无线传感器网络中覆盖性问题的研究

无线传感器网络由大量的具有低能量,短寿命并且不可靠的传感器节点组成,因此在设计无线传感器网络时一个主要的目标在于保证足够的覆盖率以及能量可靠性的前提下可以延长网络的寿命。本文介绍了几种减少能量消耗以延长网络寿命的节点调度方法,并比较了各种方法的优缺点,指出其应用环境和需要进一步研究的工作。最后在这几种算法的基础上提出了一种新的算法。     

Child Care “Packaging” Among TANF Recipients: Implications for Social Work

This article reports the results of a study that examined the ways in which current and recent TANF recipients assembled or “packaged” their child care arrangements among formal and informal providers. The findings are drawn from in-person, in-depth interviews with current and former welfare recipients. The study found that most of the respondents used multiple providers within the category of informal child care for reasons including availability, the use of a network of social acquaintances as a problem-solving resource, the need to accommodate irregular work hours, and personal trauma that contributed to a distrust of strangers in formal settings. The findings can affect the efficacy of practice with TANF recipients by contributing to social workers’ understanding of the context of decision-making regarding child care.     

基于自组网技术的燃气无线抄表系统研究

为解决现阶段燃气无线抄表系统数据采集中无线网络不稳定、路由协议复杂、自修复能力薄弱等问题，提出并设计了一种基于cc1101无线传送模块的树形拓扑结构无线自组网系统。系统中主要对数据采集层路由协议和硬件实现进行了研究和设计，分析了该燃气无线抄表系统与传统燃气无线抄表系统的不同之处，设计了该系统数据采集层网络传输协议和总体结构。无线节点建在穿透力强的433 MHz的频率下进行通信，保证了系统自组网的稳定快速。     

Toward a model of school inspections in a polycentric system

Many education systems are developing towards more lateral structures where schools collaborate in networks to improve and provide (inclusive) education. These structures call for bottom-up models of network evaluation and accountability instead of the current hierarchical arrangements where single schools are evaluated by a central agency. This paper builds on available research about network effectiveness to present evolving models of network evaluation. Network effectiveness can be defined as the achievement of positive network level outcomes that cannot be attained by individual organizational participants acting alone. Models of network evaluation need to take into account the relations between network members, the structure of the network, its processes and its internal mechanism to enforce norms in order to understand the achievement and outcomes of the network and how these may evolve over time. A range of suitable evaluation models are presented in this paper, as well as a tentative school inspection framework which is inspired by these models. The final section will present examples from Inspectorates of Education in Northern Ireland and Scotland who have developed newer inspection models to evaluate the effectiveness of a range of different networks.     

Applications and Implementation A SEPARABLE PROGRAMMING APPROACH TO EXPECTED COVERAGE: AN APPLICATION TO AMBULANCE LOCATION

The maximum expected covering location problem (MEXCLP) is reformulated using a separable programming approach. The resulting formulation—nonlinear maximum expected covering location problem (NMEXCLP)—guarantees optimality and also solves more quickly than previous heuristic approaches. NMEXCLP allows two important extensions. First, minor formulation changes allow the specification of the minimum number of times each node is to be covered in order to satisfy expected coverage criteria. Second, coverage matrices can be constructed that consider two different types of coverage simultaneously. Both extensions are useful for ambulance location problems and are demonstrated in that setting.     

A SIMULATION ANALYSIS OF THE JAPANESE JUST-IN-TIME TECHNIQUE (WITH KANBANS) FOR A MULTILINE,MULTISTAGE PRODUCTION SYSTEM

The Japanese “just-in-time with kanban” technique reduces in-process inventory to absolute minimal levels, in concert with the Japanese belief that inventory is an unnecessary evil. Due to the success of Japanese firms that employ this type of system, American firms would like to import this technique and emulate Japanese successes. But this Japanese success may be attributable not only to the just-in-time with kanban technique but also to the production environment in which the technique is employed. This paper simulates the just-in-time with kanban technique for a multiline, multistage production system in order to determine its adaptability to an American production environment that might include such characteristics as variable processing times, variable master production scheduling, and imbalances between production stages. The results have practical implications for those firms considering adoption of the Japanese technique.     

FEPA-金融时间序列自适应组合预测模型

本文报告一种金融时间序列预测的信号分析、信息融合与智能计算组合模型,简称FEPA,由针对金融时间序列（FTS）信号分析的经验模态分解（EMD）、用于数据降维的主成分分析（PCA）和用于非线性建模的人工神经网络（ANN）三部分组成。该模型首先应用滑动窗口截取原始金融时间序列最近期数据集,应用EMD分解算法把数据集分解成不同尺度的本征模态函数（IMF）,然后通过主成分分析将分解后的数据降维,提取最有信息量的特征;然后将这些特征输入到神经网络进行组合预测。本文提出的组合预测模型FEPA是基于分解-提优-合成的信息融合思想,有效提高了预测可靠性。其创新点在于：1）首次给出了EMD算法的结构化表达,提供了今后融合更多信息的算法接口;2）通过多步长预测输出深入研究EMD分解的有效信息结构;3）通过切换到更细时间框架来处理EMD的端点效应,并探索了两级时间框架下的预测效果;4）给出了金融时间序列组合预测模型的一般性架构,具有可升级性和可扩展性。并且通过滑动窗口EMD使得实证更能切近实际。通过在沪深300股指和澳大利亚股指上的实证,结果表明FEPA预测模型在沪深300股指日线和15分钟线上的预测命中率高达78%和82%,在澳大利亚股指日线上也达到了74%的命中率,经比较,明显高于文献中常见的5种模型。     

计算机无线网络安全技术探析

近年来随着无线网络的快速发展,无线网络替代传统有线网络的趋势日益明显。与此同时,无线网络的安全机制也需要得到同步完善,从而进一步实现人们安全通信的要求。论述了计算机无线网络安全所面临的主要问题,提出了解决这些无线网络安全问题的措施。     

基于最大收益的无线虚拟网络重映射算法

针对无线环境的动态性及虚拟网络请求到达与离开的随机性,使得物理网络负载分布不均衡,导致后续请求的接受率降低的问题,提出一种无线网络虚拟化中的两阶段重映射算法（two stages reconfiguration scheme for wireless network virtualization,TR-WNV）。该算法通过第1个阶段选出合适的虚拟网络进行重映射,接着在第2个阶段按照优先级对已选出的虚拟网络进行重映射,并根据离散度来判断每次映射效果。最后,通过仿真对算法进行验证,结果表明该算法在保证虚拟网络请求收益最大化同时,有效提高了虚拟网络请求接受率,实现整个底层网络的负载分布均衡。     

Two-Group Classification Using Neural Networks*

Artificial neural networks are new methods for classification. We investigate two important issues in building neural network models; network architecture and size of training samples. Experiments were designed and carried out on two-group classification problems to find answers to these model building questions. The first experiment deals with selection of architecture and sample size for different classification problems. Results show that choice of architecture and choice of sample size depend on the objective: to maximize the classification rate of training samples, or to maximize the generalizability of neural networks. The second experiment compares neural network models with classical models such as linear discriminant analysis and quadratic discriminant analysis, and nonparametric methods such as k-nearest-neighbor and linear programming. Results show that neural networks are comparable to, if not better than, these other methods in terms of classification rates in the training samples but not in the test samples.