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Liu Zhenhang, Huang Deqi, Huang Deyi, Huang Haifeng
2026,49(4):1-10, DOI:
Abstract:
To address the insufficient spatio-temporal feature perception caused by neglecting historical traffic information in existing methods,this study proposes an intersection signal control method integrating deep reinforcement learning with spatio-temporal feature modeling. The approach employs a hybrid D3QN-LSTM network architecture,which encodes multi-period traffic information into high-dimensional matrices through discrete traffic state representation. A convolutional neural network extracts spatial features,while a long short-term memory network captures temporal dependencies. A reward-feedback-driven dynamic exploration mechanism is further designed to optimize policy training. Experiments conducted on the SUMO simulation platform demonstrate that during morning peak traffic,the proposed method reduces average queue length by 49.95%,35.04% and 16.72%,and decreases cumulative waiting time by 63.03%,35.55% and 20.15% compared to fixed-timing control,conventional reinforcement learning methods and D3QN,respectively,validating the superiority of spatio-temporal feature modeling and dynamic exploration strategies. To assess algorithmic robustness,off-peak traffic flow experiments further confirm that the proposed method maintains significant advantages in both average queue length and cumulative waiting time metrics,demonstrating strong adaptability and generalizability across varying traffic load conditions.
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Tian Xiangmin, Liu Qingquan, Tang Jie, Li Zhenyu, Cao Xilong
2026,49(4):11-19, DOI:
Abstract:
The temperature measurement error caused by solar radiation, that is, the solar radiation error, can be as high as 1 K. To improve the temperature measurement accuracy, and to address the problem of high power consumption of traditional forced ventilation temperature measurement device, a temperature sensor device for brushless DC (BLDC) fan ventilation is designed, which combines natural ventilation and forced ventilation functions, effectively reducing power consumption. The computational fluid dynamics (CFD) method is utilized to conduct multi-physical field fluid-structure coupling simulation of the sensor and quantify the radiation error under different conditions, and the functional mapping relationship between wind speed and solar radiation intensity and the suction pressure of the wind turbine is established by minimizing the radiation errors, and formulate the wind turbine control strategy. The genetic algorithm-optimized BP neural network (GA-BP) algorithm optimized by genetic algorithm was compared and selected to train and fit the simulation data set, thereby constructing the radiation error correction equation. Finally, through the field comparison experiment with the 076B temperature sensor, it is shown that the radiation error of the designed temperature sensor after algorithm correction can be controlled within 0.05 K, the mean absolute error was 0.039 K, and the root mean square error was 0.045 K.
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2026,49(4):20-26, DOI:
Abstract:
Considering the problem of high failure rate and difficult maintenance of imported traveling wave tube amplifier, an indoor Ku-band 350 W solid state power amplifier was developed. A novel 16-way power dividing/combining network was proposed, which was based on novel waveguide magic T, waveguide E-plane T-junction, coplanar magic T and half-height waveguide-to-microstrip probe transition. A Ku-band 450 W power amplifier module was achieved based on 16 pieces of 35 W gallium nitride power amplifier chips and the 16-way power dividing/combining network. Then, an indoor Ku-band 350 W solid-state power amplifier was successfully developed. The measured results show that the gain is greater than 73 dB, the output power is greater than 400 W between 13.75 GHz and 14.5 GHz. After calculation, the overall efficiency is 23.65% at rated power output, which is on par with international well-known companies. This power amplifier has excellent specifications and can replace imported TWTA products completely.
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Guo Qingming, Mao Yurong, Chen Wenhui, Yang Jupeng, Zhou Xiang
2026,49(4):27-37, DOI:
Abstract:
It is a desired solution to integrate photovoltaics (PV) and battery into the high-voltage dc bus using high-gain three-port converters in renewable energy systems. Aiming at the limitations of conventional three-port converters—including restricted voltage gain, difficulty in achieving soft switching, and high voltage stress on semiconductor devices—this paper proposes a novel ultra-high voltage gain three-port converter topology based on a three-winding coupled inductor. Only one magnetic core is used so that the power density of the converter is effectively improved. The lower voltage stress and soft switching performance of semiconductor devices enable specifications with lower conduction losses to be selected, which can reduce system losses and improve efficiency. Based on the port power relationship, the proposed converter can achieve smooth switching between different operating modes. The topology and operating principles of the converter are analyzed in detail, and then the port voltage relationship, voltage/current stress, and control methods are analyzed in detail to guide parameter design. Finally, both simulation models and experimental prototypes were developed with PV input voltages ranging from 20 V to 40 V, a battery voltage of 48 V, an output voltage of 400 V, and a rated power of 400 W, validating the effectiveness of the proposed high-gain three-port converter and its control strategy.
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2026,49(4):38-48, DOI:
Abstract:
The temperature characteristic has significant influence on the dynamic performance of magnetorheological damper, so it is of great significance to study the theoretical model of temperature rise, to analyze and improve magnetorheological damper temperature characteristic. Based on the energy balance relationship of magnetorheological damper, the temperature rise theory and heat transfer mechanism of magnetorheological damper under different motion amplitudes excitation are revealed, and the temperature rise theoretical models under small and large sinusoidal harmonic motions are established.The temperature rise rule of damper is analyzed by finite element simulation. The simulation results show that the temperature of magnetorheological fluid in the damper cavity increases to 3.2℃ with small sinusoidal harmonic motion amplitude, and the temperature of magnetorheological fluid at different positions in the damper cavity is large. The temperature of magnetorheological fluid increased by 20.8℃ during the large motion amplitude, and the temperature of magnetorheological fluid at different positions in the cavity is almost equal, the theoretical model of temperature rise of magnetorheological damper is verified. It is verified by the temperature rise characteristic test, and the temperature rise test curve is consistent with the simulation and theoretical calculation results. The theoretical model has a large error in predicting the temperature rise of magnetorheological fluid under small motion amplitude, while the predicted value is more accurate under large motion amplitude. The temperature rise theoretical model not only effectively predicts the internal temperature of the damper, but also provides a theoretical basis for the structural design and engineering application of magnetorheological damper.
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Zhao Zhe, Li Bo, Xu Wenxiao, Li Yao
2026,49(4):49-60, DOI:
Abstract:
To address the issues of target detection accuracy degradation and small target miss detection caused by speckle noise interference, low signal-to-noise ratio, and multi-scale scattering characteristics of targets in Synthetic Aperture Radar images, this paper proposes a lightweight detection model named XMNet, which balances feature representation capability and real-time performance. XMNet incorporates an improved single-Head vision Transformer into the backbone network to strengthen contextual semantic correlations through global attention mechanisms. A cross-layer multi-path aggregation network is designed as the neck structure, integrating dynamic upsampling and a parallel multi-scale convolution module to optimize multi-scale feature representation. An additional high-resolution detection layer is introduced to leverage shallow high-resolution features, enhancing detail capture capability for small targets. Experiments on the MSAR-1.0 dataset demonstrate that XMNet achieves a mean average precision of 90.4% across all categories, representing an increase of 8.7% over the baseline model. Detection accuracy for small aircraft targets significantly improves by 20.1%, with only a 2-million parameter increase while achieving an inference speed of 185 FPS. When compared against nine advanced methods including FCOS and CenterNet, XMNet ranks first in comprehensive metrics balancing detection accuracy and computational efficiency. Through the design of cross-layer attention mechanisms and multi-scale feature fusion, XMNet effectively resolves the challenge of balancing feature preservation for multi-scale targets and real-time processing in SAR imagery. Its lightweight and high detection accuracy provide a viable engineering-ready solution for real-time remote sensing monitoring across various SAR platforms, demonstrating significant advantages particularly in complex scenes with dense small targets.
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Lyu Zhiyun, Guo Chenxia, Yang Ruifeng
2026,49(4):61-68, DOI:
Abstract:
To address the current limitations in data-driven remaining useful life (RUL) prediction methods for turbofan engines, which suffer from low data utilisation and constrained prediction accuracy due to inadequate exploitation of data feature information, a novel multi-scale RUL prediction model for engines is proposed. This model is termed the distributed spatio-temporal convolutional network (DSCN). The proposed method first captures linear and non-linear relatonships in engine data by calculating Pearson correlation coefficients and maximum information coefficients, thereby obtaining trend features for both stationary and non-stationary time series. Secondly, it employs a multi-scale residual fusion module to enrich data features. Building upon temporal convolutional network (TCN), it incorporates residual channel attention module (Res-CAM) and multi-head attention module (MHAM) to enhance the model′s ability to capture critical information, dynamically adjusting the weights of the data. The proposed method was experimentally validated on the FD001 and FD003 datasets within the C-MAPSS collection, yielding RMSE and Score values of 11.30 and 218.08; 12.04 and 227.65 respectively. Results indicate that this approach reduces the Score by 4.67% and 11.5% compared to the current state-of-the-art method.
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2026,49(4):69-80, DOI:
Abstract:
To address the limitations of existing CNN-based generative steganography in poor image quality and weak resistance to steganalysis, this paper proposes SSEU-Net, an improved U-Net-based steganographic architecture incorporating selective state space model, aiming to achieve high-quality image generation and secure steganography. The core contributions include: first,designing Res-SS2D module that performs quad-directional global spatial modeling on input images while maintaining linear computational complexity, thereby enhancing the visual quality of stego images; next, proposing a high-frequency feature enhancement strategy based on the observation that subtle perturbations in high-frequency regions minimally affect statistical characteristics. This strategy extracts and integrates edge features of carrier images into the encoder to guide secret information embedding into high-frequency regions, thereby reducing detectability by steganalysis; finally developing a multi-objective loss function combining PSNR and MS-SSIM for generation quality optimization, alongside introducing an L1 norm loss on low-frequency components to enforce consistency between cover and stego images in low-frequency regions, ensuring secret information is predominantly embedded in high-frequency components. Experiments demonstrate that SSEU-Net outperforms existing methods on COCO and ImageNet datasets. On ImageNet, the generated stego images achieve an average PSNR of 40.588 dB, with extracted secret images attain an average PSNR of 41.863 dB, while exhibiting strong resistance to common steganalysis.
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Su Caishan, Shi Panjing, Zhang Shuaixing, Sun Peng
2026,49(4):81-86, DOI:
Abstract:
After long-term use of Vicat softening temperature testers, problems such as temperature indication deviation and inaccurate heating rate often occur. To address this, a high-precision calibration device based on the principle of dynamic testing has been developed. This device is equipped with an auto-triggered image acquisition and temperature recording device, which can real-time capture the deformation critical point and accurately record the temperature at the moment when the sample undergoes a 1 mm deformation or is pierced by the pressure needle. Its working process includes steps such as sample placement and parameter setting, with smooth connection between each step. In practical tests, the device shows obvious advantages: at a heating rate of 12℃/6 min, the actual heating rate ranges from (11.9~12.1)℃/6 min, and the temperature indication error is (-0.1~0.2)℃; at a heating rate of 5℃/6min, the actual heating rate is in the range of (4.9~5.1)℃/6 min, and the temperature indication error is (-0.1~0.2)℃, all meeting the technical requirements. Through analysis, the standard uncertainty of temperature indication error is 0.067℃, and the standard uncertainty of heating rate error is 0.091℃/h. The innovation of this research lies in the proposal of a dynamic heating calibration method, optimization of dynamic acquisition strategy, and integration of intelligent calibration algorithms with automated processes, which can support synchronous calibration of multiple temperature points and multiple sample stations. This device effectively solves the problems existing in current calibration methods, realizes synchronous dynamic and accurate measurement of temperature and heating rate, provides a reliable guarantee for material thermal performance testing, and plays an important role in fields such as material research and development and quality control.
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Liu Pengtao, Zheng Enrang, Guo Tuo, Liu Jianguo
2026,49(4):87-95, DOI:
Abstract:
To address the issues of trailing signals generated by ultrasonic transducers under excitation pulses and reverberation interference caused by multiple reflections and scattering of acoustic waves on the tank wall, which lead to a large blind zone in ultrasonic ranging, this paper proposes the use of Linear Frequency Modulated (LFM) waves with anti-reverberation capability as the transmit signal. Furthermore, aiming at the problems of image spectrum generation resulting in target detection ambiguity and high computational load when traditional receivers directly acquire real signals, quadrature demodulation technology is adopted at the receiver end. This approach not only obtains complex signals with strong anti-interference capability but also reduces system costs. By analyzing the reverberation model of the ultrasonic level meter and comparing the ambiguity function and Q-function of CW and LFM waves through simulation, this paper concludes that LFM waves possess superior target resolution capability and better anti-reverberation performance when the target is stationary. Experiments were conducted using LFM waves as the transmit signal. At the receiver end, complex signals were obtained via quadrature demodulation, followed by matched filtering. Experimental results demonstrate that the maximum absolute measurement error of this method is less than 4 mm, and the blind zone can be reduced to 8 cm, indicating high practical value and engineering significance.
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Liu Jingyuan, Li Qi, Wu Jinglong, Zhang Zhilin
2026,49(4):96-103, DOI:
Abstract:
Existing BCI neurofeedback techniques often struggle to balance temporal and spatial resolution. Among mainstream neurofeedback methods, EEG offers millisecond-level temporal resolution but lacks precise spatial localization, whereas fMRI provides high spatial resolution but is constrained by second-level temporal delays. This trade-off in spatiotemporal resolution limits the clinical applicability of neurofeedback. To address this issue, this study proposes a hybrid wavelet neural network to model the complex nonlinear mapping between EEG signals and fMRI regional activity. The model employs parallel wavelet convolutional layers and one-dimensional convolutional layers to extract multi-resolution frequency-domain features and local time-domain features from EEG signals, respectively. A channel cross-attention mechanism is further introduced to capture nonlinear interactions between features, while a LSTM network models long-range temporal dependencies. Experimental results demonstrate that the proposed approach achieves high-precision prediction of fMRI regional dynamics across two independent datasets, significantly outperforming traditional linear models. This framework not only extends the modeling capacity of current neurofeedback “EFP” techniques but also provides a new pathway for developing neurofeedback and BCI systems with both high temporal and spatial resolution.
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Wang Sihao, Zhang Duzhen, Yang Changchang
2026,49(4):104-115, DOI:
Abstract:
Accurate lesion segmentation is crucial for early diagnosis and subsequent treatment of dermatological diseases. Existing neural networks often employ increasingly deep and complex architectures to achieve high segmentation accuracy; however, large parameter counts and high computational costs limit practical deployment. To address these challenges, a lightweight multi-scale channel interaction segmentation network (LMSCI-Net) is proposed. For each input image, a lightweight multi-scale encoding module based on channel separation and convolutional decomposition is designed, augmented by a local-global channel attention mechanism to ensure robust feature extraction while maintaining an efficient encoder. A multi-scale channel interaction enhancement module is then introduced to integrate multi-stage outputs and refine skip connections, providing the decoder with rich and precise detail information. Finally, an adaptive fusion decoding module is developed to progressively restore fine-grained details and produce accurate segmentation masks. The network is trained under a deep supervision regime and evaluated on three public skin-lesion segmentation datasets (ISIC2017, ISIC2018 and ISIC2016) as well as the PH2 dermoscopic image database. Experimental results demonstrate that, compared with the U-Net baseline, LMSCI-Net reduces parameter count and computational complexity by 99.38% and 98.78%, respectively, while maintaining high segmentation accuracy and strong generalization, thus validating its effectiveness and lightweight design.
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Chen Wenxuan, Yang Fengbao, Li Bo, Wang Xiaoxia, Ji Linna
2026,49(4):116-125, DOI:
Abstract:
UAV aerial photography is one of the mainstream object detection technologies, and this task faces problems such as small target objects, large scale changes, and complex background interference. How to improve the detection accuracy with limited computing resources is an important challenge. In order to solve the above problems, a lightweight UAV aerial target detection method was proposed. Firstly, a hierarchical dependence-aware pruning algorithm was designed to reduce the redundancy of the model. In addition, the resolution of the detection head is increased to 160×160 to enhance the detection ability of small targets, the standard convolution blocks of the network are replaced by GhostConv to reduce the computational redundancy, and the C3 module in the Neck network is redesigned by introducing the compact architecture StarNet to reduce the complexity of the feature fusion process and enhance the feature expression ability. Finally, the attention mechanism is introduced in the backbone layer to improve the feature extraction ability of the model. The experimental results show that in the VisDrone2019 dataset, the mAP_0.5 of the model is increased by 1.8%. At the same time, compared with the original model, the number of parameters is reduced by 50.4%, and the amount of computation is reduced by 35.44%. In summary, the model satisfies the requirements of the UAV platform for accuracy and lightweight in small target detection tasks.
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2026,49(4):126-135, DOI:
Abstract:
Aiming at the multi-point path planning problem for electromagnetic interference testing, a path planning method based on the combination of improved A-star algorithm and grey wolf optimization algorithm is proposed. First, the traditional A-start algorithm is improved by modifying the heuristic function and introducing a redundant point deletion strategy, thereby reducing path length and algorithm runtime. Then, the test path planning problem is transformed into a classic traveling salesman problem and solved using the improved gray wolf optimization algorithm to obtain the optimal test path. Experimental results demonstrate that compared to traditional methods, the improved approach achieves an average reduction of 4.73% in total path planning distance, 30.42% in average number of turns, 34.74% in average total turning angle, and 39.47% in average computation time. This effectively enhances testing efficiency and safety, providing a reliable solution for electromagnetic interference multi-target point testing tasks.
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He Aohui, Zhang Fengshou, Zhuang Gaoshuai, Duan Qingyang, Feng Baoyang
2026,49(4):136-147, DOI:
Abstract:
To address the issues of existing object detection models being prone to interference and resulting in insufficient accuracy when detecting barcodes in complex environments, as well as the high model complexity making deployment on low-computing-power mobile devices challenging, this study proposes a lightweight, high-precision detection algorithm called DOLN-YOLO based on YOLOv8. First, the DW-HGNetV2 architecture, reconstructed using deeply separable convolutions, is introduced as the backbone network, which enhances multi scale feature extraction capabilities while significantly reducing computational complexity. Second, the OD-C3Ghost module is constructed to replace the C2f module, enhancing dynamic perception capabilities for complex barcode deformations and further eliminating computational redundancy. Third, a lightweight shared detail enhancement detection head is designed, utilizing the gradient-strength dual-channel coordination mechanism of DEConv to enhance the model′s feature generalization capabilities, and adopts a heterogeneous convolution sharing strategy to reduce resource consumption; finally, a composite loss function NWD-PIoUV2 is proposed, combining normalized Wasserstein distance with dynamic focus PIoUV2 loss, to mitigate the optimization challenge of minor localization deviations and accelerate convergence speed. Experimental results demonstrate that, compared to the baseline model, DOLN-YOLO achieves a 0.92% improvement in mAP@0.5 and a 4.57% increase in mAP@0.5:0.95, while reducing parameters and computational costs by 58.8% and 48.6% respectively. This validates the algorithm′s superiority in detecting barcodes under complex environments. DOLN-YOLO provides a solution featuring both robust detection capability and efficient mobile deployment for logistics, healthcare, retail, and other application scenarios.
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Li Fei, Chen Pengyu, Liang Yuman, Zheng Xinyu, Wang Lie
2026,49(4):148-157, DOI:
Abstract:
To address the issues of imprecise target localization, missed detections, and false alarms caused by significant differences in target scales, diverse categories, and uneven target distribution in remote sensing images, this paper proposes an improved algorithm based on YOLOv8n, named MGD-YOLO. Firstly, the multi-scale edge-gaussian attention module （MEGA） is introduced. By integrating Gaussian smoothing, the Scharr edge operator, and a channel attention mechanism, MEGA effectively suppresses noise and enhances the feature representation of target contours in complex backgrounds. Secondly, the MDPConv structure is designed, which combines a dynamic weighted fusion mechanism with depthwise separable convolutions to overcome the fixed receptive field problem of traditional convolutions and improve the model′s ability to detect targets of varying scales. Lastly, the DLGA structure is introduced in the detection head. By dynamically allocating weights to multiple attention branches and utilizing an MLP fusion strategy, DLGA significantly improves the integration of local and global features, thereby boosting detection performance. Experimental results demonstrate that MGD-YOLO achieves a 1.6%, 2.7% and 1% increase in mAP@0.5 on the DIOR, DOTA and NWPU VHR-10 datasets, respectively, compared to YOLOv8n, thus validating its effectiveness for remote sensing image target detection tasks.
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Qin Xiaoyang, Yu Wentao, Li Lihong, Li Zhixun, Chen Biao
2026,49(4):158-168, DOI:
Abstract:
To address the widespread instability and volatility in stock price forecasting, as well as the difficulty of parameter optimization in the variational mode decomposition (VMD) algorithm, this paper proposes a two-stage combined prediction framework, CRIME-SE-VMD-VIT2M. In the first stage, the Chebyshev chaos map and lens imaging population selection strategy are introduced on the basis of the original frost ice optimization algorithm. Using SE as the fitness function, an improved CRIME-SE-VMD optimization model is constructed to enhance the global search capability and decomposition quality of parameter optimization. In the second stage, key technical indicators are selected through PCC and fused with the IMFs obtained from VMD decomposition to form a multi-dimensional feature set. Based on this, combined with the optimization results of the first stage, a VIT2M parallel dual-channel prediction model is designed and implemented to deeply extract and model multi-scale stock feature information. Experimental results show that the fitness value of CRIME-SE-VMD on four stock datasets is 0.000 318 9~0.000 703 lower than that of the comparison algorithm, demonstrating better decomposition performance. At the same time, the prediction performance of the VIT2M model on the same datasets is better than that of the comparison model, verifying its effectiveness in improving the accuracy of stock price prediction.
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Niu Jingqi, Yang Fengbao, Wang Xiaoxia
2026,49(4):169-179, DOI:
Abstract:
To address the issue of missing dynamic interactions between units caused by existing intent recognition methods failing to account for formation spatial characteristics, this paper introduces a spatiotemporal coupling mechanism and proposes a formation intent recognition method that integrates dynamic graph attention mechanisms with spatiotemporal modeling. First, a global interaction backbone network is established based on target attributes within the formation, combined with a Top-K nearest neighbor strategy to dynamically generate an adjacency matrix. This transforms the dynamically evolving formation state into a structured temporal graph. Second, a Graph Attention Network (GAT) enhanced with a Co-evolution Aware Pooling mechanism is employed to adaptively learn differentiated interaction weights between different targets, enabling rapid capture of spatial coordination features in the dynamic formation temporal graph. Finally, a Bidirectional Gated Recurrent Unit (BiGRU) augmented with a Multi Scale Attention mechanism is introduced to perform temporal analysis on the node state sequences extracted by GAT, which contain spatial coordination information. This establishes an intent recognition model that deeply integrates spatiotemporal features (STGAT-BiGRU). Simulation results show that, compared to existing methods, the proposed approach achieves average improvements of 8.78% and 8.9% in accuracy and F1 score, respectively, demonstrating its effectiveness and providing technical support for mastering situation evolution and gaining decision-making initiative.
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Yi Yong, Wang Daiqiang, Yi Zhong
2026,49(4):180-189, DOI:
Abstract:
To address the challenges of low manual efficiency and surface damage risks in contact-based measurement for precision optical slits and pinhole lenses, this paper proposes an interpolation-Zernike collaborative subpixel detection method. By enhancing edge resolution through bicubic interpolation, reducing discrete sampling errors via reconstructed orthogonal basis templates, and correcting subpixel offsets with an asymmetric Gaussian model, the method improves anti interference capabilities through dynamic thresholding and small connected-domain denoising. Simulation and experimental results demonstrate that the improved algorithm achieves a maximum detection error of 0.098 7 pixel (1.401 5 μm) for slit width and stabilizes pinhole diameter errors within 0.12 pixel (1.704 μm), representing a 62.3% accuracy improvement over traditional Zernike moment methods. Under pixel-aligned conditions, the method achieves nanoscale resolution of 0.000 2 pixel (2.84 nm), surpassing conventional micron-level limitations. The algorithm exhibits a linear positive correlation between accuracy and camera resolution, meets industrial detection standards within 3 μm under the experimental conditions, and demonstrates potential for nanometer-scale applications. This work provides an innovative solution for high-efficiency, non-destructive inspection of optical components.
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Yang Linpeng, Liu Yi, Liang Xudong, Gui Zhiguo
2026,49(4):190-203, DOI:
Abstract:
Sparse angle CT is an effective method to reduce X-ray radiation dose in clinical CT imaging. However, due to the incomplete projection caused by sparse sampling, the image reconstruction contains obvious fringe artifacts. In order to solve this problem, this paper proposes a sparse angle CT image reconstruction network based on iterative optimization deployment, IADR-Net, which adopts a unique dual-channel parallel architecture design, and includes two core components: Iterative reconstruction sub-network and global-local attention network (GLONA) detail recovery sub-network. Among them, the iterative reconstruction sub-network is based on the framework of fast iterative soft threshold algorithm, and realizes projection-to-image reconstruction through learnable nonlinear transformation and adaptive thresholding. The GLONA sub-network adopts a double-branch structure with parallel local and global features, and effectively maintains the image details through the self-adjusting fusion module. The two sub-networks work together to focus on artifact removal based on iterative expansion and detail enhancement based on attention mechanism, respectively, and finally output high-quality CT images through feature fusion. Experimental results on the Mayo dataset show that the proposed method has better performance than several representative algorithms in terms of artifact suppression and structure preservation, and provides an effective solution for clinical sparse angle CT imaging.
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2026,49(4):204-216, DOI:
Abstract:
Dense target distribution, complex backgrounds, and a large number of small objects often lead to suboptimal detection performance in remote sensing image object detection. To address these challenges, this paper proposes RSD-DETR, a remote sensing object detection algorithm based on RT-DETR. First, a lightweight multi-scale feature extraction module, Faster-CGLU, is designed by integrating a gating mechanism with partial convolution, which optimizes the aggregation of local and global feature information while reducing computational redundancy. Second, a CGA-AIFI module is constructed using cascaded group attention (CGA), which focuses on critical feature regions while suppressing irrelevant background information, thereby enhancing the interaction between the model and object features. Finally, a cross-scale dynamic feature fusion module (CS-DFFM) is designed, which performs spatial alignment and dynamic fusion of multi-scale feature maps through the dynamic scale-sequence feature fusion (DySSFF) module and the triple feature encoder (TFE) module. This effectively mitigates the loss of small object features caused by upsampling and downsampling, and enhances the network′s multi-scale feature fusion capability. Experimental results show that on the SIMD and DOTA-v1.0 datasets, the proposed algorithm reduces the number of parameters by 22.11% compared with the baseline model, and the mean average precision (mAP0.5) reaches 79.9% and 86.8% respectively, which are 2.5% and 1.7% higher than those of the baseline model. The real-time performance of the model is also improved. The detection effect is better than other classic models, and it has excellent performance.
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Wei Tingxu, Chen Ying, Li Chenghao, Ma Wenhao
2026,49(4):217-226, DOI:
Abstract:
Aiming at the challenges of remote sensing image registration such as feature extraction difficulties caused by complex environment and registration accuracy limitations caused by multi-scale geometric deformation, this paper proposes a remote sensing image registration model that integrates dual-domain features and cross-dimensional gated attention. Firstly, the multi-scale Fourier module is designed in the feature extraction stage to improve the StarNet network structure to enhance the feature extraction capability of the model by fusing the multi-scale spatial features with the frequency domain features; then, the cross-dimensional gated attention is designed so that the model can efficiently capture the contextual information in the image without sacrificing the global sensing field; secondly, the feature matching stage bidirectional parameters are obtained by applying bidirectional matching based on partial assignment matrix, and finally, the registration is completed by affine transformation. In the experiments using the aerial image dataset, the results show that when the correctly estimated keypoint scale factor is set to 0.01, 0.03 and 0.05, the registration accuracy reaches 42.8%, 85.7% and 96.9%, respectively, and the average registration time is 0.87 s, which significantly improves the accuracy and speed of remote sensing image registration.
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Zhao Lijie, Jin Mingxi, Fang Yifan, Huang Mingzhong
2026,49(4):227-235, DOI:
Abstract:
Accurate monitoring of activated sludge microorganisms is critical for maintaining the stable operation of wastewater treatment systems. However, due to their semi-transparent morphology and high similarity to the surrounding environment, these microorganisms exhibit camouflaged characteristics, rendering traditional detection methods ineffective. To address the camouflage characteristics of activated sludge microorganisms, the diversity of object scales, and the ambiguity of boundaries in complex contexts, this paper proposes a camouflaged object detection method based on multi-scale awareness and edge enhancement. The proposed method employs a multi-scale feature aware module to extract rich contextual information through parallel processing and progressive expansion of the receptive field, thereby enhancing multi-scale feature representation. An edge-aware enhancement module is introduced to fuse low-level edge details with high-level semantic information for more accurate edge feature extraction. These edge features are then integrated with the multi-scale features through an attention-guided feature module, enabling the network to focus on the positional information of edges. Finally, a context aggregation module is used to progressively aggregate multi-level features in a top-down manner, further refining the prediction and generating the final output. On the benchmark camouflaged object detection dataset and the self-constructed activated sludge microorganism camouflage dataset, the proposed method achieves improvements of 2.2%, 4.1%, and 2.1%, and 1.2%, 2.2%, and 0.6% in terms of the evaluation metrics S-measure, weighted F-measure, and E-measure, respectively. Experimental results demonstrate that the proposed method achieves superior performance over other models across all datasets.
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Zhang Zhenkai, Zhang Hao, Xin Hengfu, Yuan Hui, Tian Yanbing
2026,49(4):236-246, DOI:
Abstract:
Due to light scattering in water, underwater images commonly suffer from quality degradation. To address this issue, this paper proposes an enhancement model for turbid underwater polarized images based on an enhanced LU2Net network, validated using a self-constructed dataset. Initially, the acquired color polarization images are converted to grayscale. Complete linear polarization information is obtained by fusing the three polarization components at 0°, 45° and 90°. The degraded underwater polarized images are subsequently enhanced using the proposed enhanced LU2Net network model. Finally, enhanced images possessing richer detail features are acquired. Experimental results demonstrate that the proposed method outperforms comparative underwater image enhancement techniques including FUnIE-GAN and MLLE, in terms of both subjective and objective evaluations, as well as in the outcomes of feature point detection and Canny edge detection. Crucially, during feature point detection employing four distinct methods including ORB and AKAZE, the proposed approach consistently extracted a greater number of feature points.The proposed method achieves a 3.35% reduction in LPIPS compared to the best-performing existing method used for comparison. Furthermore, it increases the UCIQE score by 1.16% and decreases the NIQE score by 7.59% compared to the algorithm prior to enhancement. The proposed method successfully extracts clearer image edges, textures, and other fine details in turbid water environments under natural lighting conditions, thereby enhancing imaging quality in such challenging scenarios.
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Fu Yiyan, Sun Chuanmeng, Kong Xiangnian, Li Yong, Jin Hong
2026,49(4):247-256, DOI:
Abstract:
To address the challenge of balancing accuracy and real-time performance in front-end target recognition and localization for drones in complex battlefield environments with limited onboard resources, a front-end target recognition and localization method for drone operations in complex battlefield environments was developed: Using a ″backbone-neck-head″ as the basic network architecture, a non-local attention expansion module, a global multi-scale decoupled network, and a lightweight bottleneck module were introduced. Focal Loss and DIoU Loss were employed as the combined loss functions to achieve feature modeling and multi-scale detection enhancement, thereby improving the ability to capture features and enhancing accuracy; based on dependency graph-structured pruning and channel-wise knowledge distillation, a collaborative lightweight strategy was proposed, effectively reducing model complexity and improving embedded deployability. Experiments show that this method improved mAP@0.5, mAP@0.75, and mAP@0.5:0.95 by 6.0%, 7.2%, and 5.9% respectively, while reducing model parameters and GFLOPs to 17.1% and 12.0%, with precision loss controlled within 4.1%. Finally, deployment validation on embedded hardware demonstrated a frame rate of 34 fps, effectively meeting the accuracy and real-time requirements for front-end target recognition and localization during drone operations.
Volume 49, 2026 Issue 4
Research&Design
Test Systems and Modular Components
Data Acquisition
Theory and Algorithms
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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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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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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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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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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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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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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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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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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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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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 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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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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Long Biao, Yang Jun, Chen Huiping, Chen Guangrun, Zhao Peiyang
2024,47(6):157-163, DOI:
Abstract:
In order to solve the problem that the audio signal processing in the voice communication system has a large amount of data, a lot of stray signals, and the received audio signals of the frequency modulation receiver are large and small, a lightweight audio signal processing algorithm is proposed, and based on this algorithm, the audio signal receiving and automatic gain control are realized on the field programmable gate array(FPGA) platform. The algorithm combines digital down conversion technology, multistage extraction filtering technology and automatic gain control technology (AGC) technology, and is applied to the audio signal processing system. The RF analog signal received from the upper antenna is converted into baseband audio signal through analog-to-digital conversion and digital down-conversion, and the stray signal in the baseband signal is filtered through four-stage extraction filtering, reducing the complexity and power consumption of the system. At the same time, the digital AGC controls and adjusts the baseband audio signal to output a more stable audio signal. The experimental results show that the algorithm can effectively reduce the information rate from 102.4 MHz to 32 kHz, reduce the computation burden, improve the signal quality, and reduce the resource utilization of FPGA. And the automatic gain control adjustment of audio signal is realized, and the adjustment time is only 12.8 μs, which meets the power stability time of the receiver.
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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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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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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%.
