In this paper a kind of ECG signal automatic segmentation algorithm based on ECG fractal dimension trajectory is put forward.First,the ECG signal will be analyzed,then constructing the fractal dimension trajectory of ...In this paper a kind of ECG signal automatic segmentation algorithm based on ECG fractal dimension trajectory is put forward.First,the ECG signal will be analyzed,then constructing the fractal dimension trajectory of ECG signal according to the fractal dimension trajectory constructing algorithm,finally,obtaining ECG signal feature points and ECG automatic segmentation will be realized by the feature of ECG signal fractal dimension trajectory and the feature of ECG frequency domain characteristics.Through Matlab simulation of the algorithm,the results showed that by constructing the ECG fractal dimension trajectory enables ECG location of each component displayed clearly and obtains high success rate of sub-ECG,providing a basis to identify the various components of ECG signal accurately.展开更多
Visual motion segmentation(VMS)is an important and key part of many intelligent crowd systems.It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd st...Visual motion segmentation(VMS)is an important and key part of many intelligent crowd systems.It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd stampedes and crashes,which pose a serious risk to public safety and have resulted in numerous fatalities over the past few decades.Trajectory clustering has become one of the most popular methods in VMS.However,complex data,such as a large number of samples and parameters,makes it difficult for trajectory clustering to work well with accurate motion segmentation results.This study introduces a spatial-angular stacked sparse autoencoder model(SA-SSAE)with l2-regularization and softmax,a powerful deep learning method for visual motion segmentation to cluster similar motion patterns that belong to the same cluster.The proposed model can extract meaningful high-level features using only spatial-angular features obtained from refined tracklets(a.k.a‘trajectories’).We adopt l2-regularization and sparsity regularization,which can learn sparse representations of features,to guarantee the sparsity of the autoencoders.We employ the softmax layer to map the data points into accurate cluster representations.One of the best advantages of the SA-SSAE framework is it can manage VMS even when individuals move around randomly.This framework helps cluster the motion patterns effectively with higher accuracy.We put forward a new dataset with itsmanual ground truth,including 21 crowd videos.Experiments conducted on two crowd benchmarks demonstrate that the proposed model can more accurately group trajectories than the traditional clustering approaches used in previous studies.The proposed SA-SSAE framework achieved a 0.11 improvement in accuracy and a 0.13 improvement in the F-measure compared with the best current method using the CUHK dataset.展开更多
Aiming at the problem of ignoring the importance of starting point features of trajecory segmentation in existing trajectory compression algorithms,a study was conducted on the preprocessing process of trajectory time...Aiming at the problem of ignoring the importance of starting point features of trajecory segmentation in existing trajectory compression algorithms,a study was conducted on the preprocessing process of trajectory time series.Firstly,an algorithm improvement was proposed based on the segmentation algorithm GRASP-UTS(Greedy Randomized Adaptive Search Procedure for Unsupervised Trajectory Segmentation).On the basis of considering trajectory coverage,this algorithm designs an adaptive parameter adjustment to segment long-term trajectory data reasonably and the identification of an optimal starting point for segmentation.Then the compression efficiency of typical offline and online algorithms,such as the Douglas-Peucker algorithm,the Sliding Window algorithm and its enhancements,was compared before and after segmentation.The experimental findings highlight that the Adaptive Parameters GRASP-UTS segmentation approach leads to higher fitting precision in trajectory time series compression and improved algorithm efficiency post-segmentation.Additionally,the compression performance of the Improved Sliding Window algorithm post-segmentation showcases its suitability for trajectories of varying scales,providing reasonable compression accuracy.展开更多
In the vehicle trajectory application system, it is often necessary to detect whether the vehicle deviates from the specified route. Trajectory planning in the traditional route deviation detection is defined by the d...In the vehicle trajectory application system, it is often necessary to detect whether the vehicle deviates from the specified route. Trajectory planning in the traditional route deviation detection is defined by the driver through the mobile phone navigation software, which plays a more auxiliary driving role. This paper presents a method of vehicle trajectory deviation detection. Firstly, the manager customizes the trajectory planning and then uses big data technologies to match the deviation between the trajectory planning and the vehicle trajectory. Finally, it achieves the supervisory function of the manager on the vehicle track route in real-time. The results show that this method could detect the vehicle trajectory deviation quickly and accurately, and has practical application value.展开更多
车辆临近交叉口的变道行为会制约交叉口通行效率的提升。基于此,本文提出一种网联车辆环境下城市道路交通流分段协同控制方法(Segmented Cooperative cOntrol Method for Urban Road Traffic Flow,SCOM-URTF),该方法采用双层优化模型,...车辆临近交叉口的变道行为会制约交叉口通行效率的提升。基于此,本文提出一种网联车辆环境下城市道路交通流分段协同控制方法(Segmented Cooperative cOntrol Method for Urban Road Traffic Flow,SCOM-URTF),该方法采用双层优化模型,实现路段功能区动态划分和路段—交叉口交通流的协同优化。上层模型设计了一种分车道速度诱导错位变道策略(Misaligned Lane-changing with Separated Lane Speed Guidance,ML-SLSG),通过纵向空间错位排列促成左转和右转车辆的快速变道,最小化车辆变道区长度,并均衡车道组交通流量;下层模型以最小化车均延误为目标,基于动态规划法协同优化网联车辆的轨迹与交叉口信号配时参数。仿真结果表明,ML-SLSG策略能有效缩短变道长度,在低、中和高这3种交通负荷下,本文提出的车辆纵向轨迹优化模型能使交叉口车均延误减少5.9%~8.0%,且与信号配时协同优化后,车均延误可再降低3.7%~22.8%。与同类方法对比研究表明,SCOM-URTF更适合多种驾驶行为相互协调的交通环境。敏感性分析显示,更高的CAV渗透率和道路限速有助于降低车均延误;增大交叉口间距可在初期减少车均延误,但达到临界点后会出现延误反弹,而轨迹与信号的协同优化能有效遏制延误的反弹。展开更多
Field-road segmentation is one of the key tasks in the processing of the trajectory of agricultural machinery.To improve the accuracy of the field-road segmentation,this study proposed an XGBoost model based on dual f...Field-road segmentation is one of the key tasks in the processing of the trajectory of agricultural machinery.To improve the accuracy of the field-road segmentation,this study proposed an XGBoost model based on dual feature extraction and recursive feature elimination called DR-XGBoost.DR-XGBoost takes only a small amount of agricultural machine trajectory features as input.Firstly,the model adopted the dual feature extraction method we designed to rapidly expand the number of features and then adequately extract local trajectory features by the time window and feature extraction operator.Secondly,the model applies the recursive feature elimination algorithm to eliminate redundant features from the perspective of the model segmentation effect and thus reduce the computational consumption of model training.Thirdly,it trains XGBoost to complete the trajectory segmentation.To evaluate the effectiveness of DR-XGBoost,we conducted a series of experiments on a real trajectory dataset of agricultural machines.The model achieves a 98.2%Macro-F1 score on the dataset,which is 10.9%higher than the previous state-of-art.The proposal of DR-XGBoost fills the knowledge gap of trajectory feature extraction for agricultural machinery and provides a reasonable and effective feature selection scheme for the field-road segmentation problem.展开更多
文摘In this paper a kind of ECG signal automatic segmentation algorithm based on ECG fractal dimension trajectory is put forward.First,the ECG signal will be analyzed,then constructing the fractal dimension trajectory of ECG signal according to the fractal dimension trajectory constructing algorithm,finally,obtaining ECG signal feature points and ECG automatic segmentation will be realized by the feature of ECG signal fractal dimension trajectory and the feature of ECG frequency domain characteristics.Through Matlab simulation of the algorithm,the results showed that by constructing the ECG fractal dimension trajectory enables ECG location of each component displayed clearly and obtains high success rate of sub-ECG,providing a basis to identify the various components of ECG signal accurately.
基金This research work is supported by the Deputyship of Research&Innovation,Ministry of Education in Saudi Arabia(Grant Number 758).
文摘Visual motion segmentation(VMS)is an important and key part of many intelligent crowd systems.It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd stampedes and crashes,which pose a serious risk to public safety and have resulted in numerous fatalities over the past few decades.Trajectory clustering has become one of the most popular methods in VMS.However,complex data,such as a large number of samples and parameters,makes it difficult for trajectory clustering to work well with accurate motion segmentation results.This study introduces a spatial-angular stacked sparse autoencoder model(SA-SSAE)with l2-regularization and softmax,a powerful deep learning method for visual motion segmentation to cluster similar motion patterns that belong to the same cluster.The proposed model can extract meaningful high-level features using only spatial-angular features obtained from refined tracklets(a.k.a‘trajectories’).We adopt l2-regularization and sparsity regularization,which can learn sparse representations of features,to guarantee the sparsity of the autoencoders.We employ the softmax layer to map the data points into accurate cluster representations.One of the best advantages of the SA-SSAE framework is it can manage VMS even when individuals move around randomly.This framework helps cluster the motion patterns effectively with higher accuracy.We put forward a new dataset with itsmanual ground truth,including 21 crowd videos.Experiments conducted on two crowd benchmarks demonstrate that the proposed model can more accurately group trajectories than the traditional clustering approaches used in previous studies.The proposed SA-SSAE framework achieved a 0.11 improvement in accuracy and a 0.13 improvement in the F-measure compared with the best current method using the CUHK dataset.
基金Supported by the Basic Research Projects of Liaoning Provincial Department of Education(LJKQZ20222459)。
文摘Aiming at the problem of ignoring the importance of starting point features of trajecory segmentation in existing trajectory compression algorithms,a study was conducted on the preprocessing process of trajectory time series.Firstly,an algorithm improvement was proposed based on the segmentation algorithm GRASP-UTS(Greedy Randomized Adaptive Search Procedure for Unsupervised Trajectory Segmentation).On the basis of considering trajectory coverage,this algorithm designs an adaptive parameter adjustment to segment long-term trajectory data reasonably and the identification of an optimal starting point for segmentation.Then the compression efficiency of typical offline and online algorithms,such as the Douglas-Peucker algorithm,the Sliding Window algorithm and its enhancements,was compared before and after segmentation.The experimental findings highlight that the Adaptive Parameters GRASP-UTS segmentation approach leads to higher fitting precision in trajectory time series compression and improved algorithm efficiency post-segmentation.Additionally,the compression performance of the Improved Sliding Window algorithm post-segmentation showcases its suitability for trajectories of varying scales,providing reasonable compression accuracy.
文摘In the vehicle trajectory application system, it is often necessary to detect whether the vehicle deviates from the specified route. Trajectory planning in the traditional route deviation detection is defined by the driver through the mobile phone navigation software, which plays a more auxiliary driving role. This paper presents a method of vehicle trajectory deviation detection. Firstly, the manager customizes the trajectory planning and then uses big data technologies to match the deviation between the trajectory planning and the vehicle trajectory. Finally, it achieves the supervisory function of the manager on the vehicle track route in real-time. The results show that this method could detect the vehicle trajectory deviation quickly and accurately, and has practical application value.
文摘车辆临近交叉口的变道行为会制约交叉口通行效率的提升。基于此,本文提出一种网联车辆环境下城市道路交通流分段协同控制方法(Segmented Cooperative cOntrol Method for Urban Road Traffic Flow,SCOM-URTF),该方法采用双层优化模型,实现路段功能区动态划分和路段—交叉口交通流的协同优化。上层模型设计了一种分车道速度诱导错位变道策略(Misaligned Lane-changing with Separated Lane Speed Guidance,ML-SLSG),通过纵向空间错位排列促成左转和右转车辆的快速变道,最小化车辆变道区长度,并均衡车道组交通流量;下层模型以最小化车均延误为目标,基于动态规划法协同优化网联车辆的轨迹与交叉口信号配时参数。仿真结果表明,ML-SLSG策略能有效缩短变道长度,在低、中和高这3种交通负荷下,本文提出的车辆纵向轨迹优化模型能使交叉口车均延误减少5.9%~8.0%,且与信号配时协同优化后,车均延误可再降低3.7%~22.8%。与同类方法对比研究表明,SCOM-URTF更适合多种驾驶行为相互协调的交通环境。敏感性分析显示,更高的CAV渗透率和道路限速有助于降低车均延误;增大交叉口间距可在初期减少车均延误,但达到临界点后会出现延误反弹,而轨迹与信号的协同优化能有效遏制延误的反弹。
基金This work was financially supported by the National Key Research and Development Program of China(Grant No.2021YFB3901300)the National Precision Agriculture Application Project(Grant No.JZNYYY001)National Innovation Training Project for University in China(Grant No.202310019034).
文摘Field-road segmentation is one of the key tasks in the processing of the trajectory of agricultural machinery.To improve the accuracy of the field-road segmentation,this study proposed an XGBoost model based on dual feature extraction and recursive feature elimination called DR-XGBoost.DR-XGBoost takes only a small amount of agricultural machine trajectory features as input.Firstly,the model adopted the dual feature extraction method we designed to rapidly expand the number of features and then adequately extract local trajectory features by the time window and feature extraction operator.Secondly,the model applies the recursive feature elimination algorithm to eliminate redundant features from the perspective of the model segmentation effect and thus reduce the computational consumption of model training.Thirdly,it trains XGBoost to complete the trajectory segmentation.To evaluate the effectiveness of DR-XGBoost,we conducted a series of experiments on a real trajectory dataset of agricultural machines.The model achieves a 98.2%Macro-F1 score on the dataset,which is 10.9%higher than the previous state-of-art.The proposal of DR-XGBoost fills the knowledge gap of trajectory feature extraction for agricultural machinery and provides a reasonable and effective feature selection scheme for the field-road segmentation problem.
