• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.33-39
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• 2016

With recent advances in sensor technology, CMOS-based semiconductor devices and networking protocol, the areas for application of wireless sensor networks greatly expanded and diversified. Such diversification of uses for wireless sensor networks creates a multitude of beneficial possibilities for several industries. In the application of wireless sensor networks for monitoring systems' data transmission process from the sensor node to the sink node, transmission through multi-hop paths have been used. Also mobile sink techniques have been applied. However, high energy costs, unbalanced energy consumption of nodes and time gaps between the measured data values and the actual value have created a need for advancement. Therefore, this thesis proposes a new model which alleviates these problems. To reduce the communication costs due to frequent data exchange, a State Prediction Model has been developed to predict the situation of the peripheral node using a geographic autocorrelation of sensor nodes constituting the wireless sensor networks. Also, a Risk Analysis Model has developed to quickly alert the monitoring system of any fatal abnormalities when they occur. Simulation results have shown, in the case of applying the State Prediction Model, errors were smaller than otherwise. When the Risk Analysis Model is applied, the data transfer latency was reduced. The results of this study are expected to be utilized in any efficient communication method for wireless sensor network monitoring systems where all nodes are able to identify their geographic location.

• Journal of Korean Society of Forest Science
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• v.78 no.2
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• pp.228-241
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• 1989

The objectives of this study are to identify flood disasters resulted from heavy rainstorm including earth and stone-debris avalanches and also to develope the scientific data to be needed for establishing the landslide-related disaster prevention countermeasures. For this study, 5 Gun (district) regions including Booyeo, Seochun, Gongju, Boryung, and Chungyang in Chungchongnam-do of the central part of Korea, in which severe landslide damages have been triggered during 3 days from July 21 to July 23, 1987, were investigated. Mostly, landslides having death of human lives triggered from 6 a.m, to 8 a,m. on July 22, and the principal factor was proved to be the continuous heavy rain ; the continuous rainfall of internal region for 3 days measured about 300-673 mm. The structural measures for slope failure prevention countermeasures at the hollow part of upper hillslope should be required. Natural drainage network on slopes should not be disturbed in case of land use alteration, such as a chestnut planting work on hillslopes behind the houses particularly. There are so many problems in recognition of landslide disaster prevention countermeasures including evacuation exercises. More actual education of countermeasures for control of flood and landslide should be put to practice through "civil defense education" and "inhabitants' meeting." In this context, existing Erosion Control Stations of 13 regions established in each Province should not be reduced. The designation criterion and surveying processes of "Landslide Prone Site" published by Forest Administration should also be improved scientifically.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.1
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• pp.62-74
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• 2004

Baekdu-daegan is the greatest mountain chain as well as the major ecological axis of the Korean Peninsula. In recent year, however, this area is faced with the various kinds of developmental urge. To cope adequately with these problems, this study was executed to prepare synthetic and systematic management with conservation-oriented strategy for Baekdu-daegan and to suggest spatially definite zoning for the managerial area. This study is to take into consideration the traditional concepts of stream and watershed as well as the actual disturbance on Baekdu-daegan area. The study area is selected with semi-mountainous type, from Namdeokyusan to Sosagogae. To propose the process for reasonably establishing the managerial boundary adjacent to the Ridges, the analysis was carried out that ArcGIS was mainly used for its analysis with digital maps, Landsat TM image and ArcGIS Hydro Model. Landsat TM image was classified by 5 land use types such as cultivated land, urban area, barren area, water body and forest. Based on these analyses results, the managerial boundaries as alternatives from the Ridges were produced by watershed expansion process, and used for tracing the changes of areal ratio of various land use types to the relevant watersheds to search out the adequate managerial boundary. The results show that watershed expansion process could be effective tool for establishing the managerial boundary, and eighth expanded watershed toward Muju-Gun(west) and fifth expanded watershed toward Geochang-Gun(east) might be included for the adequate managerial boundary of the case site.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.457-467
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• 2018

Currently, the regulation and rules for public surveying and the UCPs (Unified Control Points) adapts those of the triangulated traverse surveying. In addition, such regulations do not take account of the unique characteristics of GNSS (Global Navigation Satellite System) surveying, thus there are difficulties in field work and data processing afterwards. A detailed procesure of GNSS processing has not yet been described either, and the verification of accuracy does not follow the generic standards. In order to propose an appropriate procedure for field surveys, we processed a short session (30 minutes) based on the scenarios similar to actual situations. The reference network in Seoul was used to process the same data span for 3 days. The temporal variation during the day was evaluated as well. We analyzed the accuracy of the estimated coordinates depending on the parameterization of tropospheric delay, which was compared with the 24-hr static processing results. Estimating the tropospheric delay is advantageous for the accuracy and stability of the coordinates, resulting in about 5 mm and 10 mm of RMSE (Root Mean Squared Error) for horizontal and vertical components, respectively. Based on the test results, we propose a procedure to estimate the daily solution and then combine them to estimate the final solution by applying the minimum constraints (no-net-translation condition). It is necessary to develop a web-based processing system using a high-end softwares. Additionally, it is also required to standardize the ID of the public control points and the UCPs for the automatic GNSS processing.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.231-238
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• 2021

SNS data can catch the user's thoughts and actions. And the trend of the search portal is a representative service that can observe the interests of users and their changes. In this paper, the relationship was analyzed by comparing statistics on narcotics incidents and the degree of exposure to narcotics related words in tweets of SNS and in the trends of search portal. It was confirmed that the trend of SNS and search portal trends was the same in the statistics of the prosecution office with a certain time difference.In addition, cluster analysis was performed to understand the meaning of tweets in which narcotics related words were mentioned. In the 50,000 tweets collected in January 2020, it was possible to find meaning related to the sale of actual drugs. Therefore, through SNS monitoring alone it is possible to monitor narcotics-related incidents and to find specific sales or purchase-related information, and this can be used in the investigation process. In the future, it is expected that crime monitoring and prediction systems can be proposed as related crime analysis may be possible not only with text but also images.

• The Journal of Bigdata
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• v.5 no.2
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• pp.17-27
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• 2020

District heating system supplies heat from low-cost high-efficiency heat production facilities to heat demand areas through a heat pipe network. For efficient heat supply system operation, it is important to accurately predict the heat demand within the region and optimize the heat production plan accordingly. In this study, a heat demand forecasting model is proposed considering real-time calorimeter information from local heat demands. Previous models considered ambient temperature and heat demand history data to predict future heat demands. To improve forecast accuracy, the proposed heat demand forecast model added big data from real-time calorimeters installed in the heat demands within the target region. By employing calorimeter information directly in the model, it is expected that the proposed forecast model is to reflect heat use pattern of each demand. Computational experiemtns based on the actual heat demand data shows that the forecast accuracy of the proposed model improved when the calorimeter big data is reflected.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.459-468
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• 2022

Long-term data analyses and bioassay experiments were conducted to assess limiting nutrients in Cheonsu Bay and Taean sea. First, long-term nutrient data (2004-2016) provided by the National Water Quality Monitoring Network were used to assess potential limiting nutrients. Analysis of the long-term data showed that the dissolved inorganic nitrogen/dissolved inorganic phosphate (DIN/DIP) ratio was mostly below 16, with N limitation being dominant. A subsequent analysis using the concentration ratios of N, P, and Si showed that N limitation was dominant during summer and autumn but that Si limitation occasionally occurred during winter and spring in relatively limited areas. However, the dominant limiting nutrient was not determined. The nutrient analysis of the field water collected during the bioassay experiment showed that DIN/DIP revealed P limitation at all stations in March and May, whereas N limitation was dominant in July and October. In the analysis using the concentration ratios of N, P, and Si, P and Si limitation appeared in March and May, but there were points with no dominant limiting nutrient. However, N limitation was dominant in July and October. In the bioassay experiment for assessment of the actual limiting nutrient, the results showed no specific limiting nutrient in March, whereas NH₄⁺ and NO₃^- showed responses in May, July, and October, which confirmed that N was a substantial limiting nutrient directly involved in phytoplankton growth during this period.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1357-1369
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• 2022

Cloud removal is an essential image processing step for any task requiring time-series optical images, such as vegetation monitoring and change detection. This paper presents a two-stage cloud removal method that combines conditional generative adversarial networks (cGANs) with regression-based calibration to construct a cloud-free time-series optical image set. In the first stage, the cGANs generate initial prediction results using quantitative relationships between optical and synthetic aperture radar images. In the second stage, the relationships between the predicted results and the actual values in non-cloud areas are first quantified via random forest-based regression modeling and then used to calibrate the cGAN-based prediction results. The potential of the proposed method was evaluated from a cloud removal experiment using Sentinel-2 and COSMO-SkyMed images in the rice field cultivation area of Gimje. The cGAN model could effectively predict the reflectance values in the cloud-contaminated rice fields where severe changes in physical surface conditions happened. Moreover, the regression-based calibration in the second stage could improve the prediction accuracy, compared with a regression-based cloud removal method using a supplementary image that is temporally distant from the target image. These experimental results indicate that the proposed method can be effectively applied to restore cloud-contaminated areas when cloud-free optical images are unavailable for environmental monitoring.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1167-1181
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• 2021

In relation to the algae bloom, four types of blue-green algae that emit toxic substances are designated and managed as harmful Cyanobacteria, and prediction information using a physical model is being also published. However, as algae are living organisms, it is difficult to predict according to physical dynamics, and not easy to consider the effects of numerous factors such as weather, hydraulic, hydrology, and water quality. Therefore, a lot of researches on algal bloom prediction using machine learning have been recently conducted. In this study, the characteristic importance of water quality factors affecting the occurrence of Cyanobacteria harmful algal blooms (CyanoHABs) were analyzed using the random forest (RF) model for Bohyeonsan Dam and Yeongcheon Dam located in Yeongcheon-si, Gyeongsangbuk-do and also predicted the occurrence of harmful blue-green algae using the machine learning and deep learning models and evaluated their accuracy. The water temperature and total nitrogen (T-N) were found to be high in common, and the occurrence prediction of CyanoHABs using artificial neural network (ANN) also predicted the actual values closely, confirming that it can be used for the reservoirs that require the prediction of harmful cyanobacteria for algal management in the future.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.39-48
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• 2022

Cyberattacks around the world continue to increase, and their damage extends beyond government facilities and affects civilians. These issues emphasized the importance of developing a system that can identify and detect cyber anomalies early. As above, in order to effectively identify cyber anomalies, several studies have been conducted to learn BGP (Border Gateway Protocol) data through a machine learning model and identify them as anomalies. However, BGP data is unbalanced data in which abnormal data is less than normal data. This causes the model to have a learning biased result, reducing the reliability of the result. In addition, there is a limit in that security personnel cannot recognize the cyber situation as a typical result of machine learning in an actual cyber situation. Therefore, in this paper, we investigate BGP (Border Gateway Protocol) that keeps network records around the world and solve the problem of unbalanced data by using SMOTE. After that, assuming a cyber range situation, an autoencoder classifies cyber anomalies and visualizes the classified data. By learning the pattern of normal data, the performance of classifying abnormal data with 92.4% accuracy was derived, and the auxiliary index also showed 90% performance, ensuring reliability of the results. In addition, it is expected to be able to effectively defend against cyber attacks because it is possible to effectively recognize the situation by visualizing the congested cyber space.