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14th IEEE International Conference on Electronic Measurement and Instruments (ICEMI)

作者： Cheng Zhongyi Yang Maoji Kang Bo Bian Xingyuan Cui Junning Harbin Inst Technol Dept Automat Test & Control Harbin 150080 Peoples R China

ISBN: (数字)9781728105109

ISBN: (纸本)9781728105109

The volume measurement accuracy of large-scale complex structural tanks in high-end large-scale equipment assembly directly affects the normal working hours of the equipment. The measurement efficiency is directly related to the development and production cycle of the equipment. In order to solve the problem of high-precision and high-efficiency in the measurement of complex structure tank volume in high-end large-scale equipment, a measurement method based on three-dimensional laser scanning which can simultaneously improve measurement accuracy and measurement efficiency is proposed. Firstly, the large-scale complex structure tank is idealized into a standard hollow cylinder structure. Secondly, the MATLAB programming simulation is used to generate the ideal point cloud data without error. Thirdly, the data is used to study the feature parameter extraction algorithm based on the least squares principle. Finally, the accuracy and effectiveness of the algorithm are verified by MATLAB simulation experiments.

关键词： Volume measurement of large-scale complex structure tanks 3D laser scanning method feature parameter extraction MATLAB simulation

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A Unified feature parameter extraction Strategy based on System Identification for the Buck Converter with linear or nonlinear loads 42

A Unified Feature Parameter Extraction Strategy based on Sys...

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42nd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Shen, Qian Ren, Lei Gong, Chunying Wang, Huizhen Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781509034741

Condition monitoring (CM) is of great significance to ensure the reliability of systems. With the wide use of power electronic equipment in multiple fields, the CM technology for power electronics has become more important. feature parameter extraction is the prerequisite to realize CM of power electronic circuits. A unified method to extract parameters of the Buck converter with different properties of loads, including linear and nonlinear loads, is proposed in this paper. The established model containing target components makes the method more effective to eliminate the accuracy influence of non-ideal factors in practical applications compared to the traditional one based on hybrid system theory. De-noising pretreatment based on wavelet transform (WT) is applied to acquired data to filter non-ideal sampling errors in practice. The method is confirmed suitable for the Buck converter operating at any allowable operation condition. Both the simulation and experiment results validate the effectiveness of the presented strategy.

关键词： feature parameter extraction the Buck converter de-noising pretreatment condition monitoring (CM)

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Research on Dual-trapezoid Curve Construction of Automobile Collision Acceleration Based on feature parameter extraction 10

Research on Dual-trapezoid Curve Construction of Automobile ...

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10th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

作者： Liu Zhixin Yang Yunsheng China Automobile Technol & Res Ctr Tianjin 300300 Peoples R China

ISBN: (纸本)9781538651148

Automobile collision acceleration is an important parameter that directly reflects the collision process of automobile. It is closely related to occupant injury and widely used in the design of automobile safety structure. Usually the collision acceleration waveform is simplified into dual-trapezoid curve, in order to analyze the relationship between automobile body structure and occupant injury. However, the construction of dual-trapezoid curve relies mainly on the subjective experience of technicians, which limits the popularization and practical application of dual-trapezoid curve. On the basis of ensuring that the real vehicle collision waveform and the dual-trapezoid curve are equivalent in terms of operation time and energy, this paper proposes a feature parameter extraction method based on real vehicle collision waveform, which can simplify the complex and ever changing collision acceleration waveform into dual-trapezoid curve in parameters. And it also conducts trolley contrast test on the original waveform and dual-trapezoid curve, and injury response and movement gesture of the dummies are well matched. The results show that the dual-trapezoid curve of collision acceleration constructed can replace the corresponding original automobile collision waveform to be applied in the development of vehicle safety system.

关键词： Crash pulse dual trapezoid curve construction feature parameter extraction

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Research on Partial Discharge Spectrum Recognition Technology Used in Power Cables Based on Convolutional Neural Networks

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INVENTIONS 2025年第2期10卷 25-25页

作者： Zhang, Zhenqing Wu, Hao Ren, Weiyin Yan, Jian Sun, Zhefu Ding, Man Lianyungang Zhiyuan Elect Power Design Co Ltd 23 Xingfu Rd Lianyungang 222000 Peoples R China State Grid Lianyungang Power Supply Co 13 Xingfu Rd Lianyungang 222000 Peoples R China Hohai Univ Sch Elect & Power Engn 8 Focheng Rd Nanjing 211100 Peoples R China

Partial discharge is an important symptom of cable aging, and timely detection of potential defects is of great significance to ensure the stability and safety of the power supply. However, due to the diversity of inspection equipment and information blockage, the staff often show blindness to the partial discharge spectrum and the defects corresponding to the spectrum. In view of this phenomenon, a partial discharge spectrum recognition method based on a convolutional neural network was developed. Firstly, a database of typical partial discharge spectrum was established, including partial amplifiers in the laboratory and at the work site, and then the convolutional neural network was used to train the defect spectral library. This paper proposes a processing technology for the on-site partial discharge spectrum;the unified grayscale image is obtained by grayscale processing, linearized stretching and size unification, and then the shape and color feature parameters are extracted according to the grayscale image, which solves the image distortion and statistical spectrum movement caused by the on-site environment or photographic angle on the user side. The partial discharge type can be obtained by comparing the processed spectrum with the database through the intelligent terminal, which greatly improves the accuracy and efficiency of on-site operations.

关键词： partial discharge partial discharge pulse phase spectrum convolutional neural networks spectral recognition feature parameter extraction

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feature parameters extraction of power transformer PD signal based on texture features in TF representation

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2017年第4期11卷 445-452页

作者： Zhou, Wenjun Liu, Yushun Li, Pengfei Wang, Yong Tian, Yan Wuhan Univ Sch Elect Engn Wuhan Peoples R China Guangzhou Power Supply Bur Co Ltd Guangzhou Guangdong Peoples R China

Ultra-high-frequency (UHF) method is an effective approach to power transformer partial discharge (PD) detection. The feature parameters extracted from UHF PD signal can be applied to insulation defect type recognition. In this study, a novel feature parameters extraction method based on texture features in time-frequency (TF) representation is proposed. PD detections of four typical insulation defects were performed on a 110kV oil-immersed power transformer. Time-domain waveform and corresponding TF representations or images of UHF PD signals were obtained. About 36 texture features were extracted from the grey-level co-occurrence matrix of TF images. The texture features were reduced into six new feature parameters by principal component analysis. These feature parameters were used as input of the support vector machine classifier for defect type recognition. The recognition accuracies of four kinds of typical defects reached 97.67, 97.00 97.67 and 98.33% proving that the proposed extracted feature parameters are suitable for insulation defect type recognition in power transformer.

关键词： feature extraction time-frequency analysis partial discharge measurement principal component analysis support vector machines power transformers feature parameter extraction texture features time-frequency representation ultrahigh-frequency method power transformer partial discharge detection time-domain waveform grey-level cooccurrence matrix principal component analysis support vector machine classifier defect type recognition

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Identification of flow regimes using back-propagation networks trained on simulated data based on a capacitance tomography sensor

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MEASUREMENT SCIENCE AND TECHNOLOGY 2004年第2期15卷 432-436页

作者： Yan, H Liu, YH Liu, CT Shenyang Univ Technol Sch Informat Sci & Engn Shenyang 110023 Peoples R China

Non-invasive techniques such as electrical capacitance tomography (ECT) are beginning to make promising contributions to control systems and are well fitted for flow-regime identification in opaque pipes or conduits. A new method of two-component flow-regime identification based on a neural network and an eight-electrode ECT sensor is proposed in this paper. Time-consuming image reconstruction and analysis are avoided. Ten feature parameters are extracted straight from the capacitance measurements and translated into regime information via a back-propagation (BP) network. The extraction of feature parameters, the architecture and the training of the BP network are given. Simulation results show that the new identification method has good precision and high speed. The use of feature parameters and the BP network for flow-regime identification is promising.

关键词： electrical capacitance tomography flow-regime identification back-propagation network feature parameter extraction

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SPEECH SIGNAL feature parameterS extraction ALGORITHM BASED ON PCNN FOR ISOLATED WORD RECOGNITION 5

SPEECH SIGNAL FEATURE PARAMETERS EXTRACTION ALGORITHM BASED ...

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5th International Conference on Audio, Language and Image Processing (ICALIP)

作者： Wang Guang-yan Zhang Yi-ming Sun Mei-lin Wang Xia Zhang Yan Tianjin Univ Commerce Sch Informat Engn Tianjin 300134 Peoples R China Hebei Univ Technol Sch Informat Engn Tianjin 300130 Peoples R China

ISBN: (纸本)9781509006540

In the isolated word speech recognition system, the extraction and matching of the characteristic parameters is the key link. This paper introduces a new feature parameter extracting methods on basis of Pulse Coupled Neural Network (PCNN) for the recognition system. By means of the visibility of speech spectrogram, the PCNN is used to extract the time series and entropy series from the spectrogram of words. Finally, by means of DTW algorithm to accomplish the task of isolated word recognition, the simulation results demonstrate the feasibility and effectiveness of the proposed algorithm.

关键词： Pulse Coupled Neural Network isolated word speech recognition feature parameter extraction spectrogram time series entropy series

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THE RESEARCH ON PALM SHAPE feature parameterS extraction

THE RESEARCH ON PALM SHAPE FEATURE PARAMETERS EXTRACTION

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7th International Conference on Wavelet Analysis and Pattern Recognition

作者： Wang, Jian-Xia Wang, Xiao-Jun Hebei Univ Sci & Technol Coll Informat Sci & Engn Shijiazhuang 050054 Hebei Peoples R China

ISBN: (纸本)9781424437276

This paper briefly introduces the recognition process on palm shape and mainly introduces feature parameter extraction process on palm shape. By statistic experiment eight feature parameters are extracted from many parameters of palm shape. They are the length of pinkie, the length of ring ringer, the length of middle finger, the length of forefinger, the length of thumb, the width of ring ringer, the width of middle finger, the width of palm. Experiment indicates that using these eight feature parameters to identify palm can reach high veracity and rapidity.

关键词： Palm shape recognition Preprocessing feature parameter feature parameter extraction Square difference

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feature extraction and Pattern Recognition Algorithm of Power Cable Partial Discharge Signal

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2023年第1期37卷 2258010-2258010页

作者： Du, Jie Mi, Jianwei Jia, Zhanpeng Mei, Jiaxiang Xidian Univ Key Lab Elect Equipment Struct Design Minist Educ Xian 710071 Peoples R China

The degree of insulation aging of power cables is closely related to their partial discharge (PD) level, so the analysis of PD signals can be used to realize the cable condition detection. However, after performing online detection of PDs on power cables, the collected signals always contain interference signals due to the influence of electromagnetic interference in the field. In order to identify each type of local discharge signal from the interference signal, this paper proposes a clustering identification algorithm for local discharge signals, which mainly involves pulse extraction, feature parameter extraction and clustering identification process. The algorithm first extracts the pulse signal by combining the amplitude-time threshold method and the time domain energy method, then obtains the feature vector of the signal according to the synchronous multi-channel method, designs a fuzzy C-mean clustering algorithm based on subtractive clustering to determine the initial clustering center to cluster the samples and finally analyzes and checks the clustering results according to the phase resolved PD (PRPD) of a single class of signals and the fit of the two-parameter Weibull distribution function. The clustering results were analyzed and examined. The experimental results show that the proposed algorithm can extract pulse signals efficiently and accurately, and the synchronous multi-channel method can characterize pulse signals better. Meanwhile, the algorithm can determine the optimal number of classes adaptively according to the clustering effectiveness function and adopt subtractive clustering to initialize the clustering center, which can approach the optimal solution faster, and can effectively cluster a variety of discharge signals, so as to realize the type identification of single-class discharge signals.

关键词： Power cable partial discharge pulse extraction feature parameter extraction fuzzy C-means clustering Weibull distribution function

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Quantification of Lithium Plating in Lithium-Ion Batteries Based on Impedance Spectrum and Artificial Neural Network

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BATTERIES-BASEL 2023年第7期9卷 350页

作者： Bai, Miao Lyu, Chao Yang, Dazhi Hinds, Gareth Harbin Inst Technol Sch Elect Engn & Automat Harbin 150001 Peoples R China Natl Phys Lab Teddington TW11 0LW Middlesex England

Accurate evaluation of the health status of lithium-ion batteries must be deemed as of great significance, insofar as the utility and safety of batteries are of concern. Lithium plating, in particular, is notoriously known to be a chemical reaction that can cause deterioration in, or even fatal hazards to, the health of lithium-ion batteries. Electrochemical impedance spectroscopy (EIS), which has distinct advantages such as being fast and non-destructive over its competitors, suffices in detecting lithium plating and thus has been attracting increasing attention in the field of battery management, but its ability of assessing quantitatively the degree of lithium plating remains largely unexplored hitherto. On this point, this work seeks to narrow that gap by proposing an EIS-based method that can quantify the degree of lithium plating. The core conception is to eventually circumvent the reliance on state-of-health measurement, and use instead the impedance spectrum to acquire an estimate on battery capacity loss. To do so, the effects of solid electrolyte interphase formation and lithium plating on battery capacity must be first decoupled, so that the mass of lithium plating can be quantified. Then, based on an impedance spectrum measurement, the parameters of the fractional equivalent circuit model (ECM) of the battery can be identified. These fractional ECM parameters are received as inputs by an artificial neural network, which is tasked with establishing a correspondence between the model parameters and the mass of lithium plating. The empirical part of the work revolves around the data collected from an aging experiment, and the validity of the proposed method is truthfully attested by dismantling the batteries, which is otherwise not needed during the actual uptake of the method.

关键词： artificial neural network lithium plating quantification equivalent circuit model parameter identification feature parameter extraction

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