This study proposes a decentralized urban traffic optimization approach by integrating Dijkstra’s algorithm with edge computing.The system models road networks as dynamic graphs,using real-time data from IoT sensors ...This study proposes a decentralized urban traffic optimization approach by integrating Dijkstra’s algorithm with edge computing.The system models road networks as dynamic graphs,using real-time data from IoT sensors to adapt routing decisions.A three-layer architecture reduces latency and im-proves scalability.Simulation results show a 42% decrease in response time and a 25%reduction in congestion compared to centralized systems.The ap-proach demonstrates high reliability and potential for smart city applications.展开更多
Quantum coherence constitutes a fundamental physical mechanism essential to the study of quantum algorithms.We study coherence and decoherence in the generalized Shor's algorithm where the register A is initialize...Quantum coherence constitutes a fundamental physical mechanism essential to the study of quantum algorithms.We study coherence and decoherence in the generalized Shor's algorithm where the register A is initialized in an arbitrary pure state,or the combined register AB is initialized in a pseudo-pure state,which encompasses the standard Shor's algorithm as a special case.We derive both lower and upper bounds on the performance of the generalized Shor's algorithm,and establish the relation between the probability of calculating the order r when register AB is initialized in a pseudo-pure state and that when register A is initialized in an arbitrary pure state.Moreover,we study coherence and decoherence in the noisy Shor's algorithm and give a lower bound on the probability that we can calculate the order r.展开更多
As an important quantum cryptanalysis algorithm,Kuperberg's algorithm efficiently addresses the dihedral hidden subgroup problem with sub-exponential acceleration.However,when dealing with large numbers,the algori...As an important quantum cryptanalysis algorithm,Kuperberg's algorithm efficiently addresses the dihedral hidden subgroup problem with sub-exponential acceleration.However,when dealing with large numbers,the algorithm demands a deeper quantum circuit depth,rendering its implementation on current quantum devices prone to noise interference and thereby significantly reducing its efficiency.To mitigate this challenge,this paper proposes a distributed Kuperberg's algorithm.It decomposes the original function into sub-functions which can be executed on different nodes in parallel.The implementation of these sub-functions necessitates a shallower quantum circuit depth and a reduced number of qubits when contrasted with the execution of the original function.Furthermore,the proposed algorithm can be directly generalized to an arbitrary number of nodes by adjusting the quantity of input qubits.The utilization of multi-node parallel processing makes the proposed algorithm a linear enhancement in query complexity relative to the original algorithm.To validate the feasibility and demonstrate the superiority of our algorithm,experiments are conducted on the Qiskit platform.展开更多
Gas hydrates are increasingly recognized as a significant unconventional energy resource and a key factor in marine geohazards and the global carbon cycle.However,accurately identifying and quantifying hydrate-bearing...Gas hydrates are increasingly recognized as a significant unconventional energy resource and a key factor in marine geohazards and the global carbon cycle.However,accurately identifying and quantifying hydrate-bearing formations remains challenging due to complex geophysical signatures and heterogeneous distribution.This study evaluates twelve supervised machine learning(ML)algorithms for two key tasks:Classification of hydrate-bearing layers and regression-based estimation of hydrate saturation,using well log and pore-water geochemical data from Site NGHP-01-19B.Two physically independent labeling frameworks are employed:One based on Archie's law using resistivity(1350 samples,29%hydratebearing),and another based on a three-phase velocity model(890 samples,25%hydrate-bearing).A diverse set of models,including tree-based ensembles(Decision Tree,Random Forest,GBDT,XGBoost,Light GBM,Cat Boost,Bagging,Ada Boost),kernel methods(SVM,SVR),instance-based learning(KNN),neural networks(MLP),and Gaussian Process models(GPR,GPC),are systematically compared using cross-validation and grid search.Ensemble methods consistently performed best in classification,with Ada Boost and GBDT,achieving test accuracies above 0.94(Archie)and 0.98(velocity-based).For regression,GPR delivered the most accurate hydrate saturation estimates(R2>0.99),while GBDT and Random Forest provided a strong balance of accuracy and computational efficiency.Notably,depth below seafloor(TDEP),though not a direct geophysical input,significantly enhanced model performance by acting as a proxy for stratigraphic and thermodynamic conditions.Group-based validation confirmed that random-sample splitting overestimates performance due to depth-wise autocorrelation,highlighting the importance of geologically informed model assessment.Overall,the consistent performance of ML models across both labeling schemes and input feature sets underscores their robustness and transferability,supporting their use as a reliable toolset for offshore gas hydrate reservoir characterization.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(PDmin)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
Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots(MRs)in both research and education.In this paper,an optimal collision-free algorithm is designed and implemented prac...Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots(MRs)in both research and education.In this paper,an optimal collision-free algorithm is designed and implemented practically based on an improved Dijkstra algorithm.To achieve this research objectives,first,the MR obstacle-free environment is modeled as a diagraph including nodes,edges and weights.Second,Dijkstra algorithm is used offline to generate the shortest path driving the MR from a starting point to a target point.During its movement,the robot should follow the previously obtained path and stop at each node to test if there is an obstacle between the current node and the immediately following node.For this aim,the MR was equipped with an ultrasonic sensor used as obstacle detector.If an obstacle is found,the MR updates its diagraph by excluding the corresponding node.Then,Dijkstra algorithm runs on the modified diagraph.This procedure is repeated until reaching the target point.To verify the efficiency of the proposed approach,a simulation was carried out on a hand-made MR and an environment including 9 nodes,19 edges and 2 obstacles.The obtained optimal path avoiding obstacles has been transferred into motion control and implemented practically using line tracking sensors.This study has shown that the improved Dijkstra algorithm can efficiently solve optimal path planning in environments including obstacles and that STEAM-based MRs are efficient cost-effective tools to practically implement the designed algorithm.展开更多
A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK ...A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.展开更多
Bidirectional Dijkstra algorithm whose time complexity is 8O(n~2) is proposed. The theory foundation is that the classical Dijkstra algorithm has not any directional feature during searching the shortest path. The alg...Bidirectional Dijkstra algorithm whose time complexity is 8O(n~2) is proposed. The theory foundation is that the classical Dijkstra algorithm has not any directional feature during searching the shortest path. The algorithm takes advantage of the adjacent link and the mechanism of bidirectional search, that is, the algorithm processes the positive search from start point to destination point and the negative search from destination point to start point at the same time. Finally, combining with the practical application of route-planning algorithm in embedded real-time vehicle navigation system (ERTVNS), one example of its practical applications is given, analysis in theory and the experimental results show that compared with the Dijkstra algorithm, the new algorithm can reduce time complexity, and guarantee the searching precision, it satisfies the needs of ERTVNS.展开更多
摘要This study proposes a decentralized urban traffic optimization approach by integrating Dijkstra’s algorithm with edge computing.The system models road networks as dynamic graphs,using real-time data from IoT sensors to adapt routing decisions.A three-layer architecture reduces latency and im-proves scalability.Simulation results show a 42% decrease in response time and a 25%reduction in congestion compared to centralized systems.The ap-proach demonstrates high reliability and potential for smart city applications.
基金supported by the National Natural Science Foundation of China(Grant Nos.12561084 and 12161056)the Natural Science Foundation of Jiangxi Province,China(Grant No.20232ACB211003)。
摘要Quantum coherence constitutes a fundamental physical mechanism essential to the study of quantum algorithms.We study coherence and decoherence in the generalized Shor's algorithm where the register A is initialized in an arbitrary pure state,or the combined register AB is initialized in a pseudo-pure state,which encompasses the standard Shor's algorithm as a special case.We derive both lower and upper bounds on the performance of the generalized Shor's algorithm,and establish the relation between the probability of calculating the order r when register AB is initialized in a pseudo-pure state and that when register A is initialized in an arbitrary pure state.Moreover,we study coherence and decoherence in the noisy Shor's algorithm and give a lower bound on the probability that we can calculate the order r.
基金Project supported by the National Natural Science Foundation of China(Grant No.62171131)the Natural Science Foundation of Fujian Province,China(Grant Nos.2022J01186 and 2023J01533)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0302901)。
摘要As an important quantum cryptanalysis algorithm,Kuperberg's algorithm efficiently addresses the dihedral hidden subgroup problem with sub-exponential acceleration.However,when dealing with large numbers,the algorithm demands a deeper quantum circuit depth,rendering its implementation on current quantum devices prone to noise interference and thereby significantly reducing its efficiency.To mitigate this challenge,this paper proposes a distributed Kuperberg's algorithm.It decomposes the original function into sub-functions which can be executed on different nodes in parallel.The implementation of these sub-functions necessitates a shallower quantum circuit depth and a reduced number of qubits when contrasted with the execution of the original function.Furthermore,the proposed algorithm can be directly generalized to an arbitrary number of nodes by adjusting the quantity of input qubits.The utilization of multi-node parallel processing makes the proposed algorithm a linear enhancement in query complexity relative to the original algorithm.To validate the feasibility and demonstrate the superiority of our algorithm,experiments are conducted on the Qiskit platform.
基金supported by the China Scholarship Council under the State Scholarship Fund(202506340082)the Key Project of Guangdong Provincial Key R&D Program(2023B1111050014)+3 种基金the Youth Promotion Project of the Natural Science Foundation of Guangdong Province(2023A1515030280)the Guangdong Basic and Applied Basic Research Foundation(2023A1515010926)the Guangzhou Science and Technology Plan Project(2024A04J9876)funded by China National Petroleum Corporation(CNPC,2024DQ02-0107)。
摘要Gas hydrates are increasingly recognized as a significant unconventional energy resource and a key factor in marine geohazards and the global carbon cycle.However,accurately identifying and quantifying hydrate-bearing formations remains challenging due to complex geophysical signatures and heterogeneous distribution.This study evaluates twelve supervised machine learning(ML)algorithms for two key tasks:Classification of hydrate-bearing layers and regression-based estimation of hydrate saturation,using well log and pore-water geochemical data from Site NGHP-01-19B.Two physically independent labeling frameworks are employed:One based on Archie's law using resistivity(1350 samples,29%hydratebearing),and another based on a three-phase velocity model(890 samples,25%hydrate-bearing).A diverse set of models,including tree-based ensembles(Decision Tree,Random Forest,GBDT,XGBoost,Light GBM,Cat Boost,Bagging,Ada Boost),kernel methods(SVM,SVR),instance-based learning(KNN),neural networks(MLP),and Gaussian Process models(GPR,GPC),are systematically compared using cross-validation and grid search.Ensemble methods consistently performed best in classification,with Ada Boost and GBDT,achieving test accuracies above 0.94(Archie)and 0.98(velocity-based).For regression,GPR delivered the most accurate hydrate saturation estimates(R2>0.99),while GBDT and Random Forest provided a strong balance of accuracy and computational efficiency.Notably,depth below seafloor(TDEP),though not a direct geophysical input,significantly enhanced model performance by acting as a proxy for stratigraphic and thermodynamic conditions.Group-based validation confirmed that random-sample splitting overestimates performance due to depth-wise autocorrelation,highlighting the importance of geologically informed model assessment.Overall,the consistent performance of ML models across both labeling schemes and input feature sets underscores their robustness and transferability,supporting their use as a reliable toolset for offshore gas hydrate reservoir characterization.
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)。
摘要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(PDmin)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This research has been funded by Scientific Research Deanship at University of Ha’il–Saudi Arabia through Project Number BA-2107.
摘要Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots(MRs)in both research and education.In this paper,an optimal collision-free algorithm is designed and implemented practically based on an improved Dijkstra algorithm.To achieve this research objectives,first,the MR obstacle-free environment is modeled as a diagraph including nodes,edges and weights.Second,Dijkstra algorithm is used offline to generate the shortest path driving the MR from a starting point to a target point.During its movement,the robot should follow the previously obtained path and stop at each node to test if there is an obstacle between the current node and the immediately following node.For this aim,the MR was equipped with an ultrasonic sensor used as obstacle detector.If an obstacle is found,the MR updates its diagraph by excluding the corresponding node.Then,Dijkstra algorithm runs on the modified diagraph.This procedure is repeated until reaching the target point.To verify the efficiency of the proposed approach,a simulation was carried out on a hand-made MR and an environment including 9 nodes,19 edges and 2 obstacles.The obtained optimal path avoiding obstacles has been transferred into motion control and implemented practically using line tracking sensors.This study has shown that the improved Dijkstra algorithm can efficiently solve optimal path planning in environments including obstacles and that STEAM-based MRs are efficient cost-effective tools to practically implement the designed algorithm.
摘要A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.
摘要Bidirectional Dijkstra algorithm whose time complexity is 8O(n~2) is proposed. The theory foundation is that the classical Dijkstra algorithm has not any directional feature during searching the shortest path. The algorithm takes advantage of the adjacent link and the mechanism of bidirectional search, that is, the algorithm processes the positive search from start point to destination point and the negative search from destination point to start point at the same time. Finally, combining with the practical application of route-planning algorithm in embedded real-time vehicle navigation system (ERTVNS), one example of its practical applications is given, analysis in theory and the experimental results show that compared with the Dijkstra algorithm, the new algorithm can reduce time complexity, and guarantee the searching precision, it satisfies the needs of ERTVNS.