An improved model of carbon nanotube conveying flow by considering comprehensive effects of Knudsen number

Although the theoretical model of carbon nanotube conveying flow has been evolving from under macroscale theory framework to under nanoscale theory framework, for now, the small-scale effects have yet to be considered thoroughly. Herein, after extending the compatibility condition, we propose an improved model. Compared with the previous models, the improved model is not only dependent on the nonlocal parameter, but also comprehensively takes all the factors related to Knudsen number, namely effective viscosity, slip boundary condition and non-uniform flow profile, into account. Based on this model, a formula of critical flow velocity is derived in addition to numerical results and our model gives a considerably decreased critical flow velocity. Besides, when Knudsen number and nonlocal parameter increase, the critical flow velocity goes down dramatically, which indicates that the effects of Knudsen number cannot be neglected, and we demonstrate that the dispute over nonlocal parameter may impair the reliability of theoretical prediction of critical flow velocity. We also find that the effects of nonlocal parameter and Knudsen number on critical flow velocity are probably uncoupled.     

An improved fuzzy preference programming to evaluate entrepreneurship orientation

This paper describes an approach to measuring the entrepreneurship orientation (EO) of firms. EO is a widely accepted way to measure the degree in which a firm is entrepreneurial. The scale has three dimensions – innovativeness, risk-taking and proactiveness – each of which is assessed using multiple items. Measuring EO is important for entrepreneurial firms and for organizations like venture capitalists, business angels, investment banks and governments investing in these firms. Both the traditional statistical and the simple approach of assessing the overall level of EO (adding item scores) have their disadvantages. The aim of this article is to discuss these disadvantages and describe how some of them can be removed by using fuzzy analytic hierarchy process (AHP), which is a multi-criteria decision-making (MCDM) method that is particularly suited to tackle multi-dimensional, fuzzy, and perception-based constructs such as EO. We first improve a fuzzy AHP and then apply it using the pairwise comparisons of three experts to evaluate the EO of 59 small to medium-sized enterprises (SMEs) and rank the firms based on their EO score. The results indicate that proactiveness is by far the most important dimension, followed by innovativeness. Furthermore, there are considerable differences when it comes to the weights of the items.     

一种改进的PDRR模型

PDRR模型是一个最常用的网络安全模型，该模型把网络体系结构划分为防护、检测、响应、恢复四个部分。文章通过论述PDRR模型所采用的网络安全技术，提出了对该模型的改进——以风险分析为核心的安全模型。     

An improved image enhancement framework based on multiple attention mechanism

Image enhancement can accentuate image feature and is necessary process in image processing. This work focuses on fusing multi-exposure image sequences low-light image enhancement. Inspired by the classical non-local means in computer vision, we proposed an improved deep neural network framework with attentions for image enhancement. Firstly, the original image was preprocessed in different dimensions. we get the edge images using an edge extracted algorithm and fusion multi exposed images to get an better initial images based on fully convolutional neural network with position and channel attention mechanism. Secondly, the head network is constructed by fully convolutional neural network. For capturing long-range dependencies between features maps, we designed a non-local attention module for head network to get better enhancement image. Finally, emerging the original images, edge image and fusion image as the input of the head network, it can enhance the images to get high-quality images. Experiments show that our framework proposed in this paper is effective and the attention mechanism play a significant hole in the network.     

An improved approach for determining the optimal orientation of orthotropic material

Several approaches for determining the optimal orientation in the topological optimization process are discussed in this paper. It is shown that the use of a strain based method may give an even worse result than the case in which the discrete orientational variables are fixed in the optimization process, because strong couplings exist among the orientational variables when the strain field is used. The stress field is less sensitive than the strain field with respect to the variations of the orientational variables. Therefore, the coupling between the orientational variables is relatively weak when the stress field is used. This explains why the stress based approaches (e.g. Suzuki and Kikuchi 1991; Díaz and Bendsøe 1992) are more efficient than the strain based method. This kind of approach will be generalized to deal with more general optimization problems. It will be shown that the new approach can not only obtain the best result for the problem as compared to the other methods, but also has the generality for various optimization problems. Several examples will be presented to support the above statements.     

关于视觉注意力选择的神经编码

本文提出了一种改进的注意力选择模型,在这个模型中,周边神经元代表初级视觉皮层的神经元,中心神经元代表更高级视觉皮层中的神经元.生理实验发现方向选择性是初级视觉皮层神经元的重要特性之一,所以模型除了考虑外部刺激的强度,也考虑了初级视觉皮层中的神经元的方向选择性.仿真结果显示改进后的模型能够选择具有不同方向选择性的目标,并且能从一个目标转移到另一个目标.和原模型相比,改进后的模型更符合生理背景.该模型的动力学分析结果,对于理解视觉神经系统的编码有一定的帮助.     

考虑用户偏好的启发式动态共乘匹配算法

针对现有算法很少考虑用户之间的共乘偏好需求,提出了一种考虑用户偏好的启发式动态共乘匹配算法。构建一个满足用户偏好需求的动态共乘匹配模型,旨在最大化系统匹配率和最小化车辆的绕行距离。算法首先根据出行请求的时间约束、车辆与用户的出行轨迹以及用户的兴趣偏好,过滤不满足用户偏好需求的车辆;其次,构建一个临时匹配图,设置边的权值为出行请求插入到车辆的当前行驶路线中的最小绕行距离;最后采用贪婪方式实现用户与车辆之间的匹配,并采用节点插入方式,将出行请求的出发地点和到达地点插入到车辆的当前行驶路线中。仿真结果表明,提出的启发式动态共乘匹配算法使车辆增加的平均绕行距离和运行时间低于现有算法,系统匹配率高于现有算法;用户的出行时间需求、兴趣偏好、信誉度等共乘需求对系统匹配率有显著影响。     

An improved likelihood model for eye tracking

While existing eye detection and tracking algorithms can work reasonably well in a controlled environment, they tend to perform poorly under real world imaging conditions where the lighting produces shadows and the person’s eyes can be occluded by e.g. glasses or makeup. As a result, pixel clusters associated with the eyes tend to be grouped together with background-features. This problem occurs both for eye detection and eye tracking. Problems that especially plague eye tracking include head movement, eye blinking and light changes, all of which can cause the eyes to suddenly disappear. The usual approach in such cases is to abandon the tracking routine and re-initialize eye detection. Of course this may be a difficult process due to missed data problem. Accordingly, what is needed is an efficient method of reliably tracking a person’s eyes between successively produced video image frames, even in situations where the person’s head turns, the eyes momentarily close and/or the lighting conditions are variable. The present paper is directed to an efficient and reliable method of tracking a human eye between successively produced infrared interlaced image frames where the lighting conditions are challenging. It proposes a log likelihood-ratio function of foreground and background models in a particle filter-based eye tracking framework. It fuses key information from even, odd infrared fields (dark and bright-pupil) and their corresponding subtractive image into one single observation model. Experimental validations show good performance of the proposed eye tracker in challenging conditions that include moderate head motion and significant local and global lighting changes. The paper presents also an eye detector that relies on physiological infrared eye responses and a modified version of a cascaded classifier.     

An enhanced network service pricing model considering network externalities

In this paper, we provide an evaluation model for marketable quality and profitability. We define the marketable quality as a qualitative aspect of profitability. Not always there is a clear tradeoff relation between marketable quality and profitability. Therefore, it is important to discover how to increase the profitability by enhancing marketable quality. We apply the real values of some leading manufacturing Japanese corporations to our proposed model to analyze its accuracy. From the analysis, we found that theoretical and real standard values of the marketable quality indicator were both 0.6. This shows that the proposed model has a good approximation. From the fair relation of network service providers and consumers, we present the network pricing guidelines for the maximum profitability, while enhancing the marketable quality. Also, we enhance our proposed network service pricing model considering network externalities.     

基于SAML的单点登录模型的改进

分析了两种典型的基于SAML的单点登录模型,针对流程复杂的缺陷进行简化,提出改进的单点登录模型,对三种模型进行比较,分析出改进模型所具有的性能优势.     

一种用于类测试的改进型EFSM模型

扩展有限状态机(EFSM)中迁移存在前置条件和相应操作,而前置条件和相应操作中变量的相互依赖性导致了EFSM中存在不可达路径,不利于基于EFSM模型的类的测试。通过把UML状态图转换成EFSM模型,提出一种消除EFSM模型不可达路径算法,从而建立一种用于面向对象软件的类测试模型,通过该模型可以应用传统的数据流和控制流分析技术对类进行测试。     

模型修正的正交模型-正交模态改进法

正交模型-正交模态法（CMCM）是一种参数修改的新方法,它具有不依赖于灵敏度分析、不需要进行迭代的特点.但是在有限元存在整体建模误差时,该方法会出现无法完成修正计算的情况,本文针对此问题进行了改进.改进后的方法可以既可以处理存在局部建模误差的情况,也可以处理存在整体建模误差的情况.本文通过梁式结构的数值算例,比较了原修正方法（CMCM）、改进后的修正方法（ICMCM）以及商业软件模型修正FEMtools的修正效果.结果表明：改进的正交模型-正交模态方法可以使分析频率更好地逼近实验值,物理参数的修改也更加准确.     

一种改进的动态合同网模型

针对基本动态合同网中存在的问题,利用遗传算法与蚁群算法融合的思想结合蚁群分工特性提出DCNP*协议.该算法利用遗传算法得到初始解,再将其转化为蚁群算法的初始信息素并利用具有分工蚁群算法中刺激度与阈值关系进行最优解求取.通过分析,该模型在动态合同网特别是在大规模的动态合同网中,能够有效地提高求解效率,并减少通信时间.     

改进的键基正交各向异性近场动力学模型

针对经典的键基近场动力学(bond-based peridynamic,BPD)模型受固定泊松比限制的问题,提出一种改进BPD模型。该模型可解除泊松比限制,并可用于分析正交各向异性单向板的变形和裂纹扩展问题。在改进BPD模型中,每根键受到轴向和横向成对力的作用,额外增加的节点转动可消除由横向力引起的附加弯矩,从而确保该模型满足角动量守恒条件。仿真结果验证所提出的改进BPD模型的精度,并展示其预测碳纤维复合材料变形和裂纹扩展的能力。     

基于全局注意力的室内人数统计模型

随着人工智能技术的爆炸式发展,机器学习、深度学习等技术在人脸识别、行人检测和视频跟踪等各个领域得到了广泛的应用,其中利用目标检测进行室内人数统计一直以来是一个热门的研究。室内监控画面存在人群相互遮挡,且目标特征模糊等问题,往往导致检测准确率低,误检率和漏检率高等情况的出现。为了解决此问题,提出了一种基于全局注意力的室内人数统计模型,引入注意力机制,对目标检测算法YOLOv3进行改进,通过提取更多小人头或模糊人头的特征来增强检测能力。实验结果表明,改进后的网络模型具有更高的召回率和平均精度。     

Improving object orientation estimates by considering multiple viewpoints

This article describes a probabilistic approach for improving the accuracy of general object pose estimation algorithms. We propose a histogram filter variant that uses the exploration capabilities of robots, and supports active perception through a next-best-view proposal algorithm. For the histogram-based fusion method we focus on the orientation of the 6 degrees of freedom (DoF) pose, since the position can be processed with common filtering techniques. The detected orientations of the object, estimated with a pose estimator, are used to update the hypothesis of its actual orientation. We discuss the design of experiments to estimate the error model of a detection method, and describe a suitable representation of the orientation histograms. This allows us to consider priors about likely object poses or symmetries, and use information gain measures for view selection. The method is validated and compared to alternatives, based on the outputs of different 6 DoF pose estimators, using real-world depth images acquired using different sensors, and on a large synthetic dataset.     

基于交叉模型的改进遗传算法

提出一种解决早熟收敛问题的改进遗传算法. 通过最小生成树聚类将种群划分为若干个子种群, 子种群内的个体之间及不同子种群间的个体之间同时进行遗传操作. 同子种群间个体的遗传操作可以保证算法的进化方向和收敛速度, 不同子种群间个体的遗传操作可以避免近亲繁殖, 提供多样性. 分别采用二进制和实数编码, 在经典的 23 个基准函数上的对比测试结果表明, 所提出算法具有较好的收敛速度和寻优能力.