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    ๊ฐœ์ธ ์‚ฌํšŒ๋ง ๋„คํŠธ์›Œํฌ ๋ถ„์„ ๊ธฐ๋ฐ˜ ์˜จ๋ผ์ธ ์‚ฌํšŒ ๊ณต๊ฒฉ์ž ํƒ์ง€

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2020. 2. ๊น€์ข…๊ถŒ.In the last decade we have witnessed the explosive growth of online social networking services (SNSs) such as Facebook, Twitter, Weibo and LinkedIn. While SNSs provide diverse benefits โ€“ for example, fostering inter-personal relationships, community formations and news propagation, they also attracted uninvited nuiance. Spammers abuse SNSs as vehicles to spread spams rapidly and widely. Spams, unsolicited or inappropriate messages, significantly impair the credibility and reliability of services. Therefore, detecting spammers has become an urgent and critical issue in SNSs. This paper deals with spamming in Twitter and Weibo. Instead of spreading annoying messages to the public, a spammer follows (subscribes to) normal users, and followed a normal user. Sometimes a spammer makes link farm to increase target accounts explicit influence. Based on the assumption that the online relationships of spammers are different from those of normal users, I proposed classification schemes that detect online social attackers including spammers. I firstly focused on ego-network social relations and devised two features, structural features based on Triad Significance Profile (TSP) and relational semantic features based on hierarchical homophily in an ego-network. Experiments on real Twitter and Weibo datasets demonstrated that the proposed approach is very practical. The proposed features are scalable because instead of analyzing the whole network, they inspect user-centered ego-networks. My performance study showed that proposed methods yield significantly better performance than prior scheme in terms of true positives and false positives.์ตœ๊ทผ ์šฐ๋ฆฌ๋Š” Facebook, Twitter, Weibo, LinkedIn ๋“ฑ์˜ ๋‹ค์–‘ํ•œ ์‚ฌํšŒ ๊ด€๊ณ„๋ง ์„œ๋น„์Šค๊ฐ€ ํญ๋ฐœ์ ์œผ๋กœ ์„ฑ์žฅํ•˜๋Š” ํ˜„์ƒ์„ ๋ชฉ๊ฒฉํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ ์‚ฌํšŒ ๊ด€๊ณ„๋ง ์„œ๋น„์Šค๊ฐ€ ๊ฐœ์ธ๊ณผ ๊ฐœ์ธ๊ฐ„์˜ ๊ด€๊ณ„ ๋ฐ ์ปค๋ฎค๋‹ˆํ‹ฐ ํ˜•์„ฑ๊ณผ ๋‰ด์Šค ์ „ํŒŒ ๋“ฑ์˜ ์—ฌ๋Ÿฌ ์ด์ ์„ ์ œ๊ณตํ•ด ์ฃผ๊ณ  ์žˆ๋Š”๋ฐ ๋ฐ˜ํ•ด ๋ฐ˜๊ฐ‘์ง€ ์•Š์€ ํ˜„์ƒ ์—ญ์‹œ ๋ฐœ์ƒํ•˜๊ณ  ์žˆ๋‹ค. ์ŠคํŒจ๋จธ๋“ค์€ ์‚ฌํšŒ ๊ด€๊ณ„๋ง ์„œ๋น„์Šค๋ฅผ ๋™๋ ฅ ์‚ผ์•„ ์ŠคํŒธ์„ ๋งค์šฐ ๋น ๋ฅด๊ณ  ๋„“๊ฒŒ ์ „ํŒŒํ•˜๋Š” ์‹์œผ๋กœ ์•…์šฉํ•˜๊ณ  ์žˆ๋‹ค. ์ŠคํŒธ์€ ์ˆ˜์‹ ์ž๊ฐ€ ์›์น˜ ์•Š๋Š” ๋ฉ”์‹œ์ง€๋“ค์„ ์ผ์ปฝ๋Š”๋ฐ ์ด๋Š” ์„œ๋น„์Šค์˜ ์‹ ๋ขฐ๋„์™€ ์•ˆ์ •์„ฑ์„ ํฌ๊ฒŒ ์†์ƒ์‹œํ‚จ๋‹ค. ๋”ฐ๋ผ์„œ, ์ŠคํŒจ๋จธ๋ฅผ ํƒ์ง€ํ•˜๋Š” ๊ฒƒ์ด ํ˜„์žฌ ์†Œ์…œ ๋ฏธ๋””์–ด์—์„œ ๋งค์šฐ ๊ธด๊ธ‰ํ•˜๊ณ  ์ค‘์š”ํ•œ ๋ฌธ์ œ๊ฐ€ ๋˜์—ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์€ ๋Œ€ํ‘œ์ ์ธ ์‚ฌํšŒ ๊ด€๊ณ„๋ง ์„œ๋น„์Šค๋“ค ์ค‘ Twitter์™€ Weibo์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ŠคํŒจ๋ฐ์„ ๋‹ค๋ฃจ๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์œ ํ˜•์˜ ์ŠคํŒจ๋ฐ๋“ค์€ ๋ถˆํŠน์ • ๋‹ค์ˆ˜์—๊ฒŒ ๋ฉ”์‹œ์ง€๋ฅผ ์ „ํŒŒํ•˜๋Š” ๋Œ€์‹ ์—, ๋งŽ์€ ์ผ๋ฐ˜ ์‚ฌ์šฉ์ž๋“ค์„ 'ํŒ”๋กœ์šฐ(๊ตฌ๋…)'ํ•˜๊ณ  ์ด๋“ค๋กœ๋ถ€ํ„ฐ '๋งž ํŒ”๋กœ์ž‰(๋งž ๊ตฌ๋…)'์„ ์ด๋Œ์–ด ๋‚ด๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•˜๊ธฐ๋„ ํ•œ๋‹ค. ๋•Œ๋กœ๋Š” link farm์„ ์ด์šฉํ•ด ํŠน์ • ๊ณ„์ •์˜ ํŒ”๋กœ์›Œ ์ˆ˜๋ฅผ ๋†’์ด๊ณ  ๋ช…์‹œ์  ์˜ํ–ฅ๋ ฅ์„ ์ฆ๊ฐ€์‹œํ‚ค๊ธฐ๋„ ํ•œ๋‹ค. ์ŠคํŒจ๋จธ์˜ ์˜จ๋ผ์ธ ๊ด€๊ณ„๋ง์ด ์ผ๋ฐ˜ ์‚ฌ์šฉ์ž์˜ ์˜จ๋ผ์ธ ์‚ฌํšŒ๋ง๊ณผ ๋‹ค๋ฅผ ๊ฒƒ์ด๋ผ๋Š” ๊ฐ€์ • ํ•˜์—, ๋‚˜๋Š” ์ŠคํŒจ๋จธ๋“ค์„ ํฌํ•จํ•œ ์ผ๋ฐ˜์ ์ธ ์˜จ๋ผ์ธ ์‚ฌํšŒ๋ง ๊ณต๊ฒฉ์ž๋“ค์„ ํƒ์ง€ํ•˜๋Š” ๋ถ„๋ฅ˜ ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ๋‚˜๋Š” ๋จผ์ € ๊ฐœ์ธ ์‚ฌํšŒ๋ง ๋‚ด ์‚ฌํšŒ ๊ด€๊ณ„์— ์ฃผ๋ชฉํ•˜๊ณ  ๋‘ ๊ฐ€์ง€ ์ข…๋ฅ˜์˜ ๋ถ„๋ฅ˜ ํŠน์„ฑ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ด๋“ค์€ ๊ฐœ์ธ ์‚ฌํšŒ๋ง์˜ Triad Significance Profile (TSP)์— ๊ธฐ๋ฐ˜ํ•œ ๊ตฌ์กฐ์  ํŠน์„ฑ๊ณผ Hierarchical homophily์— ๊ธฐ๋ฐ˜ํ•œ ๊ด€๊ณ„ ์˜๋ฏธ์  ํŠน์„ฑ์ด๋‹ค. ์‹ค์ œ Twitter์™€ Weibo ๋ฐ์ดํ„ฐ์…‹์— ๋Œ€ํ•œ ์‹คํ—˜ ๊ฒฐ๊ณผ๋Š” ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์ด ๋งค์šฐ ์‹ค์šฉ์ ์ด๋ผ๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค. ์ œ์•ˆํ•œ ํŠน์„ฑ๋“ค์€ ์ „์ฒด ๋„คํŠธ์›Œํฌ๋ฅผ ๋ถ„์„ํ•˜์ง€ ์•Š์•„๋„ ๊ฐœ์ธ ์‚ฌํšŒ๋ง๋งŒ ๋ถ„์„ํ•˜๋ฉด ๋˜๊ธฐ ๋•Œ๋ฌธ์— scalableํ•˜๊ฒŒ ์ธก์ •๋  ์ˆ˜ ์žˆ๋‹ค. ๋‚˜์˜ ์„ฑ๋Šฅ ๋ถ„์„ ๊ฒฐ๊ณผ๋Š” ์ œ์•ˆํ•œ ๊ธฐ๋ฒ•์ด ๊ธฐ์กด ๋ฐฉ๋ฒ•์— ๋น„ํ•ด true positive์™€ false positive ์ธก๋ฉด์—์„œ ์šฐ์ˆ˜ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค.1 Introduction 1 2 Related Work 6 2.1 OSN Spammer Detection Approaches 6 2.1.1 Contents-based Approach 6 2.1.2 Social Network-based Approach 7 2.1.3 Subnetwork-based Approach 8 2.1.4 Behavior-based Approach 9 2.2 Link Spam Detection 10 2.3 Data mining schemes for Spammer Detection 10 2.4 Sybil Detection 12 3 Triad Significance Profile Analysis 14 3.1 Motivation 14 3.2 Twitter Dataset 18 3.3 Indegree and Outdegree of Dataset 20 3.4 Twitter spammer Detection with TSP 22 3.5 TSP-Filtering 27 3.6 Performance Evaluation of TSP-Filtering 29 4 Hierarchical Homophily Analysis 33 4.1 Motivation 33 4.2 Hierarchical Homophily in OSN 37 4.2.1 Basic Analysis of Datasets 39 4.2.2 Status gap distribution and Assortativity 44 4.2.3 Hierarchical gap distribution 49 4.3 Performance Evaluation of HH-Filtering 53 5 Overall Performance Evaluation 58 6 Conclusion 63 Bibliography 65Docto

    ํ† ๋งˆํ†  ์ €๋‹จ ๋ฐ€์‹ ์žฌ๋ฐฐ ์‹œ ์ˆ˜๋ถ„ ์ŠคํŠธ๋ ˆ์Šค์— ์˜ํ•œ ๊ณผ์‹ค์˜ ๋‹น๋„ ์ฆ์ง„

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‹๋ฌผ์ƒ์‚ฐ๊ณผํ•™๋ถ€(์›์˜ˆ๊ณผํ•™์ „๊ณต), 2014. 2. ์ „์ฐฝํ›„.This study was conducted to increase sugar content in tomato fruits by applying water stress in high density-low truss cultivation. In the first chapter, the change in total soluble solids content (TSS) in fruits as affected by high electrical conductivity (EC) levels up to 13.7 dS m-1 of nutrient solution at different growth stages and incidence of blossom-end rot (BER) as affected by salt stress intensity and growing seasons were investigated. Growth of tomato plants was inhibited and fruit yield decreased, while soluble sugar content in fruits increased as EC of nutrient solution increased. The sugar content of tomato fruits cultivated in summer season was higher than that cultivated in winter season. BER incidence was less in winter cultivation. In the second chapter, the change in fruit quality as affected by combination of salt stress and root zone restriction was investigated to reduce the negative side effects of high EC treatment such as growth inhibition, yield loss, and higher incidence of BER. Growth and yield decreased as EC level increased and root zone volume decreased. TSS, titratable acid, and ascorbic acid in fruits increased as the root zone volume decreased. TSS increased in higher EC treatment (โ‰ฅ 4.2 dS m-1) and TA remarkably increased in the EC treatment of 7.0 dS m-1 those combined with the severe root zone restriction (container size S treatments). Glucose, fructose, citric acid, and ascorbic acid contents in tomato fruits as affected by EC level and root zone restriction were most significant when the treatments was applied at the fruit-ripening stage. Results imply that TSS in tomato fruits increased as EC levels increased and the effect is more significant when the high EC treatment is applied at the earlier stages. Fruit quality could be improved with minimized yield reduction and incidence of BER if the high EC treatment was combined with root zone restriction treatment. Cultivating tomato plants using a 200 mL-container with application of high-EC nutrient solution will be feasible for high density-low truss cultivation of high quality tomato fruits.ABSTRACT i CONTENTS iv LIST OF TABLES vi LIST OF FIGURES vii INTRODUCTION 1 LITERATURE CITED 3 CHAPTER 1. Total Soluble Solids Content in Tomato Fruits as Affected by High EC Levels at Different Growth Stages INTRODUCTION 7 MATERIALS AND METHODS 9 RESULTS AND DISCUSSION 12 LITERATURE CITED 23 CHAPTER 2. Quality Improvement of Tomato Fruits at Different Ripening Stages as Affected by Salt Stress and Root Zone Restriction INTRODUCTION 29 MATERIALS AND METHODS 32 RESULTS AND DISCUSSION 37 LITERATURE CITED 53 CONCLUSIONS 62 ABSTRACT IN KOREAN 63Maste

    Using Rank Correlation Coefficient to identify Abnormal Energy Consumption in Buildings

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    ์—๋„ˆ์ง€ ์ ˆ์•ฝ ๋ฌธ์ œ๋Š” ํ˜„์žฌ๊นŒ์ง€ ํ•ด๊ฒฐ๋˜์ง€ ์•Š์€ ๋ฌธ์ œ์ด๋ฉฐ, ์—๋„ˆ์ง€๋ฅผ ํšจ์œจ์ ์œผ๋กœ ์‚ฌ์šฉํ•˜๊ธฐ ์œ„ํ•ด ์—ฌ๋Ÿฌ ๋ฐฉ ๋ฒ•๋“ค์ด ์ œ์•ˆ๋˜์—ˆ๋‹ค. IT ๊ธฐ์ˆ ์˜ ๋ฐœ๋‹ฌ๊ณผ ๋”๋ถˆ์–ด ์„ผ์„œ, ์˜จ๋„์กฐ์ ˆ์žฅ์น˜, ์—์–ด์ปจ, ์กฐ๋ช… ๋“ฑ์˜ ๊ธฐ๊ธฐ๋“ค์„ ํ†ตํ•ฉํ•˜์—ฌ ๋ชจ๋‹ˆํ„ฐ๋งํ•˜๊ณ  ๊ด€๋ฆฌํ•˜๋Š” ๊ณต์กฐ ์‹œ์Šคํ…œ (HVAC: Heating Ventilation Air Conditioning)์ด ๊ฑด๋ฌผ์— ๋„์ž…๋˜์–ด ํ™œ ์šฉ๋˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ด ์‹œ์Šคํ…œ์„ ํ†ตํ•˜์—ฌ ์—๋„ˆ์ง€ ์†Œ๋น„์˜ ๋ฌธ์ œ์ ์„ ์ฐพ๊ณ  ์—๋„ˆ์ง€๋ฅผ ํšจ์œจ์ ์œผ๋กœ ๊ด€๋ฆฌํ•  ์ˆ˜ ์žˆ๋‹ค. ์—๋„ˆ์ง€ ํšจ์œจ์  ๊ด€๋ฆฌ๋ฅผ ์œ„ํ•˜์—ฌ ์ด์ƒ ํ˜„์ƒ์„ ํšจ์œจ์ ์œผ๋กœ ๊ฐ์ง€ํ•˜๋Š” ๊ฒƒ์— ๋Œ€ํ•œ ๋ฐฉ๋ฒ•๋“ค๋„ ๋งŽ์ด ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ๋Š” ์—๋„ˆ์ง€ ์„ผ์„œ๊ฐ„์˜ ์ „๋ ฅ ์†Œ๋ชจ ํŒจํ„ด์„ 3๊ฐœ์˜ ๋ฐด๋“œ ์˜์—ญ์œผ๋กœ ๋‚˜๋ˆ„์–ด ์ด์ƒ ํ˜„์ƒ์„ ํƒ์ง€ ํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ ๊ฐ ๋ฐด๋“œ ์˜์—ญ ๊ฐ„์˜ ๊ด€๊ณ„์— ์น˜์šฐ์ณ ๊ธฐ๊ธฐ๋“ค๊ฐ„์˜ ๋งŽ์€ ๊ด€๊ณ„๋ฅผ ํƒ์ƒ‰ํ•˜์ง€ ๋ชปํ•œ๋‹ค๋Š” ๋‹จ์ ์ด ์žˆ ์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์€ ์ „๋ ฅ ์†Œ๋ชจ ํŒจํ„ด์— ๋”ฐ๋ผ ๋ฐด๋“œ ์˜์—ญ์„ ๊ตฌ๋ถ„ํ•˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ, ์ „ ๋ฐด๋“œ ์˜์—ญ์—์„œ ๊ฐ ๊ธฐ๊ธฐ๊ฐ„์˜ ์ˆœ์œ„ ๊ด€๊ณ„ ๋ณ€ํ™”๋ฅผ ๊ด€์ฐฐํ•จ์œผ๋กœ์จ ์ด์ƒ ํ˜„์ƒ ํƒ์ง€์˜ ํšจ์œจ์„ฑ์„ ๋†’์ด๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜๊ณ  ์žˆ๋‹ค.์ด ๋…ผ๋ฌธ์€ 2016๋…„๋„ ์ •๋ถ€(๋ฏธ๋ž˜์ฐฝ์กฐ๊ณผํ•™๋ถ€)์˜ ์žฌ์›์œผ ๋กœ ์ •๋ณดํ†ต์‹ ๊ธฐ์ˆ ์ง„ํฅ์„ผํ„ฐ์˜ ์ง€์›์„ ๋ฐ›์•„ ์ˆ˜ํ–‰๋œ ์—ฐ๊ตฌ (No.B0190-16-2017,IoT ๊ธฐ๊ธฐ์˜ ๋ฌผ๋ฆฌ์  ์†์„ฑ, ๊ด€๊ณ„, ์—ญ ํ•  ๊ธฐ๋ฐ˜ Resilient/Fault-Tolerant ์ž์œจ ๋„คํŠธ์›Œํ‚น ๊ธฐ์ˆ  ์—ฐ๊ตฌ) ๋ฐ ๋ฏธ๋ž˜์ฐฝ์กฐ๊ณผํ•™๋ถ€ ๋ฐ ์ •๋ณดํ†ต์‹ ๊ธฐ์ˆ ์ง„ํฅ์„ผํ„ฐ์˜ ๋Œ€ํ•™ICT์—ฐ๊ตฌ์„ผํ„ฐ์œก์„ฑ ์ง€์›์‚ฌ์—…์˜ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋กœ ์ˆ˜ํ–‰๋˜์—ˆ ์Œ" (IITP-2015-R0992-15-1023)OAIID:RECH_ACHV_DSTSH_NO:A201620368RECH_ACHV_FG:RR00200003ADJUST_YN:EMP_ID:A001118CITE_RATE:DEPT_NM:์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€EMAIL:[email protected]_YN:CONFIRM:
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