A Quantum Spatial Graph Convolutional Network for Text Classification

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 perturbations
G 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.

Dynamic range expansion for optical frequency shift detection based on multiple harmonics

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.

Optimal Controller Design for Non-Affine Nonlinear Power Systems with Static Var Compensators for Hybrid UAVs

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.