참고문헌 (46)

1. 1. Park Y.-J. Fan S.-K.S. Hsu C.-Y. A review on fault detection and process diagnostics in industrial processes Processes 2020 8 1123 10.3390/pr8091123 

   상세보기

2. 2. Tsui K.L. Chen N. Zhou Q. Hai Y. Wang W. Prognostics and health management: A review on data driven approaches Math. Probl. Eng. 2015 2015 793161 10.1155/2015/793161 

   상세보기

3. 3. Namuduri S. Narayanan B.N. Davuluru V.S.P. Burton L. Bhansali S. Deep Learning Methods for Sensor Based Predictive Maintenance and Future Perspectives for Electrochemical Sensors J. Electrochem. Soc. 2020 167 037552 10.1149/1945-7111/ab67a8 

   상세보기

4. 4. Mazzetto M. Teixeira M. Rodrigues É.O. Casanova D. Deep learning models for visual inspection on automotive assembling line arXiv 2020 2007.01857 10.22161/ijaers.74.56 

   

5. 5. Jiang Y. Wang W. Zhao C. A machine vision-based realtime anomaly detection method for industrial products using deep learning Proceedings of the 2019 Chinese Automation Congress (CAC) Hangzhou, China 22–24 November 2019 4842 4847 

6. 6. Yang J. Li S. Wang Z. Dong H. Wang J. Tang S. Using deep learning to detect defects in manufacturing: A comprehensive survey and current challenges Materials 2020 13 5755 10.3390/ma13245755 

   상세보기

7. 7. Purohit H. Tanabe R. Ichige K. Endo T. Nikaido Y. Suefusa K. Kawaguchi Y. MIMII Dataset: Sound dataset for malfunctioning industrial machine investigation and inspection arXiv 2019 1909.09347 

8. 8. Baek W. Kim D.Y. An in-process inspection system to detect noise originating from within the interior trim panels of car doors Sensors 2020 20 630 10.3390/s20030630 

   상세보기

9. 9. Pech M. Vrchota J. Bednář J. Predictive Maintenance and Intelligent Sensors in Smart Factory Sensors 2021 21 1470 10.3390/s21041470 33672479 

   상세보기

10. 10. Ma Y. Wang C. Yang D. Wang C. Adaptive Extraction Method Based on Time-Frequency Images for Fault Diagnosis in Rolling Bearings of Motor Math. Probl. Eng. 2021 2021 6687195 10.1155/2021/6687195 

    상세보기

11. 11. Ahn H. Choi H.-L. Kang M. Moon S. Learning-Based Anomaly Detection and Monitoring for Swarm Drone Flights Appl. Sci. 2019 9 5477 10.3390/app9245477 

    상세보기

12. 12. Ahn H. Jung D. Choi H.-L. Deep generative models-based anomaly detection for spacecraft control systems Sensors 2020 20 1991 10.3390/s20071991 32252421 

    상세보기

13. 13. Glowacz A. Tadeusiewicz R. Legutko S. Caesarendra W. Irfan M. Liu H. Brumercik F. Gutten M. Sulowicz M. Daviu J.A.A. Fault diagnosis of angle grinders and electric impact drills using acoustic signals Appl. Acoust. 2021 179 108070 10.1016/j.apacoust.2021.108070 

    상세보기

14. 14. Mukkamala S. Janoski G. Sung A. Intrusion detection using neural networks and support vector machines Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN’02 (Cat. No. 02CH37290) Honolulu, HI, USA 12–17 May 2002 1702 1707 

15. 15. Lu Y. Huang Z. A new hybrid model of sparsity empirical wavelet transform and adaptive dynamic least squares support vector machine for fault diagnosis of gear pump Adv. Mech. Eng. 2020 12 1687814020922047 10.1177/1687814020922047 

    상세보기

16. 16. Pablo Fernández J. Shubitidze F. Shamatava I. Barrowes B.E. O’Neill K. Realistic subsurface anomaly discrimination using electromagnetic induction and an SVM classifier Eurasip J. Adv. Signal Process. 2010 2010 305890 10.1155/2010/305890 

    상세보기

17. 17. Malhotra P. Ramakrishnan A. Anand G. Vig L. Agarwal P. Shroff G. LSTM-based encoder-decoder for multi-sensor anomaly detection arXiv 2016 1607.00148 

18. 18. Koizumi Y. Saito S. Uematsu H. Kawachi Y. Harada N. Unsupervised detection of anomalous sound based on deep learning and the neyman–pearson lemma IEEE/ACM Trans. Audiospeechand Lang. Process. 2018 27 212 224 10.1109/TASLP.2018.2877258 

    상세보기

19. 19. Chauhan S. Vig L. Anomaly detection in ECG time signals via deep long short-term memory networks Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) Paris, France 19–21 October 2015 1 7 

20. 20. Maya S. Ueno K. Nishikawa T. dLSTM: A new approach for anomaly detection using deep learning with delayed prediction Int. J. Data Sci. Anal. 2019 8 137 164 10.1007/s41060-019-00186-0 

    상세보기

21. 21. Malhotra P. Vig L. Shroff G. Agarwal P. Long short term memory networks for anomaly detection in time series Proceedings of the ESANN 2015 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning Bruges, Belgium 22–24 April 2015 89 94 

22. 22. Gers F.A. Schmidhuber J. Cummins F. Learning to forget: Continual prediction with LSTM Neural Comput. 2000 12 2451 2471 10.1162/089976600300015015 11032042 

    상세보기

23. 23. Shi X. Chen Z. Wang H. Yeung D. Wong W. Woo W. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. CoRR arXiv 2015 1506.04214 

24. 24. Kim T.-Y. Cho S.-B. Web traffic anomaly detection using C-LSTM neural networks Expert Syst. Appl. 2018 106 66 76 10.1016/j.eswa.2018.04.004 

    상세보기

25. 25. Le N.Q.K. Huynh T.-T. Identifying SNAREs by incorporating deep learning architecture and amino acid embedding representation Front. Physiol. 2019 10 1501 10.3389/fphys.2019.01501 31920706 

    상세보기

26. 26. Bojanowski P. Grave E. Joulin A. Mikolov T. Enriching word vectors with subword information Trans. Assoc. Comput. Linguist. 2017 5 135 146 10.1162/tacl_a_00051 

    상세보기

27. 27. Lin S. Clark R. Birke R. Schönborn S. Trigoni N. Roberts S. Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model Proceedings of the ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Barcelona, Spain 4–8 May 2020 4322 4326 

28. 28. Theodoropoulos P. Spandonidis C.C. Themelis N. Giordamlis C. Fassois S. Evaluation of different deep-learning models for the prediction of a ship’s propulsion power J. Mar. Sci. Eng. 2021 9 116 10.3390/jmse9020116 

    상세보기

29. 29. Le N.Q.K. Do D.T. Hung T.N.K. Lam L.H.T. Huynh T.-T. Nguyen N.T.K. A computational framework based on ensemble deep neural networks for essential genes identification Int. J. Mol. Sci. 2020 21 9070 10.3390/ijms21239070 

    상세보기

30. 30. Cortes C. Vapnik V. Support-vector networks Mach. Learn. 1995 20 273 297 10.1007/BF00994018 

    상세보기

31. 31. MacQueen J. Some methods for classification and analysis of multivariate observations Berkeley Symp. Math. Stat. Probab. 1967 5.1 281 297 

32. 32. Altman N.S. An introduction to kernel and nearest-neighbor nonparametric regression Am. Stat. 1992 46 175 185 

    상세보기

33. 33. d’Acremont A. Fablet R. Baussard A. Quin G. CNN-based target recognition and identification for infrared imaging in defense systems Sensors 2019 19 2040 10.3390/s19092040 31052320 

    상세보기

34. 34. Chung J. Gulcehre C. Cho K. Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling arXiv 2014 1412.3555 

35. 35. LeCun Y. Bengio Y. Convolutional networks for images, speech, and time series The handbook of Brain Theory and Neural Networks MIT Press Cambridge, MA, USA 1995 Volume 3361 1995 

36. 36. Srivastava N. Hinton G. Krizhevsky A. Sutskever I. Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting J. Mach. Learn. Res. 2014 15 1929 1958 

    상세보기

37. 37. Nair V. Hinton G.E. Rectified linear units improve restricted boltzmann machines Proceedings of the 27th International Conference on International Conference on Machine Learning Haifa, Israel 21 June 2020 

38. 38. LeCun Y. Bengio Y. Hinton G. Deep learning Nature 2015 521 436 444 10.1038/nature14539 26017442 

    상세보기

39. 39. Liu W. Wen Y. Yu Z. Yang M. Large-margin softmax loss for convolutional neural networks arXiv 2017 1612.02295v4 

40. 40. Powers D.M. Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation arXiv 2020 2010.16061 

41. 41. Kingma D.P. Ba J. Adam: A method for stochastic optimization arXiv 2014 1412.6980 

42. 42. Azad M. Khaled F. Pavel M. A novel approach to classify and convert 1D signal to 2D grayscale image implementing support vector machine and empirical mode decomposition algorithm Int. J. Adv. Res. 2019 7 328 335 10.21474/IJAR01/8331 

    상세보기

43. 43. Chen X.-W. Lin X. Big data deep learning: Challenges and perspectives IEEE Access 2014 2 514 525 10.1109/ACCESS.2014.2325029 

    상세보기

44. 44. Gu J. Wang Z. Kuen J. Ma L. Shahroudy A. Shuai B. Liu T. Wang X. Wang G. Cai J. Recent advances in convolutional neural networks Pattern Recognit. 2018 77 354 377 10.1016/j.patcog.2017.10.013 

    상세보기

45. 45. Bengio Y. Lamblin P. Popovici D. Larochelle H. Greedy layer-wise training of deep networks Advances in Neural Information Processing Systems MIT Press Cambridge, MA, USA 2006 153 160 

46. 46. Bengio Y. Courville A. Vincent P. Representation learning: A review and new perspectives IEEE Trans. Pattern Anal. Mach. Intell. 2013 35 1798 1828 10.1109/TPAMI.2013.50 23787338 

    상세보기

활용도 Top5 논문

더보기

해당 논문의 주제분야에서 활용도가 높은 상위 5개 콘텐츠를 보여줍니다.
더보기 버튼을 클릭하시면 더 많은 관련자료를 살펴볼 수 있습니다.