期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到1,825篇文章
< 1 2 … 92 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Enforcing spatio-temporal collaborations in distributed predictive control under temporally relaxed STL specifications 认领 引用
1
作者 Hongbo Yang Yuanyuan Zou Shaoyuan Li 《Control Theory and Technology》 EI CSCD 2026年第2期203-213,共11页
This paper introduces spatio-temporal collaboration(STC),a novel formalism for coordinating multi-agent systems under signal temporal logic(STL).STC defines critical inter-agent dependencies that may be violated by th... This paper introduces spatio-temporal collaboration(STC),a novel formalism for coordinating multi-agent systems under signal temporal logic(STL).STC defines critical inter-agent dependencies that may be violated by the cascading delays resulting from temporal relaxation,a common method for resolving local task conflicts.To address this issue,we first analyze the propagation of task delays through dependent tasks.A time interval refinement strategy is then proposed to maintain the required collaborations.This strategy is integrated into a distributed predictive control algorithm,ensuring simultaneous satisfaction of both STL specifications and STC relations while preserving recursive feasibility and closed-loop stability.Validation via a case study demonstrates the effectiveness of proposed strategy in preventing collaboration failures. 展开更多
关键词 Collaborative control Signal temporal logic Multi-agent system Temporal relaxation Distributed predictive control
Linear-Time Temporal Logic in AI-Based Computational Tree Models 认领 引用
2
作者 Chen Liu 《逻辑学研究》 CSSCI 2026年第3期59-91,共33页
The integration of the Linear Temporal Logic(LTL)in AI-based computational tree models is an effective form of formal verification of Artificial Intelligence systems.But classical model checking methods do not scale b... The integration of the Linear Temporal Logic(LTL)in AI-based computational tree models is an effective form of formal verification of Artificial Intelligence systems.But classical model checking methods do not scale because of the state-space explosion.In this paper,we propose a novel data driven methodology backed by deep learning for efficient approximation of LTL model checking.The Temporal Convolutional Graph Network(TCGN)is a framework for deep learning that integrates graph neural networks with temporal logic in order to verify the behaviour of complex systems.We conduct experiments on synthetic and real-world datasets,showing that TCGN achieves an accuracy of 98%and outperforms traditional methods like NuSMV both in effectiveness instantaneous verification and efficiency.The TCGN can also achieve real time large scale AI system verification due to its strong robustness under adversarial perturbation. 展开更多
关键词 temporal convolutional graph network tcgn artificial intelligence systemsbut deep learning formal verification linear temporal logic ltl data driven methodology linear time temporal logic classical model checking methods
暂未订购 下载PDF
Research on Ultra-Short-Term Photovoltaic Power Forecasting Based on Parallel Architecture TCN-BiLSTM with Temporal-Spatial Attention Mechanism 认领 引用 被引量：1
3
作者 Hongbo Sun Xingyu Jiang +4 位作者 Wenyao Sun Yi Zhao Jifeng Cheng Xiaoyi Qian Guo Wang 《Energy Engineering》 EI 2026年第4期303-320,共18页
The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback ... The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback prediction method based on a parallel dual-stream Temporal Convolutional Network-Bidirectional Long Short-Term Memory(TCN-BiLSTM)architecture incorporating a spatiotemporal attention mechanism.Firstly,during data preprocessing,the optimal historical time window is determined through autocorrelation analysis while highly correlated features are selected as model inputs using Pearson correlation coefficients.Subsequently,a parallel dual-stream TCN-BiLSTM model is constructed where the TCN branch extracts localized transient features and the BiLSTM branch captures long-term periodic patterns,with spatiotemporal attention dynamically weighting spatiotemporal dependencies.Finally,Shapley Additive explanations(SHAP)additive analysis quantifies feature contribution rates and provides optimization feedback to the model.Validation using operational data from a PV power station in Northeast China demonstrates that compared to conventional deep learning models,the proposed method achieves a 17.6%reduction in root mean square error(RMSE),a 5.4%decrease in training time consumption,and a 4.78%improvement in continuous ranked probability score(CRPS),exhibiting significant advantages in both prediction accuracy and generalization capability.This approach enhances the application effectiveness of ultra-short-term PV power forecasting while simultaneously improving prediction accuracy and computational efficiency. 展开更多
关键词 Ultra-short-term forecasting temporal convolutional network bidirectional long short-term memory parallel dual-stream architecture temporal-spatial attention SHAP contribution analysis
暂未订购 下载PDF
Spatio-temporal changes in forest tree species diversity in China over the past 20 years 认领 引用 被引量：1
4
作者 Yonghong Zhang Liang Shi +7 位作者 Honglin He Qingqing Chang Jianming Deng Yan Lv Qian Xu Weihua Liu Mengyu Zhang Chenxi Li 《Journal of Forestry Research》 SCIE EI CAS CSCD 2026年第1期230-241,共12页
The latitudinal diversity gradient(LDG)is one of the most notable biodiversity patterns in biogeography.The metabolic theory of ecology(MTE)explains ecological patterns,including the LDG.However,little is known about ... The latitudinal diversity gradient(LDG)is one of the most notable biodiversity patterns in biogeography.The metabolic theory of ecology(MTE)explains ecological patterns,including the LDG.However,little is known about whether the LDG remains stable over time as climate warming progresses and whether MTE remains applicable to clarify this pattern.In this study,forest data spanning temperate,subtropical,and tropical zones across China were used to analyze long-term changes in the LDG of tree species over 2005-2020.Based on the MTE framework,spatial scales were considered to assess temperature dependence of typical forest trees species.Our results show that species richness decreased with increasing latitude,and that temperature was the primary driver of this change.Although temperature in China has significantly increased over the past two decades,the LDG of tree species has remained stable.However,there was a decrease in species richness in tropical regions over time.With predictions of the MTE,the logarithm of typical forest tree species richness exhibited negative linear relationships with the inverse of ambient temperature,indicating temperature dependence of species richness.However,the relationship remained stable and was strongly influenced by spatial scale,intensifying as spatial scale increased.The findings emphasize the important role of temperature in shaping the LDG.The effects of spatial scale,in particular,should be considered when biodiversity management plans are developed for future climate change. 展开更多
关键词 Climate warming Latitudinal diversity gradient(LDG) Metabolic theory of ecology Species richness Spatial scale Temporal dynamic
暂未订购 下载PDF
TransCarbonNet:Multi-Day Grid Carbon Intensity Forecasting Using Hybrid Self-Attention and Bi-LSTM Temporal Fusion for Sustainable Energy Management 认领 引用 被引量：1
5
作者 Amel Ksibi Hatoon Albadah +1 位作者 Ghadah Aldehim Manel Ayadi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期812-847,共36页
Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The prese... Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid. 展开更多
关键词 Carbon intensity forecasting self-attention mechanism bidirectional LSTM temporal fusion sustainable energy management smart grid optimization deep learning
暂未订购 下载PDF
A Spatial-Temporal Normalized Contrastive Embedding for Robust Motion Similarity Retrieval 认领 引用
6
作者 Seung-su Lee Young-Been Noh +1 位作者 Hwa Young Jeong Kwang-il Hwang 《Computers, Materials & Continua》 SCIE EI 2026年第9期127-158,共32页
Robust motion similarity retrieval from monocular 2D pose sequences is challenged by body-scale variation,viewpoint inconsistency,translation drift,and temporal misalignment.Existing contrastive skeleton learning meth... Robust motion similarity retrieval from monocular 2D pose sequences is challenged by body-scale variation,viewpoint inconsistency,translation drift,and temporal misalignment.Existing contrastive skeleton learning methods primarily address action recognition and rarely integrate explicit geometric canonicalization for retrievaloriented metric learning.This paper proposes a spatial-temporal normalized contrastive embedding framework that unifies structured nuisance suppression with scalable similarity representation learning.A four-stage normalization pipeline—torso-scale normalization,pelvis-centered alignment,posture-axis alignment,and phase-synchronized temporal resampling—removes geometric and temporal distortions prior to embedding.The normalized sequences are encoded using an acausal dilated temporal convolutional network trained with a hybrid contrastive objective combining NT-Xent and semi-hard triplet loss,enabling both global separation and fine-grained stylistic discrimination.A prototype-based representation further supports interpretable amateur-to-professional style mapping.Experiments on a golf swing benchmark achieve a Top-1 accuracy of 91.3%,outperforming BiLSTM and Dynamic Time Warping baselines.The framework establishes an invariant and interpretable paradigmfor motion similarity retrieval applicable to broader human movement analysis tasks. 展开更多
关键词 Motion similarity retrieval spatial-temporal normalization contrastive representation learning temporal convolutional networks(TCN) prototype-based embedding
暂未订购 下载PDF
Radial growth and its temporal stability of Dahurian larch in the southern boreal forest: Divergent trends during climate warming and warming hiatus 认领 引用
7
作者 Zhongtong Peng Enzai Du +2 位作者 Yang Tang Tao He Yuehan Tian 《Forest Ecosystems》 SCIE CAS CSCD 2026年第2期321-330,共10页
Climate warming has reshaped the structure and function of global boreal forest with expected negative impacts at its southern margins.A warming hiatus has occurred in many high-latitude regions in recent decades,but ... Climate warming has reshaped the structure and function of global boreal forest with expected negative impacts at its southern margins.A warming hiatus has occurred in many high-latitude regions in recent decades,but its impacts on tree growth in the southern boreal forest remain unclear.We sampled tree rings of Dahurian larch(Larix gmelinii)in the southern boreal forest of the Greater Khingan Mountains(GKM)and examined the trends of tree growth and its temporal stability based on the age-detrended basal area increment(BAI)for the periods of rapid warming(1962-1992)and warming hiatus(1993-2022).The results indicate that age-detrended BAI declined significantly during the warming period,while it showed no further decrease during the period of warming hiatus.Tree growth decline was associated with higher daily maximum air temperature in the main growing season and daily minimum air temperature in the non-growing season,as well as lower precipitation in the early growing season and daily minimum air temperature in the main growing season.During the warming hiatus,tree growth was positively regulated by the precipitation in the non-growing season,daily maximum air temperature in the early growing season,and daily minimum air temperature in the main growing season,but negatively affected by the daily maximum air temperature in the late growing season.Intriguingly,tree growth stability declined significantly during the warming period and recovered rapidly during the period of warming hiatus.The decline in tree growth stability was mainly explained by increasing daily minimum air temperature in the non-growing season.The recovery of tree growth stability was associated with lower precipitation in the non-growing season,higher interannual stability of daily maximum air temperature in the early growing season,higher interannual mean value and stability of daily maximum air temperature in the late growing season,and lower interannual mean value and stability of daily minimum air temperature in the main growing season.Our findings highlight a rapid recovery of tree growth stability instead of growth rate during the warming hiatus following a period of rapid warming and provide new insights into the decadal-scale resilience of the southern boreal forest in response to climate change. 展开更多
关键词 Basal Area Increment Boreal tree Dahurian larch Decadal-scale resilience Interannual variability Temperature stress Temporal stability Warming hiatus
暂未订购 下载PDF
Mapping the Cross-species Brain Connectivity Atlas and Hemispheric Asymmetry of the Temporal Pole in Humans and Macaques 认领 引用
8
作者 Qinyao Sun Shunli Zhu +6 位作者 Futing Yang Zhigang Chen Heling Li Heng Shao Hong Wang Sangma Xie Jiaojian Wang 《Neuroscience Bulletin》 SCIE CAS CSCD 2026年第1期91-106,共16页
The temporal pole(TP),one of the most expanded cortical regions in humans relative to other primates,plays a crucial role in human language processing.It is also one of the most structurally and functionally asymmetri... The temporal pole(TP),one of the most expanded cortical regions in humans relative to other primates,plays a crucial role in human language processing.It is also one of the most structurally and functionally asymmetric regions.However,whether the functional architecture of the TP is shared by humans and macaques is an open question.We used spectral clustering algorithms to define a cross-species fine-grained TP atlas with different anatomical connectivity patterns.We identified three similar subregions,two ventral and one dorsal,within the TP in both humans and macaques.The parcellation scheme for the TP was validated using functional gradient mapping,anatomical connectivity and resting-state functional connectivity pattern analysis,and functional characterization.Furthermore,in conjunction with the Allen Human Brain Atlas,we revealed the molecular basis for the functional connectivity patterns of each human TP subregion.In addition,we compared the hemispheric asymmetry in mean gray matter volume,anatomical connectivity fingerprints,and whole brain functional connectivity patterns to reveal the evolutionary differences in the TP and found different asymmetric patterns between humans and macaques.In conclusion,our findings reveal that the asymmetry in structure and connectivity may underpin the hemispheric functional specialization of the brain and provide a novel insight into understanding the evolutionary origin of the TP. 展开更多
关键词 Temporal pole Parcellation Asymmetry Evolution Human Macaque
暂未订购 下载PDF
Spatial-temporal landslide hazard analysis considering ecological and hydrological factors 认领 引用
9
作者 ZHAO Zheng LAN Hengxing +2 位作者 LI Langping BAO Han LIU Shijie 《Journal of Mountain Science》 SCIE CSCD 2026年第4期1547-1563,共17页
Ecological factors,such as vegetation roots,can enhance soil shear strength,reduce surface runoff erosion,and improve slope stability.Conversely,heavy rainfall can rapidly saturate unsaturated soil,weakening shear str... Ecological factors,such as vegetation roots,can enhance soil shear strength,reduce surface runoff erosion,and improve slope stability.Conversely,heavy rainfall can rapidly saturate unsaturated soil,weakening shear strength.As such,both ecological and hydrological processes play critical roles in shallow slope failures.In this study,we propose a spatialtemporal landslide hazard model considering ecological and hydrological factors.Specifically,ecological factors are incorporated in convolution neural network as a spatial probability module,and hydrological factors are involved in infinite slope hydrological model as a temporal probability module,finally we integrated the spatial-temporal probabilities using integration module.A case study in the Hengduan Mountain Region demonstrated that the proposed model outperforms traditional models by achieving higher prediction accuracy,with an increase in the landslide hit index,demonstrating the effectiveness of involving ecological and hydrological factors in landslide hazard analysis,offering practical suggestions for landslide risk management. 展开更多
关键词 Landslide hazard Spatial probability Temporal probability Ecological factor Hydrological factor
暂未订购 下载PDF
Spatiotemporal patterns and driving forces of dust weather events in Central Asia from 2000 to 2020 认领 引用
10
作者 LIU Yuhan ZHAO Yuanyuan +2 位作者 GAO Guanglei DING Guodong LI Ning 《Journal of Arid Land》 SCIE CAS CSCD 2026年第1期1-16,共16页
Central Asia is characterized by an arid climate and widespread desert distribution,with its sustainable development severely constrained by dust events.An objective understanding of the spatiotemporal patterns and dr... Central Asia is characterized by an arid climate and widespread desert distribution,with its sustainable development severely constrained by dust events.An objective understanding of the spatiotemporal patterns and driving forces of dust weather is highly important in this area.Based on the meteorological observations from 2000 to 2020,we examined the spatiotemporal characteristics of dust weather in the five Central Asian countries(Kazakhstan,Uzbekistan,Kyrgyzstan,Turkmenistan,and Tajikistan)via Theil-Sen trend analysis and Geodetector modeling method,quantitatively revealing the influence of environmental factors,such as temperature,precipitation,and vegetation,on the frequency of dust weather.The results showed that:(1)dust weather in Central Asia was mainly distributed in a large''dust belt''extending from west to east from northern part of the Caspian lowland desert,and concentrated in basins,plains,and other low-altitude areas.Strong dust weather mainly occurred in northern areas of the Aral Sea and southern edge of Central Asia,with a maximum annual frequency of 21.9%;(2)strong dust weather in Central Asia has fluctuated and slightly decreased since 2001.The highest frequency(1.1%)occurred in spring(from March to June);(3)from 2000 to 2020,changes such as spot shifting and shrinking occurred in the four main source areas(north of the Aral Sea,Kyzylkum Desert,Karakum Desert,and Garabogazköl Bay region),where sandstorms occurred in Central Asia,and northern Caspian lowland desert became the most important low-emission dust source in Central Asia;and(4)the combined effect of soil moisture and air temperature has the most significant influence on dust weather in Central Asia.This study provides a theoretical basis for sand prevention and sand control in Central Asia.In the future,Central Asia should focus on the rational utilization of land and water resources,and implement human interventions such as vegetation restoration and optimization of irrigation methods to curb further desertification in this area. 展开更多
关键词 Central Asia dust weather temporal and spatial distribution influencing factor Geodetector
暂未订购 下载PDF
Spatial-Temporal Variations of Bacterioplankton Community Assemblages and Functional Attributes in the Beibu Gulf,Northern South China Sea 认领 引用
11
作者 SUN Pengfei YANG Tinglong +4 位作者 LIAN Jie LI Hui WANG Weimin ZHU Zuhao TAN Yongyu 《Journal of Ocean University of China》 SCIE CAS CSCD 2026年第2期717-730,I0036-I0038,共14页
This study used amplicon sequencing of the 16S rRNA gene to explore the community composition and functional attributes of inshore and offshore bacterioplankton in the Beibu Gulf during summer and winter.Results showe... This study used amplicon sequencing of the 16S rRNA gene to explore the community composition and functional attributes of inshore and offshore bacterioplankton in the Beibu Gulf during summer and winter.Results showed a spatial and temporal variation that the inshore and offshore waters in both seasons were significantly different from each other(P<0.05).The abundance of bacterioplankton in summer was higher than winter,with a trend toward higher abundance in inshore than in the offshore.Candidatus Actinomarina,and Synechococcus were the dominant genera between inshore and offshore waters in both seasons.The richness and diversity of bacterioplankton in inshore waters were higher than in offshore waters,and were significantly higher in winter than in summer(P<0.05).The bacterioplankton community was affected by temperature and pH in inshore waters during summer.The dissolved inorganic nitrogen,nitrate,nitrite,and dissolved oxygen affected the inshore waters in winter.We found the bacterioplankton community and structure in inshore waters were mainly affected by West-Guangdong coastal current water mass and terrigenous factors in winter,and affected by terrigenous factors in summer.The Tax4Fun functional abundance analysis showed that the overall functional profiles of the bacterioplankton community in inshore and offshore waters were similar on level 1,but some main functional genes were significantly different at levels 2 and 3.These research findings have provided valuable insights into the crucial roles of bacterioplankton in the ecosystem and its biogeochemical cycles in the Beibu Gulf. 展开更多
关键词 bacterioplankton community spatial and temporal distribution function attributes environmental factors Beibu Gulf
暂未订购 下载PDF
Temporal Variability Analysis of Cortical Blood Flow in Rats with Hyperacute Cerebral Ischemia 认领 引用
12
作者 Bochao Niu Benjamin Klugah-Brown +2 位作者 Yang Xia Dezhong Yao Bharat B.Biswal 《Neuroscience Bulletin》 SCIE CAS CSCD 2026年第1期55-74,共20页
Cerebral ischemia restricts cerebral blood flow(CBF),leading to unstable hemodynamics.Past studies of ischemia mainly focused on cortical CBF reduction.However,its impact on hemodynamic changes,especially temporal var... Cerebral ischemia restricts cerebral blood flow(CBF),leading to unstable hemodynamics.Past studies of ischemia mainly focused on cortical CBF reduction.However,its impact on hemodynamic changes,especially temporal varying characteristics,remains poorly understood.Here,we collected cortical resting-state CBF in rats with left carotid artery blockage during occlusion–reperfusion,and measured the temporal variability and changes in laterality using a novel state-space method.This method was also applied to stroke EEG datasets to validate its effectiveness.After arterial occlusion,the left marginal motor,sensory,auditory,and visual cortices exhibited severe temporal variability impairments.The laterality analysis indicated that affected left regions showed inferior unilateral mean,inter-hemispheric transition probability,time fraction,and laterality duration,while the right side had a higher laterality time fraction and duration.These impairments recovered partially following blood flow restoration.Besides,the ischemic state-space metrics were positively correlated with the pre-occlusion baseline appearance.Stroke patients exhibited impaired temporal variability in the affected ischemic hemisphere.The state-space analysis revealed damaged CBF temporal variability during cerebral ischemia and predicted baseline-ischemia connections. 展开更多
关键词 State-space Cerebral blood flow Cerebral ischemia Laser speckle contrast imaging Temporal variability
暂未订购 下载PDF
DA-T3D:Distribution-Aware Cross-Modal Distillation Framework for Temporal 3D Object Detection 认领 引用
13
作者 Tianzhe Jiao Yuming Chen +2 位作者 Xiaoyue Feng Chaopeng Guo Jie Song 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期1-21,共21页
Knowledge distillation bridges the performance gap between camera-based and LiDAR-based 3D detectors by leveraging the precise geometric information from LiDAR.However,cross-modal knowledge transfer remains challengin... Knowledge distillation bridges the performance gap between camera-based and LiDAR-based 3D detectors by leveraging the precise geometric information from LiDAR.However,cross-modal knowledge transfer remains challenging due to the inherent modality heterogeneity between LiDAR and camera data,which often leads to instability during training.In this work,we find that these instabilities are closely related to distribution mismatch in the cross-modal feature space and noisy teacher signals.To address this issue,we propose a novel distribution-aware cross-modal distillation framework,named DA-T3D.Specifically,we first explicitly model the LiDAR teacher’s Bird’sEye-View(BEV)feature distribution and use the learned distribution as a statistical prior to guide the student features toward high-density and geometrically stable regions in the teacher’s BEV feature space.This ensures feature alignment in BEV space by constraining the student model’s feature distribution to match that of the LiDAR teacher model within foreground regions.Next,we further introduce response-level distillation to directly transfer the teacher’s prediction behavior to the student detection head,providing direct output-space supervision that complements feature distillation and effectively reduces modality-induced ambiguity,leading to more accurate and stable classification confidence and bounding-box regression.Furthermore,we perform temporal modeling on the distilled cross-modal features to produce fused BEV representations that capture more comprehensive scene context.Finally,we utilize the fused BEV features to generate 3D detection results.Through experiments,we validate the effectiveness and superiority of DA-T3D on the nuScenes dataset,achieving 46.7%mAP and 58.1%NDS. 展开更多
关键词 3D object detection Bird’s-Eye-View perception cross-modal knowledge distillation Dirichlet process Gaussian mixture model temporal modeling
暂未订购 下载PDF
Efficient Video Emotion Recognition via Multi-Scale Region-Aware Convolution and Temporal Interaction Sampling 认领 引用
14
作者 Xiaorui Zhang Chunlin Yuan +1 位作者 Wei Sun Ting Wang 《Computers, Materials & Continua》 SCIE EI 2026年第2期2036-2054,共19页
Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-... Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity.On the other hand,fixed-size convolution kernels are often used,which have weak perception ability for emotional regions of different scales.Therefore,this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling.In terms of space,multi-branch large-kernel stripe convolution is used to perceive emotional region features at different scales,and attention weights are generated for each scale feature.In terms of time,multi-layer odd-even down-sampling is performed on the time series,and oddeven sub-sequence interaction is performed to solve the problem of feature similarity,while reducing computational costs due to the linear relationship between sampling and convolution overhead.This paper was tested on CMU-MOSI,CMU-MOSEI,and Hume Reaction.The Acc-2 reached 83.4%,85.2%,and 81.2%,respectively.The experimental results show that the model can significantly improve the accuracy of emotion recognition. 展开更多
关键词 Multi-scale region-aware convolution temporal interaction sampling video emotion recognition
暂未订购 下载PDF
Bone adhesive with temporally-synchronized degradation for enhanced osteointegration 认领 引用
15
作者 Jun-ting Gu Zhi-ting Li +8 位作者 Yu-zhu Wang Dong-xiao Hao Gao-peng Dang Xiao-Qing Cao Franklin R.Tay Ji-hua Chen Conrado Aparicio Kai Jiao Li-na Niu 《Bone Research》 SCIE CAS CSCD 2026年第3期935-948,共14页
Bone adhesives have emerged as promising alternatives for complex fracture fixation.However,discrepancies between material degradation rates and the physiological timeline of bone healing remain a critical limitation.... Bone adhesives have emerged as promising alternatives for complex fracture fixation.However,discrepancies between material degradation rates and the physiological timeline of bone healing remain a critical limitation.Here,a polyurethane-based adhesive(TNC)was developed,synthesized from trimeric hexamethylene diisocyanate,nano-hydroxyapatite,and type I collagen.The TNC demonstrates strong initial adhesion to both wet and blood-contaminated bone surfaces and exhibits excellent biocompatibility.A distinguishing feature of TNC is its capacity to synchronize degradation with the stages of bone healing.During degradation,TNC forms a mineralized surface layer that releases calcium ions.The calcium ions activate cathepsin K,an enzyme integral to bone remodeling.This calcium-mediated mechanism accelerates TNC degradation by 1.9-fold during the remodeling phase compared to the initial phase.In a rat skull fracture model,TNC supported effective fracture stabilization and achieved favorable bone regeneration at 8 weeks after implantation.These findings demonstrate that TNC combines early mechanical stability with phasespecific degradability to facilitate bone regeneration in a temporally-controlled manner.The present work provides a framework for the development of bio-responsive bone adhesives that synchronize degradation behavior with healing phases for orthopedic applications. 展开更多
关键词 type i collagenthe fracture fixationhoweverdiscrepancies osteointegration trimeric hexamethylene diisocyanatenano hydroxyapatiteand temporally synchronized degradation polyurethane bone adhesives bone surfaces
暂未订购 下载PDF
Exploring the Temporal Degradation and Drift of AS Path Inference 认领 引用
16
作者 Xionglve Li Changsheng Hou +5 位作者 Yuzhou Huang Zhenyu Qiu Gang Hu Bingnan Hou Wei Dong Zhiping Cai 《Computers, Materials & Continua》 SCIE EI 2026年第8期833-853,共21页
The Internet inter-domain paths,i.e.,the AS paths,are important for network management,traffic engineering,and security.Due to business confidentiality,security,and privacy,the AS path information is non-public.Due to... The Internet inter-domain paths,i.e.,the AS paths,are important for network management,traffic engineering,and security.Due to business confidentiality,security,and privacy,the AS path information is non-public.Due to limited measurement resources,obtaining AS path information by measurement-based approaches is not scalable.Therefore,path inference approaches are proposed to broaden the availability of path information.These approaches assume that AS paths remain stable over a certain period of time,yet conflicting research findings question this assumption.Furthermore,the duration of the“certain period of time”is not clearly defined.Thus,we aim to address the following question:“How do the performance and temporal drift of path inference approaches evolve over time?”In this paper,we conduct a quantitative validation study and a temporal drift analysis to examine the evolution of AS path inference performance over time.The quantitative validation study shows that the minimal performance degradation is only 2.09%over eight weeks.The temporal drift analysis shows that,among the three evaluated methods,KnownPath exhibits the slowest drift,GMPI shows a moderate drift rate,and ProbInfer drifts the fastest under the current decision rule.The results provide preliminary evidence on how historical data can be leveraged despite limited measurement resources and can inform refresh-frequency decisions for path inference services under computational constraints. 展开更多
关键词 Network measurement AS path inference temporal drift
暂未订购 下载PDF
Synthesis of gunfire shock signals with controllable SRS and temporal features 认领 引用
17
作者 Yinzhong YAN Jianbin RUAN Yulong LI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第3期447-462,共16页
Aircraft-mounted weapons systems generate intense shock and vibration during combat missions,creating a highly complex,repetitive,and nonstationary environment.Avionic devices and components are susceptible to damage ... Aircraft-mounted weapons systems generate intense shock and vibration during combat missions,creating a highly complex,repetitive,and nonstationary environment.Avionic devices and components are susceptible to damage in severe gunfire shock environments and must undergo shock testing.In the absence of measured data,gunfire shock signals synthesized from Shock Response Spectrum(SRS) should serve as input excitation.However,the synthesis of gunfire shock signals presents several challenges,primarily due to the transient and repetitive nature of gunfire shock itself,as well as the inherent non-linearity in the SRS method.This paper presents a novel method for synthesizing gunfire shock signals that match SRS specifications while maintaining realistic temporal characteristics.The proposed method utilizes a shock-waveform dictionary technique to generate single-shot shock signals with controllable features including initial rise time,effective duration,and repetition intervals.These single-shot signals are then duplicated and concatenated to create multi-shot sequences,with low-frequency compensation applied to meet SRS requirements.The method's effectiveness is demonstrated through a case study simulating the M61A1 aircraft cannon firing at 4 000 rounds per minute,achieving an average error of only 0.35 d B compared to SRS specifications.Further validation across two additional cases with varied single-shot durations and repetition intervals underscores the method's generalizability.The proposed synthesis method provides a practical solution for laboratory testing of avionic equipment under gunfire shock conditions when measured data is unavailable. 展开更多
关键词 Gunfire shock environment Shock response spectrum Shock testing Shock-waveform Temporal features
暂未订购 下载PDF
Anthropogenic activities amplify spatiotemporal variations in regional ecological security patterns dominated by natural factors:Evidence from the West Liaohe River Basin,China 认领 引用
18
作者 LYU Xin LI Xiaobing +2 位作者 WANG Kai CAO Wanyu ZHANG Chenhao 《Journal of Arid Land》 SCIE CAS CSCD 2026年第5期752-773,共22页
Ecological security patterns(ESPs)represent an effective way to maintain regional ecological security and promote regional sustainable development.This study investigated the spatiotemporal variations of ESPs in the W... Ecological security patterns(ESPs)represent an effective way to maintain regional ecological security and promote regional sustainable development.This study investigated the spatiotemporal variations of ESPs in the West Liaohe River Basin(WLRB),China during 2000–2020 on the basis of five key ecosystem services(net primary production,soil conservation,habitat quality,water retention,and soil loss by wind).On the basis of the Geodetector model,we initially measured the explanatory rates of various natural and anthropogenic factors on the spatial differentiation of ecological sources and ecological corridors.The Geographically and Temporally Weighted Regression(GTWR)model was subsequently used to elucidate the driving mechanism of ESPs at the interannual scale.During 2000–2020,a"fan-shaped"ESP of"two zones,three belts,and many branches"formed in the WLRB.Natural factors dominated the spatial distribution of ESPs,and the average spatial explanation rate for ecological sources and ecological corridors was 23.86%,which was higher than that of anthropogenic activities(13.29%).However,anthropogenic activities amplified the spatiotemporal variations in ESPs.On this basis,this study proposed an ecological security protection and regulation strategy from three aspects,namely,regional priority,suitability analysis,and risk regulation,which might provide a working direction for regional practical management.This study extends the paradigm of ESP research and offers an important theoretical basis for regional ecological security,from"passive management"to"active management". 展开更多
关键词 ecological security patterns(ESPs) ecosystem services anthropogenic activities Geodetector Geographically and Temporally Weighted Regression(GTWR)model West Liaohe River Basin
暂未订购 下载PDF
Mechanisms of Spatiotemporal Interactions Between Urbanization and Ecosystem Health in Plateau-mountain Urban Areas:Insights from Central Yunnan Urban Agglomeration,China 认领 引用
19
作者 LI Chen WU Yingmei +4 位作者 QIAO Weifeng GAO Binpin WU Yan WANG Mengjiao ZHENG Kejun 《Chinese Geographical Science》 SCIE CAS CSCD 2026年第5期879-896,共18页
Urban agglomerations are key drivers of regional socioeconomic development,yet they also maintain ecosystem health(EH).Coordinating urbanization(UR)with EH is vital for sustainable development.However,the local and te... Urban agglomerations are key drivers of regional socioeconomic development,yet they also maintain ecosystem health(EH).Coordinating urbanization(UR)with EH is vital for sustainable development.However,the local and telecoupling(LTC)dynamics and their driving mechanisms remain understudied,particularly in plateau-mountain areas.Taking the Central Yunnan Urban Agglomeration(CYUA)in China,a typical plateau-mountain urban agglomeration,as a case study,we employed the Local and Telecoupling Coordination Degree(LTCCD)model and geographically and temporally weighted regression(GTWR)to analyze UR and EH coordination and identify driving mechanisms from 2000 to 2020.Our results reveal significant spatial dependence between UR and EH,with UR exerting a negative spillover effect on EH.The bivariate Moran’s I strengthened from−0.176 in 2000 to−0.212 in 2020,highlighting the need for an LTC framework.The LTCCD improved overall but exhibited spatiotemporally uneven patterns;UR lagging behind EH remained the dominant type,while core urban areas showed EH lagging behind UR,accompanied by localized LTCCD declines.Socioeconomic factors initially drove coordination,but their influence declined as ecological factors became more significant.Policy played a crucial role in guiding LTC development paths.The land system served as the key spillover medium,facilitating telecoupling coordinated development.This study provides insights into UR-EH interactions and their driving mechanisms,offering a reference for human-environment harmonization and sustainable development in plateau-mountain regions. 展开更多
关键词 local and telecoupling(LTC) coupling coordination geographically and temporally weighted regression(GTWR) land use transition Central Yunnan Urban Agglomeration(CYUA),China
暂未订购 下载PDF
An Integrated Approach to Condition-Based Maintenance Decision-Making of Planetary Gearboxes: Combining Temporal Convolutional Network Auto Encoders with Wiener Process 认领 引用 被引量：1
20
作者 Bo Zhu Enzhi Dong +3 位作者 Zhonghua Cheng Xianbiao Zhan Kexin Jiang Rongcai Wang 《Computers, Materials & Continua》 SCIE EI 2026年第1期661-686,共26页
With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance s... With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes. 展开更多
关键词 Temporal convolutional network autoencoder full lifecycle degradation experiment nonlinear Wiener process condition-based maintenance decision-making fault monitoring
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 92 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈