Distributed average computation for multiple time‐varying signals with output measurements

This paper studies the distributed average computation problem for multiple time‐varying signals with bounded inputs. Based only on relative output measurements, a pair of continuous algorithms with, respectively, static and adaptive coupling strengths are designed and utilized. From the concept of boundary layer approach, the proposed continuous algorithm with static coupling strengths can asymptotically obtain the average value of the multiple reference signals without chattering phenomenon. Furthermore, for the case of algorithms with adaptive coupling strengths, the calculation errors are uniformly ultimately bounded and exponentially converge to a small adjustable bounded set. Finally, a simulation example is presented to show the validity of the theoretical results. Copyright © 2015 John Wiley & Sons, Ltd.     

Fuzzy decision fusion of complementary experts based on evolutionary cepstral coefficients for phoneme recognition

Optimal representation of acoustic features is an ongoing challenge in automatic speech recognition research. As an initial step toward this purpose, optimization of filterbanks for the cepstral coefficient using evolutionary optimization methods is proposed in some approaches. However, the large number of optimization parameters required by a filterbank makes it difficult to guarantee that an individual optimized filterbank can provide the best representation for phoneme classification. Moreover, in many cases, a number of potential solutions are obtained. Each solution presents discrimination between specific groups of phonemes. In other words, each filterbank has its own particular advantage. Therefore, the aggregation of the discriminative information provided by filterbanks is demanding challenging task. In this study, the optimization of a number of complementary filterbanks is considered to provide a different representation of speech signals for phoneme classification using the hidden Markov model (HMM). Fuzzy information fusion is used to aggregate the decisions provided by HMMs. Fuzzy theory can effectively handle the uncertainties of classifiers trained with different representations of speech data. In this study, the output of the HMM classifiers of each expert is fused using a fuzzy decision fusion scheme. The decision fusion employed a global and local confidence measurement to formulate the reliability of each classifier based on both the global and local context when making overall decisions. Experiments were conducted based on clean and noisy phonetic samples. The proposed method outperformed conventional Mel frequency cepstral coefficients under both conditions in terms of overall phoneme classification accuracy. The fuzzy fusion scheme was shown to be capable of the aggregation of complementary information provided by each filterbank.     

概率模型检测技术在机器人路径规划中的应用

在机器人路径规划的实际应用中,针对机器人移动行为可能会受到外界环境影响的难题,提出了一种采用概率模型检测技术进行路径规划的新方法。首先,分析环境中的主要影响因素,将机器人的移动行为看作一个不确定事件,构建马尔可夫决策过程(MDP)模型。然后,采用概率计算树逻辑(PCTL)公式描述模型属性,表达机器人复杂多样的目标任务。最后,运用PRISM平台对模型进行分析和验证,得到满足属性的全局优化路径和定量数据。仿真结果表明,上述方法不仅能够保障机器人在障碍物环境中无碰撞移动,而且可以避开环境相对复杂的区域,保证机器人以最大概率完成任务。对比试验证明上述方法的正确性和有效性。     

多表达式程序设计的新型评估方法

多表达式程序设计(MEP)是应用十分广泛的自动程序设计方法。从MEP的染色体表示规则及种群演化方式来看,每个染色体中的任何基因都有可能多次被当前或其它后续种群中的其他基因引用,从而造成重复计算,耗费大量时空资源。由此提出并实现了一种新型评估方法,该新型评估方法在不改变传统MEP的染色体表示规则和种群演化方式的情况下,能够准确有效地识别演化过程中所有被重复引用的基因,从而避免了大量重复计算,显著提高了演化效率。     

Exploring creative intersections: Ernest Edmonds and his time-based generative art

This article focuses on the experience of British artist Ernest Edmonds and the influences that have informed his art practice in the past forty years. How are these influences connected? The article has an historical focus. It develops the themes within Edmonds's art and shows his connections with the ‘Systems’ artists and their forebears. In particular, the article concentrates on the encounter of Edmonds with artistic thinking about systems and process in the broad sense, as well as digital and interactive work developed from the early 1980s. As the article will demonstrate, a passion for colour, time and structure, the encounter with a number of artists inspired by Constructivism in the early 1980s, and the educational context in which Edmonds has worked from the late 1960s until the present, have offered great opportunities of interdisciplinary exchanges and ideas that had a profound impact upon the nature of his art. These influences have enabled Edmonds to explore new constructs in art through the use of technology that are a constant stimulus in his creative research, both as an artist and as an academic.     

压力隧洞设计与结构计算研究进展

针对目前压力隧洞设计现实和设计规范均不是很完善、结构计算缺乏可靠方法以及对内外水荷载作用下围岩衬砌联合承载机理研究还不深入的现状,对压力隧洞设计准则、结构计算以及内外水荷载作用下围岩衬砌联合承载机理这三个方面的国内外研究进展进行了综述,并指出存在的不足.基于研究现状的总结分析,提出了需要进一步深入研究的课题.研究成果可为压力隧洞的设计和结构计算提供参考.     

一种综合多特征的句子相似度计算方法

提出了一种综合多特征的句子相似度计算方法,该方法分别从句子的句法、词汇语义、词形三个方面来度量句子的相似度,最后将这三个方面加权整合计算得到句子的相似度。本方法综合考虑了句子的深层和表层信息,并对句子进行了词汇扩展,从而使句子相似度计算更加准确。     

基于前缀计算的序列比对研究

为了提高Smith-Waterman算法处理速度,同时不改变原算法的准确性,本文利用前缀计算方法修改Smith-Waterman算法,并进行OpenMP并行化。在多核机上测试表明,前缀计算的共享并行化使得该局部比对算法的速度得到很大的提高。     

GPU并行加速的均值偏移算法

为克服mean shift算法计算复杂度高、运行速度慢的缺点,提出一种基于GPU的快速mean shift算法.首先使用k-means算法对图像像素进行预分类,之后在预分类、下采样后缩小的数据集上进行mean shift聚类,以有效地降低算法复杂度.此外,借助GPU的通用计算功能对k-means和mean shift分别进行并行了处理.实验结果表明,通过对图像进行预处理,有效地提高了几何模板查找在强噪声、低信噪比图像中的识别率;同时,改进后的mean shift算法的运行速度提高了近40倍,满足了高速机器视觉检测的实时性要求.     

Jacobi交上的配对计算

目前已知的配对计算都是在椭圆曲线的平面模型下实现的,比如Weierstrass型曲线、Ed-wards曲线和Jacobi四次曲线。本文第一次讨论空间曲线上配对的具体计算。密码学中所关心的空间曲线主要是三维空间中的二次曲面的交,它与Edwards曲线、Jacobi四次型都有极其紧密的联系,因而研究二次曲面交上的算术与配对将促进我们对Edwards曲线、Jacobi四次曲线上的相关特性的理解。为了讨论的简洁,我们将主要分析Jacobi交,但我们的结果基本上可以类推到其他的二次曲面交上去。我们分析了Jacobi交上的几何特性,构造了Jacobi交上的有效可计算同态,并在此基础上给出了Jacobi交上配对的具体计算。