基于DRA-SSTFN耦合模型的城市供水管网结构稳定风险评估

针对供水管网结构稳定风险评估过程中部分指标数据缺省和难以量化等特征,构建了城市供水管网结构稳定风险评估的DRA-SSTFN耦合模型。该耦合模型利用模糊语义的11级划分标准定量表征供水管网各指标的风险等级和风险重要对比度,并计算出不同待评节点管段综合风险的95%置信区间范围。经耦合模型对供水节点管段进行模拟试验N=20 000次时,其仿真结果已收敛,并给出不同节点管道的动态风险区间值和正态概率分布图,可为城市基础设施规划提供借鉴。     

Real option valuation of free destination in long-term liquefied natural gas supplies

This paper presents a real option model for the valuation of destination flexibility in long-term LNG supplies. Stochastic price dynamics in the different markets is modelled through geometric Brownian motion processes. Mean reversion is considered as well as correlation between markets, but instead of the usual correlation in return shocks, a price convergence term is introduced representing the arbitrage streams between markets. Model parameters are estimated from market data on LNG prices by maximum log-likelihood. The goodness of the fit for the proposed model is tested as well as for two alternative models. Confidence intervals for the parameters are given. Results for the model are calculated by Monte Carlo simulation. Frequency distributions for the main results are plotted. The effect of the main parameters of the model is studied (i.e. price volatilities, price convergence, initial prices in the markets, mean reversion, extra transportation costs, number of alternative markets). The value of destination flexibility is found to be an important share of the value of LNG.     

Consistency of discrete Bayesian learning

Bayes’ rule specifies how to obtain a posterior from a class of hypotheses endowed with a prior and the observed data. There are three fundamental ways to use this posterior for predicting the future: marginalization (integration over the hypotheses w.r.t. the posterior), MAP (taking the a posteriori most probable hypothesis), and stochastic model selection (selecting a hypothesis at random according to the posterior distribution). If the hypothesis class is countable, and contains the data generating distribution (this is termed the “realizable case”), strong consistency theorems are known for the former two methods in a sequential prediction framework, asserting almost sure convergence of the predictions to the truth as well as loss bounds. We prove corresponding results for stochastic model selection, for both discrete and continuous observation spaces. As a main technical tool, we will use the concept of a potential: this quantity, which is always positive, measures the total possible amount of future prediction errors. Precisely, in each time step, the expected potential decrease upper bounds the expected error. We introduce the entropy potential of a hypothesis class as its worst-case entropy, with regard to the true distribution. Our results are proven within a general stochastic online prediction framework, that comprises both online classification and prediction of non-i.i.d. sequences.     

Discounting,catastrophic risks management and vulnerability modeling

Traditional discounting dramatically affects the outcome of catastrophic risk management and spatio-temporal vulnerability modeling. The misperception of discount rates produces inadequate evaluations of risk management strategies, which may provoke catastrophes and significantly contribute to the increasing vulnerability of our society. This paper analyses the implication of potential catastrophic events on the choice of discounting. In particular, it shows the necessity of using proposed equivalent undiscounted stopping time criterion and Monte Carlo based stochastic optimization procedures.     

On the Approximation of Stochastic Concurrent Constraint Programming by Master Equation

We explore the relation between the stochastic semantic associated to stochastic Concurrent Constrain Programming (sCCP) and its fluid-flow approximation. Writing the master equation for a sCCP model, we can show that the fluid flow equation is a first-order approximation of the true equation for the average. Moreover, we introduce a second-order correction and first-order equations for the variance and the covariance.     

A stochastic control model for optimal timing of climate policies

A stochastic control model is proposed as a paradigm for the design of optimal timing of greenhouse gas (GHG) emission abatement. The resolution of uncertainty concerning climate sensitivity and the technological breakthrough providing access to a carbon-free production economy are modeled as controlled stochastic jump processes. The optimal policy is characterized using the dynamic programming solution to a piecewise deterministic optimal control problem. A numerical illustration is developed with a set of parameters calibrated on recently proposed models for integrated assessment of climate policies. The results are interpreted and the insights they provide on the timing issue of climate policy are discussed.     

Modelling and estimating the reliability of stochastic dynamical systems with Markovian switching

In this paper, a general framework for the modelling of physical phenomena with stochastic dynamical systems switched by jump Markov processes is given. A methodology of the associated estimation procedures is provided. A particular attention is paid to the estimation of the underlying jump process, which is not observable.As an application, a stochastic model is proposed for the fatigue crack growth problem. The estimation of the model parameters is made on a real crack growth data set. We are thus able to simulate some crack growth paths which are used for reliability analysis through Monte Carlo techniques.     

Parameter estimation from observations of first-passage times of the Ornstein–Uhlenbeck process and the Feller process

Renewal point processes show up in many different fields of science and engineering. In some cases the renewal points become the only observable parts of an anticipated hidden random variation of some physical quantity. The hypothesis might be that a hidden random process originating from zero or some other low value only becomes visible at the time of first crossing of some given value level, and that the process is restarted from scratch immediately after the level crossing. It might then be of interest to reveal the defining properties of this hidden process from a sample of observed first-passage times. In this paper the hidden process is first anticipated as a non-stationary Ornstein–Uhlenbeck (OU) process with unknown parameters that have to be estimated only by use of the information contained in a sample of first-passage times. The estimation method is a direct application of the Fortet integral equation of the OU process. A non-stationary Feller process is considered subsequently. As the OU process, the Feller process has a known transition probability distribution that allows the formulation of the integral equation. The described integral equation estimation method also provides a subjective graphical test of the applicability of the OU process or the Feller process when applied to a reasonably large sample of observed first-passage data.

These non-stationary processes have several applications in biomedical research, for example as idealized models of the neuron membrane potential. When the potential reaches a certain threshold the neuron fires, whereupon the potential drops to a fixed initial value, from where it continuously builds up again until the next firing. Also in civil engineering there are hidden random phenomena such as internal cracking or corrosion that after some random time break through to the material surface and become observable. However, the OU process has as a model of physical phenomena the defect of not being bounded to the negative side. This defect is not present for the Feller process, which therefore may provide a useful modeling alternative to the OU process.     

组卷的遗传算法设计

提出一种改进的遗传算法作为组卷的策略.染色体采用符号编码设计,解决了遗传运算过程中满足约束条件的问题.采用非优超排序法对染色体进行评价,在选择算子的设计上,既能够复制一部分较好的个体,又体现了选择的概率性.变异概率和交叉概率能随个体的不同适应度自适应改变,同时变异概率随种群多样性自适应变化.采用基于数据仓库的最优解保存策略,使搜索结果呈现出丰富的Pareto解集.     

Simulating Emergent Properties of Coordination in Maude: the Collective Sort Case

Recent coordination languages and models are moving towards the application of techniques coming from the research context of complex systems: adaptivity and self-organization are exploited in order to tackle the openness, dynamism and unpredictability of today's distributed systems. In this area, systems are to be described using stochastic models, and simulation is a valuable tool both for analysis and design. Accordingly, in this work we focused on modelling and simulating emergent properties of coordination techniques.We first develop a framework acting as a general-purpose engine for simulating stochastic transition systems, built as a library for the Maude term rewriting system. We then evaluate this tool to a coordination problem called collective sort, where autonomous agents move tuples across different tuple spaces according to local criteria, and resulting in the emergence of the complete clustering property.