A 18-year-old male patient’s case was diagnosed as Degos’disease with pathognomonic skin lesions,accompanied by small bowel perforation,and reported here. Skin histopothological test show that the typical wedge-shap...A 18-year-old male patient’s case was diagnosed as Degos’disease with pathognomonic skin lesions,accompanied by small bowel perforation,and reported here. Skin histopothological test show that the typical wedge-shaped necrobiosis and lymphocyte inflammatory infiltration. Vessels showed narrowing and thrombosis,with lymphocyte infiltration. Degos’disease is a systemic necrotizing vasculitis. Skin biopsy can confirm its diagnosis. Severe systemic complication should be prevented. Degos’disease should be considered in the differential diagnosis of skin lesions associated with systemic involvement.展开更多
动态障碍物一直是阻碍智能体自主导航发展的关键因素,而躲避障碍物和清理障碍物是两种解决动态障碍物问题的有效方法。近年来,多智能体躲避动态障碍物(避障)问题受到了广大学者的关注,优秀的多智能体避障算法纷纷涌现。然而,多智能体清...动态障碍物一直是阻碍智能体自主导航发展的关键因素,而躲避障碍物和清理障碍物是两种解决动态障碍物问题的有效方法。近年来,多智能体躲避动态障碍物(避障)问题受到了广大学者的关注,优秀的多智能体避障算法纷纷涌现。然而,多智能体清理动态障碍物(清障)问题却无人问津,相对应的多智能体清障算法更是屈指可数。为解决多智能体清障问题,文中提出了一种基于深度确定性策略梯度与注意力Critic的多智能体协同清障算法(Multi-Agent Cooperative Algorithm for Obstacle Clearance Based on Deep Deterministic Policy Gradient and Attention Critic, MACOC)。首先,创建了首个多智能体协同清障的环境模型,定义了多智能体及动态障碍物的运动学模型,并根据智能体和动态障碍物数量的不同,构建了4种仿真实验环境;其次,将多智能体协同清障过程定义为马尔可夫决策过程(Markov Decision Process, MDP),构建了多智能体t的状态空间、动作空间和奖励函数;最后,提出一种基于深度确定性策略梯度与注意力Critic的多智能体协同清障算法,并在多智能体协同清障仿真环境中与经典的多智能体强化学习算法进行对比。实验证明,相比对比算法,所提出的MACOC算法清障的成功率更高、速度更快,对复杂环境的适应性更好。展开更多
文摘A 18-year-old male patient’s case was diagnosed as Degos’disease with pathognomonic skin lesions,accompanied by small bowel perforation,and reported here. Skin histopothological test show that the typical wedge-shaped necrobiosis and lymphocyte inflammatory infiltration. Vessels showed narrowing and thrombosis,with lymphocyte infiltration. Degos’disease is a systemic necrotizing vasculitis. Skin biopsy can confirm its diagnosis. Severe systemic complication should be prevented. Degos’disease should be considered in the differential diagnosis of skin lesions associated with systemic involvement.
文摘动态障碍物一直是阻碍智能体自主导航发展的关键因素,而躲避障碍物和清理障碍物是两种解决动态障碍物问题的有效方法。近年来,多智能体躲避动态障碍物(避障)问题受到了广大学者的关注,优秀的多智能体避障算法纷纷涌现。然而,多智能体清理动态障碍物(清障)问题却无人问津,相对应的多智能体清障算法更是屈指可数。为解决多智能体清障问题,文中提出了一种基于深度确定性策略梯度与注意力Critic的多智能体协同清障算法(Multi-Agent Cooperative Algorithm for Obstacle Clearance Based on Deep Deterministic Policy Gradient and Attention Critic, MACOC)。首先,创建了首个多智能体协同清障的环境模型,定义了多智能体及动态障碍物的运动学模型,并根据智能体和动态障碍物数量的不同,构建了4种仿真实验环境;其次,将多智能体协同清障过程定义为马尔可夫决策过程(Markov Decision Process, MDP),构建了多智能体t的状态空间、动作空间和奖励函数;最后,提出一种基于深度确定性策略梯度与注意力Critic的多智能体协同清障算法,并在多智能体协同清障仿真环境中与经典的多智能体强化学习算法进行对比。实验证明,相比对比算法,所提出的MACOC算法清障的成功率更高、速度更快,对复杂环境的适应性更好。
