Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data co...Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.展开更多
To ensure the structural integrity of life-limiting component of aeroengines,Probabilistic Damage Tolerance(PDT)assessment is applied to evaluate the failure risk as required by airworthiness regulations and military ...To ensure the structural integrity of life-limiting component of aeroengines,Probabilistic Damage Tolerance(PDT)assessment is applied to evaluate the failure risk as required by airworthiness regulations and military standards.The PDT method holds the view that there exist defects such as machining scratches and service cracks in the tenon-groove structures of aeroengine disks.However,it is challenging to conduct PDT assessment due to the scarcity of effective Probability of Detection(POD)model and anomaly distribution model.Through a series of Nondestructive Testing(NDT)experiments,the POD model of real cracks in tenon-groove structures is constructed for the first time by employing the Transfer Function Method(TFM).A novel anomaly distribution model is derived through the utilization of the POD model,instead of using the infeasible field data accumulation method.Subsequently,a framework for calculating the Probability of Failure(POF)of the tenon-groove structures is established,and the aforementioned two models exert a significant influence on the results of POF.展开更多
It is of great importance to obtain precise trace data,as traces are frequently the sole visible and measurable parameter in most outcrops.The manual recognition and detection of traces on high-resolution three-dimens...It is of great importance to obtain precise trace data,as traces are frequently the sole visible and measurable parameter in most outcrops.The manual recognition and detection of traces on high-resolution three-dimensional(3D)models are relatively straightforward but time-consuming.One potential solution to enhance this process is to use machine learning algorithms to detect the 3D traces.In this study,a unique pixel-wise texture mapper algorithm generates a dense point cloud representation of an outcrop with the precise resolution of the original textured 3D model.A virtual digital image rendering was then employed to capture virtual images of selected regions.This technique helps to overcome limitations caused by the surface morphology of the rock mass,such as restricted access,lighting conditions,and shading effects.After AI-powered trace detection on two-dimensional(2D)images,a 3D data structuring technique was applied to the selected trace pixels.In the 3D data structuring,the trace data were structured through 2D thinning,3D reprojection,clustering,segmentation,and segment linking.Finally,the linked segments were exported as 3D polylines,with each polyline in the output corresponding to a trace.The efficacy of the proposed method was assessed using a 3D model of a real-world case study,which was used to compare the results of artificial intelligence(AI)-aided and human intelligence trace detection.Rosette diagrams,which visualize the distribution of trace orientations,confirmed the high similarity between the automatically and manually generated trace maps.In conclusion,the proposed semi-automatic method was easy to use,fast,and accurate in detecting the dominant jointing system of the rock mass.展开更多
In recent years,research investigations have focused on the substantial freshwater storage in the Beaufort Gyre(BG)region due to climate change.Despite active mesoscale eddies in the area,a notable gap in understandin...In recent years,research investigations have focused on the substantial freshwater storage in the Beaufort Gyre(BG)region due to climate change.Despite active mesoscale eddies in the area,a notable gap in understanding the three-dimensional structure and induced transport has been observed.This study concentrates on the Canada Basin in the western Arctic Ocean,specifically examining a subsurface anticyclonic eddy(SAE)sampled by a Mooring A in the BG region.Hybrid Coordinate Ocean Model(HYCOM)analysis data reveal its lifecycle from February 15 to March 15,2017,marked by initiation,development,maturity,decay,and termination stages.This work extends the finding of SAE passing through Mooring A by examining its overall effects,spatiotemporal variations,and swirl transport.SAE generation through baroclinic instability,which contributes to the westward tilt of the vertical axis,is also confirmed in this study.Swirl transport induced by SAE is predominantly eastward and downward due to its trajectory and background flow.SAE temporarily weakens stratification and extends the subsurface depth but demonstrates transient effects.Moreover,SAE transports upper-layer freshwater,Pacific Winter Water,and Atlantic Water downward,emphasizing its potential influence on freshwater redistribution in the Canadian Basin.This research provides valuable insights into mesoscale eddy dynamics,revealing their role in modulating the upper water mass in the BG region.展开更多
High-performance lattice structures produced through powder bed fusion-laser beam exhibit high specific strength and energy absorption capabilities.However,a significant deviation exists between the mechanical propert...High-performance lattice structures produced through powder bed fusion-laser beam exhibit high specific strength and energy absorption capabilities.However,a significant deviation exists between the mechanical properties,service life of lattice structures,and design expectations.This deviation arises from the intense interaction between the laser and powder,which leads to the formation of numerous defects within the lattice structure.To address these issues,this paper proposes a high-performance defect detection model for metal lattice structures based on YOLOv4,called YOLO-Lattice(YOLO-L).The main objectives of this paper are as follows:(1)utilize computed tomography to construct datasets of the diamond lattice and body-centered cubic lattice structures;(2)in the backbone network of YOLOv4,employ deformable convolution to enhance the feature extraction capability of the model for small-scale defects;(3)adopt a dual-attention mechanism to suppress invalid feature information and amplify the distinction between defect and background regions;and(4)implement a channel pruning strategy to eliminate channels carrying less feature information,thereby improving the inference speed of the model.The experimental results on the diamond lattice structure dataset demonstrate that the mean average precision of the YOLO-L model increased from 96.98% to 98.8%(with an intersection over union of 0.5),and the inference speed decreased from 51.3 ms to 32.5 ms when compared to YOLOv4.Thus,the YOLO-L model can be effectively used to detect defects in metal lattice structures.展开更多
The steel-epoxy-steel sandwich structures provide enhanced corrosion resistance and fatigue resistance,making them suitable for pipeline rehabilitation with effective repair and long-term durability.However,the repair...The steel-epoxy-steel sandwich structures provide enhanced corrosion resistance and fatigue resistance,making them suitable for pipeline rehabilitation with effective repair and long-term durability.However,the repair quality can be compromised by disbond between the steel and epoxy layers,whichmay result frominsufficient epoxy injection.Conventional ultrasonic testing faces challenges in accurately locating disbond defects due to aliased echo interference at interfaces.This paper proposes a signal processing algorithm for improving the accuracy of ultrasonic reflection method for detecting disbond defects between steel and epoxy layers.First,a coati optimization algorithmvariational mode decomposition(COA-VMD)is applied to adaptively decompose the ultrasonic signals and extract the intrinsic mode function components that show high correlation with the defect-related signals.Then,by calculating the relative reflectance at the interface and establishing a quantitative evaluation index based on acoustic impedance discontinuity,the locations of disbond defects are identified.Experimental results demonstrate that this method can effectively detect the locations of disbond defects between steel and epoxy layers.展开更多
The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the probl...The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the problem,this paper proposes an integrated calibration method for structured light vision sensors.In the proposed system,the sensor is mounted on a crawler-type mobile robot,which scans and measures the center height of guardrails while in motion.However,due to external disturbances such as uneven road surfaces and vehicle vibrations,the posture of the robot may deviate,causing displacement of the sensor platform and resulting in spatial 3D measurement errors.To overcome this issue,the system integrates inertial measurement unit(IMU)data into the sensor calibration process,enabling realtime correction of posture deviations through sensor fusion.This approach achieves a unified calibration of the structured light vision system,effectively compensates for posture-induced errors,and enhances detection accuracy.A prototype was developed and tested in both laboratory and real highway environments.Experimental results demonstrate that the proposed method enables accurate center height detection of guardrails under complex road conditions,significantly reduces posture-related measurement errors,and greatly improves the efficiency and reliability of traditional detection methods.展开更多
Flexible pressure sensors have excellent prospects in applications of human-machine interfaces,artificial intelligence and human health monitoring due to their bendable and lightweight characteristics compared to rigi...Flexible pressure sensors have excellent prospects in applications of human-machine interfaces,artificial intelligence and human health monitoring due to their bendable and lightweight characteristics compared to rigid pressure sensors.However,arising from the limited compressibility of soft materials and the hardening of microstructures at the device interface,there is always a trade-off between high sensitivity and broad sensing range for most flexible pressure sensors,which results in a gradual saturation response and limits their practical applications.Herein,inspired by the distinct pressure perception function of crocodile receptors,a highly sensitive and wide-range flexible pressure sensor with multiscale microdomes and interlocked architecture is developed via a facile PS-decorated molding method.Combined with interlocked architecture,the multiscale dome-shaped structured interface enhances the compressibility of the material through structural complementarity,increases the contact area between functional materials,which compensates for the stiffness induced by the deformation of dense microscale columns.This effectively mitigates structural hardening across a wide pressure range,leading to the overall high performance of the sensor.As a result,the obtained sensor exhibits a low detection limit of 5 Pa,a high sensitivity of 6.14 kPa^(-1),a wide measurement range up to 231 kPa,short response/recovery time of 56 ms/69 ms,outstanding stability over 10,000 cycles.Considering these excellent properties,the sensor shows promising potential in health monitoring,human-computer interaction,wearable electronics.This study presents a strategy for the fabrication of flexible pressure sensors exhibiting high sensitivity and a wide pressure response range.展开更多
With the continuous expansion of digital infrastructures,malicious behaviors in host systems have become increasingly sophisticated,often spanning multiple processes and employing obfuscation techniques to evade detec...With the continuous expansion of digital infrastructures,malicious behaviors in host systems have become increasingly sophisticated,often spanning multiple processes and employing obfuscation techniques to evade detection.Audit logs,such as Sysmon,offer valuable insights;however,existing approaches typically flatten event sequences or rely on generic graph models,thereby discarding the natural parent-child process hierarchy that is critical for analyzing multiprocess attacks.This paper proposes a structure-aware threat detection framework that transforms audit logs into a unified two-dimensional(2D)spatio-temporal representation,where process hierarchy is modeled as the spatial axis and event chronology as the temporal axis.In addition,entropy-based features are incorporated to robustly capture obfuscated and non-linguistic strings,overcoming the limitations of semantic embeddings.The model’s performance was evaluated on publicly available datasets,achieving competitive results with an accuracy exceeding 95%and an F1-score of at least 0.94.The proposed approach provides a promising and reproducible solution for detecting attacks with unknown indicators of compromise(IoCs)by analyzing the relationships and behaviors of processes recorded in large-scale audit logs.展开更多
Structural Health Monitoring(SHM)plays a critical role in ensuring the safety,integrity,longevity and economic efficiency of civil infrastructures.The field has undergone a profound transformation over the last few de...Structural Health Monitoring(SHM)plays a critical role in ensuring the safety,integrity,longevity and economic efficiency of civil infrastructures.The field has undergone a profound transformation over the last few decades,evolving from traditional methods—often reliant on visual inspections—to data-driven intelligent systems.This review paper analyzes this historical trajectory,beginning with the approaches that relied on modal parameters as primary damage indicators.The advent of advanced sensor technologies and increased computational power brings a significant change,making Machine Learning(ML)a viable and powerful tool for damage assessment.More recently,Deep Learning(DL)has emerged as a paradigm shift,allowing for more automated processing of large data sets(such as the structural vibration signals and other types of sensors)with excellent performance and accuracy,often surpassing previous methods.This paper systematically reviews these technological milestones—from traditional vibration-based methods to the current state-of-the-art in deep learning.Finally,it critically examines emerging trends—such as Digital Twins and Transformer-based architectures—and discusses future research directions that will shape the next generation of SHM systems for civil engineering.展开更多
Soft pneumatic structures are promising for the actuation of soft machines,and substantial advances have occurred in their innovative design and functional verification.However,most pneumatic structures lack self-sens...Soft pneumatic structures are promising for the actuation of soft machines,and substantial advances have occurred in their innovative design and functional verification.However,most pneumatic structures lack self-sensing abilities,resulting in a lack of motion state feedback and difficulty in achieving real-time closed-loop control.Herein,a soft pneumatic composite structure(SPCS)with integrated actuation and sensing abilities is developed by combining a bellows-shaped magnetic elastomer and a wire structure.The SPCS can generate an induced voltage under deformation.The SPCS mechanical and magnetoelectric characteristics are studied comprehensively.The SPCS experimental maximum contraction is 27 mm,which is close to the theoretical and numerical results.When the SPCS is actuated by a pressure of-40 kPa,it will generate a peak induced voltage of 1.01 mV.With the increase in magnetic powder content and turns of the spiral wire,the induced voltage also increases.Additionally,two SPCSs are used to develop a self-sensing actuator,which can accurately perceive the bending direction and recognize the magnitude and direction of external force.A self-sensing soft gripper is developed,which can sense the grasping status and predict the width of grasped objects.Furthermore,a smart vehicle detection system composed of two SPCSs is proposed,which can detect the number,speed,and weight of passing vehicles.Consequently,the SPCS has numerous potential applications in soft sensors and self-sensing intelligent soft machines.展开更多
This article aims to develop a head pursuit （HP） guidance law for three-dimensional hypervelocity interception, so that the effect of the perturbation induced by seeker detection can be reduced. On the basis of a no...This article aims to develop a head pursuit （HP） guidance law for three-dimensional hypervelocity interception, so that the effect of the perturbation induced by seeker detection can be reduced. On the basis of a novel HP three-dimensional guidance model, a nonlinear variable structure guidance law is presented by using Lyapunov stability theory. The guidance law positions the interceptor ahead of the target on its tlight trajectory, and the speed of the interceptor is required to be lower than that of the target, A numerical example of maneuvering ballistic target interception verifies the rightness of the guidance model and the effectiveness of the proposed method.展开更多
Microseismic/acoustic emission(MS/AE)source localization method is crucial for predicting and controlling of potentially dangerous sources of complex structures.However,the locating errors induced by both the irregula...Microseismic/acoustic emission(MS/AE)source localization method is crucial for predicting and controlling of potentially dangerous sources of complex structures.However,the locating errors induced by both the irregular structure and pre-measured velocity are poorly understood in existing methods.To meet the high-accuracy locating requirements in complex three-dimensional hole-containing structures,a velocity-free MS/AE source location method is developed in this paper.It avoids manual repetitive training by using equidistant grid points to search the path,which introduces A*search algorithm and uses grid points to accommodate complex structures with irregular holes.It also takes advantage of the velocity-free source location method.To verify the validity of the proposed method,lead-breaking tests were performed on a cubic concrete test specimen with a size of 10 cm10 cm10 cm.It was cut out into a cylindrical empty space with a size of/6cm10 cm.Based on the arrivals,the classical Geiger method and the proposed method are used to locate lead-breaking sources.Results show that the locating error of the proposed method is 1.20 cm,which is less than 2.02 cm of the Geiger method.Hence,the proposed method can effectively locate sources in the complex three-dimensional structure with holes and achieve higher precision requirements.展开更多
As in-situ observations are sparse,targeted observations of a specific mesoscale eddy are rare.Therefore,it is difficult to study the three-dimensional structure of moving mesoscale eddies.From April to September 2014...As in-situ observations are sparse,targeted observations of a specific mesoscale eddy are rare.Therefore,it is difficult to study the three-dimensional structure of moving mesoscale eddies.From April to September 2014,an anticyclonic eddy located at 135°E-155°E,26°N-42°N was observed using 17 rapidsampling Argo floats,and the spatiotemporal variations in the three-dimensional structure were studied.The results are as follows:(1)the eddy was identified and tracked using satellite altimeter data.It had a lifetime of 269 days and an average radius of 91.5 km.The lifetime of the eddy can be divided into three phases,i.e.,the initiation,maturity,and termination phases.The depth of its influence reached 1000 m;(2)the Argo profiles were divided into seven periods(approximately 20 days in each)for composite analysis,and the composite Argo profiles and CARS2009(CSIRO Atlas of Regional Seas)climatology data were merged following the data-interpolating variational analysis(DIVA)method to reconstruct the three-dimensional structure.The temperature and salinity anomaly cores of the anticyclonic mesoscale eddy are located from 400 to 600 m.From 800 to 900 m,there is an area of low salinity at the center of the eddy.A high concentration anomaly of dissolved oxygen was located at approximately 250 m;(3)to better understand the features of the eddy and its interaction with the surroundings,we calculated the anomalous velocity of the geostrophic flow and the heat,salt,dissolved oxygen transport anomaly,and discussed the eddy's origin and its adjustments to topography.The maximum heat,salt,and oxygen transport caused by eddy were 9.37×10^11 W,3.08×10^3 kg/s,and 2.70×10^2 kg/s,which all occurred during the termination phase.This study highlights the applicability of using Argo floats to understand the three-dimensional structure thermohaline features of eddies in the North Pacific.展开更多
A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safe...A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures.展开更多
The three-dimensional structure and the seasonal variation of the North Pacific meridional overturning circulation （NPMOC） are analyzed based on the Simple Ocean Data Assimilation data and Argo profiling float data....The three-dimensional structure and the seasonal variation of the North Pacific meridional overturning circulation （NPMOC） are analyzed based on the Simple Ocean Data Assimilation data and Argo profiling float data. The NPMOC displays a multi-cell structure with four cells in the North Pacific altogether. The TC and the STC are a strong clockwise meridional cell in the low latitude ocean and a weaker clockwise meridional cell between 7°N and 18°N, respectively, while the DTC and the subpolar cell are a weaker anticlockwise meridional cell between 3°N and 15°N and a weakest anticlockwise meridional cell between 35°N and 50°N, respectively. The DTC, the TC and the STC are all of very strong seasonal variations. As to the DTC, the southward transport is strongest in fall and weakest in spring. For the TC, the northward transport is strongest in winter and weakest in spring, while the southward transport is strongest in fall and weakest in spring, which is associated with the strong southward fiow of the DTC in fall. As the STC, the northward transport is strongest in winter and weakest in summer, while the southward transport is strongest in summer and weakest in spring. This seasonal difference may be associated with the DTC. The zonal wind stress and the east-west slope of sea level play important roles in the seasonal variations of the TC, the STC and the DTC.展开更多
In order to obtain high-performance electromagnetic wave absorbers,the adjustment of structure and components is essential.Based on the above requirements,this system forms a three-dimensional frame structure consisti...In order to obtain high-performance electromagnetic wave absorbers,the adjustment of structure and components is essential.Based on the above requirements,this system forms a three-dimensional frame structure consisting of MXene and transition metal oxides(TMOs)through efficient electrostatic self-assembly.This three-dimensional network structure has rich heterojunction structures,which can cause a large amount of interface polarization and conduction losses in incident electromagnetic waves.Hollow structures cause multiple reflections and scattering of electromagnetic waves,which is also an important reason for further increasing electromagnetic wave losses.When the doping ratio is 1:1,the system has the best impedance matching,the maximum effective absorption bandwidth(EAB max)can reach 5.12 GHz at 1.7 mm,and the minimum reflection loss(RL_(min))is-50.30 dB at 1.8 mm.This provides a reference for the subsequent formation of 2D-MXene materials into 3D materials.展开更多
This paper presents a three-dimensional particle-in-cell （PIC） simulation of a Ka-band relativistic Cherenkov source with a slow wave structure （SWS） consisting of metal photonic band gap （PBG） structures. In th...This paper presents a three-dimensional particle-in-cell （PIC） simulation of a Ka-band relativistic Cherenkov source with a slow wave structure （SWS） consisting of metal photonic band gap （PBG） structures. In the simulation, a perfect match layer boundary is employed to absorb passing band modes supported by the PBG lattice with an artificial metal boundary. The simulated axial field distributions in the cross section and surface of the SWS demonstrate that the device operates in the vicinity of the π point of a TM01-1ike mode. The Fourier transformation spectra of the axial fields as functions of time and space show that only a single frequency appears at 36.27 GHz, which is in good agreement with that of the intersection of the dispersion curve with the slow space charge wave generated on the beam. The simulation results demonstrate that the SWS has good mode selectivity.展开更多
A kind of photoelectric system that is suitable to measuring and to testing the damage of the composite material intelligent structure was presented. It can measure the degree of damage of the composite intelligent st...A kind of photoelectric system that is suitable to measuring and to testing the damage of the composite material intelligent structure was presented. It can measure the degree of damage of the composite intelligent structure and it also can tell us the damage position in the structure. This system consists of two parts ： software and hardware. Experiments of the damage detection and the analysis of the composite material structure with the photoelectric system were performed, and a series of damage detection experiments was conducted. The results prove that the performance of the system is well and the effects of the measure and test are evident. Through all the experiments, the damage detection technology and test system are approved to be real-time, effective and reliable in the damage detection of the composite intelligent structure.展开更多
This paper proposes a novel idea that classifies faults into two different kinds： serious faults and small faults, and treats them with different strategies respectively. A kind of artificial neural network （ANN） i...This paper proposes a novel idea that classifies faults into two different kinds： serious faults and small faults, and treats them with different strategies respectively. A kind of artificial neural network （ANN） is proposed for detecting serious faults, and variable structure （VS） model-following control is constructed for accommodating small faults. The proposed framework takes both advantages of qualitative way and quantitative way of fault detection and accommodation. Moreover, the uncertainty case is investigated and the VS controller is modified. Simulation results of a remotely piloted aircraft with control actuator failures illustrate the performance of the developed algorithm.展开更多
基金supported by Natural Science Foundation of Qinghai Province(2025-ZJ-994M)Scientific Research Innovation Capability Support Project for Young Faculty(SRICSPYF-BS2025007)National Natural Science Foundation of China(62566050).
文摘Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.
基金supported by the National Major Science and Technology Project,China(No.J2019-Ⅳ-0007-0075)the Fundamental Research Funds for the Central Universities,China(No.JKF-20240036)。
文摘To ensure the structural integrity of life-limiting component of aeroengines,Probabilistic Damage Tolerance(PDT)assessment is applied to evaluate the failure risk as required by airworthiness regulations and military standards.The PDT method holds the view that there exist defects such as machining scratches and service cracks in the tenon-groove structures of aeroengine disks.However,it is challenging to conduct PDT assessment due to the scarcity of effective Probability of Detection(POD)model and anomaly distribution model.Through a series of Nondestructive Testing(NDT)experiments,the POD model of real cracks in tenon-groove structures is constructed for the first time by employing the Transfer Function Method(TFM).A novel anomaly distribution model is derived through the utilization of the POD model,instead of using the infeasible field data accumulation method.Subsequently,a framework for calculating the Probability of Failure(POF)of the tenon-groove structures is established,and the aforementioned two models exert a significant influence on the results of POF.
基金supported by grants from the Human Resources Development program (Grant No.20204010600250)the Training Program of CCUS for the Green Growth (Grant No.20214000000500)by the Korea Institute of Energy Technology Evaluation and Planning (KETEP)funded by the Ministry of Trade,Industry,and Energy of the Korean Government (MOTIE).
文摘It is of great importance to obtain precise trace data,as traces are frequently the sole visible and measurable parameter in most outcrops.The manual recognition and detection of traces on high-resolution three-dimensional(3D)models are relatively straightforward but time-consuming.One potential solution to enhance this process is to use machine learning algorithms to detect the 3D traces.In this study,a unique pixel-wise texture mapper algorithm generates a dense point cloud representation of an outcrop with the precise resolution of the original textured 3D model.A virtual digital image rendering was then employed to capture virtual images of selected regions.This technique helps to overcome limitations caused by the surface morphology of the rock mass,such as restricted access,lighting conditions,and shading effects.After AI-powered trace detection on two-dimensional(2D)images,a 3D data structuring technique was applied to the selected trace pixels.In the 3D data structuring,the trace data were structured through 2D thinning,3D reprojection,clustering,segmentation,and segment linking.Finally,the linked segments were exported as 3D polylines,with each polyline in the output corresponding to a trace.The efficacy of the proposed method was assessed using a 3D model of a real-world case study,which was used to compare the results of artificial intelligence(AI)-aided and human intelligence trace detection.Rosette diagrams,which visualize the distribution of trace orientations,confirmed the high similarity between the automatically and manually generated trace maps.In conclusion,the proposed semi-automatic method was easy to use,fast,and accurate in detecting the dominant jointing system of the rock mass.
基金support of the Fundamental Research Funds for the Central Universities(No.E2ET0411X2).
文摘In recent years,research investigations have focused on the substantial freshwater storage in the Beaufort Gyre(BG)region due to climate change.Despite active mesoscale eddies in the area,a notable gap in understanding the three-dimensional structure and induced transport has been observed.This study concentrates on the Canada Basin in the western Arctic Ocean,specifically examining a subsurface anticyclonic eddy(SAE)sampled by a Mooring A in the BG region.Hybrid Coordinate Ocean Model(HYCOM)analysis data reveal its lifecycle from February 15 to March 15,2017,marked by initiation,development,maturity,decay,and termination stages.This work extends the finding of SAE passing through Mooring A by examining its overall effects,spatiotemporal variations,and swirl transport.SAE generation through baroclinic instability,which contributes to the westward tilt of the vertical axis,is also confirmed in this study.Swirl transport induced by SAE is predominantly eastward and downward due to its trajectory and background flow.SAE temporarily weakens stratification and extends the subsurface depth but demonstrates transient effects.Moreover,SAE transports upper-layer freshwater,Pacific Winter Water,and Atlantic Water downward,emphasizing its potential influence on freshwater redistribution in the Canadian Basin.This research provides valuable insights into mesoscale eddy dynamics,revealing their role in modulating the upper water mass in the BG region.
基金supported by Natural Science Foundation of China(Grant No.52175488)Scientific Research Program for Young Outstanding Talent of Higher Education of Hebei Province(China)(Grant No.BJ2021045)S&T Program of Hebei(China)(Grant No.236Z1808G).
文摘High-performance lattice structures produced through powder bed fusion-laser beam exhibit high specific strength and energy absorption capabilities.However,a significant deviation exists between the mechanical properties,service life of lattice structures,and design expectations.This deviation arises from the intense interaction between the laser and powder,which leads to the formation of numerous defects within the lattice structure.To address these issues,this paper proposes a high-performance defect detection model for metal lattice structures based on YOLOv4,called YOLO-Lattice(YOLO-L).The main objectives of this paper are as follows:(1)utilize computed tomography to construct datasets of the diamond lattice and body-centered cubic lattice structures;(2)in the backbone network of YOLOv4,employ deformable convolution to enhance the feature extraction capability of the model for small-scale defects;(3)adopt a dual-attention mechanism to suppress invalid feature information and amplify the distinction between defect and background regions;and(4)implement a channel pruning strategy to eliminate channels carrying less feature information,thereby improving the inference speed of the model.The experimental results on the diamond lattice structure dataset demonstrate that the mean average precision of the YOLO-L model increased from 96.98% to 98.8%(with an intersection over union of 0.5),and the inference speed decreased from 51.3 ms to 32.5 ms when compared to YOLOv4.Thus,the YOLO-L model can be effectively used to detect defects in metal lattice structures.
基金supported by the Research Funding of Hangzhou International Innovation Institute of Beihang University(Grant No.015731201-2024KQ126)National Key R&D Program of China(Grant No.2023YFF0716600)National Natural Science Foundation of China(Grant No.62271021).
文摘The steel-epoxy-steel sandwich structures provide enhanced corrosion resistance and fatigue resistance,making them suitable for pipeline rehabilitation with effective repair and long-term durability.However,the repair quality can be compromised by disbond between the steel and epoxy layers,whichmay result frominsufficient epoxy injection.Conventional ultrasonic testing faces challenges in accurately locating disbond defects due to aliased echo interference at interfaces.This paper proposes a signal processing algorithm for improving the accuracy of ultrasonic reflection method for detecting disbond defects between steel and epoxy layers.First,a coati optimization algorithmvariational mode decomposition(COA-VMD)is applied to adaptively decompose the ultrasonic signals and extract the intrinsic mode function components that show high correlation with the defect-related signals.Then,by calculating the relative reflectance at the interface and establishing a quantitative evaluation index based on acoustic impedance discontinuity,the locations of disbond defects are identified.Experimental results demonstrate that this method can effectively detect the locations of disbond defects between steel and epoxy layers.
基金Supported by the Special Fund for Basic Scientific Research of Central-Level Public Welfare Scientific Research Institutes(2024-9007)。
文摘The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the problem,this paper proposes an integrated calibration method for structured light vision sensors.In the proposed system,the sensor is mounted on a crawler-type mobile robot,which scans and measures the center height of guardrails while in motion.However,due to external disturbances such as uneven road surfaces and vehicle vibrations,the posture of the robot may deviate,causing displacement of the sensor platform and resulting in spatial 3D measurement errors.To overcome this issue,the system integrates inertial measurement unit(IMU)data into the sensor calibration process,enabling realtime correction of posture deviations through sensor fusion.This approach achieves a unified calibration of the structured light vision system,effectively compensates for posture-induced errors,and enhances detection accuracy.A prototype was developed and tested in both laboratory and real highway environments.Experimental results demonstrate that the proposed method enables accurate center height detection of guardrails under complex road conditions,significantly reduces posture-related measurement errors,and greatly improves the efficiency and reliability of traditional detection methods.
基金supported by the National Natural Science Foundation of China(No.52175269)the Innovative Research Groups of the National Natural Science Foundation of China(No.52021003)+2 种基金Natural Science Foundation of Jilin Province of China(No.20210101052JC)Science and Technology Research Project of Education Department of Jilin Province(JJKH20231146KJ,JJKH20241262KJ)China Postdoctoral Science Foundation(2024M751086).
文摘Flexible pressure sensors have excellent prospects in applications of human-machine interfaces,artificial intelligence and human health monitoring due to their bendable and lightweight characteristics compared to rigid pressure sensors.However,arising from the limited compressibility of soft materials and the hardening of microstructures at the device interface,there is always a trade-off between high sensitivity and broad sensing range for most flexible pressure sensors,which results in a gradual saturation response and limits their practical applications.Herein,inspired by the distinct pressure perception function of crocodile receptors,a highly sensitive and wide-range flexible pressure sensor with multiscale microdomes and interlocked architecture is developed via a facile PS-decorated molding method.Combined with interlocked architecture,the multiscale dome-shaped structured interface enhances the compressibility of the material through structural complementarity,increases the contact area between functional materials,which compensates for the stiffness induced by the deformation of dense microscale columns.This effectively mitigates structural hardening across a wide pressure range,leading to the overall high performance of the sensor.As a result,the obtained sensor exhibits a low detection limit of 5 Pa,a high sensitivity of 6.14 kPa^(-1),a wide measurement range up to 231 kPa,short response/recovery time of 56 ms/69 ms,outstanding stability over 10,000 cycles.Considering these excellent properties,the sensor shows promising potential in health monitoring,human-computer interaction,wearable electronics.This study presents a strategy for the fabrication of flexible pressure sensors exhibiting high sensitivity and a wide pressure response range.
基金supported by the Nuclear Safety Research Program through Korea Foundation of Nuclear Safety(KoFONS)using the financial resource granted by the Nuclear Safety and Security Commission(NSSC)of the Republic of Korea(Grant number:2106061,50%)supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2025-25394739,Development of Security Enhancement Technology for Industrial Control Systems Based on S/HBOM Supply Chain Protection,50%).
文摘With the continuous expansion of digital infrastructures,malicious behaviors in host systems have become increasingly sophisticated,often spanning multiple processes and employing obfuscation techniques to evade detection.Audit logs,such as Sysmon,offer valuable insights;however,existing approaches typically flatten event sequences or rely on generic graph models,thereby discarding the natural parent-child process hierarchy that is critical for analyzing multiprocess attacks.This paper proposes a structure-aware threat detection framework that transforms audit logs into a unified two-dimensional(2D)spatio-temporal representation,where process hierarchy is modeled as the spatial axis and event chronology as the temporal axis.In addition,entropy-based features are incorporated to robustly capture obfuscated and non-linguistic strings,overcoming the limitations of semantic embeddings.The model’s performance was evaluated on publicly available datasets,achieving competitive results with an accuracy exceeding 95%and an F1-score of at least 0.94.The proposed approach provides a promising and reproducible solution for detecting attacks with unknown indicators of compromise(IoCs)by analyzing the relationships and behaviors of processes recorded in large-scale audit logs.
基金The authors would like to thank CNPq(Conselho Nacional de Desenvolvimento Científico e Tecnológico)—grants 407256/2022-9,303550/2025-2,402533/2023-2 and 303982/2022-5FAPEMIG(Fundação de AmparoàPesquisa do Estado de Minas Gerais)—grants APQ-00032-24 and APD-01113-25 for their financial support.
文摘Structural Health Monitoring(SHM)plays a critical role in ensuring the safety,integrity,longevity and economic efficiency of civil infrastructures.The field has undergone a profound transformation over the last few decades,evolving from traditional methods—often reliant on visual inspections—to data-driven intelligent systems.This review paper analyzes this historical trajectory,beginning with the approaches that relied on modal parameters as primary damage indicators.The advent of advanced sensor technologies and increased computational power brings a significant change,making Machine Learning(ML)a viable and powerful tool for damage assessment.More recently,Deep Learning(DL)has emerged as a paradigm shift,allowing for more automated processing of large data sets(such as the structural vibration signals and other types of sensors)with excellent performance and accuracy,often surpassing previous methods.This paper systematically reviews these technological milestones—from traditional vibration-based methods to the current state-of-the-art in deep learning.Finally,it critically examines emerging trends—such as Digital Twins and Transformer-based architectures—and discusses future research directions that will shape the next generation of SHM systems for civil engineering.
基金supported by the National Natural Science Foundation of China(Grant No.52405267)the Jiangxi Provincial Natural Science Foundation(Grant Nos.20242BAB25257,20232BAB214050)+1 种基金the China Postdoctoral Science Foundation(Grant No.2024M760877)the Natural Science Foundation of Hunan Province(Grant No.2025JJ60369)。
文摘Soft pneumatic structures are promising for the actuation of soft machines,and substantial advances have occurred in their innovative design and functional verification.However,most pneumatic structures lack self-sensing abilities,resulting in a lack of motion state feedback and difficulty in achieving real-time closed-loop control.Herein,a soft pneumatic composite structure(SPCS)with integrated actuation and sensing abilities is developed by combining a bellows-shaped magnetic elastomer and a wire structure.The SPCS can generate an induced voltage under deformation.The SPCS mechanical and magnetoelectric characteristics are studied comprehensively.The SPCS experimental maximum contraction is 27 mm,which is close to the theoretical and numerical results.When the SPCS is actuated by a pressure of-40 kPa,it will generate a peak induced voltage of 1.01 mV.With the increase in magnetic powder content and turns of the spiral wire,the induced voltage also increases.Additionally,two SPCSs are used to develop a self-sensing actuator,which can accurately perceive the bending direction and recognize the magnitude and direction of external force.A self-sensing soft gripper is developed,which can sense the grasping status and predict the width of grasped objects.Furthermore,a smart vehicle detection system composed of two SPCSs is proposed,which can detect the number,speed,and weight of passing vehicles.Consequently,the SPCS has numerous potential applications in soft sensors and self-sensing intelligent soft machines.
文摘This article aims to develop a head pursuit （HP） guidance law for three-dimensional hypervelocity interception, so that the effect of the perturbation induced by seeker detection can be reduced. On the basis of a novel HP three-dimensional guidance model, a nonlinear variable structure guidance law is presented by using Lyapunov stability theory. The guidance law positions the interceptor ahead of the target on its tlight trajectory, and the speed of the interceptor is required to be lower than that of the target, A numerical example of maneuvering ballistic target interception verifies the rightness of the guidance model and the effectiveness of the proposed method.
基金The authors wish to acknowledge financial support from the National Natural Science Foundation of China(51822407 and 51774327)Natural Science Foundation of Hunan Province in China(2018JJ1037)Innovation Driven project of Central South University(2020CX014).
文摘Microseismic/acoustic emission(MS/AE)source localization method is crucial for predicting and controlling of potentially dangerous sources of complex structures.However,the locating errors induced by both the irregular structure and pre-measured velocity are poorly understood in existing methods.To meet the high-accuracy locating requirements in complex three-dimensional hole-containing structures,a velocity-free MS/AE source location method is developed in this paper.It avoids manual repetitive training by using equidistant grid points to search the path,which introduces A*search algorithm and uses grid points to accommodate complex structures with irregular holes.It also takes advantage of the velocity-free source location method.To verify the validity of the proposed method,lead-breaking tests were performed on a cubic concrete test specimen with a size of 10 cm10 cm10 cm.It was cut out into a cylindrical empty space with a size of/6cm10 cm.Based on the arrivals,the classical Geiger method and the proposed method are used to locate lead-breaking sources.Results show that the locating error of the proposed method is 1.20 cm,which is less than 2.02 cm of the Geiger method.Hence,the proposed method can effectively locate sources in the complex three-dimensional structure with holes and achieve higher precision requirements.
基金Supported by the National Key R&D Program of China(No.2018YFC1406202)the National Natural Science Foundation of China(Nos.41830964,41976188,41605051)。
文摘As in-situ observations are sparse,targeted observations of a specific mesoscale eddy are rare.Therefore,it is difficult to study the three-dimensional structure of moving mesoscale eddies.From April to September 2014,an anticyclonic eddy located at 135°E-155°E,26°N-42°N was observed using 17 rapidsampling Argo floats,and the spatiotemporal variations in the three-dimensional structure were studied.The results are as follows:(1)the eddy was identified and tracked using satellite altimeter data.It had a lifetime of 269 days and an average radius of 91.5 km.The lifetime of the eddy can be divided into three phases,i.e.,the initiation,maturity,and termination phases.The depth of its influence reached 1000 m;(2)the Argo profiles were divided into seven periods(approximately 20 days in each)for composite analysis,and the composite Argo profiles and CARS2009(CSIRO Atlas of Regional Seas)climatology data were merged following the data-interpolating variational analysis(DIVA)method to reconstruct the three-dimensional structure.The temperature and salinity anomaly cores of the anticyclonic mesoscale eddy are located from 400 to 600 m.From 800 to 900 m,there is an area of low salinity at the center of the eddy.A high concentration anomaly of dissolved oxygen was located at approximately 250 m;(3)to better understand the features of the eddy and its interaction with the surroundings,we calculated the anomalous velocity of the geostrophic flow and the heat,salt,dissolved oxygen transport anomaly,and discussed the eddy's origin and its adjustments to topography.The maximum heat,salt,and oxygen transport caused by eddy were 9.37×10^11 W,3.08×10^3 kg/s,and 2.70×10^2 kg/s,which all occurred during the termination phase.This study highlights the applicability of using Argo floats to understand the three-dimensional structure thermohaline features of eddies in the North Pacific.
基金the National Key Research and Development Program of China(No.2023 YFC2811600)the National Natural Science Foundation of China(Nos.52301349,52088102)+1 种基金the Major Science and Technology Innovation Program of Qingdao(No.223-3-hygg-10-hy)the Qingdao Science Foundation for Post-doctoral Scientists(Nos.QDBSH20220202070,QDBSH20220201015)。
文摘A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures.
基金Supported by the National Basic Research Development Program of China(973 Program)under contract Nos 2007CB816002,2007CB816005the innovative key project of Chinese Academy of Sciences under contract No.KZCXZ-YW-201
文摘The three-dimensional structure and the seasonal variation of the North Pacific meridional overturning circulation （NPMOC） are analyzed based on the Simple Ocean Data Assimilation data and Argo profiling float data. The NPMOC displays a multi-cell structure with four cells in the North Pacific altogether. The TC and the STC are a strong clockwise meridional cell in the low latitude ocean and a weaker clockwise meridional cell between 7°N and 18°N, respectively, while the DTC and the subpolar cell are a weaker anticlockwise meridional cell between 3°N and 15°N and a weakest anticlockwise meridional cell between 35°N and 50°N, respectively. The DTC, the TC and the STC are all of very strong seasonal variations. As to the DTC, the southward transport is strongest in fall and weakest in spring. For the TC, the northward transport is strongest in winter and weakest in spring, while the southward transport is strongest in fall and weakest in spring, which is associated with the strong southward fiow of the DTC in fall. As the STC, the northward transport is strongest in winter and weakest in summer, while the southward transport is strongest in summer and weakest in spring. This seasonal difference may be associated with the DTC. The zonal wind stress and the east-west slope of sea level play important roles in the seasonal variations of the TC, the STC and the DTC.
基金supported by the National Natural Science Foundation of China(Nos.51407134,52002196)Natural Science Foundation of Shandong Province(Nos.ZR2019YQ24,ZR2020QF084)+1 种基金Taishan Scholars and Young Experts Program of Shandong Province(No.tsqn202103057)the Qingchuang Talents Induction Program of Shandong Higher Education Institution(Research and Innovation Team of Structural-Functional Polymer Composites)and Special Financial of Shandong Province(Structural Design of High-efficiency Electromagnetic Wave-absorbing Composite Materials and Construction of Shandong Provincial Talent Teams(No.37000022P990304116449)).
文摘In order to obtain high-performance electromagnetic wave absorbers,the adjustment of structure and components is essential.Based on the above requirements,this system forms a three-dimensional frame structure consisting of MXene and transition metal oxides(TMOs)through efficient electrostatic self-assembly.This three-dimensional network structure has rich heterojunction structures,which can cause a large amount of interface polarization and conduction losses in incident electromagnetic waves.Hollow structures cause multiple reflections and scattering of electromagnetic waves,which is also an important reason for further increasing electromagnetic wave losses.When the doping ratio is 1:1,the system has the best impedance matching,the maximum effective absorption bandwidth(EAB max)can reach 5.12 GHz at 1.7 mm,and the minimum reflection loss(RL_(min))is-50.30 dB at 1.8 mm.This provides a reference for the subsequent formation of 2D-MXene materials into 3D materials.
基金Project supported by the National Key Basic Research Program of China (Grant No 2007CB31040)the National Natural Science Foundation of China (Grant No 60571020)
文摘This paper presents a three-dimensional particle-in-cell （PIC） simulation of a Ka-band relativistic Cherenkov source with a slow wave structure （SWS） consisting of metal photonic band gap （PBG） structures. In the simulation, a perfect match layer boundary is employed to absorb passing band modes supported by the PBG lattice with an artificial metal boundary. The simulated axial field distributions in the cross section and surface of the SWS demonstrate that the device operates in the vicinity of the π point of a TM01-1ike mode. The Fourier transformation spectra of the axial fields as functions of time and space show that only a single frequency appears at 36.27 GHz, which is in good agreement with that of the intersection of the dispersion curve with the slow space charge wave generated on the beam. The simulation results demonstrate that the SWS has good mode selectivity.
文摘A kind of photoelectric system that is suitable to measuring and to testing the damage of the composite material intelligent structure was presented. It can measure the degree of damage of the composite intelligent structure and it also can tell us the damage position in the structure. This system consists of two parts ： software and hardware. Experiments of the damage detection and the analysis of the composite material structure with the photoelectric system were performed, and a series of damage detection experiments was conducted. The results prove that the performance of the system is well and the effects of the measure and test are evident. Through all the experiments, the damage detection technology and test system are approved to be real-time, effective and reliable in the damage detection of the composite intelligent structure.
基金This work was supported by National Natural Science Foundation of China (60574083)Key Laboratory of Process Industry Automation, Ministry ofEducation of China (PAL200514)Innovation Scientific Fund of Nanjing University of Aeronautics and Astronautics (Y0508-031)
文摘This paper proposes a novel idea that classifies faults into two different kinds： serious faults and small faults, and treats them with different strategies respectively. A kind of artificial neural network （ANN） is proposed for detecting serious faults, and variable structure （VS） model-following control is constructed for accommodating small faults. The proposed framework takes both advantages of qualitative way and quantitative way of fault detection and accommodation. Moreover, the uncertainty case is investigated and the VS controller is modified. Simulation results of a remotely piloted aircraft with control actuator failures illustrate the performance of the developed algorithm.
