• The Journal of Engineering Geology
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• v.16 no.4 s.50
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• pp.437-453
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• 2006

More than 3,000 landslides were occurred by torrential rains in Gangneung area due to the typhoon Rusa in 2002. In order to analyze the landslide origin and its geometric characteristics, 1,365 landslide data were collected from the field survey of Sacheon, Jumunjin, and Yeongok areas in which the intensive landslides took place. The average landslide size in the study area was composed of 10m width, 30m length, and $21^{\circ}{\sim}35^{\circ}$ slope angle, and the plane view of landslides A-type (i.e. wide shape of lower part) that contains approximately 50.5% of the landslides commonly occurred. In particular the area of Sacheon heavily damaged by mountain fires had more occurrence of landslides than other areas. The landslides of uniform tendency of slope direction were examined resulted from the contribution of topographic characteristics due to the weathering and wind direction during heavy rainfalls. In order to analyze the direction of joint, 249 orientation data were collected from the study area. The window method was employed to determine the characteristics of joint density in 51 locations of the study area. The results showed that many landslides occurred in the areas of joint density with the range of $0.05{\sim}0.1$.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.831-840
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• 2019

Recently, the span length of long-span bridges is getting longer. As a result, it has been suggested that a new concept called 'super long-span bridge'. In case of super long span bridges, the structure is being complicated and the importance of structural stability is being emphasized. However, until recently, the most commonly used sensors (dual axis clinometer, anemometer, strain gauge, etc.) have got limit about the bridge monitoring. Consequently, we researched the application of a Global Navigation Satellite System (GNSS) to improve the limit of the existing sensors. In this study, the dual axis clinometer, the anemometer and the strain gauge together with the GNSS were used to analyze the behavior of a super-long suspension bridge. Also, we propose the detailed method of bridge monitoring using the GNSS. This study consisted of three steps. First step calculated the absolute coordinates of the towers and the longitudinal axis direction of the study bridge using the GNSS. In second step, through the analysis of the long-term behavior in shortly after construction, we calculated the permanent displacement and evaluated the stability of main towers. Third step analyzed the behavior of bridge by the wind direction and was numerically indicated. Consequently, the bridge measurement using the GNSS appeared that the acquired data is able to easy processing according to the analysis purpose. If we will use together the existing measurement sensors with the GNSS on the maintenance of the super long-span bridge, we figure each error of measurement data and improve the monitoring system through calibration. As a result, we acquire the accurate displacement of bridge and figure the behavior of bridge. Consequently, we identified that it is able to construct the effective monitoring system.

• Korean Journal of Construction Engineering and Management
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• v.20 no.6
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• pp.126-131
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• 2019

The estimation of available solar energy at particular locations is critical to find and assess suitable locations of PV sites. The amount of PV power generation is however affected by various geographical factors (e.g., weather), which may make it difficult to identify the complex relationship between affecting factors and power outputs and to apply findings from one study to another in different locations. This study thus undertakes a regression analysis using data collected from 172 PV plants spatially distributed in Korea to identify critical weather conditions and estimate the potential power generation of PV systems. Such data also include solar radiation, precipitation, fine dust, humidity, temperature, cloud amount, sunshine duration, and wind speed. The estimated PV power generation is then compared to the actual PV power generation to evaluate prediction performance. As a result, the proposed model achieves a MAPE of 11.696(%) and an R-squred of 0.979. It is also found that the variables, excluding humidity, are all statistically significant in predicting the efficiency of PV power generation. According, this study may facilitate the understanding of what weather conditions can be considered and the estimation of PV power generation for evaluating and determining suitable locations of PV facilities.

• The Journal of the Korea Contents Association
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• v.19 no.8
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• pp.184-193
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• 2019

With the development of the information and communication technology, tourism items or products based on new technology is created or developed for tourists. This study examines tourist's satisfaction about VR program, "Secret Wind Forest", which is the story of Gotjawal, one of famous Jeju eco-tourism attraction. In order to achieve the study goal, it identified a study model from the previous studies. It collected data using survey from visitors who used VR program. The total of 227 questionnaires was utilized for data analysis. Based on the study model, it accepted five hypotheses (H1~H5), which are as follows. Firstly, individual innovativeness has a significant effect on perceived usefulness. Secondly, individual innovativeness influenced perceived usefulness, and perceived usefulness and ease influenced usage satisfaction. These results have both theoretical and practical contribution in terms of the development to tourist products using VR program. Academics can provide basic theories such as tourism activities, behaviors, and attitudes to tourism consumer-related studies on VR tourism program in terms of content application. Practically, it can help tourist marketers who want to use VR tourism program and content developers who use VR devices to construct VR program stories using tourism resources and to plan and execute contents considering the target market.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1399-1414
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• 2018

Natural forests are un-manned forests where the artificial forces of people are not applied to the formation of forests. On the other hand, artificial forests are managed by people for their own purposes such as producing wood, preventing natural disasters, and protecting wind. The artificial forests enable us to enhance economical benefits of producing more wood per unit area because it is well-maintained with the purpose of the production of wood. The distinction surveys have been performed due to different management methods according to forests. The distinction survey between natural forests and artificial forests is traditionally performed via airborne remote sensing or in-situ surveys. In this study, we suggest a classification method of forest types using satellite imagery to reduce the time and cost of in-situ surveying. A classification map of natural forest and artificial forest were generated using KOMPSAT-3, 3A, 5 data by employing artificial neural network (ANN). And in order to validate the accuracy of classification, we utilized reference data from 1/5,000 stock map. As a result of the study on the classification of natural forest and plantation forest using artificial neural network, the overall accuracy of classification of learning result is 77.03% when compared with 1/5,000 stock map. It was confirmed that the acquisition time of the image and other factors such as needleleaf trees and broadleaf trees affect the distinction between artificial and natural forests using artificial neural networks.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• Smart Structures and Systems
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• v.24 no.6
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• pp.769-781
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• 2019

Dynamic displacement response of civil structures is an important index for in-construction and in-service structural condition assessment. However, accurately measuring the displacement of large-scale civil structures such as high-rise buildings still remains as a challenging task. In order to cope with this problem, a vision-based system with the use of industrial digital camera and image processing has been developed for long-distance, remote, and real-time monitoring of dynamic displacement of supertall structures. Instead of acquiring image signals, the proposed system traces only the coordinates of the target points, therefore enabling real-time monitoring and display of displacement responses in a relatively high sampling rate. This study addresses the in-situ experimental verification of the developed vision-based system on the Canton Tower of 600 m high. To facilitate the verification, a GPS system is used to calibrate/verify the structural displacement responses measured by the vision-based system. Meanwhile, an accelerometer deployed in the vicinity of the target point also provides frequency-domain information for comparison. Special attention has been given on understanding the influence of the surrounding light on the monitoring results. For this purpose, the experimental tests are conducted in daytime and nighttime through placing the vision-based system outside the tower (in a brilliant environment) and inside the tower (in a dark environment), respectively. The results indicate that the displacement response time histories monitored by the vision-based system not only match well with those acquired by the GPS receiver, but also have higher fidelity and are less noise-corrupted. In addition, the low-order modal frequencies of the building identified with use of the data obtained from the vision-based system are all in good agreement with those obtained from the accelerometer, the GPS receiver and an elaborate finite element model. Especially, the vision-based system placed at the bottom of the enclosed elevator shaft offers better monitoring data compared with the system placed outside the tower. Based on a wavelet filtering technique, the displacement response time histories obtained by the vision-based system are easily decomposed into two parts: a quasi-static ingredient primarily resulting from temperature variation and a dynamic component mainly caused by fluctuating wind load.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.425-434
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• 2020

Background: Viral infection outbreaks are emerging public health concerns. They often exhibit seasonal patterns that could be predicted by the application of big data and bioinformatic analyses. Purpose: The purpose of this study was to identify trends in diarrhea-causing viruses such as rotavirus (Gr.A), norovirus G-I, and norovirus G-II in Cheonan, Korea. The identified related factors of diarrhea-causing viruses may be used to predict their trend and prevent their infections. Method: A retrospective analysis of 4,009 fecal samples from June 2010 to December 2019 was carried out at Dankook University Hospital in Cheonan. Reverse transcription-PCR (RT-PCR) was employed to identify virus strains. Information about seasonal patterns of infection was extracted and compared with local weather data. Results: Out of the 4,009 fecal samples tested using multiplex RT-PCR (mRT-PCR), 985 were positive for infection with Gr.A, G-I, and G-II. Out of these 985 cases, 95.3% (n = 939) were under 10 years of age. Gr.A, G-I, and G-II showed high infection rates in patients under 10 years of age. Student's t-test showed a significant correlation between the detection rate of Gr.A and the relative humidity. The detection rate of G-II significantly correlated with wind-chill temperature. Conclusion: Climate factors differentially modulate rotavirus and norovirus infection patterns. These observations provide novel insights into the seasonal impact on the pathogenesis of Gr.A, G-I, and G-II.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.1
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• pp.75-84
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• 2021

In this study, the correlation between cement quality(chemical composition, mineral composition, and compressive strength) and amount of waste alternative fuels used in the cement manufacturing process and was investigated. Cement manufacturing facility using coal, soft plastics(plastics that are easily scattered by wind power, such as vinyls), hard plastics(plastics that do not contain foreign substances, waste rubber, PP, etc.) and reclaimed oil was analised. Data was collected for 3 years from 2017 to 2019 and let the amount of fuels used as an independent variable and cement quality data as a dependent variable. As a result, depending on the type and quality of the alternative fuel has not a significant effect on the chemical composition(Cl and LSF) and mineral composition(f-CaO, C3S contents). Contrary to the concern that the compressive strength of cement would decrease, there was a significant positive correlation between amount of alternative fuel used and cement compressive strength.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.590-596
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• 2020

In the island areas where system connection with the commercial power grid is difficult, it is quite important to find a method to efficiently manage energy produced with independent microgrids. In this paper, a prosumer management system for P2P power transaction was realized through the testing the power meter and the response rate of the collected data for the power produced in the small-scale microgrids in which hybrid models of solar power and wind power were implemented. The power network of the microgrid prosumer was composed of mesh structure and the P2P power transaction was tested through the power meter and DC power transmitter in the off-grid sites which were independently constructed in three places. The measurement values of the power meter showed significant results of voltage (average): 380V + 0.9V, current (average): + 0.01A, power: 1000W (-1W) with an error range within ±1%. Stabilization of the server was also confirmed with the response rate of 0.32 sec. for the main screen, 2.61 sec. for the cumulative power generation, and 0.11 sec for the power transaction through the transmission of 50 data in real time. Therefore, the proposed system was validated as a P2P power transaction system that can be used as an independent network without transmitted by Korea Electric Power Corporation (KEPCO).