[The surface smoothing methods for three-dimensional fluorescence spectrometry based on Savitzky-Golay polynomial smoothing].

Spectral smoothing is commonly used as effective pretreatment methods in the spectral analysis. However, the conventional Savitzky-Golay polynomial smoothing methods used in two-dimensional spectral analysis can not be applied to three-dimensional spectrometry directly. In the present paper, a polynomial surface smoothing method is proposed, and the two-dimensional Savitzky-Golay polynomial smoothing methods are extended to three-dimensional fluorescence spectra. Experiment for detecting dissolved organ matter in water using three-dimensional fluorescence spectrometry was carried out, and experimental results show that the smoothing method for the three-dimensional fluorescence spectrum can effectively improve the modeling accuracy.

[Ultraviolet spectrum analysis methods for detecting the concentration of organic pollutants in water].

The absorption rate of ultraviolet could be used to measure the concentration of organic pollutant, as most of the organic pollutant has stronger absorption rate in ultraviolet region in water. In the present paper, principal component regression (PCR), partial least squares (PLS), and support vector machine (SVM) were respectively used to model a regression model after the spectrum preprocessing, such as smoothing, derivation, standard normal variate transformation (SNV), etc. Then, the concentration of organic pollutant could be measured via the ultraviolet spectrum and the regression model. In the experiments, a group of water samples from the wastewater treatment process were used to verify the effects of the various preprocessing and modeling approaches. The results showed that for the good spectrum data, direct modeling without the spectrum pretreatment could be used since the pretreatment would worsen the results. LSSVM approach is more applicable in the case of small-size samples.

[Detection of dissolved organic matter based on three-dimensional first-order derivative fluorescence spectrometry].

Based on three-dimensional first-order derivative fluorescence spectrometry, an analysis method for detecting dissolved organ matter in water is proposed in the present paper. By using simplified least squares differentiation methods presented by Savitzky and Goly, the first-order partial derivatives for emission wavelength and excitation wavelength were calculated. As the fitting polynomial has the smoothing function in the calculation of derivative spectra, a separate smoothing method is not required to remove spectrometry noise. The regression model was calculated by partial least square for 4-dimension fluorescence data including emission wavelength, excitation wavelength and their first-order derivatives. The Experimental results for detecting total organic carbon (TOC) in water show that the proposed method has obvious advantage over the conventional fluorescence spectrometry analysis methods in the aspect of the root mean square error of prediction and correlation coefficient.

Delay-dependent robust H infinity control for a class of uncertain switched systems with time delay.

For linear switched system with both parameter uncertainties and time delay, a delay-dependent sufficient condition for the existence of a new robust H infinity feedback controller was formulated in nonlinear matrix inequalities solvable by an LMI-based iterative algorithm. Compared with the conventional state-feedback controller, the proposed controller can achieve better robust control performance since the delayed state is utilized as additional feedback information and the parameters of the proposed controllers are changed synchronously with the dynamical characteristic of the system. This design method was also extended to the case where only delayed state is available for the controller. The example of balancing an inverted pendulum on a cart demonstrates the effectiveness and applicability of the proposed design methods.

Congestion control for ATM multiplexers using neural networks: multiple sources/single buffer scenario.

A new neural network based method for solving the problem of congestion control arising at the user network interface (UNI) of ATM networks is proposed in this paper. Unlike the previous methods where the coding rate for all traffic sources as controller output signals is tuned in a body, the proposed method adjusts the coding rate for only a part of the traffic sources while the remainder sources send the cells in the previous coding rate in case of occurrence of congestion. The controller output signals include the source coding rate and the percentage of the sources that send cells at the corresponding coding rate. The control methods not only minimize the cell loss rate but also guarantee the quality of information (such as voice sources) fed into the multiplexer buffer. Simulations with 150 ADPCM voice sources fed into the multiplexer buffer showed that the proposed methods have advantage over the previous methods in the aspect of the performance indices such as cell loss rate (CLR) and voice quality.