多智能体路径规划旨在解决多个智能体在同一工作空间内生成无碰撞路径的问题,是智能体无人化工作的关键支撑技术。基于回溯思想和自适应局部避障策略,提出了一种双阶段多智能体路径规划算法。在全局路径规划阶段,基于回溯思想改进的RRT*...多智能体路径规划旨在解决多个智能体在同一工作空间内生成无碰撞路径的问题,是智能体无人化工作的关键支撑技术。基于回溯思想和自适应局部避障策略,提出了一种双阶段多智能体路径规划算法。在全局路径规划阶段,基于回溯思想改进的RRT*(rapidly-exploring random trees star)算法(back tracking rapidly-exploring random trees star,BT-RRT*),减少无效父节点,并确保各智能体生成优化的无碰撞路径。在协作避障阶段,智能体依据自身的任务优先级制定局部避障策略,避开动态障碍物和其他智能体。实验结果表明,该算法可成功寻找较优路径,还可降低避障时间。展开更多
Ribonucleic acid(RNA)hybridization is widely used in popular RNA simulation software in bioinformatics.However limited by the exponential computational complexity of combin atorial problems,it is challenging to decide...Ribonucleic acid(RNA)hybridization is widely used in popular RNA simulation software in bioinformatics.However limited by the exponential computational complexity of combin atorial problems,it is challenging to decide,within an acceptable time,whether a specific RNA hybridization is effective.We hereby introduce a machine learning based technique to address this problem.Sample machine learning(ML)models tested in the training phase include algorithms based on the boosted tree(BT)random forest(RF),decision tree(DT)and logistic regression(LR),and the corresponding models are obtained.Given the RNA molecular coding training and testing sets,the trained machine learning models are applied to predict the classification of RNA hybridization results.The experiment results show that the op timal predictive accuracies are 96.2%,96.6%,96.0%and 69.8%for the RF,BT,DT and LR-based approaches,respectively,un der the strong constraint condition,compared with traditiona representative methods.Furthermore,the average computation efficiency of the RF,BT,DT and LR-based approaches are208679,269756,184333 and 187458 times higher than that o existing approach,respectively.Given an RNA design,the BT based approach demonstrates high computational efficiency and better predictive accuracy in determining the biological effective ness of molecular hybridization.展开更多
文摘提出一种新的轴承故障特征提取方法——层次模糊熵(Hierarchical Fuzzy Entropy,HFE)。层次模糊熵包括层次分析和模糊熵计算。与多尺度模糊熵相比,层次模糊熵既分析信号的低频分量又分析信号的高频分量,因而能提取更全面、准确的故障信息。改进支持向量机(Improved support vector machine based binary tree,ISVMBT)相比其他多分类器具有识别率更高的优势,因此提出了一种基于层次模糊熵和改进支持向量机的轴承故障诊断方法。首先将HFE作为故障特征提取工具,然后将所得的特征向量输入到改进支持向量机进行模式识别。通过轴承故障诊断的工程应用,表明该方法可以有效提取轴承故障特征,实现轴承不同故障类型和故障程度的准确识别。
文摘多智能体路径规划旨在解决多个智能体在同一工作空间内生成无碰撞路径的问题,是智能体无人化工作的关键支撑技术。基于回溯思想和自适应局部避障策略,提出了一种双阶段多智能体路径规划算法。在全局路径规划阶段,基于回溯思想改进的RRT*(rapidly-exploring random trees star)算法(back tracking rapidly-exploring random trees star,BT-RRT*),减少无效父节点,并确保各智能体生成优化的无碰撞路径。在协作避障阶段,智能体依据自身的任务优先级制定局部避障策略,避开动态障碍物和其他智能体。实验结果表明,该算法可成功寻找较优路径,还可降低避障时间。
基金supported by the National Natural Science Foundation of China(U1204608,61472370,61672469,61822701)
文摘Ribonucleic acid(RNA)hybridization is widely used in popular RNA simulation software in bioinformatics.However limited by the exponential computational complexity of combin atorial problems,it is challenging to decide,within an acceptable time,whether a specific RNA hybridization is effective.We hereby introduce a machine learning based technique to address this problem.Sample machine learning(ML)models tested in the training phase include algorithms based on the boosted tree(BT)random forest(RF),decision tree(DT)and logistic regression(LR),and the corresponding models are obtained.Given the RNA molecular coding training and testing sets,the trained machine learning models are applied to predict the classification of RNA hybridization results.The experiment results show that the op timal predictive accuracies are 96.2%,96.6%,96.0%and 69.8%for the RF,BT,DT and LR-based approaches,respectively,un der the strong constraint condition,compared with traditiona representative methods.Furthermore,the average computation efficiency of the RF,BT,DT and LR-based approaches are208679,269756,184333 and 187458 times higher than that o existing approach,respectively.Given an RNA design,the BT based approach demonstrates high computational efficiency and better predictive accuracy in determining the biological effective ness of molecular hybridization.
