Silicon Institute of Technology

About: Silicon Institute of Technology is a based out in . It is known for research contribution in the topics: Artificial neural network & Control theory. The organization has 286 authors who have published 529 publications receiving 4474 citations.

Power Quality Disturbance Classification Using Fuzzy C-Means Algorithm and Adaptive Particle Swarm Optimization

Abstract: This paper presents a new approach for the visual localization, detection, and classification of various nonstationary power signals using a variety of windowing techniques. Among the various windows used earlier like sine, cosine, tangent, hyperbolic tangent, Gaussian, bi-Gaussian, and complex, the modified Gaussian window is found to provide excellent normalized frequency contours of the power signal disturbances suitable for accurate detection, localization, and classification. Various nonstationary power signals are processed through the generalized S-transform with modified Gaussian 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 extended using either particle swarm optimization or genetic algorithm to refine the cluster centers.

Databases, features and classifiers for speech emotion recognition: a review

Abstract: Speech is an effective medium to express emotions and attitude through language. Finding the emotional content from a speech signal and identify the emotions from the speech utterances is an important task for the researchers. Speech emotion recognition has considered as an important research area over the last decade. Many researchers have been attracted due to the automated analysis of human affective behaviour. Therefore a number of systems, algorithms, and classifiers have been developed and outlined for the identification of emotional content of a speech from a person's speech. In this study, available literature on various databases, different features and classifiers have been taken in to consideration for speech emotion recognition from assorted languages.

Micro-Raman and XPS studies of pure ZnO ceramics

Abstract: We observed that ZnO bulk ceramics prepared by solid state reaction route at 1300 °C exhibited unexpected room temperature ferromagnetic (RTFM) property. In the absence of any magnetic impurity the cause of room temperature ferromagnetic signal in the undoped system is certainly attributed to various kinds of native defects such as oxygen vacancies ( V O ) or zinc interstitials ( I Zn ) and their clusters created inside the bulk ceramics during heating by slow step sintering schedule (SSSS). The micro-Raman investigation and X-ray photoelectron spectroscopy studies on the ZnO sample sintered at high temperature confirm the presence of such lattice defects.

Measurement and Classification of Simultaneous Power Signal Patterns With an S-Transform Variant and Fuzzy Decision Tree

Abstract: 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.