Rare event data is encountered when the events of interest occur with low frequency, and the estimators based on the cohort data only may be inefficient. However, when external information is available for the estimat...Rare event data is encountered when the events of interest occur with low frequency, and the estimators based on the cohort data only may be inefficient. However, when external information is available for the estimation, the estimators utilizing external information can be more efficient. In this paper, we propose a method to incorporate external information into the estimation of the baseline hazard function and improve efficiency for estimating the absolute risk under the additive hazards model. The resulting estimators are shown to be uniformly consistent and converge weakly to Gaussian processes. Simulation studies demonstrate that the proposed method is much more efficient. An application to a bone marrow transplant data set is provided.展开更多
Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instabili...Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.展开更多
Recognizing the specific complexities of vessel traffic flow,this comprehensive survey exclusively addresses the predictive modelling in maritime transportation,tracing the evolution from conventional statistical appr...Recognizing the specific complexities of vessel traffic flow,this comprehensive survey exclusively addresses the predictive modelling in maritime transportation,tracing the evolution from conventional statistical approaches to modern artificial intelligence(AI)techniques.The survey examines a broad range of predictive targets,including vessel volume,trajectories,velocities,destinations and traffic patterns.Through bibliometric analysis utilizing Citespace,the central research themes and technological trends characterizing the vessel traffic flow prediction domain have been identified and discussed.Our analysis indicates a clear trend towards AI-based models,highlighting their increasing dominance in enhancing predictive accuracy and efficiency.Additionally,we highlight persistent challenges,such as the integration of large datasets with traffic flow models and the critical need for real-time data analytics.The survey concludes with insights into the future of vessel traffic flow prediction research,emphasizing the potential of hybrid models that combine deep learning with statistical learning to enable more sophisticated predictive analytics to be performed.This review aims to serve as a guide for both academics and practitioners looking to maximize the use of predictive modelling in the maritime traffic sector.展开更多
Purpose-The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations(NSS).Because the conventional methods for the prediction of NSS,such as support vect...Purpose-The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations(NSS).Because the conventional methods for the prediction of NSS,such as support vector machine,particle swarm optimization,etc.,lack accuracy,robustness and efficiency,in this study,the authors propose a new method for the prediction of NSS based on recurrent neural network(RNN)with gated recurrent unit.Design/methodology/approach-This method extracts internal and external information features from the original time-series network data for the first time.Then,the extracted features are applied to the deep RNN model for training and validation.After iteration and optimization,the accuracy of predictions of NSS will be obtained by the well-trained model,and the model is robust for the unstable network data.Findings-Experiments on bench marked data set show that the proposed method obtains more accurate and robust prediction results than conventional models.Although the deep RNN models need more time consumption for training,they guarantee the accuracy and robustness of prediction in return for validation.Originality/value-In the prediction of NSS time-series data,the proposed internal and external information features are well described the original data,and the employment of deep RNN model will outperform the state-of-the-arts models.展开更多
基金partly supported by the National Natural Science Foundation of China(No.11690015,11301355,11671275,11771431 and 71501016)Key Laboratory of RCSDS,CAS(No.2008DP173182)+1 种基金Qin Xin Talents Cultivation Program(QXTCP B201705)Beijing Information Science&Technology University
文摘Rare event data is encountered when the events of interest occur with low frequency, and the estimators based on the cohort data only may be inefficient. However, when external information is available for the estimation, the estimators utilizing external information can be more efficient. In this paper, we propose a method to incorporate external information into the estimation of the baseline hazard function and improve efficiency for estimating the absolute risk under the additive hazards model. The resulting estimators are shown to be uniformly consistent and converge weakly to Gaussian processes. Simulation studies demonstrate that the proposed method is much more efficient. An application to a bone marrow transplant data set is provided.
基金Supported by the National Defense Basic Scientific Research Program of China.
文摘Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.
基金supported in part by the National Key Research and Development Program of China(Grant No.2023YFC3010803)in part by the National Natural Science Foundation of China(Grant Nos.52272424 and 52372320)+1 种基金in part by the Key Research and Development Program of Hubei Province of China(Grant No.2023BCB123)in part by the Fundamental Research Funds for the Central Universities(Grant No.104972024KFYd0039).
文摘Recognizing the specific complexities of vessel traffic flow,this comprehensive survey exclusively addresses the predictive modelling in maritime transportation,tracing the evolution from conventional statistical approaches to modern artificial intelligence(AI)techniques.The survey examines a broad range of predictive targets,including vessel volume,trajectories,velocities,destinations and traffic patterns.Through bibliometric analysis utilizing Citespace,the central research themes and technological trends characterizing the vessel traffic flow prediction domain have been identified and discussed.Our analysis indicates a clear trend towards AI-based models,highlighting their increasing dominance in enhancing predictive accuracy and efficiency.Additionally,we highlight persistent challenges,such as the integration of large datasets with traffic flow models and the critical need for real-time data analytics.The survey concludes with insights into the future of vessel traffic flow prediction research,emphasizing the potential of hybrid models that combine deep learning with statistical learning to enable more sophisticated predictive analytics to be performed.This review aims to serve as a guide for both academics and practitioners looking to maximize the use of predictive modelling in the maritime traffic sector.
基金supported by the funds of Ningde Normal University Youth Teacher Research Program(2015Q15)The Education Science Project of the Junior Teacher in the Education Department of Fujian province(JAT160532).
文摘Purpose-The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations(NSS).Because the conventional methods for the prediction of NSS,such as support vector machine,particle swarm optimization,etc.,lack accuracy,robustness and efficiency,in this study,the authors propose a new method for the prediction of NSS based on recurrent neural network(RNN)with gated recurrent unit.Design/methodology/approach-This method extracts internal and external information features from the original time-series network data for the first time.Then,the extracted features are applied to the deep RNN model for training and validation.After iteration and optimization,the accuracy of predictions of NSS will be obtained by the well-trained model,and the model is robust for the unstable network data.Findings-Experiments on bench marked data set show that the proposed method obtains more accurate and robust prediction results than conventional models.Although the deep RNN models need more time consumption for training,they guarantee the accuracy and robustness of prediction in return for validation.Originality/value-In the prediction of NSS time-series data,the proposed internal and external information features are well described the original data,and the employment of deep RNN model will outperform the state-of-the-arts models.
