We propose a novel clustering algorithm using fast global kernel fuzzy c-means-F(FGKFCM-F), where F refers to kernelized feature space. This algorithm proceeds in an incremental way to derive the near-optimal solution...We propose a novel clustering algorithm using fast global kernel fuzzy c-means-F(FGKFCM-F), where F refers to kernelized feature space. This algorithm proceeds in an incremental way to derive the near-optimal solution by solving all intermediate problems using kernel-based fuzzy c-means-F(KFCM-F) as a local search procedure. Due to the incremental nature and the nonlinear properties inherited from KFCM-F, this algorithm overcomes the two shortcomings of fuzzy c-means(FCM): sen- sitivity to initialization and inability to use nonlinear separable data. An accelerating scheme is developed to reduce the compu-tational complexity without significantly affecting the solution quality. Experiments are carried out to test the proposed algorithm on a nonlinear artificial dataset and a real-world dataset of speech signals for consonant/vowel segmentation. Simulation results demonstrate the effectiveness of the proposed algorithm in improving clustering performance on both types of datasets.展开更多
Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian fields.With the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly bein...Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian fields.With the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly being challenged.To address this challenge,we propose algorithms to detect anomalous data collected from drones to improve drone safety.We deployed a one-class kernel extreme learning machine(OCKELM)to detect anomalies in drone data.By default,OCKELM uses the radial basis(RBF)kernel function as the kernel function of themodel.To improve the performance ofOCKELM,we choose a TriangularGlobalAlignmentKernel(TGAK)instead of anRBF Kernel and introduce the Fast Independent Component Analysis(FastICA)algorithm to reconstruct UAV data.Based on the above improvements,we create a novel anomaly detection strategy FastICA-TGAK-OCELM.The method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies(ALFA)dataset.The experimental results show that compared with other methods,the accuracy of this method is improved by more than 30%,and point anomalies are effectively detected.展开更多
确定城市范围有助于城市规划、生态保护、粮食安全等方面的研究。目前对城区边界的研究存在时效性差等问题,对全球城区边界进行持续性研究具有重要意义。本文基于30m全球不透水面产品(global ISA dataset,GISA 2.0),首先利用全球30m耕...确定城市范围有助于城市规划、生态保护、粮食安全等方面的研究。目前对城区边界的研究存在时效性差等问题,对全球城区边界进行持续性研究具有重要意义。本文基于30m全球不透水面产品(global ISA dataset,GISA 2.0),首先利用全球30m耕地空间分布产品(Global 30-m spatial distribution of cropland in 2020,GCL30_2020)去除耕地分布区域。其次利用核密度估计模型和元胞自动机模型来获取初始城区边界。最后采用形态学等方法进行后处理获取2019年的全球城区边界(global urban boundary 2019,GUB2019)。将结果与哨兵二号卫星影像、现有全球城区边界产品(global urban boundaries,GUB)比较,均表现出较好的一致性。GUB2019与GUB的相关性达0.88。通过比较GUB2019和人工目视解译样本的城市面积,统计得到的交并比最高达0.902,平均交并比达0.787。GUB2019对GUB(仅到2018年)进行了时间更新,同时考虑了不透水面和耕地对城市范围的综合影响,降低了GUB的虚警。GUB2019可以为全球城市及乡村研究提供参考(下载地址:https://doi.org/10.6084/m9.figshare.26172808)。展开更多
基金Project supported by the National Research Foundation(NRF) of Korea(Nos.2013009458 and 2013068127)
文摘We propose a novel clustering algorithm using fast global kernel fuzzy c-means-F(FGKFCM-F), where F refers to kernelized feature space. This algorithm proceeds in an incremental way to derive the near-optimal solution by solving all intermediate problems using kernel-based fuzzy c-means-F(KFCM-F) as a local search procedure. Due to the incremental nature and the nonlinear properties inherited from KFCM-F, this algorithm overcomes the two shortcomings of fuzzy c-means(FCM): sen- sitivity to initialization and inability to use nonlinear separable data. An accelerating scheme is developed to reduce the compu-tational complexity without significantly affecting the solution quality. Experiments are carried out to test the proposed algorithm on a nonlinear artificial dataset and a real-world dataset of speech signals for consonant/vowel segmentation. Simulation results demonstrate the effectiveness of the proposed algorithm in improving clustering performance on both types of datasets.
基金supported by the Natural Science Foundation of The Jiangsu Higher Education Institutions of China(Grant No.19JKB520031).
文摘Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian fields.With the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly being challenged.To address this challenge,we propose algorithms to detect anomalous data collected from drones to improve drone safety.We deployed a one-class kernel extreme learning machine(OCKELM)to detect anomalies in drone data.By default,OCKELM uses the radial basis(RBF)kernel function as the kernel function of themodel.To improve the performance ofOCKELM,we choose a TriangularGlobalAlignmentKernel(TGAK)instead of anRBF Kernel and introduce the Fast Independent Component Analysis(FastICA)algorithm to reconstruct UAV data.Based on the above improvements,we create a novel anomaly detection strategy FastICA-TGAK-OCELM.The method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies(ALFA)dataset.The experimental results show that compared with other methods,the accuracy of this method is improved by more than 30%,and point anomalies are effectively detected.
文摘确定城市范围有助于城市规划、生态保护、粮食安全等方面的研究。目前对城区边界的研究存在时效性差等问题,对全球城区边界进行持续性研究具有重要意义。本文基于30m全球不透水面产品(global ISA dataset,GISA 2.0),首先利用全球30m耕地空间分布产品(Global 30-m spatial distribution of cropland in 2020,GCL30_2020)去除耕地分布区域。其次利用核密度估计模型和元胞自动机模型来获取初始城区边界。最后采用形态学等方法进行后处理获取2019年的全球城区边界(global urban boundary 2019,GUB2019)。将结果与哨兵二号卫星影像、现有全球城区边界产品(global urban boundaries,GUB)比较,均表现出较好的一致性。GUB2019与GUB的相关性达0.88。通过比较GUB2019和人工目视解译样本的城市面积,统计得到的交并比最高达0.902,平均交并比达0.787。GUB2019对GUB(仅到2018年)进行了时间更新,同时考虑了不透水面和耕地对城市范围的综合影响,降低了GUB的虚警。GUB2019可以为全球城市及乡村研究提供参考(下载地址:https://doi.org/10.6084/m9.figshare.26172808)。
