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Read on this site (6)
Yong-Hong Kuo & Andrew Kusiak. (2019) From data to big data in production research: the past and future trends. International Journal of Production Research 57:15-16, pages 4828-4853.
Read now
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Jianbo Yu & Lifeng Xi. (2009) A hybrid learning-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes. International Journal of Production Research 47:15, pages 4077-4108.
Read now
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H. Brian Hwarng
. (2005) Simultaneous identification of mean shift and correlation change in AR(1) processes. International Journal of Production Research 43:9, pages 1761-1783.
Read now
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Ruey-Shiang Guh
& Yeou-Ren Shiue. (2005) On-line identification of control chart patterns using self-organizing approaches. International Journal of Production Research 43:6, pages 1225-1254.
Read now
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H. BRIAN HWARNG. (1997) A neural network approach to identifying cyclic behaviour on control charts: a comparative study. International Journal of Systems Science 28:1, pages 99-112.
Read now
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H.
BRIAN HWARNG. (1995) Proper and effective training of a pattern recognizer for cyclic data. IIE Transactions 27:6, pages 746-756.
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Articles from other publishers (16)
Ramazan Ünlü. 2022. Research Anthology on Artificial Neural Network Applications. Research Anthology on Artificial Neural Network Applications
683
702
.
Ramazan Ünlü. (2021) Cost-oriented LSTM methods for possible expansion of control charting signals. Computers & Industrial Engineering 154, pages 107163.
Crossref
Crossref
Ramazan Ünlü. (2021) A robust data simulation technique to improve early detection performance of a classifier in control chart pattern recognition systems. Information Sciences 548, pages 18-36.
Crossref
Crossref
Ramazan Ünlü. 2020. Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering. Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering
240
258
.
Wen-An Yang & Wei Zhou. (2013) Autoregressive coefficient-invariant control chart pattern recognition in autocorrelated manufacturing processes using neural network ensemble. Journal of Intelligent Manufacturing 26:6, pages 1161-1180.
Crossref
Crossref
Siavash Haghtalab, Petros Xanthopoulos & Kaveh Madani. (2015) A robust unsupervised consensus control chart pattern recognition framework. Expert Systems with Applications 42:19, pages 6767-6776.
Crossref
Crossref
Petros Xanthopoulos & Talayeh Razzaghi. (2014) A weighted support vector machine method for control chart pattern recognition. Computers & Industrial Engineering 70, pages 134-149.
Crossref
Crossref
Shichang Du, Delin Huang & Jun Lv. (2013) Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines. Computers & Industrial Engineering 66:4, pages 683-695.
Crossref
Crossref
Wafik Hachicha & Ahmed Ghorbel. (2012) A survey of control-chart pattern-recognition literature (1991–2010) based on a new conceptual classification scheme. Computers & Industrial Engineering 63:1, pages 204-222.
Crossref
Crossref
Ussanee Purintrapiban & H.W. Corley. (2012) Neural networks for detecting cyclic behavior in autocorrelated process. Computers & Industrial Engineering 62:4, pages 1093-1108.
Crossref
Crossref
Ruey-Shiang Guh. (2010) Simultaneous process mean and variance monitoring using artificial neural networks. Computers & Industrial Engineering 58:4, pages 739-753.
Crossref
Crossref
Ruey‐Shiang Guh. (2006) On‐line Identification and Quantification of Mean Shifts in Bivariate Processes using a Neural Network‐based Approach. Quality and Reliability Engineering International 23:3, pages 367-385.
Crossref
Crossref
RUEY-SHIANG GUH. (2011) OPTIMIZING FEEDFORWARD NEURAL NETWORKS FOR CONTROL CHART PATTERN RECOGNITION THROUGH GENETIC ALGORITHMS. International Journal of Pattern Recognition and Artificial Intelligence 18:02, pages 75-99.
Crossref
Crossref
E.A. Rietman, S.A. Whitlock, M. Beachy, A. Roy & T.L. Willingham. (2001) A system model for feedback control and analysis of yield: A multistep process model of effective gate length, poly line width, and IV parameters. IEEE Transactions on Semiconductor Manufacturing 14:1, pages 32-47.
Crossref
Crossref
E.A. Rietman. (1997) Multi-step process yield control with large system models. Multi-step process yield control with large system models.
E.A. Rietman, D.J. Friedman & E.R. Lory. (1997) Pre-production results demonstrating multiple-system models for yield analysis. IEEE Transactions on Semiconductor Manufacturing 10:4, pages 469-481.
Crossref
Crossref