Ansys, Inc.

Rplushealth Limited

Xi Chen

Norman H Chang

Jimin Wen

Akhilesh Kumar

Wen-Tze Chuang

Jay Prakash Pathak

Lei Yin

Ming-Chih Shih

En Cih Yang

Bolong Zhang

Chunshui He

Ying Shiun Li

Jiang Huang

Ling Zeng

Li Chen

Fengming Yang

Chi Lei

Guo Zhao

A specification for a semi-conductor chip is received. The specification specifies a set of photomasks associated with a metal layer of the semi-conductor chip. Multiple portions of an area of the metal layer are identified. A respective image is generated for each portion of the area based on the photomasks. A respective drawn density of metal wires for each portion of the area is calculated. A trained machine learning model is invoked to predict a respective silicon density of metal wires for each respective portion of the area based on an image and a drawn density for the respective portion of the area. A silicon density for the area of the metal layer is calculated based on a combination of predicted silicon densities for the multiple portions of the area. The combination is based on an average value of the predicted silicon densities for the multiple portions of the area.

Methods and systems for predicting silicon density for a metal layer of semi-conductor chip via machine learning

A movement adjustment system based on heart rates and rating of perceived exertion (RPE) feedbacks of different users is provided, including: a user client, a heart rate monitoring device, a server, and a doctor client. The user client includes an RPE self-rating module, an RPE data acquisition module, a user-side video movement display module, and a user-side data storage module. The heart rate monitoring device includes an exercise data acquisition module. The server includes a data input module, a core processor, and a video movement library. The doctor client includes a doctor-side video movement display module, a video movement sending module, and a doctor-side data storage module. The above system according to the present disclosure adjusts, based on objective heart rate feedbacks during exercise and subjective RPE feedbacks after the exercise of different users, movements of next exercise of a patient.

Movement adjustment system based on heart rates and rating of perceived exertion feedbacks of different users

The present disclosure relates to an apparatus and method for automatically adjusting an exercise prescription based on an exercise heart rate. The apparatus includes: a heart rate detection device, configured to detect a resting heart rate and a user's real-time exercise heart rate; an exercise movement library, configured to store various exercise instruction video and audio, and provide movement guidance for the user; an exercise guidance module, configured to retrieve and display movement video and audio from the exercise movement library to the user; and a central processing unit (CPU), configured to acquire the resting heart rate and the user's real-time exercise heart rate, calculate an effective exercise target heart rate range based on the resting heart rate, determine whether the real-time exercise heart rate falls within the effective exercise target heart rate range.

Apparatus and method for automatically adjusting exercise prescription based on exercise heart rate

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Chengdu, China

Xi Chen