随着低压配电网的改造升级,台区户变关系变化频繁,为解决时有发生的用户台区挂错现象,提出一种利用改进的基于密度的点排序识别聚类结构(ordering points to identify the clustering structure,OPTICS)的台区户变关系识别和相别识别方...随着低压配电网的改造升级,台区户变关系变化频繁,为解决时有发生的用户台区挂错现象,提出一种利用改进的基于密度的点排序识别聚类结构(ordering points to identify the clustering structure,OPTICS)的台区户变关系识别和相别识别方法。首先,对配网电压序列的相关性进行定性分析,提出利用电压时序序列作为分析识别的数据基础;其次,采用改进的自适应分段聚合近似(adaptive piecewise aggregate approximation,APAA)对电压序列进行降维处理,提取能够反映电压特征的低维向量;然后利用改进的OPTICS算法对所提取的特征向量进行聚类分析,识别台区的户变关系和相别关系;最后,基于实际的台区数据进行算例分析,验证了所提方法的准确性。展开更多
Cameras can reliably detect human motions in a normal environment, but they are usually affected by sudden illumination changes and complex conditions, which are the major obstacles to the reliability and robustness o...Cameras can reliably detect human motions in a normal environment, but they are usually affected by sudden illumination changes and complex conditions, which are the major obstacles to the reliability and robustness of the system. To solve this problem, a novel integration method was proposed to combine hi-static ultra-wideband radar and cameras. In this recognition system, two cameras are used to localize the object＇s region, regions while a radar is used to obtain its 3D motion models on a mobile robot. The recognition results can be matched in the 3D motion library in order to recognize its motions. To confirm the effectiveness of the proposed method, the experimental results of recognition using vision sensors and those of recognition using the integration method were compared in different environments. Higher correct-recognition rate is achieved in the experiment.展开更多
A new human action recognition approach was presented based on chaotic invariants and relevance vector machines(RVM).The trajectories of reference joints estimated by skeleton graph matching were adopted for represent...A new human action recognition approach was presented based on chaotic invariants and relevance vector machines(RVM).The trajectories of reference joints estimated by skeleton graph matching were adopted for representing the nonlinear dynamical system of human action.The C-C method was used for estimating delay time and embedding dimension of a phase space which was reconstructed by each trajectory.Then,some chaotic invariants representing action can be captured in the reconstructed phase space.Finally,RVM was used to recognize action.Experiments were performed on the KTH,Weizmann and Ballet human action datasets to test and evaluate the proposed method.The experiment results show that the average recognition accuracy is over91.2%,which validates its effectiveness.展开更多
文摘随着低压配电网的改造升级,台区户变关系变化频繁,为解决时有发生的用户台区挂错现象,提出一种利用改进的基于密度的点排序识别聚类结构(ordering points to identify the clustering structure,OPTICS)的台区户变关系识别和相别识别方法。首先,对配网电压序列的相关性进行定性分析,提出利用电压时序序列作为分析识别的数据基础;其次,采用改进的自适应分段聚合近似(adaptive piecewise aggregate approximation,APAA)对电压序列进行降维处理,提取能够反映电压特征的低维向量;然后利用改进的OPTICS算法对所提取的特征向量进行聚类分析,识别台区的户变关系和相别关系;最后,基于实际的台区数据进行算例分析,验证了所提方法的准确性。
基金Supported by National Natural Science Foundation of China(No.50875193)
文摘Cameras can reliably detect human motions in a normal environment, but they are usually affected by sudden illumination changes and complex conditions, which are the major obstacles to the reliability and robustness of the system. To solve this problem, a novel integration method was proposed to combine hi-static ultra-wideband radar and cameras. In this recognition system, two cameras are used to localize the object＇s region, regions while a radar is used to obtain its 3D motion models on a mobile robot. The recognition results can be matched in the 3D motion library in order to recognize its motions. To confirm the effectiveness of the proposed method, the experimental results of recognition using vision sensors and those of recognition using the integration method were compared in different environments. Higher correct-recognition rate is achieved in the experiment.
基金Project(50808025) supported by the National Natural Science Foundation of ChinaProject(20090162110057) supported by the Doctoral Fund of Ministry of Education,China
文摘A new human action recognition approach was presented based on chaotic invariants and relevance vector machines(RVM).The trajectories of reference joints estimated by skeleton graph matching were adopted for representing the nonlinear dynamical system of human action.The C-C method was used for estimating delay time and embedding dimension of a phase space which was reconstructed by each trajectory.Then,some chaotic invariants representing action can be captured in the reconstructed phase space.Finally,RVM was used to recognize action.Experiments were performed on the KTH,Weizmann and Ballet human action datasets to test and evaluate the proposed method.The experiment results show that the average recognition accuracy is over91.2%,which validates its effectiveness.
