In order to enable the calibration model to be effectively transferred among multiple instruments and correct the differences between the spectra measured by different instruments, a new feature transfer model based on partial least squares regression (PLS) subspace (PLSCT) is proposed in this paper. Firstly, the PLS model of the master instrument is built, meanwhile a PLS subspace is constructed by the feature vectors. Then the master spectra and the slave spectra are projected into the PLS subspace, and the features of the spectra are also extracted at the same time. In the subspace, the pseudo predicted feature of the slave spectra is transferred by the ordinary least squares method so that it matches the predicted feature of the master spectra. Finally, a feature transfer relationship model is constructed through the feature transfer of the PLS subspace. This PLS-based subspace transfer provides an efficient method for performing calibration transfer with only a small number of standard samples. The performance of the PLSCT was compared and assessed with slope and bias correction (SBC), piecewise direct standardization (PDS), calibration transfer method based on canonical correlation analysis (CCACT), generalized least squares (GLSW), multiplicative signal correction (MSC) methods in three real datasets, statistically tested by the Wilcoxon signed rank test. The obtained experimental results indicate that PLSCT method based on the PLS subspace is more stable and can acquire more accurate prediction results.

Traditional image enhancement methods have the problems of low contrast and fuzzy details. Therefore, we propose a novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement. Firstly, multi-scale convolution is used to preprocess the image. Then, we improve the traditional Laplace edge detection operator and combine it with Gauss filter. The Gaussian filter is used to smooth the image and suppress the noise, and the edge detection is processed based on the Laplace gradient edge detector. The detail image extracted by Gauss-Laplace operator and the image with brightness enhancement are linearly weighted fused to reconstruct the image with clear detail edge and strong contrast. Experiments are carried out with detailed images in different scenes. It is compared with traditional methods to verify the effectiveness of the proposed method.

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Owing to the characteristics of time‐delay, fault randomness, uncertainty, nonlinearity and unknown interference in industrial production processes, a stochastic fuzzy predictive fault‐tolerant control algorithm is put forward based on the traditional fault‐tolerant control algorithm. The main idea of this algorithm is to integrate actuator fault into the established Takagi‐Sugeno (T‐S) model to solve actuator fault under a certain probability for the nonlinear industrial processes with time‐varying delays by combining stochastic control theory and relevant theorems. First, the actuator fault under a certain probability is considered as a T‐S model, which can be used as a description of the fault situation in the nonlinear industrial processes. Afterwards, the augmented state space model is established through integrating state deviation and output tracking error. Second, the stochastic fuzzy predictive fault‐tolerant control law can be designed on the basis of the augmented model. Meanwhile, the actuator control mode for different faults is given. If the actuator fault is under a small probability, the control mode is switched to normal control; if the actuator fault is under a large probability, the control mode is switched to fault‐tolerant control. To this end, this control algorithm can reduce energy consumption and raw material consumption. On this basis, the designed control law can be solved by using the given stochastic stability conditions in terms of linear matrix inequality. Finally, the temperature control process of a strongly nonlinear continuous stirred tank reactor is selected as a simulation object to prove the feasibility and effectivity of this algorithm.

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