@article{SchmittZhuSchmeisseretal., author = {Schmitt, Andrew L. and Zhu, Lei and Schmeißer, Dieter and Himpsel, Franz J. and Jin, Song}, title = {Metallic Single-Crystal CoSi Nanowires via Chemical Vapor Deposition of Single-Source Precursor}, language = {en} } @article{SchmittBiermanSchmeisseretal., author = {Schmitt, Andrew L. and Bierman, Matthew J. and Schmeißer, Dieter and Himpsel, Franz J. and Jin, Song}, title = {Synthesis and Properties of Single-Crystal FeSi Nanowires}, language = {en} } @inproceedings{KaliappanKoenigSchmerletal., author = {Kaliappan, Prabhu Shankar and K{\"o}nig, Hartmut and Schmerl, Sebastian and Dong, Jin Song and Zhu, Huibiao}, title = {Model-Driven Protocol Design Based on Component Oriented Modeling}, series = {Formal methods and software engineering, proceedings 12th International Conference on Formal Engineering Methods, ICFEM 2010, Shanghai, China, November 17 - 19, 2010}, booktitle = {Formal methods and software engineering, proceedings 12th International Conference on Formal Engineering Methods, ICFEM 2010, Shanghai, China, November 17 - 19, 2010}, publisher = {Springer}, address = {Berlin [u.a.]}, isbn = {978-3-642-16900-7}, pages = {613 -- 629}, language = {en} } @misc{GaoLuoPanetal., author = {Gao, Yunlong and Luo, Si-Zhe and Pan, Jin-Yan and Chen, Bai-Hua and Zhang, Yi-Song}, title = {Robust PCA Using Adaptive Probability Weighting}, series = {Acta Automatica Sinica}, volume = {47}, journal = {Acta Automatica Sinica}, number = {4}, issn = {0254-4156}, doi = {10.16383/j.aas.c180743}, pages = {825 -- 838}, abstract = {Principal component analysis (PCA) is an important method for processing high-dimensional data. In recent years, PCA models based on various norms have been extensively studied to improve the robustness. However, on the one hand, these algorithms do not consider the relationship between reconstruction error and covariance; on the other hand, they lack the uncertainty of considering the principal component to the data description. Aiming at these problems, this paper proposes a new robust PCA algorithm. Firstly, the L2,p-norm is used to measure the reconstruction error and the description variance of the projection data. Based on the reconstruction error and the description variance, the adaptive probability error minimization model is established to calculate the uncertainty of the principal component's description of the data. Based on the uncertainty, the adaptive probability weighting PCA is established. The corresponding optimization method is designed. The experimental results of artificial data sets, UCI data sets and face databases show that RPCA-PW is superior than other PCA algorithms.}, language = {en} }