• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.125-140
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• 2018

As one of the processes in the manufacturing industry, quality inspection inspects the intermediate products or final products to separate the good-quality goods that meet the quality management standard and the defective goods that do not. The manual inspection of quality in a mass production system may result in low consistency and efficiency. Therefore, the quality inspection of mass-produced products involves automatic checking and classifying by the machines in many processes. Although there are many preceding studies on improving or optimizing the process using the data generated in the production process, there have been many constraints with regard to actual implementation due to the technical limitations of processing a large volume of data in real time. The recent research studies on big data have improved the data processing technology and enabled collecting, processing, and analyzing process data in real time. This paper aims to propose the process and details of applying big data for quality inspection and examine the applicability of the proposed method to the dairy industry. We review the previous studies and propose a big data analysis procedure that is applicable to the manufacturing sector. To assess the feasibility of the proposed method, we applied two methods to one of the quality inspection processes in the dairy industry: convolutional neural network and random forest. We collected, processed, and analyzed the images of caps and straws in real time, and then determined whether the products were defective or not. The result confirmed that there was a drastic increase in classification accuracy compared to the quality inspection performed in the past.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.40 no.2
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• pp.37-42
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• 2008

Defects of corrugated board were limiting factors to the corrugated industry's growth. On-line detecting systems of defects are beneficial to increase profits of the corrugated board companies by reducing base paper consumption, increasing process reliability, and increasing productivity. By replacing naked eye inspection of defects to a defects detecting system, continuous inspection without fatigue may guarantee final products quality. The system was developed, which was consisted of line scan CCD camera, lens, illuminating parts, high speed image processor, software, various input parts, and output parts. First installation location of the system was at before fluting process after base paper unwinding, and surface of liner board was inspected by the system. Second installation location was after fluting process and combining process for liner board. Production loss includes ragged appearance of edge, irregular flute shape, wrong combination of flute, score cracking, defects in base paper, and flute cracking. The production loss was analyzed before and after the system installation at a commercial domestic mill. The production loss ratio was defined as a production loss weight per total production weight. The production loss ratio before the installation was decreased a lot from 1.28%(during 3 months before installation) to 0.76%(during 12 months after the first installation) and to 0.25%(during 6 months after the second installation).

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.56.2-56.2
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• 2015

The KMTNet Supernovae Project utilizes the large $2^{\circ}{\times}2^{\circ}$ field of view of the three KMTNet telescopes to search and monitor supernovae, especially early ones, and other optical transients. A key component of the project is to build a data pipeline with a descent latency and an early alerting system that can handle the large volume of the data in an efficient and a prompt way, while minimizing false alarms, which casts a significant challenge to the software development. Here we present the current status of their development. The pipeline utilizes a difference image analysis technique to discover candidate transient sources after making correction of image distortion. In the early phase of the program, final selection of transient sources from candidates will mainly rely on multi-filter, multi-epoch and multi-site screening as well as human inspection, and an interactive web-based system is being developed for this purpose. Eventually, machine learning algorithms, based on the training set collected in the early phase, will be used to select true transient sources from candidates.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.4
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• pp.390-395
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• 2004

The iSTMS is a web-based integrated information system supporting Pre-Service and In-Service Inspection(PSI/ISI) processes for the nuclear power plants of KHNP(Korea Hydro & Nuclear Power Co. Ltd.). The system provides a full spectrum coverage of the inspection processes from the planning stage to the final report of examination in accordance with applicable codes, standards, and regulatory requirements. The major functions of the system includes the inspection planning, examination, reporting, project control and status reporting, resource management as well as objects search and navigation. The system also provides two dimensional or three dimensional visualization interface to identify the location and geometry of components and weld areas subject to examination in collaboration with database applications. The iSIMS is implemented with commercial software packages such as database management system, 2-D and 3-D visualization tool, etc., which provide open, updated and verified foundations. This paper describes the key functions and the technologies for the implementation of the iSIMS.