• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.223-229
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• 2014

The acoustic cleaning system is widely used to remove foreign materials in factories, such as thermal power plants and incinerators. However, the acoustic cleaning systems tend to be clogged by foreign materials. In this paper, we develop a wireless sensor network for the sound wave detection in order to monitor proper operations in the acoustic cleaning systems. We observe that the developed wireless sensor network for the wave detection shows a stable operation in various industrial environments of wide temperature ranges. We also develop a data gathering device, which displays the current status of the sound generator and several values detected from the wireless sensor.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.2
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• pp.308-314
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• 2006

A line scan camera is applied to enhance COG(Chip On Glass) inspection accuracy to be measured a few micro unit. The foreign substance detection among various faulty factors has been the most difficult technology in the faulty automatic inspection step since COG pattern is very miniature and complexity. In this paper, we proposed two step area segmentation template matching method to increase matching speed. Futhermore to detect foreign substance(such as dust, scratch) with a few micro unit, the new method using gradient mask and AND operation was proposed. The proposed 2 step template matching method increased 0.3 - 0.4 second matching speed compared with conventional correlation coefficient. Also, the proposed foreign substance applied masks enhanced $5-8\%$ faulty detection rate compared with conventional no mask application method.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.873-879
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• 2023

This study explored the use of artificial intelligence(AI) to detect foreign bodies in chest X-ray images. Medical imaging, especially chest X-rays, plays a crucial role in diagnosing diseases such as pneumonia and lung cancer. With the increase in imaging tests, AI has become an important tool for efficient and fast diagnosis. However, images can contain foreign objects, including everyday jewelry like buttons and bra wires, which can interfere with accurate readings. In this study, we developed an AI algorithm that accurately identifies these foreign objects and processed the National Institutes of Health chest X-ray dataset based on the YOLOv8 model. The results showed high detection performance with accuracy, precision, recall, and F1-score all close to 0.91. Despite the excellent performance of AI, the study solved the problem that foreign objects in the image can distort the reading results, emphasizing the innovative role of AI in radiology and its reliability based on accuracy, which is essential for clinical implementation.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.1
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• pp.31-35
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• 2010

This paper presents the development of automatic defect detection program using 3D computed tomography image of small solid rocker motor. We applied the neighbor pixel comparison algorithm with beam hardening correction for the recognition of defect. We made the artificial defect specimen in order to decide a standard CT value of defect. The program was tested with 150 small solid rocket motors and it could detect the disbond, crack, foreign material and void. The program showed more reliable and faster results than human inspector's interpretation.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1816-1817
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• 2011

원자력발전소의 금속파편감시계통(LPMS : Loose Parts Monitoring System)은 원자로냉각재계통 내부에 존재할 수 있는 금속 이물질과 구조물 이완부에 의한 충격신호를 조기에 검출하여 원자로 구조물 및 핵연료 손상, 제어봉 구동장애 등을 미연에 방지하여 발전소 안전운전을 담당하는 중요 감시설비이다. LPMS는 금속 이물질이나 구조물 이완부에 의한 충격신호를 검출하기 위해 충격파에 민감한 가속도계를 원자로냉각재계통 중 금속파편이 자연적으로 모일 수 있는 각 구역의 표면에 최소 2개 이상 설치되어 있다. 원전은 규제요건에 따라 설비의 건전성 확인을 위해 24시간, 7일, 31일, 91일 마다 각 1회의 설비 건전성 시험을 수행하며, 계획예방정비기간 중에는 가속도계 주변에서 강구나 스프링 타격기를 이용한 충격시험을 통해 설비 전체의 건전성을 확인하고 있다. 설비 건전성 확인을 위해 경상운전 중에 수행하는 설비 건전성 시험에는 설비 특성상 가속도계 및 전치증폭기의 건전성을 확인할 수 없다. 따라서 본 논문에서는 경상운전 중 가속도계와 전치증폭기의 건전성을 확인할 수 있는 기법을 제시하고자 한다.

• Annual Conference of KIPS
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• 2021.11a
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• pp.640-642
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• 2021

비전 기반의 재활용-PET 선별공정에서, PET 외 물체와의 식별 성능은 물론 PET 용기 내 포함된 이물질 및 라벨, 뚜껑의 존재 여부, 색상에 대한 검출 성능은 재활용 소재 품질에 중요한 영향을 미친다. 본 연구에서는 비전 인공지능 기반의 재활용-PET 자동 선별 시스템을 제안하고, 인공지능 모델의 제작에서 감독학습의 학습 효과를 최적화하기 위한 데이터 레이블링 기법을 제안한다. 재활용대상 PET 와 이물질 파트가 포함된 용기의 컨베이어벨트 선별공정 혼입을 재현한 실험을 통해서, 재활용 소재화 물량과 순도를 최대화하기 위한 인공지능 모델 생성 방법에 대해 고찰한다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.149-156
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• 2024

This paper implemented a drill bit foreign matter inspection system based on the YOLO V3 algorithm and evaluated its performance. The study trained the YOLO V3 model using 600 training data to distinguish between the normal and foreign matter states of the drill bit. The implemented inspection system accurately analyzed the state of the drill bit and effectively detected defects through automatic inspection. The performance evaluation was performed on drill bits used more than 2,000 times, and achieved a recognition rate of 98% for determining whether resharpening was possible. The goal of foreign matter removal in the cleaning process was evaluated as 99.6%, and the automatic inspection system could inspect more than 500 drill bits per hour, which was about 4.3 times faster than the existing manual inspection method and recorded a high accuracy of 99%. These results show that the automated inspection system can dramatically improve inspection speed and accuracy, and can contribute to quality improvement and cost reduction in manufacturing sites. In future studies, it is necessary to develop more efficient and reliable inspection technology through system optimization and performance improvement.

• Journal of the Korean Society for Nondestructive Testing
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• v.14 no.2
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• pp.122-131
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• 1994

Journal bearings in the rotating machineries are vulnerable to the contamination or the insufficient supply of lubricating oil, which is likely to be the cause of unexpected shutdown or malfunction of these systems. Various destructive and nondestructive testing methods had been used for the reduction of maintenance cost and the operational safety problems due to the accidents related to bearing damages. In this experimental approach, acoustic emission monitoring is employed to the detection of incipient failure caused by intervention of foreign particles most probable in the journal bearing systems. Experimental schedules for the intervention of foreign particles was composed to be more quantitative and systematic than last study in consideration of minimum oil film thickness and particle size. The experiment was conducted under such designed conditions as inserting alumina particles to the lubrication layer in the simulated journal bearing system. Several parameters such as AE rms level, waveform, AE energy distribution and other AE event parameters are used for analysis and characterization of damage source. The results showed that the history of damage was well correlated with the changes of AE rms level and the type of damage source signal can be verified using other informations such as waveform, distributions of AE parameters etc.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2486-2491
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• 2013

There are invisible defects by naked eyes in ceramic material images such as internal stomata, cracks and foreign substances. In this paper we propose a method to detect and extract such defects from ceramic pipe weld zone by applying ART2 learning. In pre-processing, we apply Ends-in Search Stretching to enhance the intensity and then perform fuzzy binarization with triangle type membership function followed by enhanced ART2 that interacts with random input patterns to extract such invisible defects. The experiment verifies that this proposed method is sufficiently effective.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.715-722
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• 2022

If you don't regularly go to the dentist to check your teeth, it is difficult to notice cavities or various diseases of your teeth until you have pain or discomfort. Dental plaque is produced by the combination of food or foreign substances and bacteria in the mouth. Starch breaks down from the bacteria that form tartar. The acid that occurs at this time melts the enamel of the teeth and becomes a cavity. So tartar management is important. Poppyrin, the metabolism of bacteria in the mouth, reacts at 405 nm wavelengths and becomes red fluorescent, which can be seen by imaging through certain wavelength filters. By the above method, Frag and tartar are fluorescently detected and photographed with a yellow series of filters that pass wavelengths of 500 nm or more. It uses MATLAB to detect and display red fluorescence through image processing. Using the difference in voltage between normal teeth and tartar through an optical measuring circuit, it was connected to an Arduino and displayed on the LCD. This allows the user to know the presence and location of dental plaque more accurately.