• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.689-691
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• 1995

In this paper, a fault detection and isolation unit(FDIU) for a centrifugal pump system driven by DC-motor is proposed. The proposed scheme can be classified into the dedicated observer scheme(DOS). A fuzzy logic based inference engine is adopted for the isolation of each faults. Having the fuzzy inference engine, the proposed FDIU resolve a few important problems of the conventional DOSs with conventional two valued logic. The ouputs of the proposed FDIU are not "ith fault occurred" but the grade of memberships that indicate the consistency of observered symptoms(residuals) with each fault symptoms stored in the rule base. The ouputs can easily be transferred to the ranking of the fault possibilities and it will provide very useful informations in monitoring the process. The simulation results show that the FDIU has very good diagnostic ability even in the noisy environment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1330-1333
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• 1993

In this paper, first, the fuzzy Petri net inference mechanism with learning function is proposed by using the extended fuzzy Petri nets. Secondly, a control system with this new inference engine is proposed. This system can do automatically and easily the knowledge acquisition from the operator's empirical data and can also be controller adaptively under the big parameter change.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.17 no.4
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• pp.1-7
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• 2009

The surge control system in unmanned turbojet engine must be capable of accounting uncertainties from engine transient conditions, random fluctuations of key parameters such as air pressure and fuel flow and engine modeling errors. In this paper, taking into consideration of its effectiveness as well as system stability, a fuzzy PI controller is proposed. The role of the fuzzy PI controller is to stabilize the unmanned aircraft upon occurring unexpected engine surge. The proposed control scheme is proved by computer simulation using a linear engine model. The simulation results on the state space model of a small turbojet engine illustrate the proposed control system achieves the desired performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.8
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• pp.3636-3643
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• 2011

To prevent the electrical accidents in the laboratory, we identify problems for periodic inspections of the electric field and develop a fuzzy inference system that can be practically applied to check items. Focusing on electrical safety in the lab environment, we draw check items that can be applied in common and develop a standard checklist that is consistent with the laboratory electrical safety and the periodic inspections. Using the standard checklist we select the items that may contain a linguistic ambiguity and define the membership functions for these items. We also have a safety rating defined by the membership function. Using these fuzzy variables we form the fuzzy rules in the form of 'If-Then' and develop a fuzzy inference system through the fuzzy engine. From this, electrical accidents could be prevented in advance continuously by managing the intelligent and efficient inspection and electrical safety to prevent the electrical accidents in the laboratory.

• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.234-240
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• 2021

As the number of pet dog-related businesses is rising rapidly, there is an increasing need for reliable pet dog health information systems for casual pet owners, especially those caring for older dogs. Our goal is to implement a mobile pre-diagnosis system that can provide a first-hand pre-diagnosis and an appropriate coping strategy when the pet owner observes abnormal symptoms. Our previous attempt, which is based on the fuzzy C-means family in inference, performs well when only relevant symptoms are provided for the query, but this assumption is not realistic. Thus, in this paper, we propose a hybrid inference structure that combines fuzzy association memory and a double-layered fuzzy C-means algorithm to infer the probable disease with robustness, even when noisy symptoms are present in the query provided by the user. In the experiment, it is verified that our proposed system is more robust when noisy (irrelevant) input symptoms are provided and the inferred results (probable diseases) are more cohesive than those generated by the single-phase fuzzy C-means inference engine.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.5
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• pp.793-800
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• 2004

The purpose of this study is to develope a servo system with faster and more accurate response. This paper describes a method of approximate reasoning for fuzzy control of servo system based on the decomposition of $\alpha$-level fuzzy sets. We propose that fuzzy logic algorithm is a body from fuzzy inference to defuzzificaion cases where the output variable u directly is generated PWM The effectiveness for robust and faster response of the fuzzy control scheme are verified for a variable parameter by comparison with a PID control and fuzzy control A position control of DC servo system with a fuzzy logic controller is demonstrated successfully.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.7
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• pp.1520-1526
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• 2003

Design and implementation method or the 100[W] DD/AC invertor using PICl6C711 processor is described in this paper. Especially, fuzzy inference algorithm is involved in this system which can be adaptive to the environment variation. Input/output control and power consumption monitoring is controlled based on PIC16C711 processor, which compute the optimal values acquired from inference engine. Such experimental as function, efficiency, motoring are performed and experimental results showed that monitoring error is less than 2% and widely used in the area of industrial fields.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.315-320
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• 1997

Rule minimization technique adapted from rough set theory was applied to remove redundant knowledge which is not necessary to make a knowledge base. New algorithm to diagnose fault using Improved Fuzzy Cognitive Maps(I-FCMs), and Fuzzy Associative Memory(FAM) is proposed. I-FCM[22] is superior to gathering knowledge from many experts and descries dynamic behaviors of systems very well. I-FCM is not only a knowledge base, but also a inference engine. FAM has learning capability like neural network[12]. Rule minimization and composition of I-FCM and FAM make it possible to construct compact knowledge base and breaks the border between inference engine and knowledge base.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.662-665
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• 2001

As the fuzzy control is applied to servo system the hardware implementation of the fuzzy information systems requires the high speed operations, short real time control and the small size systems. The aims of this study is to develop hardware of the fuzzy information systems to be apply to servo system. In this paper, we propose a calculation method of approximate reasoning for fuzzy control based on $\alpha$-level set decomposition of fuzzy sets by quantize $\alpha$-cuts. This method can be easily implemented with analog hardware. The influence of quantization levels of $\alpha$-cuts on output from fuzzy inference engine is investigated. It is concluded that 4 quantization levels give sufficient result for fuzzy control performance of do servo system. It examined useful with experiment for dc servo system.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.441-447
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• 1998

Motivated by a recent work, a fuzzy-power-controller(FPC) is designed for the relieving-horsepower control of output variable pump with electrical proportional valve and actually implemented on the industrial excavator. In order to calculate the output power of pump with input of FPC, a linear discrete time model of load system to pump is obtained and the result is applied to control the engine-pump coupled system by software without pressure and flow sensor. The FPC controls the engine and pump coupled system by relieving horsepower control according to the change of load and the running conditions in relieving horsepower control are selected by fuzzy inference engine. A case study is peformed through the construction of the control device and installation on the excavator. It shows that the relieving-horsepower control system with the FPC, as suggested in this paper, is superior to the conventional PID controllers. And also, the excavator, with the FPC, shows that the power-loss of the coupled system is reduced and the running speed of the hydraulic actuator is enhanced.