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Cheng Hao, Li Peng, Li Weiwei, Hua Guoxiang, Yang Yue
2026,49(14):1-13, DOI:
Abstract:
To address intensified load fluctuations, enlarged peak-valley differences and capacity stress caused by large-scale electric vehicle integration in residential scenarios, this paper studies the capacity-constrained optimization of aggregated orderly charging. A grouped multi-objective optimization model is established to coordinate charging cost, total load fluctuation and charging behavior deviation, while considering group-wise energy conservation, power limits, capacity constraints and scenario feasibility. Based on a decomposition-based multi-objective evolutionary framework and success-history based adaptive differential evolution, a structure-enhanced congestion-guided method is proposed. Energy-preserving pairwise perturbation, congestion-guided evaluation and local refinement are introduced to improve the search capability under complex constraints. Mild, medium and highly capacity-tight residential scenarios are constructed for validation. The results show that the proposed method achieves the best overall performance in all five formal experimental settings. In the highly capacity-tight scenario, compared with the main benchmark method, the solution-set approximation error is reduced by about 26.1%, and the solution-set coverage quality is significantly improved. The proposed method obtains an average rank of 1 and outperforms the benchmarks in all five scenarios, with an average runtime of 47.8 s. Ablation results indicate that energy-preserving pairwise perturbation is the key mechanism for improving solution quality, while congestion-guided evaluation and local refinement further enhance the performance under tight capacity constraints. The results verify the effectiveness of the proposed model and method in achieving safe charging-load transfer, load smoothing, and charging-behavior preservation under residential capacity constraints.
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Yin Yu, Liang Jiaming, Li Baicheng, Gong Mingde, Zhang Han
2026,49(14):14-26, DOI:
Abstract:
Data center cooling systems account for a substantial proportion of overall energy consumption and are significantly affected by workload fluctuations and thermal inertia, resulting in multiple disturbances and strict physical constraints in temperature control. Conventional control methods often struggle to balance safety and energy efficiency, while existing reinforcement learning methods may generate physically infeasible actions during exploration, limiting their practical deployment. To address these issues, this paper proposes a physics-informed safe reinforcement learning method for temperature control based on an open-source simulation environment. The temperature control problem is formulated as a Markov decision process. Under the existing definitions of the state and action spaces, a dynamic safety boundary model coupling dew point temperature, system stability and air enthalpy is developed. A differentiable projection mechanism is further embedded into the policy network, enabling differentiable safety mapping of actions in the continuous action space. In year-round simulations based on EnergyPlus, the proposed method reduces the number of temperature violations to 10, increases the proportion of cold-aisle temperatures within the recommended range to 99.2%, and limits the violation severity to within 0.03℃. Meanwhile, it significantly reduces control fluctuations and improves system operating stability. While ensuring safety, the method further improves system energy efficiency. The results demonstrate that, without modifying the system structure, the proposed method enables reinforcement learning policies to effectively represent and consistently satisfy complex physical constraints, achieving a favorable balance among safety, stability, and energy efficiency. This study provides a feasible approach for the practical deployment of reinforcement learning in data center temperature control.
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Wang Haoguo, Wu Wangbing, Ni Jinhong, Yang Songkun, Wang Liming
2026,49(14):27-36, DOI:
Abstract:
In the scheduling of source network load storage in the distribution network, the dual uncertainty of meteorology and load has complex temporal dependence characteristics. If the martingale difference sequence correlation between the two is ignored, it can easily lead to conservative mismatch and interface instability risks in scheduling decisions, resulting in voltage exceeding limits. Therefore, this study proposes a robust optimization scheduling method for source network load storage considering both meteorological and load uncertainties. Firstly, a first-order autoregressive time series correlation model for meteorological forecast errors and a box interval model for load fluctuations are constructed, and a net load coupling mechanism considering dual uncertainties is established to form a joint uncertainty model that is closer to actual extreme scenarios; then, a multidimensional box shaped uncertain set with adjustable conservatism is used to characterize the fluctuation domain of net load, and the goal is to minimize the total cost of system power purchase, energy storage aging and interruptible load compensation. Taking into account constraints such as power balance, voltage safety, energy storage state of charge and interruptible load, a robust optimization scheduling model is constructed. The main problem and sub problems are iteratively solved through the column and constraint generation algorithm. Finally, design a real-time collaborative response framework for adaptive charging and discharging of energy storage and flexible adjustment of interruptible loads to address residual deviations in actual operation. The simulation results show that this method maintains the voltage of all nodes in the safe range of 0.94~1.06 per unit (p.u.) under the worst-case uncertainty scenario, and the safety margin remains positive, verifying the superiority of this method in improving the safety of the distribution network.
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Wang Zheng, Liu Qingyu, Chen Zhigang, Liu Ye, Huang Feng
2026,49(14):37-48, DOI:
Abstract:
With the deepening reliance of the socio-economy on electricity, the threat posed by extreme weather events to power system stability has become increasingly prominent. Consequently, conducting research on power system resilience assessment holds significant practical value. This study aims to construct a resilience assessment framework for power systems that accounts for the spatio-temporal characteristics of extreme weather. First, an extreme weather model incorporating spatio-temporal characteristics is developed, where the typhoon model is dynamically corrected based on historical typhoon data and real-time geographic coordinates (latitude and longitude). Second, considering factors such as power system topology, operational status and disaster response capabilities, three comprehensive indicators are proposed to evaluate the system′s resilience level across three phases: pre-disaster prediction, disaster-time endurance and post-disaster recovery. Furthermore, stochastic modeling is performed using 15 years of measured typhoon data. The IEEE RTS-79 bus system is utilized for simulation to derive resilience baseline values. From the perspective of engineering practice, three resilience enhancement strategies—increasing standby capacity, implementing load transfer and improving maintenance efficiency—are proposed and verified. This assessment framework not only reveals vulnerabilities in power systems during disaster response but also provides theoretical basis and practical guidance for enhancing power system resilience, which is of great significance for ensuring national energy security and maintaining socio-economic order.
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Li Zhongyuan, Zhang Jian, Xu Minhu, Li Lu, Jia Haifeng
2026,49(14):49-58, DOI:
Abstract:
In low-temperature environments, the battery of the substation inspection robot exhibits open circuit voltage hysteresis loop distortion, resulting in severe low-temperature drift accumulation in the estimated state of charge and significant non-stationary deviations in the predicted motion energy consumption results. Therefore, this study proposes a range enhancement strategy that integrates low-temperature discharge modeling, motion energy consumption prediction and layered compensation. Firstly, a semi empirical electrochemical model is established, and an extended Kalman filter is used to estimate the state of charge of the battery online, suppressing the cumulative drift caused by low-temperature nonlinear polarization; then, combining real-time state of charge and path planning information, output the remaining endurance time prediction result in a closed form; finally, a dual layer compensation architecture is designed. The upper layer dynamically reorders inspection task points based on the endurance tension factor and adjusts the charging trigger threshold, and the lower layer designs an energy controller based on the port controlled Hamiltonian theory, optimizing joint torque distribution through injection damping and energy shaping, experiments have shown that in low-temperature environments ranging from -18 to -8℃, this strategy can complete all 8 inspection points on a single machine, with a utilization rate of 76.2% for the state of charge, which is 43.1% to 70.5% higher than existing methods. The operation time is extended by 64.8%, and the instantaneous power variation coefficient is as low as 0.11. The current sample entropy is reduced to 0.52, verifying that this measurement can achieve accurate prediction and efficient compensation of endurance under harsh low-temperature conditions.
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Liu Kaidi, Zheng Xiangfei, Li Hongwei
2026,49(14):59-66, DOI:
Abstract:
For target tracking under conditions of inaccurate noise covariance and non-uniform sampling, this paper proposes an adaptive filter. During the measurement modeling phase, this filter incorporates an dimensionality expansion strategy to reconstruct the measurement model under non-uniform sampling, thereby effectively preventing measurement information omission and temporal mismatch issues. To achieve precise estimation of dynamic targets, this paper models the target state as an inverse Wishart distribution, where the target′s kinematic state is modeled as a Gaussian distribution and the noise covariance as an inverse Wishart distribution. By introducing a variational inference mechanism, a Bayesian recursive process is derived for optimal estimation of the target state under conditions of inaccurate noise covariance and non-uniform sampling. Simulation results validate the proposed algorithm′s robustness and estimation accuracy in complex observation environments. Compared to existing Kalman filters, it demonstrates significant advantages in handling inaccurate noise covariance and non-uniform sampling, exhibiting superior adaptability and tracking performance.
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Wang Lei, Niu Chunhui, Wang Yanlin
2026,49(14):67-74, DOI:
Abstract:
To address the limitation in measurement accuracy caused by phase mismatch in traditional single-reference signal demodulation methods, a dual-channel synchronous demodulation method is proposed to enhance the precision of a precision printed-circuit board (PCB）-based electromagnetic induction-type angular displacement sensor. By providing independent square-wave reference signals for the sine and cosine output signals of the sensor, this method achieves attenuation-free demodulation through phase matching, fundamentally eliminating the gain inconsistency induced by phase mismatch. Experimental results show that, compared with the conventional method, the proposed approach reduces the original full-cycle measurement error from ±0.0323 5° to ±0.007 6°, lowers the compensated full-cycle error from ±0.006° to ±0.003 4°, improves the dynamic range of the sensor output signal from approximately ［-16 001, 9 338］ to about ［-30 000, 30 000］, and enhances angular stability to 0.000 1°. This study provides an effective technical solution for improving the performance of high-precision PCB-based electromagnetic induction-type angular displacement sensors, offering broad prospects for engineering applications.
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2026,49(14):75-86, DOI:
Abstract:
Addressing the issues of significant scale differences in defect targets, strong background interference and potential missed detection of small and occluded targets in aerial images of transmission lines, a poly feature fusion detector based on YOLOv11n ( PFD-YOLO) is proposed. Firstly, by incorporating poly kernel Inception block （PKI Block） from poly kernel Inception network （PKINet） to enhance C3K2, the model complexity is effectively reduced while the feature extraction capability is enhanced. Secondly, a feature focusing diffusion pyramid network (FFDPN) is designed to reconstruct the feature fusion structure on the Neck side. The feature focusing module (FF) facilitates efficient interaction and contextual information propagation among features of different scales, alleviating the feature forgetting problem caused by inconsistent multi-scale features. Thirdly, a lightweight shared convolution detection head (LSCD) is introduced, utilizing cross-scale parameter sharing and group normalization strategies to significantly reduce the computational overhead of the detection head while enhancing the detection accuracy of small targets. Finally, the proposed method is validated on a self-built multi-target defect dataset of transmission lines. Experimental results show that PFD-YOLO maintains a high inference speed while improving mAP50 and mAP50.95 to 86.6% and 53.9%, respectively, representing an increase of 3.9% and 3.3% compared to the original YOLOv11n model. This effectively enhances the detection accuracy and robustness of transmission line defects and foreign objects, demonstrating good engineering application value.
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2026,49(14):87-99, DOI:
Abstract:
Aiming at the key challenges in steel surface defect detection, such as insufficient accuracy, high missed detection rate of small-sized defects and bounding box localization deviations, this paper proposes an improved steel surface defect detection model YOLOv8-CEW （closed eyes in the wild） based on YOLOv8. The model achieves performance breakthroughs in feature extraction, feature fusion, and bounding box regression through three core improvements: The integration of the CFBlock （ConvFormer block） module, the efficient up convolution block (EUCB) and the WIoU loss function. Specifically, the introduced CFBlock module is fused with the existing C2f module in the Backbone and replaces the internal bottleneck to optimize the feature capture mechanism for the unique morphologies of steel defects (slender and winding, dense and discrete), enhancing the discriminative ability of multi-scale defect features; the EUCB module replaces traditional upsampling to adapt to the drastic scale changes of defects under different shooting distances, optimize defect feature reconstruction and improve detection integrity; and the WIoU loss function dynamically adjusts the localization penalty strategy according to defect scale, strengthening the accuracy of bounding box localization. Experimental results on the NEU-DET （Northeastern University defect detection dataset） demonstrate that compared with the baseline YOLOv8 model, the improved model achieves increases of 3.2%, 1.4%, 1.1% and 0.4% in precision, recall, mAP@0.5 and mAP@0.5:0.95, respectively. Furthermore, its detection speed meets the requirements of industrial applications, indicating promising practical prospects.
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2026,49(14):100-108, DOI:
Abstract:
Hot-bar soldering (pulse reflow soldering) is widely used in the automated welding of micro-scale electronic components, where high requirements are imposed on both the temperature ramp rate and temperature control accuracy. Conventional control methods establish process models based on time-domain assumptions or equivalent analyses; however, model mismatch may result in temperature overshoot and degraded system stability. In this study, parameter identification is carried out based on nonlinear least-squares estimation, and an extended state-space model for temperature control of the hot-bar soldering process is established. An model predictive control （MPC） controller is designed, and the closed-loop simulation of the hot-bar soldering temperature control system is performed. Simulation results indicate that, compared with the conventional proportional-integral-derivative（PID） and Smith-PID controllers, the MPC controller exhibits stronger robustness under model mismatch and achieves superior disturbance rejection performance under external perturbations. Micro-cable welding experiments demonstrate that the MPC controller exhibits good temperature regulation performance, achieving a temperature rise rate of up to 240℃/s while significantly suppressing temperature overshoot. The temperature control accuracy during the welding process reaches ±2℃. After replacing welding heads with different structural sizes, the temperature control performance remains essentially unaffected, achieving precise temperature control of the hot-bar soldering process.
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Yuan Hui, Liu Jiayi, Zhang Yanshun
2026,49(14):109-116, DOI:
Abstract:
Underwater submersibles operating in emergency conditions are susceptible to ferromagnetic interference from the mother ship, leading to degraded positioning accuracy. To address this issue, a positioning error correction method for underwater submersibles based on reverse attitude update is proposed. When initial magnetic interference exists, the method first performs inertial navigation and stores the raw data. After the submersible detaches from the mother ship and the magnetic signal returns to normal, it recursively recalculates and corrects the original initial attitude angle using the inertial-phase data and reverse attitude update algorithm. Subsequently, inertial navigation and integrated navigation are re-executed with the revised initial attitude angle to eliminate the systematic errors induced by the original initial attitude angle. This method enables the estimation and compensation of both initial and process errors by leveraging the later-stage integrated navigation results. Experimental results demonstrate that, compared with the traditional integrated navigation method, after compensating for the initial heading and horizontal attitude errors of the submersible, the average position error during navigation is reduced by 87.8% and the average velocity error by 91.3%, which significantly improves the positioning performance of the integrated navigation system for underwater submersibles in emergency scenarios.
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Zhao Shuangmei, Zhou You, Pan Yizhen, Xu Xinhui, Han Shunli
2026,49(14):117-125, DOI:
Abstract:
As pivotal technologies in non-cooperative communications, signal parameter estimation and modulation recognition play a decisive role in electromagnetic signal analysis and intelligence extraction. Confronted with practical challenges including unknown signal parameters, limited model generalizability and costly post-deployment updates, this paper proposes and implements a deep learning-based blind identification method for digital modulation signals. The proposed method innovatively designs a feedforward signal processing framework that tightly integrates symbol rate estimation, adaptive sampling rate control and intelligent modulation identification. This integrated framework breaks away from the limitations of conventional paradigms that treat these functions as isolated, sequential operations, thus yielding a highly efficient and universally applicable signal processing solution. Experimental results and practical validation confirm our approach substantially reduces training expenditures while simultaneously improving generalization capacity and robustness to unknown parameters, showing strong potential for engineering applications.
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Fan Yuqi, Jin Canze, Wang Yanyang, Li Jiale, Zheng Haoyang
2026,49(14):126-139, DOI:
Abstract:
To address the issues of large collision detection scale and low collision detection efficiency in complex urban obstacle environments, which result in slow solving speeds for traditional visibility methods, a hierarchical convex hull visibility method is proposed. By pre-screening polygons in the environment using the principal convex hull, it narrows the scope of obstacles involved in visibility detection, reducing detection time. Simultaneously, a local convex hull visibility detection method is introduced. It performs polygon pair traversal to construct local convex hulls and employs bridge points for regional segmentation. This approach avoids invalid detections inherent in traditional global pair traversal, substantially improving detection efficiency. Urban environments of varying scales are constructed using real city data. Simulation comparisons are conducted against baseline path planning algorithms, related improved algorithms and existing visibility methods in the literature. Results demonstrate that the hierarchical convex hull visibility graph method rapidly generates high-quality paths, reducing planning time by 99.66%, 94.38% and 94.06% compared to traditional visibility graph methods, bidirectional rapidly-exploring random trees （Bi-RRT*） and Informed-RRT*, respectively. The proposed method effectively addresses the computational efficiency challenge for UAV path planning in complex urban environments.
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Pang Zhiyong, ia Zhigang, Bian Feng, Su Yufeng
2026,49(14):140-148, DOI:
Abstract:
Aiming at the problems of low accuracy and long inference time in bullet hole detection technology for chest ring targets in shooting sports, a lightweight algorithm based on YOLOv8 is proposed. Firstly, a MobileNetV4 backbone enhanced with a convolutional block attention module (CBAM) is designed to enhance the model′s accuracy while minimizing its computational complexity and the number of parameters. Secondly, a C2fGhost-AFPN-P2 (asymptotic feature pyramid network) feature aggregation network is proposed, which increases the network′s perception of small targets by combining the C2fGhost module, AFPN and P2 output layer. Finally, the loss function of the algorithm is set to WIoU to weaken the impact of the imbalance in the number of positive and negative samples and improve the regression accuracy of the prediction box. The experimental results on the self-made dataset show that the mAP50 is 0.973, which is 16.0% higher than the original algorithm; the number of parameters is 2.43×106, which is 19.3% lower than the original algorithm; and the GFLOP is 8.0, which is 2.4% lower than the original algorithm. The experimental results prove that M-A-YOLO has better performance in bullet hole detection.
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Zhang Ailing, Wang Houneng, Liao Xiaobing, Li Zicheng
2026,49(14):149-159, DOI:
Abstract:
In response to the problem of the denial-of-service (DoS) attacks that the network layer of cyber physical power system (CPPS) is prone to, a DoS attack detection and defense method integrating state estimation and model predictive control (MPC) is proposed. On the sensor side, a multi fading factors adaptive Kalman filter is adopted for state estimate. This algorithm overcomes the drawbacks of the traditional Kalman filter which is prone to divergence and the single fading factor adaptive Kalman filter which has low estimation accuracy, and a residual detection algorithm is designed based on the estimation results to realize attack detection. On the controller side, MPC is introduced, which calculates the control signals for the future period by using the newly received state estimation signals to compensate for the data loss caused by DoS attacks. Meanwhile the predictive control sequence is designed on the basis of Lyapunov stability theory and linear matrix inequalities (LMI), which solves the problem of system instability caused by data loss. Finally, simulation verifications are conducted on the IEEE 9-bus and IEEE 30-bus test systems. The results show that the proposed detection method can detect DoS attacks in a timely manner, and the defense method can effectively maintain the stable operation of CPPS under DoS attacks.
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Xiao Wanlin, Wang Yanbing, Liu Wugang, Hou Chuantao, Zhou Wei
2026,49(14):160-166, DOI:
Abstract:
As a core component of reusable thermal protection systems for spacecraft, ceramic fiber heat shields rely on the integrity of their borosilicate glass coatings to determine aerodynamic performance and operational safety. However, traditional 3D point cloud detection methods face challenges in defect localization and measurement due to the high similarity between complex surface textures and actual defect morphologies during inspection. To address these issues, this study proposes a two-stage measurement protocol. This method first performs 2D defect localization. Initial candidate area screening using relaxed threshold values on depth maps, followed by relative slope feature extraction to refine detection and eliminate texture interference; it then conducts 3D local measurement. Mapping localized defect regions to 3D point clouds and applying RANSAC/DBSCAN algorithms for precise dimensional analysis. Experimental results demonstrate significant improvements: compared to edge detection methods, the proposed approach achieves 83.3% defect detection accuracy (from 50% baseline) with the false positive rate reduced to 33.33%. When compared to global RANSAC/DBSCAN methods, it improves detection rate from 50% to 83.3% while reducing false positives from 50% to 33.33%, with detection capability extending to minimum 30 μm depth defects. The framework′s superior localization performance under complex texture interference is conclusively validated.
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Xing Hao, Zhang Pengfei, Qiao Shuai, Zhou Wei, Li Ningning
2026,49(14):167-176, DOI:
Abstract:
In response to the problems existing in the current research on composite damage identification, such as high feature dimensions, excessive redundant information and difficulty in distinguishing multiple damage modes, a damage identification method based on principal component analysis and Hilbert marginal spectral energy features for acoustic emission is proposed. Acoustic emission signals of epoxy resin, carbon fiber bundles, and composite were obtained through tensile experiments. Principal component analysis was used to reduce the dimension of 6 types of feature parameters and extract the principal components of high-energy fracture and frequency domain features; the Hilbert-Huang Transform was used to obtain the marginal spectrum and establish a quantitative criterion based on the concentration of band energy. The results show that the cumulative contribution rates of the first three principal components for the three types of specimens are 84.7%, 88.25% and 86.6%, respectively. These effectively extract two main damage modes: high-energy fracture (PC1) and frequency domain features (PC2). Further analysis of waveform energy using Hilbert-Huang Transform reveals that the damage energy of pure resin is concentrated in the 100~150 kHz frequency band (accounting for approximately 60%), the damage of the fiber bundle shows a three-stage evolution in the F4~F6 mid-frequency band, and the energy distribution of the composite is highly dispersed (with a dispersion mode accounting for 71%). This method can effectively distinguish matrix cracking, fiber fracture, and interface debonding, providing a quantitative identification basis for composite damage monitoring.
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2026,49(14):177-191, DOI:
Abstract:
To effectively suppress noise in intravoxel incoherent motion (IVIM) images while preserving structural information without the need for clean labels or repetitive acquisitions, this paper proposes a self-supervised denoising model based on similarity-constrained masking. Built upon the blind-spot denoising framework, the model learns from a single noisy image by integrating image neighborhood similarity with an adaptive masking strategy. In simulation experiments, at a signal-to-noise ratio(SNR) of 30, the proposed method reduces the signal mean square error (MSE) by 36.92%. Furthermore, the root mean square error (RMSE) of the parameters F, D and D_star decreases by 4.39%, 11.43% and 3.29%, respectively, while their structural similarity index measure (SSIM) increases by 2.73%, 0.21% and 3.37%. In real-world experiments, the SNR improvement ratios in gray matter (GM) and white matter (WM) regions increase by 0.40% and 3.85%, respectively. Clinical case analysis further demonstrates that the contrast-to-noise ratio of D and D_star values in the meningeal region improves from 0.064 and 0.005 to 0.271 and 0.026, respectively. Experimental results indicate that this method consistently achieves the highest and most stable contrast-to-noise ratio levels across all parameters. The proposed model effectively reduces noise while preserving tissue structure and parametric information, providing an efficient self-supervised solution for high-quality IVIM imaging and its clinical applications.
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Wang Minshui, Wang Mingchang, Sun Yongxin
2026,49(14):192-199, DOI:
Abstract:
To improve the recognition accuracy of small maritime targets in remote sensing images, a YOLO algorithm integrating multi-kernel sub-pixel enhancement and contextual attention is proposed based on the YOLO11n object detection framework. This method simultaneously employs 3×3 and 9×9 convolutional kernels to extract features with different receptive fields and adopts a super-resolution reconstruction approach to design a sub-pixel enhancement module that captures more detailed information, thereby enhancing the feature extraction capability of the backbone network. On the basis of the original C3K2 structure, a C3K2_context aggregation attention (C3K2_CAA） module is designed to capture richer contextual information, assisting the model in more accurately recognizing targets and improving detection performance in complex scenarios. The network′s detection effectiveness is validated using the publicly available SeaShips dataset. The results show that double-core sub-pixel enhancement module (DSPE）-YOLO algorithm achieves AP, mAP50 and mAP50-95 of 97.2%, 99.5% and 71.6%, respectively. Compared with the baseline YOLO11n network, the recognition accuracy increases by 4.3%, 9.4% and 4.4%, respectively. For ship detection, the AP is improved by 0.4%, 7.7%, 4.7% and 8.7% compared with PP-YOLOE-s, YOLOV6, YOLOV8-n and YOLOV10-n demonstrating excellent object detection performance.
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Zhang Yajun, Song Yanhai, Jiang Ying, Xu Yu
2026,49(14):200-210, DOI:
Abstract:
In recent years, unmanned aerial vehicles have been widely applied in the field of remote sensing image acquisition. However, in the task of target detection within remote sensing images, single-modal visible light images exhibit certain limitations, as their detection performance significantly degrades in dark environments or under adverse weather conditions such as haze. To address this issue, this paper proposes a dual-channel remote sensing image target detection network based on YOLO, named YOLO with dual-channel fusion and Mamba（YOLO-DFM）. This network integrates the Mamba architecture with the YOLO model and incorporates the dehazing module C2f with dynamic-range histogram self-attention (C2f_DHSA） to enhance the detection capability of visible light images. Additionally, we introduce a visible-infrared profound semantic fusion module (PSFM), which effectively fuses visible and infrared images, leveraging the complementary advantages of both modalities to improve the robustness and accuracy of remote sensing image target detection. For feature extraction and fusion, we propose a dual-convolution （feature multi-scale composite convolution，FMCConv） module, which combines the advantages of space-to-depth convolution (SPDConv） and deep robust feature downsampling (DRFD）. By employing an innovative feature fusion strategy and computational optimization techniques, this module enhances target detection accuracy in complex multi-scale backgrounds and is integrated into the YOLO-DFM network. We validate our proposed method on the vehicle detection in aerial imagery (VEDAI）, low-light visible-infrared paired (LLVIP） and DroneVehicle datasets. Experimental results demonstrate that, compared to the original YOLOv8, YOLO-DFM improves the mAP metric by 6.8%, 1.2% and 1.6%, respectively, significantly enhancing detection performance and confirming the model′s effectiveness. In conclusion, the YOLO-DFM network proposed in this paper provides an efficient and reliable solution for multi-modal remote sensing image target detection.
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2026,49(14):211-220, DOI:
Abstract:
Underwater concrete structures in hydraulic dams are prone to defects. Traditional manual inspection methods are inefficient and carry high risk factors. Existing underwater 3D reconstruction techniques struggle to achieve high precision due to poor image quality and complex aquatic environments. Therefore, for underwater structural defect detection, this paper proposes a 3D reconstruction solution integrating a multi-scale fusion image enhancement algorithm with an underwater refraction model. This approach combines adaptive color correction and an improved Retinex algorithm through non-subsampled shearlet transform (NSST） algorithm-based multi-scale fusion to optimize image quality, then utilizes the underwater refraction model to complete 3D reconstruction. Experiments demonstrate that this method outperforms traditional underwater image enhancement models in terms of information entropy (IE), patch-based contrast quality index (PCQI) and underwater color image quality evaluation (UCIQE) metrics.The processed images achieve an optimal IE value (7.803 6), with actual reconstruction results showing optimal detail textures and edge contours. This approach effectively enhances the reconstruction accuracy of underwater structural defects, providing reliable support for underwater inspection.
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Sun Jinhu, Wang Pengjie, Li Wei, Liu Bingyu, Du Xinhuizi
2026,49(14):221-230, DOI:
Abstract:
Medical image segmentation is one of the core tasks in medical image analysis and plays a vital role in clinical diagnosis, surgical planning, and treatment evaluation. To address the limitations of existing methods in global context modeling, local detail preservation and computational efficiency, this paper proposes a novel medical image segmentation method based on a multi-scale visual state space U-Net. This method integrates a state space modeling mechanism, a multi-scale parallel wavelet convolution module and an improved large-kernel grouped attention gating module, aiming to enhance cross-layer feature fusion capability and detailed feature representation. Furthermore, this paper introduces a hierarchical cascaded prediction strategy, which progressively generates and fuses prediction maps at multiple semantic levels of the decoder, thereby further improving the model′s robustness and adaptability to multi-scale targets. Experimental results on two public datasets, Synapse and automated cardiac diagnosis challenge (ACDC）, demonstrate that the overall performance of the proposed method surpasses existing mainstream methods, validating its effectiveness and superiority.
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2026,49(14):231-240, DOI:
Abstract:
To address the issue of complex non-uniform blurring in images captured by drones during high-speed flight and attitude jitter in dynamic road inspections, which severely degrades image quality and impacts subsequent object detection, this paper proposes an improved DeblurGAN-V2 image deblurring algorithm guided by inertial sensors. This algorithm utilizes camera inertial measurement unit (IMU) data as physical constraints, designs a gradient direction consistency loss function to enforce motion trajectory recovery, and employs dual path-aware loss to balance texture and structure. Additionally, an adaptive weighting discriminator is designed to evaluate the authenticity of generated images at different scales, thereby enhancing image quality. Experiments on a self-built drone road inspection dataset demonstrate that compared to the baseline DeblurGAN-v2 model, peak signal-to-noise ratio (PSNR） and structural similar ityindex measure (SSIM） improve by 1.52 dB and 0.056, respectively. Furthermore, in downstream vehicle object detection tasks, the false positive rate and false negative rate decrease by 9.2% and 18.1%, respectively.
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2026,49(14):241-252, DOI:
Abstract:
To address the limitations of unmanned aerial vehicle vision-and-language navigation (UAV-VLN) in multimodal feature fusion, global environment perception and long-distance planning, a history-enhanced dual-scale mixture-of-experts network is proposed. The proposed framework constructs a dual-scale structure composed of a global mixture-of-experts network and a local mixture-of-experts network. Specifically, the global network integrates linguistic descriptions with navigational map features to perform global semantic reasoning, while the local network combines visual observations and linguistic features to enable fine-grained semantic perception, thereby enhancing cross-modal understanding at different scales. To strengthen temporal modeling and long-term dependency capture, a historical memory encoder based on a matrix long short-term memory (mLSTM) network is introduced to dynamically fuse historical observations with the current state. Furthermore, a goal-progress co-prediction module is designed to jointly estimate the agent′s spatial position and task stage through a goal prediction head and a progress prediction head, thereby improving the continuity and interpretability of navigation decisions. Experiments conducted on the city-scale UAV-VLN CityNav dataset demonstrate that the proposed method achieves a success rate (SR） of 16.42% and a success weighted by path length (SPL） of 18.26%, outperforming the baseline model by 10.04% and 12.18%, respectively. These results verify that the proposed approach substantially enhances cross-modal understanding, temporal reasoning and long-distance navigation capability under urban scenarios.
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Chen Haixin, Xu Huanyu, Xu Mengxi, Zhang Lijuan, Wang Quan
2026,49(14):253-264, DOI:
Abstract:
Vehicle re-identification (Re-ID) aims to accurately identify and associate the same vehicle across different camera views to achieve cross-regional, continuous tracking and trajectory analysis. However, existing Re-ID methods often suffer from insufficient feature representation and poor robustness in complex scenarios due to camera viewpoint variations, occlusions and high intra-class similarity. To address these challenges, this study proposes a dual-branch network fusing regional features with dynamic side information. This study design a regional contextual association module (RCAM) using multi-head attention to adaptively refine key local features, and a dynamic side information embedding (D-SIE) mechanism to flexibly incorporate camera and viewpoint data. By employing CutMix augmentation, the model′s robustness and generalization are significantly enhanced. On VeRi-776, the method achieves 83.6% mAP and 97.5% Rank-1; on the 800 subset of the VehicleID dataset, the Rank-1 accuracy reaches 85.4% and the Rank-5 accuracy hits 97.8%. Cross-domain experiments also verify the strong generalization performance of the model. Experimental results demonstrate that the model proposed in this paper significantly improves the model robustness and fine-grained feature discrimination capability for the vehicle re-identification task.
Volume 49, 2026 Issue 14
The 28th Annual Meeting of the China Association for Science and Technology-Intelligent Power Weather Forecasting and Enhancement of Power System Security and Resilience
Research&Design
Intelligent Control & Performance Testing
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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2026,49(9):1-9, DOI:
Abstract:
To study the variation characteristics of sheet resistance of thin film samples under variable temperature conditions, a device integrating a Bayesian optimization fuzzy PID temperature control system and a four-probe method was designed. The Bayesian optimization algorithm was used to find the optimal parameter combination for the fuzzy controller, significantly improving the temperature control accuracy of the system and greatly reducing the experimental cost of the parameter optimization process. A highly stable constant current source circuit and a high-precision digital voltmeter were designed for the four-probe system to ensure stable current supply and accurate voltage measurement even when the resistance of the thin film changes by two to three orders of magnitude. Through an efficient two-way communication protocol between the embedded system and the host computer, the duty cycle of PWM, temperature and voltage data were synchronously transmitted in real time, achieving high-precision temperature control and real-time calculation of the sheet resistance of the thin film samples.
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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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Pan Kailin, Zhang Xiaoming, Zhang Ge, Fan Yiwei, Li Boyu
2026,49(9):10-21, DOI:
Abstract:
Vehicle geomagnetic navigation faces a key issue of magnetic interference affecting navigation accuracy during dynamic driving. Traditional fixed-parameter compensation methods struggle to adapt to interference changes under different motion states. This paper reveals the limitations of a single fixed-parameter compensation model by analyzing the relationship between motion state and magnetic compensation effect, providing a theoretical basis for the development of adaptive compensation methods. The CNN-SRU motion state recognition model constructed in the study achieved a recognition accuracy of 99.61%, with training efficiency improved by 12.8%~28.4% compared to the comparative model, and inference delay reduced by 25.4%~38.5%. Based on the recognition results, the compensation performance of the single ellipsoid fitting model was systematically evaluated, and significant differences in compensation effects under different motion states were found: The standard deviation after compensation was 49.39 nT for uniform linear motion, which exhibited the best performance; the standard deviation after compensation was 533.35 nT for steering motion due to complex interference; and the standard deviation after compensation reached 147.98 nT for acceleration motion due to strong transient interference. The research indicates that motion state is a key factor affecting the magnetic compensation effect, and fixed-parameter models cannot meet the requirements of all operating conditions. The “state recognition-compensation evaluation” framework established in this paper provides theoretical support and technical paths for the development of adaptive magnetic compensation methods.
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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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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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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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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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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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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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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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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.
