Central environmental protection inspection and carbon emission reduction: A tripartite evolutionary game model from the perspective of carbon neutrality

Since the carbon neutrality target was proposed,many countries have been facing severe challenges to carbon emission reduction sustainably.This study is conducted using a tripartite evolutionary game model to explore the impact of the central environmental protection inspection(CEPI)on driving carbon emission reduction,and to study what factors influence the strategic choices of each party and how they interact with each other.The research results suggest that local governments and manufacturing enterprises would choose strategies that are beneficial to carbon reduction when CEPI increases.When the initial willingness of all parties increases 20%,50%—80%,the time spent for the whole system to achieve stability decreases from 100%,60%—30%.The evolutionary result of“thorough inspection,regulation implementation,low-carbon management”is the best strategy for the tripartite evolutionary game.Moreover,the smaller the cost and the larger the benefit,the greater the likelihood of the three-party game stability strategy appears.This study has important guiding significance for other developing countries to promote carbon emission reduction by environmental policy.

Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.

A Railway Fastener Inspection Method Based on Abnormal Sample Generation

Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspectionmethods have insufficient detection ability in cases of imbalanced samples.To solve this problem,we propose an approach based on deep convolutional neural networks(DCNNs),which consists of three stages:fastener localization,abnormal fastener sample generation based on saliency detection,and fastener state inspection.First,a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions.Then,the foreground clip region of a fastener image is extracted by the designed fastener saliency detection network(F-SDNet),combined with data augmentation to generate a large number of abnormal fastener samples and balance the number of abnormal and normal samples.Finally,a fastener inspection model called Fastener ResNet-8 is constructed by being trained with the augmented fastener dataset.Results show the effectiveness of our proposed method in solving the problem of sample imbalance in fastener detection.Qualitative and quantitative comparisons show that the proposed F-SDNet outperforms other state-of-the-art methods in clip region extraction,reaching MAE and max F-measure of 0.0215 and 0.9635,respectively.In addition,the FPS of the fastener state inspection model reached 86.2,and the average accuracy reached 98.7%on 614 augmented fastener test sets and 99.9%on 7505 real fastener datasets.

Distributed Resource Allocation in Dispersed Computing Environment Based on UAV Track Inspection in Urban Rail Transit

With the rapid development of urban rail transit,the existing track detection has some problems such as low efficiency and insufficient detection coverage,so an intelligent and automatic track detectionmethod based onUAV is urgently needed to avoid major safety accidents.At the same time,the geographical distribution of IoT devices results in the inefficient use of the significant computing potential held by a large number of devices.As a result,the Dispersed Computing(DCOMP)architecture enables collaborative computing between devices in the Internet of Everything(IoE),promotes low-latency and efficient cross-wide applications,and meets users’growing needs for computing performance and service quality.This paper focuses on examining the resource allocation challenge within a dispersed computing environment that utilizes UAV inspection tracks.Furthermore,the system takes into account both resource constraints and computational constraints and transforms the optimization problem into an energy minimization problem with computational constraints.The Markov Decision Process(MDP)model is employed to capture the connection between the dispersed computing resource allocation strategy and the system environment.Subsequently,a method based on Double Deep Q-Network(DDQN)is introduced to derive the optimal policy.Simultaneously,an experience replay mechanism is implemented to tackle the issue of increasing dimensionality.The experimental simulations validate the efficacy of the method across various scenarios.

Automatic Road Tunnel Crack Inspection Based on Crack Area Sensing and Multiscale Semantic Segmentation

The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safetyand performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of roadtunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combinedwith a deep neural network model is an effective means to realize the localization and identification of crackdefects on the surface of road tunnels.We propose a complete set of automatic inspection methods for identifyingcracks on the walls of road tunnels as a solution to the problem of difficulty in identifying cracks during manualmaintenance. First, a set of equipment applied to the real-time acquisition of high-definition images of walls inroad tunnels is designed. Images of walls in road tunnels are acquired based on the designed equipment, whereimages containing crack defects are manually identified and selected. Subsequently, the training and validationsets used to construct the crack inspection model are obtained based on the acquired images, whereas the regionscontaining cracks and the pixels of the cracks are finely labeled. After that, a crack area sensing module is designedbased on the proposed you only look once version 7 model combined with coordinate attention mechanism (CAYOLOV7) network to locate the crack regions in the road tunnel surface images. Only subimages containingcracks are acquired and sent to the multiscale semantic segmentation module for extraction of the pixels to whichthe cracks belong based on the DeepLab V3+ network. The precision and recall of the crack region localizationon the surface of a road tunnel based on our proposed method are 82.4% and 93.8%, respectively. Moreover, themean intersection over union (MIoU) and pixel accuracy (PA) values for achieving pixel-level detection accuracyare 76.84% and 78.29%, respectively. The experimental results on the dataset show that our proposed two-stagedetection method outperforms other state-of-the-art models in crack region localization and detection. Based onour proposedmethod, the images captured on the surface of a road tunnel can complete crack detection at a speed often frames/second, and the detection accuracy can reach 0.25 mm, which meets the requirements for maintenanceof an actual project. The designed CA-YOLO V7 network enables precise localization of the area to which a crackbelongs in images acquired under different environmental and lighting conditions in road tunnels. The improvedDeepLab V3+ network based on lightweighting is able to extract crack morphology in a given region more quicklywhile maintaining segmentation accuracy. The established model combines defect localization and segmentationmodels for the first time, realizing pixel-level defect localization and extraction on the surface of road tunnelsin complex environments, and is capable of determining the actual size of cracks based on the physical coordinatesystemafter camera calibration. The trainedmodelhas highaccuracy andcanbe extendedandapplied to embeddedcomputing devices for the assessment and repair of damaged areas in different types of road tunnels.

A Systematic Review of Computer Vision Techniques for Quality Control in End-of-Line Visual Inspection of Antenna Parts

The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear,pose significant challenges to the reliability and performance of communication systems.This review paper navigates the landscape of antenna defect detection,emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection.This review paper serves as a valuable resource for researchers,engineers,and practitioners engaged in the design and maintenance of communication systems.The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures.In this study,a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented.The PRISMA principles will be followed throughout the review,and its goals are to provide a summary of recent research,identify relevant computer vision techniques,and evaluate how effective these techniques are in discovering defects during inspections.It contains articles from scholarly journals as well as papers presented at conferences up until June 2023.This research utilized search phrases that were relevant,and papers were chosen based on whether or not they met certain inclusion and exclusion criteria.In this study,several different computer vision approaches,such as feature extraction and defect classification,are broken down and analyzed.Additionally,their applicability and performance are discussed.The review highlights the significance of utilizing a wide variety of datasets and measurement criteria.The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation,such as real-time inspection systems and multispectral imaging.This review,on its whole,offers a complete study of computer vision approaches for quality control in antenna parts.It does so by providing helpful insights and drawing attention to areas that require additional exploration.

High-speed railway track components inspection framework based on YOLOv8 with high-performance model deployment

Railway inspection poses significant challenges due to the extensive use of various components in vast railway networks,especially in the case of high-speed railways.These networks demand high maintenance but offer only limited inspection windows.In response,this study focuses on developing a high-performance rail inspection system tailored for high-speed railways and railroads with constrained inspection timeframes.This system leverages the latest artificial intelligence advancements,incorporating YOLOv8 for detection.Our research introduces an efficient model inference pipeline based on a producer-consumer model,effectively utilizing parallel processing and concurrent computing to enhance performance.The deployment of this pipeline,implemented using C++,TensorRT,float16 quantization,and oneTBB,represents a significant departure from traditional sequential processing methods.The results are remarkable,showcasing a substantial increase in processing speed:from 38.93 Frames Per Second(FPS)to 281.06 FPS on a desktop system equipped with an Nvidia RTX A6000 GPU and from 19.50 FPS to 200.26 FPS on the Nvidia Jetson AGX Orin edge computing platform.This proposed framework has the potential to meet the real-time inspection requirements of high-speed railways.

A path planning method for robot patrol inspection in chemical industrial parks

Safety patrol inspection in chemical industrial parks is a complex multi-objective task with multiple degrees of freedom.Traditional pointer instruments with advantages like high reliability and strong adaptability to harsh environment,are widely applied in such parks.However,they rely on manual readings which have problems like heavy patrol workload,high labor cost,high false positives/negatives and poor timeliness.To address the above problems,this study proposes a path planning method for robot patrol in chemical industrial parks,where a path optimization model based on improved iterated local search and random variable neighborhood descent(ILS-RVND)algorithm is established by integrating the actual requirements of patrol tasks in chemical industrial parks.Further,the effectiveness of the model and algorithm is verified by taking real park data as an example.The results show that compared with GA and ILS-RVND,the improved algorithm reduces quantification cost by about 24%and saves patrol time by about 36%.Apart from shortening the patrol time of robots,optimizing their patrol path and reducing their maintenance loss,the proposed algorithm also avoids the untimely patrol of robots and enhances the safety factor of equipment.

Calibration of CO and CO2 Monitors Used in Periodic Inspection of Vehicles at Fixed Stations for Environmental Control

Global efforts for environmental cleanliness through the control of gaseous emissions from vehicles are gaining momentum and attracting increasing attention. Calibration plays a crucial role in these efforts by ensuring the quantitative assessment of emissions for informed decisions on environmental treatments. This paper describes a method for the calibration of CO/CO2 monitors used for periodic inspections of vehicles in cites. The calibration was performed in the selected ranges: 900 - 12,000 µmol/mol for CO and 2000 - 20,000 µmol/mol for CO2. The traceability of the measurement results to the SI units was ensured by using certified reference materials from CO/N2 and CO2/N2 primary gas mixtures. The method performance was evaluated by assessing its linearity, accuracy, precision, bias, and uncertainty of the calibration results. The calibration data exhibited a strong linear trend with R² values close to 1, indicating an excellent fit between the measured values and the calibration lines. Precision, expressed as relative standard deviation (%RSD), ranged from 0.48 to 4.56% for CO and from 0.97 to 3.53% for CO2, staying well below the 5% threshold for reporting results at a 95% confidence level. Accuracy measured as percent recovery, was consistently high (≥ 99.1%) for CO and ranged from 84.90% to 101.54% across the calibration range for CO2. In addition, the method exhibited minimal bias for both CO and CO2 calibrations and thus provided a reliable and accurate approach for calibrating CO/CO2 monitors used in vehicle inspections. Thus, it ensures the effectiveness of exhaust emission control for better environment.

Analysis of the Application of Bridge Rapid Inspection and Evaluation Technology

Rapid bridge inspection and evaluation mainly uses information technology to test the quality of bridge infrastructure and structures,integrates the test results with the existing management system,completes the bridge status assessment,establishes information management files to provide bridge disease problem inspection and analysis,and provides support for the application of disposal measures.This paper briefly discusses the necessity of applying rapid inspection and evaluation technology and analyzes the bridge’s rapid inspection and evaluation content,inspection system,and application process.We look forward to the future application prospects of this technology and supporting those in this field.

From manual to automated fabric inspection

Uster,Switzerland,28th March 2024–Uster Technologies offers a flexible solution to upgrade fabric inspection from manual to automated.Integration in existing production lines is quick and easy,and the data flow also brings extra benefits.It means fabric producers can significantly improve their yield with fast,accurate quality monitoring.

Defect Inspection Technology for Steel Truss Suspension Bridges

Steel truss suspension bridges are prone to developing defects after prolonged use.These defects may include corrosion of the main cable or the steel truss.To ensure the normal and safe functioning of the suspension bridge,it is necessary to inspect for defects promptly,understand the cause of the defect,and locate it through the use of inspection technology.By promptly addressing defects,the suspension bridge’s safety can be ensured.The author has analyzed the common defects and causes of steel truss suspension bridges and proposed specific inspection technologies.This research is intended to aid in the timely discovery of steel truss suspension bridge defects.

Surface form inspection with contact coordinate measurement:a review

Parts with high-quality freeform surfaces have been widely used in industries,which require strict quality control during the manufacturing process.Among all the industrial inspection methods,contact measurement with coordinate measuring machines or computer numerical control machine tool is a fundamental technique due to its high accuracy,robustness,and universality.In this paper,the existing research in the contact measurement field is systematically reviewed.First,different configurations of the measuring machines are introduced in detail,which may have influence on the corresponding sampling and inspection path generation criteria.Then,the entire inspection pipeline is divided into two stages,namely the pre-inspection and post-inspection stages.The typical methods of each sub-stage are systematically overviewed and classified,including sampling,accessibility analysis,inspection path generation,probe tip radius compensation,surface reconstruction,and uncertainty analysis.Apart from those classical research,the applications of the emerging deep learning technique in some specific tasks of measurement are introduced.Furthermore,some potential and promising trends are provided for future investigation.

Low-Cost Real-Time Automated Optical Inspection Using Deep Learning and Attention Map

The recent trends in Industry 4.0 and Internet of Things have encour-aged many factory managers to improve inspection processes to achieve automa-tion and high detection rates.However,the corresponding cost results of sample tests are still used for quality control.A low-cost automated optical inspection system that can be integrated with production lines to fully inspect products with-out adjustments is introduced herein.The corresponding mechanism design enables each product to maintain afixed position and orientation during inspec-tion to accelerate the inspection process.The proposed system combines image recognition and deep learning to measure the dimensions of the thread and iden-tify its defects within 20 s,which is lower than the production-line productivity per 30 s.In addition,the system is designed to be used for monitoring production lines and equipment status.The dimensional tolerance of the proposed system reaches 0.012 mm,and a 100%accuracy is achieved in terms of the defect reso-lution.In addition,an attention-based visualization approach is utilized to verify the rationale for the use of the convolutional neural network model and identify the location of thread defects.

Suggestions for Promoting China’s Drug Regulatory Agency to Join Pharmaceutical Inspection Co-operation Scheme-PIC/S

Objective To identify and reduce the gap between China’s drug GMP inspection and pharmaceutical inspection co-operation scheme(PIC/S)audit checklist,find out the key improvement items,and revise them pertinently,which will promote the process of China joining PIC/S.Methods The general situation of PIC/S organization and audit checklist were introduced first,and then the accession of several countries that joined the organization was analyzed.Meanwhile,the process of China’s participation in PIC/S was sorted out.After referring to the contents of PIC/S audit checklist,the problems of GMP inspection system in China were studied.Results and Conclusion There are still many problems in GMP inspection in China.Some suggestions are put forward for improvement and change,which can provide reference for the development of drug inspection agencies at all levels in China.

Minimum dose path planning for facility inspection based on the discrete Rao-combined ABC algorithm in radioactive environments with obstacles

Workers who conduct regular facility inspections in radioactive environments will inevitably be affected by radiation.Therefore,it is important to optimize the inspection path to ensure that workers are exposed to the least amount of radiation.This study proposes a discrete Rao-combined artificial bee colony(ABC)algorithm for planning inspection paths with minimum exposure doses in radioactive environments with obstacles.In this algorithm,retaining the framework of the traditional ABC algorithm,we applied the directional solution update rules of Rao algorithms at the employed bee stage and onlooker bee stage to increase the exploitation ability of the algorithm and implement discretion using the swap operator and swap sequence.To increase the randomness of solution generation,the chaos algorithm was used at the initialization stage.The K-opt operation technique was introduced at the scout bee stage to increase the exploration ability of the algorithm.For path planning in an environment with complex structural obstacles,an obstacle detour technique using a recursive algorithm was applied.To evaluate the performance of the proposed algorithm,we performed experimental simulations in three hypothetical environments and compared the results with those of improved particle swarm optimization,chaos particle swarm optimization,improved ant colony optimization,and discrete Rao’s algorithms.The experimental results show the high performance of the proposed discrete Rao-combined ABC algorithm and its obstacle detour capability.

Defect inspection of indoor components in buildings using deep learning object detection and augmented reality

Visual inspection is commonly adopted for building operation,maintenance,and safety.The durability and defects of components or materials in buildings can be quickly assessed through visual inspection.However,implementations of visual inspection are substantially time-consuming,labor-intensive,and error-prone because useful auxiliary tools that can instantly highlight defects or damage locations from images are not available.Therefore,an advanced building inspection framework is developed and implemented with augmented reality(AR)and real-time damage detection in this study.In this framework,engineers should walk around and film every corner of the building interior to generate the three-dimensional(3D)environment through ARKit.Meanwhile,a trained YOLOv5 model real-time detects defects during this process,even in a large-scale field,and the defect locations indicating the detected defects are then marked in this 3D environment.The defects areas can be measured with centimeter-level accuracy with the light detection and ranging(LiDAR)on devices.All required damage information,including defect positions and sizes,is collected at a time and can be rendered in the 2D and 3D views.Finally,this visual inspection can be efficiently conducted,and the previously generated environment can also be loaded to re-localize existing defect marks for future maintenance and change observation.Moreover,the proposed framework is also implemented and verified by an underground parking lot in a building to detect and quantify surface defects on concrete components.As seen in the results,the conventional building inspection is significantly improved with the aid of the proposed framework in terms of damage localization,damage quantification,and inspection efficiency.

Micro-Sized Pinhole Inspection with Segmented Time Reversal and High-Order Modes Cluster Lamb Waves Based on EMATs

Pinhole corrosion is difficult to discover through conventional ultrasonic guided waves inspection,particularly for micro-sized pinholes less than 1 mm in diameter.This study proposes a new micro-sized pinhole inspection method based on segmented time reversal(STR)and high-order modes cluster(HOMC)Lamb waves.First,the principle of defect echo enhancement using STR is introduced.Conventional and STR inspection experiments were conducted on aluminum plates with a thickness of 3 mm and defects with different diameters and depths.The parameters of the segment window are discussed in detail.The results indicate that the proposed method had an amplitude four times larger than of conventional ultrasonic guided waves inspection method for pinhole defect detection and could detect micro-sized pinhole defects as small as 0.5 mm in diameter and 0.5 mm in depth.Moreover,the segment window location and width(5-10 times width of the conventional excitation signal)did not affect the detection sensitivity.The combination of low-power and STR is more conducive to detection in different environments,indicating the robustness of the proposed method.Compared with conventional ultrasonic guided wave inspection methods,the proposed method can detect much smaller defect echoes usually obscured by noise that are difficult to detect with a lower excitation power and thus this study would be a good reference for pinhole defect detection.

Automated X-ray Defect Inspection on Occluded BGA Balls Using Hybrid Algorithm

Automated X-ray defect inspection of occluded objects has been an essential topic in semiconductors,autonomous vehicles,and artificial intelligence devices.However,there are few solutions to segment occluded objects in the X-ray inspection efficiently.In particular,in the Ball Grid Array inspection of X-ray images,it is difficult to accurately segment the regions of occluded solder balls and detect defects inside solder balls.In this paper,we present a novel automatic inspection algorithm that segments solder balls,and detects defects fast and efficiently when solder balls are occluded.The proposed algorithm consists of two stages.In the first stage,the defective candidates or defects are determined through the following four steps:(i)image preprocessing such as noise removal,contrast enhancement,binarization,connected component,and morphology,(ii)limiting the inspec-tion area to the ball regions and determining if the ball regions are occluded,(iii)segmenting each ball region into one or more regions with similar gray values,and(iv)determining whether there are defects or defective candidates in the regions using a weighted sum of local threshold on local variance.If there are defective candidates,the determination of defects is finally made in the following stage.In the second stage,defects are detected using the automated inspection technique based on oblique computed tomography.The 3D precision inspection process is divided into four steps:(i)obtaining 360 projection images(one image per degree)rotating the object from 0 to 360 degrees,(ii)reconstructing a 3D image from the 360 projected images,(iii)finding the center slice of gravity for solder balls from the axial slice images in the z-direction,and getting the inspection intervals between the upper bound and the lower bound from the center slice,and(iv)finally determining whether there are defects in the averaged image of solder balls.The proposed hybrid algorithm is robust for segmenting the defects inside occluded solder balls,and improves the performance of solder ball segmentation and defect detection algorithm.Experimental results show an accuracy of more than 97%.

A Privacy-Preserving Federated Learning Algorithm for Intelligent Inspection in Pumped Storage Power Station

As a distributed machine learning architecture,Federated Learning(FL)can train a global model by exchanging users’model parameters without their local data.However,with the evolution of eavesdropping techniques,attackers can infer information related to users’local data with the intercepted model parameters,resulting in privacy leakage and hindering the application of FL in smart factories.To meet the privacy protection needs of the intelligent inspection task in pumped storage power stations,in this paper we propose a novel privacy-preserving FL algorithm based on multi-key Fully Homomorphic Encryption(FHE),called MFHE-PPFL.Specifically,to reduce communication costs caused by deploying the FHE algorithm,we propose a self-adaptive threshold-based model parameter compression(SATMPC)method.It can reduce the amount of encrypted data with an adaptive thresholds-enabled user selection mechanism that only enables eligible devices to communicate with the FL server.Moreover,to protect model parameter privacy during transmission,we develop a secret sharing-based multi-key RNS-CKKS(SSMR)method that encrypts the device’s uploaded parameter increments and supports decryption in device dropout scenarios.Security analyses and simulation results show that our algorithm can prevent four typical threat models and outperforms the state-of-the-art in communication costs with guaranteed accuracy.