• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.11
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• pp.75-84
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• 2018

This study analyzed users' acceptance and intention to use in addition to needs and preferences of smart home technologies, and identified the differences in technology preference and acceptance by different factors. The subjects were residents in the 40s and 60s residing in the Seoul or suburbs of Seoul, and questionnaires were conducted in the 40s while interviews with questionnaires were conducted in the 60s. A total of 105 questionnaires were used as data, and frequency, mean, crossover, independent sample t test, one-way ANOVA and multiple regression analysis were performaed using SPSS23. The results of this study are as follows. First, hypertension, hyperlipidemia and hypercholesterolemia were the most common diseases among respondents and if there was no discomfort, they would like to continue living in the homes of the current residence. Therefore, the direction of smart home development should support the daily living and health care so that residents can live a healthy life for a long time in their living space. Second, the technologies that residents most need were a control technology of residential environments and a monitoring technology of residents' health and physiological changes. The most preferred sensor types are motion sensors and speech recognition while video cameras have a very low preference. Third, technology anxiety was the most significant factor influencing intention to accept smart home technology. The greater the technology anxiety is, the weaker the acceptance of technology. Fourth, when applying smart residential technology in homes, various resident characteristics should be considered. Age and technology intimacy were the most influential variables, and accordingly there were differences in technology preference and acceptance. Therefore, a user-friendly smart home plan should be done in the consideration of the results.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.175-182
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• 2021

The purpose of this paper is to develop a machine learning-based marine drone to prevent the farming from harmful birds such as ducks. Existing drones have been developed as marine drones to solve the problem of being lost if they collide with birds in the air or are in the sea. We designed a CNN-based learning algorithm to judge harmful birds that appear on the sea by maritime drones operating by autonomous driving. It is designed to transmit video to the control PC by connecting the Raspberry Pi to the camera for location recognition and tracking of harmful birds. After creating a map linked with the location GPS coordinates in advance at the mobile-based control center, the GPS location value for the location of the harmful bird is received and provided, so that a marine drone is dispatched to combat the harmful bird. A bird fighting drone system was designed and implemented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.146-151
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• 2021

Currently, the demand for vehicles from one family is increasing in many countries at home and abroad, reducing the number of people on the vehicle and increasing the number of vehicles on the road. The multi-passenger lane system, which is available to solve the problem of traffic congestion, is being implemented. The system allows police to monitor fast-moving vehicles with their own eyes to crack down on illegal vehicles, which is less accurate and accompanied by the risk of accidents. To address these problems, applying deep learning object recognition techniques using images from road sites will solve the aforementioned problems. Therefore, in this paper, we compare and analyze the performance of existing deep learning models, select a deep learning model that can identify real-time vehicle occupants through video, and propose a vehicle occupancy detection algorithm that complements the object-ident model's problems.

• Journal of Environmental Health Sciences
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• v.47 no.1
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• pp.101-109
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• 2021

Objectives: The objectives of this study are to introduce the development process of work situations and types in the revised regulations on wearing personal protective equipment (PPE) for hazardous chemical handlers, analyze the implementation of the regulations, and then provide basic data for future education strategies. Methods: The development process of work situations for regulation was explained through a flowchart by year. In 2018, a survey of 30 chemical managers and 201 managers and handlers was conducted based on recognition of work situations and the related regulations. In 2019, 91 chemical managers and 204 handlers were surveyed to find the degree of compliance with regulations, direction for improvement of understanding the regulations, and training methods. Results: Only 78.0% of chemical managers and 66.7% of handlers said they were aware of the regulations (p<0.05). Just 79.0% of handlers knowing the regulations said they would wear PPE in compliance with these regulations. Therefore, the best way to make workers wear proper PPE in accordance with regulations is to strengthen the promotion of education on regulations. In order to improve the quality of education, 51.7% of managers and 33.3% of handlers cited educational content (video, ppt, etc.) as the top priority. Conclusion: This study suggested that more educational opportunities should be provided and educational content should be developed in order for workers handling hazardous chemicals to wear PPE as prescribed in regulations.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.77-84
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• 2021

In this paper, we propose a system that uses image data and beacon data to classify authorized and unauthorized perosn who are allowed to enter a group facility. The image data collected through the IP camera uses YOLOv4 to extract a person object, and collects beacon signal data (UUID, RSSI) through an application to compose a fingerprinting-based radio map. Beacon extracts user location data after CNN-LSTM-based learning in order to improve location accuracy by supplementing signal instability. As a result of this paper, it showed an accuracy of 93.47%. In the future, it can be expected to fusion with the access authentication process such as QR code that has been used due to the COVID-19, track people who haven't through the authentication process.

• Journal of Advanced Technology Convergence
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• v.1 no.2
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• pp.37-48
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• 2022

This research is an empirical research of Chinese OTT Audiences on the effects of advertising model attributes on consumers' advertising perception, purchase intention, Flow and addiction. Recently, as the cosmetics market in China has grown, the role of advertising models has been highlighted, and shopping addiction caused by excessive Flow is becoming a social problem. Researchers set up a research model and tried to test which characteristics of the advertising model lead consumers to purchase, Flow and ultimately lead to addiction. Results are as follows. It was confirmed that advertisement model attributes such as recognition and attractiveness had a positive effect on viewers' advertising perception and attitude, and viewers' perceived usefulness had a positive effect on purchase intention and Flow. In addition, the purchase intention of the viewers had a positive effect on the addiction to cosmetics.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.7-12
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• 2022

The modern stage of society's development is characterized by the rapid penetration of information technologies into all spheres of life. Their use contributes to improving the quality of tourism services, as well as the competitiveness of tourism industry enterprises. The role of information technology in tourism is growing more and more every year, which determines the relevance of the study of modern trends in the use of information technology in the tourism sector. The purpose of the study is to determine the possibilities of using information technologies to improve the quality of services provided to the tourism industry under the COVID-19 restrictions. The article systematizes the main approaches to the "cluster" category and provides an original definition of the "regional tourist cluster" concept. Based on an expert survey, the main trends in the introduction of information technologies in the tourism industry under the COVID-19 restrictions have been identified, which include virtual reality and augmented reality, speech recognition technologies, photo, video, audio (contactless control technologies), mobile IT applications and Big Data technologies. It has been concluded that the vast majority of improvements in the organization of tourism services under restrictions will be based on the organization of virtual solutions and online activities. The types of tourism services will also change, and information technology will help their development and dissemination.

• Journal of IKEEE
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• v.27 no.3
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• pp.251-257
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• 2023

In this paper, we propose a dual-structured self-attention method that improves the lack of regional features of the vision transformer's self-attention. Vision Transformers, which are more computationally efficient than convolutional neural networks in object classification, object segmentation, and video image recognition, lack the ability to extract regional features relatively. To solve this problem, many studies are conducted based on Windows or Shift Windows, but these methods weaken the advantages of self-attention-based transformers by increasing computational complexity using multiple levels of encoders. This paper proposes a dual-structure self-attention using self-attention and neighborhood network to improve locality inductive bias compared to the existing method. The neighborhood network for extracting local context information provides a much simpler computational complexity than the window structure. CIFAR-10 and CIFAR-100 were used to compare the performance of the proposed dual-structure self-attention transformer and the existing transformer, and the experiment showed improvements of 0.63% and 1.57% in Top-1 accuracy, respectively.

• Journal of The Korean Association For Science Education
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• v.26 no.1
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• pp.16-24
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• 2006

The purpose of this study was to compare student-student verbal interaction from two type's experiments; problem-solving and task-solving. For this study, five 3rd grade middle school students were selected and their verbal interactions recorded via voice and video; and later transcribed. The student-student verbal interactions were classified as questions, explanations, thoughts, or metacognition fields, which were separated into deep versus surface learning approaches. For the problem-solving experiment, findings revealed that the number of verbal interactions is more than doubled and in particular, the number of verbal interactions using deep-approach is more than quadrupled from the point of problem-recognition to problem-solution. As for the task-solving experiment, findings showed that verbal interactions remained evenly distributed throughout the entire experiment. Finally, it was also discovered that students relied upon a more deep learning approach during the problem-solving experiment than the task-solving experiment.

• The Journal of the Convergence on Culture Technology
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• v.10 no.6
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• pp.799-805
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• 2024

Recently, generative artificial intelligence (AI) technology has been rapidly developing and its application fields are expanding beyond text, voice, image, object recognition, time-series forecasting, and natural language processing to the creative design field that AI was thought to be incapable of. We aim for an exploratory approach to study the cognitive model of pattern designers using generative AI. To this end, we used GPT 4o, which is the most well-known generative AI, and applied the protocol analysis method, a cognitive science research method, to the pattern design process. Four design graduate students were selected as subjects and pilot and main experiment were conducted. Voice recording and video capture were performed to collect data. The protocol method applied the concurrent protocol method, which simultaneously expresses what comes to mind while performing the task. The collected verbal data was used to classify the design process by segmenting words and developing a coding scheme to establish a framework for analysis. As a result, analysis, selection, visualization, evaluation, and optimization were discovered. We expect to present design guidelines for pattern design practice.