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Non-Defense Government Services and Operations
Published in Jay Liebowitz, The Handbook of Applied Expert Systems, 2019
Amelia A. Baldwin, Paul L. Bowen, Linda Gammill
Quest for productivity. Expert systems can be used to increase the productivity and effectiveness of experienced as well as novice personnel. Merced County, California, uses an expert system designed to make the provision of social services more efficient and effective (Kidd and Carlson, 1992). Because welfare and social services often get bogged down in the mass of rules and regulations, a similar system is being developed for the State of Pennsylvania Welfare Department. Tulare Country, California, uses a similar expert system to manage the 6000 frequently changing rules that govern welfare and social service payments. Previously, employees were unable to apply all the rules correctly; the expert system has reduced the error rate from 38% to virtually zero (Betts, 1993).
Introduction to geotechnical engineering
Published in Hsai-Yang Fang, John L. Daniels, Introductory Geotechnical Engineering, 2017
Hsai-Yang Fang, John L. Daniels
Expert systems are intelligent computer programs that are able to perform an intellectual task in a specific field as a human expert would. Systems are being applied to classification problems such as interpretation and diagnosis, as well as general problems such as planning, analysis, and design. Expert systems can be used as data management systems which facilitate correlation studies, risk analysis, and computer aided design. Information produced with these expert systems includes colorful pictorial displays and/or tabular results at any given stage of interaction. Also, the user can trace back-forth to see what has been done or may interactively alter technical and/or financial criteria and constraints. The significant advantage of the computer integrated systems is that they can lead to a greater degree of unification in the processes across many disciplines. There can be an updating of information and an expansion of capacities within both the human–computer interface as well as in the subsystems to maintain the currency of the overall system at any given time.
How to Select Rapid Prototyping and 3D Printer
Published in Rafiq Noorani, 3D Printing, 2017
In 1996, Phillipson [14] from Arizona University developed the RP Advisor that also used the MS Access. This program was compared to that of Muller using Japanese methodology of quality function deployment. As the technology expanded and the revolution of 3D printers took off, it became more difficult to select a prototyping machine that met the needs of a customer or a company. An expert system is an intelligent system that captures the human knowledge and experiences in a computer program. The system can be used to select a prototyping system and to solve other problems such as process planning, process parameter optimization, and tool design. Expert systems have been developed for part orientation and concurrent design in RP. Masood and Soo [15] developed the IRIS intelligent RP selector to assist RP users in the industry and academia to quickly select the RP system that met their requirements. The program incorporates most of the models from the United States of America, Japan, Germany, Israel, and other countries. This program is open ended and can incorporate future prototyping systems.
Application of Artificial Intelligence in Detection and Mitigation of Human Factor Errors in Nuclear Power Plants: A Review
Published in Nuclear Technology, 2023
Meenu Sethu, Bhavya Kotla, Darrell Russell, Mahboubeh Madadi, Nesar Ahmed Titu, Jamie Baalis Coble, Ronald L. Boring, Klaus Blache, Vivek Agarwal, Vaibhav Yadav, Anahita Khojandi
While most research on decision support systems is based on modern machine learning algorithms, several intelligent operator support systems that adopted knowledge-based systems (KBSs) have been reported in the literature.21,22,29,41–43 The most popular knowledge-based AI system is an expert system. An expert system is an AI program that emulates the decision-making ability of a human expert. Uhrig21 provides a survey of the different potential applications of expert systems in nuclear plants to reduce operator error and increase plant safety, reliability, and efficiency. Varde et al.44 implement a hybrid expert system combining the advantages of ANNs and KBSs to enhance the decision-making ability of the operator while coping with operational requirements, particularly during abnormal conditions. In this study, the ANN monitors the reactor’s safety status while the KBS module performs the fault diagnosis and procedure generation. Uhrig and Tsoukalas29 discuss the use of an expert system that has a neural network in its knowledge base, called a connectionist expert system, for identification of transients in NPPs that yielded great benefits in terms of speed, robustness, and knowledge acquisition. Further, they report the robust performance of hybrid neuro-fuzzy approaches that couple a rule-based expert system with pretrained ANNs using fuzzy logic in the presence of noise.
Distributed network-based structural health monitoring expert system
Published in Building Research & Information, 2021
Bello Kontagora Nuhu, Ibrahim Aliyu, Mutiu Adesina Adegboye, Je Kyeong Ryu, Olayemi Mikail Olaniyi, Chang Gyoon Lim
Damage can be considered as a modification of physical parameters such as mass, stiffness, and damping. A physical parameter can be modified by a motion attributed to a vibration within a building. Environmental factors such as temperature can also affect the status of a building. To increase the efficacy of a damage detection system, we propose the incorporation of an expert system into the SHM. Expert systems are systems that can intelligently make decisions based on available input and knowledge programmed in the input (Angeli, 2010). Expert systems are developed to be able to solve a problem with less intervention from human beings. They imitate human reasoning about a problem and use that reasoning to solve the problem. Expert systems are implemented using different methods, including Java Expert System Shell (JESS) and Fuzzy Logic.
Decision-making on HVAC&R systems selection: a critical review
Published in Intelligent Buildings International, 2018
Mehdi Shahrestani, Runming Yao, Geoffrey K Cook, Derek Clements-Croome
Camejo and Hittle (1989) introduced an expert system which included five knowledge-based subsets for HVAC&R system design. Expert systems are computer programs that seek to mimic the human reasoning (Camejo and Hittle 1989). The information about the human reasoning when addressing a specific decision is the core element of expert systems and these are held in the knowledge-based part of an expert system. In the expert system proposed by Camejo and Hittle (1989), the first subset attempted to identify a few, at least two, possible HVAC&R systems based on the limited inputs about the type of building, number of floors and rooms and the geographical location of the building. This subset estimated some of the principal parameters associated with the proposed systems. The parameters were estimated based on a fast rule of thumb, although not entirely accurate, and include installation/operation cost, required power, system life time and the required space.