• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.83-94
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• 2022

In order to actively prepare to frost damage that occurs in the process of growing crops, the spatial and temporal distribution of frost occurrence in South Korea was derived using frost observation data from 20 regions over the past 21 years (2000~2020). The main products are the number of frost days, first frost day, and last frost day by region. And the climatic trends of these results were identified by performing the Mann-Kendall trend test and Sen's slope estimator. In South Korea, a lot of frost occurs in the inland area to the west of the Taebaek and Sobaek Mountains. Relatively closer to the coastal area, the number of frost days is small, the first frost day is slow, and the last frost day is early. The east coast region has fewer frost days, the first frost day is later, and the last frost day is earlier than the west coast region. The southern sea, the southeastern sea region, and the island region rarely experience frost. As a result of the annual time series trend analysis, although South Korea is a country where climate warming is progressing, there was no trend in reducing the number of frost days and slowing the first frost day, and it was found that the last frost day is delayed by 0.5 days per year.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1195-1204
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• 2021

Recently, local torrential rain have become more frequent and severe due to abnormal climate conditions, causing a surge in human and properties damage including infrastructures along the river. In this study, water surface elevation prediction algorithm was developed using the LSTM (Long Short-term Memory) technique specialized for time series data among Machine Learning to estimate and prevent flooding of the facilities. The study area is Jamsu Bridge, the study period is 6 years (2015~2020) of June, July and August and the water surface elevation of the Jamsu Bridge after 3 hours was predicted. Input data set is composed of the water surface elevation of Jamsu Bridge (EL.m), the amount of discharge from Paldang Dam (m³/s), the tide level of Ganghwa Bridge (cm) and the number of tweets in Seoul. Complementary data were constructed by using not only structured data mainly used in precedent research but also unstructured data constructed through wordcloud, and the role of unstructured data was presented through comparison and analysis of whether or not unstructured data was used. When predicting the water surface elevation of the Jamsu Bridge, the accuracy of prediction was improved and realized that complementary data could be conservative alerts to reduce casualties. In this study, it was concluded that the use of complementary data was relatively effective in providing the user's safety and convenience of riverside infrastructure. In the future, more accurate water surface elevation prediction would be expected through the addition of types of unstructured data or detailed pre-processing of input data.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.517-524
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• 2022

This paper analyzes wave measurement using X-band navigation (ship) radar, changes in radar signal due to snowfall and precipitation, and factors that obstruct wave measurement. Data obtained from the radar installed at Sokcho Beach were used, and data from the Korea Meteorological Administration and the Korea Hydrographic and Oceanographic Agency were used for the meteorological data needed for comparative verification. Data from the Korea Meteorological Administration are measured at Sokcho Meteorological Observatory, which is about 7km away from the radar, and data from the Korea Hydrographic and Oceanographic Agency are measured at a buoy about 3km away from the radar. To this point, changes in radar signals due to rainfall or snowfall have been transmitted empirically, and there is no case of an analysis comparing the results to actual weather data. Therefore, in this paper, precipitation, snowfall data, CCTV, and radar signals from the Korea Meteorological Administration were comprehensively analyzed in time series. As a result, it was confirmed that the wave height measured by the radar according to snowfall and rainfall was reduced compared to the actual wave height, and a decrease in the radar signal strength according to the distance was also confirmed. This paper is meaningful in that it comprehensively analyzes the decrease in the signal strength of radar according to snowfall and rainfall.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.377-396
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• 2023

As the cryptocurrency market continues to grow, it has developed into a new financial market. The need for investment strategy research on the cryptocurrency market is also emerging. This study aims to conduct an empirical analysis on an investment methodology of cryptocurrency that combines short-term trading strategy and deep learning. Daily price data of the Ethereum was collected through the API of Upbit, the Korean cryptocurrency exchange. The investment performance of the experimental model was analyzed by finding the optimal parameters based on past data. The experimental model is a volatility breakout strategy(VBS), a Long Short Term Memory(LSTM) model, moving average cross strategy and a combined model. VBS is a short-term trading strategy that buys when volatility rises significantly on a daily basis and sells at the closing price of the day. LSTM is suitable for time series data among deep learning models, and the predicted closing price obtained through the prediction model was applied to the simple trading rule. The moving average cross strategy determines whether to buy or sell when the moving average crosses. The combined model is a trading rule made by using derived variables of the VBS and LSTM model using AND/OR for the buy conditions. The result shows that combined model is better investment performance than the single model. This study has academic significance in that it goes beyond simple deep learning-based cryptocurrency price prediction and improves investment performance by combining deep learning and short-term trading strategies, and has practical significance in that it shows the applicability in actual investment.

• Journal of Technology Innovation
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• v.31 no.2
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• pp.59-82
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• 2023

The purpose of this research is to conduct the empirical analysis of the short- and long-term causal relationship between public R&D investment, corporate R&D investment, and university R&D investment on economic growth in Korea. To this end, based on the time series data from 1976 to 2020, a causality test was conducted through the unit root test, cointegration test, and vector error correction model (VECM). As a result, it was found that there is a long-run equilibrium relationship between economic growth in Korea, public R&D investment, corporate R&D investment, and university R&D investment, in which a causal relationship exists in the long run. Also, while public R&D investment has a short-term effect on economic growth, corporate and university R&D investment does not have a short-term effect on economic growth. In addition, the results shows that there is a bidirectional causal relationship between economic growth and public R&D investment, corporate R&D investment and public R&D investment, and university R&D investment and public R&D investment in the short term. Through this research, it was empirically found that a highly mutual relationship exists between public R&D investment, corporate R&D investment, university R&D investment and economic growth. In order to increase the ripple effect of R&D investment on economic growth in the future, R&D investment between universities and corporations should be mutually promoted, and R&D investment by corporations should have a positive effect on public R&D investment so that public R&D investment can contribute to future economic growth.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.149-158
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• 2023

As data utilization is becoming more important recently, the importance of data centers is also increasing. However, the data center is a problem in terms of environment and economy because it is a massive power-consuming facility that runs 24 hours a day. Recently, studies using deep learning techniques to reduce power used in data centers or servers or predict traffic have been conducted from various perspectives. However, the amount of traffic data processed by the server is anomalous, which makes it difficult to manage the server. In addition, many studies on dynamic server management techniques are still required. Therefore, in this paper, we propose a dynamic server management technique based on Long-Term Short Memory (LSTM), which is robust to time series data prediction. The proposed model allows servers to be managed more reliably and efficiently in the field environment than before, and reduces power used by servers more effectively. For verification of the proposed model, we collect transmission and reception traffic data from six of Wikipedia's data centers, and then analyze and experiment with statistical-based analysis on the relationship of each traffic data. Experimental results show that the proposed model is helpful for reliably and efficiently running servers.

• Journal of the Korean Geotechnical Society
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• v.23 no.1
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• pp.23-37
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• 2007

Recently a concept of pile-tip enlarged PHC pile (Ext-PHC pile), for use in the auger-drilled construction method, has been developed and is being implemented in practice. A series of field axial load tests on both PHC and Ext-PHC piles were conducted at an experimental site. In addition, a parametric study on a number of influencing factors was made using a validated finite element model. The field axial load tests indicated an enhanced load-settlement characteristics for the Ext-PHC piles compared with the PHC piles, giving approximately 50% increase in the end bearing capacity. Also found in the results of the parametric study was that the increase in the end bearing capacity of Ext-PHC piles slightly varies with the mechanical properties of supporting ground as well as pile length, in the range of 1.25 to 1.4 time that of PHC. Overall, the results of the field tests as well as the numerical study confirmed that the end bearing capacity of PHC pile can be improved by the concept of.Ext-PHC pile.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.189-200
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• 2022

Ground subsidence on urban roads is a social issue that can lead to human and property damages. Therefore, it is crucial to detect underground cavities in advance and repair them. Underground cavity detection is mainly performed using ground penetrating radar (GPR) surveys. This process is time-consuming, as a massive amount of GPR data needs to be interpreted, and the results vary depending on the skills and subjectivity of experts. To address these problems, researchers have studied automation and quantification techniques for GPR data interpretation, and recent studies have focused on deep learning-based interpretation techniques. In this study, we described a hyperbolic event detection process based on deep learning for GPR data interpretation. To demonstrate this process, we implemented a series of algorithms introduced in the preexisting research step by step. First, a deep learning-based YOLOv3 object detection model was applied to automatically detect hyperbolic signals. Subsequently, only hyperbolic signals were extracted using the column-connection clustering (C3) algorithm. Finally, the horizontal locations of the underground cavities were determined using regression analysis. The hyperbolic event detection using the YOLOv3 object detection technique achieved 84% precision and a recall score of 92% based on AP50. The predicted horizontal locations of the four underground cavities were approximately 0.12 ~ 0.36 m away from their actual locations. Thus, we confirmed that the existing deep learning-based interpretation technique is reliable with regard to detecting the hyperbolic patterns indicating underground cavities.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.187-197
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• 2008

This study is to assess the impact of future land use change on hydrology and water quality in Gyungan-cheon watershed ($255.44km^2$) using SWAT (Soil and Water Assessment Tool) model. Using the 5 past Landsat TM (1987, 1991, 1996, 2004) and $ETM^+$ (2001) satellite images, time series of land use map were prepared, and the future land uses (2030, 2060, 2090) were predicted using CA-Markov technique. The 4 years streamflow and water quality data (SS, T-N, T-P) and DEM (Digital Elevation Model), stream network, and soil information (1:25,000) were prepared. The model was calibrated for 2 years (1999 and 2000), and verified for 2 years (2001 and 2002) with averaged Nash and Sutcliffe model efficiency of 0.59 for streamflow and determination coefficient of 0.88, 0.72, 0.68 for Sediment, T-N (Total Nitrogen), T-P (Total Phosphorous) respectively. The 2030, 2060 and 2090 future prediction based on 2004 values showed that the total runoff increased 1.4%, 2.0% and 2.7% for 0.6, 0.8 and 1.1 increase of watershed averaged CN value. For the future Sediment, T-N and T-P based on 2004 values, 51.4%, 5.0% and 11.7% increase in 2030, 70.5%, 8.5% and 16.7% increase in 2060, and 74.9%, 10.9% and 19.9% increase in 2090.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.95-106
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• 2023

Korean healing research has developed over the past 20 years along with the growing social interest in healing. The field of healing research is diverse and includes legislated natural-based healing. In this study, abstracts of 2,202 academic journals, master's, and doctoral dissertations published in KCI and RISS were collected and analyzed. As for the research method, LDA topic modeling used to classify research topics, and time-series publication trends were examined. As a result of the study, it identified that the topic of Korean healing research was connected with 5 types and 4 mediators. The five were "Healing Tourism," "Mind and Art Healing," "Forest Therapy," "Healing Space," and "Youth Restoration and Healing," and the four mediators were "Forest," "Nature," "Culture", and "Education". In addition, only legalized healing studies extracted from Korean healing research and the topics were analyzed. As a result, legalized healing research classified into four. The four types were "Healing Spatial Environment Plan", "Healing Therapy Experiment", "Agricultural Education Experiential Healing", and "Healing Tourism Factor". Forest Therapy, which has the largest amount of research in legalized healing, Agro Healing and Garden Healing which operate similar programs through plants, and Marine Healing using marine resources also analyzed. As a result, topics that show the unique characteristics of individual healing studies and topics that are considered universal in all healing studies derived. This study is significant in that it identified the overall trend of research on Korean healing facilities and programs by utilizing LDA topic modeling.