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22,862篇文章
< 1 2 … 250 >
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：
Multi-Source Traffic Information Completion and Perception Method via Graph Convolutional Neural Networks in Intelligent Connected Transportation System 认领 引用
1
作者 Pangwei Wang Jie Wang +2 位作者 Zipeng Wang Hangrui Dong Li Wang 《Computers, Materials & Continua》 SCIE EI 2026年第8期1417-1435,共19页
Traffic holographic perception refers to the real-time,high-fidelity,and multi-dimensional sensing of traffic states through the fusion of heterogeneous sensors,including cameras,radars,and connected vehicle data.The ... Traffic holographic perception refers to the real-time,high-fidelity,and multi-dimensional sensing of traffic states through the fusion of heterogeneous sensors,including cameras,radars,and connected vehicle data.The multi-source perception data obtained thereby can provide a complete digital representation of the road network for the Intelligent Transportation System(ITS).However,sensors are vulnerable to environmental interference,which can result in data loss at specific points or along arterial highways for certain periods,potentially undermining system safety and decision-making reliability.To address these challenges,a deep learning method based on Graph Convolutional Networks(GCN)and Gated Recurrent Units(GRU)is proposed,leveraging Artificial Intelligence(AI)and intelligent connected technologies for real-time acquisition of multi-sensor perception data.A feature-level fusion integrates multi-source perception data.GCN captures spatial dependencies from the road network topology,while GRU extracts temporal features from time series,enabling accurate imputation of missing traffic data.The method is evaluated at intelligent connected intersections in the Beijing High-level Autonomous Driving Demonstration Area.Results show that the accuracy of long-term traffic state completion reaches 89.36%,and the Root Mean Square Error(RMSE)is reduced by 17.2%compared to the Long Short-Term Memory(LSTM)baseline.This framework provides a practical solution for deploying traffic holographic perception technology in secure and trustworthy ITS. 展开更多
关键词 Intelligent transportation information security traffic information completion traffic holographic perception AI-driven edge computing graph convolutional neural network
暂未订购 下载PDF
Multi-source errors evaluation of machine tools:from research gaps to methodologies and applications 认领 引用 被引量：1
2
作者 Jingang Sun Yanbin Zhang +14 位作者 Xiao Ma Benkai Li Min Yang Liandi Xu Haiyuan Xin Qinglong An Lida Zhu Qingfeng Bie Xianxin Yin Shouhai Chen Guanqun Li Yusuf Suleiman Dambatta Rui Xue Zhenwei Yu Changhe Li 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第2期44-96,共53页
Multi-source errors,as critical obstacles limiting the accuracy retention and machining performance of machine tools,hold fundamental and strategic significance for achieving high-precision,high-efficiency,and high-re... Multi-source errors,as critical obstacles limiting the accuracy retention and machining performance of machine tools,hold fundamental and strategic significance for achieving high-precision,high-efficiency,and high-reliability machining in modern manufacturing systems.However,these errors typically exhibit complex characteristics such as strong coupling,time-variance,and nonlinearity,which challenge traditional methods of error identification,modeling,and compensation in terms of adaptability,real-time capability,and integration.Therefore,it is imperative to establish a systematic and intelligent multi-source error control framework.Firstly,this work systematically reviews typical error sources and their evolution mechanisms,evaluates multi-scale detection technologies including laser interferometry,double ball-bar systems,multi-sensor fusion,and vision-based systems,and constructs an intelligent error identification and evaluation framework.Next,it reviews classical modeling methods such as homogeneous transformation matrices,screw theory,thermal equilibrium models,finite element analysis,and modal analysis,compares physical modeling,data-driven,and hybrid modeling strategies,and develops an integrated multi-source error modeling architecture centered on digital twin technology and artificial intelligence.Furthermore,key technologies,including geometric error mapping and real-time compensation,online thermal error prediction and active temperature control,dynamic error suppression,and adaptive control,are summarized.A multi-level integrated error compensation architecture is proposed by combining physical models,data models,and cyber-physical synchronization.This architecture encompasses core processes such as error traceability and decoupling,dynamic prediction,real-time compensation,and closed-loop optimization,emphasizing engineering implementation mechanisms based on cyber-physical collaboration,multi-physics coupling,and multi-scale fusion,thereby effectively enhancing accuracy stability and control robustness under complex operating conditions.Finally,frontier challenges such as constructing high-fidelity coupled models from heterogeneous multi-source data,edge-cloud collaborative control,and cross-platform interoperability are discussed.The application prospects of multi-source error evaluation are also envisioned,providing theoretical foundations and technical support for the precise management and optimization of the entire lifecycle accuracy of machine tools. 展开更多
关键词 multi-source error evaluation error coupling integrated error modeling traceability and decoupling hybrid prediction closed-loop error compensation
暂未订购 下载PDF
A review on research of system dynamics and multi-source fault diagnosis of key components in high-speed train 认领 引用
3
作者 Baosen Wang Yongqiang Liu +4 位作者 Qilan Li Min Wang Qiaoying Ma Yingying Liao Shaopu Yang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第1期496-507,共12页
As China's high-speed railway technology advances,high-speed trains have emerged as a pivotal mode of transportation,instrumental in facilitating passenger and freight mobility while fostering robust regional eco-... As China's high-speed railway technology advances,high-speed trains have emerged as a pivotal mode of transportation,instrumental in facilitating passenger and freight mobility while fostering robust regional eco-nomic and trade interactions.Nonetheless,the safety of train operations remains a paramount concern,prompting extensive research into the dynamic behavior of critical components,which is essential to ensuring seamless and secure transportation services.This article commences by comprehensively reviewing the current landscape and evolutionary trajectory of dynamic model analysis for both traditional bearings and axle box bearings.Emphasis is placed on elucidating the profound influence of diverse bearing fault types on the system's kinematic state,alongside delving into the research methodologies employed in developing multi-physics field coupling models.Subsequently,it expounds on the content of investigations focusing on various wheel and track impairments,grounded in the dynamic modeling of the bearing vehicle coupling system.Concurrently,the intricate interplay between wheel-rail excitation and axle box bearing faults on the system's performance is elucidated.Concludingly,the article underscores the inadequacy of current multi-source fault diagnosis meth-odologies in tackling the intricacies of complex train operating environments,thereby highlighting its sig-nificance as a pressing and vital research agenda for the future. 展开更多
关键词 High-speed train Axle box bearing Dynamic model Wheel rail excitation Multi-source fault
暂未订购 下载PDF
Neuromorphic devices for intelligent visual perception 认领 引用
4
作者 Yixin Zhu Xiangjing Wang +4 位作者 Yuqing Hu Xinli Chen Xianhao Le Changjin Wan Qing Wan 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第1期186-219,共34页
Neuromorphic visual perception,by emulating the efficient information processing mechanisms of biological vision systems and integrating innovations in materials and device architectures,offers novel solutions for art... Neuromorphic visual perception,by emulating the efficient information processing mechanisms of biological vision systems and integrating innovations in materials and device architectures,offers novel solutions for artificial intelligence sensing.For instance,the incorporation of low-dimensional materials(e.g.,quantum dots,carbon nanotubes,and two-dimensional materials)optimizes device optoelectronic properties,while the synergistic design of organic semiconductors and oxide materials balances flexibility with complementary metal-oxide-semiconductor(CMOS)compatibility.Representative neuromorphic devices such as memristors and neuromorphic transistors address traditional vision system bottlenecks via near-sensor and in-sensor architectures in data transmission latency and energy consumption,offering a new paradigm for highly integrated,energy-efficient real-time perception.However,critical challenges—including device non-uniformity caused by material interface defects,system instability induced by memristor conductance drift,and environmental adaptability under complex illumination—remain barriers to scalable applications.This review comprehensively examines neuromorphic visual perception devices from the perspectives of device structure,operational mechanisms,materials,and applications.It explores the pivotal roles of memristors,electrolyte-gated transistors,and other neuromorphic devices in optical signal perception and information processing,with a focus on their implementations in visual perception tasks and future prospects. 展开更多
关键词 neuromorphic visual perception neuromorphic computing memristor transistor
暂未订购 下载PDF
Drive-by spatial offset detection for high-speed railway bridges based on fusion analysis of multi-source data from comprehensive inspection train 认领 引用
5
作者 Chuang Wang Jiawang Zhan +4 位作者 Nan Zhang Yujie Wang Xinxiang Xu Zhihang Wang Zhen Ni 《Railway Engineering Science》 EI 2026年第1期128-148,共21页
The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR ... The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges.Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality,cost-effectiveness,and efficiency.However,existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source,low detection accuracy,and the inability to identify lateral deformations of bridges.This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train.Firstly,dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals,and explore the hidden temporal relationships in the data.Subsequently,a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge.A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train-track-bridge interaction model,and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated.Finally,the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train.The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge,ensuring the long-term operational safety of HSR bridges. 展开更多
关键词 High-speed railway bridge Drive-by inspection Spatial offset Multi-source data fusion Deep learning
暂未订购 下载PDF
In-situ monitoring of layer-wise process quality and signal analysis for laser powder bed fusion using multi-source optical signal 认领 引用
6
作者 Di Wang Tao Tang +10 位作者 Tingyi Wang Renwu Jiang Xiaoqiang Zheng Long Zhou Laizhu Chen Wenlong Chen Pan Wang Zhiguang Zhou Ying Ma Yongqiang Yang Linqing Liu 《Additive Manufacturing Frontiers》 CAS CSCD 2026年第2期59-77,共19页
Laser powder bed fusion is a key metal additive manufacturing technology capable of fabricating geometrically complex parts,yet its reliable industrial adoption is hindered by the inherent complexity and stochastic de... Laser powder bed fusion is a key metal additive manufacturing technology capable of fabricating geometrically complex parts,yet its reliable industrial adoption is hindered by the inherent complexity and stochastic defect formation of the process.Current quality assessment is constrained by the inherent latency of offline methods and the diagnostic limitations of single-sensor monitoring.To address these challenges,this study developed a multi-source optical signal monitoring system integrating coaxial photodiodes and an off-axis industrial camera to achieve simultaneous powder spreading detection and radiation signal monitoring during LPBF layer-wise process quality monitoring.Based on the successful identification and analysis of typical detectable features,the YOLOv5s deep learning model was employed to achieve rapid and accurate detection of lack-of-powder defects during the printing process.The training results indicated that the model exhibited good performance metrics.The relationships between process parameters,typical defects,and multi-channel monitoring data were also investigated.The monitoring system achieved a spatial resolution of 300μm for in-process monitoring and demonstrated high accuracy in detecting various defect types,including lack of powder,pores,warping,stitching seams,and printing failures.Furthermore,the algorithm-detected signal anomalies exhibited good spatial correlation with the actual surface defects.Simultaneously,wavelet time-frequency analysis was employed to evaluate molten pool dynamic stability under different process parameters and to analyze energy distribution for different defects.Furthermore,3D model reconstruction from signals enabled effective correlation with actual part defects.Based on the signal-driven process optimization,complex conformal cooling molds were successfully fabricated with a grafting accuracy error of less than 0.12 mm on high-performance substrates,demonstrating the practical efficacy of the developed monitoring methodology.This study provides both a technological and a theoretical foundation for intelligent quality control in LPBF and its practical implementation in industry. 展开更多
关键词 Laser powder bed fusion Multi-source optical signal Signal analysis Layer-wise quality monitoring
暂未订购 下载PDF
A Comparative Analysis of Deep Learning Approaches for Visual Perception Benchmarks in Ship Navigation 认领 引用
7
作者 Ruolan Zhang Xingchen Ji +1 位作者 Jinichi Koue Katsutoshi Hirayama 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第2期600-616,共17页
The establishment of a reliable benchmark for evaluating model performance is critical for advancing deep learning(DL),including its application in the recognition of the ship navigation environment.Despite the steady... The establishment of a reliable benchmark for evaluating model performance is critical for advancing deep learning(DL),including its application in the recognition of the ship navigation environment.Despite the steady progress being made in object detection models across various tasks,maritime navigation presents unique challenges,such as long distances,miscellaneous objects,wide perception scales,and local conditions and features of water areas.Therefore,the improvement of DL approaches for this domain remains a significant challenge.Using a widely applicable offshore image dataset from the ship bridge,we evaluated the performance of the state-of-the-art object detection model from three perspectives:average precision,multiscale feature calculation,and intersection-over-union design,and explored the factors that may affect the model performance evaluation benchmark from the perspective of data quality,scale calculation,feature quantification,and object association.Our experiments have demonstrated that,in the context of object detection tasks within complex water surface traffic scenes,comprehensive model performance evaluation benchmarks are essential.Such benchmarks must incorporate multiple dimensions of the model. 展开更多
关键词 Long-range perception Visual navigation Dataset Multiscale detection Vision benchmark
暂未订购 下载PDF
Multi-source remote sensing unveils hydrological dynamics and drought escalation in Central Asia's arid regions 认领 引用
8
作者 WANG Yuhao ZHENG Zhijie +13 位作者 ZHU Guofeng HUANG Enwei MENG Gaojia LU Siyu QIU Dongdong CHEN Longhu LI Rui JIAO Yinying ZHAO Ling QI Xiaoyu WANG Qinqin LI Wenmin MIAO Yuxin WANG Qingyang 《Journal of Geographical Sciences》 SCIE CSCD 2026年第5期1105-1129,共25页
Despite the reported“warming-wetting”trend,Central Asia faces severe water insecurity due to climate shifts and anthropogenic activities.This study integrates multi-source remote sensing data(GRACE,TRMM,MODIS)with m... Despite the reported“warming-wetting”trend,Central Asia faces severe water insecurity due to climate shifts and anthropogenic activities.This study integrates multi-source remote sensing data(GRACE,TRMM,MODIS)with machine learning to analyze drought dynamics from 2003 to 2022 using the Water Storage Deficit Index(WSDI).Results reveal significant declines in terrestrial water storage(TWS)particularly in the Tianshan Mountains(–10.20 mm/yr)and the Central Desert(–6.19 mm/yr).Drought severity has intensified since 2014,with 67%of subregions transitioning to moderate drought.Random Forest modeling indicates that drought is no longer solely climate-driven but is increasingly dominated by anthropogenic factors(GDP,urbanization,cropland),which explain over 85%of the variability.Furthermore,the WSDI outperformed traditional indices by identifying 12 major drought events linked to deep aquifer depletion—a“hidden”structural deficit often overlooked by surface-based metrics.These findings challenge the optimistic“warming-wetting”narrative,highlighting the urgent need for storage-based management strategies to address anthropogenic groundwater depletion. 展开更多
关键词 Central Asia’s arid regions drought dynamics multi-source remote sensing GRACE hydrometeorological drivers Random Forest
暂未订购 下载PDF
How would travel contexts affect people’s perceptions and evaluations of urban green space? 认领 引用
9
作者 Yang Liu Jianying Wang +2 位作者 Mei-Po Kwan Dong Liu Liuyi Song 《Geography and Sustainability》 CSCD 2026年第2期102-115,共14页
Urban green space may impact human health through complex pathways and the effect can vary across different travel contexts.Revealing these disparities in health pathways between different travel contexts may provide ... Urban green space may impact human health through complex pathways and the effect can vary across different travel contexts.Revealing these disparities in health pathways between different travel contexts may provide essential and practical suggestions for sustainable developments in urban environments.In this study,we investi gated the impacts of travel contexts on people’s perceptions and evaluations of green space using a cross-sectional dataset collected in Hong Kong,China.Eight hundred participants in 4 representative communities were recruited through stratified sampling,and we identified 2,913 travel events from their two-day activity-travel diaries after rigorous cross-validation with GPS-derived trajectories.We also derived two green space exposure representa tions using fine-grained remote sensing imagery and 8 representative green space exposure indicators.Eighty logistical regression models and mixed-effects models were developed to investigate the associations with con trol of a range of potential uncertainties.Our results indicate solid and consistently positive associations between participants’measured green space exposure and perceived green space,and significant but variable effects of travel purposes,travel modes,and travel time on participants’perceptions and evaluations of green space.Walk ing significantly promotes participants’perceptions and positive evaluations of urban green space,buses are not significantly associated,and metro trains may depress the perception and evaluation.Our results provide solid evidence on how travel contexts may influence people’s perceptions and evaluations of urban green space and,thus,provide essential insights into environmental health studies and sustainable urban planning that consider green space as an important urban environmental setting. 展开更多
关键词 Green space Perception and evaluation Exposure measurements Travel contexts Urban planning Well-being
暂未订购 下载PDF
Tunnel boring machine jamming anticipation based on a new monitoring system for real-time perception of shield strain 认领 引用
10
作者 Yucong Pan Quansheng Liu +3 位作者 Xinlin Wu Shaohui Tang Xing Huang Yalong Jiang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第7期5562-5572,共11页
Tunnel boring machine(TBM)jamming has become one of the critical factors restricting the tunnelling speed and construction period of squeezing tunnels.To minimize the potential risk and damage of jamming accidents,a s... Tunnel boring machine(TBM)jamming has become one of the critical factors restricting the tunnelling speed and construction period of squeezing tunnels.To minimize the potential risk and damage of jamming accidents,a series of methods have been proposed to perceive TBM jamming under the condition of soft and fractured surrounding rocks.However,most of these methods cannot predict TBM jamming accurately in advance or perceive the jamming process in real-time.In the present study,a real-time monitoring system(composed of strain gauges,data acquisition,data transmission and data storage)for shield strain was developed and implemented in a TBM at the Lanzhou Water Resource Project in China.The shield strain of the double-shielded TBM was monitored and analysed continuously.The working conditions of the TBM(such as excavation or standstill,jamming or disjamming)were identifiedby analysing the characteristics of shield strain.The perception information was compared to the excavation records.The results indicate that there is good consistency between them.Combined with monitoring information and fieldsurveys,the interaction mechanisms between the shield and surrounding rock were analysed qualitatively.The jamming mechanisms of the two accidents were revealed according to the variation in stable shield strain.A criterion for anticipation TBM jamming was proposed based on the shield strain characteristics(such as trend,amplitude,and magnification).These studies provide references for real-time perception and accurate anticipation of TBM jamming in soft and fractured surrounding rock conditions. 展开更多
关键词 Tunnel boring machine(TBM)jamming Shield strain Real-time perception Anticipation criterion Monitoring system
暂未订购 下载PDF
A Bio-inspired Bubble Artificial Muscles and TacTip Perception-driven Tri-legged Robot for Obstacle Avoidance 认领 引用
11
作者 Chaoqun Xiang Zhengwei Zhong +3 位作者 Wenqiang Wu Xiaocong Chen Yisheng Guan Tao Zou 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第1期175-191,共17页
Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary... Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary Soft Tri-Legged Robot(STLR)that improves movement and obstacle-avoidance skills by using a bio-inspired pneumatic artificial muscle(Bubble Artificial Muscles)and a bio-inspired tactile sensor(TacTip).The STLR is activated by BAMs,which are flexible,pneu-matic-driven actuators that provide fine control over forward,backward,and steering movements.Obstacle identification and avoidance are facilitated by the TacTip sensor,which delivers tactile input for traversing unstructured terrains.We delineate the mechanical features of the BAMs,assess the functionality of the robot's legs,and elaborate on the incorpora-tion of the tactile sensing system.Experimental results demonstrate that the STLR can effectively achieve multi-directional flexible movement and obstacle avoidance through a cross-modal perception-actuation mechanism.This study highlights the promise of soft robotics for search and rescue,medical aid,and autonomous exploration,while delineating difficulties and opportunities for future improvements in functionality and efficiency. 展开更多
关键词 Legged robot Bio-inspired bubble artificial muscles Bio-inspired TacTip sensor Foot tactile perception Obstacle avoidance
Multi-source hydrocarbon accumulation in the faulted and deeply depressed Tangdong area,Qikou Sag,Bohai Bay Basin 认领 引用
12
作者 Xiugang Pu Guochao Xia +6 位作者 Jiangchang Dong Zhannan Shi Wenzhong Han Qianru Shi Wei Zhang Yueqi Dong Xiongying Dong 《Energy Geoscience》 EI CAS CSCD 2026年第2期85-100,共16页
Based on organic geochemical analysis data,this study examined the characteristics of multi-source hydrocarbon accumulation in the Tangdong area of Qikou Depression,Bohai Bay Basin.The results indicate that the Tangdo... Based on organic geochemical analysis data,this study examined the characteristics of multi-source hydrocarbon accumulation in the Tangdong area of Qikou Depression,Bohai Bay Basin.The results indicate that the Tangdong area contains four source rock sequences:the third member of the Dongying Formation(Ed3),the middle and lower sub-members of the first and third member of Shahejie Formation(Es 1M,Es 1L,Es 3).Both the salinity of sedimentary water bodies and the contribution of bacteria in the four source rock sequences gradual increase in the order of Es 3,Es 1M,Es 1L and Ed3,as indicated by the Ga/C30H,ETR,sterane/C30 hopane,C24TeT/C26TT,C23TT/C30H,and the carbon isotope data of group components.Additionally,certain quantities of 4-methylsterane have been detected in source rocks in the Es 3,Es 1M,and Ed3,suggesting the special contribution of dinoflagellates to these source rocks.Based on biomarker parameters and carbon isotope data,the crude oil can be categorized into classes A to D,which show complex multi-source accumulation characterized by different sources in the same well and even in the same layer.Class A crude oil occurring in the higher part of the Ed3 originates from source rocks in the Es 1L and Es 3.Class B oil existing in the lower part of the Ed3 is derived from source rocks in the Ed3.Class C oil found in the Es 1U is sourced from source rocks in the Es 1M.Class D oil present in the Es 1L is contributed indigenously by the source rocks in the Es 1L,establishing this unit as a lithologic hydrocarbon reservoir with source rocks and reservoirs in the same layer.Under the guidance of the understanding of multi-source hydrocarbon accumulation,currently,three wells have been drilled,all yielding satisfactory outcomes. 展开更多
关键词 Oil-source rock correlation Multi-source hydrocarbon accumulation Petroleum geochemistry Source rock Deeply depressed area Qikou sag Exploration well emplacement
暂未订购 下载PDF
Bio-inspired Multimodal Perception for Robotics:Bridging Neurobiological Principles and Engineering Frontiers 认领 引用
13
作者 Shuyu Wang Kui Guo 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1348-1360,共13页
To achieve human-like autonomy and adaptability in complex unstructured environments,robots must undergo a paradigm shift in multimodal perception systems by drawing inspiration from neuroscience.However,existing stud... To achieve human-like autonomy and adaptability in complex unstructured environments,robots must undergo a paradigm shift in multimodal perception systems by drawing inspiration from neuroscience.However,existing studies often remain at superficial descriptions of biological mechanisms,failing to deeply demonstrate how these principles can systematically guide innovation in robotic perception theory and technology.This review aims to bridge this gap by centering on insights from neuroscience to systematically construct a technological blueprint ranging from bio-inspired sensors to brain-like fusion algorithms.First,this review provides an in-depth analysis of the neural circuitry underlying multisensory integration in the brain,extracting engineering-ready computational principles.It then systematically examines cutting-edge sensor technologies such as neuromorphic vision and flexible electronic skin,emphasizing their applications in tasks like real-time state estimation.At the algorithmic level,the review focuses on how deep learning techniques,including variational autoencoders,cross-modal attention mechanisms,and spiking neural networks,can be employed to implement predictive coding and active perception in robotic systems.Finally,the article critically discusses core challenges in translating neuroscientific inspiration into engineering practice and outlines future directions such as neuromorphic computing,standardized embodied datasets,and machine self-body awareness.This work provides a clear development pathway for building next-generation embodied intelligence systems capable of genuine environmental perception and interaction. 展开更多
关键词 Multimodal perception Robotics Neuroscience-inspired Embodied intelligence Neuromorphic computing Predictive coding
A Survey of Key Technologies for Multi-source Heterogeneous Data in Intelligent Manufacturing 认领 引用
14
作者 Minghao Zhu Pengfei Yang +2 位作者 Bo Gao Xuehan Li Letian Wang 《Instrumentation》 2026年第1期26-39,共14页
Currently,most enterprises have adopted information software and digital equipment and gradually established digital factories.They conduct enterprise data collection and decision-support activities,generating large v... Currently,most enterprises have adopted information software and digital equipment and gradually established digital factories.They conduct enterprise data collection and decision-support activities,generating large volumes of multi-source heterogeneous data across all stages of the product life cycle.However,current data utilization methods remain simplistic,and the goal of leveraging multi-source heterogeneous data to drive manufacturing value has yet to be fully realized.To address this issue,this study first defines the concept and characteristics of multi-source heterogeneous data in intelligent manufacturing,based on an analysis of its relationship with industrial big data.Then,integrating principles from data science,a technological framework for multi-source heterogeneous data is proposed.The key technologies involved in each stage of data processing are investigated,and typical applications of such data in intelligent manufacturing are discussed.Finally,this paper analyzes the challenges and future development directions of multi-source heterogeneous data processing in intelligent manufacturing.The goal is to provide theoretical and technical support for integrating intelligent manufacturing with data science. 展开更多
关键词 intelligent manufacturing multi-source heterogeneous data feature fusion data system technological framework
暂未订购 下载PDF
High-precision classification of benthic habitat sediments in shallow waters of islands by multi-source data 认领 引用
15
作者 Qiuhua TANG Ningning LI +4 位作者 Yujie ZHANG Zhipeng DONG Yongling ZHENG Jingjing BAO Jingyu ZHANG 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2026年第1期99-108,共10页
Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications... Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications.Seabed sediment classification is one of the main contents of seabed habitat mapping.In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range,where a single data source does not fully reflect the substrate type,we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources.Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data,constructed a random forests(RF)classifier with optimal feature selection.A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island,Hainan,South China.Different seabed sediment types,such as sand,seagrass,and coral reefs were effectively identified,with an overall classification accuracy of 92%.Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources,which improved the accuracy of seabed sediment classification.Therefore,the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs. 展开更多
关键词 Wuzhizhou Island marine remote sensing coastal mapping multi-spectral remote sensing shallow water reef seabed sediment classification benthic habitat mapping multi-source data fusion random forest(RF)
暂未订购 下载PDF
The Influence of Discrimination Perception on the Psychological Resilience among Vocational High School Students:Longitudinal Mediating Effect of Vocational Identity 认领 引用
16
作者 Lingyan Zhang Yuying Yang Zhuoxuan Huang 《International Journal of Mental Health Promotion》 2026年第2期112-124,共13页
Objectives:Psychological resilience is a critical resource for vocational high school students navigating social biases and fostering mental well-being.This six-month longitudinal study investigated the developmental ... Objectives:Psychological resilience is a critical resource for vocational high school students navigating social biases and fostering mental well-being.This six-month longitudinal study investigated the developmental trajectories of discrimination perception,vocational identity,and psychological resilience in this population.It further examined the longitudinal mediating role of vocational identity in the relationship between discrimination perception and psychological resilience.Methods:A total of 526 students from five vocational high schools in Guangdong,China,were assessed via convenience sampling at two time points:baseline(T1,September 2023)and six-month follow-up(T2,March 2024).Measures of discrimination perception,psychological resilience,and vocational identity were administered.Data were analyzed using a cross-lagged panel model to test for bidirectional relationships.Results:Over the six-month period,students showed significant decreases in discrimination perception and vocational identity,but a significant increase in psychological resilience.The cross-lagged model revealed significant bidirectional relationships:discrimination perception and psychological resilience negatively predicted each other over time(β=−0.124,p<0.01;β=−0.200,p<0.001),while psychological resilience and vocational identity positively predicted each other(β=0.084,p<0.05;β=0.076,p<0.05).The mediation analysis revealed a dual-pathway mechanism.T1 discrimination perception exerted both a significant direct negative effect on T2 psychological resilience(β=−0.332,p<0.001)and a significant indirect positive effect via T1 vocational identity(indirect effect=0.020,95%CI[0.001,0.046]).This confirms a partial mediating role,indicating that vocational identity functions as a compensatory mechanism,transforming the experience of discrimination perception into a potential source of psychological resilience.Conclusions:For vocational high school students,perception of discrimination directly undermines psychological resilience,but also indirectly fosters it through the positive development of vocational identity.These findings highlight vocational identity as a pivotal mechanism in the complex relationship between social adversity and mental resilience. 展开更多
关键词 Vocational high school students vocational identity discrimination perception psychological resilience
暂未订购 下载PDF
Hybrid Deep Learning for Hydraulic Cylinder Fault Diagnosis under Complex Conditions via Multi-Source Signal Fusion 认领 引用
17
作者 Chen Yang Jianwen Yan +2 位作者 Yixiong Feng Lei Li Jianrong Tan 《Instrumentation》 2026年第1期40-56,共17页
Hydraulic presses are indispensable in automotive and aerospace manufacturing,with hydraulic cylinders serving as key components for operational safety and product quality.Internal leakage faults in hydraulic cylinder... Hydraulic presses are indispensable in automotive and aerospace manufacturing,with hydraulic cylinders serving as key components for operational safety and product quality.Internal leakage faults in hydraulic cylinders are difficult to diagnose due to the scarcity of labeled data,the complexity of fault mechanisms,and the limited representation capability of single-signal methods under variable operating conditions.To address these issues,a hybrid deep learning feature fusion model based on displacement error and pressure signal,including convolutional autoencoder,multi-head attention mechanism,residual network and bidirectional long short time series neural network(CAEMRAB),is proposed for the diagnosis and classification of leakage faults in hydraulic cylinders.A hydraulic cylinder test system simulates heavy load,variable speed,and nonlinear motion under actual operating conditions.Through the all-round deep feature decoupling of the proposed model,the multi-source signal representation ability in complex and multi-noise environments is enhanced,effectively extracting the local and global features of displacement error and pressure signal fault data and achieving efficient classification.Experimental results indicate that the proposed model achieves at least a 3.95%improvement in diagnostic accuracy compared with ablation models.In addition,it exhibits high diagnostic stability across other models,single-signal diagnosis,varying sample sizes,and complex noise conditions.These experiments fully validate the superior performance of the proposed method in terms of diagnostic accuracy,reliability,and robustness. 展开更多
关键词 hydraulic cylinder feature fusion multi-source signal hybrid deep learning deep feature decoupling
暂未订购 下载PDF
Adaptive Reinforcement Learning with Multi-Modal Perception for Autonomous Formation Control and Exploration in Large-Scale Multi-UAV Swarms 认领 引用
18
作者 Ziyuan Ma Huajun Gong Xinhua Wang 《Journal of Beijing Institute of Technology》 EI CAS 2026年第1期63-83,共21页
To address the challenge of achieving decentralized,scalable,and adaptive control for large-scale multiple unmanned aerial vehicle(multi-UAV)swarms in dynamic urban environments with obstacles and wind perturbations,w... To address the challenge of achieving decentralized,scalable,and adaptive control for large-scale multiple unmanned aerial vehicle(multi-UAV)swarms in dynamic urban environments with obstacles and wind perturbations,we proposed a hybrid framework integrating adaptive reinforcement learning(RL),multi-modal perception fusion,and enhanced pigeon flock optimization(PFO)with curiosity-driven exploration to enable robust autonomous and formation control.The framework leverages meta-learning to optimize RL policies for real-time adaptation,fuses sensor data for precise state estimation,and enhances PFO with learned leader-follower dynamics and exploration rewards to maintain cohesive formations and explore uncertain areas.For swarms of 10–30 UAVs,it achieves 34%faster convergence,61%reduced stability root mean square error(RMSE),88%fewer collisions and 85.6%–92.3%success rates in target detection and encirclement,outperforming standard multi-agent RL,pure PFO,and single-modality RL.Three-dimensional trajectory visualizations confirm cohesive formations,collision-free maneuvers,and efficient exploration in urban search-and-rescue scenarios.Innovations include meta-RL for rapid adaptation,multi-modal fusion for robust perception,and curiosity-driven PFO for scalable,decentralized control,advancing real-world multi-UAV swarm autonomy and coordination. 展开更多
关键词 multiple unmanned aerial vehicle(multi-UAV)swarm autonomous control reinforcement learning(RL) multi-modal perception pigeon flock optimization(PFO)
暂未订购 下载PDF
Optimizing Energy Efficiency in Tunnel Boring Machine Rock Breaking via Multi-source Data Fusion 认领 引用 被引量：1
19
作者 Xiaojun Yan Wencong Qi +4 位作者 Chuan Qu Minghui Ma Shanglin Liu Qian Zhang Xuesong Cheng 《Acta Mechanica Solida Sinica》 SCIE EI CSCD 2026年第4期481-493,共13页
The era of big data has profoundly transformed mechanics research,with data-driven approaches playing a vital role in modeling and optimization.This study focuses on tunnel boring machine(TBM),where the thrust-torque ... The era of big data has profoundly transformed mechanics research,with data-driven approaches playing a vital role in modeling and optimization.This study focuses on tunnel boring machine(TBM),where the thrust-torque ratio is a key determinant of their tunneling energy efficiency.However,due to the complexity of experiments and the testing requirements,obtaining sufficient high-quality data under varying geological conditions remains a major challenge in optimizing the tunneling energy efficiency of TBM.To address this,multi-cutter rotary cutting machine experiments and numerical simulations were conducted on 22 different rock types.Comprehensive datasets of normal and rolling forces were systematically collected.Using specific energy(SE)as the rock-breaking efficiency metric,we integrated physical and numerical data through a CatBoost-based fusion framework.The predictive model was initially trained on simulation data to capture the relationships among penetration,uniaxial compressive strength,tensile strength,and SE,and was subsequently fine-tuned with experimental data to develop the final fused model.Compared to models trained solely on experimental or simulated data,the fused model reduced RMSE by 37.1%and 58.6%,respectively,and improved R2by 19.0%and 44.6%,thereby enhancing both prediction accuracy and generalization capability.Furthermore,Bayesian optimization was employed to minimize SE and identify the optimal penetration.The results indicate that as rock strength increases,the optimal penetration decreases,while the corresponding minimal SE increases.These findings provide theoretical and engineering insights for improving TBM energy efficiency and parameter optimization,while establishing a robust data fusion framework for mechanical data analysis. 展开更多
关键词 Multi-source data fusion CatBoost Tunnel boring machine Multi-cutter rock-breaking Energy efficiency
暂未订购 下载PDF
Monitoring of agricultural drought based on multi-source remote sensing data in Heilongjiang Province,China 认领 引用
20
作者 Chenfa Jiang Changhui Ma +4 位作者 Sibo Duan Xiaoxiao Min Youzhi Zhang Dandan Li Xia Zhang 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第4期1716-1730,共15页
Agriculture is the foundation of socio-economic development and is highly influenced by weather and climate conditions.Drought is one of the most significant threats to agricultural development and food security.Curre... Agriculture is the foundation of socio-economic development and is highly influenced by weather and climate conditions.Drought is one of the most significant threats to agricultural development and food security.Currently,in-situ drought monitoring based on weather stations and based on remote sensing data has limitations,including infrequent updates,limited coverage,and low accuracy.This study leverages multi-source remote sensing data to monitor agricultural drought in Heilongjiang Province,China.We developed multi-source composite drought indices(MCDIs)at various timescales(3,6,9,and 12 months)by integrating precipitation,land surface temperature,soil moisture,and vegetation indices.Utilizing remote sensing data from various sources,we calculated a series of single drought indices,which are the precipitation condition index,soil moisture condition index,vegetation condition index,and temperature condition index.These are then integrated into MCDIs using a multivariable linear regression approach.The analysis reveals that MCDIs correlate more with standardized precipitation evapotranspiration index(SPEI)than single drought indices.When examining the correlation between different MCDIs and the affected area of crops and major grain production,MCDI-9 showed the highest correlation with the affected area of crops,while MCDI-12 showed the highest correlation with grain production.This suggests that these two MCDIs at different timescales are better indicators of agricultural drought.The spatio-temporal analysis of MCDI indicates that drought in Heilongjiang Province primarily occurs in early spring,gradually spreading from the Greater Khingan Mountains region to the southeastern plains.The drought gradually alleviates during the summer,ending by the autumn harvest period.Therefore,the MCDIs constructed in this study can serve as effective methods and indicators for drought monitoring in Heilongjiang Province and similar regions. 展开更多
关键词 agricultural drought spatio-temporal monitoring multi-source remote sensing data SPEI Heilongjiang Province
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 … 250 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈