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A Deep Learning–Based Bias Correction Model for Tropical Cyclone Track and Intensity towards Forecasting of the TianXing Large Weather Model 认领 引用
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作者 Shijin YUAN Xingzhou WANG +3 位作者 Bin MU Guansong WANG Zeyi NIU Hao LI 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2026年第3期612-630,共19页
Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,i... Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,its TC forecasts still require enhancement.Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods.To address this,we introduce CycloneBCNet,a deep-learning model designed to correct TianXing’s TC forecast biases by leveraging spatial and temporal data.CycloneBCNet utilizes the SimVP(simpler yet better video prediction)framework with spatial attention to highlight cyclone core regions in forecast fields.It also incorporates TC trend information(center position,maximum wind speed,and minimum sea level pressure)via an LSTM(long short-term memory)module.These TC vectors are derived from post-processed TianXing forecasts.By fusing features from forecast fields and TC vectors,CycloneBCNet corrects biases across multiple lead times.At a 96-h lead time,the track error reduces from 162.4 to 86.4 km,the wind speed error from 17.2 to 6.69 m s-1,and the pressure error from 22.2 to 9.36 hPa.Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times.Intensity corrections prioritize inner-core dynamics,particularly the eye and eyewall,while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid-to upper-level features as the forecast duration increases.These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles,including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction. 展开更多
关键词 tropical cyclone TianXing large weather model bias correction interpretability analysis deep learning-based model
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Research and Practice of a New Training Model for Software Engineering Courses Based on Generative AI and OBE Concepts 认领 引用
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作者 Shengshai Zhang Xiaodong Yu +1 位作者 Jianhui Jiang Lixiao Zhang 《计算机教育》 2026年第3期139-147,共9页
With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE ... With the advent of the AI era,how can students effectively utilize generative AI large models to assist in course learning?At the same time,how can teachers utilize generative AI tools and the teaching concept of OBE to stimulate students’innovative consciousness and teamwork ability,enabling students to identify some problems in a certain industry or field and creatively propose feasible solutions,and truly achieve the cultivation of new models in software engineering course teaching with the assistance of generative AI tools?This paper presents research and practice on a new model for cultivating software engineering courses that integrates generative AI and OBE,introduces the specific process of teaching reform and practice,and finally explains the achievements of teaching reform. 展开更多
关键词 Generative AI OBE Software engineering Teaching reform
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Coupled atmosphere-ocean data assimilation for enhanced medium-range prediction of the North Atlantic Oscillation 认领 引用
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作者 Bin MU Zijun ZHAO +2 位作者 Shijin YUAN Bo QIN Guokun DAI 《Science China Earth Sciences》 SCIE EI CSCD 2026年第7期2663-2681,共19页
Accurate prediction of the North Atlantic Oscillation(NAO)is of significant societal importance,yet it is hindered by a“weather-climate prediction gap”on the intermediate timescales that bridge these two forecasts.T... Accurate prediction of the North Atlantic Oscillation(NAO)is of significant societal importance,yet it is hindered by a“weather-climate prediction gap”on the intermediate timescales that bridge these two forecasts.This gap arises because predictability on this timescale is neither primarily determined by atmospheric initial conditions nor fully controlled by slowly evolving boundary forcings like the ocean;instead,it critically depends on the precise capture of ocean-atmosphere coupling processes.Currently,the Earth System Models(ESMs)predominantly use uncoupled data assimilation(UDA)methods,which can trigger“initialization shocks”characterized by dynamical imbalances that degrade forecast accuracy.To address this limitation,this study utilizes a nudging-based weakly coupled data assimilation(WCDA)method to improve 15-day medium-range NAO prediction through the generation of more physically coherent initial fields.Experiments are conducted within the CESM-iCTF model,a modification of the Community Earth System Model(CESM)developed by our previous work that incorporates an artificial intelligence-based convective trigger function(AI-CTF)to better simulate critical airsea interactions.Contrasting WCDA initialization with traditional UDA,our results demonstrate that WCDA reduces the 15-day NAO forecast error by 8%compared to atmosphere-only assimilation(ADA)and by 30%relative to ocean-only assimilation(ODA).Notably,WCDA forecast closely approaches the performance of the ECMWF operational system while consistently surpassing the uncoupled NCEP and UKMO forecasts.This superior performance stems from the fact that WCDA creates a physically coherent and balanced initial state,which mitigates initialization shock from air-sea inconsistencies and effectively suppresses error growth,thereby translating initial-state advantages into sustained forecast skill.We further find a positive correlation between the relative improvement of WCDA over ADA and NAO event duration,attributed to the persistent feedback provided by sea surface temperatures to the atmosphere via sensible and latent heat fluxes.Furthermore,sensitivity experiments reveal a clear temporal hand-off of predictability.During the period of 1–10 days,predictability is governed primarily by the initial atmospheric dynamical and thermal state(wind fields,temperature,and surface pressure),which is essential for suppressing nonlinear error growth in the NAO's key dipole regions.During 11–15 days,predictability becomes critically dependent on the slowly evolving ocean temperature,which acts to mitigate the amplification of error as it propagates downstream.These findings highlight the essential role of generating physically coherent coupled initial states for weather-climate prediction and offer clear scientific guidance for advancing next-generation forecast systems and optimizing future observational networks. 展开更多
关键词 Coupled data assimilation North Atlantic Oscillation NAO forecast Medium-range forecast Ocean-atmosphere interaction
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Prediction of the summertime Northwest Pacific subtropical high based on ConvLSTM 认领 引用
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作者 Fei Yang Jing Ma +3 位作者 Hongxia Lan Bin Mu Shijin Yuan Jing-Jia Luo 《Atmospheric and Oceanic Science Letters》 CAS CSCD 2026年第2期14-19,共6页
The Northwest Pacific subtropical high(NWPSH)significantly affects East Asian weather and climate,rendering the prediction of its intensity and location critically important.This study aims to evaluate the performance... The Northwest Pacific subtropical high(NWPSH)significantly affects East Asian weather and climate,rendering the prediction of its intensity and location critically important.This study aims to evaluate the performance of the Convolutional Long and Short-Term Memory(ConvLSTM)model for predicting the summertime 500 hPa geopotential height and NWPSH intensity and area at a lead time of three months,and to compare it with the dynamical models of the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS1.0)and the Canadian Seasonal to Interannual Prediction System Version 2(CanSIPSv2).The mean latitude-weighted RMSE(RMSEw),anomaly correlation coefficient(ACC),and NWPSH indices are used as evaluation metrics.For both summer mean and monthly prediction,the ConvLSTM model outperforms the two dynamical models in terms of RMSEw and ACC for the 500 hPa geopotential height over the western Pacific region.The correlation coefficients between the NWPSH intensity index predicted by the ConvLSTM model and the observations are higher than those obtained from the two dynamical models.Regarding the NWPSH area index,the ConvLSTM model shows more stable performance.Particularly in August,the improvement of the ConvLSTM model compared to the two dynamical models is more significant,indicating the robust capability in capturing late-summer circulation patterns.Therefore,the ConvLSTM model demonstrates significant potential for summer NWPSH prediction,offering a new perspective and approach for climate prediction in this region. 展开更多
关键词 ConvLSTM Northwest Pacific subtropical high Deep learning Climate prediction
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Structure-Aware Diffusion Image Outpainting for Echocardiographic Field-of-View Extension 认领 引用
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作者 Ruijia He Yixin Hu +3 位作者 Jinze Liu Qixin Zhang Duo Peng Yanyan Chen 《Computers, Materials & Continua》 SCIE EI 2026年第9期1698-1718,共21页
Transthoracic Echocardiography(TTE)often suffers from a limited field of view(FoV),which may obscure peripheral cardiac structures and hinder comprehensive visual assessment.Although FoV extension can be formulated as... Transthoracic Echocardiography(TTE)often suffers from a limited field of view(FoV),which may obscure peripheral cardiac structures and hinder comprehensive visual assessment.Although FoV extension can be formulated as outpainting,public studies on echocardiographic FoV outpainting remain scarce and are mainly represented by cGAN-based reconstruction methods such as echoGAN.However,in noisy ultrasound images with weak boundaries,the challenge is not only realistic texture synthesis,but also structural continuity across the observed-generated boundary.To address this issue,we propose a structure-aware diffusion framework for echocardiographic FoV outpainting.To the best of our knowledge,this is the first work to introduce diffusion models into this task,moving beyond the existing cGAN-based formulation.Our framework combines a diffusion baseline,a Structural Cue Encoder(SCE)for structure-sensitive conditioning,and Structure-aware Diffusion Learning(SDL)for structural regularization during denoising.Experiments show clear and consistent improvements over cGAN-based reconstruction,producing more coherent structures,smoother transitions,and better FoV extension quality. 展开更多
关键词 Field-of-view extension diffusion model image generation outpainting structure-aware learning transthoracic echocardiography
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Concurrent bug detection and localization based on deep learning 认领 引用
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作者 FENG Zuocheng ZHANG Kaiwen +1 位作者 CHEN Yiming LIU Guanjun 《井冈山大学学报（自然科学版）》 2026年第4期60-72,共13页
Concurrency bugs,which are caused by improper synchronization of shared resources in multi-threaded or distributed systems,are notoriously hard to detect and thus compromise software reliability and security.Existing ... Concurrency bugs,which are caused by improper synchronization of shared resources in multi-threaded or distributed systems,are notoriously hard to detect and thus compromise software reliability and security.Existing deep learning methods face three main limitations:the absence of large and dedicated datasets of diverse concurrency bugs;insufficient representation of concurrency semantics;and the inability of binary classification results to provide finer-grained debug information such as precise bug locations.To address these problems,this study proposes a novel method for effective concurrency bug detection and localization.A dedicated concurrency bug dataset is constructed to facilitate model training and evaluation.The method integrates a pre-trained model with a heterogeneous graph neural network(GNN)by incorporating a newly designed Concurrency-Aware Code Property Graph,which concisely and effectively characterizes concurrency semantics.To further facilitate debugging,the authors employ SubgraphX,a GNN-based interpretability method,to explore the graphs and localize concurrency bugs precisely,mapping them to specific subgraphs of source code.Experimental results show that,on average,the proposed method achieves an improvement of 10%in accuracy and precision,and 26%in recall,compared to state-of-the-art methods across diverse evaluation settings. 展开更多
关键词 concurrency bug bug detection bug localization deep learning
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TianXing:A Linear Complexity Transformer Model with Explicit Attention Decay for Global Weather Forecasting 认领 引用 被引量：2
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作者 Shijin YUAN Guansong WANG +1 位作者 Bin MU Feifan ZHOU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2025年第1期9-25,共17页
In this paper,we introduce TianXing,a transformer-based data-driven model designed with physical augmentation for skillful and efficient global weather forecasting.Previous data-driven transformer models such as Pangu... In this paper,we introduce TianXing,a transformer-based data-driven model designed with physical augmentation for skillful and efficient global weather forecasting.Previous data-driven transformer models such as Pangu-Weather,FengWu,and FuXi have emerged as promising alternatives for numerical weather prediction in weather forecasting.However,these models have been characterized by their substantial computational resource consumption during training and limited incorporation of explicit physical guidance in their modeling frameworks.In contrast,TianXing applies a linear complexity mechanism that ensures proportional scalability with input data size while significantly diminishing GPU resource demands,with only a marginal compromise in accuracy.Furthermore,TianXing proposes an explicit attention decay mechanism in the linear attention derived from physical insights to enhance its forecasting skill.The mechanism can reweight attention based on Earth's spherical distances and learned sparse multivariate coupling relationships,promptingTianXing to prioritize dynamically relevant neighboring features.Finally,to enhance its performance in mediumrange forecasting,TianXing employs a stacked autoregressive forecast algorithm.Validation of the model's architecture is conducted using ERA5 reanalysis data at a 5.625°latitude-longitude resolution,while a high-resolution dataset at 0.25°is utilized for training the actual forecasting model.Notably,the TianXing exhibits excellent performance,particularly in the Z500(geopotential height)and T850(temperature)fields,surpassing previous data-driven models and operational fullresolution models such as NCEP GFS and ECMWF IFS,as evidenced by latitude-weighted RMSE and ACC metrics.Moreover,the TianXing has demonstrated remarkable capabilities in predicting extreme weather events,such as typhoons. 展开更多
关键词 weather forecast deep learning physics augmentation linear attention
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EAAC-S2S:East Asian Atmospheric Circulation S2S Forecasting with a Deep Learning Model Considering Multi-Sphere Coupling 认领 引用
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作者 Bin MU Yuxuan CHEN +2 位作者 Shijin YUAN Bo QIN Zhenchen LIU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2025年第7期1442-1462,共21页
Subseasonal-to-seasonal(S2S)forecasting for East Asian atmospheric circulation poses significant challenges for conventional numerical weather prediction(NWP)models.Recently,deep learning(DL)models have demonstrated s... Subseasonal-to-seasonal(S2S)forecasting for East Asian atmospheric circulation poses significant challenges for conventional numerical weather prediction(NWP)models.Recently,deep learning(DL)models have demonstrated significant potential in further enhancing S2S forecasts beyond the capabilities of NWP models.However,most current DLbased S2S forecasting models largely overlook the role of global predictors from multiple spheres,such as ocean,land,and atmosphere domains,that are crucial for effective S2S forecasting.In this study,we introduce EAAC-S2S,a tailored DL model for S2S forecasting of East Asian atmospheric circulation.EAAC-S2S employs the cross-attention mechanism to couple atmospheric circulations over East Asia with representative multi-sphere(i.e.,atmosphere,land,and ocean)variables,providing pentad-averaged circulation forecasts up to 12 pentads ahead throughout all seasons.Experimental results demonstrate,on the S2S time scale,that EAAC-S2S consistently outperforms the European Centre for MediumRange Weather Forecasts(ECMWF)Ensemble Prediction System by decreasing the root-mean-square error(RMSE)by3.8%and increasing the anomaly correlation coefficient(ACC)by 8.6%,averaged across all 17 predictands.Our system also shows good skill for examples of heatwaves and the South China Sea Subtropical High Intensity Index(SCSSHII).Moreover,quantitative interpretability analysis including multi-sphere attribution and attention visualization are conducted for the first time in a DL S2S model,where the traced predictability aligns well with prior meteorological knowledge.We hope that our results have the potential to advance research in data-driven S2S forecasting. 展开更多
关键词 East Asian atmospheric circulation subseasonal-to-seasonal forecasting multi-sphere coupling deep learning interpretability
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Cluster Overlap as Objective Function 认领 引用
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作者 Pasi Fränti Claude Cariou Qinpei Zhao 《Computers, Materials & Continua》 SCIE EI 2025年第12期4687-4704,共18页
K-means uses the sum-of-squared error as the objective function to minimize within-cluster distances.We show that,as a consequence,it also maximizes between-cluster variances.This means that the two measures do not pr... K-means uses the sum-of-squared error as the objective function to minimize within-cluster distances.We show that,as a consequence,it also maximizes between-cluster variances.This means that the two measures do not provide complementary information and that using only one is enough.Based on this property,we propose a new objective function called cluster overlap,which is measured intuitively as the proportion of points shared between the clusters.We adopt the new function within k-means and present an algorithm called overlap k-means.It is an alternative way to design a k-means algorithm.A localized variant is also provided by limiting the overlap calculation to the neighboring points. 展开更多
关键词 Clustering k-means overlap measure within-cluster distance between-cluster distance arbitrary-shape clusters
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Two-Stage Early Exiting From Globality Towards Reliability 认领 引用
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作者 Jianing He Qi Zhang +1 位作者 Hongyun Zhang Duoqian Miao 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第4期1019-1032,共14页
Early exiting has shown significant potential in accelerating the inference of pre-trained language models(PLMs)by allowing easy samples to exit from shallow layers.However,existing early exiting methods primarily rel... Early exiting has shown significant potential in accelerating the inference of pre-trained language models(PLMs)by allowing easy samples to exit from shallow layers.However,existing early exiting methods primarily rely on local information from individual samples to estimate prediction uncertainty for making exiting decisions,overlooking the global information provided by the sample population.This impacts the estimation of prediction uncertainty,compromising the reliability of exiting de-cisions.To remedy this,inspired by principal component analysis(PCA),the authors define a residual score to capture the deviation of features from the principal space of the sample population,providing a global perspective for estimating prediction uncertainty.Building on this,a two-stage exiting strategy is proposed that integrates global information from residual scores with local information from energy scores at both the decision and feature levels.This strategy incorporates three-way decisions to enable more reliable exiting decisions for boundary region samples by delaying judgement.Extensive experiments on the GLUE benchmark validate that the method achieves an average speed-up ratio of 2.17×across all tasks with minimal per-formance degradation.Additionally,it surpasses the state-of-the-art E-LANG by 11%in model acceleration,along with a performance improvement of 0.6 points,demonstrating a better performance-efficiency trade-off. 展开更多
关键词 early exiting inference acceleration pre-trained language model principal component analysis three-way decisions
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Assessment of Tropical Cyclone Disaster Damage Based on Learnable Inter-City Interaction GNN 认领 引用
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作者 Shijin YUAN Laiyu YANG +2 位作者 Bin MU Bo QIN Yanjun HUANG 《Journal of Meteorological Research》 SCIE CSCD 2025年第5期1146-1166,共21页
Tropical cyclones(TCs)are one of the most frequent disastrous weather events in China,causing widespread damage.Traditional approaches for assessing TC disaster damage treat the TC affected regions as isolated units a... Tropical cyclones(TCs)are one of the most frequent disastrous weather events in China,causing widespread damage.Traditional approaches for assessing TC disaster damage treat the TC affected regions as isolated units and ignore inter-regional interactions,resulting in underestimation of complex dynamics in disaster damage assessment.In this paper,we developed an original TC disaster damage dataset,with each sample representing a unique disaster event,incorporating city-specific multi-dimensional features and damage indicators.Then,using provincial administrative divisions in China as examples,we innovatively assigned cities as nodes and constructed inter-city interaction graphs.To align with the physical interactions,a deep learning model named TC-Damage is specifically established.It includes an edge building module and a backbone.The edge building module aims to construct inter-city interaction features from multiple perspectives.The backbone employs a multi-layer Graph Neural Network(GNN)based on Graph Sample and Aggregate(GraphSAGE)and Jumping Knowledge Network(JKNet)to learn comprehensive and hierarchical features of inter-city interactions.A loss function combined with focal loss and node-level loss is proposed to address data imbalance and to enforce representation node distribution.Multiple experiments demonstrate that TC-Damage outperforms other assessment methods and effectively identifies high-contribution factors.Explainability analysis of Super Typhoon Lekima reveals that key edges are adjacent to cities with high disaster factors and social development levels and significantly overlap with edges exhibiting strong inter-city interactions. 展开更多
关键词 tropical cyclone disaster damage assessment graph neural network(GNN) inter-city interaction explainability
An Interpretable NAO Daily Prediction Model Considering Weighted Causal Effects of Physical Processes 认领 引用
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作者 Shijin YUAN Haoyu WU +3 位作者 Bin MU Yuehan CUI Bo QIN Hao LI 《Journal of Meteorological Research》 SCIE CSCD 2025年第5期1126-1145,共20页
The North Atlantic Oscillation(NAO)is a major atmospheric mode in the Northern Hemisphere,characterized by frequent fluctuations in sea level pressure(SLP)across the North Atlantic sector.In the development and evolut... The North Atlantic Oscillation(NAO)is a major atmospheric mode in the Northern Hemisphere,characterized by frequent fluctuations in sea level pressure(SLP)across the North Atlantic sector.In the development and evolution of the NAO,various dynamic physical processes such as the El Niño-Southern Oscillation(ENSO)and Madden-Julian Oscillation(MJO)influence it to different extents.Previous studies using numerical models or deep learning models for daily NAO forecasts have not accounted for the impact of these dynamic physical processes,making accurate and stable NAO forecasting still a challenge.In this study,the Varimax-Rotation Principal Component Analysis(PCA)and data-driven causal inference are used to identify key dynamic physical processes linked to the NAO.Based on these,a deep learning model called the NAO-Causal Weighted Model(NAO-CWM)is developed,which incorporates causal relationships to assign different weights to these processes,providing effective daily forecasts with a lead time of 1-14 days.Evaluation results show that NAO-CWM outperforms the advanced numerical models,offering reliable NAO forecasts and a better capturing of NAO variation trends. 展开更多
关键词 North Atlantic Oscillation Varimax-Rotation PCA causal inference deep learning weather forecasting
ESCAPE: an efficient and safe distributed UAV swarm exploration framework with collision avoidance perception 认领 引用
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作者 Yaoyang Bao Siyuan Du +5 位作者 Qingwei Jiang Yixuan Li Bochao Zhao Gang Wang Qingwen Liu Mingliang Xiong 《Autonomous Intelligent Systems》 EI 2025年第1期21-33,共13页
Significant progress has been made in distributed unmanned aerial vehicle(UAV)swarm exploration.In complex scenarios,existing methods typically rely on shared trajectory information for collision avoidance,but communi... Significant progress has been made in distributed unmanned aerial vehicle(UAV)swarm exploration.In complex scenarios,existing methods typically rely on shared trajectory information for collision avoidance,but communication timeliness issues may result in outdated trajectories being referenced when making collision avoidance decisions,preventing timely responses to the motion changes of other UAVs,thus elevating the collision risk.To address this issue,this paper proposes a new distributed UAV swarm exploration framework.First,we introduce an improved global exploration strategy that combines the exploration task requirements with the surrounding obstacle distribution to plan an efficient and safe coverage path.Secondly,we design a collision risk prediction method based on relative distance and relative velocity,which effectively assists UAVs in making timely collision avoidance decisions.Lastly,we propose a multi-objective local trajectory optimization function that considers the positions of UAVs and static obstacles,thereby planning safe flight trajectories.Extensive simulations and real-world experiments demonstrate that this framework enables safe and efficient exploration in complex environments. 展开更多
关键词 UAV Swarm Exploration Collision Avoidance Exploration Efficiency Path Planning
A Multi-View Sequence Fusion Network for Improving Tropical Cyclone Intensity Estimation 认领 引用
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作者 Shijin YUAN Guansong WANG +2 位作者 Yake ZHZNG Bin MU Feifan ZHOU 《Journal of Meteorological Research》 SCIE CSCD 2026年第3期619-635,共17页
Accurate real-time estimation of the tropical cyclone(TC)intensity is crucial for TC forecasting and disaster management.Conventional intensity estimation methods like the Dvorak technique are subjective,and most deep... Accurate real-time estimation of the tropical cyclone(TC)intensity is crucial for TC forecasting and disaster management.Conventional intensity estimation methods like the Dvorak technique are subjective,and most deep learning(DL)approaches ignore the natural variability and time lag between the actual intensity and the remote sensing cloud pattern imagery.This paper introduces a multi-view sequence fusion network(MSFN),which is a novel DL framework that enhances TC intensity estimation by combining multi-channel cloud pattern imagery,sea surface temperature(SST),and statistical factors.The MSFN features three key innovations:a rotation-equivariant convolutional neural network(CNN)encoder to extract robust cloud pattern features,a transformer-based feature interactor to capture both temporal and cross-view dependencies,and a Gated Memory Fusion(GMF)module to dynamically integrate multi-view data and enhance robustness against noise.Tested on the full 2016-2019 TC data over the western North Pacific(WNP)basin,the MSFN achieves a coefficient of determination(R2)of 0.845 and a root mean square error(RMSE)of 4.6-5.2 m s-1.When further evaluated on a strictly homogeneous subset of the same dataset,the MSFN achieves an R2 of 0.848 and an RMSE of 4.3 m s-1.Statistical analyses demonstrate that MSFN significantly outperforms the Advanced Dvorak Technique(ADT)and achieves comparable overall performance to the Satellite Consensus(SATCON)algorithm,with superior accuracy for weaker systems below 45 knots(=23.1 m s-1).Interpretability analyses and ablation studies confirm the model’s focus on meteorologically significant features and its own component synergy.The MSFN offers a reliable,interpretable tool for objective TC intensity estimation,advancing the TC forecasting capabilities. 展开更多
关键词 tropical cyclone intensity estimation deep learning satellite observation rotation-equivariant convolutional neural network(CNN) feature fusion
Improving S2S Precipitation Forecast over China via a Deep Learning Model with Multi-Sphere Causality-Linked Predictors 认领 引用
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作者 Bin MU Hao GUO +3 位作者 Shijin YUAN Yuxuan CHEN Yuehan CUI Yanjun HUANG 《Journal of Meteorological Research》 SCIE CSCD 2026年第1期254-272,共19页
Numerical models face persistent challenges in subseasonal-to-seasonal(S2S)precipitation forecasting over China due to strong precipitation variability and complex multi-sphere coupling at S2S timescales.In recent yea... Numerical models face persistent challenges in subseasonal-to-seasonal(S2S)precipitation forecasting over China due to strong precipitation variability and complex multi-sphere coupling at S2S timescales.In recent years,artificial intelligence(AI)-based post-processing has emerged as a promising approach,owing to its capacity to learn complex nonlinear relationships and correct systematic model biases from historical data.However,most existing AI-based methods neglect the spatial structure and physical interactions among multi-sphere predictors(e.g.,atmosphere,ocean,and land),limiting their ability to capture the underlying dynamics required for physical consistency.This study develops an S2S precipitation bias-correction network(S2SPre-BCNet)based on a cycle-consistent generative adversarial network(Cycle GAN),which incorporates causality-selected multi-sphere predictors as conditional inputs to improve weekly accumulated precipitation forecasts from the ECMWF S2S system over China at lead times of 1-6 weeks.Compared to the ECMWF S2S,S2SPre-BCNet reduces mean RMSE(root mean square error)by 11.6%(maximum 17.2%),increases mean ACC(anomaly correlation coefficient)by 27.2%(maximum 49.2%),and raises mean HSS(Heidke skill score)by 1.23%(maximum 2.12%).Across the case studies,S2SPre-BCNet lowers the absolute mean precipitation error by 16.4%.Additionally,interpretability analyses reveal that multi-sphere predictors contribute distinctly across lead times,and the model focuses on physically meaningful regions where precipitation dynamics are most complex,highlighting the potential of causality-informed AI for operational S2S bias correction.This study underscores that AI techniques augmented by causality-based predictor selection can effectively correct biases in forecasts produced by numerical models,enabling their use in operational forecasting. 展开更多
关键词 precipitation subseasonal-to-seasonal(S2S) bias correction causal discovery artificial intelligence
Three-dimensional dynamic probabilistic shaping high-security transmission scheme based on dual physical layer encryption for seven-core fibers 认领 引用 被引量：1
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作者 Yaya Mao Bo Liu +7 位作者 Jingrui Huang Jianxin Ren Shuaidong Chen Xiangyu Wu Yongfeng Wu Xiumin Song Zhipeng Qi Jie Cui 《Chinese Optics Letters》 SCIE EI CAS CSCD 2025年第3期14-20,共7页
In this paper,a carrier-less amplitude and phase modulation passive optical network(CAP-PON)scheme is proposed based on dynamic probabilistic shaping(DPS)and Rubik’s cube encryption in optical access networks.The key... In this paper,a carrier-less amplitude and phase modulation passive optical network(CAP-PON)scheme is proposed based on dynamic probabilistic shaping(DPS)and Rubik’s cube encryption in optical access networks.The key is generated from a novel five-dimensional entangled chaos model for dynamic probabilistic shaping and Rubik’s cube encryption.To verify the performance of the encryption scheme,an experimental demonstration of 70 Gb/s(7×10 Gb/s)encrypted DPS-3D-CAP signal transmission over 2 km weakly coupled 7-core fiber is performed.The key space of the new five-dimensional entangled chaos model reaches 10173,and the interference level reaches 100%.Experimental results show that the receiver sensitivity increases by 1.47 dB compared to the conventional uniform 3D-CAP due to the introduction of dynamic probabilistic shaping. 展开更多
关键词 CAP-PON probabilistic shaping high security chaotic encryption
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Frequency-informed transformer for real-time water pipeline leak detection 认领 引用 被引量：1
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作者 Fengnian Liu Ding Wang +1 位作者 Junya Tang Lei Wang 《Autonomous Intelligent Systems》 EI 2025年第1期241-250,共10页
Water pipeline leaks pose significant risks to urban infrastructure,leading to water wastage and potential structural damage.Existing leak detection methods often face challenges,such as heavily relying on the manual ... Water pipeline leaks pose significant risks to urban infrastructure,leading to water wastage and potential structural damage.Existing leak detection methods often face challenges,such as heavily relying on the manual selection of frequency bands or complex feature extraction,which can be both labour-intensive and less effective.To address these limitations,this paper introduces a Frequency-Informed Transformer model,which integrates the Fast Fourier Transform and self-attention mechanisms to enhance water pipe leak detection accuracy.Experimental results show that FiT achieves 99.9%accuracy in leak detection and 98.7%in leak type classification,surpassing other models in both accuracy and processing speed,with an efficient response time of 0.25 seconds.By significantly simplifying key features and frequency band selection and improving accuracy and response time,the proposed method offers a potential solution for real-time water leak detection,enabling timely interventions and more effective pipeline safety management. 展开更多
关键词 Frequency-Informed Transformer Water Pipeline Leak Detection Leak Classification Vibration Sensor Data Analysis
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