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스마트 팩토리 구현을 위한 딥러닝 기술의 품질 및 안전 분야 응용 = Application of Deep Learning Algorithms to Quality and Safety Problems for Smart Factory

박성현 2020년

논문상세정보
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' 스마트 팩토리 구현을 위한 딥러닝 기술의 품질 및 안전 분야 응용 = Application of Deep Learning Algorithms to Quality and Safety Problems for Smart Factory' 의 주제별 논문영향력
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논문영향력 요약
주제
  • aga
  • ann
  • r-cnn
  • 가속도센서
  • 딥 러닝
  • 마하효과
  • 스마트 팩토리
  • 안전
  • 품질
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
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' 스마트 팩토리 구현을 위한 딥러닝 기술의 품질 및 안전 분야 응용 = Application of Deep Learning Algorithms to Quality and Safety Problems for Smart Factory' 의 참고문헌

  • 포스코 포항제철소, ‘스마트 안전모’ 상반기 도입. 철강금속신문. Retrieved November 17
    방정환 2018 from http://www.snmnews.com/news/articleView.html?idxno=370611 [2017]
  • 청소년이 꼭 알아야 할 과학이슈 11(Season 4) Retrieved November 10
    김청한 문영운 박태균 엄태웅 이광식 이억주 이은희 이재웅 이충환 2018 from http://100.daum.net/encyclopedia/view/124XX73300006 [2016]
  • 인더스트리 4.0 시대의 스마트 팩토리 성공 사례 분석: 국내 대ㆍ중ㆍ소기업을 대상으로
    박종필 디지털융복합연구, 15(5), 107-115 [2017]
  • 이대로 휩쓸릴 수는 없다
    김유리 송해영 전통 제조강국의 자존심 4차 산업혁명의 물결, 쓸려갈 것인가 끌고 갈 것인가 – 일본편. MFG. Retrieved November 18, 2018 from http://www.ei.co.kr/content/view.asp?idx=813308 [2016]
  • 아직은 더딘 국내 스마트 팩토리. CCTV뉴스
    양대규 Retrieved November 22 from http://www.cctvnews.co.kr/news/articleView.html?idxno=79968 [2018]
  • 박은진, 김연주 “스마트공장 전망 따지는 건 시간낭비”…獨도 맨바닥서 출발. 매일경제 MBN. Retrieved October 29
    김대기 손재권 신현규 원호섭 임성현 2018 from http://news.mk.co.kr/newsRead.php?no=71125&year=2017 [2017]
  • 미국 독일 일본의 스마트 팩토리 전략. LG경제연구원. Retrieved November 17, 2018 from http://www.lgeri.com/report/view.do?idx=19544 박기혁
    나준호 최드림 강석기 [2016]
  • 공장은 거대한 로봇
    기성훈 …獨 美 日 제조혁신 전쟁 [AI, 반도체와 인간을 삼킨다]④-1 스마트공장, 데이터 분석해 생산효율성 증대. 머니투데이. Retrieved November 18, 2018 from http://news.mt.co.kr/mtview.php?no=2018022109101436114 [2018]
  • ‘리쇼어링 바람’ GE∙혼다 등 美∙日 제조 대기업, 본국으로 공장 이전, 이코노미 조선. Retrieved November 18
    김수옥 2018 from http://economychosun.com/client/ news/view.php?boardName=C24&t_num=12983 [2018]
  • [무역관 르포] 독일, 인더스트리 4.0의 현장을 가다. Retrieved November 17
    박소영 2018 from http://news.kotra.or.kr/user/globalAllBbs/kotranews/list/2/globalBbsDataAllVie w.do?dataIdx=166829&column=&search=&searchAreaCd=&searchNationCd=&searchTra deCd=&searchStartDate=&searchEndDate=&searchCategoryIdxs=&searchIndustryCateIdx =&page=1&row=100 [2018]
  • [獨 인더스트리 4.0 현장을 가다] 獨 지멘스 암베르크 공장, '소프트웨어+ 센서' 결합으로 공장 지능화. Retrieved November 7
    임근난 2018 from http://www.hellot.net/new _hellot/magazine/magazine_read.html?code=201&sub=001&idx=23462 [2015]
  • Zeiler, M. D., & Fergus, R. (2014, September). Visualizing and understanding convolutional networks. In European conference on computer vision (pp. 818-833). Springer, Cham.
  • Workplace Health and Safety Queensland (WHSQ). “Guide to machinery and equipment safety,” Retrieved August 23, 2018 from https://www.worksafe.qld.gov.au/, 2015.
  • Tool condition monitoring using K-star algorithm
    41 ( 6 ) , 2638-2643 . [2014]
  • The relation between visual acuity and illumination .
    21 ( 2 ) , 165-188 . [1937]
  • The effect of room illumination on visual acuity measurement .
    34 , 3–8 . [1999]
  • The effect of illumination and other factors on the acuity of vision
    11 ( 7 ) , 321 .. [1927]
  • Study of hybrid genetic algorithm using artificial neural network in data mining for the diagnosis of stroke disease
    3 ( 4 ) , 95-100 [2013]
  • Solution ofConstrained optimization problems by multi-objective genetic algorithm
    26 ( 10 ) , 1481-1492 [2002]
  • Single row facility layout problem using a permutation-based genetic algorithm
    213 ( 2 ) , 388- 394 [2011]
  • Predicting tanker freight rates using parsimonious variables and a hybrid artificial neural network with an adaptive genetic algorithm
    19 ( 3 ) , 538-550 . [2017]
  • Predicting remaining useful life of rotating machinery based artificial neural network
    60 ( 4 ) , 1078-1087 . [2010]
  • Performance evaluation of the inspection lighting systems in industrial auto plants
    30 ( 2 ) , 152-169 . [2001]
  • P8 . 7-Automatic Painted Surface Inspection and Defect Detection
    871-873 [2011]
  • Optimization in operations research
    Vol . 166 [1998]
  • One-against-all multi-class SVM classification using reliability measures
    Vol . 2 , pp . 849-854 ) [2005]
  • New fault recognition method for rotary machinery based on information entropy and a probabilistic neural network
    18 ( 2 ) , 337 [2018]
  • Modeling and forecasting of electricity spotprices : Computational intelligence vs classical econometrics .
    27 ( 3 ) , 301-314 . [2014]
  • Machine Safeguarding at the Point of Operation : A Guide for Finding Solutions to Machine Hazards . Oregon OSHA ( Occupational Safety & Health Division
    [2007]
  • Korea Occupational Safety and Health Agency (KOSHA).,” Report: Industrial Disaster Anal. for 2016”: pp. 763, 2016.
    pp . 763 [2016]
  • K. L.Industry 4.0
    [2018]
  • Introduction to the artificial neural networks
    [2011]
  • Intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization
    11 ( 4 ) , 4009-4029 [2011]
  • Inspection system based on artificial vision for paint defects detection on cars bodies
    [2011]
  • Injuries , illnesses , and fatal injuries in mining in 2010 .
    [2013]
  • Improving error back propagation algorithm by using cross entropy error function and adaptive learning rate
    61 ( 8 ) [2017]
  • Improved classification of surface defects for quality control of car body panels
    [2006]
  • Hybrid genetic algorithm with adaptive local search scheme
    51 , 128–141 [2006]
  • Human Factors in Engineering and Design , 7th ed
    [1993]
  • Health and Safety Executive (HSE),” The use of emery cloth on metalworking lathes,” Retrieved Feb ruary 3, 2020 from https://www.hse.gov.uk/pubns/eis2.pdf, 1993.
  • Hadad, Y. (2017). 30 amazing applications of deep learning. Retrieved November 15, 2018 from http:// www.yaronhadad.com/deep-learning-most-amazing-applications/
  • Gradient-Based Learning Applied to Document Recognition
    86 ( 11 ) , 2278-2324 . [1998]
  • Godnig, E.C. Visual Acuity under Various Illuminance Lighting Conditions. Available online: http://www.theppsc.org (accessed on 20 September 2016).
  • Gene selection and sample classification on microarray data based on adaptive genetic algorithm/k-nearest neighbor method
    38 , 4661–4667 . [2011]
  • Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN .
    163 , 104846 . [2019]
  • From concept to the introduction of Industry 4.0. International Journal of Idustrial Engineering and Management
    8 , 21 . [2017]
  • Flying accident of workpiece during lathe work
    [2020]
  • Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing
    21 ( 2 ) , 930-942 [2007]
  • Fault diagnosis system of rotating machines using continuous wavelet transform and Artificial Neural Network
    [2017]
  • Fault diagnosis system of rotating machines using Hidden Markov Model ( HMM ) . In 2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing ( IES-KCIC )
    pp . 177- 181 [2017]
  • Fault diagnosis of spur bevel gear box using artificial neural network ( ANN ) , and proximal support vector machine ( PSVM )
    10 ( 1 ) , 344-360 . [2010]
  • Fault diagnosis of single point cutting tool through vibration signal using decision tree algorithm
    5 , 1434-1441 [2014]
  • Fault detection using support vector machines and artificial neural networks , augmented by genetic algorithms .
    16 ( 2-3 ) , 373-390 . [2002]
  • Fault detection and classification using artificial neural networks
    51 ( 18 ) , 470-475 . [2018]
  • Fault Diagnosis of a Single Point Cutting Tool using Statistical Features by Random Forest Classifier
    9 ( 33 ) [2016]
  • Fast defect detection for various types of surfaces using random forest with VOV features
    16 ( 5 ) , 965-970 [2015]
  • Elite guided steady-state genetic algorithm for minimizing total tardiness in flowshops
    58 ( 2 ) , 300-306 [2010]
  • Electricity price forecasting : A review of the state-of-the-art with a look into the future
    forecasting , 30 ( 4 ) , 1030-1081 . [2014]
  • Development of a Visual Inspection Method for Defects on Metallic Surface Considering Emergent Feature
    [2016]
  • Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning
    2 [2018]
  • Deep convolutional neural networks for detection of rail surface defects .
    [2016]
  • Condition monitoring studies on spindle bearing of a lathe
    28 ( 9-10 ) , 993-1005 [2006]
  • Comparison between artificial neural network and support vector method for a fault diagnostics in rolling element bearings
    144 , 390- 397 . [2016]
  • Combined artificial neural network and genetic algorithm for cloud classification .
    [2013]
  • Classification of the rotating machine condition using artificial neural networks
    211 ( 6 ) , 439-450 [1997]
  • Chung, Y. C., & Chang, M. (2006, October). Visualization of subtle defects of car body outer panels. In 2006 SICE-ICASE International Joint Conference (pp. 4639-4642). IEEE.
  • Car body paint defect inspection using rotation invariant measure of the local variance and one-against-all support vector machine . In 2011 First International Conference on Informatics and Computational Intelligence
    pp . 244-249 [2011]
  • Bekiroğlu, S., Dede, T., & Ayvaz, Y. (2009). Implementation of different encoding types on structural optimization based on adaptive genetic algorithm. Finit e Elements in Analysis and Design, 45(11), 826-835. Brenner, M., “The True Cost of Industrial Machinery Accidents”. Retrieved April 25, 2016 from http s://www.ien.com/safety/article/20859375/the-true-cost-of-industrial-machinery-accidents, 2017.
  • Bearing fault diagnosis using wavelet packet transform , hybrid PSO and support vector machine
    97 , 1772-1783 [2014]
  • Bearing fault diagnosis based on multiscale permutation entropy and support vector machine
    14 ( 8 ) , 1343-1356 [2012]
  • B. I. S. W. A. J. I. T. , Al-Balushi , K. R. , & Al-Araimi , S. A .Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection
    16 ( 7-8 ) , 657-665 [2003]
  • Automated visual inspection of flat surface products using feature fusion
    [2009]
  • Artificial neural network based fault diagnostics of rolling element bearings using time-domain features
    17 ( 2 ) , 317-328 . [2003]
  • Artificial neural network approach for fault detection in rotary system
    8 ( 1 ) , 740-748 [2008]
  • Artificial intelligence for fault diagnosis of rotating machinery : A review
    108 , 33-47 [2018]
  • Analysis of occupational accidents : prevention through the use of additional technical safety measures for machinery
    22 ( 2 ) , 186-192 [2016]
  • An introduction to genetic algorithms .
    [1998]
  • Algorithm for the evaluation of imperfections in auto bodywork using profiles from a retroreflective image
    14 ( 2 ) , 2476-2488 . [2014]
  • A hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT , KPCA and Twin SVM
    66 , 249-261 . [2017]
  • A fast and robust convolutional neural networkbased defect detection model in product quality control
    94 ( 9-12 ) , 3465-3471 [2018]
  • 4차 산업혁명의 실체
    김희철 서울:(주)북랩 [2017]