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
Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face ...Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.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
This study investigates the multi-solution search of the optimized quantum random-walk search algorithm on the hypercube. Through generalizing the abstract search algorithm which is a general tool for analyzing the se...This study investigates the multi-solution search of the optimized quantum random-walk search algorithm on the hypercube. Through generalizing the abstract search algorithm which is a general tool for analyzing the search on the graph to the multi-solution case, it can be applied to analyze the multi-solution case of quantum random-walk search on the graph directly. Thus, the computational complexity of the optimized quantum random-walk search algorithm for the multi-solution search is obtained. Through numerical simulations and analysis, we obtain a critical value of the proportion of solutions q. For a given q, we derive the relationship between the success rate of the algorithm and the number of iterations when q is no longer than the critical value.展开更多
This paper investigates the effects of decoherence generated by broken-link-type noise in the hypercube on an optimized quantum random-walk search algorithm. When the hypercube occurs with random broken links, the opt...This paper investigates the effects of decoherence generated by broken-link-type noise in the hypercube on an optimized quantum random-walk search algorithm. When the hypercube occurs with random broken links, the optimized quantum random-walk search algorithm with decoherence is depicted through defining the shift operator which includes the possibility of broken links. For a given database size, we obtain the maximum success rate of the algorithm and the required number of iterations through numerical simulations and analysis when the algorithm is in the presence of decoherence. Then the computational complexity of the algorithm with decoherence is obtained. The results show that the ultimate effect of broken-link-type decoherence on the optimized quantum random-walk search algorithm is negative.展开更多
To address the issue of field size in random network coding, we propose an Improved Adaptive Random Convolutional Network Coding (IARCNC) algorithm to considerably reduce the amount of occupied memory. The operation o...To address the issue of field size in random network coding, we propose an Improved Adaptive Random Convolutional Network Coding (IARCNC) algorithm to considerably reduce the amount of occupied memory. The operation of IARCNC is similar to that of Adaptive Random Convolutional Network Coding (ARCNC), with the coefficients of local encoding kernels chosen uniformly at random over a small finite field. The difference is that the length of the local encoding kernels at the nodes used by IARCNC is constrained by the depth; meanwhile, increases until all the related sink nodes can be decoded. This restriction can make the code length distribution more reasonable. Therefore, IARCNC retains the advantages of ARCNC, such as a small decoding delay and partial adaptation to an unknown topology without an early estimation of the field size. In addition, it has its own advantage, that is, a higher reduction in memory use. The simulation and the example show the effectiveness of the proposed algorithm.展开更多
Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach wa...Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.展开更多
This study investigates the effects of systematic errors in phase inversions on the success rate and number of iterations in the optimized quantum random-walk search algorithm. Using the geometric description of this ...This study investigates the effects of systematic errors in phase inversions on the success rate and number of iterations in the optimized quantum random-walk search algorithm. Using the geometric description of this algorithm, a model of the algorithm with phase errors is established, and the relationship between the success rate of the algorithm, the database size, the number of iterations, and the phase error is determined. For a given database size, we obtain both the maximum success rate of the algorithm and the required number of iterations when phase errors are present in the algorithm. Analyses and numerical simulations show that the optimized quantum random-walk search algorithm is more robust against phase errors than Grover＇s algorithm.展开更多
In this paper, elitist reconstruction genetic algorithm （ERGA） based on Markov random field （MRF） is introduced for image segmentation. In this algorithm, a population of possible solutions is maintained at every ...In this paper, elitist reconstruction genetic algorithm （ERGA） based on Markov random field （MRF） is introduced for image segmentation. In this algorithm, a population of possible solutions is maintained at every generation, and for each solution a fitness value is calculated according to a fitness function, which is constructed based on the MRF potential function according to Metropolis function and Bayesian framework. After the improved selection, crossover and mutation, an elitist individual is restructured based on the strategy of restructuring elitist. This procedure is processed to select the location that denotes the largest MRF potential function value in the same location of all individuals. The algorithm is stopped when the change of fitness functions between two sequent generations is less than a specified value. Experiments show that the performance of the hybrid algorithm is better than that of some traditional algorithms.展开更多
The Very Fast Decision Tree(VFDT)algorithm is a classification algorithm for data streams.When processing large amounts of data,VFDT requires less time than traditional decision tree algorithms.However,when training s...The Very Fast Decision Tree(VFDT)algorithm is a classification algorithm for data streams.When processing large amounts of data,VFDT requires less time than traditional decision tree algorithms.However,when training samples become fewer,the label values of VFDT leaf nodes will have more errors,and the classification ability of single VFDT decision tree is limited.The Random Forest algorithm is a combinational classifier with high prediction accuracy and noise-tol-erant ability.It is constituted by multiple decision trees and can make up for the shortage of single decision tree.In this paper,in order to improve the classification accuracy on data streams,the Random Forest algorithm is integrated into the process of tree building of the VFDT algorithm,and a new Random Forest Based Very Fast Decision Tree algorithm named RFVFDT is designed.The RFVFDT algorithm adopts the decision tree building criterion of a Random Forest classifier,and improves Random Forest algorithm with sliding window to meet the unboundedness of data streams and avoid process delay and data loss.Experimental results of the classification of KDD CUP data sets show that the classification accuracy of RFVFDT algorithm is higher than that of VFDT.The less the samples are,the more obvious the advantage is.RFVFDT is fast when running in the multithread mode.展开更多
The random forest algorithm was applied to study the nuclear binding energy and charge radius.The regularized root-mean-square of error(RMSE)was proposed to avoid overfitting during the training of random forest.RMSE ...The random forest algorithm was applied to study the nuclear binding energy and charge radius.The regularized root-mean-square of error(RMSE)was proposed to avoid overfitting during the training of random forest.RMSE for nuclides with Z,N>7 is reduced to 0.816 MeV and 0.0200 fm compared with the six-term liquid drop model and a three-term nuclear charge radius formula,respectively.Specific interest is in the possible(sub)shells among the superheavy region,which is important for searching for new elements and the island of stability.The significance of shell features estimated by the so-called shapely additive explanation method suggests(Z,N)=(92,142)and(98,156)as possible subshells indicated by the binding energy.Because the present observed data is far from the N=184 shell,which is suggested by mean-field investigations,its shell effect is not predicted based on present training.The significance analysis of the nuclear charge radius suggests Z=92 and N=136 as possible subshells.The effect is verified by the shell-corrected nuclear charge radius model.展开更多
Tikhonov regularization is a powerful tool for solving linear discrete ill-posed problems.However,effective methods for dealing with large-scale ill-posed problems are still lacking.The Kaczmarz method is an effective...Tikhonov regularization is a powerful tool for solving linear discrete ill-posed problems.However,effective methods for dealing with large-scale ill-posed problems are still lacking.The Kaczmarz method is an effective iterative projection algorithm for solving large linear equations due to its simplicity.We propose a regularized randomized extended Kaczmarz(RREK)algorithm for solving large discrete ill-posed problems via combining the Tikhonov regularization and the randomized Kaczmarz method.The convergence of the algorithm is proved.Numerical experiments illustrate that the proposed algorithm has higher accuracy and better image restoration quality compared with the existing randomized extended Kaczmarz(REK)method.展开更多
A pseudo-random coding side-lobe suppression method based on CLEAN algorithm is introduced.The CLEAN algorithm mainly processes pulse compression results of a pseudo-random coding,and estimates a target＇s distance by...A pseudo-random coding side-lobe suppression method based on CLEAN algorithm is introduced.The CLEAN algorithm mainly processes pulse compression results of a pseudo-random coding,and estimates a target＇s distance by a method named interpolation method,so that we can get an ideal pulse compression result of the target,and then use the adjusted ideal pulse compression side-lobe to cut the actual pulse compression result,so as to achieve the remarkable performance of side-lobe suppression for large targets,and let the adjacent small targets appear.The computer simulations by MATLAB with this method analyze the effect of side-lobe suppression in an ideal or noisy environment.It is proved that this method can effectively solve the problem due to the side-lobe of pseudo-random coding being too high,and can enhance the radar＇s multi-target detection ability.展开更多
This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapi...This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.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financially supported by the National Natural Science Foundation of China(No.52174001)the National Natural Science Foundation of China(No.52004064)+1 种基金the Hainan Province Science and Technology Special Fund “Research on Real-time Intelligent Sensing Technology for Closed-loop Drilling of Oil and Gas Reservoirs in Deepwater Drilling”(ZDYF2023GXJS012)Heilongjiang Provincial Government and Daqing Oilfield's first batch of the scientific and technological key project “Research on the Construction Technology of Gulong Shale Oil Big Data Analysis System”(DQYT-2022-JS-750)。
文摘Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.
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(Grant No.2013CB338002)
文摘This study investigates the multi-solution search of the optimized quantum random-walk search algorithm on the hypercube. Through generalizing the abstract search algorithm which is a general tool for analyzing the search on the graph to the multi-solution case, it can be applied to analyze the multi-solution case of quantum random-walk search on the graph directly. Thus, the computational complexity of the optimized quantum random-walk search algorithm for the multi-solution search is obtained. Through numerical simulations and analysis, we obtain a critical value of the proportion of solutions q. For a given q, we derive the relationship between the success rate of the algorithm and the number of iterations when q is no longer than the critical value.
基金supported by the National Basic Research Program of China(Grant No.2013CB338002)
文摘This paper investigates the effects of decoherence generated by broken-link-type noise in the hypercube on an optimized quantum random-walk search algorithm. When the hypercube occurs with random broken links, the optimized quantum random-walk search algorithm with decoherence is depicted through defining the shift operator which includes the possibility of broken links. For a given database size, we obtain the maximum success rate of the algorithm and the required number of iterations through numerical simulations and analysis when the algorithm is in the presence of decoherence. Then the computational complexity of the algorithm with decoherence is obtained. The results show that the ultimate effect of broken-link-type decoherence on the optimized quantum random-walk search algorithm is negative.
基金supported by the National Science Foundation （NSF） under Grants No.60832001,No.61271174 the National State Key Lab oratory of Integrated Service Network （ISN） under Grant No.ISN01080202
文摘To address the issue of field size in random network coding, we propose an Improved Adaptive Random Convolutional Network Coding (IARCNC) algorithm to considerably reduce the amount of occupied memory. The operation of IARCNC is similar to that of Adaptive Random Convolutional Network Coding (ARCNC), with the coefficients of local encoding kernels chosen uniformly at random over a small finite field. The difference is that the length of the local encoding kernels at the nodes used by IARCNC is constrained by the depth; meanwhile, increases until all the related sink nodes can be decoded. This restriction can make the code length distribution more reasonable. Therefore, IARCNC retains the advantages of ARCNC, such as a small decoding delay and partial adaptation to an unknown topology without an early estimation of the field size. In addition, it has its own advantage, that is, a higher reduction in memory use. The simulation and the example show the effectiveness of the proposed algorithm.
文摘Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.
基金Project supported by the National Basic Research Program of China(Grant No.2013CB338002)
文摘This study investigates the effects of systematic errors in phase inversions on the success rate and number of iterations in the optimized quantum random-walk search algorithm. Using the geometric description of this algorithm, a model of the algorithm with phase errors is established, and the relationship between the success rate of the algorithm, the database size, the number of iterations, and the phase error is determined. For a given database size, we obtain both the maximum success rate of the algorithm and the required number of iterations when phase errors are present in the algorithm. Analyses and numerical simulations show that the optimized quantum random-walk search algorithm is more robust against phase errors than Grover＇s algorithm.
文摘In this paper, elitist reconstruction genetic algorithm （ERGA） based on Markov random field （MRF） is introduced for image segmentation. In this algorithm, a population of possible solutions is maintained at every generation, and for each solution a fitness value is calculated according to a fitness function, which is constructed based on the MRF potential function according to Metropolis function and Bayesian framework. After the improved selection, crossover and mutation, an elitist individual is restructured based on the strategy of restructuring elitist. This procedure is processed to select the location that denotes the largest MRF potential function value in the same location of all individuals. The algorithm is stopped when the change of fitness functions between two sequent generations is less than a specified value. Experiments show that the performance of the hybrid algorithm is better than that of some traditional algorithms.
文摘The Very Fast Decision Tree(VFDT)algorithm is a classification algorithm for data streams.When processing large amounts of data,VFDT requires less time than traditional decision tree algorithms.However,when training samples become fewer,the label values of VFDT leaf nodes will have more errors,and the classification ability of single VFDT decision tree is limited.The Random Forest algorithm is a combinational classifier with high prediction accuracy and noise-tol-erant ability.It is constituted by multiple decision trees and can make up for the shortage of single decision tree.In this paper,in order to improve the classification accuracy on data streams,the Random Forest algorithm is integrated into the process of tree building of the VFDT algorithm,and a new Random Forest Based Very Fast Decision Tree algorithm named RFVFDT is designed.The RFVFDT algorithm adopts the decision tree building criterion of a Random Forest classifier,and improves Random Forest algorithm with sliding window to meet the unboundedness of data streams and avoid process delay and data loss.Experimental results of the classification of KDD CUP data sets show that the classification accuracy of RFVFDT algorithm is higher than that of VFDT.The less the samples are,the more obvious the advantage is.RFVFDT is fast when running in the multithread mode.
基金Supported by Basic and Applied Basic Research Project of Guangdong Province(2021B0301030006)。
文摘The random forest algorithm was applied to study the nuclear binding energy and charge radius.The regularized root-mean-square of error(RMSE)was proposed to avoid overfitting during the training of random forest.RMSE for nuclides with Z,N>7 is reduced to 0.816 MeV and 0.0200 fm compared with the six-term liquid drop model and a three-term nuclear charge radius formula,respectively.Specific interest is in the possible(sub)shells among the superheavy region,which is important for searching for new elements and the island of stability.The significance of shell features estimated by the so-called shapely additive explanation method suggests(Z,N)=(92,142)and(98,156)as possible subshells indicated by the binding energy.Because the present observed data is far from the N=184 shell,which is suggested by mean-field investigations,its shell effect is not predicted based on present training.The significance analysis of the nuclear charge radius suggests Z=92 and N=136 as possible subshells.The effect is verified by the shell-corrected nuclear charge radius model.
基金supported by the National Natural Science Foundations of China(Nos.11571171,62073161,and 61473148)。
文摘Tikhonov regularization is a powerful tool for solving linear discrete ill-posed problems.However,effective methods for dealing with large-scale ill-posed problems are still lacking.The Kaczmarz method is an effective iterative projection algorithm for solving large linear equations due to its simplicity.We propose a regularized randomized extended Kaczmarz(RREK)algorithm for solving large discrete ill-posed problems via combining the Tikhonov regularization and the randomized Kaczmarz method.The convergence of the algorithm is proved.Numerical experiments illustrate that the proposed algorithm has higher accuracy and better image restoration quality compared with the existing randomized extended Kaczmarz(REK)method.
文摘A pseudo-random coding side-lobe suppression method based on CLEAN algorithm is introduced.The CLEAN algorithm mainly processes pulse compression results of a pseudo-random coding,and estimates a target＇s distance by a method named interpolation method,so that we can get an ideal pulse compression result of the target,and then use the adjusted ideal pulse compression side-lobe to cut the actual pulse compression result,so as to achieve the remarkable performance of side-lobe suppression for large targets,and let the adjacent small targets appear.The computer simulations by MATLAB with this method analyze the effect of side-lobe suppression in an ideal or noisy environment.It is proved that this method can effectively solve the problem due to the side-lobe of pseudo-random coding being too high,and can enhance the radar＇s multi-target detection ability.
基金the National Natural Science Foundation of China(Grant No.42274119)the Liaoning Revitalization Talents Program(Grant No.XLYC2002082)+1 种基金National Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integration(Grant No.2022YFF1400500)the Key Project of Science and Technology Commission of the Central Military Commission.
文摘This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.
