The traditional A^(*)algorithm exhibits a low efficiency in the path planning of unmanned surface vehicles(USVs).In addition,the path planned presents numerous redundant inflection waypoints,and the security is low,wh...The traditional A^(*)algorithm exhibits a low efficiency in the path planning of unmanned surface vehicles(USVs).In addition,the path planned presents numerous redundant inflection waypoints,and the security is low,which is not conducive to the control of USV and also affects navigation safety.In this paper,these problems were addressed through the following improvements.First,the path search angle and security were comprehensively considered,and a security expansion strategy of nodes based on the 5×5 neighborhood was proposed.The A^(*)algorithm search neighborhood was expanded from 3×3 to 5×5,and safe nodes were screened out for extension via the node security expansion strategy.This algorithm can also optimize path search angles while improving path security.Second,the distance from the current node to the target node was introduced into the heuristic function.The efficiency of the A^(*)algorithm was improved,and the path was smoothed using the Floyd algorithm.For the dynamic adjustment of the weight to improve the efficiency of DWA,the distance from the USV to the target point was introduced into the evaluation function of the dynamic-window approach(DWA)algorithm.Finally,combined with the local target point selection strategy,the optimized DWA algorithm was performed for local path planning.The experimental results show the smooth and safe path planned by the fusion algorithm,which can successfully avoid dynamic obstacles and is effective and feasible in path planning for USVs.展开更多
An improved version of the sparse A^(*)algorithm is proposed to address the common issue of excessive expansion of nodes and failure to consider current ship status and parameters in traditional path planning algorith...An improved version of the sparse A^(*)algorithm is proposed to address the common issue of excessive expansion of nodes and failure to consider current ship status and parameters in traditional path planning algorithms.This algorithm considers factors such as initial position and orientation of the ship,safety range,and ship draft to determine the optimal obstacle-avoiding route from the current to the destination point for ship planning.A coordinate transformation algorithm is also applied to convert commonly used latitude and longitude coordinates of ship travel paths to easily utilized and analyzed Cartesian coordinates.The algorithm incorporates a hierarchical chart processing algorithm to handle multilayered chart data.Furthermore,the algorithm considers the impact of ship length on grid size and density when implementing chart gridification,adjusting the grid size and density accordingly based on ship length.Simulation results show that compared to traditional path planning algorithms,the sparse A^(*)algorithm reduces the average number of path points by 25%,decreases the average maximum storage node number by 17%,and raises the average path turning angle by approximately 10°,effectively improving the safety of ship planning paths.展开更多
A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductiv...A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.展开更多
Virtualization is an indispensable part of the cloud for the objective of deploying different virtual servers over the same physical layer.However,the increase in the number of applications executing on the repositori...Virtualization is an indispensable part of the cloud for the objective of deploying different virtual servers over the same physical layer.However,the increase in the number of applications executing on the repositories results in increased overload due to the adoption of cloud services.Moreover,the migration of applications on the cloud with optimized resource allocation is a herculean task even though it is employed for minimizing the dilemma of allocating resources.In this paper,a Fire Hawk Optimization enabled Deep Learning Scheme(FHOEDLS)is proposed for minimizing the overload and optimizing the resource allocation on the hybrid cloud container architecture for migrating interoperability based applications This FHOEDLS achieves the load prediction through the utilization of deep CNN-GRU-AM model for attaining resource allocation and better migration of applications.It specifically adopted the Fire Hawk Optimization Algorithm(FHOA)for optimizing the parameters that influence the factors that aid in better interoperable application migration with improved resource allocation and minimized overhead.It considered the factors of resource capacity,transmission cost,demand,and predicted load into account during the formulation of the objective function utilized for resource allocation and application migration.The cloud simulation of this FHOEDLS is achieved using a container,Virtual Machine(VM),and Physical Machine(PM).The results of this proposed FHOEDLS confirmed a better resource capability of 0.418 and a minimized load of 0.0061.展开更多
Vulnerability assessment is a systematic process to identify security gaps in the design and evaluation of physical protection systems.Adversarial path planning is a widely used method for identifying potential vulner...Vulnerability assessment is a systematic process to identify security gaps in the design and evaluation of physical protection systems.Adversarial path planning is a widely used method for identifying potential vulnerabilities and threats to the security and resilience of critical infrastructures.However,achieving efficient path optimization in complex large-scale three-dimensional(3D)scenes remains a significant challenge for vulnerability assessment.This paper introduces a novel A^(*)-algorithmic framework for 3D security modeling and vulnerability assessment.Within this framework,the 3D facility models were first developed in 3ds Max and then incorporated into Unity for A^(*)heuristic pathfinding.The A^(*)-heuristic pathfinding algorithm was implemented with a geometric probability model to refine the detection and distance fields and achieve a rational approximation of the cost to reach the goal.An admissible heuristic is ensured by incorporating the minimum probability of detection(P_(D)^(min))and diagonal distance to estimate the heuristic function.The 3D A^(*)heuristic search was demonstrated using a hypothetical laboratory facility,where a comparison was also carried out between the A^(*)and Dijkstra algorithms for optimal path identification.Comparative results indicate that the proposed A^(*)-heuristic algorithm effectively identifies the most vulnerable adversarial pathfinding with high efficiency.Finally,the paper discusses hidden phenomena and open issues in efficient 3D pathfinding for security applications.展开更多
The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to an...The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.展开更多
Since the time step of the traditional finite-difference time-domain(FDTD) method is limited by the small grid size, it is inefficient when dealing with the electromagnetic problems of multi-scale structures.Therefore...Since the time step of the traditional finite-difference time-domain(FDTD) method is limited by the small grid size, it is inefficient when dealing with the electromagnetic problems of multi-scale structures.Therefore, the explicit and unconditionally stable FDTD(US-FDTD) approach has been developed to break through the limitation of Courant–Friedrich–Levy(CFL) condition.However, the eigenvalues and eigenvectors of the system matrix must be calculated before the time iteration in the explicit US-FDTD.Moreover, the eigenvalue decomposition is also time consuming, especially for complex electromagnetic problems in practical application.In addition, compared with the traditional FDTD method, the explicit US-FDTD method is more difficult to introduce the absorbing boundary and plane wave.To solve the drawbacks of the traditional FDTD and the explicit US-FDTD, a new hybrid FDTD algorithm is proposed in this paper.This combines the explicit US-FDTD with the traditional FDTD, which not only overcomes the limitation of CFL condition but also reduces the system matrix dimension, and introduces the plane wave and the perfectly matched layer(PML) absorption boundary conveniently.With the hybrid algorithm, the calculation of the eigenvalues is only required in the fine mesh region and adjacent coarse mesh region.Therefore, the calculation efficiency is greatly enhanced.Furthermore, the plane wave and the absorption boundary introduction of the traditional FDTD method can be directly utilized.Numerical results demonstrate the effectiveness, accuracy, stability, and convenience of this hybrid algorithm.展开更多
Although the phase-shift seismic processing method has characteristics of high accuracy, good stability, high efficiency, and high-dip imaging, it is not able to adapt to strong lateral velocity variation. To overcome...Although the phase-shift seismic processing method has characteristics of high accuracy, good stability, high efficiency, and high-dip imaging, it is not able to adapt to strong lateral velocity variation. To overcome this defect, a finite-difference method in the frequency-space domain is introduced in the migration process, because it can adapt to strong lateral velocity variation and the coefficient is optimized by a hybrid genetic and simulated annealing algorithm. The two measures improve the precision of the approximation dispersion equation. Thus, the imaging effect is improved for areas of high-dip structure and strong lateral velocity variation. The migration imaging of a 2-D SEG/EAGE salt dome model proves that a better imaging effect in these areas is achieved by optimized phase-shift migration operator plus a finite-difference method based on a hybrid genetic and simulated annealing algorithm. The method proposed in this paper is better than conventional methods in imaging of areas of high-dip angle and strong lateral velocity variation.展开更多
The genetic/gradient-based hybrid algorithm is introduced and used in the design studies of aeroelastic optimization of large aircraft wings to attain skin distribution,stiffness distribution and design sensitivity.Th...The genetic/gradient-based hybrid algorithm is introduced and used in the design studies of aeroelastic optimization of large aircraft wings to attain skin distribution,stiffness distribution and design sensitivity.The program of genetic algorithm is developed by the authors while the gradient-based algorithm borrows from the modified method for feasible direction in MSC/NASTRAN software.In the hybrid algorithm,the genetic algorithm is used to perform global search to avoid to fall into local optima,and then the excellent individuals of every generation optimized by the genetic algorithm are further fine-tuned by the modified method for feasible direction to attain the local optima and hence to get global optima.Moreover,the application effects of hybrid genetic algorithm in aeroelastic multidisciplinary design optimization of large aircraft wing are discussed,which satisfy multiple constraints of strength,displacement,aileron efficiency,and flutter speed.The application results show that the genetic/gradient-based hybrid algorithm is available for aeroelastic optimization of large aircraft wings in initial design phase as well as detailed design phase,and the optimization results are very consistent.Therefore,the design modifications can be decreased using the genetic/gradient-based hybrid algorithm.展开更多
The amplitude versus offset/angle(AVO/AVA)inversion which recovers elastic properties of subsurface media is an essential tool in oil and gas exploration.In general,the exact Zoeppritz equation has a relatively high a...The amplitude versus offset/angle(AVO/AVA)inversion which recovers elastic properties of subsurface media is an essential tool in oil and gas exploration.In general,the exact Zoeppritz equation has a relatively high accuracy in modelling the reflection coefficients.However,amplitude inversion based on it is highly nonlinear,thus,requires nonlinear inversion techniques like the genetic algorithm(GA)which has been widely applied in seismology.The quantum genetic algorithm(QGA)is a variant of the GA that enjoys the advantages of quantum computing,such as qubits and superposition of states.It,however,suffers from limitations in the areas of convergence rate and escaping local minima.To address these shortcomings,in this study,we propose a hybrid quantum genetic algorithm(HQGA)that combines a self-adaptive rotating strategy,and operations of quantum mutation and catastrophe.While the selfadaptive rotating strategy improves the flexibility and efficiency of a quantum rotating gate,the operations of quantum mutation and catastrophe enhance the local and global search abilities,respectively.Using the exact Zoeppritz equation,the HQGA was applied to both synthetic and field seismic data inversion and the results were compared to those of the GA and QGA.A number of the synthetic tests show that the HQGA requires fewer searches to converge to the global solution and the inversion results have generally higher accuracy.The application to field data reveals a good agreement between the inverted parameters and real logs.展开更多
In the normal operation condition, a conventional square-root cubature Kalman filter （SRCKF） gives sufficiently good estimation results. However, if the measurements are not reliable, the SRCKF may give inaccurate r...In the normal operation condition, a conventional square-root cubature Kalman filter （SRCKF） gives sufficiently good estimation results. However, if the measurements are not reliable, the SRCKF may give inaccurate results and diverges by time. This study introduces an adaptive SRCKF algorithm with the filter gain correction for the case of measurement malfunctions. By proposing a switching criterion, an optimal filter is selected from the adaptive and conventional SRCKF according to the measurement quality. A subsystem soft fault detection algorithm is built with the filter residual. Utilizing a clear subsystem fault coefficient, the faulty subsystem is isolated as a result of the system reconstruction. In order to improve the performance of the multi-sensor system, a hybrid fusion algorithm is presented based on the adaptive SRCKF. The state and error covariance matrix are also predicted by the priori fusion estimates, and are updated by the predicted and estimated information of subsystems. The proposed algorithms were applied to the vessel dynamic positioning system simulation. They were compared with normal SRCKF and local estimation weighted fusion algorithm. The simulation results show that the presented adaptive SRCKF improves the robustness of subsystem filtering, and the hybrid fusion algorithm has the better performance. The simulation verifies the effectiveness of the proposed algorithms.展开更多
Secure storage yard is one of the optimal core goals of container transportation;thus,making the necessary storage arrangements has become the most crucial part of the container terminal management systems（CTMS）.Thi...Secure storage yard is one of the optimal core goals of container transportation;thus,making the necessary storage arrangements has become the most crucial part of the container terminal management systems（CTMS）.This paper investigates a random hybrid stacking algorithm（RHSA） for outbound containers that randomly enter the yard.In the first stage of RHSA,the distribution among blocks was analyzed with respect to the utilization ratio.In the second stage,the optimization of bay configuration was carried out by using the hybrid genetic algorithm.Moreover,an experiment was performed to test the RHSA.The results show that the explored algorithm is useful to increase the efficiency.展开更多
Quantum singular value thresholding(QSVT) algorithm,as a core module of many mathematical models,seeks the singular values of a sparse and low rank matrix exceeding a threshold and their associated singular vectors.Th...Quantum singular value thresholding(QSVT) algorithm,as a core module of many mathematical models,seeks the singular values of a sparse and low rank matrix exceeding a threshold and their associated singular vectors.The existing all-qubit QSVT algorithm demands lots of ancillary qubits,remaining a huge challenge for realization on nearterm intermediate-scale quantum computers.In this paper,we propose a hybrid QSVT(HQSVT) algorithm utilizing both discrete variables(DVs) and continuous variables(CVs).In our algorithm,raw data vectors are encoded into a qubit system and the following data processing is fulfilled by hybrid quantum operations.Our algorithm requires O [log(MN)] qubits with0(1) qumodes and totally performs 0(1) operations,which significantly reduces the space and runtime consumption.展开更多
直升机低空突防已逐渐成为现代空战察打任务的核心,而低空航迹规划算法是实现该技术的关键。尽管现有的航迹规划算法已经被应用于实际低空突防任务,但基于“前端-后端”式的传统航迹规划算法依然存在规划航迹机动执行性差与复杂动态场...直升机低空突防已逐渐成为现代空战察打任务的核心,而低空航迹规划算法是实现该技术的关键。尽管现有的航迹规划算法已经被应用于实际低空突防任务,但基于“前端-后端”式的传统航迹规划算法依然存在规划航迹机动执行性差与复杂动态场景下易碰撞的缺陷。针对上述问题,本文提出一种基于Hybrid State A^(*)与增强安全管道的改进算法。首先,基于Hybrid State A^(*)算法的联合轨迹优化,可以在状态空间中完成兼顾直升机机动特性的初始航迹高效搜索,有效保证直升机航迹的可达性。其次,基于初始航迹膨胀的增强安全管道,将后端航迹优化参数限制在安全的可行域内,进而有效提升复杂动态场景下规划航迹的安全性。在实验环节,本研究结合ROS机器人仿真环境与Rviz数据可视化工具完成仿真验证,通过算法间的综合对比实验,论证了本研究所提算法对规划的航迹机动性与安全性有明显的提升。展开更多
基金Supported by the EDD of China(No.80912020104)the Science and Technology Commission of Shanghai Municipality(No.22ZR1427700 and No.23692106900).
文摘The traditional A^(*)algorithm exhibits a low efficiency in the path planning of unmanned surface vehicles(USVs).In addition,the path planned presents numerous redundant inflection waypoints,and the security is low,which is not conducive to the control of USV and also affects navigation safety.In this paper,these problems were addressed through the following improvements.First,the path search angle and security were comprehensively considered,and a security expansion strategy of nodes based on the 5×5 neighborhood was proposed.The A^(*)algorithm search neighborhood was expanded from 3×3 to 5×5,and safe nodes were screened out for extension via the node security expansion strategy.This algorithm can also optimize path search angles while improving path security.Second,the distance from the current node to the target node was introduced into the heuristic function.The efficiency of the A^(*)algorithm was improved,and the path was smoothed using the Floyd algorithm.For the dynamic adjustment of the weight to improve the efficiency of DWA,the distance from the USV to the target point was introduced into the evaluation function of the dynamic-window approach(DWA)algorithm.Finally,combined with the local target point selection strategy,the optimized DWA algorithm was performed for local path planning.The experimental results show the smooth and safe path planned by the fusion algorithm,which can successfully avoid dynamic obstacles and is effective and feasible in path planning for USVs.
基金Supported by the Tianjin University of Technology Graduate R esearch Innovation Project(YJ2281).
文摘An improved version of the sparse A^(*)algorithm is proposed to address the common issue of excessive expansion of nodes and failure to consider current ship status and parameters in traditional path planning algorithms.This algorithm considers factors such as initial position and orientation of the ship,safety range,and ship draft to determine the optimal obstacle-avoiding route from the current to the destination point for ship planning.A coordinate transformation algorithm is also applied to convert commonly used latitude and longitude coordinates of ship travel paths to easily utilized and analyzed Cartesian coordinates.The algorithm incorporates a hierarchical chart processing algorithm to handle multilayered chart data.Furthermore,the algorithm considers the impact of ship length on grid size and density when implementing chart gridification,adjusting the grid size and density accordingly based on ship length.Simulation results show that compared to traditional path planning algorithms,the sparse A^(*)algorithm reduces the average number of path points by 25%,decreases the average maximum storage node number by 17%,and raises the average path turning angle by approximately 10°,effectively improving the safety of ship planning paths.
基金supported by the Fundamental Research Funds for the Central Universities (No.3122020072)the Multi-investment Project of Tianjin Applied Basic Research(No.23JCQNJC00250)。
文摘A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.
文摘Virtualization is an indispensable part of the cloud for the objective of deploying different virtual servers over the same physical layer.However,the increase in the number of applications executing on the repositories results in increased overload due to the adoption of cloud services.Moreover,the migration of applications on the cloud with optimized resource allocation is a herculean task even though it is employed for minimizing the dilemma of allocating resources.In this paper,a Fire Hawk Optimization enabled Deep Learning Scheme(FHOEDLS)is proposed for minimizing the overload and optimizing the resource allocation on the hybrid cloud container architecture for migrating interoperability based applications This FHOEDLS achieves the load prediction through the utilization of deep CNN-GRU-AM model for attaining resource allocation and better migration of applications.It specifically adopted the Fire Hawk Optimization Algorithm(FHOA)for optimizing the parameters that influence the factors that aid in better interoperable application migration with improved resource allocation and minimized overhead.It considered the factors of resource capacity,transmission cost,demand,and predicted load into account during the formulation of the objective function utilized for resource allocation and application migration.The cloud simulation of this FHOEDLS is achieved using a container,Virtual Machine(VM),and Physical Machine(PM).The results of this proposed FHOEDLS confirmed a better resource capability of 0.418 and a minimized load of 0.0061.
基金supported by the fundings from 2024 Young Talents Program for Science and Technology Thinking Tanks(No.XMSB20240711041)2024 Student Research Program on Dynamic Simulation and Force-on-Force Exercise of Nuclear Security in 3D Interactive Environment Using Reinforcement Learning,Natural Science Foundation of Top Talent of SZTU(No.GDRC202407)+2 种基金Shenzhen Science and Technology Program(No.KCXFZ20240903092603005)Shenzhen Science and Technology Program(No.JCYJ20241202124703004)Shenzhen Science and Technology Program(No.KJZD20230923114117032)。
文摘Vulnerability assessment is a systematic process to identify security gaps in the design and evaluation of physical protection systems.Adversarial path planning is a widely used method for identifying potential vulnerabilities and threats to the security and resilience of critical infrastructures.However,achieving efficient path optimization in complex large-scale three-dimensional(3D)scenes remains a significant challenge for vulnerability assessment.This paper introduces a novel A^(*)-algorithmic framework for 3D security modeling and vulnerability assessment.Within this framework,the 3D facility models were first developed in 3ds Max and then incorporated into Unity for A^(*)heuristic pathfinding.The A^(*)-heuristic pathfinding algorithm was implemented with a geometric probability model to refine the detection and distance fields and achieve a rational approximation of the cost to reach the goal.An admissible heuristic is ensured by incorporating the minimum probability of detection(P_(D)^(min))and diagonal distance to estimate the heuristic function.The 3D A^(*)heuristic search was demonstrated using a hypothetical laboratory facility,where a comparison was also carried out between the A^(*)and Dijkstra algorithms for optimal path identification.Comparative results indicate that the proposed A^(*)-heuristic algorithm effectively identifies the most vulnerable adversarial pathfinding with high efficiency.Finally,the paper discusses hidden phenomena and open issues in efficient 3D pathfinding for security applications.
基金Supported by the National Basic Research Program of China ("973" Program)the National Natural Science Foundation of China (60872112, 10805012)+1 种基金the Natural Science Foundation of Zhejiang Province(Z207588)the College Science Research Project of Anhui Province (KJ2008B268)~~
文摘The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.
基金Project supported by the National Natural Science Foundation of China(Grant No.61571348)the Equipment Pre-Research Foundation of China(Grant No.61405180202)
文摘Since the time step of the traditional finite-difference time-domain(FDTD) method is limited by the small grid size, it is inefficient when dealing with the electromagnetic problems of multi-scale structures.Therefore, the explicit and unconditionally stable FDTD(US-FDTD) approach has been developed to break through the limitation of Courant–Friedrich–Levy(CFL) condition.However, the eigenvalues and eigenvectors of the system matrix must be calculated before the time iteration in the explicit US-FDTD.Moreover, the eigenvalue decomposition is also time consuming, especially for complex electromagnetic problems in practical application.In addition, compared with the traditional FDTD method, the explicit US-FDTD method is more difficult to introduce the absorbing boundary and plane wave.To solve the drawbacks of the traditional FDTD and the explicit US-FDTD, a new hybrid FDTD algorithm is proposed in this paper.This combines the explicit US-FDTD with the traditional FDTD, which not only overcomes the limitation of CFL condition but also reduces the system matrix dimension, and introduces the plane wave and the perfectly matched layer(PML) absorption boundary conveniently.With the hybrid algorithm, the calculation of the eigenvalues is only required in the fine mesh region and adjacent coarse mesh region.Therefore, the calculation efficiency is greatly enhanced.Furthermore, the plane wave and the absorption boundary introduction of the traditional FDTD method can be directly utilized.Numerical results demonstrate the effectiveness, accuracy, stability, and convenience of this hybrid algorithm.
基金the Open Fund(PLC201104)of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Chengdu University of Technology)the National Natural Science Foundation of China(No.61072073)the Key Project of Education Commission of Sichuan Province(No.10ZA072)
文摘Although the phase-shift seismic processing method has characteristics of high accuracy, good stability, high efficiency, and high-dip imaging, it is not able to adapt to strong lateral velocity variation. To overcome this defect, a finite-difference method in the frequency-space domain is introduced in the migration process, because it can adapt to strong lateral velocity variation and the coefficient is optimized by a hybrid genetic and simulated annealing algorithm. The two measures improve the precision of the approximation dispersion equation. Thus, the imaging effect is improved for areas of high-dip structure and strong lateral velocity variation. The migration imaging of a 2-D SEG/EAGE salt dome model proves that a better imaging effect in these areas is achieved by optimized phase-shift migration operator plus a finite-difference method based on a hybrid genetic and simulated annealing algorithm. The method proposed in this paper is better than conventional methods in imaging of areas of high-dip angle and strong lateral velocity variation.
基金Supported by the National Natural Science Foundation of China(1117202591116)
文摘The genetic/gradient-based hybrid algorithm is introduced and used in the design studies of aeroelastic optimization of large aircraft wings to attain skin distribution,stiffness distribution and design sensitivity.The program of genetic algorithm is developed by the authors while the gradient-based algorithm borrows from the modified method for feasible direction in MSC/NASTRAN software.In the hybrid algorithm,the genetic algorithm is used to perform global search to avoid to fall into local optima,and then the excellent individuals of every generation optimized by the genetic algorithm are further fine-tuned by the modified method for feasible direction to attain the local optima and hence to get global optima.Moreover,the application effects of hybrid genetic algorithm in aeroelastic multidisciplinary design optimization of large aircraft wing are discussed,which satisfy multiple constraints of strength,displacement,aileron efficiency,and flutter speed.The application results show that the genetic/gradient-based hybrid algorithm is available for aeroelastic optimization of large aircraft wings in initial design phase as well as detailed design phase,and the optimization results are very consistent.Therefore,the design modifications can be decreased using the genetic/gradient-based hybrid algorithm.
基金supported by the National Natural Science Foundation of China(U19B6003,42122029)the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX 202003)partially supported by SEG/WesternGeco Scholarship,SEG Foundation/Chevron Scholarship,and SEG/Norman and Shirley Domenico Scholarship
文摘The amplitude versus offset/angle(AVO/AVA)inversion which recovers elastic properties of subsurface media is an essential tool in oil and gas exploration.In general,the exact Zoeppritz equation has a relatively high accuracy in modelling the reflection coefficients.However,amplitude inversion based on it is highly nonlinear,thus,requires nonlinear inversion techniques like the genetic algorithm(GA)which has been widely applied in seismology.The quantum genetic algorithm(QGA)is a variant of the GA that enjoys the advantages of quantum computing,such as qubits and superposition of states.It,however,suffers from limitations in the areas of convergence rate and escaping local minima.To address these shortcomings,in this study,we propose a hybrid quantum genetic algorithm(HQGA)that combines a self-adaptive rotating strategy,and operations of quantum mutation and catastrophe.While the selfadaptive rotating strategy improves the flexibility and efficiency of a quantum rotating gate,the operations of quantum mutation and catastrophe enhance the local and global search abilities,respectively.Using the exact Zoeppritz equation,the HQGA was applied to both synthetic and field seismic data inversion and the results were compared to those of the GA and QGA.A number of the synthetic tests show that the HQGA requires fewer searches to converge to the global solution and the inversion results have generally higher accuracy.The application to field data reveals a good agreement between the inverted parameters and real logs.
基金Supported by the National Natural Science Foundation of China （50979017, NSFC60775060） the National High Technology Ship Research Project of China （GJCB09001）
文摘In the normal operation condition, a conventional square-root cubature Kalman filter （SRCKF） gives sufficiently good estimation results. However, if the measurements are not reliable, the SRCKF may give inaccurate results and diverges by time. This study introduces an adaptive SRCKF algorithm with the filter gain correction for the case of measurement malfunctions. By proposing a switching criterion, an optimal filter is selected from the adaptive and conventional SRCKF according to the measurement quality. A subsystem soft fault detection algorithm is built with the filter residual. Utilizing a clear subsystem fault coefficient, the faulty subsystem is isolated as a result of the system reconstruction. In order to improve the performance of the multi-sensor system, a hybrid fusion algorithm is presented based on the adaptive SRCKF. The state and error covariance matrix are also predicted by the priori fusion estimates, and are updated by the predicted and estimated information of subsystems. The proposed algorithms were applied to the vessel dynamic positioning system simulation. They were compared with normal SRCKF and local estimation weighted fusion algorithm. The simulation results show that the presented adaptive SRCKF improves the robustness of subsystem filtering, and the hybrid fusion algorithm has the better performance. The simulation verifies the effectiveness of the proposed algorithms.
基金Supported by the Research Grants from Shanghai Municipal Natural Science Foundation（No.10190502500） Shanghai Maritime University Start-up Funds,Shanghai Science＆Technology Commission Projects（No.09DZ2250400） Shanghai Education Commission Project（No.J50604）
文摘Secure storage yard is one of the optimal core goals of container transportation;thus,making the necessary storage arrangements has become the most crucial part of the container terminal management systems（CTMS）.This paper investigates a random hybrid stacking algorithm（RHSA） for outbound containers that randomly enter the yard.In the first stage of RHSA,the distribution among blocks was analyzed with respect to the utilization ratio.In the second stage,the optimization of bay configuration was carried out by using the hybrid genetic algorithm.Moreover,an experiment was performed to test the RHSA.The results show that the explored algorithm is useful to increase the efficiency.
基金Project supported by the Key Research and Development Program of Guangdong Province,China(Grant No.2018B030326001)the National Natural Science Foundation of China(Grant Nos.61521001,12074179,and 11890704)。
文摘Quantum singular value thresholding(QSVT) algorithm,as a core module of many mathematical models,seeks the singular values of a sparse and low rank matrix exceeding a threshold and their associated singular vectors.The existing all-qubit QSVT algorithm demands lots of ancillary qubits,remaining a huge challenge for realization on nearterm intermediate-scale quantum computers.In this paper,we propose a hybrid QSVT(HQSVT) algorithm utilizing both discrete variables(DVs) and continuous variables(CVs).In our algorithm,raw data vectors are encoded into a qubit system and the following data processing is fulfilled by hybrid quantum operations.Our algorithm requires O [log(MN)] qubits with0(1) qumodes and totally performs 0(1) operations,which significantly reduces the space and runtime consumption.
文摘直升机低空突防已逐渐成为现代空战察打任务的核心,而低空航迹规划算法是实现该技术的关键。尽管现有的航迹规划算法已经被应用于实际低空突防任务,但基于“前端-后端”式的传统航迹规划算法依然存在规划航迹机动执行性差与复杂动态场景下易碰撞的缺陷。针对上述问题,本文提出一种基于Hybrid State A^(*)与增强安全管道的改进算法。首先,基于Hybrid State A^(*)算法的联合轨迹优化,可以在状态空间中完成兼顾直升机机动特性的初始航迹高效搜索,有效保证直升机航迹的可达性。其次,基于初始航迹膨胀的增强安全管道,将后端航迹优化参数限制在安全的可行域内,进而有效提升复杂动态场景下规划航迹的安全性。在实验环节,本研究结合ROS机器人仿真环境与Rviz数据可视化工具完成仿真验证,通过算法间的综合对比实验,论证了本研究所提算法对规划的航迹机动性与安全性有明显的提升。
