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Analysis of thermal conductivity in tree-like branched networks 认领 引用
1
作者 寇建龙 陆杭军 +1 位作者 吴锋民 许友生 《Chinese Physics B》 SCIE EI CAS 2009年第4期1553-1559,共7页
Asymmetric tree-like branched networks are explored by geometric algorithms. Based on the network, an analysis of the thermal conductivity is presented. The relationship between effective thermal conductivity and geom... Asymmetric tree-like branched networks are explored by geometric algorithms. Based on the network, an analysis of the thermal conductivity is presented. The relationship between effective thermal conductivity and geometric structures is obtained by using the thermal-electrical analogy technique. In all studied cases, a clear behaviour is observed, where angle （δ,θ） among parent branching extended lines, branches and parameter of the geometric structures have stronger effects on the effective thermal conductivity. When the angle δ is fixed, the optical diameter ratio β＋ is dependent on angle θ. Moreover, γand m are not related to β＊. The longer the branch is, the smaller the effective thermal conductivity will be. It is also found that when the angle θ〈δ2, the higher the iteration m is, the lower the thermal conductivity will be and it tends to zero, otherwise, it is bigger than zero. When the diameter ratio β1 〈 0.707 and angle δ is bigger, the optimal k of the perfect ratio increases with the increase of the angle δ; when β1 〉 0.707, the optimal k decreases. In addition, the effective thermal conductivity is always less than that of single channel material. The present results also show that the effective thermal conductivity of the asymmetric tree-like branched networks does not obey Murray＇s law. 展开更多
关键词 effective thermal conductivity asymmetric tree-like branched networks geometric parameters
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Computational fluid dynamic-discrete element method coupling analysis of particle transport in branched networks 认领 引用 被引量：1
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作者 Xiaoyu Wang Jun Yao +3 位作者 Liang Gong Yang Li Yongfei Yang Hongliang Zhao 《Particuology》 SCIE EI CAS CSCD 2021年第2期140-150,共11页
An understanding of the particle transport characteristics in a branched network helps to predict the particle distribution and prevent undesired plugging in various engineering systems.Quantitative analysis of partic... An understanding of the particle transport characteristics in a branched network helps to predict the particle distribution and prevent undesired plugging in various engineering systems.Quantitative analysis of particle flow characteristics is challenging in that experiments are expensive and particle flow is difficult to detect without disturbing the flow.To overcome this difficulty,man-made fractal tree-like branched networks were built,and a coupled computational fluid dynamic and discrete element method model was applied.A series of numerical simulations was carried out to analyze the influence of fractal structure parameters of networks on the particle flow characteristics.The joint influence of inertial,shunt capacity and superposition from upstream branches on particle flow was investigated.The injection position at the inlet determined the particle velocity and its future flow path.The particle density ratio,particle size and bifurcation angle had a greater influence on the shunting of K2 branches than that in the K1 level and Nk22/Nk21 reached a maximum at 60°.Compared with a network with an even number of branches,there was a preferential branch when the branch number was odd.The preferential branch effect or asymmetry degree of the level(K2)branches had a more significant impact on particle shunting than that from the upstream branches(K1). 展开更多
关键词 Particle-fluid flow CFD-DEM coupling Branched network
Face Forgery Detection via Multi-Scale Dual-Modality Mutual Enhancement Network 认领 引用
3
作者 Yuanqing Ding Hanming Zhai +3 位作者 Qiming Ma Liang Zhang Lei Shao Fanliang Bu 《Computers, Materials & Continua》 SCIE EI 2025年第10期905-923,共19页
As the use of deepfake facial videos proliferate,the associated threats to social security and integrity cannot be overstated.Effective methods for detecting forged facial videos are thus urgently needed.While many de... As the use of deepfake facial videos proliferate,the associated threats to social security and integrity cannot be overstated.Effective methods for detecting forged facial videos are thus urgently needed.While many deep learning-based facial forgery detection approaches show promise,they often fail to delve deeply into the complex relationships between image features and forgery indicators,limiting their effectiveness to specific forgery techniques.To address this challenge,we propose a dual-branch collaborative deepfake detection network.The network processes video frame images as input,where a specialized noise extraction module initially extracts the noise feature maps.Subsequently,the original facial images and corresponding noise maps are directed into two parallel feature extraction branches to concurrently learn texture and noise forgery clues.An attention mechanism is employed between the two branches to facilitate mutual guidance and enhancement of texture and noise features across four different scales.This dual-modal feature integration enhances sensitivity to forgery artifacts and boosts generalization ability across various forgery techniques.Features from both branches are then effectively combined and processed through a multi-layer perception layer to distinguish between real and forged video.Experimental results on benchmark deepfake detection datasets demonstrate that our approach outperforms existing state-of-the-art methods in terms of detection performance,accuracy,and generalization ability. 展开更多
关键词 Face forgery detection dual branch network noise features attention mechanism multiple scale
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Numerical simulation of a gas pipeline network using computational fluid dynamics simulators 认领 引用 被引量：10
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作者 SELEZNEV Vadim 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2007年第5期755-765,共11页
This article describes numerical simulation of gas pipeline network operation using high-accuracy computational fluid dynamics (CFD) simulators of the modes of gas mixture transmission through long, multi-line pipelin... This article describes numerical simulation of gas pipeline network operation using high-accuracy computational fluid dynamics (CFD) simulators of the modes of gas mixture transmission through long, multi-line pipeline systems (CFD-simulator). The approach used in CFD-simulators for modeling gas mixture transmission through long, branched, multi-section pipelines is based on tailoring the full system of fluid dynamics equations to conditions of unsteady, non-isothermal processes of the gas mixture flow. Identification, in a CFD-simulator, of safe parameters for gas transmission through compressor stations amounts to finding the interior points of admissible sets described by systems of nonlinear algebraic equalities and inequalities. Such systems of equalities and inequalities comprise a formal statement of technological, design, operational and other constraints to which operation of the network equipment is subject. To illustrate the practicability of the method of numerical simulation of a gas transmission network, we compare computation results and gas flow parameters measured on-site at the gas transmission enter-prise. 展开更多
关键词 Long branched gas pipeline network Unsteady Non-isothermal gas flow CFD-simulator Numerical simulation Finite Volume Method Interior Point Method
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BFFNet: a bidirectional feature fusion network for semantic segmentation of remote sensing objects 认领 引用 被引量：6
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作者 Yandong Hou Zhengbo Wu +2 位作者 Xinghua Ren Kaiwen Liu Zhengquan Chen 《International Journal of Intelligent Computing and Cybernetics》 EI 2024年第1期20-37,共18页
Purpose:High-resolution remote sensing images possess a wealth of semantic information.However,these images often contain objects of different sizes and distributions,which make the semantic segmentation task challeng... Purpose:High-resolution remote sensing images possess a wealth of semantic information.However,these images often contain objects of different sizes and distributions,which make the semantic segmentation task challenging.In this paper,a bidirectional feature fusion network(BFFNet)is designed to address this challenge,which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.Design/methodology/approach:There are two main crucial elements in BFFNet.Firstly,the meanweighted module(MWM)is used to obtain the key features in the main network.Secondly,the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information.The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process.Finally,BFFNet is validated on two publicly available datasets,Potsdam and Vaihingen.Findings:In this paper,a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2-6%,respectively,compared to other mainstream segmentation networks from experimental results on two datasets.Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network.In summary,BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.Originality/value:The originality of the paper is the proposal of a BFFNet based on multi-scale and multiattention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images,especially for small objects and shadow-obscured objects. 展开更多
关键词 Remote sensing images Semantic segmentation Deep learning Branch network Multi-scale fusion
Texture branch network for chronic kidney disease screening based on ultrasound images 认领 引用 被引量：2
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作者 Peng-yi HAO Zhen-yu XU +4 位作者 Shu-yuan TIAN Fu-li WU Wei CHEN Jian WU Xiao-nan LUO 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2020年第8期1161-1170,共10页
Chronic kidney disease(CKD) is a widespread renal disease throughout the world. Once it develops to the advanced stage, serious complications and high risk of death will follow. Hence, early screening is crucial for t... Chronic kidney disease(CKD) is a widespread renal disease throughout the world. Once it develops to the advanced stage, serious complications and high risk of death will follow. Hence, early screening is crucial for the treatment of CKD. Since ultrasonography has no side effects and enables radiologists to dynamically observe the morphology and pathological features of the kidney, it is commonly used for kidney examination. In this study,we propose a novel convolutional neural network(CNN) framework named the texture branch network to screen CKD based on ultrasound images. This introduces a texture branch into a typical CNN to extract and optimize texture features. The model can automatically generate texture features and deep features from input images, and use the fused information as the basis of classification. Furthermore, we train the base part of the network by means of transfer learning, and conduct experiments on a dataset with 226 ultrasound images. Experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy of 96.01% and a sensitivity of 99.44%. 展开更多
关键词 Chronic kidney disease Ultrasound Texture branch network Transfer learning
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Study of the starting pressure gradient in branching network 认领 引用 被引量：8
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作者 LI Yan YU BoMing 《Science China(Technological Sciences)》 SCIE EI CAS 2010年第9期2397-2403,共7页
In order to increase the production of oil in low permeability reservoirs with high efficiency,it is necessary to fully understand the properties and special behaviors of the reservoirs and correctly describe the flow... In order to increase the production of oil in low permeability reservoirs with high efficiency,it is necessary to fully understand the properties and special behaviors of the reservoirs and correctly describe the flow in the reservoirs.This paper applies the branching network mode to the study of the starting pressure gradient of nonlinear Newtonian fluid(Bingham fluid)in the reservoirs with low permeability based on the fact that the fractured network may exist in the reservoirs.The proposed model for starting pressure gradient is a function of yield stress,microstructural parameters of the network.The proposed model may have the potential in further exploiting the mechanisms of flow in porous media with fractured network. 展开更多
关键词 Bingham fluid flow branching network starting pressure gradient
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