基于单导EEG的高空缺氧所致疲劳的实时检测技术研究

研究了一种基于单导EEG的高空缺氧所致疲劳的实时检测技术.采用小波包分解对所采集的EEG进行预处理,然后选取EEG数据的近似熵和Welch谱分析中30-60Hz频段的能量作为模式识别的特征向量,用Bayes概率统计识别方法进行模式识别以检测高空缺氧所致疲劳.实验结果表明,通过提取的特征量可以很好的区分正常情况和缺氧疲劳情况下的脑电模式(检测正确率高于93%).这种方法为客观、实时检测缺氧所致疲劳提供了可能.     

驾驶疲劳脑电信号的多尺度熵分析

驾驶疲劳是引起交通事故的主要因素之一.为了降低因驾驶疲劳引起的危害,探索检测驾驶疲劳的新方法,根据脑电信号的非线性特征,使用非线性动力学的多尺度熵方法来分析12名受试者处于不同驾驶状态时各导联脑电信号的多尺度熵值.实验结果表明:尽管12名受试者在不同驾驶状态时的熵值不同,但其数值特征是一致的,即非疲劳驾驶状态时的熵值高于疲劳驾驶状态时的熵值.说明多尺度熵可以区分不同的驾驶状态,可以作为检测驾驶疲劳的一个指标.     

基于小波包变换的精神疲劳多参数脑电分析

研究脑力劳动和运动引起的精神疲劳与脑电特征参数之间的相关性,以及这些特征参数在不同状态下的变化规律。通过对两种精神疲劳状态以及不疲劳状态下采集的脑电信号进行小波包分析,提取出脑电各节律并计算脑电对数能量熵,定性分析了各特征参数与不同状态间的关联性。实验结果表明,相较于不疲劳状态而言,前额叶区的脑电各节律相对功率和脑电对数能量熵在两种精神疲劳状态下均有显著变化。因此,前额叶区的脑电各节律相对功率与脑电对数能量熵可以作为衡量精神疲劳的生理指标。     

基于小波分析心理旋转测量认知疲劳

利用心理旋转现象刺激大脑做连续的认知加工,基于小波分析等时距间接测量脑神经递质的涨落,由10级小波系数谱构造出时域特征曲线作为测量结果.研究发现认知能耗的脑神经递质的表征特点是:时域曲线都呈现主峰连副峰再拖尾;主峰的斜率反比(面积正比)于(成人)年龄;副峰的出现预示着疲劳的开始.结论是认知疲劳可测而且相应的认知能耗小波模型具有普适性和特异性.     

小波包熵在脑电信号分析中的应用

为研究不同脑功能状态下脑电动态非线性特征,利用小波包变换的频率划分特性,对非平稳脑电信号进行节律提取,并计算相对小波能量,反映脑电节律间的相对能量关系。结合小波包熵分析脑电在不同大脑功能状态下的脑电复杂程度。实验结果表明,小波包分解能更精确地提取特定的脑电节律,小波包熵可以准确反映大脑活动的复杂程度。本方法也为分析其他非平稳信号提供了一种新的途径。     

基于小波系数特征融合的小鼠癫痫脑电分类

采集癫痫小鼠模型在常态与致癫状态下的脑电信号以研究其癫痫脑电的自动分类。对经过噪声和伪迹消除预处理的脑电信号进行小波变换,获得不同频率子带的小波系数,对脑电信号及与癫痫特征波相关的小波系数提取相应的线性特征（标准差）和非线性特征（样本熵）;基于这些特征及其组合使用支持向量机分类器实现分类。实验发现基于小鼠脑电本身的标准差和样本熵的分类正确率分别为59.10%和58.00%;而融合各相关小波系数的标准差或样本熵,分类正确率分别达到86.60%和88.60%;融合全部相关小波系数的线性和非线性特征后分类正确率为99.80%。这些结果说明基于小波系数特征融合的分类算法性能有显著提升,能有效实现小鼠癫痫脑电的自动分类。     

基于排列熵和CSP融合的脑电信号特征提取

脑电信号(EEG)是一种在医学领域应用非常广泛的生物电信号。单一的特征提取方法不能够多方面表示脑电信号特征,从而会给不同意识任务下运动想象脑电信号的分类带来一定困难。对此,提出一种基于离散小波变换(DWT)、排列熵(PE)和共空间模式算法(CSP)的特征提取方法(DWT-PECSP)。首先,采用db4小波基对原始脑电信号进行3层小波分解,根据左右手运动想象所处的频段重构出包含μ节律(8 Hz-12 Hz)和β节律(18 Hz-26 Hz)的频段信号;然后,分别计算出该频段信号的排列熵值和CSP方差作为特征量,并将这两组特征量进行组合;最后,将组合后的特征量输入到支持向量机(SVM)中进行分类识别。实验结果表明,该算法在2003年脑机接口竞赛的标准数据集(DataSet Ⅲ)分类上获得了较高的分类准确率(91.43%),均高于单一提取排列熵特征的准确率(71.42%)和CSP方差特征的准确率(85.71%)。通过对比近年来其他文献的特征提取方法,验证了DWT-PECSP算法能够更有效地提取运动想象脑电特征。     

二次滑动粗粒化的快速样本熵脑电情感分析

针对传统单一尺度样本熵对脑电信号(EEG)序列特征提取不明显、多尺度熵在粗粒化过程中会遗漏重要信息导致情感分类性能下降以及样本熵算法效率不高的问题,提出了一种基于二次滑动均值粗粒化的多尺度快速样本熵脑电特征提取方法。由于不同情感的脑电信号存在差异性,先采用二次滑动均值粗粒化对脑电信号进行多尺度处理,然后利用快速样本熵算法提取不同时间尺度的样本熵值作为特征向量,结合随机森林(RF)分类模型来识别不同的情感状态。提出的方法对多模态标准情感数据库DEAP进行了研究,发现大脑额区和右脑对情感比较敏感,正性、中性和负性情感在大脑侧额区获得了88.75%的平均分类准确率。实验结果表明,该方法可以有效地提取脑电特征,并且能够保证算法的效率。     

基于排列组合熵的脑电意识任务识别方法的研究

研究基于脑电信号排列组合熵的运动意识任务自动分类方法.求出时变脑电信号所对应的排列组合熵时间序列.它能很好的反映出事件相关去同步(ERD)和事件相关同步(ERS)现象,因此能有效地提取人脑想象左右手运动任务时的特征,最终利用K-近邻法模式分类方法对想象左右手运动任务进行分类决策.对国际脑机接口竞赛相关数据进行测试,最高准确率达到88.57%,最大互信息达到0.42.基于排列组合熵的脑电信号特征,可以作为脑电意识任务的有效分类依据.     

EEG信号识别中可调优化Q因子小波变换的多特征融合算法

EEG信号对脑部疾病诊断具有重要意义, 但其特征选择对信号识别准确率影响较大. 针对这个问题, 本文基于Q因子小波变换, 提出一种可调优化Q因子小波变换融合多维特征的脑电信号识别(Ad-TQWT MF)算法. 该算法首先根据小波分解后的子带信号定义能量香农熵比, 用其作为可调Q因子小波的优化评价标准; 再融合变换后信号的时域, 频域和非线性特征, 通过自适应特征选择方法构建特征子空间; 最后在该特征子空间下对脑电信号进行识别. 在BCI脑电竞赛数据集Dataset III, O3VR, X11b和S4b进行了实验, 实验结果表明: Ad-TQWT MF算法在LDA分类器中精度为89.2%, 81.2%, 83.2%和85.6%, 相比于原Q因子小波变换, 冗余特征减少10%30%, 相较于Haar和Db 4小波精度提高3%5%, 证明了Ad-TQWT MF算法的有效性.     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.