• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5842-5861
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• 2019

A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this paper proposes "An Optimal Driving Support Strategy(ODSS) based on an Genetic Algorithm for Autonomous Vehicles" which determines the optimal strategy of an autonomous vehicle by analyzing not only the external factors, but also the internal factors of the vehicle(consumable conditions, RPM levels etc.). The proposed ODSS consists of 4 modules. The first module is a Data Communication Module (DCM) which converts CAN, FlexRay, and HSCAN messages of vehicles into WAVE messages and sends the converted messages to the Cloud and receives the analyzed result from the Cloud using V2X. The second module is a Data Management Module (DMM) that classifies the converted WAVE messages and stores the classified messages in a road state table, a sensor message table, and a vehicle state table. The third module is a Data Analysis Module (DAM) which learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. The fourth module is a Data Visualization Module (DVM) which displays the optimal driving strategy and the current driving conditions on a vehicle monitor. This paper compared the DCM with existing vehicle gateways and the DAM with the MLP and RF neural network models to validate the ODSS. In the experiment, the DCM improved a loss rate approximately by 5%, compared with existing vehicle gateways. In addition, because the DAM improved computation time by 40% and 20% separately, compared with the MLP and RF, it determined RPM, speed, steering angle and lane changes faster than them.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.60-62
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• 2001

This paper shows the optimal bidding strategy determination method using Nash equilibrium concept by defining bidding strategy vector. This vector is 2-dimension vector whose components are generation amount and generation cost. Thereby we are able to make all possible strategies and their's payoff table. And then we erase dominated strategies one by one so that we obtain Nash equilibrium, the optimal bidding strategy of generation bidding game.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.4
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• pp.103-108
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• 2012

Fuel economy is an important factor in a vehicle owing to recent energy supply and environmental problems. This paper deals with fuel cell hybrid vehicles (FCHVs) and introduces a fuel economy evaluation method. The fuel economy of an FCHV depends on its power management strategy. Two rule-based power management strategies are applied to this paper and their fuel economy is evaluated based on the optimal control theory. The concept of the optimal line is also applied to this paper, which is used to compare the fuel consumption of a power management strategy to the optimal result. The two rule-based strategies are also compared to each other.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.811-816
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• 2011

In this paper, we propose a optimal design using the Evolution Strategy of a high gain $4\times4$ array antenna that have the resonant frequency of a 19.05GHz with 18.86GHz~19.26GHz bandwidth. The proposed array antenna structure is designed to be allocated equally electric power by microstrip patch power splitter. Thus the optimal array antenna with power splitter are determined by using an optimal design program based on the evolution strategy. To achieve this, an interface program between a commercial EM analysis tool and the optimal design program is constructed for implementing the evolution strategy technique that seeks a global optimum of the objective function through the iterative design process consisting of variation and reproduction. The simulation result of $4\times4$ array antenna is confirmed that the Gain is 19.36 dBi at resonance frequency 19.05GHz.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.253-253
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• 2000

Each robot plays a role of its own behavior in dynamic robot-soccer environment. One of the most necessary conditions to win a game is control of robot movement. In this paper we suggest a win strategy using Cellular Neural Network to set optimal path and cooperative behavior, which divides a soccer ground into grid-cell based ground and has robots move a next grid-cell along the optimal path to approach the moving target.

• Journal of the Korea Society of Computer and Information
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• v.20 no.9
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• pp.61-66
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• 2015

We investigate rate scheduling and power allocation problem for a delay constrained CDMA systems. Specifically, we determine an energy efficient scheduling policy, while each user maintains the short term (n time slots) average throughput. More importantly, it is shown that the optimal transmission strategy for the uplink is same as that of the downlink, called uplink and downlink duality. We then examine the performance of the optimum transmission strategy for the uplink and the downlink for various system environments.

• Proceedings of the KSR Conference
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• 1998.05a
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• pp.442-449
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• 1998

Since it is essential for traction motors to reduce size and weight to achieve given traction effort, they need high input voltage. But the lack of input voltage occurs periodically due to the characteristics of train system. Therefore traction inverters use over-modulation PWM to maximize inverter's voltage gain. On the other hand, IGBT inverters can use higher frequency twice than GTO ones, which resulted in the need for novel optimal synchronous PWM strategy. This paper suggests that linearly-compensated overmodulation/optimal synchronous PWM strategy and also the simulation results of the method for a real traction motor-intertia model are presented.

• Bulletin of the Korean Mathematical Society
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• v.56 no.6
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• pp.1467-1483
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• 2019

This paper analyzes a robust optimal reinsurance and investment strategy for an Ambiguity-Averse Insurer (AAI), who worries about model misspecification and insists on seeking robust optimal strategies. The AAI's surplus process is assumed to follow a jump-diffusion model, and he is allowed to purchase proportional reinsurance or acquire new business, meanwhile invest his surplus in a risk-free asset and a risky-asset, whose price is described by an Ornstein-Uhlenbeck process. Under the criterion for maximizing the expected exponential utility of terminal wealth, robust optimal strategy and value function are derived by applying the stochastic dynamic programming approach. Serval numerical examples are given to illustrate the impact of model parameters on the robust optimal strategies and the loss utility function from ignoring the model uncertainty.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.411-413
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• 2001

This paper discusses on an optimal bidding strategy of a generator in a competitive electricity spot market using the information of predicted spot price with some assumptions. Optimal bidding strategy of a generator is derived by solving a profit-maximizing optimization problem with a constraint where the forecasted spot price is treated as a constant value. The main advantage of this methodology is that the optimal bidding strategy of each generator can be obtained independently where the gaming characteristics of generators are merged into the forecasted spot price.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.12
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• pp.597-602
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• 2002

The restructuring of power industry is still going on all over the world for last several decades. Many kinds of restructuring model have been studied, proposed, and applied. Among those models, power pool is more popular than other. This paper assumes the power pool market structure having competitive generation sector, and a new method is presented to build a bidding strategy in that market. The utilities participating in the market have the perfect information of their cost and price functions, but they don't know which strategy to be chosen by others. To define one's strategy as a vector, we make utility's cost/price functions into discrete step functions. An utility knows only his own strategy, so he estimates the other's cost/price functions into discrete step functions. An utility knows only his own strategy, so he estimates the other's strategy using Nash equilibrium or stochastic methods. And he also has to forecast the system demand. According to this forecasting result, his payoffs can be changed. Considering these all conditions, we formulate a bidding game problem and apply noncooperative game theory to that problem for the optimal strategy or solution. Some restrictive assumption are added for simplification of solving process. A numerical example is given in Case Study to show essential features and concrete results of this approach.