• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.386-394
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• 2021

This study aims to investigate the class satisfaction of the students enrolled in between a primary and secondary teacher's college, and to identify any statistical differences among the class satisfaction factors. In order to answer these questions, a survey of 50 questionnaires were distributed to 54 primary teachers' college students and 50 secondary teacher's college ones, respectively. A survey consisted of 5 categories including class contents, instruction results, class environment, instructional supports and efficiency. To analyze data, the descriptive analysis and independent sample t-test were administerd using Jamovi statistical program. The results revealed that there was no significant difference between two groups in terms of the overall class satisfaction, even though the mean score from the secondary teachers' college students was higher than that of the primary teachers' college students. However, there was a significant difference in the class satisfaction's sub categories: class environment, instructional supports and efficiency.

• Korean Journal of Construction Engineering and Management
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• v.22 no.2
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• pp.3-11
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• 2021

Myanmar is an undeveloped country with high development value among Asian countries. Therefore, various countries including the U.S. are considering entering the market. In this respect, demand for real estate development project is forecast to grow on increased inflow of foreigners and Myanmar's economic growth. However, Myanmar is a high-risk country in terms of overseas companies, including national risk. In this study, we conducted an in-depth interview with experts (law, finance, technology, and local experts) after analyzing data on Myanmar to extract risk-causing factors. Through this, 106 risk factors were extracted, and the final risk classification system was established by conducting three-time groupings using the affinity diagramming. And the relative importance of each factor was presented using the analytic hierarchy process (AHP) technique. As a result, the country-related risk, the fund-related risk, and the pre-sale-related risk were highly important. The research results are expected to provide risk management standards to companies entering the Myanmar real estate development type project.

• Korean Journal of Construction Engineering and Management
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• v.22 no.2
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• pp.63-71
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• 2021

This paper looks into how to enhance construction project management, focusing on the change order, which is often considered one of the major causes for construction delays, disputes, and claims in the middle east construction. First, this paper categorizes the major causes of change orders. It suggests a detailed classification standard for affecting factors resulting from change orders based on a case study result of an on-going construction project in the Middle East. In particular, this paper presents a method to apply a machine learning-based feature selection to quantify the importance of change order triggers and affecting factors. As a result, the case study identifies six major change order triggers and eight affecting factors. Also, a meaningful relationship between change order triggers and affecting factors by each category is presented. This paper will contribute to setting a clear guideline for change order management for the international plant construction field while helping prevent construction delays and cost run-ups by reducing the time required for change order resolution between project owners and contractors.

• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.366-373
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• 2020

This study investigated habitat diversity changes by conducting weir operations at the Sejongbo site in the Geum River basin. High-resolution aerial photographs taken before and after the weir-opening operation were used for habitat analyses. We classified water and bar areas into 17 types, such as lotic habitats, including riffles, runs, and glides, and lentic habitats, including reservoirs, pools, bar-head backwaters, and bar ponds. Then, we estimated each habitat area quantitatively, calculated a habitat diversity index, and established a habitat diversity map. Our results indicated that after the weir-opening operation, lotic habitats and new types of shallow lentic habitats increased in the water area. In addition, for the bar area, bare lands without cultivation increased distinctively. Finally, we observed that the habitat diversity index drastically increased after the weir-opening operation, which could provide a relatively simple channel of diverse habitats.

• Journal of the Korean Regional Science Association
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• v.37 no.2
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• pp.63-82
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• 2021

This study aims to explore how manufacturing start-up activities from 2000 to 2018 have changed spatially and to predict changes in distribution patterns of future start-up activities. For the analysis, the Census on Establishments microdata from 2000 to 2018 were used, and the manufacturing industry was classified into four detailed industrial groups according to the 40 manufacturing standards presented by the Korea Institute for Industrial Economics and Trade's ISTANS. According to the results, start-up activities in industries that require high technology levels are concentrated in southern Gyeonggi region, and other start-up activities are concentrated outside of the metropolitan area. When the distribution change from 2018 to 2036, extending the trend from 2000 to 2018, it was confirmed that there was a high possibility of a rise in the hierarchy in the future in regions adjacent to regions where start-up activities occur. This study aimed to provide implications for regional policies related to fostering start-ups and creating jobs by dynamically analyzing the location pattern of manufacturing start-ups, which is a major source of job creation.

• The Journal of Korean Philosophical History
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• no.50
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• pp.171-200
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• 2016

The full-scaled study of Joseon conducted by Japan in the 1910s was part of its colonial policy, while the native Joseon studies against it contained political aspiration to recover the national rights and independence. Accordingly, the conceptual meaning of 'Joseon' varied according to its subject of speech. The establishment of modern nation-state failed along with the extinction of Korean Empire, but 'Joseon' was newly discovered within national ideology. It became a historical concept in which the experience of the past and the expectation toward the future could be united. The so-called 'Joseon Studies' was only limited to intellectuals in the academic circle, but 'Joseon' embraced the articulations from more various social agents. Furthermore, it is only natural that 'Joseon Studies' should be interpreted within the historical semantics of 'Joseon', considering the connection between concept and discourse. In his The History of Joseon Civilization, An Hwak encompassed the history from the times of ancient mythology to the contemporary times under the banner of 'Joseon'. Opposing Japanese distortion of history carried out in the name of historical positivism, he idealized Joseon history as comparable to that of the Western democracy. He extended the study of 'Joseon' into culture at large, foreshadowing a kind of Joseon philosophy. In his An Overview of Joseon Philosophical Ideas, the first description of 'Joseon philosophy' as an independent field, he proposed philosophy as one of three sources of pride in Joseon and asserted its uniqueness and originality compared to the West. It was an attempt to grasp the peculiarity of Joseon ideas from a perspective of the history of universal human civilization. He considered 'Jong'(倧) as an ideological foundation held from the ancient to the modern times, and the acceptance of Buddhism and Confucianism as beneficial to 'Joseon philosophy'. The birth of 'Joseon philosophy', the modern transformation of the traditional knowledge system, was an intellectual experiment to apply traditional knowledge to the modern disciplinary classification system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.141-148
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• 2021

This paper presents a method to detect zebra-crossing using deep learning which combines SegNet and ResNet. For the blind, a safe crossing system is important to know exactly where the zebra-crossings are. Zebra-crossing detection by deep learning can be a good solution to this problem and robotic vision-based assistive technologies sprung up over the past few years, which focused on specific scene objects using monocular detectors. These traditional methods have achieved significant results with relatively long processing times, and enhanced the zebra-crossing perception to a large extent. However, running all detectors jointly incurs a long latency and becomes computationally prohibitive on wearable embedded systems. In this paper, we propose a model for fast and stable segmentation of zebra-crossing from captured images. The model is improved based on a combination of SegNet and ResNet and consists of three steps. First, the input image is subsampled to extract image features and the convolutional neural network of ResNet is modified to make it the new encoder. Second, through the SegNet original up-sampling network, the abstract features are restored to the original image size. Finally, the method classifies all pixels and calculates the accuracy of each pixel. The experimental results prove the efficiency of the modified semantic segmentation algorithm with a relatively high computing speed.

• Journal of Wetlands Research
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• v.23 no.2
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• pp.107-115
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• 2021

In order to analyze wave propagation, tidal current, and sediment transport in the vicinity of the Gaeya open channel, it was classified into before(CASE1W) and after(CASE2W) installation of various artificial structures, and the calculation results for CASE1W and CASE2W were compared. For wave propagation, the results of incident and reflected waves were derived using the SWAN numerical model, and the tidal current velocity results were derived using the FLOW2DH numerical model for tidal current. The results of the SWAN numerical model and the FLOW2DH numerical model became the input conditions for the SEDTRAN numerical model that predicts sediment transport, and the maximum bed shear stress and suspended sediment concentration distribution near the Gaeya open channel were calculated through the SEDTRAN numerical model. As a result of the calculation of the SWAN numerical model, the wave height of CASE2W was increased by 40~50 % compared to CASE1W because the incident wave was diffracted and superimposed and the reflected wave was generated by about 7 km long northen jetty. As a result of the calculation of the FLOW2DH numerical model, According to the northen breakwater, the northen jetty and Geumrando, CASE2W was calculated 10~30 % faster than CASE1W in the tidal current of the Gaeya open channel. As a result of the calculation of the SEDTRAN numerical model, the section where the maximum bed shear stress is 1.0 N/m² or more and the suspended concentration is 80mg/L or more was widely distributed in the Gaeya open channel from the marine environment by the complex wave field(incident wave, reflected wave and tidal wave) and the installation of various artificial structures. it is believed that a sedimentation phenomenon occurred in the Gaeya open channel.

• The Journal of Korean Obstetrics and Gynecology
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• v.34 no.1
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• pp.1-14
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• 2021

Objectives: We studied for the adjustment of the patterns of 'The Diagnostic Tool for Climacteric and Postmenopausal Syndrome Pattern Identification (hereinafter CaPSPI)' (studyI) and the correlation between CaPSPI and Korean medicine doctors' diagnosis which was carried out without knowing the results of CaPSPI (studyII). Methods: The studyI followed the previous study method in 2018 (2018-3). The studyII was conducted from June 1, 2019 to July 10, 2020 with ◯◯ University Korean Medicine Hospital IRB's approval (2019-4). Doctors' diagnosis was conducted face-to-face with the subjects. Doctors' diagnosis was carried out based on the Kupperman's questionnaire, 'Diagnosis System of Oriental Medicine (hereinafter DSOM)' and four examinations (四診) records. The diagnosis was marked with 0 for 'no', 1 for 'somewhat', 2 for 'yes' and 3 for 'very yes'. The correlation between CaPSPI and the mean of doctors diagnostic scores were investigated statistically. Results: The studyI showed that heart-heat (心火) pattern was added. The Factor loading coefficient for heart-heat was 0.551 to 0.789, and the Cronbach's coefficient was 0.896. The studyII showed that the diagnosis (Kappa statistic) of two doctors showed statistically significant concordance (all eight patterns), with correlation of them were 0.3 or higher. And the correlation between the CaPSPI score and the mean of doctors' diagnostic score showed a statistically significant correlation, with liver qi depression (肝鬱) being the highest at 0.552 and dual deficiency of the heart-spleen (心脾兩虛) being the lowest at 0.301. Conclusions: Since the diagnosis results of CaPSPI showed a significant correlation with the diagnosis of Korean traditional medicine experts, it was believed that the CaPSPI results can be trusted and used for clinical purposes.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.25-34
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• 2020

In this paper, we empirically compare the effectiveness of training models to recognize beauty-related skin disease using supervised deep learning algorithms. Recently, deep learning algorithms are being actively applied for various fields such as industry, education, and medical. For instance, in the medical field, the ability to diagnose cutaneous cancer using deep learning based artificial intelligence has improved to the experts level. However, there are still insufficient cases applied to disease related to skin beauty. This study experimentally compares the effectiveness of identifying beauty-related skin disease by applying deep learning algorithms, considering CNN, ResNet, and SE-ResNet. The experimental results using these training models show that the accuracy of CNN is 71.5% on average, ResNet is 90.6% on average, and SE-ResNet is 95.3% on average. In particular, the SE-ResNet-50 model, which is a SE-ResNet algorithm with 50 hierarchical structures, showed the most effective result for identifying beauty-related skin diseases with an average accuracy of 96.2%. The purpose of this paper is to study effective training and methods of deep learning algorithms in consideration of the identification for beauty-related skin disease. Thus, it will be able to contribute to the development of services used to treat and easy the skin disease.