Explicit solution of relative entropy weighted control

We consider the minimization over probability measures of the expected value of a random variable, regularized by relative entropy with respect to a given probability distribution. In the general setting we provide a complete characterization of the situations in which a finite optimal value exists and the situations in which a minimizing probability distribution exists. Specializing to the case where the underlying probability distribution is Wiener measure, we characterize finite relative entropy changes of measure in terms of square integrability of the corresponding change of drift. For the optimal change of measure for the relative entropy weighted optimization, an expression involving the Malliavin derivative of the cost random variable is derived. The theory is illustrated by its application to several examples, including the case where the cost variable is the maximum of a standard Brownian motion over a finite time horizon. For this example we obtain an exact optimal drift, as well as an approximation of the optimal drift through a Monte-Carlo algorithm.     

Entropy of fluidized bed—a measure of particles mixing

The paper presents an attempt to define the physical entropy of the dense phase of a fluidized bed, based on liquid-like properties of fluidized systems. The quantity U was used as the analogy of temperature in classical thermodynamics. The obtained expression for physical entropy was compared to the correlation suggested for the Kolmogorov entropy in bubbling bed.     

A global energy optimization framework for 2.1D sketch extraction from monocular images

The 2.1D sketch is a layered image representation, which assigns a partial depth ordering of over-segmented regions in a monocular image. This paper presents a global optimization framework for inferring the 2.1D sketch from a monocular image. Our method only uses over-segmented image regions (i.e., superpixels) as input, without any information of objects in the image, since (1) segmenting objects in images is a difficult problem on its own and (2) the objective of our proposed method is to be generic as an initial module useful for downstream high-level vision tasks. This paper formulates the inference of the 2.1D sketch using a global energy optimization framework. The proposed energy function consists of two components: (1) one is defined based on the local partial ordering relations (i.e., figure-ground) between two adjacent over-segmented regions, which captures the marginal information of the global partial depth ordering and (2) the other is defined based on the same depth layer relations among all the over-segmented regions, which groups regions of the same object to account for the over-segmentation issues. A hybrid evolution algorithm is utilized to minimize the global energy function efficiently. In experiments, we evaluated our method on a test data set containing 100 diverse real images from Berkeley segmentation data set (BSDS500) with the annotated ground truth. Experimental results show that our method can infer the 2.1D sketch with high accuracy.     

GLCM based extraction of flame image texture features and KPCA-GLVQ recognition method for rotary kiln combustion working conditions

According to the pulverized coal combustion flame image texture features of the rotary-kiln oxide pellets sintering process,a combustion working condition recognition method based on the generalized learning vector(GLVQ) neural network is proposed.Firstly,the numerical flame image is analyzed to extract texture features,such as energy,entropy and inertia,based on grey-level co-occurrence matrix(GLCM) to provide qualitative information on the changes in the visual appearance of the flame.Then the kernel principal component analysis(KPCA) method is adopted to deduct the input vector with high dimensionality so as to reduce the GLVQ target dimension and network scale greatly.Finally,the GLVQ neural network is trained by using the normalized texture feature data.The test results show that the proposed KPCA-GLVQ classifer has an excellent performance on training speed and correct recognition rate,and it meets the requirement for real-time combustion working condition recognition for the rotary kiln process.     

试谈高可用性计算机实训室的配置与管理

基于计算机实训教学的需求,重点探讨高可用性计算机实训室的搭建要求、配置特点与管理策略。     

中国古代陶瓷雕塑的美学特征

中华民族是个古老的、有着悠久历史和灿烂文明的伟大民族,远在八千年前,中国的文明在世界就已初露曙光。漫步在漫长的历史长河边,流连于丰富的博物馆展厅里,对我国悠久的历史和灿烂的文明作一次美的巡礼该是使人感到无比的欣慰和自豪的了。美学特征乃艺术的属性,总结它不是为了停留于前人的成果上,而是为了新的创造和新的开拓。     

An intelligent stock trading system using comprehensive features

The aim of this study is to predict automatic trading decisions in stock markets. Comprehensive features (CF) for predicting future trend are very difficult to generate in a complex environment, especially in stock markets. According to related work, the relevant stock information can help investors formulate objects that may result in better profits. With this in mind, we present a framework of an intelligent stock trading system using comprehensive features (ISTSCF) to predict future stock trading decisions. The ISTSCF consists of stock information extraction, prediction model learning and stock trading decision. We apply three different methods to generate comprehensive features, including sentiment analysis (SA) that provides sensitive market events from stock news articles for sentiment indices (SI), technical analysis (TA) that yields effective trading rules based on trading information on the stock exchange for technical indices (TI), as well as the trend-based segmentation method (TBSM) that raises trading decisions from stock price for trading signals (TS). Experiments on the Taiwan stock market show that the results of employing comprehensive features are significantly better than traditional methods using numeric features alone (without textual sentiment features).     

基于灰熵关联分析的流水车间多目标调度优化及算法实现

求解流水车间多目标调度优化问题及算法适应度值分配问题, 结合灰色关联度分析方法及信息熵理论提出灰熵关联度适应值分配策略, 利用灰关联系数结合熵值权重计算适应度值, 以灰熵关联度值引导启发式算法进化. 将该方法应用到差分算法及遗传算法中解决三目标流水车间调度问题. 实验表明: 灰熵关联度适应值分配策略能够解决该问题, 可以得到分布均匀的Pareto 前端; 同时, 基于此策略的差分算法得到的解好于遗传算法的解.     

一种基于案例的多准则排序方法

针对带有间接偏好信息的多准则决策问题, 首先利用加性效用函数理论提出一种排序方法, 该方法通过构建一个简单的优化模型, 得到与间接偏好信息相容的各评价值的效用; 然后, 利用线性插值方法计算出剩下方案各评价值的效用, 进而得到所有方案的综合效用及排序; 最后, 通过实例验证了该方法的有效性和可行性.