74 research outputs found

    User Experience Enhancement on Smartphones using Wireless Communication Technologies

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2020. 8. ๋ฐ•์„ธ์›….Recently, various sensors as well as wireless communication technologies such as Wi-Fi and Bluetooth Low Energy (BLE) have been equipped with smartphones. In addition, in many cases, users use a smartphone while on the move, so if a wireless communication technologies and various sensors are used for a mobile user, a better user experience can be provided. For example, when a user moves while using Wi-Fi, the user experience can be improved by providing a seamless Wi-Fi service. In addition, it is possible to provide a special service such as indoor positioning or navigation by estimating the users mobility in an indoor environment, and additional services such as location-based advertising and payment systems can also be provided. Therefore, improving the user experience by using wireless communication technology and smartphones sensors is considered to be an important research field in the future. In this dissertation, we propose three systems that can improve the user experience or convenience by usingWi-Fi, BLE, and smartphones sensors: (i) BLEND: BLE beacon-aided fast Wi-Fi handoff for smartphones, (ii) PYLON: Smartphone based Indoor Path Estimation and Localization without Human Intervention, (iii) FINISH: Fully-automated Indoor Navigation using Smartphones with Zero Human Assistance. First, we propose fast handoff scheme called BLEND exploiting BLE as secondary radio. We conduct detailed analysis of the sticky client problem on commercial smartphones with experiment and close examination of Android source code. We propose BLEND, which exploits BLE modules to provide smartphones with prior knowledge of the presence and information of APs operating at 2.4 and 5 GHz Wi-Fi channels. BLEND operating with only application requires no hardware and Android source code modification of smartphones.We prototype BLEND with commercial smartphones and evaluate the performance in real environment. Our measurement results demonstrate that BLEND significantly improves throughput and video bitrate by up to 61% and 111%, compared to a commercial Android application, respectively, with negligible energy overhead. Second, we design a path estimation and localization system, termed PYLON, which is plug-and-play on Android smartphones. PYLON includes a novel landmark correction scheme that leverages real doors of indoor environments consisting of floor plan mapping, door passing time detection and correction. It operates without any user intervention. PYLON relaxes some requirements for localization systems. It does not require any modifications to hardware or software of smartphones, and the initial location of WiFi APs, BLE beacons, and users. We implement PYLON on five Android smartphones and evaluate it on two office buildings with the help of three participants to prove applicability and scalability. PYLON achieves very high floor plan mapping accuracy with a low localization error. Finally, We design a fully-automated navigation system, termed FINISH, which addresses the problems of existing previous indoor navigation systems. FINISH generates the radio map of an indoor building based on the localization system to determine the initial location of the user. FINISH relaxes some requirements for current indoor navigation systems. It does not require any human assistance to provide navigation instructions. In addition, it is plug-and-play on Android smartphones. We implement FINISH on five Android smartphones and evaluate it on five floors of an office building with the help of multiple users to prove applicability and scalability. FINISH determines the location of the user with extremely high accuracy with in one step. In summary, we propose systems that enhance the users convenience and experience by utilizing wireless infrastructures such as Wi-Fi and BLE and various smartphones sensors such as accelerometer, gyroscope, and barometer equipped in smartphones. Systems are implemented on commercial smartphones to verify the performance through experiments. As a result, systems show the excellent performance that can enhance the users experience.1 Introduction 1 1.1 Motivation 1 1.2 Overview of Existing Approaches 3 1.2.1 Wi-Fi handoff for smartphones 3 1.2.2 Indoor path estimation and localization 4 1.2.3 Indoor navigation 5 1.3 Main Contributions 7 1.3.1 BLEND: BLE Beacon-aided Fast Handoff for Smartphones 7 1.3.2 PYLON: Smartphone Based Indoor Path Estimation and Localization with Human Intervention 8 1.3.3 FINISH: Fully-automated Indoor Navigation using Smartphones with Zero Human Assistance 9 1.4 Organization of Dissertation 10 2 BLEND: BLE Beacon-Aided FastWi-Fi Handoff for Smartphones 11 2.1 Introduction 11 2.2 Related Work 14 2.2.1 Wi-Fi-based Handoff 14 2.2.2 WPAN-aided AP Discovery 15 2.3 Background 16 2.3.1 Handoff Procedure in IEEE 802.11 16 2.3.2 BSS Load Element in IEEE 802.11 16 2.3.3 Bluetooth Low Energy 17 2.4 Sticky Client Problem 17 2.4.1 Sticky Client Problem of Commercial Smartphone 17 2.4.2 Cause of Sticky Client Problem 20 2.5 BLEND: Proposed Scheme 21 2.5.1 Advantages and Necessities of BLE as Secondary Low-Power Radio 21 2.5.2 Overall Architecture 22 2.5.3 AP Operation 23 2.5.4 Smartphone Operation 24 2.5.5 Verification of aTH estimation 28 2.6 Performance Evaluation 30 2.6.1 Implementation and Measurement Setup 30 2.6.2 Saturated Traffic Scenario 31 2.6.3 Video Streaming Scenario 35 2.7 Summary 38 3 PYLON: Smartphone based Indoor Path Estimation and Localization without Human Intervention 41 3.1 Introduction 41 3.2 Background and Related Work 44 3.2.1 Infrastructure-Based Localization 44 3.2.2 Fingerprint-Based Localization 45 3.2.3 Model-Based Localization 45 3.2.4 Dead Reckoning 46 3.2.5 Landmark-Based Localization 47 3.2.6 Simultaneous Localization and Mapping (SLAM) 47 3.3 System Overview 48 3.3.1 Notable RSSI Signature 49 3.3.2 Smartphone Operation 50 3.3.3 Server Operation 51 3.4 Path Estimation 52 3.4.1 Step Detection 52 3.4.2 Step Length Estimation 54 3.4.3 Walking Direction 54 3.4.4 Location Update 55 3.5 Landmark Correction Part 1: Virtual Room Generation 56 3.5.1 RSSI Stacking Difference 56 3.5.2 Virtual Room Generation 57 3.5.3 Virtual Graph Generation 59 3.5.4 Physical Graph Generation 60 3.6 Landmark Correction Part 2: From Floor Plan Mapping to Path Correction 60 3.6.1 Candidate Graph Generation 60 3.6.2 Backbone Node Mapping 62 3.6.3 Dead-end Node Mapping 65 3.6.4 Final Candidate Graph Selection 66 3.6.5 Door Passing Time Detection 68 3.6.6 Path Correction 70 3.7 Particle Filter 71 3.8 Performance Evaluation 73 3.8.1 Implementation and Measurement Setup 73 3.8.2 Step Detection Accuracy 77 3.8.3 Floor Plan Mapping Accuracy 77 3.8.4 Door Passing Time 78 3.8.5 Walking Direction and Localization Performance 81 3.8.6 Impact of WiFi AP and BLE Beacon Number 84 3.8.7 Impact of Walking Distance and Speed 84 3.8.8 Performance on Different Areas 87 3.9 Summary 87 4 FINISH: Fully-automated Indoor Navigation using Smartphones with Zero Human Assistance 91 4.1 Introduction 91 4.2 Related Work 92 4.2.1 Localization-based Navigation System 92 4.2.2 Peer-to-peer Navigation System 93 4.3 System Overview 93 4.3.1 System Architecture 93 4.3.2 An Example for Navigation 95 4.4 Level Change Detection and Floor Decision 96 4.4.1 Level Change Detection 96 4.5 Real-time navigation 97 4.5.1 Initial Floor and Location Decision 97 4.5.2 Orientation Adjustment 98 4.5.3 Shortest Path Estimation 99 4.6 Performance Evaluation 99 4.6.1 Initial Location Accuracy 99 4.6.2 Real-Time Navigation Accuracy 100 4.7 Summary 101 5 Conclusion 102 5.1 Research Contributions 102 5.2 Future Work 103 Abstract (In Korean) 118 ๊ฐ์‚ฌ์˜ ๊ธ€Docto

    A Study on Influence Factor for T.D.C. Determination of Marine Diesel Engine

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    With the emergence of the problems such as air pollution and greenhouse gases over the world, IMO realized the seriousness of air pollution caused by ship and they established MARPOL 73/78 Annex VI in 1997. Annex VI includes several emission controls on air pollution. Especially, in NOx case, marine diesel engines should be certified by standard of emission controls and NOx technical codes detailed guidelines. NOx emission intensity is regulated by the output according to speed of marine diesel engines. Therefore, exact calculation of engine output is very important as well as the technique to reduce NOx. In recent years, measuring power of marine diesel engine has been obtained by using electronic pressure indicator. However, measured output has been known to be different from the one that engines generate. A device that measures the movement of piston cannot read an accurate location of the TDC, which makes it difficult to measure an accurate output of diesel engines. This fact has been confirmed through various materials. One degree difference between the location of measured TDC and that of physical TDC causes 10 percent of differences in IMEP and 25 percent of differences in heat release rate. and thus, many researchers recommend that the range of differences between the location of measured TDC and that of physical TDC should be within 0.1 degree. These days, various engines installed in ships are equipped with injection devices that inject fuels after TDC in the way to meet NOx emission controls. In this case, two pressure peak points are shown in a cylinder pressure graph. However, compression TDC on operating is indicated prior to physical TDC by heat loss and blow-by gas. The difference between compression TDC and the location of physical TDC is defined as loss angle. And if we consider loss angle, not only can measure the location of accurate TDC, but also obtain the accurate output. In this paper, loss angle is analyzed by simulation and experiment. Through a simulation, the influences of heat loss and blow-by gas had on loss angle is investigated. And confirming the value of loss angle by experimenting with an engine installed in a ship. Finally, the result of simulation with that of experiments is compared and analyzed.์ œ 1 ์žฅ ์„œ ๋ก  1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1.2 ์—ฐ๊ตฌ ๋‚ด์šฉ ์ œ 2 ์žฅ ์†์‹ค๊ฐ์˜ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 2.1 ์†์‹ค๊ฐ์˜ ์ •์˜ 2.2 ๊ธฐ์กด์˜ ์—ฐ๊ตฌ 2.3 ์••์ถ• ์‚ฌ์ดํด ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐฉ๋ฒ• ์ œ 3 ์žฅ ์••์ถ• ์‚ฌ์ดํด ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐ ๊ฒฐ๊ณผ ๊ณ ์ฐฐ 3.1 ์••์ถ• ์‚ฌ์ดํด ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋Œ€์ƒ ์—”์ง„ 3.2 ์••์ถ• ์‚ฌ์ดํด ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 3.2.1 ์••์ถ• ์‚ฌ์ดํด ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐฉ๋ฒ• 3.2.2 ์†์‹ค๊ฐ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ ์ธ์ž 3.3 ์••์ถ• ์‚ฌ์ดํด ์‹œ๋ฎฌ๋ ˆ์ด์…˜์— ์˜ํ•œ ์†์‹ค๊ฐ์˜ ๊ณ ์ฐฐ ์ œ 4 ์žฅ ์‹คํ—˜ ๋ฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ ๋น„๊ต ๊ณ ์ฐฐ 4.1 ์‹คํ—˜ 4.1.1 ์‹คํ—˜ ์žฅ์น˜ ๋ฐ ๋ฐฉ๋ฒ• 4.1.2 ์‹คํ—˜ ๊ณ„์ธก์— ์˜ํ•œ ์†์‹ค๊ฐ 4.2 ์‹คํ—˜๊ณ„์ธก ๊ฒฐ๊ณผ์™€ ์‚ฌ์ดํด ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ์˜ ๋น„๊ต ๊ณ ์ฐฐ 4.2.1 ์‚ฌ์ดํด ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 4.2.2 ์‹คํ—˜๊ณ„์ธก ๊ฒฐ๊ณผ์™€ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ์— ๊ด€ํ•œ ๋น„๊ต ๊ณ ์ฐฐ 4.2.3 ๋ธ”๋กœ์šฐ๋ฐ”์ด ๊ฐ€์Šค ์˜ˆ์ธก 4.2.4 ์†์‹ค๊ฐ์˜ ์ ์šฉ ์ œ 5 ์žฅ ๊ฒฐ ๋ก  ์ฐธ ๊ณ  ๋ฌธ

    Task-Oriented Design through Deep Reinforcement Learning

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์ง€๋Šฅํ˜•์œตํ•ฉ์‹œ์Šคํ…œํ•™๊ณผ, 2019. 2. ๊ณฝ๋…ธ์ค€.We propose a new low-cost machine-learning-based methodology which assists designers in reducing the gap between the problem and the solution in the design process. Our work applies reinforcement learning (RL) to find the optimal task-oriented design solution through the construction of the design action for each task. For this task-oriented design, the 3D design process in product design is assigned to an action space in Deep RL, and a desired 3D model is obtained by training each design action according to the task. By showing that this method achieves satisfactory design even when applied to a task pursuing multiple goals, we suggest the direction of how machine learning can contribute in design process. Also, we have validated with product designers that this methodology can assist the creative part in the process of design.1 Introduction 1 2 RelatedWorks 4 2.1 Constructive Design . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Reinforcement Learning . . . . . . . . . . . . . . . . . . . 4 3 Environment 7 3.1 Task Specification . . . . . . . . . . . . . . . . . . . . . . . 7 3.2 3D Simulation Environment . . . . . . . . . . . . . . . . . 8 3.3 Reinforcement Learning Environment . . . . . . . . . . . . 9 4 Experiment 12 4.1 Pouring Environment . . . . . . . . . . . . . . . . . . . . . 12 4.1.1 Quantitative Analysis . . . . . . . . . . . . . . . . . 13 4.1.2 Qualitative Analysis . . . . . . . . . . . . . . . . . 14 4.2 Shaking Environment . . . . . . . . . . . . . . . . . . . . . 15 4.2.1 Quantitative Analysis . . . . . . . . . . . . . . . . . 16 4.2.2 Qualitative Analysis . . . . . . . . . . . . . . . . . 17 4.3 Hybrid-Learning . . . . . . . . . . . . . . . . . . . . . . . 18 4.3.1 Quantitative Analysis . . . . . . . . . . . . . . . . . 19 4.3.2 Qualitative Analysis . . . . . . . . . . . . . . . . . 20 4.4 Contribution in Design Process . . . . . . . . . . . . . . . . 20 ii 5 Conclusion 22 5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Bibliography 24 Appendix 26 Abstract in Korean 29Maste

    On Factors Causing Partisan Confrontation in the Confirmation Hearings

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    This study aims to analyze the factors causing partisan confrontation in the confirmation hearings on executive branch appointees by focusing on the rational choices of three major players involved in the appointment process - president, opposition party, and ruling party. It assumes in the beginning that president's main goal is to realize his term and that the goal of opposition and ruling parties is to win the next presidential election, It argues that three players' rational behaviors to achieve their respective goals lead the confirmation hearing process to severe partisan conflicts. This study also analyzes the appointment process of the United States and, in so doing, identifies the reasons why partisan confrontation in the process seems to be a rare phenomenon in the country. It concludes that one of the best ways to reduce partisan conflicts in the confirmation hearing process is to strengthen the autonomy of individual legislators by freeing them from political parties' disciplines and constraints. ๋ณธ ์—ฐ๊ตฌ๋Š” ์–ด๋– ํ•œ ์›์ธ์— ์˜ํ•ด์„œ ํ•œ๊ตญ์˜ ์ธ์‚ฌ์ฒญ๋ฌธํšŒ๊ฐ€ ์ •ํŒŒ์  ๋Œ€๋ฆฝ์–‘์ƒ์„ ๋ ๊ณ  ์šด์˜๋˜๊ณ  ์žˆ๋Š”์ง€๋ฅผ ์ธ์‚ฌ์ฒญ๋ฌธํšŒ์— ์ฐธ์—ฌํ•˜๋Š” ์ฃผ์š” ํ–‰์œ„์ž๋“ค - ๋Œ€ํ†ต๋ น, ์•ผ๋‹น, ์—ฌ๋‹น-์˜ ํ•ฉ๋ฆฌ์  ์„ ํƒ์— ์ฃผ๋œ ์ดˆ์ ์„ ๋งž์ถ”์–ด ๋ฐํžˆ๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๋Œ€ํ†ต๋ น์€ ์ž์‹ ์˜ ์ •์ฑ…์ ยท์ •์น˜์  ๋น„์ „์˜ ์‹คํ˜„์„, ์•ผ๋‹น์€ ์ •๊ถŒ์˜ ํš๋“์„, ์—ฌ๋‹น์€ ์ •๊ถŒ์˜ ์žฌ์ฐฝ์ถœ์„ ์ฃผ๋œ ๋ชฉ์ ์œผ๋กœ ํ•˜๊ณ  ์žˆ๋‹ค๊ณ  ๊ฐ€์ •ํ•˜๊ณ , ์ด๋Ÿฌํ•œ ๋ชฉ์ ์„ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด ๊ฐ๊ฐ์˜ ํ–‰์œ„์ž๋“ค์ด ์„ ํƒํ•œ ํ•ฉ๋ฆฌ์  ํ–‰์œ„๊ฐ€ ์–ด๋–ป๊ฒŒ ์—ฌ๋ฐฉ์•ผ๊ณต(่ˆ‡้˜ฒ้‡ŽๅŠŸ)์˜ ์ •ํŒŒ์  ์ธ์‚ฌ์ฒญ๋ฌธํšŒ๋ฅผ ๋งŒ๋“ค์–ด ๋‚ด๊ณ  ์žˆ๋Š”์ง€ ๋…ผ์˜ํ•œ๋‹ค. ํ•œํŽธ ์ด ์—ฐ๊ตฌ๋Š” ๋ฏธ๊ตญ์˜ ์ธ์‚ฌ์ž„๋ช…๊ณผ์ •๋„ ๋ถ„์„ํ•˜๊ณ  ์žˆ๋Š”๋ฐ ์ด๋ฅผ ํ†ตํ•ด ํ•œ๊ตญ์˜ ์ธ์‚ฌ์ฒญ๋ฌธํšŒ๋ฅผ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ์•ˆ์„ ๋ชจ์ƒ‰ํ•œ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ํ•œ๊ตญ์˜ ์ธ์‚ฌ์ฒญ๋ฌธํšŒ์˜ ์ •ํŒŒ์  ์„ฑ๊ฒฉ์„ ํƒ€ํŒŒํ•˜๊ธฐ ์œ„ํ•ด ๊ฐ€์žฅ ํ•„์š”ํ•œ ๊ฒƒ์€ ์ •๋‹น์˜ ์˜ํ–ฅ๋ ฅ์„ ์ถ•์†Œ์‹œ์ผœ ์˜์›๋“ค์˜ ์ž์œจ์„ฑ์„ ์‹ ์žฅ์‹œํ‚ค๋Š” ๊ฒƒ์ด๋ผ ํ•  ์ˆ˜ ์žˆ๋‹ค

    Attenuation of reperfusion injury with angiotensin ATโ‚receptor blockade via anti-apoptotic mechanism in rat myocardial ischemic model

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜ํ•™๊ณผ ํ‰๋ถ€์™ธ๊ณผํ•™์ „๊ณต,2001.Docto

    Spiral Computed Tomography Volumetry in Use of Determination of Operative Approach in Patients with Graves disease

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜ํ•™๊ณผ, 2012. 2. ์ •์„ฑ์€.Background The purpose of this study was to access the correlation between weight of postoperative thyroid specimen and spiral computed tomography (CT) volumetry of thyroid gland of patients with Graves' disease and to know the eligibility of using CT volumetry in determining operative approach. Patients and Methods From 2009 to 2010, 56 patients with Graves' disease underwent total or subtotal thyroidectomy. An enhanced spiral CT was taken in all patients prior to the operation. Surface of the thyroid gland on 2.5mm-thick slice was calculated to obtain the volume of thyroid gland. The volume of the gland was compared to the weight of the postoperative thyroid specimen. Results Forty-two patients underwent total thyroidectomy, and 14 patients underwent subtotal thyroidectomy. Mean weight of the postoperative thyroid specimen was 43.9ยฑ33.4g, and mean volume obtained by CT volumetry was 44.2ยฑ32.8mL. A good correlation between the weight of the postoperative(๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์ ) ๊ฐ‘์ƒ์„ ์˜ ๊ทธ๋ ˆ์ด๋ธŒ์Šค๋ณ‘์€ ๊ฐ‘์ƒ์„  ๊ธฐ๋Šฅํ•ญ์ง„์ฆ์˜ ๊ฐ€์žฅ ํ”ํ•œ ์›์ธ์œผ๋กœ ๊ฐ‘์ƒ์„ ํ˜ธ๋ฅด๋ชฌ ๋ถ„๋น„๋ฅผ ์ž๊ทนํ•˜๋Š” ์ž๊ฐ€๋ฉด์—ญํ•ญ์ฒด๊ฐ€ ๊ทธ๋ ˆ์ด๋ธŒ์Šค๋ณ‘์˜ ๊ฐ€์žฅ ์ค‘์š”ํ•œ ์›์ธ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋˜ํ•œ ๊ทธ๋ ˆ์ด๋ธŒ์Šค๋ณ‘ ํ™˜์ž๋Š” ํŠน์ง•์ ์œผ๋กœ ๊ฐ‘์ƒ์„ ์˜ ์ข…๋Œ€์™€ ํ’๋ถ€ํ•œ ํ˜ˆ์•ก ๊ณต๊ธ‰์„ ๊ฐ€์ง€๊ณ  ์žˆ์–ด ์ˆ˜์ˆ ์ด ์šฉ์ดํ•˜์ง€ ์•Š์„ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ˆ˜์ˆ  ์ „ spiral CT volumetry๋ฅผ ์ด์šฉํ•ด ๊ทธ๋ ˆ์ด๋ธŒ์Šค๋ณ‘ ํ™˜์ž์˜ ๊ฐ‘์ƒ์„  ์šฉ์ ์„ ๊ณ„์‚ฐํ•˜์—ฌ ์ˆ˜์ˆ ์˜ ์šฉ์ด์„ฑ์„ ์˜ˆ์ธกํ•˜๊ณ  ์ˆ˜์ˆ  ๋ฐฉ๋ฒ•์„ ๊ฒฐ์ •ํ•˜๋Š” ์ง€ํ‘œ๋กœ์„œ์˜ ๊ฐ€๋Šฅ์„ฑ์„ ํ‰๊ฐ€ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. (๋Œ€์ƒ ๋ฐ ๋ฐฉ๋ฒ•) 2009๋…„ 1์›”๋ถ€ํ„ฐ 2010๋…„ 12์›” ์‚ฌ์ด์— ์„œ์šธ๋Œ€ํ•™๊ต๋ณ‘์› ์™ธ๊ณผ์—์„œ ๊ทธ๋ ˆ์ด๋ธŒ์Šค๋ณ‘์œผ๋กœ ๊ฐ‘์ƒ์„  ์ „์ ˆ์ œ์ˆ  ๋˜๋Š” ์•„์ „์ ˆ์ œ์ˆ ์„ ์‹œํ–‰ ๋ฐ›์€ 56๋ช…์˜ ํ™˜์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์ˆ˜์ˆ  ์ „ CT ์˜์ƒ์„ ์ด์šฉํ•ด ๊ฐ‘์ƒ์„ ์˜ ์šฉ์ ์„ ๊ณ„์‚ฐํ•˜์˜€๊ณ , ์ˆ˜์ˆ  ํ›„ ์ธก์ •ํ•œ ๊ฐ‘์ƒ์„  ํ‘œ๋ณธ์˜ ์ค‘๋Ÿ‰๊ณผ ๋น„๊ตํ•˜์˜€๋‹ค. ํ™˜์ž์˜ ์ž„์ƒ์–‘์ƒ ๋ฐ ์ˆ˜์ˆ  ์ค‘, ํ›„์˜ ๊ฒฐ๊ณผ๋Š” ์˜๋ฌด๊ธฐ๋ก์„ ํ†ตํ•˜์—ฌ ํ›„ํ–ฅ์ ์œผ๋กœ ์กฐ์‚ฌํ•˜์˜€๋‹ค. (๊ฒฐ๊ณผ) 42๋ช…์˜ ํ™˜์ž๋Š” ๊ฐ‘์ƒ์„  ์ „์ ˆ์ œ์ˆ ์„ ์‹œํ–‰ ๋ฐ›์•˜๊ณ , 14๋ช…์˜ ํ™˜์ž๋Š” ๊ฐ‘์ƒ์„  ์•„์ „์ ˆ์ œ์ˆ ์„ ์‹œํ–‰ ๋ฐ›์•˜๋‹ค. ์ ˆ์ œ๋œ ๊ฐ‘์ƒ์„  ํ‘œ๋ณธ์˜ ์ค‘๋Ÿ‰์€ 43.9ยฑ33.4g์ด์—ˆ๊ณ  CT๋ฅผ ํ†ตํ•ด ๊ณ„์‚ฐ๋œ ๊ฐ‘์ƒ์„ ์˜ ์šฉ์ ์€ 44.2ยฑ32.8mL์ด์—ˆ๋‹ค. ๊ฐ‘์ƒ์„  ํ‘œ๋ณธ์˜ ์ค‘๋Ÿ‰๊ณผ CT๋ฅผ ํ†ตํ•œ ๊ฐ‘์ƒ์„  ์šฉ์ ์„ ๋น„๊ตํ•ด ๋ณด์•˜์„ ๋•Œ ํ†ต๊ณ„ํ•™์ ์œผ๋กœ ์œ ์˜ํ•œ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์—ˆ๋‹ค.(r=0.98, p<0.001) (๊ฒฐ๋ก ) Spiral CT volumetry๋Š” ๊ทธ๋ ˆ์ด๋ธŒ์Šค๋ณ‘ ํ™˜์ž์˜ ์ˆ˜์ˆ  ์ „ ๊ฐ‘์ƒ์„  ์šฉ์ ์„ ์˜ˆ์ธกํ•˜๋Š”๋ฐ ์šฐ์ˆ˜ํ•œ ๋„๊ตฌ์ž„์„ ๋ณด์—ฌ์ฃผ์—ˆ๊ณ , ํŠนํžˆ ๊ทธ๋ ˆ์ด๋ธŒ์Šค๋ณ‘ ํ™˜์ž์—์„œ ๊ฐ‘์ƒ์„  ์ตœ์†Œ์ ˆ๊ฐœ์ˆ˜์ˆ  ์ ์šฉ์„ ๊ณ ๋ คํ• Maste

    A Quantitative Analysis of Middle School Students Behavior with a focus on Participation and Silence

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณผํ•™๊ต์œก๊ณผ, 2015. 2. ์†ก์ง„์›….๊ต์‹ค์—์„œ์˜ ๊ต์ˆ˜ํ•™์Šต์€ ๊ต์‚ฌ๊ฐ€ ์กฐ์งํ•œ ์ˆ˜์—…ํ™œ๋™์— ํ•™์ƒ๋“ค์ด ์ฐธ์—ฌํ•จ์œผ๋กœ์จ ์ด๋ฃจ์–ด์ง„๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ํ•™์ƒ๋“ค์€ ๋ฐœํ™”, ๋“ฃ๊ธฐ, ์“ฐ๊ธฐ, ์ฝ๊ธฐ ๋“ฑ์˜ ํ–‰๋™์„ ํ†ตํ•ด ์ˆ˜์—…์— ์ฐธ์—ฌํ•˜๋ฉฐ, ํŠนํžˆ ๊ณผํ•™์ˆ˜์—…์—์„œ๋Š” ์ด๋Ÿฌํ•œ ์ผ๋ฐ˜์ ์ธ ํ–‰๋™๋“ค๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๊ด€์ฐฐ์ด๋‚˜ ์ธก์ •๊ณผ ๊ฐ™์€ ํ–‰๋™๋“ค์„ ํ†ตํ•ด์„œ๋„ ์ˆ˜์—…์— ์ฐธ์—ฌํ•œ๋‹ค. ์ด์™€ ๊ฐ™์ด ํ•™์ƒ๋“ค์ด ๊ณผํ•™์ˆ˜์—…์— ์ฐธ์—ฌํ•˜๋Š” ๊ณผ์ •์—์„œ ์–ธ์–ด์  ํ–‰๋™(๋ฐœํ™”)๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋น„์–ธ์–ด์  ํ–‰๋™๋„ ํ•จ๊ป˜ ๋‚˜ํƒ€๋‚˜๋Š”๋ฐ, ์ƒ๋‹น์ˆ˜์˜ ๊ต์‚ฌ๋“ค์€ ํ•™์ƒ๋“ค์ด ๋ฐœํ™”๋ฅผ ํ•  ๋•Œ๋งŒ ์ˆ˜์—…์— ์ ๊ทน์ ์œผ๋กœ ์ฐธ์—ฌํ•œ๋‹ค๊ณ  ์ธ์‹ํ•œ๋‹ค. ๊ธฐ์กด์˜ ๊ณผํ•™๊ต์œก ์—ฐ๊ตฌ๋“ค๋„ ๋ฐœํ™”๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํ•œ ํ•™์ƒ์˜ ์ดํ•ด์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. ๋ฐ˜๋ฉด์— ๋น„์–ธ์–ด์  ํ–‰๋™๊นŒ์ง€ ๊ณ ๋ คํ•˜์—ฌ ํ•™์ƒ์˜ ์ˆ˜์—…์ฐธ์—ฌ๋ฅผ ์‚ดํŽด๋ณธ ์—ฐ๊ตฌ๋Š” ๋งค์šฐ ์ ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ์–ธ์–ด์  ํ–‰๋™(๋ฐœํ™”)๊ณผ ๋น„์–ธ์–ด์  ํ–‰๋™์„ ์ข…ํ•ฉ์ ์œผ๋กœ ๊ณ ๋ คํ•˜์—ฌ ์ค‘ํ•™์ƒ๋“ค์˜ ๊ณผํ•™์ˆ˜์—… ์ฐธ์—ฌ ์–‘์ƒ์„ ํŒŒ์•…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋ฌธ์ œ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ํ•œ ์ฐจ์‹œ์˜ ๊ณผํ•™์ˆ˜์—…๋™์•ˆ ํ•™์ƒ๋“ค์ด ์–ธ์–ด์  ํ–‰๋™(๋ฐœํ™”)๊ณผ ๋น„์–ธ์–ด์  ํ–‰๋™์„ ํ†ตํ•ด ์ˆ˜์—…์— ์ฐธ์—ฌํ•˜๋Š” ์‹œ๊ฐ„์€ ๊ฐ๊ฐ ์–ผ๋งˆ๋‚˜ ๋˜๋Š”๊ฐ€? ๋‘˜์งธ, ๊ณผํ•™์ˆ˜์—…์—์„œ ํ•™์ƒ๋“ค์€ ์ฃผ๋กœ ์–ด๋–ค ๋น„์–ธ์–ด์  ํ–‰๋™์„ ํ†ตํ•ด์„œ ์ˆ˜์—…์— ์ฐธ์—ฌํ•˜๋Š”๊ฐ€? ์…‹์งธ, ์ˆ˜์—…์— ์ฐธ์—ฌํ•˜๊ธฐ ์œ„ํ•œ ํ•™์ƒ๋“ค์˜ ํ–‰๋™์€ ๊ต์‚ฌ์˜ ๊ฐ•์˜๋ฅผ ๋“ค์„ ๋•Œ์™€ ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•  ๋•Œ์— ์–ด๋–ป๊ฒŒ ๋‹ฌ๋ผ์ง€๋Š”๊ฐ€? ๋ณธ ์—ฐ๊ตฌ๋Š” ์ตœ์ค€์˜ ๋“ฑ(2015)์ด ๊ฐœ๋ฐœํ•œ ๋น„๋””์˜ค ๋ถ„์„๋„๊ตฌ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ˆ˜๋„๊ถŒ ์†Œ์žฌ A์ค‘ํ•™๊ต์˜ ํ•œ ๊ณผํ•™์ˆ˜์—…์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ํ•™์ƒ ๊ฐœ๊ฐœ์ธ์˜ ํ–‰๋™์„ ๋น„๋””์˜ค ๋ถ„์„ํ•˜์˜€๋‹ค. ์ด ๋ถ„์„๋„๊ตฌ๋Š” ์‹ ์ฒด์  ์›€์ง์ž„๊ณผ ์‹œ์„ ์„ ๋ฐ”ํƒ•์œผ๋กœ ํ•™์ƒ์˜ 14๊ฐ€์ง€ ํ–‰๋™(์ž์œ ๋ฐœํ™”, ์Œ๋…, ๊ฒฝ์ฒญํ•˜๊ธฐ, ๋ฌต๋…, ์“ฐ๊ธฐ, ์ฃผ์˜ ์ง‘์ค‘ํ•˜๊ธฐ, ์†๋“ค๊ธฐ, ์ด๋™, ๋น„์ฐธ์—ฌ์ ์›€์ง์ž„, ๊ณผ์ œ์›€์ง์ž„, ๊ด€์ฐฐํ•˜๊ธฐ, ์ธก์ •ํ•˜๊ธฐ, ๋ถ„๋ฅ˜ํ•˜๊ธฐ, ์‹คํ—˜๋„๊ตฌ๋‹ค๋ฃจ๊ธฐ)์˜ ์ง€์†์‹œ๊ฐ„์„ ๊ธฐ๋กํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด๋ฅผ ๋ถ„์„ํ•˜์—ฌ ํ•™์ƒ์˜ ์ˆ˜์—…์ฐธ์—ฌ ์ƒํƒœ๋ฅผ 4๊ฐ€์ง€(์ฐธ์—ฌ์  ๋ฐœํ™”, ๋น„์ฐธ์—ฌ์  ๋ฐœํ™”, ์ฐธ์—ฌ์  ์นจ๋ฌต, ๋น„์ฐธ์—ฌ์  ์นจ๋ฌต)๋กœ ๋ถ„๋ฅ˜ํ•ด์ค€๋‹ค. ๋ถ„์„์˜ ๋Œ€์ƒ์ด ๋˜์—ˆ๋˜ ์ˆ˜์—…์€ ๊ฐ•์˜ ์ค‘์‹ฌ์˜ ์ผ๋ฐ˜ํ™œ๋™๊ณผ(75%) ์กฐ๋ณ„ ์‹คํ—˜ ์ค‘์‹ฌ์˜ ์‹คํ—˜ํ™œ๋™(25%)์œผ๋กœ ์ด๋ฃจ์–ด์กŒ์œผ๋ฉฐ, 4๋Œ€์˜ ์นด๋ฉ”๋ผ๋ฅผ ํ†ตํ•ด ํ–‰๋™๊ณผ ์‹œ์„  ๋“ฑ์ด ๋ชจ๋‘ ๊ด€์ฐฐ ๊ฐ€๋Šฅํ•œ 6๋ช…์˜ ํ•™์ƒ(A๏ฝžF)์„ ๋Œ€์ƒ์œผ๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ณผํ•™์ˆ˜์—…์„ ๋น„๋””์˜ค ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋Œ€๋ถ€๋ถ„์˜ ํ•™์ƒ๋“ค์€ ์ฃผ๋กœ ๋น„์–ธ์–ด์  ํ–‰๋™์„ ํ†ตํ•ด ์ˆ˜์—…์— ์ฐธ์—ฌํ–ˆ๊ณ (์ฐธ์—ฌ์  ์นจ๋ฌต 25๏ฝž69%), ์–ธ์–ด์  ํ–‰๋™(๋ฐœํ™”)์„ ํ†ตํ•œ ์ฐธ์—ฌ๋Š” ๊ฑฐ์˜ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์•˜๋‹ค(์ฐธ์—ฌ์  ๋ฐœํ™” 0๏ฝž3%). ๋‘˜์งธ, ์ฐธ์—ฌ์  ์นจ๋ฌต ์ƒํƒœ์ธ ํ•™์ƒ๋“ค์€ ๊ฒฝ์ฒญํ•˜๊ธฐ๋ฅผ ํ†ตํ•ด ์ˆ˜์—…์— ์ฐธ์—ฌํ•˜๋Š” ๋น„์ค‘์ด ๋†’์•˜๋‹ค(์ฐธ์—ฌ์  ์นจ๋ฌต ์ƒํƒœ ์ค‘ 31๏ฝž47%). ์ด์–ด์„œ ๊ต์‚ฌ ํ˜น์€ ๋ฏธ๋””์–ด ์ž๋ฃŒ ๋“ฑ์„ ์‘์‹œํ•˜๋Š” ์ฃผ์˜ ์ง‘์ค‘ํ•˜๊ธฐ(21๏ฝž36%)์™€ ์‹คํ—˜ํ–‰๋™์ธ ๊ด€์ฐฐํ•˜๊ธฐ(9๏ฝž24%)๋ฅผ ํ†ตํ•ด ์ˆ˜์—…์— ์ฐธ์—ฌํ•˜์˜€๋‹ค. ์…‹์งธ, ํ•™์ƒ๋“ค์˜ ๋น„์–ธ์–ด์  ํ–‰๋™์„ ํ†ตํ•œ ์ˆ˜์—…์ฐธ์—ฌ๊ฐ€ ์ผ๋ฐ˜ํ™œ๋™ ์ค‘(์ฐธ์—ฌ์  ์นจ๋ฌต 29๏ฝž64%)์— ๋น„ํ•ด ์‹คํ—˜ํ™œ๋™ ์ค‘(์ฐธ์—ฌ์  ์นจ๋ฌต 11๏ฝž85%)์— ์ „๋ฐ˜์ ์œผ๋กœ ๋†’์•„์กŒ๋‹ค. ์ผ๋ฐ˜ํ™œ๋™ ์ค‘์—๋Š” ํ•™์ƒ๋“ค์ด ์ฃผ๋กœ ๊ฒฝ์ฒญํ•˜๊ธฐ๋ฅผ ํ†ตํ•ด ์ˆ˜์—…์— ์ฐธ์—ฌํ•œ ๋ฐ˜๋ฉด์—(40๏ฝž59%) ์‹คํ—˜ํ™œ๋™ ์ค‘์—๋Š” ์ฃผ๋กœ ๊ด€์ฐฐํ•˜๊ธฐ๋ฅผ ํ†ตํ•ด ์ˆ˜์—…์— ์ฐธ์—ฌํ•˜์˜€๋‹ค(35๏ฝž71%). ๊ฒฐ๊ณผ์ ์œผ๋กœ ํ•™์ƒ๋“ค์€ ๊ณผํ•™์ˆ˜์—…์˜ ๋Œ€๋ถ€๋ถ„์˜ ์‹œ๊ฐ„๋™์•ˆ ์นจ๋ฌตํ•˜๊ณ  ์žˆ์—ˆ์œผ๋ฉฐ(๋ฐœํ™” 0๏ฝž19%, ์นจ๋ฌต 81๏ฝž100%), ์ฃผ๋กœ ๊ฒฝ์ฒญํ•˜๊ธฐ๋‚˜ ์ฃผ์˜ ์ง‘์ค‘ํ•˜๊ธฐ์™€ ๊ฐ™์€ ๋น„์–ธ์–ด์  ํ–‰๋™์„ ํ†ตํ•ด์„œ ์ˆ˜์—…์— ์ฐธ์—ฌํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์‹คํ—˜์ด ๋™๋ฐ˜๋œ ๊ณผํ•™์ˆ˜์—…์— ํ•™์ƒ๋“ค์ด ์–ด๋–ค ๋ฐฉ์‹์œผ๋กœ ์ฐธ์—ฌํ•˜๊ณ  ์žˆ๋Š”์ง€๋ฅผ ๋น„์–ธ์–ด์  ํ–‰๋™์— ์ดˆ์ ์„ ๋งž์ถฐ ๋ถ„์„ํ•ด ๋ด„์œผ๋กœ์จ ํ–ฅํ›„ ๊ณผํ•™์ˆ˜์—… ์ฐธ์—ฌ ํ™œ์„ฑํ™”๋ฅผ ์œ„ํ•œ ํ›„์† ์—ฐ๊ตฌ์™€ ์‹ค์ฒœ๋ฐฉ์•ˆ์˜ ํƒ์ƒ‰์„ ์œ„ํ•œ ํ† ๋Œ€๋ฅผ ๋„“ํž ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ ์˜์˜๋ฅผ ๊ฐ–๋Š”๋‹ค.์ดˆ ๋ก โ…ฐ ์ฐจ ๋ก€ โ…ณ ํ‘œ ์ฐจ๋ก€ โ…ต ๊ทธ๋ฆผ ์ฐจ๋ก€ โ…ถ 1. ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ ๋™๊ธฐ 1 1.2 ์—ฐ๊ตฌ ๋ชฉ์  3 1.3 ์šฉ์–ด์˜ ์ •์˜ 4 1.4 ์—ฐ๊ตฌ ๊ณผ์ •์˜ ๊ฐœ์š” 6 1.5 ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ 7 2. ์„ ํ–‰์—ฐ๊ตฌ์™€ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 8 2.1 ์ฐธ์—ฌ์˜ ์˜๋ฏธ 8 2.2 ์ฐธ์—ฌ์™€ ์นจ๋ฌต 13 2.3 ๊ณผํ•™๊ต์œก์—์„œ ํ–‰๋™์  ์ฐธ์—ฌ 14 2.4 ์ฐธ์—ฌ์˜ ์ธก์ • 16 2.5 ์ดˆ๋“ฑํ•™๊ต ๊ณผํ•™์ˆ˜์—…์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ํ•™์ƒ๋“ค์˜ ํ–‰๋™์  ์ฐธ์—ฌ ๋ถ„์„์„ ์œ„ํ•œ ์˜์ƒ ๋ถ„์„ ๋„๊ตฌ 20 3. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 25 3.1 ์—ฐ๊ตฌ ๋Œ€์ƒ ๋ฐ ์—ฐ๊ตฌ์ ˆ์ฐจ 25 3.2 ์ดˆ๋“ฑํ•™๊ต ๊ณผํ•™์ˆ˜์—…์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ํ•™์ƒ์˜ ํ–‰๋™์  ์ฐธ์—ฌ ์˜์ƒ ๋ถ„์„ ๋„๊ตฌ 26 4. ์—ฐ๊ตฌ๊ฒฐ๊ณผ ๋ฐ ๋…ผ์˜ 35 4.1 ํ•™์ƒ๋“ค์˜ ์ „์ฒด ์ˆ˜์—…์— ๋Œ€ํ•œ ์ˆ˜์—…์ฐธ์—ฌ ์ƒํƒœ 35 4.2 ์ˆ˜์—…์— ์ฐธ์—ฌํ•˜๋Š” ํ•™์ƒ๋“ค์—๊ฒŒ์„œ ๋‚˜ํƒ€๋‚˜๋Š” ๋น„์–ธ์–ด์  ํ–‰๋™ 37 4.3 ์ˆ˜์—…ํ™œ๋™์— ๋”ฐ๋ฅธ ํ•™์ƒ๋“ค์˜ ์ˆ˜์—…์ฐธ์—ฌ ์ƒํƒœ ๋ณ€ํ™” 40 5. ์š”์•ฝ ๋ฐ ๊ฒฐ๋ก  58 5.1 ์š”์•ฝ 58 5.2 ๊ฒฐ๋ก  ๋ฐ ์‹œ์‚ฌ์  60 5.3 ํ›„์† ์—ฐ๊ตฌ ๊ณผ์ œ 62 ์ฐธ ๊ณ  ๋ฌธ ํ—Œ 63 [๋ถ€๋ก1] 67 [๋ถ€๋ก2] 68 ABSTRACT 69Maste

    ๋ฉ€ํ‹ฐ ๋ชจ๋‹ฌ ์ •๋ณด์™€ 1์ฐจ์› ์ปจ๋ณผ๋ฃจ์…˜์„ ์ด์šฉํ•œ ๋ฏธ์„ธ๋จผ์ง€ ๋†๋„ ์˜ˆ์ธก

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€,2020. 2. ์ •๊ต๋ฏผ.Air pollution, especially from particulate matters, has become serious problem in many countries. To cope with these abrupt pollution, there has been several studies to predict the temporal concentration of air pollution using deep neural networks. However, these studies have difficulties in predicting accurately since the air quality is complexly correlated with various types of multi-modal features over a long time. In this paper, we propose a new architecture to predict air qualities of particulate matters incorporating deeply stacked 1-dimensional CNN with residual connection and attention mechanism. Specifically, 1-dimensional CNN extracts high-level features with large receptive fields and attention mechanism captures complex correlation among these features. Through extensive experiments with Seoul air pollution data and public benchmarks, we verify our architecture achieves state-of-the-art result in PM2.5 and PM10 prediction.๋Œ€๊ธฐ ์˜ค์—ผ, ํŠนํžˆ ๋ฏธ์„ธ๋จผ์ง€๋Š” ๋งŽ์€ ๊ตญ๊ฐ€์—์„œ ์‹ฌ๊ฐํ•œ ๋ฌธ์ œ๊ฐ€ ๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ธ‰๊ฒฉํ•œ ์˜ค์—ผ์— ๋Œ€์ฒ˜ํ•˜๊ธฐ ์œ„ํ•ด ๋”ฅ๋Ÿฌ๋‹์„ ์‚ฌ์šฉํ•˜์—ฌ ๋Œ€๊ธฐ ์˜ค์—ผ ๋ฌผ์งˆ์˜ ๋†๋„๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๋ช‡ ๊ฐ€์ง€ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜์–ด์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ฏธ์„ธ๋จผ์ง€ ๋†๋„๋Š” ๋‹ค์–‘ํ•œ ์œ ํ˜•์˜ ๋ฉ€ํ‹ฐ ๋ชจ๋‹ฌ ์ •๋ณด๊ฐ€ ์˜ค๋žœ ์‹œ๊ฐ„ ๋™์•ˆ ๋ณต์žกํ•˜๊ฒŒ ์—ฐ๊ด€๋˜์–ด ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๋”ฅ๋Ÿฌ๋‹์„ ์ด์šฉํ•˜์—ฌ ๋ฏธ์„ธ๋จผ์ง€ ๋†๋„๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ์˜ˆ์ธกํ•˜๋Š” ๋ฐ ์–ด๋ ค์›€์ด ์กด์žฌํ–ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ž”์กด ์—ฐ๊ฒฐ ๋ฐ ์–ดํƒ ์…˜ ๋ฉ”์ปค๋‹ˆ์ฆ˜๊ณผ ํ•จ๊ป˜ ๊นŠ์ด ์Œ“์ธ 1 ์ฐจ์› ์ปจ๋ณผ๋ฃจ์…˜์„ ์ด์šฉํ•˜์—ฌ ๋ฏธ์„ธ๋จผ์ง€ ๋†๋„๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ์ƒˆ๋กœ์šด ์•„ํ‚คํ…์ฒ˜๋ฅผ ์ œ์•ˆํ•œ๋‹ค. 1์ฐจ์› ์ปจ๋ณผ๋ฃจ์…˜์€ ๊ณ ์ฐจ์›์˜ ํŠน์ง• ๋ฒกํ„ฐ๋ฅผ ์ถ”์ถœํ•˜๋ฉฐ ์–ดํƒ ์…˜ ๋ฉ”์ปค๋‹ˆ์ฆ˜์€ ์ถ”์ถœํ•œ ๊ณ ์ฐจ์›์˜ ํŠน์ง• ๋ฒกํ„ฐ๋“ค๊ฐ„์˜ ์ƒ๊ด€ ๊ด€๊ณ„๋ฅผ ํฌ์ฐฉํ•œ๋‹ค. ์„œ์šธ ๋ฏธ์„ธ๋จผ์ง€ ๋†๋„ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•œ ์‹คํ—˜์„ ํ†ตํ•ด PM2.5 ๋ฐ PM10 ์˜ˆ์ธก์—์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์‹œํ•œ ๋ชจ๋ธ์ด ๊ธฐ์กด ๋ชจ๋ธ์„ ๋›ฐ์–ด๋„˜์—ˆ๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ–ˆ๋‹ค.Chapter 1. Introduction 1 Chapter 2. Related Works 3 Chapter 3. Model Architecture 5 Chapter 4. Experiment 11 Chapter 5. Conclusion 18 Bibliography 19 Abstract in Korean 22Maste
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