False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading fail...False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading failures,large-scale blackouts,and significant economic losses.While detecting attacks is important,accurately localizing compromised nodes or measurements is even more critical,as it enables timely mitigation,targeted response,and enhanced system resilience beyond what detection alone can offer.Existing research typically models topological features using fixed structures,which can introduce irrelevant information and affect the effectiveness of feature extraction.To address this limitation,this paper proposes an FDIA localization model with adaptive neighborhood selection,which dynamically captures spatial dependencies of the power grid by adjusting node relationships based on data-driven similarities.The improved Transformer is employed to pre-fuse global spatial features of the graph,enriching the feature representation.To improve spatio-temporal correlation extraction for FDIA localization,the proposed model employs dilated causal convolution with a gating mechanism combined with graph convolution to capture and fuse long-range temporal features and adaptive topological features.This fully exploits the temporal dynamics and spatial dependencies inherent in the power grid.Finally,multi-source information is integrated to generate highly robust node embeddings,enhancing FDIA detection and localization.Experiments are conducted on IEEE 14,57,and 118-bus systems,and the results demonstrate that the proposed model substantially improves the accuracy of FDIA localization.Additional experiments are conducted to verify the effectiveness and robustness of the proposed model.展开更多
Background and Objectives:The perception of sound in the vertical plane supports spatial hearing by enabling listeners to detect sources located above and below.Sounds originating from both the front and back elevatio...Background and Objectives:The perception of sound in the vertical plane supports spatial hearing by enabling listeners to detect sources located above and below.Sounds originating from both the front and back elevations along the mid-sagittal plane further contribute to a three-dimensional auditory experience.This study aimed to characterize the variability in vertical sound localization abilities among normal-hearing(NH)individuals using spatialized audio.Materials and Methods:Fifty-one NH participants(aged 18 to 35 years)completed three vertical localization tasks under headphones as part of a single-group,within-subject experimental study.These tasks included two-plane identification:(1)top-down localization,(2)front-back localization,and one discrimination task in the front plane.Hierarchical Cluster Analysis(HCA)was employed to identify distinct patterns in spatial localization profiles specific to the vertical-median plane.Fisher's Discriminant Function Analysis(FDA)was used to validate the accuracy of HCA and estimate classification error.Results:HCA revealed three distinct listener clusters:(1)cluster 1 with good performance across all three tasks,(2)cluster 2 with selective impairment in top-bottom identification,and(3)cluster 3 with selective deficits in front-back identification.FDA validated group membership of the clusters identified by the HCA,with a prediction accuracy of 98%.Conclusions:Individuals with clinically NH exhibited three distinct vertical localization profiles:uniform performers,those impaired in top-bottom identification,and those impaired in front-back identification.These profiles may be linked to the interplay between acoustic and non-acoustic perceptual factors.展开更多
Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficie...Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficiency of roadway infrastructure.However,existingmethods struggle in complex traffic scenarios due to static spatio-temporal embedding,restricted multi-scale temporal modeling,and weak representation of local spatial interactions.This study proposes Bi-STAT+,an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions:(1)an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations;(2)frequency-domain analysis in the temporal dimension for simultaneous high-frequency details and low-frequency trend extraction;and(3)an agent attention mechanism in the spatial dimension that enhances local feature extraction through dynamic weight allocation.Extensive experiments were performed on four distinct datasets,including two publicly benchmark datasets(PEMS04 and PEMS08)and two private datasets collected from Baotou and Chengdu,China.The results demonstrate that Bi-STAT+consistently outperforms existing methods in terms of MAE,RMSE,and MAPE,while maintaining strong robustness against missing data and noise.Furthermore,the results highlight that prediction accuracy improves significantly with higher sampling rates,providing crucial insights for optimizing real-world deployment scenarios.展开更多
The pseudo-two-dimensional(P2D)model plays an important role in exploring physicochemical mechanisms,predicting the state of health,and improving the fast charge capability for Li-ion batteries(LIBs).However,the fast ...The pseudo-two-dimensional(P2D)model plays an important role in exploring physicochemical mechanisms,predicting the state of health,and improving the fast charge capability for Li-ion batteries(LIBs).However,the fast charge leads to the lithium concentration gradient in the solid and electrolyte phases and the non-uniform electrochemical reaction at the solid/electrolyte interface.In order to decouple charge transfer reactions in LIBs under dynamic conditions,understanding the spatio-temporal resolution of the P2D model is urgently required.Till now,the study of this aspect is still insufficient.This work studies the spatio-temporal resolution for dynamic/static electrochemical impedance spectroscopy(DEIS/SEIS)on multiple scales.In detail,DEIS and SEIS with spatio-temporal resolutions are used to decouple charge transfer reactions in LIBs based on the numerical solution of the P2D model in the frequency domain.The calculated results indicate that decoupling solid diffusion requires a high spatial resolution along the r-direction in particles,decoupling electrolyte diffusion and interfacial transfer reaction requires a high spatial resolution along the x-direction,and decoupling charge transfer reactions in LIBs at an extremely low state of charge(SOC)requires an extremely high temporal resolution along the t-direction.Finally,the optimal range of spatio-temporal resolutions for DEIS/SEIS is derived,and the method to decouple charge transfer reactions with spatio-temporal resolutions is developed.展开更多
This paper proposes a tamper detection technique for semi-fragile watermarking using Quantizationbased Discrete Cosine Transform(DCT)for tamper localization.In this study,the proposed embedding strategy is investigate...This paper proposes a tamper detection technique for semi-fragile watermarking using Quantizationbased Discrete Cosine Transform(DCT)for tamper localization.In this study,the proposed embedding strategy is investigated by experimental tests over the diagonal order of the DCT coefficients.The cover image is divided into non-overlapping blocks of size 8×8 pixels.The DCT is applied to each block,and the coefficients are arranged using a zig-zag pattern within the block.In this study,the low-frequency coefficients are selected to examine the impact of the imperceptibility score and tamper detection accuracy.High accuracy of tamper detection can be achieved by checking the surrounding blocks to determine whether the corresponding block has been tampered with.The proposed tamper detection is tested under various malicious,incidental,and hybrid attacks(both incidental and malicious attacks).The experimental results demonstrate that the proposed technique achieves a Peak-Signal-to-Noise Ratio(PSNR)value of 41.2318 dB,an average Structural Similarity Index Measure(SSIM)value of 0.9768.The proposed scheme is also evaluated against malicious attacks such as copy-move,object deletion,object manipulation,and collage attacks.The proposed scheme can detect the malicious attack localization under various tampering rates.In addition,the proposed scheme can still detect tampered pixels under a hybrid attack,such as a combination ofmalicious and incidental attacks,with an average accuracy of 96.44%.展开更多
The acquisition of position information of legitimate users and jammers plays an important role in the emerging non-geostationary synchronous orbit(NGSO)satellite communications.In this paper,we study the multi-signal...The acquisition of position information of legitimate users and jammers plays an important role in the emerging non-geostationary synchronous orbit(NGSO)satellite communications.In this paper,we study the multi-signal localization problem in an uplink NGSO satellite communication system.We propose an onboard localization scheme based on multiple observations from the satellite,together with the geometric constraints of the satellite postions,the signal positions,the attitude of the satellite,and the angle-of-arrival(AoAs)of the signals.We develop a massage-passing algorithm,termed the Bayesian blind multi-signal localization(BMSL),to jointly estimate the AoAs and the signal positions.The Cramér-Rao lower bound(CRLB)is derived to characterize the fundamental performance limit of the considered localization problem.Simulation results show that the proposed BMSL algorithm can perform close to the derived CRLB and significantly outperforms its counterpart algorithms.展开更多
The stator of the maglev track plays a crucial role in the operation of the maglev system.Currently,the efficiency of maglev track inspection is limited by several factors,including the large span of elevated structur...The stator of the maglev track plays a crucial role in the operation of the maglev system.Currently,the efficiency of maglev track inspection is limited by several factors,including the large span of elevated structures,manual visual inspection,short inspection window times,and limited GPS positioning accuracy.To address these issues,this paper proposes a deep learning-based method for detecting and locating stator surface damage.This study establishes a maglev track stator surface image dataset,trains different object detection models,and compares their performance.Ultimately,YOLO and ByteTrack object tracking algorithms were chosen as the basic framework and enhanced to achieve automatic identification of high-speed maglev track stator surface damage images and track and count stator surface localization feature images.By matching the identified damaged images with their corresponding stator segment and beam segment sequence numbers,the location of the damage is pinpointed to the corresponding stator segment,enabling rapid and accurate identification and localization of complex damage to the maglev track stator surface.展开更多
This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically def...This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks(STGNNs).However,such definitions are prone to generating spurious correlations due to the dominance of periodic structures.To address this limitation,we adopt the Elastic-Band Transform(EBT)to decompose solar radiation into periodic and amplitude-modulated components,which are then modeled independently with separate graph neural networks.The periodic component,characterized by strong nationwide correlations,is learned with a relatively simple architecture,whereas the amplitude-modulated component is modeled with more complex STGNNs that capture climatological similarities between regions.The predictions from the two components are subsequently recombined to yield final forecasts that integrate both periodic patterns and aperiodic variability.The proposed framework is validated with multiple STGNN architectures,and experimental results demonstrate improved predictive accuracy and interpretability compared to conventional methods.展开更多
Granular materials exhibit complex macroscopic mechanical behaviors closely related to their microscalemicrostructural features.Traditional macroscopic phenomenological elasto-plastic models,however,usually have compl...Granular materials exhibit complex macroscopic mechanical behaviors closely related to their microscalemicrostructural features.Traditional macroscopic phenomenological elasto-plastic models,however,usually have complex formulations and lack explicit relations to these microstructural features.To avoid these limitations,this study proposes a micromechanics-based softening hyperelastic model for granular materials,integrating softening hyperelasticity withmicrostructural insights to capture strain softening,critical state,and strain localization behaviors.The model has two key advantages:(1)a clear conceptualization,straightforward formulation,and ease of numerical implementation(via Abaqus UMAT subroutine in this study);(2)explicit incorporation of micro-scale features(e.g.,contact stiffness,particle size,porosity)to reveal their influences on macroscopic responses.An isotropic directional distribution density of contacts and three specific microstructures are considered,and their softening hyperelastic constitutive modulus tensors are explicitly derived.By introducing a softening factor and critical failure energy density,the model can describe geomaterial behaviors,simulating residual strength,X-shaped shear bands,and strain localization evolution.Numerical validations in comparison with themacro-scale hyperelastic model,Abaqus Drucker-Prager model,and the experiment confirm its accuracy.Parametric studies reveal critical dependencies:a normal to tangential contact stiffness ratio of 2-8(depending on stiffness magnitude),an internal length of 2-4 mm to ensure shear band formation,and a critical failure energy density(≤10 kJ/m^(3))to trigger strain softening and localization.Influences of the specific microstructures on strain localization and softening are investigated.The model also shows mesh independence due to the introduction of an internal length.The model’s applicability is further demonstrated by slope stability analysis,capturing slip surface evolution,and load-displacement characteristics.This study develops a robust microstructure-aware hyperelastic framework to describe the mechanical behaviors of granular materials,providing multiscale insights for geotechnical engineering applications.展开更多
This paper provides a systematic review of the histochemical localization,content characteristics,and influencing factors of saponins in Pseudostellaria heterophylla based on an extensive literature survey.It provides...This paper provides a systematic review of the histochemical localization,content characteristics,and influencing factors of saponins in Pseudostellaria heterophylla based on an extensive literature survey.It provides an in-depth analysis and summary of the effects of biological factors,environmental conditions,agronomic practices,processing methods,and continuous cropping obstacles on the synthesis of P.heterophylla saponins,as well as their underlying mechanisms.Based on identified gaps in the current literature,future research directions and prospects are proposed.The findings of this review offer valuable insights for advancing the understanding of the saponin biosynthesis mechanisms in P.heterophylla and for enhancing its quality.展开更多
A multi-stage stress relaxation test was performed on a granodiorite sample to understand the deformation process prior to the macroscopic failure of brittle rocks,as well as the transient response during stress relax...A multi-stage stress relaxation test was performed on a granodiorite sample to understand the deformation process prior to the macroscopic failure of brittle rocks,as well as the transient response during stress relaxation.Distributed optical fiber sensing was used to measure strains across the sample surface by helically wrapping the single-mode fiber around the cylindrical sample.Close agreement was observed between the circumferential strains obtained from the optical fibers and the extensometer.The reconstructed full-field strain contours show strain heterogeneity from the crack closure phase,and the strains in the later deformation phase are dominantly localized within the former high-strain zone.The Gini coefficient was used to quantify the degree of strain localization and shows an initial increase during the crack closure phase,a decrease during the linear elastic phase,and a subsequent increase during the post-yielding phase.This behavior corresponds to a process of initial localization from an imperfect boundary condition,homogenization,and eventual relocalization prior to the macroscopic failure of the sample.The transient strain rate decay during the stress relaxation phase was quantified using the p-value in the“Omori-like"power law function.A higher initial stress at the onset of relaxation results in a lower p-value,indicating a slower strain rate decay.As the sample approaches macroscopic failure,the lowest p-value shifts from the most damaged zone to adjacent areas,suggesting stress redistribution or crack propagation in deformed crystalline rocks under stress relaxation conditions.展开更多
Reconfigurable intelligent surfaces(RISs)not only assist communication but also help the localization of user equipment(UE).This study focuses on indoor localization of UE with a single access point(AP)and multiple RI...Reconfigurable intelligent surfaces(RISs)not only assist communication but also help the localization of user equipment(UE).This study focuses on indoor localization of UE with a single access point(AP)and multiple RISs.First,we propose a two-stage channel estimation scheme where RIS phase shifts are tuned to obtain multiple channel soundings.In the first stage,the newtonized orthogonal matching pursuit algorithm extracts the parameters of multiple paths from the received signals.Then,the LOS path and RISreflected paths are identified.In the second stage,the estimated path gains of RIS-reflected paths with different phase shifts are utilized to determine the angle of arrival(AOA)at the RIS by obtaining the angular pseudo spectrum.Consequently,by taking the AP and RISs as reference points,the linear least squares estimator can locate UE with the estimated AOAs.Simulation results show that the proposed algorithm can realize centimeter-level localization accuracy in the discussed scenarios.Moreover,the higher accuracy of pseudo spectrum,a larger number of channel soundings,and a larger number of reference points can realize higher localization accuracy of UE.展开更多
In ultrasonic non-destructive testing of high-temperature industrial equipment,sound velocity drift induced by non-uniform temperature fields can severely compromise defect localization accuracy.Conventional approache...In ultrasonic non-destructive testing of high-temperature industrial equipment,sound velocity drift induced by non-uniform temperature fields can severely compromise defect localization accuracy.Conventional approaches that rely on room-temperature sound velocities introduce systematic errors,potentially leading to misjudgment of safety-critical components.Two primary challenges hinder current methods:first,it is difficult to monitor real-time changes in sound velocity distribution within a thermal gradient;second,traditional uniform-temperature correction models fail to capture the nonlinear dependence of material properties on temperature and their effect on ultrasonic velocity fields.Here,we propose a defect localization correction method based on multiphysics coupling.A two-dimensional coupled heat transfer–wave propagation model is established in COMSOL,and a one-dimensional steady-state heat transfer condition is used to design a numerical pulse–echo experiment in 1020 steel.Temperature-dependent material properties are incorporated,and the intrinsic relationship between sound velocity and temperature is derived,confirming consistency with classical theories.To account for gradient temperature fields,a micro-element integration algorithm discretizes the propagation path into segments,each associated with a locally computed temperature from the steady-state heat conduction solution.Defect positions are dynamically corrected through cumulative displacement along the propagation path.By integrating heat conduction and elastic wave propagation in a multiphysics framework,this method overcomes the limitations of uniform-temperature assumptions.The micro-element integration approach enables dynamic tracking of spatially varying sound velocities,offering a robust strategy to enhance ultrasonic testing accuracy in high-temperature industrial environments.展开更多
To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-r...To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-ray computed tomography were combined to obtain the strain distribution over the sample surface and internal fractures of the samples.The Gini and skewness(G-S)coefficients were used to quantify strain localization during tests,where the Gini coefficient reflects the degree of clustering of elements with high strain values,i.e.,strain localization/delocalization.The strain localization-induced asymmetry of data distribution is quantified by the skewness coefficient.A precursor to granite failure is defined by the rapid and simultaneous increase of the G-S coefficients,which are calculated from strain increment,giving an earlier warning of failure by about 8%peak stress than those from absolute strain values.Moreover,the process of damage accumulation due to stress-driven crack propagation in Beishan granite is different at various confining pressures as the stress exceeds the crack initiation stress.Concretely,strain localization is continuous until brittle failure at higher confining pressure,while both strain localization and delocalization occur at lower confining pressure.Despite the different stress conditions,a similar statistical characteristic of strain localization during the creep stage is observed.The Gini coefficient increases,and the skewness coefficient decreases slightly as the creep stress is below 95%peak stress.When the accelerated strain localization begins,the Gini and skewness coefficients increase rapidly and simultaneously.展开更多
Source localization of focal electrical activity from scalp electroencephalogram (sEEG) signal is generally modeled as an inverse problem that is highly ill-posed. In this paper, a novel source localization method is ...Source localization of focal electrical activity from scalp electroencephalogram (sEEG) signal is generally modeled as an inverse problem that is highly ill-posed. In this paper, a novel source localization method is proposed to model the EEG inverse problem using spatio-temporal long-short term memory recurrent neural networks (LSTM). The network model consists of two parts, sEEG encoding and source decoding, to model the sEEG signal and receive the regression of source location. As there does not exist enough annotated sEEG signals correspond to specific source locations, simulated data is generated with forward model using finite element method (FEM) to act as a part of training signals. A framework for source localization is proposed to estimate the source position based on simulated training data. Experiments are done on simulated testing data. The results on simulated data exhibit good robustness on noise signal, and the proposed network solves the EEG inverse problem with spatio-temporal deep network. The result show that the proposed method overcomes the highly ill-posed linear inverse problem with data driven learning.展开更多
Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LM...Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LMRB's terrestrial water storage anomaly(TWSA)and its spatio-temporal dynamics during 2002–2020.Considering the effects of natural factors and human activities,the respective contributions of climate variability and human activities to terrestrial water storage change(TWSC)were separated.Results showed that:(1)LMRB's TWSA decreased by 0.3158 cm/a.(2)TWSA showed a gradual increase in distribution from southwest of MRB to middle LMRB and from northeast of LRB to middle LMRB.TWSA positively changed in Myanmar while slightly changed in Laos and China.It negatively changed in Vietnam,Thailand and Cambodia.(3)TWSA components decreased in a descending order of soil moisture,groundwater and precipitation.(4)Natural factors had a substantial and spatial differentiated influence on TWSA over the LMRB.(5)Climate variability contributed 79%of TWSC in the LMRB while human activities contributed 21%with an increasing impact after 2008.The TWSC of upstream basin countries was found to be controlled by climate variability while Vietnam and Cambodia's TWSC has been controlled by human activities since 2012.展开更多
This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models bas...This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models based on Gaussian process assumptions.The proposed Dynamic Gaussian Process Regression(DGPR)consists of a sequence of local surrogate models related to each other.In DGPR,the time-based spatial clustering is carried out to divide the systems into sub-spatio-temporal parts whose interior has similar variation patterns,where the temporal information is used as the prior information for training the spatial-surrogate model.The DGPR is robust and especially suitable for the loosely coupled model structure,also allowing for parallel computation.The numerical results of the test function show the effectiveness of DGPR.Furthermore,the shock tube problem is successfully approximated under different phenomenon complexity.展开更多
This paper deals mainly with the existence and asymptotic behavior of traveling waves in a SIRH model with spatio-temporal delay and nonlocal dispersal based on Schauder’s fixed-point theorem and analysis techniques,...This paper deals mainly with the existence and asymptotic behavior of traveling waves in a SIRH model with spatio-temporal delay and nonlocal dispersal based on Schauder’s fixed-point theorem and analysis techniques,which generalize the results of nonlocal SIRH models without relapse and delay.In particular,the difficulty of obtaining the asymptotic behavior of traveling waves for the appearance of spatio-temporal delay is overcome by the use of integral techniques and analysis techniques.Finally,the more general nonexistence result of traveling waves is also included.展开更多
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
基金supported by National Key Research and Development Plan of China(No.2022YFB3103304).
文摘False Data Injection Attacks(FDIAs)pose a critical security threat to modern power grids,corrupting state estimation and enabling malicious control actions that can lead to severe consequences,including cascading failures,large-scale blackouts,and significant economic losses.While detecting attacks is important,accurately localizing compromised nodes or measurements is even more critical,as it enables timely mitigation,targeted response,and enhanced system resilience beyond what detection alone can offer.Existing research typically models topological features using fixed structures,which can introduce irrelevant information and affect the effectiveness of feature extraction.To address this limitation,this paper proposes an FDIA localization model with adaptive neighborhood selection,which dynamically captures spatial dependencies of the power grid by adjusting node relationships based on data-driven similarities.The improved Transformer is employed to pre-fuse global spatial features of the graph,enriching the feature representation.To improve spatio-temporal correlation extraction for FDIA localization,the proposed model employs dilated causal convolution with a gating mechanism combined with graph convolution to capture and fuse long-range temporal features and adaptive topological features.This fully exploits the temporal dynamics and spatial dependencies inherent in the power grid.Finally,multi-source information is integrated to generate highly robust node embeddings,enhancing FDIA detection and localization.Experiments are conducted on IEEE 14,57,and 118-bus systems,and the results demonstrate that the proposed model substantially improves the accuracy of FDIA localization.Additional experiments are conducted to verify the effectiveness and robustness of the proposed model.
文摘Background and Objectives:The perception of sound in the vertical plane supports spatial hearing by enabling listeners to detect sources located above and below.Sounds originating from both the front and back elevations along the mid-sagittal plane further contribute to a three-dimensional auditory experience.This study aimed to characterize the variability in vertical sound localization abilities among normal-hearing(NH)individuals using spatialized audio.Materials and Methods:Fifty-one NH participants(aged 18 to 35 years)completed three vertical localization tasks under headphones as part of a single-group,within-subject experimental study.These tasks included two-plane identification:(1)top-down localization,(2)front-back localization,and one discrimination task in the front plane.Hierarchical Cluster Analysis(HCA)was employed to identify distinct patterns in spatial localization profiles specific to the vertical-median plane.Fisher's Discriminant Function Analysis(FDA)was used to validate the accuracy of HCA and estimate classification error.Results:HCA revealed three distinct listener clusters:(1)cluster 1 with good performance across all three tasks,(2)cluster 2 with selective impairment in top-bottom identification,and(3)cluster 3 with selective deficits in front-back identification.FDA validated group membership of the clusters identified by the HCA,with a prediction accuracy of 98%.Conclusions:Individuals with clinically NH exhibited three distinct vertical localization profiles:uniform performers,those impaired in top-bottom identification,and those impaired in front-back identification.These profiles may be linked to the interplay between acoustic and non-acoustic perceptual factors.
基金partly supported by the Youth Foundation of the Inner Mongolia Natural Science Foundation[grant number 2024QN06017 and 2025MS06022]the Basic Scientific Research Business Fee Project for Universities in Inner Mongolia[grant numbers 2023XKJX019 and 2023XKJX024]the Central Guidance on Local Science and Technology Development Fund through[grant number 2024ZY0084].
文摘Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficiency of roadway infrastructure.However,existingmethods struggle in complex traffic scenarios due to static spatio-temporal embedding,restricted multi-scale temporal modeling,and weak representation of local spatial interactions.This study proposes Bi-STAT+,an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions:(1)an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations;(2)frequency-domain analysis in the temporal dimension for simultaneous high-frequency details and low-frequency trend extraction;and(3)an agent attention mechanism in the spatial dimension that enhances local feature extraction through dynamic weight allocation.Extensive experiments were performed on four distinct datasets,including two publicly benchmark datasets(PEMS04 and PEMS08)and two private datasets collected from Baotou and Chengdu,China.The results demonstrate that Bi-STAT+consistently outperforms existing methods in terms of MAE,RMSE,and MAPE,while maintaining strong robustness against missing data and noise.Furthermore,the results highlight that prediction accuracy improves significantly with higher sampling rates,providing crucial insights for optimizing real-world deployment scenarios.
基金supported by the National Natural Science Foundation of China(Nos.22479092 and 22078190)。
文摘The pseudo-two-dimensional(P2D)model plays an important role in exploring physicochemical mechanisms,predicting the state of health,and improving the fast charge capability for Li-ion batteries(LIBs).However,the fast charge leads to the lithium concentration gradient in the solid and electrolyte phases and the non-uniform electrochemical reaction at the solid/electrolyte interface.In order to decouple charge transfer reactions in LIBs under dynamic conditions,understanding the spatio-temporal resolution of the P2D model is urgently required.Till now,the study of this aspect is still insufficient.This work studies the spatio-temporal resolution for dynamic/static electrochemical impedance spectroscopy(DEIS/SEIS)on multiple scales.In detail,DEIS and SEIS with spatio-temporal resolutions are used to decouple charge transfer reactions in LIBs based on the numerical solution of the P2D model in the frequency domain.The calculated results indicate that decoupling solid diffusion requires a high spatial resolution along the r-direction in particles,decoupling electrolyte diffusion and interfacial transfer reaction requires a high spatial resolution along the x-direction,and decoupling charge transfer reactions in LIBs at an extremely low state of charge(SOC)requires an extremely high temporal resolution along the t-direction.Finally,the optimal range of spatio-temporal resolutions for DEIS/SEIS is derived,and the method to decouple charge transfer reactions with spatio-temporal resolutions is developed.
基金funded by Ministry of Higher Education Malaysia through Universiti Malaysia Pahang Al-Sultan Abdullah under Internal Research Grant(RDU233003).
文摘This paper proposes a tamper detection technique for semi-fragile watermarking using Quantizationbased Discrete Cosine Transform(DCT)for tamper localization.In this study,the proposed embedding strategy is investigated by experimental tests over the diagonal order of the DCT coefficients.The cover image is divided into non-overlapping blocks of size 8×8 pixels.The DCT is applied to each block,and the coefficients are arranged using a zig-zag pattern within the block.In this study,the low-frequency coefficients are selected to examine the impact of the imperceptibility score and tamper detection accuracy.High accuracy of tamper detection can be achieved by checking the surrounding blocks to determine whether the corresponding block has been tampered with.The proposed tamper detection is tested under various malicious,incidental,and hybrid attacks(both incidental and malicious attacks).The experimental results demonstrate that the proposed technique achieves a Peak-Signal-to-Noise Ratio(PSNR)value of 41.2318 dB,an average Structural Similarity Index Measure(SSIM)value of 0.9768.The proposed scheme is also evaluated against malicious attacks such as copy-move,object deletion,object manipulation,and collage attacks.The proposed scheme can detect the malicious attack localization under various tampering rates.In addition,the proposed scheme can still detect tampered pixels under a hybrid attack,such as a combination ofmalicious and incidental attacks,with an average accuracy of 96.44%.
基金National Key R&D Program of China under Grants 2021YFB2900404the National Natural Science Foundation of China under Grants 62371098。
文摘The acquisition of position information of legitimate users and jammers plays an important role in the emerging non-geostationary synchronous orbit(NGSO)satellite communications.In this paper,we study the multi-signal localization problem in an uplink NGSO satellite communication system.We propose an onboard localization scheme based on multiple observations from the satellite,together with the geometric constraints of the satellite postions,the signal positions,the attitude of the satellite,and the angle-of-arrival(AoAs)of the signals.We develop a massage-passing algorithm,termed the Bayesian blind multi-signal localization(BMSL),to jointly estimate the AoAs and the signal positions.The Cramér-Rao lower bound(CRLB)is derived to characterize the fundamental performance limit of the considered localization problem.Simulation results show that the proposed BMSL algorithm can perform close to the derived CRLB and significantly outperforms its counterpart algorithms.
基金supported in part by the National Natural Science Foundation of China under Grant 52432012in part by the Shanghai Science and Technology Project with 25ZR1402508。
文摘The stator of the maglev track plays a crucial role in the operation of the maglev system.Currently,the efficiency of maglev track inspection is limited by several factors,including the large span of elevated structures,manual visual inspection,short inspection window times,and limited GPS positioning accuracy.To address these issues,this paper proposes a deep learning-based method for detecting and locating stator surface damage.This study establishes a maglev track stator surface image dataset,trains different object detection models,and compares their performance.Ultimately,YOLO and ByteTrack object tracking algorithms were chosen as the basic framework and enhanced to achieve automatic identification of high-speed maglev track stator surface damage images and track and count stator surface localization feature images.By matching the identified damaged images with their corresponding stator segment and beam segment sequence numbers,the location of the damage is pinpointed to the corresponding stator segment,enabling rapid and accurate identification and localization of complex damage to the maglev track stator surface.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2023-00249743).
文摘This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks(STGNNs).However,such definitions are prone to generating spurious correlations due to the dominance of periodic structures.To address this limitation,we adopt the Elastic-Band Transform(EBT)to decompose solar radiation into periodic and amplitude-modulated components,which are then modeled independently with separate graph neural networks.The periodic component,characterized by strong nationwide correlations,is learned with a relatively simple architecture,whereas the amplitude-modulated component is modeled with more complex STGNNs that capture climatological similarities between regions.The predictions from the two components are subsequently recombined to yield final forecasts that integrate both periodic patterns and aperiodic variability.The proposed framework is validated with multiple STGNN architectures,and experimental results demonstrate improved predictive accuracy and interpretability compared to conventional methods.
基金supported by the National Natural Science Foundation of China through grant numbers 12002245 and 12172263the Science and Technology Research Program of Chongqing Municipal Education Commission through grant number KJQN202300742+1 种基金the National Natural Science Foundation of ChongqingMunicipality through grant number CSTB2025NSCQ-GPX0841Chongqing Jiaotong University through grant number F1220038.
文摘Granular materials exhibit complex macroscopic mechanical behaviors closely related to their microscalemicrostructural features.Traditional macroscopic phenomenological elasto-plastic models,however,usually have complex formulations and lack explicit relations to these microstructural features.To avoid these limitations,this study proposes a micromechanics-based softening hyperelastic model for granular materials,integrating softening hyperelasticity withmicrostructural insights to capture strain softening,critical state,and strain localization behaviors.The model has two key advantages:(1)a clear conceptualization,straightforward formulation,and ease of numerical implementation(via Abaqus UMAT subroutine in this study);(2)explicit incorporation of micro-scale features(e.g.,contact stiffness,particle size,porosity)to reveal their influences on macroscopic responses.An isotropic directional distribution density of contacts and three specific microstructures are considered,and their softening hyperelastic constitutive modulus tensors are explicitly derived.By introducing a softening factor and critical failure energy density,the model can describe geomaterial behaviors,simulating residual strength,X-shaped shear bands,and strain localization evolution.Numerical validations in comparison with themacro-scale hyperelastic model,Abaqus Drucker-Prager model,and the experiment confirm its accuracy.Parametric studies reveal critical dependencies:a normal to tangential contact stiffness ratio of 2-8(depending on stiffness magnitude),an internal length of 2-4 mm to ensure shear band formation,and a critical failure energy density(≤10 kJ/m^(3))to trigger strain softening and localization.Influences of the specific microstructures on strain localization and softening are investigated.The model also shows mesh independence due to the introduction of an internal length.The model’s applicability is further demonstrated by slope stability analysis,capturing slip surface evolution,and load-displacement characteristics.This study develops a robust microstructure-aware hyperelastic framework to describe the mechanical behaviors of granular materials,providing multiscale insights for geotechnical engineering applications.
基金Supported by Open Fund Project of the Engineering Technology Research Center of Characteristic Medicinal Plants of Fujian(PP202003).
文摘This paper provides a systematic review of the histochemical localization,content characteristics,and influencing factors of saponins in Pseudostellaria heterophylla based on an extensive literature survey.It provides an in-depth analysis and summary of the effects of biological factors,environmental conditions,agronomic practices,processing methods,and continuous cropping obstacles on the synthesis of P.heterophylla saponins,as well as their underlying mechanisms.Based on identified gaps in the current literature,future research directions and prospects are proposed.The findings of this review offer valuable insights for advancing the understanding of the saponin biosynthesis mechanisms in P.heterophylla and for enhancing its quality.
基金support of her postdoctoral research at the GFZ Helmholtz Centre for Geosciences.P.Pan acknowledges the financial support of the National Natural Science Foundation of China(Grant No.52339001)H.Hofmann and Y.Ji acknowledge the financial support of the Helmholtz Association's Initiative and Networking Fund for the Helmholtz Young Investigator Group ARES(contract number VH-NG-1516).
文摘A multi-stage stress relaxation test was performed on a granodiorite sample to understand the deformation process prior to the macroscopic failure of brittle rocks,as well as the transient response during stress relaxation.Distributed optical fiber sensing was used to measure strains across the sample surface by helically wrapping the single-mode fiber around the cylindrical sample.Close agreement was observed between the circumferential strains obtained from the optical fibers and the extensometer.The reconstructed full-field strain contours show strain heterogeneity from the crack closure phase,and the strains in the later deformation phase are dominantly localized within the former high-strain zone.The Gini coefficient was used to quantify the degree of strain localization and shows an initial increase during the crack closure phase,a decrease during the linear elastic phase,and a subsequent increase during the post-yielding phase.This behavior corresponds to a process of initial localization from an imperfect boundary condition,homogenization,and eventual relocalization prior to the macroscopic failure of the sample.The transient strain rate decay during the stress relaxation phase was quantified using the p-value in the“Omori-like"power law function.A higher initial stress at the onset of relaxation results in a lower p-value,indicating a slower strain rate decay.As the sample approaches macroscopic failure,the lowest p-value shifts from the most damaged zone to adjacent areas,suggesting stress redistribution or crack propagation in deformed crystalline rocks under stress relaxation conditions.
基金supported in part by the Fundamental Research Funds for the Central Universities under Grant 2242022k60004in part by the National Natural Science Foundation of China(NSFC)under Grants 62261160576,624B2036,W2421087,62422105+1 种基金in part by the Young Elite Scientists Sponsorship Program by CAST 2022QNRC001,and the“Zhishan”Scholars Programs of Southeast Universityin part by the Key Technologies R&D Program of Jiangsu(Prospective and Key Technologies for Industry)under Grants BE2023022,BE2023022-1 and BE2023022-2.
文摘Reconfigurable intelligent surfaces(RISs)not only assist communication but also help the localization of user equipment(UE).This study focuses on indoor localization of UE with a single access point(AP)and multiple RISs.First,we propose a two-stage channel estimation scheme where RIS phase shifts are tuned to obtain multiple channel soundings.In the first stage,the newtonized orthogonal matching pursuit algorithm extracts the parameters of multiple paths from the received signals.Then,the LOS path and RISreflected paths are identified.In the second stage,the estimated path gains of RIS-reflected paths with different phase shifts are utilized to determine the angle of arrival(AOA)at the RIS by obtaining the angular pseudo spectrum.Consequently,by taking the AP and RISs as reference points,the linear least squares estimator can locate UE with the estimated AOAs.Simulation results show that the proposed algorithm can realize centimeter-level localization accuracy in the discussed scenarios.Moreover,the higher accuracy of pseudo spectrum,a larger number of channel soundings,and a larger number of reference points can realize higher localization accuracy of UE.
基金supported by the following projects:National Natural Science Foundation of China[U24A20135]Science and Technology Program of the State Administration for Market Regulation[2024MK016]+9 种基金Basic Scientific Research Fund Project for Higher Education Institutions of Inner Mongolia(2024YXXS057)Key Project of Natural Science Foundation of Inner Mongolia[2023ZD12]2023 Inner Mongolia Autonomous Region Key R&D and Achievement Transformation Program[2023YFHH0090]Natural Science Foundation of Inner Mongolia[2022MS05006]Talent Development Fund of Inner Mongolia Autonomous RegionFundamental Research Funds for Universities[2023RCTD012]Fundamental Research Funds for Universities[2023QNJS075]Inner Mongolia Autonomous Region Postgraduate Research Innovation Project[KC2024053B]Fundamental Research Funds for Universities[2024YXXS012]Open Project of the National Key Laboratory of Special Vehicle Design and Manufacturing Integration Technology[GZ2023KF012].
文摘In ultrasonic non-destructive testing of high-temperature industrial equipment,sound velocity drift induced by non-uniform temperature fields can severely compromise defect localization accuracy.Conventional approaches that rely on room-temperature sound velocities introduce systematic errors,potentially leading to misjudgment of safety-critical components.Two primary challenges hinder current methods:first,it is difficult to monitor real-time changes in sound velocity distribution within a thermal gradient;second,traditional uniform-temperature correction models fail to capture the nonlinear dependence of material properties on temperature and their effect on ultrasonic velocity fields.Here,we propose a defect localization correction method based on multiphysics coupling.A two-dimensional coupled heat transfer–wave propagation model is established in COMSOL,and a one-dimensional steady-state heat transfer condition is used to design a numerical pulse–echo experiment in 1020 steel.Temperature-dependent material properties are incorporated,and the intrinsic relationship between sound velocity and temperature is derived,confirming consistency with classical theories.To account for gradient temperature fields,a micro-element integration algorithm discretizes the propagation path into segments,each associated with a locally computed temperature from the steady-state heat conduction solution.Defect positions are dynamically corrected through cumulative displacement along the propagation path.By integrating heat conduction and elastic wave propagation in a multiphysics framework,this method overcomes the limitations of uniform-temperature assumptions.The micro-element integration approach enables dynamic tracking of spatially varying sound velocities,offering a robust strategy to enhance ultrasonic testing accuracy in high-temperature industrial environments.
基金supported by the National Natural Science Foundation of China(Grant No.52339001).
文摘To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-ray computed tomography were combined to obtain the strain distribution over the sample surface and internal fractures of the samples.The Gini and skewness(G-S)coefficients were used to quantify strain localization during tests,where the Gini coefficient reflects the degree of clustering of elements with high strain values,i.e.,strain localization/delocalization.The strain localization-induced asymmetry of data distribution is quantified by the skewness coefficient.A precursor to granite failure is defined by the rapid and simultaneous increase of the G-S coefficients,which are calculated from strain increment,giving an earlier warning of failure by about 8%peak stress than those from absolute strain values.Moreover,the process of damage accumulation due to stress-driven crack propagation in Beishan granite is different at various confining pressures as the stress exceeds the crack initiation stress.Concretely,strain localization is continuous until brittle failure at higher confining pressure,while both strain localization and delocalization occur at lower confining pressure.Despite the different stress conditions,a similar statistical characteristic of strain localization during the creep stage is observed.The Gini coefficient increases,and the skewness coefficient decreases slightly as the creep stress is below 95%peak stress.When the accelerated strain localization begins,the Gini and skewness coefficients increase rapidly and simultaneously.
基金supported by the National Natural Science Foundation of China (No. 61672070, 61501007, 11675199, 61572004 and 81501155)the Key Project of Beijing Municipal Education Commission (No. KZ201910005008)+3 种基金general project of science and technology project of Beijing Municipal Education Commission (No. KM201610005023)the Beijing Municipal Natural Science Foundation (No. 4182005)Clinical Technology Innovation Program of Beijing Municipal Administration of Hospitals (No. XMLX201805)Beijing Municipal Science & Tech Commission (No. Z171100000117004)
文摘Source localization of focal electrical activity from scalp electroencephalogram (sEEG) signal is generally modeled as an inverse problem that is highly ill-posed. In this paper, a novel source localization method is proposed to model the EEG inverse problem using spatio-temporal long-short term memory recurrent neural networks (LSTM). The network model consists of two parts, sEEG encoding and source decoding, to model the sEEG signal and receive the regression of source location. As there does not exist enough annotated sEEG signals correspond to specific source locations, simulated data is generated with forward model using finite element method (FEM) to act as a part of training signals. A framework for source localization is proposed to estimate the source position based on simulated training data. Experiments are done on simulated testing data. The results on simulated data exhibit good robustness on noise signal, and the proposed network solves the EEG inverse problem with spatio-temporal deep network. The result show that the proposed method overcomes the highly ill-posed linear inverse problem with data driven learning.
基金National Natural Science Foundation of China,No.42161006Yunnan Fundamental Research Projects No.202201AT070094,No.202301BF070001-004+1 种基金Special Project for High-level Talents of Yunnan Province for Young Top Talents,No.C6213001159European Research Council(ERC)Starting-Grant STORIES,No.101040939。
文摘Due to water conflicts and allocation in the Lancang-Mekong River Basin(LMRB),the spatio-temporal differentiation of total water resources and the natural-human influence need to be clarified.This work investigated LMRB's terrestrial water storage anomaly(TWSA)and its spatio-temporal dynamics during 2002–2020.Considering the effects of natural factors and human activities,the respective contributions of climate variability and human activities to terrestrial water storage change(TWSC)were separated.Results showed that:(1)LMRB's TWSA decreased by 0.3158 cm/a.(2)TWSA showed a gradual increase in distribution from southwest of MRB to middle LMRB and from northeast of LRB to middle LMRB.TWSA positively changed in Myanmar while slightly changed in Laos and China.It negatively changed in Vietnam,Thailand and Cambodia.(3)TWSA components decreased in a descending order of soil moisture,groundwater and precipitation.(4)Natural factors had a substantial and spatial differentiated influence on TWSA over the LMRB.(5)Climate variability contributed 79%of TWSC in the LMRB while human activities contributed 21%with an increasing impact after 2008.The TWSC of upstream basin countries was found to be controlled by climate variability while Vietnam and Cambodia's TWSC has been controlled by human activities since 2012.
基金co-supported by the National Natural Science Foundation of China(No.12101608)the NSAF(No.U2230208)the Hunan Provincial Innovation Foundation for Postgraduate,China(No.CX20220034).
文摘This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models based on Gaussian process assumptions.The proposed Dynamic Gaussian Process Regression(DGPR)consists of a sequence of local surrogate models related to each other.In DGPR,the time-based spatial clustering is carried out to divide the systems into sub-spatio-temporal parts whose interior has similar variation patterns,where the temporal information is used as the prior information for training the spatial-surrogate model.The DGPR is robust and especially suitable for the loosely coupled model structure,also allowing for parallel computation.The numerical results of the test function show the effectiveness of DGPR.Furthermore,the shock tube problem is successfully approximated under different phenomenon complexity.
基金supported by the NSF of China(11761046)Science and Technology Plan Foundation of Gansu Province of China(20JR5RA411)Foundation of A Hundred Youth Talents Training Program of Lanzhou Jiaotong University。
文摘This paper deals mainly with the existence and asymptotic behavior of traveling waves in a SIRH model with spatio-temporal delay and nonlocal dispersal based on Schauder’s fixed-point theorem and analysis techniques,which generalize the results of nonlocal SIRH models without relapse and delay.In particular,the difficulty of obtaining the asymptotic behavior of traveling waves for the appearance of spatio-temporal delay is overcome by the use of integral techniques and analysis techniques.Finally,the more general nonexistence result of traveling waves is also included.
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
