• The Journal of Engineering Geology
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• v.29 no.3
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• pp.265-277
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• 2019

Changes in the amount, intensity, frequency, and type of precipitation, in conjunction with global warming and climate change, critically impact groundwater recharge and associated groundwater level fluctuations. Monthly gravity levels by the Gravity Recovery and Climate Experiment (GRACE) are acquired to monitor total water storage changes at regional and global scales. However, there are inherent difficulties in quantitatively relating the GRACE observations to groundwater level data due to the difficulties in spatially representing groundwater levels. Here three local interpolation methods (kriging, inverse distance weighted, and natural neighbor) were implemented to estimate the areal distribution of groundwater recharge changes in South Korea during the 2002-2016 period. The interpolated monthly groundwater recharge changes are compared with the GRACE-derived groundwater storage changes. There is a weak decrease in the groundwater recharge changes over time in both the GRACE observations and groundwater measurements, with the rate of groundwater recharge change exhibiting mean and median values of -0.01 and -0.02 cm/month, respectively.

• Informatization Policy
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• v.26 no.1
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• pp.62-82
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• 2019

This study attempts to make an empirical analysis on how mobile devices affect users in building their social relationship and if their influences are negative or positive. The purpose of this research is to explain the results by considering all the possibilities and exploring everyday lives of using mobile devices. We used the survey data from the "Research on Mobile Environment Awareness" conducted by Gyeonggi Research Institute(GRI). The main question was about the use of mobile devices and social network services (SNS) and users' opinions on using the devices. All of the 31 municipalities in Gyeonggi Province were included as a spatial range, and the final validity sample was 1,004 residents. The extent of the relationship with people is selected as a dependent variable through the multinomial logistic model and the decision tree model. As a result of the multinomial logistic analysis on the questionnaire, the characteristics of the respondents with some changes in the scope of the human relationship were found to have a significant (+) effect on conversation with family, SNS usage, residence in the rural area but not urban area, and device usage for obtaining news. The largest variable affecting the extent of relationship was the SNS usage. As the amount of SNS usage increases, the extent of the relationship also changes a lot.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.525-532
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• 2021

In the ocean, it is difficult to separate the effects of one cause due to the multiple causes, but the self-organizing map can be analyzed by adding other factors to the cluster result. Therefore, in this study, the results of the clustering of sea level data were applied to sea surface temperature. Sea level data was clustered into a total of 6 nodes. The difference between sea surface temperature and sea level height has a one-month delay, which applied sea surface temperature data a month ago to the clustered results. As a result of comparing the mean of sea surface temperature of 140 to 150°E, where the sea surface temperature was variously distributed, in the case of nodes 1, 3, and 5, it was possible to find a meandering sea surface temperature distribution that is clearly distinguished from the sea level data. While nodes 2, 4 and 6, the sea surface temperature distribution was smooth. In this study, sea surface temperature data were applied to the clustered results of sea level data, but later it is necessary to apply wind or geostrophic velocity data to compare.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1089-1098
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• 2020

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1201-1208
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• 2020

A mass of seawater with similar properties in the ocean is called a water mass, and the front is a sea area where two masses of different properties meet. The gradient algorithm is a method of extracting where the sea water temperature pixel changes rapidly assuming that the slope is large, and the place with the large slope is assumed to be a front. This method is able to process large amounts of satellite data at once. Therefore, in this study, we tried to find the front lines in the sea area around the Korean Peninsula by using a gradient algorithm. The study data used gridded sea surface temperature satellite data. The resolution was 1/4°, and the monthly average data from January 1993 to December 2018 were used. There were major five fronts representatively, China Coastal Front, South Sea Coastal Front, Kuroshio Front/ Kuroshio Extension Front, Subpolar Front and the Subarctic Front. As a result of comparing the distribution of front by season, more types of front were distributed in winter and spring than in summer and autumn, and the distribution range was wider.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1219-1230
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• 2022

Multi-spectral drones in agricultural observation require quantitative and reliable data based on physical quantities such as radiance or reflectance in crop yield analysis. In the case of remote sensing data for crop monitoring, images taken in the same area over time-series are required. In particular, biophysical data such as leaf area index or chlorophyll are analyzed through time-series data under the same reference, it can be directly analyzed. So, comparable reflectance data are required. Orthoimagery using drone images, the entire image pixel values are distorted or there is a difference in pixel values at the junction boundary, which limits accurate physical quantity estimation. In this study, reflectance and vegetation index based on drone images were calculated according to the correction method of drone images for time-series crop monitoring. comparing the drone reflectance and ground measured data for spectral characteristics analysis.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1249-1258
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• 2022

Marine debris is defined as a substance that is intentionally or inadvertently left on the shore or is introduced or discharged into the ocean, which has or is likely to have a harmful effect on the marine environments. In this study, the detection of marine debris and the analysis of the amount of change on marine debris were performed using the object detection method for an efficient method of identifying the quantity of marine debris and analyzing the amount of change. The study area is Yuho Mongdol Beach in the northeastern part of Geoje Island, and the amount of change was analyzed through images collected at 15-minute intervals for 32 days from September 12 to October 14, 2022. Marine debris detection using YOLOv5x, a one-stage object detection model, derived the performance of plastic bottles mAP 0.869 and styrofoam buoys mAP 0.862. As a result, marine debris showed a large decrease at 8-day intervals, and it was found that the quantity of Styrofoam buoys was about three times larger and the range of change was also larger.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.30-31
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• 2009

Early detection of schedule delay in field construction activities is vital to project management. It provides the opportunity to initiate remedial actions and increases the chance of controlling such overruns or minimizing their impacts. This entails project managers to design, implement, and maintain a systematic approach for progress monitoring to promptly identify, process and communicate discrepancies between actual and as-planned performances as early as possible. Despite importance, systematic implementation of progress monitoring is challenging: (1) Current progress monitoring is time-consuming as it needs extensive as-planned and as-built data collection; (2) The excessive amount of work required to be performed may cause human-errors and reduce the quality of manually collected data and since only an approximate visual inspection is usually performed, makes the collected data subjective; (3) Existing methods of progress monitoring are also non-systematic and may also create a time-lag between the time progress is reported and the time progress is actually accomplished; (4) Progress reports are visually complex, and do not reflect spatial aspects of construction; and (5) Current reporting methods increase the time required to describe and explain progress in coordination meetings and in turn could delay the decision making process. In summary, with current methods, it may be not be easy to understand the progress situation clearly and quickly. To overcome such inefficiencies, this research focuses on exploring application of unsorted daily progress photograph logs - available on any construction site - as well as IFC-based 4D models for progress monitoring. Our approach is based on computing, from the images themselves, the photographer's locations and orientations, along with a sparse 3D geometric representation of the as-built scene using daily progress photographs and superimposition of the reconstructed scene over the as-planned 4D model. Within such an environment, progress photographs are registered in the virtual as-planned environment, allowing a large unstructured collection of daily construction images to be interactively explored. In addition, sparse reconstructed scenes superimposed over 4D models allow site images to be geo-registered with the as-planned components and consequently, a location-based image processing technique to be implemented and progress data to be extracted automatically. The result of progress comparison study between as-planned and as-built performances can subsequently be visualized in the D4AR - 4D Augmented Reality - environment using a traffic light metaphor. In such an environment, project participants would be able to: 1) use the 4D as-planned model as a baseline for progress monitoring, compare it to daily construction photographs and study workspace logistics; 2) interactively and remotely explore registered construction photographs in a 3D environment; 3) analyze registered images and quantify as-built progress; 4) measure discrepancies between as-planned and as-built performances; and 5) visually represent progress discrepancies through superimposition of 4D as-planned models over progress photographs, make control decisions and effectively communicate those with project participants. We present our preliminary results on two ongoing construction projects and discuss implementation, perceived benefits and future potential enhancement of this new technology in construction, in all fronts of automatic data collection, processing and communication.

• Journal of Korean Society of Forest Science
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• v.112 no.1
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• pp.11-22
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• 2023

This study was established to provide basic information necessary for ecological management to restore the naturalness of black locust (Robinia pseudoacacia L.) plantations located in the mountains of Gyeongbuk, Korea. Using vegetation data collected from 200 black locust stands, vegetation types were classified using the TWINSPAN method, the spatial arrangement status according to the environmental gradient was identified through DCA analysis, and a synoptic table of communities was prepared based on the diagnostic species determined by determining community fidelity (Φ) for each vegetation type. The vegetation types were classified into seven types, namely, Quercus mongolica-Polygonatum odoratum var. pluriflorum type, Castanea crenata-Smilax china type, Clematis apiifolia-Lonicera japonica type, Rosa multiflora-Artemisia indica type, Quercus variabilis-Lindera glauca type, Ulmus parvifolia-Celtis sinensis type, and Prunus padus-Celastrus flagellaris type. These types usually reflected differences in complex factors such as altitude, moisture regime, successional stage, and disturbance regime. The mean relative importance value of the constituent species was highest for black locust(39.7), but oaks such as Quercus variabilis, Q. serrata, Q. mongolica, Q. acutissima, and Q. aliena were also identified as important constituent species with high relative importance values, indicating their potential for successional trends. In addition, the total percent cover of constituent species by vegetation type, life form composition, species diversity index, and indicator species were compared.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.1
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• pp.24-35
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• 2023

Rapid industrialization and urbanization have led to severe marine pollution. A Water Quality Index (WQI) has been developed to allow the effective management of marine pollution. However, the WQI suffers from problems with loss of information due to the complex calculations involved, changes in standards, calculation errors by practitioners, and statistical errors. Consequently, research on the use of artificial intelligence techniques to predict the marine and coastal WQI is being conducted both locally and internationally. In this study, six techniques (RF, XGBoost, KNN, Ext, SVM, and LR) were studied using marine environmental measurement data (2000-2020) to determine the most appropriate artificial intelligence technique to estimate the WOI of five ecoregions in the Korean seas. Our results show that the random forest method offers the best performance as compared to the other methods studied. The residual analysis of the WQI predicted score and actual score using the random forest method shows that the temporal and spatial prediction performance was exceptional for all ecoregions. In conclusion, the RF model of WQI prediction developed in this study is considered to be applicable to Korean seas with high accuracy.