30 research outputs found

    ์ƒ๋ณ€ํƒœ๋ฅผ ๊ณ ๋ คํ•œ ๋ณ€ํƒœ์œ ๊ธฐ์†Œ์„ฑ ํ˜„์ƒ ํ•ด์„

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์žฌ๋ฃŒ๊ณตํ•™๋ถ€,2002.Maste

    ์ˆ˜์ •๋œ ํ™œ์„ฑ ์Šฌ๋Ÿฌ์ง€ ๋ชจ๋ธ์— ์˜ํ•œ ๋ฏธ์ƒ๋ฌผ ์ฒด๋‚ด์ €์žฅ๊ณผ ์œ ๊ธฐ๋ฌผ ์ด์šฉ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2013. 2. ์ •ํƒœํ•™.๊ตญ๋‚ด ํ•˜์ˆ˜์ฒ˜๋ฆฌ์žฅ์˜ ๋ฐฉ๋ฅ˜์ˆ˜ ์ˆ˜์งˆ๊ธฐ์ค€์ด ๊ฐ•ํ™”๋˜๋ฉด์„œ, ํ•˜์ˆ˜์˜ ์งˆ์†Œ ๋ฐ ์ธ ๋“ฑ์˜ ์˜์–‘์—ผ๋ฅ˜์ œ๊ฑฐ๋ฅผ ์œ„ํ•œ ๊ณ ๋„์ฒ˜๋ฆฌ๋กœ์˜ ์ „ํ™˜์ด ์š”๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ์ƒ๋ฌผํ•™์  ์งˆ์†Œ ์ œ๊ฑฐ๋Š” ํƒˆ์งˆ๋ฐ˜์‘์ด ์†๋„ ๊ฒฐ์ • ๋‹จ๊ณ„๋กœ, ๊ทธ ๊ณผ์ •์—์„œ ์ฆ‰์‹œ ์ด์šฉ ๊ฐ€๋Šฅํ•œ ๊ธฐ์งˆ์ธ RBCOD๋ฅผ ์š”๊ตฌํ•œ๋‹ค. RBCOD์˜ ๋ถ€์กฑ์€ ์˜์–‘์—ผ๋ฅ˜ ์ œ๊ฑฐ์œจ์ด ๋‚ฎ์•„์ง€๋Š” ๊ฒฐ๊ณผ๋ฅผ ๊ฐ€์ ธ์˜ค๊ธฐ ๋•Œ๋ฌธ์—, ์™ธ๋ถ€ ํƒ„์†Œ์›์œผ๋กœ์„œ ๋ฉ”ํƒ„์˜ฌ, ์œ ๊ธฐ์‚ฐ ๋“ฑ์„ ๊ณต๊ธ‰ํ•˜๋Š” ๋“ฑ์˜ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜๊ธฐ๋„ ํ–ˆ๋‹ค. ํ•˜์ง€๋งŒ ์šด์˜๋น„์˜ ์ฆ๊ฐ€ ํ˜น์€ ์™ธ๋ถ€ ํƒ„์†Œ์›์ด ๋‹ค์‹œ ์˜ค์—ผ์›์œผ๋กœ ์ž‘์šฉํ•˜๋Š” ๋“ฑ์˜ ๋ถ€์ž‘์šฉ์„ ๊ฐ€์ ธ์˜ค๊ธฐ ๋•Œ๋ฌธ์— ๋Œ€์•ˆ์ด ํ•„์š”ํ•œ ์ƒํ™ฉ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ƒํ•˜์ˆ˜ ์œ ๊ธฐ๋ฌผ์˜ ๋Œ€๋ถ€๋ถ„์„ ์ฐจ์ง€ํ•˜๋Š” SBCOD๊ฐ€ ํ˜๊ธฐ, ํ˜ธ๊ธฐ, ๋ฌด์‚ฐ์†Œ์ƒํƒœ์˜ ์กฐ๊ฑด์—์„œ ๋ณ€ํ™”ํ•˜๋Š” ์–‘์ƒ์„ ์ถ”์ , ์กฐ์‚ฌํ•˜์—ฌ ํ•˜์ˆ˜ ์œ ๊ธฐ๋ฌผ์˜ ์ ์ ˆํ•œ ๊ด€๋ฆฌ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด SBCOD๋ฅผ ๋Œ€ํ‘œํ•˜๋Š” ๊ธฐ์งˆ๋กœ์„œ ์•ก์ƒ ์ „๋ถ„์ด ํ™œ์šฉ๋˜์—ˆ๊ณ , ํ™œ์„ฑ์Šฌ๋Ÿฌ์ง€๋Š” ์†Œ๊ทœ๋ชจ ์—ฐ์† ํšŒ๋ถ„์‹ ๋ฐ˜์‘์กฐ(SBR)์—์„œ ์ถฉ๋ถ„ํžˆ ์ „๋ถ„์— ์ˆœ์‘์‹œํ‚จ ๊ฒƒ์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๋ณ€ํ™” ์–‘์ƒ์„ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•˜์—ฌ, ์ „์ž์ˆ˜์šฉ์ฒด์™€ ๋ฏธ์ƒ๋ฌผ๋Ÿ‰์„ ๋‹ค๋ฅด๊ฒŒ ํ•˜์—ฌ ํšŒ๋ถ„์‹ ์‹คํ—˜์„ ์‹ค์‹œํ•˜์˜€๊ณ , ๋ฌด์‚ฐ์†Œ ์กฐ๊ฑด์˜ ๊ฒฝ์šฐ ์ „์ž์ˆ˜์šฉ์ฒด๋กœ nitrate๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๊ฐ์†Œ ๊ฒฝํ–ฅ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ด๋ฅผ ๋ชจ๋ธ์˜ ์ ์šฉ์„ ์œ„ํ•ด ASM์„ ์‚ฌ์šฉ ํ•˜์˜€๋‹ค. ์ „๋ถ„์˜ ๊ฐ€์ˆ˜๋ถ„ํ•ด๋Š” ์ „์ž ์ˆ˜์šฉ์ฒด์— ์˜ํ–ฅ์„ ๋ฐ›์ง€ ์•Š๊ณ  ํ˜ธ๊ธฐ์„ฑ, ๊ฐ„ํ— ํญ๊ธฐ ๊ทธ๋ฆฌ๊ณ  ํ˜๊ธฐ์„ฑ ์กฐ๊ฑด์˜ ๋ฐ˜์‘์—์„œ ๋ชจ๋‘ ๋™์ผํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ฏธ์ƒ๋ฌผ๋Ÿ‰์— ๋”ฐ๋ฅธ ์‹คํ—˜์—์„œ๋Š” ๋ฏธ์ƒ๋ฌผ๋Ÿ‰์ด ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ๊ฐ€์ˆ˜๋ถ„ํ•ด์œจ์ด ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ธฐ์กด์˜ ASM์—์„œ ์„ธํฌ๋‚ด ์ €์žฅํ˜„์ƒ์— ์ „์ž์ˆ˜์šฉ์ฒด๊ฐ€ ํ•„์š”ํ•˜๋‹ค๋Š” ๊ฒƒ๊ณผ๋Š” ๋‹ค๋ฅด๊ฒŒ, ์ „์ž์ˆ˜์šฉ์ฒด๊ฐ€ ์ „๋ฌดํ•œ ํ˜๊ธฐ์ƒํƒœ์—์„œ๋„ ์œ ๊ธฐ๋ฌผ์˜ ๋ฏธ์ƒ๋ฌผ์„ธํฌ ๋‚ด๋ถ€์ €์žฅ์ด ์ง„ํ–‰๋˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์„ธํฌ ๋‚ด ์ €์žฅ๋Ÿ‰์€ ๋ฏธ์ƒ๋ฌผ๋Ÿ‰์— ๋น„๋ก€ํ•˜๋Š” ๊ฒƒ์„ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ์ €์žฅ๋œ ์œ ๊ธฐ๋ฌผ์€ ์ „์ž์ˆ˜์šฉ์ฒด๊ฐ€ ์กด์žฌํ•  ๊ฒฝ์šฐ ์„ธํฌ ์ฆ์‹์— ์ด์šฉ๋˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ฆ‰ ์ „์ž์ˆ˜์šฉ์ฒด๋กœ ์งˆ์‚ฐ์„ฑ์งˆ์†Œ๋งŒ ์กด์žฌํ•˜๋Š” ๋ฌด์‚ฐ์†Œ์ƒํƒœ์—์„œ์˜ ์„ฑ์žฅ์— ํ•„์š”ํ•œ ์œ ๊ธฐ๋ฌผ์ด ์•ก์ƒ์˜ RBCOD ๋ฐ ์ฒด๋‚ด์ €์žฅ์œ ๊ธฐ๋ฌผ๋กœ๋ถ€ํ„ฐ ๊ณต๊ธ‰๋˜๋Š” ๋‘ ๊ฐ€์ง€์˜ ๊ฒฝ๋กœ๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ํ˜„์ƒ์„ ๋ฐ˜์˜ํ•˜๊ธฐ ์œ„ํ•ด์„œ ASM3 ๋ชจ๋ธ์— switching function์„ ๋„์ž…, ์ˆ˜์ •ํ•˜์—ฌ ์ ์šฉํ•œ ๊ฒฐ๊ณผ ์ „๋ถ„๋งŒ์œผ๋กœ ๊ตฌ์„ฑ๋œ ์œ ๊ธฐ๋ฌผ์ด์šฉ ์‹ค์ธก์ž๋ฃŒ์˜ ์˜๋ฏธ ์žˆ๋Š” ์„ค๋ช…์ด ๊ฐ€๋Šฅํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ด์™€ ๊ฐ™์€ ๊ณผ์ •์„ ํ†ตํ•ด ํ•œ์ •๋œ ๋ฒ”์œ„์ง€๋งŒ ๋ชจ๋ธ์˜ ๊ฐœ์„ ์ด ๊ฐ€๋Šฅํ•จ์„ ํ™•์ธ ํ•˜์˜€๋‹ค.1. ์„œ๋ก  1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1.2 ์—ฐ๊ตฌ์˜ ๋ชฉ์  2. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 2.1 ํ•˜์ˆ˜์˜ ์œ ๊ธฐ๋ฌผ ํŠน์„ฑ ๋ถ„์„ 2.2 ๊ฐ€์ˆ˜๋ถ„ํ•ด 2.3 ์ „๋ถ„์„ ์ด์šฉํ•œ ์„ ํ–‰์—ฐ๊ตฌ 2.3.1 ์ „๋ถ„์˜ ํŠน์„ฑ 2.3.2 ์ „๋ถ„์„ ์ด์šฉํ•œ ์‹คํ—˜ 2.4 ํ™œ์„ฑ์Šฌ๋Ÿฌ์ง€ ๋ชจ๋ธ (ASM) 3. ์‹คํ—˜ ์žฌ๋ฃŒ ๋ฐ ๋ฐฉ๋ฒ• 3.1 ์ „๋ถ„์˜ ์„ฑ์ƒ๊ณผ ๋ถ„์„๋ฐฉ๋ฒ• 3.2 ์Šฌ๋Ÿฌ์ง€ ์ˆœ์‘์„ ์œ„ํ•œ SBR์šด์ „ 3.3 ํšŒ๋ถ„์‹ ์‹คํ—˜ 3.4 Swiching function 4. ์‹คํ—˜ ๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ 4.1 ์Šฌ๋Ÿฌ์ง€์˜ ์ˆœ์‘ 4.2 ์šฉ์กด ์ƒํƒœ์˜ ์„ ๋ถ„ ์‹คํ—˜ 4.2.1 ํ˜ธ๊ธฐ์„ฑ๊ณผ ํ˜๊ธฐ์„ฑ์˜ ๋น„๊ต 4.2.2 ๋ฏธ์ƒ๋ฌผ๋Ÿ‰์— ๋”ฐ๋ฅธ ๋น„๊ต 4.2.3 NUR test 4.3 ASM์˜ ๊ฐœ์„  4.3.1 ๊ฐœ์„ ์˜ ๊ทผ๊ฑฐ 4.3.2 ๋ชจ๋ธ์˜ ํ™”ํ•™์–‘๋ก  ํ–‰๋ ฌ๊ณผ ๋ฐ˜์‘์†๋„์‹ 4.3.3 ๋งค๊ฐœ๋ณ€์ˆ˜์˜ ๋ณด์ •๊ณผ ์ ์šฉ 4.4 ๋ชจ๋ธ์˜ ์ ์šฉ 4.5 ๋ชจ๋ธ์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ 4.5.1 Feast ๊ธฐ๊ฐ„ 4.5.2 Famine ๊ธฐ๊ฐ„ 4.6 ๋ถ€๋ก 5. ๊ฒฐ ๋ก  6. ์ฐธ๊ณ  ๋ฌธํ—ŒMaste

    A Study on Vision-based Surface Inspection Method for Highly Noised Steel Production

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    DoctorAn automated vision-based surface inspection for highly noised steel production is proposed in this thesis.The ``highly noised'' means the surface of the steel product is covered with a lot of scales, which make the inspection difficult task. This thesis focuses the inspection method for steel wire rod. The optimal illumination structure is proposed to detect defects with depth by using double dark fields.The inspection methods for the most critical defects, roller-mark (periodic defect) and scratch (non-periodic defect) of wire rod are presented. The two kinds of defects are caused by defective rollers and occurs in a large quantity. It is essential for operators to receive early warnings of such defects and take appropriate action to avoid a large quantity of defective products.This thesis presents an efficient and real-time method for detecting defects in continuous steel wire rods. The detection algorithm is based on the undecimated discrete wavelet transform (UDWT). To overcome the noisy background of surface image, the algorithm exploits the translation invariant property of the UDWT, which improve the signal-to-noise ratio (SNR) of the image. The horizontal and vertical detail coefficients are binarized by double thresholding technique followed by the appropriate size filtering. The final defect candidates are obtained by morphological reconstructing operation.To identify periodic defects, the frequency spectrum of defect positions is analyzed. The spatial periodicity of detected defects is estimated from the inversely related periodic information of frequency spectrum.To get 1D frequency spectrum of defects, the algorithm converts 2D positional information of defects into 1D defect histogram. A robust algorithm to detect repeating impulse-like points in the spectrum uses a median and local maximum filters. The algorithm was proved to operate even when the false positive defects are over 15 times more than true positive defects, or 70 % of true positive defects are missed.To classify non-periodic defects, the classification using support vector machine (SVM) is proposed. The 14 features are extracted from binary and gray information of detected objects. The best model SVM classifier with radial basis function (RBF) kernel is obtained by 5-fold cross validation. The grid search for finding the two parameters for RBF kernel is performed. The support vectors are generated from train data and the final confusion matrix is obtained from test data, which show 85 % and 94 % of true positive and negative rate, respectively.The inspection system using the proposed method was applied to a real production line and showed the effectiveness of the method

    Buffer Policy based on High-capacity Hybrid Memories for Latency Reduction of Read/Write Operations in High-performance SSD Systems

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    Recently, an SSD with hybrid buffer memories is actively researching to reduce the overall latency in server computing systems. However, existing hybrid buffer policies caused many swapping operations in pages because it did not consider the overall latency such as read/write operations of flash chips in the SSD. This paper proposes the clock with hybrid buffer memories (CLOCK-HBM) for a new hybrid buffer policy in the SSD with server computing systems. The CLOCK-HBM constructs new policies based on unique characteristics in both DRAM buffer and NVMs buffer for reducing the number of swapping operations in the SSD. In experimental results, the CLOCK-HBM reduced the number of swapping operations in the SSD by 43.5% on average, compared with LRU, CLOCK, and CLOCK-DNV.2
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