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Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Cluster analysis & Control theory. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.


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Journal ArticleDOI
TL;DR: In this paper, a Modular Multilevel Inverter (MMI) is used to control the speed of an induction motor (IM) drive using intelligent techniques towards marine water pumping applications.
Abstract: This paper presents the design and implementation of Modular Multilevel Inverter (MMI) to control the Induction Motor (IM) drive using intelligent techniques towards marine water pumping applications. The proposed inverter is of eleven levels and has the ability to control the speed of an IM drive which is fed from solar photovoltaics. It is estimated that the energy consumed by pumping schemes in an onboard ship is nearly 50% of the total energy. Considering this fact, this paper investigates and validates the proposed control design with reduced complexity intended for marine water pumping system employing an induction motor (IM) drive and MMI. The analysis of inverter is carried out with Proportional-Integral (PI) and Fuzzy Logic (FL) based controllers for improving the performance. A comparative analysis has been made with respect to better robustness in terms of peak overshoot, settling time of the controller and Total Harmonic Distortion (THD) of the inverter. Simulations are undertaken in MATLAB/Simulink and the detailed experimental implementation is conducted with Field Programmable Gate Array (FPGA). The results thus obtained are utilized to analyze the controller performance, improved inverter output voltage, reliable induction motor speed control and power quality improvement by reduction of harmonics. The novelty of the proposed control scheme is the design and integration of MMI, IM drive and intelligent controller exclusively for marine water pumping applications.

18 citations

Journal ArticleDOI
TL;DR: The findings demonstrate that the PB-PPSO is presented and this method has high efficiency in terms of total profit and average response time for allocating the resources for the users.
Abstract: Objective: The main objective of this research is to allocate the resources with high profit and achieve high user satisfaction level in the cloud computing environment. Methods: An innovative technique called Position Balanced Parallel Particle Swarm Optimization (PB-PPSO) method is introduced for allocating resources. The main intent of PB-PPSO is to find the optimized resources for the set of tasks with less make span and minimum price. The set of rules are generated from the optimized resources for the training process. In the testing process, the resources are allocated to the new users by learning the rules from the training process. Results: PB-PPSO method shows high profit when compared to the existing methods such as Support Vector Machines (SVM) and Artificial Neural Network (ANN). In the PB-PPSO method, the optimized set of resources is determined for the set of tasks by using the particle swarm optimization algorithm. Then the rules are generated for the classification process. If the arrival rate of users is 500, the total profit is 720$ and the response time is 78ms. Based on the comparison and the results from the experiment shows the proposed approach works better than the other existing systems with high profit and less average response time. Conclusion: The findings demonstrate that the PB-PPSO is presented and this method has high efficiency in terms of total profit and average response time for allocating the resources for the users.

18 citations

Journal ArticleDOI
TL;DR: In this article, a single crystal of 4-chloro-4′methoxy benzylideneaniline (CMOBA) was grown by slow evaporation method.

18 citations

Journal ArticleDOI
TL;DR: In this article, nano-sized ZrO 2 /carbon clusters composite materials were successfully prepared by the calcination of a ZrCl 2 /starch complex under an argon atmosphere.

18 citations

Journal ArticleDOI
TL;DR: A novel method utilizing deep belief network (DBN) with grasshopper optimization algorithm (GOA) for liver disease classification is proposed, which yields 98% accuracy, 95.82% sensitivity, 97.52% specificity, 98.53% precision, and 96.8% F‐1 score in simulation process when compared with other existing techniques.
Abstract: Image processing plays a vital role in many areas such as healthcare, military, scientific and business due to its wide variety of advantages and applications. Detection of computed tomography (CT) liver disease is one of the difficult tasks in the medical field. Hand crafted features and classifications are the two types of methods used in the previous approaches, to classify liver disease. But these classification results are not optimal. In this article, we propose a novel method utilizing deep belief network (DBN) with grasshopper optimization algorithm (GOA) for liver disease classification. Initially, the image quality is enhanced by preprocessing techniques and then features like texture, color and shape are extracted. The extracted features are reduced by utilizing the dimensionality reduction method like principal component analysis (PCA). Here, the DBN parameters are optimized using GOA for recognizing liver disease. The experiments are performed on the real time and open source CT image datasets which embraces normal, cyst, hepatoma, and cavernous hemangiomas, fatty liver, metastasis, cirrhosis, and tumor samples. The proposed method yields 98% accuracy, 95.82% sensitivity, 97.52% specificity, 98.53% precision, and 96.8% F‐1 score in simulation process when compared with other existing techniques.

18 citations


Authors
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202221
2021572
2020234
2019121
2018143
2017136