Two Innovative Coalition Formation Models for Dynamic Task Allocation in Disaster Rescues

This paper addresses the problem of multi-objective coalition formation for task allocation. In disaster rescue, due to the dynamics of environments, heterogeneity and complexity of tasks as well as limited available agents, it is hard for the single-objective and single (task)-to-single (agent) task allocation approaches to handle task allocation in such circumstances. To this end, two multi-objective coalition formation for task allocation models are proposed for disaster rescues in this paper. First, through coalition formation, the proposed models enable agents to cooperatively perform complex tasks that cannot be completed by single agent. In addition, through adjusting the weights of multiple task allocation objectives, the proposed models can employ the linear programming to generate more adaptive task allocation plans, which can satisfy different task allocation requirements in disaster rescue. Finally, through employing the multi-stage task allocation mechanism of the dynamic programming, the proposed models can handle the dynamics of tasks and agents in disaster environments. Experimental results indicate that the proposed models have good performance on coalition formation for task allocation in disaster environments, which can generate suitable task allocation plans according to various objectives of task allocation.     

自动化码头运输设备充电策略优化的仿真研究

随着自动化集装箱码头成为当今码头的发展趋势,AGV(automated guided vehicle) 作为自动化码头中使用最为广泛的水平运输工具,合理制定其充电策略至关重要。针对当前自动化集装箱码头中AGV充电策略的不足,提出一种以离线充电为主在线充电为辅的充电策略,并且针对在线充电站点位置选择问题提出一种利用热区图选择有效在线充电站点的快捷方法。在使用该种充电策略情况下,通过大量仿真实验得出在码头工作任务量不变的前提下可减少AGV数量配置的结果,说明自动化集装箱码头水平搬运效率得到提高,证明该充电策略具有有效性。     

基于生成对抗网络的无载体信息隐藏

提出了一种基于生成对抗网络的无载体信息隐藏方法.发送方用伪装图像和Improved Wasserstein GAN生成一张与秘密图像相同的图像,发送方发送伪装图像,接收方收到伪装图像后用生成器生成和秘密图像视觉上相同的图像.与现有的信息隐藏方法相比,该方法不需要修改载体,能够有效地抵抗隐写分析工具的检测,能够达到秘密信息安全传输的目的.     

An Overview of Kernelization Algorithms for Graph Modification Problems

Kernelization algorithms for graph modification problems are important ingredients in parameterized computation theory. In this paper, we survey the kernelization algorithms for four types of graph modification problems, which include vertex deletion problems, edge editing problems, edge deletion problems, and edge completion problems. For each type of problem, we outline typical examples together with recent results, analyze the main techniques, and provide some suggestions for future research in this field.     

Risk reduction in a project portfolio

The positive impacts of managing projects as a portfolio are quantified by comparing the value of the integrated risk of a project portfolio and the aggregation of single project risks implemented separately. Firstly, the integrated risk is defined by proposing risky events based on set theory. Secondly, as projects interact with each other in a project portfolio, the integrated risk is evaluated by using a Bayesian network structure learning algorithm to construct an interdependent network of risks. Finally, the integrated risk of a practical case is assessed using this method, and the results show that the proposed method is an effective tool for calculating the extent of risk reduction of implementing a project portfolio and identifying the most risky project, so as to assist companies in making comprehensive decisions in the phase of portfolio selection and portfolio controlling.     

Backstepping approach to the adaptive regulator design for a one-dimensional wave equation with general input harmonic disturbance

This paper considers the parameter estimation and stabilization of an unstable one-dimensional wave equation with matched general harmonic disturbance at the controlled end. The backstepping method for infinite-dimensional system is adopted in the design of the adaptive regulator. It is shown that the resulting closed-loop system is asymptotically stable. Meanwhile, the estimated parameter is shown to be convergent to the unknown parameter as time goes to infinity.     

沪深300股指期货与现货的波动关系研究

随着高频数据获取的便利性,采用高频数据研究金融市场的波动越来越普遍。沪深300股指期货市场的迅速发展使得股指期货与现货市场的波动关系研究成为热点。采用MEM模型,对股指期货与现货市场的连续成分(连续样本路径方差)之间和跳跃成分(离散跳跃方差)之间的关系进行研究。结果表明,现货跳跃成分对期货跳跃成分的溢出效应大于期货跳跃成分对现货跳跃成分的溢出效应;现货连续成分对期货连续成分的溢出效应大于期货连续成分对现货连续成分的溢出效应。现货与期货之间的波动溢出效应主要体现在二者的连续成分上。     

Upper bounds of differential characteristic probability for unbalanced Feistel network employing DSM

In this paper, for the unbalanced Feistel network which employs diffusion matrices in a switching way, we study the fixed number of its differential active S-boxes. Firstly we obtain some lower bounds of the differential active S-boxes for m, 2m and 3m rounds of Feistel structure, respectively. By concatenating these rounds, a fixed number of differential active S-boxes for arbitrary round number is derived. Our results imply that the unbalanced Feistel network using DSM is more secure than the traditional structure.     

Nonextensivity and the <Emphasis Type="Italic">q</Emphasis>-distribution of a relativistic gas under an external electromagnetic field

We investigate the nonextensivity and the q-distribution of a relativistic gas under an external electromagnetic field. We derive an expression for the nonextensive parameter q based on the relativistic generalized Boltzmann equation, the relativistic q-H theorem and the relativistic version of the q-power-law distribution function in the nonextensive q-kinetic theory. We thus provide the connection between the parameter q≠1 and the spatial-temporal derivatives of the temperature field of the gas as well as the four-potential, and thereby present a clearly physical meaning for the nonextensivity for the relativistic gas.     

Latent Co-interests' Relationship Prediction

With the development of the social media and Internet, discovering latent information from massive information is becoming particularly relevant to improving user experience. Research efforts based on preferences and relationships between users have attracted more and more attention. Predictive problems, such as inferring friend relationship and co-author relationship between users have been explored. However, many such methods are based on analyzing either node features or the network structures separately, few have tried to tackle both of them at the same time. In this paper, in order to discover latent co-interests’ relationship, we not only consider users’ attributes but network information as well. In addition, we propose an Interest-based Factor Graph Model (I-FGM) to incorporate these factors. Experiments on two data sets (bookmarking and music network) demonstrate that this predictive method can achieve better results than the other three methods (ANN, NB, and SVM).