• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.5
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• pp.3-10
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• 2018

The purpose of this study is to estimate the attractiveness value according to the preference level on architectural aesthetic. This research starts from the concept that aesthetic is phenomenon and from the viewpoint of 'attraction value' which affects goods. Interactive internet surveys were conducted for 500 citizens of Seoul metropolitan city who are potential users of the Nodle Islet Cultural Center. Based on the scenarios and questionnaires with fuzzy models, we have examined the evaluation of architectural aesthetic and monetary willing-to-payment, and estimated the economic value by preference level of architectural aesthetic through linear regression analysis. The main results of the study are as follows: First, the economic value of the Nodle Islet Cultural Center was estimated at ?15,683.43/person. Residents of Seoul metropolitan city were willing to accept the increase in the above-mentioned amount of the facility fares when their preferred works (average 86.81 points) were constructed. (P <0/05) Second, it is confirmed that the economic value increases dramatically as the preference level of architectural aesthetic increases. Third, it is presumed that the infinite valuation of architectural aesthetic and the problem of free riding coexist in the estimation of economic valuation of architectural aesthetic for public buildings. Fourth, by mixing the fuzzy logic with contingent valuation method, starting point bias and no response biases that happened in contingent valuation could be disappeared. bias elimination must be considered seriously because another bias could be happened in full process of the research. The results of this study will serve as a basis for spreading architectural aesthetic value-oriented research from the vague and obscure aesthetic-centered discussion on the existing architectural aesthetic. In addition, it will be an opportunity to draw institutional application and utilization strategy of architectural aesthetic through architectural aesthetic value research.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.6
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• pp.1092-1098
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• 2007

Since one decade, the detection of HFPD (High frequency Partial Discharge) has been proposed as one of the effective method for the diagnosis of the power component under service in power grids. As a tool for HFPD detection, Metal Foil sensor based on the embedded technology has been commercialized for mainly power cable due to its advantages. Recently, for the on-site noise discrimination, several PA (Pulse analysis) methods have been reported and the related software, such as Neural Network and Fuzzy, have been proposed to separate the PD (Partial Discharge) signals from the noises since their wave shapes are completely different from each other. On the other hand, the relevant fundamental investigation has not yet clearly made while it is reported that the effectiveness of the current methods based on PA is dependant on the types of sensors. Moreover, regarding the identification of the vital defects introducible into the Power Cable, the direct identification of the nature of defects from the PD signals through Metal Foil coupler has not yet been realized. As a trial for solving above shortcomings, different types of software have been proposed and employed without any convincing probability of identification. In this regards, our novel algorithm 'PA Map' based on the pulse analysis is suggested to identify directly the defects inside the power cable from the HFPD signals which is output of the HFCT and metal foil sensors. This method enables to discriminate the noise and then to make the data analysis related to the PD signals. For the purpose, the HFPD detection and PA (Pulse Analysis) system have been developed and then the effect of noise discrimination has been investigated by use of the artificial defects using real scale mockup. Throughout these works, our system is proved to be capable of separating the small void discharges among the very large noises such as big air corona and ground floating discharges at the on-site as well as of identifying the concerned defects.

• Journal of Biomedical Engineering Research
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• v.21 no.4
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• pp.333-338
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• 2000

In this paper, an automated segmentation algorithm is proposed for MR brain images using T1-weighted, T2-weighted, and PD images complementarily. The proposed segmentation algorithm is composed of 3 step. In the first step, cerebrum images are extracted by putting a cerebrum mask upon the three input images. In the second step, outstanding clusters that represent inner tissues of the cerebrum are chosen among 3-dimensional(3D) clusters. 3D clusters are determined by intersecting densely distributed parts of 2D histogram in the 3D space formed with three optimal scale images. Optimal scale image is made up of applying scale space filtering to each 2D histogram and searching graph structure. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram and searching graph structure. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram. In the final step, cerebrum images are segmented using FCM algorithm with its initial centroid value as the outstanding clusters centroid value. The proposed cluster's centroid accurately. And also can get better segmentation results from the proposed segmentation algorithm with multi spectral analysis than the method of single spectral analysis.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.812-814
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• 1999

In this paper, we are applied the Genetic Algorithm (GA) to design of fuzzy logic controller (FLC) for a DC Servo-Motor Speed Control. GA is used to design of the membership functions and scaling factor of FLC. To evaluate the performances of the proposed FLC, we make an experiment on FLC for the speed control of an actual DC servo-motor system with nonlinear characteristics. Experimental results show that proposed controller have better performance than those of PD controller.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1034-1036
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• 1996

In this paper, an adaptive fuzzy logic controller is presented for auto-tunning of the scaling factors by using learning capability of neural networks. The proposed scheme consists of the FLC which includes the PI-type FLC and PD-type FLC in parallel form and the neural network which learns scale factors of FLC. Computer simulations were performed to illustrate the effectiveness of a proposed scheme. A proposed FLC controller was applied to the second order system and velocity control of the brushless DC motors. For the design of the FLC, tracking error, change of error, and acceleration error are selected as input variables of the FLC and three seal e factors were used in the parallel-type FLC. This scheme can be used to reduce the difficulty in the selection of the scale factors.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.2
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• pp.70-76
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• 2009

This paper introduces an evaluation code, which can numerically express the noise possessing degree of signals. By using this code, the best kind and setting of noise suppressing techniques can be chosen automatically. This code is applied to three kinds of specific denoising techniques; those are simple noise removing method in the count versus phase distribution, fuzzy logic method based on noise type in magnitude versus phase plot, and lastly, the technique using grouping characteristics of PD pulses in 3D plot of magnitude versus phase versus cycle. The algorithm shows good performance in the various real PD signals measured from various high voltage apparatuses in Korea.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.185-191
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• 2008

In this paper, experimental studies of a neural network (NN) control technique for non-model based position control of the x-y table robot are presented. Decentralized neural networks are used to control each axis of the x-y table robot separately. For an each neural network compensator, an inverse control technique is used. The neural network control technique called the reference compensation technique (RCT) is conceptually different from the existing neural controllers in that the NN controller compensates for uncertainties in the dynamical system by modifying desired trajectories. The back-propagation learning algorithm is developed in a real time DSP board for on-line learning. Practical real time position control experiments are conducted on the x-y table robot. Experimental results of using neural networks show more excellent position tracking than that of when PD controllers are used only.

• Nuclear Engineering and Technology
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• v.25 no.2
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• pp.222-232
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• 1993

In this paper, we present two sets of fuzzy algorithms for the steam generator level control ; one for the high power operations where the flow error is available and the other for the low power operations where the flow error is not available. These are converted to a PID type controller for the high power case and to a quadratic function form of a controller for the low power case. These controllers are implemented on the Compact Nuclear Simulator at Korea Atomic Energy Research Institute and tested by a set of four simulation experiments for each. For both cases, the results show that the total variation of the level error and of the flow error are about 50% of those by the PI controllers with about one half of the control action. For the high power case, this is mainly due to the fact that a combination of two PD type controllers in the velocity algorithm form rather than a combination of two PI type controllers in the position algorithm form is used. For the low power case, the controller is essentially a PID type with a very small integral component where the average values for the derivative component input and for the controller output are used.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.9
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• pp.114-129
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• 1996

This paper describes the whole development phase for the underwater vehicle actuating system with high hydroload torque disturbance. This includes requirement analysis, system modeling, control algorithm design, real time implementation, test and performance evaluations. As for driving control algorithms, fuzzy logic, variable structure and PD(Proportional-Differential) algorithm were designed and implemented on board controller using a single chip microprocessor. Intel 8797. And test and performance evaluation is carried out both single test and wystem integration test. We could confirm the basic performance of actuating system through the single test and gereral developing work of any actuating systems was finished with a single performance test of actuating system without system integration test. But, we suggested that system integration test be needed. System integration test is carried out using G/C HILS(Guidance and Control Hardware-In-the -Loop Simulation) which is constituted flight motion simulator, load simulator, real time host computer and the related subsystems such as inertial navigation system, power supply system and Guidance and Control Computer etc.. The most important practical contribution of this paper is that full system characteristics such as minimal control effort, enhancement of guidance and autopilot performance by the actuating system using G/C HILS technique are investigated. Through full running G/C HILS, in spite of the passing to single tests, some control algorithm resulted in failure as to stability of full system and system time frame.