In this paper, using the concatenating method, a series of local structure-preserving algorithms are obtained for the Klein-Gordon-Zakharov equation, including four multisymplectic algorithms, four local energy-preser...In this paper, using the concatenating method, a series of local structure-preserving algorithms are obtained for the Klein-Gordon-Zakharov equation, including four multisymplectic algorithms, four local energy-preserving algorithms, four local momentumpreserving algorithms;of these, local energy-preserving and momentum-preserving algorithms have not been studied before. The local structure-preserving algorithms mentioned above are more widely used than the global structure-preserving algorithms, since local preservation algorithms can be preserved in any time and space domains, which overcomes the defect that global preservation algorithms are limited to boundary conditions. In particular, under appropriate boundary conditions, local preservation laws are global preservation laws.Numerical experiments conducted can support the theoretical analysis well.展开更多
Reducing the vulnerability of a platform,i.e.,the risk of being affected by hostile objects,is of paramount importance in the design process of vehicles,especially aircraft.A simple and effective way to decrease vulne...Reducing the vulnerability of a platform,i.e.,the risk of being affected by hostile objects,is of paramount importance in the design process of vehicles,especially aircraft.A simple and effective way to decrease vulnerability is to introduce protective structures to intercept and possibly stop threats.However,this type of solution can lead to a significant increase in weight,affecting the performance of the aircraft.For this reason,it is crucial to study possible solutions that allow reducing the vulnerability of the aircraft while containing the increase in structural weight.One possible strategy is to optimize the topology of protective solutions to find the optimal balance between vulnerability and the weight of the added structures.Among the many optimization techniques available in the literature for this purpose,multiobjective genetic algorithms stand out as promising tools.In this context,this work proposes the use of a in-house software for vulnerability calculation to guide the process of topology optimization through multi-objective genetic algorithms,aiming to simultaneously minimize the weight of protective structures and vulnerability.In addition to the use of the in-house software,which itself represents a novelty in the field of topology optimization of structures,the method incorporates a custom mutation function within the genetic algorithm,specifically developed using a graph-based approach to ensure the continuity of the generated structures.The tool developed for this work is capable of generating protections with optimized layouts considering two different types of impacting objects,namely bullets and fragments from detonating objects.The software outputs a set of non-dominated solutions describing different topologies that the user can choose from.展开更多
The classical Pauli particle(CPP) serves as a slow manifold, substituting the conventional guiding center dynamics. Based on the CPP, we utilize the averaged vector field(AVF) method in the computations of drift orbit...The classical Pauli particle(CPP) serves as a slow manifold, substituting the conventional guiding center dynamics. Based on the CPP, we utilize the averaged vector field(AVF) method in the computations of drift orbits. Demonstrating significantly higher efficiency, this advanced method is capable of accomplishing the simulation in less than one-third of the time of directly computing the guiding center motion. In contrast to the CPP-based Boris algorithm, this approach inherits the advantages of the AVF method, yielding stable trajectories even achieved with a tenfold time step and reducing the energy error by two orders of magnitude. By comparing these two CPP algorithms with the traditional RK4 method, the numerical results indicate a remarkable performance in terms of both the computational efficiency and error elimination. Moreover, we verify the properties of slow manifold integrators and successfully observe the bounce on both sides of the limiting slow manifold with deliberately chosen perturbed initial conditions. To evaluate the practical value of the methods, we conduct simulations in non-axisymmetric perturbation magnetic fields as part of the experiments,demonstrating that our CPP-based AVF method can handle simulations under complex magnetic field configurations with high accuracy, which the CPP-based Boris algorithm lacks. Through numerical experiments, we demonstrate that the CPP can replace guiding center dynamics in using energy-preserving algorithms for computations, providing a new, efficient, as well as stable approach for applying structure-preserving algorithms in plasma simulations.展开更多
Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of ...Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of wind farm data.Consequently,a method for cleaning wind power anomaly data by combining image processing with community detection algorithms(CWPAD-IPCDA)is proposed.To precisely identify and initially clean anomalous data,wind power curve(WPC)images are converted into graph structures,which employ the Louvain community recognition algorithm and graph-theoretic methods for community detection and segmentation.Furthermore,the mathematical morphology operation(MMO)determines the main part of the initially cleaned wind power curve images and maps them back to the normal wind power points to complete the final cleaning.The CWPAD-IPCDA method was applied to clean datasets from 25 wind turbines(WTs)in two wind farms in northwest China to validate its feasibility.A comparison was conducted using density-based spatial clustering of applications with noise(DBSCAN)algorithm,an improved isolation forest algorithm,and an image-based(IB)algorithm.The experimental results demonstrate that the CWPAD-IPCDA method surpasses the other three algorithms,achieving an approximately 7.23%higher average data cleaning rate.The mean value of the sum of the squared errors(SSE)of the dataset after cleaning is approximately 6.887 lower than that of the other algorithms.Moreover,the mean of overall accuracy,as measured by the F1-score,exceeds that of the other methods by approximately 10.49%;this indicates that the CWPAD-IPCDA method is more conducive to improving the accuracy and reliability of wind power curve modeling and wind farm power forecasting.展开更多
To address the problem of identifying multiple types of additives in lubricating oil,a method based on midinfrared spectral band selection using the eXtreme Gradient Boosting(XGBoost)algorithm combined with the ant co...To address the problem of identifying multiple types of additives in lubricating oil,a method based on midinfrared spectral band selection using the eXtreme Gradient Boosting(XGBoost)algorithm combined with the ant colony optimization(ACO)algorithm is proposed.The XGBoost algorithm was used to train and test three additives,T534(alkyl diphenylamine),T308(isooctyl acid thiophospholipid octadecylamine),and T306(trimethylphenol phosphate),separately,in order to screen for the optimal combination of spectral bands for each additive.The ACO algorithm was used to optimize the parameters of the XGBoost algorithm to improve the identification accuracy.During this process,the support vector machine(SVM)and hybrid bat algorithms(HBA)were included as a comparison,generating four models:ACO-XGBoost,ACO-SVM,HBA-XGboost,and HBA-SVM.The results showed that all four models could identify the three additives efficiently,with the ACO-XGBoost model achieving 100%recognition of all three additives.In addition,the generalizability of the ACO-XGBoost model was further demonstrated by predicting a lubricating oil containing the three additives prepared in our laboratory and a collected sample of commercial oil currently in use。展开更多
Three dimensional(3-D)imaging algorithms with irregular planar multiple-input-multiple-output(MIMO)arrays are discussed and compared with each other.Based on the same MIMO array,a modified back projection algorithm(MB...Three dimensional(3-D)imaging algorithms with irregular planar multiple-input-multiple-output(MIMO)arrays are discussed and compared with each other.Based on the same MIMO array,a modified back projection algorithm(MBPA)is accordingly proposed and four imaging algorithms are used for comparison,back-projection method(BP),back-projection one in time domain(BP-TD),modified back-projection one and fast Fourier transform(FFT)-based MIMO range migration algorithm(FFT-based MIMO RMA).All of the algorithms have been implemented in practical application scenarios by use of the proposed imaging system.Back to the practical applications,MIMO array-based imaging system with wide-bandwidth properties provides an efficient tool to detect objects hidden behind a wall.An MIMO imaging radar system,composed of a vector network analyzer(VNA),a set of switches,and an array of Vivaldi antennas,have been designed,fabricated,and tested.Then,these algorithms have been applied to measured data collected in different scenarios constituted by five metallic spheres in the absence and in the presence of a wall between the antennas and the targets in simulation and pliers in free space for experimental test.Finally,the focusing properties and time consumption of the above algorithms are compared.展开更多
In recent years,various internet architectures,such as Integrated Services(IntServ),Differentiated Services(DiffServ),Time Sensitive Networking(TSN)and Deterministic Networking(DetNet),have been proposed to meet the q...In recent years,various internet architectures,such as Integrated Services(IntServ),Differentiated Services(DiffServ),Time Sensitive Networking(TSN)and Deterministic Networking(DetNet),have been proposed to meet the quality-of-service(QoS)requirements of different network services.Concurrently,network calculus has found widespread application in network modeling and QoS analysis.Network calculus abstracts the details of how nodes or networks process data packets using the concept of service curves.This paper summarizes the service curves for typical scheduling algorithms,including Strict Priority(SP),Round Robin(RR),Cycling Queuing and Forwarding(CQF),Time Aware Shaper(TAS),Credit Based Shaper(CBS),and Asynchronous Traffic Shaper(ATS).It introduces the theory of network calculus and then provides an overview of various scheduling algorithms and their associated service curves.The delay bound analysis for different scheduling algorithms in specific scenarios is also conducted for more insights.展开更多
This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in boa...This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.展开更多
Differential spatial modulation(DSM)is a multiple-input multiple-output(MIMO)transmission scheme.It has attracted extensive research interest due to its ability to transmit additional data without increasing any radio...Differential spatial modulation(DSM)is a multiple-input multiple-output(MIMO)transmission scheme.It has attracted extensive research interest due to its ability to transmit additional data without increasing any radio frequency chain.In this paper,DSM is investigated using two mapping algorithms:Look-Up Table Order(LUTO)and Permutation Method(PM).Then,the bit error rate(BER)performance and complexity of the two mapping algorithms in various antennas and modulation methods are verified by simulation experiments.The results show that PM has a lower BER than the LUTO mapping algorithm,and the latter has lower complexity than the former.展开更多
Although the genetic algorithm (GA) has very powerful robustness and fitness, it needs a large size of population and a large number of iterations to reach the optimum result. Especially when GA is used in complex str...Although the genetic algorithm (GA) has very powerful robustness and fitness, it needs a large size of population and a large number of iterations to reach the optimum result. Especially when GA is used in complex structural optimization problems, if the structural reanalysis technique is not adopted, the more the number of finite element analysis (FEA) is, the more the consuming time is. In the conventional structural optimization the number of FEA can be reduced by the structural reanalysis technique based on the approximation techniques and sensitivity analysis. With these techniques, this paper provides a new approximation model-segment approximation model, adopted for the GA application. This segment approximation model can decrease the number of FEA and increase the convergence rate of GA. So it can apparently decrease the computation time of GA. Two examples demonstrate the availability of the new segment approximation model.展开更多
An approach to addressing the stereo correspondence problem is presented using genetic algorithms (GAs) to obtain a dense disparity map. Different from previous methods, this approach casts the stereo matching as a mu...An approach to addressing the stereo correspondence problem is presented using genetic algorithms (GAs) to obtain a dense disparity map. Different from previous methods, this approach casts the stereo matching as a multi-extrema optimization problem such that finding the fittest solution from a set of potential disparity maps. Among a wide variety of optimization techniques, GAs are proven to be potentially effective methods for the global optimization problems with large search space. With this idea, each disparity map is viewed as an individual and the disparity values are encoded as chromosomes, so each individual has lots of chromosomes in the approach. Then, several matching constraints are formulated into an objective function, and GAs are used to search the global optimal solution for the problem. Furthermore, the coarse-to-fine strategy has been embedded in the approach so as to reduce the matching ambiguity and the time consumption. Finally, experimental results on synthetic and real images show the performance of the work.展开更多
A fully digital data acquisition system based on a field-programmable gate array(FPGA) was developed for a CsI(Tl) array at the external target facility(ETF) in the Heavy Ion Research Facility in Lanzhou(HIRFL). To pr...A fully digital data acquisition system based on a field-programmable gate array(FPGA) was developed for a CsI(Tl) array at the external target facility(ETF) in the Heavy Ion Research Facility in Lanzhou(HIRFL). To process the CsI(Tl) signals generated by γ-rays and light-charged ions, a scheme for digital pulse processing algorithms is proposed. Every step in the algorithms was benchmarked using standard γ and α sources. The scheme, which included a moving average filter, baseline restoration, leading-edge discrimination, moving window deconvolution, and digital charge comparison, was subsequently implemented on the FPGA. A good energy resolution of 5.7% for 1.33-MeV γ-rays and excellent α-γ identification using the digital charge comparison method were achieved, which satisfies CsI(Tl) array performance requirements.展开更多
Noises widely exist in interactive genetic algorithms. However, there is no effective method to solve this problem up to now. There are two kinds of noises, one is the noise existing in visual systems and the other is...Noises widely exist in interactive genetic algorithms. However, there is no effective method to solve this problem up to now. There are two kinds of noises, one is the noise existing in visual systems and the other is resulted from user’s preference mechanisms. Characteristics of the two noises are presented aiming at the application of interac- tive genetic algorithms in dealing with images. The evolutionary phases of interactive genetic algorithms are determined according to differences in the same individual’s fitness among different generations. Models for noises in different phases are established and the corresponding strategies for reducing noises are given. The algorithm proposed in this paper has been applied to fashion design, which is a typical example of image processing. The results show that the strategies can reduce noises in interactive genetic algorithms and improve the algorithm’s performance effectively. However, a further study is needed to solve the problem of determining the evolution phase by using suitable objective methods so as to find out an effective method to decrease noises.展开更多
A software defined networking(SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and o...A software defined networking(SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and optimization problems are typicallyvery complex with a huge solution space, large number of variables, and multiple objectives. Heuristic algorithms can solve theseproblems in an acceptable time but are usually limited to some particular problem circumstances. On the other hand, evolutionaryalgorithms(EAs), which are general stochastic algorithms inspired by the natural biological evolution and/or social behavior of species, can theoretically be used to solve any complex optimization problems including those found in SDNs. This paper reviewsfour types of EAs that are widely applied in current SDNs: Genetic Algorithms(GAs), Particle Swarm Optimization(PSO), Ant Colony Optimization(ACO), and Simulated Annealing(SA) by discussing their techniques, summarizing their representative applications, and highlighting their issues and future works. To the best of our knowledge, our work is the first that compares the tech-niques and categorizes the applications of these four EAs in SDNs.展开更多
The increasing overlap of core and colony populations during the anaphase of evolution may limit the performance of shifting balance genetic algorithms. To decrease such overlapping,so as to increase the local search ...The increasing overlap of core and colony populations during the anaphase of evolution may limit the performance of shifting balance genetic algorithms. To decrease such overlapping,so as to increase the local search capability of the core population,the sub-space method was used to generate uniformly distributed initial colony populations over the decision variable space. The core population was also dynamically divided,making simultaneous searching in several local spaces possible. The algorithm proposed in this paper was compared to the original one by searching for the optimum of a complicated multi-modal function. The results indicate that the solutions obtained by the modified algorithm are better than those of the original algorithm.展开更多
This paper describes the implementation of frequency-domain least mean squares (LMS) and Filtered-X algorithms and compares the performance of the frequencydomain adaptive control algorithm to a comparable timedomain ...This paper describes the implementation of frequency-domain least mean squares (LMS) and Filtered-X algorithms and compares the performance of the frequencydomain adaptive control algorithm to a comparable timedomain controller. When the frequency-domain LMS step size is allowed to vary as a function of frequency,the frequency-domain algorithm exhibits a better vibration reduction than the time-domain algorithm for the weaker frequencies in the energy spectrum.展开更多
Given a list of items and a sequence of variable-sized bins arriving one by one, it is NP-hard to pack the items into the bin list with a goal to minimize the total size of bins from the earliest one to the last used....Given a list of items and a sequence of variable-sized bins arriving one by one, it is NP-hard to pack the items into the bin list with a goal to minimize the total size of bins from the earliest one to the last used. In this paper a set of approximation algorithms is presented for cases in which the ability to preview at most k（〉=2） arriving bins is given. With the essential assumption that all bin sizes are not less than the largest item size, analytical results show the asymptotic worst case ratios of all k-bounded space and offiine algorithms are 2. Based on experiments by applying algorithms to instances in which item sizes and bin sizes are drawn independently from the continuous uniform distribution respectively in the interval [0,u] and [u,l ], averagecase experimental results show that, with fixed k, algorithms with the Best Fit packing（closing） rule are statistically better than those with the First Fit packing（closing） rule.展开更多
To establish a parallel fusion approach of processing high dimensional information, the model and criterion of multisensor fuzzy stochastic data fusion were presented. In order to design genetic algorithm fusion, the ...To establish a parallel fusion approach of processing high dimensional information, the model and criterion of multisensor fuzzy stochastic data fusion were presented. In order to design genetic algorithm fusion, the fusion parameter coding, initial population and fitness function establishing, and fuzzy logic controller designing for genetic operations and probability choosing were completed. The discussion on the highly dimensional fusion was given. For a moving target with the division of 1 64 (velocity) and 1 75 (acceleration), the precision of fusion is 0 94 and 0 98 respectively. The fusion approach can improve the reliability and decision precision effectively.展开更多
A multi-objective evolutionary optimization method (combining genetic algorithms(GAs)and game theory(GT))is presented for high lift multi-airfoil systems in aerospace engineering.Due to large dimension global op-timiz...A multi-objective evolutionary optimization method (combining genetic algorithms(GAs)and game theory(GT))is presented for high lift multi-airfoil systems in aerospace engineering.Due to large dimension global op-timization problems and the increasing importance of low cost distributed parallel environments,it is a natural idea to replace a globar optimization by decentralized local sub-optimizations using GT which introduces the notion of games associated to an optimization problem.The GT/GAs combined optimization method is used for recon-struction and optimization problems by high lift multi-air-foil desing.Numerical results are favorably compared with single global GAs.The method shows teh promising robustness and efficient parallel properties of coupled GAs with different game scenarios for future advanced multi-disciplinary aerospace techmologies.展开更多
Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safet...Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safety.This paper aims to develop hybrid support vector machine(SVM)models improved by three metaheuristic algorithms known as grey wolf optimizer(GWO),whale optimization algorithm(WOA)and sparrow search algorithm(SSA)for predicting the hard rock pillar stability.An integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM models.Five parameters including pillar height,pillar width,ratio of pillar width to height,uniaxial compressive strength and pillar stress were set as input parameters.Two global indices,three local indices and the receiver operating characteristic(ROC)curve with the area under the ROC curve(AUC)were utilized to evaluate all hybrid models’performance.The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices.Nevertheless,the performance of the SSASVM model for predicting the unstable pillar(AUC:0.899)is not as good as those for stable(AUC:0.975)and failed pillars(AUC:0.990).To verify the effectiveness of the proposed models,5 field cases were investigated in a metal mine and other 5 cases were collected from several published works.The validation results indicated that the SSA-SVM model obtained a considerable accuracy,which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability.展开更多
基金supported by the National Natural Science Foundation of China(11801277,11771213,12171245)。
文摘In this paper, using the concatenating method, a series of local structure-preserving algorithms are obtained for the Klein-Gordon-Zakharov equation, including four multisymplectic algorithms, four local energy-preserving algorithms, four local momentumpreserving algorithms;of these, local energy-preserving and momentum-preserving algorithms have not been studied before. The local structure-preserving algorithms mentioned above are more widely used than the global structure-preserving algorithms, since local preservation algorithms can be preserved in any time and space domains, which overcomes the defect that global preservation algorithms are limited to boundary conditions. In particular, under appropriate boundary conditions, local preservation laws are global preservation laws.Numerical experiments conducted can support the theoretical analysis well.
文摘Reducing the vulnerability of a platform,i.e.,the risk of being affected by hostile objects,is of paramount importance in the design process of vehicles,especially aircraft.A simple and effective way to decrease vulnerability is to introduce protective structures to intercept and possibly stop threats.However,this type of solution can lead to a significant increase in weight,affecting the performance of the aircraft.For this reason,it is crucial to study possible solutions that allow reducing the vulnerability of the aircraft while containing the increase in structural weight.One possible strategy is to optimize the topology of protective solutions to find the optimal balance between vulnerability and the weight of the added structures.Among the many optimization techniques available in the literature for this purpose,multiobjective genetic algorithms stand out as promising tools.In this context,this work proposes the use of a in-house software for vulnerability calculation to guide the process of topology optimization through multi-objective genetic algorithms,aiming to simultaneously minimize the weight of protective structures and vulnerability.In addition to the use of the in-house software,which itself represents a novelty in the field of topology optimization of structures,the method incorporates a custom mutation function within the genetic algorithm,specifically developed using a graph-based approach to ensure the continuity of the generated structures.The tool developed for this work is capable of generating protections with optimized layouts considering two different types of impacting objects,namely bullets and fragments from detonating objects.The software outputs a set of non-dominated solutions describing different topologies that the user can choose from.
基金supported by National Natural Science Foundation of China (Nos. 11975068 and 11925501)the National Key R&D Program of China (No. 2022YFE03090000)the Fundamental Research Funds for the Central Universities (No. DUT22ZD215)。
文摘The classical Pauli particle(CPP) serves as a slow manifold, substituting the conventional guiding center dynamics. Based on the CPP, we utilize the averaged vector field(AVF) method in the computations of drift orbits. Demonstrating significantly higher efficiency, this advanced method is capable of accomplishing the simulation in less than one-third of the time of directly computing the guiding center motion. In contrast to the CPP-based Boris algorithm, this approach inherits the advantages of the AVF method, yielding stable trajectories even achieved with a tenfold time step and reducing the energy error by two orders of magnitude. By comparing these two CPP algorithms with the traditional RK4 method, the numerical results indicate a remarkable performance in terms of both the computational efficiency and error elimination. Moreover, we verify the properties of slow manifold integrators and successfully observe the bounce on both sides of the limiting slow manifold with deliberately chosen perturbed initial conditions. To evaluate the practical value of the methods, we conduct simulations in non-axisymmetric perturbation magnetic fields as part of the experiments,demonstrating that our CPP-based AVF method can handle simulations under complex magnetic field configurations with high accuracy, which the CPP-based Boris algorithm lacks. Through numerical experiments, we demonstrate that the CPP can replace guiding center dynamics in using energy-preserving algorithms for computations, providing a new, efficient, as well as stable approach for applying structure-preserving algorithms in plasma simulations.
基金supported by the National Natural Science Foundation of China(Project No.51767018)Natural Science Foundation of Gansu Province(Project No.23JRRA836).
文摘Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of wind farm data.Consequently,a method for cleaning wind power anomaly data by combining image processing with community detection algorithms(CWPAD-IPCDA)is proposed.To precisely identify and initially clean anomalous data,wind power curve(WPC)images are converted into graph structures,which employ the Louvain community recognition algorithm and graph-theoretic methods for community detection and segmentation.Furthermore,the mathematical morphology operation(MMO)determines the main part of the initially cleaned wind power curve images and maps them back to the normal wind power points to complete the final cleaning.The CWPAD-IPCDA method was applied to clean datasets from 25 wind turbines(WTs)in two wind farms in northwest China to validate its feasibility.A comparison was conducted using density-based spatial clustering of applications with noise(DBSCAN)algorithm,an improved isolation forest algorithm,and an image-based(IB)algorithm.The experimental results demonstrate that the CWPAD-IPCDA method surpasses the other three algorithms,achieving an approximately 7.23%higher average data cleaning rate.The mean value of the sum of the squared errors(SSE)of the dataset after cleaning is approximately 6.887 lower than that of the other algorithms.Moreover,the mean of overall accuracy,as measured by the F1-score,exceeds that of the other methods by approximately 10.49%;this indicates that the CWPAD-IPCDA method is more conducive to improving the accuracy and reliability of wind power curve modeling and wind farm power forecasting.
基金the Beijing Natural Science Foundation(Grant No.2232066)the Open Project Foundation of State Key Laboratory of Solid Lubrication(Grant LSL-2212).
文摘To address the problem of identifying multiple types of additives in lubricating oil,a method based on midinfrared spectral band selection using the eXtreme Gradient Boosting(XGBoost)algorithm combined with the ant colony optimization(ACO)algorithm is proposed.The XGBoost algorithm was used to train and test three additives,T534(alkyl diphenylamine),T308(isooctyl acid thiophospholipid octadecylamine),and T306(trimethylphenol phosphate),separately,in order to screen for the optimal combination of spectral bands for each additive.The ACO algorithm was used to optimize the parameters of the XGBoost algorithm to improve the identification accuracy.During this process,the support vector machine(SVM)and hybrid bat algorithms(HBA)were included as a comparison,generating four models:ACO-XGBoost,ACO-SVM,HBA-XGboost,and HBA-SVM.The results showed that all four models could identify the three additives efficiently,with the ACO-XGBoost model achieving 100%recognition of all three additives.In addition,the generalizability of the ACO-XGBoost model was further demonstrated by predicting a lubricating oil containing the three additives prepared in our laboratory and a collected sample of commercial oil currently in use。
基金National Natural Science Foundation of China(No.62293493)。
文摘Three dimensional(3-D)imaging algorithms with irregular planar multiple-input-multiple-output(MIMO)arrays are discussed and compared with each other.Based on the same MIMO array,a modified back projection algorithm(MBPA)is accordingly proposed and four imaging algorithms are used for comparison,back-projection method(BP),back-projection one in time domain(BP-TD),modified back-projection one and fast Fourier transform(FFT)-based MIMO range migration algorithm(FFT-based MIMO RMA).All of the algorithms have been implemented in practical application scenarios by use of the proposed imaging system.Back to the practical applications,MIMO array-based imaging system with wide-bandwidth properties provides an efficient tool to detect objects hidden behind a wall.An MIMO imaging radar system,composed of a vector network analyzer(VNA),a set of switches,and an array of Vivaldi antennas,have been designed,fabricated,and tested.Then,these algorithms have been applied to measured data collected in different scenarios constituted by five metallic spheres in the absence and in the presence of a wall between the antennas and the targets in simulation and pliers in free space for experimental test.Finally,the focusing properties and time consumption of the above algorithms are compared.
基金supported by ZTE Industry-University-Institute Cooperation Funds。
文摘In recent years,various internet architectures,such as Integrated Services(IntServ),Differentiated Services(DiffServ),Time Sensitive Networking(TSN)and Deterministic Networking(DetNet),have been proposed to meet the quality-of-service(QoS)requirements of different network services.Concurrently,network calculus has found widespread application in network modeling and QoS analysis.Network calculus abstracts the details of how nodes or networks process data packets using the concept of service curves.This paper summarizes the service curves for typical scheduling algorithms,including Strict Priority(SP),Round Robin(RR),Cycling Queuing and Forwarding(CQF),Time Aware Shaper(TAS),Credit Based Shaper(CBS),and Asynchronous Traffic Shaper(ATS).It introduces the theory of network calculus and then provides an overview of various scheduling algorithms and their associated service curves.The delay bound analysis for different scheduling algorithms in specific scenarios is also conducted for more insights.
基金This paper is supported by the Nature Science Foundation of Heilongjiang Province.
文摘This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant No.62061024the Project of Gansu Province Science and Technology Department under Grant No.22ZD6GA055.
文摘Differential spatial modulation(DSM)is a multiple-input multiple-output(MIMO)transmission scheme.It has attracted extensive research interest due to its ability to transmit additional data without increasing any radio frequency chain.In this paper,DSM is investigated using two mapping algorithms:Look-Up Table Order(LUTO)and Permutation Method(PM).Then,the bit error rate(BER)performance and complexity of the two mapping algorithms in various antennas and modulation methods are verified by simulation experiments.The results show that PM has a lower BER than the LUTO mapping algorithm,and the latter has lower complexity than the former.
文摘Although the genetic algorithm (GA) has very powerful robustness and fitness, it needs a large size of population and a large number of iterations to reach the optimum result. Especially when GA is used in complex structural optimization problems, if the structural reanalysis technique is not adopted, the more the number of finite element analysis (FEA) is, the more the consuming time is. In the conventional structural optimization the number of FEA can be reduced by the structural reanalysis technique based on the approximation techniques and sensitivity analysis. With these techniques, this paper provides a new approximation model-segment approximation model, adopted for the GA application. This segment approximation model can decrease the number of FEA and increase the convergence rate of GA. So it can apparently decrease the computation time of GA. Two examples demonstrate the availability of the new segment approximation model.
文摘An approach to addressing the stereo correspondence problem is presented using genetic algorithms (GAs) to obtain a dense disparity map. Different from previous methods, this approach casts the stereo matching as a multi-extrema optimization problem such that finding the fittest solution from a set of potential disparity maps. Among a wide variety of optimization techniques, GAs are proven to be potentially effective methods for the global optimization problems with large search space. With this idea, each disparity map is viewed as an individual and the disparity values are encoded as chromosomes, so each individual has lots of chromosomes in the approach. Then, several matching constraints are formulated into an objective function, and GAs are used to search the global optimal solution for the problem. Furthermore, the coarse-to-fine strategy has been embedded in the approach so as to reduce the matching ambiguity and the time consumption. Finally, experimental results on synthetic and real images show the performance of the work.
基金supported by the Open Research Project of CAS Large Research InfrastructuresCAS Key Technology Talent ProgramNational Natural Science Foundations of China (Nos.U2031206 and 12273086)
文摘A fully digital data acquisition system based on a field-programmable gate array(FPGA) was developed for a CsI(Tl) array at the external target facility(ETF) in the Heavy Ion Research Facility in Lanzhou(HIRFL). To process the CsI(Tl) signals generated by γ-rays and light-charged ions, a scheme for digital pulse processing algorithms is proposed. Every step in the algorithms was benchmarked using standard γ and α sources. The scheme, which included a moving average filter, baseline restoration, leading-edge discrimination, moving window deconvolution, and digital charge comparison, was subsequently implemented on the FPGA. A good energy resolution of 5.7% for 1.33-MeV γ-rays and excellent α-γ identification using the digital charge comparison method were achieved, which satisfies CsI(Tl) array performance requirements.
基金Project 60575046 supported by the National Natural Science Foundation of China
文摘Noises widely exist in interactive genetic algorithms. However, there is no effective method to solve this problem up to now. There are two kinds of noises, one is the noise existing in visual systems and the other is resulted from user’s preference mechanisms. Characteristics of the two noises are presented aiming at the application of interac- tive genetic algorithms in dealing with images. The evolutionary phases of interactive genetic algorithms are determined according to differences in the same individual’s fitness among different generations. Models for noises in different phases are established and the corresponding strategies for reducing noises are given. The algorithm proposed in this paper has been applied to fashion design, which is a typical example of image processing. The results show that the strategies can reduce noises in interactive genetic algorithms and improve the algorithm’s performance effectively. However, a further study is needed to solve the problem of determining the evolution phase by using suitable objective methods so as to find out an effective method to decrease noises.
文摘A software defined networking(SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and optimization problems are typicallyvery complex with a huge solution space, large number of variables, and multiple objectives. Heuristic algorithms can solve theseproblems in an acceptable time but are usually limited to some particular problem circumstances. On the other hand, evolutionaryalgorithms(EAs), which are general stochastic algorithms inspired by the natural biological evolution and/or social behavior of species, can theoretically be used to solve any complex optimization problems including those found in SDNs. This paper reviewsfour types of EAs that are widely applied in current SDNs: Genetic Algorithms(GAs), Particle Swarm Optimization(PSO), Ant Colony Optimization(ACO), and Simulated Annealing(SA) by discussing their techniques, summarizing their representative applications, and highlighting their issues and future works. To the best of our knowledge, our work is the first that compares the tech-niques and categorizes the applications of these four EAs in SDNs.
基金Project 60575046 supported by the National Natural Science Foundation of China
文摘The increasing overlap of core and colony populations during the anaphase of evolution may limit the performance of shifting balance genetic algorithms. To decrease such overlapping,so as to increase the local search capability of the core population,the sub-space method was used to generate uniformly distributed initial colony populations over the decision variable space. The core population was also dynamically divided,making simultaneous searching in several local spaces possible. The algorithm proposed in this paper was compared to the original one by searching for the optimum of a complicated multi-modal function. The results indicate that the solutions obtained by the modified algorithm are better than those of the original algorithm.
文摘This paper describes the implementation of frequency-domain least mean squares (LMS) and Filtered-X algorithms and compares the performance of the frequencydomain adaptive control algorithm to a comparable timedomain controller. When the frequency-domain LMS step size is allowed to vary as a function of frequency,the frequency-domain algorithm exhibits a better vibration reduction than the time-domain algorithm for the weaker frequencies in the energy spectrum.
文摘Given a list of items and a sequence of variable-sized bins arriving one by one, it is NP-hard to pack the items into the bin list with a goal to minimize the total size of bins from the earliest one to the last used. In this paper a set of approximation algorithms is presented for cases in which the ability to preview at most k（〉=2） arriving bins is given. With the essential assumption that all bin sizes are not less than the largest item size, analytical results show the asymptotic worst case ratios of all k-bounded space and offiine algorithms are 2. Based on experiments by applying algorithms to instances in which item sizes and bin sizes are drawn independently from the continuous uniform distribution respectively in the interval [0,u] and [u,l ], averagecase experimental results show that, with fixed k, algorithms with the Best Fit packing（closing） rule are statistically better than those with the First Fit packing（closing） rule.
文摘To establish a parallel fusion approach of processing high dimensional information, the model and criterion of multisensor fuzzy stochastic data fusion were presented. In order to design genetic algorithm fusion, the fusion parameter coding, initial population and fitness function establishing, and fuzzy logic controller designing for genetic operations and probability choosing were completed. The discussion on the highly dimensional fusion was given. For a moving target with the division of 1 64 (velocity) and 1 75 (acceleration), the precision of fusion is 0 94 and 0 98 respectively. The fusion approach can improve the reliability and decision precision effectively.
文摘A multi-objective evolutionary optimization method (combining genetic algorithms(GAs)and game theory(GT))is presented for high lift multi-airfoil systems in aerospace engineering.Due to large dimension global op-timization problems and the increasing importance of low cost distributed parallel environments,it is a natural idea to replace a globar optimization by decentralized local sub-optimizations using GT which introduces the notion of games associated to an optimization problem.The GT/GAs combined optimization method is used for recon-struction and optimization problems by high lift multi-air-foil desing.Numerical results are favorably compared with single global GAs.The method shows teh promising robustness and efficient parallel properties of coupled GAs with different game scenarios for future advanced multi-disciplinary aerospace techmologies.
基金supported by the National Natural Science Foundation Project of China(Nos.72088101 and 42177164)the Distinguished Youth Science Foundation of Hunan Province of China(No.2022JJ10073)The first author was funded by China Scholarship Council(No.202106370038).
文摘Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safety.This paper aims to develop hybrid support vector machine(SVM)models improved by three metaheuristic algorithms known as grey wolf optimizer(GWO),whale optimization algorithm(WOA)and sparrow search algorithm(SSA)for predicting the hard rock pillar stability.An integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM models.Five parameters including pillar height,pillar width,ratio of pillar width to height,uniaxial compressive strength and pillar stress were set as input parameters.Two global indices,three local indices and the receiver operating characteristic(ROC)curve with the area under the ROC curve(AUC)were utilized to evaluate all hybrid models’performance.The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices.Nevertheless,the performance of the SSASVM model for predicting the unstable pillar(AUC:0.899)is not as good as those for stable(AUC:0.975)and failed pillars(AUC:0.990).To verify the effectiveness of the proposed models,5 field cases were investigated in a metal mine and other 5 cases were collected from several published works.The validation results indicated that the SSA-SVM model obtained a considerable accuracy,which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability.
