Density-based rough set model for hesitant node clustering in overlapping community detection

Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years.A notion of hesitant node（HN） is proposed. An HN contacts with multiple communities while the communications are not strong or even accidental, thus the HN holds an implicit community structure.However, HNs are not rare in the real world network. It is important to identify them because they can be efficient hubs which form the overlapping portions of communities or simple attached nodes to some communities. Current approaches have difficulties in identifying and clustering HNs. A density-based rough set model（DBRSM） is proposed by combining the virtue of densitybased algorithms and rough set models. It incorporates the macro perspective of the community structure of the whole network and the micro perspective of the local information held by HNs, which would facilitate the further ＂growth＂ of HNs in community. We offer a theoretical support for this model from the point of strength of the trust path. The experiments on the real-world and synthetic datasets show the practical significance of analyzing and clustering the HNs based on DBRSM. Besides, the clustering based on DBRSM promotes the modularity optimization.

Rough function model and rough membership function

Two pairs of approximation operators, which are the scale lower and upper approximations as well as the real line lower and upper approximations, are defined. Their properties and antithesis characteristics are analyzed. The rough function model is generalized based on rough set theory, and the scheme of rough function theory is made more distinct and complete. Therefore, the transformation of the real function analysis from real line to scale is achieved. A series of basic concepts in rough function model including rough numbers, rough intervals, and rough membership functions are defined in the new scheme of the rough function model. Operating properties of rough intervals similar to rough sets are obtained. The relationship of rough inclusion and rough equality of rough intervals is defined by two kinds of tools, known as the lower （upper） approximation operator in real numbers domain and rough membership functions. Their relative properties are analyzed and proved strictly, which provides necessary theoretical foundation and technical support for the further discussion of properties and practical application of the rough function model.

Elastoplastic Contact Mechanics Model of Rough Surface Based on Fractal Theory

Because the result of the MB fractal model contradicts with the classical contact mechanics, a revised elastoplastic contact model of a single asperity is developed based on fractal theory. The critical areas of a single asperity are scale dependent, with an increase in the contact load and contact area, a transition from elastic, elastoplastic to full plastic deformation takes place in this order. In considering the size distribution function, analytic expression between the total contact load and the real contact area on the contact surface is obtained. The elastic, elastoplastic and full plastic contact load are obtained by the critical elastic contact area of the biggest asperity and maximun contact area of a single asperity. The results show that a rough surface is firstly in elastic deformation. As the load increases, elastoplastic or full plastic deformation takes place. For constant characteristic length scale G, the slope of load-area relation is proportional to fractal dimension D. For constant fractal dimension D, the slope of load-area relation is inversely proportional to G. For constant D and G, the slope of load-area relation is inversely proportional to property of the material ~b, namely with the same load, the material of rough surface is softer, and the total contact area is larger. The contact mechanics model provides a foundation for study of the friction, wear and seal performance of rough surfaces.

Distributed hydrological models for addressing effects of spatial variability of roughness on overland flow

In this study, we investigated the origin of the overland flow roughness problem and divided the current overland flow roughness research into three types, as follows: the first type of research takes into account the effects of roughness on the volume and velocity of surface runoff, flood peaks, and the scouring capability of flows, but has not addressed the spatial variability of roughness in detail; the second type of research considers that surface roughness varies spatially with different land usage types, land-cover conditions, and different tillage forms, but lacks a quantitative study of the spatial variability; and the third type of research simply deals with the spatial variability of roughness in each grid cell or land type. We present three shortcomings of the current overland flow roughness research, including(1) the neglect of roughness in distributed hydrological models when simulating the overland flow direction and distribution,(2) the lack of consideration of spatial variability of roughness in hydrological models, and(3) the failure to distinguish the roughness formulas in different overland flow regimes. To solve these problems,distributed hydrological model research should focus on four aspects in regard to overland flow: velocity field observations, flow regime mechanisms, a basic roughness theory, and scale problems.

Fractal Prediction Model of Thermal Contact Conductance of Rough Surfaces

The thermal contact conductance problem is an important issue in studying the heat transfer of engineering surfaces, which has been widely studied since last few decades, and for predicting which many theoretical models have been established. However, the models which have been existed are lack of objectivity due to that they are mostly studied based on the statistical methodology characterization for rough surfaces and simple partition for the deformation formats of contact asperity. In this paper, a fractal prediction model is developed for the thermal contact conductance between two rough surfaces based on the rough surface being described by three-dimensional Weierstrass and Mandelbrot fractal function and assuming that there are three kinds of asperity deformation modes： elastic, elastoplastic and fully plastic. Influences of contact load and contact area as well as fractal parameters and material properties on the thermal contact conductance are investigated by using the presented model. The investigation results show that the thermal contact conductance increases with the increasing of the contact load and contact area. The larger the fractal dimension, or the smaller the fractal roughness, the larger the thermal contact conductance is. The thermal contact conductance increases with decreasing the ratio of Young＇s elastic modulus to the microhardness. The results obtained indicate that the proposed model can effectively predict the thermal contact conductance at the interface, which provide certain reference to the further study on the issue of heat transfer between contact surfaces.

Optimization and perfection of the roughing model for ferritic SUS430

Based on the characteristics of ferritic SUS430 heating and deformation,and combined with the features of the 1780 mm hot-rolling mill,a roughing model was introduced in two aspects:optimizing the rough rolling passes and improving the width control precision.Through reducing the rough rolling passes,the rough rolling time can be shortened,the precision rolling startup temperature can be raised and the yield of the hot-rolled products can be increased.Moreover,on the premise that the slab width fluctuation was great,the precision of the width control can be improved through optimizing the parameters of the hot-rolling width control model.The result shows that the optimization and perfection of the original rolling process of the stainless steel 430 series further improved its capacity and product quality.

Modeling for Collapsing Cavitation Bubble near Rough Solid Wall by Mulit-Relaxation-Time Pseudopotential Lattice Boltzmann Model

Cavitation bubble collapse near rough solid wall is modeled by the multi-relaxation-time (MRT) pseudopotential lattice Boltzmann (LB) model. The modified forcing scheme, which can achieve LB model’s thermodynamic consistency by tuning a parameter related with the particle interaction range, is adopted to achieve desired stability and density ratio. The bubble collapse near rough solid wall was simulated by the improved MRT pseudopotential LB model. The mechanism of bubble collapse is studied by investigating the bubble profiles, pressure field and velocity field evolution. The eroding effects of collapsing bubble are analyzed in details. It is found that the process and the effect of the interaction between bubble collapse and rough solid wall are affected seriously by the geometry of solid boundary. At the same time, it demonstrates that the MRT pseudopotential LB model is a potential tool for the investigation of the interaction mechanism between the collapsing bubble and complex geometry boundary.

An angular cutoff composite model for investigation on electromagnetic scattering from two-dimensional rough sea surfaces

Based on the local configuration angle division to select the corresponding method for electromagnetic scattering calculation from rough sea surface, this paper presents an angular cutoff composite model： when the local scattered angle is in the specular region that is given by an approximately 20 degrees cone around the specular direction, the Kirchhoff approximation is applied to evaluate the specular reflection, which dominates the total scattering in this region; the small perturbation method is employed to handle the diffuse reflection which is predominant as the local scattered angle is situated out of the specular region. Numerical results are compared with those of experimental and theoretical models in several configurations as a function of incident angle, wind speed, wind direction. The comparison of numerical results of other experimental and theoretical models in several configurations shows that the new composite model is robust to give accurate numerical evaluations for the sea surface scattering.

Neural Network Modeling and Prediction of Surface Roughness in Machining Aluminum Alloys

Artificial neural network is a powerful technique of computational intelligence and has been applied in a variety of fields such as engineering and computer science. This paper deals with the neural network modeling and prediction of surface roughness in machining aluminum alloys using data collected from both force and vibration sensors. Two neural network models, including a Multi-Layer Perceptron (MLP) model and a Radial Basis Function (RBF) model, were developed in the present study. Each model includes eight inputs and five outputs. The eight inputs include the cutting speed, the ratio of the feed rate to the tool-edge radius, cutting forces in three directions, and cutting vibrations in three directions. The five outputs are five surface roughness parameters. Described in detail is how training and test data were generated from real-world machining experiments that covered a wide range of cutting conditions. The results show that the MLP model provides significantly higher accuracy of prediction for surface roughness than does the RBF model.

Application of a multiregion model to the EM scattering from a rough surface with or without a target above it

An efficient multiregion model is introduced to calculate the electromagnetic scattering from a perfectly electrical conducting （PEC） rough surface with or without a PEC target above it. In the multiregion model, the rough surface is split into multiple regions depending on their position along the rough surface. Two intermediate regions are chosen as the dominant region. If a target is located above the rough surface, the target will also be included in the dominant region. The method of moments （MOM） is only adopted on the dominant region to ensure validity. Hence, the new model can greatly reduce the number of unknowns associated with full MOM analysis. The induced electric currents on the other regions are obtained by approximately considering the mutual coupling between different regions along the rough surface. Compared with the published hybrid method, this new model is not only suitable for EM scattering from a target above a rough surface but also applicable for just rough surfaces. Several numerical simulations are presented to show the validity and efficiency of the multiregion model.

不同粗糙元结构下CaCO_(3)污垢局部沉积特性

由于换热器实际的热交换过程中换热壁面并非绝对光滑,而粗糙换热壁面很容易在部分局部位置处结垢,因此基于已构建的局部析晶污垢模型,对不同粗糙元结构下CaCO_(3)污垢局部沉积特性进行数值模拟研究。对比了三种不同形状(矩形、梯形和三角形)的粗糙元,并详细分析了三角形粗糙元的相对高度和相对宽度的影响。结果表明,三种粗糙元通道内的局部污垢热阻平均值均小于光滑通道,其中三角形粗糙元的局部污垢热阻平均值最小。三种粗糙元通道局部污垢热阻沿通道长度增加均呈周期性变化,且在每个周期内均存在峰值,其中最大峰值位于粗糙元背风侧。此外,局部污垢热阻随着三角形粗糙元相对高度的增加而减小,随着相对宽度的增加而略有增加,说明相对高度对局部污垢沉积影响更大。

基于Rough Set的任务复杂度与协同模型研究

基于RoughSet(RS)理论 ,在知识的层次上 ,给出了相关任务的知识表达以及任务复杂度的概念。针对多Agent系统 ,提出了衡量Agent基于知识水平的能力、协同的冗余度等方法。运用粗糙度、信息熵和知识这三个指标 ,讨论了协同的可能条件以及必然条件。对于协同满足可能条件而不满足必然条件的情况下 ,提出了进行学习的最小知识集的概念。在此基础上 ,给出了相关任务T的静态协同模型与算法。最后 ,指出了有待解决的问题。

Rough有限格的蕴涵规则挖掘

提出了一种基于 Rough有限概念格的规则发现方法 (RRLIRD) ,以揭示数据集中的蕴涵规则 .首先引入有限概念格的简化表示形式 ,由用户选择数据集中感兴趣属性集创建概念格结构 ,提高用户的交互性和挖掘的效率 ;然后运用有限概念格与 Rough集理论相结合形成 Rough有限概念格 ,蕴涵规则则由其特有的上、下近似运算得到 ,不需计算繁琐的频繁项目集 .算法运用大型超市的交易流水数据进行仿真实验 .结果表明 ,执行时间比经典的 Apriori算法大大降低 .该算法也适用于证券行情分析和农业数据库中的病虫害分析等 .

模糊空间对象拓扑关系的Rough描述

利用Rough Set理论,在四交差模型的基础上,提出适合于描述确定和模糊区域或两个模糊区域目标间拓扑空间关系的扩展模型(4-4ID模型),并且详细讨论模糊对象之间的拓扑关系。

可传递信度模型上的Rough算子及其应用

在可传递信度模型(TBM)上定义一对Rough算子,并讨论其性质,然后将其应用于两方面:一是对同一识别框架下不同布尔代数中的任一命题进行表示,二是对TBM中的信任函数和似然函数进行Rough集解释.

Rough集理论在故障诊断专家系统中的应用研究

在传统故障诊断专家系统的基础上 ,引入Rough集理论 ,提出了基于Rough集的故障诊断专家系统模型 。

基于Rough-Fuzzy的网构软件信任度量模型

针对网构软件信任度量模型缺少自适应性和不能很好地刻画信任关系复杂性和不确定性的问题,提出基于Rough-Fuzzy的网构软件信任度量模型.将Rough-Fuzzy思想引入到对网构软件的信任度量上,并将信息熵理论引入到不同信任属性值的权值计算上,以解决信任属性值计算的不确定性问题.实验表明该度量模型对善意节点和恶意节点的信任度有明显的区分效果,能够抑制恶意节点的信任度增长.并且该模型具有良好的适应性,对于一些信任度量参数,系统可以动态地去调整并且增加或减少信任度量属性,能够很好地适应开放网络环境的动态性特点.

基于Rough Set和神经网络的CBR快捷检索方法

检索是CBR中的关键技术,直接影响CBR的推理效率和质量,检索出的案例质量的好坏直接影响着案例重用与修改的难易,该文提出先用粗糙集约简理论去除冗余的案例决策表特征,再用BP神经网络模型来实现相似案例检索,这种检索方法不需要定义案例属性之间的相似度,检索速度快。

基于Rough集理论的模态逻辑与Rough逻辑

在介绍Ｒｏｕｇｈ集的基础上，定义了Ｒｏｕｇｈ下（Ｌ）和上（Ｈ）近似算子，建立了基于Ｒｏｕｇｈ集理论的模态逻辑和Ｒｏｕｇｈ逻辑。阐述了这两种逻辑能在Ｒｏｕｇｈ集公理化基础上被统一起来，并且随着不同性质的关系Ｒ，能得到与模态逻辑相平行的各种Ｒｏｕｇｈ逻辑模型。

基于本体和Rough Set理论的知识推理模型

随着计算机的普及与快速发展,如何通过在批量数据中提取有效规则实现机器智能分类与推理,提高知识推理的有效性与准确率,成为当前知识服务领域的难题之一。文章基于本体（Ontology）的知识组织方式和粗糙集（Rough Set）理论的知识获取技术,针对批量数据的规则提取实现知识推理,提出知识推理模型ORSKM。ORSKM模型利用九元组构建领域本体,实现实例数据的语义化描述;从领域本体的属性中提取粗糙集的决策表,由决策表对领域本体关系进行领域知识知识库归约,获取领域知识相关的属性规则;通过粗糙集知识推理算法实现知识获取,最后通过实验对该知识推理模型进行验证。