• The Transactions of the Korean Institute of Power Electronics
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• v.22 no.1
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• pp.75-81
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• 2017

This paper presents a design and parallel control strategy of 1.8 kW low-voltage DC-DC converter (LDC) for mild hybrid electric vehicles to improve their power density, system efficiency, and operation stability. Topology and control scheme are important on the LDC for mild hybrid electric vehicles to achieve high system efficiency and power density because of their very low voltage and large current in input and output terminals. Therefore, the optimal topological structure and control algorithm are examined, and a detailed design methodology for the power and control stages is presented. A working sample of 1.8 kW LDC is designed and implemented by applying the adopted topology and control strategy. Experimental results indicate 92.45% of the maximum efficiency and 560 W/l of power density.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.700-702
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• 2000

The new design methodology of a hybrid fuzzy controller by means of the genetic algorithms is presented. In fuzzy controller which has been widely applied and used. in order to construct the best fuzzy rules that include adjustment of fuzzy sets, a highly skilled techniques using trial and error are required. To deal with such a problem, first, a hybrid fuzzy controller(HFC) related to the optimal estimation of control parameters is proposed. The HFC combined a PID controller with a fuzzy controller concurrently produces the better output performance than any other controller from each control output in steady state and transient state. Second, a auto-tuning algorithms is presented to automatically improve the performance of hybrid fuzzy controller, utilizing the simplified reasoning method and genetic algorithms. In addition, to obtain scaling factors and PID Parameters of HFC using GA, three kinds of estimation modes such as basic, contraction, and expansion mode are effectively utilized. The HFCs are applied to the first-order second-order process with time-delay and DC motor Computer simulations are conducted at step input and the performances of systems are evaluated and also discussed from performance indices.

• Journal of Power Electronics
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• v.11 no.4
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• pp.471-478
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• 2011

This paper addresses the establishment of a kVA-range plug-in hybrid electrical vehicle (PHEV) integration test platform and associated issues. Advancements in battery and power electronic technology, hybrid vehicles are becoming increasingly dependent on the electrical energy provided by the batteries. Minimal or no support by the internal combustion engine may result in the vehicle being occasionally unable to recharge the batteries during highly dynamic driving that occurs in urban areas. The inability to sustain its own energy source creates a situation where the vehicle must connect to the electrical grid in order to recharge its batteries. The effects of a large penetration of electric vehicles connected into the grid are still relatively unknown. This paper presents a novel methodology that will be utilized to study the effects of PHEV charging at the sub-transmission level. The proposed test platform utilizes the power hardware-in-the-loop (PHIL) concept in conjunction with high-fidelity PHEV energy system simulation models. The battery, in particular, is simulated utilizing a real-time digital simulator ($RTDS^{TM}$) which generates appropriate control commands to a power electronics-based voltage amplifier that interfaces via a LC-LC-type filter to a power grid. In addition, the PHEV impact is evaluated via another power electronic converter controlled through $dSPACE^{TM}$, a rapid control systems prototyping software.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.4
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• pp.566-578
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• 2017

For optimizing the operation plan when strike packages attack multiple targets, this article suggests a new mathematical model and a parallel hybrid genetic algorithm (PHGA) as a solution methodology. In the model, a package can assault multiple targets on a sortie and permitted the use of mixed munitions for a target. Furthermore, because the survival probability of a package depends on a flight route, it is formulated as a mixed integer programming which is synthesized the models for vehicle routing and weapon-target assignment. The hybrid strategy of the solution method (PHGA) is also implemented by the separation of functions of a GA and an exact solution method using ILOG CPLEX. The GA searches the flight routes of packages, and CPLEX assigns the munitions of a package to the targets on its way. The parallelism enhances the likelihood seeking the optimal solution via the collaboration among the HGAs.

• Journal of Navigation and Port Research
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• v.35 no.1
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• pp.83-91
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• 2011

The accuracy of forecasting is remarkably important to reduce total cost or to increase customer services, so it has been studied by many researchers. In this paper, the artificial neural network (ANN), one of the most popular nonlinear forecasting methods, is compared with autoregressive integrated moving average(ARIMA) model through performing a prediction of container traffic. It uses a hybrid methodology that combines both the linear ARIAM and the nonlinear ANN model to improve forecasting performance. Also, it compares the methodology with other models in performance for prediction. In designing network structure, this work specially applies the genetic algorithm which is known as the effectively optimal algorithm in the huge and complex sample space. It includes the time delayed neural network (TDNN) as well as multi-layer perceptron (MLP) which is the most popular neural network model. Experimental results indicate that both ANN and Hybrid models outperform ARIMA model.

• Journal of Korea Technology Innovation Society
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• v.14 no.spc
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• pp.1232-1255
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• 2011

This paper proposes a model for demand forecasting that will require less effort in the process of utilizing the new product diffusion model while also allowing for more objective and timely application. Drawing upon the theoretical foundation provided by the hype cycle model and the consumer adoption model, this proposed model makes it possible to estimate the maximum market potential based solely on bibliometrics and the scale of the early market, thereby presenting a method for supplying the major parameters required for the Bass model. Upon analyzing the forecasting ability of this model by applying it to the case of the hybrid car market, the model was confirmed to be capable of successfully forecasting results similar in scale to the market potential deduced through various other objective sources of information, thus underscoring the potentials of utilizing this model. Moreover, even the hype cycle or the life cycle can be estimated through direct linkage with bibliometrics and the Bass model. In cases where the hype cycles of other models have been observed, the forecasting ability of this model was demonstrated through simple case studies. Since this proposed model yields a maximum market potential that can also be applied directly to other growth curve models, the model presented in the following paper provides new directions in the endeavor to forecast technology diffusion and identify promising technologies through bibliometrics.

• Asia pacific journal of information systems
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• v.9 no.4
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• pp.23-40
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• 1999

This paper proposes a hybrid intelligent system that predicts the failure of firms based on the past financial performance data, combining neural network and rough set approach, We can get reduced information table, which implies that the number of evaluation criteria such as financial ratios and qualitative variables and objects (i.e., firms) is reduced with no information loss through rough set approach. And then, this reduced information is used to develop classification rules and train neural network to infer appropriate parameters. Through the reduction of information table, it is expected that the performance of the neural network improve. The rules developed by rough sets show the best prediction accuracy if a case does match any of the rules. The rationale of our hybrid system is using rules developed by rough sets for an object that matches any of the rules and neural network for one that does not match any of them. The effectiveness of our methodology was verified by experiments comparing traditional discriminant analysis and neural network approach with our hybrid approach. For the experiment, the financial data of 2,400 Korean firms during the period 1994-1996 were selected, and for the validation, k-fold validation was used.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2006.03a
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• pp.527-534
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• 2006

A real-time hybrid method, in which the experimental implementation and the numerical computation of a structure are simultaneously carried out in real-time and combined on-line, has been used as a dynamic testing technique of structure to investigate its dynamic behaviors. In this paper, an experimental hybrid method, which implements the earthquake response control of a building structure with a TLD by using only a TLD as an experimental part, is proposed and is experimentally verified through a shaking table test. In the proposed methodology, the whole building structure with a TLD is divided into the upper TLD and the lower structural parts as experimental and numerical substructures, respectively. At the moment, the control force acting between their interface is measured from the experimental TLD with shear-type load-cell which is mounted on shaking table. Shaking table vibrates the upper experimental TLD with the response calculated from the numerical substructure, which is subjected to the excitations of the measured interface control force at its top story and an earthquake input at its base. The experimental results show that the conventional method, in which both a TLD and a building model are physically manufactured and are tested, can be replaced by the proposed methodology with a simple experimental installation and a good accuracy for evaluating the control performance of a TLD.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.145-154
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• 2009

The main goal of this paper is to develop a methodology for increasing the reliability of truck OD matrices in Seoul Metropolitan Area. We propose a Hybrid Method made up of five processes by using Non-Traffic Assignment and Gradient Method. A Hybrid Method and Gradient Method have applied for comparison and estimation in Seoul Metropolitan Area. Mean Error and Root Mean Square Error of a Hybrid Method present lower than Gradient Method. The findings of this paper show that the new truck OD matrices created by a Hybrid Method are more reliable than the existing truck OD matrices in the Seoul Metropolitan Area.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.71-89
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• 2003

Although many studies demonstrate that one technique outperforms the others for a given data set, there is often no way to tell a priori which of these techniques will be most effective in the classification problems. Alternatively, it has been suggested that a better approach to classification problem might be to integrate several different forecasting techniques. This study proposes the linearly combining methodology of different classification techniques. The methodology is developed to find the optimal combining weight and compute the weighted-average of different techniques' outputs. The proposed methodology is represented as the form of mixed integer programming. The objective function of proposed combining methodology is to minimize total misclassification cost which is the weighted-sum of two types of misclassification. To simplify the problem solving process, cutoff value is fixed and threshold function is removed. The form of mixed integer programming is solved with the branch and bound methods. The result showed that proposed methodology classified more accurately than any of techniques individually did. It is confirmed that Proposed methodology Predicts significantly better than individual techniques and the other combining methods.