3 results about "Optimal composition" patented technology

The disclosure relates to a simulation method for selecting an optimal composition ratio of an oxide semiconductor and an electronic device including the oxide semiconductor. The oxide semiconductor includes: at least two elements selected from the group consisting of indium (In), gallium (Ga), zinc (Zn), tin (Sn), silver (Ag), aluminum (Al), cadmium (Cd), magnesium (Mg), antimony (Sb), silicon (Si), titanium (Ti), and zirconium (Zr); oxygen (O); and unavoidable impurities. The simulation method includes: setting a simulation target composition ratio set including various composition ratios of elements constituting an oxide semiconductor; checking whether the oxide semiconductor satisfies Equation 1, Equation 2, and Equation 3 for each of the various Equation ratios included in the simulation target Equation ratio set; and selecting a composition ratio satisfying formula 1, formula 2, and formula 3 as an optimal composition ratio. Formula 1, Formula 2, and Formula 3 may be the same as described in the specification.

The embodiment of the invention relates to the field of aluminum-based material design, and discloses an aluminum-based material design method and device based on multi-performance integrated prediction.The method comprises the steps that according to multi-performance index requirements, optimal components and technological parameters of all working procedures are determined through a multi-performance integrated predication model; the multi-performance integrated prediction model is obtained by training based on a machine learning database at least containing components, process parameters, mechanical properties and defect information, the input comprises the components and the process parameters of each process, the output comprises multi-performance indexes, and the multi-performance indexes at least comprise the mechanical properties and the defect information; performing multi-performance index experimental verification; and under the condition that the verification result does not meet the multi-performance index requirement, adjusting the current optimal component and the process parameters of each process, and performing multi-performance index experimental verification again. According to the method, the problem of intelligently optimizing the design parameters of the aluminum-based material is solved, the aluminum-based material components and process parameters meeting the requirements of multiple performance indexes can be determined, and the quality of the aluminum-based material is remarkably improved.

The application discloses a data labeling method and device, equipment and a readable storage medium, which can be applied to the field of artificial intelligence technology, and automatically labels the composition frame score of each candidate frame based on a pre-trained aesthetic large model, performs first labeling to obtain a high-score composition image, performs second labeling based on the sample labeling score of the high-score composition image fed back by a client, obtains multiple optimal composition frames and corresponding labeling score results, the first labeling is realized based on automatic labeling, and the second optimal selection is realized based on a small amount of manual labeling, that is, it is not necessary to score each candidate frame manually, the pre-trained aesthetic large model and a small amount of manual intervention are used to obtain a labeling training set for retraining the aesthetic large model, the aesthetic large model is fine-tuned based on the labeling training set, the artificial cost of constructing the aesthetic large model is reduced, and the accuracy of automatic labeling of the aesthetic large model is improved.