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Fast Algorithm for Inverse Two-Dimensional S Transform and Its Application in time-frequency Filtering for SAR Image Despeckling

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Chinese Journal of Electronics 2016年第1期25卷 100-105页

作者： GAO Fei ZHANG Ye WANG Jun SUN Jinping School of Electronic and Information Engineering Beihang University

S transform is a time-frequency representation which has been applied in various fields, yet suffers the problem of time and resource consumption. In order to overcome this problem and facilitate its application in image analysis, we introduce a fast algorithm for inverse twodimensional S transform. A two-dimensional S transform time-frequency filter for Synthetic aperture radar(SAR)image despeckling is proposed on the basis of this fast algorithm. Synthetic and actual SAR images are both used to quantitatively evaluate its performance. The proposed algorithm is compared with several classical algorithms with better results both in speckle noise reduction and detail preservation.

关键词： time-frequency representation Fast al gorithm Two-dimensional S transform time-frequency filter Synthetic aperture radar(SAR) image Speckle noise reduction

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 industry.This 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 similarity.Based 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 column.Then with periodic extension for daily and yearly electric load movement,a forecast model employing the complex matrix interpolation of the ST is introduced.The 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 2006.Result analysis indicates workability and effectiveness of the proposed method.

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

Scattering Center Modeling Using Adaptive Segmental Compressive Sampling

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Journal of Beijing Institute of Technology 2017年第4期26卷 484-493页

作者： Qifeng Li Kunyi Guo Jiaxin Wang Xinqing Sheng Tianshu Liu Center for Electromagnetic Simulation School of Information and ElectronicsBeijing Institute of Technology Science and Technology on Special System Simulation Laboratory Beijing Simulation Center

In order to deal with aliasing distortions of Doppler frequencies shown in time-frequency representation（ TFR） with aspect undersampling,an approach using adaptive segmental compressive sampling according to the aspect dependencies of the scattering centers is proposed. The random noise problem induced by compressive sampling is solved by employing a series of signal processing techniques of filtering,image transformation and Hough Transform. Three examples are presented to verify the effectiveness of this approach. The comparisons between the built models and the precise scattered fields computed by a well-validated full-wave numerical method are investigated,and the results showgood agreements between each other.

关键词： scattering center undersampling compressive sensing time-frequency representation parameter estimation

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A fast time-frequency response based differential spectral energy protection of AC microgrids including fault location

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Protection and Control of Modern Power Systems 2017年第1期2卷 331-358页

作者： O.Dharmapandit R.K.Patnaik P.K.Dash Electrical Engineering Department Institute of Technical Education and ResearchInstitute of Technical Education and ResearchSiksha O Anusandhan UniversityBhubaneswar-751030India. Electrical and Electronics Engineering Department GMR Instittute of TechnologyRajamAndhra Pradesh-532127India.

This paper proposes a pattern recognition based differential spectral energy protection scheme for ac microgrids using a Fourier kernel based fast sparse time-frequency representation(SST or simply the sparse S-Transform).The average and differential current components are passed through a change detection filter,which senses the instant of fault inception and registers a change detection point(CDP).Subsequently,if CDP is registered for one or more phases,then half cycle data samples of the average and differential currents on either side of the CDP are passed through the proposed SST technique,which generates their respective spectral energies and a simple comparison between them detects the occurrence and type of the fault.The SST technique is also used to provide voltage and current phasors and the frequency during faults which is further utilized to estimate the fault location.The proposed technique as compared to conventional differential current protection scheme is quicker in fault detection and classification,which is least effected from bias setting,has a faster relay trip response(less than one cycle from fault incipient)and a better accuracy in fault location.The significance and accuracy of the proposed scheme have been verified extensively for faults in a standard microgrid system,subjected to a large number of operating conditions and the outputs vindicate it to be a potential candidate for real time applications.

关键词： Microgrid Differential spectral energy protection time-frequency representation Fault classification Fault location

Application of Local Wave time-frequency Spectrum and Neural Networks to Fault Classification in Rotating Machine

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International Journal of Plant Engineering and Management 2005年第1期10卷 36-41页

作者： HAOZhi-hua MAXiao-jiang InstituteofVibrationEngineering DalianUniversityofTechnologyDalian116024P.R.China／／TangshanCollegeTangshan063000P.R.China InstituteofVibrationEngineering DalianUniversityofTechnologyDalian116024P.R.China

A new method of fault analysis and detection by signal classification inrotating machines is presented. The Local Wave time-frequency spectrum which is a new method forprocessing a non-stationary signal is used to produce the representation of the signal. This methodallows the decomposition of one-dimensional signals into intrinsic mode functions (IMFs) usingempirical mode decomposition and the calculation of a meaningful multi-component instantaneousfrequency. Applied to fault signals , it provides new time-frequency attributes. Then the momentsand margins of the time-frequency spectrum are calculated as the feature vectors. The probabilisticneural network is used to classify different fault modes. The accuracy and robustness of theproposed methods is investigated on signals obtained during the different fault modes (early rub,loose, misalignment of the rotor).

关键词： signal classification neural network local wave empirical modedecomposition time-frequency representation

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