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Journal articles on the topic 'Expert System Shell'

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Published: 4 June 2021
Last updated: 6 February 2022

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1

Ostrowsky, Marianne F., and Ruth C. Swezey. "An expert system shell: Expert system environment/VM." ISA Transactions 28, no. 1 (January 1989): 9–14. http://dx.doi.org/10.1016/0019-0578(89)90049-9.

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2

Yang, Jie, Chenzhou Ye, and Xiaoli Zhang. "An Expert System Shell for Fault Diagnosis." Robotica 19, no. 6 (September 2001): 669–74. http://dx.doi.org/10.1017/s0263574701003460.

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Traditional expert systems for fault diagnosis have a bottleneck in knowledge acquisition, and have limitations in knowledge representation and reasoning. A new expert system shell for fault diagnosis is presented in this paper to develop multiple knowledge models (object model, rules, neural network, case-base and diagnose models) hierarchically based on multiple knowledge. The structure of the expert system shell and the knowledge representation of multiple models are described. Diagnostic algorithms are presented for automatic modeling and hierarchical reasoning. It will be shown that the expert system shell is very effective in building diagnostic expert systems.
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3

Eugene Carter, E. "Creating a shell-based expert system." Computers & Operations Research 13, no. 2-3 (January 1986): 325–27. http://dx.doi.org/10.1016/0305-0548(86)90016-x.

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4

Koov, Merike, and Hannes Perkmann. "ExpertPriz — an integrated expert system shell." Knowledge-Based Systems 4, no. 1 (March 1991): 27–34. http://dx.doi.org/10.1016/0950-7051(91)90043-2.

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5

Merino Cisneros, Francisco Leopoldo. "Sistema experto para diagnóstico de plagas insectiles de maíz (Zea mays L.) en Centro América." Agronomía Mesoamericana 2 (June 22, 2016): 80. http://dx.doi.org/10.15517/am.v2i0.25232.

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An expert system was developed to diagnose maize insect pests in Central America. This system was established ta improve diagnostic procedures for insect pests and to facilitate the use of integrated pest management systems in maize. This work was based on acquired knowledge by experts in integrated pest management based at the Centro Agronómico Tropical de Investigación y Enseñanza (CATIE). A shell expert system specific for diagnostics was used. This expert system is capable of diagnosing 52 maize insects usingsix inference modules. Criterion used were: plant phenology, damaged organ ofthe plant, general form of the insect, specific form of the insect, general damage, specific damage, and order and family of the insecto In addition, this systemcan provide information on insectsynonyms, common names, life cycle, damage, pest location, and possible control. The system works with IBM compatible MS-DOS, employs the VP-EXPERT shell, requires 512Kb of RAM, and either two 360 Kb or one 720Kb disk drives, or a hard disk. An evaluation ofthe ease of use of the shell dedicated to diagnostics for insect pests was performed. The purpose was to compare the educationallevel of the user with the ease which the shell was used. A completely randomized design was employed with seven treatments and five observations. The treatments were: secretaries, laboratory support staff, extension agents, bachelor level personnel, M.S., and PhD. level persons. Tbe results indicated that no significant differences within the range evaluated, excluding facility of use. It was concluded that the shell presents operationalilty that can be used by various persons at different education le veis within the studied rangc. expert system was also compared with a book key (‘Las plagas invertebradas de cultivos anuales alimenticios en America Central’). A randomized complete block design was used with two treatments and 18 replications, each replication being one persono The results dcmonstrated that the expert system was superior to the book key and that the expcrt system required much less time to obtain an answer.
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6

Choi, King F. "4885705 Expert system shell for building photofinishing diagnostic systems." Expert Systems with Applications 1, no. 3 (January 1990): VI. http://dx.doi.org/10.1016/0957-4174(90)90017-o.

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7

Schneider, Victor. "A pedagogic expert system shell in Prolog." ACM SIGPLAN Notices 23, no. 6 (June 1988): 35–38. http://dx.doi.org/10.1145/44546.44548.

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8

Rodder, W., E. Reucher, and F. Kulmann. "Features of the Expert-System-Shell SPIRIT." Logic Journal of IGPL 14, no. 3 (September 6, 2006): 483–500. http://dx.doi.org/10.1093/jigpal/jzl020.

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9

Schumacher, T. "The Java-Medical Diagnostics Expert System Shell." Transfusion Medicine and Hemotherapy 28, no. 5 (2001): 292–96. http://dx.doi.org/10.1159/000053556.

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10

Prasad, B. E., T. S. Perraju, G. Uma, and P. Umarani. "An expert system shell for aerospace applications." IEEE Expert 9, no. 4 (August 1994): 56–64. http://dx.doi.org/10.1109/64.336148.

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11

Verma, A. K., K. Seetharam, and S. Baijal. "Dex – An Expert System Shell For Diagnosis." International Journal of Modelling and Simulation 19, no. 4 (January 1999): 329–33. http://dx.doi.org/10.1080/02286203.1999.11760431.

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12

Hong-Min, Zhang. "Introduction to an expert system shell - STIM." Fuzzy Sets and Systems 36, no. 1 (May 1990): 167–80. http://dx.doi.org/10.1016/0165-0114(90)90090-s.

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13

Tomić, Bojan, Jelena Jovanović, and Vladan Devedžić. "JavaDON: an open-source expert system shell." Expert Systems with Applications 31, no. 3 (October 2006): 595–606. http://dx.doi.org/10.1016/j.eswa.2005.09.085.

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14

Oyen, R. A., M. A. Keyes, and M. P. Lukas. "An Expert System Shell Embedded in the Control System." IFAC Proceedings Volumes 21, no. 13 (September 1988): 61–66. http://dx.doi.org/10.1016/s1474-6670(17)53700-3.

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15

Felföldi, János. "Work Organisation Supported by an Expert System in Agriculture." Acta Agraria Debreceniensis, no. 1 (December 4, 2001): 57–60. http://dx.doi.org/10.34101/actaagrar/1/3612.

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Expert systems are softwares that incorporate the experience of an expert and support decision makers by leading them through the thinking processes of an expert in the form of „if…then” rules. To use an expert system we have to work with knowledge expressed in a pre-determined form. To do so, we used a shell that can be purchased, and we acquired knowledge about the topic from experts through interviews and personal monitoring. Attributes that are non-measurable, such as many of the factors determining work-place organisation and revealing knowledge related to it, can be formed into words by using expert systems. The goal of the study was to present a suggested form of expert system model to help judge the level of work-place organisation.
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16

Schreiner, A., and T. Chard. "Expert Systems for the Prediction of Ovulation: Comparison of an Expert System Shell (Expertech Xi Plus) with a Program Written in a Traditional Language (BASIC)." Methods of Information in Medicine 29, no. 02 (1990): 140–45. http://dx.doi.org/10.1055/s-0038-1634775.

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AbstractThe use of an expert system shell (EXPERTECH Xi Plus) in the construction of an expert system for the diagnosis of infertility has been evaluated. A module was devised for predicting ovulation from the medical history alone. Two versions of this system were constructed, one using the expert system shell, and the other using QuickBASIC. The two systems have been compared with respect to: (1) ease of construction; (2) ease of knowledge base update; (3) help and explanation facilities; (4) diagnostic accuracy; (5) acceptability to patients and clinicians; (6) user-friendliness and ease of use; (7) use of memory space; and (8) run time. The responses of patients and clinicians were evaluated by questionnaires. The predictions made by the computer systems were compared to the conclusions reached by clinicians and to the “gold standard” of day 21 progesterone.The conclusions of this pilot study are: (1) the construction of this expert system was NOT facilitated by the use of this expert system shell; (2) update of the knowledge base was not facilitated either; (3) the expert system shell offered built-in help and explanation facilities, but as the system increased in complexity these became less useful; (4) after initial adjustment of decision thresholds the diagnostic accuracy of the system equalled that of the clinician; (5) the patient response to computer history-taking was very favorable but much less favorable to computer diagnosis; (6) the clinicians took a positive attitude to computer diagnosis; (7) the systems were easy to use; (8) the expert systems shell required much more memory space and had a much slower response time than the system written in BASIC.
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17

Lippert, Renate C. "An expert system shell to teach problem solving." TechTrends 33, no. 2 (March 1988): 22–26. http://dx.doi.org/10.1007/bf02771224.

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18

Matwin, S., S. Szpakowicz, Z. Koperczak, G. E. Kersten, and W. Michalowski. "Negoplan: an expert system shell for negotiation support." IEEE Expert 4, no. 4 (1989): 50–62. http://dx.doi.org/10.1109/64.43285.

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19

Leung, K. S., W. S. Felix Wong, and W. Lam. "Applications of a novel fuzzy expert system shell." Expert Systems 6, no. 1 (February 1989): 2–10. http://dx.doi.org/10.1111/j.1468-0394.1989.tb00070.x.

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20

Bradshaw, J. A., K. J. Carden, and D. Riordan. "Ecological applications using a novel expert system shell." Bioinformatics 7, no. 1 (1991): 79–83. http://dx.doi.org/10.1093/bioinformatics/7.1.79.

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21

Bonačic, Davor, Bruno Stiglic, and Tomaž Kolmanič. "An object language versus an expert system shell." ACM SIGPLAN OOPS Messenger 3, no. 3 (July 1992): 41–50. http://dx.doi.org/10.1145/140959.140968.

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22

Storr, A., and H. Wiedmann. "DESIS—an expert system shell for technical diagnosis." Computers in Industry 15, no. 1-2 (January 1990): 69–81. http://dx.doi.org/10.1016/0166-3615(90)90085-4.

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23

Dan, Qiu, and J. Dudeck. "MKS: An expert system shell for HIS environment." Artificial Intelligence in Medicine 2, no. 1 (March 1990): 5–19. http://dx.doi.org/10.1016/0933-3657(90)90013-h.

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24

Nian, Chee, Tan ., Chee Fai, and Tan . "A Fault Diagnosis Expert System for Commercial Bus Manufacturing process." International Journal of Engineering & Technology 7, no. 3.17 (August 1, 2018): 64. http://dx.doi.org/10.14419/ijet.v7i3.17.16624.

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Bus manufacturing is one of the important assets in automotive industries as well as mode of public transportation. The design process is difficult and long throughout time. Moreover, there are many manuals, rules and regulation according to different standard which make the standardization and design process to be difficult and time consuming. Hence, this project describes the use of an expert system shell for commercial bus design. In bus manufacturing field, design of commercial bus is heavily depending on human experts. With the help of expert system, process of design commercial bus will be shortened up to 56.5% compared to conventional way. The developed system can be used as a training module for inexperienced personnel. In this research work, the fault diagnosis system was developed by using Kappa-PC expert system shell. It is supported by object orientated technology for the MS window environment. Lastly, the developed system will be validated with a case study to verify the capability of the developed system.
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25

Mitta, Deborah A. "Formulation of Expert System Knowledge." Proceedings of the Human Factors Society Annual Meeting 33, no. 5 (October 1989): 350. http://dx.doi.org/10.1177/154193128903300525.

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Expert system knowledge represents expertise obtained through formal education, training, and/or experience. Formal education provides deep knowledge of a particular domain; experience and training result in heuristic knowledge. A knowledge base defines the range of information and understanding with which the system is capable of dealing; therefore, its information must be structured and filed for ready access. The objective of this symposium is to address the challenges associated with establishment of valid expert system knowledge, specifically, knowledge to be used by expert system shells. As expert system knowledge is obtained, structured, and stored, it is formulated. In this symposium, knowledge formulation is addressed as a three-phase process: knowledge acquisition, the mechanics associated with structuring knowledge, and knowledge porting. Knowledge acquisition is the process of extracting expertise from a domain expert. Expertise may be collected through a series of interviews between the expert and a knowledge engineer or through sessions the expert holds with an automated knowledge acquisition tool. Thus, the ultimate outcome of knowledge acquisition is a collection of raw knowledge data. The following human factors issues become apparent: documenting mental models (where mental models are the expert's conceptualization of a problem), recording cognitive problem-solving strategies, and specifying an appropriate interface between the domain expert and the acquisition methodology. The knowledge structuring process involves the refinement of raw knowledge data, where knowledge is organized and assigned a semantic structure. One issue that must be considered is how to interpret knowledge data such that formal definitions, logical relationships, and facts can be established. Finally, formulation involves knowledge porting, that is, the movement of an expert system shell's knowledge base to various other shells. The outcome of this process is a portable knowledge base, where the challenges lie in maintaining consistent knowledge, understanding the constraints inherent to a shell (the shell's ability to incorporate all relevant knowledge), and designing an acceptable user-expert system interface. The fundamental component of any expert system is its knowledge base. The issues to be presented in this symposium are important because they address three processes that are critical to the development of a knowledge base. In addition to presenting computer science challenges, knowledge base formulation also presents human factors challenges, for example, understanding cognitive problem-solving processes, representing uncertain information, and defining human-expert system interface problems. This symposium will provide a forum for discussion of both types of challenges.
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26

SHEKHAR, SHASHI, and C. V. RAMAMOORTHY. "COOP: A SELF-ASSESSMENT BASED APPROACH TO COOPERATING EXPERT SYSTEMS." International Journal on Artificial Intelligence Tools 01, no. 02 (June 1992): 175–204. http://dx.doi.org/10.1142/s0218213092000181.

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Conventional Expert System Shells do not help in developing AI programs for large applications like automated factories, which require multi-disciplinary knowledge and which are geographically distributed. To support these applications, a shell must provide tools for a knowledge-based system to (i) reason about the need for cooperation, (ii) understand global knowledge to locate relevant expert systems and (iii) select appropriate cooperation plans. Contemporary approaches like Blackboard [1], Contract-net [2] and Distributed problem solving [3] help in exploring alternative cooperation plans without any reasoning about the need for cooperation and understanding of global knowledge. Coop [4] support cooperation models to characterize three essential decisions in the cooperation process. It provides a computational method to decide if an expert system needs to consult with other expert systems. We provide techniques select appropriate cooperation plans.
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27

Jarvis, J. R. "Developing an expert system using a PC-based shell." Information Services & Use 9, no. 1-2 (January 1, 1989): 67–77. http://dx.doi.org/10.3233/isu-1989-91-207.

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28

Qiu, D., and J. Dudeck. "Temporal propositions in a HIS-oriented expert system shell." Medical Informatics 16, no. 1 (January 1991): 65–76. http://dx.doi.org/10.3109/14639239109025296.

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29

Mathkour, Hassan, Israa Al-Turaiki, and Ameur Touir. "The Development of a Bilingual Fuzzy Expert System Shell." Journal of King Saud University - Computer and Information Sciences 21 (2009): 27–44. http://dx.doi.org/10.1016/s1319-1578(09)80003-7.

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30

Leung, K. S., and M. H. Wong. "Fuzzy concepts in an object oriented expert system shell." International Journal of Intelligent Systems 7, no. 2 (February 1992): 171–92. http://dx.doi.org/10.1002/int.4550070206.

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31

Huffman, John E., Abu Masud, and Don Hommertzheim. "Essex — An intelligent advisor for expert system shell selection." Computers & Industrial Engineering 17, no. 1-4 (January 1989): 12–17. http://dx.doi.org/10.1016/0360-8352(89)90028-4.

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32

Spoor, E. R. K., and J. W. B. Vermeulen. "FRESH: expert-system shell with foundation in data modelling." Information and Software Technology 31, no. 6 (July 1989): 313–23. http://dx.doi.org/10.1016/0950-5849(89)90133-x.

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33

Leung, K. S., Y. T. So, Ares Leung, and W. S. Felix Wong. "A fuzzy expert system shell: From minicomputer to PC." Artificial Intelligence in Medicine 4, no. 1 (February 1992): 75–85. http://dx.doi.org/10.1016/0933-3657(92)90038-q.

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34

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 (February 1, 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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35

SOO, HSU LOKE. "A PROLOG-BASED CHINESE EXPERT SYSTEM." International Journal of Pattern Recognition and Artificial Intelligence 02, no. 01 (March 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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36

Jonassen, David H., and Sherwood Wang. "The Physics Tutor: Integrating Hypertext and Expert Systems." Journal of Educational Technology Systems 22, no. 1 (September 1993): 19–28. http://dx.doi.org/10.2190/7l7f-57h5-apyu-aapa.

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The Physics Tutor is a prototype intelligent tutoring system (ITS) built with “off-the-shelf” software in order to test the utility, practicality, and generalizability of the ITS concept. The tutorial information is accessed through a semantically structured hypertext which enables students to browse through the knowledge base, secure examples, and quiz themselves with practice items. The student model and expert model are encoded in several hundred rules that are evaluated by a commercial expert system shell. The output of the expert system knowledge bases is a judgment about the learners level of conceptual development and some tutorial strategies for remediating deficiencies. Because of the slowness of the system, and the enormous amount of required development time, the practicality of ITSs is called into question.
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37

Premkumar, P., and S. Kramer. "A Generalized Expert System Shell for Implementing Mechanical Design Applications: Review, Introduction, and Fundamental Concepts." Journal of Mechanisms, Transmissions, and Automation in Design 111, no. 3 (September 1, 1989): 439–42. http://dx.doi.org/10.1115/1.3259017.

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The foundations for an expert system shell for implementing mechanical design applications are presented in this paper. The shell supports facilities for knowledge acquisition, quasi-reactive planning, design evaluation, and subjective explanation. The underlying philosophy of each of these facilities and some preliminary implementation issues are discussed. A brief summary of a recent research effort and its implications on the development of a generalized expert system shell for implementing mechanical design applications are also presented.
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38

SHIN, HEUNG-YEOUL, and JAE-WON LEE. "An expert system for pneumatic design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12, no. 1 (January 1998): 3–11. http://dx.doi.org/10.1017/s0890060498121121.

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One of the biggest problems in pneumatic system design is over design. Thus, the results are excessive in costs of the initial investment and it requires too much energy. This article describes the development of expert system based pneumatic design system, PNEUDES (PNEUmatic Design Expert System), prototype that enables the user the optimal design of pneumatic system. Once the design requirements such as cylinder type and work load, etc. are input to the system, optimal cylinder specifications with standardized order-based size, valves types, and necessary accessories are all determined. Also the configuration information such as the connectivity among components and cylinder image data are supplied to the user. It can also help the novice of pneumatic design. The rule-based reasoning approach is used as a reasoning strategy with Intelligent Rule Element shell.
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39

Thirumaran M. and Banupriya P. "Dynamic Interactive Voice Response System Using Ontology and Java Expert System Shell." Procedia Computer Science 70 (2015): 107–13. http://dx.doi.org/10.1016/j.procs.2015.10.049.

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40

Bergsma, K., S. Sargent, J. Brecht, and R. Peart. "AN EXPERT SYSTEM FOR DIAGNOSING CHILLING INJURY OF VEGETABLES." HortScience 26, no. 6 (June 1991): 697C—697. http://dx.doi.org/10.21273/hortsci.26.6.697c.

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Temperature management is the most widely used method to extend the postharvest life of vegetables. Unfortunately, during less than optimal commercial conditions, certain commodities can be exposed to low, nonfreezing temperatures that may shorten their market life due to chilling injury (CI). CI is difficult to diagnose since not all commodities exhibit the same symptoms. Environmental factors may also affect the expression of CI The services of an expert are usually required to positively diagnose CI, however, experts are not always readily available, particularly during routine commercial handling. An expert system, a computer program that emulates a human expert's thought processes, will be developed to diagnose CI symptoms for several commodities. A prototype developed with Level5 Object, an expert system shell, will be presented. Diagnosis is determined by applying rules and certainty factors based on user responses to queries on the type and extent of visual symptoms. The applicability and advantages of this system will be discussed.
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41

Cradwick, Peter D. G. "Designing a practical user interface for an expert system shell." Expert Systems 8, no. 4 (November 1991): 273–76. http://dx.doi.org/10.1111/j.1468-0394.1991.tb00375.x.

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42

Ramsbottom, D. J., M. J. Adams, J. Sumiga, and J. Carroll. "Uncertainty within a commercial expert system shell for polymer analysis." Chemometrics and Intelligent Laboratory Systems 19, no. 1 (May 1993): 53–63. http://dx.doi.org/10.1016/0169-7439(93)80082-s.

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43

Tong, Wei-Guang, and Lin-Liang Zhao. "An expert system shell for analysis of real time signals." Annual Review in Automatic Programming 15 (January 1989): 65–67. http://dx.doi.org/10.1016/0066-4138(89)90012-8.

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44

Wei-Guang, Tong, and Zhao Lin-Liang. "An Expert System Shell for Analysis of Real Time Signals." IFAC Proceedings Volumes 22, no. 13 (September 1989): 65–67. http://dx.doi.org/10.1016/b978-0-08-040185-0.50016-6.

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45

Rödder, Wilhelm, Ivan Ricardo Gartner, and Sandra Rudolph. "An entropy-driven expert system shell applied to portfolio selection." Expert Systems with Applications 37, no. 12 (December 2010): 7509–20. http://dx.doi.org/10.1016/j.eswa.2010.04.095.

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46

Dunne, Morgan G., and Sean B. Dunne. "A simple expert system shell for microcomputer-aided radiographic diagnosis." Journal of Digital Imaging 3, no. 1 (February 1990): 10–14. http://dx.doi.org/10.1007/bf03168104.

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47

Kang, Sang-Seob, Sehyun Myung, and Soon-Hung Han. "A Design Expert System for Auto-Routing of Ship Pipes." Journal of Ship Production 15, no. 01 (February 1, 1999): 1–9. http://dx.doi.org/10.5957/jsp.1999.15.1.1.

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Finding the optimum route of ship pipes is a complicated and time-consuming process. Experience of designers is the main tool in this process. To reduce design man-hours and human errors an expert system shell and a geometric modeling kernel are integrated to automate the design process. Existing algorithms for routing problems have been analyzed -most of them are to solve 2-D circuit routing problems. Design of the ship piping system, especially within the engine room, is a complicated, large-scale 3-D routing problem. Methods of expert systems have been implemented to find the routes of ship pipes on the main deck of a bulk carrier. A framework of the intelligent CAD system for pipe auto-routing is suggested. The CADDS 5 of Computer vision is used as the overall CAD environment, the Nexpert Object of Neuron Data is used as the expert system shell, and the CADDS 5 ISSM is used to build user interface through which geometric models" of pipes are created and modified.
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48

Spicknall, Mark. "Developing and Using an Expert System for Planning the Production of Structural Piece-Parts." Journal of Ship Production 8, no. 03 (August 1, 1992): 163–83. http://dx.doi.org/10.5957/jsp.1992.8.3.163.

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This paper presents an example of how expert systems can be developed and used for planning structural piece-part production. First, expert systems are briefly and generically described. Then the production processes within a shipyard-like structural piece-part production facility are defined within an expert system "shell"; that is, the "objects," "attributes," and "rules" describing the production process are established and explained. Then various structural piece-parts are described to the system and the system identifies the required production processes for each described part. The inference process underlying the identification of these processes is described for each of these parts. Finally, potential applications of expert systems to other areas of shipbuilding operations are discussed.
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49

Carreño, Ricardo, Verónica Aguilar, Daniel Pacheco, Marco Antonio Acevedo, Wen Yu, and María Elena Acevedo. "An IoT Expert System Shell in Block-Chain Technology with ELM as Inference Engine." International Journal of Information Technology & Decision Making 18, no. 01 (January 2019): 87–104. http://dx.doi.org/10.1142/s0219622018500499.

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Knowledge society blockchain is one of the most powerful and recent tools to make the internet environment safer and reliable. Manufacturing has traditionally been dominated by standard designs that are mass-produced, due to the fact, that custom production causes additional costs that make it less affordable than mass production. This paper proposes to develop a designer expert system for IoT installation layout designs, using blockchain distributed system based on a machine learning, with users entering data to the expert system by a smart bot software. This expert system will work using extreme learning machine as inference engine; therefore, this is a shell to develop any expert system with fast learning. The whole system is represented by a smart contract with a value linked to the value of the expert system, the more this expert system be quoted on the web, the more the shares of the smart contract will cost.
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50

Lu, Kun Yung. "An Intelligent Monitoring System Using a ZigBee-Base Sensor Network." Applied Mechanics and Materials 182-183 (June 2012): 1393–401. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1393.

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This paper presents an intelligent monitoring system based on a ZigBee-base wireless sensor network. The proposed system includes a sensor network configuration module, an expert system shell, and an event-condition-action (ECA) engine. The sensor network configuration module is used to configure the working properties of the ZigBee components and set up the monitoring network. The expert system shell enables users to define the related events occurred at system running, the threshold of working conditions for triggering the corresponding events, and the required commands for fitting the system. The ECA engine is used to monitor the system and provide a suitable command for system fitting in a specified event being fired. The proposed system enables user to quickly establish a wireless sensor network system without well-experienced knowledge about the wireless sensor network and the expert system.
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