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Multi-source information response characteristics of surrounding rock catastrophic instability in deep roadways with four-dimensional support 认领 引用
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作者 Pengfei Yan Zhanguo Ma +5 位作者 Hongbo Li Peng Gong Haihui Zhao Chuanchuan Cai Mingshuo Xu Tianqi She 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第11期7183-7207,共25页
As coal mining progresses to greater depths,controlling the stability of surrounding rock in deep roadways has become an increasingly complex challenge.Although four-dimensional(4D)support theoretically offers unique ... As coal mining progresses to greater depths,controlling the stability of surrounding rock in deep roadways has become an increasingly complex challenge.Although four-dimensional(4D)support theoretically offers unique advantages in maintaining the stability of rock mass,the disaster evolution processes and multi-source information response characteristics in deep roadways with 4D support remain unclear.Consequently,a large-scale physical model testing system and self-designed 4D support components were employed to conduct similarity model tests on the surrounding rock failure process under unsupported(U-1),traditional bolt-mesh-cable support(T-2),and 4D support(4D-R-3)conditions.Combined with multi-source monitoring techniques,including stress–strain,digital image correlation(DIC),acoustic emission(AE),microseismic(MS),parallel electric(PE),and electromagnetic radiation(EMR),the mechanical behavior and multi-source information responses were comprehensively analyzed.The results show that the peak stress and displacement of the models are positively correlated with the support strength.The multi-source information exhibits distinct response characteristics under different supports.The response frequency,energy,and fluctuationsof AE,MS,and EMR signals,along with the apparent resistivity(AR)high-resistivity zone,follow the trend U-1>T-2>4D-R-3.Furthermore,multi-source information exhibits significantdifferences in sensitivity across different phases.The AE,MS,and EMR signals exhibit active responses to rock mass activity at each phase.However,AR signals are only sensitive to the fracture propagation during the plastic yield and failure phases.In summary,the 4D support significantlyenhances the bearing capacity and plastic deformation of the models,while substantially reducing the frequency,energy,and fluctuationsof multi-source signals. 展开更多
关键词 Physical model Deep roadway Four-dimensional(4D)support Multi-source monitoring information Catastrophic instability process
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Multi-source information fused generative adversarial network model and data assimilation based history matching for reservoir with complex geologies 认领 引用 被引量：11
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作者 Kai Zhang Hai-Qun Yu +7 位作者 Xiao-Peng Ma Jin-Ding Zhang Jian Wang Chuan-Jin Yao Yong-Fei Yang Hai Sun Jun Yao Jian Wang 《Petroleum Science》 SCIE CAS CSCD 2022年第2期707-719,共13页
For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for... For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching. 展开更多
关键词 Multi-source information Automatic history matching Deep learning Data assimilation Generative model
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Fault location of distribution networks based on multi-source information 认领 引用 被引量：8
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作者 Wenbo Li Jianjun Su +2 位作者 Xin Wang Jiamei Li Qian Ai 《Global Energy Interconnection》 EI 2020年第1期77-85,共9页
In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for th... In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or misreported.Therefore,this study considers the influence of the distributed generations(DGs)for the distribution network.This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution network.An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance. 展开更多
关键词 Internet of Things Multi-source information D-S evidence theory Binary particle swarm optimization algorithm Fault tolerance
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Enhancing train position perception through Al-driven multi-source information fusion 认领 引用 被引量：4
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作者 Haifeng Song Zheyu Sun +3 位作者 Hongwei Wang Tianwei Qu Zixuan Zhang Hairong Dong 《Control Theory and Technology》 EI CSCD 2023年第3期425-436,共12页
This paper addresses the challenge of accurately and timely determining the position of a train,with specific consideration given to the integration of the global navigation satellite system(GNSS)and inertial navigati... This paper addresses the challenge of accurately and timely determining the position of a train,with specific consideration given to the integration of the global navigation satellite system(GNSS)and inertial navigation system(INS).To overcome the increasing errors in the INS during interruptions in GNSS signals,as well as the uncertainty associated with process and measurement noise,a deep learning-based method for train positioning is proposed.This method combines convolutional neural networks(CNN),long short-term memory(LSTM),and the invariant extended Kalman filter(IEKF)to enhance the perception of train positions.It effectively handles GNSS signal interruptions and mitigates the impact of noise.Experimental evaluation and comparisons with existing approaches are provided to illustrate the effectiveness and robustness of the proposed method. 展开更多
关键词 Train positioning Deep learning Multi-source information fusion Dynamic adaptive model
Belief exponential divergence for D-S evidence theory and its application in multi-source information fusion 认领 引用 被引量：5
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作者 DUAN Xiaobo FAN Qiucen +1 位作者 BI Wenhao ZHANG An 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第6期1454-1468,共15页
Dempster-Shafer evidence theory is broadly employed in the research of multi-source information fusion.Nevertheless,when fusing highly conflicting evidence it may pro-duce counterintuitive outcomes.To address this iss... Dempster-Shafer evidence theory is broadly employed in the research of multi-source information fusion.Nevertheless,when fusing highly conflicting evidence it may pro-duce counterintuitive outcomes.To address this issue,a fusion approach based on a newly defined belief exponential diver-gence and Deng entropy is proposed.First,a belief exponential divergence is proposed as the conflict measurement between evidences.Then,the credibility of each evidence is calculated.Afterwards,the Deng entropy is used to calculate information volume to determine the uncertainty of evidence.Then,the weight of evidence is calculated by integrating the credibility and uncertainty of each evidence.Ultimately,initial evidences are amended and fused using Dempster’s rule of combination.The effectiveness of this approach in addressing the fusion of three typical conflict paradoxes is demonstrated by arithmetic exam-ples.Additionally,the proposed approach is applied to aerial tar-get recognition and iris dataset-based classification to validate its efficacy.Results indicate that the proposed approach can enhance the accuracy of target recognition and effectively address the issue of fusing conflicting evidences. 展开更多
关键词 Dempster-Shafer(D-S)evidence theory multi-source information fusion conflict measurement belief expo-nential divergence(BED) target recognition
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A multi-source information fusion method for tool life prediction based on CNN-SVM 认领 引用 被引量：1
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作者 Shuo WANG Zhenliang YU +1 位作者 Peng LIU Man Tong WANG 《Mechanical Engineering Science》 2022年第2期1-10,I0003,I0004,共10页
For milling tool life prediction and health management,accurate extraction and dimensionality reduction of its tool wear features are the key to reduce prediction errors.In this paper,we adopt multi-source information... For milling tool life prediction and health management,accurate extraction and dimensionality reduction of its tool wear features are the key to reduce prediction errors.In this paper,we adopt multi-source information fusion technology to extract and fuse the features of cutting vibration signal,cutting force signal and acoustic emission signal in time domain,frequency domain and time-frequency domain,and downscale the sample features by Pearson correlation coefficient to construct a sample data set;then we propose a tool life prediction model based on CNN-SVM optimized by genetic algorithm(GA),which uses CNN convolutional neural network as the feature learner and SVM support vector machine as the trainer for regression prediction.The results show that the improved model in this paper can effectively predict the tool life with better generalization ability,faster network fitting,and 99.85%prediction accuracy.And compared with the BP model,CNN model,SVM model and CNN-SVM model,the performance of the coefficient of determination R2 metric improved by 4.88%,2.96%,2.53%and 1.34%,respectively. 展开更多
关键词 CNN-SVM tool wear life prediction multi-source information fusion
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Multi-source information fusion:Progress and future 认领 引用 被引量：32
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作者 Xinde LI Fir DUNKIN Jean DEZERT 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2024年第7期24-58,共35页
Multi-Source Information Fusion(MSIF),as a comprehensive interdisciplinary field based on modern information technology,has gained significant research value and extensive application prospects in various domains,attr... Multi-Source Information Fusion(MSIF),as a comprehensive interdisciplinary field based on modern information technology,has gained significant research value and extensive application prospects in various domains,attracting high attention and interest from scholars,engineering experts,and practitioners worldwide.Despite achieving fruitful results in both theoretical and applied aspects over the past five decades,there remains a lack of comprehensive and systematic review articles that provide an overview of recent development in MSIF.In light of this,this paper aims to assist researchers and individuals interested in gaining a quick understanding of the relevant theoretical techniques and development trends in MSIF,which conducts a statistical analysis of academic reports and related application achievements in the field of MSIF over the past two decades,and provides a brief overview of the relevant theories,methodologies,and application domains,as well as key issues and challenges currently faced.Finally,an analysis and outlook on the future development directions of MSIF are presented. 展开更多
关键词 Multi-sensor system Information fusion Artificial intelligence Pattern recognition Human-machine integration
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A Comparison of Four Neural Networks Algorithms on Locomotion Intention Recognition of Lower Limb Exoskeleton Based on Multi-source Information 认领 引用 被引量：3
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作者 Duojin Wang Xiaoping Gu Hongliu Yu 《Journal of Bionic Engineering》 SCIE EI CSCD 2024年第1期224-235,共12页
Lower Limb Exoskeletons(LLEs)are receiving increasing attention for supporting activities of daily living.In such active systems,an intelligent controller may be indispensable.In this paper,we proposed a locomotion in... Lower Limb Exoskeletons(LLEs)are receiving increasing attention for supporting activities of daily living.In such active systems,an intelligent controller may be indispensable.In this paper,we proposed a locomotion intention recognition system based on time series data sets derived from human motion signals.Composed of input data and Deep Learning(DL)algorithms,this framework enables the detection and prediction of users’movement patterns.This makes it possible to predict the detection of locomotion modes,allowing the LLEs to provide smooth and seamless assistance.The pre-processed eight subjects were used as input to classify four scenes:Standing/Walking on Level Ground(S/WOLG),Up the Stairs(US),Down the Stairs(DS),and Walking on Grass(WOG).The result showed that the ResNet performed optimally compared to four algorithms(CNN,CNN-LSTM,ResNet,and ResNet-Att)with an approximate evaluation indicator of 100%.It is expected that the proposed locomotion intention system will significantly improve the safety and the effectiveness of LLE due to its high accuracy and predictive performance. 展开更多
关键词 Lower limb exoskeletons(LLEs) Locomotion intention ResNet Multi-source
A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM 认领 引用 被引量：2
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作者 Yili Wang Changsheng Li Xiaofeng Wang 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2024年第3期463-474,共12页
When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ... When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves. 展开更多
关键词 Penetration fuze Temporal convolutional network(TCN) Long short-term memory(LSTM) Layer counting Multi-source fusion
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Effective DOA Estimation Under Low Signal-to-Noise Ratio Based on Multi-Source Information Meta Fusion 认领 引用 被引量：4
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作者 Yun Wu Xiukun Li Zhimin Cao 《Journal of Beijing Institute of Technology》 EI CAS 2021年第4期377-396,共20页
Efficiently performing high-resolution direction of arrival(DOA)estimation under low signal-to-noise ratio(SNR)conditions has always been a challenge task in the literatures.Obvi-ously,in order to address this problem... Efficiently performing high-resolution direction of arrival(DOA)estimation under low signal-to-noise ratio(SNR)conditions has always been a challenge task in the literatures.Obvi-ously,in order to address this problem,the key is how to mine or reveal as much DOA related in-formation as possible from the degraded array outputs.However,it is certain that there is no per-fect solution for low SNR DOA estimation designed in the way of winner-takes-all.Therefore,this paper proposes to explore in depth the complementary DOA related information that exists in spa-tial spectrums acquired by different basic DOA estimators.Specifically,these basic spatial spec-trums are employed as the input of multi-source information fusion model.And the multi-source in-formation fusion model is composed of three heterogeneous meta learning machines,namely neural networks(NN),support vector machine(SVM),and random forests(RF).The final meta-spec-trum can be obtained by performing a final decision-making method.Experimental results illus-trate that the proposed information fusion based DOA estimation method can really make full use of the complementary information in the spatial spectrums obtained by different basic DOA estim-ators.Even under low SNR conditions,promising DOA estimation performance can be achieved. 展开更多
关键词 direction of arrival(DOA) signal-to-noise ratio(SNR) information fusion meta-learn-ing spatial spectrum
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GIS Platform-based Multi-source Information System for Evaluation of Cu, W and Au Resources in the Northern Qilian, China 认领 引用 被引量：1
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作者 LIChunxia FUShuixing ZHANGShoulin 《Acta Geologica Sinica(English Edition)》 SCIE EI CAS 2004年第2期558-561,共4页
Based on the information of geology, geochemistry, geophysics and remote sensing, the GIS of multi-source information is used to evaluate Cu, W and Au mineral resources in Northern Qilian, China. As the GIS evaluation... Based on the information of geology, geochemistry, geophysics and remote sensing, the GIS of multi-source information is used to evaluate Cu, W and Au mineral resources in Northern Qilian, China. As the GIS evaluation system works out in the thinking of geological prospecting, its functions include file management, graph edition, database maintenance, information inquiry and comprehensive spatial analysis as well as prospecting target prognosis. Accordingly, the GIS evaluation system can be used directly and conveniently for inquiry and analysis of visual graphs or images. 展开更多
关键词 GIS geoscientific information visualization mineral resources spatial database northern Qilian
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Multi-Source Traffic Information Completion and Perception Method via Graph Convolutional Neural Networks in Intelligent Connected Transportation System 认领 引用
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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
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Research on distribution automation security situational awareness technology based on risk transmission path and multi-source information fusion 认领 引用 被引量：1
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作者 Jingzhi Liu Hongyi Yang +2 位作者 Quanlei Qu Zhidong Liu Yang Cao 《Cybersecurity》 EI CSCD 2026年第1期99-108,共10页
It may be difcult for existing methods to make full use of the correlation and complementarity of various kinds of information when processing multi-source information.In order to accurately perceive the security situ... It may be difcult for existing methods to make full use of the correlation and complementarity of various kinds of information when processing multi-source information.In order to accurately perceive the security situation of distribution automation and ensure the safe and stable operation of distribution network,the multi-source information fusion distribution automation security situation awareness technology based on risk transmission path is studied.Based on the risk transmission path,the distribution automation security situational awareness factors are analyzed,and the main factors afecting the distribution automation security situation are divided into two dimensions:internal source and external source,and eight main awareness factors;Diferent types of sensors are set in the main areas of security situational awareness factors to collect data of diferent awareness factors.Using ant colony algorithm to optimize DS evidence fusion method,data with diferent perception factors are fused,and data fusion results with diferent perception factors are obtained.The distribution automation security situational awareness model is constructed,and the security situational awareness results are obtained based on the data fusion results of the awareness factors.If the results are higher than the set threshold,the abnormal signal can be output to determine the area where the distribution automation abnormal equipment is located.The experimental results show that the multi-source data fusion efect of this method is good,and it can accurately perceive the security status of diferent nodes of the experimental object at diferent time nodes. 展开更多
关键词 Risk transmission path Multi source information fusion Distribution automation Security situational awareness Perception factor DS evidence fusion
A comprehensive analysis method for adverse geology in tunnels based on geological information and multi-source geophysical data 认领 引用 被引量：2
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作者 Peng Wang Shi-shu Zhang +5 位作者 Wei-dong Chen Yi-guo Xue Zi-ming Qu Hua-bo Xiao Mao-xin Su Kai Zhang 《Applied Geophysics》 SCIE CSCD 2025年第1期43-52,232,共10页
Advanced geological prediction is a crucial means to ensure safety and efficiency in tunnel construction.However,diff erent advanced geological forecasting methods have their own limitations,resulting in poor detectio... Advanced geological prediction is a crucial means to ensure safety and efficiency in tunnel construction.However,diff erent advanced geological forecasting methods have their own limitations,resulting in poor detection accuracy.Using multiple methods to carry out a comprehensive evaluation can eff ectively improve the accuracy of advanced geological prediction results.In this study,geological information is combined with the detection results of geophysical methods,including transient electromagnetic,induced polarization,and tunnel seismic prediction,to establish a comprehensive analysis method of adverse geology.First,the possible main adverse geological problems are determined according to the geological information.Subsequently,various physical parameters of the rock mass in front of the tunnel face can then be derived on the basis of multisource geophysical data.Finally,based on the analysis results of geological information,the multisource data fusion algorithm is used to determine the type,location,and scale of adverse geology.The advanced geological prediction results that can provide eff ective guidance for tunnel construction can then be obtained. 展开更多
关键词 Advanced geological prediction Comprehensive analysis Geological information Transient electromagnetic Induced polarization Tunnel seismic prediction
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Multi-source errors evaluation of machine tools:from research gaps to methodologies and applications 认领 引用 被引量：1
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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
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Wireless Environmental Information Theory:A New Paradigm Toward 6G Online and Proactive Environment Intelligence Communication 认领 引用 被引量：4
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作者 Jianhua Zhang Li Yu +4 位作者 Shaoyi Liu Yichen Cai Yuxiang Zhang Hongbo Xing Tao Jiang 《Engineering》 SCIE EI CSCD 2026年第1期186-200,共15页
Channels are one of the five critical components of a communication system,and their ergodic capacity is based on all realizations of a statistical channel model.This statistical paradigm has successfully guided the d... Channels are one of the five critical components of a communication system,and their ergodic capacity is based on all realizations of a statistical channel model.This statistical paradigm has successfully guided the design of mobile communication systems from first generation(1G)to fifth generation(5G).However,this approach relies on offline channel measurements in specific environments,and thus,the system passively adapts to new environments,resulting in deviation from the optimal performance.As sixth generation(6G)expands into ubiquitous environments and pursues higher capacity,numerous sensing and artificial intelligence(AI)-based methods have emerged to combat random channel fading.However,there remains an urgent need for a proactive and online system design paradigm.From a system perspective,we propose an environment intelligence communication(EIC)based on wireless environmental information theory(WEIT)for 6G.The proposed EIC architecture operates in three steps.First,wireless environmental information(WEI)is acquired using sensing techniques.Then,leveraging WEI and channel data,AI techniques are employed to predict channel fading,thereby mitigating channel uncertainty.Finally,the communication system autonomously determines the optimal air-interface transmission strategy based on real-time channel predictions,enabling intelligent interaction with the physical environment.To make this attractive paradigm shift from theory to practice,we establish WEIT for the first time by answering three key problems:How should WEI be defined?Can it be quantified?Does it hold the same properties as statistical communication information?Subsequently,EIC aided by WEI(EIC-WEI)is validated across multiple air-interface tasks,including channel state information prediction,beam prediction,and radio resource management.Simulation results demonstrate that the proposed EIC-WEI significantly outperforms the statistical paradigm in decreasing overhead and performance optimization.Finally,several open problems and challenges,including regarding its accuracy,complexity,and generalization,are discussed.This work explores a novel and promising way for integrating communication,sensing,and AI capability in 6G. 展开更多
关键词 Sixth generation Intelligent communication Environment intelligence Wireless environmental information theory Environment sensing and reconstruction Channel prediction Digital twin channel ChannelGPT
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Deep Learning-Enhanced Human Sensing with Channel State Information: A Survey 认领 引用 被引量：1
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作者 Binglei Yue Aili Jiang +3 位作者 Chun Yang Junwei Lei Heng Liu Yin Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第1期1-28,共28页
With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State I... With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing. 展开更多
关键词 Channel State Information(CSI) human sensing human activity recognition deep learning
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A review on research of system dynamics and multi-source fault diagnosis of key components in high-speed train 认领 引用
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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
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An uncertainty evaluation for storm surge risk analysis based on information utilization efficiency 认领 引用
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作者 Guilin LIU Siyu DING +3 位作者 Shichun SONG Bokai YANG Pengyu ZHU Liping WANG 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2026年第2期545-559,共15页
With the development of methods for predicting extreme hydrological elements using probabilistic approaches,several commonly used methods have emerged for analyzing the risk of storm surge disasters,including the Annu... With the development of methods for predicting extreme hydrological elements using probabilistic approaches,several commonly used methods have emerged for analyzing the risk of storm surge disasters,including the Annual Maxima method,the Peak-Over-Threshold method,the Gumbel distribution,and the Weibull distribution.Meanwhile,and emphases have been placed on assessing and comparing the applicability and stability of these various methods.To evaluate the rationality of different methods,we an entropy uncertainty analysis method was introduced based on information utilization efficiency,in which the sample Stochastic uncertainty is measured by the ratio of information entropy before and after sampling,i.e.,the information extraction efficiency of the sampling method.Additionally,the cognitive uncertainty of the research method is assessed by the ratio of mutual information between the model and the sample to the information entropy of the sample,i.e.,the information extraction efficiency of the mathematical model.Furthermore,we incorporated the group probability calculation method,information entropy and mutual information theory to analyze and calculate the entropy uncertainty more accurately.By applying this analysis to the design wave height and the recurrence period projected in the sea area west Guangdong of China,we believed that the most reasonable hazard assessment method shall be based on the over-threshold method combined with the Pareto distribution.Conversely,the assessment method based on the process extreme value method is deemed insufficiently reasonable and requires further research. 展开更多
关键词 uncertainty storm surge information entropy mutual information group probability calculation method
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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
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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
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