,38(12):26-34,
Abstract:
Aiming at improving the accuracy and real-time performance of zoom imaging depth measurement, based on the given system design configuration, a new monocular visual depth measurement method with liquid optical control is proposed by utilizing liquid lens adjustment characteristics and neural network technology. Firstly, to eliminate the influence of optical axis drift induced by the liquid gravity factor on the measurement results, the ratio of target image area is adopted as the feature parameter. A target area calculation method based on chain code classification and strip segmentation is presented. Then, in order to describe the mapping relationship between liquid lens parameters, image feature quantity and target depth, a neural network model of liquid monocular depth measurement is constructed, and the model parameters are optimized by genetic algorithm. Furthermore, the focal power function is obtained by calibrating the parameters of the liquid lens. The neural network trained on the dataset for depth measurement has an average prediction relative error of 0.799%. Finally, an experiment is designed to test and verify the method. The average depth measurement error of targets with different distances is 2.86%, and the average measurement speed is 108.2 ms. The measurement error for targets of different shapes at a distance of 1 000 mm shall not exceed 3.60%. The results show that the monocular vision method combining liquid optical control and neural network prediction can achieve high-precision and fast depth measurement, and has good generalization performance for different shapes of objects. The research provide a new technical idea for overcoming the existing limitations of zoom imaging ranging method.
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2024,38(12):35-42,
Abstract:
The combination of sparse sampling and image restoration can not only compress data capacity, but also improve imaging speed, which is of great significance for the development of high-resolution LiDAR imaging technology. In order to improve the restoration effect of sparse sampled images, a new residual channel attention network block was designed in the paper, and the residual channel attention block was introduced into a deep unfolding network based on compressed sensing iterative soft threshold method to suppress the blurring phenomenon caused by the loss of high-frequency information in image restoration and reconstruction, forming a new method for the restoration and reconstruction of sparse sampled LiDAR images. This method combines the advantages of traditional compressed sensing reconstruction methods and neural network methods, and has a faster reconstruction speed compared to traditional compressed sensing reconstruction methods. Compared with existing neural network methods, it enhances structural insight and improves the problem of image blur in reconstruction. The validation calculations using Middlebury Stereo Data 2006 as the test dataset show that our method not only has better image reconstruction quality compared to SDA, ReconNet, TVAL3, D-AMP, and IRCNN methods, but also has higher computational efficiency; When the sparse sampling ratio is 25%, the peak signal-to-noise ratio (PSNR) of the restored image is more than 1.6 dB higher than other methods, making it an ideal method for restoring sparse LiDAR images with good overall performance.
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Chen Xuan, Bi Pengfei, Hu Zhiyuan
2024,38(12):43-53,
Abstract:
Influenced by factors such as observation conditions and acquisition scenarios, underwater optical image data usually presents the characteristics of high-dimensional small samples and is easily accompanied with noise interference. Resulting in insufficient robust performance of many dimensionality reduction methods in their recognition process. To solve this problem, we propose a novel 2DPCA method for underwater optical image recognition, called arbitrary triangle structure 2DPCA (ATS-2DPCA). On the basis of considering the relationship between reconstruction error and variance of projection data, ATS-2DPCA can successfully match the flexible robust distance metric mechanism, which effectively improves the accuracy of underwater optical image recognition under noise interference environment and achieves reasonable protection of the geometric structure of the data. In this paper, we theoretically prove the availability and convergence of the proposed method and use three underwater optical image databases for experimental verification. The optimal recognition accuracy is 89.07%, 88.52%, and 86.00%, respectively. The extensive experimental results show that ATS-2DPCA has more outstanding performance than other 2DPCA-based methods.
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2024,38(12):54-61,
Abstract:
Due to the dense presence of multiple targets and complex background information in remote sensing images, existing detection algorithms often struggle to achieve satisfactory precision in small target detection. To address this issue, we propose a small target rotation box detection algorithm for remote sensing images called DRS-YOLO, based on reparameterized generalized pyramids and dilated residuals. First, to overcome the shortcomings of the backbone network in feature extraction, we enhance semantic information by incorporating a dilated residual module into the neck of the network, building upon the YOLOv8OBB framework. Second, to improve the network's performance in detecting multi-scale targets and facilitate the flow of low-level feature information to high-level features, we replace the neck structure with a reparameterized generalized feature pyramid network for more efficient multi-scale feature fusion, which aids in capturing high-level semantics and low-level spatial details. Finally, to further enhance the network's performance in detecting small targets, we propose the SPPFI module to expand the receptive field, thereby improving detection accuracy for remote sensing targets. Experimental results demonstrate that the improved algorithm achieves an increase in detection precision of 1.5% and 9.8% on the public DIOR and HRSC2016 datasets, respectively, compared to the original YOLOv8sOBB baseline network, indicating a significant enhancement in small target detection performance for remote sensing images.
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Wu Yongze, Yu Jianfeng, Hua Chunjian, Jiang Yi, Qian Chenhao
2024,38(12):62-71,
Abstract:
The multi-dimensional defect detection of cylindrical battery shells is a critical technology for ensuring the quality and safety of lithium batteries. Due to the different processes involved in production and transportation, defects may occur in each part of the cylindrical battery shells. To solve the problem of low detection accuracy in existing methods when handling the diverse and variably scaled defects of cylindrical battery shells, this study designs an image acquisition system based on the characteristics of each part of the battery shells and constructs a multi-dimensional defect dataset. Additionally, a multi-dimensional defect detection technology is proposed based on an improved YOLOv8n. Firstly, the switchable atrous convolution is used in the C2f module to improve the multi-scale feature extraction capability. Secondly, the down sampling module is refined by combining average pooling and max pooling strategies, reducing the spatial dimensions of feature maps while retaining key information. Finally, the LSKA attention mechanism is introduced to enhance the fusion effect of multi-scale features. Experimental results show that the improved YOLOv8n model achieves an average detection accuracy of 77.4% on a custom cylindrical battery shell defect dataset, which is 4.3% higher than the original model. Furthermore, the computational load is reduced by 17%, the model size is only 6 MB, and the detection speed reaches 177 FPS, meeting the requirements for real-time industrial mass detection.
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Cao Xiyuan, Zhang Delong, Zhu Xiaolong, Zhang Zhidong, Xue Chenyang
2024,38(12):72-80,
Abstract:
Tongue diagnosis in traditional Chinese medicine (TCM) judges the deficiency and strength of internal organs as well as the vitality of functions by observing tongue features. It has the advantages of being non-invasive and convenient. Accompanied by the rapid development and wide application of computer vision technology, it is crucial to develop a model that can perform automatic detection, extraction and recognition of tongue features. Toward demands for digital tongue diagnosis in traditional Chinese medicine clinic and health monitoring, an automatic detection model for tongue tooth mark and fissure features was proposed based on improved RetinaNet. The SimPSA-ResNet and SimSPPF module were introduced into the backbone of RetinaNet to enhance the feature extraction capability and robustness of the network. Meanwhile, the multi-level feature pyramid network structure was improved to ensure that the model can better integrate information from different scales, thereby focusing more accurately on the key information pertinent to tongue features. Finally, to further streamline the model’s output, redundant output feature layers were eliminated and integrated with the Attention-guided Spatial Feature Fusion structure. This step helps retain important features while improving the utilization of information within the network. The improved RetinaNet model was trained and predicted by using the self-built tongue image dataset, and the mean average precision(mAP) reaches 94.37%, which is 2.77% higher than that of the original algorithm. Experimental results conclusively demonstrate that the improved RetinaNet model can effectively elevate the detection accuracy of tongue tooth mark and fissure features. This advancement holds tremendous potential for facilitating daily self-examination, health management and assisting doctors in diagnosis.
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Fei Zhengliang, Li Jiatian, He Feng
2024,38(12):81-89,
Abstract:
In dynamic control engineering, precise measurement of pendulum angle is crucial for achieving swing suppression. While binocular pendulum angle measurement methods rely on camera calibration results, changes in camera positions can introduce measurement deviations and pose challenges for efficient and stable measurements. To address these issues, this study proposes a monocular non-calibrated measurement method. The proposed method involves several key steps. First, continuous target image capture is combined with Kalman filtering and color space transformation to locate marker positions. Second, coordinate transformation based on cross-ratio is applied, comparing the current position with the initial position to calculate real-time pendulum angles through trigonometric transformations. Finally, a smoothing process using a combination of Gaussian and mean filtering is employed, and the results are validated against attitude sensor measurements. Experimental results demonstrate the effectiveness of the proposed method. Comparisons with binocular methods under variable-speed and constant-speed motion states yield the following findings: In variable-speed motion, the maximum pendulum angle is 1.871°, with a maximum angle error of 0.184°compared to the sensor. This represents an accuracy improvement of 0.018°over the binocular method. In constant-speed motion, the maximum pendulum angle is 3.075°, with a maximum angle error of 0.259°compared to the sensor, showing an accuracy improvement of 0.021 over the binocular method. Moreover, the computational efficiency is enhanced by 133.3%. When camera position deviations occur, the calibrated method produces maximum angle errors exceeding 0.5°compared to the sensor, while the non-calibrated method maintains errors below 0.3°. This non-calibrated approach effectively eliminates deviations, enabling accurate measurement of dynamic pendulum angles.
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Zhang Fan, Ji Chao, Song Zhiwei, Jia Xinghai, Gao Mingjiang, Cui Qichao
2024,38(12):90-102,
Abstract:
The stability of transmission lines is a crucial guarantee for the normal operation of the power grid. To prevent accidents caused by accidental contact with conductors during line construction, this paper proposes a feature extraction network based on a multi-branch dual attention mechanism, DAMF-NET, addressing the low accuracy and poor reliability of existing detection methods. This algorithm enhances the network’s focus on local features of target information by constructing a multi-branch dual attention mechanism, optimizing the feature extraction process. A multi-branch lightweight feature fusion network is proposed to reinforce the global multi-scale semantic information and feature significance under dense tasks, thereby improving the completeness of image features. A small object detection network is introduced to mitigate network scale variance and enhance the sensitivity of small object detection. By employing focal loss and EIoU optimized loss functions, the method reduces noise generated by positive and negative sample imbalance, accelerating the convergence speed of model training. Finally, a state recognition algorithm based on risk area localization is designed and deployed in the intelligent detection system of construction machinery. Experiments show that this method has better average precision compared to most current detection models, indicating its research significance in the detection of construction machinery and intelligent inspection.
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Yang Gongde, Chen Yuxiang, Wang Peng
2024,38(12):103-112,
Abstract:
To reveal the effects of different signal types, as well as their frequencies and amplitudes, on the accuracy of parameter identification and control performance in the signal injection method for permanent magnet synchronous motor，a comparative analysis through simulation and experimentation was conducted on four signal injection methods: square wave, trapezoidal wave, triangular wave, and sinusoidal wave. Firstly, according to the characteristics of the injected signals, square and trapezoidal waves are categorized as locally constant signals, whereas triangular and sinusoidal waves are categorized as globally time-varying signals. Based on the forgetting factor recursive least square, two categories of parameter identification models for signal injection methods are constructed. Secondly, simulation analysis is performed to investigate the effects of the frequency and amplitude of the two categories of injected signals on the identification results of the parameters pending identification. Comprehensively considering the identification accuracy of each parameter requiring identification, the frequency and amplitude of the injected signals is judiciously chosen. On this basis, comparative analysis of the parameter identification results is conducted and the control performance of the system is evaluated among the four signal injection methods. Finally, an experimental platform is set up to validate the parameter identification through experiments. The outcomes demonstrate that although parameter identification accuracy of locally constant signal injection is higher than that of globally time-varying signal injection, the former has a greater influence on control performance of the system compared to the latter. Among the tested signal injection methods, trapezoidal wave injection achieves the highest identification accuracy, while triangular wave injection has the least effects on the control performance of the system.
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Ma Liang, An Tengfei, Liu Wenli, Li Deen
2024,38(12):113-123,
Abstract:
To address the challenge of separating pipeline leakage signals from complex background noise and the difficulty of extracting small leakage features, a denoising method that uses the RIME to improve VMD is proposed. This method calculates the Bubble entropy value of the denoised signal for feature extraction, and then identifies the small leakage condition of the pipeline using an improved ELM optimized by RIME. First, RIME is used to improve the selection of key parameters for VMD, achieving adaptive decomposition. The mutual information entropy value between the IMFs generated by VMD is used as the fitness function value in the parameter optimization of the Rime algorithm, establishing a denoising method for water pipeline leakage signals based on RIME-VMD. Experiments have shown that compared to other heuristic optimization algorithms, the RIME-VMD method has the highest SNR of 23.922 dB, indicating that the reconstructed signal filtered by this method has the highest proportion of effective signal. The RIME-VMD method also has the lowest MAE and MSE, at 0.187 and 0.056 respectively, indicating that the reconstructed signal contains the least noise. Second, a method for extracting features from pipeline micro leakage signals using Bubble entropy is proposed, and these features are input into a model with ELM parameters optimized by RIME for pipeline leakage detection. Ultimately, the classification accuracy of the RIME-ELM model reached 95.71%, which is a 37.4% improvement compared to directly inputting fault feature vectors into the ELM, verifying the effectiveness of the proposed method.
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Xie Liangbo, Han Shen, Zhang Yukun
2024,38(12):124-134,
Abstract:
The whale optimization algorithm (WOA) is a highly competitive and efficient swarm intelligence optimization algorithm. In comparison to other intelligent optimization algorithms, WOA offers a simple structure, fewer parameters, and robust optimization capabilities. However, the conventional WOA exhibits slow convergence and falls into local optima easily. To address these issues, this paper proposes an improved whale optimization algorithm (IWOA). The algorithm adopts an adaptive update mechanism, inspired by particle swarm optimization, incorporating the individual’s historical best position during the optimization process, and dynamically adjusts the weights of the global best and individual best positions through an adaptive strategy to avoid getting trapped in local optima; at the same time, through neighborhood search strategy, neighborhood updates are carried out around the global historical optimal position in the later stage of iteration to improve the algorithm’s optimization ability. 16 typical benchmark test functions and 8 composite functions from the CEC2014 test set are selected for simulation experiments, compared to other traditional and improved swarm intelligence optimization algorithms, IWOA demonstrates superior convergence accuracy and speed, validating its effectiveness; and IWOA is applied to two engineering design problems, welding beam and pressure vessel design, compared with WOA, the economic cost is saved by 3.94% and 5.58%, respectively, verifying the effectiveness of the algorithm.
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Zhao Jingyu, Li Zhiyuan, Liu Yang, Zhang Chuanying, Bu Fanliang
2024,38(12):135-144,
Abstract:
Nonlinear elements cause significant modeling errors and control delays in speaker control processes, which affect the precise control of the speaker voice coil’s motion. This not only improves sound quality but also reduces mechanical wear and aging. This paper addresses the problems of modeling errors and control delays in the fine control of the speaker’s voice coil. We design a backstepping sliding mode controller based on an improved RBF-MLP neural network, solving the issues of control interference caused by nonlinear elements in electric speakers and the insufficient accuracy of the classical RBF network in fitting complex nonlinear models. By introducing perception layers, adaptive learning mechanisms, and generalized radial basis function, the improved RBF-MLP network reduces the mean squared error of nonlinear function fitting by more than 5% compared to the classical network, enhancing its ability to capture complex nonlinear characteristics of the speaker system and improving model fitting accuracy. A simulation environment was built to evaluate the control performance of the speaker system under different frequency, amplitude, and load conditions, focusing on control precision, system delay, and chattering problems. The experimental results show that under varying frequency and load conditions, the control delay is reduced to an average of 0.15 ms, and control errors are decreased by 39%. Furthermore, the improved control method maintains excellent robustness and stability under complex load and frequency variations. These results demonstrate the broad application potential of the improved controller in electric speaker control systems.
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Liu Xiaofeng, Xu Quangui, Jin Yan, Bo Lin
2024,38(12):145-154,
Abstract:
Aiming at the poor robustness of deep reinforcement learning for fault diagnosis in strong noise interference environments, a reinforcement learning fault diagnosis method with noise interference environment adaptation is proposed. The efficient channel attention mechanism based deep residual shrinkage network (ECA-DRSN) is taken as the basic framework of Q-network to avoid the phenomenon of gradient vanishing caused by the complex structure of Q-network. In the ECA-DRSN，the efficient channel attention mechanism is used to adaptively adjust the softening threshold，and the dilated convolution is introduced in the convolution layer of the residual shrinkage unit to obtain the fault characteristics in different scales under the noise environment. Meanwhile, the exponential linear unit is used as the activation function to further enhance the noise robustness. A quantized reward function based on signal-to-noise ratio is designed to stimulate self-directed exploratory learning of Agent. Combining the dueling Q network learning mechanism with the prioritized experience replay mechanism, the optimal diagnostic strategy of agent is generated and applied to identify the equipment fault states under noise interference environments. Example analysis results show that the recognition accuracy of bearing and gearbox faults using the method of this paper can reach 98.13% and 93.45%, respectively, and has better robustness to different intensity noise and adaptability to the environment.
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2024,38(12):155-162,
Abstract:
Motor imagery is a classic research paradigm in the field of brain computer interfaces, which aims to study the information transmission and control of external electronic devices solely through brain imagination. The Common spatial paternal algorithm is an indispensable classic feature extraction algorithm in motor imagery research. This algorithm can obtain highly discriminative features by maximizing inter class variance, thereby obtaining models with good classification performance. However, the common spatial paternal algorithm is sensitive to noise and other interferences, and requires as much inter class information as possible, resulting in poor performance in non-invasive brain imaging research. To address this problem, a data processing algorithm based on phase information in frequency domain and trend information in time domain is proposed. The phase residual sequence is constructed using the instantaneous phase sequence and the empirical mode decomposition residual component of electroencephalogram signals. The algorithm maximizes brain neural activity information while eliminating interference from external or other noise, and extracts more discriminative features through common spatial paternal algorithm, ultimately obtaining a classification model with strong recognition and generalization performance. The experimental results show that the proposed method has an average classification accuracy of 88.19% among 52 subjects, which is higher than the original sequence’s 79.67%. At the same time, it exhibits more stable classification performance in motor imagery data of different subjects, proving that the method has good recognition and generalization ability in electroencephalogram-based motor imagery classification.
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Xu Xiao, Jiang Tao, Jin Chao, Gao Jie, Lyu Yan, He Cunfu
2024,38(12):163-172,
Abstract:
High-tensile bolts are critical components in key structures such as suspension bridges and wind turbine towers, where the quality of their installation significantly impacts the stability and safety of the entire structure. However, traditional torque methods struggle to accurately measure the axial preload of bolts, making it difficult to assess structural stability effectively. To address this issue, a bolt axial force detection system was developed based on the Acoustoelastic effect, incorporating circuits such as high-voltage excitation and voltage-controlled gain. The system detects echo signals through an excitation piezoelectric sensor and utilizes a cross-correlation algorithm to calculate the time-of-flight difference, which reflects the stress state of the bolt. Stress-transition time calibration experiments were conducted, along with comparative testing between the ultrasonic and torque methods for wind power bolts. The experimental results indicate that when the axial preload of a bolt reaches 40% of its rated value, the system achieves a stress measurement error rate of ≤2.81%, with a resolution of up to 0.250 7 kN. This meets the requirements for accurate axial preload measurement. Compared with the traditional torque method, the ultrasonic approach demonstrates a clear advantage in reducing measurement error and improving resolution during bolt service, offering a reliable technical solution for stress measurement in industrial applications.
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Cheng Jiajun, Chen Han, Liu Xiangyu, Qin Jiangfan
2024,38(12):173-180,
Abstract:
With the development of biomedical field, wireless implantable devices are playing an increasingly important role. Taking into account the power loss and specific absorption rate (SAR) of electromagnetic waves in human tissues, the selection of frequency for implantable electrodes is of great significance. To address this issue, CST software is used to build a high-precision voxel model of the human head, simulate and explore the transmission characteristics of electromagnetic waves in the commonly used human communication frequency band of 400 MHz~6.5 GHz in the head tissue, analyze the distribution of electromagnetic fields, power density losses, and radiation hazard SAR values. The results show that the path loss of electromagnetic waves in human head increases with the depth of the tissue, and the channel loss in the frequency band below 3.5 GHz is smaller, which power density decreases less than 10 dB in the tissue within 25 mm of the skin. The human tissue has different absorption capabilities for different frequencies of electromagnetic radiation, and the SAR value of high-frequency electromagnetic radiation is generally smaller than that of low-frequency electromagnetic radiation. Among them, the SAR of 1.8 GHz reached the maximum value of 1.71 W/kg. Considering the power loss and electromagnetic radiation hazards, the optimal operating frequency for implantable brain electrodes varies with the depth of implantation. 2.45 GHz electromagnetic wave is suitable for implantable electrodes in the subcutaneous tissue within 5 mm of the head, and 1.8 GHz and 400 MHz are the optimal frequencies for implantable electrodes located 15 mm and 25 mm beneath the skin, with the power density increasing by 9.6% and 77.4% compared to 2.45 GHz.
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Su Sanqing, Liang Jiaxin, Wang Wei, Liao Wenkai, Zuo Fuliang, Li Junting
2024,38(12):181-189,
Abstract:
Metal-magnetic memory detection technology is a new type of non-destructive testing technology, which can identify the early damage of ferromagnetic materials. In order to promote the application of this technology in the field of bridge steel damage detection, the static bending test was carried out for the single U-rib orthotropic steel bridge deck inside the steel box girder, the magnetic signal data of the U-rib web under different load levels were collected, and the stress was obtained by combining with ANSYS finite element software, and the force-magnetic curve of the U-rib web along the height direction and the relationship curve between the stress in the stress concentration area and the magnetic signal and magnetic signal gradient were established, and the relationship between the force characteristic parameters and the magnetic characteristic parameters was analyzed. The results show that along the direction of the detection line, the normal and tangential magnetic signal values of each detection line decrease with the increase of load, and the normal magnetic signal curve fluctuates less than that of the tangential magnetic signal under the same load level, and the distribution is more gentle. The normal magnetic signal of the U-rib web has a “peak-trough” phenomenon along the height direction, which has a good correspondence with the stress distribution, and the stress concentration area can be judged by this phenomenon. The “steep rise” phenomenon of the magnetic gradient K curve in the stress concentration area can be taken as the danger warning signal of the yield strength in the actual engineering component. The μσ-λk curve of the force-magnetic correlation characteristic parameter has a good linear fit, and the stress concentration degree of the stress concentration area can be preliminarily judged according to the curve.
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2024,38(12):190-201,
Abstract:
For the problem of occlusion interference in robot grasping of occluded targets, an improved YOLO-CA-SD and semantic segmentation occluded target detection model and grasping method are proposed to complete the grasping when multiple targets and non-target objects occlude and interfere with each other. Firstly, the model adds a coordinate attention to YOLOv5l, considers the problem of detection frame matching direction based on the loss function, adds angle information between frames, and detects the original model decoupling is partially performed to reduce information loss caused by coupling. Secondly, an improved DeeplabV3+ target segmentation model was proposed. The original DeeplabV3+ backbone network was replaced by MobileNetV2 to reduce the model complexity. A CA module was added to the Atrous Spatial Pyramid Pooling structure to fuse pixel coordinate information to improve segmentation accuracy and solve the occlusion interference problem. Finally, the end rotation angle of the target poses relative to the template pose and the optimal grasping point are obtained by point cloud registration. The performance test is carried out on the self-built 2 750 commonly used tool occlusion data set. The experimental results show that the improved model improves the detection accuracy by 0.052%, 0.968%, 6.000%, and 7.400% on mAP@0.5, mAP@0.5:0.95, 60% target object occlusion rate and 60% non-target object occlusion rate datasets. The improved semantic segmentation model on this basis improves the segmentation speed and MIOU by 33.45% and 0.625%, and the ABB IRB1200 robotic arm is used to realize the experiments on the grasping of obscured targets, which verified the feasibility and practicality of the method.
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2024,38(12):202-210,
Abstract:
The accuracy of reservoir porosity prediction is crucial for assessing the storage capacity and quality of underground reservoirs. However, existing methods for porosity prediction face challenges such as limited model algorithms, low accuracy, and poor generalization. To enhance the precision of porosity prediction, this study proposes a Stacking ensemble learning method optimized by Optuna. First, gray relational analysis is used to select input parameters, including acoustic time difference, well depth, rock density, dip angle, and photoelectric absorption index. The input data is then normalized, and Optuna is employed to optimize the model parameters. Based on metrics like root mean square error, mean absolute error, and determination coefficient, random forest, support vector regression, and K-nearest neighbors are chosen as base learners for the Stacking model, with elastic net regression serving as the meta-learner. Comparative results reveal that while RF excels in handling nonlinear data, it shows instability in predictions; the Stacking model reduces RMSE by approximately 10% compared to RF. SVM demonstrates strong generalization ability but requires complex parameter tuning, with the Stacking model achieving about a 39% reduction in RMSE compared to SVM. KNN is insensitive to outliers but performs poorly on high-dimensional data, with the Stacking model lowering the error by about 21% compared to KNN. Additionally, XGBoost effectively avoids overfitting but is sensitive to outliers and requires complex tuning, with the Stacking model reducing the error by approximately 30% compared to XGBoost. Overall, the results indicate that the Optuna-optimized Stacking model significantly improves the accuracy of porosity prediction, providing a valuable reference for evaluating the oil and gas storage capacity of reservoirs.
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2024,38(12):211-217,
Abstract:
Aiming at the problem that the positioning accuracy of satellite navigation system in indoor closed places is too low due to insufficient positioning penetration and the traditional inertial guidance has a large trajectory offset in indoor pedestrian positioning, through the in-depth analysis of pedestrian trajectory projection algorithm (PDR), an improved PDR algorithm is proposed, which aims to improve the positioning accuracy in indoor positioning. The algorithm firstly designs Kalman filter and FIR filter to preprocess the sensor data to improve the data smoothing and anti-noise performance; secondly, it improves the traditional Weinberg step calculation model by adding new variables as the joint reference of step frequency detection and step calculation, which helps to reduce the cumulative error of the step estimation; and then it takes the appropriate threshold value as the pedestrian zero-speed judgment to correct the step number as well as the pedestrian’s position; finally, an extended Kalman filter (EKF) is designed to optimize the pedestrian’s position to achieve the dynamic optimization of the actual pedestrian trajectory. The simulation results show that the improved PDR algorithm significantly improves the positioning accuracy, and the average error of the pedestrian trajectory is reduced from 5.5 m to 1.2 m. Overall, the improved PDR algorithm can effectively reduce the trajectory deviation and the cumulative error, and improve the accuracy of the pedestrian positioning, which is of wide application prospect.
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Chang Hongbin, Gu Yingdong, Sun Ping, Zhang Di
2024,38(12):218-227,
Abstract:
In response to the problem of falls caused by incoordination between the walking speed of the elderly and the designated speed of the walking rehabilitation training robot during rehabilitation, this paper proposes a human-robot speed coordination anti-falling method, consisting of two parts: a falling prediction model and an anti-falling control method. First, the walking posture signals of the subject are collected by an inertial measurement unit (IMU), and a falling prediction model for the elderly is constructed using long short-term memory (LSTM) network and attention mechanism. Second, based on the falling prediction, a multi-dimensional phase space reconstruction (PSR) speed prediction model is designed for the anti-falling controller. Finally, the predicted speed of the subject is used as the target speed, and the PSR theory and model predictive control (MPC) technology are used to design an anti-falling controller for the walking rehabilitation training robot, achieving precise tracking of the subject’s walking speed and preventing falls caused by incoordination between the subject’s walking speed and the robot’s designated speed during rehabilitation training. Simulation and experimental results show that the prediction accuracy of the falling prediction model can reach 95.2%, and the lead time for falling prediction can reach 1.82 s. The human-robot speed coordination anti-falling method can effectively prevent falls caused by incoordination between the subject’s walking speed and the robot’s designated speed, enabling the subject to complete walking rehabilitation training safely.
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2024,38(12):228-236,
Abstract:
The double-exponential asymptotic sliding mode control is applied to the single-phase three-level inverter system, which exhibits a multitude of operating modes and intricate nonlinear dynamic behavior. The operational principles of the system across various modes are thoroughly analyzed, and a discrete model of the system is established utilizing the flash mapping method. The impact of system parameters on its nonlinear behavior is investigated through bifurcation diagrams and folding diagrams, leading to the identification of a two-dimensional stable operation domain for both control parameters and main circuit parameters. The stability of the system under double-exponential sliding mode control is examined using the fast-varying stability theorem, with comparisons made against bifurcation and folding diagrams for validation, as well as against other control strategies. Finally, the nonlinear behavior of the system is corroborated through time-domain waveform diagrams and their corresponding spectra under varying control parameters. The study reveals that the three-level inverter topology demonstrates more complex nonlinear dynamics; furthermore, it establishes that double-exponential asymptotic rate sliding mode control offers an expanded parameter stable domain. Specifically, the stable operational range for control parameters has been extended from 0.15~0.95 to 0.05~1.65, while shifting the unstable starting point from 1.3 (under improved exponential asymptotic rate sliding mode control) to 1.65. These research findings provide theoretical support for parameter design in implementing double-exponential asymptotic rate sliding mode control within three-level inverters.
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Bai Yixiang, Chen Zhiying, Zhang Xiulun, Liu Bixing, Chen Guoyan
2024,38(12):237-249,
Abstract:
Partial discharge ultrasonic signal monitoring is one of the commonly used methods to determine the insulation status of oil-immersed transformers. However, the on-site noise interference is difficult to avoid and often accompanied by white noise. Therefore, a denoising method based on improved variational mode decomposition and wavelet transform is proposed. Firstly, taking the kurtosis-permutation entropy criterion as the objective function, the ant colony optimization is used to determine the optimal decomposition level and penalty factor of the variational mode decomposition, and the noisy partial discharge ultrasound signal is decomposed into multiple intrinsic mode function. Then, the correlation coefficient method is used to divide the multiple intrinsic mode function into noise free function, noise containing function, and noise function. The maximum-minimum permutation entropy criterion is used as the objective function, and the ant colony optimization is used to determine the optimal wavelet threshold and propose an improved wavelet threshold function for wavelet denoising of the noisy function. Finally, the noise free function and the denoised wavelet function are reconstructed to complete the denoising of the partial discharge ultrasound signal. By denoising simulated and measured partial discharge ultrasound signals and comparing with four other denoising methods, the results show that the proposed denoising method has excellent performance. The signal-to-noise ratio and normalized correlation coefficient are average improved by 43.62% and 2.39% respectively compared with other methods, and root mean square error is average reduced by 35.46%.
Volume 38,2024 Issue 12
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Path Improvement of the A* Algorithm Based on the Flexible Rope Stretching Mechanism and AGV Autonomous Navigation
Abstract:
In complex environments characterized by multiple obstacles, the traditional A* algorithm in path planning presents the problem of redundant turning nodes. This not only increases path length and complexity but also hinders the smooth navigation of the AGV. To address these challenges, this study introduces an improved A* algorithm predicated on the tensile mechanism of a flexible rope, aimed at diminishing path nodes and augmenting trajectory smoothness. First, the mechanism of flexible rope stretching was analyzed, and critical nodes were extracted from the paths generated by the A* algorithm. Subsequently, the degeneration of non-obstacle force points was executed to minimize redundant steering nodes, followed by the sequential stretching of paths between force points, thereby streamlining the trajectory and enhancing smoothness. Ultimately, the refined A* algorithm underwent simulation experiments and was applied to AGVs for autonomous navigation path planning experiments. The simulation outcomes demonstrated that the A* algorithm, refined with the flexible rope stretching mechanism, achieved a 59.2% reduction in turning angles, a 54.2% decrease in the number of turning points, and an 11% reduction in path length, significantly simplifying and smoothing the trajectory. In the AGV navigation experiments, the optimized A* algorithm, when compared to the traditional A* algorithm, registered a 16% decrease in average angular velocity and a 33% reduction in driving turning angles, with average travel trajectory length and time reduced by 2.4% and 4%, respectively. Additionally, the average travel trajectory length and time spent are reduced by 2.4% and 4%, respectively. The experiments results show that the AGV experiences smaller node transformations and posture adjustments while following the paths planned by the improved A* algorithm, leading to smoother and more efficient movement.
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Regenerative Braking Strategies Considering Driving Cycles, Drivers and Road Information
Abstract:
The design of regenerative braking strategies requires a comprehensive consideration of multiple factors, among which vehicle driving conditions, driver characteristics and the road surface on which the vehicle is traveling have a significant impact on the regenerative braking process. In order to formulate regenerative braking strategies for electric vehicles that are adaptable to various driving conditions, improve the vehicle braking energy recovery rate and maintain braking stability, a regenerative braking strategy that comprehensively considers the influences of driving cycles, drivers and road information is proposed. Firstly, a simulation driving platform is set up to conduct driver-in-the-loop experiments and collect driving data from different drivers, thereby extracting feature parameters of driving conditions and driving styles. Then, a support vector machine (SVM) is used to train the models for identifying driving conditions and driving styles. Secondly, a road image dataset is established and a semantic segmentation network is used for road image preprocessing to remove the complex background information of the image and thereby improve the recognition efficiency. Then, a lightweight neural network, MobileNet V3, is adopted to train the road recognition model. Finally, the regenerative braking strategy base on this is formulated. The front and rear braking force distribution is optimized considering the road adhesion conditions, and a regenerative braking force correction method that takes driving cycles, driver and road information as weight factors is put forward. The simulation results show that the proposed regenerative braking strategy can take into account different driving cycles, drivers and road conditions, and further improve the vehicle energy recovery rate and braking stability.
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Research on liquid concentration detection based on microwavenear-field coaxial probe
Abstract:
Liquid concentration detection is widely used in food, environment, biomedical and other fields. Based on the microwave method, a resonant coaxial probe liquid concentration detection device was studied, designed and realized. Firstly, ethanol-water solutions of different concentrations are selected as test samples, and the effects of coupling gap, probe immersion depth and probe conductor material on detection sensitivity are simulated and analyzed. To verify the feasibility of liquid concentration detection using the designed probe, ethanol-water solutions/glucose-water solutions with a volume concentration of 0~75%/ 0~50% are measured. The experimental results show that the probe is capable of accurately measuring the liquid concentration and the detection sensitivity in different concentration ranges can be optimized by adjusting the coupling gap. In addition, in this study, the quantitative inversion model of solution concentration is constructed by combining three electromagnetic parameters, namely, resonance frequency, S11 amplitude minima and quality factor. Compared with traditional method that only use resonance frequency as indicator, relative errors of liquid concentration of ethanol-water solutions/glucose-water solutions are suppressed from 5.79%/ 3.34% to 2.19%/ 1.36%, respectively. The probe is also able to effectively differentiate a variety of transparent liquids, such as ethanol solutions, glucose solution, salt water, tap water and deionized water, etc., showing good recognition ability and data reproducibility and thus shows a wide range of potential applications.
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Adaptive Analysis and Reconstruction of Electromagnetic Railgun Acceleration by Integrating Maximum Likelihood-Wavelet and ICEEMDAN
Abstract:
Obtaining accurate projectile acceleration signals is essential for evaluating the performance of electromagnetic guns. However, the projectile is affected by different environmental factors in the chamber and out of the muzzle, which makes the acceleration signal have different modal characteristics in the bore and the muzzle stage, which leads to the failure of the conventional nonlinear non-stationary signal global processing method. Therefore, an adaptive analysis and reconstruction method of acceleration signal fusing maximum likelihood-wavelet and improved fully adaptive noise ensemble empirical mode decomposition (ICEEMDAN) is proposed in order to obtain accurate acceleration signals. Secondly, the partition signal was decomposed by ICEEMDAN to reduce the interference of harmful signals on signal parsing. Finally, the effective modal components were extracted based on the t-test for signal reconstruction to achieve accurate extraction of the effective acceleration signal. Correlation experiments show that the improvement rate of root mean square error is greater than 0, the correlation coefficient (ρ) is increased to 0.6731, and the signal-to-noise ratio (SNR) is increased to 3.8614, which avoids the problem of over-decomposition or incomplete decomposition of some regions compared with the conventional global processing methods, and realizes the accurate extraction of acceleration signals.
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Photovoltaic Array Fault Diagnosis Based on Feature Extraction and Improved Pelican Optimization Algorithm
Abstract:
Photovoltaic (PV) arrays often operate in complex and harsh environments, making them susceptible to various types and degrees of faults. To enhance the accuracy of fault diagnosis in such challenging conditions, this study proposes a novel fault diagnosis model based on feature extraction and an improved pelican optimization algorithm (IPOA) optimized support vector machine (SVM). Firstly, 15 typical fault states are simulated on the MATLAB/Simulink platform, from which a 12-dimensional fault feature vector is constructed. Kernel principal component analysis (KPCA) is then applied for feature fusion and extraction to improve feature representation capabilities. Secondly, to address the limitations of traditional pelican optimization algorithms in balancing global search and local exploitation, enhancements are introduced, including the Tent chaotic map, inertia weight, nonlinear convergence factors, and an adaptive t-distribution mutation strategy, all of which significantly improve the algorithm's optimization performance. Finally, the IPOA is used to optimize the penalty factor C and kernel parameter γ of the SVM model, establishing the IPOA-SVM PV array fault diagnosis model, which is then validated through both simulation and experimental tests. The results show that, compared to the traditional 6-dimensional feature set, the proposed 12-dimensional feature set achieves higher diagnostic accuracy. The improved model demonstrates fault diagnosis classification accuracies of 98.55% and 97.93% for simulation and experimental data, respectively, significantly outperforming other comparison models and demonstrating higher accuracy in PV array fault diagnosis.
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Fault prediction of electric vehicle charging stations based on cooperative game strategy and DBO-BiLSTM-Attention
Abstract:
Aiming at the problem of the high failure rate of electric vehicle charging piles, a fault prediction method of electric vehicle charging piles based on cooperative game strategy and dung beetle optimization algorithm-bidirectional long-term and short-term memory network-attention mechanism (DBO-BiLSTM-Attention) is proposed. Firstly, the abnormal values are processed by parameter statistical distribution map, the missing values are processed by mean filling, and the processed data are normalized. Secondly, multiple single-weighting methods are selected to calculate the feature weight, the combination weight is calculated by the cooperative game strategy, and the parameter feature matrix is amplified. Then, the DBO-BiLSTM-Attention model is built. Under the simulation experiment, the accuracy and F1 coefficient of the training set and the test set are 0.89,0.89,0.90 and 0.90, respectively. Finally, relevant comparative experiments are constructed. The results show that the proposed model has better performance and verifies the validity and rationality of the proposed model.
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Improved helmet detection algorithm for two-wheeled vehicles of RT-DETR
Abstract:
Aiming at the phenomena of leakage, false detection and low detection accuracy in complex scenes such as dense objects, small objects in distant view, etc., which occur in the helmet detection algorithm of two-wheeled vehicles, an improved RT-DETR two-wheeled vehicle helmet detection algorithm is proposed on the basis of RT-DETR-r18. Firstly, a dual cross-stage multi-scale feature fusion module (DcspBlock) is designed, and a multi-core initialization module (PKIBlock) is integrated into the cross-stage module to enhance the ability of the backbone part to capture objects of different scales in the near and far scenes; secondly, a small object detection module Decoderhead-p2 is introduced into the Encoder part of RT-DETR to enhance the model"s accuracy in small object detection; finally, the original model"s GIOU is replaced by the improved loss function MPD_Focaler-IOU, and the adjustment parameters are set to reduce the impact of positive and negative sample imbalance on the model"s performance, and the minimum vertical distance is introduced to give a better performance in the fine localization of the bounding box. Experiments show that the improved RT-DETR model improves mAP50 and mAP50-95 by 3.6% and 3.7% on the TSHW dataset, respectively, and the amount of parameters is reduced by 17.6%, which effectively improves the performance of the two-wheeled vehicle helmet detection in complex scenes.
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Lightweight rotating small target detection network adapted to remote sensing ship images
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Remote sensing images of ships is characterized by small target sizes, complex backgrounds, and significant attitude changes. Traditional ship detection algorithms focus on improving detection accuracy while neglecting model size and real-time performance, thereby limiting their practical application on resource-constrained devices. A lightweight Rotated Fusion Detection Network RFDNet adapted to remote sensing ship images is proposed to address the above problems. Considering that the remote sensing ship images are taken at a long distance, resulting in small target sizes and rich background information in the images, ACFNet is designed to improve the detection accuracy of small ship targets by fully extracting local feature information and global spatial sensing information. To avoid accuracy degradation when detecting ship targets with different attitudes, a rotating bounding box loss function is introduced, which utilizes the orientation information of rotating targets for obtaining a more accurate bounding box regression loss, thereby improving the detection performance of ship targets rotating in any direction; for the problem of increasing parameter counts brought about by increasing the accuracy of the model, a lightweight convolution is introduced into the feature fusion part, which combines the convolution, the depth separable convolution, and the channel blending to reduce the number of parameters in the model. Through comparative and ablation experiments, it has been demonstrated that RFDNet achieved mAPs of 97.63% and 81.63% on the HRSC2016 and DOTA datasets, respectively, while reducing the model parameters to 2.99M. This not only effectively improved detection accuracy but also realized a lightweight model design, providing a new insight for the application of remote sensing ship detection algorithms to resource-constrained devices.
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Ship Exhaust SO2 Ultraviolet Remote Sensing Imaging Monitoring System
张一康, 王蕊, 吴世俄, 袁浩宸, 周维, 武魁军, 何微微
Abstract:
The rapid development of the shipping industry has led to a significant increase in exhaust emissions from ships. Ship plume emissions are characterized by wide distribution and high mobility. These emissions are often hidden, uneven, and highly variable, making their regulation extremely challenging. In response to this, the present study designed and developed a high-precision, high spatiotemporal resolution UV imaging remote sensing system for the real-time, remote monitoring of SO2 emissions from ship exhausts. The system employs a three-channel design, utilizing dual-wavelength channels at 310 nm and 330 nm to eliminate interference and accurately capture SO2 signals, with a spectral channel used for cross-verification of accuracy. The monitoring system integrates the 2-IM sky background reconstruction method, a self-calibration technique, and an optical dilution effect correction algorithm, enabling the precise acquisition of optical thickness images and real-time inversion of SO2 concentrations. Additionally, through an emission rate inversion algorithm, the 2D SO2 concentration data are converted into intuitive emission rate information, further enhancing the practicality and interpretability of the monitoring data. Experimental results show that the self-calibration technique can fit calibration curves in real time with an error of only 2.35%. After optical dilution correction, the camera's detection limit reaches 3.84 ppm·m at 623 m, and it still maintains a high sensitivity of 6.24 ppm·m at 1932 m. These results fully demonstrate that the system meets the performance requirements for monitoring distant, low-concentration, mobile pollution sources. The development of this system not only provides robust technical support for the monitoring and control of marine pollutants but also aids in understanding the characteristics of ship emissions and the diffusion mechanisms of gaseous pollutants.
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Design of Miniaturized Wireless Passive Strain Sensor Array Based on Split Ring Resonator
Abstract:
Existing wireless passive strain sensors suffer from limitations such as single-direction measurement, low sensitivity and large size, making them unsuitable for strain state evaluation of large metallic structures including aircraft wings, under complex loading conditions. To address these issues, a miniaturized wireless passive strain sensor array is proposed based on split ring resonator(SRR) with the advantages of high radiation capability, low loss, and high-quality factor and the principle of trigonometric functions and vector decomposition. The proposed sensor array consisting of three sensors arranged at 120° angles can reconstruct the magnitude and direction of strain field by extracting the resonant frequency shift. After the impedance parameters of the sensor are acquired by ADS software, the sensor structure miniaturization and impedance matching optimization design are carried out by HFSS software, aiming at the target of resonant frequency optimization. In addition, "force-magnetic" coupling analysis in COMSOL software verifies the performance of sensor's strain detection. Furthermore, the fabrication of the sensor is implemented based on the above analysis and optimization. Experimental results show that the sensitivity of proposed sensor in the electrical length and width directions is —1.517 KHz/με and —0.732KHz/με , respectively. The proposed sensor array achieves a strain magnitude detection accuracy within 8.5% and a direction detection error within 10° . The sensor array can achieve strain magnitude and direction detection on metallic surface with the ability of high sensitivity, compact size, and low cost.
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Speech enhancement based on residual dilatation convolutional and gated codec networks
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The time-dependent features and context information of speech signals are crucial in speech enhancement tasks.Aiming at the problem that codec networks insufficiently capture these features,resulting in poor enhancement performance,an asymmetric residual dilatation convolutional and gated codec network (RD-EGN) is constructed.The network comprised three parts:the encoder,intermediate layer and decoder.The encoder designed a causal convolution layer structure to model the temporal feature, capture the features of different layers in the speech sequence and maintain the speech signal’s causality.The intermediate layer incorporated a residual dilated convolutional network (RDCN),which integrated dilated convolution,residual connections,and cascaded expansion blocks to endow the network with a larger receptive field.It facilitated cross-layer information transfer and extracted long-term dependency features in speech.The RDCN is combined with the long short-term memory network to capture broader context information.The decoder introduced a gating mechanism to adjust the gating degree of information flow dynamically,obtain richer global features and reconstruct enhanced speech.Ablation and performance comparison experiments were conducted on the TIMIT,UrbanSound8k,VoiceBank,and NOISE92 datasets.The results show that,RD-EGN has fewer training parameters and higher scores in SSNR and subjective evaluation metrics (CSIG,CBAK,and COVL) than CRN,AECNNand DDAEC.In objective evaluation metrics,the PESQ is increased by2.5% to 7.1%,and the STOI is increased by1% to 5.3%.RD-EGN demonstrates outstanding enhancement performance and generalization ability.
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Method on Alzheimer's Disease Prediction Based on Joint Decision-making utilizing Dual Priority Prediction Hierarchical Model
Abstract:
Accurately predicting Alzheimer's disease (AD) progression is crucial for timely treatment and intervention before advanced stage of AD. In this paper, a method on AD prediction based on joint decision-making utilizing dual priority prediction hierarchical model is proposed, which converts the three category prediction problem on AD, mild cognitive impairment (MCI) and normal cognitive (NC) into two levels of two category prediction problem. Firstly, the statistical features are extracted from the time series data of magnetic resonance imaging (MRI) and cognitive scores (CSs), which is obtained from individual historical follow-up, and the high-importance MRI volume statistical features are selected using the weighted embedded feature selection method. Then, both the NC priority prediction hierarchical model and the AD priority prediction hierarchical model are constructed. Using the selected high-importance MRI volume statistical features and CSs statistical features, these two hierarchical models are used to achieve AD/MCI/NC three category prediction. The NC and AD individuals are first predicted, and finally the MCI individuals are determined. The proposed AD prediction method is evaluated on the TADPOLE dataset. The accuracy (ACC) and macro average of F_1 score of the proposed AD prediction method are 89.29% and 88.81%, respectively. The experimental results show that the proposed method is effective and better than conventional AD prediction method.
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Design of tri-band MIMO antenna based on 5G and Wi-Fi 6E applications
Abstract:
Aiming at the problems of large size, poor port isolation and weak anti-interference ability of multi-band multiple-input multiple-output (MIMO) antennas, a tri-band MIMO antenna which can be applied to 5G n78/n79 and Wi-Fi 6E bands is designed. The antenna is composed of a slotted diamond patch and a trapezoidal floor, and combines the innovative design of a semi-circular structure and a symmetrical inverted L-shaped branch. This design not only realizes the required triple-frequency characteristics, but also effectively controls the size of the antenna to adapt to more compact application requirements. The antenna is fed by coplanar waveguide (CPW), which has the advantage of easy integration with other microwave circuits. By placing the unit antenna orthogonally and without isolating branches, the port isolation of the MIMO antenna in the required frequency band is greater than 25dB. The simulated antenna is processed and tested. The measured results show that when the return loss is less than-10 dB, the impedance bandwidth of the antenna is 3.28-3.67 GHz, 4.63-5.01 GHz and 5.67-7.65 GHz, which is suitable for 5G n78/n79, Wi-Fi 6E band. The maximum measured gain can reach 4.7 dB, the envelope correlation coefficient (ECC) is less than 0.001, and the diversity gain (DG) is greater than 9.9999. The diversity performance is good. The measured results are highly consistent with the simulation results, which verifies the effectiveness and accuracy of the design. Considering the size advantage, good isolation and excellent diversity performance of the antenna, the triple-band MIMO antenna proposed in this paper shows great application prospects in 5G and Wi-Fi 6E communication systems, which can meet the growing demand of future wireless communication and promote the development and innovation of wireless technology.
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Gear fault diagnosis method based on FBSE-ESEWT
张锐, 刘婷婷, 王燕, 付俊淋, 周卫斌, 卜二军, 王永霞, 游国栋
Abstract:
Aiming at the problem that vibration signals collected in gear fault diagnosis are often accompanied by noise interference and fault features are difficult to extract, this paper is based on Fourier-Bessel series expansion (FBSE). A noise reduction method of gear vibration signal (FBSE-ESEWT), which combines FBSE and Energy Scale Space Empirical Wavelet Transform (ESEWT), is proposed. Firstly, the frequency spectrum of the acquired gear vibration signal is obtained by using FBSE technology to replace the traditional Fourier spectrum. Then, the obtained FBSE frequency spectrum is adaptive segmtioned and screened by using the energy scale space partitioning method to accurately locate the boundary points of the effective frequency band. Then the signal components are obtained by constructing wavelet filter banks and reconstructed to reduce noise and redundant information interference. Then, in order to capture more comprehensive feature information, the processed signal is transformed by generalized S-transform to obtain time-frequency graph, and 2D convolutional neural network is input for fault diagnosis to verify the feasibility of the algorithm. Through experiments on Simulink simulation signals and actual acquisition signals, the results show that compared with the original EWT, EMD and other methods, FBSE-ESEWT has better noise reduction effect, the signal-to-noise ratio is increased by 13.96dB, and the diagnosis accuracy is up to 98.03%.
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Research on Terahertz SAR Imaging Motion Compensation Method Using Inertial Navigation Information
Abstract:
A key factor in high-resolution Synthetic Aperture Radar (SAR) imaging is that the radar operates under ideal conditions. However, the motion trajectory of radar is usually not an ideal straight line or stable, so any small motion error within the synthetic aperture time can cause image blur or distortion. For small-scale imaging scenes, due to the susceptibility of GPS to signal interference and multipath effects, the traditional motion compensation method that combines GPS and INS data is not very effective. In this scenario, this study proposes a terahertz SAR imaging motion compensation method that only utilizes inertial navigation information. This method fully utilizes the velocity information provided by the inertial navigation system, establishes a radar motion trajectory model, and effectively estimates the echo phase error in the radar line of sight direction, thereby achieving focusing on terahertz SAR imaging targets. The experiment used a SAR system with a center frequency of 0.2 THz for motion compensation, and analyzed the strong scattering points of SAR images before and after compensation. Compared with the existing technology based on GPS and INS joint motion compensation methods, the motion compensation method proposed in this study respectively improved by 0.7 dB and 0.8 dB on PSLR and ISLR. In terms of imaging speed, the motion compensation method proposed in this study also improved by 0.2%. The experimental results showed that the focusing effect of this method was better for small-scale imaging scenes, further verifying the correctness and effectiveness of the motion compensation algorithm mentioned in this study.
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Target differential attention and Transformer algorithm for infrared and low-light image fusion
Abstract:
Aiming at the problems of spectral information missing and target edge blurring in current infrared and low light level image fusion algorithms, a target difference attention algorithm and Transformer fusion algorithm for infrared and low light level image fusion are proposed. Firstly, a low-light level reconstruction network is constructed by using residual structure, and the perception loss is constructed by using VGG-16 to preserve the background texture and brightness information in the low-light level image to the maximum extent. Then, the feature extraction network is constructed by combining CNN and Transformer to extract the complete features of the image. At the same time, in the target differential attention module, the difference operation and feature extraction are carried out on the infrared image and low-light image, and the obtained infrared differential image is enhanced by the channel attention mechanism. Then the output feature map of CNN feature extraction network is added element by element to enhance the infrared target feature. Then, the high frequency and low frequency information of features are captured by gradient retention module to improve the retention of texture details. Finally, the feature reconstruction network is used to reconstruct the fused image. The experimental results show that the fusion results are not only more consistent with the human visual system, but also the objective evaluation indexes of MI and VIF are increased by 44.6% and 21.2%, respectively, compared with other fusion methods.
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Mobile lightning ranging method based on electric field analysis
Abstract:
Lightning can cause interference or harm to vehicles such as cars, trains, and flying machines, etc. Measuring the distance between lightning and moving vehicles is of positive significance for safeguarding the safety of the vehicles and the people inside them. Based on the motion scene of the moving transportation vehicle and the electric field radiation characteristics generated by lightning, Method of comparing electric field strength to electric field strength (E-E method) is established to carry out the study of lightning distance measurement and explore the feasibility of the E-E method for lightning ranging. Firstly, the E-E method is modeled by constructing the lightning electric field radiation scene, and the simulation analysis is used to compare the efficiency of the linear interpolation method and Newton's Raphson method in solving the E-E methodological model; secondly, the influence of the Doppler effect on the measurement error of the E-E method under the moving situation is analyzed, and finally, an application platform is constructed to complete the validation of the feasibility of the algorithm. After analyzing, Newton's method improves the efficiency by about 35% on the basis of linear interpolation when solving the distance value of the E-E methodological model; the measurement error is larger for the lightning signals with the reception frequency lower than 10 kHz, while the high frequency does not affect the measurement results; the Doppler effect basically does not affect the measurement results when the received signals of more than 20 kHz are measured by the E-E method under the Doppler effect; the distance measured by the application platform does not affect the measurement results; the distance measured by the application platform is not affected by the Doppler effect. results; the distance value measured by the application platform has an error of about 5% with the theoretical distance value, which indicates that the method can effectively measure the lightning distance. In addition, this kind of measurement error can be reduced by compensating the circuit and fitting the data by the least squares method.
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Evaluation Method of Measurement Uncertainty of TransducerBased on Convolution
Abstract:
As the first part of the whole testing system, the measurement uncertainty of transducer influences greatly on the uncertainty of measurement results. For this reason, the main sources of transducer uncertainty have been analyzed, and the evaluation methods have been discussed about their properties; proposes a new method to evaluate the measurement uncertainty of a transducer has been proposed based on convolution of probability density function of sources of measurement uncertainty; the method has been realized via MATLAB .Finally, the method has been successfully applied to evaluate the measurement uncertainty of a load cell, which reveals the effectiveness of the method.
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On-line fault detection method of hydraulic turbine combining PCA and adaptive K-Means clustering
Abstract:
During the operation of the bulb tubular hydropower unit, due to hydraulic factors, machinery, working conditions and other factors, it is easy to cause the runner blades and runner chamber to malfunction, which seriously affects the safe operation of the hydropower unit. Based on the analysis of the fault signal characteristics of the runner blades and runner chamber of the bulb tubular hydropower unit, an online fault detection method for hydropower units based on K-Means and Wright"s criterion is proposed. This method uses principal component analysis (PCA) to reduce the dimensionality of the vibration and noise signal characteristics of the hydropower unit, and integrates the Wright criterion to improve the traditional K-means algorithm to realize the adaptive selection of the K value, and perform online clustering of the features, which can quickly and accurately identify .The variable load state of the turbine and the failure of the metal sweeping chamber. The method proposed in this paper is applied to the fault detection of the bulb tubular unit of Wuling Electric Power’s Jinweizhou Hydropower Station. The experimental results show that the accuracy of the online fault detection using this method is 100% and the accuracy of the variable load online detection is 96.7. %, there has been no fault false positives and false negatives in the past 10 months of operation, indicating the effectiveness of the method.
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Research on positioning of mobile robot based on Laser Information
Abstract:
Aiming at the problems of slower particle convergence and poor positioning accuracy when using traditional Monte Carlo positioning algorithms in the navigation and positioning process of mobile robots, as well as low relocation efficiency after artificial kidnapping, this article gives an improved Particle filter positioning method to improve the navigation and positioning efficiency of mobile robots. First of all, it is improved on the basis of the Monte Carlo positioning algorithm and integrated into the method of adaptive region division to ensure that the region contains more effective information, reduce the convergence time of particles, and complete the preliminary coarse positioning of the robot. Then, in the particle sampling and resampling stage, the normal distribution probability model is used to update the particle weights to achieve faster and more efficient global positioning. Through experimental comparison and analysis, compared with the Monte Carlo positioning algorithm, the given method has shortened the time consumption by 4s, and the adaptive Monte Carlo positioning method in this paper can keep the positioning error at about 6cm, thus verifying the given method Effectiveness and stability.
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2017,31(1):45-50, DOI: 10.13382/j.jemi.2017.01.007
Abstract:
The concentration of nitrogen oxides (NO2, NO, N2O, etc.) in power plant is an important index of environmental protection. Aiming at the problem that the detection accuracy of nitrogen oxides concentration based on spectral analysis could be interfered by all kinds of factors, such as temperature, moisture content, tar, naphthalene, noise of electric devices, optical lens aging, interference at spectral absorption characteristics of polluting gases etc, it is difficult to improve in a single way. At first, the hardware modification is favorable for gas purification and filter. And then, the self learning and self training ability of RBF neural network can save the traditional model for the study of interference factors, and make the data processing more efficient. On the basis of a large thermal power plant’s real data in 2015, the computer simulation and analysis show that this method can improve the accuracy effectively. The overall average deviation is 0.841%.
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Wang Wen, Zhang Min, Zhu Yewen, Tang Chaofeng
2017,31(1):1-8, DOI: 10.13382/j.jemi.2017.01.001
Abstract:
Spherical joint is a commonly multi degree of freedom mechanical hinge which has many advantages such as compact structure, good flexibility, and high carrying capacity. Realization of its multi dimensional angular displacement measurement is of great significance in the prediction, feedback, and control of the system motion error. Firstly, the application of spherical joint and its structural characteristics were presented in the paper. Then, the motion description of the spherical joint and needed angles for measurement were analyzed. A review of multi dimensional angular displacement measurement method, including structural decoupling detection method, optical based detection method and magnetic field based detection method, at home and abroad was provided, Finally, the development of research on multi dimensional angular displacement measurement method for spherical joint was summarized. The focus and the difficulty of the research were pointed out, and the challenges and the breakthroughs in the key technologies were also stated.
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Liu Kun, Zhao Shuaishuai, Qu Erqing, Zhou Ying
2017,31(1):9-14, DOI: 10.13382/j.jemi.2017.01.002
Abstract:
The complex and various defects of the steel surface bring great difficulty to the feature extraction and selection. Therefore, this paper proposes a new R AdaBoost future selection method with a fusion of feature selection and sample weights updated. The proposed algorithm selects features and reduces the dimension of features via Relief feature selection according to updated samples in each cyle of AdaBoost algorithm, and uses reduced features to remove noise samples by intra class difference among samples, and then update sample library according to dynamic weight of AdaBoost. The weak classifiers are trained by the resulting optimal features, and combined to generate the final AdaBoost strong classifier, and detect and locate strip surface defects by AdaBoost two classifiers. Aiming at a variety of defects such as scratch, wrinkle, mountain, stain, etc. in the actual strip production line, the experimental results show that the proposed R AdaBoost algorithm can effectively extract features with high distinction and independence and reduce the feature dimension, and simultaneously improve the accuracy of defect detection.
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Luo Ting, Wang Xiaodong, Ma Jun, Yang Chuangyan
2021,35(12):116-125, DOI:
Abstract:
In view of the nonlinear dynamic characteristics of rolling bearing vibration signal and the low accuracy of reliability evaluation, a rolling bearing health condition assessment method based on improved cross fuzzy entropy (ICFE) and Weibull proportional hazards model (WPHM) was proposed. Firstly, the original vibration signal is decomposed by improved DLMD (Crt- DLMD), and the effective component with the most fault information is selected for reconstruction. Then, the ICFE of the reconstructed signal is calculated by using the sliding mean instead of the original coarse-grained process. Finally, the ICFE is used as the covariate of WPHM for health status assessment. The life cycle data and experiments of rolling bearing from national aeronautics and space administration (NASA) and Xi′an Jiaotong University Changxing Shengyang technology (XJTU-SY) show that the proposed method can accurately and effectively evaluate the health status of rolling bearings.
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Sun Wei, Wen Jian, Zhang Yuan, Geng Shihan
2017,31(1):15-20, DOI: 10.13382/j.jemi.2017.01.003
Abstract:
Aiming at the random error of MEMS gyroscope is the main factor that restricts its precision and application range, the Kalman filter estimation method based on regression moving average (ARMA) model is proposed in this paper. Firstly, based on the results of Allan variance analysis, the quantization noise, angle random walk and zero bias instability are the main parts of the MEMS gyroscope random noise. Then, the stability of MEMS gyroscope random noise is tested by using time series analysis. Finally, based on the random drift of the auto regressive moving average (ARMA) model, a discrete Kalman filter equation is built to actualize its error estimation and compensation. The results of static vehicle and dynamic environment of digital noise reduction and Kalman filtering compensation experiments show that the Kalman filter estimation method based on the ARMA model has more obvious advantages in MEMS Gyroscope random error compensation.
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He Lifang, Cao Li, Zhang Tianqi
2017,31(1):21-28, DOI: 10.13382/j.jemi.2017.01.004
Abstract:
Empirical mode decomposition（EMD）method attenuates the signals’ energy and generates false signals in decomposing signal noise, which leads to incorrect detection results. In order to solve this problem, a stochastic resonance method under Levy noise after denoised by EMD decomposition is presented in this paper. After decomposed by EMD, the noisy signals are handled by overlaying, averaging and resampling to meet the condition of stochastic resonance. An adaptive algorithm is used to optimize system parameters, and then the processed signal can generate stochastic resonance in bistable system to achieve precise detection. The theoretical analysis and experimental results prove that the method can detect single frequency signal and multi frequency signal under the same characteristic exponent with the Levy noise. The experimental results demonstrate that the SNR of single frequency signal can increase 14 dB in the case of SNR of -28 dB. The spectral amplitude of the 5 Hz spectrum is increased from 311.8 to 724 and 10 Hz spectrum amplitude is increased from 138.9 to 143.2. This method that reduces the residual noise energy and false signal can improve the signal energy in a complex noisy condition. Compared to EMD decomposition which cannot determine the signal components, this method can achieve the detection effect better.
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Yan Fan, Zhang Ying, Gao Ying, Tu Yongtao, Zhang Dongbo
2017,31(1):36-44, DOI: 10.13382/j.jemi.2017.01.006
Abstract:
To solve the time consuming problem of image stitching algorithm based on KAZE, a simple and effective image stitching algorithm based on AKAZE is proposed. Firstly, AKAZE feature points are extracted. Secondly, feature vectors are constructed using the M LDB descriptor and matched by computing the Hamming distance. Thirdly, wrong matches are eliminated by RANSAC and the global homography transform, and then a local projection transform is estimated using moving direct linear transformation in the overlapping regions. The image registration is achieved by combining the two transforms. Finally, the weighted fusion method fuses the images. A performance comparison test can be conducted aiming at KAZE, SIFT, SURF, ORB, BRISK. The experimental results show that the proposed algorithm has better robustness for the various transform, and the processing time is greatly reduced.
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Yin Min, Shen Ye, Jiang Lei, Feng Jing
2017,31(1):76-82, DOI: 10.13382/j.jemi.2017.01.011
Abstract:
In disaster rescue and emergency situations, node energy in sensor network is especially limited. In order to reduce unnecessary forwarding consumption, this paper presents a MANET multicast routing tree algorithm with least forwarding nodes, which is based on shortest routing tree and sub tree deletion. The algorithm is proved and analyzed in detail. Its practical distributed version is also presented. The simulation comparison shows that this distributed algorithm reduces the forwarding transmission in improved ODMRP, especially there are much more receivers in MANET. Minimum forwarding routing tree has the minimum network overhead. It is an effective way to extend the network lifetime.
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Chen Shuo, Luo Tengbin, Liu Feng, Tang Xusheng
2017,31(1):144-149, DOI: 10.13382/j.jemi.2017.01.021
Abstract:
In order to solve the low efficiency and the influence of manual factors and many other problems existed in current water meter verification, the water meter verification system using machine vision technology is proposed. And the research keynote is how to realize the template matching algorithm for rapid location of plum blossom needle and the image morphological algorithm for eliminating the bubble of wet water meter dial. Harris algorithm is used to extract the corner points of the plum blossom needle template beforehand, and the corner points of the on site image are extracted in real time. Then, the fast localization of the plum blossom needle is realized by the partial Hausdorff distance method. Finally, the effect of bubbles is eliminated by using the image morphological algorithm, and the count value of the rotating teeth of the plum blossom needle is completed. The experimental results show that the proposed system can shorten the verification time and improve the verification efficiency while ensuring the verification accuracy. The system solves the adverse effect of the bubble on the dial of the wet water meter, and it’s suitable for the verification of various types of water meters.
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Cao Xinrong, Xue Lanyan, Lin Jiawen, Yu Lun
2017,31(1):51-57, DOI: 10.13382/j.jemi.2017.01.008
Abstract:
A simple, rapid and efficient retinal vessels segmentation method is proposed. After a general analysis on gray value distribution and contrast changes of fundus images, the standardizing fundus images are obtained by using the matched filtering technique to overcome the interference of background and noise. Then, a threshold can be automatically selected to achieve the effective segmentation of blood vessels in the fundus images by estimating the proportion of the background pixels. A lot of tests show that the good performance is achieved in the public fundus images database. The experiment shows that the proposed method based on matched filtering and automatic threshold has strong practicability and high accuracy. It is useful for computer aided diagnosis of ocular diseases．
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Pan Yuehao, Song Zhihuan, Du Wangze, Wu Legang
2017,31(1):29-35, DOI: 10.13382/j.jemi.2017.01.005
Abstract:
To help nursing staff in senile apartment find the elderly fall and other actions timely, an action recognition method based on video surveillance is proposed. Firstly, the foreground images are extracted by the GMM background modeling method in HS color space. Feature extraction is performed by combining the motion features and morphological features. And action recognition can be achieved by HMM with Gaussian output. The method proposed in this paper can adapt to the changes of illumination. The method also has good robustness to the change of motion direction and motion range, and the recognition accuracy rate reaches 90%. The result shows that the method can meet the basic requirements of action recognition and the method has certain practical value.
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2017,31(1):83-91, DOI: 10.13382/j.jemi.2017.01.012
Abstract:
A fuzzy perception model is proposed to the directional sensor nodes based on the sensing characteristics of the nodes, and also the fuzzy data fusion rule is built to reduce the network uncertain region. Aiming at the problem of directional sensor network strong barrier coverage, a directional sensor network strong barrier coverage enhancement algorithm based on particle swarm optimization is proposed. The convergence rate of the algorithm is improved through the n dimensional problem be transformed into one dimensional problem. The simulation results show that, under random deployment, the perception direction of sensor nodes can be adjusted continuously. Compared with the existing algorithms, the proposed algorithm can effectively form strong barrier coverage to the target area, has a faster convergence rate, and prolongs the network lifetime.
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Zhang Gang, Bi Lujie, Jiang Zhongjun
2023,37(1):177-190, DOI: 10.13382/j.issn.1000-7105.2023.01.020
Abstract:
For the difficulties of classical bi-stable stochastic resonance (CBSR) system in amplification and detection of weak signals, an underdamped exponential tri-stable stochastic resonance (UETSR) system in a Levy noise background is proposed. The UETSR system is constructed by combining the bi-stable potential and exponential potential function, and using the property that non-Gaussian noise can effectively improve the signal-to-noise ratio. Firstly, the steady-state probability density function of the system is derived. The mean signal-to-noise ratio improvement (MSNRI) is adopted as an index to measure the stochastic resonance performance. The quantum particle swarm algorithm is used on parameters optimization. The effect of each parameter of the system on the output variation pattern of the UETSR system with different parameters α and β of Levy noise is investigated. Finally, the UETSR, CBSR and classical tri-stable stochastic resonance system (CTSR) are applied to the bearing fault diagnosis, and the amplitudes at the inner and outer ring fault frequencies after the system output increased by 197. 58, 1. 153, 18. 81 and 238. 87, 26. 63, 39. 72, respectively, compared to the input signal. The spectral level ratios of the highest peak to the second highest peak were 5. 44, 4. 03, 3. 85 and 5. 10, 3. 79, 5. 05. The experimental results show that SR phenomena can be induced by different system parameters, and the UETSR system outperformed the CBSR system and the CTSR system. The above conclusions prove that the system has excellent performance and strong practical significance
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Sun Li, Zhang Xiaofeng, Zhang Lifeng, Zhou Wenju
2017,31(1):106-111, DOI: 10.13382/j.jemi.2017.01.015
Abstract:
Velocity smoothing is one problem which is proposed in high speed machining and coal mine safety production, the aim of which is to improve machining accuracy and equipment life. Aiming at this problem, this paper proposes a stage wise model and deduces the closed form expression solution for each stage based on the relationship of acceleration and velocity, and then deduces the general solutions of cubic equation in detail for the model. Finally, the solutions are applied to the velocity smoothing. The proposed schema shows the advantages of easy to program and smoothing in transition curve when being applied for velocity smoothing in coalmine. The result demonstrates that the proposed method adapts the high speed scenarios well and has used in other several projects.
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Wan Yong, Zhang Xiaobin, Ni Weining, Zhang Wei, Sun Weifeng, Dai Yongshou
2017,31(1):99-105, DOI: DOI: 10.13382/j.jemi.2017.01.014
Abstract:
The key point of azimuthal propagation resistivity logging while drilling focuses on the structural design of the coil system. And the detection performance of azimuthal propagation resistivity LWD is mainly affected by the transmission frequency of electromagnetic wave signal, the transmitter receiver spacing, the receiver interval, the coil’s angle and the formation resistivity. The testing method of measurements is determined with different inspection requirements of azimuthal propagation resistivity LWD. According to the various constraints of the coil system under the condition of different testing method, the structure of the coil system for azimuthal propagation resistivity LWD is designed by experimental simulation method. The results provide reference for the structural design of the coil system for azimuthal propagation resistivity LWD.
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Zhou Na, Lu Changhua, Xu Tingjia, Jiang Weiwei, Du Yun
2017,31(1):139-143, DOI: 10.13382/j.jemi.2017.01.020
Abstract:
In order to improve the multi target tracking robustness and enhance the difference between the targets, this paper uses an energy minimization method for multi target tracking. Different to the existing algorithm, the algorithm focuses on the representation of the complex problem in multi target tracking as energy function model, which includes a better target segmentation strategy (similarity model). By assigns every possible solutions a cost (the “energy”), the algorithm transforms the multiple target tracking problem into an energy minimization problem. In the energy minimization optimization method, the algorithm uses the conjugate gradient algorithm and a series of jump moves to find the minimum energy value. The experimental results of open data demonstrate the effectiveness. And the quantitative analysis results show that this algorithm can improve the difference between targets or between target and background so as to obtain better robust performance compared with other algorithms.
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Chen Zhenhai, Yu Zongguang, Wei Jinghe, Su Xiaobo, Wan Shuqin
2017,31(1):132-138, DOI: 10.13382/j.jemi.2017.01.019
Abstract:
A low power, small die size 14 bit 125 MSPS pipelined ADC is presented. Switched capacitor pipelined ADC architecture is chosen for the 14 bit ADC. In order to achieve low power and compact die size, the sample and hold amplifier is removed, the 4.5 bit sub stage circuit is used in the first pipelined stage. The capacitor down scaling technique is introduced, and the current mode serial transmitter is used. A modified miller compensation technique is used in the operation amplifiers in the pipelined sub stage circuits, which offers a large bandwidth without additional current consumption. A 1.75 Gbps transmitter is introduced to drive the digital output code, which only needs 2 output pins. The ADC is fabricated in 0.18 μm 1.8 V 1P5M CMOS technology. The test results show that the 14 bit 125 MSPS ADC achieves the SNR of 72.5 dBFS and SFDR of 83.1 dB, with 10.1 MHz input at full sampling speed, while consumes the power consumption of 241 mW and occupies an area of 1.3 mm×4 mm.
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Xia Fei, Luo Zhijiang, Zhang Hao, Peng Daogang, Zhang Qian, Tang Yiwen
2017,31(1):118-124, DOI: 10.13382/j.jemi.2017.01.017
Abstract:
Aiming at the shortcoming of the low accuracy of transformer fault diagnosis, the PSO SOM LVQ（particle swarm optimization,self organizing maps,learning vector quantization） mixed neural network algorithm is presented in this paper. Firstly, the weight of SOM neural network is optimized by the method of PSO algorithm to obtain the more effective topology. Based on that, LVQ neural network is combined to cover the shortage of unsupervised learning SOM neural network. The mixed neural network algorithm combined with PSO, SOM and LVQ can improve the accuracy and reduce the error of transformer fault diagnosis. Through simulation, the three algorithms of SOM, PSO SOM and PSO SOM LVQ are compared. The comparison result show that the PSO SOM LVQ mixed neural network algorithm has the highest accuracy, and the fault diagnosis accuracy rate is 100%. Thus it can be seen, the PSO SOM LVQ mixed neural network algorithm can enhance the performance of transformer fault diagnosis effectively.
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Cao Shasha, Wu Yongzhong, Cheng Wenjuan
2017,31(1):125-131, DOI: 10.13382/j.jemi.2017.01.018
Abstract:
Musical simulation based on spectrum model is the use of acoustic theory that can achieve musical instrument’s sounds by sum of products of a series of basic functions and time varying amplitude. A new digital piano sound simulation technique is proposed by analyzing piano string vibration and damping characteristics and investigating the resonance effect of resonance box. The simulation model consists of two parts: the excitation system and the resonance system. Based on the vibration equation of the strings, the envelope modification of time domain is carried out to simulate the natural attenuation of the strings, which can make music harmonious between the notes. Then, the filter group is modeled by spectrum envelope in frequency domain to achieve the simulation of resonance system. This new method can more effectively carving voice, has better performance timbre at the same time, therefore, it makes the sound more harmonious.
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Identification method of Dongba pictograph based on topological characteristic and projection method
Xu Xiaoli, Jiang Zhanglei, Wu Guoxin, Wang Hongjun, Wang Ning
2017,31(1):150-154, DOI: 10.13382/j.jemi.2017.01.022
Abstract:
Dongba pictograph has been known as "the only living pictograph in the world".In the aspects of image recognition, content interpretation,the current English and Chinese character recognition system often can not be applied to Dongba pictograph.Concerning the difficulties in the identification of Dongba pictograph, a new character recognition is proposed. Topological features processing and projection methodcompose thefeature extraction method,then, the character recognition method based on template matching is adopted.It is showed that the feature extraction method based on the intrinsic characteristic of the pictograph,and the Dongba character recognition method based on template matching,has high accuracy through the experiment.
