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Prediction of 3D as-deposited layer geometry with sequential SDF-based spatio-temporal networks in wire arc additive manufacturing 认领 引用
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作者 Bowen Pang Zhi Zeng +6 位作者 Naixun Zhou Yiming Wu Long Chen Xuhui Zhang Jiajia Shen Qingwei Ji Ming Yin 《Additive Manufacturing Frontiers》 CAS CSCD 2026年第2期174-184,共11页
Wire arc additive manufacturing(WAAM)enables efficient fabrication of large-scale metallic components.However,the geometry of each as-deposited layer(ADL)is highly sensitive to coupled thermofluid dynamics and process... Wire arc additive manufacturing(WAAM)enables efficient fabrication of large-scale metallic components.However,the geometry of each as-deposited layer(ADL)is highly sensitive to coupled thermofluid dynamics and process fluctuations.These complex interactions make it difficult to maintain geometric stability,underscoring the need for accurate layer-wise profile predictions to support process planning,dimensional control,and defect prevention.This study presents ILPP-Net,an inter-layer profile prediction network that leverages a sequential signed distance field(SDF)representation to model the spatio-temporal evolution of multi-layer WAAM deposition.The point cloud of each layer is converted into a sequence of SDF cross-sections,which are combined with the process metadata,including global position,wire-feeding speed,deposition speed,arc current,and lateral torch offset,to predict the next-layer contour.A temporal context block(TCB)integrates geometric and process information through parametric fusion and bidirectional long short-term memory(BiLSTM),whereas a FiLM-modulated U-Net performs spatial regression to reconstruct a continuous SDF distribution.A WAAM system integrated with a 3D vision module was developed to acquire real layer datasets for training and evaluation.ILPP-Net achieves high prediction fidelity across different deposition strategies.For early thermally unstable layers(Layer 2-3),the model achieves an mloU of 0.85-0.86 with root mean square errors(RMSEs)of 0.192-0.187 mm,while performance improves for the more stabilized upper layers(Layers 7–10)to 0.89–0.90 mloU and 0.165–0.141 mm RMSE.Ablation studies show that TCB contributes to an accuracy gain of≈14.36%,highlighting the importance of temporal modeling.The proposed model was further deployed in a prediction-driven feed-forward compensation experiment,where the ILPP-based parameter adjustment significantly improved the dimensional accuracy.Relative to the open-loop deposition,the compensation strategy reduced the final height RMSE by 56.57%and the mean error by 62.4%,demonstrating the effectiveness of the model for geometry-aware adaptive control. 展开更多
关键词 Wire and arc additive manufacturing As-deposited layer Spatio-temporal deep learning Profile prediction
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Predicting Tropical Cyclone Genesis Location Using STAG-Net:A Spatio-Temporal Attention-Gated Network 认领 引用
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作者 Kalim Sattar Malik Muhammad Saad Missen +5 位作者 Syeda Zoupash Zahra Najia Saher Rab Nawaz Bashir Oumaima Saidani Shahid Kamal Muhammad I.Khan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期1112-1135,共24页
Tropical Cyclone(TC)genesis forecasting is an important aspect of early warning systems,as it allows the adoption of early warnings and mitigation plans.However,existing methods often rely on binary classification or ... Tropical Cyclone(TC)genesis forecasting is an important aspect of early warning systems,as it allows the adoption of early warnings and mitigation plans.However,existing methods often rely on binary classification or fail to capture the complex spatio-temporal dependencies that govern TC formation.To address this limitation,this study introduces STAG-Net,a novel Spatio-Temporal Attention-Gated Network designed to directly predict the geographical coordinates of TC genesis.The model uses multivariate variables of meteorological factors such as u-wind,v-wind,relative humidity,temperature,and large-scale dynamic features using a Convolutional Neural Network(CNN),Gated Recurrent Units(GRUs),and a channel-wise attention mechanism in identifying both spatial and temporal characteristics.The methodology takes the initial tropical disturbance data as an input and obtains spatial features in the ERA5 reanalysis dataset that covers 37 isobaric pressure levels.The study also investigates the effect of grid resolution on prediction performance,as four grid sizes were compared,namely 10 x 10,20×20,30×30,and 40×40.The experimental results demonstrate that STAG-Net significantly outperforms existing baselines such as the Dynamic Spatio-temporal model(DST),Spatial Attention Fusing Network(Saf-Net),and a temporal-only model.Notably,the model achieves an average MAE of 2.67°,MSE of 13.24,RMSE of 3.45,and R2 of 0.87045,corresponding to performance improvements of 9.75%,26.25%,12.92%,and 4.27%,respectively,over the baseline model.The results also indicate that the 30×30 grid configuration was found to be the most effective.The results highlight the significance of the proposed approach for the TC genesis location prediction task. 展开更多
关键词 Tropical cyclone genesis atmospheric dynamics spatio-temporal analysis deep learning reanalysis data
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Bi-STAT+:An Enhanced Bidirectional Spatio-Temporal Adaptive Transformer for Urban Traffic Flow Forecasting 认领 引用
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作者 Yali Cao Weijian Hu +3 位作者 Lingfang Li Minchao Li Meng Xu Ke Han 《Computers, Materials & Continua》 SCIE EI 2026年第2期963-985,共23页
Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficie... Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficiency of roadway infrastructure.However,existingmethods struggle in complex traffic scenarios due to static spatio-temporal embedding,restricted multi-scale temporal modeling,and weak representation of local spatial interactions.This study proposes Bi-STAT+,an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions:(1)an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations;(2)frequency-domain analysis in the temporal dimension for simultaneous high-frequency details and low-frequency trend extraction;and(3)an agent attention mechanism in the spatial dimension that enhances local feature extraction through dynamic weight allocation.Extensive experiments were performed on four distinct datasets,including two publicly benchmark datasets(PEMS04 and PEMS08)and two private datasets collected from Baotou and Chengdu,China.The results demonstrate that Bi-STAT+consistently outperforms existing methods in terms of MAE,RMSE,and MAPE,while maintaining strong robustness against missing data and noise.Furthermore,the results highlight that prediction accuracy improves significantly with higher sampling rates,providing crucial insights for optimizing real-world deployment scenarios. 展开更多
关键词 Traffic flow prediction spatio-temporal feature modeling transformer intelligent transportation deep learning
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Adaptive multi-view learning method for enhanced drug repurposing using chemical-induced transcriptional profiles, knowledge graphs, and large language models 认领 引用 被引量：1
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作者 Yudong Yan Yinqi Yang +9 位作者 Zhuohao Tong Yu Wang Fan Yang Zupeng Pan Chuan Liu Mingze Bai Yongfang Xie Yuefei Li Kunxian Shu Yinghong Li 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2025年第6期1354-1369,共16页
Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches ofte... Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches often rely on limited data sources and simplistic hypotheses,which restrict their ability to capture the multi-faceted nature of biological systems.This study introduces adaptive multi-view learning(AMVL),a novel methodology that integrates chemical-induced transcriptional profiles(CTPs),knowledge graph(KG)embeddings,and large language model(LLM)representations,to enhance drug repurposing predictions.AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning(MVL),matrix factorization,and ensemble optimization techniques to integrate heterogeneous multi-source data.Comprehensive evaluations on benchmark datasets(Fdata-set,Cdataset,and Ydataset)and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art(SOTA)methods,achieving superior accuracy in predicting drug-disease associations across multiple metrics.Literature-based validation further confirmed the model's predictive capabilities,with seven out of the top ten predictions corroborated by post-2011 evidence.To promote transparency and reproducibility,all data and codes used in this study were open-sourced,providing resources for pro-cessing CTPs,KG,and LLM-based similarity calculations,along with the complete AMVL algorithm and benchmarking procedures.By unifying diverse data modalities,AMVL offers a robust and scalable so-lution for accelerating drug discovery,fostering advancements in translational medicine and integrating multi-omics data.We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine. 展开更多
关键词 Drug repurposing Multi-view learning Chemical-induced transcriptional profile Knowledge graph Large language model Heterogeneous network
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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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Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences 认领 引用 被引量：2
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作者 Umair Muneer Butt Hadiqa Aman Ullah +3 位作者 Sukumar Letchmunan Iqra Tariq Fadratul Hafinaz Hassan Tieng Wei Koh 《Computers, Materials & Continua》 SCIE EI 2023年第3期5017-5033,共17页
Human Activity Recognition(HAR)is an active research area due to its applications in pervasive computing,human-computer interaction,artificial intelligence,health care,and social sciences.Moreover,dynamic environments... Human Activity Recognition(HAR)is an active research area due to its applications in pervasive computing,human-computer interaction,artificial intelligence,health care,and social sciences.Moreover,dynamic environments and anthropometric differences between individuals make it harder to recognize actions.This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications.It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network.Moreover,the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information.Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction.For temporal sequence,this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short TermMemory(BiLSTM)to capture longtermdependencies.Two state-of-the-art datasets,UCF101 and HMDB51,are used for evaluation purposes.In addition,seven state-of-the-art optimizers are used to fine-tune the proposed network parameters.Furthermore,this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network(CNN),where two streams use RGB data.In contrast,the other uses optical flow images.Finally,the proposed ensemble approach using max hard voting outperforms state-ofthe-art methods with 96.30%and 90.07%accuracies on the UCF101 and HMDB51 datasets. 展开更多
关键词 Human activity recognition deep learning transfer learning neural network ensemble learning spatio-temporal
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Spatio-Temporal Cellular Network Traffic Prediction Using Multi-Task Deep Learning for AI-Enabled 6G 认领 引用 被引量：1
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作者 Xiaochuan Sun Biao Wei +3 位作者 Jiahui Gao Difei Cao Zhigang Li Yingqi Li 《Journal of Beijing Institute of Technology》 EI CAS 2022年第5期441-453,共13页
Spatio-temporal cellular network traffic prediction at wide-area level plays an important role in resource reconfiguration,traffic scheduling and intrusion detection,thus potentially supporting connected intelligence ... Spatio-temporal cellular network traffic prediction at wide-area level plays an important role in resource reconfiguration,traffic scheduling and intrusion detection,thus potentially supporting connected intelligence of the sixth generation of mobile communications technology(6G).However,the existing studies just focus on the spatio-temporal modeling of traffic data of single network service,such as short message,call,or Internet.It is not conducive to accurate prediction of traffic data,characterised by diverse network service,spatio-temporality and supersize volume.To address this issue,a novel multi-task deep learning framework is developed for citywide cellular network traffic prediction.Functionally,this framework mainly consists of a dual modular feature sharing layer and a multi-task learning layer(DMFS-MT).The former aims at mining long-term spatio-temporal dependencies and local spatio-temporal fluctuation trends in data,respectively,via a new combination of convolutional gated recurrent unit(ConvGRU)and 3-dimensional convolutional neural network(3D-CNN).For the latter,each task is performed for predicting service-specific traffic data based on a fully connected network.On the real-world Telecom Italia dataset,simulation results demonstrate the effectiveness of our proposal through prediction performance measure,spatial pattern comparison and statistical distribution verification. 展开更多
关键词 the sixth generation of mobile communications technology(6G) cellular network traffic multi-task deep learning spatio-temporality
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XGBoost-Based Active Learning for Wildfire Risk Prediction 认领 引用
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作者 Hongrong Wang Hang Geng +4 位作者 Jing Yuan Wen Zhang Hanmin Sheng Qiuhua Wang Xinjian Li 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第12期3701-3721,共21页
Machine learning has emerged as a key approach in wildfire risk prediction research.However,in practical applications,the scarcity of data for specific regions often hindersmodel performance,with models trained on reg... Machine learning has emerged as a key approach in wildfire risk prediction research.However,in practical applications,the scarcity of data for specific regions often hindersmodel performance,with models trained on region-specific data struggling to generalize due to differences in data distributions.While traditional methods based on expert knowledge tend to generalize better across regions,they are limited in leveragingmulti-source data effectively,resulting in suboptimal predictive accuracy.This paper addresses this challenge by exploring how accumulated domain expertise in wildfire prediction can reduce model reliance on large volumes of high-quality data.An active learning algorithm is proposed based on XGBoost for wildfire risk assessment that autonomously identifies low-confidence predictions and seeks re-labeling through a human-in-the-loop or physics-based correction approach.This corrected data is reintegrated into the model,effectively preventing catastrophic forgetting.Experimental results demonstrate that the proposed human-in-the-loop approach significantly enhances labeling accuracy,predictive performance,and preserves the model's ability to generalize.These findings highlight the value of incorporating human expertise into machine learningmodels,offering a practical solution tomitigate data quality challenges and improvemodel reliability in wildfire risk prediction. 展开更多
关键词 Risk prediction active learning XGBoost spatio-temporal division
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An Arrhythmia Intelligent Recognition Method Based on a Multimodal Information and Spatio-Temporal Hybrid Neural Network Model 认领 引用
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作者 Xinchao Han Aojun Zhang +6 位作者 Runchuan Li Shengya Shen Di Zhang Bo Jin Longfei Mao Linqi Yang Shuqin Zhang 《Computers, Materials & Continua》 SCIE EI 2025年第2期3443-3465,共23页
Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to... Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, the spatiotemporal fusion module incorporates a spatiotemporal graph convolutional network to jointly model temporal and spatial features, uncovering complex dependencies within the Electrocardiogram data and improving the model’s ability to represent complex patterns. In experiments conducted on the MIT-BIH arrhythmia dataset, the model achieved 99.95% accuracy, 99.80% recall, and a 99.78% F1 score. The model was further validated for generalization using the clinical INCART arrhythmia dataset, and the results demonstrated its effectiveness in terms of both generalization and robustness. 展开更多
关键词 Multimodal learning spatio-temporal hybrid graph convolutional network data imbalance ECG classification
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Decoupling of Multi-View Facial Features for Cushing’s Syndrome Diagnosis 认领 引用
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作者 Changwei Song Jiaqi Qiang +5 位作者 Hongjun Liu Jianqiang Li Hui Pan Qing Zhao Jiuzuo Huang Shi Chen 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期1177-1208,共32页
Cushing’s syndrome(CS)is a rare endocrine disorder characterized by chronic hypercortisolism,and facial image-based intelligent diagnosis has emerged as a promising non-invasive approach.However,existing diagnostic m... Cushing’s syndrome(CS)is a rare endocrine disorder characterized by chronic hypercortisolism,and facial image-based intelligent diagnosis has emerged as a promising non-invasive approach.However,existing diagnostic models suffer from two core bottlenecks:inefficient fusion of deep semantic features and clinical prior features,and insufficient multi-view facial feature disentanglement without CS-specific pathophysiological constraints.To address these limitations,we propose a novel Multi-View Facial Feature Disentanglement Network(MVFFD-Net)for high-precision automatic CS diagnosis.The network takes five standard facial views(frontal,bilateral 45°oblique,and bilateral 90°lateral views)as input,with three key innovations:a bidirectional cross-attention module for synergistic fusion of deep and clinical prior features,a multi-path disentanglement architecture with multi-objective loss for separating view-invariant and view-specific features,and a graph attention fusion framework for adaptive multi-view feature integration.Unlike state-of-the-art models that mainly rely on single-view representations or generic multi-view fusion,MVFFD-Net explicitly integrates clinically guided multimodal fusion,CS-oriented feature disentanglement,and pathology-aware graph-based multi-view aggregation.Experimental results demonstrate that MVFFD-Net achieves an F1-score of 98.78%for CS diagnosis,significantly outperforming representative state-of-the-art methods in the current experimental setting.In this study,the task is defined as subject-level AI-assisted diagnosis to distinguish individuals with CS from matched controls using five standard facial views.Accordingly,the proposed framework is intended to provide non-invasive auxiliary diagnostic support rather than replace standard endocrinological evaluation and biochemical confirmation.More broadly,this clinically informed multi-view learning framework shows promising potential as a non-invasive auxiliary screening tool,though external multi-center validation remains necessary prior to large-scale clinical application. 展开更多
关键词 Cushing’s syndrome feature fusion feature disentanglement multi-view learning deep learning
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Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network 认领 引用 被引量：1
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作者 Pongsakon Promsawat Weerapan Sae-dan +2 位作者 Marisa Kaewsuwan Weerawat Sudsutad Aphirak Aphithana 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第1期579-607,共29页
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u... The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets. 展开更多
关键词 Graph neural networks convolutional neural network deep learning dynamic multi-graph spatio-temporal
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A Novel Semi-Supervised Multi-View Picture Fuzzy Clustering Approach for Enhanced Satellite Image Segmentation 认领 引用
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作者 Pham Huy Thong Hoang Thi Canh +2 位作者 Nguyen Tuan Huy Nguyen Long Giang Luong Thi Hong Lan 《Computers, Materials & Continua》 SCIE EI 2026年第3期1092-1117,共26页
Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rel... Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping. 展开更多
关键词 Multi-view clustering satellite image segmentation semi-supervised learning picture fuzzy sets remote sensing
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Multi-View & Transfer Learning for Epilepsy Recognition Based on EEG Signals 认领 引用
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作者 Jiali Wang Bing Li +7 位作者 Chengyu Qiu Xinyun Zhang Yuting Cheng Peihua Wang Ta Zhou Hong Ge Yuanpeng Zhang Jing Cai 《Computers, Materials & Continua》 SCIE EI 2023年第6期4843-4866,共24页
Epilepsy is a central nervous system disorder in which brain activity becomes abnormal.Electroencephalogram(EEG)signals,as recordings of brain activity,have been widely used for epilepsy recognition.To study epilep-ti... Epilepsy is a central nervous system disorder in which brain activity becomes abnormal.Electroencephalogram(EEG)signals,as recordings of brain activity,have been widely used for epilepsy recognition.To study epilep-tic EEG signals and develop artificial intelligence(AI)-assist recognition,a multi-view transfer learning(MVTL-LSR)algorithm based on least squares regression is proposed in this study.Compared with most existing multi-view transfer learning algorithms,MVTL-LSR has two merits:(1)Since traditional transfer learning algorithms leverage knowledge from different sources,which poses a significant risk to data privacy.Therefore,we develop a knowledge transfer mechanism that can protect the security of source domain data while guaranteeing performance.(2)When utilizing multi-view data,we embed view weighting and manifold regularization into the transfer framework to measure the views’strengths and weaknesses and improve generalization ability.In the experimental studies,12 different simulated multi-view&transfer scenarios are constructed from epileptic EEG signals licensed and provided by the Uni-versity of Bonn,Germany.Extensive experimental results show that MVTL-LSR outperforms baselines.The source code will be available on http://gffzz188fe103f8f1460asuvob09xnvu696vpq.ffgz.tsg.suse.edu.cn/didid5/MVTL-LSR. 展开更多
关键词 Multi-view learning transfer learning least squares regression epilepsy EEG signals
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WDCIP:spatio-temporal AI-driven disease control intelligent platform for combating COVID-19 pandemic 认领 引用
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作者 Siqi Wang Xiaoxiao Zhao +2 位作者 Jingyu Qiu Haofen Wang Chuang Tao 《Geo-Spatial Information Science》 SCIE EI CSCD 2024年第6期2023-2047,共25页
The outbreak and subsequent recurring waves of COVID−19 pose threats on the emergency management and people's daily life,while the large-scale spatio-temporal epidemiological data have sure come in handy in epidem... The outbreak and subsequent recurring waves of COVID−19 pose threats on the emergency management and people's daily life,while the large-scale spatio-temporal epidemiological data have sure come in handy in epidemic surveillance.Nonetheless,some challenges remain to be addressed in terms of multi-source heterogeneous data fusion,deep mining,and comprehensive applications.The Spatio-Temporal Artificial Intelligence(STAI)technology,which focuses on integrating spatial related time-series data,artificial intelligence models,and digital tools to provide intelligent computing platforms and applications,opens up new opportunities for scientific epidemic control.To this end,we leverage STAI and long-term experience in location-based intelligent services in the work.Specifically,we devise and develop a STAI-driven digital infrastructure,namely,WAYZ Disease Control Intelligent Platform(WDCIP),which consists of a systematic framework for building pipelines from automatic spatio-temporal data collection,processing to AI-based analysis and inference implementation for providing appropriate applications serving various epidemic scenarios.According to the platform implementation logic,our work can be performed and summarized from three aspects:(1)a STAI-driven integrated system;(2)a hybrid GNN-based approach for hierarchical risk assessment(as the core algorithm of WDCIP);and(3)comprehensive applications for social epidemic containment.This work makes a pivotal contribution to facilitating the aggregation and full utilization of spatio-temporal epidemic data from multiple sources,where the real-time human mobility data generated by high-precision mobile positioning plays a vital role in sensing the spread of the epidemic.So far,WDCIP has accumulated more than 200 million users who have been served in life convenience and decision-making during the pandemic. 展开更多
关键词 COVID-19 spatio-temporal artificial intelligence epidemic prevention and control platform risk assessment SIR graph autoencoder graph representation learning human mobility
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Deep Reinforcement Learning for automated scheduling of mining earthwork equipment with spatio-temporal safety constraints 认领 引用
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作者 Yanan LU Ke YOU +4 位作者 Yuxiang WANG Ying LIU Cheng ZHOU Yutian JIANG Zhangang WU 《Frontiers of Engineering Management》 CSCD 2025年第1期39-58,共20页
Large-scale machinery operated in a coordinat-ed manner in earthworks for mining constitutes high safety risks.Efficient scheduling of such machinery,factoring in safety constraints,could save time and significantly i... Large-scale machinery operated in a coordinat-ed manner in earthworks for mining constitutes high safety risks.Efficient scheduling of such machinery,factoring in safety constraints,could save time and significantly improve the overall safety.This paper develops a model of automated equipment scheduling in mining earthworks and presents a scheduling algorithm based on deep rein-forcement learning with spatio-temporal safety constraints.The algorithm not only performed well on safety parame-ters,but also outperformed randomized instances of various sizes set against real mining applications.Further,the study reveals that responsiveness to spatio-temporal safety constraints noticeably increases as the scheduling size increases.This method provides important noticeable improvements to safe automated scheduling in mining. 展开更多
关键词 deep reinforcement learning mining earthwork automated scheduling spatio-temporal safety constraints
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Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal Control 认领 引用 被引量：5
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作者 Yisha Li Ya Zhang +1 位作者 Xinde Li Changyin Sun 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第9期1987-1998,共12页
This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight... This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic efficiency.Firstly a regional multi-agent Q-learning framework is proposed,which can equivalently decompose the global Q value of the traffic system into the local values of several regions Based on the framework and the idea of human-machine cooperation,a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to realtime traffic flow densities.In order to achieve better cooperation inside each region,a lightweight spatio-temporal fusion feature extraction network is designed.The experiments in synthetic real-world and city-level scenarios show that the proposed RegionS TLight converges more quickly,is more stable,and obtains better asymptotic performance compared to state-of-theart models. 展开更多
关键词 Human-machine cooperation mixed domain attention mechanism multi-agent reinforcement learning spatio-temporal feature traffic signal control
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Unravel the spatio-temporal patterns and their nonlinear relationship with correlates of dockless shared bikes near metro stations 认领 引用 被引量：3
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作者 Zhaomin Tong Yi Zhu +3 位作者 Ziyi Zhang Rui An Yaolin Liu Meng Zheng 《Geo-Spatial Information Science》 SCIE EI CSCD 2023年第3期577-598,共22页
The dockless bike-sharing system has rapidly expanded worldwide and has been widely used as an intermodal transport to connect with public transportation.However,higher flexibility may cause an imbalance between suppl... The dockless bike-sharing system has rapidly expanded worldwide and has been widely used as an intermodal transport to connect with public transportation.However,higher flexibility may cause an imbalance between supply and demand during daily operation,especially around the metro stations.A stable and efficient rebalancing model requires spatio-temporal usage patterns as fundamental inputs.Therefore,understanding the spatio-temporal patterns and correlates is important for optimizing and rescheduling bike-sharing systems.This study proposed a dynamic time warping distance-based two-dimensional clustering method to quantify spatio-temporal patterns of dockless shared bikes in Wuhan and further applied the multiclass explainable boosting machine to explore the main related factors of these patterns.The results found six patterns on weekdays and four patterns on weekends.Three patterns show the imbalance of arrival and departure flow in the morning and evening peak hours,while these phenomena become less intensive on weekends.Road density,living service facility density and residential density are the top influencing factors on both weekdays and weekends,which means that the comprehensive impact of built-up environment attraction,facility suitability and riding demand leads to the different usage patterns.The nonlinear influence universally exists,and the probability of a certain pattern varies in different value ranges of variables.When the densities of living facilities and roads are moderate and the relationship between job and housing is relatively balanced,it can effectively promote the balanced usage of dockless shared bikes while maintaining high riding flow.The spatio-temporal patterns can identify the associated problems such as imbalance or lack of users,which could be mitigated by corresponding solutions.The relative importance and nonlinear effects help planners prioritize strategies and identify effective ranges on different patterns to promote the usage and efficiency of the bike-sharing system. 展开更多
关键词 Dockless shared bikes spatio-temporal patterns machine learning transit oriented development
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Self-Attention Spatio-Temporal Deep Collaborative Network for Robust FDIA Detection in Smart Grids 认领 引用 被引量：1
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作者 Tong Zu Fengyong Li 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第11期1395-1417,共23页
False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work u... False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams.Data-driven features,however,cannot effectively capture the differences between noisy data and attack samples.As a result,slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks.To address this problem,this paper designs a deep collaborative self-attention network to achieve robust FDIA detection,in which the spatio-temporal features of cascaded FDIA attacks are fully integrated.Firstly,a high-order Chebyshev polynomials-based graph convolution module is designed to effectively aggregate the spatio information between grid nodes,and the spatial self-attention mechanism is involved to dynamically assign attention weights to each node,which guides the network to pay more attention to the node information that is conducive to FDIA detection.Furthermore,the bi-directional Long Short-Term Memory(LSTM)network is introduced to conduct time series modeling and long-term dependence analysis for power grid data and utilizes the temporal self-attention mechanism to describe the time correlation of data and assign different weights to different time steps.Our designed deep collaborative network can effectively mine subtle perturbations from spatiotemporal feature information,efficiently distinguish power grid noise from FDIA attacks,and adapt to diverse attack intensities.Extensive experiments demonstrate that our method can obtain an efficient detection performance over actual load data from New York Independent System Operator(NYISO)in IEEE 14,IEEE 39,and IEEE 118 bus systems,and outperforms state-of-the-art FDIA detection schemes in terms of detection accuracy and robustness. 展开更多
关键词 False data injection attacks smart grid deep learning self-attention mechanism spatio-temporal fusion
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A Two-Stream Hybrid Spatio-Temporal Fusion Network For sEMG-Based Gesture Recognition 认领 引用 被引量：1
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作者 Ruiqi Han Juan Wang Jia Wang 《Instrumentation》 2024年第4期53-63,共11页
With the advancement of human-computer interaction,surface electromyography(sEMG)-based gesture recognition has garnered increasing attention.However,effectively utilizing the spatio-temporal dependencies in sEMG sign... With the advancement of human-computer interaction,surface electromyography(sEMG)-based gesture recognition has garnered increasing attention.However,effectively utilizing the spatio-temporal dependencies in sEMG signals and integrating multiple key features remain significant challenges for existing techniques.To address this issue,we propose a model named the Two-Stream Hybrid Spatio-Temporal Fusion Network(TS-HSTFNet).Specifically,we design a dynamic spatio-temporal graph convolution module that employs an adaptive dynamic adjacency matrix to explore the spatial dynamic patterns in the sEMG signals fully.Additionally,a spatio-temporal attention fusion module is designed to fully utilize the potential correlations among multiple features for the final fusion.The results indicate that the proposed TS-HSTFNet model achieves 84.96%and 88.08%accuracy on the Ninapro DB2 and Ninapro DB5 datasets,respectively,demonstrating high precision in gesture recognition.Our work emphasizes the importance of extracting spatio-temporal features in gesture recognition and provides a novel approach for multi-source information fusion. 展开更多
关键词 gesture recognition deep learning two-stream spatio-temporal feature fusion dynamic neighbor matrix
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Detection of Student Engagement in E-Learning Environments Using EfficientnetV2-L Together with RNN-Based Models 认领 引用 被引量：4
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作者 Farhad Mortezapour Shiri Ehsan Ahmadi +1 位作者 Mohammadreza Rezaee Thinagaran Perumal 《Journal on Artificial Intelligence》 2024年第1期85-103,共19页
Automatic detection of student engagement levels from videos,which is a spatio-temporal classification problem is crucial for enhancing the quality of online education.This paper addresses this challenge by proposing ... Automatic detection of student engagement levels from videos,which is a spatio-temporal classification problem is crucial for enhancing the quality of online education.This paper addresses this challenge by proposing four novel hybrid end-to-end deep learning models designed for the automatic detection of student engagement levels in e-learning videos.The evaluation of these models utilizes the DAiSEE dataset,a public repository capturing student affective states in e-learning scenarios.The initial model integrates EfficientNetV2-L with Gated Recurrent Unit(GRU)and attains an accuracy of 61.45%.Subsequently,the second model combines EfficientNetV2-L with bidirectional GRU(Bi-GRU),yielding an accuracy of 61.56%.The third and fourth models leverage a fusion of EfficientNetV2-L with Long Short-Term Memory(LSTM)and bidirectional LSTM(Bi-LSTM),achieving accuracies of 62.11%and 61.67%,respectively.Our findings demonstrate the viability of these models in effectively discerning student engagement levels,with the EfficientNetV2-L+LSTM model emerging as the most proficient,reaching an accuracy of 62.11%.This study underscores the potential of hybrid spatio-temporal networks in automating the detection of student engagement,thereby contributing to advancements in online education quality. 展开更多
关键词 Student engagement detection hybrid deep learning models computer vision EfficientNetV2-L online learningenvironments spatio-temporal classification
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