K. Karthikeyan ¹, V. Karthikeyani ²

Affiliations
1 Research and Development Centre, Bharathiar University, Coimbatore, Tamil Nadu, IN
2 Department of Computer Science, Thiruvalluvar Government Arts College, Rasipuram, Tamil Nadu, IN

Abstract

This paper proposes the method of Ontology Based Concept Hierarchy Extraction of Web Data. This helps to extract Concept Hierarchy efficient way for ontology construction. It is very useful for learning the ontology from the text in more efficient way. In General, Natural Language is Complexity and Uncertainty. The existing system used either Statistical based learning or logic based learning Techniques. Statistical based learning techniques gives solution only for complexity and Logic based techniques gives solution for uncertainty alone. But the Statistical Relational Learning Techniques give solution for both Complexity and Uncertainty. So, our proposed system uses Statistical Relational Learning Technique, named Markov Logic Network. Markov Logic Network is a technique in which identify the concept in the domain and order the candidate terms in hierarchical way. An experimental result provides the best concept hierarchy extractions compared to the state-of-art methods.Ontology

Keywords

Concept Hierarchy Extraction, Hearst Pattern, Markov Logic Network, Ontology, Semantic Web.

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G. Somasekhar ¹, K. Karthikeyan ²

Affiliations
1 SCSE, VIT University, Vellore – 632006, Tamil Nadu, IN
2 SAS, VIT University, Vellore – 632006, Tamil Nadu, IN

Abstract

Data matching provides valuable information relevant to complex decisions about programs or policies. For example, information about peer influences on teen behavior, achieved through data matching, can help people decide what kinds of programs would discourage early pregnancy, teenage drinking, and delinquency. If the data used in the data matching process has the big data characteristics and could not be processed on a single machine then it is termed as big data matching, where traditional data matching methods fail. Dedoop1 is the latest tool developed for big data matching. It needs a pair of clusters as input. The state-of-the-art big data matching techniques have a common disadvantage which leads to expensive redundant similarity computations. We focus on the selection process of the pair of clusters given as input to the Dedoop. This approach avoids unnecessary similarity computations. Entire data is subjected to prior selection process before entering into the Dedoop. The technique of canopy clustering is combined with the unique linkage pairs formation technique to solve the redundancy problem. A test sample of article-author information is usedto match the authors related to common subject. Though the basic pre data matching redundancy avoidance approach (BPRA2) solves the problem to some extent, it has some limitations. In addition to considerable preprocessing overhead, it does not solve scalability and incremental issues. The proposed PRAMR approach reduces the preprocessing overhead in BPRA to ‘m’ times. As the PRAMR uses the big data technique ‘MapReduce’, the scalability and incremental issues are solved. Hadoop is used for MapReduce jobs. The results are compared with the BPRA and Kolb’s approach3. In section IV and V, it is proved that PRAMR is more efficient than the state-of-the-art techniques. PRAMR shows improvementcompared to BPRA and Kolb’s approach giving a better solution to the overlapping clusters problem.

Keywords

Big Data Matching, Canopy Clustering, Data Point, Overlapping Clusters, Redundancy, Similarity Roller

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K. Karthikeyan ¹, P. K. Dhal ¹

Affiliations
1 Department of Electrical and Electronics Engineering, Vel Tech Dr. RR and Dr. SR Technical University, Chennai - 600095, Tamil Nadu, IN

Abstract

In this paper, the advancement in small signal stability via the optimal placement of Static Synchronous Compensator (STATCOM) and its performance is considered. The Eigen values are employed to analyze the stability of the WSCC system and the analysis has been done by PSAT software. It has been studied through light, medium, heavy, heavy one loading conditions unlike the existing system. At heavy one loading condition, the stability of the system collapses and the system has positive Eigen values. The system with STATCOM has the ability to work with stability though it undergoes heavy one loading conditions compared to the system without STATCOM and the positive Eigen values in the system is nil. The outcome of the proposed approach shows that STATCOM enhances the small signal stability excellently in the power system network.

Keywords

Eigen Value, PSAT, Small Signal Stability, STATCOM, WSCC

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C. R. Chidambaram ¹, Jainam Shah ¹, A. Sai Kumar ¹, K. Karthikeyan ¹

Affiliations
1 Structural Engineering, SMBS, VIT University, Chennai Campus, Chennai - 600127, Tamil Nadu, IN

Abstract

Progressive collapse is one of the main reasons for the failure of structure. It occurs due to removal/ damage of a column or a shear wall by fire, blast or vehicle impact. In this study, aG+7 moment resisting steel frame residential building was analysed using ETABS to predict the sensitivity of the structure to progressive collapse due to fire loads. Columns at different levels were given a temperature of 550̊ C with reduced material properties and yield strength as per code IS 800. Progressive collapse load combination was adoptedas per GSA guidelines. Corner, edge, intermediate and re-entrant columns were removed separately at alternate storeys. The lower storeys were found to be more susceptible than the upper storeys. The structure may be redesigned to avoid progressive collapse, with a significant increase in steel consumption. This study can be useful for important structures..

Keywords

ETABS, Fire Load, GSA Guidelines, Moment Resistingsteel Frame, Progressive Collapse.

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K. Karthikeyan ¹, P. K. Dhal ¹

Affiliations
1 Department of Electrical and Electronics Engineering, Vel Tech Dr. RR & Dr. SR Technical University, 42-Avadi- Vel Tech Road,Chennai - 6000062, Tamil Nadu, IN

Abstract

In this paper, transient stability of a multi machine system is evaluated when the system is experienced a three phase fault and the stability of multi machine system is recovered via optimal placement and tuning of STATCOM. Here, the parameters of the STATCOM are tuned by Biogeography based optimization and the STATCOM is positioned optimally in the system. The performance of the tuned system is compared with various operational conditions like without STATCOM, with STATCOM (untuned) and with two STATCOM (untuned). The time domain simulations and the analysis of the multi machine system are done by PSAT (Power System Analysis Toolbox). When the multi machine system is subjected to a three phase fault, voltage stability of the system is collapsed. The multi machine system with STATCOM (tuned by Biogeography based optimization) regains the stability and gives better voltage profile than the system with STATCOM (untuned), with two STATCOM (untuned) and the system without STATCOM. The results are evaluated with the other operational conditions of the system and it proves that the system has the transient stability when the STATCOM (tuned by Biogeography based optimization) is applied.

Keywords

Biogeography Based Optimization, STATCOM, Transient Stability, WSCC

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