Abstract: An overhead hoist transfer apparatus includes a rail assembly including a straight rail having an empty section, and a curved rail having a curved empty section; an engine including a first LSD having first and second wheels at two sides respectively; and a second LSD having third and fourth wheels at two sides respectively; a moving carriage driven by the engine and suspended from the rail assembly; first and second guide wheels disposed on the first LSD; third and fourth guide wheels disposed on the second LSD; and two guide boards disposed above a joining point of the straight rail and the curved rail. An elevation of the guide boards is equal to that of the guide wheels. The guide board includes a straight edge and a curved edge.

Abstract: A powered industrial truck includes a lateral movement assembly including four sliding members and four pivotal members both on a wheeled carriage, four links having a first end pivotably secured to the sliding member and a second end pivotably secured to either end of the pivotal member, a motor shaft having two ends pivotably secured to the pivotal members respectively, a first electric motor on one frame member, and four mounts attached to the sliding members respectively; two lift assemblies including a second electric motor, a shaft having two ends rotatably secured to the sliding members respectively, two gear trains at the ends of the shaft respectively, a first gear connected to the second electric motor, a second gear on the shaft, and a first roller chain on the first and second gears; two electric attachments on the platform and being laterally moveable, each attachment. The mount has rollers.

Abstract: The present disclosure discloses a method and an apparatus for classifying a brain anomaly and apparatus based on a 3D MRI image, wherein the classifying method comprises: receiving a to-be-processed 3D MRI image, performing a convolution operation on an imaging sequence corresponding to the 3D MRI image based on a first neural network algorithm to obtain segment masks; and performing a classification operation on the imaging sequence corresponding to the 3D MRI image based on a second neural network algorithm and the segment masks to obtain a classification result of the brain anomaly. Supported by the technologies of artificial intelligence and big data processing, embodiments of the present disclosure enable classification of a brain anomaly shown in the received MRI image through training an MRI recognition model, thereby effectively improving classification accuracy of the brain anomaly and further enhancing diagnosis accuracy of the brain anomaly based on the MRI image.

Abstract: The present disclosure discloses a method and an apparatus for classifying a brain anomaly and apparatus based on a 3D MRI image, wherein the classifying method comprises: receiving a to-be-processed 3D MRI image, performing a convolution operation on an imaging sequence corresponding to the 3D MRI image based on a first neural network algorithm to obtain segment masks; and performing a classification operation on the imaging sequence corresponding to the 3D MRI image based on a second neural network algorithm and the segment masks to obtain a classification result of the brain anomaly. Supported by the technologies of artificial intelligence and big data processing, embodiments of the present disclosure enable classification of a brain anomaly shown in the received MRI image through training an MRI recognition model, thereby effectively improving classification accuracy of the brain anomaly and further enhancing diagnosis accuracy of the brain anomaly based on the MRI image.