74 research outputs found
User Experience Enhancement on Smartphones using Wireless Communication Technologies
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ ๋ณด๊ณตํ๋ถ, 2020. 8. ๋ฐ์ธ์
.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
๊ฐ์ฌ์ ๊ธDocto
A Study on Influence Factor for T.D.C. Determination of Marine Diesel Engine
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.์ 1 ์ฅ ์ ๋ก
1.1 ์ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์
1.2 ์ฐ๊ตฌ ๋ด์ฉ
์ 2 ์ฅ ์์ค๊ฐ์ ์ด๋ก ์ ๋ฐฐ๊ฒฝ
2.1 ์์ค๊ฐ์ ์ ์
2.2 ๊ธฐ์กด์ ์ฐ๊ตฌ
2.3 ์์ถ ์ฌ์ดํด ์๋ฎฌ๋ ์ด์
๋ฐฉ๋ฒ
์ 3 ์ฅ ์์ถ ์ฌ์ดํด ์๋ฎฌ๋ ์ด์
๋ฐ ๊ฒฐ๊ณผ ๊ณ ์ฐฐ
3.1 ์์ถ ์ฌ์ดํด ์๋ฎฌ๋ ์ด์
๋์ ์์ง
3.2 ์์ถ ์ฌ์ดํด ์๋ฎฌ๋ ์ด์
3.2.1 ์์ถ ์ฌ์ดํด ์๋ฎฌ๋ ์ด์
๋ฐฉ๋ฒ
3.2.2 ์์ค๊ฐ์ ๋ฏธ์น๋ ์ํฅ ์ธ์
3.3 ์์ถ ์ฌ์ดํด ์๋ฎฌ๋ ์ด์
์ ์ํ ์์ค๊ฐ์ ๊ณ ์ฐฐ
์ 4 ์ฅ ์คํ ๋ฐ ์๋ฎฌ๋ ์ด์
๊ฒฐ๊ณผ ๋น๊ต ๊ณ ์ฐฐ
4.1 ์คํ
4.1.1 ์คํ ์ฅ์น ๋ฐ ๋ฐฉ๋ฒ
4.1.2 ์คํ ๊ณ์ธก์ ์ํ ์์ค๊ฐ
4.2 ์คํ๊ณ์ธก ๊ฒฐ๊ณผ์ ์ฌ์ดํด ์๋ฎฌ๋ ์ด์
๊ฒฐ๊ณผ์ ๋น๊ต ๊ณ ์ฐฐ
4.2.1 ์ฌ์ดํด ์๋ฎฌ๋ ์ด์
4.2.2 ์คํ๊ณ์ธก ๊ฒฐ๊ณผ์ ์๋ฎฌ๋ ์ด์
๊ฒฐ๊ณผ์ ๊ดํ ๋น๊ต ๊ณ ์ฐฐ
4.2.3 ๋ธ๋ก์ฐ๋ฐ์ด ๊ฐ์ค ์์ธก
4.2.4 ์์ค๊ฐ์ ์ ์ฉ
์ 5 ์ฅ ๊ฒฐ ๋ก
์ฐธ ๊ณ ๋ฌธ
Task-Oriented Design through Deep Reinforcement Learning
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ์ตํฉ๊ณผํ๊ธฐ์ ๋ํ์ ์ง๋ฅํ์ตํฉ์์คํ
ํ๊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
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 ์ฐ๊ตฌ๋ ์ด๋ ํ ์์ธ์ ์ํด์ ํ๊ตญ์ ์ธ์ฌ์ฒญ๋ฌธํ๊ฐ ์ ํ์ ๋๋ฆฝ์์์ ๋ ๊ณ ์ด์๋๊ณ ์๋์ง๋ฅผ ์ธ์ฌ์ฒญ๋ฌธํ์ ์ฐธ์ฌํ๋ ์ฃผ์ ํ์์๋ค - ๋ํต๋ น, ์ผ๋น, ์ฌ๋น-์ ํฉ๋ฆฌ์ ์ ํ์ ์ฃผ๋ ์ด์ ์ ๋ง์ถ์ด ๋ฐํ๋ ๊ฒ์ ๋ชฉ์ ์ผ๋ก ํ๋ค. ์ด ์ฐ๊ตฌ๋ ๋ํต๋ น์ ์์ ์ ์ ์ฑ
์ ยท์ ์น์ ๋น์ ์ ์คํ์, ์ผ๋น์ ์ ๊ถ์ ํ๋์, ์ฌ๋น์ ์ ๊ถ์ ์ฌ์ฐฝ์ถ์ ์ฃผ๋ ๋ชฉ์ ์ผ๋ก ํ๊ณ ์๋ค๊ณ ๊ฐ์ ํ๊ณ , ์ด๋ฌํ ๋ชฉ์ ์ ๋ฌ์ฑํ๊ธฐ ์ํด ๊ฐ๊ฐ์ ํ์์๋ค์ด ์ ํํ ํฉ๋ฆฌ์ ํ์๊ฐ ์ด๋ป๊ฒ ์ฌ๋ฐฉ์ผ๊ณต(่้ฒ้ๅ)์ ์ ํ์ ์ธ์ฌ์ฒญ๋ฌธํ๋ฅผ ๋ง๋ค์ด ๋ด๊ณ ์๋์ง ๋
ผ์ํ๋ค. ํํธ ์ด ์ฐ๊ตฌ๋ ๋ฏธ๊ตญ์ ์ธ์ฌ์๋ช
๊ณผ์ ๋ ๋ถ์ํ๊ณ ์๋๋ฐ ์ด๋ฅผ ํตํด ํ๊ตญ์ ์ธ์ฌ์ฒญ๋ฌธํ๋ฅผ ๊ฐ์ ํ๊ธฐ ์ํ ๋ฐฉ์์ ๋ชจ์ํ๋ค. ๊ฒฐ๋ก ์ ์ผ๋ก ํ๊ตญ์ ์ธ์ฌ์ฒญ๋ฌธํ์ ์ ํ์ ์ฑ๊ฒฉ์ ํํํ๊ธฐ ์ํด ๊ฐ์ฅ ํ์ํ ๊ฒ์ ์ ๋น์ ์ํฅ๋ ฅ์ ์ถ์์์ผ ์์๋ค์ ์์จ์ฑ์ ์ ์ฅ์ํค๋ ๊ฒ์ด๋ผ ํ ์ ์๋ค
Attenuation of reperfusion injury with angiotensin ATโreceptor blockade via anti-apoptotic mechanism in rat myocardial ischemic model
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :์ํ๊ณผ ํ๋ถ์ธ๊ณผํ์ ๊ณต,2001.Docto
Spiral Computed Tomography Volumetry in Use of Determination of Operative Approach in Patients with Graves disease
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ์ํ๊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 ) ๊ฐ์์ ์ ๊ทธ๋ ์ด๋ธ์ค๋ณ์ ๊ฐ์์ ๊ธฐ๋ฅํญ์ง์ฆ์ ๊ฐ์ฅ ํํ ์์ธ์ผ๋ก ๊ฐ์์ ํธ๋ฅด๋ชฌ ๋ถ๋น๋ฅผ ์๊ทนํ๋ ์๊ฐ๋ฉด์ญํญ์ฒด๊ฐ ๊ทธ๋ ์ด๋ธ์ค๋ณ์ ๊ฐ์ฅ ์ค์ํ ์์ธ์ผ๋ก ์๋ ค์ ธ ์๋ค. ๋ํ ๊ทธ๋ ์ด๋ธ์ค๋ณ ํ์๋ ํน์ง์ ์ผ๋ก ๊ฐ์์ ์ ์ข
๋์ ํ๋ถํ ํ์ก ๊ณต๊ธ์ ๊ฐ์ง๊ณ ์์ด ์์ ์ด ์ฉ์ดํ์ง ์์ ๊ฐ๋ฅ์ฑ์ด ๋๋ค.
๋ณธ ์ฐ๊ตฌ์์๋ ์์ ์ spiral CT volumetry๋ฅผ ์ด์ฉํด ๊ทธ๋ ์ด๋ธ์ค๋ณ ํ์์ ๊ฐ์์ ์ฉ์ ์ ๊ณ์ฐํ์ฌ ์์ ์ ์ฉ์ด์ฑ์ ์์ธกํ๊ณ ์์ ๋ฐฉ๋ฒ์ ๊ฒฐ์ ํ๋ ์งํ๋ก์์ ๊ฐ๋ฅ์ฑ์ ํ๊ฐํ๊ณ ์ ํ์๋ค. (๋์ ๋ฐ ๋ฐฉ๋ฒ) 2009๋
1์๋ถํฐ 2010๋
12์ ์ฌ์ด์ ์์ธ๋ํ๊ต๋ณ์ ์ธ๊ณผ์์ ๊ทธ๋ ์ด๋ธ์ค๋ณ์ผ๋ก ๊ฐ์์ ์ ์ ์ ์ ๋๋ ์์ ์ ์ ์ ์ ์ํ ๋ฐ์ 56๋ช
์ ํ์๋ฅผ ๋์์ผ๋ก ์์ ์ CT ์์์ ์ด์ฉํด ๊ฐ์์ ์ ์ฉ์ ์ ๊ณ์ฐํ์๊ณ , ์์ ํ ์ธก์ ํ ๊ฐ์์ ํ๋ณธ์ ์ค๋๊ณผ ๋น๊ตํ์๋ค. ํ์์ ์์์์ ๋ฐ ์์ ์ค, ํ์ ๊ฒฐ๊ณผ๋ ์๋ฌด๊ธฐ๋ก์ ํตํ์ฌ ํํฅ์ ์ผ๋ก ์กฐ์ฌํ์๋ค. (๊ฒฐ๊ณผ) 42๋ช
์ ํ์๋ ๊ฐ์์ ์ ์ ์ ์ ์ ์ํ ๋ฐ์๊ณ , 14๋ช
์ ํ์๋ ๊ฐ์์ ์์ ์ ์ ์ ์ ์ํ ๋ฐ์๋ค. ์ ์ ๋ ๊ฐ์์ ํ๋ณธ์ ์ค๋์ 43.9ยฑ33.4g์ด์๊ณ CT๋ฅผ ํตํด ๊ณ์ฐ๋ ๊ฐ์์ ์ ์ฉ์ ์ 44.2ยฑ32.8mL์ด์๋ค. ๊ฐ์์ ํ๋ณธ์ ์ค๋๊ณผ CT๋ฅผ ํตํ ๊ฐ์์ ์ฉ์ ์ ๋น๊ตํด ๋ณด์์ ๋ ํต๊ณํ์ ์ผ๋ก ์ ์ํ ์๊ด๊ด๊ณ๋ฅผ ๊ฐ์ง๊ณ ์์๋ค.(r=0.98, p<0.001) (๊ฒฐ๋ก ) Spiral CT volumetry๋ ๊ทธ๋ ์ด๋ธ์ค๋ณ ํ์์ ์์ ์ ๊ฐ์์ ์ฉ์ ์ ์์ธกํ๋๋ฐ ์ฐ์ํ ๋๊ตฌ์์ ๋ณด์ฌ์ฃผ์๊ณ , ํนํ ๊ทธ๋ ์ด๋ธ์ค๋ณ ํ์์์ ๊ฐ์์ ์ต์์ ๊ฐ์์ ์ ์ฉ์ ๊ณ ๋ คํ Maste
A Quantitative Analysis of Middle School Students Behavior with a focus on Participation and Silence
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณผํ๊ต์ก๊ณผ, 2015. 2. ์ก์ง์
.๊ต์ค์์์ ๊ต์ํ์ต์ ๊ต์ฌ๊ฐ ์กฐ์งํ ์์
ํ๋์ ํ์๋ค์ด ์ฐธ์ฌํจ์ผ๋ก์จ ์ด๋ฃจ์ด์ง๋ค. ์ผ๋ฐ์ ์ผ๋ก ํ์๋ค์ ๋ฐํ, ๋ฃ๊ธฐ, ์ฐ๊ธฐ, ์ฝ๊ธฐ ๋ฑ์ ํ๋์ ํตํด ์์
์ ์ฐธ์ฌํ๋ฉฐ, ํนํ ๊ณผํ์์
์์๋ ์ด๋ฌํ ์ผ๋ฐ์ ์ธ ํ๋๋ค๋ฟ๋ง ์๋๋ผ ๊ด์ฐฐ์ด๋ ์ธก์ ๊ณผ ๊ฐ์ ํ๋๋ค์ ํตํด์๋ ์์
์ ์ฐธ์ฌํ๋ค.
์ด์ ๊ฐ์ด ํ์๋ค์ด ๊ณผํ์์
์ ์ฐธ์ฌํ๋ ๊ณผ์ ์์ ์ธ์ด์ ํ๋(๋ฐํ)๋ฟ๋ง ์๋๋ผ ๋น์ธ์ด์ ํ๋๋ ํจ๊ป ๋ํ๋๋๋ฐ, ์๋น์์ ๊ต์ฌ๋ค์ ํ์๋ค์ด ๋ฐํ๋ฅผ ํ ๋๋ง ์์
์ ์ ๊ทน์ ์ผ๋ก ์ฐธ์ฌํ๋ค๊ณ ์ธ์ํ๋ค. ๊ธฐ์กด์ ๊ณผํ๊ต์ก ์ฐ๊ตฌ๋ค๋ ๋ฐํ๋ฅผ ์ค์ฌ์ผ๋ก ํ ํ์์ ์ดํด์ ์ด์ ์ ๋ง์ถ๊ณ ์๋ค. ๋ฐ๋ฉด์ ๋น์ธ์ด์ ํ๋๊น์ง ๊ณ ๋ คํ์ฌ ํ์์ ์์
์ฐธ์ฌ๋ฅผ ์ดํด๋ณธ ์ฐ๊ตฌ๋ ๋งค์ฐ ์ ๋ค.
์ด์ ๋ณธ ์ฐ๊ตฌ๋ ์ธ์ด์ ํ๋(๋ฐํ)๊ณผ ๋น์ธ์ด์ ํ๋์ ์ข
ํฉ์ ์ผ๋ก ๊ณ ๋ คํ์ฌ ์คํ์๋ค์ ๊ณผํ์์
์ฐธ์ฌ ์์์ ํ์
ํ๊ณ ์ ํ์๋ค. ์ฐ๊ตฌ๋ฌธ์ ๋ ๋ค์๊ณผ ๊ฐ๋ค. ์ฒซ์งธ, ํ ์ฐจ์์ ๊ณผํ์์
๋์ ํ์๋ค์ด ์ธ์ด์ ํ๋(๋ฐํ)๊ณผ ๋น์ธ์ด์ ํ๋์ ํตํด ์์
์ ์ฐธ์ฌํ๋ ์๊ฐ์ ๊ฐ๊ฐ ์ผ๋ง๋ ๋๋๊ฐ? ๋์งธ, ๊ณผํ์์
์์ ํ์๋ค์ ์ฃผ๋ก ์ด๋ค ๋น์ธ์ด์ ํ๋์ ํตํด์ ์์
์ ์ฐธ์ฌํ๋๊ฐ? ์
์งธ, ์์
์ ์ฐธ์ฌํ๊ธฐ ์ํ ํ์๋ค์ ํ๋์ ๊ต์ฌ์ ๊ฐ์๋ฅผ ๋ค์ ๋์ ์คํ์ ์ํํ ๋์ ์ด๋ป๊ฒ ๋ฌ๋ผ์ง๋๊ฐ?
๋ณธ ์ฐ๊ตฌ๋ ์ต์ค์ ๋ฑ(2015)์ด ๊ฐ๋ฐํ ๋น๋์ค ๋ถ์๋๊ตฌ๋ฅผ ์ด์ฉํ์ฌ ์๋๊ถ ์์ฌ A์คํ๊ต์ ํ ๊ณผํ์์
์์ ๋ํ๋๋ ํ์ ๊ฐ๊ฐ์ธ์ ํ๋์ ๋น๋์ค ๋ถ์ํ์๋ค. ์ด ๋ถ์๋๊ตฌ๋ ์ ์ฒด์ ์์ง์๊ณผ ์์ ์ ๋ฐํ์ผ๋ก ํ์์ 14๊ฐ์ง ํ๋(์์ ๋ฐํ, ์๋
, ๊ฒฝ์ฒญํ๊ธฐ, ๋ฌต๋
, ์ฐ๊ธฐ, ์ฃผ์ ์ง์คํ๊ธฐ, ์๋ค๊ธฐ, ์ด๋, ๋น์ฐธ์ฌ์ ์์ง์, ๊ณผ์ ์์ง์, ๊ด์ฐฐํ๊ธฐ, ์ธก์ ํ๊ธฐ, ๋ถ๋ฅํ๊ธฐ, ์คํ๋๊ตฌ๋ค๋ฃจ๊ธฐ)์ ์ง์์๊ฐ์ ๊ธฐ๋กํ ์ ์๋ค. ๊ทธ๋ฆฌ๊ณ ์ด๋ฅผ ๋ถ์ํ์ฌ ํ์์ ์์
์ฐธ์ฌ ์ํ๋ฅผ 4๊ฐ์ง(์ฐธ์ฌ์ ๋ฐํ, ๋น์ฐธ์ฌ์ ๋ฐํ, ์ฐธ์ฌ์ ์นจ๋ฌต, ๋น์ฐธ์ฌ์ ์นจ๋ฌต)๋ก ๋ถ๋ฅํด์ค๋ค. ๋ถ์์ ๋์์ด ๋์๋ ์์
์ ๊ฐ์ ์ค์ฌ์ ์ผ๋ฐํ๋๊ณผ(75%) ์กฐ๋ณ ์คํ ์ค์ฌ์ ์คํํ๋(25%)์ผ๋ก ์ด๋ฃจ์ด์ก์ผ๋ฉฐ, 4๋์ ์นด๋ฉ๋ผ๋ฅผ ํตํด ํ๋๊ณผ ์์ ๋ฑ์ด ๋ชจ๋ ๊ด์ฐฐ ๊ฐ๋ฅํ 6๋ช
์ ํ์(A๏ฝF)์ ๋์์ผ๋ก ๋ถ์ํ์๋ค.
๊ณผํ์์
์ ๋น๋์ค ๋ถ์ํ ๊ฒฐ๊ณผ๋ ๋ค์๊ณผ ๊ฐ๋ค. ์ฒซ์งธ, ๋๋ถ๋ถ์ ํ์๋ค์ ์ฃผ๋ก ๋น์ธ์ด์ ํ๋์ ํตํด ์์
์ ์ฐธ์ฌํ๊ณ (์ฐธ์ฌ์ ์นจ๋ฌต 25๏ฝ69%), ์ธ์ด์ ํ๋(๋ฐํ)์ ํตํ ์ฐธ์ฌ๋ ๊ฑฐ์ ๋ํ๋์ง ์์๋ค(์ฐธ์ฌ์ ๋ฐํ 0๏ฝ3%). ๋์งธ, ์ฐธ์ฌ์ ์นจ๋ฌต ์ํ์ธ ํ์๋ค์ ๊ฒฝ์ฒญํ๊ธฐ๋ฅผ ํตํด ์์
์ ์ฐธ์ฌํ๋ ๋น์ค์ด ๋์๋ค(์ฐธ์ฌ์ ์นจ๋ฌต ์ํ ์ค 31๏ฝ47%). ์ด์ด์ ๊ต์ฌ ํน์ ๋ฏธ๋์ด ์๋ฃ ๋ฑ์ ์์ํ๋ ์ฃผ์ ์ง์คํ๊ธฐ(21๏ฝ36%)์ ์คํํ๋์ธ ๊ด์ฐฐํ๊ธฐ(9๏ฝ24%)๋ฅผ ํตํด ์์
์ ์ฐธ์ฌํ์๋ค. ์
์งธ, ํ์๋ค์ ๋น์ธ์ด์ ํ๋์ ํตํ ์์
์ฐธ์ฌ๊ฐ ์ผ๋ฐํ๋ ์ค(์ฐธ์ฌ์ ์นจ๋ฌต 29๏ฝ64%)์ ๋นํด ์คํํ๋ ์ค(์ฐธ์ฌ์ ์นจ๋ฌต 11๏ฝ85%)์ ์ ๋ฐ์ ์ผ๋ก ๋์์ก๋ค. ์ผ๋ฐํ๋ ์ค์๋ ํ์๋ค์ด ์ฃผ๋ก ๊ฒฝ์ฒญํ๊ธฐ๋ฅผ ํตํด ์์
์ ์ฐธ์ฌํ ๋ฐ๋ฉด์(40๏ฝ59%) ์คํํ๋ ์ค์๋ ์ฃผ๋ก ๊ด์ฐฐํ๊ธฐ๋ฅผ ํตํด ์์
์ ์ฐธ์ฌํ์๋ค(35๏ฝ71%).
๊ฒฐ๊ณผ์ ์ผ๋ก ํ์๋ค์ ๊ณผํ์์
์ ๋๋ถ๋ถ์ ์๊ฐ๋์ ์นจ๋ฌตํ๊ณ ์์์ผ๋ฉฐ(๋ฐํ 0๏ฝ19%, ์นจ๋ฌต 81๏ฝ100%), ์ฃผ๋ก ๊ฒฝ์ฒญํ๊ธฐ๋ ์ฃผ์ ์ง์คํ๊ธฐ์ ๊ฐ์ ๋น์ธ์ด์ ํ๋์ ํตํด์ ์์
์ ์ฐธ์ฌํ๊ณ ์์๋ค.
๋ณธ ์ฐ๊ตฌ๋ ์คํ์ด ๋๋ฐ๋ ๊ณผํ์์
์ ํ์๋ค์ด ์ด๋ค ๋ฐฉ์์ผ๋ก ์ฐธ์ฌํ๊ณ ์๋์ง๋ฅผ ๋น์ธ์ด์ ํ๋์ ์ด์ ์ ๋ง์ถฐ ๋ถ์ํด ๋ด์ผ๋ก์จ ํฅํ ๊ณผํ์์
์ฐธ์ฌ ํ์ฑํ๋ฅผ ์ํ ํ์ ์ฐ๊ตฌ์ ์ค์ฒ๋ฐฉ์์ ํ์์ ์ํ ํ ๋๋ฅผ ๋ํ ์ ์๋ค๋ ์ ์์ ์์๋ฅผ ๊ฐ๋๋ค.์ด ๋ก โ
ฐ
์ฐจ ๋ก โ
ณ
ํ ์ฐจ๋ก โ
ต
๊ทธ๋ฆผ ์ฐจ๋ก โ
ถ
1. ์๋ก 1
1.1 ์ฐ๊ตฌ ๋๊ธฐ 1
1.2 ์ฐ๊ตฌ ๋ชฉ์ 3
1.3 ์ฉ์ด์ ์ ์ 4
1.4 ์ฐ๊ตฌ ๊ณผ์ ์ ๊ฐ์ 6
1.5 ์ฐ๊ตฌ์ ํ๊ณ 7
2. ์ ํ์ฐ๊ตฌ์ ์ด๋ก ์ ๋ฐฐ๊ฒฝ 8
2.1 ์ฐธ์ฌ์ ์๋ฏธ 8
2.2 ์ฐธ์ฌ์ ์นจ๋ฌต 13
2.3 ๊ณผํ๊ต์ก์์ ํ๋์ ์ฐธ์ฌ 14
2.4 ์ฐธ์ฌ์ ์ธก์ 16
2.5 ์ด๋ฑํ๊ต ๊ณผํ์์
์์ ๋ํ๋๋ ํ์๋ค์ ํ๋์ ์ฐธ์ฌ ๋ถ์์ ์ํ ์์ ๋ถ์ ๋๊ตฌ 20
3. ์ฐ๊ตฌ ๋ฐฉ๋ฒ 25
3.1 ์ฐ๊ตฌ ๋์ ๋ฐ ์ฐ๊ตฌ์ ์ฐจ 25
3.2 ์ด๋ฑํ๊ต ๊ณผํ์์
์์ ๋ํ๋๋ ํ์์ ํ๋์ ์ฐธ์ฌ ์์ ๋ถ์ ๋๊ตฌ 26
4. ์ฐ๊ตฌ๊ฒฐ๊ณผ ๋ฐ ๋
ผ์ 35
4.1 ํ์๋ค์ ์ ์ฒด ์์
์ ๋ํ ์์
์ฐธ์ฌ ์ํ 35
4.2 ์์
์ ์ฐธ์ฌํ๋ ํ์๋ค์๊ฒ์ ๋ํ๋๋ ๋น์ธ์ด์ ํ๋ 37
4.3 ์์
ํ๋์ ๋ฐ๋ฅธ ํ์๋ค์ ์์
์ฐธ์ฌ ์ํ ๋ณํ 40
5. ์์ฝ ๋ฐ ๊ฒฐ๋ก 58
5.1 ์์ฝ 58
5.2 ๊ฒฐ๋ก ๋ฐ ์์ฌ์ 60
5.3 ํ์ ์ฐ๊ตฌ ๊ณผ์ 62
์ฐธ ๊ณ ๋ฌธ ํ 63
[๋ถ๋ก1] 67
[๋ถ๋ก2] 68
ABSTRACT 69Maste
๋ฉํฐ ๋ชจ๋ฌ ์ ๋ณด์ 1์ฐจ์ ์ปจ๋ณผ๋ฃจ์ ์ ์ด์ฉํ ๋ฏธ์ธ๋จผ์ง ๋๋ ์์ธก
ํ์๋
ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ ๋ณด๊ณตํ๋ถ,2020. 2. ์ ๊ต๋ฏผ.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.๋๊ธฐ ์ค์ผ, ํนํ ๋ฏธ์ธ๋จผ์ง๋ ๋ง์ ๊ตญ๊ฐ์์ ์ฌ๊ฐํ ๋ฌธ์ ๊ฐ ๋์๋ค. ์ด๋ฌํ ๊ธ๊ฒฉํ ์ค์ผ์ ๋์ฒํ๊ธฐ ์ํด ๋ฅ๋ฌ๋์ ์ฌ์ฉํ์ฌ ๋๊ธฐ ์ค์ผ ๋ฌผ์ง์ ๋๋๋ฅผ ์์ธกํ๋ ๋ช ๊ฐ์ง ์ฐ๊ตฌ๊ฐ ์งํ๋์ด์๋ค. ๊ทธ๋ฌ๋ ๋ฏธ์ธ๋จผ์ง ๋๋๋ ๋ค์ํ ์ ํ์ ๋ฉํฐ ๋ชจ๋ฌ ์ ๋ณด๊ฐ ์ค๋ ์๊ฐ ๋์ ๋ณต์กํ๊ฒ ์ฐ๊ด๋์ด ์๊ธฐ ๋๋ฌธ์ ๋ฅ๋ฌ๋์ ์ด์ฉํ์ฌ ๋ฏธ์ธ๋จผ์ง ๋๋๋ฅผ ์ ํํ๊ฒ ์์ธกํ๋ ๋ฐ ์ด๋ ค์์ด ์กด์ฌํ๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ์์กด ์ฐ๊ฒฐ ๋ฐ ์ดํ ์
๋ฉ์ปค๋์ฆ๊ณผ ํจ๊ป ๊น์ด ์์ธ 1 ์ฐจ์ ์ปจ๋ณผ๋ฃจ์
์ ์ด์ฉํ์ฌ ๋ฏธ์ธ๋จผ์ง ๋๋๋ฅผ ์์ธกํ๋ ์๋ก์ด ์ํคํ
์ฒ๋ฅผ ์ ์ํ๋ค. 1์ฐจ์ ์ปจ๋ณผ๋ฃจ์
์ ๊ณ ์ฐจ์์ ํน์ง ๋ฒกํฐ๋ฅผ ์ถ์ถํ๋ฉฐ ์ดํ ์
๋ฉ์ปค๋์ฆ์ ์ถ์ถํ ๊ณ ์ฐจ์์ ํน์ง ๋ฒกํฐ๋ค๊ฐ์ ์๊ด ๊ด๊ณ๋ฅผ ํฌ์ฐฉํ๋ค. ์์ธ ๋ฏธ์ธ๋จผ์ง ๋๋ ๋ฐ์ดํฐ๋ฅผ ์ด์ฉํ ์คํ์ ํตํด PM2.5 ๋ฐ PM10 ์์ธก์์ ๋ณธ ๋
ผ๋ฌธ์์ ์ ์ํ ๋ชจ๋ธ์ด ๊ธฐ์กด ๋ชจ๋ธ์ ๋ฐ์ด๋์๋ค๋ ๊ฒ์ ํ์ธํ๋ค.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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