Nonmonotone Adaptive Barzilai-Borwein Gradient Algorithm for Compressed Sensing

We study a nonmonotone adaptive Barzilai-Borwein gradient algorithm for l1-norm minimization problems arising from compressed sensing. At each iteration, the generated search direction enjoys descent property and can be easily derived by minimizing a local approximal quadratic model and simultaneously taking the favorable structure of the l1-norm. Under some suitable conditions, its global convergence result could be established. Numerical results illustrate that the proposed method is promising and competitive with the existing algorithms NBBL1 and TwIST

Calibration method of center of rotation under the displaced detector scanning for industrial CT

For computed tomography, projection center of rotation (COR) is a significant reconstruction parameter which needs to be precisely measured before an image reconstruction. Otherwise, serious flame-shaped artifacts will arise in the CT images. In particular, when a displaced detector scanning mode is used for the inspection of a large object, the line connecting the X-ray focus and the COR is not perpendicular to the detector, making the calibration of the COR more difficult. In this paper, a new calibration method of the COR based on the symmetrical property of a sinogram is proposed. According to this method, the possible COR is enumerated among a range and the original sinogram is transformed onto a virtual detector. Then all the variances between the sum of the projection data on the left half side of the virtual detector and that on the right half side of the virtual detector are calculated. Finally, the accurate COR is determined by seeking out the minimum value of the variances. The proposed method does not need any dedicated phantom to complete the calibration, but rather directly makes use of the sinogram of the scanned sample to search the COR. Because all the projection data collected by a detector unit is summed up to perform the calibration, the algorithm is not sensitive to random noise, which has been proved by the simulated data with high-level noise and the experimental data

Microstructure, Micro-Indentation, and Scratch Behavior of Cr Films Prepared on Al alloys by Using Magnetron Sputtering

In this study, closed-field unbalanced magnetron sputtering (CFUMS) was employed to deposit pure Cr films on soft substrate of 2024 Al alloy. The effects of deposition powers and biases on the microstructures and mechanical performance of Cr films were systematically investigated by using X-ray diffraction (XRD), scanning electron microscope (SEM), micro-indentation and scratch test. Results showed that all the Cr films had a strong (110) preferred orientation and anisotropic surface morphology with columnar structures. The size of Cr particles was in the range of 50–350 nm, increasing with larger target power and higher biases. The hardness of Cr films was between 3.3 and 4.8 GPa, which was much higher than the Al alloy substrate (1.44 GPa). The Young’s modulus of Cr film could reach a maximum value of 169 GPa at 2.0 kW/70 V. The critical load increased when increasing the power but decreased with higher bias, achieving a maximum value of 53.83 N at 2.0 kW/10 V. The adhesive failure mechanism of Cr film was mainly attributed to the plastic deformation of softer Al substrate

Modelling and Prediction of Random Delays in NCSs Using Double-Chain HMMs

This paper is concerned with the modelling and prediction of random delays in networked control systems. The stochastic distribution of the random delay in the current sampling period is assumed to be affected by the network state in the current sampling period as well as the random delay in the previous sampling period. Based on this assumption, the double-chain hidden Markov model (DCHMM) is proposed in this paper to model the delays. There are two Markov chains in this model. One is the hidden Markov chain which consists of the network states and the other is the observable Markov chain which consists of the delays. Moreover, the delays are also affected by the hidden network states, which constructs the DCHMM-based delay model. The initialization and optimization problems of the model parameters are solved by using the segmental K-mean clustering algorithm and the expectation maximization algorithm, respectively. Based on the model, the prediction of the controller-to-actuator (CA) delay in the current sampling period is obtained. The prediction can be used to design a controller to compensate the CA delay in the future research. Some comparative experiments are carried out to demonstrate the effectiveness and superiority of the proposed method