• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.379-390
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• 2005

In this paper, we introduce a new combinatorial auction mechanism - N-Bilateral Optimized Combinatorial Auction (N-BOCA). N-BOCA is a flexible iterative combinatorial auction model that offers optimized trading for multi-suppliers and multi-purchasers in the supply chain. We design the N-BOCA system from the perspectives of architecture, protocol, and trading strategy. Under the given N-BOCA architecture and protocol, auctioneers and bidders have diverse decision strategies for winner determination. This needs flexible modeling environments. Hence, we propose an optimization modeling agent for bid and auctioneer selection. The agent has the capability to automatic model formulation for Integer Programming modeling. Finally, we show the viability of N-BOCA through prototype and experiments. The results say both higher allocation efficiency and effectiveness compared with I-to-N general combinatorial auction mechanisms.

• KIEAE Journal
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• v.16 no.3
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• pp.89-94
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• 2016

Purpose: This study aimed at developing an artificial neural network (ANN) model to predict the optimal start moment of the setback temperature during the normal occupied period of a building. Method: For achieving this objective, three major steps were conducted: the development of an initial ANN model, optimization of the initial model, and performance tests of the optimized model. The development and performance testing of the ANN model were conducted through numerical simulation methods using transient systems simulation (TRNSYS) and matrix laboratory (MATLAB) software. Result: The results analysis in the development and test processes revealed that the indoor temperature, outdoor temperature, and temperature difference from the setback temperature presented strong relationship with the optimal start moment of the setback temperature; thus, these variables were used as input neurons in the ANN model. The optimal values for the number of hidden layers, number of hidden neurons, learning rate, and moment were found to be 4, 9, 0.6, and 0.9, respectively, and these values were applied to the optimized ANN model. The optimized model proved its prediction accuracy with the very storing statistical correlation between the predicted values from the ANN model and the simulated values in the TRNSYS model. Thus, the optimized model showed its potential to be applied in the control algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1505-1509
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• 2005

This paper introduces a new architecture of genetically optimized self-organizing fuzzy polynomial neural networks by means of information granulation. The conventional SOFPNNs developed so far are based on mechanisms of self-organization and evolutionary optimization. The augmented genetically optimized SOFPNN using Information Granulation (namely IG_gSOFPNN) results in a structurally and parametrically optimized model and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNN. With the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The GA-based design procedure being applied at each layer of genetically optimized self-organizing fuzzy polynomial neural networks leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, a collection of the specific subset of input variables, and the number of membership function) available within the network. To evaluate the performance of the IG_gSOFPNN, the model is experimented with using gas furnace process data. A comparative analysis shows that the proposed IG_gSOFPNN is model with higher accuracy as well as more superb predictive capability than intelligent models presented previously.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.33-38
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• 2021

This paper presents a comparative study between the parameter-optimized Pacejka model and artificial neural network model for the tire force estimation. The two different approaches are investigated and compared in this study. First, offline optimization is conducted based on Pacejka Magic Formula model to determine the proper parameter set for the minimization of tire force error between the model and test data set. Second, deep neural network model is used to fit the model to the tire test data set. The actual tire forces are measured using MTS Flat-Track test platform and the measurements are used as the reference tire data set. The focus of this study is on the applicability of machine learning technique to tire force estimation. It is shown via the regression results that the deep neural network model is more effective in describing the tire force than the parameter-optimized Pacejka model.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.6
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• pp.14-20
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• 2015

The Korea ministry of land, infrastructure and transport set the goal of cutting greenhouse gas emissions from the transport sector by 34.3% relative to the business as usual scenario by 2020. In order to achieve this goal, support is being given to education and information regarding eco-driving. As a practical measure, however, a vehicle control strategy for decreasing fuel consumptions and emissions is necessary. Therefore, this paper presents an optimized driving model in order to decrease fuel consumption. Scenarios were established by driving mode. The speed profile for each scenario applied to Comprehensive Modal Emission Model and then each fuel consumption was estimated. Scenarios and speed variation with the least fuel consumption were derived by comparing the fuel consumptions of scenarios. The optimized driving model was developed by the derived the results. The speed profiles of general driver were collected by field test. The speed profile of the developed model and the speed profile of general driver were compared and then fuel consumptions for each speed profile were analyzed. The fuel consumptions for optimized driving were decreased by an average of 11.8%.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.6
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• pp.72-80
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• 2013

For the proper cooling of in-wheel motor, the cooling channel should have the characteristics which are low pressure drop and adequate cooling oil supply to motor part. In this study, the flow performance of cooling channel for in-wheel motor was evaluated and the shape of the channel was optimized. First, the pressure drop and flow distribution characteristics of the initial channel model were evaluated using numerical analysis. Also, by the result of analysis and design modification, 4 design parameters of the channel were selected. Second, using the Taguchi optimal method, the cooling channel was optimized. In the method, nine models with different levels of the design parameters were generated and the flow characteristics of each models was estimated. Base on the result, the main effect of the design parameters was founded and optimized model was obtained. For the optimized model, the pressure drop and oil flow rate were about 0.196 bar and 0.207 L/min, respectively. The pressure drop decreased by about 0.3 bar and the oil flow rate to the motor part increased by about 0.2 L/min compared to the initial model.

• Journal of Magnetics
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• v.22 no.2
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• pp.257-265
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• 2017

This paper presents a 2-D analytical method to calculate the back EMF of the axial flux permanent magnet synchronous generator (AFPMSG) with coreless stator and dual rotor having magnets mounted on both sides of rotor yoke. Furthermore, in order to reduce the no load voltage total harmonics distortion (VTHD), the initial model of the coreless AFPMSG is optimized by using a developed analytical method. Optimization using the 2-D analytical method reduces the optimization time to less than a minute. The back EMF obtained by using the 2-D analytical method is verified by a time stepped 3-D finite element analysis (FEA) for both the initial and optimized model. Finally, the VTHD, output torque and torque ripples of both the initial and optimized models are compared with 3D-FEA. The result shows that the optimized model reduces the VTHD and torque ripples as compared to the initial model. Furthermore, the result also shows that output torque increases as the result of the optimization.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.303-311
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• 2012

In this study, we introduce a dynamic process model as well as the design methodology of optimized fuzzy controller for its efficient application to vacuum production system to produce a semiconductor, solar module and display and so on. In a vacuum control field, PID control method is widely used from the viewpoint of simple structure and preferred performance. But, PID control method is very sensitive to the change of environment of control system as well as the change of control parameters. Therefore, it's difficult to get a preferred performance results from target system which has a complicated structure and lots of nonlinear factors. To solve such problem, we propose the design methodology of an optimized fuzzy PID controller through a following series of steps. First a dynamic characteristic of the target system is analyzed through a series of experiments. Second the process model is built up and its characteristic is compared with real process. Third, the optimized fuzzy PID controller is designed using genetic algorithms. Finally, the fuzzy controller is applied to target system and then its performance is compared with that of other conventional controllers(PID, PI, and Fuzzy PI controller). The performance of the proposed fuzzy controller is evaluated in terms of auto-tuned control parameters and output responses considered by ITAE index, overshoot, rise time and steady state time.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.454-457
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• 1997

The purpose of this study is to develop an optimal model, using existing models, that is able to estimate the amount of spatter utilizing artificial neural network in the short circuit transfer mode of gas metal arc (GMA) welding. The amount of spatter generated during welding can become a barometer which represents the process stability of metal transfer in GMA welding, and it depends on some factors which constitute a periodic waveforms of welding current and arc voltage in short circuit GMA welding. So, the 12 factors, which could express the characteristics for the waveforms, and the amount of spatter are used as input and output variables of the neural network, respectively. Two neural network models to estimate the amount of spatter are proposed: A neural network model, where arc extinction is not considered, and a combined neural network model where it is considered. In order to reduce the calculation time it take to produce an output, the input vector and hidden layers for each model are optimized using the correlation coefficients between each factor and the amount of spattcr. The est~mation performance of each optimized model to the amount of spatter IS assessed and compared to the est~mation performance of the model proposed by Kang. Also, through the evaluation for the estimation performance of each optimized model, it is shown that the combined neural network model can almost perfectly predict the amount of spatter.

• Journal of The Korean Society of Agricultural Engineers
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• v.47 no.3
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• pp.3-13
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• 2005

The conceptual rainfall-runoff models are used to predict complex hydrological effects of a basin. However, to obtain reliable results, there are some difficulties and problems in choosing optimum model, calibrating, and verifying the chosen model suitable for hydrological characteristics of the basin. In this study, Genetic Algorithm and SCE-UA method as global optimization methods were applied to compare the each optimization technique and to analyze the application for the rainfall-runoff models. Modified TANK model that is used to calculate outflow for watershed management and reservoir operation etc. was optimized as a long term rainfall-runoff model. And storage-function model that is used to predict real-time flood using historical data was optimized as a short term rainfall-runoff model. The optimized models were applied to simulate runoff on Pyeongchang-river watershed and Bocheong-stream watershed in 2001 and 2002. In the historical data study, the Genetic Algorithm and the SCE-UA method showed consistently good results considering statistical values compared with observed data.