• Journal of the Korean earth science society
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• v.41 no.6
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• pp.557-574
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• 2020

The purpose of this study is to analyze the cause of high PM2.5 mass concentrations in Cheongju for the period of non-Asian dust days using the weather chart, the stream lines at 850 hPa, the backward trajectory, and the weather and air quality model. As a result of analyzing the time series of PM2.5 concentrations and weather charts for the episodic days in Cheongju, the weather patterns were shown in related to long-range transport of PM2.5 from China or surrounding areas. In fact, in the PM2.5 time series, 60-80 ㎍ m-3, which is more than 2-3 times higher than the concentration attributed to Cheongju activities, was observed as a background concentration related to long-range transport. The distribution of high PM2.5 concentration was typically dependent on the locations of the high and low pressures above the ground while the upper jet stream passed through the Korean Peninsula. Consequently, the high PM2.5 concentration in Cheongju is due to massive air pollutants in the form of smog originated from industrial, household and energy combustion sources of Beijing and other nearby regions of China. These air pollutants move along a fast zonal wind caused by the atmospheric pressure arrangement. high concentration of PM2.5 in Cheongju City is because the mass of air pollutants in the form of smog generated from industrial, household and energy combustion origins in Beijing or other nearby regions of China move along a fast wind speed zone according to the atmospheric pressure arrangement of long-distance transportation. Air pollutants including PM2.5 show an M-shaped pattern that passes through the topography of the Cheongju basin from north to south as a belt or band-shaped pollutant. The ground high pressure according to the above-ground high pressure expansion area and cut-off low or low pressure arrangement, or the bands in the form of river stems appear in a gradual incremental pattern that changes into a U-shape under the influence of the wind.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.6
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• pp.603-611
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• 2020

This study collected and analyzed transportation card data in order to better understand the operation and usage of city buses in Ulsan Metropolitan City in Korea. The analysis used quantitative and qualitative indicators according to the characteristics of the data, and also the categories were classified as general status, operational status, and satisfaction. The existing city bus survey method has limitations in terms of survey scale and in the survey process itself, which incurs various types of errors as well as requiring a lot of time and money to conduct. In particular, the bus means indicators calculated using transportation card data were analyzed to compensate for the shortcomings of the existing operational status survey methods that rely entirely on site surveys. The city bus index calculated by using the transportation card data involves quantitative operation status data related to the user, and this results in the advantage of being able to conduct a complete survey without any data loss in the data collection process. We took the transportation card data from the entire city bus network of Ulsan Metropolitan City on Wednesday April 3, 2019. The data included information about passenger numbers/types, bus types, bus stops, branches, bus operators, transfer information, and so on. From the data analysis, it was found that a total of 234,477 people used the city bus on the one day, of whom 88.6% were adults and 11.4% were students. In addition, the stop with the most passengers boarding and alighting was Industrial Tower (10,861 people), A total of 20,909 passengers got on and off during the peak evening period of 5 PM to 7 PM, and 13,903 passengers got on and off the No. 401 bus route. In addition, the top 26 routes in terms of the highest number of passengers occupied 50% of the total passengers, and the top five bus companies carried more than 70% of passengers, while 62.46% of the total routes carried less than 500 passengers per day. Overall, it can be said that this study has great significance in that it confirmed the possibility of replacing the existing survey method by analyzing city bus use by using transportation card data for Ulsan Metropolitan City. However, due to limitations in the collection of available data, analysis was performed only on one matched data, attempts to analyze time series data were not made, and the scope of analysis was limited because of not considering a methodology for efficiently analyzing large amounts of real-time data.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.42-51
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• 2020

This study was performed to support the 6th forest basic planning by Korea Forest Service as predicting the change of forestland area by the transition of land use type in the future over 35 years in South Korea. It is very important to analyze upcoming forestland area change for future forest planning because forestland plays a basic role to predict forest resources change for afforestation, production and management in the future. Therefore, the transitional interaction between land use types in future of South Korea was predicted in this study using econometrical models based on past trend data of land use type and related variables. The econometrical model based on maximum discounted profits theory for land use type determination was used to estimate total quantitative change by forestland, agricultural land and urban area at national scale using explanatory variables such as forestry value added, agricultural income and population during over 46 years. In result, it was analyzed that forestland area would decrease continuously at approximately 29,000 ha by 2027 while urban area increases in South Korea. However, it was predicted that the forestland area would be started to increase gradually at 170,000 ha by 2050 because urban area was reduced according to population decrement from 2032 in South Korea. We could find out that the increment of forestland would be attributed to social problems such as urban hollowing and localities extinction phenomenon by steep decrement of population from 2032. The decrement and increment of forestland by unbalanced population immigration to major cities and migration to localities might cause many social and economic problems against national sustainable development, so that future strategies and policies for forestland should be established considering such future change trends of land use type for balanced development and reasonable forestland use and conservation.

• Journal of Dental Rehabilitation and Applied Science
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• v.36 no.4
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• pp.254-261
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• 2020

Purpose: The purpose of this study was to assess the marginal and internal fit of interim crowns fabricated by two different manufacturing method (subtractive manufacturing technology and additive manufacturing technology). Materials and Methods: Forty study models were fabricated with plasters by making an impression of a master model of the maxillary right first molar for ceramic crown. On each study model, interim crowns (n = 40) were fabricated using three types of 3D printers (Meg-printer 2; Megagen, Zenith U; Dentis, and Zenith D; Dentis) and one type milling machine (imes-icore 450i; imes-icore GmbH). The internal of the interim crowns were filled with silicon and fitted to the study model. Internal scan data was obtained using an intraoral scanner. The fit of interim crowns were evaluated in the margin, absolute margin, axial, cusp, and occlusal area by using the superimposition of 3D scan data (Geomagic control X; 3D Systems). The Kruskal-wallis test, Mann-Whitney U test and Bonferroni correction method were used to compare the results among groups (α = 0.05). Results: There was no significant difference in the absolute marginal discrepancy of the temporary crown manufactured by three 3D printers and one milling machine (P = 0.812). There was a significant difference between the milling machine and the 3D printer in the axial and occlusal area (P < 0.001). The temporary crown with the milling machine showed smaller axial gap and higher occlusal gap than 3D printer. Conclusion: Since the marginal fit of the temporary crown produced by three types of 3D printers were all with in clinically acceptable range (< 120 ㎛), it can be sufficiently used for the fabrication of the temporary crown.

• Korean Journal of Mineralogy and Petrology
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• v.33 no.4
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• pp.349-359
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• 2020

The Ulsan Fault Zone (UFZ) of NNW trend is developed in the Gyeongsang Basin, the southeastern part of the Korean Peninsula, and the Quaternary faults have been found around the UFZ. The faults generally thrust the Bulguksa igneous rocks of Late Cretaceous-Early Tertiary upon the Quaternary deposits or are developed within the Quaternary deposits. They mainly show the reverse-slip sense of top-to-the west movement. The lines connecting the their outcrop sites show a zigzag-form which is similar to the orientation of their fault surfaces which show the various trends, like (W)NW, N-S, (E)NE, ENE trends. The E-W trending dextral strike(-slip) fault is found in the Quaternary deposits of the Singye-ri valley. It cuts the N-S trending reverse fault and are cut by the N-S trending thrust fault again. Two types of at least two times of Quaternary tectonic movements related to the formation of neotectonic fault zone in the Singye-ri valley are considered from such the geometric and kinematic characteristics of Quaternary faults. One is the reverse faulting of N-S trend by the E-W directed 1st compression and associated the strike-slip tear faulting of E-W trend, and then the thrust faulting of N-S trend by the E-W directed 2nd compression. The other is the reverse faulting of N-S trend, and then the dextral strike-slip faulting of E-W trend by the NW-SE directed compression, and then the thrust faulting of N-S trend. In this paper is suggested the development history of Singye-ri neotectonic fault zone on the basis of the various orientations of Quaternary fault surfaces around the UFZ, and the zigzag-form connecting line of their outcrop sites, and the compressive arc-shaped lineaments which convex to the west reported recently in the Yangsan Fault Zone.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.312-326
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• 2020

Generation of weather forecasts at 100 m resolution through a statistical downscaling process was implemented by Korea Meteorological Administration Post- Processing (KMAPP) system. The KMAPP data started to be used in various industries such as hydrologic, agricultural, and renewable energy, sports, etc. Cheorwon area and Jeonbuk area have horizontal planes in a relatively wide range in Korea, where there are many complex mountainous areas. Cheorwon, which has a large number of in-situ and remotely sensed phenological data over large-scale rice paddy cultivation areas, is considered as an appropriate area for verifying KMAPP prediction performance in agricultural areas. In this study, the performance of predicting KMAPP temperature changes according to ecological changes in agricultural areas in Cheorwon was compared and verified using KMA and National Center for AgroMeteorology (NCAM) observations. Also, during the heat wave in Jeonbuk Province, solar radiation forecast was verified using Automated Synoptic Observing System (ASOS) data to review the usefulness of KMAPP forecast data as input data for application models such as livestock heat stress models. Although there is a limit to the need for more cases to be collected and selected, the improvement in post-harvest temperature forecasting performance in agricultural areas over ordinary residential areas has led to indirect guesses of the biophysical and phenological effects on forecasting accuracy. In the case of solar radiation prediction, it is expected that KMAPP data will be used in the application model as detailed regional forecast data, as it tends to be consistent with observed values, although errors are inevitable due to human activity in agricultural land and data unit conversion.

• Journal of Ginseng Culture
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• v.3
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• pp.54-73
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• 2021

The Natural Products Research Institute (NPRI, 生藥硏究所), an institution affiliated with Keijo Imperial University (京城帝國大學), was the predecessor of the NPRI at Seoul National University and a comprehensive research institute that focused on ginseng research during the Japanese colonial era. It was established under the leadership of Noriyuki Sugihara (杉原德行), a professor of the second lecture in pharmacology at the College of Medicine in Keijo Imperial University. Prof. Sugihara concentrated on studying Korean ginseng and herbal medicine beginning in 1926 when the second lecture of pharmacology was established. In addition to Prof. Sugihara, who majored in medicine and pharmacology, Kaku Tenmin (加來天民), an assistant professor who majored in pharmacy; Tsutomu Ishidoya (石戶谷勉), a lecturer who majored in agriculture and forestry; and about 36 researchers actively worked in the laboratory before the establishment of the NPRI in 1939. Among these personnel, approximately 14 Korean researchers had basic medical knowledge, derived mostly from specialized schools, such as medical, dental, and pharmaceutical institutions. As part of the initiative to explore the medicinal herbs of Joseon, the number of Korean researchers increased beginning in 1930. This increase started with Min Byung-Ki (閔丙祺) and Kim Ha-sik (金夏植). The second lecture of pharmacology presented various research results in areas covering medicinal plants in Joseon as well as pharmacological actions and component analyses of herbal medicines. It also conducted joint research with variousinstitutions. Meanwhile, in Gaesong (開城), the largest ginseng-producing area in Korea, the plan for the Pharmaceutical Industry Complex was established in 1935. This was a large-scale project aimed at generating profits through research on and the mass production of drugs and the reformation of the ginseng industry under collaboration among the Gaesong Ministry, Kwandong (關東) military forces, Keijo Imperial University, and private organizations. In 1936 and 1938, the Gyeonggi Provincial Medicinal Plant Research Institute (京畿道立 藥用植物硏究所) and the Herb Garden of Keijo Imperial University (京城帝國大學 藥草園) and Pharmaceutical Factory were established, respectively. These institutions merged to become Keijo Imperial University's NPRI, which wasthen overseen by Prof. Sugihara as director. Aside from conducting pharmacological research on ginseng, the NPRI devoted efforts to the development and sale of ginseng-based drugs, such as Sunryosam (鮮麗蔘), and the cultivation of ginseng. In 1941, the Jeju Urban Test Center (濟州島試驗場) was established, and an insecticide called Pancy (パンシ) was produced using Jeju-do medicinal herbs. However, even before research results were published in earnest, Japanese researchers, including Prof. Sugihara, hurriedly returned to Japan in 1945 because of the surrender of Japanese forces and the liberation of Korea. The NPRI was handed over to Seoul National University and led by Prof. Oh Jin-Sup (吳鎭燮), a former medical student at Keijo Imperial University. Scholars such as Woo Lin-Keun (禹麟根) and Seok Joo-Myung (石宙明) worked diligently to deal with the Korean pharmaceutical industry.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.55-67
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• 2021

Hornbeams (Carpinus spp.), which are widely distributed in South Korea, are recognized as one of the most abundant species at climax stage in the temperate forests. Although the distribution and vegetation structure of the C. laxiflora community have been reported, little ecological information of C. tschonoskii is available. Little effort was made to examine the distribution shift of these species under the future climate conditions. This study was conducted to predict potential shifts in the distribution of C. laxiflora and C. tschonoskii in 2050s and 2090s under the two sets of climate change scenarios, RCP4.5 and RCP8.5. The MaxEnt model was used to predict the spatial distribution of two species using the occurrence data derived from the 6th National Forest Inventory data as well as climate and topography data. It was found that the main factors for the distribution of C. laxiflora were elevation, temperature seasonality, and mean annual precipitation. The distribution of C. tschonoskii, was influenced by temperature seasonality, mean annual precipitation, and mean diurnal rang. It was projected that the total habitat area of the C. laxiflora could increase by 1.05% and 1.11% under RCP 4.5 and RCP 8.5 scenarios, respectively. It was also predicted that the distributional area of C. tschonoskii could expand under the future climate conditions. These results highlighted that the climate change would have considerable impact on the spatial distribution of C. laxiflora and C. tschonoskii. These also suggested that ecological information derived from climate change impact assessment study can be used to develop proper forest management practices in response to climate change.