基于多特征组合的构音障碍语音识别

为提高构音障碍识别准确率,提出一种基于多特征组合的构音障碍语音识别方法.利用遗传算法进行特征选择,从语音的韵律特征、频谱特征、人耳听觉特征、嗓音质量特征和声道模型特征等5类特征组合成的多特征组合中选择出分类准确率最高的特征子集,通过SVM分类器对选择出的特征进行识别.在Torgo声学和发音数据库对不同的语音刺激类型进行...     

一种基于多分类器融合的人体运动行为识别模型

为了提高基于智能设备的人体日常行为识别的准确率,针对不同智能设备内置加速度传感器获取的三轴加速度信息,提出了一种基于多分类器融合的行为识别MCF(Multiple Classifier Fusion)模型。针对5种日常行为(静止、散步、跑步、上楼及下楼),优选出与每种行为相关度高的特征集,用于训练对每种行为识别效果最佳的5个基分类器,并采用一个融合器对5个基分类器的输出进行融合处理,得到最终行为识别结果。该模型对这5种行为的平均识别准确率和可信度分别达到96.84%和97.41%,能有效进行用户行为识别。     

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。     

基于DTCWT的运动想象脑电特征提取

针对脑电信号采用单一特征识别存在自适应性差和识别率低等问题，提出一种基于双树复小波(DTCWT)的多特征融合的左右手运动想象脑电特征提取方法。对原始脑电信号进行DTCWT变换提取最佳时频段；对所提取的信号频段进行希尔伯特变换与Lempel-Ziv复杂度计算，将得到的时-频域特征与非线性特征组合为特征向量；采用线性判别分析(LDA)完成运动想象任务的分类。实验采用BCI CompetitionⅢ竞赛数据对该方法进行验证，仿真结果表明其识别准确率明显提高，最高可达89.84%。     

高可信度加权的多分类器融合行为识别模型

为了提高基于智能移动设备的人体日常行为识别准确率,提出一种高可信度加权的多分类器融合行为识别模型（MCFM）。针对不同智能设备内置加速度传感器获取的三轴加速度信息,优选出与人体行为相关度高的特征集作为该模型的输入,将决策树、支持向量机以及反向传播（BP）神经网络三个基分类器通过高可信度加权投票算（HRWV）法训练出一个新的融合分类器。实验结果表明,所提出的分类器融合模型能有效提高行为识别的准确率,对静止、散步、跑步、上楼及下楼五种日常行为的平均识别准确率达到94.88%。     

基于平均影响值和支持向量机的小麦病害识别

为了提高小麦病害识别准确率,提出了一种基于平均影响值思想和支持向量机的小麦病害识别方法.首先,使用阈值分割方法对小麦叶片图像进行分割;其次,利用灰度共生矩阵和颜色矩提取病害的纹理和颜色特征;再次,将平均影响值思想应用于支持向量机回归进行特征变量优选;最后,将优选出的特征变量作为支持向量机的输入向量进行识别.实验结果表明,经过特征变量优选,特征变量个数由14个减少到7个,识别准确率达到96.25％,为农田小麦病害识别提供了有效方法.     

基于复合时空特征的人体行为识别方法

为了有效地表征人体行为中的姿势信息和运动信息,提高行为识别算法的准确率,提出一种融合三维方向梯度直方图特征与光流直方图特征的复合时空特征,并利用其进行人体行为识别.首先采用复合时空特征综合描述三维时空局部区域的像素分布和像素变化;然后构建复合时空特征词典,并根据该特征词典完成对人体行为序列特征集合的描述;最后采用主题模型构建人体行为识别算法,对行为序列中提取的复合时空特征进行分类,实现人体行为的识别.实验结果表明:该方法能有效地提高人体行为识别准确率.     

一种改进的GRU-InFCN人体行为识别模型

为了克服传统机器学习方法在采用传感器数据进行人体行为识别领域上识别效果对人工特征选取依赖严重、识别准确率不高等问题,提出一种改进的全卷积神经网络和多层循环神经网络并联的深度学习模型(GRU-InFCN),并对传感器数据特征进行自动提取,实现人体动作的识别。该模型通过多尺度卷积神经网络和双层GRU网络(Gated Recurrent Unit,GRU)分别对传感器数据进行特征提取,将特征矩阵在矩阵维度上进行特征拼接再通过Softmax完成特征分类。实验结果表明,在开源人体行为识别(HAR)数据集上采用该方法进行人体行为识别,准确率达到了97.76%。该模型在取得高准确率的同时,避免了复杂的信号预处理和特征工程。     

基于Hu矩和纹理特征结合的人体异常行为识别

为了提高人体异常行为识别的准确率,采用了一种将多特征结合的异常行为识别算法,主要包括对步行、快跑、慢跑、拳击、双手挥舞、鼓掌六种异常行为进行识别。首先从视频流中提取出人体轮廓,然后从所得的轮廓中提取Hu矩特征与纹理特征。最后通过模板匹配的方法,采用马氏距离度量所需识别的当前行为特征向量与标准模板行为的特征向量之间的相似性,并通过设置相应的阈值判定该行为所属类别。实验证明,该方法比提取单一特征的方法识别率高,且具有一定的实用价值。     

基于多特征和Stacking算法的Android恶意软件检测方法

Android由于其广泛的普及率使得其平台上的恶意软件数量不断增加,针对目前大部分方法采用单一特征和单一算法进行检验,准确率不高的不足,提出了一种基于多特征与Stacking算法的静态检测方法,该方法能够弥补这两方面的不足. 首先使用多种特征信息组成特征向量,并且使用Stacking集成学习算法组合Logistic,SVM,k近邻和CART决策树多个基本算法,再通过训练样本进行学习形成分类器. 实验结果表明,相对于使用单一特征和单一算法其识别准确率得到提高,可达94.05%,该分类器对测试样本拥有较好的识别性能.     

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.