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 hav...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.展开更多
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 chang...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.展开更多
In using the PGCEVD （Poisson-Gumbel Compound Extreme Value Distribution） model to calculate return values of typhoon wave height, the quantitative selection of the threshold has blocked its application. By analyzing...In using the PGCEVD （Poisson-Gumbel Compound Extreme Value Distribution） model to calculate return values of typhoon wave height, the quantitative selection of the threshold has blocked its application. By analyzing the principle of the threshold selection of PGCEVD model and in combination of the change point statistical methods, this paper proposes a new method for quantitative calculation of the threshold in PGCEVD model. Eleven samples from five engineering points in several coastal waters of Guangdong and Hainan, China, are calculated and analyzed by using PGCEVD model and the traditional Pearson type III distribution （P-III） model, respectively. By comparing the results of the two models, it is shown that the new method of selecting the optimal threshold is feasible. PGCEVD model has more stable results than that of P-III model and can be used for the return wave height in every direction.展开更多
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...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.展开更多
Reference evapotranspiration(ET_(0))is a vital component in hydrometeorological research and is widely applied to various aspects,such as water resource management,hydrological modeling,irrigation deployment,and under...Reference evapotranspiration(ET_(0))is a vital component in hydrometeorological research and is widely applied to various aspects,such as water resource management,hydrological modeling,irrigation deployment,and understanding and predicting the influence of hydrologic cycle variations on future climate and land use changes.Quantifying the influence of various meteorological variables on ET_(0) is not only helpful for predicting actual evapotranspiration but also has important implications for understanding the impact of global climate change on regional water resources.Based on daily data from 69 meteorological stations,the present study analyzed the spatiotemporal pattern of ET_(0) and major contributing meteorological variables to ET_(0) from 1960 to 2017 by the segmented re-gression model,Mann-Kendall test,wavelet analysis,generalized linear model,and detrending method.The results showed that the annual ET_(0) declined slightly because of the combined effects of the reduction in solar radiation and wind speed and the increase in vapor pressure deficit(VPD)and average air temperature in the Loess Plateau(LP)during the past 58 yr.Four change points were detected in 1972,1990,1999,and 2010,and the annual ET_(0) showed a zigzag change trend of‘increasing-decreasing-increasing-decreasing-increasing’.Wind speed and VPD played a leading role in the ET_(0) changes from 1960 to 1990 and from 1991 to 2017,respectively.This study confirms that the dominant meteorological factors affecting ET_(0) had undergone significant changes due to global climate change and vegetation greening in the past 58 years,and VPD had become the major factor controlling the ET_(0) changes on the LP.The data presented herein will contribute to increasing the accuracy of predictions on future changes in ET_(0).展开更多
In recent years, intensifying waterlogging, salt water intrusion, wetland loss, and ecosystem degradation in Chinese delta cities and adjacent regions have generated the pressing need to create an urban form that is s...In recent years, intensifying waterlogging, salt water intrusion, wetland loss, and ecosystem degradation in Chinese delta cities and adjacent regions have generated the pressing need to create an urban form that is suited to both current and future climates incorporating sea level rise. However, adaptation planning uptake is slow. This is particularly unfortunate because patterns of urban form interact with mean sea level rise （MSLR） in ways that reduce or intensify its impact. There are currently two main barriers that are significant in arresting the implementation of adaptation planning with reference to the MSLR projections composed of geomorphologic MSLR projections and eustatic MSLR projections from global climate warming, and making a comprehensive risk assessment of MSLR projections. The present review shows recent progresses in mapping MSLR projections and their risk assessment approaches on Chinese delta cities, and then a perspective of adapting these cities to MSLR projections as following six aspects. 1） The geomorphologic MSLR projections are contributed by the natural tectonic subsidence projections and the MSLR projections by anthropogenic geomorphologic change. The former needs to be updated in a global framework. The latter is accumulated by land subsidence from underground water depletion, water level fall caused by the erosion of riverbeds from a sediment supply decline attributed to the construction of watershed dams, artificial sand excavation, water level raise by engineering projects including land reclamation, deep waterway regulation, and fresh water reservoirs. 2） Controlling MSLR projections by anthropogenic geomorphologic changes. 3） The IPCC AR5 RCPs MSLRs scenarios are expected to be projected to the local eustatic MSLR projections on the Chinese deltas. 4） The MSLR projections need to be matched to a local elevation datum. 5） Modeling approaches of regional river-sea numerical with semi- analytical hydrodynamics, estuarine channel network, system dynamics and adaptation points are perspective. 6） Adaptation planning to MSLR projections requires a comprehensive risk assessment of the risk of flood, fresh water supply shortage, coastal erosion, wetland loss, siltation of ports and waterway in Chinese delta cities and adjacent regions.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
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
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
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 i...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.展开更多
This paper utilizes a change-point estimator based on <span>the </span><span style="font-style:italic;">φ</span><span>-</span><span>divergence. Since </span>&...This paper utilizes a change-point estimator based on <span>the </span><span style="font-style:italic;">φ</span><span>-</span><span>divergence. Since </span><span "=""><span>we seek a </span><span>near perfect</span><span> translation to reality, then locations of parameter change within a finite set of data have to be accounted for since the assumption of </span><span>stationary</span><span> 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.</span></span>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
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
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 f...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.展开更多
Time series segmentation has attracted more interests in recent years,which aims to segment time series into different segments,each reflects a state of the monitored objects.Although there have been many surveys on t...Time series segmentation has attracted more interests in recent years,which aims to segment time series into different segments,each reflects a state of the monitored objects.Although there have been many surveys on time series segmentation,most of them focus more on change point detection(CPD)methods and overlook the advances in boundary detection(BD)and state detection(SD)methods.In this paper,we categorize time series segmentation methods into CPD,BD,and SD methods,with a specific focus on recent advances in BD and SD methods.Within the scope of BD and SD,we subdivide the methods based on their underlying models/techniques and focus on the milestones that have shaped the development trajectory of each category.As a conclusion,we found that:(1)Existing methods failed to provide sufficient support for online working,with only a few methods supporting online deployment;(2)Most existing methods require the specification of parameters,which hinders their ability to work adaptively;(3)Existing SD methods do not attach importance to accurate detection of boundary points in evaluation,which may lead to limitations in boundary point detection.We highlight the ability to working online and adaptively as important attributes of segmentation methods,the boundary detection accuracy as a neglected metrics for SD methods.展开更多
Cloud computing is an increasingly popular paradigm for accessing computing resources. For marketing application, this paper proposes a dynamic model of customer interpurchase time with geometric distribution. This mo...Cloud computing is an increasingly popular paradigm for accessing computing resources. For marketing application, this paper proposes a dynamic model of customer interpurchase time with geometric distribution. This model considers that there is a change point in interpurchase time and two types of probability density functions are demonstrated （time decreasing before changing; time increasing after changing）. With the description of change point, Bernoulli and Poisson distributions also are discussed in the model construction.展开更多
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 ...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.展开更多
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 fiel...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.展开更多
The paper considers the problem of testing for a change point in the parameters of AR（p） models.It is shown that the asymptotically limiting distribution of the residual CUSUM of squares test（RCUSQ） is still the s...The paper considers the problem of testing for a change point in the parameters of AR（p） models.It is shown that the asymptotically limiting distribution of the residual CUSUM of squares test（RCUSQ） is still the sup of a standard Brownian bridge under null hypothesis.We also show via simulations that our asymptotic results provide good approximations in finite samples.展开更多
This paper mainly investigated the basic information about non-stationary trend change point patterns. After performing the investigation, the corresponding results show the existence of a trend, its magnitude, and ch...This paper mainly investigated the basic information about non-stationary trend change point patterns. After performing the investigation, the corresponding results show the existence of a trend, its magnitude, and change points in 24-hourly annual maximum series (AMS) extracted from monthly maximum series (MMS) data for thirty years (1986-2015) rainfall data for Uyo metropolis. Trend analysis was performed using Mann-Kendall (MK) test and Sen’s slope estimator (SSE) used to obtain the trend magnitude, while the trend change point analysis was conducted using the distribution-free cumulative sum test (CUSUM) and the sequential Mann-Kendall test (SQMK). A free CUSUM plot date of change point of rainfall trend as 2002 at 90% confidence interval was obtained from where the increasing trend started and became more pronounced in the year 2011, another change point year from the SQMK plot with the trend intensifying. The SSE gave an average rate of change in rainfall as 2.1288 and 2.16 mm/year for AMS and MMS time series data respectively. Invariably, the condition for Non-stationary concept application is met for intensity-duration-frequency modeling.展开更多
基金supported by the National Natural Science Foundation of China (41071206)the Key Technologies R&D Program of China during the 11th Five-Year Plan period (2008BADA7B05)
文摘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.
基金Supported by the National Natural Science Foundation of China（10471126）.
文摘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.
基金supported by the National Natural Science Foundation of China(Grant No.10902039)the Major Project Research of the Ministry of Railways of the People's Republic of China(Grant No.2010-201)
文摘In using the PGCEVD （Poisson-Gumbel Compound Extreme Value Distribution） model to calculate return values of typhoon wave height, the quantitative selection of the threshold has blocked its application. By analyzing the principle of the threshold selection of PGCEVD model and in combination of the change point statistical methods, this paper proposes a new method for quantitative calculation of the threshold in PGCEVD model. Eleven samples from five engineering points in several coastal waters of Guangdong and Hainan, China, are calculated and analyzed by using PGCEVD model and the traditional Pearson type III distribution （P-III） model, respectively. By comparing the results of the two models, it is shown that the new method of selecting the optimal threshold is feasible. PGCEVD model has more stable results than that of P-III model and can be used for the return wave height in every direction.
文摘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.
基金Under the auspices of the Chinese Academy of Sciences(CAS)Strategic Leading Science and Technology Project Category A(No.XDA23100203)National Natural Science Foundation of China(No.42071144,41501093,41771118)+1 种基金Key Research and Development Program of China(No.2016YFC0501601)Fundamental Research Funds for the Central Universities(No.GK202003060)。
文摘Reference evapotranspiration(ET_(0))is a vital component in hydrometeorological research and is widely applied to various aspects,such as water resource management,hydrological modeling,irrigation deployment,and understanding and predicting the influence of hydrologic cycle variations on future climate and land use changes.Quantifying the influence of various meteorological variables on ET_(0) is not only helpful for predicting actual evapotranspiration but also has important implications for understanding the impact of global climate change on regional water resources.Based on daily data from 69 meteorological stations,the present study analyzed the spatiotemporal pattern of ET_(0) and major contributing meteorological variables to ET_(0) from 1960 to 2017 by the segmented re-gression model,Mann-Kendall test,wavelet analysis,generalized linear model,and detrending method.The results showed that the annual ET_(0) declined slightly because of the combined effects of the reduction in solar radiation and wind speed and the increase in vapor pressure deficit(VPD)and average air temperature in the Loess Plateau(LP)during the past 58 yr.Four change points were detected in 1972,1990,1999,and 2010,and the annual ET_(0) showed a zigzag change trend of‘increasing-decreasing-increasing-decreasing-increasing’.Wind speed and VPD played a leading role in the ET_(0) changes from 1960 to 1990 and from 1991 to 2017,respectively.This study confirms that the dominant meteorological factors affecting ET_(0) had undergone significant changes due to global climate change and vegetation greening in the past 58 years,and VPD had become the major factor controlling the ET_(0) changes on the LP.The data presented herein will contribute to increasing the accuracy of predictions on future changes in ET_(0).
基金Acknowledgments This study was financially supported by the Shanghai Science and Technology Committee （10dz1210600）, the National Sea Welfare Project （201005019-09）, the Natural Science Foundation of China （41476075, 41340044）, and the China Geological Survey （12120115043101 ）.
文摘In recent years, intensifying waterlogging, salt water intrusion, wetland loss, and ecosystem degradation in Chinese delta cities and adjacent regions have generated the pressing need to create an urban form that is suited to both current and future climates incorporating sea level rise. However, adaptation planning uptake is slow. This is particularly unfortunate because patterns of urban form interact with mean sea level rise （MSLR） in ways that reduce or intensify its impact. There are currently two main barriers that are significant in arresting the implementation of adaptation planning with reference to the MSLR projections composed of geomorphologic MSLR projections and eustatic MSLR projections from global climate warming, and making a comprehensive risk assessment of MSLR projections. The present review shows recent progresses in mapping MSLR projections and their risk assessment approaches on Chinese delta cities, and then a perspective of adapting these cities to MSLR projections as following six aspects. 1） The geomorphologic MSLR projections are contributed by the natural tectonic subsidence projections and the MSLR projections by anthropogenic geomorphologic change. The former needs to be updated in a global framework. The latter is accumulated by land subsidence from underground water depletion, water level fall caused by the erosion of riverbeds from a sediment supply decline attributed to the construction of watershed dams, artificial sand excavation, water level raise by engineering projects including land reclamation, deep waterway regulation, and fresh water reservoirs. 2） Controlling MSLR projections by anthropogenic geomorphologic changes. 3） The IPCC AR5 RCPs MSLRs scenarios are expected to be projected to the local eustatic MSLR projections on the Chinese deltas. 4） The MSLR projections need to be matched to a local elevation datum. 5） Modeling approaches of regional river-sea numerical with semi- analytical hydrodynamics, estuarine channel network, system dynamics and adaptation points are perspective. 6） Adaptation planning to MSLR projections requires a comprehensive risk assessment of the risk of flood, fresh water supply shortage, coastal erosion, wetland loss, siltation of ports and waterway in Chinese delta cities and adjacent regions.
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.
基金Supported by Foundation of Education Department of Shaanxi Provincial Government（2010JK561） Supported by Basic Research Foundation of Xi’an Polytechnic University（2010JC07）+1 种基金 Supported by the Special Funds of the National Natural Science Foundation of China（11026135） Supported by Chinese Ministry of Education Funds for Young Scientists（10YJC910007）
文摘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.
文摘This paper utilizes a change-point estimator based on <span>the </span><span style="font-style:italic;">φ</span><span>-</span><span>divergence. Since </span><span "=""><span>we seek a </span><span>near perfect</span><span> translation to reality, then locations of parameter change within a finite set of data have to be accounted for since the assumption of </span><span>stationary</span><span> 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.</span></span>
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 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funded by the National Key Research and Development Program of China(2016YFC0503705)
文摘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.
基金This work is supported by the National Key Research and Development Program of China(2022YFF1203001)National Natural Science Foundation of China(Nos.62072465,62102425)the Science and Technology Innovation Program of Hunan Province(Nos.2022RC3061,2023RC3027).
文摘Time series segmentation has attracted more interests in recent years,which aims to segment time series into different segments,each reflects a state of the monitored objects.Although there have been many surveys on time series segmentation,most of them focus more on change point detection(CPD)methods and overlook the advances in boundary detection(BD)and state detection(SD)methods.In this paper,we categorize time series segmentation methods into CPD,BD,and SD methods,with a specific focus on recent advances in BD and SD methods.Within the scope of BD and SD,we subdivide the methods based on their underlying models/techniques and focus on the milestones that have shaped the development trajectory of each category.As a conclusion,we found that:(1)Existing methods failed to provide sufficient support for online working,with only a few methods supporting online deployment;(2)Most existing methods require the specification of parameters,which hinders their ability to work adaptively;(3)Existing SD methods do not attach importance to accurate detection of boundary points in evaluation,which may lead to limitations in boundary point detection.We highlight the ability to working online and adaptively as important attributes of segmentation methods,the boundary detection accuracy as a neglected metrics for SD methods.
基金supported by the National Science Council of Taiwan under Grant No. NSC 99-2410-H-156-013 and NSC 98-2410-H-156-021
文摘Cloud computing is an increasingly popular paradigm for accessing computing resources. For marketing application, this paper proposes a dynamic model of customer interpurchase time with geometric distribution. This model considers that there is a change point in interpurchase time and two types of probability density functions are demonstrated （time decreasing before changing; time increasing after changing）. With the description of change point, Bernoulli and Poisson distributions also are discussed in the model construction.
文摘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.
基金National Natural Science Foundations of China(No. 61074140,No. 60974094)Young Teacher Development Support Project of Shandong University of Technology,China
文摘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.
基金Supported by NSFC(70783094)Supported by XUST(2010039)
文摘The paper considers the problem of testing for a change point in the parameters of AR（p） models.It is shown that the asymptotically limiting distribution of the residual CUSUM of squares test（RCUSQ） is still the sup of a standard Brownian bridge under null hypothesis.We also show via simulations that our asymptotic results provide good approximations in finite samples.
文摘This paper mainly investigated the basic information about non-stationary trend change point patterns. After performing the investigation, the corresponding results show the existence of a trend, its magnitude, and change points in 24-hourly annual maximum series (AMS) extracted from monthly maximum series (MMS) data for thirty years (1986-2015) rainfall data for Uyo metropolis. Trend analysis was performed using Mann-Kendall (MK) test and Sen’s slope estimator (SSE) used to obtain the trend magnitude, while the trend change point analysis was conducted using the distribution-free cumulative sum test (CUSUM) and the sequential Mann-Kendall test (SQMK). A free CUSUM plot date of change point of rainfall trend as 2002 at 90% confidence interval was obtained from where the increasing trend started and became more pronounced in the year 2011, another change point year from the SQMK plot with the trend intensifying. The SSE gave an average rate of change in rainfall as 2.1288 and 2.16 mm/year for AMS and MMS time series data respectively. Invariably, the condition for Non-stationary concept application is met for intensity-duration-frequency modeling.
