Compression-based Facies Modelling
Mathematical Geosciences, 2023 AbstractSimple object- or pixel-based facies models use facies proportions as the constraining input parameter to be honored in the output model. The resultant interconnectivity of the facies bodies is an unconstrained output property of the modelling, and if the objects being modelled are geometrically representative in three dimensions, commonly ...
Tom Manzocchi +3 more
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Failure analysis of CFRP laminates subjected to Compression After Impact: FE simulation using discrete interface elements [PDF]
, 2013 This paper presents a model for the numerical simulation of impact damage, permanent indentation and compression after impact (CAI) in CFRP laminates.
Bouvet, Christophe +2 more
core +1 more source
Roulette: A Pruning Framework to Train a Sparse Neural Network From Scratch
IEEE Access, 2021 Due to space and inference time restrictions, finding an efficient and sparse sub-network from a dense and over-parameterized network is critical for deploying neural networks on edge devices.
Qiaoling Zhong +3 more
doaj +1 more source
To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference [PDF]
2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom), 2018 8 pages, To appear in ISPA ...
Qin, Q +8 more
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Modelling Mammographic Compression of the Breast [PDF]
, 2008 We have developed a biomechanical model of the breast to simulate compression during mammographic imaging. The modelling framework was applied to a set of MR images of the breasts of a volunteer. Images of the uncompressed breast were segmented into skin and pectoral muscle, from which a finite element (FE) mesh of the left breast was generated using a
Jae-Hoon Chung +3 more
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A Neural Network Model Compression Approach Based on Deep Feature Map Transfer
IEEE Access, 2020 Neural network is widely used in computer vision. However, with the continuous expansion of the application field, high-precision large parameter neural network model is difficult to deploy on small equipment with limited resources.
Zhibo Guo +4 more
doaj +1 more source
Image compression with anisotropic diffusion [PDF]
, 2008 Compression is an important field of digital image processing where well-engineered methods with high performance exist. Partial differential equations (PDEs), however, have not much been explored in this context so far. In our paper we introduce a novel
Welk, Martin +19 more
core +1 more source
SANA: Sensitivity-Aware Neural Architecture Adaptation for Uniform Quantization
Applied Sciences, 2023 Uniform quantization is widely taken as an efficient compression method in practical applications. Despite its merit of having a low computational overhead, uniform quantization fails to preserve sensitive components in neural networks when applied with ...
Mingfei Guo, Zhen Dong, Kurt Keutzer
doaj +1 more source
Comparison of the effect of locking vs standard screws on the mechanical properties of bone-plate constructs in a comminuted diaphyseal fracture model [PDF]
, 2012 The purpose of this study was to compare the mechanical properties of bone-plate constructs with locking compression plates (LCP) used either with standard screws or with locking screws on an experimental model of comminuted ...
Autefage, André +4 more
core +1 more source
Filter Pruning with Convolutional Approximation Small Model Framework
Computation, 2023 Convolutional neural networks (CNNs) are extensively utilized in computer vision; however, they pose challenges in terms of computational time and storage requirements. To address this issue, one well-known approach is filter pruning.
Monthon Intraraprasit +1 more
doaj +1 more source