19 research outputs found

    ์ƒˆ๋กœ์šด ์†Œ์‹ค ์ฑ„๋„์„ ์œ„ํ•œ ์ž๊ธฐ๋™ํ˜• ๊ตฐ ๋ณตํ˜ธ๊ธฐ ๋ฐ ๋ถ€๋ถ„ ์ ‘์† ๋ณต๊ตฌ ๋ถ€ํ˜ธ ๋ฐ ์ผ๋ฐ˜ํ™”๋œ ๊ทผ ํ”„๋กœํ† ๊ทธ๋ž˜ํ”„ LDPC ๋ถ€ํ˜ธ์˜ ์„ค๊ณ„

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2019. 2. ๋…ธ์ข…์„ .In this dissertation, three main contributions are given asi) new two-stage automorphism group decoders (AGD) for cyclic codes in the erasure channel, ii) new constructions of binary and ternary locally repairable codes (LRCs) using cyclic codes and existing LRCs, and iii) new constructions of high-rate generalized root protograph (GRP) low-density parity-check (LDPC) codes for a nonergodic block interference and partially regular (PR) LDPC codes for follower noise jamming (FNJ), are considered. First, I propose a new two-stage AGD (TS-AGD) for cyclic codes in the erasure channel. Recently, error correcting codes in the erasure channel have drawn great attention for various applications such as distributed storage systems and wireless sensor networks, but many of their decoding algorithms are not practical because they have higher decoding complexity and longer delay. Thus, the AGD for cyclic codes in the erasure channel was introduced, which has good erasure decoding performance with low decoding complexity. In this research, I propose new TS-AGDs for cyclic codes in the erasure channel by modifying the parity check matrix and introducing the preprocessing stage to the AGD scheme. The proposed TS-AGD is analyzed for the perfect codes, BCH codes, and maximum distance separable (MDS) codes. Through numerical analysis, it is shown that the proposed decoding algorithm has good erasure decoding performance with lower decoding complexity than the conventional AGD. For some cyclic codes, it is shown that the proposed TS-AGD achieves the perfect decoding in the erasure channel, that is, the same decoding performance as the maximum likelihood (ML) decoder. For MDS codes, TS-AGDs with the expanded parity check matrix and the submatrix inversion are also proposed and analyzed. Second, I propose new constructions of binary and ternary LRCs using cyclic codes and existing two LRCs for distributed storage system. For a primitive work, new constructions of binary and ternary LRCs using cyclic codes and their concatenation are proposed. Some of proposed binary LRCs with Hamming weights 4, 5, and 6 are optimal in terms of the upper bounds. In addition, the similar method of the binary case is applied to construct the ternary LRCs with good parameters. Also, new constructions of binary LRCs with large Hamming distance and disjoint repair groups are proposed. The proposed binary linear LRCs constructed by using existing binary LRCs are optimal or near-optimal in terms of the bound with disjoint repair group. Last, I propose new constructions of high-rate GRP LDPC codes for a nonergodic block interference and anti-jamming PR LDPC codes for follower jamming. The proposed high-rate GRP LDPC codes are based on nonergodic two-state binary symmetric channel with block interference and Nakagami-mm block fading. In these channel environments, GRP LDPC codes have good performance approaching to the theoretical limit in the channel with one block interference, where their performance is shown by the channel threshold or the channel outage probability. In the proposed design, I find base matrices using the protograph extrinsic information transfer (PEXIT) algorithm. Also, the proposed new constructions of anti-jamming partially regular LDPC codes is based on follower jamming on the frequency-hopped spread spectrum (FHSS). For a channel environment, I suppose follower jamming with random dwell time and Rayleigh block fading environment with M-ary frequnecy shift keying (MFSK) modulation. For a coding perspective, an anti-jamming LDPC codes against follower jamming are introduced. In order to optimize the jamming environment, the partially regular structure and corresponding density evolution schemes are used. A series of simulations show that the proposed codes outperforms the 802.16e standard in the presence of follower noise jamming.์ด ๋…ผ๋ฌธ์—์„œ๋Š”, i) ์†Œ์‹ค ์ฑ„๋„์—์„œ ์ˆœํ™˜ ๋ถ€ํ˜ธ์˜ ์ƒˆ๋กœ์šด ์ด๋‹จ ์ž๊ธฐ๋™ํ˜• ๊ตฐ ๋ณตํ˜ธ๊ธฐ , ii) ๋ถ„์‚ฐ ์ €์žฅ ์‹œ์Šคํ…œ์„ ์œ„ํ•œ ์ˆœํ™˜ ๋ถ€ํ˜ธ ๋ฐ ๊ธฐ์กด์˜ ๋ถ€๋ถ„ ์ ‘์† ๋ณต๊ตฌ ๋ถ€ํ˜ธ(LRC)๋ฅผ ์ด์šฉํ•œ ์ด์ง„ ํ˜น์€ ์‚ผ์ง„ ๋ถ€๋ถ„ ์ ‘์† ๋ณต๊ตฌ ๋ถ€ํ˜ธ ์„ค๊ณ„๋ฒ•, ๋ฐ iii) ๋ธ”๋ก ๊ฐ„์„ญ ํ™˜๊ฒฝ์„ ์œ„ํ•œ ๊ณ ๋ถ€ํšจ์œจ์˜ ์ผ๋ฐ˜ํ™”๋œ ๊ทผ ํ”„๋กœํ† ๊ทธ๋ž˜ํ”„(generalized root protograph, GRP) LDPC ๋ถ€ํ˜ธ ๋ฐ ์ถ”์  ์žฌ๋ฐ ํ™˜๊ฒฝ์„ ์œ„ํ•œ ํ•ญ์žฌ๋ฐ ๋ถ€๋ถ„ ๊ท ์ผ (anti-jamming paritally regular, AJ-PR) LDPC ๋ถ€ํ˜ธ๊ฐ€ ์—ฐ๊ตฌ๋˜์—ˆ๋‹ค. ์ฒซ๋ฒˆ์งธ๋กœ, ์†Œ์‹ค ์ฑ„๋„์—์„œ ์ˆœํ™˜ ๋ถ€ํ˜ธ์˜ ์ƒˆ๋กœ์šด ์ด๋‹จ ์ž๊ธฐ๋™ํ˜• ๊ตฐ ๋ณตํ˜ธ๊ธฐ๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ตœ๊ทผ ๋ถ„์‚ฐ ์ €์žฅ ์‹œ์Šคํ…œ ํ˜น์€ ๋ฌด์„  ์„ผ์„œ ๋„คํŠธ์›Œํฌ ๋“ฑ์˜ ์‘์šฉ์œผ๋กœ ์ธํ•ด ์†Œ์‹ค ์ฑ„๋„์—์„œ์˜ ์˜ค๋ฅ˜ ์ •์ • ๋ถ€ํ˜ธ ๊ธฐ๋ฒ•์ด ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋งŽ์€ ๋ณตํ˜ธ๊ธฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๋†’์€ ๋ณตํ˜ธ ๋ณต์žก๋„ ๋ฐ ๊ธด ์ง€์—ฐ์œผ๋กœ ์ธํ•ด ์‹ค์šฉ์ ์ด์ง€ ๋ชปํ•˜๋‹ค. ๋”ฐ๋ผ์„œ ๋‚ฎ์€ ๋ณตํ˜ธ ๋ณต์žก๋„ ๋ฐ ๋†’์€ ์„ฑ๋Šฅ์„ ๋ณด์ผ ์ˆ˜ ์žˆ๋Š” ์ˆœํ™˜ ๋ถ€ํ˜ธ์—์„œ ์ด๋‹จ ์ž๊ธฐ ๋™ํ˜• ๊ตฐ ๋ณตํ˜ธ๊ธฐ๊ฐ€ ์ œ์•ˆ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํŒจ๋ฆฌํ‹ฐ ๊ฒ€์‚ฌ ํ–‰๋ ฌ์„ ๋ณ€ํ˜•ํ•˜๊ณ , ์ „์ฒ˜๋ฆฌ ๊ณผ์ •์„ ๋„์ž…ํ•œ ์ƒˆ๋กœ์šด ์ด๋‹จ ์ž๊ธฐ๋™ํ˜• ๊ตฐ ๋ณตํ˜ธ๊ธฐ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•œ ๋ณตํ˜ธ๊ธฐ๋Š” perfect ๋ถ€ํ˜ธ, BCH ๋ถ€ํ˜ธ ๋ฐ ์ตœ๋Œ€ ๊ฑฐ๋ฆฌ ๋ถ„๋ฆฌ (maximum distance separable, MDS) ๋ถ€ํ˜ธ์— ๋Œ€ํ•ด์„œ ๋ถ„์„๋˜์—ˆ๋‹ค. ์ˆ˜์น˜ ๋ถ„์„์„ ํ†ตํ•ด, ์ œ์•ˆ๋œ ๋ณตํ˜ธ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๊ธฐ์กด์˜ ์ž๊ธฐ ๋™ํ˜• ๊ตฐ ๋ณตํ˜ธ๊ธฐ๋ณด๋‹ค ๋‚ฎ์€ ๋ณต์žก๋„๋ฅผ ๋ณด์ด๋ฉฐ, ๋ช‡๋ช‡์˜ ์ˆœํ™˜ ๋ถ€ํ˜ธ ๋ฐ ์†Œ์‹ค ์ฑ„๋„์—์„œ ์ตœ๋Œ€ ์šฐ๋„ (maximal likelihood, ML)๊ณผ ๊ฐ™์€ ์ˆ˜์ค€์˜ ์„ฑ๋Šฅ์ž„์„ ๋ณด์ธ๋‹ค. MDS ๋ถ€ํ˜ธ์˜ ๊ฒฝ์šฐ, ํ™•์žฅ๋œ ํŒจ๋ฆฌํ‹ฐ๊ฒ€์‚ฌ ํ–‰๋ ฌ ๋ฐ ์ž‘์€ ํฌ๊ธฐ์˜ ํ–‰๋ ฌ์˜ ์—ญ์—ฐ์‚ฐ์„ ํ™œ์šฉํ•˜์˜€์„ ๊ฒฝ์šฐ์˜ ์„ฑ๋Šฅ์„ ๋ถ„์„ํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ, ๋ถ„์‚ฐ ์ €์žฅ ์‹œ์Šคํ…œ์„ ์œ„ํ•œ ์ˆœํ™˜ ๋ถ€ํ˜ธ ๋ฐ ๊ธฐ์กด์˜ ๋ถ€๋ถ„ ์ ‘์† ๋ณต๊ตฌ ๋ถ€ํ˜ธ (LRC)๋ฅผ ์ด์šฉํ•œ ์ด์ง„ ํ˜น์€ ์‚ผ์ง„ ๋ถ€๋ถ„ ์ ‘์† ๋ณต๊ตฌ ๋ถ€ํ˜ธ ์„ค๊ณ„๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ดˆ๊ธฐ ์—ฐ๊ตฌ๋กœ์„œ, ์ˆœํ™˜ ๋ถ€ํ˜ธ ๋ฐ ์—ฐ์ ‘์„ ํ™œ์šฉํ•œ ์ด์ง„ ๋ฐ ์‚ผ์ง„ LRC ์„ค๊ณ„ ๊ธฐ๋ฒ•์ด ์—ฐ๊ตฌ๋˜์—ˆ๋‹ค. ์ตœ์†Œ ํ•ด๋ฐ ๊ฑฐ๋ฆฌ๊ฐ€ 4,5, ํ˜น์€ 6์ธ ์ œ์•ˆ๋œ ์ด์ง„ LRC ์ค‘ ์ผ๋ถ€๋Š” ์ƒํ•œ๊ณผ ๋น„๊ตํ•ด ๋ณด์•˜์„ ๋•Œ ์ตœ์  ์„ค๊ณ„์ž„์„ ์ฆ๋ช…ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๋น„์Šทํ•œ ๋ฐฉ๋ฒ•์„ ์ ์šฉํ•˜์—ฌ ์ข‹์€ ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ์‚ผ์ง„ LRC๋ฅผ ์„ค๊ณ„ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ทธ ์™ธ์— ๊ธฐ์กด์˜ LRC๋ฅผ ํ™œ์šฉํ•˜์—ฌ ํฐ ํ•ด๋ฐ ๊ฑฐ๋ฆฌ์˜ ์ƒˆ๋กœ์šด LRC๋ฅผ ์„ค๊ณ„ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ LRC๋Š” ๋ถ„๋ฆฌ๋œ ๋ณต๊ตฌ ๊ตฐ ์กฐ๊ฑด์—์„œ ์ตœ์ ์ด๊ฑฐ๋‚˜ ์ตœ์ ์— ๊ฐ€๊นŒ์šด ๊ฐ’์„ ๋ณด์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, GRP LDPC ๋ถ€ํ˜ธ๋Š” Nakagami-mm ๋ธ”๋ก ํŽ˜์ด๋”ฉ ๋ฐ ๋ธ”๋ก ๊ฐ„์„ญ์ด ์žˆ๋Š” ๋‘ ์ƒํƒœ์˜ ์ด์ง„ ๋Œ€์นญ ์ฑ„๋„์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ฑ„๋„ ํ™˜๊ฒฝ์—์„œ GRP LDPC ๋ถ€ํ˜ธ๋Š” ํ•˜๋‚˜์˜ ๋ธ”๋ก ๊ฐ„์„ญ์ด ๋ฐœ์ƒํ–ˆ์„ ๊ฒฝ์šฐ, ์ด๋ก ์  ์„ฑ๋Šฅ์— ๊ฐ€๊นŒ์šด ์ข‹์€ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ค€๋‹ค. ์ด๋Ÿฌํ•œ ์ด๋ก  ๊ฐ’์€ ์ฑ„๋„ ๋ฌธํ„ฑ๊ฐ’์ด๋‚˜ ์ฑ„๋„ outage ํ™•๋ฅ ์„ ํ†ตํ•ด ๊ฒ€์ฆํ•  ์ˆ˜ ์žˆ๋‹ค. ์ œ์•ˆ๋œ ์„ค๊ณ„์—์„œ๋Š”, ๋ณ€ํ˜•๋œ PEXIT ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ™œ์šฉํ•˜์—ฌ ๊ธฐ์ดˆ ํ–‰๋ ฌ์„ ์„ค๊ณ„ํ•œ๋‹ค. ๋˜ํ•œ AJ-PR LDPC ๋ถ€ํ˜ธ๋Š” ์ฃผํŒŒ์ˆ˜ ๋„์•ฝ ํ™˜๊ฒฝ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ถ”์  ์žฌ๋ฐ์ด ์žˆ๋Š” ํ™˜๊ฒฝ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ๋‹ค. ์ฑ„๋„ ํ™˜๊ฒฝ์œผ๋กœ MFSK ๋ณ€๋ณต์กฐ ๋ฐฉ์‹์˜ ๋ ˆ์ผ๋ฆฌ ๋ธ”๋ก ํŽ˜์ด๋”ฉ ๋ฐ ๋ฌด์ž‘์œ„ํ•œ ์ง€์† ์‹œ๊ฐ„์ด ์žˆ๋Š” ์žฌ๋ฐ ํ™˜๊ฒฝ์„ ๊ฐ€์ •ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์žฌ๋ฐ ํ™˜๊ฒฝ์œผ๋กœ ์ตœ์ ํ™”ํ•˜๊ธฐ ์œ„ํ•ด, ๋ถ€๋ถ„ ๊ท ์ผ ๊ตฌ์กฐ ๋ฐ ํ•ด๋‹น๋˜๋Š” ๋ฐ€๋„ ์ง„ํ™” (density evolution, DE) ๊ธฐ๋ฒ•์ด ํ™œ์šฉ๋œ๋‹ค. ์—ฌ๋Ÿฌ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ๋Š” ์ถ”์  ์žฌ๋ฐ์ด ์กด์žฌํ•˜๋Š” ํ™˜๊ฒฝ์—์„œ ์ œ์•ˆ๋œ ๋ถ€ํ˜ธ๊ฐ€ 802.16e์— ์‚ฌ์šฉ๋˜์—ˆ๋˜ LDPC ๋ถ€ํ˜ธ๋ณด๋‹ค ์„ฑ๋Šฅ์ด ์šฐ์ˆ˜ํ•จ์„ ๋ณด์—ฌ์ค€๋‹ค.Contents Abstract Contents List of Tables List of Figures 1 INTRODUCTION 1.1 Background 1.2 Overview of Dissertation 1.3 Notations 2 Preliminaries 2.1 IED and AGD for Erasure Channel 2.1.1 Iterative Erasure Decoder 2.1.1 Automorphism Group Decoder 2.2. Binary Locally Repairable Codes for Distributed Storage System 2.2.1 Bounds and Optimalities of Binary LRCs 2.2.2 Existing Optimal Constructions of Binary LRCs 2.3 Channels with Block Interference and Jamming 2.3.1 Channels with Block Interference 2.3.2 Channels with Jamming with MFSK and FHSS Environment. 3 New Two-Stage Automorphism Group Decoders for Cyclic Codes in the Erasure Channel 3.1 Some Definitions 3.2 Modification of Parity Check Matrix and Two-Stage AGD 3.2.1 Modification of the Parity Check Matrix 3.2.2 A New Two-Stage AGD 3.2.3 Analysis of Modification Criteria for the Parity Check Matrix 3.2.4 Analysis of Decoding Complexity of TS-AGD 3.2.5 Numerical Analysis for Some Cyclic Codes 3.3 Construction of Parity Check Matrix and TS-AGD for Cyclic MDS Codes 3.3.1 Modification of Parity Check Matrix for Cyclic MDS Codes . 3.3.2 Proposed TS-AGD for Cyclic MDS Codes 3.3.3 Perfect Decoding by TS-AGD with Expanded Parity Check Matrix for Cyclic MDS Codes 3.3.4 TS-AGD with Submatrix Inversion for Cyclic MDS Codes . . 4 New Constructions of Binary and Ternary LRCs Using Cyclic Codes and Existing LRCs 4.1 Constructions of Binary LRCs Using Cyclic Codes 4.2 Constructions of Linear Ternary LRCs Using Cyclic Codes 4.3 Constructions of Binary LRCs with Disjoint Repair Groups Using Existing LRCs 4.4 New Constructions of Binary Linear LRCs with d โ‰ฅ 8 Using Existing LRCs 5 New Constructions of Generalized RP LDPC Codes for Block Interference and Partially Regular LDPC Codes for Follower Jamming 5.1 Generalized RP LDPC Codes for a Nonergodic BI 5.1.1 Minimum Blockwise Hamming Weight 5.1.2 Construction of GRP LDPC Codes 5.2 Asymptotic and Numerical Analyses of GRP LDPC Codes 5.2.1 Asymptotic Analysis of LDPC Codes 5.2.2 Numerical Analysis of Finite-Length LDPC Codes 5.3 Follower Noise Jamming with Fixed Scan Speed 5.4 Anti-Jamming Partially Regular LDPC Codes for Follower Noise Jamming 5.4.1 Simplified Channel Model and Corresponding Density Evolution 5.4.2 Construction of AJ-PR-LDPC Codes Based on DE 5.5 Numerical Analysis of AJ-PR LDPC Codes 6 Conclusion Abstract (In Korean)Docto

    Estimation of manoeuvring coefficients of a submerged body by parameter identification

    No full text
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธๅคงๅญธๆ ก ๅคงๅญธ้™ข :้€ ่ˆนๆตทๆด‹ๅทฅๅญธ็ง‘,1996.Docto

    The Effects of Social Relation, Living Arrangement, Residential Area on Elderly Depression

    No full text
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๋ณด๊ฑด๋Œ€ํ•™์› ๋ณด๊ฑดํ•™๊ณผ(๋ณด๊ฑด์ •์ฑ…๊ด€๋ฆฌํ•™์ „๊ณต), 2021.8. ๊น€์ฐฝ์—ฝ.์‚ฌํšŒ์  ๊ด€๊ณ„๋Š” ๋‹ค์–‘ํ•œ ๊ฒฝ๋กœ๋ฅผ ํ†ตํ•ด ๊ฑด๊ฐ•์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฉฐ, ๋ฌธํ™”์  ํ™˜๊ฒฝ๊ณผ ์‚ฌํšŒ๊ฒฝ์ œ์  ์กฐ๊ฑด์€ ์‚ฌํšŒ์  ๊ด€๊ณ„๊ฐ€ ๊ฑด๊ฐ•์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ธฐ์ œ๋ฅผ ๋‘˜๋Ÿฌ์‹ผ ๋งฅ๋ฝ์„ ๊ตฌ์„ฑํ•œ๋‹ค. ๋…ธ์ธ์˜ ์‚ฌํšŒ์  ๊ด€๊ณ„๊ฐ€ ์šฐ์šธ์— ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค๋Š” ์‚ฌ์‹ค์€ ๋„๋ฆฌ ์•Œ๋ ค์ ธ ์žˆ์œผ๋‚˜, ๋…ธ์ธ์˜ ๊ฑด๊ฐ•์„ ํšจ๊ณผ์ ์œผ๋กœ ๊ฐœ์„ ํ•˜ ๋ ค๋ฉด ์ด๋Ÿฌํ•œ ํšจ๊ณผ๊ฐ€ ๊ตฌ์ฒด์ ์ธ ์ƒํ™ฉ๊ณผ ์กฐ๊ฑด์— ๋”ฐ๋ผ ์–ด๋–ป๊ฒŒ ๋‹ฌ๋ผ์ง€๋Š”์ง€๋ฅผ ์ดํ•ดํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋…ธ์ธ์˜ ์‚ฌํšŒ์  ๊ด€๊ณ„๊ฐ€ ์ž‘๋™ํ•˜๋Š” ๋งฅ๋ฝ์— ์ดˆ์ ์„ ๋งž์ถ”์–ด, ์นœํ•œ ์‚ฌ๋žŒ๊ณผ์˜ ๋งŒ๋‚จ๊ณผ ๋‹จ์ฒด ์ฐธ์—ฌ๊ฐ€ ์šฐ์šธ์— ๋ฏธ์น˜๋Š” ํšจ๊ณผ๊ฐ€ ๋…๊ฑฐ ์—ฌ๋ถ€ ๋ฐ ๊ฑฐ์ฃผ ์ง€์—ญ์— ๋”ฐ๋ผ ์–ด๋–ป๊ฒŒ ๋‹ฌ๋ผ์ง€๋Š”์ง€๋ฅผ ํ™•์ธ ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ์ž๋ฃŒ๋กœ๋Š” ๊ณ ๋ นํ™”์—ฐ๊ตฌํŒจ๋„์กฐ์‚ฌ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์˜€์œผ๋ฉฐ, ์ผ๋ฐ˜ํ™” ์ถ”์ • ๋ฐฉ์ •์‹ ๋ชจํ˜•์„ ํ†ตํ•ด ์‚ฌํšŒ์  ๊ด€๊ณ„์˜ ํšจ๊ณผ์— ๋Œ€ํ•œ ๋…๊ฑฐ ์—ฌ๋ถ€์˜ ์กฐ์ ˆ ํšจ๊ณผ๋ฅผ ๊ฒ€์ •ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋Œ€์ƒ์ž๋ฅผ ๊ฑฐ์ฃผ ์ง€์—ญ์— ๋”ฐ๋ผ ๋Œ€๋„์‹œ, ์ค‘์†Œ ๋„์‹œ, ๋†์–ด์ดŒ์œผ๋กœ ๋‚˜๋ˆ„์–ด ํ•˜์œ„์ง‘๋‹จ ๋ถ„์„์„ ํ†ตํ•ด ์ง€์—ญ ๊ฐ„ ๋น„๊ต๋ฅผ ์ˆ˜ํ–‰ ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋†์–ด์ดŒ ์ง€์—ญ์€ ๋‹ค๋ฅธ ์ง€์—ญ์— ๋น„ํ•ด ์‚ฌํšŒ๊ฒฝ์ œ์ ์œผ๋กœ ์—ด์•…ํ•œ ์กฐ๊ฑด์— ์ฒ˜ํ•ด ์žˆ์—ˆ๋‹ค. ๋‘˜์งธ, ์นœํ•œ ์‚ฌ๋žŒ๊ณผ์˜ ๋งŒ๋‚จ์€ ์šฐ์šธ์„ ๋‚ฎ์ถ”์—ˆ์œผ๋‚˜, ๋‹จ์ฒด ์ฐธ์—ฌ๋Š” ์นœํ•œ ์‚ฌ๋žŒ๊ณผ์˜ ๋งŒ๋‚จ์„ ๋ณด์ •ํ•œ ์ƒํƒœ์—์„œ ์˜คํžˆ๋ ค ์šฐ์šธ์— ๋ถ€์ •์ ์ธ ํšจ๊ณผ๋ฅผ ๋ณด์˜€๋‹ค. ํ•˜์œ„์ง‘๋‹จ ๋ถ„์„์—์„œ ๋†์–ด์ดŒ ์ง€์—ญ์€ ์นœํ•œ ์‚ฌ๋žŒ๊ณผ์˜ ๋งŒ๋‚จ์˜ ํšจ๊ณผ๊ฐ€ ๋Œ€๋„์‹œ๋ณด๋‹ค ๊ฐ•ํ–ˆ๊ณ , ๋‹จ์ฒด ์ฐธ์—ฌ์˜ ๋ถ€์ •์  ํšจ๊ณผ๋„ ๋†์–ด์ดŒ ์ง€์—ญ์—์„œ๋งŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์…‹์งธ, ๋…๊ฑฐ๋Š” ์นœํ•œ ์‚ฌ๋žŒ๊ณผ์˜ ๋งŒ๋‚จ์˜ ํšจ๊ณผ์—๋งŒ ์กฐ์ ˆ ํšจ๊ณผ๋ฅผ ๋ณด์˜€์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ํšจ๊ณผ๋Š” ๋†์–ด์ดŒ ์ง€์—ญ์—์„œ๋งŒ ์œ ์˜ํ•˜์˜€๋‹ค. ์ฆ‰ ๋†์–ด์ดŒ ์ง€์—ญ์—์„œ ์นœํ•œ ์‚ฌ๋žŒ๊ณผ์˜ ๋งŒ๋‚จ์˜ ๊ธ์ •์ ์ธ ํšจ๊ณผ๋Š” ๋น„๋…๊ฑฐ๋…ธ์ธ์— ๋น„ํ•ด ๋…๊ฑฐ๋…ธ์ธ์—์„œ ๋” ์•ฝํ•ด์กŒ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์— ๊ธฐ์ดˆํ•˜์—ฌ ๋‹ค์Œ ์‚ฌํ•ญ์„ ์ œ์–ธํ•˜์˜€๋‹ค. ์ฒซ์งธ, ์šฐ์šธ์— ๋Œ€ํ•œ ์‚ฌํšŒ์  ๊ด€๊ณ„์˜ ํšจ๊ณผ๋Š” ๋งฅ๋ฝ์— ๋”ฐ๋ผ ๋ณ€๋™ํ•˜๋ฏ€๋กœ, ์‚ฌํšŒ์  ๊ด€๊ณ„๋ฅผ ํ†ตํ•œ ์ค‘์žฌ๋ฅผ ๊ธฐํšํ•˜๊ณ  ์‹คํ–‰ํ•  ๋•Œ๋Š” ๋งฅ๋ฝ๊ณผ ์กฐ๊ฑด์„ ์ถฉ๋ถ„ํžˆ ๊ณ ๋ คํ•˜๋Š” ์„ฌ์„ธํ•œ ์ ‘๊ทผ์ด ํ•„์š”ํ•˜๋‹ค. ๋‘˜์งธ, ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์šฐ์šธ์— ๋Œ€ํ•œ ์‚ฌํšŒ์  ๊ด€๊ณ„์˜ ํšจ๊ณผ์— ๋ณตํ•ฉ์ ์ธ ๋งฅ๋ฝ์ด ์ž‘๋™ํ•œ๋‹ค๋Š” ๊ฒƒ์„ ๊ณ„๋Ÿ‰์ ์œผ๋กœ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ ์œผ๋‚˜, ๋งฅ๋ฝ์ด ์ž‘๋™ํ•˜๋Š” ๊ฒฝ๋กœ์™€ ๊ธฐ์ œ๋ฅผ ๋ฐํžˆ๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•์„ ํ†ตํ•œ ์ถ”๊ฐ€์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์…‹์งธ, ๋…ธ์ธ์˜ ์‚ฌํšŒ์  ๊ด€๊ณ„๊ฐ€ ์ง‘๋‹จ ๊ฐ„์˜ ๊ฑด๊ฐ• ๋ถˆํ‰๋“ฑ์„ ์™„ํ™”ํ•˜๋Š” ํšจ๊ณผ๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ์œผ๋ฏ€๋กœ, ๊ฑด๊ฐ• ๊ฒฉ์ฐจ๋ฅผ ์ขํžˆ๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋†์–ด์ดŒ ์ง€์—ญ๊ณผ ๋…๊ฑฐ๋…ธ์ธ์˜ ์‚ฌํšŒ๊ฒฝ์ œ์  ์กฐ๊ฑด์„ ๊ฐœ์„ ํ•ด์•ผ ํ•œ๋‹ค.Social relations affect health through various path, and cultural environment and socioeconomic conditions constitute the context surrounding the mechanism in which social relations affect health. Although it is widely known that the social relationship of the elderly affects depression, it is necessary to understand how these effects vary according to specific circumstances and conditions in order to improve the health of the elderly effectively. This study focused on the context in which the social relationship of the elderly works, and investigated how the effects of contact with neighborhoods and group participation on depression vary depending on the residential area and the living arangement. The study used the Korean Longitudinal Study of Aging (KLoSA) and verified the moderating effect of the living alone status on the effect of social relations through the generalized estimation equation(GEE) model. And the subjects were divided into large cities, medium-small cities, and rural areas according to the residential area, and the comparison between the regions was performed through subgroup analysis. The results of this study are as follows: First, rural areas were under relatively disadvantaged socioeconomic conditions compared to other regions. Second, contact with neighborhoods lowered depression, but group participation showed a negative effect on depression while adjusting contact with neighborhoods. In the subgroup analysis, the effect of contact with neighborhoods in rural areas was stronger than that of large cities, and the negative effect of group participation was also found only in rural areas. Third, living arrangement showed moderating effect only on the effect of contact with neighborhoods, and this effect was significant only in rural areas. In other words, the positive effect of contact with neighborhoods in rural areas has become weaker in the elderly living alone than in the elderly living with family. Based on the results, the following suggestions were made. First, since the effects of social relationships on depression vary according to context, a delicate approach is needed to fully consider contexts and conditions when planning and executing interventions through social relationships. Second, this study identified quantitatively that complex contexts work on the effects of social relationships on depression, but further studies are needed through various methods to clarify the path and mechanism of contextual effect. Third, since the social relationship of the elderly has a limit to the effect of alleviating the health inequality among the groups, the socioeconomic conditions of the rural areas and elderly living alone should be improved in order to close the health gap.์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ 1 ์ œ 2 ์ ˆ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 3 1. ์‚ฌํšŒ์  ๊ด€๊ณ„๊ฐ€ ๊ฑด๊ฐ•์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 3 2. ์‚ฌํšŒ์  ๊ด€๊ณ„์˜ ๋งฅ๋ฝ์œผ๋กœ์„œ์˜ ์‚ฌํšŒ์ž๋ณธ 4 3. ์‚ฌํšŒ์  ๊ด€๊ณ„์™€ ์šฐ์šธ 6 4. ๊ฐ€๊ตฌ ํ˜•ํƒœ์™€ ๊ฑฐ์ฃผ ์ง€์—ญ์ด ๊ตฌ์„ฑํ•˜๋Š” ๋งฅ๋ฝ 7 ์ œ 3 ์ ˆ ์—ฐ๊ตฌ ๊ฐ€์„ค 9 ์ œ 2 ์žฅ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 12 ์ œ 1 ์ ˆ ์ž๋ฃŒ์› ๋ฐ ๋ณ€์ˆ˜ ์ •์˜ 12 ์ œ 2 ์ ˆ ๋ถ„์„ ๋ฐฉ๋ฒ• 15 ์ œ 3 ์žฅ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 18 ์ œ 1 ์ ˆ ๊ธฐ์ˆ  ๋ถ„์„ 18 ์ œ 2 ์ ˆ ์ผ๋ฐ˜ํ™” ์ถ”์ • ๋ฐฉ์ •์‹ ๋ชจํ˜• ๋ถ„์„ 23 1. ๊ฐ€์ƒ๊ด€๊ตฌ์กฐ ์„ ํƒ 23 2. ์ „์ฒด ํ‘œ๋ณธ ๋ถ„์„ 23 3. ํ•˜์œ„์ง‘๋‹จ ๋ถ„์„ 26 4. ๋ฏผ๊ฐ๋„ ๋ถ„์„ 29 ์ œ 4 ์žฅ ๋…ผ์˜ ๋ฐ ๊ฒฐ๋ก  30 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ ๊ณ ์ฐฐ 30 1. ๋…ธ์ธ์˜ ์‚ฌํšŒ๊ฒฝ์ œ์  ํŠน์„ฑ๊ณผ ์‚ฌํšŒ์  ๊ด€๊ณ„ 30 2. ๋…ธ์ธ์˜ ์šฐ์šธ๊ณผ ์—ฐ๊ด€๋œ ์š”์ธ 32 3. ์‚ฌํšŒ์  ๊ด€๊ณ„์™€ ์šฐ์šธ 34 4. ๋…๊ฑฐ์˜ ์กฐ์ ˆ ํšจ๊ณผ 37 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ์˜์˜์™€ ํ•œ๊ณ„ 40 ์ œ 3 ์ ˆ ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 42 ์ฐธ๊ณ ๋ฌธํ—Œ 45 Abstract 51์„

    A Robust and Efficient Rao-Blackwellized Particle Filter for Nonlinear and Nongaussian SLAM Problem

    No full text
    DoctorIntelligent mobility is a fundamental requirement of autonomous robots. To achieve this, mobile robots should learn the model of environments while estimating their poses. Thus, the simultaneous localization and mapping (SLAM) has been a major topic in the robotics application over the last decades. Most of researches presented so far in the SLAM field use laser range finders or vision sensors. The fundamental reason of this is that performance of the SLAM algorithm depends on the sensor performance in principle. A main goal of this thesis is to develop an efficient and robust SLAM algorithm that can be used in a practical application for personal service robots. A consistent SLAM algorithm is usually computationally heavy. On the other hand, the map learning using the noisy and short-range sensor is more difficult than the high performance sensors and it increases the computational cost in general to maintain the filter consistency with estimation accuracy. So, this thesis proposes algorithms to improve both the computational efficiency and the filter consistency, based on the RBPF framework. In advance, this thesis contributes to solve the SLAM problem that specifically addresses the noisy sonar sensor in the RBPF framework that can handle a multi-hypotheses tracking with non-Gaussianity. We first present the straightforward approach to improve a robustness and efficiency of the RBPF framework. To estimate the uncertainty in both the robot's pose and the map precisely, our approach introduces the scaled unscented transformation technique, which is able to estimate the mean and the covariance to a higher order of accuracy than the linearized techniques. Thus, even if there is a large bearing uncertainty, higher order information about the state distribution can be represented well, and this benefit produces the robustness to the sensor noise. On the other hand, since the filter inconsistency of the RBPF based SLAM algorithm due to a lack of hypotheses is the issue, we propose a novel approach considering a loop closing event in the resampling process of the particle filter after defining the problem thoroughly. We also propose an algorithm for speeding up RBPF that uses an optimal proposal distribution. This algorithm can significantly improve the computational efficiency while maintaining a performance of the standard algorithm due to an assistance of the Gaussian mixture filter in the proposal computation. The optimal proposal of RBPF is still widely spread because of the large sensor noise and intermittent nature of the sonar feature. This increases a required number of particles for learning the consistent map. To solve the problem, we incorporate prior information of structured environments so that the feature is constrained. Finally we present a RBPF-SLAM solution based on the ceiling mosaic approach using a single web-cam. The most attractive factor of this approach is its practicability in typical indoors, because the sensor reading from the ceiling is not influenced by moving objects on the ground. Aforementioned our approaches allow a robot to build a consistent map with estimating its pose accurately, even though a low resolution, short-range noisy sensor is used with less number of particles
    corecore