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Hybrid adaptive machine learning approach for detection and mitigation of GNSS spoofing through enhanced osprey optimization algorithm 认领 引用
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作者 KOTI Sushmitha SANDHYA Rachamalla 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期1059-1080,共22页
Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,veloci... Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,velocity,and time(PVT).Several GNSS devices are exposed to detect spoofing attacks due to the use of unsafe locations.In addition,misleading signals are intentionally used to generate timing and position,and GNSS signal spoofing provides a constant risk to consumers.In past works,the implementation of the Global Positioning System(GPS)in autonomous vehicle navigation might be endangered by spoofing.To mitigate these issues,this task develops a hybrid machine-learning method for mitigating and detecting GNSS spoofing attacks.The developed model is processed with three phases:data collection,feature extraction,and detection.Initially,the required data is taken from the standard resource.Then,the data is given to the feature extraction phase.The features of the data are retrieved using the principal component analysis(PCA)and t-distributed stochastic neighbor embedding(t-SNE)model.The features obtained from the collected data are transferred to the detection phase.In the final phase,the GNSS spoofing detection and mitigation is executed using a machine learning method called as hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP).Enhanced osprey optimization algorithm(EOOA)is utilized for optimizing the variables to enhance the efficacy of models and achieves greater performance than other standard models. 展开更多
关键词 Global Navigation Satellite System(GNSS) detection and mitigation of GNSS t-distributed stochastic neighbor embedding(t-SNE) enhanced osprey optimization algorithm principal component analysis(PCA) hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP)
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Inversion of Rayleigh wave dispersion curves based on the Osprey Optimization Algorithm 认领 引用
2
作者 Zhi Li Hang-yu Yue +3 位作者 De-xi Ma Yu Fu Jing-yang Ni Jin-jun Pi 《Applied Geophysics》 SCIE CSCD 2025年第3期804-819,896,897,共16页
In Rayleigh wave exploration,the inversion of dispersion curves is a crucial step for obtaining subsurface stratigraphic information,characterized by its multi-parameter and multi-extremum nature.Local optimization al... In Rayleigh wave exploration,the inversion of dispersion curves is a crucial step for obtaining subsurface stratigraphic information,characterized by its multi-parameter and multi-extremum nature.Local optimization algorithms used in dispersion curve inversion are highly dependent on the initial model and are prone to being trapped in local optima,while classical global optimization algorithms often suffer from slow convergence and low solution accuracy.To address these issues,this study introduces the Osprey Optimization Algorithm(OOA),known for its strong global search and local exploitation capabilities,into the inversion of dispersion curves to enhance inversion performance.In noiseless theoretical models,the OOA demonstrates excellent inversion accuracy and stability,accurately recovering model parameters.Even in noisy models,OOA maintains robust performance,achieving high inversion precision under high-noise conditions.In multimode dispersion curve tests,OOA effectively handles higher modes due to its efficient global and local search capabilities,and the inversion results show high consistency with theoretical values.Field data from the Wyoming region in the United States and a landfill site in Italy further verify the practical applicability of the OOA.Comprehensive test results indicate that the OOA outperforms the Particle Swarm Optimization(PSO)algorithm,providing a highly accurate and reliable inversion strategy for dispersion curve inversion. 展开更多
关键词 surface wave exploration dispersion curve inversion Osprey Optimization Algorithm Particle Swarm Optimization geophysical inversion
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An Enhanced Osprey Optimization-Based Interpretable Deep Learning Framework for Predicting Coal Spontaneous Combustion Temperatures 认领 引用
3
作者 Rui Yan Botao Fan +2 位作者 Xueqi Qu Cuihuan Ren Xu Zhou 《Computers, Materials & Continua》 SCIE EI 2026年第10期876-902,共27页
Accurate prediction of coal spontaneous combustion(CSC)temperatures is crucial for safe coal mine production.To further improve the accuracy of CSC temperature prediction and the interpretability of the model,this stu... Accurate prediction of coal spontaneous combustion(CSC)temperatures is crucial for safe coal mine production.To further improve the accuracy of CSC temperature prediction and the interpretability of the model,this study proposes an interpretable Chebyshev chaotic mapping-Lévy flight-Enhanced pinhole imaging inverse learning-Adaptive weighted Osprey Optimization Algorithm optimized Bidirectional Long Short-Term Memory(CLEA-OOA-BiLSTM)framework for predicting CSC temperatures.First,we optimized the Osprey Optimization Algorithm(OOA)by incorporating the Chebyshev chaotic map,Lévy flights,enhanced pinhole imaging backpropagation,and an adaptive weighting strategy,thereby developing the CLEA-OOA algorithm.Through comparative experiments using eight benchmark test functions and four heuristic algorithms,we verified that CLEA-OOA achieves superior convergence accuracy and convergence speed.Subsequently,using data from the Dongtan Coal Mine as the subject of study,we employed CLEA-OOA to perform adaptive optimization of the BiLSTM hyperparameters.Using Spearman’s correlation analysis,C2H4/C2H6,CO,C2H4,CO/ΔO2,and O2(%)were identified as key indicators for predicting the CSC temperatures.The results show that the CLEA-OOA-BiLSTM model achieves an R2of 0.98,which is higher than that of all comparison models.The model’s MSE,RMSE,MAE,and MAPE are 11.26%,3.36%,2.73%,and 2.74%,respectively,demonstrating the model’s excellent error control capabilities.The results of the global and local SHapley Additive exPlanations(SHAP)interpretability analysis indicate that C2H4/C2H6and CO are key contributing factors to the model’s decision-making,consistent with the oxidation mechanisms of CSC.The model was validated using coal mine data from multiple locations in Inner Mongolia,Shanxi,and Anhui,with R2consistently reaching 0.985,demonstrating strong cross-regional generalization capabilities and practical engineering value.This framework provides a new method for CSC early warning and the intelligent development of mines. 展开更多
关键词 Coal spontaneous combustion coal mine safety interpretable deep learning temperature prediction BiLSTM improved osprey optimization algorithm SHAP analysis
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