Accuracy Improvement of Mutual Integration Mechanism Driven Algorithms for Boom Cable Force Recognition DOI Creative Commons

HaoYu Zhang,

Yang Yang, He Zhang

и другие.

Research Square (Research Square), Год журнала: 2023, Номер unknown

Опубликована: Дек. 6, 2023

Abstract Accurate measurement of cable tension is crucial for real-time monitoring bridge systems, preventing potential risks, and ensuring safety continuous operation. However, traditional often faces the challenge accuracy when dealing with complex elastic boundary conditions. This article uses 9 finite element model suspension cables conditions as data force identification, heuristic algorithms to achieve identification goal minimizing frequency actual frequency. Based on recognition results process, reasons inaccurate forces under boundaries were analyzed, a mutual fusion mechanism was proposed improve identification. The show that reduces maximum relative error in by 12.6%, significantly improving accuracy, most initial 5%, meeting needs practical engineering. In addition, non parametric test statistical method also proves introduction has significant impact value tension. Finally, verified through from three engineering meet requirements. provides new technical solution intelligent accurate long beams, broad application prospects.

Язык: Английский

Adaptive Ant Colony Optimization with Sub-Population and Fuzzy Logic for 3D Laser Scanning Path Planning DOI Creative Commons
Junfang Song, Yuanyuan Pu, Xiaoyu Xu

и другие.

Sensors, Год журнала: 2024, Номер 24(4), С. 1098 - 1098

Опубликована: Фев. 8, 2024

For the precise measurement of complex surfaces, determining position, direction, and path a laser sensor probe is crucial before obtaining exact measurements. Accurate surface hinges on modifying overtures planning scan point displacement to optimize alignment its velocity accuracy. This manuscript proposes 3D scanning technique that utilizes adaptive ant colony optimization with sub-population fuzzy logic (SFACO), which involves consideration layout, attitude, planning. Firstly, this study based four-coordinate measuring machine paired probe. The instrument used establish coordinate system, relationship between them transformed. readings each axis object being measured under normal attitude are then reversed through system transformation, thus resulting in optimal attitude. nominal distance matrix, demonstrates significance created all points be measured. Subsequently, ACO algorithm integrates multiple swarm dynamic domain structures suggested enhance algorithm’s performance by refining utilizing operators. efficacy verified experiments 13 popular TSP benchmark datasets, thereby demonstrating complexity SFACO approach. Ultimately, problem addressed employing proposed conjunction matrix.

Язык: Английский

Процитировано

3
Multi-strategy learning-based particle swarm optimization algorithm for COVID-19 threshold segmentation DOI
Donglin Zhu, Jiaying Shen,

Yangyang Zheng

и другие.

Computers in Biology and Medicine, Год журнала: 2024, Номер 176, С. 108498 - 108498

Опубликована: Апрель 30, 2024

Язык: Английский

Процитировано

3
Identification of fruit using a flexible tactile sensor array DOI
Lihua Cai, Hongyao Chen, Xue Zuo

и другие.

Instrumentation Science & Technology, Год журнала: 2024, Номер unknown, С. 1 - 20

Опубликована: Янв. 25, 2024

This article aims to address the issue of low recognition accuracy in existing sorting robots caused by lighting, occlusion, and environmental factors. A fruit method based on a flexible tactile sensor array is described. enables robot directly perceive attributes objects identify fruits using gripper, facilitating intelligent sorting. novel utilized construct hand information acquisition platform, which collects time series data for fruits. Principal component analysis then employed dimensionality reduction, followed development an improved particle swarm optimization support vector machine model. Through experimental study, optimized model compared with four other models, demonstrating better classification performance. The achieves average up 98.10% five types comparison between algorithm, genetic grid search algorithm reveals superior performance new approach. In future, this expected be implemented industrial automatic production lines. Furthermore, will further refined enhance efficiency.

Язык: Английский

Процитировано

1
Thermal resistance optimization of ultra-thin vapor chamber based on data-driven model and metaheuristic algorithm DOI

Guimin Ye,

Yuxuan Sheng,

Yaping Zou

и другие.

International Communications in Heat and Mass Transfer, Год журнала: 2024, Номер 153, С. 107382 - 107382

Опубликована: Март 14, 2024

Язык: Английский

Процитировано

1
The Practice of Cooperative Learning in Music Education: Optimization and Improvement of Learning Strategies DOI Open Access
Bo Sun, Asta Rauduvaitė,

Haoyue Sun

и другие.

Transactions on Comparative Education, Год журнала: 2024, Номер 6(2)

Опубликована: Янв. 1, 2024

This study explores the practice of cooperative learning in music education with aim optimizing and enhancing students' strategies. Cooperative has shown remarkable effect by adopting strategies such as group grouping cooperation, interaction cooperation skills training, project practice, evaluation feedback. The assessment results showed that not only helped students improve their musical skills, but also promoted development non-musical abilities, teamwork communication skills. In addition, can enhance interest motivation learning. However, there are some challenges differences willingness to cooperate coordination problems process. view these challenges, this paper puts forward corresponding countermeasures suggestions, establishing clear rules providing necessary training. Looking future, continuous educational concepts technologies, application will have a broader prospect, it is expected further comprehensive quality through innovative teaching methods technical means.

Язык: Английский

Процитировано

1
Application of Local Search Particle Swarm Optimization Based on the Beetle Antennae Search Algorithm in Parameter Optimization DOI Creative Commons
Feng Teng,

Shuwei Deng,

Qianwen Duan

и другие.

Actuators, Год журнала: 2024, Номер 13(7), С. 270 - 270

Опубликована: Июль 17, 2024

Intelligent control algorithms have been extensively utilized for adaptive controller parameter adjustment. While the Particle Swarm Optimization (PSO) algorithm has several issues: slow convergence speed requiring a large number of iterations, tendency to get trapped in local optima, and difficulty escaping from them. It is also sensitive distribution solution space, where uneven can lead inefficient contraction. On other hand, Beetle Antennae Search (BAS) robust, precise, strong global search capabilities. However, its limitation lies focusing on single individual. As iterations increases, step size decays, causing it stuck extrema preventing escape. Although setting fixed or larger initial avoid this, results poor stability. The PSO algorithm, which targets population, help BAS increase diversity address deficiencies. Conversely, characteristics aid finding optimal early optimization process, accelerating convergence. Therefore, considering combination leverage their respective advantages enhance overall performance. This paper proposes an improved W-K-BSO, integrates strategy into phase PSO. By leveraging chaotic mapping, enhances population accelerates speed. Additionally, adoption linearly decreasing inertia weight performance, while coordinated contraction factor regulates Furthermore, influence beetle antennae position increments particles incorporated, along with establishment new velocity update rules. Simulation experiments conducted nine benchmark functions demonstrate that W-K-BSO consistently exhibits significantly improves ability escape precision, stability across various dimensions, enhancements ranging 7 9 orders magnitude compared algorithm. Application PID Pointing Tracking System (PTS) reduced system stabilization time by 28.5%, confirming algorithm’s superiority competitiveness.

Язык: Английский

Процитировано

1
Advancing photovoltaic system design: An enhanced social learning swarm optimizer with guaranteed stability DOI
Lingyun Deng, Sanyang Liu

Computers in Industry, Год журнала: 2024, Номер 164, С. 104209 - 104209

Опубликована: Ноя. 8, 2024

Язык: Английский

Процитировано

1
Hybrid particle swarm optimization with adaptive learning strategy DOI
Lanyu Wang, D. Tian,

Xiaorui Gou

и другие.

Soft Computing, Год журнала: 2024, Номер 28(17-18), С. 9759 - 9784

Опубликована: Июль 18, 2024

Язык: Английский

Процитировано

0
Hybrid Intelligent Model for Estimating the Cost of Huizhou Replica Traditional Vernacular Dwellings DOI Creative Commons

Jian Huang,

Wei Huang,

Wei Quan

и другие.

Buildings, Год журнала: 2024, Номер 14(9), С. 2623 - 2623

Опубликована: Авг. 24, 2024

Amidst the backdrop of rural revitalization and cultural renaissance, there is a surge in construction demand for replica traditional vernacular dwellings. Traditional cost estimation methods struggle to meet need rapid precise due complexity inherent their construction. To address this challenge, study aims enhance accuracy efficiency by innovatively developing an Adaptive Self-Explanatory Convolutional Neural Network (ASCNN) model, tailored specific needs dwellings Huizhou region. The ASCNN model employs Random Forest filter key features, inputs these into CNN estimation, utilizes Particle Swarm Optimization (PSO) optimize parameters, thereby improving predictive accuracy. decision-making process thoroughly interpreted through SHAP value analysis, ensuring credibility transparency. During collected analyzed bidding control price data from 98 empirical results demonstrate that exhibits outstanding performance on test set, with Root Mean Square Error (RMSE) 9828.06 yuan, Absolute Percentage (MAPE) 0.6%, Coefficient Determination (R2) as high 0.989, confirming model’s strong generalization capability. Through further identifies factors such floor plan layout, roof area, column material coefficient are central prediction. proposed not only significantly improves dwellings, but also enhances its transparency interpretation methods, providing reliable basis related investment decisions. findings offer valuable references insights buildings other regions worldwide.

Язык: Английский

Процитировано

0
Random Shared Local Dominator Guided Particle Swarm Optimization DOI Creative Commons

Gong-Wei Song,

H.Y. Cao,

Lang Zhang

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Сен. 3, 2024

Abstract Guiding exemplar selection plays a crucial role in assisting particle swarm optimization (PSO) to gain satisfactory performance. To improve the effectiveness helping PSO solve complex problems with high and efficiency deteriorates due serious diversity loss, this paper devises random shared local dominator guided scheme (RSLDG) for PSO, leading simple yet effective variant named RSLDG-PSO. In contrast existing studies, where each can only follow guidance of best position within its area, RSLDG-PSO first randomly partitions whole into several sub-swarms then identifies sub-swarm. Then, all these positions are collected together form pool particles learn. Subsequently, particle, is chosen stochastically from pool, along own historical experience, guide learning. way, highly diverse considerably promising exemplars provided update swarm. Furthermore, alleviate sensitivity parameters, an adaptive adjustment strategy sub-swarm size, dynamic adjusting two coefficients. With above schemes, expectedly maintains good balance between search convergence traverse solution space.

Язык: Английский

Процитировано

0