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Trélat, Sophie, and Michel-Olivier Sturtzer. "Predicting explosion and blast effects: A multi-scale experimental approach." International Journal of Safety and Security Engineering 9, no. 3 (November 30, 2019): 356–70. http://dx.doi.org/10.2495/safe-v9-n4-356-370.
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Rots, Jan G., Francesco Messali, Rita Esposito, Valentina Mariani, and Samira Jafari. "Multi-Scale Approach towards Groningen Masonry and Induced Seismicity." Key Engineering Materials 747 (July 2017): 653–61. http://dx.doi.org/10.4028/www.scientific.net/kem.747.653.
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In the last years, the induced seismicity in the northern part of the Netherlands has considerably increased. The existing building stock was not designed for seismic loading, and it is characterised by very slender walls, limited cooperation between walls and floors, and use of cavity walls. As a consequence, the validation of analytical and numerical models for the assessment of unreinforced masonry buildings and the characterisation of the masonry at both material and structural level have become of great importance. An extensive large-scale testing program was performed at the Delft University of Technology in 2015 to create benchmarks for the validation of the numerical and analytical models. The attention was mainly devoted to a terraced house typology, which was widely adopted for housing in the period 1960-1980, and focused on the characterisation of the typology at various levels: material, connection, component and assemblage level. The experimental tests at component and assemblage levels were also reproduced by nonlinear finite element analysis, validated and calibrated against the data available from the testing campaign at material level. In this paper, an overview description of performed experiments and numerical analyses is provided; specific devotion is given to the main outcomes of the campaign and to the lessons learned by the experimental evidences for improving the numerical models.
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Jia, Yonghong, Mingting Zhou, and Ye Jinshan. "OBJECT-ORIENTED CHANGE DETECTION BASED ON MULTI-SCALE APPROACH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 517–22. http://dx.doi.org/10.5194/isprsarchives-xli-b7-517-2016.
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The change detection of remote sensing images means analysing the change information quantitatively and recognizing the change types of the surface coverage data in different time phases. With the appearance of high resolution remote sensing image, object-oriented change detection method arises at this historic moment. In this paper, we research multi-scale approach for high resolution images, which includes multi-scale segmentation, multi-scale feature selection and multi-scale classification. Experimental results show that this method has a stronger advantage than the traditional single-scale method of high resolution remote sensing image change detection.
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Jia, Yonghong, Mingting Zhou, and Ye Jinshan. "OBJECT-ORIENTED CHANGE DETECTION BASED ON MULTI-SCALE APPROACH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 517–22. http://dx.doi.org/10.5194/isprs-archives-xli-b7-517-2016.
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The change detection of remote sensing images means analysing the change information quantitatively and recognizing the change types of the surface coverage data in different time phases. With the appearance of high resolution remote sensing image, object-oriented change detection method arises at this historic moment. In this paper, we research multi-scale approach for high resolution images, which includes multi-scale segmentation, multi-scale feature selection and multi-scale classification. Experimental results show that this method has a stronger advantage than the traditional single-scale method of high resolution remote sensing image change detection.
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Cai, Lei, and Shuiwang Ji. "A Multi-Scale Approach for Graph Link Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3308–15. http://dx.doi.org/10.1609/aaai.v34i04.5731.
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Deep models can be made scale-invariant when trained with multi-scale information. Images can be easily made multi-scale, given their grid-like structures. Extending this to generic graphs poses major challenges. For example, in link prediction tasks, inputs are represented as graphs consisting of nodes and edges. Currently, the state-of-the-art model for link prediction uses supervised heuristic learning, which learns graph structure features centered on two target nodes. It then learns graph neural networks to predict the existence of links based on graph structure features. Thus, the performance of link prediction models highly depends on graph structure features. In this work, we propose a novel node aggregation method that can transform the enclosing subgraph into different scales and preserve the relationship between two target nodes for link prediction. A theory for analyzing the information loss during the re-scaling procedure is also provided. Graphs in different scales can provide scale-invariant information, which enables graph neural networks to learn invariant features and improve link prediction performance. Our experimental results on 14 datasets from different areas demonstrate that our proposed method outperforms the state-of-the-art methods by employing multi-scale graphs without additional parameters.
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Mastropasqua, Luca, Alessandro Donazzi, and Stefano Campanari. "A Multi-Scale Modelling Approach and Experimental Calibration Applied to Commercial SOFCs." ECS Transactions 78, no. 1 (May 30, 2017): 2645–58. http://dx.doi.org/10.1149/07801.2645ecst.
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Lo Monte, Francesco, Roberto Felicetti, Alberto Meda, and Anna Bortolussi. "Assessment of concrete sensitivity to fire spalling: A multi-scale experimental approach." Construction and Building Materials 212 (July 2019): 476–85. http://dx.doi.org/10.1016/j.conbuildmat.2019.03.332.
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Leighton, M., T. Nicholls, M. De la Cruz, R. Rahmani, and H. Rahnejat. "Combined lubricant–surface system perspective: Multi-scale numerical–experimental investigation." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 231, no. 7 (December 12, 2016): 910–24. http://dx.doi.org/10.1177/1350650116683784.
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Frictional losses are one of the main causes of reduced energy efficiency in all machines and mechanisms. In particular, there is mounting pressure upon manufacturers of all forms of vehicle to comply with increasingly stringent legislation and directives with regard to harmful emissions. Therefore, reduction of friction has become an imperative issue. The traditional approach of dealing with surface material and lubricant formulation in isolation has been replaced by a lubricant–surface system approach. This paper presents multi-scale experimentation from nano/meso-scale lateral force microscopy of ultra-thin surface adsorbed films through to micro-scale precision sliding tribometry to investigate lubricant–surface friction optimisation within the mixed regime of lubrication, using lubricants with different organic and inorganic friction modifying species. These affect the parameters of the system, commonly used as input to models for mixed and boundary regimes of lubrication. Therefore, the precise measurement of these parameters at different physical scales is important. The study also makes use of detailed numerical predictions at micro-scale through combined solution of the average Reynolds equation as well as interaction of wetted asperities in mixed and boundary regimes of lubrication. Good agreement is found between the predictions and measurements at micro-scale tribometric interactions. Furthermore, the same trends are observed in testing across the physical scales.
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Castro-Triguero, Rafael, Enrique Garcia-Macias, Erick Saavedra Flores, M. I. Friswell, and Rafael Gallego. "Multi-scale model updating of a timber footbridge using experimental vibration data." Engineering Computations 34, no. 3 (May 2, 2017): 754–80. http://dx.doi.org/10.1108/ec-09-2015-0284.
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Purpose The purpose of this paper is to capture the actual structural behavior of the longest timber footbridge in Spain by means of a multi-scale model updating approach in conjunction with ambient vibration tests. Design/methodology/approach In a first stage, a numerical pre-test analysis of the full bridge is performed, using standard beam-type finite elements with isotropic material properties. This approach offers a first structural model in which optimal sensor placement (OSP) methodologies are applied to improve the system identification process. In particular, the effective independence (EFI) method is used to determine the optimal locations of a set of sensors. Ambient vibration tests are conducted to determine experimentally the modal characteristics of the structure. The identified modal parameters are compared with those values obtained from this preliminary model. To improve the accuracy of the numerical predictions, the material response is modeled by means of a homogenization-based multi-scale computational approach. In a second stage, the structure is modeled by means of three-dimensional solid elements with the above material definition, capturing realistically the full orthotropic mechanical properties of wood. A genetic algorithm (GA) technique is adopted to calibrate the micromechanical parameters which are either not well-known or susceptible to considerable variations when measured experimentally. Findings An overall good agreement is found between the results of the updated numerical simulations and the corresponding experimental measurements. The longitudinal and transverse Young's moduli, sliding and rolling shear moduli, density and natural frequencies are computed by the present approach. The obtained results reveal the potential predictive capabilities of the present GA/multi-scale/experimental approach to capture accurately the actual behavior of complex materials and structures. Originality/value The uniqueness and importance of this structure leads to an intensive study of its structural behavior. Ambient vibration tests are carried out under environmental excitation. Extraction of modal parameters is obtained from output-only experimental data. The EFI methodology is applied for the OSP on a large-scale structure. Information coming from several length scales, from sub-micrometer dimensions to macroscopic scales, is included in the material definition. The strong differences found between the stiffness along the longitudinal and transverse directions of wood lumbers are incorporated in the structural model. A multi-scale model updating approach is carried out by means of a GA technique to calibrate the micromechanical parameters which are either not well-known or susceptible to considerable variations when measured experimentally.
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Tian, Yan, Xun Wang, Jiachen Wu, Ruili Wang, and Bailin Yang. "Multi-scale Hierarchical Residual Network for Dense Captioning." Journal of Artificial Intelligence Research 64 (January 30, 2019): 181–96. http://dx.doi.org/10.1613/jair.1.11338.
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Recent research on dense captioning based on the recurrent neural network and the convolutional neural network has made a great progress. However, mapping from an image feature space to a description space is a nonlinear and multimodel task, which makes it difficult for the current methods to get accurate results. In this paper, we put forward a novel approach for dense captioning based on hourglass-structured residual learning. Discriminant feature maps are obtained by incorporating dense connected networks and residual learning in our model. Finally, the performance of the approach on the Visual Genome V1.0 dataset and the region labelled MS-COCO (Microsoft Common Objects in Context) dataset are demonstrated. The experimental results have shown that our approach outperforms most current methods.
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Tremmel, Stephan, Max Marian, Benedict Rothammer, Tim Weikert, and Sandro Wartzack. "Designing Amorphous Carbon Coatings Using Numerical and Experimental Methods within a Multi-Scale Approach." Defect and Diffusion Forum 404 (October 2020): 77–84. http://dx.doi.org/10.4028/www.scientific.net/ddf.404.77.
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Amorphous carbon coatings have the potential to effectively reduce friction and wear in tribotechnical systems. The appropriate application of amorphous carbon layers requires both, a very good understanding of the tribological system and knowledge of the relationships between the fabrication of the coatings and their properties. In technical practice, however, the coatings’ development and their selection on the one hand and the design of the tribological system and its environment on the other hand are usually very strongly separated. The present work therefore aims to motivate the integrated development of tribotechnical systems with early consideration of the potential of amorphous carbon coatings. An efficient integrated development process is presented, which makes it possible to determine the boundary conditions and the load collective of the tribological system based upon an overall system and to derive the requirements for a tailored coating. In line with the nature of tribology, this approach must cover several scales. In this respect, the development process follows a V-model. The left branch of the V-model is mainly based upon a simulation chain including multibody and contact simulations. The right branch defines an experimental test chain comprising coating characterization to refine the contact simulation iteratively and tribological testing on different levels to validate the function fulfillment. Within this contribution, the outlined approach is illustrated by two use cases, namely the cam/tappet-pairing and the total knee replacement.
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Yu, Hang, Jiulu Gong, and Derong Chen. "Object Detection Using Multi-Scale Balanced Sampling." Applied Sciences 10, no. 17 (September 1, 2020): 6053. http://dx.doi.org/10.3390/app10176053.
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Detecting small objects and objects with large scale variants are always challenging for deep learning based object detection approaches. Many efforts have been made to solve these problems such as adopting more effective network structures, image features, loss functions, etc. However, for both small objects detection and detecting objects with various scale in single image, the first thing should be solve is the matching mechanism between anchor boxes and ground-truths. In this paper, an approach based on multi-scale balanced sampling(MB-RPN) is proposed for the difficult matching of small objects and detecting multi-scale objects. According to the scale of the anchor boxes, different positive and negative sample IOU discriminate thresholds are adopted to improve the probability of matching the small object area with the anchor boxes so that more small object samples are included in the training process. Moreover, the balanced sampling method is proposed for the collected samples, the samples are further divided and uniform sampling to ensure the diversity of samples in training process. Several datasets are adopted to evaluate the MB-RPN, the experimental results show that compare with the similar approach, MB-RPN improves detection performances effectively.
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Muhammad, Wazir, and Supavadee Aramvith. "Multi-Scale Inception Based Super-Resolution Using Deep Learning Approach." Electronics 8, no. 8 (August 13, 2019): 892. http://dx.doi.org/10.3390/electronics8080892.
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Single image super-resolution (SISR) aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) image. In order to address the SISR problem, recently, deep convolutional neural networks (CNNs) have achieved remarkable progress in terms of accuracy and efficiency. In this paper, an innovative technique, namely a multi-scale inception-based super-resolution (SR) using deep learning approach, or MSISRD, was proposed for fast and accurate reconstruction of SISR. The proposed network employs the deconvolution layer to upsample the LR image to the desired HR image. The proposed method is in contrast to existing approaches that use the interpolation techniques to upscale the LR image. Primarily, interpolation techniques are not designed for this purpose, which results in the creation of undesired noise in the model. Moreover, the existing methods mainly focus on the shallow network or stacking multiple layers in the model with the aim of creating a deeper network architecture. The technique based on the aforementioned design creates the vanishing gradients problem during the training and increases the computational cost of the model. Our proposed method does not use any hand-designed pre-processing steps, such as the bicubic interpolation technique. Furthermore, an asymmetric convolution block is employed to reduce the number of parameters, in addition to the inception block adopted from GoogLeNet, to reconstruct the multiscale information. Experimental results demonstrate that the proposed model exhibits an enhanced performance compared to twelve state-of-the-art methods in terms of the average peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) with a reduced number of parameters for the scale factor of 2 × , 4 × , and 8 × .
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Hamed, Elham, Iwona Jasiuk, Andrew Yoo, YikHan Lee, and Tadeusz Liszka. "Multi-scale modelling of elastic moduli of trabecular bone." Journal of The Royal Society Interface 9, no. 72 (January 25, 2012): 1654–73. http://dx.doi.org/10.1098/rsif.2011.0814.
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We model trabecular bone as a nanocomposite material with hierarchical structure and predict its elastic properties at different structural scales. The analysis involves a bottom-up multi-scale approach, starting with nanoscale (mineralized collagen fibril) and moving up the scales to sub-microscale (single lamella), microscale (single trabecula) and mesoscale (trabecular bone) levels. Continuum micromechanics methods, composite materials laminate theory and finite-element methods are used in the analysis. Good agreement is found between theoretical and experimental results.
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Sabato, A., P. Poozesh, P. Avitabile, and C. Niezrecki. "Experimental modal analysis of a utility-scale wind turbine blade using a multi-camera approach." Journal of Physics: Conference Series 1149 (December 2018): 012005. http://dx.doi.org/10.1088/1742-6596/1149/1/012005.
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XU, BIN, YUAN YAN TANG, BIN FANG, and ZHAO WEI SHANG. "MULTI-SCALE GRADIENT INVARIANT FOR FACE RECOGNITION UNDER VARYING ILLUMINATION." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 08 (December 2012): 1256016. http://dx.doi.org/10.1142/s0218001412560162.
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In this paper, a novel approach derived from image gradient domain called multi-scale gradient faces (MGF) is proposed to abstract multi-scale illumination-insensitive measure for face recognition. MGF applies multi-scale analysis on image gradient information, which can discover underlying inherent structure in images and keep the details at most while removing varying lighting. The proposed approach provides state-of-the-art performance on Extended YaleB and PIE: Recognition rates of 99.11% achieved on PIE database and 99.38% achieved on YaleB which outperforms most existing approaches. Furthermore, the experimental results on noised Yale-B validate that MGF is more robust to image noise.
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Zhang, Yue, Chuan Cai Liu, and Jian Zou. "Using Multi-Scale Gaussian Derivatives for Appearance-Based Recognition." Applied Mechanics and Materials 513-517 (February 2014): 1561–64. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1561.
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This paper addresses a novel global appearance-based approach to recognize objects in images by using multi-scale Gaussian derivatives (GDs). Because the GDs distributions of filtered images are almost picked, this obstacles obtaining discriminative binned distributions for each image. For this reason, we execute k-means clustering on each scale of pooled Gaussian derivative set of the instances come from all classes to yield k-cluster centroids for partitioning feature space, thus generating normalized binned marginal distributions for all training and testing samples, which are holistically adaptive to underlying distributions. On similarity matching, we identify each image with a point of product multinomial manifold with boundary, and use the direct sum of geodesic distance metric for sets of binned marginal densities. The promising experimental results on Zurich buildings database (ZuBuD) validate the feasibility and effectiveness of our approach.
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Wertheim, Kenneth Y., Bhanwar Lal Puniya, Alyssa La Fleur, Ab Rauf Shah, Matteo Barberis, and Tomáš Helikar. "A multi-approach and multi-scale platform to model CD4+ T cells responding to infections." PLOS Computational Biology 17, no. 8 (August 3, 2021): e1009209. http://dx.doi.org/10.1371/journal.pcbi.1009209.
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Immune responses rely on a complex adaptive system in which the body and infections interact at multiple scales and in different compartments. We developed a modular model of CD4+ T cells, which uses four modeling approaches to integrate processes at three spatial scales in different tissues. In each cell, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models. Cell population dynamics are described by an agent-based model and systemic cytokine concentrations by ordinary differential equations. A Monte Carlo simulation algorithm allows information to flow efficiently between the four modules by separating the time scales. Such modularity improves computational performance and versatility and facilitates data integration. We validated our technology by reproducing known experimental results, including differentiation patterns of CD4+ T cells triggered by different combinations of cytokines, metabolic regulation by IL2 in these cells, and their response to influenza infection. In doing so, we added multi-scale insights to single-scale studies and demonstrated its predictive power by discovering switch-like and oscillatory behaviors of CD4+ T cells that arise from nonlinear dynamics interwoven across three scales. We identified the inflamed lymph node’s ability to retain naive CD4+ T cells as a key mechanism in generating these emergent behaviors. We envision our model and the generic framework encompassing it to serve as a tool for understanding cellular and molecular immunological problems through the lens of systems immunology.
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Matsubara, Masahiko, Massimo Celino, Philip S. Salmon, and Carlo Massobrio. "Atomic Scale Modelling of Materials: A Prerequisite for any Multi-Scale Approach to Structural and Dynamical Properties." Solid State Phenomena 139 (April 2008): 141–50. http://dx.doi.org/10.4028/www.scientific.net/ssp.139.141.
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We describe two examples of application focusing on first-principles molecular dynamics as an effective tool to unravel the atomic-scale structure of condensed-matter systems. The first application is on disordered network-forming materials and the second is on silicon-doped fullerenes. We show that an accurate modelling of interatomic forces based on density functional theory, when combined with an account of the temperature evolution, is an unavoidable prerequisite for analyzing and interpreting experimental results on a quantitative basis. In the case of disordered systems, we describe the basic structural features of amorphous GeSe4 and highlight the predominant chemical order in this system. The effect of adding or removing an electron charge on the stability of Si-doped fullerenes is exemplified by considering the finite temperature evolution of heterofullerenes.
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Zhu, Deju, Xiaotong Zhang, Yunfu Ou, and Mengying Huang. "Experimental and numerical study of multi-scale tensile behaviors of Kevlar® 49 fabric." Journal of Composite Materials 51, no. 17 (October 6, 2016): 2449–65. http://dx.doi.org/10.1177/0021998316671573.
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Kevlar® 49 fabrics have excellent performances such as high elastic modulus and high impact resistance, which are widely used in ballistic systems, aerospace, fabric reinforced composite materials and other fields. The present work studied the multi-scale mechanical behaviors of Kevlar® 49 in the forms of fiber, yarn and fabric subjected to uniaxial tension. The experimental results showed that the material mechanical properties are dependent on structural size scale and gage length of samples. The tensile strengths decrease with increasing gage length and structural size scale from fiber to yarn, and to fabric, and follow Weibull distribution by conducting statistical analysis, which is used to quantify the degree of variability in the tensile strengths of fiber and yarn with different gage lengths. At last, user-defined subroutines (UMAT) in ANSYS were implemented to simulate the tensile behaviors of single yarn and fabric by using the constitutive models of fiber and yarn, respectively, which considered their Weibull distribution in tensile strength. This probabilistic approach can simulate the multi-scale tensile behaviors of Kevlar® 49 accurately and reveal the mechanisms of deformation and failure process based on the various size scales. This approach is also applicable to study the multi-scale behaviors of other fabrics once their properties and Weibull parameters are determined.
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Xu, Lei, YuanChen Huang, Chao Zhao, and Sung Kyu Ha. "Progressive failure prediction of woven fabric composites using a multi-scale approach." International Journal of Damage Mechanics 27, no. 1 (August 25, 2016): 97–119. http://dx.doi.org/10.1177/1056789516663613.
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Finite element representative unit cell models are established for the study of progressive failure of woven fabrics: plain weave, twill weave, and satin weave. A multi-scale approach ranging from the meso-scale to micro-scale regime is used, providing the failure observation inside the constituents. The constituent stresses of the fiber and matrix in the warp and fill tows of the woven fabric unit cell are calculated using micromechanics. Correlations between meso-scale tow stresses and micro-scale constituent stresses are established by using stress amplification factors. After calculating micro-scale stresses, the micromechanics of failure damage model is employed to determine the progressive damage statuses in each constituent of woven fabric composites. For the matrix of tows, a volume-averaging homogenization method is utilized to eliminate damage localization by smearing local damages over the whole matrix region of the unit cell. Subsequently, the ultimate strength is predicted for woven composites with different tow architectures. The prediction results are compared with the experimental values, and good agreement is observed.
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MINH, C. HA, FR BOUSSU, A. IMAD, T. KANIT, and D. CRÉPIN. "MULTI-SCALE MODEL TO PREDICT THE BALLISTIC IMPACT BEHAVIOR OF MULTI-LAYER PLAIN-WOVEN FABRICS." International Journal of Computational Methods 11, no. 03 (June 2014): 1343011. http://dx.doi.org/10.1142/s0219876213430111.
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This paper presents a multi-scale model that can predict the ballistic impact behavior of multi-layer plain-woven fabrics using the finite element method (FEM). Multi-layer fabrics of 30.5 × 30.5 cm, woven by high performance yarns Kevlar® 29 3000 denier, are impacted by a 0.3 fragment simulating projectile (FSP). Using a multi-scale approach, behavior of multi-layer fabrics subjected to different impact velocities is numerically analyzed. Ballistic limit of the fabric can also be predicted. The multi-scale model shows an effective gain of computation time in comparison with current mesoscopic ones. Computational results show a good agreement with experimental data.
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Hurley, R. C., S. A. Hall, and J. P. Wright. "Multi-scale mechanics of granular solids from grain-resolved X-ray measurements." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 473, no. 2207 (November 2017): 20170491. http://dx.doi.org/10.1098/rspa.2017.0491.
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This work discusses an experimental technique for studying the mechanics of three-dimensional (3D) granular solids. The approach combines 3D X-ray diffraction and X-ray computed tomography to measure grain-resolved strains, kinematics and contact fabric in the bulk of a granular solid, from which continuum strains, grain stresses, interparticle forces and coarse-grained elasto-plastic moduli can be determined. We demonstrate the experimental approach and analysis of selected results on a sample of 1099 stiff, frictional grains undergoing multiple uniaxial compression cycles. We investigate the inter-particle force network, elasto-plastic moduli and associated length scales, reversibility of mechanical responses during cyclic loading, the statistics of microscopic responses and microstructure–property relationships. This work serves to highlight both the fundamental insight into granular mechanics that is furnished by combined X-ray measurements and describes future directions in the field of granular materials that can be pursued with such approaches.
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Land, Sander, Steven A. Niederer, William E. Louch, Ole M. Sejersted, and Nicolas P. Smith. "Integrating multi-scale data to create a virtual physiological mouse heart." Interface Focus 3, no. 2 (April 6, 2013): 20120076. http://dx.doi.org/10.1098/rsfs.2012.0076.
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While the virtual physiological human (VPH) project has made great advances in human modelling, many of the tools and insights developed as part of this initiative are also applicable for facilitating mechanistic understanding of the physiology of a range of other species. This process, in turn, has the potential to provide human relevant insights via a different scientific path. Specifically, the increasing use of mice in experimental research, not yet fully complemented by a similar increase in computational modelling, is currently missing an important opportunity for using and interpreting this growing body of experimental data to improve our understanding of cardiac function. This overview describes our work to address this issue by creating a virtual physiological mouse model of the heart. We describe the similarities between human- and mouse-focused modelling, including the reuse of VPH tools, and the development of methods for investigating parameter sensitivity that are applicable across species. We show how previous results using this approach have already provided important biological insights, and how these can also be used to advance VPH heart models. Finally, we show an example application of this approach to test competing multi-scale hypotheses by investigating variations in length-dependent properties of cardiac muscle.
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Ha, Sung Kyu, Lei Xu, Chao Zhao, and Matthias DeMonte. "Progressive failure prediction of short fiber reinforced composites using a multi-scale approach." Journal of Composite Materials 52, no. 27 (April 22, 2018): 3785–801. http://dx.doi.org/10.1177/0021998318770252.
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A hybrid multi-scale approach combining a virtual mesoscale volume element (representative volume element) and a microscale finite element representative unit cell is developed, for progressive failure prediction of short fiber reinforced composites. The representative volume element represents the fiber orientation and distribution of the whole composites, from which the global mechanical behavior can be estimated. The representative unit cell captures the local mechanical response of each short fiber by transforming global strains to local strains. The constituent strains of the fiber, matrix, and interface are calculated from local strains using representative unit cell. Correlations between mesoscale local strains and microscale constituent strains are established using strain amplification factors. After computing microscale stresses, a progressive damage model is employed to determine the damage status of all constituents. A homogenization method is employed to eliminate damage localization in the matrix and interface. The predicted stress–strain curves are compared with experimental results, and good agreement is also achieved.
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Torii, Ryo, Rallia-Iliana Velliou, David Hodgson, and Vivek Mudera. "Modelling multi-scale cell–tissue interaction of tissue-engineered muscle constructs." Journal of Tissue Engineering 9 (January 2018): 204173141878714. http://dx.doi.org/10.1177/2041731418787141.
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Expectation on engineered tissue substitute continues to grow, and for an effective development of a functional tissue and to control its quality, cellular mechanoresponse plays a key role. Although the mechanoresponse – in terms of cell–tissue interaction across scales – has been understood better in recent years, there are still technical limitations to quantitatively monitor the processes involved in the development of both native and engineered tissues. Computational (in silico) studies have been utilised to complement the experimental limitations and successfully applied to the prediction of tissue growth. We here review recent activities in the area of combined experimental and computational analyses of tissue growth, especially in the tissue engineering context, and highlight the advantages of such an approach for the future of the tissue engineering, using our own case study of predicting musculoskeletal tissue engineering construct development.
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Wu, Chunyi, Gaochao Xu, Jia Zhao, and Yan Ding. "A novel large-scale task processing approach for big data across multi-domain." Advances in Mechanical Engineering 10, no. 12 (December 2018): 168781401881495. http://dx.doi.org/10.1177/1687814018814955.
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Large-scale task processing for big data based on cloud computing has become a research hotspot nowadays. Many traditional task processing approaches in single domain based on cloud computing have been presented successively. Unfortunately, it is limited to some extent due to the type, price, and storage location of substrate resource. Based on this argument, a large-scale task processing approach for big data in multi-domain has been proposed in this work. While the serious problem of overheads in computation and data transmission still exists in task processing across multi-domain, to overcome this problem, a virtual network mapping algorithm based on multi-objective particle swarm optimization in multi-domain is proposed. Based on Pareto dominance theory, a fast non-dominated selection method for the optimal virtual network mapping scheme set is presented and crowding degree comparison method is employed for the final optimal mapping scheme, which contributes to the load balancing and minimization of bandwidth resource cost in data transmission. Cauchy mutation is introduced to accelerate convergence of the algorithm. Eventually, the large-scale tasks are processed efficiently. Experimental results show that the proposed approach can effectively reduce the additional consumption of computing and bandwidth resources, and greatly decrease the task processing time.
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Mulas, Maria Gabriella, Dario Coronelli, and Luca Martinelli. "Multi-scale modelling approach for the pushover analysis of existing RC shear walls—Part II: Experimental verification." Earthquake Engineering & Structural Dynamics 36, no. 9 (2007): 1189–207. http://dx.doi.org/10.1002/eqe.676.
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Cancellieri, Dominique, Valérie Leroy-Cancellieri, Xavier Silvani, and Frédéric Morandini. "New experimental diagnostics in combustion of forest fuels: microscale appreciation for a macroscale approach." Natural Hazards and Earth System Sciences 18, no. 7 (July 16, 2018): 1957–68. http://dx.doi.org/10.5194/nhess-18-1957-2018.
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Abstract. In modelling the wildfire behaviour, good knowledge of the mechanisms and the kinetic parameters controlling the thermal decomposition of forest fuel is of great importance. The kinetic modelling is based on the mass-loss rate, which defines the mass-source term of combustible gases that supply the flames and influences the propagation of wildland fires. In this work, we investigated the thermal degradation of three different fuels using a multi-scale approach. Lab-scale experimental diagnostics such as thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), use of the cone calorimeter (CC) or Fire Propagation Apparatus (FPA) led to valuable results for modelling the thermal degradation of vegetal fuels and allowed several upgrades of pyrolysis models. However, this work remains beyond large-scale conditions of a wildland or forest fire. In an effort to elaborate on the kinetic models under realistic natural fire conditions, a mass-loss device specifically designed for the field scale has been developed. The paper presents primary results gained using this new device, during large-scale experiments of controlled fires. The mass-loss records obtained on a field scale highlight the influence of the chemical composition and the structure of plants. Indeed, two species with similar chemical and morphological characteristics exhibit similar mass-loss rates, whereas the third presents different thermal behaviour. The experimental data collected at a field scale led to a new insight about thermal degradation processes of natural fuel when compared to the kinetic laws established in TGA. These new results provide a global description of the kinetics of degradation of Mediterranean forest fuels. The results led to a proposed thermal degradation mechanism that has also been validated on a larger scale.
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Montero, Marc Villa, Ehsan Barjasteh, Harsh K. Baid, Cody Godines, Frank Abdi, and Kamran Nikbin. "Multi-Scale Impact and Compression-After-Impact Modeling of Reinforced Benzoxazine/Epoxy Composites using Micromechanics Approach." Journal of Multiscale Modelling 08, no. 01 (February 22, 2017): 1750002. http://dx.doi.org/10.1142/s1756973717500020.
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A multi-scale micromechanics approach along with finite element (FE) model predictive tool is developed to analyze low-energy-impact damage footprint and compression-after-impact (CAI) of composite laminates which is also tested and verified with experimental data. Effective fiber and matrix properties were reverse-engineered from lamina properties using an optimization algorithm and used to assess damage at the micro-level during impact and post-impact FE simulations. Progressive failure dynamic analysis (PFDA) was performed for a two step-process simulation. Damage mechanisms at the micro-level were continuously evaluated during the analyses. Contribution of each failure mode was tracked during the simulations and damage and delamination footprint size and shape were predicted to understand when, where and why failure occurred during both impact and CAI events. The composite laminate was manufactured by the vacuum infusion of the aero-grade toughened Benzoxazine system into the fabric preform. Delamination footprint was measured using C-scan data from the impacted panels and compared with the predicated values obtained from proposed multi-scale micromechanics coupled with FE analysis. Furthermore, the residual strength was predicted from the load-displacement curve and compared with the experimental values as well.
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Wan, Yuchai, Zhongshu Zheng, Ran Liu, Zheng Zhu, Hongen Zhou, Xun Zhang, and Said Boumaraf. "A Multi-Scale and Multi-Level Fusion Approach for Deep Learning-Based Liver Lesion Diagnosis in Magnetic Resonance Images with Visual Explanation." Life 11, no. 6 (June 18, 2021): 582. http://dx.doi.org/10.3390/life11060582.
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Many computer-aided diagnosis methods, especially ones with deep learning strategies, of liver cancers based on medical images have been proposed. However, most of such methods analyze the images under only one scale, and the deep learning models are always unexplainable. In this paper, we propose a deep learning-based multi-scale and multi-level fusing approach of CNNs for liver lesion diagnosis on magnetic resonance images, termed as MMF-CNN. We introduce a multi-scale representation strategy to encode both the local and semi-local complementary information of the images. To take advantage of the complementary information of multi-scale representations, we propose a multi-level fusion method to combine the information of both the feature level and the decision level hierarchically and generate a robust diagnostic classifier based on deep learning. We further explore the explanation of the diagnosis decision of the deep neural network through visualizing the areas of interest of the network. A new scoring method is designed to evaluate whether the attention maps can highlight the relevant radiological features. The explanation and visualization make the decision-making process of the deep neural network transparent for the clinicians. We apply our proposed approach to various state-of-the-art deep learning architectures. The experimental results demonstrate the effectiveness of our approach.
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Olevsky, Eugene. "Sintering of Tailored Powder Components: Multi-Scale Modeling and Experimentation." Advances in Science and Technology 45 (October 2006): 510–15. http://dx.doi.org/10.4028/www.scientific.net/ast.45.510.
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A true multi-scale approach is applied for the development of a new meso-macro methodology for modeling of sintering. Sintering distortions of multi-layer and capillary-porous composite powder structures are determined based upon the continuum theory of sintering implemented in a finite-element code. The macroscopic constitutive parameters of the powder material are obtained on the basis of the meso-scale simulations of a realistic grain-pore structure. The model follows both the densification and the damage development during sintering using a new fracture criterion for the prediction of macroscopic strength in sintering. The simulation results are compared with the experimental data on sintering of zirconia, alumina, and Cu powder composites. The modeling results contribute to the development of multi-layer powder metalceramic technologies used for fabrication of components for thermal management of electronic circuitry and wireless communication appliances.
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Guo, Rui Bin, Tao Guan, Dong Xiang Zhou, Ke Ju Peng, and Wei Hong Fan. "Efficient Multi-Scale Registration of 3D Reconstructions Based on Camera Center Constraint." Advanced Materials Research 998-999 (July 2014): 1018–23. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.1018.
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Recent approaches for reconstructing 3D scenes from image collections only produce single scene models. To build a unified scene model that contains multiple subsets, we present a novel method for registration of 3D scene reconstructions in different scales. It first normalizes the scales of the models building on similarity reconstruction by the constraint of the 3D position of shared cameras. Then we use Cayley transform to fit the matrix of coordinates transformation for the models in normalization scales. The experimental results show the effectiveness and scalability of the proposed approach.
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Lin, Xian, Xinyi Zou, Dong An, Bruce W. Krakauer, and Mingfang Zhu. "Multi-Scale Modeling of Microstructure Evolution during Multi-Pass Hot-Rolling and Cooling Process." Materials 14, no. 11 (May 29, 2021): 2947. http://dx.doi.org/10.3390/ma14112947.
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In this work, a 6-pass hot-rolling process followed by air cooling is studied by means of a coupled multi-scale simulation approach. The finite element method (FEM) is utilized to obtain macroscale thermomechanical parameters including temperature and strain rate. The microstructure evolution during the recrystallization and austenite (γ) to ferrite (α) transformation is simulated by a mesoscale cellular automaton (CA) model. The solute drag effect is included in the CA model to take into account the influence of manganese on the γ/α interface migration. The driving force for α-phase nucleation and growth also involves the contribution of the deformation stored energy inherited from hot-rolling. The simulation renders a clear visualization of the evolving grain structure during a multi-pass hot-rolling process. The variations of the nonuniform, deformation-stored energy field and carbon concentration field are also reproduced. A detailed analysis demonstrates how the parameters, including strain rate, grain size, temperature, and inter-pass time, influence the different mechanisms of recrystallization. Grain refinement induced by recrystallization and the γ→α phase transformation is also quantified. The simulated final α-fraction and the average α-grain size agree reasonably well with the experimental microstructure.
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Hu, Luo Kai, Yan Lin Cheng, and Chao Liang. "A Horizontal Segmentation Based Ontology Parallel Query Approach." Advanced Materials Research 760-762 (September 2013): 1978–81. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1978.
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The performance of ontology query has become one of the bottlenecks of the large-scale bulk applications. Firstly OWL ontology files stored into the database in the form of triple table using Oracle 11g semantic technology. And then we designed and implemented the ontology parting method based on horizontal segmentation. Thirdly, several typical ontology query operations were achieved based on the multi-threading technology. Experimental results show that the parallel query methods described herein significantly improve query performance.
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Nóbrega, Rodolfo Luiz Bezerra, Gabriele Lamparter, Harold Hughes, Alphonce Chenjerayi Guzha, Ricardo Santos Silva Amorim, and Gerhard Gerold. "A multi-approach and multi-scale study on water quantity and quality changes in the Tapajós River basin, Amazon." Proceedings of the International Association of Hydrological Sciences 377 (April 16, 2018): 3–7. http://dx.doi.org/10.5194/piahs-377-3-2018.
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Abstract. We analyzed changes in water quantity and quality at different spatial scales within the Tapajós River basin (Amazon) based on experimental fieldwork, hydrological modelling, and statistical time-trend analysis. At a small scale, we compared the river discharge (Q) and suspended-sediment concentrations (SSC) of two adjacent micro-catchments (< 1 km2) with similar characteristics but contrasting land uses (forest vs. pasture) using empirical data from field measurements. At an intermediary scale, we simulated the hydrological responses of a sub-basin of the Tapajós (Jamanxim River basin, 37 400 km2), using a hydrological model (SWAT) and land-use change scenario in order to quantify the changes in the water balance components due to deforestation. At the Tapajós' River basin scale, we investigated trends in Q, sediments, hydrochemistry, and geochemistry in the river using available data from the HYBAM Observation Service. The results in the micro-catchments showed a higher runoff coefficient in the pasture (0.67) than in the forest catchment (0.28). At this scale, the SSC were also significantly greater during stormflows in the pasture than in the forest catchment. At the Jamanxim watershed scale, the hydrological modelling results showed a 2 % increase in Q and a 5 % reduction of baseflow contribution to total Q after a conversion of 22 % of forest to pasture. In the Tapajós River, however, trend analysis did not show any significant trend in discharge and sediment concentration. However, we found upward trends in dissolved organic carbon and NO3- over the last 20 years. Although the magnitude of anthropogenic impact has shown be scale-dependent, we were able to find changes in the Tapajós River basin in streamflow, sediment concentration, and water quality across all studied scales.
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Kim, Hyung Yong, Ji Won Yoon, Sung Jun Cheon, Woo Hyun Kang, and Nam Soo Kim. "A Multi-Resolution Approach to GAN-Based Speech Enhancement." Applied Sciences 11, no. 2 (January 13, 2021): 721. http://dx.doi.org/10.3390/app11020721.
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Recently, generative adversarial networks (GANs) have been successfully applied to speech enhancement. However, there still remain two issues that need to be addressed: (1) GAN-based training is typically unstable due to its non-convex property, and (2) most of the conventional methods do not fully take advantage of the speech characteristics, which could result in a sub-optimal solution. In order to deal with these problems, we propose a progressive generator that can handle the speech in a multi-resolution fashion. Additionally, we propose a multi-scale discriminator that discriminates the real and generated speech at various sampling rates to stabilize GAN training. The proposed structure was compared with the conventional GAN-based speech enhancement algorithms using the VoiceBank-DEMAND dataset. Experimental results showed that the proposed approach can make the training faster and more stable, which improves the performance on various metrics for speech enhancement.
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Zhang, Pei-Lin, Bing Li, Shuang-Shan Mi, Ying-Tang Zhang, and Dong-Sheng Liu. "Bearing Fault Detection Using Multi-Scale Fractal Dimensions Based on Morphological Covers." Shock and Vibration 19, no. 6 (2012): 1373–83. http://dx.doi.org/10.1155/2012/438789.
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Vibration signals acquired from bearing have been found to demonstrate complicated nonlinear characteristics in literature. Fractal geometry theory has provided effective tools such as fractal dimension for characterizing the vibration signals in bearing faults detection. However, most of the natural signals are not critical self-similar fractals; the assumption of a constant fractal dimension at all scales may not be true. Motivated by this fact, this work explores the application of the multi-scale fractal dimensions (MFDs) based on morphological cover (MC) technique for bearing fault diagnosis. Vibration signals from bearing with seven different states under four operations conditions are collected to validate the presented MFDs based on MC technique. Experimental results reveal that the vibration signals acquired from bearing are not critical self-similar fractals. The MFDs can provide more discriminative information about the signals than the single global fractal dimension. Furthermore, three classifiers are employed to evaluate and compare the classification performance of the MFDs with other feature extraction methods. Experimental results demonstrate the MFDs to be a desirable approach to improve the performance of bearing fault diagnosis.
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Calneryte, Dalia, Rimantas Barauskas, Daiva Milasiene, Rytis Maskeliunas, Audrius Neciunas, Armantas Ostreika, Martynas Patasius, and Andrius Krisciunas. "Multi-scale finite element modeling of 3D printed structures subjected to mechanical loads." Rapid Prototyping Journal 24, no. 1 (January 2, 2018): 177–87. http://dx.doi.org/10.1108/rpj-05-2016-0074.
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Purpose The purpose of this paper is to investigate the influence of geometrical microstructure of items obtained by applying a three-dimensional (3D) printing technology on their mechanical strength. Design/methodology/approach Three-dimensional printed items (3DPI) are composite structures of complex internal constitution. The buildup of the finite element (FE) computational models of 3DPI is based on a multi-scale approach. At the micro-scale, the FE models of representative volume elements corresponding to different additive layer heights and different thicknesses of extruded fibers are investigated to obtain the equivalent non-linear nominal stress–strain curves. The obtained results are used for the creation of macro-scale FE models, which enable to simulate the overall structural response of 3D printed samples subjected to tensile and bending loads. Findings The validation of the models was performed by comparing the computed results against the experimental ones, where satisfactory agreement has been demonstrated within a marked range of thicknesses of additive layers. Certain inadequacies between computed against experimental results were observed in cases of thinnest and thickest additive layers. The principle explanation of the reasons of inadequacies takes into account the poorer quality of mutual adhesion in case of very thin extruded fibers and too-early solidification effect. Originality/value Flexural and tensile experiments are simulated by FE models that are created with consideration to microstructure of 3D printed samples.
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Prüger, Stefan, Ashutosh Gandhi, and Daniel Balzani. "Influence of microstructure morphology on multi-scale modeling of low-alloyed TRIP-steels." Engineering Computations 35, no. 2 (April 16, 2018): 499–528. http://dx.doi.org/10.1108/ec-01-2017-0009.
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Purpose The purpose of this study is to quantify the impact of the variation of microstructural features on macroscopic and microscopic fields. The application of multi-scale methods in the context of constitutive modeling of microheterogeneous materials requires the choice of a representative volume element (RVE) of the considered microstructure, which may be based on some idealized assumptions and/or on experimental observations. In any case, a realistic microstructure within the RVE is either computationally too expensive or not fully accessible by experimental measurement techniques, which introduces some uncertainty regarding the microstructural features. Design/methodology/approach In this paper, a systematical variation of microstructural parameters controlling the morphology of an RVE with an idealized microstructure is conducted and the impact on macroscopic quantities of interest as well as microstructural fields and their statistics is investigated. The study is carried out under macroscopically homogeneous deformation states using the direct micro-macro scale transition approach. Findings The variation of microstructural parameters, such as inclusion volume fraction, aspect ratio and orientation of the inclusion with respect to the overall loading, influences the macroscopic behavior, especially the micromechanical fields significantly. Originality/value The systematic assessment of the impact of microstructural parameters on both macroscopic quantities and statistics of the micromechanical fields allows for a quantitative comparison of different microstructure morphologies and a reliable identification of microstructural parameters that promote failure initialization in microheterogeneous materials.
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Clayton, J. D., M. Guziewski, J. P. Ligda, R. B. Leavy, and J. Knap. "A Multi-Scale Approach for Phase Field Modeling of Ultra-Hard Ceramic Composites." Materials 14, no. 6 (March 14, 2021): 1408. http://dx.doi.org/10.3390/ma14061408.
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Diamond-silicon carbide (SiC) polycrystalline composite blends are studied using a computational approach combining molecular dynamics (MD) simulations for obtaining grain boundary (GB) fracture properties and phase field mechanics for capturing polycrystalline deformation and failure. An authentic microstructure, reconstructed from experimental lattice diffraction data with locally refined discretization in GB regions, is used to probe effects of local heterogeneities on material response in phase field simulations. The nominal microstructure consists of larger diamond and SiC (cubic polytype) grains, a matrix of smaller diamond grains and nanocrystalline SiC, and GB layers encasing the larger grains. These layers may consist of nanocrystalline SiC, diamond, or graphite, where volume fractions of each phase are varied within physically reasonable limits in parametric studies. Distributions of fracture energies from MD tension simulations are used in the phase field energy functional for SiC-SiC and SiC-diamond interfaces, where grain boundary geometries are obtained from statistical analysis of lattice orientation data on the real microstructure. An elastic homogenization method is used to account for distributions of second-phase graphitic inclusions as well as initial voids too small to be resolved individually in the continuum field discretization. In phase field simulations, SiC single crystals may twin, and all phases may fracture. The results of MD calculations show mean strengths of diamond-SiC interfaces are much lower than those of SiC-SiC GBs. In phase field simulations, effects on peak aggregate stress and ductility from different GB fracture energy realizations with the same mean fracture energy and from different random microstructure orientations are modest. Results of phase field simulations show unconfined compressive strength is compromised by diamond-SiC GBs, graphitic layers, graphitic inclusions, and initial porosity. Explored ranges of porosity and graphite fraction are informed by physical observations and constrained by accuracy limits of elastic homogenization. Modest reductions in strength and energy absorption are witnessed for microstructures with 4% porosity or 4% graphite distributed uniformly among intergranular matrix regions. Further reductions are much more severe when porosity is increased to 8% relative to when graphite is increased to 8%.
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Braud, I., P. A. Ayral, C. Bouvier, F. Branger, G. Delrieu, J. Le Coz, G. Nord, et al. "Multi-scale hydrometeorological observation and modelling for flash flood understanding." Hydrology and Earth System Sciences 18, no. 9 (September 26, 2014): 3733–61. http://dx.doi.org/10.5194/hess-18-3733-2014.
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Abstract. This paper presents a coupled observation and modelling strategy aiming at improving the understanding of processes triggering flash floods. This strategy is illustrated for the Mediterranean area using two French catchments (Gard and Ardèche) larger than 2000 km2. The approach is based on the monitoring of nested spatial scales: (1) the hillslope scale, where processes influencing the runoff generation and its concentration can be tackled; (2) the small to medium catchment scale (1–100 km2), where the impact of the network structure and of the spatial variability of rainfall, landscape and initial soil moisture can be quantified; (3) the larger scale (100–1000 km2), where the river routing and flooding processes become important. These observations are part of the HyMeX (HYdrological cycle in the Mediterranean EXperiment) enhanced observation period (EOP), which will last 4 years (2012–2015). In terms of hydrological modelling, the objective is to set up regional-scale models, while addressing small and generally ungauged catchments, which represent the scale of interest for flood risk assessment. Top-down and bottom-up approaches are combined and the models are used as "hypothesis testing" tools by coupling model development with data analyses in order to incrementally evaluate the validity of model hypotheses. The paper first presents the rationale behind the experimental set-up and the instrumentation itself. Second, we discuss the associated modelling strategy. Results illustrate the potential of the approach in advancing our understanding of flash flood processes on various scales.
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Braud, I., P. A. Ayral, C. Bouvier, F. Branger, G. Delrieu, J. Le Coz, G. Nord, et al. "Multi-scale hydrometeorological observation and modelling for flash-flood understanding." Hydrology and Earth System Sciences Discussions 11, no. 2 (February 11, 2014): 1871–945. http://dx.doi.org/10.5194/hessd-11-1871-2014.
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Abstract. This paper presents a coupled observation and modelling strategy aiming at improving the understanding of processes triggering flash floods. This strategy is illustrated for the Mediterranean area using two French catchments (Gard and Ardèche) larger than 2000 km2. The approach is based on the monitoring of nested spatial scales: (1) the hillslope scale, where processes influencing the runoff generation and its concentration can be tackled; (2) the small to medium catchment scale (1–100 km2) where the impact of the network structure and of the spatial variability of rainfall, landscape and initial soil moisture can be quantified; (3) the larger scale (100–1000 km2) where the river routing and flooding processes become important. These observations are part of the HyMeX (Hydrological Cycle in the Mediterranean Experiment) Enhanced Observation Period (EOP) and lasts four years (2012–2015). In terms of hydrological modelling the objective is to set up models at the regional scale, while addressing small and generally ungauged catchments, which is the scale of interest for flooding risk assessment. Top-down and bottom-up approaches are combined and the models are used as "hypothesis testing" tools by coupling model development with data analyses, in order to incrementally evaluate the validity of model hypotheses. The paper first presents the rationale behind the experimental set up and the instrumentation itself. Second, we discuss the associated modelling strategy. Results illustrate the potential of the approach in advancing our understanding of flash flood processes at various scales.
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Gahlen, P., S. Fröbel, A. Karbach, D. Gabriel, and M. Stommel. "Experimental multi-scale approach to determine the local mechanical properties of foam base material in polyisocyanurate metal panels." Polymer Testing 93 (January 2021): 106965. http://dx.doi.org/10.1016/j.polymertesting.2020.106965.
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Xiao, Degui, Qilei Chen, and Shanshan Li. "A Multi-Scale Cascaded Hierarchical Model for Image Labeling." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 09 (November 2016): 1660005. http://dx.doi.org/10.1142/s0218001416600053.
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Image labeling is an important and challenging task in the area of graphics and visual computing, where datasets with high quality labeling are critically needed. In this paper, based on the commonly accepted observation that the same semantic object in images with different resolutions may have different representations, we propose a novel multi-scale cascaded hierarchical model (MCHM) to enhance general image labeling methods. Our proposed approach first creates multi-resolution images from the original one to form an image pyramid and labels each image at different scale individually. Next, it constructs a cascaded hierarchical model and a feedback circle between image pyramid and labeling methods. The original image labeling result is used to adjust labeling parameters of those scaled images. Labeling results from the scaled images are then fed back to enhance the original image labeling results. These naturally form a global optimization problem under scale-space condition. We further propose a desirable iterative algorithm in order to run the model. The global convergence of the algorithm is proven through iterative approximation with latent optimization constraints. We have conducted extensive experiments with five widely used labeling methods on five popular image datasets. Experimental results indicate that MCHM improves labeling accuracy of the state-of-the-art image labeling approaches impressively.
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Nnolim, Uche. "Adaptive Multi-Scale Entropy Fusion De-Hazing Based on Fractional Order." Journal of Imaging 4, no. 9 (September 6, 2018): 108. http://dx.doi.org/10.3390/jimaging4090108.
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This paper describes a proposed fractional filter-based multi-scale underwater and hazy image enhancement algorithm. The proposed system combines a modified global contrast operator with fractional order-based multi-scale filters used to generate several images, which are fused based on entropy and standard deviation. The multi-scale-global enhancement technique enables fully adaptive and controlled color correction and contrast enhancement without over exposure of highlights when processing hazy and underwater images. This in addition to the illumination/reflectance estimation coupled with global and local contrast enhancement. The proposed algorithm is also compared with the most recent available state-of-the-art multi-scale fusion de-hazing algorithm. Experimental comparisons indicate that the proposed approach yields a better edge and contrast enhancement results without a halo effect, without color degradation, and is faster and more adaptive than all other algorithms from the literature.
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Chen, Yuhao, Alexander Wong, Yuan Fang, Yifan Wu, and Linlin Xu. "Deep Residual Transform for Multi-scale Image Decomposition." Journal of Computational Vision and Imaging Systems 6, no. 1 (January 15, 2021): 1–5. http://dx.doi.org/10.15353/jcvis.v6i1.3537.
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Multi-scale image decomposition (MID) is a fundamental task in computer vision and image processing that involves the transformation of an image into a hierarchical representation comprising of different levels of visual granularity from coarse structures to fine details. A well-engineered MID disentangles the image signal into meaningful components which can be used in a variety of applications such as image denoising, image compression, and object classification. Traditional MID approaches such as wavelet transforms tackle the problem through carefully designed basis functions under rigid decomposition structure assumptions. However, as the information distribution varies from one type of image content to another, rigid decomposition assumptions lead to inefficiently representation, i.e., some scales can contain little to no information. To address this issue, we present Deep Residual Transform (DRT), a data-driven MID strategy where the input signal is transformed into a hierarchy of non-linear representations at different scales, with each representation being independently learned as the representational residual of previous scales at a user-controlled detail level. As such, the proposed DRT progressively disentangles scale information from the original signal by sequentially learning residual representations. The decomposition flexibility of this approach allows for highly tailored representations cater to specific types of image content, and results in greater representational efficiency and compactness. In this study, we realize the proposed transform by leveraging a hierarchy of sequentially trained autoencoders. To explore the efficacy of the proposed DRT, we leverage two datasets comprising of very different types of image content: 1) CelebFaces and 2) Cityscapes. Experimental results show that the proposed DRT achieved highly efficient information decomposition on both datasets amid their very different visual granularity characteristics.
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Jurczuk, Krzysztof, Marcin Czajkowski, and Marek Kretowski. "Multi-GPU approach to global induction of classification trees for large-scale data mining." Applied Intelligence 51, no. 8 (January 14, 2021): 5683–700. http://dx.doi.org/10.1007/s10489-020-01952-5.
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AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.
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Chang, Ting-Cheng, and Hui Wang. "A self-testing cloud model for multi-criteria group decision making." Engineering Computations 33, no. 6 (August 1, 2016): 1767–83. http://dx.doi.org/10.1108/ec-08-2015-0258.
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Purpose – The purpose of this paper is to select the best scaling coefficient during the quantitative-qualitative conversion. Design/methodology/approach – Cloud model can describe the qualitative concept of randomness and fuzziness, achieve uncertain transition between qualitative and quantitative in the field of multi-criteria group decision and has been receiving widespread attention. This paper discusses scale conversion issues of the cloud model when evaluating qualitative information. In order to improve the accuracy of the evaluation on multi-attribute decision problems based on uncertainty of natural linguistic information, this paper proposes a method of self-testing cloud model based on a composite scale (with the exponential scale and the scale as a basis). Findings – Through experimental verification results show that under composite scale, the best suitable selection of can effectively improve the accuracy and reliability of decision results. Originality/value – This research presents a new approach to determine the suitable value for coefficient based on uncertain knowledge of natural multi-criteria group decision making, and gives concrete steps and examples. This method has positive significance to improve the quality of qualitative and quantitative conversion based on cloud model.
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Dayananda, Chaitra, Jae-Young Choi, and Bumshik Lee. "Multi-Scale Squeeze U-SegNet with Multi Global Attention for Brain MRI Segmentation." Sensors 21, no. 10 (May 12, 2021): 3363. http://dx.doi.org/10.3390/s21103363.
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In this paper, we propose a multi-scale feature extraction with novel attention-based convolutional learning using the U-SegNet architecture to achieve segmentation of brain tissue from a magnetic resonance image (MRI). Although convolutional neural networks (CNNs) show enormous growth in medical image segmentation, there are some drawbacks with the conventional CNN models. In particular, the conventional use of encoder-decoder approaches leads to the extraction of similar low-level features multiple times, causing redundant use of information. Moreover, due to inefficient modeling of long-range dependencies, each semantic class is likely to be associated with non-accurate discriminative feature representations, resulting in low accuracy of segmentation. The proposed global attention module refines the feature extraction and improves the representational power of the convolutional neural network. Moreover, the attention-based multi-scale fusion strategy can integrate local features with their corresponding global dependencies. The integration of fire modules in both the encoder and decoder paths can significantly reduce the computational complexity owing to fewer model parameters. The proposed method was evaluated on publicly accessible datasets for brain tissue segmentation. The experimental results show that our proposed model achieves segmentation accuracies of 94.81% for cerebrospinal fluid (CSF), 95.54% for gray matter (GM), and 96.33% for white matter (WM) with a noticeably reduced number of learnable parameters. Our study shows better segmentation performance, improving the prediction accuracy by 2.5% in terms of dice similarity index while achieving a 4.5 times reduction in the number of learnable parameters compared to previously developed U-SegNet based segmentation approaches. This demonstrates that the proposed approach can achieve reliable and precise automatic segmentation of brain MRI images.