• Environmental and Resource Economics Review
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• v.18 no.2
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• pp.279-309
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• 2009

Greenhouse gas emissions should be precisely forecast to reduce the emissions from industrial production processes. This study calculated the direct and indirect $CO_2$ emission intensities of 401 industries using the Input-Output tables 2003 and statistical data on the amount of energy use. This study had some limitations in drawing study findings because overseas data were used given the lack of domestic data. Other limiting factors included the oil distribution problems in the oil refinery sector, re-review of carbon neutral, and insufficient consideration of waste treatment. Nonetheless, this study is very meaningful since the direct and indirect $CO_2$ emission intensities of 401 industries were calculated. Specifically, this study considered from the zero-waste perspective the effects of waste, which attract interest worldwide since coke gas and gas from the steel industry are obtained as byproducts for the first time in Korea. According to the results of the analysis of $CO_2$ emission intensity per industry, typical industries whose indirect $CO_2$ emission intensity is high include crude steel making, Remicon, steel wire rods & track rail, cast iron, and iron reinforcing rods & bar steel. These industries produce products using the raw materials produced in the industrial sector whose $CO_2$ emission intensity is high. The representative industries whose direct $CO_2$ emission intensity is high include cement, pig iron, lime & plaster products, andcoal-based compounds. These industries extract raw ore from nature and refine them into raw materials that are useful in other industries. The findings in this study can be effectively used for the following case: estimation of target $CO_2$ emission reduction level reflecting each industrial sector's characteristics, calculation of potential emission reduction of each policy to reduce $CO_2$ emissions, identification of a firm's $CO_2$ emission level, and setting of the target level of emission reduction. Moreover, the findings in this study can be utilized widely in fields such as System of integrated Environmental and Economic Accounting(SEEA) and Material Flow Analysis(MFA) as the current topic of research in Korea.

• Journal of the Korean Society for Marine Environment & Energy
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• v.19 no.2
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• pp.87-98
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• 2016

During the period between July 3 and 27 of 2009, water samples were collected from the Russian coast at a depth of 30m from 26 stations (including Ulleung and Japan basins) onboard the Russian survey vessel R/V Lavrentyev following 4 lines (D, R, E, and A). The samples were analyzed for nutrients and chlorophyll a contents. All parameters exhibited higher values in warm waters than in cold waters ($NH_4:1.8-fold$, $PO_4:1.8-fold$, $SiO_2:1.2-fold$, and chlorophyll-${\alpha}$:1.9-fold), except nitrates, which was 1.4-fold higher in cold waters than in warm waters. The horizontal distribution of ammonia, phosphate, and chlorophyll-${\alpha}$ was very similar to each other and showed the highest values in the waters near Russia, where a upwelling influence of cold current and bottom water prevails, while relatively low distribution was observed at the Ulleung Basin. On the other hand, nitrates showed the highest concentration at the Ulleung Basin, which is under the direct influence of the Tsushima warm water, and showed a gradual decrease northward. The N/P ratio showed the highest value in the Tsushima middle water, rather than in the North Korean Cold Water, the Tsushima Warm Water was the primary source of nitrate flow into the East Sea. However, the average concentration of phosphate in the warm waters was < $0.2{\mu}M$, thereby limiting phytoplankton growth, while a high concentration of phosphate in cold waters showed a direct correlation with chlorophyll-${\alpha}$. The results of principal component analysis for the identification of primary factors that influence the marine environment showed that principal component I was water temperature and principal component II was influenced chlorophyll-${\alpha}$ and nutrients. Therefore, Study area has greatest influenced by water temperature, and clearly distinct cold and warm water regions were observed in the East Sea.

• Economic and Environmental Geology
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• v.38 no.3 s.172
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• pp.305-317
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• 2005

This study addresses the preliminary results of rock slope stability analyses including hazard assessments for slope failure conducted on the selected sections of rural road cut slope which are about 4 km long. The study area is located in the Mt. Chuntae northeast of Busan and mainly composed of Cretaceous rhyolitic ash-flow tuff', fallout tuff, rhyolitc and andesite. The volcanic rock mass in the area has a number of discontinuities that produce a potentially unstable slope, as the present cut slope is more than 70 degrees in most of the slope sections. Discontinuity geometry data were collected at selected 8 scanline sections and analyzed to estimate important discontinuity geometry parameters to perform rock slope kinematic and block theory analyses. Kinematic analysis for plane sliding has resulted in maximum safe slope angles greater than $65^{\circ}$ for most of the discontinuities. For most of the wedges, maximum safe cut slope angles greater than $45^{\circ}$ were obtained. Maximum safe slope angles greater than 80" were obtained fur most of the discontinuities in the toppling case. The block theory analysis resulted in the identification of potential key blocks (type II) in the SL4, SL5, SL6 and SL8 sections. The chance of sliding taking place through a type ll block under a combined gravitational and external loading is quite high in the investigated area. The results support in-field observations of a potentially unstable slope that could become hazardous under external forces. The results obtained through limit equilibrium slope stability analyses show how a stable slope can become an unstable slope as the water pressure acting on joints increases and how a stable slope under Barton's shear strength criterion can fail as the worst case scenario of using Mohr-Coulomb criterion.

• Radiation Oncology Journal
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• v.18 no.1
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• pp.67-72
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• 2000

Background :The authors have developed a Digital image chart(DIC) and digital Radiotherapy Record System (DRRS). We have evaluated the DIC and DRRS for reliability, usefulness, ease of use, and efficiency. Materials and Methods :The basic design of the DIC and DRRS was to build an digital image database of radiation therapy Patient records for a more efficient and timely flow of critical image information throughout the department. This system is a submit of comprehensive radiation oncology management system (C-ROMS) and composed of a picture archiving and communication system (PACS), a radiotherapy information database, and a radiotherapy imaging database. The DIC and DRRS were programmed using Delphi under a Windows 95 environment and is capable of displaying the digital images of patients identification photos, simulation films, radiotherapy setup, diagnostic radiology images, gross lesion Photos, and radiotherapy Planning isodose charts with beam arrangements. Twenty-three clients in the department are connected by Ethernet (10 Mbps) to the central image server (Sun Ultra-sparc 1 workstation). Results :From the introduction of this system in February 1998 through December 1999, we have accumulated a total of 15,732 individual images for 2,556 patients. We can organize radiation therapy in a 'paperless' environment in 120 patients with breast cancer. Using this system, we have succeeded in the prompt, accurate, and simultaneous access to patient care information from multiple locations throughout the department. This coordination has resulted in improved operational efficiency within the department. Conclusion :The authors believe that the DIC and DRRS has contributed to the improvement of radiation oncology department efficacy as well as to time and resource savings by providing necessary visual information throughout the department conveniently and simultaneously. As a result, we can also achieve the 'paperless' and 'filmless' practice of radiation oncology with this system.

• Journal of the Korean Geographical Society
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• v.50 no.6
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• pp.571-605
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• 2015

This research revealed that 'Yipjiamlyu' in the Mukedeng's map is geographically 'a beginning point of underf low,' whose location is on the Heishigou's riverbed(E.L. 1,840m) in the NNE side of Daegakbong peak, and that 'Tomungangweon'(Heishigou) is one of the upstream reach of the Sungari River, which, according to historical documents and my fieldwork, Mukedeng also knew at the time of Yimjin(1712) Boundary Making and Demarcation(YBMD). These findings suggest the need to reinterpret the processes of YBMD. Mukedeng set up the Baekdusanjeonggyeobi on the mistaken assumptions on the linkage of 'Yipjiamlyu' and Tumen River. It should have been set up on the Daeyeonjibong peak. Mukedeng found the 'Yipjiamlyu' on the riverbed of 'Tomungangweon'(Heishigou), went downstream, and realized that this river did not flow into the Tumen River. During the search for the source of Tumen River, he found a water stream, and regarded it as the source of Tumen River. He speculated that the water at the 'Yipjiamlyu' flows through the underground to reappear at the his 'identified' source of Tumen River. Consequently, he adjured the construction of demarcation from Baekdusanjeonggyeobi through 'Yipjiamlyu' to the his 'identified' source of Tumen River. The water stream pointed as the source of Tumen River, however, was not part of the upstream reach of Tumen River. Actually, Korean officials, who were in charge of establishing boundary features, set up the demarcation from Baekdusanjeonggyeobi through Huanghuasongdianzi to the true source of Tumen River identified by themselves, which Mukedeng had not intended. The ambiguity of the location of 'Yipjiamlyu' caused a difference between Mukedeng's original request and Korean officials' implementation in the boundary demarcation. Throughout the whole processes of YBMD, Korea(Joseon) and China(Qing) both mistook the real geography of the river system. Their understanding on Yalu River system was correct. But the identification of the spring source of the Tumen River by Korean participants was the only geographically correct result related on this river system in YBMD.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.