This paper introduces a novel chattering-free terminal sliding mode control(SMC)strategy to address chaotic behavior in permanent magnet synchronous generators(PMSG)for offshore wind turbine systems.By integrating an ...This paper introduces a novel chattering-free terminal sliding mode control(SMC)strategy to address chaotic behavior in permanent magnet synchronous generators(PMSG)for offshore wind turbine systems.By integrating an adaptive exponential reaching law with a continuous barrier function,the proposed approach eliminates chattering and ensures robust performance under model uncertainties.The methodology combines adaptive SMC with dynamic switching to estimate and compensates for unknown uncertainties,providing smooth and stable control.Finally,the performance and effectiveness of the proposed approach are compared with those of a previous study.展开更多
In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantl...In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantly degrades image quality.Traditional denoising methods,typically based on filter techniques,often face challenges related to inefficiency and limited adaptability.To address these limitations,this study proposes a novel SAR image denoising algorithm based on an enhanced residual network architecture,with the objective of enhancing the utility of SAR imagery in complex electromagnetic environments.The proposed algorithm integrates residual network modules,which directly process the noisy input images to generate denoised outputs.This approach not only reduces computational complexity but also mitigates the difficulties associated with model training.By combining the Transformer module with the residual block,the algorithm enhances the network's ability to extract global features,offering superior feature extraction capabilities compared to CNN-based residual modules.Additionally,the algorithm employs the adaptive activation function Meta-ACON,which dynamically adjusts the activation patterns of neurons,thereby improving the network's feature extraction efficiency.The effectiveness of the proposed denoising method is empirically validated using real SAR images from the RSOD dataset.The proposed algorithm exhibits remarkable performance in terms of EPI,SSIM,and ENL,while achieving a substantial enhancement in PSNR when compared to traditional and deep learning-based algorithms.The PSNR performance is enhanced by over twofold.Moreover,the evaluation of the MSTAR SAR dataset substantiates the algorithm's robustness and applicability in SAR denoising tasks,with a PSNR of 25.2021 being attained.These findings underscore the efficacy of the proposed algorithm in mitigating speckle noise while preserving critical features in SAR imagery,thereby enhancing its quality and usability in practical scenarios.展开更多
This study addresses the adaptive control and function projective synchronization problems between 2D Rulkov discrete-time system and Network discrete-time system. Based on backstepping design with three controllers, ...This study addresses the adaptive control and function projective synchronization problems between 2D Rulkov discrete-time system and Network discrete-time system. Based on backstepping design with three controllers, a systematic, concrete and automatic scheme is developed to investigate the function projective synchronization of discretetime chaotic systems. In addition, the adaptive control function is applied to achieve the state synchronization of two discrete-time systems. Numerical results demonstrate the effectiveness of the proposed control scheme.展开更多
This paper presents an energy management optimization system based on an adaptive functional state model of battery aging for internal combustion engine vehicles(ICEVs).First,the functional characteristics of batterie...This paper presents an energy management optimization system based on an adaptive functional state model of battery aging for internal combustion engine vehicles(ICEVs).First,the functional characteristics of batteries in ICEVs are investigated.Then,an adaptive functional state model is proposed to represent battery aging throughout the entire battery service life.A battery protection scheme is developed,including over-discharge and graded over-current protection to improve battery safety.A model-based energy management strategy is synthesized to comprehensively optimize fuel economy,battery life preservation,and vehicle performance.The performance of the proposed scheme was examined under comprehensive test scenarios based on field and bench tests.The results show that the proposed energy management algorithm can effectively improve fuel economy.展开更多
In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive fun...In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive functional control(AFPFC) scheme for multivariable nonlinear systems was proposed.Firstly,multivariable nonlinear systems were described based on Takagi-Sugeno(T-S) fuzzy models;assuming that the antecedent parameters of T-S models were kept,the consequent parameters were identified on-line by using the weighted recursive least square(WRLS) method.Secondly,the identified T-S models were linearized to be time-varying state space model at each sampling instant.Finally,by using linear predictive control technique the analysis solution of the optimal control law of AFPFC was established.The application results for pH neutralization process show that the absolute error between the identified T-S model output and the process output is smaller than 0.015;the tracking ability of the proposed AFPFC is superior to that of non-AFPFC(NAFPFC) for pH process without disturbances,the overshoot of the effluent pH value of AFPFC with disturbances is decreased by 50% compared with that of NAFPFC;when the process parameters of AFPFC vary with time the integrated absolute error(IAE) performance index still retains to be less than 200 compared with that of NAFPFC.展开更多
Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the g...Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.展开更多
Characteristic Basis Function Method (CBFM) is a novel approach for analyzing the ElectroMagnetic (EM) scattering from electrically large objects. Based on dividing the studied object into small blocks, the CBFM is su...Characteristic Basis Function Method (CBFM) is a novel approach for analyzing the ElectroMagnetic (EM) scattering from electrically large objects. Based on dividing the studied object into small blocks, the CBFM is suitable for parallel computing. In this paper, a static load balance parallel method is presented by combining Message Passing Interface (MPI) with Adaptively Modified CBFM (AMCBFM). In this method, the object geometry is partitioned into distinct blocks, and the serial number of blocks is sent to related nodes according to a certain rule. Every node only needs to calculate the information on local blocks. The obtained results confirm the accuracy and efficiency of the proposed method in speeding up solving large electrical scale problems.展开更多
In order to analyze skull variation in the genus Macaca ,seventy seven craniodental variables were taken from eleven species.They were first defined seven functional units comprising three anatomical regions.Twenty s...In order to analyze skull variation in the genus Macaca ,seventy seven craniodental variables were taken from eleven species.They were first defined seven functional units comprising three anatomical regions.Twenty seven variables were finally selected to carry out the morphology of the whole skull.The data,organized in these ways,were examined to discover variations between and within the various species.The methods used were Principal Components Analysis (PCA) and Discriminant Function Analysis (DFA).PCAs of the functional units anatomical regions,and the whole skull provided similar,though not identical,separations of species clusters in both sexes separately.These differences in structure could be related to size,sexual dimorphism,diet,ecology,classification and phylogeny.The question of size should have been easy to settle.Unfortunately,this is not the case.In this study where the raw data are measurements of the specimens,the main differences should be size.However,the size differences seem to occur in both the first and second (independent) multivariate axes.In some analyses the size differences between the species are biggest and appear in the first axis.In other analyses it is the separation between the sexes (and these too are largely size) that are the biggest and appear in the first axis.Yet in other analyses,both of these size separations,though still orthogonal to one another,present in the combination of the first two axes.This certainly implies that a single axis of body size is not present and that shape differences have not been isolated form size differences.It also implies that sexual dimorphism is a complex matter.As a result,the question of the relationships between the species is therefore also complex.One cluster of species that includes M fascicularis,M sinica and M radiata was significantly isolated from all others regardless of level of analysis.This relationship is quite different from that proposed on the anatomy of the reproductive organs (Delson,1980;Fooden,1976,1980).展开更多
To improve image quality under low illumination conditions,a novel low-light image enhancement method is proposed in this paper based on multi-illumination estimation and multi-scale fusion(MIMS).Firstly,the illuminat...To improve image quality under low illumination conditions,a novel low-light image enhancement method is proposed in this paper based on multi-illumination estimation and multi-scale fusion(MIMS).Firstly,the illumination is processed by contrast-limited adaptive histogram equalization(CLAHE),adaptive complementary gamma function(ACG),and adaptive detail preserving S-curve(ADPS),respectively,to obtain three components.Then,the fusion-relevant features,exposure,and color contrast are selected as the weight maps.Subsequently,these components and weight maps are fused through multi-scale to generate enhanced illumination.Finally,the enhanced images are obtained by multiplying the enhanced illumination and reflectance.Compared with existing approaches,this proposed method achieves an average increase of 0.81%and 2.89%in the structural similarity index measurement(SSIM)and peak signal-to-noise ratio(PSNR),and a decrease of 6.17%and 32.61%in the natural image quality evaluator(NIQE)and gradient magnitude similarity deviation(GMSD),respectively.展开更多
Motivated by the autopilot of an unmanned aerial vehicle（UAV） with a wide flight envelope span experiencing large parametric variations in the presence of uncertainties, a fuzzy adaptive tracking controller（FATC） ...Motivated by the autopilot of an unmanned aerial vehicle（UAV） with a wide flight envelope span experiencing large parametric variations in the presence of uncertainties, a fuzzy adaptive tracking controller（FATC） is proposed. The controller consists of a fuzzy baseline controller and an adaptive increment, and the main highlight is that the fuzzy baseline controller and adaptation laws are both based on the fuzzy multiple Lyapunov function approach, which helps to reduce the conservatism for the large envelope and guarantees satisfactory tracking performances with strong robustness simultaneously within the whole envelope. The constraint condition of the fuzzy baseline controller is provided in the form of linear matrix inequality（LMI）, and it specifies the satisfactory tracking performances in the absence of uncertainties. The adaptive increment ensures the uniformly ultimately bounded（UUB） predication errors to recover satisfactory responses in the presence of uncertainties. Simulation results show that the proposed controller helps to achieve high-accuracy tracking of airspeed and altitude desirable commands with strong robustness to uncertainties throughout the entire flight envelope.展开更多
Partial differential equations(PDEs)are important tools for scientific research and are widely used in various fields.However,it is usually very difficult to obtain accurate analytical solutions of PDEs,and numerical ...Partial differential equations(PDEs)are important tools for scientific research and are widely used in various fields.However,it is usually very difficult to obtain accurate analytical solutions of PDEs,and numerical methods to solve PDEs are often computationally intensive and very time-consuming.In recent years,Physics Informed Neural Networks(PINNs)have been successfully applied to find numerical solutions of PDEs and have shown great potential.All the while,solitary waves have been of great interest to researchers in the field of nonlinear science.In this paper,we perform numerical simulations of solitary wave solutions of several PDEs using improved PINNs.The improved PINNs not only incorporate constraints on the control equations to ensure the interpretability of the prediction results,which is important for physical field simulations,in addition,an adaptive activation function is introduced.By introducing hyperparameters in the activation function to change the slope of the activation function to avoid the disappearance of the gradient,computing time is saved thereby speeding up training.In this paper,the m Kd V equation,the improved Boussinesq equation,the Caudrey–Dodd–Gibbon–Sawada–Kotera equation and the p-g BKP equation are selected for study,and the errors of the simulation results are analyzed to assess the accuracy of the predicted solitary wave solution.The experimental results show that the improved PINNs are significantly better than the traditional PINNs with shorter training time but more accurate prediction results.The improved PINNs improve the training speed by more than 1.5 times compared with the traditional PINNs,while maintaining the prediction error less than 10~(-2)in this order of magnitude.展开更多
Compared with the quad-rotor unmanned aerial vehicle (UAV), the coaxial twelve-rotor UAV has stronger load carrying capacity, higher driving ability and stronger damage resistance. This paper focuses on its robust ada...Compared with the quad-rotor unmanned aerial vehicle (UAV), the coaxial twelve-rotor UAV has stronger load carrying capacity, higher driving ability and stronger damage resistance. This paper focuses on its robust adaptive control. First, a mathematical model of a coaxial twelve-rotor is established. Aiming at the problem of model uncertainty and external disturbance of the coaxial twelve-rotor UAV, the attitude controller is innovatively adopted with the combination of a backstepping sliding mode controller (BSMC) and an adaptive radial basis function neural network (RBFNN). The BSMC combines the advantages of backstepping control and sliding mode control, which has a simple design process and strong robustness. The RBFNN as an uncertain observer, can effectively estimate the total uncertainty. Then the stability of the twelve-rotor UAV control system is proved by Lyapunov stability theorem. Finally, it is proved that the robust adaptive control strategy presented in this paper can overcome model uncertainty and external disturbance effectively through numerical simulation and prototype of twelve-rotor UAV tests.展开更多
Gap acceptance theory is broadly used for evaluating unsignalized intersections in developed coun tries. Intersections with no specific priority to any move ment, known as uncontrolled intersections, are common in Ind...Gap acceptance theory is broadly used for evaluating unsignalized intersections in developed coun tries. Intersections with no specific priority to any move ment, known as uncontrolled intersections, are common in India. Limited priority is observed at a few intersections, where priorities are perceived by drivers based on geom etry, traffic volume, and speed on the approaches of intersection. Analyzing such intersections is complex because the overall traffic behavior is the result of drivers, vehicles, and traffic flow characteristics. Fuzzy theory has been widely used to analyze similar situations. This paper describes the application of adaptive neurofuzzy interface system （ANFIS） to the modeling of gap acceptance behavior of rightturning vehicles at limited priority Tintersections （in India, vehicles are driven on the left side of a road）. Field data are collected using video cameras at four Tintersections having limited priority. The data extracted include gap/lag, subject vehicle type, conflicting vehicle type, and driver＇s decision （accepted/rejected）. ANFIS models are developed by using 80 % of the extracted data （total data observations for major road right turning vehicles are 722 and 1,066 for minor road right turning vehicles） and remaining are used for model vali dation. Four different combinations of input variables are considered for major and minor road right turnings sepa rately. Correct prediction by ANFIS models ranges from 75.17 % to 82.16 % for major road right turning and 87.20 % to 88.62 % for minor road right turning. Themodels developed in this paper can be used in the dynamic estimation of gap acceptance in traffic simulation models.展开更多
Bloom Filters(BFs)are compact and probabilistic data structures designed for efficient set membership queries.They offer high query and storage efficiency,making them particularly useful in network and distributed sys...Bloom Filters(BFs)are compact and probabilistic data structures designed for efficient set membership queries.They offer high query and storage efficiency,making them particularly useful in network and distributed systems.However,the scalability of BFs in accommodating“big data”is limited by increased false positive rates,inflexible hash functions,and inefficient matching with dynamic datasets.To address these limitations,we introduce the Extensible Bloom Filter(EBF),which incorporates a flexible expansion mechanism and an adaptive hash function generation scheme.The EBF design features a set of BF vectors that expand according to the rate of incoming data,with each vector sized to suit the characteristics of the data.Adaptive hash functions,derived from common base matrices,streamline the process by leveraging strong inter-hash relationships.This reduces overhead and simplifies queries across multiple BF vector sizes.Performance evaluations have shown that the EBF consistently achieves a low false positive rate and minimal query time,even amid dynamic data arrivals and large data sets.With its extensibility and adaptability,the EBF provides a robust solution for applications requiring dynamic set representations with stringent accuracy requirements.It enhances the capabilities of network and distributed systems,making them more efficient in handling complex data scenarios.展开更多
The machinery fault signal is a typical non-Gaussian and non-stationary process. The fault signal can be described by SaS distribution model because of the presence of impulses.Time-frequency distribution is a useful ...The machinery fault signal is a typical non-Gaussian and non-stationary process. The fault signal can be described by SaS distribution model because of the presence of impulses.Time-frequency distribution is a useful tool to extract helpful information of the machinery fault signal. Various fractional lower order(FLO) time-frequency distribution methods have been proposed based on fractional lower order statistics, which include fractional lower order short time Fourier transform(FLO-STFT), fractional lower order Wigner-Ville distributions(FLO-WVDs), fractional lower order Cohen class time-frequency distributions(FLO-CDs), fractional lower order adaptive kernel time-frequency distributions(FLO-AKDs) and adaptive fractional lower order time-frequency auto-regressive moving average(FLO-TFARMA) model time-frequency representation method.The methods and the exiting methods based on second order statistics in SaS distribution environments are compared, simulation results show that the new methods have better performances than the existing methods. The advantages and disadvantages of the improved time-frequency methods have been summarized.Last, the new methods are applied to analyze the outer race fault signals, the results illustrate their good performances.展开更多
Accuracy of a simulation strongly depends on the grid quality. Here, quality means orthogonality at the boundaries and quasi-orthogonality within the critical regions, smoothness, bounded aspect ratios and solution ad...Accuracy of a simulation strongly depends on the grid quality. Here, quality means orthogonality at the boundaries and quasi-orthogonality within the critical regions, smoothness, bounded aspect ratios and solution adaptive behaviour. It is not recommended to refine the parts of the domain where the solution shows little variation. It is desired to concentrate grid points and cells in the part of the domain where the solution shows strong gradients or variations. We present a simple, effective and com- putationally efficient approach for quadrilateral mesh adaptation. Several numerical examples are presented for supporting our claim.展开更多
Lactococcus lactis,a major starter culture in the dairy industry,has been widely applied in food fermentation.While current research has primarily focused on evaluating its role during fermentation,genomic investigati...Lactococcus lactis,a major starter culture in the dairy industry,has been widely applied in food fermentation.While current research has primarily focused on evaluating its role during fermentation,genomic investigations into its genetic diversity and functional adaptability remain limited.In this study,199 L.lactis strains isolated from Chinese traditional artisanal cheeses(72 bovine,71 goat,and 56 yak milk cheese isolates)were subjected to comparative genomic analysis.Genomic characteristic analysis indicated that bovine milk strains possess larger genomes and the highest number of unique genes.Functional characterization further demonstrated notable differences in carbohydrate metabolism among strains from different sources,with yak milk strains enriched in enzymes involved in complex polysaccharide degradation,including members of the carbohydrate esterases family.Moreover,strains from different sources exhibit distinct strategies for lactose hydrolysis and metabolic utilization,reflecting adaptive evolution to their specific nutritional niches.Analysis of the antibiotic resistance profile suggests that L.lactis predominantly harbors glycopeptide and lincosamide resistance genes,encompassing four distinct resistance mechanisms.Collectively,this study reveals the genetic diversity and adaptive evolution of L.lactis strains from different sources and identifies key genes associated with carbohydrate degradation,lactose metabolism,and antibiotic resistance,providing concrete genetic evidence for the selection of efficient and safe industrial fermentation strains.展开更多
In order to discern dental morphometric variations among the Asian colobines, residuals of the colobines, derived from allometric baselines formed by the Asian macaques （Macaca）, were analyzed with Principal Compone...In order to discern dental morphometric variations among the Asian colobines, residuals of the colobines, derived from allometric baselines formed by the Asian macaques （Macaca）, were analyzed with Principal Components Analysis and Euclidean Distances. Results indicated that the widely accepted view that the colobines possess relatively smaller front teeth than the macaques is only the case for the first incisors. The colobines show relatively smaller molars than the macaques. Such profiles may be related to the differences in dietary preferences between the two major groups of the Asian Old World monkeys. The magnitude of such differences is not as great as usullay assumed for the two groups that contain both African and Asian taxa. In other words, the two Asian cercopithecoid groups may have homogenously been shaped by the tectonic modifications and climate alterations in the past five million years. There exist marked differences among the Asian colobines when each of the genera is compared with macaques; the dental profile reflects not only the variation in geographic distribution but also in phylogenetic divergence. Thus, the snub-nosed monkeys （Rhinopithecus） and the gray langurs （Semnopithecus） are characterized by relatively larger molars than the other colobines - larger even than those of the macaques. The differences among Asian colobines, depicted by Euclidean Distances, seems to reflect the relationship of the phylogeny and evolution between colobines and cercopithecines.展开更多
The trajectory optimization of an unpowered reentry vehicle via artificial emotion memory optimization(AEMO)is discussed.Firstly,reentry dynamics are established based on multiple constraints and parameterized control...The trajectory optimization of an unpowered reentry vehicle via artificial emotion memory optimization(AEMO)is discussed.Firstly,reentry dynamics are established based on multiple constraints and parameterized control variables with finite dimensions are designed.If the constraint is not satisfied,a distance measure and an adaptive penalty function are used to address this scenario.Secondly,AEMO is introduced to solve the trajectory optimization problem.Based on the theories of biology and cognition,the trial solutions based on emotional memory are established.Three search strategies are designed for realizing the random search of trial solutions and for avoiding becoming trapped in a local minimum.The states of the trial solutions are determined according to the rules of memory enhancement and forgetting.As the iterations proceed,the trial solutions with poor quality will gradually be forgotten.Therefore,the number of trial solutions is decreased,and the convergence of the algorithm is accelerated.Finally,a numerical simulation is conducted,and the results demonstrate that the path and terminal constraints are satisfied and the method can realize satisfactory performance.展开更多
Plasticity is a natural property of living organisms that is crucial for adaptation and evolution.Over the last decades,the availability of sophisticated neuroimaging techniques（in particular,functional magnetic reso...Plasticity is a natural property of living organisms that is crucial for adaptation and evolution.Over the last decades,the availability of sophisticated neuroimaging techniques（in particular,functional magnetic resonance imaging（f MRI）,and transcranial magnetic stimulation（TMS））,has made it possible to explore in vivo the on-line functioning of brain and its plasticity.However,展开更多
文摘This paper introduces a novel chattering-free terminal sliding mode control(SMC)strategy to address chaotic behavior in permanent magnet synchronous generators(PMSG)for offshore wind turbine systems.By integrating an adaptive exponential reaching law with a continuous barrier function,the proposed approach eliminates chattering and ensures robust performance under model uncertainties.The methodology combines adaptive SMC with dynamic switching to estimate and compensates for unknown uncertainties,providing smooth and stable control.Finally,the performance and effectiveness of the proposed approach are compared with those of a previous study.
文摘In the field of image processing,the analysis of Synthetic Aperture Radar(SAR)images is crucial due to its broad range of applications.However,SAR images are often affected by coherent speckle noise,which significantly degrades image quality.Traditional denoising methods,typically based on filter techniques,often face challenges related to inefficiency and limited adaptability.To address these limitations,this study proposes a novel SAR image denoising algorithm based on an enhanced residual network architecture,with the objective of enhancing the utility of SAR imagery in complex electromagnetic environments.The proposed algorithm integrates residual network modules,which directly process the noisy input images to generate denoised outputs.This approach not only reduces computational complexity but also mitigates the difficulties associated with model training.By combining the Transformer module with the residual block,the algorithm enhances the network's ability to extract global features,offering superior feature extraction capabilities compared to CNN-based residual modules.Additionally,the algorithm employs the adaptive activation function Meta-ACON,which dynamically adjusts the activation patterns of neurons,thereby improving the network's feature extraction efficiency.The effectiveness of the proposed denoising method is empirically validated using real SAR images from the RSOD dataset.The proposed algorithm exhibits remarkable performance in terms of EPI,SSIM,and ENL,while achieving a substantial enhancement in PSNR when compared to traditional and deep learning-based algorithms.The PSNR performance is enhanced by over twofold.Moreover,the evaluation of the MSTAR SAR dataset substantiates the algorithm's robustness and applicability in SAR denoising tasks,with a PSNR of 25.2021 being attained.These findings underscore the efficacy of the proposed algorithm in mitigating speckle noise while preserving critical features in SAR imagery,thereby enhancing its quality and usability in practical scenarios.
基金supported by the Natural Science Foundation of China under Grant Nos.10747141 and 10735030Zhejiang Provincial Natural Science Foundation under Grant No.605408+3 种基金Ningbo Natural Science Foundation under Grant Nos.2007A610049 and 2008A61001National Basic Research Program of China (973 Program 2007CB814800)Programme for Changjiang Scholars and Innovative Research Team in University (IRT0734)K.C.Wong Magna Fund in Ningbo University
文摘This study addresses the adaptive control and function projective synchronization problems between 2D Rulkov discrete-time system and Network discrete-time system. Based on backstepping design with three controllers, a systematic, concrete and automatic scheme is developed to investigate the function projective synchronization of discretetime chaotic systems. In addition, the adaptive control function is applied to achieve the state synchronization of two discrete-time systems. Numerical results demonstrate the effectiveness of the proposed control scheme.
基金supported by National Natural Science Foundation of China(Grant No.52002209)Beijing Nova Program,and the State Key Laboratory of Automotive Safety and Energy(Grant No.KFY2210).
文摘This paper presents an energy management optimization system based on an adaptive functional state model of battery aging for internal combustion engine vehicles(ICEVs).First,the functional characteristics of batteries in ICEVs are investigated.Then,an adaptive functional state model is proposed to represent battery aging throughout the entire battery service life.A battery protection scheme is developed,including over-discharge and graded over-current protection to improve battery safety.A model-based energy management strategy is synthesized to comprehensively optimize fuel economy,battery life preservation,and vehicle performance.The performance of the proposed scheme was examined under comprehensive test scenarios based on field and bench tests.The results show that the proposed energy management algorithm can effectively improve fuel economy.
基金Project(2007AA04Z162) supported by the National High-Tech Research and Development Program of ChinaProjects(2006T089, 2009T062) supported by the University Innovation Team in the Educational Department of Liaoning Province, China
文摘In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive functional control(AFPFC) scheme for multivariable nonlinear systems was proposed.Firstly,multivariable nonlinear systems were described based on Takagi-Sugeno(T-S) fuzzy models;assuming that the antecedent parameters of T-S models were kept,the consequent parameters were identified on-line by using the weighted recursive least square(WRLS) method.Secondly,the identified T-S models were linearized to be time-varying state space model at each sampling instant.Finally,by using linear predictive control technique the analysis solution of the optimal control law of AFPFC was established.The application results for pH neutralization process show that the absolute error between the identified T-S model output and the process output is smaller than 0.015;the tracking ability of the proposed AFPFC is superior to that of non-AFPFC(NAFPFC) for pH process without disturbances,the overshoot of the effluent pH value of AFPFC with disturbances is decreased by 50% compared with that of NAFPFC;when the process parameters of AFPFC vary with time the integrated absolute error(IAE) performance index still retains to be less than 200 compared with that of NAFPFC.
基金funded by the National Natural Science Foundation of China(General Program:No.52074314,No.U19B6003-05)National Key Research and Development Program of China(2019YFA0708303-05)。
文摘Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.
文摘Characteristic Basis Function Method (CBFM) is a novel approach for analyzing the ElectroMagnetic (EM) scattering from electrically large objects. Based on dividing the studied object into small blocks, the CBFM is suitable for parallel computing. In this paper, a static load balance parallel method is presented by combining Message Passing Interface (MPI) with Adaptively Modified CBFM (AMCBFM). In this method, the object geometry is partitioned into distinct blocks, and the serial number of blocks is sent to related nodes according to a certain rule. Every node only needs to calculate the information on local blocks. The obtained results confirm the accuracy and efficiency of the proposed method in speeding up solving large electrical scale problems.
文摘In order to analyze skull variation in the genus Macaca ,seventy seven craniodental variables were taken from eleven species.They were first defined seven functional units comprising three anatomical regions.Twenty seven variables were finally selected to carry out the morphology of the whole skull.The data,organized in these ways,were examined to discover variations between and within the various species.The methods used were Principal Components Analysis (PCA) and Discriminant Function Analysis (DFA).PCAs of the functional units anatomical regions,and the whole skull provided similar,though not identical,separations of species clusters in both sexes separately.These differences in structure could be related to size,sexual dimorphism,diet,ecology,classification and phylogeny.The question of size should have been easy to settle.Unfortunately,this is not the case.In this study where the raw data are measurements of the specimens,the main differences should be size.However,the size differences seem to occur in both the first and second (independent) multivariate axes.In some analyses the size differences between the species are biggest and appear in the first axis.In other analyses it is the separation between the sexes (and these too are largely size) that are the biggest and appear in the first axis.Yet in other analyses,both of these size separations,though still orthogonal to one another,present in the combination of the first two axes.This certainly implies that a single axis of body size is not present and that shape differences have not been isolated form size differences.It also implies that sexual dimorphism is a complex matter.As a result,the question of the relationships between the species is therefore also complex.One cluster of species that includes M fascicularis,M sinica and M radiata was significantly isolated from all others regardless of level of analysis.This relationship is quite different from that proposed on the anatomy of the reproductive organs (Delson,1980;Fooden,1976,1980).
基金supported by the National Key R&D Program of China(No.2022YFB3205101)NSAF(No.U2230116)。
文摘To improve image quality under low illumination conditions,a novel low-light image enhancement method is proposed in this paper based on multi-illumination estimation and multi-scale fusion(MIMS).Firstly,the illumination is processed by contrast-limited adaptive histogram equalization(CLAHE),adaptive complementary gamma function(ACG),and adaptive detail preserving S-curve(ADPS),respectively,to obtain three components.Then,the fusion-relevant features,exposure,and color contrast are selected as the weight maps.Subsequently,these components and weight maps are fused through multi-scale to generate enhanced illumination.Finally,the enhanced images are obtained by multiplying the enhanced illumination and reflectance.Compared with existing approaches,this proposed method achieves an average increase of 0.81%and 2.89%in the structural similarity index measurement(SSIM)and peak signal-to-noise ratio(PSNR),and a decrease of 6.17%and 32.61%in the natural image quality evaluator(NIQE)and gradient magnitude similarity deviation(GMSD),respectively.
文摘Motivated by the autopilot of an unmanned aerial vehicle（UAV） with a wide flight envelope span experiencing large parametric variations in the presence of uncertainties, a fuzzy adaptive tracking controller（FATC） is proposed. The controller consists of a fuzzy baseline controller and an adaptive increment, and the main highlight is that the fuzzy baseline controller and adaptation laws are both based on the fuzzy multiple Lyapunov function approach, which helps to reduce the conservatism for the large envelope and guarantees satisfactory tracking performances with strong robustness simultaneously within the whole envelope. The constraint condition of the fuzzy baseline controller is provided in the form of linear matrix inequality（LMI）, and it specifies the satisfactory tracking performances in the absence of uncertainties. The adaptive increment ensures the uniformly ultimately bounded（UUB） predication errors to recover satisfactory responses in the presence of uncertainties. Simulation results show that the proposed controller helps to achieve high-accuracy tracking of airspeed and altitude desirable commands with strong robustness to uncertainties throughout the entire flight envelope.
文摘Partial differential equations(PDEs)are important tools for scientific research and are widely used in various fields.However,it is usually very difficult to obtain accurate analytical solutions of PDEs,and numerical methods to solve PDEs are often computationally intensive and very time-consuming.In recent years,Physics Informed Neural Networks(PINNs)have been successfully applied to find numerical solutions of PDEs and have shown great potential.All the while,solitary waves have been of great interest to researchers in the field of nonlinear science.In this paper,we perform numerical simulations of solitary wave solutions of several PDEs using improved PINNs.The improved PINNs not only incorporate constraints on the control equations to ensure the interpretability of the prediction results,which is important for physical field simulations,in addition,an adaptive activation function is introduced.By introducing hyperparameters in the activation function to change the slope of the activation function to avoid the disappearance of the gradient,computing time is saved thereby speeding up training.In this paper,the m Kd V equation,the improved Boussinesq equation,the Caudrey–Dodd–Gibbon–Sawada–Kotera equation and the p-g BKP equation are selected for study,and the errors of the simulation results are analyzed to assess the accuracy of the predicted solitary wave solution.The experimental results show that the improved PINNs are significantly better than the traditional PINNs with shorter training time but more accurate prediction results.The improved PINNs improve the training speed by more than 1.5 times compared with the traditional PINNs,while maintaining the prediction error less than 10~(-2)in this order of magnitude.
基金Supported by the National Natural Science Foundation of China(No.11372309,61304017)Youth Innovation Promotion Association(No.2014192)+1 种基金the Provincial Special Funds Project of Science and Technology Cooperation(No.2017SYHZ0024)the Key Technology Development Project of Jilin Province(No.20150204074GX)
文摘Compared with the quad-rotor unmanned aerial vehicle (UAV), the coaxial twelve-rotor UAV has stronger load carrying capacity, higher driving ability and stronger damage resistance. This paper focuses on its robust adaptive control. First, a mathematical model of a coaxial twelve-rotor is established. Aiming at the problem of model uncertainty and external disturbance of the coaxial twelve-rotor UAV, the attitude controller is innovatively adopted with the combination of a backstepping sliding mode controller (BSMC) and an adaptive radial basis function neural network (RBFNN). The BSMC combines the advantages of backstepping control and sliding mode control, which has a simple design process and strong robustness. The RBFNN as an uncertain observer, can effectively estimate the total uncertainty. Then the stability of the twelve-rotor UAV control system is proved by Lyapunov stability theorem. Finally, it is proved that the robust adaptive control strategy presented in this paper can overcome model uncertainty and external disturbance effectively through numerical simulation and prototype of twelve-rotor UAV tests.
基金partially funded by Department of Science and Technology (DST), Govt. of Indiaproject SR/ FTP/ETA-61/2010
文摘Gap acceptance theory is broadly used for evaluating unsignalized intersections in developed coun tries. Intersections with no specific priority to any move ment, known as uncontrolled intersections, are common in India. Limited priority is observed at a few intersections, where priorities are perceived by drivers based on geom etry, traffic volume, and speed on the approaches of intersection. Analyzing such intersections is complex because the overall traffic behavior is the result of drivers, vehicles, and traffic flow characteristics. Fuzzy theory has been widely used to analyze similar situations. This paper describes the application of adaptive neurofuzzy interface system （ANFIS） to the modeling of gap acceptance behavior of rightturning vehicles at limited priority Tintersections （in India, vehicles are driven on the left side of a road）. Field data are collected using video cameras at four Tintersections having limited priority. The data extracted include gap/lag, subject vehicle type, conflicting vehicle type, and driver＇s decision （accepted/rejected）. ANFIS models are developed by using 80 % of the extracted data （total data observations for major road right turning vehicles are 722 and 1,066 for minor road right turning vehicles） and remaining are used for model vali dation. Four different combinations of input variables are considered for major and minor road right turnings sepa rately. Correct prediction by ANFIS models ranges from 75.17 % to 82.16 % for major road right turning and 87.20 % to 88.62 % for minor road right turning. Themodels developed in this paper can be used in the dynamic estimation of gap acceptance in traffic simulation models.
基金supported in part by the National Natural Science Foundation of China(Nos.62025201 and 62472167)in part by the Hunan Provincial Natural Science Foundation of China(Nos.2024JJ3014 and 2024JJ5165)in part by the Key Research and Development Program of Hunan Province(No.2023GK2001).
文摘Bloom Filters(BFs)are compact and probabilistic data structures designed for efficient set membership queries.They offer high query and storage efficiency,making them particularly useful in network and distributed systems.However,the scalability of BFs in accommodating“big data”is limited by increased false positive rates,inflexible hash functions,and inefficient matching with dynamic datasets.To address these limitations,we introduce the Extensible Bloom Filter(EBF),which incorporates a flexible expansion mechanism and an adaptive hash function generation scheme.The EBF design features a set of BF vectors that expand according to the rate of incoming data,with each vector sized to suit the characteristics of the data.Adaptive hash functions,derived from common base matrices,streamline the process by leveraging strong inter-hash relationships.This reduces overhead and simplifies queries across multiple BF vector sizes.Performance evaluations have shown that the EBF consistently achieves a low false positive rate and minimal query time,even amid dynamic data arrivals and large data sets.With its extensibility and adaptability,the EBF provides a robust solution for applications requiring dynamic set representations with stringent accuracy requirements.It enhances the capabilities of network and distributed systems,making them more efficient in handling complex data scenarios.
基金supported by the National Natural Science Foundation of China(61261046,61362038)the Natural Science Foundation of Jiangxi Province(20142BAB207006,20151BAB207013)+2 种基金the Science and Technology Project of Provincial Education Department of Jiangxi Province(GJJ14738,GJJ14739)the Research Foundation of Health Department of Jiangxi Province(20175561)the Science and Technology Project of Jiujiang University(2016KJ001,2016KJ002)
文摘The machinery fault signal is a typical non-Gaussian and non-stationary process. The fault signal can be described by SaS distribution model because of the presence of impulses.Time-frequency distribution is a useful tool to extract helpful information of the machinery fault signal. Various fractional lower order(FLO) time-frequency distribution methods have been proposed based on fractional lower order statistics, which include fractional lower order short time Fourier transform(FLO-STFT), fractional lower order Wigner-Ville distributions(FLO-WVDs), fractional lower order Cohen class time-frequency distributions(FLO-CDs), fractional lower order adaptive kernel time-frequency distributions(FLO-AKDs) and adaptive fractional lower order time-frequency auto-regressive moving average(FLO-TFARMA) model time-frequency representation method.The methods and the exiting methods based on second order statistics in SaS distribution environments are compared, simulation results show that the new methods have better performances than the existing methods. The advantages and disadvantages of the improved time-frequency methods have been summarized.Last, the new methods are applied to analyze the outer race fault signals, the results illustrate their good performances.
文摘Accuracy of a simulation strongly depends on the grid quality. Here, quality means orthogonality at the boundaries and quasi-orthogonality within the critical regions, smoothness, bounded aspect ratios and solution adaptive behaviour. It is not recommended to refine the parts of the domain where the solution shows little variation. It is desired to concentrate grid points and cells in the part of the domain where the solution shows strong gradients or variations. We present a simple, effective and com- putationally efficient approach for quadrilateral mesh adaptation. Several numerical examples are presented for supporting our claim.
基金supported by the National Key Research and Development Project(2022YFD2100703)the National Natural Science Foundation of China(32394051 and U23A20259)the Fundamental Research Funds for the Central Universities(JUSRP622013).
文摘Lactococcus lactis,a major starter culture in the dairy industry,has been widely applied in food fermentation.While current research has primarily focused on evaluating its role during fermentation,genomic investigations into its genetic diversity and functional adaptability remain limited.In this study,199 L.lactis strains isolated from Chinese traditional artisanal cheeses(72 bovine,71 goat,and 56 yak milk cheese isolates)were subjected to comparative genomic analysis.Genomic characteristic analysis indicated that bovine milk strains possess larger genomes and the highest number of unique genes.Functional characterization further demonstrated notable differences in carbohydrate metabolism among strains from different sources,with yak milk strains enriched in enzymes involved in complex polysaccharide degradation,including members of the carbohydrate esterases family.Moreover,strains from different sources exhibit distinct strategies for lactose hydrolysis and metabolic utilization,reflecting adaptive evolution to their specific nutritional niches.Analysis of the antibiotic resistance profile suggests that L.lactis predominantly harbors glycopeptide and lincosamide resistance genes,encompassing four distinct resistance mechanisms.Collectively,this study reveals the genetic diversity and adaptive evolution of L.lactis strains from different sources and identifies key genes associated with carbohydrate degradation,lactose metabolism,and antibiotic resistance,providing concrete genetic evidence for the selection of efficient and safe industrial fermentation strains.
文摘In order to discern dental morphometric variations among the Asian colobines, residuals of the colobines, derived from allometric baselines formed by the Asian macaques （Macaca）, were analyzed with Principal Components Analysis and Euclidean Distances. Results indicated that the widely accepted view that the colobines possess relatively smaller front teeth than the macaques is only the case for the first incisors. The colobines show relatively smaller molars than the macaques. Such profiles may be related to the differences in dietary preferences between the two major groups of the Asian Old World monkeys. The magnitude of such differences is not as great as usullay assumed for the two groups that contain both African and Asian taxa. In other words, the two Asian cercopithecoid groups may have homogenously been shaped by the tectonic modifications and climate alterations in the past five million years. There exist marked differences among the Asian colobines when each of the genera is compared with macaques; the dental profile reflects not only the variation in geographic distribution but also in phylogenetic divergence. Thus, the snub-nosed monkeys （Rhinopithecus） and the gray langurs （Semnopithecus） are characterized by relatively larger molars than the other colobines - larger even than those of the macaques. The differences among Asian colobines, depicted by Euclidean Distances, seems to reflect the relationship of the phylogeny and evolution between colobines and cercopithecines.
基金supported by the Defense Science and Technology Key Laboratory Fund of Luoyang Electro-optical Equipment Institute,Aviation Industry Corporation of China(6142504200108).
文摘The trajectory optimization of an unpowered reentry vehicle via artificial emotion memory optimization(AEMO)is discussed.Firstly,reentry dynamics are established based on multiple constraints and parameterized control variables with finite dimensions are designed.If the constraint is not satisfied,a distance measure and an adaptive penalty function are used to address this scenario.Secondly,AEMO is introduced to solve the trajectory optimization problem.Based on the theories of biology and cognition,the trial solutions based on emotional memory are established.Three search strategies are designed for realizing the random search of trial solutions and for avoiding becoming trapped in a local minimum.The states of the trial solutions are determined according to the rules of memory enhancement and forgetting.As the iterations proceed,the trial solutions with poor quality will gradually be forgotten.Therefore,the number of trial solutions is decreased,and the convergence of the algorithm is accelerated.Finally,a numerical simulation is conducted,and the results demonstrate that the path and terminal constraints are satisfied and the method can realize satisfactory performance.
文摘Plasticity is a natural property of living organisms that is crucial for adaptation and evolution.Over the last decades,the availability of sophisticated neuroimaging techniques（in particular,functional magnetic resonance imaging（f MRI）,and transcranial magnetic stimulation（TMS））,has made it possible to explore in vivo the on-line functioning of brain and its plasticity.However,
