The first-principles calculations based on density functional theory are used to obtain structural, mechanical, and electronic properties of Zr-Te compounds. The optimized structural parameters are consistent with the available experimental data. The calculated mechanical properties and formation energy show that the Zr-Te compounds are all mechanically and thermodynamically stable. The bulk modulus B, shear modulus G, Young's modulus E, Debye temperature Θ D , and sound velocity v m are listed, which are positively correlated with the increasing of atomic fraction of Zr. The behaviors of density of states of Zr-Te compounds are obtained. Furthermore, the electronic properties are discussed to clarify the bonding characteristics of compounds. The electronic characteristics demonstrate that the Zr-Te systems with different phases are both covalent and metallic.
X-ray flaw detection is widely used in non-destructive area. The intuitive defect information can be obtained through the X-ray film, which is usually digitized into high greyscale image by a 12-bit or 16-bit, called super 8-bit, industrial scanner. When an ordinary 8bit monitor displays a super 8-bit greyscale image, it appears loss of detail information, blurring of the image appears and other problems. Therefore, in this paper, a pseudocolour enhancement algorithm for displaying high-bit RAW images on low-bit monitor was proposed according to the chromatographic mapping relationship based on the visual characteristics of human eye. First, a high grey-scale pseudo-colour enhancement algorithm, called HOTM-HGL, based on hot metal coding was proposed based on the standard film, whose enhancement effect is better than current mainstream algorithms. Second, aiming at the non-standard film, a new stretching function called RAW-Optical-Stretching was reconstructed to improve HOTM-HGL algorithm, called HOTM-HGLS algorithm, whose display effect on ordinary monitors was improved in further. Finally, HOTM-HGLS algorithm was applied in the detection of X-ray film defect, which was convenient for the capture of defect information in the weld. Compared with the existing algorithms, various indicators have been greatly improved, enriching the amount of information and strengthening the image recognition effects.
Maximum a posteriori estimation (MAP) with Dirichlet prior has been shown to be effective in improving the parameter learning of Bayesian networks when the available data are insufficient. Given no extra domain knowledge, uniform prior is often considered for regularization. However, when the underlying parameter distribution is non-uniform or skewed, uniform prior does not work well, and a more informative prior is required. In reality, unless the domain experts are extremely unfamiliar with the network, they would be able to provide some reliable knowledge on the studied network. With that knowledge, we can automatically refine informative priors and select reasonable equivalent sample size (ESS). In this paper, considering the parameter constraints that are transformed from the domain knowledge, we propose a Constrained adjusted Maximum a Posteriori (CaMAP) estimation method, which is featured by two novel techniques. First, to draw an informative prior distribution (or prior shape), we present a novel sampling method that can construct the prior distribution from the constraints. Then, to find the optimal ESS (or prior strength), we derive constraints on the ESS from the parameter constraints and select the optimal ESS by cross-validation. Numerical experiments show that the proposed method is superior to other learning algorithms.
Electrothermal actuation is one of the main actuation mechanisms and has been employed to make scanning microelectromechanical systems (MEMS) mirrors with large scan range, high fill factor, and low driving voltage, but there exist long-term drifting issues in electrothermal bimorph actuators whose causes are not well understood. In this paper, the stability of an Al/SiO2 bimorph electrothermal MEMS mirror operated in both static and dynamic scan mode has been studied. Particularly, the angular drifts of the MEMS mirror plate were measured over 90 h at different temperatures in the range of 50–150 °C. The experiments show that the temporal drift of the mirror plate orientation largely depends on the temperature of the electrothermal bimorph actuators. Interestingly, it is found that the angular drift changes from falling to rising as the temperature increases. An optimal operating temperature between 75 °C to 100 °C for the MEMS mirror is identified. At this temperature, the MEMS mirror exhibited stable scanning with an angular drift of less than 0.0001°/h.
In order to overcome the problems of poor accuracy and high complexity of current classification algorithm for non-equilibrium data set, this paper proposes a decision tree classification algorithm for non-equilibrium data set based on random forest. Wavelet packet decomposition is used to denoise non-equilibrium data, and SNM algorithm and RFID are combined to remove redundant data from data sets. Based on the results of data processing, the non-equilibrium data sets are classified by random forest method. According to Bootstrap resampling method with certain constraints, the majority and minority samples of each sample subset are sampled, CART is used to train the data set, and a decision tree is constructed. Obtain the final classification results by voting on the CART decision tree classification. Experimental results show that the proposed algorithm has the characteristics of high classification accuracy and low complexity, and it is a feasible classification algorithm for non-equilibrium data set.
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