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In spectrometer measurement, it is very important to accurately calibrate the wavelength of all target characteristic spectra. Although wavelength calibration methods have long been investigated, no techniques have been designed for the scanning, double-layer, secondary diffraction, linear-array CCD spectrometer, to the best of our knowledge. Based on the grating diffraction equation and experimental results, a mathematical model of wavelength calibration was established for the scanning, double-layer, secondary diffraction, linear-array CCD spectrometer. In this study, a robust, full-screen, wavelength calibration algorithm is proposed, based on the related working principle and the requirements of both accuracy and robustness. The detailed steps are as follows. First, we established a wavelength calibration model at central pixels, following the grating diffraction equation. Then, according to the relationship between the difference in pixels and the feedback values of the grating ruler, a model was established to show the association between these factors. Finally, we combined the two models and built a full-screen wavelength calibration model. We theoretically and experimentally demonstrate that the proposed calibration algorithm is an excellent calibration tool, which can conveniently and accurately calibrate the wavelengths of central and non-central pixels at the same time.

Nowadays, pitch motors play an important role in many manufacturing plants. To ensure the other components run normally, it is urgent to automatically monitor the running state of pitch motors and early warning faults to avoid huge losses at a later period. Based on the normal behavior modeling technique, this paper studies the status monitoring of the pitch motors. Based on the fact that the state of the motor varies with time, we propose to train an echo state network with the SCADA data to predict the temperature of the pitch motor. Subsequently, the EWMA (exponentially weighted moving average) technique is used to set the alarm limit lines of each parameter. By employing some real data collected in a wind farm in China to conduct experiments, the results show that in comparison with several other methods, the proposed method can more effectively identify and early warn the faults of the pitch motor.

This paper empirically evaluates two kinds of features, which are extracted, respectively, with traditional statistical methods and convolutional neural networks (CNNs), in order to improve the performance of seismic patch image classification. In the latter case, feature vectors, named “CNN-features”, were extracted from one trained CNN model, and were then used to learn existing classifiers, such as support vector machines. In this case, to learn the CNN model, a technique of transfer learning using synthetic seismic patch data in the source domain, and real-world patch data in the target domain, was applied. The experimental results show that CNN-features lead to some improvements in the classification performance. By analyzing the data complexity measures, the CNN-features are found to have the strongest discriminant capabilities. Furthermore, the transfer learning technique alleviates the problems of long processing times and the lack of learning data.

Rapid new microvascular network induction was critical for bone regeneration, which required the spatiotemporal delivery of growth factors and transplantation of endothelial cells. In this study, the linear poly(d,l-lactic-co-glycolic acid)-b-methoxy poly(ethylene glycol) (PLGA-mPEG) block copolymer microspheres were prepared for simultaneously delivering vascular endothelial growth factor (VEGF) and monocyte chemotactic protein-1 (MCP-1). Then, vascular endothelial cells (VECs) with growth factor loaded microspheres were composited into a star-shaped PLGA-mPEG block copolymer solution. After this, composite hydrogel (microspheres ratio: 5 wt%) was formed by increasing the temperature to 37 °C. The release profiles of VEGF and MCP-1 from composite hydrogels in 30 days were investigated to confirm the different simultaneous delivery systems. The VECs exhibited a good proliferation in the composite hydrogels, which proved that the composite hydrogels had a good cytocompatibility. Furthermore, in vivo animal experiments showed that the vessel density and the mean vessel diameters increased over weeks after the composite hydrogels were implanted into the necrosis site of the rabbit femoral head. The above results suggested that the VECs-laden hydrogel composited with the dual-growth factor simultaneous release system has the potential to enhance angiogenesis in bone tissue engineering.