• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.3
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• pp.293-299
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• 2022

Recently, various stakeholder are interested in microplastic to cause pollution of the marine's ecosystem and effort to conduct study of product's life cycle to reduce pollution of marine's ecosystem. The micorplastic refer to materials of the nano- to micro- sized units and it can be classified into primary and secondary. The primary microplastic mean the manufactured for use in the specific field such as the microbead of the cosmetic or cleanser. also, secondary mean the unintentionally generated during use of the product such as the textile crumb by the doing the laundry. Tire and Road Wear Particles(TRWPs) are also defined as secondary microplastic. Typically, TRWPs are created by friction between the tread compound's rubber of the tire and the surface of the road du ring the driving cars. Most of the generated TRWPs exist on the roadside and some of them were carried to marine by the rainwater. In this study, we perform the quantitative analysis of the TRWPs existed in fine dust at the roadside. So, we collected the dust from the roadside in Chungcheongnam-do's C site with a movement of 1,300 cars per the hour. The collected samples were separated according to size and density. And shape analysis was performed using the Scanning Electron Microscope(SEM). We were possible to discover a lot of TRWPs at the fine dust of the 100 ± 20 ㎛. And we analysis it u sing the Thermo Gravimetric Analysis(TGA) and Gas Chromatography/Mass Spectrometer(GC/MS) for the quantitative components from the tire. As a result, it was confirmed that TRWPs generated from the roadside fine dust were included the 0.21 %, and the tire and road components in the generated TRWPs consisted of the 3:7 ratio.

• Journal of Aerospace System Engineering
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• v.17 no.6
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• pp.1-8
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• 2023

Research, such as developing alternative energy in the transportation field, including aviation, is being actively conducted to solve the issue of current climate change. Interest in ammonia fuel as a carbon free energy (CFE) source is increasing due to the ease of liquefaction and transportation and similarity in energy density to that of methanol. However, explosiveness and toxicity of ammonia make it difficult to handle. Therefore, in this study, stable ammonia production was attempted using relatively easy-to-handle urea water solution (UWS). High temperature steam was used to promote the hydrolysis of ammonia. In order to determine the causes for ammonia production below the theoretical equivalent ratio, it was suggested that there were not enough collisions to promote the hydrolysis based on the kinetic theory of gases. The hydrolysis of unreacted isocyanic acid (HNCO) was tested according to the change in water supply. As a result, an increased amount of ammonia produced was confirmed. The increased amount of ammonia produced in a certain section was dependent on the steam temperature and the flow rate of water supplied.

• Journal of Environmental Impact Assessment
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• v.26 no.2
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• pp.127-139
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• 2017

Measurements of net ecosystem exchange (NEE) of $CO_2$ based on the eddy covariance technique provide reasonable carbon balance estimates in response to local environmental conditions. In South Korea, the forest ecosystems cover approximately 64% of the total area, thereby strongly affecting regional carbon balances. Cultivated croplands that cover about 17% of the total area should also be considered when calculating the carbon balance of the country. In this study, our objectives were (a) to quantify the range and seasonal variation of NEE at forest ecosystems, including deciduous, coniferous, and mixed forests, and agricultural ecosystems, including rice paddies and a potato field, in South Korea and (b) to compare NEE at ten Fluxnet sites that have the same or similar ecosystems as found in South Korea. The results showed that the forest and agricultural ecosystems were carbon sinks. In Korea, NEE at the forest ecosystems varied between -31 and $-362gC/m^2/yr$, and NEE at the croplands ranged from -210 to $-248gC/m^2/growing$ season. At the deciduous forest, NEE reached low values in late spring, early summer, and early autumn, while at the coniferous forest, it reached low values in spring, early summer, and mid autumn. The young mixed forest was a much stronger carbon sink than the old-growth deciduous and coniferous forests. During each crop growing season, beet had the lowest NEE value within six crops, followed by wither wheat, maize, rice, potato, and soybean. These results will be useful for designing and applying management strategies for the reduction of $CO_2$ emissions.

• Geophysics and Geophysical Exploration
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• v.19 no.1
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• pp.29-36
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• 2016

Recently, the studies about rock physics model (RPM) in shale reservoir are widely performed. In shale reservoir, the degree of the maturity can be estimated by kerogen and GOR (Gas-Oil Ratio). The researches on the rock physics model of shale reservoir with the amount of kerogen have been actively carried out but not with GOR. Thus, in this study, we analyzed the changes in seismic velocity and density, and AVO (Amplitude Variation with Offset) response depending on changes in GOR and the amount of kerogen. Since the shale consists of plate-like particles, it has vertical transverse isotropy (VTI). Therefore we estimated the seismic velocity and density by using Backus averaging method and analyzed AVO responses based on these estimated properties. The results of analysis showed that the changes in the velocity with the GOR variation are small but the velocity changes with the variation in kerogen amount are relatively larger. In case, GOR 180 (Litre/Litre) which is boundary between heavy oil and light oil, when volume fraction of kerogen increased from 5% to 35%, the P-wave velocity normal to the layering increased 51%. That is, it helps estimating maturity of kerogen through the velocity. Meanwhile, when rates of oil-gas mixture are large, the effect of GOR variation on the velocity change became larger. In case volume fraction of kerogen is 5%, the P-wave velocity normal to the layering was estimated $1.46km/s^2$ in heavy oil (GOR 40) but $1.36km/s^2$ in light oil (GOR 300). The AVO responses analysis showed class 4 regardless of the GOR and amount of kerogen because variation of poisson's ratio is small. Therefore, shale reservoir has possibility to have class 4.

• Journal of Animal Environmental Science
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• v.17 no.sup
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• pp.7-20
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• 2011

This study was conducted to analyze the mixing ratio of raw feed materials for the methane mitigation and also to identify the minimum rearing density for improving the productivity of beef calves as eco-friendly fodder. Raw materials used in this study for the formulation of feed for methane reduction were crushed corn and alfalfa along with other 21 species. In addition, to investigate the appropriate rearing density, 12 Hanwoo calves with average weight of 150 kg was selected and experiment was conducted for four months. Methane gas emission (Bo) is about 3-4 times less in TMR 4 compared to TMR 1, 2 and 3. Feed price calculated for TMR 4 ration was also affordable. In addition, all TMRs showed a normal ruminal pH. Disappearance rate was observed to be lower in TMR 4 as compared to TMR 1, 2 and 3, but methane production decreased by 24 to 37%. The result showed improved total body weight, average daily gain and feed conversion ratio in rearing low-density ($18m^2/head$), and general treatment ($9m^2/head$) compared to overcrowding treatment ($6m^2/head$). In addition, blood components (total protein, glucose, AST, ALT and GGT factors) involved in health and disease treatments and health-related nutrition metabolism are lower in the low-density and general treatment compared to the high density treatment. Postural development (development of body size) i.e., weight, height and width significantly increased in the low and general density treatment compared to high density treatment. Especially excellent improvement was observed in low-density treatment than the general treatment. Moisture content, colonic bacteria and coccidium are higher in low and high density treatments than in the general treatment. The adequacy for beef rearing density is considered to be more desirable in an area more than $6m^2/head$. In conclusion, present study suggests that possibility of methane reduction through adjusting mixed feed ration. Also, rearing density is also an important factor in the growth and development of beef calves.

• Horticultural Science & Technology
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• v.31 no.6
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• pp.720-725
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• 2013

When highly shrinkable materials such as coir dust are major component of root media, the degrees of compaction during container filling of root media severely influences the physical properties of root media. It results in the changes in total porosity (TP), container capacity (CC) and air-filled porosity (AFP). This research was conducted to secure the fundamental information in changes of soil physical properties as influenced by the compaction of root media during container filling. To achieve this, three root media were formulated by blending coir dust (CD) with expanded rice hull (CD + ERH, 8:2, v/v), carbonized rice hull (CD + CRH, 6:4) and ground and raw pine bark (CD + GRPB, 8:2). Based on the optimum bulk density, the amount of root media filled into 6.0, 7.5, 8.5, 10.5 and 12.5 cm were adjusted to 90, 100, 110, 120 and 130% based on the weight of root media. Then the changes in TP, CC, and AFP were measured. Elevation of the packing amount of root media in all sizes of pot resulted in the decrease of TP. But the decrease was more severe in CD + ERH and CD + CRH than those in CD + GRPB. The CC also decreased gradually as the packing amounts were elevated in three root media, but the decreases were severe as the container sizes became larger. The AFP decreased drastically by the elevation of the packing amount of root media in all sizes of pot. The AFP was the highest in CD + CRH medium when pot sizes were smaller than 7 cm, but that was the highest in CD + ERH when the pot sizes were larger than 8.5 cm among the 3 root media tested. In this research, the elevation of packing amount of three root media influenced more severely the AFP rather than CC. This result indicates that the packing amount should be controlled to maintain appropriate level of AFP because AFP rather than CC influence severely crop growth. The results obtained through this study can be used to predict the changes in physical properties of root media as influenced by packing amount in various sizes of pots.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.1
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• pp.15-24
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• 2015

$CH_4$ is a trace gas and one of the key greenhouse gases, which requires continuous and systematic monitoring. The application of eddy covariance technique for $CH_4$ flux measurement requires a fast-response, laser-based spectroscopy. The eddy covariance measurements have been used to monitor $CO_2$ fluxes and their data processing procedures have been standardized and well documented. However, such processes for $CH_4$ fluxes are still lacking. In this note, we report the first measurement of $CH_4$ flux in a rice paddy by employing the eddy covariance technique with a recently commercialized wavelength modulation spectroscopy. $CH_4$ fluxes were measured for five consecutive days before and after the rice transplanting at the Gimje flux monitoring site in 2012. The commercially available $EddyPro^{TM}$ program was used to process these data, following the KoFlux protocol for data-processing. In this process, we quantified and documented the effects of three key corrections: (1) frequency response correction, (2) air density correction, and (3) spectroscopic correction. The effects of these corrections were different between daytime and nighttime, and their magnitudes were greater with larger $CH_4$ fluxes. Overall, the magnitude of $CH_4$ flux increased on average by 20-25% after the corrections. The National Center for AgroMeteorology (www.ncam.kr) will soon release an updated KoFlux program to public users, which includes the spectroscopic correction and the gap-filling of $CH_4$ flux.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.278-283
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• 2019

3 kinds of fine powder, Fe-2%Ni alloy powder(N Ltd.) and Fe+2%Ni mixed powder(B Ltd. and S Ltd.), were fabricated into sintered compacts of bending strength specimens and ring type specimens by metal injection molding, debinding and controlling sintering conditions (reduction and sintering atmospheres, sintering temperature, sintering time and cooling rates). Density and magnetic properties of the sintered compacts were evaluated with the following conclusions. (1) When each compact was hold at 1123K for 3.6ks in H2 and sintered at 1623K for 14.4ks in Ar, the density of N, B and S Ltd.'s sintered compacts were measured as 96, 99 and 99%, and oxygen/carbon contents were measured as 0.0041%O/0.0006%C, 0.0027%O/0.0022%C, and 0.160%O/0.0026%C, respectively. (2) Magnetic characteristics of B Ltd. compact in Ar with the best results showed $B_{25}=14.3KG$, $B_r=7.75KG$, and $H_c=2.1Oe$, but not enough as those made by melting process. (3) Magnetic properties of B Ltd. compact which were sintered at 1673K for 14.4ks in Ar gas, and cooled at $0.83Ks^{-1}$ to 1123K and then cooled at $0.083Ks^{-1}$ down to room temperature were measured as $B_{25}=14.8KG$, $B_r=8.3KG$, and $H_c=1.3Oe$, almost similar to those made by melting process. Objected soft magnetic materials properties were obtained through sintering process by controlling sintering conditions (reduction condition, sintering atmosphere, sintering temperature and sintering time) and cooling rates.

• 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.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.