On the convective nature of the instability of a front undergoing a supercritical Turing bifurcation

Fronts are traveling waves in spatially extended systems that connect two different spatially homogeneous rest states. If the rest state behind the front undergoes a supercritical Turing instability, then the front will also destabilize. On the linear level, however, the front will be only convectively unstable since perturbations will be pushed away from the front as it propagates. In other words, perturbations may grow but they can do so only behind the front. It is of interest to show that this behavior carries over to the full nonlinear system. It has been successfully done in a case study by Ghazaryan and Sandstede [A. Ghazaryan, B. Sandstede, Nonlinear convective instability of Turing-unstable fronts near onset: a case study, SIAM J. Appl. Dyn. Syst. 6 (2007) 319–347]. In the present paper, analogous results are obtained for the same system as in Ghazaryan and Sandstede (2007), but for a different parameter regime.     

Multispectral landuse classification using neural networks and support vector machines: one or the other,or both?

Land use classification is an important part of many remote sensing applications. A lot of research has gone into the application of statistical and neural network classifiers to remote‐sensing images. This research involves the study and implementation of a new pattern recognition technique introduced within the framework of statistical learning theory called Support Vector Machines (SVMs), and its application to remote‐sensing image classification. Standard classifiers such as Artificial Neural Network (ANN) need a number of training samples that exponentially increase with the dimension of the input feature space. With a limited number of training samples, the classification rate thus decreases as the dimensionality increases. SVMs are independent of the dimensionality of feature space as the main idea behind this classification technique is to separate the classes with a surface that maximizes the margin between them, using boundary pixels to create the decision surface. Results from SVMs are compared with traditional Maximum Likelihood Classification (MLC) and an ANN classifier. The findings suggest that the ANN and SVM classifiers perform better than the traditional MLC. The SVM and the ANN show comparable results. However, accuracy is dependent on factors such as the number of hidden nodes (in the case of ANN) and kernel parameters (in the case of SVM). The training time taken by the SVM is several magnitudes less.     

Joint wall clutter mitigation and data-driven model for through-wall detection

Wall effects such as the strong front wall return and the propagation of the signal as it travels through the wall must be taken into account in through-wall detection. First of all, compensation of through-wall propagation effects such as attenuation and dispersion should be considered in imaging procedure assuming effective removal of the strong front wall return (called wall clutter). However, the wave backscattered from the target behind the wall is usually submerged in the wall clutter. In addition, the wall parameters are unknown in practical situation. Therefore, how to detect a target behind the wall without prior knowledge of wall characteristics is becoming a new challenge that is difficult to solve. In this article, a novel method jointing wall clutter mitigation and data-driven model (DDM) is proposed to address this new challenge. First, the total echo is decomposed into wall, target, and noise subspaces using singular value decomposition (SVD), while the image entropy is introduced as an evaluation criterion for selecting the sub-spaces boundary, and therefore the wall clutter is mitigated. Then, through-wall detection problem is cast as a regression one using DDM model and the regression problem is solved by means of Support Vector Machine (SVM). It is demonstrated that the proposed method can efficiently detect the target behind the wall without prior knowledge of its characteristics.     

基于粒子群的多目标优化算法

论文提出了一种新的基于粒子群的多目标优化算法。用搜索过程中所发现非劣解的一部分构成精英集,将其作为粒子群的历史最佳,引导粒子群的搜索,并通过小生境技术和部分变异的方法来提高非劣解集的多样性和分散性。对三个典型多目标测试函数所作实验的结果验证了该方法的有效性和快速性,结果还表明:该方法所得非劣解集在分散性、错误率和逼近程度等量化指标上优于FFGA、SPEA、PAES、NSGA等方法,是一种非常有潜力的多目标优化方法。     

轨道车辆防全向错位前面板防爬器性能仿真

运用LS DYNA对一种新型防全向错位前面板防爬器抑制横向滑移和垂向爬升的能力进行仿真,通过模拟2节编组列车车辆间水平初始偏转为2°和3°的碰撞工况,分析比较传统前面板防爬器和新型防全向错位前面板防爬器对横向运动的抑制能力。在此基础上,仿真分析车辆间初始垂向偏移分别为20 和40 mm的碰撞工况,检验新型防爬器的垂向防爬能力。仿真结果表明,在碰撞工况下,新型防全向错位前面板防爬器的横向运动和垂向运动抑制能力均优于传统面板防爬器,可在车辆设计中推广使用。     

A hybrid ray tracer for rendering polygon and volume data

Volume rendering, a technique for visualizing sampled functions of three spatial dimensions by computing 2-D projections of a colored semitransparent volume, is extended to handle polygonally defined objects. A hybrid ray-tracing algorithm, whereby rays are simultaneously cast through a set of polygons and a volume data array, is used. Samples of each are drawn at equally spaced intervals along the rays, and the resulting colors and opacities are composited together in depth-sorted order. To avoid aliasing of polygonal edges at modest computational expense, a form of selective supersampling is used. To avoid errors in visibility at polygon-volume intersections, special treatment is given to volume samples lying immediately in front of and behind polygons. The cost, image quality, and versatility of the algorithm are evaluated using data from 3-D medical imaging applications     

A multi-objective artificial physics optimization algorithm based on ranks of individuals

This paper proposes a multi-objective artificial physics optimization algorithm based on individuals’ ranks. Using a Pareto sorting based technique and incorporating the concept of neighborhood crowding degree, evolutionary individuals in the search space are evaluated at first. Then each individual is assigned a unique serial number in terms of its performance, which affects the mass of the individual. Thereby, the population evolves towards the direction of the Pareto-optimal front. Synchronously, the presented approach has good diversity, such that the population is spread evenly on the Pareto front. Results of simulation on a number of difficult test problems show that the proposed algorithm, with less evolutionary generations, is able to find a better spread of solutions and better convergence near the true Pareto-optimal front compared to classical multi-objective evolutionary algorithms (NSGA, SPEA, MOPSO) and to simple multi-objective artificial physics optimization algorithm.     

An incremental ant colony optimization based approach to task assignment to processors for multiprocessor scheduling

Optimized task scheduling is one of the most important challenges to achieve high performance in multiprocessor environments such as parallel and distributed systems. Most introduced task-scheduling algorithms are based on the so-called list scheduling technique. The basic idea behind list scheduling is to prepare a sequence of nodes in the form of a list for scheduling by assigning them some priority measurements, and then repeatedly removing the node with the highest priority from the list and allocating it to the processor providing the earliest start time (EST). Therefore, it can be inferred that the makespans obtained are dominated by two major factors: (1) which order of tasks should be selected (sequence subproblem); (2) how the selected order should be assigned to the processors (assignment subproblem). A number of good approaches for overcoming the task sequence dilemma have been proposed in the literature, while the task assignment problem has not been studied much. The results of this study prove that assigning tasks to the processors using the traditional EST method is not optimum; in addition, a novel approach based on the ant colony optimization algorithm is introduced, which can find far better solutions.     

几何自适应参数曲面网格生成

为满足有限元分析的需要,针对参数曲面提出一种几何自适应的网格生成方法.通过黎曼度量控制下的曲面约束Delaunay三角化获得曲面中轴,将其用于自动识别曲面邻近特征,并通过曲率计算自动识别曲率特征;根据邻近特征和曲率特征,融合传统网格尺寸控制技术控制边界曲线离散,并创建密度场;结合映射法和前沿推进技术对组合参数曲面生成几何自适应的网格.实验结果表明,该方法能够处理复杂的几何外形,生成的网格具有很好的自适应效果和质量.     

基于模糊C-均值聚类的TSP演化算法

提出了一种基于FCM聚类的TSP演化算法。该算法以聚类中心为新的结点组成一个简单的TSP问题,用演化算法寻求其最短路径。在最短路径中,对于每一聚类,可寻求其距前面的聚类和后面的聚类最近的两结点之间的最短距离,若其中的结点较多,则再次演化得到其最短路径,若结点较少,则可用Warshall算法可得到最短路径。通过三个阶段的演化可得到较好的结果。     

一种基于决策图贝叶斯网络的强度Pareto进化算法

提出了一种基于决策图贝叶斯网络的强度Pareto进化算法,该算法把贝叶斯概率模型结合到多目标进化算法中,通过构造和学习网络来替代传统进化算法中的交叉重组和变异等遗传操作,避免对大量参数的人工设置和重要构造块的破坏．求解多目标背包问题的仿真结果表明,所提算法可以快速收敛到较好的Pareto前沿,有很强的鲁棒性．     

A hermeneutic interpretation of a controlled laboratory experiment: a case study of decision‐making with a group support system

We conduct a case study of a laboratory experiment involving a group support system and explain how it went awry. We take the perspectives of the experiment's human subjects and the researchers themselves as the basis on which to interpret what happened in the experiment. We interpret the researchers as imputing, to the human subjects, the ‘conduit model’ of communication and the ‘calculator model’ of human information processing, which together constitute an instance of Ricoeur's hermeneutic ‘world behind the text’. We interpret the human subjects as importing, into the laboratory, their socially constructed world of personal friends, their histories and even their popular culture – a world that is an instance of Ricoeur's hermeneutic ‘world in front of the text’. We explain the experiment's going awry as following from the researchers' not accounting for, much less being aware of, the disparity between the two worlds. In taking the human subjects and the researchers seriously as human beings, we make recommendations about how such experiments might be better conducted, particularly in information systems research.     

A multi-model selection framework for unknown and/or evolutive misclassification cost problems

In this paper, we tackle the problem of model selection when misclassification costs are unknown and/or may evolve. Unlike traditional approaches based on a scalar optimization, we propose a generic multi-model selection framework based on a multi-objective approach. The idea is to automatically train a pool of classifiers instead of one single classifier, each classifier in the pool optimizing a particular trade-off between the objectives. Within the context of two-class classification problems, we introduce the “ROC front concept” as an alternative to the ROC curve representation. This strategy is applied to the multi-model selection of SVM classifiers using an evolutionary multi-objective optimization algorithm. The comparison with a traditional scalar optimization technique based on an AUC criterion shows promising results on UCI datasets as well as on a real-world classification problem.     

混合型网络中DDoS攻击的入侵扫描研究

现有网络中网络地址转换（NAT）的存在使得其后网络中的主机对外部网络变得不可见,IPv6庞大的地址空间也使得攻击者利用传统的随机地址扫描策略很难找到有漏洞主机。概述当前DDoS攻击的基本原理,具体分析了随着因特网体系结构的变化,网络NAT等设施的出现对DDoS攻击所带来的影响。针对传统理论在研究DDoS攻击过程中的一些不足,提出了一种基于搜索引擎技术和Teredo服务的新型扫描策略,以及对NAT后主机实施DDoS攻击的具体方法。仿真实验证明这种新型DDoS入侵攻击更加有效,对复杂网络环境的适应性也更强。     

Expert Systems and the "Myth" of Symbolic Reasoning

Elements of the artificial intelligence approach to expert systems offer great productivity advantages over traditional approaches to application systems development, even though the end result may be a program employing no AI techniques. These productivity advantages are the hidden truths behind the "myth" that symbolic reasoning programs are better than ordinary ones.     

Nonequilibrium kinetics in oxygen dissociation behind a shock wave front

The processes of thermally nonequilibrium chemical kinetics in shock waves with no equilibrium between translational and vibrational degrees of molecular freedom are examined. Equations describing thermal nonequilibrium are presented. The calculations show considerable divergence from thermal equilibrium behind the shock wave front both in stationary and nonstationary conditions.     

货物招标采购网络信息系统的设计与研究

公司在多年的招标过程中形成了比较严谨的招投标管理流程,利用公司业已存在的局域网络,建立相应的数据库,采用B/S(browse/server)的方式,将前端事务处理的逻辑与后端的多维数据库处理制定的原则结合起来协同工作,极大地提高了招标、投标的效率。     

基于视差平面分割的移动机器人障碍物地图构建方法

作为自主移动机器人地表障碍物探测（GPOD）技术的一部分,提出了一种利用双目摄像机的视差图像 获取信息来构建机器人前方障碍物栅格地图的方法. 该方法融合了3 维立体视觉技术以及2 维图像处理技术,前者 依据视差图的直方图信息对视差图像进行自适应平面分割,把每个平面看作是3 维场景中的实物切片进而提取障碍 物3 维信息,后者通过计算各平面上的障碍物信息曲线来提取障碍物信息,把立体视觉数据从视差图像空间变换到 2 维的障碍物地图空间. 给出了该方法构建障碍物地图的整体过程,试验结果证明了该算法的有效性和精确性.     

Front delineation and tracking with multiple underwater vehicles

This study describes a method for detecting and tracking ocean fronts using multiple autonomous underwater vehicles (AUVs). Multiple vehicles, equally spaced along the expected frontal boundary, complete near parallel transects orthogonal to the front. Two different techniques are used to determine the location of the front crossing from each individual vehicle transect. The first technique uses lateral gradients to detect when a change in the observed water property occurs. The second technique uses a measure of the vertical temperature structure over a single dive to detect when the vehicle is in upwelling water. Adaptive control of the vehicles ensure they remain perpendicular to the estimated front boundary as it evolves over time. This method was demonstrated in several experiment periods totaling weeks, in and around Monterey Bay, CA, in May and June of 2017. We compare the two front detection methods, a lateral gradient front detector and an upwelling front detector using the Vertical Temperature Homogeneity Index. We introduce two metrics to evaluate the adaptive control techniques presented. We show the capability of this method for repeated sampling across a dynamic ocean front using a fleet of three types of platforms: short‐range Iver AUVs, Tethys‐class long‐range AUVs, and Seagliders. This method extends to tracking gradients of different properties using a variety of vehicles.     

一种用于RFID传感阅读器的载波抵消射频前端方案

为了改善传统RFID(射频识别)传感阅读器体制的载波泄漏问题、提高射频前端动态范围和解调信号的质量,提出了一种采用自适应载波抑制技术的新型射频收发前端电路方案。计算机仿真结果表明,采用该方案可以获得60dB以上的载波抑制性能改善;实验验证结果表明,采用该方案获得约42dB的载波抑制性能及动态范围改善,解调信号的波形失真显著降低;现场实测结果证明,系统能够有效抑制强背景反射干扰,有效阅读距离最长达11米。