检索结果-内蒙古大学图书馆

Design-manufacturing-evaluation integrated of microwave absorption metastructure based on large variation genetic-ant colony fusion optimization algorithm and additive manufacturing

引用

COMPOSITES COMMUNICATIONS 2025年 54卷

作者： Huang, Yixing Zhao, Pengzhen Sun, Yingjian Liao, Haitao Dong, Huaiyu Gao, Shuailong Li, Qun Zhao, Tian Li, Ying Beijing Inst Technol Inst Adv Struct Technol Beijing Key Lab Lightweight Multifunct Composite M Beijing 100081 Peoples R China Shandong Univ Aeronaut Sch Chem Engn & Safety Binzhou 256600 Peoples R China

Absorbing materials and structures are widely used in military and civilian fields, such as electromagnetic wave protection and stealth technology. However, current materials face challenges including high mass, limited loadbearing capacity, and the lack of integrated material-structure designs. In this study, we combined additive manufacturing technology with a hybrid optimization algorithm, integrating large mutation genetic and ant colony algorithms, to develop a novel lightweight honeycomb structure. The optimized structure achieved broadband absorption over 91.84 % of the 2 GHz-40 GHz range and demonstrated superior mechanical performance, with a maximum deflection of 13.72 mm and a load capacity of 2.87 kN. These findings present an innovative strategy for designing lightweight structures that effectively balance electromagnetic absorption and mechanical durability.

关键词： Design-manufacturing-evaluation integrated Large variation genetic and ant colony fusion optimization algorithm Microwave absorbing metastructure

来源：评论

学校读者我要写书评

暂无评论

Framework design and empirical analysis of intelligent scheduling system for high-altitude photovoltaic power generation based on mixed optimization of long-nosed raccoon optimization algorithm and black winged kite optimization algorithm (COA-BKA)

引用

DISCOVER COMPUTING 2025年第1期28卷 1-33页

作者： Hu, Heng Xiong, Xiaoming Wang, Shuang Yan, Taidong Zhang, Yuancheng Gan, Shuang Three Gorges Grp Sichuan Daofu Renewable Energy Co Chengdu 626400 Sichuan Peoples R China Three Gorges Grp Sichuan ABa Renewable Energy Co L Chengdu 624600 Sichuan Peoples R China

This study proposes an intelligent scheduling system for high-altitude photovoltaic power generation, utilizing a hybrid optimization approach that combines the Long-nosed Raccoon optimization algorithm (COA) and the Black-winged Kite optimization algorithm (BKA) (COA-BKA). The goal is to enhance scheduling accuracy, stability, and response speed under the unique environmental conditions of high-altitude regions, such as fluctuating light intensity, extreme temperatures, and dynamic load demands. In experimental comparisons with traditional algorithms like Particle Swarm optimization (PSO) and Genetic algorithm (GA), COA-BKA achieved a scheduling accuracy of 0.98, outperforming PSO (0.92) and GA (0.90). COA-BKA also demonstrated superior convergence speed, reaching the optimal solution by the 50th iteration, while PSO and GA required more iterations (80 and 100, respectively). Additionally, COA-BKA completed scheduling in just 4.5 s, significantly faster than PSO (6.3 s) and GA (7.2 s). The system effectively handled fluctuating light intensity and load demand changes, showcasing its robust adaptability. These results suggest that COA-BKA provides a highly efficient and stable solution for intelligent scheduling in high-altitude photovoltaic power systems, improving operational efficiency and reducing costs, while offering significant advancements for real-time optimization in smart grids.

关键词： Plateau photovoltaic power generation Intelligent scheduling Long nosed raccoon optimization algorithm Black winged kite optimization algorithm Hybrid optimization COA-BKA optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Early Warning of College Students' Ideological and Political Course Performance Using an optimization algorithm

引用

JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2025年第2期29卷 389-395页

作者： Chen, Yuehua Hebei Chem & Pharmaceut Coll Dept Marxism Teaching 88 Fangxing Rd Shijiazhuang 050026 Peoples R China

With the reform of teaching methods, hybrid online and offline teaching modes have been used increasingly in college courses. In this setting, the factors affecting academic performance are more complex, making it more challenging to predict students' performance. Therefore, there is an urgent need for higher- performance prediction algorithms. This study briefly analyzed college students' learning in ideological and political courses. Then, the learning features of college students in the courses were extracted using the Super Star platform and teaching system. Feature selection was carried out based on the information gain rate, while the training set was balanced using the synthetic minority oversampling technique (SMOTE). Moreover, the seagull optimization algorithm (SOA) was applied to optimize the hyperparameters of eXtreme Gradient Boosting (XGBoost) to develop the SOA-XGBoost algorithm for early warning of performance. Experiments were performed on the collected datasets. It was found that the effect of the SOA-XGBoost algorithm on the early warning of performance improved significantly following SMOTE processing. The F1- value reached 0.955 and the area under the curve value was 0.976. The SOA exhibited superior performance in hyperparameter optimization compared with other algorithms such as the grid search. The SOA-XGBoost algorithm also achieved the best results in early warning of performance. These results confirm the effectiveness of the proposed SOA-XGBoost algorithm for early warning of performance, and the method can be widely applied in practice.

关键词： ideological and political course performance warning optimization algorithm XGBoost

来源：评论

学校读者我要写书评

暂无评论

Industrial defect detection and location based on greedy particle swarm optimization algorithm

引用

INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2025年第3期40卷 214-221页

作者： Tang, Yaorui Yang, Bo Peng, Hong Luo, Xiaohui Xihua Univ Sch Comp & Software Engn Chengdu Peoples R China

Particle swarm optimization algorithm is a branch of evolutionary computing and can search for a better solution in a given feature space. This paper introduces the particle swarm optimization algorithm and greedy strategy for the defect detection and location of industrial components and then proposes a greedy particle swarm optimization algorithm. This paper employs sparse random projection to map the vast high-dimensional information to the low-dimensional space while maintaining the relative distance between the data. This employment helps to accelerate the training and prediction speed of the model and remove some unimportant features or noise. This algorithm first adopts particle swarm optimization (PSO) to initialize the cluster centers in an effort to minimize the maximum distance between all unlabeled data points and these centers. The algorithm then utilizes the greedy strategy to select a batch of data points to represent the corresponding features of the normal image, thereby improving the coverage of the model to the data. Experiments have shown that the results of most categories of data sets are close to or better than the current existing methods, especially in defect detection. In terms of defect localization for object categories, our method achieves a pixel-level anomaly localization index (AUROC) of 98.3% on the MVTec AD dataset. [GRAPHICS]

关键词： Evolutionary computing optimization algorithm feature extraction greedy strategy industrial defect detection and location

来源：评论

学校读者我要写书评

暂无评论

Estimation of suspended sediment load utilizing a super-optimized deep learning approach informed by the red fox optimization algorithm

引用

EARTH SCIENCE INFORMATICS 2025年第3期18卷 1-17页

作者： Malekpour, Mohammad Mahdi Ahmadi, Mohammad Mehdi Gugliotta, Marcello Tabari, Mahmoud Mohammad Rezapour Qaderi, Kourosh Shahid Bahonar Univ Kerman Fac Agr Dept Water Engn Kerman Iran Martin Luther Univ Halle Wittenberg Inst Geosci & Geog Halle An Der Saale Germany Univ Mazandaran Fac Engn & Technol Dept Civil Engn Babolsar Mazandaran Iran

The suspended sediment load transported by rivers can be estimated using various methodologies, including those based on artificial intelligence. In this study, we employed the Long Short-Term Memory (LSTM) model to estimate the suspended sediment concentration in the Mississippi River, United States of America. The input variables for the LSTM model included river discharge, water depth, suspended sediment load, and flow velocity. To enhance the model's performance, the input data and initial parameters were optimized using the Red Fox optimization (RFO) algorithm, resulting in a super-optimized LSTM model (SLSTM-RFO) developed through a two-phase optimization process. Additionally, sediment load estimations were conducted using alternative models, specifically the Artificial Neural Network (ANN) and Generalized Regression Neural Network (GRNN) models. The performance of these models was assessed using five performance indicators, the correlation coefficient (R2), root mean square error (RMSE), mean absolute error (MAE), Nash and Sutcliffe efficiency (NS), and RMSE observations standard deviation ratio (RSR), demonstrating that the SLSTM-RFO model significantly outperformed the other models. Specifically, the SLSTM-RFO yielded improved estimation results, achieving reductions in error (RMSE) of 73.30%, 81.50%, and 82.56% compared to the LSTM, ANN, and GRNN models, respectively.

关键词： Artificial neural network Long short-term memory (LSTM) optimization algorithm Mississippi River Sedimentation concentration

来源：评论

学校读者我要写书评

暂无评论

Enhancing neurological disease diagnostics: fusion of deep transfer learning with optimization algorithm for acute brain stroke prediction using facial images

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-21页

作者： Alrowais, Fadwa Alqahtani, Mohammed Khan, Jahangir Ben Miled, Achraf Albalawneh, Da'ad Alkharashi, Abdulwhab Al Zanin, Samah Marzouk, Radwa Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia Univ Bisha Coll Comp & Informat Technol Dept Informat Syst & Cyber Secur Bisha 61922 Saudi Arabia King Khalid Univ Appl Coll Mahayil Dept Comp Sci Abha Saudi Arabia Northern Border Univ Coll Sci Dept Comp Sci Ar Ar 73213 Saudi Arabia Univ Tabuk Univ Coll Umluj Dept Comp Sci Tabuk Saudi Arabia Saudi Elect Univ Coll Comp & Informat Dept Comp Sci Riyadh Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Kharj Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh Saudi Arabia

Stroke is a main risk to life and fitness in current society, particularly in the aging population. Also, the stroke is recognized as a cerebrovascular accident. It contains a nervous illness, which can result from haemorrhage or ischemia of the brain veins, and regular mains to assorted motor and cognitive damages that cooperate with functionality. Screening for stroke comprises physical examination, history taking, and valuation of risk features like age or certain cardiovascular illnesses. Symptoms and signs of stroke include facial weakness. Even though computed tomography (CT) and magnetic resonance imaging (MRI) are standard diagnosis techniques, artificial intelligence (AI) systems have been constructed based on these methods, which deliver fast detection. AI is gaining high attention and is being combined into numerous areas with medicine to enhance the accuracy of analysis and the quality of patient care. This paper proposes an enhancing neurological disease diagnostics fusion of transfer learning for acute brain stroke prediction using facial images (ENDDFTL-ABSPFI) method. The proposed ENDDFTL-ABSPFI method aims to enhance brain stroke detection and classification models using facial imaging. Initially, the image pre-processing stage applies the fuzzy-based median filter (FMF) model to eliminate the noise in input image data. Furthermore, fusion models such as Inception-V3 and EfficientNet-B0 perform the feature extraction. Moreover, the hybrid of convolutional neural network and bidirectional long short-term memory (CNN-BiLSTM) model is employed for the brain stroke classification process. Finally, the multi-objective sailfish optimization (MOSFO)-based hyperparameter selection process is carried out to optimize the classification outcomes of the CNN-BiLSTM model. The simulation validation of the ENDDFTL-ABSPFI technique is investigated under the Kaggle dataset concerning various measures. The comparative evaluation of the ENDDFTL-ABSPFI technique portra

关键词： Fusion of transfer learning Acute brain stroke prediction Facial images optimization algorithm Image pre-processing

来源：评论

学校读者我要写书评

暂无评论

Research on optimization algorithm in grid engineering survey data acquisition and processing based on cloud computing 5

Research on optimization algorithm in grid engineering surve...

引用

5th International Conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Zhang, Jiyong Sun, Xiaohu Huang, Bing Liu, Donghui Ren, Peixiang State Grid Economics and Technology Research Institute Co. Ltd. Beijing 102209 China

ISBN: (纸本)9781510691667

With the increasing scale of power grids and the growing demand for intelligence, efficient and accurate collection and processing of grid engineering survey data has become critical. This study aims to explore the optimization application of cloud computing technology in grid engineering data collection and processing. We design and implement an optimization algorithm based on cloud computing, which integrates several technologies such as distributed computing, big data storage and management, cluster monitoring and security management. Through comparative experiments, our algorithm significantly outperforms traditional methods in terms of data processing speed, achieving at least a four-fold improvement;in terms of data accuracy, the error rate is controlled at a lower level of 0.2%-0.3%. In addition, our method demonstrates good scalability and cost-effectiveness. This study not only provides an efficient data processing solution for power grid engineering, but also lays a solid foundation for the intelligent and digital transformation of future power grids. © 2025 SPIE.

关键词： cloud computing grid engineering optimization algorithm survey data

来源：评论

学校读者我要写书评

暂无评论

optimization algorithm-based Elman neural network controller for continuous stirred tank reactor process model

引用

CURRENT SCIENCE 2021年第8期120卷 1324-1333页

作者： Baranilingesan, I KPR Inst Engn & Technol Dept Elect & Elect Engn Coimbatore 641407 Tamil Nadu India

A continuous stirred tank reactor (CSTR) is a standout nonlinear system among the most essential units of chemical industries. In this article, an Elman neural network is designed to analyse the characteristics of nonlinear behaviour of the CSTR system. The data generated employing the state-space model of CSTR are used to train the designed Elman neural network controller and the controller parameters are optimally tuned by the proposed hybrid swarm intelligence-based optimization algorithm. Two different hybridizations have been developed, including DPSO, DGSA and hybrid DPSO-DGSA and successfully employed in controller tuning. The significance of the proposed controller is validated by a comparative analysis made with conventional methods and the performance is experimentally demonstrated using MATLAB software.

关键词： Continuous stirred tank reactor neural network controller nonlinear behaviour optimization algorithm process control

来源：评论

学校读者我要写书评

暂无评论

optimization algorithm based on kinetic-molecular theory

引用

Journal of Central South University 2013年第12期20卷 3504-3512页

作者：范朝冬欧阳红林张英杰艾朝阳 College of Electrical and Information Engineering Hunan University College of Information Science and Engineering Hunan University Institute of Cognitive Science Hunan University

Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular theory(KMTOA) is proposed. In the KMTOA three operators are designed: attraction, repulsion and wave. The attraction operator simulates the molecular attraction, with the molecules moving towards the optimal ones, which makes possible the optimization. The repulsion operator simulates the molecular repulsion, with the molecules diverging from the optimal ones. The wave operator simulates the thermal molecules moving irregularly, which enlarges the searching spaces and increases the population diversity and global searching ability. Experimental results indicate that KMTOA prevails over other algorithms in the robustness, solution quality, population diversity and convergence speed.

关键词： optimization algorithm heuristic search algorithm kinetic-molecular theory diversity convergence

来源：评论

学校读者我要写书评

暂无评论

optimization algorithm for Reduction the Size of Dixon Resultant Matrix:A Case Study on Mechanical Application

引用

Computers, Materials & Continua 2019年第2期58卷 567-583页

作者： Shang Zhang Seyedmehdi Karimi Shahaboddin Shamshirband Amir Mosavi College of Computer and Information Technology China Three Gorges UniversityYichangChina Department of Mathematics Jouybar branchIslamic Azad UniversityJouybarIran Department for Management of Science and Technology Development Ton Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Information Technology Ton Duc Thang UniversityHo Chi Minh CityVietnam Department Institute of Structural Mechanics Bauhaus University of WeimarGermany Institute of Automation Kando Kalman Faculty of Electrical EngineeringObuda University1431 BudapestHungary

In the process of eliminating variables in a symbolic polynomial system,the extraneous factors are referred to the unwanted parameters of resulting *** paper aims at reducing the number of these factors via optimizing the size of Dixon *** optimal configuration of Dixon matrix would lead to the enhancement of the process of computing the resultant which uses for solving polynomial *** do so,an optimization algorithm along with a number of new polynomials is introduced to replace the polynomials and implement a complexity ***,the monomial multipliers are optimally positioned to multiply each of the ***,through practical implementation and considering standard and mechanical examples the efficiency of the method is evaluated.

关键词： Dixon resultant matrix symbolic polynomial system elimination theory optimization algorithm computational complexity

来源：评论

学校读者我要写书评

暂无评论

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

请选择保存的检索档案：

请选择收藏分类：

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

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

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：