• Journal of the Korean Geotechnical Society
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• v.37 no.5
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• pp.5-17
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• 2021

In this study, the shear behavior between pile-sandy soil interface was quantified based on series of rigorous ring shear test results. Ring shearing test was carried out to observe the shear behavior prior to failure and behavior at residual state between most commonly used pile materials - steel and concrete - and Jumunjin sand. The test was set to clarify the shear behavior under various confinement conditions and soil densities. The test results were converted in to representative friction angles for various test materials. Additional numerical analysis was executed to validate the accuracy of the test results. Based on the test results and the numerical validation, it was found that due to the dilative and contractive nature of sand, its interface behavior can be categorized in to two different types : soils with higher densities tend to show peak shear stress and moves on to residual state, while on the other hand, soils with lower densities tend to show bilinear load-transfer curves along the interface. However, the relative density and the confining stress was found to affect the friction angle only in the small train range, and converges as it progresses to large deformation. This study established a large deformation analysis method which can successfully simulate and predict the large deformation behavior such as ring shear tests. Moreover, the friction angle derived from the ring shear test result and verified by numerical analysis can be applied to numerical analysis and actual design of various pile foundations.

• Knowledge Management Research
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• v.22 no.2
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• pp.145-165
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• 2021

As of late December 2019, the spread of COVID-19 pandemic began which put the entire world in panic. In order to overcome the crisis and minimize any subsequent damage, the government as well as its affiliated institutions must maximize effects of pre-existing policy support and introduce a holistic response plan that can reflect this changing situation- which is why it is crucial to analyze social topics and people's interests. This study investigates people's major thoughts, attitudes and topics surrounding COVID-19 pandemic through the use of social media and big data. In order to collect public opinion, this study segmented time period according to government countermeasures. All data were collected through NAVER blog from 31 December 2019 to 12 December 2020. This research applied TF-IDF keyword extraction and LDA topic modeling as text-mining techniques. As a result, eight major issues related to COVID-19 have been derived, and based on these keywords, this research presented policy strategies. The significance of this study is that it provides a baseline data for Korean government authorities in providing appropriate countermeasures that can satisfy needs of people in the midst of COVID-19 pandemic.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.1
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• pp.115-132
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• 2021

This study collected data on COVID-19 research papers published in international journals by Korean authors from WoS. Bibliographical analysis was performed on subject categories, institutions, funder distribution and so on. In addition, open access and journal review speed were also analyzed, which play an important role in facilitating academic publishing and distribution. The results showed that COVID-19-related papers published in international journals by Korean authors in 2020 included more papers on some specific fields, such as medicine, biology, and multidisciplinary. These researchers have published lots of papers not only in foreign journals but also in domestic English journals. 94% of papers were open access, and gold open access, which is available immediately after publication, was about 70% of the total. The COVID-19 orthopedic papers produced by Korean researchers were collected from PubMed and analyzed, and the average of review days was about 24 days. The analysis, including open access and review speed, showed that there has been an atmosphere of cooperation in the academic publishing ecosystem after the COVID-19 crisis. It would be desirable to continue this cooperation and address chronic problems in academic publishing system, such as promoting the publication of gold open access and reviewing efficiency.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.320-330
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• 2020

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.167-182
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• 2022

The accident data attributes of the traffic accident report are used not only in traditional traffic safety-related research to identify the cause of traffic accidents, but also as basis data for the development of the automated vehicle driving performance verification scenarios. However, since the data attributes of the traffic accident report are limited for the purpose of reconstructing the traffic situation and developing scenarios, this study aims to provide the directions for improvement of traffic accident report, ultimately for its expanded usability for the automated vehicle test scenarios. The directions for improvement of the traffic accident report are provided by categorizing the traffic situation before the accident (pre-crash), the situation immediately before or during the accident (on-crash), and the situation after the accident (post-crash), respectively. Additional data items or data processing methods are presented. Furthermore, data elements that can be extracted from the traffic accident process data in the unstructured narrative form are explored and provided.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.57-64
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• 2022

If the inside of a building collapses due to a disaster such as fire, collapse, or natural disaster, the physical security inside the building is likely to become ineffective. Here, physical security is needed to minimize the human casualties and physical damages in the collapsed building. Therefore, this paper proposes an algorithm to minimize the damage in a disaster situation by fusing existing research that detects obstacles and collapsed areas in the building and a deep learning-based object detection algorithm that minimizes human casualties. The existing research uses a single camera to determine whether the corridor environment in which the robot is currently located has collapsed and detects obstacles that interfere with the search and rescue operation. Here, objects inside the collapsed building have irregular shapes due to the debris or collapse of the building, and they are classified and detected as obstacles. We also propose a method to detect rescue requesters-the most important resource in the disaster situation-and minimize human casualties. To this end, we collected open-source disaster images and image data of disaster situations and calculated the accuracy of detecting rescue requesters in disaster situations through various deep learning-based object detection algorithms. In this study, as a result of analyzing the algorithms that detect rescue requesters in disaster situations, we have found that the YOLOv4 algorithm has an accuracy of 0.94, proving that it is most suitable for use in actual disaster situations. This paper will be helpful for performing efficient search and rescue in disaster situations and achieving a high level of physical security, even in collapsed buildings.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.33 no.3
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• pp.242-247
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• 1988

To elucidate the relationship of the quality of aromatic tobaccos to their chemical constituents, certain chemical components and leaf quality by price were compared among cured leaves produced under different soil moisture levels during growing season. As the soil moisture increased, plant height and the length and width of largest leaf increased. days to flower was shortened and total chlorophyll and carotenoid content of green leaf decreased. As the soil moisture increased, leaf quality was deteriorated. The content of nicotine, pet. ether ext. and total nitrogen increased with slight increment of nonvolatile organic acids and higher fatty acids, but ash content and pH of cured leaves decreased under high soil moisture content. Volatile organic acids such as 3-methyl pentanoic acid, the main compounds contributing to the aroma of oriental tobacco, and most volatile neutrals decreased conspicuously under high soil moisture. The content of pet. ether ext., volatile organic acids, volatile neutrals, ash and pH of cured leaves were found to be the appropriate factors for the quality evaluation of aromatic leaves grown under under different soil moisture.

• The Journal of Engineering Geology
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• v.33 no.1
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• pp.15-25
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• 2023

The effects of forest fires on the activity of microbial communities in topsoil and subsoil were investigated. Samples were collected from Korean forest soils comprising mainly igneous and sedimentary rocks. Analysis of beta-glucosidase, found higher microbial activity in sedimentary rocks than in igneous rocks. Enzyme activity was not observed immediately after fire, but was restored over time. The enzyme activity of subsoil was inhibited by 33~46% compared with that in the topsoil, regardless of soil damage. The effect of fire on the availability of microbial substrate was investigated using EcoPlate. The percentages of average well color development values of damaged and normal topsoil were 52.7~56.8% and 62.3~83.6%, respectively. Forest fires appear to affect the diversity and substrate availability of the subsoil microbial community by accelerating the decomposition of soil organic matter. The Shanon index, representing microbial biodiversity, was high in the topsoil of all samples; it was higher for soil microorganisms in sedimentary rocks than in igneous rocks, and higher in topsoil than in subsoil.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.27-40
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• 2023

With the increase in the spread of smart devices and the impact of COVID-19, the consumption of media contents through smart devices has significantly increased. Along with this trend, the amount of media contents viewed through OTT platforms is increasing, that makes contents recommendations on these platforms more important. Previous contents-based recommendation researches have mostly utilized metadata that describes the characteristics of the contents, with a shortage of researches that utilize the contents' own descriptive metadata. In this paper, various text data including titles and synopses that describe the contents were used to recommend similar contents. KLUE-RoBERTa-large, a Korean language model with excellent performance, was used to train the model on the text data. A dataset of over 20,000 contents metadata including titles, synopses, composite genres, directors, actors, and hash tags information was used as training data. To enter the various text features into the language model, the features were concatenated using special tokens that indicate each feature. The test set was designed to promote the relative and objective nature of the model's similarity classification ability by using the three contents comparison method and applying multiple inspections to label the test set. Genres classification and hash tag classification prediction tasks were used to fine-tune the embeddings for the contents meta text data. As a result, the hash tag classification model showed an accuracy of over 90% based on the similarity test set, which was more than 9% better than the baseline language model. Through hash tag classification training, it was found that the language model's ability to classify similar contents was improved, which demonstrated the value of using a language model for the contents-based filtering.

• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.460-468
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• 2023

Developmental Dysplasia of the Hip (DDH) is a pathological condition commonly occurring during the growth phase of infants. It acts as one of the factors that can disrupt an infant's growth and trigger potential complications. Therefore, it is critically important to detect and treat this condition early. The traditional diagnostic methods for DDH involve palpation techniques and diagnosis methods based on the detection of keypoints in the hip joint using X-ray or ultrasound imaging. However, there exist limitations in objectivity and productivity during keypoint detection in the hip joint. This study proposes a deep learning model-based keypoint detection method using X-ray and ultrasound imaging and analyzes the performance of keypoint detection using various deep learning models. Additionally, the study introduces and evaluates various data augmentation techniques to compensate the lack of medical data. This research demonstrated the highest keypoint detection performance when applying the residual network 152 (ResNet152) model with simple & complex augmentation techniques, with average Object Keypoint Similarity (OKS) of approximately 95.33 % and 81.21 % in X-ray and ultrasound images, respectively. These results demonstrate that the application of deep learning models to ultrasound and X-ray images to detect the keypoints in the hip joint could enhance the objectivity and productivity in DDH diagnosis.