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Hu Yue, Fan Jianhua, Hu Yongyang, Wei Xianglin, Li Xu
2025,39(5):1-10,
Abstract:
To address the problem that the increased positioning errors in wireless positioning for intelligent vehicles caused by non-line-of-sight (NLOS) signals, a robust UWB/IMU fusion positioning methodology based on reliable identification of NLOS signals is proposed. Firstly, the coarse NLOS identification is conducted based on a support vector machine （SVM） learning model and a multi-sensor consistency mathematical model respectively. Subsequently, the fine NLOS identification model based on D-S evidence theory is designed to effectively integrate the results of the aforementioned models at the decision level. Finally, a multi-sensor adaptive fusion positioning method based on factor graph is proposed to dynamically adjust the fusion model according to the results of NLOS identification, in order to achieve robust positioning for intelligent vehicles in NLOS environments. The results of real vehicle experiments indicate that, in terms of NLOS identification performance, compared with the conventional SVM model, the proposed method improves the precision, recall and accuracy by 6.97%, 5.37% and 6.36% respectively. In terms of positioning performance, compared with the existing conventional least squares positioning method, the proposed method reduces the root mean square error, the maximum error, and the standard deviation by 12.55%, 63.40%, and 13.23%, respectively, effectively improving the positioning accuracy and robustness of intelligent vehicles in NLOS environments, and overcoming the shortcomings of traditional methods in low positioning accuracy and poor reliability in NLOS environments.
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Hu Chonglin, Gao Shang, Wang Hao, Yang Shangke, Jiang Jian
2025,39(5):11-18,
Abstract:
In response to industry challenges such as low sensitivity, limited detection area, and poor linearity in conventional methods for measuring ice thickness on aircraft surfaces, this study proposes a sensor array based on the nested split-ring resonator (NSRR) structure. This structure is characterized by strong radiation capability, low loss, high quality factor, strong field confinement effect, and ease of miniaturization. A sensor array composed of 72 nested split-ring resonator (NSRR) units (11×11 mm) was designed and fabricated, achieving a compact footprint of 88×99 mm. This system quantifies ice thickness by monitoring shifts in resonance frequency. ADS equivalent circuit simulations revealed a deterministic relationship between the NSRR’s equivalent capacitance and the array’s resonance frequency. HFSS electromagnetic simulations further demonstrated the array’s capability to detect media with varying dielectric constants and measure ice thickness at millimeter-scale resolution, with a simulated sensitivity of 23.46 MHz/mm. Experimental results further validate a strong linear relationship between the resonance frequency and average ice thickness, with a coefficient of determination (R2) of 0.989. The maximum detection sensitivity reaches 21.15 MHz/mm, with a maximum relative error of less than 5%. These findings demonstrate that the proposed sensor facilitates large-area, quantitative detection of average ice thickness on structural surfaces, offering advantages such as high sensitivity, extensive coverage, low cost, and scalability.
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Mei Xiaojun, Wu Huafeng, Chen Xinqiang, Xian Jiangfeng, Wu Zhongdai
2025,39(5):19-28,
Abstract:
Node localization is a critical technique for acquiring location information and has emerged as a fundamental technology within wireless sensor networks (WSNs). Localization accuracy in wireless sensor networks (WSNs) can deteriorate due to uncertainties in the transmit power (TP) and path loss exponent (PLE). To address this challenge, a coarse-to-fine third-order localization method (CFTL) is proposed. First, TP uncertainty is mitigated using differential forms. The problem is then reformulated into a natural constant-based least squares estimation (NC-LSE) framework through first-order Taylor expansion and logarithmic transformations, with coarse-grained positions obtained via a linear unbiased estimation method. Second, an optimization function with PLE as the variable is constructed, and the puma optimization (PO) algorithm is employed to estimate the PLE. Third, the optimized PLE is incorporated into the differential-based generalized trust region subproblem (DGTRS) framework, and the fine-grained position is calculated using the bisection method. Additionally, the generalized inverse theorem for block matrices is applied to derive the Cram-r-Rao lower bound (CRLB) under dual-parameter uncertainty, assessing the algorithm’s effectiveness. Simulation and practical results demonstrate that the proposed method enhances localization accuracy by at least 10.96% and up to 32.18% compared to existing methods across various conditions.
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Jiang Meijuan, Liu Xixiang, Sheng Guangrun
2025,39(5):29-39,
Abstract:
To address the issues of the large errors in received signal strength indicator (RSSI)-based ranging methods and the limited adaptability of the iterative method based on the scaling by majorizing a complicated function (SAMCOF), which cannot adapt to the instability of distance measurements and degrading localization precision, this paper proposes an adaptive weighting algorithm based on the extended Kalman filter (EKF) and multidimensional scaling (MDS). The algorithm first fuses the distance measurements obtained from RSSI and acceleration information using EKF to obtain optimized distance states. Then, the weights for different communication node pairs are dynamically adjusted based on the confidence of the distance states in the covariance matrix, and an optimized distance matrix is constructed for MDS-MAP positioning to obtain the initial positions. Finally, the SMACOF-based iterative optimization method is employed to refine the initial positions, reduce the negative impact of incomplete link observations and enhance positioning accuracy. Simulation experiments show that the proposed localization algorithm outperforms MDS-MAP, vMDS, and wMDS in various network distributions, communication radii, node numbers, and noise levels, improving positioning precision and robustness in dynamic networks. Additionally, the semi-physical experiment results of the positioning system based on ZigBee CC2530 validate the effectiveness of the algorithm’s effectiveness in both indoor and outdoor scenarios, overcoming the limitations of traditional methods in complex environments.
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Wang Xian, Gao Shang, Yang Shangke, Ma Lijun, Jiang Jian, Lei Yifei
2025,39(5):40-50,
Abstract:
Existing wireless passive strain sensors suffer from limitations such as single-direction measurement, low sensitivity and large size, making them unsuitable for strain state evaluation of large metallic structures including aircraft wings, under complex loading conditions. To address these issues, a miniaturized wireless passive strain sensor array is proposed based on split ring resonator(SRR) with the advantages of high radiation capability, low loss, and high quality factor and the principle of trigonometric functions and vector decomposition. The proposed sensor array consisting of three sensors arranged at 120° angles can reconstruct the magnitude and direction of strain field by extracting the resonant frequency shift. After the impedance parameters of the sensor are acquired by ADS software, the sensor structure miniaturization and impedance matching optimization design are carried out by HFSS software, aiming at the target of resonant frequency optimization. In addition, “force magnetic” coupling analysis in COMSOL software verifies the performance of sensor’s strain detection. Furthermore, the fabrication of the sensor is implemented based on the above analysis and optimization. Experimental results show that the sensitivity of proposed sensor in the electrical length and width directions is -1.517 and -0.732 kHz/με, respectively. The proposed sensor array achieves a strain magnitude detection accuracy within 8.5% and a direction detection error within 10°. The sensor array can achieve strain magnitude and direction detection on metallic surface with the ability of high sensitivity, compact size, and low cost.
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Zhang Yongxian, Chen Jingqi, Guan Fengjing
2025,39(5):51-58,
Abstract:
Ultra-wideband (UWB) technology applied in indoor localization is susceptible to non-line-of-sight, which will lead to a decrease in localization accuracy. To address this problem, a new UWB localization method is proposed. The coordinates of the labels are initially estimated using the intersection classification method. This approach is then combined with an adaptive covariance Kalman filter to optimize the estimated coordinates and ultimately reduce positioning errors. The intersection classification process involves defining circles with base stations as centers and the distances between the base stations and the tag as radii. The number of intersections between the base station circles is classified, and various methods such as line intersections, weighted circles, and weighted centroids are employed to calculate the tag′s initial position, referred to as the rough coordinates. To further refine the positioning, residuals are introduced to adjust the process noise and measurement noise parameters in the Kalman filter. Additionally, a two-stage forgetting factor is incorporated to update the covariance matrix. The rough coordinates serve as input to the adaptive covariance Kalman filter, which then produces the optimized tag coordinates. Experimental results demonstrate that method effectively reduces the maximum positioning error to 14.2 cm, with an average error of 7.65 cm and a total error variance of 2.47 cm. These improvements significantly enhance the accuracy and robustness of UWB-based indoor positioning systems, meeting the stringent demands of indoor localization applications.
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Jiang Xinyi, Qiao Guifang, Nie Xingang, Gao Chunhui, Tian Rongjia
2025,39(5):59-66,
Abstract:
The accuracy performance of industrial robots largely affects their applications in various fields. To realize the pose measurement and parameter calibration of parallel robots, a pose measurement system based on draw-wire sensors is designed. In order to meet the accuracy performance requirements, the pose measurement accuracy compensation method based on this system is proposed. Firstly, the kinematic model of the pose measurement system is established according to its mechanical structure. Secondly, the draw wire sensors error and structural parameter error of the pose measurement system are analyzed. The measurement error of the draw-wire sensor is reduced to less than 0.1mm by error fitting. Finally, the measurement accuracy and calibration effect of the posture measurement system are verified through experiments. The experimental results show that the average positioning error and attitude error of the posture measurement system after calibration are 0.216 mm and 0.055°, respectively. The proposed posture measurement system is applied to calibrate the 6 DOF Stewart parallel robot. The average pose error of the calibrated 6 DOF Stewart parallel robot is reduced from (2.706 mm, 1.067°) to (0.778 mm, 0.493°). Therefore, the proposed posture measurement system based on draw-wire sensors can satisfy the requirements of pose measurement and kinematic parameter calibration for parallel robots.
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Huang Chao, Huang Yuxin, Yang Zebin, Zhang Yi
2025,39(5):67-74,
Abstract:
In environments where GPS signals are unavailable, SLAM algorithms relying solely on visual-inertial odometry can achieve local accurate positioning, but they suffer from significant accumulated errors during long-distance movements, leading to decreased positioning accuracy. Although GPS can provide global location information, its performance is often unstable in complex environments such as urban canyons, tunnels, and indoor spaces, where signals are easily blocked or interfered with, limiting its applicability. To address aforementioned issues, the VIG-SLAM algorithm is proposed, which integrates a tightly-coupled visual/inertial/odometer positioning system with GPS data. First, a GPS accuracy factor model and anomaly detection mechanism are developed to evaluate and dynamically select high-quality GPS data suitable for fusion. Second, an improved adaptive time difference compensation strategy is proposed to solve the problem of timestamp mismatch between GPS and VIW systems. At the same time, the weight of GPS signal is dynamically adjusted in time difference compensation to improve positioning accuracy and robustness in complex environments. Finally, a global pose graph optimization model with GPS constraints is constructed, using GPS global positioning information as a global constraint to complement VIW local positioning, achieving robust positioning in large-scale environments. The proposed method’s effectiveness is validated on public datasets and real-world experimental scenarios, with results showing that the average positioning accuracy of VIG-SLAM algorithm improves by at least 15% compared to current mainstreamvisual SLAM algorithms, demonstrating strong robustness and accuracy advantages.
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Hu Zijian, Sun Changhe, Hei Chuang, Luo Mingzhang, Du Guofeng
2025,39(5):75-83,
Abstract:
Under the “double carbon” goal, steel structures are widely used in a variety of large-scale industrial fields. However, steel structures are susceptible to various weld defects due to production process, environment and other factors during connection welding. These defects reduce the stability and service life of the steel structure. Timely detection of defects is important for damage assessment and repair. Aiming at the existing limitations of detecting weld defects in steel structures, it is difficult to balance the detection resolution and detection range. A sixteen-channel high-resolution piezoelectric ultrasonic transducer linear array is designed. Firstly, the structure of the piezoelectric ultrasonic transducer linear array is determined through the optimization of the theoretical model and the device is fabricated. Secondly, in order to test the performance of the developed line array probe, the electrical impedance and ultrasonic performance are tested. The test results show that the deviation between the theoretical design resonant frequency and the experimental test results is less than 5%, and the average -6 dB relative bandwidth of each array element is 79.33%. A broadband linear array probe is successfully designed and developed through the design of acoustic impedance matching layers and wedges. Finally, the linear array probe was used to detect and analyze three types of hole defects with internal diameters of 2 000, 1 000 and 500 μm respectively in a 25 mm thick standard welded test block. The results show that all can be effectively identified and the detection resolution is better than 500 μm, providing technical support for the accurate detection and early warning of tiny defects in thick steel plate welds.
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Cao Ruikang, Li Yansong, Geng Cong, Liu Yilun, Liu Jun
2025,39(5):84-94,
Abstract:
Optical current sensor (OCS) is very sensitive to temperature changes, and temperature changes lead to errors in its measurement, which makes it difficult to meet the requirements of power system metering. Therefore, accurate prediction of OCS measurement errors caused by temperature changes is of great significance for monitoring its operational stability and ensuring the safe operation of the power system. Since the OCS output current is strongly nonlinear due to the influence of temperature, this paper proposes a radial basis Koopman-Kalman prediction algorithm for nonlinear power systems, which solves the problem that the OCS output current is difficult to predict under the influence of temperature due to strong nonlinearity. Firstly, the nonlinear OCS output current state quantities are mapped into the high-dimensional space to form an extended state by the radial basis function (RBF), and the extended state is decomposed by the extended dynamic mode decomposition (EDMD) algorithm to calculate the approximate Koopman-Kalman algorithm in the high-dimensional space. Koopman operator approximation matrix. Secondly, the approximated Koopman operator is used for batch prediction in the high-dimensional linear space. Finally, Kalman filtering is used to update the correction to the last prediction of the batch prediction to follow the state change of the system. The OCS temperature-current data obtained from experimental measurements are used for experiments, and the results show that the mean square error （MSE） of the prediction algorithm proposed in this paper is reduced by more than 90% in comparison with both the standard Koopman prediction and the LSTM prediction for different temperature variations, which proves the effectiveness of the proposed algorithm.
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2025,39(5):95-102,
Abstract:
Aiming at the autonomous parking environment in which the map building and localization accuracy are limited by using only LIDAR sensors, a map building and localization method I-LOAM based on the inertial measurement unit (IMU) tightly coupled with LIDAR is proposed for autonomous vehicle parking scenarios.Firstly, IMU pre-integration of the point cloud data is performed at the front-end, point cloud preprocessing to remove the ground point cloud and reduce the point cloud scale to ensure the efficiency of laser odometry. Secondly, an S-ICP algorithm based on sample consensus initial alignment (SAC-IA) coarse processing and optimized iterative closest point (ICP) fine alignment is proposed, which complements the tightly coupled positioning algorithm with IMU and LiDAR to provide the best solution for autonomous parking. The S-ICP algorithm is complementary to the tightly coupled IMU and LiDAR localization algorithm, providing a more flexible and accurate map building and localization solution for the autonomous parking system. Then, the map is constructed by fusing IMU information, laser odometry and loopback detection information at the back-end. Compared with the LeGO-LOAM algorithm, the proposed algorithm’s rms error is reduced by 45%, 3% and 6% in outdoor, indoor and straight road scenarios, respectively, with better accuracy and robustness, which provides an accurate and reliable solution for map building and localization tasks in autonomous parking environments.
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Chen Guangqiu, Dai Yuhang, Duan Jin, Huang Dandan
2025,39(5):103-116,
Abstract:
Aiming at the problems of spectral information missing and target edge blurring in current infrared and low light level image fusion algorithms, a target difference attention algorithm and Transformer fusion algorithm for infrared and low light level image fusion are proposed. Firstly, a low-light level reconstruction network is constructed by using residual structure, and the perception loss is constructed by using VGG-16 to preserve the background texture and brightness information in the low-light level image to the maximum extent. Then, the feature extraction network is constructed by combining CNN and Transformer to extract the complete features of the image. At the same time, in the target differential attention module, the difference operation and feature extraction are carried out on the infrared image and low-light image, and the obtained infrared differential image is enhanced by the channel attention mechanism. Then the output feature map of CNN feature extraction network is added element by element to enhance the infrared target feature. Then, the high frequency and low frequency information of features are captured by gradient retention module to improve the retention of texture details. Finally, the feature reconstruction network is used to reconstruct the fused image. The experimental results show that the fusion results are not only more consistent with the human visual system, but also the objective evaluation indexes of MI and VIF are increased by 44.6% and 21.2%, respectively, compared with other fusion methods.
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Zhang Junxin, Hu Mei, Zhong Wenan, Sun Leyuan, Hu Peng, Ye Xin, Yan Zheng
2025,39(5):117-124,
Abstract:
With the rapid expansion of space launch infrastructure, modern launch sites face the critical challenge of parallel task scheduling for multiple launch vehicles (LVs) undergoing concurrent testing. The process involves sequential phases—LV arrival, testing, final assembly, transfer, and propellant loading/launch—each requiring dedicated or shared test facilities. Due to variations in LV configurations, certain test areas are mutually exclusive, while others have limited capacity (typically accommodating only one LV at a time). Under these constraints, achieving efficient multi mission parallel scheduling to minimize total completion time has become an urgent operational requirement. Analysis of domestic and international research since 2000 reveals that traditional methods, such as dual-code network diagrams, are inadequate for parallel mission planning. Conventional approaches like the critical path method (CPM) and value chain analysis lack robust quantitative capabilities for handling complex resource conflicts. To address these limitations in China’s space launch scheduling, this study proposes a genetic algorithm (GA)-based framework with a dual-layer encoding scheme. The algorithm dynamically adjusts population size and iteration counts based on the number of parallel missions, while the fitness function directly corresponds to the scheduling objective: minimizing mission duration under facility constraints. Case studies demonstrate the method’s efficacy. For a scenario involving five LVs, the algorithm generates optimized parallel schedules in under one minute, significantly outperforming manual dual-code network diagram construction. The proposed approach exhibits notable universality and extensibility: the encoding scheme can be customized to accommodate diverse LV workflows, enhancing practical applicability.
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Zhang Chuanying, Zhao Jingyu, Liu Yang, Bu Fanliang
2025,39(5):125-133,
Abstract:
To address the limitations of the fixed beamformer in the generalized sidelobe canceller (GSC) in suppressing sidelobe interference and processing non-stationary speech signals in complex environments, this paper proposes an improved GSC-based speech enhancement method utilizing a parameterized beamformer.The proposed method employs a dynamic tuning mechanism to flexibly balance and adjust between the delay-and-sum (DS) beamformer and the super-directive (SD) beamformer, effectively suppressing sidelobe interference and enhancing the robustness and adaptability of the GSC in complex acoustic environments. Furthermore, a cross-correlation coefficient is introduced to regulate the step size of the adaptive filter weight update, mitigating the over-attenuation issue caused by variations in speech signals and improving the processing accuracy for non-stationary speech signals. Simulation experiments were conducted in MATLAB to evaluate the performance of the proposed method under various noise conditions, including Babble noise, music noise, and white noise. The performance of the proposed method was compared with that of the traditional GSC and the GSC with the least mean square (LMS) algorithm. The evaluation was carried out from multiple perspectives, including 3D beam patterns, noise reduction effects under different background noise and parameter conditions, and the effectiveness of the cross-correlation coefficient. Quantitative analysis was performed using performance metrics such as segmental signal-to-noise ratio (SNR) and perceptual evaluation of speech quality (PESQ).The results demonstrate that the proposed method significantly outperforms the traditional GSC in terms of noise reduction performance and speech clarity. In environments with Babble noise, music noise, and white noise, the segmental SNR improved to 11.02, 6.14, and 10.33 dB, respectively, while the PESQ values increased to 3.65, 3.20, and 3.25, respectively. By adjusting the parameters, the proposed method achieves optimal noise reduction effects in different noise environments, validating its effectiveness and superiority in complex acoustic scenarios.
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Li Taochang, Li Jianzhang, Hou Limin, Jin Haibo
2025,39(5):134-143,
Abstract:
The paper proposes a robot adaptive admittance control method based on the multi-agent deep deterministic policy gradient (MA-DDPG) to address the issue of low trajectory accuracy in fixed-parameter active compliance control caused by modeling errors, such as uncertainty in robot internal parameters. Firstly, an admittance controller is established based on the robot model. Secondly, by integrating the DDPG algorithm with the admittance control framework, an adaptive admittance controller is developed, wherein the DDPG-based agent dynamically generates optimal admittance parameters. To address issues of slow convergence and unsatisfactory control performance, the concept of multiple agents is introduced into the adaptive admittance control algorithm, with each agent responsible for optimizing an individual admittance control parameter. The MA-DDPG algorithm, based on a centralized training and distributed execution architecture, is employed to optimize the admittance controller parameters. Finally, the feasibility and effectiveness of the proposed method are validated through a comparative analysis between the impact of deep reinforcement learning simulation training and the experimental outcomes of adaptive admittance control on the anticipated trajectory. The experimental data demonstrate that in comparison with adaptive admittance control based on alternative deep reinforcement learning algorithms, the proposed method exhibits a 65.88% improvement in convergence speed and a 63.35% enhancement in trajectory accuracy.
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Chen Guangqiu, Ren Tianrong, Duan Jin, Huang Dandan
2025,39(5):144-154,
Abstract:
To solve the problems of poor segmentation effect and unclear target edge segmentation of single-modal visible RGB image semantic segmentation at night or in the environment of light change, and there are still many shortcomings in the existing cross-modal semantic segmentation networks when obtaining global context information and fusing cross-modal features. This paper proposed a cross-modal semantic segmentation algorithm based on dual-branch multi-scale feature fusion. The Segformer is used as the backbone network to extract features and capture long-distance dependencies. The feature enhancement module is used to improve the contrast of shallow feature maps and the discrimination of edge information. The effective attention enhancement module and cross-modal feature fusion module are used to model the relationship between pixels of different modal feature maps, aggregate complementary information, and give full play to the advantages of cross-modal features. Finally, the lightweight All-MLP decoder was used to reconstruct the image and predict the segmentation result. Compared with the mainstream algorithms in the existing literature, the proposed algorithm has the best evaluation indicators on the MFNet urban street view dataset, and the mAcc and mIoU reach 76.9%and 59.8%respectively. Experimental results show that the proposed algorithm can effectively improve the problem of unclear target edge contour segmentation and improve the accuracy of image segmentation when dealing with complex scenes.
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Zhao Shen, Zhu Haoran, Zhou Chao, Li Wei, Zhang Rui
2025,39(5):155-165,
Abstract:
In the application of detecting and locating low-altitude drones using acoustic signals, the existing planar microphone arrays face problems such as low directional resolution and poor interference resistance. The research focuses on addressing these challenges by establishing a stochastic three-dimensional(3D) array optimization design model with multiple constraints, suitable for drone direction finding. Additionally, an optimization-solving method based on an elite-tournament selection strategy is proposed. Based on a four-ring 3D array configuration, the model minimizes the peak side-lobe level as the optimization objective while setting array structural constraints and limiting the beam main lobe width. A multi-parameter constraint optimization model for stochastic 3D arrays is constructed. Furthermore, an elite-tournament selection strategy is proposed for optimizing the solution process. The elite strategy and tournament strategy are combined into a multi-fusion selection strategy, which is applied during the iterative process of genetic algorithm optimization. This combination enhances the convergence rate of the algorithm and its global search capability, leading to the achievement of the optimized array configuration. Simulation and experimental results show that the direction finding pattern of the target array exhibits fewer false detection points, demonstrating improved noise immunity and spatial resolution. Compared to the four-ring 3D array, the target array reduces the detection failure rate for low-altitude drones by 4.33%, and the azimuth and elevation angle errors decrease by 1.54° and 0.73°, respectively. The maximum detection distance is improved by 12 m.
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2025,39(5):166-176,
Abstract:
Aiming at the problems of complex background environment and insufficient detection accuracy of small target defects in surface defect detection of wind turbines, an efficient surface defect detection method for wind turbines is proposed. Firstly, a backbone network with feature extraction and fusion capabilities is constructed, and an improved channel attention is introduced in the residual part to help the network better extract feature information. Secondly, a new generation of convolution deformation module is used for output, so that the model can better capture the correlation between space and time in the input data, simplify the model and improve the detection speed. Finally, an efficient spatial-depth information conversion module is introduced in the down-sampling part of the model to reduce the spatial dimension in the input feature map to the channel dimension, retain the salient features while reducing the loss of fine-grained information, and further improve the ability of the model to detect small targets.The experimental results show that compared with the YOLOv7 network, the accuracy of the proposed network is improved by 3.5%, the recall rate is improved by 2.3%, and the average accuracy is improved by 3.1% when the intersection over union is 0.5.In the data set 2 with better image quality, the accuracy rate reaches 96%, the recall rate reaches 94%, and the average accuracy reaches 96.7% when IoU is 0.5. The proposed model has obvious advantages in solving the problem of false detection and missed detection, and has faster detection speed. It is more suitable for application in the actual detection environment and has good engineering application prospects.
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Zhao Haijun, Chen Huayue, Cui Mengtian
2025,39(5):177-187,
Abstract:
In this paper, transmitted signal leakage(TSL) and propagation delay in wireless communication system are studied; Firstly, a baseband digital model of TSL pollution is established, the time variation of the polluted channel and the propagation delay to the receiver is considered in this model, and the TSL time-varying channel based on the first-order autoregressive model is analyzed and established. Secondly, the complex gain estimation of TSL time-varying channel is obtained based on the least mean square algorithm, and a discrete time observation model is established to implement the compensation of TSL pollution and propagation delay. The asymptotic performance expression of the compensation algorithm in the case of uncompensated fractional delay is obtained. Finally, a digital compensation algorithm based on the joint estimation of complex channel time-varying gain and fractional delay is proposed by analyzing the complex channel gain estimation and fractional delay influence in the case of synchronization. The simulation experiment results show that the proposed joint estimation compensation algorithm is not only effective and robust, but also has better signal interference ratio performance and lower complexity than the multi-tap LMS solution and other advanced schemes.
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Estimation of lithium battery health state based on optimal reconstructed health factor and RIME-SVR
Yang Dongxiao, Wang He, Dang Hongyu, Yuan Yuxuan, He Jiegong
2025,39(5):188-196,
Abstract:
In order to improve the estimation accuracy of lithium battery state of health (SOH), a novel estimation method combining the optimal reconstruction of Health Indicators and RIME-optimized support vector regression (RIME-SVR) is proposed. First, three measurable Health Indicators are extracted from the charging and discharging process of lithium batteries, and their correlation with SOH is verified using the Pearson method. Subsequently, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm is employed to decompose and reconstruct the health indicators. The optimal reconstruction method is determined through experimental validation, effectively reducing the interference of data noise and capacity recovery on SOH estimation. Finally, an SVR estimation model optimized by the RIME algorithm is established. The experiments are conducted using NASA battery degradation data. The results show that compared with particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms, RIME exhibits faster convergence speed and stronger global search capability when optimizing SVR parameters, significantly enhancing model performance. Moreover, the lithium battery SOH estimation model based on the optimal reconstruction of health indicators and RIME-SVR outperforms other models in the comparative experiments in terms of three indicators, achieving higher estimation accuracy and fitting degree. When the optimally reconstructed health indicator Dtv1+Ti1+Tdv1 is used as input, the model’s average mean absolute error (MAE) is below 0.37, root mean squared error (RMSE) is below 0.55, and the coefficient of determination is higher than 0.92, indicating good universality and robustness.
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Ran Ning, Shi Gaolang, Zhang Shaokang, HaoJinyuan
2025,39(5):197-207,
Abstract:
Remote sensing small target images often suffer from issues such as overly dense targets, small target sizes, and difficulty in feature extraction, leading to low detection accuracy for existing object detection algorithms. To address these problems, this paper proposes an SBC-YOLOv8 algorithm for remote sensing small target detection based on YOLOv8 and incorporates the SAHI slicing method. First, the SAHI slicing method is applied to slice the remote sensing small target images, effectively mitigating the problems of excessive target density and small sizes. Second, a Space-to-Depth module is added to the Backbone of YOLOv8 to enhance the network’s feature extraction capability, effectively addressing the challenge of extracting small target features. Then, a BiFPN feature fusion method is employed, and the original P5 layer is replaced with the P2 layer, strengthening the network’s multi-scale feature fusion ability and improving detection accuracy. Finally, the CSP-OmniFusion module is adopted to further address the difficulty of extracting remote sensing small target features. Experimental results show that, compared to the original YOLOv8 algorithm, the SBC-YOLOv8 algorithm with SAHI improvements yields a 23.4% and 18.3% increase in mAP@0.5 on the validation and test sets of the VisDrone2019 dataset, respectively; mAP@0.5∶0.95 increases by 17.4% and 12.4%, respectively. Additionally, on the CARPK and HRSID datasets, mAP@0.5 increases by 1.6% and 1%, and mAP@0.5∶0.95 increases by 6.1% and 2.7%, respectively. Therefore, the proposed algorithm effectively improves the detection performance of remote sensing small target images.
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Liu Yang, Liu Sirui, Xu Xiaomiao, Wang Zhujun
2025,39(5):208-216,
Abstract:
To address issues such as detail loss and inconsistent results across different brightness regions in the Zero-DCE network, an unsupervised low-light image enhancement algorithm based on the enhanced depth curve estimation network (EnDCE-Net) is proposed. This algorithm explores the potential mapping relationship between low-light images and unpaired normal-light images to achieve significant improvements in image quality under low-light conditions. First, a novel feature extraction network is introduced, which integrates multiple skip connections and convolutional layers, allowing for the effective fusion of low-level and high-level features. This enables the network to learn the key features of low-light images and enhances its ability to process them. Second, a set of joint no-reference loss functions is designed, emphasizing brightness-related features during the optimization process, which facilitates more efficient parameter updates and enhances the overall quality and effectiveness of the image enhancement. To evaluate the effectiveness of the proposed algorithm, comparative experiments were conducted on five publicly available datasets. Compared to the suboptimal algorithm Zero-DCE, the PSNR and SSIM on the reference dataset SICE were improved by 9.4% and 21%, respectively. On the no-reference datasets LIME, DICM, MEF, and NPE, the NIQE scores reached 4.04, 3.04, 3.35, and 3.83, respectively. The experimental results demonstrate that the proposed algorithm outperforms others, producing enhanced images with natural colors, balanced brightness, and clear details. Both subjective visual assessments and objective quantitative metrics show significant improvements over the competing algorithms, highlighting the excellence and advancement of the proposed method in image enhancement.
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Lei Yongheng, Wang Liming, Yang Ting, Fu Yiyuan, Liu Yixuan, Ye Fei
2025,39(5):217-226,
Abstract:
An improved online calibration method for weather radar based on ground clutter reflectivity has been proposed, aiming to promptly detect and correct measurement errors in reflectivity caused by radar hardware failures or performance degradation, thereby enhancing the accuracy of quantitative precipitation estimation. Utilizing the Gabella weather radar clutter identification algorithm, the unfiltered reflectivity data is labeled for ground clutter. Based on this, a fixed-dimensional ground clutter data matrix is obtained through resampling, and the frequency of ground clutter occurrence is statistically analyzed. The cumulative probability density function of the ground clutter data is calculated, and the reflectivity value corresponding to its 95% distribution is selected as the monitoring value to identify stable ground clutter data. The baseline value of the ground clutter reflectivity is then obtained using the metal sphere calibration method. Finally, the relative calibration adjustment value of the radar is calculated by the difference between the monitoring value and the baseline value. Sensitivity tests were conducted on the selection range of ground clutter data, range correction, and large-scale precipitation conditions, optimizing the screening threshold and significantly improving the sensitivity and reliability of online radar system bias calibration. Experimental results from the CINRAD/SA-D weather radar at Changsha Lianhuashan showed that on March 28, 2024, under large-scale precipitation conditions, the average hourly relative calibration adjustment value of the radar was 0.22 dB, with a standard deviation of 0.76 dB. The daily relative calibration adjustment value standard deviation in March was 0.75 dB, and a change in radar calibration constant of approximately 2 dB was effectively monitored from March 8 to 9. On September 11, the ground clutter reflectivity baseline value obtained from the metal sphere fixed-point scan was compared with the theoretical value, revealing a radar system bias of -0.13 dB and a standard deviation of 0.26 dB. This method effectively enhances the sensitivity and reliability of online monitoring of weather radar system bias, providing a feasible technical approach for the online calibration of weather radar.
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Liu Xingmou, Wei Chao, Yang Hui, Xiao Yao, Yang Ning
2025,39(5):227-240,
Abstract:
To investigate the fault states of automotive electronically controlled gear pumps under complex working conditions, it is necessary to achieve real-time monitoring of gear pump vibrations and extract the evolutionary characteristics of fault signals. Firstly, a dynamic mathematical model of gear pump was established, and the source and characteristics of gear pump vibration in normal operation and fault states were clarified through theoretical analysis, providing a theoretical basis for fault diagnosis. Then, based on the operating characteristics of the gear pump, an oil pump gear failure experiment platform was designed and built, which simulated the gear failure mode under various working conditions, conducted time-frequency analysis of the collected signals, and extracted the optimal indicators that reflected the fault information. parameter. Finally, conventional data processing methods and improved empirical modal decomposition of fully adaptive noise ensembles are used to compare and diagnose different fault modes under various operating conditions. The research results show that under the premise of ensuring the decomposition efficiency and fault identification accuracy, the proposed improved fully adaptive noise ensemble empirical modal decomposition method has increased by an average of 28.45% in decomposition time, and the fault of gear pumps at high speeds. The recognition accuracy rate has increased by 1.29%. The reliability and accuracy of this method are verified, and the theoretical basis and engineering reference for gear pump status monitoring and fault diagnosis technology are provided.
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Chen Xiao, Yan Hao, Zeng Zhaoyou
2025,39(5):241-250,
Abstract:
To address the issue of low accuracy caused by the single extraction of deep features in current bird sound recognition methods, this study proposed a DenseNet based bird sound recognition method with feature fusion. First, the Mel-spectrogram was extracted from bird sound signals as the network input. Then, DenseNet was used as the base network, and convolutional block attention module was integrated into the standard convolutional layer of all dense blocks dense blocks. The convolutional block attention module learns the feature representation of training set, determines the importance and correlation of different levels of bird song feature information, and further weights and fuses them according to channel and spatial dimensions, making the network pay more attention to the important feature channels and spatial positions in bird song features. Then, adding dropout block algorithm after the standard convolutional layer of dense blocks promotes the network to learn features from different regions in a more balanced manner, improves the network’s adaptability to new bird song data, and enables the network to better capture common features in the data. Subsequently, a deep feature extraction branch using transformer encoder was established for DenseNet to enhance the network’s ability to capture global information and long-distance dependencies in birdsong features. Finally, the deep features extracted by the two branches are fused to enrich the information content of the deep features. This method was tested in seven sets on the Xeno-Canto data set. Experimental results on the test data set show that the proposed method achieves an average accuracy of 88.65%, which is 10.83% higher than the EMSCNN method, 20.14% higher than the AlexNet method, 16.3% higher than the VGGNet method, and 4.28% higher than DenseNet. The experiment proved the effectiveness and progressiveness of the proposed method. It outperforms other comparative deep learning methods in terms of recognition performance and effectiveness.
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Zhang Dengpan, Lan Zheng, Du Yiheng
2025,39(5):251-261,
Abstract:
Short-term wind power prediction is crucial for power system scheduling and operational security. However, the accuracy of such predictions is severely compromised by the inherent strong randomness and non-stationarity of wind power data, as well as limitations in existing methods, including insufficient shape-preserving capability in data preprocessing, modal aliasing, and inefficient parameter optimization in prediction models. To address these issues, this paper proposes a novel hybrid framework combining a piecewise cubic hermite interpolating polynomial (PCHIP) with variational mode decomposition (VMD) for data preprocessing and a sparrow search algorithm (SSA)-optimized long short-term memory (LSTM) network for prediction. First, abnormal values in raw wind power data are identified and repaired using PCHIP, which preserves the local monotonicity and curvature of the original sequence through Hermite interpolation. Second, the preprocessed data are decomposed into four intrinsic mode components (IMFs) via VMD to capture multi-scale temporal features. Finally, the stabilized IMF sequences are input into the SSA-LSTM wind power forecasting model to yield prediction outcomes. Experimental validation using 21-day measured power data from a wind farm demonstrates that the proposed model achieves a fitting degree of 0.989 1 with actual values, improving prediction accuracy by 5.558% compared to conventional LSTM, thereby verifying the effectiveness and superiority of the method.
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2025,39(5):262-269,
Abstract:
The high pilot overhead of channel estimation (CE) poses a significant challenge that hinders the wider application of reconfigurable intelligent surfaces (RIS) in wireless systems. Two timescale CE strategy can reduce the pilot overhead effectively by leveraging the semi-stationary characteristic of the base station (BS)-RIS channels. However, this strategy is unsuitable for real-time CE due to its reliance on an iterative optimization algorithm for BS-RIS CE, which entails high computational complexity. This paper reconsiders the optimization method within the framework of two timescale CE strategy for BS-RIS channels. Firstly, after completing the received pilot data matrix, the CE equations are simplified to a second-order nonlinear rank-one optimization problem. Subsequently, the complex-valued matrix of received pilots in the gradient equations is decomposed in blocks and represented in real terms, and an optimization method based on principal eigenvalue approximation is proposed. The proposed method establishes a semi-closed-form relation between the received pilots and the channel parameters. For the scenario of Rician channel and typical antenna configurations, the simulation results show that the proposed method has lower computational complexity compared to the referenced iterative method. And it can reduce more than 85% pilot overhead when the channel coherent time of BS-RIS is 4 times of RIS-User channels. When the signal-to-noise ratio (SNR) of received pilots is less than 16 dB, the estimation accuracy surpasses that of the iterative algorithm. Consequently, the proposed CE method is more competitive in scenarios requiring high real-time CE or the scenarios that RIS is located far away from BS and close to users.
Volume 39,2025 Issue 5
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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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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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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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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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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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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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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.
