• Journal of the Daesoon Academy of Sciences
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• v.35
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• pp.427-476
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• 2020

Park Wudang formally registered Daesoon Jinrihoe in 1969. While it shares ideas and history with other Jeungsanist groups, this paper argues that its organizational profile is unique. The two major institutional structures, the bonbu (headquarters) and the bangmyeon (branch) have together created space for the rapid development of this Korean new religion. The bonbu is a centralized hierarchy, while the bangmyeon exhibits the strong loyalty and cohesiveness of the clan. Running throughout both structural forms is the lineage concept, which is conceived here as an articulating paradigm able to operate in different organizational forms. This finely-balanced institutional structure makes a major contribution to Daesoon Jinrihoe's ability to fulfill its religious mission. The first side of this balance is the headquarters, which includes the core organization based in Yeoju as well as some outside temples and training centers. All of these were established under the direction of the Lord of Principle, the Dojeon, Park Wudang. Park Wudang also fixed the Dao Constitution, the Doheon, which serves as a blueprint for governance. From the Central Council to the various institutions for propagation, guidance, and auditing, current management practices conform closely to Park Wudang's organization vision. The second aspect of Daesoon Jinrihoe's organization is the branch structure. The larger branches, such as Yeongwol and Geumreung, are complex organizations in their own rights. The paper concludes by characterizing the two major axes of headquarters and branch as organizational types. Using Robert Quinn and Kim Cameron's institutional typology, the paper concludes that the bonbu is a classic centralized hierarchy with its focus on efficiency. The bangmyeon, in contrast, with its high level of group identity and spirit, comes approximates the clan institutional structure.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1015-1023
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• 2022

The Entry-Descent-Landing process of a lander involves many environmental and technical challenges. To solve these problems, recently, terrestrial relative navigation (TRN) technology has been essential for landers. TRN is a technology for estimating the position and attitude of a lander by comparing Inertial Measurement Unit (IMU) data and image data collected from a descending lander with pre-built reference data. In this paper, we present a method for generating descent dataset and extracting landmarks, which are key elements for developing TRN technologies to be used on Mars. The proposed method generates IMU data of a descending lander using a simulated Mars landing trajectory and generates descent images from high-resolution ortho-map and digital elevation map through a ray tracing technique. Landmark extraction is performed by an area-based extraction method due to the low-textured surfaces on Mars. In addition, search area reduction is carried out to improve matching accuracy and speed. The performance evaluation result for the descent dataset generation method showed that the proposed method can generate images that satisfy the imaging geometry. The performance evaluation result for the landmark extraction method showed that the proposed method ensures several meters of positioning accuracy while ensuring processing speed as fast as the feature-based methods.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.2
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• pp.171-182
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• 2022

InSAR (Interferometry SAR) technique is a technique that uses complex data to obtain phase difference information from two or more SAR image data, and enables high-resolution image extraction, surface change detection, elevation measurement, and glacial change observation. In many countries, research on the InSAR technique is being conducted in various fields of study such as volcanic activity detection, glacier observation in Antarctica, and ground subsidence analysis. In this study, a case of large ground settlement due to groundwater level drawdown during tunnelling was introduced, and ground settlement analyses using InSAR technique and numerical analysis method were compared. The maximum settlement and influence radius estimated by the InSAR technique and numerical method were found to be quite similar, which confirms the reliability of the InSAR technique. Through this case study, it was found that the InSAR technique reliable to use for estimating ground settlement and can be used as a key technology to identify the long-term ground settlement history in the absence of measurement data.

• Journal of the Korean Geotechnical Society
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• v.37 no.12
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• pp.47-56
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• 2021

The cryosuction (negative pore pressure) in freezing soils causes groundwater migration from the frozen fringe to freezing front for ice lens formation. Frost heave and heaving pressure by ice lens cause damage to ground infrastructure. In order to prevent damage by the frost heave, various frost susceptibility criteria have been proposed. The SP (Segregation Potential) is the most widely used classification criterion for frost susceptibility in cold regions. The expansion of the ice lens by the migration of the groundwater is a key role in frost heave mechanism, and thus it is necessary to evaluate the hydraulic conductivity. In this paper, soil mixtures of coarse-fines (sand-silt) were prepared in various weight fractions and used for frost heave and column permeability test. For each case, the SP and the hydraulic conductivity were derived and correlations were analyzed. As a results, the transition threshold of the SP and the hydraulic conductivity were shown at 20% and 50% of the silt weight fraction, respectively. Although there are difference between these transition thresholds, these two coefficients show a specific correlation. In the future, additional study should be conducted for detailed analysis of the threshold transition values between SP and hydraulic conductivity.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.59-73
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• 2022

Open peer review intends to help review a manuscript in a transparent and accountable manner, opening up the identity of a reviewer and authors in a scholarly journal. Although some research about open peer review, for example, authors' understanding of open peer review, exists, research about editors' perspectives of a scholarly journal in Korean domestic science and technology fields has not been found. Editors' views may include whether an academic journal plans to adopt open peer review, when they might adopt it, or what the possible benefits, challenges, and issues in adopting open peer review would be. This paper presents a survey scheme, data, and analysis of Korean editors' perspectives on open peer review. Specifically, we designed the online survey questionnaire, collected the survey data from journal editors, and analyzed the survey results to explore editors' understanding of open peer review. We then compared our research with previous work, such as our Focused Group Interview and other similar domestic and foreign analysis. This study result is expected to help make an open peer review policy for public institutes to provide essential services for scholarly journals, including a scholarly peer review system. Academic society may also get some insights in adopting the open peer review method in the peer-review process.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.5
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• pp.30-42
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• 2022

Due to the increase in construction of tunnels, the burdens of maintenance works for tunnel structures have been increasing in Korea. In addition, the increase of traffic volume and aging of materials also threatens the safety of tunnel facilities, therefore, maintenance costs are expected to increase significantly in the future. Accordingly, automated condition assessment technologies like image-based tunnel scanning system for inspection and diagnosis of tunnel facilities have been proposed. For image-based tunnel scanning system, it is key to create a planar image through stitching of multiple images captured by tunnel scanning system. In this study, performance of feature-based stitching algorithms suitable for stitching tunnel scanning images was evaluated. In order to find a suitable algorithm SIFT, ORB, and BRISK are compared. The performance of the proposed algorithm was determined by the number of feature extraction, calculation speed, accuracy of feature matching, and image stitching result. As for stitching performance, SIFT algorithm was the best in all parts of tunnel image. ORB and BRISK also showed satisfactory performance and short calculation time. SIFT can be used to generate precise planar images. ORB and BRISK also showed satisfactory stitching results, confirming the possibility of being used when real-time stitching is required.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.543-553
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• 2021

As a key policy for achieving the goal of reducing GHG in the building sector, Korea has enforced the mandatory certification of zero-energy buildings for new buildings in the public sector from 2020. This study evaluated a policy to achieve Net Zero by identifying the trend of changes in building energy performance according to policy and presenting a methodology to analyze the current performance state of energy technology applied to buildings. The final goal was to help stakeholders apply appropriate energy technologies for new buildings. For this study, data collected on building energy efficiency certification over the last four years have shown a gradual increase in energy performance. In addition, K-means cluster analysis was used to analyze the performance status of energy technologies applied to buildings. The high and low clusters of education and office facilities were used to analyze the comparative group (2016-2020, 2020). As a result, the solar module area in both high and low clusters of education facilities increased by 261.1% and 283.5%. In contrast, the solar module area decreased by both high and low clusters of office facilities. The most passive and active technologies showed an increase in energy performance.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.135-144
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• 2021

Recently, data-driven decision-making technology has become a key technology leading the data industry, and machine learning technology for this requires high-quality training datasets. However, real-world data contains missing values for various reasons, which degrades the performance of prediction models learned from the poor training data. Therefore, in order to build a high-performance model from real-world datasets, many studies on automatically imputing missing values in initial training data have been actively conducted. Many of conventional machine learning-based imputation techniques for handling missing data involve very time-consuming and cumbersome work because they are applied only to numeric type of columns or create individual predictive models for each columns. Therefore, this paper proposes a new data imputation technique called 'Denoising Self-Attention Network (DSAN)', which can be applied to mixed-type dataset containing both numerical and categorical columns. DSAN can learn robust feature expression vectors by combining self-attention and denoising techniques, and can automatically interpolate multiple missing variables in parallel through multi-task learning. To verify the validity of the proposed technique, data imputation experiments has been performed after arbitrarily generating missing values for several mixed-type training data. Then we show the validity of the proposed technique by comparing the performance of the binary classification models trained on imputed data together with the errors between the original and imputed values.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.153-161
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• 2022

Physical activities are decreasing and sitting time is increasing due to the automation, smartization, and intelligence of necessary household items throughout daily life. Recent healthcare studies have reported that the likelihood of obesity, diabetes, cardiovascular disease, and early death increases in proportion to sitting time. In this paper, we develop a sitting posture correction cushion in real time using capacitive pressure sensor based on conductive textile. It develops a pressure sensor using conductive textile, a key component of the posture correction cushion, and develops a low power-based pressure measurement circuit. It provides a function to transmit sensor values measured in real time to smartphones using BLE short-range wireless communication on the posture correction cushion, and develops a mobile application to check the condition of the sitting posture through these sensor values. In the mobile app, you can visualize your sitting posture and check it in real time, and if you keep it in the wrong posture for a certain period of time, you can notify it through an alarm. In addition, it is possible to visualize the sitting time and posture accuracy in a graph. Through the correction cushion in this paper, we experiment with how effective it is to correct the user's posture by recognizing the user's sitting posture, and present differentiation and excellence compared to other product.

• Journal of the Korean Society of Radiology
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• v.16 no.1
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• pp.1-6
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• 2022

Deep learning is a collection of algorithms that enable learning by summarizing the key contents of large amounts of data; it is being developed to diagnose lesions in the medical imaging field. To evaluate the accuracy of the cerebral hemorrhage diagnosis, we used a convolutional neural network (CNN) to derive the diagnostic accuracy of cerebral parenchyma computed tomography (CT) images and the cerebral parenchyma CT images of areas where cerebral hemorrhages are suspected of having occurred. We compared the accuracy of CNN with different numbers of hidden layers and discovered that CNN with more hidden layers resulted in higher accuracy. The analysis results of the derived CT images used in this study to determine the presence of cerebral hemorrhages are expected to be used as foundation data in studies related to the application of artificial intelligence in the medical imaging industry.