Multi-voltage X-ray image sequence fusion based on selection of valid region

A key step is to extract valid information region in the fusion of multi-voltage X-ray image sequence for complicated components. To improve the self-adaption of extraction, a method is presented in this paper. In this paper, the valid informa-tion region is selected by the grey level interval, which is computed by the optimization of image quality evaluation model. The model is based on the histogram equalization and the grey level interval. Then, every valid region of images at different voltages is extracted and they are fused according their grey level transformation function. The fusion image contains completed struc-ture information of the component. The fusion experiment of a cylinder head shows the effectiveness of the presented method.

Design of a multi-voltage probe system for ICRF antenna coupling resistance measurement on EAST

A multi-voltage probe array system is designed to measure the coupling resistance of an ion cyclotron resonance frequency antenna. In the process of the antenna coupling resistance data extraction, the minimization algorithm, the original Levenberg–Marquardt algorithm, is replaced by the Broyden–Fletcher–Goldfarb–Shanno algorithm to achieve more stable and accurate results. Moreover, a simple model of the multi-voltage probe array was applied to simulate the performance of the Kalman filter, and to optimize the distance and position of the probes and probe number to mitigate the influence of the system noise on the rebuilt results. During the EAST experiment in 2019, a four-voltage probe array was applied to measure the coupling resistance of line 6 during high confined mode discharge. The measurement results by the multivoltage probe array system and the voltage/current probe pair show a good agreement.

Enhancing 3D Reconstruction Accuracy of FIB Tomography Data Using Multi‑voltage Images and Multimodal Machine Learning

FIB-SEM tomography is a powerful technique that integrates a focused ion beam(FIB)and a scanning electron microscope(SEM)to capture high-resolution imaging data of nanostructures.This approach involves collecting in-plane SEM imagesand using FIB to remove material layers for imaging subsequent planes,thereby producing image stacks.However,theseimage stacks in FIB-SEM tomography are subject to the shine-through effect,which makes structures visible from theposterior regions of the current plane.This artifact introduces an ambiguity between image intensity and structures in thecurrent plane,making conventional segmentation methods such as thresholding or the k-means algorithm insufficient.Inthis study,we propose a multimodal machine learning approach that combines intensity information obtained at differentelectron beam accelerating voltages to improve the three-dimensional(3D)reconstruction of nanostructures.By treatingthe increased shine-through effect at higher accelerating voltages as a form of additional information,the proposed methodsignificantly improves segmentation accuracy and leads to more precise 3D reconstructions for real FIB tomography data.