Senthilkumar M

Bio: Senthilkumar M is an academic researcher from VIT University. The author has contributed to research in topics: Scheduling (computing) & Big data. The author has an hindex of 4, co-authored 6 publications receiving 107 citations.

Review of Health Prognostics and Condition Monitoring of Electronic Components.

Abstract: To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted.

A Survey on Job Scheduling in Big Data

Abstract: Big Data Applications with Scheduling becomes an active research area in last three years. The Hadoop framework becomes very popular and most used frameworks in a distributed data processing. Hadoop is also open source software that allows the user to effectively utilize the hardware. Various scheduling algorithms of the MapReduce model using Hadoop vary with design and behavior, and are used for handling many issues like data locality, awareness with resource, energy and time. This paper gives the outline of job scheduling, classification of the scheduler, and comparison of different existing algorithms with advantages, drawbacks, limitations. In this paper, we discussed various tools and frameworks used for monitoring and the ways to improve the performance in MapReduce. This paper helps the beginners and researchers in understanding the scheduling mechanisms used in Big Data.

Energy aware task scheduling using hybrid firefly - GA in big data

Abstract: Task scheduling is important of research in big data and it is made in two traditions user level and system level; in user level issues with scheduling between the service provider and customer; in system level issues in scheduling with resources management in the data centre. The drawbacks of various existing methods to increase in power consumption of data centres have become a significant issue. Now the MapReduce clusters constitute a major piece of the data centre for big data applications. Simply the absolute size, high fault-tolerant nature and low utilisation levels make them less energy efficient. The complexity of scheduling increases when there is an increase in the size of the task, it becomes very tedious to perform scheduling effectively. The drawback with existing scheduling algorithm generates higher computational cost and less efficient; the multi-objective scheduling with cloud computing makes it difficult to resolve the problem in the case of complex tasks. These are the primary drawbacks of several existing works, which prompt us to manage this research on task scheduling in cloud computing.

Analysing the concrete compressive strength using Pearson and Spearman

Abstract: Spearman's rank relationship coefficient is a nonparametric (dispersion free) rank measurement. Spearman's coefficient is not a measure of the direct relationship between two factors, as a few ”analysts” proclaim. Pearson's relationship coefficient is the covariance of the two factors separated by the result of their standard deviations. The possibility of the paper is to look at the estimations of Pearson's concrete compressive strength coefficient and Spearman's rank relationship coefficient and in addition their factual hugeness for various arrangements of information depicting provincial records of the financial advancement. In this, the Spearman correlation coefficient is based on the ranked values for each variable rather than the raw data. Spearman correlation is often used to evaluate relationships involving ordinal variables. The Pearson and Spearman's method is compared with the rank coefficient in the concrete compressive strength.

Memory efficient multi key (MEMK) generation scheme for secure transportation of sensitive data over cloud and IoT devices

Abstract: A new variant of RSA has been proposed called Memory Efficient Multi Key (MEMK) generation scheme. For sensitive data, our scheme will aid in exchanging the information between cloud to IoT and IoT to IoT devices. When cryptography belongs to the asymmetric type, then it has public and private keys. For memory efficiency, our scheme reuses the RSA scheme with a Diophantine form of the nonlinear equation. Moreover, our scheme performance comparatively performs well and this mainly due to the use of RSA public key alone. Due to this, our MEMK does not require multiplicative inverse function or Extended Euclid's algorithm. Finally, we have made an experimental result on various phases of MEMK PKC such as key generation, encryption, and decryption by varying the N-bit modulo bits from 1K to 10K.

A Review on IGBT Module Failure Modes and Lifetime Testing

Abstract: This article focuses on failure modes and lifetime testing of IGBT modules being one of the most vulnerable components in power electronic converters. IGBT modules have already located themselves in the heart of many critical applications, such as automotive, aerospace, transportation, and energy. They are required to work under harsh operational and environmental conditions for extended target lifetime that may reach 30 to 40 years in some applications. Therefore, addressing the reliability of IGBT modules is of paramount importance. The paper provides a comprehensive review on IGBT modules dominant failure modes, and long-term reliability. A detailed discussion on accelerated testing, and lifetime and degradation characterization considering thermo-mechanical stress is also presented in details.

Energy Efficient Smart Buildings Using Coordination Among Appliances Generating Large Data

Abstract: Internet of Things based smart grids (SGs) represent a vision of future power systems which helps to provide electricity in a smart and user friendly way. Demand side management is one of the most important component of a SG which allows energy consumers to change their electricity consumption patterns to reduce the electricity consumption cost. In this paper, we propose a home energy management system which helps to achieve our desired objectives: reduced electricity consumption cost, peak to average ratio and maximize user comfort. For this purpose, we have proposed a scheduling technique which is a hybrid of already existing optimization techniques: bacteria foraging algorithm and harmony search algorithm and is named as hybrid bacterial harmony (HBH) algorithm. Being producer of electricity units to the consumers, a utility establishes an incentive based pricing tariff; we, on top of it have employed seasonal time of use tariff which allows consumers to take decisions regarding their consumption patterns. Moreover, we introduce the concept of coordination among smart appliances using dynamic programming (DP) approach. The coordination among appliances is achieved by the help of the large data generated from the appliances of multiple homes with the joint work of heuristic techniques and DP. The resultant coordination not only reduces the electricity cost but also increases the user comfort. At last, we evaluate the performance of our proposed energy management system using our proposed optimization technique HBH. To comparatively evaluate the performance of our proposed technique, we compare it with already existing techniques. Simulation results validate that the proposed technique effectively accomplish the desired objectives while considering the consumer comfort.

Data analysis using box and whisker plot for lung cancer

Abstract: In statistical analysis, we have a collection of data, with the use of these data, we have to do analysis based on our requirements. With the collection of data using Statistical analysis, we deal collection, analysis, presentation and organizing the data. With the help of statistical analysis, we can find underlying patterns, relationships, and trends between data samples. The R system for statistical computing is an environment for data analysis and graphics. Here we are going to implement boxplot method and control chart methods for Lung cancer dataset. With the help of boxplot, we can easily make relations between samples and we can find the outliers.