This article studies the existence and uniqueness of the mild solution of a family of control systems with a delay that are governed by the nonlinear fractional evolution differential equations in Banach spaces.Moreov...This article studies the existence and uniqueness of the mild solution of a family of control systems with a delay that are governed by the nonlinear fractional evolution differential equations in Banach spaces.Moreover,we establish the controllability of the considered system.To do so,first,we investigate the approximate controllability of the corresponding linear system.Subsequently,we prove the nonlinear system is approximately controllable if the corresponding linear system is approximately controllable.To reach the conclusions,the theory of resolvent operators,the Banach contraction mapping principle,and fixed point theorems are used.While concluding,some examples are given to demonstrate the efficacy of the proposed results.展开更多
In this article,we study the approximate controllability of neutral partial differential equations with Hilfer fractional derivative and not instantaneous impulses effects.By using the Sadovskii's fixed point theo...In this article,we study the approximate controllability of neutral partial differential equations with Hilfer fractional derivative and not instantaneous impulses effects.By using the Sadovskii's fixed point theorem,fractional calculus and resolvent operator functions,we prove the approximate controllability of the considered system.展开更多
As data analysis often incurs significant communication and computational costs,these tasks are increasingly outsourced to cloud computing platforms.However,this introduces privacy concerns,as sensitive data must be t...As data analysis often incurs significant communication and computational costs,these tasks are increasingly outsourced to cloud computing platforms.However,this introduces privacy concerns,as sensitive data must be transmitted to and processed by untrusted parties.To address this,fully homomorphic encryption(FHE)has emerged as a promising solution for privacy-preserving Machine-Learning-as-a-Service(MLaaS),enabling computation on encrypted data without revealing the plaintext.Nevertheless,FHE remains computationally expensive.As a result,approximate homomorphic encryption(AHE)schemes,such as CKKS,have attracted attention due to their efficiency.In our previous work,we proposed RP-OKC,a CKKS-based clustering scheme implemented via TenSEAL.However,errors inherent to CKKS operations—termed CKKS-errors—can affect the accuracy of the result after decryption.Since these errors can be mitigated through post-decryption rounding,we propose a data pre-scaling technique to increase the number of significant digits and reduce CKKS-errors.Furthermore,we introduce an Operation-Error-Estimation(OEE)table that quantifies upper-bound error estimates for various CKKS operations.This table enables error-aware decryption correction,ensuring alignment between encrypted and plaintext results.We validate our method on K-means clustering using the Kaggle Customer Segmentation dataset.Experimental results confirm that the proposed scheme enhances the accuracy and reliability of privacy-preserving data analysis in cloud environments.展开更多
As power systems expand,solving the unit commitment problem(UCP)becomes increasingly challenging due to the curse of dimensionality,and traditional methods often struggle to balance computational efficiency and soluti...As power systems expand,solving the unit commitment problem(UCP)becomes increasingly challenging due to the curse of dimensionality,and traditional methods often struggle to balance computational efficiency and solution optimality.To tackle this issue,we propose a problem-structure-informed quantum approximate optimization algorithm(QAOA)framework that fully exploits the quantum advantage under extremely limited quantum resources.Specifically,we leverage the inherent topological structure of power systems to decompose large-scale UCP instances into smaller subproblems,which are solvable in parallel by limited number of qubits.This decomposition not only circumvents the current hardware limitations of quantum computing but also achieves higher performance as the graph structure of the power system becomes more sparse.Consequently,our approach can be extended to future power systems that are larger and more complex.展开更多
Stratified flow is a common phenomenon in horizontal tubes of two-phase flow systems. However, the existing methods for calculating the wetted angle of the flat interface model and the central angle of the two-circle ...Stratified flow is a common phenomenon in horizontal tubes of two-phase flow systems. However, the existing methods for calculating the wetted angle of the flat interface model and the central angle of the two-circle model rely on solving implicit transcendental equations, which require iterative numerical root-finding methods,thereby introducing computational complexity and inefficiency. This paper proposes the high-precision explicit approximate solutions for the two models, directly correlating the geometric parameters with the flow parameters, thus significantly enhancing the efficiency and accuracy of two-phase flow analysis.展开更多
为构建综合能源系统安全域(integrated energy system security region,IESSR),该文提出一种基于多项式混沌展开(polynomial chaos expansion,PCE)的IESSR边界(IESR boundary,IESSRB)近似方法,借助该方法可得IESSRB的多项式逼近表达式...为构建综合能源系统安全域(integrated energy system security region,IESSR),该文提出一种基于多项式混沌展开(polynomial chaos expansion,PCE)的IESSR边界(IESR boundary,IESSRB)近似方法,借助该方法可得IESSRB的多项式逼近表达式。首先,根据IESSRB的边界拓扑特性,构建系统的IESSR边界点搜索优化模型;然后,根据PCE,对IESSR边界点搜索优化模型进行参数化处理,构建IESSRB搜索的参数化优化模型;进一步地,根据IESSRB搜索的参数化优化模型的KKT条件,将IESSRB的参数化优化模型转化为高维参数化非线性方程组;在此基础上,借助广义Galerkin投影构建关于近似IESSRB的多项式逼近系数的Galerkin投影方程组,通过求解该方程组可得IESSRB的多项式逼近系数,从而获得IESSRB的多项式逼近表达式;为进一步降低Galerkin投影方程组求解复杂度,提出多项式分段近似IESSRB方法,在提高IESSRB近似精度的同时,提升了IESSRB近似的计算效率;最后,通过IES E39-G20测试系统和IES E118-G96测试系统对所提方法进行分析、验证。结果表明,所提方法可实现IESSR的准确、有效构建。展开更多
To reduce vehicle emissions in road networks, a new signal coordination algorithm based on approximate dynamic programming （ADP） is developed for two intersections. Taking the Jetta car as an experimental vehicle, f...To reduce vehicle emissions in road networks, a new signal coordination algorithm based on approximate dynamic programming （ADP） is developed for two intersections. Taking the Jetta car as an experimental vehicle, field tests are conducted in Changchun Street of Changchun city and vehicle emission factors in complete stop and uniform speed states are collected. Queue lengths and signal light colors of approach lanes are selected as state variables, and green switch plans are selected as decision variables of the system. Then the calculation model of the optimization index during the planning horizon is developed based on the basis function method of the ADP. The temporal-difference algorithm is employed to update the weighting factor vector of the approximate function. Simulations are conducted in Matlab and the results show that the established algorithm outperforms the conventional coordination algorithm in reducing vehicle emissions by 8.2%. Sensitive analysis of the planning horizon length on the evaluation index is also conducted and the statistical results show that the optimal length of the planning horizon is directly proportional to the traffic load.展开更多
Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data po...Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data points which use a query point as one of their k nearest neighbors. To answer the RNNk of queries efficiently, the properties of the Voronoi cell and the space-dividing regions are applied. The RNNk of the given point can be found without computing its nearest neighbors every time by using the rank Voronoi cell. With the elementary RNNk query result, the candidate data points of reverse nearest neighbors can he further limited by the approximation with sweepline and the partial extension of query region Q. The approximate minimum average distance （AMAD） can be calculated by the approximate RNNk without the restriction of k. Experimental results indicate the efficiency and the effectiveness of the algorithm and the approximate method in three varied data distribution spaces. The approximate query and the calculation method with the high precision and the accurate recall are obtained by filtrating data and pruning the search space.展开更多
文摘This article studies the existence and uniqueness of the mild solution of a family of control systems with a delay that are governed by the nonlinear fractional evolution differential equations in Banach spaces.Moreover,we establish the controllability of the considered system.To do so,first,we investigate the approximate controllability of the corresponding linear system.Subsequently,we prove the nonlinear system is approximately controllable if the corresponding linear system is approximately controllable.To reach the conclusions,the theory of resolvent operators,the Banach contraction mapping principle,and fixed point theorems are used.While concluding,some examples are given to demonstrate the efficacy of the proposed results.
基金Supported by Shandong University of Finance and Economics 2023 International Collaborative Projectsthe National Natural Science Foundation of China(Grant No.62073190)。
文摘In this article,we study the approximate controllability of neutral partial differential equations with Hilfer fractional derivative and not instantaneous impulses effects.By using the Sadovskii's fixed point theorem,fractional calculus and resolvent operator functions,we prove the approximate controllability of the considered system.
基金funded by National Science and Technology Council,Taiwan,grant numbers are 110-2401-H-002-094-MY2 and 112-2221-E-130-001.
文摘As data analysis often incurs significant communication and computational costs,these tasks are increasingly outsourced to cloud computing platforms.However,this introduces privacy concerns,as sensitive data must be transmitted to and processed by untrusted parties.To address this,fully homomorphic encryption(FHE)has emerged as a promising solution for privacy-preserving Machine-Learning-as-a-Service(MLaaS),enabling computation on encrypted data without revealing the plaintext.Nevertheless,FHE remains computationally expensive.As a result,approximate homomorphic encryption(AHE)schemes,such as CKKS,have attracted attention due to their efficiency.In our previous work,we proposed RP-OKC,a CKKS-based clustering scheme implemented via TenSEAL.However,errors inherent to CKKS operations—termed CKKS-errors—can affect the accuracy of the result after decryption.Since these errors can be mitigated through post-decryption rounding,we propose a data pre-scaling technique to increase the number of significant digits and reduce CKKS-errors.Furthermore,we introduce an Operation-Error-Estimation(OEE)table that quantifies upper-bound error estimates for various CKKS operations.This table enables error-aware decryption correction,ensuring alignment between encrypted and plaintext results.We validate our method on K-means clustering using the Kaggle Customer Segmentation dataset.Experimental results confirm that the proposed scheme enhances the accuracy and reliability of privacy-preserving data analysis in cloud environments.
文摘As power systems expand,solving the unit commitment problem(UCP)becomes increasingly challenging due to the curse of dimensionality,and traditional methods often struggle to balance computational efficiency and solution optimality.To tackle this issue,we propose a problem-structure-informed quantum approximate optimization algorithm(QAOA)framework that fully exploits the quantum advantage under extremely limited quantum resources.Specifically,we leverage the inherent topological structure of power systems to decompose large-scale UCP instances into smaller subproblems,which are solvable in parallel by limited number of qubits.This decomposition not only circumvents the current hardware limitations of quantum computing but also achieves higher performance as the graph structure of the power system becomes more sparse.Consequently,our approach can be extended to future power systems that are larger and more complex.
基金supported by the General Research Fund from the Research Grants Council of the Hong Kong Special Administrative Region of China (No. PolyU 15210624)。
文摘Stratified flow is a common phenomenon in horizontal tubes of two-phase flow systems. However, the existing methods for calculating the wetted angle of the flat interface model and the central angle of the two-circle model rely on solving implicit transcendental equations, which require iterative numerical root-finding methods,thereby introducing computational complexity and inefficiency. This paper proposes the high-precision explicit approximate solutions for the two models, directly correlating the geometric parameters with the flow parameters, thus significantly enhancing the efficiency and accuracy of two-phase flow analysis.
文摘为构建综合能源系统安全域(integrated energy system security region,IESSR),该文提出一种基于多项式混沌展开(polynomial chaos expansion,PCE)的IESSR边界(IESR boundary,IESSRB)近似方法,借助该方法可得IESSRB的多项式逼近表达式。首先,根据IESSRB的边界拓扑特性,构建系统的IESSR边界点搜索优化模型;然后,根据PCE,对IESSR边界点搜索优化模型进行参数化处理,构建IESSRB搜索的参数化优化模型;进一步地,根据IESSRB搜索的参数化优化模型的KKT条件,将IESSRB的参数化优化模型转化为高维参数化非线性方程组;在此基础上,借助广义Galerkin投影构建关于近似IESSRB的多项式逼近系数的Galerkin投影方程组,通过求解该方程组可得IESSRB的多项式逼近系数,从而获得IESSRB的多项式逼近表达式;为进一步降低Galerkin投影方程组求解复杂度,提出多项式分段近似IESSRB方法,在提高IESSRB近似精度的同时,提升了IESSRB近似的计算效率;最后,通过IES E39-G20测试系统和IES E118-G96测试系统对所提方法进行分析、验证。结果表明,所提方法可实现IESSR的准确、有效构建。
基金The National High Technology Research and Development Program of China (863 Program ) (No. 2011AA110304 )the National Natural Science Foundation of China (No. 50908100)
文摘To reduce vehicle emissions in road networks, a new signal coordination algorithm based on approximate dynamic programming （ADP） is developed for two intersections. Taking the Jetta car as an experimental vehicle, field tests are conducted in Changchun Street of Changchun city and vehicle emission factors in complete stop and uniform speed states are collected. Queue lengths and signal light colors of approach lanes are selected as state variables, and green switch plans are selected as decision variables of the system. Then the calculation model of the optimization index during the planning horizon is developed based on the basis function method of the ADP. The temporal-difference algorithm is employed to update the weighting factor vector of the approximate function. Simulations are conducted in Matlab and the results show that the established algorithm outperforms the conventional coordination algorithm in reducing vehicle emissions by 8.2%. Sensitive analysis of the planning horizon length on the evaluation index is also conducted and the statistical results show that the optimal length of the planning horizon is directly proportional to the traffic load.
基金Supported by the National Natural Science Foundation of China (60673136)the Natural Science Foundation of Heilongjiang Province of China (F200601)~~
文摘Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data points which use a query point as one of their k nearest neighbors. To answer the RNNk of queries efficiently, the properties of the Voronoi cell and the space-dividing regions are applied. The RNNk of the given point can be found without computing its nearest neighbors every time by using the rank Voronoi cell. With the elementary RNNk query result, the candidate data points of reverse nearest neighbors can he further limited by the approximation with sweepline and the partial extension of query region Q. The approximate minimum average distance （AMAD） can be calculated by the approximate RNNk without the restriction of k. Experimental results indicate the efficiency and the effectiveness of the algorithm and the approximate method in three varied data distribution spaces. The approximate query and the calculation method with the high precision and the accurate recall are obtained by filtrating data and pruning the search space.
