针对航空电缆电弧故障因特征隐蔽性和危害性强引发的飞行安全隐患等问题提出一种新型检测方法。首先参考行业标准模拟飞行环境搭建试验平台完成数据采集。再采用北方苍鹰算法优化自适应噪声完备集合经验模态分解方法(Complete Ensemble ...针对航空电缆电弧故障因特征隐蔽性和危害性强引发的飞行安全隐患等问题提出一种新型检测方法。首先参考行业标准模拟飞行环境搭建试验平台完成数据采集。再采用北方苍鹰算法优化自适应噪声完备集合经验模态分解方法(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)将故障电弧电流分解为不同本征模态函数分量并对其提取多尺度模糊熵、时域、频域组合特征。最后设计秃鹰搜索-随机森林算法(Bald Eagle Search and Random Forest,BES-RF)进行电弧故障检测,结果表明:检测准确率达98.05%,相比传统分解方法与检测算法准确率提高3.5%、4.7%,验证该方法的有效性。展开更多
The joint-bolt-African Vulture optimization algorithm(AVOA)model is proposed for the design of building connections to improve the stability of steel beam-to-column connections.For this algorithm,the type of steel is ...The joint-bolt-African Vulture optimization algorithm(AVOA)model is proposed for the design of building connections to improve the stability of steel beam-to-column connections.For this algorithm,the type of steel is first determined,and the number of bolts needed by the corresponding steel type is referenced in Eurocode 3.Then,the bearing capacity of the joint can be calculated.The joint-bolt-AVOA model is established by substituting the bolt number required by the steel into the algorithm to obtain the optimal bolt number required while ensuring joint stability.The results show that the number of bolts required by the joint-bolt-AVOA model based on the stability of steel is lower than that calculated by Eurocode 3.Therefore,AVOA can effectively optimize the number of bolts needed in building connections and save resources.展开更多
The integration of IoT and Deep Learning(DL)has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management(PHM).Electrocardiograms(ECGs)are widely used for cardio...The integration of IoT and Deep Learning(DL)has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management(PHM).Electrocardiograms(ECGs)are widely used for cardiovascular disease(CVD)diagnosis,but fluctuating signal patterns make classification challenging.Computer-assisted automated diagnostic tools that enhance ECG signal categorization using sophisticated algorithms and machine learning are helping healthcare practitioners manage greater patient populations.With this motivation,the study proposes a DL framework leveraging the PTB-XL ECG dataset to improve CVD diagnosis.Deep Transfer Learning(DTL)techniques extract features,followed by feature fusion to eliminate redundancy and retain the most informative features.Utilizing the African Vulture Optimization Algorithm(AVOA)for feature selection is more effective than the standard methods,as it offers an ideal balance between exploration and exploitation that results in an optimal set of features,improving classification performance while reducing redundancy.Various machine learning classifiers,including Support Vector Machine(SVM),eXtreme Gradient Boosting(XGBoost),Adaptive Boosting(AdaBoost),and Extreme Learning Machine(ELM),are used for further classification.Additionally,an ensemble model is developed to further improve accuracy.Experimental results demonstrate that the proposed model achieves the highest accuracy of 96.31%,highlighting its effectiveness in enhancing CVD diagnosis.展开更多
Intelligent construction has become an inevitable trend in the development of the construction industry.In the excavation project,using machine learning methods for early warning can improve construction efficiency an...Intelligent construction has become an inevitable trend in the development of the construction industry.In the excavation project,using machine learning methods for early warning can improve construction efficiency and quality and reduce the chances of damage in the excavation process.An interpretable gradient boosting based ensemble learning framework enhanced by the African Vultures Optimization Algorithm(AVOA)was proposed and evaluated in estimating the diaphragm wall deflections induced by excavation.We investigated and compared the performance of machine learning models in predicting deflections induced by excavation based on a database generated by finite element simulations.First,we exploratively analyzed these data to discover the relationship between features.We used several state-of-the-art intelligent models based on gradient boosting and several simple models for model selection.The hyperparameters for all models in evaluation are optimized using AVOA,and then the optimized models are assembled into a unified framework for fairness assessment.The comprehensive evaluation results show that the AVOA-CatBoost built in this paper performs well(RMSE=1.84,MAE=1.18,R2=0.9993)and cross-validation(RMSE=2.65±1.54,MAE=1.17±0.23,R2=0.998±0.002).In the end,in order to improve the transparency and usefulness of the model,we constructed an interpretable model from both global and local perspectives.展开更多
针对到达时间差定位(Time difference of arrival,TDOA)和到达角定位(Angle of arrival,AOA)联合定位,提出了基于准反射学习机制和并行机制改进的非洲秃鹫定位算法。改进的非洲秃鹫算法在对定位模型最大似然的适应度函数寻优和迭代过程...针对到达时间差定位(Time difference of arrival,TDOA)和到达角定位(Angle of arrival,AOA)联合定位,提出了基于准反射学习机制和并行机制改进的非洲秃鹫定位算法。改进的非洲秃鹫算法在对定位模型最大似然的适应度函数寻优和迭代过程中,引入准反射机制以丰富种群多样性和加快收敛速度,在一定程度上也平衡了探索和开发能力;引入并行机制,通过一个种群的最优个体指导另一种群,加快收敛速度,增强了寻优性能。实验结果看,将改进的非洲秃鹫算法与非洲秃鹫算法(AVOA)、改进的哈里斯鹰算法(IHHO)、混沌麻雀搜索优化算法(CSSOA)、鸽群优化算法(PIO)、疯狂自适应樽海鞘算法(CASSA)进行对比,在基准函数和定位模型的求解上,都表现出了更快的收敛速度、更准确的定位精度和更好的稳定性。展开更多
文摘针对航空电缆电弧故障因特征隐蔽性和危害性强引发的飞行安全隐患等问题提出一种新型检测方法。首先参考行业标准模拟飞行环境搭建试验平台完成数据采集。再采用北方苍鹰算法优化自适应噪声完备集合经验模态分解方法(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)将故障电弧电流分解为不同本征模态函数分量并对其提取多尺度模糊熵、时域、频域组合特征。最后设计秃鹰搜索-随机森林算法(Bald Eagle Search and Random Forest,BES-RF)进行电弧故障检测,结果表明:检测准确率达98.05%,相比传统分解方法与检测算法准确率提高3.5%、4.7%,验证该方法的有效性。
文摘The joint-bolt-African Vulture optimization algorithm(AVOA)model is proposed for the design of building connections to improve the stability of steel beam-to-column connections.For this algorithm,the type of steel is first determined,and the number of bolts needed by the corresponding steel type is referenced in Eurocode 3.Then,the bearing capacity of the joint can be calculated.The joint-bolt-AVOA model is established by substituting the bolt number required by the steel into the algorithm to obtain the optimal bolt number required while ensuring joint stability.The results show that the number of bolts required by the joint-bolt-AVOA model based on the stability of steel is lower than that calculated by Eurocode 3.Therefore,AVOA can effectively optimize the number of bolts needed in building connections and save resources.
基金funded by Researchers Supporting ProjectNumber(RSPD2025R947),King Saud University,Riyadh,Saudi Arabia.
文摘The integration of IoT and Deep Learning(DL)has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management(PHM).Electrocardiograms(ECGs)are widely used for cardiovascular disease(CVD)diagnosis,but fluctuating signal patterns make classification challenging.Computer-assisted automated diagnostic tools that enhance ECG signal categorization using sophisticated algorithms and machine learning are helping healthcare practitioners manage greater patient populations.With this motivation,the study proposes a DL framework leveraging the PTB-XL ECG dataset to improve CVD diagnosis.Deep Transfer Learning(DTL)techniques extract features,followed by feature fusion to eliminate redundancy and retain the most informative features.Utilizing the African Vulture Optimization Algorithm(AVOA)for feature selection is more effective than the standard methods,as it offers an ideal balance between exploration and exploitation that results in an optimal set of features,improving classification performance while reducing redundancy.Various machine learning classifiers,including Support Vector Machine(SVM),eXtreme Gradient Boosting(XGBoost),Adaptive Boosting(AdaBoost),and Extreme Learning Machine(ELM),are used for further classification.Additionally,an ensemble model is developed to further improve accuracy.Experimental results demonstrate that the proposed model achieves the highest accuracy of 96.31%,highlighting its effectiveness in enhancing CVD diagnosis.
基金National Natural Science Foundation of China(Grant Nos.42107214 and 52130905).
文摘Intelligent construction has become an inevitable trend in the development of the construction industry.In the excavation project,using machine learning methods for early warning can improve construction efficiency and quality and reduce the chances of damage in the excavation process.An interpretable gradient boosting based ensemble learning framework enhanced by the African Vultures Optimization Algorithm(AVOA)was proposed and evaluated in estimating the diaphragm wall deflections induced by excavation.We investigated and compared the performance of machine learning models in predicting deflections induced by excavation based on a database generated by finite element simulations.First,we exploratively analyzed these data to discover the relationship between features.We used several state-of-the-art intelligent models based on gradient boosting and several simple models for model selection.The hyperparameters for all models in evaluation are optimized using AVOA,and then the optimized models are assembled into a unified framework for fairness assessment.The comprehensive evaluation results show that the AVOA-CatBoost built in this paper performs well(RMSE=1.84,MAE=1.18,R2=0.9993)and cross-validation(RMSE=2.65±1.54,MAE=1.17±0.23,R2=0.998±0.002).In the end,in order to improve the transparency and usefulness of the model,we constructed an interpretable model from both global and local perspectives.
文摘针对到达时间差定位(Time difference of arrival,TDOA)和到达角定位(Angle of arrival,AOA)联合定位,提出了基于准反射学习机制和并行机制改进的非洲秃鹫定位算法。改进的非洲秃鹫算法在对定位模型最大似然的适应度函数寻优和迭代过程中,引入准反射机制以丰富种群多样性和加快收敛速度,在一定程度上也平衡了探索和开发能力;引入并行机制,通过一个种群的最优个体指导另一种群,加快收敛速度,增强了寻优性能。实验结果看,将改进的非洲秃鹫算法与非洲秃鹫算法(AVOA)、改进的哈里斯鹰算法(IHHO)、混沌麻雀搜索优化算法(CSSOA)、鸽群优化算法(PIO)、疯狂自适应樽海鞘算法(CASSA)进行对比,在基准函数和定位模型的求解上,都表现出了更快的收敛速度、更准确的定位精度和更好的稳定性。
