Does Biochar Addition Influence the Change Points of Soil Phosphorus Leaching?

Phosphorus change point indicating the threshold related to P leaching, largely depends on soil properties. Increasing data have shown that biochar addition can improve soil retention capacity of ions. However, we have known little about weather biochar amendment influence the change point of P leaching. In this study, two soils added with 0, 5, 10, 20, and 50 g biochar kg-1 were incubated at 25℃ for 14 d following adjusting the soil moisture to 50% water-holding capacity （WHC）. The soils with different available P values were then obtained by adding a series of KH2PO4 solution （ranging from 0 to 600 mg P kg-1 soil）, and subjecting to three cycles of drying and rewetting. The results showed that biochar addition significantly lifted the P change points in the tested soils, together with changes in soil pH, organic C, Olen-P and CaC12-P but little on exchangeable Ca and Mg, oxalate-extractable Fe and Al. The Olsen-P at the change points ranged from 48.65 to 185.07 mg kg-1 in the alluvial soil and 71.25 to 98.65 mg kg^-1 in the red soil, corresponding to CaCl2-P of 0.31-6.49 and 0.18-0.45 mg L~, respectively. The change points of the alluvial soil were readily changed by adding biochar compared with that of the red soil. The enhancement of change points was likely to be explained as the improvement of phosphate retention ability in the biochar-added soils.

Detecting Change Points in Polynomial Regression Models with an Application to Cable Data Sets

In this paper, the Schwarz Information Criterion (SIC) is used to detect the change points in polynomial regression models. Switching quadratic regression models with same amount of model deviation and switching polynomial regression models with different amount of model deviation for different segments of regression are considered. The number of separate regimes and their corresponding regression orders are assume to be known. The method is then applied to cable data sets and the change points are successfully detected.

RATE OF CONVERGENCE FOR MULTIPLE CHANGEPOINTS ESTIMATION OF MOVING-AVERAGE PROCESSES

In this paper, the least square estimator in the problem of multiple change points estimation is studied. Here, the moving-average processes of ALNQD sequence in the mean shifts are discussed. When the number of change points is known, the rate of convergence of change-points estimation is derived. The result is also true for p-mixing, φ-mixing, a-mixing, associated and negatively associated sequences under suitable conditions.

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.

Wavelet Detection and Estimation of Change Points in Nonparametric Regression Models under Random Design

A wavelet method of detection and estimation of change points in nonparametric regression models under random design is proposed. The confidence bound of our test is derived by using the test statistics based on empirical wavelet coefficients as obtained by wavelet transformation of the data which is observed with noise. Moreover, the consistence of the test is proved while the rate of convergence is given. The method turns out to be effective after being tested on simulated examples and applied to IBM stock market data.

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.

Testing for Change Points in Partially Linear Models

In this paper we provide a method to test the existence of the change points in the nonparametric regression function of partially linear models with conditional heteroscedastic variance. We propose the test statistic and establish its asymptotic properties under some regular conditions. Some simulation studies are given to investigate the performance of the proposed method in finite samples. Finally, the proposed method is applied to a real data for illustration.

Comparative Analysis of Climatic Change Trend and Change-Point Analysis for Long-Term Daily Rainfall Annual Maximum Time Series Data in Four Gauging Stations in Niger Delta

The aim of this study is to establish the prevailing conditions of changing climatic trends and change point dates in four selected meteorological stations of Uyo, Benin, Port Harcourt, and Warri in the Niger Delta region of Nigeria. Using daily or 24-hourly annual maximum series (AMS) data with the Indian Meteorological Department (IMD) and the modified Chowdury Indian Meteorological Department (MCIMD) models were adopted to downscale the time series data. Mann-Kendall (MK) trend and Sen’s Slope Estimator (SSE) test showed a statistically significant trend for Uyo and Benin, while Port Harcourt and Warri showed mild trends. The Sen’s Slope magnitude and variation rate were 21.6, 10.8, 6.00 and 4.4 mm/decade, respectively. The trend change-point analysis showed the initial rainfall change-point dates as 2002, 2005, 1988, and 2000 for Uyo, Benin, Port Harcourt, and Warri, respectively. These prove positive changing climatic conditions for rainfall in the study area. Erosion and flood control facilities analysis and design in the Niger Delta will require the application of Non-stationary IDF modelling.

Detecting Climate Change Trend, Size, and Change Point Date on Annual Maximum Time Series Rainfall Data for Warri, Nigeria

The study focused on the detection of indicators of climate change in 24-hourly annual maximum series (AMS) rainfall data collected for 36 years (1982-2017) for Warri Township, using different statistical methods yielded a statistically insignificant positive mild trend. The IMD and MCIMD downscaled model’s time series data respectively produced MK statistics varying from 1.403 to 1.4729, and 1.403 to 1.463 which were less than the critical Z-value of 1.96. Also, the slope magnitude obtained showed a mild increasing trend in variation from 0.0189 to 0.3713, and 0.0175 to 0.5426, with the rate of change in rainfall intensity at 24 hours duration as 0.4536 and 0.42 mm/hr.year (4.536 and 4.2 mm/decade) for the IMD and the MCIMD time series data, respectively. The trend change point date occurred in the year 2000 from the distribution-free CUSUM test with the trend maintaining a significant and steady increase from 2010 to 2015. Thus, this study established the existence of a trend, which is an indication of a changing climate, and satisfied the condition for rainfall Non-stationary intensity-duration-frequency (NS-IDF) modeling required for infrastructural design for combating flooding events.

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.

Discrete Software Reliability Growth Modeling for Errors of Different Severity Incorporating Change-point Concept

Several software reliability growth models （SRGM） have been developed to monitor the reliability growth during the testing phase of software development. In most of the existing research available in the literatures, it is considered that a similar testing effort is required on each debugging effort. However, in practice, different types of faults may require different amounts of testing efforts for their detection and removal. Consequently, faults are classified into three categories on the basis of severity： simple, hard and complex. This categorization may be extended to r type of faults on the basis of severity. Although some existing research in the literatures has incorporated this concept that fault removal rate （FRR） is different for different types of faults, they assume that the FRR remains constant during the overall testing period. On the contrary, it has been observed that as testing progresses, FRR changes due to changing testing strategy, skill, environment and personnel resources. In this paper, a general discrete SRGM is proposed for errors of different severity in software systems using the change-point concept. Then, the models are formulated for two particular environments. The models were validated on two real-life data sets. The results show better fit and wider applicability of the proposed models as to different types of failure datasets.

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.

The SCUSUM Estimation of Variance Change Point in Linear Process

This paper considers the estimation problem of a variance change-point in linear process.Consistency of a SCUSUM type change-point estimator is proved and its rate of convergence is established.The mean-unknown case is also considered.

Consistency of the <i>φ</i>-Divergence Based Change Point Estimator

This paper utilizes a change-point estimator based on the φ-divergence. Since we seek a near perfect translation to reality, then locations of parameter change within a finite set of data have to be accounted for since the assumption of stationary model is too restrictive especially for long time series. The estimator is shown to be consistent through asymptotic theory and finally proven through simulations. The estimator is applied to the generalized Pareto distribution to estimate changes in the scale and shape parameters.

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.

Flow regime changes in three catchments with different landforms following ecological restoration in the Chinese Loess Plateau

The Chinese Loess Plateau is known as one of the most severe soil erosion regions in the world.Two ecological restoration projects,i.e.,the integrated soil conservation project since the 1970s and the''Grain for Green''project since 1999,have been progressively implemented to control the soil erosion in this area.Ecological restoration has greatly changed flow regime over the past five decades.However,the mechanism of how flow regime responds to ecological restoration among landforms remains poorly understood.In this study,we investigated the temporal dynamics of flow regime in three catchments,i.e.,Wuqi,Honghe and Huangling hydrological stations,respectively representing the loess hilly-gully,loess table-gully and rocky mountain(covered by secondary forest)areas in the Chinese Loess Plateau,using daily hydrological data during the 1960s–2010s.The nonparametric Mann-Kendall test,Pettitt's test and daily flow series were used to investigate the changes of flow regime.Significantly negative trends of annual streamflow were detected at the Wuqi and Honghe stations,except for the Huangling station.The annual baseflow at the Wuqi station showed a significantly positive trend whereas a significantly negative trend was observed at the Honghe station,and there was no significant trend at the Huangling station.It was interesting that baseflow index significantly increased during the whole period in all catchments.However,the trends and change points of daily flow series derived by different percentages of exceedance and extreme series in different consecutive days varied among individuals.Based on the change points analysis of annual streamflow,we divided data series into three periods,i.e.,the baseline period(from 1959 and 1963 to 1979,PI),the integrated soil conservation period(1980–1999,PII)and the''Grain for Green''period(2000–2011,PIII).We found that streamflow decreased due to the reduction of high streamflow(exceeding 5%of time within a year)and median streamflow(50%)in PII and PIII at the Wuqi and Honghe stations.However,low flow(95%)increased in PII and PIII at the Wuqi station while decreased at the Honghe station.Streamflow change at the Huangling station was more stable,thus potentially resulting in much less soil erosion in the forestry area than in the other areas.The great improvement in ecological environment on the Chinese Loess Plateau revealed the advantages of ecological restoration in reducing flood amount and compensating streamflow at a regional scale.

Damage Position of Concrete Beam Based on Test of Even Change Point

The quantitative analysis of damage position of concrete beam is established.The method of damage position free of models used in this paper is based on the variations of strain mode and test of even change point.The discrete model and the position criterion are presented.Numerical example is conducted,the result demonstrates that the method of damage position is correct and effective.

Recognition Method for Change Point of Traffic Flow Linear Regressions

Recognition method of traffic flow change point was put forward based on traffic flow theory and the statistical change point analysis of multiple linear regressions. The method was calibrated and tested with the field data of Liantong Road of Zibo city to verify the validity and the feasibility of the theory. The results show that change point method of multiple linear regression can make out the rule of quantitative changes in traffic flow more accurately than ordinary methods. So, the change point method can be applied to traffic information management system more effectively.