Adaptive quadtree simulation of shallow flows with wet-dry fronts over complex topography

Many natural terrains have complicated surface topography. The simulation of steep-fronted flows that occur after heavy rainfall flash floods or as inundation from dyke breaches is usually based on the non-linear shallow water equations in hyperbolic conservation form. Particular challenges to numerical modellers are posed by the need to balance correctly the flux gradient and source terms in Godunov-type finite volume shock-capturing schemes and by the moving wet-dry boundary as the flood rises or falls. This paper presents a Godunov-type shallow flow solver on adaptive quadtree grids aimed at simulating flood flows as they travel over natural terrain. By choosing the stage and discharge as dependent variables in the hyperbolic non-linear shallow water equations, a new deviatoric formulation is derived that mathematically balances the flux gradient and source terms in cases where there are wet-dry fronts. The new formulation is more general in application than previous a priori approaches. Three benchmark tests are used to validate the solver, and include steady flow over a submerged hump, flow disturbances propagating over an elliptical-shaped hump, and free surface sloshing motions in a vessel with a parabolic bed. The model is also used to simulate the propagation of a flood due to a dam break over an initially dry floodplain containing three humps.     

The DVS model: a new concept for heavy gas dispersion by water curtain

Numerous accidents involving dangerous substances may unfortunately occur, namely because of the importance of the transport of dangerous materials, of the storage and use of dangerous products on industrial sites. Several kinds of barriers can be used to protect or reduce the consequences of these accidents, such as the water curtains which mitigate the atmospheric dispersion of toxic clouds. Nevertheless, few studies assessing their effectiveness exist and models predicting the concentrations downstream of water curtains are scarce. In this context, a new concept, based on the representation of the water curtain as a Discontinuous Virtual Source is proposed. From this concept, the DVS model aims to predict on one hand the influence of the ‘peacock tail hose’ (vertical and upwards water curtain) on the dispersion of a toxic cloud, on the other hand, to predict the concentrations downwind of the water barrier.The bases of the model are set out and the results of the simulation, are presented and compared with experimental values of an ammonia plume dispersion in presence of the peacock tail spray.     

Taylor dispersion in shallow micro-channels: aspect ratio effect

We consider the axial dispersion of a liquid flowing through a micro-channel, which has a slender shallow cross-section. A lubrication approximation allows for an analytical analysis of the effect of cross-section and aspect ratio on the dispersion process. Numerical simulations are performed in order to verify the approximate solution.     

Roe-type schemes for dense gas flow computations

Dense gas dynamics studies the dynamic behavior of gases in the thermodynamic region close to the liquid–vapor critical point, where the perfect gas law is no longer valid, and has to be replaced by more complex equations of state. In such a region, some fluids, known as the Bethe–Zel’dovich–Thompson fluids, can exhibit non-classical nonlinearities, such as expansion shocks, and mixed shock-fan waves. In the present work, the problem of choosing a suitable numerical scheme for dense gas flow computations is addressed. In particular, some extensions of classical Roe’s scheme to real gas flows are reviewed and their performances are evaluated for flow problems involving non-classical nonlinearities. A simplification to Roe’s linearization procedure is proposed, which does not satisfy the U-property exactly, but significantly reduces complexity and computational costs. Such simplification introduces an additional error O(δx²), with δx the mesh size, with respect to the first-order accurate Roe’s scheme, and O(δx⁶) with respect to its higher-order MUSCL extensions. Numerical experiments, concerning a one-dimensional dense gas shock tube, supersonic flow of a BZT gas past a forward-facing step, and transonic dense gas flow through a turbine cascade, show a negligible influence of the adopted linearization procedure on the solution accuracy, whereas it significantly affects computational efficiency.     

Autonomous objects: A natural model for complex applications

Object-oriented database systems are emerging as the next generation DBMSs for advanced applications, e.g., VLSI design, mechanical CAD/CAM, software engineering, etc. However, a close analysis indicates that the requirements imposed by these application domains cannot be met by an object-oriented model that relies purely onpassive objects. In this work we go beyond the conventionalsingle-thread-of-control paradigm of passive object models and propose a model ofactive objects which can autonomously initiate responses to environmental changes.Autonomous objects cooperate with each other by synchronous orasynchronous message passing—giving rise to themultiple-thread-of-control in such an environment. It is shown howevents—to which active objects react—can be incorporated into this model. We propose a nondeterministic computational model for the individual active objects that allows the autonomous reaction upon events. We show that this very sparse extension to an object-oriented model gives rise to several high-level features which can be controlled by events. The object-oriented paradigm allows one to isolate the rules according to which events are being raised. This leads to a potentially rather efficient execution model compared to existing relational concepts, which are typically globally-defined event trigger mechanisms.     

A discrete Boltzmann equation model for two-phase shallow granular flows

In this paper a Discrete Boltzmann Equation model (hereinafter DBE) is proposed as solution method of the two-phase shallow granular flow equations, a complex nonlinear partial differential system, resulting from the depth-averaging procedure of mass and momentum equations of granular flows. The latter, as e.g. a debris flow, are flows of mixtures of solid particles dispersed in an ambient fluid.The reason to use a DBE, instead of a more conventional numerical model (e.g. based on Riemann solvers), is that the DBE is a set of linear advection equations, which replaces the original complex nonlinear partial differential system, while preserving the features of its solutions. The interphase drag function, an essential characteristic of any two-phase model, is accounted for easily in the DBE by adding a physically based term. In order to show the validity of the proposed approach, the following relevant benchmark tests have been considered: the 1D simple Riemann problem, the dam break problem with the wet–dry transition of the liquid phase, the dry bed generation and the perturbation of a state at rest in 2D. Results are satisfactory and show how the DBE is able to reproduce the dynamics of the two-phase shallow granular flow.     

浅层CNN网络构建的噪声比例估计模型

目的 利用深度卷积神经网络（deep convolutional neural network,DCNN）构建的非开关型随机脉冲噪声（random-valued impulse noise,RVIN）降噪模型在降噪效果和执行效率上均比主流的开关型RVIN降噪算法更有优势,但在实际应用中,这类基于训练（数据驱动）的降噪模型,其性能却受制于能否对待降噪图像受噪声干扰的严重程度进行准确的测定（即存在数据依赖问题）。为此,提出了一种基于浅层卷积神经网络的快速RVIN噪声比例预测（noise ratio estimation,NRE）模型。方法 该预测模型的主要任务是检测待降噪图像中的噪声比例值并将其作为反映图像受噪声干扰严重程度的指标,依据NRE预测模型的检测结果可以自适应调用相应预先训练好的特定区间DCNN降噪模型,从而快速且高质量地完成图像降噪任务。结果 分别在10幅常用图像和50幅纹理图像两个测试集上进行测试,并与现有的主流RVIN降噪算法中的检测模块进行对比。在常用图像测试集上,本文所提出的NRE预测模型的预测准确性最高。相比于噪声比例预测精度排名第2的算法, NRE预测模型在噪声比例预测值均方根误差上低0.6% 2.4%。在50幅纹理图像测试集上,NRE模型的均方根误差波动范围最小,表明其稳定性最好。通过在1幅大小为512×512像素图像上的总体平均执行时间来比较各个算法执行效率的优劣,NRE模型执行时间仅为0.02 s。实验数据表明：所提出的NRE预测模型在受各种不同噪声比例干扰的自然图像上均可以快速而稳定地测定图像中受RVIN噪声干扰的严重程度,非盲的DCNN降噪模型与其联用后即可无缝地转化为盲降噪算法。结论 本文RVIN噪声比例预测模型在各个噪声比例下具有鲁棒的预测准确性,与基于DCNN的非开关型RVIN深度降噪模型配合使用后能妥善解决DCNN网络模型固有的数据依赖问题。     

An Eulerian-Lagrangian model for dense particle clouds

The interaction of a shock wave with a layer of particles, a topic important for the formation of dust clouds in air, has been analyzed by numerical simulation. The modelling method used are the Eulerian-Lagrangian and the Eulerian-Eulerian approaches, the latter is traditionally used for this type of simulation. Using Eulerian-Lagrangian simulation, it has been possible to include the effects of particle-particle and particle-wall collisions in a realistic and direct manner. Results are mainly shown as snap-shots of particle positions during the simulations and statistics for the particle displacement and collisions. The results show that collisions influence the process of particle cloud formation significantly.     

深层聚合残差密集网络的超声图像左心室分割

目的 超声图像是临床医学中应用最广泛的医学图像之一,但左心室超声图像一般具有强噪声、弱边缘和组织结构复杂等问题,其图像分割难度较大。临床上需要一种效率高、质量好的超声图像左心室分割算法。本文提出一种基于深层聚合残差密集网络（deep layer aggregation for residual dense network,DLA-RDNet）的超声图像左心室分割算法。方法 对获取的超声图像进行形态学操作,定位目标区域,得到目标图像。构建残差密集网络（residual dense network,RDNet）用于提取图像特征,并将RDNet得到的层次信息通过深层聚合（deep layer aggregation,DLA）的方式紧密融合到一起,得到分割网络DLA-RDNet,用于实现对超声图像左心室的精确分割。通过深监督（deep supervision,DS）方式为网络剪枝,简化网络结构,提升网络运行速度。结果 数据测试集的实验结果表明,所提算法平均准确率为95.68%,平均交并比为97.13%,平均相似性系数为97.15%,平均垂直距离为0.31 mm,分割轮廓合格率为99.32%。与6种分割算法相比,所提算法的分割精度更高。在测试阶段,每幅图像仅需不到1 s的时间即可完成分割,远远超出了专业医生的分割速度。结论 提出了一种深层聚合残差密集神经网络对超声图像左心室进行分割,通过主、客观对比实验表明本文算法的有效性,能够较对比方法更实时准确地对超声图像左心室进行分割,符合临床医学中超声图像左心室分割的需求。     

A hierarchical dense deformable model for 3D face reconstruction from skull

3D face reconstruction from skull has been investigated deeply by computer scientists in the past two decades because it is important for identification. The dominant methods construct 3D face from the soft tissue thickness measured at a set of landmarks on skull. The quantity and position of the landmarks are very vital for 3D face reconstruction, but there is no uniform standard for the selection of the landmarks. Additionally, the acquirement of the landmarks on skull is difficult without manual assistance. In this paper, an automatic 3D face reconstruction method based on a hierarchical dense deformable model is proposed. To construct the model, the skull and face samples are acquired by CT scanner and represented as dense triangle mesh. Then a non-rigid dense mesh registration algorithm is presented to align all the samples in point-to-point correspondence. Based on the aligned samples, a global deformable model is constructed, and three local models are constructed from the segmented patches of the eye, nose and mouth. For a given skull, the globe and local deformable models are iteratively matched with it, and the reconstructed facial surface is obtained by fusing the globe and local reconstruction results. To validate the presented method, a measurement in the coefficient domain of a face deformable model is defined. The experimental results indicate that the proposed method has good performance for 3D face reconstruction from skull.     

A novel compression framework of the dense point-cloud model for cultural heritage artifacts

Multimedia Tools and Applications - With the help of laser scanner, the accurate digital information of cultural relics can be obtained. However, how to transfer the enormous and dense data by an...     

A guaranteed prediction model for complex linear systems

The method of approximation in the norm is used to design a linear predictor for the output of a complex linear system at a given future time. It is shown that the computed output is an optimal linear transformation of some variables denoted the "most important" states of the system. The proposed method allows to obtain the system output prediction as a signal belonging to a guaranteed uncertainty tube.     

基于地貌和地形的积雪仿真模型

针对三维气象灾害研究平台中雪灾、雪崩的仿真提出一种积雪仿真模型,有助于灾害分析人员对这类气象灾害进行预防。利用基于RGB颜色向量距离的彩色图像分割算法区分图像纹理中不同的地貌;然后在已分类的地貌上,使用依据力学原理推导出的公式判断不同地形上能否形成积雪;最后依据计算结果对场景纹理图像进行处理,呈现出积雪覆盖的效果。实验表明,利用该模型可以在三维气象灾害仿真平台中产生积雪效果。     

一种基于虚拟P2SP的应用层混合组播模型

为了提高应用层组播数据分发的效率,提出了一种基于虚拟P2SP(peer to server & peer)架构的应用层混合组播模型VPHM。该模型构建了一个四层的数据分发结构,从上至下为服务节点层、超级节点层、虚拟服务层和peer节点层。上三层组成组播域,保证了组播的广度,下两层组成VP2SP(virtual P2SP)域,确保了组播的深度。仿真实验结果表明,该模型具有较低的平均端到端时延和较小的控制开销,适用于高服务质量的大规模应用层组播应用环境。     

A novel dense capsule network based on dense capsule layers

Applied Intelligence - Capsule network, which performs feature presentations for classification tasks via novel capsule forms, has attracted more and more attention. However, its performance on...     

A knowledge base model for complex forging die machining

Recent evolutions on forging process induce more complex shape on forging die. These evolutions, combined with High Speed Machining (HSM) process of forging die lead to important increase in time for machining preparation. In this context, an original approach for generating machining process based on machining knowledge is proposed in this paper. The core of this approach is to decompose a CAD model of complex forging die in geometrical features. Technological data and topological relations are aggregated to a geometrical feature in order to create machining features. Technological data, such as material, surface roughness and form tolerance are defined during forging process and dies design. These data are used to choose cutting tools and machining strategies. Topological relations define relative positions between the surfaces of the die CAD model. After machining features identification cutting tools and machining strategies currently used in HSM of forging die, are associated to them in order to generate machining sequences. A machining process model is proposed to formalize the links between information imbedded in the machining features and the parameters of cutting tools and machining strategies. At last machining sequences are grouped and ordered to generate the complete die machining process. In this paper the identification of geometrical features is detailed. Geometrical features identification is based on machining knowledge formalization which is translated in the generation of maps from STL models. A map based on the contact area between cutting tools and die shape gives basic geometrical features which are connected or not according to the continuity maps. The proposed approach is illustrated by an application on an industrial study case which was accomplished as part of collaboration.     

A data model and algebra for probabilistic complex values

We present a probabilistic data model for complex values. More precisely, we introduce probabilistic complex value relations, which combine the concept of probabilistic relations with the idea of complex values in a uniform framework. We elaborate a model-theoretic definition of probabilistic combination strategies, which has a rigorous foundation on probability theory. We then define an algebra for querying database instances, which comprises the operations of selection, projection, renaming, join, Cartesian product, union, intersection, and difference. We prove that our data model and algebra for probabilistic complex values generalizes the classical relational data model and algebra. Moreover, we show that under certain assumptions, all our algebraic operations are tractable. We finally show that most of the query equivalences of classical relational algebra carry over to our algebra on probabilistic complex value relations. Hence, query optimization techniques for classical relational algebra can easily be applied to optimize queries on probabilistic complex value relations.     

IPH-TRIM3D-PCLake: A three-dimensional complex dynamic model for subtropical aquatic ecosystems

This paper presents IPH-TRIM3D-PCLake, a three-dimensional complex dynamic model for subtropical aquatic ecosystems. It combines a spatially explicit hydrodynamic model with a water-quality and biotic model of ecological interactions. The software, which is freely available for research purposes, has a graphical user-friendly interface and a flexible design that allows the user to vary the complexity of the model. It also has built-in analysis tools such as Monte Carlo sensitivity analysis, a genetic algorithm for calibration, and plotting tools.     

Visual-FIR: A tool for model identification and prediction of dynamical complex systems

A new platform for the fuzzy inductive reasoning (FIR) methodology has been designed and developed under the MATLAB environment. The new tool, named Visual-FIR, allows the identification of dynamic systems models in a user-friendly environment. FIR offers a pattern-based approach to modeling and predicting either univariate or multivariate time series, obtaining very good results when applied to various areas such as control, biology, and medicine. However, the available implementation of FIR was such that new code had to be developed for each new application studied. Visual-FIR resolves this limitation and offers a high-efficiency implementation. Furthermore, the Visual-FIR platform presents a new vision of the methodology based on process blocks and adds new features, increasing the overall capabilities of the FIR methodology. The DAMADICS benchmark problem is addressed in this research using the Visual-FIR approach.