“主导·主体”教学模式在配置路由选择协议实验中的应用

针对多数计算机网络实验课以学生为中心的实际情况,在分析其缺点的基础上,文章提出将"主导.主体"教学模式应用于计算机网络原理实验课中,并以配置路由选择协议为实验案例,探讨"主导.主体"教学模式在其中的应用,为该教学模式在计算机网络其他实验中的应用提供借鉴意义。     

对五个问题的回答

自公元前六世纪毕达哥拉斯在数学中提出单子概念开始,数学中就出现了不可分量的概念。此文在对第一个问题的回答中对不可分量下了严格的定义。在对第二个问题的回答中指出:将有理数与有理数组成的空集合结合在一起排序,这是实数理论的基础,是引进无理数的基本办法。作者只是发展了这一办法,定义了个由三元组表示的偏心的区间套组成的Dedekind分割并确定了不等式0<<,m∈N。这样是一个标准的无穷小,数学上的不可分割的量和毕达哥拉斯所期望的单子。在对第三个问题的回答中指明我们现在不用Lebesque测度,只讨论区间和区间套的测度。并指出:我们已在先行的文章中证明了“实数集R的测度为零”,而在Lebesque测度中认定“实数集R的测度大于零”。这是Lebesque测度中的头号悖论。在对第四个问题的回答中指出无理数e和π满足不等式:0相似文献   

Development of a Web-based RCM system for the driverless Rubber-Tired K-AGT system

The Korean Railroad Research Institute (KRRI) developed the rubber-tired AGT system (Model: K-AGT) between 1999 and 2005. The K-AGT is a light rail transit system does not require a driver and generally operates on an elevated railroad for transporting passengers. Accidents caused by driverless vehicles can severely affect social confidence, safety and economy. Therefore, it is very important to minimize the occurrences of such faults, and to accurately perform detailed maintenance tasks and thoroughly investigate the cause of any repeated failures. This research develops the web-based reliability centered maintenance (RCM) system for the K-AGT train system. The framework of the RCM system is based on performing a failure mode and effects analyses (FMEA) procedure on all the sub-systems in the K-AGT system. Out of the devices that have a low reliability, the high failure ranked devices are included high on the list for performing the overall maintenance plans. Through registration of historical failure data and the reliability indexes, the results of the FMEA can be updated. Such a process is repeated continuously and can achieve very accurate predictions for device operational lifetimes and failure rates. Also, the RCM system is designed so that workers can refer to the expert system for the latest procedures to perform the required diagnosis and repair of any failure. The overall RCM system consists of a failure/task management system, a preventive maintenance system, an expert system, a material management system, and an approval system. This research describes the development of the preventive maintenance system and the expert system that have been produced because these are the main functions for the RCM system. This paper was recommended for publication in revised form by Associate Editor Dae-Eun Kim Myung-Won Suh is a Professor of Mechanical Engineering. During 1986–1988, he worked for Ford motor company as researcher. From 1989–1995, he worked in technical center of KIA motors. He took a BS degree in Mechanical Engineering from Seoul National University and an MS degree in Mechanical Engineering from KAIST, South Korea. He obtained his Doctorate at the University of Michigan, USA, in 1989. His research areas include structure and system optimization, advanced safety vehicle and reliability analysis & optimization. Chul-Ho Bae is a PhD candidate at Sungkyunkwan University in Suwon, South Korea. He accomplished fellowship work as researcher at Mississippi State University, USA, in 2003 and 2005. He worked in Institute of Advanced Machi-nery and Technology (IMAT) as a Research Assistant in 2004. He was a part time Lecturer in computer aided Mechanical Engineering of Ansan College of Technology, Suwon Science College, and Osan College during 2004–2005. He took a BS Degree in Mechanical Design and an MS Degree in Mechanical Engineering from the Sungkyunkwan University. His research interests include computer aided engineering, reliability engineering, and optimization.     

Visual Exploration of Dynamic Multichannel EEG Coherence Networks

Electroencephalography (EEG) coherence networks represent functional brain connectivity, and are constructed by calculating the coherence between pairs of electrode signals as a function of frequency. Visualization of such networks can provide insight into unexpected patterns of cognitive processing and help neuroscientists to understand brain mechanisms. However, visualizing dynamic EEG coherence networks is a challenge for the analysis of brain connectivity, especially when the spatial structure of the network needs to be taken into account. In this paper, we present a design and implementation of a visualization framework for such dynamic networks. First, requirements for supporting typical tasks in the context of dynamic functional connectivity network analysis were collected from neuroscience researchers. In our design, we consider groups of network nodes and their corresponding spatial location for visualizing the evolution of the dynamic coherence network. We introduce an augmented timeline‐based representation to provide an overview of the evolution of functional units (FUs) and their spatial location over time. This representation can help the viewer to identify relations between functional connectivity and brain regions, as well as to identify persistent or transient functional connectivity patterns across the whole time window. In addition, we introduce the time‐annotated FU map representation to facilitate comparison of the behaviour of nodes between consecutive FU maps. A colour coding is designed that helps to distinguish distinct dynamic FUs. Our implementation also supports interactive exploration. The usefulness of our visualization design was evaluated by an informal user study. The feedback we received shows that our design supports exploratory analysis tasks well. The method can serve as a first step before a complete analysis of dynamic EEG coherence networks.     

Multiple kernel clustering based on centered kernel alignment

Multiple kernel clustering (MKC), which performs kernel-based data fusion for data clustering, is an emerging topic. It aims at solving clustering problems with multiple cues. Most MKC methods usually extend existing clustering methods with a multiple kernel learning (MKL) setting. In this paper, we propose a novel MKC method that is different from those popular approaches. Centered kernel alignment—an effective kernel evaluation measure—is employed in order to unify the two tasks of clustering and MKL into a single optimization framework. To solve the formulated optimization problem, an efficient two-step iterative algorithm is developed. Experiments on several UCI datasets and face image datasets validate the effectiveness and efficiency of our MKC algorithm.     

基于RCM需求的CMMS系统设计

根据以可靠性为中心的维修(RCM)的信息需求,计算机维修管理系统(CMMS)由设备的原始/运行/维修信息管理、财务信息管理、报表和数据输出与显示等子系统组成.各子系统管理的数据通过数据库进行存储,并按设备ID号索引.系统采用B/S模式,其构成含本地和远程数据采集、网络连接与服务器、及Web浏览器管理终端等.     

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

The analysis of ocean and atmospheric datasets offers a unique set of challenges to scientists working in different application areas. These challenges include dealing with extremely large volumes of multidimensional data, supporting interactive visual analysis, ensembles exploration and visualization, exploring model sensitivities to inputs, mesoscale ocean features analysis, predictive analytics, heterogeneity and complexity of observational data, representing uncertainty, and many more. Researchers across disciplines collaborate to address such challenges, which led to significant research and development advances in ocean and atmospheric sciences, and also in several relevant areas such as visualization and visual analytics, big data analytics, machine learning and statistics. In this report, we perform an extensive survey of research advances in the visual analysis of ocean and atmospheric datasets. First, we survey the task requirements by conducting interviews with researchers, domain experts, and end users working with these datasets on a spectrum of analytics problems in the domain of ocean and atmospheric sciences. We then discuss existing models and frameworks related to data analysis, sense‐making, and knowledge discovery for visual analytics applications. We categorize the techniques, systems, and tools presented in the literature based on the taxonomies of task requirements, interaction methods, visualization techniques, machine learning and statistical methods, evaluation methods, data types, data dimensions and size, spatial scale and application areas. We then evaluate the task requirements identified based on our interviews with domain experts in the context of categorized research based on our taxonomies, and existing models and frameworks of visual analytics to determine the extent to which they fulfill these task requirements, and identify the gaps in current research. In the last part of this report, we summarize the trends, challenges, and opportunities for future research in this area. (see http://www.acm.org/about/class/class/2012 )     

This is Not Entertainment: Experiencing the Dream House

Testing the spatiotemporal limits of experience and art installation, Dream House dwells upon a physical interaction between bodily movement and synthesised frequencies to prompt a variable sound spectrum and a specific interior atmosphere. Ted Krueger highlights the primacy of first-person experience within a spatial continuum, and in particular an experience of a nonvisual and interactive nature. Copyright © 2008 John Wiley & Sons, Ltd.     

川渝气区中心井站管理模式

中国石油西南油气田公司是我国首个以天然气生产为主的千万吨级大油气田公司,管理着川渝地区油气田1400多个井站,井站具有点多、面广、战线长等特点。近年来,随着该公司天然气产量日益增长,井站不断增多,操作员工数量相对不足,出现了人力资源跟不上产量递增这一现实困难,成为制约该公司快速发展的主要因素之一。选择新的劳动组织管理模式来缓解矛盾、解决问题、突破瓶颈迫在眉睫。为此,以科学发展观为指导,在对该公司所属部分气田充分调研的基础上,结合井下节流、井控系统等新工艺、新技术在气田广泛运用的实际,运用比较研究的方法,创新了中心井站管理模式。分析了该管理模式实施需具备的“软、硬”件要求,阐述了试点单位在实施此模式后取得的成效和暴露的问题,思考了下步全面推进中心井站管理模式需完善的内容和措施。通过数据比对和实例佐证,结论认为：该管理模式的顺利实施将更加体现以人为本,实现节能降耗,达到挖潜增效,有效缓解生产规模快速发展与人力资源相对紧缺的矛盾。     

发电设备点检周期模型

给出了点检定修策略的技术内涵以及适用性准则。针对目前发电设备点检周期决策缺乏理论支持、只能依据经验进行判定的现状,基于延迟时间模型理论,以设备潜在故障状态以及P-F间隔为出发点,建立了单部件系统点检周期决策的费用模型及风险模型。考虑不同故障模式导致的后果和企业的风险偏好,以平均单位时间检修费用最小化为目标,建立了点检周期决策费用模型;以功能故障发生的风险小于可接受水平为目标,建立了点检周期决策风险模型。所建立的模型适用于单部件系统精密点检以及解体点检周期的决策问题。给出了实际应用算例,证明了点检周期决策模型的有效性。