• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.139-151
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• 2017

The habitat preference of roe deers(Capreolus pygargus) in Jeju island, South Korea was analyzed by using their occurrence probability in MaxEnt model in this study. Totally 490 surveying data were gathered and 15 environmental variables were chosen for the model in which 6 variables out of 15 ones were filtered and finally removed because of there being higher correlation(over 0.7 in correlation coefficient). According to the modeling, roe deers were known to prefer the area ranging from 200 to 700 meter and over 1,500 meter in sea level, where there were not many dominant tree and/or dominant vegetation with low density so that understory vegetation can grow well with plentiful sunlight and can be used as a food of herbivore like roe deers. Otherwise, the region ranging from 700 to 1,500 meter was mostly covered with high density vegetation which cut off sunlight trying to penetrate through the dominant vegetation. It can cause a lower density of vegetation on surface, which can not attract to roe deers.

• Journal of Korean Library and Information Science Society
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• v.52 no.2
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• pp.25-44
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• 2021

In a situation where OA papers are increasing, POI, which indexes how open the research activities of individual researchers are, is drawing attention. This study investigated the existence of OA papers and the OA method published in international academic journals by domestic LIS researchers, and derived the researchers' POI based on this. In addition, by examining the relationship between the POI index and the researcher's amount of research papers, the research sub field, and the foreign co-authors, it was analyzed whether these factors are relevant to the researcher's POI. As a result, there were 492 papers by 82 researchers whose OA status and method were normally identified through Unpaywall. Second, only 20.7% of papers published in international journals were open accessed, and almost cases were gold and green methods. Third, there were many papers in text mining in medical journals, and the papers opened in the green method are open in institutional repositories of foreign co-authors or transnational subject repositories such as PMC. Third, the POI index was relatively higher for researchers in the field of informetrics, machine learning than other fields. In addition, it was analyzed that the presence or absence of overseas co-authors is related to OA.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.561-568
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• 2022

The purpose of this study is to explore the narrative of VR disaster and safety Education introduced by disaster film narrative. VR(Virtual Reality) is be suitable technology for disaster and safety Education due to media characteristics as 'immersion', 'presence', 'interactivity', 'pleasure'. Disaster film narrative is able to be worth VR disaster and safety education as a variety of stories and educational effect. For this study, examine a theoretical study and a visiting research of 'Busan 119 Safety & Experience Center'. This study concludes that Firstly need to introduce catharsis effect, Secondly, build 'interactive narratives' that ensure active participation of users, Thirdly, introduce an 'adventure game' narrative element, Fourthly, introduce a hero-shaped narrative in which the user becomes a one-man hero, And lastly, need education as use user's multiple access and group experience learning. Therefore, This thesis is of academic value in that it suggest a desirable new direction of narrative in VR disaster and safety education.

• Journal of Korean Home Economics Education Association
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• v.35 no.2
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• pp.1-20
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• 2023

The purpose of this study is to extract and analyze ecological citizenship elements in the middle school home economics textbook used in the 2015 national curriculum for the introduction of ecological transformation education in the 2022 national curriculum. As a result of the analysis, the content analysis of the ecological citizenship factor was validated by six experts who are incumbent middle school home economics teachers, and the S-CVI value was 0.97, ensuring the validity of the ecological citizenship factor analysis. The results of analyzing 242 ecological citizenship factors extracted from home economics textbooks are as follows. According to the content area of the 2015 national home economics curriculum, the 'human development and family' area had the highest presence of ecological citizenship factors followed by the 'resource management and self-reliance' area and the 'home life and safety' area. Among the categories of ecological citizenship factors, 'value⋅attitude' was the most frequent, followed by 'process⋅function' and 'knowledge⋅understanding'. For each textbook composition system, ecological citizenship elements were extracted in the order of pictures, text, activities, and supplementary materials. There was a significant variation in the number of ecological citizenship factors among publishers, indicating the importance of the textbook writers' perception, interpretation, and direction of writing. Based on these analysis results, ecological citizenship teaching and learning activities applicable to home economics education were presented. This study highlights the potential for practicing ecological citizenship education in line with the new orientation of the curriculum on ecological transformation education through home economics education. Furthermore, it provides valuable baseline data for the development and implementation of textbooks for the 2022 national curriculum.

• Journal of the Korean Geotechnical Society
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• v.39 no.10
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• pp.41-48
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• 2023

The presence of abnormalities in the subgrade of roads poses safety risks to users and results in significant maintenance costs. In this study, we aimed to experimentally evaluate the temperature distributions in abnormal areas of subgrade materials using infrared cameras and analyze the data with machine learning techniques. The experimental site was configured as a cubic shape measuring 50 cm in width, length, and depth, with abnormal areas designated for water and air. Concrete blocks covered the upper part of the site to simulate the pavement layer. Temperature distribution was monitored over 23 h, from 4 PM to 3 PM the following day, resulting in image data and numerical temperature values extracted from the middle of the abnormal area. The temperature difference between the maximum and minimum values measured 34.8℃ for water, 34.2℃ for air, and 28.6℃ for the original subgrade. To classify conditions in the measured images, we employed the image analysis method of a convolutional neural network (CNN), utilizing ResNet-101 and SqueezeNet networks. The classification accuracies of ResNet-101 for water, air, and the original subgrade were 70%, 50%, and 80%, respectively. SqueezeNet achieved classification accuracies of 60% for water, 30% for air, and 70% for the original subgrade. This study highlights the effectiveness of CNN algorithms in analyzing subgrade properties and predicting subsurface conditions.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.439-449
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• 2024

Recent rapid advancements in voice generation technology have enabled the natural synthesis of voices using text alone. However, this progress has led to an increase in malicious activities, such as voice phishing (voishing), where generated voices are exploited for criminal purposes. Numerous models have been developed to detect the presence of synthesized voices, typically by extracting features from the voice and using these features to determine the likelihood of voice generation.This paper proposes a new model for extracting voice features to address misuse cases arising from generated voices. It utilizes a deep learning-based audio codec model and the pre-trained natural language processing model BERT to extract novel voice features. To assess the suitability of the proposed voice feature extraction model for voice detection, four generated voice detection models were created using the extracted features, and performance evaluations were conducted. For performance comparison, three voice detection models based on Deepfeature proposed in previous studies were evaluated against other models in terms of accuracy and EER. The model proposed in this paper achieved an accuracy of 88.08%and a low EER of 11.79%, outperforming the existing models. These results confirm that the voice feature extraction method introduced in this paper can be an effective tool for distinguishing between generated and real voices.

• Journal of the Korea Computer Graphics Society
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• v.30 no.1
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• pp.1-10
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• 2024

In immersive virtual environments including mixed reality (MR) and virtual reality (VR), avatars or agents, which are virtual humans, are being studied and applied in various ways as factors that increase users' social presence. Recently, studies are being conducted to apply generative AI as an agent to improve user learning effects or suggest a collaborative environment in an immersive virtual environment. This study proposes a novel method for interface application of a virtual assistant agent (VAA) using OpenAI's ChatGPT in an immersive virtual environment including VR and MR. The proposed method consists of an information agent that responds to user queries and a control agent that controls virtual objects and environments according to user needs. We set up a development environment that integrates the Unity 3D engine, OpenAI, and packages and development tools for user participation in MR and VR. Additionally, we set up a workflow that leads from voice input to the creation of a question query to an answer query, or a control request query to a control script. Based on this, MR and VR experience environments were produced, and experiments to confirm the performance of VAA were divided into response time of information agent and accuracy of control agent. It was confirmed that the interface application of the proposed VAA can increase efficiency in simple and repetitive tasks along with user-friendly features. We present a novel direction for the interface application of an immersive virtual environment through the proposed VAA and clarify the discovered problems and limitations so far.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.6
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• pp.67-84
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• 2023

In some areas of the Republic of Korea, the designation and management of conservation areas do not adequately reflect regional characteristics and often impose behavioral regulations without considering the local context. One prominent example is the Busan EDC area. As a result, conflicts may arise, including large-scale civil complaints, regarding the conservation and utilization of these areas. Therefore, for the efficient designation and management of protected areas, it is necessary to consider various ecosystem factors, changes in land use, and regional characteristics. In this study, we specifically focused on the Busan EDC area and applied machine learning techniques to analyze the habitat of regional species. Additionally, we employed Explainable Artificial Intelligence techniques to interpret the results of our analysis. To analyze the regional characteristics of the waterfront area in the Busan EDC district and the habitat of migratory birds, we used bird observations as dependent variables, distinguishing between presence and absence. The independent variables were constructed using land cover, elevation, slope, bridges, and river depth data. We utilized the XGBoost (eXtreme Gradient Boosting) model, known for its excellent performance in various fields, to predict the habitat probabilities of 11 bird species. Furthermore, we employed the SHapley Additive exPlanations technique, one of the representative methodologies of XAI, to analyze the relative importance and impact of the variables used in the model. The analysis results showed that in the EDC business district, as one moves closer to the river from the waterfront, the likelihood of bird habitat increases based on the overlapping habitat probabilities of the analyzed bird species. By synthesizing the major variables influencing the habitat of each species, key variables such as rivers, rice fields, fields, pastures, inland wetlands, tidal flats, orchards, cultivated lands, cliffs & rocks, elevation, lakes, and deciduous forests were identified as areas that can serve as habitats, shelters, resting places, and feeding grounds for birds. On the other hand, artificial structures such as bridges, railways, and other public facilities were found to have a negative impact on bird habitat. The development of a management plan for conservation areas based on the objective analysis presented in this study is expected to be extensively utilized in the future. It will provide diverse evidential materials for establishing effective conservation area management strategies.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.4
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• pp.17-34
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• 2023

Camera trapping has been used as a non-invasive survey method that minimizes anthropogenic disturbance to ecosystems. Nevertheless, it is labor-intensive and time-consuming, requiring researchers to quantify species and populations. In this study, we aimed to improve the preprocessing of camera trapping data by utilizing an object detection algorithm. Wildlife monitoring using unmanned sensor cameras was conducted in a forested urban forest and a green space on a university campus in Cheonan City, Chungcheongnam-do, Korea. The collected camera trapping data were classified by a researcher to identify the occurrence of species. The data was then used to test the performance of the YOLO-X object detection algorithm for wildlife detection. The camera trapping resulted in 10,500 images of the urban forest and 51,974 images of green spaces on campus. Out of the total 62,474 images, 52,993 images (84.82%) were found to be false positives, while 9,481 images (15.18%) were found to contain wildlife. As a result of wildlife monitoring, 19 species of birds, 5 species of mammals, and 1 species of reptile were observed within the study area. In addition, there were statistically significant differences in the frequency of occurrence of the following species according to the type of urban greenery: Parus varius(t = -3.035, p < 0.01), Parus major(t = 2.112, p < 0.05), Passer montanus(t = 2.112, p < 0.05), Paradoxornis webbianus(t = 2.112, p < 0.05), Turdus hortulorum(t = -4.026, p < 0.001), and Sitta europaea(t = -2.189, p < 0.05). The detection performance of the YOLO-X model for wildlife occurrence was analyzed, and it successfully classified 94.2% of the camera trapping data. In particular, the number of true positive predictions was 7,809 images and the number of false negative predictions was 51,044 images. In this study, the object detection algorithm YOLO-X model was used to detect the presence of wildlife in the camera trapping data. In this study, the YOLO-X model was used with a filter activated to detect 10 specific animal taxa out of the 80 classes trained on the COCO dataset, without any additional training. In future studies, it is necessary to create and apply training data for key occurrence species to make the model suitable for wildlife monitoring.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.767-773
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• 2023

To In this study, we sought to evaluate related factors affecting lung volume and their significance in pulmonary function and ventilation disorders. As experimental subjects, 206 normal adult men and women who underwent a low-dose chest CT scan and a spirometry test were selected at the same time. The experimental method was to measure lung volume using lung CT images obtained through a low-dose chest CT scan using deep learning-based AVIEW. Measurements were made using the LCS automatic diagnosis program. In addition, the results of measuring lung function were obtained using a spirometer, and gender and BMI were selected as related factors that affect lung volume, and significance was evaluated through an independent sample T-test with lung volume. As a result of the experiment, it was confirmed that in evaluating lung volume according to gender, all lung volumes of men were larger than all lung volumes of women. he result of an independent samples T-test using the respective average values for gender and lung volume showed that all lung volumes were larger in men than in women, which was significant (p<0.001). And in the evaluation of lung volume according to BMI index, it was confirmed that all lung volumes of adults with a BMI index of 24 or higher were larger than all lung volumes of adults with a BMI index of less than 24. However, the independent samples T-test using the respective average values for BMI index and lung volume did not show a significant result that all lung volumes were larger in BMI index 24 or higher than in BMI index less than 24 (p<0.055). In the evaluation of lung volume according to the presence or absence of pulmonary ventilation impairment, it was confirmed that all lung volumes of adults with normal pulmonary function ventilation were larger than all lung volumes of adults with pulmonary ventilation impairment. And as a result of the independent sample T-test using the respective average values for the presence or absence of pulmonary ventilation disorder and lung volume, the result was significant that all lung volumes were larger in adults with normal pulmonary function ventilation than in adults with pulmonary function ventilation disorder (p <0.001). Lung volume and spirometry test results are the most important indicators in evaluating lung health, and using these two indicators together to evaluate lung function is the most accurate evaluation method. Therefore, it is expected that this study will be used as basic data by presenting the average lung volume for adults with normal ventilation and adults with impaired lung function and ventilation in similar future studies on lung volume and vital capacity testing.