• Korean Security Journal
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• no.16
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• pp.161-187
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• 2008

The purpose of this study is to suggest a development method of current Korean security system by analyzing the problems shown in the performance of security work in relation to the terrorism, which is enlarging in the word, from various aspects. In order to perform the study, the researcher considered the basic theory concerned to current Korean law concerned to security, principle and methodology of security, terror and new terrorism. The researcher performed the study by selecting qualitative case study focused on Park Geun-Hye case. Through the study, the methods to develop Korean security system are as follows. First, from the legal aspect, it is necessary to establish the law concerned to terrorism prevention and important person security. Moreover, it is necessary to search for the development of private security by revising Security Industry Act, which is a legal ground of private security. Second, it is necessary to improve and reinforce education & training program, which is not still divided in detail from the aspect of private security cultivation. Moreover, it is necessary to activate personal protection work and enlarge market through Security Industry Act and make an effort to change social recognition over security, which is devaluated in the society. From the viewpoint, national license about private security shall be adopted. The department of president security, which is a representative of official security, shall transfer the advanced technology to private security organization. Third, from the aspect of operation, the operation of security based on SCE principle, human shield principle, the nearest person's protection principle, body extension principle, linear protection principle and evacuation priority principle is required. Therefore, the priority shall be given to preventive security and thorough security plan shall be made for the operation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Journal of the Society of Disaster Information
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• v.15 no.1
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• pp.1-11
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• 2019

Purpose: This study intends to determine how disaster prevention characteristics of important state-designated wooden cultural properties in Gyeongsangnam-do vary according to the surrounding environments and to examine disaster prevention measures for wooden cultural properties that fit their surrounding environments accordingly. Method: The designation status and characteristics of cultural properties in Gyeongsangnam-do and Gyeongsangbuk-do were identified, and the damage status of cultural properties in Gyeongsangnam-do and Gyeongsangbuk-do was reviewed based on the history of disasters. Also, the disaster prevention environments for 58 state-designated wooden cultural properties in Gyeongsangnam-do and Gyeongsangbuk-do were analyzed separately into mountainous area, rural area and urban area, topographic characteristics were drawn. Results: For cultural properties located in urban areas, it was found that security guards were arranged properly and disaster prevention training was carried out well. In addition, access condition to the cultural properties was adequate; prompt access to such properties was possible. In rural areas, flame retardant works have been undertaken properly and many cultural properties were found to be located on a flat ground. Mountainous areas had highly inadequate access condition to cultural properties and disasters occurred most frequently in these areas in the past. Conclution: First, for wooden cultural properties located in urban areas, it is necessary to secure the self-defense fire service manpower for an initial response and reinforce the disaster prevention education. Second, for wooden cultural properties located in rural areas, prevention projects such as insect control project and disaster prevention insurance should be carried out in order to protect the cultural properties. Third, as for wooden cultural properties located in mountainous areas, it is necessary to prepare establish to reinforce self-response capability.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.3
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• pp.185-200
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• 2021

The Marine Environment QA/QC management system has been operated since 2010 to secure the reliability of data and improve the analysis capabilities of measurement and analysis institutions. From 2010 to 2020, the cumulative number of measurement and analysis institutions participated in the QA/QC management system was 266. And the number of certificates issued by the ministry of oceans and fisheries is 182. A total of 32 reference materials for proficiency testing and interlaboratory comparisons have been developed. They were first developed focusing on items (Nutrients, COD) commonly analyzed in marine environmental measuring network, marine pollution impact surveys, sea area utilization impact assessment, deepsea water surveys, and information network on fishing ground environments. In addition, it is time to expand the filed of the QA/QC management system, such as seawater temperature, salinity, PCBs and PAHs in sediments, which are mainly analyzed in most monitoring programs. On-site assessment has been conducted for 162 laboratories according to ISO/IEC 17025 to evaluate their conformity of the quality management system and deficiency. In terms of management and technology requirements, about 4.2% of organizations showed insufficient division of duties among employees 8.7% of them revealed the lack of employee training. By test item, about 6.3% of organizations showed the lack of standard substance management and the state of the cleaning glassware was pointed out in about 5.4% of them. The QA/QC management system should be continuously supplemented by identifying the causes of nonconformities and area for improvement.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.2
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• pp.217-230
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• 2022

The adequate control of TBM face pressure is of vital importance to maintain face stability by preventing face collapse and surface settlement. An EPB shield TBM excavates the ground by applying face pressure with the excavated soil in the pressure chamber. One of the challenges during the EPB shield TBM operation is the control of face pressure due to difficulty in managing the excavated soil. In this study, the face pressure of an EPB shield TBM was predicted using the geological and operational data acquired from a domestic TBM tunnel site. Four machine learning algorithms: KNN (K-Nearest Neighbors), SVM (Support Vector Machine), RF (Random Forest), and XGB (eXtreme Gradient Boosting) were applied to predict the face pressure. The model comparison results showed that the RF model yielded the lowest RMSE (Root Mean Square Error) value of 7.35 kPa. Therefore, the RF model was selected as the optimal machine learning algorithm. In addition, the feature importance of the RF model was analyzed to evaluate appropriately the influence of each feature on the face pressure. The water pressure indicated the highest influence, and the importance of the geological conditions was higher in general than that of the operation features in the considered site.

• Journal of International Area Studies (JIAS)
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• v.14 no.4
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• pp.27-52
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• 2011

The regions of Central Asia have each acquired an elevated strategic importance in the new security paradigm of post-September 1lth. Comprised of five states, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan, Central Asia's newly enhanced strategic importance stems from several other factors, ranging from trans-national threats posed by Islamic extremism, drug production and trafficking, to the geopolitical threats inherent in the region's location as a crossroads between Russia, Southwest Asia and China. Although the U.S. military presence in the region began before September 11th, the region became an important platform for the projection of U.S. military power against the Taliban in neighboring Afghanistan. The analysis goes on to warn that 'with US troops already in place to varying extents in Central Asian states, it becomes particularly important to understand the faultlines, geography, and other challenges this part of the world presents'. The Kyrgyz military remains an embryonic force with a weak chain of command, the ground force built to Cold War standards, and an almost total lack of air capabilities. Training, discipline and desertion - at over 10 per cent, the highest among the Central Asian republics - continue to present major problems for the creation of combat-effective armed forces. Kyrgyzstan has a declared policy of national defence and independence without the use of non-conventional weapons. Kyrgyzstan participates in the regional security structures, such as the Collective Security Treaty Organisation (CSTO) and the Shanghai Co-operation Organisation (SCO) but, in security matters at least, it is dependent upon Russian support. The armed forces are poorly trained and ill-equipped to fulfil an effective counter-insurgency or counter-terrorist role. The task of rebuilding is much bigger, and so are the stakes - the integrity and sovereignty of the Kyrgyz state. Only democratization, the fight against corruption, reforms in the military and educational sectors and strategic initiatives promoting internal economic integration and national cohesion hold the key to Kyrgyzstan's lasting future

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.2
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• pp.81-93
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• 2023

The purpose of this study is to clarify the background of the plans and the spatial characteristics of the garden at the Anglo-Korean School, an educational institution established in Gaeseong in 1906 by Yun Chi-ho and the American Methodist Church. The time scope of the study is from 1906, when the school was opened, to the early 1920s, when the basic building structure of the school was completed. The spatial scope is the school complex, located in Gaeseong, and its affiliated facilities. The contents of the study include the planning background and purpose, spatial layout, and plants used in the school garden. This study reviewed Yun Ch'i-ho's papers and Warren A. Candler's papers at Emory University, documents, photos, and maps produced in the early 20th century. The results show that the school garden was first mentioned at the school's opening and that with a strong will, Yun Chi-ho insisted on establishing a school garden. The garden was located around the engineering department building and was divided into several sections and lots. Economic plants, such as fruit trees, comprised the garden and were sourced from the Methodist Church of the South, USA. This study reveals that the garden at the Anglo-Korean School functioned as a training ground for agriculture and horticulture education and was differentiated from Seowon, a traditional Korean academy that symbolically spaced Neo-Confucianism and that emphasized the views of the surrounding nature during the Joseon Dynasty.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Journal of the Korean Geotechnical Society
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• v.39 no.12
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• pp.37-46
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• 2023

Dynamic soil properties are essential factors for predicting the detailed behavior of the ground. However, there are limitations to gathering soil samples and performing additional experiments. In this study, we used an artificial neural network (ANN) to predict dynamic soil properties based on static soil properties. The selected static soil properties were soil cohesion, internal friction angle, porosity, specific gravity, and uniaxial compressive strength, whereas the compressional and shear wave velocities were determined for the dynamic soil properties. The Levenberg-Marquardt and Bayesian regularization methods were used to enhance the reliability of the ANN results, and the reliability associated with each optimization method was compared. The accuracy of the ANN model was represented by the coefficient of determination, which was greater than 0.9 in the training and testing phases, indicating that the proposed ANN model exhibits high reliability. Further, the reliability of the output values was verified with new input data, and the results showed high accuracy.

• Korean Journal of Radiology
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• v.21 no.1
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• pp.88-100
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• 2020

Objective: We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images. Materials and Methods: A fully convolutional network-based segmentation system was developed using a training dataset of 883 CT scans from 467 subjects. Axial CT images obtained at the inferior endplate level of the 3rd lumbar vertebra were used for the analysis. Manually drawn segmentation maps of the skeletal muscle, visceral fat, and subcutaneous fat were created to serve as ground truth data. The performance of the fully convolutional network-based segmentation system was evaluated using the Dice similarity coefficient and cross-sectional area error, for both a separate internal validation dataset (426 CT scans from 308 subjects) and an external validation dataset (171 CT scans from 171 subjects from two outside hospitals). Results: The mean Dice similarity coefficients for muscle, subcutaneous fat, and visceral fat were high for both the internal (0.96, 0.97, and 0.97, respectively) and external (0.97, 0.97, and 0.97, respectively) validation datasets, while the mean cross-sectional area errors for muscle, subcutaneous fat, and visceral fat were low for both internal (2.1%, 3.8%, and 1.8%, respectively) and external (2.7%, 4.6%, and 2.3%, respectively) validation datasets. Conclusion: The fully convolutional network-based segmentation system exhibited high performance and accuracy in the automatic segmentation of abdominal muscle and fat on CT images.