• 1
  • Current Issue
  • Online First
  • Adopt
  • Most Downloaded
  • Archive
    Select All
    Display Method:: |
    Volume 37,2023 Issue 7
    • Liang Jingyuan, Chen Minghui, Wang Huiqin, Ke Xizheng

      2023,37(7):1-16, DOI:


      The transmission of optical signals in atmospheric turbulent channels causes signal fading and light intensity flicker, and the baseband signal cannot be recovered correctly by fixed threshold detection at the receiving end, so adaptive adjustment of the received signal decision threshold is required. Adaptive threshold detection technology can effectively suppress the atmospheric turbulence effect, which is an important means to improve the bit error rate performance of optical wireless communication systems and enhance system reliability. Its detection performance is mainly optimized and improved for the threshold detection algorithm and feedback mechanism. Reviewing the development process of adaptive threshold detection technology, starting from the structure of optical wireless communication system, deriving the optimal decision threshold model for the received signal based on Bayesian maximum likelihood estimation and maximum posterior probability criteria, and the realization of baseband signal demodulation by comparing the received signal with the optimal decision threshold. The typical adaptive threshold detection models based on the minimum mean square error filter, Kalman filter and fading Kalman filter are analyzed, which are suitable for stationary input signals and non-stationary input signals respectively. At the same time, the related research work of Xi′ an University of Technology using high-order cumulants instead of traditional second-order statistics in the field of adaptive threshold detection is introduced. Finally, the future development trend of this field is summarized and foreseen, which can provide some reference for the future research and development of adaptive threshold detection technology for optical wireless communication.

    • Guan Cunhe, Xu Gaobin, Wang Huanzhang, Zhang Yu, Ma Yuanming

      2023,37(7):17-25, DOI:


      A reliability assessment model based on the total probability formula is proposed to address the potential fatigue failure and fracture failure of MEMS acceleration sensors under complex stress conditions. The model accomplishes reliability modeling of the device in vibration, impact, and vibration-impact coupled environments. The model includes the Wiener processes and the homogeneous poisson random processes, which describe the fatigue damage of the device in the vibration environment and the random impact of the device, respectively. Furthermore, the influence of the amplitude of random impacts on the device degradation rate is considered. The correlation between multiple failure modes is reflected by the sudden increase in fatigue damage generated by the device under impact stress. A comparative analysis was conducted to compare the reliability models considering the independent and coupled effects of vibration and impact. The results demonstrate that the reliability model considering the coupled effects of vibration and impact provides more meaningful guidance in the evaluation.

    • Lu Xingzhi, Dong Mingli, Zhang Xu, He Yanlin

      2023,37(7):26-32, DOI:


      Aiming at the problem of deficient demodulation precision in fiber Bragg grating ( FBG) demodulation system caused by wavelength drift in a variable temperature environment, a grating method for FBG sensing system based on Fabry-Perot (F-P) standard for nonlinear wavelength drift calibration was proposed. In this paper, the FBG demodulation system based on the F-P standard is established, the wavelength calibration process based on the nonlinear error compensation of the F-P standard is designed, and the simulation experiment system of the FBG demodulation system in the practical application environment of aerospace is built, and the experimental tests are carried out in the constant temperature and variable temperature environment. The experimental results show that the demodulation stability of the sensor system is within ±0. 6 pm and ±1. 1 pm under constant temperature and variable temperature, and the wavelength drift of the sensor system demodulation decreases from 20. 3 pm to 0. 7 pm under variable temperature. The accuracy and stability of the method are verified, which provides a reference for structural health monitoring in aerospace and other fields.

    • Pan Zhiyuan, Bu Fanliang

      2023,37(7):33-41, DOI:


      A DV-Hop (distance vector-hop) localization algorithm based on dung beetle algorithm optimization was proposed for the problem of significant localization error of traditional DV-Hop algorithm in wireless sensor networks. Firstly, the dual communication radius was introduced to refine the number of hop nodes, then the average hop size of anchor nodes was calculated using the minimum mean square error criterion, and the mean of the improved average hop size was taken as the average hop size of each unknown node, finally, a weighting factor was introduced to optimize the fitness function, and the dung beetle optimization algorithm was used for coordinate calculation instead of the trilateral measurement method. The simulation results show that the proposed algorithm improves the average positioning error by 55. 69%, 59. 61% and 67. 59%, and the error variance by 52. 41%, 45. 58% and 36. 87% than the classical DV-Hop algorithm, which has good positioning accuracy and better stability.

    • Chen Xianghong, Shi Fanping, Yang Peng, Peng Wei, Yang Xiaoqiang, Yan Junshan

      2023,37(7):42-52, DOI:


      This article proposes a high-precision batch temperature measurement system and testing method for low temperature measurement efficiency of high-precision temperature sensors, the system with the ability testing multiple cores. This article proposes classification method, binary scanning method, and successive approximation method to calibrate the platinum resistance PT100 for the low testing accuracy of platinum resistance PT100. After calibration, the maximum temperature measurement error value in the temperature range of -65 ℃ to 145 ℃ is changed from 0. 412 ℃ to 0. 021 ℃ , and the temperature measurement accuracy is improved by 94. 9%. Simultaneously, the stability, temperature measurement time, and temperature measurement accuracy of the system are modeled and simulated. Firstly, Heat transfer modeling is conducted for the outer wall to the inner wall and convective heat transfer of cold fluid in internal cavity of the thermostatic device respectively. The simulation result shows that the thermostatic device can achieve heat balance in only 75. 2 seconds. Then, the tested circuit heating up, the base heating up, and the calibrated platinum resistance is modeled by comprehensive thermal simulation. The simulation result shows that the temperature measurement accuracy of the system can reach to 0. 016 ℃ . Finally, the external and internal temperatures of several classic high-precision temperature sensors are tested and verified, and the result shows that the system can test temperature sensors with a temperature measurement accuracy of 0. 031 ℃ , which can well meet the temperature measurement requirements.

    • Hu Yong, Lyu Huiyan, Li Shaorong, Zou Li, Ren Yinghui, Liang Jie

      2023,37(7):53-61, DOI:


      The geological conditions of the slopes on both sides of the reservoir area of the hydropower station are complex and changeable. The stable working condition of the slopes is closely related to the safe operation of the hydropower station. In this paper, the MEMS attitude sensors (gyroscopes and accelerometers) are arranged on the slopes by the array mode at the left slope cover layer of the reservoir area of the Yalong River Guandi Hydropower Station. Using the initial alignment, a real-time slope stability data acquisition system is established to determine the navigation target parameters′ attitude, orientation, velocity and position. The initial values of philosophy, bearing, speed and position are calculated. Then the acceleration and angular momentum under the load system acquired by the MEMS attitude sensors are converted to the navigation system in real-time through the attitude matrix, which is used to calculate the slope change attitude matrix and displacement in real time to carry out experimental research. The experimental results show that applying the MEMS attitude sensor to slope displacement monitoring can achieve real-time acquisition of slope surface displacement data. The displacement data acquisition accuracy can reach the millimetre level to meet the actual needs of engineering. The research results are of great significance to the real-time monitoring and analysis of the stable state of slope surface displacement.

    • Chen Mingyun, He Yigang, Zhao Yingying

      2023,37(7):62-71, DOI:


      In order to reduce the diagnostic complexity and cost of the three-level neutral-point-clamped ( NPC) rectifier, a fault diagnosis method for the open-circuit fault of power switches was proposed. This method was based on the characteristics of switching states. Firstly, the cumulative values of some switching states in half a current cycle were selected as the diagnostic variables, which were combined with the self-adaptive threshold and the phase of currents to realize the faulty bridge arm detection. Then, the outerswitch fault-tolerant control based on reactive current injection was implemented and the threshold was updated. Finally, according to the change of diagnostic variables, the faulty switch was located during fault-tolerant process. The proposed method can realize both singleswitch and double-switch open-circuit fault diagnosis without additional sensors, complicated calculations and diagnostic rules. It has the advantages of simple implementation and low cost. The experimental results verify the effectiveness and robustness of the proposed method.

    • Liu Chongpei, Sun Wei, Liu Jian, Yang Hui, Zhang Xing, Fan Shimeng

      2023,37(7):72-80, DOI:


      Aiming at the low accuracy of object six-degree-of-freedom ( 6D) pose estimation in scenes with interferences such as illumination changes, distance changes, background clutter, and occlusions, a mixed channel attention module (MCA) is proposed, which combines multi-scale feature fusion and attention mechanisms. Based on MCA, a category-level object 6D pose estimation method (MCA6D) is further constructed. The key steps include object instance segmentation, feature extraction and optimization based on MCA, object model reconstruction based on prior shape, and pose estimation based on point cloud registration. Relevant experiments show that our method achieves 86. 3% (5°2 cm), 73. 4% (5°5 cm) and 39. 2% (5°2 cm), 43. 3% (5°5 cm) mean average precision in the public datasets CAMERA and REAL, respectively, which is ahead of mainstream methods such as NOCS, SPD, and SGPA. At the same time, the practical experiment shows that the proposed method can accurately estimate the 6D pose of the object in scenes with interference, such as illumination changes, distance changes, background clutter, and occlusions.

    • Wang Shi, Zhu Xiaoying, Sun Hao, Zhang Min, Bian Tingyue

      2023,37(7):81-92, DOI:


      In multi-user multi-channel cognitive radio networks for the internet of things (CR-IoT), a spectrum co-sensing-allocation strategy based on edge computing is proposed. To evaluate the performance of the proposed strategy, a performance evaluation system (PES) capable of quantifying the quality of service (QoS) of cognitive users and the interference to primary users is developed. In the PES, a Markov model is established to describe the CR-IoT system state based on queuing theory. The performance metrics of heterogeneous cognitive users with configurable parameters can be analyzed independently. Thus, various spectrum sensing-allocation strategy can be evaluated using the proposed PES. Numerical results show that the proposed edge computing strategy outperforms central computing strategy in QoS of cognitive users and the quantified interference to primary user. It proves that the PES can help regulators design various spectrum sensing-allocation strategy.

    • Fan Tian′e, Tang Xin, Lei Haoran, Li Penghua

      2023,37(7):93-103, DOI:


      With the wide application of lithium-ion battery systems in electric vehicles, the safety issue caused by short-circuit fault of battery pack is becoming more serious. Therefore, the studies on state monitoring of battery pack and fault diagnosis are receiving more attention. To deal with the issues of low generality, poor anti-interference capacity and critical inconsistency of battery pack existed in non-model-based fault diagnosis methods, a short-circuit fault diagnosis method based on statistical analysis and density clustering is proposed for battery packs in this paper. Firstly, the fault information of battery pack is extracted by using the relative entropy of kernel density estimation (KDE) and correlation coefficient, based on a forgetting mechanism. The fault information is used to identify the changes of batteries’ voltage and temperature caused by short-circuit fault. Then, the short-circuit battery can be automatically identified by adopting the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The robustness of the proposed method is validated under conditions of noise interference and serious inconsistency. Furthermore, the effectiveness of the proposed method is verified under different short-circuit degree with 1, 5 and 10 Ω short-circuit resistors, and the accuracy of short-circuit fault diagnosis can reach 92. 17% in the case of a 10 Ω short-circuit resistor. By comparative analysis, the results show that the proposed diagnosis method can effectively detect and locate short-circuit batteries, and the more severe the fault, the shorter the diagnosis time required.

    • Li Wei, Gao Lin

      2023,37(7):104-112, DOI:


      At this stage, the image deep learning algorithm cannot detect the chronological process problem. In this paper, the artificial assembly process of the mountain board assembly of knitting machinery is studied, and the MS-RetinaNet object detection algorithm is proposed. Using the idea of natural language processing for reference, the Swing-Transformer structure is introduced to retain the hierarchy of CNN structure, make up for the lack of high-level semantic information fusion in CNN structure, and enhance the ability to learn overall and details. The improved GIoU Loss is used to increase the judgment factor formula, mitigate the impact of loss calculation degradation, and optimize the regression effect of the bounding box. According to the multi-scale target parameters, the best anchor frame ratio is adopted to improve the recall rate and detection accuracy. The chronological detector is designed to enable the algorithm to distinguish the sequence and logical relationship of the target. The experimental results show that the algorithm AP can reach 90. 3%, which is more than 2% higher than the current mainstream algorithm. The detection speed of a single image is about 46 ms, meeting the chronological detection requirements of the process flow, and the overall performance is superior.

    • Cui Yongdan, Yu Jing, Li Jizhen, Cai Jinhui, Kong Ming

      2023,37(7):113-120, DOI:


      Aiming at the problems of solid thread gauges including difficult to process, high cost, easy wear and cumbersome verification procedures, a digital measurement model establishment and parameter calculation method of thread gauge based on three-dimensional point cloud was proposed. In order to realize the digitization of the physical thread gauge. First, the 3D digital model was obtained, and the key parameters of the model such as large diameter, middle diameter, small diameter, pitch and tooth profile angle were obtained. Secondly, the single error and comprehensive error of the model are analyzed, and the relative error of each parameter is less than 5%. Finally, the uncertainty and extended uncertainty of each parameter of the digital model are calculated, which proves the accuracy of the digital measurement model of thread gauge. The results provide a research basis for the digital development of thread gauge and have reference value for the digital transformation of measurement industry.

    • Ran Ning, Zhang Jiaming, Yang Hongfei, Hao Zhenming, Hao Jinyuan

      2023,37(7):121-130, DOI:


      A semantic network-based network search algorithm is proposed to address the problems of slow planning speed and high memory occupation of raster map-based path planning techniques in the face of large maps and high-resolution maps. Firstly, a semantic partitioning network is used to pre-sample the raster map, secondly, the optimal path range is formed by widening the optimal path through imagery expansion to improve the robustness of the algorithm, finally, the feature map of the semantic network is used to guide the planning of the search algorithm, which speeds up the path planning of the high-resolution raster map. Experimental simulations show that the network search algorithm reduces the time by an average of 72. 5%, the number of traversal points by an average of 51. 6%, and the path length by an average of 0. 73% compared to the traditional search algorithm, and the network search algorithm can effectively speed up the path search and reduce the memory occupation.

    • Li Bin, Guo Ziqiang, Gao Peng

      2023,37(7):131-139, DOI:


      Aiming at the problems of the northern goshawk optimization algorithm (NGO), such as low convergence accuracy and easy to fall into local optimum, an improved northern goshawk optimization algorithm (INGO) is proposed and applied to the fault diagnosis of photovoltaic array. Firstly, circle mapping, adaptive weight factor and Levy flight strategy are used to improve the INGO. Combined with Gaussian detection mechanism and hybrid kernel extreme learning machine ( HKELM), the INGO-HKELM fault diagnosis model is built. Secondly, the INGO algorithm is compared with the NGO, the particle swarm optimization algorithm ( PSO), and the whale optimization algorithm (WOA) on the test functions, which shows that it has advantages in optimization ability and stability. Then, the operating characteristics of photovoltaic arrays under different operating states are analyzed, and a 5-D fault feature vector is proposed as the input of data. Finally, the four algorithms are used to optimize the kernel parameters of HKELM and achieve fault classification. The results show that the proposed method can accurately detect abnormal states of photovoltaic modules, and the accuracy of INGO-HKELM model reaches 93. 74%, which verifies the effectiveness and feasibility of the proposed algorithm.

    • Xing Fangyi, Xu Cheng, Gao Hongwei

      2023,37(7):140-147, DOI:


      In order to solve the problems of the ORB image feature detection algorithm under non-uniform illumination, such as overly clustered feature points and low accuracy of feature matching, we propose an efficient and high-precision illumination adaptive ORB image feature matching algorithm. The oFAST feature points of the image to be measured are extracted using the adaptive threshold, and the number of feature points in the low illumination or high exposure area is further increased through the uniform distribution of the optimized quadtree decomposition method. Then, feature matching is performed according to Hamming distance, and the improved RANSAC algorithm is used to eliminate mis-matching, so as to improve the matching accuracy of the feature points in the ORB algorithm. The experimental results show that for data sets with obvious illumination changes, compared with ORB, MA, Y-ORB and SORB algorithms, the average feature distribution uniformity of our proposed algorithm is improved by 13. 1%, the feature extraction time is saved by 26. 3%, and the comprehensive evaluation index is improved by 18. 5%. It can efficiently complete feature matching under complex environment changes, and has strong application value in the fields of target recognition and 3D reconstruction.

    • Zheng Xiaoyu, Dong Zhixu, Yang Heran, Sun Xingwei, Liu Yin

      2023,37(7):148-155, DOI:


      In order to solve the problem that local specular reflection is easy to occur during the measurement of objects with strong reflection surface, which affects the measurement accuracy. The digital micro mirror is added to the structural light measurement optical path and the measurement system is designed and built, including the digital micro mirror, CCD camera, projector and other devices. The matching of the system, the parameter calibration of camera and projector and the phase calculation are completed. The coordinate mapping relationship between the digital micromirror device unit and the camera pixel unit is established by using the sparrow search algorithm to optimize the BP neural network. The mapping error is 0. 583 pixels. An adaptive mask generation method based on PID controller is proposed, and the measurement experiment of the measure block with strong reflective surface is carried out. The experiment shows that the method can effectively reduce the gray level of the overexposed area, and achieve high dynamic range imaging. The proposed method can provide theoretical support for the three-dimensional measurement of strongly reflective surfaces.

    • Zhu Yifeng, Jia Xiaolei, Zhou Feishan, Zhang Ziyang, Li Yan

      2023,37(7):156-165, DOI:


      In order to improve the control performance of single-phase five-level rectifier, a dual closed-loop integrated control method based on non-delay power observer was studied. In this method, a virtual current signal reconstruction algorithm is proposed and combined with the improved generalized integration algorithm to construct a non-delay power observer, which is applied to the model predictive power control of the inner loop of the rectifier to observe the required power in real time, while the linear distraction control is used instead of the traditional proportional integration algorithm by linear distraction control in the outer voltage loop. The experimental results show that the time required for the control method to retrace the given value after the power mutation is shortened by 5~ 7 ms, and the voltage fluctuation on the DC side is reduced by 4. 4% and 4. 8% under the grid voltage drop and load disturbance conditions, respectively. Compared with the traditional method, this method not only reduces the overshoot of rectifier, improves the dynamic performance of the predicted power control of the inner loop model, but also effectively enhances the anti-interference ability of the voltage outer loop.

    • Wang Lingyun, Li Tingyi, Li Yang, Wan Xudong, Tong Huamin

      2023,37(7):166-176, DOI:


      The proportion of armor clamp rust in aerial images of power transmission lines is rich in details and irregularly distributed. To overcome problems such as local information loss, low accuracy, and slow speed in the segmentation detection process, a DeepLabV3+- based semantic segmentation model for armor clamp rust is proposed. The backbone network is replaced with a lightweight improved MobileNetV3 network to speed up computation, and an adaptive feature pyramid (AFP) structure is proposed to merge multiple scales. A feature fusion atrous spatial pyramid pooling ( FEF-ASPP) structure is proposed, combined with the FRN layer to strengthen pixel relationships without reducing resolution. Finally, the loss function is optimized to improve the effectiveness of the operator. Experiments show that the mIoU and mPA reach 87. 15% and 96. 64%, respectively, which is an improvement of 3. 09% and 4. 29% compared to the original model. The parameter quantity is only 48% of the original model, and the inference time is only 15. 94 ms, reducing the requirement for device computing power and achieving high-efficiency, high-precision, and lightweight segmentation detection of armor clamp rust in power transmission equipment.

    • Zhao Pu, Wu Yi

      2023,37(7):177-185, DOI:


      For the lack of detection accuracy for human keypoints, it is improved on the basis network of KAPAO (keypoints and pose as objects). The generalization of network is improved by the enhance data method of PoseTrans ( pose transformation); for the lack of characteristic fusion capabilities, the BiFPN (Bi-directional feature network) module is designed to fully integrate different semantic characteristic to improve the integration ability of deep semantics information and shallow semantic information; the adaptive expansion convolution module is designed to adaptive fusion different expansion rates of output branch during the network output phase, it effectively obtains the global information of the image; in order to retain the optimal key point prediction box, the traditional NMS is replaced by SDR-NMS ( soft DIOU relocation non-maximum suppression ) during the post-processing part of the network. The experimental results show that the AP score was increased by 4. 8%, the AP was 68. 6%, and the detection speed was 19. 1 ms. The accuracy and detection speed of network have better performance.

    • Wang Yiwen, Wang Weili, Liu Xianchao, Hu Weiqin

      2023,37(7):186-195, DOI:


      Accurate prediction of wind speed is of great significance for safe operation and efficient power generation of wind farms. Aiming at the inherent defects of the single decomposition strategy used in existing literatures in wind speed prediction and the unstable effect of the optimized prediction model, a hybrid prediction model combining two-stage decomposition and iJaya-ELM is proposed. First, ICEEMDAN decomposition is performed on the original wind speed sequence, and 12 components are obtained, and reconstructed into high frequency terms, middle frequency terms and low frequency terms based on the permutation entropy. Then, the high frequency term is filtered by singular spectrum decomposition to remove the sequence noise. An improved Jaya algorithm, iJaya, is proposed to obtain the optimal connection weights and thresholds of ELM. Finally, the predictive results of each component are linearly integrated to obtain the final results. The model is validated by wind speed data of wind farm in Gansu province of China, and its robustness and universality are tested by wind speed data of Xinjiang region. The experimental results show that the iJaya algorithm is of strong optimization accuracy and stability, and the two-stage decomposition can deeply excavate the characteristics of wind speed series. The hybrid model can effectively improve the wind speed prediction accuracy, and the average absolute error and mean square error are 0. 067 9 and 0. 134 5, respectively.

    • Wu Kunpeng, Shi Jie

      2023,37(7):196-204, DOI:


      In the double-sided shearing process, the steel plate alignment process requires manual visual observation of the laser beam allowance, which is complex in operation and subjective judgments that affect data accuracy. Therefore, in this paper, an automatic alignment system for double-sided steel plate shearing based on machine vision is designed, which relies on multiple sets of area array cameras distributed along the roller table to collect the status data of the steel plate on the roller table. Using on-site measurement data and the target width of the steel plate, two virtual cutting lines are calibrated, eliminating the dependence on traditional auxiliary laser lines. At the same time, a cascaded steel plate object extraction model is adopted in the system, and the step-by-step extraction idea of “rough first and then fine” is adopted to improve the accuracy of steel plate edge detection. The movement distance is converted based on the relationship between the steel plate contour position and the virtual shear line position, thereby controlling the magnetic centering device to complete the steel plate centering process and improving the automation of the double sided shear process. The actual application results show that the system has a measurement error of less than 5 mm for the width of steel plates, and an automatic control centering error of less than 10 mm, meeting the automatic control requirements of enterprises.

    • Wang Lei, He Kun, Li Zongshuai, Chang Dongrun

      2023,37(7):205-212, DOI:


      Aiming at the problem of poor generalization ability of fault diagnosis of traditional deep learning network model under variable working conditions, a fault identification method based on the fusion of transfer learning bidirectional long short memory network and attention mechanism ( TLBA) is proposed. Divide the original fault data into source domain and target domain, and construct a bidirectional long short-term memory network (BA) model that integrates attention mechanisms, and then use this model to learn source domain data features. Finally, transfer learning is used to further optimize and adjust the network parameters of the BA model by learning the data in the target domain, and finally the fault classification identification model in the target domain is obtained. Taking the aircraft wing beam fault as an example, the results show that compared with the traditional fault diagnosis method BiLSTM-Attention, the comprehensive evaluation index F1-score of this method is improved by 3. 4%, and the average fault diagnosis accuracy is above 91%. At the same time, the fault classification results under variable operating conditions are relatively stable.

    • Ye Fei, Luo Xingzhi, Song Yongchun, Ding Guocheng, Yang Xiaozhi, Tan Shoubiao

      2023,37(7):213-220, DOI:


      As an indispensable key component of power transmission lines, power fittings provide a guarantee for stable power transmission. Once the electric power fittings have defects, it will bring huge hidden dangers, causing damage to transmission facilities or even large-scale power failure, affecting people’ s production and life. The traditional power transmission line maintenance mainly depends on manual on-site maintenance, which is not only dangerous, but also difficult to detect. The continuous progress of AI recognition technology provides a better method for the defect recognition of electric power fittings. At present, the target recognition accuracy of Faster R-CNN is high, but it is relatively low for small target objects such as screws. Firstly, the features are extracted and marked by the double feature fusion operator, then input into the improved Faster R-CNN model with the introduction of mixed attention mechanism for feature re extraction. The features with high coincidence degree are fused, and the defects are classified and recognized, which can effectively identify the screws in the small power fittings. The experiment shows that the improved Faster R-CNN based on dual feature fusion in this paper has obvious improvement effect compared with the traditional Faster R-CNN and YOLO. The average accuracy of the model is improved by 5%, and the average accuracy is improved by 11%, which also ensures the real-time performance of the algorithm identification. It has a good detection effect on small electrical fittings such as screws.

    • Zhou Xiaofa, Zhang Yuegang, Fang Yu, Yang Hao, Xia Yanfeng, Wu Xin, Fan Diqing, Sun Boyang

      2023,37(7):221-229, DOI:


      The sag of conductor and ground wire is one of the key indicators for the construction quality and safety operation of the overhead transmission line. Aiming at the shortcomings of current sag measurement method’s accuracy and convenience, a mathematical model is proposed based on laser ranging and grating angle measurement technology for the sag measurement of the line. On the basis of this model, a data quality evaluation optimization algorithm which the similarity measure combined with the Newton interpolation method is put forward to realize the data set compensation and sag calculation for the case of insufficient robustness of the single conductor’s sag measurement of the multi-split line. Compared with point cloud extraction power line method, only a small amount of measurement data is needed for the proposed method to fit the line model and calculate the sag value. In order to verify the proposed method and compare with current sag measurement methods, a 220 kV double-split line is used in a test, and the result shows that the maximum sag error rate of the optimized data is only 1. 47%, which proves that the proposed method’ s measurement accuracy can meet the requirement of engineering sites, and can improve the security and efficiency of the overhead transmission line’s sag measurement.

    • Yan Xiaoheng, Li Chaozheng, Chen Weihua, Wang Shuai

      2023,37(7):230-242, DOI:


      The corrosion detection of main ground electrode in underground coal mine is crucial to the safety of the staff. Traditional manual visual inspection detection by transporting the electrode overground is time-consuming and the damage magnitude cannot be confirmed. Therefore, a new online corrosion detection method based on ultrasonic SH wave echo characteristics is proposed. Firstly, the dispersion equation of SH wave in the liquid-immersed plate structures is derived. The zero-order SH wave with small dispersion and long propagation distance is selected as the detection excitation signal, and the finite element model of liquid-immersed plate structure is constructed to determine the optimal ultrasonic excitation frequency. Secondly, the coupled flow-solid-acoustic multi-physical field detection model of the main grounding electrode in coal mines is constructed and the corrosion detection ability of ultrasonic SH0 wave on the main ground electrode is studied by simulation, then the echo signal characteristics under 1 ~ 5 mm corrosion depth and 5 ~ 25 mm corrosion radius are analyzed. Finally, the non-destructive testing system of the main ground electrode in coal mine is constructed, and experimental verification is carried out. The experimental results prove the feasibility of the proposed method, the corrosion location error is 5. 64% on a 500 mm×375 mm×5 mm main grounding plate, the corrosion signal amplitude is positively correlated with the magnitude of corrosion defects. The research provides an effective method for corrosion location and damage assessment of underground coal mine main ground electrode.

    • Lu Zhaogang, Zhu Xuan

      2023,37(7):243-250, DOI:


      Aiming at the problem that the screen data of roll grinder can only be obtained by manual transcription, we designed a method of automatic identification and recording of key parameters of roll grinder screen. The specially designed smart camera is installed above the CNC screen of the grinder, and the camera structure is designed with the angle of 45° “L”, which can take photos of the CNC screen without affecting the work of the master. Firstly, the screen image is registered and corrected by edge positioning and perspective transformation. Secondly, grinder parameters in the image were identified by the trained YOLOv5 model. Finally, the key parameters of the grinder are imported into the database to complete the real-time recording and transmission of the parameters, so as to provide timely and accurate key equipment parameters for the adjustment of related subsequent production processes. In addition, in view of the common Moire pattern phenomenon in screen images, the design of polarization window and Moire pattern removal algorithm is combined to effectively filter Moire pattern, which significantly reduces the influence of Moire pattern on the recognition accuracy. Since the system has been running for half a year, the recognition rate of grinding machine screen data has exceeded 99%, which significantly reduces labor intensity and manual error, and improves productivity.

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

      2017,31(1):45-50, DOI: 10.13382/j.jemi.2017.01.007


      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


      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


      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


      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.

    • He Lifang, Cao Li, Zhang Tianqi

      2017,31(1):21-28, DOI: 10.13382/j.jemi.2017.01.004


      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.

    • Pan Yuehao, Song Zhihuan, Du Wangze, Wu Legang

      2017,31(1):29-35, DOI: 10.13382/j.jemi.2017.01.005


      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.

    • Yan Fan, Zhang Ying, Gao Ying, Tu Yongtao, Zhang Dongbo

      2017,31(1):36-44, DOI: 10.13382/j.jemi.2017.01.006


      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.

    • Chen Shuo, Luo Tengbin, Liu Feng, Tang Xusheng

      2017,31(1):144-149, DOI: 10.13382/j.jemi.2017.01.021


      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.

    • Yin Min, Shen Ye, Jiang Lei, Feng Jing

      2017,31(1):76-82, DOI: 10.13382/j.jemi.2017.01.011


      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.

    • Cao Xinrong, Xue Lanyan, Lin Jiawen, Yu Lun

      2017,31(1):51-57, DOI: 10.13382/j.jemi.2017.01.008


      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


      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


      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.

    • 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


      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.

    • 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


      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.

    • Zhou Na, Lu Changhua, Xu Tingjia, Jiang Weiwei, Du Yun

      2017,31(1):139-143, DOI: 10.13382/j.jemi.2017.01.020


      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.

    • Chen Zhenhai, Yu Zongguang, Wei Jinghe, Su Xiaobo, Wan Shuqin

      2017,31(1):132-138, DOI: 10.13382/j.jemi.2017.01.019


      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


      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


      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.

    • Jiang Zhanglei, Xu Xiaoli

      2017,31(1):155-160, DOI: 10.13382/j.jemi.2017.01.023


      Rotating machinery is the most widely used mechanical equipment in production, and its transmission system is an important part of the rotating machinery. Whether the rotating machinery transmission system running in safe and stable state has an important impact on the economy and society, so, researched on running stability deterioration of rotating machinery is significant. Rotor test rig has typical transmission structure of rotating machinery, and feature parameter extraction method faced to running stability deteriorationof its transmission system has been proposed.This paper describes the evolutionary trajectory of stable running state deterioratedto the unstable running state, and deterioration evolutionary matrix has been proposed based on higher order cumulant method. A feature parameter extractionhas been used to depict evolutionary track faced to running stability deterioration of rotating machinery based on characterization parameters of the state deterioration trend such as mean value of stability deterioration and varianceof stability deterioration. The vibration data of the actual operating state of rotor test rigare collected, and experimental verification is carried out based on the method.The results show that this method can describe the overall trend of the operation statesface to the transmission system of the rotating machinery.

    • Ouyang Yiming, Chen Jingwen, Liang Huaguo, Huang Zhengfeng, Du Gaoming, An Xin

      2017,31(1):92-98, DOI: 10.13382/j.jemi.2017.01.013


      When congestion occurs in the network, VOQ routers still suffer a certain degree of head of line blocking (HoL) problem in the network on chip (NoC).Aiming at this issue, we propose the load balancing AVOQ router architecture. Firstly, the VOQ mechanism is kept to deal with the HoL problem. Secondly, a flexible output port can be picked up in the routing computing module, making sure that the dada is ported out to the less congested road and a single virtual channel (VC) can read the packet adaptively, so that the less congested flow in the downstream can be transmitted. The experimental results show that, compared to the VC router and the VOQ router, AVOQ router reduces the average latency by 83.2% and 57.1% and improves the throughput by 72.7% and 33.3% at most, while the area overhead and power consumption are affordable. By the use of above adaptive mechanism, the network load is balanced and the congestion is relieved, and the appearing of the HoL is decreased. Moreover, the impact of HoL is eliminated as long as it appears,and the network performance is improved greatly.

    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

    Press search
    Search term
    From To