Abstract: The present disclosure provides a self-learning collaborative control method for active steering and yaw moment for a motor vehicle, including a first step of constructing fundamental formulas which are stored in a vehicle ECU, and a second step of calculating an active steering angle ?C and a yaw moment Mc on line by the vehicle ECU according to following sub-steps during a driving process of the motor vehicle, and controlling a driving state of the motor vehicle according to ?C and Mc. The second step includes a first sub-step of collecting raw real-time parameter values, a second sub-step of performing calculation by the identifier and the control target reference model, a third sub-step of calculating ?C and Mc. The present disclosure can realize the self-learning collaborative control of active steering and yaw moment without requiring a system control model and correct a driver's steering operation.

Abstract: In the field of MRI, a model-based MRI image reconstruction technique is provided. The model-based reconstruction technique increases the performance of Time-of-Flight MRA. In a learning phase, a model is calculated from a sufficiently large set of data acquired at both low and high magnetic fields, using deep learning strategies. In a clinical phase, the model is applied to measured data generating high MR image quality.

Abstract: A local shimming device and a method for compensation of non-uniformity of a main magnetic field are disclosed, wherein the local shimming device comprises: a local coil, arranged to cover a local region of an examination subject, to receive a magnetic resonance signal from the local region; multiple shim coils, wherein the multiple shim coils are integrated in the local coil or arranged to surround at least a part of the local coil; and a shimming controller, configured to control at least one shim coil of the multiple shim coils, according to a strength distribution of a main magnetic field in the local region, to provide a field distribution for compensating a component of the main magnetic field in the local region.

Abstract: In the field of MRI, a model-based MRI image reconstruction technique is provided. The model-based reconstruction technique increases the performance of Time-of-Flight MRA. In a learning phase, a model is calculated from a sufficiently large set of data acquired at both low and high magnetic fields, using deep learning strategies. In a clinical phase, the model is applied to measured data generating high MR image quality.