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Shanmugapriya, A.
- Dynamic Voltage Management System with Advanced Disaster Controlling Techniques and GSM Technology
Abstract Views :185 |
PDF Views:3
Authors
Affiliations
1 Department of ECE, Pollachi Institute of Engineering and Technology, Coimbatore, IN
1 Department of ECE, Pollachi Institute of Engineering and Technology, Coimbatore, IN
Source
Programmable Device Circuits and Systems, Vol 9, No 7 (2017), Pagination: 155-158Abstract
A novel photovoltaic fed Dynamic Voltage Restorer (PV-DVR) is proposed to mitigate deep voltage sags, voltage swells and outages on a low voltage residential distribution system during both daytime and night time. In addition to the voltage regulation, the proposed PV-DVR reduces the energy consumption from the utility grid by is connecting the utility grid from the load through the semiconductor switches, when the PV system generates excessive or equal power to the required load demand during the day time. However, the reduction of energy consumption is always desirable for the reduction of panel tariff and global warming gasses
Keywords
GSM, Wireless Sensor Network, Vibration Sensor.
References
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- A Hybrid Fuzzy C-Means Clustering with Artificial Fish Swarm Algorithm Optimization for Data Mining with Cascade RMC-FSVM
Abstract Views :319 |
PDF Views:0
Authors
Affiliations
1 Sri Ramakrishna College of Arts and Science for Women, Coimbatore, IN
2 Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, Coimbatore, IN
1 Sri Ramakrishna College of Arts and Science for Women, Coimbatore, IN
2 Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, Coimbatore, IN
Source
Fuzzy Systems, Vol 10, No 3 (2018), Pagination: 57-63Abstract
Different endeavours have been made to enhance Support Vector Machines (SVMs) in light of various situations of certifiable issues. SVMs are the purported benchmarking neural system innovation propelled by the aftereffects of statistical learning theory. Among them, considering specialists' learning has been affirmed to enable SVMs to manage noisy data to acquire more helpful outcomes. For instance, SVMs with monotonicity constraints and with the Tikhonov regularization method, otherwise called Regularized Monotonic SVM (RMCSVM) consolidate imbalance requirements into SVMs in view of the monotonic property of real-world issues, and the Tikhonov regularization technique is additionally connected to guarantee that the arrangement is one of a kind and limited. These sorts of SVMs are additionally alluded to as knowledge-oriented SVMs. Be that as it may, explaining SVMs with monotonicity imperatives will require significantly more calculation than SVMs. In this exploration, at in the first place, Cascade RMC-FSVM can be spread over different processors with modified map decreased system over overhead and requires far less memory, deciding suitable noise or outlier is basic with traditional FSVMs, as these can impactsly affect the learning performance, and clustering functions can diminish the impacts of noise or outliers. This investigation proposed a Hybrid Fuzzy C-Means (HFCM) clustering with Artificial Fish School Algorithm (AFSA) strategies to observationally identify that damaging the monotonic condition in the training data can lessen the generalizability and monotonicity of RMC-FSVM. The consequently chosen virtual sets are performed in view of the technique of AFSA with least and most extreme of certain arbitrary subsets of the input dataset is considered as the fitness estimation of AFSA. The proposed work's judgment is done in the matlab simulation environment from which it is affirmed that the proposed examine technique tends to give a decent outcome when contrasted with the current methodologies, for the most part as for the precise knowledge learning.Keywords
Monotonicity, Outlier Detection, Optimal Virtual Pair Selection, Artificial Fish School Algorithm, Fuzzy C-Means, Clustering.References
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