地震微观前兆预报网络系统设计研究(1)——地震模型

地震预报是一个极大挑战性的世界难题.为了准确地预报地震必须跟踪地震的整个孕育过程.例如利用基岩传感器网络收集地震微观前兆的各种信息,包括基岩应力、应变和破裂强度的变化、基岩振动状态的变化、引力波和冲击波强度的变化、地热温度分布的变化、电磁场的变化等.经过传感器数据融合和信号处理提取有用信息,然后由数据处理中心做出决策判断.提出了地震微观前兆预报网络系统设计的一整套详细方案,包括地震模型、基岩传感器网络和数据融合以及信号处理技术和网络系统设计.本文介绍地震模型的建立     

北斗卫星在智慧城市建设中的应用评述

在“数字城市”的概念后提出了“智慧城市”,用来解决或减轻城市化和经济转型所造成的社会问题。随着我国北斗卫星组网日益完善,以及物联网、云计算等技术的快速发展,使城市运行管理更加智慧化提供了可能性。通过智慧交通、智慧减灾、智慧社区等北斗项目积极探索“智慧城市”的建设,使北斗真正融入百姓生活,逐渐建立我国北斗在社会中的置信度、认可度。对北斗卫星顺利投入我国全方位、多维度应用领域具有重要意义,以便更好服务于城市建设和管理,进一步推进城市和谐发展。     

Quasi-birth-and-death processes with restricted transitions and its applications

In this paper, we identify a class of quasi-birth-and-death (QBD) processes where the transitions to higher (respectively lower) levels are restricted to occur only from (respectively to) a subset of the phase space. These restrictions induce a specific structure in the R or G matrix of the QBD process, which can be exploited to reduce the time required to compute these matrices. We show how this reduction can be achieved by first defining and solving a censored process, and then solving a Sylvester matrix equation. To illustrate the applicability and computational gains obtained with this approach, we consider several examples where the referred structures either arise naturally or can be induced by adequately modeling the system at hand. The examples include the general MAP/PH/1 queue, a priority queue with two customer classes, an overflow queueing system and a wireless relay node.     

“智慧消防”建设与发展的思考

随着全球掀起建设“智慧城市”的热潮,“智慧消防”建设作为“智慧城市”建设的组成部分,得到了各级公安消防机构的关注.探讨了“智慧消防”的发展背景、概念、与“数字消防”的关系和建设需求,并构建了“智慧消防”平台框架和提出了重点建设项目内容,希望能给“智慧消防”建设提供一些启示.     

Analysis of a large number of Markov chains competing for transitions

We consider the behaviour of a stochastic system composed of several identically distributed, but non independent, discrete-time absorbing Markov chains competing at each instant for a transition. The competition consists in determining at each instant, using a given probability distribution, the only Markov chain allowed to make a transition. We analyse the first time at which one of the Markov chains reaches its absorbing state. When the number of Markov chains goes to infinity, we analyse the asymptotic behaviour of the system for an arbitrary probability mass function governing the competition. We give conditions that ensure the existence of the asymptotic distribution and we show how these results apply to cluster-based distributed storage when the competition is handled using a geometric distribution.     

Probabilistic systems coalgebraically: A survey

We survey the work on both discrete and continuous-space probabilistic systems as coalgebras, starting with how probabilistic systems are modeled as coalgebras and followed by a discussion of their bisimilarity and behavioral equivalence, mentioning results that follow from the coalgebraic treatment of probabilistic systems. It is interesting to note that, for different reasons, for both discrete and continuous probabilistic systems it may be more convenient to work with behavioral equivalence than with bisimilarity.     

浅谈智慧城市管理系统设计与实施难点

本文介绍了智慧城市管理系统设计的原则和实施难点,把实施过程中容易出错或者难以把握的地方,进行了详细的描述。     

大型复杂项目管理在智慧医疗项目中的应用

围绕大项目整体管理,首先从大项目特征、计划过程、实施和控制等方面展开论述,详细介绍了如何化解在项目实践中遇到的项目工作复杂度高、整体协调不一致、团队实施绩效低等问题,确保项目整体通过验收并如期上线,获得了较好的社会效益和经济效益,最后对项目整体管理中的几点不足进行了总结。     

Parallel and interacting Markov chain Monte Carlo algorithm

In many situations it is important to be able to propose N independent realizations of a given distribution law. We propose a strategy for making N parallel Monte Carlo Markov chains (MCMC) interact in order to get an approximation of an independent N-sample of a given target law. In this method each individual chain proposes candidates for all other chains. We prove that the set of interacting chains is itself a MCMC method for the product of N target measures. Compared to independent parallel chains this method is more time consuming, but we show through examples that it possesses many advantages. This approach is applied to a biomass evolution model.     

Markov Chain Approach to Probabilistic Guidance for Swarms of Autonomous Agents

Motivated by biological swarms occurring in nature, there is recent interest in developing swarms comprised completely of engineered agents. The main challenge for developing swarm guidance laws compared to earlier formation flying and multi‐vehicle coordination approaches is the sheer number of agents involved. While formation flying applications might involve up to 10 to 20 agents, swarms are desired to contain hundreds to many thousands of agents. In order to deal with the sheer size, the present paper makes a break with past deterministic methods, and considers the swarm as a statistical ensemble for which guidance can be performed from a probabilistic point of view. The probability‐based approach takes advantage of the law of large numbers, and leads to computationally tractable and implementable swarm guidance laws. Agents following a probabilistic guidance algorithm make statistically independent probabilistic decisions based solely on their own state, which ultimately guides the swarm to the desired density distribution in the configuration space. Two different synthesis methods are introduced for designing probabilistic guidance laws. The first is based on the Metropolis‐Hastings (M‐H) algorithm, and the second is based on using linear matrix inequalities (LMIs). The M‐H approach ensures convergent swarm behavior subject to enforced desired motion constraints, while the LMI approach additionally ensures exponential convergence with a prescribed decay rate, and allows minimization of a cost function that reflects fuel expenditure. In addition, both algorithms endow the swarm with the property of self‐repair, and the capability to strictly enforce zero‐probability keep‐out regions. This last property requires a slight generalization of the Perron‐Frobenius theory, and can be very useful in swarm applications that contain regions where no agents are allowed to go. Simulation examples are given to illustrate the methods and demonstrate desired properties of the guided swarm.     

Tight bounds for the cover time of multiple random walks

We study the cover time of multiple random walks on undirected graphs G=(V,E). We consider k parallel, independent random walks that start from the same vertex. The speed-up is defined as the ratio of the cover time of a single random walk to the cover time of these k random walks. Recently, Alon et al. (2008) [5] derived several upper bounds on the cover time, which imply a speed-up of Ω(k) for several graphs; however, for many of them, k has to be bounded by O(logn). They also conjectured that, for any 1?k?n, the speed-up is at most O(k) on any graph. We prove the following main results:

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We present a new lower bound on the speed-up that depends on the mixing time. It gives a speed-up of Ω(k) on many graphs, even if k is as large as n.

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We prove that the speed-up is O(klogn) on any graph. For a large class of graphs we can also improve this bound to O(k), matching the conjecture of Alon et al.

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We determine the order of the speed-up for any value of 1?k?n on hypercubes, random graphs and degree restricted expanders. For d-dimensional tori with d>2, our bounds are tight up to logarithmic factors.

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Our findings also reveal a surprisingly sharp threshold behaviour for certain graphs, e.g., the d-dimensional torus with d>2 and hypercubes: there is a value T such that the speed-up is approximately min{T,k} for any 1?k?n.

     

A theoretical framework for simulated annealing

Simulated Annealing has been a very successful general algorithm for the solution of large, complex combinatorial optimization problems. Since its introduction, several applications in different fields of engineering, such as integrated circuit placement, optimal encoding, resource allocation, logic synthesis, have been developed. In parallel, theoretical studies have been focusing on the reasons for the excellent behavior of the algorithm. This paper reviews most of the important results on the theory of Simulated Annealing, placing them in a unified framework. New results are reported as well.This research was sponsored by SRC and DARPA monitored by SNWSC under contract numbers N00039-87-C-012 and N00039-88-C-0292.     

Risk-sensitive probability for Markov chains

The probability distribution of a Markov chain is viewed as the information state of an additive optimization problem. This optimization problem is then generalized to a product form whose information state gives rise to a generalized notion of probability distribution for Markov chains. The evolution and the asymptotic behavior of this generalized or “risk-sensitive” probability distribution is studied in this paper and a conjecture is proposed regarding the asymptotic periodicity of risk-sensitive probability and proved in the two-dimensional case. The relation between a set of simultaneous non-linear and the set of periodic attractors is analyzed.     

Sequential Activity Profiling: Latent Dirichlet Allocation of Markov Chains

To provide a parsimonious generative representation of the sequential activity of a number of individuals within a population there is a necessary tradeoff between the definition of individual specific and global representations. A linear-time algorithm is proposed that defines a distributed predictive model for finite state symbolic sequences which represent the traces of the activity of a number of individuals within a group. The algorithm is based on a straightforward generalization of latent Dirichlet allocation to time-invariant Markov chains of arbitrary order. The modelling assumption made is that the possibly heterogeneous behavior of individuals may be represented by a relatively small number of simple and common behavioral traits which may interleave randomly according to an individual-specific distribution. The results of an empirical study on three different application domains indicate that this modelling approach provides an efficient low-complexity and intuitively interpretable representation scheme which is reflected by improved prediction performance over comparable models.Mark Girolami is a Reader in the Department of Computing Science at the University of Glasgow. In 2000 he was the TEKES visiting professor at the Laboratory of Computing and Information Science in Helsinki University of Technology. In 1998 and 1999 Dr. Girolami was a research fellow at the Laboratory for Advanced Brain Signal Processing in the Brain Science Institute, RIKEN, Wako-Shi, Japan. He has been a visiting researcher at the Computational Neurobiology Laboratory (CNL) of the Salk Institute. This year (2005) he will take up an MRC funded Discipline Hopping Award in the Department of Biochemistry. Mark Girolami holds a degree in Mechanical Engineering from the University of Glasgow (1985), and a Ph.D. in Computing Science from the University of Paisley (1998). Dr. Girolami was a development engineer with IBM from 1985 until 1995 when he left to pursue an academic career.Ata Kabán received the B.Sc. degree with honours (1999) in computer science from the University “Babes-Bolyai” of Cluj-Napoca, Romania, and the Ph.D. degree in computer science (2001) from the University of Paisley, UK. She is a lecturer in the School of Computer Science of the University of Birmingham. Her current interests concern probabilistic modelling, machine learning and their applications to automated data analysis. She has been a visiting researcher at Helsinki University of Technology (June–December 2000 and in the summer of 2003). Prior to her career in Computer Science, she received the B.A. degree in musical composition (1994) and the M.A. (1995) and Ph.D. (1999) degrees in musicology from the Music Academy “Gh. Dima” of Cluj-Napoca, Romania.     

Analysis of an identification algorithm arising in the adaptive estimation of Markov chains

We investigate an algorithm applied to the adaptive estimation of partially observed finite-state Markov chains. The algorithm utilizes the recursive equation characterizing the conditional distribution of the state of the Markov chain, given the past observations. We show that the process “driving” the algorithm has a unique invariant measure for each fixed value of the parameter, and following the ordinary differential equation method for stochastic approximations, establish almost sure convergence of the parameter estimates to the solutions of an associated differential equation. The performance of the adaptive estimation scheme is analyzed by examining the induced controlled Markov process with respect to a long-run average cost criterion. This research was supported in part by the Air Force Office of Scientific Research under Grant AFOSR-86-0029, in part by the National Science Foundation under Grant ECS-8617860 and in part by the DoD Joint Services Electronics Program through the Air Force Office of Scientific Research (AFSC) Contract F49620-86-C-0045.     

连续时间Markov决策过程互模拟等价及逻辑保持

模型检测中,Markov决策过程可以建模具有不确定性的系统,然而状态空间爆炸问题将会影响系统验证的成败与效率,互模拟等价可以用于系统状态的简约.在强互模拟关系的基础上,给出Markov决策过程模型弱互模拟等价关系的概念,导出了连续时间Markov决策过程及其内嵌离散时间Markov决策过程互模拟等价关系的内在联系;在强互模拟等价关系逻辑特征保持的基础上,给出弱互模拟等价关系下的逻辑保持性质,证明了弱互模拟等价的两个状态,同时满足除下一步算子外的连续随机逻辑公式,从而可以将原模型中的验证问题转换为简约后模型的验证问题,提高验证的效率.     

车牌识别中先验知识的嵌入及字符分割方法

针对车牌字符分割过程中先验知识嵌入困难,分割过程对于前期车牌定位依赖较强的问题,提出了一种新的先验知识嵌入方法及其对应的字符分割算法。给定一种类型的车牌,利用字符的可能排列方式定义马尔可夫链中的状态,可以将车牌字符分割转化为一组马尔可夫链的前向识别过程。结合连通分量提取及垂直投影分割算法,可以有效地获取车牌的最优分割结果及其置信度。在实际应用中,该算法不依赖于前期的精确定位,对粗定位后的图像即可进行快速有效地分割。该方法统一了不同类型车牌的先验知识嵌入方法,降低了编码复杂度。在中国车牌及马来西亚车牌上的实验结果均证明,该方法有效地提高了车牌字符分割的性能。