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
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
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
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
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
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
Highlights By conjugating the same anti-N monoclonal antibody(mAb4-mAb1)with colloidal gold or fluorescent microspheres,this study developed two rapid point-of-care antigen immunochromatographic strips for the detecti...Highlights By conjugating the same anti-N monoclonal antibody(mAb4-mAb1)with colloidal gold or fluorescent microspheres,this study developed two rapid point-of-care antigen immunochromatographic strips for the detection of porcine deltacoronavirus.The fluorescent microsphere-based lateral flow test strip demonstrated a sensitivity of 10~(1.7)TCID_(50)/0.1 mL,which is fourfold higher than that of the colloidal gold-based assay.Porcine deltacoronavirus(PDCoV)is a recently identified enteric coronavirus that causes an acute infectious disease in piglets,leading to diarrhea,vomiting,dehydration,and mortality(Hu et al.2015).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
An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, clo...An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, cloud data de-noising optimization, construction, display and operation of three-dimensional model, model editing, profile generation, calculation of goaf volume and roof area, Boolean calculation among models and interaction with the third party soft ware. Concerning this system with a concise interface, plentiful data input/output interfaces, it is featured with high integration, simple and convenient operations of applications. According to practice, in addition to being well-adapted, this system is favorably reliable and stable.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
A comprehensive study was conducted to differentiate cardiovascular disease (CVD) subjects from non-CVD subjects using short recording electrocardiogram (ECG) of 244 Malaysian adults in The MalaysianCohort project. An...A comprehensive study was conducted to differentiate cardiovascular disease (CVD) subjects from non-CVD subjects using short recording electrocardiogram (ECG) of 244 Malaysian adults in The MalaysianCohort project. An automated peak detection algorithm to detect nine fiducialpoints of electrocardiogram (ECG) was developed. Forty-eight features wereextracted in both time and frequency domains, including statistical featuresobtained from heart rate variability and Poincare plot analysis. These includefive new features derived from spectrum counts of five different frequencyranges. Feature selection was then made based on p-value and correlationmatrix. Selected features were used as input for five classifiers of artificialneural network (ANN), k-nearest neighbors (kNN), support vector machine(SVM), discriminant analysis (DA), and decision tree (DT). Results showedthat six features related to T wave were statistically significant in distinguishingCVD and non-CVD groups. ANN had performed the best with 94.44% specificity and 86.3% accuracy, followed by kNN with 80.56% specificity, 86.49%sensitivity and 83.56% accuracy. The novelties of this study were in providingalternative solutions to detect P-onset, P-offset, T-offset as well as QRS-onsetpoints using discrete wavelet transform method. Additionally, two out of thefive newly proposed spectral features were significant in differentiating bothgroups, at frequency ranges of 1–10 Hz and 5–10 Hz. The prediction outcomeswere also comparable to previous related studies and significantly importantin using ECG to predict cardiac-related events among CVD and non-CVDsubjects in the Malaysian population.展开更多
Human object interaction(HOI)recognition plays an important role in the designing of surveillance and monitoring systems for healthcare,sports,education,and public areas.It involves localizing the human and object tar...Human object interaction(HOI)recognition plays an important role in the designing of surveillance and monitoring systems for healthcare,sports,education,and public areas.It involves localizing the human and object targets and then identifying the interactions between them.However,it is a challenging task that highly depends on the extraction of robust and distinctive features from the targets and the use of fast and efficient classifiers.Hence,the proposed system offers an automated body-parts-based solution for HOI recognition.This system uses RGB(red,green,blue)images as input and segments the desired parts of the images through a segmentation technique based on the watershed algorithm.Furthermore,a convex hullbased approach for extracting key body parts has also been introduced.After identifying the key body parts,two types of features are extracted.Moreover,the entire feature vector is reduced using a dimensionality reduction technique called t-SNE(t-distributed stochastic neighbor embedding).Finally,a multinomial logistic regression classifier is utilized for identifying class labels.A large publicly available dataset,MPII(Max Planck Institute Informatics)Human Pose,has been used for system evaluation.The results prove the validity of the proposed system as it achieved 87.5%class recognition accuracy.展开更多
With the rapid development of communication and computer,the individual identification technology of communication equipment has been brought to many application scenarios.The identification of the same type of electr...With the rapid development of communication and computer,the individual identification technology of communication equipment has been brought to many application scenarios.The identification of the same type of electronic equipment is of considerable significance,whether it is the identification of friend or foe in military applications,identity determination,radio spectrum management in civil applications,equipment fault diagnosis,and so on.Because of the limited-expression ability of the traditional electromagnetic signal representation methods in the face of complex signals,a new method of individual identification of the same equipment of communication equipment based on deep learning is proposed.The contents of this paper include the following aspects:(1)Considering the shortcomings of deep learning in processing small sample data,this paper provides a universal and robust feature template for signal data.This paper constructs a relatively complete signal template library from multiple perspectives,such as time domain and transform domain features,combined with high-order statistical analysis.Based on the inspiration of the image texture feature,characteristics of amplitude histogram of signal and the signal amplitude co-occurrence matrix(SACM)are proposed in this paper.These signal features can be used as a signal fingerprint template for individual identification.(2)Considering the limitation of the recognition rate of a single classifier,using the integrated classifier has achieved better generalization ability.The final average accuracy of 5 NRF24LE1 modules is up to 98%and solved the problem of individual identification of the same equipment of communication equipment under the condition of the small sample,low signal-to-noise ratio.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 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financially supported by the National Key Research and Development Program of China(2021YFF0703600)。
文摘Highlights By conjugating the same anti-N monoclonal antibody(mAb4-mAb1)with colloidal gold or fluorescent microspheres,this study developed two rapid point-of-care antigen immunochromatographic strips for the detection of porcine deltacoronavirus.The fluorescent microsphere-based lateral flow test strip demonstrated a sensitivity of 10~(1.7)TCID_(50)/0.1 mL,which is fourfold higher than that of the colloidal gold-based assay.Porcine deltacoronavirus(PDCoV)is a recently identified enteric coronavirus that causes an acute infectious disease in piglets,leading to diarrhea,vomiting,dehydration,and mortality(Hu et al.2015).
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.
基金Project(51274250)supported by the National Natural Science Foundation of ChinaProject(2012BAK09B02-05)supported by the National Key Technology R&D Program during the 12th Five-year Plan of China
文摘An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, cloud data de-noising optimization, construction, display and operation of three-dimensional model, model editing, profile generation, calculation of goaf volume and roof area, Boolean calculation among models and interaction with the third party soft ware. Concerning this system with a concise interface, plentiful data input/output interfaces, it is featured with high integration, simple and convenient operations of applications. According to practice, in addition to being well-adapted, this system is favorably reliable and stable.
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 study was supported by the Ministry of Education Malaysia’s Fundamental Research Grant Scheme FRGS/1/2019/TK04/UKM/02/4TMC research was funded by a top-down grant from the Ministry of Education Malaysia(Grant Number PDE48).
文摘A comprehensive study was conducted to differentiate cardiovascular disease (CVD) subjects from non-CVD subjects using short recording electrocardiogram (ECG) of 244 Malaysian adults in The MalaysianCohort project. An automated peak detection algorithm to detect nine fiducialpoints of electrocardiogram (ECG) was developed. Forty-eight features wereextracted in both time and frequency domains, including statistical featuresobtained from heart rate variability and Poincare plot analysis. These includefive new features derived from spectrum counts of five different frequencyranges. Feature selection was then made based on p-value and correlationmatrix. Selected features were used as input for five classifiers of artificialneural network (ANN), k-nearest neighbors (kNN), support vector machine(SVM), discriminant analysis (DA), and decision tree (DT). Results showedthat six features related to T wave were statistically significant in distinguishingCVD and non-CVD groups. ANN had performed the best with 94.44% specificity and 86.3% accuracy, followed by kNN with 80.56% specificity, 86.49%sensitivity and 83.56% accuracy. The novelties of this study were in providingalternative solutions to detect P-onset, P-offset, T-offset as well as QRS-onsetpoints using discrete wavelet transform method. Additionally, two out of thefive newly proposed spectral features were significant in differentiating bothgroups, at frequency ranges of 1–10 Hz and 5–10 Hz. The prediction outcomeswere also comparable to previous related studies and significantly importantin using ECG to predict cardiac-related events among CVD and non-CVDsubjects in the Malaysian population.
基金This research was supported by a grant(2021R1F1A1063634)of the Basic Science Research Program through the National Research Foundation(NRF)funded by the Ministry of Education,Republic of Korea.
文摘Human object interaction(HOI)recognition plays an important role in the designing of surveillance and monitoring systems for healthcare,sports,education,and public areas.It involves localizing the human and object targets and then identifying the interactions between them.However,it is a challenging task that highly depends on the extraction of robust and distinctive features from the targets and the use of fast and efficient classifiers.Hence,the proposed system offers an automated body-parts-based solution for HOI recognition.This system uses RGB(red,green,blue)images as input and segments the desired parts of the images through a segmentation technique based on the watershed algorithm.Furthermore,a convex hullbased approach for extracting key body parts has also been introduced.After identifying the key body parts,two types of features are extracted.Moreover,the entire feature vector is reduced using a dimensionality reduction technique called t-SNE(t-distributed stochastic neighbor embedding).Finally,a multinomial logistic regression classifier is utilized for identifying class labels.A large publicly available dataset,MPII(Max Planck Institute Informatics)Human Pose,has been used for system evaluation.The results prove the validity of the proposed system as it achieved 87.5%class recognition accuracy.
基金This work was supported by the National natural science foundation of China(No:62071057)Beijing nature fund(No:3182028).The support is gratefully acknowledged.
文摘With the rapid development of communication and computer,the individual identification technology of communication equipment has been brought to many application scenarios.The identification of the same type of electronic equipment is of considerable significance,whether it is the identification of friend or foe in military applications,identity determination,radio spectrum management in civil applications,equipment fault diagnosis,and so on.Because of the limited-expression ability of the traditional electromagnetic signal representation methods in the face of complex signals,a new method of individual identification of the same equipment of communication equipment based on deep learning is proposed.The contents of this paper include the following aspects:(1)Considering the shortcomings of deep learning in processing small sample data,this paper provides a universal and robust feature template for signal data.This paper constructs a relatively complete signal template library from multiple perspectives,such as time domain and transform domain features,combined with high-order statistical analysis.Based on the inspiration of the image texture feature,characteristics of amplitude histogram of signal and the signal amplitude co-occurrence matrix(SACM)are proposed in this paper.These signal features can be used as a signal fingerprint template for individual identification.(2)Considering the limitation of the recognition rate of a single classifier,using the integrated classifier has achieved better generalization ability.The final average accuracy of 5 NRF24LE1 modules is up to 98%and solved the problem of individual identification of the same equipment of communication equipment under the condition of the small sample,low signal-to-noise ratio.
