About: Dr. B.C. Roy Engineering College, Durgapur is a based out in . It is known for research contribution in the topics: Electric power system & Wireless sensor network. The organization has 385 authors who have published 683 publications receiving 7213 citations.
Topics: Electric power system, Wireless sensor network, Microstrip antenna, Particle swarm optimization, Antenna (radio)
Papers published on a yearly basis
••01 Feb 2014
TL;DR: The ability to continually change and learning capability is the driving power of NFS methodologies and will be the key for future intelligent applications.
Abstract: This paper surveys neuro fuzzy systems (NFS) development using classification and literature review of articles for the last decade (2002-2012) to explore how various NFS methodologies have been developed during this period. Based on the selected journals of different NFS applications and different online database of NFS, this article surveys and classifies NFS applications into ten different categories such as student modeling system, medical system, economic system, electrical and electronics system, traffic control, image processing and feature extraction, manufacturing and system modeling, forecasting and predictions, NFS enhancements and social sciences. For each of these categories, this paper mentions a brief future outline. This review study indicates mainly three types of future development directions for NFS methodologies, domains and article types: (1) NFS methodologies are tending to be developed toward expertise orientation. (2) It is suggested that different social science methodologies could be implemented using NFS as another kind of expert methodology. (3) The ability to continually change and learning capability is the driving power of NFS methodologies and will be the key for future intelligent applications.
TL;DR: Simulation results show that GWO has better tuning capability than CLPSO, EPSDE and other similar population-based optimization techniques.
Abstract: In this article an attempt has been made to solve load frequency control (LFC) problem in an interconnected power system network equipped with classical PI/PID controller using grey wolf optimization (GWO) technique. Initially, proposed algorithm is used for two-area interconnected non-reheat thermal-thermal power system and then the study is extended to three other realistic power systems, viz. (i) two-area multi-units hydro-thermal, (ii) two-area multi-sources power system having thermal, hydro and gas power plants and (iii) three-unequal-area all thermal power system for better validation of the effectiveness of proposed algorithm. The generation rate constraint (GRC) of the steam turbine is included in the system modeling and dynamic stability of aforesaid systems is investigated in the presence of GRC. The controller gains are optimized by using GWO algorithm employing integral time multiplied absolute error (ITAE) based fitness function. Performance of the proposed GWO algorithm has been compared with comprehensive learning particle swarm optimization (CLPSO), ensemble of mutation and crossover strategies and parameters in differential evolution (EPSDE) and other similar meta-heuristic optimization techniques available in literature for similar test system. Moreover, to demonstrate the robustness of proposed GWO algorithm, sensitivity analysis is performed by varying the operating loading conditions and system parameters in the range of ± 50 % . Simulation results show that GWO has better tuning capability than CLPSO, EPSDE and other similar population-based optimization techniques.
TL;DR: The proposed ensemble SVM-based method could be used as an efficient and cost-effective method for sleep staging with the advantage of reducing stress and burden imposed on subjects.
Abstract: The present work aims at automatic identification of various sleep stages like, sleep stages 1, 2, slow wave sleep (sleep stages 3 and 4), REM sleep and wakefulness from single channel EEG signal. Automatic scoring of sleep stages was performed with the help of pattern recognition technique which involves feature extraction, selection and finally classification. Total 39 numbers of features from time domain, frequency domain and from non-linear analysis were extracted. After extraction of features, SVM based recursive feature elimination (RFE) technique was used to find the optimum number of feature subset which can provide significant classification performance with reduced number of features for the five different sleep stages. Finally for classification, binary SVMs were combined with one-against-all (OAA) strategy. Careful extraction and selection of optimum feature subset helped to reduce the classification error to 8.9% for training dataset, validated by k-fold cross-validation (CV) technique and 10.61% in the case of independent testing dataset. Agreement of the estimated sleep stages with those obtained by expert scoring for all sleep stages of training dataset was 0.877 and for independent testing dataset it was 0.8572. The proposed ensemble SVM-based method could be used as an efficient and cost-effective method for sleep staging with the advantage of reducing stress and burden imposed on subjects.
TL;DR: In this article, a teaching learning based optimization (TLBO) approach is proposed to minimize power loss and energy cost by optimal placement of capacitors in radial distribution systems, where learners improve their knowledge or ability through the teaching methodology of teacher and in second part learners increase their knowledge by interactions among themselves.
Abstract: This paper presents teaching learning based optimization (TLBO) approach to minimize power loss and energy cost by optimal placement of capacitors in radial distribution systems. The proposed algorithm is based on two basic concept of education namely teaching phase and learning phase. In first phase, learners improve their knowledge or ability through the teaching methodology of teacher and in second part learners increase their knowledge by interactions among themselves. To check the feasibility, the proposed method is applied on standard 22, 69, 85 and 141 bus radial distribution systems. Numerical experiments are included to demonstrate that the proposed TLBO can obtain better quality solution than many existing techniques like genetic algorithm (GA), particle swarm optimization (PSO), direct search algorithm (DSA) and mixed integer linear programming (MILP) approach.
TL;DR: A novel quasi-oppositional teaching learning based optimization (QOTLBO) methodology in order to find the optimal location of distributed generator to simultaneously optimize power loss, voltage stability index and voltage deviation of radial distribution network is presented.
Abstract: This paper presents a novel quasi-oppositional teaching learning based optimization (QOTLBO) methodology in order to find the optimal location of distributed generator to simultaneously optimize power loss, voltage stability index and voltage deviation of radial distribution network. The basic disadvantage of the original teaching learning based optimization (TLBO) algorithm is that it gives a near optimal solution rather than an optimal one in a limited iteration cycles. In this paper, opposition based learning (OBL) and quasi OBL concepts are introduced in original TLBO algorithm for improving the convergence speed and simulation results of TLBO. In order to show the effectiveness and superiority, the proposed algorithms are tested on 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results of the proposed methods are compared with those obtained by other artificial intelligence techniques like GA/PSO, GA, PSO and loss sensitivity factor simulated annealing (LSFSA). The results show that the QOTLBO surpasses the other techniques in terms of solution quality.
Showing all 385 results
|Provas Kumar Roy||36||176||4123|
|Chandan Kumar Ghosh||26||143||2424|
|Hemant A. Patil||20||215||1826|
|Anup Kumar Bhattacharjee||20||206||1614|
|Dakshina Ranjan Kisku||17||120||1032|
|Partha Pratim Bhattacharya||12||67||556|
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