一种基于语言评价信息的多指标群聚类方法

针对具有语言评价信息的多指标群聚类分析问题,提出一种基于二元语义信息处理的最大树聚类方法.首先描述了具有语言评价信息的多指标群聚类问题,并介绍了近年来国际上最新发展的二元语义概念及其算子;然后基于二元语义信息处理的方法,将专家给出的语言评价信息进行"量化"集结,并依据传统的最大树聚类分析方法的基本思路,给出了解决基于语言评价信息的最大树聚类方法的计算步骤;最后通过一个算例说明了所提出方法的有效性.     

Group decision making based on multiple types of linguistic preference relations

In this paper, we investigate group decision making problems with multiple types of linguistic preference relations. The paper has two parts with similar structures. In the first part, we transform the uncertain additive linguistic preference relations into the expected additive linguistic preference relations, and present a procedure for group decision making based on multiple types of additive linguistic preference relations. By using the deviation measures between additive linguistic preference relations, we give some straightforward formulas to determine the weights of decision makers, and propose a method to reach consensus among the individual preferences and the group’s opinion. In the second part, we extend the above results to group decision making based on multiple types of multiplicative linguistic preference relations, and finally, a practical example is given to illustrate the application of the results.     

Cognitive diversity and team performance in a complex multiple task environment

This article examines the multiple effects of cognitive diversity in teams operating complex human-machine-systems. The study employed a PC-based multiple-task environment, called the Cabin Air Management System, which models a process control task in the operational context of a spacecraft's life support system. Two types of cognitive diversity were examined: system understanding and team specialization. System understanding referred to the depth of understanding team members were given during training (low-level procedure-oriented vs. high level knowledge-oriented training). Team specialization referred to the degree to which knowledge about system fault scenarios was distributed between team members (specialized vs. non-specialized). A total of 72 participants took part in the study. After having received 4.5 h of training on an individual basis, participants completed a 1-h experimental session, in which they worked in two-person teams on a series of fault scenarios of varying difficulty. Measures were taken of primary and secondary task performance, system intervention and information sampling strategies, system knowledge, subjective operator state, communication patterns and conflict. The results provided evidence for the benefits of cognitive diversity with regard to system understanding. This manifested itself in better primary task performance and more efficient manual system control. No advantages were found for cognitive diversity with regard to specialization. There was no effect of cognitive diversity on intra-team conflict, with conflict levels generally being very low. The article concludes with a discussion of the implications of the findings for the engineering of cognitive diversity in teams operating complex human-machine-systems.     

Cognitive diversity and team performance in a complex multiple task environment

This article examines the multiple effects of cognitive diversity in teams operating complex human-machine-systems. The study employed a PC-based multiple-task environment, called the Cabin Air Management System, which models a process control task in the operational context of a spacecraft's life support system. Two types of cognitive diversity were examined: system understanding and team specialization. System understanding referred to the depth of understanding team members were given during training (low-level procedure-oriented vs. high level knowledge-oriented training). Team specialization referred to the degree to which knowledge about system fault scenarios was distributed between team members (specialized vs. non-specialized). A total of 72 participants took part in the study. After having received 4.5 h of training on an individual basis, participants completed a 1-h experimental session, in which they worked in two-person teams on a series of fault scenarios of varying difficulty. Measures were taken of primary and secondary task performance, system intervention and information sampling strategies, system knowledge, subjective operator state, communication patterns and conflict. The results provided evidence for the benefits of cognitive diversity with regard to system understanding. This manifested itself in better primary task performance and more efficient manual system control. No advantages were found for cognitive diversity with regard to specialization. There was no effect of cognitive diversity on intra-team conflict, with conflict levels generally being very low. The article concludes with a discussion of the implications of the findings for the engineering of cognitive diversity in teams operating complex human-machine-systems.     

基于云发生算法的犹豫语言多准则决策方法

犹豫语言集比传统语言变量能更好地表示不确定决策信息,但因其具有非均匀和离散的特点,使用时难以找到对应的解析词运算规则和比较方法。对此,利用云模型的云滴生成算法,通过数值模拟方法解决犹豫语言信息的运算与比较问题,提出了相应的多准则决策方法,并通过算例分析了其有效性。     

基于多QoS约束的数据网格任务调度算法研究

针对数据网格环境下的多QoS约束任务调度问题,提出了一种基于最早完成时间与QoS相识度的数据网格任务调度算法(data grid task scheduling algorithm based on Min-min and QoS similarity,MS-GTSA).该算法将最早完成时间与S-GTSA算法相结合,在任务调度过程中,选取任务QoS约束与资源QoS匹配最佳,且完成时间最早的一项优先进行调度.在满足任务最佳QoS匹配的同时,时间跨度得到了较大的改善.仿真结果表明,该算法有效降低了任务调度的时间跨度,在综合性能上较S-GTSA算法有所提高.     

基于多种云环境的任务调度算法综述

任务调度算法是云计算资源分配部署的核心方法。针对当前云计算发展面临的任务需求和数据量指数级增长的问题,重点对任务调度算法进行了系统的梳理和归纳,以云环境为分类依据,研究分析了单云、联盟云、混合云、多云四类调度算法。在单云环境中,从传统启发式、元启发式以及混合式任务调度算法角度进行阐述。在联盟云、混合云、多云环境中,从工作流和独立任务调度算法角度进行阐述。通过比较,总结了现有算法的优点、缺点以及优化性能,并形成结论性意见和开放性问题,为未来对容器云、数据云以及兼顾资源分配与任务调度算法的研究奠定基础。     

Emotional speaker recognition in real life conditions using multiple descriptors and i-vector speaker modeling technique

Multimedia Tools and Applications - Emotional speaker recognition under real life conditions becomes an urgent need for several applications. This paper proposes a novel approach using multiple...     

一种基于二元语义信息处理的多属性群决策方法

为了解决评价信息为语言信息的多属性群决策问题,结合VIKOR方法,提出一种基于二元语义信息处理的多属性群决策方法．该方法使用二元语义信息集结算子获得决策群组的决策信息,通过最大化群效用和最小化个体遗憾来获得决策者满意的折衷方案．该方法计算简单,便于理解,可有效避免信息的丢失和扭曲,并可克服理想解方法不能反映出各方案与正负理想解的接近程度的不足．最后,算例计算结果表明了该方法的有效性．     

Large-scale group decision making with multiple stakeholders based on probabilistic linguistic preference relation

A large number of stakeholders take part in the process of decision making, namely, large-scale group decision making (LGDM) problems. Every stakeholder utilises a linguistic preference relation (LPR) to represent her/his preference information for alternatives. Then, a probabilistic LPR (PLPR) is established to represent the group preference. However, some stakeholders may only provide partial preference information about the alternatives. Thus, a PLPR with incomplete probabilities can be used to manage LGDM problems in complex environments. Based on the defined expected multiplicative consistency of PLPR, a probability computation model is established by mathematical programming to derive the missing probabilities of PLPR. In addition, an iterative algorithm to improve the consistency is proposed to obtain the PLPR with satisfactory consistency. Finally, a real-world investment decision-making problem with multiple stakeholders is solved to demonstrate the effectiveness of the proposed method.     

基于顺序扩展一致性包算法的多无人机分布式任务分配

以异构多无人机协同执行复杂的耦合多任务为背景,提出一种求解分布式任务分配问题非死锁的顺序扩展一致性包算法.首先,建立考虑任务载荷资源、任务时序、威胁区等约束条件的时序多任务分配模型;其次,对一致性包算法的任务包构建过程和冲突消解规则进行扩展,并设计一种基于有向图深度优先搜索的方法进行任务方案的死锁检测和修正,以实现无冲突和无死锁的任务分配;然后,将关联任务之间的时序约束转化为软时间窗约束,利用顺序分层的策略进行求解;最后,为了提高任务分配结果的可靠性,采用Dubins曲线路径将航路规划耦合到任务分配中.仿真实验表明,所提出的算法能够快速有效地求解异构多无人机分布式耦合多任务分配问题,具备良好的最优性和时效性.     

基于云模型具有语言评价信息的多属性群决策研究

针对多属性群决策中具有语言评价信息偏好的表示与集结的关键问题．研究了基于云模型的决策专家个体偏好表示、偏好集结和方案优选方法．首先采用云模型表示决策者给出的自然语言评价信息,而属性和决策者权重大小则用云的语气运算表示;然后用浮动云进行偏好集结,根据云模型的相对距离进行方案的排序和优选．此方法可充分表达评价语言的模糊性和随机性,具有较大的客观性．     

An efficient approach to understanding and predicting the effects of multiple task characteristics on performance

Abstract

In ergonomics there is often a need to identify and predict the separate effects of multiple factors on performance. A cost-effective fractional factorial approach to understanding the relationship between task characteristics and task performance is presented. The method has been shown to provide sufficient independent variability to reveal and predict the effects of task characteristics on performance in two domains. The five steps outlined are: selection of performance measure, task characteristic identification, task design for user trials, data collection, regression model development and task characteristic analysis. The approach can be used for furthering knowledge of task performance, theoretical understanding, experimental control and prediction of task performance.

Practitioner Summary: A cost-effective method to identify and predict the separate effects of multiple factors on performance is presented. The five steps allow a better understanding of task factors during the design process.     

Automatic assignment of reviewers in an online peer assessment task based on social interactions

Online peer assessment tasks are very popular and have unique characteristics that improve learning and encourage social interactions in a distance education environment. Unfortunately, social factors have usually been ignored in the process of selecting reviewers for online peer assessment tasks. We hypothesise that this fact could have some influence on the lack of engagement and participation by some learners. For this reason, we propose an approach in which social network analysis techniques, expert criteria, and Bayesian reasoning are applied to select reviewers with the objective of increasing participation in peer review tasks. The approach is divided into two elements. On the one hand, we have developed an influence diagram template that structures a set of proposed social network analysis variables in accordance with expert criteria. This influence diagram template can be easily updated for any course simply by eliciting a minimal set of parameters. On the other hand, we have instantiated the proposed influence diagram template to produce an influence diagram network to quantify the quality of reviewer assignment for an online peer assessment task. In an online experiment, we verified that the consideration of social factors can increase participation in a peer assessment task.     

基于PCA和多约简SVM的多级说话人辨识

提出一种基于主成分分析（PCA）和多约简支持向量机（SVM）的多级说话人辨识方法。首先用PCA对注册说话人进行快速粗判决,再用多约简SVM进行最后决策。此多约简SVM有两个约简步骤,即用PCA和样本选择算法分别减少训练数据的维数和个数。理论分析和实验结果表明：该方法可以大大减少系统的存储量和计算量,提高训练和识别时间,并具有较好的鲁棒性。     

基于扩展VIKOR的不确定语言多属性群决策方法

针对不确定语言多属性群决策问题,提出了一种基于扩展多准则妥协优化解(VIKOR)的决策方法。该方法首先借鉴二元语义表示模型定义了不确定语言短语的运算规则及其距离公式,并使用运算规则集结了专家给出的不确定语言评价信息;然后结合属性的效益型和成本型特征分别定义了其正负理想点,通过将VIKOR法扩展到不确定语言环境来获取方案的折衷排序结果;最后,通过算例分析表明了所提出方法的有效性和优越性。     

基于CFCC和相位信息的鲁棒性说话人辨识

传统的说话人识别中,人们往往认为人耳对相位信息不敏感而忽略了相位信息对语音识别的影响。为了验证相位信息对说话人识别的影响,提出了一种提取相位特征参数的方法。分别在纯净语音和带噪语音条件下,基于高斯混合模型,通过将相位特征参数与耳蜗倒谱系数（CFCC）相结合,研究了相位信息对说话人辨识性能的影响。实验结果标明：相位信息在说话人识别中也有着重要的作用,将其应用于说话人辨识系统,可明显提高系统的识别率和鲁棒性。     

基于PLDA的说话人识别时变鲁棒性问题研究

随着时间的变化,人的声音也会发生变化。这对说话人的识别带来了一定的影响。通过研究发现,说话人识别的性能与时间有着线性变化的规律。传统的说话人识别系统使用GMM-UBM模型并不能很好地学习出线性变化规律。由于概率线性判别分析(PLDA)对于类内与类间有着很好的线性区分度,所以为了解决线性变化的问题,选择概率线性判别分析的方法学习说话人识别中时变的线性变化规律。从实验结果看出,PLDA对于说话人识别的识别鲁棒性具有很好的提升。