Using deep neural network with small dataset to predict material defects

Deep neural network (DNN) exhibits state-of-the-art performance in many fields including microstructure recognition where big dataset is used in training. However, DNN trained by conventional methods with small datasets commonly shows worse performance than traditional machine learning methods, e.g. shallow neural network and support vector machine. This inherent limitation prevented the wide adoption of DNN in material study because collecting and assembling big dataset in material science is a challenge. In this study, we attempted to predict solidification defects by DNN regression with a small dataset that contains 487 data points. It is found that a pre-trained and fine-tuned DNN shows better generalization performance over shallow neural network, support vector machine, and DNN trained by conventional methods. The trained DNN transforms scattered experimental data points into a map of high accuracy in high-dimensional chemistry and processing parameters space. Though DNN with big datasets is the optimal solution, DNN with small datasets and pre-training can be a reasonable choice when big datasets are unavailable in material study

WRD-Net: Water Reflection Detection Using A Parallel Attention Transformer

In contrast to symmetry detection, Water Reflection Detection (WRD) is less studied. We treat this topic as a Symmetry Axis Point Prediction task which outputs a set of points by implicitly learning Gaussian heat maps and explicitly learning numerical coordinates. We first collect a new data set, namely, the Water Reflection Scene Data Set (WRSD). Then, we introduce a novel Water Reflection Detection Network, i.e., WRD-Net. This network is built on top of a series of Parallel Attention Vision Transformer blocks with the Atrous Spatial Pyramid (ASP-PAViT) that we deliberately design. Each block captures both the local and global features at multiple scales. To our knowledge, neither the WRSD nor the WRD-Net has been used for water reflection detection before. To derive the axis of symmetry, we perform Principal Component Analysis (PCA) on the points predicted. Experimental results show that the WRD-Net outperforms its counterparts and achieves the true positive rate of 0.823 compared with the human annotation.</p

Application of deep transfer learning to predicting crystal structures of inorganic substances

Convolutional neural network (CNN) consists of shallow learning machine and automatic feature extractor. The feature extractor of a well-trained CNN on a big dataset can be reused in related tasks with small datasets. This technique is called deep transfer learning which not only bypasses manual feature engineering but also improves the generalization of new models. In this study, we attempted to predict crystal structures of inorganic substances, a challenge for material science, with CNN and transfer learning. CNNs were trained on a big dataset of 228 k compounds from open quantum materials database (OQMD). The feature extractors of the well-trained CNNs were reused for extracting features on a phase prototypes dataset (containing 17 k inorganic substances and involving 170 crystal structures) and two high-entropy alloy datasets. The extracted features were then fed into random forest classifier as input. High classification accuracy (above 0.9) was achieved in three datasets. The visualization of the extracted features proved the effectiveness of the transferable feature extractors. This method can be easily adopted in quickly building machine learning models of good performance without resorting to time-consuming manual feature engineering routes

WRD-Net: Water Reflection Detection Using A Parallel Attention Transformer

In contrast to symmetry detection, Water Reflection Detection (WRD) is less studied. We treat this topic as a Symmetry Axis Point Prediction task which outputs a set of points by implicitly learning Gaussian heat maps and explicitly learning numerical coordinates. We first collect a new data set, namely, the Water Reflection Scene Data Set (WRSD). Then, we introduce a novel Water Reflection Detection Network, i.e., WRD-Net. This network is built on top of a series of Parallel Attention Vision Transformer blocks with the Atrous Spatial Pyramid (ASP-PAViT) that we deliberately design. Each block captures both the local and global features at multiple scales. To our knowledge, neither the WRSD nor the WRD-Net has been used for water reflection detection before. To derive the axis of symmetry, we perform Principal Component Analysis (PCA) on the points predicted. Experimental results show that the WRD-Net outperforms its counterparts and achieves the true positive rate of 0.823 compared with the human annotation.</p

Transformation from human-readable documents and archives in arc welding domain to machine-interpretable data

The capability of extracting useful information from documents and further transferring into knowledge is essential to advance technology innovations in industries. However, the overwhelming majority of scientific literature primarily published as unstructured human-readable formats is incompatible for machine analysis via contemporary artificial intelligence (AI) methods that effectively discovers knowledge from data. Therefore, the extraction approach transforming of unstructured data are fundamental in establishing state-of-the-art digital knowledge-based platforms. In this paper, we integrated multiple Python libraries and developed a method as a cohesive package for automated data extraction and quick processing to convert unstructured documents into machine-interpretable data. Transformed data can be further incorporated with AI analytical methods. The output files have shown excellent quality of digitalised data without major flaws in terms of context inconsistency. All scripts were written in Python with functional modules providing easy accessibility and proficiency to achieve objectives. Eventually, the finalised well-structured data can be implemented for further knowledge discovery

(a) Plot of the analytical intraband current <em>j</em>_2D(ψ) (blue solid line), its hyperbolic tangent approximation (black dash–dotted line) and its relativistic approximation (red dashed line)

<p><strong>Figure 2.</strong> (a) Plot of the analytical intraband current <em>j</em>_2D(ψ) (blue solid line), its hyperbolic tangent approximation (black dash–dotted line) and its relativistic approximation (red dashed line). (b)–(d) Plots of analytical intraband current <em>j</em>_2D(<em>t</em>) when ψ(<em>t</em>) = ψ₀sech(<em>t</em>/<em>t</em>₀)cos(5<em>t</em>/<em>t</em>₀) (blue solid line), for ψ₀ = 0.2, 1 and 3 respectively, corresponding to the three black dots in (a). The same curves but using the tanh and the relativistic approximations are also shown by the black dash–dotted and the red dashed lines, respectively.</p> <p><strong>Abstract</strong></p> <p>We propose an electrically tunable graphene-based metamaterial that shows a large nonlinear optical response at THz frequencies. The responsible nonlinearity comes from the intraband current, which we are able to calculate analytically. We demonstrate that the proposed metamaterial supports stable 2D spatial solitary waves. Our theoretical approach is not restricted to graphene, but can be applied to all materials exhibiting a conical dispersion supporting massless Dirac fermions.</p

Phase-to-phase wave parameters measurement of distribution lines based on BP networks

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(a) Soliton profiles of the fundamental mode (<em>n</em> = 0) and two higher order modes (<em>n</em> = 1 and <em>n</em> = 2) as a function of dimensionless radius <em>R</em>, for <em>q</em> = −0.7

<p><strong>Figure 3.</strong> (a) Soliton profiles of the fundamental mode (<em>n</em> = 0) and two higher order modes (<em>n</em> = 1 and <em>n</em> = 2) as a function of dimensionless radius <em>R</em>, for <em>q</em> = −0.7. (b) Same as (a), but for <em>q</em> = −0.1. (c), (d) 3D plots of the fundamental and the second-order soliton of (a), (b), respectively.</p> <p><strong>Abstract</strong></p> <p>We propose an electrically tunable graphene-based metamaterial that shows a large nonlinear optical response at THz frequencies. The responsible nonlinearity comes from the intraband current, which we are able to calculate analytically. We demonstrate that the proposed metamaterial supports stable 2D spatial solitary waves. Our theoretical approach is not restricted to graphene, but can be applied to all materials exhibiting a conical dispersion supporting massless Dirac fermions.</p

Prediction of re-oxidation behaviour of ultra-low carbon steel by different slag series

A kinetic model was developed using FactSage Macro Processing to simulate the re-oxidation of ultra-low carbon steel via different oxidising slags. The calculated results show good agreement with experimental laboratory thermal simulation data. Therefore, the model can be used to predict the change behaviour of slag-metal-inclusion in the re-oxidation reaction of liquid steel. It can provide prediction and guidance for an accurate secondary oxidation control process. During the slag re-oxidation process, when the oxygen in the steel is supersaturated and the slag is low in oxidation, it can easily form stick-like and dendritic shape inclusions of Al2O3 in steel. As the (FeO) content increases in slag, the oxygen transfer from slag to steel is evident, and the inclusion size increases, showing clusters and spherical shapes. In addition, supersaturated oxygen in steel easily forms unstable Al2O3-TiOx inclusions with [Ti]. As the components of liquid steel tend to be uniform, the Al2O3-TiOx inclusions will decompose and disappear, forming stable Al2O3 and TiO2 inclusions. The number of inclusions can be reduced by increasing the basicity and the ratio of CaO to Al2O3 in the initial slag

(a) Schematics of graphene conical dispersion with doping

<p><strong>Figure 1.</strong> (a) Schematics of graphene conical dispersion with doping. Intraband and interband optical transitions are indicated. (b) Geometry of the proposed multilayer metamaterial. The structure is made up of alternating graphene–silica–silicon layers, with total thickness <em>L</em>, much smaller than the wavelength of the THz beam. Each layer of graphene is doped by using an applied gate voltage <em>V</em>_g.</p> <p><strong>Abstract</strong></p> <p>We propose an electrically tunable graphene-based metamaterial that shows a large nonlinear optical response at THz frequencies. The responsible nonlinearity comes from the intraband current, which we are able to calculate analytically. We demonstrate that the proposed metamaterial supports stable 2D spatial solitary waves. Our theoretical approach is not restricted to graphene, but can be applied to all materials exhibiting a conical dispersion supporting massless Dirac fermions.</p