联合回归估计的优良性

讨论了联合回归估计的性质，证明了它是最小二乘估计的压缩估计，且有比它更好的抗干扰性和稳健性。     

面板堆石坝下游坝坡稳定可靠度分析

应用考虑条块间水平作用力的条分法，在非线性破坏准则的基础上，结合堆石面板坝的破坏模式，构造了完整的面板堆石坝下游坝坡稳定可靠度分析模型，并结合实例进行了计算．所得结果合理，表明该模型比较符合实际．     

一类时滞神经网络系统的局部稳定性与分枝

给出一类时滞神经网络系统的零解的局部稳定判定准则，并给出该系统出现Ｈｏｐｆ分枝的参数条件     

井楼油田零区浅薄层稠油试验区注蒸汽开采综合评价

井楼油田零区试验区是典型的浅薄层特稠油注蒸汽开采先导试验区,经历了蒸汽吞吐和蒸汽驱两个阶段.本文从油藏地质特点和注蒸汽开采特点出发,系统阐述了这类油藏注蒸汽开采的技术策略,从油层动用状况、开发指标和经济指标三个方面对注蒸汽开采效果进行了综合评价.     

深厚覆盖层岩组划分及主要工程地质问题

深厚覆盖具有结构松散、岩性不连续，在水平和垂直方向有较大变化、成因类型复杂、物理力学性质呈较大不均匀性的特点。深厚覆盖层可按其成因、物质组成、结构特征及厚度变化等作为岩组划分的依据。宝兴水电站坝址覆盖层按此依据可划分为６大岩组。     

Disturbance Attenuation and Trajectory Tracking via a Reduced-order Output Feedback Controller for Robot Manipulators

This paper presents a solution to the problem of trajectory tracking with external disturbance attenuation in robotics systems via a reduced-order output feedback controller, without velocity measurement. The proposed control law ensures both semi-global asymptotic stability of the closed-loop system and external disturbance attenuation. The approach is based on the notion of the L₂-gain and requires to solve two algebraic Riccati inequalities. The proposed controller design is illustrated by a simulation example.     

Approximate similarity retrieval with M-trees

Motivated by the urgent need to improve the efficiency of similarity queries, approximate similarity retrieval is investigated in the environment of a metric tree index called the M-tree. Three different approximation techniques are proposed, which show how to forsake query precision for improved performance. Measures are defined that can quantify the improvements in performance efficiency and the quality of approximations. The proposed approximation techniques are then tested on various synthetic and real-life files. The evidence obtained from the experiments confirms our hypothesis that a high-quality approximated similarity search can be performed at a much lower cost than that needed to obtain the exact results. The proposed approximation techniques are scalable and appear to be independent of the metric used. Extensions of these techniques to the environments of other similarity search indexes are also discussed. Received July 7, 1998 / Accepted October 13, 1998     

Control of Discrete Fuzzy Systems: Uncertainty and Guaranteed Performance

Robust control designs for a discrete Takagi and Sugeno (T-S) fuzzy model are proposed. The T-S fuzzy model contains (possibly fast) time-varying uncertainty. First, a switching-type robust control is shown to stabilize the T-S fuzzy model asymptotically. Second, a saturation-type robust control is shown to render the T-S model practically stable. In both designs, only the bound of uncertainty is needed. The effectiveness of proposed designs is analyzed rigorously and illustrated by simulations.     

Making Beliefs Coherentl. The Subtraction and Addition Strategies.

The notion of epistemic coherence is interpreted as involving not only consistency but also stability. The problem how to consolidate a belief system, i.e., revise it so that it becomes coherent, is studied axiomatically as well as in terms of set-theoretical constructions. Representation theorems are given for subtractive consolidation (where coherence is obtained by deleting beliefs) and additive consolidation (where coherence is obtained by adding beliefs).     

Applying Plan Recognition Algorithms To Program Understanding

Program understanding is often viewed as the task of extracting plans and design goals from program source. As such, it is natural to try to apply standard AI plan recognition techniques to the program understanding problem. Yet program understanding researchers have quietly, but consistently, avoided the use of these plan recognition algorithms. This paper shows that treating program understanding as plan recognition is too simplistic and that traditional AI search algorithms for plan recognition are not suitable, as is, for program understanding. In particular, we show (1) that the program understanding task differs significantly from the typical general plan recognition task along several key dimensions, (2) that the program understanding task has particular properties that make it particularly amenable to constraint satisfaction techniques, and (3) that augmenting AI plan recognition algorithms with these techniques can lead to effective solutions for the program understanding problem.