Frequent flood disasters caused by climate change may lead to tremendous economic and human losses along inland waterways.Emergency response and rescue vessels(ERRVs)play an essential role in minimizing losses and pro...Frequent flood disasters caused by climate change may lead to tremendous economic and human losses along inland waterways.Emergency response and rescue vessels(ERRVs)play an essential role in minimizing losses and protecting lives and property.However,the path planning of ERRVs has mainly depended on expert experiences instead of rational decision making.This paper proposes an improved artificial potential field(APF)algorithm to optimize the shortest path for ERRVs in the rescue process.To verify the feasibility of the proposed model,eight tests were carried out in two water areas of the Yangtze River.The results showed that the improved APF algorithm was efficient with fewer iterations and that the response time of path planning was reduced to around eight seconds.The improved APF algorithm performed better in the ERRV’s goal achievement,compared with the traditional algorithm.The path planning method for ERRVs proposed in this paper has theoretical and practical value in flood relief.It can be applied in the emergency management of ERRVs to accelerate flood management efficiency and improve capacity to prevent,mitigate,and relieve flood disasters.展开更多
The composite field multiplication is an important and complex module in symmetric cipher algorithms, and its realization performance directly restricts the processing speed of symmetric cipher algorithms. Based on th...The composite field multiplication is an important and complex module in symmetric cipher algorithms, and its realization performance directly restricts the processing speed of symmetric cipher algorithms. Based on the characteristics of composite field multiplication in symmetric cipher algorithms and the realization principle of its reconfigurable architectures, this paper describes the reconfigurable composite field multiplication over GF（（2^8）k） （k=1,2,3,4） in RISC （reduced instruction set computer） processor and VLIW （very long instruction word） processor architecture, respectively. Through configuration, the architectures can realize the composite field multiplication over GF（2^8）, GF （（2^8）2）, GF（（28）3） and GF（（28）4） flexibly and efficiently. We simulated the function of circuits and synthesized the reconfigurable design based on the 0.18 μm CMOS （complementary metal oxide semiconductor） standard cell library and the comparison with other same kind designs. The result shows that the reconfigurable design proposed in the paper can provide higher efficiency under the premise of flexibility.展开更多
With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater envir...With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater environments.However,nowadays AUVs generally have drawbacks such as weak endurance,low intelligence,and poor detection ability.The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks.To improve the underwater operation ability of the AUV,this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm.In response to the limitations of a single algorithm,an optimization scheme is proposed to improve the artificial potential field ant colony(APF-AC)algorithm.Compared with traditional ant colony and comparative algorithms,the APF-AC reduced the path length by 1.57%and 0.63%(in the simple environment),8.92%and 3.46%(in the complex environment).The iteration time has been reduced by approximately 28.48%and 18.05%(in the simple environment),18.53%and 9.24%(in the complex environment).Finally,the improved APF-AC algorithm has been validated on the AUV platform,and the experiment is consistent with the simulation.Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV,and shows a higher safety.展开更多
Modular inverse arithmetic plays an important role in elliptic curve cryptography. Based on the analysis of Montgomery modular inversion algorithm, this paper presents a new dual-field modular inversion algorithm, and...Modular inverse arithmetic plays an important role in elliptic curve cryptography. Based on the analysis of Montgomery modular inversion algorithm, this paper presents a new dual-field modular inversion algorithm, and a novel scalable and unified architecture for Montgomery inverse hardware in finite fields GF(p) and GF(2n) is proposed. Furthermore, this architecture based on the new modular inversion algorithm has been verified by modeling it in Verilog-HDL, and accomplished it under 0.18 μm CMOS technology. The result indicates that our work has better performance and flexibility than other works.展开更多
Determining the optimum location of facilities is critical in many fields,particularly in healthcare.This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 20...Determining the optimum location of facilities is critical in many fields,particularly in healthcare.This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019(COVID-19)pandemic.The used model is the most appropriate among the three most common location models utilized to solve healthcare problems(the set covering model,the maximal covering model,and the P-median model).The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear constraints.The model is used to determine the optimum location of field hospitals for COVID-19 risk reduction.The designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper Egypt.In this case study,a discrete binary gaining–sharing knowledge-based optimization(DBGSK)algorithm is proposed.The DBGSK algorithm is based on how humans acquire and share knowledge throughout their life.The DBGSK algorithm mainly depends on two junior and senior binary stages.These two stages enable DBGSK to explore and exploit the search space efficiently and effectively,and thus it can solve problems in binary space.展开更多
In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone ...In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.展开更多
Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index ...Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index of RP method for the three-dimensional finite element model(FEM) has been given.By taking the electric field of aluminum reduction cell(ARC) as the research object,the performance of two classical RP methods,which are Al-NASRA and NGUYEN partition(ANP) algorithm and the multi-level partition(MLP) method,has been analyzed and compared.The comparison results indicate a sound performance of ANP algorithm,but to large-scale models,the computing time of ANP algorithm increases notably.This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration.To obtain the satisfied speed and the precision,an improved dynamic self-adaptive ANP(DSA-ANP) algorithm has been proposed.With consideration of model scale,complexity and sub-RP stage,the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight,and then dynamically adds these connected elements.The proposed algorithm has been applied to the finite element analysis(FEA) of the electric field simulation of ARC.Compared with the traditional ANP algorithm,the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s.This proves the superiority of the improved algorithm on computing time performance.展开更多
To overcome the shortcomings of the traditional artificial potential field method in mobile robot path planning, an improved artificial potential field model （IAPFM） was established, then a new path planning method ...To overcome the shortcomings of the traditional artificial potential field method in mobile robot path planning, an improved artificial potential field model （IAPFM） was established, then a new path planning method combining the IAPFM with optimization algorithm （trust region algorithm） is proposed. Attractive force between the robot and the target location, and repulsive force between the robot and the obstacles are both converted to the potential field intensity; and filled potential field is used to guide the robot to go out of the local minimum points ; on this basis, the effect of dynamic obstacles velocity and the robot＇s velocity is consid thers and the IAPFM is established, then both the expressions of the attractive potential field and the repulsive potential field are obtained. The trust region algorithm is used to search the minimum value of the sum of all the potential field inten- sities within the movement scope which the robot can arrive in a sampling period. Connecting of all the points which hare the minimum intensity in every sampling period constitutes the global optimization path. Experiment result shows that the method can meet the real-time requirement, and is able to execute the mobile robot path planning task effectively in the dynamic environment.展开更多
为加快末端物流配送的效率,提出一种配送无人机的航迹规划问题。针对传统快速搜索随机树(rapidlysearch random tree,RRT)算法在航迹规划中存在的盲目性和路径不平滑等问题,将人工势场法(artificial potential field,APF)与Informed-RRT...为加快末端物流配送的效率,提出一种配送无人机的航迹规划问题。针对传统快速搜索随机树(rapidlysearch random tree,RRT)算法在航迹规划中存在的盲目性和路径不平滑等问题,将人工势场法(artificial potential field,APF)与Informed-RRT^(*)算法融合,提出一种自适应步长增长策略的改进APF-Informed-RRT^(*)算法。首先在选择新节点时,考虑到障碍物和目标点的影响,提出一种自适应步长增长策略来解决采样的盲目性;其次采用三次B样条对拐点处进行平滑处理;最后分别采用RRT^(*)算法、Informed-RRT^(*)算法和改进APF-Informed-RRT^(*)算法在两种环境中进行仿真实验。结果表明,改进APF-Informed-RRT^(*)算法相较于RRT^(*)算法和Informed-RRT^(*)算法,在运行时间、迭代次数以及路径平滑上都得到提升。展开更多
This paper introduces the principle of genetic algorithm and the basic method of solving Markov random field parameters.Focusing on the shortcomings in present methods,a new method based on genetic algorithms is propo...This paper introduces the principle of genetic algorithm and the basic method of solving Markov random field parameters.Focusing on the shortcomings in present methods,a new method based on genetic algorithms is proposed to solve the parameters in the Markov random field.The detailed procedure is discussed.On the basis of the parameters solved by genetic algorithms,some experiments on classification of aerial images are given.Experimental results show that the proposed method is effective and the classification results are satisfactory.展开更多
Optimization efficiencies and mechanisms of simulated annealing, genetic algorithm, differential evolution and downhill simplex differential evolution are compared and analyzed. Simulated annealing and genetic algorit...Optimization efficiencies and mechanisms of simulated annealing, genetic algorithm, differential evolution and downhill simplex differential evolution are compared and analyzed. Simulated annealing and genetic algorithm use a directed random process to search the parameter space for an optimal solution. They include the ability to avoid local minima, but as no gradient information is used, searches may be relatively inefficient. Differential evolution uses information from a distance and azimuth between individuals of a population to search the parameter space, the initial search is effective, but the search speed decreases quickly because differential information between the individuals of population vanishes. Local downhill simplex and global differential evolution methods are developed separately, and combined to produce a hybrid downhill simplex differential evolution algorithm. The hybrid algorithm is sensitive to gradients of the object function and search of the parameter space is effective. These algorithms are applied to the matched field inversion with synthetic data. Optimal values of the parameters, the final values of object function and inversion time is presented and compared.展开更多
Genetic algorithm finite element method (GA FEM) is applied to the study of tectonic stress field of part of East Asia area. From the observed stress distribution, 2 D elastic plane stress inversion is made to dedu...Genetic algorithm finite element method (GA FEM) is applied to the study of tectonic stress field of part of East Asia area. From the observed stress distribution, 2 D elastic plane stress inversion is made to deduce the boundary forces and investigate controlling factors. It is suggested that the continent continent collision is the dominant factor controlling the Chinese tectonic stress field. The ocean continent convergence along the subduction zone is an important factor. There exists tensile boundary force along the marginal sea.展开更多
基金The National Natural Science Foundation of China(Grant No.72274052)the National Natural Science Foundation of China(Grant No.72174173).
文摘Frequent flood disasters caused by climate change may lead to tremendous economic and human losses along inland waterways.Emergency response and rescue vessels(ERRVs)play an essential role in minimizing losses and protecting lives and property.However,the path planning of ERRVs has mainly depended on expert experiences instead of rational decision making.This paper proposes an improved artificial potential field(APF)algorithm to optimize the shortest path for ERRVs in the rescue process.To verify the feasibility of the proposed model,eight tests were carried out in two water areas of the Yangtze River.The results showed that the improved APF algorithm was efficient with fewer iterations and that the response time of path planning was reduced to around eight seconds.The improved APF algorithm performed better in the ERRV’s goal achievement,compared with the traditional algorithm.The path planning method for ERRVs proposed in this paper has theoretical and practical value in flood relief.It can be applied in the emergency management of ERRVs to accelerate flood management efficiency and improve capacity to prevent,mitigate,and relieve flood disasters.
基金Supported by the National Natural Science Foundation of China(61202492,61309022,61309008)the Natural Science Foundation for Young of Shaanxi Province(2013JQ8013)
文摘The composite field multiplication is an important and complex module in symmetric cipher algorithms, and its realization performance directly restricts the processing speed of symmetric cipher algorithms. Based on the characteristics of composite field multiplication in symmetric cipher algorithms and the realization principle of its reconfigurable architectures, this paper describes the reconfigurable composite field multiplication over GF（（2^8）k） （k=1,2,3,4） in RISC （reduced instruction set computer） processor and VLIW （very long instruction word） processor architecture, respectively. Through configuration, the architectures can realize the composite field multiplication over GF（2^8）, GF （（2^8）2）, GF（（28）3） and GF（（28）4） flexibly and efficiently. We simulated the function of circuits and synthesized the reconfigurable design based on the 0.18 μm CMOS （complementary metal oxide semiconductor） standard cell library and the comparison with other same kind designs. The result shows that the reconfigurable design proposed in the paper can provide higher efficiency under the premise of flexibility.
基金supported by Research Program supported by the National Natural Science Foundation of China(No.62201249)the Jiangsu Agricultural Science and Technology Innovation Fund(No.CX(21)1007)+2 种基金the Open Project of the Zhejiang Provincial Key Laboratory of Crop Harvesting Equipment and Technology(Nos.2021KY03,2021KY04)University-Industry Collaborative Education Program(No.201801166003)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.SJCX22_1042).
文摘With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater environments.However,nowadays AUVs generally have drawbacks such as weak endurance,low intelligence,and poor detection ability.The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks.To improve the underwater operation ability of the AUV,this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm.In response to the limitations of a single algorithm,an optimization scheme is proposed to improve the artificial potential field ant colony(APF-AC)algorithm.Compared with traditional ant colony and comparative algorithms,the APF-AC reduced the path length by 1.57%and 0.63%(in the simple environment),8.92%and 3.46%(in the complex environment).The iteration time has been reduced by approximately 28.48%and 18.05%(in the simple environment),18.53%and 9.24%(in the complex environment).Finally,the improved APF-AC algorithm has been validated on the AUV platform,and the experiment is consistent with the simulation.Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV,and shows a higher safety.
基金Supported by the National High Technology Research and Development Program of China (863 Program) (No. 2008AA01Z103)
文摘Modular inverse arithmetic plays an important role in elliptic curve cryptography. Based on the analysis of Montgomery modular inversion algorithm, this paper presents a new dual-field modular inversion algorithm, and a novel scalable and unified architecture for Montgomery inverse hardware in finite fields GF(p) and GF(2n) is proposed. Furthermore, this architecture based on the new modular inversion algorithm has been verified by modeling it in Verilog-HDL, and accomplished it under 0.18 μm CMOS technology. The result indicates that our work has better performance and flexibility than other works.
基金funded by Deanship of Scientific Research,King Saud University,through the Vice Deanship of Scientific Research.
文摘Determining the optimum location of facilities is critical in many fields,particularly in healthcare.This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019(COVID-19)pandemic.The used model is the most appropriate among the three most common location models utilized to solve healthcare problems(the set covering model,the maximal covering model,and the P-median model).The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear constraints.The model is used to determine the optimum location of field hospitals for COVID-19 risk reduction.The designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper Egypt.In this case study,a discrete binary gaining–sharing knowledge-based optimization(DBGSK)algorithm is proposed.The DBGSK algorithm is based on how humans acquire and share knowledge throughout their life.The DBGSK algorithm mainly depends on two junior and senior binary stages.These two stages enable DBGSK to explore and exploit the search space efficiently and effectively,and thus it can solve problems in binary space.
基金This work was supported by the Postdoctoral Fund of FDCT,Macao(Grant No.0003/2021/APD).Any opinions,findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor.
文摘In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.
基金Project(61273187)supported by the National Natural Science Foundation of ChinaProject(61321003)supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China
文摘Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index of RP method for the three-dimensional finite element model(FEM) has been given.By taking the electric field of aluminum reduction cell(ARC) as the research object,the performance of two classical RP methods,which are Al-NASRA and NGUYEN partition(ANP) algorithm and the multi-level partition(MLP) method,has been analyzed and compared.The comparison results indicate a sound performance of ANP algorithm,but to large-scale models,the computing time of ANP algorithm increases notably.This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration.To obtain the satisfied speed and the precision,an improved dynamic self-adaptive ANP(DSA-ANP) algorithm has been proposed.With consideration of model scale,complexity and sub-RP stage,the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight,and then dynamically adds these connected elements.The proposed algorithm has been applied to the finite element analysis(FEA) of the electric field simulation of ARC.Compared with the traditional ANP algorithm,the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s.This proves the superiority of the improved algorithm on computing time performance.
基金Supported by the National High Technology Research and Development Programme of China（ No. 2006AA04Z245 ） and China Postdoctoral Science Foundation （ No. 200904500988 ）.
文摘To overcome the shortcomings of the traditional artificial potential field method in mobile robot path planning, an improved artificial potential field model （IAPFM） was established, then a new path planning method combining the IAPFM with optimization algorithm （trust region algorithm） is proposed. Attractive force between the robot and the target location, and repulsive force between the robot and the obstacles are both converted to the potential field intensity; and filled potential field is used to guide the robot to go out of the local minimum points ; on this basis, the effect of dynamic obstacles velocity and the robot＇s velocity is consid thers and the IAPFM is established, then both the expressions of the attractive potential field and the repulsive potential field are obtained. The trust region algorithm is used to search the minimum value of the sum of all the potential field inten- sities within the movement scope which the robot can arrive in a sampling period. Connecting of all the points which hare the minimum intensity in every sampling period constitutes the global optimization path. Experiment result shows that the method can meet the real-time requirement, and is able to execute the mobile robot path planning task effectively in the dynamic environment.
文摘为加快末端物流配送的效率,提出一种配送无人机的航迹规划问题。针对传统快速搜索随机树(rapidlysearch random tree,RRT)算法在航迹规划中存在的盲目性和路径不平滑等问题,将人工势场法(artificial potential field,APF)与Informed-RRT^(*)算法融合,提出一种自适应步长增长策略的改进APF-Informed-RRT^(*)算法。首先在选择新节点时,考虑到障碍物和目标点的影响,提出一种自适应步长增长策略来解决采样的盲目性;其次采用三次B样条对拐点处进行平滑处理;最后分别采用RRT^(*)算法、Informed-RRT^(*)算法和改进APF-Informed-RRT^(*)算法在两种环境中进行仿真实验。结果表明,改进APF-Informed-RRT^(*)算法相较于RRT^(*)算法和Informed-RRT^(*)算法,在运行时间、迭代次数以及路径平滑上都得到提升。
文摘This paper introduces the principle of genetic algorithm and the basic method of solving Markov random field parameters.Focusing on the shortcomings in present methods,a new method based on genetic algorithms is proposed to solve the parameters in the Markov random field.The detailed procedure is discussed.On the basis of the parameters solved by genetic algorithms,some experiments on classification of aerial images are given.Experimental results show that the proposed method is effective and the classification results are satisfactory.
文摘Optimization efficiencies and mechanisms of simulated annealing, genetic algorithm, differential evolution and downhill simplex differential evolution are compared and analyzed. Simulated annealing and genetic algorithm use a directed random process to search the parameter space for an optimal solution. They include the ability to avoid local minima, but as no gradient information is used, searches may be relatively inefficient. Differential evolution uses information from a distance and azimuth between individuals of a population to search the parameter space, the initial search is effective, but the search speed decreases quickly because differential information between the individuals of population vanishes. Local downhill simplex and global differential evolution methods are developed separately, and combined to produce a hybrid downhill simplex differential evolution algorithm. The hybrid algorithm is sensitive to gradients of the object function and search of the parameter space is effective. These algorithms are applied to the matched field inversion with synthetic data. Optimal values of the parameters, the final values of object function and inversion time is presented and compared.
文摘Genetic algorithm finite element method (GA FEM) is applied to the study of tectonic stress field of part of East Asia area. From the observed stress distribution, 2 D elastic plane stress inversion is made to deduce the boundary forces and investigate controlling factors. It is suggested that the continent continent collision is the dominant factor controlling the Chinese tectonic stress field. The ocean continent convergence along the subduction zone is an important factor. There exists tensile boundary force along the marginal sea.
