The performance and price of copper-based micro linear products are determined by the diameter uniformity.How to accurately detect the wire diameter of long-length copper based micro linear products without cutting or...The performance and price of copper-based micro linear products are determined by the diameter uniformity.How to accurately detect the wire diameter of long-length copper based micro linear products without cutting or damage has always been a technical concern for production enterprises.Herein,a novel approach was developed for nondestructive detection of the average diameter at any given segment of a long copper wire by assessing the adsorption capacity of arginine on its surface.The amount of adsorbent on the surface of the copper wire exhibits a positive correlation with the area,which can be detected by extractive electrospray ionization mass spectrometry(EESI-MS)after online elution with ammonia.The experimental results demonstrated that the analysis can be completed within 15 min,with a good linear relationship between copper wires with different diameters and the adsorption capacity of arginine.The linear correlation coefficient R2was 0.995,the relative standard deviation was 1.10%-2.81%,and the detection limit reached 2.5μm(length of segment=4 cm),showing potential applications for facile measurement of the average diameter of various metal wires.展开更多
Wheat quality detection is essential to ensure the safety ofwheat circulation and storage.The traditional wheat quality detection methods mainly include artificial sensory evaluation and physicochemical index analysis...Wheat quality detection is essential to ensure the safety ofwheat circulation and storage.The traditional wheat quality detection methods mainly include artificial sensory evaluation and physicochemical index analysis,which are difficult to meet the requirements for high accuracy and efficiency in modern wheat quality detection due to the disadvantages of subjectivity,destruction of sample integrity and low efficiency.With the rapid development of optical technology,various optical-based methods,using near-infrared spectroscopy technology,hyperspectral imaging technology and terahertz,etc.,have been proposed for wheat quality detection.These methods have the characteristics of nondestructiveness and high efficiency which make them popular in wheat quality detection in recent years.In this paper,various state-of-the-art optical-based techniques of wheat quality detection are analyzed and summarized in detail.Firstly,the principle and process of common optical non-destructive detection methods for wheat quality are introduced.Then,the optical techniques used in these detection methods are divided into seven categories,and the comparison of these technologies and their advantages and disadvantages are further discussed.It shows that terahertz technology is regarded as the most promising wheat quality detection method compared with other optical detection technologies,because it can not only detect most types of wheat deterioration,but also has higher accuracy and efficiency.Finally,the research of optical technology in wheat quality detection is prospected.The future research of optical technology-based wheat quality detection mainly includes the construction of wheat quality optical detection standardization database,the fusion of multiple optical detection technologies and multiple quality index information,the improvement of the anti-interference of optical technology and the industrialization of optical inspection technology for wheat quality.These studies are of great significance to improve the detection technology of wheat and ensure the storage safety of wheat in the future.展开更多
We present a non-destructive method (NDM) to identify minute quantities of high atomic number (<em>Z</em>) elements in containers such as passenger baggage, goods carrying transport trucks, and environment...We present a non-destructive method (NDM) to identify minute quantities of high atomic number (<em>Z</em>) elements in containers such as passenger baggage, goods carrying transport trucks, and environmental samples. This method relies on the fact that photon attenuation varies with its energy and properties of the absorbing medium. Low-energy gamma-ray intensity loss is sensitive to the atomic number of the absorbing medium, while that of higher-energies vary with the density of the medium. To verify the usefulness of this feature for NDM, we carried out simultaneous measurements of intensities of multiple gamma rays of energies 81 to 1408 keV emitted by sources<sup> 133</sup>Ba (half-life = 10.55 y) and <sup>152</sup>Eu (half-life = 13.52 y). By this arrangement, we could detect minute quantities of lead and copper in a bulk medium from energy dependent gamma-ray attenuations. It seems that this method will offer a reliable, low-cost, low-maintenance alternative to X-ray or accelerator-based techniques for the NDM of high-Z materials such as mercury, lead, uranium, and transuranic elements etc.展开更多
This paper tries to address the problem of binary CT image reconstruction in non-destructive detection with an algorithm based on compressed sensing(CS) and Otsu's method, which could reconstruct binary CT image o...This paper tries to address the problem of binary CT image reconstruction in non-destructive detection with an algorithm based on compressed sensing(CS) and Otsu's method, which could reconstruct binary CT image of test object from incomplete detection data. According to binary CT image characteristics, we employ Splitbregman method based on L1/2regularization to solve piecewise constant region reconstruction. To improve the reconstructed image quality from incomplete detection data, we utilize a priori knowledge and Otsu's method as the optimization constraint. In our study, we make numerical simulation to investigate our proposed method,and compare reconstructed results from different reconstruction methods. Finally, the experimental results demonstrate that the proposed method could effectively reduce noise and suppress artifacts, and reconstruct high-quality binary image from incomplete detection data.展开更多
Cemented paste backfill(CPB)is a technology that achieves safe mining by filling the goaf with waste rocks,tailings,and other materials.It is an inevitable choice to deal with the development of deep and highly diffic...Cemented paste backfill(CPB)is a technology that achieves safe mining by filling the goaf with waste rocks,tailings,and other materials.It is an inevitable choice to deal with the development of deep and highly difficult mines and meet the requirements of environmental protection and safety regulations.It promotes the development of a circular economy in mines through the development of lowgrade resources and the resource utilization of waste,and extends the service life of mines.The mass concentration of solid content(abbreviated as“concentration”)is a critical parameter for CPB.However,discrepancies often arise between the on-site measurements and the pre-designed values due to factors such as groundwater inflow and segregation within the goaf,which cannot be evaluated after the solidification of CPB.This paper innovatively provides an in-situ non-destructive approach to identify the real concentration of CPB after curing for certain days using hyperspectral imaging(HSI)technology.Initially,the spectral variation patterns under different concentration conditions were investigated through hyperspectral scanning experiments on CPB samples.The results demonstrate that as the CPB concentration increases from 61wt%to 73wt%,the overall spectral reflectance gradually increases,with two distinct absorption peaks observed at 1407 and 1917 nm.Notably,the reflectance at 1407 nm exhibited a strong linear relationship with the concentration.Subsequently,the K-nearest neighbors(KNN)and support vector machine(SVM)algorithms were employed to classify and identify different concentrations.The study revealed that,with the KNN algorithm,the highest accuracy was achieved when K(number of nearest neighbors)was 1,although this resulted in overfitting.When K=3,the model displayed the optimal balance between accuracy and stability,with an accuracy of 95.03%.In the SVM algorithm,the highest accuracy of 98.24%was attained with parameters C(regularization parameter)=200 and Gamma(kernel coefficient)=10.A comparative analysis of precision,accuracy,and recall further highlighted that the SVM provided superior stability and precision for identifying CPB concentration.Thus,HSI technology offers an effective solution for the in-situ,non-destructive monitoring of CPB concentration,presenting a promising approach for optimizing and controlling CPB characteristic parameters.展开更多
Moisture content of corn directly affects its quality and storage time,and the rapid on-line detection of the moisture content of corn ears not threshed or in vivo in the fields is required.Because of the special shap...Moisture content of corn directly affects its quality and storage time,and the rapid on-line detection of the moisture content of corn ears not threshed or in vivo in the fields is required.Because of the special shape of corn ear,the rapid,low cost and non-destructive bioelectrical impedance measurement is more suitable for its moisture content detection.Using the four-electrode method with the Agilent E4980A precision LCR meter,the electrical impedance spectroscopies of the sweet corn ears and waxy corn ears at different moisture contents were acquired.The frequency range of the detection was from 20 Hz to 2 MHz and to enhance the contact,the attached-type electrodes were wrapped in cotton soaked with 0.1%NaCl solution.The impedance data over the frequency range from 300 Hz to 5 kHz were used to obtain the parameters of the bio-impedance Cole-Cole model.The results showed a good linear correlation(coefficient of determination R2=0.960)between the equivalent parallel resistance R∞of sweet corn ear and the moisture content value determined by standard chemical method.The research proved that the bioelectrical impedance spectroscopy can be used for detecting the moisture content of corn ear.展开更多
In order to achieve the non-destructive detection of the firmness of Korla fragrant pear during the ripening period,the characteristic variables integrating the parallel equivalent inductance(Lp),quality factor(Q),par...In order to achieve the non-destructive detection of the firmness of Korla fragrant pear during the ripening period,the characteristic variables integrating the parallel equivalent inductance(Lp),quality factor(Q),parallel equivalent capacitance(Cp),dissipation factor(D),parallel equivalent resistance(Rp)and impedance(Z)were formulated through principal component analysis(PCA).Further,based on the characteristic variables,the models were established for predicting the firmness of Korla fragrant pear by using the generalized regression neural network(GRNN)and back-propagation neural network(BPNN).The results showed that firmness has significant correlations with the six electrical parameters.The first two principal components(PCs)were selected as the characteristic variables of the electrical parameters.GRNN exhibited the best performance in predicting firmness(R2=0.9628,RMSE=0.383).The results could provide important references for non-destructive detection of the quality of Korla fragrant pear.展开更多
Based on the analysis technology of visible/near infrared diffuse reflectance spectroscopy a portable and nondestructive detector was designed to test comprehensive quality of red globe grape bunches in the growth per...Based on the analysis technology of visible/near infrared diffuse reflectance spectroscopy a portable and nondestructive detector was designed to test comprehensive quality of red globe grape bunches in the growth period.The detector included spectrum acquisition probe,spectrometer,lithium battery,halogen lamp light source,advanced RISC machines(ARM)board and peripheral circuit.Based on microsoft foundation classes(MFC)development tool,the real-time analysis and processing software of the detector was written by C++language.The optimal partial least squares regression(PLSR)detection model of multi-quality parameters was implanted into the hardware device.This paper selected the red globe grapes bunches in the growth period as the research samples,collected the visible/near infrared diffuse reflectance spectrum information,and then used the established PLSR model to detect the soluble solid content(SSC),total acid(TA)and pH of the samples to generate comprehensive quality parameter.So as to realize the nondestructive detecting of comprehensive quality of red globe grapes bunches in the growth period.In conclusion,the detector could realize real-time and non-destructive detecting of red globe grapes bunches in growth period aiming at the comprehensive quality.展开更多
During the storage of composite propellants, the migration of plasticizers and other unbonded additives at the interfaces of liner adhesives has garnered significant attention in understanding liner failure mechanisms...During the storage of composite propellants, the migration of plasticizers and other unbonded additives at the interfaces of liner adhesives has garnered significant attention in understanding liner failure mechanisms, aging processes, and safety performance. However, there is currently no non-destructive and quantitative detection method for migration of plasticizers in propellant liner. In this study, we developed a HTPB sensing liner by incorporating conductive fillers-namely carbon black(CB), carbon nanotubes(CNTs), and graphene nanoplatelets(GNP)-into the HTPB matrix. The synergistic interaction between CNTs and GNP facilitates the formation of a tunneling conductive network that imparts electrical conductivity to the HTPB liner. To elucidate the functional relationship between conductivity and nitroglycerin(NG) migration, we applied the HTPB sensing liner onto double base propellant surfaces and measured both the conductivity of the sensing layer and NG migration during a 71°C accelerated aging experiment. The results shows that when CNTs/GNP content reaches 3wt%, there is an exponential correlation between conductivity and NG migration with a fitting degree of 0.9652;the average response sensitivity of ΔR/R0 relative to NG migration is calculated as 41.69, with an average deviation of merely5.67% between NG migrations derived from conductivity fittings compared to those obtained via TGA testing results. Overall, this sensing liner exhibits excellent capabilities for detecting NG migration nondestructively and quantitatively while offering a novel approach for assessing interfacial component migrations as well as debonding defects in propellants-a promising avenue for future self-monitoring strategies regarding propellant integrity.展开更多
Optoelectronic terahertz generation and detection play a key role in the applications of non-destructive testing,which involves different areas such as physics,biological,material science,imaging,explosions detection,...Optoelectronic terahertz generation and detection play a key role in the applications of non-destructive testing,which involves different areas such as physics,biological,material science,imaging,explosions detection,astronomy applications,semiconductor technology and superconductiong electronics. In this article,we present a reviewof the principle and performance of typical terahertz sources,detectors and non-destructive testing applications. On this basis,the newdevelopment and trends of terahertz radiation detectors are also discussed.展开更多
In this study,newly harvested and aged rice seeds were analyzed to determine their aging process,identify the difference between artificially and naturally aged seeds,and develop a rapid,accurate,and non-destructive d...In this study,newly harvested and aged rice seeds were analyzed to determine their aging process,identify the difference between artificially and naturally aged seeds,and develop a rapid,accurate,and non-destructive detection method for water status and water distribution of rice seed with different vigor.To this end,an artificially accelerated aging test was conducted on the newly harvested rice seeds.Then,low-field nuclear magnetic resonance(LF-NMR)technology was applied to test the new(Shennong No.9816,2018),old(Shennong No.9816,2017),and artificially aged seeds(Shennong No.9816,2018).A standard germination test was conducted for three types of seeds.Finally,the differences of water status and distribution between rice seeds of different vigor were analyzed based on the standard germination test results and wave spectrometry information collected using LF-NMR.The results indicated that new seeds,old seeds,and the artificially accelerated aging rice seeds all exhibited two water phases,and the vigor of rice seeds after the artificial accelerated aging test was lower than that of new seeds.There were significant differences between the frequencies of bound water at the time of the peak and the time at the end of the peak for the three types of seeds.The two times showed an increasing trend for rice seeds with poor vigor,indicating that the ability of the water in the rice seeds having poor vigor to combine with other substances was weakened.There were significant differences between the distributions of free water peak end time for the three types of seeds.All the rice seeds with poor vigor exhibited a decreasing trend at this time,indicating that the freedom of free water inside the rice seed samples with poor vigor was weakened.The total water content of the artificially aged seeds and the aged seeds was higher than that of the new seeds,but the free water content increased from artificially aged seeds to new seeds to aged seeds.This indicates that LF-NMR technology is an effective detection method that can simply compare the differences in seed vitality with respect to water distribution as well as differentiate the seed internal water content of artificially aged and naturally aged seeds.展开更多
This article takes the actual construction project of a certain concrete bridge project as an example to analyze the application of acoustic non-destructive testing technology in its detection.It includes an overview ...This article takes the actual construction project of a certain concrete bridge project as an example to analyze the application of acoustic non-destructive testing technology in its detection.It includes an overview of a certain bridge construction project studied and acoustic non-destructive testing technology and the application of acoustic non-destructive testing technology in actual testing.This analysis hopes to provide some guidelines for acoustic non-destructive testing of modern concrete bridge projects.展开更多
Metal foils have emerged as one of the promising materials for anode-free batteries due to their high energy density and scalability in production.The unclear lithium plating/stripping kinetics of metal foil current c...Metal foils have emerged as one of the promising materials for anode-free batteries due to their high energy density and scalability in production.The unclear lithium plating/stripping kinetics of metal foil current collectors in anode-free batteries was addressed by using the non-destructive distribution of relaxation times(DRT)analysis to systematically investigate the lithium transport behavior of 14 metal foils and its correlation with electrochemical performance.By integrating energy-dispersive spectro scopy(EDS),cyclic voltammetry(CV),and galvanostatic testing,the exceptional properties of indium(In),tin(Sn),and silver(Ag)were revealed:the Li-In alloying reaction exhibits high reversibility,Li-Sn alloys demonstrate outstanding cycling stability,and the Li-Ag solid-solution mechanism provides an ideal lithium deposition interface on the silver substrate.The DRT separates the polarization internal resistance of lithium ions passing through the SEI layer(R_(sei),τ2)and the polarization internal resistance of lithium ions undergoing charge transfer reaction at the electrolyte/electrode interface(R_(ct),τ3)by decoupling the electrochemical impedance spectroscopy(EIS).For the first time,the correlation betweenτ2,τ3,and the cycle life/Coulombic efficiency of alloy/solid-solution metals was established,while non-alloy metals are not suitable for this method due to differences in lithium deposition mechanisms.This study not only illuminates the structure-property relationship governing the lithium kinetics of metal foil electrodes but also provides a novel non-destructive analytical strategy and theoretical guidance for the rational design of stable anodes in high-energy-density batteries,facilitating the efficient screening and optimization of anode-free battery.展开更多
A non invasive ultrasonic method is used to detect whether or not the frozen fish has suffered a partial or total accidental thawing. The time of flight and the peak to peak amplitude of the ultrasonic signals backsca...A non invasive ultrasonic method is used to detect whether or not the frozen fish has suffered a partial or total accidental thawing. The time of flight and the peak to peak amplitude of the ultrasonic signals backscattered by fish are recorded during thawing. The comparison of the evolution curves and images corresponding to first and second thawing shows indicators of accidental thawing. The monitoring of thawing process showed that its assessment can be reduced to the measurement of the water content lost by fish. The attempt to replace the original water lost by fish in first thawing is analyzed. The influence of the transducer frequency on fish thawing evaluation is tested.展开更多
Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakt...Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy.展开更多
In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds...In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds in current intelligent inspection algorithms,this paper proposes CG-YOLOv8,a lightweight and improved model based on YOLOv8n for PCB surface defect detection.The proposed method optimizes the network architecture and compresses parameters to reduce model complexity while maintaining high detection accuracy,thereby enhancing the capability of identifying diverse defects under complex conditions.Specifically,a cascaded multi-receptive field(CMRF)module is adopted to replace the SPPF module in the backbone to improve feature perception,and an inverted residual mobile block(IRMB)is integrated into the C2f module to further enhance performance.Additionally,conventional convolution layers are replaced with GSConv to reduce computational cost,and a lightweight Convolutional Block Attention Module based Convolution(CBAMConv)module is introduced after Grouped Spatial Convolution(GSConv)to preserve accuracy through attention mechanisms.The detection head is also optimized by removing medium and large-scale detection layers,thereby enhancing the model’s ability to detect small-scale defects and further reducing complexity.Experimental results show that,compared to the original YOLOv8n,the proposed CG-YOLOv8 reduces parameter count by 53.9%,improves mAP@0.5 by 2.2%,and increases precision and recall by 2.0%and 1.8%,respectively.These improvements demonstrate that CG-YOLOv8 offers an efficient and lightweight solution for PCB surface defect detection.展开更多
This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagno...This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency.To this end,a total of 3234 high-resolution images(2400×1080)were collected from three major rice diseases Rice Blast,Bacterial Blight,and Brown Spot—frequently found in actual rice cultivation fields.These images served as the training dataset.The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone,thereby resulting in both model compression and improved inference speed.Additionally,YOLOv5-P,based on PP-PicoDet,was configured as a comparative model to quantitatively evaluate performance.Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance,with an mAP 0.5 of 89.6%,mAP 0.5–0.95 of 66.7%,precision of 91.3%,and recall of 85.6%,while maintaining a lightweight model size of 6.45 MB.In contrast,YOLOv5-P exhibited a smaller model size of 4.03 MB,but showed lower performance with an mAP 0.5 of 70.3%,mAP 0.5–0.95 of 35.2%,precision of 62.3%,and recall of 74.1%.This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.展开更多
Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone t...Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.展开更多
The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce...The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.展开更多
基金supported by the National Natural Science Foundation of China(No.22422402)National Key Research and Development Program of China(No.2022YFF0705300)Key Research and Development Program of Jiangxi Province(No.20232BBG70004)。
文摘The performance and price of copper-based micro linear products are determined by the diameter uniformity.How to accurately detect the wire diameter of long-length copper based micro linear products without cutting or damage has always been a technical concern for production enterprises.Herein,a novel approach was developed for nondestructive detection of the average diameter at any given segment of a long copper wire by assessing the adsorption capacity of arginine on its surface.The amount of adsorbent on the surface of the copper wire exhibits a positive correlation with the area,which can be detected by extractive electrospray ionization mass spectrometry(EESI-MS)after online elution with ammonia.The experimental results demonstrated that the analysis can be completed within 15 min,with a good linear relationship between copper wires with different diameters and the adsorption capacity of arginine.The linear correlation coefficient R2was 0.995,the relative standard deviation was 1.10%-2.81%,and the detection limit reached 2.5μm(length of segment=4 cm),showing potential applications for facile measurement of the average diameter of various metal wires.
基金supported by the scientific and technological key project in Henan Province (No.212102210148)Open fund of Key Laboratory of Grain Information Processing and Control (No.KFJJ-2018-101)
文摘Wheat quality detection is essential to ensure the safety ofwheat circulation and storage.The traditional wheat quality detection methods mainly include artificial sensory evaluation and physicochemical index analysis,which are difficult to meet the requirements for high accuracy and efficiency in modern wheat quality detection due to the disadvantages of subjectivity,destruction of sample integrity and low efficiency.With the rapid development of optical technology,various optical-based methods,using near-infrared spectroscopy technology,hyperspectral imaging technology and terahertz,etc.,have been proposed for wheat quality detection.These methods have the characteristics of nondestructiveness and high efficiency which make them popular in wheat quality detection in recent years.In this paper,various state-of-the-art optical-based techniques of wheat quality detection are analyzed and summarized in detail.Firstly,the principle and process of common optical non-destructive detection methods for wheat quality are introduced.Then,the optical techniques used in these detection methods are divided into seven categories,and the comparison of these technologies and their advantages and disadvantages are further discussed.It shows that terahertz technology is regarded as the most promising wheat quality detection method compared with other optical detection technologies,because it can not only detect most types of wheat deterioration,but also has higher accuracy and efficiency.Finally,the research of optical technology in wheat quality detection is prospected.The future research of optical technology-based wheat quality detection mainly includes the construction of wheat quality optical detection standardization database,the fusion of multiple optical detection technologies and multiple quality index information,the improvement of the anti-interference of optical technology and the industrialization of optical inspection technology for wheat quality.These studies are of great significance to improve the detection technology of wheat and ensure the storage safety of wheat in the future.
文摘We present a non-destructive method (NDM) to identify minute quantities of high atomic number (<em>Z</em>) elements in containers such as passenger baggage, goods carrying transport trucks, and environmental samples. This method relies on the fact that photon attenuation varies with its energy and properties of the absorbing medium. Low-energy gamma-ray intensity loss is sensitive to the atomic number of the absorbing medium, while that of higher-energies vary with the density of the medium. To verify the usefulness of this feature for NDM, we carried out simultaneous measurements of intensities of multiple gamma rays of energies 81 to 1408 keV emitted by sources<sup> 133</sup>Ba (half-life = 10.55 y) and <sup>152</sup>Eu (half-life = 13.52 y). By this arrangement, we could detect minute quantities of lead and copper in a bulk medium from energy dependent gamma-ray attenuations. It seems that this method will offer a reliable, low-cost, low-maintenance alternative to X-ray or accelerator-based techniques for the NDM of high-Z materials such as mercury, lead, uranium, and transuranic elements etc.
基金Supported by the National Natural Science Foundation of China(Nos.61401049 and 61201346)Postdoctoral Science Foundation of China(No.2014M560703)+1 种基金Chongqing Postdoctoral Science Foundation(No.Xm2014105)the Fundamental Research Funds for the Central Universities(Nos.CDJZR14125501 and 106112015CDJRC121103)
文摘This paper tries to address the problem of binary CT image reconstruction in non-destructive detection with an algorithm based on compressed sensing(CS) and Otsu's method, which could reconstruct binary CT image of test object from incomplete detection data. According to binary CT image characteristics, we employ Splitbregman method based on L1/2regularization to solve piecewise constant region reconstruction. To improve the reconstructed image quality from incomplete detection data, we utilize a priori knowledge and Otsu's method as the optimization constraint. In our study, we make numerical simulation to investigate our proposed method,and compare reconstructed results from different reconstruction methods. Finally, the experimental results demonstrate that the proposed method could effectively reduce noise and suppress artifacts, and reconstruct high-quality binary image from incomplete detection data.
基金funded by the National Natural Science Foundation of China(Nos.52474165 and 52522404)。
文摘Cemented paste backfill(CPB)is a technology that achieves safe mining by filling the goaf with waste rocks,tailings,and other materials.It is an inevitable choice to deal with the development of deep and highly difficult mines and meet the requirements of environmental protection and safety regulations.It promotes the development of a circular economy in mines through the development of lowgrade resources and the resource utilization of waste,and extends the service life of mines.The mass concentration of solid content(abbreviated as“concentration”)is a critical parameter for CPB.However,discrepancies often arise between the on-site measurements and the pre-designed values due to factors such as groundwater inflow and segregation within the goaf,which cannot be evaluated after the solidification of CPB.This paper innovatively provides an in-situ non-destructive approach to identify the real concentration of CPB after curing for certain days using hyperspectral imaging(HSI)technology.Initially,the spectral variation patterns under different concentration conditions were investigated through hyperspectral scanning experiments on CPB samples.The results demonstrate that as the CPB concentration increases from 61wt%to 73wt%,the overall spectral reflectance gradually increases,with two distinct absorption peaks observed at 1407 and 1917 nm.Notably,the reflectance at 1407 nm exhibited a strong linear relationship with the concentration.Subsequently,the K-nearest neighbors(KNN)and support vector machine(SVM)algorithms were employed to classify and identify different concentrations.The study revealed that,with the KNN algorithm,the highest accuracy was achieved when K(number of nearest neighbors)was 1,although this resulted in overfitting.When K=3,the model displayed the optimal balance between accuracy and stability,with an accuracy of 95.03%.In the SVM algorithm,the highest accuracy of 98.24%was attained with parameters C(regularization parameter)=200 and Gamma(kernel coefficient)=10.A comparative analysis of precision,accuracy,and recall further highlighted that the SVM provided superior stability and precision for identifying CPB concentration.Thus,HSI technology offers an effective solution for the in-situ,non-destructive monitoring of CPB concentration,presenting a promising approach for optimizing and controlling CPB characteristic parameters.
基金This research was supported by the Special Fund for Agro-scientific Research in the Public Interest(20120302)the Program of the Co-Construction with Beijing and the Specialized Research Fund for the Doctoral Program of Higher Education(20130008110035).
文摘Moisture content of corn directly affects its quality and storage time,and the rapid on-line detection of the moisture content of corn ears not threshed or in vivo in the fields is required.Because of the special shape of corn ear,the rapid,low cost and non-destructive bioelectrical impedance measurement is more suitable for its moisture content detection.Using the four-electrode method with the Agilent E4980A precision LCR meter,the electrical impedance spectroscopies of the sweet corn ears and waxy corn ears at different moisture contents were acquired.The frequency range of the detection was from 20 Hz to 2 MHz and to enhance the contact,the attached-type electrodes were wrapped in cotton soaked with 0.1%NaCl solution.The impedance data over the frequency range from 300 Hz to 5 kHz were used to obtain the parameters of the bio-impedance Cole-Cole model.The results showed a good linear correlation(coefficient of determination R2=0.960)between the equivalent parallel resistance R∞of sweet corn ear and the moisture content value determined by standard chemical method.The research proved that the bioelectrical impedance spectroscopy can be used for detecting the moisture content of corn ear.
基金supported by the National Natural Science Foundation of China(Grant No.32202139,32260618)the Tarim University President Fund Project(Grant No.TDZKCQ201902,TDZKSS202109)+2 种基金the Innovation Research Team Project of President’s Fund of Tarim University(Grant No.TDZKCX202203)Xinjiang Production&Construction Group Key Laboratory of Agricultural Products Processing in Xinjiang South(Grant No.AP1905)the“Strong Youth”Key Talents of Scientific and Technological Innovation(Grant No.2021CB039)and the authors also acknowledge all of the persons who assisted in this writing.
文摘In order to achieve the non-destructive detection of the firmness of Korla fragrant pear during the ripening period,the characteristic variables integrating the parallel equivalent inductance(Lp),quality factor(Q),parallel equivalent capacitance(Cp),dissipation factor(D),parallel equivalent resistance(Rp)and impedance(Z)were formulated through principal component analysis(PCA).Further,based on the characteristic variables,the models were established for predicting the firmness of Korla fragrant pear by using the generalized regression neural network(GRNN)and back-propagation neural network(BPNN).The results showed that firmness has significant correlations with the six electrical parameters.The first two principal components(PCs)were selected as the characteristic variables of the electrical parameters.GRNN exhibited the best performance in predicting firmness(R2=0.9628,RMSE=0.383).The results could provide important references for non-destructive detection of the quality of Korla fragrant pear.
基金This study was funded by National Natural Science Foundation of China(32072302)Hubei Provincial(China)Natural Science Foundation(2012FKB02910)Hubei Research and Development Program(2011BHB016)。
文摘Based on the analysis technology of visible/near infrared diffuse reflectance spectroscopy a portable and nondestructive detector was designed to test comprehensive quality of red globe grape bunches in the growth period.The detector included spectrum acquisition probe,spectrometer,lithium battery,halogen lamp light source,advanced RISC machines(ARM)board and peripheral circuit.Based on microsoft foundation classes(MFC)development tool,the real-time analysis and processing software of the detector was written by C++language.The optimal partial least squares regression(PLSR)detection model of multi-quality parameters was implanted into the hardware device.This paper selected the red globe grapes bunches in the growth period as the research samples,collected the visible/near infrared diffuse reflectance spectrum information,and then used the established PLSR model to detect the soluble solid content(SSC),total acid(TA)and pH of the samples to generate comprehensive quality parameter.So as to realize the nondestructive detecting of comprehensive quality of red globe grapes bunches in the growth period.In conclusion,the detector could realize real-time and non-destructive detecting of red globe grapes bunches in growth period aiming at the comprehensive quality.
基金funded by Zhijian Laboratory Open Fund,Rocket Force University of Engineering(Grant No.2023-ZJSYS-KF01-03).
文摘During the storage of composite propellants, the migration of plasticizers and other unbonded additives at the interfaces of liner adhesives has garnered significant attention in understanding liner failure mechanisms, aging processes, and safety performance. However, there is currently no non-destructive and quantitative detection method for migration of plasticizers in propellant liner. In this study, we developed a HTPB sensing liner by incorporating conductive fillers-namely carbon black(CB), carbon nanotubes(CNTs), and graphene nanoplatelets(GNP)-into the HTPB matrix. The synergistic interaction between CNTs and GNP facilitates the formation of a tunneling conductive network that imparts electrical conductivity to the HTPB liner. To elucidate the functional relationship between conductivity and nitroglycerin(NG) migration, we applied the HTPB sensing liner onto double base propellant surfaces and measured both the conductivity of the sensing layer and NG migration during a 71°C accelerated aging experiment. The results shows that when CNTs/GNP content reaches 3wt%, there is an exponential correlation between conductivity and NG migration with a fitting degree of 0.9652;the average response sensitivity of ΔR/R0 relative to NG migration is calculated as 41.69, with an average deviation of merely5.67% between NG migrations derived from conductivity fittings compared to those obtained via TGA testing results. Overall, this sensing liner exhibits excellent capabilities for detecting NG migration nondestructively and quantitatively while offering a novel approach for assessing interfacial component migrations as well as debonding defects in propellants-a promising avenue for future self-monitoring strategies regarding propellant integrity.
基金supported by the Cooperative Innovation Center of Terahertz Science , the National Basic Research Program of China (Grant No. 2014CB339800)the National Natural Science Foundation of China (Grant Nos. 61138001, 61420106006)+1 种基金the Program for Changjiang Scholars and Innovative Research Team in University (grant No. IRT13033)the Major National Development Project of Scientific Instruments and Equipment of China (Grant No. 2011YQ150021)
文摘Optoelectronic terahertz generation and detection play a key role in the applications of non-destructive testing,which involves different areas such as physics,biological,material science,imaging,explosions detection,astronomy applications,semiconductor technology and superconductiong electronics. In this article,we present a reviewof the principle and performance of typical terahertz sources,detectors and non-destructive testing applications. On this basis,the newdevelopment and trends of terahertz radiation detectors are also discussed.
基金This project was supported by National Natural Science Foundation of China(Grant No.31701318)National Natural Science Foundation of China Projects of International Cooperation and Exchanges(Grant No.31811540396)Basic Research Project of Education Department of Liaoning Province(Grant No.LSNJC201916).
文摘In this study,newly harvested and aged rice seeds were analyzed to determine their aging process,identify the difference between artificially and naturally aged seeds,and develop a rapid,accurate,and non-destructive detection method for water status and water distribution of rice seed with different vigor.To this end,an artificially accelerated aging test was conducted on the newly harvested rice seeds.Then,low-field nuclear magnetic resonance(LF-NMR)technology was applied to test the new(Shennong No.9816,2018),old(Shennong No.9816,2017),and artificially aged seeds(Shennong No.9816,2018).A standard germination test was conducted for three types of seeds.Finally,the differences of water status and distribution between rice seeds of different vigor were analyzed based on the standard germination test results and wave spectrometry information collected using LF-NMR.The results indicated that new seeds,old seeds,and the artificially accelerated aging rice seeds all exhibited two water phases,and the vigor of rice seeds after the artificial accelerated aging test was lower than that of new seeds.There were significant differences between the frequencies of bound water at the time of the peak and the time at the end of the peak for the three types of seeds.The two times showed an increasing trend for rice seeds with poor vigor,indicating that the ability of the water in the rice seeds having poor vigor to combine with other substances was weakened.There were significant differences between the distributions of free water peak end time for the three types of seeds.All the rice seeds with poor vigor exhibited a decreasing trend at this time,indicating that the freedom of free water inside the rice seed samples with poor vigor was weakened.The total water content of the artificially aged seeds and the aged seeds was higher than that of the new seeds,but the free water content increased from artificially aged seeds to new seeds to aged seeds.This indicates that LF-NMR technology is an effective detection method that can simply compare the differences in seed vitality with respect to water distribution as well as differentiate the seed internal water content of artificially aged and naturally aged seeds.
文摘This article takes the actual construction project of a certain concrete bridge project as an example to analyze the application of acoustic non-destructive testing technology in its detection.It includes an overview of a certain bridge construction project studied and acoustic non-destructive testing technology and the application of acoustic non-destructive testing technology in actual testing.This analysis hopes to provide some guidelines for acoustic non-destructive testing of modern concrete bridge projects.
基金supported by the Quzhou Science and Technology Bureau Project(2023D023,2023D030,2023D002,and2024D028)the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(LZY23B030002)+3 种基金the Shijiazhuang Shangtai Technology Co.,Ltd.Hebei Provincial Department of Science and Technology(24291101Z)the International Cooperation Projects of Sichuan Provincial Department of Science and Technology(2021YFH0126)the Sichuan Provincial Science and Technology Department's key research project(2023YFG0203)。
文摘Metal foils have emerged as one of the promising materials for anode-free batteries due to their high energy density and scalability in production.The unclear lithium plating/stripping kinetics of metal foil current collectors in anode-free batteries was addressed by using the non-destructive distribution of relaxation times(DRT)analysis to systematically investigate the lithium transport behavior of 14 metal foils and its correlation with electrochemical performance.By integrating energy-dispersive spectro scopy(EDS),cyclic voltammetry(CV),and galvanostatic testing,the exceptional properties of indium(In),tin(Sn),and silver(Ag)were revealed:the Li-In alloying reaction exhibits high reversibility,Li-Sn alloys demonstrate outstanding cycling stability,and the Li-Ag solid-solution mechanism provides an ideal lithium deposition interface on the silver substrate.The DRT separates the polarization internal resistance of lithium ions passing through the SEI layer(R_(sei),τ2)and the polarization internal resistance of lithium ions undergoing charge transfer reaction at the electrolyte/electrode interface(R_(ct),τ3)by decoupling the electrochemical impedance spectroscopy(EIS).For the first time,the correlation betweenτ2,τ3,and the cycle life/Coulombic efficiency of alloy/solid-solution metals was established,while non-alloy metals are not suitable for this method due to differences in lithium deposition mechanisms.This study not only illuminates the structure-property relationship governing the lithium kinetics of metal foil electrodes but also provides a novel non-destructive analytical strategy and theoretical guidance for the rational design of stable anodes in high-energy-density batteries,facilitating the efficient screening and optimization of anode-free battery.
文摘A non invasive ultrasonic method is used to detect whether or not the frozen fish has suffered a partial or total accidental thawing. The time of flight and the peak to peak amplitude of the ultrasonic signals backscattered by fish are recorded during thawing. The comparison of the evolution curves and images corresponding to first and second thawing shows indicators of accidental thawing. The monitoring of thawing process showed that its assessment can be reduced to the measurement of the water content lost by fish. The attempt to replace the original water lost by fish in first thawing is analyzed. The influence of the transducer frequency on fish thawing evaluation is tested.
基金funded by Key research and development Program of Henan Province(No.251111211200)National Natural Science Foundation of China(Grant No.U2004163).
文摘Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy.
基金funded by the Joint Funds of the National Natural Science Foundation of China(U2341223)the Beijing Municipal Natural Science Foundation(No.4232067).
文摘In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds in current intelligent inspection algorithms,this paper proposes CG-YOLOv8,a lightweight and improved model based on YOLOv8n for PCB surface defect detection.The proposed method optimizes the network architecture and compresses parameters to reduce model complexity while maintaining high detection accuracy,thereby enhancing the capability of identifying diverse defects under complex conditions.Specifically,a cascaded multi-receptive field(CMRF)module is adopted to replace the SPPF module in the backbone to improve feature perception,and an inverted residual mobile block(IRMB)is integrated into the C2f module to further enhance performance.Additionally,conventional convolution layers are replaced with GSConv to reduce computational cost,and a lightweight Convolutional Block Attention Module based Convolution(CBAMConv)module is introduced after Grouped Spatial Convolution(GSConv)to preserve accuracy through attention mechanisms.The detection head is also optimized by removing medium and large-scale detection layers,thereby enhancing the model’s ability to detect small-scale defects and further reducing complexity.Experimental results show that,compared to the original YOLOv8n,the proposed CG-YOLOv8 reduces parameter count by 53.9%,improves mAP@0.5 by 2.2%,and increases precision and recall by 2.0%and 1.8%,respectively.These improvements demonstrate that CG-YOLOv8 offers an efficient and lightweight solution for PCB surface defect detection.
文摘This study proposes a lightweight rice disease detection model optimized for edge computing environments.The goal is to enhance the You Only Look Once(YOLO)v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency.To this end,a total of 3234 high-resolution images(2400×1080)were collected from three major rice diseases Rice Blast,Bacterial Blight,and Brown Spot—frequently found in actual rice cultivation fields.These images served as the training dataset.The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the backbone,thereby resulting in both model compression and improved inference speed.Additionally,YOLOv5-P,based on PP-PicoDet,was configured as a comparative model to quantitatively evaluate performance.Experimental results demonstrated that YOLOv5-V2 achieved excellent detection performance,with an mAP 0.5 of 89.6%,mAP 0.5–0.95 of 66.7%,precision of 91.3%,and recall of 85.6%,while maintaining a lightweight model size of 6.45 MB.In contrast,YOLOv5-P exhibited a smaller model size of 4.03 MB,but showed lower performance with an mAP 0.5 of 70.3%,mAP 0.5–0.95 of 35.2%,precision of 62.3%,and recall of 74.1%.This study lays a technical foundation for the implementation of smart agriculture and real-time disease diagnosis systems by proposing a model that satisfies both accuracy and lightweight requirements.
基金National Science and Technology Council,the Republic of China,under grants NSTC 113-2221-E-194-011-MY3 and Research Center on Artificial Intelligence and Sustainability,National Chung Cheng University under the research project grant titled“Generative Digital Twin System Design for Sustainable Smart City Development in Taiwan.
文摘Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.
基金supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.
