• The KSFM Journal of Fluid Machinery
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• v.10 no.3 s.42
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• pp.33-38
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• 2007

Fan selection procedure and fan noise evaluation method are presented for the system electronic cooling by combining FNM(Flow Network Model) and fan noise correlation model. Internal flow paths and distribution in electronic system we analyzed by using the FNM with the flow resistances for flow elements of the system. Based on the fan operation point predicted from the FNM analysis results, the present fan noise model predicts overall sound power, pressure levels and spectrum. The predictions of the flow distribution, the fan operation and the noise level in electronic system by the present method are well agreed with 3-D CFD and actual test results.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.354-358
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• 2006

This study is concerned with the part-machine grouping(PMG) based on the non-binary part-machine incidence matrix in which real manufacturing Factors such as the operation sequences with multiple visits to the same machine and production volumes of parts are incorporated and each entry represents actual moves due to different operation sequences. The proposed approach adopts Fuzzy ART neural network to quickly create the initial part families and their associated machine cells. To enhance the poor solution due to category proliferation inherent to most artificial neural networks, a supplementary procedure reassigning parts and machines is added. To show effectiveness of the proposed approach to large-size PMG problems, a psuedo-replicated clustering procedure is designed and implemented.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.7
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• pp.1036-1044
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• 1995

In this paper, a band-pass filter is implemented with the diffusion and difference processes by using the diffusion neural network model. The center frequency of this band-pass filter can be varied by iterations of the diffusion and difference operations, and the selectivity can be determined by iterations of the difference operation. We propose an efficient algorithm that can generate various band-pass filters using arbitrary diffusion and difference iterations. This algorithm needs only simple operations of diffusion and difference.

• Proceedings of the KSME Conference
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• 2000.04b
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• pp.205-212
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• 2000

Induction motors. having axial cooling channels in stator and rotor are designed for better cooling performance. Traction motors are one of those examples. And, thermal analysis gain more attention with the Increased demand of the motors, for reliable operation and life prolongation. was Induced to effective thermal conductivity through modeling. Through. fundamental comparison experiment, heat source experiment and transient state experiment, the induction motor using inverter was examined to produce heat source with frequency level and traced to thermal variation at starting and stopping. And thermal analysis using thermal network was compared with a transient state experiment.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.6
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• pp.168-176
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• 1996

A diagnosis system which can monitor tool breakage and chipping in real time was developed using a DSP(Digital Signal Processor) board in face milling operation. AR modelling and band energy method were used to extract the feature of tool states from cutting force signals. Artificial neural network embedded on DSP board discriminates different patterns from features got after signal processing. The features extracted from AR modelling are more accurate for the malfunction of a process than those from band energy method, even though the computing speed of the former is slow. From the processed features, we can construct the real time diagnosis system which monitors malfunction by using a DSP board having a parallel processing capability.

• Nuclear Engineering and Technology
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• v.30 no.3
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• pp.222-234
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• 1998

The Westinghouse OT$\Delta$T DNB protection logic heavily restricts the operation region by applying the same logic for a full range of operating pressure in order to maintain its simplicity. In this work, a fuzzy neural network method is used to estimate the DNB protection limit using the measured average temperature and pressure of a reactor core. Fuzzy system parameters are optimized by a hybrid learning method. This algorithm uses a gradient descent algorithm to optimize the antecedent parameters and a least-squares algorithm to solve the consequent parameters. The proposed method is applied to Yonggwang 3&4 nuclear power plants and the proposed method has 5.99 percent larger thermal margin than the conventional OT$\Delta$T trip logic. This simple algorithm provides a good information for the nuclear power plant operation and diagnosis by estimating the DNB protection limit each time step.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.59-64
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• 2002

An expert system, to assist dam managers for five dams along the Saikawa River, has been developed with a primary objective of achieving swift and accurate reservoir operation decision-makings during floods. The expert system is capable of supporting on decision-makings upon establishment of flood management procedure and release/storage planning. Furthermore, an attempt was made to improve reservoir inflow prediction models for better supporting capability. As a result, accuracy on prediction of inflow up to 7 hours ahead was improved, which is important for flood management of the five dams, using neural network. The neural network inflow prediction models were developed for each types of floods caused by frontal rainfalls, snowmelt and typhoons, after extracting relevant meteorological factors for each.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.4
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• pp.11-19
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• 1996

Recently, fuzzy and neural network techniques have been successfully applied to control of complex and ill-defined system in a wide variety of areas, such as robot, water purification, automatic train operation system and automatic container crane operation system, etc. In this paper, we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feedforward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand, feedforward neural networks provide salient features, such as learning and parallelism. In the proposed neuro-fuzzy controller, the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error backpropagation algorithm as a learning rule, while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally, the effectiveness of the proposed controller is verified through computer simulation of an inverted pendulum system.

• Proceedings of the KSR Conference
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• 1998.05a
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• pp.31-38
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• 1998

Guideway Transportation will provide the break-through to solve the traffic problems, which a lot of big cities have over the world. APM (Automated People Mover) system, Guideway Transportation, is classified by PRT, GRT, and MRT according to the capacity of passenger to be carried by its system. In the Application of PRT (Personal Rapid Transit) system, the enact analysis of the traffic problem on the area to be applied should be preceded. PRT system is characterized by Off-Line and Demand Operation. The Network of PRT system depends on the Load Time and Load Pattern. The operation of PRT system will have move efficient way when it is applied to the network with evenly distributed Load Time and Load Pattern.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.6
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• pp.125-132
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• 2017

Generally, in machining operations, the required machining performance can be obtained by properly combining several machining parameters properly. In this research, we construct a simulation model, which that predicts the relationship between the input variables and output variables in the turning operation. Input variables necessary for the turning operation include cutting speed, feed, and depth of cut. Surface roughness and electrical current consumption are used as the output variables. To construct the simulation model, an Artificial Neural Network (ANN) is employed. With theIn ANN, training is necessary to find appropriate weights, and the Ant Colony Optimization (ACO) technique is used as a training tool. EspeciallyIn particular, for the continuous domain, ACOR is adopted and athe related algorithm is developed. Finally, the effects of the algorithm on the results are identified and analyzsed.