城市水系统关联模型研究

为了理清城市水系统中多因素间的复杂关系,对系统解决城市水问题提供帮助,基于水系统理论与城市水系统模拟相关研究,提出了城市水系统中水-经济社会-生态环境各因素之间相互作用和反馈的关联模型理论框架,建立了城市水系统演变过程中由针对经济发展的正反馈回路和针对可持续性的负反馈回路共同控制的新模式,构建了模拟城市水-经济社会-生态环境内在联系的城市水系统关联模型。以武汉市为例,应用建立的城市水系统关联模型,采用2001—2017年的历史统计数据,建立了武汉市城市水-经济社会-生态环境各要素之间互馈作用的模拟模型,预测了武汉市的城市发展轨迹,并与武汉市2030年的远期规划进行比较,验证了模型的有效性,表明城市水系统模型能够揭示城市水-经济社会-生态环境多要素的耦合驱动机制,体现了节水技术与绿色发展相关政策两大因子导向下的生态环境、经济社会协同发展的良好预期。     

电力工程中的地质灾害危险性评估

我国幅员辽阔,地质环境条件多样且复杂,导致了地质灾害发育强烈,种类繁多、危害较大。地质灾害与工程建设活动密切相关,既相互作用,相互依存,又相互影响[1]。电力工程项目遍布全国,特别是输电线路大多为人烟稀少的山区分布,线路长,跨越的地质地貌区域复杂,存在的地质灾害多种多样,或多或少面临着各种地质灾害的严重威胁[2]。通过电力工程地质灾害危险性评估工作的现状及评估工作流程的介绍,进一步分析电力工程地质灾害的危险性评估工作中存在的问题并提出了相应的看法。     

An implicit enthalpy scheme for one-phase Stefan problems

A compact finite-difference scheme to solve one-phase Stefan problems in one dimension is described. Numerical experiments indicate that the moving interface is obtained withO(t) accuracy when 3–4 iterations per time step are used to solve the nonlinear implicit scheme. The scheme can be adapted to ADI methods in higher dimensions.     

结构化对等网络特性及关键技术分析

本文简述了对等网络的概念及分类,分析了结构化对等网络特性、存在问题和关键技术,指出结构化对等网络的发展前景。     

Reduced order model predictive control for constrained discrete‐time linear systems

A reduced order model predictive control (MPC) is discussed for constrained discrete‐time linear systems. By employing a decomposition method for finite‐horizon linear systems, an MPC law is obtained from a reduced order optimization problem. The decomposition enables us to construct pairs of initial state and control sequence which have large influence on system responses, and it also characterizes the standard LQ control. The MPC law is obtained based on a combination of the LQ control and dominant input sequences over the prediction horizon. The proposed MPC method is illustrated with numerical examples. Copyright © 2011 John Wiley & Sons, Ltd.     

Alternative Method of Solution of the Regulator Equation: L2‐Space Approach

An alternative method for the proof of solvability of the differential equation that is a part of the regulator equation which arises from the solution of the output regulation problem. The proof uses the L²‐space based theory of solutions of partial differential equations for the case of the linear output regulation problem. In the nonlinear case, a sequence of linear equations is defined so that their solutions converge to the solution of the nonlinear problem. This is proved using the Banach Contraction Theorem. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Group technology based adaptive cell formation using predator-prey genetic algorithm

In recent years, there has been a considerable growth of application of group technology in cellular manufacturing. This has led to investigation of the primary cell formation problem (CFP), both in classical and soft-computing domain. Compared to more well-known and analytical techniques like mathematical programming which have been used rigorously to solve CFPs, heuristic approaches have yet gained the same level of acceptance. In the last decade we have seen some fruitful attempts to use evolutionary techniques like genetic algorithm (GA) and Ant Colony Optimization to find solutions of the CFP. The primary aim of this study is to investigate the applicability of a fine grain variant of the predator-prey GA (PPGA) in CFPs. The algorithm has been adapted to emphasize local selection strategy and to maintain a reasonable balance between prey and predator population, while avoiding premature convergence. The results show that the algorithm is competitive in identifying machine-part clusters from the initial CFP matrix with significantly less number of iterations. The algorithm scaled efficiently for large size problems with competitive performance. Optimal cluster identification is then followed by removal of the bottleneck elements to give a final solution with minimum inter-cluster transition cost. The results give considerable impetus to study similar NP-complete combinatorial problems using fine-grain GAs in future.     

A comparative study between dynamic adapted PSO and VNS for the vehicle routing problem with dynamic requests

Combinatorial optimization problems are usually modeled in a static fashion. In this kind of problems, all data are known in advance, i.e. before the optimization process has started. However, in practice, many problems are dynamic, and change while the optimization is in progress. For example, in the dynamic vehicle routing problem (DVRP), new orders arrive when the working day plan is in progress. In this case, routes must be reconfigured dynamically while executing the current simulation. The DVRP is an extension of a conventional routing problem, its main interest being the connection to many real word applications (repair services, courier mail services, dial-a-ride services, etc.). In this article, a DVRP is examined, and solving methods based on particle swarm optimization and variable neighborhood search paradigms are proposed. The performance of both approaches is evaluated using a new set of benchmarks that we introduce here as well as existing benchmarks in the literature. Finally, we measure the behavior of both methods in terms of dynamic adaptation.     

Nodal ant colony optimization for solving profit based unit commitment problem for GENCOs

This paper proposes a nodal ant colony optimization (NACO) technique to solve profit based unit commitment problem (PBUCP). Generation companies (GENCOs) in a competitive restructured power market, schedule their generators with an objective to maximize their own profit without any regard for system social benefit. Power and reserve prices become important factors in decision process. Ant colony optimization that mimics the behavior of ants foraging activities is suitably implemented to search the UCP search space. Here a search space consisting of optimal combination of binary nodes for unit ON/OFF status is represented for the movement of the ants to maintain good exploration and exploitation search capabilities. The proposed model help GENCOs to make decisions on the quantity of power and reserve that must be put up for sale in the markets and also to schedule generators in order to receive the maximum profit. The effectiveness of the proposed technique for PBUCP is validated on 10 and 36 generating unit systems available in the literature. NACO yields an increase of profit, greater than 1.5%, in comparison with the basic ACO, Muller method and hybrid LR-GA.     

非部分嵌套信息结构的LQG动态队决策问题的可解性（II）*

本文讨论非嵌套信息结构的递阶队决策问题和控制均享信息结构的LQG分散控制问题,文中首先证明这两类问题的信息结构都是“部分控制嵌入”的,进而给出最优策略(或控制)存在的充要条件和当此条件成立时最优策略(或控制)的表达式。