Few-shot point cloud 3D object detection(FS3D)aims to identify and locate objects of novel classes within point clouds using knowledge acquired from annotated base classes and a minimal number of samples from the nove...Few-shot point cloud 3D object detection(FS3D)aims to identify and locate objects of novel classes within point clouds using knowledge acquired from annotated base classes and a minimal number of samples from the novel classes.Due to imbalanced training data,existing FS3D methods based on fully supervised learning can lead to overfitting toward base classes,which impairs the network’s ability to generalize knowledge learned from base classes to novel classes and also prevents the network from extracting distinctive foreground and background representations for novel class objects.To address these issues,this thesis proposes a category-agnostic contrastive learning approach,enhancing the generalization and identification abilities for almost unseen categories through the construction of pseudo-labels and positive-negative sample pairs unrelated to specific classes.Firstly,this thesis designs a proposal-wise context contrastive module(CCM).By reducing the distance between foreground point features and increasing the distance between foreground and background point features within a region proposal,CCM aids the network in extracting more discriminative foreground and background feature representations without reliance on categorical annotations.Secondly,this thesis utilizes a geometric contrastive module(GCM),which enhances the network’s geometric perception capability by employing contrastive learning on the foreground point features associated with various basic geometric components,such as edges,corners,and surfaces,thereby enabling these geometric components to exhibit more distinguishable representations.This thesis also combines category-aware contrastive learning with former modules to maintain categorical distinctiveness.Extensive experimental results on FS-SUNRGBD and FS-ScanNet datasets demonstrate the effectiveness of this method with average precision exceeding the baseline by up to 8%.展开更多
A bridge project is taken as an example to analyze the application of bearing capacity detection and evaluation.This article provides a basic overview of the project,the application of bearing capacity detection techn...A bridge project is taken as an example to analyze the application of bearing capacity detection and evaluation.This article provides a basic overview of the project,the application of bearing capacity detection technology,and the bearing capacity assessment analysis.It is hoped that this analysis can provide a scientific reference for the load-bearing capacity detection and evaluation work in bridge engineering projects,thereby achieving a scientific assessment of the overall load-bearing capacity of the bridge engineering structure.展开更多
In the unstructured litchi orchard,precise identification and localization of litchi fruits and picking points are crucial for litchi-picking robots.Most studies adopt multi-step methods to detect fruit and locate pic...In the unstructured litchi orchard,precise identification and localization of litchi fruits and picking points are crucial for litchi-picking robots.Most studies adopt multi-step methods to detect fruit and locate picking points,which are slow and struggle to cope with complex environments.This study proposes a YOLOv8-iGR model based on YOLOv8n-pose improvement,integrating end-to-end network for both object detection and key point detection.Specifically,this study considers the influence of auxiliary points on picking point and designs four litchi key point strategies.Secondly,the architecture named iSaE is proposed,which combines the capabilities of CNN and attention mechanism.Subsequently,C2f is replaced by Generalized Efficient Layer Aggregation Network(GELAN)to reduce model redundancy and improve detection accuracy.Finally,based on RFAConv,RFAPoseHead is designed to address the issue of parameter sharing in large convolutional kernels,thereby more effectively extracting feature information.Experimental results demonstrate that YOLOv8-iGR achieves an AP of 95.7%in litchi fruit detection,and the Euclidean distance error of picking points is less than 8 pixels across different scenes,meeting the requirements of litchi picking.Additionally,the GFLOPs of the model are reduced by 10.71%.The accuracy of the model’s localization for picking points was tested through field picking experiments.In conclusion,YOLOv8-iGR exhibits outstanding detection performance along with lower model complexity,making it more feasible for implementation on robots.This will provide technical support for the vision system of the litchi-picking robot.展开更多
To automatically detecting whether a person is wearing mask properly,we propose a face mask detection algorithm based on hue-saturation-value(HSV)+histogram of oriented gradient(HOG)features and support vector machine...To automatically detecting whether a person is wearing mask properly,we propose a face mask detection algorithm based on hue-saturation-value(HSV)+histogram of oriented gradient(HOG)features and support vector machines(SVM).Firstly,human face and five feature points are detected with RetinaFace face detection algorithm.The feature points are used to locate to mouth and nose region,and HSV+HOG features of this region are extracted and input to SVM for training to realize detection of wearing masks or not.Secondly,RetinaFace is used to locate to nasal tip area of face,and YCrCb elliptical skin tone model is used to detect the exposure of skin in the nasal tip area,and the optimal classification threshold can be found to determine whether the wear is properly according to experimental results.Experiments show that the accuracy of detecting whether mask is worn can reach 97.9%,and the accuracy of detecting whether mask is worn correctly can reach 87.55%,which verifies the feasibility of the algorithm.展开更多
The detection of outliers and change points from time series has become research focus in the area of time series data mining since it can be used for fraud detection, rare event discovery, event/trend change detectio...The detection of outliers and change points from time series has become research focus in the area of time series data mining since it can be used for fraud detection, rare event discovery, event/trend change detection, etc. In most previous works, outlier detection and change point detection have not been related explicitly and the change point detections did not consider the influence of outliers, in this work, a unified detection framework was presented to deal with both of them. The framework is based on ALARCON-AQUINO and BARRIA's change points detection method and adopts two-stage detection to divide the outliers and change points. The advantages of it lie in that: firstly, unified structure for change detection and outlier detection further reduces the computational complexity and make the detective procedure simple; Secondly, the detection strategy of outlier detection before change point detection avoids the influence of outliers to the change point detection, and thus improves the accuracy of the change point detection. The simulation experiments of the proposed method for both model data and actual application data have been made and gotten 100% detection accuracy. The comparisons between traditional detection method and the proposed method further demonstrate that the unified detection structure is more accurate when the time series are contaminated by outliers.展开更多
Change point detection becomes increasingly important because it can support data analysis by providing labels to the data in an unsupervised manner.In the context of process data analytics,change points in the time s...Change point detection becomes increasingly important because it can support data analysis by providing labels to the data in an unsupervised manner.In the context of process data analytics,change points in the time series of process variables may have an important indication about the process operation.For example,in a batch process,the change points can correspond to the operations and phases defined by the batch recipe.Hence identifying change points can assist labelling the time series data.Various unsupervised algorithms have been developed for change point detection,including the optimisation approachwhich minimises a cost functionwith certain penalties to search for the change points.The Bayesian approach is another,which uses Bayesian statistics to calculate the posterior probability of a specific sample being a change point.The paper investigates how the two approaches for change point detection can be applied to process data analytics.In addition,a new type of cost function using Tikhonov regularisation is proposed for the optimisation approach to reduce irrelevant change points caused by randomness in the data.The novelty lies in using regularisation-based cost functions to handle ill-posed problems of noisy data.The results demonstrate that change point detection is useful for process data analytics because change points can produce data segments corresponding to different operating modes or varying conditions,which will be useful for other machine learning tasks.展开更多
A performance assisted enhancement Kalman filtering algorithm(PAE-KF) for GPS/INS integration navigation in urban areas was presented in this work. The aim of this PAE-KF algorithm was to prevent "deep contaminat...A performance assisted enhancement Kalman filtering algorithm(PAE-KF) for GPS/INS integration navigation in urban areas was presented in this work. The aim of this PAE-KF algorithm was to prevent "deep contamination" caused by error GPS data. This filtering algorithm effectively combined fault estimation of raw GPS data and nonholonomic constraint of vehicle. In fault estimation, a change point detection algorithm based on abrupt change model was proposed. Statistical tool was then used to infer the future bound of GPS data, which can detect faults in GPS raw data. If any kinds of faults were detected, dead reckoning mechanism begins to compute current position. Nonholonomic constraint condition of vehicle was used to estimate velocity of vehicle and change point detection was added into classic Kalman filtering structure. Experiment on vehicle shows that even when the GPS signals are unavailable for a period of time, this method can also output high accuracy data.展开更多
A highly sensitive electrochemiluminescence-polymerase chain reaction (ECL-PCR) method for K-ras point mutation detection is developed. Briefly, K-ras oncogene was amplified by a Ru(bpy)3(2+) (TBR)-labeled forward and...A highly sensitive electrochemiluminescence-polymerase chain reaction (ECL-PCR) method for K-ras point mutation detection is developed. Briefly, K-ras oncogene was amplified by a Ru(bpy)3(2+) (TBR)-labeled forward and a biotin-labeled reverse primer, and followed by digestion with MvaI restriction enzyme, which only cut the wild-type amplicon containing its cutting site. The digested product was then adsorbed to the streptavidin-coated microbead through the biotin label and detected by ECL assay. The experiment results showed that the different genotypes can be clearly discriminated by ECL-PCR method. It is useful in point mutation detection, due to its sensitivity, safety, and simplicity.展开更多
Trend analysis and change point detection in a time series are frequent analysis tools.Change point detection is the identification of abrupt variation in the process behaviour due to natural or artificial changes,whe...Trend analysis and change point detection in a time series are frequent analysis tools.Change point detection is the identification of abrupt variation in the process behaviour due to natural or artificial changes,whereas trend can be defined as estimation of gradual departure from past norms.We analyze the time series data in the presence of trend,using Cox-Stuart methods together with the change point algorithms.We applied the methods to the nearsurface wind speed time series for Australia as an example.The trends in near-surface wind speeds for Australia have been investigated based upon our newly developed wind speed datasets,which were constructed by blending observational data collected at various heights using local surface roughness information.The trend in wind speed at 10 m is generally increasing while at 2 m it tends to be decreasing.Significance testing,change point analysis and manual inspection of records indicate several factors may be contributing to the discrepancy,such as systematic biases accompanying instrument changes,random data errors(e.g.accumulation day error)and data sampling issues.Homogenization technique and multiple-period trend analysis based upon change point detections have thus been employed to clarify the source of the inconsistencies in wind speed trends.展开更多
Data Matrix(DM)codes have been widely used in industrial production.The reading of DM code usually includes positioning and decoding.Accurate positioning is a prerequisite for successful decoding.Traditional image pro...Data Matrix(DM)codes have been widely used in industrial production.The reading of DM code usually includes positioning and decoding.Accurate positioning is a prerequisite for successful decoding.Traditional image processing methods have poor adaptability to pollution and complex backgrounds.Although deep learning-based methods can automatically extract features,the bounding boxes cannot entirely fit the contour of the code.Further image processing methods are required for precise positioning,which will reduce efficiency.Because of the above problems,a CenterNet-based DM code key point detection network is proposed,which can directly obtain the four key points of the DM code.Compared with the existing methods,the degree of fitness is higher,which is conducive to direct decoding.To further improve the positioning accuracy,an enhanced loss function is designed,including DM code key point heatmap loss,standard DM code projection loss,and polygon Intersection-over-Union(IoU)loss,which is beneficial for the network to learn the spatial geometric characteristics of DM code.The experiment is carried out on the self-made DM code key point detection dataset,including pollution,complex background,small objects,etc.,which uses the Average Precision(AP)of the common object detection metric as the evaluation metric.AP reaches 95.80%,and Frames Per Second(FPS)gets 88.12 on the test set of the proposed dataset,which can achieve real-time performance in practical applications.展开更多
The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic reconnaissance.Existing methodologies predominantly rely on prior information or heavily constrai...The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic reconnaissance.Existing methodologies predominantly rely on prior information or heavily constrained models,posing challenges for non-cooperative applications.This paper introduces a novel approach to model MFRs using a Bayesian network,where the conditional probability density function is approximated by an autoregressive kernel mixture network(ARKMN).Utilizing the estimated probability density function,a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse trains.Simulation results affirm the proposed method's efficacy in modeling MFRs,outperforming the state-of-the-art in pulse train denoising and change point detection.展开更多
Time series segmentation has attracted more interests in recent years,which aims to segment time series into different segments,each reflects a state of the monitored objects.Although there have been many surveys on t...Time series segmentation has attracted more interests in recent years,which aims to segment time series into different segments,each reflects a state of the monitored objects.Although there have been many surveys on time series segmentation,most of them focus more on change point detection(CPD)methods and overlook the advances in boundary detection(BD)and state detection(SD)methods.In this paper,we categorize time series segmentation methods into CPD,BD,and SD methods,with a specific focus on recent advances in BD and SD methods.Within the scope of BD and SD,we subdivide the methods based on their underlying models/techniques and focus on the milestones that have shaped the development trajectory of each category.As a conclusion,we found that:(1)Existing methods failed to provide sufficient support for online working,with only a few methods supporting online deployment;(2)Most existing methods require the specification of parameters,which hinders their ability to work adaptively;(3)Existing SD methods do not attach importance to accurate detection of boundary points in evaluation,which may lead to limitations in boundary point detection.We highlight the ability to working online and adaptively as important attributes of segmentation methods,the boundary detection accuracy as a neglected metrics for SD methods.展开更多
The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in ...The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in the per-frame 3D posture estimation from two-dimensional(2D)mapping to 3D mapping.Firstly,by examining the relationship between the movements of different bones in the human body,four virtual skeletons are proposed to enhance the cyclic constraints of limb joints.Then,multiple parameters describing the skeleton are fused and projected into a high-dimensional space.Utilizing a multi-branch network,motion features between bones and overall motion features are extracted to mitigate the drift error in the estimation results.Furthermore,the estimated relative depth is projected into 3D space,and the error is calculated against real 3D data,forming a loss function along with the relative depth error.This article adopts the average joint pixel error as the primary performance metric.Compared to the benchmark approach,the estimation findings indicate an increase in average precision of 1.8 mm within the Human3.6M sample.展开更多
Fringe projection profilometry(FPP)has been widely applied to non-contact three-dimensional measurement in industries owing to its high accuracy and speed.The point cloud,which is a measurement result of the FPP syste...Fringe projection profilometry(FPP)has been widely applied to non-contact three-dimensional measurement in industries owing to its high accuracy and speed.The point cloud,which is a measurement result of the FPP system,typically contains a large number of invalid points caused by the background,ambient light,shadows,and object edge regions.Research on noisy point detection and elimination has been conducted over the past two decades.However,existing invalid point removal methods are based on image intensity analysis and are only applicable to simple measurement backgrounds that are purely dark.In this paper,we propose a novel invalid point removal framework that consists of two aspects:(1)A convolutional neural network(CNN)is designed to segment the foreground from the background of different intensity conditions in FPP measurement circumstances to remove background points and the most discrete points in background regions.(2)A two-step method based on the fringe image intensity threshold and a bilateral filter is proposed to eliminate the small number of discrete points remaining after background segmentation caused by shadows and edge areas on objects.Experimental results verify that the proposed framework(1)can remove background points intelligently and accurately in different types of complex circumstances,and(2)performs excellently in discrete point detection from object regions.展开更多
Taking Shanghai as an example,this study obtained the online travel notes data from Xiaohongshu and Qunar in the past 10 years to construct the Shanghai tourist flow network(STFN)and used the methods of change point d...Taking Shanghai as an example,this study obtained the online travel notes data from Xiaohongshu and Qunar in the past 10 years to construct the Shanghai tourist flow network(STFN)and used the methods of change point detection(CPD)and complex network analysis(CNA)to reveal the spatial structure characteristics of Shanghai tourism flow and the dynamic evolution process of STFN.The results showed that:(1)In the past 10 years,Shanghai tourist market had experienced a process of evolution from stable and orderly to short-term fluc-tuation and then gradual recovery,and the year of 2019 was the turning point of tourist flow network evolution.(2)The small-world and approximate scale-free characteristics of STFN were verified,and the network changed from disassortative to temporary assortative,showing a development trend of external expansion and internal separation.(3)While the centrality indicators of tourist flow network remained stable as a whole,the attention to cultural nodes was also increasing with the emergence of new nodes;(4)In terms of spatial connection,new popular nodes emerged and the relationship between them and the surrounding nodes was strengthened;(5)The spatial pattern of tourist flow network presented an inverted“V”shape and gradually expanded to southwest and southeast,forming a network with core nodes as the center and radiating outward.At the same time,newly emerging nodes at the periphery had formed relatively independent clusters.展开更多
Objective:To observe the electrosensitization of Sifeng(EX-UE10)in children with constipation due to excessive heat in intestine.Methods:The meridian values of Sifeng(EX-UE10)in 80 children with constipation due to ex...Objective:To observe the electrosensitization of Sifeng(EX-UE10)in children with constipation due to excessive heat in intestine.Methods:The meridian values of Sifeng(EX-UE10)in 80 children with constipation due to excessive heat in intestine and in 80 healthy children were measured using a traditional Chinese medicine(TCM)meridian detector,and the variation rule of the point meridian values was analyzed by SPSS version 26.0 statistical software.Results:The meridian values of Sifeng(EX-UE10)of the index finger,middle finger,and ring finger in the observation group were statistically different from those in the control group(P<0.01).There was no statistical difference in the meridian value of Sifeng(EX-UE10)of the little finger between the two groups(P>0.05).Conclusion:Electrosensitization occurs at Sifeng(EX-UE10)of the index finger,middle finger,and ring finger in children with constipation(syndrome of excessive heat in intestine),and thus the treatment can focus on stimulating the index finger,middle finger,and ring finger.展开更多
Based on the coded and non-coded targets, the targets are extracted from the images according to their size, shape and intensity etc., and thus an improved method to identify the unique identity（D） of every coded ta...Based on the coded and non-coded targets, the targets are extracted from the images according to their size, shape and intensity etc., and thus an improved method to identify the unique identity（D） of every coded target is put forward and the non-coded and coded targets are classified. Moreover, the gray scale centroid algorithm is applied to obtain the subpixel location of both uncoded and coded targets. The initial matching of the uncoded target correspondences between an image pair is established according to similarity and compatibility, which are based on the ID correspondences of the coded targets. The outliers in the initial matching of the uncoded target are eliminated according to three rules to finally obtain the uncoded target correspondences. Practical examples show that the algorithm is rapid, robust and is of high precision and matching ratio.展开更多
Human pose recognition and estimation in video is pervasive.However,the process noise and local occlusion bring great challenge to pose recognition.In this paper,we introduce the Kalman filter into pose recognition to...Human pose recognition and estimation in video is pervasive.However,the process noise and local occlusion bring great challenge to pose recognition.In this paper,we introduce the Kalman filter into pose recognition to reduce noise and solve local occlusion problem.The core of pose recognition in video is the fast detection of key points and the calculation of human steering angles.Thus,we first build a human key point detection model.Frame skipping is performed based on the Hamming distance of the hash value of every two adjacent frames in video.Noise reduction is performed on key point coordinates with the Kalman filter.To calculate the human steering angle,current state information of key points is predicted using the optimal estimation of key points at the previous time.Then human steering angle can be calculated based on current and previous state information.The improved SENet,NLNet and GCNet modules are integrated into key point detection model for improving accuracy.Tests are also given to illustrate the effectiveness of the proposed algorithm.展开更多
Isogeometric analysis(IGA)is introduced to establish the direct link between computer-aided design and analysis.It is commonly implemented by Galerkin formulations(isogeometric Galerkin,IGA-G)through the use of nonuni...Isogeometric analysis(IGA)is introduced to establish the direct link between computer-aided design and analysis.It is commonly implemented by Galerkin formulations(isogeometric Galerkin,IGA-G)through the use of nonuniform rational B-splines(NURBS)basis functions for geometric design and analysis.Another promising approach,isogeometric collocation(IGA-C),working directly with the strong form of the partial differential equation(PDE)over the physical domain defined by NURBS geometry,calculates the derivatives of the numerical solution at the chosen collocation points.In a typical IGA,the knot vector of the NURBS numerical solution is only determined by the physical domain.A new perspective on the IGAmethod is proposed in this study to improve the accuracy and convergence of the solution.Solving the PDE with IGA can be regarded as fitting the load function defined on the NURBS geometry(right-hand side)with derivatives of the NURBS numerical solution(left-hand side).Moreover,the design of the knot vector has a close relationship to theNURBS functions to be fitted in the area of data fitting in geometric design.Therefore,the detected feature points of the load function are integrated into the initial knot vector of the physical domainto construct thenewknot vector of thenumerical solution.Then,they are connected seamlessly with the IGA-C framework for its great potential combining the accuracy and smoothness merits with the computational efficiency,which we call isogeometric collocation by fitting load function(IGACL).In numerical experiments,we implement our method to solve 1D,2D,and 3D PDEs and demonstrate the improvement in accuracy by comparing it with the standard IGA-C method.We also verify the superiority in the accuracy of our knot selection scheme when employed in the IGA-G method,which we call isogeometric Galerkin by fitting load function(IGA-GL).展开更多
Seam image processing is the basis of the realization of automatic laser vision seam tracking system, and it has become one of the important research directions. Adding windows processing, gray processing, fast median...Seam image processing is the basis of the realization of automatic laser vision seam tracking system, and it has become one of the important research directions. Adding windows processing, gray processing, fast median filtering, binary processing and image edge extraction are used to pretreat the seam image. In the post-processing of seam image, the feature points of the target image are succesfully detected by using center line extraction and feature points detection algorithm based on slope analysis. The whole processing time is less than 150 ms, and the real-time processing of seam image can be implemented.展开更多
文摘Few-shot point cloud 3D object detection(FS3D)aims to identify and locate objects of novel classes within point clouds using knowledge acquired from annotated base classes and a minimal number of samples from the novel classes.Due to imbalanced training data,existing FS3D methods based on fully supervised learning can lead to overfitting toward base classes,which impairs the network’s ability to generalize knowledge learned from base classes to novel classes and also prevents the network from extracting distinctive foreground and background representations for novel class objects.To address these issues,this thesis proposes a category-agnostic contrastive learning approach,enhancing the generalization and identification abilities for almost unseen categories through the construction of pseudo-labels and positive-negative sample pairs unrelated to specific classes.Firstly,this thesis designs a proposal-wise context contrastive module(CCM).By reducing the distance between foreground point features and increasing the distance between foreground and background point features within a region proposal,CCM aids the network in extracting more discriminative foreground and background feature representations without reliance on categorical annotations.Secondly,this thesis utilizes a geometric contrastive module(GCM),which enhances the network’s geometric perception capability by employing contrastive learning on the foreground point features associated with various basic geometric components,such as edges,corners,and surfaces,thereby enabling these geometric components to exhibit more distinguishable representations.This thesis also combines category-aware contrastive learning with former modules to maintain categorical distinctiveness.Extensive experimental results on FS-SUNRGBD and FS-ScanNet datasets demonstrate the effectiveness of this method with average precision exceeding the baseline by up to 8%.
文摘A bridge project is taken as an example to analyze the application of bearing capacity detection and evaluation.This article provides a basic overview of the project,the application of bearing capacity detection technology,and the bearing capacity assessment analysis.It is hoped that this analysis can provide a scientific reference for the load-bearing capacity detection and evaluation work in bridge engineering projects,thereby achieving a scientific assessment of the overall load-bearing capacity of the bridge engineering structure.
基金supported by Natural Science Foundation of Guangdong Province(Grant No.2025A1515011771)Guangzhou Science and Technology Plan Project(Grant No.2024E04J1242,2023B01J0046)+2 种基金Guangdong Provincial Department of Science and Technology(Grant No.2023A0505050130)Key Projects of Guangzhou Science and Technology Program(Grant No.2024B03J1357)Natural Science Foundation of China(Grant No.61863011,32071912).
文摘In the unstructured litchi orchard,precise identification and localization of litchi fruits and picking points are crucial for litchi-picking robots.Most studies adopt multi-step methods to detect fruit and locate picking points,which are slow and struggle to cope with complex environments.This study proposes a YOLOv8-iGR model based on YOLOv8n-pose improvement,integrating end-to-end network for both object detection and key point detection.Specifically,this study considers the influence of auxiliary points on picking point and designs four litchi key point strategies.Secondly,the architecture named iSaE is proposed,which combines the capabilities of CNN and attention mechanism.Subsequently,C2f is replaced by Generalized Efficient Layer Aggregation Network(GELAN)to reduce model redundancy and improve detection accuracy.Finally,based on RFAConv,RFAPoseHead is designed to address the issue of parameter sharing in large convolutional kernels,thereby more effectively extracting feature information.Experimental results demonstrate that YOLOv8-iGR achieves an AP of 95.7%in litchi fruit detection,and the Euclidean distance error of picking points is less than 8 pixels across different scenes,meeting the requirements of litchi picking.Additionally,the GFLOPs of the model are reduced by 10.71%.The accuracy of the model’s localization for picking points was tested through field picking experiments.In conclusion,YOLOv8-iGR exhibits outstanding detection performance along with lower model complexity,making it more feasible for implementation on robots.This will provide technical support for the vision system of the litchi-picking robot.
基金National Natural Science Foundation of China(No.519705449)。
文摘To automatically detecting whether a person is wearing mask properly,we propose a face mask detection algorithm based on hue-saturation-value(HSV)+histogram of oriented gradient(HOG)features and support vector machines(SVM).Firstly,human face and five feature points are detected with RetinaFace face detection algorithm.The feature points are used to locate to mouth and nose region,and HSV+HOG features of this region are extracted and input to SVM for training to realize detection of wearing masks or not.Secondly,RetinaFace is used to locate to nasal tip area of face,and YCrCb elliptical skin tone model is used to detect the exposure of skin in the nasal tip area,and the optimal classification threshold can be found to determine whether the wear is properly according to experimental results.Experiments show that the accuracy of detecting whether mask is worn can reach 97.9%,and the accuracy of detecting whether mask is worn correctly can reach 87.55%,which verifies the feasibility of the algorithm.
基金Project(2011AA040603) supported by the National High Technology Ressarch & Development Program of ChinaProject(201202226) supported by the Natural Science Foundation of Liaoning Province, China
文摘The detection of outliers and change points from time series has become research focus in the area of time series data mining since it can be used for fraud detection, rare event discovery, event/trend change detection, etc. In most previous works, outlier detection and change point detection have not been related explicitly and the change point detections did not consider the influence of outliers, in this work, a unified detection framework was presented to deal with both of them. The framework is based on ALARCON-AQUINO and BARRIA's change points detection method and adopts two-stage detection to divide the outliers and change points. The advantages of it lie in that: firstly, unified structure for change detection and outlier detection further reduces the computational complexity and make the detective procedure simple; Secondly, the detection strategy of outlier detection before change point detection avoids the influence of outliers to the change point detection, and thus improves the accuracy of the change point detection. The simulation experiments of the proposed method for both model data and actual application data have been made and gotten 100% detection accuracy. The comparisons between traditional detection method and the proposed method further demonstrate that the unified detection structure is more accurate when the time series are contaminated by outliers.
基金support by the Federal Ministry for Economic Affairs and Climate Action of Germany(BMWK)within the Innovation Platform“KEEN-Artificial Intelligence Incubator Laboratory in the Process Industry”(Grant No.01MK20014T)The research of L.B.is supported by the Swedish Research Council Grant VR 2018-03661。
文摘Change point detection becomes increasingly important because it can support data analysis by providing labels to the data in an unsupervised manner.In the context of process data analytics,change points in the time series of process variables may have an important indication about the process operation.For example,in a batch process,the change points can correspond to the operations and phases defined by the batch recipe.Hence identifying change points can assist labelling the time series data.Various unsupervised algorithms have been developed for change point detection,including the optimisation approachwhich minimises a cost functionwith certain penalties to search for the change points.The Bayesian approach is another,which uses Bayesian statistics to calculate the posterior probability of a specific sample being a change point.The paper investigates how the two approaches for change point detection can be applied to process data analytics.In addition,a new type of cost function using Tikhonov regularisation is proposed for the optimisation approach to reduce irrelevant change points caused by randomness in the data.The novelty lies in using regularisation-based cost functions to handle ill-posed problems of noisy data.The results demonstrate that change point detection is useful for process data analytics because change points can produce data segments corresponding to different operating modes or varying conditions,which will be useful for other machine learning tasks.
基金Projects(90820302,60805027)supported by the National Natural Science Foundation of ChinaProject(2011BAK15B06)supported by the National Science and Technology Support Program,China+1 种基金Project(2013M541003)supported by the China Postdoctoral Science FoundationProject(2012YQ090208)supported by the Special-Funded Program on National Key Scientific Instruments and Equipment Development
文摘A performance assisted enhancement Kalman filtering algorithm(PAE-KF) for GPS/INS integration navigation in urban areas was presented in this work. The aim of this PAE-KF algorithm was to prevent "deep contamination" caused by error GPS data. This filtering algorithm effectively combined fault estimation of raw GPS data and nonholonomic constraint of vehicle. In fault estimation, a change point detection algorithm based on abrupt change model was proposed. Statistical tool was then used to infer the future bound of GPS data, which can detect faults in GPS raw data. If any kinds of faults were detected, dead reckoning mechanism begins to compute current position. Nonholonomic constraint condition of vehicle was used to estimate velocity of vehicle and change point detection was added into classic Kalman filtering structure. Experiment on vehicle shows that even when the GPS signals are unavailable for a period of time, this method can also output high accuracy data.
文摘A highly sensitive electrochemiluminescence-polymerase chain reaction (ECL-PCR) method for K-ras point mutation detection is developed. Briefly, K-ras oncogene was amplified by a Ru(bpy)3(2+) (TBR)-labeled forward and a biotin-labeled reverse primer, and followed by digestion with MvaI restriction enzyme, which only cut the wild-type amplicon containing its cutting site. The digested product was then adsorbed to the streptavidin-coated microbead through the biotin label and detected by ECL assay. The experiment results showed that the different genotypes can be clearly discriminated by ECL-PCR method. It is useful in point mutation detection, due to its sensitivity, safety, and simplicity.
文摘Trend analysis and change point detection in a time series are frequent analysis tools.Change point detection is the identification of abrupt variation in the process behaviour due to natural or artificial changes,whereas trend can be defined as estimation of gradual departure from past norms.We analyze the time series data in the presence of trend,using Cox-Stuart methods together with the change point algorithms.We applied the methods to the nearsurface wind speed time series for Australia as an example.The trends in near-surface wind speeds for Australia have been investigated based upon our newly developed wind speed datasets,which were constructed by blending observational data collected at various heights using local surface roughness information.The trend in wind speed at 10 m is generally increasing while at 2 m it tends to be decreasing.Significance testing,change point analysis and manual inspection of records indicate several factors may be contributing to the discrepancy,such as systematic biases accompanying instrument changes,random data errors(e.g.accumulation day error)and data sampling issues.Homogenization technique and multiple-period trend analysis based upon change point detections have thus been employed to clarify the source of the inconsistencies in wind speed trends.
基金funded by the Youth Project of National Natural Science Foundation of China(52002031)the General Project of Shaanxi Province Science and Technology Development Planned Project(2023-JC-YB-600)+1 种基金Postgraduate Education and Teaching Research University-Level Project of Central University Project(300103131033)the Transportation Research Project of Shaanxi Transport Department(23-108 K).
文摘Data Matrix(DM)codes have been widely used in industrial production.The reading of DM code usually includes positioning and decoding.Accurate positioning is a prerequisite for successful decoding.Traditional image processing methods have poor adaptability to pollution and complex backgrounds.Although deep learning-based methods can automatically extract features,the bounding boxes cannot entirely fit the contour of the code.Further image processing methods are required for precise positioning,which will reduce efficiency.Because of the above problems,a CenterNet-based DM code key point detection network is proposed,which can directly obtain the four key points of the DM code.Compared with the existing methods,the degree of fitness is higher,which is conducive to direct decoding.To further improve the positioning accuracy,an enhanced loss function is designed,including DM code key point heatmap loss,standard DM code projection loss,and polygon Intersection-over-Union(IoU)loss,which is beneficial for the network to learn the spatial geometric characteristics of DM code.The experiment is carried out on the self-made DM code key point detection dataset,including pollution,complex background,small objects,etc.,which uses the Average Precision(AP)of the common object detection metric as the evaluation metric.AP reaches 95.80%,and Frames Per Second(FPS)gets 88.12 on the test set of the proposed dataset,which can achieve real-time performance in practical applications.
基金supported by the National Natural Science Foundation of China under Grant 62301119。
文摘The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic reconnaissance.Existing methodologies predominantly rely on prior information or heavily constrained models,posing challenges for non-cooperative applications.This paper introduces a novel approach to model MFRs using a Bayesian network,where the conditional probability density function is approximated by an autoregressive kernel mixture network(ARKMN).Utilizing the estimated probability density function,a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse trains.Simulation results affirm the proposed method's efficacy in modeling MFRs,outperforming the state-of-the-art in pulse train denoising and change point detection.
基金This work is supported by the National Key Research and Development Program of China(2022YFF1203001)National Natural Science Foundation of China(Nos.62072465,62102425)the Science and Technology Innovation Program of Hunan Province(Nos.2022RC3061,2023RC3027).
文摘Time series segmentation has attracted more interests in recent years,which aims to segment time series into different segments,each reflects a state of the monitored objects.Although there have been many surveys on time series segmentation,most of them focus more on change point detection(CPD)methods and overlook the advances in boundary detection(BD)and state detection(SD)methods.In this paper,we categorize time series segmentation methods into CPD,BD,and SD methods,with a specific focus on recent advances in BD and SD methods.Within the scope of BD and SD,we subdivide the methods based on their underlying models/techniques and focus on the milestones that have shaped the development trajectory of each category.As a conclusion,we found that:(1)Existing methods failed to provide sufficient support for online working,with only a few methods supporting online deployment;(2)Most existing methods require the specification of parameters,which hinders their ability to work adaptively;(3)Existing SD methods do not attach importance to accurate detection of boundary points in evaluation,which may lead to limitations in boundary point detection.We highlight the ability to working online and adaptively as important attributes of segmentation methods,the boundary detection accuracy as a neglected metrics for SD methods.
基金supported by the Medical Special Cultivation Project of Anhui University of Science and Technology(Grant No.YZ2023H2B013)the Anhui Provincial Key Research and Development Project(Grant No.2022i01020015)the Open Project of Key Laboratory of Conveyance Equipment(East China Jiaotong University),Ministry of Education(KLCE2022-01).
文摘The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in the per-frame 3D posture estimation from two-dimensional(2D)mapping to 3D mapping.Firstly,by examining the relationship between the movements of different bones in the human body,four virtual skeletons are proposed to enhance the cyclic constraints of limb joints.Then,multiple parameters describing the skeleton are fused and projected into a high-dimensional space.Utilizing a multi-branch network,motion features between bones and overall motion features are extracted to mitigate the drift error in the estimation results.Furthermore,the estimated relative depth is projected into 3D space,and the error is calculated against real 3D data,forming a loss function along with the relative depth error.This article adopts the average joint pixel error as the primary performance metric.Compared to the benchmark approach,the estimation findings indicate an increase in average precision of 1.8 mm within the Human3.6M sample.
基金Supported by National Defense Basic Scientific Research Program of China(Grant No.JCKY2021602B032)。
文摘Fringe projection profilometry(FPP)has been widely applied to non-contact three-dimensional measurement in industries owing to its high accuracy and speed.The point cloud,which is a measurement result of the FPP system,typically contains a large number of invalid points caused by the background,ambient light,shadows,and object edge regions.Research on noisy point detection and elimination has been conducted over the past two decades.However,existing invalid point removal methods are based on image intensity analysis and are only applicable to simple measurement backgrounds that are purely dark.In this paper,we propose a novel invalid point removal framework that consists of two aspects:(1)A convolutional neural network(CNN)is designed to segment the foreground from the background of different intensity conditions in FPP measurement circumstances to remove background points and the most discrete points in background regions.(2)A two-step method based on the fringe image intensity threshold and a bilateral filter is proposed to eliminate the small number of discrete points remaining after background segmentation caused by shadows and edge areas on objects.Experimental results verify that the proposed framework(1)can remove background points intelligently and accurately in different types of complex circumstances,and(2)performs excellently in discrete point detection from object regions.
基金The Key Project of National Natural Science Foundation of China(42130510)。
文摘Taking Shanghai as an example,this study obtained the online travel notes data from Xiaohongshu and Qunar in the past 10 years to construct the Shanghai tourist flow network(STFN)and used the methods of change point detection(CPD)and complex network analysis(CNA)to reveal the spatial structure characteristics of Shanghai tourism flow and the dynamic evolution process of STFN.The results showed that:(1)In the past 10 years,Shanghai tourist market had experienced a process of evolution from stable and orderly to short-term fluc-tuation and then gradual recovery,and the year of 2019 was the turning point of tourist flow network evolution.(2)The small-world and approximate scale-free characteristics of STFN were verified,and the network changed from disassortative to temporary assortative,showing a development trend of external expansion and internal separation.(3)While the centrality indicators of tourist flow network remained stable as a whole,the attention to cultural nodes was also increasing with the emergence of new nodes;(4)In terms of spatial connection,new popular nodes emerged and the relationship between them and the surrounding nodes was strengthened;(5)The spatial pattern of tourist flow network presented an inverted“V”shape and gradually expanded to southwest and southeast,forming a network with core nodes as the center and radiating outward.At the same time,newly emerging nodes at the periphery had formed relatively independent clusters.
文摘Objective:To observe the electrosensitization of Sifeng(EX-UE10)in children with constipation due to excessive heat in intestine.Methods:The meridian values of Sifeng(EX-UE10)in 80 children with constipation due to excessive heat in intestine and in 80 healthy children were measured using a traditional Chinese medicine(TCM)meridian detector,and the variation rule of the point meridian values was analyzed by SPSS version 26.0 statistical software.Results:The meridian values of Sifeng(EX-UE10)of the index finger,middle finger,and ring finger in the observation group were statistically different from those in the control group(P<0.01).There was no statistical difference in the meridian value of Sifeng(EX-UE10)of the little finger between the two groups(P>0.05).Conclusion:Electrosensitization occurs at Sifeng(EX-UE10)of the index finger,middle finger,and ring finger in children with constipation(syndrome of excessive heat in intestine),and thus the treatment can focus on stimulating the index finger,middle finger,and ring finger.
基金The National Natural Science Foundation of China(No50475041)
文摘Based on the coded and non-coded targets, the targets are extracted from the images according to their size, shape and intensity etc., and thus an improved method to identify the unique identity（D） of every coded target is put forward and the non-coded and coded targets are classified. Moreover, the gray scale centroid algorithm is applied to obtain the subpixel location of both uncoded and coded targets. The initial matching of the uncoded target correspondences between an image pair is established according to similarity and compatibility, which are based on the ID correspondences of the coded targets. The outliers in the initial matching of the uncoded target are eliminated according to three rules to finally obtain the uncoded target correspondences. Practical examples show that the algorithm is rapid, robust and is of high precision and matching ratio.
基金supported by the National Natural Science Foundation of China(Nos.72101026,61621063)the State Key Laboratory of Intelligent Control and Decision of Complex Systems.
文摘Human pose recognition and estimation in video is pervasive.However,the process noise and local occlusion bring great challenge to pose recognition.In this paper,we introduce the Kalman filter into pose recognition to reduce noise and solve local occlusion problem.The core of pose recognition in video is the fast detection of key points and the calculation of human steering angles.Thus,we first build a human key point detection model.Frame skipping is performed based on the Hamming distance of the hash value of every two adjacent frames in video.Noise reduction is performed on key point coordinates with the Kalman filter.To calculate the human steering angle,current state information of key points is predicted using the optimal estimation of key points at the previous time.Then human steering angle can be calculated based on current and previous state information.The improved SENet,NLNet and GCNet modules are integrated into key point detection model for improving accuracy.Tests are also given to illustrate the effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China under Grant Nos.61872316,62272406,61932018the National Key R&D Plan of China under Grant No.2020YFB1708900.
文摘Isogeometric analysis(IGA)is introduced to establish the direct link between computer-aided design and analysis.It is commonly implemented by Galerkin formulations(isogeometric Galerkin,IGA-G)through the use of nonuniform rational B-splines(NURBS)basis functions for geometric design and analysis.Another promising approach,isogeometric collocation(IGA-C),working directly with the strong form of the partial differential equation(PDE)over the physical domain defined by NURBS geometry,calculates the derivatives of the numerical solution at the chosen collocation points.In a typical IGA,the knot vector of the NURBS numerical solution is only determined by the physical domain.A new perspective on the IGAmethod is proposed in this study to improve the accuracy and convergence of the solution.Solving the PDE with IGA can be regarded as fitting the load function defined on the NURBS geometry(right-hand side)with derivatives of the NURBS numerical solution(left-hand side).Moreover,the design of the knot vector has a close relationship to theNURBS functions to be fitted in the area of data fitting in geometric design.Therefore,the detected feature points of the load function are integrated into the initial knot vector of the physical domainto construct thenewknot vector of thenumerical solution.Then,they are connected seamlessly with the IGA-C framework for its great potential combining the accuracy and smoothness merits with the computational efficiency,which we call isogeometric collocation by fitting load function(IGACL).In numerical experiments,we implement our method to solve 1D,2D,and 3D PDEs and demonstrate the improvement in accuracy by comparing it with the standard IGA-C method.We also verify the superiority in the accuracy of our knot selection scheme when employed in the IGA-G method,which we call isogeometric Galerkin by fitting load function(IGA-GL).
基金The work was supported by National Natural Science Foundation of China （No. 50975195）.
文摘Seam image processing is the basis of the realization of automatic laser vision seam tracking system, and it has become one of the important research directions. Adding windows processing, gray processing, fast median filtering, binary processing and image edge extraction are used to pretreat the seam image. In the post-processing of seam image, the feature points of the target image are succesfully detected by using center line extraction and feature points detection algorithm based on slope analysis. The whole processing time is less than 150 ms, and the real-time processing of seam image can be implemented.
