• 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 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 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 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.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.17-32
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• 2023

The entrepreneurial activities of college students play a significant role in modern economic and social development, particularly as a solution to the changing economic landscape and youth unemployment issues. Introducing innovative ideas and technologies into the market through entrepreneurship can contribute to sustainable economic growth and social value. Additionally, the entrepreneurial intentions of college students are shaped by various factors, making it crucial to deeply understand and appropriately support these elements. To this end, this study systematically explores the importance and impact of role models through a multiple serial mediation analysis. Through a survey of 300 college students, the study analyzed how two psychological variables, growth mindset and entrepreneurial self-efficacy, mediate the influence of role models on entrepreneurial intentions. The presence and success stories of role models were found to enhance the growth mindset of college students, which in turn boosts their entrepreneurial self-efficacy and ultimately strengthens their entrepreneurial intentions. The analysis revealed that exposure to role models significantly influences the formation of a growth mindset among college students. This mindset fosters a positive attitude towards viewing challenges and failures in entrepreneurship as learning opportunities. Such a mindset further enhances entrepreneurial self-efficacy, thereby strengthening the intention to engage in entrepreneurial activities. This research offers insights by integrating various theories, such as mindset theory and social learning theory, to deeply understand the complex process of forming entrepreneurial intentions. Practically, this study provides important guidelines for the design and implementation of college entrepreneurship education. Utilizing role models can significantly enhance students' entrepreneurial intentions, and educational programs can strengthen students' growth mindset and entrepreneurial self-efficacy by sharing entrepreneurial experiences and knowledge through role models. In conclusion, this study provides a systematic and empirical analysis of the various factors and their complex interactions that impact the entrepreneurial intentions of college students. It confirms that psychological factors like growth mindset and entrepreneurial self-efficacy play a significant role in shaping entrepreneurial intentions, beyond mere information or technical education. This research emphasizes that these psychological factors should be comprehensively considered when developing and implementing policies and programs related to college entrepreneurship education.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.41 no.2
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• pp.1-10
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• 2023

This study aims to explore the significance of historic changes and cultural characteristics of landscape architecture in Korean Seowon. Seowon refers to educational private institutes that also served as Confucian shrines and were prevalent during the mid-to-late Joseon dynasty. Seowon comprised three distinct functional spaces: a shrine, a school, and a garden. The concept of Seowon's garden extended beyond designed landscapes to include the surrounding natural environment. The importance of landscape architecture in Seowon is rooted in its connection to the educational philosophy of these institutes. During the Joseon dynasty, scholars revered nature as a manifestation of Confucian ideals, and they believed that close engagement with nature was integral to self-discipline and learning. This research investigated fifteen relatively well-preserved garden in South Korea and conducted a comprehensive analysis of their gardens. The analysis revealed two key findings. Firstly, gardens in Seowon were actively designed and constructed during the early phase of Seowon culture but gradually diminished after the 17th century. This can be attributed to the shift in Seowon's purpose, with a greater emphasis on its religious function over education. Consequently, the significance and presence of landscape architecture in Seowon, which was closely related with its Confucianist education, declined. Secondly, the study explored the historical backgrounds of each Seowon's landscape architecture and found that many of them were designed or influenced by individuals who were later memorialized and deified in the Seowon's shrines. The landscape architecture created by these predecessors was carefully preserved by the faculties and students as a form of respect. Therefore, landscape architecture in Korean Seowon not only conveys the institutional purpose as an educational hub for the local society but also reflects the institute's strong relationship with the figures they worship as shrines.