• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.483-487
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• 1994

In this paper, we analize the effect of a steering water used in a unified phase I-phase II semi-infinite constrained optimization algorithm and present a new algorithm based on the facts that when the point x is far away from the feasible region where all the constraints are satisfied, reaching to the feasible region is more important than minimizing the cost function and that when the point x is near the region, it is more efficient to try to reach the feasible region and to minimize the cost function concurrently. Also, the angle between the search direction vector and the gradient of the cost function is considered when the steering parameter value is computed. Even though changing the steering parameter does not change the rate of convergence of the algorithm, we show through some examples that the proposed algorithm performs better than the other algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.176-181
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• 1992

In this paper, an adaptive generalized predictive control(GPC) algorithm which minimizes a N-stage cost function is proposed. The resulting controller is based on GPC algorithm and can be used in unknown plant parameters as the parameters of one step ahead predictor are estimated by recursive least squares method. The estimated parameters are extended to G,P, and F amtrix which contain the parameters of N step ahead predictors. And the minimization of cost function assuming no constraints on future controls results in the projected control increment vector. Hence this adaptive GPC algorithm can be used for either unknown system or varing system parameters, and it is also shown through simulations that the algorithm is robust to the variation of system parameters. This adaptive GPC scheme is shown to have the same stability properties as the deterministic GPC, and requires small amount of calculation compared to other adaptive algorithms which minimize N-stage cost function. Especially, in case that the maximum output horizon is 1, the proposed algorithm can be applicable to direct adaptive GPC.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.7
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• pp.808-813
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• 1999

A fuzzy learning method to control an unstable and multivariable system is presented in this paper, Because the multivariable system has generally a coupling effect between the inputs and outputs, it is difficult to find its modeling equation or parameters. If the system is unstable, initial condition rules are needed to make it stable because learning is nearly impossible. Therefore, this learning method uses the initial rules and introduces a cost function composed of the actual error and error-rate of each output without the modeling equation. To minimize the cost function, we experimentally got the Jacobian matrix in the operating point of the system. From the Jacobian matrix, we can find the direction of the convergence in the learning, and the optimal control rules are finally acquired when the fuzzy rules are updated by changing the portion of the errors and error rates.

• Journal of the Korean Regional Science Association
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• v.11 no.2
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• pp.1-17
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• 1995

Regional production structres are investigated through an estimation of three-input(labor, capital and material) translog cost function. The estimation uses the pooled data in which time series data of 1970-1990 are combined with cross-sectional data corresponding to firm sizes. The empirical finding are the following: (1) the factors are Allen substitutes each otner in general except Pusan and a couple of regionss, and (2) estimates are very small in its absolute value irrespective of the regions. The low elasticity estimates of this study implies that substitytability among the three inputs are very weak in the regional manufacturing production compared to that in the national production. Seoul-Kyungki metropolitan area produces not only the smallest elasticities but relatively stable estimates without much fluctuation between the sub-regions of the area.

• 전기의세계
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• v.24 no.5
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• pp.72-80
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• 1975

In this paper an application of the Monte Carlo method to optimum circuit design is discussed. T. Tsuda and T. Kiyono's algorithm based on the Monte Carlo method for solving multiple simul-taneous nonlinear equations is generalized to apply it to finding solutions of the constrained nonlinear optimization problem. The generalized algorithm derived here is directly applied to economical circuit design. In the cirsuit design, the object function is a cost function which is related to the cost of each circuit component. The constraint is the variance of the total system expressed by the variances of each circuit component. The design is to be determined so that the circuit has specified drift reliability with minimum cost. A practical example of economical circuit design and a general nonlinear function minimization is presented with food results.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.7
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• pp.839-846
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• 1999

This paper presents an efficient algorithm for determining the location, size and number of capacitors in unbalanced radial distribution system. The objective function formulated consists of two terms: cost for energy loss and cost related to capacitor purchase and capacitor installation. The cost function associated with capacitor placement is considered as step function due to banks of standard discrete capacities. Genetic algorithms(GA) are used to obtain the population is derived. The strings in each population consist of the bus number index and size of capacitors to be installed. In order to determine the number of capacitor placement, the length mutation operator is used. Its efficiency is proved through the application in unbalanced radial distribution systems made of 10 buses with 9 distribution lines and 25 buses with 24 distribution lines.

• Proceedings of the Safety Management and Science Conference
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• 2002.11a
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• pp.17-23
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• 2002

Value can be defined as function divided by cost. The ultimate goal of VE (Value Engineering) is to simultaneously reduce cost and to enhance function in given projects. In general, there are eight phases (Selection, Investigation, Speculation, Evaluation, Development. Presentation, Implementation, and Audit) to perform VE. This paper presents analysis of important factors for VE in construction fields using QFD (Quality Function Deployment) technique. QFD was introduced in 1972 to help design supertanker in Mitsubish's shipyards in Kob, Japan and formally defined by the American Supplier Institute in 1987 as a system for translating consumer requirements into appropriate company requirements at every stage, from research, through product design and development, to manufacture, distribution, installation, and marketing, sales and services. The objective of this paper is to provide and analyze the trend on the viewpoint of efficient value engineering of field workers. The data are collected by questionnaires. The samples for this study are chosen from 13 companies in Korea during 2 months (2002. 7 ∼ 2002. 8). The results of this study will play an important role not only for the efficient value engineering but also for preparing of life cycle cost analysis.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.3
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• pp.239-246
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• 2003

Individual items are produced continuously from an industrial process. Each item is checked to determine whether it satisfies a lower screening limit for the quality characteristic which is the weight of an expensive ingredient. If it does, it is sold at a regular price; if it does not, it is reprocessed or sold at a reduced price. The process mean may be adjusted to a higher value in order to reduce the proportion of the nonconforming items. Using a higher process mean, however, may result in a higher production cost. In this paper, the optimal process mean and lower screening limit are determined in situations where the probability that an item functions well is given by a logistic function of the quality characteristic. Profit models are constructed which involve four price/cost components; selling prices, cost from an accepted nonconforming item, and reprocessing and inspection costs. Methods of finding the optimal process mean and lower screening limit are presented and numerical examples are given.

• Journal of Power Electronics
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• v.19 no.2
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• pp.497-508
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• 2019

The predictive speed control (PSC) strategy can realize the simultaneous control of speed and current by using one cost function. As a model-based control method, the performance of the PSC is vulnerable to model mismatches such as load torque disturbances and parameter uncertainties. To solve this problem, this paper presents a robust predictive speed control (RPSC) strategy for surface-mounted permanent magnet synchronous motor (SPMSM) drives. The proposed RPSC uses extended state observers (ESOs) to estimate the lumped disturbances caused by load torque changes and parameter mismatches. The observer-based prediction model is then compensated by using the estimated disturbances. The introduction of ESOs can achieve robustness against predictive model uncertainties. In addition, a modified cost function is designed to further suppress load torque disturbances. The performance of the proposed RPSC scheme has been corroborated by experimental results under the condition of load torque changes and parameter mismatches.

• Journal of Power Electronics
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• v.19 no.1
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• pp.168-178
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• 2019

Discrete space vector modulation (DSVM) is an effective method to improve the steady-state performance of the finite control set predictive control for permanent magnet synchronous motor drive systems. However, it requires complex computations due to the presence of numerous virtual voltage vectors. This paper proposes an improved finite control set model-free predictive control using DSVM to reduce the computational burden. First, model-free deadbeat current control is used to generate the reference voltage vector. Then, based on the principle that the voltage vector closest to the reference voltage vector minimizes the cost function, the optimal voltage vector is obtained in an effective way which avoids evaluation of the cost function. Additionally, in order to implement double-objective control, a two-level decisional cost function is designed to sequentially reduce the stator currents tracking error and the inverter switching frequency. The effectiveness of the proposed control is validated based on experimental tests.