651,067 research outputs found
A thermoviscoplastic model with damage for simultaneous hot/cold forging analysis
A constitutive model is presented for simultaneous hot/cold forming processes of steels. The phenomenological material theory is based on an enhanced rheological model and accounts temperature dependently for nonlinear hardening, thermally activated recovery effects, an improved description of energy storage and dissipation during plastic deformations, and damage evolution as well. A thermomechanically consistent treatment of dissipative heating due to inelastic deformations, recovery processes and damage mechanisms is applied. The constitutive model is implemented into a commercial FE-code. The material parameters of the effective model response are identified for a low alloyed steel and validated by means of a simultaneous hot/cold forging process
Joint Regression and Ranking for Image Enhancement
Research on automated image enhancement has gained momentum in recent years,
partially due to the need for easy-to-use tools for enhancing pictures captured
by ubiquitous cameras on mobile devices. Many of the existing leading methods
employ machine-learning-based techniques, by which some enhancement parameters
for a given image are found by relating the image to the training images with
known enhancement parameters. While knowing the structure of the parameter
space can facilitate search for the optimal solution, none of the existing
methods has explicitly modeled and learned that structure. This paper presents
an end-to-end, novel joint regression and ranking approach to model the
interaction between desired enhancement parameters and images to be processed,
employing a Gaussian process (GP). GP allows searching for ideal parameters
using only the image features. The model naturally leads to a ranking technique
for comparing images in the induced feature space. Comparative evaluation using
the ground-truth based on the MIT-Adobe FiveK dataset plus subjective tests on
an additional data-set were used to demonstrate the effectiveness of the
proposed approach.Comment: WACV 201
Damage localization using experimental modal parameters and topology optimization
This work focuses on the developement of a damage detection and localization tool using the Topology Optimization feature of MSC.Nastran. This approach is based on the correlation of a local stiness loss and the change in modal parameters due to damages in structures. The loss in stiness is accounted by the Topology Optimization approach for updating undamaged numerical models towards similar models with embedded damages. Hereby, only a mass penalization and the changes in experimentally obtained modal parameters are used as objectives. The theoretical background for the implementation of this method is derived and programmed in a Nastran input file and the general feasibility of the approach is validated numerically, as well as experimentally by updating a model of an experimentally tested composite laminate specimen. The damages have been introduced to the specimen by controlled low energy impacts and high quality vibration tests have been conducted on the specimen for dierent levels of damage. These supervised experiments allow to test the numerical diagnosis tool by comparing the result with both NDT technics and results of previous works (concerning shifts in modal parameters due to damage). Good results have finally been archieved for the localization of the damages by the Topology Optimization
Monitoring and management of power transmission dynamics in an industrial smart grid
This article is a position paper whose purpose is to give the context for presentations in a special session at PowerTech 2013. The special session is being proposed by the EU FP7 Real-Smart Consortium, a Marie Curie Industry-Academic Pathways and Partnerships project. The paper gives an overview of topics on modeling, monitoring and management of power transmission dynamics with participation from large industrial loads. © 2013 IEEE
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