A research team from the Ningbo Institute of Materials Technology and Engineering of the Chinese Academy of Sciences has developed a new method to enhance the efficiency of dynamics modeling for industrial robots, tackling long-standing bottlenecks in real-time torque computation.
A research team led by Prof. SUN Youwen from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has developed two innovative artificial intelligence systems to enhance the safety and efficiency of fusion energy experiments.
A research team from the Shenyang Institute of Automation (SIA) of the Chinese Academy of Sciences has developed a novel online measurement method based on multi-camera vision and regional pre-segmentation. This new approach enables real-time, high-precision inspection of the spatial dimensions of flanged and jointed tubes.
A research team led by Prof. LI Hai from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has developed a novel deep learning framework that significantly improves the accuracy and interpretability of detecting neurological disorders through speech.
Dr. LIN Mingqiang's group from the Fujian Institute of Research on the Structure of Matter of the Chinese Academy of Sciences proposed an ensemble convolutional neural network based transfer learning framework for achievable lithium-ion battery health perception in flexible charging procedure.
On June 18, the Lower Hybrid Current Drive system — a critical subsystem of the Comprehensive Research Facility for Fusion Technology — successfully passed expert testing and was officially accepted, marking a significant milestone for the project.
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