Reliability-based design optimization of offshore wind turbine support structures using RBF surrogate model

Yu, Changhai; Lv, Xiaolong; Huang, Dan; Jiang, Dongju

doi:10.1007/s11709-023-0976-8

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…RBFNN is composed of an input layer (IL), a hidden layer (HL), and an output layer (OL) (Yadav Rajesh et al, 2023;Yu et al, 2023). The construction of HL space is to use radial basis functions as the basis of hidden layer units.…”

Section: Ssa-rbfnn Modelmentioning

confidence: 99%

A Model for Predicting Physical Health of College Students Based on Semantic Web and Deep Learning Under Cloud Edge Collaborative Architecture

Wang,

Zhang,

Tang

et al. 2024

International Journal on Semantic Web and Information Systems

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A model for predicting physical health of college students based on semantic web and deep learning under cloud edge collaborative architecture is proposed to address the issue of most physical health prediction models being unable to fully describe the characteristics of sports performance changes and having large prediction errors. Firstly, the authors design a measurement data analysis system based on cloud edge collaboration architecture to improve data analysis efficiency. Then, they preprocess the data on the edge side, such as missing samples, and extract data features using an equal dimensional dynamic GOM model. Finally, they deploy the RBFNN-SSA model in the cloud center, input the characteristics of each indicator into the model for predictive analysis, and obtain the physical health status of college students. Based on the physical health test data of Hohai University from 2018 to 2021, an experimental analysis was conducted. The results showed that all three intervention measures had significant effects on maintaining and improving the physical health level of college students.

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Section: Ssa-rbfnn Modelmentioning

confidence: 99%

A Model for Predicting Physical Health of College Students Based on Semantic Web and Deep Learning Under Cloud Edge Collaborative Architecture

Wang,

Zhang,

Tang

et al. 2024

International Journal on Semantic Web and Information Systems

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Layer-wise Learning Rate Optimization for Task-Dependent Fine-Tuning of Pre-trained Models: An Evolutionary Approach

Bu,

Liu,

Huang

et al. 2024

ACM Trans. Evol. Learn. Optim.

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The superior performance of large-scale pre-trained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT), has received increasing attention in both academic and industrial research and has become one of the current research hotspots. A pre-trained model refers to a model trained on large-scale unlabeled data, whose purpose is to learn general language representation or features for fine-tuning or transfer learning in subsequent tasks. After pre-training is complete, a small amount of labeled data can be used to fine-tune the model for a specific task or domain. This two-stage method of “pre-training+fine-tuning” has achieved advanced results in natural language processing (NLP) tasks. Despite widespread adoption, existing fixed fine-tuning schemes that adapt well to one NLP task may perform inconsistently on other NLP tasks given that different tasks have different latent semantic structures. In this paper, we explore the effectiveness of automatic fine-tuning pattern search for layer-wise learning rates from an evolutionary optimization perspective. Our goal is to use evolutionary algorithms to search for better task-dependent fine-tuning patterns for specific NLP tasks than typical fixed fine-tuning patterns. Experimental results on two real-world language benchmarks and three advanced pre-training language models show the effectiveness and generality of the proposed framework.

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A Multi-Performance Reliability Evaluation Approach Based on the Surrogate Model with Cluster Mixing Weight

Fan,

Wang,

Zhang

et al. 2024

Applied Sciences

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Kriging surrogate model has extracted extensive attention in reliability evaluation, owing to its excellent applicability and operability nowadays, which confronts with difficulties in balancing the efficiency and accuracy for complicated mechanical assets with multiple failure modes. Consequently, this paper devises a multi-performance reliability analysis approach within the surrogate model framework, particularly innovative in its use of cluster mixing weight. Specifically, high-value test points are selected to fit the surrogate model after sorting the samples referring to the corresponding values; then, a cluster-based active learning strategy is employed to accomplish rapid convergence, and the particle swarm algorithm is utilized to optimize relevant parameters. Afterwards, the mixing weight for every performance referring to the contributions to the final reliability is determined, and the failure probability is subsequently predicted. Furthermore, the superiority of the proposed approach with the clustering surrogate model and mixing weight, compared with traditional sampling as well as other surrogate models, has been verified via case studies, contributing to overcoming the multi-performance reliability analysis oriented to complicated mechanical assets.

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Reliability-based design optimization of offshore wind turbine support structures using RBF surrogate model

Cited by 3 publications

References 40 publications

A Model for Predicting Physical Health of College Students Based on Semantic Web and Deep Learning Under Cloud Edge Collaborative Architecture

A Model for Predicting Physical Health of College Students Based on Semantic Web and Deep Learning Under Cloud Edge Collaborative Architecture

Layer-wise Learning Rate Optimization for Task-Dependent Fine-Tuning of Pre-trained Models: An Evolutionary Approach

A Multi-Performance Reliability Evaluation Approach Based on the Surrogate Model with Cluster Mixing Weight

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