Abstract
Investigators publish new report on artificial intelligence. According to news reporting out of Tokyo, Japan, by NewsRx editors, research stated, “The control of the velocity of a high-speed laser- induced microjet is crucial in applications such as needle-free injection. Previous studies have indicated that the jet velocity is heavily influenced by the volumes of secondary cavitation bubbles generated through laser absorption.” Financial supporters for this research include Japan Society For The Promotion of Science; Japan Science And Technology Agency. Our news editors obtained a quote from the research from Tokyo University of Agriculture and Technology: “However, there has been a lack of investigation of the relationship between the positions of secondary cavitation bubbles and the jet velocity. In this study, we investigate the effects of secondary cavitation on the jet velocity of laser-induced microjets extracted using explainable artificial intelligence (XAI). An XAI is used to classify the jet velocity from images of secondary cavitation and to extract features from the images through visualization of the classification process. For this purpose, we run 1000 experiments and collect the corresponding images. The XAI model, which is a feedforward neural network (FNN), is trained to classify the jet velocity from the images of secondary cavitation bubbles. After achieving a high classification accuracy, we analyze the classification process of the FNN.”
NETL
NSTL