• Journal of Korea Trade
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• 제25권3호
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• pp.48-64
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• 2021

Purpose - This study examined the effect of tariff cuts on productivity in Korea's manufacturing industries and the effect of initial productivity level before tariff cuts on productivity improvement after tariff cuts. We also attempted to identify whether import-driven or export-driven factors are more important for productivity improvement, especially in low productivity industries. Design/methodology - Since tariff reduction is a policy decision that can affect cross-industry, its impact is spread across all industries beyond the scope of a single firm through the input and output network of industry structure. Accordingly, we proposed a new method to measure the change in productivity to reflect the impact of tariff cuts across industries. Through an Armington CGE analysis, changes in endogenous variables can be directly measured after the exogenous shock of tariff reduction, and the amount of movements in productivity triggered by tariff cuts can also be calculated. We can thus assess the effectiveness of exogenous policy, such as tariff cuts, through the difference between the benchmark and counterfactual values of endogenous variables. Findings - This study confirmed that tariff reduction positively affected productivity improvement in Korea's manufacturing industries. It also confirmed that productivity gains occur in Korea's leading export industries. Finally, greater productivity gains were recorded in the group with additional high-export-share or high-import-share conditions for low productivity industries. These results are, in a limited sense, consistent with the existing studies that emphasize the importance of exports and imports on productivity improvement, especially for low productivity industries. Originality/value - The results of our experiments are different from those of non-CGE studies, which measure the industry-level change in productivity with dummy coefficients, in terms of directly calculating the amount of change in productivity. In addition, we propose that the Armington CGE model is more appropriate than the Melitz CGE model to directly measure the productivity after tariff cuts. This is because the Melitz CGE model assumes the given specific productivity density, which does not change after an overall drop of tariffs. To the best of our knowledge, this approach to directly calculating productivity by reflecting the impact of tariff reduction across industries through CGE analysis, is unprecedented in this literature.

• 자원ㆍ환경경제연구
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• 제33권3호
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• pp.291-314
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• 2024

국제해사기구(IMO)의 환경규제 강화에 따라 친환경 선박의 수요가 증가하고 있으며, 특히 LNG 추진선박이 조명받고 있다. 이에 따라 항만의 경쟁력 확보 및 LNG 추진선박의 활성화를 위해 LNG 벙커링 인프라 구축이 필요한 시점이다. 그러나 현재 관련 인프라를 구축한 국내 항만은 전무한 실정이며, 선행연구는 LNG 벙커링 산업 차원에서의 경제적 효과만을 중심으로 진행되었다. 이에 본 연구에서는 2015년 지역연관산업표를 활용하여 부산항에 LNG 벙커링 인프라를 구축할 경우 지역 내 경제적 파급효과에 대해 살펴보았다. 2023년 기준 부산항에 LNG 벙커링 인프라를 구축할 경우, 예상 사업비는 2조 1,091억 원이었다. 부문별 평균 생산유발계수는 1.223, 평균 부가가치유발계수는 0.372, 평균 취업유발계수는 7.58로 분석되었다.

• 보건의료산업학회지
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• 제6권3호
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• pp.219-229
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• 2012

In these days, the interest on medical industry is increasing around the world. This paper attempts to estimate the economic effects of the medical and measuring instrument industry through the Input-Output Analysis. Especially, 78*78 Sector Tables were used as the first analysis tool. So then, 79*79 Sector Tables adjusted were used for that industry. The main analysis tools of this study are comparing and analyzing backward and forward linkage effect, the induced effect of the self industry and other industries and the induced coefficients such as products, value-added, employee's pay, sales surplus, employment. According to the result of analysis, the medical and measuring instrument industry has great economic impacts which affects the major macroeconomic factors such as production and backward linkage effect. And the induced effects of the self medical and measuring instrument industry are significant compared to other industries in aspects of production, employee's pay and sales surplus.

• 비파괴검사학회지
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• 제23권6호
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• pp.622-634
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• 2003

This paper is concerned with a computational method for recovering a crack shape of steam generator tubes of nuclear plants. Problems on the shape identification are discussed arising in the characterization of a structural defect in a conductor using data of eddy current inspection. A surface defect on the generator tube ran be detected as a probe impedance trajectory by scanning a pancake type coil. First, a mathematical model of the inspection process is derived from the Maxwell's equation. Second, the input and output relation is given by the approximate model by virtue of the hybrid use of the finite element and boundary element method. In that model, the crack shape is characterized by the unknown coefficients of the B-spline function which approximates the crack shape geometry. Finally, a parameter estimation technique is proposed for recovering the crack shape using data from the probe coil. The computational experiments were successfully tested with the laboratory data.

• 디지털융복합연구
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• 제6권2호
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• pp.97-105
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• 2008

This paper tries to measure the spill-over effect of the production and investment of telecommunication service industry (hereafter telecommunication industry), using the most recent data of 2003 input-output tables. The results are summarized as follows. First, the industries which have the biggest spill-over effect from the production of telecommunication industry is miscellaneous business service (including the sale commission of telecommunication service), other engineering services (including royalty), and business consumption. Second, the production of telecommunication industry induces more value-added, and less production, less import, and less employment than related industries such as radio and television equipment, communications and broadcasting equipment, and computer and peripheral equipment. Third, while the investment of telecommunication service amounts to 15% of its production, the effect of the investment on production, value-added, consumption, and employment reaches 70% of that of its production. The policy implication of this paper is that the telecommunication industry contributes to overall economy mainly through its investment.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2002년도 봄 학술발표회 논문집
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• pp.997-1002
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• 2002

Traditional prediction models have been developed with a fixed equation form based on the limited number of data and parameters. If new data is quite different from original data, then the model should update not only its coefficients but also its equation form. However, artificial neural network (ANN) does not need a specific equation form. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. Therefore, the purpose of this paper is to develop the I-PreConS (Intelligent system for PREdiction of CONcrete Strength using ANN) that provides in-place strength information of the concrete to facilitate concrete form removal and scheduling for construction.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2004년도 춘계 학술발표회 제16권1호
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• pp.446-449
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• 2004

Traditional prediction models have been developed with a fixed equation from based on the limited number of data and parameters. If new data is quite different from original data, then the model should update not only its coefficients but also its equation form. However, artificial neural network dose not need a specific equation form. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. Therefore, the purpose of this study is to verify faith and application of prediction system of concrete strength using artificial neural networks through mock-up test.

• 보건의료산업학회지
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• 제7권3호
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• pp.57-69
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• 2013

In these days, the interest on health industry is increasing around the world. This paper attempts to estimate the economic effects of the Cosmetic Industrydusing the Input-Output Analysis. Especially, 78*78 Sector Tables were used as the first analysis tool. So then, 79*79 Sector Tables adjusted were used for that industry. The main analysis tools of this study are comparing and analyzing backward and forward linkage effects, the induced effects of the self industry and other industries and the induced coefficients such as product, value-added, job and employment. According to the result of analysis, the cosmetic industry has great economic impacts which affects the major macroeconomic factors such as product, value added and backward linkage effect. And the induced effects of the self cosmetic industry are significant compared to other industries in aspects of product, value-added, and employment.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 B
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• pp.797-799
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• 1999

In this paper, we design the Multi-FNN(Fuzzy-Neural Networks) using HCM Method. The proposed Multi-FNN uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. Also, We use HCM(Hard C-Means) method of clustering technique for improvement of output performance from pre-processing of input data. The parameters such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. We use the training and testing data set to obtain a balance between the approximation and the generalization of our model. Several numerical examples are used to evaluate the performance of the our model. From the results, we can obtain higher accuracy and feasibility than any other works presented previously.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.420-423
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• 2001

In this paper, the Multi-FNNs(Fuzzy-Neural Networks) architecture is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNNs architecture uses simplified inference and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNNs according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNNs model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model we use the time series data for gas furnace and the NOx emission process data of gas turbine power plant.