{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T08:10:19Z","timestamp":1726042219514},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030295127"},{"type":"electronic","value":"9783030295134"}],"license":[{"start":{"date-parts":[[2019,8,24]],"date-time":"2019-08-24T00:00:00Z","timestamp":1566604800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-29513-4_88","type":"book-chapter","created":{"date-parts":[[2019,8,23]],"date-time":"2019-08-23T13:04:00Z","timestamp":1566565440000},"page":"1222-1233","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Channel-Wise Reconstruction-Based Anomaly Detection Framework for Multi-channel Sensor Data"],"prefix":"10.1007","author":[{"given":"Mingu","family":"Kwak","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seoung Bum","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,8,24]]},"reference":[{"issue":"3","key":"88_CR1","doi-asserted-by":"publisher","first-page":"036015","DOI":"10.1088\/1741-2560\/8\/3\/036015","volume":"8","author":"DF Wulsin","year":"2011","unstructured":"Wulsin, D.F.: Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement. J. Neural Eng. 8(3), 036015 (2011)","journal-title":"J. Neural Eng."},{"key":"88_CR2","first-page":"1","volume":"2016","author":"K Wang","year":"2016","unstructured":"Wang, K.: Research on healthy anomaly detection model based on deep learning from multiple time-series physiological signals. Sci. Program. 2016, 1\u20139 (2016)","journal-title":"Sci. Program."},{"key":"88_CR3","unstructured":"Motoi, K., Tanaka, S.: Evaluation of a new sensor system for ambulatory monitoring of human posture and walking speed using accelerometers and gyroscope. In: SICE 2003 Annual Conference, vol. 2, pp. 1232\u20131235. IEEE (2003)"},{"key":"88_CR4","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.eswa.2017.06.011","volume":"87","author":"SS Khan","year":"2017","unstructured":"Khan, S.S.: Detecting unseen falls from wearable devices using channel-wise ensemble of autoencoders. Expert Syst. Appl. 87, 280\u2013290 (2017)","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"88_CR5","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1016\/j.aei.2015.03.001","volume":"29","author":"R Akhavian","year":"2015","unstructured":"Akhavian, R.: Construction equipment activity recognition for simulation input modeling using mobile sensors and machine learning classifiers. Adv. Eng. Inform. 29(4), 867\u2013877 (2015)","journal-title":"Adv. Eng. Inform."},{"key":"88_CR6","doi-asserted-by":"publisher","first-page":"2092","DOI":"10.1016\/j.proeng.2014.12.452","volume":"97","author":"T Praveenkumar","year":"2014","unstructured":"Praveenkumar, T.: Fault diagnosis of automobile gearbox based on machine learning techniques. Procedia Eng. 97, 2092\u20132098 (2014)","journal-title":"Procedia Eng."},{"key":"88_CR7","first-page":"3995","volume":"15","author":"J Yang","year":"2015","unstructured":"Yang, J., Nguyen, M.N.: Deep convolutional neural networks on multichannel time series for human activity recognition. IJCAI 15, 3995\u20134001 (2015)","journal-title":"IJCAI"},{"key":"88_CR8","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yan, W.: Time series classification from scratch with deep neural networks: a strong baseline. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1578\u20131585. IEEE (2017)","DOI":"10.1109\/IJCNN.2017.7966039"},{"issue":"1","key":"88_CR9","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1007\/s11704-015-4478-2","volume":"10","author":"Y Zheng","year":"2016","unstructured":"Zheng, Y.: Exploiting multi-channels deep convolutional neural networks for multivariate time series classification. Front. Comput. Sci. 10(1), 96\u2013112 (2016)","journal-title":"Front. Comput. Sci."},{"key":"88_CR10","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. arXiv preprint, 1610-02357 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"88_CR11","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.neucom.2017.04.070","volume":"262","author":"S Ahmad","year":"2017","unstructured":"Ahmad, S.: Unsupervised real-time anomaly detection for streaming data. Neurocomputing 262, 134\u2013147 (2017)","journal-title":"Neurocomputing"},{"key":"88_CR12","doi-asserted-by":"crossref","unstructured":"Ferdousi, Z.: Unsupervised outlier detection in time series data. In: 22nd International Conference on Data Engineering Workshops 2006, Proceedings, pp. x121\u2013x121. IEEE (2006)","DOI":"10.1109\/ICDEW.2006.157"},{"key":"88_CR13","doi-asserted-by":"crossref","unstructured":"Vahdatpour, A.: Unsupervised discovery of abnormal activity occurrences in multi-dimensional time series, with applications in wearable systems. In: Proceedings of the 2010 SIAM International Conference on Data Mining, pp. 641\u2013652. Society for Industrial and Applied Mathematics (2010)","DOI":"10.1137\/1.9781611972801.56"},{"key":"88_CR14","doi-asserted-by":"crossref","unstructured":"Sakurada, M.: Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, p. 4. ACM (2014)","DOI":"10.1145\/2689746.2689747"},{"issue":"2","key":"88_CR15","doi-asserted-by":"publisher","first-page":"36","DOI":"10.3390\/jimaging4020036","volume":"4","author":"BR Kiran","year":"2018","unstructured":"Kiran, B.R.: An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos. J. Imaging 4(2), 36 (2018)","journal-title":"J. Imaging"},{"issue":"Dec","key":"88_CR16","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent, P.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11(Dec), 3371\u20133408 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"88_CR17","first-page":"52","volume-title":"Lecture Notes in Computer Science","author":"Jonathan Masci","year":"2011","unstructured":"Masci, J., Meier, U.: Stacked convolutional auto-encoders for hierarchical feature extraction. In: International Conference on Artificial Neural Networks, pp. 52\u201359. Springer, Heidelberg (2011)"},{"key":"88_CR18","unstructured":"Mao, X.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: Advances in Neural Information Processing Systems, pp. 2808\u20132810 (2016)"},{"key":"88_CR19","doi-asserted-by":"crossref","unstructured":"He, K.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"88_CR20","unstructured":"Srivastava, R.K.: Training very deep networks. In: Advances in Neural Information Processing Systems, 2377\u20132385 (2015)"},{"key":"88_CR21","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.patrec.2017.07.016","volume":"105","author":"M Ribeiro","year":"2018","unstructured":"Ribeiro, M.: A study of deep convolutional auto-encoders for anomaly detection in videos. Pattern Recogn. Lett. 105, 13\u201322 (2018)","journal-title":"Pattern Recogn. Lett."},{"key":"88_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"88_CR23","doi-asserted-by":"crossref","unstructured":"Han, D.: Comparison of commonly used image interpolation methods. In: ICCSEE, Hangzhou, pp. 1556\u20131559 (2013)","DOI":"10.2991\/iccsee.2013.391"},{"key":"88_CR24","unstructured":"Rasmus, A., Raiko, T.: Denoising autoencoder with modulated lateral connections learns invariant representations of natural images. arXiv preprint (2014). \n                  arXiv:1412.7210"},{"key":"88_CR25","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413\u2013422. IEEE (2008)","DOI":"10.1109\/ICDM.2008.17"},{"issue":"2","key":"88_CR26","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1145\/335191.335388","volume":"29","author":"M Breunig","year":"2000","unstructured":"Breunig, M.: LOF: identifying density-based local outliers. ACM Sigmod Rec. 29(2), 93\u2013104 (2000)","journal-title":"ACM Sigmod Rec."},{"key":"88_CR27","unstructured":"Dozat, T.: Incorporating nesterov momentum into adam (2016)"},{"key":"88_CR28","unstructured":"Sutskever, I., Martens, J.: On the importance of initialization and momentum in deep learning. In: International Conference on Machine Learning, pp. 1139\u20131147 (2013)"},{"key":"88_CR29","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256 (2010)"},{"key":"88_CR30","unstructured":"Chollet, F.: Keras (2015)"},{"issue":"Oct","key":"88_CR31","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(Oct), 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"88_CR32","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1177\/096228029200100105","volume":"1","author":"IT Joliffe","year":"1992","unstructured":"Joliffe, I.T.: Principal component analysis and exploratory factor analysis. Stat. Methods Med. Res. 1(1), 69\u201395 (1992)","journal-title":"Stat. Methods Med. Res."},{"key":"88_CR33","first-page":"583","volume-title":"Lecture Notes in Computer Science","author":"Bernhard Sch\u00f6lkopf","year":"1997","unstructured":"Sch\u00f6lkopf, B., Smola, A.: Kernel principal component analysis. In: International Conference on Artificial Neural Networks, pp. 583\u2013588. Springer, Heidelberg (1997)"}],"container-title":["Advances in Intelligent Systems and Computing","Intelligent Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-29513-4_88","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,23]],"date-time":"2019-08-23T13:27:21Z","timestamp":1566566841000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-29513-4_88"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,24]]},"ISBN":["9783030295127","9783030295134"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-29513-4_88","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2019,8,24]]},"assertion":[{"value":"24 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IntelliSys","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Proceedings of SAI Intelligent Systems Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"London","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"intellisys2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/saiconference.com\/IntelliSys","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}