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Teng Feiyu, Zhang Hong, Ye Mengting, Sun Lingyu, Zhan Chunyan, Jiang Mingshun
2026,40(8):1-15,
Abstract:
The pipeline transportation system serves as the “energy artery” of national economic development, and its structural integrity is directly related to the safety of the energy, chemical, and infrastructure sectors. However, influenced by harsh environments and corrosive media, pipelines are highly susceptible to corrosion damages. During long term service, corrosion damage poses a significant challenge, as it is difficult to monitor effectively, thereby becoming a bottleneck in pipeline network. The piezoelectric ultrasonic guided wave (UGW) corrosion monitoring technology has been applied to achieve full-coverage, in-situ, and online monitoring of corrosion damage and structural health conditions in pipelines under service. This promotes the transition of pipeline maintenance strategies from periodic inspection to online monitoring and condition-based maintenance, through which the operational safety of pipelines is ensured and maintenance costs are reduced. In this review, first, the theory of UGW in pipelines is introduced. Then, the development status of system design and key technologies include damage characterization, identification and environmental adaptation is summarized and analyzed, the applications in oil and gas, chemical, and municipal fields are reviewed. Finally, the challenges and development trends of piezoelectric UGW techniques for pipeline corrosion monitoring are summarized.
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2026,40(8):16-25,
Abstract:
To address the ill-posed inverse problem, reconstruction artifacts, and blurred boundaries in electromagnetic tomography (EMT) imaging of fine cracks on planar metal components, an image reconstruction network named SE_ASPP_UNet is proposed. The network integrates squeeze-and-excitation (SE) and atrous spatial pyramid pooling (ASPP) modules into the U-shaped network (U_Net) framework. The SE module enhances crack-related channel responses, while the ASPP module extracts multi-scale contextual features in the bottleneck layer, improving the representation of cracks with different lengths and orientations. A combined binary cross-entropy and Tversky loss is adopted to improve the reconstruction of slender crack structures. Based on a regular strip-like crack simulation dataset jointly constructed using COMSOL and MATLAB, comparative experiments with various reconstruction methods, loss-function ablation experiments, and network-module ablation experiments were conducted. SE_ASPP_UNet achieved an average Dice coefficient of 0.836 0 and an average connected component error (CC_Err) of 0.055 2 on the simulation test set, showing favorable region overlap and topological connectivity. After fine-tuning with a small number of measured samples, measured validation on 6061 aluminum alloy plates showed that the method could recover crack boundaries and spatial locations. Out-of-distribution experiments further indicated that the model retained a certain ability to recover the macroscopic propagation trend of irregular strip-like cracks unseen during training. These results suggest that SE_ASPP_UNet can support EMT image reconstruction of fine cracks in planar metals.
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Zhang Bing, Chen Qiang, Ni Wei, Wu Chengrui, Shi Mingjiang, Huang Jingsheng, Wu Xiaohui, Xiao Yingying, Liu Mingyang
2026,40(8):26-35,
Abstract:
In the oil and gas industry, the acoustic detection of internal leaks in pressurized valves is constrained by environmental noise interference, making it difficult for detection algorithms to discern the temporal characteristics of internal leaks, leading to false alarms for valve internal leaks. To address the drawback of identifying key temporal characteristics of internal leak acoustic signals, we propose the feature mode decomposition (FMD)-Transformer-ResNet internal leak detection method. This method utilizes FMD, fully considering the periodicity and impact characteristics of internal leak acoustic signals, to purify and reconstruct the internal leak acoustic source, overcoming the shortcomings of traditional signal decomposition techniques in dealing with internal leak acoustic signals, such as mode mixing. Subsequently, a Gaussian mixture model (GMM) is used to assign sensitivity weights to the internal leak acoustic source signals. These weights enhance the ability of the Transformer to recognize key temporal characteristics of internal leak acoustic signals. Combined with a residual neural network classifier (ResNet), it achieves high-precision classification and identification of internal leak conditions. Experimental results show that the proposed FMD-Transformer-ResNet valve internal leak detection method achieves an optimal recognition accuracy of up to 97% for internal leaks, surpassing the recognition accuracy of similar methods such as CNN-GRU-Attention and CNN-LSTM-Attention. The proposed method forms a new perspective for valve internal leak condition recognition research, focusing on “signal purification, feature weighting, and accurate prediction,” and holds promising engineering application prospects.
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2026,40(8):36-47,
Abstract:
To address the challenges of strong nonstationarity, severe noise interference, and insufficient detection accuracy of arc fault signals under complex operating conditions, this paper proposes a series arc fault detection method that integrates optimized feature enhancement with physics-informed deep modeling. First, an improved dream optimization algorithm (IDOA) is introduced to adaptively optimize the parameters in wavelet energy spectrum reconstruction, enabling the enhancement of arc-sensitive frequency bands and improving signal representation capability. Subsequently, recurrence plot (RP) is employed to perform time-frequency mapping on the reconstructed signals, transforming one-dimensional nonstationary signals into two-dimensional structural features to characterize their nonlinear dynamic evolution. In the feature modeling stage, a time-frequency collaborative receptive field attention (TF-CRFA) mechanism is constructed to jointly model transient characteristics in the time domain and energy distribution in the frequency domain, thereby achieving adaptive enhancement of critical arc features. Furthermore, a physics-informed Transformer (PI-Transformer) is designed by incorporating physical priors, such as high-frequency energy mutation and nonstationarity of arc signals, into the model architecture and loss function, enabling unified global temporal dependency modeling and physical consistency constraints. Experimental results demonstrate that the proposed method outperforms comparative models in terms of detection accuracy, noise robustness, and adaptability to complex conditions and potential for engineering applications in arc fault detection.
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Han Chenglong, Xu Chi, Li Lin, Liu Huan
2026,40(8):48-57,
Abstract:
Robotic tactile perception has become a core capability for realizing robotic embodied intelligence, yet faces challenges such as poor dynamic adaptation, overlapping perceptual states, and stringent real-time requirements. To address this, this paper employs a spatial-temporal decoupling-based deep learning framework, and proposes a Transformer-Mamba-based classification method for robotic tactile perception. Firstly, a multi-scale feature enhancement module based on an efficient channel attention mechanism is designed to strengthen the spatial-temporal multi-scale features of tactile perception data. Secondly, a feature extraction module based on Transformer-Mamba is constructed to extract spatial and temporal features from tactile perception data. Herein, Transformer serves as the core for extracting spatial features, while Mamba serves as the core for extracting temporal features, thereby reducing the algorithmic inference time. Furthermore, we design a feature classifier based on gated fusion, which reduces feature loss and improves the algorithm’s stability by fusing cross-stage features. Finally, we conduct experiments to validate the proposed algorithm using three public datasets: Tactile-event-corl, Multimodality slip detection, and Dataset for SCT-CNN. The results demonstrate that the proposed algorithm achieves an accuracy of 98.72%, precision of 98.87%, recall of 98.85%, and F1 score of 98.85%, representing improvements of 1.67%, 1.41%, 1.70% and 1.65% respectively over existing algorithms, while reducing the average inference time by 90%.
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Lu Jinbo, Fan Lihua, Qian Yongliang, Liu Xicheng, Pei Zhen
2026,40(8):58-68,
Abstract:
Aiming at the problems of low accuracy and random noise in angular velocity signals of conventional Micro-Electro-Mechanicals gyroscopes, a signal processing method based on multivariate variational mode decomposition (MVMD) optimized by four-vector intelligent metaheuristic (FVIM) is proposed in this paper. Firstly, the collected angular velocity signals are processed by MVMD, and divided into two components of frequency and energy. Secondly, the FVIM is utilized to optimize the two key parameters of modal decomposition, namely the number of modes k and the penalty factor α, so as to obtain the optimal combination of parameters. Then, the signals are decomposed into multiple intrinsic mode function (IMF) components, and sample entropy (SPN) combined with cosine similarity is taken as the joint decision criterion to classify the IMF components into noise components, aliasing components and effective components. Furthermore, the wavelet transform (WT) algorithm is adopted to process the aliasing components, and the processed components are reconstructed with the screened effective components to obtain the denoised angular velocity signals. Finally, experimental verification is conducted by applying the proposed method to both simulation signals and actual measured signals. The results show that the root mean square error is reduced by 57.1% during simulation signal processing, and the random walk values of the measured Y-axis and Z-axis angular velocity signals are decreased by 76.9% and 56.2% respectively.
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Liu Xingmou, Chen Minyi, Yang Ning, Xiao Yao
2026,40(8):69-80,
Abstract:
To address the issues of insufficient generalization ability in traditional single-index diagnosis methods under complex operating conditions, and the opacity of the decision-making process in conventional machine learning models (the “black box” problem) for transformer DC bias condition assessment, this paper proposes an accurate assessment model based on multi-dimensional feature fusion and Shapley additive explanations (SHAP) interpretability analysis. First, based on the analysis of the vibration response mechanism of transformers under different degrees of DC bias, a 14-dimensional feature system fusing time-domain statistics, frequency-domain harmonic components, and specific physical mechanism features (such as weighted odd-even harmonic energy ratio and spectral entropy) is extracted and constructed. Next, a recursive feature elimination (RFE) strategy based on SHAP importance is utilized to adaptively screen the feature set, effectively eliminating redundant features. On this basis, the extreme gradient boosting (XGBoost) algorithm is adopted to build the assessment model, and Bayesian optimization is employed to adaptively tune the model hyperparameters, achieving precise identification of DC bias severity levels. Experimental verification based on multiple measurement points indicates that the optimized model can adaptively select the optimal feature subset for different sensor locations, achieving an assessment accuracy of over 96.2% across all tested points. Finally, the SHAP framework is introduced to quantitatively analyze the model’s decision-making mechanism from both global and local perspectives. The interpretability results demonstrate a high consistency between the model’s decision basis and the physical laws of vibration transmission and DC bias. This method demonstrates excellent interpretability while ensuring high diagnostic accuracy, providing a transparent and reliable solution for the engineering application of data-driven models in the field of transformer condition assessment.
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Li Hongxu, Feng Yongfu, Feng Xiaoming, Wang Xinghui, Li Shiyu, Yang Minhao
2026,40(8):81-91,
Abstract:
Lidar, widely used in practical applications, is often affected by background light noise and system shot noise, leading to a decline in signal quality and difficulty in extracting valid information. To address this issue, this paper proposes a dual noise filtering method combining local mean decomposition (LMD) and successive variational mode decomposition (SVMD), which is applied to the field of lidar cloud layer detection. The method employs LMD to decompose the original signal and uses sample entropy to categorize the signal into low-frequency and mixed signals. Subsequently, SVMD is applied to the mixed signals, and mutual information is utilized to further segregate the signals into high-frequency and low-frequency effective signals. Finally, the low-frequency and low-frequency effective signals are reconstructed into a denoised signal. The denoising performance of the proposed method is verified using both simulated and measured data. The experimental results show that for the Bumps signal with an input SNR of -6 dB, the output SNR of the proposed method reaches 8.49 dB, and the root mean square error (RMSE) is as low as 0.67. For the lidar simulated echo signal with an input SNR of 9 dB, the output SNR is 19.344 8 dB and the RMSE is 5.181 7×10-12, which exhibits significant advantages over the methods such as WT, LMD, EMD-WT, SVMD, and EEMD-GWO-SVD. Moreover, in the application of lidar cloud parameter detection, the denoised signal from this method is closest to the original signal in terms of cloud base and top heights, effectively preserving signal details and features, which is beneficial for enhancing the inversion accuracy of lidar data.
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Chen Yanping, Zhou Qiao, Li Jianpeng, He Dongnan, Cai Linjun, Zhou Jun, Peng Hui, Liu Yu, Zou Mengqiang
2026,40(8):92-99,
Abstract:
To solve the problems of noise interference and error accumulation drift in low-cost MEMS IMU in pedestrian positioning applications, a tightly coupled inertial navigation method integrating Transformer and error-state Kalman filter (ESKF) is proposed. The central innovation of this approach is the synergistic integration of deep learning perception and classical physical constraints, alongside a novel adaptive observation noise adjustment mechanism driven by confidence weighting. Firstly, the multi head self-attention mechanism of Transformer is utilized to replace traditional recurrent neural networks to more effectively capture long-range temporal dependencies from raw IMU data, thereby achieving high-precision two-dimensional velocity regression. Secondly, a tightly coupled strategy is designed to map the Transformer network’s predicted velocity and its confidence into ESKF observation updates. Furthermore, a velocity error feedback mechanism is constructed using dual zero-velocity update (ZUPT) methods to correct heading drift and gyroscope bias. Experimental results based on the publicly available RoNIN dataset demonstrate that this method can effectively correct speed estimations, thereby enhancing overall trajectory accuracy. Compared with traditional RNN/LSTM algorithms, the proposed method exhibits superior robustness and the calculated trajectory matches the actual trajectory better. The 95th percentile of the absolute trajectory error and relative trajectory error were optimally reduced to 2.99 meters and 3.25 meters, respectively. Compared with the other two methods, the maximum positioning error has been reduced by the least 3.31 meters, representing a decrease of approximately 50.38%. The proposed method effectively improves the positioning accuracy of inertial pedestrian navigation, providing a certain reference for the autonomous positioning of consumer mobile devices in complex indoor environments.
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Zhang Yonghao, Piao Linhua, Sun Jiahao, Wang Jiaxuan
2026,40(8):100-110,
Abstract:
To address the issues of insufficient robustness and poor pressure stability in flexible multimodal sensors, this paper proposes a flexible temperature-pressure dual-mode sensor (FTPDMS) based on a sawtooth-shaped resistive film. Mathematical models for the sensor’s temperature-pressure response were established using COMSOL Multiphysics and NI Multisim, and the structural parameters were optimised. Using PI as the sputtering mask, the FTPDMS was fabricated via a process involving metal magnetron sputtering, peeling, and Polydimethylsiloxane（PDMS）elastomer casting. Experimental results indicate that the sensor’s sensitivity within the pressure ranges of 7 Pa to 1 kPa, 1 to 60, and 60 kPa to 100 kPa is -4.59, -0.1 and -0.23 mV/kPa respectively, with non linearity of 10.13%, 7.75% and 11.68% respectively. The pressure response time is approximately 303 ms, the lower pressure limit is 7 Pa, the hysteresis is 3.6%, and the repeatability is ≥2 000 cycles; the signal remains stable during constant-pressure testing. Within the temperature range of 27 ℃~67 ℃, the sensitivity is 0.41 mV/℃, the non-linearity is 6.25%, and the temperature response time is approximately 1.5 s. This sensor offers advantages such as a wide measurement range (100 kPa), high sensitivity (-4.59 mV/kPa), fast response (303 ms), high robustness and pressure stability (≥2 000 cycles), and low hysteresis (3.6%). Furthermore, its fabrication process is simple, cost-effective and environmentally friendly, making it suitable for applications in medical health monitoring, electronic skin and smart wearable devices.
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Nie Ziyun, Li Zhuorui, Li Xiang, Ma Jun
2026,40(8):111-121,
Abstract:
In existing rolling bearing dynamics modeling, the evolution of the impact force when the rolling element passes through the outer ring defect is not fully considered, resulting in systematic deviations in the analysis of the overall dynamic response characteristics of the bearing, making it difficult to accurately characterize its true performance status under actual operating conditions. To address this, a dynamic modeling method for local faults in rolling bearings that characterizes impact response is proposed. First, the motion process of the rolling element traversing an outer ring defect is analyzed, and a corresponding time-varying displacement excitation model is constructed accordingly. Second, based on the momentum theorem, the impact force function related to the defect length, depth, and rotational speed when the rolling element impacts the rear edge of the outer ring defect is derived. Integrating Hertzian contact theory, a dynamic model for local faults in rolling bearings that reflects impact response characteristics is constructed. Finally, taking the N6205 deep groove ball bearing as an example, the established model was verified through simulation and experiment, and the effects of different defect lengths, depths, and rotational speeds on the bearing’s dynamic characteristics were analyzed. The results show that, under operating conditions of 500 and 1 000 r/min, the errors between the simulated signal’s double-impact time interval and first harmonic characteristic and the theoretical values are both less than 0.6%, and the maximum error with the measured data does not exceed 3.23%. Simultaneously, the local impact generated during the contact between the rolling element and the outer ring defect has a significant impact on the bearing’s vibration response. This study can provide a reference for rolling bearing fault diagnosis and life assessment.
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Xiao Jie, Yu Shuiqin, Wang Zhiyong, Feng Yi, Xing Kan, Zhai Huaming
2026,40(8):122-132,
Abstract:
To mitigate thermal error effects on the linear axis positioning accuracy of gear grinding machines and enhance gear tooth surface quality and consistency, a thermal error prediction method based on the golden jackal optimization (GJO) algorithm and long short-term memory (LSTM) neural network is proposed. Using the H650GA grinding machine’s linear axis as the research object, PT100 temperature sensors and an XL-80 laser interferometer were employed to collect temperature and thermal error data under various working conditions. The fuzzy exponent, iteration count, and termination conditions of the Fuzzy C-Means clustering algorithm were optimized using the genetic algorithm, and key temperature-sensitive points were selected through Grey Relational Analysis. The GJO algorithm was employed to optimize the number of hidden layer nodes, initial learning rate, and L2 regularization coefficient of the LSTM network. A GJO-LSTM thermal error prediction model was constructed using training set data. Finally, the predictive performance of the GJO-LSTM model was validated with test set data. Experimental results demonstrated that compared to GA-LSTM, LSTM, and BP models, the GJO-LSTM model reduces RMSE by 30.3%, 42.8%, and 54.8%, and MAE by 36.2%, 41.1%, and 45.4%, respectively, confirming its superior prediction accuracy and robustness.
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Xu Guoming, Cao Xuyan, Zhang Beibei, Yang Yumu, Li Jiayi, Sun Aocheng
2026,40(8):133-145,
Abstract:
To address the challenges of small target omission and dense occlusion interference in ship detection from high-resolution optical remote sensing images, this paper proposes a dense ship target detection algorithm based on global scene understanding and local detail perception. First, to enhance the backbone network’s capability in extracting features of small targets, a fine-grained perception module (FGPM) is designed. Its multi-scale parallel feature extraction strategy and dual-attention enhancement mechanism effectively alleviate the inherent information loss of small targets while improving the signal-to-noise ratio between the foreground and the complex background; Second, a multi-scale global and local (MGLF) joint Feature fusion module is designed to synergistically optimize global scene understanding and local detail perception, enhancing the model’s robustness and adaptive capability in complex regions; Finally, ADown downsampling is introduced to replace traditional convolutions, preventing excessive loss of contextual information during the feature map size reduction process. Experimental results on the DIOR and NWPU datasets demonstrate that the proposed algorithm achieves improvements of 1% to 5% in precision, recall, and mean average precision compared to the baseline method, effectively validating its ability to ensure detection accuracy while reducing false detections and missed detections in optical remote sensing-based dense ship detection.
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Li Songtao, Jia Ye, Guo Jinchuan, Zheng Yi
2026,40(8):146-157,
Abstract:
Addressing the challenges of insufficient modeling of complex nonlinear mixing effects by traditional physical models in hyperspectral unmixing (HU) and the weak generalization capability of purely data-driven methods due to the lack of physical priors, this study proposes an unmixing method (M2S2) that integrates mechanistic constraints with multi-scale spectral-spatial joint learning. The aim is to resolve the failure of hyperspectral sub-pixel analysis caused by nonlinear mixing effects in complex scenarios. Accordingly, this paper first constructs physical constraint conditions for the endmember abundances, including non-negativity, sum-to-one, and sparsity. Subsequently, distinct core modules are designed to hierarchically extract features from both spectral and spatial data, leveraging multi-scale pooling structures and multi-channel attention mechanisms to highlight the saliency of hyperspectral mixed information and enhance the model’s unmixing performance. Comparative experiments on multiple datasets demonstrate that the proposed M2S2 method improves hyperspectral unmixing performance by approximately 6.8% compared to other methods. The findings validate the effectiveness of combining physical mechanistic priors with deep learning models and conducting feature enhancement through multi-scale spectral-spatial joint learning, offering a novel approach for addressing the challenges of nonlinear hyperspectral unmixing.
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Feng Linsong, Wen Yinghong, Wang Xinyi, Liu Hao, Zhang Dan, Li Meng
2026,40(8):158-170,
Abstract:
A train electromagnetic compatibility risk assessment model based on combined weighting and deep neural networks is proposed to address the complex electromagnetic environment of high-speed railway signaling equipment and the limited capability of conventional pass/fail electromagnetic compatibility (EMC) tests in supporting fault precursor identification. First, a Cube risk evaluation system with threat, vulnerability, and consequence dimensions is established. A three-layer progressive assessment architecture is constructed. Second, subjective and objective weights are fused using analytic hierarchy process (AHP), entropy weight method, and game theory. A static assessment model is built by combining technique for order preference by similarity to ideal solution (TOPSIS) and cloud model for risk ranking and uncertainty characterization. Third, a multilayer perceptron network is designed to output comprehensive risk physical quantities and cloud model features under four operation scenarios, enabling dynamic risk prediction under unknown conditions. Finally, surge immunity tests on axle counter equipment validate the model. The model’s discrimination of the optimal grounding scheme matches the measured results. The proposed model system has good reliability and engineering applicability, providing quantitative decision support for EMC risk management in rail transit.
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Liu Jianhua, Dai Wei, Wang Lanhao, Fan Ruipeng, Li Jielong
2026,40(8):171-183,
Abstract:
The working environment of vibrating screens is complex and variable, and the distribution of vibration data is easily disturbed and changes, leading to a decline in the generalization ability of the model. Additionally, the occurrence frequencies of different fault types vary, resulting in differences in the number of samples between fault categories. To address above issues, a cross-domain fault diagnosis method for vibrating screens with class imbalance, combining cloud oversampling and domain adaptation geometric constructive network, is proposed. Firstly, by integrating feature extraction methods from signal processing and statistical analysis, a joint analysis of time domain, frequency domain, and time-frequency domain is conducted to extract key statistical features that comprehensively reflect the dynamic characteristics of vibration signals. Secondly, to balance the number of samples of each class in the dataset, the uncertainty transformation mechanism of the cloud model is introduced to enhance minority class samples in the cloud feature space. Finally, the maximum mean and covariance difference is introduced to reduce the global feature distribution difference, constructing a domain adaptation geometric constructive network fault diagnosis model, thereby enhancing the model’s generalization ability. Taking the actual industrial cross-domain data of vibrating screen as an example, the results show that, compared with cross-domain and class-imbalanced fault diagnosis methods such as convolutional neural networks （CNN）, random vector functional link neural network （RVFLNN）, and active broad transfer for class-imbalanced learning （ABTCI）, the average fault diagnosis accuracy of this method is above 0.93. Furthermore, its classification accuracy in various diagnostic tasks is also higher than these methods, further demonstrating the effectiveness and superior generalization ability of this method.
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2026,40(8):184-196,
Abstract:
To enhance segmentation accuracy and robustness for complex road structures under severe occlusion and fragmentation in high-resolution remote sensing images, this study proposes MSAR-Net—an attention refinement network integrating multi-scale residual learning and efficient dual-kernel perception. The network adopts an encoder-decoder architecture, embedding a Multi-Scale Residual Learning module within the LinkNet50 backbone. This module partitions input features and processes them through heterogeneous-kernel cascading to achieve cross-scale semantic fusion, expanding receptive fields while preserving feature consistency. An efficient Dual-Kernel Attention Module is designed to jointly model spatial and channel dependencies via dual parallel convolutions with adaptive fusion mechanisms, strengthening long-range contextual modeling capabilities. During training, a combined Dice loss and BCE loss mitigates class imbalance and enhances recognition of thin road structures. Evaluated on three benchmark remote sensing road datasets (Massachusetts, DeepGlobe, and CHN6-CUG), MSAR-Net achieves F1 scores of 0.802 5, 0.782 3, and 0.781 7, with IoU values of 0.670 1, 0.642 4, and 0.641 6 respectively. Key metrics surpass state-of-the-art methods by average margins of 0.72% (F1) and 1.20% (IoU). Ablation studies and large-scale image tests confirm superior generalization capability and topological preservation in complex urban scenarios with dense occlusions. The framework effectively mitigates limitations of conventional methods regarding insufficient receptive fields and road fragmentation while maintaining computational efficiency, providing a precise, stable, and generalizable solution for high-resolution remote sensing road extraction.
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Shi Yuxing, Wang Hongjun, Jin Zhilei, Han Fengxia
2026,40(8):197-206,
Abstract:
Carbon fiber reinforced polymer (CFRP) is widely used in aerospace and other fields. However, assembly drilling is easy to cause stress concentration, resulting in local damage expansion and bearing capacity reduction. It is urgent to study the mechanism of local damage generation and expansion of carbon fiber composite plates and predict their failure. A multi-scale analysis method for tensile progressive damage of carbon fiber composites is proposed, and the whole process numerical simulation prediction of micro-macro mechanical properties and tensile damage of CFRP laminates are realized. Firstly, the representative volume element (RVE) is established, and the periodic boundary conditions are set to obtain the mechanical properties of the material. Then, the finite element model of CFRP laminates is established, and the progressive damage multi-scale prediction model is established by using the stress form Hashin damage criterion and the linear damage evolution based on fracture energy. The tensile simulation of laminates with holes was carried out. The established progressive damage model can accurately predict the load-displacement curve of laminates, and the error between the maximum load and the test result is only within 9.3%. Finally, the effects of different hole depths on the tensile ultimate load and damage behavior of perforated laminates were studied. The results show that the ultimate bearing capacity of laminates decreases significantly with the increase of hole depth. The damage starts from the matrix cracking of the 90°plies at the edge of the hole, and gradually expands to the 0 °plies in the hole area. As the load increases, the damage is further transmitted to the internally intact 0 °main bearing layer that has not been drilled, resulting in continuous degradation of the bearing capacity. The increase of the hole depth not only reduces the effective bearing area, but also changes the initial position and evolution path of fiber damage in the 0 °plies, weakens the inter-layer load redistribution ability and accelerates the structural failure.
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Chen Liyang, Wang Wenyu, Du Zelei, Zhu Zhoutao, Li Yuhang, Chen Zhangping, Kong Yaguang
2026,40(8):207-215,
Abstract:
Aiming at the challenge of efficiently and accurately evaluating the stress state of plate components in fields such as aerospace and energy equipment during processing and service, a high-precision ultrasonic stress detection and 3D visualization analysis system based on critically refracted longitudinal (Lcr) waves has been developed. This system is designed to provide a non-destructive, rapid, and quantitative technical means for assessing the stress state of components. Based on the acoustoelastic effect of Lcr waves, a quantitative relationship model between stress and the difference in ultrasonic wave propagation time was established. The system adopts a hardware-software co-design architecture: the hardware integrates a high-voltage spike pulse excitation circuit with a peak voltage of 300 V, an AD module with a maximum sampling frequency of 500 MS/s, and an ultrasonic probe and wedge combination that meets the first critical angle incidence condition. The software optimizes waveform data using wavelet hybrid threshold denoising technology, combines the cross-correlation method to calculate the acoustic time difference, and achieves accurate stress resolution while also providing 3D visualization of the stress field. In experimental validation on Q235 steel specimens, the system achieved a measurement error of less than ±10 MPa within a stress range of 0 MPa to 150 MPa, with a single-point measurement time under 0.5 seconds. It demonstrated significant advantages in measurement accuracy, efficiency, and ease of operation, enabling high-precision non-destructive testing of stresses in plate components and meeting the requirements for efficiency and reliability in engineering field stress detection.
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2026,40(8):216-227,
Abstract:
To address the challenges of difficult target detection and degraded detection performance in remote sensing images caused by adverse weather conditions such as rain, snow, and fog, this study proposes a remote sensing target detection model integrated with image enhancement, namely you only look once-image detail enhancement (YOLO-IDE), based on the YOLOv11 architecture. Firstly, inspired by the GFFA dehazing network, an image detail enhancement (IDE) module is designed to mitigate the impact of degraded features on the detection task. The enhancement module is integrated with the detection network into an end-to-end architecture to improve target detection performance. Meanwhile, the original C3k2 module in the YOLOv11 backbone is replaced with the CSP efficient dual layer aggregation networks (CSP-EDLAN) module. By adopting dual convolution techniques, the proposed module enhances feature representation while alleviating excessive computational consumption. Furthermore, the original Conv module is replaced with a receptive field dual attention convolution (RFDAConv) module, which expands the receptive field to suppress background noise and thereby improve target detection accuracy. The three components function in sequential coordination, first improving the quality of input images, then optimizing feature representation capability, and finally denoising deep-level features to refine outputs. Finally, experiments are conducted on both real-world and synthetic datasets under adverse weather conditions. The results show that, compared with the baseline YOLOv11 model, the proposed model achieves improvements of 2.4% and 2.3% in mAP@0.5, as well as 4% and 1.3% in mAP@0.5:0.95, respectively. These results verify the effectiveness and feasibility of the proposed method.
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Gou Yang, Li Cong, Zheng Fanghua, Fu Xin, Ye Shenhai
2026,40(8):228-239,
Abstract:
Enhancing the bandwidth and output power of Tonpilz underwater acoustic transducers is critical for improving sonar detection performance. Constrained by limited space in underwater platforms, conventional methods—such as adjusting transducer dimensions or adding acoustic matching layers—are impractical for boosting power output and broadening operational bandwidth. This study therefore proposes a novel radiating head structure design combined with wideband impedance matching. Parameter optimization of the structure was conducted via finite element analysis. Subsequently, an active power calculation model incorporating residual vibration damping resistors was established by extending the conventional Krimholtz-Leedom-Matthaei (KLM) equivalent circuit model with an impedance matching network. Finally, component parameters of the network were calculated using an adaptive Gauss-Newton algorithm, with the objective of minimizing the average impedance across the target frequency band. Experimental results demonstrate that, compared to traditional transducers, the proposed design achieves a 4~13 dB increase in transmitting voltage response (TVR) within the operational bandwidth, triples the active power output, broadens the bandwidth by 1.5 times, and reduces residual vibration interference in excitation signals by 90%. These improvements significantly enhance the acoustic radiation characteristics of Tonpilz transducers, providing a solid foundation for advancing the performance of domestically produced underwater acoustic detection equipment.
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Xu Luyao, Xu Zhenying, Yang Yulong, Yu Chenchen, Chen Zhuyan
2026,40(8):240-250,
Abstract:
To address the critical limitation of single-point temporal monitoring data in characterizing porosity during laser cladding, this study proposes a porosity prediction framework based on multi-view temporal modeling. A ViT-based multi-view temporal fusion architecture is developed, which integrates “intra-view temporal modeling + inter-view information interaction” and incorporates residual attention connections to enhance gradient flow and feature diversity in deep networks. In single-view temporal modeling, an independent feature learning module (IFLM) is employed to extract image and thermal field features, while a temporal dependency encoding module (TDEM) captures long-range dependencies to form high-quality view-level temporal representations. For multi-view information interaction, a cross-view attention module (CVAM) is designed to guide feature exchange, and a multi-view fusion encoding module (MVFEM) is introduced to achieve unified encoding and global context modeling, thereby balancing interactive and integrative fusion. Results show that CVAM improves prediction accuracy to 97%, while MVFEM further increases it to 98%, the overall framework achieves accuracy in porosity prediction is 98%, significantly improved compared to existing methods.
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Yang Guangyu, Xing Xuening, Wang Yajing, Shen Jin, Liu Wei, Ming Hu, Ma Longfei, Zhang Qi
2026,40(8):251-265,
Abstract:
Neural network shows certain superiority in the field of dynamic light scattering inversion. However, the robustness and accuracy of the inversion results can be influenced by different training samples and network models. To solve this problem, an inversion method combining information bias weighting (IBW), convolutional neural network (CNN), and Transformer encoder (CNN-Transformer) is proposed. This method establishes a three-layer framework consisting of training sample generation, local feature extraction, and global dependency modeling. It uses the particle size distribution (PSD) obtained from IBW inversion as prior information to generate reliable training samples and utilizes the CNN-Transformer model to extract local features of the electric field autocorrelation function and perform global dependency modeling, thereby achieving an accurate PSD. The inversion results of simulated data with four different widths indicate that, compared with Tikhonov-PGRNN and IBW, the CNN-Transformer method achieves higher accuracy, stronger noise resistance, and better bimodal resolution for different particle systems. The experimental data inversion further validates the conclusions drawn from the simulated data. The peak position errors of 100, 203, 555 nm unimodal and 33 nm/203 nm, 300 nm/502 nm bimodal particle systems inverted by CNN-Transformer are 0, 1.48%, 0.90%, and 2.42%/0.06%, 1.27%/0.86% respectively, which are better than the inversion results of Tikhonov-PGRNN and IBW.
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Geng Liqing, Zhu Xinying, Wang Ke, Hu Jianghong
2026,40(8):266-274,
Abstract:
To address issues such as artifacts and blurred details in super-resolution images of chip surface defects generated by traditional super-resolution generative adversarial network （SRGAN), this paper proposes a method for chip surface image super-resolution reconstruction based on an improved SRGAN. Firstly, the pyramid context extraction module (PCEM) is introduced into the generator network. This module effectively integrates multi-level information and details of the image through the pooling and concatenation of multi-scale feature maps, combined with a contextual attention mechanism. This significantly enhances the super-resolution reconstruction performance, resulting in clearer details in the generated images. Secondly, the rectangular self-calibration module (RSCM) is introduced into the generator network. By modeling context in horizontal and vertical directions, it adaptively calibrates key regions with rectangular geometric features, thereby effectively extracting the structural edges and directional textures of chip images and enhancing the model’s ability to represent chip structural information. Finally, the mobile network version 3 Small (MobileNetV3 Small) is introduced into the adversarial network. This makes the super-resolution reconstruction model more lightweight, lowers hardware deployment requirements, and simultaneously strengthens feature extraction capabilities. Experimental results demonstrate that the improved algorithm increases the peak signal-to-noise ratio (PSNR) by 5.53 dB and improves the structural similarity index (SSIM) by 4.2%, effectively enhancing image quality. This provides a novel solution for the deployment of high-precision chip surface defect detection technology in industrial scenarios.
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Chen Xianyi, Luo Xinghuan, Zhao Jingyu
2026,40(8):275-288,
Abstract:
To address the issues of uniform repulsive influence range in traditional artificial potential field methods that lead to path redundancy, low planning efficiency, and susceptibility to local optima in complex multi-obstacle environments, an improved artificial potential field path planning method based on adaptive elliptical repulsive field is proposed. First, an adaptive elliptical repulsive field theory is constructed, utilizing LiDAR to obtain real-time geometric features of obstacles and dynamically adjusting the elliptical repulsive influence range based on multi-dimensional factors. Second, a virtual target point generation method based on dual-circle strategy is designed, determining the optimal escape direction through geometric construction to effectively solve the local optimum problem. Finally, the algorithm performance is validated through simulation experiments and mobile robot physical platform verification. Simulation results show that in complex discrete obstacle environments, compared with existing improved methods, the proposed algorithm reduces runtime by 48.19%, path length by 4.67%, and path deviation by 5.65%, with significantly lower escape mechanism trigger frequency. Physical experiment validation demonstrates that compared with existing improved methods, the proposed algorithm achieves optimization of 20.15%, 14.95%, and 5.95% in runtime, path length, and path deviation respectively. Experimental results prove that the improved algorithm exhibits excellent performance in path planning efficiency, safety, and environmental adaptability, with good robustness and real-time performance, capable of meeting the path planning requirements of mobile robots in complex multi-obstacle environments.
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Zhou Ling, Huang Qian, Chen Ying, He Ling, Zhu Haoyuan
2026,40(8):289-300,
Abstract:
To address the issues of frequent random disturbances during the operation of wind turbine key components and the difficulty in identifying early-stage faults, this study proposes a wind turbine key component anomaly early-warning method based on a bidirectional long short-term memory (BiLSTM) network integrated with single-head attention (SA),whose hyperparameters are optimized by the RIME optimization algorithm (RIME).First, the supervisory control and data acquisition (SCADA) data of wind turbines are preprocessed, and a combined method of interquartile range and data binning is adopted to eliminate abnormal data. Second, the gray relational analysis method is applied, with the main bearing temperature and generator stator temperature set as the reference sequences respectively; relevant operational parameters with high correlation are selected as comparison sequences to construct the multi-dimensional input feature set of the model. Subsequently, the Single-Head Attention mechanism is introduced to capture long-term dependencies in the time series, and the RIME algorithm is used to optimize the hyperparameters of the BiLSTM model, thereby establishing a RIME-BiLSTM-SA multi-input single-output anomaly early-warning model. Furthermore, the exponentially weighted moving-average (EWMA) method is employed to analyze the residuals between the predicted values and actual values of the model, so as to determine the early-warning threshold. Finally, the proposed method is validated using measured SCADA data from a wind farm in East China. The results show that the RIME-BiLSTM-SA early-warning model proposed in this study can generate valid early-warning signals 12 hours prior to the occurrence of main bearing temperature anomalies and 32.5 hours prior to the occurrence of generator stator temperature anomalies.
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Shao Ziqiang, Wei Lisheng, Wu Tao
2026,40(8):301-312,
Abstract:
Aiming at the problems of multi-scale change and small target density, feature degradation caused by dynamic illumination and weather interference, as well as boundary box missing detection and positioning offset under overlapping occlusion, an efficient traffic image detection algorithm based on improved yolov11n is proposed. Firstly, the C3k2-DConv module is constructed by embedding dynamic convolution (DConv) in the C3k2 module of the network, which improves the sensory field expansion efficiency and dynamic feature capture capability; Secondly, the dynamic feature enhancement ASPP-LSKA module is designed to replace the backbone network spatial pyramid pooling-fast (SPPF) module with atrous spatial pyramid pooling (ASPP) module for dynamic capture of multi-scale context features and incorporates large separable kernel attention (LSKA) for more effective capture of long-range feature interactions; Additionally, the neck network is reconstructed by adding shallow feature maps to the neck layer to enhance the model’s perception ability of small targets, and combined with space to depth convolution (SPDConv) to alleviate the loss of fine-grained information in the downsampling process; Finally, Soft-NMS and DIoU-NMS are fused and replace the original NMS, which achieves retaining more bounding boxes while utilizing the distance information between the bounding boxes in order to further optimize the selection of bounding boxes and improve the bounding box accuracy. The experimental results showed that on the SODA-10M dataset, compared with the YOLOv11n model, the average detection accuracy mAP@0.5 and mAP@0.5:0.95 values increased by 4.1% and 6.7%, respectively; On the BDD100K dataset, the average detection accuracy mAP@0.5 and mAP0.5:0.95 values increased by 6.3% and 7.2%, respectively; On the VisDrone2019 dataset, the average detection accuracy mAP@0.5 and mAP@0.5:0.95 values increased by 8.5% and 6.8%, respectively. It can be seen that the proposed algorithm is effective and generalizable in traffic scene detection.
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2026,40(8):313-326,
Abstract:
Electronic products are developing toward miniaturization, complexity, and high performance. As a core functional carrier, the design density and manufacturing precision requirements of printed circuit boards (PCB) continue to increase, resulting in defects that are smaller and more similar to background features. Achieving high-precision, real-time defect detection while controlling costs is currently a critical technical challenge that needs to be addressed. To this end, this paper proposes a lightweight, high-precision detection framework based on an improved YOLOv11—global semantic exchange YOLO (GSE-YOLO). First, a deformable feature fusion (DFF) module is designed to dynamically adjust the receptive field and deformable convolution sampling positions, enhancing feature extraction and information interaction to provide rich feature representation. Next, a multi-layer exchange feature pyramid network (MRFPN) is constructed to retain shallow edge details guided by multi-layer semantics while reinforcing deep semantic representation. At the same time, a high-level semantic space is built to enable cross-scale and cross-location feature interaction, improving defect detection and perception capabilities in complex backgrounds. In addition, considering the characteristics of PCB defects, which are often tiny and appear in complex backgrounds, the YOLOv11 detection head (DH) is optimized to focus on small target regions, balancing accuracy and efficiency. On the HRIPCB public dataset, the model achieves 98.2% mAP@0.5 with 5.87×106 parameters and 13.4 GFLOPs computational cost, demonstrating its effectiveness and deployment potential for industrial PCB defect detection.
Volume 40,2026 Issue 8
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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.
