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An intelligent process monitoring approach, which consists of process trend recognition and fault detection, is presented in this paper. This method incorporates wavelet transform, symbolic representation of data trend, pattern recognition, and Hidden Markov model (HMM) for intelligent reasoning. A simulation example and an industrial case study have shown the value of this approach. [ABSTRACT FROM AUTHOR]

This paper is concerned with dynamic modeling, prediction and analysis of cell cytotoxicity. A real-time cell electronic sensing (RT-CES) system has been used for label-free, dynamic measurements of cell responses to toxicant. Cells were grown onto the surfaces of the microelectronic sensors. Changes in cell number expressed as cell index (CI) have been recorded on-line as time series. The CI data are used for dynamic modeling in this paper. The developed models are verified using data that do not participate in the modeling. Optimal multi-step ahead predictions are calculated and compared with the actual CI. A new framework for dynamic cytotoxicity system analysis is established. Through the analysis of the system impulse response, we have observed that there are considerably similarities between the impulse response curves and the raw dynamic data, but there are also some striking differences between the two, particularly in terms of the initial and final cell killing effects. It is shown that dynamic modeling has great potential in modeling cell dynamics in the presence of toxicant and predicting the response of the cells. [ABSTRACT FROM AUTHOR]