Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance de...Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.展开更多
The reaction of pp → pK^＋A is a very good channel to study N^＊ resonances through their KA decay mode, because there is no mixing of isospin I = 1/2 and I = 3/2 due to isospin conservation. In this work, we extend ...The reaction of pp → pK^＋A is a very good channel to study N^＊ resonances through their KA decay mode, because there is no mixing of isospin I = 1/2 and I = 3/2 due to isospin conservation. In this work, we extend a resonance model, which can reproduce the total cross section very well, to offer differential cross section information about this reaction. It can serve as a reference to build the scheduled hadron detector at Lanzhou Cooler Storage Ring （CSR）. Experiment measurement of these differential cross sections in the future will supply us more constraints on the model and help us understanding the strangeness production dynamics better.展开更多
基金Supported by the National Natural Science Foundation of China （61273160）, the Natural Science Foundation of Shandong Province of China （ZR2011FM014） and the Fundamental Research Funds for the Central Universities （10CX04046A）.
文摘Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.
基金The project partly supported by National Natural Science Foundation of China under Grant Nos. 10225525 and 10435080 and Knowledge Innovation Project of the Chinese Academy of Sciences under Grant No. KJCX2-SW-N02. We thank H.C. Chiang, G.M. Jin, X.G. Li, J.Y. Liu, P.N. Shen, J.J. Xie, H.S. Xu, and W.L. Zhan for useful discussions.
文摘The reaction of pp → pK^＋A is a very good channel to study N^＊ resonances through their KA decay mode, because there is no mixing of isospin I = 1/2 and I = 3/2 due to isospin conservation. In this work, we extend a resonance model, which can reproduce the total cross section very well, to offer differential cross section information about this reaction. It can serve as a reference to build the scheduled hadron detector at Lanzhou Cooler Storage Ring （CSR）. Experiment measurement of these differential cross sections in the future will supply us more constraints on the model and help us understanding the strangeness production dynamics better.
