{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:50:45Z","timestamp":1761598245225,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,8,23]]},"DOI":"10.1145\/3394486.3403328","type":"proceedings-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T23:03:55Z","timestamp":1597964635000},"page":"2775-2783","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["A Self-Evolving Mutually-Operative Recurrent Network-based Model for Online Tool Condition Monitoring in Delay Scenario"],"prefix":"10.1145","author":[{"given":"Monidipa","family":"Das","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]},{"given":"Mahardhika","family":"Pratama","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]},{"given":"Tegoeh","family":"Tjahjowidodo","sequence":"additional","affiliation":[{"name":"KU Leuven, Leuven, Belgium"}]}],"member":"320","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments. In SIAM International Conference on Data Mining (SDM).","author":"Ashfahani Andri","year":"2019","unstructured":"Andri Ashfahani and Mahardhika Pratama . 2019 . Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments. In SIAM International Conference on Data Mining (SDM). Andri Ashfahani and Mahardhika Pratama. 2019. Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments. In SIAM International Conference on Data Mining (SDM)."},{"key":"e_1_3_2_2_2_1","volume-title":"Searching for exotic particles in high-energy physics with deep learning. Nature communications","author":"Baldi Pierre","year":"2014","unstructured":"Pierre Baldi , Peter Sadowski , and Daniel Whiteson . 2014. Searching for exotic particles in high-energy physics with deep learning. Nature communications , Vol. 5 ( 2014 ), 4308. Pierre Baldi, Peter Sadowski, and Daniel Whiteson. 2014. Searching for exotic particles in high-energy physics with deep learning. Nature communications, Vol. 5 (2014), 4308."},{"key":"e_1_3_2_2_3_1","volume-title":"Proceedings of the 12th International LAMDAMAP Conference","author":"Brecher C","year":"2017","unstructured":"C Brecher , M Klatte , and F Tzanetos . 2017 . Analysis of spatial and temporal dependencies of the TCP-dislocation measurement for the assessment of the thermo-elastic behavior of 3-axis machine tools . In Proceedings of the 12th International LAMDAMAP Conference , Bristol, UK. 122--132. C Brecher, M Klatte, and F Tzanetos. 2017. Analysis of spatial and temporal dependencies of the TCP-dislocation measurement for the assessment of the thermo-elastic behavior of 3-axis machine tools. In Proceedings of the 12th International LAMDAMAP Conference, Bristol, UK. 122--132."},{"key":"e_1_3_2_2_4_1","unstructured":"Yunpeng Chen Jianan Li Huaxin Xiao Xiaojie Jin Shuicheng Yan and Jiashi Feng. 2017. Dual path networks. In Advances in Neural Information Processing Systems. 4467--4475.  Yunpeng Chen Jianan Li Huaxin Xiao Xiaojie Jin Shuicheng Yan and Jiashi Feng. 2017. Dual path networks. In Advances in Neural Information Processing Systems. 4467--4475."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-009-2110-z"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8851757"},{"key":"e_1_3_2_2_7_1","volume-title":"MUSE-RNN: A Multilayer Self-Evolving Recurrent Neural Network for Data Stream Classification. In 2019 IEEE International Conference on Data Mining (ICDM). IEEE.","author":"Das M","year":"2019","unstructured":"M Das , M Pratama , S Savitri , and J Zhang . 2019 b . MUSE-RNN: A Multilayer Self-Evolving Recurrent Neural Network for Data Stream Classification. In 2019 IEEE International Conference on Data Mining (ICDM). IEEE. M Das, M Pratama, S Savitri, and J Zhang. 2019 b. MUSE-RNN: A Multilayer Self-Evolving Recurrent Neural Network for Data Stream Classification. In 2019 IEEE International Conference on Data Mining (ICDM). IEEE."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5781"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.136"},{"key":"e_1_3_2_2_10_1","volume-title":"Compose: A semisupervised learning framework for initially labeled nonstationary streaming data","author":"Dyer Karl B","year":"2014","unstructured":"Karl B Dyer , Robert Capo , and Robi Polikar . 2014 . Compose: A semisupervised learning framework for initially labeled nonstationary streaming data . IEEE transactions on neural networks and learning systems, Vol. 25 , 1 (2014), 12--26. Karl B Dyer, Robert Capo, and Robi Polikar. 2014. Compose: A semisupervised learning framework for initially labeled nonstationary streaming data. IEEE transactions on neural networks and learning systems, Vol. 25, 1 (2014), 12--26."},{"volume-title":"Deep learning","author":"Goodfellow Ian","key":"e_1_3_2_2_11_1","unstructured":"Ian Goodfellow , Yoshua Bengio , Aaron Courville , and Yoshua Bengio . 2016. Deep learning . Vol. 1 . MIT press Cambridge . Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. 2016. Deep learning. Vol. 1. MIT press Cambridge."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2015.08.019"},{"key":"e_1_3_2_2_13_1","volume-title":"Lifelong Learning with Dynamically Expandable Networks. In International Conference on Learning Representations (ICLR). 1--11","author":"Lee Jeongtae","year":"2018","unstructured":"Jeongtae Lee , Jaehong Yun , Sungju Hwang , and Eunho Yang . 2018 . Lifelong Learning with Dynamically Expandable Networks. In International Conference on Learning Representations (ICLR). 1--11 . Jeongtae Lee, Jaehong Yun, Sungju Hwang, and Eunho Yang. 2018. Lifelong Learning with Dynamically Expandable Networks. In International Conference on Learning Representations (ICLR). 1--11."},{"key":"e_1_3_2_2_14_1","volume-title":"Condition monitoring of face milling tool using K-star algorithm and histogram features of vibration signal. Engineering science and technology, an international journal","author":"Madhusudana CK","year":"2016","unstructured":"CK Madhusudana , Hemantha Kumar , and S Narendranath . 2016. Condition monitoring of face milling tool using K-star algorithm and histogram features of vibration signal. Engineering science and technology, an international journal , Vol. 19 , 3 ( 2016 ), 1543--1551. CK Madhusudana, Hemantha Kumar, and S Narendranath. 2016. Condition monitoring of face milling tool using K-star algorithm and histogram features of vibration signal. Engineering science and technology, an international journal, Vol. 19, 3 (2016), 1543--1551."},{"key":"e_1_3_2_2_15_1","volume-title":"Predicting tool life in turning operations using neural networks and image processing. Mechanical systems and signal processing","author":"Miko\u0142ajczyk T","year":"2018","unstructured":"T Miko\u0142ajczyk , K Nowicki , A Bustillo , and D Yu Pimenov . 2018. Predicting tool life in turning operations using neural networks and image processing. Mechanical systems and signal processing , Vol. 104 ( 2018 ), 503--513. T Miko\u0142ajczyk, K Nowicki, A Bustillo, and D Yu Pimenov. 2018. Predicting tool life in turning operations using neural networks and image processing. Mechanical systems and signal processing, Vol. 104 (2018), 503--513."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.precisioneng.2016.12.011"},{"key":"e_1_3_2_2_17_1","volume-title":"Online tool condition monitoring based on parsimonious ensemble","author":"Pratama Mahardhika","year":"2018","unstructured":"Mahardhika Pratama , Eric Dimla , Tegoeh Tjahjowidodo , Witold Pedrycz , and Edwin Lughofer . 2018. Online tool condition monitoring based on parsimonious ensemble . IEEE Transactions on Cybernetics ( 2018 ). Mahardhika Pratama, Eric Dimla, Tegoeh Tjahjowidodo, Witold Pedrycz, and Edwin Lughofer. 2018. Online tool condition monitoring based on parsimonious ensemble. IEEE Transactions on Cybernetics (2018)."},{"key":"e_1_3_2_2_18_1","volume-title":"Progressive neural networks. arXiv preprint arXiv:1606.04671","author":"Rusu Andrei A","year":"2016","unstructured":"Andrei A Rusu , Neil C Rabinowitz , Guillaume Desjardins , Hubert Soyer , James Kirkpatrick , Koray Kavukcuoglu , Razvan Pascanu , and Raia Hadsell . 2016. Progressive neural networks. arXiv preprint arXiv:1606.04671 ( 2016 ). Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell. 2016. Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016)."},{"key":"e_1_3_2_2_19_1","volume-title":"35th International Conference on Machine Learning. 4548--4557","author":"Serr\u00e0 Joan","year":"2018","unstructured":"Joan Serr\u00e0 , D'idac Sur'is , Marius Miron , and Alexandros Karatzoglou . 2018 . Overcoming catastrophic forgetting with hard attention to the task . In 35th International Conference on Machine Learning. 4548--4557 . Joan Serr\u00e0, D'idac Sur'is, Marius Miron, and Alexandros Karatzoglou. 2018. Overcoming catastrophic forgetting with hard attention to the task. In 35th International Conference on Machine Learning. 4548--4557."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974010.98"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2011.10.018"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1784\/204764215815848401"},{"key":"e_1_3_2_2_23_1","volume-title":"Stochastic configuration networks: Fundamentals and algorithms","author":"Wang Dianhui","year":"2017","unstructured":"Dianhui Wang and Ming Li. 2017. Stochastic configuration networks: Fundamentals and algorithms . IEEE transactions on cybernetics, Vol. 47 , 10 ( 2017 ), 3466--3479. Dianhui Wang and Ming Li. 2017. Stochastic configuration networks: Fundamentals and algorithms. IEEE transactions on cybernetics, Vol. 47, 10 (2017), 3466--3479."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.3390\/s16060795"}],"event":{"name":"KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Virtual Event CA USA","acronym":"KDD '20"},"container-title":["Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403328","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394486.3403328","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:01:49Z","timestamp":1750197709000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403328"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,20]]},"references-count":24,"alternative-id":["10.1145\/3394486.3403328","10.1145\/3394486"],"URL":"https:\/\/doi.org\/10.1145\/3394486.3403328","relation":{},"subject":[],"published":{"date-parts":[[2020,8,20]]},"assertion":[{"value":"2020-08-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}