检索结果-南通市图书馆

Optimal structural design for a certain near-space composite propeller of airship using adaptive region division blending model

引用

Chinese Journal of Aeronautics 2024年第5期37卷 301-316页

作者： Weigang AN Tianyu LIN Shigen WANG School of Aeronautics Northwestern Polytechnical UniversityXi’an 710072China

Near-space airship is a frontier and hotspot in current military research and development,and the near-space composite propeller is the key technology for its development.In order to obtain higher aerodynamic efficiency at an altitude of 22 km,a certain near-space composite propeller is designed as a long and slender aerodynamic shape with a 10 m diameter,which brings many challenges to the composite structure design.The initial design is obtained by the composite structure variable stiffness design method using based on fixed region division blending model.However,it weighs 23.142 kg,exceeding the required 20 kg.In order to meet the structural design requirements of the propeller,a variable stiffness design method using the adaptive region division blending model is proposed in this paper.Compared with the methods using the fixed region division blending model,this method optimizes region division,stacking thickness and stacking sequence in a single level,considering the coupling effect among them.Through a more refined region division,this method can provide a more optimal design for composite tapered structures.Additionally,to improve the efficiency of optimization subjected to manufacturing constraints,a hierarchical penalty function is proposed to quickly filter out the solutions that do not meet manufacturing constraints.The above methods combined with a genetic algorithm(GA)using specific encoding are adopted to optimize the near-space composite propeller.The optimal design of the structure weighs 18.831 kg,with all manufacturing constraints and all structural response constraints being satisfied.Compared with the initial design,the optimal design has a more refined region division,and achieves a weight reduction of 18.6%.This demonstrates that a refined region division can significantly improve the mechanical performance of the composite tapered structure.

关键词： Structural optimization Composite tapered structure genetic algorithm Adaptive region division Manufacturing constraint Near-space composite propeller

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A New Double Layer Multi-Secret Sharing Scheme

引用

China Communications 2024年第1期21卷 297-309页

作者： Elavarasi Gunasekaran Vanitha Muthuraman Department of Computer Applications Alagappa UniversityKaraikudiIndia

Cryptography is deemed to be the optimum strategy to secure the data privacy in which the data is encoded ahead of time before sharing it.Visual Secret Sharing(VSS)is an encryption method in which the secret message is split into at least two trivial images called’shares’to cover it.However,such message are always targeted by hackers or dishonest members who attempt to decrypt the message.This can be avoided by not uncovering the secret message without the universal share when it is presented and is typically taken care of,by the trusted party.Hence,in this paper,an optimal and secure double-layered secret image sharing scheme is proposed.The proposed share creation process contains two layers such as threshold-based secret sharing in the first layer and universal share based secret sharing in the second layer.In first layer,genetic algorithm(GA)is applied to find the optimal threshold value based on the randomness of the created shares.Then,in the second layer,a novel design of universal share-based secret share creation method is proposed.Finally,Opposition Whale Optimization algorithm(OWOA)-based optimal key was generated for rectange block cipher to secure each share.This helped in producing high quality reconstruction images.The researcher achieved average experimental outcomes in terms of PSNR and MSE values equal to 55.154225 and 0.79365625 respectively.The average PSNRwas less(49.134475)and average MSE was high(1)in case of existing methods.

关键词： genetic algorithm oppositional whale optimization algorithm rectangle block cipher secret sharing scheme shares universal share

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Using Machine Learning to Identify and Optimize Sensitive Parameters in Urban Flood Model Considering Subsurface Characteristics

引用

International Journal of Disaster Risk Science 2024年第1期15卷 116-133页

作者： Hengxu Jin Yu Zhao Pengcheng Lu Shuliang Zhang Yiwen Chen Shanghua Zheng Zhizhou Zhu Key Laboratory of VGE of Ministry of Education Nanjing Normal UniversityNanjing 210023China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China

This study presents a novel method for optimizing parameters in urban flood models,aiming to address the tedious and complex issues associated with parameter optimization.First,a coupled one-dimensional pipe network runoff model and a two-dimensional surface runoff model were integrated to construct an interpretable urban flood model.Next,a principle for dividing urban hydrological response units was introduced,incorporating surface attribute features.The K-means algorithm was used to explore the clustering patterns of the uncertain parameters in the model,and an artificial neural network(ANN)was employed to identify the sensitive parameters.Finally,a genetic algorithm(GA) was used to calibrate the parameter thresholds of the sub-catchment units in different urban land-use zones within the flood model.The results demonstrate that the parameter optimization method based on K-means-ANN-GA achieved an average Nash-Sutcliffe efficiency coefficient(NSE) of 0.81.Compared to the ANN-GA and K-means-deep neural networks(DNN) methods,the proposed method better characterizes the runoff generation and flow processes.This study demonstrates the significant potential of combining machine learning techniques with physical knowledge in parameter optimization research for flood models.

关键词： Artifcial neural network Coupled urban fooding model genetic algorithm K-means algorithm Subcatchment delineation Uncertain parameters

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Comparison of Parallel genetic algorithm and Particle Swarm Optimization for Parameter Calibration in Hydrological Simulation

引用

Data Intelligence 2023年第4期5卷 904-922页

作者： Xinyu Zhang Yang Li Genshen Chu University of Science and Technology Beijing Beijing 100083China Engineering Research Center of Intelligent Supercomputing Beijing100083

Parameter calibration is an important part of hydrological simulation and affects the final simulation results.In this paper,we introduce heuristic optimization algorithms,genetic algorithm(GA)to cope with the complexity of the parameter calibration problem,and use particle swarm optimization algorithm(PsO)as a comparison.For large-scale hydrological simulations,we use a multilevel parallel parameter calibration framework to make full use of processor resources,and accelerate the process of solving high-dimensional parameter calibration.Further,we test and apply the experiments on domestic supercomputers.The results of parameter calibration with GA and PSO can basically reach the ideal value of 0.65 and above,with PSO achieving a speedup of 58.52 on TianHe-2 supercomputer.The experimental results indicate that using a parallel implementation on multicore CPUs makes high-dimensional parameter calibration in large-scale hydrological simulation possible.Moreover,our comparison of the two algorithms shows that the GA obtains better calibration results,and the PSO has a more pronounced acceleration effect.

关键词： Hydrologic simulation Parameter calibration genetic algorithm Particle swarm optimization

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A Multi-Object genetic algorithm for the Assembly Line Balance Optimization in Garment Flexible Job Shop Scheduling

引用

Intelligent Automation & Soft Computing 2023年第8期37卷 2421-2439页

作者： Junru Liu Yonggui Lv Hangzhou Dianzi University Hangzhou310018China

Numerous clothing enterprises in the market have a relatively low efficiency of assembly line planning due to insufficient optimization of bottleneck stations.As a result,the production efficiency of the enterprise is not high,and the production organization is not up to expectations.Aiming at the problem of flexible process route planning in garment workshops,a multi-object genetic algorithm is proposed to solve the assembly line bal-ance optimization problem and minimize the machine adjustment path.The encoding method adopts the object-oriented path representation method,and the initial population is generated by random topology sorting based on an in-degree selection mechanism.The multi-object genetic algorithm improves the mutation and crossover operations according to the characteristics of the clothing process to avoid the generation of invalid offspring.In the iterative process,the bottleneck station is optimized by reasonable process splitting,and process allocation conforms to the strict limit of the station on the number of machines in order to improve the compilation efficiency.The effectiveness and feasibility of the multi-object genetic algorithm are proven by the analysis of clothing cases.Compared with the artificial allocation process,the compilation efficiency of MOGA is increased by more than 15%and completes the optimization of the minimum machine adjustment path.The results are in line with the expected optimization effect.

关键词： Assembly line balance topological order genetic algorithm compilation efficiency pre-production scheduling

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Optimization of a Single Flash Geothermal Power Plant Powered by a Trans-Critical Carbon Dioxide Cycle Using genetic algorithm and Nelder-Mead Simplex Method

引用

Energy Engineering 2023年第2期120卷 263-275页

作者： Yashar Aryanfar Jorge Luis García Alcaraz Department of Electric Engineering and Computation Autonomous University of Ciudad JuárezCiudad JuárezChihuahuaMéxico Department of Industrial Engineering and Manufacturing Autonomous University of Ciudad JuárezCiudad JuárezChihuahuaMéxico

The usage of renewable energies,including geothermal energy,is expanding rapidly worldwide.The low efficiency of geothermal cycles has consistently highlighted the importance of recovering heat loss for these cycles.This paper proposes a combined power generation cycle(single flash geothermal cycle with trans-critical CO_(2) cycle)and simulates in the EES(Engineering Equation Solver)software.The results show that the design parameters of the proposed system are significantly improved compared to the BASIC single flash cycle.Then,the proposed approach is optimized using the genetic algorithm and the Nelder-Mead Simplex method.Separator pressure,steam turbine output pressure,and CO_(2) turbine inlet pressure are three assumed variable parameters,and exergy efficiency is the target parameter.In the default operating mode,the system exergy efficiency was 32%,increasing to 39%using the genetic algorithm and 37%using the Nelder-Mead method.

关键词： Optimization geothermal genetic algorithm Nelder-Mead simplex exergy efficiency

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

genetic algorithm in seismic waveform inversion and its application in deep seismic sounding data interpretation

引用

Acta Seismologica Sinica(English Edition) 2006年第2期19卷 163-172页

作者：王夫运张先康 Geophysical Exploration Center China Earthquake Administration Zhengzhou 450002 China

A genetic algorithm of body waveform inversion is presented for better understanding of crustal and upper mantle structures with deep seismic sounding （DSS） waveform data. General reflection and transmission synthetic seismogram algorithm, which is capable of calculating the response of thin alternating high and low velocity layers, is applied as a solution for forward modeling, and the genetic algorithm is used to find the optimal solution of the inverse problem. Numerical tests suggest that the method has the capability of resolving low-velocity layers, thin alternating high and low velocity layers, and noise suppression. Waveform inversion using P-wave records from Zeku, Xiahe and Lintao shots in the seismic wide-angle reflection/refraction survey along northeastern Qinghai-Xizang （Tibeteau） Plateau has revealed fine structures of the bottom of the upper crust and alternating layers in the middle/lower crust and topmost upper mantle.

关键词： genetic algorithm waveform inversion numerical test deep seismic sounding fine crustal structure

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

genetic Nelder-Mead neural network algorithm for fault parameter inversion using GPS data

引用

Geodesy and Geodynamics 2022年第4期13卷 386-398页

作者： Leyang Wang Ranran Xu Fengbin Yu Faculty of Geomatics East China University of TechnologyNanchang 330013China Faculty of Industrial Safety and Occupational Health Jiangsu College of Safety TechnologyXuzhou 221000China Key Laboratory of Mine Environmental Monitoring and Improving Around Poyang Lake Ministry of Natural ResourcesNanchang 330013China BGI Engineering Consultants Ltd. Beijing 100038China

The traditional genetic algorithm(GA)has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters.Therefore,this article considers the combination of GA with other non-linear algorithms in order to improve the inversion precision of GA.This paper proposes a genetic Nelder-Mead neural network algorithm(GNMNNA).This algorithm uses a neural network algorithm(NNA)to optimize the global search ability of GA.At the same time,the simplex algorithm is used to optimize the local search capability of the GA.Through numerical examples,the stability of the inversion algorithm under different strategies is explored.The experimental results show that the proposed GNMNNA has stronger inversion stability and higher precision compared with the existing algorithms.The effectiveness of GNMNNA is verified by the BodrumeKos earthquake and Monte Cristo Range earthquake.The experimental results show that GNMNNA is superior to GA and NNA in both inversion precision and computational stability.Therefore,GNMNNA has greater application potential in complex earthquake environment.

关键词： Fault parameter inversion genetic algorithm Nelder-Mead simplex algorithm Neural network algorithm

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

genetic algorithm optimization for finned channel performance

引用

Applied Mathematics and Mechanics(English Edition) 2007年第12期28卷 1597-1604页

作者： S.S.Mousavi K.Hooman S.J.Mousavi Production Department SAIPA CompanyTehranIran School of Engineering the University of QueenslandBrisbaneAustralia Knowledge Engineering Research Center Aerospace Industrial OrganizationTehranIran

Compared to a smooth channel, a finned channel provides a higher heat transfer coefficient; increasing the fin height enhances the heat transfer. However, this heat transfer enhancement is associated with an increase in the pressure drop. This leads to an increased pumping power requirement so that one may seek an optimum design for such systems. The main goal of this paper is to define the exact location and size of fins in such a way that a minimal pressure drop coincides with an optimal heat transfer based on the genetic algorithm. Each fin arrangement is considered a solution to the problem （an individual for genetic algorithm）. An initial population is generated randomly at the first step. Then the algorithm has been searched among these solutions and made new solutions iteratively by its functions to find an optimum design as reported in this article.

关键词： Nusselt number pressure drop genetic algorithm optimum design baffle

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and genetic algorithms (GA) in South Algeria

引用

Computers, Materials & Continua 2024年第6期79卷 4725-4740页

作者： Djeldjli Halima Benatiallah Djelloul Ghasri Mehdi Tanougast Camel Benatiallah Ali Benabdelkrim Bouchra Material Sciences Department Faculty of Material SciencesMathematics and Computer ScienceLaboratory of Sustainable Development and Computer Science(LSDCS)University of AdrarAdrar01000Algeria Department of Civil Engineering University of Sistan and BaluchestanZahedan98167-45845Iran Laboratory of Computer Production and Maintenance Engineering(LGIPM)University of LorraineChieulles57070France Laboratory of Energy Environment and Information System(LEEIS) University of AdrarAdrar01000Algeria

When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and Bechar.The proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN model.The GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory.The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

关键词： Solar energy systems genetic algorithm neural networks hybrid adaptive neuro fuzzy inference system solar radiation

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：