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    ํ˜„์žฅ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ๋Šฅ๋ ฅ์„ ํ™•์žฅํ•˜๊ธฐ ์œ„ํ•œ ์ž์œ ๋„ ๋†’์€ ์…€ํ”„ ํŠธ๋ž˜ํ‚น ๊ธฐ์ˆ ์˜ ๋””์ž์ธ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2019. 2. ์„œ์ง„์šฑ.Collecting and tracking data in everyday contexts is a common practice for both individual self-trackers and researchers. The increase in wearable and mobile technologies for self-tracking encourages people to gain personal insights from the data about themselves. Also, researchers exploit self-tracking to gather data in situ or to foster behavioral change. Despite a diverse set of available tracking tools, however, it is still challenging to find ones that suit unique tracking needs, preferences, and commitments. Individual self-tracking practices are constrained by the tracking tools' initial design, because it is difficult to modify, extend, or mash up existing tools. Limited tool support also impedes researchers' efforts to conduct in situ data collection studies. Many researchers still build their own study instruments due to the mismatch between their research goals and the capabilities of existing toolkits. The goal of this dissertation is to design flexible self-tracking technologies that are generative and adaptive to cover diverse tracking contexts, ranging from personal tracking to research contexts. Specifically, this dissertation proposes OmniTrack, a flexible self-tracking approach leveraging a semi-automated tracking concept that combines manual and automated tracking methods to generate an arbitrary tracker design. OmniTrack was implemented as a mobile app for individuals. The OmniTrack app enables self-trackers to construct their own trackers and customize tracking items to meet their individual needs. A usability study and a field development study were conducted with the goal of assessing how people adopt and adapt OmniTrack to fulfill their needs. The studies revealed that participants actively used OmniTrack to create, revise, and appropriate trackers, ranging from a simple mood tracker to a sophisticated daily activity tracker with multiple fields. Furthermore, OmniTrack was extended to cover research contexts that enclose manifold personal tracking contexts. As part of the research, this dissertation presents OmniTrack Research Kit, a research platform that allows researchers without programming expertise to configure and conduct in situ data collection studies by deploying the OmniTrack app on participants' smartphones. A case study in deploying the research kit for conducting a diary study demonstrated how OmniTrack Research Kit could support researchers who manage study participants' self-tracking process. This work makes artifacts contributions to the fields of human-computer interaction and ubiquitous computing, as well as expanding empirical understanding of how flexible self-tracking tools can enhance the practices of individual self-trackers and researchers. Moreover, this dissertation discusses design challenges for flexible self-tracking technologies, opportunities for further improving the proposed systems, and future research agenda for reaching the audiences not covered in this research.์ผ์ƒ์˜ ๋งฅ๋ฝ์—์„œ ๋ฐ์ดํ„ฐ๋ฅผ ๋ชจ์œผ๋Š” ํ™œ๋™์ธ ์…€ํ”„ ํŠธ๋ž˜ํ‚น(self-tracking)์€ ๊ฐœ์ธ๊ณผ ์—ฐ๊ตฌ์˜ ์˜์—ญ์—์„œ ํ™œ๋ฐœํžˆ ํ™œ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์›จ์–ด๋Ÿฌ๋ธ” ๋””๋ฐ”์ด์Šค์™€ ๋ชจ๋ฐ”์ผ ๊ธฐ์ˆ ์˜ ๋ฐœ๋‹ฌ๋กœ ์ธํ•ด ์‚ฌ๋žŒ๋“ค์€ ๊ฐ์ž์˜ ์‚ถ์— ๋Œ€ํ•ด ๋งํ•ด์ฃผ๋Š” ๋ฐ์ดํ„ฐ๋ฅผ ๋” ์‰ฝ๊ฒŒ ์ˆ˜์ง‘ํ•˜๊ณ , ํ†ต์ฐฐํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ์—ฐ๊ตฌ์ž๋“ค์€ ํ˜„์žฅ(in situ) ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ•˜๊ฑฐ๋‚˜ ์‚ฌ๋žŒ๋“ค์—๊ฒŒ ํ–‰๋™ ๋ณ€ํ™”๋ฅผ ์ผ์œผํ‚ค๋Š” ๋ฐ์— ์…€ํ”„ ํŠธ๋ž˜ํ‚น์„ ํ™œ์šฉํ•œ๋‹ค. ๋น„๋ก ์…€ํ”„ ํŠธ๋ž˜ํ‚น์„ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ๋„๊ตฌ๋“ค์ด ์กด์žฌํ•˜์ง€๋งŒ, ํŠธ๋ž˜ํ‚น์— ๋Œ€ํ•ด ๋‹ค์–‘ํ™”๋œ ์š”๊ตฌ์™€ ์ทจํ–ฅ์„ ์™„๋ฒฝํžˆ ์ถฉ์กฑํ•˜๋Š” ๊ฒƒ๋“ค์„ ์ฐพ๋Š” ๊ฒƒ์€ ์‰ฝ์ง€ ์•Š๋‹ค. ๋Œ€๋ถ€๋ถ„์˜ ์…€ํ”„ ํŠธ๋ž˜ํ‚น ๋„๊ตฌ๋Š” ์ด๋ฏธ ์„ค๊ณ„๋œ ๋ถ€๋ถ„์„ ์ˆ˜์ •ํ•˜๊ฑฐ๋‚˜ ํ™•์žฅํ•˜๊ธฐ์— ์ œํ•œ์ ์ด๋‹ค. ๊ทธ๋ ‡๊ธฐ ๋•Œ๋ฌธ์— ์‚ฌ๋žŒ๋“ค์˜ ์…€ํ”„ ํŠธ๋ž˜ํ‚น์— ๋Œ€ํ•œ ์ž์œ ๋„๋Š” ๊ธฐ์กด ๋„๊ตฌ๋“ค์˜ ๋””์ž์ธ ๊ณต๊ฐ„์— ์˜ํ•ด ์ œ์•ฝ์„ ๋ฐ›์„ ์ˆ˜๋ฐ–์— ์—†๋‹ค. ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ, ํ˜„์žฅ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ•˜๋Š” ์—ฐ๊ตฌ์ž๋“ค๋„ ์ด๋Ÿฌํ•œ ๋„๊ตฌ์˜ ํ•œ๊ณ„๋กœ ์ธํ•ด ์—ฌ๋Ÿฌ ๋ฌธ์ œ์— ๋ด‰์ฐฉํ•œ๋‹ค. ์—ฐ๊ตฌ์ž๋“ค์ด ๋ฐ์ดํ„ฐ๋ฅผ ํ†ตํ•ด ๋‹ตํ•˜๊ณ ์ž ํ•˜๋Š” ์—ฐ๊ตฌ ์งˆ๋ฌธ(research question)์€ ๋ถ„์•ผ๊ฐ€ ๋ฐœ์ „ํ• ์ˆ˜๋ก ์„ธ๋ถ„๋˜๊ณ , ์น˜๋ฐ€ํ•ด์ง€๊ธฐ ๋•Œ๋ฌธ์— ์ด๋ฅผ ์œ„ํ•ด์„œ๋Š” ๋ณต์žกํ•˜๊ณ  ๊ณ ์œ ํ•œ ์‹คํ—˜ ์„ค๊ณ„๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ํ•˜์ง€๋งŒ ํ˜„์กดํ•˜๋Š” ์—ฐ๊ตฌ์šฉ ์…€ํ”„ ํŠธ๋ž˜ํ‚น ํ”Œ๋žซํผ๋“ค์€ ์ด์— ๋ถ€ํ•ฉํ•˜๋Š” ์ž์œ ๋„๋ฅผ ๋ฐœํœ˜ํ•˜์ง€ ๋ชปํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฐ„๊ทน์œผ๋กœ ์ธํ•ด ๋งŽ์€ ์—ฐ๊ตฌ์ž๋“ค์ด ๊ฐ์ž์˜ ํ˜„์žฅ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ์—ฐ๊ตฌ์— ํ•„์š”ํ•œ ๋””์ง€ํ„ธ ๋„๊ตฌ๋“ค์„ ์ง์ ‘ ๊ตฌํ˜„ํ•˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉํ‘œ๋Š” ์ž์œ ๋„ ๋†’์€---์—ฐ๊ตฌ์  ๋งฅ๋ฝ๊ณผ ๊ฐœ์ธ์  ๋งฅ๋ฝ์„ ์•„์šฐ๋ฅด๋Š” ๋‹ค์–‘ํ•œ ์ƒํ™ฉ์— ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š”---์…€ํ”„ ํŠธ๋ž˜ํ‚น ๊ธฐ์ˆ ์„ ๋””์ž์ธํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋ณธ๊ณ ์—์„œ๋Š” ์˜ด๋‹ˆํŠธ๋ž™(OmniTrack)์ด๋ผ๋Š” ๋””์ž์ธ ์ ‘๊ทผ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์˜ด๋‹ˆํŠธ๋ž™์€ ์ž์œ ๋„ ๋†’์€ ์…€ํ”„ ํŠธ๋ž˜ํ‚น์„ ์œ„ํ•œ ๋ฐฉ๋ฒ•๋ก ์ด๋ฉฐ, ๋ฐ˜์ž๋™ ํŠธ๋ž˜ํ‚น(semi-automated tracking)์ด๋ผ๋Š” ์ปจ์…‰์„ ๋ฐ”ํƒ•์œผ๋กœ ์ˆ˜๋™ ๋ฐฉ์‹๊ณผ ์ž๋™ ๋ฐฉ์‹์˜ ์กฐํ•ฉ์„ ํ†ตํ•ด ์ž„์˜์˜ ํŠธ๋ž˜์ปค๋ฅผ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋จผ์ € ์˜ด๋‹ˆํŠธ๋ž™์„ ๊ฐœ์ธ์„ ์œ„ํ•œ ๋ชจ๋ฐ”์ผ ์•ฑ ํ˜•ํƒœ๋กœ ๊ตฌํ˜„ํ•˜์˜€๋‹ค. ์˜ด๋‹ˆํŠธ๋ž™ ์•ฑ์€ ๊ฐœ๊ฐœ์ธ์ด ์ž์‹ ์˜ ํŠธ๋ž˜ํ‚น ๋‹ˆ์ฆˆ์— ๋งž๋Š” ํŠธ๋ž˜์ปค๋ฅผ ์ปค์Šคํ„ฐ๋งˆ์ด์ง•ํ•˜์—ฌ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. ๋ณธ๊ณ ์—์„œ๋Š” ์‚ฌ๋žŒ๋“ค์ด ์–ด๋–ป๊ฒŒ ์˜ด๋‹ˆํŠธ๋ž™์„ ์ž์‹ ์˜ ๋‹ˆ์ฆˆ์— ๋งž๊ฒŒ ํ™œ์šฉํ•˜๋Š”์ง€ ์•Œ์•„๋ณด๊ณ ์ž ์‚ฌ์šฉ์„ฑ ํ…Œ์ŠคํŠธ(usability testing)์™€ ํ•„๋“œ ๋ฐฐํฌ ์—ฐ๊ตฌ(field deployment study)๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ฐธ๊ฐ€์ž๋“ค์€ ์˜ด๋‹ˆํŠธ๋ž™์„ ํ™œ๋ฐœํžˆ ์ด์šฉํ•ด ๋‹ค์–‘ํ•œ ๋””์ž์ธ์˜ ํŠธ๋ž˜์ปคโ€”์•„์ฃผ ๋‹จ์ˆœํ•œ ๊ฐ์ • ํŠธ๋ž˜์ปค๋ถ€ํ„ฐ ์—ฌ๋Ÿฌ ๊ฐœ์˜ ํ•„๋“œ๋ฅผ ๊ฐ€์ง„ ๋ณต์žกํ•œ ์ผ์ผ ํ™œ๋™ ํŠธ๋ž˜์ปค๊นŒ์ง€โ€”๋“ค์„ ์ƒ์„ฑํ•˜๊ณ , ์ˆ˜์ •ํ•˜๊ณ , ํ™œ์šฉํ•˜์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ, ์˜ด๋‹ˆํŠธ๋ž™์„ ํ˜„์žฅ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ์—ฐ๊ตฌ์— ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ์—ฐ๊ตฌ ํ”Œ๋žซํผ ํ˜•ํƒœ์˜ '์˜ด๋‹ˆํŠธ๋ž™ ๋ฆฌ์„œ์น˜ ํ‚ท(OmniTrack Research Kit)'์œผ๋กœ ํ™•์žฅํ•˜์˜€๋‹ค. ์˜ด๋‹ˆํŠธ๋ž™ ๋ฆฌ์„œ์น˜ ํ‚ท์€ ์—ฐ๊ตฌ์ž๋“ค์ด ํ”„๋กœ๊ทธ๋ž˜๋ฐ ์–ธ์–ด ์—†์ด ์›ํ•˜๋Š” ์‹คํ—˜์„ ์„ค๊ณ„ํ•˜๊ณ  ์˜ด๋‹ˆํŠธ๋ž™ ์•ฑ์„ ์ฐธ๊ฐ€์ž๋“ค์˜ ์Šค๋งˆํŠธํฐ์œผ๋กœ ๋ฐฐํฌํ•  ์ˆ˜ ์žˆ๋„๋ก ๋””์ž์ธ๋˜์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์˜ด๋‹ˆํŠธ๋ž™ ๋ฆฌ์„œ์น˜ ํ‚ท์„ ์ด์šฉํ•ด ์ผ์ง€๊ธฐ๋ก ์—ฐ๊ตฌ(diary study)๋ฅผ ์ง์ ‘ ์ˆ˜ํ–‰ํ•˜์˜€๊ณ , ์ด๋ฅผ ํ†ตํ•ด ์˜ด๋‹ˆํŠธ๋ž™ ์ ‘๊ทผ๋ฒ•์ด ์–ด๋–ป๊ฒŒ ์—ฐ๊ตฌ์ž๋“ค์˜ ์—ฐ๊ตฌ ๋ชฉ์ ์„ ์ด๋ฃจ๋Š” ๋ฐ์— ๋„์›€์„ ์ค„ ์ˆ˜ ์žˆ๋Š”์ง€ ์ง์ ‘ ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํœด๋จผ-์ปดํ“จํ„ฐ ์ธํ„ฐ๋ž™์…˜(Human-Computer Interaction) ๋ฐ ์œ ๋น„์ฟผํ„ฐ์Šค ์ปดํ“จํŒ…(Ubiquitous Computing) ๋ถ„์•ผ์— ๊ธฐ์ˆ ์  ์‚ฐ์ถœ๋ฌผ๋กœ์จ ๊ธฐ์—ฌํ•˜๋ฉฐ, ์ž์œ ๋„ ๋†’์€ ์…€ํ”„ ํŠธ๋ž˜ํ‚น ๋„๊ตฌ๊ฐ€ ์–ด๋–ป๊ฒŒ ๊ฐœ์ธ๊ณผ ์—ฐ๊ตฌ์ž๋“ค์„ ๋„์šธ ์ˆ˜ ์žˆ๋Š”์ง€ ์‹ค์ฆ์ ์ธ ์ดํ•ด๋ฅผ ์ฆ์ง„ํ•œ๋‹ค. ๋˜ํ•œ, ์ž์œ ๋„ ๋†’์€ ์…€ํ”„ํŠธ๋ž˜ํ‚น ๊ธฐ์ˆ ์— ๋Œ€ํ•œ ๋””์ž์ธ์  ๋‚œ์ œ, ์—ฐ๊ตฌ์—์„œ ์ œ์‹œํ•œ ์‹œ์Šคํ…œ์— ๋Œ€ํ•œ ๊ฐœ์„ ๋ฐฉ์•ˆ, ๋งˆ์ง€๋ง‰์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ ๋‹ค๋ฃจ์ง€ ๋ชปํ•œ ๋‹ค๋ฅธ ์ง‘๋‹จ์„ ์ง€์›ํ•˜๊ธฐ ์œ„ํ•œ ํ–ฅํ›„ ์—ฐ๊ตฌ ๋…ผ์ œ์— ๋Œ€ํ•˜์—ฌ ๋…ผ์˜ํ•œ๋‹ค.Abstract CHAPTER 1. Introduction 1.1 Background and Motivation 1.2 Research Questions and Approaches 1.2.1 Designing a Flexible Self-Tracking Approach Leveraging Semiautomated Tracking 1.2.2 Design and Evaluation of OmniTrack in Individual Tracking Contexts 1.2.3 Designing a Research Platform for In Situ Data Collection Studies Leveraging OmniTrack 1.2.4 A Case Study of Conducting an In Situ Data Collection Study using the Research Platform 1.3 Contributions 1.4 Structure of this Dissertation CHAPTER 2. Related Work 2.1 Background on Self-Tracking 2.1.1 Self-Tracking in Personal Tracking Contexts 2.1.2 Utilization of Self-Tracking in Other Contexts 2.2 Barriers Caused by Limited Tool Support 2.2.1 Limited Tools and Siloed Data in Personal Tracking 2.2.2 Challenges of the Instrumentation for In Situ Data Collection 2.3 Flexible Self-Tracking Approaches 2.3.1 Appropriation of Generic Tools 2.3.2 Universal Tracking Systems for Individuals 2.3.3 Research Frameworks for In Situ Data Collection 2.4 Grounding Design Approach: Semi-Automated Tracking 2.5 Summary of Related Work CHAPTER3 DesigningOmniTrack: a Flexible Self-Tracking Approach 3.1 Design Goals and Rationales 3.2 System Design and User Interfaces 3.2.1 Trackers: Enabling Flexible Data Inputs 3.2.2 Services: Integrating External Trackers and Other Services 3.2.3 Triggers: Retrieving Values Automatically 3.2.4 Streamlining Tracking and Lowering the User Burden 3.2.5 Visualization and Feedback 3.3 OmniTrack Use Cases 3.3.1 Tracker 1: Beer Tracker 3.3.2 Tracker 2: SleepTight++ 3.3.3 Tracker 3: Comparison of Automated Trackers 3.4 Summary CHAPTER 4. Understanding HowIndividuals Adopt and Adapt OmniTrack 4.1 Usability Study 4.1.1 Participants 4.1.2 Procedure and Study Setup 4.1.3 Tasks 4.1.4 Results and Discussion 4.1.5 Improvements A_er the Usability Study 4.2 Field Deployment Study 4.2.1 Study Setup 4.2.2 Participants 4.2.3 Data Analysis and Results 4.2.4 Reflections on the Deployment Study 4.3 Discussion 4.3.1 Expanding the Design Space for Self-Tracking 4.3.2 Leveraging Other Building Blocks of Self-Tracking 4.3.3 Sharing Trackers with Other People 4.3.4 Studying with a Broader Audience 4.4 Summary CHAPTER 5. Extending OmniTrack for Supporting In Situ Data Collection Studies 5.1 Design Space of Study Instrumentation for In-Situ Data Collection 5.1.1 Experiment-Level Dimensions 5.1.2 Condition-Level Dimensions 5.1.3 Tracker-Level Dimensions 5.1.4 Reminder/Trigger-Level Dimensions 5.1.5 Extending OmniTrack to Cover the Design Space 5.2 Design Goals and Rationales 5.3 System Design and User Interfaces 5.3.1 Experiment Management and Collaboration 5.3.2 Experiment-level Configurations 5.3.3 A Participants Protocol for Joining the Experiment 5.3.4 Implementation 5.4 Replicated Study Examples 5.4.1 Example A: Revisiting the Deployment Study of OmniTrack 5.4.2 Example B: Exploring the Clinical Applicability of a Mobile Food Logger 5.4.3 Example C: Understanding the Effect of Cues and Positive Reinforcement on Habit Formation 5.4.4 Example D: Collecting Stress and Activity Data for Building a Prediction Model 5.5 Discussion 5.5.1 Supporting Multiphase Experimental Design 5.5.2 Serving as Testbeds for Self-Tracking Interventions 5.5.3 Exploiting the Interaction Logs 5.6 Summary CHAPTER 6. Using the OmniTrack Research Kit: A Case Study 6.1 Study Background and Motivation 6.2 OmniTrack Configuration for Study Instruments 6.3 Participants 6.4 Study Procedure 6.5 Dataset and Analysis 6.6 Study Result 6.6.1 Diary Entries 6.6.2 Aspects of Productivity Evaluation 6.6.3 Productive Activities 6.7 Experimenter Experience of OmniTrack 6.8 Participant Experience of OmniTrack 6.9 Implications 6.9.1 Visualization Support for Progressive, Preliminary Analysis of Collected Data 6.9.2 Inspection to Prevent Misconfiguration 6.9.3 Providing More Alternative Methods to Capture Data 6.10 Summary CHAPTER 7. Discussion 7.1 Lessons Learned 7.2 Design Challenges and Implications 7.2.1 Making the Flexibility Learnable 7.2.2 Additive vs. Subtractive Design for Flexibility 7.3 Future Opportunities for Improvement 7.3.1 Utilizing External Information and Contexts 7.3.2 Providing Flexible Visual Feedback 7.4 Expanding Audiences of OmniTrack 7.4.1 Supporting Clinical Contexts 7.4.2 Supporting Self-Experimenters 7.5 Limitations CHAPTER 8. Conclusion 8.1 Summary of the Approaches 8.2 Summary of Contributions 8.2.1 Artifact Contributions 8.2.2 Empirical Research Contributions 8.3 Future Work 8.3.1 Understanding the Long-term E_ect of OmniTrack 8.3.2 Utilizing External Information and Contexts 8.3.3 Extending the Input Modality to Lower the Capture Burden 8.3.4 Customizable Visual Feedback 8.3.5 Community-Driven Tracker Sharing 8.3.6 Supporting Multiphase Study Design 8.4 Final Remarks APPENDIX A. Study Material for Evaluations of the OmniTrack App A.1 Task Instructions for Usability Study A.2 The SUS (System Usability Scale) Questionnaire A.3 Screening Questionnaire for Deployment Study A.4 Exit Interview Guide for Deployment Study A.5 Deployment Participant Information APPENDIX B Study Material for Productivity Diary Study B.1 Recruitment Screening Questionnaire B.2 Exit Interview Guide Abstract (Korean)Docto

    A study of pixel variation method for vibration measurement with high speed camera

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    The main focus of this study is to validate the authenticity of vibration measurement using high speed camera. High speed camera with the capability of immense frame rates opens up the possibility of capturing slight pixel differences in continuous frame. Thus, the vibrations on an object could be captured in units of pixels. Through image tracking processes camera parameters and image pixel coordinates were obtained. Then the frequency analysis from the data was performed based on the Fast Fourier Transform (FFT) method. Using this technique pure sound waves of 50, 100, and 200 Hz were generated and successfully recovered. After recovering pure sound waves, experiment to recognize multi frequency sound with high speed camera and PTV (Particle Tracking Velocimetry) algorithm has been conducted. To simulate natural environment conditions simultaneous frequency and frequency sweep were generated. Simultaneous frequency was combination of 50, 62.5, 75, 87.5, and 100 Hz frequencies, and frequency sweep was progressing starting from 0 to 2000 Hz. Then the vibration caused by the frequencies were captured by high speed camera, and the measured vibration data were used to reconstruct the original data. To evaluate the authenticity of the experiment human voice were captured instead of using pure sound waves. As a result, high speed camera and PTV algorithm method showed promising results of reconstructing human voice as well. From all the conducted experiments of pure sound frequency, multi-frequency, simultaneous frequency, frequency sweep and human voice tests the integration of high speed camera and PTV algorithm showed promising results of measuring small vibrations caused by sound. Next phase of the experiment is to measure depth information using a single high speed camera and three laser pointers. Depth information is essential when measuring vibrations because it contains 3 dimensional coordinate data. Yet a single high speed camera is not sufficient to retrieve depth information from an image. Thus, lasers are used to support measuring depth of vibration. Three lasers are shot with a triangular formation on to a mirror surface. Then when the mirror is subjected to a external force, such as vibrational force, the triangular laser formation reflected off the mirror will start to deform or drift. The deformation of triangular laser points can be used to derive the depth information of subjected vibration on the mirror. The changing displacement between the three laser points can be calculated using PTV algorithm. Integrating the high speed camera images and laser point deformations can then be used to verify or reveal the vibration on the mirror. Then neural network is applied to enhance the accuracy and speed of measuring vibration. Furthermore, the application of neural network introduces the feasibility of universal vibration measuring technique.์ œ1์žฅ ์„œ ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ ๋ชฉ์  2 1.3 ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 4 ์ œ2์žฅ ์ง„๋™์ธก์ • 5 2.1 ์ง„๋™์ด๋ก  5 2.2 ์ง„๋™์ธก์ • 8 2.2.1 ์ผ๋ฐ˜์ ์ธ ์ง„๋™์ธก์ • ๋ฐฉ๋ฒ• ๋ฐ ๋ชฉ์  8 2.2.2 ์ง„๋™์˜ ํŠน์ง• 9 2.3 ์ง„๋™์ธก์ •์— ์˜ํ•œ ์Œ์› ๋ณต์› ๊ธฐ์กด ์—ฐ๊ตฌ 12 ์ œ3์žฅ ์นด๋ฉ”๋ผ ์˜์ƒ๊ธฐ๋ฐ˜ 2์ฐจ์› ๋ฏธ์„ธ์ง„๋™ ์ธก์ • 14 3.1 PIV, PTV ์›๋ฆฌ 14 3.2 PIV, PTV ๊ธฐ๋ฐ˜ ์ง„๋™ ํ•ด์„ ์•Œ๊ณ ๋ฆฌ๋“ฌ 21 3.3 2์ฐจ์› ๋ฏธ์„ธ์ง„๋™ ๊ธฐ๋ฐ˜ ์Œ์› ๊ฐ€์‹œํ™” 23 3.3.1 ์‹œ๊ฐ์  ์Œ์› ๋ณต์›์˜ ์žฅ์  23 3.3.2 ์Œ์› ๋ณต์› ๋ฐฉ๋ฒ• 24 3.3.2.1 ๋‹จ์ฃผํŒŒ์ˆ˜ ๋ณต์› 25 3.3.2.2 ๋‹ค์ฃผํŒŒ์ˆ˜ ๋ณต์› 29 3.3.3 ์Œ์„ฑ ๋ณต์› ์‹คํ—˜ ๋ฐ ๊ฒฐ๊ณผ 35 ์ œ4์žฅ ์นด๋ฉ”๋ผ ์˜์ƒ๊ธฐ๋ฐ˜ 3์ฐจ์› ๋ฏธ์„ธ์ง„๋™ ์ธก์ • 47 4.1 ๋‰ด๋Ÿด ๋„คํŠธ์›Œํฌ 47 4.1.1 ๊ฐœ์š” 47 4.1.2 ์‹ ๊ฒฝ๋ง ์•Œ๊ณ ๋ฆฌ๋“ฌ 47 4.2 ์•Œ๊ณ ๋ฆฌ๋“ฌ ๊ฒ€์ฆ 50 4.2.1 ํผ์…‰ํŠธ๋ก  50 4.2.2 XOR์—ฐ์‚ฐ 54 4.2.3 ์˜์ƒ๊ธฐ๋ฐ˜ ๋‰ด๋Ÿด ๋„คํŠธ์›Œํฌ 57 4.3 ๋ ˆ์ด์ € ๋ฐ˜์‚ฌ๋ฒ•์— ์˜ํ•œ 3์ฐจ์› ๋ฏธ์„ธ์ง„๋™ ์ธก์ • 58 4.3.1 ์‹คํ—˜์žฅ์น˜ ๋ฐ ๋ฐฉ๋ฒ• 58 4.3.2 ์‹คํ—˜๊ฒฐ๊ณผ 64 4.4 ์•Œ๊ณ ๋ฆฌ๋“ฌ ์ถ”๊ฐ€๊ฒ€์ฆ 79 4.4.1 ํ•™์Šต๋ฐฉ์‹ 79 4.4.2 ๊ฒฐ๊ณผ 80 ์ œ5์žฅ ๊ฒฐ๋ก  84 ์ฐธ ๊ณ  ๋ฌธ ํ—Œ 8

    Epidemiologic Survey of Head and Neck Cancers in Korea

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    Head and neck cancers have never been systematically studied for clinical purposes yet in Korea. This epidemiological survey on head and neck cancer patients was undertaken from January to December 2001 in 79 otorhinolaryngology resident-training hospitals nationwide. The number of head and neck cancer patients was 1,063 cases in the year. The largest proportion of cases arose in the larynx, as many as 488 cases, which accounted for 45.9%. It was followed by, in order of frequency, oral cavity (16.5%), oropharynx (10.0%), and hypopharynx (9.5%). The male:female ratio was 5:1, and the mean age was 60.3 yr. Surgery was the predominant treatment modality in head and neck cancers: 204 (21.5%) cases were treated with only surgery, 198 (20.8%) cases were treated with surgery and radiotherapy, 207 cases (21.8%) were treated with combined therapy of surgery, radiotherapy, and chemotherapy. Larynx and hypopharynx cancers had a stronger relationship with smoking and alcohol drinking than other primary site cancers. Of them, 21 cases were found to be metastasized at the time of diagnosis into the lung, gastrointestinal tract, bone, or brain. Coexisting second primary malignancies were found in 23 cases. At the time of diagnosis, a total of 354 cases had cervical lymph node metastasis accounting for 42.0%.ope

    A Study on Prevention Maintenance of Container Terminal Equipments for Increasing Terminal Productivity

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    ์ˆ˜์ถœ์ž…์˜ ๊ต๋Ÿ‰ ์—ญํ• ์„ ๋‹ด๋‹นํ•˜๋Š” ํ•ญ๋งŒ์—์„œ ์ˆ˜์ถœ์ž… ๋ฌผ๋™๋Ÿ‰์„ ์ฒ˜๋ฆฌํ•˜๋Š” ํ•ญ๋งŒ์‹œ์„ค ๋ฏธํก์ด ๋ฌธ์ œ๊ฐ€ ๋˜๊ณ  ์žˆ์œผ๋ฉฐ ์ด๋Ÿฌํ•œ ์  ๋•Œ๋ฌธ์— ๊ณ ๋ถ€๊ฐ€๊ฐ€์น˜๋ฅผ ์ฐฝ์ถœํ•  ์ˆ˜ ์—†์„ ๋ฟ ์•„๋‹ˆ๋ผ ์ด์ƒ์‚ฐ๋Œ€๋น„ ๋ฌผ๋ฅ˜๋น„์˜ ๋น„์œจ์ด ๋ฏธ๊ตญ๊ณผ ์ผ๋ณธ์— ๋น„ํ•ด ๋งค์šฐ ๋†’๊ณ  ๋ฌผ๋ฅ˜ ํ™˜๊ฒฝ์ด ์—ด์•…ํ•œ ๊ฒƒ์ด ํ˜„์‹ค์ด๋‹ค. ๋”ฐ๋ผ์„œ ํ•ญ๋งŒ์˜ ๊ฒฝ์Ÿ๋ ฅ ๊ฐ•ํ™”๋ฅผ ์œ„ํ•ด ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์˜ ์ž๋™ํ™”๋ฅผ ๋ชฉ์ ์œผ๋กœ ํ™œ๋ฐœํ•˜๊ฒŒ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜๊ณ  ์žˆ์œผ๋ฉฐ ๊ตญ์ฑ…์‚ฌ์—…์œผ๋กœ ์‹ ํ•ญ, ๋งˆ์‚ฐํ•ญ, ๊ด‘์–‘ํ•ญ 3๋‹จ๊ณ„ ๊ณต์‚ฌ๊ฐ€ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ ํ•ญ๋งŒ์— ์„ค์น˜๋œ ํ•˜์—ญ์žฅ๋น„์˜ ํšจ์œจ์„ฑ์„ ๋†’์ด๊ธฐ ์œ„ํ•ด ์ œ์–ด์‹œ์Šคํ…œ์—๋„ ๋งŽ์€ ์—ฐ๊ตฌ๊ฐœ๋ฐœ์ด ์ด๋ฃจ์–ด์ ธ ์ตœ์‹  ํ•˜์—ญ์žฅ๋น„๊ฐ€ ๋„์ž…๋˜๊ณ  ์žˆ๋‹ค. ํ•˜์—ญ์žฅ๋น„๋Š” ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์—์„œ ํ™”๋ฌผ์„ ๋น ๋ฅธ ์‹œ๊ฐ„ ๋‚ด์— ์–‘ํ™”์™€ ์ ํ™”, ๋ฐ˜์ถœ๊ณผ ๋ฐ˜์ž…, ์•ผ๋“œ ๋‚ด์—์„œ ์งง์€ ์‹œ๊ฐ„์— ์›ํ•˜๋Š” ์œ„์น˜๋กœ ์ด์†กํ•˜๋Š” ์šด๋ฐ˜๊ธฐ๊ธฐ๋ฅผ ๋งํ•œ๋‹ค. ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์˜ ์ƒ์‚ฐ์„ฑ์€ ํ„ฐ๋ฏธ๋„๋‚ด ํ•˜์—ญ์žฅ๋น„ ๊ฐ€๋™๋ฅ , ์ฆ‰ ๊ณ ์žฅ๋ฅ ์— ํฌ๊ฒŒ ์˜์กด๋˜๊ณ  ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ์œผ๋ฉฐ ๋ณต์žกํ•œ ํ•˜์—ญ์žฅ๋น„์˜ ๊ตฌ์กฐ ๋ฐ ํ™”๋ฌผ ์ค‘๋Ÿ‰์˜ ๋ณ€ํ™” ํŠน์„ฑ์— ๋”ฐ๋ผ ๋‹ค์–‘ํ•˜๊ฒŒ ๊ณ ์žฅ์ด ๋ฐœ์ƒ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ํ•˜์—ญ์žฅ๋น„์˜ ๊ณ ์žฅ์€ ๊ธฐ๊ณ„์  ํ”ผ๋กœ๋„ ๋ˆ„์ , ์ „๋™๊ธฐ ์ˆ˜๋ช…๋‹จ์ถ•, ์ œ์–ด์‹œ์Šคํ…œ ์†์ƒ, ์™€์ด์–ด๋กœํ”„ ์ˆ˜๋ช…๋‹จ์ถ•, ํ•˜์—ญ์ž‘์—…์ž์˜ ์•ˆ์ „์„ฑ ์œ„ํ˜‘๊ณผ ๊ฐ™์€ ๋ฌธ์ œ๋ฅผ ํ•ญ์ƒ ๋‚ดํฌํ•˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„ ์ƒ์‚ฐ์„ฑ ํ–ฅ์ƒ์„ ์œ„ํ•ด ํ•˜์—ญ์žฅ๋น„์˜ ํŠน์„ฑ์— ๋งž๋Š” ์ฒ ์ €ํ•œ ์˜ˆ๋ฐฉ๋ณด์ „์„ ์‹ค์‹œํ•˜์—ฌ ํ•˜์—ญ์ž‘์—…์ค‘์— ๋ฐœ์ƒํ•˜๋Š” ๊ณ ์žฅ์„ ๋ฐฉ์ง€ํ•˜๊ณ  ์‚ฌ๊ณ ๋ฅผ ๋ฏธ์—ฐ์— ๋ฐฉ์ง€ํ•จ์œผ๋กœ์จ ํ•˜์—ญ์ƒ์‚ฐ์„ฑ์„ ๋†’์ด๋Š” ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•˜๊ณ ์ž ํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ณ ์žฅ์„ ๋ฏธ๋ฆฌ ์˜ˆ์ง€ํ•จ์œผ๋กœ์จ ๊ณ ์žฅ๋ฐœ์ƒ์„ ์ตœ์†Œํ™”ํ•˜์—ฌ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์˜ ์ƒ์‚ฐ์„ฑ ์ œ๊ณ ์™€ ํ•˜์—ญ์žฅ๋น„์˜ ์‹ ๋ขฐ์„ฑ์„ ๋†’์ด๊ณ  ๊ถ๊ทน์ ์œผ๋กœ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์˜ ์ „์ฒด ์ƒ์‚ฐ์„ฑ์„ ๋†’์ž„์œผ๋กœ์จ ๋Œ€์™ธ ์‹ ์ธ๋„๋ฅผ ๋†’์ด๋Š”๋ฐ ๊ธฐ์—ฌํ•˜๊ณ ์ž ํ•œ๋‹ค. ํ•ญ๋งŒํ•˜์—ญ์žฅ๋น„์˜ ๊ฐ€๋™์— ๋”ฐ๋ฅธ ์‹ ๋ขฐ์„ฑ์€ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์—์„œ ์ƒ์‚ฐ์„ฑ ์ œ๊ณ ์™€ ๊ฒฝ์Ÿ๋ ฅ ๊ฐ•ํ™”์— ์ค‘์š”ํ•œ ๋ณ€์ˆ˜๋กœ ์ž‘์šฉํ•˜๊ณ  ์žˆ์œผ๋ฉฐ ํ•˜์—ญ์žฅ๋น„์˜ ๊ณ ์žฅ์€ ์ž‘์—…์ค‘๋‹จ์„ ์˜๋ฏธํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๊ณ„ํš๋œ ์ƒ์‚ฐ์„ฑ์— ์ฐจ์งˆ์ด ์ƒ๊ธฐ๋Š” ๊ฒƒ์€ ๋‹น์—ฐํ•œ ๊ฒฐ๊ณผ์ด๋‹ค. ์ƒ์‚ฐ์„ฑ ์ €ํ•˜๋Š” ํ•ด๋‹น ์šด์˜์‚ฌ์—๋„ ๋งŽ์€ ์–ด๋ ค์›€์„ ๊ฐ€์ ธ๋‹ค์ฃผ์ง€๋งŒ ์„ ๋ฐ•์˜ ์ฒด์„ &#8228์ฒดํ™”๋กœ ์—ฐ๊ฒฐ๋˜๋ฏ€๋กœ ํ•ญ๋งŒ๊ณผ ๊ตญ๊ฐ€๋ฌผ๋ฅ˜๊ฒฝ์Ÿ๋ ฅ๊ฐ•ํ™”์— ํฐ ์†์‹ค์„ ๊ฐ€์ ธ๋‹ค์ค€๋‹ค. ๋ณด์ „ํ˜•ํƒœ๋Š” ๋ณด์ „์š”์›์˜ ์ ‘๊ทผ์„ฑ, ๋ณด์ „์‹œ๊ฐ„, ์šด์˜ํšจ์œจ์„ฑ์„ ์„ค๊ณ„์‹œ์ ๋ถ€ํ„ฐ ์ ์šฉํ•ด์•ผํ•˜๊ณ  ์žฅ์น˜์˜ ์‹ ๋ขฐ์„ฑ์ด ๊ฒ€์ฆ๋œ ๋ถ€ํ’ˆ์„ ์‚ฌ์šฉํ•˜์—ฌ ์ตœ์ ์˜ ๋ณด์ „์ฃผ๊ธฐ์„ค์ •์— ํ•จ์ˆ˜๋ฅผ ๊ทผ๊ฑฐ๋กœ ํ•˜์—ฌ ์‹ ๋ขฐ๋„์™€ ๊ณ ์žฅ๋ฅ  ํ•จ์ˆ˜์— ์˜ํ•œ ์ตœ์ ์˜ ๋ณด์ „์ฃผ๊ธฐ๋ฅผ ์„ค์ • ํ•˜์—ฌ์•ผ ํ•œ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ํ•ญ๋งŒ์˜ ์ƒ์‚ฐ์„ฑ ํ‰๊ฐ€๋ชจํ˜•์„ ์‹œ์„ค, ์ธ๋ ฅ, ์„ ์„์œผ๋กœ ๊ตฌ๋ถ„ํ•˜์˜€์œผ๋‚˜ ํ‰๊ฐ€๋ชจํ˜•์— ๊ฐ™์€ ์„ ์„์ด๋ผ๋„ ์„ ๋ฐ•์˜ ํฌ๊ธฐ์™€ ํ•˜์—ญ์ž‘์—…๋Ÿ‰, ํ•˜์—ญ์žฅ๋น„์˜ ๋Œ“์ˆ˜์™€ ์ธ๋ ฅ์˜ ํˆฌ์ž…์ด ๋‹ค๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚˜๋ฏ€๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ƒ์‚ฐ์„ฑ ํ‰๊ฐ€๋ชจํ˜•์— ์„ ๋ฐ•๋‹น ์ƒ์‚ฐ์„ฑ ํ‰๊ฐ€๋ฅผ ๋‹ฌ๋ฆฌํ•˜์˜€๋‹ค. ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์˜ ์šด์˜์ž๋ฃŒ๋ฅผ ๊ทผ๊ฑฐ๋กœ ์„ ์„์ ‘์•ˆ์œจ, ์ด์„ ์„์ƒ์‚ฐ์„ฑ, ์ˆœ์„ ์„์ƒ์‚ฐ์„ฑ, ์ด์žฅ๋น„์ƒ์‚ฐ์„ฑ, ์ˆœ์žฅ๋น„์ƒ์‚ฐ์„ฑ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋ฏผ๊ฐ๋„ ๋ถ„์„์„ ํ†ตํ•˜์—ฌ ๋ฏผ๊ฐ๋„์™€ ๊ณ ์žฅ๋ฅ  ๊ด€๊ณ„๋กœ๋ถ€ํ„ฐ ์ž‘์—…์ค‘๋‹จ ๋น„์œจ์ด ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฐ ํŠธ๋ฆฌ ํฌ๋ ˆ์ธ ๊ณ ์žฅ์ด ๋ฏผ๊ฐ๋„๊ฐ€ ๋†’์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ƒ์‚ฐ์„ฑ์˜ ํ‰๊ฐ€๋ชจํ˜•์— ํ•˜์—ญ์žฅ๋น„ ๊ณ ์žฅ์— ๋Œ€ํ•œ ๋ถ€๋ถ„์„ ํฌํ•จํ•˜์˜€์œผ๋ฉฐ, ํ•˜์—ญ์žฅ๋น„์˜ ๊ฐ€๋™์ค‘ ๊ณ ์žฅ์„ ์ตœ์†Œ๋กœ ํ•œ๋‹ค๋ฉด ํ•˜์—ญ์ž‘์—…์ค‘๋‹จ ๋น„์œจ์„ ์ค„์ผ ์ˆ˜ ์žˆ๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ํ˜„์žฌ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์—์„œ ํ•˜์—ญ์žฅ๋น„ ๋ถ€ํ’ˆ๋“ค์˜ ๊ต์ฒด์™€ ๋ณด์ „์ฃผ๊ธฐ๋ฅผ ์„ค์ •ํ•œ ๋ชจ๋‹ˆํ„ฐ๋ง์ด ๋˜์–ด ์žˆ๋‹ค๊ณ ๋Š” ํ•˜์ง€๋งŒ, ์ด๋Ÿฐ ๋ชจ๋‹ˆํ„ฐ๋ง์€ ์ œ์ž‘์‚ฌ์˜ ๊ถŒ๊ณ  ์‚ฌํ•ญ์— ๋งž์ถฐ์„œ ํ”„๋กœ๊ทธ๋žจ๋˜์–ด ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์‹ค์ œ ํ”„๋กœ๊ทธ๋žจ์— ๋งž๊ฒŒ ๊ต์ฒด์™€ ๋ณด์ „์ด ์ด๋ฃจ์–ด์ง€๊ธฐ๋Š” ํ˜„์‹ค์ ์œผ๋กœ ์–ด๋ ค์šด ๋ถ€๋ถ„๋“ค์ด ๋งŽ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„ ๋‚ด๋ถ€ํ™˜๊ฒฝ๊ณผ ์ฃผ์œ„์—ฌ๊ฑด์ด ์ฃผ๋กœ ์„ ๋ฐ•์˜ ์ฒด ํ•ญ๋งŒ์žฅ๋น„์˜ ์šด์˜์‹œ ์ธ๊ฐ„๊ณตํ•™์ ์ธ ์ธก๋ฉด์ด ์ ์ ˆํžˆ ๊ณ ๋ ค๋˜์–ด์•ผ ํ•œ๋‹ค. ์šด์ „์ž์˜ ์˜์—ญ์ธ ์šด์ „์‹ค์˜ ์•ˆ๋ฝํ•œ ๋ฐฐ์น˜, ๋ณด์ „์š”์›์ด ์ž‘์—…ํšจ์œจ์„ ๋†’์ผ ์ˆ˜ ์žˆ๋Š” ์„ค๊ณ„ ๋“ฑ ๋งŽ์€ ๋ถ€๋ถ„์— ์ด๋ฅด๊ธฐ๊นŒ์ง€ ํ•˜์—ญ์žฅ๋น„๋Š” ๋ฐœ์ „์„ ํ•  ๊ฒƒ์ด๋‹ค. ํ•˜์—ญ์žฅ๋น„์˜ ์‹œ์Šคํ…œ์ด ๋ฐœ์ „๋˜์–ด ๊ฐ€๋Š” ๊ฒƒ๊ณผ ๋ฐœ ๋งž์ถ”์–ด ๊ณ ๋„์˜ ์‹ ๋ขฐ์„ฑ์ด ํ™•๋ณด๋˜์–ด์•ผ ํ•˜๋ฉฐ ์ด๋ฅผ ์œ„ํ•ด์„œ๋Š” ๊ณ ์žฅ๋ณด์ „์—๋„ ์ „๋ฌธ๊ฐ€์˜ ์ง„๋‹จ๊ณผ ์ˆ˜๋ฆฌ๊ฐ€ ์š”๊ตฌ๋œ๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 1.2 ์—ฐ๊ตฌ๋‚ด์šฉ๊ณผ ๊ตฌ์„ฑ 3 ์ œ 2 ์žฅ ํ•˜์—ญ์žฅ๋น„ ๋ณด์ „ ๋ฐ ์ ๊ฒ€ 6 2.1 ๊ฐœ์š” 7 2.2 ๊ฐ ํŠธ๋ฆฌ ํฌ๋ ˆ์ธ 7 2.2.1 ๊ฐ ํŠธ๋ฆฌ ํฌ๋ ˆ์ธ์˜ ๋ณด์ „ 12 2.2.2 ๊ฐ ํŠธ๋ฆฌ ํฌ๋ ˆ์ธ์˜ ์ ๊ฒ€ 17 2.3 ํŠธ๋žœ์Šคํผ ํฌ๋ ˆ์ธ 23 2.3.1 ํŠธ๋žœ์Šคํผ ํฌ๋ ˆ์ธ์˜ ๋ณด์ „ 28 2.3.2 ํŠธ๋žœ์Šคํผ ํฌ๋ ˆ์ธ์˜ ์ ๊ฒ€ 29 2.4 ์•ผ๋“œ ํŠธ๋ž™ํ„ฐ 31 2.4.1 ์•ผ๋“œ ํŠธ๋ž™ํ„ฐ์˜ ๋ณด์ „ 31 2.4.2 ์•ผ๋“œ ํŠธ๋ž™ํ„ฐ์˜ ์ ๊ฒ€ 33 2.5 ๊ธฐํƒ€ ํ•˜์—ญ์žฅ๋น„ 34 2.6 ๊ณ ์ฐฐ ๋ฐ ์š”์•ฝ 37 ์ œ 3 ์žฅ ํ•˜์—ญ์žฅ๋น„์˜ ๊ณ ์žฅ 39 3.1 ํ•˜์—ญ์žฅ๋น„์˜ ์‚ฌ๊ณ ์‚ฌ๋ก€ 39 3.1.1 ๊ธฐ๊ณ„๋ถ„์•ผ๊ณ ์žฅ์— ์˜ํ•œ ์‚ฌ๊ณ  39 3.1.2 ์ „๊ธฐ๋ถ„์•ผ๊ณ ์žฅ์— ์˜ํ•œ ์‚ฌ๊ณ  40 3.2 ํ•˜์—ญ์žฅ๋น„์˜ ๋ณด์ „๊ด€๋ฆฌ 44 3.2.1 ์˜ˆ๋ฐฉ๋ณด์ „ 46 3.2.2 ์‚ฌํ›„๋ณด์ „ 53 3.3 ๊ณ ์žฅ๋ถ„์„ 54 3.3.1 ๊ณ ์žฅ๋ฅ ๊ณผ ๊ณ ์žฅํ™•๋ฅ ๋ฐ€๋„ํ•จ์ˆ˜ 54 3.3.2 ์š•์กฐ๊ณก์„  59 3.4 ๋ณด์ „์„ฑ 65 3.4.1 ํ‰๊ท ์ˆ˜๋ช…๊ณผ ํ‰๊ท ๊ณ ์žฅ 65 3.4.2 ํ•˜์—ญ์žฅ๋น„์˜ ๋ณด์ „์„ฑ 69 3.4.3 ํ•˜์—ญ์žฅ๋น„์˜ ๊ฐ€๋™์„ฑ 71 3.5 ํ•˜์—ญ์žฅ๋น„์˜ ๊ณ ์žฅ ๋ถ„์„ 73 3.5.1 ๊ฐ ํŠธ๋ฆฌ ํฌ๋ ˆ์ธ์˜ ๊ณ ์žฅ 73 3.5.2 ํŠธ๋žœ์Šคํผ ํฌ๋ ˆ์ธ์˜ ๊ณ ์žฅ 82 3.5.3 ์•ผ๋“œ ํŠธ๋ž™ํ„ฐ์˜ ๊ณ ์žฅ 87 3.5.4 ํ•˜์—ญ์žฅ๋น„์˜ ๊ณ ์žฅ ๋ถ„์„๊ฒฐ๊ณผ 88 3.6 ๊ณ ์ฐฐ ๋ฐ ์š”์•ฝ 89 ์ œ 4 ์žฅ ํ•˜์—ญ์žฅ๋น„์˜ ๊ณ ์žฅ๋ฅ  ๋ฐ ์ƒ์‚ฐ์„ฑ 91 4.1 ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์˜ ์ƒ์‚ฐ์„ฑ ๋ชจํ˜• 91 4.1.1 ์ƒ์‚ฐ์„ฑ ํ‰๊ฐ€๊ฒฝํ–ฅ 91 4.1.2 ์ƒ์‚ฐ์„ฑ ์ธก์ •๋ชจํ˜• 94 4.2 ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์˜ ์ƒ์‚ฐ์„ฑ ๋ถ„์„ 97 4.2.1 ์ƒ์‚ฐ์„ฑ ๋ฏผ๊ฐ๋„ 97 4.2.2 ์ƒ์‚ฐ์„ฑ ๋ถ„์„ 98 4.2.3 1๊ฐœ ์„ ๋ฐ•๋‹น ์ƒ์‚ฐ์„ฑ ๋ถ„์„ 99 4.3 ๊ณ ์ฐฐ ๋ฐ ์š”์•ฝ 101 ์ œ 5 ์žฅ ํ•˜์—ญ์žฅ๋น„ ์˜ˆ๋ฐฉ๋ณด์ „์— ์˜ํ•œ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์˜ ์ƒ์‚ฐ์„ฑ ํ–ฅ์ƒ 102 5.1 ์˜ˆ๋ฐฉ๋ณด์ „์ฃผ๊ธฐ ๊ฐœ์„ ์— ์˜ํ•œ ๋ฐฉ๋ฒ• 102 5.1.1 ์˜ˆ๋ฐฉ๋ณด์ „ ์ฃผ๊ธฐ 102 5.1.2 ์˜ˆ๋ฐฉ๋ณด์ „ ์ฃผ๊ธฐ์„ค์ • 104 5.2 ์˜ˆ์ง€๋ณด์ „์— ์˜ํ•œ ๋ฐฉ๋ฒ• 105 5.2.1 ์ƒํƒœ๊ณต๊ฐ„๊ธฐ๋ฒ•์— ์˜ํ•œ ์˜ˆ์ง€๋ณด์ „ 106 5.2.2 ๊ณ ์žฅ์˜ˆ์ง€ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 112 5.2.3 ์ƒ์‚ฐ์„ฑ ๋น„๊ต ๊ฒฐ๊ณผ 123 5.3 ๊ณ ์ฐฐ ๋ฐ ์š”์•ฝ 124 ์ œ 6 ์žฅ ๊ฒฐ๋ก  126 ์ฐธ๊ณ ๋ฌธํ—Œ 12

    Performance evaluation of Volume PTV based on Affine transformation and measurement the wake of a circular cylinder

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    The paper discusses about the performances of the constructed affine transformation based tomographic PTV algorithm. Before commencing the performance tests on the tomographic PTV, the performance tests were carried out for the artificial images of which data were numerically generated for the two-dimensional Taylor-Green Vortex. The two-dimensional vector fields were calculated for these artificial images by changing several factors such as, particle maximum movement(PM), particle neighbors(PN), particle number of density and particle diameter. For the tests of the two-dimensional cases, two-dimensional affine transformation was used. Three-dimensional vector fields were also calculated by using the artificial images of a three-dimensional ring vortex, in which three-dimensional affine transformation was used. For the reconstruction of the artificial particles, the MART(multiplicative algebraic reconstruction technique) was used for the tests the tomographic PTV algorithm. The three-dimensional distribution of the particles and their locations were reconstructed by the use of this MART method. After confirming the performances, the constructed algorithm was tested for the wake flows of a circular cylinder(Reynolds number = 630), through which the flow features obtained by the conventional tomographic PIV and by the constructed tomographic PTV were compared, and eventually the performances of the constructed algorithm was evaluated qualitatively.Abstract 1 ์ œ1์žฅ ์„œ ๋ก  3 1.1 ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  3 1.2 ์—ฐ๊ตฌ๋ชฉ์  ๋ฐ ๊ตฌ์„ฑ 5 ์ œ2์žฅ Volume PTV ์™€ Tomo PIV์˜ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 6 2.1 PIV ๋ฐ PTV 6 2.2 3์ฐจ์› ๊ณ„์ธก๋ฐฉ๋ฒ• ๋ฐ ์›๋ฆฌ 7 2.3 Affine ๋ณ€ํ™˜ ๊ธฐ๋ฐ˜ Volume PTV 9 2.3.1 Affine ๋ณ€ํ™˜ 9 2.3.2 ํ™•๋ฅ ์ผ์น˜๋ฒ• 12 2.3.3 Deformation PIV์™€ Affine PTV์˜ ์„ฑ๋Šฅ๋น„๊ต 14 2.3.4 Volume PTV 26 2.4 Tomographical Reconstruction Method (ART & MART method) 28 ์ œ3์žฅ Volume PTV์˜ ๊ตฌ์ถ• ๋ฐ ์„ฑ๋Šฅํ‰๊ฐ€ 32 3.1 ๊ฐ€์ƒ์˜์ƒ ์ž…์ž์ƒ์„ฑ 32 3.2 ๊ฐ€์ƒ์˜์ƒ ์œ ๋™์žฅ 32 3.3 ๊ณ„์‚ฐ๊ฒฐ๊ณผ 34 3.4 ๊ฒฐ๋ก  ๋ฐ ๊ณ ์ฐฐ 46 ์ œ4์žฅ Volume PTV๋ฅผ ์ด์šฉํ•œ ์‹ค๋ฆฐ๋” ํ›„๋ฅ˜ ๊ณ„์ธก 47 4.1 ์‹คํ—˜์žฅ์น˜ ๋ฐ ์‹คํ—˜๋ฐฉ๋ฒ• 47 4.2 ์‹คํ—˜๊ฒฐ๊ณผ 49 ์ œ5์žฅ ๊ฒฐ๋ก  ๋ฐ ๊ณ ์ฐฐ 54 ์ฐธ๊ณ ๋ฌธํ—Œ 5

    Laryngeal Neurophysiology

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    ope

    A Case of Actinomycosis of the Neck

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    Actinomycosis of cerivcofacial region is an uncommon disease and presents as an abscess or chronic lesion mimicking malignancy, tuberculosis, or fungal lesion. Actinomycosis is difficult to diagnose because of fastidious nature of the organism in culture and general lack of familiarity with the disease. So, a high index of suspicion is required to make an accurate and timely diagnosis. We present a case of male patient with actinomycosis of submandibular triangle complaining of neck mass.ope

    An Efficient Workflow Management Scheme with Explicit Business Rules

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    In this paper, we have identified and classified various workflow operational rules. There are many business rules involved in the operation of workflow systems within the enterprise business environments. The rules are defined as ECA (Event-Condition- Action) rules and integrated with workflow systems with the active DB technology. Operational rules are categorized into task dispatching rules, dynamic process adaptation rules, exception handling rules, event-based monitoring rules, and external domain business rules. By adopting rule-based approach, the modification of business rules for process management can be easier. With the explicit management of business rules, the reasoning process of organizations can be formalized and managed transparently, which enables rapid and clear decision-making

    Histology and Stroboscopic Findings after Injection of Artecollโ“‡ and Restylaneโ“‡ into Paralyzed Canine Vocal Fold๏ผšin vivo Canine Study

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    Background and Objectives๏ผšThe aims of this study are to introduce Artecoll and Restaylane, that have been available for facial augmentation, as new materials for injection laryngoplasty, to investigate the mucosal wave of true vocal folds after the injection of these two materials into the true vocal fold, and to assess its biocompatibility and durability. Subjects and Method๏ผšAfter complete paralysisof the right recurrent laryngeal nerve of 6 Beagle dogs, the dogs were divided into the Artecoll injection group and the Restylane injection group, and Artecoll or Restylane was injected into vocalis muscle and vocal ligament. The recurrent laryngeal nerve of the opposite side was stimulated, the posterior commissure was sutured, and the mucosal wave of true vocal folds was examined by stroboscopy in in vivo canine study 1, 3, and 6 months after the injection. And, histopathological change of the injected materials after total laryngectomy wasexamined by hematoxylin and eosin (H & E) staining and masson trichrome staining. Results๏ผšIn both the Artecoll injection and the Restylane injection groups, the mucosal wave of true vocal folds was detected by stroboscopic examination until 6 months after the injection, and the difference of the mucosal wave of true vocal folds between these two groups was difficult to detect. Histological studies revealed that the injected materials remained in the vocal ligament and vocalis muscle and theses two materials were resorbed with time, Artecoll showing less resorption. These two materials were biocompatible and, particularly, Restylane showed less foreign body reaction. Conclusion๏ผšSince both Artecoll and Restylane are biocompatible and durable, they areconsidered as the suitable material for injection larygoplasty, and additional long-term studies are required.ope

    Retrograde Analysis of Complications of Jejunal Free Flap after Total Pharyngo-Laryngo-Cervical Esophagectomy in Advanced Hypopharyngeal Cancer Treatment

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    Hypophayngeal cancers are usually diagnosed in advanced stages and in many cases, they need total pharyngocervical esophagectomy and surgical reconstruction. Among many surgical reconstructive methods, jejunal free flap has anatomical and functional advantages such as tubed nature, peristaltic activity, excellent blood supply. In this study we analysed the surgical procedure and complications of jejunal free flap after total pharyngo-cervical esophagectomy. 20 cases performed jejunal free flap from 1995 to 2007 at Severance Hospital were reviewed. According to time of onset, early and late complications were reviewed. Surgical procedure was reviewed with operation record. Oral diet tolerance was reviewed on the basis of pharyngogram and subjective symptoms. The most common complication was stricture, and it occurred in 40% of cases and 63% of them were managed with conservative care. As early complication, fistula formation was all managed with conservative care. Oral feeding tolerance after jejunal free flap was 65% and 7 patients were tolerable to general diet. In our study, stricture was the most common complication and its management is important in post op oral diet tolerance.ope
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