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Journal articles on the topic 'Knowledge-Based Expert System'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Khator, Rajesh Kumar. "Expert System using Knowledge Based System." International Journal of Computer Sciences and Engineering 7, no. 6 (2019): 52–55. http://dx.doi.org/10.26438/ijcse/v7i6.5255.

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2

DiStefano, Joseph J., Parker Waechter, Philip Abramowitz, Hsiao-Te Su, and Thuvan N. Nguyen. "Expert System-Knowledge Based Biomodeling." IFAC Proceedings Volumes 27, no. 1 (1994): 243–48. http://dx.doi.org/10.1016/s1474-6670(17)46220-3.

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3

Alkoc, Ediz, and Fuat Erbatur. "SITE EXPERT: a prototype knowledge-based expert system." Engineering Construction and Architectural Management 5, no. 3 (1998): 238–51. http://dx.doi.org/10.1046/j.1365-232x.1998.00047.x.

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4

ALKOC, EDIZ, and FUAT ERBATUR. "SITE EXPERT: a prototype knowledge‐based expert system." Engineering, Construction and Architectural Management 5, no. 3 (1998): 238–51. http://dx.doi.org/10.1108/eb021078.

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5

Mockler, Robert J., and D. G. Dologite. "Teaching Expert Knowledge-Based System Development." Journal of Education for Business 63, no. 7 (1988): 300–306. http://dx.doi.org/10.1080/08832323.1988.10118481.

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6

Billatos, S. B., and J. A. Webster. "Knowledge-Based Expert System for Ballscrew Grinding." Journal of Engineering for Industry 115, no. 2 (1993): 230–35. http://dx.doi.org/10.1115/1.2901654.

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This paper discusses the ballscrew grinding process and provides a newly structured knowledge-based expert system (KBES) developed to automate the process based on fuzzy pattern recognition techniques for shape control with recommendations for further optimization considering practical machining constraints. The KBES offers several advantages including the use of semiskilled operators who can monitor several machines simultaneously, the consistent production of high quality parts, and the significant reduction in cycle time due to automatic adjustment of steadies. A new stepper motor steady design and in-process measuring system are also described.

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7

Schub, Adolf, and Maad El Madlaji. "A Knowledge-Based Expert System for Earthmoving." Structural Engineering International 2, no. 2 (1992): 131–33. http://dx.doi.org/10.2749/101686692780615996.

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8

Chow, K. C. Ander, and W. E. Watt. "A knowledge-based expert system for flood frequency analysis." Canadian Journal of Civil Engineering 17, no. 4 (1990): 597–609. http://dx.doi.org/10.1139/l90-068.

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Single-station flood frequency analysis is an important element in hydrotechnical planning and design. In Canada, no single statistical distribution has been specified for floods; hence, the conventional approach is to select a distribution based on its fit to the observed sample. This selection is not straightforward owing to typically short record lengths and attendant sampling error, magnified influence of apparent outliers, and limited evidence of two populations. Nevertheless, experienced analysts confidently select a distribution for a station based only on a few heuristics. A knowledge-based expert system has been developed to emulate these expert heuristics. It can perform data analyses, suggest an appropriate distribution, detect outliers, and provide means to justify a design flood on physical grounds. If the sample is too small to give reliable quantile estimates, the system performs a Bayesian analysis to combine regional information with station-specific data. The system was calibrated and tested for 52 stations across Canada. Its performance was evaluated by comparing the distributions selected by experts with those given by the developed system. The results indicated that the system can perform at an expert level in the task of selecting distributions. Key words: flood frequency, expert system, single-station, fuzzy logic, inductive reasoning, production system.

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9

Parng, Kuocheng A., and Vernon S. Ellingstad. "MENUDA: A Knowledge-Based Menu Design Expert System." Proceedings of the Human Factors Society Annual Meeting 31, no. 12 (1987): 1315–19. http://dx.doi.org/10.1177/154193128703101203.

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An experimental knowledge-based menu design assistant (MENUDA) was developed to aid the design of menu systems. A conceptual model was first developed to provide a structured construct to organize knowledge of menu system design from the available literature, and to serve as a paradigm for the development of the MENUDA system. The knowledge base and the user interface of the MENUDA system were developed under an interactive microcomputer environment supported by Texas Instruments' Personal Consultant Plus. The current version of the MENUDA system is described in the paper. In addition, the methodology used to derive rules in the MENUDA knowledge base and the appropriateness of employing a knowledge-based expert system approach to providing user interface design guidelines are discussed.

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10

Arbaiy, Nureize, Shafiza Eliza Sulaiman, Norlida Hassan, and Zehan Afizah Afip. "Integrated Knowledge Based Expert System for Disease Diagnosis System." IOP Conference Series: Materials Science and Engineering 226 (August 2017): 012097. http://dx.doi.org/10.1088/1757-899x/226/1/012097.

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11

M Abdelsalam, Hassan, Muhammad Nazri Borhan, Abdalrhman Milad, et al. "Web-Based Knowledge Evolution for Thinking Green Transportation Using Expert Systems." International Journal of Engineering & Technology 7, no. 2.29 (2018): 837. http://dx.doi.org/10.14419/ijet.v7i2.29.14267.

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In many developing nations, it is difficult for development planners and novice engineers to determine the point of transportation service and usability based only on the master plan of the city. On the other hand, nowadays managers in the organizations use computerized support for making decisions on a day-to-day basis. The experts are capable of identifying problems easily and finding solutions fast but novice engineers are unable to do so. This expert system is designed to fulfill the gap that exist between the urban design experts and user who wish to use Green transportation system namely students, decision makers and engineers. Verification, process validation and comparison of the system with similar systems showed that the system is reliable and practical. In order to create the expert system, sources related to Expert system concerning transportation have been studied and interviews with experts have been conducted. The proposed expert system is developed using Visual Studio as the building tool and SQL as the query tool. The knowledge base of thinking green transportation [T Green T] includes three modules and each module may contain a number of sub-modules, parameters, strategies, and criteria. The system incorporate rules and images for various types of measurements. Lastly, several recommendations for maintaining and improving the system are suggested. The development’s suitability can be characterized using websites of other modes of transportation such as bicycles and pedestrian traffic.

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12

Wu, Su Yan, and Wen Bo Li. "Research on Knowledge Service Oriented Expert Management System." Applied Mechanics and Materials 519-520 (February 2014): 1568–71. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.1568.

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The distinctive characteristics of knowledge service oriented expert management system needs to have instant, on-demand service, accurate service, comprehensive services, and personalized service. In order to meet the requirements of the establishment of this system, this paper studied the construction of expert resources and expert retrieval services , and The crowdsourcing technology-based open expert body build, experts of different heterogeneous data sources access to information based expert body interaction semantics expert retrieval method

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13

Rubin, Stuart H. "Knowledge-Based Software Prototyping and Reuse." International Journal on Artificial Intelligence Tools 06, no. 01 (1997): 127–47. http://dx.doi.org/10.1142/s0218213097000074.

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Models are executable prototypes. Modeling is closely tied to simulation, which refers to the exercise of a model over a variable parametric space. Model simulations not only provide the engineer with feedback pertaining to the validity of a proposed design, but additionally allow competing designs to be compared on one or more parameters (i.e., sensitivity analysis). Models are defined from a base of several hundred primitive constructs. These constructs can define additional constructs hierarchically. An expertn–system was constructed, which retrieves software for reuse. This expert system is itself reusable and consists of many sub-systems – any one of which can invoke any other. A key feature is that any expertn–system need never be modified, for purposes of reuse, once saved in a repository. Rather, it communicates all information back to the caller and lets the caller decide how and when to use it. Thus, blocks in an expertn–system have very low coupling (i.e., no off-model connections). In addition, expertn–systems are, as their name suggests, organized in a hierarchy. This means that very complex decision-making systems can be called into play with minimal effort. Growing the repository is equivalent to learning.

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14

Hanna, Paulette B., Awad S. Hanna, and Thomas A. Papagiannakis. "Knowledge-based advisory system for flexible pavement routine maintenance." Canadian Journal of Civil Engineering 20, no. 1 (1993): 154–63. http://dx.doi.org/10.1139/l93-016.

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One of the most pressing problems facing Canada is the condition of roadway infrastructure. Making good maintenance decisions requires years of practical experience and judgement. Expert systems have great potential for solving pavement maintenance problems that usually require significant human expertise for solution. Expert systems, also known as knowledge-based systems, have been used as a means for conveying pavement maintenance knowledge gained through research and field experience to individuals responsible for maintaining asphalt pavements. An expert system is defined as an interactive computer program which documents judgement, experience, intuition, and other information in order to provide knowledgeable advice.This paper describes the development of PMAS, a pavement maintenance advisory system, which can assist highway engineers in planning effective flexible or asphalt concrete pavement maintenance strategies. The system uses two alternative commercial expert system shells. The system questions the user in a multiple-choice format in everyday English and (or) by using pictures. The user responds by selecting one or more of the choices provided by the system. At the end of the consultation session, the system displays the most appropriate maintenance strategy along with its life expectancy. PMAS facilitates the decision-making process and could serve as a consultant for field engineers. Key words: expert system, knowledge-based system, pavement maintenance.

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15

W, Yunanri, Doddy Teguh Yuwono, and Ika Safitri Windarti. "SYSTEMATIC OF EXPERT SYSTEM BASE ON SOFTWARE AND CALCULATION METHOD." Jurnal Informatika, Teknologi dan Sains 2, no. 1 (2020): 1–6. http://dx.doi.org/10.51401/jinteks.v2i1.552.

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The expert system is a system that seeks to adopt human knowledge into technology, so that technology can solve problems as is usually done by experts. A good expert system is designed to solve a particular problem by imitating the work of the experts. With expert systems, the layman can solve quite complicated problems, because in fact the problem can only be solved with the help of experts. For experts, the expert system will also assist its activities as a highly experienced assistant. Currently there are many studies that raise cases about expert systems. This study aims to create systematic on expert systems based on a collection of expert system cases and classify them based on the tools used, implementation types and frequently used methods.

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16

Hansen, James V., and William F. Messier. "A knowledge-based expert system for auditing advanced computer systems." European Journal of Operational Research 26, no. 3 (1986): 371–79. http://dx.doi.org/10.1016/0377-2217(86)90139-6.

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17

ADESUSI, O. M., O. R. ADETUNJI, A. F. ADEKOYA, S. B. ADEJUYIGBE, and S. O. IPADEOLA. "A KNOWLEDGE-BASED EXPERT SYSTEM FOR SILICATE GLASS PLANT MAINTENANCE." Journal of Natural Sciences Engineering and Technology 14, no. 2 (2018): 116–25. http://dx.doi.org/10.51406/jnset.v14i2.1761.

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A Knowledge-Based Expert System (KBES) which will aid diagnosis of plant faults within Silicate Glass Plant domain was developed. Knowledge was elicited using a combination of techniques (protocol-generation, teach and teach back, protocol analysis techniques) and root cause failure analy-sis was used to identify primary cause of fault as they were presented by the domain experts. The elicited knowledge were structured and coded in form of rules using C Language Integrated Produc-tion System â€žCLIPS SHELLâ€Ÿ. The KBES developed comprised a main system and four sub-systems with utility files. Performance evaluation was carried out on the KBES for consistency and exactness. Field tests were conducted and the data acquired were analyzed using some maintenance parame-ters. Results obtained when the KBES was used in the plantâ€Ÿs faults diagnosis compared with when it was not used showed that with expert system in use, an average reduction in plant downtime of 36.62% was obtained for field test carried out. Also, 24.28% and 34.03% were obtained for average increase in Plant Availability and Total Plant Maintenance Productivity respectively.

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18

Sai Srinadh, K. V. "Knowledge based Expert System for Welding Defect Analysis." Indian Welding Journal 39, no. 4 (2006): 32. http://dx.doi.org/10.22486/iwj.v39i4.178444.

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19

Golumbic, Martin C., Moshe Markovich, Shalom Tsur, and Uri J. Schild. "A Knowledge-Based Expert System for Student Advising." IEEE Transactions on Education E-29, no. 2 (1986): 120–24. http://dx.doi.org/10.1109/te.1986.5570612.

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20

Hsu, Y. Y., and J. L. Chen. "Distribution planning using a knowledge-based expert system." IEEE Transactions on Power Delivery 5, no. 3 (1990): 1514–19. http://dx.doi.org/10.1109/61.57995.

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21

Bai, Yong, and Serji N. Amirkhanian. "Knowledge‐Based Expert System for Concrete Mix Design." Journal of Construction Engineering and Management 120, no. 2 (1994): 357–73. http://dx.doi.org/10.1061/(asce)0733-9364(1994)120:2(357).

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22

Voelker, J. A., and G. B. Ratica. "The genesis of a knowledge-based expert system." IBM Systems Journal 25, no. 2 (1986): 181–89. http://dx.doi.org/10.1147/sj.252.0181.

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23

Cheng, Edmond D. H. "Wind data generator: A knowledge-based expert system." Journal of Wind Engineering and Industrial Aerodynamics 38, no. 2-3 (1991): 101–8. http://dx.doi.org/10.1016/0167-6105(91)90032-r.

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24

Sun, Tao, and Hai Bo Liu. "Design of Fault Diagnosis Expert System of Transformer." Applied Mechanics and Materials 291-294 (February 2013): 2557–61. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.2557.

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The transformer fault diagnosis expert system design knowledge representation and reasoning mechanisms are the key issue. Characteristics of transformer fault diagnosis system based on human experts, learning on the basis of the human expert diagnosis of transformer faults, to build a transformer fault diagnosis expert system of systems architecture, knowledge representation and reasoning mechanisms for a more detailed analysis and discussion.

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Yang, Zong Xiao, Tian Xiao, Lei Song, and Guan Qiang Dong. "Expressway Event Management Expert System Based on Ontology and Rule Reasoning." Advanced Materials Research 601 (December 2012): 361–68. http://dx.doi.org/10.4028/www.scientific.net/amr.601.361.

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Response and decision-making support in expressway emergency management and rescue greatly affects some key factors like emergency rescue plan, emergency duration time, effect range, economy and time loss. Experts system is a kind of intelligent program, using knowledge and reasoning to solve the complex problem that only experts can work out, it is a popular and efficient decision-support means, has some practical features that manual way cannot compare with and offers a modem scientific measure in incident management. Expert system can provide high-efficiency, rapid, exact and powerful assistance in decision support. In order to deal with various possible emergencies promptly and efficiently and therefore ensure the safety and unblocked state of the expressway, develop the expert system has become a trend of emergency rescue and management. In expert system,The knowledge representation is the key basic issues has the important influence to construction of the knowledge base and the reasoning of the expert system. So there has the practical significance to further research on knowledge representation of expressway accident management system, According to features of different knowledge, in exsiting expert systems different knowledge representation methods were adopted such as frame, production rule, procedure, first-order predicate logic, etc. This paper focus on the ontology’s application in the field of expressway emergency rescue and propose a expressway event managemnet expert system based on ontology and rule reasoning.

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Tavana, Madjid. "Knowledge-Based Expert System Development and Validation with Petri Nets." Journal of Information & Knowledge Management 07, no. 01 (2008): 37–46. http://dx.doi.org/10.1142/s0219649208001907.

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Expert systems (ESs) are complex information systems that are expensive to build and difficult to validate. Numerous knowledge representation strategies such as rules, semantic networks, frames, objects and logical expressions are developed to provide high-level abstraction of a system. Rules are the most commonly used form of knowledge representation and they are derived from popular techniques such as decision trees and decision tables. Despite their huge popularity, decision trees and decision tables are static and cannot model the dynamic requirements of a system. In this study, we propose Petri Nets (PNs) for dynamic system representation and rule derivation. PNs with their graphical and precise nature and their firm mathematical foundation are especially useful for building ESs that exhibit a variety of situations, including: sequential execution, conflict, concurrency, synchronisation, merging, confusion, or prioritisation. We demonstrate the application of our methodology in the design and development of a medical diagnostic expert system.

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Simonovic, Slobodan P. "Knowledge-based systems and operational hydrology." Canadian Journal of Civil Engineering 18, no. 1 (1991): 1–11. http://dx.doi.org/10.1139/l91-001.

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Knowledge-based systems were brought to the attention of hydrologists almost a decade ago. The application of knowledge-based systems technology is natural and appropriate for the field of hydrology because it contains numerous procedures developed from theory, actual practice, and experience. The emphasis of the present paper is on demystifying knowledge-based systems of artificial intelligence. After a detailed review of the most important applications to the field of hydrology, the original concept for applying knowledge-based technology is presented. The discussion ends with the list of possible benefits from the application of knowledge-based technology. An expert system for the selection of a suitable method for flow measurement in open channels is used as a case study to illustrate the discussion in the paper. The system has been designed for potential use in Environment Canada. Key words: expert system, water resources, hydrology, flow measurements.

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Barai, Sudhikumar, and Padmesh Charan Pandey. "KNOWLEDGE BASED EXPERT SYSTEM APPROACH TO INSTRUMENTATION SELECTION (INSEL)." TRANSPORT 19, no. 4 (2004): 171–76. http://dx.doi.org/10.3846/16484142.2004.9637971.

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The selection of appropriate instrumentation for any structural measurement of civil engineering structure is a complex task. Recent developments in Artificial Intelligence (AI) can help in an organized use of experiential knowledge available on instrumentation for laboratory and in‐situ measurement. Usually, the instrumentation decision is based on the experience and judgment of experimentalists. The heuristic knowledge available for different types of measurement is domain dependent and the information is scattered in varied knowledge sources. The knowledge engineering techniques can help in capturing the experiential knowledge. This paper demonstrates a prototype knowledge based system for INstrument SELection (INSEL) assistant where the experiential knowledge for various structural domains can be captured and utilized for making instrumentation decision. In particular, this Knowledge Based Expert System (KBES) encodes the heuristics on measurement and demonstrates the instrument selection process with reference to steel bridges. INSEL runs on a microcomputer and uses an INSIGHT 2+ environment.

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Wang, Chao, Chunlan Jiang, Liang Mao, and Xinyi Cheng. "Knowledge Based Performance Analysis Expert System for Fragmentation Ammunitions." Information Technology Journal 13, no. 3 (2014): 508–14. http://dx.doi.org/10.3923/itj.2014.508.514.

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Mansyur, Resdiansyah, Riza Atiq O. K. Rahmat, Amiruddin Ismail, and Mohamad Raduan Kabit. "Knowledge Based Expert Advisory System for Transport Demand Management." International Journal on Advanced Science, Engineering and Information Technology 1, no. 6 (2011): 652. http://dx.doi.org/10.18517/ijaseit.1.6.130.

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Al-Ani, Ibrahiem Abdul Razak, Lariyah Mohd Sidek, M. N. Mohamed Desa, and N. E. Ahmad Basri. "Knowledge-based Expert System for Stormwater Management in Malaysia." Journal of Environmental Science and Technology 5, no. 5 (2012): 381–88. http://dx.doi.org/10.3923/jest.2012.381.388.

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Nan, Cen, Faisal Khan, and M. Tariq Iqbal. "Real-time fault diagnosis using knowledge-based expert system." Process Safety and Environmental Protection 86, no. 1 (2008): 55–71. http://dx.doi.org/10.1016/j.psep.2007.10.014.

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Craw, Susan M., Robin A. Boswell, and Raymond C. Rowe. "Automated knowledge refinement for rule-based formulation expert system." Pharmaceutical Science & Technology Today 2, no. 9 (1999): 383–85. http://dx.doi.org/10.1016/s1461-5347(99)00182-0.

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Burnashev, R. A., Ismail Amer, and A. I. Enikeev. "Expert system building tools based on dynamically updated knowledge." Journal of Physics: Conference Series 1352 (October 2019): 012008. http://dx.doi.org/10.1088/1742-6596/1352/1/012008.

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KIMES, DANIEL S., PATRICK R. HARRISON, and P. ANN RATCLIFFE. "A knowledge-based expert system for inferring vegetation characteristics." International Journal of Remote Sensing 12, no. 10 (1991): 1987–2020. http://dx.doi.org/10.1080/01431169108955233.

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Humphrey, S. M. "A knowledge-based expert system for computer-assisted indexing." IEEE Expert 4, no. 3 (1989): 25–38. http://dx.doi.org/10.1109/64.43268.

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Harris, Regina M., Susan G. Hill, and Robert J. Lysaght. "Operator Workload Knowledge-Based Expert System Tool (OWLKNEST) Demonstration." Proceedings of the Human Factors Society Annual Meeting 33, no. 17 (1989): 1140–41. http://dx.doi.org/10.1518/107118189786758049.

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Objectives The Operator Workload Knowledge-based Expert System Tool (OWLKNEST) is a tool that provides guidance in selecting the most appropriate technique(s) for estimating or predicting Operator Workload (OWL). This demonstration will provide hands-on usage for interested parties in utilizing OWLKNEST to determine the most appropriate OWL technique for their particular situation, interpreting the resulting outputs, and performing sensitivity analysis to assess the impact of changing responses. Description of subject matter A variety of OWL estimation techniques are available, but information about these techniques and their appropriateness for particular situations may be difficult to assimilate. Furthermore, information on practical issues concerning the applicability of a particular tool is often unavailable—e.g., time, cost, personnel issues, etc. The selection of the appropriate technique(s) for assessing OWL is complex and beyond the scope of many practitioners. Therefore, an expert system approach was selected to provide a tool which can serve as a clearinghouse of knowledge as well as providing recommendations for workload estimation techniques. OWLKNEST requires an IBM PC type microcomputer equipped with a minimum of 640 Kb memory and two floppy diskette drives of at least 360Kb. Exsys Professional, a rule-based, backward-chaining system, is the expert system shell used for OWLKNEST. OWLKNEST incorporates an hierarchical logic structure that quickly focuses on the most applicable technique(s) and minimizes the number of questions posed by the system. It is based upon a taxonomy which divides OWL techniques into analytical and empirical techniques. OWLKNEST utilizes a question-and-answer dialogue to facilitates use by inexperienced analysts and is supplemented by embedded help features. The inputs are fed into the expert system, which applies rules and knowledge based on user information. The result is a suggested list of appropriate techniques. The user can obtain brief descriptions of the recommended technique(s) including references and descriptions of prior applications. OWLKNEST assumes that users have at least a fundamental knowledge of OWL concepts but can be novice computer and expert system users. The development of OWLKNEST was sponsored by the Army Research Institute and free copies of the runtime version of OWLKNEST are available to interested parties. Additional details about OWLKNEST are available in Hill and Harris, 1989 and Harris et al, 1989. More complete information about utilizing OWLKNEST can be obtained in the user's guide—Handbook for Operation of the OWLKNEST Tool (HOOT) (Harris et al., 1989). Importance The increasing criticality of performing OWL assessments throughout the system development cycle has Id to the recognition that a comprehensive, easy-to-use tool to recommend the appropriate OWL techniques is needed. OWLKNEST provides this capability and supplies information on the appropriate OWL techniques based on individual needs and resources. Possible Applications OWLKNEST will be useful in military application as well as other application areas where prediction and evaluation of operator workload is of interest. Similarly, the workload techniques are not application specific, but can be used in a variety of domains. OWLKNEST can provide the information and guidance of an appropriate selection of workload techniques for a broad range of applications.

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Yoon, YoungOhe, Lynn L. Peterson, and Paul R. Bergstresser. "Desknet: The dermatology expert system with knowledge-based network." Neural Networks 1 (January 1988): 477. http://dx.doi.org/10.1016/0893-6080(88)90501-1.

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Batanov, Dentcho, Nagen Nagarur, and Prapan Nitikhunkasem. "EXPERT-MM: A knowledge-based system for maintenance management." Artificial Intelligence in Engineering 8, no. 4 (1993): 283–91. http://dx.doi.org/10.1016/0954-1810(93)90012-5.

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40

Chang, Te-Chuan, C. William Ibbs, and Keith C. Crandall. "A fuzzy logic system for expert systems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 2, no. 3 (1988): 183–93. http://dx.doi.org/10.1017/s0890060400000640.

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Using the theory of fuzzy sets, this paper develops a fuzzy logic reasoning system as an augmentation to a rule-based expert system to deal with fuzzy information. First, fuzzy set theorems and fuzzy logic principles are briefly reviewed and organized to form a basis for the proposed fuzzy logic system. These theorems and principles are then extended for reasoning based on knowledge base with fuzzy production rules. When an expert system is augmented with the fuzzy logic system, the inference capability of the expert system is greatly expanded; and the establishment of a rule-based knowledge base becomes much easier and more economical. Interpretations of the system’s power and possible future research directions conclude the paper.

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Xia, Jian Sheng. "Research on Expert System for Ball Mill Type Selection Based on Knowledge." Advanced Materials Research 538-541 (June 2012): 3029–33. http://dx.doi.org/10.4028/www.scientific.net/amr.538-541.3029.

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The ball mill grinding process is an important grinding process, the type selection and design of the grinding system is a complex process, so the previous design needs to rely on the experience of experts. It uses the expert system principles and case-based reasoning technology in the grinding system selection, to improve the design efficiency.

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Moisan, Sabine. "Generating Knowledge-Based System Generators." International Journal of Intelligent Information Technologies 6, no. 1 (2010): 1–17. http://dx.doi.org/10.4018/jiit.2010100901.

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This article investigates software engineering techniques for designing and reengineering knowledge-based system generators, focusing on inference engines and domain specific languages. Indeed, software development of knowledge-based systems is a difficult task. We choose a software engineering approach to favor code reuse, evolution, and maintenance. We propose a software platform named Lama to design the different elements necessary to produce a knowledge-based system. This platform offers software toolkits (mainly component frameworks) to build interfaces, inference engines, and expert languages. We have used the platform to build several KBS generators for various tasks (planning, classification, model calibration) in different domains. The approach appears well fitted to knowledge-based system generators; it allows developers a significant gain in time, as well as it improves software readability and safeness.

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Chan, W. T., and Lawrence C. C. Koe. "A Knowledge-Based Framework for the Diagnosis of Sludge Bulking in the Activated Sludge Process." Water Science and Technology 23, no. 4-6 (1991): 847–55. http://dx.doi.org/10.2166/wst.1991.0536.

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Despite advances in the understanding of the activated sludge process for treating wastewater, the operation of an activated sludge process plant - in particular, the detection, diagnosis and remedy of operational problems - still involves a significant amount of qualitative knowledge derived from empirical observations. Expert systems can be of assistance to plant operators in problem diagnosis by automating the problem-solving behavior of human experts and retrieving the appropriate chunks of qualitative knowledge from a large collection of such knowledge as the context of the problem dictates. A new generation of expert systems shell delivers better performance by providing (i) an object-centred framework with interesting computational properties to organize the considerable amounts of information about the physical world, (ii) flexible, context-dependent and programmable inference strategies to better model the problem-solving behavior of human experts, (iii) a reliable means of integrating numeric and symbolic computation, and (iv) a means for the expert system application to couple its inference procedure with its ability to interact with events in the real world through sensors and actuators. A prototype expert system employing a new generation expert system shell has been developed for diagnosing the sludge bulking problem in the activated sludge process. The paper discusses the knowledge representation scheme employed in the prototype, which is general enough to be extended to cover other operational problems occurring in sewage treatment plants. A study was performed to validate the knowledge in the prototype by comparing the conclusions of a panel of human experts reported in the literature with those of the prototype in response to a wide range of operating conditions. The study shows close agreement between the two sets of conclusions.

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SOO, HSU LOKE. "A PROLOG-BASED CHINESE EXPERT SYSTEM." International Journal of Pattern Recognition and Artificial Intelligence 02, no. 01 (1988): 17–24. http://dx.doi.org/10.1142/s0218001488000030.

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This paper presents the design and implementation of a Chinese Expert System Shell which is based on a Chinese Prolog interpreter. The system is divided into three parts: the knowledge acquisition module, the knowledge application module and the inference engine. The knowledge engineer defines the syntax of the language to be used by himself and by the users when they interact with the system. The natural language interface is table driven and can be modified easily. The system also caters for the case when the domain expert finds it difficult to articulate the rules, but is able to give examples. An inductive engine is included to extract rules from examples.

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Furuta, H., N. Shiraishi, M. Umano, and K. Kawakami. "Knowledge-based expert system for damage assessment based on fuzzy reasoning." Computers & Structures 40, no. 1 (1991): 137–42. http://dx.doi.org/10.1016/0045-7949(91)90466-y.

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Schwarz, G., R. Grims, E. Rumpl, G. Rom, G. Pfurtscheller, and V. Haase. "BRAINDEX: An Interactive, Knowledge-Based System Supporting Brain Death Diagnosis." Methods of Information in Medicine 29, no. 03 (1990): 193–99. http://dx.doi.org/10.1055/s-0038-1634782.

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AbstractBRAINDEX (Brain-Death Expert System) is an interactive, knowledge-based expert system offering support to physicians in decision making concerning brain death. The physician is given the possibility of communicating in almost natural language and, therefore, in terms with which he is familiar. This updated version of the system is implemented on an IBM-PC/AT with the expert system shell PC-PLUS and consists of about 430 rules. The determination of brain death is realized with backward chaining and for the optional coma-scaling a forward-chaining mechanism is used.

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Andrianof, Harkamsyah. "Design Of Expert System To Diagnose Sexually Infected Diseases In Human Using The Backward Chaining Method (Perancangan Sistem Pakar Untuk Mendiagnosa Penyakit Menular Seksual Pada Manusia Dengan Penerapan Metode Backward Chaining)." Jurnal KomtekInfo 6, no. 2 (2019): 135–43. http://dx.doi.org/10.35134/komtekinfo.v6i2.52.

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Expert systems (expert systems) in general is trying to adopt a system of human knowledge into a computer, so the computer can resolve the problem as was done by the experts. Or in other words, the expert system is a system designed and implemented with the help of a specific programming language to be able to resolve the problem as done by experts. In this case I tried to implement an expert system to diagnose sexually transmitted disease from the symptoms and the causes of Sexually Transmitted Disease. The purpose of this paper is to build a knowledge-based system on Sexually Transmitted Disease using backward chaining method that will be displayed in the form of a website using PHP programming and MySQL database.

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Han, Li Fen, Shou Yan Zhong, Wei Feng Ding, and Tan Xiang Liu. "Research on Knowledge-Based Injection Mould Project Design System." Advanced Materials Research 383-390 (November 2011): 1411–16. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1411.

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The intelligent design technology based on the Expert System (ES) was applied into injection mould design. The experts’ knowledge and experience related to the injection mould project design (IMPD) was expressed in the form of production rule. The knowledge base of intelligent project design of injection mould was constructed. The knowledge automatic reasoning via CLIPS (C Language Integrated Production System) knowledge deducing machine was realized. The achievement of the injection mould project’s intelligent design was reached. The research on the injection mould intelligent design based on CLIPS was made. The intelligent design system for injection mould project (IDSIMP) was developed. The injection mould design project could be provided to the injection mould design engineers via the system. The system is of better application value on engineering.

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Qin, H., J. Xiao, X. Gao, and H. Wang. "Horse-Expert: An aided expert system for diagnosing horse diseases." Polish Journal of Veterinary Sciences 19, no. 4 (2016): 907–15. http://dx.doi.org/10.1515/pjvs-2016-0112.

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Abstract In contrast to the rapid development of the horse husbandry in China, the ability of horse veterinarians to diagnose diseases has not been improved and only a few domain experts have considerable expertise. At present, many expert systems have been developed for diseases diagnosis, but few for horse diseases diagnosis have been studied in depth. This paper presents the design and development of a computer-aided expert system for diagnosing horse diseases. We suggest an approach for diagnosis of horse diseases based on the analysis of diagnostic characteristics and the experiential knowledge of domain experts. It is based on using evidence-weighted uncertainty reasoning theory, which is a combination of evidence theory and an uncertainty pass algorithm of confidence factors. It enables drawing of inferences with atypical clinical signs and the uncertainty of the user’s subjective understanding. It reduces the influence of subjective factors on diagnostic accuracy. The system utilizes a user friendly interface for users and requests a confidence factor from users when feedback is given to the system. Horse-Expert combines the confidence factors with weight factors assigned to clinical signs by experts during the knowledge acquisition process to make diagnostic conclusions. The system can diagnose 91 common horse diseases, and provides suggestions for appropriate treatment options. In addition, users can check the medical record through statistical charts. The system has been tested in seven demonstration areas of Xinjiang province in northwestern China. By constantly maintaining and updating the knowledge base, the system has potential application in veterinary practice.

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Yin, Xiao Yu, Xian Ping Xie, Zhen Li, et al. "Design of Mine Ventilation Systems Based on Artificial Intelligence." Applied Mechanics and Materials 614 (September 2014): 107–12. http://dx.doi.org/10.4028/www.scientific.net/amm.614.107.

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Expert systems, or knowledge based systems, are programs in which the answer to a user-posed question is reached by logical or plausible inference rather than strictly by calculation, although calculation routines can form a major part of an expert system. Based on the integration of expert system technology and optimization technology, an intelligent computer aided design method for mine ventilation systems is proposed in this paper. Firstly, the structure and control algorithm of the intelligent design system are explored. Secondly, the knowledge types required for the mine ventilation expert system and the acquiring method of knowledge are discussed. Finally, the inference method of this expert system is put forward.

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