The Chinese University of Hong Kong

Chenglin Li

Shih-Chi Chen

Ji Wang

Xiaogang Wang

Hongsheng Li

XiangBo Liu

In some embodiments, a control system, a control method and a storage medium are provided. In the method, first motion information of a machine acquired by a first sensor is received; the first motion information is inputted into a deep learning model to obtain a model output, the deep learning model comprising a convolutional neural network (CNN) and a long short-term memory (LSTM); the deep learning model is trained using the first motion information and second motion information acquired by a second sensor; the first sensor and the second sensor having different ways of detecting information and processing the detected information. The model output is used to control the machine.

Control system, control method and computer storage medium

The present application discloses a microtome. The microtome includes: a blade cutting a soft material in a first direction, the first direction being a feeding direction of the soft material; a blade holder holding the blade; an actuator providing a vibration in a second direction along a cutting edge of the blade; and a frequency-tunable resonator driven by the actuator into vibration and fixedly connected to the blade holder to transfer the vibration to the blade holder and the blade, the resonator having a tunable resonant frequency in the second direction.

Microtome

A compliant apparatus for nano-manufacture, including a stage for supporting the objects to be nano-manufactured. The stage includes at least one flexural beam and at least one actuator coupled to the flexural beam; and the actuator is configured to generate and apply axial loads onto the flexural beam, such that a natural frequency of the flexural beam is shifted in response to the generated axial loads, so as to allow trade-offs between the natural frequency and a stroke of the stage for nano-manufacturing the objects.

Growing community of inventors

Hong Kong, China

Chenglin Li