The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo...The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.展开更多
Vacancy defects,as fundamental disruptions in metallic lattices,play an important role in shaping the mechanical and electronic properties of aluminum crystals.However,the influence of vacancy position under coupled t...Vacancy defects,as fundamental disruptions in metallic lattices,play an important role in shaping the mechanical and electronic properties of aluminum crystals.However,the influence of vacancy position under coupled thermomechanical fields remains insufficiently understood.In this study,transmission and scanning electron microscopy were employed to observe dislocation structures and grain boundary heterogeneities in processed aluminum alloys,suggesting stress concentrations and microstructural inhomogeneities associated with vacancy accumulation.To complement these observations,first-principles calculations and molecular dynamics simulations were conducted for seven single-vacancy configurations in face-centered cubic aluminum.The stress response,total energy,density of states(DOS),and differential charge density were examined under varying compressive strain(ε=0–0.1)and temperature(0–600 K).The results indicate that face-centered vacancies tend to reduce mechanical strength and perturb electronic states near the Fermi level,whereas corner and edge vacancies appear to have weaker effects.Elevated temperatures may partially restore electronic uniformity through thermal excitation.Overall,these findings suggest that vacancy position exerts a critical but position-dependent influence on coupled structure-property relationships,offering theoretical insights and preliminary experimental support for defect-engineered aluminum alloy design.展开更多
To address the challenges of high-precision optical surface defect detection,we propose a novel design for a wide-field and broadband light field camera in this work.The proposed system can achieve a 50°field of ...To address the challenges of high-precision optical surface defect detection,we propose a novel design for a wide-field and broadband light field camera in this work.The proposed system can achieve a 50°field of view and operates at both visible and near-infrared wavelengths.Using the principles of light field imaging,the proposed design enables 3D reconstruction of optical surfaces,thus enabling vertical surface height measurements with enhanced accuracy.Using Zemax-based simulations,we evaluate the system’s modulation transfer function,its optical aberrations,and its tolerance to shape variations through Zernike coefficient adjustments.The results demonstrate that this camera can achieve the required spatial resolution while also maintaining high imaging quality and thus offers a promising solution for advanced optical surface defect inspection.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
In order to investigate the effect of die wall thickness on morphologies of defect band,a stepped mold with a wall thickness of 5 mm,4 mm,3 mm,2 mm,and 1 mm was designed to carry out high pressure die casting experime...In order to investigate the effect of die wall thickness on morphologies of defect band,a stepped mold with a wall thickness of 5 mm,4 mm,3 mm,2 mm,and 1 mm was designed to carry out high pressure die casting experiments with AlSi10 MgMn alloy.For castings with wall thickness of 2-4 mm,the ratio of the mean defect band width(w)and mean grain size(d)in the defect band(w/d)ranges 7-18,while it increases to 24.47 for the 5 mm-thick casting.This difference is related with the filling speed and the distribution of externally solidified crystals(ESCs).The mold flow analysis indicates that the filling speed decreases from 25.41 m·s^(-1)to 11.07 m·s^(-1)when wall thickness increases from 2 mm to 5 mm.Due to the decreasing filling speed along the wall thickness,ESCs gradually diffuse from the center to the defect band,which keep the shear strength in the defect band at a high-level during filling.Meanwhile,the shear strength generated during the filling also decreases as the shear rate drops.Finally,the defect bands in the 5 mm-thick region become widen and indistinct,and the porosity is as high as 5.25%.展开更多
Photosynthesis is a promising method for H_(2)O_(2)production,but its application in pure water is limited by slow oxidation kinetics and rapid photocarrier recombination of photocatalysts.Herein,a novel defective car...Photosynthesis is a promising method for H_(2)O_(2)production,but its application in pure water is limited by slow oxidation kinetics and rapid photocarrier recombination of photocatalysts.Herein,a novel defective carbon nitride photocatalyst(D-C3-xN4)containing the C vacancies and the frustrated Lewis pairs(B and N of cyano group)is designed for H_(2)O_(2)photosynthesis,and the role of C vacancies on the electron transfer mechanism during photocatalysis is systematically investigated.The D-C_(3-x)N_(4) exhibits a H_(2)O_(2)production rate of 140.1μmol·g^(-1)·h^(-1) in pure water,which is 87.6 times that of C_(3)N_(4).Such superior performance for H_(2)O_(2)photosynthesis is found to arise from the C vacancies and frustrated Lewis pairs(FLPs).The C vacancies have strong electron-trapping ability,which greatly enhances the separation of photocarriers.The C vacancies can also effectively reduce O_(2)to*OOH via a proton-coupled process,which significantly accelerates the O_(2)reduction kinetics.Meanwhile,the FLPs show an outstanding catalytic activity for H_(2)O oxidation.This study not only provides a new structure for highly active photocatalysts,but also deepens the understanding of the electron transfer mechanism of photocatalysts with trapped sites.展开更多
While thermal air exfoliation is widely used to prepare graphitic carbon nitride(g-C_(3)N_(4))nanosheets,the effects of calcination conditions and atmosphere on their electronic structure and photocatalytic CO_(2)redu...While thermal air exfoliation is widely used to prepare graphitic carbon nitride(g-C_(3)N_(4))nanosheets,the effects of calcination conditions and atmosphere on their electronic structure and photocatalytic CO_(2)reduction reaction(CO_(2)RR)performance remain systematically unexplored.We prepared g-C_(3)N_(4)nanosheets with varying thickness and defects by controlling exfoliation parameters.The obtained nanosheets calcined longest in air exhibited highest CO_(2)RR activity,twice that of bulk g-C_(3)N_(4).The comprehensive analysis of structural characterizations indicates the thickness of g-C_(3)N_(4)nanosheets became thinner,and the defects increased as the calcination time increased.The N vacancies(N_(v))and O-doping caused by N_(2) and O_(2)from air,respectively,enable valence band elevation(N_(v))and conduction band depression(O-doping)that collectively redistribute the electronic structure.Nitrogen/oxygen dual-defects generated impurity levels,reduced the work function and band gap of g-C_(3)N_(4)nanosheets,and served as shallow traps for photogenerated e^(-).The results of in-situ spectroscopy indicate these increased effective e^(-)are enriched around of N atoms to react with the adsorbed CO_(2).During the CO_(2)reduction process,the N_(v) promoted the formation of*COOH,and this dual-defect co-promoted the*CO desorption,resulting in the improved CO_(2)RR activity.These results comprehensively analyze the regulatory effect of thermal air calcination on the electronic structure of g-C_(3)N_(4),providing valuable insights for designing g-C_(3)N_(4)nanosheets based photocatalysts for CO_(2)RR.展开更多
Automatic surface defect detection is a critical technique for ensuring product quality in industrial casting production.While general object detection techniques have made remarkable progress over the past decade,cas...Automatic surface defect detection is a critical technique for ensuring product quality in industrial casting production.While general object detection techniques have made remarkable progress over the past decade,casting surface defect detection still has considerable room for improvement.Lack of sufficient and high-quality data has become one of the most challenging problems for casting surface defect detection.In this paper,we construct a new casting surface defect dataset(CSDD)containing 2100 high-resolution images of casting surface defects and 56356 defects in total.The class and defect region for each defect are manually labeled.We conduct a series of experiments on this dataset using multiple state-of-the-art object detection methods,establishing a comprehensive set of baselines.We also propose a defect detection method based on YOLOv5 with the global attention mechanism and partial convolution.Our proposed method achieves superior performance compared to other object detection methods.Additionally,we also conduct a series of experiments with multiple state-of-the-art semantic segmentation methods,providing extensive baselines for defect segmentation.To the best of our knowledge,the CSDD has the largest number of defects for casting surface defect detection and segmentation.It would benefit both the industrial vision research and manufacturing applications.Dataset and code are available at https://github.com/Kerio99/CSDD.展开更多
In integrated circuit(IC)manufacturing,fast,nondestructive,and precise detection of defects in patterned wafers,realized by bright-field microscopy,is one of the critical factors for ensuring the final performance and...In integrated circuit(IC)manufacturing,fast,nondestructive,and precise detection of defects in patterned wafers,realized by bright-field microscopy,is one of the critical factors for ensuring the final performance and yields of chips.With the critical dimensions of IC nanostructures continuing to shrink,directly imaging or classifying deep-subwavelength defects by bright-field microscopy is challenging due to the well-known diffraction barrier,the weak scattering effect,and the faint correlation between the scattering cross-section and the defect morphology.Herein,we propose an optical far-field inspection method based on the form-birefringence scattering imaging of the defective nanostructure,which can identify and classify various defects without requiring optical super-resolution.The technique is built upon the principle of breaking the optical form birefringence of the original periodic nanostructures by the defect perturbation under the anisotropic illumination modes,such as the orthogonally polarized plane waves,then combined with the high-order difference of far-field images.We validated the feasibility and effectiveness of the proposed method in detecting deep subwavelength defects through rigid vector imaging modeling and optical detection experiments of various defective nanostructures based on polarization microscopy.On this basis,an intelligent classification algorithm for typical patterned defects based on a dual-channel AlexNet neural network has been proposed,stabilizing the classification accuracy ofλ/16-sized defects with highly similar features at more than 90%.The strong classification capability of the two-channel network on typical patterned defects can be attributed to the high-order difference image and its transverse gradient being used as the network’s input,which highlights the polarization modulation difference between different patterned defects more significantly than conventional bright-field microscopy results.This work will provide a new but easy-to-operate method for detecting and classifying deep-subwavelength defects in patterned wafers or photomasks,which thus endows current online inspection equipment with more missions in advanced IC manufacturing.展开更多
Infectious bone defects represent a substantial challenge in clinical practice,necessitating the deployment of advanced therapeutic strategies.This study presents a treatment modality that merges a mild photothermal t...Infectious bone defects represent a substantial challenge in clinical practice,necessitating the deployment of advanced therapeutic strategies.This study presents a treatment modality that merges a mild photothermal therapy hydrogel with a pulsed drug delivery mechanism.The system is predicated on a hydrogel matrix that is thermally responsive,characteristic of bone defect sites,facilitating controlled and site-specific drug release.The cornerstone of this system is the incorporation of mild photothermal nanoparticles,which are activated within the temperature range of 40–43°C,thereby enhancing the precision and efficacy of drug delivery.Our findings demonstrate that the photothermal response significantly augments the localized delivery of therapeutic agents,mitigating systemic side effects and bolstering efficacy at the defect site.The synchronized pulsed release,cooperated with mild photothermal therapy,effectively addresses infection control,and promotes bone regeneration.This approach signifies a considerable advancement in the management of infectious bone defects,offering an effective and patient-centric alternative to traditional methods.Our research endeavors to extend its applicability to a wider spectrum of tissue regeneration scenarios,underscoring its transformative potential in the realm of regenerative medicine.展开更多
The accuracy and reliability of non-destructive testing(NDT)approaches in detecting interior corrosion problems are critical,yet research in this field is limited.This work describes a novel way to monitor the structu...The accuracy and reliability of non-destructive testing(NDT)approaches in detecting interior corrosion problems are critical,yet research in this field is limited.This work describes a novel way to monitor the structural integrity of steel gas pipelines that uses advanced numerical modeling techniques to anticipate fracture development and corrosion effects.The objective is to increase pipeline dependability and safety through more precise,real-time health evaluations.Compared to previous approaches,our solution provides higher accuracy in fault detection and quantification,making it ideal for pipeline integritymonitoring in real-world applications.To solve this issue,statistical analysis was conducted on the size and directional distribution features of about 380,000 sets of internal corrosion faults,as well as simulations of erosion and wear patterns on bent pipes.Using real defectmorphologies,we developed a modeling framework for typical interior corrosion flaws.We evaluated and validated the applicability and effectiveness of in-service inspection processes,as well as conducted on-site comparison tests.The results show that(1)the length and width of corrosion defects follow a log-normal distribution,the clock orientation follows a normal distribution,and the peak depth follows a Freundlich EX function distribution pattern;(2)pipeline corrosion defect data can be classified into three classes using the K-means clustering algorithm,allowing rapid and convenient acquisition of typical size and orientation characteristics of internal corrosion defects;(3)the applicability range and boundary conditions of various NDT techniques were verified,establishing comprehensive selection principles for internal corrosion defect detection technology;(4)on-site inspection results showed a 31%The simulation and validation platform for typical interior corrosion issues greatly enhances the accuracy and reliability of detection data.展开更多
The service life of internal combustion engines is significantly influenced by surface defects in cylinder liners.To address the limitations of traditional detection methods,we propose an enhanced YOLOv8 model with Sw...The service life of internal combustion engines is significantly influenced by surface defects in cylinder liners.To address the limitations of traditional detection methods,we propose an enhanced YOLOv8 model with Swin Transformer as the backbone network.This approach leverages Swin Transformer's multi-head self-attention mechanism for improved feature extraction of defects spanning various scales.Integrated with the YOLOv8 detection head,our model achieves a mean average precision of 85.1%on our dataset,outperforming baseline methods by 1.4%.The model's effectiveness is further demonstrated on a steel-surface defect dataset,indicating its broad applicability in industrial surface defect detection.Our work highlights the potential of combining Swin Transformer and YOLOv8 for accurate and efficient defect detection.展开更多
Current research on localized raceway defects of angular contact ball bearings(ACBB)mainly focuses on assuming that localized raceway defects are cube-shaped defects characterized using a half-sine displacement excita...Current research on localized raceway defects of angular contact ball bearings(ACBB)mainly focuses on assuming that localized raceway defects are cube-shaped defects characterized using a half-sine displacement excitation function.However,the assumption of a cube-shaped defect cannot accurately reflect the morphological characteristics of a localized raceway defect,and the half-sine displacement excitation function cannot be used to accurately describe the relationship between the geometric positions of rolling element and raceway in the region of localized raceway defects.In this study,a comprehensive dynamic model of an ACBB considering a three-dimensional localized raceway defect is established based on the nonlinear Hertz contact theory in conjunction with the outer raceway control theory using the improved Newton–Raphson iteration method.Three localized raceway defect distribution types,namely symmetric,offset,and deflection distributions,are considered.The established model is verified by comparing the results of the proposed model with those of existing literature.The dynamic characteristics of the ACBB were analyzed by investigating the effects of the geometrical size and distribution types on the time-varying contact angles,contact forces,and diagonal stiffness of the ACBB.The investigation results show that the appearance of localized raceway defect leads to the time-varying curves of contact angles,contact forces and diagonal stiffness havingΛ-and V-shaped mutations in some time intervals;The variation tendencies of theΛ-and V-shaped mutations are significant with the increase in defect radial depth H,defect axial width a and angular distanceθ_b.The increase in defect eccentric distance L is beneficial to the rolling elements disengaging from the defect area and it can weaken the influence of localized raceway defect on the time-varying contact and stiffness characteristics of ACBB.The time-varying contact and stiffness characteristics appear to change significantly when the defect deflection angleα_βincrease toα_γ.The results of this study provide a theoretical basis for the fault diagnosis of localized raceway defects in ACBB.展开更多
The detection of surface defects in concrete bridges using deep learning is of significant importance for reducing operational risks,saving maintenance costs,and driving the intelligent transformation of bridge defect...The detection of surface defects in concrete bridges using deep learning is of significant importance for reducing operational risks,saving maintenance costs,and driving the intelligent transformation of bridge defect detection.In contrast to the subjective and inefficient manual visual inspection,deep learning-based algorithms for concrete defect detection exhibit remarkable advantages,emerging as a focal point in recent research.This paper comprehensively analyzes the research progress of deep learning algorithms in the field of surface defect detection in concrete bridges in recent years.It introduces the early detection methods for surface defects in concrete bridges and the development of deep learning.Subsequently,it provides an overview of deep learning-based concrete bridge surface defect detection research from three aspects:image classification,object detection,and semantic segmentation.The paper summarizes the strengths and weaknesses of existing methods and the challenges they face.Additionally,it analyzes and prospects the development trends of surface defect detection in concrete bridges.展开更多
Rational design of defected carbons adjacent to nitrogen(N)dopants is a fascinating but challenging approach for enhancing the catalytic performance of N-doped carbon.Meanwhile,the combined effect of heteroatom doping...Rational design of defected carbons adjacent to nitrogen(N)dopants is a fascinating but challenging approach for enhancing the catalytic performance of N-doped carbon.Meanwhile,the combined effect of heteroatom doping and defect engineering can efficiently increase the oxygen reduction reaction(ORR)ability of inactive carbons through charge redistribution.Herein,we report that an enhanced built-in electric field caused by the combined effect of N-doping and carbon defects in the twodimensional(2D)mesoporous N-doped carbon nano flakes(NCNF)is a promising technique for improving ORR performance.As a result,the NCNF exhibits more promising ORR activity than Pt/C and similar performance with reported robust catalysts.Comprehensive experimental and theoretical investigations suggest that topologically defected carbon adjacent to the graphitic valley nitrogen is a real active site,rendering optimal energy for the adsorption of ORR intermediates and lowering the total energy barrier for ORR.Also,NCNF-based Zn-air batteries exhibited an excellent power density and specific capacity of~121.10 mW cm^(-2)and~679.86 mA h g_(Zn)^(-1),respectively.This study not only offers new insights into defected carbons with graphitic valley N for ORR but also proposes novel catalyst design principles and provides a solid grasp of the built-in electric field effect on the ORR performance of defective catalysts.展开更多
Aiming at the problems of low detection efficiency and difficult positioning of traditional steel surface defect detection methods,a lightweight steel surface defect detection model based on you only look once version...Aiming at the problems of low detection efficiency and difficult positioning of traditional steel surface defect detection methods,a lightweight steel surface defect detection model based on you only look once version 7(YOLOv7)is proposed.First,a cascading style sheets(CSS)block module is proposed,which uses more lightweight operations to obtain redundant information in the feature map,reduces the amount of computation,and effectively improves the detection speed.Secondly,the improved spatial pyramid pooling with cross stage partial convolutions(SPPCSPC)structure is adopted to ensure that the model can also pay attention to the defect location information while predicting the defect category information,obtain richer defect features.In addition,the convolution operation in the original model is simplified,which significantly reduces the size of the model and helps to improve the detection speed.Finally,using efficient intersection over union(EIOU)loss to focus on high-quality anchors,speed up convergence and improve positioning accuracy.Experiments were carried out on the Northeastern University-defect(NEU-DET)steel surface defect dataset.Compared with the original YOLOv7 model,the number of parameters of this model was reduced by 40%,the frames per second(FPS)reached 112,and the average accuracy reached 79.1%,the detection accuracy and speed have been improved,which can meet the needs of steel surface defect detection.展开更多
The advancement of aqueous magnesium ion energy storage devices encounters limitations due to the substantial hydration radius of magnesium ions(Mg^(2+))and their strong electrostatic interaction with the primary mate...The advancement of aqueous magnesium ion energy storage devices encounters limitations due to the substantial hydration radius of magnesium ions(Mg^(2+))and their strong electrostatic interaction with the primary material.Consequently,this study successfully developed a MnS/MnO heterostructure through a straightforward hydrothermal and annealing method,marking its initial application in aqueous magnesium ion capacitors(AMICs).The fabricated MnS/MnO heterostructure,characterized by S defects,also generates Mn defects via in-situ initiation of early electrochemical processes.This unique dual ion defects MnS/MnO heterostructure(DID-MnS/MnO)enables the transformation of MnS and MnO,initially not highly active electrochemically for Mg^(2+),into cathode materials exhibiting high electrochemical activity and superior performance.Moreover,DID-MnS/MnO enhances conductivity,improves the kinetics of surface redox reactions,and increases the diffusion rate of Mg^(2+).Furthermore,this study introduces a dual energy storage mechanism for DID-MnS/MnO,which,in conjunction with dual ion defects,offers additional active sites for Mg^(2+)insertion/deinsertion in the host material,mitigating volume expansion and structural degradation during repeated charge-discharge cycles,thereby significantly enhancing cycling reversibility.As anticipated,using a three-electrode system,the developed DID-MnS/MnO demonstrated a discharge specific capacity of 237.9 mAh/g at a current density of 0.1 A/g.Remarkably,the constructed AMIC maintained a capacity retention rate of 94.3%after 10000 cycles at a current density of 1.0 A/g,with a specific capacitance of 165.7 F/g.Hence,DID-MnS/MnO offers insightful perspectives for designing alternative clean energy sources and is expected to contribute significantly to the advancement of the clean energy sector.展开更多
Osteogenesis imperfecta(OI)is a group of diseases caused by defects in type I collagen processing which result in skeletal fragility.While these disorders have been regarded as defects in osteoblast function,the role ...Osteogenesis imperfecta(OI)is a group of diseases caused by defects in type I collagen processing which result in skeletal fragility.While these disorders have been regarded as defects in osteoblast function,the role of matrix-embedded osteocytes in OI pathogenesis remains largely unknown.Homozygous human SP7(c.946 C>T,R316C)mutation results in a recessive form of OI characterized by fragility fractures,low bone mineral density and osteocyte dendrite defects.To better understand how the OI-causing R316C mutation affects the function of SP7,we generated Sp7^(R342C)knock-in mice.Consistent with patient phenotypes,Sp7^(R342C/R342C)mice demonstrate increased cortical porosity and reduced cortical bone mineral density.Sp7^(R342C/R342C)mice show osteocyte dendrite defects,increased osteocyte apoptosis,and intracortical bone remodeling with ectopic intracortical osteoclasts and elevated osteocyte Tnfsf11 expression.展开更多
Based on inspection data,the authors analyze and summarize the main types and distribution characteristics of tunnel structural defects.These defects are classified into three types:surface defects,internal defects,an...Based on inspection data,the authors analyze and summarize the main types and distribution characteristics of tunnel structural defects.These defects are classified into three types:surface defects,internal defects,and defects behind the structure.To address the need for rapid detection of different defect types,the current state of rapid detection technologies and equipment,both domestically and internationally,is systematically reviewed.The research reveals that surface defect detection technologies and equipment have developed rapidly in recent years.Notably,the integration of machine vision and laser scanning technologies have significantly improved detection efficiency and accuracy,achieving crack detection precision of up to 0.1 mm.However,the non-contact rapid detection of internal and behind-the-structure defects remains constrained by hardware limitations,with traditional detection remaining dominant.Nevertheless,phased array radar,ultrasonic,and acoustic vibration detection technologies have become research hotspots in recent years,offering promising directions for detecting these challenging defect types.Additionally,the application of multisensor fusion technology in rapid detection equipment has further enhanced detection capabilities.Devices such as cameras,3D laser scanners,infrared thermal imagers,and radar demonstrate significant advantages in rapid detection.Future research in tunnel inspection should prioritize breakthroughs in rapid detection technologies for internal and behind-the-structure defects.Efforts should also focus on developing multifunctional integrated detection vehicles that can simultaneously inspect both surface and internal structures.Furthermore,progress in fully automated,intelligent systems with precise defect identification and real-time reporting will be essential to significantly improve the efficiency and accuracy of tunnel inspection.展开更多
Achieving high-quality perovskite films without surface defects is regarded as a crucial target for the development of durable high-performance perovskite solar cells.Additive engineering is commonly employed to simul...Achieving high-quality perovskite films without surface defects is regarded as a crucial target for the development of durable high-performance perovskite solar cells.Additive engineering is commonly employed to simultaneously control the growth of perovskite crystals and passivate defects.Here,4-(trifluoromethyl)benzoic anhydride(4-TBA)composed of benzene rings functionalized with carbonyl and trifluoromethyl groups was used as an example additive to study the characteristics of additives used for producing high-quality perovskites and controlling their surface properties.The interaction between4-TBA and perovskite precursor materials was investigated using density functional theory(DFT)simulations.The electron-rich carbonyl group efficiently passivated the under-coordinated lead-ion defects.Additionally,hydrogen bonding between trifluoromethyl and organic cations prevents the generation of cation vacancies.Because of its intrinsic hydrophobicity,the trifluoromethyl group simultaneously improves the moisture and heat stability of the film.4-TBA serves as a universal modifier for various perovskite compositions.The power conversion efficiency(PCE)of inverted perovskite solar cells(PSCs)based on methylammonium(MA)with 4-TBA was improved from 16.15%to 19.28%.Similarly,the PCE of inverted PSCs based on a cesium formamidinium MA(CsFAMA)perovskite film increased from20.72%to 23.58%,upon addition of 4-TBA.Furthermore,the moisture and thermal stability of 4-TBAtreated films and devices was significantly enhanced,along with prolonged device performance.Our work provides guidance on selecting the structure and functional groups that are essential for surface defect passivation and the production of high-quality perovskites.展开更多
基金supported by the State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
基金supported by the Research Project on Strengthening the Construction of an Important Ecological Security Barrier in Northern China by Higher Education Institutions in the Inner Mongolia Autonomous Region(STAQZX202313)the Inner Mongolia Autonomous Region Education Science‘14th Five-Year Plan’2024 Annual Research Project(NGJGH2024635).
文摘Vacancy defects,as fundamental disruptions in metallic lattices,play an important role in shaping the mechanical and electronic properties of aluminum crystals.However,the influence of vacancy position under coupled thermomechanical fields remains insufficiently understood.In this study,transmission and scanning electron microscopy were employed to observe dislocation structures and grain boundary heterogeneities in processed aluminum alloys,suggesting stress concentrations and microstructural inhomogeneities associated with vacancy accumulation.To complement these observations,first-principles calculations and molecular dynamics simulations were conducted for seven single-vacancy configurations in face-centered cubic aluminum.The stress response,total energy,density of states(DOS),and differential charge density were examined under varying compressive strain(ε=0–0.1)and temperature(0–600 K).The results indicate that face-centered vacancies tend to reduce mechanical strength and perturb electronic states near the Fermi level,whereas corner and edge vacancies appear to have weaker effects.Elevated temperatures may partially restore electronic uniformity through thermal excitation.Overall,these findings suggest that vacancy position exerts a critical but position-dependent influence on coupled structure-property relationships,offering theoretical insights and preliminary experimental support for defect-engineered aluminum alloy design.
基金supported by the Jilin Science and Technology Development Plan(20240101029JJ)the following study:synchronized high-speed detection of surface shape and defects in the grinding stage of complex surfaces(KLMSZZ202305)+3 种基金for the high-precision wide dynamic large aperture optical inspection system for fine astronomical observation by the National Major Research Instrument Development Project(62127901)for ultrasmooth manufacturing technology of large diameter complex curved surface by the National Key R&D Program(2022YFB3403405)for research on the key technology of rapid synchronous detection of surface shape and subsurface defects in the grinding stage of large diameter complex surfaces by the International Cooperation Project(2025010157)The Key Laboratory of Optical System Advanced Manufacturing Technology,Chinese Academy of Sciences(2022KLOMT02-04)also supported this study.
文摘To address the challenges of high-precision optical surface defect detection,we propose a novel design for a wide-field and broadband light field camera in this work.The proposed system can achieve a 50°field of view and operates at both visible and near-infrared wavelengths.Using the principles of light field imaging,the proposed design enables 3D reconstruction of optical surfaces,thus enabling vertical surface height measurements with enhanced accuracy.Using Zemax-based simulations,we evaluate the system’s modulation transfer function,its optical aberrations,and its tolerance to shape variations through Zernike coefficient adjustments.The results demonstrate that this camera can achieve the required spatial resolution while also maintaining high imaging quality and thus offers a promising solution for advanced optical surface defect inspection.
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.
基金supported by the National Natural Science Foundation of China(No.52474396 and 52175284)the National Key Research and Development Program of China(Grant No.2022YFB3404201)。
文摘In order to investigate the effect of die wall thickness on morphologies of defect band,a stepped mold with a wall thickness of 5 mm,4 mm,3 mm,2 mm,and 1 mm was designed to carry out high pressure die casting experiments with AlSi10 MgMn alloy.For castings with wall thickness of 2-4 mm,the ratio of the mean defect band width(w)and mean grain size(d)in the defect band(w/d)ranges 7-18,while it increases to 24.47 for the 5 mm-thick casting.This difference is related with the filling speed and the distribution of externally solidified crystals(ESCs).The mold flow analysis indicates that the filling speed decreases from 25.41 m·s^(-1)to 11.07 m·s^(-1)when wall thickness increases from 2 mm to 5 mm.Due to the decreasing filling speed along the wall thickness,ESCs gradually diffuse from the center to the defect band,which keep the shear strength in the defect band at a high-level during filling.Meanwhile,the shear strength generated during the filling also decreases as the shear rate drops.Finally,the defect bands in the 5 mm-thick region become widen and indistinct,and the porosity is as high as 5.25%.
基金supported by the Shaanxi Sanqin Scholars Innovation Team,the Science and Technology Project of Yan’an City(No.2023-CYL-193)the Key Science Research Plan of Department of Education in Shaanxi Province(No.23JS070)the Science Research Training Project(No.CLXZ2207).
文摘Photosynthesis is a promising method for H_(2)O_(2)production,but its application in pure water is limited by slow oxidation kinetics and rapid photocarrier recombination of photocatalysts.Herein,a novel defective carbon nitride photocatalyst(D-C3-xN4)containing the C vacancies and the frustrated Lewis pairs(B and N of cyano group)is designed for H_(2)O_(2)photosynthesis,and the role of C vacancies on the electron transfer mechanism during photocatalysis is systematically investigated.The D-C_(3-x)N_(4) exhibits a H_(2)O_(2)production rate of 140.1μmol·g^(-1)·h^(-1) in pure water,which is 87.6 times that of C_(3)N_(4).Such superior performance for H_(2)O_(2)photosynthesis is found to arise from the C vacancies and frustrated Lewis pairs(FLPs).The C vacancies have strong electron-trapping ability,which greatly enhances the separation of photocarriers.The C vacancies can also effectively reduce O_(2)to*OOH via a proton-coupled process,which significantly accelerates the O_(2)reduction kinetics.Meanwhile,the FLPs show an outstanding catalytic activity for H_(2)O oxidation.This study not only provides a new structure for highly active photocatalysts,but also deepens the understanding of the electron transfer mechanism of photocatalysts with trapped sites.
基金supported by the National Natural Science Foundation of China(Nos.62004143 and 22502150)the Key Project of Scientific Research Plan of Hubei Provincial Department of Education(No.D20241501)+5 种基金the China Postdoctoral Science Foundation(No.2024M762505)the Postdoctoral Fellowship Program(Grade C)(No.GZC20250787)the China Postdoctoral Science Foundation-Hubei Joint Support Program(No.2025T032HB)the Scientific Research Fund Project of Wuhan Institute of Technology(Nos.K2024053,K2025102,and 25QD010)the Open Fund of the Guangdong Provincial Key Laboratory of Electronic Functional Materials and Devices(No.EFMD2024006Z)the Innovation Project of Engineering Research Center of Phosphorus Resources Development and Utilization of Ministry of Education(No.LCX202404).
文摘While thermal air exfoliation is widely used to prepare graphitic carbon nitride(g-C_(3)N_(4))nanosheets,the effects of calcination conditions and atmosphere on their electronic structure and photocatalytic CO_(2)reduction reaction(CO_(2)RR)performance remain systematically unexplored.We prepared g-C_(3)N_(4)nanosheets with varying thickness and defects by controlling exfoliation parameters.The obtained nanosheets calcined longest in air exhibited highest CO_(2)RR activity,twice that of bulk g-C_(3)N_(4).The comprehensive analysis of structural characterizations indicates the thickness of g-C_(3)N_(4)nanosheets became thinner,and the defects increased as the calcination time increased.The N vacancies(N_(v))and O-doping caused by N_(2) and O_(2)from air,respectively,enable valence band elevation(N_(v))and conduction band depression(O-doping)that collectively redistribute the electronic structure.Nitrogen/oxygen dual-defects generated impurity levels,reduced the work function and band gap of g-C_(3)N_(4)nanosheets,and served as shallow traps for photogenerated e^(-).The results of in-situ spectroscopy indicate these increased effective e^(-)are enriched around of N atoms to react with the adsorbed CO_(2).During the CO_(2)reduction process,the N_(v) promoted the formation of*COOH,and this dual-defect co-promoted the*CO desorption,resulting in the improved CO_(2)RR activity.These results comprehensively analyze the regulatory effect of thermal air calcination on the electronic structure of g-C_(3)N_(4),providing valuable insights for designing g-C_(3)N_(4)nanosheets based photocatalysts for CO_(2)RR.
基金supported by the National Natural Science Foundation of China(U23B2060,62088102)the Key Research and Development Program of China(2020AAA0108305).
文摘Automatic surface defect detection is a critical technique for ensuring product quality in industrial casting production.While general object detection techniques have made remarkable progress over the past decade,casting surface defect detection still has considerable room for improvement.Lack of sufficient and high-quality data has become one of the most challenging problems for casting surface defect detection.In this paper,we construct a new casting surface defect dataset(CSDD)containing 2100 high-resolution images of casting surface defects and 56356 defects in total.The class and defect region for each defect are manually labeled.We conduct a series of experiments on this dataset using multiple state-of-the-art object detection methods,establishing a comprehensive set of baselines.We also propose a defect detection method based on YOLOv5 with the global attention mechanism and partial convolution.Our proposed method achieves superior performance compared to other object detection methods.Additionally,we also conduct a series of experiments with multiple state-of-the-art semantic segmentation methods,providing extensive baselines for defect segmentation.To the best of our knowledge,the CSDD has the largest number of defects for casting surface defect detection and segmentation.It would benefit both the industrial vision research and manufacturing applications.Dataset and code are available at https://github.com/Kerio99/CSDD.
基金funded by National Natural Science Foundation of China(Grant Nos.52130504,52305577,and 52175509)the Key Research and Development Plan of Hubei Province(Grant No.2022BAA013)+4 种基金the Major Program(JD)of Hubei Province(Grant No.2023BAA008-2)the Interdisciplinary Research Program of Huazhong University of Science and Technology(2023JCYJ047)the Innovation Project of Optics Valley Laboratory(Grant No.OVL2023PY003)the Postdoctoral Fellowship Program(Grade B)of China Postdoctoral Science Foundation(Grant No.GZB20230244)the fellowship from the China Postdoctoral Science Foundation(2024M750995)。
文摘In integrated circuit(IC)manufacturing,fast,nondestructive,and precise detection of defects in patterned wafers,realized by bright-field microscopy,is one of the critical factors for ensuring the final performance and yields of chips.With the critical dimensions of IC nanostructures continuing to shrink,directly imaging or classifying deep-subwavelength defects by bright-field microscopy is challenging due to the well-known diffraction barrier,the weak scattering effect,and the faint correlation between the scattering cross-section and the defect morphology.Herein,we propose an optical far-field inspection method based on the form-birefringence scattering imaging of the defective nanostructure,which can identify and classify various defects without requiring optical super-resolution.The technique is built upon the principle of breaking the optical form birefringence of the original periodic nanostructures by the defect perturbation under the anisotropic illumination modes,such as the orthogonally polarized plane waves,then combined with the high-order difference of far-field images.We validated the feasibility and effectiveness of the proposed method in detecting deep subwavelength defects through rigid vector imaging modeling and optical detection experiments of various defective nanostructures based on polarization microscopy.On this basis,an intelligent classification algorithm for typical patterned defects based on a dual-channel AlexNet neural network has been proposed,stabilizing the classification accuracy ofλ/16-sized defects with highly similar features at more than 90%.The strong classification capability of the two-channel network on typical patterned defects can be attributed to the high-order difference image and its transverse gradient being used as the network’s input,which highlights the polarization modulation difference between different patterned defects more significantly than conventional bright-field microscopy results.This work will provide a new but easy-to-operate method for detecting and classifying deep-subwavelength defects in patterned wafers or photomasks,which thus endows current online inspection equipment with more missions in advanced IC manufacturing.
基金supported by the National Natural Science Foundation of China(32171354,82222015,82171001)The National Key Research and Development Program of China2023YFC2413600Research Funding from West China School/Hospital of Stomatology,Sichuan University(No.RCDWIS2023-1).
文摘Infectious bone defects represent a substantial challenge in clinical practice,necessitating the deployment of advanced therapeutic strategies.This study presents a treatment modality that merges a mild photothermal therapy hydrogel with a pulsed drug delivery mechanism.The system is predicated on a hydrogel matrix that is thermally responsive,characteristic of bone defect sites,facilitating controlled and site-specific drug release.The cornerstone of this system is the incorporation of mild photothermal nanoparticles,which are activated within the temperature range of 40–43°C,thereby enhancing the precision and efficacy of drug delivery.Our findings demonstrate that the photothermal response significantly augments the localized delivery of therapeutic agents,mitigating systemic side effects and bolstering efficacy at the defect site.The synchronized pulsed release,cooperated with mild photothermal therapy,effectively addresses infection control,and promotes bone regeneration.This approach signifies a considerable advancement in the management of infectious bone defects,offering an effective and patient-centric alternative to traditional methods.Our research endeavors to extend its applicability to a wider spectrum of tissue regeneration scenarios,underscoring its transformative potential in the realm of regenerative medicine.
基金The“13th Five-Year Plan”National Science and Technology Major Project,2016ZX05052,Changchao QiThe China National Petroleum Corporation Science and Technology Project,2021DJ6505,Changchao Qi.
文摘The accuracy and reliability of non-destructive testing(NDT)approaches in detecting interior corrosion problems are critical,yet research in this field is limited.This work describes a novel way to monitor the structural integrity of steel gas pipelines that uses advanced numerical modeling techniques to anticipate fracture development and corrosion effects.The objective is to increase pipeline dependability and safety through more precise,real-time health evaluations.Compared to previous approaches,our solution provides higher accuracy in fault detection and quantification,making it ideal for pipeline integritymonitoring in real-world applications.To solve this issue,statistical analysis was conducted on the size and directional distribution features of about 380,000 sets of internal corrosion faults,as well as simulations of erosion and wear patterns on bent pipes.Using real defectmorphologies,we developed a modeling framework for typical interior corrosion flaws.We evaluated and validated the applicability and effectiveness of in-service inspection processes,as well as conducted on-site comparison tests.The results show that(1)the length and width of corrosion defects follow a log-normal distribution,the clock orientation follows a normal distribution,and the peak depth follows a Freundlich EX function distribution pattern;(2)pipeline corrosion defect data can be classified into three classes using the K-means clustering algorithm,allowing rapid and convenient acquisition of typical size and orientation characteristics of internal corrosion defects;(3)the applicability range and boundary conditions of various NDT techniques were verified,establishing comprehensive selection principles for internal corrosion defect detection technology;(4)on-site inspection results showed a 31%The simulation and validation platform for typical interior corrosion issues greatly enhances the accuracy and reliability of detection data.
基金supported by the Scientific and technological key project in Henan Province 22210224002the Natural Science Foundation of Henan Polytechnic University B2021-38.
文摘The service life of internal combustion engines is significantly influenced by surface defects in cylinder liners.To address the limitations of traditional detection methods,we propose an enhanced YOLOv8 model with Swin Transformer as the backbone network.This approach leverages Swin Transformer's multi-head self-attention mechanism for improved feature extraction of defects spanning various scales.Integrated with the YOLOv8 detection head,our model achieves a mean average precision of 85.1%on our dataset,outperforming baseline methods by 1.4%.The model's effectiveness is further demonstrated on a steel-surface defect dataset,indicating its broad applicability in industrial surface defect detection.Our work highlights the potential of combining Swin Transformer and YOLOv8 for accurate and efficient defect detection.
基金Supported by National Natural Science Foundation of China(Grant No.52075554)Hunan Provincial Natural Science Foundation of China(Grant No.2022JJ20070)+1 种基金Innovation-Driven Research Program of Central South University of China(Grant No.2023CXQD049)State Key Laboratory of High Performance Complex Manufacturing of China(Grant No.ZZYJKT2021-07)。
文摘Current research on localized raceway defects of angular contact ball bearings(ACBB)mainly focuses on assuming that localized raceway defects are cube-shaped defects characterized using a half-sine displacement excitation function.However,the assumption of a cube-shaped defect cannot accurately reflect the morphological characteristics of a localized raceway defect,and the half-sine displacement excitation function cannot be used to accurately describe the relationship between the geometric positions of rolling element and raceway in the region of localized raceway defects.In this study,a comprehensive dynamic model of an ACBB considering a three-dimensional localized raceway defect is established based on the nonlinear Hertz contact theory in conjunction with the outer raceway control theory using the improved Newton–Raphson iteration method.Three localized raceway defect distribution types,namely symmetric,offset,and deflection distributions,are considered.The established model is verified by comparing the results of the proposed model with those of existing literature.The dynamic characteristics of the ACBB were analyzed by investigating the effects of the geometrical size and distribution types on the time-varying contact angles,contact forces,and diagonal stiffness of the ACBB.The investigation results show that the appearance of localized raceway defect leads to the time-varying curves of contact angles,contact forces and diagonal stiffness havingΛ-and V-shaped mutations in some time intervals;The variation tendencies of theΛ-and V-shaped mutations are significant with the increase in defect radial depth H,defect axial width a and angular distanceθ_b.The increase in defect eccentric distance L is beneficial to the rolling elements disengaging from the defect area and it can weaken the influence of localized raceway defect on the time-varying contact and stiffness characteristics of ACBB.The time-varying contact and stiffness characteristics appear to change significantly when the defect deflection angleα_βincrease toα_γ.The results of this study provide a theoretical basis for the fault diagnosis of localized raceway defects in ACBB.
基金supported by the Key Research and Development Program of Shaanxi Province-International Science and Technology Cooperation Program Project (No.2020KW-001)the Contract for Xi'an Municipal Science and Technology Plan Project-Xi'an City Strong Foundation Innovation Plan (No.21XJZZ0074)the Key Project of Graduate Student Innovation Fund at Xi'an University of Posts and Telecommunications (No.CXJJZL2023013)。
文摘The detection of surface defects in concrete bridges using deep learning is of significant importance for reducing operational risks,saving maintenance costs,and driving the intelligent transformation of bridge defect detection.In contrast to the subjective and inefficient manual visual inspection,deep learning-based algorithms for concrete defect detection exhibit remarkable advantages,emerging as a focal point in recent research.This paper comprehensively analyzes the research progress of deep learning algorithms in the field of surface defect detection in concrete bridges in recent years.It introduces the early detection methods for surface defects in concrete bridges and the development of deep learning.Subsequently,it provides an overview of deep learning-based concrete bridge surface defect detection research from three aspects:image classification,object detection,and semantic segmentation.The paper summarizes the strengths and weaknesses of existing methods and the challenges they face.Additionally,it analyzes and prospects the development trends of surface defect detection in concrete bridges.
基金supported by the National Natural Science Foundation of China(22262010,22062005,22165005,U20A20128)Guangxi Science and Technology Fund for Distinguished HighTalent Introduction Program(AC22035091)Guangxi Science Fund for Distinguished Young Scholars(2019GXNSFFA245016)。
文摘Rational design of defected carbons adjacent to nitrogen(N)dopants is a fascinating but challenging approach for enhancing the catalytic performance of N-doped carbon.Meanwhile,the combined effect of heteroatom doping and defect engineering can efficiently increase the oxygen reduction reaction(ORR)ability of inactive carbons through charge redistribution.Herein,we report that an enhanced built-in electric field caused by the combined effect of N-doping and carbon defects in the twodimensional(2D)mesoporous N-doped carbon nano flakes(NCNF)is a promising technique for improving ORR performance.As a result,the NCNF exhibits more promising ORR activity than Pt/C and similar performance with reported robust catalysts.Comprehensive experimental and theoretical investigations suggest that topologically defected carbon adjacent to the graphitic valley nitrogen is a real active site,rendering optimal energy for the adsorption of ORR intermediates and lowering the total energy barrier for ORR.Also,NCNF-based Zn-air batteries exhibited an excellent power density and specific capacity of~121.10 mW cm^(-2)and~679.86 mA h g_(Zn)^(-1),respectively.This study not only offers new insights into defected carbons with graphitic valley N for ORR but also proposes novel catalyst design principles and provides a solid grasp of the built-in electric field effect on the ORR performance of defective catalysts.
基金supported by the National Natural Science Foundation of China(No.62103298)the Natural Science Foundation of Hebei Province(No.F2018209289)。
文摘Aiming at the problems of low detection efficiency and difficult positioning of traditional steel surface defect detection methods,a lightweight steel surface defect detection model based on you only look once version 7(YOLOv7)is proposed.First,a cascading style sheets(CSS)block module is proposed,which uses more lightweight operations to obtain redundant information in the feature map,reduces the amount of computation,and effectively improves the detection speed.Secondly,the improved spatial pyramid pooling with cross stage partial convolutions(SPPCSPC)structure is adopted to ensure that the model can also pay attention to the defect location information while predicting the defect category information,obtain richer defect features.In addition,the convolution operation in the original model is simplified,which significantly reduces the size of the model and helps to improve the detection speed.Finally,using efficient intersection over union(EIOU)loss to focus on high-quality anchors,speed up convergence and improve positioning accuracy.Experiments were carried out on the Northeastern University-defect(NEU-DET)steel surface defect dataset.Compared with the original YOLOv7 model,the number of parameters of this model was reduced by 40%,the frames per second(FPS)reached 112,and the average accuracy reached 79.1%,the detection accuracy and speed have been improved,which can meet the needs of steel surface defect detection.
基金supported by the National Natural Science Foundation of China(Nos.52071171,52202248)Liaoning BaiQianWan Talents Program(LNBQW2018B0048)+8 种基金Shenyang Science and Technology Project(21-108-9-04)Key Research Project of Department of Education of Liaoning Province(LJKZZ20220015)the Research Fund for the Doctoral Program of Liaoning Province(2022-BS-114)Chunhui Program of the Ministry of Education of the People’s Republic of China(202201135)Australian Research Council(ARC)through Future Fellowship(FT210100298,FT210100806)Discovery Project(DP220100603)Linkage Project(LP210100467,LP210200504,LP210200345,LP220100088)Industrial Transformation Training Centre(IC180100005)schemes,and the Australian Government through the Cooperative Research Centres Projects(CRCPXIII000077)the Australian Renewable Energy Agency(ARENA)as part of ARENA’s Transformative Research Accelerating Commercialisation Program(TM021).
文摘The advancement of aqueous magnesium ion energy storage devices encounters limitations due to the substantial hydration radius of magnesium ions(Mg^(2+))and their strong electrostatic interaction with the primary material.Consequently,this study successfully developed a MnS/MnO heterostructure through a straightforward hydrothermal and annealing method,marking its initial application in aqueous magnesium ion capacitors(AMICs).The fabricated MnS/MnO heterostructure,characterized by S defects,also generates Mn defects via in-situ initiation of early electrochemical processes.This unique dual ion defects MnS/MnO heterostructure(DID-MnS/MnO)enables the transformation of MnS and MnO,initially not highly active electrochemically for Mg^(2+),into cathode materials exhibiting high electrochemical activity and superior performance.Moreover,DID-MnS/MnO enhances conductivity,improves the kinetics of surface redox reactions,and increases the diffusion rate of Mg^(2+).Furthermore,this study introduces a dual energy storage mechanism for DID-MnS/MnO,which,in conjunction with dual ion defects,offers additional active sites for Mg^(2+)insertion/deinsertion in the host material,mitigating volume expansion and structural degradation during repeated charge-discharge cycles,thereby significantly enhancing cycling reversibility.As anticipated,using a three-electrode system,the developed DID-MnS/MnO demonstrated a discharge specific capacity of 237.9 mAh/g at a current density of 0.1 A/g.Remarkably,the constructed AMIC maintained a capacity retention rate of 94.3%after 10000 cycles at a current density of 1.0 A/g,with a specific capacitance of 165.7 F/g.Hence,DID-MnS/MnO offers insightful perspectives for designing alternative clean energy sources and is expected to contribute significantly to the advancement of the clean energy sector.
基金support from the National Institute of Health(K99AR081897,R00AR081897)M.N.W.acknowledges funding support from the National Institute of Health(P01DK011794,R01DK116716)+1 种基金the Smith Family Foundation Odyssey Award,and the Chen Institute Massachusetts General Hospital Research Scholar(2024-2029)awardμCT and bone histomorphometry were performed by the Center for Skeletal Research at Massachusetts General Hospital,a NIH-funded program(P30AR066261 and AR075042)led by Mary Bouxsein and Marie Demay.
文摘Osteogenesis imperfecta(OI)is a group of diseases caused by defects in type I collagen processing which result in skeletal fragility.While these disorders have been regarded as defects in osteoblast function,the role of matrix-embedded osteocytes in OI pathogenesis remains largely unknown.Homozygous human SP7(c.946 C>T,R316C)mutation results in a recessive form of OI characterized by fragility fractures,low bone mineral density and osteocyte dendrite defects.To better understand how the OI-causing R316C mutation affects the function of SP7,we generated Sp7^(R342C)knock-in mice.Consistent with patient phenotypes,Sp7^(R342C/R342C)mice demonstrate increased cortical porosity and reduced cortical bone mineral density.Sp7^(R342C/R342C)mice show osteocyte dendrite defects,increased osteocyte apoptosis,and intracortical bone remodeling with ectopic intracortical osteoclasts and elevated osteocyte Tnfsf11 expression.
文摘Based on inspection data,the authors analyze and summarize the main types and distribution characteristics of tunnel structural defects.These defects are classified into three types:surface defects,internal defects,and defects behind the structure.To address the need for rapid detection of different defect types,the current state of rapid detection technologies and equipment,both domestically and internationally,is systematically reviewed.The research reveals that surface defect detection technologies and equipment have developed rapidly in recent years.Notably,the integration of machine vision and laser scanning technologies have significantly improved detection efficiency and accuracy,achieving crack detection precision of up to 0.1 mm.However,the non-contact rapid detection of internal and behind-the-structure defects remains constrained by hardware limitations,with traditional detection remaining dominant.Nevertheless,phased array radar,ultrasonic,and acoustic vibration detection technologies have become research hotspots in recent years,offering promising directions for detecting these challenging defect types.Additionally,the application of multisensor fusion technology in rapid detection equipment has further enhanced detection capabilities.Devices such as cameras,3D laser scanners,infrared thermal imagers,and radar demonstrate significant advantages in rapid detection.Future research in tunnel inspection should prioritize breakthroughs in rapid detection technologies for internal and behind-the-structure defects.Efforts should also focus on developing multifunctional integrated detection vehicles that can simultaneously inspect both surface and internal structures.Furthermore,progress in fully automated,intelligent systems with precise defect identification and real-time reporting will be essential to significantly improve the efficiency and accuracy of tunnel inspection.
基金supported by a Research Grant of Pukyong National University(2023)。
文摘Achieving high-quality perovskite films without surface defects is regarded as a crucial target for the development of durable high-performance perovskite solar cells.Additive engineering is commonly employed to simultaneously control the growth of perovskite crystals and passivate defects.Here,4-(trifluoromethyl)benzoic anhydride(4-TBA)composed of benzene rings functionalized with carbonyl and trifluoromethyl groups was used as an example additive to study the characteristics of additives used for producing high-quality perovskites and controlling their surface properties.The interaction between4-TBA and perovskite precursor materials was investigated using density functional theory(DFT)simulations.The electron-rich carbonyl group efficiently passivated the under-coordinated lead-ion defects.Additionally,hydrogen bonding between trifluoromethyl and organic cations prevents the generation of cation vacancies.Because of its intrinsic hydrophobicity,the trifluoromethyl group simultaneously improves the moisture and heat stability of the film.4-TBA serves as a universal modifier for various perovskite compositions.The power conversion efficiency(PCE)of inverted perovskite solar cells(PSCs)based on methylammonium(MA)with 4-TBA was improved from 16.15%to 19.28%.Similarly,the PCE of inverted PSCs based on a cesium formamidinium MA(CsFAMA)perovskite film increased from20.72%to 23.58%,upon addition of 4-TBA.Furthermore,the moisture and thermal stability of 4-TBAtreated films and devices was significantly enhanced,along with prolonged device performance.Our work provides guidance on selecting the structure and functional groups that are essential for surface defect passivation and the production of high-quality perovskites.
