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.

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.

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.

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.

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.

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.

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.

Rolling Gaussian Process Regression with Application to Regime Shifts

Gaussian Process Regression (GPR) can be applied to the problem of estimating a spatially-varying field on a regular grid, based on noisy observations made at irregular positions. In cases where the field has a weak time dependence, one may desire to estimate the present-time value of the field using a time window of data that rolls forward as new data become available, leading to a sequence of solution updates. We introduce “rolling GPR” (or moving window GPR) and present a procedure for implementing that is more computationally efficient than solving the full GPR problem at each update. Furthermore, regime shifts (sudden large changes in the field) can be detected by monitoring the change in posterior covariance of the predicted data during the updates, and their detrimental effect is mitigated by shortening the time window as the variance rises, and then decreasing it as it falls (but within prior bounds). A set of numerical experiments is provided that demonstrates the viability of the procedure.

Analysis of the merged quasi-geoid of neighbouring areas

Employing the merged quasi-geoid, we analyses the causes of systematic errors in modelling of quasigeoid of neighbouring areas in the paper, and the efficient method is introduced to improve the accuracy of quasi-geoid. First, the systematic error is weakened with the moving window method applied to established quasi-geoids in two adjacent regions, and the accuracy of the merged quasi-geoid in the stitching region is checked using the measured GPS benchmark data; Second, the whole quasi-geoid is recomputed with data obtained from two adjacent regions if the accuracy of the quasi-geoid obtained from the first step in the stitching region is low; Finally, the quasi-geoids in two adjacent regions are recomputed respectively using GPS benchmark data of own region and adjacent region with the same solution if the accuracy of whole quasi-geoid obtained from the second step also is low. Actual data sets from Sichuan Province and Chongqing City are employed to test the moving window method. It is shown that the quasi-geoid models with high resolution and accuracy were obtained.

A novel predict-prevention quality control method of multi-stage manufacturing process towards zero defect manufacturing

Zero defection manufacturing (ZDM) is the pursuit of the manufacturing industry. However, there is a lack of the implementation method of ZDM in the multi-stage manufacturing process (MMP). Implementing ZDM and controlling product quality in MMP remains an urgent problem in intelligent manufacturing. A novel predict-prevention quality control method in MMP towards ZDM is proposed, including quality characteristics monitoring, key quality characteristics prediction, and assembly quality optimization. The stability of the quality characteristics is detected by analyzing the distribution of quality characteristics. By considering the correlations between different quality characteristics, a deep supervised long-short term memory (SLSTM) prediction network is built for time series prediction of quality characteristics. A long-short term memory-genetic algorithm (LSTM-GA) network is proposed to optimize the assembly quality. By utilizing the proposed quality control method in MMP, unqualified products can be avoided, and ZDM of MMP is implemented. Extensive empirical evaluations on the MMP of compressors validate the applicability and practicability of the proposed method.

Online Power System Voltage Stability Index for LCC HVDC Using Local Measurements

This paper proposes an online power system voltage stability index(PVSI Online)that quantifies power system voltage stability in real time between an AC network and LCC HVDC using only local measurements.Previous methods relied on using telecommunications to inform a change in a predetermined AC network model.The proposed method uses local measurements of voltage and current from the HVDC using a newly developed,numerically robust technique called Moving Window Covariance to detect a change in the AC network in real time.These network parameters are fed to a newly devised index that accurately models and captures the dynamics of the interaction between an LCC HVDC and AC network.The index is used to quantify stability,but also to advise network operation.In an onset of instability due to network degradation,the proposed index can provide a power order value for the HVDC to restore stability.All as a self-contained module that can be implemented within a HVDC scheme.

Spectral Insights into Microdynamics of Thermoresponsive Polymers from the Perspective of Two-dimensional Correlation Spectroscopy

Generalized two-dimensional correlation spectroscopy （2DCOS） and its derivate technique, perturbation correlation moving window （PCMW）, have found great potential in studying a series of physico-chemical phenomena in stimuli-responsive polymeric systems. By spreading peaks along a second dimension, 2DCOS can significantly enhance spectral resolution and discern the sequence of group dynamics applicable to various external perturbation-induced spectroscopic changes, especially in infrared （IR）, near-infrared （NIR） and Raman spectroscopy. On the basis of 2DCOS synchronous power spectra changing, PCMW proves to be a powerful tool to monitor complicated spectral variations and to find transition points and ranges. This article reviews the recent work of our research group in the application of 2DCOS and PCMW in thermoresponsive polymers, mainly focused on liquid crystalline polymers and lower critical solution temperature （LCST）-type polymers. Details of group motions and chain conformational changes upon temperature perturbation can thus be elucidated at the molecular level, which contribute to the understanding of their phase transition nature.

Fault mode probability factor based fault-tolerant control for dissimilar redundant actuation system

This paper presents a Fault Mode Probability Factor（FMPF） based Fault-Tolerant Control（FTC） strategy for multiple faults of Dissimilar Redundant Actuation System（DRAS）composed of Hydraulic Actuator（HA） and Electro-Hydrostatic Actuator（EHA）. The long-term service and severe working conditions can result in multiple gradual faults which can ultimately degrade the system performance, resulting in the system model drift into the fault state characterized with parameter uncertainty. The paper proposes to address this problem by using the historical statistics of the multiple gradual faults and the proposed FMPF to amend the system model with parameter uncertainty. To balance the system model precision and computation time, a Moving Window（MW） method is used to determine the applied historical statistics. The FMPF based FTC strategy is developed for the amended system model where the system estimation and Linear Quadratic Regulator（LQR） are updated at the end of system sampling period. The simulations of DRAS system subjected to multiple faults have been performed and the results indicate the effectiveness of the proposed approach.

Unexpected ecotone dynamics of a sand dune vegetation complex following water table decline

Aims Central Hungarian inland dune ranges harbor heterogeneous grassland vegetation with an extensive network of ecotones,arranged perpendicular to topography-driven hydrologic gradients.The area suffers from severe aridification due to climate change and local anthropogenic factors,which have led to a dramatic decline of the water table.As a result,groundwater is no longer reachable for lowlying plant communities;thus,we expect they are bound to undergo profound changes.This study investigates how the plant communities respond to this changing environment over time by monitoring ecotones,since they are frequently the hotspots of ecosystem change.We monitored five ecotones along permanent belt transects for 15 years to characterize their dynamic response,and to identify the internal structural changes of the plant communities the ecotones delimit.Methods Ecotones were delineated with the split moving window technique.The dynamics of two ecotone parameters,location and contrast,were analyzed with linear regression models incorporating two independent variables:study year as a measure of time since the loss of groundwater,and precipitation as a possible driver of interannual variations.The internal changes of the patches separated by the ecotones were analyzed using plant functional groups.Important Findings Precipitation had no detectable effect on the ecotone descriptors,but study year influenced ecotones in an unusual fashion.The position of the ecotones appeared to be very stable in time;their dynamics are stationary,not directional as we predicted.The contrasts had clear tendencies;two ecotones disappeared,one new one was formed and two ecotones showed no trend.The internal changes of the patches over time were dramatic,showing a shift toward more xeric and more open plant assemblages in most stretches of the transects.Thus,the dynamic response of the vegetation was not patch expansion vs.shrinking,but fusion vs.division,which profoundly restructured the vegetation pattern.Analysis of plant functional groups revealed that the trends of the ecotone contrasts could be traced back to internal changes of the patches and not to processes within ecotones.Hence,in situations where stationary ecotone dynamics prevail,ecotone position may be a poor indicator of the effects of strong directional environmental changes.However,in this study we show that ecotone contrast can serve as a sensitive tool for monitoring landscape pattern transformations in these cases.Also,this highlights the long-term nature of ecotone responses,which can have implications in landscape planning and restoration measures.