286 research outputs found

    G-CSC Report 2010

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    The present report gives a short summary of the research of the Goethe Center for Scientific Computing (G-CSC) of the Goethe University Frankfurt. G-CSC aims at developing and applying methods and tools for modelling and numerical simulation of problems from empirical science and technology. In particular, fast solvers for partial differential equations (i.e. pde) such as robust, parallel, and adaptive multigrid methods and numerical methods for stochastic differential equations are developed. These methods are highly adanvced and allow to solve complex problems.. The G-CSC is organised in departments and interdisciplinary research groups. Departments are localised directly at the G-CSC, while the task of interdisciplinary research groups is to bridge disciplines and to bring scientists form different departments together. Currently, G-CSC consists of the department Simulation and Modelling and the interdisciplinary research group Computational Finance

    Advances in Data Mining Knowledge Discovery and Applications

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    Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

    Model-based myoelectric control of robots for assistance and rehabilitation

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    The first anthropomorphic robots and exoskeletons were developed with the idea of combining man and machine into an intimate symbiotic unit that can perform as one joint system. A human-robot interface consists of processes of two different nature: (1) the physical interaction (pHRI) between the device and its user and (2) the exchange of cognitive information (cHRI) between the human and the robot. To achieve the symbiosis between the two actors, both need to be optimized. The evolution of mechanical design and the introduction of new materials pushed pHRI to new frontiers on ergonomics and assistance performance. However, cHRI still lacks on this direction because is more complicated: it requires communication from the cognitive processes occuring in the human agent to the robot, e.g. intention detection; but also from the robot to the human agent, e.g. feedback modalities such as haptic cues. A possible innovation is the inclusion of the electromyographic signal, the command signal from our brain to the musculoskeletal system for the movement, in the robot control loop. The aim of this thesis was to develop a real-time control framework for an assistive device that can generate the same force produced by the muscles. To do this, I incorporated in the robot control loop a detailed musculoskeletal model that estimates the net torque at the joint level by taking as inputs the electromyography signals and kinematic data. This module is called myoprocessor. Here I present two applications of this control approach: the first was implemented on a soft wearable arm exosuit in order to evaluate the adaptation of the controller on different motion and loads. The second one, was a generation of myoprocessor-driven force field on a planar robot manipulandum in order to study the modularity changes of the musculoskeletal system. Both applications showed that the device controlled by myoprocessor works symbiotically with the user, by reducing the muscular activity and preserving the motor performance. The ability of seamlessly combining musculoskeletal force estimators with assistive devices opens new avenues for assisting human movement both in healthy and impaired individuals

    Conferentie informatiewetenschap 2003, Technische Universiteit Eindhoven, 20 november 2003 : proceedings

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    Conferentie informatiewetenschap 2003, Technische Universiteit Eindhoven, 20 november 2003 : proceedings

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    Privacy-Protecting Techniques for Behavioral Data: A Survey

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    Our behavior (the way we talk, walk, or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions. Hence, techniques to protect individuals privacy against unwanted inferences are required. To consolidate knowledge in this area, we systematically reviewed applicable anonymization techniques. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. Our analysis shows that some behavioral traits (e.g., voice) have received much attention, while others (e.g., eye-gaze, brainwaves) are mostly neglected. We also find that the evaluation methodology of behavioral anonymization techniques can be further improved

    A novel numerical framework for simulation of multiscale spatio-temporally non-linear systems in additive manufacturing processes.

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    New computationally efficient numerical techniques have been formulated for multi-scale analysis in order to bridge mesoscopic and macroscopic scales of thermal and mechanical responses of a material. These numerical techniques will reduce computational efforts required to simulate metal based Additive Manufacturing (AM) processes. Considering the availability of physics based constitutive models for response at mesoscopic scales, these techniques will help in the evaluation of the thermal response and mechanical properties during layer-by-layer processing in AM. Two classes of numerical techniques have been explored. The first class of numerical techniques has been developed for evaluating the periodic spatiotemporal thermal response involving multiple time and spatial scales at the continuum level. The second class of numerical techniques is targeted at modeling multi-scale multi-energy dissipative phenomena during the solid state Ultrasonic Consolidation process. This includes bridging the mesoscopic response of a crystal plasticity finite element framework at inter- and intragranular scales and a point at the macroscopic scale. This response has been used to develop an energy dissipative constitutive model for a multi-surface interface at the macroscopic scale. An adaptive dynamic meshing strategy as a part of first class of numerical techniques has been developed which reduces computational cost by efficient node element renumbering and assembly of stiffness matrices. This strategy has been able to reduce the computational cost for solving thermal simulation of Selective Laser Melting process by ~100 times. This method is not limited to SLM processes and can be extended to any other fusion based additive manufacturing process and more generally to any moving energy source finite element problem. Novel FEM based beam theories have been formulated which are more general in nature compared to traditional beam theories for solid deformation. These theories have been the first to simulate thermal problems similar to a solid beam analysis approach. These are more general in nature and are capable of simulating general cross-section beams with an ability to match results for complete three dimensional analysis. In addition to this, a traditional Cholesky decomposition algorithm has been modified to reduce the computational cost of solving simultaneous equations involved in FEM simulations. Solid state processes have been simulated with crystal plasticity based nonlinear finite element algorithms. This algorithm has been further sped up by introduction of an interfacial contact constitutive model formulation. This framework has been supported by a novel methodology to solve contact problems without additional computational overhead to incorporate constraint equations averting the usage of penalty springs

    Dagstuhl News January - December 2008

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    "Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

    The Impact of Digital Technologies on Public Health in Developed and Developing Countries

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    This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

    Improving Access and Mental Health for Youth Through Virtual Models of Care

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    The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial
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