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Sun Fei, Cao Yuhe, Cui Te, Ren Chao
2024,38(4):1-8,
Abstract:
The underwater swimming manipulator (USM) is a new type of underwater robot composed of an underwater snake robot and several thrusters. The USM system has the characteristics of high nonlinear and uncertainty, and its dynamic model is difficult to establish accurately. Therefore, it is challenging to achieve high precision stabilization control of USM. To solve this problem, this paper designs a dynamic control framework based on feedback linearization and adaptive radial basis function neural network (RBFNN) for USM stabilization control. Firstly, the structure of the USM platform is introduced, the dynamic model of the USM is established based on the Lagrange equation, and the model of the vector thrust system is derived. Then, a dynamic controller based on feedback linearization and RBFNN is designed, and the weight of RBFNN is updated adaptively by backstepping method. Among them, the weight adaptive updating RBFNN is used to estimate the unmodeled part of the system, parameter errors and external disturbances, so as to compensate the dynamics controller. In addition, in order to convert the generalized forces and torques provided by the dynamic controller into the control inputs of each actuator, a thrust distribution strategy is given. Finally, lake experiments are carried out to stabilize the I-shape and C-shape of USM respectively. Compared with traditional methods, the steady-state errors of the proposed control scheme under both configurations are less than 0.08 m and 10°, which verifies the effectiveness of the proposed 6-DOF USM stabilization controller.
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Jiang Yinyu, Ding Yong, Zuo Feng, Lu Wenke
2024,38(4):9-17,
Abstract:
Aiming at the problem of temperature drift of Hall effect force sensor, a new temperature compensation model of chaotic adaptive whale optimized BP neural network (CIWOA-BP) was proposed. This model uses Cubic mapping as the initial whale population generation method to improve the quality and distribution uniformity of the population. The adaptive weight was introduced to adjust the shrinking and bounding mechanism of the whale to improve the global search ability and convergence of the algorithm. The CIWOA algorithm is used to optimize the initial weights and thresholds of the back propagation (BP) neural network, so that the model has better measurement accuracy and stability. Research results indicate that after temperature compensation, the temperature coefficient of sensitivity for the Hall effect force sensor decreases from 5.08×10-3/℃ to 9.8×10-5/℃, reducing by two order of magnitude. The temperature-induced relative error decreases from 19.82% before compensation to 0.38%, which is reduced by over 52 times, effectively mitigating the influence of temperature on measurement results.
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Bao Changhao, Gao Xinjian, Wang Wenli, Wang Xin, Gao Jun
2024,38(4):18-26,
Abstract:
Atmospheric polarization mode are stable natural attributes with widespread applications in navigation, detection, and other fields. However, due to the influence of natural environments and surrounding structures, the polarization information obtained at the same time is often local and discontinuous, impacting its practical use. Existing methods mainly focus on repairing large-scale images of atmospheric polarization mode, resulting in limited accuracy in restoring high-frequency signals and causing edge blurring. To address this issue, this paper proposes a method of soft segmentation and synthesis for polarization information, which avoids the loss of high-frequency signals by redundantly segmenting and synthesizing the polarization information, thereby mining the high-frequency signal features in each local region. Additionally, based on the spatiotemporal continuity of atmospheric polarization mode, reasonable inference is made to ensure consistency between the reconstructed information and the real information, thereby generating complete and continuous atmospheric polarization information. Experimental results demonstrate that this method effectively reconstructs missing polarization information in atmospheric polarization mode. In practical reconstruction experiments where cloud interference exceeds 40%, the proposed method shows a 26% improvement in SSIM and a 12% improvement in PSNR compared to other methods.
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Liu Qingtao, Wang Zijun, Zhang Yulong, Zhang Yichao, Zhao Bin, Yin Enhuai, Lyu Jingxiang
2024,38(4):27-36,
Abstract:
Laser measurement enables efficient non-contact real-time measurement and finds extensive application in the field of 3D printing. However, laser measurement is susceptible to interference from various factors such as measurement condition and the external environment, which are complex and difficult to quantify and analyze. Therefore, based on the principle of direct laser triangulation and an analysis of the factors affecting measurement accuracy, this paper proposes a 3D printing in-machine measurement error correction method integrating self-attention and residual neural network (SRNN). Firstly, the factors that affect measurement accuracy are used as input variables to collect laser measurement values and obtain a sample dataset. Then, residual network is employed to extract deep-level features from the sample data, and a self-attention mechanism is introduced to establish connections between influencing factors, resulting in weighted extracted features. Subsequently, the weighted features are learned through a fully connected network to obtain the predicted values of measurement errors. Based on this predicted value, the measurement errors are corrected. A laser in-machine measurement system is built, and experimental verification is conducted using three types of color cards (red, green, and purple) made of the same material. The results show that, compared to convolutional neural network (CNN) and self-attention neural network (SelfNN), the method proposed in this paper achieves the smallest mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), exhibits the best stability, and yields correction results that are closest to the ground truth. After the laser measurement result is calibrated, the error is reduced from the original ±28 μm to below ±9 μm, significantly enhancing the accuracy and stability of 3D printing laser in-machine measurement.
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Zhang Lijun, Cao Jiangtao, Ji Xiaofei, Wang Tianhao
2024,38(4):37-45,
Abstract:
At present, most of the research on students’ classroom learning status focus on single-person online monitoring, and the monitoring of offline classroom with multiple students and complex environment is still in the exploratory stage. A monitoring system for students’ classroom learning status was designed for offline education to monitor students’ classroom attendance and fatigue state of students’ faces in real time. First, DSFD face detection algorithm combined with ResNet deep residual network was used to recognize students’ faces and record students’ attendance. Then, ERT regression tree set algorithm combined with head pose estimation was used to detect the fatigue behavior of yawning and drowsiness. Then, the improved YOLOv5 object detection algorithm added CBAM module was used to detect students’ closed eyes behavior. Finally, a complete set of integrated attendance, fatigue detection of student classroom learning state monitoring system is formed. In the actual classroom test environment, the system can accurately calculate the students’ attendance, and can real-time monitor the fatigue state of yawning, lower head and closed eyes on the face of the students. The detection accuracy rate is more than 90%, and the detection speed is about 14.1 fps, which proves that the system has important use value.
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Wang Lucai, Chen Chunjiang, Zou Yiwen, Xie Ting
2024,38(4):46-54,
Abstract:
Microalgae microscopic image target detection technology is one of the important research directions in fields such as biology and environmental monitoring. The dataset of microalgae images captured by electron microscope exhibits a long-tail data issue. Traditional methods for microalgae detection are notoriously labor-intensive, time-consuming, and heavily influenced by operator expertise. In this context, combining methods to address the long-tail distribution, this paper proposes a target detection algorithm called DDM-YOLO, which combines delayed resampling and knowledge distillation. The approach involves data augmentation for microalgae microscopic images and utilizes delayed resampling for long-tail data. In the second stage, reverse resampling is applied to focus on the challenging minority class samples, thereby enhancing the performance of target detection. Additionally, a lightweight target detection network architecture is designed, and knowledge distillation is employed to reduce model complexity and computational requirements. Experimental outcomes reveal that the DDM-YOLO algorithm achieves an mAP@0.5/% of 77.1%, surpassing the YOLOv5s algorithm by a notable 6.1%. The model parameter size is 3.88 megabytes, a significant 45.4% decrease. This proposed method significantly enhances performance on microalgae microscopic image data and efficiently performs target detection under resource-constrained conditions, substantially reducing the workload of detection personnel.
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Fan Xiaoyu, Liu Tao, Wang Zhenya, Tao Jia, Zhu Zhenjun
2024,38(4):55-65,
Abstract:
Deep learning has made significant progress in fault diagnosis, but it is mostly an end-to-end intelligent diagnosis with limited application in signal denoising. This article proposes a denoising method based on fully convolutional network (FCN). Firstly, the overall model adopts the encoder decoder architecture, where the encoder part consists of three convolutional layers and the decoder part consists of four deconvolution layers. Secondly, residual connections were introduced to constrain the learning objectives of the model, allowing the model to focus more on noise information during propagation. And in order to enhance the feature extraction ability of the model, non-local blocks (NLB) are introduced in the encoder and decoder. Then, through simulation signal comparison experiments, select the hyperparameters of the network and compare them with current mainstream noise reduction methods to preliminarily verify the noise reduction effect of the model. Finally, the denoising effect of the proposed method was compared and verified through practical cases. The results showed that the method proposed in this paper achieved good application effects in both intuitive observation and denoising performance indicators, and can effectively improve the accuracy of fault diagnosis.
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Sun Wei, Zeng Haoting, Zhang Xiaorui, Wang Yu, Ye Jianfeng
2024,38(4):66-75,
Abstract:
In the field of unmanned vehicles, point cloud strength and ground constraints play a very important role in mapping and positioning under large-scale environment. However, existing laser SLAM algorithms only consider geometric features when constructing maps, and neglect point cloud intensity information and ground constraints, resulting in blurry mapping details and drifting in the Z-axis direction, thereby reducing the accuracy of SLAM systems. To this end, this paper proposes a laser SLAM optimization algorithm based on point cloud intensity and ground constraints. Based on the ground measurement model, it is proposed to construct local conditional ground constraints, which not only improves the accuracy of ground point extraction but also reduces the drifting in the Z-axis direction; introducing point cloud intensity information to improve the reliability of non-ground point clustering, further improving mapping accuracy and positioning stability. A feature extraction method based on local smoothness is proposed, in which by introducing intensity factors to rank intensity features, features with consistent intensity information are selected preferentially, enhancing the robustness of feature extraction. The pose is optimized and estimated by constructing strength residuals based on a spherical strength map, together with geometric residuals, effectively solving the problem of blurring in map details in odometry. The matching distance and intensity difference based on feature projection are used to remove interference from dynamic point clouds, further improving the robustness of SLAM systems. Experiments on the public dataset KITTI and real scenarios have shown that the proposed algorithm has higher mapping and positioning accuracies by introducing ground constraints and point cloud strength information. Compared to the LVI-SAM algorithm that outperforms traditional LIO-SAM algorithm, the proposed algorithm in this paper is improved by 54.5% in accuracy, providing a reliable solution for SLAM tasks of unmanned vehicles in large-scale environment.
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2024,38(4):76-84,
Abstract:
A distributed adaptive iterative learning control strategy is proposed for the formation problem of nonlinear multi-agent systems with unknown time-varying parameters. Firstly, the uncertain parameters of the system are expanded through Fourier series, and a convergent series sequence is employed to handle the truncation error resulting from the Fourier series expansion. Combined with the formation error during the operation of the multi-agent system, the adaptive iterative learning control law and parameter update law are derived. Secondly, for scenarios where the dynamics of the leader are unknown to most agents, a new auxiliary control is designed to compensate for the unknown dynamics and avoid unknown bounded interference. Then, based on the Lyapunov energy function, it is proved that the formation error of the multi-agent system tends to be zero within a limited time as the number of iterations increases under the action of the designed control law. Finally, this control strategy is applied to multi-UAV formation systems, and its effectiveness is validated through the construction of a semi-physical experimental platform. Experimental results demonstrate that this control method can ensure rapid formation of the required formation by multiple agents, and each agent can accurately track the desired trajectory within a limited time. The proposed method fully considers the parameter uncertainty and anti-interference ability of multi-agent systems, providing an effective approach for the precise control of complex multi-agent systems in practical applications.
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Zhang Yi, Sun Jinlin, Ding Shihong, Chang Yafei, Xing Gaoyong
2024,38(4):85-93,
Abstract:
To address the issue of output voltage disturbance in Buck converters under complex environments with load fluctuations, a composite adaptive prescribed performance control scheme is proposed to enhance control effectiveness. Initially, an adaptive law is utilized to predict and estimate the nonlinear function containing the load term within the model. Concurrently, a parallel estimation model is constructed during the adaptive law update process to acquire prediction errors, which are then integrated with tracking errors to design an adaptive parameter update law. Subsequently, a generalized proportional-integral observer is employed to estimate the remaining uncertainties and external disturbances, which are compensated for within the control law. Finally, combining command-filtering backstepping control and specified-time prescribed performance control techniques, a composite adaptive prescribed performance control scheme for Buck converters is proposed. The presented scheme ensures high-precision prediction of load fluctuations, preventing output voltage from exceeding the prescribed function range during sudden events, and also demonstrates the signal convergence within the closed-loop control system. Experimental results indicate that the composite adaptive prescribed performance control, when faced with a sudden reduction in load, limits the system’s maximum voltage deviation to 0.376 V, a 78.7% decrease compared to the traditional adaptive backstepping control’s 1.773 V, thereby validating the effectiveness and superiority of the proposed scheme.
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Zhou Weiwei, Gao Yin, Wu Yifang, Li Jun
2024,38(4):94-107,
Abstract:
An edge-preserving smoothing algorithm based on local Gaussian mean-difference variation is proposed to address the issue of detail not being preserved during the process of image smoothing. Firstly, a local Gaussian mean-difference variational operator is established by statistical analysis. To differentiate between structure and texture, the operator is employed to quantify the difference between the local gradient and the gradient after Gaussian filtering. Secondly, a local Gaussian mean-difference variational smoothing model is developed, and a sparse solution is used to produce the initial smooth image. Finally, an isolated noise removal model is suggested to address the issue of texture residue in images with complex texture. The model adjusts pixel values using an adaptive window and eliminates texture residue from the initial smooth image without changing the structure. It has been demonstrated through subjective and objective experiments that this algorithm produces smoothing results of superior quality than traditional algorithms. Evaluation indicators improved by 0.7% overall. Extended experiments verify the algorithm's applicability and efficiency enhancement potential across various visual tasks, including compression artifact removal, HDR tone mapping, image dehazing, and accelerated Laplacian pyramid.
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Zhao Baiting, Zhang Chen, Jia Xiaofen
2024,38(4):108-116,
Abstract:
Aiming at the current problems of low efficiency and poor detection accuracy of steel surface defects, a model, named ECC-YOLO, is proposed for steel surface defects detection based on YOLOv7. Firstly, in order to improve the capability of feature map information characterization of the backbone network, a feature enhancement module ConvNeXt is introduced, which enhances the feature extraction capability of the model for fine cracks by fusing the depth separable convolution and the large kernel convolution, secondly, a C2fFB module is designed, which enhances the capability of extracting the feature information of the target and at the same time, reduces the computational volume and parameter complexity of the model significantly. Finally, the MPCE module is designed with the help of the ECA attention mechanism to weaken the interference of the complex background information on the steel surface defect detection and improve the detection efficiency. Finally, extensive experimental results show that the mAP of the model of ECC-YOLO reaches 77.2% on the NEU-DET dataset, and compared with YOLOv7, the detection accuracy of ECC-YOLO is improved by 10.1%, and the number of model parameters is reduced by 9.3%, which gives the model a better comprehensive performance in steel surface defect detection.
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Liu Yanli, Wang Hao, Li Jiayuan, Zhang Fan
2024,38(4):117-127,
Abstract:
The series arc fault is mainly caused by poor contact of the electrical contact points in the circuit, which is one of the main causes of electric vehicle fires, directly threatening the life safety of the occupants. In order to study it, an experimental platform for DC series arc fault of electric vehicles was established. The voltage signals of the power supply terminal were obtained under various working conditions, and the impact of arc faults on the power supply terminal voltage was analyzed. When constructing the detection model, the paper used a convolutional neural network, introduced a lightweight convolution operation, and considered its limitations in practical applications. Combining conventional convolution and lightweight convolution operations, a preliminary model for arc fault detection was constructed. Then, with the scale and accuracy of the network as the evaluation index, the genetic algorithm with elite preservation strategy was used to search for the external structure and internal parameters of the model. Finally, the model AFDNet suitable for arc fault detection (AFD) of electric vehicles was established. The detection accuracy of the model is 93.73%, and the running time on the embedded device Jetson Nano（JN） is 10.82 ms. After establishing the model, the paper compared the search results of the algorithm with other network structures in relation to network size, accuracy, and real-time performance, verified the validity of the results obtained by the search algorithm. By comparing AFDNet with other detection methods, it was proven that the performance of the electric vehicle arc fault detection model was superior.
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2024,38(4):128-139,
Abstract:
Aiming at the problems for which the first predicting time (FPT) of bearing remaining useful life (RUL) is based on subjective selection and maintenance risks caused by predictive lag. A stochastic configuration networks (SCNs)-based bearing residual life prediction method is proposed. Firstly, the complementary ensemble empirical mode decomposition (CEEMD) is used to decompose the original bearing horizontal vibration signal, then extract its time-domain and frequency-domain signals to construct fusion features. Secondly, the health state is divided by wavelet clustering to find the appropriate FPT, and the health data set is constructed by combining the characteristics of the energy response bearing degradation. The prediction is made by SCNs network offline modeling, and the prediction results are corrected according to the slope of the fitted curve and the RMSE index. Through experimental analysis, the comprehensive score of the proposed method is as high as 0.83, and the mean absolute deviation (MAD) and standard deviation (SD) of the error percentage are 5.26 and 3.38. Compared with other prediction methods, the proposed method has higher prediction accuracy.
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Chen Guangqiu, Yin Wenqing, Wen Qizhang, Zhang Chenjie, Duan Jin
2024,38(4):140-150,
Abstract:
Aiming at the problem that a single intensity image lacks polarization information and cannot provide sufficient scene information under bad weather conditions, this paper proposes a dual-attention mechanism to generate an adversarial network for fusion of intensity and polarization images. The algorithmic network consists of a generator containing an encoder, a fusion module and a decoder and a discriminator. First, the source image is fed into the encoder of the generator, after a convolutional layer and dense block for feature extraction, then feature fusion is performed in the texture enhancement fusion module containing the attention mechanism and finally the fused image is obtained by the decoder. The discriminator is mainly composed of two convolutional modules and two attention modules, and the generator network parameters are iteratively optimised by constant gaming during the network training process, so that the generator outputs a high-quality fused image that retains the sparse features of the polarimetric image without losing the intensity image information. Experiments show that the fused images obtained by this method are subjectively richer in texture information and more in line with the visual perception of the human eye, and that the SD is improved by about 18.5% and the VIF by about 22.4% in the objective evaluation index.
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2024,38(4):151-160,
Abstract:
The dropper clamp bolt is an important component of railway power supply line, which can affect the flow quality of electric locomotive. Therefore, this paper improves the SSD algorithm: Firstly, a lightweight neural network MobileNetV3 is introduced for front-end feature extraction to reduce the model complexity and improve the detection speed; secondly, CA attention mechanism to replace the SE module of the linear bottleneck layer with inverted residuals structure, aggregate the position information in the two directions of space, and the adjusted feature layer can capture the global remote feature information. Finally, the feature fusion module for reconstructing the feature layer is designed to adjust the small target detection layer to improve the detection effect of small targets. This paper also expands the training sample with CycleGAN to solve the problem of insufficient data set. The experimental results show that the model complexity of the improved algorithm decreased, and mAP @ 0.5 and FPS reached 95.5% and 81 fps, respectively. This study helps the transformation of catenary detection instruments to small mobile embedded devices.
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Xu Heng, Liu Hu, Shao Hui, Sun Long, Hu Yuxia, Meng Fanyu
2024,38(4):161-175,
Abstract:
To mitigate the influence of non line of sight (NLoS) errors in ultra-wideband (UWB) ranging, this study presents a method that utilizes a genetic algorithm backpropagation neural network (GA-BP) for error identification and optimization. This method effectively detects and rectifies ranging errors and system deviations occurring in the NLoS propagation link, and subsequently improves the ranging outcomes through the application of Kalman filtering (KF). On this basis, this paper proposes a weighted concentric circle clustering localization (WCCGT) method to address the problem of no intersection or multiple intersection points in multilateral positioning caused by ranging errors. The method solves the problem of no intersection points through weighted concentric circle generation (WCCG). Then, it uses the mean shift clustering localization method to achieve a localization solution and improve localization accuracy. The experimental results show that the improved ranging optimization method effectively reduces the ranging error in the NLoS propagation link, and the ranging accuracy based on UWB is improved by more than 60%. Analyze through static positioning experiments and dynamic experiments, the positioning results of the WCCGT method were compared with the least squares (LS) method. The proposed method can achieve a positioning accuracy of 10.78 cm in NLoS environments, and the positioning performance has been improved by 17.32%.
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Yang Jiapei, Wang Yu, Peng Guangjian, Bai Qing, Liu Xin, Jin Baoquan
2024,38(4):176-186,
Abstract:
In order to meet the demand of voiceprint recognition in flammable and explosive environment. A linear Sagnac interference optical fiber acoustic sensor system has been designed. Speech data was denoised using the Wiener filtering algorithm, and pitch features were extracted through three-level clipping. Speaker samples were screened using dynamic time warping, and Mel-frequency cepstral coefficients were extracted as features. Voiceprint recognition experiments were conducted utilizing the Gaussian mixture model-expectation maximization algorithm, concurrently investigating the frequency response characteristics of the optical fiber acoustic sensor system and their relationship with voiceprint features. The influence of the amplitude of acquired speech on voiceprint recognition outcomes was studied. Experimental results demonstrate that the system can realize the sound signal perception in the frequency range of 300~3 500 Hz. When the sound amplitude decreases from 0.9 to 0.15 V, the difference between the maximum and second-largest log-likelihood values drops from 35.5 to 10.9, the recognition result changed from success to failure. Repetition experiments show that, at a distance of 2 meters from the sound source along a 10-kilometer sensing fiber, the system accurately recognizes 400 speech segments of 3 to 5 seconds duration, unrelated to any specific text, achieving an overall identification accuracy rate of 94.75%. This system holds promise as a solution for voiceprint recognition in applications such as equipment fault diagnosis and emergency response within flammable and explosive environments.
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Ji Xiaofei, Zhao Shuai, Song Jinghao, Cui Tong
2024,38(4):187-194,
Abstract:
Person re-identification is highly used in the areas of traffic management, searching for lost people, etc. It is hard for existing algorithms to deal with the problem of human pose change, occlusion and feature misalignment, and a pose-guided and feature-fused pedestrian re-recognition algorithm is proposed. The proposed algorithm includes three branches, including global branch, global branch based on pose estimation guidance, and local alignment branch. The global branch extracts the global features of pedestrians and captures the coarse-grained information of pedestrians. The global branch based on posture estimation guidance uses the posture estimation network guidance model to focus on the global visible area of pedestrians and reduce the interference of occlusion to pedestrian recognition. Local alignment branch uitilizes a pose estimation algorithm to establish aligned local features while distinguishing visible local regions to reduce occlusion as well as the influence of postural changes. Through a multi-branch structure, integrated local characteristics with global ones to augment feature diversity is achieved and enhanced model robustness. Finally, network training is conducted using cross-entropy and triplet loss functions. The viability of the proposed algorithm is validated by the test results on Market-1501 and DukeMTMC-ReID datasets, during which the Rank-1 and mAP of the DukeMTMC-ReID dataset reached 91.2% and 81.8%, respectively, which has a better practicality.
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Chen Yuxuan, Xie Dailiang, Cui Lishui, Xu Ya, Huang Zhenwei, Liu Tiejun
2024,38(4):195-201,
Abstract:
To address various challenges associated with conventional laminar flowmeters (LFM), such as inadequate linearity, significant length-diameter, inconvenient processing and use, and susceptibility to fluid-induced effects, a novel annular-gap laminar element structure is proposed, drawing inspiration from the double-cone flowmeter. This innovative design is accompanied by a comprehensive elucidation of its measurement principles and an analysis of the sources contributing to non-linear pressure losses within the flow conduit. Central to this design is the maintenance of coaxial alignment between the outer jacket cylinder and the cone and circular cylindrical, resulting in a flow channel characterized by concentric circular annual. Computational fluid dynamics (CFD) simulations were leveraged to ascertain the optimal cone angle of the conical guiding structure and establish the dimensional parameters of the laminar element. Furthermore, pressure taps were strategically positioned within the fully developed laminar segment of the flow channel, thereby theoretically mitigating localized losses at the inlet and outlet typical of conventional capillary-type LFM, as well as kinetic energy dissipation within the laminar development region. Experimental validation involved the fabrication of three distinct test specimens with varying gap dimensions, followed by rigorous testing. Results revealed that for flow rates below 53 mL/min, the measurement error of the laminar element remained within an acceptable margin of 3%. Likewise, within the flow rate range at (130~6 189) mL/min, the measurement error was constrained within the range of ±2%. Notably, a robust linear relationship between pressure drop and flow rate was observed, affirming the efficacy of the proposed design in circumventing the non-linear influences inherent in traditional LFM. The elucidation asserts the structural efficacy of annular gap laminar flow elements in effectively mitigating the nonlinear influences characteristic of traditional LFM. Simultaneously, it highlights the adaptability of the measured flow range, which can vary with alterations in gap size.
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Xia Yankun, Kou Jianqiang, Li Xinyang
2024,38(4):202-216,
Abstract:
Aiming at the problem that vibration signal of inter-turn short circuit fault in permanent magnet synchronous motor (PMSM) is easily affected by noise and it is difficult to accurately extract the fault feature of it, an improved whale optimization algorithm (IWOA) optimized variational mode decomposition (VMD) denoising method is proposed and applied to vibration signal of inter-turn short circuit fault in PMSM. Firstly, the nonlinear convergence factor, adaptive weight and the Cauchy operator are introduced into the traditional whale optimization algorithm, and the IWOA algorithm is used to optimize the VMD parameters to achieve adaptive signal decomposition. Secondly, according to the principle of selecting the optimal intrinsic mode function based on multi-scale permutation entropy and variance contribution rate, the signal components are divided into the noise-dominated components and effective signal components. The noise-dominated components are denoised by the non-local mean filtering (NLM). Finally, the denoised and effective signal components are reconstructed as denoised signal. A motor short circuit fault model is established using ANSYS finite element software, and a short circuit fault experimental platform is built. Using this method to denoise the simulated and measured signals, it is further compared with many denoising methods such as wavelet threshold denoising method. The signal to noise ratio of the simulated signal is improved from 8 dB to 20.273 8 dB, and the signal to noise ratio of the measured signal is improved by 77.01% compared with wavelet threshold denoising method, which proved the effectiveness and practicality of the proposed method.
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Chen Xinyu, Huang Zhenwei, Mei Jie, Wang Junxian, 邹剑秋, Xie Dailiang
2024,38(4):217-224,
Abstract:
At present, China has not yet publicized the national calibration specification of respiratory tester, which makes it difficult to carry out the metrological traceability of various parameters of respiratory tester. Some self-compiled calibration methods for gas flow calibration have the problem that the flow calibration point is affected by the size of the plunger, which in turn limits the flow calibration range. To solve this problem, this paper proposes a calibration method based on the standard meter method and plunger method for static flow and tidal volume, respectively, and integrates two sets of pipelines and designs a calibration system. The basic principle of flow measurement of the ventilator detector is introduced, the calibration method is proposed based on this principle and the device is actually built and tested, and the test results show that the static flow measurement range of this system is 5~200 SLPM, with the extended uncertainty Ur(Qv)=0.602% (k=2); the tidal volume measurement range is 0~2 000 mL, with the extended uncertainty Ur(V)=0.174% (k=2), and the relative error meets the technical specifications, and this system can realize the calibration of respiratory tester calibration of flow parameters. The design of this calibration system lays the foundation for the research and development of the calibration device of respiratory tester and provides certain reference significance for the establishment of the calibration specification of respiratory tester.
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Fang Zhihong, Wang Libo, Zhu Yu, Zhang Yin, Wang Fangfang, Sun Haixuan, Xu Huafeng, Guo Pan
2024,38(4):225-233,
Abstract:
The heat exchange tubes of the steam generator, as a key component of the pressure boundary in the primary circuit of the high temperature gas-cooled reactor nuclear power plant, plays an important role in heat exchange and radiation barrier, and its structural integrity seriously affects the safe operation of nuclear power. In response to the in-service detection difficulties of this type of special structure heat exchange tubes, a dedicated electromagnetic ultrasonic guided wave automatic detection system has been designed, a magnetic field enhanced electromagnetic ultrasonic guided wave probe with built in single point detection has been developed, a five axis linkage multi degree of freedom automatic transport device with modular components has been developed, a dynamic positioning method for tube holes based on machine vision has been proposed, a full-scale simulation test platform for steam generator was established, and positioning accuracy test and defects detection test were carried out. The experimental results indicate that the designed automated detection system can achieve high-precision positioning and automatic walking of target tube holes at any position, and can identify the notch defects at the weld of dissimilar steel and about 60 m away from the detection end on the simulator. The effective detection range covers the entire length of the heat exchange tubes, which is expected to provide technical support for the quality and health evaluation of the special structure heat exchange tubes of the steam generator in high temperature gas-cooled reactor nuclear power plants.
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Yang Yuqiang, Zhang Yuying, Gao Jiale, Bian Yuan, Mu Xiaoguang, Wang Ji, Yang Wenhu, Wang Wenhua
2024,38(4):234-240,
Abstract:
In this paper, a novel all-fiber temperature sensor based on Fabry-Perot interferometer (FPI) and Michelson interferometer (MI) is proposed and fabricated. The sensor is formed by sequentially fusion-splicing a single-mode fiber, a segment of suspended-core fiber (SCF), and another segment of single-mode fiber, with each connection having an offset. The reflecting surfaces formed by the fusion and cutting constitute the FPI and MI, and the optical path of MI is about 2 times (slightly more than 2 times) that of FPI, so the two interferometers produce the first-order harmonic vernier effect. The experimental results show that the double envelopes with obvious first-order harmonic Vernier effect appears in the interference spectrum of the sensor, and with the increase of temperature, the double envelopes of the interference spectrum gradually red-shifts, and the red-shift amount is much greater than that of a single MI. In the temperature range of 20 ℃~120 ℃, the temperature sensitivity of the sensor is 208 pm/℃, about 20.8 times that of a single MI, and about 245 times that of a single FPI. The temperature rising and falling experiments and multiple measurement experiments at the same temperature show that the sensor has good repeatability and stability. The sensor realizes the parallel connection of FPI and MI within the same optical fiber, with a total length of the sensor head being only about 584.4 μm. Additionally, it features an all-fiber tip structure, making it particularly suitable for high-temperature detection in small space environments.
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Zhou Yong, Liu Hongbin, Hou Yadong
2024,38(4):241-247,
Abstract:
The multi-scale feature pyramid can alleviate the problems of semantic segmentation in complex traffic scenes, such as missing segmentation, wrong segmentation and unclear boundary segmentation. However, the existing multi-scale feature pyramid has to downsample the feature maps and sacrifice the spatial detail information for rich semantic information, leading to the limited accuracy of the final segmentation result. Aiming at this problem, a feature enhancement module is proposed to further reinforce similar features based on cosine similarity between different vectors before downsampling, alleviating the negative influence of downsampling. In addition, combined with the principle of dilated convolution and strip convolution, the large convolution kernel is modified to build a new multi-scale feature pyramid module for semantic information with different scales and larger receptive fields. The proposed segmentation method is real-time and efficient, and can meet the requirements of automatic driving. Experiments on the VOC2012 dataset show that the mIoU of the proposed method reaches 74.36%, and the FPS reaches 43, which is superior than the current prevailing semantic segmentation methods.
Volume 38,2024 Issue 4
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Multi-modal Information Fusion with sequential for Fault Diagnosis of Power Transformer
Abstract:
Aiming at the problem of variability in multimodal data and missing samples, we propose a multi-modal information fusion method (MIF) based on vibration and infrared image data for effective and speediness evaluation of power transformer fault status. First, the bidirectional gated recurrent unit extracts the feature from the text data of the vibration, frequency image of the vibration, and infrared image of the power transformer, and obtains the feature vector of difference modal. And then, the cross-attention mechanism builds the relationship between the difference modal for obtaining the fusion feature. Finally, the convolution layer and full connected layer output the fault status of the power transformer. The experiment data come from the 10kV power transformer, which contains the vibration signal and infrared images. The experiment result shows that the MIF obtains more reliable diagnostic results, and provides a method basis for fault detection based on the multi-modal data of the power transformer.
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Retinal Vascular Segmentation Algorithm Based on Full Scale Dense Convolutional U-shaped Networks
Abstract:
A dense cascade convolution and self attention feature aggregation network was constructed for the segmentation of retinal vascular images, addressing the difficulties in segmenting small blood vessels and the occurrence of fractures during the segmentation process. The network utilizes multi-scale dense convolution combined with self attention mechanism; To better extract complex feature information of retinal small blood vessels, a dense aggregation module is constructed as the backbone network of the U-shaped network; Embedding self attention patches and multi-scale aggregation modules at the bottom layer of the network to enhance receptive fields and obtain high-dimensional semantic feature information; The feature aggregation module is used in the skip connection part of the model to improve the segmentation accuracy of the model. The experimental results show that on the DRIVE public dataset, the F1 score of the network reaches 83.19%, the accuracy ACC score reaches 97.11%, and the AUC value reaches 98.94%; On the CHASE-DB1 and STARE datasets, compared with Unet, DUNet, SA Unet, and FR Unet networks, the AUC index of this network has achieved the best results so far. Using this network for retinal vessel segmentation, the accuracy and robustness of segmentation have been improved to varying degrees, achieving excellent results in small vessel segmentation and its generalization ability.
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Fault Diagnosis Based on Contrastive Learning under Time-Varying Small Sample Conditions
Abstract:
In the context of time-varying operating conditions, fault diagnosis often exhibits high dynamism, while the limited model learning under small samples makes the issue more challenging. For the above situation, a fault diagnosis method based on contrastive deep convolutional networks is proposed. Firstly, considering the characteristic of small data samples, take advantage of differences in vibration data distribution caused by speed changes, and naturally realize data enhancement without manual operation. Subsequently, in the process of data processing, the vibration data of the same healthy state at different rotational speeds are used as positive samples, while the vibration data from different health states are used as negative samples. The key features are extracted by comparing the similarity between the samples so as to reduce the distance between the positive samples while increasing the distance between the negative samples. Finally, the feature extractor is trained and optimized by comparative training method, where a weighted combination of contrastive loss and cross-entropy loss is used as the composite loss function, enabling the model to effectively perform classification tasks while learning feature representations. The method is applied to two different bearing failure datasets at different time-varying rotational speeds for case studies respectively. The experimental results show that the proposed model not only performs well in the feature extraction and classification tasks, but also realizes high accuracy fault diagnosis under both data scarcity and time-varying speed conditions. It is verified that the proposed model shows high feasibility and effectiveness in dealing with time-varying small-sample data, and outperforms other advanced diagnostic methods.
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Wear Prediction of O-sealing in Active Seals of Actuator Based on Physical Models and Statistical Analysis
Abstract:
In actual service environments, 40% of the total failures that occur in aircraft are caused by sealing leaks, and the quality of their sealing performance directly affects the functionality, performance, and reliability of the product. However, the lack of wear monitoring of sealing rings on aircraft makes it difficult to evaluate the health status of sealing rings. To address this issue, this paper proposes a method for predicting the wear of active O-sealing based on physical model and statistical analysis. Firstly, a mechanism analysis and parameter measurement are conducted on the wear of the sealing ring. Secondly, Abaqus finite element software is used to simulate and study the motion process of the sealing ring, obtaining the contact stress of the sealing ring. Then, statistical analysis is conducted on the cumulative stroke of each servo flight to obtain the probability statistical curve of the stroke. Combined with the Holm Archard wear model, the probability distribution curve of the wear volume is obtained. Finally, based on the relationship between time and travel, a mathematical model between wear volume and time is established. Multiple samples are used for validation, and the results show that the probability of the actual wear volume within the predicted density function"s 3sigma range is 95.83%, proving that the model in this paper has a high probability of predicting the volume wear of the O-sealing for active seals.
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Multichannel weight fusion and wavelet decomposition method for detecting epileptic spines
Abstract:
Automated detection of spikes in EEG is a current research focus and is important for epilepsy diagnosis. There are two main types of existing detection methods: signal analysis and machine learning. The former is sensitive to outliers, and the robustness of the latter algorithms to different data has not been fully validated. In addition, most of the current studies are based on single-channel EEG, which is usually susceptible to artefactual interference. Aiming at the problems of the existing algorithms and combining the characteristics of spikes, this paper proposes a spike detection algorithm based on multi-channel data weight fusion and wavelet decomposition, which adopts feature fusion based on the weights of multi-channel data to achieve data reinforcement of spikes, and finally performs wavelet decomposition of the data and detects the spikes using the mode maxima. Experimentally verified, the algorithm achieves the precise detection of interictal spike wave, and the diagnostic accuracy can reach more than 92.3%, which provides a informative method for the detection of epileptic spike wave.
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Research Progress on Noise Models in Optical Wireless Communication systems
Abstract:
Optical wireless communication, as a high-speed, high bandwidth, and high security communication technology, has received widespread attention. However, noise, as an important limiting factor of system performance, can cause system signal distortion and signal-to-noise ratio to decrease, thereby affecting communication quality. Therefore, in order to reduce the impact of noise on signals and design and adjust communication systems more effectively, it is necessary to understand and simulate the behavior of noise, and noise models are an important tool for studying noise behavior. This article systematically studies the noise and noise models in optical wireless communication systems. Firstly, the article analyzes each type of typical noise introduced in optical wireless communication systems from the perspectives of signal source, channel, and destination. Then, the research progress on corresponding noise and its noise models at home and abroad was summarized, and the mechanism of noise generation was analyzed, And corresponding noise models were provided. In addition, the characteristics and limitations of relevant models were summarized. Finally, key suppression technologies for various types of noise were summarized, and further research directions in this field were discussed, which can provide theoretical support and inspiration for the development of optical wireless communication technology and system design and optimization in this field.
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YOLOV8 algorithm is improved in the defect detection of wind turbine blades applications
Abstract:
As a critical component of wind turbines, defects in wind turbine blades pose a significant threat to their operation. To improve the precision and recall rates of defect detection in wind turbine blades, this paper proposes an Efficient Multi-Scale Convolution module (EMSConv) to replace the convolutional modules in residual blocks for grouped convolution, targeting the YOLOv8n network. Multiple attention mechanisms from Dynamic Head are introduced in the detection head, leveraging the synergy between multiple self-attention mechanisms across feature layers for scale, spatial, and task awareness, thereby enhancing the representational capability of the object detection module. By integrating Inner-IoU, Wise-IoU, and MPDIoU, a novel Inner-Wise-MPDIoU is proposed to replace CIoU, improving the network"s detection accuracy. Tested on a custom dataset of wind turbine blade defects, the experiments show that the proposed YOLOv8-EDI achieves a mAP50 value of 81.0% on this dataset, a 2.3% improvement over the original YOLOv8n; the recall rate reached 76.8%, a 3.7% improvement; and the computational complexity of the network structure was reduced by 5.5%. Compared with the original model, YOLOv8-EDI exhibits superior localization ability and detection accuracy for wind turbine blade defects, meeting the demands of industrial-scale batch detection.
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Signal detection method for magnetic flux leakage small defects based on composite backbone network
Abstract:
Magnetic flux leakage (MFL) internal detection is the core technology of pipeline internal detection, which is crucial to ensuring the safe transportation of pipelines. Due to the long-term underground or deep sea environment of pipelines, there are many small defects on the surface of pipelines. Due to the limited information available on small defects, traditional deep learning defect detection methods have difficulty achieving satisfactory detection results for small defects. A composite backbone network-based signal detection method for small magnetic leakage defects is proposed. First, a data enhancement method called background compression is proposed to compress background signals and thus enhance key features of small defects. Secondly, an adaptive positive and negative sample allocation strategy is designed to address the issue of uneven positive and negative sample allocation for small defects in the region proposal network. Finally, a multi-branch high-resolution feature extraction network for small defects is proposed, which uses a multi-branch composite structure to obtain high-resolution features for feature fusion, thereby improving the network's utilization of small defect texture information. The proposed method is validated using pipeline data from a test site, and the experimental results show that the proposed method is effective, achieving a detection accuracy of 90.3%, with an 8.4% mAP improvement compared to the best results.
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Flowrate measurement model for plug flow based on multi-sensor information fusion
Abstract:
Gas-liquid two-phase flow, pervasive in energy and chemical industries, presents significant measurement challenges due to its inherently complex and dynamic nature. Accurate quantification of flow parameters remains elusive, given the variability in phase distribution and interaction dynamics. To address the need for accurate gas-liquid two-phase flow measurement, a novel sensor design is introduced. Leveraging the acoustic emission sensor's capability to detect flow-induced noise and the pronounced variation in near-infrared absorption across different media, the proposed sensor is specifically tailored for slug flow characterization in two-phase systems. Acoustic emission probes were dually installed in both the venturi pipe and its extension section, complemented by dual near-infrared photodetectors positioned within the venturi pipe. A synchronized acoustic emission and near-infrared acquisition system was developed. Utilizing this setup, 54 datasets of slug flow were meticulously gathered on the high-precision gas-liquid two-phase loop at Hebei University. Through integrated processing, characteristic parameters of the two-phase flow were successfully extracted. Time-domain analysis was employed to extract the standard deviation and skewness from the acoustic emission and near-infrared datasets. In conjunction with parameter fitting techniques, a predictive model for two-phase flow was formulated, followed by comprehensive error analysis. Through verification, the relative deviation of 92.6% of the predicted flow value is within ±20%. The results show that the multi-sensor information fusion scheme based on acoustic emission sensor and near-infrared sensor provides a new way to study the flow characteristics of gas-liquid two-phase flow.
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Review of Research on Theory and Technology of Pulsed Eddy Current Testing for Detection of Defects
Abstract:
Due to the advantages of non-contact, high efficiency, and rich information content of the detection signal, pulsed eddy current technology is widely used in the defect detection of industrial products, especially those with cladding, heterogeneous, and multi-layer conductive structures that are extremely difficult to detect. The design and optimization of pulsed eddy current probe structure is the key to improve the detection sensitivity and accuracy, and a lot of research work has been carried out around the design and optimization of the probe structure; different materials, structures, and defects of different types or shapes need to be characterized by the use of appropriate features, and the selection and analysis of the features is also the key to the research of pulsed eddy current technology; in addition. In addition, since the pulse eddy current detection signal is greatly affected by the lift-off height, the accurate detection of defects with unknown lift-off height faces great challenges, so the suppression of the lift-off effect is also one of the research focuses. In this paper, the research progress of pulsed eddy current detection technology is reviewed from the aspects of probe design and development, selection and analysis of feature quantity, and suppression of lift-off effect, etc. At the same time, in order to better promote the development of pulsed eddy current detection technology, the paper, on the basis of the existing research, makes an outlook on the design of pulsed eddy current probe, feature quantity analysis, and the suppression of lift-off effect.
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Intelligent detection of transmission line construction machinery based on DAMF-NET
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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Research on Distributed Control of Freight Train Based on Cascade Disturbance
Abstract:
In response to the tracking control challenges faced by freight trains under multi-source disturbances, this paper proposes a fixed-time replacement sliding mode control method based on a cascaded disturbance observer. Initially, a multi-mass dynamic model that takes into account the inter-vehicular forces is constructed. To address the scenario where both matched and unmatched disturbances coexist, a cascaded structure disturbance observer is designed to estimate multi-source disturbances concurrently, thereby relaxing the traditional prerequisite of disturbance observers that dictates disturbances must vary slowly. Utilizing the disturbance observation data, the train dynamics model under unmatched disturbances is transformed into a matched mode. Ultimately, a distributed control strategy based on the alternative sliding mode approach is put forward. Simulation results demonstrate that the proposed cascaded disturbance observer can accurately estimate multi-source disturbances within 0.5 seconds. Compared with traditional research on train tracking control, the proposed control strategy manages to swiftly handle a series of destabilizing issues caused by multi-source disturbances. While ensuring the stability of inter-vehicular forces, it achieves robust tracking control of both speed and displacement indices. Relative to conventional control methods, the system convergence time is enhanced by more than 5 seconds, reflecting superior real-time performance and robustness.
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Localization feature extraction method for obscured drogue
Abstract:
In autonomous aerial refueling, the circular structure of the drogue refueling port is often used to assist target positioning. Still, the complex background interference and oiled plug obscuring significantly reduce the accuracy of circular feature extraction. To address the background interference problem, an adaptive mean filter is designed to obtain the center of mass of refueling ports to obtain the accurate set of edge points in a smaller range using the imaging operation. To address the oiled plug obscuring problem, an outlier elimination algorithm based on convex hull detection is proposed to enhance the anti-interference performance of feature extraction. An iterative reweighted least squares based on geometric distance is proposed to optimize elliptic targets. On the simulation platform, the influence of K value on the fitting accuracy and efficiency of the iterative reweighted least squares algorithm is emphatically analyzed. At the same time, the accuracy and anti-occlusion performance of the fitting algorithm are tested. The average error of the algorithm is less than 0.5% when there is no occlusion and less than 2% when the occlusion rate is 50%. Finally, the feature extraction experiment of the actual drogue is carried out. Compared with other classical algorithms, the accuracy is improved by 49.3%, the average extraction error is 0.79%, the average processing time is 13.9ms, and the extraction error is controlled within 2% under the special case that the drogue is obscured. Experimental results show that the positioning feature extraction method of drogue meets the requirements of accuracy, rapidity and robustness of image processing for autonomous aerial refueling, and can improve the success rate of autonomous aerial refueling docking and reduce the probability of accidents.
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Research on sound source localization method using data fusion of multiple microphone arrays
Abstract:
In Time Difference of Arrival (TDOA)-based sound source localization methods, the estimation accuracy of TDOA is constrained and limited by the sampling frequency. A low sampling frequency can lead to significant errors between estimated and true time delays, further resulting in complete deviation between estimated and actual positions of distant sound sources. Additionally, using a single microphone array for sound source localization results in significant errors, failing to meet practical application requirements.To address these issues, an improved second-order cross-correlation algorithm based on cubic spline interpolation for TDOA estimation is proposed. Cubic spline interpolation is employed after the first cross-correlation to increase the sampling frequency, followed by the second cross-correlation to obtain TDOA. Simulation results under conditions of 10-fold interpolation demonstrate that this method reduces the TDOA error from 7.6% to 0.6%.Subsequently, a sound source localization method based on data fusion of multiple microphone arrays is proposed. The localization results of multiple microphone arrays are fused to obtain the final position of the sound source. Taking a quaternion cross array as an example, 10 points in space are selected to compare the far-field localization performance of single, dual, and quaternion microphone arrays. The results indicate that the localization error of a single microphone array can exceed 1 meter when the sound source is distant, while the localization errors of dual and quaternion microphone arrays are generally within 0.3 meters and 0.2 meters, respectively, significantly improving localization accuracy.。
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Fault Detection and Line Selection Method of Series Arc Fault in Frequency Converter Load Circuit
Abstract:
The high temperature of series arc fault is one of the main causes of electrical fire. Aiming at the problem that there is no effective protection method for the series arc fault in the load circuit of industrial frequency converter, a new method of fault detection and line selection for the series arc fault was proposed. First, the series arc fault experiments in different lines were carried out for the load circuit of three-phase frequency converter commonly used in industrial field. Second, the improved variational mode decomposition based on the principle of energy convergence was used to adaptively decompose the A-phase current signal at the front end of the frequency converter into multiple modal components. After multiplying the single modal component by the energy coefficient, the feature enhancement signals of multiple current signals were reconstructed, and the feature matrix was established. Third, the feature matrix was divided into blocks, and the kernel principal component analysis was used to reduce the dimension of each block matrix, and the matrix composed of the reduced dimension signal was reduced twice to construct the fault feature vector. Finally, the support vector machine optimized by the pelican optimization algorithm was used to detect the series arc fault and select the fault line. The results show that the proposed method can realize the fault detection and line selection of the series arc fault in six lines of the whole circuit of the frequency converter only by analyzing the A-phase current signal at the front end of the frequency converter, and the accuracy of fault detection and line selection is more than 98%.
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Design of microstrip antenna and its application in partial discharge detection
Abstract:
Partial discharge of electrical equipment is not only the main factor of insulation deterioration, but also an important parameter to effectively characterize insulation defects. The accurate detection of partial discharge can detect the potential faults that endanger the safety of equipment in time. In order to solve the problems that rigid structure, small size structure and wide bandwidth performance of the existing microstrip antenna sensors are difficult to balance, the UHF detection method based on antenna is studied in this paper, and a new flexible microstrip antenna sensor is developed considering antenna size and bandwidth. Using polyimide as base material, the narrow-band characteristics are improved by using partial flooring technology, and the beveled meandering technology is introduced to expand the working bandwidth while keeping the antenna area unchanged. Aiming at the problem of unstable antenna performance caused by single size parameter adjustment in the process of size optimization, the nonlinear relationship between antenna size parameters and working bandwidth is studied, and the mathematical model between them is established using radial basis function (RBF) neural network. The improved beluga whale optimization (IBWO) algorithm is used to optimize the size of antenna with the maximum working bandwidth as the target. The simulation results show that the size of the new flexible microstrip antenna is reduced by 59.59%. The operating bandwidth is increased from 0.598GHz~0.6GHz to 0.3GHz~3GHz, which fully covers the design requirements of partial discharge detection. By simulating partial discharge test and comparing with Archimedean spiral antenna and three-dimensional spiral antenna, the results show that the new flexible microstrip antenna has more reliable detection performance.
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Comparative analysis of the electromagnetic force characteristics of inter-turn short circuits and low-voltage side outlet short circuits in transformer windings
Abstract:
Power transformers are critical components of the electrical grid, and the insulation status of their windings is directly linked to the operational safety and reliability of power supply. Short circuits at the low-voltage side outlets can easily damage the inter-turn insulation, leading to inter-turn short circuit faults in the windings. To further study the transient processes of inter-turn short circuits and low-voltage side outlet short circuits in transformer windings, this research investigates the weak links in heat generation and mechanical stresses, and elucidates the mechanisms of insulation failure development. An electromagnetic-force coupled model, congruent with the actual structural dimensions of the transformer, was developed. Utilizing finite element simulation software, the electromagnetic transient processes of the windings under various operational conditions were examined. A comparative analysis of the electromagnetic force distribution characteristics was conducted to explore the influence of different fault conditions on winding insulation. The results indicate that during inter-turn short circuit faults in transformer windings, the current in the short-circuited turn significantly exceeds that in three-phase outlet short circuits. Compared to normal load conditions, the peak currents through the short-circuited turns and overall windings increased by 5318% and 3314%, respectively; the maximum magnetic field intensity rose by 1511% and 2111%, and the peak electromagnetic force density on the winding turns surged by 5210% and 11489%. Inter-turn short circuit faults in transformer windings are likely to severely damage the insulation, whereas three-phase outlet short circuits can lead to unstable deformations and degradation of insulation.
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Cascaded Equalization Control of Lithium Batteries Based on Variable Discourse Domain Fuzzy PID Algorithm
Abstract:
During the long-term use of lithium battery packs, there is a problem of inconsistent voltages among the series-connected individual bat-teries. To solve this problem, a cascade bidirectional Cuk equalization circuit system based on variable domain fuzzy PID control is pro-posed in this paper. In this system, a cascade bidirectional Cuk equalization circuit is employed to achieve equalization between non-adjacent cells, and the variable domain fuzzy PID algorithm is exploited to enhance the voltage equalization speed. To verify the fea-sibility and superiority of the system, simulation models of the traditional bidirectional Cuk equalization circuit and the cascaded bidirec-tional Cuk equalization circuit were designed in MATLAB/Simulink. The comparison of simulation results under the control of the fuzzy PID algorithm and the variable domain fuzzy PID algorithm reveals that the balancing time of the cascade bidirectional Cuk balancing topology based on the variable domain fuzzy PID algorithm is decreased by 64.29% compared with the traditional bidirectional Cuk bal-ancing topology without a control algorithm. The equalization time of the cascade bidirectional Cuk equalization topology without a con-trol algorithm is reduced by 50.82%. The balancing time of the cascade bidirectional Cuk balancing topology based on the fuzzy PID algorithm is reduced by 14.29%. According to the analysis of the simulation results, it is demonstrated that the cascade bidirectional Cuk equalization circuit system based on fuzzy PID control in the variable theory domain can augment the voltage equalization rate of the bat-tery.
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Research on Video Output Function Detection System for ADC Based on GMSL2
Abstract:
The Advanced Driving Assistance System Domain Controller is responsible for processing and analyzing data from various sensors. However, as the number of in-vehicle cameras continues to grow, various stages within the Domain Controller, including deserialization, serialization, and image processing, may encounter frame loss and pixel anomalies, which can adversely affect the results of image processing. To accurately evaluate the GMSL2 video output functionality of the ADC, a dual-channel GMSL2 video capture and video quality comparison system has been researched. The system involves a hardware card design that initially deserializes the GMSL2 video signal into MIPI CSI-2 signals. Subsequently, a bridging IC separates the MIPI signals into LVDS and CMOS signals recognizable by the FPGA. The XLINX XC7K325T-FFG900main control chip is then utilized for FPGA logic design, enabling the parsing of MIPI signals, conversion of YUV422 to RGB888 video format, DDR3 buffering, and PCIe 2.0×8 bus transmission. Finally, by integrating image feature extraction, digital tube threading recognition algorithms, and the RGB weighted Euclidean color difference formula, the system achieves detection of frame loss and color differences in the video. The experimental results indicate that this system can collect dual-channel YUV422 8-bit, 4K, 30FPS video data from the GMSL2 interface in real time, and conduct a quantitative analysis to determine whether the video output from the intelligent driving domain controller has issues with frame loss and color differences, thereby distinguishing between qualified and unqualified device under test. This has increased the reliability of the test results for the video input and output functions of the Advanced Driving Assistance System Domain Controller.
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Online compensation of acceleration on error while drilling based on MICOA
Abstract:
（研究的问题）To improve the measurement accuracy of the downhole accelerometer, a method for online compensation of accelerometer errors based on a magnetic-inertial coati optimization algorithm is designed. （研究的过程和方法）Firstly, an error compensation model is established based on the sources of error; the constraint conditions of the gravity angle and the magnetic-gravity angle are established using a gyroscope and a magnetometer; the difference between the true value of the acceleration and the modulus of the theoretical value is set as the objective function. Secondly, based on the coati optimization algorithm, the initial search boundary for error parameters is determined according to the recursive gravity acceleration, and the boundary is narrowed based on the relative distance among the current error parameters, the optimal error parameters, and the boundary values; a boundary point selection is designed to screen the initial error parameters, enabling the algorithm to initially search in the direction of high-quality solutions while retaining some inferior solutions to increase the diversity of error parameters; in the global exploration stage of the algorithm, parameters are designed to adjust the search range of accelerometer error parameters based on the error between the current error parameters and the average error parameters. Finally, the ratio of the modulus of gravity is set as the threshold for deep development, and a Gaussian mutation vector is constructed to enable the accelerometer error parameters to break out of local optima. （研究结果）Experimental results show that after MICOA compensation, the accelerometer error decreases, and the range of inclination angle decreases by approximately 62.5%; at different drilling angles, the root mean square error and standard deviation of the inclination angle can be maintained below 1°.
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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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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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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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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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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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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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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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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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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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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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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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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.
