Patterned-based time series segmentation (PTSS) is an important task for many time series data mining applications. In this paper, according to the characteristics of PTSS, a generalized model is proposed for PTSS. Fi...Patterned-based time series segmentation (PTSS) is an important task for many time series data mining applications. In this paper, according to the characteristics of PTSS, a generalized model is proposed for PTSS. First, a new inter-pretation for PTSS is given by comparing this problem with the prototype-based clustering (PC). Then, a novel model, called clustering-inverse model (CI-model), is presented. Finally, two algorithms are presented to implement this model. Our experimental results on artificial and real-world time series demonstrate that the proposed algorithms are quite effective.展开更多
Titanium dioxides have been extensively investigated as promising anodes for Lithium ion batteries(LIBs)because of the high–rate capacity and cyclability,as well as the improved safety over graphite anode(1,2)However...Titanium dioxides have been extensively investigated as promising anodes for Lithium ion batteries(LIBs)because of the high–rate capacity and cyclability,as well as the improved safety over graphite anode(1,2)However,as a typical insertion–type anode,anatase TiO2 exhibits low conductivity(10–12S cm-1 for electron conductivity[3]and 10–17–10–10 cm2 s1 for Li+ion diffusion coefficient[4])and poor specific capacity(only accommodate<0.5 Li per bulk TiO2 unit[5]),severely limiting its practical applications.展开更多
Studying on the anode materials with high energy densities for next-generation lithium-ion batteries(LIBs) is the key for the wide application for electrochemical energy storage devices.Ti-based compounds as promising...Studying on the anode materials with high energy densities for next-generation lithium-ion batteries(LIBs) is the key for the wide application for electrochemical energy storage devices.Ti-based compounds as promising anode materials are known for their outstanding high-rate capacity and cycling stability as well as improved safety over graphite. However, Ti-based materials still suffer from the low capacity, thus largely limiting their commercialized application. Here, we present an overview of the recent development of Ti-based anode materials in LIBs, and special emphasis is placed on capacity enhancement by rational design of hybrid nanocomposites with conversion-/alloying-type anodes. This review is expected to provide a guidance for designing novel Ti-based materials for energy storage and conversion.展开更多
In many data stream mining applications, traditional density estimation methods such as kemel density estimation, reduced set density estimation can not be applied to the density estimation of data streams because of ...In many data stream mining applications, traditional density estimation methods such as kemel density estimation, reduced set density estimation can not be applied to the density estimation of data streams because of their high computational burden, processing time and intensive memory allocation requirement. In order to reduce the time and space complexity, a novel density estimation method Dm-KDE over data streams based on the proposed algorithm m-KDE which can be used to design a KDE estimator with the fixed number of kernel components for a dataset is proposed. In this method, Dm-KDE sequence entries are created by algorithm m-KDE instead of all kemels obtained from other density estimation methods. In order to further reduce the storage space, Dm-KDE sequence entries can be merged by calculating their KL divergences. Finally, the probability density functions over arbitrary time or entire time can be estimated through the obtained estimation model. In contrast to the state-of-the-art algorithm SOMKE, the distinctive advantage of the proposed algorithm Dm-KDE exists in that it can achieve the same accuracy with much less fixed number of kernel components such that it is suitable for the scenarios where higher on-line computation about the kernel density estimation over data streams is required. We compare Dm-KDE with SOMKE and M-kernel in terms of density estimation accuracy and running time for various stationary datasets. We also apply Dm-KDE to evolving data streams. Experimental results illustrate the effectiveness of the pro- posed method.展开更多
文摘Patterned-based time series segmentation (PTSS) is an important task for many time series data mining applications. In this paper, according to the characteristics of PTSS, a generalized model is proposed for PTSS. First, a new inter-pretation for PTSS is given by comparing this problem with the prototype-based clustering (PC). Then, a novel model, called clustering-inverse model (CI-model), is presented. Finally, two algorithms are presented to implement this model. Our experimental results on artificial and real-world time series demonstrate that the proposed algorithms are quite effective.
基金supported by the National Natural Science Foundation of China (51772163)the State Key Laboratory of New Ceramic and Fine Processing Tsinghua University (KF201801)
文摘Titanium dioxides have been extensively investigated as promising anodes for Lithium ion batteries(LIBs)because of the high–rate capacity and cyclability,as well as the improved safety over graphite anode(1,2)However,as a typical insertion–type anode,anatase TiO2 exhibits low conductivity(10–12S cm-1 for electron conductivity[3]and 10–17–10–10 cm2 s1 for Li+ion diffusion coefficient[4])and poor specific capacity(only accommodate<0.5 Li per bulk TiO2 unit[5]),severely limiting its practical applications.
基金supported by the National Natural Science Foundation of China (Nos. 51472137 and 51772163)
文摘Studying on the anode materials with high energy densities for next-generation lithium-ion batteries(LIBs) is the key for the wide application for electrochemical energy storage devices.Ti-based compounds as promising anode materials are known for their outstanding high-rate capacity and cycling stability as well as improved safety over graphite. However, Ti-based materials still suffer from the low capacity, thus largely limiting their commercialized application. Here, we present an overview of the recent development of Ti-based anode materials in LIBs, and special emphasis is placed on capacity enhancement by rational design of hybrid nanocomposites with conversion-/alloying-type anodes. This review is expected to provide a guidance for designing novel Ti-based materials for energy storage and conversion.
基金This work was supported in part by the National Natural Science Foundation of China （Grants Nos. 61170122, 61272210）, by Japan Society for the Promotion of Sciences （JSPS）, by the Natural Science Foundation of Jiangsu Province （BK2011417, BK2011003）, by Jiangsu 333 Expert Engineering Grant （BRA201114-2）, and by 2011 and 2012 Postgraduate Student＇s Creative Research Funds of Jiangsu Province （CXZZ11-0483, CXZZ12-0759）.
文摘In many data stream mining applications, traditional density estimation methods such as kemel density estimation, reduced set density estimation can not be applied to the density estimation of data streams because of their high computational burden, processing time and intensive memory allocation requirement. In order to reduce the time and space complexity, a novel density estimation method Dm-KDE over data streams based on the proposed algorithm m-KDE which can be used to design a KDE estimator with the fixed number of kernel components for a dataset is proposed. In this method, Dm-KDE sequence entries are created by algorithm m-KDE instead of all kemels obtained from other density estimation methods. In order to further reduce the storage space, Dm-KDE sequence entries can be merged by calculating their KL divergences. Finally, the probability density functions over arbitrary time or entire time can be estimated through the obtained estimation model. In contrast to the state-of-the-art algorithm SOMKE, the distinctive advantage of the proposed algorithm Dm-KDE exists in that it can achieve the same accuracy with much less fixed number of kernel components such that it is suitable for the scenarios where higher on-line computation about the kernel density estimation over data streams is required. We compare Dm-KDE with SOMKE and M-kernel in terms of density estimation accuracy and running time for various stationary datasets. We also apply Dm-KDE to evolving data streams. Experimental results illustrate the effectiveness of the pro- posed method.
