• 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.

• Korean Journal of Radiology
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• v.20 no.8
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• pp.1275-1284
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• 2019

Objective: To develop algorithms using convolutional neural networks (CNNs) for automatic segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) and compare them with conventional algorithms, including a thresholding-based segmentation. Materials and Methods: Between September 2005 and August 2015, 429 patients presenting with acute cerebral ischemia (training:validation:test set = 246:89:94) were retrospectively enrolled in this study, which was performed under Institutional Review Board approval. Ground truth segmentations for acute ischemic lesions on DWI were manually drawn under the consensus of two expert radiologists. CNN algorithms were developed using two-dimensional U-Net with squeeze-and-excitation blocks (U-Net) and a DenseNet with squeeze-and-excitation blocks (DenseNet) with squeeze-and-excitation operations for automatic segmentation of acute ischemic lesions on DWI. The CNN algorithms were compared with conventional algorithms based on DWI and the apparent diffusion coefficient (ADC) signal intensity. The performances of the algorithms were assessed using the Dice index with 5-fold cross-validation. The Dice indices were analyzed according to infarct volumes (< 10 mL, ≥ 10 mL), number of infarcts (≤ 5, 6-10, ≥ 11), and b-value of 1000 (b1000) signal intensities (< 50, 50-100, > 100), time intervals to DWI, and DWI protocols. Results: The CNN algorithms were significantly superior to conventional algorithms (p < 0.001). Dice indices for the CNN algorithms were 0.85 for U-Net and DenseNet and 0.86 for an ensemble of U-Net and DenseNet, while the indices were 0.58 for ADC-b1000 and b1000-ADC and 0.52 for the commercial ADC algorithm. The Dice indices for small and large lesions, respectively, were 0.81 and 0.88 with U-Net, 0.80 and 0.88 with DenseNet, and 0.82 and 0.89 with the ensemble of U-Net and DenseNet. The CNN algorithms showed significant differences in Dice indices according to infarct volumes (p < 0.001). Conclusion: The CNN algorithm for automatic segmentation of acute ischemic lesions on DWI achieved Dice indices greater than or equal to 0.85 and showed superior performance to conventional algorithms.

• Journal of the Korean Society of Radiology
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• v.85 no.4
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• pp.714-726
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• 2024

Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%-80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors' experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases.

• Journal of Popular Narrative
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• v.25 no.1
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• pp.55-85
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• 2019

In this article, we examine the real meaning behind the stories in which the N-po Generation (Millennial Generation) is depicted, through the observation of entertainment programs, TV series, and movies. This could be an opportunity to investigate the aspects of emotional customs of our era, which have been described by television media as portraying the complex and multifaceted reality in the most mundane and popular manner while influencing the public. Problems with youth unemployment, the polarization of life, and instability are not only global issues but situations that specifically occur in South Korea. It is thus vital to pay attention to the inner side of the N-po Generation who enjoy Sohwakhaeng (small but certain happiness) by eating alone as the placebo effect of this tough reality. This is an agenda that should be viewed as a problem in the fundamental design of South Korean society. The consciousness of the problem shown in the TV series has been drawing attention. The TV series Because depicts a love narrative that concentrates on emotions in a relationship that started between housemates due to poverty and housing problems, leading to marriage. Thus, the TV series persuasively dramatized 'confluent love' in the N-po Generation. In the movie , Miso can be regarded as a symbol that represents the emergence of a new generation of cultural sensitivity. There is a suggestion in the sequence of that identifies the pursuit of taste with the discovery of identity. The TV series is a growth narrative that deals heavily with youth unemployment, temporary workers, fragmented families, and dating violence. The housemates in find emotional stability through interaction with each other, and courageously approach their individual problems. In the process, images of women, who are empathetic towards others and are willing to jointly solve their problems, are calmly depicted to reveal a story of growth revolving around a ground emotional community. The current problem that South Korean society should contemplate is how to be fully human beyond mere survival, and how to further seek the conditions of human existence. In that sense, what we should pursue is a notion of 'publicness', which can put several generations together. Because of the reality that confliction between generations must be triggered, in order to make a passage of sympathizing, mass media's sensitivity training becomes more important. This may be the duty of mass media.