• KIEAE Journal
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• v.15 no.4
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• pp.105-110
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• 2015

Purpose: This study aimed at finding the optimal input variables of the artificial neural network-based predictive model for the optimal controls of the indoor temperature environment. By applying the optimal input variables to the predictive model, the required time for restoring the current indoor temperature during the setback period to the normal setpoint temperature can be more precisely calculated for the cooling season. The precise prediction results will support the advanced operation of the cooling system to condition the indoor temperature comfortably in a more energy-efficient manner. Method: Two major steps employing the numerical computer simulation method were conducted for developing an ANN model and finding the optimal input variables. In the first process, the initial ANN model was intuitively determined to have input neurons that seemed to have a relationship with the output neuron. The second process was conducted for finding the statistical relationship between the initial input variables and output variable. Result: Based on the statistical analysis, the optimal input variables were determined.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.23 no.12
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• pp.782-790
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• 2011

Typical temperature control methods of a cooler for machine tools are hot-gas bypass and compressor variable speed control. The hot-gas bypass system has been widely used to control the cooler temperature in many general industrial fields. On the contrary, the compressor variable speed control is focused on special fields such as aerospace and high precision machine tools which need high precision control. The variable speed control system usually has two control variables such as target temperature and superheat. In other words, the variable speed control system is basically multi-input multi-output(MIMO) system. In spite of MIMO system, the proportional integral derivative(PID) feedback control methodology that based on single-input single-output (SISO) system is generally used for designing the variable speed control system. Therefore, it is inevitable to describe transfer functions for dynamic behaviors of every controlled variables and decide the PID gains with tremendous iteration process. Moreover, the designed PID gains do not provide optimum system performances. To solve these problems, high performance controller design method based on a state space model is suggested in this paper. An optimum controller is designed to minimize both control errors and energy inputs. This method was more simple to describe dynamic behaviors and easier to design the cooler controller which is MIMO system.

• The Transactions of the Korea Information Processing Society
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• v.6 no.3
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• pp.781-791
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• 1999

DD(Decision Diagrams) is an efficient operational data structure for an optimal expression of boolean functions. In a graph-based synthesis using DD, the goal of optimization decreases representation space for boolean functions. This paper represents boolean functions using OPKFDD(Ordered Pseudo-Kronecker Functional Decision Diagrams) for a graph-based synthesis and is based on the number of nodes as the criterion of DD size. For a property of OPKFDD that is able to select one of different decomposition types for each node, OPKFDD is variable in its size by the decomposition types selection of each node and input variable order. This paper proposes a method for generating OPKFDD efficiently from the current BDD(Binary Decision Diagram) Data structure and an algorithm for minimizing one. In the multiple output functions, the relations of each function affect the number of nodes of OPKFDD. Therefore this paper proposes a method to decide the input variable order considering the above cases. Experimental results of comparing with the current representation methods and the reordering methods for deciding input variable order are shown.

• International Journal of Precision Engineering and Manufacturing
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• v.2 no.1
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• pp.43-55
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• 2001

A continuously variable transmission(CVT) mechanism composed of one differential gear unit and one continuously variable unit(CVU) can be classified according to the coupling of CVU and the direction of power flows. The mechanism has many advantages which are the decrease of CVT size, the increase of overall efficiency, the extension of speed ratio range and generation of geared neutral. The CVT mechanism considered here is the input coupled type which combines the functions of a 2K-H I type differential gear unit and a V-belt type CVU. One shaft of the CVU is connected directly to the input shaft and another shaft of it is linked to the differential gear unit. It is shown that some fundamental relations(speed ratios, power flows and efficiencies) for twelve mechanisms previously described are valid by various experimental studies, six of them produce a power circulation and the others produce a power split. Some useful comparisons between theoretical analysis and experimental results are presented. General properties also are discussed, which connect following power flow modes : (a) power circulation mode; (b) power split mode.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.6-8
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• 2005

In this paper, we discuss model identification of nonlinear data using GAs-based Fuzzy Polynomial Neural Networks(GAs-FPNN). Fuzzy Polynomial Neural Networks(FPNN) is proposed model based Group Method Data Handling(GMDH) and Neural Networks(NNs). Each node of FPNN is expressed Fuzzy Polynomial Neuron(FPN). Network structure of nonlinear data is created using Genetic Algorithms(GAs) of optimal search method. Accordingly, GAs-FPNN have more inflexible than the existing models (in)from structure selecting. The proposed model select and identify its for optimal search of Genetic Algorithms that are no. of input variables, input variable numbers and consequence structures. The GAs-FPNN model is select tuning to input variable number, number of input variable and the last part structure through optimal search of Genetic Algorithms. It is shown that nonlinear data model design using Genetic Algorithms based FPNN is more usefulness and effectiveness than the existing models.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.792-798
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• 2021

In this paper, an algorithm for determining the coagulant input rate in the drug-injection tank during the process of the water purification plant was derived through big data analysis and prediction based on artificial intelligence. In addition, analysis of big data technology and AI algorithm application methods and existing academic and technical data were reviewed to analyze and review application cases in similar fields. Through this, the goal was to develop an algorithm for determining the coagulant input rate and to present the optimal input rate through autonomous driving simulator and pilot operation of the coagulant input process. Through this study, the coagulant injection rate, which is an output variable, is determined based on various input variables, and it is developed to simulate the relationship pattern between the input variable and the output variable and apply the learned pattern to the decision-making pattern of water plant operating workers.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.3
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• pp.49-61
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• 2015

We study the problem of multiple response optimization (MRO) and focus on the selection of input levels which will produce desirable output quality. We propose an interactive multiple objective optimization approach to the input design. The earlier interactive methods utilized for MRO communicate with the decision maker only using the response variable values, in order to improve the current response values, thereby resulting in the corresponding design solution automatically. In their interaction steps of preference articulation, no account is taken of any active changes in design variable values. On the contrary, our approach permits the decision maker to change the design variable values in its interaction stage, which makes possible the consideration of the preference or economics of the design variable side. Using some typical value functions, we also demonstrate that our method converges reasonably well to the known optimal solutions.

• Journal of Industrial Technology
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• v.32 no.A
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• pp.115-118
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• 2012

Recently, there are a lot of papers in order to replace the electrolytic capacitor into the film capacitor in output of PFC(Power Factor Correction). However, the film capacitor, which has capacitance of low values, causes a large ripple voltage in output of PFC. The LED drivers are connected series in the output of PFC and affected by the magnitude of voltage ripple. In this paper, we have compared the fixed frequency method with the variable frequency for the constant-current control and propose the control method to avoid the sub-harmonic oscillation in the variable input voltage. An 80W PFC, using film capacitors instead of electrolytic capacitors, and LED driver has been built and compared the fixed frequency control method with the variable frequency control method.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.524-529
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• 2006

In this paper, we propose a method to design variable structure systems with sliding sector for multi-input multi-output systems with mismatched uncertainties in the state matrix. For the uncertain systems we define sliding sectors within which a norm of the state decreases with zero input despite of mismatched uncertainties. Using the notion of the sliding sector we give simple design algorithms of variable structure control laws that can reduce the chattering. Finally, we give a design example in order to show the effectiveness of our method.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.7
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• pp.773-783
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• 1992

There have been several control schemes for the single input systems with unmeasurable state variables using variable structure control(VSC) theory. However, each of them is a study on the systems which can be represented in the phase canonical form or non-phase canonical form dynamic equation separately. As these control algorithms have difficulties in practical application by its theoretical limitations, in this paper we propose a new VSC theory which overcomes those limitations, in this paper we propose a new VSC theory which overcomes those limitations of proposed schemes. This new control scheme can be realized for the general linear systems which have unmeasurable state variables. And the switching function of this VSS algorithm consists of measurable state variable function(reduced-order switching function) and its derivatives. Also in the construction of control imput only measurable state variables are used.