Delhi Technological University

About: Delhi Technological University is a education organization based out in New Delhi, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 4427 authors who have published 6761 publications receiving 71035 citations. The organization is also known as: Delhi College of Engineering & DTU.

Microstructure and mechanical properties of thermoelectric nanostructured n-type silicon-germanium alloys synthesized employing spark plasma sintering

Abstract: Owing to their high thermoelectric (TE) figure-of-merit, nanostructured Si80Ge20 alloys are evolving as a potential replacement for their bulk counterparts in designing efficient radio-isotope TE generators. However, as the mechanical properties of these alloys are equally important in order to avoid in-service catastrophic failure of their TE modules, we report the strength, hardness, fracture toughness, and thermal shock resistance of nanostructured n-type Si80Ge20 alloys synthesized employing spark plasma sintering of mechanically alloyed nanopowders of its constituent elements. These mechanical properties show a significant enhancement, which has been correlated with the microstructural features at nano-scale, delineated by transmission electron microscopy.

Application of Group Method of Data Handling model for software maintainability prediction using object oriented systems

Abstract: Object-oriented methodology has emerged as most prominent in software industry for application development. Maintenance phase begins once the product is delivered and by software maintainability we mean the ease with which existing software could be modified during maintenance phase. We can improve and control software maintainability if we can predict it in the early phases of software life cycle using design metrics. Predicting the maintainability of any software has become critical with the increasing importance of software maintenance. Many authors have practiced and proved theoretical validation followed by empirical evaluation using statistical and experimental techniques for evaluating the relevance of any given metrics suite using many models. In this paper, we have presented an empirical study to evaluate the effectiveness of novel technique called Group Method of Data Handling (GMDH) for the prediction of maintainability over other models. Although many metrics have been proposed in the literature, software design metrics suite proposed by Chidamber et al. and revised by Li et al. have been selected for this study. Two web-based customized softwares developed using C# Language have been used for empirical study. Source code of old and new versions for both applications were collected and analysed against modifications made in every class. The changes were counted in terms of number of lines added, deleted or modified in the classes belonging to new version with respect to the classes of old version. Finally values of metrics were combined with “change” in order to generate data points. Hence, in this study an attempt has been made to evaluate and examine the effectiveness of prediction models for the purpose of software maintainability using real life web based projects. Three models using Feed Forward 3-Layer Back Propagation Network (FF3LBPN), General Regression Neural Network (GRNN) and GMDH are developed and performance of GMDH is compared against two others i.e. FF3LBPN and GRNN. With the aid of this empirical analysis, we can safely suggest that software professionals can use OO metric suite to predict the maintainability of software using GMDH technique with least error and best precision in an object oriented paradigm.

A review on rumour prediction and veracity assessment in online social network

Abstract: In the present era, the social network is used as an important medium for sharing thoughts and opinions of an individual. The main reason behind this is, it provides a fast-spreading of information among the public easily, requiring a very low cost of access. This leads to having online social media as one of the stepping stones to encourage false content and influencing public opinion and its decision. Rumour is one of the prominent forms of misleading information on social media and should be detected as early as possible for avoiding their significant effects. Due to these reasons, the researchers have put their keen interest in developing an effective rumour detection framework in the last years. In this paper, we mainly focused on six main aspects. Firstly, we discuss rumours from a definition perspective that have been considered in the state-of-the-art and describe the generalized model of rumour detection. Secondly, we discuss how to get access to data from different social media platforms, and presents various state-of-the-art methods to gather these data, as well as publicly available datasets. Third, we describe a different set of features that have been considered in rumour detection approaches. Fourth, we provide deep insight into the various methods used to employ rumour detection and its veracity assessment on multimedia data (Text and Images) with some practical implications. Whereas in the fifth aspect, the constraints of the study have been discussed. Finally, we concluded with useful findings and suggested future directions.

Photovoltaic power forecasting methods in smart power grid

Abstract: Due to increasing global warming and depletion of conventional resources it is important to think of new types of energy sources to have clean and sufficient energy for sustainable growth. Photovoltaic (PV) power generation is playing important role in minimizing the shortage of power demand and providing clean energy to smart power grid. PV energy is growing and getting connected in distributed manner to the smart power grid. Forecasting of PV generation would play a vital role in the interconnection of the PV generators to smart power grid as this is intermittent in nature, depend on weather conditions and distributed throughout the grid. A comprehensive review of the PV forecasting methods in terms of their performances has been done in this paper. This paper presents a broad spectrum of the issues related to forecasting of PV generation at centralized as well as distributed level and also discusses its importance in smart power grid.