134 research outputs found
What should medical students know about artificial intelligence in medicine?
Artificial intelligence (AI) is expected to affect various fields of medicine substantially and has the potential to improve many aspects of healthcare. However, AI has been creating much hype, too. In applying AI technology to patients, medical professionals should be able to resolve any anxiety, confusion, and questions that patients and the public may have. Also, they are responsible for ensuring that AI becomes a technology beneficial for patient care. These make the acquisition of sound knowledge and experience about AI a task of high importance for medical students. Preparing for AI does not merely mean learning information technology such as computer programming. One should acquire sufficient knowledge of basic and clinical medicines, data science, biostatistics, and evidence-based medicine. As a medical student, one should not passively accept stories related to AI in medicine in the media and on the Internet. Medical students should try to develop abilities to distinguish correct information from hype and spin and even capabilities to create thoroughly validated, trustworthy information for patients and the public.ope
๊ณ ๋ฐ๋ ๋ฌด์ ๋ ๋์ ์ ์ก ํฅ์ ๊ธฐ๋ฒ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ, 2017. 8. ์ต์ฑํ.๋ฌด์ ํต์ ์ ๋ํ ์์๊ฐ ์ฆ๊ฐํจ์ ๋ฐ๋ผ, Wi-Fi๋ก ํํ ์๋ ค์ง IEEE 802.11 ํ์ค ๊ธฐ๋ฐ ๋ฌด์ ๋(WLAN, Wireless Local Area Network)์ ์ด๋์์๋ ์ฐพ์๋ณผ ์ ์๋ ๊ธฐ์ ๋ก ๊ฑฐ๋ญ๋ฌ๋ค. ์ด๋ก ์ธํด ๋ฌด์ ๋์ ๊ณ ๋ฐํ, ์ฆ ๊ณต๊ฐ์ ์ผ๋ก ์ธ์ ํ ๋ง์ AP(Access Point)์ STA(station)๋ค์ด ๋์ผํ ์ฃผํ์ ์ฑ๋์ ์ฌ์ฉํ๋ฉฐ ์ด๋ก ์ธํด ํ ๋จ๋ง์ด ์ป์ ์ ์๋ ์ฑ๋ฅ์ด ์ ํ๋๋ ํ์์ด ๋๋๋ฌ์ง๊ณ ์๋ค. ๋ฐ๋ผ์ ์ด๋ฌํ ๊ณ ๋ฐ๋ ๋ฌด์ ๋ ํ๊ฒฝ์์๋ ๋จ์ผ ์ ์ก์ ๋ํ ์คํํธ๋ผ ํจ์จ ๋ฟ๋ง ์๋๋ผ ์ฃผํ์ ์์์ ๊ณต๊ฐ ์ฌ์ฌ์ฉ(spatial reuse)์ ์ค์์ฑ ๋ํ ๊ฐ์กฐ๋๋ค. ์ฆ, ํน์ ๊ณต๊ฐ ๋ด์์ ์ผ๋ง๋ ๋ง์ ๋์ ์ ์ก์ด ๊ฐ๋ฅํ์ง๊ฐ ์ค์ํ ์ด์๋ก ์๋ฆฌ๋งค๊นํ๊ณ ์๋ค.
๋ณธ ํ์๋
ผ๋ฌธ์์๋ ๊ณ ๋ฐ๋ ๋ฌด์ ๋ ํ๊ฒฝ์์ ๋ ๋ง์ ๋์ ์ ์ก์ ์ฑ๊ณต์ํค๊ธฐ ์ํ์ฌ ๋ค์๊ณผ ๊ฐ์ ์ธ ๊ฐ์ง ์ ๋ต์ ๊ณ ๋ คํ๋ค.
(i) ๋งค์ฒด์ ๊ทผ์ ์ด(MAC, Medium Access Control) ๊ณ์ธต์ ACK(Acknowledgment) ๋ฐ CTS(Clear-To-Send) ํ๋ ์์ ๋ํ ์ก์ ์ ๋ ฅ ์ ์ด, (ii) ๋ฐ์กํ ๊ฐ์ง ์๊ณ๊ฐ(CST, Carrier-Sense Threshold) ์ ์, (iii) ๋์ ์ก์ ๋ฐ ์์ (STR, Simultaneous Transmit and Receiver), ์ฆ ๋์ผ๋์ญ ์ ์ด์ค ํต์ (in-band full duplex).
์ฒซ๋ฒ์งธ๋ก, ๋ณธ ํ์ ๋
ผ๋ฌธ์์๋ ๋ฐ์ดํฐ ํ๋ ์์ ์ํ ๋์ผ ์ฑ๋ ๊ฐ์ญ(CCI, Co-Channel Interference)๋ณด๋ค ๋ ์กฐ๋ช
๋์ด ์๋ MAC ACK ํ๋ ์์ ์ํด ๋ฐ์ํ๋ CCI์ ์ฃผ๋ชฉํ๋ค. ํ๋ฅ ์ ๊ธฐํ ๋ถ์(stochastic geometry analysis)์ ๊ธฐ๋ฐ์ผ๋ก ACK ํ๋ ์์ ์ก์ ์ ๋ ฅ ์กฐ์ ์ ํ์์ฑ์ ํ์ธํ์์ผ๋ฉฐ, ์ด๋ฅผ ๋ฐํ์ผ๋ก ๋์ ACK ํ๋ ์ ์ก์ ์ ๋ ฅ ์ ์ด ์๊ณ ๋ฆฌ์ฆ์ธ Quiet ACK(QACK)์ ์ ์ํ๋ค. QACK์ ๋ฐ์ดํฐ ํ๋ ์ ์์ ์ค ์ํ๋๋ CCI ๊ฒ์ถ ๋ฐ CCI ์ ๋ ฅ ์ถ์ ๊ธฐ๋ฒ๊ณผ ACK ํ๋ ์ ์ ์ก ํต๊ณ๋ฅผ ํ์ฉํ์ฌ ์ธ๋ฐํ๊ณ ์ ์ํ๊ฒ ACK ํ๋ ์์ ์ก์ ์ ๋ ฅ์ ์กฐ์ ํ๋ค. ๋๋ถ์ด, QACK์ ๋ฐํ์ผ๋ก CTS ํ๋ ์ ์ก์ ์ ๋ ฅ์ ์กฐ์ ํ์ฌ ๋ ๋ง์ ๋์ ์ ์ก์ด ์๋๋ ์ ์๊ฒ ํ๋ Quiet CTS(QCTS)๋ผ๋ ์๊ณ ๋ฆฌ์ฆ ๋ํ ์ ์ํ๋ค.
QACK์ ์คํ ๊ฐ๋ฅ์ฑ๊ณผ ์ฑ๋ฅ์ SDR(Software-Defined Radio) ๊ธฐ๋ฐ ํ๋กํ ํ์
์ ํตํด ๊ฒ์ฆํ๋ฉฐ ๊ธฐ์กด ๋ฐฉ์ ๋๋น ์ฝ 1.5๋ฐฐ ๋์ ์์จ์ ์ป์ ์ ์์์ ํ์ธํ๋ค. ๋ณด๋ค ์ผ๋ฐ์ ์ธ ๋ฌด์ ๋ ํ๊ฒฝ์์์ QACK ๋ฐ QCTS์ ์ฑ๋ฅ์ ns-3๋ฅผ ์ฌ์ฉํ ๋ค์ํ ์๋ฎฌ๋ ์ด์
์ ํตํด ํ๊ฐํ๋ค.
๋ค์์ผ๋ก, ๋์์ ๋ ๋ง์ ๋์ ์ ์ก์ด ์๋๋ ์ ์๋๋ก ๊ฐ์ญ์(interferer node)๊ณผ ๋ชฉ์ ๋
ธ๋(destination node)์ ๋ฐ๋ผ CST๋ฅผ ์ ์ดํ๋ โโCST ์ ์ ๋ฐฉ๋ฒ, FACT(Fine-grained Adaptation of Carrier-sense Threshold)๋ฅผ ์ ์ํ๋ค. ์ ์ํ๋ ๋ฐฉ๋ฒ์ ๋ฌด์ ๋ ํ์ค์์ ์ด๋ฏธ ์ ์๋์ด ์๋ ๊ธฐ๋ฅ์ ์ฌ์ฉํ๋ฏ๋ก ์์ฉ ๋ฌด์ ๋ ๊ธฐ๊ธฐ์์ ์ฝ๊ฒ ๊ตฌํํ ์ ์๋ค. ๋ํ FACT ๋ฐ ๋ค๋ฅธ CST ์ ์ ๊ธฐ๋ฒ๊ณผ ํจ๊ป ๋์ํ ์ ์๋ CCA(Clear Channel Assessment) ์ค๋ฒํค๋ ๊ฐ์ ๊ธฐ๋ฒ์ ์ ์ํ๋ฉฐ, ์ ์ํ ๊ธฐ๋ฒ๋ค์ ์ฑ๋ฅ์ ns-3 ์๋ฎฌ๋ ์ด์
์ ํตํด ๋น๊ตํ๊ฐํ๋ค. ์๋ฎฌ๋ ์ด์
๊ฒฐ๊ณผ๋ฅผ ํตํด ์ ์ํ ๋ฐฉ๋ฒ์ด ๊ธฐ์กด ๋ฐฉ๋ฒ์ ๋นํด ๋คํธ์ํฌ ์ ์ฒด ์์จ์ ํฐ ํญ์ผ๋ก ํฅ์์ํฌ ์ ์์์ ํ์ธํ๋ค.
๋ง์ง๋ง์ผ๋ก, ๋ฌด์ ๋์์ STR์ ๊ฐ๋ฅํ๊ฒํ๋ ์๋ก์ด MAC ํ๋กํ ์ฝ, ์ฆ MASTaR(MAC Protocol for Access points in Simultaneous Transmit and Receive mode)๋ฅผ ๊ธฐ์กด ๋ฌด์ ๋ ํ์ค์ ์ค์ํ๋ ๋ฐฉ๋ฒ์ผ๋ก ์ ์ํ๋ค. ๋ํ MASTaR ๋์์ ์ํด ํ์ํ ๋ฌผ๋ฆฌ๊ณ์ธต์์ ๋์งํธ ์๊ฐ ๊ฐ์ญ ์์(SIC, Self-Interference Cancellation) ์ ๋ต์ ์ ์ํ๋ฉฐ ๊ทธ ์คํ ๊ฐ๋ฅ์ฑ๊ณผ ์ฑ๋ฅ์ 3์ฐจ์ ๊ด์ ์ถ์ (3D-ray tracing) ๊ธฐ๋ฐ ์๋ฎฌ๋ ์ด์
์ ํตํด ๋ค์ํ ์ธก๋ฉด์์ ํ๊ฐํ๋ค. ์๋ฎฌ๋ ์ด์
๊ฒฐ๊ณผ๋ ํ์ฌ ๋ฌด์ ๋ MAC ํ๋กํ ์ฝ๋ณด๋ค ์ต๋ 2.58๋ฐฐ ๋์ ์์จ์ด MASTaR๋ฅผ ํตํด ์ป์ด์ง ์ ์์์ ๋ณด์ธ๋ค.
์์ฝํ๋ฉด, ๋ณธ ํ์๋
ผ๋ฌธ์์๋ ACK ๋ฐ CTS ํ๋ ์์ ์ก์ ์ ๋ ฅ ์ ์ด ์๊ณ ๋ฆฌ์ฆ๊ณผ CST ์ ์ ๋ฐ STR์ ์ํ ํ๋กํ ์ฝ์ ์ ์ํ๋ค. ์ ์ํ ์๊ณ ๋ฆฌ์ฆ ๋ฐ ํ๋กํ ์ฝ์ ์คํ ๊ฐ๋ฅ์ฑ๊ณผ ์ฑ๋ฅ์ ์์น ํด์, 3์ฐจ์ ๊ด์ ์ถ์ , ns-3 ๊ธฐ๋ฐ ์์คํ
์์ค(system-level) ์๋ฎฌ๋ ์ด์
, SDR ๊ธฐ๋ฐ ํ๋กํ ํ์
๋ฑ ๋ค์ํ ๋ฐฉ๋ฒ๋ก ์ ํตํด ์
์ฆํ๋ค.With increasing demand for wireless connectivity, IEEE 802.11 wireless local area network (WLAN), a.k.a. Wi-Fi, has become ubiquitous and continues to grow in number. This leads to the high density of WLAN, where many access points (APs) and client stations (STAs) operate on the same frequency channel. In a densely deployed WLAN, greater emphasis is placed on the importance of spatial reuse as well as spectral efficiency. In other words, it is of particular importance how many simultaneous transmissions are possible in a given area.
In this dissertation, we consider the following three strategies to increase the number of successful simultaneous transmissions: (i) Transmit power control for medium
access control (MAC) acknowledgment (ACK) and clear-to-send (CTS) frames, (ii) carrier sense threshold (CST) adaptation, and (iii) simultaneous transmit and receive (STR), i.e., in-band full-duplex communication.
First, this dissertation sheds light on the co-channel interference (CCI) caused by 802.11 MAC ACK frames, which has been less studied than the CCI caused by data frames. Based on stochastic geometry analysis, we propose Quiet ACK (QACK), a dynamic transmit power control algorithm for ACK frames. Fine-grained transmit power adjustment is enabled by CCI detection and CCI power estimation in the middle of a
data frame reception. A power control algorithm for clear-to-send (CTS) frame transmission, namely Quiet CTS (QCTS) is also proposed based on QACK. Our prototype using software-defined radio shows the feasibility and performance gain of QACK, i.e., 1.5X higher throughput than the legacy 802.11 WLAN. The performance of QACK and QCTS is further evaluated in more general WLAN environments via extensive simulations
using ns-3.
Second, a fine-grained CST adaptation method, which controls CST depending on both interferer and destination nodes, is proposed to improve spatial reuse in WLAN. The proposed method utilizes pre-defined functions in the WLAN standard, thus making itself easily implementable in commercial WLAN devices. Supplementary clear channel assessment (CCA) method is also proposed to further enhance network performance
by reducing CCA overhead. The performance of the proposed methods is comparatively evaluated via ns-3 simulation. Simulation results show that the proposed methods significantly improve network throughput compared with the legacy method.
Finally, a novel MAC protocol that enables STR in 802.11 WLAN, namely MASTaR, is proposed based on standard-compliant methods. Also, a digital self-interference cancellation (SIC) strategy is proposed to support the operation of MASTaR. The feasibility and the performance of MASTaR are extensively evaluated via 3D ray tracing-based simulation. The simulation results demonstrate that significant performance enhancement,e.g., up to 2.58X higher throughput than the current 802.11 MAC protocol, can be achieved by an STR-capable access point.
In summary, we propose an algorithm for ACK and CTS transmission power control and two protocols each for CST adaptation and STR which enhance the efficiency of WLAN by enriching simultaneous transmission. The feasibility and the performance of the algorithm and protocols are demonstrated via various methodologies including numerical analysis, 3D ray-tracing, ns-3 based system-level simulation, and prototype using a software-defined radio.1 Introduction 1
1.1 Motivation 1
1.2 Overview of Existing Approaches 3
1.2.1 Transmit power control for CCI reduction 3
1.2.2 CST adaptation for better spatial reuse 3
1.2.3 MAC protocol for STR in WLAN 4
1.3 Main Contributions 7
1.3.1 Quiet ACK: ACK Transmit Power Control 7
1.3.2 FACT: CST adaptation scheme 8
1.3.3 MASTaR: MAC protocol for STR in WLAN 8
1.4 Organization of the Dissertation 9
2 Quiet ACK: ACK Transmit Power Control in IEEE 802.11 WLANs 10
2.1 Introduction 10
2.2 Numerical Analysis 12
2.2.1 System Model 13
2.2.2 AISR Expansion by ACK Power Control 18
2.2.3 Optimization of ACK Outage Tolerance 19
2.3 QACK: Proposed ACK power Control 21
2.3.1 CCI Detection and CCI Power Estimation 22
2.3.2 Link Margin Estimation 26
2.3.3 ACK Power Adjustment 29
2.3.4 Conditional QACK Enabling/Disabling 30
2.4 Prototyping-Based Feasibility Evaluation 30
2.4.1 Feasibility of CCI Detection and CCI Power Estimation 30
2.4.2 Throughput Enhancement by QACK 33
2.5 Simulation-based Performance Evaluation 34
2.5.1 Two BSS Topology 35
2.5.2 Multiple BSS Environment 38
2.5.3 Coexistence with Legacy Devices 41
2.6 Quiet CTS: Proposed CTS Power Control 41
2.6.1 Problem Statement 41
2.6.2 CTS Power Control 42
2.6.3 Relationship with Quiet ACK 44
2.6.4 Simulation Results 45
2.7 Summary 48
3 FACT: Fine-Grained Adaptation of Carrier Sense Threshold in IEEE 802.11 WLANs 49
3.1 Introduction 49
3.2 Preliminaries 50
3.2.1 IEEE 802.11h Transmit Power Control (TPC) 50
3.2.2 IEEE 802.11ah Basic Service Set (BSS) Color 52
3.3 FACT: Proposed CST Adaptation Scheme 52
3.3.1 Basic Principle 53
3.3.2 Challenges and Solutions 54
3.3.3 Specification 54
3.3.4 Transmit Power Adjustment 56
3.3.5 Conditional Update of CST 57
3.4 Blind CCA and Backoff Compensation 57
3.4.1 Blind CCA 58
3.4.2 Backoff Compensation 59
3.5 Performance Evaluation 59
3.6 Summary 63
4 MASTaR: MAC Protocol for Access Points in Simultaneous Transmit and Receive Mode 64
4.1 Introduction 64
4.2 Preliminaries 68
4.2.1 Explicit Block ACK 68
4.2.2 Capture Effect 69
4.3 MASTaR: Proposed MAC Protocol 70
4.3.1 PTX Identification 70
4.3.2 Initial Training 73
4.3.3 Link Map Management 73
4.3.4 Secondary Transmission 74
4.4 Feasibility Study 76
4.4.1 Analog SIC and Channel Modeling 76
4.4.2 Digital SIC for WLAN 79
4.5 Performance Evaluation 83
4.5.1 Simulation with UDP Data Traffic 87
4.5.2 Simulation with Voice and Data Traffic 100
4.6 Summary 102
5 Concluding Remarks 103
5.1 Research Contributions 103
5.2 Future Work 104
Abstract (In Korean) 110Docto
ํ๊ตญ์ง์ญ๋๋ฐฉ๊ณต์ฌ ์ด์๊ธ ์ฌ๋ก๋ฅผ ์ค์ฌ์ผ๋ก
This study began with the recognition that the reason why the reporting system-based heat rates could not be raised in a timely manner and what factors influence the rate decision. Accordingly, by empirically analyzing factors influencing the heat rates decision process through quantified data, there is a purpose to seek ways to improve the regulatory system for inducing consumers to use heat reasonably based on accurate price signals and promoting a stable financial structure of Integrated energy providers. To this end, the theoretical background of rate regulation was reviewed, and the factors influencing the heat rates decision process were analyzed through quantified data by dividing them into cost, socio-economic, and political factors.
There have been many studies related to heat rates. However, it was the theoretical discussion on the heat rates regulation method and its effect on the heat rates decision case study were mainly conducted, and the factors that influence the heat rates decision process were not often analyzed with a quantitative method. This study conducted an empirical analysis using specific data such as the annual heat rates increase rate, so I tried to suggest alternatives of the regulatory system by finding that the reason why heat rates could not be increased in a timely manner was influenced by political factors such as elections other than cost factors.
The results obtained through this study are as follows.
First, heat rates were affected by LNG price fluctuations among the cost factors. Through this, it was found that the required cost was recovered based on the principle of โservice cost-orientedโ rate decision.
Second, it was confirmed that whether the National Assembly member election was conducted or not had an influence on the heat rates decision among the political factors. These results support the mixed model of Cnudde and McCrone that policy-making factors are influenced by political variables to some extent, and Aliison's bureaucratic model, in which the government's policy-making is a political activity through compromise and bargaining among the participants.
Through these research results, I would like to present the following policy implications.
First, it is necessary to stipulate a clear concept for reporting the adjustment of heat rates. The current heat rates adjustment is a report system, but after the report, the government and the Integrated energy industry have been confused over whether the government will accept it. The government has argued that acceptance is needed, but the Integrated energy industry has argued that acceptance is not necessary. After the report, if the formal requirements stipulated in the law are satisfied, the report is deemed to have been reported, or if it is prescribed as a report that requires acceptance, it is considered to be accepted if the processing time exceeds that period.
Second, there is a need to improve the system so that the cost of Integrated energy providers other than Korea District Heating Corporation can be properly reflected in rates. Currently, every company can calculate heat rates up to 110% of the market standard rate (heat rate applied to the majority of households receiving district heating and cooling, which is actually the heat rates of Korea District Heating Corporation). As a result, small and medium-sized private Integrated energy company that use expensive fuels or newly enter the market will not be able to recover adequate costs, which will inevitably deteriorate management conditions. Although it is not necessary to operate an indiscriminate rate system for each business operator, it seems necessary to reflect the appropriate cost.
Third, it is necessary to operate an independent decision-making agency to minimize political influence on the heat rates decision process. Suppressing the increase in public utility charges may be inevitable to stabilize the lives of ordinary people, but if it is excessive, it will not be able to induce reasonable heat use for consumers, and the conditions of business operators will inevitably deteriorate. In particular, unlike electricity rates, heat rates are in a situation where public and private businesses are operating together, so it is necessary to discuss the operation of these independent regulatory agency in depth.
The limitations of this study and follow-up studies are suggested as follows.
First, this study analyzed the impact on the heat rate of Korea District Heating Corporation, one of the business operator, so it was not possible to confirm whether the appropriate cost was collected through the heat rates for each business operator. Accordingly, it is necessary to conduct additional follow-up studies comparing the actual input cost and heat rates adjustment details even for some of the private business, even if it is not the target of all Integrated energy businesses.
Second, in setting variables, it is expected that more meaningful research results will be drawn if variables representing policy changes in the Integrated energy industry and the factors for reducing fuel costs due to the development of high-efficiency facilities according to technological development and improvement of the operation process are reflected.
Nevertheless, for the necessity of comparative analysis research on the process of determining utilities other than electricity, which was pointed out as a limitation of existing studies related to the decision of public utility charges, heat rates analysis was performed through quantified data. And as a result, it has confirmed that political factors such as elections are reflected through the process of determining heat rates.
In the future, through follow-up research that complements the limitations of the study, I expect that Integrated energy companies including Korea District Heating Corporation expand the supply of district energy in stable management conditions, actively respond to climate change, improve energy savings and the convenience of people's lives.๋ณธ ์ฐ๊ตฌ๋ ์ ๊ณ ์ ๊ธฐ๋ฐ์ ์ด์๊ธ์ด ์ ๊ธฐ์ ์ธ์๋์ง ๋ชปํ๋ ์ด์ ์ ์๊ธ๊ฒฐ์ ์ ์ํฅ์ ๋ฏธ์น๋ ์์ธ์ ๋ฌด์์ธ๊ฐ? ๋ผ๋ ์ธ์์์ ์์๋์๋ค. ์ด์ ๋ฐ๋ผ ์ด์๊ธ ๊ฒฐ์ ๊ณผ์ ์ ์ํฅ์ ๋ฏธ์น๋ ์์๋ฅผ ๊ณ๋ํ๋ ๋ฐ์ดํฐ๋ฅผ ํตํด ์ค์ฆ์ ์ผ๋ก ๋ถ์ํจ์ผ๋ก์จ ์๋น์์๊ฒ ์ ํํ ๊ฐ๊ฒฉ ์๊ทธ๋์ ์
๊ฐํ ํฉ๋ฆฌ์ ์ธ ์ด์ฌ์ฉ์ ์ ๋ํ๊ณ , ์ง๋จ์๋์ง์ฌ์
์์ ์์ ์ ์ฌ๋ฌด๊ตฌ์กฐ๋ฅผ ๋๋ชจํ๊ธฐ ์ํ ์ด์๊ธ ๊ท์ ์ฒด๊ณ์ ๊ฐ์ ๋ฐฉ์์ ๋ชจ์ํ๋๋ฐ ๋ชฉ์ ์ด ์๋ค. ์ด๋ฅผ ์ํด ์๊ธ๊ท์ ์ ์ด๋ก ์ ๋ฐฐ๊ฒฝ์ ๊ฒํ ํ๊ณ , ์ด์๊ธ ๊ฒฐ์ ๊ณผ์ ์ ์ํฅ์ ๋ฏธ์น๋ ์์ธ์ ์๊ฐ์ , ์ฌํ๊ฒฝ์ ์ , ์ ์น์ ์์ธ์ผ๋ก ๋๋์ด ๊ณ๋ํ๋ ๋ฐ์ดํฐ๋ฅผ ํตํด ๋ถ์ํ์๋ค.
๊ทธ๊ฐ ์ด์๊ธ๊ณผ ๊ด๋ จํ ์ฐ๊ตฌ๋ค์ ๋ง์ด ์์๋ค. ๊ทธ๋ฌ๋ ์ฃผ๋ก ์ด์๊ธ ๊ท์ ๋ฐฉ์์ ๋ํ ์ด๋ก ์ ๋
ผ์์ ์ด์๊ธ ๊ฒฐ์ ๊ณผ์ ์ ๋ฏธ์น๋ ์ํฅ์ ๋ํ์ฌ ์ฌ๋ก๋ฅผ ์ค์ฌ์ผ๋ก ์งํ๋์์ผ๋ฉฐ ์ด์๊ธ ๊ฒฐ์ ๊ณผ์ ์ ์ํฅ์ ๋ฏธ์น๋ ์์ธ์ ๊ณ๋์ ์ธ ๊ธฐ๋ฒ์ผ๋ก ๋ถ์ํ ๊ฒฝ์ฐ๋ ๋ง์ง ์์๋ค. ๋ณธ ์ฐ๊ตฌ๋ ์ฐ๋๋ณ ์ด์๊ธ ์ธ์์จ ๋ฑ ๊ตฌ์ฒด์ ๋ฐ์ดํฐ๋ฅผ ํ์ฉํ ์ค์ฆ์ ๋ถ์์ ํตํ์ฌ ์ด์๊ธ์ด ์ ๊ธฐ์ ์ธ์๋์ง ๋ชปํ๋ ์ด์ ๊ฐ ์๊ฐ์ ์์ธ ์ด์ธ ์ ๊ฑฐ์ ๊ฐ์ ์ ์น์ ์์ธ์ ์ํฅ์ ๋ฐ๊ณ ์์์ ๋ฐํ ํฉ๋ฆฌ์ ์ด์๊ธ ๊ท์ ์ฒด๊ณ์ ๋ํ ๊ฐ์ ๋ฐฉ์์ ์ ์ํ๊ณ ์ ํ์๋ค.
๋ณธ ์ฐ๊ตฌ๋ฅผ ํตํด ์ป์ ๊ฒฐ๊ณผ๋ ๋ค์๊ณผ ๊ฐ๋ค.
์ฒซ์งธ, ์ด์๊ธ์ ์๊ฐ์ ์์ธ ์ค LNG ๊ฐ๊ฒฉ ๋ณ๋์ ์ํฅ์ ๋ฐ์๋ค. ์ด๋ฅผ ํตํด ์ด์๊ธ์ โ์๋น์ค ์๊ฐ์ฃผ์โ ์๊ธ๊ฒฐ์ ์์น์ ๊ธฐ๋ฐํ์ฌ ์์ ์๊ฐ๋ฅผ ํ์ํ๊ณ ์์์ ์ ์ ์์๋ค.
๋์งธ, ์ ์น์ ์์ธ ์ค ๊ตญํ์์์ ๊ฑฐ ์ค์์ฌ๋ถ๊ฐ ์ด์๊ธ ๊ฒฐ์ ์ ์ํฅ์ ๋ฏธ์น ๊ฒ์ผ๋ก ํ์ธ๋์๋ค. ์ด ๊ฐ์ ๊ฒฐ๊ณผ๋ ์ ์ฑ
๊ฒฐ์ ์์ธ์ด ์ ์น์ ๋ณ์์ ์ํด ์ด๋ ์ ๋ ์ํฅ์ ๋ฐ๋๋ค๋ Cnudde์ McCrone์ ํผํฉ๋ชจํ๊ณผ ์ ๋ถ์ ์ ์ฑ
๊ฒฐ์ ์ ์ฐธ์ฌ์๋ค๊ฐ์ ํํ๊ณผ ํฅ์ ์ ์ํ์ฌ ์ด๋ฃจ์ด์ง๋ ์ ์น์ ํ๋์ด๋ผ๋ Allison์ ๊ด๋ฃ์ ์น๋ชจํ์ ๋ท๋ฐ์นจํ๋ ๊ฒ์ผ๋ก ์ฌ๊ฒจ์ง๋ค.
์ด ๊ฐ์ ์ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ํตํด ๋ค์๊ณผ ๊ฐ์ ์ ์ฑ
์ ์์ฌ์ ์ ์ ์๊ณ ์ ํ๋ค.
์ฒซ์งธ, ์ด์๊ธ ์กฐ์ ์ ๊ณ ์ ๋ํ ๋ช
ํํ ๊ฐ๋
์ ๊ท์ ํํ ํ์๊ฐ ์๋ค. ํํ ์ด์๊ธ ์กฐ์ ์ ์ ๊ณ ์ ์ด๋, ์ ๊ณ ์ดํ ์ ๋ถ์ ์๋ฆฌ ์ฌ๋ถ๋ฅผ ๋๋ฌ์ธ๊ณ ์ ๋ถ์ ์ง๋จ์๋์ง์
๊ณ๊ฐ ํผ์ ์ ๋น์ด์๋ค. ์ ๋ถ๋ ์๋ฆฌ๊ฐ ํ์ํ๋ค๋ ์
์ฅ์ด๊ณ , ์ง๋จ์๋์ง์
๊ณ๋ ์๋ฆฌ๊ฐ ํ์ํ์ง ์๋ค๊ณ ์ฃผ์ฅํด ์๋ค. ์ ๊ณ ์ดํ ๋ฒ๋ น์ ๊ท์ ๋ ํ์์์ ์๊ฑด์ ์ถฉ์กฑํ๋ฉด ์ ๊ณ ๋ ๊ฒ์ผ๋ก ๋ณด๊ฑฐ๋, ์๋ฆฌ๊ฐ ํ์ํ ์ ๊ณ ๋ก ๊ท์ ํ๋, ์ฒ๋ฆฌ์ํ์ ๋๊ณ ๊ทธ ๊ธฐ๊ฐ์ ์ด๊ณผํ๋ ๊ฒฝ์ฐ ์๋ฆฌ๋ฅผ ํ ๊ฒ์ผ๋ก ๊ฐ์ฃผํ๋ ๋ฐฉ์ ๋ฑ์ด ๋์์ด ๋ ๊ฒ์ด๋ค.
๋์งธ, ํ๊ตญ์ง์ญ๋๋ฐฉ๊ณต์ฌ ์ด์ธ ์ง๋จ์๋์ง์ฌ์
์์ ์๊ฐ๋ฅผ ์๊ธ์ ์ ์ ํ๊ฒ ๋ฐ์ํ ์ ์๋๋ก ์ ๋๋ฅผ ๊ฐ์ ํ ํ์๊ฐ ์๋ค. ํ์ฌ, ๋ชจ๋ ์ฌ์
์๋ ์์ฅ๊ธฐ์ค์๊ธ(์ง์ญ๋๋๋ฐฉ์ ๊ณต๊ธ๋ฐ๋ ์ธ๋ ์ค 50%์ด์ ๋๋ค์์ ์ธ๋์ ์ ์ฉ๋๋ ์ด์๊ธ์ผ๋ก ์ฌ์ค์ ํ๊ตญ์ง์ญ๋๋ฐฉ๊ณต์ฌ์ ์ด์๊ธ์ ์๋ฏธ)์ 110%๊น์ง๋ฅผ ์๊ธ ์ํ์ผ๋ก ํ์ฌ ์ด์๊ธ์ ์ฐ์ ํ ์ ์๋ค. ์ด๋ก ์ธํด ๊ฐ๋น์ผ ์ฐ๋ฃ๋ฅผ ์ฐ๊ฑฐ๋ ์ ๊ท๋ก ์์ฅ์ ์ง์
ํ ์ค์ํ ๋ฏผ๊ฐ ์ง๋จ์๋์ง์ฌ์
์๋ ์ ์ ์๊ฐ๋ฅผ ํ์ํ์ง ๋ชปํด ๊ฒฝ์์ฌ๊ฑด์ด ์
ํ๋ ์ ๋ฐ์ ์์ ๊ฒ์ด๋ค. ์ฌ์
์๋ณ๋ก ๋ฌด๋ถ๋ณํ ์๊ธ์ฒด๊ณ๊ฐ ์ด์๋์ด์๋ ์ ๋์ง๋ง, ์ ์ ์๊ฐ๋ฅผ ์ ๋๋ก ๋ฐ์ํ๋ ๊ฒ์ด ๋ฌด์๋ณด๋ค ์ค์ํ๋ค๊ณ ์๊ฐ๋๋ค.
์
์งธ, ์ด์๊ธ ๊ฒฐ์ ๊ณผ์ ์ ๋ฏธ์น๋ ์ ์น์ ์ํฅ๋ ฅ์ ์ต์ํํ๊ธฐ ์ํ ๋
๋ฆฝ์ ์ธ ์์ฌ๊ฒฐ์ ๊ธฐ๊ตฌ๋ฅผ ์ด์ํ ํ์๊ฐ ์๋ค. ๊ณต๊ณต์๊ธ์ ์ธ์ ์ต์ ๋ ์๋ฏผ์ํ ์์ ์ ์ํด ๋ถ๊ฐํผํ ๋ฉด๋ ์๊ฒ ์ง๋ง ์ง๋์น ๊ฒฝ์ฐ ์๋น์์๊ฒ๋ ํฉ๋ฆฌ์ ์ธ ์ด์ฌ์ฉ์ ์ ๋ํ ์ ์๊ณ , ์ฌ์
์์ ๊ฒฝ์์ฌ๊ฑด์ ์
ํ๋ ์ ๋ฐ์ ์์ ๊ฒ์ด๋ค. ํนํ, ์ด์๊ธ์ ์ ๊ธฐ์๊ธ๊ณผ ๋ฌ๋ฆฌ ๊ณต๊ณต๊ณผ ๋ฏผ๊ฐ์ฌ์
์๊ฐ ํจ๊ป ์ฌ์
์ ์์ํ๊ณ ์๋ ์ํฉ์ด๊ธฐ ๋๋ฌธ์ ์ด๋ฌํ ๋
๋ฆฝ๊ท์ ๊ธฐ๊ตฌ์ ์ด์์ ๋ํด์๋ ์ฌ๋์๊ฒ ๋
ผ์๋ฅผ ํด ๋ณผ ํ์๊ฐ ์์ ๊ฒ์ด๋ค.
๋ณธ ์ฐ๊ตฌ์ ํ๊ณ์ ๋ฐ ํ์ ์ฐ๊ตฌ์ ๋ํด ๋ค์๊ณผ ๊ฐ์ด ์ ์ธํ๊ณ ์ ํ๋ค.
์ฒซ์งธ, ๋ณธ ์ฐ๊ตฌ๋ ์ง๋จ์๋์ง์ฌ์
์ ์ค ํ๋์ธ ํ๊ตญ์ง์ญ๋๋ฐฉ๊ณต์ฌ์ ์ด์๊ธ์ ๋ฏธ์น๋ ์ํฅ์ ๋ถ์ํ ๊ฒ์ผ๋ก ์ฌ์
์๋ณ๋ก ์ด์๊ธ์ ํตํ ์ ์ ์๊ฐ ํ์์ฌ๋ถ๋ ํ์ธ์ด ๋ถ๊ฐํ์๋ค. ์ด์ ๋ฐ๋ผ, ์ง๋จ์๋์ง์ฌ์
์ ์ ์ฒด ๋์์ด ์๋๋๋ผ๋ ํ๊ตญ์ง์ญ๋๋ฐฉ๊ณต์ฌ๋ฅผ ์ ์ธํ ๋ฏผ๊ฐ ์ฌ์
์ ์ค ์ผ๋ถ๋ฅผ ๋์์ผ๋ก ์ค์ ํฌ์
์๊ฐ์ ์ด์๊ธ ์กฐ์ ๋ด์ญ์ ๋น๊ตํด ๋ณด๋ ์ถ๊ฐ์ ์ธ ํ์ ์ฐ๊ตฌ๊ฐ ์งํ๋ ํ์๊ฐ ์๊ฒ ๋ค.
๋์งธ, ๋ณ์์ ์ค์ ์ ์์ด ๊ธฐ์ ๋ฐ์ ์ ๋ฐ๋ฅธ ๊ณ ํจ์จ ์ค๋น ๊ฐ๋ฐ, ์ด์ ํ๋ก์ธ์ค ๊ฐ์ ๋ฑ์ผ๋ก ์ธํ ์ฐ๋ฃ๋น ์ ๊ฐ์์ธ ๋ฐ ์ง๋จ์๋์ง์ฐ์
์ ์ ์ฑ
์ ๋ณํ๋ฅผ ๋ํํ๋ ๋ณ์๋ฅผ ๋ฐ์ํ๋ค๋ฉด ๋์ฑ ์๋ฏธ์๋ ์ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ๋์ถํ ๊ฒ์ผ๋ก ๊ธฐ๋๋๋ค.
๊ทธ๋ผ์๋ ๋ถ๊ตฌํ๊ณ , ๊ณต๊ณต์๊ธ ๊ฒฐ์ ๊ณผ ๊ด๋ จํ ๊ธฐ์กด ์ฐ๊ตฌ์ ํ๊ณ๋ก ์ง์ ๋ ์ ๊ธฐ์๊ธ ์ด์ธ์ ๋ค๋ฅธ ๊ณต๊ณต์๊ธ ๊ฒฐ์ ๊ณผ์ ๋น๊ต๋ถ์ ์ฐ๊ตฌ ํ์์ฑ์ ๋ํ์ฌ ์ด์๊ธ ๋ถ์์ ๊ณ๋ํ๋ ๋ฐ์ดํฐ๋ฅผ ํตํด ์ํํ์๊ณ , ๊ทธ ๊ฒฐ๊ณผ ์ด์๊ธ ๊ฒฐ์ ๊ณผ์ ์์๋ ์ ๊ฑฐ์ ๊ฐ์ ์ ์น์ ์์ธ์ด ๋ฐ์๋๊ณ ์์์ ํ์ธํ๋ค๋ ์ ์์ ํฐ ์์๊ฐ ์๋ค๊ณ ํ๊ฒ ๋ค.
ํฅํ, ์ฐ๊ตฌ์ ํ๊ณ๋ฅผ ๋ณด์ํ๋ ์ถ๊ฐ์ ์ธ ์ฐ๊ตฌ๋ฅผ ํตํด ํ๊ตญ์ง์ญ๋๋ฐฉ๊ณต์ฌ๋ฅผ ํฌํจํ ์ง๋จ์๋์ง ์ฌ์
์๊ฐ ์์ ์ ์ธ ๊ฒฝ์์ฌ๊ฑด ์์์ ์ง๋จ์๋์ง๋ฅผ ํ๋ ๋ณด๊ธํ์ฌ, ๊ธฐํ๋ณํ์ ๋ฅ๋์ ์ผ๋ก ๋์ํ๊ณ ์๋์ง ์ ๊ฐ๊ณผ ๊ตญ๋ฏผ์ํ์ ํธ์ต์ฆ์ง์ ๋์ฑ ์ด๋ฐ์งํ ์ ์๊ธฐ๋ฅผ ๊ธฐ๋ํด ๋ณธ๋ค.์ 1 ์ฅ ์ ๋ก 1
์ 1 ์ ์ฐ๊ตฌ์ ๋ฐฐ๊ฒฝ๊ณผ ๋ชฉ์ 1
์ 2 ์ ์ฐ๊ตฌ์ ๋์๊ณผ ๋ฐฉ๋ฒ 2
์ 2 ์ฅ ์ง๋จ์๋์ง์ฌ์
๋ฐ ์ด์๊ธ ์ ๋ 4
์ 1 ์ ์ง๋จ์๋์ง์ฌ์
์ ๊ฐ์ 4
์ 2 ์ ์ง๋จ์๋์ง์ฌ์
์ ํํฉ 7
์ 3 ์ ์ด์๊ธ ์ ๋ 15
์ 3 ์ฅ ์ด๋ก ์ ๋
ผ์ ๋ฐ ์ ํ์ฐ๊ตฌ 20
์ 1 ์ ์๊ธ์ ์ ๋ถ๊ท์ 20
1. ์๊ธ๊ท์ ์ ์์น 20
2. ์ด๋ก ์ ๊ณ ์ฐฐ 21
3. ์๊ธ๊ท์ ์ ๊ด๋ จ๋ ์ ํ์ฐ๊ตฌ 23
์ 2 ์ ์ ์ฑ
๊ฒฐ์ 25
1. ์ด์๊ธ์ ์ ์ฑ
ํน์ฑ 25
2. ์ด๋ก ์ ๊ณ ์ฐฐ 26
3. ์ ์ฑ
๊ฒฐ์ ๊ณผ ๊ด๋ จ๋ ์ ํ์ฐ๊ตฌ 29
์ 3 ์ ๊ธฐ์กด ์ฐ๊ตฌ์์ ์ฐจ๋ณ์ฑ 30
์ 4 ์ฅ ์ด์๊ธ ๊ฒฐ์ ๊ณผ์ ์ ์ํธ์์ฉ ๋ถ์ 31
์ 1 ์ ์ด์๊ธ ๊ฒฐ์ ์ ์์น๊ณผ ์ ์ฐจ 31
1. ์ด์๊ธ ๊ฒฐ์ ์ ์์น 31
2. ์ด์๊ธ ๊ฒฐ์ ์ ์ฐจ 33
์ 2 ์ ์ดํด๊ด๊ณ์ 34
1. ํ๊ตญ์ง์ญ๋๋ฐฉ๊ณต์ฌ 34
2. ์ฐ์
ํต์์์๋ถ 35
3. ๊ธฐํ์ฌ์ ๋ถ 35
4. ์ด์ต์ง๋จ 36
์ 3 ์ ์ด์๊ธ ๊ฒฐ์ ๊ณผ์ ์ฌ๋ก ๋ถ์ 38
1. ์ด์๊ธ ๊ฒฐ์ ์ฌ๋ก 38
2. ์ ๊ฒฐ 40
์ 5 ์ฅ ์ด์๊ธ ๊ฒฐ์ ์์ธ ๋ถ์ 42
์ 1 ์ ๋ถ์ํ๊ณผ ์ฐ๊ตฌ๊ฐ์ค 42
์ 2 ์ ๋ณ์์ ์ ์ 44
1. ๋
๋ฆฝ๋ณ์ 44
2. ์ข
์๋ณ์ 47
์ 3 ์ ๋ถ์๋ฐฉ๋ฒ 49
์ 4 ์ ๋ถ์๊ฒฐ๊ณผ 49
1. ๊ธฐ๋ณธ ํต๊ณ๊ฐ 49
2. ๋ค์ค๊ณต์ ์ฑ์ ์ง๋จ 50
3. ๋ค์คํ๊ท๋ถ์ ๊ฒฐ๊ณผ 52
4. ๊ฐ์ค์ ๊ฒํ 54
์ 6 ์ฅ ๊ฒฐ ๋ก 58
์ 1 ์ ์ฐ๊ตฌ๊ฒฐ๊ณผ์ ์์ฝ 58
์ 2 ์ ์ฐ๊ตฌ์ ์ ์ฑ
์ ํจ์ ๋ฐ ํ๊ณ 60
์ฐธ๊ณ ๋ฌธํ 63
Abstract 66์
์จ๋๊ฐ๊ฒฝ๋๊ธฐํฌํ ์ ๊ฐ๋ํน์ฑ์ ๋ฏธ์น๋์ํฅ
๊ฒฝ๋๊ธฐํฌํ ์ ํ์ฅ ์ ์ฉ์ ์์ด ๋ฌธ์ ๊ฐ ๋ ์ ์๋ ์ฌ๋ฌ ์์ธ ์ค ์จ๋์ ๋ํ ์ฐ๊ตฌ๊ฐ ํ์ฌ๊น์ง๋ ์ด๋ฃจ์ด์ง์ง ์์๋ค. ๊ทธ๋์ ๋ณธ ๋
ผ๋ฌธ์์๋ ์จ๋๊ฐ ๊ฒฝ๋๊ธฐํฌํ ์ ํน์ฑ ํนํ ๊ฐ๋์ ๋ฏธ์น๋ ์ํฅ์ ํ์
ํ๊ธฐ ์ํด ์ฐ๊ตฌ๊ฐ ์ด๋ฃจ์ด์ก๋ค. ์ผ๋ฐ ์ฝํฌ๋ฆฌํธ๋ ์จ๋์ ๋ฐ๋ผ ํ์ค์ฝํฌ๋ฆฌํธ์ ์์ค์ฝํฌ๋ฆฌํธ๋ก ๋๋์ด ์จ๋์ ๋ํ ํ์ง ๊ฒ์ฆ ๋ฐฉ๋ฒ์ด๋ ํ์ฉ๊ธฐ์ค์ด ์์ด ์์์์ ํผํด๋ฅผ ์ต์ํ ์ํฌ ์ ์์ง๋ง ๊ฒฝ๋๊ธฐํฌํ ์ ์จ๋์ ๋ํ ๊ตฌ์ฒด์ ์ธ ๊ธฐ์ค์ด ์์ด ์ค๋ฌด์ ์ฉ์ ์ด๋ ค์์ ๊ฒช๊ณ ์๋ค.
๋ณธ ์ฐ๊ตฌ์์๋ ํ์ฅ์์ ๊ฐ์ฅ ๋ง์ด ์ฌ์ฉ๋ ์ ์๋ ๋จ์ ์ค๋ 6 kN/mยณ, 8 kN/mยณ, 10 kN/mยณ์ ๋ฐ๋ฅธ ๋ฐฐํฉ ์กฐ๊ฑด์ ์ค์ ํ์ฌ ๊ฒฝ๋๊ธฐํฌํ ๊ณต์์ฒด๋ฅผ ์ ์ํ ํ ์ฐ๋ฆฌ๋๋ผ์ ๊ธฐ์จํน์ฑ์ ๊ณ ๋ คํ ๊ฐ๊ธฐ ๋ค๋ฅธ ์จ๋์์ ์์ํ์ฌ ๊ฐ๋ ํน์ฑ์ ํ์
ํ๊ธฐ ์ํด ์ผ์ถ์์ถ์ํ์ ์ค์ํ์๋ค. ์จ๋์ ๋ฐ๋ฅธ ๊ฒฝ๋๊ธฐํฌํ ์ ์นจํ๋์ ์์๋ณด๊ธฐ ์ํด ๊ณต์์ฒด์ ์ํฌ๋ ๊ด์ฐฐ์ํ๊ณผ SEM ๋ถ์์ ํตํ ์จ๋์ ๋ฐ๋ฅธ ๊ฒฝ๋๊ธฐํฌํ ๋ด์ ๊ธฐํฌ ํํ ๋ณํ๋ฅผ ์์๋ณด๊ธฐ ์ํด ํน์ ์ฌ์ง์ดฌ์๋ ์ค์๋์๋ค. ๋ํ ๊ฒฝ๋๊ธฐํฌํ ์ ๊ด๋ฌผ์กฐ์ฑ์ ํ์ธํ๊ธฐ ์ํด X-์ ํ์ ์ ์ค์ํ์๊ณ ์งํ์์ ์ํ ์ค์ผ์ ์ฉ์ถ์ ๋ํ ํ์ธ์ ์ํ์ฌ ์ฉ์ถ์ํ์ ์ค์ํ์๋ค. ๊ฐ๊ฐ์ ์คํ๊ฒฐ๊ณผ๋ฅผ ๋ถ์ํ์ฌ ์์์จ๋๊ฐ ๊ฒฝ๋๊ธฐํฌํ ์ ์์ถ๊ฐ๋, ์นจํ๋ ๋ฐ ๊ธฐํฌ ํ์ฑ ๋ฑ์ ์ด๋ค ์ํฅ์ ๋ฏธ์น๋์ง ๊ทธ ํน์ฑ์ ํ์
ํ์๋ค. ์ต์ข
์ ์ผ๋ก ๊ฒฝ๋๊ธฐํฌํ ์ ํ์ง์ ๋ณด์ฅํ ์ ์๋ ์์ ๊ฐ๋ฅํ ํ์ฉ์จ๋ ๊ธฐ์ค์ ๋ณธ ์ฐ๊ตฌ๋ฅผ ํตํด ์ ์ํ์๋ค.์ 1 ์ฅ ์ ๋ก 1
1.1 ์ฐ๊ตฌ์ ๋ฐฐ๊ฒฝ 1
1.2 ์ฐ๊ตฌ์ ๋ชฉ์ ๋ฐ ๋ฒ์ 2
์ 2 ์ฅ ๊ธฐ์กด ์ฐ๊ตฌ 3
2.1 ๊ฒฝ๋๊ธฐํฌํ 3
2.1.1 ํ์์ฑ 3
2.1.2 ์ญํ์ ํน์ฑ 3
2.2 ์จ๋ 13
2.2.1 ์จ๋ ๋ณํ์ ๋ฐ๋ฅธ ์ํฅ 13
2.2.2 ๊ตญ๋ด์ฐ๊ตฌ 15
2.2.3 ๊ตญ์ธ์ฐ๊ตฌ 18
2.2.4 ๊ตญ๋ด์ธ ๊ธฐ์ค๋น๊ต 19
์ 3 ์ฅ ์ค๋ด์ํ 22
3.1 ์ฌ๋ฃ 22
3.1.1 ์๋ฉํธ 22
3.1.2 ๋ชจ๋ 22
3.1.3 ๊ธฐํฌ์ 23
3.1.4 ๋ฌผ 26
3.2 ๋ฐฐํฉ๊ณํ 26
3.3 ๊ณต์์ฒด ์ ์ 28
3.4 ์ํ์จ๋ 30
3.4.1 ์คํ๋ณ์ 30
3.4.2 ์ํ์กฐ๊ฑด 30
3.4.3 ์์์ฅ์น 31
3.4.4 ์ผ์ถ์์ถ์ํ 32
3.4.5 ์ํฌ๋ ๊ด์ฐฐ์ํ 34
3.4.6 ์ฌ์ง์ดฌ์ 36
3.4.7 ์ฉ์ถ์ํ 36
์ 4 ์ฅ ์ํ ๊ฒฐ๊ณผ ๋ฐ ๋ถ์ 37
4.1 ์ผ์ถ์์ถ๊ฐ๋ 37
4.2 ๊ณต์์ฒด ์ํฌ๋ ๋ณํ ๋ฐ ์ธํ๋ณํ 48
4.3 ์ฌ์ง์ดฌ์ 55
4.3.1 EDS๋ฅผ ์ด์ฉํ ๊ตฌ์ฑ์ฑ๋ถ ๋ถ์ 55
4.4 ๊ตฌ์ฑ์์ ๋ฐ ์ค๊ธ์ ์ฉ์ถ๋ 57
์ 5 ์ฅ ๊ฒฐ ๋ก 64
์ฐธ๊ณ ๋ฌธํ 6
Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis
We aimed to predict molecular subtypes of breast cancer using radiomics signatures extracted from synthetic mammography reconstructed from digital breast tomosynthesis (DBT). A total of 365 patients with invasive breast cancer with three different molecular subtypes (luminal A + B, luminal; HER2-positive, HER2; triple-negative, TN) were assigned to the training set and temporally independent validation cohort. A total of 129 radiomics features were extracted from synthetic mammograms. The radiomics signature was built using the elastic-net approach. Clinical features included patient age, lesion size and image features assessed by radiologists. In the validation cohort, the radiomics signature yielded an AUC of 0.838, 0.556, and 0.645 for the TN, HER2 and luminal subtypes, respectively. In a multivariate analysis, the radiomics signature was the only independent predictor of the molecular subtype. The combination of the radiomics signature and clinical features showed significantly higher AUC values than clinical features only for distinguishing the TN subtype. In conclusion, the radiomics signature showed high performance for distinguishing TN breast cancer. Radiomics signatures may serve as biomarkers for TN breast cancer and may help to determine the direction of treatment for these patients.ope
Pathologic Complete Response Prediction after Neoadjuvant Chemoradiation Therapy for Rectal Cancer Using Radiomics and Deep Embedding Network of MRI
Assessment of magnetic resonance imaging (MRI) after neoadjuvant chemoradiation therapy (nCRT) is essential in rectal cancer staging and treatment planning. However, when predicting the pathologic complete response (pCR) after nCRT for rectal cancer, existing works either rely on simple quantitative evaluation based on radiomics features or partially analyze multi-parametric MRI. We propose an effective pCR prediction method based on novel multi-parametric MRI embedding. We first seek to extract volumetric features of tumors that can be found only by analyzing multiple MRI sequences jointly. Specifically, we encapsulate multiple MRI sequences into multi-sequence fusion images (MSFI) and generate MSFI embedding. We merge radiomics features, which capture important characteristics of tumors, with MSFI embedding to generate multi-parametric MRI embedding and then use it to predict pCR using a random forest classifier. Our extensive experiments demonstrate that using all given MRI sequences is the most effective regardless of the dimension reduction method. The proposed method outperformed any variants with different combinations of feature vectors and dimension reduction methods or different classification models. Comparative experiments demonstrate that it outperformed four competing baselines in terms of the AUC and F1-score. We use MRI sequences from 912 patients with rectal cancer, a much larger sample than in any existing work.ope
Performance of deep learning-based algorithm for detection of ileocolic intussusception on abdominal radiographs of young children
The purpose of this study was to develop and test the performance of a deep learning-based algorithm to detect ileocolic intussusception using abdominal radiographs of young children. For the training set, children (โค5 years old) who underwent abdominal radiograph and ultrasonography (US) for suspicion of intussusception from March 2005 to December 2017 were retrospectively included and divided into control and intussusception groups according to the US results. A YOLOv3-based algorithm was developed to recognize the rectangular area of the right abdomen and to diagnose intussusception. For the validation set, children (โค5 years old) who underwent both radiograph and US from January to August 2018 with the suspicion of intussusception were included. Diagnostic performances of an algorithm and radiologists were compared. Total 681 children including 242 children in intussusception group were included in the training set and 75 children including 25 children in intussusception group were included in the validation set. The sensitivity of the algorithm was higher compared with that of the radiologists (0.76 vs. 0.46, pโ=โ0.013), while specificity was not different between the algorithm and the radiologists (0.96 vs. 0.92, pโ=โ0.32). Deep learning-based algorithm can aid screening of intussusception using abdominal radiography in young children.ope
Diagnosis of thyroid nodules on ultrasonography by a deep convolutional neural network
The purpose of this study was to evaluate and compare the diagnostic performances of the deep convolutional neural network (CNN) and expert radiologists for differentiating thyroid nodules on ultrasonography (US), and to validate the results in multicenter data sets. This multicenter retrospective study collected 15,375 US images of thyroid nodules for algorithm development (n = 13,560, Severance Hospital, SH training set), the internal test (n = 634, SH test set), and the external test (n = 781, Samsung Medical Center, SMC set; n = 200, CHA Bundang Medical Center, CBMC set; n = 200, Kyung Hee University Hospital, KUH set). Two individual CNNs and two classification ensembles (CNNE1 and CNNE2) were tested to differentiate malignant and benign thyroid nodules. CNNs demonstrated high area under the curves (AUCs) to diagnose malignant thyroid nodules (0.898-0.937 for the internal test set and 0.821-0.885 for the external test sets). AUC was significantly higher for CNNE2 than radiologists in the SH test set (0.932 vs. 0.840, P < 0.001). AUC was not significantly different between CNNE2 and radiologists in the external test sets (P = 0.113, 0.126, and 0.690). CNN showed diagnostic performances comparable to expert radiologists for differentiating thyroid nodules on US in both the internal and external test sets.ope
Diagnostic Performance of Deep Learning-Based Lesion Detection Algorithm in CT for Detecting Hepatic Metastasis from Colorectal Cancer
Objective: To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiologists.
Materials and methods: This clinical retrospective study used 4386-slice computed tomography (CT) images and labels from a training cohort (502 patients with colorectal cancer [CRC] from November 2005 to December 2010) to train the DLLD for detecting liver metastasis, and used CT images of a validation cohort (40 patients with 99 liver metastatic lesions and 45 patients without liver metastasis from January 2011 to December 2011) for comparing the performance of the DLLD with that of readers (three abdominal radiologists and three radiology residents). For per-lesion binary classification, the sensitivity and false positives per patient were measured.
Results: A total of 85 patients with CRC were included in the validation cohort. In the comparison based on per-lesion binary classification, the sensitivity of DLLD (81.82%, [81/99]) was comparable to that of abdominal radiologists (80.81%, p = 0.80) and radiology residents (79.46%, p = 0.57). However, the false positives per patient with DLLD (1.330) was higher than that of abdominal radiologists (0.357, p < 0.001) and radiology residents (0.667, p < 0.001).
Conclusion: DLLD showed a sensitivity comparable to that of radiologists when detecting liver metastasis in patients initially diagnosed with CRC. However, the false positives of DLLD were higher than those of radiologists. Therefore, DLLD could serve as an assistant tool for detecting liver metastasis instead of a standalone diagnostic tool.ope
Current State and Strategy for Establishing a Digitally Innovative Hospital: Memorial Review Article for Opening of Yongin Severance Hospital
The emergence of new technologies, especially digital transformation, influences the entire society, including the medical aspects. Therefore, the concept of digital hospital has been emerging. Here we present the Yongin Severance Hospital, which has developed various novel solutions to serve as foundations for the establishment of a digitally innovative hospital. Further strategies have also been provided to implement consistent and long-term planning.ope
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