The Experts below are selected from a list of 326010 Experts worldwide ranked by ideXlab platform
P.k. Dash - One of the best experts on this subject based on the ideXlab platform.
-
Comparison of modified teaching–learning-based optimization and extreme learning machine for classification of multiple Power Signal disturbances
Neural Computing and Applications, 2016Co-Authors: P. K. Nayak, P.k. Dash, S. Mishra, Ranjeeta BisoiAbstract:This paper presents a modified TLBO (teaching–learning-based optimization) approach for the local linear radial basis function neural network (LLRBFNN) model to classify multiple Power Signal disturbances. Cumulative sum average filter has been designed for localization and feature extraction of multiple Power Signal disturbances. The extracted features are fed as inputs to the modified TLBO-based LLRBFNN for classification. The performance of the proposed modified TLBO-based LLRBFNN model is compared with the conventional model in terms of convergence speed and classification accuracy. Also, an extreme learning machine (ELM) approach is used to optimize the performance of the proposed LLRBFNN and is compared with the TLBO method in classifying the multiple Power Signal disturbances. The classification results reveal that although the TLBO approach produces slightly better accuracy in comparison with the ELM approach, the latter is much faster in implementation, thus making it suitable for processing large quantum of Power Signal disturbance data.
-
nonstationary Power Signal time series data classification using lvq classifier
Soft Computing, 2014Co-Authors: Birendra Biswal, Shazia Hasan, Milan Biswal, P.k. DashAbstract:A new approach to time-frequency analysis and pattern recognition of non-stationary Power Signals is proposed in this paper. In this manuscript, visual localization, detection and classification of non-stationary Power Signals are achieved using wavelet packet decomposition and automatic pattern recognition is carried out through learning vector quantization neural network. The wavelet packet decomposition (WPD) of the non-stationary Power Signals is carried out to extract the coefficients at multiple level of decomposition. The relevant features for pattern classification are derived from the time-scale information obtained by WPD. The extracted features are used to classify different Power quality disturbances by using learning vector quantization neural net. Various non-stationary Power Signal waveforms are considered to verify the applicability of the proposed technique.
-
measurement and classification of simultaneous Power Signal patterns with an s transform variant and fuzzy decision tree
IEEE Transactions on Industrial Informatics, 2013Co-Authors: Milan Biswal, P.k. DashAbstract:This paper proposes a new scheme for measurement, identification, and classification of various types of Power quality (PQ) disturbances. The proposed method employs a fast variant of S-Transform (ST) algorithm for the extraction of relevant features, which are used to distinguish among different PQ events by a fuzzy decision tree (FDT)-based classifier. Various single as well as simultaneous Power Signal disturbances have been simulated to demonstrate the efficiency of the proposed technique. The simulation result implies that the proposed scheme has a higher recognition rate while classifying simultaneous PQ faults, unlike other methods. The Fast dyadic S-transform (FDST) algorithm for accurate time-frequency localization, Decision Tree algorithms for optimal feature selection, Fuzzy decision rules to complement overlapping patterns, robust performance under different noise conditions and a relatively simple classifier methodology are the strengths of the proposed scheme.
-
a Signal processing adaptive algorithm for nonstationary Power Signal parameter estimation
International Journal of Adaptive Control and Signal Processing, 2013Co-Authors: Shazia Hasan, P.k. Dash, Sarita NandaAbstract:SUMMARY This paper presents the design and analysis of an adaptive algorithm for tracking the amplitude, phase and frequency of the fundamental, harmonics and interharmonics present in time-varying Power sinusoid in white noise. If frequency, amplitude and phase of the multiple sinusoids become nonstationary, they are estimated as an unconstrained optimization problem using robust and low complexity multi-objective Gauss–Newton algorithm. The presented algorithm deals with frequency drift and can accurately estimate frequency variation, amplitude and phase variation, as well as harmonic amplitude and phase variations. Further, the learning parameters in the proposed algorithm are tuned iteratively to provide faster convergence and better accuracy. The excellent tracking capability of proposed multi-objective Gauss–Newton algorithm is shown through simulation and experimental results in a nonstationary environment. Copyright © 2012 John Wiley & Sons, Ltd.
-
A hybrid ant colony optimization technique for Power Signal pattern classification
Expert Systems with Applications, 2011Co-Authors: Birendra Biswal, P.k. Dash, Satyasis MishraAbstract:Research highlights? Power Signal disturbance features are extracted using Time-time transform. ? Fuzzy C-means clustering uses the Power Signal features to form a decision tree. ? A hybrid Ant colony optimization technique is used to update the cluster centres. ? This approach improves processing speed and Power quality classification accuracy. ? The present approach is superior to other time-frequency and evolutionary techniques. This paper presents a novel clustering and pattern classification of Power Signal disturbances using a variant of S-transform, which is termed as a phase corrected wavelet transform. This variant is obtained by taking the inverse Fourier transform of S-transform and is known as time-time transform (TT-transform). The output from the TT-transform based Power Signal processing is a set of relevant features that is used for visual localization, detection, and disturbance pattern classification. The TT-transform is a method of dividing a primary time series into a set of secondary, time-localized time series, through use of a translatable, scalable Gaussian window. These secondary time series resemble ordinary windowed time series, except that higher frequencies are more strongly concentrated around the midpoint of the Gaussian, as compared with lower frequencies. In this paper the TT-transform is generalized to accommodate arbitrary scalable windows. The generalized TT-transform can be useful in resolving the times of event initiations when used jointly with a related time-frequency distribution, the generalized S-transform.The extracted features are the input to a fuzzy C-means clustering algorithm (FCA) to generate a decision tree for Power Signal disturbance pattern classification. To improve the pattern classification of the fuzzy C-means decision tree, the cluster centers are updated using a hybrid ant colony optimization technique (HACO). Further a comparative assessment of Power Signal disturbance pattern classification accuracy for different population based optimization approach like the genetic algorithm (GA) and particle swarm optimization technique are presented in this paper. The various computational simulations presented in this paper reveal significant improvement in the pattern classification accuracy, the average number of function evaluations and processing time, etc.
Birendra Biswal - One of the best experts on this subject based on the ideXlab platform.
-
nonstationary Power Signal time series data classification using lvq classifier
Soft Computing, 2014Co-Authors: Birendra Biswal, Shazia Hasan, Milan Biswal, P.k. DashAbstract:A new approach to time-frequency analysis and pattern recognition of non-stationary Power Signals is proposed in this paper. In this manuscript, visual localization, detection and classification of non-stationary Power Signals are achieved using wavelet packet decomposition and automatic pattern recognition is carried out through learning vector quantization neural network. The wavelet packet decomposition (WPD) of the non-stationary Power Signals is carried out to extract the coefficients at multiple level of decomposition. The relevant features for pattern classification are derived from the time-scale information obtained by WPD. The extracted features are used to classify different Power quality disturbances by using learning vector quantization neural net. Various non-stationary Power Signal waveforms are considered to verify the applicability of the proposed technique.
-
Power Signal disturbance identification and classification using a modified frequency slice wavelet transform
IET Generation Transmission & Distribution, 2014Co-Authors: Birendra Biswal, Sukumar MishraAbstract:This study presents a novel approach to localise, detect and classify non-stationary Power Signal disturbances using a modified frequency slice wavelet transform (MFSWT). MFSWT is an extension of frequency slice wavelet transform (FSWT), which provides frequency-dependant resolution with additional window parameters for better localisation of the spectral characteristics. An advantage of the MFSWT is attributed to the fact that the modulating sinusoids are fixed with respect to the time axis, whereas a localising scalable modified Gaussian window dilates and translates. Several practical Power Signals are considered for visual analysis using MFSWT, and the disturbance patterns are appropriately localised with unique signature corresponding to each type. This work also evaluates the detection capability of the proposed methodology and a comparison with earlier FSWT and Hilbert transform to show the superiority of proposed technique in detecting the Power quality disturbances. A probabilistic neural network (PNN) based classifier is used for identifying the various disturbance classes. The spread parameter of the Gaussian activation function in PNN is tuned and its effect on the classification at different strengths of noise is studied.
-
A hybrid ant colony optimization technique for Power Signal pattern classification
Expert Systems with Applications, 2011Co-Authors: Birendra Biswal, P.k. Dash, Satyasis MishraAbstract:Research highlights? Power Signal disturbance features are extracted using Time-time transform. ? Fuzzy C-means clustering uses the Power Signal features to form a decision tree. ? A hybrid Ant colony optimization technique is used to update the cluster centres. ? This approach improves processing speed and Power quality classification accuracy. ? The present approach is superior to other time-frequency and evolutionary techniques. This paper presents a novel clustering and pattern classification of Power Signal disturbances using a variant of S-transform, which is termed as a phase corrected wavelet transform. This variant is obtained by taking the inverse Fourier transform of S-transform and is known as time-time transform (TT-transform). The output from the TT-transform based Power Signal processing is a set of relevant features that is used for visual localization, detection, and disturbance pattern classification. The TT-transform is a method of dividing a primary time series into a set of secondary, time-localized time series, through use of a translatable, scalable Gaussian window. These secondary time series resemble ordinary windowed time series, except that higher frequencies are more strongly concentrated around the midpoint of the Gaussian, as compared with lower frequencies. In this paper the TT-transform is generalized to accommodate arbitrary scalable windows. The generalized TT-transform can be useful in resolving the times of event initiations when used jointly with a related time-frequency distribution, the generalized S-transform.The extracted features are the input to a fuzzy C-means clustering algorithm (FCA) to generate a decision tree for Power Signal disturbance pattern classification. To improve the pattern classification of the fuzzy C-means decision tree, the cluster centers are updated using a hybrid ant colony optimization technique (HACO). Further a comparative assessment of Power Signal disturbance pattern classification accuracy for different population based optimization approach like the genetic algorithm (GA) and particle swarm optimization technique are presented in this paper. The various computational simulations presented in this paper reveal significant improvement in the pattern classification accuracy, the average number of function evaluations and processing time, etc.
-
Non-stationary Power Signal classification using local linear radial basis function neural networks
International Journal of Knowledge-based and Intelligent Engineering Systems, 2009Co-Authors: Birendra Biswal, P.k. Dash, Sukumar MishraAbstract:Our work provides an effective feature based method for analyzing both steady state and short duration non-stationary Power Signal disturbances using a Local Linear Radial Basis Function Neural Network (LLRBFNN). In contrast to the normalized probabilistic neural network (PNN), the proposed LLRBFNN is an excellent approximation network, which performs the classification task with minimal amount of computational Power than probabilistic neural network (PNN). The difference of the LLRBFNN with conventional Radial Basis Function Neural Network (RBFNN) is that a local linear model replaces the connection of weights between the hidden layer and output layer of conventional RBFNN. Both normalized time domain and frequency domain features are used for the training purpose. It is noticed that spectral entropy is an effective frequency domain feature for non-stationary Power Signal classification. Moreover the local linear model provides a robust model for network learning, which is not prone to local linear points. This is supported by the observation that both the LLRBFNN model and the global search optimization techniques like Genetic algorithm provide similar results.
-
NaBIC - TT-ACO based Power Signal classifier
2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009Co-Authors: Birendra Biswal, P.k. Dash, Milan Biswal, M. V. Nageswara RaoAbstract:This paper intends to propose a novel clustering method based on ant colony (AC) algorithm. A new approach called TT-transform based time frequency analysis is used in processing the non-stationary Power Signal disturbances. The time-time transform is the inverse Fourier transform of S-transform. The proposed model is demonstrated using feature vector from the domain of Power Signal analysis, yielding promising results. Visual localization, detection and classification of non-stationary Power Signals problem is carried out through TT-transform to generate time-frequency contours for extracting relevant features and certain pertinent feature vectors are applied to the Fuzzy C-means Algorithm with ant colony optimization for Power Signal classification. From simulation results, it is shown that the proposed algorithm has superior performance when compared to particle swarm algorithm.
Jovitha Jerome - One of the best experts on this subject based on the ideXlab platform.
-
Power Signal DISTURBANCE CLASSIFICATION USING WAVELET BASED NEURAL NETWORK
ASEAN Journal on Science and Technology for Development, 2017Co-Authors: S. Suja, Jovitha JeromeAbstract:In this paper, the Power Signal disturbances are detected using discrete wavelet transform (DWT) and categorized using neural networks. This paper presents a prototype of Power quality disturbance recognition system. The prototype contains three main components. First a simulator is used to generate Power Signal disturbances. The second component is a detector which uses the technique of DWT to detect the Power Signal disturbances. DWT is used to extract disturbance features in the Power Signal. The third component is neural network architecture to classify the Power Signal disturbances.
-
pattern recognition of Power Signal disturbances using s transform and tt transform
International Journal of Electrical Power & Energy Systems, 2010Co-Authors: S. Suja, Jovitha JeromeAbstract:This paper deals with the identification of Power Signal disturbances using the S Transform and TT Transform. The various Power Signal disturbances are simulated using MATLAB. These Power Signal disturbances are subjected to S Transform and TT Transform. The results of the transformation are generated as a pattern. It was found that the patterns obtained for each of the Power Signal disturbance is unique in nature. Because of this unique pattern generated for each disturbances the identification of the disturbance could be done with accuracy.
-
Power Signal disturbance classification using wavelet based neural network
Serbian Journal of Electrical Engineering, 2007Co-Authors: S. Suja, Jovitha JeromeAbstract:In this paper, the Power Signal disturbances are detected using discrete wavelet transform (DWT) and categorized using neural networks. This paper presents a prototype of Power quality disturbance recognition system. The prototype contains three main components. First a simulator is used to generate Power Signal disturbances. The second component is a detector which uses the technique of DWT to detect the Power Signal disturbances. DWT is used to extract disturbance features in the Power Signal. These coefficients obtained from DWT are further subjected to statistical manipulations for increasing the detection accuracy. The third component is neural network architecture to classify the Power Signal disturbances with increased accuracy of detection.
B. K. Panigrahi - One of the best experts on this subject based on the ideXlab platform.
-
Non-stationary Power Signal processing for pattern recognition using HS-transform
Applied Soft Computing, 2009Co-Authors: Birendra Biswal, P.k. Dash, B. K. PanigrahiAbstract:A new approach to time-frequency transform and pattern recognition of non-stationary Power Signals is presented in this paper. In the proposed work visual localization, detection and classification of non-stationary Power Signals are achieved using hyperbolic S-transform known as HS-transform and automatic pattern recognition is carried out using GA based Fuzzy C-means algorithm. Time-frequency analysis and feature extraction from the non-stationary Power Signals are done by HS-transform. Various non-stationary Power Signal waveforms are processed through HS-transform with hyperbolic window to generate time-frequency contours for extracting relevant features for pattern classification. The extracted features are clustered using Fuzzy C-means algorithm and finally the algorithm is optimized using genetic algorithm to refine the cluster centers. The average classification accuracy of the disturbances is 93.25% and 95.75% using Fuzzy C-means and genetic based Fuzzy C-means algorithm, respectively.
-
Power Signal classification using dynamic wavelet network
Applied Soft Computing, 2009Co-Authors: Birendra Biswal, P.k. Dash, B. K. Panigrahi, J. B. V. ReddyAbstract:A new approach to classification of non-stationary Power Signals based on dynamic wavelet has been considered. This paper proposes a model for non-stationary Power Signal disturbance classification using dynamic wavelet networks (DWN). A DWN is a combination of two sub-networks consisting of a wavelet layer and adaptive probabilistic network. The DWN has the capability of automatic adjustment of learning cycles for different classes of Signals, for minimizing error. DWN models are specifically suitable for application in dynamic environments with time varying non-stationary Power Signals. The test results showed accurate classification, fast and adaptive learning mechanism, fast processing time and overall model effectiveness in classifying various non-stationary Power Signals. The classification result of the DWN has been compared with that of the probabilistic neural network (PNN).
-
A new approach to time-frequency analysis and pattern recognition of non-stationary Power Signal disturbances
Engineering Intelligent Systems for Electrical Engineering and Communications, 2006Co-Authors: P.k. Dash, Birendra Biswal, B. K. PanigrahiAbstract:The paper presents a new approach to localize, detect and classify Power Signal disturbance problems using S-transforms (phase corrected wavelet transform) and a rule based expert system. The S-transform is an extension of the ideas of the continuous wavelet transform (CWT) and is based on a moving and scalable localizing gaussian window. This transform has some desirable characteristics that are absent in the continuous wavelet transform. The S-transform is unique in that it provides frequency-dependant resolution while maintaining a direct relationship with the Fourier spectrum. These advantages of the S-transform are due to the fact that the modulating sinusoids are fixed with respect to the time axis, whereas the localizing scalable gaussian window dilates and translates. Several Power Signal transient disturbances like voltage sag, voltage swell, flicker, harmonic distortions are taken for analysis using both S-transforms and wavelet transforms to prove the superiority of the former over the later. Automated classification software is developed using Artificial Intelligence technique like Expert Systems to provide very accurate identification of Power quality events.
Kea-tiong Tang - One of the best experts on this subject based on the ideXlab platform.
-
A miniature electronic nose system based on an MWNT–polymer microsensor array and a low-Power Signal-processing chip
Analytical and Bioanalytical Chemistry, 2014Co-Authors: Shih-wen Chiu, Ting-i Chou, Hsin Chen, Kea-tiong TangAbstract:This article introduces a Power-efficient, miniature electronic nose (e-nose) system. The e-nose system primarily comprises two self-developed chips, a multiple-walled carbon nanotube (MWNT)–polymer based microsensor array, and a low-Power Signal-processing chip. The microsensor array was fabricated on a silicon wafer by using standard photolithography technology. The microsensor array comprised eight interdigitated electrodes surrounded by SU-8 “walls,” which restrained the material–solvent liquid in a defined area of 650 × 760 μm^2. To achieve a reliable sensor-manufacturing process, we used a two-layer deposition method, coating the MWNTs and polymer film as the first and second layers, respectively. The low-Power Signal-processing chip included array data acquisition circuits and a Signal-processing core. The MWNT–polymer microsensor array can directly connect with array data acquisition circuits, which comprise sensor interface circuitry and an analog-to-digital converter; the Signal-processing core consists of memory and a microprocessor. The core executes the program, classifying the odor data received from the array data acquisition circuits. The low-Power Signal-processing chip was designed and fabricated using the Taiwan Semiconductor Manufacturing Company 0.18-μm 1P6M standard complementary metal oxide semiconductor process. The chip consumes only 1.05 mW of Power at supply voltages of 1 and 1.8 V for the array data acquisition circuits and the Signal-processing core, respectively. The miniature e-nose system, which used a microsensor array, a low-Power Signal-processing chip, and an embedded k -nearest-neighbor-based pattern recognition algorithm, was developed as a prototype that successfully recognized the complex odors of tincture, sorghum wine, sake, whisky, and vodka. Figure The miniature e-nose device prototype
-
A miniature electronic nose system based on an MWNT–polymer microsensor array and a low-Power Signal-processing chip
Analytical and bioanalytical chemistry, 2014Co-Authors: Shih-wen Chiu, Ting-i Chou, Hsin Chen, Kea-tiong TangAbstract:This article introduces a Power-efficient, miniature electronic nose (e-nose) system. The e-nose system primarily comprises two self-developed chips, a multiple-walled carbon nanotube (MWNT)–polymer based microsensor array, and a low-Power Signal-processing chip. The microsensor array was fabricated on a silicon wafer by using standard photolithography technology. The microsensor array comprised eight interdigitated electrodes surrounded by SU-8 “walls,” which restrained the material–solvent liquid in a defined area of 650 × 760 μm2. To achieve a reliable sensor-manufacturing process, we used a two-layer deposition method, coating the MWNTs and polymer film as the first and second layers, respectively. The low-Power Signal-processing chip included array data acquisition circuits and a Signal-processing core. The MWNT–polymer microsensor array can directly connect with array data acquisition circuits, which comprise sensor interface circuitry and an analog-to-digital converter; the Signal-processing core consists of memory and a microprocessor. The core executes the program, classifying the odor data received from the array data acquisition circuits. The low-Power Signal-processing chip was designed and fabricated using the Taiwan Semiconductor Manufacturing Company 0.18-μm 1P6M standard complementary metal oxide semiconductor process. The chip consumes only 1.05 mW of Power at supply voltages of 1 and 1.8 V for the array data acquisition circuits and the Signal-processing core, respectively. The miniature e-nose system, which used a microsensor array, a low-Power Signal-processing chip, and an embedded k-nearest-neighbor-based pattern recognition algorithm, was developed as a prototype that successfully recognized the complex odors of tincture, sorghum wine, sake, whisky, and vodka.