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Xie Tianyu, He Yunze, Deng Haiping, Deng Baoyuan, Song Dianyi, Fang Xuelin
2026,40(6):1-11,
Abstract:
Cardiopulmonary resuscitation (CPR) is critical for improving the survival rate of patients with cardiac arrest. Traditional CPR training and assessment mainly rely on manual observation, which suffers from strong subjectivity and a lack of quantitative metrics. Moreover, existing methods show insufficient accuracy in handling the severe limb occlusions and fine-grained motion capture that are characteristic of CPR. To address these challenges, this paper proposes a CPR pose estimation algorithm based on an improved YOLOv13n-Pose model. The proposed method is validated on a self-constructed CPR pose estimation dataset covering multiple subjects and scenarios.First, to tackle the severe self-occlusion formed by the rescuer’s arms and torso, a Bi-Level routing attention (BRA) mechanism is introduced, which enhances the network’s global feature extraction capability in complex scenes through dynamic sparse perception. Second, a hybrid enhanced upsample (HEUpsample) module is designed to replace the neck upsampling structure. By incorporating spatial offset and channel shuffle strategies, the module reduces detail loss during feature map upsampling and improves keypoint localization accuracy. Experimental results demonstrate that the improved model achieves mAP scores of 95.8% and 88.5% for CPR action recognition and pose estimation on the CPR dataset, representing improvements of 2.3% and 3.6% over the baseline model, respectively. Compared with mainstream methods, the proposed approach maintains a high inference speed while reducing computational cost, and it also demonstrates strong generalization ability on the public OCHuman dataset.
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Liu Le, Gu Linglong, Li Ziyang, Fang Yiming
2026,40(6):12-23,
Abstract:
Aiming at the problems of high network complexity, large parameter size, and difficulty in adapting to the complex environment and stringent real-time requirements of continuous casting production lines in current general detection algorithms, this paper proposes a lightweight continuous casting slab detection algorithm named SGI-YOLOv7 (YOLOv7 with StarNet, GSConv-based Slim-neck, and Inner-SIoU loss function). Firstly, a dedicated dataset for slab detection is constructed based on images collected from the continuous casting production line. Secondly, StarNet is adopted to replace the backbone network of YOLOv7 (you only look once version 7), utilizing star operations to achieve efficient feature mapping, which effectively reduces the model’s parameter scale and computational complexity. Thirdly, a lightweight Slim-neck structure based on group shuffle convolution (GSConv) is introduced to achieve efficient fusion and compression of cross-level features. Simultaneously, an inner scale-aware intersection over union (Inner-SIoU) loss function is designed based on auxiliary bounding boxes, which enhances the model’s adaptability and convergence speed in scenarios with significant aspect ratio variations through direction-aware gradient optimization and a multi-scale regression mechanism. Finally, comparative experimental results on the self-built slab dataset show that the proposed SGI-YOLOv7 algorithm maintains high detection accuracy (mAP@0.5 is 96.0%) while reducing parameters by 35.6% and computational cost by 65.6% compared to the baseline YOLOv7 model, with an inference speed reaching 82 frames per second. This significantly alleviates the hardware deployment burden and achieves efficient, lightweight, real-time detection of continuous casting slabs in industrial scenarios.
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Han Hong, Wang Jinming, Zhang Fei, Li Zhonghu
2026,40(6):24-34,
Abstract:
To address the large parameter count, high computational overhead, and poor minor-defect detection in existing wind turbine blade surface anomaly detection models, this paper designs a lightweight detection model named CDFNet. The model takes YOLOv10n as the baseline and introduces a feature enhancement downsampling (FED) module into the backbone to enhance fine-grained feature extraction for complex anomaly regions. A cross-dimensional collaborative attention (CCA) mechanism integrates spatial location with channel semantic information to improve minor-defect localization and recognition. The method also replaces the original neck with a fast bidirectional feature pyramid network (FBiFPN) to boost multi-scale feature fusion efficiency and reduce model complexity. Experiments on a self-built dataset of wind turbine blade surface anomalies show that CDFNet achieves a mAP@0.5 of 83.2%, which is 6.4 percentage points higher than the baseline. CDFNet contains 2.02×106 parameters, a 25.5% reduction compared to the baseline. For minor defects, CDFNet improves detection accuracy over the baseline by 21.3 percentage points for cracks, 11.6 for missing diversion strips, and 11.1 for surface damages. These results demonstrate that CDFNet strikes a favorable balance between detection accuracy and model lightweightness, making it suitable for wind turbine blade surface anomaly detection.
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Guo Jiacheng, Luo Xiaozhong, Zhong Han
2026,40(6):35-48,
Abstract:
Dense and non-uniform haze in real-world scenes exhibits complex spatial variations, which significantly degrades the generalization ability of existing deep image dehazing models, leading to unstable detail recovery and high deployment costs. To address these challenges, this paper proposes a dual-path structurally re-parameterized dehazing network (re-parameterized large kernel dehaze, RepLKDehaze). The proposed framework consists of a large-kernel structural re-parameterization subnet (large-kernel rep subnet, RepLK) and a full-resolution attention subnet (full-resolution attention subnet, FRA). The former is built upon a trimmed UniRepLKNet-s encoder-decoder backbone, where multi-branch representations are introduced via dilated re-parameterization during training and equivalently merged into a single large-kernel convolution at inference, enabling efficient modeling of global haze distribution. The latter operates at full spatial resolution and incorporates residual channel attention to compensate for local texture and edge information loss caused by downsampling. By fusing features from both paths, RepLKDehaze achieves a balanced reconstruction between global consistency and fine-grained detail preservation. Experimental results demonstrate that although RepLKDehaze does not achieve the best performance across all metrics on synthetic datasets such as RESIDE-SOTS, it consistently outperforms state-of-the-art methods on real-world datasets that better reflect realistic imaging degradation. Specifically, it achieves 17.66 dB/0.613/0.494 (PSNR/SSIM/LPIPS) on Dense-Haze, 21.66 dB/0.716/0.254 on NH-Haze 2020, and 22.74 dB/0.857/0.169 on NH-Haze 2021. The proposed method shows clear advantages in color fidelity, structural preservation, and residual haze suppression. These results indicate that the proposed dual-path structurally re-parameterized dehazing network effectively enhances robustness in real dense non-uniform haze scenarios while maintaining deployment efficiency, providing a practical solution for real-world hazy-scene visual perception tasks.
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Zhang Licheng, Wang Lei, Song Yifei, Xie Yaohua, Zhou Zhuping, Chen Chuncheng
2026,40(6):49-63,
Abstract:
Pedestrian detection in complex environments presents a critical technical challenge for autonomous driving systems. This paper proposes a pedestrian target detection method based on multimodal feature fusion of infrared and visible light images. First, to address the challenge of cross-modal feature fusion, we propose an infrared-visible image fusion algorithm (DIVIF) based on a multi-source diffusion model. The algorithm incorporates a reversible feature decoupling module (IGCM) to capture target information distributions across modalities and extract large-scale structural features, along with a lightweight local capture module (LLCM) to enhance detail fidelity through attention-driven dynamic interaction. A global-local iterative optimization mechanism is introduced to achieve effective feature transfer and deep fusion. Second, for low-illumination scenarios, we propose an improved YOLOv8n detection network with lightweight architecture and attention mechanisms. The model integrates MobileNetV4 and GSConv into the backbone and neck networks for efficiency, while incorporating EMA attention to strengthen key feature extraction and MPDIoU loss to optimize convergence. Furthermore, a complete pedestrian detection system implementation is designed for practical applications. Experimental results demonstrate that the proposed method effectively fuses multimodal features, achieving precise detection in low-light and occluded scenarios. Compared to mainstream detection networks, our algorithm outperforms in all evaluation metrics.
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2026,40(6):64-77,
Abstract:
Currently, the two-dimensional principal component analysis (2DPCA) method has been widely applied in facial recognition due to its inherent low-covariance matrix characteristic. However, in the real world, captured facial images are often subject to noise interference, leading to issues such as unstable feature extraction and the inability to effectively perceive key discriminative information between categories in many existing 2DPCA-based methods. To overcome these limitations, a novel robust discriminative 2DPCA (RD-2DPCA) method is proposed and applied to facial recognition. First, this method introduces a prototype-driven momentum contrastive learning mechanism. Through momentum updates and online clustering, it constructs semantically consistent positive-negative sample pairs to reduce intra-class divergence, thereby forming a more separable discriminative subspace projection. Second, an adaptive sine angle constraint is embedded in the projection. This constraint ensures a strong association between the solution and the weighted covariance matrix that preserves the global data structure, while further mitigating the influence of outliers on the final projection direction. Finally, to address the optimization problem, a combined strategy of Riemannian gradient updates and a custom non-greedy iterative algorithm ensures stable convergence to a robust optimal solution guided by discriminative principles. To validate the feasibility of this method, experiments were conducted on three publicly available facial datasets, achieving optimal recognition accuracies of 73.90%, 61.31%, and 87.74% respectively. Comprehensive experimental results demonstrate that RD-2DPCA exhibits superior performance compared to single-stage or pure 2DPCA variants in terms of discriminative capability, robustness, and convergence stability. This fully validates the method’s interference resistance and practical application value in complex observation environments.
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Huang Yong, Gan Shiming, Xing Manxi, Wang Rui, Sun Zhenbang
2026,40(6):78-88,
Abstract:
The morphological characteristics of the weld joint are a core indicator for assessing the quality of aluminum alloy welds. To address issues in the morphological detection of aluminum alloy weld joints, specifically specular reflection and the uneven brightness of light stripes caused by surface contamination after welding, which lead to distortions in extracting the centerline, an anti-interference detection method using binocular vision combined with line-structured light and feature-assisted stickers is proposed. This approach integrates a U-Net neural network for welding zone localization with an improved skeletonization-gray-gravity method for sub-pixel extraction of the light stripe centerline, enabling high-quality stripe image acquisition under complex interference. The results demonstrate that the proposed method effectively suppresses interference from plate reflection and contamination, with a consistent positioning accuracy exceeding 99% for the welding zone. This approach achieves high precision 3D reconstruction of the joint morphology, with relative errors in the detection of weld reinforcement and width not exceeding 6.0% and 4.0%, respectively, significantly enhancing the analytical accuracy and robustness of the stripe information. To investigate the relationship between process parameters and joint morphology quality in laser-metal inert gas (MIG) hybrid welding of aluminum alloy, experiments were conducted on 6-mm thick 6061 aluminum alloy plates using eight sets of parameters with different energy ratios. The joint quality was evaluated according to the ISO 10042:2018 international standard. The results indicate that as the energy ratio increases, the weld reinforcement and width gradually decrease and stabilize. When the energy ratio between the laser and the MIG arc ranges from 1.01 to 1.36, the joint quality meets the ISO 10042:2018 Level B requirements, indicating excellent joint quality. This confirms the reliability of the proposed method for detecting joint morphological features in complex environments, providing technical support for the automated assessment of aluminum alloy joint quality.
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Wang Yu, Wei Lisheng, Zhang Pinggai, Hu Baoling
2026,40(6):89-100,
Abstract:
Aiming at the problem that the defect detection model of printed circuit boards in the environment of limited computing resources of industrial edge devices is difficult to balance lightweight and detection accuracy, a lightweight PCB defect detection algorithm based on YOLOv12n is proposed. Firstly, the design of the fusion token mixer adopts the C3K2-CF module of depthwise separable convolution, which not only reduces the number of parameters but also enhances the perception ability for small targets. Secondly, a new feature fusion enhancement network FFEN is proposed to ensure the integrity of the target information in cross-scale feature transfer, thereby strengthening the model’s ability to pay attention to the target. Secondly, a feature-focused fusion module FFF is designed to fully integrate multi-scale context information and enhance the model’s ability to extract complex features. Finally, to address the issue of missed detection of small targets in PCB defect detection, the task-aligned dynamic detection head TADDH is introduced. This enabled the information exchange channel between the localization and classification dual detection heads to be established, thereby improving the detection accuracy of the model for small-sized defects. The experimental results show that, compared with the original YOLOv12n model, the improved algorithm achieves an mAP@0.5 of 98.6% on the PKU-Market-PCB dataset with data augmentation. The stricter mAP@0.5:0.95 metric has been improved by 7.2%. The parameter count and computational cost have been reduced by 47.2% and 16.9%, respectively. On the Deep PCB public dataset, the improved model achieved a detection accuracy of 97.5% mAP@0.5, representing a 2.5% improvement over mAP@0.5:0.95. It can be seen from this that the improved algorithm effectively achieves high-precision PCB defect detection and meets the detection requirements of generalized small targets in industrial scenarios.
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Guo Wenhao, Hao Bin, Zhang Fei
2026,40(6):101-115,
Abstract:
Underwater biological target detection technology is of great significance to the development of numerous fields, such as marine ecological protection and fishery resource exploitation. To improve the accuracy and speed of underwater biological detection and address the high parameter count in existing models, a lightweight algorithm, YOLOv11-PiA, based on improved YOLOv11, is proposed. Firstly, the C3k2 module is improved by introducing partial convolution (PConv), resulting in the C3k2_PConv module, which effectively reduces the model’s parameter count and computational complexity while enhancing detection accuracy and speed. Secondly, the C2PSA_iRMB module, which combines the C2PSA module with the inverted residual block attention mechanism (iRMB), is proposed. This module strengthens feature information at multiple depths to enhance feature representation, thereby improving detection accuracy. Finally, the traditional convolutional downsampling module in the baseline model is replaced with the lightweight downsampling module (ADown), further reducing the model’s parameter count and improving detection efficiency. Experimental results on the RUOD and DUO underwater biological datasets show that the proposed YOLOv11-PiA model achieves mAP@0.5 values of 85.6% and 82.6%, with frames per second (FPS) reaching 97 and 116 fps, respectively. Compared with the original YOLOv11n model, YOLOv11-PiA increases mAP@0.5 by 2.8% and 2.3%, and improves FPS by 9 and 13 fps, respectively. Meanwhile, the parameter count, floating-point operations per second (FLOPs), and model size are reduced by 29.8%, 22.2%, and 29.1%, respectively. The comprehensive performance of YOLOv11-PiA outperforms mainstream target detection models such as RTDETR and YOLOv12, enabling fast and accurate detection of underwater organisms.
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Jia Qi, Wang Guolei, Lu Dunmin
2026,40(6):116-125,
Abstract:
Automatic detection of tiny defects on coating surfaces holds significant importance in manufacturing quality control. Addressing the critical need for quality inspection of coating surfaces on large-scale aircraft panels, this paper proposes a machine vision-based multi-scale defect collaborative detection method, focusing on solving the challenge of synchronously identifying cross-scale defects including millimeter-scale pits, fisheyes, and bubbles as well as centimeter-scale scratches and sags. This paper first systematically analyzed the shortcomings of existing detection methods in terms of synchronous recognition of multi-scale defects and balancing real-time performance with high precision, and conducted research accordingly. The core contributions completed include: constructed a multi-light source collaborative image acquisition platform and established a coating image dataset covering five typical defect types; designed a composite image preprocessing method that integrates nonlinear illumination correction and multi-scale enhancement, effectively addressing brightness gradient anomalies caused by local reflections and sudden brightness changes; and proposed an improved GCSS-YOLOv8 deep learning model to enhance detection performance for defects of different scales. Experimental results show that the proposed method achieves a mean Average Precision (mAP@0.5) of 91.4% on the self-built dataset, which is 1.9% higher than the original YOLOv8 model, verifying its effectiveness and practical value.
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2026,40(6):126-137,
Abstract:
Addressing the challenges of large parameter sizes, slow inference speeds, and the difficulty in balancing model lightweighting with detection performance in existing steel surface defect detection models, this paper proposes a UAL-YOLO model based on YOLOv11n. Firstly, a parameter-free unified 3D attention module (UAM) is integrated into the backbone. By deriving 3D weights from second-order statistics, it identifies outlier defect features via linear separability, significantly enhancing the signal-to-noise ratio of semantic information. Secondly, a novel attention-guided fusion (AGF) neck network is designed, which utilizes deep features to generate guiding weights, intelligently and selectively aggregating multi-scale features to improve the efficiency and accuracy of feature fusion. Finally, a lightweight efficient offset-based up sampler (LEO) is developed using content-aware offsets to recover fine textures of slender cracks and minute pits. This approach improves the extraction of low-contrast edges and effectively minimizes the missed detection rate of tiny defects in industrial scenarios. Experimental results demonstrate that compared to the baseline YOLOv11 model, the UAL-YOLO model achieves a 2.1% improvement in mAP@0.5 and a 3.7% increase in recall rate on the NEU-DET dataset, while reducing the number of parameters, computational load, and storage capacity by 26.36%, 15.87%, and 23.64%, respectively. The UAL-YOLO model significantly enhances precision while achieving notable lightweighting, providing a new solution for the deployment of high-precision, low-cost steel defect detection technologies in industrial scenarios.
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Liu Liansheng, Dong Chenglong, Zhang Haijun, Ma Xin, Sun Hao, Peng Yu
2026,40(6):138-149,
Abstract:
Electronic measurement instruments have a critical demand for real-time protocol analysis of high-speed interconnect buses. They require high-speed processing capabilities for gigahertz-level signal acquisition and full protocol visibility from the physical layer to the transaction layer in protocol analysis to support high-precision diagnostics with nanosecond-level jitter. However, traditional CPU-based software solutions are constrained by operating system scheduling latency and cache jitter, making low-latency processing challenging. Commercial protocol analyzers are costly and lack embedded flexibility. To address these issues, this article leverages the hardware parallelism and real-time advantages of FPGAs, considering the importance of the PCIe 2.0 protocol in measurement systems, and proposes a full-stack real-time analysis architecture. This architecture aims to break the throughput bottleneck of traditional software solutions while avoiding high costs. In the descrambling part, an advanced prediction pipeline design is proposed to achieve efficient descrambling. The system achieves full protocol coverage from the physical layer to the transaction layer through data preprocessing, parallel decoding, advanced prediction pipeline descrambling, and state machine parsing. Specifically, 128-bit parallel preprocessing accomplishes symbol alignment and data down-sampling, 80-bit processing completes 8b/10b parallel decoding and advanced prediction pipeline descrambling, and three types of dedicated state machine parsers are responsible for protocol field extraction and CRC verification. Experimental results on the Kintex UltraScale series FPGA platform show that the system operates stably at 312.5 MHz, supporting real-time analysis of 5 GT/s line-rate data. The processing latency from input data to detection results is 22 clock cycles (approximately 70 ns). The CRC verification achieves a 100% match rate, with resource utilization of only 1.39% LUTs and 0.24% FFs. The results demonstrate that the proposed architecture meets practical application requirements in terms of latency, resource overhead, and functional completeness.
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Tang Haodong, Pan Shuguo, Tao Xianlu, Gao Wang, Zhang Chi
2026,40(6):150-158,
Abstract:
Accurately obtaining the motion state of a vehicle is of great significance for the estimation of vehicle trajectory and position, especially in environments with severe satellite signal occlusion (such as urban canyons, wooded areas, tunnels, etc.). However, existing vehicle motion estimation methods driven by dynamic models rely too much on modeling accuracy and are difficult to achieve accurate estimation under some extreme working conditions. Therefore, in order to accurately obtain vehicle motion state information without relying on the accuracy of dynamic models, this paper proposes a hierarchical vehicle state estimation method based on the crested porcupine optimization long short-term memory network. Firstly, by analyzing the basic physical characteristics of vehicle dynamics, a hierarchical estimation strategy for longitudinal velocity and lateral velocity is designed. Then, a dataset composed of various driving conditions is constructed for training, and during the training process, the crested porcupine optimization algorithm is used to optimize parameters such as learning rate and number of iterations. Finally, virtual simulation tests are conducted to verify the estimation method under multiple working conditions and multiple road surface conditions. The results show that this method effectively improves the estimation accuracy. Compared with model-driven methods, the estimation accuracy of lateral velocity, longitudinal velocity, and yaw rate is improved by 92%, 73%, and 52% respectively, and the overall performance is better than the traditional Kalman filtering algorithm based on dynamic models.
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Liu Chan, Chen Yangyi, Wu Jianbo, Huang Siyang, Wu Peng, Ba Junhao, Zhao Yongjie, Huang Honghe
2026,40(6):159-168,
Abstract:
To address passive reconnaissance requirements in distributed detection scenarios such as drone swarms, traditional time difference of arrival (TDOA) localization faces two fundamental challenges. Firstly, high bandwidth consumption from cross-node raw signal transmission for correlation calculations. Secondly, clock synchronization constraints that inherently limit positioning accuracy. The TDOA measurements include clock synchronization errors among different receiving nodes, and improving synchronization precision would significantly increase costs. To address these issues, a differential time difference of arrival (DTDOA) localization algorithm is proposed for time-division ad hoc networks. The method first calculates the differential time of arrival from different radiation sources to the local node within each node. Then, observation equations are established across nodes based on the differential time difference of arrival. Finally, the target position is solved using a damped Gauss-Newton iteration method. Experiments demonstrate that the proposed algorithm eliminates clock synchronization errors through a differential observation structure, and its Cram-r-Rao lower bound depends solely on correlation calculation errors. Furthermore, it avoids the process of transmitting raw signals between nodes, significantly reducing network bandwidth overhead.
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2026,40(6):169-178,
Abstract:
In order to obtain high-precision 3D mapping for domestic cleaning robots and address the limitations of traditional planar calibration methods—such as overfitting and low calibration accuracy, an improved extrinsic calibration method based on three-cylinder geometric constraints is proposed. Side-scan data from three fixed congruent cylinders using the two different sensors are obtained at different positions by varying the robot’s poses. For the three elliptical contours generated by the intersection of the LDS sensor, the RANSAC algorithm is employed to extract the center points of these contours and two randomly selected points on each contour. For the 3D point cloud of the cylinder surfaces captured via the ToF depth sensor, the central axes of the three cylinders are fitted using the RANSAC algorithm. Spatial geometric constraints are established using the three center points from the elliptical contours and the distances from the two randomly selected points on each contour to their corresponding cylinder axes, forming a system of nonlinear optimization equations for extrinsic parameter estimation. To enhance solving stability, an improved optimization strategy integrating the Powell algorithm and the quasi-Newton BFGS algorithm is proposed, effectively addressing the dependency on initial parameter values. Simulation experiments are conducted to analyze the impact of initial values and Gaussian noise. The proposed method achieves an average rotation error of 0.37° and an average translation error of 3.2 mm, demonstrating rapid convergence even with significant initial offsets. Finally, real experiments and comparative 3D mapping tests are performed to verify the effectiveness of the algorithm. The results show that the proposed method is insensitive to initial parameter values, offers operational simplicity and high calibration accuracy, and possesses significant value for practical engineering applications.
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Huang Junfen, Wang Guilong, Xue Long, Cao Yingyu, Liu Xuecheng
2026,40(6):179-188,
Abstract:
In order to enhance the stair-climbing robot’s capability for rapid detection and precise localization of stairs in indoor environments, a stair detection and localization method based on a rotating 2D LiDAR was proposed. A hardware platform consisting of a servo motor, a 2D LiDAR, a Raspberry Pi upper computer, and a DJI Type A developer board was constructed. On this basis, for various relative positional relationships between the robot and the stairs, a stair detection algorithm based on point cloud region-growing clustering and principal component analysis (PCA) was proposed to extract multi-level step facade features and compute the ascent direction angle of the stairs. This enabled the determination of the angle between the stair ascent direction and the robot’s X-axis in the X-Y plane across multiple scenarios. Using the center point of the first-step facade as a key localization parameter, a PCA-based plane fitting algorithm was employed to obtain critical parameters such as step height (H), length (L), and the coordinates of the first-step facade center point, thereby achieving precise stair localization. A total of 240 experiments were conducted across four types of stair scenarios with varying robot-stair relative positions. The results showed that the absolute deviation in stair ascent direction detection ranged from 0.03° to 2.35°, with an average deviation within 1.5°. Localization experiments in different stair scenarios revealed that the maximum positioning deviation of the first-step facade center point in the robot coordinate system was 0.024 m in the X-direction and 0.062 m in the Y-direction. The experimental results demonstrate that the proposed method achieves high detection accuracy and positioning precision across various stair scenarios, providing reliable perception and localization support for autonomous cross-floor navigation of stair-climbing robots in complex indoor environments.
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Zeng Chaoyang, Liu Ping, Luo Haoxuan, Liu Mingjie, Piao Changhao
2026,40(6):189-200,
Abstract:
Traditional risk assessment methods mostly rely on deterministic indicators, making it hard to dynamically quantify risks from the uncertainty of surrounding vehicle behaviors. To address this limitation, this study proposes a risk assessment approach integrating information entropy-based uncertainty quantification and driving style. Specifically, information entropy quantifies the uncertainty of vehicle motion states, while different driving styles are converted into quantifiable risk values for driving risk evaluation. Firstly, an information entropy-based method assesses the uncertainty of vehicle motion states for real-time risk perception. Secondly, K-means clustering is applied to vehicle speed, jerk, and time headway to identify diverse driving styles. A weighted mechanism is then established to fuse driving style and information entropy for the proposed risk assessment model. Finally, experiments are conducted under congested and highway scenarios. The correlation between evaluated risks and actual vehicle energy consumption is analyzed, and comparisons are made with the artificial potential field method, deterministic risk assessment method, and learning-based risk assessment method. Results show that the proposed method reliably detects risks in various events. In terms of response time, it is reduced by an average of 50.55%, 86.35%, and 81.85% compared with the other three methods, respectively. Meanwhile, its computation time is significantly lower than that of the artificial potential field method and the learning-based risk method, demonstrating the effectiveness and application value of the proposed method in driving risk assessment.
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Yu Ming, Chen Junjie, Lu Haotian
2026,40(6):201-210,
Abstract:
In order to solve the problem that existing degradation models for nonlinear electromechanical systems under intermittent faults fail to simultaneously consider the effects of imperfect maintenance and degradation coupling, which leads to a decline in prediction accuracy, a remaining useful life prediction method based on intermittently composite degradation model is proposed for nonlinear electromechanical system with intermittent faults. Firstly, the bond graph model of the nonlinear electromechanical system is established for fault detection and isolation, and an augmented water flow optimizer algorithm is developed for intermittent fault estimation. Secondly, a hybrid intermittent fault feature is extracted based on the tumbling window to comprehensively describe the intermittent fault trend. The intermittently composite degradation model is proposed to predict the remaining useful life of faulty component in the situation where the imperfect maintenance and degradation coupling effect exist simultaneously. The model divides the degradation process of the faulty components into internal and external degradation, and describes the effects of degradation coupling and imperfect maintenance through coupling factors and a geometric reduction of intensity model. Experimental results show that the proposed method achieves cumulative relative accuracy of 97.53% and 96.80% in predicting the remaining useful life of multiple intermittent faults, which is superior to the particle swarm optimization algorithm and the water flow optimization algorithm. Additionally, in comparison with degradation models considering only degradation coupling effects and only imperfect maintenance effects, the proposed method demonstrates significant improvement in prediction accuracy.
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Lu Yuankai, Li Shuting, Chen Zhiqiang, He Zuen, Li Yangsen, Wu Haibin
2026,40(6):211-221,
Abstract:
Large-scale components in aerospace and shipbuilding industries often feature extensive V-groove seams requiring filling processes such as welding or glue dispensing. To achieve precise identification and tracking of groove seam features on large-scale components, a multi-station segmented automatic tracking method based on a mobile manipulator is proposed. This approach involves sequentially capturing multi-frame point cloud images of the V-groove seam along its trajectory. A local 3D point cloud model of the component is then generated by stitching the point cloud data using a registration algorithm combining key feature points (ISS) and the point-to-plane iterative closest point (ICP) algorithm. Subsequently, a novel surface variation feature descriptor (SVFD) is proposed to characterize surface concavity and convexity, enabling the identification of the V-groove point cloud set. Groove seam feature points are then accurately acquired based on symmetry constraints and distance constraints. A uniform and discrete seam path is generated by fitting these feature points with NURBS curves and performing equal-arc-length interpolation. To meet process requirements, the tool end-effector orientation (e.g., glue gun pose) at each path point is further planned. Finally, considering both the manipulator’s manipulability and the mobile platform’s pose constraints, the desired path for the mobile platform is planned using an equidistant offset strategy. A hardware experimental platform was established, and glue dispensing experiments were conducted on four distinct types of V-groove seam features. Experimental results demonstrate that the average groove seam trajectory extraction error does not exceed 0.857 mm, and the average mobile platform path tracking error is within 8.577 mm, confirming the method’s capability for precise extraction and tracking of large component seams. This technique is applicable not only to welding and glue dispensing but also to tracking analogous structural features on other large-scale components.
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2026,40(6):222-231,
Abstract:
Since its invention, the scanning tunneling microscope (STM) has played a pivotal role in surface science and nanotechnology due to its exceptional atomic-resolution imaging capability. In recent years, although high-end STMs have enabled breakthroughs under extreme conditions, their high cost and operational complexity hinder widespread adoption in education and routine inspection. This paper presents a compact, cost-effective, and high-performance portable STM system developed entirely in-house. The system comprises a piezoelectric scanner, coarse approach stepper motor, low-noise signal detection circuit, STM32H7-based control unit, and a Qt-based user interface, all built from the ground up. A modular microscope body integrates a passive vibration isolation system, and the mechanical decoupling of the scanner and motor enhances imaging stability. Experimental results demonstrate high signal precision, with controller voltage resolution better than 0.6 mV. The stepper motor operates reliably, with a no-load starting voltage of 17 V (down) to 24 V (up) and step sizes ranging from 0.33 μm (17 V) to 2.01 μm (80 V). The custom-developed scanner initiates movement at 20 V (down) to 29 V (up), with step sizes from 0.21 μm (17 V) to 1.26 μm (80 V). The scanner structure is compact and rigid, with measured thermal drift rates of 31.5 pm/min (X-Y) and 42.3 pm/min (Z) on a ground-floor lab, and 44.8 pm/min (X-Y) and 56.2 pm/min (Z) on a third-floor desktop, reflecting excellent structural and electronic stability. Ultimately, the system achieves atomic-resolution imaging under ambient conditions, with high imaging consistency and control precision, making it suitable for scientific research, teaching, and industrial surface characterization.
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Wang Hongjun, Ge Henglin, Pang Jianjun, Yue Yubin, Xie Long
2026,40(6):232-244,
Abstract:
Aiming at the problem of insufficient recognition accuracy of traditional tool wear monitoring methods under multi-physics coupling conditions and complex working conditions, this paper proposes a cross-working-condition tool wear identification method based on Mel-frequency cepstral coefficients (MFCC) and joint distribution alignment (JDA), designed to address the challenge of weak tool wear monitoring capability across varying operational conditions. First, spindle vibration signals are collected, and MFCC is employed to extract features from these vibration signals, compressing high-frequency information while preserving critical mid-to-low frequency characteristics. Second, a transfer learning model based on joint distribution alignment is constructed, which integrates source domain labeled data with target domain unlabeled data for joint training, thereby achieving knowledge transfer of tool wear patterns across different working conditions. This model aligns output distributions through cross-entropy loss, aligns feature distributions using maximum mean discrepancy (MMD) loss, and incorporates a self-training pseudo-label generation mechanism to optimize the utilization efficiency of target domain data. On the NASA milling dataset, the proposed method achieves a recognition accuracy of 93.33% in cross-working-condition scenarios involving variations in feed rate, cutting depth, and spindle speed, significantly outperforming traditional methods based on Fourier transform and wavelet transform. When applied to the CTB milling test, the model achieved a recognition accuracy of 93.25%. The proposed method effectively resolves the challenge of tool wear state recognition under cross-working-condition environments, providing reliable technical support for tool condition monitoring in industrial applications, and possesses significant engineering application value.
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Yang Zhen, Feng Feng, Li Xin, Zhang Junbo, Zhang Qiaochu, Liu Haoyuan
2026,40(6):245-261,
Abstract:
The occurrence of coal-rock dynamic disasters is closely related to the damage state of coal. To achieve real-time monitoring of the crack development trend within coal underload, a localization model for internal fractures based on an equivalent electromagnetic radiation source is constructed. This study explores a monitoring method for fracture development in loaded coal and its corresponding solution algorithm. Additionally, a uniaxial loading experiment is conducted to analyze the effects of coal sample origin and loading rate on the proposed monitoring method, thereby verifying its general applicability. The results indicate that the electromagnetic radiation (EMR) generated by loaded coal originates from both electric-type and magnetic-type sources. The combined effects of these sources contribute to EMR emission during the expansion and closure of internal cracks in the coal. The constructed equivalent EMR source model for charged fractures can be utilized for monitoring coal damage. By employing a triaxial fluxgate sensor, an EMR monitoring array for loaded coal samples can be established. The non-coplanar arrangement of the sensors proves more effective than the coplanar configuration in mitigating error amplification effects. The BFGS-integrated pathfinding strategy (BIPDS) enhances the accuracy of solving typical multimodal functions by more than tenfold compared to traditional algorithms, while reducing the standard deviation by approximately 99%, thereby providing algorithmic support for the proposed radiation source localization model based on vertical distance error. The fracture monitoring model determines a through-crack inclination angle of 78°, with its spatial position closely matching the actual crack distribution. Under different experimental conditions, the variation trends of the synthesized EMR vector intensity remain consistent, indicating that coal sample origin and loading rate do not affect the monitoring performance. The method demonstrates reliable monitoring effectiveness and general applicability. These findings contribute to understanding the damage evolution mechanism of loaded coal and provide technical support for advancing research on the prevention and control of coal-rock dynamic disasters.
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Li Wenhai, Guo Kai, Sun Weichao, Tang Zhenhao
2026,40(6):262-270,
Abstract:
In field in-situ testing of airborne electronic countermeasure equipment, the positioning process of test points suffers from low efficiency and high time consumption. This paper proposes an automatic test point positioning method based on airframe point cloud features. Using the aircraft landing gear as a key reference, this method requires only a one-time acquisition of the test point’s position in the airframe coordinate system, which can then be combined with LiDAR point cloud data to achieve automatic test point positioning at any location. This process involves key steps such as point cloud mapping and cropping, coordinate transformation between the airframe and the LiDAR, and transformation matrix solving. The paper conducts measurement experiments on actual aircraft equipment in an outdoor environment. The results show that the absolute error between the measured positions and the actual values is less than 10 cm, with a relative error not exceeding 1%, demonstrating high reliability and feasibility. This advancement can promote the development of equipment testing technology toward unmanned and intelligent operations.
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Liu Chunxi, Xu Chen, Li Lei, Chang Honghe
2026,40(6):271-282,
Abstract:
To address the challenges of current stress optimization and efficiency improvement in diode-clamped hybrid three-level dual active bridge converters, this study proposes a dual-phase-shifting control scheme based on minimum current stress optimization. The improved snake swarm optimization algorithm is employed to search for optimal phase-shifting combinations that minimize current stress. First, to overcome the snake swarm algorithm’s susceptibility to local optima and high parameter sensitivity, the Northern Condor Algorithm’s predation mechanism is introduced to enhance global search capability and reduce parameter sensitivity, thereby improving convergence stability and search accuracy for optimal phase-shifting combinations. Additionally, constraint conditions based on current stress, power, and voltage deviations are incorporated into the penalty function to further narrow the search range. Experimental validation through simulation platforms demonstrates that the proposed control method reduces current stress by 24% at low power levels and 13% at high power levels compared to the Lagrange multiplier method. These results confirm the effectiveness of the improved strategy in minimizing current stress, validating the feasibility of the proposed control approach, and enhancing energy transfer characteristics.
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Guo Suna, Wang Qianhui, Han Jiawen, Fang Lide
2026,40(6):283-289,
Abstract:
To the problems of low computational efficiency, strong grid dependence, and insufficient real-time performance in the traditional computational fluid dynamics (CFD) method for flowmeter performance analysis, deep learning technology was introduced to construct a flowmeter performance prediction method based on a spatiotemporal sequence model PredRNN_V2. In the task of predicting the internal flow field of a unilateral Venturi flowmeter, the iterative flow field evolution during the steady-state CFD solution process was modeled as a spatiotemporal sequence prediction problem, and a complete process from data acquisition, preprocessing to model training and evaluation was constructed. ConvLSTM, PredRNN, and PredRNN_V2 were selected for comparison, and mean squared error (MSE), structural similarity index (SSIM), learned perceptual image patch similarity (LPIPS), and peak signal-to-noise ratio (PSNR) were used as evaluation indicators to quantitatively assess the prediction performance. The results showed that the MSE output by the PredRNN_V2 model increased the most slowly, with an average PSNR of 36.8, significantly higher than PredRNN and ConvLSTM. The average SSIM of PredRNN_V2 was 0.92, and the LPIPS was 0.069, indicating that its prediction results had lower noise and clearer cloud boundary, and its prediction accuracy was significantly superior to the other two models while maintaining a high computational efficiency. The relative error between the prediction results of the PredRNN_V2 model and the simulation results was within ±5%, indicating the reliability and accuracy of the model in predicting the flow field.
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Wei Jichen, Wan Fayu, Xia Minfeng, Shi Liyuan, Chen Jiawen, Chen Xiaohe
2026,40(6):290-298,
Abstract:
A binocular vision based probe package pad rapid alignment technique is proposed to address the challenge of rapid positioning between probes and chip packaging pad in electrostatic discharge (ESD) testing of charged device models (CDM). Firstly, a set of image datasets for chip packaging is constructed, and a lightweight L-YOLOv8n algorithm is used to train the model as the front-end recognition of the chip under test. Then, the sub-pixel segmentation and positioning of the pad are completed through the image processing module, and the three-dimensional coordinate information of the pad is accurately obtained based on visual difference and stereo matching. Finally, the information is sent to the main control board of the CDM ESD testing system to control the stepper motor and achieve fast and accurate alignment between the probe and pad. The experimental results show that the system has an mean average precision (mAP) of over 99% for various packaged chips such as BGA and SOP under small sample training conditions, and achieves complete recognition of pad and accurate extraction of three-dimensional coordinates. The positioning accuracy in the horizontal two-dimensional direction reaches 0.01 mm, and the depth direction accuracy is 0.1 mm. At the same time, the system has significant advantages in low cost and strong environmental adaptability, without the need for manual alignment of probes with pad for testing. The rapid and accurate alignment of probe pad can be completed with only camera equipment and motion control modules, which is easy to deploy and suitable for CDM testing in multiple scenarios and various chip packaging types.
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Wang Chao, Yang Huiyue, Xiao Wei, He Zhijie, Wang Fandong
2026,40(6):299-307,
Abstract:
Frequency estimation is a key technology in the field of measurement instrumentation. Traditional periodogram and multiple signal classification (MUSIC) algorithms suffer from severe performance degradation under low signal-to-noise ratio environments, failing to meet the precision requirements of instrumentation measurements. This paper proposes a frequency estimation method based on multi-scale residual networks (MultiFR). First, parallel 3×1 and 5×1 convolutional kernels extract features at different scales, enhancing the ability to capture both local details and global patterns. Then, residual connections mechanisms are introduced to overcome the gradient vanishing problem in deep networks, ensuring effective feature propagation. The L2 norm squared loss function is adopted to achieve precise measurement of the deviation between the network output spectrum and the ideal spectrum. Finally, a dynamic SNR training strategy is utilized to improve the model’s adaptability under different noise levels, and a high-resolution frequency representation output module is implemented to achieve accurate frequency peak localization. Experimental results demonstrate that the proposed method achieves improved frequency resolution capability. Under extremely low SNR conditions of 0, MultiFR achieves a miss detection rate of 22.2% and a Chamfer distance of 0.145, representing relative improvements of 4.1% and 5.8% respectively compared to the DeepFreq method, and outperforming mainstream methods such as PSnet, MUSIC, and periodogram. Validation with Coriolis flowmeter signals confirms the feasibility, effectiveness, and robustness of the proposed method under low SNR conditions.
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Hui Dengfeng, Shamala K. Subramaniam, Lili Nurliyana binti Abdullah, Abdullah bin Muhammed, Liu Hongyan, Wang Miao, Wang Zijun, Hui Yuchun, Zhu Junyu
2026,40(6):308-322,
Abstract:
In standardized examination-rooms, candidates are prohibited from using personal timekeeping devices, while wall-mounted BPC time-code radio-controlled clocks (RCCs) in complex buildings often suffer from reception blind spots, unstable synchronization, and frequent battery replacement. To address this, a unified time-keeping scheme combining a “life-time powered mechanical RCC” with a “local BPC miniature transmitter” is proposed. On the terminal side, the WBC-E16A exam clock retains its original appearance, but its two AA cells are upgraded to a 2S4P low-self-discharge AA pack, and an mWh-level energy model is derived from constant-resistance discharge tests and in-situ power measurements. On the infrastructure side, a≤1.5 W USB 5 V transmitter is developed that acquires UTC from multiple NTP servers over campus WiFi and rebroadcasts the standard BPC code inside classrooms. Tests with ten clocks in a fully enclosed underground space show that, in BPC reception blind spots, all terminals automatically complete their first synchronization within 2~10 minutes. In a four-week field trial with thirty clocks in a teaching building, the automatic synchronization success rate increases from about 70% to 100% after enabling the local transmitter. Discharge and consumption measurements indicate that Nanfu IoT AA cells provide about 34.9 Wh of usable energy above 2.2 V; combined with a daily consumption of 14.8~16.5 mWh and a 1%~2% self-discharge rate, a single 2S4P pack can support at least five years of operation without battery replacement. The scheme has been deployed in about 7 848 terminals across 22 provincial-level regions, and the proposed Life-Time RCC plus local BPC blind-spot compensation enables long-term, second-level synchronization of mechanical wall clocks while greatly reducing maintenance and inspection workload and significantly enhancing the robustness of examination-room timekeeping.
Volume 40,2026 Issue 6
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2017,31(1):45-50, DOI: 10.13382/j.jemi.2017.01.007
Abstract:
The concentration of nitrogen oxides (NO2, NO, N2O, etc.) in power plant is an important index of environmental protection. Aiming at the problem that the detection accuracy of nitrogen oxides concentration based on spectral analysis could be interfered by all kinds of factors, such as temperature, moisture content, tar, naphthalene, noise of electric devices, optical lens aging, interference at spectral absorption characteristics of polluting gases etc, it is difficult to improve in a single way. At first, the hardware modification is favorable for gas purification and filter. And then, the self learning and self training ability of RBF neural network can save the traditional model for the study of interference factors, and make the data processing more efficient. On the basis of a large thermal power plant’s real data in 2015, the computer simulation and analysis show that this method can improve the accuracy effectively. The overall average deviation is 0.841%.
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Wang Wen, Zhang Min, Zhu Yewen, Tang Chaofeng
2017,31(1):1-8, DOI: 10.13382/j.jemi.2017.01.001
Abstract:
Spherical joint is a commonly multi degree of freedom mechanical hinge which has many advantages such as compact structure, good flexibility, and high carrying capacity. Realization of its multi dimensional angular displacement measurement is of great significance in the prediction, feedback, and control of the system motion error. Firstly, the application of spherical joint and its structural characteristics were presented in the paper. Then, the motion description of the spherical joint and needed angles for measurement were analyzed. A review of multi dimensional angular displacement measurement method, including structural decoupling detection method, optical based detection method and magnetic field based detection method, at home and abroad was provided, Finally, the development of research on multi dimensional angular displacement measurement method for spherical joint was summarized. The focus and the difficulty of the research were pointed out, and the challenges and the breakthroughs in the key technologies were also stated.
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Liu Kun, Zhao Shuaishuai, Qu Erqing, Zhou Ying
2017,31(1):9-14, DOI: 10.13382/j.jemi.2017.01.002
Abstract:
The complex and various defects of the steel surface bring great difficulty to the feature extraction and selection. Therefore, this paper proposes a new R AdaBoost future selection method with a fusion of feature selection and sample weights updated. The proposed algorithm selects features and reduces the dimension of features via Relief feature selection according to updated samples in each cyle of AdaBoost algorithm, and uses reduced features to remove noise samples by intra class difference among samples, and then update sample library according to dynamic weight of AdaBoost. The weak classifiers are trained by the resulting optimal features, and combined to generate the final AdaBoost strong classifier, and detect and locate strip surface defects by AdaBoost two classifiers. Aiming at a variety of defects such as scratch, wrinkle, mountain, stain, etc. in the actual strip production line, the experimental results show that the proposed R AdaBoost algorithm can effectively extract features with high distinction and independence and reduce the feature dimension, and simultaneously improve the accuracy of defect detection.
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Luo Ting, Wang Xiaodong, Ma Jun, Yang Chuangyan
2021,35(12):116-125, DOI:
Abstract:
In view of the nonlinear dynamic characteristics of rolling bearing vibration signal and the low accuracy of reliability evaluation, a rolling bearing health condition assessment method based on improved cross fuzzy entropy (ICFE) and Weibull proportional hazards model (WPHM) was proposed. Firstly, the original vibration signal is decomposed by improved DLMD (Crt- DLMD), and the effective component with the most fault information is selected for reconstruction. Then, the ICFE of the reconstructed signal is calculated by using the sliding mean instead of the original coarse-grained process. Finally, the ICFE is used as the covariate of WPHM for health status assessment. The life cycle data and experiments of rolling bearing from national aeronautics and space administration (NASA) and Xi′an Jiaotong University Changxing Shengyang technology (XJTU-SY) show that the proposed method can accurately and effectively evaluate the health status of rolling bearings.
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Sun Wei, Wen Jian, Zhang Yuan, Geng Shihan
2017,31(1):15-20, DOI: 10.13382/j.jemi.2017.01.003
Abstract:
Aiming at the random error of MEMS gyroscope is the main factor that restricts its precision and application range, the Kalman filter estimation method based on regression moving average (ARMA) model is proposed in this paper. Firstly, based on the results of Allan variance analysis, the quantization noise, angle random walk and zero bias instability are the main parts of the MEMS gyroscope random noise. Then, the stability of MEMS gyroscope random noise is tested by using time series analysis. Finally, based on the random drift of the auto regressive moving average (ARMA) model, a discrete Kalman filter equation is built to actualize its error estimation and compensation. The results of static vehicle and dynamic environment of digital noise reduction and Kalman filtering compensation experiments show that the Kalman filter estimation method based on the ARMA model has more obvious advantages in MEMS Gyroscope random error compensation.
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He Lifang, Cao Li, Zhang Tianqi
2017,31(1):21-28, DOI: 10.13382/j.jemi.2017.01.004
Abstract:
Empirical mode decomposition（EMD）method attenuates the signals’ energy and generates false signals in decomposing signal noise, which leads to incorrect detection results. In order to solve this problem, a stochastic resonance method under Levy noise after denoised by EMD decomposition is presented in this paper. After decomposed by EMD, the noisy signals are handled by overlaying, averaging and resampling to meet the condition of stochastic resonance. An adaptive algorithm is used to optimize system parameters, and then the processed signal can generate stochastic resonance in bistable system to achieve precise detection. The theoretical analysis and experimental results prove that the method can detect single frequency signal and multi frequency signal under the same characteristic exponent with the Levy noise. The experimental results demonstrate that the SNR of single frequency signal can increase 14 dB in the case of SNR of -28 dB. The spectral amplitude of the 5 Hz spectrum is increased from 311.8 to 724 and 10 Hz spectrum amplitude is increased from 138.9 to 143.2. This method that reduces the residual noise energy and false signal can improve the signal energy in a complex noisy condition. Compared to EMD decomposition which cannot determine the signal components, this method can achieve the detection effect better.
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Yan Fan, Zhang Ying, Gao Ying, Tu Yongtao, Zhang Dongbo
2017,31(1):36-44, DOI: 10.13382/j.jemi.2017.01.006
Abstract:
To solve the time consuming problem of image stitching algorithm based on KAZE, a simple and effective image stitching algorithm based on AKAZE is proposed. Firstly, AKAZE feature points are extracted. Secondly, feature vectors are constructed using the M LDB descriptor and matched by computing the Hamming distance. Thirdly, wrong matches are eliminated by RANSAC and the global homography transform, and then a local projection transform is estimated using moving direct linear transformation in the overlapping regions. The image registration is achieved by combining the two transforms. Finally, the weighted fusion method fuses the images. A performance comparison test can be conducted aiming at KAZE, SIFT, SURF, ORB, BRISK. The experimental results show that the proposed algorithm has better robustness for the various transform, and the processing time is greatly reduced.
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Cao Xinrong, Xue Lanyan, Lin Jiawen, Yu Lun
2017,31(1):51-57, DOI: 10.13382/j.jemi.2017.01.008
Abstract:
A simple, rapid and efficient retinal vessels segmentation method is proposed. After a general analysis on gray value distribution and contrast changes of fundus images, the standardizing fundus images are obtained by using the matched filtering technique to overcome the interference of background and noise. Then, a threshold can be automatically selected to achieve the effective segmentation of blood vessels in the fundus images by estimating the proportion of the background pixels. A lot of tests show that the good performance is achieved in the public fundus images database. The experiment shows that the proposed method based on matched filtering and automatic threshold has strong practicability and high accuracy. It is useful for computer aided diagnosis of ocular diseases．
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Yin Min, Shen Ye, Jiang Lei, Feng Jing
2017,31(1):76-82, DOI: 10.13382/j.jemi.2017.01.011
Abstract:
In disaster rescue and emergency situations, node energy in sensor network is especially limited. In order to reduce unnecessary forwarding consumption, this paper presents a MANET multicast routing tree algorithm with least forwarding nodes, which is based on shortest routing tree and sub tree deletion. The algorithm is proved and analyzed in detail. Its practical distributed version is also presented. The simulation comparison shows that this distributed algorithm reduces the forwarding transmission in improved ODMRP, especially there are much more receivers in MANET. Minimum forwarding routing tree has the minimum network overhead. It is an effective way to extend the network lifetime.
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Chen Shuo, Luo Tengbin, Liu Feng, Tang Xusheng
2017,31(1):144-149, DOI: 10.13382/j.jemi.2017.01.021
Abstract:
In order to solve the low efficiency and the influence of manual factors and many other problems existed in current water meter verification, the water meter verification system using machine vision technology is proposed. And the research keynote is how to realize the template matching algorithm for rapid location of plum blossom needle and the image morphological algorithm for eliminating the bubble of wet water meter dial. Harris algorithm is used to extract the corner points of the plum blossom needle template beforehand, and the corner points of the on site image are extracted in real time. Then, the fast localization of the plum blossom needle is realized by the partial Hausdorff distance method. Finally, the effect of bubbles is eliminated by using the image morphological algorithm, and the count value of the rotating teeth of the plum blossom needle is completed. The experimental results show that the proposed system can shorten the verification time and improve the verification efficiency while ensuring the verification accuracy. The system solves the adverse effect of the bubble on the dial of the wet water meter, and it’s suitable for the verification of various types of water meters.
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Zhang Gang, Bi Lujie, Jiang Zhongjun
2023,37(1):177-190, DOI: 10.13382/j.issn.1000-7105.2023.01.020
Abstract:
For the difficulties of classical bi-stable stochastic resonance (CBSR) system in amplification and detection of weak signals, an underdamped exponential tri-stable stochastic resonance (UETSR) system in a Levy noise background is proposed. The UETSR system is constructed by combining the bi-stable potential and exponential potential function, and using the property that non-Gaussian noise can effectively improve the signal-to-noise ratio. Firstly, the steady-state probability density function of the system is derived. The mean signal-to-noise ratio improvement (MSNRI) is adopted as an index to measure the stochastic resonance performance. The quantum particle swarm algorithm is used on parameters optimization. The effect of each parameter of the system on the output variation pattern of the UETSR system with different parameters α and β of Levy noise is investigated. Finally, the UETSR, CBSR and classical tri-stable stochastic resonance system (CTSR) are applied to the bearing fault diagnosis, and the amplitudes at the inner and outer ring fault frequencies after the system output increased by 197. 58, 1. 153, 18. 81 and 238. 87, 26. 63, 39. 72, respectively, compared to the input signal. The spectral level ratios of the highest peak to the second highest peak were 5. 44, 4. 03, 3. 85 and 5. 10, 3. 79, 5. 05. The experimental results show that SR phenomena can be induced by different system parameters, and the UETSR system outperformed the CBSR system and the CTSR system. The above conclusions prove that the system has excellent performance and strong practical significance
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Pan Yuehao, Song Zhihuan, Du Wangze, Wu Legang
2017,31(1):29-35, DOI: 10.13382/j.jemi.2017.01.005
Abstract:
To help nursing staff in senile apartment find the elderly fall and other actions timely, an action recognition method based on video surveillance is proposed. Firstly, the foreground images are extracted by the GMM background modeling method in HS color space. Feature extraction is performed by combining the motion features and morphological features. And action recognition can be achieved by HMM with Gaussian output. The method proposed in this paper can adapt to the changes of illumination. The method also has good robustness to the change of motion direction and motion range, and the recognition accuracy rate reaches 90%. The result shows that the method can meet the basic requirements of action recognition and the method has certain practical value.
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2017,31(1):83-91, DOI: 10.13382/j.jemi.2017.01.012
Abstract:
A fuzzy perception model is proposed to the directional sensor nodes based on the sensing characteristics of the nodes, and also the fuzzy data fusion rule is built to reduce the network uncertain region. Aiming at the problem of directional sensor network strong barrier coverage, a directional sensor network strong barrier coverage enhancement algorithm based on particle swarm optimization is proposed. The convergence rate of the algorithm is improved through the n dimensional problem be transformed into one dimensional problem. The simulation results show that, under random deployment, the perception direction of sensor nodes can be adjusted continuously. Compared with the existing algorithms, the proposed algorithm can effectively form strong barrier coverage to the target area, has a faster convergence rate, and prolongs the network lifetime.
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Wan Yong, Zhang Xiaobin, Ni Weining, Zhang Wei, Sun Weifeng, Dai Yongshou
2017,31(1):99-105, DOI: DOI: 10.13382/j.jemi.2017.01.014
Abstract:
The key point of azimuthal propagation resistivity logging while drilling focuses on the structural design of the coil system. And the detection performance of azimuthal propagation resistivity LWD is mainly affected by the transmission frequency of electromagnetic wave signal, the transmitter receiver spacing, the receiver interval, the coil’s angle and the formation resistivity. The testing method of measurements is determined with different inspection requirements of azimuthal propagation resistivity LWD. According to the various constraints of the coil system under the condition of different testing method, the structure of the coil system for azimuthal propagation resistivity LWD is designed by experimental simulation method. The results provide reference for the structural design of the coil system for azimuthal propagation resistivity LWD.
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Sun Li, Zhang Xiaofeng, Zhang Lifeng, Zhou Wenju
2017,31(1):106-111, DOI: 10.13382/j.jemi.2017.01.015
Abstract:
Velocity smoothing is one problem which is proposed in high speed machining and coal mine safety production, the aim of which is to improve machining accuracy and equipment life. Aiming at this problem, this paper proposes a stage wise model and deduces the closed form expression solution for each stage based on the relationship of acceleration and velocity, and then deduces the general solutions of cubic equation in detail for the model. Finally, the solutions are applied to the velocity smoothing. The proposed schema shows the advantages of easy to program and smoothing in transition curve when being applied for velocity smoothing in coalmine. The result demonstrates that the proposed method adapts the high speed scenarios well and has used in other several projects.
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Zhou Na, Lu Changhua, Xu Tingjia, Jiang Weiwei, Du Yun
2017,31(1):139-143, DOI: 10.13382/j.jemi.2017.01.020
Abstract:
In order to improve the multi target tracking robustness and enhance the difference between the targets, this paper uses an energy minimization method for multi target tracking. Different to the existing algorithm, the algorithm focuses on the representation of the complex problem in multi target tracking as energy function model, which includes a better target segmentation strategy (similarity model). By assigns every possible solutions a cost (the “energy”), the algorithm transforms the multiple target tracking problem into an energy minimization problem. In the energy minimization optimization method, the algorithm uses the conjugate gradient algorithm and a series of jump moves to find the minimum energy value. The experimental results of open data demonstrate the effectiveness. And the quantitative analysis results show that this algorithm can improve the difference between targets or between target and background so as to obtain better robust performance compared with other algorithms.
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Chen Zhenhai, Yu Zongguang, Wei Jinghe, Su Xiaobo, Wan Shuqin
2017,31(1):132-138, DOI: 10.13382/j.jemi.2017.01.019
Abstract:
A low power, small die size 14 bit 125 MSPS pipelined ADC is presented. Switched capacitor pipelined ADC architecture is chosen for the 14 bit ADC. In order to achieve low power and compact die size, the sample and hold amplifier is removed, the 4.5 bit sub stage circuit is used in the first pipelined stage. The capacitor down scaling technique is introduced, and the current mode serial transmitter is used. A modified miller compensation technique is used in the operation amplifiers in the pipelined sub stage circuits, which offers a large bandwidth without additional current consumption. A 1.75 Gbps transmitter is introduced to drive the digital output code, which only needs 2 output pins. The ADC is fabricated in 0.18 μm 1.8 V 1P5M CMOS technology. The test results show that the 14 bit 125 MSPS ADC achieves the SNR of 72.5 dBFS and SFDR of 83.1 dB, with 10.1 MHz input at full sampling speed, while consumes the power consumption of 241 mW and occupies an area of 1.3 mm×4 mm.
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Xia Fei, Luo Zhijiang, Zhang Hao, Peng Daogang, Zhang Qian, Tang Yiwen
2017,31(1):118-124, DOI: 10.13382/j.jemi.2017.01.017
Abstract:
Aiming at the shortcoming of the low accuracy of transformer fault diagnosis, the PSO SOM LVQ（particle swarm optimization,self organizing maps,learning vector quantization） mixed neural network algorithm is presented in this paper. Firstly, the weight of SOM neural network is optimized by the method of PSO algorithm to obtain the more effective topology. Based on that, LVQ neural network is combined to cover the shortage of unsupervised learning SOM neural network. The mixed neural network algorithm combined with PSO, SOM and LVQ can improve the accuracy and reduce the error of transformer fault diagnosis. Through simulation, the three algorithms of SOM, PSO SOM and PSO SOM LVQ are compared. The comparison result show that the PSO SOM LVQ mixed neural network algorithm has the highest accuracy, and the fault diagnosis accuracy rate is 100%. Thus it can be seen, the PSO SOM LVQ mixed neural network algorithm can enhance the performance of transformer fault diagnosis effectively.
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Cao Shasha, Wu Yongzhong, Cheng Wenjuan
2017,31(1):125-131, DOI: 10.13382/j.jemi.2017.01.018
Abstract:
Musical simulation based on spectrum model is the use of acoustic theory that can achieve musical instrument’s sounds by sum of products of a series of basic functions and time varying amplitude. A new digital piano sound simulation technique is proposed by analyzing piano string vibration and damping characteristics and investigating the resonance effect of resonance box. The simulation model consists of two parts: the excitation system and the resonance system. Based on the vibration equation of the strings, the envelope modification of time domain is carried out to simulate the natural attenuation of the strings, which can make music harmonious between the notes. Then, the filter group is modeled by spectrum envelope in frequency domain to achieve the simulation of resonance system. This new method can more effectively carving voice, has better performance timbre at the same time, therefore, it makes the sound more harmonious.
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Identification method of Dongba pictograph based on topological characteristic and projection method
Xu Xiaoli, Jiang Zhanglei, Wu Guoxin, Wang Hongjun, Wang Ning
2017,31(1):150-154, DOI: 10.13382/j.jemi.2017.01.022
Abstract:
Dongba pictograph has been known as "the only living pictograph in the world".In the aspects of image recognition, content interpretation,the current English and Chinese character recognition system often can not be applied to Dongba pictograph.Concerning the difficulties in the identification of Dongba pictograph, a new character recognition is proposed. Topological features processing and projection methodcompose thefeature extraction method,then, the character recognition method based on template matching is adopted.It is showed that the feature extraction method based on the intrinsic characteristic of the pictograph,and the Dongba character recognition method based on template matching,has high accuracy through the experiment.
