Moving Window Average Research Articles

Vegetation and Evapotranspiration Responses to Increased Atmospheric Vapor Pressure Deficit across the Global Forest

A forest is vulnerable to drought and plays important roles in the regulation of carbon and water cycling in a terrestrial ecosystem. Atmospheric vapor pressure deficit (VPD) has been identified as an increasingly major factor in plant functioning and has been established as a main contributor to recent drought-induced plant mortality, independent of other drivers associated with climate change. However, most previous studies have focused on the effects of climate warming and CO2 enrichment on vegetation growth, without considering the effects of an increased VPD on vegetation growth and evapotranspiration (ET) in forest ecosystems. This could lead to a large uncertainty in estimating the variability in forest carbon sinks. Based on the long-term satellite data, we investigated the response of the leaf area index (LAI) and ET to the VPD via a partial correlation analysis in this study. We also examined the temporal variability in the partial coefficients within a ten-year moving window. The results showed that over 50% of the region displayed a negative partial correlation between the LAI, ET, and VPD, and those pixels were mainly concentrated in North America and the plains of Eastern Europe. Regions with a negative trend of partial correlation in both the LAI and ET are mostly located in the plains of Eastern Europe and the Siberian Plain of western Russia, while the positive trend is mainly in South America. The plains of Eastern Europe are becoming drier, which was proved by the interannual trend of the Standardized Precipitation Evapotranspiration Index (SPEI) and soil water content (SWC). Additionally, the LAI and ET in those areas exhibited a significant positive correlation with the SWC based on the moving window average. This study suggests that the role of the VPD on vegetation will become increasingly prominent in the context of future climate change for the forest ecosystem.

Statistical downscaling of GCMs wind speed data for trend analysis of future scenarios: a case study in the Lombardy region

Near-surface wind speed is a key climatic variable, affecting many sectors, such as energy production, air pollution, and natural hazard. Lombardy region of Italy is among the European areas with lowest average wind speed, leading generally to low air quality and wind energy potential. However, it is also one of the most affected area by tornadoes in Italy. Here we investigate possible changes in wind circulation as due to prospective global warming. We analysed wind speed WS under future scenarios (SSP1-2.6 and SSP5-8.5) from six Global Climate Models (GCMs) until 2100, tuned against observed WS data. We employed a statistical downscaling method, namely Stochastic Time Random Cascade (STRC) to correct locally GCMs outputs. Three statistical tests, i.e. Linear Regression, Mann Kendall, Moving Window Average, were carried out to analyse future trends of: annual WS averages, 95th quantile (as an indicator of large WS), and the number of days of calm wind per year (NWC). The proposed STRC algorithm can successfully adjust the mean, standard deviation, and autocorrelation structure of the GCM outputs. No strong trends are found for the future. The chosen variables would all display non-stationarity, and the 95th percentile display a positive trend for most of the stations. Concerning NWC, notable discrepancies among GCMs are seen. The STRC algorithm can be used to successfully adjust GCMs outputs to reflect locally observed data and to then generate credible long-term scenarios for WSs as a tool for decision-making.

An Experimental Study on Trajectory Tracking Control of Torpedo-like AUVs Using Coupled Error Dynamics

In this paper, we propose a trajectory tracking controller with experimental verification for torpedo-like autonomous underwater vehicles (AUVs) with underactuation characteristics. The proposed controller overcomes the underactuation problem by designing the desired error dynamics in a coupled form using state variables in body-fixed and world coordinates. Unlike the back-stepping control requiring high-order derivatives of state variables, the proposed controller only requires the first derivatives of the states, which can alleviate noise magnification issues due to differentiation. We adopt time delay estimation to estimate the dynamics indirectly using control inputs and vehicle outputs, making the proposed controller relatively easy to apply without requiring the all of the vehicle dynamics. We also address some practical issues that commonly arise in experimental environments: handling measurement noises and actuation limits. To mitigate the effects of noise on the controller, a filtering technique using a moving window average is employed. Additionally, to account for the actuation limits, we design an anti-windup structure that takes into consideration the nonlinearity between the thrusting force and rotating speed of the thruster. We verify the tracking performance of the proposed controller through experimentation using an AUV. The experimental results show that the 3D motion control of the proposed controller exhibits an RMS error of 0.3216 m and demonstrate that the proposed controller achieves accurate tracking performance, making it suitable for survey missions that require tracking errors of less than one meter.

A moving window average method for internal fault detection of power transformers

Power transformers are among the mos]t critical components of power systems and should receive reliable protection against faults. Differential relays are widely used in the protection of power transformers. However, these relays are susceptible to mistaking the inrush currents, which are generated during the turning on of transformers. This paper presents a new method for detecting inrush currents based on the moving window averaging of current. The study found that using the Blackman Harris window rather than other windows in this method significantly reduces fault detection time. The proposed method is robust to strong noises such as white Gaussian noise. Finally, the proposed method was compared with the methods commonly used in industrial protection relays. In this comparison, it was demonstrated that the method offers several advantages over conventional techniques, including the ability to detect faults with high second harmonic and under current transformer saturation and to function properly in modern transformers.

Real-time processing methods to characterize streamwise vortices

One of the bottlenecks of an active control of turbulent boundary layers based on particle image velocimetry (PIV) measurements relates to the ability to process PIV images and extract useful for control information in real-time, on a time scale relevant for the selected control strategy. We propose two methodologies to extract useful information from PIV measurements in the y−z spanwise - wall normal cross-section, based on characterisation of properties such as size and strength of streamwise vortices. The vortices are created by active vortex generators embedded in a fully turbulent boundary layer. The proposed methodologies combine vortex identification and characterisation methods that use instantaneous PIV realisations to extract centers and strengths of streamwise vortices. The main purpose is to compare a standard vortex identification method that provides a robust and accurate estimate with another, ad-hoc method that is less robust or accurate but has a large computational speed gain potential. For demonstration purposes we use PIV measurements obtained at the wind tunnel facility downstream active vortex generators (Carlier and Stanislas, 2005; Foucaut et al., 2014). The first algorithm uses the Q-criteria and the integration of vorticity of each extracted vortex. This robust and accurate method requires the full spatially resolved PIV field followed by the computationally expensive spatial derivatives calculations and an integration. The second algorithm utilises the prior knowledge about the presence and shape of streamwise vortices expected in the measurement, therefore uses only a single line velocity profile estimate and can be parallelized to obtain few horizontal velocity profiles at different wall-normal distances. We compare the two methods and discuss their potential in term of computation times and robustness. Results show that in a turbulent boundary layer, a moving window average of a number of instantaneous fields is needed for both methods.

Modeling the neurodynamic organizations and interactions of teams

Across-brain neurodynamic organizations arise when teams perform coordinated tasks. We describe a symbolic electroencephalographic (EEG) approach that identifies when team neurodynamic organizations occur and demonstrate its utility with scientific problem solving and submarine navigation tasks. Each second, neurodynamic symbols (NS) were created showing the 1–40 Hz EEG power spectral densities for each team member. These data streams contained a performance history of the team’s across-brain neurodynamic organizations. The degree of neurodynamic organization was calculated each second from a moving window average of the Shannon entropy over the task. Decreased NS entropy (i.e., greater neurodynamic organization) was prominent in the ~16 Hz EEG bins during problem solving, while during submarine navigation, the maximum NS entropy decreases were ~10 Hz and were associated with establishing the ship’s location. Decreased NS entropy also occurred in the 20–40 Hz bins of both teams and was associated with uncertainty or stress. The highest mutual information levels, calculated from the EEG values of team dyads, were associated with decreased NS entropy, suggesting a link between these two measures. These studies show entropy and mutual information mapping of symbolic EEG data streams from teams can be useful for identifying organized across-brain team activation patterns.

State of the Art in the Development of Adaptive Soft Sensors based on Just-in-Time Models

Data-driven soft sensors have gained popularity due to availability of the recorded historical plant data. The success stories of the implementations of soft sensors, however, involved some practical difficulties. Even if a good soft sensor is successfully developed, its predictive performance will gradually deteriorate after a certain time due to changes in the state of plants and process characteristics, such as catalyst deactivation and sensor and process drifts due to equipment ageing, fouling, clogging and wear, changes of raw materials and so on. To get soft sensor automatically updated, different kinds of methods have been introduced, such as Kalman filter, moving window average, recursive and ensemble methods. However, these methods have some drawbacks which motivate the development and implementation of just-in-time (JIT) model based adaptive soft sensor. This paper aims to report the current status of adaptive soft sensors based on just-in-time modelling approach. Critical review and discussion on the original and modified algorithms of the JIT modelling approach are presented. Proposed topics for future research and development are also outlined to provide a road map on the developing improved and more practical adaptive soft sensors based on JIT models.

Y-Spect: A Multi-Method Gamma Spectrometry Analysis Program

To accomplish a more accurate, precise and correct interpretation and analysis of spectrum data collecting from a gamma spectrometry counting system, a fully interactive computer code, named Y-Spect, has been developed by using the Delphi 7.0 programming language. The code combines several popular methods for peak search, i.e.: Mariscotti, Phillips-Marlow, Robertson et al ., Routti-Prussin, Black, Sterlinski, Savitzky-Golay and Block et al . Any combinations of those methods can be chosen during a peak searching process, which can be performed in automatic or manual mode. Moving Window Average- and Savitzky-Golay-methods are available for spectrum data smoothing. Peak fitting is done by using a non-linear least square method of Levenberg-Marquardt for either a pure Gaussian peak shape or one with an additional Right/Left Tail function. Other than standard features, such as: peak identification and determination of: continuum, region of interest (ROI), and peak area, etc., Y-Spect has also a special feature which can predict the existence of escape- and/or sum peaks that belong to a certain radioisotope. Aside from displaying the complete spectrum graph, including: singlet or multiplet ROIs and peak identifications, Y-Spect can also display the first- or second-derivative of the spectrum data. Data evaluation is given as: isotope names, peak energy, Net-Count(-Rate), etc. Y-Spect is provided with a complete ENDF/B-VII.0 gamma-ray library file that contains of 16089 gamma energy lines from 1420 different radioisotopes. Other general specifications are: maximum number of: spectrum's channels = 16*1024; ROIs = 2*1024; ROI’s width = 2*1024 channels; Overlapping peaks (multiplet) = 20; Identified isotopes = 3*1024, and Isotope library's energy lines = 16*1024. Received: 16 January 2013; Revised: 21 April 2013; Accepted: 25 April 2013

Recent (1980–2009) evidence of climate change in the upper Karakoram, Pakistan

We investigate here recent (1980–2009) climate variability in the upper Karakoram, Northern Pakistan, of particular interest given the peculiar glacier behavior during the last two decades. Differently from other glacierized regions in the Hindu Kush–Karakoram–Himalaya region, glaciers in the Karakoram display limited ice thinning, and in some cases advancing has been detected. Climate analysis is required to describe recent (i.e., last three decades) variability, to aid highlighting of the factors driving glacier evolution. Starting from monthly data, we analyze seasonal values of total precipitation, number of wet days, maximum (max) and minimum (min) air temperature, max precipitation in 24 h, and cloud cover for 17 weather stations in the upper Karakoram, clustered within three climatic regions as per use of principal components analysis. We detect possible nonstationarity in each of these regions by way of (1) linear regression, (2) moving window average, and (3) Mann–Kendall test, also in progressive form, to detect the onset date of possible trends. We then evaluate linear correlation coefficients between Northern Atlantic Oscillation (NAO) index and climate variables to assess effectiveness of teleconnections, claimed recently to affect climate in this area. Also, we compare temperature within the investigated zone against global temperature anomalies, to evidence enhanced warming within this area. We found mostly nonsignificant changes of total precipitation, unless for few stations displaying increase in Chitral-Hindu Kush region and Northwest Karakoram, or Gilgit area, and decrease in Western Himalaya, Kotli region. Max precipitation is mostly unchanged, unless for slight increase in Chitral and Gilgit areas, and slight decrease in Kotli region. Number of wet days is mostly increasing in Gilgit area, and decreasing in Chitral area, with no clear signal in Kotli region. Min temperatures increase always but during Summer, when decreasing values are detected, especially for Gilgit and Chitral regions. Max temperatures are found to increase everywhere. Cloud cover is significantly increasing in Gilgit area, but decreasing otherwise, especially in Kotli region. Max temperature regime is significantly positively correlated against global thermal anomaly, while min temperature regime is nonsignificantly negatively correlated. Max and min temperatures seem mostly negatively correlated to NAO. Some dependence of trend intensity for the considered variables against altitude is found, different for each region, suggesting that investigation of weather variables at the highest altitudes is warranted to discriminate further climate variability in the area.

Application of the Hilbert–Huang Transform to the Estimation of Air-Sea Turbulent Fluxes

The Hilbert-Huang transform (HHT) is applied to analyzing the turbulent time series obtained within the atmospheric boundary layer over the ocean. A method based on the HHT is introduced to reduce the influence of non-turbulent motions on the eddy-covariance based flux by removing non-turbulent modes from the time series. The scale dependence of the flux is examined and a gap mode is identified to distinguish between turbulent modes and non-turbulent modes. To examine the effectiveness of this method it is compared with three conventional methods (block average, moving-window average, and multi-resolution decomposition). The data used are from three sonic anemometers installed on a moored buoy at about 6, 4 and 2.7 m height above the sea surface. For each method, along-wind and cross-wind momentum fluxes and sensible heat fluxes at the three heights are calculated. According to the assumption of a constant-flux layer, there should be no significant difference between the fluxes at the three heights. The results show that the fluxes calculated using HHT exhibit a smaller difference and higher correlation than the other methods. These results support the successful application of HHT to the estimation of air-sea turbulent fluxes.

Design of atmospheric composition monitor based on ultraviolet optical absorption technology

An open path atmospheric composition monitor is designed based on ultraviolet differential absorption technology. Dark current correction and diode response correction are used to improve the detection limit and Savitzky-Golay filter is used to improve the measurement accuracy. The experimental results show that the designed system has the ability to measure NO and NO2 in real time with reasonable accuracy. The detection limit of the system is about 0.25 ppm for NO and 0.28 ppm for NO2. When the concentration level of the target gases is below 100 ppm, the system has good linearity and high measurement accuracy, i.e., the measurement accuracy is about 2% for NO and about 4% for NO2. The detection limit of dark current can be improved by about 5 to 10 ppb, and the correction of diode response can improve the detection limit by around 30 ppb. Moving window average can improve the detection limit at low concentration levels but will generate more errors at higher concentration levels. Generally, the designed system meets the requirement of measuring multi-species air pollutants in real time and accurately.

Incorporating retrospective clustering into a prospective cusum methodology for anthrax: Evaluating the effects of disease expectation

We analysed livestock anthrax in Kazakhstan from 1960–2006, using a prospective CUSUM to examine the affects of expectation on the detection of spatio-temporal clusters. Three methods for deriving baselines were used for CUSUM; a standard z-score, AVG, a spatially-weighted z-score derived from Local Moran’s I, LISA, and a moving-window average, MWA. LISA and AVG elicited alarm signals in the second year that did not return below threshold during the 47-year period, while MWA signaled an alarm at year four and relented at year fifteen. The number of spatial clusters elicited varied: LISA n = 16, AVG n = 11, and MWA n = 3, although there were clusters present around Shymkent, in south-central Kazakhstan, in each method. The results illustrate that the selection of a baseline with an unknown background population has a significant effect on the ability to detect the onset of clusters in space and in time when employing a CUSUM methodology.

The influence of providing feedback on force production and within-participant reproducibility during maximal voluntary exertions for the anterior deltoid, middle deltoid, and infraspinatus

The normalization of electromyographic signals to a maximum voluntary reference contraction is common practice within the ergonomics research paradigm. However, there is a lack of support for a common protocol for obtaining maximum repeatable exertions. Specifically, there is minimal evidence to support the use of providing force magnitude feedback during the production of voluntary maximum exertions (MVE) in terms of both signal amplitude and repeatability. Therefore, the purpose of this study was to determine (1) if an MVE force magnitude feedback protocol increased both the force exerted and corresponding muscle activity, (2) if force magnitude feedback improved the within-participant reproducibility of the force or activity observed, and (3) if the surface electromyography (sEMG) signal processing method affected the repeatability of determining peak muscle activity. Seventeen participants completed a series of MVEs; first without feedback of the forces they produced, then with feedback of the forces they were producing, and again without feedback to determine if providing force feedback influenced their ability to produce a maximum force. Hand force and sEMG from the anterior deltoid, middle deltoid, and infraspinatus were measured during each exertion. The results showed that the highest forces and muscle activities were achieved when force feedback was provided. Force magnitude feedback resulted in a 7–22% increase in magnitude (for force and activity) and a decrease of 11–46% in the coefficient of variation specifying an improvement in the within-participant reproducibility. Signal processing techniques also affected within-participant reproducibility, however to a much lesser extent. The peak value from a 500-ms moving window average of the linear enveloped or root mean squared sEMG was the most reproducible technique tested.