President Luo Haocai of the China Society for Human Rights Studies （CSHRS）, who is also former vice chair-man of the Chinese People＇s Political Consultative Conference （CPPCC） National Committee, led a CSHRS del-...President Luo Haocai of the China Society for Human Rights Studies （CSHRS）, who is also former vice chair-man of the Chinese People＇s Political Consultative Conference （CPPCC） National Committee, led a CSHRS del- egation to visit Heilongjiang, Jilin and Liaoning provinces from Sept. 10 to 16, 2012, in order to know more about human rights research and practice as well as promote related training and education in northeast China. Li Buyun, honorary member of the Chinese Academy of Social Sciences （CASS）,展开更多
In the heart of Antananarivo,Madagascar’s sundrenched capital,Joseph Andrinirina flashes a nervous smile as he inspects a faulty car engine at the start of another day of work at his garage.“Since I started this bus...In the heart of Antananarivo,Madagascar’s sundrenched capital,Joseph Andrinirina flashes a nervous smile as he inspects a faulty car engine at the start of another day of work at his garage.“Since I started this business six months ago,I enjoy being my own boss.I now earn a steady income,support my family,and provide jobs for others,”he told ChinAfrica.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
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
To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The...To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The inspection robot utilizes multiple sensors to monitor key parameters of the fans,such as vibration,noise,and bearing temperature,and upload the data to the monitoring center.The robot’s inspection path employs the improved A^(*)algorithm,incorporating obstacle penalty terms,path reconstruction,and smoothing optimization techniques,thereby achieving optimal path planning for the inspection robot in complex environments.Simulation results demonstrate that the improved A^(*)algorithm significantly outperforms the traditional A^(*)algorithm in terms of total path distance,smoothness,and detour rate,effectively improving the execution efficiency of inspection tasks.展开更多
1.C.Li,"Holding‘China Inc.’Together:The CCP and The Rise of China’s Yangqi",The China Quarterly,Vol.228,2016,pp.927-949.2.J.Gao,"‘Bypass the Lying Mouths’:How Does the CCP Tackle Information Distor...1.C.Li,"Holding‘China Inc.’Together:The CCP and The Rise of China’s Yangqi",The China Quarterly,Vol.228,2016,pp.927-949.2.J.Gao,"‘Bypass the Lying Mouths’:How Does the CCP Tackle Information Distortion at Local Levels?",The China Quarterly,Vol.228,2016,pp.950-969.3.C.Sorace,"Party Spirit Made Flesh:The Production of Legitimacy in the Aftermath of the 2008Sichuan Earthquake",The China Journal,Vol.76,2016,pp.41-62.4.Y.Yeo,"Complementing the Local Discipline Inspection Commissions of the CCP:Empowerment of the Central Inspection Groups",Journal of Contemporary China,Vol.25,No.97,2016,pp.59-74.展开更多
The China comprehensive inspection train(CIT)is designed for evaluating railway infrastructure to ensure safe railway operations.The CIT integrates an array of inspection devices,capable of simultaneously assessing ra...The China comprehensive inspection train(CIT)is designed for evaluating railway infrastructure to ensure safe railway operations.The CIT integrates an array of inspection devices,capable of simultaneously assessing railway health condition parameters.The CIT450,representing the second generation,can reach a top speed of 450 km/h with inspection on the infrastructure.This paper begins by outlining the global evolution of inspection trains.It then focuses on the critical technologies underlying the CIT450,which include:(1)real-time inspection data acquisition with spatial and temporal synchronization;(2)intelligent fusion and centralized management of multi-source inspection data,enabling remote supervision of the inspection process;(3)technologies in inspecting track,train–track interaction,catenary,signalling systems,and train operating environment;and(4)AI-driven analysis and correlation of inspection data.The future developmental directions for comprehensive inspection trains are discussed finally.The CIT450’s approach to real-time railway health monitoring can enrich traditional inspection means,operational,and maintenance methods by enhancing inspection efficiency and automating railway maintenance.展开更多
Aims and Scope Asian Journal of Social Pharmacy(AJSP)is peer-reviewed quarterly English journal jointly hosted by Herbal Font Pharmaceutical Limited and Shenyang Pharmaceutical University.AJSP is dedicated to providin...Aims and Scope Asian Journal of Social Pharmacy(AJSP)is peer-reviewed quarterly English journal jointly hosted by Herbal Font Pharmaceutical Limited and Shenyang Pharmaceutical University.AJSP is dedicated to providing researchers,pharmacists,administrators,and educators working within the field of pharmacy worldwide with a platform of communication in the advancement and development in social pharmacy.The journal welcomes original contributions in pharmacy-related research including policies,regulations and laws,administration,monitoring,inspection,surveillance,utilization,formulary analysis,drug manufacturing,drug marketing,drug R&D,pharmacy practice,clinical pharmacy,pharmacoeconomics,and modernization of traditional Chinese medicine.To expedite the dissemination of findings from latest research,the journal receives rapid research report.Rapid research reports can be published within 3 months on submission,which does not preclude publication of full length reports of the research work in other journals.展开更多
Precision steel balls are critical components in precision bearings.Surface defects on the steel balls will significantly reduce their useful life and cause linear or rotational transmission errors.Human visual inspec...Precision steel balls are critical components in precision bearings.Surface defects on the steel balls will significantly reduce their useful life and cause linear or rotational transmission errors.Human visual inspection of precision steel balls demands significant labor work.Besides,human inspection cannot maintain consistent quality assurance.To address these limitations and reduce inspection time,a convolutional neural network(CNN)based optical inspection system has been developed that automatically detects steel ball defects using a novel designated vertical mechanism.During image detection processing,two key challenges were addressed and resolved.They are the reflection caused by the coaxial light onto the ball center and the image deformation appearing at the edge of the steel balls.The special vertical rotating mechanism utilizing a spinning rod along with a spiral track was developed to enable successful and reliable full steel ball surface inspection during the rod rotation.The combination of the spinning rod and the spiral rotating component effectively rotates the steel ball to facilitate capturing complete surface images.Geometric calculations demonstrate that the steel balls can be completely inspected through specific rotation degrees,with the surface fully captured in 12 photo shots.These images are then analyzed by a CNN to determine surface quality defects.This study presents a new inspection method that enables the entire examination of steel ball surfaces.The successful development of this innovative automated optical inspection system with CNN represents a significant advancement in inspection quality control for precision steel balls.展开更多
This paper comprehensively explores the impulsive on-orbit inspection game problem utilizing reinforcement learning and game training methods.The purpose of the spacecraft is to inspect the entire surface of a non-coo...This paper comprehensively explores the impulsive on-orbit inspection game problem utilizing reinforcement learning and game training methods.The purpose of the spacecraft is to inspect the entire surface of a non-cooperative target with active maneuverability in front lighting.First,the impulsive orbital game problem is formulated as a turn-based sequential game problem.Second,several typical relative orbit transfers are encapsulated into modules to construct a parameterized action space containing discrete modules and continuous parameters,and multi-pass deep Q-networks(MPDQN)algorithm is used to implement autonomous decision-making.Then,a curriculum learning method is used to gradually increase the difficulty of the training scenario.The backtracking proportional self-play training framework is used to enhance the agent’s ability to defeat inconsistent strategies by building a pool of opponents.The behavior variations of the agents during training indicate that the intelligent game system gradually evolves towards an equilibrium situation.The restraint relations between the agents show that the agents steadily improve the strategy.The influence of various factors on game results is tested.展开更多
Reticular structures are the basis of major infrastructure projects,including bridges,electrical pylons and airports.However,inspecting and maintaining these structures is both expensive and hazardous,traditionally re...Reticular structures are the basis of major infrastructure projects,including bridges,electrical pylons and airports.However,inspecting and maintaining these structures is both expensive and hazardous,traditionally requiring human involvement.While some research has been conducted in this field of study,most efforts focus on faults identification through images or the design of robotic platforms,often neglecting the autonomous navigation of robots through the structure.This study addresses this limitation by proposing methods to detect navigable surfaces in truss structures,thereby enhancing the autonomous capabilities of climbing robots to navigate through these environments.The paper proposes multiple approaches for the binary segmentation between navigable surfaces and background from 3D point clouds captured from metallic trusses.Approaches can be classified into two paradigms:analytical algorithms and deep learning methods.Within the analytical approach,an ad hoc algorithm is developed for segmenting the structures,leveraging different techniques to evaluate the eigendecomposition of planar patches within the point cloud.In parallel,widely used and advanced deep learning models,including PointNet,PointNet++,MinkUNet34C,and PointTransformerV3,are trained and evaluated for the same task.A comparative analysis of these paradigms reveals some key insights.The analytical algorithm demonstrates easier parameter adjustment and comparable performance to that of the deep learning models,despite the latter’s higher computational demands.Nevertheless,the deep learning models stand out in segmentation accuracy,with PointTransformerV3 achieving impressive results,such as a Mean Intersection Over Union(mIoU)of approximately 97%.This study highlights the potential of analytical and deep learning approaches to improve the autonomous navigation of climbing robots in complex truss structures.The findings underscore the trade-offs between computational efficiency and segmentation performance,offering valuable insights for future research and practical applications in autonomous infrastructure maintenance and inspection.展开更多
Purpose–This paper aims to systematically review the evolution of inspection technologies and equipment for heavy-haul railway infrastructure,with a focus on China’s Shuohuang Railway and Daqin Railway.It summarizes...Purpose–This paper aims to systematically review the evolution of inspection technologies and equipment for heavy-haul railway infrastructure,with a focus on China’s Shuohuang Railway and Daqin Railway.It summarizes the technological progression from traditional manual inspections to integrated and intelligent inspection systems,analyzes their practical application outcomes and outlines future research directions to support the safe,efficient and sustainable operation of heavy-haul railways.Design/methodology/approach–The study employs a combination of historical and empirical analysis,primarily drawing on academic literature and operational data from Shuohuang Railway.The development of inspection technologies is categorized into two distinct phases:traditional inspection and integrated inspection.The comprehensive effectiveness of these technologies is evaluated based on actual inspection efficiency,defect detection capability,cost savings and other relevant data.Findings–The adoption of integrated inspection vehicles has significantly improved inspection efficiency and accuracy.In 2014,the world’s first heavy-haul integrated inspection vehicle enabled synchronous multidisciplinary inspections,greatly reducing reliance on manual labor.By 2024,the intelligent heavy-haul integrated inspection vehicle further enhanced detection precision by 30%.Practical applications demonstrate that the annual number of track defects decreased from 25,000 to 3,800,while the track quality index(TQI)remained stable below 6 mm.Additionally,annual maintenance costs were reduced by more than 40 m yuan.Originality/value–This paper provides the first systematic review of the development of inspection technologies for heavy-haul railway infrastructure,highlighting China’s leading achievements in integrated and intelligent inspection.It clarifies the practical value of these technologies in enhancing safety,reducing costs and optimizing maintenance operations.Furthermore,it proposes future directions for development,including system integration,onboard computing capabilities and unmanned operations,offering valuable insights for technological innovation and policymaking in the field.展开更多
Objective To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features,providing novel insights into the early screening of lung cancer.Methods This study included p...Objective To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features,providing novel insights into the early screening of lung cancer.Methods This study included patients with pulmonary nodules diagnosed at the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from November 1,2019 to December 31,2024,as well as patients with lung cancer diagnosed in the Oncology Departments of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and Longhua Hospital during the same period.The facial image information of patients with pulmonary nodules and lung cancer was collected using the TFDA-1 tongue and facial diagnosis instrument,and the facial diagnosis features were extracted from it by deep learning technology.Statistical analysis was conducted on the objective facial diagnosis characteristics of the two groups of participants to explore the differences in their facial image characteristics,and the least absolute shrinkage and selection operator(LASSO)regression was used to screen the characteristic variables.Based on the screened feature variables,four machine learning methods:random forest,logistic regression,support vector machine(SVM),and gradient boosting decision tree(GBDT)were used to establish lung cancer classification models independently.Meanwhile,the model performance was evaluated by indicators such as sensitivity,specificity,F1 score,precision,accuracy,the area under the receiver operating characteristic(ROC)curve(AUC),and the area under the precision-recall curve(AP).Results A total of 1275 patients with pulmonary nodules and 1623 patients with lung cancer were included in this study.After propensity score matching(PSM)to adjust for gender and age,535 patients were finally included in the pulmonary nodule group and the lung cancer group,respectively.There were significant differences in multiple color space metrics(such as R,G,B,V,L,a,b,Cr,H,Y,and Cb)and texture metrics[such as gray-levcl co-occurrence matrix(GLCM)-contrast(CON)and GLCM-inverse different moment(IDM)]between the two groups of individuals with pulmonary nodules and lung cancer(P<0.05).To construct a classification model,LASSO regression was used to select 63 key features from the initial 136 facial features.Based on this feature set,the SVM model demonstrated the best performance after 10-fold stratified cross-validation.The model achieved an average AUC of 0.8729 and average accuracy of 0.7990 on the internal test set.Further validation on an independent test set confirmed the model’s robust performance(AUC=0.8233,accuracy=0.7290),indicating its good generalization ability.Feature importance analysis demonstrated that color space indicators and the whole/lip Cr components(including color-B-0,wholecolor-Cr,and lipcolor-Cr)were the core factors in the model’s classification decisions,while texture indicators[GLCM-angular second moment(ASM)_2,GLCM-IDM_1,GLCM-CON_1,GLCM-entropy(ENT)_2]played an important auxiliary role.Conclusion The facial image features of patients with lung cancer and pulmonary nodules show significant differences in color and texture characteristics in multiple areas.The various models constructed based on facial image features all demonstrate good performance,indicating that facial image features can serve as potential biomarkers for lung cancer risk prediction,providing a non-invasive and feasible new approach for early lung cancer screening.展开更多
The growing importance of maintaining and extending the functional lifespan of reinforced concrete structures has resulted in an increased emphasis on non-destructive testing techniques as essential tools for evaluati...The growing importance of maintaining and extending the functional lifespan of reinforced concrete structures has resulted in an increased emphasis on non-destructive testing techniques as essential tools for evaluating structural conditions.Non-destructive testing procedures offer a notable benefit in assessing the uniformity,homogeneity,ability to withstand compression,durability,and degree of corrosion in reinforcing bars within reinforced concrete structures.This study aimed to evaluate the existing condition of partially constructed residential buildings in Rewari district,located in the state of Haryana.The reinforced concrete structure of the building had been completed eight years ago,however,the project was abruptly stopped.Prior to recommencing the construction,it is important to assess the present state of the structure in order to evaluate the deterioration in Reinforced Cement Concrete(RCC).The building’s state was evaluated by visually inspecting the building,conducting on-site examinations,and analyzing samples in a laboratory.The findings emphasize the assessment of the robustness and durability of concrete to ascertain the degree of deterioration and degradation in the structure.The study incorporates visual inspection,and non-destructive evaluation utilizing different instruments to evaluate the corrosion condition of reinforcing bars.In addition,selected RCC columns,beams,and slabs undergo chemical testing.It has been observed that the strength results and chemical results were within permissible limits.展开更多
To meet the demand for intelligent and unmanned development in thermal power plants,an intelligent inspection system has been designed.This system efficiently performs inspection tasks and monitors the operational par...To meet the demand for intelligent and unmanned development in thermal power plants,an intelligent inspection system has been designed.This system efficiently performs inspection tasks and monitors the operational parameters of key equipment in real-time.The collected data is uploaded to the monitoring center,allowing operation and maintenance personnel to access equipment information promptly.Data analysis is used to provide fault warning and diagnosis for critical equipment.The system employs the Pure Pursuit algorithm,which effectively avoids obstacles and ensures path continuity and stability.Simulation results show that the Pure Pursuit algorithm significantly improves the navigation accuracy and task efficiency of the inspection robot,ensuring the reliability of thermal power plant inspections.展开更多
Performance-based warranties(PBWs)are widely used in industry and manufacturing.Given that PBW can impose financial burdens on manufacturers,rational maintenance decisions are essential for expanding profit margins.Th...Performance-based warranties(PBWs)are widely used in industry and manufacturing.Given that PBW can impose financial burdens on manufacturers,rational maintenance decisions are essential for expanding profit margins.This paper proposes an optimization model for PBW decisions for systems affected by Gamma degradation processes,incorporating periodic inspection.A system performance degradation model is established.Preventive maintenance probability and corrective renewal probability models are developed to calculate expected warranty costs and system availability.A benefits function,which includes incentives,is constructed to optimize the initial and subsequent inspection intervals and preventive maintenance thresholds,thereby maximizing warranty profit.An improved sparrow search algorithm is developed to optimize the model,with a case study on large steam turbine rotor shafts.The results suggest the optimal PBW strategy involves an initial inspection interval of approximately 20 months,with subsequent intervals of about four months,and a preventive maintenance threshold of approximately 37.39 mm wear.When compared to common cost-minimization-based condition maintenance strategies and PBW strategies that do not differentiate between initial and subsequent inspection intervals,the proposed PBW strategy increases the manufacturer’s profit by 1%and 18%,respectively.Sensitivity analyses provide managerial recommendations for PBW implementation.The PBW strategy proposed in this study significantly increases manufacturers’profits by optimizing inspection intervals and preventive maintenance thresholds,and manufacturers should focus on technological improvement in preventive maintenance and cost control to further enhance earnings.展开更多
Twenty years ago,during an inspection tour of the pollutionbesieged village Yucun in Anji County,east China’s Zhejiang Province,then provincial Party Secretary Xi Jinping proposed that“lucid waters and lush mountain...Twenty years ago,during an inspection tour of the pollutionbesieged village Yucun in Anji County,east China’s Zhejiang Province,then provincial Party Secretary Xi Jinping proposed that“lucid waters and lush mountains are invaluable assets”–a seminal insight now known as the“Two Mountains”Theory.展开更多
Sewer pipe condition assessment by performing regular inspections is crucial for ensuring the systems’effective operation and maintenance.CCTV(closed-circuit television)is widely employed in North America to examine ...Sewer pipe condition assessment by performing regular inspections is crucial for ensuring the systems’effective operation and maintenance.CCTV(closed-circuit television)is widely employed in North America to examine the internal conditions of sewage pipes.Due to the extensive inventory of pipes and associated costs,it is not practical for municipalities to conduct inspections on each sanitary sewage pipe section.According to the ASCE(American Society of Civil Engineers)infrastructure report published in 2021,combined investment needs for water and wastewater systems are estimated to be$150 billion during 2016-2025.Therefore,new solutions are needed to fill the trillion-dollar investment gap to improve the existing water and wastewater infrastructure for the coming years.ML(machine learning)based prediction model development is an effective method for predicting the condition of sewer pipes.In this research,sewer pipe inspection data from several municipalities are collected,which include variables such as pipe material,age,diameter,length,soil type,slope of construction,and PACP(Pipeline Assessment Certification Program)score.These sewer pipe data exhibit a severe imbalance in pipes’PACP scores,which is considered the target variable in the development of models.Due to this imbalanced dataset,the performance of the sewer prediction model is poor.This paper,therefore,aims to employ oversampling and hyperparameter tuning techniques to treat the imbalanced data and improve the model’s performance significantly.Utility owners and municipal asset managers can utilize the developed models to make more informed decisions on future inspections of sewer pipelines.展开更多
The manufacturing processes of casing rings are prone to multi-type defects such as holes,cracks,and porosity,so ultrasonic testing is vital for the quality of aeroengine.Conventional ultrasonic testing requires manua...The manufacturing processes of casing rings are prone to multi-type defects such as holes,cracks,and porosity,so ultrasonic testing is vital for the quality of aeroengine.Conventional ultrasonic testing requires manual analysis,which is susceptible to human omission,inconsistent results,and time-consumption.In this paper,a method for automated detection of defects is proposed for the ultrasonic Total Focusing Method(TFM)inspection of casing rings based on deep learning.First,the original datasets of defect images are established,and the Mask R-CNN is used to increase the number of defects in a single image.Then,the YOLOX-S-improved lightweight model is proposed,and the feature extraction network is replaced by Faster Net to reduce redundant computations.The Super-Resolution Generative Adversarial Network(SRGAN)and Convolutional Block Attention Module(CBAM)are integrated to improve the identification precision.Finally,a new test dataset is created by ultrasonic TFM inspection of an aeroengine casing ring.The results show that the mean of Average Precision(m AP)of the YOLOX-S-improved model reaches 99.17%,and the corresponding speed reaches 77.6 FPS.This study indicates that the YOLOX-S-improved model performs better than conventional object detection models.And the generalization ability of the proposed model is verified by ultrasonic B-scan images.展开更多
文摘President Luo Haocai of the China Society for Human Rights Studies （CSHRS）, who is also former vice chair-man of the Chinese People＇s Political Consultative Conference （CPPCC） National Committee, led a CSHRS del- egation to visit Heilongjiang, Jilin and Liaoning provinces from Sept. 10 to 16, 2012, in order to know more about human rights research and practice as well as promote related training and education in northeast China. Li Buyun, honorary member of the Chinese Academy of Social Sciences （CASS）,
文摘In the heart of Antananarivo,Madagascar’s sundrenched capital,Joseph Andrinirina flashes a nervous smile as he inspects a faulty car engine at the start of another day of work at his garage.“Since I started this business six months ago,I enjoy being my own boss.I now earn a steady income,support my family,and provide jobs for others,”he told ChinAfrica.
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.
文摘To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The inspection robot utilizes multiple sensors to monitor key parameters of the fans,such as vibration,noise,and bearing temperature,and upload the data to the monitoring center.The robot’s inspection path employs the improved A^(*)algorithm,incorporating obstacle penalty terms,path reconstruction,and smoothing optimization techniques,thereby achieving optimal path planning for the inspection robot in complex environments.Simulation results demonstrate that the improved A^(*)algorithm significantly outperforms the traditional A^(*)algorithm in terms of total path distance,smoothness,and detour rate,effectively improving the execution efficiency of inspection tasks.
文摘1.C.Li,"Holding‘China Inc.’Together:The CCP and The Rise of China’s Yangqi",The China Quarterly,Vol.228,2016,pp.927-949.2.J.Gao,"‘Bypass the Lying Mouths’:How Does the CCP Tackle Information Distortion at Local Levels?",The China Quarterly,Vol.228,2016,pp.950-969.3.C.Sorace,"Party Spirit Made Flesh:The Production of Legitimacy in the Aftermath of the 2008Sichuan Earthquake",The China Journal,Vol.76,2016,pp.41-62.4.Y.Yeo,"Complementing the Local Discipline Inspection Commissions of the CCP:Empowerment of the Central Inspection Groups",Journal of Contemporary China,Vol.25,No.97,2016,pp.59-74.
基金supported by the National Natural Science Foundation of China(Grant No.52272427)the Technology Research and Development Program of China National Railway Group(Grant No.K2021T015)Development Plan of China Academy of Railway Sciences Corporation Ltd.(Grant No.2022YJ256)。
文摘The China comprehensive inspection train(CIT)is designed for evaluating railway infrastructure to ensure safe railway operations.The CIT integrates an array of inspection devices,capable of simultaneously assessing railway health condition parameters.The CIT450,representing the second generation,can reach a top speed of 450 km/h with inspection on the infrastructure.This paper begins by outlining the global evolution of inspection trains.It then focuses on the critical technologies underlying the CIT450,which include:(1)real-time inspection data acquisition with spatial and temporal synchronization;(2)intelligent fusion and centralized management of multi-source inspection data,enabling remote supervision of the inspection process;(3)technologies in inspecting track,train–track interaction,catenary,signalling systems,and train operating environment;and(4)AI-driven analysis and correlation of inspection data.The future developmental directions for comprehensive inspection trains are discussed finally.The CIT450’s approach to real-time railway health monitoring can enrich traditional inspection means,operational,and maintenance methods by enhancing inspection efficiency and automating railway maintenance.
文摘Aims and Scope Asian Journal of Social Pharmacy(AJSP)is peer-reviewed quarterly English journal jointly hosted by Herbal Font Pharmaceutical Limited and Shenyang Pharmaceutical University.AJSP is dedicated to providing researchers,pharmacists,administrators,and educators working within the field of pharmacy worldwide with a platform of communication in the advancement and development in social pharmacy.The journal welcomes original contributions in pharmacy-related research including policies,regulations and laws,administration,monitoring,inspection,surveillance,utilization,formulary analysis,drug manufacturing,drug marketing,drug R&D,pharmacy practice,clinical pharmacy,pharmacoeconomics,and modernization of traditional Chinese medicine.To expedite the dissemination of findings from latest research,the journal receives rapid research report.Rapid research reports can be published within 3 months on submission,which does not preclude publication of full length reports of the research work in other journals.
文摘Precision steel balls are critical components in precision bearings.Surface defects on the steel balls will significantly reduce their useful life and cause linear or rotational transmission errors.Human visual inspection of precision steel balls demands significant labor work.Besides,human inspection cannot maintain consistent quality assurance.To address these limitations and reduce inspection time,a convolutional neural network(CNN)based optical inspection system has been developed that automatically detects steel ball defects using a novel designated vertical mechanism.During image detection processing,two key challenges were addressed and resolved.They are the reflection caused by the coaxial light onto the ball center and the image deformation appearing at the edge of the steel balls.The special vertical rotating mechanism utilizing a spinning rod along with a spiral track was developed to enable successful and reliable full steel ball surface inspection during the rod rotation.The combination of the spinning rod and the spiral rotating component effectively rotates the steel ball to facilitate capturing complete surface images.Geometric calculations demonstrate that the steel balls can be completely inspected through specific rotation degrees,with the surface fully captured in 12 photo shots.These images are then analyzed by a CNN to determine surface quality defects.This study presents a new inspection method that enables the entire examination of steel ball surfaces.The successful development of this innovative automated optical inspection system with CNN represents a significant advancement in inspection quality control for precision steel balls.
文摘This paper comprehensively explores the impulsive on-orbit inspection game problem utilizing reinforcement learning and game training methods.The purpose of the spacecraft is to inspect the entire surface of a non-cooperative target with active maneuverability in front lighting.First,the impulsive orbital game problem is formulated as a turn-based sequential game problem.Second,several typical relative orbit transfers are encapsulated into modules to construct a parameterized action space containing discrete modules and continuous parameters,and multi-pass deep Q-networks(MPDQN)algorithm is used to implement autonomous decision-making.Then,a curriculum learning method is used to gradually increase the difficulty of the training scenario.The backtracking proportional self-play training framework is used to enhance the agent’s ability to defeat inconsistent strategies by building a pool of opponents.The behavior variations of the agents during training indicate that the intelligent game system gradually evolves towards an equilibrium situation.The restraint relations between the agents show that the agents steadily improve the strategy.The influence of various factors on game results is tested.
基金funded by the spanish Ministry of Science,Innovation and Universities as part of the project PID2020-116418RB-I00 funded by MCIN/AEI/10.13039/501100011033.
文摘Reticular structures are the basis of major infrastructure projects,including bridges,electrical pylons and airports.However,inspecting and maintaining these structures is both expensive and hazardous,traditionally requiring human involvement.While some research has been conducted in this field of study,most efforts focus on faults identification through images or the design of robotic platforms,often neglecting the autonomous navigation of robots through the structure.This study addresses this limitation by proposing methods to detect navigable surfaces in truss structures,thereby enhancing the autonomous capabilities of climbing robots to navigate through these environments.The paper proposes multiple approaches for the binary segmentation between navigable surfaces and background from 3D point clouds captured from metallic trusses.Approaches can be classified into two paradigms:analytical algorithms and deep learning methods.Within the analytical approach,an ad hoc algorithm is developed for segmenting the structures,leveraging different techniques to evaluate the eigendecomposition of planar patches within the point cloud.In parallel,widely used and advanced deep learning models,including PointNet,PointNet++,MinkUNet34C,and PointTransformerV3,are trained and evaluated for the same task.A comparative analysis of these paradigms reveals some key insights.The analytical algorithm demonstrates easier parameter adjustment and comparable performance to that of the deep learning models,despite the latter’s higher computational demands.Nevertheless,the deep learning models stand out in segmentation accuracy,with PointTransformerV3 achieving impressive results,such as a Mean Intersection Over Union(mIoU)of approximately 97%.This study highlights the potential of analytical and deep learning approaches to improve the autonomous navigation of climbing robots in complex truss structures.The findings underscore the trade-offs between computational efficiency and segmentation performance,offering valuable insights for future research and practical applications in autonomous infrastructure maintenance and inspection.
基金supported by 2020 Science and Technology Innovation Project of Shuo-Huang Railway Development Company(SHTL-20-12).
文摘Purpose–This paper aims to systematically review the evolution of inspection technologies and equipment for heavy-haul railway infrastructure,with a focus on China’s Shuohuang Railway and Daqin Railway.It summarizes the technological progression from traditional manual inspections to integrated and intelligent inspection systems,analyzes their practical application outcomes and outlines future research directions to support the safe,efficient and sustainable operation of heavy-haul railways.Design/methodology/approach–The study employs a combination of historical and empirical analysis,primarily drawing on academic literature and operational data from Shuohuang Railway.The development of inspection technologies is categorized into two distinct phases:traditional inspection and integrated inspection.The comprehensive effectiveness of these technologies is evaluated based on actual inspection efficiency,defect detection capability,cost savings and other relevant data.Findings–The adoption of integrated inspection vehicles has significantly improved inspection efficiency and accuracy.In 2014,the world’s first heavy-haul integrated inspection vehicle enabled synchronous multidisciplinary inspections,greatly reducing reliance on manual labor.By 2024,the intelligent heavy-haul integrated inspection vehicle further enhanced detection precision by 30%.Practical applications demonstrate that the annual number of track defects decreased from 25,000 to 3,800,while the track quality index(TQI)remained stable below 6 mm.Additionally,annual maintenance costs were reduced by more than 40 m yuan.Originality/value–This paper provides the first systematic review of the development of inspection technologies for heavy-haul railway infrastructure,highlighting China’s leading achievements in integrated and intelligent inspection.It clarifies the practical value of these technologies in enhancing safety,reducing costs and optimizing maintenance operations.Furthermore,it proposes future directions for development,including system integration,onboard computing capabilities and unmanned operations,offering valuable insights for technological innovation and policymaking in the field.
基金National Natural Science Foundation of China(82305090)Shanghai Municipal Health Commission(20234Y0168)National Key Research and Development Program of China (2017YFC1703301)。
文摘Objective To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features,providing novel insights into the early screening of lung cancer.Methods This study included patients with pulmonary nodules diagnosed at the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from November 1,2019 to December 31,2024,as well as patients with lung cancer diagnosed in the Oncology Departments of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and Longhua Hospital during the same period.The facial image information of patients with pulmonary nodules and lung cancer was collected using the TFDA-1 tongue and facial diagnosis instrument,and the facial diagnosis features were extracted from it by deep learning technology.Statistical analysis was conducted on the objective facial diagnosis characteristics of the two groups of participants to explore the differences in their facial image characteristics,and the least absolute shrinkage and selection operator(LASSO)regression was used to screen the characteristic variables.Based on the screened feature variables,four machine learning methods:random forest,logistic regression,support vector machine(SVM),and gradient boosting decision tree(GBDT)were used to establish lung cancer classification models independently.Meanwhile,the model performance was evaluated by indicators such as sensitivity,specificity,F1 score,precision,accuracy,the area under the receiver operating characteristic(ROC)curve(AUC),and the area under the precision-recall curve(AP).Results A total of 1275 patients with pulmonary nodules and 1623 patients with lung cancer were included in this study.After propensity score matching(PSM)to adjust for gender and age,535 patients were finally included in the pulmonary nodule group and the lung cancer group,respectively.There were significant differences in multiple color space metrics(such as R,G,B,V,L,a,b,Cr,H,Y,and Cb)and texture metrics[such as gray-levcl co-occurrence matrix(GLCM)-contrast(CON)and GLCM-inverse different moment(IDM)]between the two groups of individuals with pulmonary nodules and lung cancer(P<0.05).To construct a classification model,LASSO regression was used to select 63 key features from the initial 136 facial features.Based on this feature set,the SVM model demonstrated the best performance after 10-fold stratified cross-validation.The model achieved an average AUC of 0.8729 and average accuracy of 0.7990 on the internal test set.Further validation on an independent test set confirmed the model’s robust performance(AUC=0.8233,accuracy=0.7290),indicating its good generalization ability.Feature importance analysis demonstrated that color space indicators and the whole/lip Cr components(including color-B-0,wholecolor-Cr,and lipcolor-Cr)were the core factors in the model’s classification decisions,while texture indicators[GLCM-angular second moment(ASM)_2,GLCM-IDM_1,GLCM-CON_1,GLCM-entropy(ENT)_2]played an important auxiliary role.Conclusion The facial image features of patients with lung cancer and pulmonary nodules show significant differences in color and texture characteristics in multiple areas.The various models constructed based on facial image features all demonstrate good performance,indicating that facial image features can serve as potential biomarkers for lung cancer risk prediction,providing a non-invasive and feasible new approach for early lung cancer screening.
文摘The growing importance of maintaining and extending the functional lifespan of reinforced concrete structures has resulted in an increased emphasis on non-destructive testing techniques as essential tools for evaluating structural conditions.Non-destructive testing procedures offer a notable benefit in assessing the uniformity,homogeneity,ability to withstand compression,durability,and degree of corrosion in reinforcing bars within reinforced concrete structures.This study aimed to evaluate the existing condition of partially constructed residential buildings in Rewari district,located in the state of Haryana.The reinforced concrete structure of the building had been completed eight years ago,however,the project was abruptly stopped.Prior to recommencing the construction,it is important to assess the present state of the structure in order to evaluate the deterioration in Reinforced Cement Concrete(RCC).The building’s state was evaluated by visually inspecting the building,conducting on-site examinations,and analyzing samples in a laboratory.The findings emphasize the assessment of the robustness and durability of concrete to ascertain the degree of deterioration and degradation in the structure.The study incorporates visual inspection,and non-destructive evaluation utilizing different instruments to evaluate the corrosion condition of reinforcing bars.In addition,selected RCC columns,beams,and slabs undergo chemical testing.It has been observed that the strength results and chemical results were within permissible limits.
文摘To meet the demand for intelligent and unmanned development in thermal power plants,an intelligent inspection system has been designed.This system efficiently performs inspection tasks and monitors the operational parameters of key equipment in real-time.The collected data is uploaded to the monitoring center,allowing operation and maintenance personnel to access equipment information promptly.Data analysis is used to provide fault warning and diagnosis for critical equipment.The system employs the Pure Pursuit algorithm,which effectively avoids obstacles and ensures path continuity and stability.Simulation results show that the Pure Pursuit algorithm significantly improves the navigation accuracy and task efficiency of the inspection robot,ensuring the reliability of thermal power plant inspections.
基金supported by the National Natural Science Foundation of China(71871219).
文摘Performance-based warranties(PBWs)are widely used in industry and manufacturing.Given that PBW can impose financial burdens on manufacturers,rational maintenance decisions are essential for expanding profit margins.This paper proposes an optimization model for PBW decisions for systems affected by Gamma degradation processes,incorporating periodic inspection.A system performance degradation model is established.Preventive maintenance probability and corrective renewal probability models are developed to calculate expected warranty costs and system availability.A benefits function,which includes incentives,is constructed to optimize the initial and subsequent inspection intervals and preventive maintenance thresholds,thereby maximizing warranty profit.An improved sparrow search algorithm is developed to optimize the model,with a case study on large steam turbine rotor shafts.The results suggest the optimal PBW strategy involves an initial inspection interval of approximately 20 months,with subsequent intervals of about four months,and a preventive maintenance threshold of approximately 37.39 mm wear.When compared to common cost-minimization-based condition maintenance strategies and PBW strategies that do not differentiate between initial and subsequent inspection intervals,the proposed PBW strategy increases the manufacturer’s profit by 1%and 18%,respectively.Sensitivity analyses provide managerial recommendations for PBW implementation.The PBW strategy proposed in this study significantly increases manufacturers’profits by optimizing inspection intervals and preventive maintenance thresholds,and manufacturers should focus on technological improvement in preventive maintenance and cost control to further enhance earnings.
文摘Twenty years ago,during an inspection tour of the pollutionbesieged village Yucun in Anji County,east China’s Zhejiang Province,then provincial Party Secretary Xi Jinping proposed that“lucid waters and lush mountains are invaluable assets”–a seminal insight now known as the“Two Mountains”Theory.
文摘Sewer pipe condition assessment by performing regular inspections is crucial for ensuring the systems’effective operation and maintenance.CCTV(closed-circuit television)is widely employed in North America to examine the internal conditions of sewage pipes.Due to the extensive inventory of pipes and associated costs,it is not practical for municipalities to conduct inspections on each sanitary sewage pipe section.According to the ASCE(American Society of Civil Engineers)infrastructure report published in 2021,combined investment needs for water and wastewater systems are estimated to be$150 billion during 2016-2025.Therefore,new solutions are needed to fill the trillion-dollar investment gap to improve the existing water and wastewater infrastructure for the coming years.ML(machine learning)based prediction model development is an effective method for predicting the condition of sewer pipes.In this research,sewer pipe inspection data from several municipalities are collected,which include variables such as pipe material,age,diameter,length,soil type,slope of construction,and PACP(Pipeline Assessment Certification Program)score.These sewer pipe data exhibit a severe imbalance in pipes’PACP scores,which is considered the target variable in the development of models.Due to this imbalanced dataset,the performance of the sewer prediction model is poor.This paper,therefore,aims to employ oversampling and hyperparameter tuning techniques to treat the imbalanced data and improve the model’s performance significantly.Utility owners and municipal asset managers can utilize the developed models to make more informed decisions on future inspections of sewer pipelines.
基金supported by the Postdoctoral Fellowship Program of CPSF,China(No.GZC20232015)the China Postdoctoral Science Foundation(No.2024M752499)+3 种基金the Postdoctoral Project of Hubei Province,China(No.2024HBBHCXA076)the Wuhan East Lake New Technology Development Zone Open List Project,China(No.2022KJB128)the National Natural Science Foundation of China(No.51875428)the Fundamental Research Funds for the Central Universities,China(No.104972024RSCbs0013)。
文摘The manufacturing processes of casing rings are prone to multi-type defects such as holes,cracks,and porosity,so ultrasonic testing is vital for the quality of aeroengine.Conventional ultrasonic testing requires manual analysis,which is susceptible to human omission,inconsistent results,and time-consumption.In this paper,a method for automated detection of defects is proposed for the ultrasonic Total Focusing Method(TFM)inspection of casing rings based on deep learning.First,the original datasets of defect images are established,and the Mask R-CNN is used to increase the number of defects in a single image.Then,the YOLOX-S-improved lightweight model is proposed,and the feature extraction network is replaced by Faster Net to reduce redundant computations.The Super-Resolution Generative Adversarial Network(SRGAN)and Convolutional Block Attention Module(CBAM)are integrated to improve the identification precision.Finally,a new test dataset is created by ultrasonic TFM inspection of an aeroengine casing ring.The results show that the mean of Average Precision(m AP)of the YOLOX-S-improved model reaches 99.17%,and the corresponding speed reaches 77.6 FPS.This study indicates that the YOLOX-S-improved model performs better than conventional object detection models.And the generalization ability of the proposed model is verified by ultrasonic B-scan images.
