• Land and Housing Review
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• v.12 no.3
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• pp.11-26
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• 2021

With the increasing demand for office space, there have been questions on how office rent distribution produces a change in the urban spatial structure in Seoul. The purpose of this paper is to investigate a relative price gradient and to present a time-series model that can quantitatively explain the dynamic changes in the urban spatial structure. The analysis was dealt with office rent above 3,306 m² for the past 10 years from 1Q 2010 to 4Q 2019 within Seoul. A modified repeat sales model was employed. The main findings are briefly summarized as follows. First, according to the estimates of the office price gradient in the three major urban centers of Seoul, the CBD remained at a certain level with little change, while those in the GBD and the YBD continued to increase. This result reveals that the urban form of Seoul has shifted from monocentric to polycentric. This shows that the spatial distribution of companies has gradually accelerated decentralized concentration implying that the business networks have become significant. Second, contrary to small and medium-sized office buildings that have undertaken no change in the gradient, large office buildings have seen an increase in the gradient. The relative price gradients in small and medium-sized buildings were inversely proportional among the CBD, the GBD, and the YBD, implying their heterogeneous submarkets by office rent movements. Presumably, those differences in the submarkets were attributed to investment attraction, industrial competition, and the credit and preference of tenants. The findings are consistent with the hierarchical system identified in the Seoul 2030 Plan as well as the literature about Seoul's urban form. This research claims that the proposed method, based on the modified repeat sales model, is useful in understanding temporal dynamic changes. Moreover, the findings can provide implications for urban growth strategies under rapidly changing market conditions.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.531-542
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• 2021

Open-pit mines require constant monitoring as they can cause surface changes and environmental disturbances. In open-pit mines, there is little vegetation at the mining site and can be monitored using InSAR (Interferometric Synthetic Aperture Radar) coherence imageries. In this study, activities occurring in mine were analyzed by applying the recently developed InSAR coherence-based NDAI (Normalized Difference Activity Index). The March 5 Youth Mine is a North Korean mine whose development has been expanded since 2008. NDAI analysis was performed with InSAR coherence imageries obtained using Sentinel-1 SAR images taken at 12-day intervals in the March 5 Youth Mine. First, the area where the elevation decreased by about 75.24 m and increased by about 9.85 m over the 14 years from 2000 was defined as the mining site and the tailings piles. Then, the NDAI images were used for time series analysis at various time intervals. Over the entire period (2017-2019), average mining activity was relatively active at the center of the mining area. In order to find out more detailed changes in the surface activity of the mine, the time interval was reduced and the activity was observed over a 1-year period. In 2017, we analyzed changes in mining operations before and after artificial earthquakes based on seismic data and NDAI images. After the large-scale blasting that occurred on 30 April 2017, activity was detected west of the mining area. It is estimated that the size of the mining area was enlarged by two blasts on 30 September 2017. The time-averaged NDAI images used to perform detailed time-series analysis were generated over a period of 1 year and 4 months, and then composited into RGB images. Annual analysis of activity confirmed an active region in the northeast of the mining area in 2018 and found the characteristic activity of the expansion of tailings piles in 2019. Time series analysis using NDAI was able to detect random surface changes in open-pit mines that are difficult to identify with optical images. Especially in areas where in situ data is not available, remote sensing can effectively perform mining activity analysis.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of Space Technology and Applications
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• v.1 no.3
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• pp.283-301
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• 2021

In order to satisfy the intellectual curiosity of mankind to explore the unknown, National Aeronautics and Space Administration (NASA) in the United States and European Space Agency (ESA) in Europe are embarking on various R&D under the motto of the grand dream of pioneering space into a safe and sustainable environment. In the 2020s and 30s, it is expected that advanced giant observation equipment will be in operation, such as the development of a 10-meter-class telescope in space. In Korea, following the development of the 0.15 m Near-Infrared Imaging Spectrometer (NISS), Korea Astronomy and Space Science Institute (KASI) is also participating a 0.2 m Spectro-Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer (SPHEREx) as an international cooperation partner in small exploration telescope. However, domestic experience in the development and operation of the space telescopes is still insufficient, and there is no plan with long-term prospects for constructing telescopes. In order to answer questions about the unknown world that mankind has not experienced using our own equipment, planning and preparation for the construction of a space telescope through close cooperation among industry-university-institute-government is urgently needed. In this paper, the necessity, background, development goals, and expected effects of the development of the Korean Space Telescope are summarized conceptually, and a working group (WG) is also proposed. In the WG activities, Korea shall take the lead in establishing the Korean-style space telescope development plan, and will start a valuable step to establish the national direction in the field of space astronomy and related technologies. We hope that the WG will be another milestone in Korea's space development.

• Fashion & Textile Research Journal
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• v.24 no.6
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• pp.667-685
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• 2022

This paper aims to investigate 4D printing materials for soft robots. 4D printing is a targeted evolution of the 3D printed structure in shape, property, and functionality. It is capable of self-assembly, multi-functionality, and self-repair. In addition, it is time-dependent, printer-independent, and predictable. The shape-shifting behaviors considered in 4D printing include folding, bending, twisting, linear or nonlinear expansion/contraction, surface curling, and generating surface topographical features. The shapes can shift from 1D to 1D, 1D to 2D, 2D to 2D, 1D to 3D, 2D to 3D, and 3D to 3D. In the 4D printing auxetic structure, the kinetiX is a cellular-based material design composed of rigid plates and elastic hinges. In pneumatic auxetics based on the kirigami structure, an inverse optimization method for designing and fabricating morphs three-dimensional shapes out of patterns laid out flat. When 4D printing material is molded into a deformable 3D structure, it can be applied to the exoskeleton material of soft robots such as upper and lower limbs, fingers, hands, toes, and feet. Research on 4D printing materials for soft robots is essential in developing smart clothing for healthcare in the textile and fashion industry.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.491-501
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• 2022

Bentonite has been proposed as a buffer and backfill material for high-level radioactive waste repository. Under such repository environment conditions, bentonite is subjected to combined thermal, hydrological, mechanical, and chemical processes. This study evaluates the feasibility of applying X-ray CT technology on the characterization of bentonite under hydration conditions using a newly developed testing cell. The cylindrical cell is made of platic material, with a removable cap to place the sample, enabling to apply vertical pressure on the sample and to measure swelling pressure. The hydration test was carried out with a sample made of Gyeonju bentonite, with a dry density of 1.4 g/cm³, and a water content of 20%. The sample had a diameter of 27.5 mm and a height of 34 mm. During the test, water was injected at a constant pressure of 0.207 MPa, and lasted for 7 days. After one day of hydration, bentonite swelled and filled out the space inside the cell. Moreover, CT histograms showed how the hydration process induced an initial increase and later progressive decrease on the density of the sample. Detailed profiles of the mean CT value, CT standard deviation, and CT gradient provided more details on the hydration process of the sample and showed how the bottom and top regions exhibited a decrease on density while the middle region showed an increase, especially during the first two days of hydration. Later, the differences in CT values with respect to the initial state decreased, and were small at the end of testing. The formation and later reduction of cracks was also characterized through CT scanning.

• The Journal of Engineering Geology
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• v.33 no.2
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• pp.275-291
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• 2023

Global warming has made the polar regions more accessible, leading to increased demand for the construction of new resource-development plants in oil-rich permafrost regions. The selection of locations of resource-development plants in permafrost regions should consider the surface displacement resulting from thawing and freezing of the active layer of permafrost. However, few studies have considered surface displacement in the selection of optimal locations of resource-development plants in permafrost region. In this study, Analytic Hierarchy Process (AHP) analysis using a range of geospatial information variables was performed to select optimal locations for the construction of oil-sands development plants in the permafrost region of southern Athabasca, Alberta, Canada, including consideration of surface displacement. The surface displacement velocity was estimated by applying the Small BAseline Subset Interferometric Synthetic Aperture Radar technique to time-series Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar images acquired from February 2007 to March 2011. ERA5 reanalysis data were used to generate geospatial data for air temperature, surface temperature, and soil temperature averaged for the period 2000~2010. Geospatial data for roads and railways provided by Statistics Canada and land cover maps distributed by the North American Commission for Environmental Cooperation were also used in the AHP analysis. The suitability of sites analyzed using land cover, surface displacement, and road accessibility as the three most important geospatial factors was validated using the locations of oil-sand plants built since 2010. The sensitivity of surface displacement to the determination of location suitability was found to be very high. We confirm that surface displacement should be considered in the selection of optimal locations for the construction of new resource-development plants in permafrost regions.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.1
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• pp.55-73
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• 2024

Unicorn companies are attracting attention around the world as they are recognized for their high corporate value in a short period of time as an innovative business models. Their growth process presents good lessons for the startup ecosystem and have a positive impact on national economic development and job creation. However, previous studies related to unicorn companies are focused on 'event studies' and 'case studies' such as characteristics of founders, environmental factors, business models and success/failure cases of companies already recognized as unicorns rather than a multifaceted approach. The occurrence of unicorn companies and Macroscopic analysis of related factors is lacking. Against this background, this study are considering the characteristics of unicorns examined through previous research and the current status unicorns with a high proportion of technology companies, the purpose was to analyze the impact of the country's technological competitiveness, such as 'technology human resource index', 'R&D index', and 'technology infrastructure index', on the increase in unicorn companies. For statistical analysis, data published by various international organizations, the Bank of Korea, and Statistics Korea from 2017 to 2020 and unicorn company data compiled by CB Insights were used as panel data for 44 countries to be tested by multiple regression analysis. As a result of the study, it was confirmed that the number of science majors had a positive (+) effect on the increase of unicorn companies in the case of technology human resource index, and in the case of R&D index, the total amount of R&D investment had a positive (+) effect on the increase of unicorn companies, while the number of Triad Patents Families and the number of scientific and technological papers published had a negative (-) effect on the increase of unicorn companies. Finally, in the case of technology infrastructure index, it was confirmed that the number of the world's 500th-ranked universities had a positive (+) effect on the increase of unicorn companies. This study is the first to reveal the causal relationship between national technological competitiveness and unicorn company growth based on country-specific and time-series empirical data, which were insufficiently covered in previous studies. and compared to the UN's ranking of the global industrial competitiveness index and the OECD's total R&D investment by country, Korea is considered to have technological and growth potential, while the number of unicorn companies driving growth as leaders of the innovative economy is relatively small, so the research results can be used when establishing policies to discover and foster unicorn companies in the future.

• Economic and Environmental Geology
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• v.8 no.3
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• pp.117-124
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• 1975

Wet chemical analysis (for $MnO_2$, MnO, and $H_2O$(+)) and electron microprobe analysis (for $Fe_2O_3$ and PbO) give $MnO_2$ 74.91, MnO 11.33, $Fe_2O_3$ (total Fe) 4.19, PbO 0.03, $H_2O$ (+) 9.46, sum 99.92%. 'Available oxygen determined by oxalate titration method is allotted to $MnO_2$ from total Mn, and the remaining Mn is calculated as MnO. Traces of Ba, Ca, Mg, K, Cu, Zn, and Al were found. Li and Na were not found. The existence of (OH) is verified from the infrared absorption spectra. The analysis corresponds to the formula $Mn^{4+}{_{4.85}}(Mn^{2+}{_{0.90}}Fe^{3+}{_{0.30}})_{1.20}O_{8.09}(OH)_{5.91}$, on the basis of O=14, 'or ideally $Mn^{4+}{_{5-x}}(Mn^{2+},Fe^{3+})_{1+x}O_{8}(OH)_{6}$ ($x{\approx}0.2$). X-ray single crystal study could not be made because of the distortion of single crystals. But the x-ray powder pattern is satisfactorily indexed by an orthorhombic cell with a 9.324, b 14.05, c $7.956{\AA}$., Z=4. The indexed powder diffraction lines are 9.34(s) (100), 7.09(s) (020), 4.62(m) (200, 121), 4.17(m) (130), 3.547(s) (112), 3.212(vw) (041), 3.101(s) (300), 2.597(w) (013), 2.469(m) (331), 2.214(vw)(420), 2.098(vw) (260), 2.014 (vw) (402), 1.863(w) (500), 1.664(w) (314), 1.554(vw) (600), 1.525(m) (601), 1.405(m) (0.10.0). DTA curve shows the endothermic peaks at $250-370^{\circ}C$ and $955^{\circ}C$. The former is due to the dehydration: and oxidation forming$(Mn,\;Fe)_2O_3$(cubic, a $9.417{\AA}$), and the latter is interpreted as the formation of a hausmannite-type oxide (tetragonal, a 5.76, c $9.51{\AA}$) from $(Mn,\;Fe)_2O_3$. Infrared absorption spectral curve shows Mn-O stretching vibrations at $515cm^{-1}$ and $545cm^{-1}$, O-H bending vibration at $1025cm^{-1}$ and O-H stretching vibration at $3225cm^{-1}$. Opaque. Reflectance 13-15%. Bireflectance distinct in air and strong in oil. Reflection pleochroism changes from whitish to light grey. Between crossed nicols, color changes from yellowish brown with bluish tint to grey in air and yellowish brown to grey through bluish brown in oil. No internal reflections. Etching reactions: HCl(conc.) and $H_2SO_4+H_2O_2$-grey tarnish; $SnCl_2$(sat.)-dark color; $HNO_3$(conc.)-grey color; $H_2O_2$-tarnish with effervescence. It is black in color. Luster dull. Cleavage one direction perfect. Streak brownish black to dark brown. H. (Mohs) 2-3, very fragile. Specific gravity 3.59(obs.), 3.57(calc.). It occurs as radiating groups of flakes, flower-like aggregates, colloform bands, dendritic or arborescent masses composed of fine grains in the cementation zone of the supergene manganese oxide deposits of the Janggun mine, Bonghwa-gun, southeastern Korea. Associated minerals are calcite, nsutite, todorokite, and some undetermined manganese dioxide minerals. The name is for the mine, the first locality. The mineral and name were approved before publication by the Commission on New Minerals and Mineral Names, I.M.A.