State Estimation Moving Window Gradient Iterative Algorithm for Bilinear Systems Using the Continuous Mixed p-norm Technique

This paper studies the parameter estimation problems of the nonlinear systems described by the bilinear state space models in the presence of disturbances.A bilinear state observer is designed for deriving identification algorithms to estimate the state variables using the input-output data.Based on the bilinear state observer,a novel gradient iterative algorithm is derived for estimating the parameters of the bilinear systems by means of the continuous mixed p-norm cost function.The gain at each iterative step adapts to the data quality so that the algorithm has good robustness to the noise disturbance.Furthermore,to improve the performance of the proposed algorithm,a dynamicmoving window is designed which can update the dynamical data by removing the oldest data and adding the newestmeasurement data.A numerical example of identification of bilinear systems is presented to validate the theoretical analysis.

On-line Fault Diagnosis in Industrial Processes Using Variable Moving Window and Hidden Markov Model

An integrated framework is presented to represent and classify process data for on-line identifying abnormal operating conditions. It is based on pattern recognition principles and consists of a feature extraction step, by which wavelet transform and principal component analysis are used to capture the inherent characteristics from process measurements, followed by a similarity assessment step using hidden Markov model (HMM) for pattern comparison. In most previous cases, a fixed-length moving window was employed to track dynamic data, and often failed to capture enough information for each fault and sometimes even deteriorated the diagnostic performance. A variable moving window, the length of which is modified with time, is introduced in this paper and case studies on the Tennessee Eastman process illustrate the potential of the proposed method.

Double Moving Window MPCA for Online Adaptive Batch Monitoring

Online monitoring of chemical process performance is extremely important to ensure the safety of a chemical plant and consistently high quality of products. Multivariate statistical process control has found wide applications in process performance analysis, monitoring and fault diagnosis using existing rich historical database.In this paper, we propose a simple and straight forward multivariate statistical modeling based on a moving window MPCA (multiway principal component analysis) model along the time and batch axis for adaptive monitoring the progress of batch processes in real-time. It is an extension to minimum window MPCA and traditional MPCA.The moving window MPCA along the batch axis can copy seamlessly with variable run length and does not need to estimate any deviations of the ongoing batch from the average trajectories. It replaces an invariant fixed-model monitoring approach with adaptive updating model data structure within batch-to-batch, which overcomes the changing operation condition and slows time-varying behaviors of industrial processes. The software based on moving window MPCA has been successfully applied to the industrial polymerization reactor of polyvinyl chloride (PVC) process in the Jinxi Chemical Company of China since 1999.

Analysis of Landscape Patterns in the Urban-Rural Integrated Area Based on Moving Window: A Case Study of Sanshui District in Foshan City

The urban-rural integrated area in Sanshui District of Foshan City was selected for research, and the impact of landscape pattern around the No.269 provincial highway was analyzed based on the land-use data in 2014 using the spatial analysis in GIS and the moving window method. The results showed that:(1) within the scope of a 2 km-range buffer zone, with a low degree of heterogeneity, land for construction use and water area were the dominant land-use types, while with a high degree of fragmentation, cultivated land, wooded land, grassland, garden land, land for other farm uses, and land unused were scattered;(2) the 250-m square moving window could well detect the change characteristics of landscape pattern on both sides of the road;(3) the gradient analysis of landscape pattern in urban-rural integrated area, which was conducted with the aid of a 750-m transect on both sides of the road, indicated that there were significant differences between landscape indexes both in the urban-rural integrated area and on both sides of the road;(4) the road that had an obvious cutting and fragmentation impact on the landscape was an important factor leading to the increasing fragmentation and heterogeneity to regional landscapes.

Recurring Multi-layer Moving Window Approach to Forecast Day-ahead and Week-ahead Load Demand Considering Weather Conditions

The incorporation of weather variables is crucial in developing an effective demand forecasting model because electricity demand is strongly influenced by weather conditions.The dependence of demand on weather conditions may change with time during a day.Therefore,the time stamped weather information is essential.In this paper,a multi-layer moving window approach is proposed to incorporate the significant weather variables,which are selected using Pearson and Spearman correlation techniques.The multi-layer moving window approach allows the layers to adjust their size to accommodate the weather variables based on their significance,which creates more flexibility and adaptability thereby improving the overall performance of the proposed approach.Furthermore,a recursive model is developed to forecast the demand in multi-step ahead.An electricity demand data for the state of New South Wales,Australia are acquired from the Australian Energy Market Operator and the associated results are reported in the paper.The results show that the proposed approach with dynamic incorporation of weather variables is promising for day-ahead and week-ahead load demand forecasting.

Modelling of a Class of Nonstationary Time Series with Kalman Filter Using Moving Window

In this paper a method for modelling and forecasting of a class of nonstationary time series with Kalman filter using moving window is proposed. The procedure of the method is as follows: in terms of parameter estimation during recursive process by using LSM, the state space equation is constructed, then the Kalman filter using moving window is made to get the data with reduced level of observation noise. Finally, the precise parameter estimation can be obtained by using the LSM again. The algorithm is carried on recursively. Good results for estimating and forecasting are shown by simulation, examples. The algorithm of Kalman filter using moving window proposed by us is introduced in this paper, which can guarantee the precision and convergence of Kalman filter.

Land use induced blurring of forest-grassland transition in north-west Himalaya-A case study using Moving Split Window boundary detection technique

Transition zone or ecotone is a unique community sandwiched between two communities/ecosystems/biomes.These ecotones in Himalaya remained unexplored for many ecological aspects like biodiversity,phyto-sociology,boundary detection and even impact of change in land use pattern(anthropogenic activity).The most accepted and widespread technique called as Moving Split Window(MSW) technique is used for detection of vegetation and environmental boundaries at four different sites in the lesser stratum of north-west Himalaya.All the four sites were at different distances from the nearest human inhabited area.Anthropogenic activities like grazing,herb collection,wood collection etc.were common at proximal sites.Such activities have led to the change in land use pattern.In this study,we have tried to work out the impact of the change in land use pattern(human interference) on the vegetation and basic environmental parameters like soil pH,electrical conductivity and moisture on forestgrassland ecotone in north-west Himalaya.Data on mountain steepness was also collected and analyzed.The dissimilarity profile using the statistical tool Squared Euclidian Distance(SED) indicated that species turnover locations increase with the increase in distance of ecotones from human settlements.The ecotones at distant locations from human villages are characterized with blunt as well as sharp peaks for vegetation data,however,conditions are reverse in case of the proximal sites.The study also reveals that as the distance between the ecotone and human settlements increases,the complex conditions like multiple vegetation boundaries prevails on the transitions.In this regard,land use induced blurring of forest-grassland transition in north-west Himalaya is summed up in the study.

Toward real-time digital pulse process algorithms for CsI(Tl)detector array at external target facility in HIRFL-CSR

A fully digital data acquisition system based on a field-programmable gate array(FPGA) was developed for a CsI(Tl) array at the external target facility(ETF) in the Heavy Ion Research Facility in Lanzhou(HIRFL). To process the CsI(Tl) signals generated by γ-rays and light-charged ions, a scheme for digital pulse processing algorithms is proposed. Every step in the algorithms was benchmarked using standard γ and α sources. The scheme, which included a moving average filter, baseline restoration, leading-edge discrimination, moving window deconvolution, and digital charge comparison, was subsequently implemented on the FPGA. A good energy resolution of 5.7% for 1.33-MeV γ-rays and excellent α-γ identification using the digital charge comparison method were achieved, which satisfies CsI(Tl) array performance requirements.

Spatial Pattern of Cotton Yield Variability and Its Response to Climate Change in Cotton Belt of Pakistan

Cotton is a revenue source for cotton-producing countries;as the second-largest crop in Pakistan,it significantly contributes to its economy.Over the past few decades,cotton productivity has become unstable in Pakistan,and climate change is one of the main factors that impact cotton yield.Due to climate change,it becomes very important to understand the change trend and its impact on cotton yield at the regional level.Here,we investigate the relationship of standardized cotton yield variability with the variability of climate factors using a 15-yr moving window.The piecewise regression was fitted to obtain the trend-shifting point of climate factors.The results show that precipitation has experienced an overall decreasing trend of–0.64 mm/yr during the study period,with opposing trends of–1.39 mm/yr and 1.52 mm/yr before and after the trend-shifting point,respectively.We found that cotton yield variability increased at a rate of 0.17%/yr,and this trend was highly correlated with the variability of climate factors.The multiple regression analysis explains that climate variability is a dominant factor and controlled 81%of the cotton production in the study area from 1990 to 2019,while it controlled 73%of the production from 1990 to 2002 and 84%from 2002 to 2019.These findings reveal that climate factors affact the distinct spatial pattern of changes in cotton yield variability at the tehsil level.

Nonlinear dynamics in Divisia monetary aggregates:an application of recurrence quantification analysis

We construct recurrence plots(RPs)and conduct recurrence quantification analysis(RQA)to investigate the dynamic properties of the new Center for Financial Stability(CFS)Divisia monetary aggregates for the United States.In this study,we use the lat-est vintage of Divisia aggregates,maintained within CFS.We use monthly data,from January 1967 to December 2020,which is a sample period that includes the extreme economic events of the 2007–2009 global financial crisis.We then make comparisons between narrow and broad Divisia money measures and find evidence of a nonlinear but reserved possible chaotic explanation of their origin.The application of RPs to broad Divisia monetary aggregates encompasses an additional drift structure around the global financial crisis in 2008.Applying the moving window RQA to the growth rates of narrow and broad Divisia monetary aggregates,we identify periods of changes in data-generating processes and associate such changes to monetary policy regimes and financial innovations that occurred during those times.

Changes of Residential Land Density and Spatial Pattern from 1989 to 2004 in Jinan City,China

Urban sprawl is driven by a myriad of factors, the predominant one of which is the development of residential land. Selecting part of Jinan City for a case study, we use the landscape metric of percent of landscape (PLAND) to capture residential land growth and density changes in 1989, 1996 and 2004 to illuminate the dynamic process of residential land development. The results indicate that the moving window method and the landscape metrics method are efficient ways to describe residential land density. The residential land showed the greatest change among the built-up land with 1995.68 ha from 1989 to 2004, which is mainly transformed from agriculture land and green space. The urban center area of study area is primarily covered with medium density residential land, and surrounded by high density residential land. The development pattern of residential land exhibited both fill-in (new growth occurs through infilling the free spaces within the developed area) and sprawl processes, influenced by a series of factors, such as urban development policy, conservation of springs, recreational and aesthetic amenities. The findings of the study will help to guide urban planning with a focus on the management and protection of the environment and resources.

Assessment of forest geospatial patterns over the three giant forest areas of China

Geospatial patterns of forest fragmentation over the three traditional giant forested areas of China （Northeastern, southwestern and Southern China） were analyzed comparatively and reported based on a 250-m resolution land cover dataset. Specifically, the spatial patterns of forest fragmentation were characterized by combining geospatial metrics and forest fragmentation models. The driving forces resulting in the differences of the forest spatial patterns were also investigated. Results suggested that forests in southwest China had the highest severity of forest fragmentation, followed by south region and northeast region. The driving forces of forest fragmentation in China were primarily the giant population and improper exploitation of forests. In conclusion, the generated information in the study provided valuable insights and implications as to the fragmentation patterns and the conservation of hiodiversity or genes, and the use of the chosen geospatial metrics and forest fragmentation models was quite useful for depicting forest fragmentation patterns.

Telecoupled land-use changes in distant countries

International food trade has become a key driving force of agricultural land-use changes in trading countries, which has influenced food production and the global environment. Researchers have studied agricultural land-use changes and related environmental issues across multi-trading countries together, but most studies rely on statistic data without spatial attributes. However, agricultural land-use changes are spatially heterogeneous. Uncovering spatial attributes can reveal more critical information that is of scientific significance and has policy implications for enhancing food security and protecting the environment. Based on an integrated framework of telecoupling （socioeconomic and environmental interactions over distances）, we studied spatial attributes of soybean land changes within and among trading countries at the same time. Three distant countries -- Brazil, China, and the United States -- constitute an excellent example of telecoupled systems through the process of soybean trade. Our results presented the spatial distribution of soybean land changes-- highlighting the hotspots of soybean gain and soybean loss, and indicated these changes were spatially clustered, different across multi-spatial scales, and varied among the trading countries. Assisted by the results, global challenges like food security and biodiversity loss within and among trading countries can be targeted and managed efficiently. Our work provides simul- taneously spatial information for understanding agricultural land-use changes caused by international food trade globally, highlights the needs of coordination among trading countries, and promotes global sustainability.

Rank-size Distributions of Chinese Cities: Macro and Micro Patterns

A large number of studies have been conducted to find a better fit for city rank-size distributions in different countries. Many theoretical curves have been proposed, but no consensus has been reached. This study argues for the importance of examining city rank-size distribution across different city size scales. In addition to focusing on macro patterns, this study examines the micro patterns of city rank-size distributions in China. A moving window method is developed to detect rank-size distributions of cities in different sizes incrementally. The results show that micro patterns of the actual city rank-size distributions in China are much more complex than those suggested by the three theoretical distributions examined(Pareto, quadratic, and q-exponential distributions). City size distributions present persistent discontinuities. Large cities are more evenly distributed than small cities and than that predicted by Zipf′s law. In addition, the trend is becoming more pronounced over time. Medium-sized cities became evenly distributed first and then unevenly distributed thereafter. The rank-size distributions of small cities are relatively consistent. While the three theoretical distributions examined in this study all have the ability to detect the overall dynamics of city rank-size distributions, the actual macro distribution may be composed of a combination of the three theoretical distributions.

Comparative analysis of volatile constituents between herbal pair flos lonicerae-caulis lonicerae and its single herbs

Gas chromatography-mass spectrometry(GC-MS) and the chemometric resolution method(alternative moving window factor analysis,AMWFA) were used for comparative analysis of volatile constituents in herbal pair(HP) flos lonicerae-caulis lonicerae(FL-CL) and its single herbs.The temperature-programmed retention index(PTRI) was also employed for the identification of compounds.In total,44,39,and 50 volatile chemical components in volatile oil of FL,CL and HP FL-CL were separately determined qualitatively and quantitatively,accounting for 87.22%,94.54% and 90.08% total contents of volatile oil of FL,CL and HP FL-CL,respectively.The results show that there are 32 common volatile constituents between HP FL-CL and single herb FL,33 common volatile constituents between HP FL-CL and single herb CL,and 10 new constituents in the volatile oil of HP FL-CL.

Effective Elastic Thickness of the Lithosphere in the Mariana Subduction Zone and Surrounding Regions and Its Implications for Their Tectonics

To understand the rheology,structure,and tectonics of the lithosphere in the Mariana subduction zone and surrounding regions,we calculated the effective elastic thickness of the lithosphere(Te)in these areas using the improved moving window admittance technique(MWAT)method.We find that smaller data grid spacing can better reflect Te variations in the subduction zone.The Te of the study region ranges from 0 to 47 km.The Te is reduced from 40 km on the seaward side of the outer-rise region to 1-2 km along the trench axis.The lithospheric breaking distance from the trench axis ranges from 0 to 250 km.We suggest that the intermediate Te values in seamounts and high Te values on the seaward side of the outer-rise region respectively reflect the‘fossil’rheological state and current lithospheric strength of the Pacific plate.The faulting induced by the downward bending of subducting plate not only ruptures the lithosphere but also contributes to the mantle serpentinization,significantly reducing the lithospheric strength.The largest breaking distance of the Ogasawara Plateau may be due to the increase in the mass load of the subducting plate in the Ogasawara Plateau and the significant horizontal bending force in the plate caused by the resistance of seamounts to subduction.Furthermore,a good positive correlation exists between the breaking distance and subduction dip angle along the trench axis.We suggest that the subducting plate with a larger breaking distance is likely to form a larger subduction angle.

Online quantitative analysis of soluble solids content in navel oranges using visible-nearinfrared spectroscopy and variable selection methods

Variable selection is applied widely for visible-near infrared(Vis-NIR)spectroscopy analysis of internal quality in fruits.Different spectral variable selection methods were compared for online quantitative analysis of soluble solids content(SSC)in navel oranges.Moving window partial least squares(MW-PLS),Monte Carlo uninformative variables elimination(MC-UVE)and wavelet transform(WT)combined with the MC-UVE method were used to select the spectral variables and develop the calibration models of online analysis of SSC in navel oranges.The performances of these methods were compared for modeling the Vis NIR data sets of navel orange samples.Results show that the WT-MC-UVE methods gave better calibration models with the higher correlation cofficient(r)of 0.89 and lower root mean square error of prediction(RMSEP)of 0.54 at 5 fruits per second.It concluded that Vis NIR spectroscopy coupled with WT-MC-UVE may be a fast and efective tool for online quantitative analysis of SSC in navel oranges.

AO-MW-PLS method applied to rapid quantification of teicoplanin with near-infrared spectroscopy

Teicoplanin(TCP)is an important lipoglycopeptide antibiotic produced by fermenting Acti-noplanes teichomyceticus.The change in TCP concentration is important to measure in the fermentation process.In this study,a reagent-free and rapid quantification method for TCP in the TCP-Tris-HCl mixture samples was developed using near infrared(NIR)spectroscopy by focusing our attention on the fermentation process for TCP.The absorbance optimization(AO)partial least squares(PLS)was proposed and integrated with the moving window(MW)PLS,which is called AO-MW-PLS method,to select appropriate wavebands.Amodel set that includes various wavebands that were equivalent to the optimal AO-MW-PLS waveband was,proposed based on statistical considerations.The public region of all equivalent wavebands was just one of the equivalent wavebands.The obtained public regions were 1540-1868 nm for TCP and 1114-1310 nm for Tris.The root-mean-square error and correlation coeficient for leave-one-out cross validation were 0.046 mg mL^(-1)and 0.9998 mg mL^(-1)for TCP,and 0.235 mg mL^(-1)and 0.9986 mg mL^(-1)for Tris,respectively.All the models achieved highly accurate prediction effects,and the selected wavebands provided valuable references for designing specialized spectrometers.This study provided a valuable reference for further application of the proposed methods to TCP fermentation broth and to other spectroscopic analysis fields.

Geographical classification of Nanfeng mandarin by near infrared spectroscopy coupled with chemometrics methods

Near infrared spectroscopy(NIRS),coupled with principal component analysis and wavelength selection techniques,has been sed to develop a robust and reliable reduced-spectrum classifi-cation model for determining the geographical origins of Nanfeng mandarins.The application of the changeable size moving window principal component analysis(CSMWPCA)provided a notably improved lassification model,with correct classification rates of 92.00%,100.00%,90.00%,100.00%,100.00%,100.00%and 100.00%for Fujian,Guangxi,Hunan,Baishe,Baofeng,Qiawan,Sanxi samples,respectively,as well as,a total dassification rate of 97.52%in the wavelength range from 1007 to 1296 nm.To test and apply the proposed method,the procedure was applied to the analysis of 59 samples in an independent test set.Good identification results(correct rate of 96.61%)were also received.The improvement achieved by the application of CSMWPCA method was particularly remarkable when taking the low complexities of the final model(290 variables)into account.The results of the study showed the great potential of NIRS as a fast,nondestructive and environmentally acceptable method for the rapid and reliable determination for geographical classifcation of Nanfeng mandarins.

Selection of stable equivalent wavebands for near-infrared spectroscopic analysis of total nitrogen in soil

The selection of stable wavebands for the near-infrared(NIR)spectroscopic analysis of total nitrogen(TN)in soil was accomplished by using an improved moving window partial leastsquares(MWPLS)method.A new modeling approach was performed based on randomness,similarity and stability,which produced an objective,stable and practical model.Based on the MWPLS method,a search was in the overali scanning region from 400 to 2498 nm,and the optimal waveband was identified to be 1424 to 282 nm.A model space that includes 41 wave-bands that are equivalent to the optimal waveband was then proposed.The public range of the 41equivalent optimal wavebands was 1590 to 1870 nm,which contained suficient TN information.The wavebands of 1424 to 2282 nm,1590 to 1870nm,and the long NIR region 1100 to 2498 nmall achieved satisfactory validation ffects.However,the public waveband of 1590 to 1870 nm hadonly a minimum number of wavelengths,which significantly reduced the method complexity.Various equivalent wavebands serve as guidelines for designing spect roscopic instruments.Thesewavebands could address the restrictions of position and the number of wavelengths in instru-ment design.