• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.210-213
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• 2010

최근 사용자의 멀티미디어에 대한 요구의 증가가 VOD (Video-on-Demand) 서비스를 발전시키게 되었다. VOD는 엔터테인먼트나 원격 교육, 광고 및 정보 등 많은 분야에서 사용되고 있다. 이러한 VOD 서비스는 많은 디스크 I/O와 네트워크 I/O를 요구하며 기존 웹 서버 시스템과 비교했을 때 오랜 시간동안 서비스를 해야 하는 특징을 가지고 있다. 또한 VOD 서비스는 많은 네트워크와 디스크의 대역폭을 요구하며, 서비스의 QoS에 민감해서 사용자 응답시간이 길어지면 사용자 요청의 취소율이 높아지게 된다. 따라서 불만족스러운 서비스의 증가로 네트워크 부하만 증가하게 된다. 이러한 기존 웹 서버 환경과는 다른 부하의 패턴이 있는 VOD 서비스 환경에서는 부하를 균형적으로 분배하여 서비스의 QoS를 높이는 것이 매우 중요하다. 본 논문에서는 분산 VOD 시스템 환경에서 부하를 효율적으로 분산하기 위해 계층형 분산 VOD 시스템 모델과 사용자 요청 패턴의 히스토리와 유전 알고리즘을 기반으로 한 스케줄러를 제안한다. 본 논문에서 제안한 계층형 분산 VOD 시스템 모델은 서버들을 지역적으로 분산하고 제어 서버를 지역마다 설치하여 지역에 있는 VOD 서버들을 관리하도록 구성한다. 사용자 요청을 지역 서버군 내에서 분산시키기 위해서 히스토리를 기반으로 한 유전 알고리즘을 사용한다. 이러한 히스토리 정보를 기반으로 유전 알고리즘의 적합도 함수에 적용하여 VOD 시스템을 위한 유전 알고리즘과 유전 연산을 구현한다. 본 논문에서 제안한 부하 분산 알고리즘은 VOD 서비스 환경에서 사용자 요구에 대한 부하를 보다 정확하게 예측하여 부하를 분산할 수 있다. 본 논문에서 제안한 계층형 분산 VOD 시스템의 부하 분산 알고리즘의 성능을 테스트하기 위해 OPNET 기반 시뮬레이터를 구현한다. 라운드로빈(round-robin) 방식과 랜덤(random) 방식과의 비교 실험을 통해 본 논문에서 제안한 부하 분산 알고리즘의 성능을 평가한다. 비교 실험을 통해 본 논문에서 제안한 알고리즘이 보다 안정적인 QoS를 제공하는 것을 보여준다.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1075-1084
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• 2022

The use of Hi-Tech in cultural heritage preservation and the promotion of cultural heritage values in general, particularly artifacts, opens new opportunities for attracting tourists while also posing a challenge due to the need to reward high-quality excursions to visitors historical and cultural values. Building Information Modeling (BIM) and Hi-Tech in new building management have been widely adopted in the construction industry; however, Historic Building Information Modeling (HBIM) is an exciting challenge in 3D modeling and building management. For those reasons, the Scan-to-HBIM approach involves generating an HBIM model for existing buildings from the point cloud data collected by Terrestrial 3D Laser Scanner integrated with Virtual Reality (VR), Augmented Reality (AR), contributes to spatial historic sites simulation for virtual experiences. Therefore, this study aims to (1) generate the application of Virtual Reality, Augmented Reality to Historic Building Information Modeling - based workflows in a case study which is a monument in the city; (2) evaluate the application of these technologies to improve awareness of visitors related to the promotion of historical values by surveying the experience before and after using this application. The findings shed light on the barriers that prevent users from utilizing technologies and problem-solving solutions. According to the survey results, after experiencing virtual tours through applications and video explanations, participant's perception of the case study improved. When combined with emerging Hi-Tech and immersive interactive games, the Historic Building Information Modeling helps increase information transmission to improve visitor awareness and promote heritage values.

• Proceedings of the ESK Conference
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• 1997.04a
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• pp.56-60
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• 1997

The study of human erroneous actions has traditionally taken place along two different lines of approach. One has been concerned with finding and explaining the causes of erroneous actions, such as studies in the psychology of "error". The other has been concerned with the qualitative and quantitative prediction of possible erroneous actions, exemplified by the field of human reliability analysis (HRA). Another distinction is also that the former approach has been dominated by an academic point of view, hence emphasising theories, models, and experiments, while the latter has been of a more pragmatic nature, hence putting greater emphasis on data and methods. We have been developing a method to make predictions about error modes. The input to the method is a detailed task description of a set of scenarios for an experiment. This description is then analysed to characterise thd nature of the individual task steps, as well as the conditions under which they must be carried out. The task steps are expressed in terms of a predefined set of cognitive activity types. Following that each task step is examined in terms of a systematic classification of possible error modes and the likely error modes are identified. This effectively constitutes a qualitative analysis of the possibilities for erroneous action in a given task. In order to evaluate the accuracy of the predictions, the data from a large scale experiment were analysed. The experiment used the full-scale nuclear power plant simulator in the Halden Man-Machine Systems Laboratory (HAMMLAB) and used six crews of systematic performance observations by experts using a pre-defined task description, as well as audio and video recordings. The purpose of the analysis was to determine how well the predictions matiched the actually observed performance failures. The results indicated a very acceptable rate of accuracy. The emphasis in this experiment has been to develop a practical method for qualitative performance prediction, i.e., a method that did not require too many resources or specialised human factors knowledge. If such methods are to become practical tools, it is important that they are valid, reliable, and robust.

• Journal of Korean society of Dental Hygiene
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• v.22 no.6
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• pp.551-561
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• 2022

Objectives: This study aimed to investigate the perceptions regarding oral muscle strength training for elder people among dental professionals. Methods: The study participants were selected using non-probability sampling methods, as dentists and dental hygienists with more than 3 years of work experience at dental institutions. A total of 15 participants were selected, including 6 dentists, 4 clinical dental hygienists, and 5 public dental hygienists. Interviews were conducted in June and July 2022, and two focus group interviews were conducted for each group. The first round was face-to-face and the second round was conducted through an online video conference. Results: Through focus group interviews, five factors were obtained; lack of awareness, value of training, factors necessary for implementation, performance status, and obstructive factors. It was found that most study participants had a negative perception regarding the application of oral muscle strength training for elder people due to the lack of information and education on the subject. However, the benefit of oral muscle strength training was positively recognized; adequate educational material, appropriate compensation, adequate time, and availability of patients will be necessary for proper training. Conclusions: It is necessary to improve the perceptions regarding the importance of strengthening oral muscles for the elder people among dentists and dental hygienists. In addition, high-quality educational material that can be easily comprehended and practiced should be developed for effective training.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1135-1138
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• 2011

안드로이드 시장이 급성장함에 따라 안드로이드 기반 어플리케이션에 대한 수요가 많아지고 있다. 그로 인해 3D 그래픽적 기능을 요구하는 어플리케이션에 대한 요구도 늘어났다. 그러나 안드로이드 단말기에서는 3D 처리능력이 데스크 탑과 비교하여 성능과 서비스에 한계가 있다. 그렇기 때문에 VNC(Virtual Network Computing)를 사용하여 고성능의 그래픽을 요구하는 프로그램을 실행할 수 있게 된다. VNC 란 클라이언트에서 데스크 탑으로 접속하여 원격으로 제어하고 그 결과를 그래픽으로 확인하는 프로그램이다. 그러나 기존의 Android VNC 는 해상도가 낮고, 이미지 전송 속도가 느리기 때문에 3D 렌더링 이미지 처리가 불가능했다. 또한 Android VNC 는 인터페이스가 불편하여 입력 오류가 많아 사용이 불편했다는 단점 등이 지적되어 왔다. 본 논문에서는 이를 개선하기 위한 Android Turbo VNC 를 제안한다. Android Turbo VNC는 libjpeg-turbo 코덱을 적용하여 3D 이미지 부분에서 기존의 Android VNC 에 비하여 약 80~120%의 이미지 개선과 이미지 압축률을 4 배정도 높여 CAD 와 같은 고성능의 그래픽을 요구하는 프로그램의 사용을 가능하게 한다. 그리고 Android Turbo VNC 에서는 기존 Android VNC 의 불편한 UI 를 개선하였다. 클라우드 서버에서는 CAD, Document, Game, Video, General 총 5 가지의 프로그램을 서비스하여 그에 맞는 테마 별 UI 를 제공한다. libjpeg-turbo 코덱의 적용을 통해 Android-Turbo VNC 는 수십 장의 설계 도면을 굳이 들고 다니지 않더라도 하나의 테블릿 PC 안에서 보는 것이 가능하게 된다. 테마별 UI 중 CAD 테마는 3D CAD 를 사용하는 산업현장에서 적극적으로 활용될 것으로 기대된다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.301-302
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• 2021

After the Korona 19, "social distancing" was implemented, existing "pop culture" or entertainment programs were unable to communicate in both directions and declined. Since then, "Untact content" has shown its potential to grow due to untouch performances such as BTS' "Bangbangcon" and the rapid growth of Netflix, a global OTT (online video service). In addition, most of the global and Untact content is online and digital, which means a huge amount of big data will be poured out. Therefore, analyzing the big data poured out during the distribution of untact content will help us identify consumers' needs, and the growth expectations will also be high. Therefore, we would like to explore the research cases that have been conducted in existing studies regarding the subject of the study and analyze how big data can affect the distribution of content in the Untact era.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.277-280
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• 2021

Recently as various contents are mass produced based on high accessibility, the media contents market is more active. Users want to find content that suits their taste, and each platform is competing for personalized recommendations for content. For an efficient recommendation system, high-quality metadata is required. Existing platforms take a method in which the user directly inputs the metadata of an image. This will waste time and money processing large amounts of data. In this paper, for media hyperpersonalization recommendation, keyframes are extracted based on the YCrCb color model of the video based on movie trailers, movie genres are distinguished through supervised learning of artificial intelligence and In the future, we would like to propose a utilization plan for generating metadata.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1325-1328
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• 2022

언제 어디서든 한 손으로 미디어 콘텐츠를 소비할 수 있게 해주는 모바일 기기들이 기존 전통적 미디어 콘텐츠 단말기였던 TV나 데스크톱 PC들을 대체하게 되면서 세로형 영상 콘텐츠에 대한 수요가 나날이 높아져 가고 있다. 이와 더불어 모바일 단말기 제조사들은 서로 간의 경쟁에서 앞서기 위해 제품 차별화 전략을 수립하고 모바일 사용자들의 요구 사항을 세세하게 맞추기 위한 결과, 저마다 다른 디스플레이 해상도 규격을 가진 모바일 기기들이 생산되고 있는 상황이다. 이에 미디어 콘텐츠 제작자들은 기존 가로형 영상 콘텐츠와 더불어 새롭게 요구되는 세로형 영상 콘텐츠들을 저마다 다른 해상도 규격에 맞추는데 많은 시간과 비용을 투자하고 있다. 더 나아가 모바일 단말기 해상도 규격과 맞지 않는 영상 콘텐츠를 시청하게 될 경우, 모바일 사용자 입장에서는 디스플레이 전체 영역을 뷰포트로 잡을 수 없어 시청 만족도가 떨어질 수 있다. 이에 본 논문은 한 번의 콘텐츠 제작을 통해서도 추가 비용 없이 다양한 디스플레이 규격을 가진 단말기들에 대해 맞춤형 콘텐츠 서비스 제공을 가능하게 하여 미디어 콘텐츠 소비자들에게 충분한 시청 몰입감을 제공해줄 수 있는 단말 적응적 미디어 화면비 변환 시스템을 제안한다. 단말 적응적 미디어 화면비 변환 시스템은 딥러닝 네트워크 모델과 이미지 관련 라이브러리를 기반으로 하여 설계한 시스템이며, 사용자가 시청하기 원하는 영역을 판단하고, 사용자가 원하는 뷰포트 종횡비에 따라 해당 영역을 잘라내어 사용자가 원하는 세로형 영상 콘텐츠를 제공해준다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.191-193
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• 2022

The advent of the 4th Industrial Revolution due to the trigger of digital technologies such as artificial intelligence and big data has caused many changes in society, culture, and industry. However, traditional markets in each region are not responding quickly to new distribution environments and consumer changes. In particular, in the case of traditional markets in Jeju, regional characteristics such as marketing strategies for tourists visiting Jeju have not been utilized. Therefore, this study proposes the establishment of a smart traditional market based on big data and artificial intelligence that utilizes the regional characteristics of Jeju. The research contents include customer profiling through visitor big data analysis, providing tourist movement results through traffic analysis, providing real-time popular product charts, and developing video-based fire and crime prevention functions.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.455-462
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• 2020

Noise which is unwanted sound is a serious pollutant that can affect human health, as well as the working and living environment if exposed to humans. However, current noise management on the construction project is generally conducted after the noise exceeds the regulation standard, which increases the conflicts with inhabitants near the construction site and threats to the safety and productivity of construction workers. To overcome the limitations of the current noise management methods, the activities of construction equipment which is the main source of construction noise need to be managed throughout the construction period in real-time. Therefore, this paper proposed a framework for automatically detecting noise sources in construction sites in real-time based on convolutional neural networks (CNNs) according to the following four steps: (i) Step 1: Definition of the noise sources; (ii) Step 2: Data preparation; (iii) Step 3: Noise source classification using the audio CNN; and (iv) Step 4: Noise source detection using the visual CNN. The short-time Fourier transform (STFT) and temporal image processing are used to contain temporal features of the audio and visual data. In addition, the AlexNet and You Only Look Once v3 (YOLOv3) algorithms have been adopted to classify and detect the noise sources in real-time. As a result, the proposed framework is expected to immediately find construction activities as current noise sources on the video of the construction site. The proposed framework could be helpful for environmental construction managers to efficiently identify and control the noise by automatically detecting the noise sources among many activities carried out by various types of construction equipment. Thereby, not only conflicts between inhabitants and construction companies caused by construction noise can be prevented, but also the noise-related health risks and productivity degradation for construction workers and inhabitants near the construction site can be minimized.