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
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
InGaAs high electron mobility transistors （HEMTs） on InP substrate with very good device performance have been grown by mental organic chemical vapor deposition （MOCVD）. Room temperature Hall mobilities of the 2-D...InGaAs high electron mobility transistors （HEMTs） on InP substrate with very good device performance have been grown by mental organic chemical vapor deposition （MOCVD）. Room temperature Hall mobilities of the 2-DEG are measured to be over 8 700 cm^2/V-s with sheet carrier densities larger than 4.6× 10^12 cm^ 2. Transistors with 1.0 μm gate length exhibits transconductance up to 842 mS/ram. Excellent depletion-mode operation, with a threshold voltage of-0.3 V and IDss of 673 mA/mm, is realized. The non-alloyed ohmic contact special resistance is as low as 1.66×10^-8 Ω/cm^2, which is so far the lowest ohmic contact special resistance. The unity current gain cut off frequency （fT） and the maximum oscillation frequency （fmax） are 42.7 and 61.3 GHz, respectively. These results are very encouraging toward manufacturing InP-based HEMT by MOCVD.展开更多
In the COVID-19 pandemic situation,the need to adopt cloud computing(CC)applications by education institutions,in general,and higher education(HE)institutions,in particular,has especially increased to engage students ...In the COVID-19 pandemic situation,the need to adopt cloud computing(CC)applications by education institutions,in general,and higher education(HE)institutions,in particular,has especially increased to engage students in an online mode and remotely carrying out research.The adoption of CC across various sectors,including HE,has been picking momentum in the developing countries in the last few years.In the Indian context,the CC adaptation in the HE sector(HES)remains a less thoroughly explored sector,and no comprehensive study is reported in the literature.Therefore,the aim of the present study is to overcome this research vacuum and examine the factors that impact the CC adoption(CCA)by HE institutions(HEIs)in India.The scope of the study is limited to public universities(PUs)in India.There are,in total,465 Indian PUs and among these 304 PUs,(i.e.,65%PUs)are surveyed using questionnaire-based research.The study has put forth a novel integrated technology adoption framework consisting of the Technology Acceptance Model(TAM),Technology-Organization-Environment(TOE),and Diffusion of Innovation(DOI)in the context of the HES.This integrated TAM-TOE-DOI framework is utilized in the study to analyze eleven hypotheses concerning factors of CCA that have been tested using structural equation modelling(SEM)and confirmatory factor analysis(CFA).The findings reveal that competitive advantage(CA),technology compatibility(TC),technology readiness(TR),senior leadership support,security concerns,government support,and vendor support are the significant contributing factors of CCA by Indian PUs.The study contends that whereas the rest of the factors positively affect the PUs’intention towards CCA,security concerns are a significant reason for the reluctance of these universities against adopting CC.The findings demonstrated the application of an integrated TAM-TOE-DOI framework to assess determining factors of CCA in Indian PUs.Further,the study has given useful insights into the successful CCA by Indian PUs,which will facilitat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.
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.
基金Project(Z132012A001)supported by the Technical Basis Research Program in Science and Industry Bureau of ChinaProject(61201028,60876009)supported by the National Natural Science Foundation of China
文摘InGaAs high electron mobility transistors （HEMTs） on InP substrate with very good device performance have been grown by mental organic chemical vapor deposition （MOCVD）. Room temperature Hall mobilities of the 2-DEG are measured to be over 8 700 cm^2/V-s with sheet carrier densities larger than 4.6× 10^12 cm^ 2. Transistors with 1.0 μm gate length exhibits transconductance up to 842 mS/ram. Excellent depletion-mode operation, with a threshold voltage of-0.3 V and IDss of 673 mA/mm, is realized. The non-alloyed ohmic contact special resistance is as low as 1.66×10^-8 Ω/cm^2, which is so far the lowest ohmic contact special resistance. The unity current gain cut off frequency （fT） and the maximum oscillation frequency （fmax） are 42.7 and 61.3 GHz, respectively. These results are very encouraging toward manufacturing InP-based HEMT by MOCVD.
文摘In the COVID-19 pandemic situation,the need to adopt cloud computing(CC)applications by education institutions,in general,and higher education(HE)institutions,in particular,has especially increased to engage students in an online mode and remotely carrying out research.The adoption of CC across various sectors,including HE,has been picking momentum in the developing countries in the last few years.In the Indian context,the CC adaptation in the HE sector(HES)remains a less thoroughly explored sector,and no comprehensive study is reported in the literature.Therefore,the aim of the present study is to overcome this research vacuum and examine the factors that impact the CC adoption(CCA)by HE institutions(HEIs)in India.The scope of the study is limited to public universities(PUs)in India.There are,in total,465 Indian PUs and among these 304 PUs,(i.e.,65%PUs)are surveyed using questionnaire-based research.The study has put forth a novel integrated technology adoption framework consisting of the Technology Acceptance Model(TAM),Technology-Organization-Environment(TOE),and Diffusion of Innovation(DOI)in the context of the HES.This integrated TAM-TOE-DOI framework is utilized in the study to analyze eleven hypotheses concerning factors of CCA that have been tested using structural equation modelling(SEM)and confirmatory factor analysis(CFA).The findings reveal that competitive advantage(CA),technology compatibility(TC),technology readiness(TR),senior leadership support,security concerns,government support,and vendor support are the significant contributing factors of CCA by Indian PUs.The study contends that whereas the rest of the factors positively affect the PUs’intention towards CCA,security concerns are a significant reason for the reluctance of these universities against adopting CC.The findings demonstrated the application of an integrated TAM-TOE-DOI framework to assess determining factors of CCA in Indian PUs.Further,the study has given useful insights into the successful CCA by Indian PUs,which will facilitat
