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
To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense,dynamic,unpredictable obstacles challenging conventional methods—this p...To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense,dynamic,unpredictable obstacles challenging conventional methods—this paper proposes a hybrid algorithm integrating Q-learning and improved A*-Artificial Potential Field(A-APF).Centered on theQ-learning framework,the algorithmleverages safety-oriented guidance generated byA-APF and employs a dynamic coordination mechanism that adaptively balances exploration and exploitation.The proposed system comprises four core modules:(1)an environment modeling module that constructs grid-based obstacle maps;(2)an A-APF module that combines heuristic search from A*algorithm with repulsive force strategies from APF to generate guidance;(3)a Q-learning module that learns optimal state-action values(Q-values)through spraying robot-environment interaction and a reward function emphasizing path optimality and safety;and(4)a dynamic optimization module that ensures adaptive cooperation between Q-learning and A-APF through exploration rate control and environment-aware constraints.Simulation results demonstrate that the proposed method significantly enhances path safety in complex underground mining environments.Quantitative results indicate that,compared to the traditional Q-learning algorithm,the proposed method shortens training time by 42.95% and achieves a reduction in training failures from 78 to just 3.Compared to the static fusion algorithm,it further reduces both training time(by 10.78%)and training failures(by 50%),thereby improving overall training efficiency.展开更多
A novel approach for collision-free path planning of a multiple degree-of-freedom(DOF)articulated robot in a complex environment is proposed.Firstly,based on visual neighbor point(VNP),a numerical artificial potential...A novel approach for collision-free path planning of a multiple degree-of-freedom(DOF)articulated robot in a complex environment is proposed.Firstly,based on visual neighbor point(VNP),a numerical artificial potential field is constructed in Cartesian space,which provides the heuristic information,effective distance to the goal and the motion direction for the motion of the robot joints.Secondly,a genetic algorithm,combined with the heuristic rules,is used in joint space to determine a series of contiguous configurations piecewise from initial configuration until the goal configuration is attained.A simulation shows that the method can not only handle issues on path planning of the articulated robots in environment with complex obstacles,but also improve the efficiency and quality of path planning.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
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
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
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
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(Grant No.52374156).
文摘To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense,dynamic,unpredictable obstacles challenging conventional methods—this paper proposes a hybrid algorithm integrating Q-learning and improved A*-Artificial Potential Field(A-APF).Centered on theQ-learning framework,the algorithmleverages safety-oriented guidance generated byA-APF and employs a dynamic coordination mechanism that adaptively balances exploration and exploitation.The proposed system comprises four core modules:(1)an environment modeling module that constructs grid-based obstacle maps;(2)an A-APF module that combines heuristic search from A*algorithm with repulsive force strategies from APF to generate guidance;(3)a Q-learning module that learns optimal state-action values(Q-values)through spraying robot-environment interaction and a reward function emphasizing path optimality and safety;and(4)a dynamic optimization module that ensures adaptive cooperation between Q-learning and A-APF through exploration rate control and environment-aware constraints.Simulation results demonstrate that the proposed method significantly enhances path safety in complex underground mining environments.Quantitative results indicate that,compared to the traditional Q-learning algorithm,the proposed method shortens training time by 42.95% and achieves a reduction in training failures from 78 to just 3.Compared to the static fusion algorithm,it further reduces both training time(by 10.78%)and training failures(by 50%),thereby improving overall training efficiency.
文摘A novel approach for collision-free path planning of a multiple degree-of-freedom(DOF)articulated robot in a complex environment is proposed.Firstly,based on visual neighbor point(VNP),a numerical artificial potential field is constructed in Cartesian space,which provides the heuristic information,effective distance to the goal and the motion direction for the motion of the robot joints.Secondly,a genetic algorithm,combined with the heuristic rules,is used in joint space to determine a series of contiguous configurations piecewise from initial configuration until the goal configuration is attained.A simulation shows that the method can not only handle issues on path planning of the articulated robots in environment with complex obstacles,but also improve the efficiency and quality of path planning.
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 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.
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。
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.
