含约束的基于模型的诊断系统

在诊断空间中如何选取理想诊断是诊断系统面临的一个重要问题。在实际的诊断过程中,人们会利用限制条件排除不太可能的诊断,或者利用强制条件选取较优的诊断。按照这个思想,作者提出含约束的基于模型的诊断系统,通过增加依赖于应用领域的约束控制诊断空间,这是一种能够融入计算过程的选择诊断的机制,同时作者在系统拓扑结构的基础上给出了选取理想约束的理论依据。     

Interruptibility as a constraint on hybrid systems

It is widely mooted that a plausible computational cognitive model should involve both symbolic and connectionist components. However, sound principles for combining these components within a hybrid system are currently lacking; the design of such systems is oftenad hoc. In an attempt to ameliorate this we provide a framework of types of hybrid systems and constraints therein, within which to explore the issues. In particular, we suggest the use of system independent constraints, whose source lies in general considerations about cognitive systems, rather than in particular technological or task-based considerations. We illustrate this through a detailed examination of an interruptibility constraint: handling interruptions is a fundamental facet of cognition in a dynamic world. Aspects of interruptions are delineated, as are their precise expression in symbolic and connectionist systems. We illustrate the interaction of the various constraints from interruptibility in the different types of hybrid systems. The picture that emerges of the relationship between the connectionist and the symbolic within a hybrid system provides for sufficient flexibility and complexity to suggest interesting general implications for cognition, thus vindicating the utility of the framework.     

Time-Constrained Keyframe Selection Technique

In accessing large collections of digitized videos, it is often difficult to find both the appropriate video file and the portion of the video that is of interest. This paper describes a novel technique for determining keyframes that are different from each other and provide a good representation of the whole video. We use keyframes to distinguish videos from each other, to summarize videos, and to provide access points into them. The technique can determine any number of keyframes by clustering the frames in a video and by selecting a representative frame from each cluster. Temporal constraints are used to filter out some clusters and to determine the representative frame for a cluster. Desirable visual features can be emphasized in the set of keyframes. An application for browsing a collection of videos makes use of the keyframes to support skimming and to provide visual summaries.     

Dynamic problems of shape optimal design for shallow curved plates

The problem of optimal structural design of shallow thin-walled elements such as curved rectangular plates are formulated and solved for dynamic conditions. The distribution of the initial curvature of shallow plates in a nonstrained state is taken as the control function. Dynamic compliance is considered as the minimized performance functional. Optimality conditions are derived for the distributed parameter system considered and applied for the construction of the analytical solution. The rigorous analysis of extremum conditions and behavioural equations shows that the initial optimization problem is decomposed into several problems of classical structural analysis, which can be successfully solved analytically. Some optimal designs obtained for rectangular plates under stretching and bending, and a plate lying on an elastic foundation and subjected to lateral forces are presented. Received: November 27, 1998     

美军武器装备激励约束定价机制研究

在分析美军武器装备约束激励定价的3种机制,即成本激励约束定价、技术激励定价和进度激励约束定价的方法和标准基础上,概括了美军武器装备激励约束定价的主要特点,提出了我军建立武器装备激励约束定价机制的几点思考。     

Social-welfare based task allocation for multi-robot systems with resource constraints

This paper aims to propose a distributed task allocation algorithm for a team of robots that have constraints on energy resources and operate in an unknown dynamic environment. The objective of the allocation is to maximize task completion ratio while minimizing resource usage. The approach we propose is inspired by the social welfare in economics that helps extend the combined operational lifetime of the team by balancing resource consumptions among robots. This social welfare based task allocation method positions a robot team appropriately in preparedness for dynamic future events and enables to achieve the objectives of the system flexibly depending on the application context. Our simulation-based experiments show that the proposed algorithm outperforms a typical market-based approach in various scenarios.     

LEARNING AND VERIFYING SAFETY CONSTRAINTS FOR PLANNERS IN A KNOWLEDGE‐IMPOVERISHED SYSTEM

We present a Bayesian approach to learning flexible safety constraints and subsequently verifying whether plans satisfy these constraints. Our approach, called the Safety Constraint Learner/Checker (SCLC), infers safety constraints from a single expert demonstration trace and minimal background knowledge, and applies these constraints to the solutions proposed by multiple planning agents in an integrated and heterogeneous ensemble. The SCLC calculates how much to blame plan fragments (partial solutions) generated by the individual planning agents. This information is used when composing these fragments into a final overall plan. In particular, fragments whose safety violations exceed a threshold are rejected. This facilitates the generation of safe plans. We have integrated the SCLC within the Generalized Integrated Learning Architecture, which was designed for Defense Advanced Research Projects Agency (DARPA)’s Integrated Learning (IL) program. The main goal of the IL program is to promote the development and success of sophisticated systems that learn to solve challenging real‐world problems based on a simple demonstration by a human expert and exiguous domain knowledge. We present experimental results showing the advantages of the SCLC on two multiagent problem‐solving tasks that were benchmark applications in DARPA’s IL program.     

Computation of probabilistic linear programming problems involving normal and log-normal random variables with a joint constraint

A method for solving single- and multi-objective probabilistic linear programming problems with a joint constraint is presented. It is assumed that the parameters in the probabilistic linear programming problems are random variables, and the probabilistic problem is converted to an equivalent deterministic mathematical programming problem. In this paper the parameters are generally considered as normal and log-normal random variables. A non-linear programming method is used to solve the single-objective deterministic problem, and a fuzzy programming method is used to solve the multi-objective deterministic problem. Finally, a numerical example is presented to illustrate the methodology.     

Solving multiple processor and multiple resource constrained scheduling problems using a genetic algorithm approach

We consider the multiprocessor scheduling of unit time tasks with precedence constraints and finite set of limited resources. Each task demands some amount of resources for its execution and the total demand for each kind of resources must not exceed a certain limit at any instant of time. Our objective is to find out the minimum time schedule which satisfies the partial orders and the resource usage constraints. We have applied Genetic Algorithm for the present problem. We have shown that the Genetic Algorithm is quite superior to the First Fit Decreasing method.     

A mobility constraint model to infer sensor behaviour in forest fire risk monitoring

Wireless sensor networks (WSNs) play an important role in forest fire risk monitoring. Various applications are in operation. However, the use of mobile sensors in forest risk monitoring remains largely unexplored. Our research contributes to fill this gap by designing a model which abstracts mobility constraints within different types of contexts for the inference of mobile sensor behaviour. This behaviour is focused on achieving a suitable spatial coverage of the WSN when monitoring forest fire risk. The proposed mobility constraint model makes use of a Bayesian network approach and consists of three components: (1) a context typology describing different contexts in which a WSN monitors a dynamic phenomenon; (2) a context graph encoding probabilistic dependencies among variables of interest; and (3) contextual rules encoding expert knowledge and application requirements needed for the inference of sensor behaviour. As an illustration, the model is used to simulate the behaviour of a mobile WSN to obtain a suitable spatial coverage in low and high fire risk scenarios. It is shown that the implemented Bayesian network within the mobility constraint model can successfully infer behaviour such as sleeping sensors, moving sensors, or deploying more sensors to enhance spatial coverage. Furthermore, the mobility constraint model contributes towards mobile sensing in which the mobile sensor behaviour is driven by constraints on the state of the phenomenon and the sensing system.