National Institute of Technology, Hamirpur

About: National Institute of Technology, Hamirpur is a education organization based out in Mandi, India. It is known for research contribution in the topics: Wireless sensor network & Thermoelastic damping. The organization has 1331 authors who have published 2960 publications receiving 41946 citations. The organization is also known as: NIT Hamirpur.

A review on genetic algorithm: past, present, and future

Abstract: In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

Biosensors and their applications - A review.

Abstract: The various types of biosensors such as enzyme-based, tissue-based, immunosensors, DNA biosensors, thermal and piezoelectric biosensors have been deliberated here to highlight their indispensable applications in multitudinous fields. Some of the popular fields implementing the use of biosensors are food industry to keep a check on its quality and safety, to help distinguish between the natural and artificial; in the fermentation industry and in the saccharification process to detect precise glucose concentrations; in metabolic engineering to enable in vivo monitoring of cellular metabolism. Biosensors and their role in medical science including early stage detection of human interleukin-10 causing heart diseases, rapid detection of human papilloma virus, etc. are important aspects. Fluorescent biosensors play a vital role in drug discovery and in cancer. Biosensor applications are prevalent in the plant biology sector to find out the missing links required in metabolic processes. Other applications are involved in defence, clinical sector, and for marine applications.

Review of software tools for hybrid renewable energy systems

Abstract: Hybrid energy systems are being utilized for supplying electrical energy in urban, rural and remote areas to overcome the intermittence of solar and wind resources. A hybrid renewable energy system incorporates two or more electricity generation options based on renewable energy or fossil fuel unit. The techno-economic analysis of the hybrid system is essential for the efficient utilization of renewable energy resources. Due to multiple generation systems, hybrid system analysis, is quite complex and requires to be analyzed thoroughly. This requires software tools for the design, analysis, optimization, and economic viability of the systems. In this paper, 19 softwares with their main features and current status are presented. The softwares studied are HOMER, Hybrid2, RETScreen, iHOGA, INSEL, TRNSYS, iGRHYSO, HYBRIDS, RAPSIM, SOMES, SOLSTOR, HySim, HybSim, IPSYS, HySys, Dymola/Modelica, ARES, SOLSIM, and HYBRID DESIGNER. The research work related to hybrid systems carried out using these softwares at different locations worldwide is also reviewed. The main objective of the paper is to provide the current status of these softwares to provide basic insight for a researcher to identify and utilize suitable tool for research and development studies of hybrid systems. The capabilities of different softwares are also highlighted. The limitations, availability and areas of further research have also been identified.

Solar radiation prediction using Artificial Neural Network techniques: A review

Abstract: Solar radiation data plays an important role in solar energy research. These data are not available for location of interest due to absence of a meteorological station. Therefore, the solar radiation has to be predicted accurately for these locations using various solar radiation estimation models. The main objective of this study is to review Artificial Neural Network (ANN) based techniques in order to identify suitable methods available in the literature for solar radiation prediction and to identify research gaps. The study shows that Artificial Neural Network techniques predict solar radiation more accurately in comparison to conventional methods. The prediction accuracy of ANN models is found to be dependent on input parameter combinations, training algorithm and architecture configurations. Further research areas in ANN technique based methodologies are also identified in the present study.