• Journal of the Korean Applied Science and Technology
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• v.34 no.4
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• pp.962-973
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• 2017

Concerns about an air pollution are gradually increasing at home and abroad. The automotive and fuel researchers are trying to reduce emissions and greenhouse gases of vehicles through a research on new engine designs and innovative after-treatment systems using clean fuels (eco-alternative fuel) and fuel quality improvements. In this paper, we stduy the emission characteristics of greenhouse gases on seven vehicles using gasoline, diesel, and LPG by legal test mode in domestic and abroad.(Urban mode, Highway mode, rapidly acceleration and deceleration, using air conditioner, low temperature condition) Regardless of fuels, most of the greenhouse gases tend to show the worst results in cold FTP-75 mode. In the case of A vehicles (2.0 MPI) and B vehicles (2.4 GDI) using a gasoline fuel, the factors that increase greenhouse gases are in order of a rapidly acceleration and deceleration, using air conditioner, low temperature condition. But G vehicles(LPLi) have different emission characteristics from another vehicles. In the case of A vehicles (2.0 w/o DPF) and B vehicles (2.2 with DPF) using a diesel fuel, the factors that increase greenhouse gases are in order of a rapidly acceleration and deceleration, using air conditioner, low temperature condition. However, the factor of F vehicles are in order of low temperature condition, using air conditioner, rapidly acceleration and deceleration. In conclusion, it will be an effective method to apply different technologies of emission reduction for each fuel.

• Journal of Life Science
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• v.29 no.6
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• pp.671-678
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• 2019

Antibacterial and antioxidant activities of plant sources have attracted a wide range of interest across the world over the last decade. This is due to the growing concern for safe and alternative sources of antibacterial and antioxidant agents. In this study, we focused on the antibacterial and antioxidant activities and the chemical composition of a methanol extract from Viburnum sargentii seeds. The chemical composition was determined by gas chromatography-mass spectroscopy (GC-MS), and the antibacterial activity was screened by a disc diffusion assay. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were determined using the microbroth dilution and spread plate method, respectively. The V. sargentii extract showed growth inhibition activity on all tested Gram-positive (Listeria monocytogenes, Staphylococcus aureus, and Staphylococcus saprophyticus) and Gram-negative (Escherichia coli, Pseudomonas putida, and Proteus vulgaris) pathogenic bacteria. The MIC and MBC ranged from 0.156~1.25 mg/ml for Gram-positive and 0.625~5.0 mg/ml for Gram-negative tested bacteria. The GC-MS results revealed the presence of several phytochemicals such as ${\beta}-sitosterol$ and vitamin E, which are known for their pharmacological applications. The antioxidant activities of V. sargentii extract were investigated by three different methods: the 2,2-diphenyl-1-picrylhydrazyl free radical scavenging assay, the reducing power assay, and the total antioxidant capacity assay. The results showed a concentration-dependent antioxidant potential for all three used methods. In sum, our findings suggest that the methanol extract of V. sargentii seeds has the potential to inhibit the growth of pathogenic bacteria and provide antioxidant compounds, making it therefore worthy of further investigation.

• 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.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.