Search results for "vector [form factor]"

Texture advection on discontinuous flows

2015

Texture advection techniques, which transport textures using a velocity field, are used to visualize the dynamics of a flow on a triangle mesh. Some flow phenomena lead to velocity fields with discontinuities that cause the deformation of the texture which is not properly controlled by these techniques. We propose a method to detect and visualize discontinuities on a flow, keeping consistent texture advection at both sides of the discontinuity. The method handles the possibility that the discontinuity travels across the domain of the flow with arbitrary velocity, estimating its speed with least-squares approximation. The technique is tested with different sample scenarios and with two avala…

The Influence of the Electric Field on the Development of the Swirling Flame Velocity Field and Combustion Characteristics

2008

Dynamic mode decomposition of magnetohydrodynamic bubble chain flow in a rectangular vessel

2021

We demonstrate the first application of dynamic mode decomposition (DMD) to bubble flow with resolved dynamic liquid/gas boundaries. Specifically, we have applied DMD to the output of numerical simulations for a system where chains of bubbles ascend through a rectangular liquid metal vessel. Flow patterns have been investigated in the vessel and bubble reference frames. We show how gas flow rate and applied magnetic affect bubble wake flow and larger-scale flow structures within the liquid metal vessel by examining the velocity field mode statistics over trajectory time and total flow time as well as the computed mode velocity fields. The results of this proof-of-concept study indicate that…

Lie algebra on the transverse bundle of a decreasing family of foliations

2010

Abstract J. Lehmann-Lejeune in [J. Lehmann-Lejeune, Cohomologies sur le fibre transverse a un feuilletage, C.R.A.S. Paris 295 (1982), 495–498] defined on the transverse bundle V to a foliation on a manifold M, a zero-deformable structure J such that J 2 = 0 and for every pair of vector fields X , Y on M: [ J X , J Y ] − J [ J X , Y ] − J [ X , J Y ] + J 2 [ X , Y ] = 0 . For every open set Ω of V, J. Lehmann-Lejeune studied the Lie Algebra L J ( Ω ) of vector fields X defined on Ω such that the Lie derivative L ( X ) J is equal to zero i.e., for each vector field Y on Ω : [ X , J Y ] = J [ X , Y ] and showed that for every vector field X on Ω such that X ∈ K e r J , we can write X = ∑ [ Y ,…

2020

The scientific and practical fields-especially high-performance sports-increasingly request a stronger focus be placed on individual athletes in human movement science research. Machine learning methods have shown efficacy in this context by identifying the unique movement patterns of individuals and distinguishing their intra-individual changes over time. The objective of this investigation is to analyze biomechanically described movement patterns during the fatigue-related accumulation process within a single training session of a high number of repeated executions of a ballistic sports movement-specifically, the frontal foot kick (mae-geri) in karate-in expert athletes. The two leading r…

A genetic integrated fuzzy classifier

2005

This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells. The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good performance and robustness of the integrated classifier strategies.

No-Reference 3D Mesh Quality Assessment Based on Dihedral Angles Model and Support Vector Regression

2016

International audience; 3D meshes are subject to various visual distortions during their transmission and geometrical processing. Several works have tried to evaluate the visual quality using either full reference or reduced reference approaches. However, these approaches require the presence of the reference mesh which is not available in such practical situations. In this paper, the main contribution lies in the design of a computational method to automatically predict the perceived mesh quality without reference and without knowing beforehand the distortion type. Following the no-reference (NR) quality assessment principle, the proposed method focuses only on the distorted mesh. Specific…

FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention

2019

International audience; Skin lesion segmentation in dermoscopic images is still a challenge due to the low contrast and fuzzy boundaries of lesions. Moreover, lesions have high similarity with the healthy regions in terms of appearance. In this paper, we propose an accurate skin lesion segmentation model based on a modified conditional generative adversarial network (cGAN). We introduce a new block in the encoder of cGAN called factorized channel attention (FCA), which exploits both channel attention mechanism and residual 1-D kernel factorized convolution. The channel attention mechanism increases the discriminability between the lesion and non-lesion features by taking feature channel int…

Talent identification in soccer using a one-class support vector machine

2019

Abstract Identifying potential future elite athletes is important in many sporting events. The successful identification of potential future elite athletes at an early age would help to provide high-quality coaching and training environments in which to optimize their development. However, a large variety of different skills and qualities are needed to succeed in elite sports, making talent identification generally a complex and multifaceted problem. Due to the rarity of elite athletes, datasets are inherently imbalanced, making classical statistical inference difficult. Therefore, we approach talent identification as an anomaly detection problem. We trained a nonlinear one-class support ve…

A Machine Learning Approach for Fall Detection and Daily Living Activity Recognition

2019

The number of older people in western countries is constantly increasing. Most of them prefer to live independently and are susceptible to fall incidents. Falls often lead to serious or even fatal injuries which are the leading cause of death for elderlies. To address this problem, it is essential to develop robust fall detection systems. In this context, we develop a machine learning framework for fall detection and daily living activity recognition. We use acceleration and angular velocity data from two public databases to recognize seven different activities, including falls and activities of daily living. From the acceleration and angular velocity data, we extract time- and frequency-do…