(Weak) Dual hyper <Emphasis Type="Italic">K</Emphasis>-ideals

In this note by considering the notion of (weak) dual hyper K-ideal, we obtain some related results. After that we determine the relationships between (weak) dual hyper K-ideals and (weak) hyper K-ideals. Finally, we give a characterization of hyper K-algebras of order 3 or 4 based on the (weak) dual hyper K-ideals.     

一种基于K-Means局部最优性的高效聚类算法

K-Means聚类算法只能保证收敛到局部最优,从而导致聚类结果对初始代表点的选择非常敏感.许多研究工作都着力于降低这种敏感性.然而,K-Means的局部最优和结果敏感性却构成了K-MeanSCAN聚类算法的基础.K-MeanSCAN算法对数据集进行多次采样和K-Means预聚类以产生多组不同的聚类结果,来自不同聚类结果的子簇之间必然会存在交集.算法的核心思想是,利用这些交集构造出关于子簇的加权连通图,并根据连通性合并子簇.理论和实验证明,K-MeanScan算法可以在很大程度上提高聚类结果的质量和算法的效率.     

Continuous K-Nearest Neighbor Query for Moving Objects with Uncertain Velocity

One of the most important queries in spatio-temporal databases that aim at managing moving objects efficiently is the continuous K-nearest neighbor (CKNN) query. A CKNN query is to retrieve the K-nearest neighbors (KNNs) of a moving user at each time instant within a user-given time interval [t _s , t _e ]. In this paper, we investigate how to process a CKNN query efficiently. Different from the previous related works, our work relieves the past assumption, that an object moves with a fixed velocity, by allowing that the velocity of the object can vary within a known range. Due to the introduction of this uncertainty on the velocity of each object, processing a CKNN query becomes much more complicated. We will discuss the complications incurred by this uncertainty and propose a cost-effective P² KNN algorithm to find the objects that could be the KNNs at each time instant within the given query time interval. Besides, a probability-based model is designed to quantify the possibility of each object being one of the KNNs. Comprehensive experiments demonstrate the efficiency and the effectiveness of the proposed approach.

On the strict logic foundation of fuzzy reasoning

This paper focuses on the logic foundation of fuzzy reasoning. At first, a new complete first-order fuzzy predicate calculus system K^* corresponding to the formal system L^* is built. Based on the many-sort system K_ms^* corresponding to K^*, the triple I methods of FMP and FMT for fuzzy reasoning and their consistency are formalized, thus fuzzy reasoning is put completely and rigorously into the logic framework of fuzzy logic.The author is indebted to anonymous referee for his useful comments which have helped to improve the paper.     

Partial match queries in relaxed multidimensional search trees

Partial match queries arise frequently in the context of large databases, where each record contains a distinct multidimensional key, that is, the key of each record is aK-tuple of values. The components of a key are called thecoordinates orattributes of the key. In a partial match query we specify the value ofs attributes, 0<s<K, and leave the remainingK —s attributes unspecified. The goal is to retrieve all the records in the database that match the specified attributes. In this paper we present several results about the average performance and variance of partial matches in relaxedK-dimensional trees (search trees and digital tries). These data structures are variants of the well knownK d-trees andK d-tries. In relaxed trees the sequence of attributes used to guide a query is explicitly stored at the nodes of the tree and randomly generated and, in general, will be different for different search paths. In the standard variants, the sequence of attributes that guides a query examines the attributes in a cyclic fashion, fixed and identical for all search paths. We show that the probabilistic analysis of the relaxed multidimensional trees is very similar to that of standardK d-trees andK d-tries, and also to the analysis of quadtrees. In fact, besides the average cost and variance of partial match in relaxedK d-trees andK d-tries, we also obtain the variance of partial matches in two-dimensional quadtrees. We also compute the average cost of partial matches in other relaxed multidimensional digital tries, namely, relaxedK d-Patricia and relaxedK d-digital search trees. This research was supported by Acciones Integradas Hispano-Austríacas HU1997-0016 (Austrian-Spanish Scientific Exchange Program). The first author was also supported by ESPRIT LTR 20244 (ALCOM IT), CICYT TIC97-1475-CE, DGES PB95-0787 (KOALA), and CIRIT 1997SGR-00366 (SGR). The second author was also supported by the Austrian Research Society (FWF) under Project P12599-MAT. Online publication October 13, 2000.     

Geno-mathematical identification of the multi-layer perceptron

In this paper, we will focus on the use of the three-layer backpropagation network in vector-valued time series estimation problems. The neural network provides a framework for noncomplex calculations to solve the estimation problem, yet the search for optimal or even feasible neural networks for stochastic processes is both time consuming and uncertain. The backpropagation algorithm—written in strict ANSI C—has been implemented as a standalone support library for the genetic hybrid algorithm (GHA) running on any sequential or parallel main frame computer. In order to cope with ill-conditioned time series problems, we extended the original backpropagation algorithm to a K nearest neighbors algorithm (K-NARX), where the number K is determined genetically along with a set of key parameters. In the K-NARX algorithm, the terminal solution at instant t can be used as a starting point for the next t, which tends to stabilize the optimization process when dealing with autocorrelated time series vectors. This possibility has proved to be especially useful in difficult time series problems. Following the prevailing research directions, we use a genetic algorithm to determine optimal parameterizations for the network, including the lag structure for the nonlinear vector time series system, the net structure with one or two hidden layers and the corresponding number of nodes, type of activation function (currently the standard logistic sigmoid, a bipolar transformation, the hyperbolic tangent, an exponential function and the sine function), the type of minimization algorithm, the number K of nearest neighbors in the K-NARX procedure, the initial value of the Levenberg–Marquardt damping parameter and the value of the neural learning (stabilization) coefficient α. We have focused on a flexible structure allowing addition of, e.g., new minimization algorithms and activation functions in the future. We demonstrate the power of the genetically trimmed K-NARX algorithm on a representative data set.     

Intuitionistic fuzzy left k-ideals of semirings

We introduce the notion of intuitionistic fuzzy left k-ideals of semirings and investigate their properties and connections with left k-ideals of the corresponding semirings. Next we give some important characterizations of intuitionistic fuzzy left k-ideals of different type and describe various methods of constructions of such intuitionistic fuzzy sets. Finally, we propose some natural classification of intuitionistic fuzzy left k-ideals. The research work was supported by PUCIT.     

一种基于彩色编码技术的基序发现算法

从DNA序列中发现基序是生物计算中的一个重要问题,序列条数K=20包含基序用例的序列条数k=16的(l,d)-(K-k)问题(记作(l,d)-(20-16)问题)是目前生物学家十分关注的基序发现问题.针对该问题提出了一种基于彩色编码技术的SDA(sample-driven algorithm)搜索算法--彩色编码基序搜索算法(color coding motif finding algorithm,简称CCMF算法).它利用彩色编码技术将该问题转化为(l,d)-(16-16)问题,再采用分治算法和分支定界法来求解.在解决将(l,d)-(20-16)问题转化为(l,d)-(16-16)问题时,CCMF算法利用彩色编码技术将4 845个组合降低到403个着色,这将极大地提高算法的整体运行效率.使用模拟数据和生物数据进行测试的结果表明,CCMF算法能够快速发现所有(l,d)-(20-16)问题的基序模型和基序用例,具有优于其他算法的综合性能评价,能够用于真实的基序发现问题.同时,通过修改着色方案,CCMF算法可以用于求解一般的(l,d)-(K-k)问题,其中,k＜K.     

A note on Lindenmayer systems,Szilard languages,spectra, and equivalence

We investigate the Szilard languages and the spectra of Lindenmayer systems through the generalization of Lindenmayer systems toK-iteration grammars and context-sensitiveK-iteration grammars. Various decidability and undecidability results are presented with respect to the evaluation of Szilard languages and spectra for particularK-iteration grammars. Further, two different definitions of the Szilard and spectral equivalence of twoK-iteration grammars are investigated.Work carried out under a National Research Council of Canada Grant No. A-7700.     

基于熵加权K-means全局信息聚类的高光谱图像分类

目的 高光谱图像波段数目巨大，导致在解译及分类过程中出现“维数灾难”的现象。针对该问题，在K-means聚类算法基础上，考虑各个波段对不同聚类的重要程度，同时顾及类间信息，提出一种基于熵加权K-means全局信息聚类的高光谱图像分类算法。方法 首先，引入波段权重，用来刻画各个波段对不同聚类的重要程度，并定义熵信息测度表达该权重。其次，为避免局部最优聚类，引入类间距离测度实现全局最优聚类。最后，将上述两类测度引入K-means聚类目标函数，通过最小化目标函数得到最优分类结果。结果 为了验证提出的高光谱图像分类方法的有效性，对Salinas高光谱图像和Pavia University高光谱图像标准图中的地物类别根据其光谱反射率差异程度进行合并，将合并后的标准图作为新的标准分类图。分别采用本文算法和传统K-means算法对Salinas高光谱图像和Pavia University高光谱图像进行实验，并定性、定量地评价和分析了实验结果。对于图像中合并后的地物类别，光谱反射率差异程度大，从视觉上看，本文算法较传统K-means算法有更好的分类结果；从分类精度看，本文算法的总精度分别为92.20%和82.96%， K-means算法的总精度分别为83.39%和67.06%，较K-means算法增长8.81%和15.9%。结论 提出一种基于熵加权K-means全局信息聚类的高光谱图像分类算法，实验结果表明，本文算法对高光谱图像中具有不同光谱反射率差异程度的各类地物目标均能取得很好的分类结果。     

An Efficient Parallel Strategy for ComputingK-Terminal Reliability and Finding Most Vital Edges in 2-Trees and Partial 2-Trees

In this paper, we first develop a parallel algorithm for computingK-terminal reliability, denoted byR(G_K), in 2-trees. Based on this result, we can also computeR(G_K) in partial 2-trees using a method that transforms, in parallel, a given partial 2-tree into a 2-tree. Finally, we solve the problem of finding most vital edges with respect toK-terminal reliability in partial 2-trees. Our algorithms takeO(log n) time withC(m, n) processors on a CRCW PRAM, whereC(m, n) is the number of processors required to find the connected components of a graph withmedges andnvertices in logarithmic time.     

A discrete-time dynamic K-winners-take-all neural circuit

A discrete-time mathematical model of K-winners-take-all (KWTA) neural circuit that can quickly identify the K-winning from N neurons, where 1K<N, whose input signals are larger than that of remaining N−K neurons, is given and analyzed. A functional block scheme of the circuit is presented. For N competitors, such circuit is composed of N feedforward and one feedback hard-limiting neurons that are used to determine the dynamic shift of input signals. The circuit has low computational and hardware implementation complexity, high speed of signal processing, can process signals of any finite range, possesses signal order preserving property and does not require resetting and corresponding supervisory circuit that additionally increases a speed of signal processing.     

Platoon架构下VANETs车间通信过程及性能分析

智能车辆编组platoon的稳定运行需要车辆间实时可靠的信息传输来保证.针对应用专用短程通信（DSRC）技术来实现车载自组织网路（VANETs）车间通信的platoon架构,提出了一种车间通信网络性能的分析方法,分别对platoon组内智能车辆间通信和多个platoons组间通信的过程进行了分析.该方法采用长度有限的M/G/1/K排队模型分析数据包到达媒体访问控制（MAC）层的排队过程,得到不同网络负载下缓冲区排队状态的平稳分布;并且在缓冲区可能处于空闲状态时,利用Markov模型分析方法得到不同车辆位置的车间通信性能.研究结果表明：网络数据流量、信道条件、MAC层缓冲区排队过程、争用信道退避过程以及platoon参数对车间通信的包传输时延和包丢失概率等网络性能有显著影响,数值分析结果验证了专用短程通信技术下的车间通信具有较小的信息传输延时,能够满足platoon稳定运行的要求.     

一种云环境下的大数据Top-K查询方法

Top-K查询在搜索引擎、电子商务等领域有着广泛的应用.Top-K查询从海量数据中返回最符合用户需求的前K个结果,主要目的是消除信息过载带来的负面影响.大数据背景下的Top-K查询,给数据管理和分析等方面带来新的挑战.结合MapReduce的特点,从数据划分、数据筛选等方面对云环境下的大数据Top-K查询问题进行深入研究.实验结果表明,该方法具有良好的性能和扩展性.     

一种半监督K均值多关系数据聚类算法

提出了一种半监督K均值多关系数据聚类算法.该算法在K均值聚类算法的基础上扩展了其初始类簇的选择方法和对象相似性度量方法,以用于多关系数据的半监督学习.为了获取高性能,该算法在聚类过程中充分利用了标记数据、对象属性及各种关系信息.多关系数据库Movie上的实验结果验证了该算法的有效性.     

Yule’s Characteristic K Revisited

The measure of lexical repetition constitutes one of the variables used to determine the lexical richness of literary texts, a value further employed in authorship attribution studies. Although most of the constants for lexical richness actually depend on text length, Yule’s characteristic is considered to be highly reliable for being text length independent. It is not the aim of this paper questioning the validity of K to measure the lexical repeat-rate, nor to evaluate its usefulness in authorship studies, but to review the most accurate procedure to calculate its value in the light of the lack of standardization found in the specific literature. At the same time, the peculiar calculation of Yule’s K by TACT is explained. Our study suggests that standardization will certainly help improve the studies where K is employed.     

A Generalization of Local Fuzzy Structures

The class of bounded residuated lattice ordered monoids Rl-monoids) contains as proper subclasses the class of pseudo BL-algebras (and consequently those of pseudo MV-algebras, BL-algebras and MV-algebras) and of Heyting algebras. In the paper we introduce and investigate local bounded Rl-monoids which generalize local algebras from the above mentioned classes of fuzzy structures. Moreover, we study and characterize perfect bounded Rl-monoids.     

Harmony K-means algorithm for document clustering

Fast and high quality document clustering is a crucial task in organizing information, search engine results, enhancing web crawling, and information retrieval or filtering. Recent studies have shown that the most commonly used partition-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm can generate a local optimal solution. In this paper we propose a novel Harmony K-means Algorithm (HKA) that deals with document clustering based on Harmony Search (HS) optimization method. It is proved by means of finite Markov chain theory that the HKA converges to the global optimum. To demonstrate the effectiveness and speed of HKA, we have applied HKA algorithms on some standard datasets. We also compare the HKA with other meta-heuristic and model-based document clustering approaches. Experimental results reveal that the HKA algorithm converges to the best known optimum faster than other methods and the quality of clusters are comparable.     

New result on chromaticity of K4-homeomorphic graphs

A K₄-homeomorphic graph or simply K₄-homeomorph, denoted by K₄(a, b, c, d, e, f), is the graph obtained when the six edges of a complete graph with four vertices (K₄) are subdivided into a, b, c, d, e, f segments, respectively. Each subdivided edge is called a path and its length is the number of resulting segments. The result in this paper completes the study on the chromaticity of K₄-homeomorphs with exactly two paths of length 2.     

Efficient algorithms for counting parameterized list H-colorings

We study the fixed parameter tractability of the counting version of a parameterization of the restrictive list H-coloring problem. The parameterization is defined by fixing the number of preimages of a subset C of the vertices in H through a weight assignment K on C. We show the fixed parameter tractability of counting the number of list (H,C,K)-colorings, for the case in which (H,C,K) is simple. We introduce the concept of compactor and a new algorithmic technique, compactor enumeration, that allow us to design fixed parameter algorithms for parameterized counting problems.