公共事业管理专业读书（文献阅读）训练探析

专业读书训练是公共事业管理专业的重要教学内容。本文在探讨专业读书教学目的和意义的同时,并提出了读书训练的原则、内容和方法,旨在帮助公共事业管理专业学生形成未来的公共事务管理者所需的职业素养。     

基于神经网络的超精密机床伺服进给非线性模型辨识

以双轴Ｔ型结构的超精密金刚石车床的伺服进给系统为研究对象,采用自构造神经网络技术,建立了系统的非线性动态数学模型,为系统非线性控制与补偿提供参考模型     

THE CONCEPT OF NUCLEAR LEARNING

This article seeks to elucidate the concept of nuclear learning. It explores both the “nuclear” and the “learning” aspects of the concept. On the nuclear side, it distinguishes between learning basic facts about nuclear arms and drawing inferences about the larger implications of those facts. On the learning side, it discusses three issues: whether to use the term in a normative or value-neutral manner; the difference between learning that leads to a change in means versus learning that leads to a re-evaluation of ends; and whether learning only takes place at the level of individuals or whether there can also be learning by collective entities. The article argues there is no universal best answer to these questions and that the particular concept of learning that should be employed depends on the goals of the analyst. If the goal is to reduce the chances of nuclear war, however, one type of learning that will be important to consider is whether there is shared, cross-national learning.     

Learning in counterinsurgency: what do we really know?

Since the publication in 2002 of John Nagl's Learning to Eat Soup with a Knife, organizational learning has been widely presumed an important ingredient for success in counterinsurgency. But sampling the literature from before and after this time shows remarkably little analytical treatment of the issue of learning and even confusion over what it may mean. This article considers the theories, hypotheses, research strategies, threats to validity, methods of measurement, treatments of time, and general lack of statistical analysis in the work to date and recommends a course for future research.     

新疆石河子内初班学生的学习适应性研究

为深入了解内初班学生的学习适应性状况,本研究分别在石河子市四所内初班承办学校中,严格按照分层抽样的方法选取了294名学生作为被试,采用《学习适应性测验》量表探求内初班学生的学习适应性特征。结果显示:石河子内初班学生的学习适应性与全国常模基本一致,学习适应不良主要集中在学习技术和学习态度上。在性别上、年级上差异不显著,民考民和民考汉学生的学习适应性在全量表上差异显著,总体上民考民学生的学习适应性优于民考汉学生。     

利用深度学习的硬件计数器复用估计算法

利用深度学习方法,为硬件计数器复用(multiplexing,MPX)提供结果精度更高的估计模型。通过对MPX估计得到的结果与实际采集的真实数据进行相似性分析,证明相同程序多次运行之间得到的硬件计数值是线性相关的。采用神经网络多层感知器(multilayer perceptron,MLP)和双向门控神经网络(bidirectional gated recurrent unit, Bi-GRU)这2种深度学习模型,对MPX数据进行拟合。基于动态时间规整(dynamic time warping, DTW),提出一个全新的评估MPX数据精度的指标DTW-cost。实验结果表明,同时收集15个硬件事件数据时,MLP方法拟合得到的13个高性能计算应用平均准确率比现有使用最广的固定插值法高出10.53%,最多可提升19.8%;而在MLP表现较差的事件上,Bi-GRU方法得到的平均准确率提升了28.8%。     

Bayesian strategies for dynamic pricing in e‐commerce

E‐commerce platforms afford retailers unprecedented visibility into customer purchase behavior and provide an environment in which prices can be updated quickly and cheaply in response to changing market conditions. This study investigates dynamic pricing strategies for maximizing revenue in an Internet retail channel by actively learning customers' demand response to price. A general methodology is proposed for dynamically pricing information goods, as well as other nonperishable products for which inventory levels are not an essential consideration in pricing. A Bayesian model of demand uncertainty involving the Dirichlet distribution or a mixture of such distributions as a prior captures a wide range of beliefs about customer demand. We provide both analytic formulas and efficient approximation methods for updating these prior distributions after sales data have been observed. We then investigate several strategies for sequential pricing based on index functions that consider both the potential revenue and the information value of selecting prices. These strategies require a manageable amount of computation, are robust to many types of prior misspecification, and yield high revenues compared to static pricing and passive learning approaches. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

无监督特征学习的人体活动识别

针对人的局限性可能会导致在提取特征中丢失重要信息,从而影响最终的识别效果问题,提出无监督特征学习技术的惯性传感器特征提取方法。其核心思想是使用无监督特征学习方法学习多个特征映射,再将所有特征映射拼接起来形成最终的特征计算方法。其优点是不会造成重要信息的损失,而且可以显著减少所使用的无监督特征学习模型的规模。为了验证所提出的特征提取方法在活动识别中的有效性,运用一个公开的活动识别数据集,使用三种常用无监督模型进行特征提取,并使用支持向量机进行活动识别。实验结果表明,特征提取方法取得了良好的效果,与其他方法相比具有一定的优势。     

无人机自驾仪硬件加固方案设计与可靠性分析

自驾仪是无人机实现自主飞行与自主完成各项任务的核心器件。现有商用无人机自驾仪大多没有进行硬件加固,直接用来执行重大任务时有一定风险。通过分析可知自驾仪组成模块中对安全性和可靠性影响最大的模块为控制解算器。根据逐步提高的容错需求,使用复位器、计数器、反相器、选择器等简单器件以及在芯片内部添加简单代码,分别设计了单机复位加固方案、双机热备加固方案、硬件切换和软件切换双机互备加固方案。重点研究了加固方案的可靠性随时间的变化关系,并进行了对比分析。对加固方案的工作机制进行了模拟,分析了这些方案在处理故障时的系统异常输出时间等容错特性。计算表明,这些加固方案可以显著提高系统的可靠性,其中双机互备加固方案的可靠性最高。该研究对于指导高可靠性自驾仪设计时在容错效果与复杂度、成本等方面进行折中具有较大的参考意义。     

矩阵变换特征与码序列联合学习的卷积码识别方法

现有基于深度学习的卷积码识别方法仍存在参数规模较大、识别性能较弱等不足。针对该问题,提出了一种基于矩阵变换特征与码序列联合学习的卷积码识别方法。将接收到的码字序列排列成矩阵形式,利用软信息剔除可靠性较低的码字,通过一种新的矩阵变换算法得到特征矩阵。在识别时,将原始码字矩阵和特征矩阵输入到具有多模态数据联合学习能力的网络模型,在神经网络中完成特征的提取融合与卷积码的识别。仿真结果表明,所提方法性能明显优于现有基于深度学习的识别方法,特别是对于高码率卷积码;当码率较低时,同样优于传统识别方法。当信噪比达到5 dB时,25种不同参数卷积码的识别率均可达到100%。