Sensor modelling, optimization and control engineering
7
Scopus Publications
74
Scholar Citations
3
Scholar h-index
2
Scholar i10-index
Scopus Publications
Automatic Skin Tumour Segmentation Using Prioritized Patch Based Region–A Novel Comparative Technique A. Ashwini, S. Murugan IETE Journal of Research, 2023 Skin tumour detection plays a key factor in medical research. Nowadays, tumour detection process is of crucial importance as the number of persons affected is increasing substantially. The aim of this research work is to develop a new approach for efficient image enhancement and tumour detection from other unaffected regions on computed tomographic skin images. This work is mainly related to medical application methods on computed tomography (CT) skin tumour images that have been designed and implemented efficiently. The initial method, which was based on the quality of image, enhanced the medical image performance. Normally, these images are very noise sensitive and create difficulty in handling procedures. Proper care has to be taken which involves the introduction of pre-processing algorithms like enhancement techniques and filters. According to this, Anisotropic Diffusion Filtering (ADF) followed by Recursive Mean Separate Histogram Equalization (RMSHE) algorithm was introduced to improve the contrast of tumour images. In the second method, Public Contour Metric Based Segmentation (PCMBS) Mapping and Prioritized Patch Based Region Segmentation (PPBRS) Algorithm is proposed for skin tumour segmentation. These techniques are performed in CT skin tumour image which are benign or malignant. Overall accuracy of 98.5% and 95.4% is obtained for various benign and malignant tumours, respectively, in MATLAB 2018a software.
Optimized Convolutional Neural Network-Based Adaptive Controller for 6-Phase DFIG-Based Wind Energy Systems Arunkumar Azhakappan, Agees Kumar Chellappan, Murugan Sethuramalingam Electric Power Components and Systems, 2023 AbstractThis article proposes an optimized convolutional neural network-based adaptive control scheme (OCAC) for DFIG-based wind energy conversion systems. While linear systems can function successfully with the help of a PI controller, the behavior of the system becomes unstable when physical variations are present, rendering the PI controller ineffective. The purpose of this research is to guarantee that the proposed OCAC acquires self-adaptation under all conditions. By accounting for crucial circumstances such as changes in wind speed, fluctuations in generator parameters, and asymmetrical grid faults, the efficiency of OCAC control is proven. The hyperparameters of the deep convolutional neural network are optimized using the flower pollination algorithm, which boosts the network’s speed and precision. In comparison to the non-optimized technique, an overall improvement of 6.3% in training accuracy was attained through the use of the optimized method. Furthermore, the suggested OCAC would forecast the next systemic state and update control strategies of DFIG-based wind energy systems in real-time. The effectiveness of the OCAC is assessed with five different state-of-the-art algorithms under two distinct test scenarios. The simulation was conducted using MATLAB software. A comparison with a PID controller showed that the total harmonic distortion of the grid current decreased by 16.57% and that of the generator current decreased by 12.07%.Keywords: six-phase DFIGwind energy systemsconvolutional neural networkcontrol strategiesflower pollination optimization algorithm DISCLOSURE STATEMENTThe authors have no relevant financial or non-financial interests to disclose.AUTHORS' CONTRIBUTIONSArunkumar A: Conceptualization, formal analysis, investigation, project administration, and resources; Agees Kumar C: Conceptualization, methodology, analysis, and writing draft; Murugan S: Technical editing, proofreading, and project administration.AVAILABILITY OF DATA AND MATERIALThe datasets gathered during the current study are available from the corresponding author upon reasonable request.Additional informationFundingThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Notes on contributorsArunkumar AzhakappanArunkumar Azhakappan received the B.E degree in electrical and electronics engineering from Anna university, Chennai, India in 2005 and M.E degree in power electronics and drives from Anna university, Chennai, India in 2007, where he is currently working as an assistant professor in Arunachala College of Engineering for women, Kanyakumari, India. His research interests include analysis, control, protection and application of power converters in renewable energy and distributed generation units and motor drives.Agees Kumar ChellappanAgees Kumar Chellappan received the B.E. degree in electronics and instrumentation engineering from the National Engineering College, Kovilpatti, India, the M.E. degree in process control and instrumentation from Annamalai University, Chidambaram, India, and the Ph.D. degree from the Faculty of Electrical and Electronics Engineering, Anna University, Chennai. He is currently a Professor with the department of EEE, Arunachala College of Engineering for Women, Vellichanthai, India. His current research interests include multi-objective optimization, power electronics, electrical drives, and soft computing.Murugan SethuramalingamMurugan Sethuramalingam received his Ph.D. degree in Faculty of Electrical Engineering and M.E degree in Control and Instrumentation Engineering in 2018 and 2005 respectively from Anna University, Chennai. He received the B.E degree in Electronics and Instrumentation Engineering from Manonmaniam Sundaranar University, Tirunelveli in 2002. In 2005 he joined as a lecturer at Kamaraj College of Engineering and Technology, Virudhunagar. From 2007-2010, he serviced in Francis Xavier Engineering College, Tirunelveli. He currently serves as a Professor at Thamirabharani Engineering College, Tirunelveli in the department of Electronics and Communication Engineering. He has published more than 20 journal papers and 2 patents. His research area includes sensor optimization, electrical measurements and instrumentation, soft computing algorithms and control systems.
Enhancing the linearity characteristics of photoelectric displacement sensor based on extreme learning machine method Murugan Sethuramalingam, Umayal Subbiah Photonic Sensors, 2015 Photoelectric displacement sensors rarely possess a perfectly linear transfer characteristic, but always have some degree of non-linearity over their range of operation. If the sensor output is nonlinear, it will produce a whole assortment of problems. This paper presents a method to compensate the nonlinearity of the photoelectric displacement sensor based on the extreme learning machine (ELM) method which significantly reduces the amount of time needed to train a neural network with the output voltage of the optical displacement sensor and the measured input displacement to eliminate the nonlinear errors in the training process. The use of this proposed method was demonstrated through computer simulation with the experimental data of the sensor. The results revealed that the proposed method compensated the presence of nonlinearity in the sensor with very low training time, lowest mean squared error (MSE) value, and better linearity. This research work involved less computational complexity, and it behaved a good performance for nonlinearity compensation for the photoelectric displacement sensor and has a good application prospect.
An evolutionary optimized algorithm approach to compensate the non-linearity in linear variable displacement transducer characteristics S. Murugan, S.P. Umayal Journal of Electrical Engineering and Technology, 2014 Linearization of transducer characteristic plays a vital role in electronic instrumentation because all transducers have outputs nonlinearly related to the physical variables they sense. If the transducer output is nonlinear, it will produce a whole assortment of problems. Transducers rarely possess a perfectly linear transfer characteristic, but always have some degree of non-linearity over their range of operation. Attempts have been made by many researchers to increase the range of linearity of transducers. This paper presents a method to compensate nonlinearity of Linear Variable Displacement Transducer (LVDT) based on Extreme Learning Machine (ELM) method, Differential Evolution (DE) algorithm and Artificial Neural Network (ANN) trained by Genetic Algorithm (GA). Because of the mechanism structure, LVDT often exhibit inherent nonlinear input-output characteristics. The best approximation capability of optimized ANN technique is beneficial to this. The use of this proposed method is demonstrated through computer simulation with the experimental data of two different LVDTs. The results reveal that the proposed method compensated the presence of nonlinearity in the displacement transducer with very low training time, lowest Mean Square Error (MSE) value and better linearity. This research work involves less computational complexity and it behaves a good performance for nonlinearity compensation for LVDT and has good application prospect.
A Novel Nonlinearity Compensation Method to Improve the Linearity Characteristics of Constant Temperature Anemometer Using Optimized Evolutionary Algorithms International Journal of Applied Engineering Research, 2014
RECENT SCHOLAR PUBLICATIONS
Optimized Convolutional Neural Network-Based Adaptive Controller for 6-Phase DFIG-Based Wind Energy Systems A Azhakappan, AK Chellappan, M Sethuramalingam Electric Power Components and Systems 51 (19), 2262-2283 , 2023 2023 Citations: 2
Automatic skin tumour segmentation using prioritized patch based region–a novel comparative technique A Ashwini, S Murugan IETE Journal of Research 69 (1), 137-148 , 2023 2023 Citations: 30
Brain tumor findings in patient with a novel cascaded function R Remya, S Murugan, KP Geetha Signal, Image and Video processing 16 (6), 1533-1540 , 2022 2022 Citations: 3
GNVDF: A GPU-accelerated Novel Algorithm for Finding Frequent Patterns Using Vertical Data Format Approach and Jagged Array P Sumathi, S Murugan International Journal of Modern Education and Computer Science (IJMECS) 13 … , 2021 2021 Citations: 2
A series of exponential function, as a novel methodology in detecting brain tumor R Remya, KP Geetha, S Murugan Biomedical signal processing and control 62, 102158 , 2020 2020 Citations: 8
A METHOD AND DEVICE: REAL TIME MONITORING OF PATIENTS DSM Dr. G. Rajakumar, Dr. N. Muthukumaran, Dr. T. Samraj Lawrence, Dr.S ... IN Patent 201,941,044,451 , 2019 2019
Generation of Electric Power using Wind Energy through Moving Vehicles TVJYM S.Murugan, M.Marimuthu, N.R.Shakthy International Journal of Advanced Research in Electrical, Electronics and … , 2019 2019
A fuzzy logic controlled solar power generation with integrated maximum power point tracking using multi-level inverter A Ravi, S Murugan, JD Sathiyaraj, R Aandal International Journal of Applied Engineering Research. 10, 2315-2326 , 2019 2019 Citations: 17
WDO Based Security Constrained Unit Commitment with Flexible Set for Variable Wind Power M Sumathy, AG Saravanan, S Murugan, AA Manuela ICON (Integrating Concepts) 3 (1), 7 , 2018 2018
Configuration and Augmentation of Solar Humidifier System KSSS S.Murugan, B.Rupanraj International Journal of Research in Advanced Technology 3 (3), 1-4 , 2018 2018
Nonlinearity error compensation of Venturi flow meter using evolutionary optimization algorithms S Murugan, SP Umayal, K Srinivasan, M Aruna IJIRST 3 (7) , 2016 2016 Citations: 1
Enhancing the linearity characteristics of photoelectric displacement sensor based on extreme learning machine method M Sethuramalingam, U Subbiah Photonic Sensors 5 (1), 24-31 , 2015 2015 Citations: 3
An Evolutionary Optimized Nonlinearity Compensation Technique to Improve the Linearity Characteristics of Thermocouple Sensor SM M S Devi International Journal of Applied Engineering Research 10 (1), 945-951 , 2015 2015
Nonlinearity compensation of linear variable displacement transducer based on differential evolution algorithm S Murugan, SP Umayal Australian Journal of Basic and Applied Sciences 8 (16), 1-10 , 2015 2015 Citations: 2
An evolutionary optimized algorithm approach to compensate the non-linearity in linear variable displacement transducer characteristics S Murugan, SP Umayal Journal of Electrical Engineering & Technology 9 (6), 2142-2153 , 2014 2014 Citations: 3
A Novel Nonlinearity Compensation Method to Improve the Linearity Characteristics of Constant Temperature Anemometer Using Optimized Evolutionary Algorithms S Murugan, SP Umayal International Journal of Applied Engineering Research 9 (23), 21639-21656 , 2014 2014 Citations: 1
A review on enhancing the linearity characteristic of different types of transducers-a comparative study S Murugan, SP Umayal Int. J. Mod. Eng. Res 3, 1186-1191 , 2013 2013 Citations: 2
A Review on Enhancing the Linearity Characteristic of different types of Transducers- A Comparative Study’ SPU S. Murugan International Conference on Instrumentation, Communication, Control and … , 2013 2013
A Case Study on Enhancing the Performance of Linear Variable Differential Transformer by using ANN Based Temperature Compensation Circuits SPU S. Murugan European Journal of Scientific Research 96 (1), 7-16 , 2013 2013
Sensor Fusion in Time Triggered Network SG S. Murugan Recent Advances in Industrial Automation and Networking- RAIN 2005 , 2005 2005
MOST CITED SCHOLAR PUBLICATIONS
Automatic skin tumour segmentation using prioritized patch based region–a novel comparative technique A Ashwini, S Murugan IETE Journal of Research 69 (1), 137-148 , 2023 2023 Citations: 30
A fuzzy logic controlled solar power generation with integrated maximum power point tracking using multi-level inverter A Ravi, S Murugan, JD Sathiyaraj, R Aandal International Journal of Applied Engineering Research. 10, 2315-2326 , 2019 2019 Citations: 17
A series of exponential function, as a novel methodology in detecting brain tumor R Remya, KP Geetha, S Murugan Biomedical signal processing and control 62, 102158 , 2020 2020 Citations: 8
Brain tumor findings in patient with a novel cascaded function R Remya, S Murugan, KP Geetha Signal, Image and Video processing 16 (6), 1533-1540 , 2022 2022 Citations: 3
Enhancing the linearity characteristics of photoelectric displacement sensor based on extreme learning machine method M Sethuramalingam, U Subbiah Photonic Sensors 5 (1), 24-31 , 2015 2015 Citations: 3
An evolutionary optimized algorithm approach to compensate the non-linearity in linear variable displacement transducer characteristics S Murugan, SP Umayal Journal of Electrical Engineering & Technology 9 (6), 2142-2153 , 2014 2014 Citations: 3
Optimized Convolutional Neural Network-Based Adaptive Controller for 6-Phase DFIG-Based Wind Energy Systems A Azhakappan, AK Chellappan, M Sethuramalingam Electric Power Components and Systems 51 (19), 2262-2283 , 2023 2023 Citations: 2
GNVDF: A GPU-accelerated Novel Algorithm for Finding Frequent Patterns Using Vertical Data Format Approach and Jagged Array P Sumathi, S Murugan International Journal of Modern Education and Computer Science (IJMECS) 13 … , 2021 2021 Citations: 2
Nonlinearity compensation of linear variable displacement transducer based on differential evolution algorithm S Murugan, SP Umayal Australian Journal of Basic and Applied Sciences 8 (16), 1-10 , 2015 2015 Citations: 2
A review on enhancing the linearity characteristic of different types of transducers-a comparative study S Murugan, SP Umayal Int. J. Mod. Eng. Res 3, 1186-1191 , 2013 2013 Citations: 2
Nonlinearity error compensation of Venturi flow meter using evolutionary optimization algorithms S Murugan, SP Umayal, K Srinivasan, M Aruna IJIRST 3 (7) , 2016 2016 Citations: 1
A Novel Nonlinearity Compensation Method to Improve the Linearity Characteristics of Constant Temperature Anemometer Using Optimized Evolutionary Algorithms S Murugan, SP Umayal International Journal of Applied Engineering Research 9 (23), 21639-21656 , 2014 2014 Citations: 1
A METHOD AND DEVICE: REAL TIME MONITORING OF PATIENTS DSM Dr. G. Rajakumar, Dr. N. Muthukumaran, Dr. T. Samraj Lawrence, Dr.S ... IN Patent 201,941,044,451 , 2019 2019
Generation of Electric Power using Wind Energy through Moving Vehicles TVJYM S.Murugan, M.Marimuthu, N.R.Shakthy International Journal of Advanced Research in Electrical, Electronics and … , 2019 2019
WDO Based Security Constrained Unit Commitment with Flexible Set for Variable Wind Power M Sumathy, AG Saravanan, S Murugan, AA Manuela ICON (Integrating Concepts) 3 (1), 7 , 2018 2018
Configuration and Augmentation of Solar Humidifier System KSSS S.Murugan, B.Rupanraj International Journal of Research in Advanced Technology 3 (3), 1-4 , 2018 2018
An Evolutionary Optimized Nonlinearity Compensation Technique to Improve the Linearity Characteristics of Thermocouple Sensor SM M S Devi International Journal of Applied Engineering Research 10 (1), 945-951 , 2015 2015
A Review on Enhancing the Linearity Characteristic of different types of Transducers- A Comparative Study’ SPU S. Murugan International Conference on Instrumentation, Communication, Control and … , 2013 2013
A Case Study on Enhancing the Performance of Linear Variable Differential Transformer by using ANN Based Temperature Compensation Circuits SPU S. Murugan European Journal of Scientific Research 96 (1), 7-16 , 2013 2013
Sensor Fusion in Time Triggered Network SG S. Murugan Recent Advances in Industrial Automation and Networking- RAIN 2005 , 2005 2005
