• 스마트미디어저널
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• 제5권3호
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• pp.30-34
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• 2016

Nowadays, there are a lot of Korean documents, which often need to be identified in one of printed or handwritten text. Early methods for the identification use structural features, which can be simple and easy to apply to text of a specific font, but its performance depends on the font type and characteristics of the text. Recently, the bag-of-words model has been used for the identification, which can be invariant to changes in font size, distortions or modifications to the text. The method based on bag-of-words model includes three steps: word segmentation using connected component grouping, feature extraction, and finally classification using SVM(Support Vector Machine). In this paper, bag-of-words model based method is proposed using SURF(Speeded Up Robust Feature) for the identification of machine printed and handwritten text in Korean documents. The experiment shows that the proposed method outperforms methods based on structural features.

• 한국정밀공학회지
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• 제31권6호
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• pp.491-495
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• 2014

We developed a compact high-precision slot-die coating machine for thin-film deposition on a flexible substrate. For smooth and precise coating, air-bearing and linear motor system were employed to minimize velocity ripple. The gap control mechanism is specially designed to have repeatability of gap between nozzle and substrate under 1 ${\mu}m$. Due to extremely precise gap control, the machine can coat thin-films down to 50 nm with $200mm{\times}100mm$ size. A thin film of Ag nano-particle ink is coated for demonstration.

• 한국기계가공학회지
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• 제21권2호
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• pp.137-143
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• 2022

Composite materials, being light-weight and of high mechanical strength, are increasingly used in various industries such as the aerospace, automobile, sporting-goods manufacturing, and ship-building industries. Recently, manufacturing of composite materials using 3D printers has increased. 3D-printed composite materials are made in free-form and adapted for end-use by adjusting the fiber content and orientation. However, research on the machining of 3D printed composite materials is limited. The aim of this study is to develop a machine learning method to select machining conditions for machining of 3D-printed composite materials. The composite material was composed of Onyx and carbon fibers and stacked sequentially. The experiments were performed using the following machining conditions: spindle speed, feed rate, depth of cut, and machining direction. Cutting forces of the different machining conditions were measured by milling the composite materials. PCA, a method of machine learning, was developed to select the machining conditions and will be used in subsequent experiments under various machining conditions.

• 반도체디스플레이기술학회지
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• 제18권3호
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• pp.25-30
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• 2019

In this paper, we have analyzed manufacturing problems of the scaffold with pores using FDM 3D printer and PLGA. We suggested the solutions using 3SC practical TRIZ. We selected the final solution used machine learning. We reduced number of experiments using most influential factor after analysis print factors. We printed the scaffold and measured pore size. We created the regression model using python tensorflow. The print condition data of measured pore size was used as training data. We predicted the pore size of printed condition using regression model. We printed the scaffold using the predicted the print condition data. We quantitatively compare the predicted scaffold pore size data and the measured scaffold pore size data. We got satisfactory result.

• 한국정보과학회논문지:소프트웨어및응용
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• 제30권10호
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• pp.904-911
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• 2003

본 논문에서는 우편봉투 상에 기입된 인쇄체 한글 주소와 필기체 한글 주소를 효과적으로 구분할 수 있는 방법을 제안한다. 문자인식 모듈을 포함하는 각종 응용 시스템에서 입력 영상이 인쇄체인지 필기체인지 구분하는 것은 매우 중요하다. 이는 대부분의 경우 인쇄체 영상과 필기체 영상이 갖는 특징이 상이하여, 각 영상에서의 문자 및 문자열 분리 방법, 문자 인식 방법 둥이 매우 상이하게 개발되기 때문이다. 본 논문에서 제안한 구분 방법은 연결요소 추출 및 병합, 특징 추출, 영상 구분 순으로 수행된다. 연결요소 추출 및 병합 단계에서는 입력영상으로부터 연결요소를 추출한 후 일부 연결요소들에 대하여 병합을 시도하며, 특징 추출 단계에서는 병합결과 얻어진 연결요소들의 그룹들로부터 폭과 위치에 관련된 특징을 추출하고, 영상 구분 단계에서는 추출한 특징을 입력으로 제공받는 다충퍼셉트론을 사용하여 구분을 시도한다. 제안한 방법의 우수성을 증명하기 위해 실제 우편물로부터 추출된 3,147개의 한글 주소 영상을 사용하여 실험한 결과, 98.85%의 구분률을 보여주었다.

• 전기학회논문지
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• 제61권3호
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• pp.472-478
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• 2012

The inspection machine in PCB (printed circuit board) assembly line checks assembly errors by inspecting the images inside of the component inspection region. The component inspection region consists of region of component package and region of soldering. It is necessary to extract the regions automatically for auto-teaching system of the inspection machine. We propose an image segmentation method to extract the component inspection regions automatically from images of PCB. The acquired image is transformed to HSI color model, and then segmented by several regions by clustering method. We develop a modified K-means algorithm to increase the accuracy of extraction. The heuristics generating the initial clusters and merging the final clusters are newly proposed. The vertical and horizontal projection is also developed to distinguish the region of component package and region of soldering. The experimental results are presented to verify the usefulness of the proposed method.

• 한국산학기술학회논문지
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• 제10권3호
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• pp.554-559
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• 2009

본 논문에서는 인쇄체 숫자를 인식하기 위해 가중 원형 정합(weighted template matching) 방법을 제안한다. 원형 정합은 입력 영상 전체를 하나의 전역적인 특징으로 처리하는 데 반해, 제안된 가중 원형 정합은 패턴의 특징이 나타나는 국부적인 영역에 해밍 거리(Hamming distance)의 가중치를 두어 패턴 특징을 강조하여 숫자 패턴의 인식률을 높인다. 실험에서는 기존의 원형 정합을 사용했을 때, 오류 역전파 신경망을 사용했을 때와 가중 원형 정합을 사용했을 때의 혼돈 행렬(confusion matrix)을 각각 서로 비교한다. 실험 결과는 본 논문에서 제안한 방법에 의해 인쇄체 숫자의 인식률이 크게 향상된 것을 보인다.

• 펄프종이기술
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• 제45권3호
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• pp.1-8
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• 2013

Hanji has been used mainly for preservation paper because of superior mechanical properties. However, it was not used in printing for inkjet and laser printer-printed letters. In this study, machine-made Hanji was prepared with five different contents of paper mulberry 20, 40, 60, 80 and 100% and managed by same pressure calendering. By increasing of paper mulberry contents, tearing index and folding endurance of machine-made Hanji increased because of increased fiber-to-fiber bonding. Printability property of machine-made Hanji improved by decreasing of paper mulberry contents. After 20 hours accelerated aging, the initial folding endurance of machine-made Hanji was reduced by approximately one-fourth degree. Between 40 and 100% contents of paper mulberry was showed similar levels about preservation property. The machine-made Hanji of paper mulberry 60% content was suitable for permanence and printability properties using preservation paper and printer-printed letters.

• 한국컴퓨터정보학회논문지
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• 제21권2호
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• pp.97-103
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• 2016

This paper deals with the cycle time minimization problem that determines the productivity in printed circuit board (PCB) with n components using the m placement machines. This is known as production cycle time determination problem (PCTDP). The polynomial time algorithm to be obtain the optimal solution has been unknown yet, therefore this hard problem classified by NP-complete. This paper gets the initial assignment result with the machine has minimum unit placement time per each component firstly. Then, the balancing process with reallocation from overhead machine to underhead machine. Finally, we perform the swap optimization and get the optimal solution of cycle time $T^*$ within O(mn) computational complexity. For experimental data, the proposed algorithm can be obtain the same result as integer programming+branch-and-bound (IP+B&B) and B&B.

• 반도체디스플레이기술학회지
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• 제18권4호
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• pp.46-50
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• 2019

In this paper, We used the regression model of machine learning for improve the print quantity problem when which print scaffold with 400 ㎛ pore using FDM 3d printer. We have difficult to experiment with changing all factors in the field. So we reduced print quantity by selected two factors that most impact the pore size. We printed and measured scaffold 5 times under same conditions. We created regression model using scaffold pore size and print conditions. We predicted pore size of untested print condition using the regression model. After print scaffold with 400 ㎛ pore, we printed scaffold 5 times under same conditions. We compare the predicted scaffold pore size and the measured scaffold pore size. We confirmed that error is less than 1 % and we verified the results quantitatively.