In the past few years, much and much attention has been paid to the method for solving non-convex programming. Many convergence results are obtained for bounded sets. In this paper, we get global convergence results f...In the past few years, much and much attention has been paid to the method for solving non-convex programming. Many convergence results are obtained for bounded sets. In this paper, we get global convergence results for non-convex programming in unbounded sets under suitable conditions.展开更多
In recent years, it has shown that a generalized thresholding algorithm is useful for inverse problems with sparsity constraints. The generalized thresholding minimizes the non-convex p-norm based function with p <...In recent years, it has shown that a generalized thresholding algorithm is useful for inverse problems with sparsity constraints. The generalized thresholding minimizes the non-convex p-norm based function with p < 1, and it penalizes small coefficients over a wider range meanwhile applies less bias to the larger coefficients.In this work, on the basis of two-level Bregman method with dictionary updating(TBMDU), we use the modified thresholding to minimize the non-convex function and propose the generalized TBMDU(GTBMDU) algorithm.The experimental results on magnetic resonance(MR) image simulations and real MR data, under a variety of sampling trajectories and acceleration factors, consistently demonstrate that the proposed algorithm can efficiently reconstruct the MR images and present advantages over the previous soft thresholding approaches.展开更多
The support vector machine(SVM)is a classical machine learning method.Both the hinge loss and least absolute shrinkage and selection operator(LASSO)penalty are usually used in traditional SVMs.However,the hinge loss i...The support vector machine(SVM)is a classical machine learning method.Both the hinge loss and least absolute shrinkage and selection operator(LASSO)penalty are usually used in traditional SVMs.However,the hinge loss is not differentiable,and the LASSO penalty does not have the Oracle property.In this paper,the huberized loss is combined with non-convex penalties to obtain a model that has the advantages of both the computational simplicity and the Oracle property,contributing to higher accuracy than traditional SVMs.It is experimentally demonstrated that the two non-convex huberized-SVM methods,smoothly clipped absolute deviation huberized-SVM(SCAD-HSVM)and minimax concave penalty huberized-SVM(MCP-HSVM),outperform the traditional SVM method in terms of the prediction accuracy and classifier performance.They are also superior in terms of variable selection,especially when there is a high linear correlation between the variables.When they are applied to the prediction of listed companies,the variables that can affect and predict financial distress are accurately filtered out.Among all the indicators,the indicators per share have the greatest influence while those of solvency have the weakest influence.Listed companies can assess the financial situation with the indicators screened by our algorithm and make an early warning of their possible financial distress in advance with higher precision.展开更多
Bacterial flagellar filament can undergo a stress-induced polymorphic phase transition in both vitro and vivo environments.The filament has 12 different helical forms(phases) characterized by different pitch lengths a...Bacterial flagellar filament can undergo a stress-induced polymorphic phase transition in both vitro and vivo environments.The filament has 12 different helical forms(phases) characterized by different pitch lengths and helix radii.When subjected to the frictional force of flowing fluid,the filament changes between a left-handed normal phase and a right-handed semi-coiled phase via phase nucleation and growth.This paper develops non-local finite element method(FEM) to simulate the phase transition under a displacement-controlled loading condition(controlled helix-twist).The FEM formulation is based on the Ginzburg-Landau theory using a one-dimensional non-convex and non-local continuum model.To describe the processes of the phase nucleation and growth,viscosity-type kinetics is also used.The non-local FEM simulation captures the main features of the phase transition:two-phase coexistence with an interface of finite thickness,phase nucleation and phase growth with interface propagation.The non-local FEM model provides a tool to study the effects of the interfacial energy/thickness and loading conditions on the phase transition.展开更多
Waterside creatures or aquatic organisms use a fin or web to generate a thrust force. These fins or webs have a non-convex section, referred to as a non-convex shape. We investigate the drag force acting on ...Waterside creatures or aquatic organisms use a fin or web to generate a thrust force. These fins or webs have a non-convex section, referred to as a non-convex shape. We investigate the drag force acting on a non-convex plate during unsteady motion. We perform the experiment in a water tank during free fall. We fabricate the non-convex plate by cutting isosceles triangles from the side of a convex hexagonal plate. The base angle of the triangle is between 0° to 45°. The base angle is 0 indicates the convex hexagonal thin plate. We estimate the drag coefficient with the force balance acting on the model based on the image analysis technique. The results indicate that increasing the base angle by more than 30° increased the drag coefficient. The drag coefficient during unsteady motion changed with the growth of the vortex behind the model. The vortex has small vortices in the shear layer, which is related to the Kelvin-Helmholtz instabilities.展开更多
The proliferation of deep learning(DL)has amplified the demand for processing large and complex datasets for tasks such as modeling,classification,and identification.However,traditional DL methods compromise client pr...The proliferation of deep learning(DL)has amplified the demand for processing large and complex datasets for tasks such as modeling,classification,and identification.However,traditional DL methods compromise client privacy by collecting sensitive data,underscoring the necessity for privacy-preserving solutions like Federated Learning(FL).FL effectively addresses escalating privacy concerns by facilitating collaborative model training without necessitating the sharing of raw data.Given that FL clients autonomously manage training data,encouraging client engagement is pivotal for successful model training.To overcome challenges like unreliable communication and budget constraints,we present ENTIRE,a contract-based dynamic participation incentive mechanism for FL.ENTIRE ensures impartial model training by tailoring participation levels and payments to accommodate diverse client preferences.Our approach involves several key steps.Initially,we examine how random client participation impacts FL convergence in non-convex scenarios,establishing the correlation between client participation levels and model performance.Subsequently,we reframe model performance optimization as an optimal contract design challenge to guide the distribution of rewards among clients with varying participation costs.By balancing budget considerations with model effectiveness,we craft optimal contracts for different budgetary constraints,prompting clients to disclose their participation preferences and select suitable contracts for contributing to model training.Finally,we conduct a comprehensive experimental evaluation of ENTIRE using three real datasets.The results demonstrate a significant 12.9%enhancement in model performance,validating its adherence to anticipated economic properties.展开更多
Slater选举是最优化问题,也是NP-hard问题,此类问题一般被认为不存在多项式时间的算法。考虑到其求解的复杂度与回答集求解的复杂度是一致的,为此,提出一种利用回答集程序(Answer Set Programming,ASP)求解Slater选举的新方法。首先,使...Slater选举是最优化问题,也是NP-hard问题,此类问题一般被认为不存在多项式时间的算法。考虑到其求解的复杂度与回答集求解的复杂度是一致的,为此,提出一种利用回答集程序(Answer Set Programming,ASP)求解Slater选举的新方法。首先,使用饱和技术为Slater选举建立逻辑上等价的ASP模型;其次,对模型进行正确性证明;最后,调用回答集求解器DLV求解Slater选举的具体实例,并在实验结果中说明其可行性。该方法不仅可求解Slater选举问题,而且在ASP中所使用的饱和技术还为其他同类的最优化问题提供了一种新的逻辑表示途径。展开更多
研究了基于下行无线信息和能量协同传输(simultaneous wireless-information and power-transfer,SWIPT)大规模多输入单输出(multiple-input and single-output,MISO)系统的吞吐率优化问题.该系统为时分双工(time division duplex,TDD)...研究了基于下行无线信息和能量协同传输(simultaneous wireless-information and power-transfer,SWIPT)大规模多输入单输出(multiple-input and single-output,MISO)系统的吞吐率优化问题.该系统为时分双工(time division duplex,TDD)模式,同时移动站采用先收集后传输的协议.在下行信噪比(signal-to-noise ratio,SNR)和移动站的传输功率约束下,为实现上行吞吐率的最大化,对功率分配系数和下行传输时间进行了联合优化,由于该问题为非凸优化问题,采用基于拉格朗日乘子的梯度算法进行优化.最后,通过与单独优化下行传输时间算法的比较,验证了该联合优化算法的优越性.展开更多
该文研究带时间窗约束的车辆路径问题(Vehicle Routing Problem with Time Windows,VRPTW),这是一个典型的NP-Hard问题。针对传统粒子群算法求解带时间窗约束的车辆路径问题容易陷入局部最优的缺陷,提出了一种基于多策略方法改进的粒子...该文研究带时间窗约束的车辆路径问题(Vehicle Routing Problem with Time Windows,VRPTW),这是一个典型的NP-Hard问题。针对传统粒子群算法求解带时间窗约束的车辆路径问题容易陷入局部最优的缺陷,提出了一种基于多策略方法改进的粒子群算法(Multi-Strategy improved particle Swarm Optimization Algorithm,MSPSO)来解决该问题。该算法采用惯性权重递减策略,使得算法在前期的全局搜索和后期的局部搜索都能够有良好的表现,通过引入随机选择策略更新粒子最优位置,可以增加解空间的多样性,有效避免算法陷入局部最优。最后通过测试Solomon Benchmark算例的结果,在25个客户的C103数据集上MSPSO算法对比RWPSO算法的行驶距离降低了38.29,对比S-PSO算法在C103、R103这两个数据集与最优解误差分别降低了1.76%和3.99%。在50个客户C1系列数据集上MSPSO算法对比PSO算法行驶距离分别减少了14.26、45.66、67.7,与数据集的最优解误差基本能保持在1%以内。从实验结果可以证明MSPSO算法在求解VRPTW问题方面具有优越性和有效性。展开更多
This paper focuses on the sensor subset optimization problem in time difference of arrival(TDOA) passive localization scenario. We seek for the best sensor combination by formulating a non-convex optimization problem,...This paper focuses on the sensor subset optimization problem in time difference of arrival(TDOA) passive localization scenario. We seek for the best sensor combination by formulating a non-convex optimization problem, which is to minimize the trace of covariance matrix of localization error under the condition that the number of selected sensors is given. The accuracy metric is described by the localization error covariance matrix of classical closed-form solution, which is introduced to convert the TDOA nonlinear equations into pseudo linear equations. The non-convex optimization problem is relaxed to a standard semi-definite program(SDP) and efficiently solved in a short time. In addition, we extend the sensor selection method to a mixed TDOA and angle of arrival(AOA) localization scenario with the presence of sensor position errors. Simulation results validate that the performance of the proposed sensor selection method is very close to the exhaustive search method.展开更多
The exact minimax penalty function method is used to solve a noncon- vex differentiable optimization problem with both inequality and equality constraints. The conditions for exactness of the penalization for the exac...The exact minimax penalty function method is used to solve a noncon- vex differentiable optimization problem with both inequality and equality constraints. The conditions for exactness of the penalization for the exact minimax penalty function method are established by assuming that the functions constituting the considered con- strained optimization problem are invex with respect to the same function η （with the exception of those equality constraints for which the associated Lagrange multipliers are negative these functions should be assumed to be incave with respect to η）. Thus, a threshold of the penalty parameter is given such that, for all penalty parameters exceeding this threshold, equivalence holds between the set of optimal solutions in the considered constrained optimization problem and the set of minimizer in its associated penalized problem with an exact minimax penalty function. It is shown that coercivity is not suf- ficient to prove the results.展开更多
One class of effective methods for the optimization problem with inequality constraints are to transform the problem to a unconstrained optimization problem by constructing a smooth potential function. In this paper, ...One class of effective methods for the optimization problem with inequality constraints are to transform the problem to a unconstrained optimization problem by constructing a smooth potential function. In this paper, we modifies a dual algorithm for constrained optimization problems and establishes a corresponding improved dual algorithm; It is proved that the improved dual algorithm has the local Q-superlinear convergence; Finally, we performed numerical experimentation using the improved dual algorithm for many constrained optimization problems, the numerical results are reported to show that it is valid in practical computation.展开更多
Fault detection technique is introduced with similarity measure. The characteristics of conventional similarity measure based on fuzzy number are discussed. With the help of distance measure, similarity measure is con...Fault detection technique is introduced with similarity measure. The characteristics of conventional similarity measure based on fuzzy number are discussed. With the help of distance measure, similarity measure is constructed explicitly. The designed distance-based similarity measure is applicable to general fuzzy membership functions including non-convex fuzzy membership function, whereas fuzzy number-based similarity measure has limitation to calculate the similarity of general fuzzy membership functions. The applicability of the proposed similarity measure to general fuzzy membership structures is proven by identifying the definition. To decide fault detection of flight system, the experimental data （pitching moment coefficients and lift coefficients） are transformed into fuzzy membership functions. Distance-based similarity measure is applied to the obtained fuzzy membership functions, and similarity computation and analysis are obtained with the fault and normal operation coefficients.展开更多
Unmanned Aerial Vehicle(UAV)has emerged as a promising novel application for the Sixth-Generation(6G)wireless communication by leveraging more favorable Line-of-Sight(Lo S)propagation.However,the jamming resistance by...Unmanned Aerial Vehicle(UAV)has emerged as a promising novel application for the Sixth-Generation(6G)wireless communication by leveraging more favorable Line-of-Sight(Lo S)propagation.However,the jamming resistance by exploiting UAV’s mobility is a new challenge in the UAV-ground communication.This paper investigates the trajectory planning problem in an UAV communication system,where the UAV is operated by a Ground Control Unit(GCU)to perform certain tasks in the presence of multiple jammers with imperfect power and location information.To ensure the reliability of the GCU-to-UAV link,we formulate the problem as a non-convex semi-infinite optimization,aiming to maximize the average worst-case Signal-toInterference-plus-Noise Ratio(SINR)over a given flight duration by designing the robust trajectory of the UAV under stringent energy availability constraints.To handle this problem efficiently,we develop an iterative algorithm for the solution with the aid of S-procedure and Successive Convex Approximation(SCA)method.Numerous results demonstrate the efficacy of our proposed algorithm and offer some useful design insights to practical system.展开更多
The efficient antenna scheduling strategy for data relay satellites(DRSs)is essential to optimize the throughput or delay of the satellite data relay network.However,these two objectives conflict with each other since...The efficient antenna scheduling strategy for data relay satellites(DRSs)is essential to optimize the throughput or delay of the satellite data relay network.However,these two objectives conflict with each other since the user satellites(USs)with higher priorities take up more transmission time of DRSs’antennas for greater throughput but the USs storing more packets cause a severer waiting delay to the whole network.To balance the conflicting metrics for meeting the delay-throughput integrated requirements,we formulate the antenna scheduling as a stochastic non-convex fractional programming,which is challenging to be solved.For the tractability,we equivalently transform the fractional programming to a parametric problem and implement the Lyapunov drift to guarantee the constraint of mean rate stability.By proposing a delay and throughput tradeoff based antenna scheduling algorithm,we further transform the parametric problem to a solvable weight matching problem.Simulation results reveal the feasible region of the preference control parameter for integrated QoS cases and its variation relationship with network delay and throughput.展开更多
This paper presents a decentralized control strategy for the scheduling of electrical energy activities of a microgrid composed of smart homes connected to a distributor and exchanging renewable energy produced by ind...This paper presents a decentralized control strategy for the scheduling of electrical energy activities of a microgrid composed of smart homes connected to a distributor and exchanging renewable energy produced by individually owned distributed energy resources. The scheduling problem is stated and solved with the aim of reducing the overall energy supply from the grid, by allowing users to exchange the surplus renewable energy and by optimally planning users' controllable loads. We assume that each smart home can both buy/sell energy from/to the grid taking into account time-varying non-linear pricing signals. Simultaneously, smart homes cooperate and may buy/sell locally harvested renewable energy from/to other smart homes. The resulting optimization problem is formulated as a non-convex non-linear programming problem with a coupling of decision variables in the constraints. The proposed solution is based on a novel heuristic iterative decentralized scheme algorithm that suitably extends the Alternating Direction Method of Multipliers to a non-convex and decentralized setting. We discuss the conditions that guarantee the convergence of the presented algorithm. Finally, the application of the proposed technique to a case study under several scenarios shows its effectiveness.展开更多
Martensite domain formation, evolution and annihilation are widely observed in stress-induced phase transformation of superelastic NiTi polycrystalline shape memory alloys. By the calculation of the thermodynamic driv...Martensite domain formation, evolution and annihilation are widely observed in stress-induced phase transformation of superelastic NiTi polycrystalline shape memory alloys. By the calculation of the thermodynamic driving force and the incorporation of friction kinetics of the interface, the domain morphology and its evolution were successfully simulated by the interface-tracking technique. The computational results agree well with the experimental observation of tensile strips. Based on theoretical and computational results, we discussed the effects of critical driving force and the existence of metastability on the transition between different domain patterns.展开更多
基金The NNSF (10071031) of China China Postdoctoral Science Foundation.
文摘In the past few years, much and much attention has been paid to the method for solving non-convex programming. Many convergence results are obtained for bounded sets. In this paper, we get global convergence results for non-convex programming in unbounded sets under suitable conditions.
基金the National Natural Science Foundation of China(Nos.6136200161365013 and 51165033)+3 种基金the Natural Science Foundation of Jiangxi Province(Nos.20132BAB211030 and 20122BAB211015)the Technology Foundation of Department of Education in Jiangxi Province(Nos.GJJ 13061 and GJJ14196)the National Postdoctoral Research Funds(No.2014M551867)the Jiangxi Advanced Projects for Postdoctoral Research Funds(No.2014KY02)
文摘In recent years, it has shown that a generalized thresholding algorithm is useful for inverse problems with sparsity constraints. The generalized thresholding minimizes the non-convex p-norm based function with p < 1, and it penalizes small coefficients over a wider range meanwhile applies less bias to the larger coefficients.In this work, on the basis of two-level Bregman method with dictionary updating(TBMDU), we use the modified thresholding to minimize the non-convex function and propose the generalized TBMDU(GTBMDU) algorithm.The experimental results on magnetic resonance(MR) image simulations and real MR data, under a variety of sampling trajectories and acceleration factors, consistently demonstrate that the proposed algorithm can efficiently reconstruct the MR images and present advantages over the previous soft thresholding approaches.
文摘The support vector machine(SVM)is a classical machine learning method.Both the hinge loss and least absolute shrinkage and selection operator(LASSO)penalty are usually used in traditional SVMs.However,the hinge loss is not differentiable,and the LASSO penalty does not have the Oracle property.In this paper,the huberized loss is combined with non-convex penalties to obtain a model that has the advantages of both the computational simplicity and the Oracle property,contributing to higher accuracy than traditional SVMs.It is experimentally demonstrated that the two non-convex huberized-SVM methods,smoothly clipped absolute deviation huberized-SVM(SCAD-HSVM)and minimax concave penalty huberized-SVM(MCP-HSVM),outperform the traditional SVM method in terms of the prediction accuracy and classifier performance.They are also superior in terms of variable selection,especially when there is a high linear correlation between the variables.When they are applied to the prediction of listed companies,the variables that can affect and predict financial distress are accurately filtered out.Among all the indicators,the indicators per share have the greatest influence while those of solvency have the weakest influence.Listed companies can assess the financial situation with the indicators screened by our algorithm and make an early warning of their possible financial distress in advance with higher precision.
基金supported by the Hong Kong University of Science and Technology and the National Natural Science Foundation of China (10902013)
文摘Bacterial flagellar filament can undergo a stress-induced polymorphic phase transition in both vitro and vivo environments.The filament has 12 different helical forms(phases) characterized by different pitch lengths and helix radii.When subjected to the frictional force of flowing fluid,the filament changes between a left-handed normal phase and a right-handed semi-coiled phase via phase nucleation and growth.This paper develops non-local finite element method(FEM) to simulate the phase transition under a displacement-controlled loading condition(controlled helix-twist).The FEM formulation is based on the Ginzburg-Landau theory using a one-dimensional non-convex and non-local continuum model.To describe the processes of the phase nucleation and growth,viscosity-type kinetics is also used.The non-local FEM simulation captures the main features of the phase transition:two-phase coexistence with an interface of finite thickness,phase nucleation and phase growth with interface propagation.The non-local FEM model provides a tool to study the effects of the interfacial energy/thickness and loading conditions on the phase transition.
文摘Waterside creatures or aquatic organisms use a fin or web to generate a thrust force. These fins or webs have a non-convex section, referred to as a non-convex shape. We investigate the drag force acting on a non-convex plate during unsteady motion. We perform the experiment in a water tank during free fall. We fabricate the non-convex plate by cutting isosceles triangles from the side of a convex hexagonal plate. The base angle of the triangle is between 0° to 45°. The base angle is 0 indicates the convex hexagonal thin plate. We estimate the drag coefficient with the force balance acting on the model based on the image analysis technique. The results indicate that increasing the base angle by more than 30° increased the drag coefficient. The drag coefficient during unsteady motion changed with the growth of the vortex behind the model. The vortex has small vortices in the shear layer, which is related to the Kelvin-Helmholtz instabilities.
基金supported by the National Natural Science Foundation of China(Nos.62072411,62372343,62402352,62403500)the Key Research and Development Program of Hubei Province(No.2023BEB024)the Open Fund of Key Laboratory of Social Computing and Cognitive Intelligence(Dalian University of Technology),Ministry of Education(No.SCCI2024TB02).
文摘The proliferation of deep learning(DL)has amplified the demand for processing large and complex datasets for tasks such as modeling,classification,and identification.However,traditional DL methods compromise client privacy by collecting sensitive data,underscoring the necessity for privacy-preserving solutions like Federated Learning(FL).FL effectively addresses escalating privacy concerns by facilitating collaborative model training without necessitating the sharing of raw data.Given that FL clients autonomously manage training data,encouraging client engagement is pivotal for successful model training.To overcome challenges like unreliable communication and budget constraints,we present ENTIRE,a contract-based dynamic participation incentive mechanism for FL.ENTIRE ensures impartial model training by tailoring participation levels and payments to accommodate diverse client preferences.Our approach involves several key steps.Initially,we examine how random client participation impacts FL convergence in non-convex scenarios,establishing the correlation between client participation levels and model performance.Subsequently,we reframe model performance optimization as an optimal contract design challenge to guide the distribution of rewards among clients with varying participation costs.By balancing budget considerations with model effectiveness,we craft optimal contracts for different budgetary constraints,prompting clients to disclose their participation preferences and select suitable contracts for contributing to model training.Finally,we conduct a comprehensive experimental evaluation of ENTIRE using three real datasets.The results demonstrate a significant 12.9%enhancement in model performance,validating its adherence to anticipated economic properties.
文摘Slater选举是最优化问题,也是NP-hard问题,此类问题一般被认为不存在多项式时间的算法。考虑到其求解的复杂度与回答集求解的复杂度是一致的,为此,提出一种利用回答集程序(Answer Set Programming,ASP)求解Slater选举的新方法。首先,使用饱和技术为Slater选举建立逻辑上等价的ASP模型;其次,对模型进行正确性证明;最后,调用回答集求解器DLV求解Slater选举的具体实例,并在实验结果中说明其可行性。该方法不仅可求解Slater选举问题,而且在ASP中所使用的饱和技术还为其他同类的最优化问题提供了一种新的逻辑表示途径。
文摘研究了基于下行无线信息和能量协同传输(simultaneous wireless-information and power-transfer,SWIPT)大规模多输入单输出(multiple-input and single-output,MISO)系统的吞吐率优化问题.该系统为时分双工(time division duplex,TDD)模式,同时移动站采用先收集后传输的协议.在下行信噪比(signal-to-noise ratio,SNR)和移动站的传输功率约束下,为实现上行吞吐率的最大化,对功率分配系数和下行传输时间进行了联合优化,由于该问题为非凸优化问题,采用基于拉格朗日乘子的梯度算法进行优化.最后,通过与单独优化下行传输时间算法的比较,验证了该联合优化算法的优越性.
文摘该文研究带时间窗约束的车辆路径问题(Vehicle Routing Problem with Time Windows,VRPTW),这是一个典型的NP-Hard问题。针对传统粒子群算法求解带时间窗约束的车辆路径问题容易陷入局部最优的缺陷,提出了一种基于多策略方法改进的粒子群算法(Multi-Strategy improved particle Swarm Optimization Algorithm,MSPSO)来解决该问题。该算法采用惯性权重递减策略,使得算法在前期的全局搜索和后期的局部搜索都能够有良好的表现,通过引入随机选择策略更新粒子最优位置,可以增加解空间的多样性,有效避免算法陷入局部最优。最后通过测试Solomon Benchmark算例的结果,在25个客户的C103数据集上MSPSO算法对比RWPSO算法的行驶距离降低了38.29,对比S-PSO算法在C103、R103这两个数据集与最优解误差分别降低了1.76%和3.99%。在50个客户C1系列数据集上MSPSO算法对比PSO算法行驶距离分别减少了14.26、45.66、67.7,与数据集的最优解误差基本能保持在1%以内。从实验结果可以证明MSPSO算法在求解VRPTW问题方面具有优越性和有效性。
基金supported by the National Natural Science Foundation of China under Grant (61631015, 61501354 61471395 and 61501356)the Key Scientific and Technological Innovation Team Plan (2016KCT-01)the Fundamental Research Funds of the Ministry of Education (7215433803 and XJS16063)
文摘This paper focuses on the sensor subset optimization problem in time difference of arrival(TDOA) passive localization scenario. We seek for the best sensor combination by formulating a non-convex optimization problem, which is to minimize the trace of covariance matrix of localization error under the condition that the number of selected sensors is given. The accuracy metric is described by the localization error covariance matrix of classical closed-form solution, which is introduced to convert the TDOA nonlinear equations into pseudo linear equations. The non-convex optimization problem is relaxed to a standard semi-definite program(SDP) and efficiently solved in a short time. In addition, we extend the sensor selection method to a mixed TDOA and angle of arrival(AOA) localization scenario with the presence of sensor position errors. Simulation results validate that the performance of the proposed sensor selection method is very close to the exhaustive search method.
文摘The exact minimax penalty function method is used to solve a noncon- vex differentiable optimization problem with both inequality and equality constraints. The conditions for exactness of the penalization for the exact minimax penalty function method are established by assuming that the functions constituting the considered con- strained optimization problem are invex with respect to the same function η （with the exception of those equality constraints for which the associated Lagrange multipliers are negative these functions should be assumed to be incave with respect to η）. Thus, a threshold of the penalty parameter is given such that, for all penalty parameters exceeding this threshold, equivalence holds between the set of optimal solutions in the considered constrained optimization problem and the set of minimizer in its associated penalized problem with an exact minimax penalty function. It is shown that coercivity is not suf- ficient to prove the results.
基金Supported by the National 863 Project (2003AA002030)
文摘One class of effective methods for the optimization problem with inequality constraints are to transform the problem to a unconstrained optimization problem by constructing a smooth potential function. In this paper, we modifies a dual algorithm for constrained optimization problems and establishes a corresponding improved dual algorithm; It is proved that the improved dual algorithm has the local Q-superlinear convergence; Finally, we performed numerical experimentation using the improved dual algorithm for many constrained optimization problems, the numerical results are reported to show that it is valid in practical computation.
基金Project supported by the Second Stage of Brain Korea and Korea Research Foundation
文摘Fault detection technique is introduced with similarity measure. The characteristics of conventional similarity measure based on fuzzy number are discussed. With the help of distance measure, similarity measure is constructed explicitly. The designed distance-based similarity measure is applicable to general fuzzy membership functions including non-convex fuzzy membership function, whereas fuzzy number-based similarity measure has limitation to calculate the similarity of general fuzzy membership functions. The applicability of the proposed similarity measure to general fuzzy membership structures is proven by identifying the definition. To decide fault detection of flight system, the experimental data （pitching moment coefficients and lift coefficients） are transformed into fuzzy membership functions. Distance-based similarity measure is applied to the obtained fuzzy membership functions, and similarity computation and analysis are obtained with the fault and normal operation coefficients.
文摘Unmanned Aerial Vehicle(UAV)has emerged as a promising novel application for the Sixth-Generation(6G)wireless communication by leveraging more favorable Line-of-Sight(Lo S)propagation.However,the jamming resistance by exploiting UAV’s mobility is a new challenge in the UAV-ground communication.This paper investigates the trajectory planning problem in an UAV communication system,where the UAV is operated by a Ground Control Unit(GCU)to perform certain tasks in the presence of multiple jammers with imperfect power and location information.To ensure the reliability of the GCU-to-UAV link,we formulate the problem as a non-convex semi-infinite optimization,aiming to maximize the average worst-case Signal-toInterference-plus-Noise Ratio(SINR)over a given flight duration by designing the robust trajectory of the UAV under stringent energy availability constraints.To handle this problem efficiently,we develop an iterative algorithm for the solution with the aid of S-procedure and Successive Convex Approximation(SCA)method.Numerous results demonstrate the efficacy of our proposed algorithm and offer some useful design insights to practical system.
基金supported in part by the Natural Science Foundation of China under Grant U19B2025,Grant 61725103,Grant 61701363,Grant 61931005,and Grant 62001347.
文摘The efficient antenna scheduling strategy for data relay satellites(DRSs)is essential to optimize the throughput or delay of the satellite data relay network.However,these two objectives conflict with each other since the user satellites(USs)with higher priorities take up more transmission time of DRSs’antennas for greater throughput but the USs storing more packets cause a severer waiting delay to the whole network.To balance the conflicting metrics for meeting the delay-throughput integrated requirements,we formulate the antenna scheduling as a stochastic non-convex fractional programming,which is challenging to be solved.For the tractability,we equivalently transform the fractional programming to a parametric problem and implement the Lyapunov drift to guarantee the constraint of mean rate stability.By proposing a delay and throughput tradeoff based antenna scheduling algorithm,we further transform the parametric problem to a solvable weight matching problem.Simulation results reveal the feasible region of the preference control parameter for integrated QoS cases and its variation relationship with network delay and throughput.
基金supported by European Regional Development Fund in the "Apulian Technology Clusters SMARTPUGLIA 2020"Program
文摘This paper presents a decentralized control strategy for the scheduling of electrical energy activities of a microgrid composed of smart homes connected to a distributor and exchanging renewable energy produced by individually owned distributed energy resources. The scheduling problem is stated and solved with the aim of reducing the overall energy supply from the grid, by allowing users to exchange the surplus renewable energy and by optimally planning users' controllable loads. We assume that each smart home can both buy/sell energy from/to the grid taking into account time-varying non-linear pricing signals. Simultaneously, smart homes cooperate and may buy/sell locally harvested renewable energy from/to other smart homes. The resulting optimization problem is formulated as a non-convex non-linear programming problem with a coupling of decision variables in the constraints. The proposed solution is based on a novel heuristic iterative decentralized scheme algorithm that suitably extends the Alternating Direction Method of Multipliers to a non-convex and decentralized setting. We discuss the conditions that guarantee the convergence of the presented algorithm. Finally, the application of the proposed technique to a case study under several scenarios shows its effectiveness.
文摘Martensite domain formation, evolution and annihilation are widely observed in stress-induced phase transformation of superelastic NiTi polycrystalline shape memory alloys. By the calculation of the thermodynamic driving force and the incorporation of friction kinetics of the interface, the domain morphology and its evolution were successfully simulated by the interface-tracking technique. The computational results agree well with the experimental observation of tensile strips. Based on theoretical and computational results, we discussed the effects of critical driving force and the existence of metastability on the transition between different domain patterns.
