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555 results on '"Sivakumaran, N."'

5. Control of Feed Drives in CNC Machine Tools Using Artificial Immune Adaptive Strategy

Author

Lavanya, A., Revathi, S., Sivakumaran, N., Rajkumar, K., Kumar, Amit, Series Editor, Suganthan, Ponnuthurai Nagaratnam, Series Editor, Haase, Jan, Series Editor, Senatore, Sabrina, Editorial Board Member, Gao, Xiao-Zhi, Editorial Board Member, Mozar, Stefan, Editorial Board Member, Srivastava, Pradeep Kumar, Editorial Board Member, Sarveshwaran, Velliangiri, editor, Chen, Joy Iong-Zong, editor, and Pelusi, Danilo, editor

Published
2023
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7. Role of Machine Learning Algorithms on Alzheimer Disease Prediction

Author

Krishna Kumar, V., Geetha Devasena, M. S., Gopu, G., Sivakumaran, N., 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, Jacob, I. Jeena, editor, Kolandapalayam Shanmugam, Selvanayaki, editor, and Bestak, Robert, editor

Published
2022
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11. Lower Limb Amputees Rehabilitation: IOT-Based Real-Time Human Movement

Author

Arun Ganesh, K., Sivakumaran, N., Kumaravel, S., 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, Komanapalli, Venkata Lakshmi Narayana, editor, Sivakumaran, N., editor, and Hampannavar, Santoshkumar, editor

Published
2021
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12. Minisun IDEEA Life Gait for Gait Analysis

Author

Singh, Avanish Kumar, Sivakumaran, N., Kumaravel, S., 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, Komanapalli, Venkata Lakshmi Narayana, editor, Sivakumaran, N., editor, and Hampannavar, Santoshkumar, editor

Published
2021
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13. Optimal Pi Controller Tuning for a Multivariable Flotation Column by Hyperbolic Tangent Weighted Error Function Using GA

Author

Kota, Venkata Kiran Kumar, Sivakumaran, N., Arulmozhi, 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, Komanapalli, Venkata Lakshmi Narayana, editor, Sivakumaran, N., editor, and Hampannavar, Santoshkumar, editor

Published
2021
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31. A NOVEL DEEP LEARNING MODEL FOR AUTOMATED CRIME PREDICTION

Author

Kalai Selvan and Sivakumaran N

Abstract

Smart Policing is the need of the hour as exponentially increase in crime rates is observed across the country, to intelligently deploying forces / rescue resources in preventing comes from happening and to minimize the effect of crimes. A crime is defined as a deliberate act that results in bodily or psychological harm as well as property damage or loss. Law enforcing agencies strive to come up with efficient prevention measures since the amount and variety of criminal acts are growing at an alarming rate. As technology scales up, the criminal activities are absolutely alarming, leaving the conventional criminal identification techniques incompetent. Today, the Police personnel across each country immensely reliant on digital information such as images, emails, video and etc. of crime scenes to accelerate investigation. It is urgent to devise technologies to achieve better system modelling that use machine learning algorithms in order to assist for easy and quick prediction of crimes. This article proposes a model that has convolutional neural networks, Long Short Term Memory (LSTM) and Multiplicative Attention. Simulation results of the proposed idea exhibit impressive prediction when conventional models such as Deep Learning for Real-Time Crime Forecasting (DLRTCF), Linear Regression (LR), Additive Regression (AR) and Decision Stump (DS) respectively. The prediction occurs at an average processing time between 1150ms and 2118ms which is highly significant.

Published
2023

36. sj-docx-1-tim-10.1177_01423312221147553 – Supplemental material for An enhanced dynamic soft sensor–based online estimation of missing data for water distribution system with inherent disturbances

Author

Mohamed Hussain, K, Sivakumaran, N, Sankaranarayanan, S, Radhakrishnan, TK, and Swaminathan, G

Subjects
FOS: Materials engineering, FOS: Other engineering and technologies, 91299 Materials Engineering not elsewhere classified, 99999 Engineering not elsewhere classified
Abstract

Supplemental material, sj-docx-1-tim-10.1177_01423312221147553 for An enhanced dynamic soft sensor–based online estimation of missing data for water distribution system with inherent disturbances by K Mohamed Hussain, N Sivakumaran, S Sankaranarayanan, TK Radhakrishnan and G Swaminathan in Transactions of the Institute of Measurement and Control

Published
2023
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37. Design and Implementation of the FOIMC Using the PSO Algorithm for the Coupled Tank System

Author

Sateesh Kumar Vavilala, Vinopraba Thirumavalavan, Radhakrishnan T. K., and Sivakumaran N.

Abstract

In this work, a fractional order internal model controller (FOIMC) is developed for the level control of a coupled tank system. The parameters of the FOIMC are optimized by the particle swarm optimization (PSO) algorithm. The controller parameters obtained by the PSO algorithm, are compared with the gray wolf optimizer (GWO) and the whale optimization algorithms (WOA), and the PSO algorithm gave better results. The performance of the proposed FOIMC is compared with the state of the art. The experimental results obtained from the laboratory setup of the cylindrical tank system using the proposed FOIMC are also presented. The step responses from the simulation and the experimental results, indicated that the proposed FOIMC has good time domain performance than the state of the art. The proposed controller showed better robustness to a change in the operating point. The novelty of the present work is, for the first time, a comparative study of latest swarm algorithms is done for the tuning of FOIMC parameters.

Published
2022

40. Missing data estimation and IoT‐based flyby monitoring of a water distribution system: Conceptual and experimental validation.

Author

Sankaranarayanan, S., Swaminathan, G., Radhakrishnan, T.K., and Sivakumaran, N.

Subjects
MISSING data (Statistics), WATER distribution, KALMAN filtering, FAULT location (Engineering), MUNICIPAL water supply
Abstract

Summary: Achieving uninterrupted water supply to the consumer node is considered to be the significant aspect for urban water distribution system (WDS). Under circumstances such as sensor failures or large sampling interval, the intermittent vital data are ignored, which lead to the missing data problem. In this work, an enhanced version of Kalman filter (KF) is proposed termed to be customized KF (CKF). The proposed CKF is equipped to handle the state‐ and input‐dependent noises, which are amplified based on the input and states in WDS. These noises corrupt the measured response from the WDS added to the conventional sensor and model uncertainties. In this case, a real‐world‐existing WDS is considered to test the credibility of the proposed algorithm. This reduces the complexity of the computation and the sampling rate of the measured head level, and the flows are considered to be random. The study indicates that the proposed CKF performs better in estimating the missing data voids with the noise‐corrupted measurements. To have the continuous monitoring accessible in remote sections, the vital parameters are monitored through Internet of Things (IoT) in android platform. The estimated data from the MATLAB and the sensor data of the consumer demands are monitored through user‐defined android application. This technique provides the advantage of monitoring the WDS in flyby condition and also even in the presence of any sensor failures, which also helps to localize the fault location. [ABSTRACT FROM AUTHOR]

Published
2023
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