查看更多>>摘要：Fringe Projection Profilometry (FPP) is a cost-effective and non-invasive technology that has been shown to measure finer features. In this work, we developed an in-situ FPP method to measure the dynamic topography of powder bed and printed layer during Laser Powder Bed Fusion (LPBF) additive manufacturing (AM) process. A systematic study towards developing a comprehensive framework of LPBF-specific FPP is demonstrated to enhance and evaluate the performance of applying FPP for in-situ LPBF monitoring, including 1) a modified sensor model with localized correction; 2) improved phase unwrapping with FFT filtering 3) quantitative uncertainty analysis; and 4) experimental validation with ex-situ characterization. The developed LPBF-specific FPP system and methods are implemented on a commercial LPBF-AM machine, achieving better accuracy, more robustness, and increased field of view while maintaining sufficient measurement range and decent resolution, in contrast to literature methods. The established FPP framework will facilitate the development of closed-loop control strategies for advancing LPBF based AM.
查看更多>>摘要：Due to the strategic importance of satellites, the safety and reliability of satellites have become more important. Sensors that monitor satellites generate lots of multivariate time series, and the abnormal patterns in the multivariate time series may imply malfunctions. The existing anomaly detection methods for multivariate time series have poor effects when processing the data with few dimensions or sparse relationships between sequences. This paper proposes an unsupervised anomaly detection model based on the variational Transformer to solve the above problems. The model uses the Transformer's self-attention mechanism to capture the potential correlations between sequences and capture the multi-scale temporal information through the improved positional encoding and up-sampling algorithm. Then, the model comprehensively considers the extracted features through the residual variational autoencoder to perform effective anomaly detection. Experimental results on a real dataset and two public datasets show that the proposed method is superior to the mainstream and state-ofthe-art methods.
查看更多>>摘要：The ability of magnetic field distortion (MFD) and magnetic attractive force (MAF) sensors to detect both the inner and outer carburized layers of the furnace tube was investigated in the study. They were employed for quantitative nondestructive evaluation of carburized layer depth in furnace tubes of Cr35Ni45NbMA. It was found that the carburized layer on the outer surface had the dominant effect on the responses of MFD and MAF sensors compared to the carburized layer on the inner surface. Parabolic dependency of the responses of MFD and MAF sensors on the carburized layer at the outer surface was concluded. The accuracy of the proposed sensors was verified by experiments. It was found that MFD sensor was more suitable for the detection of the outer carburized layer of furnace tube than MAF sensor, while PSD obtained by MAF could be used for qualitative evaluation of the depth of inner carburized layer.
查看更多>>摘要：The development of wireless communication leads to the gradual reduction of spectrum resources. As an essential part of cognitive radio (CR) technology, wideband spectrum sensing can detect whether the current authorized spectrum is being occupied, which is the prerequisite for dynamic spectrum allocation. In this paper, by utilizing the correlation of cyclic prefixes in the frequency domain, we propose a novel spectrum sensing algorithm applied to wideband scenarios with the existence of noise uncertainty (NU). Based on the proposed algorithm, we construct the dual-threshold cooperative spectrum sensing framework by integrating the multiuser cooperative model and dual-threshold detection model to enhance the effectiveness and reliability of spectrum sensing. With the parameters of 5G New Radio (5G-NR), we consider a cooperative spectrum sensing scheme to evaluate system performance. The simulation results demonstrate that the proposed framework exhibits better wideband spectrum sensing performance than traditional algorithms and effectively reduces the overhead of system.
查看更多>>摘要：A design method of spectral tunable radiation source with supercontinuum laser as the radiation source and digital micro-mirror device (DMD) as the spectral modulation device is proposed. Adopt the design idea of reducing the relative aperture of the spectral imaging system to improve the imaging quality, use cylindrical collimating beam expanding system to replace the collimating objective in the traditional spectral imaging system, and the relative aperture of the system is reduced in the dispersion direction. According to the test results, in narrow-band mode, the maximum full width at half maximum (FWHM) of monochromatic light bandwidth in the spectrum range of 450-1000 nm is 3.86 nm. In the broad-band mode, four typical stellar color temperature of T = 2600 K, 3500 K, 7000 K, 11,000 K are simulated, and the simulation results show that the accuracy of the star spectrum is better than 3.1% at 2600 K, 4.2% at 3500 K, 2.8% at 7000 K and 5.1% at 11,000 K.
查看更多>>摘要：In this paper, a synthesis of a novel grass-like Pt-doped NiCo2O4 nanocomposite (GL Pt-doped NiCo2O4) is demonstrated. The characterization of fabricated nanocomposite was performed using the techniques such as scanning electron microscope (SEM), Energy-dispersive X-ray spectroscopy (EDX) and X-ray diffraction (XRD). Further, this nanocomposite was used for the modification of screen-printed electrode (SPE). A suitable electrocatalytic behavior was obtained for GL Pt-doped NiCo2O4/SPE to detect amlodipine (AML) selectively and sensitively in presence of different routine interfering ions and physiological compounds. The AML determination showed broad linearity (0.07 to 350 mu M) using differential pulse voltammetry (DPV), and limit of detection (LOD) and limit of quantification (LOQ) of 0.009 and 0.027 mu M were obtained respectively. Moreover, acceptable recovery for AML by the present modified electrode was obtained in drug and biological samples.
查看更多>>摘要：Automated vehicles are prone to traffic accidents in severe weather conditions. Real-time vehicle detection can improve the driving safety of automated vehicles. This paper proposes a new vehicle detection method based on multi-sensor fusion to improve the vehicle detection performance in severe weather conditions. First, an efficient vehicle target extraction method from the radar is proposed that uses supervised learning to train a classifier based on LightGBM. This method does not require complex prior knowledge to determine the target segmentation threshold and transforms the target extraction into a data-driven classification. The vehicle target extraction method based on LightGBM has 95.5% accuracy and a 96% true positive rate. Second, we estimate the potential area of vehicles from infrared images according to the distribution of radar targets and predict the region of interest (ROI) of vehicles based on pixel regression. The ROI extraction method based on radar can avoid complicated calculations and interference of heat sources in the environment, which will greatly improve the speed and accuracy of ROI extraction. Radar-based ROI extraction only takes 4 ms, which is much lower than image-based ROI extraction. Finally, four new Haar-like feature templates are designed to improve the vehicle detection performance, which can improve the detection accuracy by 2.9%. This method has a 92.4% detection accuracy and a 43 Fps detection speed in the mad test, which significantly improves the vehicle detection performance in severe weather.
NSTL
Elsevier