基于LDAP技术的企业基础信息平台构建

针对企业信息系统对信息共享、数据一致性和统一认证等需求，文中提出了基于LDAP技术构建企业基础信息平台的一种构想。并对基础信息平台构建模式、信息组织方式、开发环境的建立以及客户端程序设计等问题进行了深入探讨并给出了相应的解决方案。     

城市居住区详细规划中的生态设计

首先提出在居住区规划中进行生态设计的理念及这一设计要依据的原則和生态学原理,然后概括出其涉及的主要内容与具体设计方法.     

提高规划与管理的科学性,促进小城镇健康发展

提出了科学的城镇规划与管理的基本任务，进而从四个方面阐述了如何把握这个基本任务促进本城镇经济与社会健康、迅速而稳步地发展：１）科学地研究城镇的性质与规模；２）合理地安排城镇用地布局；３）切实地制定近期建设规划；４）宜人地创造本镇风貌特色。     

1991 年安徽特大洪灾对城市规划科学性的检验

以合肥、芜湖、蚌埠、马鞍山等市及三河、撮镇等集镇为例说明总体规划和防洪专业规划的制定与洪涝灾害的受灾程度密切相关，指出城市规划中考虑防灾的必要性和措施。     

对合肥市郊县绿化造林的一点看法

对合肥市郊县的绿化造林现状作了分析，提出了切实可行的措施。     

Cashing in on Caching: An Architecture for Time-Bounded Knowledge-Based Problem Solving

Knowledge-based computing, in general, suffers from an inherent open-endedness that precludes its application in time-bounded domains where an answer must be computed within a stipulated time limit. We examine a two-way improvement of the shortcomings: a knowledge representation scheme that provides easy access to relevant knowledge and thereby reduces search time, and a reasoning scheme that is algorithmic in nature and thus makes computational requirements meaningfully estimable.In this work, we offer a cache-based architecture that is capable of both storing knowledge in different formats (e.g. rules, cases), and invoking an appropriate reasoning scheme to fit the available computing time. The cache helps in retrieving the most relevant pieces of knowledge (not only exact matches) required for solving a given problem. This cache relies on a reasoning tactic, knowledge interpolation, that can generate a solution from two near-matches in an algorithmic way, to generate time-bounded solutions. We illustrate the design of such a cache for solving resource allocation problems in the domain of shortwave radio transmission and evaluate its performance in observing imposed temporal bounds.     

理性遗传算法及其在运动规划中的应用

为提高理性遗传算法遗传信忠的完备性、算法全局收敛性以及算法的整体结构，给出了一个更一般化的理性算子和算法结构，证明了算法的全局收敛性．理论分析和在运动规划问题中的应用结果验证了理性遗传算法的有效性．     

微粒群优化算法在相关新产品组合投入的应用

引入新产品最佳投入期和相关收益的概念，在产品生命周期量化描述前提下，提出一个非线性半无限规划的相关新产品组合投入模型，并用微粒群优化算法(PS0)求解，仿真实例表明，微粒群优化算法在求解组合优化问题时简便且易于实现，具有很强的实用性。     

Optimal Ordered Problem Solver

We introduce a general and in a certain sense time-optimal way of solving one problem after another, efficiently searching the space of programs that compute solution candidates, including those programs that organize and manage and adapt and reuse earlier acquired knowledge. The Optimal Ordered Problem Solver (OOPS) draws inspiration from Levin's Universal Search designed for single problems and universal Turing machines. It spends part of the total search time for a new problem on testing programs that exploit previous solution-computing programs in computable ways. If the new problem can be solved faster by copy-editing/invoking previous code than by solving the new problem from scratch, then OOPS will find this out. If not, then at least the previous solutions will not cause much harm. We introduce an efficient, recursive, backtracking-based way of implementing OOPS on realistic computers with limited storage. Experiments illustrate how OOPS can greatly profit from metalearning or metasearching, that is, searching for faster search procedures.