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.

Formula for calculating spatial similarity degrees between point clouds on multi-scale maps taking map scale change as the only independent variable

The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity degree/relation in multi-scale map spaces and then proposes a model for calculating the degree of spatial similarity between a point cloud at one scale and its gener- alized counterpart at another scale. After validation, the new model features 16 points with map scale change as the x coordinate and the degree of spatial similarity as the y coordinate. Finally, using an application for curve fitting, the model achieves an empirical formula that can calculate the degree of spatial similarity using map scale change as the sole independent variable, and vice versa. This formula can be used to automate algorithms for point feature generalization and to determine when to terminate them during the generalization.

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.

Change-Point Detection for General Nonparametric Regression Models

A number of statistical tests are proposed for the purpose of change-point detection in a general nonparametric regression model under mild conditions. New proofs are given to prove the weak convergence of the underlying processes which assume remove the stringent condition of bounded total variation of the regression function and need only second moments. Since many quantities, such as the regression function, the distribution of the covariates and the distribution of the errors, are unspecified, the results are not distribution-free. A weighted bootstrap approach is proposed to approximate the limiting distributions. Results of a simulation study for this paper show good performance for moderate samples sizes.

ON DETECTION OF CHANGE POINTS USING MEAN VECTORS

In this paper,the authors consider the problem of change points within the framework of model selection and propose a procedure for estimating the locations of change points when the number of change points is known.The strong consistency of this procedure is also established. The problem of detecting change points is discussed within the framework of the simultaneous test procedure.The case where the number of change points is unknown will be discussed in another paper.

基于自编码器的多功能雷达工作状态切换点检测方法

先进多功能雷达可以实现复杂的工作状态调制和灵活的波束调度,为识别不同的雷达工作状态并检测状态之间的切换点带来了巨大的挑战.为此提出了一种对多功能雷达层次化模型中的“符号−脉冲”层中的调制参数级工作状态的种切换点检测方法.所提出算法基于自编码器,并对真实电磁环境中观测非理想的情况进行设计,不仅可以在无监督的情况下准确地检测出工作状态切换点,并且不需要对数据的分布情况进行假设.仿真实验验证了本文提出算法相较于切换点检测算法的有效性和优越性,对于0~20%的脉冲丢失情况相较于对比方法具有更好的性能,在20%脉冲丢失的极端情况下,也可以达到0.7的F_(1)-score.

有限相依随机序列的非贝叶斯变点最优监测

本文研究了有限相依样本序列的非贝叶斯变点检测问题.通过引入非负动态随机控制线,我们不仅构造并证明了两个最优控制图,而且还得到了比原定义更容易计算的Lorden测度和Pollak测度的最小值的表达式.

基于BCUSUM的多参数变点估计

文章基于递归残差的逆序特征和隔离检测研究了回归模型多参数变点的检测方法。首先,构建带有变点的回归模型,考虑到多元正向CUSUM检验能防止协变量均值与偏移量正交时损失功效,但其变点检测效果并不理想的情况,引入修正的检验统计量BCUSUM。其次,结合快速高效的隔离检测技术,提出MCPDP算法用于估计变点数目及位置。最后,模拟结果表明,所提出的方法能较好地控制检验水平,有更高的功效;评价结果显示,MCPDP算法在变点估计性能方面表现较优;实例分析表明,交通流变点符合实际交通情况,验证了该方法的有效性,且所构建的模型可以作为交通参数确定性经验关系的一种修正。

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.

基于多突变点与模板匹配的用电设备在线状态监测方法

针对现阶段用电设备状态监测技术存在的处理速度较慢、准确率较低等问题,文中基于多突变点检测和模板匹配策略提出了一种用电设备在线状态监测方法。该方法在缓冲区模型和滑动窗口模型的基础上,利用多路搜索树突变点检测(Ternary Search Tree and Kolmogorov-Smirnov,TSTKS)算法形成窗口维度和缓冲区维度的特征向量,通过两种维度的模板匹配实现用电设备的运行状态匹配和状态切换时刻定位。基于家用电冰箱的仿真实验结果表明,所提方法具有检测速度快、准确率高等优点,可为用电设备状态监测领域提供参考。

基于Potree结构的建筑物激光点云与BIM点云的变化检测

建筑变化检测可对建筑管理及决策提供有力支持。目前建筑变化检测的难点是完成检测任务所需计算成本高。为了提高检测效率,提出一种基于Potree结构的建筑物激光点云与建筑信息模型(building information modelling,BIM)点云的变化检测方法。该方法将实时获取的激光点云,与建设初期规划的BIM进行比较,检测和识别出二者之间的差异,作为建筑变化检测的结果。实验结果表明,与基于可修改嵌套八叉树结构方法比较,本文提出的方法在保证完整性、准确度等不损失的情况下,在时间复杂度上降低了22.05%。

短包隐蔽通信下抗检测-干扰式攻击策略

为应对潜在的检测-干扰式恶意攻击,提出了一种基于短包隐蔽通信的抗检测-干扰攻击策略,通过隐藏无线传输行为的存在来实现抗干扰。考虑到干扰机采用Shewhart变点检测实时判断源节点的传输行为,当判决源节点正在传输数据包后启动人工干扰。为隐藏无线传输行为,源节点随机选择一个起始时刻以低功率、低速率传输有限长编码数据包。数据包发送功率越小、编码长度越短,隐蔽性越强,抗检测-干扰式攻击性能越好,但发送功率小和编码长度短使得接收端数据包译码错误概率较大。以有效传输速率最大为目标对数据包发送功率和编码长度进行优化设计。结果表明,存在最优的发送功率和编码长度使系统在传输隐蔽性和可靠性之间取得最优折衷。数值仿真结果验证了理论分析,证明了所提方案的有效性以及优化数据包编码长度和发送功率的必要性。

船舶柴油机运行参数异常检测及分析

为保障船舶安全高效运行,提出4种船舶柴油机运行参数异常检测方法。该技术在空间向量统计的异常检测基础上,进一步提出新的方法监测运行参数时间序列的趋势异常。通过提取运行参数数据演化过程的趋势和规律,可以更早、更准确地识别异常趋势,为设备管理提供决策支持。实验结果表明,所提出的异常检测技术能够有效提高船舶柴油机异常检测的效率和准确性,及早发现安全隐患。

基于影像密集匹配点云的建筑物变化检测方法

采用影像密集匹配点云开展变化检测能有效发现建筑物高度的变化。提出了一种融合深度神经网络与空间体素的影像密集匹配点云建筑物变化检测方法。首先构建注意力驱动的建筑物点云提取深度神经网络,再分别利用布料模拟滤波算法和植被指数剔除地面点和植被点云,最后通过空间体素比较提取建筑物点云变化。实验结果表明,该方法的漏检率为0%,且虚警率比传统方法降低了48.64%,说明其具有大幅提升违章建筑发现效率的潜力,能够满足实际生产需求。

基于优化变点-四分位法的光伏异常数据检测

针对光伏电站运行原始数据中异常数据占比高、数据总体质量差的特点,对数据的异常识别与清洗是进行数据分析、预测的前提。为此,分析了光伏电站辐射强度-功率异常数据的特征和来源,提出一种基于滑动标准差曲线线性拟合的变点检测法,以及一种变点-四分位联合的光伏功率异常数据识别算法。利用多个光伏电站数据验证了所提算法的有效性和普适性,实现了对零散型、堆积型等各类异常数据的良好检测。

基于多时相Landsat影像的河流边滩提取研究

河流边滩是因河流沉积作用在岸边形成的沉积物,其变化可以间接反映出河流形态及其周围环境的演变。河流边滩的动态监测对于河流生态系统研究和航道管理有着非常重要的意义。针对河流边滩与部分建筑物影像光谱相似而难以区分的问题,基于Landsat影像中边滩与河流的光谱、空间和物候等多维差异化特征,提出了一种新的河流边滩提取方法,综合利用多时相水体指数的二值变化检测方法和面向对象分析方法,成功提取了赣江中下游的河流边滩。一方面丰富了河流边滩监测的方法,为地理学研究提供新的观察视角;另一方面为赣江流域生态系统建设、环境治理和航运规划管理提供更丰富的河流边滩数据。

基于空间体素表达与影像目标验证的建筑物三维变化检测分析

针对复杂城乡区域违法建筑检测效率低、虚警和楼层加盖等漏检多的难题,本文结合三维点云空间体素表达与二维影像目标识别,提出一种适用于无人机影像密集匹配点云的建筑物三维变化检测方法。首先,对两时相的无人机密集匹配点云进行配准、地面点滤波等预处理;然后,对点云构造八叉树,逐层比较体素获取变化点云;最后,将变化点云聚类后逐个按正射视角投影生成二维影像,利用ConvNext网络进行建筑物验证,得到建筑物变化检测结果。本文实验在对象级上进行检查效果评价,在正确率达到87.18%的情况下,实现了100%的检测完整率。结果表明本文方法能够有效提升违章建筑发现效率,满足实际生产需求。

SIGNAL DETECTION OF GLOBAL CLIMATE CHANGE AND EXTERNAL FORCING FACTORS

In this paper,we displayed one-dimensional climate signals,such as global temperature variation,Southern Oscillation Index and variation of external forcing factors,on a two- dimensional time-scale plane using compactly supported wavelet decomposition.Using the lag- correlation analysis method,and interpretative variance analysis method,and phase comparison method to the wavelet analysis result,we not only gained the variation on different scales to the global temperature and El Nino signals,the location of the jump point and intrinsic scale of these series,but also indicated the magnitude,extent and time of the effect of external forcing factors on them.We also put forward reasonable explanation to the main variation of recent 140 years.