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Wei Qin, Pi Jianyong, Zhang Song, Liu Wei
2026,49(15):1-14, DOI:
Abstract:
To address the poor performance of existing search-and-rescue detection methods in complex environments, this study proposes an enhanced algorithm named DAAS-RT-DETR. Based on the real-time detection transformer (RT-DETR) framework, the method first applies dual convolution kernels (DualConv) to strengthen multi-scale feature extraction. The global attention mechanism (GAM) is then incorporated into the attention-based intrascale feature interaction (AIFI) module to jointly capture spatial and channel dependencies. An adaptive gated fusion module (AGFM) is further introduced to improve feature interaction efficiency, and the loss function is replaced with scale-based dynamic loss (SBD Loss) to achieve more accurate object localization.Experimental results show that the proposed algorithm improves mAP50 and mAP50-95 by 4.6% and 4.2% on the AiTV dataset, and by 6.3% and 6.2% on the self-built RES dataset, respectively, while requiring fewer parameters. These gains highlight the strong practical potential of the method for real-world search-and-rescue missions.
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Zhao Wensong, Ding Shuchen, He Meng, Bi Kaitao, Jin Xin
2026,49(15):15-24, DOI:
Abstract:
To address the overshoot, oscillation and slow disturbance recovery of the acoustic-pressure envelope in ultrasonic transdermal drug delivery caused by load variation, pure time delay and actuator saturation, an interval type-2 fuzzy self-tuning incremental proportional-integral-derivative (PID) control method considering load uncertainty is proposed. Based on the Butterworth-Van Dyke (BVD) equivalent circuit and envelope baseband modeling, the controlled plant is approximated as a second-order system with pure time delay. Gaussian interval type-2 fuzzy sets, Karnik-Mendel type-reduction and an anti-windup mechanism are introduced to realize bounded online tuning of PID parameters. Simulation results show that, compared with conventional PID, the proposed method reduces root mean square error (RMSE), peak overshoot, settling time and disturbance recovery time by 15.4%, 62.1%, 38.2% and 41.9%, respectively. Compared with type-1 fuzzy PID, interval type-2 fuzzy self-tuning incremental PID (IT2-FPID） further reduces peak overshoot, settling time and disturbance recovery time by 58.6%, 26.8% and 24.7%, respectively. Experimental results show that IT2-FPID outperforms the comparison methods and can achieve fast, stable and robust control for the acoustic-pressure envelope in ultrasonic drug delivery.
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2026,49(15):25-35, DOI:
Abstract:
Electromagnetic simulation plays a crucial role in microwave filter design, but traditional numerical methods are computationally expensive and struggle to handle the many-to-many mapping problem from design parameters to electromagnetic responses. To rapidly and accurately establish parametric models for microwave filters, this paper proposes a data-driven surrogate model based on a hybrid architecture combining conditional variational autoencoder (CVAE) and Transformer. Firstly, an S-parameter response database for a fourth-order cavity filter was constructed using electromagnetic simulation software. Subsequently, a hybrid CVAE-Transformer model was built based on this database. The model introduces a probabilistic latent space and designs an innovative composite loss function that incorporates a hierarchical decibel-domain adaptive weight mechanism, achieving a high-fidelity mapping from structural parameters to the probability distribution of S11 parameters. Experimental results show that compared to several benchmark models, the surrogate model exhibits optimal performance, with a mean squared error (MSE) as low as 0.002 324 and a correlation coefficient as high as 0.996 6. Compared to traditional electromagnetic simulation methods, the prediction speed is improved by more than three orders of magnitude. Therefore, the rapid and accurate prediction capability of the surrogate model proposed in this paper has significant theoretical guidance and practical engineering application value for the robust design, yield analysis, and optimization design of microwave filters.
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Zhang Nana, Ren Xuhu, Xu Chenyang, Zhao Wenjing, Nie Zhixi
2026,49(15):36-44, DOI:
Abstract:
To address the challenges of significant difficulty and insufficient accuracy in locating the centerline of small-diameter buried pipelines in urban underground short and medium-distance scenarios, this paper proposes an improved Rauch-Tung Striebel (RTS) filtering and smoothing algorithm aimed at enhancing positioning precision. The method is based on a combined navigation scheme using an inertial navigation system and an odometer. It initially employs an extended Kalman filter for forward data fusion to suppress error divergence. Subsequently, an innovative improved RTS backward smoothing algorithm is proposed. By introducing a smoothed "error state", it directly corrects the positioning information and sensor parameters. Through sensor parameter compensation and secondary filtering optimization, the positioning accuracy is improved. To validate the algorithm′s effectiveness, simulation experiments were conducted. The results indicate that this method can achieve pipeline centerline mapping accuracy on the order of 10-3, fully meeting the engineering application requirements for pipeline in-line inspection positioning. This provides more reliable data support for the safe operation and maintenance of pipelines.
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2026,49(15):45-59, DOI:
Abstract:
To precisely address the critical issue of internal shot peening residue in slender tubes, which affects the service safety of high-end equipment, and to meet the stringent requirements for full-cycle quality assurance of core components in fields such as aerospace and precision medical devices, this study overcomes the limitations of existing nondestructive testing technologies. For the first time in the context of detecting shot peening residue inside slender tubes, this paper proposes combining eddy current testing technology with a lock-in amplifier and designs an integrated shot peening residue detection system based on field programmable gate array （FPGA） and a lock-in amplifier. Using an eddy current sensor as the probing tool, the system is inserted into the tube to capture the electromagnetic response generated by the residual shot. Based on cross-correlation theory, the lock-in amplifier enables precise extraction of weak phase signals. These signals are then quantified by a high-precision data acquisition module controlled by an FPGA, and finally, residue identification and location of the shot are achieved through phase variation characteristics. The system overcomes the application limitations of conventional ultrasonic testing, radiographic testing and single-mode eddy current testing. It can adapt to different detection scenarios through multi-frequency excitation, improving detection accuracy from the centimeter level to the millimeter level. It effectively detects residual shot of different sizes, requires no coupling agent and is unaffected by tube geometry or shot adhesion conditions. Additionally, the system offers fast detection speed, high efficiency, compact size, low cost, no radiation pollution, relaxed environmental requirements and ease of on-site deployment. A practical test platform was built to validate the detection of steel shot residue of various sizes inside aluminum-alloy slender tubes under different excitation frequencies. The results show that the detection system can effectively identify residual shots inside the tubes under different excitation frequencies. The average error in shot location detection is only 2.38 mm, with a standard deviation of 1.35 mm, and the signal-to-noise ratio is also improved compared to traditional eddy current testing methods.
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Zhan Keyi, Zhang Chunlin, Wu Kai, Pan Yi, Li Yue
2026,49(15):60-66, DOI:
Abstract:
Fault diagnosis of aero-engines is of great significance to flight safety and operational efficiency. However, in practical applications, large-scale labeled fault data are difficult to obtain, leading to overfitting and insufficient generalization ability of traditional models under small-sample conditions. To address this challenge, this study introduces an advanced tabular generation model for sensor data synthesis, aiming to achieve data augmentation in small-sample scenarios. The constructed tabular generation model is extended from a tabular prior fitting network. It can quickly fit data distributions without additional training, and exhibits higher efficiency and adaptability compared with existing tabular generation methods. The prediction model is trained and evaluated using UniXDE.DTM, which is an advanced data modeling software. Experimental results show that the proposed method can effectively expand aero-engine fault samples, significantly improve diagnostic performance under small-sample conditions, and achieve optimal results in comparison with various mainstream generation models. This research provides an efficient and feasible solution for small-sample fault diagnosis, and demonstrates the application potential of large tabular models in the intelligent operation and maintenance of key industrial equipment.
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Miao Qiang, Tang Yujing, Zhou Zhenglei, Zhang Yujie, Yang Zhou
2026,49(15):67-75, DOI:
Abstract:
Electricity theft seriously threatens the secure and stable operation of power systems. With the continuous upgrading of metering systems, the availability of multi-source data has been significantly improved, providing favorable conditions for electricity theft detection research. However, existing methods still suffer from the limitations of single-dimensional data utilization and the difficulty of effectively capturing complex temporal dependencies. To address these challenges, this paper proposes a parallel convolutional neural network self-attention network (PCSA-Net) for electricity theft detection, which aims to exploit the complex coupling relationships among heterogeneous electricity, gas and water consumption data. In the proposed framework, multi-dimensional heterogeneous time-series data are first normalized to ensure the consistency and reliability of model inputs. PCSA-Net is composed of parallel convolutional neural networks for independent feature extraction from different energy sequences, convolutional layers for cross-channel feature fusion, and a self-attention mechanism for temporal dependency modeling and feature weighting. To alleviate the impact of class imbalance in electricity theft detection, a weighted binary cross-entropy loss function is employed during model training. Extensive experiments conducted on multiple datasets with different anomaly ratios demonstrate that PCSA-Net consistently outperforms traditional and deep learning baseline methods in terms of area under curve (AUC) and weighted F1-score (WF1), confirming its effectiveness in complex multi-source environments.
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Liu Junjie, Wu Jie, Feng Yannan, Guo Nanxuan
2026,49(15):76-83, DOI:
Abstract:
To address the problem that software clock recovery in high-speed serial links is prone to misjudging the number of symbols between transition edges under severe jitter conditions, which further causes cumulative phase distortion in the recovered clock, an anti-distortion software clock recovery method based on phase error correction is proposed. In the software clock recovery process, the phase difference between the reference clock and the recovered clock is monitored in real time to identify and correct symbol-number decision errors between transition edges. Comparative experiments with a commercial oscilloscope show that, under conditions of high random jitter and strong duty-cycle distortion, the symbol-rate estimation error of the commercial oscilloscope can reach 0.3%, whereas the relative error of the proposed method remains below 0.005%, yielding an approximately 60-fold improvement in measurement accuracy. The results demonstrate that the proposed method can effectively suppress phase distortion in the recovered clock and improve the stability and robustness of symbol-rate estimation under severe jitter conditions.
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Zhang Yuanpeng, Liu Xuan, Hong Yingping
2026,49(15):84-93, DOI:
Abstract:
To address challenges in test recovery of missile-borne equipments—including uncertain recovery zones, inability to manually initiate terminal operations, unstable data transmission links and power constraints—the paper proposes and implements a self-initiating positioning and recovery system utilizing Beidou-3 radio navigation satellite service/radio determination satellite service (radio navigation satellite service (RNSS)/radio determination satellite service (RDSS)) coordination. The system adopts a centralized control architecture, employing RNSS as the primary positioning method while activating RDSS short message communication under specific recovery conditions. A five-state state machine coordination mechanism is established for "standby-awakened-flight-recovery-landing hold" operations, ensuring reliable transmission of position data and critical parameters. Additionally, a self-initiating hold circuit combining "tilt switch physical triggering+microcontroller self-hold" is designed to handle long-term storage and high-dynamic impact scenarios during launch. Motion feature analysis further distinguishes transportation disturbances from launch processes. Prototype testing demonstrates the system′s stable performance in self-initiation, state switching, RNSS positioning and RDSS data transmission. Static positioning tests show an average error of approximately 0.45 m with maximum errors below 0.85 m. Short message communication maintains an overall reception success rate of at least 86.7% in both open and obstructed environments.
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Li Zihao, Zheng Qingchun, Zhu Peihao
2026,49(15):94-106, DOI:
Abstract:
Addressing the critical challenges of weak edge representation, complex multi-scale backgrounds, and inefficient model structures in UAV-based small object detection, this paper proposes YOLO-GML—a lightweight yet powerful detector evolved from YOLOv8n. The model integrates three key innovations: LearnableDoGFilter and GaussianFilter module to enhance target contours and suppress background interference; multi-scale variable kernel convolution (MKConv） module for hierarchical receptive field fusion of local and global contexts; and a lightweight architecture achieved by pruning deep downsampling stages and optimizing the detection Head. Extensive experiments on the VisDrone2019 dataset demonstrate that YOLO-GML not only significantly reduces the parameter count to only 2.044×106 down from 3.008×106 but also achieves superior detection accuracy, with mAP50 rising from 33.633% to 42.818% and mAP50.95 from 19.628% to 25.830%. This work establishes a new leading balance between lightweight design and high precision, offering an efficient and practical solution for real-time UAV vision applications.
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Lu Siyuan, Feng Jiao, Zhang Zhizhong, Li Peng
2026,49(15):107-116, DOI:
Abstract:
In communication testing, signal sources are required to generate broadband and diverse test signals; however, the effective output bandwidth is often limited by the system clock frequency. To extend the effective output bandwidth of multiple test signals, this paper designs and implements a multi mode baseband signal parallel generation system based on field-programmable gate array (FPGA）. The proposed system adopts a signal-oriented parallel generation architecture and supports the oversampled real-time generation of various baseband vector signals, including additive white Gaussian noise (AWGN), amplitude shift keying (ASK), phase shift keying (PSK), frequency shift keying (FSK), single-tone/multi-tone, chirp and comb signals. In addition, the system supports combined output of selected signal types as well as flexible configuration of multiple parameters. Experimental results demonstrate that, under a 250 MHz operating clock, the proposed system achieves an effective output bandwidth exceeding 670 MHz, while maintaining continuous modulation phase and good waveform quality, with an error vector magnitude (EVM) below 2%. The proposed system provides a feasible FPGA-based hardware implementation for the flexible generation of broadband and multi-mode baseband test signals.
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Liu Ming, Wang Xinqian, Zhao Chaofan, Zhao Luyao, Gao Xiangchuan
2026,49(15):117-124, DOI:
Abstract:
Addressing the challenging issue of error rate performance analysis and optimization in non-coherent massive single-input multiple-output (SIMO） systems, a differential amplitude phase shift keying (DAPSK） constellation design scheme based on evolutionary computation is proposed. Initially, the constellation structure is initialized according to the user modulation order and DAPSK modulation characteristics. Subsequently, an evolutionary computation framework is introduced for iterative optimization. During the evolutionary process, the initial constellation distribution is adjusted by maximizing the minimum Euclidean distance between joint constellation points, thereby reducing the computational complexity of subsequent searches. The amplitude and phase of user symbols are used as chromosome encoding parameters. Under the constraints of total power and user sub-constellation sets, the bit error rate (BER） obtained from Monte Carlo simulations is minimized as the fitness function. Candidate schemes are generated through selection, crossover and mutation operations, and the population size and evolutionary generations are dynamically adjusted to enhance convergence efficiency, ultimately obtaining the optimal constellation design scheme. Simulation results demonstrate that, compared to the baseline scheme, the proposed scheme achieves a system BER reduction of approximately 1 to 2 orders of magnitude under medium to high signal-to-noise ratio conditions and with large-scale antenna configurations. The research results demonstrate that the proposed scheme not only improve system performance but also effectively reduce detection complexity, providing a feasible solution for ultra-reliable low-latency industrial internet of things communication.
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Qiang Haijun, Cai Yinhui, Zhang Jian, Wang Jianyu, Miao Qiang
2026,49(15):125-132, DOI:
Abstract:
In response to the issues of low efficiency, susceptibility to missed and false detections in traditional manual inspection of spherical shut-off valve bolts, as well as the insufficient capability of standard object detection algorithms in identifying subtle defects, this paper proposes an improved YOLOv5s based model for bolt state detection. The model enhances feature focus on critical regions by incorporating the convolutional block attention module (CBAM） attention mechanism, optimizes detail preservation in multi-scale feature fusion through the content-aware ReAssembly of features (CARAFE） upsampling operator, and employs depthwise separable convolution (DWConv） to achieve lightweight network design. These improvements collectively enhance the identification accuracy of four bolt states: normal, loose, dislodged and offset. Experimental results on a dataset collected from hydropower plant sites show that the proposed model achieves mAP@0.5 of 93.54%, representing a 2.91% improvement over the original YOLOv5s model. Furthermore, it outperforms mainstream detection algorithms such as faster regions with convolutional neural networks (Faster R-CNN） and single shot MultiBox detector (SSD） in overall performance, demonstrating its capability to perform accurate and efficient abnormal state detection.
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Shuai Yihui, Ma Jiaqing, Zhan Yu, He Zhiqin, Wu Qinmu
2026,49(15):133-142, DOI:
Abstract:
To address the issues of chattering and imprecise phase compensation of the low-pass filter in traditional sliding mode observer-based sensorless control of permanent magnet synchronous motors, this paper proposes a higher-order sliding mode observer with an exponential-power switching reaching law. First, a piecewise composite function is employed to replace the sign function, effectively suppressing the inherent high-frequency chattering of the system； second, by integrating the exponential reaching law and the power reaching law, an exponential-power switching reaching law is designed to further enhance the dynamic performance and convergence efficiency of the system； furthermore, a saturation function is introduced to replace the gain of the original exponential term, mitigating the chattering caused by the exponential term near the sliding surface； finally, simulation experiments are conducted to validate the proposed method. The results show that, compared with three other typical control strategies, the errors in rotational speed and torque have been significantly reduced and the rotor position is tracked rapidly and accurately. The proposed algorithm effectively suppresses system chattering while preserving its fast response characteristics, achieving precise estimation of rotor position and speed.
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Zhao Wenjing, Ren Xuhu, Ma Longfei, Zhang Nana, Sun Shenglan
2026,49(15):143-153, DOI:
Abstract:
Aiming at the problem that traditional integral transform is easy to produce "ripple wave" interference in cable defect location, resulting in misjudgment and missed detection, this paper proposes a cable location compensation method based on frequency domain weighting of reflection coefficient spectrum. Firstly, the cable distribution parameter model is established, and the positioning principle based on the reflection coefficient spectrum is derived. Secondly, the attenuation constant is extracted by the resonance point and the function is fitted, and then the frequency domain weighted repair compensation function is constructed to realize the exponential gain compensation in the low frequency band and the Blackman window suppression in the high frequency band, which can effectively smooth the positioning curve and suppress the boundary oscillation. The simulation and experimental results show that the proposed method can reduce the waveform complexity in both single-defect and multi-defect scenarios, significantly eliminate the "ripple wave", make the "mutation peak" of weak defects clear and distinguishable, and has high positioning accuracy and strong anti-noise performance, which can provide a reliable basis for cable condition-based maintenance.
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Jiang Yucheng, Xue Chunjiang, Wang Yanlan, Zhang Wei
2026,49(15):154-160, DOI:
Abstract:
Aiming at the problem of measurement accuracy of the straightness of deep hole tubes, a measurement method based on the principle of optimized by the geometric approximation searching (GAS） algorithm is proposed. Firstly, the position of the center of each cross-section is obtained step by step by the measurement device. Secondly, the least squares (LS) algorithm is used to fit the center of each cross-section to locate the reference axis. Finally, the fitted axis and straightness obtained by the LS algorithm are taken as the initial parameters, and the GAS algorithm is used to carry out the optimized iterative calculation to obtain more accurate straightness error. At the same time, the measurement software developed by laboratory virtual instrument engineering workbench (LabVIEW） can monitor the data in real time. The experimental results show that compared with the single use of the LS algorithm, the measurement accuracy of the optimized iterative combination algorithm (LS+GAS) improved by 5.24% to 19.27%, and the uncertainty of the measurement results is reduced by an average of 24.82%. The algorithm of optimized iterative combination shows high accuracy and good robustness in fitting the axis, and combined with the self-propelled device, it can significantly improve the efficiency of straightness measurement.
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Liu Shuo, Ding Haozhan, Ma Jiying
2026,49(15):161-172, DOI:
Abstract:
Improving the prediction accuracy of wind power is of great significance to ensure the safety of large-scale wind power integration and the efficient operation of energy systems. However, the inherent non-stationarity and strong randomness of wind power series bring great challenges to prediction, and the existing models are still insufficient in the depth and breadth of feature extraction. Therefore, this paper proposes a combined prediction model based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) combined with empirical wavelet transform (EWT) decomposition data, bidirectional time series convolution network (BiTCN) and bidirectional gated recurrent unit (BiGRU) prediction. The hybrid exploration selection strategy and spiral ascent factor are used to improve the global and local search performance of the dream optimization algorithm (DOA), and to search the optimal hyperparameters of ICEEMDAN and the combined model. Aiming at the noise residual problem of high-frequency components in ICEEMDAN, EWT is used to perform secondary decomposition denoising on the high-frequency components with the heaviest noise. Subsequently, the processed components are input into the combined prediction network composed of BiTCN-BiGRU to synergistically capture the multi-scale local features and long-term bidirectional dependencies of the sequence. Finally, the prediction results of each component are aggregated to obtain the final prediction value. Through the verification of the data set in this paper, the ICEEMDAN-EWT and BiTCN-BiGRU models based on improved dream optimization algorithm (IDOA） optimization are reduced to 0.73 in the average absolute percentage error, and the average absolute error and root mean square error are reduced to 2.5 and 1.46, respectively, with high prediction accuracy.
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Shuai Junhui, Wang Heng, Feng Jiao, Xie Shijun
2026,49(15):173-184, DOI:
Abstract:
To address the problem of low allocation efficiency caused by the strong coupling of power, bandwidth and timeslot resources in low earth orbit (LEO) satellite beam hopping systems, which further restricts the improvement of user satisfaction, this paper proposes a joint power-bandwidth allocation scheme for LEO satellite beam hopping systems based on Rainbow deep Q-network (DQN) deep reinforcement learning. First, a multi-dimensional state space is constructed by comprehensively considering features such as channel state information, queue state information and service requirements, and a joint optimization model for timeslot, power and bandwidth resources is established with the goal of maximizing user satisfaction. Second, considering the variable user demands and user states in the LEO satellite environment, the DQN is adopted for timeslot allocation learning, and the trust region interior point method is used to output power allocation to meet user requirements. Finally, to achieve and accelerate the convergence of the algorithm in the task, the Rainbow DQN algorithm is introduced in this paper.By integrating the optimization characteristics of multiple technologies, it rapidly generates the joint timeslot-power-bandwidth allocation strategy for the LEO satellite system. Simulation results show that the proposed resource allocation scheme can optimize the joint power and bandwidth configuration of the LEO satellite beam hopping system, improve user satisfaction and algorithm training stability, and effectively verify the application advantages of multi-technology fusion reinforcement learning in discrete action spaces.
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Liu Hui, Yuan Liang, Xiao Wendong
2026,49(15):185-192, DOI:
Abstract:
To address the non-uniform spatial distribution of pseudo point clouds, where point density is excessively high at close range but remains relatively sparse at long range, the resulting distance-dependent disparity can cause inconsistent proposal feature representations across different ranges and consequently degrade overall detection performance. To mitigate this issue, we introduce two components, namely geometric enhancement module(GEM) and adaptive region-of-interest attention fusion (ARAF) module. In GEM, a region-wise resampling strategy is adopted: voxel-level downsampling is applied to near-range regions to remove redundant points and suppress noise, while the sparse point distribution in far-range regions is preserved to maintain essential geometric structures. In ARAF, region importance is modeled at the region of interest (RoI) level by predicting adaptive weights and performing weighted fusion of features from real point clouds and pseudo point clouds. This design enables the network to dynamically emphasize far-range point cloud information according to object distance, thereby alleviating performance degradation caused by insufficient feature expressiveness in long-range scenarios. Experiments on the KITTI benchmark show that the proposed method improves the three evaluation metrics by 3.19%, 1.90% and 1.75%, respectively.
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Xie Yukun, Liu Qiang, Gao Mingwang, Cao Wanli, Ma Yuanjie
2026,49(15):193-205, DOI:
Abstract:
Fatigue driving detection demands high real-time performance and reliability, yet existing object detection models suffer from large parameter counts and high computational complexity, making them difficult to deploy on in-vehicle edge devices. To tackle this problem, this paper proposes Flat-YOLO, an ultra-lightweight detection model tailored for in-vehicle environments, which reduces parameter redundancy through progressive flattened channel scaling and incorporates two complementary modules, bounding box-aware channel self-attention (BBoxCSA) and IoU-aware multi-scale fusion (IoUMSF), to enhance edge representation and multi-scale fusion. On the self-constructed driver drowsiness detection (DDD) dataset (9 751 images, 7 classes), Flat-YOLO reduces parameters by 69.4% compared to YOLOv8n while maintaining comparable mAP@0.5:0.95, with precision and recall improvements of 0.3% and 1.0%, respectively; compared to YOLOv8s, it achieves a 91.7% parameter reduction and 76.8% computational cost reduction. Class-wise results further demonstrate that for safety-critical categories, Flat-YOLO improves recall for head_low by 4.9% (0.951 vs 0.902) and for open_eyes by 2.2% (0.913 vs 0.891); for fine-grained fatigue cues, it enhances mouth′s mAP@0.5:0.95 by 0.9% (0.479 vs 0.470) and yawn′s precision by 3.3% (0.947 vs 0.914). Furthermore, on the public yawning detection dataset （YawDD） dataset, Flat-YOLO achieves an mAP@0.5:0.95 of 0.901 (compared to 0.862 for YOLOv8n), validating the cross-dataset generalization capability of the proposed method.
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Zhang Wei, Li Rongxin, Chen Gan, Xie Mingshan
2026,49(15):206-217, DOI:
Abstract:
Refraction and specular reflection from transparent objects severely interfere with depth-camera-based 3D measurement, resulting in holes, artifacts and noise in depth maps, which further leads to incomplete object point clouds and biased 6D grasp pose estimation. Existing depth completion methods often struggle to achieve a good trade-off between accuracy and efficiency. To address these issues, this paper proposes spectral and edge-aware depth completion network （SEDNet）, a lightweight depth completion network for robotic grasping. SEDNet first encodes robust joint representations via an RGB-D adaptive gated fusion module. It then employs an edge-aware confidence module to predict a depth confidence map, which suppresses noise in low-confidence regions during multi-scale feature fusion. A shifted-window mechanism is introduced in the downsampling stages to enhance cross-window interaction and preserve structural continuity. On the decoder side, SEDNet combines skip connections with a multi-scale spectral attention module, where differentiable wavelet transforms and dynamic spatial attention enable enhanced reconstruction of fine details and object boundaries. Experiments on the public TransCG dataset demonstrate that, compared with the mainstream baseline depth filler net （DFNet）, our method reduces root-mean-square error (RMSE）, relative error (REL） and mean absolute error (MAE） by 50.00%, 25.93% and 50.00%, respectively, while achieving an inference time of only 0.020 s per frame. These results indicate that SEDNet significantly improves depth completion accuracy while meeting the real-time requirements of robotic grasping, validating the effectiveness and practicality of the proposed method.
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2026,49(15):218-228, DOI:
Abstract:
Aiming at the problems of difficulty in detail recovery at low sampling rates, insufficient utilization of historical feature information and blocking artifacts in current image compressed sensing reconstruction methods based on deep unfolding networks, this paper proposes a diffusion and dual-attention alternating optimization network (DDANet) for image compressed sensing reconstruction. The network combines the diffusion model with the deep unfolding framework by designing an alternating iteration mechanism between the diffusion denoising sub-stage and dual-attention sub-stage, which achieves collaborative enhancement of data distribution priors and cross-stage feature fusion, improving the recovery quality at low sampling rates. The dual-attention sub-stage includes a cross-stage channel attention (CSCA) module and a window sparse attention (WSA) module, which enhance image reconstruction details by fusing cross-stage features and exploiting local self-similarity. To address blocking artifacts, this paper proposes a coordinate spatial self-correction (CSSC) module, which suppresses block boundary discontinuity artifacts through the collaborative action of spatial, coordinate, and self-calibration branches, ensuring global consistency of reconstructed images. Experimental results show that on the Set11 test set, when the sampling rate is 1%, the proposed method improves peak signal-to-noise ratio (PSNR） by 0.61 dB compared with diffusion message passing deep unfolding networks （DMP-DUN） which also uses diffusion model, and by 1.31 dB compared with optimization-inspired cross-attention transformer unfolding framework(OTCUF）; when the sampling rate is 10%, PSNR improves by 1.22 and 1.77 dB compared with OTCUF and unrolling fixed-point continuous network(UFC-Net） respectively, indicating that the method has significant advantages at low sampling rates.
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Liang Dongxin, Huang Zhiyong, Shi Xiaotao, Jiang Wei, Zhu Jiazhi
2026,49(15):229-237, DOI:
Abstract:
To address the challenges of low detection accuracy and weak small-target recognition capability for Chinese sturgeon in underwater sonar images caused by noise interference, this study proposes an improved YOLOv8n-based sonar detection model. The methodology integrates three key innovations: a hierarchical attention fusion block module is designed to achieve adaptive fusion of local and global features through a hierarchical attention mechanism; a pinwheel-shaped convolution module is introduced, employing a multi-branch convolution fusion strategy with a Gaussian-distributed receptive field to enhance differential feature extraction for small targets; a lightweight shared convolutional separated BN (batch normalization) detection head is developed to balance detection accuracy and inference efficiency. Experiments are conducted on a self-constructed Chinese sturgeon sonar dataset, partitioned into training, validation, and test sets at an 8∶1∶1 ratio. Experimental results demonstrate that compared to the baseline YOLOv8n model, the proposed approach improves mAP@0.5 by 1.0%, precision by 2.6%, and recall by 0.3%, while reducing parameters by 0.58×106 and floating pointoperations by 0.7 GFLOPs. Ablation studies confirm that the synergistic integration of the three modules significantly enhances model performance. Comparative experiments show that the proposed model outperforms mainstream object detection models in detection accuracy under stable parameter complexity. The improved model achieves notable enhancements in both detection precision and efficiency for Chinese sturgeon in complex underwater noise environments, providing an effective technical solution for underwater sonar behavior monitoring. This work holds significant application value for Chinese sturgeon conservation, wild training in artificial breeding, and population restoration efforts.
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Ma Yuanjie, Liu Qiang, Gao Mingwang, Cao Wanli, Xie Yukun
2026,49(15):238-250, DOI:
Abstract:
To address the problem that coal and gangue have similar appearances under complex working conditions, and that illumination fluctuation, motion blur, and occlusion easily lead to low detection accuracy as well as high false detection and missed detection rates, a coal gangue object detection method based on ECA-DEIM is proposed. In the backbone network, a lightweight multi-scale attention module is introduced to enhance the capture capability for multi-scale targets. A coal-gangue spatial refinement and selective correction module is constructed to improve the spatial representation capability of similar targets. An adaptive dynamic normalization module is designed to enhance the generalization ability of the model. Experimental results show that, on a self-built dataset, the mAP@［0.5:0.95］ and mAP@0.5 reach 52.3% and 82.5%, respectively, which are 2.2% and 2.4% higher than those of DEIM, while the number of parameters is reduced by 19.9%. The missed detection rate is reduced to 3.95%, the false detection rate is decreased by 4.3%, and the sorting accuracy reaches 93.1%. The results indicate that the proposed method has good detection accuracy, robustness, and engineering applicability under complex working conditions.
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Wang Zheng, Liu Sunjun, Zhang Xiao, Liu Qu, Tao Yiting
2026,49(15):251-264, DOI:
Abstract:
To address the challenges of limited feature extraction capability, dense distribution of small objects, and drastic target scale variations that lead to low detection accuracy in unmanned aerial vehicle (UAV) aerial imagery, this paper proposes an improved UAV object detection algorithm based on real-time detection transformer (RT-DETR). Firstly, in the backbone network, a C2f-EncoderBlock feature enhancement module is designed to restructure the network architecture. By introducing a frequency-spatial domain collaborative perception mechanism and utilizing fast Fourier transform （FFT） to capture global illumination and texture information in the frequency domain, this module effectively resolves the difficulty of feature extraction in low-resolution and blurred images while significantly reducing the number of model parameters. In the detection head, a multi-scale small object enhancement pyramid structure is designed to effectively address the issues of missed detections for small objects and coarse feature fusion in the original model through lossless downsampling and multi-dimensional feature interaction. Finally, a focaler minimum point distance intersection over union (Focaler-MPDIoU) loss function incorporating a hard-sample focusing mechanism is designed to replace the original generalized intersection over union (GIoU). This function not only improves the geometric localization accuracy of dense targets by minimizing corner distances but also enhances the model′s ability to mine hard samples, such as tiny or occluded objects, through a dynamic re-weighting strategy, thereby further improving detection accuracy. Experimental results on the VisDrone2019 dataset demonstrate that the improved algorithm achieves a 3.9% increase in mAP50 compared to the baseline model, while reducing the parameter count by 24.75%, effectively enhancing UAV object detection performance.
Volume 49, 2026 Issue 15
Application of Artificial Intelligence in Electronic Measurement
Sensor and Non-electricity Measurement
Research&Design
Theory and Algorithms
Information Technology & Image Processing
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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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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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Xue Xianbin, Tan Beihai, Yu Rong, Zhong Wuchang
2024,47(6):1-7, DOI:
Abstract:
Urban intersections are accident-prone sections. For intelligent networked vehicles, it is very important to carry out risk detection and collision warning during driving to ensure the safety of driving. This paper proposes a traffic risk field model considering traffic signal constraints for urban intersections with traffic lights, and designs a three-level collision warning method based on this model. Firstly, a functional scenario is constructed according to the potential conflict risk points of urban intersections, and the vehicle risk field model is carried out considering the constraint effect of traffic signal. In order to solve the problem of collision warning, a three-level conflict area is proposed to be divided by the index, and the collision risk of the main vehicle is measured according to the position of the potential energy field around the main vehicle by calculating the corresponding field strength around the main vehicle. The experimental results show that the designed model can accurately warn the interfering vehicles entering the potential energy field of the main vehicle, the warning success rate can reach 100%, and the false alarm rate is only 3.4%, which proves the reliability and effectiveness of the proposed method.
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Wei Jinwen, Tan Longming, Guo Zhijun, Tan Jingyuan, Hou Yanchen
2024,47(6):8-13, DOI:
Abstract:
To address the issue of low accuracy in indoor static target positioning with existing single-antenna ultra-high frequency RFID technology, this paper proposes a new RFID localization method based on an antenna boresight signal propagation model. The method first determines the height position of the target through vertical antenna scanning; secondly, it adjusts the antenna height to match that of the target and then performs stepwise rotational scanning to identify the target′s azimuth angle; furthermore, it utilizes a Sparrow Search Algorithm optimized back propagation neural network to establish a path loss model for ranging purposes; finally, it integrates the height, azimuth angle, and distance data to complete the target positioning. Experimental results show that in indoor environment testing, the proposed method has an average positioning error of 7.2 cm, which meets the positioning requirements for items in general indoor scenarios.
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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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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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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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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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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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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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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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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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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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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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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.
