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In-Sensor Reservoir Computing Employing Reconfigurable Optoelectronic Transistors for Multi-Task Learning 认领 引用
1
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-Source Fusion with Patch-Guided Multi-Task Learning for Power Prediction of Offshore Wind Farm Clusters 认领 引用
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作者 Weijia Tang Qiang Li Ningyu Zhang 《Energy Engineering》 EI 2026年第7期371-388,共18页
Large-scale offshore wind farm clusters(OWFCs)have been increasingly connected to the power grid,and requires advanced forecasting models to enhance the prediction accuracy of OWFC's power output.This paper propos... Large-scale offshore wind farm clusters(OWFCs)have been increasingly connected to the power grid,and requires advanced forecasting models to enhance the prediction accuracy of OWFC's power output.This paper proposes a multi-source fusion with patch-guided multi-task learning for power prediction of offshore wind farm clusters.Unlike traditional graph-based approaches that rely on predefined topological relationships,which are limited in capturing the highly similar but rapidly changing meteorological conditions among closely spaced offshore farms,the proposed model employs a parameter-sharing multi-task learning network to achieves both independence and correlation among offshore wind farm clusters,followed by utilizing a dynamically weighted multi-task loss function to gradually optimize the network parameters.Moreover,the proposed model applies the patch-guided feature learning module further enables natural alignment and fusion of multi-source data.To demonstrate the performance of the proposed model,experiments were conducted on offshore wind farm clusters in three different regions.The results show that the proposed model can obtain an average accuracy improvement of around 24.31%for MAE and 19.l4%for RMSE,ensuring prediction accuracy and robustness. 展开更多
关键词 Wind farm clusters patch-based feature learning dynamic weight loss function
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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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Robust Graph Diffusion for Multi-Task Learning in Ultra-High Voltage Direct Current Monitoring Systems 认领 引用
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作者 Shaojie Wang Chuliang Xue +3 位作者 Hui Li Rui Zhu Xiaoping Ren Limei Hu 《Journal of Beijing Institute of Technology》 EI CAS 2026年第2期150-158,共9页
In this work,we investigate the problem of multi-task learning(MTL)in ultra-high voltage direct current(UHVDC)monitoring systems.Considering the measurements are affected by wireless channel impairments,typically char... In this work,we investigate the problem of multi-task learning(MTL)in ultra-high voltage direct current(UHVDC)monitoring systems.Considering the measurements are affected by wireless channel impairments,typically characterized by block fading and link noise.Such channel imperfections significantly degrade the performance of distributed estimation in real-world power system environments.Based on the graph signal processing method,we propose the multi-task robust decoupled diffusion least mean square algorithm(MT-RDDLMS).Specifically,a decoupled adapt-then-combine strategy is introduced to reduce the influence of wireless channels on data exchange among measurement units.Moreover,an average estimation method with an adaptive smoothing factor is developed to further suppress link noise and enhance estimation accuracy.Simulation results confirm the robustness and effectiveness of the proposed algorithm under realistic wireless channel conditions. 展开更多
关键词 graph signal processing distributed multitask learning ultra-high voltage direct cur-rent(UHVDC)systems average estimation
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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://gffzz723e88f798a44b18hqfkobk9xvkn06wnu.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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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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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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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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DEEP NEURAL NETWORKS COMBINING MULTI-TASK LEARNING FOR SOLVING DELAY INTEGRO-DIFFERENTIAL EQUATIONS 认领 引用 被引量：1
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作者 WANG Chen-yao SHI Feng 《数学杂志》 2025年第1期13-38,共26页
Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay di... Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data. 展开更多
关键词 Delay integro-differential equation Multi-task learning parameter sharing structure deep neural network sequential training scheme
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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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A Multi-Task Learning Framework for Joint Sub-Nyquist Wideband Spectrum Sensing and Modulation Recognition 认领 引用
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作者 Dong Xin Stefanos Bakirtzis +1 位作者 Zhang Jiliang Zhang Jie 《China Communications》 SCIE EI CSCD 2025年第1期128-138,共11页
The utilization of millimeter-wave frequencies and cognitive radio(CR)are promising ways to increase the spectral efficiency of wireless communication systems.However,conventional CR spectrum sensing techniques entail... The utilization of millimeter-wave frequencies and cognitive radio(CR)are promising ways to increase the spectral efficiency of wireless communication systems.However,conventional CR spectrum sensing techniques entail sampling the received signal at a Nyquist rate,and they are not viable for wideband signals due to their high cost.This paper expounds on how sub-Nyquist sampling in conjunction with deep learning can be leveraged to remove this limitation.To this end,we propose a multi-task learning(MTL)framework using convolutional neural networks for the joint inference of the underlying narrowband signal number,their modulation scheme,and their location in a wideband spectrum.We demonstrate the effectiveness of the proposed framework for real-world millimeter-wave wideband signals collected by physical devices,exhibiting a 91.7% accuracy in the joint inference task when considering up to two narrowband signals over a wideband spectrum.Ultimately,the proposed data-driven approach enables on-the-fly wideband spectrum sensing,combining accuracy,and computational efficiency,which are indispensable for CR and opportunistic networking. 展开更多
关键词 automated modulation classification cognitive radio convolutional neural networks deep learning spectrum sensing sub-Nyquist sampling
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Nuclear mass based on the multi-task learning neural network method 认领 引用 被引量：24
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作者 Xing-Chen Ming Hong-Fei Zhang +3 位作者 Rui-Rui Xu Xiao-Dong Sun Yuan Tian Zhi-Gang Ge 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2022年第4期96-103,共8页
The global nuclear mass based on the macroscopic-microscopic model was studied by applying a newly designed multi-task learning artificial neural network(MTL-ANN). First, the reported nuclear binding energies of 2095 ... The global nuclear mass based on the macroscopic-microscopic model was studied by applying a newly designed multi-task learning artificial neural network(MTL-ANN). First, the reported nuclear binding energies of 2095 nuclei(Z ≥ 8, N ≥ 8) released in the latest Atomic Mass Evaluation AME2020 and the deviations between the fitting result of the liquid drop model(LDM)and data from AME2020 for each nucleus were obtained.To compensate for the deviations and investigate the possible ignored physics in the LDM, the MTL-ANN method was introduced in the model. Compared to the single-task learning(STL) method, this new network has a powerful ability to simultaneously learn multi-nuclear properties,such as the binding energies and single neutron and proton separation energies. Moreover, it is highly effective in reducing the risk of overfitting and achieving better predictions. Consequently, good predictions can be obtained using this nuclear mass model for both the training and validation datasets and for the testing dataset. In detail, the global root mean square(RMS) of the binding energy is effectively reduced from approximately 2.4 MeV of LDM to the current 0.2 MeV, and the RMS of Sn, Spcan also reach approximately 0.2 MeV. Moreover, compared to STL, for the training and validation sets, 3-9% improvement can be achieved with the binding energy, and 20-30% improvement for Sn, Sp;for the testing sets, the reduction in deviations can even reach 30-40%, which significantly illustrates the advantage of the current MTL. 展开更多
关键词 Macroscopic–microscopic model Binding energy Neural network Multi-task learning
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Convective Storm VIL and Lightning Nowcasting Using Satellite and Weather Radar Measurements Based on Multi-Task Learning Models 认领 引用 被引量：3
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作者 Yang LI Yubao LIU +3 位作者 Rongfu SUN Fengxia GUO Xiaofeng XU Haixiang XU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2023年第5期887-899,共13页
Convective storms and lightning are among the most important weather phenomena that are challenging to forecast.In this study,a novel multi-task learning(MTL)encoder-decoder U-net neural network was developed to forec... Convective storms and lightning are among the most important weather phenomena that are challenging to forecast.In this study,a novel multi-task learning(MTL)encoder-decoder U-net neural network was developed to forecast convective storms and lightning with lead times for up to 90 min,using GOES-16 geostationary satellite infrared brightness temperatures(IRBTs),lightning flashes from Geostationary Lightning Mapper(GLM),and vertically integrated liquid(VIL)from Next Generation Weather Radar(NEXRAD).To cope with the heavily skewed distribution of lightning data,a spatiotemporal exponent-weighted loss function and log-transformed lightning normalization approach were developed.The effects of MTL,single-task learning(STL),and IRBTs as auxiliary input features on convection and lightning nowcasting were investigated.The results showed that normalizing the heavily skew-distributed lightning data along with a log-transformation dramatically outperforms the min-max normalization method for nowcasting an intense lightning event.The MTL model significantly outperformed the STL model for both lightning nowcasting and VIL nowcasting,particularly for intense lightning events.The MTL also helped delay the lightning forecast performance decay with the lead times.Furthermore,incorporating satellite IRBTs as auxiliary input features substantially improved lightning nowcasting,but produced little difference in VIL forecasting.Finally,the MTL model performed better for forecasting both lightning and the VIL of organized convective storms than for isolated cells. 展开更多
关键词 convection/lightning nowcasting multi-task learning geostationary satellite weather radar U-net model
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Multi-task learning for seismic elastic parameter inversion with the lateral constraint of angle-gather difference 认领 引用 被引量：3
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作者 Pu Wang Yi-An Cui +4 位作者 Lin Zhou Jing-Ye Li Xin-Peng Pan Ya Sun Jian-Xin Liu 《Petroleum Science》 SCIE EI CAS CSCD 2024年第6期4001-4009,共9页
Pre-stack seismic inversion is an effective way to investigate the characteristics of hydrocarbon-bearing reservoirs.Multi-parameter application is the key to identifying reservoir lithology and fluid in pre-stack inv... Pre-stack seismic inversion is an effective way to investigate the characteristics of hydrocarbon-bearing reservoirs.Multi-parameter application is the key to identifying reservoir lithology and fluid in pre-stack inversion.However,multi-parameter inversion may bring coupling effects on the parameters and destabilize the inversion.In addition,the lateral recognition accuracy of geological structures receives great attention.To address these challenges,a multi-task learning network considering the angle-gather difference is proposed in this work.The deep learning network is usually assumed as a black box and it is unclear what it can learn.However,the introduction of angle-gather difference can force the deep learning network to focus on the lateral differences,thus improving the lateral accuracy of the prediction profile.The proposed deep learning network includes input and output blocks.First,angle gathers and the angle-gather difference are fed into two separate input blocks with Res Net architecture and Unet architecture,respectively.Then,three elastic parameters,including P-and S-wave velocities and density,are simultaneously predicted based on the idea of multi-task learning by using three separate output blocks with the same convolutional network layers.Experimental and field data tests demonstrate the effectiveness of the proposed method in improving the prediction accuracy of seismic elastic parameters. 展开更多
关键词 Seismic inversion Multi-task learning network Angle gathers Lateral accuracy Elastic parameter
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MMLUP: Multi-Source & Multi-Task Learning for User Profiles in Social Network 认领 引用 被引量：2
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作者 Dongjie Zhu Yuhua Wang +5 位作者 Chuiju You Jinming Qiu Ning Cao Chenjing Gong Guohua Yang Helen Min Zhou 《Computers, Materials & Continua》 SCIE EI 2019年第9期1105-1115,共11页
With the rapid development of the mobile Internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media data.How to integ... With the rapid development of the mobile Internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media data.How to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services,marketing,and recommendation systems.In this paper,we propose Multi-source&Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a user.Firstly,we design their own feature extraction models for multiple heterogeneous data sources.Secondly,we design a shared layer to fuse multiple heterogeneous data sources as general shared representation for multi-task learning.Thirdly,we design each task’s own unique presentation layer for discriminant output of specific-task.Finally,we design a weighted loss function to improve the learning efficiency and prediction accuracy of each task.Our experimental results on more than 5000 Sina Weibo users demonstrate that our approach outperforms state-of-the-art baselines for inferring gender,age and region of social media users. 展开更多
关键词 User profiles multi-source multi-task learning social network
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Serial structure multi-task learning method for predicting reservoir parameters 认领 引用 被引量：2
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作者 Xu Bin-Sen Li Ning +4 位作者 Xiao Li-Zhi Wu Hong-Liang Feng-Zhou Wang Bing Wang Ke-Wen 《Applied Geophysics》 SCIE CSCD 2022年第4期513-527,604,共15页
Buiding data-driven models using machine learning methods has gradually become a common approach for studying reservoir parameters.Among these methods,deep learning methods are highly effective.From the perspective of... Buiding data-driven models using machine learning methods has gradually become a common approach for studying reservoir parameters.Among these methods,deep learning methods are highly effective.From the perspective of multi-task learning,this paper uses six types of logging data—acoustic logging(AC),gamma ray(GR),compensated neutron porosity(CNL),density(DEN),deep and shallow lateral resistivity(LLD)and shallow lateral resistivity(LLS)—that are inputs and three reservoir parameters that are outputs to build a porosity saturation permeability network(PSP-Net)that can predict porosity,saturation,and permeability values simultaneously.These logging data are obtained from 108 training wells in a medium₋low permeability oilfield block in the western district of China.PSP-Net method adopts a serial structure to realize transfer learning of reservoir-parameter characteristics.Compared with other existing methods at the stage of academic exploration to simulating industrial applications,the proposed method overcomes the disadvantages inherent in single-task learning reservoir-parameter prediction models,including easily overfitting and heavy model-training workload.Additionally,the proposed method demonstrates good anti-overfitting and generalization capabilities,integrating professional knowledge and experience.In 37 test wells,compared with the existing method,the proposed method exhibited an average error reduction of 10.44%,27.79%,and 28.83%from porosity,saturation,permeability calculation.The prediction and actual permeabilities are within one order of magnitude.The training on PSP-Net are simpler and more convenient than other single-task learning methods discussed in this paper.Furthermore,the findings of this paper can help in the re-examination of old oilfield wells and the completion of logging data. 展开更多
关键词 Deep learning Multi-task learning Reservoir-parameter prediction
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