Netaji Subhash Engineering College

About: Netaji Subhash Engineering College is a based out in . It is known for research contribution in the topics: Cloud computing & Fuzzy logic. The organization has 302 authors who have published 576 publications receiving 3260 citations.

Fuzzy clustering algorithms for unsupervised change detection in remote sensing images

Abstract: In this paper, we propose a context-sensitive technique for unsupervised change detection in multitemporal remote sensing images. The technique is based on fuzzy clustering approach and takes care of spatial correlation between neighboring pixels of the difference image produced by comparing two images acquired on the same geographical area at different times. Since the ranges of pixel values of the difference image belonging to the two clusters (changed and unchanged) generally have overlap, fuzzy clustering techniques seem to be an appropriate and realistic choice to identify them (as we already know from pattern recognition literatures that fuzzy set can handle this type of situation very well). Two fuzzy clustering algorithms, namely fuzzy c-means (FCM) and Gustafson-Kessel clustering (GKC) algorithms have been used for this task in the proposed work. For clustering purpose various image features are extracted using the neighborhood information of pixels. Hybridization of FCM and GKC with two other optimization techniques, genetic algorithm (GA) and simulated annealing (SA), is made to further enhance the performance. To show the effectiveness of the proposed technique, experiments are conducted on two multispectral and multitemporal remote sensing images. A fuzzy cluster validity index (Xie-Beni) is used to quantitatively evaluate the performance. Results are compared with those of existing Markov random field (MRF) and neural network based algorithms and found to be superior. The proposed technique is less time consuming and unlike MRF does not require any a priori knowledge of distributions of changed and unchanged pixels.

Teaching learning based optimization for economic load dispatch problem considering valve point loading effect

Abstract: This paper presents a novel teaching learning based optimization (TLBO) technique to solve economic load dispatch (ELD) of the thermal unit without considering transmission losses. The proposed methodology can take care of ELD considering nonlinearity such as valve point loading. The objective of economic load dispatch is to determine the optimal power generation of the units to meet the load demand, such that the overall cost of generation is minimized, while satisfying different operational constraints. TLBO is a recently developed evolutionary algorithm based on two basic concepts of education namely teaching phase and learning phase. At first, learners improve their knowledge through the teaching methodology of teacher and finally learners increase their knowledge by interactions among themselves. The effectiveness of the proposed algorithm has been verified on three different test systems with equality and inequality constraints. Compared with the other existing techniques demonstrates the superiority of the proposed algorithm.

An overview of global climate changing in current scenario and mitigation action

Abstract: Climate changing is a global threat to the world. There are so many reasons behind this problem. One of the major reasons is carbon emissions in atmosphere. The causes for this global threat are many, among them GHG (green house gas emission) is one of them. Also deforestation, land use change, sulfate aerosol and black carbon are the other major reason leading to the ozone layer depletion and changing climate. Due to the carbon emission atmosphere is being polluted and also so many disasters happen routinely. Atmosphere is getting hot day by day. Due to this unnatural and sudden temperature rise, glaciers are melting, so sudden flash floods occur. Agricultural sector is also suffering due to the global warming effects. This will also affect the productivity of grains world wide. Climate changing increases land and as well as sea temperature and alters precipitation quantity and patterns. As a result increasing the global average sea level, risk of coastal erosions, etc. climate change will be an added stress for the fisheries and aquaculture sectors. Effects will also be severe on coasts and marine ecosystems. Extreme events like drought, flood may also happen due to these impacts. This paper elaborately present the current situation of climate changing and the causes of its vulnerable effects, also the mitigation action of climate changing are also discussed.

Resilient supplier selection using AHP-TOPSIS-QFD under a fuzzy environment

Abstract: The aim of this research is to develop a quantitative approach that handles the conflicts between different decision makers and measures the performance of the suppliers in a manufacturing system to select a resilient supplier. In this paper, a fuzzy-multi-criterion group decision making approach is conceived to develop a resilient supplier selection process. In the proposed methodology, distance based optimization methodology, that is, TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) integrated with fuzzy system, identifies the features of general selection criteria. Since manufacturer criteria and resiliency criteria are mostly based on the subjective judgment of ‘technical requirement’ and ‘customer requirement’, AHP (Analytic Hierarchy Process) and QFD (Quality Function Deployment) methodology have been integrated in AHP-QFD methodology. Finally, a supplier selection index is calculated in which the decision maker’s attitude plays an important role. The model produce...

Transmission Line Fault Classification and Location Using Wavelet Entropy and Neural Network

Abstract: The ability to locate the faults as well as to identify the type of fault in overhead transmission lines is of prime importance for the economic operation of modern power systems. An expert system based on an artificial neural network for fault classification and distance estimation is proposed in this article. The power system network has been simulated using EMTP/ATP software, and signal analysis has been performed in MATLAB environment (The MathWorks, Natick, Massachusetts, USA). Various types of faults have been simulated at different locations along the transmission line. The faulty voltage signals have been analyzed through wavelet transform using the Db4 mother wavelet. The entropies of the wavelet decompositions have been fed to the neural networks for classification and fault distance evaluation. The suggested technique is proven to be successful for classification and location of the faults.