Fast connected-component labeling

Labeling of connected components in a binary image is one of the most fundamental operations in pattern recognition: labeling is required whenever a computer needs to recognize objects (connected components) in a binary image. This paper presents a fast two-scan algorithm for labeling of connected components in binary images. We propose an efficient procedure for assigning provisional labels to object pixels and checking label equivalence. Our algorithm is very simple in principle, easy to implement, and suitable for hardware and parallel implementation. We show the correctness of our algorithm, analyze its complexity, and compare it with other labeling algorithms. Experimental results demonstrated that our algorithm is superior to conventional labeling algorithms.     

Evaluation of connected-component labeling algorithms for distributed-memory systems

Connected component labeling is a key step in a wide-range of applications, such as community detection in social networks and coherent structure identification in massively-parallel scientific simulations. There have been several distributed-memory connected component algorithms described in literature; however, little has been done regarding their scalability analysis. Theoretical and experimental results are presented for five algorithms: three that are direct implementations of previous approaches, one that is an implementation of a previous approach that is optimized to reduce communication, and one that is a novel approach based on graph contraction. Under weak scaling and for certain classes of graphs, the graph contraction algorithm scales consistently better than the four other algorithms. Furthermore, it uses significantly less memory than two of the alternative methods and is of the same order in terms of memory as the other two.     

一种基于信念状态压缩的实时POMDP算法

针对求解部分可观察马尔可夫决策过程（POMDP）信念状态空间是NP难问题．提出一种信念状态空间压缩（BSSC）算法．将信念状态空间的高维压缩到低维,利用动态贝叶斯网络对状态转移函数、观察函数和报酬函数进行压缩。降低求解规模,达到实时决策的目的．对比实验表明,所提出的算法可以快速求解最优策略和最优值函数．     

Interactive algorithms for improving incomplete linguistic preference relations based on consistency measures

Incomplete linguistic preference relations (InLPRs) are generally inevitable in group decision making problems due to several reasons. Two vital issues of InLPRs are the consistency and the estimation of missing entries. The initial InLPR may be not consistent, which means that some of its entries do not reflect the real opinions of the experts accurately. Thus, there are deviations between some initial provided values and real opinions. Therefore, it is valuable to elicit the providers to realize and repair the deviations. In this paper, we discuss the consistency and the completing algorithms of InLPRs by interacting with the experts. Servicing as the minimum condition of consistency, the weak consistency of InLPRs is defined and a weak consistency reaching algorithm is designed to guarantee the logical correctness of InLPRs. Then two distinct completing algorithms are presented to estimate the missing entries. The former not only estimates all possible linguistic terms and represents them by the extended hesitant fuzzy linguistic terms sets but also keeps weak consistency during the computing procedures. The later can automatically revise the existing entries using the new opinions supplemented by the experts during interactions. All the proposed algorithms interact with the experts to elicit and mine their actual opinions more accurately. A real case study is also presented to clarify the advantages of our proposal. Moreover, these algorithms can serve as assistant tools for the experts to present their preferences.     

基于改进遗传算法的最小生成树算法

以图论和改进遗传算法为基础,提出了一种求最小生成树的遗传算法。该算法采用二进制表示最小树问题,并设计出相应的适应度函数、算子以及几种控制策略,以提高执行速度和进化效率。传统算法一次只能得到一个候选解。用该算法对其求解,可以在较短的时间内以较高的概率获得多个候选解。应用实例表明该算法优于传统算法。     

笛卡尔网格生成中的相交算法

研究三维空间笛卡尔网格与三角形面网格的相交判断算法—-ADT(Alternating Digital Tree)算法和KD (K-Dimensional)树算法,分别用球体模型和飞机模型对ADT与KD树进行分析,比较二者快速相交判断时的查找效率.结果表明:同一种模型下ADT比KD树平衡,树的深度小;ADT的查找效率明显比KD树高;影响KD树查找时间的主要因素是查找次数.整体来看,在快速相交判断中采用ADT性能更高.     

Decision trees and genetic algorithms for condition monitoring forecasting of aircraft air conditioning

Unscheduled maintenance of aircraft can cause significant costs. The machine needs to be repaired before it can operate again. Thus it is desirable to have concepts and methods to prevent unscheduled maintenance. This paper proposes a method for forecasting the condition of aircraft air conditioning system based on observed past data. Forecasting is done in a point by point way, by iterating the algorithm. The proposed method uses decision trees to find and learn patterns in past data and use these patterns to select the best forecasting method to forecast future data points. Forecasting a data point is based on selecting the best applicable approximation method. The selection is done by calculating different features/attributes of the time series and then evaluating the decision tree. A genetic algorithm is used to find the best feature set for the given problem to increase the forecasting performance. The experiments show a good forecasting ability even when the function is disturbed by noise.     

基于云模型的决策表规则约简

通过对决策表的转换,将规则映射成为云向量,用云向量数字特征间的相似度来度量决策表规则间的等价关系.基于此,提出了基于云模型的决策表规则约简算法,不仅解决了粗集基于严格属性匹配的等价关系不能区分相似关系,也克服了基于模糊集等价关系依赖专家先验知识、对属性值随机性分布考虑不足的缺点.实验说明了该算法的高效性.     

Optimizing airline passenger prescreening systems with Bayesian decision models

The Transportation Security Agency provides airline security in the United States using a variety of measures including a computer based passenger prescreening system. This paper develops Bayesian decision models of two prescreening systems: one that places ticketed passengers into two classifications (fly and no-fly), and a three classification system that includes potential flight. Using a parameterized cost structure, and the expected monetary value decision criteria, this paper develops optimal levels of undesirable personal characteristics that should place people into the various categories. The models are explored from both the government perspective and the passenger's perspective.     

依存关系上的中文名词性谓词识别研究

首次实现了一个基于依存关系的中文名词性谓词识别平台,作为语义角色标注的前提,谓词识别的结果直接影响语义角色标注的性能。使用两种方法进行实验：一种是基于传统的特征向量的方法在Chinese Nombank 的转换语料上进行了系统实验,对各种词法特征、结构特征及其组合进行了测试,标准语料上F1值达到89.65,自动语料上达到81.27。另一种是使用树核的方法进行探索性实验,在标准语料和自动语料上分别得到84.62和80.93的F1值。     

Genetic algorithms for video segmentation

The current paper presents a new genetic algorithm (GA)-based method for video segmentation. The proposed method is specifically designed to enhance the computational efficiency and quality of the segmentation results compared to standard GAs. The segmentation is performed by chromosomes that independently evolve using distributed genetic algorithms (DGAs). However, unlike conventional DGAs, the chromosomes are initiated using the segmentation results of the previous frame, instead of random values. Thereafter, only unstable chromosomes corresponding to moving object parts are evolved by crossover and mutation. As such, these mechanisms allow for effective solution space exploration and exploitation, thereby improving the performance of the proposed method in terms of speed and segmentation quality. These advantages were confirmed based on experiments where the proposed method was successfully applied to both synthetic and natural video sequences.     

Optimizing self-organizing overlay network using evolutionary approach

Self-organizing overlay networks are emerging as next generation networks capable of adapting to the needs of applications at runtime. Applications performance significantly depends on the structure and behaviors of the underlying self-organizing overlay networks. To achieve desired performance, not only the logical overlay topology but also the behaviors of nodes in this overlay network need to be optimized. Moreover, self-organizing overlay networks are extremely dynamic, unreliable and often large-scale. It is therefore important to design new optimizing approaches to meet these challenges. In this paper, we present an evolutionary optimization methodology for self-organizing overlay network. The optimizations of self-organizing overlay networks are modeled as dynamically evolutionary process, in which the nodes interact with each other, change their internal structures and alter their external links to improve the collective performance. To design appropriate fitness functions and rules that guides the direction of the evolution, overlay network can reach a stable state with desired global application performance eventually. Such a methodology leads to our distributed algorithms for proximity-based overlay topology maintenance and Peer-to-Peer living media streaming, in which every node in the overlay network rewires their behaviors and connectivity according to local available information and embedded rules. These algorithms are shown to perform well using simulations.     

Genetic algorithms and neural networks: optimizing connections and connectivity

Genetic algorithms are a robust adaptive optimization method based on biological principles. A population of strings representing possible problem solutions is maintained. Search proceeds by recombining strings in the population. The theoretical foundations of genetic algorithms are based on the notion that selective reproduction and recombination of binary strings changes the sampling rate of hyperplanes in the search space so as to reflect the average fitness of strings that reside in any particular hyperplane. Thus, genetic algorithms need not search along the contours of the function being optimized and tend not to become trapped in local minima. This paper is an overview of several different experiments applying genetic algorithms to neural network problems. These problems include

1. (1) optimizing the weighted connections in feed-forward neural networks using both binary and real-valued representations, and

2. (2) using a genetic algorithm to discover novel architectures in the form of connectivity patterns for neural networks that learn using error propagation.

Future applications in neural network optimization in which genetic algorithm can perhaps play a significant role are also presented.     

RTI时间管理的一种新型动态尺度标注算法

时间管理算法是决定RTI时间管理服务性能的关键。为解决时间管理中常用的Frederick算法计算GALT(greatest available logical time)时可能出现死锁以及仿真系统消息延迟等问题,定义了联邦成员尺度的概念,并结合时间前瞻量的动态调整思想,提出了动态尺度标注算法,并对其进行了分析。分析表明该算法不但减少了消息的延迟时间,还解决了时间管理中的死锁问题。通过在制导弹药飞行视景仿真系统上测试,表明算法改善了仿真效果,提高了仿真系统性能。     

A new approximation algorithm for labeling points with circle pairs

We study the NP-hard problem of labeling points with maximum-radius circle pairs: given n point sites in the plane, find a placement for 2n interior-disjoint uniform circles, such that each site touches two circles and the circle radius is maximized. We present a new approximation algorithm for this problem that runs in time and O(n) space and achieves an approximation factor of (≈1.486+ε), which improves the previous best bound of 1.491+ε.     

Enhancing the effectiveness of Ant Colony Decision Tree algorithms by co-learning

Data mining and visualization techniques for high-dimensional data provide helpful information to substantially augment decision-making. Optimization techniques provide a way to efficiently search for these solutions. ACO applied to data mining tasks – a decision tree construction – is one of these methods and the focus of this paper. The Ant Colony Decision Tree (ACDT) approach generates solutions efficiently and effectively but scales poorly to large problems. This article merges the methods that have been developed for better construction of decision trees by ants. The ACDT approach is tested in the context of the bi-criteria evaluation function by focusing on two problems: the size of the decision trees and the accuracy of classification obtained during ACDT performance. This approach is tested in co-learning mechanism, it means agents–ants can interact during the construction decision trees via pheromone values. This cooperation is a chance of getting better results. The proposed methodology of analysis of ACDT is tested in a number of well-known benchmark data sets from the UCI Machine Learning Repository. The empirical results clearly show that the ACDT algorithm creates good solutions which are located in the Pareto front. The software that implements the ACDT algorithm used to generate the results of this study can be downloaded freely from http://www.acdtalgorithm.com.     

An efficient Dijkstra-like labeling method for computing shortest odd/even paths

We show how the problem of determining shortest paths of even or odd length between two specified vertices in a graph G = (V, E) can be reduced to the problem of finding a shortest alternating path with respect to a specific matching in a related graph H. This problem can be solved by a Dijkstra-like labeling procedure of complexity O(|V|²) respectively O(|E|log|V|). Interpreting this procedure appropriately the method can then be applied directly on the given graph G.     

An assessment of the effectiveness of decision tree methods for land cover classification

Choice of a classification algorithm is generally based upon a number of factors, among which are availability of software, ease of use, and performance, measured here by overall classification accuracy. The maximum likelihood (ML) procedure is, for many users, the algorithm of choice because of its ready availability and the fact that it does not require an extended training process. Artificial neural networks (ANNs) are now widely used by researchers, but their operational applications are hindered by the need for the user to specify the configuration of the network architecture and to provide values for a number of parameters, both of which affect performance. The ANN also requires an extended training phase.In the past few years, the use of decision trees (DTs) to classify remotely sensed data has increased. Proponents of the method claim that it has a number of advantages over the ML and ANN algorithms. The DT is computationally fast, make no statistical assumptions, and can handle data that are represented on different measurement scales. Software to implement DTs is readily available over the Internet. Pruning of DTs can make them smaller and more easily interpretable, while the use of boosting techniques can improve performance.In this study, separate test and training data sets from two different geographical areas and two different sensors—multispectral Landsat ETM+ and hyperspectral DAIS—are used to evaluate the performance of univariate and multivariate DTs for land cover classification. Factors considered are: the effects of variations in training data set size and of the dimensionality of the feature space, together with the impact of boosting, attribute selection measures, and pruning. The level of classification accuracy achieved by the DT is compared to results from back-propagating ANN and the ML classifiers. Our results indicate that the performance of the univariate DT is acceptably good in comparison with that of other classifiers, except with high-dimensional data. Classification accuracy increases linearly with training data set size to a limit of 300 pixels per class in this case. Multivariate DTs do not appear to perform better than univariate DTs. While boosting produces an increase in classification accuracy of between 3% and 6%, the use of attribute selection methods does not appear to be justified in terms of accuracy increases. However, neither the univariate DT nor the multivariate DT performed as well as the ANN or ML classifiers with high-dimensional data.     

ID3算法的一种改进算法

决策树是归纳学习和数据挖掘的重要方法,通常用来形成分类器和预测模型。ID3算法是决策树中的核心算法,文章针对ID3算法倾向于取值较多的属性的缺点,引进用户兴趣度对ID3算法作了改进,并通过实验对改进前后的算法进行了比较,实验表明,改进后的算法是有效的。     

Parallel breadth-first search algorithms for trees and graphs

Parallel Breadth-First Search (BFS) algorithms for ordered trees and graphs on a shared memory model of a Single Instruction-stream Multiple Data-stream computer are proposed. The parallel BFS algorithm for trees computes the BFS rank of eachnode of an ordered tree consisting of n nodes in time of 0(β log n) when 0(n ^1+1/β) processors are used, β being an integer greater than or equal to 2. The parallel BFS algorithm for graphs produces Breadth-First Spanning Trees (BFSTs) of a directedgraph G having n nodes in time 0(log d.log n) using 0(n ³) processors, where d is the diameter of G If G is a strongly connected graph or a connected undirected graph the BFS algorithm produces n BFSTs, each BFST having a different start node.