• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.6
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• pp.1572-1578
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• 2007

The specified wastes consist of waste acid, waste alkali, waste oil, waste organic solvent, waste resin, dust, sludge, infectious waste, and others. Among these specified wastes, a great portion is liquid phase wastes. The purpose of this study is to develop the high temperature and high pressure (HTHP) treatment system for decomposition of the liquid phase specified waste (LPSW). For this, we analyzed the physical and chemical properties of the LPSW such as density, proximate analysis, ultimate analysis, heating values, and designed 0.3 ton/day HTHP treatment system. The LPSW tested in this experiment were prepared by adding TCE(trichloroethylene) and toluene to liquid phase waste which was brought into the commercial waste treatment company. The average density of waste oil (25 samples), waste resin (5 samples), and waste solvent (12 samples) was 0.99 g/mL, 0.91 g/mL, and 0.93 g/mL, respectively. And the average lower heating value of waste oil, waste resin, and waste solvent was 8,294 kcal/kg, 5,809 kcal/kg, and 7,462 kcal/kg, respectively. The DRE (Destruction & Removal Efficiency) of TCE and toluene were 99.95% and 99.73% at atmospheric pressure conditions and that were 99.99% and 99.82% at pressurized conditions, respectively. These results showed that TCE/toluene mixtures were properly decomposed over about 99.73% of DRE by the HTHP treatment system and pressurized conditions were more effective to destroy those pollutants than atmospheric pressure conditions. Also these systems could be directly applied to industries which try to treat the liquid phase specified waste within the regulation limit.

• Korea Trade Review
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• v.42 no.5
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• pp.137-159
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• 2017

This study divides manufacturing in 18 countries including Korea, China, Japan and OECD countries into 11 areas and estimates and compares the technological efficiency of each industry. The traditional view of productivity is to increase production capacity through technological innovation or process innovation, but it is also influenced by the technological efficiency of production process. A Stochastic Frontier Production Model (SFM) is a representative method for estimating the technical efficiency of such production. First, as a result of estimating the production function by setting the output variable as total output or value-added, in both cases, the output increased significantly in all manufacturing sectors as inputs of labor, capital, and intermediate increased. On the other hand, R&D investment has a large impact on output in chemical, electronics, and machinery industries. Next, as a result of estimating the technological efficiency through the production function, when the total output is set as the output variable, the overall average of each sector is 0.8 or more, showing mostly high efficiency. However, when value-added was set, Japan had the highest level in most manufacturing sectors, while other countries were lower than the efficiency of the total output. Comparing the three countries of Korea, China and Japan, Japan showed the highest efficiency in most manufacturing sectors, and Korea was about half or one third of Japan and China was lower than Korea. However, in the food and electronics sectors, China is higher than Korea, indicating that China's production efficiency has greatly improved. As such, Korea is not able to narrow its gap with Japan relatively faster than China's rapid growth. Therefore, various policy supports are needed to promote technology development. In addition, in order to improve manufacturing productivity, it is necessary to shift to an economic structure that can raise technological efficiency as well as technology development.

• The Journal of Engineering Geology
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• v.33 no.4
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• pp.543-553
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• 2023

The current status of domestic a agalmatolite mines in South Korea was investigated with a view to establishing a stable supply of agalmatolite and managing its demand. Most mined agalmatolite deposits were formed through hydrothermal alteration of Mesozoic volcanic rocks. The physical characteristics of pyrophyllite, the main constituent mineral of agalmatolite, are as follows: specific gravity 2.65~2.90, hardness 1~2, density 1.60~1.80 g/cm³, refractoriness ≥29, and color white, gray, grayish white, grayish green, yellow, or yellowish green. Among the chemical components of domestic agalmatolite, SiO₂ and Al₂O₃ contents are respectively 58.2~67.2 and 23.1~28.8 wt.% for pyrophyllite, 49.2~72.6 and 16.5~31.0 wt.% for pyrophyllite + dickite, 45.1 and 23.3 wt.% for pyrophyllite + illite, 43.1~82.3 and 11.4~35.8 wt.% for illite, and 37.6~69.0 and 19.6~35.3 wt.% for dickite. Domestic agalmatolite mines are concentrated mainly in the southwest and southeast of the Korean Peninsula, with some occurring in the northeast. Twenty-one mines currently produce agalmatolite in South Korea, with reserves in the order of Jeonnam (45.6%) > Chungbuk (30.8%) > Gyeongnam (13.0%) > Gangwon (4.8%), and Gyeongbuk (4.8%). The top 10 agalmatolite-producing mines are in the order of the Central Resources Mine (37.9%) > Wando Mine (25.6%) > Naju Ceramic Mine (13.4%) > Cheongseok-Sajiwon Mine (5.4%) > Gyeongju Mine (5.0%) > Baekam Mine (5.0%) > Minkyung-Nohwado Mine (3.3%) > Bugok Mine (2.3%) > Jinhae Pylphin Mine (2.2%) > Bohae Mine. Agalmatolite has low thermal conductivity, thermal expansion, thermal deformation, and expansion coefficients, low bulk density, high heat and corrosion resistance, and high sterilization and insecticidal efficiency. Accordingly, it is used in fields such as refractory, ceramic, cement additive, sterilization, and insecticide manufacturing and in filling materials. Its scope of use is expanding to high-tech industries, such as water treatment ceramic membranes, diesel exhaust gas-reduction ceramic filters, glass fibers, and LCD panels.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.