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    Volume 47, 2024 Issue 6
      Research&Design
    • Xue Xianbin, Tan Beihai, Yu Rong, Zhong Wuchang

      2024,47(6):1-7, DOI:

      Abstract:

      Urban intersections are accident-prone sections. For intelligent networked vehicles, it is very important to carry out risk detection and collision warning during driving to ensure the safety of driving. This paper proposes a traffic risk field model considering traffic signal constraints for urban intersections with traffic lights, and designs a three-level collision warning method based on this model. Firstly, a functional scenario is constructed according to the potential conflict risk points of urban intersections, and the vehicle risk field model is carried out considering the constraint effect of traffic signal. In order to solve the problem of collision warning, a three-level conflict area is proposed to be divided by the index, and the collision risk of the main vehicle is measured according to the position of the potential energy field around the main vehicle by calculating the corresponding field strength around the main vehicle. The experimental results show that the designed model can accurately warn the interfering vehicles entering the potential energy field of the main vehicle, the warning success rate can reach 100%, and the false alarm rate is only 3.4%, which proves the reliability and effectiveness of the proposed method.

    • Wei Jinwen, Tan Longming, Guo Zhijun, Tan Jingyuan, Hou Yanchen

      2024,47(6):8-13, DOI:

      Abstract:

      To address the issue of low accuracy in indoor static target positioning with existing single-antenna ultra-high frequency RFID technology, this paper proposes a new RFID localization method based on an antenna boresight signal propagation model. The method first determines the height position of the target through vertical antenna scanning; secondly, it adjusts the antenna height to match that of the target and then performs stepwise rotational scanning to identify the target′s azimuth angle; furthermore, it utilizes a Sparrow Search Algorithm optimized back propagation neural network to establish a path loss model for ranging purposes; finally, it integrates the height, azimuth angle, and distance data to complete the target positioning. Experimental results show that in indoor environment testing, the proposed method has an average positioning error of 7.2 cm, which meets the positioning requirements for items in general indoor scenarios.

    • Wang Huiquan, Wei Zhipeng, Ma Xin, Xing Haiying

      2024,47(6):14-19, DOI:

      Abstract:

      To solve the problem of low control accuracy of the tidal volume emergency ventilation for lower air pressure at high altitudes, we propose a dual-loop PID tidal volume control system, which utilizes a pressure-compensated PID controller to adjust fan speed, supplemented by an integral-separate PID controller in order to achieve precise control of airflow velocity.Compared with single-loop PID control, the rapid response and no overshooting are observed in the performance tests of the dual-loop control system at an altitude of 4 370 m and atmospheric pressure of 59 kPa, in addition, the output error of the average airflow velocity decrease to 3.19% (the maximum error is 4.1%), which is superior to that of current clinical equipment. Our work offers an effective solution for high-altitude emergency ventilator tidal volume control, and contributes important insights to the development of ventilation control technology in special environments.

    • Fang Xin, Shen Lan, Li Fei, Lyu Fangxing

      2024,47(6):20-27, DOI:

      Abstract:

      The high-frequency measurement data of underground vibration signals can record more specific details about the dynamic response of drilling tools, which is helpful for analyzing and diagnosing abnormal vibrations underground. However, the high-frequency measurement generates a large amount of measurement data, resulting in significant storage pressure for underground vibration measurement equipment. The proposed method uses compressed sensing technology to selectively collect and store sparse underground vibration data and then recover high-frequency measurement results through a signal reconstruction algorithm. In the process of realizing this method, an innovative method of constructing a layered Fourier dictionary against spectrum leakage is proposed, and an improved OMP signal reconstruction algorithm based on layered tracking is researched and realized, which greatly reduces the time required for signal recovery. Simulation and experimental test results demonstrate the method′s effectiveness, achieving a system compression ratio of 18.9 and a reconstruction error of 52.1 dB. The proposed method may greatly reduce the data storage pressure of the measuring equipment in the underground, and provides a new way to obtain high-frequency measurement data of underground vibration.

    • Wu Jing, Cao Bingyao

      2024,47(6):28-33, DOI:

      Abstract:

      With the increasing demand for satellite network, vehicle-connected network, industrial network and other service simulation, this paper proposes a multi-session delay damage simulation method based on delay range strategy to build flexible software network damage simulation, aiming at the problems of small number of analog links, low flexibility and high resource occupation of traditional dedicated channel damage instruments. In this method, the delay damage of each session flow is identified and controlled independently, and the multi-queue merging architecture based on time delay strategy is adopted to reduce the resource consumption. The experimental results show that compared with the traditional dedicated device and simulation software NetEm, the proposed method supports the independent delay configuration of million-level links, increases the number of session streams from ten to one million, and reduces the memory consumption by at least 85% under each bandwidth, which meets the requirements of large scale and accuracy, and greatly reduces the system cost.

    • Feng Zhibo, Zhu Yanming, Liu Wenzhong, Zhang Junjie, Li Yingchun

      2024,47(6):34-40, DOI:

      Abstract:

      The data bits and spread spectrum codes of the spaceborne spread-spectrum transponder are asynchronous. Due to the influence of transmission system noise and Doppler frequency shift, it can cause attenuation of peak values related to receiving and transmitting spread spectrum codes, leading to a decrease in capture performance. Traditional capture techniques often have problems such as high algorithm complexity, slow capture speed, and difficulty adapting to the requirements of large frequency offsets of hundreds of kilohertz. This article proposes a spread spectrum sequence search method that truncates the spread spectrum sequence into two segments for correlation operations, and combines the signal squared sum FFT loop for a large frequency offset locking, effectively suppressing the attenuation of correlation peaks and improving pseudocode capture performance. MATLAB simulation and FPGA board level testing show that the proposed spread spectrum signal capture scheme can resist Doppler frequency shifts of up to ±300 kHz, with an average capture time of about 95 ms. In addition, the FPGA implementation of this algorithm saves about 47% of LUT, 43% of Register, and more than half of DSP and BRAM resources compared to traditional structures, making it of great application value in resource limited real-time communication systems.

    • Theory and Algorithms
    • Yang Yi, Aimen Malik, Yuan Ruifu, Wang Keping

      2024,47(6):41-49, DOI:

      Abstract:

      Hydraulic support pillar pressure prediction has been a pivotal basis for decision-making in the mining process. It has been one of the fundamental pieces of information for ensuring the stability of the surrounding rock. However, although the pressure of hydraulic support pillars followed certain patterns, it couldn’t be predicted using simple mathematical models. Additionally, during the mining process, issues such as the support detaching the roof, roof fragmentation, and sensor detection errors introduced a significant amount of random noise, turning the pressure data into a non-stationary time series. This significantly complicated the pressure prediction. Based on the Transformer model, this paper proposed a differencing non-stationary Transformer model, which introduced differencing normalization and de-normalization operations in the Transformer′s Encoder and Decoder, respectively, to enhance the stationarity of the series. At the same time, a de-stationary attention mechanism was deployed within the Transformer to calculate the correlations between sequence elements, which thereby enhanced the model′s predictive capabilities. Comparative experiments on a real coal mine support pillar dataset showed that the differencing non-stationary Transformer model proposed in this paper achieved a prediction performance of 0.674, which was significantly better than LSTM, Transformer, and non stationary Transformer models.

    • Peng Duo, Luo Bei, Chen Jiangxu

      2024,47(6):50-57, DOI:

      Abstract:

      Aiming at the non-range-ranging location problem of multi-storey WSN structures, a three-dimensional indoor multi-storey structure location algorithm IAODV-HOP algorithm based on improved Tianying is proposed in the field of large-scale indoor multi-storey structure location for some large commercial supermarkets, hospitals, teaching buildings and so on. Firstly, the nodes are divided into three types of communication radius to refine the number of hops, and the average hop distance of the nodes is modified by using the minimum mean square error and the weight factor. Secondly, the IAO algorithm is used to optimize the coordinates of unknown nodes, and the population is initialized by the best point set strategy, which solves the problem that the quality and diversity of the population are difficult to guarantee due to the random distribution of the initial population in the Tianying algorithm. In addition, the golden sine search strategy is added to the local search to improve the position update mode of the population, and enhance the local search ability of the algorithm. Through simulation experiments, compared with traditional 3D-DV-Hop, PSO-3DDV-Hop, N3-3DDV-Hop and N3-ACO-3DDV-Hop, the normalized average positioning error of the proposed algorithm IAODV-HOP is reduced by 70.33%, 62.67%, 64% and 53.67%, respectively. It has better performance, better stability and higher positioning accuracy.

    • Ma Dongyin, Wang Xinping, Li Weidong

      2024,47(6):58-63, DOI:

      Abstract:

      Aiming at the Automatic Train Operation of high-speed train,an algorithm based on BAS-PSO optimized auto disturbance rejection control (ADRC) is used to design speed tracking controller.The ADRC is designed based on the train dynamics model,ITAE is used as the objective function,and the parameters are tuned by BAS-PSO.CRH380A train parameters are selected, The tracking effect of BAS-PSO, PSO and improved shark optimized ADRC algorithm on the target speed curve of the train is compared by MATLAB simulation,The tracking error of the train target speed curve based on the BAS-PSO optimized ADRC algorithm is kept in the range of ±0.4 km/h,which is closer to the target speed curve than the other two algorithms.The results show that the ADRC based on BAS-PSO optimization has the advantages of small tracking error and strong anti-interference ability.

    • Li Ya, Wang Weigang, Zhang Yuan, Liu Ruipeng

      2024,47(6):64-70, DOI:

      Abstract:

      A task offloading strategy based on Vehicle Edge Computing (VEC) is designed to meet the requirements of complex vehicular tasks in terms of latency, energy consumption, and computational performance, while reducing network resource competition and consumption. The goal is to minimize the long-term cost balancing between task processing latency and energy consumption. The task offloading problem in vehicular networks is modeled as a Markov Decision Process (MDP). An improved algorithm, named LN-TD3, is proposed building upon the traditional Twin Delayed Deep Deterministic Policy Gradient (TD3). This improvement incorporates Long Short-Term Memory (LSTM) networks to approximate the policy and value functions. The system state is normalized to accelerate network convergence and enhance training stability. Simulation results demonstrate that LN-TD3 outperforms both fully local computation and fully offloaded computation by more than two times. In terms of convergence speed, LN-TD3 exhibits approximately a 20% improvement compared to DDPG and TD3.

    • Fan Shuaixin, Gu Yuhai, Zou Zhi, Cui Yue

      2024,47(6):71-78, DOI:

      Abstract:

      Feature matching is often used to calculate pose information in visual measurement, but there is no available algorithm for designing feature matching for infrared active targets. In order to achieve matching of infrared active targets with different distributions, this paper proposes a general two-stage feature Point matching method. The first stage is coarse registration. First, the convex hull of the image feature point set is detected to obtain the outermost points. Fast coarse registration is achieved by constructing a triangle feature set and using Mahalanobis distance to calculate and search for similar triangles. The second stage is precise matching. First, the Euler angle is calculated through coarse matching features to avoid the 180° rotational symmetry of the matching results. In order to solve the problem of possible missing feature points after coarse registration, the epipolar constraint fine matching strategy is adopted to make full use of the existing features. Match the geometric information of feature points to effectively achieve accurate matching of remaining points. Theoretical analysis and experiments show that under the rotational symmetry point set and the non-rotation symmetry point set composed of 13 infrared luminescent points, this method can efficiently match within the absolute rotation range of 0°~40°, and the experimental test limit performance can reach 50°, and has good robustness to the occlusion of feature points in actual scenes. The experimental results verify its adaptability and stability, and has high practical value.

    • Zhou Jianxin, Zhang Lihong, Sun Tenghao

      2024,47(6):79-85, DOI:

      Abstract:

      Aiming at the problems that the standard honey badger algorithm (HBA) is easy to fall into local optimum, low search accuracy and slow convergence speed, a honey badger algorithm based on elite differential mutation (EDVHBA) is proposed. The elite solution searched by the two optimization strategies in the standard HBA is combined with differential mutation to generate a new elite solution. The use of three elite solutions to guide the next iteration of the population can increase the diversity of the algorithm solution and prevent the algorithm from falling into premature convergence. At the same time, the nonlinear density factor is improved and a new position update strategy is introduced to improve the convergence speed and optimization accuracy of the algorithm. In order to verify the performance of the algorithm, simulation experiments are carried out on eight classical test functions. The results show that compared with other swarm intelligence algorithms and improved HBA, EDVHBA can find the optimal value 0 in the unimodal function, and converge to the ideal optimal value in the multimodal function after about 50 iterations, which verifies that EDVHBA has better optimization performance.

    • Information Technology & Image Processing
    • Zhang Huimin, Li Feng, Huang Weijia, Peng Shanshan

      2024,47(6):86-93, DOI:

      Abstract:

      A lightweight improved model CAM-YOLOX is designed based on YOLOX to address the issues of false alarms of land targets and missed detections of shore targets encountered in ship target detection in large scene Synthetic Aperture Radar(SAR)images in near-shore scenes. Firstly, embed Coordinate Attention Mechanism in the backbone to enhance ship feature extraction and maintain high detection performance; Secondly, add a shallow branch to the Feature Pyramid Network structure to enhance the ability to extract small target features; Finally, in the feature fusion network, Shuffle unit was used to replace CBS and stacked Bottleneck structures in CSPLayer, achieving model compression. Experiments are carried out on the LS-SSDD-v1.0 remote sensing dataset. The experimental results show that compared with the original algorithm, the improved algorithm in this paper has the precision increased by 5.51%, the recall increased by 3.68%, and the number of model parameters decreased by 16.33% in the near-shore scene ship detection. The proposed algorithm can effectively suppress false alarms on land and reduce the missed detection rate of ships on shore without increasing the number of model parameters.

    • Ma Zhewei, Zhou Fuqiang, Wang Shaohong

      2024,47(6):94-99, DOI:

      Abstract:

      A feature point extraction algorithm based on adaptive threshold and an improved quadtree homogenization strategy are proposed to address the issue of low positioning accuracy or low matching logarithms of the SLAM system caused by the ORB-SLAM2 algorithm extracting fewer feature points in dark environments or environments with fewer textures, resulting in system crashes. Firstly, based on the brightness of the image, FAST (Features from Accelerated Seed Test) feature points are extracted using adaptive thresholds. Then, an improved quadtree homogenization strategy is used to eliminate and compensate the feature points of the image, completing feature point selection. The experimental results show that the improved feature point extraction algorithm increases the number of matching pairs by 17.6% and SLAM trajectory accuracy by 49.8% compared to the original algorithm in dark and textured environments, effectively improving the robustness and accuracy of the SLAM system.

    • Zhang Fubao, Wu Ting, Zhao Chunfeng, Wei Xianliang, Liu Susu

      2024,47(6):100-108, DOI:

      Abstract:

      In real-time detection of saw chain defects based on machine vision, factors like oil contamination and dust impact image brightness and quality, leading to a decrease in the feature extraction capability of the object detection network. In this paper, an automated saw chain defect detection method that combines low-light enhancement and the YOLOv3 algorithm is proposed to ensure the accuracy of saw chain defect detection in complex environments. In the system, the RRDNet network is used to adaptively enhance the brightness of the saw chain image and restore the detailed features in the dark areas of the image. The improved YOLOv3 algorithm is used for defect detection. FPN structure is added with a feature output layer, the a priori bounding box parameters are re-clustered using the K-means clustering algorithm, and the GIoU loss function is introduced to improve the object defect detection accuracy. Experimental results demonstrate that this approach significantly improve image illumination and recover image details. The mAP value of the improved YOLOv3 algorithm is 92.88%, which is a 14% improvement over the original YOLOv3. The overall leakage rate of the system eventually reduces to 3.2%, and the over-detection rate also reduces to 9.1%. The method proposed in this paper enables online detection of saw chain defects in low-light scenarios and exhibits high detection accuracy for various defects.

    • Online Testing and Fault Diagnosis
    • Zhang Bian, Tian Ruyun, Han Weiru, Peng Yuxin

      2024,47(6):109-115, DOI:

      Abstract:

      In order to solve the problems that the traditional SPD life alarm characterization method can not clearly correspond to the real life state of SPD, and the remaining life model characterized by a single degradation related parameter has poor predictability, a multi-parameter SPD life remote monitoring system based on STM32 is designed. With STM32 as the main controller, the important parameters such as surge current, leakage current, surface temperature and tripping status of SPD are collected in real time, and the status information is uploaded to the One net cloud platform through the BC20 wireless communication module. The One net cloud platform displays and stores the multi-parameter data of SPD in real time, and provides data management and analysis. The SVM classification model is used to judge whether SPD is damaged and the BO-LSTM prediction model is used to predict the remaining life of SPD. Based on the positioning function of BC20, the real-time geographic location of SPD can be viewed on the host computer. The results show that the root mean square error and average absolute error of the BO-LSTM prediction model are 0.001 3 and 0.001 8, and the system can monitor the SPD status in real time, effectively predict the remaining life value of SPD, and give early warning in time.

    • Shi Shujie, Zhao Fengqiang, Wang Bo, Yang Chenhao, Zhou Shuai

      2024,47(6):116-122, DOI:

      Abstract:

      Rolling bearings play an important role in rotating machinery. If a fault occurs, it can cause equipment shutdown, and in severe cases, endanger the safety of on-site personnel. Therefore, it is necessary to diagnose the fault. In response to the difficulty in extracting fault features of rolling bearings and the low accuracy of traditional classification methods, this paper proposes a fault diagnosis method based on Set Empirical Mode Decomposition (EEMD) energy entropy and Golden Jackal Optimization Algorithm (GJO) optimized Kernel Extreme Learning Machine (KELM), achieving the goal of extracting fault features of rolling bearings and correctly classifying them. Through experimental data validation, this method can extract the fault information features hidden in the original signal of rolling bearings, with a diagnostic accuracy of up to 98.47%.

    • Zhan Huiqiang, Zhang Qi, Mei Jianing, Sun Xiaoyu, Lin Mu, Yao Shunyu

      2024,47(6):123-130, DOI:

      Abstract:

      Aiming at the force test in low-speed pressurized wind tunnel, the original data source of aerodynamic characteristic curve is analyzed. With the balance signal, flow field state and model attitude as the main objects, combined with the test control process, the abnormal detection methods and strategies of the test data are studied from the dimensions of single point data vector, single test data matrix and multi-test data set in the same period, and an expert system for abnormal data detection is designed and developed based on this core knowledge base. The system inference engine automatically detects online during the test, and realizes the pre-detection and pre-diagnosis of the original data through data identification, rule reasoning, logical reasoning and knowledge iteration. The experimental application results show that the expert system is highly sensitive to the detection of abnormal types such as abnormal bridge pressure, linear segment jump point and zero point detection, which guides the direction of abnormal data analysis and improves the efficiency of problem data investigation.

    • Data Acquisition
    • Xu Lijie, Wang Yanlin, Chen Qingshan

      2024,47(6):131-136, DOI:

      Abstract:

      In order to realize high-precision real-time detection of large-stroke precision optical focusing components In order to realize high-precision real-time detection of large-stroke precision optical focusing components, high-precision long-displacement sensors based on axial eddy current effect are studied. A long-displacement eddy current probe simulation model is established for linearity testing, an eddy current sensor test system is built for accuracy experiments, and the long-displacement eddy current sensor is connected to the precision optical focusing assembly. The experimental results show that while the measurable displacement reaches 24 mm, the linearity is better than 1%, the resolution is better than 0.5 μm, the accuracy is better than 1 μm, and the high-precision long-displacement eddy current sensor meets the requirements of the precision optical focusing assembly.

    • Cheng Dongxu, Wang Ruizhen, Zhou Junyang, Zhang Kai, Zhang Pengfei

      2024,47(6):137-142, DOI:

      Abstract:

      For the tobacco industry, there is currently no detection device and method for detecting the heating temperature and temperature uniformity of heated cigarette smoking sets. In order to solve the temperature measurement needs of micro rod-shaped heating sheets in a narrow space, this article developed a cigarette heating rod thermometer, and designed a new structure suitable for temperature measurement of cigarette heating rods. In order to verify the accuracy and reliability of the measurement results of the cigarette heating rod thermometer, uncertainty analysis of the thermometer was performed. The analysis results are based on the "GB/T 13283-2008 Accuracy Level of Detection Instruments and Display Instruments for Industrial Process Measurement and Control" standard. The measurement range is 100 ℃~400 ℃, meeting the requirements of level 0.1. The final experiment verified that the heating temperature field of different cigarettes can be effectively measured.

    • Xu Ziqiang, Li Cheng, Mu Lianbo, Wang Suilin, Liu Jianjun

      2024,47(6):143-150, DOI:

      Abstract:

      To improve the positioning accuracy of the leakage application of the direct buried hot water heating pipe network by acoustic method, based on the analysis of the applicability of various wavelet threshold functions, an improved threshold function noise reduction method is proposed. This method can theoretically overcome the constant deviation of the soft threshold function and the shortcomings of the hard threshold function signal oscillation. Through setting adjustment parameters, improving the noise reduction ability, and retaining the signal of the region less than the threshold point to avoid effective signal loss. The experiment was carried out in a large direct buried hot water circulation pipe network. The research showed that the leakage positioning error was within ±1 m and the positioning accuracy reached 0.11%~3.49%. Finally, the acoustic leakage detection method was adopted in a practical engineering case of a Beijing heating system. The leakage location error is 0.37%~0.66%, and the positioning accuracy has efficiently is improved.

    • Zhang Xinyu, Fan Ximei, Li Zhonghu, Li Jing, Wang Jinming

      2024,47(6):151-156, DOI:

      Abstract:

      Ultrasonic phased array theory and Total Focusing Method are introduced to identify internal defects of thick-walled pipelines,the image is reconstructed. Sparse Matrix Capture technology is used to reduce data volume and improve imaging efficiency, it simulated the ultrasonic phased array total focus imaging of thick-walled pipelines with the outer diameter of 550mm and the wall thickness of 65 mm by the finite element method. The results show that when the excitation center frequency is 5 MHz, the element width is 0.5 mm, the element spacing is 1 mm, the number of array elements is 32. the effectively of the image of Sparse Matrix Capture-Total Focusing Method is 74.81% higher than that of Full Matrix Capture-Total Focusing Method, which is improves the imaging speed and meets the requirements of rapid imaging.

    • Long Biao, Yang Jun, Chen Huiping, Chen Guangrun, Zhao Peiyang

      2024,47(6):157-163, DOI:

      Abstract:

      In order to solve the problem that the audio signal processing in the voice communication system has a large amount of data, a lot of stray signals, and the received audio signals of the frequency modulation receiver are large and small, a lightweight audio signal processing algorithm is proposed, and based on this algorithm, the audio signal receiving and automatic gain control are realized on the field programmable gate array(FPGA) platform. The algorithm combines digital down conversion technology, multistage extraction filtering technology and automatic gain control technology (AGC) technology, and is applied to the audio signal processing system. The RF analog signal received from the upper antenna is converted into baseband audio signal through analog-to-digital conversion and digital down-conversion, and the stray signal in the baseband signal is filtered through four-stage extraction filtering, reducing the complexity and power consumption of the system. At the same time, the digital AGC controls and adjusts the baseband audio signal to output a more stable audio signal. The experimental results show that the algorithm can effectively reduce the information rate from 102.4 MHz to 32 kHz, reduce the computation burden, improve the signal quality, and reduce the resource utilization of FPGA. And the automatic gain control adjustment of audio signal is realized, and the adjustment time is only 12.8 μs, which meets the power stability time of the receiver.

    • Li Hui, Hu Dengfeng, Zhang Kai, Zou Borong, Liu Wei

      2024,47(6):164-172, DOI:

      Abstract:

      In signal generation algorithms, a large number of labeled signal samples are needed for network training, but it is usually difficult to obtain signals carrying message information markers in bulk. To address this problem, this paper proposes a method based on CycleGAN and transfer learning, which realizes the generation of Enhanced LORAN signals without the need for a large number of signals and the corresponding messages as markers and uses migration learning to generate them quickly with a small number of measured signals. The structure of the CycleGAN includes two generators and two discriminators, using the Enhanced LORAN signals and message data sets that do not need to be one-to-one correspondence, so that the generator learns the interconversion relationship between the two data sets, and realises that the input message data can generate the Enhanced LORAN signals corresponding to it, for the characteristics of the Enhanced LORAN signal, the network model is improved using a one-dimensional convolution, residual network, and self-attention mechanism. Experimentally confirmed, it is confirmed that the mean square error of the signal generated by this paper with the measured data is 0.015 3, the average Pearson correlation coefficient is 0.984 3, and the accuracy of the contained message information is 99.02%. To verify the universality of the algorithm, this paper validates the algorithm on PSK, ASK, and FSK datasets, and the experimental results show that the generated signals satisfy the expectations and provide a new idea for signal modulation and demodulation with unknown parameters.

    • Qiu Yanbo, Chu Kaibin, Zhang Ji, Feng Chengtao

      2024,47(6):173-181, DOI:

      Abstract:

      In order to improve the image quality of font generation and reduce the labour cost of font design, a method for few-shot font generation based on multilevel channel attention network is proposed. Firstly, the method acquires important local features through the style-aware attention module; then a multilevel attention mechanism is designed, where shallower layers can only observe the local features of the image, while deeper layers can observe all the features of the image, and new stylistic features are constructed by aggregating the local features of different levels. Finally, a content loss function, a style loss function and a L1 loss function are used to optimise the parameters of the model and stabilise the training of the network so that the generated images are consistent with the target font in terms of content and style. The experimental results show that the method has a strong generalisation to fonts of unknown style and fonts of unknown content. Compared to other methods, the proposed method shows better experimental results that maintain the integrity of the content structure and the accuracy of the font style.

    • Chen Haoan, Li Hui, Huang Rui, Fu Pingbo, Zhang Jian

      2024,47(6):182-189, DOI:

      Abstract:

      Facing the challenges of regulating unmanned aerial vehicles (UAV), and based on an YOLOv5-Lite improved model, this paper incorporates an exponential moving sample weight function that dynamically allocates loss function weights to the model during the training iteration. Through model computations, we achieve real-time UAV tracking using a two-degree-of-freedom servo platform. Furthermore, video capture, model calculations, and servo control are all performed locally on a Raspberry Pi 4B.The optimized model maintains the original model's parameter count while achieving a mAP@.5:.95 score of 70.2%, representing a 1.5% improvement over the baseline model. Real-time inference on the Raspberry Pi yields an average speed of 2.1 frames per second (FPS), demonstrating increased processing efficiency. Simultaneously, the Raspberry Pi controls a servo platform via the I2C protocol to track UAV targets, ensuring real-time dynamic monitoring of UAVs. This optimization enhances system reliability and offers superior practical value.

    • Zhou Guoliang, Zhang Daohui, Guo Xiaoping

      2024,47(6):190-196, DOI:

      Abstract:

      The gesture recognition method based on surface electromyography and pattern recognition has a broad application prospect in the field of rehabilitation hand. In this paper, a hand gesture recognition method based on surface electromyography (sEMG) is proposed to predict 52 hand movements. In order to solve the problem that surface EMG signals are easily disturbed and improve the classification effect of surface EMG signals, TiCNN-DRSN network is proposed, whose main function is to better identify the noise and reduce the time for filtering the noise. Ti is a TiCNN network, in which convolutional kernel Dropout and minimal batch training are used to introduce training interference to the convolutional neural network and increase the generalization of the model; DRSN is a deep residual shrinkage network, which can effectively eliminate redundant signals in sEMG signals and reduce signal noise interference. TiCNN-DRSN has achieved high anti-noise and adaptive performance without any noise reduction pretreatment. The recognition rate of this model on Ninapro database reaches 97.43% 0.8%.

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    • Model predictive control of sewage treatment process based on POD-LSTM

      马会彪, 曾静

      Abstract:

      In order to solve the problem of high computational cost of model predictive control when solving nonlinear optimization problems in large nonlinear systems such as wastewater treatment, this paper proposes a reduced-order neural network model predictive control algorithm applied to wastewater treatment benchmark. First, for large-scale nonlinear and strongly coupled systems in wastewater treatment, the intrinsic orthogonal decomposition method is used to construct a reduced-order process model to reduce the complexity of the nonlinear system. Then, the long short-term memory network is used to approximate the reduced-order system, thereby solving the problem that the reduced-order system is difficult to express explicitly. Finally, a model predictive controller is designed based on this reduced-order system to achieve efficient control of wastewater treatment. Experimental results show that while ensuring good control effect, the proposed reduced-order neural network model predictive control strategy significantly reduces the computational time compared with the model predictive control strategy of the first principle model of wastewater treatment.

      • 1
    • Knee osteoarthritis based on improved Swin Transformer X-ray image automatic diagnosis

      许超, 王云健, 刘洋, 卢雪梅, 丁勇

      Abstract:

      Knee osteoarthritis is a common disease in the elderly population, which is highly disabling. Automatic diagnosis of knee osteoarthritis based on deep learning algorithm has important application value. Therefore, an automatic diagnosis algorithm of knee osteoarthritis based on improved Swin Transformer model is proposed. The transfer learning is protected by replacing the global average pooling layer of the neck network with a two-layer fully connected layer plus ReLU activation function. Adding full connection layer and Tanh activation function to the head network to combine more nonlinear features; In the process of data preprocessing and model training, data enhancement is realized by relying on Albumentations library and adding Mixup module respectively. The experimental results show that the proposed algorithm can effectively improve the classification accuracy of X-ray images of knee osteoarthritis, and the diagnostic accuracy reaches 76.0 % on the public data set of Kaggle website. At the same time, the generalization experiments on other X-ray image data sets of knee osteoarthritis and medical image data sets in different fields show that it has good generalization ability, which further proves the effectiveness of the proposed algorithm.

      • 1
    • Research of active SLAM with composite exploration points

      董哲明, 孙中元

      Abstract:

      To improve the efficiency of internal inspections in power plants, this paper proposes an inspection scheme based on intelligent robots. Given the complexity of power plant environments, achieving efficient and accurate autonomous mapping by robots in unknown settings is crucial. We designed an active SLAM (Simultaneous Localization and Mapping) method using composite exploration points, incorporating plane segmentation and vector synthesis to guide exploration trajectories, thereby reducing map uncertainty from random exploration. The boundary point evaluation function is enhanced by considering boundary length gain to improve exploration efficiency. The method involves using plane segmentation to search for boundary points around the target, with an evaluation function based on movement distance and boundary length to determine the optimal boundary point with the largest exploration range. Composite exploration points are created through vector synthesis of the optimal boundary and target points, guiding the robot for simultaneous mapping and tracking. Real-time positioning and mapping technology is used to construct the current environmental grid map, achieving target point tracking and autonomous mapping through sequential exploration points. By setting new target points, tracking and expanding the mapping range are achieved. The proposed algorithm exhibits a tendency towards exploration, performing depth-first search on the grid map while considering the traction effect of target points, thereby avoiding multiple trajectory overlaps and loops. Experimental results demonstrate that this method achieves target tracking and high-precision mapping in unknown environments with fewer exploration steps and shorter paths.

      • 1
    • Low-light object detection algorithm based on image feature enhancement

      黄玉龙, 张晓玲

      Abstract:

      Low illumination environments can lead to situations such as inconspicuous image target features and severe noise interference, which affect the detection performance of the object detector.To address the above problems, a multi-scale image feature enhancement module (FEM) is constructed, and in conjunction with YOLOv8s object detection network, an end-to-end low-light image object detection method FE-YOLO is constructed.Firstly, FEM is employed to extract feature information from the input image at three different scales and efficiently fuse them to obtain an enhanced image with rich feature representation.Then, in the neck network of YOLOv8s, a target feature enhancement module (TFE) is incorporated. TFE works by suppressing background noise information in higher-level features, thereby accentuating the representation capacity of target features.The experimental results indicate that the proposed method achieved superior detection accuracy. On the low-light image object detection dataset ExDark, the average precision (mAP) reached 75.63%, representing a 3.03% improvement compared to the original YOLOv8s algorithm.

      • 1
    • Design of FPGA image edge detection system based on Sobel

      宋倩男, 刘光柱

      Abstract:

      As research and development in the field of machine vision continue to advance, the requirements for image processing have become more complex and diverse. Edge information detection is particularly important when processing real-time images. This paper designs an FPGA image edge detection system based on the Sobel algorithm, capable of real-time video image acquisition, processing, and display. Adaptive threshold and non-maximum suppression algorithms are used, combined with an 8-direction Sobel edge detection algorithm to improve detection accuracy. The Sobel edge detection algorithm is validated and implemented in hardware before and after improvement. A pipeline design is adopted to generate a sliding window to accelerate image processing and enhance the real-time performance of image processing. Hardware synthesis experiments show that the FPGA image edge detection system based on the Sobel algorithm can efficiently achieve image edge detection of video streams, improving image processing speed by 57%, providing comprehensive edge detail detection, enhancing video image processing efficiency, and can be used for target recognition and tracking research.

      • 1
    • Diffusion model-based staining normalization for colorectal image

      李子成, 贾 伟, 赵雪芬, 高宏娟

      Abstract:

      Existing staining normalization methods are unable to accurately extract the complex structural features of colorectal pathological images, resulting in the loss of partial structural information and the inability to generate high-quality staining-normalized colorectal pathological images. To address this issue, a staining normalization method for colorectal pathological images based on a conditional diffusion model is proposed. The proposed method includes conditional diffusion model and image feature reconstruction In conditional diffusion model,firstly, the Markov chain forward process is employed to add noise to the original colorectal pathological images. Then, the noisy images and conditional images are input into an enhanced denoising network for denoising. During this process, an enhanced activation module is utilized to learn the deep features of the colorectal pathological images and capture more contextual information. A skip-connection spatial attention module is introduced between the encoder and decoder to accurately extract the positional spatial information of the colorectal pathological images. Finally, a pyramid feature extraction module is designed to extract the features of the multi-scale conditional images and generated images, and a reconstruction loss function is constructed to optimize the performance of the entire network. Experimental results demonstrate that compared with existing methods, the proposed staining normalization method can generate higher-quality staining-normalized colorectal pathological images on public datasets GlaS and CRAG.

      • 1
    • Research on adolescent schizophrenia EEG recognition based on MSAPNet

      廉小亲, 王梓桐, 高超, 马虢春, 刘春权, 关文洋

      Abstract:

      Aiming at the problem that it is difficult to fully extract deeper features using single-scale convolution and traditional ReLU activation function when using deep learning models to identify EEG signals of adolescent schizophrenia. Put forward a kind of multi-scale convolutional neural network model with adaptive ReLU(MSAPNet) for adolescent patients with schizophrenia and healthy adolescent brain electrical signal classification. Firstly, a multi-scale cascade module is used to extract the input 3D feature matrix containing the original EEG spatial information. Secondly, the features at different levels were fused through the designed feature fusion module. Multi-scale down sampling module is then used to decrease the dimension of feature maps. Finally, using the classification module to complete identification and detection of disease. The experimental results show that the MSAPNet of disease identification accuracy, sensitivity, specificity and accurate rates and F1 score can be achieved respectively 97.21%, 97.51%, 96.86%, 97.29% and 97.40%, compared with the related research has better detection performance, proved the effectiveness of the proposed method.

      • 1
    • Research on remote calibration method of electrical parameters based on modern communication technology

      王思云, 陈铭明, 李志新, 卢树峰, 鲍进

      Abstract:

      On the basis of existing ways of realizing remote calibration and traceability of measuring instruments, a remote calibration method of electrical parameters based on modern communication technology is proposed. The calibration method combines the remote calibration method with non-physical standards as the transmission object and the standard source method of DC voltage source calibration, which places the standards in the laboratory rather than transmitting them to the field, solving the problems of long calibration period and difficult to measure additional errors of the traditional electrical parameter calibration method. Based on the principle of time-frequency calibration by the satellite co-vision method, the standard voltage source and the calibrated electrical parameters are converted into reliable digital quantities for the remote transmission of the electrical parameters, and the transmission and traceability chain of the electrical parameters is established; the reference voltage remote calibration module of the AD conversion module is designed, and a model of the remote self-calibration of the AD conversion module is established, and the remote calibration algorithm of the electrical parameters based on the satellite co-vision is investigated, and the remote self-calibration algorithm of the A/D conversion is also investigated. The remote self-calibration algorithm of analog-to-digital conversion is studied, and the conversion results of high-precision electric parameter acquisition module are corrected. After data analysis, its accuracy is 0.1 level.

      • 1
    • Construction of swarm unmanned aerial vehicle cooperative remote sensing simulation system under emergency scenarios

      陈何伟

      Abstract:

      In unexpected emergency response scenarios, it is necessary to quickly obtain global situational images of the scene for subsequent assessment and decision-making. Swarm UAVs have the advantages of large number, low cost and fast imaging, and are widely used in military fields. This paper explores the application of swarm UAV cooperative reconnaissance to the field of emergency remote sensing, and constructs a swarm UAV remote sensing digital simulation and verification system, which researches and simulates and verifies the swarm UAV's formation coordination, airway planning, and cooperative splicing of multi-channel video. Aiming at the problem of unstable overlap rate between multiple video frames, an adaptive dynamic sampling algorithm is proposed to maintain the idempotence of the overall efficiency of the splicing algorithm under different overlap rates. Subsequently, for the unstable characteristics of video streams in response scenes, a breakpoint re-splicing algorithm is proposed to ensure that the availability of the algorithm can be maintained at the expense of splicing accuracy in poor shooting environments. The results show that: swarm UAVs can construct a global situational image of the scene in quasi real-time, and this paper can provide technical support for the application of swarm UAVs in the field of remote sensing in emergency response.

      • 1
    • Surface defect segmentation of condensing copper pipe based on feature optimization

      张雨涵, 陈春梅, 邓豪, 刘桂华

      Abstract:

      To address the issue of insufficient accuracy in defect segmentation caused by weak expression of surface defect characteristics on condenser copper tubes and feature confusion between similar defects, a feature-optimized method for surface defect segmentation on condenser copper tubes is proposed. Firstly, to address the problem of indistinct surface defects on condenser copper pipes, the method utilizes an attention optimization module based on the defect area attention enhancement strategy to enhance the feature expression ability of defects and suppress background feature expression. Secondly, through the use of dilation convolutions with varying rates and the integration of feature map optimization technology, cross-domain semantic capture of pixels is achieved and resolve the issue of feature confusion between similar defects. Finally, a multi-scale feature enhancement fusion method based on feature alignment is established to improve the model's detection ability for defects at different scales. Multiple sets of comparative experiments are conducted on images of condenser copper tubes which are captured in real production line environments, and the results show that the proposed method achieves the balance between the precision and the number of parameters when solving the above problems, and achieves a good segmentation effect. The algorithm achieves an average intersection over union of 80.53 % and a Dice coefficient of 88.94 %, with the model size being only 25 MB.

      • 1
    • Research on wave impact pressure sensor based on piezoelectric fiber composites

      黄禾艺, 李正农, 任志刚, 范楠贵

      Abstract:

      Wave impact pressure is one of the important physical quantities in ocean engineering. The traditional method of measuring wave impact pressure using pressure sensors has single measurement results and poor stability. Therefore, it is necessary to find a new measuring element to replace pressure sensors. In this paper, a macro fiber composite material (MFC) is used to measure the wave impact pressure of the model for a wave impact on a vertical plate. In order to verify the feasibility and accuracy of this measurement method, the measured values of wave impact pressure measured by MFC were compared with the calculated values of empirical formulas commonly used in ocean engineering. The experimental results show that the measurement results of MFC are in good agreement with the calculation results of our country's specifications; compared with our country's specifications, among all the effective data generated by the five incident wave heights, only the average error of the incident wave height of 5 cm exceeds 10 %, which is 18.25 %. The average error of the incident wave height of 12 cm is the smallest, with the smallest being 2.56 %.

      • 1
    • Steel surface defect detection algorithm based on improved YOLOv8n

      徐森

      Abstract:

      Addressing the issues of diverse defect types, significant size variations on steel surfaces, as well as the complexity and limited accuracy of existing models, an improved steel surface defect detection algorithm based on YOLOv8n was proposed. Initially, Omni-dimensional Dynamic Convolution (ODConv) was introduced in the feature extraction module to efficiently capture multi-dimensional features, thereby reducing information loss. Subsequently, an Asymptotic Feature Pyramid Network (AFPN) structure was embedded in the feature fusion module to facilitate direct interaction between non-adjacent level features, alleviating semantic gaps and enhancing the quality of feature integration. Lastly, the original CIoU loss was supplanted by Wise-IoUv3, which employs a dynamic non-monotonic focusing mechanism in the loss function, to optimize bounding box regression. This adjustment expedited network convergence while boosting detection precision. Extensive experiments conducted on the NEU-DET dataset demonstrated that the refined YOLOv8-ODAW network model achieved a 7.3% increase in mAP50%, a 21.95% decrease in GFLOPs compared to the original model, exhibiting superior localization capability and detection accuracy for steel surface defects. The detection speed met the requirements of industrial inspection standards.

      • 1
    • Research on vertical handover algorithm of heterogeneous wireless network based on Dueling-DQN

      李敏之, 李转怀

      Abstract:

      In view of the fact that the network selection algorithm in the heterogeneous wireless network has few quality of service indicators, and the frequent switching of users is becoming more and more serious, In this paper, a vertical handover method for heterogeneous wireless networks based on subjective and objective weighting combined with improved deep reinforcement learning is proposed. Firstly, a software-defined network architecture sup-porting heterogeneous wireless networks was proposed; Secondly, an attribute weighting algorithm combining subjective and objective weighting was proposed; Finally, the network selection problem is solved by using Dueling-DQN. The simulation results show that the proposed algorithm reduces the number of switching times by 11.25%, 13.34%, 18.76% and 13.75% respectively under different user types of networks, and increases the throughput by 16.64%. Therefore, the algorithm proposed in this paper effectively avoids ping-pong switching, reduces the number of switching times and improves the throughput.

      • 1
    • An improved YOLOv5-based surface defect detection technology for aluminum ingot alloys

      胡 波, 王 佳 欣, 杨 青, 陈 婷, 杨 明

      Abstract:

      Aiming at the problems of irregular morphology and suboptimal detection performance of surface defect on aluminum ingot alloys, an improved YOLOv5-based defect detection method is proposed. Firstly, The Res2Net feature extraction network block is employed to replace the CSPDarknet53 module of the baseline model, which can effectively detect the multi-scale defect. Secondly, the CBAM convolutional attention module is introduced into the backbone network of YOLOv5 to enhance the representational ability of defect features. Finally, the over-parameterized reparameterization convolutional blocks are used to substitute for the 3×3 convolutional blocks in the backbone and neck networks so as to reduce the model"s inference latency. Experimental results compared with the traditional target detection methods demonstrate the improved method achieves a mAP of 75.8% for defect detection, which is a significant improvement both in detection accuracy and inference speed, and can well satisfy the tasks and demands of practical industrial production.

      • 1
    • Research on aero-engine gas path fault diagnosis based on CNN-BES-ELM

      蔡开龙

      Abstract:

      Aiming at the airway fault problems occurring during the operation of aero-engine, an aero-engine airway fault diagnosis model based on Convolutional Neural Network (CNN) combined with Bald Eagle Search Algorithm (BES) Optimized Extreme Learning Machine (ELM) is proposed. The aero-engine airway data are learned by CNN and the fault features hidden in the data are extracted, the BES algorithm is introduced to optimize the weights and biases of the ELM, and the optimized ELM is used to classify the abstract features extracted by the CNN, so as to achieve the purpose of fault diagnosis. The experimental results show that the CNN-BES-ELM-based model achieves an average accuracy of 97.80%, which is 2.7%, 5.4%and 7.35% higher than that of CNN-ELM, CNN and ELM, respectively, and compared with commonly used deep learning models such as Deep Belief Network (DBN) and Stacked Auto Encoder (SAE), the accuracy is improved by 5.4% and 3.4%; and still retains more than 90% accuracy in noise environments such as random noise, Gaussian noise and pretzel noise, which overall shows good diagnostic performance, generalization ability and noise immunity, and provides a theoretical basis for its practical application in aero-engine airway fault diagnosis.

      • 1
    • Fan blade defect detection algorithm based on improved YOLOv8

      李冰, 张易牧, 魏乐涛, 王月

      Abstract:

      As an important component of wind turbines, blades are easily affected by the natural environment, leading to damage such as erosion, cracks, and detachment of rubber coats, thereby affecting the efficiency of wind power generation and the safe operation of the unit. A modified YOLOv8 fan blade defect detection algorithm is proposed to address the issue of low accuracy in detecting blade defects in complex environments. The single module SPPF in the backbone feature extraction network is integrated into the LSKA attention mechanism to enhance the network's attention to important features and improve the performance of the model; Secondly, the Neck section adopts a weighted bidirectional feature pyramid Bi-FPN structure, and use FasterBlock to improve the C2f module. The Bi-YOLOv8 fast lightweight network structure is proposed to enhance the multi-scale feature fusion ability of the model and improve the accuracy of small target detection; Finally, the Inner-iou method, which assists in calculating the loss of bounding boxes, is used to optimize the loss function and improve the accuracy and generalization ability of the model's defect detection. Through the experiment of defect detection on the image of fan blades, the results show that the proposed method improves the accuracy rate of defect detection by 7.3%, mAP50 by 3.3%, and reduces the number of parameters by 27%.

      • 1
    • Improve the Small Target Detection Algorithm of YOLOv8 UAV Aerial Image

      程换新, 吕玉凯, 骆晓玲, 池荣虎

      Abstract:

      Aiming at the problems of low feature extraction capability and scale diversity in UAV aerial images, an improved YOLOv8 object detection algorithm for UAV aerial images is proposed. Firstly, P2 layer is added to enhance the small target detection capability of the model. Secondly, the bidirectional feature alignment fusion method is designed to improve the neck. Combining the idea of feature alignment module and bidirectional feature pyramid, the multi-scale fusion capability of the model is improved to achieve a more complete feature fusion. Then, bi-level routing-spatial attention module is designed and added to the backbone. By connecting the bi-level routing attention module and spatial attention module, the feature capturing ability of the target is strengthened. Finally, the loss function Focaler-XIoU is designed to solve the influence of sample difficulty distribution on border regression, and enhance the stability and detection effect of the model. The experimental results show that the improved network model has improved the VisDrone dataset mAP50 by 9.2%, which has better detection effect than the current mainstream target detection algorithm, and can well complete the UAV aerial image detection task.

      • 1
    • Research on the prediction of the remaining life of internal corrosion in oilfield water injection pipelines

      骆正山, 杜丹

      Abstract:

      In order to estimate the remaining safe service life of the pipeline, the extreme gradient boosting algorithm model besed on grey correlating analysis was proposed. Grey Relational Analysis (GRA) was used to calculate and rank the correlation values between each influencing factor and the remaining life, and the data of the influencing factors with high correlation were preferably input into the eXtreme Gradient Boosting (XGBoost) algorithm for the prediction of the remaining life of corroded pipelines. Taking an oilfield water injection pipeline as an example, the results showed that the Root Mean Square Error (RMSE) was 0.012, the Mean Absolute Error (MAE) was 0.068, and the goodness of fit (R2) was 0.999, compared with the other three prediction models, the results showed that the prediction accuracy and generalization performance of the model constructed in this paper were better.

      • 1
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    Display Method:: |
      Online Testing and Fault Diagnosis
    • Meng Xiangzhong, Wang Weixi

      2016,39(11):183-186, DOI:

      Abstract:

      For the problem that temperature sensors of mine belt transport system can’t be inspected in real time and on site, design a based on Smith estimated compensation control for portable underground conveyer temperature sensor detection device. The working principle of the device is introduced in detail, the hardware design of thermostat, intrinsically safe power supply circuit and the drive system based on the Smith prediction control is expounded and the flow chart of main program software is given in this paper. The experimental result and field operation results show that the device has the characteristics of convenient carrying, accurate measurement, real time and on site detection and so on.

    • Gu Youyi, Jiang Lixing, Sun Zhenxiong, Wang Li, Wang Ancheng

      2019,42(10):1-5, DOI:

      Abstract:

      Outdoor baseline is the special length standard in the field of surveying and mapping, it can be used to verify the addition and multiplication constants of the total station and other photoelectric rangefinders. In order to ensure the authenticity, accuracy and reliability of verification results, conducting outdoor baseline traceability periodically is essential. At present, direct measurement by 24 m invar tape or high precision electro-optical measurement is mainly used to achieve the traceability of outdoor baseline in China, a large number of experimental facts have shown that there are still system errors between the quantity transfer of baselines in China and abroad. With the rapid development of China′s manufacturing industry and the proposal of “made in China 2025”, the traditional traceability technology of outdoor baseline is difficult to meet the increasingly high precision requirements, and it is urgent to achieve the outdoor baseline precision ranging. Combined with the current research situation at home and abroad, optical interferometry by Vaisala interference comparator, direct measurement by 24 m invar tape and high precision electro-optical measurement are summarized,the advantages and disadvantages of the three methods are deeply analyzed. Last but not least, some thoughts and suggestions are put forward for the future outdoor baseline field construction in China.

    • Li Songsong, Ping Dongyue, Zhang Chenchen, Xia Wenze, Guo Zhonghui

      2019,42(10):6-11, DOI:

      Abstract:

      Electromagnetic acoustic transducer(EMAT)sensors are widely concerned because they do not need couplers and can be used in high-temperature environments. However, there are problems such as low energy conversion efficiency, small amplitude of ultrasonic echo signal, and easy to be disturbed by noise, which need to be further studied. According to the working mechanism of the EMAT, through analyzing the characteristics of the echo signal of ultrasonic Lamb determines the index of receiving circuit, design and optimize the impedance matching circuit, preamplifier circuit and low-pass filter circuit, and design the digital bandpass filter for further signal processing. Firstly, the preamplifier circuit is simulated to make it capable of receiving small signals. Then a three-stage MFB low pass filter circuit is adopted to eliminate the spatial coupling noise. The EMAT flaw detection test of aluminum plate was made and built by circuit hardware. The results show that the designed receiver circuit can detect lamb wave well, which provides a foundation for the development of the testing system.

    • Yang Xunyong, Yang Fashun, Hu Rui, Chen Xiao, Ma Kui

      2019,42(10):43-47, DOI:

      Abstract:

      Take the high-voltage high-power chip TO-3 package structure with operating voltage of 70V and output current of 9A as an example, the three-dimensional package model is first established based on the thermal analysis software Flo THERM, and the thermal characteristics of the package model is simulated and analyzed. Secondly, comparative analysis is carried out for the presence/absence of substrates, different substrate materials, and different package materials. Finally, the temperature of the package is studied according to the thickness of the bonding layer, the power and the thickness of the substrate, and a package with optimized heat dissipation is obtained. The simulation results show that The higher the thermal conductivity of the substrate material and the package casing, the better the heat dissipation effect. As the thickness of the bonding layer and the power of the chip increase, the temperature of the chip gradually increases. As the thickness of the substrate increases, the temperature of the chip decreases. The heat dissipation effect is optimal when the substrate material is copper, the package casing is BeO, and the bonding layer is AuSn20.

    • Bai Kezong, Dang Yupu

      2019,42(10):82-85, DOI:

      Abstract:

      The near-drilling tool can effectively overcome the shortcomings of the conventional measurement while drilling system, which is very suitable for use in complex formations or thin reservoirs, and has the advantages of convenient field assembly and low operating cost. Near-bit azimuth gamma measurement system is the core of its geological steering control. In this paper, two important modules of near-bit azimuth gamma ray measurement system are introduced in detail: sector measurement module and gamma counting processing module. Field experiments show that the system can quickly indicate the direction of the tool crossing the horizon, meet the requirements of field operation, and has a certain market value.

    • Sensor and Non-electricity Measurement
    • Tao Hongbo, Fang Yong, Xu Guanghong, Bu Dongyao, Jin Yanliang

      2016,39(11):126-130, DOI:

      Abstract:

      In this paper, a remote monitoring system for air quality in vehicle is designed based on Kalman filter. The microprocessor Exynos 4412 is used as the controller and signal processer in this system. The data of PM2.5, PM10, formaldehyde, temperature and humidity are obtained by gas sensors.The mean filteringis used to eliminate pulse noiseof data and the Kalman filtering is used to calculatethe optimal estimation values. Thesedatadrawgraphics are displayed on theAndroid boardand sentto mobile terminal for remote real time monitoring.When the data is more than the normal threshold, the mobile terminal is able to give the acousto optic alarm and control the car air system. The experimental results show that the system can effectively improve the accuracy of pollution monitoring and mobility.

    • He Changgen

      2019,42(10):12-15, DOI:

      Abstract:

      The on-line monitoring device of transmission line in Xinjiang Province cannot work normally in the extremely cold environment, which brings some difficulties to the operation and maintenance of transmission line. In this paper, the implementation scheme of power tapping from OPGW was put forward. Based on EMTP simulation platform, 500 kV ac transmission line model was established, which simulated the induced voltage and current, and the dual insulation mode is proposed. The results prove that the induced voltage and current of ground wire are positively correlated with the spacing and load power, and are basically unaffected by the soil resistivity; when the span is 300 m and the load power is 500 MW, the output power of the ground wire energy-collecting device is 137.8 W, fully meeting the power supply demand of on-line monitoring device in the transmission line; under the condition of long-term extremely cold temperature -45 ℃, by using nano porous silica insulation membrane and infrared radiation heating, it still can ensure ideal temperature conditions,which is not less than -30 ℃; after the insulation measures are adopted for the power supply, the mean rate of no failure in online monitoring device is increased from 85.9 to 99.6%, and the reliability is significantly increased. The research results provide a design idea of stable power supply for on-line monitoring devices in extremely cold regions.

    • Liu Wei, Wang Yun

      2019,42(10):38-42, DOI:

      Abstract:

      Wireless power transmission technology is the key technology that enables special robots to achieve lightweight sustainable work. The magnetic coupling resonant wireless charging, must consider the impact of metal obstacles on the transmission system. The metal eddy current effect is equivalent to mutual inductance coupling circuit, and the transmission system is modeled by coupling circuit theory. The transmission equation of voltage gain coefficient and the expression of energy loss of metal obstacles to transmission system are derived. With the change of coupling coefficient, the transmission system still has frequency splitting, critical coupling, over-coupling and under-coupling. Given the parameters of the transmission system, the critical coupling coefficient Kc=1.54×10-4. When there are metal obstacles, K′c=1.09×10-2. which lead to the reduction of voltage gain, the shift of resonance frequency, the increase of coupling coefficient and the decrease of coupling performance in wireless transmission system. The change of output voltage in the presence of metal barrier is obtained by simulation.

    • Yu Zhenzhong, Zhou Feng

      2019,42(10):16-21, DOI:

      Abstract:

      In order to solve the problem that the surplus torque′s strong interference to the electric load simulator and affects the tracking accuracy, the fuzzy PID control method based on particle swarm optimization is applied to the design of motor controller. Firstly, the mathematical model of the electric loading system is established based on the analysis of the structure and working principle of the loading motor, and feedforward compensation is deduced by the principle of structural invariability; Secondly, because of conventional PID controller cannot deal with the complex nonlinear environment by changing parameters, and the fuzzy PID quantization factor scale factor is difficult to adjust by experience, a compound control strategy based on fuzzy PID and particle swarm optimization algorithm is proposed. Finally, the simulation result shows that the proposed control strategy is superior to the conventional fuzzy PID controller.

    • Zheng Ying, Zheng Xianfeng, Cheng Jingjie

      2019,42(10):48-51, DOI:

      Abstract:

      The engine mount system is an important part of the vehicle, Play the role of supporting engine, blocking vibration and improving ride comfort in the process of driving This paper studies the relationship between the stiffness of the suspension element and the inherent characteristics of the suspension system by modeling and simulation of the automobile engine. In Adams software, a three-dimensional model of the engine suspension system is established, simulation analysis is conducted, and the intrinsic frequency and vibration modal energy decoupling distribution of the suspension system is obtained in the Vibration vibration module. The analysis shows that the vibration isolation effect is poor. With the stiffness of suspended rubber pad as the optimized parameter, a new energy decoupling distribution is obtained, and the decoupling rate in the main vibration direction reaches more than 80%. Compared with the initial data, the decoupling degree is greatly improved, which shows that the optimized data has obvious effect on the vibration isolation of the engine, and verifies the feasibility of the optimized design method.

    • Information Technology & Image Processing
    • Zhao Hongliang, Guo Youmin, Wang Jianxin, Yang Jun

      2024,47(1):130-135, DOI:

      Abstract:

      In order to solve the problems of low accuracy and slow detection speed of obstacle detection in the complex rail transit background, an improved object detection network model of YOLOv5 was proposed. Firstly, a lightweight Transformer backbone EMO based on attention mechanism was used to replace some modules in the original backbone of YOLOv5, which not only ensured the lightweight, but also improved the accuracy and stability of the model. Secondly, Focal-EIoU is used to replace the CIoU loss function in YOLOv5 to solve the problems of low training efficiency and slow convergence speed caused by CIoU. Finally, the lightweight upsampling operator CARAFE is used to replace the original upsampling layer in the YOLOv5 algorithm, which has a larger receptive field without introducing too many parameters and computational cost, and improves the detection accuracy and detection speed. Experimental results show that compared with the original YOLOv5 network model, the mean average precision of the proposed method is improved by 11.1%, the precision is improved by 13%, the recall is improved by 11.4%, and the detection speed reaches 60.7 frames per second. The proposed method shows good performance in the target detection task, and effectively enhances the detection performance of the target detection model in the context of rail transit.

    • Lin Rongxia

      2019,42(10):33-37, DOI:

      Abstract:

      In order to improve the real-time control ability of biped robot, a real-time obstacle avoidance position control method for biped robot based on reinforcement learning is proposed. Taking the stability of biped walking as the control objective function, the real-time path dynamics model of biped robot is constructed. The acceleration and inertia moment of the robot′s centroid motion are taken as the controlled object. The effective collision sub-model is used to plan the real-time obstacle avoidance path of biped robot, and the collision sub-model and swing sub-model are combined to adjust the error correction parameters of biped robot. The fuzzy reinforcement learning tracking method is used to control the error gain of biped robot, and the real time obstacle avoidance position control of biped robot is realized. The simulation results show that the proposed method can avoid obstacles in real time and improve the adaptive control ability of biped robot.

    • Wu Shaolei, Xiao Jianhong, Feng Yu, Shi Liang, Zhang Liang

      2019,42(10):22-27, DOI:

      Abstract:

      Due to the high level penetration of several types of distributed energy source, storage system needs to be added in modern power system to suppress power flow fluctuation. In order to realize high efficiency bidirectional power conversion under the circumstance of wide input and output voltage range in energy storage system, a bidirectional buck-boost DC/DC converter based on multi-mode control is studied in this paper. When the input voltage exceeds the output voltage above a certain threshold, valley current mode control is employed and the converter operates as buck region. When the output voltage exceeds the input voltage above a certain threshold, peak current mode control is used and the converter operates as boost region. Furthermore, when the intput voltage is similar to the output voltage, phase-shift mode control is adopted to obtain seamless transition and the converter operates in a manner of buck and boost combination. In order to realize high accurary output and fast dynamics, Type II compensator is employed. By the approach of small portion overlaping between Buck region and Boost region, the discontinuity happened at region transition in traditional Buck-Boost converter is eliminated. 97.5% peak efficiency is achieved in the 1 kW hardware prototype and the transition between Buck region and Boost region is smooth.

    • Wang Xuejun, Gu Jinliang, Luo Hong′e, Xia Yan, Li Baoming

      2019,42(10):110-114, DOI:

      Abstract:

      In order to measure and study the contact impedance of the contact interface between the central rail and the electromagnetic launching system, a low impedance measurement system at low frequency is developed. The system measures the vector voltage at both ends of the impedance to be measured and the standard impedance separately by impedance transformation method. The real and imaginary parts of the vector voltage are separated by phase sensitive detection method based on free axis. The magnitude of the impedance to be measured is calculated according to the relationship between the standard impedance voltage and the impedance voltage to be measured. The system uses STM32 controller as signal generator and controller, adopts Kelvin four-wire method to reduce the influence of lead resistance and contact resistance, and uses software compensation method to reduce measurement error. The measurement results show that the measuring system can work normally within 10 kHz and has high accuracy in measuring resistance within 100 Ω.

    • Liu Baohang, Wang Bingsen, Li Ziqi

      2019,42(10):28-32, DOI:

      Abstract:

      Wireless power transmission (WPT) based on the magnetically-coupled resonant is a hot topic in the research and development in modern electrical technology. It is the most likely to be a technology to provide solutions for wireless power supply of electrical equipment, such as household appliances, various consumer electronic products, smart wearable devices and embedded medical devices. The WPT is a flexible access and transmission for real-time power supply. In order to get rid of the shackles of the traditional power cord, the limitation of the space and distance of the power supply mode is solved. Using coupled-mode theory, the two identical helical coils are built with the height of 20 cm, the diameter of 60 cm and the number of turns of 5.25. Both coils are made of hollow copper wire. The expected resonant frequency given is 7.65 MHz, which is about 4.5% off from the measured resonance at 8 MHz. Control the class E power amplifier(PA) gate with an 8 MHz square wave signal, The effects of the nonlinear and linear shut capacitance have been considered in the drain of PA, it is important to predict the performance when an external capacitor is necessary to add for the optimal Class-E mode. Impedance matching is designed to maximum the power transfer from the drain of PA to the transmission coil. The system achieves the maximum transmission distance of 6 meters on the theoretical basis of the transmitting coil of diameter of 60 cm. According to experimental analysis, the system has a great improvement in transmission distance.

    • Liu Nian, Zhou Yan, Zu Jiakui

      2019,42(10):120-125, DOI:

      Abstract:

      The bus-based distributed structure has been applied to the field of drones because of its simple structure, easy expansion, and maintainability. The research group takes unmanned helicopter as the research object, and carries on the technical upgrade on the basis of the original centralized flight control system. A design scheme of distributed flight control system based on CAN bus is proposed. It describes the overall design scheme, hardware design, communication mechanism, software development and implementation of distributed system based on CAN bus, and carries out semi-physical simulation verification. The test results show that the actual bandwidth occupied by CAN bus is 10.5%, and there is no frame loss. The synchronization accuracy of communication reference clock is less than 150 μs, which can fully meet the requirements of flight control system. Through the whole process of flight semi physical simulation, it is proved that the distributed flight control system based on CAN bus can meet the real-time, reliability and other control requirements of unmanned helicopter flight control system, and the design meets the engineering requirements.

    • Zhu Jiangbo, Zhao Zhiheng, Liu Yang, Ma Jiayi, Sun Lei

      2019,42(10):52-57, DOI:

      Abstract:

      A new black and white color selection system based on ZYNQ-7000 series fully-programmable SoC is designed to solve the problem of slow detection speed and low integration of traditional black and white color selection system. Using hardware and software co-design methods, firstly linear CCD image acquisition, grayscale correction, threshold comparison and valve output are implemented on the PL side. Then using the AXI interface to cache the compensated grayscale value, valve output signal, and setup parameters to the DDR3 SDRAM memory, and finally The PS side is ported to an embedded Linux operating system for real-time display and human-computer interaction. The experimental results show that the system can effectively measure the eigenvalue parameters of the materials with screening, and carry out the selection, the operation is stable and reliable, and meets the requirements of industrial applications.

    • Li Zicong, Zeng Yuhang, Xiong Xiaoming

      2019,42(10):126-131, DOI:

      Abstract:

      In recent years, convolutional neural networks have done a great job in many machine vision tasks. However, existing software implementations are not well implemented in portable devices. A convolutional neural network system based on Xilinx all-programmable SoC is designed to accelerate the convolutional operation in parallel, which only need few design resource and implement fast detection system. The system uses multi-stage pipeline technology and input data reuse to improve calculation efficiency. The hardware part completes convolutional network calculation, and the software part finish the image preprocessing and post-image detection preprocessing, thereby improving operation efficiency. The system can implements the convolution operation with different size, mean pooling operation and the non-maximum suppression algorithm, which achieves accurate positioning of multiple faces in the picture. The experimental results show that the average calculation rate of the system is 0.19 Gops/s at the operating frequency of 100 MHz,and the power consumption is only 4.07% of the general purpose CPU.

    • Research&Design
    • Bi Lige, Tong Xiaolin, Li Hanchao

      2017,40(6):1-4, DOI:

      Abstract:

      When projectile launched by ground operations for weather modification did not burst in the air and land, it would damage to the ground personnel or important facilities. In order to avoid the damage, using remote sensing image, global positioning system and geographic information technology, this paper designed and developed an analysis system for safe firing area of antiaircraft guns and rocket. The system extracts the object information from the high resolution remote sensing image of Spot in the system database and the GPS positioning information of the operation terminal system. Combined with the trajectory parameters of different types of rocket and antiaircraft guns, this article analyzed the safe firing area. The results of firing area analysis are intuitive, reliable, convenient and quick operation, providing a scientific basis for safety of ground operations for weather modification.

    • Yang Fan, Ma Lixin

      2019,42(10):58-62, DOI:

      Abstract:

      In order to improve the working efficiency of the parallel H-bridge active filter, when the power supply voltage is distorted, a more accurate harmonic compensation current is calculated in a shorter time, and an accurate compensation command is provided for the deadbeat control. A control strategy combining adaptive harmonic detection algorithm with carrier phase shift sinusoidal pulse width modulation (CPS-SPWM). Firstly, the reference compensation current is calculated quickly and accurately by the adaptive harmonic detection algorithm. Then, the bridge voltage stability is realized by the outer loop voltage PI control, and the internal loop equalization proportional control is used to realize the same capacitor voltage of the sub-module on the bridge arm. CPS-SPWM provides a more accurate modulated wave signal for active filters. Through simulation experiments, it is verified that the control strategy has higher stability and accuracy than traditional methods.

    Editor in chief:Prof. Sun Shenghe

    Inauguration:1980

    ISSN:1002-7300

    CN:11-2175/TN

    Domestic postal code:2-369

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