• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.161-163
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• 2021

In an apartment or private building, it is difficult for the user to operate the elevator button in a similar situation with luggage in both hands. In an environment where human contact must be minimized due to a highly infectious virus such as COVID-19, it is inevitable to operate an elevator based on untact. This paper proposes an operating system capable of operating the elevator by using the user's voice and image processing through the user's face without pressing the elevator button. The elevator can be operated to a designated floor without pressing a button by detecting the face of a person entering the elevator by detecting the person's face from the camera installed in the elevator, matching the information registered in advance. When it is difficult to recognize a person's face, it is intended to enhance the convenience of elevator use in an untouched environment by controlling the floor of the elevator using the user's voice through a microphone and automatically recording access information.

• Journal of the Korean Society of School Health
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• v.36 no.3
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• pp.103-112
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• 2023

Purpose: The COVID-19 pandemic, which emerged in late 2019, had a profound impact on global public health and disrupted the daily lives of people worldwide. Particularly, university students faced a challenging situation as their university life underwent a drastic transformation due to long-term remote learning and isolation measures. This study aimed to investigate the relationship between changes in daily life during the 2020 COVID-19 pandemic and depressive symptoms among university students aged between 19 and 29 in Korea. Methods: We analyzed data from the nationally representative 2020 Community Health Survey (CHS). Among the 229,269 participants, 9,279 university students aged 19-29, either enrolled or on leave, were selected. After excluding 401 cases with missing values, the final sample comprised 8,878 individuals. Using multivariate logistic regression with a complex sample design, we explored the association between daily life changes during the COVID-19 pandemic and depressive symptoms. Results: Changes in daily life during the COVID-19 pandemic was associated with depressive symptoms in Korean university students aged 19 to 29, even after adjusting for sociodemographic characteristics, health-related factors, and COVID-19-related aspects (OR=1.28, 95% CI=1.09~1.50). Conclusion: Our study suggests that when examining the impact of COVID-19 on health issues, it is crucial to consider the changes in daily life caused by the pandemic. These findings can provide insights into the psychological well-being of university students during times of crisis.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.958-963
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• 2023

Lately, there has been increasing research on the prediction of motion performance using artificial intelligence for the safe design and operation of ships. However, compared to conventional ships, research on small fishing boats is insufficient. In this paper, we propose a model that estimates the motion response essential for calculating the motion performance of small fishing boats using a deep neural network. Hydrodynamic analysis was conducted on 15 small fishing boats, and a database was established. Environmental conditions and main particulars were applied as input data, and the response amplitude operators were utilized as the output data. The motion response predicted by the trained deep neural network model showed similar trends to the hydrodynamic analysis results. The results showed that the high-frequency motion responses were predicted well with a low error. Based on this study, we plan to extend existing research by incorporating the hull shape characteristics of fishing boats into a deep neural network model.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Journal of Korean Home Economics Education Association
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• v.34 no.1
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• pp.35-57
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• 2022

This study developed an 'Eco-friendly Clothing Life Cycle' class applying gamification. And the effect of and learners' satisfaction on the class were examined after implementation. The developed class was applied to 40 sophomore students from "A" high school in Gyeonggi-do from February 3, 2022, to February 10, 2022, in a total of 4 sessions. The class was conducted in the stages of production-distribution-consumption-disposal, and was conducted in a way that a mission is solved after learning in Gather Town. It is designed so that learners continuously repeat learning until they accomplish the mission. The learners completed pre-class and post-class questionnaires. And a focus group interview was conducted with a randomly selected group of three learners. According to the pre-/post-class test comparison, the gamification class on the theme of "Eco-friendly Clothing Life Cycle" was found not to have a significant effect on learners' immersion or self-directed learning attitudes. However, in the case of the learners with high levels of non-immersion tendency, the level of immersion in the class increased, and the satisfaction level was positively associated with the level of immersion and self-directed attitude. Learners expressed 'concern' and 'expectation' about the gamification class, and said that although the developed class was using a 'new teaching method', 'appropriate use' was necessary. And learners were evaluated this class as a 'student-centered class' and acknowledged that it allowed 'self-directed learning'. The teacher who implemented the class said that this class was more effective in attracting students' expectations and interests compared to the conventional classes, and that the class in the meta-verse environment was perceived as a new type of class in the non-face-to-face era. The teacher also mentioned that when applied to the actual educational field, a detailed design is needed that allows the learners to proceed smoothly, and the role of the teacher in the class was more important. And the teacher also mentioned that the class should be properly designed so that the expectations given by the 'game' do not obscure the essence of the class.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.745-755
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• 2017

University campuses try to satisfy students' demands. Therefore, the physical environment of university campuses keeps changing. Due to the increasing the number of students and shortage of school building facilities, it is necessary to improve the facilities and create new space in the campus, in order to solve this problem and improve the universities. At the moment, the learning method of university students is evolving toward discussion sessions involving fellow students and presentations utilizing multimedia facilities. In response to these changes, a new type of independent space has appeared, which is called the Group Study Room. In this study, we analyze the spatial characteristics of the Group Study Room. Also, through an IPA(Importance Performance Analysis), this study tries to examine the relationship between the spatial characteristics and users' satisfaction. This analysis is expected to reveal the importance of the spatial characteristics of the group study rooms from the users' point of view. This research can be used for facility planning in universities in the future. The primary aim of this study is to analyze the physical environment of the Group Study Rooms of K university. The secondary aim of this study is to manifest the characteristics of these new learning spaces and to check the space elements of the group study rooms for the sake of efficient planning and management.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.2
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• pp.85-92
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• 2023

Respiratory infections such as COVID-19 mainly occur within enclosed spaces. The presence or absence of abnormal symptoms of respiratory infectious diseases is judged through initial symptoms such as fever, cough, sneezing and difficulty breathing, and constant monitoring of these early symptoms is required. In this paper, image matching correction was performed for the RGB camera module and the thermal imaging camera module, and the temperature of the thermal imaging camera module for the measurement environment was calibrated using a blackbody. To detection the target recommended by the standard, a deep learning-based object recognition algorithm and the inner canthus recognition model were developed, and the model accuracy was derived by applying a dataset of 100 experimenters. Also, the error according to the measured distance was corrected through the object distance measurement using the Lidar module and the linear regression correction module. To measure the performance of the proposed model, an experimental environment consisting of a motor stage, an infrared thermography temperature screening system and a blackbody was established, and the error accuracy within 0.28℃ was shown as a result of temperature measurement according to a variable distance between 1m and 3.5 m.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.2
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• pp.174-188
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• 2004

In the life environment changed with not only the quality and the price of the products but also the material abundance, it is the most crucial factor for the strategy of product sales to investigate consumer's sensibility and preference degree. In this perspective, it is necessary to design and merchandise the products in cope with each consumer's sensibility and needs as well as its functional aspects. In this paper, we propose the Fashion Design Recommender Agent System (FDRAS-pro) for textile design applying collaborative filtering personalization technique as one of the methods of material development centered on consumer's sensibility and preference. For a collaborative filtering system based on textile, Representative-Attribute Neighborhood is adopted to determine the number or neighbors that will be used for preferences estimation. Pearson's Correlation Coefficient is used to calculate similarity weights among users. We build a database founded on the sensibility adjectives to develop textile designs by extracting the representative sensibility adjectives from users' sensibility and preferences about textile designs. FDRAS-pro recommends textile designs to a customer who has a similar propensity about textile. To investigate the sensibility and emotion according to the effect of design factors, fertile designs were analyzed in terms of 9 design factors, such as, motif source, motif-background ratio, motif variation, motif interpretation, motif arrangement, motif articulation, hue contrast, value contrast, chroma contrast. Finally, we plan to conduct empirical applications to verify the adequacy and the validity of our system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.5
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• pp.2124-2132
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• 2011

The purpose of this research is to design and develop an evaluation system with a view to improving teaching skills in the u-Campus environment. To achieve the purpose, this research analyzed various theories on the effectiveness of teaching and teaching evaluation system, extracting several evaluation factors which are to be included in the system. Based on these evaluation factors, this research developed a prototype by reflecting teaching assessment, coaching, self-reflection, portfolio, supervisor selection, user authority in sharing the portfolio and ubiquitous technology. This prototype differs from the existing evaluation methods in that it can enlarge the extent of supervisors other than professors according to their own selecting and can reduce the burden of assessment task, thus improve their teaching skills. This teaching evaluation system using portfolio and ubiquitous technology will be an innovative alternative to conventional assessment methods and will be of great help to qualitative progress in higher level education.

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.75-85
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• 2007

One of important factors for improving the learning achievement of students in computer programming education is to provide plenty of opportunities of problem-solving experiences through variety forms of assignments, However, for the most cases, evaluation of programming assignments is performed manually by instructors and automated tools for the accurate evaluation are not equipped at the present time. Under this restricted environment instructors need much work and time to grade assignments so that instructors could not deliver sufficient programming assignments to students, In order to overcome this problem. au automated programming assignment evaluation system is needed that would enable instructors to evaluate assignments easily in an effective and consistent way and also to detect any plagiarism activities among students in program source codes readily, Accordingly, in this paper we design and implement a Web-based programming assignment grading system that allows instructors to evaluate program performance automatically as well as to evaluate program styles and piagiarism easily with appropriate feedback.