字典序多目标多阶段决策问题的嘉量解法

建立了字典序多目标多阶段决策模型，并用嘉量原理解决字典序多目标多阶段决策问题．在字典序多目标多阶段决策的赋嘉量有向图中，从任意出发点到终点的嘉量等于出发点经过中间状态的诸顶点到终点的嘉量摹和,这个摹和与出发点之前、终点之后的状态无关．在计算过程中，将合取作为摹和，常义加法作为摹乘，则字典序多目标多阶段决策问题的递推运算转换为多阶段的摹矩阵运算．     

数据融合综述

新兴的数据融合科学内容丰富且应用广泛,正确理解融合算法与层次分类,能帮助我们避免数据富有但信息贫乏的情况发生.     

高速铁路基本走向决策方法和模型

分析了高速铁路基本走向方案比选特点，应用较为成熟的AHP主观赋权法作为指标权重分配计算方法，运用多目标决策分析原理，综合多种系统评价方法提出了一种高速铁路基本走向决策方法，并建立了优选模型，决策者可以根据具体情况灵活地进行方案的评价与排序，使方案决策具有针对性。实践证明该决策方法和模型能利用全部信息，排序结果能够比较客观地符合高速铁路基本走向选择的实际情况。     

A robust decision feedback equalizer for ATSC DTV receivers

Least mean square （LMS） decision feedback equalizer （DFE） is preferred as an effective solution to coping with inter-symbol interference （ISI） for ATSC digital television （DTV） receivers. In DTV transmission environment, echo delay often covers several hundreds symbols, which leads to very large-scale equalizer. One consequence of the large-scale equalizer is the very slow convergence, which combined with error propagation, inherent drawback of DFE, seriously deteriorates the performance of the receivers, especially in severe channels. More working modes and corresponding robust control mechanism were given to help the equalizer converge to the stable state smoothly. Simulation results show that the improved equalizer can perform better, especially in the severe channels.     

基于列车用户优先级的频谱共享博弈算法

不同业务优先级高速铁路用户的爆炸式业务需求与稀缺频谱资源的矛盾日益突出。针对运行于不同地形环境、在相同eNB覆盖范围内的高速铁路次用户的频谱共享博弈问题进行研究。基于次用户承载业务的不同优先级,提出模糊综合评判-优先级调度算法,将次用户间的频谱竞争建模为非合作博弈,通过次服务的频谱效用函数、主系统的频谱交易价格函数实现次用户间的差异化动态频谱共享。仿真结果表明,算法较好地反映了真实的频谱共享情形,可以更加准确地对次用户频谱需求进行业务优先级划分,提高频谱利用率的同时保证了其最大效用。     

中外反恐立法的比较分析与完善

当前,恐怖活动犯罪的社会危害性日益凸显,威胁着全球范围内的和平稳定秩序。我国一直在国际法层面和国内法层面对反恐立法进行探索,尤其是2011年《关于加强反恐怖工作的决定》的出台,意味着我国已经确定了以此决定为核心的独立式立法模式,我国反恐立法水平达到了新的高度。与国外相比,我国在立法内容、罪名设置、相关程序法等方面仍存在诸多不足。本文将通过对国外反恐立法内容比较总结,在本土化资源话语体系下,对外国立法经验进行选择性借鉴。     

Effects of Friends' Information Interaction on Travel Decisions

Many existing studies focus on the effects of traffic information from ATIS on travel choices. Research on the effect of friends' travel information on people's travel choice is uncommon, yet needed. In this paper, behavioral experiments are conducted to discuss what the effects of information interaction with friends can bring on individual travel decision behavior. The experimental results concluded that when the rate of friends' information interaction reaches 33.3%, the whole system is at an optimal level. Furthermore, users reach an optimum when the rate of friends' travel information interaction is 75%. The average time and cost per person is increasing when the whole system optimum transfers to the user optimum. Moreover, adjustments of departure time at the system optimum are more dispersed than at the user optimum, while travel choice changes are more concentrated.     

Training Needs of Coastal Resources Decision Makers in Ohio's Lake Erie Basin

This article describes results from a study focused on informational and knowledge needs of local coastal resources decision makers in the Ohio Lake Erie basin. New information was obtained through a series of focus groups of local decision makers and key training providers in the basin. The results suggest that training providers and local coastal resources decision makers have differing perceptions of knowledge and information needs and training venues. The results suggest a role for state and federal agency training providers as coordinators and facilitators of an enhanced learning network among decision makers, including support of an information clearinghouse, adoption of appropriate e-based learning modalities, and regionally based learning and practice networks to improve the function of the training market to meet local decision maker needs.     

Intra-household bargaining for school trip accompaniment of children: A group decision approach

This paper tests a group decision-making model to examine the school travel behavior of students 6–18 years old in the Minneapolis-St. Paul Metropolitan area. The school trip information of 1737 two-parent families with a student is extracted from Travel Behavior Inventory data collected by the Metropolitan Council between the Fall 2010 and Spring 2012. The model has four distinct characteristics including: (1) considering the student explicitly in the model, (2) allowing for bargaining or negotiation within households, (3) quantifying the intra-household interaction among family members, and (4) determining the decision weight function for household members. This framework also covers a household with three members, namely, a father, a mother, and a student, and unlike other studies it is not limited to dual-worker families. To test the hypotheses we build two models, each with and without the group-decision approach. The models are separately built for different age groups, namely students 6–12 and 12–18 years old. This study considers a wide range of variables such as work status of parents, age and gender of students, mode of travel, and distance to school. The findings of this study demonstrate that the elasticities of the two modeling approaches differ not only in the value, but in the sign in some cases. In 63% of the cases the unitary household model underestimates the results. More precisely, the elasticities of the unitary household model are as much as 2 times more than that of the group-decision model in 20% of cases. This is a direct consequence of model misspecification that misleads both long- and short-term policies where the intra-household bargaining and interaction is overlooked in travel behavior models.     

基于分类决策树的城市内部迁居人群通勤时间预测模型

针对迁居个体长距离、长时间的通勤现象，以南京迁居人群通勤出行多源数据为基础，综合考虑迁居人群个人家庭属性、迁居属性、建成环境属性及通勤属性，建立迁居人群通勤时间多元线性回归模型，分析各属性对通勤时间的影响. 结合多元线性回归和Logistic 回归确定决策树模型入选变量和通勤时间离散值，利用C4.5 算法建立迁居人群通勤时间决策树，以决策树结构揭示不同背景下迁居个体通勤时间模式特征. 结果显示，相较于个人家庭属性，职住同区、通勤出发时刻、住房类型、通勤距离、通勤方式及社区至主干道的便捷程度对迁居个体通勤时长的影响更为显著，侧面反映社区微观建成环境优化政策更利于提升迁居个体通勤效率.