As a core component of the network,web applications have become one of the preferred targets for attackers because the static configuration of web applications simplifies the exploitation of vulnerabilities by attacke...As a core component of the network,web applications have become one of the preferred targets for attackers because the static configuration of web applications simplifies the exploitation of vulnerabilities by attackers.Although the moving target defense(MTD)has been proposed to increase the attack difficulty for the attackers,there is no solo approach can cope with different attacks;in addition,it is impossible to implement all these approaches simultaneously due to the resource limitation.Thus,the selection of an optimal defense strategy based on MTD has become the focus of research.In general,the confrontation of two players in the security domain is viewed as a stochastic game,and the reward matrices are known to both players.However,in a real security confrontation,this scenario represents an incomplete information game.Each player can only observe the actions performed by the opponent,and the observed actions are not completely accurate.To accurately describe the attacker’s reward function to reach the Nash equilibrium,this work simulated and updated the strategy selection distribution of the attacker by observing and investigating the strategy selection history of the attacker.Next,the possible rewards of the attacker in each confrontation via the observation matrix were corrected.On this basis,the Nash-Q learning algorithm with reward quantification was proposed to select the optimal strategy.Moreover,the performances of the Minimax-Q learning algorithm and Naive-Q learning algorithm were compared and analyzed in the MTD environment.Finally,the experimental results showed that the strategy selection algorithm can enable defenders to select a more reasonable defensive strategy and achieve the maximum possible reward.展开更多
Network maintenance strategy selection is a multi-objective decision making topic. It mostly depends on the uncertainty and fuzziness of decision makers and conditions. In this paper, based on analytic hierarchy proce...Network maintenance strategy selection is a multi-objective decision making topic. It mostly depends on the uncertainty and fuzziness of decision makers and conditions. In this paper, based on analytic hierarchy process(AHP) and technique for order preference by similarity to ideal solution(TOPSIS), TOPSIS partial order method is proposed to choose the optimal maintenance strategy. This method uses AHP to determine the weights of evaluation indexes. The optimal maintenance strategy choice is given as an example to demonstrate the effectiveness of the method.展开更多
In the task of Facial Expression Recognition(FER),data uncertainty has been a critical factor affecting performance,typically arising from the ambiguity of facial expressions,low-quality images,and the subjectivity of...In the task of Facial Expression Recognition(FER),data uncertainty has been a critical factor affecting performance,typically arising from the ambiguity of facial expressions,low-quality images,and the subjectivity of annotators.Tracking the training history reveals that misclassified samples often exhibit high confidence and excessive uncertainty in the early stages of training.To address this issue,we propose an uncertainty-based robust sample selection strategy,which combines confidence error with RandAugment to improve image diversity,effectively reducing overfitting caused by uncertain samples during deep learning model training.To validate the effectiveness of the proposed method,extensive experiments were conducted on FER public benchmarks.The accuracy obtained were 89.08%on RAF-DB,63.12%on AffectNet,and 88.73%on FERPlus.展开更多
The level of genetic variation within a breeding population affects the effectiveness of selection strategies for genetic improvement.The relationship between genetic variation level within Pinus tabuliformis breeding...The level of genetic variation within a breeding population affects the effectiveness of selection strategies for genetic improvement.The relationship between genetic variation level within Pinus tabuliformis breeding populations and selection strategies or selection effectiveness is not fully investigated.Here,we compared the selection effectiveness of combined and individual direct selection strategies using half-and full-sib families produced from advanced-generation P.tabuliformis seed orchard as our test populations.Our results revealed that,within half-sib families,average diameter at breast height(DBH),tree height,and volume growth of superior individuals selected by the direct selection strategy were higher by 7.72%,18.56%,and 31.01%,respectively,than those selected by the combined selection strategy.Furthermore,significant differences(P<0.01)were observed between the two strategies in terms of the expected genetic gains for average tree height and volume.In contrast,within full-sib families,the differences in tree average DBH,height,and volume between the two selection strategies were relatively minor with increase of 0.17%,2.73%,and 2.21%,respectively,and no significant differences were found in the average expected genetic gains for the studied traits.Half-sib families exhibited greater phenotypic and genetic variation,resulting in improved selection efficiency with the direct selection strategy but also introduced a level of inbreeding risk.Based on genetic distance estimates using molecular markers,our comparative seed orchard design analysis showed that the Improved Adaptive Genetic Programming Algorithm(IAPGA)reduced the average inbreeding coefficient by 14.36% and 14.73% compared to sequential and random designs,respectively.In conclusion,the combination of the direct selection strategy with IAPGA seed orchard design aimed at minimizing inbreeding offered an efficient approach for establishing advanced-generation P.tabuliformis seed orchards.展开更多
A mixed strategy of the exit selection in a pedestrian evacuation simulation with multi-exits is constructed by fusing the distance-based and time-based strategies through a cognitive coefficient, in order to reduce t...A mixed strategy of the exit selection in a pedestrian evacuation simulation with multi-exits is constructed by fusing the distance-based and time-based strategies through a cognitive coefficient, in order to reduce the evacuation imbalance caused by the asymmetry of exits or pedestrian layout, to find a critical density to distinguish whether the strategy of exit selection takes effect or not, and to analyze the exit selection results with different cognitive coefficients. The strategy of exit selection is embedded in the computation of the shortest estimated distance in a dynamic parameter model, in which the concept of a jam area layer and the procedure of step-by-step expending are introduced. Simulation results indicate the characteristics of evacuation time gradually varying against cognitive coefficient and the effectiveness of reducing evacuation imbalance caused by the asymmetry of pedestrian or exit layout. It is found that there is a critical density to distinguish whether a pedestrian jam occurs in the evacuation and whether an exit selection strategy is in effect. It is also shown that the strategy of exit selection has no effect on the evacuation process in the no-effect phase with a low density, and that evacuation time and exit selection are dependent on the cognitive coefficient and pedestrian initial density in the in-effect phase with a high density.展开更多
The severe environmental problems and the demand for energy urgently require electrocatalysis to replace Haber-Bosch for the nitrogen reduction reaction(NRR).The descriptors and important properties of single-atom and...The severe environmental problems and the demand for energy urgently require electrocatalysis to replace Haber-Bosch for the nitrogen reduction reaction(NRR).The descriptors and important properties of single-atom and homonuclear double-atom catalysts have been preliminarily explored,but the relationship between the inherent properties and catalytic activity of heteronuclear double-atom catalysts with better performance remains unclear.Therefore,it is very significant to explore the prediction expressions of catalytic activity of heteronuclear double-atom catalysts based on their inherent properties and find the rule for selecting catalytic centers.Herein,by summarizing the free energy for the key steps of NRR on 55 catalysts calculated through the first-principle,the expressions of predicting the free energy and the corresponding descriptors are deduced by the machine learning,and the strategy for selecting the appropriate catalytic center is proposed.The selection strategy for the central atom of heteronuclear double-atom catalysts is that the atomic number of central B atom should be between group VB and VIIIB,and the electron difference between central A atom and B atom should be large enough,and the selectivity of NRR or hydrogen evolution reaction(HER)could be calculated through the prediction formula.Moreover,five catalysts are screened to have low limiting potential and excellent selectivity,and are further analyzed by electron transfer.This work explores the relationship between the inherent properties of heteronuclear double-atom catalysts and the catalytic activity,and puts forward the rules for selecting the heteronuclear double-atom catalytic center,which has guiding significance for the experiment.展开更多
In this article,we introduce a new bi-directional dual-relay selection strategy with its bit error rate(BER)performance analysis.During the first step of the proposed strategy,two relays out of a set of N relay-nodes ...In this article,we introduce a new bi-directional dual-relay selection strategy with its bit error rate(BER)performance analysis.During the first step of the proposed strategy,two relays out of a set of N relay-nodes are selected in a way to optimize the system performance in terms of BER,based on the suggested algorithm which checks if the selected relays using the maxmin criterion are the best ones.In the second step,the chosen relay-nodes perform an orthogonal space-time coding scheme using the two-phase relaying protocol to establish a bi-directional communication between the communicating terminals,leading to a significant improvement in the achievable coding and diversity gain.To further improve the overall system performance,the selected relay-nodes apply also a digital network coding scheme.Furthermore,this paper discusses the analytical approximation of the BER performance of the proposed strategy,where we prove that the analytical results match almost perfectly the simulated ones.Finally,our simulation results show that the proposed strategy outperforms the current state-of-the-art ones.展开更多
Nowadays, the technology of renewable sources grid-connection and DC transmission has a rapid development. And phasor measurement units(PMUs) become more notable in power grids, due to the necessary of real time monit...Nowadays, the technology of renewable sources grid-connection and DC transmission has a rapid development. And phasor measurement units(PMUs) become more notable in power grids, due to the necessary of real time monitoring and close-loop control applications. However, the PMUs data quality issue affects applications based on PMUs a lot. This paper proposes a simple yet effective method for recovering PMU data. To simply the issue, two different scenarios of PMUs data loss are first defined. Then a key combination of preferred selection strategies is introduced. And the missing data is recovered by the function of spline interpolation. This method has been tested by artificial data and field data obtained from on-site PMUs. The results demonstrate that the proposed method recovers the missing PMU data quickly and accurately. And it is much better than other methods when missing data are massive and continuous. This paper also presents the interesting direction for future work.展开更多
Within the scope of dual distribution channel(DDC)modes—ME&T-C and M-T&E-C,a game model designed for channel members was proposed.Based on this game model,the game equilibrium under both centralized and decen...Within the scope of dual distribution channel(DDC)modes—ME&T-C and M-T&E-C,a game model designed for channel members was proposed.Based on this game model,the game equilibrium under both centralized and decentralized decisionmaking situations was analyzed,the channel members' and overall revenues of two modes under the same decision-making situation are compared,and the influence of demand shift coefficient to the overall and members' revenue was also studied through example analysis.Based on the comparison and analysis of the revenue yielded from the two DDC modes,it's discovered that within a certain hypothetical range,the M-T&E-C mode seems to be a better option for the manufacturer than the ME&T-C mode.Therefore,this discovery can be served as a theoretical reference for manufacturers when choosing the optimal DDC mode in real life.展开更多
Hierarchical Task Network(HTN)planning is a powerful technique in artificial intelligence for handling complex problems by decomposing them into hierarchical task structures.However,achieving optimal solutions in HTN ...Hierarchical Task Network(HTN)planning is a powerful technique in artificial intelligence for handling complex problems by decomposing them into hierarchical task structures.However,achieving optimal solutions in HTN planning remains a challenge,especially in scenarios where traditional search algorithms struggle to navigate the vast solution space efficiently.This research proposes a novel technique to enhance HTN planning by integrating the Ant Colony Optimization(ACO)algorithm into the refinement process.The Ant System algorithm,inspired by the foraging behavior of ants,is well-suited for addressing optimization problems by efficiently exploring solution spaces.By incorporating ACO into the refinement phase of HTN planning,the authors aim to leverage its adaptive nature and decentralized decision-making to improve plan generation.This paper involves the development of a hybrid strategy called ACO-HTN,which combines HTN planning with ACO-based plan selection.This technique enables the system to adaptively refine plans by guiding the search towards optimal solutions.To evaluate the effectiveness of the proposed technique,this paper conducts empirical experiments on various domains and benchmark datasets.Our results demonstrate that the ACO-HTN strategy enhances the efficiency and effectiveness of HTN planning,outperforming traditional methods in terms of solution quality and computational performance.展开更多
Solving the path planning problem of Autonomous Underwater Vehicles(AUVs)is crucial for reducing energy waste and improving operational efficiency.However,two main challenges hinder further development:Firstly,existin...Solving the path planning problem of Autonomous Underwater Vehicles(AUVs)is crucial for reducing energy waste and improving operational efficiency.However,two main challenges hinder further development:Firstly,existing algorithms often treat this as a single-objective optimization problem,whereas in reality,it should be multi-objective,considering factors such as distance,safety,and smoothness simultaneously.Secondly,the limited availability of optimization results arises due to they are single-path,which fail to meet real-world conditions.To address these challenges,first of all,an improved AUV path planning model is proposed,in which the collisions of path and obstacles are classified more specifically.Subsequently,a novel Altruistic Nurturing Algorithm(ANA)inspired by natural altruism is introduced.In the algorithm,nurturing cost considering Pareto rank and crowd distance is introduced as guidance of evolution to avoid futile calculation,abandonment threshold is self-adaptive with descendant situation to help individuals escape from local optima and double selection strategy combining crowd and k-nearest neighbors selection helps to get a better-distributed Pareto front.Experimental results comparing ANA with existing algorithms in AUV path planning demonstrate its superiority.Finally,a user-friendly interface,the Multi-Objective AUV Path Planner,is designed to provide users with a group of paths for informed decisionmaking.展开更多
This gene pyramiding strategy is based on the idea of efficiently pyramiding genes of interest by crosses and selection to obtain a population with favorable alleles from different breeds or lines, which is called an ...This gene pyramiding strategy is based on the idea of efficiently pyramiding genes of interest by crosses and selection to obtain a population with favorable alleles from different breeds or lines, which is called an ideal population. We investigate impacts of some factors on the pyramiding efficiencies by simulation. These factors include selection strategies （the breeding value selection, the molecular scores selection and the index selection）, proportion selected （2, 10 and 20%）, recombination rates between adjacent target genes （0.1, 0.3 and 0.5） and different mating types （the random mating and the positive assortative mating avoiding sib mating）. The results show that： （1） The more recombination rate and the lower proportion male selected, the better pyramiding efficiency; （2） the ideal population is obtained via various selection strategies, while different selection strategies are suitable for different breeding objectives. From the perspective of pyramiding target genes merely, the molecular scores selection is the best one, for the purpose of pyramiding target genes and recovering genetic background of the target trait, the index selection is the best one, while from the saving cost point of view, the breeding value selection is the best one; （3） the positive assortative mating is more efficient for gene pyramiding compared with the random mating in the terms of the number of generations of intercross for getting the ideal population.展开更多
Tunnel seismic detection methods are effective for obtaining the geological structure around the tunnel face,which is critical for safe construction and disaster mitigation in tunnel engineering.However,there is often...Tunnel seismic detection methods are effective for obtaining the geological structure around the tunnel face,which is critical for safe construction and disaster mitigation in tunnel engineering.However,there is often a lack of accuracy in the acquired geological information and physical properties ahead of the tunnel face in the current tunnel seismic detection methods.Thus,we apply a frequency-domain acoustic full-waveform inversion(FWI)method to obtain high-resolution results for the tunnel structure.We discuss the influence of the frequency group selection strategy and the tunnel observation system settings regarding the inversion results and determine the structural imaging and physical property parameter inversion of abnormal geological bodies ahead of the tunnel face.Based on the conventional strategies of frequency-domain acoustic FWI,we propose a frequency group selection strategy that combines a low-frequency selection covering the vertical wavenumber and a high-frequency selection of antialiasing.This strategy can effectively obtain the spatial structure and physical parameters of the geology ahead of the tunnel face and improve the inversion resolution.In addition,by linearly increasing the side length of the tunnel observation system,we share the influence of the length of the two sides of the observation systems of different tunnels on the inversion results.We found out that the inversion results are the best when the side length is approximately five times the width of the tunnel face,and the influence of increasing the side observation length beyond this range on the inversion results can be ignored.Finally,based on this approach,we invert for the complex multi-stratum model,and an accurate structure and physical property parameters of the complex stratum ahead of the tunnel face are obtained,which verifies the feasibility of the proposed method.展开更多
A selective moving window partial least squares(SMW-PLS) soft sensor was proposed in this paper and applied to a hydro-isomerization process for on-line estimation of para-xylene(PX) content. Aiming at the high freque...A selective moving window partial least squares(SMW-PLS) soft sensor was proposed in this paper and applied to a hydro-isomerization process for on-line estimation of para-xylene(PX) content. Aiming at the high frequency of model updating in previous recursive PLS methods, a selective updating strategy was developed. The model adaptation is activated once the prediction error is larger than a preset threshold, or the model is kept unchanged.As a result, the frequency of model updating is reduced greatly, while the change of prediction accuracy is minor.The performance of the proposed model is better as compared with that of other PLS-based model. The compromise between prediction accuracy and real-time performance can be obtained by regulating the threshold. The guidelines to determine the model parameters are illustrated. In summary, the proposed SMW-PLS method can deal with the slow time-varying processes effectively.展开更多
We propose a novel parameter value selection strategy for the Lüsystem to construct a chaotic robot to accomplish the complete coverage path planning(CCPP)task.The algorithm can meet the requirements of high rand...We propose a novel parameter value selection strategy for the Lüsystem to construct a chaotic robot to accomplish the complete coverage path planning(CCPP)task.The algorithm can meet the requirements of high randomness and coverage rate to perform specific types of missions.First,we roughly determine the value range of the parameter of the Lüsystem to meet the requirement of being a dissipative system.Second,we calculate the Lyapunov exponents to narrow the value range further.Next,we draw the phase planes of the system to approximately judge the topological distribution characteristics of its trajectories.Furthermore,we calculate the Pearson correlation coefficient of the variable for those good ones to judge its random characteristics.Finally,we construct a chaotic robot using variables with the determined parameter values and simulate and test the coverage rate to study the relationship between the coverage rate and the random characteristics of the variables.The above selection strategy gradually narrows the value range of the system parameter according to the randomness requirement of the coverage trajectory.Using the proposed strategy,proper variables can be chosen with a larger Lyapunov exponent to construct a chaotic robot with a higher coverage rate.Another chaotic system,the Lorenz system,is used to verify the feasibility and effectiveness of the designed strategy.The proposed strategy for enhancing the coverage rate of the mobile robot can improve the efficiency of accomplishing CCPP tasks under specific types of missions.展开更多
Accurately estimating the overlap between quantum states is a fundamental task in quantum information processing.While various strategies using distinct quantum measurements have been proposed for overlap estimation,t...Accurately estimating the overlap between quantum states is a fundamental task in quantum information processing.While various strategies using distinct quantum measurements have been proposed for overlap estimation,the lack of experimental benchmarks on estimation precision limits strategy selection in different situations.Here we compare the performance of four practical strategies for overlap estimation,including tomography-tomography,tomographyprojection,Schur collective measurement and optical swap test using photonic quantum systems.We encode the quantum states on the polarization and path degrees of freedom of single photons.The corresponding measurements are performed by photon detection on certain modes following single-photon mode transformation or two-photon interference.We further propose an adaptive strategy with optimized precision in full-range overlap estimation.Our results shed new light on extracting the parameter of interest from quantum systems,prompting the design of efficientquantum protocols.展开更多
With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are non-dominant.Therefore,the selection of individuals and the retention of elite individuals are important.Ex...With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are non-dominant.Therefore,the selection of individuals and the retention of elite individuals are important.Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems.Thus,this work proposes an improved many-objective pigeon-inspired optimization(ImMAPIO)algorithm with multiple selection strategies to solve many-objective optimization problems.Multiple selection strategies integrating hypervolume,knee point,and vector angles are utilized to increase selection pressure to the true Pareto Front.Thus,the accuracy,convergence,and diversity of solutions are improved.ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III,GrEA,MOEA/D,RVEA,and many-objective Pigeon-inspired optimization algorithm.Experimental results indicate the superiority of ImMAPIO on these test functions.展开更多
This study focuses on the problem of multitarget tracking.To address the existing problems of current tracking algorithms,as manifested by the time consumption of subgroup separation and the uneven group size of unman...This study focuses on the problem of multitarget tracking.To address the existing problems of current tracking algorithms,as manifested by the time consumption of subgroup separation and the uneven group size of unmanned aerial vehicles(UAVs)for target tracking,a multitarget tracking control algorithm under local information selection interaction is proposed.First,on the basis of location,number,and perceived target information of neighboring UAVs,a temporary leader selection strategy is designed to realize the local follow-up movement of UAVs when the UAVs cannot fully perceive the target.Second,in combination with the basic rules of cluster movement and target information perception factors,distributed control equations are designed to achieve a rapid gathering of UAVs and consistent tracking of multiple targets.Lastly,the simulation experiments are conducted in two-and three-dimensional spaces.Under a certain number of UAVs,clustering speed of the proposed algorithm is less than 3 s,and the equal probability of the UAV subgroup size after group separation is over 78%.展开更多
The rapid growth of marine applications leads to a significant increase in Maritime Devices(MDs).Traditional shore-based maritime communication networks face limitations,such as overloaded and transmission distance to...The rapid growth of marine applications leads to a significant increase in Maritime Devices(MDs).Traditional shore-based maritime communication networks face limitations,such as overloaded and transmission distance to provide network services for MDs.Unmanned Aerial Vehicles(UAVs)act as relays that can expand coverage and enhance the quality of service for offshore communication networks.We consider a multi-UAV-assisted Offshore Internet of Things(mUAV-OloT),and formulate a throughput maximization problem by jointly optimizing channel allocation,Leader MD(LMD)selection,UAV-LMD association,and LMD-MD association.Firstly,we propose the Hypergraph-based Two-Stage Matching(HTSM)algorithm where a Hypergraph-based LMD Selection(HLMDS)strategy is employed to identify the set of LMDs.Secondly,the Kuhn-Munkres algorithm is used to optimize the UAV-LMD association and a Weighted Threedimensional Hypergraph Matching(WTHM)algorithm is designed to solve the LMD-MD association and channel allocation.Numerical results show that the HTSM algorithm outperforms benchmark algorithms regarding throughput.展开更多
Ontology evolution is the timely adaptation of ontologies to changing requirements, which is becoming more and more important as ontologies become widely used in different fields. This paper shows how to address the p...Ontology evolution is the timely adaptation of ontologies to changing requirements, which is becoming more and more important as ontologies become widely used in different fields. This paper shows how to address the problem of evolving ontologies with less manual case-based reasoning using an automatic selection mechanism. An automatic ontology evolution strategy selection framework is presented that automates the evolution. A minimal change impact algorithm is also developed for the framework. The method is shown to be effective in a case study.展开更多
基金This paper is supported by the National Key R&D Program of China(2017YFB0802703)the National Nature Science Foundation of China(61602052).
文摘As a core component of the network,web applications have become one of the preferred targets for attackers because the static configuration of web applications simplifies the exploitation of vulnerabilities by attackers.Although the moving target defense(MTD)has been proposed to increase the attack difficulty for the attackers,there is no solo approach can cope with different attacks;in addition,it is impossible to implement all these approaches simultaneously due to the resource limitation.Thus,the selection of an optimal defense strategy based on MTD has become the focus of research.In general,the confrontation of two players in the security domain is viewed as a stochastic game,and the reward matrices are known to both players.However,in a real security confrontation,this scenario represents an incomplete information game.Each player can only observe the actions performed by the opponent,and the observed actions are not completely accurate.To accurately describe the attacker’s reward function to reach the Nash equilibrium,this work simulated and updated the strategy selection distribution of the attacker by observing and investigating the strategy selection history of the attacker.Next,the possible rewards of the attacker in each confrontation via the observation matrix were corrected.On this basis,the Nash-Q learning algorithm with reward quantification was proposed to select the optimal strategy.Moreover,the performances of the Minimax-Q learning algorithm and Naive-Q learning algorithm were compared and analyzed in the MTD environment.Finally,the experimental results showed that the strategy selection algorithm can enable defenders to select a more reasonable defensive strategy and achieve the maximum possible reward.
基金the Weapons and Equipment Preresearch Fund(No.9140A27040414JB34079)the Specialized Research Fund for the Doctoral Program of the Military Education(No.2015JY354)
文摘Network maintenance strategy selection is a multi-objective decision making topic. It mostly depends on the uncertainty and fuzziness of decision makers and conditions. In this paper, based on analytic hierarchy process(AHP) and technique for order preference by similarity to ideal solution(TOPSIS), TOPSIS partial order method is proposed to choose the optimal maintenance strategy. This method uses AHP to determine the weights of evaluation indexes. The optimal maintenance strategy choice is given as an example to demonstrate the effectiveness of the method.
文摘In the task of Facial Expression Recognition(FER),data uncertainty has been a critical factor affecting performance,typically arising from the ambiguity of facial expressions,low-quality images,and the subjectivity of annotators.Tracking the training history reveals that misclassified samples often exhibit high confidence and excessive uncertainty in the early stages of training.To address this issue,we propose an uncertainty-based robust sample selection strategy,which combines confidence error with RandAugment to improve image diversity,effectively reducing overfitting caused by uncertain samples during deep learning model training.To validate the effectiveness of the proposed method,extensive experiments were conducted on FER public benchmarks.The accuracy obtained were 89.08%on RAF-DB,63.12%on AffectNet,and 88.73%on FERPlus.
基金financially supported by the Biological BreedingNational Science and Technology Major Project(2023ZD0405806)the National Key R&D Program for the 14th Five-Year Plan in China(2022YFD2200304).
文摘The level of genetic variation within a breeding population affects the effectiveness of selection strategies for genetic improvement.The relationship between genetic variation level within Pinus tabuliformis breeding populations and selection strategies or selection effectiveness is not fully investigated.Here,we compared the selection effectiveness of combined and individual direct selection strategies using half-and full-sib families produced from advanced-generation P.tabuliformis seed orchard as our test populations.Our results revealed that,within half-sib families,average diameter at breast height(DBH),tree height,and volume growth of superior individuals selected by the direct selection strategy were higher by 7.72%,18.56%,and 31.01%,respectively,than those selected by the combined selection strategy.Furthermore,significant differences(P<0.01)were observed between the two strategies in terms of the expected genetic gains for average tree height and volume.In contrast,within full-sib families,the differences in tree average DBH,height,and volume between the two selection strategies were relatively minor with increase of 0.17%,2.73%,and 2.21%,respectively,and no significant differences were found in the average expected genetic gains for the studied traits.Half-sib families exhibited greater phenotypic and genetic variation,resulting in improved selection efficiency with the direct selection strategy but also introduced a level of inbreeding risk.Based on genetic distance estimates using molecular markers,our comparative seed orchard design analysis showed that the Improved Adaptive Genetic Programming Algorithm(IAPGA)reduced the average inbreeding coefficient by 14.36% and 14.73% compared to sequential and random designs,respectively.In conclusion,the combination of the direct selection strategy with IAPGA seed orchard design aimed at minimizing inbreeding offered an efficient approach for establishing advanced-generation P.tabuliformis seed orchards.
基金Project supported by the National Basic Research Program of China(Grant No.2012CB725400)the National Natural Science Foundation of China(Grant No.11172035)+2 种基金the Fundamental Research Funds for the Central Universities of China(Grant No.2013JBM046)the China Postdoctoral Science Foundation(Grant Nos.20090460184 and 201003036)the Talent Foundation of Beijing Jiaotong University,China(Grant No.2012RC026)
文摘A mixed strategy of the exit selection in a pedestrian evacuation simulation with multi-exits is constructed by fusing the distance-based and time-based strategies through a cognitive coefficient, in order to reduce the evacuation imbalance caused by the asymmetry of exits or pedestrian layout, to find a critical density to distinguish whether the strategy of exit selection takes effect or not, and to analyze the exit selection results with different cognitive coefficients. The strategy of exit selection is embedded in the computation of the shortest estimated distance in a dynamic parameter model, in which the concept of a jam area layer and the procedure of step-by-step expending are introduced. Simulation results indicate the characteristics of evacuation time gradually varying against cognitive coefficient and the effectiveness of reducing evacuation imbalance caused by the asymmetry of pedestrian or exit layout. It is found that there is a critical density to distinguish whether a pedestrian jam occurs in the evacuation and whether an exit selection strategy is in effect. It is also shown that the strategy of exit selection has no effect on the evacuation process in the no-effect phase with a low density, and that evacuation time and exit selection are dependent on the cognitive coefficient and pedestrian initial density in the in-effect phase with a high density.
基金supports by the National Natural Science Foundation of China(NSFC,52271113)the Natural Science Foundation of Shaanxi Province,China(2020JM 218)+1 种基金the Fundamental Research Funds for the Central Universities(CHD300102311405)HPC platform,Xi’an Jiaotong University。
文摘The severe environmental problems and the demand for energy urgently require electrocatalysis to replace Haber-Bosch for the nitrogen reduction reaction(NRR).The descriptors and important properties of single-atom and homonuclear double-atom catalysts have been preliminarily explored,but the relationship between the inherent properties and catalytic activity of heteronuclear double-atom catalysts with better performance remains unclear.Therefore,it is very significant to explore the prediction expressions of catalytic activity of heteronuclear double-atom catalysts based on their inherent properties and find the rule for selecting catalytic centers.Herein,by summarizing the free energy for the key steps of NRR on 55 catalysts calculated through the first-principle,the expressions of predicting the free energy and the corresponding descriptors are deduced by the machine learning,and the strategy for selecting the appropriate catalytic center is proposed.The selection strategy for the central atom of heteronuclear double-atom catalysts is that the atomic number of central B atom should be between group VB and VIIIB,and the electron difference between central A atom and B atom should be large enough,and the selectivity of NRR or hydrogen evolution reaction(HER)could be calculated through the prediction formula.Moreover,five catalysts are screened to have low limiting potential and excellent selectivity,and are further analyzed by electron transfer.This work explores the relationship between the inherent properties of heteronuclear double-atom catalysts and the catalytic activity,and puts forward the rules for selecting the heteronuclear double-atom catalytic center,which has guiding significance for the experiment.
基金This work was supported by College of Engineering and Technology,the American University of the Middle East,Kuwait.Homepage:https://www.aum.edu.kw.
文摘In this article,we introduce a new bi-directional dual-relay selection strategy with its bit error rate(BER)performance analysis.During the first step of the proposed strategy,two relays out of a set of N relay-nodes are selected in a way to optimize the system performance in terms of BER,based on the suggested algorithm which checks if the selected relays using the maxmin criterion are the best ones.In the second step,the chosen relay-nodes perform an orthogonal space-time coding scheme using the two-phase relaying protocol to establish a bi-directional communication between the communicating terminals,leading to a significant improvement in the achievable coding and diversity gain.To further improve the overall system performance,the selected relay-nodes apply also a digital network coding scheme.Furthermore,this paper discusses the analytical approximation of the BER performance of the proposed strategy,where we prove that the analytical results match almost perfectly the simulated ones.Finally,our simulation results show that the proposed strategy outperforms the current state-of-the-art ones.
基金supported in part by National Natural Science Foundation of China(NSFC)(51627811,51707064)Project Supported by the National Key Research and Development Program of China(2017YFB090204)Project of State Grid Corporation of China(SGTYHT/16-JS-198)
文摘Nowadays, the technology of renewable sources grid-connection and DC transmission has a rapid development. And phasor measurement units(PMUs) become more notable in power grids, due to the necessary of real time monitoring and close-loop control applications. However, the PMUs data quality issue affects applications based on PMUs a lot. This paper proposes a simple yet effective method for recovering PMU data. To simply the issue, two different scenarios of PMUs data loss are first defined. Then a key combination of preferred selection strategies is introduced. And the missing data is recovered by the function of spline interpolation. This method has been tested by artificial data and field data obtained from on-site PMUs. The results demonstrate that the proposed method recovers the missing PMU data quickly and accurately. And it is much better than other methods when missing data are massive and continuous. This paper also presents the interesting direction for future work.
基金Scientific Research and Innovation Project of Shanghai Municipal Education Commission,China(No.14ZS151)Humanities and Social Sciences Youth Fund Project of the Ministry of Education,China(No.12YJC630157)Technical Innovation Project of Shanghai Textile(Group)Co.,Ltd.,China(No.2013-zx-12)
文摘Within the scope of dual distribution channel(DDC)modes—ME&T-C and M-T&E-C,a game model designed for channel members was proposed.Based on this game model,the game equilibrium under both centralized and decentralized decisionmaking situations was analyzed,the channel members' and overall revenues of two modes under the same decision-making situation are compared,and the influence of demand shift coefficient to the overall and members' revenue was also studied through example analysis.Based on the comparison and analysis of the revenue yielded from the two DDC modes,it's discovered that within a certain hypothetical range,the M-T&E-C mode seems to be a better option for the manufacturer than the ME&T-C mode.Therefore,this discovery can be served as a theoretical reference for manufacturers when choosing the optimal DDC mode in real life.
基金supported by the Ministry of Science and High Education of the Russian Federation by the grant 075-15-2022-1137supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R323),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Hierarchical Task Network(HTN)planning is a powerful technique in artificial intelligence for handling complex problems by decomposing them into hierarchical task structures.However,achieving optimal solutions in HTN planning remains a challenge,especially in scenarios where traditional search algorithms struggle to navigate the vast solution space efficiently.This research proposes a novel technique to enhance HTN planning by integrating the Ant Colony Optimization(ACO)algorithm into the refinement process.The Ant System algorithm,inspired by the foraging behavior of ants,is well-suited for addressing optimization problems by efficiently exploring solution spaces.By incorporating ACO into the refinement phase of HTN planning,the authors aim to leverage its adaptive nature and decentralized decision-making to improve plan generation.This paper involves the development of a hybrid strategy called ACO-HTN,which combines HTN planning with ACO-based plan selection.This technique enables the system to adaptively refine plans by guiding the search towards optimal solutions.To evaluate the effectiveness of the proposed technique,this paper conducts empirical experiments on various domains and benchmark datasets.Our results demonstrate that the ACO-HTN strategy enhances the efficiency and effectiveness of HTN planning,outperforming traditional methods in terms of solution quality and computational performance.
基金supported by the Guangzhou City School Joint Found Project(SL2022A03J01009)the National Nature Science Foundation of China(Grant No.51975135)the Natural Science Foundation of Guangdong Province(2018A030310063).
文摘Solving the path planning problem of Autonomous Underwater Vehicles(AUVs)is crucial for reducing energy waste and improving operational efficiency.However,two main challenges hinder further development:Firstly,existing algorithms often treat this as a single-objective optimization problem,whereas in reality,it should be multi-objective,considering factors such as distance,safety,and smoothness simultaneously.Secondly,the limited availability of optimization results arises due to they are single-path,which fail to meet real-world conditions.To address these challenges,first of all,an improved AUV path planning model is proposed,in which the collisions of path and obstacles are classified more specifically.Subsequently,a novel Altruistic Nurturing Algorithm(ANA)inspired by natural altruism is introduced.In the algorithm,nurturing cost considering Pareto rank and crowd distance is introduced as guidance of evolution to avoid futile calculation,abandonment threshold is self-adaptive with descendant situation to help individuals escape from local optima and double selection strategy combining crowd and k-nearest neighbors selection helps to get a better-distributed Pareto front.Experimental results comparing ANA with existing algorithms in AUV path planning demonstrate its superiority.Finally,a user-friendly interface,the Multi-Objective AUV Path Planner,is designed to provide users with a group of paths for informed decisionmaking.
基金supported by the National Major Special Project of China on New Varieties Cultivation for Transgenic Organisms (2009ZX08009-146B)by the National Non-profit Institute Research Grant,China (2012cj-2)
文摘This gene pyramiding strategy is based on the idea of efficiently pyramiding genes of interest by crosses and selection to obtain a population with favorable alleles from different breeds or lines, which is called an ideal population. We investigate impacts of some factors on the pyramiding efficiencies by simulation. These factors include selection strategies （the breeding value selection, the molecular scores selection and the index selection）, proportion selected （2, 10 and 20%）, recombination rates between adjacent target genes （0.1, 0.3 and 0.5） and different mating types （the random mating and the positive assortative mating avoiding sib mating）. The results show that： （1） The more recombination rate and the lower proportion male selected, the better pyramiding efficiency; （2） the ideal population is obtained via various selection strategies, while different selection strategies are suitable for different breeding objectives. From the perspective of pyramiding target genes merely, the molecular scores selection is the best one, for the purpose of pyramiding target genes and recovering genetic background of the target trait, the index selection is the best one, while from the saving cost point of view, the breeding value selection is the best one; （3） the positive assortative mating is more efficient for gene pyramiding compared with the random mating in the terms of the number of generations of intercross for getting the ideal population.
基金supported by the National Natural Science Foundation of China(41704146)the Fundamental Research Funds for National Universities,China University of Geosciences(Wuhan)(CUGL180816)。
文摘Tunnel seismic detection methods are effective for obtaining the geological structure around the tunnel face,which is critical for safe construction and disaster mitigation in tunnel engineering.However,there is often a lack of accuracy in the acquired geological information and physical properties ahead of the tunnel face in the current tunnel seismic detection methods.Thus,we apply a frequency-domain acoustic full-waveform inversion(FWI)method to obtain high-resolution results for the tunnel structure.We discuss the influence of the frequency group selection strategy and the tunnel observation system settings regarding the inversion results and determine the structural imaging and physical property parameter inversion of abnormal geological bodies ahead of the tunnel face.Based on the conventional strategies of frequency-domain acoustic FWI,we propose a frequency group selection strategy that combines a low-frequency selection covering the vertical wavenumber and a high-frequency selection of antialiasing.This strategy can effectively obtain the spatial structure and physical parameters of the geology ahead of the tunnel face and improve the inversion resolution.In addition,by linearly increasing the side length of the tunnel observation system,we share the influence of the length of the two sides of the observation systems of different tunnels on the inversion results.We found out that the inversion results are the best when the side length is approximately five times the width of the tunnel face,and the influence of increasing the side observation length beyond this range on the inversion results can be ignored.Finally,based on this approach,we invert for the complex multi-stratum model,and an accurate structure and physical property parameters of the complex stratum ahead of the tunnel face are obtained,which verifies the feasibility of the proposed method.
基金Supported by the National Natural Science Foundation of China(61203133,61203072)the Open Project Program of the State Key Laboratory of Industrial Control Technology(ICT1214)
文摘A selective moving window partial least squares(SMW-PLS) soft sensor was proposed in this paper and applied to a hydro-isomerization process for on-line estimation of para-xylene(PX) content. Aiming at the high frequency of model updating in previous recursive PLS methods, a selective updating strategy was developed. The model adaptation is activated once the prediction error is larger than a preset threshold, or the model is kept unchanged.As a result, the frequency of model updating is reduced greatly, while the change of prediction accuracy is minor.The performance of the proposed model is better as compared with that of other PLS-based model. The compromise between prediction accuracy and real-time performance can be obtained by regulating the threshold. The guidelines to determine the model parameters are illustrated. In summary, the proposed SMW-PLS method can deal with the slow time-varying processes effectively.
基金Project supported by the National Natural Science Foundation of China(Nos.61973184 and 61473179)the Natural Science Foundation of Shandong Province,China(No.ZR2021MF072)。
文摘We propose a novel parameter value selection strategy for the Lüsystem to construct a chaotic robot to accomplish the complete coverage path planning(CCPP)task.The algorithm can meet the requirements of high randomness and coverage rate to perform specific types of missions.First,we roughly determine the value range of the parameter of the Lüsystem to meet the requirement of being a dissipative system.Second,we calculate the Lyapunov exponents to narrow the value range further.Next,we draw the phase planes of the system to approximately judge the topological distribution characteristics of its trajectories.Furthermore,we calculate the Pearson correlation coefficient of the variable for those good ones to judge its random characteristics.Finally,we construct a chaotic robot using variables with the determined parameter values and simulate and test the coverage rate to study the relationship between the coverage rate and the random characteristics of the variables.The above selection strategy gradually narrows the value range of the system parameter according to the randomness requirement of the coverage trajectory.Using the proposed strategy,proper variables can be chosen with a larger Lyapunov exponent to construct a chaotic robot with a higher coverage rate.Another chaotic system,the Lorenz system,is used to verify the feasibility and effectiveness of the designed strategy.The proposed strategy for enhancing the coverage rate of the mobile robot can improve the efficiency of accomplishing CCPP tasks under specific types of missions.
基金supported by National Natural Science Foundation of China(GrantsNo.U24A2017,No.12347104 and No.12461160276)the National Key Researchand Development Program of China(Grants No.2023YFC2205802)+1 种基金Natural Science Foundation of Jiangsu Province(Grants No.BK20243060 and No.BK20233001)in part by State Key Laboratory of Advanced Optical Communication Systems and Networks,China.
文摘Accurately estimating the overlap between quantum states is a fundamental task in quantum information processing.While various strategies using distinct quantum measurements have been proposed for overlap estimation,the lack of experimental benchmarks on estimation precision limits strategy selection in different situations.Here we compare the performance of four practical strategies for overlap estimation,including tomography-tomography,tomographyprojection,Schur collective measurement and optical swap test using photonic quantum systems.We encode the quantum states on the polarization and path degrees of freedom of single photons.The corresponding measurements are performed by photon detection on certain modes following single-photon mode transformation or two-photon interference.We further propose an adaptive strategy with optimized precision in full-range overlap estimation.Our results shed new light on extracting the parameter of interest from quantum systems,prompting the design of efficientquantum protocols.
基金This work was supported by the National Key Research and Development Program of China(No.2018YFC1604000)the National Natural Science Foundation of China(Nos.61806138,61772478,U1636220,61961160707,and 61976212)+2 种基金the Key R&D Program of Shanxi Province(High Technology)(No.201903D121119)the Key R&D Program of Shanxi Province(International Cooperation)(No.201903D421048)the Key R&D Program(International Science and Technology Cooperation Project)of Shanxi Province,China(No.201903D421003).
文摘With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are non-dominant.Therefore,the selection of individuals and the retention of elite individuals are important.Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems.Thus,this work proposes an improved many-objective pigeon-inspired optimization(ImMAPIO)algorithm with multiple selection strategies to solve many-objective optimization problems.Multiple selection strategies integrating hypervolume,knee point,and vector angles are utilized to increase selection pressure to the true Pareto Front.Thus,the accuracy,convergence,and diversity of solutions are improved.ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III,GrEA,MOEA/D,RVEA,and many-objective Pigeon-inspired optimization algorithm.Experimental results indicate the superiority of ImMAPIO on these test functions.
文摘This study focuses on the problem of multitarget tracking.To address the existing problems of current tracking algorithms,as manifested by the time consumption of subgroup separation and the uneven group size of unmanned aerial vehicles(UAVs)for target tracking,a multitarget tracking control algorithm under local information selection interaction is proposed.First,on the basis of location,number,and perceived target information of neighboring UAVs,a temporary leader selection strategy is designed to realize the local follow-up movement of UAVs when the UAVs cannot fully perceive the target.Second,in combination with the basic rules of cluster movement and target information perception factors,distributed control equations are designed to achieve a rapid gathering of UAVs and consistent tracking of multiple targets.Lastly,the simulation experiments are conducted in two-and three-dimensional spaces.Under a certain number of UAVs,clustering speed of the proposed algorithm is less than 3 s,and the equal probability of the UAV subgroup size after group separation is over 78%.
基金supported by the National Natural Science Foundation of China(Nos.51939001,62371085,62122069,and 62071431)the Fundamental Research Funds for the Central Universities(No.3132023514)the FDCT-MOST Joint Project(No.0066/2019/AMJ).
文摘The rapid growth of marine applications leads to a significant increase in Maritime Devices(MDs).Traditional shore-based maritime communication networks face limitations,such as overloaded and transmission distance to provide network services for MDs.Unmanned Aerial Vehicles(UAVs)act as relays that can expand coverage and enhance the quality of service for offshore communication networks.We consider a multi-UAV-assisted Offshore Internet of Things(mUAV-OloT),and formulate a throughput maximization problem by jointly optimizing channel allocation,Leader MD(LMD)selection,UAV-LMD association,and LMD-MD association.Firstly,we propose the Hypergraph-based Two-Stage Matching(HTSM)algorithm where a Hypergraph-based LMD Selection(HLMDS)strategy is employed to identify the set of LMDs.Secondly,the Kuhn-Munkres algorithm is used to optimize the UAV-LMD association and a Weighted Threedimensional Hypergraph Matching(WTHM)algorithm is designed to solve the LMD-MD association and channel allocation.Numerical results show that the HTSM algorithm outperforms benchmark algorithms regarding throughput.
基金Supported by the National Key Basic Research and Development (973) Program of China (No. 2007CB310605)the National Natural Science Foundation of China (Nos.60802035 and 60902050)Funds for Creative Research Groups of China (No.60821001)
文摘Ontology evolution is the timely adaptation of ontologies to changing requirements, which is becoming more and more important as ontologies become widely used in different fields. This paper shows how to address the problem of evolving ontologies with less manual case-based reasoning using an automatic selection mechanism. An automatic ontology evolution strategy selection framework is presented that automates the evolution. A minimal change impact algorithm is also developed for the framework. The method is shown to be effective in a case study.
