Mapping in the dynamic environment is an important task for autonomous mobile robots due to the unavoidable changes in the workspace. In this paper, we propose a framework for RGBD SLAM in low dynamic environment, whi...Mapping in the dynamic environment is an important task for autonomous mobile robots due to the unavoidable changes in the workspace. In this paper, we propose a framework for RGBD SLAM in low dynamic environment, which can maintain a map keeping track of the latest environment. The main model describing the environment is a multi-session pose graph, which evolves over the multiple visits of the robot. The poses in the graph will be pruned when the 3D point scans corresponding to those poses are out of date. When the robot explores the new areas, its poses will be added to the graph. Thus the scans kept in the current graph will always give a map of the latest environment. The changes of the environment are detected by out-of-dated scans identification module through analyzing scans collected at different sessions. Besides, a redundant scans identification module is employed to further reduce the poses with redundant scans in order to keep the total number of poses in the graph with respect to the size of environment. In the experiments, the framework is first tuned and tested on data acquired by a Kinect from laboratory environment. Then the framework is applied to external dataset acquired by a Kinect II from a workspace of an industrial robot in another country, which is blind to the development phase, for further validation of the performance. After this two-step evaluation, the proposed framework is considered to be able to manage the map in date in dynamic or static environment with a noncumulative complexity and acceptable error level.展开更多
密码协议是安全共享网络资源的机制和规范,是构建网络安全环境的基石,其安全性对整个网络环境的安全起着至关重要的作用。提出了采用Colored Petri Nets(CPN,着色Petri网)分析密码协议的新方法。采用新方法对TMN协议的多次并发会话通信...密码协议是安全共享网络资源的机制和规范,是构建网络安全环境的基石,其安全性对整个网络环境的安全起着至关重要的作用。提出了采用Colored Petri Nets(CPN,着色Petri网)分析密码协议的新方法。采用新方法对TMN协议的多次并发会话通信进行形式化建模,模型依据会话配置和会话顺序进行功能单元划分,采用on-the-fly方法生成攻击路径。采用状态空间搜索技术,发现了该协议的多次并发会话不安全状态,并获得了新的攻击模式。展开更多
Purpose： This research aims to identify product search tasks in online shopplng ana analyze the characteristics of consumer multi-tasking search sessions. Design/methodology/approach： The experimental dataset contai...Purpose： This research aims to identify product search tasks in online shopplng ana analyze the characteristics of consumer multi-tasking search sessions. Design/methodology/approach： The experimental dataset contains 8,949 queries of 582 users from 3,483 search sessions. A sequential comparison of the Jaccard similarity coefficient between two adjacent search queries and hierarchical clustering of queries is used to identify search tasks. Findings： （1） Users issued a similar number of queries （1.43 to 1.47） with similar lengths （7.3-7.6 characters） per task in mono-tasking and multi-tasking sessions, and （2） Users spent more time on average in sessions with more tasks, but spent less time for each task when the number of tasks increased in a session. Research limitations： The task identification method that relies only on query terms does not completely reflect the complex nature of consumer shopping behavior.Practical implications： These results provide an exploratory understanding of the relationships among multiple shopping tasks, and can be useful for product recommendation and shopping task prediction. Originality/value： The originality of this research is its use of query clustering with online shopping task identification and analysis, and the analysis of product search session characteristics.展开更多
基金This work is supported by the National Natural Science Foundation of China （Grant No. NSFC： 61473258, U 1509210）, and the Joint Centre for Robotics Research （JCRR） between Zhejiang University and the University of Technology, Sydney.
文摘Mapping in the dynamic environment is an important task for autonomous mobile robots due to the unavoidable changes in the workspace. In this paper, we propose a framework for RGBD SLAM in low dynamic environment, which can maintain a map keeping track of the latest environment. The main model describing the environment is a multi-session pose graph, which evolves over the multiple visits of the robot. The poses in the graph will be pruned when the 3D point scans corresponding to those poses are out of date. When the robot explores the new areas, its poses will be added to the graph. Thus the scans kept in the current graph will always give a map of the latest environment. The changes of the environment are detected by out-of-dated scans identification module through analyzing scans collected at different sessions. Besides, a redundant scans identification module is employed to further reduce the poses with redundant scans in order to keep the total number of poses in the graph with respect to the size of environment. In the experiments, the framework is first tuned and tested on data acquired by a Kinect from laboratory environment. Then the framework is applied to external dataset acquired by a Kinect II from a workspace of an industrial robot in another country, which is blind to the development phase, for further validation of the performance. After this two-step evaluation, the proposed framework is considered to be able to manage the map in date in dynamic or static environment with a noncumulative complexity and acceptable error level.
文摘密码协议是安全共享网络资源的机制和规范,是构建网络安全环境的基石,其安全性对整个网络环境的安全起着至关重要的作用。提出了采用Colored Petri Nets(CPN,着色Petri网)分析密码协议的新方法。采用新方法对TMN协议的多次并发会话通信进行形式化建模,模型依据会话配置和会话顺序进行功能单元划分,采用on-the-fly方法生成攻击路径。采用状态空间搜索技术,发现了该协议的多次并发会话不安全状态,并获得了新的攻击模式。
基金supported by the National Science Foundation of China(NSFC)Grant(No.71373015)
文摘Purpose： This research aims to identify product search tasks in online shopplng ana analyze the characteristics of consumer multi-tasking search sessions. Design/methodology/approach： The experimental dataset contains 8,949 queries of 582 users from 3,483 search sessions. A sequential comparison of the Jaccard similarity coefficient between two adjacent search queries and hierarchical clustering of queries is used to identify search tasks. Findings： （1） Users issued a similar number of queries （1.43 to 1.47） with similar lengths （7.3-7.6 characters） per task in mono-tasking and multi-tasking sessions, and （2） Users spent more time on average in sessions with more tasks, but spent less time for each task when the number of tasks increased in a session. Research limitations： The task identification method that relies only on query terms does not completely reflect the complex nature of consumer shopping behavior.Practical implications： These results provide an exploratory understanding of the relationships among multiple shopping tasks, and can be useful for product recommendation and shopping task prediction. Originality/value： The originality of this research is its use of query clustering with online shopping task identification and analysis, and the analysis of product search session characteristics.
