Phage display is a technology of gene expression and screening, it is widely used in the fields of defining antigen epitopes, signal transduction, genetic treatment, parasites research and tumor targeted therapy. Brea...Phage display is a technology of gene expression and screening, it is widely used in the fields of defining antigen epitopes, signal transduction, genetic treatment, parasites research and tumor targeted therapy. Breast cancer is the most common cancer in women, we can obtain peptides specially associated with breast cancer by using phage display technology, and this method has great potential in early diagnosis of breast cancer and development new targeted drugs.展开更多
Bionic hands are promising devices for assisting individuals with hand disabilities in rehabilitation robotics.Controlled primarily by bioelectrical signals such as myoelectricity and EEG,these hands can compensate fo...Bionic hands are promising devices for assisting individuals with hand disabilities in rehabilitation robotics.Controlled primarily by bioelectrical signals such as myoelectricity and EEG,these hands can compensate for lost hand functions.However,developing model-based controllers for bionic hands is challenging and time-consuming due to varying control parameters and unknown application environments.To address these challenges,we propose a model-free approach using reinforcement learning(RL)for designing bionic hand controllers.Our method involves mimicking real human hand motion with the bionic hand and employing a human hand motion decomposition technique to learn complex motions from simpler ones.This approach significantly reduces the training time required.By utilizing real human hand motion data,we design a multidimensional sampling proximal policy optimization(PPO)algorithm that enables efficient motion control of the bionic hand.To validate the effectiveness of our approach,we compare it against advanced baseline methods.The results demonstrate the quick learning capabilities and high control success rate of our method.展开更多
基金Supported by a grant from Science and Technology Planning Project ofGuangdong Province(No:2010B031600066)
文摘Phage display is a technology of gene expression and screening, it is widely used in the fields of defining antigen epitopes, signal transduction, genetic treatment, parasites research and tumor targeted therapy. Breast cancer is the most common cancer in women, we can obtain peptides specially associated with breast cancer by using phage display technology, and this method has great potential in early diagnosis of breast cancer and development new targeted drugs.
基金supported by Su Yan Yuan(“Development and industrialization of intelligent multi-degree-of-freedom arm based on perceptual fusion and collaborative control"(Su Yan Yuan[2019]No.107))Shanghai DianJi University(“Research on flexible joint and adaptive control technology for new upper limb prosthesis"(scientific research start-up fund project of Shanghai DianJi University)and“Research on robot intelligent grasping technology based on visual touch fusion in unstructured environment"(Science and technology[2020]No.79 of Shanghai DianJi University)).
文摘Bionic hands are promising devices for assisting individuals with hand disabilities in rehabilitation robotics.Controlled primarily by bioelectrical signals such as myoelectricity and EEG,these hands can compensate for lost hand functions.However,developing model-based controllers for bionic hands is challenging and time-consuming due to varying control parameters and unknown application environments.To address these challenges,we propose a model-free approach using reinforcement learning(RL)for designing bionic hand controllers.Our method involves mimicking real human hand motion with the bionic hand and employing a human hand motion decomposition technique to learn complex motions from simpler ones.This approach significantly reduces the training time required.By utilizing real human hand motion data,we design a multidimensional sampling proximal policy optimization(PPO)algorithm that enables efficient motion control of the bionic hand.To validate the effectiveness of our approach,we compare it against advanced baseline methods.The results demonstrate the quick learning capabilities and high control success rate of our method.
