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Utilizing Continuous Kernels for Processing Irregularly and Inconsistently Sampled Data With Position-Dependent Features
(2023-03-13)
Continuous Kernels have been a recent development in convolutional neural networks. Such kernels are used to process data sampled at different resolutions as well as irregularly and inconsistently sampled data. Convolutional neural networks have the property of translational invariance (e.g., features are detected regardless of their position in the measurement domain), which is unsuitable for certain types of data, where the position of detected features is relevant. However, the capabilities of continuous kernels ...
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On the Challenges toward Realization of Functionally Graded Structures by Electron Beam Melting—Fe-Base Shape Memory Alloy and Stainless Steel
(ASTM International, 2020-09-22)
In the present study, an iron-manganese-aluminum-nickel (Fe-Mn-Al-Ni) shape memory alloy was processed on an austenitic steel (AISI 304) build platform by electron beam melting in order to study the feasibility of realizing functionally graded structures consisting of two different materials (i.e., a functional and a structural material). Compression specimens consisting of the processed shape memory alloy and the austenitic build platform in equal parts were investigated. The microstructure was analyzed in the ...
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Fast parallel quasi-static time series simulator for active distribution grid operation with pandapower
(IET, 2021)
The increasing penetration from intermittent renewable distributed energy resources in distribution grid brings along challenges in grid operation and planning. To evaluate the impact on the grid voltage profile, grid losses, and discrete actions from assets (e.g. transformer tap changes), quasi-static simulation is an appropriate method. Quasi-static time series and Monte-Carlo simulation requires a tremendous number of power flow calculations (PFCs), which can be significantly accelerated with a parallel High-Performance ...