运动训练信息管理系统的构建与实现

本文旨在开发一套基于B/S的运动训练信息管理系统,该系统主要具备人员管理、系统管理、训练器具管理、训练管理等主要功能,并且能够辅助教练员针对运动训练过程中产生的大量数据信息做出有效的处理,对运动员训练状态及日后体育竞技能力做出科学的评价、分析,从而帮助教练员做出适当的决策。     

一种改进的OFDM系统符号定时同步算法

文章在研究Schmidl＆Cox同步算法的基础上,提出了一种改进的符号定时同步算法。改进后的算法通过对原算法训练符号的加权,消除了定时同步中的峰值平台问题。仿真结果显示,该算法的定时同步性能优于原算法。     

异步协作通信中三协作方信道与延迟估计算法

无线通信是采用多节点协作分集方式发送信号的,故具有很大的优势,但缺点是所传输的信号是异步的,因此采用协作分集方式发送信号亟需消除这种异步的影响。为此,给出了块平坦衰落信道延迟估计算法,采用训练序列附加在信息码上作为循环前缀,通过循环前缀来获得已知符号,再利用已知符号来消除异步。结果表明,该方案完全能消除异步,信道估计达到了克拉默拉奥下限,误码率和MRC（最大比合并）方式相同,且带有完全的分集阶数,初步达到了消除信号异步影响的目的。     

基于HLA的自行高炮CGF的研究与设计

CGF是分布式交互作战仿真的重要支撑技术,在武器系统模拟训练平台中起着极其重要的作用。首先介绍了基于HLA框架实现自行高炮CGF的设计思想和原则,并结合自行高炮作战模拟训练过程,构建了基于HLA的自行高炮CGF体系结构和行为模型。在自行高炮CGF智能决策实现方面,对自行高炮CGF实体智能行为仿真的方法进行了理论探索,最后给出了自行高炮CGF系统的仿真实现方法和一个高炮CGF系统。对于以后基于HLA的自行高炮模拟训练系统中自行高炮CGF的开发具有很大参考价值。     

基于条件随机场的复杂背景图像文字分割

图像中的文字提取是处理文字信息的关键步骤。提出一种基于条件随机场（CRFs）的分割方法。针对背案复杂的情况,精心选择特征,设计分类器,计算在给定观测数据情况下,像素标签的条件概率分布。这样,有效地避免了计算联合分布所带来的问题（如马尔可夫随机场）。与传统的分类器相比,该方法更加注重像素以及标签之间互相影响程度以及合理性。试验结果表明,与其他方法相比,CRFs的文字分割结果有明显的提高。     

Recent developments in Dual Resource Constrained (DRC) system research

Real world manufacturing systems are usually constrained by both machine and human resources. Human operators are often the constraining resource and transfer between workstations to process jobs when required. This kind of system is known as a Dual Resource Constrained (DRC) system and presents additional technical challenges which must be considered during planning and scheduling. These technical challenges can be categorised into the five main dimensions of job release mechanisms, job dispatching, worker flexibility, worker assignment and transfer costs. This paper aims to provide an overview of recent developments in DRC research concerned with each of these areas and also discusses some possible approaches to solving the resource scheduling problem in a DRC system. The focus is on materials published after 1995 and up to 2009. Previous reviews on DRC systems are commented on and followed by a review of recent works associated with each of the five dimensions of DRC system research. Advancements made and new methodologies proposed are discussed and future research directions are identified.     

A simultaneous multi-issue negotiation through autonomous agents

Automated negotiation through autonomous agents has become increasingly important since the advent of e-marketplace. A deadlock may arise in which both negotiators refuse to disclose more information; then, mediation is required. We propose an agent-based sealed-bid design in which both agents simultaneously submit their respective offers to the mediate agent, and construct an efficient negotiation strategy which can reach an agreement aiming to maximize their owner’s utility. An important contribution of this paper is that we consider negotiators not necessarily to conflict over all issues, such as quantity. Our experimental results demonstrate that the proposed automated negotiation strategy is efficient in terms of the Pareto-efficiency of the negotiated contracts, the joint utility obtained, and the equality of both agents. In addition, since disclosure of information is reduced greatly, the design can discourage counter-speculation and effectively control fraud and misrepresentation to a certain extent. The method we proposed is simple and stable in the context of a game and the model could be well suited for practical agent applications.     

微机保护在供配电实训仿真系统中的应用

微机保护是变电所实现综合自动化的基础。在对微机保护系统的基本组成和特点介绍之后,具体分析了它在供配电实训仿真系统的应用和功能。     

一种利用叠加训练序列降低OFDM系统PAR的方法

峰均比(PAR)过高是正交频分复用(OFDM)技术亟待解决的关键问题之一。在基于判决指导信道估计的叠加训练序列OFDM系统中,提出一种选择性叠加训练序列降低系统PAR的方法。该方法使用多个相互独立的训练序列分别与数据序列叠加,选择其中PAR性能最好的用于传输,接收端完成信道估计的同时消除了训练序列对数据序列的影响,因此没有增加接收端的复杂度。计算机仿真结果表明:该方法在不增加接收端复杂度的前提下,有效降低了系统PAR,且对信道估计性能不造成任何影响,因此该方法可用于实际系统中。     

基于FCM与KKT条件的增量学习方法

增量学习方法的思想是仅利用部分相关的样本集参与训练,即能够保留历史样本知识,又能够不断地吸收新的知识,提高机器学习效率和精度,解决了大量样本训练时间长和存储空间不足的问题。因此,如何有效地丢弃大量无效的样本点是增量学习算法研究的重点。文中提出了一种FCM(Fuzzy C-Means)和KKT(Karush-KuhnTucker)条件结合的增量学习方法,分别从历史样本集和新增样本集两个阶段对无效样本进行过滤,利用余下的样本进行训练。最后,利用UCI数据库中的4组数据进行实验分析,结果证明训练精度与全数据样本的训练精度几乎完全拟合。