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Ye Xiamei, Li Peng, Feng Jiao, Zhang Zhizhong, Guo Xiaoxu
2025,48(3):1-9, DOI:
Abstract:
Variable fractional delay filters are widely used in delay compensation technologies due to their ability to achieve arbitrary fractional delay transformations. However, due to the high complexity of solving its filter coefficients and poor adaptability, its application in engineering is severely limited. To address this challenge, this paper proposes a method to flexibly change the performance of fractional time-delay filters by adjusting the filter parameters under the condition of low computational complexity, and completes the FPGA simulation verification. The method precisely controls the window shape by adjusting the width factor of the window function, thereby optimizing the time-frequency characteristics of the filter at different orders and providing more accurate frequency selectivity compared to traditional methods. In addition, this paper adopts the orthogonal triangular decomposition least squares matrix method to solve the filter coefficients, and the designed filter requires only one matrix inverse under the condition of guaranteeing the accuracy of group delay, which effectively avoids the complex mathematical operations such as partial derivatives and double integration. Simulation results show that the method proposed in this paper reduces the computational complexity by one order of magnitude compared with the existing methods under the condition of maintaining the same delay accuracy, with the maximum magnitude error reaching -104 dB and the maximum group delay error reaching 2.34×10-4. FPGA verification results show that the design method has low hardware computational resource consumption, greatly improving the efficiency of the design of the Farrow filter.
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Wang Wencheng, Yu Zhike, Zheng Shihan
2025,48(3):10-17, DOI:
Abstract:
The 20th Central Committee′s Third Plenary Session emphasized the comprehensive implementation of water conservancy reform tasks, with a focus on residential drinking water as a key livelihood task. The coagulation process is a critical step in drinking water treatment. Due to the significant time delay characteristic of the coagulation process, conventional PID control cannot achieve satisfactory results for control systems with frequent changes in raw water quality. Therefore, a linear active disturbance rejection controller (LADRC), which does not rely on an accurate system model, is applied to the system. An extended observer is used to estimate and compensate for disturbances in the coagulation control system. Additionally, an adaptive Smith controller, combining a Smith predictor with a fuzzy controller, is designed to mitigate the impact of large time delays on control effectiveness, leading to the proposed Fuzzy-Smith-LADRC controller. To address the difficulties in adjusting the controller parameters, an improved dung beetle optimization algorithm (MSIDBO) is introduced for parameter tuning. This improved algorithm optimizes issues such as uneven initial population distribution and the tendency to fall into local optima found in the DBO algorithm, enabling MSIDBO to converge quickly and better balance global exploration and local exploitation capabilities. When the system model is accurate, this control method reduces the settling time by 279 s and decreases overshoot by 8% compared to PID control, it also reduces settling time by 40 s compared to DMC control. When the system model changes, it demonstrates better anti-disturbance and robustness compared to LADRC.
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2025,48(3):18-25, DOI:
Abstract:
Addressing the trajectory tracking problem of a quadrotor UAV, a command filter backstepping control strategy based on predefined time is proposed to mitigate the impact of model uncertainties and unknown external disturbances on system stability. Firstly, a predefined-time disturbance observer is designed to accurately estimate the system uncertainties and unknown external disturbances in real-time. Secondly, to effectively alleviate the "differential explosion" issue in the backstepping control strategy, a predefined-time command filter is designed. Based on this, position and attitude controllers are further designed using the backstepping method, enhancing the system′s control accuracy and response speed. Finally, the Lyapunov theory is employed to verify the stability of the proposed control strategy. Simulation experiments validate the effectiveness and superiority of the proposed control strategy.
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Chen Zihuan, Zhou Zhanmin, Wang Xin, Yu Qi, Li Xu
2025,48(3):26-34, DOI:
Abstract:
Aiming at the problems of insufficient consideration of time delay in the modeling process of servo motor drive system, lack of analysis given in the premise of system stability in the calibration of control gain, and relatively high conservatism of system stability criterion, this paper proposes a time delay related stability criterion with lower conservatism. Firstly, the time-varying delay is considered in the inertia structure model and the state space equations of the time-delayed closed-loop systems are established by designing the corresponding controllers, and then the Lyapunov generalized functional analysis method is used in combination with the techniques of free weight matrix, time delay splitting and integral inequality to reduce the conservatism of the stability criterion. The applicable time delay range of the controller gain for system stability guaranteed by this stability criterion is derived by running the MATLAB program, and it is concluded that this paper′s criterion improves the upper bound of the maximum time delay for system stability by 46.33%, which verifies that this paper′s criterion has a lower conservatism. The stability analysis of this paper provides theoretical reference for the analysis and control of more complex servo motor drive systems.
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Zhou Qian, Li Jianwei, Pei Haodong
2025,48(3):35-42, DOI:
Abstract:
In response to the problems such as the limited applicable scenarios of the automatic white balance algorithm (AWB), an adaptive white balance algorithm based on histogram adjustment is proposed, and the proposed algorithm is implemented in hardware using a field-programmable gate array (FPGA) to meet the real-time processing requirements of embedded systems. The algorithm statistically calculates the histograms of different color channels in a color image, uses the similarity of histogram shapes between channels as the judgment condition, and combines an adaptive histogram adjustment strategy to perform white balance correction for images in different scenes. The experimental results demonstrate that this algorithm exhibits excellent adaptability in both colorful and image scenarios containing large areas of monochromatic blocks. Compared with traditional white balance algorithms, the image color restoration effect is remarkable, the average accuracy of calibration is increased by 6%, and it can achieve real-time processing at a resolution of 1 280×720@30 fps on embedded devices, presenting a promising prospect for engineering applications.
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2025,48(3):43-51, DOI:
Abstract:
FairMOT, a multi-object tracking algorithm, proposes a balanced learning strategy between the detection branch and the re-identification branch, effectively balancing the tasks of object detection and re-identification, thereby improving tracking accuracy. However, due to the limited feature extraction capability of its DLA34 backbone network, the model′s tracking performance often declines in complex real-world scenarios, leading to missed detections and incorrect tracking. To enhance the backbone network′s feature extraction capability, this paper designs a deep aggregated backbone network based on an element-wise multiplication structure and proposes the FairMOT-Star algorithm. This algorithm leverages the principle of hidden dimension enhancement brought by the element-wise multiplication structure to achieve concise and efficient object feature extraction. Additionally, EIoU_Loss is used as the regression loss function for the bounding box regression task, more precisely describing the positional and shape relationships between detection boxes and ground truth boxes, thus improving prediction accuracy. In the matching and association part, the Kalman filter algorithm predicts target motion information, and the Hungarian algorithm associates and matches targets and trajectories across frames in the temporal dimension. Experimental tests on the MOT16 dataset achieved an MOTA accuracy of 86.0%. The model′s weight parameters amount to 19.59 M, reducing parameter count by 9.7% compared to the FairMOT model, while increasing MOTA accuracy by 3.5%, effectively optimizing the computational parameters and tracking accuracy of the FairMOT algorithm.
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2025,48(3):52-59, DOI:
Abstract:
Aiming at the limitations of small targets in remote sensing images, such as complex image background, dense distribution of small targets, and diverse target scales, this paper proposes an improved algorithm based on YOLOv8n. Firstly, a multi-scale null attention module is designed to introduce a multi-scale null attention mechanism in the backbone network in combination with the C2f module to effectively capture multi-scale semantic information and reduce the redundancy of the self-attention mechanism; secondly, a residual fast convolution module is designed to reduce the model computation and improve the feature extraction capability; finally, the PIoU v2-Iou loss function is used instead of the CIOU loss function to improve the detection accuracy of the model. The experimental results on DOTA, RSOD and VisDrone2019 datasets show that the improved YOLOv8n model improves the mAP by 2.7%、3.3% and 3.8%, respectively, and reduces the computation by 0.5 GFLOPs compared with the original model YOLOv8n, which validates the effectiveness of the new algorithm.
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Xu Ming, Wang Fengfu, Long Wen
2025,48(3):60-73, DOI:
Abstract:
To tackle complex numerical optimization problems, this paper proposes an improved hiking optimization algorithm based on Cauchy distribution operator and random differential mutation strategy (CDHOA). The algorithm enhances diversity through effective population initialization, balances global search with local exploitation using the inverse cumulative Cauchy distribution operator, and employs a random differential mutation strategy to boost exploitation and reduce local optima risks. Experimental results show the average performance of CDHOA on the CEC2017 test set is better than that of eight comparison algorithms. The statistical test further confirmed that the performance difference was significant. Nine representative test functions are selected from the CEC2017 test set, and the effectiveness of the three enhancement strategies in the algorithm is verified by comparative experiments. Additionally, it is applied to the parameter identification of photovoltaic model, and a small root mean square error of 2.43×10-3 is achieved, which has the best result of all comparison algorithms. In two kinds of engineering design problems, the algorithm achieves the minimum objective function value, which is better than the comparison algorithms. Overall，CDHOA performs well in global search ability, convergence speed and accuracy, which effectively improves the performance of solving complex numerical optimization problems.
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Gang Shuai, Liu Peisheng, Guo Xiwang
2025,48(3):74-82, DOI:
Abstract:
To address the issues of large parameter quantity, high computational complexity, and high resource demands on the computing platform for the steel surface defect detection model, a lightweight improved algorithm has been proposed. Firstly, using ShuffleNetV2 as the improved backbone layer has achieved remarkable results in reducing model complexity and computational load. Secondly, a sufficiently flexible and lightweight channel attention mechanism (CA) was incorporated after the SPPF module, while the bidirectional feature pyramid network (BiFPN) was utilized to enhance feature fusion, thereby improving the efficiency of feature information flow. Finally, the lightweight dual convolution kernel (DualConv) was employed to replace the convolution layer in C2f, and the parameter quantity was reduced through the grouping convolution strategy. Experimental results indicate that, compared with the original YOLOv8n, the improved model achieves lightweighting while maintaining detection accuracy. The parameter quantity is 56.2% of the original, and the volume and computational load have decreased to 3.6 MB and 4.8 GFLOPs, respectively, representing a reduction of 42.86% and 41.47% compared to the original model. The lightweighting of the model reduces the deployment cost and is suitable for practical deployment and application.
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Jia Baohui, Su Jiacheng, Gao Yuan
2025,48(3):83-91, DOI:
Abstract:
To address the issues of inconsistent feature distributions and the influence of noise components on the transfer effect in gearbox vibration data collected under different operating conditions, this paper proposes a deep transfer learning fault diagnosis method that integrates an attention mechanism with domain adversarial transfer networks. First, labeled and unlabeled vibration signals are constructed into datasets using a fixed-length data segmentation method. Second, to reduce the negative transfer impact caused by noisy samples, a convolutional block attention module (CBAM) and a discriminative loss term are used to assist the feature extractor in extracting discriminative features and enhancing the classification decision boundary. Finally, to solve the problem of inconsistent data feature distributions, a multi-kernel maximum mean discrepancy (MK-MMD) is employed to align the global distributions of the source and target domains, and an adversarial mechanism is used to align the subdomain distributions between the two domains. Experimental validation on a publicly available variable-condition gearbox fault dataset demonstrates that the proposed method achieves an average recognition accuracy of over 96.25%. A comparison with other diagnostic methods further validates the effectiveness and superiority of the proposed approach.
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2025,48(3):92-99, DOI:
Abstract:
To address the high false alarm rate and delayed fault detection in traditional principal component analysis (PCA) methods for industrial process fault detection, this paper proposes an iterative modeling-based sliding window PCA fault detection method. To improve detection real-time performance, an iterative approach is used during the modeling process to progressively remove outlier samples from the PCA model data, optimizing the PCA model. To reduce the false alarm rate, a sliding observation window is employed to count the number of outlier samples, and a second confidence limit is constructed for fault detection. To enhance fault detection accuracy, a composite statistic is used as the detection index, which considers anomalies in both the principal component direction and the residual space. To validate the effectiveness of the proposed method, simulation experiments were conducted using numerical examples and the Tennessee-Eastman (TE) process. In the numerical examples, the fault detection accuracy reached 89.20% with a false alarm rate of 1.33%. For the TE process, the fault detection accuracy reached 99.39% with a false alarm rate of 3.12%.
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Jia Zhuzhi, Kang Yunjuan, Zhu Hongyu, Zhang Bo, Song Xiangjin
2025,48(3):100-111, DOI:
Abstract:
When diagnosing rotor bar faults using motor current signature analysis, the fault characteristic frequencies and amplitudes on both sides of the fundamental frequency are crucial parameters for determining whether a fault has occurred and its severity. The diagnostic capability of FFT algorithm heavily depends on the length of the analyzed data. Although the least squares prony analysis algorithm has short-time data analysis capabilities, it is highly sensitive to noise levels and suffer from insufficient fault feature extraction, and failure may occur when the motor operates at low frequencies and low loads. To address these issues, an improved method combining singular value decomposition and LS-PA algorithms for diagnosing rotor bar faults is proposed. Initially, the SVD matrix is reconstructed using truncated data of original current signal, and effective order is determined based on the difference quotient of singular values. Subsequently, pre-processes technique is used to moderately suppress noise in stator current signal. Finally, the LS-PA algorithm is applied to identify and diagnose fault features from the preprocessed signal. Finite element simulation and experimental results demonstrate that the proposed method can effectively suppress signal noise and has the diagnostic performance of short-time data with high resolution. It achieves stable diagnosis of rotor bar faults under full load conditions, from light to full load, both in constant frequency and variable frequency power supply scenarios, outperforming traditional FFT methods.
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Xu Yihang, Wang Ping, Yang Yuan, Wang Yong
2025,48(3):112-117, DOI:
Abstract:
In order to improve the detection distance and section location accuracy of rail fracture with ultrasonic guided wave and ensure the safety of rail during service. In this study, Barker code encoding and Kaiser window function were used to modulate the excitation signal, expand the excitation signal time width, enhance the energy of the transmitted signal, and improve the resolution of the defect signal through pulse compression processing of the detected signal. The finite element simulation was carried out, the rail 3D simulation model was built, and the rail fracture damage was simulated. When the rail fracture degree is 20%, the peak-to-peak ratio of the main wave packet signal and the adjacent wave packet signal of the direct wave signal is 1.065 by using multi-period sine wave signal excitation, while the peak-to-peak ratio of the main wave packet signal and the adjacent wave packet signal is increased to 2.542 by using Barker13 coded signal excitation. The characteristics of defect echo signal under different fracture degrees are analyzed, and the amplitude of the echo signal intensity changes above 40 dB by Barker13 coded excitation signal. The finite element simulation shows that the encoded excitation and pulse compression technology can effectively improve the resolution and signal amplitude of the detected signal. Combined with the offline long-distance rail detection experiment, the detection signal obtained by using multi-period sine wave signal has complex components and difficult calculation of time difference. However, pulse compression technology can effectively improve the signal-to-noise ratio of detection signal and improve the signal resolution of the initial wave signal and the echo signal, and the accuracy rate of section positioning is 99.3%. Coding excitation and pulse compression technology can improve the amplitude of detection signal and the accuracy of detection and positioning effectively, and provide technical means for long-distance rail section positioning.
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2025,48(3):118-127, DOI:
Abstract:
Student classroom behavior recognition is important for improving the quality of teaching and learning. Most of the current mainstream research is based on video or sensor technologies, however, these methods suffer from privacy invasion and high cost, which constrain their wide application. Therefore, this paper proposes a method of student classroom behavior recognition based on WiFi CSI. This method first collected CSI signals for four typical classroom behaviors (hands up、stand up、sit down and turn over books) in a real classroom environment. Then combined with the characteristics of WiFi CSI data, Hampel filter and wavelet transform are used to denoise the CSI signal, and all subcarrier features are fused by designing principal component analysis algorithm. Subsequently, the CSI action intervals are intercepted according to the fusion features designing the local outlier factor detection algorithm, and the intercepted CSI signals are converted into amplitude energy maps by introducing three-dimensional mapping. Finally, the amplitude energy map dataset is feature extracted and classification recognized by designing a transfer learning model based on residual network. The experimental results show that the accuracy of the method is 98.89% and 99.07% in the step classroom and small classroom, and it can reach more than 98% in the test for different people. It is proved that the method has high recognition accuracy and good robustness, which provides a new idea for the research of student classroom behavior recognition.
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Zhou Mengying, Zhang Xuewu, Zeng Pengyuan, Jiang Yaxin
2025,48(3):128-137, DOI:
Abstract:
Addressing the issues of urban drainage pipeline defects being susceptible to background interference, the variability of characteristic scales, and the low detection accuracy and high false positive rate of existing detection models, this paper presents an improved defect detection algorithm based on YOLOv8. Initially, the DSK module is designed and embedded within the C2f module of the backbone network to expand the receptive field and improve the ability to extract multi-scale defect features. Subsequently, the Slim-neck network structure is introduced to refine the neck network, effectively utilizing and fusing defect feature information, which also contributes to the lightweightification of the model. Finally, the FocalEIOU loss function is adopted to enhance the detection performance for smaller defect targets and the convergence speed of the model. Experimental results on a pipeline defect dataset indicate that the proposed improved algorithm achieves a mean average precision (mAP) of 67.5% at a detection rate of 70.4 fps. Compared to the original YOLOv8 algorithm, the mAP value and detection speed are respectively increased by 3.8% and 1.7 fps, demonstrating superior detection performance. For the purpose of practical application, this paper has developed a system software capable of real-time detection of pipeline defects based on an improved algorithm. Through actual project detection, the enhanced algorithm proposed in this paper has been validated to meet the requirements of high precision and real-time detection for the task of urban drainage pipeline defect inspection.
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Xia Juntao, Yan Mingxuan, Yang Xinqi, Zhang Xiaojun, Tao Zhi
2025,48(3):138-144, DOI:
Abstract:
The use of low-dose CT scans has significantly reduced radiation exposure during examinations. However, this reduction has led to increased noise and artifacts in CT images, compromising image quality and diagnostic accuracy, which can affect physicians′ judgment during the diagnostic process. Recent advancements in generative models have demonstrated excellent performance in addressing these issues. Nonetheless, these models still face challenges, such as generating confusion and structural deficiencies during the generation process. To tackle these problems, a conditional diffusion denoising network model has been developed. This model incorporates a trainable curve matching module to correct different noise levels and includes a joint loss function. Experimental results indicate that the proposed algorithm achieves superior denoising outcomes compared to comparative algorithms, with test results of PSNR 35.70 and SSIM 0.912 8 on the dataset, representing the optimal performance among the selected methods. Additionally, it demonstrates good generalization across low-dose CT images with varying radiation doses, maintaining excellent denoising levels.
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Dong Runhua, Chang Qing, Kong Pengwei, Wang Yaoli
2025,48(3):145-153, DOI:
Abstract:
Regarding the perennial safety production problems that factories constantly encounter, such as the strict prohibition of smoke and fire in the workshop area, the need for constant attention to the behavioral safety of workers, and whether workers wear masks in adverse working condition scenarios, an improved worker behavior and fire detection algorithm FDH-DETR based on RT-DETR was proposed. Firstly, through the fusion of the Deep Faster feature depth fusion module and FasterNet, the number of parameters and the amount of computation of the algorithm were reduced. Secondly, through the DRBC3 module size convolution kernel conversion mechanism, the inference cost of the model was decreased. Finally, through the HiLo-AIFI high-low frequency scale within feature interaction module, the extraction ability of high-low frequency features was enhanced. Experimental results indicate that the improved algorithm achieved an average accuracy of 93.8%, a reduction of 31.6% in parameters, a reduction of 61.4% in computation, and an FPS of 150 frames per second. Inference experiments were conducted in real working condition scenarios, verifying the effectiveness of the algorithm.
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Lyu Bo, Zhou Rong, Zhang Tianyu, Pu Mengyang
2025,48(3):154-160, DOI:
Abstract:
Indoor location technology based on WiFi fingerprint database has attracted much attention because of its high precision and easy deployment, while the quality of offline fingerprint database is a key factor to determine the location accuracy. To solve the problem of high acquisition cost of offline fingerprint database, a denoising fingerprint database enhancement model (FASRGAN-DAE) based on denoising autoencoder super resolution generation adductive network is proposed. The method enhances the location accuracy by enhancing the sparse fingerprint database. Specifically, firstly, the fingerprint data is mapped to the corresponding fingerprint image; then, on the basis of deleting the batch normalization layer (BN layer), the generator network improves the perception loss function to generate high-resolution fingerprint images, and reduces the hidden layer and output layer of the autoencoder to improve the quality of the generated images. Meanwhile, in the discriminator network, the BN layer is deleted and the output of the convolutional layer is used as the authenticity score of the input image. The mean square error loss function is used to optimize the discriminator network to enhance the ability of distinguishing between real and generated images. Finally, the fingerprint image is restored to the fingerprint data through the mapping module to realize the enhancement of the fingerprint database. Through the localization experiment in the real underground parking lot environment, compared with the original fingerprint database, the average localization error was reduced by 5.69% after FASRGAN-DAE enhanced data.
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Huang Yanguo, Peng Jian, Fang Minjie, Wu Shuiqing
2025,48(3):161-171, DOI:
Abstract:
In complex road environments, existing algorithms for road multi-target detection suffer from poor recognition performance, large number of parameters, and high computational complexity, making them unsuitable for deployment on resource-limited mobile devices. To address these issues, a lightweight road multi-target detection algorithm combining non-adjacent features is proposed based on YOLOv7-tiny. First, the design of the Tiny-AFPN combines non-adjacent features of different scales, reducing the loss of features caused by scale differences and achieving richer cross-scale information interaction. Secondly, with the introduction of DSConv, the ELAN was redesigned and named ELAN-DS, improving the expression of features while optimizing the efficient layer aggregation network and reducing the complexity of the model. Finally, the use of the MPDIoU loss function improves the accuracy of bounding box regression and enhances the network′s target detection capabilities. In the experiments on SODA10M, compared with the original YOLOv7-tiny model, the improved algorithm increased accuracy, mAP@0.5, and recall by 1.4%, 1.4%, and 5.9%, respectively. It also reduced the number of parameters and computation by 8.2% and 41.5%, respectively. This effectively reduces the number of parameters and the computational complexity, substantially improves the detection speed of the model, and provides the possibility for deployment on edge devices.
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Li Peikun, Li Feng, Ge Zhongxian, Zhang Ting
2025,48(3):172-179, DOI:
Abstract:
Affected by factors such as water attenuation and scattering, underwater optical images suffer from severe color distortion, blurriness, and other issues, resulting in a significant decline in quality and poor target resolution,which in turn is not conducive to carrying out underwater target detection tasks.To address the above problems, in order to improve the accuracy of underwater target detection and reduce the incidence of misdetection and missed detection, this paper proposes an underwater target detection algorithm based on improved YOLOv8n: ESA-YOLOv8. Firstly， the algorithm introduces the ESP module in C2f to improve the Bottleneck structure, the ESP module optimizes network efficiency and reduces the number of model parameters and computations in YOLOv8n;secondly, a small target detection layer is added to improve the detection capability of underwater small targets; finally, the lightweight up-sampling operator CARAFE and the attention mechanism ECA are successively introduced into the neck network to improve the target detection accuracy and realize the enhancement of up-sampling feature fusion.The experimental results show that on the underwater biological dataset DUO,the ESA-YOLOv8 algorithm designed in this paper achieves a mAP@0.5 of 84.7% and a mAP@0.5：0.95 of 65.5%, with a reduction in model parameters. These results represent an improvement of 1.7% and 1.8%, respectively, compared to the base model YOLOv8n.The high accuracy detection results and the reduction in the number of model parameters demonstrate the effectiveness of the improved YOLOv8n and its potential application in underwater target detection.
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Zhao Shuai, Zhang Chuntang, Fan Chunling
2025,48(3):180-187, DOI:
Abstract:
Obtaining a panoramic view of the seafloor through image stitching is of great significance for understanding deep-sea topography and geomorphology. Due to the challenges posed by the deep-sea environment, seafloor image features are often blurred, making the continuous stitching of sequential images require a stable and efficient stitching network. To address the issue, this paper proposes a deep-sea sequential image stitching network called AP-LG, which combines an improved ALIKED with LightGlue. Firstly, Deformable ConvNets v2 is used to replace the original deformable convolutional networks in ALIKED, introducing an adjustment mechanism to enhance the network′s feature capture capability. Then, multi-scale feature fusion is achieved through feature pyramid networks, improving robustness of the network to environmental changes. Finally, LightGlue is employed as the core feature matching network, and based on homography estimation strategies, continuous alignment and stitching of multiple sequential images are achieved. The experimental results indicate that on the UIEBD and DISD datasets, the AP-LG network achieved matching rates of 32.91% and 49.41%, respectively, enabling 86.00% and 93.60% of the image pairs to be matched with over 100 valid feature points. The proposed method can stably extract seafloor image features, achieve feature matching, and effectively complete the stitching of sequential seafloor images.
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Construction method of regional fingerprint database of ground current field strength based on RBFNN
Zhang Zhicheng, Su Zhong, Zhao Hui, Li Fei, Sun Zhenzhen
2025,48(3):188-196, DOI:
Abstract:
The ground current field characterized by field strength is widely applied in fields such as geophysical exploration, seismic monitoring, and through-earth communication. However, due to the variability of regional ground current field strength, it is challenging to establish a regional fingerprint database for ground current field strength. This paper proposes a method for constructing a regional fingerprint database of ground current field strength based on RBFNN. By employing time-division cross-injection to construct a regional ground current field and using orthogonal electrodes to detect ground current field signals at different detection points, fingerprint features of the ground current field strength are extracted. RBFNN is used to fit the field strength variation function model in Kriging interpolation, and Kriging interpolation is then employed to estimate fine-grained fingerprint features of the ground current field strength. Based on the estimation results, a regional fingerprint database of ground current field strength is constructed. An experiment to construct the fingerprint database was conducted in a natural environment of 150 m×50 m. The results show that the constructed fine-grained (0.1 m×0.1 m) regional fingerprint database of ground current field strength achieves an average construction accuracy of 89.84%, with the highest accuracy reaching 95.46%.
Volume 48, 2025 Issue 3
Research&Design
Theory and Algorithms
Online Testing and Fault Diagnosis
Data Acquisition
Information Technology & Image Processing
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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%.
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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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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.
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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.
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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.
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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.
