• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.679-683
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• 2007

Statistical learning theory by Vapnik consists of support vector machine(SVM), support vector regression(SVR), and support vector clustering(SVC) for classification, regression, and clustering respectively. In this algorithms, SVC is good clustering algorithm using support vectors based on Gaussian kernel function. But, similar to SVM and SVR, SVC needs to determine kernel parameters and regularization constant optimally. In general, the parameters have been determined by the arts of researchers and grid search which is demanded computing time heavily. In this paper, we propose a differential evolution based SVC(DESVC) which combines differential evolution into SVC for efficient selection of kernel parameters and regularization constant. To verify improved performance of our DESVC, we make experiments using the data sets from UCI machine learning repository and simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.591-593
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• 2022

This paper proposes a partial offloading system for minimizing application service processing latency in an FEC (Fog/Edge Computing) environment, and it analyzes the offloading effect of the proposed system against local-only and edge-server-only processing based on network load. A partial offloading algorithm based on reconstruction linearization of multi-branch structures is included in the proposed system, as is an optimal collaboration algorithm between mobile devices and edge servers [1,2]. The experiment was conducted by applying layer scheduling to a logical CNN model with a DAG topology. When compared to local or edge-only executions, experimental results show that the proposed system always provides efficient task processing strategies and processing latency.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.1
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• pp.83-90
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• 2002

This paper proposed the strategy learning method by means of the fusion of Back-Propagation neural network and Q learning algorithm for two-person, deterministic janggi board game. The learning process is accomplished simply through the playing each other. The system consists of two parts of move generator and search kernel. The one consists of move generator generating the moves on the board, the other consists of back-propagation and Q learning plus $\alpha$$\beta$ search algorithm in an attempt to learn the evaluation function. while temporal difference learns the discrepancy between the adjacent rewards, Q learning acquires the optimal policies even when there is no prior knowledge of effects of its moves on the environment through the learning of the evaluation function for the augmented rewards. Depended on the evaluation function through lots of games through the learning procedure it proved that the percentage won is linearly proportional to the portion of learning in general.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.3
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• pp.21-29
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• 2005

The most of automated negotiation systems are dependent upon negotiators' offers, as negotiation is going on. Particularly, the preference, evaluation function and negotiation strategy are variously changed at every negotiation round by the negotiator and have an effect on the counter offers. Therefore, this study proposed the automated negotiation methodology or negotiation model which makes the negotiator's participation minimize. To minimize negotiator's participation, the preference of negotiator was predicted by the ratio of seller and buyer's count offers and the evaluation function of negotiator was also predicted by least squares approximation method at every negotiation round. The predicted evaluation function was evaluated and selected by $R^2$ value, coefficient of determination. Finally the optimal counter offers were generated by the genetic algorithm using the predicted preference and value function.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.5
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• pp.187-198
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• 2008

This paper develops a framework for efficient resource management within the grid service environment. Resource management is the core element of the grid service; therefore, GridRMF(Grid Resource Management Framework) is modeled and developed in order to respond to such variable characteristics of resources as accordingly as possible. GridRMF uses the participation level of grid resource as a basis of its hierarchical management. This hierarchical management divides managing domains into two parts: VMS(Virtual Organization Management System) for virtual organization management and RMS(Resource Management System) for metadata management. VMS mediates resources according to optimal virtual organization selection mechanism, and responds to malfunctions of the virtual organization by LRM(Local Resource Manager) automatic recovery mechanism. RMS, on the other hand, responds to load balance and fault by applying resource status monitoring information into adaptive performance-based task allocation algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.767-773
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• 2014

An economic ship routing means to sail a ship with a goal of minimizing the fuel consumption by utilizing weather forecast information, and there have been various systems which have been recently studied. For a successful economic ship routing system, it is needed to properly control an engine power or change a geographical path considering weather forecast. An optimal geographical path is difficult to be determined, though, because it is a minimal dynamic-cost path search problem where the actual fuel consumption is dynamically variable by the weather condition when the ship will pass the area. In this paper, we propose an geographical path-search algorithm based on evolutionary strategy to efficiently search a good quality solution out of tremendous candidate solutions. We tested our approach with the shortest path-based sailing method over seven testing routes and observed that the former reduced the estimated fuel consumption than the latter by 1.82% on average and the maximum 2.49% with little difference of estimated time of arrival. In particular, we observed that our method can find a path to avoid bad weather through a case analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.4B
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• pp.412-421
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• 2011

An economic ship routing means to sail a ship with a goal of minimizing the fuel consumption by utilizing weather forecast information, and many such systems have been recently developed. Most of them assume that sailing is carried out with a constraint like a fixed output of engine-power or a fixed sailing speed. However, if the output of engine-power is controlled, it is possible to reduce the fuel consumption by sailing a ship under a relatively good weather condition. In this paper, we propose a novel economic ship routing system which can search optimal outputs of the engine-power for each part of a path by employing an evolutionary strategy. In addition, we develope an $A^*$ algorithm to find the shortest path and a method to enhance the degree of curve representation. These make the proposed system applicable to an arbitrary pair of departure and destination points. We compared our proposed system with another existing system not controlling output of the engine-power over 36 scenarios in total, and observed that the former reduced the estimated fuel consumption than the latter by 1.3% on average and the maximum 5.6% with little difference of estimated time of arrival.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1575-1580
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• 2011

A key factor in the study of hybrid vehicle is to enhance the usability of energy. The paper introduces the monitor and controlling technology of hybrid vehicle that can process the relevant information considering the structure of power system and driving strategies simultaneously, and can monitor its results. This technology, so called HEV algorithm analysis, has been applied to PRIUS THS made by Toyota Co. LTD. This model is adapted to parallel hybrid type. It has a somewhat comlex structure, but has several merits. It's energy loss is lower when conversing. and also it is easily applied to the conventional vehicle having a gasoline engine without any overall changing of its structure, and so on. This monitor and controlling technology is very useful to study on the various driving strategies of hybrid vehicle for maximizing the usability between engine and electric motor.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.93-104
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• 2024

Demand response is a strategy that encourages customers to adjust their consumption patterns at times of peak demand with the aim to improve the reliability of the power grid and minimize expenses. The integration of renewable energy sources into smart grids poses significant challenges due to their intermittent and unpredictable nature. Demand response strategies, coupled with reinforcement learning techniques, have emerged as promising approaches to address these challenges and optimize grid operations where traditional methods fail to meet such kind of complex requirements. This research focuses on investigating the application of reinforcement learning algorithms in demand response for renewable energy integration. The objectives include optimizing demand-side flexibility, improving renewable energy utilization, and enhancing grid stability. The results emphasize the effectiveness of demand response strategies based on reinforcement learning in enhancing grid flexibility and facilitating the integration of renewable energy.

• Journal of Korean Society of Steel Construction
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• v.18 no.6
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• pp.677-686
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• 2006

In this paper, a more practical and realistic method is proposed to establish the lifetime optimum maintenance strategies of the deteriorating bridges considering the life-cycle performance as well as life-cycle cost. The genetic algorithm is applied to generate the set of maintenance scenarios that is the multi-objective combinatorial optimization problem related to lifetime performance and cost as separate objective functions, and the technique to select optimum tradeoff maintenance scenario is presented. Optimum maintenance scenarios could be generated not only at the individual member level but also at the system level of the bridge. Through the analytical results of applying the proposed methodology to the existing bridge, it is expected that the methodology will be effectively used to determine the optimum maintenance strategy for introducing a real preventive maintenance system and overcoming the limits of existing maintenance methods.