• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.11
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• pp.51-57
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• 2014

This paper presents an algorithmic type computing technique of process coefficient in predicting model of temperature for reheating furnace and also suggests a design method of neural network model to find an adequate value of process coefficient for arbitrary operating conditions including test conditons. The proposed neural network use furnace temperature, line speed and slab information as input variables, and process coefficient is output variable. Reasonable process coefficients can be obtained by an algorithmic procedure proposed in this paper using process data gathered at test conditons. Also, neural network model output equal process coefficient under same input conditions. This means that adquate process coefficients can be found by only computing neural network model without additive test even if operating conditions vary.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1042-1045
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• 2003

In this paper, we propose the speaker identification system that uses vowel that has speaker's characteristic. System is divided to speech feature extraction part and speaker identification part. Speech feature extraction part extracts speaker's feature. Voiced speech has the characteristic that divides speakers. For vowel extraction, formants are used in voiced speech through frequency analysis. Vowel-a that different formants is extracted in text. Pitch, formant, intensity, log area ratio, LP coefficients, cepstral coefficients are used by method to draw characteristic. The cpestral coefficients that show the best performance in speaker identification among several methods are used. Speaker identification part distinguishes speaker using Neural Network. 12 order cepstral coefficients are used learning input data. Neural Network's structure is MLP and learning algorithm is BP (Backpropagation). Hidden nodes and output nodes are incremented. The nodes in the incremental learning neural network are interconnected via weighted links and each node in a layer is generally connected to each node in the succeeding layer leaving the output node to provide output for the network. Though the vowel extract and incremental learning, the proposed system uses low learning data and reduces learning time and improves identification rate.

• Journal of IKEEE
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• v.23 no.4
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• pp.1353-1359
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• 2019

It is presented in this paper that the static output feedback (SOF) pole-assignment problem of some linear time-invariant systems can be completely resolved by parametrization in real Grassmann space. For the real Grassmannian parametrization, the so-called Plucker matrix is utilized as a linear matrix formula formulated from the SOF variable's coefficients of a characteristic polynomial constrained in Grassmann space. It is found that the exact SOF pole assignability is determined by the linear independency of columns of Plucker sub-matrix and by full-rank of that sub-matrix. It is also presented that previous diverse pole-assignment methods and various computation algorithms of the real SOF gains for 2-input, 2-output, 4^th order systems are unified in a deterministic way within this real Grassmannian parametrization method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.5
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• pp.52-61
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• 1993

This paper deals with adaptive control method of a robot manipulator with one-flexible link. ARMA model is used as a prediction and estimation model, and adaptive control scheme consists of parameter estimation part and adaptive controller. Parameter estimation part estimates ARMA model's coefficients by using recursive least-squares(RLS) algorithm and generates the predicted output. Variable forgetting factor (VFF) is introduced to achieve an efficient estimation, and adaptive controller consists of reference model, error dynamics model and minimum prediction error controller. An optimal input is obtained by minimizing input torque, it's successive input change and the error between the predicted output and the reference output.

• Korean Journal of Community Nutrition
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• v.8 no.5
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• pp.763-769
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• 2003

The food-service industry in Korea has experienced remarkable growth during the past few decades. The objectives of this study were to analyze the influence of the food-service industry upon the national economy by using an input-output analysis and to find the industrial position of the food service industry. This paper analysed the economic effect of the food-service industry using 168 items arranged in a transaction table based on producer's prices in the 1995 input-output tables. The results of this study showed that the food-service industry had a major influence on the national economy of Korea. Based on the calculation of the following five coefficients; Korea's production inducement coefficient ranked as 50, its import inducement coefficient ranked as 28, its value added inducement coefficient ranked as 32, its worker inducement coefficient ranked as 2 and its employee inducement coefficient per final demand ranked as 5 in a total of 168 industries.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.884-892
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• 2011

The analysis of the tourist industry in relation to the general industries is of high use as a means to measure an economic effectiveness as the interest in the policy of service industry increases. From the Input-Output Tables of both 2007 and 2008, Inverse Matrix Coefficients, Imports Requirement Coefficients, and Value Added Requirement Coefficients have been derived. As a result of analysis, the main indexes of the industry-related analysis have almost no differences as compared with those of the 1980s. In spite of the reduction in the scope of the tourist industry in this paper, it is estimated that the reason why the above-mentioned result has been derived is that the influence of today's tourist industry grows bigger than that of the past. In the future studies, the agreement on the classification of tourist industry is requested. In addition, all kinds of calculations have to be derived in general, and the general parts of the tourist industry have to be analyzed in details.

• Journal of Information Technology Services
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• v.17 no.3
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• pp.37-51
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• 2018

Recently, cloud computing market is growing geometrically in both private and public area, and many global companies in various domains have developed and provided cloud computing services. In such situation, Korean government made multiple plans for domestic cloud computing industry. However, most research institutes have focused on market size and status information, which makes actual effectiveness of cloud computing service hard to recognize. In this study, we define cloud computing Industry by rearranging Input-Output table published by the Bank of Korea to use Input-Output Analysis. The Input-Output Analysis was devised in 1963 by Leontief and it is used in many fields of study until now. It produces various coefficients that are able to identify production-inducing effect, value-added effect, labor-inducing effect, front and rear chain effect. The analysis results show that production-inducing effect, front and rear chain effect of cloud computing industry is low compared to other industries. However, cloud computing Industry possesses relatively high value-added effect and labor-inducing effect. It is because industry magnitude of cloud computing is smaller than other industries such as manufacturing, chemical industries. The economic effects of the cloud computing industry are not remarkable, but this result is significant to emerging markets and industries and presents the fresh way of analyzing cloud computing research field.

• KDI Journal of Economic Policy
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• v.12 no.4
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• pp.3-20
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• 1990

The multisector model is designed to analyze and forecast structural change in industrial output, employment, capital and relative price as well as macroeconomic change in aggregate income, interest rate, etc. This model has 25 industrial sectors, containing about 1,300 equations. Therefore, this model is characterized by detailed structural disaggregation at the sectoral level. Individual industries are based on many of the economic relationships in the model. This is what distinguishes a multisector model from a macroeconomic model. Each industry is a behavioral agent in the model for industrial investment, employment, prices, wages, and intermediate demand. The strength of the model lies in the simulating the interactions between different industries. The result of its simulation will be introduced in the next paper. In this paper, we only introduce the structure of the multisector model and the coefficients of the equations. The multisector model is a dynamic model-that is, it solves year by year into the future using its own solutions for earlier years. The development of a dynamic, year-by-year solution allows us to combine the change in structure with a consideration of the dynamic adjustment required. These dynamics have obvious advantages in the use of the multisector model for industrial planning. The multisector model is a medium-term and long-term model. Whereas a short-term model can taken the labor supply and capital stock as given, a long-term model must acknowledge that these are determined endogenously. Changes in the medium-term can be analyzed in the context of long-term structural changes. The structure of this model can be summarized as follow. The difference in domestic and world prices affects industrial structure and the pattern of international trade; domestic output and factor price affect factor demand; factor demand and factor price affect industrial income; industrial income and relative price affect industrial consumption. Technical progress, as measured in terms of total factor productivity and relative price affect input-output coefficients; input-output coefficients and relative price determine the industrial input cost; input cost and import price determine domestic price. The differences in productivity and wage growth among different industries affect the relative price.

• 전기의세계
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• v.31 no.5
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• pp.361-367
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• 1982

The problem of parameter identification for multivariable stochastic linear systems from output measurements, which are corrupted by noises, is considered. A modified Luenberger's input/output canonical form is used for reducing the number of unknown coefficients. A computationally and conceptionally simple systematic procedure for parameter estimation is obtained using output correlation method. The estimates are shown to be asymptotically normal, unbiased and consistent. Numerical examples are presented to illustrate the identification method.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.597-600
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• 1997

In this paper, the authors propose a new method for linearizing a nonlinear dynamical system by use of polynomial compensation. In this method, an M-sequence is applied to the nonlinear system and the crosscorrelation function between the input and the output gives us every crosssections of Volterra kernels of the nonlinear system up to 3rd order. We construct a polynomial compensation function from comparison between lst order Volterra kernel and high order kernels. The polynomial compensation function is, in this case, of third order whose coefficients are variable depending on the amplitude of the input signal. Once we can get compensation function of nonlinear system, we can construct a linearization scheme of the nonlinear system. That is. the effect of second and third order Volterra kernels are subtracted from the output, thus we obtain a sort of linearized output. The authors applied this method to a saturation-type nonlinear system by simulation, and the results show good agreement with the theoretical considerations.