Deep learning has emerged as a powerful tool for predicting the remaining useful life(RUL)of batteries,contingent upon access to ample data.However,the inherent limitations of data availability from traditional or acc...Deep learning has emerged as a powerful tool for predicting the remaining useful life(RUL)of batteries,contingent upon access to ample data.However,the inherent limitations of data availability from traditional or accelerated life testing pose significant challenges.To mitigate the prediction accuracy issues arising from small sample sizes in existing intelligent methods,we introduce a novel data augmentation framework for RUL prediction.This framework harnesses the inherent high coincidence of degradation patterns exhibited by lithium-ion batteries to pinpoint the knee point,a critical juncture marking a significant shift in the degradation trajectory.By focusing on this critical knee point,we leverage the power of normalizing flow models to generate virtual data,effectively augmenting the training sample size.Additionally,we integrate a Bayesian Long Short-Term Memory network,optimized with Box-Cox transformation,to address the inherent uncertainty associated with predictions based on augmented data.This integration allows for a more nuanced understanding of RUL prediction uncertainties,offering valuable confidence intervals.The efficacy and superiority of the proposed framework are validated through extensive experiments on the CS2 dataset from the University of Maryland and the CrFeMnNiCo dataset from our laboratory.The results clearly demonstrate a substantial improvement in the confidence interval of RUL predictions compared to pre-optimization,highlighting the ability of the framework to achieve high-precision RUL predictions even with limited data.展开更多
DDoS attacks represent one of the most pervasive and evolving threats in cybersecurity,capable of crippling critical infrastructures and disrupting services globally.As networks continue to expand and threats become m...DDoS attacks represent one of the most pervasive and evolving threats in cybersecurity,capable of crippling critical infrastructures and disrupting services globally.As networks continue to expand and threats become more sophisticated,there is an urgent need for Intrusion Detection Systems(IDS)capable of handling these challenges effectively.Traditional IDS models frequently have difficulties in detecting new or changing attack patterns since they heavily depend on existing characteristics.This paper presents a novel approach for detecting unknown Distributed Denial of Service(DDoS)attacks by integrating Sliced Iterative Normalizing Flows(SINF)into IDS.SINF utilizes the Sliced Wasserstein distance to repeatedly modify probability distributions,enabling better management of high-dimensional data when there are only a few samples available.The unique architecture of SINF ensures efficient density estimation and robust sample generation,enabling IDS to adapt dynamically to emerging threats without relying heavily on predefined signatures or extensive retraining.By incorporating Open-Set Recognition(OSR)techniques,this method improves the system’s ability to detect both known and unknown attacks while maintaining high detection performance.The experimental evaluation on CICIDS2017 and CICDDoS2019 datasets demonstrates that the proposed system achieves an accuracy of 99.85%for known attacks and an F1 score of 99.99%after incremental learning for unknown attacks.The results clearly demonstrate the system’s strong generalization capability across unseen attacks while maintaining the computational efficiency required for real-world deployment.展开更多
The detection of gravitational waves by the LIGO-Virgo-KAGRA collaboration has ushered in a new era of observational astronomy,emphasizing the need for rapid and detailed parameter estimation and population-level anal...The detection of gravitational waves by the LIGO-Virgo-KAGRA collaboration has ushered in a new era of observational astronomy,emphasizing the need for rapid and detailed parameter estimation and population-level analyses.Traditional Bayesian inference methods,particularly Markov chain Monte Carlo,face significant computational challenges when dealing with the high-dimensional parameter spaces and complex noise characteristics inherent in gravitational wave data.This review examines the emerging role of simulation-based inference methods in gravitational wave astronomy,with a focus on approaches that leverage machine-learning techniques such as normalizing flows and neural posterior estimation.We provide a comprehensive overview of the theoretical foundations underlying various simulation-based inference methods,including neural posterior estimation,neural ratio estimation,neural likelihood estimation,flow matching,and consistency models.We explore the applications of these methods across diverse gravitational wave data processing scenarios,from single-source parameter estimation and overlapping signal analysis to testing general relativity and conducting population studies.Although these techniques demonstrate speed improvements over traditional methods in controlled studies,their model-dependent nature and sensitivity to prior assumptions are barriers to their widespread adoption.Their accuracy,which is similar to that of conventional methods,requires further validation across broader parameter spaces and noise conditions.展开更多
Monocular 3D object detection is challenging due to the lack of accurate depth information.Some methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input t...Monocular 3D object detection is challenging due to the lack of accurate depth information.Some methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input to augment the RGB images.Depth-based methods attempt to convert estimated depth maps to pseudo-LiDAR and then use LiDAR-based object detectors or focus on the perspective of image and depth fusion learning.However,they demonstrate limited performance and efficiency as a result of depth inaccuracy and complex fusion mode with convolutions.Different from these approaches,our proposed depth-guided vision transformer with a normalizing flows(NF-DVT)network uses normalizing flows to build priors in depth maps to achieve more accurate depth information.Then we develop a novel Swin-Transformer-based backbone with a fusion module to process RGB image patches and depth map patches with two separate branches and fuse them using cross-attention to exchange information with each other.Furthermore,with the help of pixel-wise relative depth values in depth maps,we develop new relative position embeddings in the cross-attention mechanism to capture more accurate sequence ordering of input tokens.Our method is the first Swin-Transformer-based backbone architecture for monocular 3D object detection.The experimental results on the KITTI and the challenging Waymo Open datasets show the effectiveness of our proposed method and superior performance over previous counterparts.展开更多
Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately ...Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.展开更多
The application of multiple unmanned aerial vehicles(UAVs)for the pursuit and capture of unauthorized UAVs has emerged as a novel approach to ensuring the safety of urban airspace.However,pursuit UAVs necessitate the ...The application of multiple unmanned aerial vehicles(UAVs)for the pursuit and capture of unauthorized UAVs has emerged as a novel approach to ensuring the safety of urban airspace.However,pursuit UAVs necessitate the utilization of their own sensors to proactively gather information from the unauthorized UAV.Considering the restricted sensing range of sensors,this paper proposes a multi-UAV with limited visual field pursuit-evasion(MUV-PE)problem.Each pursuer has a visual field characterized by limited perception distance and viewing angle,potentially obstructed by buildings.Only when the unauthorized UAV,i.e.,the evader,enters the visual field of any pursuer can its position be acquired.The objective of the pursuers is to capture the evader as soon as possible without collision.To address this problem,we propose the normalizing flow actor with graph attention critic(NAGC)algorithm,a multi-agent reinforcement learning(MARL)approach.NAGC executes normalizing flows to augment the flexibility of policy network,enabling the agent to sample actions from more intricate distributions rather than common distributions.To enhance the capability of simultaneously comprehending spatial relationships among multiple UAVs and environmental obstacles,NAGC integrates the“obstacle-target”graph attention networks,significantly aiding pursuers in supporting search or pursuit activities.Extensive experiments conducted in a high-precision simulator validate the promising performance of the NAGC algorithm.展开更多
Runoff observation uncertainty is a key unsolved issue in the hydrology community.Existing studies mainly focused on observation uncertainty sources and their impacts on simulation performance,but the impacts on chang...Runoff observation uncertainty is a key unsolved issue in the hydrology community.Existing studies mainly focused on observation uncertainty sources and their impacts on simulation performance,but the impacts on changes of flow regime characteristics remained rare.This study detects temporal changes in 16 flow regime metrics from five main components(i.e.,magnitude,frequency of events,variability,duration,and timing),and evaluates the effects of observation uncertainty on trends of flow regime metrics by adopting a normal distribution error model and using uncertainty width,significant change rate of slopes,coefficient of variation,and degree of deviation.The daily runoff series from 1971 to 2020 at five hydrological stations(i.e.,Huangheyan,Tangnaihai,and Lanzhou in the Yellow River Source Region,Xianyang in the Weihe River Catchment,and Heishiguan in the Yiluo River Catchment)in the water conservation zone of Yellow River are collected for our study.Results showed that:(1)Flow regimes showed significant increases in the low flow magnitude,and significant decreases in the high and average flow magnitude,variability and duration at all the five stations.The magnitude,variability and duration metrics decreased significantly,and the frequency metrics increased significantly at Heishiguan.The low flow magnitude and timing metrics increased significantly,while the high flow magnitude,frequency and variability metrics decreased significantly at Xianyang.The low flow magnitude and high flow timing metrics increased significantly,while the low flow frequency,high flow magnitude and variability metrics decreased significantly in the Yellow River Source Region.(2)Observation uncertainty remarkably impacted the changes of 28.75% of total flow regime metrics at all the stations.The trends of 11.25% of total metrics changed from significance to insignificance,while those of 17.5% of total metrics changed from insignificance to significance.For the rest metrics,the trends remained the same,i.e.,significant(18.75%)and insignificant(52.50%)trends.(3)Observation uncertainty had the greatest impacts on the frequency metrics,especially at Xianyang,followed by duration,variability,timing and magnitude metrics.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62227814,52205040,22279070,and U21A20170)the Natural Science Basic Research Program of Shaanxi(2023-JC-QN-0140)+3 种基金the Young Talent Fund of Xi’an Association for Science and Technology(Grant No.959202313096)the Key Projects of the Shaanxi Province Natural Science Foundation(Grant No.2025JC-QYXQ-038)the Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems(Grant No.GZKF-202430)the National Key Research and Development Program of China(Grant No.2024YFB3311204)。
文摘Deep learning has emerged as a powerful tool for predicting the remaining useful life(RUL)of batteries,contingent upon access to ample data.However,the inherent limitations of data availability from traditional or accelerated life testing pose significant challenges.To mitigate the prediction accuracy issues arising from small sample sizes in existing intelligent methods,we introduce a novel data augmentation framework for RUL prediction.This framework harnesses the inherent high coincidence of degradation patterns exhibited by lithium-ion batteries to pinpoint the knee point,a critical juncture marking a significant shift in the degradation trajectory.By focusing on this critical knee point,we leverage the power of normalizing flow models to generate virtual data,effectively augmenting the training sample size.Additionally,we integrate a Bayesian Long Short-Term Memory network,optimized with Box-Cox transformation,to address the inherent uncertainty associated with predictions based on augmented data.This integration allows for a more nuanced understanding of RUL prediction uncertainties,offering valuable confidence intervals.The efficacy and superiority of the proposed framework are validated through extensive experiments on the CS2 dataset from the University of Maryland and the CrFeMnNiCo dataset from our laboratory.The results clearly demonstrate a substantial improvement in the confidence interval of RUL predictions compared to pre-optimization,highlighting the ability of the framework to achieve high-precision RUL predictions even with limited data.
基金supported by the National Science and Technology Council,Taiwan with grant numbers NSTC 112-2221-E-992-045,112-2221-E-992-057-MY3,and 112-2622-8-992-009-TD1.
文摘DDoS attacks represent one of the most pervasive and evolving threats in cybersecurity,capable of crippling critical infrastructures and disrupting services globally.As networks continue to expand and threats become more sophisticated,there is an urgent need for Intrusion Detection Systems(IDS)capable of handling these challenges effectively.Traditional IDS models frequently have difficulties in detecting new or changing attack patterns since they heavily depend on existing characteristics.This paper presents a novel approach for detecting unknown Distributed Denial of Service(DDoS)attacks by integrating Sliced Iterative Normalizing Flows(SINF)into IDS.SINF utilizes the Sliced Wasserstein distance to repeatedly modify probability distributions,enabling better management of high-dimensional data when there are only a few samples available.The unique architecture of SINF ensures efficient density estimation and robust sample generation,enabling IDS to adapt dynamically to emerging threats without relying heavily on predefined signatures or extensive retraining.By incorporating Open-Set Recognition(OSR)techniques,this method improves the system’s ability to detect both known and unknown attacks while maintaining high detection performance.The experimental evaluation on CICIDS2017 and CICDDoS2019 datasets demonstrates that the proposed system achieves an accuracy of 99.85%for known attacks and an F1 score of 99.99%after incremental learning for unknown attacks.The results clearly demonstrate the system’s strong generalization capability across unseen attacks while maintaining the computational efficiency required for real-world deployment.
基金supported by the National Key Research and Development Program of China(2021YFC2203004)the National Natural Science Foundation of China(NSFC)(12405076,12247187,and 12147103)+1 种基金the National Astronomical Data Center(NADC2023YDS-01)the Fundamental Research Funds for the Central Universities.
文摘The detection of gravitational waves by the LIGO-Virgo-KAGRA collaboration has ushered in a new era of observational astronomy,emphasizing the need for rapid and detailed parameter estimation and population-level analyses.Traditional Bayesian inference methods,particularly Markov chain Monte Carlo,face significant computational challenges when dealing with the high-dimensional parameter spaces and complex noise characteristics inherent in gravitational wave data.This review examines the emerging role of simulation-based inference methods in gravitational wave astronomy,with a focus on approaches that leverage machine-learning techniques such as normalizing flows and neural posterior estimation.We provide a comprehensive overview of the theoretical foundations underlying various simulation-based inference methods,including neural posterior estimation,neural ratio estimation,neural likelihood estimation,flow matching,and consistency models.We explore the applications of these methods across diverse gravitational wave data processing scenarios,from single-source parameter estimation and overlapping signal analysis to testing general relativity and conducting population studies.Although these techniques demonstrate speed improvements over traditional methods in controlled studies,their model-dependent nature and sensitivity to prior assumptions are barriers to their widespread adoption.Their accuracy,which is similar to that of conventional methods,requires further validation across broader parameter spaces and noise conditions.
基金supported in part by the Major Project for New Generation of AI (2018AAA0100400)the National Natural Science Foundation of China (61836014,U21B2042,62072457,62006231)the InnoHK Program。
文摘Monocular 3D object detection is challenging due to the lack of accurate depth information.Some methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input to augment the RGB images.Depth-based methods attempt to convert estimated depth maps to pseudo-LiDAR and then use LiDAR-based object detectors or focus on the perspective of image and depth fusion learning.However,they demonstrate limited performance and efficiency as a result of depth inaccuracy and complex fusion mode with convolutions.Different from these approaches,our proposed depth-guided vision transformer with a normalizing flows(NF-DVT)network uses normalizing flows to build priors in depth maps to achieve more accurate depth information.Then we develop a novel Swin-Transformer-based backbone with a fusion module to process RGB image patches and depth map patches with two separate branches and fuse them using cross-attention to exchange information with each other.Furthermore,with the help of pixel-wise relative depth values in depth maps,we develop new relative position embeddings in the cross-attention mechanism to capture more accurate sequence ordering of input tokens.Our method is the first Swin-Transformer-based backbone architecture for monocular 3D object detection.The experimental results on the KITTI and the challenging Waymo Open datasets show the effectiveness of our proposed method and superior performance over previous counterparts.
基金This work was supported in part by the National Key R&D Program of China 2021YFE0110500in part by the National Natural Science Foundation of China under Grant 62062021in part by the Guiyang Scientific Plan Project[2023]48-11.
文摘Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.
基金supported in part by the National Natural Science Foundation of China(62373380)。
文摘The application of multiple unmanned aerial vehicles(UAVs)for the pursuit and capture of unauthorized UAVs has emerged as a novel approach to ensuring the safety of urban airspace.However,pursuit UAVs necessitate the utilization of their own sensors to proactively gather information from the unauthorized UAV.Considering the restricted sensing range of sensors,this paper proposes a multi-UAV with limited visual field pursuit-evasion(MUV-PE)problem.Each pursuer has a visual field characterized by limited perception distance and viewing angle,potentially obstructed by buildings.Only when the unauthorized UAV,i.e.,the evader,enters the visual field of any pursuer can its position be acquired.The objective of the pursuers is to capture the evader as soon as possible without collision.To address this problem,we propose the normalizing flow actor with graph attention critic(NAGC)algorithm,a multi-agent reinforcement learning(MARL)approach.NAGC executes normalizing flows to augment the flexibility of policy network,enabling the agent to sample actions from more intricate distributions rather than common distributions.To enhance the capability of simultaneously comprehending spatial relationships among multiple UAVs and environmental obstacles,NAGC integrates the“obstacle-target”graph attention networks,significantly aiding pursuers in supporting search or pursuit activities.Extensive experiments conducted in a high-precision simulator validate the promising performance of the NAGC algorithm.
基金National Key Research and Development Program of China,No.2021YFC3201102National Natural Science Foundation of China,No.42071041,No.42171047。
文摘Runoff observation uncertainty is a key unsolved issue in the hydrology community.Existing studies mainly focused on observation uncertainty sources and their impacts on simulation performance,but the impacts on changes of flow regime characteristics remained rare.This study detects temporal changes in 16 flow regime metrics from five main components(i.e.,magnitude,frequency of events,variability,duration,and timing),and evaluates the effects of observation uncertainty on trends of flow regime metrics by adopting a normal distribution error model and using uncertainty width,significant change rate of slopes,coefficient of variation,and degree of deviation.The daily runoff series from 1971 to 2020 at five hydrological stations(i.e.,Huangheyan,Tangnaihai,and Lanzhou in the Yellow River Source Region,Xianyang in the Weihe River Catchment,and Heishiguan in the Yiluo River Catchment)in the water conservation zone of Yellow River are collected for our study.Results showed that:(1)Flow regimes showed significant increases in the low flow magnitude,and significant decreases in the high and average flow magnitude,variability and duration at all the five stations.The magnitude,variability and duration metrics decreased significantly,and the frequency metrics increased significantly at Heishiguan.The low flow magnitude and timing metrics increased significantly,while the high flow magnitude,frequency and variability metrics decreased significantly at Xianyang.The low flow magnitude and high flow timing metrics increased significantly,while the low flow frequency,high flow magnitude and variability metrics decreased significantly in the Yellow River Source Region.(2)Observation uncertainty remarkably impacted the changes of 28.75% of total flow regime metrics at all the stations.The trends of 11.25% of total metrics changed from significance to insignificance,while those of 17.5% of total metrics changed from insignificance to significance.For the rest metrics,the trends remained the same,i.e.,significant(18.75%)and insignificant(52.50%)trends.(3)Observation uncertainty had the greatest impacts on the frequency metrics,especially at Xianyang,followed by duration,variability,timing and magnitude metrics.
