Objective: To observe the effect of the joint injury of the distal radio-ulnar joint. Methods: 60 patients with Distal Radioulnar Joint (DRUJ) injury were divided into observation group and control group according to ...Objective: To observe the effect of the joint injury of the distal radio-ulnar joint. Methods: 60 patients with Distal Radioulnar Joint (DRUJ) injury were divided into observation group and control group according to random number method. 30 cases were included in each of the two groups.Before and after treatment in patients with Visual Analogue Scale (Visual Analogue Scale, VAS) score, forearm pronation and supination electromyographic activity, methods of electric integral value (integral electromyogram, iEMG) and Wrist in patients with self assessment Scale (Patient - Rated Wrist Evaluation, PRWE) score evaluation, comparison, and the clinical observation on diagnosis of disease and curative effect of traditional Chinese medicine standard (assessment process by blind method).Results: compared with the two groups before and after treatment, VAS score decreased, forearm pronation and postpronation activity increased, iEMG value increased, and PRWE scale score decreased (all P < 0.05), and the curative effect of the treatment group was better than that of the control group (P < 0.05). The total effective rate of the treatment group [93.3% (28/30)] was higher than that of the control group [50%(15/30), P < 0.05].Conclusion: the combined exercise training of muscle and bone setting technique can effectively alleviate the pain of patients with radial ulnar joint injury, improve the rotation of the forearm, increase the recruitment of the anterior rotatory muscle, and improve the wrist function of patients, and the effect is better than if combined with forearm support fixation.展开更多
The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. H...The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.展开更多
TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving i...TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving intrusion monitoring and interception.To address the challenges of data acquisition,real-world deployment,and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks,we propose an innovative swarm intelligencebased UAV pursuit-evasion control framework,namely“Boids Model-based DRL Approach for Pursuit and Escape”(Boids-PE),which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning(DRL).The Boids model,which simulates collective behavior through three fundamental rules,separation,alignment,and cohesion,is adopted in our work.By integrating Boids model with the Apollonian Circles algorithm,significant improvements are achieved in capturing UAVs against simple evasion strategies.To further enhance decision-making precision,we incorporate a DRL algorithm to facilitate more accurate strategic planning.We also leverage self-play training to continuously optimize the performance of pursuit UAVs.During experimental evaluation,we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios,customizing the state space,action space,and reward function models for each scenario.Extensive simulations,supported by the PyBullet physics engine,validate the effectiveness of our proposed method.The overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks,providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions.展开更多
基金Key project of nature fund of anhui department of education(No.KJ2018a0273).
文摘Objective: To observe the effect of the joint injury of the distal radio-ulnar joint. Methods: 60 patients with Distal Radioulnar Joint (DRUJ) injury were divided into observation group and control group according to random number method. 30 cases were included in each of the two groups.Before and after treatment in patients with Visual Analogue Scale (Visual Analogue Scale, VAS) score, forearm pronation and supination electromyographic activity, methods of electric integral value (integral electromyogram, iEMG) and Wrist in patients with self assessment Scale (Patient - Rated Wrist Evaluation, PRWE) score evaluation, comparison, and the clinical observation on diagnosis of disease and curative effect of traditional Chinese medicine standard (assessment process by blind method).Results: compared with the two groups before and after treatment, VAS score decreased, forearm pronation and postpronation activity increased, iEMG value increased, and PRWE scale score decreased (all P < 0.05), and the curative effect of the treatment group was better than that of the control group (P < 0.05). The total effective rate of the treatment group [93.3% (28/30)] was higher than that of the control group [50%(15/30), P < 0.05].Conclusion: the combined exercise training of muscle and bone setting technique can effectively alleviate the pain of patients with radial ulnar joint injury, improve the rotation of the forearm, increase the recruitment of the anterior rotatory muscle, and improve the wrist function of patients, and the effect is better than if combined with forearm support fixation.
基金supported in part by the National Science Foundation of China (61973247, 61673315, 62173268)the Key Research and Development Program of Shaanxi (2022GY-033)+2 种基金the Nationa Postdoctoral Innovative Talents Support Program of China (BX20200272)the Key Program of the National Natural Science Foundation of China (61833015)the Fundamental Research Funds for the Central Universities (xzy022021050)。
文摘The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.
文摘TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving intrusion monitoring and interception.To address the challenges of data acquisition,real-world deployment,and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks,we propose an innovative swarm intelligencebased UAV pursuit-evasion control framework,namely“Boids Model-based DRL Approach for Pursuit and Escape”(Boids-PE),which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning(DRL).The Boids model,which simulates collective behavior through three fundamental rules,separation,alignment,and cohesion,is adopted in our work.By integrating Boids model with the Apollonian Circles algorithm,significant improvements are achieved in capturing UAVs against simple evasion strategies.To further enhance decision-making precision,we incorporate a DRL algorithm to facilitate more accurate strategic planning.We also leverage self-play training to continuously optimize the performance of pursuit UAVs.During experimental evaluation,we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios,customizing the state space,action space,and reward function models for each scenario.Extensive simulations,supported by the PyBullet physics engine,validate the effectiveness of our proposed method.The overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks,providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions.
