• Korean Journal of Construction Engineering and Management
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• v.22 no.2
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• pp.88-96
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• 2021

There have been many efforts to identify and eliminate the direct causes of the construction accident, but many accidents are still occurring in the construction industry. The main reason for the construction accident is not because of ignorance of the causes, but because of safety insensitivity. This paper analyzes why construction workers feel safety insensitivity and how safety insensitivity varies depending on their age and work experience. A survey of 103 construction workers confirmed that systematic safety education is the most important factor in minimizing safety insensitivity of construction workers. On the other hand, economic reasons such as cost reduction were identified as the most tempting factor to increase safety insensitivity. In addition, the survey results showed that those in their 40s and 50s, the largest proportion of construction workers, have a significantly higher level of safety insensitivity than those in their 20s and 60s. These findings are expected to be valuable source that can be used to prevent construction safety accidents.

• Korean Journal of Food Science and Technology
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• v.54 no.3
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• pp.264-271
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• 2022

In this study, the nutritional properties of sterilized and non-heat-pressed raw perilla oil (SRPO) were studied and its potential as a functional food was evaluated. The copper, cobalt, and calcium levels were high in sterilized and SRPO. The total polyphenol content and ABTS radical scavenging activity were the highest in SRPO, whereas nitrite scavenging activity was the highest in 45℃ cold pressed perilla oil (CPPO). The above results confirmed that sterilized and non-heat-pressed perilla oil had high mineral and total polyphenol contents, as well as ABTS radical scavenging activity and nitrite scavenging ability. The peroxide value of SRPO decreased as the storage period increased, and the acid value of low-temperature pressed perilla oil over 65℃ (LPPO) significantly increased. This work also provided an opportunity to develop a new method for manufacturing perilla oil, and it is hoped that these experiments will form a basis for the commercialization of perilla oil.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.269-279
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• 2022

Purpose: The purpose of this study is to develop a deep learning-based personal protective equipment detection model for disaster prevention at construction sites, and to apply it to actual construction sites and to analyze the results. Method: In the method of conducting this study, the dataset on the real environment was constructed and the developed personal protective equipment(PPE) detection model was applied. The PPE detection model mainly consists of worker detection and PPE classification model.The worker detection model uses a deep learning-based algorithm to build a dataset obtained from the actual field to learn and detect workers, and the PPE classification model applies the PPE detection algorithm learned from the worker detection area extracted from the work detection model. For verification of the proposed model, experimental results were derived from data obtained from three construction sites. Results: The application of the PPE recognition model to construction site brings up the problems related to mis-recognition and non-recognition. Conclusions: The analysis outcomes were produced to apply the object recognition technology to a construction site, and the need for follow-up research was suggested through representative cases of worker recognition and non-recognition, and mis-recognition of personal protective equipment.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.81-92
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• 2021

The purpose of this study is to present an approach to the "Digital Health Passport" (DHP), which will be the most important in the change of the travel industry among the sudden environmental changes brought about by COVID-19. To this end, this study reviewed a variety of empirical literature on DHP, and proposed a framework for DHP based on literature review. The framework is composed of travel intention, health information provision intention, and new technology acceptance/adoption of tourists. First, in terms of travel intention, providing information to DHP should not undermine the travel intention of the travelers. It should be possible to facilitate the travelers' enjoyment by using the information provided by the traveler. In addition, there is a need to assure that the data provided by travelers is managed in a reliable way. Second, it is necessary to understand why the travelers want to provide additional personal information (information disclosure), rather than seeing healthcare information only in terms of mandatory information provision. Finally, from the perspective of new technology, it is necessary to understand the intention of travelers to use/adopt DHP. The key implication of this work is that it proposed a DHP framework for realizing the travel bubble to predict and respond to foreign travelers' behaviors.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.319-325
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• 2021

With the advancement of wireless technology and the rapid growth of the infrastructure of mobile communication technology, systems applying AI-based platforms are drawing attention from users. In particular, the system that understands users' tastes and interests and recommends preferred items is applied to advanced e-commerce customized services and smart homes. However, there is a problem that these recommendation systems are difficult to reflect in real time the preferences of various users for tastes and interests. In this research, we propose a Fuzzy-AHP-based movies recommendation system using the Gated Recurrent Unit (GRU) language model to address a problem. In this system, we apply Fuzzy-AHP to reflect users' tastes or interests in real time. We also apply GRU language model-based models to analyze the public interest and the content of the film to recommend movies similar to the user's preferred factors. To validate the performance of this recommendation system, we measured the suitability of the learning model using scraping data used in the learning module, and measured the rate of learning performance by comparing the Long Short-Term Memory (LSTM) language model with the learning time per epoch. The results show that the average cross-validation index of the learning model in this work is suitable at 94.8% and that the learning performance rate outperforms the LSTM language model.

• Korean Association of Arts Management
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• no.59
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• pp.73-99
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• 2021

Free admission policies have been gradually extended for the public to use cultural facilities free of charge, which lowered the barriers to use those facilities and has a great effect on increasing visitor demands. However, the annual number of visitors which is open to the public isn't quite accurate and varies from institution to institution, which means just a head count. Especially people counter overestimates the number of visitors because it counts visitors in duplicate. Therefore, the purpose of this study is to prepare effective way of grasping the number of visitors using cultural infrastructure. First, comparable number of visitors should be measured by defining the notion of visitor clearly, which has been measured vaguely. Secondly, the problem of duplicate count, which is considered the most problematic, should be solved. Thirdly, the various analysis of visitor behavior should be conducted to provide a high-quality service. To work out the problems above, new measurement will be presented here. This study suggests a state-of-the-art wireless signal-based measurement that could eliminate the duplicate data by collecting MAC address -smart device's distinct signal value. And it also could analyze diverse visitor behaviors by understanding a flow of visitor traffic, duration of stay and revisitation. I would like to examine the possibility and effectiveness of this new measurement by testing it.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.171-177
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• 2022

In this paper, we propose a service method that can provide insight into multi-source agricultural data, way to cluster environmental factor which supports data analysis according to time flow, and curate crop environmental factors. The proposed curation service consists of four steps: collection, preprocessing, storage, and analysis. First, in the collection step, the service system collects and organizes multi-source agricultural data by using an OpenAPI-based web crawler. Second, in the preprocessing step, the system performs data smoothing to reduce the data measurement errors. Here, we adopt the smoothing method for each type of facility in consideration of the error rate according to facility characteristics such as greenhouses and open fields. Third, in the storage step, an agricultural data integration schema and Hadoop HDFS-based storage structure are proposed for large-scale agricultural data. Finally, in the analysis step, the service system performs DTW-based time series classification in consideration of the characteristics of agricultural digital data. Through the DTW-based classification, the accuracy of prediction results is improved by reflecting the characteristics of time series data without any loss. As a future work, we plan to implement the proposed service method and apply it to the smart farm greenhouse for testing and verification.

• Journal of Korean Elementary Science Education
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• v.40 no.4
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• pp.443-459
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• 2021

In this study, a TPACK-based STEAM program was developed and applied under the theme of excretory organs in the 'Structure and Function of Our Body' of the elementary science curriculum. The program was produced and conducted through curriculum analysis and learning goal detailing, learning environment analysis, teaching·learning method and technology selection, TPACK elements arrangement and teaching·learning material development, application and effectiveness verification. Teacher's TPACK considered in STEAM program design process is content knowledge (appearance and work of excretory organs), pedagogical knowledge (STEAM, problem-based learning, research learning, discussion learning, cooperative learning, scientific writing) and technology knowledge (3D printer and smart device application technology). The program consisted of a total of 8 hours of project learning activities and was applied to 29 students in the fifth grade as an experimental group. A program of the same theme developed mainly from textbooks was applied to 27 students in the fifth grade of a comparison group. As a result of the application of the program, the experimental group showed significant improvement in creative problem-solving ability and scientific attitude compared to the comparison group, and the class satisfaction with the STEAM program was also high. However, there was no significant difference in academic achievement ability.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.193-206
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• 2021

Artificial Intelligence (AI) has been inserted into people's lives through Intelligent Virtual Assistants (IVA), like Alexa. Moreover, intelligent systems have expanded to design studios. This research delves into designers' perspectives on developing AI-based practices and examines the challenges of adopting future intelligent design assistants. We surveyed UX/UI professionals in Brazil to understand how they use IVAs and AI design tools. We also explored a scenario featuring the use of Alexa Sensei, a hypothetical voice-controlled AI-based design assistant mixing Alexa and Adobe Sensei characteristics. The findings indicate respondents have had limited opportunities to work with AI, but they expect intelligent systems to improve the efficiency of the design process. Further, majority of the respondents predicted that they would be able to collaborate creatively with AI design systems. Although designers anticipated challenges in natural language interaction, those who already adopted IVAs were less resistant to the idea of working with Alexa Sensei as an AI design assistant.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.189-196
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• 2023

As the manufacturing paradigm shifts, various collaborative robots are creating new markets. Demand for collaborative robots is increasing in all industries for the purpose of easy operation, productivity improvement, and replacement of manpower who do simple tasks compared to existing industrial robots. However, accidents frequently occur during work caused by collaborative robots in industrial sites, threatening the safety of workers. In order to construct an industrial site through robots in a human-centered environment, the safety of workers must be guaranteed, and there is a need to develop a collaborative robot guard system that provides reliable communication without the possibility of dispatch. It is necessary to double prevent accidents that occur within the working radius of cobots and reduce the risk of safety accidents through sensors and computer vision. We build a system based on OPC UA, an international protocol for communication with various industrial equipment, and propose a collaborative robot guard system through image analysis using ultrasonic sensors and CNN (Convolution Neural Network). The proposed system evaluates the possibility of robot control in an unsafe situation for a worker.