Issue 1 • Date Jan 2000
This paper proposes a solution methodology for a missile defense problem involving the sequential allocation of defensive resources over a series of engagements. The problem is cast as a dynamic programming/Markovian decision problem, which is computationally intractable by exact methods because of its large number of states and its complex modeling issues. We employed a neuro-dynamic programming framework, whereby the cost-to-go function is approximated using neural network architectures that are trained on simulated data. We report on the performance obtained using several different training methods, and we compare this performance with the optimal approach View full abstract»
Characterizing a confidence space for discrete event timings for fault monitoring using discrete sensing and actuation signalsPage(s): 52 - 66
Many manufacturing systems are controlled using discrete sensors and actuators. The changes in values of the corresponding control I/O signals are events. The timing and sequencing relationships of these events can be used to determine whether a system is operating as expected, or whether a fault may have occurred. We present a method of learning intervened timing relationships using observations from a correctly operating system. The sample statistics of the observation characteristic of correct system operation are used to create a confidence space of possible timing relationships of the underlying system. Any timing relationship used as a specification of correct observation for fault monitoring will result in some level of false alarms and missed detections among all the possible relationships in the confidence space; the timing relationships can be chosen to minimize the worst case total of the false alarm and missed detection costs over the confidence space. Simulations are used to evaluate the performance of the chosen timing relationship over a range of perturbed systems View full abstract»
We consider the problem of constructing optimal and near-optimal test sequences for multiple fault diagnosis. The computational complexity of solving the optimal multiple-fault isolation problem is super exponential, that is, it is much more difficult than the single-fault isolation problem, which, by itself, is NP-hard. By employing concepts from information theory and AND/OR graph search and by exploiting the single fault testing strategies of Pattipati et al. (1990), we present several test sequencing algorithms for the multiple fault isolation problem. These algorithms provide a trade-off between the degree of suboptimality and computational complexity. Furthermore, we present novel diagnostic strategies that generate a diagnostic directed graph, instead of a traditional diagnostic tree, for multiple fault diagnosis. Using this approach, the storage complexity of the overall diagnostic strategy reduces substantially. The algorithms developed herein have been successfully applied to several real-world systems View full abstract»
In many cases, a single view of an object may not contain sufficient features to recognize it unambiguously. This paper presents a new online recognition scheme based on next view planning for the identification of an isolated 3D object using simple features. The scheme uses a probabilistic reasoning framework for recognition and planning. Our knowledge representation scheme encodes feature based information about objects as well as the uncertainty in the recognition process. This is used both in the probability calculations as well as in planning the next view. Results clearly demonstrate the effectiveness of our strategy for a reasonably complex experimental set View full abstract»
In this paper, two models predicting mean time until next failure based on Bayesian approach are presented. Times between failures follow Weibull distributions with stochastically decreasing ordering on the hazard functions of successive failure time intervals, reflecting the tester's intent to improve the software quality with each corrective action. We apply the proposed models to actual software failure data and show they give better results under sum of square errors criteria as compared to previous Bayesian models and other existing times between failures models. Finally, we utilize likelihood ratios criterion to compare new model's predictive performance View full abstract»
The use of patterns allows proven design expertise to be captured, communicated and reused in areas such as architecture and object-oriented programming. The artificial intelligence discipline of behavior-based robotics is an area in which such design expertise has undoubtedly accumulated, although it has not been captured and formalized. Consequently, the creation of these robotic systems remains a relatively unstructured process requiring a large amount of trial-and-error development. There is therefore a strong case for representing the previous design expertise which has been developed in this field. This paper describes how this may be achieved through the use of design patterns View full abstract»
We apply a signed distance ranking method for fuzzy numbers to a critical path method for activity-on-edge (AOE) networks. We use signed distance ranking to define ordering simply, which means we can use both positive and negative values to define ordering. The primary result obtained in the paper is the use of signed distance ranking of fuzzy numbers obtaining Properties 3 and 4. We conclude that the fuzzy AOE network is an extension of the crisp AOE network, and thus the fuzzy critical path in a fuzzy AOE network, under some conditions, is the same as the crisp critical path in a crisp AOE network View full abstract»
The optimization of such complex systems as manufacturing systems often necessitates the use of simulation. In this paper, the use of evolutionary algorithms is suggested for the optimization of simulation models. Several types of variables are taken into account. The reduction of computing cost is achieved through the parallelization of this method, which allows several simulation experiments to be run simultaneously. Emphasis is put on a distributed approach where several computers manage both their own local population of solutions and their own simulation experiments, exchanging solutions using a migration operator. After a first evaluation through a mathematical function with a known optimum, the benefits of this new approach are demonstrated through the example of a transport lot sizing and transporter allocation problem in a manufacturing flow shop system, which is solved using a distributed software implemented on a network of eight Sun workstations View full abstract»
Aims & Scope
The fields of systems engineering and human machine systems: systems engineering includes efforts that involve issue formulation, issue analysis and modeling, and decision making and issue interpretation at any of the lifecycle phases associated with the definition, development, and implementation of large systems.
This Transactions ceased production in 2012. The current retitled publication is IEEE Transactions on Systems, Man, and Cybernetics: Systems.
Meet Our Editors
Dr. Witold Pedrycz
University of Alberta