首页|Researchers from North China Electric Power University Detail New Studies and Fi ndings in the Area of Machine Learning (Learnable Bilevel Optimization Method fo r Electrical Capacitance Tomography)
Researchers from North China Electric Power University Detail New Studies and Fi ndings in the Area of Machine Learning (Learnable Bilevel Optimization Method fo r Electrical Capacitance Tomography)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting from Beijing, People's Republic of C hina, by NewsRx journalists, research stated, "The positive role of electrical c apacitance tomography technology depends on high -precision tomographic images. Despite its success, one of the main barriers is the low -quality tomogram." Financial support for this research came from National Key Research and Develop- ment Program of China. The news correspondents obtained a quote from the research from North China Elec tric Power University, "A new learnable bilevel optimization imaging method is p roposed to address this problem in this study, in which the image prior and mode l parameters can be learned from the collected datasets. The upper level optimiz ation problem learns the regularization parameter under the constraint of the lo wer level optimization problem that implements image reconstruction. A new lower level optimization problem with the introduced machine learning prior is built, which leverages the prior knowledge from collected datasets, imaging targets an d imaging mechanisms. The machine learning prior is learned through extreme lear ning machine, and the training is reformulated into a fractional optimization pr oblem with the physical mechanisms of imaging as a constraint. A new optimizer i s proposed to solve the learnable bilevel optimization imaging problem. The effe ctiveness has been demonstrated by the reconstruction of higher precision images and better noise immunity in comparison with advanced imaging techniques."
BeijingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningNorth China Electric Power University
NETL
NSTL
