Ming Rao

Bio: Ming Rao is an academic researcher from Rutgers University. The author has contributed to research in topics: Real-time Control System & Agent architecture. The author has an hindex of 1, co-authored 1 publications receiving 4 citations.

Integrated intelligent system for real-time control

Abstract: Chemical intelligent process control is a new interdisciplinary field which extensively applies the knowledge of computer science, electrical engineering as well as system science to chemical processes. Two expert systems for process control have been developed by using expert system tool OPS5. IDSOC (Intelligent Decisionmaker for Problem-solving Strategy of Optimal Control) is developed to help us handle various complicated decisionmaking problems in optimal control. It can modify its knowledge base and deal with uncertainty information. The configuration of reasoning at three levels and utilization of "filter rules" enable IDSOC to run very efficiently. IDSCA (Intelligent Direction Selector for the Controllers' Action in multiloop control systems) is a production system, which can not only perform heuristic reasoning in designing process control systems but also test and modify its results adaptively. Meta-level control strategy provides the efficient management for knowledge. A new architecture for implementing chemical intelligent process control systems is also developed. The construction of intelligent systems is one of the most important techniques among artificial intelligence research tasks. Our goal is to develop an integrated intelligent system to accomplish the real-time control of chemical and biochemical processes. An integrated intelligent system is a large knowledge integration environment that consists of several symbolic reasoning systems (expert systems) and numerical computation packages. These software programs are controlled by a meta-system, which manages the selection, operation and communication of these programs. This new architecture can serve as a universal configuration to develop high-performance intelligent process control systems for may complicated industrial applications in real-world domains. The configuration of the integrated intelligent system has attracted significant attention from both industry and academia, and is expected to lead to a new era for the application of AI techniques to real-world chemical intelligent process control problems. Our experience from developing chemical intelligent process control systems also indicates that new knowledge may be generated from the process of developing knowledge-based systems, which may complement the knowledge of both artificial intelligence techniques and the related application domains.

Selected M-Related Dissertations Bibliography

Abstract: The following are citations selected by title and abstract as being related to AI, resulting from a computer search, using BRS Information Technologies, of the Dissertation Abstracts Online database produced by University Microfilms International (UMI).The online file includes abstracts, which are not published in this listing, but the citations below do include reference to the published Dissertation Abstracts International (DAI), which contains the abstracts. Other elements of the citation are author; university, degree, and, if available, number of pages; title; UMI order number and year-month of DAI; and one or more DAI subject descriptors chosen by the author of the dissertation. The listing may include masters abstracts, denoted by MAI (Masters Abstracts International) instead of DAI. References are sorted by subject descriptor; in the event that there is more than one descriptor, the first is used. Within each descriptor, entries are sorted by author.Unless otherwise specified, paper or microform copies of dissertations may be ordered from University Microfilms International, Dissertation Copies, Post Office Box 1764, Ann Arbor, MI 48106; telephone for U.S. (except Michigan, Hawaii, Alaska): 1-800-521-3042, for Canada: 1-800-268-6090. Price lists and other ordering and shipping information are in the introduction to the published DAI. Agriculture, Forestry and Wildlife