基于OWL-S的服务发现算法研究

Web服务的大量涌现对服务发现提出了挑战,UDDI上基于关键字和简单分类的服务发现机制已经不能很好地满足需要。本文在分析现有相关研究的基础上提出了基于OWL-S的We b服务发现改进方法,通过引入OWL-S的过程模型实现了对服务中原子服务和子服务的匹配,提高了查准率;提出了构建依赖关系网的算法,通过对图中结点标注颜色的方法实实现了对满足用户需求的服务过程的发现。算法不仅返回满足用户需要的匹配结果,还可以在匹配不成功时给用户建议得到全部要求的额外输入。     

基于多粒度服务库的SOA参考模型及其应用

针对传统的面向服务架构中服务管理、发现、组合等效率较低的问题,通过分析面向服务架构中的粒度思想,提出一种基于多粒度服务库的面向服务架构参考模型.首先,讨论多粒度服务库的概念,并给出服务库的粒化算法;其次,对所提模型的总体结构特别是粒度计算引擎进行了详细的介绍,阐述粒度计算引擎的主要功能,给出一个基于多粒度服务库的服务发现算法;然后,提出一种基于多粒度服务库的服务组合算法,同时说明SOA参考模型的作用;最后,结合具体应用实例验证该模型的有效性.     

基于序列挖掘的Web服务推荐研究

提出了一种全新的服务发现方法.其核心思想是通过从以往服务组合序列中发现高频率出现的组合序列集, 然后利用该序列集进行服务推荐.给出了服务推荐系统框架;对序列模式算法进行了改进,以适应连续序列挖掘的需求, 并描述了服务推荐的匹配算法;最后通过在一个原型系统的性能测试证明服务推荐方法是可行和有效的.     

一种基于语义Web服务的匹配算法研究

传统的 Web 服务发现只是简单的基于适的 Web 服务成为主要问题.在服务使用 OWL-S 描述的前提下,服务匹配通常认为是本体概念的匹配,给出了一种基于语义 Web 服务的匹配算法,通过服务发布者提供的服务和服务消费者寻求的服务的关系来进行匹配算法研究,提出了相应的服务匹配技术,详细介绍了服务请求时进行服务匹配使用的匹配算法,达到服务发布者和请求者可以实现语义的匹配.最后对服务匹配算法进行了实现,提高了服务的查准率和查全率.     

一种基于信任的多Agent供应链协商关系网

Agent技术已被广泛用于供应链伙伴的协商。协商前如何选择协商Agent对提高协商效率有着重要的意义。提出了一种基于信任的多Agent协商关系网及其形成和更新算法,并对该协商关系网的特点进行了深入的研究。模拟表明,提出的协商关系网能有效地促进Agent之间的协商,提高协商成功率。     

基于柯西烟花算法的大规模服务组合优化

随着Web服务的日益丰富,如何动态地从大量候选服务集中选择出整体性能高的组合服务已成为服务组合优化领域亟待解决的问题。为稳定、高效地解决大数据集下的服务组合问题,提出一种柯西烟花算法。该算法在搜索过程中引入柯西变异算子增强了算法的全局搜索能力;采用精英候选策略有效降低了算法的时间开销。实验结果验证了该算法在处理大规模服务组合优化问题时的可行性和稳定性。     

一种基于语义的服务搜索与匹配方法

Web服务是构建基于面向服务架构(SOA)的应用系统所需的主流分布式计算技术.作者此前提出了一种语义增强服务库,支持基于语义的服务注册、搜索和组装.讨论该服务库中所使用的一种基于语义的服务搜索与匹配方法.该算法不仅考虑了功能性属性而且引入了非功能性属性来进行服务匹配.算法利用所建立的本体对服务进行语义标注,并通过对OWL-S进行扩展,增加了非功能性属性支持.论文给出了服务的定义,详细介绍了服务搜索与匹配的步骤,并以一个服务的搜索为例对算法的使用进行了说明.最后把本文提出的算法与基于关键字匹配的服务搜索算法进行了比较分析,结果表明本文所提出的算法搜索更加准确.论文最后简介了基于该算法的语义库的基本架构及功能.     

基于过程划分技术的服务组合拆分方法

针对集中式服务组合内的中心控制器瓶颈问题,提出一种基于过程划分技术的非集中式服务组合构建方法。首先,利用类型有向图对业务过程进行建模;然后,基于图转换的方法提出分组算法,根据分组算法对过程模型进行拆分;最后,根据拆分后的结果来构建非集中式服务组合。经实验测试,分组算法对模型1的耗时与单线程算法相比降低了21.4%,构建的非集中式服务组合拥有更低响应时间和更高吞吐量。实验结果表明,所提方法能有效地拆分服务组合中的业务过程,所构建的非集中式服务组合能提升服务性能。     

面向结构化布局设计的产品布局多色模型

为实现产品原理方案向结构化布局映射,提出产品布局元层次关系网及产品布局多色模型.首先在布局模块及其属性关系网的基础上构建布局元层次关系网;然后应用多色集合的个人颜色、统一颜色,给出产品布局元层次关系网的数学描述,形成产品布局多色模型,并提出推理算法;再利用个人颜色、统一颜色之间的围道布尔矩阵推理原理,进行产品结构布局设计过程中功能—运动分配—布局模块之间映射过程的形式化描述.应用该模型进行结构化布局设计,可有效地支持产品结构布局设计的公理化;最后在Pro/E平台中利用VC.net 2003工具进行二次开发,并以数控机床结构布局设计为例验证了文中模型和设计方法的可行性和有效性.     

一种基于语义Web服务的匹配算法研究

传统的Web服务发现只是简单的基于关键字的语法匹配,查询得到的服务往往不是用户想要的,如何正确定位合适的Web服务成为主要问题。在服务使用OWL-S描述的前提下,服务匹配通常认为是本体概念的匹配,给出了一种基于语义Web服务的匹配算法,通过服务发布者提供的服务和服务消费者寻求的服务的关系来进行匹配算法研究,提出了相应的服务匹配技术,详细介绍了服务请求时进行服务匹配使用的匹配算法,达到服务发布者和请求者可以实现语义的匹配。最后对服务匹配算法进行了实现,提高了服务的查准率和查全率。     

基于SRN/HMM的非特定人连续手语识别系统

手语识别是通过计算机提供一种有效而准确的机制将手语翻译成文本或语音。目前最新发展水平的手语识别系统在实际应用中应解决非特定人连续手语问题。提出一种将连续手语识别分解成各弧立词识别的分治方法，用于非特定人连续手语识别。把精简循环网（simple recurrent network,简称SRN）作为连续手语的段边界检测器，把SRN分段结果作为隐马可夫模型（hidden Markov models,简称HMM）框架中的状态输入，在HMM框架里使用网格Viterbi算法搜索出一条最佳手语词路径。实验结果表明，该方法的识别效果比单纯使用HMM要好。     

Model-based synthesis of plucked string instruments by using aclass of scattering recurrent networks

A physical modeling method for electronic music synthesis of plucked-string tones by using recurrent networks is proposed. A scattering recurrent network (SRN) which is used to analyze string dynamics is built based on the physics of acoustic strings. The measured vibration of a plucked string is employed as the training data for the supervised learning of the SRN. After the network is well trained, it can be regarded as the virtual model for the measured string and used to generate tones which can be very close to those generated by its acoustic counterpart. The "virtual string" corresponding to the SRN can respond to different "plucks" just like a real string, which is impossible using traditional synthesis techniques such as frequency modulation and wavetable. The simulation of modeling a cello "A"-string demonstrates some encouraging results of the new music synthesis technique. Some aspects of modeling and synthesis procedures are also discussed.     

A Chinese sign language recognition system based on SOFM/SRN/HMM

In sign language recognition (SLR), the major challenges now are developing methods that solve signer-independent continuous sign problems. In this paper, SOFM/HMM is first presented for modeling signer-independent isolated signs. The proposed method uses the self-organizing feature maps (SOFM) as different signers' feature extractor for continuous hidden Markov models (HMM) so as to transform input signs into significant and low-dimensional representations that can be well modeled by the emission probabilities of HMM. Based on these isolated sign models, a SOFM/SRN/HMM model is then proposed for signer-independent continuous SLR. This model applies the improved simple recurrent network (SRN) to segment continuous sign language in terms of transformed SOFM representations, and the outputs of SRN are taken as the HMM states in which the lattice Viterbi algorithm is employed to search the best matched word sequence. Experimental results demonstrate that the proposed system has better performance compared with conventional HMM system and obtains a word recognition rate of 82.9% over a 5113-sign vocabulary and an accuracy of 86.3% for signer-independent continuous SLR.     

无回路网络中最短路问题的高效算法

无回路网络是一类重要的网络,给出在无回路网络中求解最短路树形图和任意顶点对间最短路的高效算法。该算法将顶点进行重新编号,结合广度优先探索法,从源顶点出发依次搜索每个顶点的所有出弧,并在弧的头部进行权值变换操作,可以得到最短路树形图和任意顶点对间最短路,算法复杂度分别为O(m)和O(m(n-m1/2))。该算法思想简便、复杂度低、易于操作。 关键词：     

Beyond feedforward models trained by backpropagation: a practical training tool for a more efficient universal approximator.

Cellular simultaneous recurrent neural network (SRN) has been shown to be a function approximator more powerful than the multilayer perceptron (MLP). This means that the complexity of MLP would be prohibitively large for some problems while SRN could realize the desired mapping with acceptable computational constraints. The speed of training of complex recurrent networks is crucial to their successful application. This work improves the previous results by training the network with extended Kalman filter (EKF). We implemented a generic cellular SRN (CSRN) and applied it for solving two challenging problems: 2-D maze navigation and a subset of the connectedness problem. The speed of convergence has been improved by several orders of magnitude in comparison with the earlier results in the case of maze navigation, and superior generalization has been demonstrated in the case of connectedness. The implications of this improvements are discussed.     

Simple recurrent networks learn context-free and context-sensitive languages by counting

It has been shown that if a recurrent neural network (RNN) learns to process a regular language, one can extract a finite-state machine (FSM) by treating regions of phase-space as FSM states. However, it has also been shown that one can construct an RNN to implement Turing machines by using RNN dynamics as counters. But how does a network learn languages that require counting? Rodriguez, Wiles, and Elman (1999) showed that a simple recurrent network (SRN) can learn to process a simple context-free language (CFL) by counting up and down. This article extends that to show a range of language tasks in which an SRN develops solutions that not only count but also copy and store counting information. In one case, the network stores information like an explicit storage mechanism. In other cases, the network stores information more indirectly in trajectories that are sensitive to slight displacements that depend on context. In this sense, an SRN can learn analog computation as a set of interdependent counters. This demonstrates how SRNs may be an alternative psychological model of language or sequence processing.     

Comparing Simple Recurrent Networks and n-Grams in a Large Corpus

The increased availability of text corpora and the growth of connectionism has stimulated a renewed interest in probabilistic models of language processing in computational linguistics and psycholinguistics. The Simple Recurrent Network (SRN) is an important connectionist model because it has the potential to learn temporal dependencies of unspecified length. In addition, many computational questions about the SRN's ability to learn dependencies between individual items extend to other models. This paper will report on experiments with an SRN trained on a large corpus and examine the ability of the network to learn bigrams, trigrams, etc., as a function of the size of the corpus. The performance is evaluated by an information theoretic measure of prediction (or guess) ranking and output vector entropy. With enough training and hidden units the SRN shows the ability to learn 5 and 6-gram dependencies, although learning an n-gram is contingent on its frequency and the relative frequency of other n-grams. In some cases, the network will learn relatively low frequency deep dependencies before relatively high frequency short ones if the deep dependencies do not require representational shifts in hidden unit space.     

基于移动Ad hoc网络的服务管理研究

本文基于移动Ad hoc网络的广播特性、分簇的普遍性等特点,在分析分布式服务发现协议GSD等的基础上,提出了新的服务发现机制,该机制考虑了各节点处理能力、存储能力以及所提供的服务种类等不可能完全相同的特性,克服了GSD同等对待所有节点的缺欠;另外,文中提出了基于优先级和两步资源预约的服务调度和QoS分配方式,依据服务所请求的QoS级别和资源情况来确定合适的端到端QoS级别,保证资源的充分利用,并给出了算法的一个实际应用的例子,该算法考虑了资源的抢占和两步方式的资源预约,比普通的确定QoS级别的算法具有更高的预约成功率,而比仅依据优先级的算法具有更高的资源利用率。     

Training Winner-Take-All Simultaneous Recurrent Neural Networks

The winner-take-all (WTA) network is useful in database management, very large scale integration (VLSI) design, and digital processing. The synthesis procedure of WTA on single-layer fully connected architecture with sigmoid transfer function is still not fully explored. We discuss the use of simultaneous recurrent networks (SRNs) trained by Kalman filter algorithms for the task of finding the maximum among N numbers. The simulation demonstrates the effectiveness of our training approach under conditions of a shared-weight SRN architecture. A more general SRN also succeeds in solving a real classification application on car engine data     

服务覆盖网中保证QoS的服务路由算法

针对服务覆盖网的服务路径选择问题,提出改进的KCP算法。在后继结点选择时,使用节点延迟、剩余资源、链路带宽、延迟等表征节点和链路性能的多项指标,组合成综合性能函数;引入调节因子兼顾负载均衡和节点复用率。仿真试验表明,此算法能够有效地进行服务路由选择,并且达到良好的负载均衡和高节点复用率。