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Li Bo, Cui Zhanbo, Wang Jin, Wang Jiawei, Tang Hongxia, Li Yuxiao
2026,40(3):1-13,
Abstract:
The advanced flight control system realizes the full time and full authority of the aircraft′s flying attitude control, which directly determines the flight quality and makes it the primary object of aircraft fault prediction and health management. Due to the high degree of functional coupling, complex structural cross-link relationship, and diversified degradation failure modes of flight control system, it is difficult to extract fault characteristics, poor real-time life prediction, and difficult to verify health assessment model. The extraction of fault characteristics and relationships in flight control systems is a key technology in the current PHM field of flight control systems. By screening out key information that accurately represents faults from massive flight data, the accuracy and efficiency of fault identification can be improved. Remaining life prediction is another key technology of the PHM system. It predicts the later performance trend through equipment operation and maintenance data to achieve the purpose of early fault warning. In the engineering application of PHM technology in the flight control system, the establishment of the model is the key to whether the system can be realized. The real-time performance and visualization of the system also constantly put forward new requirements for the update and upgrade of the model. Therefore, this paper conducts a review from three aspects: multiple extraction of fault information of the flight control system, the research status of RUL prediction, and the research status of PHM engineering of the flight control system.
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Zeng Xianyang, Liang Yuansheng, Yu Hao, Yang Hongli
2026,40(3):14-26,
Abstract:
In grid map environments, path planning algorithms based on Voronoi diagrams offer good globality and completeness. However, the resulting paths often suffer from excessive turning points, significant redundant paths, poor followability, and low planning efficiency in dynamic obstacle environments. To address these shortcomings, this paper proposes a path planning algorithm (BV-R-GDWA) that integrates an improved Voronoi skeleton diagram with the dynamic windowing approach (DWA). This algorithm first utilizes key point extraction and topology reconstruction techniques from the Voronoi skeleton diagram, combined with an obstacle inflation model and inflection point screening mechanism, to replan the initial path, resulting in a shorter and smoother globally optimized path within safety distance constraints. During the local planning phase, this paper innovatively designs a dynamic weighted global path guidance function, enabling the robot to adaptively adjust its tracking strategy based on the deviation between its current position and the global path. Experimental results show that in simple environments, compared with the Voronoi skeleton graph method, the proposed global path algorithm reduces planning time, path length, and the number of turning points by 26.3%, 12.9%, and 27.3%, respectively. In complex dynamic environments, the BV-R-GDWA algorithm can still maintain high planning efficiency and path quality, showing good robustness and adaptability. The main innovation of this paper is the proposed key point extraction and dynamic weight guidance mechanism, which achieves an effective balance between global path safety and local obstacle avoidance real-time performance. It has important theoretical significance and engineering application value for improving the navigation performance of mobile robots in complex scenarios.
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Tian Hongkun, Jiang Nan, Li Suyuan, Ren Tao
2026,40(3):27-35,
Abstract:
Aiming at the challenges of complex background interference, random distribution and large differences in transmission line scales in UAV transmission line detection tasks, this paper proposes an algorithm framework based on multi-scale feature enhancement. The framework is based on multi-modal feature fusion input and integrates three core modules: the multi-scale feature enhancement module (multi-scale feature enhance, MSFE) is used to capture transmission line features of different scales; the dual-path interactive attention module (dual-path interactive attention, DPIA) focuses on key transmission line regions through a dual-path mechanism of channel and space; the adaptive feature fusion module (adaptive feature fusion, AFF) dynamically balances the semantic information of the encoder and the edge information of the decoder, aiming to improve the detection robustness in complex scenarios. Experimental results show that the proposed method performs excellently in various complex scenarios such as foggy days, snowy days, and low light. Compared with existing methods, all indicators have achieved significant improvements. Ablation experiments fully verify the effectiveness of each module: on the basis of the baseline model, IoU, Robj, Recall, Precision, and F1-Score are increased by 12.56%, 5.53%, 4.54%, 3.03%, and 3.78% respectively, indicating that they are crucial for enhancing the detection capability of the model. Qualitative analysis results further confirm that the algorithm can accurately locate the slender structure of transmission lines in complex scenarios and effectively suppress background noise. Therefore, the method in this paper has practical application value for UAV transmission line detection tasks.
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Zhong Yi, Feng Hao, Yin Chenbo, Wang Jun, Yan Shikuan, Sun Lin
2026,40(3):36-45,
Abstract:
To address the issues of path redundancy and susceptibility to local optima in traditional path planning methods, this paper proposes an improved dung beetle optimization (IDBO) algorithm for efficient flight path planning of unmanned aerial vehicle (UAV) inspection of construction hoisting machinery. The core improvements include three mechanisms: A population initialization strategy based on a good point set to enhance spatial uniformity and coverage of initial solutions; an exponential decay formula to dynamically adjust the perturbation factor for adaptive balance between global exploration and local exploitation; and a hybrid Cauchy-Gaussian mutation mechanism to mutate stagnant populations, thereby inhibiting premature convergence and enhancing global search performance. Experimental results on benchmark test sets demonstrate that the proposed IDBO algorithm outperforms comparable algorithms in both convergence speed and solution accuracy, securing the top comprehensive ranking. For the UAV inspection application, a comprehensive evaluation model was formulated, integrating critical factors including path length, energy consumption, and threat cost. Simulations conducted within a realistic 3D construction site model populated with multiple hoisting machinery confirm that the paths planned by IDBO not only effectively avoid obstacles but also yield significant improvements in the objective function value. Specifically, in three scenarios of varying complexity, the performance improved by 11.47%, 7.23%, and 9.17%, respectively, when compared to the baseline method. Consequently, the proposed IDBO algorithm provides an effective and robust solution for autonomous UAV path planning in complex construction environments characterized by multiple obstacles, numerous inspection targets, and multi-dimensional costs, demonstrating considerable application potential.
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Chen Ruilin, Cao Hui, Zheng Xiaodong, Yan Dapeng, Xue Shuangsi, Qu Kai, Ji Shengchang
2026,40(3):46-57,
Abstract:
The safe and stable operation of substation equipment is paramount for power system reliability. In recent years, UAV inspection has emerged as a crucial maintenance tool in substations due to its efficiency and enhanced safety. However, inherent UAV noise, coupled with ambient environmental sounds, often mixes significantly with the vital acoustic signatures of operational equipment. This severe interference substantially hinders acoustic-based equipment status detection and early fault prognosis. To address this challenge and efficiently isolate substation equipment sounds from such complex mixtures, a multi-scale gated source separation network (GSN) model is proposed. The GSN model adopts an encoder-separator-decoder architecture: its encoder incorporates parallel multi-scale 1D depthwise separable convolutions to capture rich features across various temporal scales; the separator constructs a dual-path structure, comprising a local temporal modeling and a global contextual modeling, integrating their outputs via a gated fusion mechanism; the decoder employs layer-by-layer 1D transposed convolutions with skip connections to reconstruct the time domain signal. Experimental validation was conducted on a tripartite mixed dataset comprising substation equipment sounds, UAV noise, and environmental background noise. Results indicate that GSN has superior performance compared to mainstream models. GSN achieved improvements in SI-SDR by 0.8~7.1 dB, SIR by 1.3~9.7 dB, and PCC by 0.032~0.297. Furthermore, GSN demonstrated notable advantages in training convergence speed and stability. The GSN model effectively suppresses complex interference and faithfully reconstructs target equipment sound sources, thereby providing high-quality signals for acoustic inspection of substation equipment.
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Zhang Kun, Bai Junzhe, Ouyang Peng, Xu Junzhe, Zhang Peijian, Fei Minrui, Hua Liang
2026,40(3):58-70,
Abstract:
With the rapid development of drone aerial photography technology, the demand for precise recognition of targets such as infrastructure in low-altitude scenarios has been increasingly growing. However, traditional object detection and semantic segmentation methods still have shortcomings in boundary delineation and distinguishing between similar instances. To address these issues, this paper proposes an improved multimodal feature fusion instance segmentation network, MFFISNet, to enhance the fineness and robustness of target segmentation in drone remote sensing images. The method in this paper includes three main innovations: a dual-path input structure is constructed, utilizing both RGB images and DSM information to enrich multimodal feature representation; for the DSM branch, HWF-LM and DMSCA are introduced, significantly enhancing the model’s ability to represent elevation and structural information; FGCA mechanism is proposed to achieve efficient fusion of cross-modal features, thereby improving instance segmentation accuracy in complex scenarios. On the Drone-OrthoSeg dataset, MFFISNet achieved a bounding box mAP of 42.63% and mask mAP of 42.69%; on the NWPU VHR-10 dataset, the results were 77.86% and 72.59%, respectively; and on the foggy maritime scenarios of the FoggyShipInsseg dataset, it also achieved good performance of 63.86% and 59.69%. Experimental results indicate that the proposed method outperforms existing advanced methods in both accuracy and robustness, providing efficient and reliable technical support for automated detection and measurement of infrastructure in low-altitude scenarios.
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Chen Jing, Lin Tongyu, Yin Cunyi, Jiang Hao, Zheng Shaocong
2026,40(3):71-80,
Abstract:
To address the issues of insufficient positioning accuracy, high cost, and the requirement for dedicated onboard positioning modules in existing indoor UAV positioning schemes, this paper proposes a high-precision three-dimensional (3D) UAV positioning system based on WiFi channel state information (WiFi-CSI) sniffing. Specifically, the system deploys a number of low-cost ESP32-S3 sensors to construct a UAV sensing network, which directly sniffs the WiFi signals transmitted by the UAV and parses the CSI signals from them. Combined with the altitude information acquired by time of flight (ToF) sensors, this system achieves a lightweight and high-precision positioning solution that eliminates the need for dedicated positioning receiving modules on the UAV. Considering CSI characteristics at different altitude layers, we innovatively design a hierarchical training strategy and a sparse neural regression network (SIRD). Fusing the SE attention mechanism, Inception multi-scale convolution, and residual connections, the network establishes the mapping between CSI and 2D coordinates. 3D positioning is finally realized by fusing predicted 2D coordinates with ToF altitude. Experimental results verify the effectiveness of the proposed system are the mean localization error (MLE) is 0.107 9 m, the localization mean squared error (LMSE) is 0.100 3 m, and the coefficient of determination R2 is 0.956 2. Compared with existing indoor UAV positioning methods, the proposed method achieves a significant improvement in positioning accuracy, providing a low-cost and high-precision solution for indoor UAV applications.
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Zhang Dandan, Song Rui, Ren Chao, Liu Gaohua, Li Shuai, Li Wei
2026,40(3):81-92,
Abstract:
Three-dimensional segmentation decomposes complex power environments into semantic and instance-level regions, enabling precise spatial awareness for power robots in automated and safer live-line operations. However, existing methods face high 3D data acquisition costs and limited real-time processing, hindering practical deployment. To address these challenges, we introduce the first low-cost, rapid 3D segmentation framework using only multi-view monocular images and text prompts, without requiring 3D sensors. Specifically, we leverage Grounded-SAM2’s robust generalization and a custom color-annotation scheme to produce RGB segmentation masks for each view. These masks are then fed into the Spann3r reconstruction model, fusing geometry and color to reconstruct a dense scene point cloud. Candidate point sets corresponding to power equipment are extracted via color filtering, and a density-constrained DBSCAN clustering step separates individual instances while suppressing noise. Experimental validation in real-world power scenarios shows that, using only monocular RGB imagery, our method achieves over 92.6% mean intersection over union (mIoU) and over 93.4% mean accuracy (mAcc) for 3D segmentation. It offers the low-cost, and fast solution for the intelligent operation and maintenance of power robots without 3D sensor support.
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Wang Rui, Chen Tingmu, Zeng Huixiong, Wu Yuxing, Gao Yin
2026,40(3):93-105,
Abstract:
A system of collaborative control for uncalibrated robotic arms, grounded in 3D vision guidance, is put forward herein. This addresses three central issues in the control system of industrial robotic arms: inadequacy in human-machine collaboration precision, latency in dynamic response, and deficiency of safety constraints. Lightweight deep-learning perception and real-time inverse kinematic control are innovatively integrated. The perception layer employs the MediaPipe Lite convolutional network to detect 33 key human-body points at 30 fps.It also concurrently fuses the spatial coordinates from the depth camera, generating accurate 3D joint data. The mapping layer initiates a dynamic shoulder-reference calibration mechanism. This overcomes the traditional reliance on calibration and combines dual constraints of the working space, ensuring motion safety. The control layer features a geometric closed inverse-solution model (real-time solution in 0.8 ms). Through a two-threaded asynchronous architecture, it separates attitude detection from joint control, thoroughly resolving the bottleneck of response delay. Experimental validation indicates that on the JAKA Zu3 platform, the system attains a reduced terminal-trajectory tracking error, an extremely low action-delay rate, and a zero joint-overtravel rate. It suits the dynamic scenario of operators with heights ranging from 160 to 190 cm.In the future, it can find applications in settings like automotive assembly lines and nuclear-waste treatment, offering a highly adaptable human-machine collaboration model for flexible manufacturing.
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Huang Rong, Wang Yufu, Zhou Shuo, Tang Diyin, Ma Yongle, Guo Yufang
2026,40(3):106-113,
Abstract:
With the large-scale application of electric vehicles, accurate prediction of battery remaining capacity has become a core requirement to ensure driving range, safety, and economic efficiency. Traditional battery capacity degradation prediction methods mainly rely on aging experiment calibration or large-scale data-driven learning, making them difficult to adapt to complex real-world operating conditions. To address this issue, this paper proposes a mechanism-data hybrid-driven method for battery capacity loss prediction. On one hand, the Arrhenius model is employed to quantitatively describe the long-term dominant effects of temperature and cumulative ampere-hour throughput on capacity degradation from a mechanistic perspective. On the other hand, an LSTM network is used to capture dynamic perturbations in capacity loss under complex real-world operating conditions. Finally, the predicted battery capacity loss and its confidence interval are obtained. The proposed method is validated using charging data collected from electric vehicles under natural driving conditions, demonstrating its effectiveness in predicting capacity degradation in practical scenarios. Experimental results show that under the condition of training using only the first 30% of historical capacity degradation data, the method achieved a mean absolute error (MAE) of 0.73% and a root mean square error (RMSE) of 0.96% for subsequent capacity loss prediction, with the maximum error controlled within 2.18%. Overall, the prediction performance is superior to that of a standalone Arrhenius model and a pure LSTM model. The results indicate that the proposed mechanism-data hybrid prediction method can achieve high-precision and stable capacity loss predictions under real-world, complex onboard conditions, providing an engineering-applicable solution for assessing the health status and managing the lifespan of power batteries.
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Lyu Yi, Zhu Zhirong, Zhao Tianyu
2026,40(3):114-123,
Abstract:
Neural networks employed for fault diagnosis in complex scenarios often face challenges like strong noise interference and incomplete information from individual sensors, leading to degraded diagnostic performance. To address this issue, a cross-sensor convolutional neural network with dual residual and feature adaptation model is proposed. Firstly, during the data feature extraction process, a dual-ring residual module is utilized to alleviate the gradient vanishing problem during training. Subsequently, the convolutional block attention module attention mechanism is introduced to enhance the model's ability to focus on critical features. Then, a feature optimization and reconstruction module is utilized to improve the efficiency of feature learning and the capability of feature expression. Thereafter, an adaptive feature fusion module is employed to adaptively fuse high-level features extracted from different sensors. Finally, the fused features are classified through a global average pooling layer, a fully connected layer, and a Softmax function to accomplish the fault diagnosis task. The results demonstrate that the proposed method effectively integrates multi-sensor data features and exhibits robustness against noise of varying intensities. The average diagnostic accuracy of the model reaches 97.48% under noise levels ranging from -2 to -18 dB, showing an improvement of 1.88% compared to using a single sensor. This study provides an effective reference for solving fault diagnosis problems in complex scenarios.
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Ran Ning, Shi Gaolang, Zhang Haoyu, Zhang Shaokang, Hao Jinyuan
2026,40(3):124-134,
Abstract:
To address the limitations of existing object detection algorithms in remote sensing small object detection—such as large model size, high computational complexity, and low detection accuracy—this paper proposes a lightweight remote sensing detection algorithm based on YOLO11, named EDB-YOLO11. The algorithm introduces improvements from two aspects: network architecture and loss function. First, the EMBC module is designed to replace the original C3K2 module, which effectively enhances the feature representation capability of the network. Second, a novel Downsample module is employed instead of the traditional convolution-based downsampling, which reduces the number of parameters and computational cost while improving the feature extraction ability of the backbone. Third, BiFPN is adopted to replace the original PANet for feature fusion, significantly enhancing the network’s multi-scale feature integration efficiency. Finally, a new loss function called Focal-WIoU is proposed by combining the advantages of the Focal mechanism and WIoU loss. This design enables the model to focus more on high-quality training samples and reduce the impact of low-quality samples, thereby improving overall detection accuracy. Experimental results show that EDB-YOLO11 reduces the number of parameters by 27.18% and the computational cost by 16.43%. On the VisDrone2019 dataset, the mAP@0.5 increases by 3.4%, and the mAP@0.5：0.95 improves by 1.8%. On the generalized remote sensing datasets SIMD and MAR20, the mAP@0.5 improves by 3.8% and 0.3%, respectively, while the mAP@0.5：0.95 improves by 3.0% and 0.2%, respectively. These results demonstrate the effectiveness and superiority of the proposed EDB-YOLO11 algorithm in remote sensing small object detection tasks.
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Zheng Wenxue, Li Jiayin, Pan Zhenrong, Liu Bin, Zhang Zhi
2026,40(3):135-142,
Abstract:
Due to the complex internal environment of pipelines and the minute dimensions of cracks, crack detection signals exhibit a low signal-to-noise ratio. To address the challenge of high-SNR detection for crack defects on both inner and outer pipeline walls, a balanced field electromagnetic crack detection method employing simultaneous clock sources for excitation and reference signals is proposed. The detection principle of balanced field electromagnetic technology for internal and external wall cracks in pipelines is elaborated. The influence of frequency difference noise between excitation and reception signals is analysed through theoretical and numerical simulations. A balanced field electromagnetic crack detection system based on FPGA digital orthogonal demodulation is developed, with software design completed for synchronising excitation and reference signals. Experimental validation confirms the system’s detection capability, demonstrating its ability to detect surface cracks ranging from 1 to 7 mm and buried cracks from 1 to 4 mm. Surface crack detection signals exhibit a single peak and valley characteristic, while buried crack signals display a double peak characteristic. Field crack detection pulling tests on pipelines demonstrated that the system consumes less than 1.1 W of power, achieving a signal-to-noise ratio exceeding 41.9 dB for surface crack detection and over 36 dB for buried crack detection. This method is suitable for inline inspection of crack defects on both the inner and outer walls of pipelines.
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Zhu Jiangyan, Ma Jun, Wu Jiande, Xiong Xin
2026,40(3):143-154,
Abstract:
In complex systems, condition monitoring data consist of multi-source spatiotemporal information collected from multiple sensors. To effectively capture temporal degradation patterns and spatial correlations among measured variables, we propose an adaptive temporal-spatial feature fusion neural network (TSTFNN) based on a dual-stream structure. This framework incorporates parallel temporal and spatial streams to extract temporal dependencies and spatial correlations separately. To overcome the limitations of traditional dot-product self-attention, which often neglects time-series continuity, a convolutional self-attention mechanism is implemented, enhancing the capacity of model to capture sequential continuity and subtle temporal variations. A multi scale convolutional neural network further extracts spatial correlation features across variables, improving global perception capabilities. During feature fusion, an adaptive weighting mechanism enables dynamic integration of temporal and spatial features. To optimize predictive performance, a joint loss function, combining constrained mean squared error (MSE) and feature balance loss, is introduced, facilitating the collaborative learning of temporal and spatial features. Finally, experimental results based on NASA′s C-MAPSS benchmark dataset demonstrate that the proposed method outperforms various state-of-the-art (SOTA) models in terms of multi-source data RUL prediction accuracy.
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2026,40(3):155-163,
Abstract:
To address the challenges of high algorithmic complexity and implementation difficulty when identifying arcing in high-speed trains using current and voltage signals, as well as the high cost of arc recognition through high-speed camera imaging, a novel method based on port impedance is proposed for pantograph-catenary arc detection. First, the influence of airflow velocity under actual operating conditions on the arc voltage gradient is considered, and the voltage gradient is optimized to establish an arc voltage model that more accurately reflects real train operation conditions. Then, to facilitate more accurate simulation, the pantograph-catenary system is equivalently modeled as a two-port network consisting of system impedance and arc impedance under conditions of minimal load fluctuation, and a port impedance model incorporating airflow effects is developed. Subsequently, the feasibility of the optimized arc model is analyzed using the PSCAD/EMTDC simulation platform and a constructed experimental pantograph-catenary arc simulation setup. A support vector machine (SVM) arc recognition model optimized by the dream optimization algorithm is employed to identify the arc, thereby validating the feasibility of port impedance-based arc recognition. Finally, the port impedance-based arc recognition method is comprehensively compared with three traditional methods—current signal, voltage signal, and arc image recognition—across six evaluation criteria to verify its practicality and cost-effectiveness.
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Li Yufeng, 1Liang Kun, Yu Tian, Chen Yuren, Ba Xiaohui
2026,40(3):164-175,
Abstract:
The GNSS time-frequency transfer receiver is widely used in time synchronization systems and nodes across various critical fields, where precise calibration of internal delay is essential for achieving high-accuracy time-frequency transfer. To address nanosecond-level time-frequency transfer requirements, experimental research is conducted on absolute and differential calibration methods for BeiDou time-frequency transfer receivers. First, the principles and differences of step-by-step, integrity absolute, and differential calibration methods are compared and analyzed. Based on GNSS anechoic chamber and common-clock-difference scenery, implementation plans are designed accordingly. Calibration comparison experiments are conducted for different types of BeiDou time-frequency transfer receivers at B1I, B1C, B2a, B3I, and L1 C/A to evaluate measurement uncertainty and analyze various calibration methods. The experimental results demonstrated that the measurement uncertainties of the step-by-step, integrity, and differential calibration methods are better than 0.71, 0.98 and 1.27 ns, respectively. The delay calibration values within each frequency point are consistent within the uncertainty ranges of the three calibration methods, verifying the effectiveness of the calibration comparison experiment and the superiority of the integrity absolute calibration method, forming a time-frequency signal calibration capability with sub-nanosecond uncertainty levels.
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Yan He, Shi Xiaoliang, Wu Chaohua, Zeng Zhi, Luo Wei, Sun Cunfu
2026,40(3):176-188,
Abstract:
Addressing the issues of low accuracy, false positives, and missed detections in metal surface defect detection in industrial field production, as well as the challenges faced by metal surface defect detection, such as difficulty in distinguishing small targets, strong interference from complex backgrounds, and significant noise impact, especially in the areas of small target detection and multi-scale feature extraction, this study proposes the GMS2-YOLO model to improve the accuracy of target defect detection by combining multi-scale feature extraction, feature fusion enhancement, and a separated batch normalization detection head. Firstly, the GhostConv is combined with the HGBlock module, and the GHGnet network structure is used as the backbone network to improve the model’s ability to extract the detailed features of the target defects. Then, in the innovative fusion of MANet and StarBlock in the neck structure, MSNet is used to replace the C3k2 module to achieve more diversified and rich gradient flow information and enhance the extraction and fusion ability of the model. Secondly, the YOLOv11n detection head is redesigned, and the separated batch normalization detection head is used to realize the interaction and fusion of information at different levels, so as to accurately identify the defect target. Finally, using the new loss function WIoU, the accuracy of defect detection is improved by increasing the attention to medium-quality images. The experimental results show that the mean average precision mAP@0.5 index of this method on the Self-Dataset dataset reaches 80.8%, which is 3.6% higher than that of YOLOv11n. The mAP@0.5：0.95 index reached 53.5%, which was 8.5% higher than that of YOLOv11n. In addition, in the NEU-DET dataset, the AP value of the small defect CR in the dataset reached 68.3%, which was 15.8% higher than that of YOLOv11n. The proposed model greatly improves the accuracy of defect detection, and has obvious advantages in solving the problems of false detection and missed detection. In addition, compared with other mainstream models, the improved model improves the detection accuracy without losing more inference speed, and has good prospects for engineering applications.
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2026,40(3):189-197,
Abstract:
An analog-to-information converter (AIC) is an emerging method for compressive sampling that overcomes the limitations imposed by the Nyquist rate. The classical AIC employs a pseudo-random (PN) sequence at the Nyquist conversion rate to mix the input signal, then down-samples it by spreading the sparse signal across the entire frequency band, enabling the extraction of low-frequency information. However, in wireless communications applications, signals are often bandpass. The excessively high PN conversion rate leads to excessive sampling redundancy and significant non-ideal effects, degrading the quality of the reconstructed signal. To address these issues, this paper proposes a new design method for ultra-low-rate PN sequences based on bandpass sampling theory. This method uses the actual bandwidth of the input signal rather than its maximum frequency to determine the PN sequence rate. Based on this method, we constructed a novel bandpass random demodulation (RD) AIC architecture that employs a Sigma Delta ADC as its core component. By decreasing the PN sequence switching rate, the architecture effectively minimizes non-ideal effects associated with high-speed PN switching, ensuring the integrity of bandpass signal information while significantly improving the compression ratio and the reconstruction signal-to-noise ratio (RSNR). Experimental results show that for an input signal with a frequency range of 780~790 kHz, the proposed bandpass RD AIC can lower the PN sequence conversion rate to 52 kHz. This advancement achieves a sampling compression ratio of 30 and an average RSNR of 68 dB. Compared with the latest RD Sigma Delta AIC design, the proposed architecture improves the sampling compression ratio by 7 times and enhances the RSNR by 146 dB.
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Hou Limin, Gao Yixin, Shi Chen
2026,40(3):198-207,
Abstract:
Aimed at the issues of structural complexity and high computational burden for permanent magnet synchronous motor (PMSM) traditional coupled control methods, a novel finite-time leader-following speed cooperative control method based on a multi-agent system is proposed. Relying on an undirected communication topology, a distributed finite-time speed consensus protocol is constructed based on the multi-agent mathematical model of PMSMs. Meanwhile, in order to deal with uncertain disturbances and unmodeled dynamics in the system, a super-twisting extended state observer (STESO) is introduced for real-time estimation, and the observation results are incorporated into the consensus protocol for compensation, thereby deriving the desired q-axis current. Furthermore, by constructing a Lyapunov function, rigorous theoretical proof of the finite-time convergence of the proposed controller is provided, along with an upper bound estimate of the convergence time. Finally, comparative experiments with the deviation coupling control algorithm are carried out on an experimental platform comprising three PMSM speed regulation systems. Results show that under conditions of speed variation, load changes, and forward-reverse rotation, the proposed method exhibits superior performance the maximum speed jitter is reduced from 3 r/min with the traditional method to below 0.5 r/min, synchronization error is significantly decreased, and the speed can recover smoothly and rapidly to the set value under load disturbances. Experimental results verify that the proposed scheme achieves high synchronization accuracy, fast convergence, and strong robustness, and provides an effective solution for high-performance multi-motor cooperative control.
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Xu Peng, Cao Fang, Li Dezhi, Ma Haonan, Li Huijuan1, Peng Xinxin, Wang Xiaojun, Wan Shibin
2026,40(3):208-219,
Abstract:
The modular multilevel converter (MMC) is widely utilized in high-voltage direct current (HVDC) transmission systems due to its modular design, scalability, and fault tolerance. Although conventional model predictive control (MPC) offers the advantages of a fast dynamic response and simple implementation, its application is limited by high computational burden and insufficient circulating current suppression. To address these issues, a hybrid model predictive control (H-MPC) strategy is proposed, which combines an improved indirect MPC with a fractional-order quasi-proportional-integral-resonant (FO-QPIλR) controller. The improved indirect MPC optimizes control objectives and simplifies the rolling optimization process, significantly reducing the computational burden while avoiding the complexity of weighting factor tuning in conventional MPC, thereby achieving fast current tracking and submodule capacitor voltage balancing. Meanwhile, the FO-QPIλR controller exhibits better dynamic performance and robustness than a traditional PI controller, effectively suppressing the circulating current without the need for decoupling. To validate the effectiveness of the proposed strategy, a comparison with the conventional indirect MPC strategy was conducted. Simulation results show that the circulating current amplitude is reduced by 80% and the submodule capacitor voltage fluctuation is decreased by 9%; experimental results further demonstrate a 53% reduction in circulating current amplitude and a 10% reduction in submodule capacitor voltage fluctuation. The simulation and experimental results indicate that the proposed hybrid control strategy, while maintaining the fast dynamic response and high-quality output current of MPC, significantly suppresses circulating current harmonics and enhances the submodule capacitor voltage balancing capability, thus verifying the effectiveness and superiority of the strategy.
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Chang Huaiqing, Wang Guiping, Zhang Kai, Zhao Shanmeng, Guan Limin
2026,40(3):220-230,
Abstract:
To address the insufficient feature extraction capability of the PointRCNN object detection algorithm in complex scenes, we first design a local-global attention module (LGAM). LGAM computes attention weights from the local geometric relationships between each central point and its neighbors, enabling effective fusion of local features. Simultaneously, global contextual features are captured via a bilinear regularization method, and local and global features are then fused for collaborative optimization. Next, we introduce a multi-scale kernel convolutional attention module (MKCAM), which dynamically aggregates multi-scale features by parallelizing standard and dilated convolutions and incorporates a channel-pooled spatial attention mechanism. Both LGAM and MKCAM are cascaded into the original PointRCNN point-cloud encoding network to enhance its feature extraction capacity. Furthermore, to mitigate misdetections caused by the fixed IoU threshold in traditional non-maximum suppression (NMS), we propose a fuzzy NMS that adaptively assigns IoU thresholds based on object size and scene density. By integrating the improved point-cloud encoder with fuzzy NMS, we present an enhanced PointRCNN algorithm. Experimental results on the KITTI dataset show accuracy improvements of 1.05%, 3.43%, and 1.33% for cars, pedestrians, and cyclists, respectively. On our self-collected roadside dataset with sparse point clouds, detection accuracies for the three classes increased by 1.3%, 2.71%, and 2.9%, respectively, confirming the effectiveness and generalization ability of the proposed method.
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Zhang Qingzheng, Zhang Shihai, Qu Chongnian, Guo Xiaosai
2026,40(3):231-239,
Abstract:
A parameter identification method based on a lightweight RT-DETR model and a spline interpolation method is proposed for the instrument intelligent inspection system developed for the offshore platform wellhead control panel. To achieve lightweight model and high-precision targets extraction, the backbone and neck networks of RT-DETR are reconstructed and optimized. The cross-stage local illumination enhancement module (CSP-IEM) and the fast enhanced hybrid aggregation module (FEMAM) are introduced to improve illumination robustness and neck network detection efficiency. A matching-aware loss function (MAL) is designed to preserve matching quality information. The ablation experimental results show that the model achieves a mean average precision (mAP) of 76.1% at 0.5, reduces the number of parameters and computation by 30.73% and 25.79%, respectively, and achieves a frame rate of 216 fps. Based on the key targets recognition and image processing methods for instruments, a method using spline interpolation to calculate instrument readings is proposed. The experiment of reading instrument parameters from on-site collected images shows that the spline interpolation method reduces the mean relative error and mean global error by 45.5% and 48.3%, respectively compared to the local angle method. Comprehensive experiments have demonstrated that the proposed instrument parameter identification method meets the requirements for deployment under limited on-site computing resources, as well as the needs for detection accuracy and real-time performance.
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2026,40(3):240-249,
Abstract:
This study proposed a ship fire identification approach that integrates an attention mechanism, a convolutional neural network (CNN) and a bidirectional long short term memory network (BiLSTM) to address the low accuracy of existing methods. A three-deck ferry fire simulation model was constructed using the fire dynamics simulator (FDS), and sensors were used to collect temperature, carbon monoxide, and visibility data from the simulated ship fire process. A CNN was employed to extract longitudinal features from fire data, while dimensionality reduction was used to compress data length and to reduce the number of model training parameters. A cascaded deep learning neural network based on BiLSTM was established to extract transverse features from fire data, where an attention mechanism was incorporated at the output layer. Furthermore, to accelerate convergence, an improved grey wolf optimization algorithm was developed by integrating the chaotic game algorithm. The improved algorithm was applied to optimize the CNN-BiLSTM-Attention model, which was subsequently utilized to perform ship fire identification experiments under two scenarios. The experimental results indicated that, despite the imbalance in ship fire data samples, the proposed approach outperformed other fire classification methods, achieving 100% fire identification accuracy and satisfying practical engineering requirements.
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Lu Yong, Yan Liheng, Xie Xiaoxiao, Chen Jiaxin, Li Longjie
2026,40(3):250-261,
Abstract:
To address the challenges of insufficient nonlinear inversion accuracy and low convergence efficiency in grounding grid defect detection using the transient electromagnetic apparent resistivity method, this study proposes an intelligent hybrid inversion approach that integrates the global search capability of genetic algorithms (GA) with the local optimization of Newton’s method. To overcome the limitations of traditional GA, such as slow convergence and low sensitivity to small-scale defects, a “data-driven and model-constrained” inversion framework is established. This framework employs reactive mechanisms in GA, including tournament selection and dynamic crossover and mutation, to mitigate the “black-box mapping” limitations of purely data-driven models and achieve interpretable searches in the initial solution space. High-quality initial values obtained from the global search are then used as inputs for Newton’s method, fundamentally resolving the “initial value sensitivity” issue of traditional iterative approaches and establishing a collaborative inversion strategy of “global pre-search and local refinement.”Experimental results demonstrate that, under scenarios involving 2 000 to 10 000 data groups, the hybrid algorithm achieves an average total computation time of 7.2~35.8 seconds, representing a reduction of 4.8~12.6 seconds compared to the combined time of traditional GA and Newton’s method (12.0~48.4 seconds). Iterative efficiency is significantly improved, with cases terminating at the preset maximum iteration limit (100 cycles) reduced by 33.6% compared to Newton’s method, and the proportion of valid solutions obtained within fewer than 20 iterations increased by 45-4%. The inversion accuracy is notably superior, with an average error of 6.020 7×10-8, reflecting reductions of 83.65% and 98.95% compared to traditional iterative methods and GA, respectively. Finally, field scaled-model experiments confirm that the proposed method can effectively identify grounding grid topologies and detect hidden defects such as fractures and gaps, significantly enhancing detection accuracy and efficiency under complex operational conditions compared to single-method approaches.
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Wu Guoxin, Qi Yinyan, Zuo Yunbo, Dong Yuanqiu, Chen Xuanyu
2026,40(3):262-272,
Abstract:
Aero-engine blades, as one of the core high-precision components of the engine, require rapid and accurate surface measurement to ensure engine performance. Accurate extraction of laser stripe centerlines is a critical step in three-dimensional measurement, directly affecting measurement accuracy. To address the limitations of existing center extraction methods under uneven stripe width, high curvature, and noisy conditions, this study proposes a centerline extraction algorithm combining normal-guided extremum method with an improved spatial grayscale centroid propagation approach to optimize extraction precision. The method first estimates the centerline position using the Steger method; potential key feature regions are then identified via the extremum method, and weighted spatial grayscale centroid calculation along the normal direction is applied to obtain the initial stripe center. An improved eight-neighborhood angular selection is subsequently used to screen effective points, and outliers are removed using the 3σ criterion, ultimately yielding precise center coordinates. Comparative experiments demonstrate that the algorithm achieves a root mean square error (RMSE) of 0.058 pixel on standard test images, effectively preserving stripe details; 1 frame processing time is approximately 0.755 ms, achieving a 6-fold speed improvement over the Steger method and a 3-fold improvement over the conventional grayscale method, effectively addressing the curvature-induced centerline tilt problem. Furthermore, tests on blurred samples and images with added Gaussian noise show a 3.7% increase in fitting accuracy and a 6.9% decrease in RMSE compared with traditional methods, indicating superior robustness and practical applicability. The proposed algorithm provides effective technical support for high-precision measurement applications in aerospace manufacturing and optical precision instruments.
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Deng Guiwen, Li Jin, Xing Wanjun, Wu Wenbin
2026,40(3):273-281,
Abstract:
In the integrated application scenario of synthetic aperture radar (SAR) and communication, SAR imaging is typically implemented using the traditional range-Doppler algorithm (RDA). However, this method encounters geometric distortion in imaging scenarios with large squint angles. To address this issue, a synthetic aperture imaging method for OFDM signals based on the extended Omega-K algorithm (EWKA) is proposed. This method applies the traditional Omega-K algorithm to OFDM signal-based synthetic aperture imaging. After removing the cyclic prefix (CP) from the echo signals, two-dimensional pulse compression is performed in the range and azimuth directions. Phase shift compensation is applied to correct phase deviations caused by squint conditions.Subsequently, Stolt interpolation in the two-dimensional frequency domain is used to decouple higher-order phase coupling between the range and azimuth directions, and Doppler frequency offset compensation is performed to achieve the final two-dimensional imaging. Finally, simulation experiments on single-point and multi-point targets were conducted in both side-looking and large squint scenarios. Comparative simulation results show that, in side-looking scenarios, the EWKA-based imaging algorithm improves the azimuth PSLR from -12.32 dB to -17.024 dB, an enhancement of approximately 38.1%, while maintaining comparable range performance. In squint scenarios, the traditional RDA-based imaging algorithm suffers from defocusing and fails to achieve satisfactory imaging, whereas the EWKA-based algorithm maintains good focus. The simulation results verify the feasibility and effectiveness of the EWKA-based imaging algorithm under both side-looking and large squint conditions. The proposed EWKA-based OFDM signal synthetic aperture imaging algorithm not only resolves the defocusing issue encountered in traditional RDA-based methods in large squint scenarios but also enhances azimuth performance in side-looking scenarios, significantly expanding the applicability of OFDM signal-based synthetic aperture imaging.
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Wang Mengdi, Ma Chao, Wang Shaohong, Li Mu, Xu Haowen, Zhang Haiyang
2026,40(3):282-290,
Abstract:
To achieve real-time gait recognition under multi-user, multi-cadence, and multi-gait-type conditions, a gait recognition method integrating a cycle-adaptive mechanism and knowledge distillation is proposed. This method comprehensively considers both recognition accuracy and real-time performance, aiming to enhance the adaptability of lower-limb exoskeleton systems in dynamic and complex environments. First, a cycle-adaptive sliding window mechanism based on spectrum analysis is designed to dynamically adjust the window length according to the signal’s periodicity, precisely adapting to variations in users and gait cadences. Then, a graph neural network (GNN) is used as the teacher model to fully extract the spatiotemporal relationships in multi-channel IMU data, and a multi-layer perceptron (MLP) model is used as the student model. Knowledge transfer is achieved through knowledge distillation. Experiments were conducted using five types of gait data for comparison and validation. Under the same data conditions, the adaptive sliding window scheme improved the overall classification accuracy from 91.6% to 94.2%, an increase of 2.6%. By combining hard labels with soft labels from the teacher model and optimizing the distillation loss function parameters, the student model learned the rich features and information from the teacher model, improving accuracy by 1.94%. Meanwhile, the average recognition time per window for the student model was reduced from 17.4 ms (teacher model) to 4.9 ms, significantly enhancing real-time responsiveness. Experimental results show that the method demonstrates good recognition stability and generalization across multiple users, cadences, and gait types, combining high accuracy and low latency, with strong practicality and deployment value. It is suitable for scenarios such as lower-limb exoskeletons that require high real-time performance and recognition accuracy.
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Qi Qi, Zhang Binbin, Fan Jinbiao, Jiang Shengyuan
2026,40(3):291-300,
Abstract:
During the star-soil penetrative exploration process, the detector operates under high-overload conditions. The mechanical sensing module, typically located at the foremost part of the detector, experiences the most severe stress environment. To prevent damage from high overloads, the module requires potting. However, while potting materials enhance the module’s impact resistance, they introduce nonlinearities in the amplitude-frequency response. This paper addresses this issue by proposing the ‘co-planar excitation’ impact calibration method and the data processing method of ‘order-adaptive identification NLS-Wolfe line search’. First, leveraging the principle of symmetry, the reference sensor and the module under calibration are mounted symmetrically on the movable table of a shock amplifier. Dynamic calibration of the mechanical sensing module is achieved under co-planar excitation pulses. During the calibration data processing, the order of the transfer function is determined through order-adaptive identification, and the transfer function parameters are identified using the NLS-Wolfe line search identification module. Results show that after compensation, the operating frequency bandwidth with amplitude error within ±5% is effectively extended to 12.356 kHz, and the correlation of the main pulse reaches 97.69%.
Volume 40,2026 Issue 3
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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.
