The quality of hot-rolled steel strip is directly affected by the strip crown.Traditional machine learning models have shown limitations in accurately predicting the strip crown,particularly when dealing with imbalanc...The quality of hot-rolled steel strip is directly affected by the strip crown.Traditional machine learning models have shown limitations in accurately predicting the strip crown,particularly when dealing with imbalanced data.This limitation results in poor production quality and efficiency,leading to increased production costs.Thus,a novel strip crown prediction model that uses the Boruta and extremely randomized trees(Boruta-ERT)algorithms to address this issue was proposed.To improve the accuracy of our model,we utilized the synthetic minority over-sampling technique to balance the imbalance data sets.The Boruta-ERT prediction model was then used to select features and predict the strip crown.With the 2160 mm hot rolling production lines of a steel plant serving as the research object,the experimental results showed that 97.01% of prediction data have an absolute error of less than 8 lm.This level of accuracy met the control requirements for strip crown and demonstrated significant benefits for the improvement in production quality of steel strip.展开更多
Sampling-based planning algorithms play an important role in high degree-of-freedom motion planning(MP)problems,in which rapidly-exploring random tree(RRT)and the faster bidirectional RRT(named RRT-Connect)algorithms ...Sampling-based planning algorithms play an important role in high degree-of-freedom motion planning(MP)problems,in which rapidly-exploring random tree(RRT)and the faster bidirectional RRT(named RRT-Connect)algorithms have achieved good results in many planning tasks.However,sampling-based methods have the inherent defect of having difficultly in solving planning problems with narrow passages.Therefore,several algorithms have been proposed to overcome these drawbacks.As one of the improved algorithms,Rapidlyexploring random vines(RRV)can achieve better results,but it may perform worse in cluttered environments and has a certain environmental selectivity.In this paper,we present a new improved planning method based on RRT-Connect and RRV,named adaptive RRT-Connect(ARRT-Connect),which deals well with the narrow passage environments while retaining the ability of RRT algorithms to plan paths in other environments.The proposed planner is shown to be adaptable to a variety of environments and can accomplish path planning in a short time.展开更多
This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapi...This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.展开更多
Credit card fraud is a wide-ranging issue for financial institutions, involving theft and fraud committed using a payment card. In this paper, we explore the application of linear and nonlinear statistical modeling an...Credit card fraud is a wide-ranging issue for financial institutions, involving theft and fraud committed using a payment card. In this paper, we explore the application of linear and nonlinear statistical modeling and machine learning models on real credit card transaction data. The models built are supervised fraud models that attempt to identify which transactions are most likely fraudulent. We discuss the processes of data exploration, data cleaning, variable creation, feature selection, model algorithms, and results. Five different supervised models are explored and compared including logistic regression, neural networks, random forest, boosted tree and support vector machines. The boosted tree model shows the best fraud detection result (FDR = 49.83%) for this particular data set. The resulting model can be utilized in a credit card fraud detection system. A similar model development process can be performed in related business domains such as insurance and telecommunications, to avoid or detect fraudulent activity.展开更多
针对基本的快速搜索随机树(rapidly-exploring random tree,RRT)算法用于路径规划时存在的树扩展无导向性、密集障碍物区域规划效率低、局部区域节点聚集等问题,提出一种新的RRT改进算法。该算法采用增强的目标偏向策略,并引入可变的权...针对基本的快速搜索随机树(rapidly-exploring random tree,RRT)算法用于路径规划时存在的树扩展无导向性、密集障碍物区域规划效率低、局部区域节点聚集等问题,提出一种新的RRT改进算法。该算法采用增强的目标偏向策略,并引入可变的权值系数,提高随机树扩展的导向性和灵活性;同时采用局部节点过滤机制,过滤局部区域内聚集的节点;最后,使用节点直连策略对初始路径进行优化处理。仿真实验的结果表明,改进的RRT算法规划路径的速度更快且生成的路径质量更高,充分证明了改进算法的有效可行性。展开更多
针对传统RRT(Rapidly-exploring Random Tree)算法在进行机械臂路径规划时存在的采样随机性过大、搜索效率低下、所规划的路径曲折等问题,提出一种基于采样区域限制的改进RRT(Sampling Area Restriction RRT,SAR-RRT)算法。首先,针对随...针对传统RRT(Rapidly-exploring Random Tree)算法在进行机械臂路径规划时存在的采样随机性过大、搜索效率低下、所规划的路径曲折等问题,提出一种基于采样区域限制的改进RRT(Sampling Area Restriction RRT,SAR-RRT)算法。首先,针对随机性过大的问题,通过引入目标偏置策略来增强随机树的目标导向性,并采用球形采样区域以及角度限制策略对算法的采样进行约束,减少算法对无用空间区域的探索。其次,为提升算法的搜索效率,对随机树的节点扩展进行自适应优化,采用多步长扩展,使算法能够充分利用环境与障碍物的信息,同时利用贪婪思想加快随机树的收敛从而缩短路径的生成时间。最后,对初始规划出的路径进行二次优化处理,在去除路径中的冗余点后以三次B样条曲线对路径进行平滑处理,提升所规划路径的质量。实验结果表明,在2维及3维场景下,SAR-RRT算法均可以顺利完成路径规划任务。对比传统RRT算法,改进算法总体上使路径长度降低27.73%,规划时间缩短85.25%,采样点数减少87.19%且所生成的路径更加平滑。展开更多
针对快速扩展随机树(Rapidly-exploring Random Tree,RRT)算法及其衍生算法路径规划时间长且规划效率低的问题,提出RRT算法与人工势场法结合的PAAPF-RRT机械臂路径规划算法,旨在最短的时间、最小的迭代次数内,在静态环境中找到连接起始...针对快速扩展随机树(Rapidly-exploring Random Tree,RRT)算法及其衍生算法路径规划时间长且规划效率低的问题,提出RRT算法与人工势场法结合的PAAPF-RRT机械臂路径规划算法,旨在最短的时间、最小的迭代次数内,在静态环境中找到连接起始点与终点的最优路径。首先,引入基于地图障碍物分布评估策略和采样区域优化策略,根据地图的障碍物分布、数量调整算法的步长以及偏向概率。然后,伴随随机树的生长,更新随机点的采样区域,保证随机树向目标点生长。其次,将RRT算法与人工势场法结合,当随机树与障碍物发生碰撞时,使用人工势场法引导随机树节点生长避开障碍物,解决了RRT算法随机树生长到障碍物附近且朝目标点生长的方向被障碍物遮挡时随机树无法生长的问题。最后,利用节点修剪策略,把算法生成的初始路径中的冗余节点进行修剪,得到拐点更少、路径更简洁的优化路径。实验结果表明,PAAPF-RRT算法在路径规划时间上对于RRT算法、GB-RRT算法以及RRT*算法分别减少了93.64%、73.58%、93.28%,在迭代次数方面分别下降了91.40%、79.64%、90.58%,在路径长度方面只占其他3种算法的79.34%、86.21%、95.58%。展开更多
针对快速搜索随机树(rapidly-exploring random tree,RRT)算法的随机采样特征导致的收敛速度慢、路径冗余度高、采样点利用率低问题,给出一种新的解决方法。首先,根据图复杂度公式,计算出图的复杂度后确定目标偏执概率,建立偏置概率自...针对快速搜索随机树(rapidly-exploring random tree,RRT)算法的随机采样特征导致的收敛速度慢、路径冗余度高、采样点利用率低问题,给出一种新的解决方法。首先,根据图复杂度公式,计算出图的复杂度后确定目标偏执概率,建立偏置概率自适应模型;其次,在首次规划好路线后,路径中仍存在一些不必要的拐点与棱角,针对传统路径裁剪依赖局部搜索策略,可能导致次优解生成,提出PRM-Dijkstra(probabilistic roadmap-dijkstra)算法对路径进行裁剪,将改进RRT算法生成的树节点利用PRM算法相互连接起来,通过Dijkstra算法计算出一条最优路径;最后,改进RRT算法与PRM-Dijkstra种算法优势相结合,在保证有一条路径的前提下,最大概率的寻找最优路径。通过复杂图下仿真避障实验,结果显示:改进RRT算法在节点生成数量与规划用时相较传统RRT算法平均减少80%,相较于Goal-bias RRT算法均减少40%。并通过机器人操作系统(robot operating system,ROS)下的MoveIt!集成开发平台进行现实环境下避障实验,验证了算法的可行性与有效性。展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52074085,U21A20117 and U21A20475)the Fundamental Research Funds for the Central Universities(Grant No.N2004010)the Liaoning Revitalization Talents Program(XLYC1907065).
文摘The quality of hot-rolled steel strip is directly affected by the strip crown.Traditional machine learning models have shown limitations in accurately predicting the strip crown,particularly when dealing with imbalanced data.This limitation results in poor production quality and efficiency,leading to increased production costs.Thus,a novel strip crown prediction model that uses the Boruta and extremely randomized trees(Boruta-ERT)algorithms to address this issue was proposed.To improve the accuracy of our model,we utilized the synthetic minority over-sampling technique to balance the imbalance data sets.The Boruta-ERT prediction model was then used to select features and predict the strip crown.With the 2160 mm hot rolling production lines of a steel plant serving as the research object,the experimental results showed that 97.01% of prediction data have an absolute error of less than 8 lm.This level of accuracy met the control requirements for strip crown and demonstrated significant benefits for the improvement in production quality of steel strip.
基金supported in part by the National Science Foundation of China(61976175,91648208)the Key Project of Natural Science Basic Research Plan in Shaanxi Province of China(2019JZ-05)。
文摘Sampling-based planning algorithms play an important role in high degree-of-freedom motion planning(MP)problems,in which rapidly-exploring random tree(RRT)and the faster bidirectional RRT(named RRT-Connect)algorithms have achieved good results in many planning tasks.However,sampling-based methods have the inherent defect of having difficultly in solving planning problems with narrow passages.Therefore,several algorithms have been proposed to overcome these drawbacks.As one of the improved algorithms,Rapidlyexploring random vines(RRV)can achieve better results,but it may perform worse in cluttered environments and has a certain environmental selectivity.In this paper,we present a new improved planning method based on RRT-Connect and RRV,named adaptive RRT-Connect(ARRT-Connect),which deals well with the narrow passage environments while retaining the ability of RRT algorithms to plan paths in other environments.The proposed planner is shown to be adaptable to a variety of environments and can accomplish path planning in a short time.
基金the National Natural Science Foundation of China(Grant No.42274119)the Liaoning Revitalization Talents Program(Grant No.XLYC2002082)+1 种基金National Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integration(Grant No.2022YFF1400500)the Key Project of Science and Technology Commission of the Central Military Commission.
文摘This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.
文摘Credit card fraud is a wide-ranging issue for financial institutions, involving theft and fraud committed using a payment card. In this paper, we explore the application of linear and nonlinear statistical modeling and machine learning models on real credit card transaction data. The models built are supervised fraud models that attempt to identify which transactions are most likely fraudulent. We discuss the processes of data exploration, data cleaning, variable creation, feature selection, model algorithms, and results. Five different supervised models are explored and compared including logistic regression, neural networks, random forest, boosted tree and support vector machines. The boosted tree model shows the best fraud detection result (FDR = 49.83%) for this particular data set. The resulting model can be utilized in a credit card fraud detection system. A similar model development process can be performed in related business domains such as insurance and telecommunications, to avoid or detect fraudulent activity.
文摘针对基本的快速搜索随机树(rapidly-exploring random tree,RRT)算法用于路径规划时存在的树扩展无导向性、密集障碍物区域规划效率低、局部区域节点聚集等问题,提出一种新的RRT改进算法。该算法采用增强的目标偏向策略,并引入可变的权值系数,提高随机树扩展的导向性和灵活性;同时采用局部节点过滤机制,过滤局部区域内聚集的节点;最后,使用节点直连策略对初始路径进行优化处理。仿真实验的结果表明,改进的RRT算法规划路径的速度更快且生成的路径质量更高,充分证明了改进算法的有效可行性。
文摘针对传统RRT(Rapidly-exploring Random Tree)算法在进行机械臂路径规划时存在的采样随机性过大、搜索效率低下、所规划的路径曲折等问题,提出一种基于采样区域限制的改进RRT(Sampling Area Restriction RRT,SAR-RRT)算法。首先,针对随机性过大的问题,通过引入目标偏置策略来增强随机树的目标导向性,并采用球形采样区域以及角度限制策略对算法的采样进行约束,减少算法对无用空间区域的探索。其次,为提升算法的搜索效率,对随机树的节点扩展进行自适应优化,采用多步长扩展,使算法能够充分利用环境与障碍物的信息,同时利用贪婪思想加快随机树的收敛从而缩短路径的生成时间。最后,对初始规划出的路径进行二次优化处理,在去除路径中的冗余点后以三次B样条曲线对路径进行平滑处理,提升所规划路径的质量。实验结果表明,在2维及3维场景下,SAR-RRT算法均可以顺利完成路径规划任务。对比传统RRT算法,改进算法总体上使路径长度降低27.73%,规划时间缩短85.25%,采样点数减少87.19%且所生成的路径更加平滑。
文摘针对快速扩展随机树(Rapidly-exploring Random Tree,RRT)算法及其衍生算法路径规划时间长且规划效率低的问题,提出RRT算法与人工势场法结合的PAAPF-RRT机械臂路径规划算法,旨在最短的时间、最小的迭代次数内,在静态环境中找到连接起始点与终点的最优路径。首先,引入基于地图障碍物分布评估策略和采样区域优化策略,根据地图的障碍物分布、数量调整算法的步长以及偏向概率。然后,伴随随机树的生长,更新随机点的采样区域,保证随机树向目标点生长。其次,将RRT算法与人工势场法结合,当随机树与障碍物发生碰撞时,使用人工势场法引导随机树节点生长避开障碍物,解决了RRT算法随机树生长到障碍物附近且朝目标点生长的方向被障碍物遮挡时随机树无法生长的问题。最后,利用节点修剪策略,把算法生成的初始路径中的冗余节点进行修剪,得到拐点更少、路径更简洁的优化路径。实验结果表明,PAAPF-RRT算法在路径规划时间上对于RRT算法、GB-RRT算法以及RRT*算法分别减少了93.64%、73.58%、93.28%,在迭代次数方面分别下降了91.40%、79.64%、90.58%,在路径长度方面只占其他3种算法的79.34%、86.21%、95.58%。
