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135 results on '"Tripathi, M. M."'

1. Thermal-Electrochemical Modeling and Analysis of Different Cathode-Anode pairs for Lithium-ion Battery

Author

SHARMA, S., PANWAR, A. K., and TRIPATHI, M. M.

Subjects
anode, batteries, cathode, electrochemical, electrodes, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Computer engineering. Computer hardware, TK7885-7895
Abstract

This article represents a computational approach for the estimation of the characteristics of lithium-ion batteries for a 2D electrochemical model of cylindrical type lithium-ion battery using COMSOL software. Various input parameters used in the model formulation have been obtained from experimental data as well as published work. For the evaluation of battery performance, an electrochemical-thermal mathematical model has been designed to determine the electrochemical parameters such as rate capability, charge/discharge characteristic, ragone curve. Further, the electrochemical characteristics have been characterized with the help of cyclic voltammetry (CV) test which indicates the uniform electrochemical reactivity and transport properties of the designed battery. Two types of electrode pairs in which LiMn2O4 as cathode materials form LiMn2O4 (LMO)/Graphite and LMO/Carbon (MCMB) are designed and these electrode pairs are simulated and evaluated to determine the characteristics of battery material using charge and discharge curve at different current loads (C-rates) as well as ragone curve. The simulation results validate the electrochemical behavior of the proposed 2D model. Also, the LMO/graphite electrode pair shows better discharging and charging capacity, high energy, and power curve as compared to the LMO/Carbon (MCMB) electrode pair.

Published
2021
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4. Zero Initialization of modified Gated Recurrent Encoder-Decoder Network for Short Term Load Forecasting

Author

Vedanshu and Tripathi, M M

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Single layer Feedforward Neural Network(FNN) is used many a time as a last layer in models such as seq2seq or could be a simple RNN network. The importance of such layer is to transform the output to our required dimensions. When it comes to weights and biases initialization, there is no such specific technique that could speed up the learning process. We could depend on deep network initialization techniques such as Xavier or He initialization. But such initialization fails to show much improvement in learning speed or accuracy. In this paper we propose Zero Initialization (ZI) for weights of a single layer network. We first test this technique with on a simple RNN network and compare the results against Xavier, He and Identity initialization. As a final test we implement it on a seq2seq network. It was found that ZI considerably reduces the number of epochs used and improve the accuracy. The developed model has been applied for short-term load forecasting using the load data of Australian Energy Market. The model is able to forecast the day ahead load accurately with error of 0.94%.

Published
2018

5. Priority-Based Buffer Management Technique Against Dropping Attack in Opportunistic Networks

Author

Srivastav, Satyendra Kumar, Tripathi, M. M., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Bashir, Ali Kashif, editor, Fortino, Giancarlo, editor, Khanna, Ashish, editor, and Gupta, Deepak, editor

Published
2022
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6. Electricity Price Forecasting Using LSTM Network and K-Means Clustering by Considering the Effect of Wind Power Generation

Author

Varanasi, Jyothi, Tripathi, M. M., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Chanda, Chandan Kumar, editor, Szymanski, Jerzy R., editor, Sikander, Afzal, editor, Mondal, Pranab Kumar, editor, and Acharjee, Dulal, editor

Published
2022
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7. Hybrid Forecasting Model Based on Nonlinear Auto-Regressive Exogenous Network, Fourier Transform, Self-organizing Map and Pattern Recognition Model for Hour Ahead Electricity Load Forecasting

Author

Nida-e-Falak, Tripathi, M. M., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Harvey, David, editor, Kar, Haranath, editor, Verma, Shekhar, editor, and Bhadauria, Vijaya, editor

Published
2021
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8. Human Action Recognition Using a New Hybrid Descriptor

Author

Mishra, Om, Kavimandan, Pranoti S., Tripathi, M. M., Kapoor, Rajiv, Yadav, Kalpana, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Harvey, David, editor, Kar, Haranath, editor, Verma, Shekhar, editor, and Bhadauria, Vijaya, editor

Published
2021
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9. Predictive Analysis of Wind Turbine Output Power Using Support Vector Machine(SVM) Based on Genetic Algorithm(GA)

Author

Srivastava, Tushar, Tripathi, M. M., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Harvey, David, editor, Kar, Haranath, editor, Verma, Shekhar, editor, and Bhadauria, Vijaya, editor

Published
2021
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13. Enhanced Electrical and Mechanical Characteristics Monitored by Various Software for Lithium-Ion Battery Tests

Author

Sharma, Snigdha, Panwar, Amrish K., Tripathi, M. M., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Sikander, Afzal, editor, Acharjee, Dulal, editor, Chanda, Chandan Kumar, editor, Mondal, Pranab Kumar, editor, and Verma, Piyush, editor

Published
2020
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14. Predictive Analysis of NARX, NLIO, and RNN Networks for Short-Term Wind Power Forecasting

Author

Srivastava, Tushar, Tripathi, M. M., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Jain, Vanita, editor, Chaudhary, Gopal, editor, Taplamacioglu, M. Cengiz, editor, and Agarwal, M. S., editor

Published
2020
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15. PSO-Tuned ANN-Based Prediction Technique for Penetration of Wind Power in Grid

Author

Kumar, Vijay, Pal, Yash, Tripathi, M. M., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Singh, Pradeep Kumar, editor, Kar, Arpan Kumar, editor, Singh, Yashwant, editor, Kolekar, Maheshkumar H., editor, and Tanwar, Sudeep, editor

Published
2020
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16. Parameter Optimization Using PSO for Neural Network-Based Short-Term PV Power Forecasting in Indian Electricity Market

Author

Yadav, Harendra Kumar, Pal, Yash, Tripathi, M. M., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Singh, Pradeep Kumar, editor, Kar, Arpan Kumar, editor, Singh, Yashwant, editor, Kolekar, Maheshkumar H., editor, and Tanwar, Sudeep, editor

Published
2020
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20. Short-Term Solar Power Forecasting Using Random Vector Functional Link (RVFL) Network

Author

Aggarwal, Arpit, Tripathi, M. M., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Perez, Gregorio Martinez, editor, Tiwari, Shailesh, editor, Trivedi, Munesh C., editor, and Mishra, Krishn K., editor

Published
2018
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37. Development of Artificial Intelligence Techniques for Solar PV Power Forecasting for Dehradun Region of India.

Author

Verma, Amit, Upadhyay, K. G., and Tripathi, M. M.

Subjects
SOLAR energy, PHOTOVOLTAIC power generation, ARTIFICIAL intelligence, SUPERVISED learning, RANDOM forest algorithms, STANDARD deviations
Abstract

The need of reducing emission of carbon dioxide became possible with the increased penetration of solar photovoltaic (PV) power generation. The variable and intermittent nature of solar PV power generation affects the stability of the distribution grid. It comes to be necessary to forecast the generated PV power to avoid such type of uncertain conditions. This paper presents the empirical comparisons of six developed supervised learning algorithms to predict the solar power generation for the Dehradun region in India. The algorithms namely multiple linear regression (MLP), ridge regression, decision tree (DT), random forest (RF), support vector machine (SVM) and K-nearest neighbor (KNN) are modified in accordance with the higher prediction accuracy. The detailed empirical comparisons of results are discussed on the basis of mean absolute percentage error (MAPE) and root mean squared error (RMSE). It is found that the best performance of RF method with MAPE as 2.2790% and RMSE as 0.8792%. Tree based algorithms have shown the improved performance among all the methods while SVM and ridge techniques perform quietly low. [ABSTRACT FROM AUTHOR]

Published
2021

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