In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the op...In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.展开更多
In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and red...In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.展开更多
The melting process of a phase change material(PCM) inside a capsule can be promising in the thermal management of spacecraft. Such spacecraft operate under various gravity conditions, but previous studies have mostly...The melting process of a phase change material(PCM) inside a capsule can be promising in the thermal management of spacecraft. Such spacecraft operate under various gravity conditions, but previous studies have mostly considered the influence of gravity conditions on the constrained melting process of a PCM and not on its unconstrained melting process. In this study, a numerical model was constructed to comprehensively analyze the constrained and unconstrained melting processes of a PCM inside a spherical capsule under low-gravity conditions. After validation, the model was then applied to investigating the effects of low-gravity conditions on the evolution of velocity, temperature, melt layer thickness, heat transfer, liquid fraction, and total melting time. For the unconstrained melting process, low-gravity conditions weaken buoyancy-driven natural convection and slow down the solid PCM downward trend, thereby limiting the melting rate. In addition, the melt layer thickness does not increase linearly with decreasing gravity. Specifically, the increase in melt layer thickness is smaller by about 1.06 mm when the gravity drops from 0.4g to 0.2g compared to when it drops from 0.2g to 0.1g. The local heat flux in the contact melting area gradually decreases with the reduction of gravity during the unconstrained melting process. During the constrained melting process, notable oscillations in the local heat flux were observed. Decreasing the gravity from g to 0g increased the total melting times of the constrained and unconstrained melting processes by 417% and 621%, respectively.展开更多
Solving constrained multi-objective optimization problems(CMOPs)is a challenging task due to the presence of multiple conflicting objectives and intricate constraints.In order to better address CMOPs and achieve a bal...Solving constrained multi-objective optimization problems(CMOPs)is a challenging task due to the presence of multiple conflicting objectives and intricate constraints.In order to better address CMOPs and achieve a balance between objectives and constraints,existing constrained multi-objective evolutionary algorithms(CMOEAs)predominantly focus on devising various strategies by leveraging the relationships between objectives and constraints,and the designed strategies usually are effective for the problems with simple constraints.However,these methods most ignore the relationship between decision variables and constraints.In fact,the essence of optimization is to find appropriate decision variables to meet various complex constraints.Therefore,it is hoped that the problem can be analyzed from the perspective of decision variables,so as to obtain more excellent results.Based on the above motivation,this paper proposes a decision variables classification approach,according to the relationship between decision variables and constraints,variables are divided into constraint-related(CR)variables and constraintindependent(CI)variables.Consequently,by optimizing these two types of variables independently,the population can sustain a favorable balance between feasibility and diversity.Furthermore,specific offspring generation strategies are proposed for the two categories of decision variables in order to achieve rapid convergence while maintaining population diversity.Experimental results on 31 test problems as well as 20 real-world problems demonstrate that the proposed algorithm is competitive compared to some state-of-the-art constrained multi-objective optimization algorithms.展开更多
Dear Editor,This letter addresses the formation control problem for constrained underactuated autonomous underwater vehicles (AUVs). The feasibility condition of the virtual control law is eliminated by introducing a ...Dear Editor,This letter addresses the formation control problem for constrained underactuated autonomous underwater vehicles (AUVs). The feasibility condition of the virtual control law is eliminated by introducing a nonlinear state dependence function (NSDF) that transforms the state of each AUV in the formation.展开更多
Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectiv...Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.展开更多
Dear Editor,This letter proposes a novel Nash bargaining solution-based multiobjective model predictive control(MPC)scheme to deal with the interaction force control and the path-following problem of the constrained i...Dear Editor,This letter proposes a novel Nash bargaining solution-based multiobjective model predictive control(MPC)scheme to deal with the interaction force control and the path-following problem of the constrained interactive robot.Considering the elastic interaction force model,a mechanical trade-off always exists between the interaction force and position,which means that neither force nor path following can satisfy their desired demands completely.Based on this consideration,two irreconcilable control specifications,the force object function and the position track object function,are proposed,and a new multi-objective MPC scheme is then designed.展开更多
The research,fabrication and development of piezoelectric nanofibrous materials offer effective solutions to the challenges related to energy consumption and non-renewable resources.However,enhancing their electrical ...The research,fabrication and development of piezoelectric nanofibrous materials offer effective solutions to the challenges related to energy consumption and non-renewable resources.However,enhancing their electrical output still remains a significant challenge.Here,a strategy of inducing constrained phase separation on single nanofibers via shear force was proposed.Employing electrospinning technology,a polyacrylonitrile/polyvinylidene difluoride(PAN/PVDF)nanofibrous membrane was fabricated in one step,which enabled simultaneous piezoelectric and triboelectric conversion within a single-layer membrane.Each nanofiber contained independent components of PAN and PVDF and exhibited a rough surface.The abundant frictional contact points formed between these heterogeneous components contributed to an enhanced endogenous triboelectric output,showcasing an excellent synergistic effect of piezoelectric and triboelectric response in the nanofibrous membrane.Additionally,the component mass ratio influenced the microstructure,piezoelectric conformation and piezoelectric performance of the PAN/PVDF nanofibrous membranes.Through comprehensive performance comparison,the optimal mass ratio of PAN to PVDF was determined to be 9∶1.The piezoelectric devices made of the optimal PAN/PVDF nanofibrous membranes with rough nanofiber surfaces generated an output voltage of 20 V,which was about 1.8 times that of the smooth one at the same component mass ratio.The strategy of constrained phase separation on the surface of individual nanofibers provides a new approach to enhance the output performance of single-layer piezoelectric nanofibrous materials.展开更多
Constrained Friction Processing(CFP),a novel friction-based technique,has been developed to efficiently process fine-grained magnesium(Mg)rods,expanding the potential applications of biodegradable Mg alloys in medical...Constrained Friction Processing(CFP),a novel friction-based technique,has been developed to efficiently process fine-grained magnesium(Mg)rods,expanding the potential applications of biodegradable Mg alloys in medical implants.This study investigates the enhancement of mechanical properties through the implementation of multiple pass CFP(MP-CFP)in comparison to the conventional single pass CFP.The results reveal a substantial improvement in compressive yield strength(CYS),ultimate compressive strength,and failure plastic strain by 11%,28%,and 66%,respectively.A comprehensive analysis of material evolution during processing and the effects of the final microstructure on mechanical properties was conducted.The intricate material flow behavior during the final plunge stage of MP-CFP results in a reduced intensity of local basal texture and macrotexture.The diminished intensity of basal texture,combined with a low geometrical compatibility factor at the top of the rod after MP-CFP,effectively impedes slip transfer across grain boundaries.This leads to a local strain gradient along the compression direction,ultimately contributing to the observed enhancement in mechanical properties.The Mg-0.5Zn0.3Ca(wt.%)alloy,after texture modification by MP-CFP,exhibits a competitive CYS compared with other traditional methods,highlighting the promising application potential of MP-CFP.展开更多
Pore structure directly affects the occurrence and migration of shale hydrocarbon,and the lack of research on the mechanism of the pore structure is an important reason for the hindrance of shale hydrocarbon explorati...Pore structure directly affects the occurrence and migration of shale hydrocarbon,and the lack of research on the mechanism of the pore structure is an important reason for the hindrance of shale hydrocarbon exploration.By analysing the geochemistry and reservoir characteristics of Jurassic lacustrine shales in Sichuan Basin,this study recovers their paleoenvironments and further discusses paleoenvironmental constraints on pore structure.The results show that the Lower Jurassic lacustrine shales in the Sichuan Basin are in a warm and humid semi-anoxic to anoxic lake environment with high productivity,a strong stagnant environment,and a rapid sedimentation rate,with water depths ranging from about 11.54-55.22 m,and a mixture of type Ⅱ/Ⅲ kerogen is developed.In terms of reservoir characteristics,they are dominated by open-slit pores,and the pores are relatively complex.The percentage of mesopores is the highest,while the percentage of macropores is the lowest.Further analysis shows that paleoclimate controls the overall pore complexity and surface relaxation of shales by influencing the weathering rate of mother rocks.Paleoredox conditions control the proportion and complexity of shale pores by influencing TOC content.The research results will provide theoretical basis for improving the exploration efficiency of lacustrine shale resources and expanding exploration target areas.展开更多
Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying...Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying prediction uncertainty is hence crucial for robust geoscientific decision-making.This study proposes a novel deep learning framework,the Spatially Constrained Variational Autoencoder(SC-VAE),for denoising geochemical survey data with integrated uncertainty quantification.The SC-VAE incorporates spatial regularization,which enforces spatial coherence by modeling inter-sample relationships directly within the latent space.The performance of the SC-VAE was systematically evaluated against a standard Variational Autoencoder(VAE)using geochemical data from the gold polymetallic district in the northwestern part of Sichuan Province,China.Both models were optimized using Bayesian optimization,with objective functions specifically designed to maintain essential geostatistical characteristics.Evaluation metrics include variogram analysis,quantitative measures of spatial interpolation accuracy,visual assessment of denoised maps,and statistical analysis of data distributions,as well as decomposition of uncertainties.Results show that the SC-VAE achieves superior noise suppression and better preservation of spatial structure compared to the standard VAE,as demonstrated by a significant reduction in the variogram nugget effect and an increased partial sill.The SC-VAE produces denoised maps with clearer anomaly delineation and more regularized data distributions,effectively mitigating outliers and reducing kurtosis.Additionally,it delivers improved interpolation accuracy and spatially explicit uncertainty estimates,facilitating more reliable and interpretable assessments of prediction confidence.The SC-VAE framework thus provides a robust,geostatistically informed solution for enhancing the quality and interpretability of geochemical data,with broad applicability in mineral exploration,environmental geochemistry,and other Earth Science domains.展开更多
Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior knowledge.This is achieved by leveraging the property of adversarial examples.That is,when generated from a surrogate model,they re...Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior knowledge.This is achieved by leveraging the property of adversarial examples.That is,when generated from a surrogate model,they retain their features if applied to other models due to their good transferability.However,adversarial examples often exhibit overfitting,as they are tailored to exploit the particular architecture and feature representation of source models.Consequently,when attempting black-box transfer attacks on different target models,their effectiveness is decreased.To solve this problem,this study proposes an approach based on a Regularized Constrained Feature Layer(RCFL).The proposed method first uses regularization constraints to attenuate the initial examples of low-frequency components.Perturbations are then added to a pre-specified layer of the source model using the back-propagation technique,in order to modify the original adversarial examples.Afterward,a regularized loss function is used to enhance the black-box transferability between different target models.The proposed method is finally tested on the ImageNet,CIFAR-100,and Stanford Car datasets with various target models,The obtained results demonstrate that it achieves a significantly higher transfer-based adversarial attack success rate compared with baseline techniques.展开更多
Switched systems play an imperative role in modeling many real industrial systems with abrupt changes.Due to possible exposure to unreliable and complex physical environments,switching dynamics may simultaneously face...Switched systems play an imperative role in modeling many real industrial systems with abrupt changes.Due to possible exposure to unreliable and complex physical environments,switching dynamics may simultaneously face multiple faults,including the unexpected controller disconnect,the temporary mismatch between subsystems and desired corresponding controllers,and the intermittent disordering of mode transitions.These commonly arising faults may result in severe and detrimental impacts on the reliability and convergence of the closed-loop solution,thereby bringing significant yet challenging issues to be tackled.This paper provides the first attempt to investigate the stabilization problem for a class of constrained switched linear systems with multiple faults under mode-dependent dwell time(MDT).From a set-theory perspective,we demonstrate a critical necessary and sufficient stability condition for switched systems without uncertainties.Moreover,the non-conservative stability criterion is further extended to the perturbed switched systems with rigorous proof.A switching communication network example verifies the validity of the theoretical result and demonstrates their advantages.展开更多
The electricity-hydrogen integrated energy system(EH-IES)enables synergistic operation of electricity,heat,and hydrogen subsystems,supporting renewable energy integration and efficient multi-energy utilization in futu...The electricity-hydrogen integrated energy system(EH-IES)enables synergistic operation of electricity,heat,and hydrogen subsystems,supporting renewable energy integration and efficient multi-energy utilization in future low carbon societies.However,uncertainties from renewable energy and load variability threaten system safety and economy.Conventional chance-constrained programming(CCP)ensures reliable operation by limiting risk.However,increasing source-load uncertainties that can render CCP models infeasible and exacerbate operational risks.To address this,this paper proposes a risk-adjustable chance-constrained goal programming(RACCGP)model,integrating CCP and goal programming to balance risk and cost based on system risk assessment.An intelligent nonlinear goal programming method based on the state transition algorithm(STA)is developed,along with an improved discretized step transformation,to handle model nonlinearity and enhance computational efficiency.Experimental results show that the proposed model reduces costs while controlling risk compared to traditional CCP,and the solution method outperforms average sample sampling in efficiency and solution quality.展开更多
This paper addresses the time-varying formation-containment(FC) problem for nonholonomic multi-agent systems with a desired trajectory constraint, where only the leaders can acquire information about the desired traje...This paper addresses the time-varying formation-containment(FC) problem for nonholonomic multi-agent systems with a desired trajectory constraint, where only the leaders can acquire information about the desired trajectory. Input the fixed time-varying formation template to the leader and start executing, this process also needs to track the desired trajectory, and the follower needs to converge to the convex hull that the leader crosses. Firstly, the dynamic models of nonholonomic systems are linearized to second-order dynamics. Then, based on the desired trajectory and formation template, the FC control protocols are proposed. Sufficient conditions to achieve FC are introduced and an algorithm is proposed to resolve the control parameters by solving an algebraic Riccati equation. The system is demonstrated to achieve FC, with the average position and velocity of the leaders converging asymptotically to the desired trajectory. Finally, the theoretical achievements are verified in simulations by a multi-agent system composed of virtual human individuals.展开更多
The shear wave(S-wave)velocity is a critical rock elastic parameter in shale reservoirs,especially for evaluating shale fracability.To effectively supplement S-wave velocity under the condition of no actual measuremen...The shear wave(S-wave)velocity is a critical rock elastic parameter in shale reservoirs,especially for evaluating shale fracability.To effectively supplement S-wave velocity under the condition of no actual measurement data,this paper proposes a physically-data driven method for the S-wave velocity prediction in shale reservoirs based on the class activation mapping(CAM)technique combined with a physically constrained two-dimensional Convolutional Neural Network(2D-CNN).High-sensitivity log curves related to S-wave velocity are selected as the basis from the data sensitivity analysis.Then,we establish a petrophysical model of complex multi-mineral components based on the petrophysical properties of porous medium and the Biot-Gassmann equation.This model can help reduce the dispersion effect and constrain the 2D-CNN.In deep learning,the 2D-CNN model is optimized using the Adam,and the class activation maps(CAMs)are obtained by replacing the fully connected layer with the global average pooling(GAP)layer,resulting in explainable results.The model is then applied to wells A,B1,and B2 in the southern Songliao Basin,China and compared with the unconstrained model and the petrophysical model.The results show higher prediction accuracy and generalization ability,as evidenced by correlation coefficients and relative errors of 0.98 and 2.14%,0.97 and 2.35%,0.96 and 2.89%in the three test wells,respectively.Finally,we present the defined C-factor as a means of evaluating the extent of concern regarding CAMs in regression problems.When the results of the petrophysical model are added to the 2D feature maps,the C-factor values are significantly increased,indicating that the focus of 2D-CNN can be significantly enhanced by incorporating the petrophysical model,thereby imposing physical constraints on the 2D-CNN.In addition,we establish the SHAP model,and the results of the petrophysical model have the highest average SHAP values across the three test wells.This helps to assist in proving the importance of constraints.展开更多
Sentiment analysis,a cornerstone of natural language processing,has witnessed remarkable advancements driven by deep learning models which demonstrated impressive accuracy in discerning sentiment from text across vari...Sentiment analysis,a cornerstone of natural language processing,has witnessed remarkable advancements driven by deep learning models which demonstrated impressive accuracy in discerning sentiment from text across various domains.However,the deployment of such models in resource-constrained environments presents a unique set of challenges that require innovative solutions.Resource-constrained environments encompass scenarios where computing resources,memory,and energy availability are restricted.To empower sentiment analysis in resource-constrained environments,we address the crucial need by leveraging lightweight pre-trained models.These models,derived from popular architectures such as DistilBERT,MobileBERT,ALBERT,TinyBERT,ELECTRA,and SqueezeBERT,offer a promising solution to the resource limitations imposed by these environments.By distilling the knowledge from larger models into smaller ones and employing various optimization techniques,these lightweight models aim to strike a balance between performance and resource efficiency.This paper endeavors to explore the performance of multiple lightweight pre-trained models in sentiment analysis tasks specific to such environments and provide insights into their viability for practical deployment.展开更多
This paper investigates a class of constrained distributed zeroth-order optimization(ZOO) problems over timevarying unbalanced graphs while ensuring privacy preservation among individual agents. Not taking into accoun...This paper investigates a class of constrained distributed zeroth-order optimization(ZOO) problems over timevarying unbalanced graphs while ensuring privacy preservation among individual agents. Not taking into account recent progress and addressing these concerns separately, there remains a lack of solutions offering theoretical guarantees for both privacy protection and constrained ZOO over time-varying unbalanced graphs.We hereby propose a novel algorithm, termed the differential privacy(DP) distributed push-sum based zeroth-order constrained optimization algorithm(DP-ZOCOA). Operating over time-varying unbalanced graphs, DP-ZOCOA obviates the need for supplemental suboptimization problem computations, thereby reducing overhead in comparison to distributed primary-dual methods. DP-ZOCOA is specifically tailored to tackle constrained ZOO problems over time-varying unbalanced graphs,offering a guarantee of convergence to the optimal solution while robustly preserving privacy. Moreover, we provide rigorous proofs of convergence and privacy for DP-ZOCOA, underscoring its efficacy in attaining optimal convergence without constraints. To enhance its applicability, we incorporate DP-ZOCOA into the federated learning framework and formulate a decentralized zeroth-order constrained federated learning algorithm(ZOCOA-FL) to address challenges stemming from the timevarying imbalance of communication topology. Finally, the performance and effectiveness of the proposed algorithms are thoroughly evaluated through simulations on distributed least squares(DLS) and decentralized federated learning(DFL) tasks.展开更多
Altermagnetism,a recently identified class of collinear magnetism,combines key features of antiferromagnets and ferromagnets.Despite having zero net magnetization,altermagnetic materials exhibit anomalous transport ef...Altermagnetism,a recently identified class of collinear magnetism,combines key features of antiferromagnets and ferromagnets.Despite having zero net magnetization,altermagnetic materials exhibit anomalous transport effects,including the anomalous Hall,Nernst,and thermal Hall effects,as well as magneto-optical Kerr and Faraday effects.These phenomena,previously thought unique to ferromagnets,are dictated by symmetry,as confirmed by density functional theory(DFT)calculations.However,an effective model-based approach to verify these symmetry constraints remains unavailable.In this Letter,we construct a k·ρ model for d-wave altermagnets CuX_(2)(X=F,Cl)using spin space group representations and apply it to calculate the anomalous Hall effect.The symmetry-imposed transport properties predicted by the model are in agreement with the DFT results,providing a foundation for further investigation into symmetry-restricted transport phenomena in altermagnetic materials.展开更多
In recent years,numerous recurrent neural network(RNN)models have been reported for solving time-dependent nonlinear optimization problems.However,few existing RNN models simultaneously involve nonlinear equality cons...In recent years,numerous recurrent neural network(RNN)models have been reported for solving time-dependent nonlinear optimization problems.However,few existing RNN models simultaneously involve nonlinear equality constraints,direct discretization,and noise suppression.This limitation presents challenges when existing models are applied to practical engineering problems.Additionally,most current discrete-time RNN models are derived from continuous-time models,which may not perform well for solving essentially discrete problems.To handle these issues,a robust direct-discretized RNN(RDD-RNN)model is proposed to efficiently realize time-dependent optimization constrained by nonlinear equalities(TDOCNE)in the presence of various time-dependent noises.Theoretical analyses are provided to reveal that the proposed RDD-RNN model possesses excellent convergence and noise-suppressing capability.Furthermore,numerical experiments and manipulator control instances are conducted and analyzed to validate the superior robustness of the proposed RDD-RNN model under various time-dependent noises,particularly quadratic polynomial noise.Eventually,small target detection experiments further demonstrate the practicality of the RDD-RNN model in image processing applications.展开更多
基金Supported by the National Natural Science Foundation of China(12071133)Natural Science Foundation of Henan Province(252300421993)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B110005)。
文摘In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.
基金supported by the National Natural Science Foundation of China under Grant No.61972040the Science and Technology Research and Development Project funded by China Railway Material Trade Group Luban Company.
文摘In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.
基金supported by the National Natural Science Foundation of China (Grant No.52376181)。
文摘The melting process of a phase change material(PCM) inside a capsule can be promising in the thermal management of spacecraft. Such spacecraft operate under various gravity conditions, but previous studies have mostly considered the influence of gravity conditions on the constrained melting process of a PCM and not on its unconstrained melting process. In this study, a numerical model was constructed to comprehensively analyze the constrained and unconstrained melting processes of a PCM inside a spherical capsule under low-gravity conditions. After validation, the model was then applied to investigating the effects of low-gravity conditions on the evolution of velocity, temperature, melt layer thickness, heat transfer, liquid fraction, and total melting time. For the unconstrained melting process, low-gravity conditions weaken buoyancy-driven natural convection and slow down the solid PCM downward trend, thereby limiting the melting rate. In addition, the melt layer thickness does not increase linearly with decreasing gravity. Specifically, the increase in melt layer thickness is smaller by about 1.06 mm when the gravity drops from 0.4g to 0.2g compared to when it drops from 0.2g to 0.1g. The local heat flux in the contact melting area gradually decreases with the reduction of gravity during the unconstrained melting process. During the constrained melting process, notable oscillations in the local heat flux were observed. Decreasing the gravity from g to 0g increased the total melting times of the constrained and unconstrained melting processes by 417% and 621%, respectively.
基金supported in part by the National Natural Science Foundation of China(U23A20340,62176238,62476254,62106230)the Key Research and Development Projects of the Ministry of Science and Technology of China(2022YFD2001200)+3 种基金the Natural Science Foundation Project of Henan Province(242300420277)the Key Research and Development Program of Henan(251111113900)the Frontier Exploration Projects of Longmen Laboratory(LMQYTSKT031)Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Open Fund Project(BDIC-2023-B-005).
文摘Solving constrained multi-objective optimization problems(CMOPs)is a challenging task due to the presence of multiple conflicting objectives and intricate constraints.In order to better address CMOPs and achieve a balance between objectives and constraints,existing constrained multi-objective evolutionary algorithms(CMOEAs)predominantly focus on devising various strategies by leveraging the relationships between objectives and constraints,and the designed strategies usually are effective for the problems with simple constraints.However,these methods most ignore the relationship between decision variables and constraints.In fact,the essence of optimization is to find appropriate decision variables to meet various complex constraints.Therefore,it is hoped that the problem can be analyzed from the perspective of decision variables,so as to obtain more excellent results.Based on the above motivation,this paper proposes a decision variables classification approach,according to the relationship between decision variables and constraints,variables are divided into constraint-related(CR)variables and constraintindependent(CI)variables.Consequently,by optimizing these two types of variables independently,the population can sustain a favorable balance between feasibility and diversity.Furthermore,specific offspring generation strategies are proposed for the two categories of decision variables in order to achieve rapid convergence while maintaining population diversity.Experimental results on 31 test problems as well as 20 real-world problems demonstrate that the proposed algorithm is competitive compared to some state-of-the-art constrained multi-objective optimization algorithms.
基金supported by the National Natural Science Foundation of China(62073094)the Fundamental Research Funds for the Central Universities(3072024GH0404)
文摘Dear Editor,This letter addresses the formation control problem for constrained underactuated autonomous underwater vehicles (AUVs). The feasibility condition of the virtual control law is eliminated by introducing a nonlinear state dependence function (NSDF) that transforms the state of each AUV in the formation.
基金supported in part by the National Natural Science Foundation of China(62173255,62188101)Shenzhen Key Laboratory of Control Theory and Intelligent Systems(ZDSYS20220330161800001)
文摘Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.
基金supported by the National Natural Science Foundation of China(62303095)the Natural Science Foundation of Sichuan Province(2023NSFSC0872).
文摘Dear Editor,This letter proposes a novel Nash bargaining solution-based multiobjective model predictive control(MPC)scheme to deal with the interaction force control and the path-following problem of the constrained interactive robot.Considering the elastic interaction force model,a mechanical trade-off always exists between the interaction force and position,which means that neither force nor path following can satisfy their desired demands completely.Based on this consideration,two irreconcilable control specifications,the force object function and the position track object function,are proposed,and a new multi-objective MPC scheme is then designed.
基金National Natural Science Foundation of China(No.52373281)National Energy-Saving and Low-Carbon Materials Production and Application Demonstration Platform Program,China(No.TC220H06N)。
文摘The research,fabrication and development of piezoelectric nanofibrous materials offer effective solutions to the challenges related to energy consumption and non-renewable resources.However,enhancing their electrical output still remains a significant challenge.Here,a strategy of inducing constrained phase separation on single nanofibers via shear force was proposed.Employing electrospinning technology,a polyacrylonitrile/polyvinylidene difluoride(PAN/PVDF)nanofibrous membrane was fabricated in one step,which enabled simultaneous piezoelectric and triboelectric conversion within a single-layer membrane.Each nanofiber contained independent components of PAN and PVDF and exhibited a rough surface.The abundant frictional contact points formed between these heterogeneous components contributed to an enhanced endogenous triboelectric output,showcasing an excellent synergistic effect of piezoelectric and triboelectric response in the nanofibrous membrane.Additionally,the component mass ratio influenced the microstructure,piezoelectric conformation and piezoelectric performance of the PAN/PVDF nanofibrous membranes.Through comprehensive performance comparison,the optimal mass ratio of PAN to PVDF was determined to be 9∶1.The piezoelectric devices made of the optimal PAN/PVDF nanofibrous membranes with rough nanofiber surfaces generated an output voltage of 20 V,which was about 1.8 times that of the smooth one at the same component mass ratio.The strategy of constrained phase separation on the surface of individual nanofibers provides a new approach to enhance the output performance of single-layer piezoelectric nanofibrous materials.
基金Ting Chen thanks the China Scholarship Council for the Award of a Fellowship(No.202006230137)Benjamin Klusemann ac-knowledges funding by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)-project number 544306307+1 种基金Banglong Fu acknowledge the financial support of the National Natural Science Foundation of China(Grant No.52405386)State Key Laboratory of Precision Welding&Joining of Materials and Structures(Grant No.MSWJ-24M13).
文摘Constrained Friction Processing(CFP),a novel friction-based technique,has been developed to efficiently process fine-grained magnesium(Mg)rods,expanding the potential applications of biodegradable Mg alloys in medical implants.This study investigates the enhancement of mechanical properties through the implementation of multiple pass CFP(MP-CFP)in comparison to the conventional single pass CFP.The results reveal a substantial improvement in compressive yield strength(CYS),ultimate compressive strength,and failure plastic strain by 11%,28%,and 66%,respectively.A comprehensive analysis of material evolution during processing and the effects of the final microstructure on mechanical properties was conducted.The intricate material flow behavior during the final plunge stage of MP-CFP results in a reduced intensity of local basal texture and macrotexture.The diminished intensity of basal texture,combined with a low geometrical compatibility factor at the top of the rod after MP-CFP,effectively impedes slip transfer across grain boundaries.This leads to a local strain gradient along the compression direction,ultimately contributing to the observed enhancement in mechanical properties.The Mg-0.5Zn0.3Ca(wt.%)alloy,after texture modification by MP-CFP,exhibits a competitive CYS compared with other traditional methods,highlighting the promising application potential of MP-CFP.
基金supported from the Opening fund of State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development(33550000-22-ZC0613-0297)National Natural Science Foundation of China(42102196)the Natural Science Basis Research Plan in Shaanxi Province of China(2022JM-147).
文摘Pore structure directly affects the occurrence and migration of shale hydrocarbon,and the lack of research on the mechanism of the pore structure is an important reason for the hindrance of shale hydrocarbon exploration.By analysing the geochemistry and reservoir characteristics of Jurassic lacustrine shales in Sichuan Basin,this study recovers their paleoenvironments and further discusses paleoenvironmental constraints on pore structure.The results show that the Lower Jurassic lacustrine shales in the Sichuan Basin are in a warm and humid semi-anoxic to anoxic lake environment with high productivity,a strong stagnant environment,and a rapid sedimentation rate,with water depths ranging from about 11.54-55.22 m,and a mixture of type Ⅱ/Ⅲ kerogen is developed.In terms of reservoir characteristics,they are dominated by open-slit pores,and the pores are relatively complex.The percentage of mesopores is the highest,while the percentage of macropores is the lowest.Further analysis shows that paleoclimate controls the overall pore complexity and surface relaxation of shales by influencing the weathering rate of mother rocks.Paleoredox conditions control the proportion and complexity of shale pores by influencing TOC content.The research results will provide theoretical basis for improving the exploration efficiency of lacustrine shale resources and expanding exploration target areas.
基金supported by the National Natural Science Foundation of China(Nos.42530801,42425208)the Natural Science Foundation of Hubei Province(China)(No.2023AFA001)+1 种基金the MOST Special Fund from State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences(No.MSFGPMR2025-401)the China Scholarship Council(No.202306410181)。
文摘Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying prediction uncertainty is hence crucial for robust geoscientific decision-making.This study proposes a novel deep learning framework,the Spatially Constrained Variational Autoencoder(SC-VAE),for denoising geochemical survey data with integrated uncertainty quantification.The SC-VAE incorporates spatial regularization,which enforces spatial coherence by modeling inter-sample relationships directly within the latent space.The performance of the SC-VAE was systematically evaluated against a standard Variational Autoencoder(VAE)using geochemical data from the gold polymetallic district in the northwestern part of Sichuan Province,China.Both models were optimized using Bayesian optimization,with objective functions specifically designed to maintain essential geostatistical characteristics.Evaluation metrics include variogram analysis,quantitative measures of spatial interpolation accuracy,visual assessment of denoised maps,and statistical analysis of data distributions,as well as decomposition of uncertainties.Results show that the SC-VAE achieves superior noise suppression and better preservation of spatial structure compared to the standard VAE,as demonstrated by a significant reduction in the variogram nugget effect and an increased partial sill.The SC-VAE produces denoised maps with clearer anomaly delineation and more regularized data distributions,effectively mitigating outliers and reducing kurtosis.Additionally,it delivers improved interpolation accuracy and spatially explicit uncertainty estimates,facilitating more reliable and interpretable assessments of prediction confidence.The SC-VAE framework thus provides a robust,geostatistically informed solution for enhancing the quality and interpretability of geochemical data,with broad applicability in mineral exploration,environmental geochemistry,and other Earth Science domains.
基金supported by the Intelligent Policing Key Laboratory of Sichuan Province(No.ZNJW2022KFZD002)This work was supported by the Scientific and Technological Research Program of Chongqing Municipal Education Commission(Grant Nos.KJQN202302403,KJQN202303111).
文摘Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior knowledge.This is achieved by leveraging the property of adversarial examples.That is,when generated from a surrogate model,they retain their features if applied to other models due to their good transferability.However,adversarial examples often exhibit overfitting,as they are tailored to exploit the particular architecture and feature representation of source models.Consequently,when attempting black-box transfer attacks on different target models,their effectiveness is decreased.To solve this problem,this study proposes an approach based on a Regularized Constrained Feature Layer(RCFL).The proposed method first uses regularization constraints to attenuate the initial examples of low-frequency components.Perturbations are then added to a pre-specified layer of the source model using the back-propagation technique,in order to modify the original adversarial examples.Afterward,a regularized loss function is used to enhance the black-box transferability between different target models.The proposed method is finally tested on the ImageNet,CIFAR-100,and Stanford Car datasets with various target models,The obtained results demonstrate that it achieves a significantly higher transfer-based adversarial attack success rate compared with baseline techniques.
基金supported in part by the Natural Sciences and Engineering Research Council of Canada(NSERC)supported by the National Natural Science Foundation of China under Grant 62303403Zhejiang Provincial Natural Science Foundation of China under Grants LR25F030004 and LQ24F030022。
文摘Switched systems play an imperative role in modeling many real industrial systems with abrupt changes.Due to possible exposure to unreliable and complex physical environments,switching dynamics may simultaneously face multiple faults,including the unexpected controller disconnect,the temporary mismatch between subsystems and desired corresponding controllers,and the intermittent disordering of mode transitions.These commonly arising faults may result in severe and detrimental impacts on the reliability and convergence of the closed-loop solution,thereby bringing significant yet challenging issues to be tackled.This paper provides the first attempt to investigate the stabilization problem for a class of constrained switched linear systems with multiple faults under mode-dependent dwell time(MDT).From a set-theory perspective,we demonstrate a critical necessary and sufficient stability condition for switched systems without uncertainties.Moreover,the non-conservative stability criterion is further extended to the perturbed switched systems with rigorous proof.A switching communication network example verifies the validity of the theoretical result and demonstrates their advantages.
基金Project(2022YFC2904502)supported by the National Key Research and Development Program of ChinaProject(62273357)supported by the National Natural Science Foundation of China。
文摘The electricity-hydrogen integrated energy system(EH-IES)enables synergistic operation of electricity,heat,and hydrogen subsystems,supporting renewable energy integration and efficient multi-energy utilization in future low carbon societies.However,uncertainties from renewable energy and load variability threaten system safety and economy.Conventional chance-constrained programming(CCP)ensures reliable operation by limiting risk.However,increasing source-load uncertainties that can render CCP models infeasible and exacerbate operational risks.To address this,this paper proposes a risk-adjustable chance-constrained goal programming(RACCGP)model,integrating CCP and goal programming to balance risk and cost based on system risk assessment.An intelligent nonlinear goal programming method based on the state transition algorithm(STA)is developed,along with an improved discretized step transformation,to handle model nonlinearity and enhance computational efficiency.Experimental results show that the proposed model reduces costs while controlling risk compared to traditional CCP,and the solution method outperforms average sample sampling in efficiency and solution quality.
文摘This paper addresses the time-varying formation-containment(FC) problem for nonholonomic multi-agent systems with a desired trajectory constraint, where only the leaders can acquire information about the desired trajectory. Input the fixed time-varying formation template to the leader and start executing, this process also needs to track the desired trajectory, and the follower needs to converge to the convex hull that the leader crosses. Firstly, the dynamic models of nonholonomic systems are linearized to second-order dynamics. Then, based on the desired trajectory and formation template, the FC control protocols are proposed. Sufficient conditions to achieve FC are introduced and an algorithm is proposed to resolve the control parameters by solving an algebraic Riccati equation. The system is demonstrated to achieve FC, with the average position and velocity of the leaders converging asymptotically to the desired trajectory. Finally, the theoretical achievements are verified in simulations by a multi-agent system composed of virtual human individuals.
基金supported by the National Natural Science Foundation of China(Nos.42374150,42374152)Natural Science Foundation of Shandong Province(ZR2020MD050).
文摘The shear wave(S-wave)velocity is a critical rock elastic parameter in shale reservoirs,especially for evaluating shale fracability.To effectively supplement S-wave velocity under the condition of no actual measurement data,this paper proposes a physically-data driven method for the S-wave velocity prediction in shale reservoirs based on the class activation mapping(CAM)technique combined with a physically constrained two-dimensional Convolutional Neural Network(2D-CNN).High-sensitivity log curves related to S-wave velocity are selected as the basis from the data sensitivity analysis.Then,we establish a petrophysical model of complex multi-mineral components based on the petrophysical properties of porous medium and the Biot-Gassmann equation.This model can help reduce the dispersion effect and constrain the 2D-CNN.In deep learning,the 2D-CNN model is optimized using the Adam,and the class activation maps(CAMs)are obtained by replacing the fully connected layer with the global average pooling(GAP)layer,resulting in explainable results.The model is then applied to wells A,B1,and B2 in the southern Songliao Basin,China and compared with the unconstrained model and the petrophysical model.The results show higher prediction accuracy and generalization ability,as evidenced by correlation coefficients and relative errors of 0.98 and 2.14%,0.97 and 2.35%,0.96 and 2.89%in the three test wells,respectively.Finally,we present the defined C-factor as a means of evaluating the extent of concern regarding CAMs in regression problems.When the results of the petrophysical model are added to the 2D feature maps,the C-factor values are significantly increased,indicating that the focus of 2D-CNN can be significantly enhanced by incorporating the petrophysical model,thereby imposing physical constraints on the 2D-CNN.In addition,we establish the SHAP model,and the results of the petrophysical model have the highest average SHAP values across the three test wells.This helps to assist in proving the importance of constraints.
文摘Sentiment analysis,a cornerstone of natural language processing,has witnessed remarkable advancements driven by deep learning models which demonstrated impressive accuracy in discerning sentiment from text across various domains.However,the deployment of such models in resource-constrained environments presents a unique set of challenges that require innovative solutions.Resource-constrained environments encompass scenarios where computing resources,memory,and energy availability are restricted.To empower sentiment analysis in resource-constrained environments,we address the crucial need by leveraging lightweight pre-trained models.These models,derived from popular architectures such as DistilBERT,MobileBERT,ALBERT,TinyBERT,ELECTRA,and SqueezeBERT,offer a promising solution to the resource limitations imposed by these environments.By distilling the knowledge from larger models into smaller ones and employing various optimization techniques,these lightweight models aim to strike a balance between performance and resource efficiency.This paper endeavors to explore the performance of multiple lightweight pre-trained models in sentiment analysis tasks specific to such environments and provide insights into their viability for practical deployment.
基金supported in part by the National Key Research and Development Program of China(2022ZD0120001)the National Natural Science Foundation of China(62233004,62273090,62073076)the Jiangsu Provincial Scientific Research Center of Applied Mathematics(BK20233002)
文摘This paper investigates a class of constrained distributed zeroth-order optimization(ZOO) problems over timevarying unbalanced graphs while ensuring privacy preservation among individual agents. Not taking into account recent progress and addressing these concerns separately, there remains a lack of solutions offering theoretical guarantees for both privacy protection and constrained ZOO over time-varying unbalanced graphs.We hereby propose a novel algorithm, termed the differential privacy(DP) distributed push-sum based zeroth-order constrained optimization algorithm(DP-ZOCOA). Operating over time-varying unbalanced graphs, DP-ZOCOA obviates the need for supplemental suboptimization problem computations, thereby reducing overhead in comparison to distributed primary-dual methods. DP-ZOCOA is specifically tailored to tackle constrained ZOO problems over time-varying unbalanced graphs,offering a guarantee of convergence to the optimal solution while robustly preserving privacy. Moreover, we provide rigorous proofs of convergence and privacy for DP-ZOCOA, underscoring its efficacy in attaining optimal convergence without constraints. To enhance its applicability, we incorporate DP-ZOCOA into the federated learning framework and formulate a decentralized zeroth-order constrained federated learning algorithm(ZOCOA-FL) to address challenges stemming from the timevarying imbalance of communication topology. Finally, the performance and effectiveness of the proposed algorithms are thoroughly evaluated through simulations on distributed least squares(DLS) and decentralized federated learning(DFL) tasks.
基金supported by the National Natural Science Foundation of China(Grant No.12274117)the Natural Science Foundation of Henan(Grant No.242300421214)+4 种基金the Program for Innovative Research Team(in Science and Technology)in the University of Henan Province(Grant No.24IRTSTHN025)the Open Fund of Guangdong Provincial Key Laboratory of Nanophotonic Manipulation(No.202502)Guangdong S&T Program(No.2023B1212010008)the High-Performance Computing Center of Henan Normal Universitysupported by the U.S.DOE,Office of Science(Grant No.DE-FG02-05ER46237)。
文摘Altermagnetism,a recently identified class of collinear magnetism,combines key features of antiferromagnets and ferromagnets.Despite having zero net magnetization,altermagnetic materials exhibit anomalous transport effects,including the anomalous Hall,Nernst,and thermal Hall effects,as well as magneto-optical Kerr and Faraday effects.These phenomena,previously thought unique to ferromagnets,are dictated by symmetry,as confirmed by density functional theory(DFT)calculations.However,an effective model-based approach to verify these symmetry constraints remains unavailable.In this Letter,we construct a k·ρ model for d-wave altermagnets CuX_(2)(X=F,Cl)using spin space group representations and apply it to calculate the anomalous Hall effect.The symmetry-imposed transport properties predicted by the model are in agreement with the DFT results,providing a foundation for further investigation into symmetry-restricted transport phenomena in altermagnetic materials.
基金supported in part by the National Key Research and Development Program of China(2023YFC3011100)the National Natural Science Foundation of China(62476294)+1 种基金the Science and Technology Planning Project of Guangdong Province,China(2021B1212040017)the Guangdong Basic and Applied Basic Research Foundation(2025A1515010377,2023A1515110697).
文摘In recent years,numerous recurrent neural network(RNN)models have been reported for solving time-dependent nonlinear optimization problems.However,few existing RNN models simultaneously involve nonlinear equality constraints,direct discretization,and noise suppression.This limitation presents challenges when existing models are applied to practical engineering problems.Additionally,most current discrete-time RNN models are derived from continuous-time models,which may not perform well for solving essentially discrete problems.To handle these issues,a robust direct-discretized RNN(RDD-RNN)model is proposed to efficiently realize time-dependent optimization constrained by nonlinear equalities(TDOCNE)in the presence of various time-dependent noises.Theoretical analyses are provided to reveal that the proposed RDD-RNN model possesses excellent convergence and noise-suppressing capability.Furthermore,numerical experiments and manipulator control instances are conducted and analyzed to validate the superior robustness of the proposed RDD-RNN model under various time-dependent noises,particularly quadratic polynomial noise.Eventually,small target detection experiments further demonstrate the practicality of the RDD-RNN model in image processing applications.
