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Wang Min, Shi Minghang, Hong Mei, Li Yongshun, Tian Zikang
2025,48(1):1-7, DOI:
Abstract:
A path planning method based on an improved synchronous bidirectional A-star algorithm and grey wolf optimization algorithm is proposed for the multi objective cruising path planning problem of unmanned boats. Firstly, the traditional A-star algorithm has been improved by introducing a synchronous bidirectional search strategy and dynamic weight adjustment, reducing path redundancy points and algorithm computation time. Then, the cruise path planning problem is transformed into a classic traveling salesman problem and solved using an improved grey wolf optimization algorithm to obtain the optimal cruise path. The experimental results show that the method proposed in this paper is superior to traditional methods in terms of total distance, number of turns, and computation time in path planning. It can effectively improve the cruising efficiency and safety of unmanned boats and provide a reliable solution for multi-target point cruising tasks of unmanned boats.
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Wang Yongqi, Xiao Dengyu, Hu Man, Qin Yi, Wu Fei
2025,48(1):8-19, DOI:
Abstract:
The high cost of collecting and processing high-quality motor fault data samples has resulted in the collection of newly unlabeled data samples. Domain adaptation has emerged as a promising approach to process and recognize new unlabeled data with the help of existing data. This has led to a surge of interest in domain adaptation in the field of fault diagnosis. In the field of electric machine fault diagnosis based on domain adaptation, two issues require attention. A conflict arises in the gradients of multiple tasks when employing the conventional domain adaptation framework. And, the existing methods rarely study the migration task between complex states. In light of the aforementioned issues, this paper puts forth AMDA motor fault diagnosis method based on multi-task alignment, with the aim of providing a solution to the aforementioned problems. The AMDA method employs a feature extractor comprising a multi-task one-dimensional convolutional layer, a batch normalization layer, and a pooling layer, to extract the higher-order features of the source and target domains. Subsequently, a combination of an adversarial-based approach and a distributional difference metric-based approach is utilized to reduce the distributional differences of data features. Finally, a multi-task learning approach based on gradient alignment is introduced to balance and optimize the three tasks: fault classifier, domain discriminator, and distributional difference metric. By reducing the conflicting gradients among the tasks, this approach ultimately enables the development of a domain adaptation fault diagnosis model for acoustic signals of electric motors based on multi task learning. Cross-operational state fault diagnosis tests are conducted under multiple test setups using the proposed AMDA method, and the test results demonstrate that the AMDA method effectively accomplishes the migration task between stable operational state (Stable), start operational state (Start), and European driving cycle state (NEDC) in the acoustic signal. Based on cross-operational state electric motor fault diagnosis tests, the highest diagnosis accuracies reach 91.49%. Furthermore, the performance of AMDA method is significantly higher than that of other methods in the two comparison tests, which offer valuable insights for research and engineering applications.
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Yang Tong, Wei Weimin, Fu Chengcheng, Yang Tiancheng, Xue Mei
2025,48(1):20-28, DOI:
Abstract:
Due to the absorption and scattering of light by water characteristics, underwater images usually present problems such as blurred details and low resolution. In order to improve the clarity of underwater images, an underwater image super-resolution reconstruction method that improves SRResNet is proposed. This method introduces the hybrid attention mechanism into the deep residual network to enhance the clarity of underwater images. Secondly, the structural similarity loss function is introduced to better protect image content, improve image quality, and make the training results more consistent with human visual perception. Experimental results show that the underwater image super-resolution reconstruction method based on improved SRResNet can effectively deal with problems such as blurred underwater images and low resolution. Compared to various other underwater image reconstruction methods on different datasets, this method improved the PSNR by 0.69 dB to 2.43 dB and the SSIM by 2.66% to 7.17%, demonstrating superior performance across all metrics.
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2025,48(1):29-38, DOI:
Abstract:
Aiming at the current problems of insufficient obstacle detection accuracy, slow detection speed, large number of model parameters and poor detection of small target obstacles in the complex environment of urban roads, an improved YOLOv8n lightweight urban driving road obstacle detection algorithm is proposed. Firstly, the MRObstacle urban road obstacle target detection dataset is produced to extend the types and numbers of obstacle detection; secondly, a new SPS_C2f backbone network is designed to improve the backbone network, to reduce the number of network parameters and to improve the detection speed, and the M_ECA attention module is added to the Neck portion of the network, to improve the network detection speed and the feature expression ability; thirdly, the BiFPN is integrated with a feature pyramid and a small target detection algorithm is added to the network. feature pyramid and adding a small target detection head to better capture the features of small-sized obstacles; finally, using the loss function MPDIoU that optimises the values of the bounding box width and height to improve the performance of the network bounding box regression. Compared with the original YOLOv8n algorithm, the mAP0.5 metric of this algorithm is improved by 2.04% to 97.12%, the FPS value is improved by 12.08 fps to 107.45 fps, and the volume of the network parameter is reduced by 10% to 2.73 MB.This algorithm improves the detection accuracy and speed while decreasing the number of parameters, and it can be better applied to the urban road obstacle detection task.
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Cao Jie, Wen Li, Tang Min, Guo Yijing, Gu Changzhan
2025,48(1):39-45, DOI:
Abstract:
Aiming at the current problems of limited stability and accuracy and high complexity of each solution in non-contact vital signs measurement, a non-contact vital signs measurement method based on matched filtering is designed to achieve low computational complexity and maintain optimality estimation. Five samples are tested in an office environment, and the results show the effectiveness of the proposed method to reduce the vital sign measurement errors due to people′s body movements in real environments. As an example, for sample 4, the variance of heart rate decreases from 2 825 to 82 in the smoothness design, and the root mean square error of heart rate decreases from 16 to 4 in the accuracy tracking calibration design. Clinical experiments are further compared with the current medical reference standards, and the results show that the respiratory rate error is within 1 bpm, while the heart rate measurements are better, which makes it potentially useful.
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Hu Bo, Xie Jun, Liu Junjie, Wang Hu
2025,48(1):46-54, DOI:
Abstract:
Motor imagery (MI) EEG signals are more difficult to recognize due to the inclusion of long, continuous eigenvalues as well as their own strong individual variability and low signal-to-noise ratio. In this study, we propose a model that combines a convolutional neural network (CNN) with a Transformer, aiming to effectively decode and classify motor imagery EEG signals. The method takes the original multichannel motor imagery EEG signals as input, and learns the local features of the entire one-dimensional temporal and spatial convolutional layers by firstly performing a convolutional operation on the temporal domain of the signals in the first temporal convolutional layer, and then subsequently performing a convolutional operation on the null domain of the signals in the second spatial convolutional layer. Next, the temporal features are smoothed by averaging the pooling layers along the temporal dimension and passing all the feature channels at each time point to the attention mechanism to extract the global correlations in the local temporal features. Finally, a simple classifier module based on a fully connected layer is used to classify the EEG signals for prediction. Through experimental validation on the publicly available BCI competition dataset IV-2a and dataset IV-2b, the results show that the model can effectively classify MI EEG signals with average classification accuracies of up to 80.95% and 84.79%, which is an improvement of 6.45% and 4.31% in comparison to the EEGNet network, respectively, and effectively improves motor imagery evoked potential signals of the brain-computer interface performance.
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2025,48(1):55-63, DOI:
Abstract:
To address the challenges in mask detection for faces in dense crowd scenarios, particularly due to information loss from crowd occlusion, small detection targets, and low resolution, improved YOLOv8 algorithm for dense crowd mask detection is proposed. This approach replaces the FPN structure in YOLOv8 with a GD mechanism to solve the issue of missing cross-layer information transmission. The GD mechanism uses a unified module to collect and integrate information from all layers, enabling lossless cross-layer information transmission and enhancing the network′s feature extraction capabilities. The ODconv module is inserted into the GD mechanism to learn the information transmitted by GD along four dimensions, improving the model′s detection accuracy for low-resolution images and small targets. Additionally, a SCSBD is introduced to lighten the YOLOv8 detection head, which occupies a significant proportion, thereby balancing the model size. Experimental results show that the improved network has increased precision, recall, and mean average precision by 13.6%、1.5% and 6.9%, respectively, with an 81.1% accuracy in mask detection on faces. The model′s weight file is only 25 MB, and its size is between YOLOv8s and Gold-YOLO-S, achieving a balance between size and accuracy.
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An Longhui, Wang Manli, Zhang Changsen
2025,48(1):64-75, DOI:
Abstract:
To solve the problem of conveyor belt tear detection in the special operating environment of underground mines, a lightweight detection algorithm based on line laser assistance and improved YOLOv7 is proposed. Firstly, considering that the conveyor belt tear is mainly small targets, the largest detection layer is not needed, thus simplifying the network model to reduce the model size and the number of parameters. In addition, the dynamic non monotonic FM-based Wise-IoU loss function is adopted to make the model pay more attention to common quality samples and improve the model detection performance. Then, the LAMP pruning method is used to improve the model′s computing speed and reduce the computing complexity, achieving the lightweight of the detection network. The channel knowledge distillation is used to improve the model accuracy without loss, and finally, the model is accelerated by TensorRT to achieve faster detection speed. The experimental results show that compared with the benchmark model, the improved model has a parameter number and computing volume reduced by 86.8% and 49.2%, respectively, mAP@0.5:0.95 reached 62.4%, and the detection speed was improved by 151.0 fps, the model size was reduced from 71.3 MB to 12.8 MB. After the improvement, the model has improved the accuracy and real-time detection of conveyor belt tear faults.
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Wang Junkai, Zhang Xiaoyu, Liu Xiangbin, Guo Rong
2025,48(1):76-83, DOI:
Abstract:
In the absence of disturbance information, the chattering effect in terminal sliding mode control of a permanent magnet synchronous motor (PMSM) becomes more pronounced. To reduce the impact of unknown uncertainties and disturbances on control performance, this paper presents an improved adaptive fast terminal sliding mode control method based on a disturbance observer. First, a mathematical model of the PMSM is developed, accounting for parameter uncertainties and load disturbances, and a nonsingular fast terminal sliding surface is designed to enhance the system′s response speed. A disturbance observer is then utilized to estimate system uncertainties and unknown disturbances, with an adaptive gain introduced in the sliding mode controller to compensate for estimation errors, achieving adaptive robust control without requiring a known upper bound for disturbances. This improved adaptive control strategy dynamically compensates for disturbances, strengthening the system′s adaptability to unknown disturbances. Simulation and experimental results demonstrate that, in the absence of disturbance information, the proposed method effectively suppresses chattering caused by sliding mode control, improves the robustness of the PMSM system, and significantly enhances control accuracy and dynamic performance.
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Song Yu, Gao Gang, Liang Chao, Xu Junsheng
2025,48(1):84-91, DOI:
Abstract:
Aiming at the problem that traditional grey wolf algorithm is prone to local optimality in 3D path planning, an improved grey wolf algorithm is proposed in this paper. Firstly, the environment of the three-dimensional threat region is modeled, and the total cost function of UAV flight is specified under the constraint conditions. Secondly, chaotic sequences and quasi-reverse learning strategies were added to the initialization of grey wolf population, which increased the diversity of species and the search scope of unknown domain, and improved the adaptive weight factors to update individual positions, thus speeding up the convergence speed. Finally, in order to avoid falling into local optimization, particle swarm optimization algorithm is introduced to balance global development and local convergence. The experimental results show that compared with the other three typical path planning algorithms, the improved gray wolf algorithm can find a safe and feasible path, and has a stable optimization ability.
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Yang Li, Yang Chenchen, Yang Genghuang, Duan Hailong, Deng Jingwei
2025,48(1):92-99, DOI:
Abstract:
Aiming at the problems of false detection and missing detection in the complex background of photovoltaic cell defect detection, an improved YOLOv8 based photovoltaic cell defect detection algorithm was proposed. Firstly, the bidirectional feature pyramid network is used as the feature fusion mechanism to achieve multi-scale feature fusion through top-down and top-down paths. Secondly, the context aggregation module is introduced into the neck network, and the context information of different receptive fields is obtained by using the cavity convolution of different cavity convolution rates, which helps the model to identify small targets more accurately, and thus improves the target detection performance of the model. Finally, the boundary frame loss function is optimized and its weight factor is adjusted continuously to improve the convergence speed and efficiency of the model. The experimental results show that compared with the detection network of YOLOv8 algorithm, the recall rate and average accuracy are respectively increased by 10.4% and 1.8%, and the detection frame rate reaches 270 fps, ensuring the lightweight requirements of real-time detection and subsequent deployment. The improved algorithm can carry out robust detection of photovoltaic cell defects under complex background.
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Tang Jijie, Liu Jinguang, Ou Xiaofang
2025,48(1):100-110, DOI:
Abstract:
To address the problems of accuracy and efficiency limitations in traditional methods of studying vehicle trajectories and accelerate the promotion of digital road traffic management, this paper proposes a vehicle trajectory extraction method at intersections based on multi-target tracking optimization. First, based on the YOLOv8s algorithm framework, a multi-branch convolution strategy was introduced and an image processing method combining standard convolution and depthwise separable convolution was designed to improve the robustness of the model to different scenes and maintain a stable frame rate. Then, the loss function of the DeepSORT algorithm is improved by accurately quantifying the angle difference and distance loss to increase the convergence speed of the model and the accuracy of handling irregular objects. Finally, the accurate extraction of vehicle trajectories is ensured by deriving the conversion relationship between the pixel coordinate system and the real-world coordinate system. The experimental results show that the improved model has improved mAP, recall rate and MOTA by 2.9%、5.6% and 0.7% respectively compared with the original model, and the number of encoding transformations (IDS) has decreased by 64%. The frame rate can be kept stable during detection. And by deriving the conversion relationship between the pixel coordinate system and the real-world coordinate system, the vehicle′s trajectory information in the surveillance video can be accurately extracted. This provides methodological support for in-depth research on vehicle characteristics and road traffic risks, and has high practical application value.
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Fang Ming, Zhang Jiao, Xu Jing, Wang Yitan
2025,48(1):111-118, DOI:
Abstract:
In order to solve the problems such as the large amount of YOLOv8 parameters affecting the detection speed, this paper proposes a lightweight leather defect detection algorithm based on the YOLOv8 framework by using automotive seat leather as a sample for defect detection on the surface of automotive seats. Firstly, the original backbone network of YOLOv8 is replaced with the lightweight network StarNet, which achieves the mapping of high-dimensional and nonlinear feature spaces through star arithmetic, thus demonstrating impressive performance and low latency with a compact network structure and low energy consumption. Secondly, the original detection head is replaced with a lightweight shared convolutional detection head, which allows for a significant reduction in the number of parameters through the use of shared convolution, making the model lighter so that it can be easily deployed on resource-constrained devices. Finally, the C2f module of the neck network is replaced by the C2f_Star module, which fuses feature maps of different scales while the network is more lightweight to improve the accuracy and robustness of target detection. Experimental validation of the model on the home-made HSV-Leather dataset shows that the improved YOLOv8-Leather detection model outperforms the YOLOv8n model. Compared to the YOLOv8n model, the improved model reduces the number of parameters by 57%, improves the detection speed by 20%, reduces the model weights by 52%, and reduces the computation by 53%. The experiment verifies the feasibility of the improved model in solving the problem of leather surface defect detection.
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Fu Mingkai, Wang Shaohong, Ma Chao
2025,48(1):119-128, DOI:
Abstract:
Gait recognition is a key technology for lower limb exoskeleton robots, and accurate gait recognition plays a crucial role in the flexible control of these robots. To address the randomness in gait characteristics (such as walking speed and stride length) across different individuals and within the same individual, this paper proposes a gait phase recognition method based on an improved SECBAM-Densenet network model.Firstly, two inertial measurement units were placed on the tibia and the rectus femoris muscle of the thigh to collect gait data from 200 participants performing four gait tasks: walking forward, turning, ascending stairs and descending stairs. After filtering and resampling the data for preprocessing, the processed data were used as input to the proposed model. Finally, the SECBAM-Densenet model was used to classify the gait phases. The results show that the improved SECBAM-Densenet model achieved an average recognition accuracy of 95.76% in different gait phases within the same individual, which represents an improvement of 0.66% to 21.22% compared to other models. For different individuals, the recognition accuracy for each phase was higher than 94%.These experimental results indicate that the proposed model can be applied in the field of gait phase recognition, providing experimental reference for the flexible control of lower limb exoskeleton robots.
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Yuan Ye, Tang Chunyang, Zhang Boxuan, Li Qiang
2025,48(1):129-136, DOI:
Abstract:
In response to the problem of low accuracy in individual identification of communication radiation sources under channel noise interference, a communication radiation source individual identification method that integrates time-frequency characteristics is proposed by utilizing the difference in channel noise interference suppression effect of signal mapping to different time-frequency domains. Firstly, extract I/Q, power spectrum, and wavelet spectrum information from the radiation source signal, and fuse the time-frequency information of the signal through one-dimensional convolution in both horizontal and vertical directions; then, the channel attention module and spatial attention module are used to fuse time-frequency features; finally, M-ResNeXt network is used to achieve individual identification of radiation sources under channel noise interference. The experimental results show that under the interference of three channel noises, Gaussian white noise with a signal-to-noise ratio (SNR) of 15 dB, Rayleigh, and Rician, the recognition accuracy of the proposed time-frequency feature fusion method reaches 97.6%、97.7%、and 98.5% respectively. Even when facing unknown noise interference at an SNR of 15 dB, it can still achieve a recognition accuracy of over 97.7%. Therefore, the time-frequency feature fusion method can significantly improve the accuracy and robustness of individual communication radiation source identification.
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Li Jie, Zhang Xinyue, Tu Jingmin, Chen Jiwen, Li Li
2025,48(1):137-144, DOI:
Abstract:
Aiming at the recognition problem caused by the complex background and the high similarity of commodity packaging in the vending machine scene, a commodity recognition method combining multi-scale attention mechanism and metric learning is proposed. Firstly, based on the ResNet hierarchical structure, multi-head self-attention is introduced to fully exploit the advantages of multi-scale feature extraction of convolutional neural network (CNN) and the global modeling ability of Transformer, and a new multi-scale hollow attention is designed to make the model focus on local features such as trademark shape and local texture in similar packaging, as well as context global features. Secondly, a down-sampling multi-scale feature fusion strategy is designed to effectively improve the multi-scale feature expression ability of the algorithm. Finally, ArcFace loss function is used to enhance the recognition ability of the model. In order to verify the effectiveness of the proposed method, a commodity data set in a real scene is constructed, which is collected by the top-view camera of the vending cabinet. The experimental results show that the MAP @ 1 accuracy of this method on the Commodity 553 dataset reaches 87.4%, which is better than the current mainstream recognition methods and can achieve more accurate commodity recognition.
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Wu Huidong, Liu Licheng, Pan Dan
2025,48(1):145-153, DOI:
Abstract:
To address the low diagnostic accuracy of Alzheimer′s disease (AD) caused by the subtle complexity and spatial heterogeneity of brain lesions in structural magnetic resonance imaging (sMRI) of AD patients, a hybrid architecture that combines the strengths of convolutional neural networks (CNN) and Transformers is proposed for the AD diagnosis. First, a multi-view feature encoder is designed, in which a view local feature extractor with integrated hybrid attention mechanisms is employed to extract complementary information from the coronal, sagittal, and axial views of sMRI. The semantic representation of lesion regions is further enhanced through a multi-view information interaction learning strategy. Second, a cascaded multi-scale fusion subnetwork is designed to progressively fuse multi-scale feature map information, enhancing discriminative ability. Finally, a Transformer encoder is used to model the global feature representation of full-brain sMRI. Results on the Alzheimer′s disease neuroimaging initiative (ADNI) dataset show that the proposed method in this paper achieves classification accuracies of 94.05% for AD and 81.59% for mild cognitive impairment (MCI) conversion prediction, outperforming several existing methods.
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Yang Ruijun, Zhang Hao, Ye Jing
2025,48(1):154-165, DOI:
Abstract:
Aiming at the large model parameters and slow detection speed encountered by current lightweight target detection algorithms when applied to the task of detecting military aircraft in remote sensing images, this study proposes a lightweight detection algorithm for military aircraft targets based on YOLOv8n, named LeYOLO-MARs. The algorithm introduces an optimized inverted bottleneck module to replace the traditional bottleneck in the backbone network, reducing computational requirements while maintaining feature extraction capabilities and improving processing speed. In the neck network, a fast pyramid architecture is integrated to reduce the number of convolutional layers, enhance the efficiency of semantic information sharing, and decrease lock and wait times, while also considering limited parallelization opportunities and architectural complexity. A lightweight decoupled detection head, simplified through pointwise convolution, is employed, alongside the use of Inner-SIoU as the new localization regression loss function, which enhances the ability to learn from small target samples and accelerates the convergence of bounding box regression. Moreover, the algorithm incorporates a lightweight pyramid compression attention mechanism, effectively combining local and global attention to establish long-range channel dependencies. Experimental results demonstrate that the improved algorithm achieves a detection accuracy of 95.7%, 0.4% higher than the baseline model, while reducing model parameters by 43% and computational load by 63%, marking a notable improvement in detection performance compared to mainstream algorithms and enabling high-quality real-time detection of military aircraft targets.
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Tian Qing, Zhao Yu, Zhang Zheng, Yang Qiang
2025,48(1):166-174, DOI:
Abstract:
Underwater range-gated imaging technology is not affected by ambient light and has the advantage of long operating distances, making it a field of interest for many researchers. However, underwater gated images face issues such as uneven lighting distribution and high noise levels, which impair image clarity. In response to these challenges, this paper introduces an Enhanced Zero-DCE++ algorithm, building on the existing low-light enhancement algorithm Zero-DCE++. Initially, an improved kernel selection module is incorporated, replacing standard convolution and ReLU activation functions with depthwise separable convolution and ReLU6, to address overexposure issues in certain areas of underwater gated images. Furthermore, an improved HWAB half-wavelet attention module utilizing CBAM instead of the DAU dual attention unit is employed to differentiate between noise and real features in the wavelet domain, enhancing feature distinction and improving imaging clarity. Lastly, an ADNet noise reduction module is added to effectively suppress noise following low-light enhancement by Zero-DCE++. Experiments on a self collected underwater gated dataset demonstrate that the Enhanced Zero-DCE++ model achieves approximately 0.65 dB improvement in peak signal-to-noise ratio and a 0.23 increase in image information entropy compared to the Zero-DCE++ model, proving the model′s effectiveness and feasibility.
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2025,48(1):175-185, DOI:
Abstract:
To address the issue of inadequate target detection performance in the visual perception systems of autonomous vehicles, particularly under complex weather conditions such as fog and rain that introduce environmental noise, we propose a joint optimization target detection algorithm based on adaptive image denoising and multiple attention mechanisms(DMC-YOLO).An image denoising network has been constructed that combines the dark channel prior algorithm with ACE image enhancement technology to improve image quality in challenging weather conditions. Additionally, this network is integrated with the YOLOv8 backbone, utilizing SCDonw convolution to replace standard convolution. By incorporating point convolution and depth convolution, the aim is to reduce computational costs while obtaining richer down-sampling information.The SEAM attention module is employed to merge local and global information within the network. Furthermore, the SA detection head is introduced to emphasize contextual features, allowing for the retention of more detailed information. To enhance the network′s adaptability to various complex environments, linear interval mapping is incorporated into the loss function for reconstructing IoU.Experimental results indicate that, compared to the baseline model, the average accuracy of the improved algorithm increases by 2.9% while reducing the number of parameters by 15%. This effectively enhances the ability of autonomous vehicles to recognize targets in complex environments.The deployment outcomes on EC-R3588SPC and Nvidia Jetson NX edge devices are promising, fulfilling real-time detection requirements even under challenging weather conditions.
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Cao Jianfang, Peng Cunhe, Chen Zhiqiang, Yang Zhuolin
2025,48(1):186-196, DOI:
Abstract:
Aiming at texture problems, contour similarity among fresco image characters, large differences in fresco character features in different scenes, complex background noise, and confusing classification, an improvement strategy for ResNet convolutional neural network is proposed. Firstly, the larger 7×7 convolutional kernel in the input layer of the model is separated into three series-connected 3×3 small convolutional kernels stacked in the backbone, and 2×2 average pooling and maximum pooling are used for add feature fusion to replace the original maximum pooling operation, which enhances the model′s representative ability. Secondly, a multi-scale efficient spatial channel attention module is designed, based on the ECA channel attention module, the spatial attention module is connected in series, and the original 3×3 convolutional kernel in the spatial module is replaced by the SK attention module, which fuses the multi-scale information to capture the global long-distance dependency, and reduces the interference of background noise. Finally, a cellular aggregation structure is proposed to perform ADD operation on the output information in the neighboring block blocks as inputs to the subsequent layers, capturing both low-level and high-level features to enhance the circulation of contextual information. The experimental results show that the model achieves 96.51%、96.65%、96.67% and 96.63% in accuracy、precision、recall and F1 value, respectively. Relative to the original model ResNet-18 accuracy is improved by 9.76%, and compared with mainstream classification algorithms classification accuracy, generalization ability, and stability are all improved, which can efficiently and accurately identify the type of mural belonging to the mural, which is of significant value for cultural heritage preservation and art history aspects of the research.
Volume 48, 2025 Issue 1
Research&Design
Theory and Algorithms
Data Acquisition
Information Technology & Image Processing
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The defect segmentation and evaluation method of DR image using VA-UNet
Abstract:
The tension clamp plays the role of connecting wires and carrying current in the transmission line, and its crimping quality is directly related to the safe and effective operation of the power grid. In order to solve the problems of complex operation and high personnel requirements in the DR defect detection of tension clamp crimping, a DR image defect evaluation method using VA-UNet segmentation technology was proposed. Firstly, the semantic segmentation model VA-UNet for DR image defects in tension clamps is studied, VGG16 with significant image feature extraction and analysis ability is selected as the backbone network, multi-scale feature fusion is enhanced by integrating spatial pyramid pooling structure ASPP, and mixed loss function is introduced to accelerate the model convergence and improve the segmentation accuracy. Then, a grading method combining the model prediction segmentation results and related quantitative analysis was studied to realize the hazard severity assessment of DR defects in tension clamp crimping, which provided a reference for the subsequent wire clamp treatment. Based on the data set preparation and the analysis of test evaluation indicators, the relevant ablation experiments showed that the mIoU and mPA of VA-UNet reached 84.14% and 91.58%, respectively, which were significantly higher than those of the original model. The experiment of assessing the severity of DR defects in tension clamp crimping shows that the method is scientific and practical.
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Research on super-resolution reconstruction algorithm based on gated image
Abstract:
Laser range gating technology can break through the limitations of traditional imaging in complex environments such as rain, snow and fog, low light and inverse glare, but the generated gated image is a low-quality grayscale map, which lacks color information and is difficult to distinguish between the subject and the background, so super-resolution reconstruction technology is needed to focus on the reconstruction of edge information and spatial details to improve the visual effect. Due to the lack of color and rich texture information in the gated image, the traditional feature extraction method is prone to redundant features, which affects the reconstruction efficiency. In order to solve the above problems, this paper proposes a bi-aggregation deep feature extraction network. Firstly, shallow feature extraction was carried out by spatial and channel reconstruction convolution (SCConv) to improve the information content and solve the redundancy problem. Secondly, a new deep feature extraction module was des
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Research on complete coverage path planning algorithm for intelligent cleaning robots
Abstract:
To address the issues of path redundancy and low coverage in intelligent cleaning robot path planning, a hybrid algorithm for full coverage path planning is proposed, which integrates the semi-spring artificial potential field method, A* algorithm, and dynamic update strategy. This aims to solve the problem effectively. The A* algorithm is used for initial path planning, while the semi-spring artificial potential field method is employed for local obstacle avoidance to reduce local optima issues. The dynamic coverage value update strategy optimizes path priority based on real-time coverage, improving coverage efficiency and reducing redundant coverage. Additionally, the dynamic weight adjustment mechanism based on fuzzy logic enables the algorithm to adaptively adjust coverage and obstacle avoidance weights in complex environments. Simulation results show that the hybrid algorithm outperforms the traditional artificial potential field-based comparison algorithm and the comparison algorithm without dynamic coverage value update strategy in scenarios with regular obstacle distribution. Specifically, path length is reduced by 2.8% and 7.1%, and redundant coverage is reduced by 49.6% and 71.3%, respectively. In scenarios with irregular obstacle distribution, path length is reduced by 12.7% and 29.4%, and redundant coverage is reduced by 36.7% and 60.2%, respectively. In narrow road scenarios, path length is reduced by 32.8% and 25.5%, redundant coverage is reduced by 66.7% and 60.8%, and coverage rate is improved by 3.6% and 8.1%.
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Anomaly Detection Method for Monitoring Data of High Core-Wall Rockfill Dams Based on DSVDD
Abstract:
Reservoir dams, as critical infrastructures for flood control, hydropower generation, and agricultural irrigation, pose significant risks if breached, making regular safety monitoring essential. However, monitoring data often contains anomalies due to environmental factors, system malfunctions, and abnormal behaviors of the monitored objects. Detecting these anomalies is vital for effective data analysis and early risk identification to ensure the dam safety. Existing anomaly detection methods typically focus on gross outliers and overlook subtle, gradual anomalies. This paper proposes a gradual anomaly detection method based on Deep Support Vector Data Description (DSVDD). The method constructs multi-dimensional monitoring parameters as samples and trains an autoencoder using DSVDD to map the input data to a compact hypersphere. The anomaly score is derived from the distance of the input sample from the center of the hypersphere. The proposed method is validated using laser alignment data from a high core-wall rockfill dam, and the results demonstrate its superior performance compared to other methods.
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Research on mobile robot dynamic obstacle avoidance by fusing A* and DWA algorithms
Abstract:
To solve the problems of low search efficiency, interspersed obstacles, unsmooth paths, and inability to avoid unknown obstacles in the traditional A* algorithm during path planning, a mobile robot dynamic obstacle avoidance method integrating the improved A* algorithm with the improved DWA algorithm is proposed. In the improved A* algorithm, the global obstacle occupancy ratio is introduced, dynamic weight coefficients are added to the heuristic function, the search field is optimized, the safe distance is set to remove redundant nodes, and third-order Bessel curves are added to smooth the paths, and the cost subfunctions of the target points are added to the DWA algorithm and the coefficients of the cost function are dynamically adjusted. The dynamic obstacle avoidance of the mobile robot is realized. Simulation results show that in different environments, compared with the traditional A* algorithm and the improved algorithm in literature [24], the path length of the improved A* algorithm in this paper is shortened by an average of 5.14% and 1.01%, the search nodes are reduced by 57.05% and 36.59%, and the planning time is reduced by 34.39% and 8.49%, respectively; the improved fusion algorithm in this paper is reduced by an average of 5.14% and 1.01%, the search node is reduced by 57.05% and 36.59%, and the planning time is reduced by 34.39% and 8.49%, respectively. and the fusion algorithm in literature [24], the path length is shortened by 19.89% and 1.82% on average, and the planning time is reduced by 53.66% and 13.01% on average, respectively. It is proved that the improved fusion algorithm proposed in this paper effectively shortens the planned path length and time, and is able to realize real-time obstacle avoidance in complex dynamic environments to satisfy the high efficiency and safety of mobile robots during traveling.
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Interpretable rotating machinery fault diagnosis based on data enhancement
Abstract:
To address the challenges of sparse labels and insufficient data in mechanical fault diagnosis while enhancing diagnostic performance and model interpretability, this paper introduces a time-frequency convolutional neural network (TF-CNN) model embedded with time-frequency analysis. By integrating advanced data augmentation techniques with a convolutional architecture leveraging time-frequency transformation, the model effectively extracts multi-scale, key time-frequency features from vibration signals. The time-frequency convolutional layer combines the physical interpretability of time-frequency analysis with the autonomous feature extraction capabilities of convolutional neural networks, enabling precise identification of critical signal characteristics and fault diagnosis through classification mechanisms. Experimental validation on the CWRU dataset demonstrates that the TF-CNN model achieves a diagnostic accuracy of 99.8%, significantly outperforming baseline methods. Additionally, frequency response analysis confirms the model's ability to emphasize key signal frequency bands, further strengthening its physical interpretability. By seamlessly integrating the strengths of time-frequency analysis and deep learning, the TF-CNN model offers an innovative, efficient, and interpretable approach to industrial fault diagnosis. This work provides valuable insights and practical guidance for advancing fault diagnosis techniques, paving the way for robust applications in complex industrial scenarios.
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The temperature bias correction based on the SWGU-ConvLSTM model
Abstract:
I designed a novel deep learning network model called SWGU-ConvLSTM for temperature bias correction, which incorporates a U-Net and bidirectional adversarial network architecture. The model utilizes the ConvLSTM module to extract local information and the SwinTransform module for global information. The IAFF module fuses the output features of both ConvLSTM and SwinTransform, employing U-shaped connections and skip connections to better integrate shallow and deep information, while capturing information at different scales. The model serves as both a generator and discriminator for bidirectional adversarial training, enhancing its learning and predictive capabilities. Using ECMWF"s publicly available TIGGE numerical model data as the corrected data and ERA5 reanalysis data as the label data, the model corrects 6-hour temperature forecasts. Experimental results indicate that the proposed SWGU-ConvLSTM model significantly outperforms other comparative models in metrics such as MSE, MAE, and SSIM.
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Non-contact large-stroke position and attitude measurement system for parallel robots
Abstract:
To meet the demand for non-contact synchronous measurement of the large-range six-degree-of-freedom pose of a parallel robot's moving platform, a pose measurement system composed of a laser and visual components is proposed. The system utilizes image processing technology to output the two-dimensional coordinates of light spots on the detection surface, achieving precise pose measurement of parallel robot platforms through the cooperation of lasers and vision components. Subsequently, the solution method of the measurement system is studied, and the pose of the target is derived based on analytical geometry methods. Finally, experimental tests are conducted using a high-precision parallel robot. The results indicate that the system achieves a root mean square error of less than 0.1 mm in position measurement and less than 0.07° in attitude measurement. The average relative errors of position and attitude measurements are 0.76% and 2.56%, respectively. This research provides a novel and effective solution for large-stroke pose measurement technology, with broad application prospects in robotic motion control, precision manufacturing, and scientific experiments.
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Multimodal 3D Object Detection Method Based on ConvNeXt and Deformable Cross Attention
Abstract:
In recent years, with the rapid development of new energy vehicles, 3D object detection, as a core foundation of autonomous driving technology, has become increasingly important. Strategies that integrate multimodal information, such as radar point clouds and images, can significantly enhance the accuracy and robustness of object detection. Inspired by BEVDet, this paper proposes an improved multimodal fusion 3D object detection method based on the BEV (Bird"s Eye View) perspective. The method employs a ConvNeXt network combined with an FPN-DCN structure to efficiently extract image features and utilizes a deformable cross-attention mechanism to achieve deep fusion of image and point cloud data, thereby further enhancing the detection accuracy of the model. Experiments on the nuScenes autonomous driving dataset demonstrate the superior performance of our model, with an NDS of 64.9% on the test set, significantly outperforming most existing detection methods.
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Continuous non-invasive blood pressure prediction based on multiple physiological parameters
闫硕, 吴阳, 王世锋, 王慧泉, 何佳, 刘星, 王通, 陈杰, 李晓鹏, 李颖苇, 欧宗锟
Abstract:
At present, because the cuff method of blood pressure measurement cannot work in the high pressure environment, in order to solve the problem of blood pressure measurement in the high pressure environment, we proposed a k-Nearest Neighbor continuous non-invasive blood pressure prediction model combining pulse wave transit time and heart rate variability. In this study, rabbits were pressurized from normal pressure to a depth of 1000 m in a high-pressure chamber. During this process, electrocardiogram, photoplethysmography and invasive blood pressure of rabbits were collected. The average absolute error ± standard deviation of the k-Nearest Neighbor model for the prediction of the systolic and diastolic blood pressure and mean blood pressure of rabbit 1 is 2.2±1.5 mmHg and 1.9±1.4 mmHg, respectively. The results of SBP and DBP for rabbit 2 were 1.7±1.3 mmHg and 1.7±1.5 mmHg respectively. The results show that the method proposed in this paper achieves good results in predicting blood pressure of different individuals under high pressure environment, and provides ideas for blood pressure monitoring under high pressure.
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IDBO-SA-LSTM based rolling force prediction for cold continuous rolling
Abstract:
In order to solve the problems of low prediction accuracy of the traditional rolling force model and the dung beetle optimizer (DBO) algorithm's tendency to fall into local optimal solutions, a rolling force prediction model based on the improved dung beetle optimisation algorithm combined with the self-attention (SA) mechanism for long and short-term memory (LSTM) networks is proposed. An improved dung beetle optimizer (IDBO) algorithm is obtained by adding the golden sine algorithm and dynamic weight coefficients and introducing the Circle chaotic mapping, and by combining the LSTM network with the SA mechanism, the IDBO-SA-LSTM cold rolling force prediction model is established, and compared with other models. Six different benchmark functions are used for testing, and simulation experiments show that IDBO algorithm has faster convergence speed and optimization accuracy than the sparrow search algorithm, the dung beetle optimization algorithm, the grey wolf search algorithm and so on. The rolling force prediction experiments are carried out using 6554 field operation data of a two-stand cold rolling unit, and the results show that the prediction error indexes of the IDBO-SA-LSTM algorithm are smaller than the other comparative models, and the IDBO-SA-LSTM algorithm can predict the rolling force within ±4% with the hit rate of 99%, with high model accuracy and good generalization ability.
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A highly robust sector current differential protection criterion under noisy disturbance environments
Abstract:
With the development of renewable-energy-dominated power system, current differential protection, a key technology to ensure the safe operation of power systems, has attracted widespread attention regarding its reliability. However, the influence of noise interference in the process of information transmission is often ignored in the existing studies when discussing current differential protection, which may lead to the failure of protection action. Deeply analyzes the influence of complex noise disturbance composed of Gaussian white noise and impulse noise on the operation characteristics of current differential protection, based on the ρ-plane theory, and proposes a new sector criterion. Compared with the traditional circular criterion, the proposed sector criterion can provide a larger braking area, and has superior anti-misoperation and anti-braking performance under different signal-to-noise ratio, which can be proved by theoretical analysis and simulation experiments. The sector criterion reduces the mal-operation rate by about 50% compared to traditional criterion, and can ensure that both the mal-operation rate and the refusal operation rate are far less than 10-5, even under complex noise disturbances with a low signal-to-noise ratio (30dB). It provides a high-reliability solution for current differential protection in the distribution network, which has important theoretical and practical significance for ensuring the safe and stable operation of the new power system.
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Simulation of correlation between the array data by separating in the angular domain
Abstract:
To address the difficulty of analyzing qualitatively and measuring quantitatively the correlation of underwater acoustic array element domain data before and after angular domain separation through experiments, a simulation method is proposed to perform correlation simulation analysis on the input and separated array element domain data in the azimuth and distance directions. Some typical seafloor echoes are used as inputs to the linear array from different directions as the simulation model. The correlation index between the input and the separated data is simulated. By pre-emphasizing on the ends, the simulation results show that the data of the typical seafloor echo input has better correlation with the data of the angular bandpass filter output in the azimuth directions. The results show that the correlation coefficient of reef bottom echoes has increased from 0.9881 to 0.9998, the correlation coefficient of sand and mud bottom echoes has increased from 0.9342 to 0.9967, and the correlation coefficient of mud bottom echoes has increased from 0.8388 to 0.9581.
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UAV information collection optimization algorithm for node dynamic priority
Abstract:
In distributed IoT application scenarios such as environmental monitoring, nodes often have different priorities due to the different regional importance of node monitoring and the amount of data collected. The dynamic change of node priority will make the UAV frequently replace the target node of data acquisition, resulting in prolonged task completion time and unwarranted waste of energy. Therefore, we propose a joint optimization algorithm of UAV task completion time and energy consumption based on DDQN for distributed IoT application scenarios with dynamic priority of nodes. During the training process, the UAV learns the optimal strategy under the constraints of task completion time, energy consumption and avoiding node data overflow. The simulation results show that compared with the maximum priority strategy and greedy strategy, the task completion time of the proposed algorithm is reduced by 9.2 % and 15.1 % respectively, and the energy consumption is reduced by 10 % and 16.3 % respectively. Compared with the DQN method, the proposed algorithm converges faster and the training process is more stable.
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Decoding Strategies for Dysarthric Speech Recognition
Abstract:
Dysarthric speech arises from neurological disorders that cause motor impairments in the articulatory organs, resulting in abnormal pronunciation and prosody, which pose significant challenges to traditional ASR systems. To address these issues, this paper proposes an innovative algorithm that combines a multi-level representation fusion decoding strategy with hotword boosting technology. Built upon the Transformer-based encoder-decoder architecture, the approach improves the conventional single-view decoding method by introducing multi-level representation fusion. This is achieved through three distinct fusion strategies, which effectively enhance the model's ability to comprehend complex sentences and contextual information. Additionally, to further improve the recognition accuracy of dysarthric speech, hotword boosting is integrated into the beam search decoding process to assign higher weights to key terms. The results demonstrate that the proposed method significantly reduces the WER compared to other baseline models. Specifically, compared to the Whisper baseline model, the WER on the UASpeech dataset decreased from 38.31% to 27.18%, and on the TORGO dataset, it decreased from 16.38% to 12.67%. This highlights the effectiveness of the proposed method in improving the accuracy of dysarthric speech recognition.
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An X-ray prohibited Detection Method Based on Anchor-aided and Granular level multi-Scale features
Abstract:
Aiming at the current problems of occlusion, noise interference and low detection accuracy between prohibited X-ray objects, a contraband detection model integrating anchor-aided training strategy and fine-grained multi-scale features was proposed based on the YOLOv8s network. In the network, the C3_Res2Net module is used to replace the C2f module. By integrating features at different levels to enhance multi-scale, the receptive field range of the network layer is increased, and features at the fine-grained level are obtained to solve the problem of low detection accuracy caused by occlusion between contraband items;The sliding average Slide Loss target category loss function and the improved border loss function are used to try to assign higher weights to difficult samples, which reduces the competitiveness of high-quality anchor frames while reducing the harmful gradients generated by low-quality examples. At the same time, the focus is on anchor frames of ordinary quality to improve the overall performance of the detector and make it have better anti-noise interference ability; In the early stages of training, the ATSS (Adaptive Training Sample Selection) and Task-Aligned Assigner collaborative label assignment strategies are used, leveraging anchor-based preset information to stabilize model training; In the later training stages, an anchor-aided training strategy further enhances detection accuracy by exploiting the respective advantages of various anchor networks; The improved model was trained and tested on the public SIXray and HiXray datasets, achieving mAP50 scores of 94.9% and 83.7%, and mAP50:95 scores of 73.1% and 52.2%, respectively. The results demonstrate that the proposed model offers high accuracy and stability in contraband detection.
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Non-synchronous Measurement of Arrays In Strong Interference Environments
Abstract:
The estimation of the relative acoustic transfer func-tion is a prerequisite for implementing mul-ti-reference non-synchronous measurements with a microphone array. In industrial test scenarios, strong background noise poses a significant challenge, resulting in substantial errors in multi-reference non-synchronous measurements. To address such issue, an identification approach for the relative acoustic transfer function matrix based on total least squares is established. The cross-spectral matrix obtained by scanning the microphone array is com-pleted, and acoustic holography imaging is achieved using the partial field derived from the decomposi-tion of the spectral correlation matrix. The results indicate that the proposed method can effectively mitigate the influence of background noise on the completion of the spectral correlation matrix of mi-crophone measurements under low signal-to-noise ratio scenarios. When the signal-to-noise ratio is below 10 dB, the error can be reduced by more than 2%, thereby enhancing the non-synchronous imag-ing accuracy of the microphone array.
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Improved RT-DETR-based Method for Traffic Sign Recognition in Ex-treme Weather
Abstract:
To address the issues of decreased detection accuracy and difficulty in small object recognition caused by blurred traffic signs in extreme weather conditions such as rain, fog, and snow, this paper proposes a traffic sign detection algorithm based on an improved RT-DETR. First, data augmentation is applied to the TT100K dataset under simulated extreme weather conditions to enhance the model"s ability to recognize traffic signs in these environments. Second, the Ortho attention mechanism is introduced into the backbone network, which uses orthogonal filters to reduce feature redundancy and prioritize essential channel information, thereby improving the model"s detection accuracy for small objects. Additionally, a High-level Screening-feature Pyramid Network (HS-FPN) replaces the Cross-scale Contextual Feature Mixer (CCFM) in the original model. HS-FPN filters and merges low-level feature information using high-level features, enhancing the model"s detection accuracy for low-contrast and blurred targets in extreme weather conditions. Experimental results show that the proposed improved algorithm achieves an average detection accuracy of 87.84%, an improvement of 2.37% over the original RT-DETR model, while reducing the parameter count to 18.22M, an 8.4% reduction compared to the original model. The model demonstrates higher accuracy in recognizing small objects and targets under extreme weather, contributing significantly to passenger safety.
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Finite Element Analysis of Dual-Plane Linear Array Electromagnetic Tomography
Abstract:
This paper proposes a detection technology of Dual-Plane Linear Array Electromagnetic Tomography, aimed at enhancing the traffic management and congestion control capabilities of traffic authorities, especially for traffic accident detection on mountain highways. Considering the characteristic of the road sides being extended, the paper designs a sensor structure composed of two parallel planar coil arrays, each linearly arranged with five coils. A three-dimensional finite element method is employed to simulate the feasibility of Dual-Plane Linear Array Electromagnetic Tomography for detecting traffic accidents and to analyze the distribution characteristics of the electromagnetic field in the object field as well as the characteristics of the sensitivity maps. The paper studies the effect in reconstructing vehicle distribution using LBP algorithm, Tikhonov Regularization algorithm, and Landweber algorithm. The simulation results indicate that calculating the electrical conductivity sensitivity requires the extraction of the electric field in the x and y axes, while calculating the magnetic permeability sensitivity matrix requires the extraction of the magnetic field in the z-axis. Meanwhile, it can determine the electrical parameter distribution of the conductors by measuring boundary voltages, but the intensity of the magnetic permeability sensitivity matrix in the object field is significantly higher than that of the electrical conductivity sensitivity matrix, which indicates Dual-Plane Linear Array Electromagnetic Tomography is suitable for capturing boundary magnetic fields to reconstruct images. Furthermore, by comparing the image reconstruction of vehicles in six different positions using the three algorithms, it is found that using the Landweber algorithm provides the best results in reconstructing the magnetic permeability distribution images of vehicles, with the minimum Image Error (IE) of 0.905 and the maximum Correlation Coefficient (CC) of 0.547. Therefore, Dual-Plane Linear Array Electromagnetic Tomography is feasible in simulation and can effectively be used for the magnetic permeability distribution image reconstruction of road vehicles, possessing potential application value in improving road traffic safety management and reducing traffic congestion.
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Simultaneous Localization and Mapping of unmanned forklift based on improved DLO algorith
Abstract:
Stacking unmanned forklifts are responsible for the stacking and picking of goods in warehousing and logistics management. In industrial environments, it is necessary to develop fast and accurate state estimation and environment perception algorithms in order to achieve autonomous navigation movement of unmanned forklift trucks. However, when using LiDAR odometer for state estimation, problems such as inaccurate mapping and pose drift are often encountered. Therefore, a method of simultaneous localization and mapping of pileup unmanned forklift based on improved DLO algorithm is proposed. Firstly, the motion model provided by the Inertial measurement Unit and the point cloud data of the multi-line LiDAR are combined to perform a prior estimation of the initial pose of the forklift. Then, through the front end of DLO SLAM, the generalized least square method is used to scan and match, and the pose of the forklift is estimated in real time and the map is constructed. Finally, the back-end pose optimization and loop detection of HDL-Graph-SLAM are used to further improve the accuracy of map reconstruction. Experimental results show that the proposed scheme can effectively suppress map drift and error accumulation in dynamic environment. Compared with DLO SLAM, the localization accuracy is improved by 60.9 % and compared with Cartographer algorithm by 56.9 %. At the same time, the stability is also significantly improved to meet the requirements of stacking unmanned forklifts for accurate and efficient simultaneous localization and mapping.
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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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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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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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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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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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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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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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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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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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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.
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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.
