An efficient two-step quantum key distribution （QKD） protocol with orthogonal product states in the （×）（n ≥3） Hilbert space is presented. In this protocol, the particles in the orthogonal product states fo...An efficient two-step quantum key distribution （QKD） protocol with orthogonal product states in the （×）（n ≥3） Hilbert space is presented. In this protocol, the particles in the orthogonal product states form two particle sequences. The sender, Alice, first sends one sequence to the receiver, Bob. After Bob receives the first particle sequence, Alice and Bob check eavesdropping by measuring a fraction of particles randomly chosen. After ensuring the security of the quantum channel, Alice sends the other particle sequence to Bob. By making an orthogonal measurement on the two particle sequences, Bob can obtain the information of the orthogonal product states sent by Alice. This protocol has many distinct features such as great capacity, high efficiency in that it uses all orthogonal product states in distributing the key except those chosen for checking eavesdroppers.展开更多
Aiming at the problem of soft sensing modeling for chemical process with strong nonlinearity and complexity,a soft sensing modeling method based on kernel-based orthogonal projections to latent structures(K-OPLS)is pr...Aiming at the problem of soft sensing modeling for chemical process with strong nonlinearity and complexity,a soft sensing modeling method based on kernel-based orthogonal projections to latent structures(K-OPLS)is proposed.Orthogonal projections to latent structures(O-PLS)is a general linear multi-variable data modeling method.It can eliminate systematic variations from descriptive variables(input)that are orthogonal to response variables(output).In the framework of O-PLS model,K-OPLS method maps descriptive variables to high-dimensional feature space by using“kernel technique”to calculate predictive components and response-orthogonal components in the model.Therefore,the K-OPLS method gives the non-linear relationship between the descriptor and the response variables,which improves the performance of the model and enhances the interpretability of the model to a certain extent.To verify the validity of K-OPLS method,it was applied to soft sensing modeling of component content of debutane tower base butane(C4),the quality index of the key product output for industrial fluidized catalytic cracking unit(FCCU)and H 2S and SO 2 concentration in sulfur recovery unit(SRU).Compared with support vector machines(SVM),least-squares support-vector machine(LS-SVM),support vector machine with principal component analysis(PCA-SVM),extreme learning machine(ELM),kernel based extreme learning machine(KELM)and kernel based extreme learning machine with principal component analysis(PCA-KELM)methods under the same conditions,the experimental results show that the K-OPLS method has superior modeling accuracy and good model generalization ability.展开更多
基金Project supported by the National Natural Science Foundation of China （Grant No 60373059）, the Doctoral Programs Foundation of the Ministry of Education of China （Grant No 20040013007）, and the Major Research plan of the National Natural Science Foundation of China （Grant No 90604023）.
文摘An efficient two-step quantum key distribution （QKD） protocol with orthogonal product states in the （×）（n ≥3） Hilbert space is presented. In this protocol, the particles in the orthogonal product states form two particle sequences. The sender, Alice, first sends one sequence to the receiver, Bob. After Bob receives the first particle sequence, Alice and Bob check eavesdropping by measuring a fraction of particles randomly chosen. After ensuring the security of the quantum channel, Alice sends the other particle sequence to Bob. By making an orthogonal measurement on the two particle sequences, Bob can obtain the information of the orthogonal product states sent by Alice. This protocol has many distinct features such as great capacity, high efficiency in that it uses all orthogonal product states in distributing the key except those chosen for checking eavesdroppers.
基金National Natural Science Foundation of China(No.51467008)。
文摘Aiming at the problem of soft sensing modeling for chemical process with strong nonlinearity and complexity,a soft sensing modeling method based on kernel-based orthogonal projections to latent structures(K-OPLS)is proposed.Orthogonal projections to latent structures(O-PLS)is a general linear multi-variable data modeling method.It can eliminate systematic variations from descriptive variables(input)that are orthogonal to response variables(output).In the framework of O-PLS model,K-OPLS method maps descriptive variables to high-dimensional feature space by using“kernel technique”to calculate predictive components and response-orthogonal components in the model.Therefore,the K-OPLS method gives the non-linear relationship between the descriptor and the response variables,which improves the performance of the model and enhances the interpretability of the model to a certain extent.To verify the validity of K-OPLS method,it was applied to soft sensing modeling of component content of debutane tower base butane(C4),the quality index of the key product output for industrial fluidized catalytic cracking unit(FCCU)and H 2S and SO 2 concentration in sulfur recovery unit(SRU).Compared with support vector machines(SVM),least-squares support-vector machine(LS-SVM),support vector machine with principal component analysis(PCA-SVM),extreme learning machine(ELM),kernel based extreme learning machine(KELM)and kernel based extreme learning machine with principal component analysis(PCA-KELM)methods under the same conditions,the experimental results show that the K-OPLS method has superior modeling accuracy and good model generalization ability.
