• 자원환경지질
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• 제47권3호
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• pp.237-246
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• 2014

본 연구는 산업연관표의 투입산출표와 거시계량경제모형을 연계하여 산업부문에 발생한 충격의 효과가 거시변수에 미치는 효과를 예측할 수 있도록 모형화 하였으며, 모형을 통해 광산품 부문의 수입대체에 따른 경제적 효과를 추정하였다. 구축한 모형의 전반적인 개요는 거시계량경제모형을 구축하고 산업연관표를 통해 도출된 피용자보수를 국민계정상 가처분소득의 대리변수로 이용하여 두 분석틀을 연계하였다. 거시계량경제모형은 한국은행이 작성한 1997년 기준 모형을 근간으로 하여 최근의 한국은행 모형을 통해 수정 및 보완하여 2011년 기준의 연간 거시계량경제모형을 구축하였으며, 산업연관분석과 연계되는 가처분소득과 총취업자수 부분을 수정도입하였다. 산업연관표 부분은 2005년~2011년의 통합대분류 경상표 및 그 계수표를 이용하였다. 구축된 모형을 적용하여 광산품 중간투입 중 1%의 수입대체가 미치는 경제효과를 추정하였는데, 2011년 기준으로 GDP 0.00073% 증가, 경상수지 0.01040% 증가, 실업률 0.00233%p 감소의 효과가 나타났다. 본 연구는 거시변수를 이용하여 지출측면 중심으로 활용되던 거시계량경제모형에 산업연관표를 활용함으로써 산업 수준의 충격과 투입부문을 고려할 수 있도록 하였다는 점에 의의가 있다.

• 수산경영론집
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• 제46권1호
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• pp.109-119
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• 2015

This paper is to estimate the economic effect of benefits of the R&D and recreational fishing as well as input-output analysis in the Tae-an Trial Sea Farm Project(TTSFP). We use B/C model to indicate the effects of economic valuation. B/C analyses model consists of Benefit Cost Ratio(BCR), Net Present Value(NPV) and Internal Ration of Return(IRR). Using 5.5% discounting rates and the survey data, the sub-models show economically feasible in the all of analysis and analyzed the results as follows. NPV is 42,147 million won, BCR is 3.29 and IRR is 34.30%. This study attempts to apply input-output(I-O) analysis in connecting the economic effect of TTSFP. I-O model was constructed, focusing on three effects; the production-inducing effect, the value-added-inducing effect and employment-inducing effect. There are positive effects on economic value and job creation in Tae-an and Nation.

• KIEAE Journal
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• 제15권4호
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
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• pp.233-242
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• 1995

In this paper, we introduce the identification model of dynamic system using the neural networks, We propose two identification models. The output of the parallel identification model is a linear combination of its past values as well as those of the input. The series-parallel model is a linear combination of the past values in the input and output of the plant. To generate stable adaptive laws, we prove that the series-parallel model is found to be proferable.

• 대한토목학회논문집
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• 제14권4호
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• pp.859-866
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• 1994

대부분의 중소규모 하천 유역에서는 수위 또는 유량 등의 실측자료가 빈약한 상태이기 때문에 홍수시의 유출량을 정확히 추정하고, 홍수피해를 경감시키기 위한 적절한 대책을 세우기가 매우 어려운 실정이다. 본 연구에서는 부산지방에 위치한 온천천 유역을 대상으로 하여 중소 하천 유역에 대한 선형 이산화 입력-출력 모형의 매개변수를 추정하고자 하였으며, 1993년 6월부터 실측한 유출량 자료와 저류함수법에 의해 산정한 유효강우량을 사용하여 선형 이산화 입력-출력 모형의 매개변수를 최소자승법과 상관함수법에 의해 추정하였다. 선형 이산화 입력-출력 모형에 의한 유출 수문곡선은 실측 수문곡선을 잘 재현한 것으로 나타났다. 또한 추정된 매개변수와 강우자료만을 사용하여 모의발생시킨 수문곡선 역시 실측 수문곡선과 잘 일치함을 알 수 있었다.

• 산업공학
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• 제22권1호
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• pp.44-55
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• 2009

We propose a method for selection of input-output factors in DEA. It is designed to select better combinations of input-output factors that are well suited for evaluating substantial performance of DMUs. Several selected DEA models with different input-output factors combinations are evaluated, and the relationship between the computed efficiency scores and a single performance criterion of DMUs is investigated using decision tree. Based on the results of decision tree analysis, a relatively better DEA model can be chosen, which is expected to well represent the true performance of DMUs. We illustrate the effectiveness of the proposed method by applying it to the efficiency evaluation of 101 listed companies in steel and metal industry.

• 동력기계공학회지
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• 제6권3호
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• pp.49-56
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• 2002

This study suggests a trajectory tracking controller composed of an input output linearization compensator and a linear controller. The input output linearization compensator is derived from the nonlinear equations of a pneumatic control system and it algebraically transforms a nonlinear system dynamics into a linear one, so that input output characteristics of the control system is linearized regardless of the variation of the operating point and linear control techniques can be applied. The results of nonlinear simulations show that the proposed controller tracks the given trajectories more accurately than a state feedback controller does.

• 한국지능시스템학회논문지
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• 제5권2호
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• pp.70-78
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• 1995

입출력 데이터를 이용한 퍼지모델의 설정은 구조 설정과 변수 설정으로 나누어진다. 본 논문에서는 기존 방법의 문제점을 해결하고 퍼지모델의 이러한 구조와 변수를 설정하는 새로운 방법을 제안한다. 입출력 데이터가 주어지면, 후건부 변수는 선형성과연속성을 고려하여 휴(Hough) 변환과클러스터링 방법에 의해 각각 설정된다. 또한 경사 하강법(Gradient descent method)을 사용하여 퍼지모델 변수의 미세조정을 행한다. 마지막으로 단일 입출력 시스템에 대하여 시뮬레이션을 통해 제안된 방법의 유효성을 보인다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.26-26
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• 2023

General circulation models (GCMs) are widely used in hydrological prediction, however their coarse grids make them unsuitable for regional analysis, therefore a downscaling method is required to utilize them in hydrological assessment. As one of the downscaling methods, convolutional neural network (CNN)-based downscaling has been proposed in recent years. The aim of this study is to generate the process of dynamic downscaling using CNNs by applying GCM output as input and RCM output as label data output. Prediction accuracy is compared between different input datasets, and model structures. Several input datasets with key atmospheric variables such as precipitation, temperature, and humidity were tested with two different formats; one is two-dimensional data and the other one is three-dimensional data. And in the model structure, the hyperparameters were tested to check the effect on model accuracy. The results of the experiments on the input dataset showed that the accuracy was higher for the input dataset without precipitation than with precipitation. The results of the experiments on the model structure showed that substantially increasing the number of convolutions resulted in higher accuracy, however increasing the size of the receptive field did not necessarily lead to higher accuracy. Though further investigation is required for the application, this paper can contribute to the development of efficient downscaling method with CNNs.

• 산업경영시스템학회지
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• 제20권44호
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• pp.129-134
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• 1997

The purpose of this study is to estimate the distribution rate of R&D input on R&D output in major manufacturing industrial sector. The distribution rate is estimated on time-series data for the period 1980 to 1996. The data used in this study can be divided into the two categories. 1) R&D output data (Patent, Utility) 2) R&D input data (R&D expenditure, R&D workers) The raw data of R&D expenditure is transformed into R&D stock. And the specific production function is used to represent the interaction between R&D input and output. The production function shows the maximum rate of R&D output that can be achieved by certain given, technologically possible, R&D input combinations. The main findings can be summarized as follows. 1) There was a diminishing return between R&D input and output$(\alpha+\beta<1). 2) R&D output growth was more affected by R&D expenditures than R&D workers. 3) R&D workers were more contributed highly to Patent granted than Utility model.