• Title/Summary/Keyword: automated inspection system

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Comparison of an Automated Most-Probable-Number Technique TEMPO®TVC with Traditional Plating Methods PetrifilmTM for Estimating Populations of Total Aerobic Bacteria with Livestock Products (축산물가공품에서 건조필름법과 TEMPO®TVC 검사법의 총세균수 비교분석)

  • Kim, Young-Jo;Wee, Sung-Hwan;Yoon, Ha-Chung;Heo, Eun-Jeong;Park, Hyun-Jeong;Kim, Ji-Ho;Moon, Jin-San
    • Journal of Food Hygiene and Safety
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    • v.27 no.1
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    • pp.103-107
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    • 2012
  • We compared between an automated most-probable-number technique $TEMPO^{(R)}$TVC and traditional plating methods $Petrifilm^{TM}$ for estimating populations of total aerobic bacteria in various livestock products. 257 samples randomly selected in local retail stores and 87 samples inoculated with $E.$ $coli$ ATCC 25922, $Staphylococcus$ $aureus$ ATCC 12868 were tested in this study. The degree of agreement was estimated according to the CCFRA (Campden and Chorleywood Food Research Association Group) Guideline 29 and the agreement indicates the difference of two kinds methods is lower than 1 log base 10($log_{10}$). The samples of hams, jerky products, ground meat products, milks, ice creams, infant formulas, and egg heat formed products were showed above 95% in the agreement of methods. In contrast, proportion of agreement on meat extract products, cheeses and sausages were 93.1%, 92.1%, 89.1%, respectively. One press ham and five sausages containing spice and seasoning, two pork cutlets containing spice and bread crumbs, two meat extract product and two natural cheeses and one processing cheese with a high fat content, and one ice cream containing chocolate of all samples showed the discrepancy. Our result suggest that $TEMPO^{(R)}$TVC system is efficient to analyses total aerobic bacteria to compare manual method in time-consuming and laborious process except livestock products having limit of detection.

Field Application of a Cable NDT System for Cable-Stayed Bridge Using MFL Sensors Integrated Climbing Robot (누설자속센서를 탑재시킨 이동로봇을 이용한 사장교 케이블 비파괴검사 시스템의 현장 적용)

  • Kim, Ju-Won;Choi, Jun-Sung;Lee, Eun-Chan;Park, Seung-Hee
    • Journal of the Korean Society for Nondestructive Testing
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    • v.34 no.1
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    • pp.60-67
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    • 2014
  • In this study, an automated cable non-destructive testing(NDT) system was developed to monitor the steel cables that are a core component of cable-stayed bridges. The magnetic flux leakage(MFL) method, which is suitable for ferromagnetic continuum structures and has been verified in previous studies, was applied to the cable inspection. A multi-channel MFL sensor head was fabricated using hall sensors and permanent magnets. A wheel-based cable climbing robot was fabricated to improve the accessibility to the cables, and operating software was developed to monitor the MFL-based NDT research and control the climbing robot. Remote data transmission and robot control were realized by applying wireless LAN communication. Finally, the developed element techniques were integrated into an MFL-based cable NDT system, and the field applicability of this system was verified through a field test at Seohae Bridge, which is a typical cable-stayed bridge currently in operation.

Deep Learning Models for Autonomous Crack Detection System (자동화 균열 탐지 시스템을 위한 딥러닝 모델에 관한 연구)

  • Ji, HongGeun;Kim, Jina;Hwang, Syjung;Kim, Dogun;Park, Eunil;Kim, Young Seok;Ryu, Seung Ki
    • KIPS Transactions on Software and Data Engineering
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    • v.10 no.5
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    • pp.161-168
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    • 2021
  • Cracks affect the robustness of infrastructures such as buildings, bridge, pavement, and pipelines. This paper presents an automated crack detection system which detect cracks in diverse surfaces. We first constructed the combined crack dataset, consists of multiple crack datasets in diverse domains presented in prior studies. Then, state-of-the-art deep learning models in computer vision tasks including VGG, ResNet, WideResNet, ResNeXt, DenseNet, and EfficientNet, were used to validate the performance of crack detection. We divided the combined dataset into train (80%) and test set (20%) to evaluate the employed models. DenseNet121 showed the highest accuracy at 96.20% with relatively low number of parameters compared to other models. Based on the validation procedures of the advanced deep learning models in crack detection task, we shed light on the cost-effective automated crack detection system which can be applied to different surfaces and structures with low computing resources.

Computer Vision-based Automated Adhesive Quality Inspection Model of Exterior Insulation and Finishing System (컴퓨터 비전 기반 외단열 공사의 접착제 도포품질 감리 자동화 모델)

  • Yoon, Sebeen;Kang, Mingyun;Jang, Hyounseung;Kim, Taehoon
    • Journal of the Korea Institute of Building Construction
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    • v.23 no.2
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    • pp.165-173
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    • 2023
  • This research proposed a model for automatically monitoring the quality of insulation adhesive application in external insulation construction. Upon case implementation, the area segmentation model demonstrated a 92.3% accuracy, while the area and distance calculation accuracies of the proposed model were 98.8% and 96.7%, respectively. These findings suggest that the model can effectively prevent the most common insulation defect, insulation failure, while simultaneously minimizing the need for on-site supervisory personnel during external insulation construction. This, in turn, contributes to the enhancement of the external insulation system. Moving forward, we plan to gather construction images of various external insulation methods to refine the image segmentation model's performance and develop a model capable of automatically monitoring scenarios with a considerable number of insulation materials in the image.

Development of Machine Vision System and Dimensional Analysis of the Automobile Front-Chassis-Module

  • Lee, Dong-Mok;Yang, Seung-Han;Lee, Sang-Ryong;Lee, Young-Moon
    • Journal of Mechanical Science and Technology
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    • v.18 no.12
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    • pp.2209-2215
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    • 2004
  • In the present research work, an automated machine vision system and a new algorithm to interpret the inspection data has been developed. In the past, the control of tolerance of front-chassis-module was done manually. In the present work a machine vision system and required algorithm was developed to carryout dimensional evaluation automatically. The present system is used to verify whether the automobile front-chassis-module is within the tolerance limit or not. The directional ability parameters related with front-chassis-module such as camber, caster, toe and king-pin angle are also determined using the present algorithm. The above mentioned parameters are evaluated by the pose of interlinks in the assembly of an automobile front-chassis-module. The location of ball-joint center is important factor to determine these parameters. A method to determine the location of ball-joint center using geometric features is also suggested in this paper. In the present work a 3-D best fitting method is used for determining the relationship between nominal design coordinate system and the corresponding feature coordinate system.

A Study on Big Data Processing Technology Based on Open Source for Expansion of LIMS (실험실정보관리시스템의 확장을 위한 오픈 소스 기반의 빅데이터 처리 기술에 관한 연구)

  • Kim, Soon-Gohn
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.14 no.2
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    • pp.161-167
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    • 2021
  • Laboratory Information Management System(LIMS) is a centralized database for storing, processing, retrieving, and analyzing laboratory data, and refers to a computer system or system specially designed for laboratories performing inspection, analysis, and testing tasks. In particular, LIMS is equipped with a function to support the operation of the laboratory, and it requires workflow management or data tracking support. In this paper, we collect data on websites and various channels using crawling technology, one of the automated big data collection technologies for the operation of the laboratory. Among the collected test methods and contents, useful test methods and contents useful that the tester can utilize are recommended. In addition, we implement a complementary LIMS platform capable of verifying the collection channel by managing the feedback.

Implementation of recognition system on extracting inferior goods of radiation fin (방열판 불량품 추출을 위한 식별 시스템 구현)

  • Sim, Woo-Sung;Huh, Do-Geun;Lee, Yong-Sik
    • Journal of Institute of Control, Robotics and Systems
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    • v.6 no.1
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    • pp.91-97
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    • 2000
  • In this paper, the illuminator is designed to recognize the shape and the existence of holes of radiation fin in the point that the light reflection characteristics are different according to the roughness of the material. The threshold value, the positions of holes and the black pixel nembers in the positon are obtained under the illuminator, in accordance with the reference image, by applying binary conversion and hole segmentation algorithm, as they are suggested in this paper, The existence and shape of hole are recognized by calculating the distance and feature value in the test image, which is obtained from the parameters of reference image. It is programmed to apply to GUI(Graphic User the Interface) in windows. More than 98% of recognition rate is shown, as it is applied to three different sizes of the radiation fin.

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Contrast Enhancement for Defects Extraction from Seel-tube X-ray Images (결함추출을 위한 강판튜브 엑스선 영상의 명암도 향상)

  • Hwang, Jung-Won;Hwang, Jae-Ho
    • Proceedings of the IEEK Conference
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    • 2007.07a
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    • pp.361-362
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    • 2007
  • We propose a contrast-controlled feature detection approach for steel radiograph image. X-ray images are low contrast, dark and high noise image. So, It is not simple to detect defects directly in automated radiography inspection system. Contrast enhancement, histogram equalization and median filter are the most frequently used techniques to enhance the X-ray images. In this paper, the adaptive control method based on contrast limited histogram equalization is compared with several histogram techniques. Through comparative analysis, CLAHE(contrast controlled adaptive histogram equalization) can enhance detection of defects better.

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Development of Software for the Preventive Maintenance (설비 예방보전을 위한 소프트웨어 개발)

  • 이장원;김원중
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.21 no.46
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    • pp.229-240
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    • 1998
  • In these days, we are facing the needs of the times that the product have to be converted from quantity to quality. Most companys now pay more attention to maximize efficiency of facilities with introducing high priced equipments, those equipments are too automated and electronic that partial failure or short time stop leads to the stop of producing process in the whole factory. Consequently, effective maintenance management of production equipment becomes a only solution against problems. This thesis develops a preventive maintenance software that grasp a failure sign through daily inspection with management item, Machine-Capability index and estimate a trend, determine the time of preventive maintenance. The software is useful to the plant operator in analyzing information easily and controlling preventive maintenance operations efficiently.

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Implementation of an Automated Visual Inspection System of PCB using CAD Information (CAD 정보를 이용한 PCB 자동시각검사 시스템 구현)

  • Heo Se-Heung;Park Byun-Joon;Hahn Kwang-Soo;Choi Joon-Soo
    • Proceedings of the Korean Information Science Society Conference
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    • 2006.06b
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    • pp.340-342
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    • 2006
  • 기존의 PCB 자동시각검사 시스템은 창조 비교 방법을 위한 참조 영상(골든 영상) 획득에 많은 어려움을 겪고 있다. 이러한 문제를 해결하기 위하여 본 연구에서는 PCB 제작에 사용되는 CAD 파일을 이용하여 참조 영상을 생성함으로써 학습을 좀 더 쉽고 간??하게 할 수 있는 시스템을 구현하였다.

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