The rudder mechanism of the X-rudder autonomous underwater cehicle(AUV)is relatively complex,and fault diagnosis capability is an important guarantee for its task execution in complex underwater environments.However,t...The rudder mechanism of the X-rudder autonomous underwater cehicle(AUV)is relatively complex,and fault diagnosis capability is an important guarantee for its task execution in complex underwater environments.However,traditional fault diagnosis methods currently rely on prior knowledge and expert experience,and lack accuracy.In order to improve the autonomy and accuracy of fault diagnosis methods,and overcome the shortcomings of traditional algorithms,this paper proposes an X-steering AUV fault diagnosis model based on the deep reinforcement learning deep Q network(DQN)algorithm,which can learn the relationship between state data and fault types,map raw residual data to corresponding fault patterns,and achieve end-to-end mapping.In addition,to solve the problem of few X-steering fault sample data,Dropout technology is introduced during the model training phase to improve the performance of the DQN algorithm.Experimental results show that the proposed model has improved the convergence speed and comprehensive performance indicators compared to the unimproved DQN algorithm,with precision,recall,F_(1-score),and accuracy reaching up to 100%,98.07%,99.02%,and 98.50% respectively,and the model’s accuracy is higher than other machine learning algorithms like back propagation,support vector machine.展开更多
Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment.Reinforcement Learning methods enable a st...Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment.Reinforcement Learning methods enable a state action function in mobile robots suited to their environment.During trial-and-error interaction with its surroundings,it helps a robot tofind an ideal behavior on its own.The Deep Q Network(DQN)algorithm is used in TurtleBot 3(TB3)to achieve the goal by successfully avoiding the obstacles.But it requires a large number of training iterations.This research mainly focuses on a mobility robot’s best path prediction utilizing DQN and the Artificial Potential Field(APF)algorithms.First,a TB3 Waffle Pi DQN is built and trained to reach the goal.Then the APF shortest path algorithm is incorporated into the DQN algorithm.The proposed planning approach is compared with the standard DQN method in a virtual environment based on the Robot Operation System(ROS).The results from the simulation show that the combination is effective for DQN and APF gives a better optimal path and takes less time when compared to the conventional DQN algo-rithm.The performance improvement rate of the proposed DQN+APF in comparison with DQN in terms of the number of successful targets is attained by 88%.The performance of the proposed DQN+APF in comparison with DQN in terms of average time is achieved by 0.331 s.The performance of the proposed DQN+APF in comparison with DQN average rewards in which the positive goal is attained by 85%and the negative goal is attained by-90%.展开更多
基金Supported by the National Natural Science Foundation of China under Grant Nos.52071099,52071104National Key Project of Research and Development Program under Grant No.2021YFC2801300Research Fund from National Key Laboratory of Autonomous Marine Vehicle Technology under Grant No.2023-SXJQR-SYSJJ01.
文摘The rudder mechanism of the X-rudder autonomous underwater cehicle(AUV)is relatively complex,and fault diagnosis capability is an important guarantee for its task execution in complex underwater environments.However,traditional fault diagnosis methods currently rely on prior knowledge and expert experience,and lack accuracy.In order to improve the autonomy and accuracy of fault diagnosis methods,and overcome the shortcomings of traditional algorithms,this paper proposes an X-steering AUV fault diagnosis model based on the deep reinforcement learning deep Q network(DQN)algorithm,which can learn the relationship between state data and fault types,map raw residual data to corresponding fault patterns,and achieve end-to-end mapping.In addition,to solve the problem of few X-steering fault sample data,Dropout technology is introduced during the model training phase to improve the performance of the DQN algorithm.Experimental results show that the proposed model has improved the convergence speed and comprehensive performance indicators compared to the unimproved DQN algorithm,with precision,recall,F_(1-score),and accuracy reaching up to 100%,98.07%,99.02%,and 98.50% respectively,and the model’s accuracy is higher than other machine learning algorithms like back propagation,support vector machine.
文摘Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment.Reinforcement Learning methods enable a state action function in mobile robots suited to their environment.During trial-and-error interaction with its surroundings,it helps a robot tofind an ideal behavior on its own.The Deep Q Network(DQN)algorithm is used in TurtleBot 3(TB3)to achieve the goal by successfully avoiding the obstacles.But it requires a large number of training iterations.This research mainly focuses on a mobility robot’s best path prediction utilizing DQN and the Artificial Potential Field(APF)algorithms.First,a TB3 Waffle Pi DQN is built and trained to reach the goal.Then the APF shortest path algorithm is incorporated into the DQN algorithm.The proposed planning approach is compared with the standard DQN method in a virtual environment based on the Robot Operation System(ROS).The results from the simulation show that the combination is effective for DQN and APF gives a better optimal path and takes less time when compared to the conventional DQN algo-rithm.The performance improvement rate of the proposed DQN+APF in comparison with DQN in terms of the number of successful targets is attained by 88%.The performance of the proposed DQN+APF in comparison with DQN in terms of average time is achieved by 0.331 s.The performance of the proposed DQN+APF in comparison with DQN average rewards in which the positive goal is attained by 85%and the negative goal is attained by-90%.
文摘基于超高效液相色谱-四极杆静电场轨道阱质谱(UPLC-Q-Exactive Orbitrap-MS)技术及网络药理学探讨化浊散结除痹方治疗痛风性关节炎(gouty arthritis,GA)的药效物质及潜在机制。采用UPLC-Q-Exactive Orbitrap-MS技术鉴定化浊散结除痹方药物成分,对其有效成分进行定性分析,共鉴定出化浊散结除痹方中184个有效成分;通过PharmMapper在线数据库筛选有效成分靶点897个,在OMIM、GeneCards、CTD等数据库获取GA相关的疾病靶点491个,进行韦恩分析后获得二者的交集靶点60个,通过Cytoscape平台构建“成分靶点-GA靶点”网络图,利用STRING数据库构建蛋白-蛋白互作网络,筛选出16个核心靶点,将核心靶点进行基因本体论(Gene Ontology,GO)与京都基因和基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)信号通路的富集分析,并构建“成分-靶点-通路”网络图,发现该方治疗GA的主要有效成分为酚类、黄酮类、生物碱类、萜类化合物,关键靶点有SRC、MMP3、MMP9、REN、ALB、IGF1R、PPARG、MAPK1、HPRT1、CASP1,通过GO分析发现其治疗GA主要涉及脂质反应、细菌反应、生物刺激的反应等生物过程,通过KEGG分析发现其治疗GA相关的通路有脂质和动脉粥样硬化、中性粒细胞胞外诱捕网、IL-17等。综上,该研究揭示了酚类、黄酮类、生物碱类、萜类化合物可能是化浊散结除痹方治疗GA的核心药效物质,其药效机制可能与SRC、MMP3、MMP9等靶点及脂质和动脉粥样硬化、中性粒细胞胞外诱捕网、IL-17等通路相关。
