Time-domain Green dyadics for temporally dispersive, bi-isotropic media

Time-domain Green dyadics for linear, homogeneous, temporally dispersive bi-isotropic media are presented. Complex time-dependent electromagnetic field is introduced. Approximation to the complex field from an electric point dipole in an unbounded bi-isotropic medium with respect to the slowly varying components (second forerunner approximation) is obtained. Numerical examples are presented. Surface integral equations for the tangential components of the electromagnetic fields are derived for two standard scattering problems

Quantization of spinor field in the Schwarzschild spacetime and spin sums for solutions of the Dirac equation

We discuss the problem of canonical quantization of a free massive spinor field in the Schwarzschild spacetime. It is shown that a consistent procedure of canonical quantization of the field can be carried out without taking into account the internal region of the black hole, the canonical commutation relations in the resulting theory hold exactly and the Hamiltonian has the standard form. Spin sums are obtained for solutions of the Dirac equation in the Schwarzschild spacetime.Comment: 19 pages, 1 figur

Transient electromagnetic wave propagation in laterally discontinuous, dispersive media

This paper concerns propagation of transient electromagnetic waves in laterally discontinuous dispersive media. The approach, used here, employs a component decomposition of all fields. Specifically, the propagation operator that maps a transverse field on one plane to another plane is specified. Expansion of this mapping near the wave front determines the precursor or forerunner of the problem

Anomaly segmentation model for defects detection in electroluminescence images of heterojunction solar cells

Efficient defect detection in solar cell manufacturing is crucial for stable green energy technology manufacturing. This paper presents a deep-learning-based automatic detection model SeMaCNN for classification and semantic segmentation of electroluminescent images for solar cell quality evaluation and anomalies detection. The core of the model is an anomaly detection algorithm based on Mahalanobis distance that can be trained in a semi-supervised manner on imbalanced data with small number of digital electroluminescence images with relevant defects. This is particularly valuable for prompt model integration into the industrial landscape. The model has been trained with the on-plant collected dataset consisting of 68 748 electroluminescent images of heterojunction solar cells with a busbar grid. Our model achieves the accuracy of 92.5%, F1 score 95.8%, recall 94.8%, and precision 96.9% within the validation subset consisting of 1049 manually annotated images. The model was also tested on the open ELPV dataset and demonstrates stable performance with accuracy 94.6% and F1 score 91.1%. The SeMaCNN model demonstrates a good balance between its performance and computational costs, which make it applicable for integrating into quality control systems of solar cell manufacturing

Algorithms for design optimization of chemistry of hard magnetic alloys using experimental data

A multi-dimensional random number generation algorithm was used to distribute chemical concentrations of each of the alloying elements in the candidate alloys as uniformly as possible while maintaining the prescribed bounds on the minimum and maximum allowable values for the concentration of each of the alloying elements. The generated candidate alloy compositions were then examined for phase equilibria and associated magnetic properties using a thermodynamic database in the desired temperature range. These initial candidate alloys were manufactured, synthesized and tested for desired properties. Then, the experimentally obtained values of the properties were fitted with a multi-dimensional response surface. The desired properties were treated as objectives and were extremized simultaneously by utilizing a multi-objective optimization algorithm that optimized the concentrations of each of the alloying elements. This task was also performed by another conceptually different response surface and optimization algorithm for double-checking the results. A few of the best predicted Pareto optimal alloy compositions were then manufactured, synthesized and tested to evaluate their macroscopic properties. Several of these Pareto optimized alloys outperformed most of the candidate alloys on most of the objectives. This proves the efficacy of the combined meta-modeling and experimental approach in design optimization of the alloys. A sensitivity analysis of each of the alloying elements was also performed to determine which of the alloying elements contributes the least to the desired macroscopic properties of the alloy. These elements can then be replaced with other candidate alloying elements such as not-so-rare earth elements

Self-Organizing Maps for Pattern Recognition in Design of Alloys

A combined experimental\u2013computational methodology for accelerated design of AlNiCo-type permanent magnetic alloys is presented with the objective of simultaneously extremizing several magnetic properties. Chemical concentrations of eight alloying elements were initially generated using a quasirandom number generator so as to achieve a uniform distribution in the design variable space. It was followed by manufacture and experimental evaluation of these alloys using an identical thermo-magnetic protocol. These experimental data were used to develop meta-models capable of directly relating the chemical composition with desired macroscopic properties of the alloys. These properties were simultaneously optimized to predict chemical compositions that result in improvement of properties. These data were further utilized to discover various correlations within the experimental dataset by using several concepts of artificial intelligence. In this work, an unsupervised neural network known as selforganizing maps was used to discover various patterns reported in the literature. These maps were also used to screen the composition of the next set of alloys to be manufactured and tested in the next iterative cycle. Several of these Pareto-optimized predictions out-performed the initial batch of alloys. This approach helps significantly reducing the time and the number of alloys needed in the alloy development process