The data generated from non-Euclidean domains and its graphical representation(with complex-relationship object interdependence)applications has observed an exponential growth.The sophistication of graph data has pose...The data generated from non-Euclidean domains and its graphical representation(with complex-relationship object interdependence)applications has observed an exponential growth.The sophistication of graph data has posed consequential obstacles to the existing machine learning algorithms.In this study,we have considered a revamped version of a semi-supervised learning algorithm for graph-structured data to address the issue of expanding deep learning approaches to represent the graph data.Additionally,the quantum information theory has been applied through Graph Neural Networks(GNNs)to generate Riemannian metrics in closed-form of several graph layers.In further,to pre-process the adjacency matrix of graphs,a new formulation is established to incorporate high order proximities.The proposed scheme has shown outstanding improvements to overcome the deficiencies in Graph Convolutional Network(GCN),particularly,the information loss and imprecise information representation with acceptable computational overhead.Moreover,the proposed Quantum Graph Convolutional Network(QGCN)has significantly strengthened the GCN on semi-supervised node classification tasks.In parallel,it expands the generalization process with a significant difference by making small random perturbationsG of the graph during the training process.The evaluation results are provided on three benchmark datasets,including Citeseer,Cora,and PubMed,that distinctly delineate the superiority of the proposed model in terms of computational accuracy against state-of-the-art GCN and three other methods based on the same algorithms in the existing literature.展开更多
Sensors based on optical resonators often have their measurement range limited by their cavity linewidth,particularly in the measurement of time-varying signals.This paper introduces a method for optical frequency shi...Sensors based on optical resonators often have their measurement range limited by their cavity linewidth,particularly in the measurement of time-varying signals.This paper introduces a method for optical frequency shift detection using multiple harmonics to expand the dynamic range of sensors based on optical resonators.The proposed method expands the measurement range of optical frequency shift beyond the cavity linewidth while maintaining measurement accuracy.The theoretical derivation of this method is carried out based on the equation of motion for an optical resonator and the recursive relationship of the Bessel function.Experimental results show that the dynamic range is expanded to 4 times greater than the conventional first harmonic method while still maintaining accuracy.Furthermore,we present an objective analysis of the correlation between the expansion factor of the method and the linewidth and free spectrum of the optical resonator.展开更多
As an important branch of federated learning,vertical federated learning(VFL)enables multiple institutions to train on the same user samples,bringing considerable industry benefits.However,VFL needs to exchange user f...As an important branch of federated learning,vertical federated learning(VFL)enables multiple institutions to train on the same user samples,bringing considerable industry benefits.However,VFL needs to exchange user features among multiple institutions,which raises concerns about privacy leakage.Moreover,existing multi-party VFL privacy-preserving schemes suffer from issues such as poor reli-ability and high communication overhead.To address these issues,we propose a privacy protection scheme for four institutional VFLs,named FVFL.A hierarchical framework is first introduced to support federated training among four institutions.We also design a verifiable repli-cated secret sharing(RSS)protocol(32)-sharing and combine it with homomorphic encryption to ensure the reliability of FVFL while ensuring the privacy of features and intermediate results of the four institutions.Our theoretical analysis proves the reliability and security of the pro-posed FVFL.Extended experiments verify that the proposed scheme achieves excellent performance with a low communication overhead.展开更多
A generalized non-affine nonlinear power system model is presented for a single machine bus power system with a Static Var Compensator(SVC)or State Var System(SVS)for hybrid Unmanned Aerial Vehicles(UAVs).The model is...A generalized non-affine nonlinear power system model is presented for a single machine bus power system with a Static Var Compensator(SVC)or State Var System(SVS)for hybrid Unmanned Aerial Vehicles(UAVs).The model is constructed by differential algebraic equations on the MATLAB-Simulink platform with the programming technique of its S-Function.Combining the inverse system method and the Linear Quadratic Regulation(LQR),an optimized SVC controller is designed.The simulations under three fault conditions show that the proposed controller can effectively improve the power system transient performance.展开更多
基金supported by the National Key Research and Development Program of China(2018YFB1600600)the National Natural Science Foundation of China under(61976034,U1808206)the Dalian Science and Technology Innovation Fund(2019J12GX035).
文摘The data generated from non-Euclidean domains and its graphical representation(with complex-relationship object interdependence)applications has observed an exponential growth.The sophistication of graph data has posed consequential obstacles to the existing machine learning algorithms.In this study,we have considered a revamped version of a semi-supervised learning algorithm for graph-structured data to address the issue of expanding deep learning approaches to represent the graph data.Additionally,the quantum information theory has been applied through Graph Neural Networks(GNNs)to generate Riemannian metrics in closed-form of several graph layers.In further,to pre-process the adjacency matrix of graphs,a new formulation is established to incorporate high order proximities.The proposed scheme has shown outstanding improvements to overcome the deficiencies in Graph Convolutional Network(GCN),particularly,the information loss and imprecise information representation with acceptable computational overhead.Moreover,the proposed Quantum Graph Convolutional Network(QGCN)has significantly strengthened the GCN on semi-supervised node classification tasks.In parallel,it expands the generalization process with a significant difference by making small random perturbationsG of the graph during the training process.The evaluation results are provided on three benchmark datasets,including Citeseer,Cora,and PubMed,that distinctly delineate the superiority of the proposed model in terms of computational accuracy against state-of-the-art GCN and three other methods based on the same algorithms in the existing literature.
基金supported by the National Natural Science Foundation of China(NSFC)(No.52305621)Foundation Research Project of Shanxi Province(No.202203021212156)Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement(No.201905D121001002)。
文摘Sensors based on optical resonators often have their measurement range limited by their cavity linewidth,particularly in the measurement of time-varying signals.This paper introduces a method for optical frequency shift detection using multiple harmonics to expand the dynamic range of sensors based on optical resonators.The proposed method expands the measurement range of optical frequency shift beyond the cavity linewidth while maintaining measurement accuracy.The theoretical derivation of this method is carried out based on the equation of motion for an optical resonator and the recursive relationship of the Bessel function.Experimental results show that the dynamic range is expanded to 4 times greater than the conventional first harmonic method while still maintaining accuracy.Furthermore,we present an objective analysis of the correlation between the expansion factor of the method and the linewidth and free spectrum of the optical resonator.
基金This work was supported in part by ZTE Industry⁃University⁃Institute Co⁃operation Funds under Grant No.202211FKY00112Open Research Proj⁃ects of Zhejiang Lab under Grant No.2022QA0AB02Natural Science Foundation of Sichuan Province under Grant No.2022NSFSC0913.
文摘As an important branch of federated learning,vertical federated learning(VFL)enables multiple institutions to train on the same user samples,bringing considerable industry benefits.However,VFL needs to exchange user features among multiple institutions,which raises concerns about privacy leakage.Moreover,existing multi-party VFL privacy-preserving schemes suffer from issues such as poor reli-ability and high communication overhead.To address these issues,we propose a privacy protection scheme for four institutional VFLs,named FVFL.A hierarchical framework is first introduced to support federated training among four institutions.We also design a verifiable repli-cated secret sharing(RSS)protocol(32)-sharing and combine it with homomorphic encryption to ensure the reliability of FVFL while ensuring the privacy of features and intermediate results of the four institutions.Our theoretical analysis proves the reliability and security of the pro-posed FVFL.Extended experiments verify that the proposed scheme achieves excellent performance with a low communication overhead.
文摘A generalized non-affine nonlinear power system model is presented for a single machine bus power system with a Static Var Compensator(SVC)or State Var System(SVS)for hybrid Unmanned Aerial Vehicles(UAVs).The model is constructed by differential algebraic equations on the MATLAB-Simulink platform with the programming technique of its S-Function.Combining the inverse system method and the Linear Quadratic Regulation(LQR),an optimized SVC controller is designed.The simulations under three fault conditions show that the proposed controller can effectively improve the power system transient performance.
