• Journal of Korea Water Resources Association
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• v.51 no.spc
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• pp.1057-1066
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• 2018

Climate change has been influenced on extreme precipitation events, which are major driving causes of flooding. Especially, most of extreme water-related disasters in Korea occur from floods induced by extreme precipitation events. However, future climate change scenarios simulated with Global Circulation Models (GCMs) or Reigonal Climate Models (RCMs) are limited to the application on medium and small size rivers and urban watersheds due to coarse spatial and temporal resolutions. Therefore, the current study introduces the state-of-the-art approaches and procedures of statistical downscaling techniques to resolve this limitation It is expected that the temporally downscaled data allows frequency analysis for the future precipitation and estimating the design precipitation for disaster prevention.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.241-247
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• 2019

The purpose of this study was to develop and apply a to identify the effects of problem solving process and communication for nursing students. The subjects consisted of one group of 71 fourth year nursing students of D Colleage To test the application effect, a one group pre-post test design was applied. This study showed significantly higher problem solving process(t=-12.6, p<.001), and communication(t=-9.91, p<.001). Learning satisfaction was positively affected. Simulation program is one of the effective teaching and learning methods. It is recommended to develpo a scenario for simulation education applied various cases which are undergoing clinical testing, so as to apply it to the nursing training education.

• Journal of Ocean Engineering and Technology
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• v.34 no.6
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• pp.394-405
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• 2020

To assess the station-keeping performance of floating structures in the Arctic region, the ice load should be considered along with other environmental loads induced by waves, wind, and currents. However, present methods for performance evaluation in the time domain are not effective in terms of time and cost. An ice load generation module is proposed based on the experimental data measured at the KRISO ice model basin. The developed module was applied to a time domain simulation. Using the results of a captive model test conducted in multiple directions, the statistical characteristics of ice loads were analyzed and processed so that an ice load corresponding to an arbitrary angle of the structure could be generated. The developed module is connected to commercial dynamic analysis software (OrcaFlex) as an external force input. Station-keeping simulation in the time domain was conducted for the same floating structure used in the model test. The mooring system was modeled and included to reflect the designed operation scenario. Simulation results show the effectiveness of the proposed ice generation module and its application to station-keeping performance evaluation. Considering the generated ice load, the designed structure can maintain a heading angle relative to ice up to 4°. Station-keeping performance is enhanced as the heading angle conforms to the drift direction. It is expected that the developed module will be used as a platform to verify station-keeping algorithms for Arctic floating structures with a dynamic positioning system.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.3
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• pp.328-338
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• 2021

A study on common modular design based on open standards to reduce the life cycle cost of ground weapon system is underway. Since the ground weapon system includes major mission equipment such as fire control system, it is essential to apply the concept of fault tolerance through automatic reconfiguration and blocking unspecified equipment through connectivity control. However, it is difficult to generalize due to the difference in operating characteristics for each system. In this paper, we propose a plug-and-play framework, which includes plug-and-play architecture and mechanism. The proposed method can be used in common by the application of each component as it is divided into a common service layer. In addition, the proposed connectivity control and autonomous reconfiguration method facilitates reflection of operating characteristics for each system. We constructed a verification environment that can simulate ground weapon systems and components, and verified that the proposed framework works through scenario-based functional tests.

• (The)Study of the Eastern Classic
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• no.43
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• pp.33-64
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• 2011

The historical drama played on TV in 2003, Daejanggeum is originally based on the short historical record of lady doctor of the palace from the [Jungjong record] of Josun. The drama mixed fiction and historic record well together draw enormous interest and became a novel, musical and animation for children. Also the location of shooting drama became a theme park to attract travelers and the name 'Daejanggeum' was used for products to create great additional value. Most of all, the drama then was exported to overseas and became the representing drama of Korea. Therefore, drama is the representing piece that proved the success of historic data with its application as various modern cultural contents. The analysis of success reason of showed the creation of new modern woman character, fresh selection of the item that suits well in the time of desiring wellbeing, the strong drama scenario with different story development compared to previous historic drama. Also, it used 'one source multi use' method prior to the broadcasting and prepared production of various cultural contents. This success of Daejanggeum means a lot from the point of 'modern acceptance of tradition' to tradition researchers.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.179-188
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• 2022

There have been many successful researches on a bearing fault diagnosis based on Deep Learning, but there is still a critical issue of the data distribution difference between training data and test data from their different working conditions causing performance degradation in applying those methods to the machines in the field. As a solution, a data adaptation method has been proposed and showed a good result, but each and every approach is strictly limited to a specific applying scenario or presupposition, which makes it still difficult to be used as a real-world application. Therefore, in this study, we have proposed a method that, using a data transformation with MFCCs and a simple CNN architecture, can perform a robust diagnosis on a target domain data without an additional learning or tuning on the model generated from a source domain data and conducted an experiment and analysis on the proposed method with the CWRU bearing dataset, which is one of the representative datasests for bearing fault diagnosis. The experimental results showed that our method achieved an equal performance to those of transfer learning based methods and a better performance by at least 15% compared to that of an input transformation based baseline method.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.3
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• pp.47-62
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• 2022

The Ministry of Environment has been working to reduce the impact on biodiversity, ecosystems, and social costs caused by soil runoff from highland Agricultural fields by setting up non-point pollution source management districts. To reduce soil loss, runoff path reduction technology has been applied, but it has been less cost effective. In addition, non-point pollution sources cause environmental conflicts in downstream areas, and recently highland Agricultural fields are becoming vulnerable to climate change. The Ministry of Environment is promoting the optimal management plan in earnest to convert arable land into forests and grasslands, but since non-point pollution is not a simple environmental problem, it is necessary to approach it from the aspect of NbS(Nature-Based Solution). In this study, a scenario for applying the nature-based solution was established for three subwatersheds west of Haean-myeon, Yanggu-gun, Gangwon-do. The soil loss distribution was spatialized through GeoWEPP and the amount of soil loss was compared for the non-point pollution reduction effect of mixed forests and grasslands. When cultivated land with a slope of 20% or more and ginseng fields were restored to perennial grasslands and mixed forests, non-point pollution reduction effects of about 32% and 29.000 tons compared to the current land use were shown. Also, it was confirmed that mixed forest rather than perennial grassland is an effective nature-based solution to reduce non-point pollution.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3950-3969
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• 2021

Radio Frequency (RF)-based indoor localization technologies play significant roles in various Internet of Things (IoT) services (e.g., location-based service). Most such technologies require that all the devices comply with a specified technology (e.g., WiFi, ZigBee, and Bluetooth). However, this requirement limits its application scenarios in today's IoT context where multiple devices complied with different standards coexist in a shared environment. To bridge the gap, in this paper, we propose a cross-technology localization approach, which is able to localize target nodes using a different type of devices. Specifically, the proposed framework reuses the existing WiFi infrastructure without introducing additional cost to localize Non-WiFi device (i.e., ZigBee). The key idea is to leverage the interference between devices that share the same operating frequency (e.g., 2.4GHz). Such interference exhibits unique patterns that depend on the target device's location, thus it can be leveraged for cross-technology localization. The proposed framework uses Principal Components Analysis (PCA) to extract salient features of the received WiFi signals, and leverages Dynamic Time Warping (DTW), Gradient Boosting Regression Tree (GBRT) to improve the robustness of our system. We conduct experiments in real scenario and investigate the impact of different factors. Experimental results show that the average localization accuracy of our prototype can reach 1.54m, which demonstrates a promising direction of building cross-technology technologies to fulfill the needs of modern IoT context.

• The Journal of the Korea Contents Association
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• v.22 no.9
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• pp.76-84
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• 2022

In this paper, in order to compose the visual perception cognitive function training content that can be linked with the IPUZZLE image block, an interactive content device that utilizes photos and videos of smartphones. Four areas of visual memory, visual continuity, spatial relationship, and visual discrimination were derived and the content operation, application method, and scenario were written. It was intended to continuously give and induce children's desire to participate in training by designing the content image and developing the existing learning terrain visual and perceptual cognitive function training materials in the form of mobile mini-games. Experiential activities were conducted for general children and their guardians using the developed contents, and the results were found to be significant in terms of concentration, effect, and effect compared to basic puzzle toys. It is expected that this thesis will be a meaningful data for the study of cognitive function improvement activities based on digital toys and contents.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.