1,263 research outputs found

    ๊ตญ๊ฐ€GIS ์ค‘์žฅ๊ธฐ ์ •์ฑ…๋ฐฉํ–ฅ ์—ฐ๊ตฌ(Vision and policy issues for national geographic information system in Korea)

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    ๋…ธํŠธ : ์ด ์—ฐ๊ตฌ๋ณด๊ณ ์„œ์˜ ๋‚ด์šฉ์€ ๊ตญํ† ์—ฐ๊ตฌ์›์˜ ์ž์ฒด ์—ฐ๊ตฌ๋ฌผ๋กœ์„œ ์ •๋ถ€์˜ ์ •์ฑ…์ด๋‚˜ ๊ฒฌํ•ด์™€๋Š” ์ƒ๊ด€์—†์Šต๋‹ˆ๋‹ค

    CNN YOLO๋ฅผ ์ด์šฉํ•œ ์—ดํ™”์ƒ๊ธฐ๋ฐ˜ ์˜์ƒ๊ณผ ์ ๊ตฌ๋ฆ„ ๊ธฐ๋ฐ˜ ์—”๋“œ๋ฐ€ ๊ฐ์‹œ ์‹œ์Šคํ…œ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„๊ณตํ•™๋ถ€, 2022. 8. ์•ˆ์„ฑํ›ˆ.As adoption of smart-factory system in manufacturing becoming inevitable, autonomous monitoring system in the field of machining has become viral nowadays. Among various methods in autonomous monitoring, vision-based monitoring is the most sought-after. This system uses vision sensors integrated with detection models developed through deep learning. However, the disadvantage of being greatly affected by optical conditions, such as ambient lighting or reflective materials, critically affects the performance in terms of monitoring. Instead of vision sensors, LiDAR, which provides depth map by measuring light returning time using infrared radiation (IR) directly to the object, can be complementary method. The study presents a LiDAR ((Light Detection and Ranging)-based end mill state monitoring system, which renders strengths of both vision and LiDAR detecting. This system uses point cloud and IR intensity data acquired by the LiDAR while object detection algorithm developed based on deep learning is engaged during the detection stage. The point cloud data is used to detect and determine the length of the endmill while the IR intensity is used to detect the wear present on the endmill. Convolutional neural network based You Only Look Once (YOLO) algorithm is selected as an object detection algorithm for real-time monitoring. Also, the quality of point cloud has been improved using data prep-processing method. Finally, it is verified that end mill state has been monitored with high accuracy at the actual machining environment.์ œ์กฐ ๋ถ„์•ผ์—์„œ ์Šค๋งˆํŠธ ํŒฉํ† ๋ฆฌ ์‹œ์Šคํ…œ์˜ ๋„์ž…์œผ๋กœ ์ธํ•ด ๊ฐ€๊ณต ๊ณผ์ •์˜ ๋ฌด์ธ ๋ชจ๋‹ˆํ„ฐ๋ง ์‹œ์Šคํ…œ์ด ํ•„์—ฐ์ ์œผ๋กœ ๋„์ž…๋˜๊ณ  ์žˆ๋‹ค. ๋ฌด์ธ ๋ชจ๋‹ˆํ„ฐ๋ง์˜ ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ• ์ค‘ ๋น„์ „ ๊ธฐ๋ฐ˜ ๋ชจ๋‹ˆํ„ฐ๋ง์ด ๊ฐ€์žฅ ๋งŽ์ด ์“ฐ์ด๊ณ  ์žˆ๋‹ค. ํ•ด๋‹น ๋น„์ „ ๊ธฐ๋ฐ˜ ์‹œ์Šคํ…œ์˜ ๊ฒฝ์šฐ ๋”ฅ ๋Ÿฌ๋‹์„ ํ†ตํ•ด ๊ฐœ๋ฐœ๋œ ๊ฐ์ง€ ๋ชจ๋ธ๊ณผ ํ†ตํ•ฉ๋œ ๋น„์ „ ์„ผ์„œ๋ฅผ ์‚ฌ์šฉํ•œ๋‹ค. ํ•˜์ง€๋งŒ ์ฃผ๋ณ€ ์กฐ๋ช…์ด๋‚˜ ๋ฐ˜์‚ฌ ๋ฌผ์งˆ๊ณผ ๊ฐ™์€ ๊ด‘ํ•™์  ์กฐ๊ฑด์— ํฌ๊ฒŒ ์˜ํ–ฅ์„ ๋ฐ›๋Š” ๋‹จ์ ์€ ๋ชจ๋‹ˆํ„ฐ๋ง ์ธก๋ฉด์—์„œ ์„ฑ๋Šฅ์— ์น˜๋ช…์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ธฐ์— ์ด๋ฅผ ๋ณด์™„ํ•˜๋Š” ๋Œ€์•ˆ์ด ํ•„์š”ํ•˜๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ๋น„์ „ ์„ผ์„œ ๋Œ€์‹  ์ ์™ธ์„ (IR)์„ ๋ฌผ์ฒด์— ์ง์ ‘ ์กฐ์‚ฌํ•˜์—ฌ ๋น›์˜ ์™•๋ณต ์‹œ๊ฐ„์„ ์ธก์ •ํ•˜์—ฌ ๊นŠ์ด ์ •๋ณด๋ฅผ ์ธก์ •ํ•˜๋Š” LiDAR๋ฅผ ์ด์šฉํ•˜์—ฌ ๋น„์ „ ์„ผ์„œ์˜ ํ•œ๊ณ„๋ฅผ ๋ณด์™„ํ•˜๋Š” ์‹œ์Šคํ…œ์„ ์†Œ๊ฐœํ•œ๋‹ค. ๋˜ํ•œ ๋น„์ „๊ณผ LiDAR ๊ฐ์ง€์˜ ์žฅ์ ์„ ๋ชจ๋‘ ์ œ๊ณตํ•˜๋Š” LiDAR ๊ธฐ๋ฐ˜ ์—”๋“œ๋ฐ€ ์ƒํƒœ ๋ชจ๋‹ˆํ„ฐ๋ง ์‹œ์Šคํ…œ์„ ์ œ์‹œํ•œ๋‹ค. ์ด ์‹œ์Šคํ…œ์€ LiDAR์—์„œ ํš๋“ํ•œ ์  ๊ตฌ๋ฆ„ ์ •๋ณด ๋ฐ IR ๊ฐ•๋„ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜๋ฉฐ, ๋”ฅ ๋Ÿฌ๋‹์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ฐœ๋ฐœ๋œ ๊ฐ์ฒด ๊ฐ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๊ฐ์ง€ ๋‹จ๊ณ„์™€ ์—”๋“œ๋ฐ€์˜ ๊ธธ์ด๋ฅผ ๊ฐ์ง€ํ•˜๊ณ  ์ธก์ •ํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ๋˜๋ฉฐ IR ๊ฐ•๋„๋Š” ์—”๋“œ๋ฐ€์— ์กด์žฌํ•˜๋Š” ๋งˆ๋ชจ ํ˜น์€ ํŒŒ์† ์ •๋ณด๋ฅผ ๊ฐ์ง€ํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ๋œ๋‹ค. ์‹ค์‹œ๊ฐ„ ๋ชจ๋‹ˆํ„ฐ๋ง์„ ์œ„ํ•œ ๊ฐ์ฒด ๊ฐ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ YOLO(You Only Look Once) ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ์ปจ๋ณผ๋ฃจ์…˜ ์‹ ๊ฒฝ๋ง์ด ์„ ํƒ๋˜์—ˆ์œผ๋ฉฐ ๋ฐ์ดํ„ฐ ์ „์ฒ˜๋ฆฌ๋ฅผ ํ†ตํ•ด ํฌ์ธํŠธ ํด๋ผ์šฐ๋“œ์˜ ํ’ˆ์งˆ์„ ํ–ฅ์ƒ์‹œ์ผฐ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์‹ค์ œ ๊ฐ€๊ณต ํ™˜๊ฒฝ์—์„œ ์—”๋“œ๋ฐ€ ์ƒํƒœ๋ฅผ ๋†’์€ ์ •ํ™•๋„๋กœ ๋ชจ๋‹ˆํ„ฐ๋งํ•˜๋Š” ๊ณผ์ •์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค.1. Introduction . 1 1.1 Tool monitoring in CNC machines 1 1.2 LiDAR and point cloud map. 5 1.3 IR intensity application 7 2. System modelling 9 2.1 End mill monitoring system overview 9 2.2 Hardware setup . 11 2.3 End mill failure monitoring 15 2.4 YOLO setup 18 3. Data processing . 19 3.1 Confidence score. 19 3.2 Noise removal 20 3.3 Point cloud accumulation. 22 3.4 IR intensity monitoring 26 4. Experiments and results . 28 4.1 Data gathering 28 4.2 Training 30 4.3 Results . 32 5. Conclusion . 39 Reference 41 Abstract (In Korean) 43์„

    ๋‹ค์ค‘ ๋ณดํ–‰์ž ์ธ์ง€๋ฅผ ์œ„ํ•œ ์„ผ์„œ ์œตํ•ฉ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ฐœ๋ฐœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€,2019. 8. ์ด๊ฒฝ์ˆ˜.ํ™˜๊ฒฝ ์„ผ์„œ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ณดํ–‰์ž๋ฅผ ์ธ์ง€ํ•˜๊ณ  ์ถ”์ ํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์•ˆ์ „ํ•œ ๋„์‹ฌ ์ž์œจ์ฃผํ–‰์„ ์œ„ํ•ด ๊ฐ€์žฅ ์ค‘์š”ํ•œ ๊ธฐ์ˆ  ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์ƒ์—…์šฉ ๋น„์ „ ์„ผ์„œ, ๋ผ์ด๋‹ค ์„ผ์„œ, ๊ทธ๋ฆฌ๊ณ  ๋””์ง€ํ„ธ ์ง€๋„ ์ •๋ณด๋ฅผ ์œตํ•ฉํ•ด ๋ณดํ–‰์ž๋ฅผ ์ถ”์ ํ•˜๋Š” ์ƒˆ๋กœ์šด ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์‹œํ•œ๋‹ค. ์ƒ์—…์šฉ ๋น„์ „ ์„ผ์„œ๋Š” ๋ณดํ–‰์ž๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ํƒ์ง€ํ•˜๋Š” ๋ฐ˜๋ฉด ๋ผ์ด๋‹ค ์„ผ์„œ๋Š” ๊ฑฐ๋ฆฌ๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ์ธก์ •ํ•œ๋‹ค. ๋ณธ ์‹œ์Šคํ…œ์€ ์ƒ์—…์šฉ ๋น„์ „ ์„ผ์„œ๋ฅผ ์ด์šฉํ•ด ๋ณดํ–‰์ž๋ฅผ ํƒ์ง€ํ•˜๋ฉฐ, ๋ผ์ด๋‹ค ์„ผ์„œ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ƒํƒœ ์ถ”์ • ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œ์ผฐ๋‹ค. ๋˜ํ•œ ๋””์ง€ํ„ธ ์ง€๋„๋ฅผ ์ด์šฉํ•ด ๋ผ์ด๋‹ค ์„ผ์„œ์˜ ๊ด€์‹ฌ ์˜์—ญ์„ ์„ค์ •ํ•˜์˜€๋‹ค. ํƒ์ง€ ๊ฒฐ๊ณผ๋Š” ์„œ์šธ๋Œ€ํ•™๊ต ์บ ํผ์Šค์—์„œ ์•ฝ 4600ํ”„๋ ˆ์ž„ ์ฃผํ–‰ ๋ฐ์ดํ„ฐ๋กœ, ์ถ”์ •์˜ ์ •ํ™•์„ฑ์€ ์ฃผํ–‰ ์‹คํ—˜์„ ํ†ตํ•ด ๊ฒ€์ฆํ•˜์—ฌ ๋ณต์žกํ•œ ๋„์‹ฌ ์ฃผํ–‰ ์ƒํ™ฉ์—์„œ๋„ ๋ณธ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ์œ ์šฉํ•จ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค.Pedestrian detection and tracking algorithm using environmental sensors is one of the most fundamental technology for safe urban autonomous driving. This paper presents a novel sensor fusion algorithm for multi pedestrian tracking using commercial vision sensor, LiDAR sensor, and digital HD map. The commercial vision sensor effectively detects pedestrian, whereas LiDAR sensor accurately measures a distance. Our system uses commercial vision sensor as detector and utilize LiDAR sensor to enhance estimation. In addition, digital HD map is utilized to properly define Region of Interest (ROI) of LiDAR sensor point cloud data. The detection performance is validated by about 4600 frames of SNU campus driving data and estimation accuracy is calculated through driving experiment. The proposed algorithm can be utilized for autonomous driving vehicles in various urban driving situationChapter 1 Introductionโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ1 1.1 Motivationโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ1 1.2 Previous Researchโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ3 1.3 Contributionsโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ4 1.4 Thesis Outline โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ5 Chapter 2 System Architecture โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ6 2.1 Vehicle Sensor Configurationโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ6 2.2 Fusion Architectureโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ8 Chapter 3 Vision Track Management & Filteringโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ9 3.1 Filtering for Target Trackingโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ10 3.1.1 Process Modelโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ10 3.1.2 Measurement modelโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ13 3.2 Data Associationโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ14 Chapter 4 Vision Guided LiDAR Track Management & Filteringโ€ฆโ€ฆ15 4.1 Cluster Validationโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ17 4.2 Filtering for Target Trackingโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ18 4.2.1 Process Modelโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ18 4.2.2 Measurement modelโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ18 4.3 Track Management Ruleโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ19 Chapter 5 Fusion Methodโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ20 5.1 Track Associationโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ20 5.2 State Fusionโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ21 Chapter 6 Experimental Resultโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ22 6.1 Track Initializing and Association Probability along Longitudinal distance โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ23 6.2 Detection & Association Rate in SNU Campus Driving Dataโ€ฆ25 6.3 Error of Statesโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ26 Chapter 7 Conclusion โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ28 Bibliographyโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ29 ๊ตญ๋ฌธ ์ดˆ๋กโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ32Maste

    A Study on the Process of Establishing Church Vision through the Vision Room: The Case of Incheon Shinkwang Church in Korea

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    The purpose of this study is to suggest a systematic process in establishing churchโ€™s vision by emphasizing the importance of having a clear vision in church growth and by communicating with the church members in the midst of a situation where stable ministry transition is required due to the replacement of the senior pastor now that 50 years has passed since the church was planted. For many years, the Korean church has experienced steady growth and revival. However, from the 21st century, the growth is slowing down and is stagnant. One of the reasons is the conflict between the former senior pastor and the following senior pastor which is rising to the surface as a problem among Korean churches. To resolve this conflict along with the chaos of transitions in churches and assemble church membersโ€™ hearts as one, there is a need for process of developmental vision establishment. This research focused on providing a systematic process of establishing visions that are effectively applicable to Korean churches through studying the importance of visions from the Bible, investigating on the books and case from Ansan Dongsan church which offers stable vision in transition of generations, and analyzing the refined process of vision established through researcherโ€™s one year ministry field at Incheon Shinkwang church. The researcher aims to introduce the legitimacy of establishing visions with the church members in Korean churches and to deliver the specific application method to the ministers who dream for church revival through the process of vision establishment

    The Formation of a Church Community United with a Local Community: The Case of Vision Village in Changwon, Kyungsangnamdo

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    The purpose of this thesis project is to build a platform for a united community of church and local residents. In the community, the church expands its boundaries so as to become embedded in the local resident community. Nowadays, Korean churches have lost the public character and the ability to fulfill their social roles and functions in the community. As a result, the churches have low reputation and influence in society, and they face the crisis of reductions in the number of churches and its members. For the sake of overcoming these problems of Korean churches, the thesis studies biblical ecclesiology and summarizes the internal and external problems that Korean churches are facing. A biblical and practical solution will be offered based on these studies. In order to provide a practical solution, the writer compiled geographical, historical, and social data on two villages, Dorae and Hyundo, located in Oesan-ro, Buk-myoun, Uichang-gu, Changwon-si, Kyoungsangnam-do, Korea, and administered a survey to collect data on the opinions of the residents. The practical solution, called Vision Village, will be summarized based on this research. Vision Village is being built as a united, local Christian community, in Kyoungsangnam-do, Korea and will be presented as a solution to the Korean churches

    ๋Œ€ํ•œ๋ฏผ๊ตญ 2050 LEDS์™€ 2030 INDC ์ˆ˜๋ฆฝ ๊ฑฐ๋ฒ„๋„Œ์Šค ๋น„๊ต๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ™˜๊ฒฝ๊ณ„ํšํ•™๊ณผ, 2021.8. ์œค์ˆœ์ง„.IPCC ์ง€๊ตฌ์˜จ๋‚œํ™” 1.5ยฐC ํŠน๋ณ„๋ณด๊ณ ์„œ์— ๋”ฐ๋ฅด๋ฉด ํŒŒ๋ฆฌํ˜‘์ • ๋ชฉํ‘œ ๋‹ฌ์„ฑ๊ณผ ๊ธฐํ›„์œ„๊ธฐ ๋Œ€์‘์„ ์œ„ํ•ด 2030๋…„๊นŒ์ง€ ์ด์‚ฐํ™”ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์„ 2010๋…„ ๋Œ€๋น„ 45% ๊ฐ์ถ•ํ•ด์•ผ ํ•˜๋ฉฐ, 2050๋…„๊นŒ์ง€ ํƒ„์†Œ์ค‘๋ฆฝ(Net-zero) ์ƒํƒœ๋ฅผ ์ด๋ฃจ์–ด์•ผ ํ•œ๋‹ค. ๋ชจ๋“  ๋‹น์‚ฌ๊ตญ์€ ์ „ ์ง€๊ตฌ ์˜จ๋„์ƒ์Šน์„ 1.5โ„ƒ๊นŒ์ง€ ์ œํ•œํ•˜๋Š” ํŒŒ๋ฆฌํ˜‘์ •์˜ ๋ชฉํ‘œ์— ๋”ฐ๋ผ 2050๋…„ ์žฅ๊ธฐ์ €ํƒ„์†Œ๋ฐœ์ „์ „๋žต(Long-term Low greenhouse gas Emission Development Strategies, ์ดํ•˜ LEDS)์„ ์ˆ˜๋ฆฝํ•˜์—ฌ 2020๋…„๊นŒ์ง€ ๊ตญ์ œ์‚ฌํšŒ์— ์ œ์ถœํ•ด์•ผ ํ–ˆ๋‹ค. ์ด์— ๋”ฐ๋ผ ๋Œ€ํ•œ๋ฏผ๊ตญ์€ ์ง€๋‚œ 2019๋…„ 3์›”, ํ•™๊ณ„ยท์‚ฐ์—…๊ณ„ยท์‹œ๋ฏผ์‚ฌํšŒยท์ฒญ๋…„ ๋“ฑ 7๊ฐœ์˜ ๋ถ„๊ณผ 69๋ช…์œผ๋กœ ๊ตฌ์„ฑ๋œ ๋ฏผ๊ฐ„ํ˜‘์˜์ฒด์ธ 2050 ์ €ํƒ„์†Œ ์‚ฌํšŒ ๋น„์ „ ํฌ๋Ÿผ์„ ๊ตฌ์„ฑํ•˜์—ฌ LEDS ์ˆ˜๋ฆฝ์„ ์œ„ํ•œ ๋…ผ์˜๋ฅผ ํ•ด๋‚˜๊ฐ”๋‹ค. ๋…ผ์˜๋ฅผ ํ†ตํ•ด ๋‚˜์˜จ ๊ฒ€ํ† ์•ˆ์„ ๋ฐ”ํƒ•์œผ๋กœ ์•ฝ 1๋…„ ๋™์•ˆ 2050 LEDS ์ˆ˜๋ฆฝ์— ๊ด€ํ•œ ์‚ฌํšŒ์  ๋…ผ์˜๊ณผ์ •์„ ๊ฑฐ์ณค๊ณ  2020๋…„ 12์›” ์ •๋ถ€๋Š” ๋Œ€ํ•œ๋ฏผ๊ตญ 2050 LEDS๋ฅผ UN์— ์ œ์ถœํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. 2050 LEDS ์ˆ˜๋ฆฝ๊ณผ์ •์—์„œ ์ฃผ๋ชฉํ•  ๋งŒํ•œ ์‚ฌ์‹ค์€ ๊ธฐ์กด์˜ ๊ธฐํ›„๋ณ€ํ™” ์ •์ฑ… ์ˆ˜๋ฆฝ๊ณผ์ • ๋•Œ์™€๋Š” ๋‹ฌ๋ฆฌ ์ฒญ๋…„์ด ์ตœ์ดˆ๋กœ ๋ถ„๊ณผ์œ„์›์œผ๋กœ ์ฐธ์—ฌํ–ˆ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” "๊ธฐ์กด์˜ 2030 ๊ตญ๊ฐ€๊ฒฐ์ •๊ธฐ์—ฌ(Intended Nationally Determined Contribution, ์ดํ•˜ INDC) ์ˆ˜๋ฆฝ๊ณผ์ •๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ 2050 LEDS ์ˆ˜๋ฆฝ์„ ์œ„ํ•œ ์˜์‚ฌ๊ฒฐ์ •๊ณผ์ •์—์„œ๋Š” ์–ด๋– ํ•œ ๋ณ€ํ™”๊ฐ€ ์žˆ์—ˆ๊ณ  ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๊ฐ€ ์–ด๋–ป๊ฒŒ 2050 LEDS ์ˆ˜๋ฆฝ๊ณผ์ •์—์„œ ์ฒญ๋…„์„ธ๋Œ€์˜ ์ฐธ์—ฌ๋ฅผ ๋งŒ๋“ค์—ˆ์œผ๋ฉฐ, ๊ฒฐ๊ตญ ์ฒญ๋…„์„ธ๋Œ€ ์ฐธ์—ฌ๊ฐ€ ์ฃผ๋Š” ์˜์˜๋Š” ๋ฌด์—‡์ธ๊ฐ€?โ€๋ผ๋Š” ์งˆ๋ฌธ์„ ๊ธฐ์ดˆ๋กœ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ ์งˆ๋ฌธ์— ๊ฐ€์žฅ ์ ์ ˆํžˆ ๋Œ€๋‹ตํ•  ์ˆ˜ ์žˆ๋Š” ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์œผ๋กœ์„œ ์‚ฌ๋ก€ ์—ฐ๊ตฌ๋ฅผ ์„ ํƒํ–ˆ์œผ๋ฉฐ ๊ณผ์ •์ถ”์ ๋ฒ•์„ ํ†ตํ•ด ์‚ฌ๋ก€์˜ ์ธ๊ณผ๊ด€๊ณ„์™€ ๊ทธ ๊ณผ์ •์„ ๊ทœ๋ช…ํ–ˆ๋‹ค. ๋˜ํ•œ, ๊ธฐํ›„๋ณ€ํ™” ๊ฑฐ๋ฒ„๋„Œ์Šค๋ฅผ ๊ฐ๊ด€์ ์œผ๋กœ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด์„œ ํ˜‘๋ ฅ์  ๊ฑฐ๋ฒ„๋„Œ์Šค ์ด๋ก ์„ ํ† ๋Œ€๋กœ ํ•˜์—ฌ ๊ฑฐ๋ฒ„๋„Œ์Šค ๋ถ„์„ ํ‹€์„ ๊ตฌ์„ฑํ•œ ํ›„ ์ด์— ๊ธฐ์ดˆํ•˜์—ฌ ์—ฐ๊ตฌ ๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ์„ค๋ช…ํ–ˆ๋‹ค. ์ž๋ฃŒ ์ˆ˜์ง‘ ๋ฐฉ๋ฒ•์œผ๋กœ๋Š” ๋ฌธํ—Œ์กฐ์‚ฌ, ์ฐธ์—ฌ๊ด€์ฐฐ, 21๋ช…์˜ ํ•ต์‹ฌ ์ดํ•ด๋‹น์‚ฌ์ž์™€์˜ ์‹ฌ์ธต๋ฉด์ ‘์„ ์ทจํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, 2030 INDC์— ๋น„ํ•ด 2050 LEDS ์ˆ˜๋ฆฝ๊ณผ์ •์—์„œ๋Š” ์ฐธ์—ฌ ์ดํ•ด๋‹น์‚ฌ์ž์˜ ๋ฒ”์œ„๊ฐ€ ํ™•์žฅ๋˜์—ˆ์œผ๋ฉฐ ์ „๋ฐ˜์ ์œผ๋กœ ์‚ฌํšŒ์  ๋…ผ์˜์˜ ์‹œ๊ฐ„์ด ๋Š˜์–ด๋‚ฌ๋‹ค. ๊ทธ ์ „์— ๋น„ํ•ด ์ผ๋ฐ˜ ์‹œ๋ฏผ์ด ์ฐธ์—ฌํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐํšŒ๊ฐ€ ํ™•๋Œ€๋˜์—ˆ๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ํ•˜์ง€๋งŒ ๋”์šฑ ํˆฌ๋ช…์„ฑ ์žˆ๋Š” ์ •๋ณด์˜ ๊ณต๊ฐœ์™€ ์‹œ๋ฏผ์˜ ์–ธ์–ด๋กœ ๊ตฌ์„ฑ๋œ ์ •๋ณด ์ „๋‹ฌ์ด ํ•„์š”ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ–ˆ๋‹ค. 2050 LEDS ์ˆ˜๋ฆฝ๊ณผ์ •์—์„œ ์ฒญ๋…„์„ธ๋Œ€๋Š” ํฌ๋Ÿผ ๋‚ด๋ถ€์™€ ํฌ๋Ÿผ ์™ธ๋ถ€์—์„œ์˜ ์ฐธ์—ฌ๋ฅผ ํ†ตํ•ด ํƒ„์†Œ์ค‘๋ฆฝ์— ๊ด€ํ•œ ์˜๊ฒฌ์„ ๊ฐœ์ง„ํ–ˆ๋‹ค. ์ฒญ๋…„๋“ค์€ ํ˜•ํ‰์„ฑ๊ณผ ์ˆ™์˜์„ฑ ์ธก๋ฉด์—์„œ LEDS ์ˆ˜๋ฆฝ์„ ์œ„ํ•œ ๊ฑฐ๋ฒ„๋„Œ์Šค์— ํ•œ๊ณ„๊ฐ€ ์žˆ์—ˆ์Œ์—๋„, ๋Œ€ํ•œ๋ฏผ๊ตญ์˜ 2050 ํƒ„์†Œ์ค‘๋ฆฝ ์„ ์–ธ์— ํฐ ์˜ํ–ฅ์„ ๋ฏธ์ณค๊ณ  ๋‹ค์–‘ํ•œ ์ดํ•ด๋‹น์‚ฌ์ž๋“ค์ด ์ฐธ์—ฌํ•  ์ˆ˜ ์žˆ๋Š” ๊ฑฐ๋ฒ„๋„Œ์Šค ํ๋ฆ„์„ ๋งŒ๋“ค์—ˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ฐœ๊ฒฌํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์˜์˜๊ฐ€ ์žˆ๋‹ค. ์ฒซ์งธ, ๊ฑฐ๋ฒ„๋„Œ์Šค ๋ถ„์„ ํ‹€์„ ํ™œ์šฉํ•ด ํŠน์ • ๊ธฐํ›„๋ณ€ํ™” ์˜์‚ฌ๊ฒฐ์ • ์‚ฌ๋ก€๋ฅผ ๋ถ„์„ํ•˜๋ฉฐ ํ–ฅํ›„ ๊ธฐํ›„๋ณ€ํ™” ์ •์ฑ…์—์„œ ๊ฐ€์žฅ ์ค‘์š”ํ•œ ์‹œ์ž‘์ ์ด ๋  2050 LEDS, ์ฆ‰ ํƒ„์†Œ์ค‘๋ฆฝ ์ „๋žต ์ˆ˜๋ฆฝ์˜ ๊ฑฐ๋ฒ„๋„Œ์Šค๋ฅผ ํ‰๊ฐ€ํ–ˆ๋‹ค๋Š” ์ ์ด๋‹ค. ๋‘˜์งธ, ๊ฑฐ๋ฒ„๋„Œ์Šค ์ด๋ก ๊ณผ ๊ณผ์ •์ถ”์ ๋ฒ•์„ ์ ์šฉํ•ด ์‚ฌ๋ก€ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์„ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•œ ์ด๋ก ์  ์ ‘๊ทผ์„ ์ทจํ–ˆ๋‹ค๋Š” ์ ์ด๋‹ค. ์…‹์งธ, ์ฒญ๋…„์„ธ๋Œ€์˜ ์ฐธ์—ฌ๊ฐ€ ๊ตญ๋‚ด ์ •๋ถ€ ๊ธฐํ›„๋ณ€ํ™” ์ •์ฑ… ์ˆ˜๋ฆฝ๊ณผ์ •์—์„œ ์˜ํ–ฅ์„ ์ค„ ์ˆ˜ ์žˆ๋Š” ์ค‘์š”ํ•œ ์š”์†Œ๋ผ๋Š” ์ ์— ์ฃผ๋ชฉํ•˜๊ณ  ์–ด๋– ํ•œ ๋ฐฉ์‹์œผ๋กœ ๊ทธ๋Ÿฐ ํšจ๊ณผ๊ฐ€ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š”์ง€๋ฅผ ์‚ฌ๋ก€๋ฅผ ํ†ตํ•ด ์ถ”์ ํ–ˆ๋‹ค๋Š” ์ ์ด๋‹ค. ํ–ฅํ›„ ๊ธฐํ›„๋ณ€ํ™” ์ •์ฑ…์—์„œ๋Š” ์ฒญ๋…„๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋‹ค์–‘ํ•œ ์ดํ•ด๋‹น์‚ฌ์ž๋“ค์ด ์ฐธ์—ฌํ•  ์ˆ˜ ์žˆ๋Š” ๊ฑฐ๋ฒ„๋„Œ์Šค์™€ ์ด๋ฅผ ์œ„ํ•œ ์‹œ์Šคํ…œ ๊ตฌ์ถ•์ด ์š”๊ตฌ๋œ๋‹ค. ๋˜ํ•œ, ๊ธฐํ›„๋ณ€ํ™” ์ •์ฑ… ์ˆ˜๋ฆฝ ๋ฐ ์‚ฌํšŒ์  ๋…ผ์˜๊ณผ์ •์—์„œ ํˆฌ๋ช…์„ฑ ์žˆ๋Š” ์ •๋ณด ์ „๋‹ฌ์˜ ์ค‘์š”์„ฑ์ด ํ•„์š”ํ•˜๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ์™€ ์˜์˜๋ฅผ ํ†ตํ•ด ํ–ฅํ›„ ๊ธฐํ›„๋ณ€ํ™” ์ •์ฑ… ์ˆ˜๋ฆฝ๊ณผ์ •์—์„œ ํšจ๊ณผ์ ์ธ ๊ฑฐ๋ฒ„๋„Œ์Šค ์ฒด๊ณ„๋ฅผ ๊ตฌ์ถ•ํ•˜๊ณ  ์šด์˜ํ•จ์œผ๋กœ์จ ์ผ๋ฐ˜์‹œ๋ฏผ์˜ ๊ธฐํ›„๋ณ€ํ™” ์ •์ฑ… ์ˆ˜์šฉ์„ฑ์„ ๋†’์ด๋Š” ๋ฐ ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค.The IPCC Special Report on Global Warming of 1.5ยฐC claims that global human-caused carbon dioxide (CO2) emissions must decline by 45% from 2010 levels by 2030 in order to reach carbon neutrality (Net Zero) objective in 2050. Accordingly, all concerned countries have to formulate their Long-Term Low greenhouse gas Emission Development Strategies (LEDS) under the Paris Agreement and communicate them to the United Nations Framework Convention on Climate Change (UNFCCC) until 2020. In March 2019, the South Korean government established the 2050 Low-carbon Vision Forum, constituted of 69 members distributed in 7 branches including academia, industry, civil society, and youth. After more than one year of social discussion, and based on the draft establishing the foundations of South Koreaโ€™s 2050 LEDS, the government finally submitted its final strategy to the UNFCCC in December 2020. It is particularly noteworthy that, unlike previous climate change policy establishment processes in South Korea, the formation of the 2050 LEDS policy included the young generation. This thesis examines the differences between the 2030 Intended Nationally Determined Contribution (INDC) and the 2050 LEDS decision-making processes in the South Korean context. It particularly investigates the role of the governance changes that contributed to the participation of the youth in the debate and the implications of this participation for the 2050 LEDS decision-making process. To address these questions and identify potential causal relationships, a case study approach using qualitative research and process tracing methodologies is adopted. A particular focus is made on climate governance, and a new framework based on the collaborative governance theory is applied. Data collection methods include surveys of literature, participatory observations, and in-depth interviews with 21 key stakeholders. The analysis results show that the scope of the participating stakeholders and the time for social discussion were larger in the 2050 LEDS establishing process than in the 2030 INDC establishing process. In other words, there have been more opportunities for ordinary citizens to participate in climate change-related debates during the building of the 2050 LEDS. Nevertheless, it is essential to keep information on climate change transparent, open, and understandable for all citizens. In the 2050 LEDS establishing process, the youth generation expressed their opinions on carbon neutrality through active participation inside and outside the forum. Despite some certain limitations of the governance system for the decision-making process in terms of equity and deliberation, the youth has had a significant influence in establishing South Koreaโ€™s 2050 carbon neutrality declaration and contributed to creating a harmonious governance structure for various stakeholders to participate in. This study has several implications. First, the governance analysis framework was used to analyze specific climate change-related decision-making cases and to evaluate the governance of 2050 LEDS โ€“ i.e., carbon-neutral strategy, the most important starting point for future climate change policies. Second, a theoretical approach was adopted to apply the qualitative case study methodology through governance theory and process tracing methods. Third, it has been verified that the participation of the youth generation has a central role in influencing the national government's climate change policy. It has also been observed how this influence has been demonstrated in different cases. Future climate change policies require the establishment of governance systems where not only young people but also various stakeholders can participate. It also identified the importance of guaranteeing transparent information in the process of climate change policymaking and social discussion. This study highlights the necessity for effective governance systems to be built and operated in the process of decision-making climate change policies in the future, with the ultimate goal of contributing to increasing the acceptability of climate change policies for citizens.โ… . ์„œ๋ก  1 1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1 2. ์—ฐ๊ตฌ ๋ชฉ์  5 โ…ก. ๋…ผ์˜ ๋ฐฐ๊ฒฝ๊ณผ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  7 1. ๋…ผ์˜ ๋ฐฐ๊ฒฝ 7 1) ๊ธฐํ›„๋ณ€ํ™” ํ˜‘์ƒ ๋ฐฐ๊ฒฝ๊ณผ ํŒŒ๋ฆฌํ˜‘์ • ์ฑ„ํƒ 7 2) INDC์™€ LEDS 10 3) ์ฒญ๋…„์„ธ๋Œ€์™€ ๊ธฐํ›„๋ณ€ํ™” 15 2. ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  23 1) ๊ฑฐ๋ฒ„๋„Œ์Šค ์ด๋ก  23 2) ๊ธฐํ›„๋ณ€ํ™”์™€ ๊ฑฐ๋ฒ„๋„Œ์Šค 24 3) ์„ ํ–‰์—ฐ๊ตฌ์™€ ์—ฐ๊ตฌ ๋ถ„์„ ํ‹€ 28 โ…ข. ์—ฐ๊ตฌ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 36 1. ์—ฐ๊ตฌ ๋ฒ”์œ„ 36 2. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 37 3. ์ž๋ฃŒ ์ˆ˜์ง‘ ๋ฐฉ๋ฒ• 39 โ…ฃ. ๋ถ„์„ ๊ฒฐ๊ณผ 47 1. 2050 LEDS์™€ 2030 INDC ์ˆ˜๋ฆฝ ๊ฑฐ๋ฒ„๋„Œ์Šค ๋น„๊ต 47 1) 2030 INDC์™€ 2050 LEDS ๊ตญ์ œ๋ฒ•์ƒ ํŠน์ง• 47 2) 2030 INDC ์ˆ˜๋ฆฝ๊ณผ์ • 51 3) 2050 LEDS ์ˆ˜๋ฆฝ๊ณผ์ • 59 4) 2050 LEDS์™€ 2030 INDC ๊ฑฐ๋ฒ„๋„Œ์Šค ๋น„๊ต ๋ถ„์„ 86 2. ๊ธฐํ›„๋ณ€ํ™” ๊ฑฐ๋ฒ„๋„Œ์Šค์—์„œ์˜ ์ฒญ๋…„์„ธ๋Œ€ 92 1) 2050 LEDS ์˜์‚ฌ๊ฒฐ์ • ๊ณผ์ •์—์„œ ์ฒญ๋…„ ์ฐธ์—ฌ ์˜์˜์™€ ๊ณผ์ œ 92 2) ๊ธฐํ›„๋ณ€ํ™” ๊ฑฐ๋ฒ„๋„Œ์Šค์™€ ์ฒญ๋…„์„ธ๋Œ€ ์ฐธ์—ฌ 98 โ…ค. ๊ฒฐ๋ก  101 1. ์—ฐ๊ตฌ ์š”์•ฝ 101 2. ์—ฐ๊ตฌ ์˜์˜์™€ ์‹œ์‚ฌ์  103 3. ํ•œ๊ณ„ ๋ฐ ์ถ”ํ›„ ์—ฐ๊ตฌ๊ณผ์ œ 106 ์ฐธ๊ณ ๋ฌธํ—Œ 109 ๋ถ€๋ก 115 Abstract 122์„

    Future development strategies for KODISA journals: overview of 2016 and strategic plans for the future

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    Purpose โ€“ With the rise of the fourth industrial revolution, it has converged with the existing industrial revolution to give shape to increased accessibility of knowledge and information. As a result, it has become easier for scholars to actively pursue and compile research in various fields. This current study aims to focus and assess the current standing of KODISA: the Journal of Distribution Science (JDS), International Journal of Industrial Distribution & Business (IJIDB), the East Asian Journal of Business Management (EAJBM), the Journal of Asian Finance, Economics and Business (JAFEB) in a rapidly evolving era. Novel strategies for creating the future vision of KODISA 2020 will also be examined. Research design, data, and methodology โ€“ The current research will analyze published journals of KODISA in order to offer a vision for the KODISA 2020 future. In part 1, this paper will observe the current address of the KODISA journal and its overview of past achievements. Next, part 2 will discuss the activities that will be needed for journals of KODISA, JDS, IJIDB, EAJBM, JAFEB to branch out internationally and significant journals will be statistically analyzed in part 3. The last part 4 will offer strategies for the continued growth of KODISA and visions for KODISA 2020. Results โ€“ Among the KODISA publications, IJIDB was second, JDS was 23rd (in economic publications of 54 journals), and EAJBM was 22nd (out of 79 publications in management field journals). This shows the high quality of the KODISA publication journals. According to 2016 publication analysis, JDS, IJIDB, etc. each had 157 publications, 15 publications, 16 publications, and 28 publications. In the case of JDS, it showed an increase of 14% compared to last year. Additionally, JAFEB showed a significant increase of 68%. This shows that compared to other journals, it had a higher rate of paper submission. IJIDB and EAJBM did not show any significant increases. In JDS, it showed many studies related to the distribution, management of distribution, and consumer behavior. In order to increase the status of the KODISA journal to a SCI status, many more international conferences will open to increase its international recognition levels. Second, the systematic functions of the journal will be developed further to increase its stability. Third, future graduate schools will open to foster future potential leaders in this field and build a platform for innovators and leaders. Conclusions โ€“ In KODISA, JDS was first published in 1999, and has been registered in SCOPUS February 2017. Other sister publications within the KODISA are preparing for SCOPUS registration as well. KODISA journals will prepare to be an innovative journal for 2020 and the future beyond
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