• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.11a
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• pp.135-135
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• 2004

• IE interfaces
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• v.8 no.3
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• pp.231-239
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• 1995

일반적으로 공정설계자는 실제 절삭을 위하여 각 공정의 표준 절삭조건에 대하여 적적한 보정을 수행한다. 이러한 보정과정에서 사용되는 지식은 경험에 바탕을 둔 것이므로 이의 시스템화는 경험 지향적인 방법론(Experience-Oriented Method)을 요구한다. 본 논문에서는 밀링 공정을 대상으로, 검색된 표준 절삭조건에 대하여 최적의 절삭조건을 결정하기 위한 방법과 제안된 방법에 의해 개발된 시스템을 소개한다.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2959-2961
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• 1999

A position control algorithm for a inverted pendulum is studied. The proposed algorithm is based on a fuzzy theory and Generalized Radial Basis Function(GRBF). The conventional fuzzy methods need expert's knowledges or human experiences. The GRBF, which is an optimization algorithm, tunes automatically the input-output membership parameters and fuzzy rules. The simulation is presented to illustrate the approaches.

• Annual Conference of KIPS
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• 2009.04a
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• pp.1311-1313
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• 2009

이동성을 가지는 네트워크 환경이 발생하면서 이를 지원하기 위한 이동 네트워크(NEtwork MObility) 프로토콜이 설계되었다. 하지만 표준 이동 네트워크 기술은 라우팅시에 최적화된 경로를 통하여 패킷이 전달되지 못하는 문제점이 있다. 이를 해결하기 위한 이동 네트워크 경로 최적화 기법이 많이 연구되고 있는데 멀티호밍(multihoming) 환경을 고려하여 경로 최적화를 할 경우 더욱 좋은 성능을 향상을 기대할 수 있다. 이 논문에서는 멀티호밍 이동네트워크 환경에서 내부 경로 최적화를 통하여 성능을 향상시킬 수 있는 기법을 제시한다.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06a
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• pp.152-154
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• 2012

스마트폰은 배터리를 사용하는 기기이기 때문에 전력 최적화가 매우 중요한 사안이다. 특별히, 많은 에너지가 소모되는 3G 네트워크 인터페이스에서, 불필요하게 대기하며 발생하는 Tail 에너지를 줄이기 위한 연구가 활발히 진행되어 왔다. 기존의 연구들은 사용자와 응용의 특성을 고려하지 않고 전송 예측 방법에 대해서도 논하고 있지 않아 실제 시스템에 적용하는데 한계가 존재한다. 본 논문에서는, 국내 망환경에서 적용 가능한 3G 모델을 통해 사용자의 응용 별 사용 경향성을 파악하고, 응용 별로 서로 다른 Tail 지속 시간을 선택하여 에너지를 최적화 할 수 있는 방법을 제시한다. 본 기법을 적용하였을 때, 10%의 지연 증가를 준수하며, 평균 34%의 네트워크 에너지를 줄일 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.376-379
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• 1996

Synthesis for qualitative analysis in connection with quantitative analysis from the pinch design method, EVOP and Operations Research is proposed for the optimal synthesis of heat exchanger networks, that is through of the transportation model of the linear programming for synthesizing chemical processing systems, to determine the location of pinch points, the stream matches and the corresponding heat flowrate exchanged at each match. In the second place, according to the optimization, the optimal design of heat transfer enhancement is carried on a fixed optimum heat exchanger network structure, in which this design determines optimal operational parameters and the chosen type of heat exchangers as well. Finally, the method of this paper is applied to the study of the optimal synthetic design of heat exchanger network of constant-decompress distillation plants.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.22-25
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• 2002

The Mass production of ceramic heater has encountered with the estimation for the proper parameters of the printing conditions. This paper presents a method to estimate the squeegee pressure. It uses resistance distribution from the trial run with approximate squeegee pressure which comes from statistical process control (SPC). Then, the resistance distribution and its total resistance are input to the backpropagation neural networks that can recognize resistance's distribution patterns. The value of output network derived from the input value can identify to the appropriate squeegee pressure. The experimental results are demonstrated In ensure the efficiency and the reliability of this method with the accuracy 96.75 percent. Indeed, embedded on this method will aid us to reduce the loss from the normal mass production.

• Transactions on Electrical and Electronic Materials
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• v.18 no.2
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• pp.111-118
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• 2017

The increased diversity of different types of energy sources requires moving towards smart distribution networks. This paper proposes a probabilistic DG (distributed generation) units planning model to determine technology type, capacity and location of DG units while simultaneously allocating ESS (energy storage systems) based on pre-determined capacities. This problem is studied in a wind integrated power system considering loads, prices and wind power generation uncertainties. A suitable method for DG unit planning will reduce costs and improve reliability concerns. Objective function is a cost function that minimizes DG investment and operational cost, purchased energy costs from upstream networks, the defined cost to reliability index, energy losses and the investment and degradation costs of ESS. Electrical load is a time variable and the model simulates a typical radial network successfully. The proposed model was solved using the DICOPT solver under GAMS optimization software.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1239-1242
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• 2003

The neural networks are a famous model to learn the nonlinear function or nonlinear system. The main point of neural network is that the difference actual output from desired output is used to update weights. Usually, the gradient descent method is used for the learning process. On training process, if learning rate is too large, neural networks hardly guarantee convergence of neural networks. On the other hand, if learning rate is too small, the training spends much time. Therefore, one major problem in use of neural networks are to decrease the teaming time while neural networks are guaranteed convergence. In this paper, we suggest the model of fuzzy logic to neural networks to calibrate learning rate. This method is to tune learning rate dynamically according to error and demonstrates the optimization of training.

• 전기의세계
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• v.23 no.3
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• pp.43-52
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• 1974

The matrix inversion is very inefficient for computing direct solutions of the large spare systems of linear equations that arise in many network problems as a large electrical power system. Optimally ordered triangular factorization of sparse matrices is more efficient and offers the other important computational advantages in some applications with this method. The direct solutions are computed from sparse matrix factors instead of a full inverse matrix, thereby gaining a significant advantage is speed and computer memory requirements. In this paper, it is shown that the sparse matrix method is superior to the inverse matrix method to solve the linear equations of large sparse networks. In addition, it is shown that the sparse matrix method is superior to the inverse matrix method to solve the linear equations of large sparse networks. In addition, it is shown that the solutions may be applied directly to sove the load flow in an electrical power system. The result of this study should lead to many aplications including short circuit, transient stability, network reduction, reactive optimization and others.