A gait control method for a biped robot based on the deep Q-network (DQN) algorithm is proposed to enhance the stability of walking on uneven ground. This control strategy is an intelligent learning method of posture ...A gait control method for a biped robot based on the deep Q-network (DQN) algorithm is proposed to enhance the stability of walking on uneven ground. This control strategy is an intelligent learning method of posture adjustment. A robot is taken as an agent and trained to walk steadily on an uneven surface with obstacles, using a simple reward function based on forward progress. The reward-punishment (RP) mechanism of the DQN algorithm is established after obtaining the offline gait which was generated in advance foot trajectory planning. Instead of implementing a complex dynamic model, the proposed method enables the biped robot to learn to adjust its posture on the uneven ground and ensures walking stability. The performance and effectiveness of the proposed algorithm was validated in the V-REP simulation environment. The results demonstrate that the biped robot's lateral tile angle is less than 3° after implementing the proposed method and the walking stability is obviously improved.展开更多
以深度学习框架为基础,提出了一种时空联合供水管网漏损检测模型。该模型首先运用Node2Vec算法求解不同时间段内节点特征;其次,通过模糊C-均值聚类法,利用管网模型节点特征进行分区。最后,以不同时间段的压力敏感度作为输入,漏损位置的...以深度学习框架为基础,提出了一种时空联合供水管网漏损检测模型。该模型首先运用Node2Vec算法求解不同时间段内节点特征;其次,通过模糊C-均值聚类法,利用管网模型节点特征进行分区。最后,以不同时间段的压力敏感度作为输入,漏损位置的分区号作为标签,通过深度信念神经网络进行训练,并通过训练后的模型对管网漏损位置进行检测。在实例分析中,以A市实际供水管网拓扑结构进行验证,利用MATLAB-Open Water Analytics toolbox联合编程建模,结果表明,各个时间段的检测效果均较优,正确率均达到为80%以上。因此,该模型能够有效地检测管网漏损。展开更多
We develop a picosecond widely tunable laser in a deep-ultraviolet region from 175 nm to 210 nm, generated by two stages of frequency doubling of a 80-MHz mode-locked picosecond Ti：sapphire laser. A β-BaB2O4 walk-of...We develop a picosecond widely tunable laser in a deep-ultraviolet region from 175 nm to 210 nm, generated by two stages of frequency doubling of a 80-MHz mode-locked picosecond Ti：sapphire laser. A β-BaB2O4 walk-off compensation configuration and a KBe2BO3F2 prism-coupled device are adopted for the generation of second harmonic and fourth harmonics, respectively. The highest power is 3.72 mW at 193 nm, and the fluctuation at 2.85 mW in 130 rain is less than ±2%.展开更多
Aims: Steadily the clinicians of our team in inflammatory bowel disease encounter ulcerative colitis patients that develop deep ulcers during their treatment. Currently, these practitioners are only equipped with thei...Aims: Steadily the clinicians of our team in inflammatory bowel disease encounter ulcerative colitis patients that develop deep ulcers during their treatment. Currently, these practitioners are only equipped with their grade of expertise in inflammatory domains to decide what new therapy maybe use in such cases. Encouraged by the limited knowledge of this frequent pathology, we seek to determine the molecular conditions underlying the recurrent formation of deep ulcerations in certain group of patients. Method: The goal of this strategy is to expose differences between groups of patients based on similarities computed by random walk graph kernels and performing functional inference on those differences. Results: We apply the methodology to a cohort of eleven miRNA microarrays of ulcerative colitis patients. Our results showed how the group of ulcerative colitis patients with presence of deep ulcers is topologically more similar (0.35) than ulcerative colitis patients (0.18) to control. Such topological constraint drove functional inference to complete the information that clinicians need. Conclusions: Our analyses reveal highly interpretable in the guidance of practitioners to eventually correct initial therapies of ulcerative colitis patients that develop deep ulcers. The methodology can provide them with useful molecular hypotheses necessaries prior to make any decision on the newest course of the treatment.展开更多
Identifying the association between metabolites and diseases will help us understand the pathogenesis of diseases,which has great significance in diagnosing and treating diseases.However,traditional biometric methods ...Identifying the association between metabolites and diseases will help us understand the pathogenesis of diseases,which has great significance in diagnosing and treating diseases.However,traditional biometric methods are time consuming and expensive.Accordingly,we propose a new metabolite-disease association prediction algorithm based on DeepWalk and random forest(DWRF),which consists of the following key steps:First,the semantic similarity and information entropy similarity of diseases are integrated as the final disease similarity.Similarly,molecular fingerprint similarity and information entropy similarity of metabolites are integrated as the final metabolite similarity.Then,DeepWalk is used to extract metabolite features based on the network of metabolite-gene associations.Finally,a random forest algorithm is employed to infer metabolite-disease associations.The experimental results show that DWRF has good performances in terms of the area under the curve value,leave-one-out cross-validation,and five-fold cross-validation.Case studies also indicate that DWRF has a reliable performance in metabolite-disease association prediction.展开更多
针对经典的节点相似性链路预测算法只考虑网络拓扑结构或者节点属性信息的问题,使用词嵌入模型Word2vec学习得到节点文本属性信息的表示,进而改进TADW(text-associated deep walk)算法,弥补其语义信息表示能力的不足.基于改进的TADW图...针对经典的节点相似性链路预测算法只考虑网络拓扑结构或者节点属性信息的问题,使用词嵌入模型Word2vec学习得到节点文本属性信息的表示,进而改进TADW(text-associated deep walk)算法,弥补其语义信息表示能力的不足.基于改进的TADW图嵌入方法提出一种融合网络拓扑结构和节点属性信息的相似性指标,并基于此相似性指标提出链路预测算法.在三个真实数据集上的实验结果表明所提出算法可以提高预测精度,并具有更好的鲁棒性,同时使用图嵌入的方法有效解决了网络数据的稀疏性问题.展开更多
基金Supported by the National Ministries and Research Funds(3020020221111)
文摘A gait control method for a biped robot based on the deep Q-network (DQN) algorithm is proposed to enhance the stability of walking on uneven ground. This control strategy is an intelligent learning method of posture adjustment. A robot is taken as an agent and trained to walk steadily on an uneven surface with obstacles, using a simple reward function based on forward progress. The reward-punishment (RP) mechanism of the DQN algorithm is established after obtaining the offline gait which was generated in advance foot trajectory planning. Instead of implementing a complex dynamic model, the proposed method enables the biped robot to learn to adjust its posture on the uneven ground and ensures walking stability. The performance and effectiveness of the proposed algorithm was validated in the V-REP simulation environment. The results demonstrate that the biped robot's lateral tile angle is less than 3° after implementing the proposed method and the walking stability is obviously improved.
文摘以深度学习框架为基础,提出了一种时空联合供水管网漏损检测模型。该模型首先运用Node2Vec算法求解不同时间段内节点特征;其次,通过模糊C-均值聚类法,利用管网模型节点特征进行分区。最后,以不同时间段的压力敏感度作为输入,漏损位置的分区号作为标签,通过深度信念神经网络进行训练,并通过训练后的模型对管网漏损位置进行检测。在实例分析中,以A市实际供水管网拓扑结构进行验证,利用MATLAB-Open Water Analytics toolbox联合编程建模,结果表明,各个时间段的检测效果均较优,正确率均达到为80%以上。因此,该模型能够有效地检测管网漏损。
基金supported by the State Key Program for Basic Research of China (Grant No. 2010CB630706)National High Technology Research and Development Program of Chinathe National Natural Science Foundation of China (Grant No. 61138004)
文摘We develop a picosecond widely tunable laser in a deep-ultraviolet region from 175 nm to 210 nm, generated by two stages of frequency doubling of a 80-MHz mode-locked picosecond Ti：sapphire laser. A β-BaB2O4 walk-off compensation configuration and a KBe2BO3F2 prism-coupled device are adopted for the generation of second harmonic and fourth harmonics, respectively. The highest power is 3.72 mW at 193 nm, and the fluctuation at 2.85 mW in 130 rain is less than ±2%.
文摘Aims: Steadily the clinicians of our team in inflammatory bowel disease encounter ulcerative colitis patients that develop deep ulcers during their treatment. Currently, these practitioners are only equipped with their grade of expertise in inflammatory domains to decide what new therapy maybe use in such cases. Encouraged by the limited knowledge of this frequent pathology, we seek to determine the molecular conditions underlying the recurrent formation of deep ulcerations in certain group of patients. Method: The goal of this strategy is to expose differences between groups of patients based on similarities computed by random walk graph kernels and performing functional inference on those differences. Results: We apply the methodology to a cohort of eleven miRNA microarrays of ulcerative colitis patients. Our results showed how the group of ulcerative colitis patients with presence of deep ulcers is topologically more similar (0.35) than ulcerative colitis patients (0.18) to control. Such topological constraint drove functional inference to complete the information that clinicians need. Conclusions: Our analyses reveal highly interpretable in the guidance of practitioners to eventually correct initial therapies of ulcerative colitis patients that develop deep ulcers. The methodology can provide them with useful molecular hypotheses necessaries prior to make any decision on the newest course of the treatment.
文摘Identifying the association between metabolites and diseases will help us understand the pathogenesis of diseases,which has great significance in diagnosing and treating diseases.However,traditional biometric methods are time consuming and expensive.Accordingly,we propose a new metabolite-disease association prediction algorithm based on DeepWalk and random forest(DWRF),which consists of the following key steps:First,the semantic similarity and information entropy similarity of diseases are integrated as the final disease similarity.Similarly,molecular fingerprint similarity and information entropy similarity of metabolites are integrated as the final metabolite similarity.Then,DeepWalk is used to extract metabolite features based on the network of metabolite-gene associations.Finally,a random forest algorithm is employed to infer metabolite-disease associations.The experimental results show that DWRF has good performances in terms of the area under the curve value,leave-one-out cross-validation,and five-fold cross-validation.Case studies also indicate that DWRF has a reliable performance in metabolite-disease association prediction.
文摘针对经典的节点相似性链路预测算法只考虑网络拓扑结构或者节点属性信息的问题,使用词嵌入模型Word2vec学习得到节点文本属性信息的表示,进而改进TADW(text-associated deep walk)算法,弥补其语义信息表示能力的不足.基于改进的TADW图嵌入方法提出一种融合网络拓扑结构和节点属性信息的相似性指标,并基于此相似性指标提出链路预测算法.在三个真实数据集上的实验结果表明所提出算法可以提高预测精度,并具有更好的鲁棒性,同时使用图嵌入的方法有效解决了网络数据的稀疏性问题.
