1,726,330 research outputs found
IIT Hyderabad team wins TCS CodeVita contest
Get PDFAdarsh Pugalia and Shalin Shah, from IIT Hyderabad bagged CodeVita’s 1st prize of 10,000, at the contest held in
Hyderabad last week.
A two-member team from IIT Hyderabad emerged on top at the TCS CodeVita contest for engineering and
science students.
The team, comprising Adarsh Pugalia and Shalin Shah, from IIT Hyderabad bagged CodeVita’s 1st prize
of $10,000, at the contest held in
Hyderabad last week, a Tata Consultancy Services (TCS) release on Monday said
Adarsh Nim M23SLAP0003 Assignment.xlsx
No full textThe data is just a sample to understand how figshare work.</p
GPU-accelerated depth map generation for X-ray simulations of complex CAD geometries
Get PDFInteractive x-ray simulations of complex computer-aided design (CAD) models can provide valuable insights for better interpretation of the defect signatures such as porosity from x-ray CT images. Generating the depth map along a particular direction for the given CAD geometry is the most compute-intensive step in x-ray simulations. We have developed a GPU-accelerated method for real-time generation of depth maps of complex CAD geometries. We preprocess complex components designed using commercial CAD systems using a custom CAD module and convert them into a fine user-defined surface tessellation. Our CAD module can be used by different simulators as well as handle complex geometries, including those that arise from complex castings and composite structures. We then make use of a parallel algorithm that runs on a graphics processing unit (GPU) to convert the finely-tessellated CAD model to a voxelized representation. The voxelized representation can enable heterogeneous modeling of the volume enclosed by the CAD model by assigning heterogeneous material properties in specific regions. The depth maps are generated from this voxelized representation with the help of a GPU-accelerated ray-casting algorithm. The GPU-accelerated ray-casting method enables interactive (> 60 frames-per-second) generation of the depth maps of complex CAD geometries. This enables arbitrarily rotation and slicing of the CAD model, leading to better interpretation of the x-ray images by the user. In addition, the depth maps can be used to aid directly in CT reconstruction algorithms.This proceeding may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This proceeding appeared in Grandin, Robert J., Gavin Young, Stephen D. Holland, and Adarsh Krishnamurthy. "GPU-accelerated depth map generation for X-ray simulations of complex CAD geometries." In AIP Conference Proceedings, vol. 1949, no. 1, p. 190002. AIP Publishing LLC, 2018, and may be found at
DOI: 10.1063/1.5031636.
Copyright 2018 Author(s).
Posted with permission
Discovering your business and soft skills: efficacy of psychology-based tools
No full textHoy en día, cada vez más empresas y organizaciones utilizan diferentes tipos de tests de personalidad con el fin de identificar a los candidatos ideales para sus nuevas vacantes, pero ésta no es la única razón. En la mayoría de los casos, el objetivo de estas pruebas es, además, facilitar el trabajo de los departamentos implicados, filtrando y descartando automáticamente a un gran número de candidatos, cuyo perfil no se ajuste a los requisitos del puesto en cuestión. Este tipo de tests intentan predecir de qué es capaz el aspirante y su potencial de desarrollo identificando, por ejemplo, aptitudes como liderazgo, innovación, etc. Pero, ¿son de verdad capaces de identificar cómo se desenvuelve la persona en el ambiente de trabajo, o su capacidad para transmitir a la hora de hablar en público? ¿Hasta qué punto son precisos estos test de personalidad? Algunos lo ven como un complemento a las entrevistas, los cuales pueden - o no - jugar a favor del candidato, pero, ¿hay alguna otra manera de conocer las aptitudes de los aspirantes de una manera más precisa y creíble? Adarsh Arora, emprendedor y profesor del Instituto Tecnológico de Illinois, propone una nueva plataforma llamada TruAccolades. Esta propuesta llevada a cabo junto con alumnos de la universidad permite que los usuarios -desde etapas tempranas puedan adquirir diferentes insignias en relación a su trabajo académico y/o profesional, a través del feedback real de otras personas.---ABSTRACT---Nowadays, more and more companies and organizations use different types of personality assessments in order to identify ideal candidates for their new vacancies, but this is not the only reason. In most cases, the aim of these tests is also to facilitate the work of the departments involved by automatically filtering and discarding a large number of candidates whose profile does not match with the requirements of the position in question. This type of assessments attempt to predict what the aspirant is capable of and his/her potential by identifying, for example, skills such as leadership, innovation, etc. But are they really capable of identifying how the people perform in the work environment, or their ability to transmit when speaking in public? To what extent are these personality tests accurate? Some see it as a complement to interviews, which may – or may not – play into the candidate’s favor, but is there any other way of knowing the applicants’ skills in a more accurate and credible way? Adarsh Arora, entrepreneur and professor at the Illinois Institute of Technology, proposes a new platform called TruAccolades. This proposal, carried out together with students from this university, allows users -from early stages- to acquire different accolades in relation to their academic and/or professional work, through other people’s real feedback
Applying explainable artificial intelligence models for understanding depression among IT workers
No full textArtificial Intelligence (AI) systems are getting better and better as each day goes on, but due to the increased complexity of the models that are being used, we are unable to understand how these decisions are being made by the system. Explainable Artificial Intelligence (XAI) is a subfield of AI that aims to provide intelligible explanations to the end-user. This study evaluates people who are at risk of mental illness and detects early signs of depressive symptoms, using XAI approaches.</p
Incorporation of composite defects from ultrasonic NDE into CAD and FE models
Get PDFFiber-reinforced composites are widely used in aerospace industry due to their combined properties of high strength and low weight. However, owing to their complex structure, it is difficult to assess the impact of manufacturing defects and service damage on their residual life. While, ultrasonic testing (UT) is the preferred NDE method to identify the presence of defects in composites, there are no reasonable ways to model the damage and evaluate the structural integrity of composites. We have developed an automated framework to incorporate flaws and known composite damage automatically into a finite element analysis (FEA) model of composites, ultimately aiding in accessing the residual life of composites and make informed decisions regarding repairs. The framework can be used to generate a layer-by-layer 3D structural CAD model of the composite laminates replicating their manufacturing process. Outlines of structural defects, such as delaminations, are automatically detected from UT of the laminate and are incorporated into the CAD model between the appropriate layers. In addition, the framework allows for direct structural analysis of the resulting 3D CAD models with defects by automatically applying the appropriate boundary conditions. In this paper, we show a working proof-of-concept for the composite model builder with capabilities of incorporating delaminations between laminate layers and automatically preparing the CAD model for structural analysis using a FEA software.This proceeding may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This proceeding appeared in Bingol, Onur Rauf, Bryan Schiefelbein, Robert J. Grandin, Stephen D. Holland, and Adarsh Krishnamurthy. "Incorporation of composite defects from ultrasonic NDE into CAD and FE models." AIP Conference Proceedings 1806, no. 1, (2017): 150004. , and may be found at DOI: 10.1063/1.4974728. Posted with permission.</p
Mental stress detection from ultra-short heart rate variability using explainable graph convolutional network with network pruning and quantisation
No full textThis study introduces a novel pruning approach based on explainable graph convolutional networks, strategically amalgamating pruning and quantisation, aimed to tackle the complexities associated with existing machine learning and deep learning models for stress detection using ultra-short heart rate variability analysis. These complexities often impede the implementation ability of such models on resource-limited devices. The proposed method exhibits exceptional performance, demonstrating high accuracy (97.75%) and efficiency (97.66%) on the WESAD dataset, along with an impressive accuracy (94.48%) and efficiency (94.39%) on the SWELL dataset. Importantly, the runtime complexity saw a significant reduction, down by 63.4% and 69.34% compared to the original model. The proposed method's notable advantage lies in its ability to retain nearly all of the initial model's performance with negligible loss, even when the pruning levels are below 60%. This innovative approach, thus, offers a promising solution for effective stress detection, specifically designed to operate smoothly on devices with limited resources.</p
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