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Microseismic signal processing and rockburst disaster identification:A multi-task deep learning and machine learning approach 认领 引用 被引量：1
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作者 Chunchi Ma Weihao Xu +3 位作者 Xuefeng Ran Tianbin Li Hang Zhang Dongwei Xing 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第1期441-456,共16页
Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely id... Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters. 展开更多
关键词 Underground engineering Microseismic signal processing Deep learning Multi-task Rockburst identification
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Multi-task U-net inversion of synthetic look-ahead logging-while-drilling data 认领 引用
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作者 Shun Zhang Wen-Xiu Zhang +3 位作者 Wen-Xuan Chen Peng-Fei Liang Wen-Yang Wang Xing-Han Li 《Petroleum Science》 SCIE EI CAS CSCD 2026年第4期1908-1928,共21页
Electromagnetic look-ahead logging while drilling instruments detect the electrical characteristics of undrilled formations,enabling proactive decision-making.Real-time geological insight ahead of the drill bit is cri... Electromagnetic look-ahead logging while drilling instruments detect the electrical characteristics of undrilled formations,enabling proactive decision-making.Real-time geological insight ahead of the drill bit is critical for effective geosteering.This study introduces a multi-task U-net neural network that simultaneously inverts multiple formation parameters real-time.Six datasets,each corresponding to different electromagnetic components,were used to train six specialized neural networks.All networks exhibited rapid convergence and successfully inverted 60,000 sample in 15 s,satisfying real-time requirements.Residual and relative error analyses reveal that the multi-component network delivers the highest accuracy.Sensitivity analysis shows that coaxial and coplanar components are more sensitive to conductivity variations,whereas coaxial and cross-components excel at resolving interface positions.The yy component displays the strongest sensitivity to anisotropy.Compared with the traditional Levenberg-Marquardt algorithm,the proposed method demonstrates improved accuracy and efficiency.Moreover,the Levenberg-Marquardt inversion with the neural network output as initial models further enhances accuracy.Benchmark comparisons reveal that the multi-task U-net outperforms various mainstream machine learning and deep learning models,including LSTM,FCN,ResNet,and XGBoost,in both inversion accuracy and generalization.Moreover,sensitivity analyses to noise and near-bit geological complexity reveal that,while the proposed model experiences some performance degradation under high noise levels or highly heterogeneous backgrounds,it maintains strong robustness under moderate noise conditions and achieves reliable inversion results in two-layer geological settings.These results establish the multi-task U-net as a fast,accurate,and robust tool for real-time electromagnetic look-ahead inversion in geosteering applications. 展开更多
关键词 Multi-task U-net Look-ahead Anisotropy Multiple components Inversion
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In-Sensor Reservoir Computing Employing Reconfigurable Optoelectronic Transistors for Multi-Task Learning 认领 引用
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作者 Shanshan Jiang Hainan Zhang +4 位作者 Kesheng Wang Shuo Cheng Can Fu Huanhuan Wei Gang He 《Rare Metals》 SCIE EI CAS CSCD 2026年第4期629-639,共11页
The edge deployment of artificial intelligence has driven the exploitation of compact,energy-efficient information processing systems that integrate sensing,memory,and multi-task processing functions.However,conventio... The edge deployment of artificial intelligence has driven the exploitation of compact,energy-efficient information processing systems that integrate sensing,memory,and multi-task processing functions.However,conventional vision systems suffer from significant energyime overhead,extra hardware costs,and an unaffordable algorithm.Herein,we demonstrate an in-sensor computing system employing reconfigurable optoelectronic transistors(ROETs)for multi-task learning.These transistors exhibit reconfigurable volatile and nonvolatile characteristics under both optical and electrical stimuli.Capitalizing on this reconfigurability,we establish an in-sensor reservoir computing(RC)system operating in multi-signal modes:volatile dynamics function as the reservoir,whereas nonvolatile properties configure the readout layer.The abundant optoelectronic reservoir states display exceptional feature separability and prolonged stability in the ambient atmosphere.Such a reliable RC system successfully achieves multi-task processing of images.Notably,under the optoelectronic coordination mode,it effectively alleviates feature degradation while sustaining consistently high recognition accuracy.Furthermore,the system exhibits remarkable dynamic information processing capabilities,achieving recognition accuracies of 89.02%for dynamic gestures and 96.04%for moving vehicles recognition,respectively.Supplemental functionalities,including light adaptation and image sharpening,are also implemented.This work presents a configurable multimodal platform featuring a flexible in-sensor reservoir computing architecture,providing a potential solution for efficient multi-task processing. 展开更多
关键词 in-sensor reservoir computing multi-task learning neuromorphic applications optoelectronic transistors reconfigurable devices
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Cooperative Beam Selection for RIS-Aided Terahertz MIMO Networks via Multi-Task Learning 认领 引用
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作者 Ma Xinying Chen Gong Wang Xiaofei 《China Communications》 SCIE EI CSCD 2026年第2期211-227,共17页
Reconfigurable intelligent surface(RIS)have been cast as a promising alternative to alleviate blockage vulnerability and enhance coverage capability for terahertz(THz)communications.Owing to large-scale array elements... Reconfigurable intelligent surface(RIS)have been cast as a promising alternative to alleviate blockage vulnerability and enhance coverage capability for terahertz(THz)communications.Owing to large-scale array elements at transceivers and RIS,the codebook based beamforming can be utilized in a computationally efficient manner.However,the codeword selection for analog beamforming is an intractable combinatorial optimization(CO)problem.To this end,by taking the CO problem as a classification problem,a multi-task learning based analog beam selection(MTL-ABS)framework is developed to implement cooperative beam selection concurrently at transceivers and RIS.In addition,residual network and self-attention mechanism are used to combat the network degradation and mine intrinsic THz channel features.Finally,the network convergence is analyzed from a blockwise perspective,and numerical results demonstrate that the MTL-ABS framework greatly decreases the beam selection overhead and achieves near optimal sum-rate compared with heuristic search based counterparts. 展开更多
关键词 beam selection multi-task learning reconfigurable intelligent surface(RIS) terahertz(THz)communications
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A surface emphasized multi-task learning framework for surface property predictions:A case study of magnesium intermetallics 认领 引用
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作者 Gaoning Shi Yaowei Wang +3 位作者 Kun Yang Yuan Qiu Hong Zhu Xiaoqin Zeng 《Journal of Magnesium and Alloys》 SCIE EI CAS CSCD 2026年第1期216-227,共12页
Surface properties of crystals are critical in many fields,including electrochemistry and photoelectronics,the efficient prediction of which can expedite the design and optimization of catalysts,batteries,alloys etc.H... Surface properties of crystals are critical in many fields,including electrochemistry and photoelectronics,the efficient prediction of which can expedite the design and optimization of catalysts,batteries,alloys etc.However,we are still far from realizing this vision due to the rarity of surface property-related databases,especially for multicomponent compounds,due to the large sample spaces and limited computing resources.In this work,we present a surface emphasized multi-task crystal graph convolutional neural network(SEM-CGCNN)to predict multiple surface properties simultaneously from crystal structures.The model is evaluated on a dataset of 3526 surface energies and work functions of binary magnesium intermetallics obtained through first-principles calculations,and obvious improvements are observed both in efficiency and accuracy over the original CGCNN model.By transferring the pre-trained model to the datasets of pure metals and other intermetallics,the fine-tuned SEM-CGCNN outperforms learning from scratch and can be further applied to other surface properties and materials systems.This study could be a paradigm for the end-to-end mapping of atomic structures to anisotropic surface properties of crystals,which provides an efficient framework to understand and screen materials with desired surface characteristics. 展开更多
关键词 Graph neural networks Multi-task learning Surface energy Work function Intermetallic compounds Mg alloy
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Multi-task hierarchical network for semantic understanding of air traffic controller-pilot communication 认领 引用
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作者 Xiaoxiao ZHANG Qihan DENG +4 位作者 Yang YANG Shengsheng QIAN Yi HUI Yanbo ZHU Kaiquan CAI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第3期499-515,共17页
Flight situational awareness in civil aviation relies on the semantic understanding of both the key details and the full picture from the Air Traffic Controller(ATCo)and pilot communication.This paper proposes a novel... Flight situational awareness in civil aviation relies on the semantic understanding of both the key details and the full picture from the Air Traffic Controller(ATCo)and pilot communication.This paper proposes a novel end-to-end Multi-Task Hierarchical Network(MTHN)for automatically understanding ATCo-pilot communication,handling slot filling,role detection,and intent recognition at different levels while adaptively integrating them.Specifically,we introduce a wordbased knowledge-masked slot distillation module that constructs an ATC knowledge base to dynamically mask keywords during teacher-student distillation.Considering the distinct intent differences between ATCos and pilots,we design a sentence-based role-aware intent attention module that extracts role label space vectors as context to enrich intent representations.To exploit the complementarity across different semantic levels in ATCo-pilot communication,we explicitly develop an adaptive bi-interaction flow module that dynamically explores semantic dependencies among tasks.Extensive experiments on real-world datasets collected in China show the superior performance of MTHN,compared to state-of-the-art baselines in both general natural language understanding and ATC-specific text processing.Our results highlight that MTHN achieves 99.26%,97.25%,and 96.22%accuracy across key slots,as well as 96.59%accuracy in speaker role classification.Moreover,it can perceive multi-label deep intents behind sentences.These analytical findings demonstrate the potential to reduce human errors in high-concurrency ATCo-pilot interactions under dense operational conditions. 展开更多
关键词 Air traffic control Attention mechanism Hierarchical modeling Multi-task learning Semantic understanding
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Synthetic Data-Driven Multi-Task Framework for UAV Detection and Classification 认领 引用
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作者 ZHANG Jingkai LI Xinde +2 位作者 WEI-WANG Zichao WANG Ziyao MA Ke 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第1期209-220,共12页
With the rapid development of unmanned aerial vehicle(UAV)technology,there is an increasingly urgent demand for intelligent detection,identification,and performance parameter inference techniques.However,existing UAV ... With the rapid development of unmanned aerial vehicle(UAV)technology,there is an increasingly urgent demand for intelligent detection,identification,and performance parameter inference techniques.However,existing UAV datasets face severe challenges including high acquisition costs,labor-intensive annotation,and data scarcity,and lack methods for directly predicting functional performance from single images.This paper proposes a UAV analysis framework based on synthetic data generation and multi-task deep learning.We construct an adaptive dataset generation system based on Unreal Engine 5,incorporating UAV size classification,adaptive distance adjustment algorithms,and enhanced 3D-to-2D coordinate transformation techniques for automated high-quality synthetic data generation.We design a multi-task collaborative learning network integrating visual information,distance information,and uncertainty quantification modules,supporting UAV detection,parameter prediction,functional classification,and size prediction.Experimental results demonstrate high similarity between synthetic and real data(Fréchet inception distance is 21.05),with parameter prediction achieving mean absolute percentage errors of 29.1%and 27.0%for maximum speed and altitude respectively,and military-civilian classification accuracy reaching 62.5%.This method provides a low-cost,efficient solution for intelligent UAV analysis with considerable practical value. 展开更多
关键词 unmanned aerial vehicle(UAV) synthetic dataset generation multi-task learning parameter prediction uncertainty quantification
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TCR-RoadNet:A Transformer-Enhanced Multi-Task Deep Learning Architecture for Real-Time Road Damage Detection and Segmentation 认领 引用
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作者 Olzhas Olzhayev Bakhytzhan Kulambayev Azizah Suliman 《Computers, Materials & Continua》 SCIE EI 2026年第9期1024-1053,共30页
Automated road damage detection is a critical component of intelligent transportation systems,enabling efficient infrastructure maintenance and improved traffic safety.However,existing approaches often suffer from lim... Automated road damage detection is a critical component of intelligent transportation systems,enabling efficient infrastructure maintenance and improved traffic safety.However,existing approaches often suffer from limited contextual understanding,insufficient segmentation accuracy,and suboptimal real-time performance.This study presents TCR-RoadNet,a transformer-enhanced multi-task deep learning architecture designed for simultaneous road damage detection and segmentation in real-world driving environments.The proposed framework integrates a multi-scale convolutional backbone with a Transformer Context Refinement(TCR)module to capture both fine-grained structural details and long-range spatial dependencies across feature scales.To further enhance performance,a Decoupled Detection Head(DDH)is employed to stabilize localization and classification learning,while a Classification Refinement Module(CRM)improves inter-class discrimination using region-based feature enhancement.In addition,a Boundary-Aware Segmentation Head is introduced to generate precise damage contours by incorporating edge-sensitive learning mechanisms.The model is evaluated on the RDD2022 dataset,complemented by an extended dataset containing additional road images collected from Kazakhstan to increase environmental diversity and robustness.Experimental results demonstrate that the proposed method achieves 0.9416 precision,0.9235 recall,and 0.8718 mAP@50,along with a segmentation performance of 0.8129 mean Intersection over Union(mIoU),while maintaining real-time inference at 57 FPS.Comparative analysis and ablation studies confirm that each architectural component contributes to consistent performance gains in both detection and segmentation tasks.Qualitative results further illustrate the robustness of the proposed framework under varying lighting,weather,and road conditions.The proposed approach offers a scalable and efficient solution for real-time road condition monitoring and intelligent infrastructure management systems. 展开更多
关键词 Road damage detection pavement defect segmentation transformer computer vision multi-task deep learning intelligent transportation systems
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Task-Structured Curriculum Learning for Multi-Task Distillation:Enhancing Step-by-Step Knowledge Transfer in Language Models 认领 引用
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作者 Ahmet Ezgi Aytug Onan 《Computers, Materials & Continua》 SCIE EI 2026年第3期1647-1673,共27页
Knowledge distillation has become a standard technique for compressing large language models into efficient student models,but existing methods often struggle to balance prediction accuracy with explanation quality.Re... Knowledge distillation has become a standard technique for compressing large language models into efficient student models,but existing methods often struggle to balance prediction accuracy with explanation quality.Recent approaches such as Distilling Step-by-Step(DSbS)introduce explanation supervision,yet they apply it in a uniform manner that may not fully exploit the different learning dynamics of prediction and explanation.In this work,we propose a task-structured curriculum learning(TSCL)framework that structures training into three sequential phases:(i)prediction-only,to establish stable feature representations;(ii)joint prediction-explanation,to align task outputs with rationale generation;and(iii)explanation-only,to refine the quality of rationales.This design provides a simple but effective modification to DSbS,requiring no architectural changes and adding negligible training cost.We justify the phase scheduling with ablation studies and convergence analysis,showing that an initial prediction-heavy stage followed by a balanced joint phase improves both stability and explanation alignment.Extensive experiments on five datasets(e-SNLI,ANLI,CommonsenseQA,SVAMP,and MedNLI)demonstrate that TSCL consistently outperforms strong baselines,achieving gains of+1.7-2.6 points in accuracy and 0.8-1.2 in ROUGE-L,corresponding to relative error reductions of up to 21%.Beyond lexical metrics,human evaluation and ERASERstyle faithfulness diagnostics confirm that TSCL produces more faithful and informative explanations.Comparative training curves further reveal faster convergence and lower variance across seeds.Efficiency analysis shows less than 3%overhead in wall-clock training time and no additional inference cost,making the approach practical for realworld deployment.This study demonstrates that a simple task-structured curriculum can significantly improve the effectiveness of knowledge distillation.By separating and sequencing objectives,TSCL achieves a better balance between accuracy,stability,and explanation quality.The framework generalizes across domains,including medical NLI,and offers a principled recipe for future applications in multimodal reasoning and reinforcement learning. 展开更多
关键词 Knowledge distillation curriculum learning language models multi-task learning step-by-step learning
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PRIME:A Physics-Guided Residual Integrated Framework for Multi-Task Aircraft Engine Diagnostics 认领 引用
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作者 Ouail Mjahed Soukaina Mjahed 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期333-370,共38页
Accurate aircraft engine diagnostics is essential for ensuring operational safety and enabling predictive maintenance under heterogeneous operating conditions.Although deep learning models can effectively capture high... Accurate aircraft engine diagnostics is essential for ensuring operational safety and enabling predictive maintenance under heterogeneous operating conditions.Although deep learning models can effectively capture high-dimensional multivariate sensor dynamics,purely data-driven approaches often entangle operating-condition variability with degradation-sensitive patterns,which limits robustness and generalization.This paper introduces PRIME,a physics-guided residual integrated framework for multi-task aircraft engine diagnostics.Rather than embedding explicit thermodynamic equations or physical constraints into the optimization process,PRIME relies on a physically motivated residual decomposition strategy that separates operating-condition-driven nominal behavior from degradation-sensitive sensor deviations.Specifically,nominal responses are estimated from operating-condition representations and subtracted from observed sensor signals to isolate fault-relevant residual patterns.These residual representations are then processed by a hybrid temporal architecture combining temporal convolutional networks and transformer-based self-attention,enabling joint modeling of local degradation signatures and long-range temporal dependencies.Within a unified optimization framework,PRIME simultaneously performs Fault Detection(FD),Fault Type Classification(FTC),and Health State Estimation(HSE).Extensive experiments on NASA C-MAPSS,NCMAPSS,and the ALFA dataset show consistent and statistically significant improvements over strong baseline models.For FD and FTC,PRIME achieves gains of approximately 2%-4%over the strongest neural baselines evaluated under the same protocol,with larger margins over classical machine learning approaches.For HSE,PRIME yields more faithful degradation trajectories,leading to systematic reductions in estimation error across single-and multi-regime datasets.When Remaining Useful Life(RUL)is projected from the learned health trajectory through a thresholdbased mechanism,the resulting estimates also improve substantially,with RMSE reductions of up to about 22%under complex operating conditions.These results show that physics-guided residual disentanglement improves robustness,interpretability,and multi-task diagnostic performance.More broadly,they support the view that HSE provides a useful latent degradation representation for downstream prognostic assessment,even though RUL is not directly optimized by the model. 展开更多
关键词 Physics-guided learning residual modeling multi-task learning aircraft engine diagnostics prognostics and health management
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Short-Term Rolling Prediction of Tropical Cyclone Intensity Based on Multi-Task Learning with Fusion of Deviation-Angle Variance and Satellite Imagery 认领 引用 被引量：1
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作者 Wei TIAN Ping SONG +5 位作者 Yuanyuan CHEN Yonghong ZHANG Liguang WU Haikun ZHAO Kenny Thiam Choy LIM KAM SIAN Chunyi XIANG 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2025年第1期111-128,共18页
Tropical cyclones(TCs)are one of the most serious types of natural disasters,and accurate TC activity predictions are key to disaster prevention and mitigation.Recently,TC track predictions have made significant progr... Tropical cyclones(TCs)are one of the most serious types of natural disasters,and accurate TC activity predictions are key to disaster prevention and mitigation.Recently,TC track predictions have made significant progress,but the ability to predict their intensity is obviously lagging behind.At present,research on TC intensity prediction takes atmospheric reanalysis data as the research object and mines the relationship between TC-related environmental factors and intensity through deep learning.However,reanalysis data are non-real-time in nature,which does not meet the requirements for operational forecasting applications.Therefore,a TC intensity prediction model named TC-Rolling is proposed,which can simultaneously extract the degree of symmetry for strong TC convective cloud and convection intensity,and fuse the deviation-angle variance with satellite images to construct the correlation between TC convection structure and intensity.For TCs'complex dynamic processes,a convolutional neural network(CNN)is used to learn their temporal and spatial features.For real-time intensity estimation,multi-task learning acts as an implicit time-series enhancement.The model is designed with a rolling strategy that aims to moderate the long-term dependent decay problem and improve accuracy for short-term intensity predictions.Since multiple tasks are correlated,the loss function of 12 h and 24 h are corrected.After testing on a sample of TCs in the Northwest Pacific,with a 4.48 kt root-mean-square error(RMSE)of 6 h intensity prediction,5.78 kt for 12 h,and 13.94 kt for 24 h,TC records from official agencies are used to assess the validity of TC-Rolling. 展开更多
关键词 tropical cyclone intensity structure rolling prediction multi-task
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AS-SOMTF:A novel multi-task learning model for water level prediction by satellite remoting 认领 引用
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作者 Xin Su Zijian Qin +3 位作者 Weikang Feng Ziyang Gong Christian Esposito Sokjoon Lee 《Digital Communications and Networks》 SCIE EI CSCD 2025年第5期1554-1566,共13页
Satellite communication technology has emerged as a key solution to address the challenges of data transmission in remote areas.By overcoming the limitations of traditional terrestrial communication networks,it enable... Satellite communication technology has emerged as a key solution to address the challenges of data transmission in remote areas.By overcoming the limitations of traditional terrestrial communication networks,it enables long-distance data transmission anytime and anywhere,ensuring the timely and accurate delivery of water level data,which is particularly crucial for fishway water level monitoring.To enhance the effectiveness of fishway water level monitoring,this study proposes a multi-task learning model,AS-SOMTF,designed for real-time and comprehensive prediction.The model integrates auxiliary sequences with primary input sequences to capture complex relationships and dependencies,thereby improving representational capacity.In addition,a novel timeseries embedding algorithm,AS-SOM,is introduced,which combines generative inference and pooling operations to optimize prediction efficiency for long sequences.This innovation not only ensures the timely transmission of water level data but also enhances the accuracy of real-time monitoring.Compared with traditional models such as Transformer and Long Short-Term Memory(LSTM)networks,the proposed model achieves improvements of 3.8%and 1.4%in prediction accuracy,respectively.These advancements provide more precise technical support for water level forecasting and resource management in the Diqing Tibetan Autonomous Prefecture of the Lancang River,contributing to ecosystem protection and improved operational safety. 展开更多
关键词 Fish passages Water-level prediction Time series forecasting Multi-task learning Hierarchical clustering Satellite communication
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Joint Retrieval of PM2.5 Concentration and Aerosol Optical Depth over China Using Multi-Task Learning on FY-4A AGRI 认领 引用
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作者 Bo LI Disong FU +4 位作者 Ling YANG Xuehua FAN Dazhi YANG Hongrong SHI Xiang’ao XIA 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2025年第1期94-110,共17页
Aerosol optical depth(AOD)and fine particulate matter with a diameter of less than or equal to 2.5μm(PM2.5)play crucial roles in air quality,human health,and climate change.However,the complex correlation of AOD–... Aerosol optical depth(AOD)and fine particulate matter with a diameter of less than or equal to 2.5μm(PM2.5)play crucial roles in air quality,human health,and climate change.However,the complex correlation of AOD–PM2.5and the limitations of existing algorithms pose a significant challenge in realizing the accurate joint retrieval of these two parameters at the same location.On this point,a multi-task learning(MTL)model,which enables the joint retrieval of PM2.5concentration and AOD,is proposed and applied on the top-of-the-atmosphere reflectance data gathered by the Fengyun-4A Advanced Geosynchronous Radiation Imager(FY-4A AGRI),and compared to that of two single-task learning models—namely,Random Forest(RF)and Deep Neural Network(DNN).Specifically,MTL achieves a coefficient of determination(R2)of 0.88 and a root-mean-square error(RMSE)of 0.10 in AOD retrieval.In comparison to RF,the R2increases by 0.04,the RMSE decreases by 0.02,and the percentage of retrieval results falling within the expected error range(Within-EE)rises by 5.55%.The R2and RMSE of PM2.5retrieval by MTL are 0.84 and 13.76μg m~(-3)respectively.Compared with RF,the R2increases by 0.06,the RMSE decreases by 4.55μg m~(-3),and the Within-EE increases by 7.28%.Additionally,compared to DNN,MTL shows an increase of 0.01 in R2and a decrease of 0.02 in RMSE in AOD retrieval,with a corresponding increase of 2.89%in Within-EE.For PM2.5retrieval,MTL exhibits an increase of 0.05 in R2,a decrease of 1.76μg m~(-3)in RMSE,and an increase of 6.83%in Within-EE.The evaluation suggests that MTL is able to provide simultaneously improved AOD and PM2.5retrievals,demonstrating a significant advantage in efficiently capturing the spatial distribution of PM2.5concentration and AOD. 展开更多
关键词 AOD PM2.5 FY-4A multi-task learning joint retrieval
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DKP-ADS:Domain knowledge prompt combined with multi-task learning for assessment of foliar disease severity in staple crops 认领 引用 被引量：1
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作者 Yujiao Dan Xingcai Wu +5 位作者 Ya Yu Ziang Zou R.D.S.M Gunarathna Peijia Yu Yuanyuan Xiao Qi Wang 《The Crop Journal》 SCIE CAS CSCD 2025年第6期1939-1954,共16页
Staple crops are the cornerstone of the food supply but are frequently threatened by plant diseases.Effective disease management,including disease identification and severity assessment,helps to better address these c... Staple crops are the cornerstone of the food supply but are frequently threatened by plant diseases.Effective disease management,including disease identification and severity assessment,helps to better address these challenges.Currently,methods for disease severity assessment typically rely on calculating the area proportion of disease segmentation regions or using classification networks for severity assessment.However,these methods require large amounts of labeled data and fail to quantify lesion proportions when using classification networks,leading to inaccurate evaluations.To address these issues,we propose an automated framework for disease severity assessment that combines multi-task learning and knowledge-driven large-model segmentation techniques.This framework includes an image information processor,a lesion and leaf segmentation module,and a disease severity assessment module.First,the image information processor utilizes a multi-task learning strategy to analyze input images comprehensively,ensuring a deep understanding of disease characteristics.Second,the lesion and leaf segmentation module employ prompt-driven large-model technology to accurately segment diseased areas and entire leaves,providing detailed visual analysis.Finally,the disease severity assessment module objectively evaluates the severity of the disease based on professional grading standards by calculating lesion area proportions.Additionally,we have developed a comprehensive database of diseased leaf images from major crops,including several task-specific datasets.Experimental results demonstrate that our framework can accurately identify and assess the types and severity of crop diseases,even without extensive labeled data.Codes and data are available at http://gffzz723e88f798a44b18hfvobxknfq50k6ob6.ffgz.tsg.suse.edu.cn/. 展开更多
关键词 Domain knowledge Prompt-driven Multi-task learning Staple crop Assessment of disease severity
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MolP-PC:a multi-view fusion and multi-task learning framework for drug ADMET property prediction 认领 引用 被引量：1
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作者 Sishu Li Jing Fan +2 位作者 Haiyang He Ruifeng Zhou Jun Liao 《Chinese Journal of Natural Medicines》 SCIE CAS CSCD 2025年第11期1293-1300,共8页
The accurate prediction of drug absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties represents a crucial step in early drug development for reducing failure risk.Current deep learning approaches... The accurate prediction of drug absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties represents a crucial step in early drug development for reducing failure risk.Current deep learning approaches face challenges with data sparsity and information loss due to single-molecule representation limitations and isolated predictive tasks.This research proposes molecular properties prediction with parallel-view and collaborative learning(MolP-PC),a multi-view fusion and multi-task deep learning framework that integrates 1D molecular fingerprints(MFs),2D molecular graphs,and 3D geometric representations,incorporating an attention-gated fusion mechanism and multi-task adaptive learning strategy for precise ADMET property predictions.Experimental results demonstrate that MolP-PC achieves optimal performance in 27 of 54 tasks,with its multi-task learning(MTL)mechanism significantly enhancing predictive performance on small-scale datasets and surpassing single-task models in 41 of 54 tasks.Additional ablation studies and interpretability analyses confirm the significance of multi-view fusion in capturing multi-dimensional molecular information and enhancing model generalization.A case study examining the anticancer compound Oroxylin A demonstrates MolP-PC’s effective generalization in predicting key pharmacokinetic parameters such as half-life(T0.5)and clearance(CL),indicating its practical utility in drug modeling.However,the model exhibits a tendency to underestimate volume of distribution(VD),indicating potential for improvement in analyzing compounds with high tissue distribution.This study presents an efficient and interpretable approach for ADMET property prediction,establishing a novel framework for molecular optimization and risk assessment in drug development. 展开更多
关键词 Molecular ADMET prediction Multi-view fusion Attention mechanism Multi-task deep learning
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Skillful bias correction of offshore near-surface wind field forecasting based on a multi-task machine learning model 认领 引用 被引量：1
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作者 Qiyang Liu Anboyu Guo +5 位作者 Fengxue Qiao Xinjian Ma Yan-An Liu Yong Huang Rui Wang Chunyan Sheng 《Atmospheric and Oceanic Science Letters》 CSCD 2025年第5期28-35,共8页
Accurate short-term forecast of offshore wind fields is still challenging for numerical weather prediction models.Based on three years of 48-hour forecast data from the European Centre for Medium-Range Weather Forecas... Accurate short-term forecast of offshore wind fields is still challenging for numerical weather prediction models.Based on three years of 48-hour forecast data from the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System global model(ECMWF-IFS)over 14 offshore weather stations along the coast of Shandong Province,this study introduces a multi-task learning(MTL)model(TabNet-MTL),which significantly improves the forecast bias of near-surface wind direction and speed simultaneously.TabNet-MTL adopts the feature engineering method,utilizes mean square error as the loss function,and employs the 5-fold cross validation method to ensure the generalization ability of the trained model.It demonstrates superior skills in wind field correction across different forecast lead times over all stations compared to its single-task version(TabNet-STL)and three other popular single-task learning models(Random Forest,LightGBM,and XGBoost).Results show that it significantly reduces root mean square error of the ECMWF-IFS wind speed forecast from 2.20 to 1.25 m s−1,and increases the forecast accuracy of wind direction from 50%to 65%.As an explainable deep learning model,the weather stations and long-term temporal statistics of near-surface wind speed are identified as the most influential variables for TabNet-MTL in constructing its feature engineering. 展开更多
关键词 Forecast bias correction Wind field Multi-task learning Feature engineering Explainable AI
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A multi-task learning method for blast furnace gas forecasting based on coupling correlation analysis and inverted transformer 认领 引用
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作者 Sheng Xie Jing-shu Zhang +2 位作者 Da-tao Shi Yang Guo Qi Zhang 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2025年第10期3280-3297,共18页
Accurate forecasting of blast furnace gas(BFG)production is an essential prerequisite for reasonable energy scheduling and management to reduce carbon emissions.Coupling forecasting between BFG generation and consumpt... Accurate forecasting of blast furnace gas(BFG)production is an essential prerequisite for reasonable energy scheduling and management to reduce carbon emissions.Coupling forecasting between BFG generation and consumption dynamics was taken as the research object.A multi-task learning(MTL)method for BFG forecasting was proposed,which integrated a coupling correlation coefficient(CCC)and an inverted transformer structure.The CCC method could enhance key information extraction by establishing relationships between multiple prediction targets and relevant factors,while MTL effectively captured the inherent correlations between BFG generation and consumption.Finally,a real-world case study was conducted to compare the proposed model with four benchmark models.Results indicated significant reductions in average mean absolute percentage error by 33.37%,achieving 1.92%,with a computational time of 76 s.The sensitivity analysis of hyperparameters such as learning rate,batch size,and units of the long short-term memory layer highlights the importance of hyperparameter tuning. 展开更多
关键词 Byproduct gases forecasting Coupling correlation coefficient Multi-task learning Inverted transformer Bi-directional long short-term memory Blast furnace gas
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Explainable AI Based Multi-Task Learning Method for Stroke Prognosis 认领 引用
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作者 Nan Ding Xingyu Zeng +1 位作者 Jianping Wu Liutao Zhao 《Computers, Materials & Continua》 SCIE EI 2025年第9期5299-5315,共17页
Predicting the health status of stroke patients at different stages of the disease is a critical clinical task.The onset and development of stroke are affected by an array of factors,encompassing genetic predispositio... Predicting the health status of stroke patients at different stages of the disease is a critical clinical task.The onset and development of stroke are affected by an array of factors,encompassing genetic predisposition,environmental exposure,unhealthy lifestyle habits,and existing medical conditions.Although existing machine learning-based methods for predicting stroke patients’health status have made significant progress,limitations remain in terms of prediction accuracy,model explainability,and system optimization.This paper proposes a multi-task learning approach based on Explainable Artificial Intelligence(XAI)for predicting the health status of stroke patients.First,we design a comprehensive multi-task learning framework that utilizes the task correlation of predicting various health status indicators in patients,enabling the parallel prediction of multiple health indicators.Second,we develop a multi-task Area Under Curve(AUC)optimization algorithm based on adaptive low-rank representation,which removes irrelevant information from the model structure to enhance the performance of multi-task AUC optimization.Additionally,the model’s explainability is analyzed through the stability analysis of SHAP values.Experimental results demonstrate that our approach outperforms comparison algorithms in key prognostic metrics F1 score and Efficiency. 展开更多
关键词 Explainable AI stroke prognosis multi-task learning AUC optimization
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A Survey of Cooperative Multi-agent Reinforcement Learning for Multi-task Scenarios 认领 引用 被引量：7
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作者 Jiajun CHAI Zijie ZHAO +1 位作者 Yuanheng ZHU Dongbin ZHAO 《Artificial Intelligence Science and Engineering》 2025年第2期98-121,共24页
Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-... Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-robot control.Empowering cooperative MARL with multi-task decision-making capabilities is expected to further broaden its application scope.In multi-task scenarios,cooperative MARL algorithms need to address 3 types of multi-task problems:reward-related multi-task,arising from different reward functions;multi-domain multi-task,caused by differences in state and action spaces,state transition functions;and scalability-related multi-task,resulting from the dynamic variation in the number of agents.Most existing studies focus on scalability-related multitask problems.However,with the increasing integration between large language models(LLMs)and multi-agent systems,a growing number of LLM-based multi-agent systems have emerged,enabling more complex multi-task cooperation.This paper provides a comprehensive review of the latest advances in this field.By combining multi-task reinforcement learning with cooperative MARL,we categorize and analyze the 3 major types of multi-task problems under multi-agent settings,offering more fine-grained classifications and summarizing key insights for each.In addition,we summarize commonly used benchmarks and discuss future directions of research in this area,which hold promise for further enhancing the multi-task cooperation capabilities of multi-agent systems and expanding their practical applications in the real world. 展开更多
关键词 multi-task multi-agent reinforcement learning large language models
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MAMGBR: Group-Buying Recommendation Model Based on Multi-Head Attention Mechanism and Multi-Task Learning 认领 引用
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作者 Zongzhe Xu Ming Yu 《Computers, Materials & Continua》 SCIE EI 2025年第8期2805-2826,共22页
As the group-buying model shows significant progress in attracting new users,enhancing user engagement,and increasing platform profitability,providing personalized recommendations for group-buying users has emerged as... As the group-buying model shows significant progress in attracting new users,enhancing user engagement,and increasing platform profitability,providing personalized recommendations for group-buying users has emerged as a new challenge in the field of recommendation systems.This paper introduces a group-buying recommendation model based on multi-head attention mechanisms and multi-task learning,termed the Multi-head Attention Mechanisms and Multi-task Learning Group-Buying Recommendation(MAMGBR)model,specifically designed to optimize group-buying recommendations on e-commerce platforms.The core dataset of this study comes from the Chinese maternal and infant e-commerce platform“Beibei,”encompassing approximately 430,000 successful groupbuying actions and over 120,000 users.Themodel focuses on twomain tasks:recommending items for group organizers(Task Ⅰ)and recommending participants for a given group-buying event(Task Ⅱ).In model evaluation,MAMGBR achieves an MRR@10 of 0.7696 for Task I,marking a 20.23%improvement over baseline models.Furthermore,in Task II,where complex interaction patterns prevail,MAMGBR utilizes auxiliary loss functions to effectively model the multifaceted roles of users,items,and participants,leading to a 24.08%increase in MRR@100 under a 1:99 sample ratio.Experimental results show that compared to benchmark models,such as NGCF and EATNN,MAMGBR’s integration ofmulti-head attentionmechanisms,expert networks,and gating mechanisms enables more accurate modeling of user preferences and social associations within group-buying scenarios,significantly enhancing recommendation accuracy and platform group-buying success rates. 展开更多
关键词 Group-buying recommendation multi-head attention mechanism multi-task learning
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