MANUFACTURING, DESIGNING AND ANALYSIS OF TRANSFORMER CORE
Authors: Jaimin Prajapati, Aayush Tripathi, Vineeta S. Chauhan
Abstract
Transformer is based on the principal of mutual induction from primary winding to secondary winding due to alternating current. The design calculation of transformer is a hectic process when done manually. Since the calculation is long and interdependent with each steps the occurrence of error is more likely so use of computation system can minimize the error. For this use of MATLAB to have a software to design the transformer providing the design parameter is the main objective of the paper. With the Graphical user interface (GUI) and app designer helps to simplify the process and make it more user friendly which provide space for input values and options to choose. The software make calculation regarding the dimension, efficiency and manufacturing cost of the specified transformer. In this research paper,manufacturing, designing and analysis of transformer coreis done. The paper includes data collection and simulation about transformer core, Electrical steel, LV & HV windings and simulation is done in MATLAB software. Core is manufactured using CRGO steel. The transformer designing process includes Customer’s requirement, Material selection, Dimensioning, LV-HV windings, etc. By this data collection and simulation, we were able to simulate Transformer Designing using C language in MATLAB software.
Multi-Keyword Search with a Threshold for Cloud-Based Group Data Sharing
Authors: Dr. K. Nagi Reddy, Mr.Mohd. Mateen Ahmed, Ms.B.Nagalakshmi
Abstract
Transformer is based on the principal of mutual induction from primary winding to secondary winding due to alternating current. The design calculation of transformer is a hectic process when done manually. Since the calculation is long and interdependent with each steps the occurrence of error is more likely so use of computation system can minimize the error. For this use of MATLAB to have a software to design the transformer providing the design parameter is the main objective of the paper. With the Graphical user interface (GUI) and app designer helps to simplify the process and make it more user friendly which provide space for input values and options to choose. The software make calculation regarding the dimension, efficiency and manufacturing cost of the specified transformer. In this research paper,manufacturing, designing and analysis of transformer coreis done. The paper includes data collection and simulation about transformer core, Electrical steel, LV & HV windings and simulation is done in MATLAB software. Core is manufactured using CRGO steel. The transformer designing process includes Customer’s requirement, Material selection, Dimensioning, LV-HV windings, etc. By this data collection and simulation, we were able to simulate Transformer Designing using C language in MATLAB software.
Speech Emotion Recognition Through Neural Networks
Authors: Ashapriyadarshini. M, M. Harika, P. Sowmika, N. Pavan kumar, M. Susanna
Abstract
Clarity and intelligibility in speech signal demands removal of noise and interference associated with the signal at the source. This poses further challenge when the speech signal is colored with human emotions. Recognizing emotions in speech signals have always been a tough task. In this work, we have taken a different approachforpreprocessing and enhancing the emotional speech signal adaptively before classification.Artificial Neural network based Multilayer perceptron (MLP) classifier is used to recognize neutral, calm, happy, sad, angry, disgust, fear and surprise emotion as against neutral voices using Mel-frequency cepstrum coefficients (MFCC), chroma and Mel features. Ryerson Audio-Visual Database of EmotionalSpeech and Song (RAVDESS)The accuracy has improved to approximately 75% with enhanced signal.In this work, we would like to analyze basic emotions of a human being like calm, happy, fearful, disgust etc. from emotional speech signals. We use Multi Layer Perceptron Classifier to categorize the given data into respective groups. Mel-frequency cepstrum coefficients (MFCC), chroma and Mel features are extracted from the speech signals and are used to train the MLP classifier. For achieving this objective, we use python libraries like Librosa, sklearn, pyaudio, NumPy and soundfile to analyse the speech modulations and recognize the emotion.