首页|Studies from Chinese Academy of Sciences Further Understanding of Machine Learni ng (Machine Learning Assisted Characterization of Local Bubble Properties and It s Coupling With the Emms Bubbling Drag)
Studies from Chinese Academy of Sciences Further Understanding of Machine Learni ng (Machine Learning Assisted Characterization of Local Bubble Properties and It s Coupling With the Emms Bubbling Drag)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news originating from Beijing, People's Re public of China, by NewsRx correspondents, research stated, "Empirical correlati ons for bubble diameter and velocity are incapable of predicting the local bubbl e behaviors fairly because the impact of local hydrodynamics on bubbles in fluid ized beds. Based on image processing, a novel bubble identification method with an adaptive threshold was proposed to distinguish and characterize bubbles in fl uidized beds." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Youth Innovation Promotion Association of the Chinese Academ y of Sciences, Transformational Technologies for Clean Energy and Demonstration Strategic Priority Research Program of the Chinese Academy of Sciences. Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, "The information regarding bubble properties and local hydrodynami cs can thus be extracted using the big data from highly resolved simulations. Ac cordingly, the deep neural network was trained to accurately predict local bubbl e properties, where the inputs were determined by performing correlation analysi s and a random forest algorithm. We found Reynolds number, voidage, and relative coordinates are the dominant factors, and a four-variable choice was demonstrat ed to output satisfactory performance for predicting local bubble diameter and v elocity."
BeijingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningChinese Academy of Sciences
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