• Journal of the Korean Applied Science and Technology
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• v.34 no.1
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• pp.1-11
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• 2017

Bio-oil has attracted considerable interest as one of the promising renewable energy resources because it can be used as a feedstock in conventional petroleum refineries for the production of high value chemicals or next-generation hydrocarbon fuels. Zeolites have been shown to effectively promote cracking reactions during pyrolysis resulting in highly deoxygenated and hydrocarbon-rich compounds and stable pyrolysis oil products. In this study, catalytic pyrolysis was applied to upgrade bio-oil from yellow poplar and then fuel characteristics of upgraded bio-oil was investigated. Yellow Poplar(500 g) which ground 0.3~1.4 mm was processed into bio-oil by catalytic pyrolysis for 1.64 seconds at $465^{\circ}C$ with Control, Blaccoal, Whitecoal, ZeoliteY and ZSM-5. Under the catalyst conditions, bio-oil productions decreased from 54.0%(Control) to 51.4 ~ 53.5%, except 56.2%(Blackcoal). HHV(High heating value) of upgraded bio-oil was more lower than crude bio-oil while the water content increased from 37.4% to 37.4 ~ 45.2%. But the other properties were improved significantly. Under the upgrading conditions, ash and TAN(Total Acid Number) is decrease and particularly important as transportation fuel, the viscosity of bio-oil decreased from 6,933 cP(Control) to 2,578 ~ 4,627 cP. In addition, ZeoliteY was most effective on producing aromatic hydrocarbons and decreasing of from the catalytic pyrolysis.

• Journal of the Mineralogical Society of Korea
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• v.30 no.3
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• pp.93-102
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• 2017

Heavy mineral provide an important information for sediment provenance as well as a potential submarine mineral resources. We compared the heavy mineral characteristics between Southeastern Yellow Sea Mud (SEYSM) and Southwestern Cheju Island Mud (SWCIM) surface sediments. We separated heavy minerals from 28 surface sediments in each mudbelt, and then carried out stereo-microscopic, field-emission scanning electron microscopic, energy dispersive spectroscopic and electron probe microanalysis to characterize the type, abundance, mineralogical properties and distribution pattern of heavy mineral. Amphibole and epidote, which are two major heavy minerals, account for more than 70% of total heavy minerals. Zircon and sphene contents are more abundant in SEYSM, whereas apatite and rutile contents are more abundant in SWCIM. Monazite only occurs in some area of SEYSM. Sphene and monazite content decrease to the south in SEYSM. Both garnet-zircon index (GZi) and rutile-zircon index (RuZi) are low in SEYSM but high in SWCIM. Amphiboles in SEYSM primarily correspond to hornblende, however those in SWCIM represent variable composition from pargasite, tshermakite, hornblende to tremolite. Garnets in SEYSM have high Mg and low Ca, but those in SWCIM have low Mg with variable Ca. Different heavy mineral characteristics between SEYSM and SWCIM suggests that sediments in each mudbelt have different provenances. Although this study implies that SEYSM sediment may mostly come from nearby Korean western rivers such as the Keum and Han rivers, this study does not suggest any idea of the source area of SWCIM sediment. Further study is needed to interpret the provenance and transportation mechanism of mudbelt sediments through the heavy mineral research for the river sediments flowing into the Yellow Sea and much more marine sediments.

• Korean journal of applied entomology
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• v.61 no.4
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• pp.605-614
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• 2022

Two major problems currently threaten human survival on Earth: climate change and the rapid aging of the population in developed countries. Climate change is a result of the increase in greenhouse gas (GHG) concentrations in the atmosphere due to the increase in the use of fossil fuels owing to economic and transportation development. The rapid increase in the age of the population is a result of the rise in life expectancy due to the development of biomedical science and technology and the improvement of personal hygiene in developed countries. To avoid irreversible global climate change, it is necessary to quickly transition from the current fossil fuel-based economy to a zero-carbon renewable energy-based economy that does not emit GHGs. To achieve this goal, the dairy and livestock industry, which generates the most GHGs in the agricultural sector, must transition to using low-carbon emission production methods while simultaneously increasing consumers' preference for low-carbon diets. Although 77% of currently available arable land globally is used to produce livestock feed, only 37% and 18% of the proteins and calories that humans consume come from dairy and livestock farming and industry. Therefore, using edible insects as a protein source represents a good alternative, as it generates less GHG and reduces water consumption and breeding space while ensuring a higher feed conversion rate than that of livestock. Additionally, utilizing the functionality of medicinal insects, such as silkworms, which have been proven to have certain health enhancement effects, it is possible to develop functional foods that can prevent or delay the onset of currently incurable degenerative diseases that occur more frequently in the elderly. Insects are among the first animals to have appeared on Earth, and regardless of whether humans survive, they will continue to adapt, evolve, and thrive. Therefore, the use of various edible and medicinal insects, including silkworms, in industry will provide an important foundation for human survival and prosperity on Earth in the near future by resolving the current two major problems.

• 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.