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    Volume 38,2024 Issue 2
    • Li Guangrui, Liu Qiong, Zhang Yiqing, Zhang Xinyao, Huang Jingxu, Fu Jian

      2024,38(2):1-9,

      Abstract:

      In response to the technical challenges posed by the varying sizes, haphazard arrangement, and overlapping occlusion of prohibited items in security X-ray images, we propose an enhanced HRNet-based multi-scale feature fusion network model. This model aims to achieve automatic segmentation and recognition of prohibited items in images. In the encoding stage, we leverage the multi-resolution parallel network architecture of HRNet to extract multi-scale features, addressing the diverse scale of prohibited items in security X-ray images. In the decoding stage, a multi-level feature aggregation module is introduced that uses data-dependent upsampling instead of bilinear interpolation. upsampling to reduce information loss during aggregation, thus ensuring a more comprehensive representation of the features of the features extracted in the coding stage for a more complete characterisation of objects. In the overall architecture of the network, a de-obscuration module based on the attention mechanism is embedded to strengthen the edge-awareness ability of the model, alleviate the problem of serious overlapping occlusion of items in security X-ray images, and improve the segmentation and recognition accuracy of the model. By experimenting on the public dataset of PIDray security check images, the results show that the average intersection ratio of 73.15%, 69.47%, and 58.33% are achieved in the three validation subsets of Easy, Hard, and Hidden, respectively, which are 0.49%, 1.17%, and 5.69%, respectively, and the overall average intersection ratio is improved by about 2.45%.

    • Chen Sirui, Sun Xingwei, Yang Heran, Dong Zhixu, Liu Yin

      2024,38(2):10-18,

      Abstract:

      In order to accurately determine the degree of abrasion of abrasive belts in grinding screw rotors, the degree of abrasion of abrasive belts is identified according to the law of change of the color characteristics and texture characteristics of the surface image in the abrasion process of abrasive belts. The texture features and color features of the surface image of the abrasive belt after the grinding process are extracted, and the abrasive belt wear degree is classified according to the surface roughness of the screw rotor in different grinding time periods. The classification performance of the support vector machine is greatly affected by its own kernel function and penalty function, so it is proposed to optimize the kernel parameter and penalty parameter of the support vector machine by using the aquila optimizer optimization algorithm, and to establish the wear degree model of AO-SVM sand belt image recognition. The experiment is completed by utilizing a self-developed special belt grinding device for screw rotors. The grinding parameters are set as follows: the linear speed of the grinding belt is 10 m/s, the axial feed speed of the workpiece is 50 mm/min, the cylinder pressure of the tensioning wheel is 0.35 MPa, the cylinder pressure of the active wheel is 0.5 MPa, and the grinding time is 25 min. The recognition accuracy of AO-SVM for the abrasive belt wear degree model reaches 92.5%, which is improved by 5.0% and 3.6% compared to the random forest algorithm (RFC) and the XGboost classification algorithm, respectively, and the convergence speed is fast. The degree of abrasive belt wear can be identified by the AO-SVM model through the color feature change and texture feature change of the surface image of the abrasive belt, which can effectively avoid excessive abrasive belt wear and damage to the workpiece, and provide theoretical guidance for judging the degree of abrasive belt wear and the time to change the abrasive belt when the abrasive belt is used to grind the screw rotor.

    • Shan Huilin, Lyu Zongkui, Fu Xiangwei, Hu Yuxiang, Duan Xiuxian, Zhang Yinsheng

      2024,38(2):19-29,

      Abstract:

      Aiming at the existing remote sensing image target detection methods with low detection accuracy for small-size aircraft targets, inaccurate feature information transfer and insufficient information interaction, a remote sensing image aircraft target detection method based on discriminative feature extraction and context-awareness is proposed. A backbone network with a discriminative feature extraction module is designed to enhance feature extraction for multi-scale aircraft targets; an adaptive feature enhancement module is introduced to selectively focus on small targets and optimize the transfer of feature information and information interaction; and a feature fusion up-sampling module is designed to perform up-sampling operations on the feature maps to improve the accuracy of high-level semantic information. The detection accuracy on the DOTAv1 dataset reaches 95.2%, which is 3.7% to 18% higher than that of mainstream algorithms such as YOLOv5s, SCRDet, ASSD. In addition, the detection speed and the number of model parameters of the proposed method are 147 frames per second and 13.4 M, respectively. Compared with the current mainstream algorithms, the proposed method has strong competitiveness and meets the real-time detection requirements of aircraft targets in the background of remote sensing.

    • Lu Pengtao, Jiang Wen, Huang Ju, Sun Shuifa, Wang Fangyi

      2024,38(2):30-39,

      Abstract:

      The recognition of vessel names (license plates) plays a crucial role in waterway transportation systems. Addressing the challenge of identifying vessel names in inland waterways, where targets are relatively small and vessels are observed at significant angular inclinations on both sides of the waterway, we propose an automatic ship name identification (ASNI) framework based on the differentiable binarization (DB) natural scene text detection algorithm and the convolutional recurrent neural network (CRNN) text recognition algorithm. ASNI comprises three main components: ship name detection, text image correction, and recognition. The text image correction component consists of a ship name correction module and a super-resolution reconstruction module. Firstly, the framework utilizes the DB algorithm to perform adaptive scale fusion processing on the candidate regions of vessel names in images, generating feature maps. Feature mapping is used to predict and generate binary images to identify connected regions, thereby obtaining regions of interest (ROI) containing vessel names. Subsequently, after ship name detection, a ship name correction module is introduced to rectify irregular text within the ROI using perspective transformation. Furthermore, a super-resolution reconstruction module is designed to enhance the resolution of the corrected vessel name images. Finally, the CRNN algorithm is employed to recognize vessel names within the corrected text images in the ROI, yielding the ultimate results. Through training and testing on the ship license plate (SLP) dataset specific to inland waterways, experimental results demonstrate that the ASNI framework achieves an average accuracy of 87.50% in vessel recognition, representing a 3.12% improvement over the baseline framework. The framework presented in this paper effectively addresses issues related to low resolution and angular inclinations leading to inaccurate vessel recognition. Compared to the baseline framework, ASNI exhibits superior recognition performance.

    • Guo Jianglong, Jiang Qing, Cao Songxiao, Song Tao

      2024,38(2):40-48,

      Abstract:

      A machine vision based surface defect detection method for microchannel flow channel plates is proposed to meet the demand for automatic detection of complex surface defects in industrial automation production. This method focuses on common pit and damage defects in the CV holes and expansion valve holes of the flow channel plate. Firstly, the ROI region is extracted through Hoff circle detection to eliminate background interference. Gaussian filtering is used to filter the ROI image, and binarization and morphological corrosion operations are used to filter out interference noise to highlight defect features. Then, the Two-Pass algorithm and seed filling method are used to calculate the connected domain to achieve pit defect detection. Use circle search to find the inner and outer circles of the hole end surface, unfold the circular ring, and use Canny edge detection operator to search for the defect contour, screen the contour area to achieve the detection of damaged defects. Through comparative experiments, it has been verified that the method proposed in this paper has a higher detection rate in the detection of defect samples in the runner plate compared to traditional surface defect detection methods. The method proposed in this article has been validated to have a stable defect detection rate of over 92% on the surface of the flow channel plate, and the algorithm has fast processing speed and strong robustness, achieving fast, non-contact high-precision detection and meeting the requirements of industrial automation.

    • Chen Fafa, Dong Haifei, He Xiangyang, Chen Baojia

      2024,38(2):49-57,

      Abstract:

      In order to lighten the rust image segmentation network model and eliminate the interference of non single feature background and similar feature backgrounds such as rust liquid, this paper replaces the encoded part of the U-Net network model with the MobilenetV3_large network, imports the pre-trained weights of the MobilenetV3_large network based on the ImageNet dataset, and replaces the ordinary convolution of the decoded part of the U-Net network model with a deep separable residual convolution. And add the attention-oriented AG module and the Dropout mechanism in the process of upsampling. Experimental results demonstrate that the improved U-Net network model designed in this paper exhibits significant advantages in rust image segmentation under non-uniform feature background and similar feature background interference such as rust liquids. The model size is reduced by 81.18% compared to the original U-Net network model, resulting in a decrease of floating point calculations by 98.34%. Additionally, the detection efficiency has improved by 3.27 times, increasing from less than 6 frames/s to 19 frames/s. While the network model is lightweight, the accuracy of the network model is 95.54%, which is 5.04% higher than the original U_Net network model.

    • Song Jiasheng, Li Haotian

      2024,38(2):58-66,

      Abstract:

      The location of target corner points in an image is the key data for implementing many computer vision tasks. In order to overcome the data redundancy problem arising from traditional detection algorithms, an edge-based corner point target detection method in the scale space is proposed. First, a grouped multilayer scale space is constructed, and multiple smoothed images are obtained after projecting the original image into it. At the same time, the defined edge operator is applied to detect all edges in the smoothed image to obtain multiple sets of pixel points stored in order, and the transformation to larger scales is stopped when the number of point sets is stable. Then, at the current scale, the indicator values of each element in the point set reflecting its corner intensity are calculated. The support set interval of corner points is detected according to the variation pattern of these indicator values, and the final target corner points are determined in this interval using a Gaussian fitting function. Experiments show that the method is able to detect the target corner points with significant features and their angles, where the accuracy of the synthesized images is at the pixel level and the average error to figure ratio in the application case is about 1.5/100.

    • Liu Le, Zhang Xiaosong, Huang Feng, Fang Yiming

      2024,38(2):67-75,

      Abstract:

      In order to tackle the challenges associated with small character regions, complex lighting variations, and poor image quality in the identification of slab numbers in steel continuous casting production lines, a two-stage algorithm is proposed for slab number detection and recognition, utilizing deep learning techniques. Firstly, datasets for slab number detection and recognition are prepared based on the collected slab images of a continuous casting production line. Secondly, in the slab number detection stage, an AD-PAN feature fusion structure based on the DBNet algorithm is designed to enhance the multi-scale feature fusion capability and expand the receptive field of the detection algorithm, thereby improving the localization accuracies of the slab numbers. Thirdly, in the slab number recognition stage, the proposed algorithm incorporates a SPIN correction network and a SVTR slab number recognition network for end-to-end training, enabling them to actively transform input brightness and improve color distortion among characters and between characters and backgrounds. Finally, comparative experiments are conducted on self-made datasets for slab number detection and recognition, and experimental results demonstrate that the algorithm proposed in this study is capable of effectively locating slab at different positions on the roller table and robustly recognizing slab numbers in complex backgrounds. The Hmean value for slab number detection is 97.92%, and the accuracy for slab number recognition is 97.33%, confirming the high accuracy of slab number detection and recognition achieved by the proposed algorithm.

    • Li Jian, Liu Tianyi, Wang Jialin, Liu Shan, Huang Xinjing

      2024,38(2):76-84,

      Abstract:

      The structural deformation of subsea pipelines increases the risk of pipeline fracture, which can cause significant economic losses. This paper proposes a pipeline displacement and deformation detection method based on a spherical inner detector equipped with a multi-channel magnetic sensor array. The stress in the pipe wall and the change of the magnetic flux density inside the pipeline caused by the displacement deformation of the pipeline are analyzed according to the magneto-mechanical effect. The structure layout of the detector and the magnetic signal acquisition scheme are designed. The collection of spatial rotational magnetic flux density information inside the pipeline is realized, and the rotational magnetic signal is converted into translational magnetic signal in the pipeline coordinate system. By extracting the magnetic data on the contour lines inside the pipeline, multiple channels of signals are integrated to increase the space sampling coverage of the magnetic signal inside the pipeline. Finally, internal inspection tests were conducted on pipeline vertical and equivalent lateral loading-induced displacement deformation. The results showed that under longitudinal force deformation state, the magnetic flux density longitudinal component dispersion decreased by 136.1 μT on the contour line inside the pipeline; under equivalent lateral force deformation state, the magnetic flux density lateral component dispersion decreased by 123.1 μT on the contour line inside the pipeline. The magnetic flux density dispersion measured by the spherical internal detector can reflect the displacement deformation of the pipeline in different directions, and the larger the deformation of the pipeline, the smaller the magnetic flux density dispersion.

    • Liu Kewei, Hu Yinchun, Zhang He

      2024,38(2):85-91,

      Abstract:

      In pipeline transportation, the measurement results of turbine flowmeter are easily deviated by unsteady flow. In order to guide the selection and correction of turbine flowmeter under pulsating flow condition, it is necessary to estimate the maximum pulsation error of turbine flowmeter under pulsation conditions. Ignoring the change of fluid pressure and temperature, based on dynamic characteristics of turbine flowmeter and conservation of energy, the upper limit formula of sinusoidal pulsating flow error is derived by calculating the work ratio of pulsating flow and steady flow to the impeller in one pulsating period. A CFD simulation model of turbine flowmeter was established based on 6DOF model and UDF. The air flow test platform is built, the working grade standard device is used as the reference, and two flowmeters are connected by a tube section with built-in rectifiers. Compared with the flow value under constant flow, the error limit under each pulsating condition is calculated. The maximum pulsation difference between the results of the formula and CFD simulation is 0.281%, and 0.224% between the results of the formula and the air flow experiments. The upper limit formula for pulsation error is proved its accuracy when compared to the results of CFD simulation and air flow experiment. The pulsation error upper limit formula is proved to be accurate, and can be directly used to estimate and correct the value under the condition that the pulsation frequency is greater than 10 Hz.

    • Chen Xiaoxuan, Zou Yang, Weng Zuchen, Lin Jinjia, Lin Xinliang, Zhang Yunxiao

      2024,38(2):92-100,

      Abstract:

      Extracting feature parameters from the recovery voltage polarization spectrum is currently a widely adopted method for evaluating the status of transformer oil-paper insulation. However, the polarization spectrum is prone to anomalous feature data due to factors such as working condition interference and artificial errors, which seriously reduces the accuracy of the evaluation. In response to the above issues, this paper proposed a recovery voltage data cleaning method based on local outlier factor (LOF) and improved K-nearest neighbor (IKNN). Firstly, Maximum recovery voltage Urmax, the initial slope Sr and dominant time constant tcdom of the recovery voltage polarization spectrum were selected as aging feature parameters, and anomalous feature data in the non-standard polarization spectrum were identified and filtered out based on the LOF algorithm. Secondly, the Fuzzy C-means (FCM) clustering algorithm was used to reduce the interference of noise points on the KNN algorithm, and the correlations between various features were highlighted by weighted Euclidean distance scale. Then, a data filling model architecture based on IKNN was constructed to fill in missing feature data. Finally, multiple sets of measured data were incorporated to validate the effectiveness of the proposed data cleaning method. The results indicate that the accuracy of status evaluation after data cleaning has increased by about 50% compared to the original data, which effectively improves the quality of transformer recovery voltage data and lays a solid foundation for accurate perception of transformer operation status.

    • Liu Zechao, Li Jingzhao, Zheng Changlu, Wang Guofeng

      2024,38(2):101-111,

      Abstract:

      To guarantee the safety control performance of monorail cranes operating in complex track conditions within deep mines, enhancing the accuracy and reliability of dynamic inclination recognition for monorail cranes is necessary. Therefore, this paper proposes a dynamic inclination recognition method of monorail crane based on DFFRLS-AUKF algorithm. Firstly, an adaptive smoothing filtering algorithm is used to filter the acceleration and velocity data collected in real-time to avoid the interference of environmental noise and ensure the integrity of the data. Secondly, the track curvature model is established to achieve the accurate analysis of the entire working conditions of the track, and based on the filtered data, a reliable track curvature value is obtained by combining the dynamic recursive least squares of forgetting factor (DFFRLS) algorithm with the dynamic forgetting factor. Finally, based on the calculated track curvature, the unscented Kalman filter (UKF) is improved by using the Sage-Husa noise estimator, which achieves the self-adaptation of the dynamic Adaptive adjustment of dynamic inclination recognition, and the accuracy of emotional inclination recognition is improved. Experiments show that the proposed DFFRLS-AUKF algorithm improves the dynamic inclination recognition accuracy by 25.25% and 39.5% on average compared with the traditional algorithm during the testing of monorail crane in monorail section 1 and monorail section 2, which demonstrates that the DFFRLS-AUKF algorithm has good accuracy and reliability under different track conditions, and effectively guarantees the safety of monorail crane driving under complex track conditions.

    • Liu Xiaosong, Xu Zaixiang, Shan Zebiao, Xu Enda, Lyu Yue

      2024,38(2):112-119,

      Abstract:

      In the background of strong impulse noise, the performance of fractional low-order statistics delay estimation method is degraded and the prior knowledge of noise is required. In order to solve the problem, a new time delay estimation method based on second-order fractional low order covariance is proposed. Firstly, the bounded nonlinear sigmoid function is used to process the signal with impulse noise, so that the additional impulse noise can be fully compressed without affecting the time delay information carried by useful signals. Then, the second-order fractional low-order covariance operation is carried out on the processed signals of receival and transmission, that is, after obtaining the self-fractional low-order covariance of the transmitted signals and the mutual fractional low-order covariance of the received and transmitted signals, the mutual fractional low-order covariance of the two is calculated again, thus, the effect of impulse noise can be further suppressed. Finally, the effectiveness of the proposed method is verified by simulation experiments. The results show that the proposed method is free from the restriction that the fractional low-order covariance index is less than the characteristic index of Alpha stable distribution noise, and has higher estimation accuracy than the fractional low-order covariance method. The simulation experiment results show that under the generalized signal-to-noise ratio of -10 dB, the delay estimation takes 0.056 0 s and the accuracy reaches 97.76%.

    • Chen Wanzhi, Zhang Guoman, Wang Tianyuan

      2024,38(2):120-130,

      Abstract:

      Considering the problem that the sparse features in the existing traffic anomaly detection models are easily ignored by the feature selection algorithms, a traffic anomaly detection method based on feature coupling generalization (FCG) was proposed. First, the DBSCAN density clustering algorithm was used to remove outliers in the data to reduce the impact of the anomalies on the subsequent FCG algorithm. Second, the minimal-redundancy-maximal-relevance (mRMR) algorithm was used to sort the data features, and the most influential features for classification were selected to generate the class-distinguishing features (CDF) in the FCG algorithm, in order to enhance the classification ability. The K-nearest neighbors (KNN) algorithm was used to fill in the missing values in CDF to maintain data integrity. Then, the data were grouped according to attack categories, and the features were sorted using the mRMR algorithm respectively, and the sparse features with instance-distinguishing ability in the data of each attack category were selected as the example-distinguishing feature (EDF) in the FCG algorithm. The degree of coupling between the two features in the anomaly detection data and the upper concept of EDF were used to transform EDF into more generalized features. Finally, the processed data were fed into the random forest (RF) model based on Bayesian optimization (BO) parameters for classification and identification. Through simulation experiments on the NSL-KDD dataset, the accuracy reached 91.79%, which verifies the proposed method has a good detection performance.

    • Sun Bin, Ni Shuang, Zhu Qing, Chen Xiaohui

      2024,38(2):131-138,

      Abstract:

      This paper addresses the optimization design problem of electromagnetic conductivity sensors for low conductivity measurements. The working principle of electromagnetic conductivity sensors is analyzed, and a physical model is constructed. The influence of excitation signal parameters, magnetic core dimensions, core spacing, excitation coil turns, and receiver coil turns on the output voltage is considered. An improved model for electromagnetic conductivity measurement is proposed, taking into account the multiplicity of excitation frequencies. Specifically, the excitation frequency directly affects the output voltage and also modifies the magnetic permeability of the core through the magnetostrictive effect, thereby influencing the output voltage. Theoretical analyses of the main parameters, such as frequency, spacing, and turns, are conducted using a comparative approach with experimental results. It is found that it exists an optimal frequency range where the output voltage remains relatively stable despite fluctuations in frequency. By adjusting the spacing between the two magnetic cores, coupling voltage between them is reduced, resulting in a higher proportion of effective signals and improved accuracy. The accuracy of the theoretical model is verified through experimental validation. Furthermore, the optimized parameters are employed in the design of a conductivity probe, which is calibrated using conductivity standard solutions. Comparative experiments are conducted with a German-made conductivity meter from Bode to calculate the Pearson correlation coefficient, demonstrating the accuracy and high reliability of the optimized model.

    • Jiang Jinye, Feng Hao, Chang Xiaodan, Yin Chenbo, Cao Donghui, Li Chunbiao, Xie Jiaxue

      2024,38(2):139-147,

      Abstract:

      Nonlinear friction negatively impacts the dynamic and static performance of hydraulic servo systems in robotic excavators, leading to issues such as trajectory creep, flat peaks, and steady-state errors. The traditional LuGre friction model, which relies solely on velocity and internal bristle state variables that cannot be accurately measured, fails to comprehensively describe the complex friction characteristics of excavator hydraulic servo systems. Considering the position, velocity, and direction of the excavator hydraulic servo system, we propose an enhanced LuGre friction model and introduce a velocity threshold to address the instability issue of the elastic bristle average deformation state observer in the friction model. Secondly, to address the issues of traditional optimization algorithms getting stuck in local optimal solutions and having slow convergence speeds, the basic particle swarm optimization algorithm has been enhanced. This enhancement involves the introduction of inertia weight, asynchronous change, and elite mutation operations to accurately and rapidly identify the six unknown parameters in the improved LuGre friction model. Subsequently, using the identified friction model, a friction compensation controller based on the principle of structural invariance is designed. Three different operating condition trajectory tracking experiments were conducted on a 23-ton excavator. The conventional proportional-integral-differential controller exhibits the highest tracking error, with the maximum tracking error for the triangular trajectory reaching 29.68 mm. In contrast, the feedforward friction compensation controller, which is based on the enhanced LuGre model, achieves a significantly lower error of 9.70 mm, representing a 67.31% reduction in error. The experimental results demonstrate that the proposed friction compensation controller significantly enhances the trajectory tracking accuracy of the excavator.

    • He Ning, Yang Ziqi, Qian Cheng

      2024,38(2):148-159,

      Abstract:

      The state of health (SOH) is an important index for battery management system, and accurate SOH estimation is of great significance for ensuring safe and stable operation of battery. Extracting reliable and effective health features to describe the aging state of battery and constructing accurate and stable estimation model are the main problems we face at present. In order to improve the accuracy of SOH estimation, a fuzzy entropy and particle filter (PF) based SOH estimation method for lithium-ion battery is proposed. Firstly, the fuzzy entropy value is extracted as the aging characteristic of the battery by analyzing the discharge voltage data during the aging process. Secondly, a non-parametric state-space model to describe the aging characteristics of lithium-ion battery is constructed based on the metabolic grey model (MGM) and the temporal convolutional network (TCN). Finally, the closed-loop SOH estimation of lithium-ion battery is realized by PF. In addition, the proposed SOH estimation method is validated using the NASA lithium-ion battery datasets and compared with other methods in the field. The results show that the maximum estimation error of the proposed method is about 5%, the estimation accuracy is improved by about 50% compared with similar methods, and the proposed method exhibits good robustness under different training cycles, which verifies the feasibility and superiority of the proposed method.

    • Wang Ziwei, Tao Xu, Li Hui, Shi Zhenting, Zhang Jian, Xu Yulong

      2024,38(2):160-170,

      Abstract:

      Due to the presence of interference between users, power allocation problems in wireless communication networks are often non convex and require a huge amount of computation. The current graph neural network (GNN) has become an effective computational method used to solve this problem. In order to maximize network transmission speed while reducing computational complexity, a flexible duplex network graph representation method that incorporates device and communication connection attributes into GNN is proposed, and a corresponding flexible duplex graph neural network (FD-GNN) model is constructed. For the first time, the distance between node pairs, channel gain, and neighbors are introduced as dynamic thresholds into FD-GNN to adapt to dynamic environments. Excluding channel state information of neighbors in GNN, pruning edges in FD-GNN reduces computation time and network complexity. Simulation results show that the proposed threshold setting method based on channel gain neighbors has the best performance and reaches 97% of the weighted minimum mean square error (WMMSE), reducing the training time required by 24% compared to Full GANN. The proposed threshold based effectively reduces the time complexity of GNN operations and improves the effectiveness of the algorithm.

    • Hua Chengcheng, Chai Lining, Zhou Zhanfeng, Chen Xu, Liu Jia

      2024,38(2):171-181,

      Abstract:

      The existence of virtual reality motion sickness is a key factor restricting the further development of the VR technology industry, the study of neural activity related to virtual reality motion sickness and its accurate detection is the premise to solve this problem, neural activity in resting-state virtual reality motion sickness missing from previous studies. Therefore, this study uses the resting Electroencephalogram(EEG)signals before and after the virtual reality motion sickness exposure task, and proposes the virtual reality motion sickness EEG characteristics as indicators to realize the detection of virtual reality motion sickness. First, the variational mode decomposition is performed on the EEG signals of five electrodes selected by statistical analysis in this paper, namely Fp1, Fp2, F8, T7 and T8, and extract the sample entropy, permutation entropy and center frequency from the selected modal components. Then, two stages of feature selection are performed by statistical tests and ReliefF algorithm. Finally, the selected feature vectors are sent to the support vector machine for classification, then the automatic detection of motion sickness in virtual reality is realized. The results showed that the accuracy, sensitivity and specificity of this method reached 98.3%, 98.5% and 98.1%, respectively, and the area under the ROC curve reached 1, it is superior to other methods, which proves the advantages and effectiveness of this method in the automatic detection of EEG signals in virtual reality motion sickness.

    • Li Xin, Qu Zhongjie, Li Zipeng, Yin Liyong, Su Rui

      2024,38(2):182-189,

      Abstract:

      Mild cognitive impairment (MCI) is a key stage in the diagnosis of senile dementia, and the characteristics of electrical brain (EEG) signals can reflect the cognitive status of MCI patients and help achieve early diagnosis. In the process of EEG feature extraction, most existing studies use fixed time windows to complete segmentation processing for each rhythm of EEG, ignoring the feature differences of different rhythms, thus affecting the diagnostic effect. In view of this problem, a new combined sliding window optimization algorithm is proposed, which improves the construction method of zero model by iterative amplitude adjustment Fourier transform (IAAFT), so as to evaluate the brain dynamic characteristics KPLI. By adopting a variety of sliding window combinations for EEG frequency band signals and guiding them with KPLI indicators, the best sliding window combinations suitable for different frequency bands are obtained. Based on the best sliding window combination, the phase lag index (PLI) is extracted from each band combination, the continuous wavelet transform (CWT) feature is performed, and the MCI diagnosis is realized through the ResNet-MLP dual-channel classification network. The results show that diagnostic classification was achieved for 88 subjects (32 MCI patients, 36 Alzheimer’s disease patients, and 20 healthy control) using a personalized combination band sliding window, and the classification accuracy was 82.2%, which is 10% higher than the classification of fixed window (72.2% classification accuracy is obtained). The results showed that based on the individualized EEG rhythm feature combination, the features of MCI could be better extracted, and the accuracy and specificity of the diagnosis of mild cognitive impairment could be improved, which was an effective EEG feature extraction method.

    • Wang Faguang, Yang Wei, Li Dongfa, Liu Chen, Liu Hanbiao, Li Lei

      2024,38(2):190-198,

      Abstract:

      The detection performance of resonant force sensor depends on the geometry dimensions, structure configuration and sensing mechanism of the resonant sensitive element. At present, the method of improving the detection performance by simply reducing the size has been in the bottleneck period. In order to study and develop a novel resonant force sensor, coordinate the contradiction between nonlinear vibration and resonant structure detection performance, explore a more sensitive sensing mechanism and improve its detection performance, a piezoelectric driven resonant magnetically coupled cantilever force sensor is proposed. Firstly, the design and theoretical modeling of the structure of the magnetically coupled cantilever beam are carried out. The influence of the external pressure on the vibration characteristics of the magnetically coupled cantilever beam structure is analyzed theoretically. With the increase of the pressure, the distance between the magnetically coupled cantilever beams decreases and the resonance frequency increases. Secondly, the experiment verifies the advantages of the bifurcation jump dynamic behavior, which increases the maximum amplitude by 2.8 times compared with the resonance of a single resonant beam. Then, two pressure detection schemes based on the bifurcation jump characteristics and frequency doubling response are studied, and the pressure detection is realized by using the critical frequency of the bifurcation jump and the high order response frequency when the mode is coupled. The sensitivity and linearity are analyzed. The experimental results show that the amplitude change of the detection scheme based on the bifurcation jump characteristic is obvious, which is about 5 times that of the detection scheme based on the frequency doubling characteristic, and it is easy to detect and overcome the adverse effects of nonlinear factors. The detection scheme based on frequency doubling response has high output sensitivity, which is about 4 times that of the detection scheme based on bifurcation jump, and large signal-to-noise ratio, which provides a certain reference value for designing resonant force sensors with different detection principles.

    • Zhao Haijun, Chen Huayue, Cui Mengtian

      2024,38(2):199-210,

      Abstract:

      Aiming at the data acquisition in large sparse sensor networks, a network architecture using the ubiquitous existence of mobile agents in environment to connect sparse sensors and a 2-dimensional grid random walk analysis model are proposed in this paper. The proposed sensor network model consists of three abstract layers, namely, bottom layer composed of wireless sensors, the middle layer composed of various transportation agents, and the top layer composed of access points/central repositories. The specific implementation principle is that the mobile transport agents located in the middle layer collect data from the wireless sensors distributing at the bottom layer and buffer the data, and after wandering transport, finally deliver the data collected from the wireless sensors at the bottom layer to the necessary access points at the top layer for necessary storage and processing, so as to achieve the data acquisition of the entire sensor network. The theoretical analysis and simulation experiment results show that the proposed sensor netwoks model based on mobile transport agents not only has robustness and scalability, but also has obvious advantages over base station network model and Ad-hoc network model in terms of sensor power consumption, data success rate and infrastructure invested cost.

    • Yang Jinxian, Yin Fengshuai

      2024,38(2):211-218,

      Abstract:

      To solve the problem of low output accuracy of MEMS gyro in MWD, an online gyro error compensation method based on magnetic-gravity ephemera algorithm (MGMA) is proposed. Firstly, the error source of GYRO while drilling is analyzed and the error compensation model is derived. Secondly, the objective function of MEMS accelerometer is obtained by using the cross product of gravity vector. In addition, considering the adverse effects of strong vibration and impact on the accelerometer during drilling, the relative error constraint of the magnetic mode value is designed based on the strong anti-vibration ability of MEMS magnetometer. Then, on the basis of MA, the search upper and lower bounds are determined adaptively according to the relationship between the gyro and the magnetometer output, aiming at the constant change of gyro error parameters under the influence of harsh environment while drilling. The relative error of gravity mode value is used to design the inertia weight and balance the global exploration and local development ability of the algorithm. Finally, according to the relative error of the magnetic-gravity mode value, the variation perturbation strategy is introduced in the children to reduce the possibility of falling into the local optimal. The experimental results show that the gyro error after MGMA compensation is obviously reduced, and the well inclination error is reduced from 9.75° to 1.52°, and compared with PSO and MA algorithm, it has the advantages of fast speed and high precision.

    • Peng Feitong, Xu Kai, Wu Shixun, Huang Deqing

      2024,38(2):219-230,

      Abstract:

      Aiming at the randomness and complexity of jointless track circuit faults, the single diagnosis model has the problems of one-sided extraction features and unreasonable empirical design of model structure. A fault diagnosis method based on intelligent optimization deep network is proposed. Firstly, the fault feature set is established by six voltage detection quantities of the track circuit signal centralized monitoring system. The convolutional neural network (CNN) is used to extract the feature space information, and the long short-term memory network (LSTM) is used to extract the time feature information, so that the features extracted by track circuit fault diagnosis have both spatial and temporal information. At the same time, the genetic algorithm (GA) is introduced to optimize the structure and parameters of the aforementioned deep neural network, and the output weight of the feature level of the two combined networks is further optimized by combining the Q-learning method in reinforcement learning. Finally, the multi-layer perceptron (MLP) is used to fit and correct the classification error of the deep network to improve the fault diagnosis accuracy of the model for the track circuit. The simulation results show that the recognition rate of the fault diagnosis of the track circuit using the intelligent optimized deep network model can reach 99.28% compared with the single model and the refined design combination model. The evaluation index is improved, and the fault diagnosis accuracy is higher. It is proved that the intelligent optimized deep network can further improve the fault diagnosis performance of the track circuit.

    • Zhong Yingchun, Tian Zhihao

      2024,38(2):231-240,

      Abstract:

      UWB (Ultra-Wide Band, UWB) technology is one of the main approaches for obtaining the location coordinates of unmanned equipment while inspecting in large enclosed spaces with multiple obstacles. Currently, the design of UWB location base stations topology mainly relies on manual methods, which have many shortcomings such as low design efficiency, unknown design effect, and difficult determination of cost-effectiveness. To address these issues, this paper proposes a scientific and complete topological evaluation system of UWB location base stations, and a universal automated design method for the topology of UWB location base stations in large enclosed spaces. The method is based on simulation, genetic algorithm and topological evaluation system to automatically generate the optimal topology scheme suitable for practical application scenarios, including the optimal number and location information of base stations. The experimental results show that: 1) The topology generated by the universal design approach has a better comprehensive performance than the topology with the largest number of base stations, with an increase of 6.2% in overall performance; it also has a better comprehensive performance than the topology with the smallest number of base stations, with an increase of 21.2% in overall performance. 2) The topology generated by the universal design method has a better comprehensive performance than the serrated topology designed by humans, with an increase of 11.4% in overall performance and a design efficiency increase of 92.9%. Therefore, for the scenario of multiple obstacles in large enclosed spaces, the approach proposed in this paper can design a topology with better overall performance and higher cost-effectiveness in a shorter time, providing a foundation for unmanned equipment to conduct autonomous patrols and inspections in such large enclosed spaces.

    • Wang Qianqian, Su Han, Lin Zhen, Wang Haitao, Lan Kun

      2024,38(2):241-248,

      Abstract:

      Testability verification and FMEA analysis of equipment consider single faults and use sampling methods to determine fault samples traditionally, without considering the different failure modes of redundant modules and the coverage rate of faults. Aiming at the requirements and difficulties of testability verification for launch vehicle electronic equipment, including redundant function verification requirements and testing coverage requirement etc., the functional degradation FMEA analysis method and evaluation method of coverage rate of fault modes was proposed on the basis of the general testability verification method of equipment, and a testability verification and evaluation method suitable for the launch vehicle electronic equipment was formed. An electronic equipment on the launch vehicle was taken as an example for modeling. The results proved that the method proposed in this paper effectively identified the redundant faults, and incorporate the fault modes of redundant modules into the evaluation of testability indicators. The faults of redundant modules were considered in the evaluation of testability indicators, and the verification of redundant functions of launch vehicle electronic equipment was assessed. The proposed fault coverage evaluation method and sample size supplement method effectively improve the coverage of testability verification for fault modes. It is suitable for testability verification and evaluation of launch vehicle electronic equipment, and can be extended to the electrical system of launch vehicle.

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    • A New Method for Dynamic Magnetic Resonance Image Reconstruction Combining Wavelet Frame and Low-rank

      俞智慧

      Abstract:

      Dynamic Magnetic Resonance Imaging (DMRI) is an imaging technology that acquires images through continuous scanning to capture their changes over time and space. Applying compressed sensing technology to DMRI tends to result in unsatisfactory visual quality of the reconstructed magnetic resonance images. Therefore, to address the deficiencies of compressed sensing in DMRI reconstruction, a reconstruction model based on low-rank and sparse decomposition is proposed by using L1 norm to characterize the sparsity of magnetic resonance image data and utilizing low-rank to describe the intrinsic correlation of dynamic magnetic resonance image sequences. This effectively reduces artifacts in dynamic magnetic resonance imaging. In the modeling phase, the sparse component is modeled using the L1 norm, while the low-rank component is modeled using the nuclear norm. In the model optimization phase, a wavelet framework regularization method is introduced, and the reconstruction model is transformed into a non-smooth convex optimization problem, which is then solved by using a momentum-accelerated proximal gradient method. Finally, experiments are conducted on cardiac cine, cardiac perfusion, and phantom membrane image data to verify the effectiveness of the proposed model. The experimental results show that the average PSNR and the average SSIM of the proposed method reach 33.7090 dB and 0.9660 at a sampling ratio of 30%,respectively,which further improves the reconstruction accuracy of the dynamic magnetic resonance image.

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    • Target detection of workshop tools based on sparse learnable proposal

      刘珍兵, 孙巧榆, 王述文, 夏嘉伟

      Abstract:

      Aiming at the significant size discrepancies and various shapes among different models of workshop tools, a workshop tool detection method based on sparse learnable proposal is proposed. Firstly, sparse representation and learnable proposal mechanism are integrated to improve the robustness of the model and reduce the required parameters in the detection process. Secondly, Swin-Transformer structure is introduced to enhance the global and detail learning ability of the model, which can effectively overcome the shortcomings of traditional convolution neural network in high-level semantic information fusion. Thirdly, an improved multi-scale feature fusion network architecture is used to improve the detection ability of the model for various scale targets according to effective fusion of different scale features. Finally, multi-head attention and dynamic convolution are combined to establish a more precise and detailed connection between different feature layers, thereby furtherly improving the accuracy of target detection. The CIoU loss function is applied to make the regression prediction of the boundary box more comprehensive and accurate by considering the location, scale and shape information. The experimental results show that the average detection accuracy of the proposed method for workshop tool detection reaches 91%, which is at least 2.3% higher than the current mainstream methods. At the same time, the detection speed of a single picture is about 53ms, which meets the needs of real-time detection and reflects the excellent comprehensive performance.

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    • Control Method of Low Ripple Adjustable DC Regulated Power Supply Based on Buck-Boost Inverter circuit

      谈宜雯, 张小平, 李毅凡

      Abstract:

      In order to overcome the deficiencies of the traditional low ripple DC regulated power supply's main circuit topology, such as complex circuit structure and large size, a new type of adjustable DC regulated power supply topology based on Buck-Boost inverter circuit is proposed to replace the two links of high frequency inverter and transformer step-up in the traditional structure with the Buck-Boost inverter link, which can be significantly simplified in terms of the circuit structure compared with the traditional structure. Compared with the conventional structure, this structure can be simplified significantly in terms of circuit structure. In order to realize the requirement of low ripple and high stability DC voltage output from this new type of adjustable DC regulated power supply, a composite control method based on proportional-vector proportional-integral is proposed for the Buck-Boost inverter circuit, that is, the capacitor voltage and inductor current in the Buck-Boost inverter circuit are taken as the state variables, and the inductor current is taken as the inner loop of the control, and the capacitor voltage is the outer loop of the control, and the two closed loops are decoupled by adopting proportional-vector proportional-integral composite control method. The decoupling control of the two state variables is realized by adopting the proportional-integral-vector proportional-integral composite control method for the two closed loops, and finally the effect is verified by simulation and experiment, and at the same time compared and analyzed with the traditional low ripple DC regulated power supply. The results show that the new adjustable DC regulated power supply topology based on Buck-Boost inverter circuit proposed in the paper and the control method proposed for the topology not only have small output DC voltage ripple, high steady-state accuracy and good dynamic performance, but also have the features of simple circuit structure and arbitrary adjustable output DC voltage, which are of good application value.

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    • Deep learning-based classification and identification of fiber optic microseismic signals

      金姝, 罗家童, 高雅, 俞本立, 张书金, 甄胜来

      Abstract:

      Microseismic monitoring technology can give the spatial location of rock body rupture or instability in real time and accurately, and has become one of the important means of early warning for disasters such as coal and gas herniation and tunnel rock explosion. Aiming at the problem of complex environment and weak signals difficult to be recognized effectively in underground engineering, a microseismic signal recognition method combining convolutional neural network and Transformer (T_CNN) is proposed. Six kinds of signals in tunnel engineering in a western region are collected by using fiber-optic acceleration sensors, and the signals are input into the model for training and verification after band-pass filtering for noise reduction and Fourier transform. Convolutional neural network in the model is utilized for feature extraction, focusing on the key information based on Transformer, and the final multi-classification results are derived by multilayer perceptron. The results show that the classification accuracy of the T_CNN-based model reaches 98.09% and converges faster. Compared with the current state-of-the-art residual neural network, the accuracy is improved by 6.2%, and the precision, recall, and F1 score are improved by 0.036, 0.023, and 0.033, respectively, which confirms the superiority of the algorithm in practical engineering applications. In addition, the energy of the fiber microseismic signal can also be estimated more accurately after the fiber microseismic signal is input into the model after the feature transformation, which further verifies that the model has good application prospects.

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    • Relative localization between robots based on UWB bearing

      宁恒, 刘冉, 郭林, 蓝发籍, 左建, 肖宇峰

      Abstract:

      Relative localization is a prerequisite for multiple robots in unknown environments to accomplish collaborative tasks such as formation, exploration, and rescue. A relative localization method based on Ultra-Wideband (UWB) bearing is proposed for positioning between robots in unknown infrastructure-free environments where satellite signals are blocked. The proposed method uses a sliding window to intercept the inter-robot bearing observations and motion trajectories over a period of time, construct the bearing cost function, and estimate the relative pose between the robots by minimizing the cost function. However, the non-convexity of the function leads traditional optimization algorithms to fall into local optimal solutions. Therefore, Sparrow Search Algorithm (SSA) is used to optimize the cost function for the relative localization between robots. To reduce the effect of UWB bearing measurement errors, the SSA-estimated pose and odometry information are fused by a back-end pose graph optimization algorithm to achieve more accurate relative positioning. The experimental results show that the method is able to achieve an average translation error of 0.32m and an average rotation error of 2.1° in an indoor environment with a size of 12m×6m.

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    • Point Cloud Classification Based on PointCloudTransformer and Optimized Ensemble Learning

      于喜俊, 段勇

      Abstract:

      Aiming at the difficulty of extracting features and classifying 3D point clouds due to their irregularity and disorder, this paper proposes a 3D point cloud classification method that integrates deep learning and ensemble learning. Firstly, the deep learning model PointCloudTransformer is trained to extract point cloud features and to construct a set of base classifiers. Subsequently, we design a base classifier selection model for ensemble learning that takes the diversity and average overall accuracy of the base classifiers as the optimization objectives and proposes a binary multi-objective beluga optimization algorithm to optimize the base classifier model and obtain the ensemble pruning scheme set. Finally, the majority voting method is used to ensemble the classification results of each base classifier combination on the test set to obtain the optimal base classifier combination, and an ensemble learning model of point cloud classification based on multi-objective optimization ensemble pruning is obtained. Experimental results on the ModelNet40 point cloud classification dataset demonstrate that the method in this paper achieves higher ensemble accuracy with a smaller ensemble scale and can accurately classify multi-class 3D point clouds.

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    • A planar filtering patch antenna with controllable radiation nulls

      王丽黎, 徐亚妮

      Abstract:

      In order to meet the needs of integrated and multi-functional RF front-end devices, a planar filter patch antenna with controllable radiation nulls is designed for WLAN 5GHz band. The antenna is based on a microstrip patch antenna fed by a coaxial probe. The upper surface is a metal radiation patch fused with a ribbon and a rectangular slot, and the lower surface is trial-grounded. The symbiotic strip is located on both sides of the wide edge of the radiation patch, and the rectangular groove is etched on the radiation patch and located on both sides of the coaxial feed point. The two structures introduce a radiation null at the low frequency and high frequency of the passband respectively, which makes the antenna realize the filter response and good radiation characteristics. At the same time, the position of the two radiation nulls is freely controllable, which improves the flexibility of the design. The simulation and test results show that the center frequency of the filter antenna is 5.25 GHz, the relative bandwidth is 14% (4.89~5.62GHz), and the two radiation zeros are located at 4.55GHz and 6.05GHz respectively. The average gain in the working passband is about 7dBi, and the out-of-band suppression level is greater than 19dBi. The test results are consistent with the simulation results. The design of the filter antenna does not introduce additional filter network, saves the overall size of the antenna, and has low profile, light weight, compact structure, and good filtering and radiation performance.

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    • Metal Surface Defect Recognition Method Based on CNN with PGW-Attention

      赵云亮, 唐东林, 何媛媛, 丁超, 杨洲

      Abstract:

      To address the challenges in detecting dispersed and fine defects on metal surfaces, convolutional neural networks (CNNs) often fall short due to their limited ability to capture global features, leading to missed detections and loss of detail in identifying defects such as oxidation particles, cracks, and scratches. Although Transformers are capable of capturing comprehensive global information, the extensive computation required can be costly. In pursuit of an efficient and accurate method for metal surface defect detection, this study introduces a novel network architecture, the DPG-Transformer, which synergistically combines the local feature extraction capabilities of CNNs with the global modeling strengths of Transformer. This integration is facilitated through the use of depthwise separable convolutions (DW-Conv) and Pooling Grid Window Attention Mechanisms (PGW-Attention). The effectiveness of the DPG-Transformer was validated on both a proprietary metal defect dataset (ST-DET) and a public dataset (NEU-CLS), achieving defect detection accuracies of 99.3% and 99.6%, respectively, and outperforming several classic networks in terms of accuracy, computational efficiency, and floating-point operations. Additionally, visualization experiments demonstrated that the DPG-Transformer more comprehensively extracts defect features associated with corrosion and scaling compared to CNN models, and more precisely focuses on the global features of elongated cracks and scratches than Transformer models. The results indicate that the DPG-Transformer not only reduces computational load and complexity but also enhances the comprehensive and precise detection of metal surface defects, making it a highly suitable approach for practical applications in metal surface defect detection.

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    • Path planning of robot arm based on APF-informed-RRT* algorithm with bidirectional target bias

      刘小松, 康磊, 单泽彪, 朱焕海, 刘云清

      Abstract:

      In view of the problems of large search randomness, poor target bias and path tortuousness in the current robotic arm path planning algorithm, an APF-informed-RRT* algorithm based on bidirectional target bias was proposed. Firstly, probabilistic adaptive target bias strategy is introduced based on bidirectional informed-RRT* to reduce the randomness of search and improve sampling efficiency. Secondly, for path expansion, the artificial potential field method is integrated into the two-way search tree to reduce the number of iterations of the algorithm. At the same time, in the path growth stage, the dynamic step growth strategy is adopted, that is, the step size is dynamically adjusted according to the expansion trend of the search tree, so as to avoid local optimization and speed up the path search time. Finally, the redundant nodes are removed by the principle of triangle inequality, and then the path is smoothed by B-spline curve to obtain the optimal planning path. The simulation and comparison experiments with bidirectional RRT*, bidirectional informed-RRT* and bidirectional P-RRT* are carried out in 3D environment. Compared with bidirectional RRT*, the time is saved by 41% and the number of sampling points is reduced by 63%. Compared with two-way informed-RRT*, 58% less time and 68% fewer samples are collected. Compared with bidirectional P-RRT*, it saves 30% in time and 60% in sampling quantity.

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    • High order model-free adaptive iterative learning control for speed control of hydraulic anchor drill

      朱敏, 卜旭辉, 梁嘉琪

      Abstract:

      Aiming at the problem of high-precision control of rotational speed in the rotary system of hydraulic anchor drilling rigs in the presence of parameter uncertainty and nonlinear constraints, a model-free adaptive iterative learning-based rotational speed control scheme for the rotary system of hydraulic anchor drilling rigs is proposed by taking advantage of the repetitive nature of the drilling rig operation. First, the state space model of the drill rig slewing control system about the rotational speed is constructed. Secondly, the dynamic linearization technique is used to construct the equivalent linear mapping relationship between the hydraulic motor and the servo valve current in the iterative domain of the drilling rig slewing system, and the model-free adaptive iterative learning speed control design method is proposed based on the historical servo valve current input and hydraulic motor rotary angle output data collected by the system. The asymptotic convergence of the rotational speed tracking error of the hydraulic anchor drilling rig slewing system along the data direction as well as in the direction of repeated operations is then given theoretically. Finally, the effectiveness of the algorithm is verified by joint simulation using MATLAB software and AMEsim platform. The results show that compared with the traditional PID algorithm and the iterative learning control algorithm, the proposed algorithm is able to realize the high-precision control of the drilling rig speed by using only the measurable data without the need of a known anchor drilling rig system model, and it still has a good adaptive and anti-jamming ability in the face of the sudden external disturbances and the fluctuation of the oil temperature.

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    • Study on measurement method for runway friction coefficient: construction and verification of estimation model with specific condition

      牛亚东, 朱华波, 周围

      Abstract:

      With the rapid development of China's civil aviation industry, the safety of aircraft takeoff and landing is an important consideration and challenge faced by the airport authorities. Friction, which is generated on the contact surface between the tire and runway, is a crucial factor in ensuring the safe landing of aircraft. Measuring the friction coefficient is a vital task for resolving the issue of measuring runway friction coefficient in China. In this paper, we present a finite element method to quantify the runway friction coefficient. We perform a multi-physical field coupling analysis of tire-runway interactions utilizing ABAQUS. This allows us to obtain the correlation between the friction coefficient and the tread friction (shear) stress under varying load, pressure, and speed conditions. By analyzing the trend of friction stress variation under both univariate and multivariate operating conditions, and subsequently employing the fitting method to reverse solve the friction coefficient, it is possible to establish models for estimating the friction coefficient under differing operating conditions. This leads us to the achievement of a measurement method that solely relies on tread friction stress to assess friction coefficient within specific operating conditions. Finally, a tester for determining the friction coefficient of airport runways has been employed to validate an estimation model in accordance with standard working conditions. Six experiments were conducted at a distance of 3000 metres, and as a result, the discrepancies between the estimation outcomes and the actual measurements ranged from 2.47% to 4.13%. This study affirms the validity and accuracy of the finite element method-based estimation model and presents a stimulating framework for investigating the measurement technique of runway friction coefficient.

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    • Research on Flexible LC Wireless Humidity Sensor Based on GO/Mxene

      张小勇, 寇海荣, 尚珍珍, 杨立波

      Abstract:

      A flexible LC wireless humidity sensor based on GO/Mxene is investigated. Its purpose is to compensate for the limitations of a single material and meet the requirements for passive sensing and bending performance in applications. The principle of the sensor is analysed, and a flexible cross finger electrode antenna based on polyimide is designed. The resonant frequency of the antenna is 146MHz, and the electric field distribution on the antenna is obtained using simulation software. GO/MXene is prepared. The surface morphology and microstructure of GO/MXene are characterized using scanning electron microscopy and energy spectrum analyze, and its structure and constituent elements of GO/MXene are verified. The humidity sensor is fabricated by placing the prepared GO/MXene as a humidity sensitive material at the strongest field strength of the antenna. The performance of the sensor is tested. The results showed that the sensor has high sensitivity. In the relative humidity(RH) range of 20-70% RH, the sensitivity of the sensor reached 90.51 kHz/% RH, and in the relative humidity range of 70-95% RH, the sensitivity reached 651.86 kHz/% RH. At the same time, the sensor has good stability and response time performance, which can monitor human respiration. The Sensor has considerable application prospects in fields such as health detection and robot skin.

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    • Research on surface defect detection of wind turbine based on lightweight convolutional network

      杨宇龙, 张银胜, 陈昕, 段修贤, 吉茹, 单慧琳

      Abstract:

      The power generation efficiency and service life of wind turbines are related to their surface integrity. This study aims to address the issue of inaccurate detection results and long detection time in traditional surface defect detection methods for wind turbines. A surface defect detection model for wind turbines is designed. Firstly, lightweight convolution technology is integrated into the model, effectively enhancing the ability of information exchange between channels and improving the detection effect of small-sized defects through richer feature information; Secondly, the visual attention network module has been introduced into the backbone network, enriching the contextual information and improving the feature extraction ability of convolutional neural networks; Then, a coordinated attention mechanism is introduced into the neck network to capture the location information of defects through spatial orientation; Finally, modify the loss function of bounding box regression to WIoU to develop an appropriate gradient gain allocation strategy. The experimental results show that the improved detection model improves the detection accuracy by 4.14% compared to the original model, significantly enhancing the detection ability of small defects; At the same time, the parameter count of the improved model was reduced by 2.29M, while the parameter count was reduced by 6.2G. The detection speed was significantly improved, meeting the real-time detection requirements of the model.

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    • On-line fault detection method of hydraulic turbine combining PCA and adaptive K-Means clustering

      徐雄, 林海军, 刘悠勇, 胡边

      Abstract:

      During the operation of the bulb tubular hydropower unit, due to hydraulic factors, machinery, working conditions and other factors, it is easy to cause the runner blades and runner chamber to malfunction, which seriously affects the safe operation of the hydropower unit. Based on the analysis of the fault signal characteristics of the runner blades and runner chamber of the bulb tubular hydropower unit, an online fault detection method for hydropower units based on K-Means and Wright"s criterion is proposed. This method uses principal component analysis (PCA) to reduce the dimensionality of the vibration and noise signal characteristics of the hydropower unit, and integrates the Wright criterion to improve the traditional K-means algorithm to realize the adaptive selection of the K value, and perform online clustering of the features, which can quickly and accurately identify .The variable load state of the turbine and the failure of the metal sweeping chamber. The method proposed in this paper is applied to the fault detection of the bulb tubular unit of Wuling Electric Power’s Jinweizhou Hydropower Station. The experimental results show that the accuracy of the online fault detection using this method is 100% and the accuracy of the variable load online detection is 96.7. %, there has been no fault false positives and false negatives in the past 10 months of operation, indicating the effectiveness of the method.

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    • Research on positioning of mobile robot based on Laser Information

      焦传佳, 江 明, 孙龙龙 童胜杰 徐印赟

      Abstract:

      Aiming at the problems of slower particle convergence and poor positioning accuracy when using traditional Monte Carlo positioning algorithms in the navigation and positioning process of mobile robots, as well as low relocation efficiency after artificial kidnapping, this article gives an improved Particle filter positioning method to improve the navigation and positioning efficiency of mobile robots. First of all, it is improved on the basis of the Monte Carlo positioning algorithm and integrated into the method of adaptive region division to ensure that the region contains more effective information, reduce the convergence time of particles, and complete the preliminary coarse positioning of the robot. Then, in the particle sampling and resampling stage, the normal distribution probability model is used to update the particle weights to achieve faster and more efficient global positioning. Through experimental comparison and analysis, compared with the Monte Carlo positioning algorithm, the given method has shortened the time consumption by 4s, and the adaptive Monte Carlo positioning method in this paper can keep the positioning error at about 6cm, thus verifying the given method Effectiveness and stability.

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    • Gaussian process enhanced robust cubature Kalman filter and application in integrated navigation

      崔冰波, 吉峰, 孙宇, 魏新华

      Abstract:

      The observable degree of navigation state has a significant effect on the state estimation of GNSS/INS. In order to improve the accuracy of heading of land vehicle, an improved robust cubature Kalman filter (RCKF) method is proposed. First, the resampling-free sigma-point update framework is employed to separate the cubature point update from the Gaussian information limitation, and thus improving the propagation efficiency of the information contained in instantiated points in the iteratively filtering period. Secondly, in order to improve the heading of land vehicle when it travels along a straight-line, the Gaussian process (GP) is introduced into the uncertainty calibration of moment approximation of system model based on state observability analysis. Simulation results indicate that GP-RCKF improves the heading angle obviously when the state observability is weak, and compared with RCKF the heading is improved by 28.9%.

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    • Research on Traffic Sign Recognition Technology Based on YOLOv5 Algorithm

      吕禾丰, 陆华才

      Abstract:

      Aiming at the low detection accuracy of traditional traffic sign recognition algorithms,a traffic sign recognition method with improved YOLOv5 algorithm is proposed.First,improve the loss function of the YOLOv5 algorithm,use the EIOU loss function instead of the GIOU loss function used by the YOLOv5 algorithm to optimize the training model,improve the accuracy of the algorithm, and achieve faster identification of the target,then use the weighted Cluster NMS to improve the YOLOv5 itself.The weighted NMS algorithm improves the accuracy of generating the detection frame.The experimental results show that the mAP value of the model trained on the CCTSDB traffic sign dataset produced by Changsha University of Science and Technology by the improved YOLOv5 algorithm reaches 84.35%,which is 6.23% higher than the original YOLOv5 algorithm.Therefore,the improved YOLOv5 algorithm has higher accuracy in traffic sign recognition and can be better applied to practice.

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    • A visual measurement method of large inter-frame deformation material

      李慧, 顾永刚, 翟超

      Abstract:

      To solve the decorrelation phenomenon of digital image correlation when measures large inter-frame deformation, a DIC full-field measurement method based on bilateral feature matching is proposed. Firstly, the ORB (Oriented FAST and Rotated BRIEF) algorithm is used to extract and match the feature point pairs to ensure that a large number of matching point pairs can be quickly extracted. Secondly, to deal with the problem of false matching, a bilateral feature matching is proposed based on the grid-based motion statistics method to eliminate false matching point pairs. Finally, the deformation parameters of each point of interest are independently estimated and provided to the inverse compositional Gauss-Newton algorithm to iteratively obtain the final deformation results. The robustness and accuracy of the proposed method are verified by simulation and large compression tests on the polymer foam. Results demonstrate that the real deformation can be measured by this method is about 40%., which is suitable for measuring large inter-frame compression deformation, and shows great potential in measuring other large inter-frame deformation.

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    • Study On the Concept of Space Time Keeping System

      刘民, 王乾娟, 李军

      Abstract:

      To solve the problem that there is no rule to unify time for wider space such as solar system, a new rule for time uniformity is proposed in this paper. Space Time-keeping system (STKS) evolves from the concept of Space Metrology which is based on the theory of General Relativity, and meets the convention of time unit and the beginning of time. It uses the cesium atomic clock to measure proper time and pulsars to measure coordinate time, unifying time through the coordinate time on the origin of solar system barycentre coordinate. The viewpoint of relative time denies the uniqueness of standard time. For different local area or coordinates, when looking from each other, one’s measurement of the other’s time interval would be uneven, showing a curved coordinate axis. While on the viewpoint of absolute time, the standard time is unique and different timing devices could be synchronized by dissemination technology. The concept of STKS will revolutionize the traditional viewpoint of absolute time. With a customized feedback mechanism designed, it would improve time keeping technology to a more stable scale.

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    • Research on pressure magnetic measurement system based on j-a model of force-magnetic coupling

      刘欣, 封皓, 杨洋, 赵亚丽

      Abstract:

      A pressure measurement method for in-service vessel based on magneto-mechanical effect is proposed. Non-intrusive and non-contact pressure measurement is realized by using the stable correspondence between magnetic signals outside the vessel and the stress on the vessel wall. The relationship between magnetic permeability and stress of steel in weak geomagnetic field is analyzed by using J-A coupling model, and the feasibility of using external magnetic field of vessel to measure internal pressure is theoretically proved. A multi-channel synchronous magnetic signal acquisition system is designed, which can simultaneously acquire the magnetic signals of three components of the multi-sensor. Experiments are carried out to verify the performance and advantages of this method. The results show that in the range of 0-3 MPa, there is a good linear relationship between the magnetic field near the surface and the internal pressure of the vessel. The sensitivity to the pressure change of the magnetic field at different parts of the vessel surface is different, so multi-point deployment and calibration optimization are required. For low carbon steel pressure vessel with an outer diameter of 275 mm and a wall thickness of 7.5 mm, the sensitivity of magnetic measurement can reach up to 131.4 mGs/MPa. Via axis-symmetrically arranging a pair of sensors and adding their output up, the influence of rotating vessel on measurement accuracy can be significantly weakened.

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    Display Method:: |
    • Yan Yue, Jiang Yun, Yan Shi

      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%.

    • 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.

    • 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.

    • 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.

    • 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.

    • 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.

    • 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.

    • 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.

    • 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.

    • 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.

    • 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.

    • 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.

    • Zhang Juwei, Wang Yu

      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.

    • 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

    • 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.

    • 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.

    • 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.

    • 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.

    • 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.

    • 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.

    Editor in chief:Prof. Peng Xiyuan

    Edited and Published by:Journal of Electronic Measurement and Instrumentation

    International standard number:ISSN 1000-7105

    Unified domestic issue:CN 11-2488/TN

    Domestic postal code:80-403

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