• Proceedings of the Korean Nuclear Society Conference
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• 1996.11a
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• pp.73-78
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• 1996

In this study, we proposed a fuzzy gain scheduler with intelligent learning algorithm for a reactor control. In the proposed algorithm, we used the gradient descent method to learn the rule bases of a fuzzy algorithm. These rule bases are learned toward minimizing an objective function, which is called a performance cost function. The objective of fuzzy gain scheduler with intelligent learning algorithm is the generation of adequate gains, which minimize the error of system. The condition of every plant is generally changed as time gose. That is, the initial gains obtained through the analysis of system are no longer suitable for the changed plant. And we need to set new gains, which minimize the error stemmed from changing the condition of a plant. In this paper, we applied this strategy for reactor control of nuclear power plant (NPP), and the results were compared with those of a simple PI controller, which has fixed gains. As a result, it was shown that the proposed algorithm was superior to the simple PI controller.

• Journal of Mechanical Science and Technology
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• v.17 no.4
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• pp.484-491
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• 2003

For most of linear time-varying (LTV) systems, it is difficult to design time-varying controllers in analytic way. Accordingly, by approximating LTV systems as uncertain linear time-invariant, control design approaches such as robust control have been applied to the resulting uncertain LTI systems. In particular, a robust control method such as quantitative feedback theory (QFT) has an advantage of guaranteeing the frozen-time stability and the performance specification against plant parameter uncertainties. However, if these methods are applied to the approximated linear. time-invariant (LTI) plants with large uncertainty, the resulting control law becomes complicated and also may not become ineffective with faster dynamic behavior. In this paper, as a method to enhance the fast dynamic performance of LTV systems with bounded time-varying parameters, the approximated uncertainty of time-varying parameters are reduced by the proposed QFT parameter-scheduling control design based on radial basis function (RBF) networks.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.825-827
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• 1989

A heuristic model which determines the scheduling of serial flowshops with minimization of the makespan is proposed for an idealized batch chemical plant. It generates an initial sequence by heuristic reasoning and improves it recursively until no improvement is possible. The heuristic reasoning is based on Johnson's Rule which gives the sequence with the minimum makespan for a two-unit flowshop. The evolutionary step searches the neighborhood of the current sequence for sequences with lower makespan. The robustness of this model is also examined by comparing the minimum makespan of literature examples with the theoretical one.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.446-451
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• 1992

In this paper, we propose a method to manage production system easily for operators when either equipments or products are changed. And we explain the scheduling AI tool which realizes the proposal method. The tool's knowledge expression consists of models, rules, mathematical expression and fuzzy logic. The model expresses the relations between products and manufacture, and properties of products. The models are separated into three type, equipment model, operation model, and product model. These models are classified by applicable fields as the assembly process or continuous plant process, The model expression of each type is based on object oriented paradigm. We report systems utilizing our approach.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.95-96
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• 1986

시스템을 입력과 출력값 만으로 제어하고자 할 경우에는, 플랜트의 파라메타를 추정하면서 제어해 나가야 할 것이다. 이러한 경우에는, 귀환제어나 최적제어 형태로는 여러가지 문제점이 발견되어서, 최근에 적응제어가 많이 연구되고 있다. 이에는 Gain-Scheduling 방법, Self-tuning regulator 방법 및 model reference adaptive control 방법이 있다. Gain-Scheduling 방법은 미지의 파라메타가 plant에 있을지라도, 이를 즉시 예측할 수 있을 경우 보조변수 추정을 통하여 이득을 조절하여 시스템을 안정시키는 것이고, self tuning regulator는 보조변수를 직접 조정하여 시스템을 제어한다. 또 model reference adaptive control 방법은 기준모델을 정하여, 이에 따라 관측기 등을 통하여, 플랜트의 파라메타를 추정 제어해 나가는 것이다. 이때 기준 모델의 출력과 플랜트 출력사이의 오차를 어떻게 할 것인가? 추정되는 파라메타와 오차와의 대수관계 및 차수 등, 그 한계 해석이 최근의 MRAC 설계연구에 큰 과제가 되어 왔다. 이에 본 연구에서는 신호합성 및 해석에 뛰어난 기능이 있는 Walsh 함수를 이용하여, 간단한 Micro computer의 도움으로, 오차 함수를 합성하고, 미지의 파라메타를 추정하여, 시스템의 adaptive filter설계에의 가능성에 대하여 연구하고자 한다. 또 이를 실제 예를 들어 고찰하였다.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.5
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• pp.426-432
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• 2000

A novel mixed-integer linear programming model for the short-term scheduling of a sequential multipurpose batch plant is addressed. First, a time slot domain to each unit is introduced. By assigning each time slot to a product, we obtain the production sequence that minimizes makespan. For multiple-unit assignment problem where a few parallel units with the same function exist, production paths are defined for the distinction of the same stage with a different unit. As a second issue, the model adapted for sequence dependent changeover is presented. For a time slot of a unit, if a product is assigned to the time slot and a different product is assigned to the adjacent time slot, the changeover time considering this situation is included. The performance of the proposed models are illustrated through two examples.

• IE interfaces
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• v.14 no.4
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• pp.385-393
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• 2001

In this paper, a scheme of scheduling a bottleneck operation is presented for production planning of make-to-order. We focus on the problem of capacity-dependent processing time in which processing time of the bottleneck operation is not fixed, but varies with job sequence or equipment capacity. For this, a genetic algorithm is applied for job sequencing with an objective function of mean square of weighted deviation. An experimental study is implemented in power transformer plant and results are compared with those of the EDD rule. It shows that the genetic algorithm is relatively good for most cases.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.6
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• pp.103-112
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• 2001

The development of infrared thermometry has led many researchers to use plant temperatures, and specifically the temperature of the crop canopy in the field, for estimating the water stress of a crop. The purpose of this study was to evaluate the role of leaf temperature in irrigation scheduling. An experiment was carried out in a greenhouse with chinese cabbage. Leaf temperature was measured with infrared thermometry and evapotranspiration of the crop was measured by lysimeters. Influence of the difference between leaf temperature and air temperature on crop evapotranspiration was evaluated under varying water stress condition. A further objective was to evaluate the effect of other climatic variables on the relationship between evapotranspiration and temperature difference between leaf and air. A statistical model for estimating evapotranspiration using the temperature difference, relative humidity. and radiation was developed and tested. Crop water stress index was calculated using vapour pressure deficit and the temperature difference. Relations between the crop water stress index and crop evapotranspiration was tested. The index was closely related with evapotranspiration.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.858-860
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• 1998

Daily generation scheduling system (DGSC) is a main tool in the power system operation. In Korea, a DGSC was developed in 1982 and it was updated continuously. However, as new type of generators and the number of constraints are introduced. it is very difficult to use the old DGSC. This paper presents a proposal for the development of new DGSC. In the Proposed proposal, line flow constraint and dispatch of combined cycle plant are included.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.312-317
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• 1998

Reliable maintenance scheduling of main equipments is a crucial problem in thermal power stations in order to skirt overall losses of power generation resulted from severe failures of the equipments. A reasonable method was proposed to determine the maintenance scheduling of whole pump system in thermal power stations in order to reduce the maintenance cost by keeping the present avail-ability of the pump system throughout the operation. The dimensional reduction method was used to solve problems encountered due to few data which involved many operational factors in failure rate of pumps. The problem of bandlimited nature of data with time was solved by extrapolating future failures from presently available actual data with an aid of Weibull distribution. The results of the analysis identified the most suitable maintenance intervals of each pump type accordingly and hence reduce the cost of unnecessary maintenance with an acceptable range in the overall system availability.