大型呼叫中心话务量预测

为进行呼叫中心的坐席数估计和后续的排班工作,分析了历史话务量数据的特点,总结出影响大型呼叫中心话务量的因素,并用这些影响因素的不同组合来预测话务量,通过结果的对比分析得出相对最优的话务量预测模型.在此模型的基础上分别采用BP神经网络算法和支持向量机算法(LS-SVM)对话务量进行了预测,通过分析和比较结果表明,BP神经网络比支持向量机算法更适合对大型呼叫中心话务量的预测.     

呼叫中心分块回归话务量预测

为获得前瞻性话务量数据,解决呼叫中心坐席安排的问题,实现人力资源合理配置,分析历史话务量特性,提出了基于支持向量机和[K]近邻算法的分块回归（SKBR）话务量预测模型。将话务量按日期类型分为工作日话务量、周末话务量以及节假日话务量,采用不同的模型预测相应的话务量。以某省电力呼叫中心话务量为例,在Matlab平台上进行实验。结果证明,相比SVM模型和改进寻参方法的SVM模型,SKBR模型在预测准确性上有所提升。     

大型呼叫中心排班算法的研究

为了实现对大型呼叫中心系统人力资源的合理配置,从而极大提升呼叫中心生产力并使得运营成本得到最小化,提出了智能排班算法并实现了大型呼叫中心智能排班系统.分析了排班的本质及其所面临的问题,利用外点法抽象除了拟合坐席预测曲线的数学模型,并基于粒子群优化机制对坐席预测结果进行了分段自适应的拟合,最后采用队列轮循法生成了班表.实践结果表明,利用该算法所生成的班表规律高,能很好地适应呼叫中心的需求.     

呼叫中心排班优化模型的研究

呼叫中心排班系统的优化研究是在话务员和坐席资源约束下,根据服务需求时间序列,合理安排资源,以提高呼叫接入服务质量、提高话务员的个性化满意度和降低运营成本的多目标复杂约束的优化问题。根据客服质量与话务员满意度相关的指标和约束,引入了服务水平、排班成本、话务员疲劳度、排班公平性和话务员意愿等因素。基于对满足服务水平的班组数量的预测,依据对外的规划经营和对内的管理调度的区别,将排班决策分为班次设计和班组指派两大具有递进关系的子模型。利用改进型PSO算法和旋转排班法进行求解,并且根据排班结果和评价参数对规划调度参数进行反馈调整。最后通过仿真分析说明了本文方法在兼顾客服满意度和话务员满意度的呼叫中心排班决策系统中有很好的适应性。     

遗传算法优化BP神经网络的混沌时间序列预测

为提高BP神经网络预测模型对混沌时间序列的预测精度,将改进的遗传算法和BP神经网络结合,提出了一种基于改进遗传算法优化BP神经网络的混沌时间序列预测方法。利用改进的遗传算法优化BP神经网络的权值和阈值,训练BP神经网络预测模型求得最优解。将该模型应用到几个典型的非线性系统进行预测仿真,验证了该算法的有效性,与BP神经网络预测模型的预测结果进行了比较,仿真结果表明该方法对混沌时间序列具有更好的非线性拟合能力和更高的预测精度。     

CPSO算法优化神经网络重构的网络流量预测

为了提高网络流量的预测精度,提出了一种混沌粒子群算法优化相空间重构和神经网络的网络流量预测模型(CPSO-BPNN)。利用混沌粒子群算法对BP神经网络初始参数、延迟时间、嵌入维数进行优化,根据延迟时间、嵌入维数对网络流量数据进行重构,BP神经网络根据初始参数进行训练建立网络流量预测模型,通过仿真实验对模型性能进行测试。结果表明,CPSO-BPNN可以准确描述网络流量的复杂变化趋势,提高了网络流量的预测精度。     

模糊神经网络在肺癌诊断中的应用

目的:综合运用模糊数学和神经网络知识构建一个模糊神经网络模型,用于肺癌诊断,提高诊断率.方法:以实际肺癌诊断病例中的一部分为训练样本,首先利用隶属函数对样本数据进行模糊化处理,再输入基于BP算法的神经网络,对网络进行训练.用训练好的网络对余下的样本进行预测,并将预测结果与病理专家的诊断以及普通的BP神经网络预测结果进行比较.结果:模糊神经网络模型的预测结果与病理专家的诊断精度相当接近,优于普通的BP神经网络预测结果.     

Adaboost算法改进BP神经网络预测研究

针对传统BP神经网络容易陷入局部极小、预测精度低的问题,提出使用Adaboost算法和BP神经网络相结合的方法,提高网络预测精度和泛化能力。该方法首先对样本数据进行预处理并初始化测试数据分布权值;然后通过选取不同的隐含层节点数、节点传递函数、训练函数、网络学习函数构造出不同类型的BP弱预测器并对样本数据进行反复训练;最后使用Adaboost算法将得到的多个BP神经网络弱预测器组成新的强预测器。对UCI数据库中数据集进行仿真实验,结果表明本方法比传统BP网络预测平均误差绝对值减少近50%,提高了网络预测精度,为神经网络预测提供借鉴。     

基于量子粒子群神经网络的太阳黑子数预测磁

为了提高太阳黑子数的预测精度,论文提出了一种基于量子粒子群神经网络预测太阳黑子数的模型（QPSO-BP 网络）。首先基于前18个太阳周（1755～1953）的年均值,利用量子粒子群算法优化 BP 神经网络的权值和阀值,完成网络训练训;然后对第19太阳周（1954～2013）年均值进行预测,检验模型的预测能力。与普通 BP 神经网络预测的对比结果表明,该模型在逼近能力和预测精度两方面均有明显提高,从而表明基于量子粒子群优化的训练方法对于提高神经网络预测能力具有一定潜力。     

基于差分进化的BP神经网络预测混沌时间序列

针对BP神经网络预测模型收敛速度慢和容易陷入局部极小值的缺点,将差分进化算法和神经网络结合起来,提出了一种基于差分进化算法的BP神经网络预测混沌时间序列的方法,利用差分进化算法的全局寻优能力对BP神经网络的权值和阈值进行优化,然后训练BP神经网络预测模型求得最优解,将该预测方法用到3个典型的混沌时间序列进行算法的有效性验证,并与BP算法的预测精度进行了比较,仿真结果表明该方法对混沌时间序列预测具有更好的非线性拟合能力和更高的预测准确性。     

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.