• 제어로봇시스템학회논문지
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• 제22권4호
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• pp.247-252
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• 2016

In this paper, we propose a new defect detection technology for micro-defect on the surface of steel products. Due to depth and size of microscopic defect, slop of surface and vibration of strip, the conventional optical method cannot guarantee the detection performance. To solve the above-mentioned problems and increase signal to noise ratio, a novel retro-schlieren method that consists of retro reflector and knife edge is proposed. Moreover dual switching lighting method is also applied to distinguish uneven micro defects and surface noise. In proposed method, defective regions are represented by a black and white pattern. This pattern is detected by a defect detection algorithm with Gabor filter. Experimental results by simulator for sample defects of cold rolled steel show that the proposed method is effective.

• Journal of Biosystems Engineering
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• 제26권2호
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• pp.169-176
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• 2001

Defect of apple was depreciated the product value and causes storage disease seriously. To detect the defect of ‘Fuji’apple with machine vision system, the optical characteristics of defect should be investigated. In this research, absorbance spectra of defect were acquired by spectrophotometer in the range of visible and NIR region(400∼1,100nm) and L*a*b* color values were also acquired by colorimeter. NIR machine vision system was constructed with B&W camera, frame grabber, 16 tungsten-halogen lamps, variable focal length lens and NIR bandpass filter which was mounted to lens outward. Average gray values of defect at 15 NIR wavelength were acquired and the significant NIR wavelength was selected by comparing Mahalanobis distance between sound and defective apple. As the result of Mahalanobis distance analysis, the significant wavelength to discriminate the defectives in ‘Fuji’apple were found to be 720nm for scab and 970nm for bruise and cuts and 920nm was also effective regardless of defective types.

• 품질경영학회지
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• 제25권3호
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• pp.74-85
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• 1997

In this paper we calculate the subgroup size necessary for detecting the change of percent defective with several detection probabilities for orginal population fraction nonconforming p, changed population fraction nonconforming $p^*$, and the ratio k=$p^*$/p in the usage of p control charts. From our calculation we can know the error level of normal a, pp.oximation in detection probability calculation and recommend the subgroup size with lower error levels of normal a, pp.oximation, and then we show the reasonable subgroup size necessary for p, $p^*$, k, and the detection probability of the change of fraction nonconforming in a process. The information that we here show in tables will be useful when p control chart users decide the subgroup size in the p control chart users decide the subgroup size in the p control chart.

• 반도체디스플레이기술학회지
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• 제22권2호
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• pp.40-44
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• 2023

The defect detection based on deep learning requires minimal loss and high accuracy to pinpoint product defects. In this paper, we confirm the loss rate of deep learning training based on disc-shaped artificial scaffold images. It is intended to compare the performance of Cross-Entropy functions used in object detection algorithms. The model was constructed using normal, defective artificial scaffold images and category cross entropy and sparse category cross entropy. The data was repeatedly learned five times using each loss function. The average loss rate, average accuracy, final loss rate, and final accuracy according to the loss function were confirmed.

• 한국항행학회논문지
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• 제20권3호
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• pp.210-217
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• 2016

전 세계 공항의 이동지역 내 이물질인 FOD (foreign object defective)에 의한 피해 량이 연간 2억불에 달하고 있다. 2000년 샤를드골 공항에서는 FOD로 인한 133명의 인명피해가 발생하기도 했다. 국내에도 각 공항별로 FOD로 인한 사고발생 및 장비의 수리 등 직 간접적인 피해가 발생하고 있는 상황이다. 이에 항공안전기술개발 사업의 일환으로 공항 내 항공기 이동지역 이물질 자동탐지 시스템의 개발이 진행 중에 있다. 분석 결과에 의하면 운용 방식의 특성상 민간 공항의 경우 고정식 감시를 요구했고 군 공항은 이동식을 선호했다. 본 논문에서는 군 민간 FOD 탐지 시스템의 요구조건을 파악하여 국내 조건에 맞는 최소 성능 사양을 분석하였다.

• Journal of Ginseng Research
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• 제43권4호
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• pp.684-691
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• 2019

Background: We have reported that defective nef and gag genes are induced in HIV-1-infected patients treated with Korean Red Ginseng (KRG). Methods: To investigate whether KRG treatment and highly active antiretroviral therapy (HAART) affect genetic defects in the pol gene, we amplified and sequenced a partial pol gene (p-pol) containing the integrase portion (1.2 kb) by nested PCR with sequential peripheral blood mononuclear cells over 20 years and compared it with those patients at baseline, in control patients, those taking ginseng-based combination therapy (GCT; KRG plus combinational antiretroviral therapy) and HAART alone. We also compared our findings to look for the full-length pol gene (pol) (3.0-kb) Results: Twenty-patients infected with subtype B were treated with KRG for $116{\pm}58months$ in the absence of HAART. Internal deletion in the pol gene (${\Delta}pol$) was significantly higher in the KRG group (11.9%) than in the control group and at baseline; its detection was significantly inhibited during GCT as much as during HAART. In addition, the ${\Delta}pol$ in p-pol significantly depended on the duration of KRG treatment. In pol, the proportion of ${\Delta}pol$ was significantly higher in the KRG group (38.7%) than in the control group, and it was significantly inhibited during GCT and HAART. In contrast, the proportion of stop codon appeared not to be affected by KRG treatment. The PCR success rate was significantly decreased with longer GCT. Conclusion: The proportion of ${\Delta}pol$ depends on template size as well as KRG treatment. HAART decreases the detection of ${\Delta}pol$.

• 스마트미디어저널
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• 제13권2호
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• pp.85-94
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• 2024

Artificial Intelligence of Things (AIoT), which combines AI and the Internet of Things (IoT), has recently gained popularity. Deep neural networks (DNNs) have achieved great success in many applications. Deploying complex AI models on embedded boards, nevertheless, may be challenging due to computational limitations or intelligent model complexity. This paper focuses on an AIoT-based system for smart sewing automation using edge devices. Our technique included developing a detection model and a decision tree for a sufficient testing scenario. YOLOv5 set the stage for our defective sewing stitches detection model, to detect anomalies and classify the sewing patterns. According to the experimental testing, the proposed approach achieved a perfect score with accuracy and F1score of 1.0, False Positive Rate (FPR), False Negative Rate (FNR) of 0, and a speed of 0.07 seconds with file size 2.43MB.

• Smart Structures and Systems
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• 제30권3호
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.

• 품질경영학회지
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• 제16권1호
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• pp.8-14
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• 1988

Because of demand pattern variability and Product Liability pursuance for products, it is necessary to convert from Product Liability Defence to Product Liability Prevention. For aggravation of company environment, automation, mechanization and FMS are required, to reduce quality cost in this situation, we present the following two alternatives. (1) We solidify the PL policy by process improvement. (2) We set up sensor equipment for defective detection in its early stage.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2011년도 추계학술대회
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• pp.382-386
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• 2011

RFID 태그 생산 분야에서 RFID 칩 본딩 과정 이후에 RFID 태그 불량 검출 기능을 수행하는 불량 검출 시스템 개발이 요구되어 왔다. 그러나 RFID 태그의 특징을 이해하면서 제대로 된 설계 개념을 가지고 구현된 시스템을 설계하기가 어렵고 사소한 기능의 변화에도 시스템을 처음부터 설계를 해야 하는 어려움이 있었다. 이 논문에서는 RFID 태그 불량 검출 기능을 수행하는 불량검출 시스템을 UML을 이용하여 객체지향 기법으로 설계하고 UML로 설계된 모델링을 객체지향을 지원하는 비주얼 언어인 LabVIEW OOP로 적용하는 방법을 제시한다. UML과 LabVIEW OOP로 설계되고 구현된 불량검출 시스템에 대한 성능과 시스템의 기능 변화에 따른 재설계 기법에 대한 기법도 제안한다.