Change Point Detection for Process Data Analytics Applied to a Multiphase Flow Facility

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.

DM Code Key Point Detection Algorithm Based on CenterNet

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.

Non-iterative image feature matching algorithm based on reference point correspondences

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.

Probabilistic modeling of multifunction radars with autoregressive kernel mixture network

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.

Unsupervised Time Series Segmentation: A Survey on Recent Advances

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.

Multi-Branch High-Dimensional Guided Transformer-Based 3D Human Posture Estimation

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.

Analysis of Bridge-Bearing Capacity Detection and Evaluation Technology

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.

On-line outlier and change point detection for time series

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.

Performance assisted enhancement based on change point detection and Kalman filtering

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.

Highly sensitive ECL-PCR method for detection of K-ras point mutation

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.

Change Point Detection and Trend Analysis for Time Series

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.

New Perspective to Isogeometric Analysis:Solving Isogeometric Analysis Problem by Fitting Load Function

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).

Face mask detection algorithm based on HSV+HOG features and SVM

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.

Study on the image processing of laser vision seam tracking system

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.

An Intelligent Framework for Recognizing Social Human-Object Interactions

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.

Cardiovascular Disease Prediction Among the Malaysian Cohort Participants Using Electrocardiogram

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.

Individual Identification of Electronic Equipment Based on Electromagnetic Fingerprint Characteristics

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.

Abnormal driving behavior identification based on direction and position offsets

Abnormal driving behavior identification( ADBI) has become a research hotspot because of its significance in driver assistance systems. However,current methods still have some limitations in terms of accuracy and reliability under severe traffic scenes. This paper proposes a new ADBI method based on direction and position offsets,where a two-factor identification strategy is proposed to improve the accuracy and reliability of ADBI. Self-adaptive edge detection based on Sobel operator is used to extract edge information of lanes. In order to enhance the efficiency and reliability of lane detection,an improved lane detection algorithm is proposed,where a Hough transform based on local search scope is employed to quickly detect the lane,and a validation scheme based on priori information is proposed to further verify the detected lane. Experimental results under various complex road conditions demonstrate the validity of the proposed ADBI.

Design of Hybrid Compensators for Power Quality Improvement of Oilfield Drilling Electrical System

With large-scale use of kinds of motors, oilfield drilling electrical system always accompanied by serious power quality problem, including reactive current, harmonics current and grid voltage distortion, which would greatly threaten the safety and proper working of the whole system. This paper focuses on a power quality improvement project to solve these problems. A hybrid compensating scheme, including an active compensator and a passive compensator, is carried out. Because of the specificity of oilfield drilling electrical system, compensators are redesigned against features of this application background. And then the current detection point arrangement of this hybrid system is also taken into consideration to build the whole system much more effective and reliable. Now the improvement project is already implemented in application field, and the power quality of the system is greatly improved.

Integration system research and development for three-dimensional laser scanning information visualization in goaf

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.