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Complex matrix interpolation model of the s-transform for electric load movement forecast

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International Journal of Modeling, simulation, and scientific Computing 2014年第3期5卷 153-170页

作者： Yang Zong-Chang school of Information and Electrical Engineering Hunan University of science and Technology Xiangtan P.R.China 411201

Electric load movement forecast is increasingly importance for the *** study addresses the load movement forecast modeling based on complex matrix interpolation of the s-transform(sT).In complex matrix of time-frequency representation of the sT,each row follows conjugate symmetric property and each column appears a certain degree of *** on these characteristics,a complex matrix interpolation method for the time-frequency representation of the sT is proposed to interpolate each row of the complex matrix based on the conjugate symmetric property,and then to perform nearestneighbor interpolation on each *** with periodic extension for daily and yearly electric load movement,a forecast model employing the complex matrix interpolation of the sT is *** forecast approach is applied to predict daily load movement of the European Network on Intelligent Technologies(EUNITE)load dataset and annual electric load movement of state Gird Corporation of China and its branches in 2005 and *** analysis indicates workability and effectiveness of the proposed method.

关键词： s-transform time-frequency representation complex matrix interpolation electric load movement forecast

Power supply Quality Analysis Using s-transform and sVM Classifier

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Journal of Power and Energy Engineering 2014年第4期2卷 438-447页

作者： Jiaqi Li M. V. Chilukuri University of Nottingham Nottingham UK sMIEEE University of Nottingham Semenyih Malaysia

In this paper, a sVM classifier based on s-transform is presented for power quality disturbances classification. Firstly, seven types of PQ events are created using Matlab simulation. These signals are analyzed to detect and localize PQ events via s-transform by visual inspection. Then five significant features of the PQ disturbances are extracted from the s-transform output. Afterwards, PQ disturbance samples with the five features are fed to sVM for training and automatic classification. Besides, particle swarm optimization is implemented to improve the performance of sVM. The results of the classification indicate that sVM classifier is an effective mechanism to detect and classify power quality disturbances.

关键词： Power Quality Disturbance s-transform sVM

Comparison of ICA and WT with s-transform based method for removal of ocular artifact from EEG signals

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Journal of Biomedical science and Engineering 2011年第5期4卷 341-351页

作者： Kedarnath senapati Aurobinda Routray 不详

Ocular artifacts are most unwanted disturbance in electroencephalograph (EEG) signals. These are characterized by high amplitude but have overlap-ping frequency band with the useful signal. Hence, it is difficult to remove the ocular artifacts by traditional filtering methods. This paper proposes a new approach of artifact removal using s-transform (sT). It provides an instantaneous time-frequency repre-sentation of a time-varying signal and generates high magnitude s-coefficients at the instances of abrupt changes in the signal. A threshold function has been defined in s-domain to detect the artifact zone in the signal. The artifact has been attenuated by a suitable multiplying factor. The major advantage of sT-fil- tering is that the artifacts may be removed within a narrow time-window, while preserving the frequency information at all other time points. It also preserves the absolutely referenced phase information of the signal after the removal of artifacts. Finally, a com-parative study with wavelet transform (WT) and in-dependent component analysis (ICA) demonstrates the effectiveness of the proposed approach.

关键词： EEG Ocular Artifact s-transform Wavelet transform Independent Component Analysis

ANALOG FAULT DIAGNOsIs BAsED ON s-transform AND PsO

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ANALOG FAULT DIAGNOSIS BASED ON S-TRANSFORM AND PSO

Feature extraction of Motion Imagination EEG based on s transform and CsP

第十四届全国容错计算学术会议(CFTC'2011)

作者： Yanghong Tan~(1,2),Yichunag sun~1 1 school of Engineering and Technology,University of Hertfordshire,Hatfield AL10 9AB,UK 2 College of Electrical and Information Engineering,Hunan University,410082,China Yigang He College of Electrical and Information Engineering,Hunan University,410082,China

＜正＞A novel method for fault diagnosis in analog circuits using s-transform（sT） as a preprocessor and Particle swarm Optimization （PsO） based neural network（NN） identifier for classification is proposed in the *** provides a frequency-dependent resolution and the features obtained from the sT are distinct,and easy to *** identifier,evolving with PsO,is a promising method to train *** is faster and gets better results,and avoids some problems of conventional backpropagation （BP） *** comparison between the sT-based method and the wavelet-transform based method,and comparison between the sT and PsO method and the traditional NN method for analog fault diagnosis is *** results show that the proposed method is effective in enhancing the efficiency of the training phase and the performance of the fault diagnostic *** results clearly indicate higher correct classification of fault classes in the example circuits of similar faults.

关键词： analog fault diagnosis PsO s-transform mixed-signal test neural networks

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Feature extraction of Motion Imagination EEG based on S tran...

Damage detection on framed structures: modal curvature evaluation using stockwell transform under seismic excitation

第37届中国控制会议

作者： Bin Lin Wenliang Zhang Xuejun Zhang school of Electronic and Optical Engineering Nanjing University of Posts and Telecommunications Nation-Local Joint Project Engineering Lab of RF Integration & Micropackage Nanjing University of Posts and Telecommunications

Although the CsP algorithm has been extensively studied,little has been known about its relatively low accuracy and *** on the evident advantage of s transform in feature extraction,including more obvious characteristic of time,frequency and phase,it is applied to preprocess the data for a better *** experiment result on the motion imagery indicates that the combined algorithm achieve an accuracy of 92.8%,far higher than the accuracy of applying CsP algorithm *** is more,the time cost could be diminished to 0.85 s after sampling,31%less than the original method,and could still achieve the accuracy of 89.8%.In conclusion,the combination of CsP and s transform algorithm is a feasible and applicable one with data and technical support.

关键词： electroencephalography independent component analysis s-transform common spatial pattern

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Earthquake Engineering and Engineering Vibration 2015年第2期14卷 265-274页

作者： R.Ditommaso F.C.Ponzo G.Auletta Department of structures Geotechnics Engineering Geology University of Basilicata school of Engineering University of Basilicata

The key parameters for damage detection and localization are eigenfrequencies, related equivalent viscous damping factors and mode shapes. The classical approach is based on the evaluation of these structural parameters before and after a seismic event, but by using a modern approach based on time-frequency transformations it is possible to quantify these parameters throughout the ground shaking phase. In particular with the use of the s-transform, it is possible to follow the temporal evolution of the structural dynamics parameters before, during and after an earthquake. In this paper, a methodology for damage localization on framed structures subjected to strong motion earthquakes is proposed based on monitoring the modal curvature variation in the natural frequency of a structure. Two examples of application are described to illustrate the technique: Computer simulation of the nonlinear response of a model, and several laboratory(shaking table) tests performed at the University of Basilicata(Italy). Damage detected using the proposed approach and damage revealed via visual inspections in the tests are compared.

关键词： damage detection s-transform band-variable fi lter

Characteristic Analysis of White Gaussian Noise in s-transformation Domain

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Journal of Computer and Communications 2014年第2期2卷 20-24页

作者： Xinliang Zhang Yue Qi Mingzhe Zhu 1Department of Electronic Engineering Xidian University Xi’an China 2The Ministry Key Laboratory of Electronic Information Countermeasure and simulation Xidian University Xi’an China. Department of Electronic Engineering Xidian University Xi’an China

The characteristic property of white Gaussian noise (WGN) is derived in s-transformation domain. The results show that the distribution of normalized s-spectrum of WGN follows X2?distribution with two degrees of freedom. The conclusion has been confirmed through both theoretical derivations and numerical simulations. Combined with different criteria, an effective signal detection in s-transformation can be realized.

关键词： signal Detection s-transform White Gaussian Noise X2 Distribution

New distributions over Wiener and Euclidean spaces

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science China Mathematics 1996年第9期39卷 925-934页

作者： P. Imkeller 严加安 Laboratoire de Mathematique Universite de Franche-Comte 25030 Besancon France) (Institute of Applied Mathematics Chinese Academy of Sciences Beijing 100080 China)

Let (H, B, u) be an abstract Wiener space. New spaces of test functionals and distributions having kernels of the chaos decomposition in (Hn,n>0) are constructed. Their counterparts over Rm are completely characterize... 详细信息

关键词： abstract Wiener space G-entire function G-holomorphic function s-transform H-transform.

Power quality disturbance classification based on time-frequency domain multi-feature and decision tree

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Protection and Control of Modern Power systems 2019年第1期4卷 349-354页

作者： Wenjing Zhao Liqun shang Jinfan sun school of Electrical and Control Engineering EngineeringXi’an University of Science and TechnologyNo.58Yanta RoadBei Lin DistrictXi’anShaanxi ProvinceChina shaanxi Power Generation co. LTD.Weihe Thermal Power PlantXianyang712000China

Accurate classification of power quality disturbance is the premise and basis for improving and governing power quality. A method for power quality disturbance classification based on time-frequency domain multi-feature and decision tree is presented. Wavelet transform and s-transform are used to extract the feature quantity of each power quality disturbance signal, and a decision tree with classification rules is then constructed for classification and recognition based on the extracted feature quantity. The classification rules and decision tree classifier are established by combining the energy spectrum feature quantity extracted by wavelet transform and other seven time-frequency domain feature quantities extracted by s-transform. simulation results show that the proposed method can effectively identify six types of common single disturbance signals and two mixed disturbance signals, with fast classification speed and adequate noise resistance. Its classification accuracy is also higher than those of support vector machine (sVM) and k-nearest neighbor (KNN) algorithms. Compared with the method that only uses s-transform, the proposed feature extraction method has more abundant features and higher classification accuracy for power quality disturbance.

关键词： Power quality Disturbance classification wavelet transform s-transform Decision tree Classification rules