Multi-agent and driving behavior based rear-end collision alarm modeling and simulating

Distance-between-vehicle-measurement is the only factor in traditional car rear-end alarm system. To address the above problem, this paper proposes an alarming model based on multi-agent systems (MAS) and driving behavior. It consists of four different types of agents that can either work alone or collaborate through a communications protocol on the basis of the extended KQML. The rear-end alarming algorithm applies the Bayes decision theory to calculate the probability of collision and prevent its occurrence real-time. The learning algorithm of driving behavior based on ensemble artificial neural network (ANN) and the decision procedure based on Bayes’ theory are also described in this paper. Both autonomy and reliability are enhanced in the proposed system. The effectiveness and robustness of the model have been confirmed by the simulated experiments.     

Multi‐Unit Bayesian Auction with Demand or Budget Constraints

We consider the problem of revenue maximization on multi‐unit auctions where items are distinguished by their relative values; any pair of items has the same ratio of values to all buyers. As is common in the study of revenue maximizing problems, we assume that buyers' valuations are drawn from public known distributions and they have additive valuations for multiple items. Our problem is well motivated by sponsored search auctions, which made money for Google and Yahoo! in practice. In this auction, each advertiser bids an amount b_i to compete for ad slots on a web page. The value of each ad slot corresponds to its click‐through‐rate, and each buyer has her own per‐click valuations, which is her private information. Obviously, a strategic bidder may bid an amount that is different with her true valuation to improve her utility. Our goal is to design truthful mechanisms avoiding this misreporting. We develop the optimal (with maximum revenue) truthful auction for a relaxed demand model (where each buyer i wants at most d_i items) and a sharp demand model (where buyer i wants exactly d_i items). We also find an auction that always guarantees at least half of the revenue of the optimal auction when the buyers are budget constrained. Moreover, all of the auctions we design can be computed efficiently, that is, in polynomial time.     

Refined entanglement concentration for electron-spin entangled cluster states with quantum-dot spins in optical microcavities

We present a refined entanglement concentration protocol (ECP) for an arbitrary unknown less-entangled four-electron-spin cluster state by exploring the optical selection rules derived from the quantum-dot spins in one-sided optical microcavities. In our ECP, the parties obtain not only the four-electron-spin systems in the partial entanglement with two unknown parameters, but also the less-entangled two-electron-spin systems in the first step. Utilizing the above preserved systems as the resource for the second step of our ECP, the parties can obtain a standard cluster state by keeping the robust odd-parity instances with two parity-check gates. Meanwhile, the systems in the rest three instances can be used as the resource in the next round of our ECP. The success probability of our ECP is largely increased by iteration of the ECP process. Moreover, all the coefficients of our ECP are unknown for the parties without assistance of extra single electron-spin, so our ECP maybe has good applications in quantum communication network in the future.     

试析Web程序中常见字符串问题的处理

从PHP实现字符串的正确输出入手,分析了PHP中相关字符串处理函数在Web程序开发中发挥的重要作用。     

Evolutionary level set method for structural topology optimization

This paper proposes an evolutionary accelerated computational level set algorithm for structure topology optimization. It integrates the merits of evolutionary structure optimization (ESO) and level set method (LSM). Traditional LSM algorithm is largely dependent on the initial guess topology. The proposed method combines the merits of ESO techniques with those of LSM algorithm, while allowing new holes to be automatically generated in low strain energy within the nodal neighboring region during optimization. The validity and robustness of the new algorithm are supported by some widely used benchmark examples in topology optimization. Numerical computations show that optimization convergence is accelerated effectively.     

Finite-time weighted average consensus with respect to a monotonic function and its application

The consensus state is an important and fundamental quantity for consensus problems of multi-agent systems, which indicates where all the dynamical agents reach. In this paper, weighted average consensus with respect to a monotonic function, which means that the trajectories of the monotonic function along the state of each agent reach the weighted average of their initial values, is studied for a group of kinematic agents with time-varying topology. By constructing a continuous nonlinear distributed protocol, such a consensus problem can be solved in finite time even though the time-varying topology involves unconnected graphs. Then the distributed protocol is employed to compute the maximum-likelihood estimation of unknown parameters over sensor networks. Compared with the existing results, the estimate scheme proposed here may reduce the costs of data communication, storage memory, book-keeping and computational overheads.     

综合业务和区分业务比较研究

为了在Internet上提供有服务质量等级和保证的服务,现在已经提出的在传输层上的实现方案包括综合业务（Integtated Services）和区分业务（Differentiated Services)。本文分析了两种方案的体系结构、工作原理和服务类型,比较了二者在设计思想、实现机制、QoS特性等方面的差异和优缺点,介绍了两种模型相互结合的扩展体系结构及最新研究进展。     

大纯滞后过程的采样控制及其工程实现中的若干问题

提出了采样控制机制的新观点,介绍控制器参数确定的新方法,分析了采样控制的质量,指出了在工程实施应考虑的若干问题。     

一个基于加权和组合降维的web文本分类系统

设计了一个web文本分类系统,采用了基于统计分析和粗糙集组合的方法进行降维;降维时考虑了属性的位置信息,采用加权方式标注属性的不同重要性,以达到提高分类速度和分类准确度的目的。