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
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
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
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
In this paper, we propose a novel anomaly detection method for data centers based on a combination of graphstructure and abnormal attention mechanism. The method leverages the sensor monitoring data from targetpower s...In this paper, we propose a novel anomaly detection method for data centers based on a combination of graphstructure and abnormal attention mechanism. The method leverages the sensor monitoring data from targetpower substations to construct multidimensional time series. These time series are subsequently transformed intograph structures, and corresponding adjacency matrices are obtained. By incorporating the adjacency matricesand additional weights associated with the graph structure, an aggregation matrix is derived. The aggregationmatrix is then fed into a pre-trained graph convolutional neural network (GCN) to extract graph structure features.Moreover, both themultidimensional time series segments and the graph structure features are inputted into a pretrainedanomaly detectionmodel, resulting in corresponding anomaly detection results that help identify abnormaldata. The anomaly detection model consists of a multi-level encoder-decoder module, wherein each level includesa transformer encoder and decoder based on correlation differences. The attention module in the encoding layeradopts an abnormal attention module with a dual-branch structure. Experimental results demonstrate that ourproposed method significantly improves the accuracy and stability of anomaly detection.展开更多
Cracks,potholes,and other defects often occur on infrastructure such as bridges,among which cracks are one of the most frequent defects.They have diverse shapes and are difficult to detect.Traditional manual inspectio...Cracks,potholes,and other defects often occur on infrastructure such as bridges,among which cracks are one of the most frequent defects.They have diverse shapes and are difficult to detect.Traditional manual inspection methods are inefficient and have low accuracy,while automated inspection machines are bulky and inconvenient to carry and use.Based on the shortcomings of existing detection technologies,this paper proposes a portable structural surface crack detection system based on the Android platform using a portable hand-held image acquisition device.The system captures cracks on the structure's surface and obtains high-definition crack images.Then,these images are transmitted to portable smartphone terminals through Wi-Fi.Next,the image is pre-processed using weighted averaging,grayscale linear transformation,and adaptive median filtering.Then,the improved Canny edge detection algorithm is applied to identify crack information,and the edge segmentation algorithm is used to determine the crack width.Finally,based on camera calibration,the pixels are converted into the length data required for actual measurement.The results show that the system is easy to operate,and it not only has crack storage and tracking functions,but also can effectively measure the crack width on the surface of components.The measurement accuracy of this system reaches the sub-pixel level,and in actual testing,compared with the crack width gauge,the maximum relative error does notexceed6.25%.展开更多
Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficient...Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.展开更多
Structural damage detection(SDD)remains highly challenging,due to the difficulty in selecting the optimal damage features from a vast amount of information.In this study,a tree model-based method using decision tree a...Structural damage detection(SDD)remains highly challenging,due to the difficulty in selecting the optimal damage features from a vast amount of information.In this study,a tree model-based method using decision tree and random forest was employed for feature selection of vibration response signals in SDD.Signal datasets were obtained by numerical experiments and vibration experiments,respectively.Dataset features extracted using this method were input into a convolutional neural network to determine the location of structural damage.Results indicated a 5%to 10%improvement in detection accuracy compared to using original datasets without feature selection,demonstrating the feasibility of this method.The proposed method,based on tree model and classification,addresses the issue of extracting effective information from numerous vibration response signals in structural health monitoring.展开更多
Palladium diselenide(PdSe_(2)),a novel two-dimensional(2D)material with a unique pentagonal crystal structure including anisotropic properties,has emerged as a highly promising candidate for developing the next genera...Palladium diselenide(PdSe_(2)),a novel two-dimensional(2D)material with a unique pentagonal crystal structure including anisotropic properties,has emerged as a highly promising candidate for developing the next generation photoelectronic devices.In this review,firstly,we have shed light on key figures of merit for polarization detection.After that,this review mainly highlights the structural and electronic properties of PdSe_(2)focusing on its strong polarization sensitivity,tunable bandgap,and excellent environmental stability,making it ideal for developing the photoelectronic devices such as broadband photodetectors and their further applications in polarization detection-based imaging systems.We also discuss challenges in scalable synthesis,material stability,and integration with other low-dimensional materials,offering future research directions to optimize PdSe_(2)for commercial applications.Owing to the outstanding optoelectronic properties of PdSe_(2),it stands at the forefront of optoelectronic materials,poised to enable new innovations in polarization photodetection.展开更多
Infrared(IR)spectroscopy,a technique within the realm of molecular vibrational spectroscopy,furnishes distinctive chemical signatures pivotal for both structural analysis and compound identification.A notable challeng...Infrared(IR)spectroscopy,a technique within the realm of molecular vibrational spectroscopy,furnishes distinctive chemical signatures pivotal for both structural analysis and compound identification.A notable challenge emerges from the misalignment between the mid-IR light wavelength range and molecular dimensions,culminating in a constrained absorption cross-section and diminished vibrational absorption coefficients(Supplementary data).展开更多
Chirality,a common phenomenon in nature,appears in structures ranging from galaxies and condensed matter to atomic nuclei.There is a persistent demand for new,high-precision methods to detect chiral structures,particu...Chirality,a common phenomenon in nature,appears in structures ranging from galaxies and condensed matter to atomic nuclei.There is a persistent demand for new,high-precision methods to detect chiral structures,particularly at the microscale.Here,we propose a novel method,vortex Mössbauer spectroscopy,for probing chiral structures.By leveraging the orbital angular momentum carried by vortex beams,this approach achieves high precision in detecting chiral structures at scales ranging from nanometers to hundreds of nanometers.Our simulation shows the ratio of characteristic lines in the Mössbauer spectra of ^(57)Fe under vortex beams exhibits differences of up to four orders of magnitude for atomic structures with different arrangements.Additionally,simulations reveal the response of ^(229m)Th chiral structures to vortex beams with opposite angular momenta differs by approximately 49-fold.These significant spectral variations indicate that this new vortex Mössbauer probe holds great potential for investigating the microscopic chiral structures and interactions of matter.展开更多
Structural Health Monitoring(SHM)systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity.There is a need for more efficient techniques...Structural Health Monitoring(SHM)systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity.There is a need for more efficient techniques to detect defects,as traditional methods are often prone to human error,and this issue is also addressed through image processing(IP).In addition to IP,automated,accurate,and real-time detection of structural defects,such as cracks,corrosion,and material degradation that conventional inspection techniques may miss,is made possible by Artificial Intelligence(AI)technologies like Machine Learning(ML)and Deep Learning(DL).This review examines the integration of computer vision and AI techniques in Structural Health Monitoring(SHM),investigating their effectiveness in detecting various forms of structural deterioration.Also,it evaluates ML and DL models in SHM for their accuracy in identifying and assessing structural damage,ultimately enhancing safety,durability,and maintenance practices in the field.Key findings reveal that AI-powered approaches,especially those utilizing IP and DL models like CNNs,significantly improve detection efficiency and accuracy,with reported accuracies in various SHM tasks.However,significant research gaps remain,including challenges with the consistency,quality,and environmental resilience of image data,a notable lack of standardized models and datasets for training across diverse structures,and concerns regarding computational costs,model interpretability,and seamless integration with existing systems.Future work should focus on developing more robust models through data augmentation,transfer learning,and hybrid approaches,standardizing protocols,and fostering interdisciplinary collaboration to overcome these limitations and achieve more reliable,scalable,and affordable SHM systems.展开更多
As technologies related to power equipment fault diagnosis and infrared temperature measurement continue to advance,the classification and identification of infrared temperature measurement images have become crucial ...As technologies related to power equipment fault diagnosis and infrared temperature measurement continue to advance,the classification and identification of infrared temperature measurement images have become crucial in effective intelligent fault diagnosis of various electrical equipment.In response to the increasing demand for sufficient feature fusion in current real-time detection and low detection accuracy in existing networks for Substation fault diagnosis,we introduce an innovative method known as Gather and Distribution Mechanism-You Only Look Once(GD-YOLO).Firstly,a partial convolution group is designed based on different convolution kernels.We combine the partial convolution group with deep convolution to propose a new Grouped Channel-wise Spatial Convolution(GCSConv)that compensates for the information loss caused by spatial channel convolution.Secondly,the Gather and Distribute Mechanism,which addresses the fusion problem of different dimensional features,has been implemented by aligning and sharing information through aggregation and distribution mechanisms.Thirdly,considering the limitations in current bounding box regression and the imbalance between complex and simple samples,Maximum Possible Distance Intersection over Union(MPDIoU)and Adaptive SlideLoss is incorporated into the loss function,allowing samples near the Intersection over Union(IoU)to receive more attention through the dynamic variation of the mean Intersection over Union.The GD-YOLO algorithm can surpass YOLOv5,YOLOv7,and YOLOv8 in infrared image detection for electrical equipment,achieving a mean Average Precision(mAP)of 88.9%,with accuracy improvements of 3.7%,4.3%,and 3.1%,respectively.Additionally,the model delivers a frame rate of 48 FPS,which aligns with the precision and velocity criteria necessary for the detection of infrared images in power equipment.展开更多
Biometric template protection is essential for finger-based authentication systems,as template tampering and adversarial attacks threaten the security.This paper proposes a DCT-based fragile watermarking scheme incorp...Biometric template protection is essential for finger-based authentication systems,as template tampering and adversarial attacks threaten the security.This paper proposes a DCT-based fragile watermarking scheme incorporating AI-based tamper detection to improve the integrity and robustness of finger authentication.The system was tested against NIST SD4 and Anguli fingerprint datasets,wherein 10,000 watermarked fingerprints were employed for training.The designed approach recorded a tamper detection rate of 98.3%,performing 3–6%better than current DCT,SVD,and DWT-based watermarking approaches.The false positive rate(≤1.2%)and false negative rate(≤1.5%)were much lower compared to previous research,which maintained high reliability for template change detection.The system showed real-time performance,averaging 12–18 ms processing time per template,and is thus suitable for real-world biometric authentication scenarios.Quality analysis of fingerprints indicated that NFIQ scores were enhanced from 2.07 to 1.81,reflecting improved minutiae clarity and ridge structure preservation.The approach also exhibited strong resistance to compression and noise distortions,with the improvements in PSNR being 2 dB(JPEG compression Q=80)and the SSIM values rising by 3%–5%under noise attacks.Comparative assessment demonstrated that training with NIST SD4 data greatly improved the ridge continuity and quality of fingerprints,resulting in better match scores(260–295)when tested against Bozorth3.Smaller batch sizes(batch=2)also resulted in improved ridge clarity,whereas larger batch sizes(batch=8)resulted in distortions.The DCNN-based tamper detection model supported real-time classification,which greatly minimized template exposure to adversarial attacks and synthetic fingerprint forgeries.Results demonstrate that fragile watermarking with AI indeed greatly enhances fingerprint security,providing privacy-preserving biometric authentication with high robustness,accuracy,and computational efficiency.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
Feasibility of a wave propagation-based active crack detection technique for nondestructive evaluations （NDE） of concrete structures with surface bonded and embedded piezoelectric-ceramic （PZT） patches was studied...Feasibility of a wave propagation-based active crack detection technique for nondestructive evaluations （NDE） of concrete structures with surface bonded and embedded piezoelectric-ceramic （PZT） patches was studied. At first, the wave propagation mechanisms in concrete were analyzed. Then, an active sensing system with integrated actuators/sensors was constructed. One PZT patch was used as an actuator to generate high frequency waves, and the other PZT patches were used as sensors to detect the propagating wave. Scattered wave signals from the damage can be obtained by subtracting the baseline signal of the intact structure from the recorded signal of the damaged structure. In the experimental study, progressive cracked damage inflicted artificially on the plain concrete beam is assessed by using both lateral and thickness modes of the PZT patches. The results indicate that with the increasing number and severity of cracks, the magnitude of the sensor output decreases for the surface bonded PZT patches, and increases for the embedded PZT patches.展开更多
Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operat...Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operation of high-speed trains.For this reason,a rail internal defect detection method based on an enhanced network structure and module design using ultrasonic images is proposed in this paper.First,a data augmentation method was used to extend the existing image dataset to obtain appropriate image samples.Second,an enhanced network structure was designed to make full use of the high-level and low-level feature information in the image,which improved the accuracy of defect detection.Subsequently,to optimize the detection performance of the proposed model,the Mish activation function was used to design the block module of the feature extraction network.Finally,the pro-posed rail defect detection model was trained.The experimental results showed that the precision rate and F1score of the proposed method were as high as 98%,while the model’s recall rate reached 99%.Specifically,good detec-tion results were achieved for different types of defects,which provides a reference for the engineering application of internal defect detection.Experimental results verified the effectiveness of the proposed method.展开更多
The development of robust damage detection methods for offshore structures is crucial to prevent catastrophes caused by structural failures. In this research, we developed an Improved Modal Strain Energy （IMSE） meth...The development of robust damage detection methods for offshore structures is crucial to prevent catastrophes caused by structural failures. In this research, we developed an Improved Modal Strain Energy （IMSE） method for detecting damage in offshore platform structures based on a traditional modal strain energy method （the Stubbs index method）. The most significant difference from the Stubbs index method was the application of modal frequencies. The goal was to improve the robustness of the traditional method. To demonstrate the effectiveness and practicality of the proposed IMSE method, both numerical and experimental studies were conducted for different damage scenarios using a jacket platform structure. The results demonstrated the effectiveness of the IMSE method in damage location when only limited, spatially incomplete, and noise-polluted modal data is available. Comparative studies showed that the IMSE index outperformed the Stubbs index and exhibited stronger robustness, confirming the superiority of the proposed approach.展开更多
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 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 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 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 Science and Technology Project of China Southern Power Grid Company,Ltd.(031200KK52200003)the National Natural Science Foundation of China(Nos.62371253,52278119).
文摘In this paper, we propose a novel anomaly detection method for data centers based on a combination of graphstructure and abnormal attention mechanism. The method leverages the sensor monitoring data from targetpower substations to construct multidimensional time series. These time series are subsequently transformed intograph structures, and corresponding adjacency matrices are obtained. By incorporating the adjacency matricesand additional weights associated with the graph structure, an aggregation matrix is derived. The aggregationmatrix is then fed into a pre-trained graph convolutional neural network (GCN) to extract graph structure features.Moreover, both themultidimensional time series segments and the graph structure features are inputted into a pretrainedanomaly detectionmodel, resulting in corresponding anomaly detection results that help identify abnormaldata. The anomaly detection model consists of a multi-level encoder-decoder module, wherein each level includesa transformer encoder and decoder based on correlation differences. The attention module in the encoding layeradopts an abnormal attention module with a dual-branch structure. Experimental results demonstrate that ourproposed method significantly improves the accuracy and stability of anomaly detection.
基金Supported by Shaanxi Provincial Key Research and Development Program(2024GX-YBXM-288)the National Natural Science Foundation of China(52172324)+1 种基金Beilin District Science and Technology Program(GX2350)the Special Fund Project for Basic Research Business Expenses of Central level Public Welfare Research Institutes(2023-9062)。
文摘Cracks,potholes,and other defects often occur on infrastructure such as bridges,among which cracks are one of the most frequent defects.They have diverse shapes and are difficult to detect.Traditional manual inspection methods are inefficient and have low accuracy,while automated inspection machines are bulky and inconvenient to carry and use.Based on the shortcomings of existing detection technologies,this paper proposes a portable structural surface crack detection system based on the Android platform using a portable hand-held image acquisition device.The system captures cracks on the structure's surface and obtains high-definition crack images.Then,these images are transmitted to portable smartphone terminals through Wi-Fi.Next,the image is pre-processed using weighted averaging,grayscale linear transformation,and adaptive median filtering.Then,the improved Canny edge detection algorithm is applied to identify crack information,and the edge segmentation algorithm is used to determine the crack width.Finally,based on camera calibration,the pixels are converted into the length data required for actual measurement.The results show that the system is easy to operate,and it not only has crack storage and tracking functions,but also can effectively measure the crack width on the surface of components.The measurement accuracy of this system reaches the sub-pixel level,and in actual testing,compared with the crack width gauge,the maximum relative error does notexceed6.25%.
基金supported by the Research and Development Center of Transport Industry of New Generation of Artificial Intelligence Technology(Grant No.202202H)the National Key R&D Program of China(Grant No.2019YFB1600702)the National Natural Science Foundation of China(Grant Nos.51978600&51808336).
文摘Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.
基金supported by the Project of Guangdong Province High Level University Construction for Guangdong University of Technology(Grant No.262519003)the College Student Innovation Training Program of Guangdong University of Technology(Grant Nos.S202211845154 and xj2023118450384).
文摘Structural damage detection(SDD)remains highly challenging,due to the difficulty in selecting the optimal damage features from a vast amount of information.In this study,a tree model-based method using decision tree and random forest was employed for feature selection of vibration response signals in SDD.Signal datasets were obtained by numerical experiments and vibration experiments,respectively.Dataset features extracted using this method were input into a convolutional neural network to determine the location of structural damage.Results indicated a 5%to 10%improvement in detection accuracy compared to using original datasets without feature selection,demonstrating the feasibility of this method.The proposed method,based on tree model and classification,addresses the issue of extracting effective information from numerous vibration response signals in structural health monitoring.
基金supported by the National Key Research and Development Program of China under Grant No.2019YFB 2203400the“111 project”under Grant No.B20030.
文摘Palladium diselenide(PdSe_(2)),a novel two-dimensional(2D)material with a unique pentagonal crystal structure including anisotropic properties,has emerged as a highly promising candidate for developing the next generation photoelectronic devices.In this review,firstly,we have shed light on key figures of merit for polarization detection.After that,this review mainly highlights the structural and electronic properties of PdSe_(2)focusing on its strong polarization sensitivity,tunable bandgap,and excellent environmental stability,making it ideal for developing the photoelectronic devices such as broadband photodetectors and their further applications in polarization detection-based imaging systems.We also discuss challenges in scalable synthesis,material stability,and integration with other low-dimensional materials,offering future research directions to optimize PdSe_(2)for commercial applications.Owing to the outstanding optoelectronic properties of PdSe_(2),it stands at the forefront of optoelectronic materials,poised to enable new innovations in polarization photodetection.
基金supported by National Natural Science Foundation of China(Grant No.:32301161)the Natural Scientific Foundation of Hunan Province,China(Grant No.:2023JJ60052)+3 种基金the Scientific Research Project of Hunan Provincial Health Commission,China(Grant No.:202112062218,20190161)the Scientific Research Project of Hunan Provincial Department of Education,China(Grant No.:22B0455)the Clinical“4310”Project of the University of South China,China(Grant No.:20224310NHYCG02)the Doctoral Scientific Research Foundation of University of South China,China(Grant No.:200XQD042).
文摘Infrared(IR)spectroscopy,a technique within the realm of molecular vibrational spectroscopy,furnishes distinctive chemical signatures pivotal for both structural analysis and compound identification.A notable challenge emerges from the misalignment between the mid-IR light wavelength range and molecular dimensions,culminating in a constrained absorption cross-section and diminished vibrational absorption coefficients(Supplementary data).
基金supported in part by the National Key R&D Program(Grant No.2023YFA1606900)the National Natural Science Foundation of China(Grant No.12235003)。
文摘Chirality,a common phenomenon in nature,appears in structures ranging from galaxies and condensed matter to atomic nuclei.There is a persistent demand for new,high-precision methods to detect chiral structures,particularly at the microscale.Here,we propose a novel method,vortex Mössbauer spectroscopy,for probing chiral structures.By leveraging the orbital angular momentum carried by vortex beams,this approach achieves high precision in detecting chiral structures at scales ranging from nanometers to hundreds of nanometers.Our simulation shows the ratio of characteristic lines in the Mössbauer spectra of ^(57)Fe under vortex beams exhibits differences of up to four orders of magnitude for atomic structures with different arrangements.Additionally,simulations reveal the response of ^(229m)Th chiral structures to vortex beams with opposite angular momenta differs by approximately 49-fold.These significant spectral variations indicate that this new vortex Mössbauer probe holds great potential for investigating the microscopic chiral structures and interactions of matter.
文摘Structural Health Monitoring(SHM)systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity.There is a need for more efficient techniques to detect defects,as traditional methods are often prone to human error,and this issue is also addressed through image processing(IP).In addition to IP,automated,accurate,and real-time detection of structural defects,such as cracks,corrosion,and material degradation that conventional inspection techniques may miss,is made possible by Artificial Intelligence(AI)technologies like Machine Learning(ML)and Deep Learning(DL).This review examines the integration of computer vision and AI techniques in Structural Health Monitoring(SHM),investigating their effectiveness in detecting various forms of structural deterioration.Also,it evaluates ML and DL models in SHM for their accuracy in identifying and assessing structural damage,ultimately enhancing safety,durability,and maintenance practices in the field.Key findings reveal that AI-powered approaches,especially those utilizing IP and DL models like CNNs,significantly improve detection efficiency and accuracy,with reported accuracies in various SHM tasks.However,significant research gaps remain,including challenges with the consistency,quality,and environmental resilience of image data,a notable lack of standardized models and datasets for training across diverse structures,and concerns regarding computational costs,model interpretability,and seamless integration with existing systems.Future work should focus on developing more robust models through data augmentation,transfer learning,and hybrid approaches,standardizing protocols,and fostering interdisciplinary collaboration to overcome these limitations and achieve more reliable,scalable,and affordable SHM systems.
基金Science and Technology Department of Jilin Province(No.20200403075SF)Education Department of Jilin Province(No.JJKH20240148KJ).
文摘As technologies related to power equipment fault diagnosis and infrared temperature measurement continue to advance,the classification and identification of infrared temperature measurement images have become crucial in effective intelligent fault diagnosis of various electrical equipment.In response to the increasing demand for sufficient feature fusion in current real-time detection and low detection accuracy in existing networks for Substation fault diagnosis,we introduce an innovative method known as Gather and Distribution Mechanism-You Only Look Once(GD-YOLO).Firstly,a partial convolution group is designed based on different convolution kernels.We combine the partial convolution group with deep convolution to propose a new Grouped Channel-wise Spatial Convolution(GCSConv)that compensates for the information loss caused by spatial channel convolution.Secondly,the Gather and Distribute Mechanism,which addresses the fusion problem of different dimensional features,has been implemented by aligning and sharing information through aggregation and distribution mechanisms.Thirdly,considering the limitations in current bounding box regression and the imbalance between complex and simple samples,Maximum Possible Distance Intersection over Union(MPDIoU)and Adaptive SlideLoss is incorporated into the loss function,allowing samples near the Intersection over Union(IoU)to receive more attention through the dynamic variation of the mean Intersection over Union.The GD-YOLO algorithm can surpass YOLOv5,YOLOv7,and YOLOv8 in infrared image detection for electrical equipment,achieving a mean Average Precision(mAP)of 88.9%,with accuracy improvements of 3.7%,4.3%,and 3.1%,respectively.Additionally,the model delivers a frame rate of 48 FPS,which aligns with the precision and velocity criteria necessary for the detection of infrared images in power equipment.
文摘Biometric template protection is essential for finger-based authentication systems,as template tampering and adversarial attacks threaten the security.This paper proposes a DCT-based fragile watermarking scheme incorporating AI-based tamper detection to improve the integrity and robustness of finger authentication.The system was tested against NIST SD4 and Anguli fingerprint datasets,wherein 10,000 watermarked fingerprints were employed for training.The designed approach recorded a tamper detection rate of 98.3%,performing 3–6%better than current DCT,SVD,and DWT-based watermarking approaches.The false positive rate(≤1.2%)and false negative rate(≤1.5%)were much lower compared to previous research,which maintained high reliability for template change detection.The system showed real-time performance,averaging 12–18 ms processing time per template,and is thus suitable for real-world biometric authentication scenarios.Quality analysis of fingerprints indicated that NFIQ scores were enhanced from 2.07 to 1.81,reflecting improved minutiae clarity and ridge structure preservation.The approach also exhibited strong resistance to compression and noise distortions,with the improvements in PSNR being 2 dB(JPEG compression Q=80)and the SSIM values rising by 3%–5%under noise attacks.Comparative assessment demonstrated that training with NIST SD4 data greatly improved the ridge continuity and quality of fingerprints,resulting in better match scores(260–295)when tested against Bozorth3.Smaller batch sizes(batch=2)also resulted in improved ridge clarity,whereas larger batch sizes(batch=8)resulted in distortions.The DCNN-based tamper detection model supported real-time classification,which greatly minimized template exposure to adversarial attacks and synthetic fingerprint forgeries.Results demonstrate that fragile watermarking with AI indeed greatly enhances fingerprint security,providing privacy-preserving biometric authentication with high robustness,accuracy,and computational efficiency.
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.
基金Funded by the National Natural Science Foundation of China (51178305)the Key Projects in the Science & Technology Pillar Program of Tianjin (11ZCKFSF00300)
文摘Feasibility of a wave propagation-based active crack detection technique for nondestructive evaluations （NDE） of concrete structures with surface bonded and embedded piezoelectric-ceramic （PZT） patches was studied. At first, the wave propagation mechanisms in concrete were analyzed. Then, an active sensing system with integrated actuators/sensors was constructed. One PZT patch was used as an actuator to generate high frequency waves, and the other PZT patches were used as sensors to detect the propagating wave. Scattered wave signals from the damage can be obtained by subtracting the baseline signal of the intact structure from the recorded signal of the damaged structure. In the experimental study, progressive cracked damage inflicted artificially on the plain concrete beam is assessed by using both lateral and thickness modes of the PZT patches. The results indicate that with the increasing number and severity of cracks, the magnitude of the sensor output decreases for the surface bonded PZT patches, and increases for the embedded PZT patches.
基金Supported by National Natural Science Foundation of China(Grant No.61573233)Guangdong Provincial Natural Science Foundation of China(Grant No.2021A1515010661)Guangdong Provincial Special Projects in Key Fields of Colleges and Universities of China(Grant No.2020ZDZX2005).
文摘Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operation of high-speed trains.For this reason,a rail internal defect detection method based on an enhanced network structure and module design using ultrasonic images is proposed in this paper.First,a data augmentation method was used to extend the existing image dataset to obtain appropriate image samples.Second,an enhanced network structure was designed to make full use of the high-level and low-level feature information in the image,which improved the accuracy of defect detection.Subsequently,to optimize the detection performance of the proposed model,the Mish activation function was used to design the block module of the feature extraction network.Finally,the pro-posed rail defect detection model was trained.The experimental results showed that the precision rate and F1score of the proposed method were as high as 98%,while the model’s recall rate reached 99%.Specifically,good detec-tion results were achieved for different types of defects,which provides a reference for the engineering application of internal defect detection.Experimental results verified the effectiveness of the proposed method.
基金Supported by the National Natural Science Foundation of China （51209189, 51379196）, and the Natural Science Foundation of Shandong Province （ZR2013 EEQ006, ZR2011 EL049）
文摘The development of robust damage detection methods for offshore structures is crucial to prevent catastrophes caused by structural failures. In this research, we developed an Improved Modal Strain Energy （IMSE） method for detecting damage in offshore platform structures based on a traditional modal strain energy method （the Stubbs index method）. The most significant difference from the Stubbs index method was the application of modal frequencies. The goal was to improve the robustness of the traditional method. To demonstrate the effectiveness and practicality of the proposed IMSE method, both numerical and experimental studies were conducted for different damage scenarios using a jacket platform structure. The results demonstrated the effectiveness of the IMSE method in damage location when only limited, spatially incomplete, and noise-polluted modal data is available. Comparative studies showed that the IMSE index outperformed the Stubbs index and exhibited stronger robustness, confirming the superiority of the proposed approach.
