Due to the compromise of the security of the underlying system or machine stonng the key, exposure of the private key can be a devastating attack on a cryptosystem. Key insulation is an important technique to protect ...Due to the compromise of the security of the underlying system or machine stonng the key, exposure of the private key can be a devastating attack on a cryptosystem. Key insulation is an important technique to protect private keys. To deal with the private （signing） key exposure problem in identity-based signature systems, we propose an identity-based threshold key-insulated signature （IBTKIS） scheme. It strengthens the security and flexibility of existing identity-based key-insulated signature schemes. Our scheme＇ s security is proven in the random oracle model and rests on the hardness of the computational Diffie-Helhnan problem in groups equipped with a pairing. To the best of our knowledge, it is the first IBTKIS scheme up to now.展开更多
LS-SVM （least squares support vector machines） are a class of kemel machines emphasizing on primal-dual aspects in a constrained optimization framework. LS-SVMs aim at extending methodologies typical of classical su...LS-SVM （least squares support vector machines） are a class of kemel machines emphasizing on primal-dual aspects in a constrained optimization framework. LS-SVMs aim at extending methodologies typical of classical support vector machines for problems beyond classification and regression. This paper describes a methodology that was developed for the prediction of the critical flashover voltage of polluted insulators by using a LS-SVM. The methodology uses as input variables characteristics of the insulator such as diameter, height, creepage distance, form factor and equivalent salt deposit density. The estimation offlashover performance of polluted insulators is based on field experience and laboratory tests are invaluable as they significantly reduce the time and labour involved in insulators design and selection. The majority of the variables to be predicted are dependent upon several independent variables. The results from this work are useful to predict the contamination severity, critical flashover voltage as a function of contamination severity, arc length, and especially to predict the flashover voltage. The validity of the approach was examined by testing several insulators with different geometries. Moreover, the performance of the proposed approach with other intelligence method based on ANN （artificial neural networks） is compared. It can be concluded that the LS-SVM approach has better generalization ability that assist the measurement and monitoring of contamination severity, flashover voltage and leakage current.展开更多
基金Supported by the National Natural Science Foundation of China （No. 60970111, 61133014, 60903189, 60903020）.
文摘Due to the compromise of the security of the underlying system or machine stonng the key, exposure of the private key can be a devastating attack on a cryptosystem. Key insulation is an important technique to protect private keys. To deal with the private （signing） key exposure problem in identity-based signature systems, we propose an identity-based threshold key-insulated signature （IBTKIS） scheme. It strengthens the security and flexibility of existing identity-based key-insulated signature schemes. Our scheme＇ s security is proven in the random oracle model and rests on the hardness of the computational Diffie-Helhnan problem in groups equipped with a pairing. To the best of our knowledge, it is the first IBTKIS scheme up to now.
文摘LS-SVM （least squares support vector machines） are a class of kemel machines emphasizing on primal-dual aspects in a constrained optimization framework. LS-SVMs aim at extending methodologies typical of classical support vector machines for problems beyond classification and regression. This paper describes a methodology that was developed for the prediction of the critical flashover voltage of polluted insulators by using a LS-SVM. The methodology uses as input variables characteristics of the insulator such as diameter, height, creepage distance, form factor and equivalent salt deposit density. The estimation offlashover performance of polluted insulators is based on field experience and laboratory tests are invaluable as they significantly reduce the time and labour involved in insulators design and selection. The majority of the variables to be predicted are dependent upon several independent variables. The results from this work are useful to predict the contamination severity, critical flashover voltage as a function of contamination severity, arc length, and especially to predict the flashover voltage. The validity of the approach was examined by testing several insulators with different geometries. Moreover, the performance of the proposed approach with other intelligence method based on ANN （artificial neural networks） is compared. It can be concluded that the LS-SVM approach has better generalization ability that assist the measurement and monitoring of contamination severity, flashover voltage and leakage current.
