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Volumn , Issue , 2012, Pages 205-228

A temporal probabilistic approach for continuous tool condition monitoring

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EID: 84891811719     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-4666-2095-7.ch011     Document Type: Chapter
Times cited : (2)

References (20)
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    • Jun-Hong, Z., Chee Khiang, P., Lewis, F. L., & Zhao-Wei, Z. (2009). Intelligent diagnosis and prognosis of tool wear using dominant feature identification. IEEE Transactions on Industrial Informatics, 5(4), 454-464. doi:10.1109/TII.2009.2023318
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    • Jun-Hong, Z.1    Chee Khiang, P.2    Lewis, F.L.3    Zhao-Wei, Z.4
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    • Fitting hidden semi-Markov models to breakpoint rainfall data
    • doi:10.1239/jap/1085496598
    • Sansom, J., & Thomson, P. (2001). Fitting hidden semi-Markov models to breakpoint rainfall data. Journal of Applied Probability, 38, 142-157. doi:10.1239/jap/1085496598
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    • Sansom, J.1    Thomson, P.2
  • 18
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    • Hidden semi Markov models
    • doi:10.1016/j.artint.2009.11.011
    • Yu, S. Z. (2010). Hidden semi Markov models. Artificial Intelligence, 174(2), 215-243. doi:10.1016/j.artint.2009.11.011
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    • January, doi:10.1109/LSP.2002.806705
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    • Yu, S.Z.1    Kobayashi, H.2
  • 20
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    • A comparative study of feature selection for hidden Markov model-based micro-milling tool wear monitoring
    • Zhu, K. P., Hong, G. S., & Wong, Y. S. (2008). A comparative study of feature selection for hidden Markov model-based micro-milling tool wear monitoring. Mining Science and Technology, 12(3), 348-369.
    • (2008) Mining Science and Technology , vol.12 , Issue.3 , pp. 348-369
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.