• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.213-216
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• 2005

In this research, we explore how to manage the transition of technology generations considering incremental innovation of the existing technology generation. Firms can slow down decaying of the existing technology by continuous incremental improvements rather than introducing a new generation technology at the first time if the former strategy is better. We characterize optimal technology transition problem by setting up an optimal control model. The model which is originally designed and solved by Thompson(1968) as a ‘Machine maintenance problem’ has been cited to build the main body of our model. With this analytical model, we derive optimal ‘incremental innovation’ strategy which is considering transition to the next technology. Our analysis indicates that there exists an unique ‘stopping incremental innovation timing’. Before the point of time, the decision maker should make his effort at a maximum level to enhance the current technology. However from the stopping timing to the final time horizon where the new technology is introduced, it is found that not to invest to the current technology any more is optimal.

• Bulletin of the Korean Mathematical Society
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• v.48 no.4
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• pp.737-757
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• 2011

This work is concerned with an optimal selling rule for a large position of stock in a market. Selling a large block of stock in a short period typically depresses the market, which would result in a poor filling price. In addition, the large selling intensity makes the regime more likely to be poor state in the market. In this paper, regime switching and depressing terms associated with selling intensity are considered on a set of geometric Brownian models to capture movements of underlying asset. We also consider the liquidation strategy to sell much smaller number of shares in a long period. The goal is to maximize the overall return under state constraints. The corresponding value function with the selling strategy is shown to be a unique viscosity solution to the associated HJB equations. Optimal liquidation rules are characterized by a finite difference method. A numerical example is given to illustrate the result.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.700-705
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• 2004

Clustering of incomplete data using the Autoencoder and the Fuzzy c-Means(PCM) is proposed in this paper. The Proposed algorithm, called Optimal Completion Autoencoder Fuzzy c-Means(OCAEFCM), utilizes the Autoencoder Neural Network (AENN) and the Gradiant-based FCM (GBFCM) for optimal completion of missing data and clustering of the reconstructed data. The proposed OCAEFCM is applied to the IRIS data and a data set from a financial institution to evaluate the performance. When compared with the existing Optimal Completion Strategy FCM (OCSFCM), the OCAEFCM shows 18％-20％ improvement of performance over OCSFCM.

• Journal of applied mathematics & informatics
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• v.40 no.5_6
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• pp.857-871
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• 2022

In this work, we formulated a mathematical model for divorce in marriage and extended in to an optimal control model. Firstly, we qualitatively established the model positivity and boundedness. Also we saw sensitivity analysis of the model and identified the positive and negative indices parameters. An optimal control model were developed by incorporating three time dependent control strategies (couple relationship education, reducing getting married too young & consulting separators to renew their marriage) on the deterministic model. The Pontryagin's maximum principle were used for the derivation of necessary conditions of the optimal control problem. Finally, with Newton's forward and backward sweep method numerical simulation were performed on optimality system by considering four integrated strategies. So that we reached to a result that using all three strategies simultaneously (the strategy D) is an optimal control in order to effectively control marriage divorce over a specified period of time. From this we conclude that, policymakers and stakeholders should use the indicated control strategy at a time in order to fight against Divorce in a population.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1079-1085
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• 2000

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). An immune system is the living bodys self-protection and self-maintenance system. these features can be applied to decision making of the optimal swarm behavior in a dynamically changing environment. For applying immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody, and control parameter as a T-cell, respectively. When the environmental condition (antigen) changes, a robot selects an appropriate behavior strategy (antibody). And its behavior strategy is stimulated and suppressed by other robots using communication (immune network). Finally, much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and immune network hypothesis, and it is used for decision making of the optimal swarm strategy. Adaptation ability of the robot is enhanced by adding T-cell model as a control parameter in dynamic environments.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.591-597
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• 2001

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). Immune system is living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For applying immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other robot using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control school is based on clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.3
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• pp.85-97
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• 2000

In this paper, static and dynamic shift control stategies of CVT speed ratio are suggested. For the static shift control, in order to operate engine on the optimal operating region, a fuzzy control logic is used. In the fuzzy logic, S- factor that is defined as a degree of sportiness is introduced. Simulation results show that the static shift control strategy based on the fuzzy logic selects the optimal operating point automatically between the economy and the sporty mode corresponding to the driver's desire and the driving condition. For the dynamic shift control strategy, a shift speed map is suggested which determines the shift sped as fast or slow based on Δi, the difference between the desired speed ratio id and the actual speed ratio i, and throttle opening. It is seen from the simulation results that the CVT shift speed is determined by the dynamic shift control strategy to provide appropriate performance and comfort for the driver's demand and driving condition. Additionally, experiments are performed to investigate the dynamic performance of the shift speed for the lift foot up. From the experimental results, it is found that improved shift feeling can be obtained by the dynamic shift control strategy when lift foot up occurs.

• Journal of Power Electronics
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• v.19 no.2
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• pp.549-559
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• 2019

This paper proposes an adaptive sliding mode control (ASMC) strategy with an enhanced optimal reaching law (EORL) for the robust current tracking control of the boost converter based hybrid power source (HPS) in an electric vehicle (EV). A conventional ASMC strategy based on state observers and the hysteresis control method is used to realize the current tracking control for the boost converter based HPS. Then a novel enhanced exponential reaching law is proposed to improve the ASMC. Moreover, an enhanced exponential reaching law is optimized by particle swarm optimization. Finally, the adaptive control factor is redesigned based on the EORL. Simulations and experiments are established to validate the ASMC strategy with the EORL. Results show that the ASMC strategy with the EORL has an excellent current tracking control effect for the boost converter based HPS. When compared with the conventional ASMC strategy, the convergence time of the ASMC strategy with the EORL can be effectively improved. In EV applications, the ASMC strategy with the EORL can achieve robust current tracking control of the boost converter based HPS. It can guarantee the active and stable power distribution for boost converter based HPS.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.631-636
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• 2018

This paper proposed an optimal operation strategy for a hybrid energy storage system (HESS) with a lithium-ion battery and lead-acid battery for mild hybrid electric vehicles (mild HEVs). The proposed mild HEV system is targeted to mount the electric motor and the battery to a conventional internal combustion engine vehicle. Because the proposed mild HEV includes the motor and energy storage device of small capacity, the system focuses on low system cost and small size. To overcome these limitations, it is necessary to use a lead acid battery which is used for a vehicle. Thus, it is possible to use more energy using HESS with a lithium battery and a lead storage battery. The HESS, which combines the lithium-ion battery and the secondary battery in parallel, can achieve better performance by using the two types of energy storage systems with different characteristics. However, the system requires an operation strategy because accurate and selective control of the batteries for each situation is necessary. In this paper, an optimal operation strategy is proposed considering characteristics of each energy storage system, state-of-charge (SOC), bidirectional converters, the desired output power, and driving conditions in the mild HEV system. The performance of the proposed system is evaluated through several case studies with respect to energy capacity, SOC, battery characteristic, and system efficiency.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1163-1168
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• 2006

Manufacturing technology for the production of high value-added fine chemical products is emphasized and getting more attention as the diversified interests of customers and the demand of high quality products are getting bigger and bigger everyday. Thus, the development of advanced batch processes, which is the preferred and most appropriate way of producing these types of products, and the related technologies are becoming more important. Therefore, high-precision batch distillation is one of the important elements in the successful manufacturing of fine chemicals, and the importance of the process operation strategy with quality assurance cannot be overemphasized. Accordingly, proposing a process structure explanation and operation strategy of such processes including batch processes and batch distillation would be of great value. We investigate optimal operation strategy and production planning of multi-purpose plants consisting of batch processes and batch distillation for the manufacturing of fine chemical products. For the short-term scheduling of a sequential multi-purpose batch plant consisting of batch distillation under MPC and UIS policy, we proposed a MILP model based on a priori time slot allocation. Also, we consider that the waste product of being produced on batch distillation is recycled to the batch distillation unit for the saving of raw materials. The developed methodology will be especially useful for the design and optimal operations of multi-purpose and multiproduct plants that is suitable for fine chemical production.