Lanxiang Sun

Wavelet denoising method for laser-induced breakdown spectroscopy

The wavelet threshold denoising method is an effective noise suppression approach for noisy laser-induced breakdown spectroscopy spectrum. The wavelet threshold denoising method is influenced by several key issues such as the choice of wavelet, the choice of decomposition level, threshold selection, and the choice of thresholding functions. In this paper, the double threshold optimization models of semi-soft thresholding function are established firstly. Next, on the basis of grey relational analysis and Euclid closeness of fuzzy theory, a method of amending the double thresholds of semi-soft thresholding function is put forward. The performance of the proposed method is verified by analysis of both synthetic and observed signals. The limit of detection values are reduced by more than 50% and the signal to noise ratios are improved by a factor of two by using the proposed method.

A Method for Resolving Overlapped Peaks in Laser-Induced Breakdown Spectroscopy (LIBS)

Spectral peak overlapping is a basic problem in analytical data processing of laser-induced breakdown spectroscopy (LIBS). Curve fitting is the typical method of resolving overlapped peaks. For preventing ambiguous fitting, appropriate initial values must be known. The aim of this work was to present a method that could be used to determine appropriate initial values of the curve-fitting method by using fractional differential theory. According to the variation of characteristic points of Lorentzian peaks at different fractional differential orders, parameter estimators were obtained that were used to calculate the initial values of the curve-fitting method. As it is a widely used optimization method, the Levenberg–Marquardt method was used in curve fitting. Simulation and LIBS experimental results proved that the proposed method of the initial value estimation can effectively resolve the overlapped peaks in LIBS data processing.

Selection of spectral data for classification of steels using laser-induced breakdown spectroscopy

Principal component analysis (PCA) combined with artificial neural networks was used to classify the spectra of 27 steel samples acquired using laser-induced breakdown spectroscopy. Three methods of spectral data selection, selecting all the peak lines of the spectra, selecting intensive spectral partitions and the whole spectra, were utilized to compare the influence of different inputs of PCA on the classification of steels. Three intensive partitions were selected based on experience and prior knowledge to compare the classification, as the partitions can obtain the best results compared to all peak lines and the whole spectra. We also used two test data sets, mean spectra after being averaged and raw spectra without any pretreatment, to verify the results of the classification. The results of this comprehensive comparison show that a back propagation network trained using the principal components of appropriate, carefully selected spectral partitions can obtain the best results. A perfect result with 100% classification accuracy can be achieved using the intensive spectral partitions ranging of 357-367 nm.

Application of Stand-off Double-Pulse Laser-Induced Breakdown Spectroscopy in Elemental Analysis of Magnesium Alloy*

In this study, a stand-off and collinear double pulse laser-induced breakdown spectroscopy (DP LIBS) system was designed, and the magnesium alloy samples at a distance of 2.5 m away from the LIBS system were measured. The effect of inter-pulse delay on spectra was studied, and the signal enhancement was observed compared to the single pulse LIBS (SP LIBS). The morphology of the ablated crater on the sample indicated a higher efficiency of surface pretreatment in DP LIBS. The calibration curves of Ytterbium (Y) and Zirconium (Zr) were investigated. The square of the correlation coefficient of the calibration curve of element Y reached up to 0.9998.

An Image Auxiliary Method for Quantitative Analysis of Laser-Induced Breakdown Spectroscopy

Improving both the stability and accuracy of laser-induced breakdown spectroscopy (LIBS) is an issue in quantitative analysis. For certain environments outside of the laboratory, consistently and exactly maintaining the distance from the optical system to the sample surface is difficult, and fluctuations of this distance severely affect the stability of the spectrum. In this work, the principal components of the plasma images are extracted and used to correct the spectral line intensities as an auxiliary method to reduce spectral fluctuation. The presented image auxiliary method is combined with univariate analysis and multivariate analysis, and the element concentrations of Cu, Mn, V, and Cr in steel samples are analyzed. For univariate analysis, all the determination coefficients ( R2) of the four elements exceed 0.99, whereas the average relative standard deviations (RSDs) of the intensities decrease from 30.45, 23.14, 27.03, and 22.04%, to 2.13, 3.38, 2.49, and 3.58%, respectively. For the multivariate analysis, the R2 values for Cu, Mn, V, and Cr also all exceed 0.99, and the average RSDs of the predicted concentrations of the validation samples decrease to 2.87, 3.82, 2.86, and 6.51%, respectively.

A design method of robust optimal PI controller with saturation link for different processes

In this paper, a new design method of robust optimal proportional-integral (PI) controller with saturation characteristic is presented for different processes. Closed-loop feedback system is the nonlinear robust control system which can be equivalently transformed into robust linear switch system (RLSS) or robust autonomous switch system (RASS). The nonlinear robust optimal control problem can be equivalently transformed into nonlinear constraint optimization (NLCO) problem via the common Lyapunov functions (CLF) theorem, interval polynomial stable criterion and inference, and Lyapunov theorem. Therefore, robust optimal PI controller with saturation characteristic is obtained from solving the NLCO problem. Simulation results show the effectiveness and usefulness of the proposed design method.

A method derived from genetic algorithm, principal component analysis and artificial neural networks to enhance classification capability of laser-Induced breakdown spectroscopy

Selection of characteristic lines is a critical work for both qualitative and quantitative analysis of laser-induced breakdown spectroscopy; it usually needs a lot of time and effort. A novel method combining genetic algorithm, principal component analysis and artificial neural networks (GA-PCA-ANN) is proposed to automatically extract the characteristic spectral segments from the original spectra, with ample feature information and less interference. On the basis of this method, three selection manners: selecting the whole spectral range, optimizing a fixed-length segment and optimizing several non-fixed-length sub-segments were analyzed; and their classification results of steel samples were compared. It is proved that selecting a fixed-length segment with an appropriate segment length achieves better results than selecting the whole spectral range; and selecting several non-fixed-length sub-segments obtains the best result with smallest amount of data. The proposed GA-PCA-ANN method can reduce the workload of analysis, the usage of bandwidth and cost of spectrometers. As a result, it can enhance the classification capability of laser-induced breakdown spectroscopy.

Design of optimal PI controller with dead-zone link for low-order processes with time delay

In this paper, a design method of optimal proportional-integral (PI) controller with dead-zone link is presented for low-order processes with time delay. Closed-loop system is the nonlinear control system with the dead-zone link. The feedback system can be equivalently transformed into the linear switch system (LSS) or autonomous switch system (ASS). The optimal control problem is equivalently transformed into nonlinear constraint optimization (NLCO) problem via common Lyapunov functions (CLF) theorem and Lyapunov theorems. Therefore, optimal PI controller can be obtained by solving the NLCO problem. Simulation results show effectiveness and usefulness of theproposed design method.

A design method of optimal PI controller with saturation characteristic for second-order processes

In this paper, a design method of optimal proportional-integral (PI) controller with saturation characteristic is presented for second-order processes. Closed-loop system can be seen as the nonlinear control system because of PI controllers saturation characteristic. The closed-loop feedback system is equivalently transformed into linear switch system (LSS) or autonomous switch system (ASS). The optimal control problem is equivalently transformed into nonlinear constraint optimization (NLCO) problem via the common Lyapunov functions (CLF) theorem and Lyapunov theorems. Therefore, optimal PI controller parameters can be obtained from solving the NLCO problem. Simulation results show effectiveness and usefulness of the proposed design method.

A POSSIBLE POTENTIAL RELATIVE STABILITY ASSESSMENT METHOD FOR LINEAR CONTROL SYSTEMS

Traditional assessment method applies phase margin and amplitude margin of open loop transfer function to assess relative stability of linear systems. This paper presents a possible potential relative stability assessment method (RSAM) for linear systems. The relative stability can be qualitative described by relative stable distance (RSD) which is directly calculated by first column of Routh array or Hurwitz determinant. The RSD may be possible to indicate relative stability of closed- loop system, and RSD may be used to design optimal controller for linear systems. Several numerical examples show the proposed method’s potential usefulness to certain extent.