• The KIPS Transactions:PartB
• /
• v.11B no.7 s.96
• /
• pp.855-860
• /
• 2004

This paper describes knowledge base optimization of an intelligent diagnosis system based on fuzzy relational products(IDS-DAAP) for the diseases with acute abdominal Pain. The knowledge base of IDS-DAAP is composed of the fuzzy rules and the fuzzy membership functions. The author here proposes an advanced intelligent diagnosis system (A-lDS-DAAP) in which the fuzzy rule generation algorithm is applied. Comparing with previous IDS-DAAP and IDS-DAAP-NN, a modified approach with A-IDS-DAAP shows that it improves the diagnosis rate and reduces the time to diagnose.

• Proceedings of the IEEK Conference
• /
• 2003.07d
• /
• pp.1295-1298
• /
• 2003

The steam generator level in a PWR is very difficult to control particularly at low power. And the constant control gain and time value are not adaptive in steam generator level controller. In normal operation constant control gain and time value have no problem. But there is problem at low power. So variable control gains based on the temperature are required. The best control gain is decided by the experienced knowledge. A fuzzy gain tuner is used for the gain tuning. In the design of fuzzy gain-tuner processing, the experienced knowledge is employed for making fuzzy rules.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.11a
• /
• pp.711-715
• /
• 1996

This paper describes an automated computer-aided engineering (CAE) system for micromachines whose size range 10 to 10$^3$${\mu}{\textrm}{m}$. An automatic finite element mesh generation technique, which is based on the fuzzy knowledge processing and computational geometry techniques, is incorporated into the system, together with one of commercial finite clement (FE) analysis codes, MARC, and one of commercial solid modelers, Designbase. The system allows a geometry model of concern to be a automatically converted to different FE models, depending on physical phenomena to be analyzed, i.e. electrostatic analysis, stress analysis, modal analysis and so on. The FE analysis models are then exported to the FE analysis code, and then analyses are performed. This system is successfully applied to an electrostatic micro actuator.

• Journal of the Korea Society for Simulation
• /
• v.11 no.3
• /
• pp.13-22
• /
• 2002

This paper describes an automated evaluation of electrostatic field for micro motors whose sizes range 10 to 103um. Electric field modeling in micro motors has been generally restricted to in-plane two-dimensional finite element analysis (FEA). In this paper, the actual three-dimensional geometry of the micro motor is considered. An automatic FE mesh generation technique, which is based on the fuzzy knowledge processing and computational geometry techniques, is incorporated in the system, together with one of commercial FE analysis codes and one of commercial solid modelers. The system allows a geometry model of concern to be automatically converted to different FE models, depending on physical phenomena to be analyzed, electrostatic analysis and stress analysis and so on. The FE models are then exported to the FE analysis code, and then analyses are peformed. Then, analytical analysis and FE analysis about the torque generated by electrostatic micro motor are performed. The starting torque is proportional to $V^2$, the calculated starting torque from the two-dimensional analytical solutions are three times larger than those from the three-dimensional FE solutions.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.5
• /
• pp.1289-1296
• /
• 1997

To build up a knowledge-based system in an Artifical Inerligence System, selecting an appropriate set of rules is one of the key provlems. In this paper, we discuss a new method for exteacting fuzzy rules diredtly from fuzzy membdrchip function dat for pattern classifcation. The fuzzy rules with variable fuzzy recions are defined by sharing fuzzy space in fuzzy grid.Tehse rules are extracted form memberchop function. Them, optimal input vari-ables for the rules are determined using the number of extracted rules as a criterion. The method is compared with neural networks using Ishibuchi. Finally, in order to demonstrate the cffectiveness of the present method, simulation results are shown.

• Journal of the Institute of Convergence Signal Processing
• /
• v.3 no.4
• /
• pp.42-48
• /
• 2002

The case that cannot show the satisfactory control results with a modeling error and a shortage of related knowledge about a plant is if a fuzzy controller designed based on the plant model or the experience applies to an actual plant. We must adjust the scale factor which is a controller again in order to improve control performance in case of this and needs a lot of time and costs because this regulation process is carried out with a trial and error way We proposes the fuzzy controller that an automatic control adjust scale factors according to fuzzy logic and normalizer in this paper We confirmed that an automatic adjusted fuzzy controller displayed good performance than the fuzzy controller that scale factors was fixed through simulation. We implemented the controller using the DSP processor and applied in a hydraulic servo system. And then we observed an experimental results.

• The KIPS Transactions:PartB
• /
• v.15B no.2
• /
• pp.147-158
• /
• 2008

Fuzzy Cognitive Map (FCM) and Bayesian Belief Network (BBN) are two major frameworks for modeling, representing and reasoning about causal knowledge. Despite their extensive use in causal knowledge engineering, there is no reported work which compares their respective roles. This paper aims to fill the gap by providing a qualitative comparison of the two frameworks through a systematic analysis based on some inherent features of the frameworks. We proposed a set of comparison criteria which covers the entire process of causal knowledge engineering, including modeling, representation, and reasoning. These criteria are usability, expressiveness, reasoning capability, formality, and soundness. The results of comparison have revealed some important facts about the characteristics of FCM and BBN, which will help to determine how FCM and BBN should be used, with respect to each other, in causal knowledge engineering.

• Journal of the Korea Safety Management & Science
• /
• v.4 no.4
• /
• pp.97-108
• /
• 2002

This study deals with the application of knowledge-based engineering and a methodology for the assessment & measurement of reliability, availability, maintainability, and safety of industrial systems using fault-tree representation. A fuzzy methodology for fault-tree evaluation seems to be an alternative solution to overcome the drawbacks of the conventional approach. To improve the quality of results, the membership functions must be approximated based on heuristic considerations. Conventionally, it is not always easy to obtain a system reliability for components with different individual failure probability density functions(p.d.f.), We utilize fuzzy set theory to solve the adequacy of the conventional probability in accounting and processing of built-in uncertainties in the probabilistic data. The purpose of this study is to propose the framework of knowledge-based engineering through integrating the various sources of knowledge involved in a FTA.

• Journal of Internet Computing and Services
• /
• v.4 no.4
• /
• pp.45-51
• /
• 2003

In general, the values for attribute appearing in fuzzy object-oriented data models are represented by the fuzzy sets. If it can allow the attribute values in the fuzzy object-oriented data models to be represented by the interval-valued fuzzy sets, then it can allow the fuzzy object-oriented data models to represent the attribute values in more flexible manner. The attribute values of frames appearing in the inheritance structure of the fuzzy object-oriented data models are calculated by a prloritized conjunction operation using interval-valued fuzzy sets. This approach can be applied to knowledge and information processing in which degree of membership is represented as not the conventional fuzzy sets but the interval-valued fuzzy sets.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.6 no.1
• /
• pp.58-63
• /
• 2006

This paper describes a novel automated analysis system for bellows of piping system. An automatic finite element (FE) mesh generation technique, which is based on the fuzzy theory and computational geometry technique, is incorporated into the system, together with one of commercial FE analysis codes and one of commercial solid modelers. In this system, a geometric model, i.e. an analysis model, is first defined using a commercial solid modelers for 3-D shell structures. Node is generated if its distance from existing node points is similar to the node spacing function at the point. The node spacing function is well controlled by the fuzzy knowledge processing. The Delaunay triangulation technique is introduced as a basic tool for element generation. The triangular elements are converted to quadrilateral elements. Practical performances of the present system are demonstrated through several analysis for bellows of piping system.