基于HSI彩色空间模型的域替换降噪算法

对彩色数字图像进行降噪处理时,为兼顾标量处理法的简便性和矢量处理法的有效性,提出一种对彩色空间域实行替换的方法。采用均值滤波法在RGB域内进行滤波处理,将图像数据从RGB域转换到HSI域,用滤波处理后的H域、S域替换含有噪声的H域、 S域。对由双目内窥镜采集的实际彩色图像进行实验,结果证明该域替换法可以结合标量处理和矢量处理的优点,达到比一般标量处理法及简单矢量处理法更好的降噪效果。     

The (strong) rainbow connection numbers of Cayley graphs on Abelian groups

A path in an edge-colored graph G, whose adjacent edges may have the same color, is called a rainbow path if no two edges of the path are colored the same. The rainbow connection number rc(G) of G is the minimum integer i for which there exists an i-edge-coloring of G such that every two distinct vertices of G are connected by a rainbow path. The strong rainbow connection number src(G) of G is the minimum integer i for which there exists an i-edge-coloring of G such that every two distinct vertices u and v of G are connected by a rainbow path of length d(u,v). In this paper, we give upper and lower bounds of the (strong) rainbow connection numbers of Cayley graphs on Abelian groups. Moreover, we determine the (strong) rainbow connection numbers of some special cases.     

SFFS-PC-NN optimized by genetic algorithm for dynamic prediction of financial distress with longitudinal data streams

Recently, research of financial distress prediction has become increasingly urgent. However, existing static models for financial distress prediction are not able to adapt to the situation that the sample data flows constantly with the lapse of time. Financial distress prediction with static models does not meet the demand of the dynamic nature of business operations. This article explores the theoretical and empirical research of dynamic modeling on financial distress prediction with longitudinal data streams from the view of individual enterprise. Based on enterprise’s longitudinal data streams, dynamic financial distress prediction model is constructed by integrating financial indicator selection by using sequential floating forward selection method, dynamic evaluation of enterprise’s financial situation by using principal component analysis at each longitudinal time point, and dynamic prediction of financial distress by using back-propagation neural network optimized by genetic algorithm. This model’s ex-ante prediction efficiently combines its ex-post evaluation. In empirical study, three listed companies’ half-year longitudinal data streams are used as the sample set. Results of dynamic financial distress prediction show that the longitudinal and dynamic model of enterprise’s financial distress prediction is more effective and feasible than static model.     

基于数字图像处理的虚拟输入方法

针对当前嵌入式设备输入方法存在的高成本低体验问题,提出了一种运行于嵌入式系统中基于数字图像处理的虚拟输入方法.该方法利用嵌入式设备附加的摄像设备,对用户在虚拟键盘上的按键过程进行图像采集,通过角点识别和图像分割进行按键识别,以便通过采集到的图像定位出用户点击的目标按键,从而实现信息输入过程.通过实例验证了该方法的可行性...     

基于Web Service的异构集成查询方案

提出一种基于Web Service的异构集成综合查询系统,采用B/S和C/S相结合的多层分布式体系结构,能够有效提高系统的实用性,并屏蔽数据的异构性和各种系统模式的差别,仿真实验结果表明,该设计方案提升了异构系统的可集成性,且能实现用户透明查询的功能,保证数据源的安全。     

基于入度调整的结构化P2P网络拥塞控制算法

针对结构化对等网络中存在的由查询负载不均衡所导致的拥塞问题,提出一种基于动态入度调整的拥塞控制算法。算法根据节点查询负载的情况,动态判定出可能存在的热点,并将热点周围的节点组织起来,通过调整每个节点入连接的数量对负载进行调整。为了克服节点处理能力的异构性,算法将热点组中的节点进一步划分为多个能力组,并在能力组内和组间分别进行调整。模拟结果表明,该算法在处理突发流量的过程中可以有效地减少查询失败的次数,降低拥塞产生的影响。     

桥式起重机精准定位在线轨迹规划方法设计及应用

针对工业桥式起重机的异步电机难以准确跟踪给定轨迹,从而导致离线轨迹规划算法定位误差很大的问题,论文提出一种基于相平面分析的在线轨迹规划算法.这种方法利用激光测距仪采集位置信息,在起重机运行过程中,根据台车与目标位置的距离来优化轨迹不同阶段的运行时间,并实时调整台车轨迹的有关参数,以实现台车精准定位的目标.实验结果表明,这种方法可以很好地兼顾负载快速消摆与台车准确定位两方面的要求,有利于提升桥式起重机的运送效率.论文将这种在线轨迹规划方法成功应用于标准32 t工业桥式起重机,并取得了良好的运行效果.     

翼伞系统在较大风场中的归航控制

翼伞系统是一类特殊的柔翼飞行器,由于其飞行速度较低,容易受到风场的影响.针对翼伞系统在较大风场中难以准确跟踪归航轨迹、实现精确着陆,因此将风场中平均风的影响在轨迹规划中予以考虑,采用一种改进的粒子群算法(particle swarm optimization,PSO)优化分段归航轨迹;将紊流的影响作为外界的干扰,由线性自抗扰控制器(linear active disturbance rejection controller,LADRC)进行修正.仿真结果表明,该归航控制方法对提高翼伞系统在较大风场环境下的抗风性能和归航精度有重要意义.     

CAMAS: A cluster-aware multiagent system for attributed graph clustering

Attributed graphs describe nodes via attribute vectors and also relationships between different nodes via edges. To partition nodes into clusters with tighter correlations, an effective way is applying clustering techniques on attributed graphs based on various criteria such as node connectivity and/or attribute similarity. Even though clusters typically form around nodes with tight edges and similar attributes, existing methods have only focused on one of these two data modalities. In this paper, we comprehend each node as an autonomous agent and develop an accurate and scalable multiagent system for extracting overlapping clusters in attributed graphs. First, a kernel function with a tunable bandwidth factor δ is introduced to measure the influence of each agent, and those agents with highest local influence can be viewed as the “leader” agents. Then, a novel local expansion strategy is proposed, which can be applied by each leader agent to absorb the most relevant followers in the graph. Finally, we design the cluster-aware multiagent system (CAMAS), in which agents communicate with each other freely under an efficient communication mechanism. Using the proposed multiagent system, we are able to uncover the optimal overlapping cluster configuration, i.e. nodes within one cluster are not only connected closely with each other but also with similar attributes. Our method is highly efficient, and the computational time is shown that nearly linearly dependent on the number of edges when δ ∈ [0.5, 1). Finally, applications of the proposed method on a variety of synthetic benchmark graphs and real-life attributed graphs are demonstrated to verify the systematic performance.     

A SAS macro for target dose estimation by reinforced urn processes in phase I clinical trials

The reinforced urn processes (RUPs) approach can estimate the target dose on the basis of the prior distribution function precisely and conveniently without the requirements about the explicit-estimated dose-response curve and the posterior complicated inference. The application of the RUPs approach was not discussed from the perspective of phase I clinical trial in the previous studies which just focused on the theory and methodology. And the modification of the traditional RUPs design should be considered for the purposes of ethnics and efficiency. A SAS macro was designed to explore the appropriate parameter settings according to the simulation outcomes in different situations and apply the RUPs approach for two state processes in phase I clinical trail with the modified RUPs design. The posterior estimation can be obtained precisely and efficiently with application of SAS program following the appropriate workflow and determination rule which were described in the example.