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PARETO FRONT CAPTURE USING DETERMINISTIC OPTIMIZATION METHODS IN MULTI-CRITERION AERODYNAMIC DESIGN 认领 引用 被引量：1
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作者 唐智礼 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2006年第2期81-86,共6页
Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary a... Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary algorithms （EAs） and the Pareto front concept are used to solve practical design problems in industry for its robustness in capturing convex, concave, discrete or discontinuous Pareto fronts of multi-objective optimization problems. However, the process is time-consuming. Therefore, deterministic optimization methods are introduced to capture the Pareto front, and the types of the captured Pareto front are explained. Numerical experiments show that the deterministic optimization method is a good alternative to EAs for capturing any convex and some concave Pareto fronts in multi-criterion aerodynamic optimization problems due to its efficiency. 展开更多
关键词 multi-criterion design Pareto front deterministic optimization methods aerodynamics
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Analysis of Effective Optimization Methods for Building Construction Technology Management 认领 引用
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作者 Zongbing Lu 《Frontiers Research of Architecture and Engineering》 2019年第3期19-22,共4页
During the construction process of the construction project,the construction technology management work can improve the overall quality of the project construction.In the context of increasingly fierce competition in ... During the construction process of the construction project,the construction technology management work can improve the overall quality of the project construction.In the context of increasingly fierce competition in the construction market,construction enterprises should strengthen the management of construction technology,enhance their technical level and market competitiveness,and promote the development of the construction market[1].The paper mainly analyzes the optimization methods of build-Keywords:ing construction technology management. 展开更多
关键词 Construction engineering Construction technology management Optimization methods
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AI Model Compression Methods: A Distribution-Aware Residual Entropy Quantization 认领 引用
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作者 Nikita Sakovich Dmitry Aksenov +1 位作者 Ekaterina Pleshakova Sergey Gataullin 《Computers, Materials & Continua》 SCIE EI 2026年第8期854-864,共11页
We introduce the DARE-Q(Distribution-Aware Residual Entropy Quantization)method—a post-training quantization method for neural network weights designed to reduce bit-width with minimal degradation of model quality.Un... We introduce the DARE-Q(Distribution-Aware Residual Entropy Quantization)method—a post-training quantization method for neural network weights designed to reduce bit-width with minimal degradation of model quality.Unlike traditional approaches that solely optimize the mean squared error of weight approximation,DARE-Q additionally considers the entropy of the quantization residual,allowing for control over the statistical properties of the resulting error.The method is based on channel-wise symmetric uniform quantization with scaling based on a combined loss function that includes L2 distortion and entropy regularization.The DARE-Q method is implemented as a compact DAREQuantLinear module which can be easily integrated into standard transformer pipelines without changing the inference logic or using specific kernels.The experimental analysis was conducted on the language models facebook/opt-125m and facebook/opt-350m,which contain approximately 125 and 350 million parameters.The quality of the models was assessed using the standard perplexity metric(PPL)computed on the wikitext-2-raw-v1 dataset.DARE-Q is completely data-free and does not require model retraining or calibration data,which makes it the only viable option in privacy-sensitive or confidential environments where access to the original training data is restricted—precisely the setting where methods such as GPTQ and AWQ cannot be applied.The observed increase in PPL relative to data-dependent baselines reflects this fundamental trade-off rather than a shortcoming of the approach.By leveraging per-channel scale selection and a combined loss function,DARE-Q provides a flexible trade-off between approximation accuracy and quantization error structure,creating an attractive algorithmic basis for further improvement of model compression methods. 展开更多
关键词 Artificial intelligence large language models mathematical optimization methods model compression quantization methods information theory high-performance computing
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Erratum to: Deep learning technique based efficient optimizationmethod for cone dielectric energy generator 认领 引用
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作者 Demin Zhao Aoyu Xiao +4 位作者 Zewen Gu Menghang Chen Guoqiang Xu Bin Zhao Jianlin Liu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期142-142,共1页
The name of Bin Zhao in the original published Chinese section is“赵斌”,which should be corrected to“赵彬”.
关键词 deep learning bin zhao cone dielectric energy generator optimization method
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Optimization of Thermoplastic Elastomer (TPE) Components for Aerospace Structures Using Computerized Data-Driven Design 认领 引用
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作者 Adwaa Mohammed Abdulmajeed Duaa Abdul Rida Musa +2 位作者 Ola Abdul Hussain Emad Kadum Njim Royal Madan 《Computers, Materials & Continua》 SCIE EI 2026年第6期766-794,共29页
A data-driven optimization framework that integrates machine learning surrogate models,finite element analysis(FEA),and a multi-objective optimization algorithm is used in this study for developing thermoplastic elast... A data-driven optimization framework that integrates machine learning surrogate models,finite element analysis(FEA),and a multi-objective optimization algorithm is used in this study for developing thermoplastic elastomer(TPE)parts for aerospace applications.By using FEA simulations and experiments,a database of input design parameters(e.g.,geometry and structural shape modifier)is generated.Afterwards,we train surrogate models(e.g.,Gaussian Process Regression,neural networks)to approximate mappings from design space to performance space.Finally,we propose Pareto-optimal TPE designs using the surrogate embedded in a multi-objective optimization loop(such as NSGA-Ⅱ or gradient-based methods).The novelty of this approach is demonstrated by employing highly simplified surrogate models,including an artificial neural network(ANN)with 10 hidden neurons trained on analytically generated synthetic data.The proposed methodology has been validated using an aerospace-related case study:a vibration-damping plate.Compared with the baseline configuration,Pareto-optimal designs identified by the proposed framework achieved a reduction in maximum deflection of 23%-28%and a reduction in von Mises stress of 18%-24%,depending on the selected trade-off solution,as the number of full FEA simulations required for optimization was reduced from 500 to 50.This framework enables faster design of TPE components for aerospace systems.Validation against high-fidelity ANSYS simulations showed a mean error of~1.18%and a maximum deviation of~2.6%. 展开更多
关键词 TPE aerospace structures surrogate modelling data-driven optimization multi-objective optimization method FEM
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Robust Pose Graph Optimization Against Outliers Using Consistency Credibility Factor 认领 引用
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作者 Jie Cai Guoliang Wei +1 位作者 Wangyan Li Yaolei Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第5期1044-1046,共3页
Dear Editor,Pose graph optimization(PGO)is a popular optimization approach that plays a crucial role in the simultaneous localization and mapping(SLAM)back-end.However,when incorrect loop closure constraints(referred ... Dear Editor,Pose graph optimization(PGO)is a popular optimization approach that plays a crucial role in the simultaneous localization and mapping(SLAM)back-end.However,when incorrect loop closure constraints(referred to as outliers)are present in the SLAM front-end,the standard PGO algorithm fails catastrophically and can not return an accurate map.To address this issue,this letter proposes a novel algorithm that leverages classical optimization methods to effectively handle outliers.The proposed algorithm introduces a new formulation that incorporates a credibility factor model,which improves the robustness of the optimization process.Additionally,an innovative consistency classification algorithm is developed to detect outliers.Extensive experiments are conducted on multiple benchmark datasets to evaluate the consistency and accuracy of the proposed algorithm. 展开更多
关键词 graph optimization pgo pose graph optimization outliers consistency classification robustness optimization approach credibility factor classical optimization methods
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Review of Power Module Automatic Layout Optimization Methods in Electric Vehicle Applications 认领 引用 被引量：10
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作者 Puqi Ning Huakang Li +1 位作者 Yunhao Huang Yuhui Kang 《Chinese Journal of Electrical Engineering》 EI CSCD 2020年第3期8-24,共17页
The layout of power modules is a crucial design consideration,especially for silicon carbide devices.For electrical layout,optimizing the parasitic parameters can improving switching loss and dynamic behavior.For ther... The layout of power modules is a crucial design consideration,especially for silicon carbide devices.For electrical layout,optimizing the parasitic parameters can improving switching loss and dynamic behavior.For thermal layout,reducing the thermal resistance and controlling thermal capacitance can reduce the local hot point.Conventional layout design iterations are based on human knowledge and experience.But the major drawback of manual design methods is a limited choice of candidates,large time consumption and also the lack of consistency.With the introduce of automatic layout design,these challenges can be overcome which in the meanwhile alleviates current and temperature imbalance.By reviewing element representation,placement,routing,fitness evaluation,and the optimization algorithm approaches,a state-of-the-art power module layout design method for electric vehicle applications is introduced. 展开更多
关键词 Automatic layout design power modules optimization method
Optimization-Based Approaches to Uncertainty Analysis of Structures Using Non-Probabilistic Modeling:A Review 认领 引用
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作者 Yoshihiro Kanno Izuru Takewaki 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第4期115-152,共38页
Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization.This paper provides a review on optimization-based methods for uncertainty analysis,with focusing... Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization.This paper provides a review on optimization-based methods for uncertainty analysis,with focusing attention on specific properties of adopted numerical optimization approaches.We collect and discuss the methods based on nonlinear programming,semidefinite programming,mixed-integer programming,mathematical programming with complementarity constraints,difference-of-convex programming,optimization methods using surrogate models and machine learning techniques,and metaheuristics.As a closely related topic,we also overview the methods for assessing structural robustness using non-probabilistic uncertainty modeling.We conclude the paper by drawing several remarks through this review. 展开更多
关键词 Uncertainty non-probabilisticmodeling optimization methods bound for structural response robustness
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A high output power 340 GHz balanced frequency doubler designed based on linear optimization method 认领 引用
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作者 LIU Zhi-Cheng ZHOU Jing-Tao +5 位作者 MENG Jin WEI Hao-Miao YANG Cheng-Yue SU Yong-Bo JIN Zhi JIA Rui 《红外与毫米波学报》 SCIE EI CAS CSCD 北大核心 2025年第2期184-191,共8页
In this paper,a linear optimization method(LOM)for the design of terahertz circuits is presented,aimed at enhancing the simulation efficacy and reducing the time of the circuit design workflow.This method enables the ... In this paper,a linear optimization method(LOM)for the design of terahertz circuits is presented,aimed at enhancing the simulation efficacy and reducing the time of the circuit design workflow.This method enables the rapid determination of optimal embedding impedance for diodes across a specific bandwidth to achieve maximum efficiency through harmonic balance simulations.By optimizing the linear matching circuit with the optimal embedding impedance,the method effectively segregates the simulation of the linear segments from the nonlinear segments in the frequency multiplier circuit,substantially improving the speed of simulations.The design of on-chip linear matching circuits adopts a modular circuit design strategy,incorporating fixed load resistors to simplify the matching challenge.Utilizing this approach,a 340 GHz frequency doubler was developed and measured.The results demonstrate that,across a bandwidth of 330 GHz to 342 GHz,the efficiency of the doubler remains above 10%,with an input power ranging from 98 mW to 141mW and an output power exceeding 13 mW.Notably,at an input power of 141 mW,a peak output power of 21.8 mW was achieved at 334 GHz,corresponding to an efficiency of 15.8%. 展开更多
关键词 linear optimization method(LOM) three-dimensional electromagnetic model(3D-EM) Harmonic impedance optimization Schottky planar diode Terahertz frequency doubler
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Optimization Methods for Mixed Integer Weakly Concave Programming Problems 认领 引用 被引量：2
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作者 Zhi-you Wu Fu-sheng Bai +1 位作者 Yong-jian Yang Feng Jiang 《Journal of the Operations Research Society of China》 EI 2014年第2期195-222,共28页
In this paper,we consider a class of mixed integer weakly concave programming problems(MIWCPP)consisting of minimizing a difference of a quadratic function and a convex function.A new necessary global optimality condi... In this paper,we consider a class of mixed integer weakly concave programming problems(MIWCPP)consisting of minimizing a difference of a quadratic function and a convex function.A new necessary global optimality conditions for MIWCPP is presented in this paper.A new local optimization method for MIWCPP is designed based on the necessary global optimality conditions,which is different from the traditional local optimization method.A global optimization method is proposed by combining some auxiliary functions and the new local optimization method.Furthermore,numerical examples are also presented to show that the proposed global optimization method for MIWCPP is efficient. 展开更多
关键词 Global optimality conditions Local optimization method Global optimization method Mixed integer weakly concave programming problems
Hybrid AI-IoT Framework with Digital Twin Integration for Predictive Urban Infrastructure Management in Smart Cities 认领 引用 被引量：1
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作者 Abdullah Alourani Mehtab Alam +2 位作者 Ashraf Ali Ihtiram Raza Khan Chandra Kanta Samal 《Computers, Materials & Continua》 SCIE EI 2026年第1期462-493,共32页
The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often... The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities. 展开更多
关键词 Smart cities digital twin AI-IOT framework predictive infrastructure management edge computing reinforcement learning optimization methods federated learning urban systems modeling smart governance
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Optimization Methods for Box-Constrained Nonlinear Programming Problems Based on Linear Transformation and Lagrange Interpolating Polynomials 认领 引用
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作者 Zhi-You Wu Fu-Sheng Bai Jing Tian 《Journal of the Operations Research Society of China》 EI CSCD 2017年第2期193-218,共26页
In this paper,an optimality condition for nonlinear programming problems with box constraints is given by using linear transformation and Lagrange interpolating polynomials.Based on this condition,two new local optimi... In this paper,an optimality condition for nonlinear programming problems with box constraints is given by using linear transformation and Lagrange interpolating polynomials.Based on this condition,two new local optimization methods are developed.The solution points obtained by the new local optimization methods can improve the Karush–Kuhn–Tucker(KKT)points in general.Two global optimization methods then are proposed by combining the two new local optimization methods with a filled function method.Some numerical examples are reported to show the effectiveness of the proposed methods. 展开更多
关键词 Nonlinear programming Optimality conditions Linear transformation Lagrange interpolating polynomials Global optimization method
Kernel Function-Based Primal-Dual Interior-Point Methods for Symmetric Cones Optimization 认领 引用
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作者 ZHAO Dequan ZHANG Mingwang 《Wuhan University Journal of Natural Sciences》 CAS 2014年第6期461-468,共8页
In this paper, we present a large-update primal-dual interior-point method for symmetric cone optimization（SCO） based on a new kernel function, which determines both search directions and the proximity measure betwe... In this paper, we present a large-update primal-dual interior-point method for symmetric cone optimization（SCO） based on a new kernel function, which determines both search directions and the proximity measure between the iterate and the center path. The kernel function is neither a self-regular function nor the usual logarithmic kernel function. Besides, by using Euclidean Jordan algebraic techniques, we achieve the favorable iteration complexity O（ √r（1/2）（log r）^2 log（r/ ε））, which is as good as the convex quadratic semi-definite optimization analogue. 展开更多
关键词 symmetric cones optimization Kernel function Interior-point method polynomial complexity
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Flow Ripple Reduction of Axial Piston Pump for Multiple Working Statuses with Fast Structural Optimization Method 认领 引用
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作者 Yuanzhi Xu Renyuan Wang +3 位作者 Ming Deng Zhengyi Gu Yan Xie Zongxia Jiao 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2025年第6期562-583,共22页
The flow ripple caused by an axial piston pump may lead to pipe vibrations and lower hydraulic component reliability,which are of particular concern in hydraulic systems.The valve plate of the pump is considered the p... The flow ripple caused by an axial piston pump may lead to pipe vibrations and lower hydraulic component reliability,which are of particular concern in hydraulic systems.The valve plate of the pump is considered the part most related to flow ripple,and its structural design is an important topic.In this study,an analytical model for the axial piston pump flow ripple was established and verified using a numerical analysis with computational fluid dynamics(CFD)calculations.Moreover,a parametric analysis of the valve plate was performed to investigate the critical parameters and their ranges.A fast optimization method,the rotation vector optimization method(RVOM),was proposed for the valve plate design and compared with the currently used optimization methods to prove its efficiency.As a constant-pressure pump works in different states of swashplate angle,outlet pressure,and pump speed,an optimization principle for the entire working status was proposed to achieve the overall reduction performance.A test rig for an aircraft hydraulic pump was established,and validation experiments were conducted.It was determined that the optimized pump could achieve reduction at multiple working statuses,and the largest pressure pulsation reduction ratios for the typical speed and speed sweep tests reached 64.7%and 71.7%,respectively.The model and method proposed in this study are proven to be effective and accurate. 展开更多
关键词 Axial piston pump Optimization method Flow ripple Valve plate design Multiple working statuses
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Generative Adversarial Networks for Image Super-Resolution: A Survey 认领 引用
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作者 Ziang Wu Xuanyu Zhang +3 位作者 Yinbo Yu Qi Zhu Jerry Chun-Wei Lin Chunwei Tian 《Computers, Materials & Continua》 SCIE EI 2026年第6期72-117,共46页
Image super-resolution is a significant area in the field of image processing,with broad applications across multiple domains.In recent years,advancements in Generative Adversarial Networks(GANs)have led to an increas... Image super-resolution is a significant area in the field of image processing,with broad applications across multiple domains.In recent years,advancements in Generative Adversarial Networks(GANs)have led to an increased adoption of GAN-based methods in image super-resolution,yielding remarkable results.However,there is still a limited amount of research that systematically and comprehensively summarizes the various GAN-based techniques for image super-resolution.This paper provides a comparative study that elucidates the application differences of GANs in this field.We begin by reviewing the development of GANs and introducing their popular variants used in image applications.Subsequently,we systematically analyze the theoretical motivations,implementation approaches,and technical distinctions of GAN-based optimization methods and discriminative learning from three perspectives:supervised,semi-supervised,and unsupervised learning.We examine these methods concerning their integration of different network architectures,prior knowledge,loss functions,and multitask strategies.Furthermore,we conduct a systematic comparison of state-of-the-art GAN methods through quantitative and qualitative analyses using publicly available super-resolution datasets.In addition to traditional metrics such as PSNR and SSIM in our quantitative analysis,we also consider complexity and running time as reference standards to better align the evaluation with practical application demands.Finally,we identify several challenges currently faced by GANs in the domain of image super-resolution,including issues related to training stability and the need for improved evaluation metrics.We outline future research directions aimed at enhancing the robustness and efficiency of GAN-based super-resolution techniques,emphasizing the importance of integration with other machine learning frameworks to further advance this exciting field. 展开更多
关键词 SISR GANs optimization methods and discriminative learning
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Open-Source Codes of Topology Optimization: A Summary for Beginners to Start Their Research 认领 引用 被引量：12
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作者 Yingjun Wang Xinqing Li +1 位作者 Kai Long Peng Wei 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第10期1-34,共34页
Topology optimization(TO),a numerical technique to find the optimalmaterial layoutwith a given design domain,has attracted interest from researchers in the field of structural optimization in recent years.For beginner... Topology optimization(TO),a numerical technique to find the optimalmaterial layoutwith a given design domain,has attracted interest from researchers in the field of structural optimization in recent years.For beginners,opensource codes are undoubtedly the best alternative to learning TO,which can elaborate the implementation of a method in detail and easily engage more people to employ and extend the method.In this paper,we present a summary of various open-source codes and related literature on TO methods,including solid isotropic material with penalization(SIMP),evolutionary method,level set method(LSM),moving morphable components/voids(MMC/MMV)methods,multiscale topology optimization method,etc.Simultaneously,we classify the codes into five levels,fromeasy to difficult,depending on their difficulty,so that beginners can get started and understand the form of code implementation more quickly. 展开更多
关键词 Topology optimization open-source code optimization methods code classification beginners
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Modeling and Optimization for Scheduling of Chemical Batch Processes 认领 引用 被引量：7
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作者 钱宇 潘明 黄亚才 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 2009年第1期1-7,共7页
Chemical batch processes have become significant in chemical manufacturing. In these processes, large numbers of chemical products are produced to satisfy human demands in daily life. Recently, economy globalization h... Chemical batch processes have become significant in chemical manufacturing. In these processes, large numbers of chemical products are produced to satisfy human demands in daily life. Recently, economy globalization has resulted, in growing worldwide competitions in tradi.tional chemical .process industry. In order to keep competitive in the global marketplace, each company must optimize its production management and set up a reactive system for market fluctuation. Scheduling is the core of production management in chemical processes. The goal of this paper is to review the recent developments in this challenging area. Classifications of batch scheduling problems and optimization methods are introduced. A comparison of six typical models is shown in a general benchmark example from the literature. Finally, challenges and applications in future research are discussed. 展开更多
关键词 chemical batch processes scheduling optimization methods
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On the Use of the Nelder-Mead Simplex Method in Control Design and Systems Theory 认领 引用
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作者 Laura Menini Corrado Possieri Antonio Tornambe 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期186-204,共19页
The Nelder-Mead simplex method is a well-known algorithm enabling the minimization of functions that are not available in closed-form and that need not be differentiable or convex.Furthermore,it is particularly parsim... The Nelder-Mead simplex method is a well-known algorithm enabling the minimization of functions that are not available in closed-form and that need not be differentiable or convex.Furthermore,it is particularly parsimonious on the number of function evaluations,thus making it preferable to convex optimization paradigms in the case,common when dealing with control design problems,that the objective function of the optimization problem is non-differentiable,non-convex,and its closed-form is not available or difficult to be computed analytically.The main goal of this paper is to show how the joint use of the Nelder-Mead simplex method and the Morrison algorithm can be successfully used to solve relevant and challenging control problems that cannot be easily solved using analytic methods.In particular,it is shown how the problems of strong stabilization,static output feedback stabilization,and design of robust controllers having fixed structure can be framed as optimization problems,which,in turn,can be efficiently solved by coupling the two above mentioned algorithms.The performance of this procedure is compared with state-of-the-art techniques on dozens of static output feedback benchmark case studies,and its effectiveness is demonstrated by several examples. 展开更多
关键词 Optimization method robust controller static output feedback strong stabilizability
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Inequalities for warped product submanifolds in Kenmotsu space forms 认领 引用
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作者 DONG Jinlin CHENG Lijuan ZHU Yecheng 《纯粹数学与应用数学》 2026年第2期376-392,共17页
In the study of submanifold theory,one of the important problems is to nd relationship between the main extrinsic invariants and the main intrinsic invariants of a submanifold.By optimization methods on warped product... In the study of submanifold theory,one of the important problems is to nd relationship between the main extrinsic invariants and the main intrinsic invariants of a submanifold.By optimization methods on warped product semi-slant submanifolds,We obtain some results on warped product semi-slant submanifolds and establish two inequalities between the intrinsic invariants and the extrinsic invariants,for generalized normalizedδ-Casorati curvatures of warped product semi-slant submanifolds in Kenmotsu space forms. 展开更多
关键词 Kenmotsu manifold Casorati curvature optimization methods
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Sequential ensemble optimization based on general surrogate model prediction variance and its application on engine acceleration schedule design 认领 引用 被引量：6
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作者 Yifan YE Zhanxue WANG Xiaobo ZHANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2021年第8期16-33,共18页
The Efficient Global Optimization(EGO)algorithm has been widely used in the numerical design optimization of engineering systems.However,the need for an uncertainty estimator limits the selection of a surrogate model.... The Efficient Global Optimization(EGO)algorithm has been widely used in the numerical design optimization of engineering systems.However,the need for an uncertainty estimator limits the selection of a surrogate model.In this paper,a Sequential Ensemble Optimization(SEO)algorithm based on the ensemble model is proposed.In the proposed algorithm,there is no limitation on the selection of an individual surrogate model.Specifically,the SEO is built based on the EGO by extending the EGO algorithm so that it can be used in combination with the ensemble model.Also,a new uncertainty estimator for any surrogate model named the General Uncertainty Estimator(GUE)is proposed.The performance of the proposed SEO algorithm is verified by the simulations using ten well-known mathematical functions with varying dimensions.The results show that the proposed SEO algorithm performs better than the traditional EGO algorithm in terms of both the final optimization results and the convergence rate.Further,the proposed algorithm is applied to the global optimization control for turbo-fan engine acceleration schedule design. 展开更多
关键词 Cross-validation Efficient global optimization Engine acceleration schedule design Ensemble of surrogate models Gas turbine engine Optimization methods Surrogate-based optimization
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