Informatics Publishing Limited

S. Asokkumar ¹, S. Thangavel ²

Affiliations
1 Management Studies, Mahendra Engineering College, Mallasamuthiram, Namakkal (Dt), IN
2 Department of EEE, K.S.R. College of Technology, Tiruchengode, Namakkal (Dt), IN

Abstract

In many data mining applications such as the discovery of association rules, strong rules, and many other important discovery tasks, mining frequent item sets is a fundamental and essential problem. Methods have been implemented for mining frequent item sets using a prefix-tree structure, for storing compressed information GenMax is used for mining maximal frequent item sets. It uses a technique called progressive focusing to perform maximal checking, and differential set propagation to perform fast frequency computation. Genmax algorithm was not implemented for closed frequent item set.
The proposal in this paper present an improved index based enhancement on Genmax algorithm for effective fast and less memory utilized pruning of maximal frequent item and closed frequent item sets. The extension induces a search tree on the set of frequent closed item sets thereby we can completely enumerate closed item sets without duplications. The memory use of mining the maximal frequent item set does not depend on the number of frequent closed item sets. The proposed model reduce the number of disk I/Os and make frequent item set mining scale to large transactional databases. Experimental results shows a comparison of improved index based GenMax and existing GenMax for efficient pruning of maximal frequent and closed frequent item sets in terms of item precision and fastness.

Keywords

Index Mining, Frequent Item Set, Genmax, Association Rules, Data Mining, Transactional Databases.

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S. Thangavel ¹, S. Sujin David Rajan ¹, B. C. Kevin ¹

Affiliations
1 Department of Bio-technology, Udaya School of Engineering, Kanyakumari, IN

Abstract

Introducing newly Designed biodigesters for domestic and industrial use to process biosolids (organic wastes) by microbes to rapid the process. Digester gas (60% to 65% methane), ammonia and manure (boil) are the byproduct of this process. With proper treatment, this methane can be used in an internal combustion engine to drive a generator and make electricity. Also it can be used for cooking and heating purposes in rural areas. Biol consists of a mix of manure and water that has fermented in the biodigester. Biol is a liquid fertilizer that can completely replace chemical fertilizer. It can be sprayed on crops as a foliar fertilizer, or can be placed directly on the soil or into irrigation canals. Producers report an increase in crop production from 30% to 50%. In addition, biol protects against insects and helps plants recover from damage by frost. Ammonia released from this can be used for garden plants to grow. The microbes such as Pseudozyma spp. yeasts , Strains of P.antarctica , etc has more degradation activity which can degrade bioplastics . The Microbial Fuel Cell can be Installed within this digester to promote considerable amount of electricity. The purpose of this study is to determine the technical and economic feasibility of generating energy from the methane using these kind of biodigesters.

Keywords

Pseudozyma spp , P.antarctica , Biosolids, Biol, Biodigesters.

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S. Thangavel ¹, U. Dhanalakshmi ²

Affiliations
1 Department of EEE, K. S. Rangasamy College of Technology, Tiruchengode, Tamilnadu, IN
2 Department of EEE, K. S. Rangasamy College of Technology, Tiruchengode, Tamilnadu, IN

Abstract

Power system networks are becoming more interconnected and complicated. Therefore, the control centers feel the necessity of robust and scalable methods for power system state estimation that maintain performance suitably for large-scale systems. Utilities recently have been operating with less amount of money invested in transmission infrastructure and enhanced economic pressure contributing towards occurrence of blackouts. So blackout is avoided by the exact state of power system is required in order to take efficient corrective and preventive action. State estimation help in getting a good picture of system state. State estimation is a methodology that provides the best possible approximation for the state of a system by processing the available information therefore it can provide a real time data for many of the central control and dispatch functions in a power system. This paper presents a Phasor Measurement Unit (PMU) based state estimation in classic HVDC link using Weighted Least Square (WLS) algorithm. The algorithm and simulation are renowned with MATLAB software.

Keywords

Phasor Measurement Unit (PMU), State Estimation, Weighted Least Square (WLS) algorithm, High Voltage Direct Current (HVDC) Links.

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K. M. Muhammed Nabil ¹, P. Hare Prasaad ², T. Narendra Reddy ³, Prakash Vinod ³, S. Senthil Kumar ¹, S. Thangavel ²

Affiliations
1 Vellore Institute of Technology (VIT), Vellore, IN
2 Kongu Engineering College(KEC), Erode, IN
3 NMTC Dept., CMTI, Bengaluru, IN

Abstract

Due to the current structure of digital factory, it is necessary to build the smart factory to upgrade the manufacturing industry. This paper provides brief review on Smart factory and its implementation on traditional factory. They adopt the combination of physical technology and cyber technology and deeply integrate previously independent discrete systems making the involved technologies more complex and precise than they are now. Furthermore, a hierarchical architecture of the smart factory was proposed first, and then the key technologies were analyzed from the aspects of the physical resource layer, the network layer, and the data application layer. We have discussed the various features of the Smart Factory through an factory experts and example from the already existed smart factory. The Smart Factory architecture serves as solution pattern for the conception of modern production plants, which are characterized by mechatronic changeability, individualized mass production and internal and external networking. In addition, we discussed the major issues and potential solutions to key emerging technologies, such as Internet of Things (IoT), big data, and cloud computing, which are embedded in the manufacturing process. Finally, we discuss the main limitation of the Smart factory and its ongoing research towards overcoming the limitations toward the future.

Keywords

Industry 4.0, Manufacturing Servitization, Industrial Big Data, Smart Factory, Real Time Factory, Internet Of Things, Cyber Physical System, Cyber-Physical System.

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