• Journal of Korea Water Resources Association
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• v.51 no.spc
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• pp.1135-1148
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• 2018

Previous studies on reservoir operation have been assumed that the climate in the future would be similar to that in the past. However, in the presence of climate non-stationarity, Robust Optimization (RO) which finds the feasible solutions under broader uncertainty is necessary. RO improves the existing optimization method by adding a robust term to the objective function that controls the uncertainty inherent due to input data instability. This study proposed Robust-SDP that combines Stochastic Dynamic Programming (SDP) and RO to estimate dam operation rules while coping with climate non-stationarity. The future inflow series that reflect climate non-stationarity were synthetically generated. We then evaluated the capacity of the dam operation rules obtained from the past inflow series based on six evaluation indicators and two decision support schemes. Although Robust-SDP was successful in reducing the incidence of extreme water scarcity events under climate non-stationarity, there was a trade-off between the number of extreme water scarcity events and the water scarcity ratio. Thus, it is proposed that decision-makers choose their optimal rules in reference to the evaluation results and decision support illustrations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.659-662
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• 1998

In this paper, lower and upper possibility distributions are identified to reflect two extreme opinions in portfolio selection problems based on upper and lower possibility distributions are formalized as quadratic programming problems. Portfolios for compromising two extreme opinions from upper and lower possibility distributions and balancing the opinions of a group of experts can be obtained by quadratic optimization problems, respectively.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.200-201
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• 2010

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.804-809
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• 2001

Optimal design of a portable structure which supports impact loading is presented. The structure requires impact loading capability, stiffness and minimum weight for portability. A collapsible tripod structure with locking mechanism is suggested. Taguchi method has been used to identify the most important design variables and the initial design. Subsequent optimization yields additional weight reduction under stress and displacement constrains.

• Journal of Industrial Technology
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• v.26 no.A
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• pp.173-180
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• 2006

Scheduling for shipbuilding processes has many alternative solutions since it has long time horizon and handles many jobs. In this paper, an shipbuilding scheduling system for outfitting and painting is presented based on constraint satisfaction techniques(CST). We provide a field based model and an efficient solution methodology by using ILOG Scheduler and Solver. ILOG Solver support extensive optimization goals easily. We do verify the improvement and efficiency of the schedule generated by the scheduling system presented in this paper.

• 한국정보디스플레이학회:학술대회논문집
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• 2002.08a
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• pp.449-451
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• 2002

We reported the algorithm of driving scheme that enhances moving picture property by improving gray-to-gray response time. Here, we report result of simulation for estimation of driving voltage to reduce response time, and experimental result. We investigated optimization of algorithm so that minimum size of LUT can support to reducing the gray-to-gray response time within 1 frame period, and with single algorithm it is possible to apply the algorithm to various kinds of LC material. So in our system there is no external EEPROM.

• Proceeding of EDISON Challenge
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• 2016.03a
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• pp.389-396
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• 2016

In order to maximize the speed of Theo Jansen Mechanism in an given design space and prototyping material, the trajectory path was maximized according to several literature reviews, and the lower leg was elongated maximally in order to minimize the shift between support phase and transfer phase.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.843-848
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• 1999

Support vector machine(SVM) is a new and very promising classification and regression technique developed by Bapnik and his group at AT&T Bell laboratories. However it has failed to establish itself as common machine learning tool. This is partly due to the fact that SVM is not easy to implement and its standard implementation requires the optimization package for quadratic programming. In this paper we present simple iterative Kernl Adatron algorithm for nonparametric regression which is easy to implement and guaranteed to converge to the optimal solution and compare it with neural networks and projection pursuit regression.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.282-287
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• 2021

A multicore system must be able to take full advantage of the program's instruction and data parallelism. This study introduces the data replication technique as a support technique to maximize the program's instruction and data parallelism. Instruction level parallelism can be limited by data dependency. In this case, if data is replicated to each processor core and used, instruction level parallelism can be used to the maximum. The technique proposed in this study can maximize the performance improvement effect when applied to scientific applications such as matrix multiplication operation.

• Journal of Korean Tunnelling and Underground Space Association
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• v.10 no.2
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• pp.153-166
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• 2008

This paper concerns the development of a knowledge-based tunnel design system within the framework of artificial neural networks (ANNs). The system is aimed at expediting a routine tunnel design works such as determination of support patterns and stability analysis of selected support patterns. A number of sub-modules for determination of support patterns and stability assessment were developed and implemented to the system. It is shown that the ANNs trained with the results of 2D and 3D numerical analyses can be generalized with a reasonable accuracy, and that the ANN based tunnel design concept is a robust tool for tunnel design optimization. The details of the system architecture and the ANNs development are discussed in this paper.