• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.21 no.1
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• pp.29-37
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• 2017

A general optimal recovery problem is to approximate a value of a linear operator on a subset (class) in linear space from a value of another linear operator (called information), measured with an error in given metric. We use this formulation to investigate the classical computerized tomography problem of inversion of the noisy Radon transform.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.8
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• pp.585-592
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• 1989

This paper presents an efficient algorithm for the reactive planning of transmission network under normal operating conditions. The optimal operation of a power system is a prerequisite to obtain the optimal investment planning. The operation problem is decomposed into a P-optimization module and a Q-optimization module, but both modules use the same objective function of generation cost. In the investment problem, a new variable decomposition technique is adopted which can operate the operation and the investment variables. The optimization problem is solved by using the gradient projection method (GPM).

• Journal of the Korea Safety Management & Science
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• v.1 no.1
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• pp.135-143
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• 1999

This paper discusses an optimal burn-in procedure to minimize total costs based on the assumption that the failure rate pattern follows a bimodal mixed Weibull distribution. The procedure will consider warranty period as a factor of the total expected bum-in cost. A cost model is formulated to find the optimal burn-in time that minimizes the expected burn-in cost. Conditional reliability for warranty period will be discussed. An illustrative example is included to show how to use the cost model in practice.

• Proceedings of the Safety Management and Science Conference
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• 2006.11a
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• pp.427-435
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• 2006

We study on the consistency of AHP. It is research that extend of SAW methods by [1]. For tools that measure judgment of inconsistency eigenvector methods, we research consistency that introduced consistency ratio by Saaty. in general, the higher consistence of compare matrix the bigger error within matrix. In this paper, we use the AHP for the optimal decision making. By this method, we have optimal decision making numenical example which three models of any domestic motors companies.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.52
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• pp.109-116
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• 1999

Flow Shop with Multiple Processors(FSMP) scheduling involves sequencing jobs in a flow shop where, at any processing stage, there exists one or more identical processors. Any methodology to determine the optimal mean flow time for this type of problem is NP-complete. This necessitates the use of sub-optimal heuristic methods to address problems of moderate to large scale. This paper presents global lower bounds on FSMP mean flow time problems which may be used to assess the quality of heuristic solutions when the optimal solution is unknown.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.719-728
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• 1999

This paper discusses an optimal burn-in procedure to minimize total costs based on the assumption that the failure rate pattern follows a bimodal mixed Weibull distribution. The procedure will consider warranty period as a factor of the total expected burn-in cost. A cost model is formulated to find the optimal burn-in time that minimizes the expected burn-in cost. Conditional reliability for warranty period will be discussed. An illustrative example is included to show how to use the cost model in prctice.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.114-116
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• 2015

This paper presents a Stochastic Time-Cost Tradeoff analysis system (STCT) that identifies optimal construction methods for activities, hence reducing the project completion time and cost simultaneously. It makes use of schedule information obtained from critical path method (CPM), applies alternative construction methods data obtained from estimators to respective activities, computes an optimal set of genetic algorithm (GA) parameters, executes simulation based GA experiments, and identifies near optimal solution(s). A test case verifies the usability of STCT.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.315-317
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• 2001

This paper studies for the optimal operation of BESS. The goal must be optimized electricity charge of the customer sides owned time-of-use rates in this paper. Therefore, the least of cost is caused by BESS installation, Multi-Pass Dynamic Programming (MPDP) algorithm is applied to the customer for the optimal operation determination in this paper. It is to solve the optimal solution under the constraints. No matter how become one stage in general, problem is divided into several stage in series in this algorithm. Regardless of the decision step, MPDP is only accomplished based on the state of stage in the present. To investigate the efficiencies of the algorithm, it is applied the typical load curve to the cutomer owned Time-Of-Use(TOU). Result shows that the maximun economic benefits of the battery energy storage system can be achieved by the purposed algorithm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.10
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• pp.1050-1061
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• 2014

Airborne radar usually use MPRF(medium PRF) waveforms to detect and track look-down or all-directions air-to-air targets. To use MPRF waveforms, airborne radar have to operate optimal PRF schedules and select optimal PRFs able to detect look-down air-to-air targets, overcoming look-down clutter and resolving range/velocity ambiguities. In this paper, we propose a real-time search method for the optimal(and sub-optimal) PRFs able to detect and track targets in real-time change of situation for MPRF pulsed-Doppler airborne radars.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.654-656
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• 1998

This paper presents an optimal identification method of nonlinear and complex system that is based on fuzzy-neural network(FNN). The FNN used simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM Algorithm to find initial parameters of membership function. And then to obtain optimal parameters, we use the genetic algorithm. Genetic algorithm is a random search algorithm which can find the global optimum without converging to local optimum. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance of the FNN, we use the time series data for 9as furnace and the sewage treatment process.