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.
In this paper, we present the concept of generalization in constructing windows for subsequence matching and propose a new subsequence matching method. GeneralMatch, based on the generalization. The earlier work of Faloutsos et al.(FRM in short) causes a lot of false alarms due to lack of the point-filtering effect. DualMatch, which has been proposed by the authors, improves performance significantly over FRM by exploiting the point filtering effect, but it has the problem of having a smaller maximum window size (half that FRM) given the minimum query length. GeneralMatch, an improvement of DualMatch, offers advantages of both methods: it can use large windows like FRM and, at the same time, can exploit the point-filtering effect like DualMatch. GeneralMatch divides data sequences into J-sliding windows (generalized sliding windows) and the query sequence into J-disjoint windows (generalized disjoint windows). We formally prove that our GeneralMatch is correct, i.e., it incurs no false dismissal. We also prove that, given the minimum query length, there is a maximum bound of the window size to guarantee correctness of GeneralMatch. We then propose a method of determining the value of J that minimizes the number of page accesses, Experimental results for real stock data show that, for low selectivities ($10^{-6}~10^{-4}$), GeneralMatch improves performance by 114% over DualMatch and by 998% iver FRM on the average; for high selectivities ($10^{-6}~10^{-4}$), by 46% over DualMatch and by 65% over FRM on the average.
Journal of the Korean association of regional geographers
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v.4
no.2
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pp.219-234
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1998
This study examines spatial characteristics of residential distribution and occupational composition of Korean emmigrants who live in China, United States, and Japan. The data used are The Conditions of Korean Emmigrants published by Ministry of Foreign Affairs in 1995. Analysis method used this data are as follows: 1) to clarify the tendency and spatial distribution of Korean emmigrants in each nation. 2) to grasp the residential distribution of Korean emmigrants in China, United States, and Japan where many Koreans have lived. 3) to analyze the occupational composition and its spatial characteristics of Korean emmigrants. The main findindgs obtained are summarized as follows: Farmer, forester, stock farmer, and fishery occupy over 50% of the employees of Korean emmigrants in China; traders and other employees occupy about three-fourths of the employees of Korean emmigrants in United States; and other employees occupy about 80% of the employees of Korean emmigrants in Japan. Therefore, the ratio of occupational composition of Korean emmigrants was influenced by emmigration motive, level of economic development of emmigration nation, restrictive condition for ethnic minority, and social status before emmigration etc. Specialized occupational composition in region where many Korean emmigrants in each nation lived was that the highest specialized occupation in region including primary city in population scale is trader; the highest specialized occupation in region including secondary city is manufacturing employee, and the highest specialized occupation in region including third city has transitional characteristics of each region including primary and secondary city. And professional occupation such as lawyer, doctor, religionist and educator appeared to the region including primary city. Finally, Korean emmigrants in United States and Japan contributed to the revitalization of inner city areas. And potential ethnic organization can be seen in church, mass media, and enterprise association in United States, but it can be seen in community shopping association in Japan, Because American society is based on Christianity but Japanese society is not.
This paper discusses an effective processing of similarity search that supports time warping in large sequence database. Time warping enables finding sequences with similar patterns even when they are of different length, Previous methods fail to employ multi-dimensional indexes without false dismissal since the time warping distance does not satisfy the triangular inequality. They have to scan all the database, thus suffer from serious performance degradation in large database. Another method that hires the suffix tree also shows poor performance due to the large tree size. In this paper we propose a new novel method for similarity search that supports time warping Our primary goal is to innovate on search performance in large database without false dismissal. to attain this goal ,we devise a new distance function $D_{tw-Ib}$ consistently underestimates the time warping distance and also satisfies the triangular inequality, $D_{tw-Ib}$ uses a 4-tuple feature vector extracted from each sequence and is invariant to time warping, For efficient processing, we employ a distance function, We prove that our method does not incur false dismissal. To verify the superiority of our method, we perform extensive experiments . The results reveal that our method achieves significant speedup up to 43 times with real-world S&P 500 stock data and up to 720 times with very large synthetic data.
Considering worldwide efforts to mitigate repercussions of climate change, the South Korean government has declared to reach net zero by 2050 to achieve a carbon-neutral sustainable society. For full implementation of NDCs, the government has actively reflected its forestry sector into these strategies. Since coarse woody debris (CWD) in forests represents an enduring carbon storage, it is of particular significance to determine characteristics of changes in carbon stocks of CWD by utilizing data on dead trees monitored in permanent sample plots within national forest inventories (NFIs). In this study, therefore, both occurrence and carbon stocks of CWD were estimated in such plots using data on CWD from the 5th, 6th, and 7th NFIs. Subsequently, characteristics of changes in carbon stocks over time were analyzed. Based on the analysis of 2,021 plots available for monitoring in each NFI of Gangwon Province, the volume of CWD (m3 ha-1) was found to be 4.71 in the 5th NFI and 4.09 in the 6th NFI. However, the volume of CWD declined to 3.09 in the 7th NFI. Moreover, the annual carbon stocks of CWD (ton C ha-1) were estimated to be 0.67 in 2009, 0.64 in 2014, and 0.41 in 2019, showing a downward trend over time. This study provides a basis for future research to investigate long-term changes and estimate carbon stocks of CWD in South Korea forests.
Purpose - I analyzes risk-to-performance evaluated in the market using data from sale and lease back. Specifically, I analyze from the perspective of financial institutions that purchase sale and lease back based on the cases of investment by ship investment companies and acquisition of ships. Design/methodology/approach - I use 49 sale and lease back data from 2017 to 2019 for empirical analysis. Findings - The main results of this paper are as follows. First, after sale and lease back of domestic ships, the average amount of sales by the leased shipping company is 25.1 billion won, the average amount of investment by the purchased financial institution is 14.6 billion won (60%) and the average length of the ship is nine years. In ship finance, sale and lease back is deemed to be appropriately used as a means of restructuring for a large amount of money. Second, the main risk factor for sale and lease back of domestic ships is credit risk and can be measured in VaR in practice. As a result of the empirical analysis, the average credit risk burden ratio is 9%. As a major risk factor, low creditworthiness of restructuring companies is the key. Third, as a result of measuring the profitability of financial institutions that purchase sale and lease back of domestic ships at a net current price, it has an average value of 300 million won, but the deviation by case is very large. Fourth, the risk adjusted performance of sale and lease back of domestic ships is 0.54 on average compared to the total risk capital, and 0.52 compared to the stock-risk capital, and as with profitability earlier, the deviation of each case is very large and misaligned. In order to boost the sale and lease back market for large and long-term assets, in order to overcome low profitability as a prerequisite for future participation of commercial purchased financial institutions, it is expected that purchase decisions based on expectations versus risk will be necessary. Research implications or Originality - The results of this paper are expected to broaden the understanding of sale and lease back and foster the ability to assess long-term risk and performance. Based on this, it is believed that rapid restructuring of companies through sale and lease back of large amounts of long-term assets will greatly increase the utility of the domestic financial market.
This study used a non-parametric linear program, Data Envelopment Analysis to compare the efficiency of South Korean and Chinese banks from 2000 to 2008, which is said to be the reformation period of their financial structure. The sample banks were 10 commercial banks and 6 regional banks in Korea, and 4 state-owned commercial banks and 11 stock commercial banks in China. The main objective of our research is to compare their efficiency, as well as the changes in efficiency periodically according to the types of the banks. According to the periodical analysis, both of the countries showed steady increase in efficiency. This shows that finance restructure and merging were positive factors for bank's efficiency during the revolution of finance structure. The study showed that between Korea and China, the bank of Korea has higher efficiency than that of China. Although the reconstruction period happened around the same time, due to the earlier acceleration period to opening Korea's financial market, made the difference in efficiency.
Inyoung Jang;Heon Mo Jeong;Sang-Hak Han;Na-Hyun Ahn;Dukyeop Kim;Sung-Ryong Kang
Journal of Wetlands Research
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v.25
no.4
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pp.417-425
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2023
As climate change gets severe, the ecosystem acts as an important carbon sink, therefore efforts are being made to utilize these functions to mitigate climate change. In this study, we inventoried and analyzed the previous studies related to carbon storage and flux by ecosystem type (forest, cropland, wetland, grassland, and settlement) and carbon pool (aboveground and belowground biomass, dead wood, Litter, soil organic carbon, and ecosystem) in Korean ecosystems. We also collected the results of previous studies and calculated the average value of carbon storage and flux for each ecosystem type and carbon pool. As a result, we found that most (66%) of Korea's carbon storage and fluxes studies were conducted in forests. Based on the results of forest studies, we estimated the storage by carbon stock. We found that much carbon is stored in vegetation (aboveground: 4,018.32 gC m-2 and belowground biomass: 4,095.63 gC m-2) and soil (4,159.43 gC m-2). In particular, a large amount of carbon is stored in the forest understory. For other ecosystem types, it was impossible to determine each carbon pool's storage and flux due to data limitations. However, in the case of soil organic carbon storage, the data for forests and grasslands were comparable, showing that both ecosystems store relatively similar amounts of carbon (4,159.43 gC m-2, 4,023.23 gC m-2, respectively). This study confirms the need to study carbon in rather diverse ecosystem types.
Kim, Sung Hyun;Choi, Joon Ki;Kim, Jae Seok;Jang, Ah Reum;Lee, Jae Ho;Cha, Kyung Jin;Lee, Sang Won
Journal of Intelligence and Information Systems
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v.24
no.4
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pp.137-154
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2018
Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.
Category management has been implemented to enhance competitiveness in the food distribution industry since 2000 in Korea. This study helps to understand why suppliers achieve better or worse performance than competitors in a category. The major objective of this article is to explore which category tactics are effective to have influence on category performance when suppliers as a category captain implement category management with variety enhancer categories like shampoo, toothpaste, and detergent. The Nielsen data were analyzed using regression and Chow test. The empirical results that were varied upon the store type and market position found out which specific actions on product assortments, pricing, shelving, and product replenishment can increase category sales. Specifically, in the case of market leader in large supermarket, the significant indicators of category sales with respect to category tactics are the out-of-stock rate, the variance across brand shares, the forward inventory, and the days supply of a product. However, in the case of follower in large supermarket, the significant indicators of category sales are the variance across brand shares, the forward inventory, and the days supply of a product. On the other hand, in the case of small supermarket, the significant factors on category sales for both market leader and follower are the retail distribution rate, the variance across brand shares, the forward inventory, and the days supply of a product category. In sum, regardless of the store type and market position, dominant brands in a category, the forward inventory, and short days supply of a product improved performance in all categories. Critical difference is that the out-of-stock rate acted as a key ingredient for the market leader between large and small supermarket and the retail distribution rate for the follower between large and small supermarket. This article presents some theoretical and managerial implications of the empirical results and finalizes the paper by addressing limitations and future research directions.
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