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Structural Damage Identification Using Ensemble Deep Convolutional Neural Network Models 认领 引用 被引量：5
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作者 Mohammad Sadegh Barkhordari Danial Jahed Armaghani Panagiotis G.Asteris 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第2期835-855,共21页
The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visualmethods,which may result in an unreliable damage characterization due to inspector subje... The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visualmethods,which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of expertise.As a result,a robust,reliable,and repeatable method of damage identification is required.Ensemble learning algorithms for identifying structural damage are evaluated in this article,which use deep convolutional neural networks,including simple averaging,integrated stacking,separate stacking,and hybridweighted averaging ensemble and differential evolution(WAE-DE)ensemblemodels.Damage identification is carried out on three types of damage.The proposed algorithms are used to analyze the damage of 4585 structural images.The effectiveness of the ensemble learning techniques is evaluated using the confusion matrix.For the testing dataset,the confusion matrix achieved an accuracy of 94 percent and a minimum recall of 92 percent for the best model(WAE-DE)in distinguishing damage types as flexural,shear,combined,or undamaged. 展开更多
关键词 Machine learning ensemble learning algorithms convolutional neural network damage assessment structural damage
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Stochastic Hybrid Rumor Control:A Data-Driven Ensemble Learning Control Algorithm 认领 引用
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作者 ZHONG Xiaojing ZENG Jiaxin +3 位作者 XIANG Wendi CARABALLO Tomás DENG Feiqi PENG Yuqing 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2026年第4期1436-1462,共27页
To explore the impact of various groups and methods on rumor propagation,the authors propose a‘Double-Refutation(DR)and Double-Blocking(DB)'rumor control strategy.This strategy combines external refutation via me... To explore the impact of various groups and methods on rumor propagation,the authors propose a‘Double-Refutation(DR)and Double-Blocking(DB)'rumor control strategy.This strategy combines external refutation via media reports,internal refutation by counteracting individuals,and both continuous and impulse blocking methods.By leveraging multi-synergy and aiming to minimize control costs,the authors propose stochastic optimal hybrid control strategies for rumor containment.Additionally,to enhance the response speed of the control strategy,the authors introduce an ensemble learning algorithm as a substitute for theoretical solutions.Numerical simulations demonstrate that the trained ensemble learning control algorithm can quickly identify sub-optimal control strategies for rumor spreading,with costs only 4.1%higher than those of the optimal control theory. 展开更多
关键词 Double refutation and blocking control mechanism dual-layer propagation model hybrid optimal control stacking ensemble learning control algorithm stochastic control
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The study of intelligent algorithm in particle identification of heavy-ion collisions at low and intermediate energies 认领 引用 被引量：2
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作者 Gao-Yi Cheng Qian-Min Su +1 位作者 Xi-Guang Cao Guo-Qiang Zhang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2024年第2期170-182,共13页
Traditional particle identification methods face timeconsuming,experience-dependent,and poor repeatability challenges in heavy-ion collisions at low and intermediate energies.Researchers urgently need solutions to the... Traditional particle identification methods face timeconsuming,experience-dependent,and poor repeatability challenges in heavy-ion collisions at low and intermediate energies.Researchers urgently need solutions to the dilemma of traditional particle identification methods.This study explores the possibility of applying intelligent learning algorithms to the particle identification of heavy-ion collisions at low and intermediate energies.Multiple intelligent algorithms,including XgBoost and TabNet,were selected to test datasets from the neutron ion multi-detector for reaction-oriented dynamics(NIMROD-ISiS)and Geant4 simulation.Tree-based machine learning algorithms and deep learning algorithms e.g.TabNet show excellent performance and generalization ability.Adding additional data features besides energy deposition can improve the algorithm’s performance when the data distribution is nonuniform.Intelligent learning algorithms can be applied to solve the particle identification problem in heavy-ion collisions at low and intermediate energies. 展开更多
关键词 Heavy-ion collisions at low and intermediate energies Machine learning Ensemble learning algorithm Particle identification Data imbalance
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Adaptive Fault Detection Scheme Using an Optimized Self-healing Ensemble Machine Learning Algorithm 认领 引用
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作者 Levent Yavuz Ahmet Soran +2 位作者 AhmetÖnen Xiangjun Li S.M.Muyeen 《CSEE Journal of Power and Energy Systems》 SCIE EI CSCD 2022年第4期1145-1156,共12页
This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to detect.Rather than usin... This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to detect.Rather than using traditional machine learning(ML)algorithms or hybrid signal processing techniques,a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML algorithms.In the proposed method,the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization(PSO)weights.For this purpose,power system failures are simulated by using the PSCA D-Python co-simulation.One of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored information.Therefore,the proposed technique will be able to work on different systems,topologies,or data collections.The proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect. 展开更多
关键词 Decision tree(DT) ensemble machine learning algorithm fault detection islanding operation k-Nearest Neighbor(kNN) linear discriminant analysis(LDA) logistic regression(LR) Naive Bayes(NB) self-healing algorithm
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