Knowledge Engineering Oriented Intelligent Expert System Development Platform

2012 ◽  
Author(s):  
Tie-niu Song ◽  
Ting-yu Lin ◽  
Mu Gu ◽  
Xu-dong Chai ◽  
Tan Li ◽  
...  
Keyword(s):  
Expert System ◽  
Knowledge Engineering ◽  
System Development ◽  
Development Platform

Study on Fault Diagnosis Expert System of Aircraft Power-Supply System

2014 ◽  
Vol 945-949 ◽  
pp. 2617-2622 ◽  
Author(s):  
Xu Yan Zhuang ◽  
Ya Yun Xu ◽  
Yong Bao
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Knowledge Base ◽  
Power Supply ◽  
Power Supply System ◽  
System Development ◽  
Inference Engine ◽  
Supply System ◽  
Development Platform ◽  
Development Tool

We always regard aircraft power supply system as the "blood system" of aircraft. It plays a very important role in aircraft work. In view of its fault diagnosis present situation and in order to improve fault diagnosis efficiency, we put forward to use expert system development tool CLIPS to build up fault diagnosis expert system. In this paper, we choose the power-supply system of Cirrus SR20 as diagnosis object, and choose CLIPS as development tool to build up knowledge base and inference engine. By using Eclips development platform to write interface programs and using CLIPS JNI to call CLIPS programs we successfully complete the expert system total performance including knowledge base, inference engine and interface.

Wearable expert system development: definitions, models and challenges for the future

2017 ◽  
Vol 51 (3) ◽  
pp. 235-258 ◽  
Author(s):  
Fabio Sartori ◽  
Riccardo Melen
Keyword(s):  
Expert System ◽  
Knowledge Engineering ◽  
System Development ◽  
Knowledge Bases ◽  
Content Type ◽  
Domain Experts ◽  
Points Of View ◽  
Input Variables ◽  
Distinguishing Features ◽  
Over Time

Purpose A wearable expert system (WES) is an expert system designed and implemented to obtain input from and give outputs to wearable devices. Among its distinguishing features are the direct cooperation between domain experts and users, and the interaction with a knowledge maintenance system devoted to dynamically update the knowledge base taking care of the evolving scenario. The paper aims to discuss these issues. Design/methodology/approach The WES development method is based on the Knowledge Acquisition Framework based on Knowledge Artifact (KAFKA) framework. KAFKA employs multiple knowledge artifacts, each devoted to the acquisition and management of a specific kind of knowledge. The KAFKA framework is introduced from both the conceptual and computational points of view. An example is given which demonstrates the interaction, within this framework, of taxonomies, Bayesian networks and rule-based systems. An experimental assessment of the framework usability is also given. Findings The most interesting characteristic of WESs is their capability to evolve over time, due both to the measurement of new values for input variables and to the detection of new input events, that can be used to modify, extend and maintain knowledge bases and to represent domains characterized by variability over time. Originality/value WES is a new and challenging concept, dealing with the possibility for a user to develop his/her own decision support systems and update them according to new events when they arise from the environment. The system fully supports domain experts and users with no particular skills in knowledge engineering methodologies, to create, maintain and exploit their expert systems, everywhere and when necessary.

scholarly journals RANCANG BANGUN SISTEM PAKAR PENANGGULANGAN PENYAKIT KEDELAI

2016 ◽  
Vol 25 (1) ◽  
pp. 117
Author(s):  
Indah Puji Astuti ◽  
Irman Hermadi ◽  
Agus Buono ◽  
Kikin H Mutaqin
Keyword(s):  
Expert System ◽  
Time Allocation ◽  
Knowledge Engineering ◽  
System Development ◽  
Prototype System ◽  
Engineering Process ◽  
Detection And Identification ◽  
Development Life Cycle ◽  
Soybean Diseases ◽  
Very High

Early detection and identification of soybean diseases is important to support better productivity of soybean. The demand for the availability of an expert on soybean disease is very high, especially for the beginners in the field of agriculture. However, the number and time allocation of the experts are not adequate to serve farmers located in different geographical areas. Therefore, an expert system is proposed as a solution to use as a diagnostic tool for soybean diseases just like a human expert. It will be even easier when the system is implemented into an Android-based application to be used anywhere and anytime. The objective of this study was to analyze and design an expert system for early identification of soybean diseases. This study was adopting the Expert System Development Life Cycle (ESDLC) approach. The stages were project initialization, knowledge engineering process, and implementation. The study was started with the project initialization phase that conducted in September 2014 and the completion of the implementationphase in August 2015. The results of research were in the form of document analysis and prototype system.

Application of a Bayesian network to integrated circuit tester diagnosis

1995 ◽  
Vol 9 (1) ◽  
pp. 51-65 ◽  
Author(s):  
Daniel Mittelstadt ◽  
Robert Paasch ◽  
Bruce D’Ambrosio
Keyword(s):  
Expert System ◽  
Real World ◽  
Integrated Circuit ◽  
Procedural Knowledge ◽  
Knowledge Engineering ◽  
Bayesian Belief Network ◽  
Belief Networks ◽  
Diagnostic Problem ◽  
Ic Testing ◽  
Time Limitations

AbstractResearch efforts to implement a Bayesian belief-network-based expert system to solve a real-world diagnostic problem – the diagnosis of integrated circuit (IC) testing machines – are described. The development of several models of the IC tester diagnostic problem in belief networks also is described, the implementation of one of these models using symbolic probabilistic inference (SPI) is outlined, and the difficulties and advantages encountered are discussed. It was observed that modeling with interdependencies in belief networks simplifies the knowledge engineering task for the IC tester diagnosis problem, by avoiding procedural knowledge and focusing on the diagnostic component’s interdependencies. Several general model frameworks evolved through knowledge engineering to capture diagnostic expertise that facilitated expanding and modifying the networks. However, model implementation was restricted to a small portion of the modeling, that of contact resistance failures, which were due to time limitations and inefficiencies in the prototype inference software we used. Further research is recommended to refine existing methods, in order to speed evaluation of the models created in this research. With this accomplished, a more complete diagnosis can be achieved.

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