SVM在空中目标威胁值评估中的应用

构建了基于回归型支持向量机的空中目标威胁值评估模型,确定了基于粗糙集理论的空中目标威胁因素,并建立了数据离散化、归一化的标准。将常用的4种核函数应用于对空中目标威胁值的估计,并通过基于网格法和遗传算法的搜索方法找到模型的最优参数。最后通过Matlab对算例进行了仿真验证。     

Single batch machine scheduling with deliveries

We consider the problem of scheduling a set of n jobs on a single batch machine, where several jobs can be processed simultaneously. Each job j has a processing time p_j and a size s_j. All jobs are available for processing at time 0. The batch machine has a capacity D. Several jobs can be batched together and processed simultaneously, provided that the total size of the jobs in the batch does not exceed D. The processing time of a batch is the largest processing time among all jobs in the batch. There is a single vehicle available for delivery of the finished products to the customer, and the vehicle has capacity K. We assume that K = rD, where and r is an integer. The travel time of the vehicle is T; that is, T is the time from the manufacturer to the customer. Our goal is to find a schedule of the jobs and a delivery plan so that the service span is minimized, where the service span is the time that the last job is delivered to the customer. We show that if the jobs have identical sizes, then we can find a schedule and delivery plan in time such that the service span is minimum. If the jobs have identical processing times, then we can find a schedule and delivery plan in time such that the service span is asymptotically at most 11/9 times the optimal service span. When the jobs have arbitrary processing times and arbitrary sizes, then we can find a schedule and delivery plan in time such that the service span is asymptotically at most twice the optimal service span. We also derive upper bounds of the absolute worst‐case ratios in both cases. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 470–482, 2015     

基于稀疏贝叶斯学习的DOA估计

针对基于l1范数约束的稀疏表示DOA(Direction Of Arrival)估计算法对初始参数较为敏感的问题,提出了一种基于稀疏贝叶斯学习的DOA估计算法。首先通过信号来波方向的空间采样构造冗余字典,将阵列信号处理中的DOA估计信号模型转化为压缩感知中的稀疏重构信号模型。然后基于经验贝叶斯推理的方法,将待估计的稀疏系数值用方差未知的联合高斯分布描述,而未知的方差值决定了待估计系数的稀疏性。通过观测数据估计得到未知的方差,进而得到信号的DOA估计值。仿真结果表明,提出的算法有较高估计精度,并且对非相干信源和相干信源都具有较好的估计性能。     

主动学习与自学习的中文命名实体识别

命名实体识别是信息抽取中的一项基础性任务,如何利用丰富的未标注语料来提高实体识别的指标是该领域一个重要的研究方向。基于条件随机场提出一种将主动学习与自学习相结合的方法——SACRF,通过设置置信度函数和2-Gram频度阈值来选取样本,并采用人工与自动相结合的方式进行标注来扩展训练语料。实验表明,该方法在提高实体识别的精确率和召回率的同时,能够显著地降低人工标注的工作量。     

一种具有遗忘特性的在线学习算法框架

基于凸优化中的对偶理论,提出了一种具有遗忘特性的在线学习算法框架。其中,Hinge函数的Fenchel对偶变换是将基本学习问题由批量学习转化为在线学习的关键。新的算法过程是通过以不同方式提升含有约束变量的对偶问题实现的:(1)梯度提升;(2)贪婪提升。回顾了以往的相关研究工作,并指出了与之的区别与联系。人造数据集和真实数据集上的实验结果证实了算法框架的有效性。算法可以很好地处理数据流中的分类面漂移问题,为设计和分析新的在线学习算法提供了一个新的思路。     

中国近代编辑出版工作者的矛盾心理分析

中国近代编辑出版是在旧制度已经衰败、新制度尚未建立的历史背景下发展起来的。西方文明与中国古老文明的冲突、旧思想与新思想的较量、公共利益与个体利益的矛盾必然存在。中国近代编辑出版工作者面对西学与中学、雅文化与俗文化、社会效益与经济效益的选择时,存在着矛盾心理,并为中国文化的近代化作出了贡献。     

高师生专业适应性与学习自我效能感的关系研究

运用《专业适应性量表》和《学习自我效能感问卷》,对600名高师生进行调查,研究结果是:(1)高师生专业适应性略高于平均水平,但并未达到理想状况。(2)专业承诺与专业自我效能感因子在性别上存在显著差异;专业适应性及其各个因子在年级上存在显著差异;专业承诺与专业自我效能感因子及专业适应性总体水平在科类上存在显著差异;非独生子女的专业适应性及其专业承诺、专业学习动力、专业自我效能水平显著高于独生子女的水平;担任过班干部的高师生的专业承诺、专业学习行为、专业自我效能感水平显著高于未担任过班干部的高师生。(3)高师生专业适应性及其各个因子与学习自我效能感及其各个因子均存在显著正相关,高师生的专业适应性能解释学习自我效能感54.2%的方差变异。     

中学学习型教师团队建设探索——以华山中学为例

学习型组织具有五个要素,即自我超越、改善心智模式、建立共同愿景、团队学习、系统思考,学习型教师团队同样要符合这些特征。为此,华山中学开展了积极探索,提出创建学习型教师团队的关键是:建立共同愿景,提升生命力;改变学习方式,增强学习力;变革管理模式,培养创造力。初步营造出了凝心聚力实现又好又快发展的良好教育氛围,促进了学校教育实践的全面开展。     

国内关于少数民族学生自主学习研究述评——基于2005-2013年相关论文的统计与分析

文章梳理归纳了2005—2013年间国内在少数民族学生自主学习的研究成果。对收集到的68篇相关文献从研究概况、研究内容、研究方法、研究对象几个方面进行了分析,并依据主要的研究内容和方法将本领域研究大体分为两个阶段：起步阶段（2005-2008）和发展阶段（2009-2013）。通过分析发现目前仍存在数量较少、层次低、重复研究多、研究深度与广度不够等不足。     

Single machine just‐in‐time scheduling problems with two competing agents

In scheduling problems with two competing agents, each one of the agents has his own set of jobs to be processed and his own objective function, and both share a common processor. In the single‐machine problem studied in this article, the goal is to find a joint schedule that minimizes the total deviation of the job completion times of the first agent from a common due‐date, subject to an upper bound on the maximum deviation of job completion times of the second agent. The problem is shown to be NP‐hard even for a nonrestrictive due‐date, and a pseudopolynomial dynamic program is introduced and tested numerically. For the case of a restrictive due‐date (a sufficiently small due‐date that may restrict the number of early jobs), a faster pseudopolynomial dynamic program is presented. We also study the multiagent case, which is proved to be strongly NP‐hard. A simple heuristic for this case is introduced, which is tested numerically against a lower bound, obtained by extending the dynamic programming algorithm. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 61: 1–16, 2014