• Korean Journal of Heritage: History & Science
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• v.56 no.4
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• pp.132-158
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• 2023

The five-story and seven-story stone pagodas at Cheongnyangsaji temple site in Gongju are located under the Sambulbong peak of Gyeryongsan mountain, and are known to have been built of the middle in Goryeo dynasty. As the two pagodas in which two types of Baekje stone pagoda coexist in one era, their historical and academic value are recognized. The seven-story pagoda was overturned by robbery in 1944, and as a result, the five-story pagoda was tilted. Although the two pagodas were restored in 1961, structural instability was continuously raised. In this study, measurement data accumulated from May 2021 to March 2022, and seasonal characteristics were reviewed, and the micro behavior of pagodas were analyzed according to temperature and precipitation during the same period. As a result, the micro thermoelastic behavior was repeated according to the daily temperature change in all sensors, and both the slope and the displacement showed microscale behavior. In the inclinometer, moisture containing the surface and inside of the stones repeated expansion and contraction due to temperature change, showing the micro movements. In particular, the upper part of the five-story pagoda moved up to 3.89° to the northwest, and the seven-story pagoda tilted up to 0.078° to the northeast. The maximum displacements were recorded as 0.127 and 0.149 mm in the five-story and the seven-story pagoda, respectively. These values tended to return to the original position at the end of the measurement, but did not recover completely, indicating a state requiring precise monitoring. The result obtained through the study can be used as basic data for the stable conservation of the two stone pagodas. Based on the behavioral characteristics considering various environmental factors should be analyzed, and the preventive conservation through the maintenance of measurement system built this time should be continued.

• The Journal of the Petrological Society of Korea
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• v.6 no.2
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• pp.77-85
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• 1997

The Early Proterozoic reworked rock association occurs within the Preacmbrian high grade metamorphic rocks in the area of Daqingshan, Inner Molgolia. In this association, the various large scale ductile deformation belts, form a nappe structure where the foliation steeply dips to north and the lineation ($340^{circ}-30^{\circ}$) plunges at $45^{\circ}55^{\circ}$. This result indicates the subduction/extension with northern part thrusting over the southern part at high angle. The southern subducted microlithon has the characteristics of prograde metamorphism. The northern thrusted microlithon shows the evidence of retrograde metamorphism with decreasing pressure and increasing temperature. The main rock types of Early Proterozoic Moyites are biotite adamellite and syenogranites occurring in the form of small batholiths or stocks and alkali-feldspar granites in veins. The biotite adamellites are progressively contacted with the Archean and Early Proterozoic rocks and contain a great deal of enclaves of metamorphosed rocks, suggesting an anatexis origin. The geochemical characteristics of moyites show the typical features of anatexis granite. At middle to late Early Proterozoic time, the continent-continent collision formed the large scale thrusting and imbrication of Archean basement rocks. According to the mineral assemblage and thermobarometer of Paria et al. (1988) give the following P-T condition : up-faulted block; $700-710^{\circ}C$, 0.72-0.78 Gpa (early stage) and $600^{\circ}C$, 0.44 Gpa (late stage), footwall block; $620^{\circ}C$, 0.8 Gpa (early stage), $620-840^{\circ}C$, 0.64-0.45 Gpa (peak) and $620-630^{\circ}C$, 0.35Gpa (late stage). These results suggest a clockwise P-T-t path (jin et al., 1991, 1994). According to the depth-temperature model in the comperature subduction zone and the experimental data of Wyllie et al. (1983), we propose a tectonic-magmatic-thermal model to account for metamorphism-anatexis of moyite occurring in subduction-shear zone.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.2035-2046
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• 2021

Glacier movement speed is the most basic measurement for glacial dynamics research and is a very important indicator in predicting sea level rise due to climate change. In this study, the two-dimensional velocity measurements of Campbell Glacier located in Terra Nova Bay in East Antarctica were observed through the SAR offset tracking technique. For this purpose, domestic KOMPSAT-5 SAR satellite images taken on July 9, 2021 and August 6, 2021 were acquired. The Multi-kernel SAR offset tracking proposed through previous studies is a technique to obtain the optimal result that satisfies both resolution and precision. However, since offset tracking is repeatedly performed according to the size of the kernel, intensive computational power and time are required. Therefore, in this study, we strategically proposed a coarse-to-fine offset tracking approach. Through coarse-to-fine SAR offset tracking, it is possible to obtain a result with improved observation precision (especially, about 4 times in azimuth direction) while maintaining resolution compared to general offset tracking results. Using this proposed technique, a two-dimensional velocity measurements of Campbell Glacier were generated. As a result of analyzing the two-dimensional movement velocity image, it was observed that the grounding line of Campbell Glacier exists at approximately latitude -74.56N. The flow velocity of Campbell Glacier Tongue analyzed in this study (185-237 m/yr) increased compared to that of 1988-1989 (140-240 m/yr). And compared to the flow velocity (181-268 m/yr) in 2010-2012, the movement speed near the ground line was similar, but it was confirmed that the movement speed at the end of the Campbell Glacier Tongue decreased. However, there is a possibility that this is an error that occurs because the study result of this study is an annual rate of glacier movement that occurred for 28 days. For accurate comparison, it will be necessary to expand the data in time series and accurately calculate the annual rate. Through this study, the two-dimensional velocity measurements of the glacier were observed for the first time using the KOMPSAT-5 satellite image, a domestic X-band SAR satellite. It was confirmed that the coarse-to-fine SAR offset tracking approach of the KOMPSAT-5 SAR image is very useful for observing the two-dimensional velocity of glacier movements.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.3
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• pp.147-153
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• 2013

There exist various types of the WEC (Wave Energy Converter), and among them, the point absorber is the most popularly investigated type. However, it is difficult to find examples of systematically measured data analysis for the design of the point absorber type of power buoy in the world. The study investigates the wave load acting on the point absorber type resonance power buoy wave energy extraction system proposed by Kweon et al. (2010). This study analyzes the time series spectra with respect to the three-year wave data (2002.05.01~2005.03.29) measured using the pressure type wave gage at the seaside of north breakwater of Hupo harbor located in the east coast of the Korean peninsula. From the analysis results, it could be deduced that monthly wave period and wave height variations were apparent and that monthly wave powers were unevenly distributed annually. The average wave steepness of the usual wave was 0.01, lower than that of the wind wave range of 0.02-0.04. The mode of the average wave period has the value of 5.31 sec, while mode of the wave height of the applicable period has the value of 0.29 m. The occurrence probability of the peak period is a bi-modal type, with a mode value between 4.47 sec and 6.78 sec. The design wave period can be selected from the above four values of 0.01, 5.31, 4.47, 6.78. About 95% of measured wave heights are below 1 m. Through this study, it was found that a resonance power buoy system is necessary in coastal areas with low wave energy and that the optimal design for overcoming the uneven monthly distribution of wave power is a major task in the development of a WEF (Wave Energy Farm). Finding it impossible to express the average spectrum of the usual wave in terms of the standard spectrum equation, this study proposes a new spectrum equation with three parameters, with which basic data for the prediction of the power production using wave power buoy and the fatigue analysis of the system can be given.

• Journal of the Korean earth science society
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• v.22 no.5
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• pp.388-404
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• 2001

In order to examine the relative accuracy of satellite observations and model reanalyses about lower stratospheric temperature trends, two satellite-observed Microwave Sounding Unit (MSU) channel 4 (Ch 4) brightness temperature data and two GCM (ECMWF and GEOS) reanalyses during 1981${\sim}$1993 have been intercompared with the regression analysis of time series. The satellite data for the period of 1980${\sim}$1999 are MSU4 at nadir direction and SC4 at multiple scans, respectively, derived in this study and Spencer and Christy (1993). The MSU4 temperature over the globe during the above period shows the cooling trend of -0.35 K/decade, and the cooling over the global ocean is 1.2 times as much as that over the land. Lower stratospheric temperatures during the common period (1981${\sim}$1993) globally show the cooling in MSU4 (-0.14 K/decade), SC4 (-0.42 K/decade) and GEOS (-0.15 K/decade) which have strong annual cycles. However, ECMWF shows a little warming and weak annual cycle. The 95% confidence intervals of the lower stratospheric temperature trends are greater than those of midtropospheric (channel 2) trends, indicating less confidence in Ch 4. The lapse rate in the trend between the above two atmospheric layers is largest over the northern hemispheric land. MSU4 has low correlation with ECMWF over the globe, and high value with GEOS near the Korean peninsula. Lower correlations (r < 0.6) between MSU4 and SC4 (or ECMWF) occur over $30^{\circ}$N latitude belt, where subtropical jet stream passes. Temporal correlation among them over the globe is generally high (r > 0.6). Four kinds of lower stratospheric temperature data near the Korean peninsula commonly show cooling trends, of which the SC4 values (-0.82 K/decade) is the largest.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.199-207
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• 2016

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for practical forecasting of forest fire danger rate by reflecting the climate change for the time period of 2000yrs. Forest fire in South Korea is highly influenced by humidity, wind speed, temperature, and precipitation. To effectively forecast forest fire occurrence, we developed a forest fire danger rating model using weather factors associated with forest fire in 2000yrs. Forest fire occurrence patterns were investigated statistically to develop a forest fire danger rating index using times series weather data sets collected from 76 meteorological observation centers. The data sets were used for 11 years from 2000 to 2010. Development of the national forest fire occurrence probability model used a logistic regression analysis with forest fire occurrence data and meteorological variables. Nine probability models for individual nine provinces including Jeju Island have been developed. The results of the statistical analysis show that the logistic models (p<0.05) strongly depends on the effective and relative humidity, temperature, wind speed, and rainfall. The results of verification showed that the probability of randomly selected fires ranges from 0.687 to 0.981, which represent a relatively high accuracy of the developed model. These findings may be beneficial to the policy makers in South Korea for the prevention of forest fires.

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.559-566
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• 2007

The generation of precise digital elevation model (DEM) is very important in coastal area where time series are especially required. Although a LIDAR system is useful in coastal regions, it is not yet popular in Korea mainly because of its high surveying cost and national security reasons. Recently, precise DEM has been made using radar interferometry and waterline methods. One of these methods, spaceborne imaging radar interferometry has been widely used to measure the topography and deformation of the Earth. We acquired ALOS PALSAR FBD mode (Fine Beam Dual) data for evaluating the quality of interferograms and their coherency. We attempted to construct DEM using ALOS PALSAR pairs - One pair is 2007/05/22 and 2007/08/22, another pair is 2007/08/22 and 2007/10/22 with respective perpendicular baseline of 820 m, 312m and respective height sensitivity of 75 m and 185m at southern of Ganghwa tidal flat, Siwha- and Hwaong-lake over west coastal of Korea peninsula. Ganghwa tidal flat has low coherence between 0.3 and 0.5 of 2007/05/22 and 2007/08/22 pair. However, Siwha-lake and Hwaong-lake areas have a higher coherence value (From 0.7 and 0.9) than Ganghwa tidal area. The reason of difference coherence value is tidal condition between tidal flat area (Ganghwa) and reclaimed zone (Siwha-lake and Hwaong-lake). Therefore, DEM was constructed by ALOS PALSAR pair over Siwha-lake and Hwaong-lake. If the temporal baseline is enough short to maintain the coherent phases and height sensitivity is enough small, we will be able to successfully construct a precise DEM over coastal area. From now on, more ALOS PALSAR data will be needed to construct precise DEM of West Coast of Korea peninsular.

• Maritime Security
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• v.1 no.1
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• pp.273-310
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• 2020

This study aims at empirically analyzing the overall mechanism of the "Gray Zone Strategy", which has begun to be used as one of Chinese major maritime security strategies in maritime conflicts surrounding the South China Sea and East China Sea since early 2010, and comparing the resulting conflict patterns in those reg ions. To this end, I made the following two hypotheses about Chinese gray zone strategy. The hypotheses that I have argued in this study are the first, "The marine gray zone strategy used by China shows different structures of implementation in the South China Sea and the East China Sea, which are major conflict areas.", the second, "Therefore, the patterns of disputes in the South China Sea and the East China Sea also show a difference." In order to examine this, I will classify Chinese gray zone strategy mechanisms multi-dimensionally in large order, 1) conflict trends and frequency of strategy execution, 2) types and strengths of strategy, 3) actors of strategy execution, and 4) response methods of counterparts. So, I tried to collect data related to this based on quantitative modeling to test these. After that, about 10 years of data pertaining to this topic were processed, and a research model was designed with a new categorization and operational definition of gray zone strategies. Based on this, I was able to successfully test all the hypotheses by successfully comparing the comprehensive mechanisms of the gray zone strategy used by China and the conflict patterns between the South China Sea and the East China Sea. In the conclusion, the verified results were rementioned with emphasizing the need to overcome the security vulnerabilities in East Asia that could be caused by China's marine gray zone strategy. This study, which has never been attempted so far, is of great significance in that it clarified the intrinsic structure in which China's gray zone strategy was implemented using empirical case studies, and the correlation between this and maritime conflict patterns was investigated.

• 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.