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Yin Zhuoyi, Yuan Fang, Cui Hanwen, Liu Sheng, Zhang Zhaofu, He Xiaoyuan
2026,40(1):1-19,
Abstract:
With the continuous advancement of integrated circuit technology, advanced manufacturing and packaging have greatly enhanced chip performance and integration density. However, large-scale structures, high-density interconnects, and multi-chip stacking also intensify reliability issues such as warpage, raising the risk of failure. To improve the stability and yield of semiconductor processes, accurate warpage measurement and analysis have become essential for process control. This paper provides a systematic review of the definition, measurement methods, and recent progress related to warpage in semiconductor packaging. The concept of warpage and the corresponding measurement standards are first clarified based on domestic and international specifications. Major measurement techniques—including contact methods, laser scanning, shadow moir, interferometry, fringe projection, and digital image correlation—are then introduced and compared in terms of their principles, advantages, and limitations. Literature statistics indicate that shadow moir is the most widely used method, while digital image correlation is rapidly growing due to its capability for strain and coefficient of thermal expansion measurement and strong scalability; fringe projection is also increasingly adopted for its high flexibility. Finally, this paper summarizes the current status of commercial warpage-measurement systems in terms of accuracy, field of view, and measurement principles, and outlines potential improvement directions for domestic instruments. This review aims to support the selection of warpage-measurement methods and the optimization of semiconductor processes, promoting the development of measurement technologies and the broader industry.
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2026,40(1):20-32,
Abstract:
The recycling of retired power batteries is about to reach a peak. The secondary use of retired batteries can effectively avoid the waste of resources and environmental pollution. State of health (SOH) assessment is a key evaluation index for the secondary use. But traditional methods for estimating the SOH have the disadvantages of time and energy consumption. This paper proposes a rapid estimation strategy for the SOH of retired batteries based on resting voltage curves. After discharging the retired batteries to the same voltage lower limit, there will be differences in the state of charge (SOC) of different SOH batteries. Eventually, the resting voltage curves of different SOH batteries varies. By analyzing the health characteristics from this resting voltage difference, this paper achieves rapid estimation of the SOH of retired batteries. There are outliers in data collection due to acquisition errors, which can lead to incorrect training of regression algorithm models in the training set. In order to address this problem, this paper analyzes the impact of the number of outliers on the accuracy of regression algorithm estimation of SOH. This paper proposes using the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm to identify these outliers. The identifications of outliers can avoid the impact of outliers on the accuracy of SOH estimation model and effectively improve the accuracy of SOH estimation.
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Pang Zheyuan, Yang Kun, Song Zhengxiang, Meng Jinhao
2026,40(1):33-42,
Abstract:
To rapidly and efficiently evaluate the state of health (SOH) of substation backup power batteries and address the issue of low estimation accuracy in data-driven methods due to the lack of actual operational data, this paper proposes a SOH estimation method for substation batteries that combines electrochemical characteristics and Gaussian process regression (GPR). Traditional studies that use characteristic parameters obtained from single aging experiments struggle to accurately reflect the actual aging conditions of lead-acid batteries used in substation backup power. Starting from the electrochemical essence of the battery, this method designs float charging and cyclic aging experiments to collect electrochemical impedance spectroscopy (EIS) data under different aging mechanisms. Subsequently, highly representative electrochemical characteristic parameters are extracted using Pearson correlation analysis and grey relational analysis. The combination of these two experimental aging characteristics more closely approximates the actual aging characteristics of the battery, effectively improving the quality and efficiency of the training data and reducing the amount of training data required. Finally, these extracted characteristic parameters are used to train the GPR model to achieve accurate SOH estimation for actual substation batteries. The results show that the absolute error (AE) in estimating the SOH of randomly selected substation batteries is less than 0.08, with an average absolute error (MAE) of 0.033 0 and a root mean square error (RMSE) of 0.038 6. This method does not require the collection of actual data and can effectively estimate the SOH of substation batteries with a small amount of experimental data.
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Wu Wenjin, Wu Jing, Zhan Wenfa, Zha Shenlong, Su Jianhui
2026,40(1):43-50,
Abstract:
Owing to the shortcomings of current state of charge (SOC) estimation algorithms, such as poor stability and large error, a new algorithm based on the integration of adaptive extended Kalman filter (AEKF) and short term memory (LSTM) based on real vehicle cloud discharge data was proposed to predict SOC of small-power electric vehicles. Adaptive forgetting factor least square method (AFFRLS) was used to identify the second order RC equivalent circuit model parameters of the battery. Secondly, the real-time discharge data collected by the cloud is taken as the research target, and the AEKF-LSTM fusion algorithm is used to predict the battery SOC of small-power electric vehicles. The AEKF-LSTM fusion algorithm takes the terminal voltage, current, temperature at the current moment and the SOC of the battery at the previous moment as inputs, and uses the updated SOC as the output to train the estimation model. Finally, compare the battery SOC prediction results of the AEKF-LSTM fusion algorithm and the single AEKF algorithm with the actual SOC values. The experimental results show that the root mean square error (RMSE) of the AEKF-LSTM fusion algorithm is 0.005 8 V, and the mean absolute error (MAE) is 0.004 1 V. Compared with the AEKF algorithm, its RMSE is reduced by 0.008 7 V and its MAE is reduced by 0.116 4 V, and both RMSE and MAE of AEKF-LSTM fusion algorithm are less than 0.6%. It is proved that the fusion algorithm has high estimation accuracy and strong robustness.
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Cheng Zhuming, Chang Xianlei, Hu Xuefeng, Zhao Gongchen, Wang Chao
2026,40(1):51-60,
Abstract:
The unscented Kalman filter (UKF) is a commonly used algorithm for estimating the state of charge (SOC) of lithium-ion batteries. However, in practical applications, due to uncertainties such as external environmental temperature variations, battery capacity degradation, and non-Gaussian process noise, further improvements in algorithm performance are required to ensure more accurate estimation. Thus, an improved unscented Kalman filter algorithm (PO-RUKF) is proposed. Firstly, H∞ filtering is introduced into the UKF to enhance robustness, mitigating the effects of various disturbances. Secondly, the parrot optimization algorithm is employed to adaptively adjust the process noise covariance matrix of the UKF, addressing the issue of prior determination of filter noise parameters and thereby improving filtering accuracy. Finally, experimental validation is conducted using two publicly available datasets from the university of Maryland under FUDS and HPPC conditions. The results demonstrate that under varying temperatures, battery capacity degradation states, and different operating conditions, the improved algorithm achieves higher SOC estimation accuracy compared to traditional UKF and robust UKF algorithm, with an average absolute error of less than 0.50% and a root mean square error of less than 0.56%. Additionally, the improved algorithm exhibits stronger robustness and universality. It is proved that the proposed method can provide more reliable and effective technical support for SOC estimation of lithium ion batteries.
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Lei Xiaoben, Hu Xinhua, Wang Hao
2026,40(1):61-69,
Abstract:
The state of charge (SOC) prediction of aviation nickel-cadmium batteries is a critical technology for ensuring the safe operation of aircraft. To address the issues of insufficient accuracy and poor environmental adaptability in traditional prediction models, this study proposes a hybrid WOA-RF prediction model that combines the whale optimization algorithm (WOA) with random forest (RF). Firstly, an initial prediction model is constructed based on the random forest regression algorithm, leveraging its multi-decision tree ensemble advantage to handle nonlinear features. Secondly, the whale optimization algorithm is introduced to globally optimize the core hyperparameters of the random forest, resolving the inefficiency of manual parameter tuning and thereby enhancing the model’s prediction accuracy and generalization capability. To validate the model’s performance, discharge cycle experiments were conducted under different temperature conditions (20 ℃, 0 ℃, -10 ℃, -20 ℃), and the prediction results of the WOA-RF model were compared with those of traditional RF, backpropagation neural network (BPNN), support vector regression (SVR), as well as particle swarm optimization RF (PSO-RF) and genetic algorithm-optimized RF (GA-RF) models. The experimental results show that under standard temperature conditions, the WOA-RF model achieves a mean absolute error (MAE) of 1.22%, a coefficient of determination (R2) of 0.986, and a root mean square error (RMSE) of 1.56%, outperforming the comparison models. In low-temperature environments, the WOA-RF model maintains an MAE below 1.5%, an RMSE below 1.8%, and an R2 above 0.975, demonstrating stronger environmental robustness. The conclusion indicates that the WOA-RF model effectively improves the accuracy and stability of SOC prediction, making it particularly suitable for monitoring the state of nickel-cadmium batteries under extreme aviation operating conditions. This provides reliable technical support for battery management systems.
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2026,40(1):70-80,
Abstract:
Accurate estimation of the state of health (SOH) of lithium-ion batteries is crucial for optimizing battery design. However, accurate SOH estimation remains challenging due to the complex degradation mechanism within the battery. Therefore, a SOH estimation method based on charging voltage curve and physics-informed neural network (PINN) is proposed. Firstly, Spearman correlation analysis is used to extract battery aging characteristics from the constant current segment of the charging voltage curve and establish a partial differential equation model for battery SOH degradation. Secondly, using a neural network with added physics-informed constraints to approximate the implicit model. Then, the weighted average and convergence acceleration techniques of the weighted mean of vectors (INFO) algorithm are utilized to optimize the PINN hyperparameters and improve the estimation accuracy of the method. Finally, this method is used for SOH estimation on three publicly available datasets: MIT, CALCE, and NASA. The results show that the average RMSE of the proposed method on the MIT test set with changes in charging strategy is 0.271 6%. Compared with long short-term memory network (LSTM), convolutional neural network (CNN) and baseline neural network (BNN) methods, the error is reduced by 80.74%, 57.48% and 74.73%, respectively. The estimation accuracy on both the CALCE and NASA test sets is above 97%. This proves that the method has high estimation accuracy and good robustness to changes in electrode materials and experimental conditions.
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Guo Xiangwei, Yuan Jianglong, Chen Gang, Wang Chen, Su Jiawen
2026,40(1):81-90,
Abstract:
Accurate state of health (SOH) is of great significance for safe operation of Li-ion battery storage systems. Aiming at the shortcomings of the current SOH estimation methods in terms of poor applicability, large computational load and low accuracy, a SOH estimation method for lithium batteries based on improved domain adaptive transfer learning is proposed. First, a new SOH indicator based on time interval for equal charging voltage difference is designed, which can simulate the random constant current charging segments and simplify the input parameters of the SOH estimation model. Second, by introducing adaptive transfer learning and combining the GRU network characteristics, a GRU network based on an improved domain adaptive transfer learning is proposed to reduce the negative transfer and network training load. Again, based on the new SOH indicator and neural network, the SOH estimation is realized. Finally, the proposed estimation method is validated based on the test data of the self-built experimental platform. The verification results show that, compared with the method based on traditional domain adaptive transfer learning, the mean absolute error and root mean square error of the proposed method are reduced by 3.0% and 3.8% respectively when the test current is 0.75 C. A reduction of 5.8% and 4.3% was achieved at a test current of 0.5 C. Compared with the estimation method based on fine-tuned transfer learning, the error is reduced by 22.9% and 17.4% respectively when the test rate is 0.75 C. At a test current of 0.5 C, the reductions are 25.8% and 14.7%, respectively.
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Pang Xiaoqiong, Li Xiao, Li Xiaojie, Zhang Xin
2026,40(1):91-101,
Abstract:
To ensure the reliability and safety of lithium-ion battery pack operation, it is very important to accurately and robustly predict its remaining useful life (RUL). However, the inconsistency within the battery pack will accelerate the process of degradation, which increases the difficulty of RUL prediction. At the same time, the traditional numerical prediction method is difficult to adapt to the needs of different security and emergency levels. Therefore, this study proposes a scheme combining battery pack inconsistency evaluation and RUL interval prediction. Firstly, based on the voltage and temperature data, multiple health indicators (HI) reflecting the inconsistency of battery pack were extracted. Secondly, the sample entropy method is used to objectively weight these HIs to evaluate the inconsistency of the battery pack. Then, the inconsistency evaluation results were incorporated into the health indicator system and processed by fuzzy information granulation (FIG) to provide upper and lower bounds for interval prediction. Finally, the long-term and short-term memory (LSTM) neural network was used modeling, taking the upper and lower bound sequences processed by FIG as inputs and the upper and lower bound sequences of capacity as outputs, and the point prediction and interval prediction of RUL is achieved. The experimental results show that this strategy can effectively evaluate the inconsistency of battery pack, and the evaluation results are highly correlated with the degree of battery pack degradation. In addition, for the training data at different starting points, the error of point prediction results is controlled within 0.32 Ah, the comprehensive evaluation criterion P for interval prediction is higher than 1.97, indicating the feasibility and effectiveness of the prediction method.
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Wang Jianqiu, He Yongtai, Pu Dongling, Wang Xiaodan
2026,40(1):102-109,
Abstract:
To address the overfitting and unreliable uncertainty estimation of purely data-driven approaches, this paper proposes a hybrid physics-data framework (Phys + GPR) for battery prognostics. First, a three-segment empirical model, derived from the early, accelerated, and linear degradation stages of lithium-ion batteries, is employed to extract a physics-based capacity prior. The residuals between measured capacity and the prior are then modelled by a two-stage heteroscedastic Gaussian process regression (GPR), Stage 1 estimates the residual mean, Stage 2 estimates the input-dependent variance. A TreeBagger random-forest regressor further refines the mean prediction, and β-calibration is applied on the training set to scale the predictive intervals, ensuring a reliable 90% coverage throughout the battery lifetime. Leave-one-battery-out (LOBO) cross-validation on NASA cells B0005, B0006, B0007 and B0018 shows that Phys + GPR achieves R2>0.93 for all cells, with a 90% prediction-interval coverage probability (PICP) of 70%~92% and a mean prediction-interval width (MPIW) of 0.085~0.10 Ah—significantly outperforming pure GPR, single-exponential + GPR and SVR baselines. The results demonstrate superior cross-battery generalisation, interpretable physics priors and robust uncertainty quantification, providing high-confidence support for battery health management and online RUL prediction.
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Zhang Chaolong, Wang Anxiang, Zhang Yan, Cheng Kaixin, Zhou Yujie
2026,40(1):110-119,
Abstract:
To address the current challenge in accurately estimating the state of health (SOH) of lithium-ion battery packs, a high-precision SOH estimation method integrating multi-scale features of the overall degradation and individual cell inconsistencies of the battery pack is designed. In this method, a deep learning model convolutional neural network Kolmogorov-Arnold network-Bahdanau attention (CNN-KAN-BA) combining a convolutional neural network (CNN), a Kolmogorov-Arnold network (KAN), and a Bahdanau attention (BA) mechanism is proposed. In the proposed SOH estimation process, systematic aging experiments are first conducted on a six-cell series-connected 18650 battery pack to obtain full life-cycle data. Then, the incremental energy analysis (IEA) method is adopted to extract the incremental energy curve length feature that characterizes the overall degradation of the battery pack. Simultaneously, the median absolute deviation of individual cell voltages within the pack and the temperature kurtosis are calculated as key individual features reflecting the evolution of inconsistency. Thereby, a multi-scale feature set that comprehensively describes the coordinated “overall-individual” degradation of the battery pack is constructed. The CNN-KAN-BA estimation model is trained using the features from the training data and is validated with the test data. The results show that this method can achieve high-precision SOH estimation, with a mean absolute error of 0.587 4%, a root mean square error of 0.699 0%, and an average coefficient of determination higher than 98%, all of which are superior to other common SOH estimation methods. The proposed method can effectively solve the problem of precise SOH estimation for lithium-ion battery packs.
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Deng Yuao, Wang Danhao, Pan Junzhen, Peng Daogang
2026,40(1):120-132,
Abstract:
Efficient power generation of photovoltaic cells plays a crucial role in promoting green and low-carbon circular development. To address the challenges posed by complex backgrounds and small target sizes in photovoltaic cell defect images, this paper proposes an improved defect detection model based on YOLO11n, named CCMW-YOLO11n. Firstly, a cross stage partial improvement (CSP-I) module is introduced into the backbone network of YOLO11n. This module integrates the multi-head self attention (MHSA), convolutional gated linear unit (CGLU), and conventional convolution (Conv), balancing global information perception and local feature extraction, thereby enhancing the extraction of multi-scale features. Secondly, the content-aware reassembly of features (CARAFE) upsampling technique is employed during the feature fusion stage. This method adaptively reorganizes feature maps and enhances details, effectively preserving fine-grained features and improving the model’s detection performance on complex targets. Additionally, the mixed aggregation net enhancement (MAN-E) module is incorporated into the neck network to further strengthen feature representation capabilities. Finally, addressing the limitations of the CIoU loss function in the baseline model, a novel bounding box regression loss function named Wise-Inner-SIoU is proposed by combining WIoUv3, Inner-IoU, and SIoU, optimizing the regression performance of the model. Experimental results demonstrate that the improved CCMW-YOLO11n model achieves a 9.6% increase in recall rate, with mAP@0.5 and mAP@0.5:0.95 reaching 91.0% and 61.1%, respectively, representing improvements of 3.1% and 2.0% over the baseline model, thereby realizing efficient detection of photovoltaic cell defects.
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Liu Shengjian, Liu Liansheng, Zhang Yiwei, Peng Yu
2026,40(1):133-142,
Abstract:
The output bandwidth of the arbitrary waveform generator (AWG) is limited by the bandwidth of the digital to analog converter (DAC). The frequency interleaved DAC (FI-DAC) could enhance bandwidth enhancement effectively. However, non-ideal characteristics of analog components cause peak amplitude frequency errors in the edge frequency bands of FI-DAC. These errors reduce the flatness of the output signal and degrade system performance. To address this issue, this article proposes an improved FI-DAC precalibrator that focuses on addressing the peak nonlinear amplitude frequency errors. Firstly, the linear phase error between the two channels of the FI-DAC is calibrated. Secondly, the algorithm utilizes support vector regression (SVR) to realize a precalibrator by formulating a regression model for the initial calibration of amplitude frequency errors. Thirdly, the algorithm integrates locally weighted learning (LWL) to assign adaptive weights to the edge frequency band. Finally, with a single-channel DAC sampling rate of 1.25 GSa/s, the application of the FI-DAC achieves an output bandwidth of 850 MHz, improving the signal output frequency range. Experimental results show that the minimum flatness within the passband is -0.061 dB, and the maximum flatness is 0.032 dB, which is close to the ideal flatness of 0 dB. Further validation is conducted on the 5 GSa/s experimental platform. Compared with other algorithms, the SVR-LWL precalibrator achieves higher accuracy in calibrating peak amplitude frequency errors in the FI-DAC.
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Zhao Kaihui, Tu Linxuan, Jia Lin, Huang Yishan, He Jing
2026,40(1):143-155,
Abstract:
A novel model free adaptive high-order sliding mode control strategy based on data-driven extended sliding mode observer is proposed to address the problem of PMSM drive system over reliance on accurate models and poor robustness in the face of load disturbances. Firstly, convert the motor motion equation into a discrete partial form dynamic linearization model. Secondly, a new controller is constructed that integrates the advantages of partial format model free adaptive control and discrete-time high-order sliding mode control; Simultaneously design a data-driven extended nonsingular discrete terminal sliding mode observer to observe disturbances in real time and input them into the controller to compensate for tracking errors. Then, based on the motor output speed and input reference current data within the sliding time window, an improved pseudo ladder real-time estimation algorithm is constructed to enhance the tracking ability of time-varying parameters, and achieve data-driven control based on the second-order partial range model. Finally, through simulation and experimental comparison with traditional methods under sudden changes in operating conditions, the results show that this method can shorten the convergence time of the motor by 35% and reduce the average waveform distortion caused by load disturbances by 18.4%, effectively ensuring the stable and efficient operation of PMSM and verifying the reliability and superiority of the proposed method.
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Li Mengjie, Xing Hongyan, Wu Han
2026,40(1):156-168,
Abstract:
To enhance the detection performance of weak targets against sea clutter, this paper proposes a hybrid detection method. This method integrates improved variational mode decomposition (IVMD) combined with wavelet packet multi-threshold decomposition (WPD), and a long short-term memory (LSTM) network optimized by the Harris Hawks optimization (HHO) algorithm. The IVMD, whose optimal parameters are determined adaptively by adaptive particle swarm optimization (APSO), is employed to precisely decompose the sea clutter signal into several intrinsic mode functions (IMFs). For the high-frequency IMFs containing strong noise, a multi-band wavelet packet decomposition and layered threshold denoising strategy is designed within the WPD framework to effectively suppress noise while preserving weak target characteristics. The Harris Hawks optimization algorithm is utilized to optimize the hyperparameters of the LSTM model, thereby enhancing its capability for nonlinear time-series modeling within the complex sea clutter environment. By combining phase space reconstruction with the denoised signals, the accuracy and anti-interference capability of target detection are significantly improved. Experiments using the real-world IPIX radar dataset from McMaster University, Canada, demonstrate that the proposed method markedly improves detection accuracy under both high and low signal-to-noise ratio conditions. Compared to traditional LSTM-based methods, the detection capability is improved by at least 35%.
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Li Jiaheng, Chang Zhiyuan, Fan Wei, Chen Chao, Xu Zhenying
2026,40(1):169-179,
Abstract:
Bearings are critical transmission components in rotating machinery whose operating conditions directly affect equipment safety and efficiency, making real-time monitoring and accurate prediction of remaining useful life (RUL) essential for preventing failures. Although deep learning-based self-attention models are widely used for life prediction, their reliance on feature embeddings and positional encoding hinders the capture of subtle degradation changes. Embedded Gaussian masks improve detection of delicate local degradation features, but their cubic computational complexity with data length limits practical efficiency. To overcome these issues, this study proposes a collaborative framework that integrates state-space model (SSM) with attention mechanisms. By incorporating wavelet transforms and cepstral filtering into the state-space process, the new feature tokenization module replaces traditional embeddings to enhance degradation representation. A gating-based dynamic selection algorithm then analyzes feature evolution, trend fluctuations, and noise resistance in real time to intelligently extract key degradation indicators, while a lightweight multi-scale attention module decodes life mapping by merging local vibration characteristics with global degradation patterns and reducing computational load. Comparative experiments on the PRONOSTIA dataset (conditions 1 and 2) and full-life test data from Jiangsu Lianyy Measurement and Control Technology Co., Ltd. show MAE improvements of 11.4%, 20%, and 15.4% and RMSE enhancements of 15.2%, 18.5%, and 27.4%, with ablation studies confirming up to a 55.6% boost in computational efficiency.
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Research on preview control of interconnected systems with equivalent input disturbance compensation
Li Sheng, Lan Yonghong, Luo Zhao
2026,40(1):180-190,
Abstract:
To address the problem of degraded tracking performance of traditional preview control in interconnected continuous-time systems under external disturbances, a decentralized robust preview control (PC) method based on equivalent input disturbance (EID) compensation is proposed. First, an EID estimator is designed to achieve online estimation and compensation of external disturbances acting on each subsystem, thereby effectively reducing their influence on system performance. Then, a new equality constraint based on preview information is introduced to construct a decentralized preview controller, and an augmented error system is formulated to transform the controller design problem into the stabilization problem of the error system. Finally, by combining Lyapunov stability theory, linear matrix inequality tools, and singular value decomposition techniques, sufficient conditions are derived to guarantee that the closed-loop system is asymptotically stable and satisfies the H∞ performance indices, and the controller and observer gains are obtained. Numerical simulations on a coupled inverted pendulum platform show that the proposed control strategy reduces the maximum tracking error caused by disturbances from 0.315 7 to 0.048 3, shortens the recovery time to steady state from 213 ms to 25 ms, and decreases the overshoot from 19.58% to 2.06%. These results demonstrate that the proposed method is significantly superior to traditional preview control and sliding mode control, verifying its effectiveness and advantages in disturbance rejection and tracking performance.
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Guo Zhongyu, Yue Yuqi, Chen Juan
2026,40(1):191-200,
Abstract:
In the complex process industry, due to the lack of online detection instruments or harsh production environments, some key variables are difficult to measure and cannot be measured online. Therefore, research on soft sensing modeling of these variables is needed in process industries. Currently, deep learning based soft sensing modeling mostly focuses on feature modeling from a single perspective, neglecting valuable information from other perspectives, resulting in low accuracy of the prediction model. To address this issue, this paper proposes an industrial soft sensing modeling method based on a multi-view dual graph attention (Mv-DGAT) network. This method first constructs a multi-view framework, builds a spatial graph attention (SGAT) network based on the maximal information coefficient to complete the spatial perspective, and constructs a temporal graph attention (TGAT) network based on a multi-level temporal graph structure and the long short-term memory (LSTM)network to establish the temporal perspective. Secondly, the multi-head attention mechanism is used for spatiotemporal feature fusion prediction. Finally, the cosine similarity is introduced to evaluate the complementarity between views and suppress redundant features. The proposed method was tested on a publicly available dataset of real industrial processes. The experimental results showed that the proposed method has high prediction accuracy, with determination coefficients R2 reaching 0.85 and root mean square error reduced by more than 10% compared to the comparison model.
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Li Song, Gao Song, Zeng Qinggang, Xin Xiang, Tang Hongdou, Wu Jiahao, Yuan Lin
2026,40(1):201-214,
Abstract:
To efficiently achieve in-depth detection of the deposit-bedrock interface of potentially unstable slopes, a ground-penetrating radar for the unmanned aerial vehicle was developed. The principle of the stepped-frequency continuous wave ground-penetrating radar prototype system was analyzed, and this study demonstrated the key parameters of the unmanned aerial vehicle-borne ground-penetrating radar prototype system. Based on the measurement principle, scanning frequency mode, and the feature of being able to perform time-domain reflectometry measurement of the vector network analyzer, this study selected the lightweight N9914A handheld vector network analyzer to develop a radar transceiver. Specifically, the handheld terminal is used as the master computer, the acquisition software is deployed and the written script file is imported. And vector network analyzer is controlled through the network interface to automatically store data files by time. A small-sized (66 cm×10 cm×0.1 cm) low-frequency air-coupled antenna was developed to achieve the radiation and reception of the stepped-frequency continuous radar waves, and a prototype system of low-frequency unmanned aerial vehicle-borne ground-penetrating radar based on a handheld vector network analyzer was integrated. Through performance and transceiver function tests of the prototype system, the results show that the working bandwidth of the system reaches 20 to 150 MHz and the signal transmission power is greater than 5 W, and its transceiver function meets the requirements of the experimental design. The prototype system was mounted on an unmanned aerial vehicle, and field tests were conducted at the Dayangping landslide site in Xiaoshui Town, Yingshan County, Sichuan Province. The results show that the depth of the reflection interface in the 7.7′B scan image of the survey line is approximately 11.5 m (the relative dielectric constant of the soil is 9), which is consistent with the stratified interface of silty clay and mudstone in the known geological section, meeting the requirement of a detection depth greater than 10 m.
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Yang Yanfei, Bo Kai, Zhao Qiankai, Huang Daochun, Chen Junquan
2026,40(1):215-227,
Abstract:
To resolve the design conflict between low heat flux density requirements in superconducting motor multi-layer insulation material and structural compactness demands, this study proposes a multi-objective optimization method for multilayer insulation based on an improved layer by layer model (LBL) and non-dominated sorting genetic algorithm. First, we enhanced the conventional LBL model’s computational accuracy by incorporating key parameters—spacer optical properties, reflective screen aperture ratio, and a dynamic adaptive coefficient—derived from fundamental radiative, gaseous, and solid conductive heat transfer equations. Second, we constructed a variable-density MLI model with up to four distinct density zones, accounting for relative heat transfer contributions. Finally, employing a non-dominated sorting genetic algorithm with layer counts per density zone as design variables and the improved LBL model as fitness function, we optimized the system under layer-count constraints per zone and total layer count, yielding the Pareto frontier through population evolution. Based on this, we further analyzed the relationships governing MLI heat flux density in relation to three key design parameters:the number of density zones, layer count per density zone, and layer density. Concurrently, we assessed the regulatory effects of variable-density configurations on heat flux distribution. Results demonstrate that the optimized solutions span heat flux densities of 0.42 to 3.11 W/m2 and thicknesses of 5.5 to 43.0 mm, encompassing four configuration types:uniform-density layouts, and variable-density configurations with two, three, or four distinct density zones. By adjusting the number of density zones and the layer density and number of layers in the density zones, multi-layer insulation material optimization can be achieved, reducing the difficulty of subsequent construction.
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Gao Weiliang, Li Baopeng, Peng Haijun, Wu Zhenghua, Guo Weibo
2026,40(1):228-237,
Abstract:
Power line targets (high-voltage lines) are difficult to detect in low-altitude aircraft collision avoidance systems due to their slender physical dimensions and small electromagnetic scattering cross-section, which severely constrains flight safety. Conventional detection methods such as optical radar, millimeter-wave radar, and lidar are limited by factors like visibility, transmission power, and atmospheric conditions, resulting in restricted detection range, low recognition probability, and high false alarm rates. To address these issues, an airborne power line detection system based on a dual-polarization technical scheme is designed. This system utilizes the differential scattering characteristics of power lines under horizontal and vertical polarization in the L-band. It extracts the dual-polarization amplitude and polarization tilt angle from the power line echoes as polarization feature vectors, which are combined with traditional Doppler detection state vectors to form an augmented state vector for target detection. A cascaded classifier, employing support vector machines (SVM) and convolutional neural networks (CNN), is constructed to extract both explicit and implicit features of power line targets. The classifier is trained with 2 000 sets of real detection data to enhance the accurate recognition probability of power line targets in complex ground clutter. Flight tests conducted in field and mountainous environments demonstrate that the proposed power line detection system operates effectively all-weather and all-day, achieving a correct recognition probability of over 92% for power line targets within a range of 1 500 meters, which indicates broad potential for engineering applications
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Huang Chao, Yang Zebin, Huang Yuxin
2026,40(1):238-246,
Abstract:
Regarding the issues of non-smooth paths, unstable obstacle avoidance, and getting stuck in local optima in mobile robot path planning, a new adaptive crayfish optimization algorithm (ACOA) based on the traditional crayfish optimization algorithm (COA) is proposed. The algorithm improves robot path planning. First, a Piecewise chaotic map is used to set up the population. It adds diversity and randomness to the population and helps the algorithm search better globally. Next, an adaptive temperature adjustment is used. This helps crayfish change their behaviors to fit the situation and balances global planning and local searching to speed up the convergence of the algorithm. Finally, a special fitness function is designed. It helps robots avoid obstacles better and creates smoother paths. Tests show that the improved algorithm works well in different complex environments. It gives shorter paths, faster convergence, and fewer turns. Compared with the COA algorithm, the ACOA algorithm reduces the path length by 3.9%, 5.3%, and 17.3% in the three maps, and the inflection points are reduced by 40%, 33%, and 45%, respectively. It also smooths paths and better avoids obstacles.
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Wang Zhi, Li Zhonghu, Zhang Xinyu, Wang Jinming, Yang Liqing
2026,40(1):247-255,
Abstract:
To address the issues of large data volume for storage and transmission of ultrasonic guided wave full focus imaging signals for pipeline defects and low detection efficiency, a full focus imaging method based on compressive sensing and sparse matrix is studied. Firstly, six types of greedy algorithms are employed to perform compression and reconstruction on pipeline echo data. The study analyzes the influence of reconstruction algorithms on the reconstruction accuracy of simulated signals, selects the optimal reconstruction algorithm, and verifies that compressed sensing algorithms can break the constraints of the Nyquist theorem. Then, the sparsity is calculated to determine the optimal sparse basis. The measurement matrix is constructed by analyzing both the incoherence with the optimal sparse basis and the curvature effect of the pipeline. Compared with the random Gaussian matrix, the curvature-weighted measurement matrix can reduce the impact of the pipeline’s curvature effect, thereby improving the signal reconstruction accuracy and the quality of total focusing imaging. Finally, the optimal scheme is applied to conduct single-defect total focusing imaging and double-defect total focusing imaging respectively on the full-matrix data and sparse-matrix data obtained through compression and reconstruction. The results show that the sparse matrix total focusing imaging algorithm based on compressed sensing can reduce the total time for total focusing imaging and compressed sensing signal processing by 60% while ensuring accuracy. This method effectively improves imaging speed and detection efficiency, and simultaneously reduces the requirements for the hardware performance of the detection system.
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Shi Yanqiong, Xu Yizhe, Yang Yonghui, Lu Rongsheng
2026,40(1):256-268,
Abstract:
Traditional focus measure algorithms typically convert color images into grayscale prior to processing, which inevitably leads to the loss of chromatic information and consequently reduces the accuracy of focus assessment. To address this limitation, this study proposes a focus measure algorithm based on spatial chromatic dispersion and color gradients. First, the Euclidean distances between color vectors of adjacent pixels within a local window in the RGB space are calculated to construct a set of pixel-wise chromatic differences, and the product of the sum and variance of this set is defined as the spatial chromatic dispersion. Second, a spatial correlation matrix is constructed using the gradient values of the RGB channels of the color image, and the trace of this matrix is adopted as the color gradient measure. Finally, the spatial chromatic dispersion and color gradient are modeled as the prior distribution and likelihood function, respectively, and a Gaussian posterior distribution is derived using Bayesian statistics to serve as the focus measure function.The proposed algorithm enhances the accuracy of focus evaluation for color images and accelerates peak response, while also improving discrimination in weak-texture regions and areas with limited color information. Experimental results show that, compared with several mainstream methods, the proposed algorithm achieves improvements of 9% and 15% in peak sensitivity, curve steepness, and flat-region fluctuation on simulated and real images, respectively. When applied to 3D reconstruction, the algorithm attains the best performance in terms of RMSE and CORR on simulated datasets, and the relative depth-value error on real images does not exceed 4.6%. These findings demonstrate that the proposed algorithm exhibits superior focus measurement performance and can significantly improve the accuracy of 3D reconstruction.
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Zhang Quan, Liu Tiantian, Liu Yangyi, Duan Chang, Li Yan, Peng Bo
2026,40(1):269-278,
Abstract:
Vehicle re-identification technology plays a crucial role in intelligent transportation systems. Its accurate and efficient performance is decisive for significantly enhancing urban traffic safety and efficiency. However, complex weather conditions such as fog can lead to reduced imaging visibility, severely distorting vehicle appearance information. Existing vehicle re-identification methods still suffer from lower average precision and inadequate generalization capabilities under these conditions. To address these issues, a method that integrates multi-scale features for vehicle re-identification in foggy weather has been proposed. This method aims to enhance the re-identification accuracy and robustness on real-world data under foggy conditions. This method is divided into two branches: image dehazing and vehicle Re-ID. By leveraging the concept of shared weights, this approach balances the two tasks, enabling the model to extract stable and representative features from foggy images. The image dehazing module utilizes a two-stage restoration and pyramid enhancement technique to generate clear images, providing key features of vehicles in foggy conditions, there-by reducing the impact of haze on the accuracy of Re-ID. The vehicle Re-ID module leverages a feature pyramid and convolutional block attention mechanism to capture richer and more significant features across different scales, enhancing the entire branch’s Re-ID capability. Experiments were conducted on the FVRID dataset, comparing this method with various other Re-ID approaches. The results showed that on real-world data, the mean average precision reached 83.32%, and the cumulative matching characteristic at rank 1 was 94.70%. Both metrics outperformed other methods, indicating that the proposed multi-scale feature fusion method for foggy weather vehicle Re-ID significantly improves performance under such conditions, demonstrating stronger accuracy and generalization capability. This research not only advances the current state of technology for foggy weather vehicle re-identification but also provides valuable insights for future studies. As the demand increases for applications such as intelligent traffic management and autonomous driving systems, this improved re-identification method holds great promise for advancing these related fields.
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Chen Xu, Zheng Xiaoliang, Xue Sheng, Pang Jingyu, Yang Zhiqiang
2026,40(1):279-288,
Abstract:
The complex working environment of coal mine drilling operations, characterized by intense illumination, water mist, and dust interference, poses significant challenges for accurate identification of drilling procedures. To enhance intelligent procedure recognition during coal mine drilling operations, this study proposes an inertial data-driven drilling procedure identification method. Firstly, the inertial response characteristics of the drill tail during various procedures were systematically analyzed. An inertial response measurement module was developed and its deployment scheme formulated. Secondly, a bidirectional long short-term memory (BiLSTM) network was employed to extract temporal features from inertial data. The classical Transformer Encoder network was improved to construct a dual-modal Transformer（DMT） feature extraction network specifically for inertial data. A BiLSTM-DMT network was designed to effectively capture features from dual-modal inertial data, enabling intelligent perception and identification of drilling procedures. Finally, field measurements were conducted underground, yielding an inertial dataset encompassing seven typical drilling procedures. The neuron configuration of the BiLSTM network was optimized through comparative analysis of multiple fusion models. Experimental results demonstrate that the proposed method achieves 98.87% recognition accuracy in training instances and 96.26% in engineering applications, significantly outperforming existing comparative algorithms. This confirms the method’s effectiveness in identifying drilling procedures with high accuracy and robustness, thereby establishing a novel technical pathway for intelligent procedure recognition in coal mine drilling operations.
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Ma Jingjie, Yao Zhenjian, Zhao Yuxing, Shi Bo
2026,40(1):289-297,
Abstract:
To solve the interference of square-wave signals in the phase-shift characteristic calibration of square-wave powered pressure sensors, a reliable calibration method for the phase-shift characteristics of airborne square-wave powered pressure sensors based on sinusoidal excitation is proposed. With the Hilbert transform envelope extraction algorithm, the sinusoidal response signal is identified first from the output of the pressure sensor. Then, the zero offset of the sinusoidal response signal is eliminated by combining robust local mean decomposition algorithm and fuzzy entropy index. Finally, the initial phase of the pressure sensor is estimated based on the four-parameter sinusoidal fitting algorithm, and the phase shift characteristic is calibrated according to the initial phase of the reference pressure sensor. The calibration performance of the proposed method is evaluated by simulation and sinusoidal pressure calibration experiments. The simulation results show that the average relative error of the phase shift characteristic obtained by the proposed method under sinusoidal pressure excitation of different frequencies is about 0.594%, which is less than that obtained by traditional methods such as empirical mode decomposition (24.543%), and extremum envelope method (1.553%). The calibration experiments prove that this method can effectively solve the coupled problem of modal interference caused by sinusoidal excitation signal and square-wave powered signal, leading to inaccurate extraction of sinusoidal responses signal, and achieves reliable calibration of phase shift characteristic of airborne square-wave powered pressure sensors.
Volume 40,2026 Issue 1
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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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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.
