• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.139-152
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• 2023

Recently, there has been an increasing interest in the migration of young people to local areas. These young people are discovering and reinterpreting local resources to open local businesses while generating new value in local activities and businesses. This study was initiated with the recognition that fundamental solutions are needed for these young people to settle and sustain residence in the local area. Edward Relph stated that 'place' is a fundamental attribute that influences human existence in the world and is the source of stability and identity formation for individuals. This is deeply related to the psychology of young people who have migrated to local areas. These young people accept the unfamiliar 'space' as a 'place' to form stability and personal identity. Therefore, this study utilized PhotoVoice methodology to examine the process and key factors of place identity formation among migrant youths. As a result, the study identified factors that enable young people to settle in local areas and sustain residence while recognizing elements the local government should focus on to support and address the influx of young people. The results of this study can serve as a foundation for addressing the declining population in local areas through the formation of a relationship population and spur the inflow of young people to local cities in the future.

• Asia Marketing Journal
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• v.15 no.3
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• pp.97-115
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• 2013

This paper was motivated by two gaps in the extant literature on brand portfolio planning. First, research has shown that, as the number of products connected to a brand increases, the extended product receives more favorable evaluations. However, this result was obtained by comparing two brands with different number of products while controlling the brands' breadths. Hence one may question if the above result would hold when the brand is narrow as well as broad. Second, the literature has investigated the effect of brand breadth on the perceived fit and evaluations of an extended product within a relatively limited range ("narrow vs. broad") and not considered the case of a "very broad" brand. To address these gaps, we propose two hypotheses: 1) the effects of the number of products associated with a brand on the perceived fit and evaluations of a moderately far brand extension are moderated by the brand's breadth (H1); and 2) the relationship between a brand's breadth and a moderately far extension's perceived fit and evaluations looks like an inverse-U shape (H2). Study 1 was conducted to test H1. Study 1 employed a 2 × 2 within-subjects design in which the first factor was the number of products (small (2) or large (5)), and the second factor was brand breadth (narrow or broad). We measured brand breadth as the perceived similarity among products associated with a brand. Participants provided the perceived fit and evaluations of an extended product. Study 2 was conducted to test H2 as well as to replicate Study 1 in a more general setting and with different products. It employed a 2 × 3 within-subjects design, in which the first factor was the number of products (small (2) or large (5)), and the second factor was brand breadth (narrow, broad, or very broad). The results from two experiments support both hypotheses. This paper contributes to the literature on brand extensions in two ways. First, it broadens our understanding of the effects of product number and brand breadth on extended product evaluations by considering the two factors jointly. Second, we believe this study to be the first to present evidence that brand breadth can exert an inverted U-shape effect on the perceived fit and evaluations of an extended product. The results also offer implications for marketers. First, marketers should heed the finding that adding similar products to a narrow brand does not help the brand's extension launch. Second, the finding that the relationship between brand breadth and extended product evaluations might not be linear provides practical implications. While a narrow brand should not keep launching close extensions, nor should a broad brand continue producing far extensions to broaden its breadth. A firm with a broad corporate or family brand might want to consider introducing a new brand instead of adding dissimilar products under the brand umbrella.

• The Journal of Sustainable Design and Educational Environment Research
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• v.22 no.4
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• pp.9-17
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• 2023

This study explores school location, school environment, educational conditions, and appropriate scale of schools in the context of Sejong City's development area and identifies effective school establishment plans based on the analysis of the needs of the educational community. The research questions for this study include, first, what is the change trend in the number of students as a result of the opening of schools in the development area of Sejong City to the present, and what differences are there between Sejong and other new cities? Second, what challenges arise in school location due to the occurrence of oversized schools and undergraduate institutions? Third, what challenges arise in school location that would limit the ability to create a safe school environment? Fourth, what aspects need to be improved in school location decisions to promote proper placement? A survey was conducted among parents and faculty members to collect data. The findings revealed that first, when establishing a school, identifying an appropriate location for the school was the top priority of the respondents. The second was the proximity of the school to dense housing, with a parent drop zone next to the school site. Third, to address the issue of lack of playgrounds and special class and care classes, respondents called for various measures such as securing school sites within a certain area. Finally, integrated operation schools and school facilities are required in preparation for decreasing school-age populations due to low birth rates.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.207-216
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• 2024

As big data was built due to the 4th Industrial Revolution, personalized services increased rapidly. As a result, the amount of personal information collected from online services has increased, and concerns about users' personal information leakage and privacy infringement have increased. Online service providers provide privacy policies to address concerns about privacy infringement of users, but privacy policies are often misused due to the long and complex problem that it is difficult for users to directly identify risk items. Therefore, there is a need for a method that can automatically check whether the privacy policy is safe. However, the safety verification technique of the conventional blacklist and machine learning-based privacy policy has a problem that is difficult to expand or has low accessibility. In this paper, to solve the problem, we propose a safety verification technique for the privacy policy using the GPT-3.5 API, which is a generative artificial intelligence. Classification work can be performed evenin a new environment, and it shows the possibility that the general public without expertise can easily inspect the privacy policy. In the experiment, how accurately the blacklist-based privacy policy and the GPT-based privacy policy classify safe and unsafe sentences and the time spent on classification was measured. According to the experimental results, the proposed technique showed 10.34% higher accuracy on average than the conventional blacklist-based sentence safety verification technique.

• 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 Practical Engineering Education
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• v.16 no.1_spc
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• pp.41-49
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• 2024

The advancement of artificial intelligence on a global scale is significantly transforming life. In the field of education, there is a strong emphasis on actively utilizing AI and fostering creatively integrated talents with diverse knowledge. In alignment with this trend, there is a paradigm shift in AI education across primary, middle, high school, as well as university and graduate education. Leading AI schools and specialized high schools are dedicated to enhancing students' AI capabilities, while universities integrate AI into software courses or establish new AI departments to nurture talent. In AI-integrated education graduate programs, national efforts are underway to educate instructors from various disciplines on applying AI technology to the curriculum. In this context, specialized high schools are also restructuring their departments to cultivate technological talent in AI, tailored to students' characteristics and career paths. While the current education focuses primarily on the fundamental concepts and technologies of AI, there is a need to address the aspect of developing practical problem-solving skills. Therefore, this research aims to compare and analyze essential educational courses in AI-leading schools, AI-integrated high schools, AI high schools, university AI departments, and AI-integrated education graduate programs. The goal is to propose the necessary educational courses for AI education in specialized high schools, with the expectation that a more advanced curriculum in AI education can be established in specialized high schools through this effort.

• Smart Media Journal
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• v.13 no.5
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• pp.52-61
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• 2024

The onset of the COVID-19 pandemic has accelerated social transformations across various nations. These changes, particularly prominent in the corporate and industrial sectors, have necessitated a shift towards increased remote activities, fundamentally altering societal structures. Within this context, the concept of the Metaverse, a virtual world existing since the early 2000s but previously underrecognized, began to gain widespread recognition. In South Korea, major tech companies such as Naver, Kakao, and Coupang have long normalized remote working, with new employee orientations also taking place on Metaverse platforms. Beyond the IT sector, institutions requiring large gatherings, such as schools, have adopted the Metaverse for hosting major events like welcome ceremonies and informational sessions. This phenomenon suggests that the Metaverse is not merely a transient social trend but is gradually integrating into the daily lives of the general populace, serving as a significant social connector. This study explores the potential of Metaverse-enabled design thinking and methodologies to address the Environmental, Social, and Governance (ESG) challenges faced by Korean society. Specifically, the research focuses on developing solutions for social issues related to pets in Korea.

• Journal of the Korean Society of Radiology
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• v.18 no.2
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• pp.127-133
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• 2024

The elbow joint is made up of three different bones. X-rays or other radiological exams are commonly used to diagnose elbow injuries or disorders caused by physical activity and external forces. Previous research on the elbow joint reported a new examination method that meets the imaging evaluation criteria in the tilt position by Z-axis elevation of the forearm. Therefore, this study aims to design an optimized instrument and develop an aid applicable to other upper extremity exams. After completing the 2D drawing and 3D modeling design, the final design divided into four parts was fabricated with a 3D printer using ABS plastic and assembled. The developed examination aid consists of a four-stage Z-axis elevation tilt angle function (0°, 5°, 10°, and 15°) and can rotate and fixate 360° in 1-degree increments. It was designed to withstand a maximum equivalent stress of 56.107 Pa and a displacement of 1.6548e-5 mm through structural analysis to address loading issues caused by cumulative frequency of use and physical utilization. In addition to X-ray exams of the elbow joint, the developed aid can be used for shoulder function tests by rotating the humerus and also be applied to MRI and CT exams as it is made of non-metallic materials. It will contribute to the accuracy and efficiency of medical imaging diagnosis through clinical applications of various devices and medical imaging exams in the future.

• Journal of IKEEE
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• v.28 no.2
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• pp.150-157
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• 2024

In this paper, we propose a novel algorithm for predicting the number of apples on an apple tree using a deep learning-based object detection model and a polynomial regression model. Measuring the number of apples on an apple tree can be used to predict apple yield and to assess losses for determining agricultural disaster insurance payouts. To measure apple fruit load, we photographed the front and back sides of apple trees. We manually labeled the apples in the captured images to construct a dataset, which was then used to train a one-stage object detection CNN model. However, when apples on an apple tree are obscured by leaves, branches, or other parts of the tree, they may not be captured in images. Consequently, it becomes difficult for image recognition-based deep learning models to detect or infer the presence of these apples. To address this issue, we propose a two-stage inference process. In the first stage, we utilize an image-based deep learning model to count the number of apples in photos taken from both sides of the apple tree. In the second stage, we conduct a polynomial regression analysis, using the total apple count from the deep learning model as the independent variable, and the actual number of apples manually counted during an on-site visit to the orchard as the dependent variable. The performance evaluation of the two-stage inference system proposed in this paper showed an average accuracy of 90.98% in counting the number of apples on each apple tree. Therefore, the proposed method can significantly reduce the time and cost associated with manually counting apples. Furthermore, this approach has the potential to be widely adopted as a new foundational technology for fruit load estimation in related fields using deep learning.