• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.379-387
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• 2015

A custom power device provides an integrated solution to the present problems that are faced by the utilities and power distribution. In this paper, a new controller is designed which is connected to a multiconverter unified power quality conditioner (MC-UPQC) for improving the power quality issues adopted modified synchronous reference frame (MSRF) theory with Fuzzy logic control (FLC) technique. This newly designed controller is connected to a source in order to compensate voltage and current in two feeders. The expanded concept of UPQC is multi converter-UPQC; this system has a two-series voltage source inverter and one shunt voltage source inverter connected back to back. This configuration will helps mitigate any type of voltage / current fluctuations and power factor correction in power distribution network to improve power quality issues. In the proposed system the power can be conveyed from one feeder to another in order to mitigate the voltage sag, swell, interruption and transient response of the system. The control strategies of multi converter- UPQC are designed based on the modified synchronous reference frame theory with fuzzy logic controller. The fast dynamics response of dc link capacitor is achieved with the help of Fuzzy logic controller. Different types of fault conditions are taken and simulated for the analysis and the results are compared with the conventional method. The relevant simulation and compensation performance analysis of the proposed multi converter-UPQC with fuzzy logic controller is performed.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1529-1532
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• 2007

Failure Mode, Effects, and Criticality Analysis (FMECA) is an extension of FMEA which includes a criticality analysis. The criticality analysis is used to chart the probability of failure modes against the severity of their consequences. The result highlights failure modes with relatively high probability and severity of consequences, allowing remedial effort to be directed where it will produce the greatest value. However, there are several limitations. Measuring severity of failure consequences is subjective and linguistic. Since The result of FMECA only gives qualitative and quantitative informations, it should be re-analysed to prioritize critical units. Fuzzy set theory has been introduced by Lotfi A. Zadeh (1965). It has extended the classical set theory dramatically. Based on fuzzy set theory, fuzzy logic has been developed employing human reasoning process. IF-THEN fuzzy rule based assessment approach can model the expert's decision logic appropriately. Fault tree analysis (FTA) is one of most common fault modeling techniques. It is widely used in many fields practically. In this paper, a simple fault tree analysis is proposed to measure the severity of components. Fuzzy rule based assessment method interprets linguistic variables for determination of critical unit priorities. An rail-way transforming system is analysed to describe the proposed method.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.3
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• pp.46-54
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• 1997

In this paper, we psropose the feedback linearization technique for a nonlinear system using genetic algorithms (GAs) and fuzzy logic system. The proposed control scheme approximates the nonlinear term of a nonlinear system using the fuzzy logic system and computes the control input for cancelling the nonlinear term. Then in the fuzzy logic system, the number and shape of membership function of the premise aprt will be tuned to minimize the control error boundary using GAs. And the parameters of the consequence of fuzzy rule will be tuned by the adaptive laws based on lyapunov stability theory in order to guarantee the closed loop stability of control system. The evolution of fuzzy logic system is processed during the on-line adaptive control. The effectiveness of proposed method will be demonstrated by computer simulation of simple nonlinear sytem.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.04a
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• pp.171-175
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• 1998

This work presents an estimation for stress status using biosignal and fuzzy theory. Stress is estimated by 'coin-build' experiment with two type, relax and stress status. The estimator uses five biosignals, fuzzy logic to combine these signals and physiological knowledge. The system was tested in 10 records of healthy indivisuals and acheived a template of a stress progress. This work presents an estimation for stress status using biosignal and fuzzy theory. Stress is estimated by 'coin-build' experiment with two type, relax and stress status. The estimator uses five biosignals, fuzzy logic to combine these signals and physiological knowledge. The system was tested in 10 records of healthy indivisuals and acheived a template of a stress progress.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.171-177
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• 2011

The decision making process is based on accurate and timely available information. To obtain precise information from the internet is becoming more difficult due to the continuous increase in vagueness and uncertainty from online information resources. This also poses a problem for blind people who desire the full use from online resources available to other users for decision making in their daily life. Ontology is considered as one of the emerging technology of knowledge representation and information sharing today. Fuzzy logic is a very popular technique of artificial intelligence which deals with imprecision and uncertainty. The classical ontology can deal ideally with crisp data but cannot give sufficient support to handle the imprecise data or information. In this paper, we incorporate fuzzy logic with heavyweight ontology to solve the imprecise information extraction problem from heterogeneous misty sources. Fuzzy ontology consists of fuzzy rules, fuzzy classes and their properties with axioms. We use Fuzzy OWL plug-in of Protege to model the fuzzy ontology. A prototype is developed which is based on OWL-2 (Web Ontology Language-2), PAL (Protege Axiom Language), and fuzzy logic in order to examine the effectiveness of the proposed system.

• Transactions of the Korean Society of Automotive Engineers
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• v.6 no.5
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• pp.138-145
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• 1998

Recently, TCS(Traction Control System) is attracting attention, because it maintains traction ability and steerability of vehicles on low-$\mu$ surface roads by controlling the slip rate between tire and road surface. The development of TCS control law is difficult due to the highly nonlinearity and uncertainty involved in TCS. A fuzzy logic approach is appealing for TCS. In this paper, fuzzy logic controller for TCS is introduced and evaluated by the computer simulation with 8 DOF vehicle model. The result indicate that the fuzzy logic TCS improves vehicle's stability and steerability.

• Journal of the Korean Institute of Navigation
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• v.8 no.1
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• pp.15-42
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• 1984

Many studies have been done in the field of fuzzy logic theory, but it's application to the ship's steering system is few until this date. This paper is to survey the effect of application of fuzzy logic control by new compositional rule of Inference to the ship's steering system. The controller is made up of a set of Linguistic Control Rules which are conditional linguistic statements connecting the inputs and output, and take the inputs derived from deviation angle and it's angular velocity. The Linguistic Control Rules are implemented on the digital computer to verify the performance of the fuzzy logic controller and simulations have been done in six cases of initial condition and disturbance type. Consequently, it was proved that the ship's steering system by introducing the F.L.C. is performed efficiently and less energy loss system compared with the conventional autopilot.

• Korean Journal of Remote Sensing
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• v.21 no.2
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• pp.145-162
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• 2005

To improve the accuracy of land-cover discrimination in SAB data classification, this paper presents a methodology that includes feature extraction and fusion steps with multi-temporal SAR data. Three features including average backscattering coefficient, temporal variability and coherence are extracted from multi-temporal SAR data by considering the temporal behaviors of backscattering characteristics of SAR sensors. Dempster-Shafer theory of evidence(D-S theory) and fuzzy logic are applied to effectively integrate those features. Especially, a feature-driven heuristic approach to mass function assignment in D-S theory is applied and various fuzzy combination operators are tested in fuzzy logic fusion. As experimental results on a multi-temporal Radarsat-1 data set, the features considered in this paper could provide complementary information and thus effectively discriminated water, paddy and urban areas. However, it was difficult to discriminate forest and dry fields. From an information fusion methodological point of view, the D-S theory and fuzzy combination operators except the fuzzy Max and Algebraic Sum operators showed similar land-cover accuracy statistics.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.822-825
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• 1993

A fuzzy logic controller derived from the variable structure control (VSC) theory is designed. Unlike the conventional design of the fuzzy controller, we do not fuzzify the error and the rate of error, but fuzzify the sliding surface. After the fuzzy sliding surface is introduced, the fuzzy rules are defined based on the sliding control theory. It will be shown this sliding mode fuzzy controller is a kind of VSC that introduces the boundary layer in the switching surface and that the control input is continuously approximated in the layer. As a result we can guarantee the stability and the robustness by the help of VSC, which were difficult to insure in the past fuzzy controllers. Simulation results for the inverted pendulum will show the validity.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1066-1070
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• 2003

Recently technologies have created new principle and theory but the PID control system remains its popularity as the PID controller contains simple structure, including maintenance and parameter adjustment being so simple. Thus, this paper proposes auto tune PID by fuzzy logic controller based on FPGA which to achieve real time and small size circuit board. The digital PID controller design to consist of analog to digital converter which use chip TDA8763AM/3 (10 bit high-speed low power ADC), digital to analog converter which use two chip DAC08 (8 bit digital to analog converters) and fuzzy logic tune digital PID processor embedded on chip FPGA XC2S50-5tq-144. The digital PID processor was designed by fundamental PID equation which architectures including multiplier, adder, subtracter and some other logic gate. The fuzzy logic tune digital PID was designed by look up table (LUT) method which data storage into ROM refer from trial and error process. The digital PID processor verified behavior by the application program ModelSimXE. The result of simulation when input is units step and vary controller gain ($K_p$, $K_i$ and $K_d$) are similarity with theory of PID and maximum execution time is 150 ns/action at frequency are 30 MHz. The fuzzy logic tune digital PID controller based on FPGA was verified by control model of level control system which can control level into model are correctly and rapidly. Finally, this design use small size circuit board and very faster than computer and microcontroller.