• Korean Journal of Construction Engineering and Management
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• v.13 no.5
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• pp.94-102
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• 2012

Recently, the global financial crisis and the increasing number of unsold houses in Korea are construction companies to assess their efficiency. The most important factor in analyzing the efficiency of a company is the input-output variable. However, systematic stud the core input-output variables, which have a great influence on the efficiency analysis. Thus, to the core input-output variables for efficiency analysis of construction companies, this study propose a model that includes all combinations of input-output variables and to find the core input-output variables using the Data Envelopment Analysis(DEA) model and Principal Component Analysis(PCA). Existing research and theories were studied variables and 21 models were established to measure efficiency. were obtained that the core input and output variable in 2006 the number of employees and sales. For 2008, the core input variable was capital stock and the core output variable was quarterly net profit. For 2010, the core input variable was fixed asset and the core output variable was sales. Through obtaining the variables that greatly affect the efficiency of construction companies, it is considered that individual construction companies will be able to prepare a priority strategy to enhance efficiency.

• The Journal of Fisheries Business Administration
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• v.43 no.3
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• pp.75-87
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• 2012

Today, the Korean fisheries is undergoing significant hardships, both domestically and internationally. While declining amount of catch, ascending international oil prices and others pose a compelling challenge to the fishing sector, the ever strengthening influence of international institutions related to fisheries and international trade organizations also compel to bring about myriad of changes in the realm of fishery products. Against the backdrop, this study attempted to examine the fisheries catch, aquaculture, service, processing fields in terms of its rippling effect and of how the industry has been changed by analyzing the past and present through an input-output analysis. As for research methods, 168 items of the input-output tables in 2000, 2005, 2009, and 2010 were integrated to form and classify 32 sectors (28 basic sectors + catch, aquaculture, fishery service, processed fishery products) so as to generate production inducement coefficient, sensitivity coefficient, and impact coefficient. The analysis results revealed that : though the linkage effect of fishery industry was not very sizable, the impact coefficient of the processed fishery products was high; the consumption and investment coefficient sector among production inducement coefficient was on an upturn trend ; the export coefficient was tended to decline. In the future research, it is necessary to carry out a study based on the integration of detailed classification (404 sector) and a study and analysis of fishery industry by different regions through the inter-regional input-output tables. The fishery industry is one of the crucial industries in Korea. The fishery industry is not only important in its own right but also significant as it exerts influence over other industries. Therefore, it is required that there should be more investment and supports for the development of the fishery industry, and pay efforts to ensure that the investment and development could lead to mutual growth for both the fishery and other various industries.

• KDI Journal of Economic Policy
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• v.46 no.2
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• pp.45-74
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• 2024

The import content of export (ICE) has served as an indicator of global integration for several decades. It is defined as the share of imported products embodied in exports and can be interpreted as the relative degree of the utilization of global production network (GPN) over the domestic supply chain (DSC) in terms of 'value-added.' This paper proposes two new indicators of global integration. They are defined as the ratios of imports (foreign products) to gross output (domestic products) generated by exports and can be interpreted as the relative degrees of the utilization of GPN over DSC in terms of 'production.' Both indicators are easy to compute and can be compared between years, between countries, between industries, and between groups of industries. The paper applies the new indicators to the recent edition of the OECD's Input-Output Database. Finally, the paper shows that the recent slowdown in international trade is mostly due to the decrease in the international trade of intermediate goods, with significant implications regarding the future of global integration.

• Journal of the Korean Society for Railway
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• v.19 no.6
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• pp.815-825
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• 2016

The inter-industrial inducement effects of the Korean railway services on the output, value-added, and imports of 383 industrial sectors of the Korean economy have been computed by the input-output analysis technique utilizing the '2013 Input-Output Statistics', which was published most recently in December 2015 by the Bank of Korea. The research results are, then, compared and contrasted with the former research results with the '2003 Input-0utput Statistics', which were published in the Journal of the Korean Society for Railway, August, 2008. The economic inducement effects computed in this study are as follows. The Korean railway service industry produced \5,841 billion worth of passenger and freight railroad services in the year 2013, and it induced \8,511 billion worth of output, \84 billion worth of value-added, and \1,050 billion worth of imports for Korean industry as a whole.; the production inducement multiplier, value-added multiplier, and imports inducement multiplier of 2013 results are shown to be quite different from 2003 multipliers.; it has been shown that it is necessary to utilize the most recent Input-Output Statistics for the computation of the most recent inter-industrial analysis.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.84-99
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• 2014

In this study, determinant input-output variables are identified for calculating Data Envelopment Analysis (DEA) efficiency scores relating to evaluating the efficiency of government-sponsored research and development (R&D) projects. In particular, this study proposes a systematic framework of design and analysis of experiments, called "all possible DEAs", for pinpointing DEA determinant input-output variables. In addition to correlation analyses, two modified measures of time series analysis are developed in order to check the similarities between a DEA complete data structure (CDS) versus the rest of incomplete data structures (IDSs). In this empirical analysis, a few DEA determinant input-output variables are found to be associated with a typical public R&D performance evaluation logic model, especially oriented to a mid- and long-term performance perspective. Among four variables, only two determinants are identified : "R&D manpower" ($x_2$) and "Sales revenue" ($y_1$). However, it should be pointed out that the input variable "R&D funds" ($x_1$) is insignificant for calculating DEA efficiency score even if it is a critical input for measuring efficiency of a government-sonsored R&D project from a practical point of view a priori. In this context, if practitioners' top priority is to see the efficiency between "R&D funds" ($x_1$) and "Sales revenue" ($y_1$), the DEA efficiency score cannot properly meet their expectations. Therefore, meticulous attention is required when using the DEA application for public R&D performance evaluation, considering that discrepancies can occur between practitioners' expectations and DEA efficiency scores.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.2
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• pp.371-383
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• 1997

This paper propose a fuzzy regression method using fuzzy neural networks when a membership value is attached to each input-output pair. First, a method of linear fuzzy regression analysis is described by interpreting the reliability of each input-output pair as its membership values. Next, an architecture of fuzzy neural networks with fuzzy weights and fuzzy biases is shown. The fuzzy neural network maps a crisp input vector to a fuzzy output. A cost function is defined using the fuzzy output from the fuzzy neural network and the corresponding target output with a membership value. A learning algorithm is derived from the cost function. The derived learning algorithm trains the fuzzy neural network so that the level set of the fuzzy output includes the target output. Last, the proposed method is illustrated by computer simulations on numerical examples.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.680-684
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• 2005

This paper presents a data-based stability analysis of a MIMO linear time-invariant discrete-time system, as an extension of the previous results for a SISO system. In the MIMO case, a similar discussion as in the case of a SISO system is also applied, except that an augmented input and output space is considered whose dimension is determined in relation to both the orders of the input and output vectors and the numbers of inputs and outputs. As certain subspaces of the input and output space, both output data space and closed-loop data space are defined, which contain all the behaviors of a system, respectively, with zero input in open-loop and with a control input in closed-loop. Then, we can derive the data-based stability conditions, in which the open-loop stability can be checked by using a data matrix whose column vectors span the output data space and the closed-loop stability can also be checked by using a data matrix whose column vectors span the closed-loop data space.

• Proceedings of the Technology Innovation Conference
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• 1997.07a
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• pp.160-171
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• 1997

This paper starts out by reviewing the literature that in different ways utilizes patent data as a output of R&D investment. The main focus, however, is an analysis of time-lag between R&D input and output. To achieve this research objective, the basic data associated with the R&D input(expenditure, researchers) and output(patent, utilities) for the past 15 years, from 1980 to 1994, in the areas of electrical-electronic, mechanical and chemical industries have been collected. And the raw output data were altered it to objective data using Laspeyres approach and analyzed using multiple regression analysis, especially stepwise regression analysis. The result of this study can be summarized as follows: a) The time-lag between R&D input and output is from 1 to 4 years. This result is equal to the research conclusion of the existing foreign studies. b) It was found that the time-lag of patents was longer than of utility models. c) It was showed that the time-lag of electrical-electronic, mechanical industry was longer than the chemical one.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.999-1004
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• 2004

In this study, we identify contribution of structure-borne-noise of vehicle HVAC system using Multi-Dimensional spectral analysis (MDSA) method. Firstly, to identify the applicability of MDSA method, the case of HVAC system was modeled with four input / single output system. The four inputs which is given vibration data is composed of blower, evaporator, heater and duct. The single output is noise data from driver's seat. When the blower motor is operating, we analyze the contributions of four input / single output. As a result of experiment, we identify efficiency of systems modeled with four input / single output through ordinary coherence function (OCF) and multiple coherence function (MCF).

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.17-27
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• 2020

With the continuous development and application of Internet technology, in recent years, new technologies such as cloud computing, big data, the Internet of Things, and AI are becoming more and more familiar to the general public. The development of a digital society has entered a new period of development. In this paper, we used on the 2018 annual data of 50 listed companies with blockchain concept stocks in China. Using data envelopment analysis (DEA) to study and analyze the input-output efficiency, it can be concluded that the input-output efficiency of 50 listed companies is very different. Inefficient companies are as high as 62%. Most companies have a large room for improvement in input-output efficiency due to uneconomical scale or inefficient technology. In order to better improve the company's input-output efficiency, one must improve the efficiency of resource utilization, optimize the company's research and development costs and the input and management of technical personnel; the second is to increase technological innovation and business innovation.