一种多级运动目标检测算法

为了提高Javed等提出的运动目标三级检测算法的稳定性,文中对其处理方法做出改进;在像素级处理阶段,采用每一个像素点及其邻域组成的集合作为特征矢量来描述图像,对YUV格式的彩色图像的不同颜色分量分别建立混合高斯模型,得到背景模型后,计算出Sobel边缘检测得到的边缘点的统计分布;在区域级处理阶段,将彩色图像分割与背景建模结合起来,得到具有精确边缘的运动目标,并利用边缘信息消除干扰目标;实验结果表明,即使在前景纹理、颜色比较一致且与背景对比不是很明显的情况下,改进后的方法也能完整地检测出运动目标.     

Phase change materials in the ternary system NH4Cl+CaCl2+H2O

Solubility isotherms of the ternary system (NH₄Cl+CaCl₂+H₂O) were elaborately determined at T= (273.15 and 298.15) K by using the isothermal method. In the equilibrium phase diagram, there are two solubility branches corresponding to the solid phases CaCl₂⋅6H₂O and NH₄Cl. Invariant point compositions are 36.32 wt% CaCl₂ and 3.4 wt% NH₄Cl at 273.15 K, and 45.86 wt% CaCl₂ and 5.22 wt% NH₄Cl at 298.15 K. A Pitzer-Simonson-Clegg thermodynamic model was applied to represent the thermodynamic properties of this ternary system and to construct a partial phase diagram of the ternary system at temperatures between (273.15 and 323.15) K. It was found in the predicted solubility phase diagram that the double salt 2NH₄Cl⋅CaCl₂⋅3H₂O, found by other authors at (323.1 and 348.1) K, will disappear at temperatures below 298.15 K. Besides, it was found that there are two peritectic points in the ternary system with peritectic temperatures at 299.65 K and 298.15 K, and the former peritectic point falls just on the line between the composition points of NH₄Cl and CaCl₂⋅6H₂O. According to phase rule, a solution made of this point will begin to crystallize at 299.65 K and end at 298 K and therefore can be acted as a “pseudo eutectic” phase change material (PCM). A heat storing and releasing experiment of 50 grams of the PCM was carried out, obtaining a satisfying result.     

基于移动平均线组合和支持向量机的股市趋势研究

证券投资是一种高风险、高收益的理财方式,而对市场运行趋势的良好把握有助于投资者回避风险、把握机会。提出基于多条移动平均线斜率组合和支持向量机算法,判断当前市场运行趋势,从而采取相应的投资策略。模拟实验证明,该方法取得了良好的效果。     

H.264帧内预测模式选择快速算法的研究

提出一种适用于H.264帧内预测的快速算法,利用相邻像素间的梯度筛选预测模式来避免不必要的预测模式计算。实验结果表明:用全I帧编码,该算法在图像质量和输出码率基本不变的情况下,编码时间大约节省了60%。     

A computer-enabled solution procedure for food wholesalers’ distribution decision in cities with a circular transportation infrastructure

This paper addresses the food distribution decision from a wholesaler's perspective, as the wholesalers are often faced with thorny issues such as when, where, how to deliver products at the lowest costs to best satisfy the retailers’ changing needs. This research relies on a distribution problem encountered at Northern Grocery Company in Beijing as an example to develop a two-stage solution procedure and subsequent computer-enabled programs for identifying efficient vehicle routing alternatives. The solution process, which integrates heuristic search algorithms and an integer programming model, can be applied to a wide range of distribution problems with specified route durations and a circular transportation network structure as exemplified by City of Beijing.     

A new feature selection method for Gaussian mixture clustering

With the wide applications of Gaussian mixture clustering, e.g., in semantic video classification [H. Luo, J. Fan, J. Xiao, X. Zhu, Semantic principal video shot classification via mixture Gaussian, in: Proceedings of the 2003 International Conference on Multimedia and Expo, vol. 2, 2003, pp. 189-192], it is a nontrivial task to select the useful features in Gaussian mixture clustering without class labels. This paper, therefore, proposes a new feature selection method, through which not only the most relevant features are identified, but the redundant features are also eliminated so that the smallest relevant feature subset can be found. We integrate this method with our recently proposed Gaussian mixture clustering approach, namely rival penalized expectation-maximization (RPEM) algorithm [Y.M. Cheung, A rival penalized EM algorithm towards maximizing weighted likelihood for density mixture clustering with automatic model selection, in: Proceedings of the 17th International Conference on Pattern Recognition, 2004, pp. 633-636; Y.M. Cheung, Maximum weighted likelihood via rival penalized EM for density mixture clustering with automatic model selection, IEEE Trans. Knowl. Data Eng. 17(6) (2005) 750-761], which is able to determine the number of components (i.e., the model order selection) in a Gaussian mixture automatically. Subsequently, the data clustering, model selection, and the feature selection are all performed in a single learning process. Experimental results have shown the efficacy of the proposed approach.     

A modified particle swarm optimization via particle visual modeling analysis

A particle is treated as a whole individual in all researches on particle swarm optimization (PSO) currently, these are not concerned with the information of every particle’s dimensional vector. A visual modeling method describing particle’s dimensional vector behavior is presented in this paper. Based on the analysis of visual modeling, the reason for premature convergence and diversity loss in PSO is explained, and a new modified algorithm is proposed to ensure the rational flight of every particle’s dimensional component. Meanwhile, two parameters of particle-distribution-degree and particle-dimension-distance are introduced into the proposed algorithm in order to avoid premature convergence. Simulation results of the new PSO algorithm show that it has a better ability of finding the global optimum, and still keeps a rapid convergence as with the standard PSO.     

Recognizing control chart patterns with neural network and numerical fitting

Control chart has been widely used to determine whether the state of machining process is stable or not, and pattern recognition technology is often used to automatically judge the changing modes of control chart. It is because that the abnormal patterns of a control chart can reveal the potential problem of machining quality. In order to improve the recognition rate and efficiency of control chart patterns, a neural network-numerical fitting (NN-NF) model is proposed to recognize different control chart patterns. A back propagation (BP) network is first used to recognize control chart patterns preliminarily. And then, numerical fitting method is adopted to estimate the parameters and specific types of the patterns, which is different from the traditional neural network-based control chart pattern recognition methods. Based on this, Monte Carlo simulation is used to generate training and testing data samples. The results of simulated experiment show that training time of this NN-NF model can be reduced. At the same time, the recognition rate can also be improved. At last, a real example is also provided to illustrate the NN-NF model.     

An intelligent fault diagnosis method based on wavelet packer analysis and hybrid support vector machines

In this paper, a new intelligent method for the fault diagnosis of the rotating machinery is proposed based on wavelet packet analysis (WPA) and hybrid support machine (hybrid SVM). In fault diagnosis for mechanical systems, information about stability and mutability can be further acquired through WPA from original signal. The faulty vibration signals obtained from a rotating machinery are decomposed by WPA via Dmeyer wavelet. A new multi-class fault diagnosis algorithm based on 1-v-r SVM approach is proposed and applied to rotating machinery. The extracted features are applied to hybrid SVM for estimating fault type. Compared to conventional back-propagation network (BPN), the superiority of the hybrid SVM method is shown in the success of fault diagnosis. The test results of hybrid SVM demonstrate that the applying of energy criterion to vibration signals after WPA is a very powerful and reliable method and hence estimating fault type on rotating machinery accurately and quickly.