Design sensitivity analysis of nonlinear dynamic response of structural and mechanical systems

This paper describes a unified variational theory for design sensitivity analysis of nonlinear dynamic response of structural and mechanical systems for shape, nonshape, material and mechanical properties selection, as well as control problems. The concept of an adjoint system, the principle of virtual work and a Lagrangian-Eulerian formulation to describe the deformations and the design variations are used to develop a unified view point. A general formula for design sensitivity analysis is derived and interpreted for usual performance functionals. Analytical examples are utilized to demonstrate the use of the theory and give insights for application to more complex problems that must be treated numerically.Derivatives The comma notation for partial derivatives is used, i.e. G,_u = G/u. An upper dot represents material time derivative, i.e. ü = ²u/t². A prime implies derivative with respect to the time measured in the reference time-domain, i.e. u = du/d.     

Supporting collaborative software development over GitHub

GitHub is a web‐based, distributed Software Configuration Management (SCM) system build over Git, which enables developers to host shared repositories over the Internet and access them from any location, at any time. It helps developers to effectively orchestrate their activities over shared codebases by capturing direct conflicts arising because of concurrent editing on the same shared artifact. However, SCM systems have limited support for capturing inconsistencies arising because of indirect conflicts which arise because of software dependency relationships that exist between related artifacts, and lead to the introduction of syntactic and semantic inconsistencies in codebases. In this paper, we propose a novel collaborative software development (CSD) tool named, Collaboration Over GitHub (COG), that provides real‐time information about arising direct and indirect conflicts among collaborative developers, working over GitHub, through a collection of workspace awareness widgets. These widgets provide people‐centric information about direct and indirect collaborators over GitHub. Resource‐centric information about current and conflicting activities of real‐time collaborators is captured and propagated to others, based on the dependency relationships between software artifacts being manipulated by them. COG uses dependency graphs to store and process dependency relationship information which is required to ascertain information about indirect conflicts. Notably, the most important novel contribution of COG is that it not only captures indirect conflicts that lead to the introduction of syntactic inconsistencies but also changes that lead to semantic inconsistencies in the codebase. It also does so at finer levels of granularity, with changes to individual method's body being traced, thereby capturing statement‐level conflicts as well. Copyright © 2016 John Wiley & Sons, Ltd.     

OPTIMIZING THE ROTATION AND TRANSLATION OF FINGERPRINT IMAGES USING GENETIC ALGORITHM

In the minutiae-based fingerprint authentication system, the minutiae in the query image are required to be matched with the minutiae of the reference image that is stored in the database. Ideally, the minutiae extracted from the different impressions of the same fingerprint must match with each other, but practically, because of displacement, rotation, and other linear/ nonlinear distortions, minutiae extracted from different impressions of the same fingerprint do not match with each other. In order to maximize the number of matching minutiae, the alignment of the two fingerprints is required. Correctly aligning the fingerprints requires the translation and rotation to be recovered exactly. In this article, a new genetic-algorithm (GA)-based relative alignment algorithm for the alignment of reference and query fingerprint images is proposed. With the proposed algorithm there is no need to find the reference core or delta point because reliable detection of these reference points is a difficult task. In the proposed algorithm, all the three parameters x, y (translation), and θ (rotational) have been optimized separately. In order to improve the processing time, two acceleration steps have also been implemented. The experiments conducted on the FVC2002/Db1_a database reveal that a high accuracy has been achieved with the proposed method.     

Reckoner for health risk and insurance premium using adaptive neuro-fuzzy inference system

The paper demonstrates an efficient use of intelligent system for solving the classification problem in the sector of health insurance. A model based on adaptive neuro-fuzzy inference system (ANFIS) is proposed to deal with the fuzziness in the real-life environments. This approach enables the interpretation of majority of health factors of an insurance seeker through a set of fuzzy rules to determine the degree of risk to an individual. A fuzzy neural network has been trained with fuzzy inputs like age, occupation, family size, smoking habits, drinking habits, diabetes history, heart disease and other relevant inputs of individual for risk calculation. The model gets importance in health insurance sector because risk determination is fuzzy in nature, and fuzzy calculations are done more accurately by machines rather than human beings especially for the problems which are repetitive in nature and have large number of vague parameters. The proposed model can help the insurance seeker to identify the degree of risk he is having if he is not taking health insurance. This serves a dual purpose of attracting the insurance seeker to acquire the insurance and facilitate generating business to insurance company. Indicative results are presented and discussed in detail in terms of accuracy and solution interpretability.     

A parallel built-in self-diagnostic method for nontraditional faults of embedded memory arrays

In this paper, we propose a built-in self-diagnostic march-based algorithm that identifies faulty memory cells based on a recently introduced nontraditional fault model. It is developed based on the DiagRSMarch algorithm, which is a diagnostic algorithm to identify traditional faults for embedded memory arrays. A minimal set of additional operations is added to DiagRSMarch for identifying the nontraditional faults without affecting the diagnostic coverage of the traditional faults. The embedded memory arrays are accessed using a bidirectional serial interfacing architecture which minimizes the routing overhead introduced by the diagnosis hardware. Using the concepts of the bidirectional interfacing technique, parallel testing, and redundant-tolerant operations, the diagnostic process can be accomplished efficiently at-speed with minimal hardware overhead.     

High‐Performance Microwave‐Derived Multi‐Principal Element Alloy Coatings for Tribological Application

The authors report the development of Al_xCoCrFeNi (x = 0.1 to 3) high entropy alloy (HEA) coatings using a simple and straightforward microwave technique. The microstructure of the developed coatings is composed of a cellular structure and diffused interface with the substrate. The microstructure of the HEA coatings varies as a direct function of Al content. An increase in Al fraction shows structural transformation from FCC to BCC along with the evolution of σ and B₂ as the major secondary phases. The diffusion of Mo from the substrate enhances the mixing entropy and promotes σ‐phase formation. The HEA coatings show significantly high hardness compared to SS316L substrate steel (227 HV) with a maximum value of 726 HV observed for three‐molar composition. The fracture toughness exhibits an inverse correlation with the Al fraction with the highest value of around 49 MPa m^1/2 observed for Al_0.1CoCrFeNi coating. The equimolar coating composition shows lowest erosion rates among all the tested samples due to optimum combination of the mechanical properties. The erosion resistance of the equimolar coating is 2 to 5 times higher than steel substrate and around 1.5 times higher than the non‐equimolar counterparts depending upon the impingement angles.

     

Systematic Uncertainty Evaluation of the Cesium Fountain Primary Frequency Standard at NPL India

CSIR-NPL, India (NPLI) has developed its first Cesium atomic fountain (NPLI-CsF1). The indigenously developed fountain frequency standard is now fully operational and its frequency is now being evaluated along with all systematic and statistical uncertainties. Seven frequency evaluations have been recently conducted to estimate fountain frequency with respect to UTC (Coordinate Universal Time). The fountain frequency is compared with the Hydrogen MASER frequency which is contributing to UTC and hence fountain frequency is evaluated with respect to the UTC The fountain frequency is stable to few parts in 10¹⁵ at <1 day averaging time.     

A survey on channel coding techniques for 5G wireless networks

Telecommunication Systems - Although 4G (fourth generation) i.e. LTE (long term evolution) systems are now in use world-wide. But today’s 4G systems have some challenges left such as spectrum...     

Generalized intuitionistic fuzzy soft power aggregation operator based on t-norm and their application in multicriteria decision-making

The aim of this paper is to develop some new power aggregation operators for intuitionistic fuzzy (IF) soft numbers. The aggregation operators are named as IF soft power averaging (IFSPA) operator, weighted IFSPA (WIFSPA) operator, ordered WIFSPA operator, IF soft power geometric (IFSPG) operator, and weighted and ordered weighted IFSPG aggregation operators. The salient features of these operators are discussed in detail. Further, these operators are extended to its generalized version and called generalized IFSPA or geometric aggregation operators. Then, we utilized these operators to develop an approach to solve the decision-making problem under IF soft set environment and demonstrated with an illustrative example. A comparative analysis of existing approaches has been done for showing the validity of the proposed work.     

High performance and safe flight of full‐scale helicopters from takeoff to landing with an ensemble of planners

Autonomous flight of unmanned full‐size rotor‐craft has the potential to enable many new applications. However, the dynamics of these aircraft, prevailing wind conditions, the need to operate over a variety of speeds and stringent safety requirements make it difficult to generate safe plans for these systems. Prior work has shown results for only parts of the problem. Here we present the first comprehensive approach to planning safe trajectories for autonomous helicopters from takeoff to landing. Our approach is based on two key insights. First, we compose an approximate solution by cascading various modules that can efficiently solve different relaxations of the planning problem. Our framework invokes a long‐term route optimizer, which feeds a receding‐horizon planner which in turn feeds a high‐fidelity safety executive. Secondly, to deal with the diverse planning scenarios that may arise, we hedge our bets with an ensemble of planners. We use a data‐driven approach that maps a planning context to a diverse list of planning algorithms that maximize the likelihood of success. Our approach was extensively evaluated in simulation and in real‐world flight tests on three different helicopter systems for duration of more than 109 autonomous hours and 590 pilot‐in‐the‐loop hours. We provide an in‐depth analysis and discuss the various tradeoffs of decoupling the problem, using approximations and leveraging statistical techniques. We summarize the insights with the hope that it generalizes to other platforms and applications.