计算机网络信息管理及其安全防护策略

文章以计算机网络信息管理工作为研究对象,从计算机网络信息管理内容的分类、计算机网络信息管理工作中的安全问题分析以及计算机网络信息安全防护策略分析这三个方面入手,对其进行了较为详细的分析与阐述,并据此论证了做好这一工作在进一步提升计算机网络信息管理质量与管理效率的过程中所起到的至关重要的作用与意义。     

带平衡约束矩形布局优化问题的深度强化学习算法

带平衡约束的矩形布局问题源于卫星舱设备布局设计,属于组合优化问题。深度强化学习利用奖赏机制,通过数据训练实现高性能决策优化。针对布局优化问题,提出一种基于深度强化学习的新算法DAR及其扩展算法IDAR。DAR用指针网络输出定位顺序,再利用定位机制给出布局结果,算法的时间复杂度是O（n³）;IDAR算法在DAR的基础上引入迭代机制,算法时间复杂度是O（n⁴）,但能给出更好的结果。测试表明DAR算法具有较好的学习能力,用小型布局问题进行求解训练所获得的模型,能有效应用在大型问题上。在两个大规模典型算例的对照实验中,提出算法分别超出和接近目前最优解,具有时间和质量上的优势。     

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。     

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.     

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。     

A Heuristic Approach to the Discovery of Macro-Operators

This paper describes a heuristic approach to the discovery of useful macro-operators (macros) in problem solving. The approach has been implemented in a program, MACLEARN, that has three parts: macro-proposer, static filter, and dynamic filter. Learning occurs during problem solving, so that performance improves in the course of a single problem trial. Primitive operators and macros are both represented within a uniform representational framework that is closed under composition. This means that new macros can be defined in terms of others, which leads to a definitional hierarchy. The representation also supports the transfer of macros to related problems. MACLEARN is embedded in a supporting system that carries out best-first search. Experiments in macro learning were conducted for two classes of problems: peg solitaire (generalized Hi-Q puzzle), and tile sliding (generalized Fifteen puzzle). The results indicate that MACLEARN'S filtering heuristics all improve search performance, sometimes dramatically. When the system was given practice on simpler training problems, it learned a set of macros that led to successful solutions of several much harder problems.     

Probability densities for conditional statistics in the cubic sensor problem

This paper applies the techniques of Malliavin’s stochastic calculus of variations to Zakai’s equation for the one-dimensional cubic sensor problem in order to study the existence of densities of conditional statistics. Let {X _t} be a Brownian motion observed by a cubic sensor corrupted by white noise, and let denote the unnormalized conditional estimate of φ(X _i ). If φ₁,...,φ _n are linearly independent, and if , it is shown that the probability distribution of admits a density with respect to Lebesgue measure for anyn. This implies that, at any fixed time, the unnormalized conditional density cannot be characterized by a finite set of sufficient statistics. Research supported in part by NSF Grant No. MCS-8301880 and by the Institute for Mathematics and It Applications, Minneapolis, Minnesota.     

曝光数据流优化问题的蒙特卡罗算法

集成工艺中控制图形发生器产生掩模图形的数据流文件的数据排列顺序优化,是一个其结点数可达数千个之多的大规模旅行商问题(从镜头架的位置,即每个矩形的矩心为结点),常规算法早已不适用。本文采用多阶段蒙特卡罗算法并加以改进,补充了“分层优化模式”,邻接结点的预合并和在各个阶段末提取共同子路径并删去相关结点等一系列逐步缩小问题规模的措施,形成了一个实用的优化算法。相应的软件已成功地应用于薄膜磁头的数据流文件的优化。