Root Mean Square Estimation Research Articles

Evaluation of dimensional accuracy and degree of polymerization of stereolithography photopolymer resin under different postpolymerization conditions: An in vitro study

Statement of problemThe appropriate postpolymerization of stereolithography (SLA) resins with the least effect on dimensional accuracy and with optimal polymerization is unclear. PurposeThe purpose of this in vitro study was to investigate the dimensional accuracy and degree of polymerization of a photopolymer resin for SLA with different postpolymerizing times and temperatures. Material and methodsSixty 1.5-mm-thick specimens were made from clear photopolymer resin with a 3D printer to simulate a maxillary complete denture. They were postpolymerized for different periods (15 and 30 minutes) at 3 different temperatures (40 °C, 60 °C, and 80 °C). Both prepolymerization and postpolymerization gap sizes for each specimen were measured at 5 different locations under a stereomicroscope. The tissue surface was scanned before and after polymerization, and the images were superimposed. The deviation was analyzed by using computer-aided design (CAD) software; root mean square estimates (RMSE) and color map data were obtained. Fourier transform infrared spectrometry was used to determine the degree of conversion (DC) of all specimens. The Kruskal-Wallis and Mann-Whitney tests were used to calculate the difference value of the gap sizes (α=.05). One-way ANOVA and the Tukey test were used for RMSE and DC (α=.05). ResultsThe smallest average change in gap sizes was found at 15 minutes and 40 °C, and the largest change at 30 minutes and 80 °C. The lowest RMSE was obtained at 30 minutes and 40 °C (P<.05). On the color map, a uniform deposited layer was created at 15 minutes and 40 °C and 30 minutes and 40 °C. The highest DC was found at 30 minutes and 60 °C, which differed significantly from 15 minutes and 40 °C (P<.05). The lowest degree of polymerization was found at 30 minutes and 40 °C. ConclusionsThe polymerizing temperature exerted a greater effect than polymerizing time, with lower temperatures leading to improved fit and tissue surface accuracy. The recommended parameters for SLA polymerization are 15 minutes and 40 °C. These conditions offered high dimensional accuracy, favorable surface tissue adaptation, and satisfactory DC.

Simulation Study of Adjusted Spatial Weighting Method to Estimate Missing Rainfall Data

Missing value especially in environmental study is a common problem including in rainfall modelling. Incomplete data will affect the accuracy and efficiency in any modelling process. In this study, simulation method is used to demonstrate the efficiency of the old normal ratio inverse distance correlation weighting method (ONRIDCWM) in solving missing rainfall data. The simulation study is used to identify the best parameters for correlation power of p, percentage of missing value and sample size, n of the ONRIDCWM through simulating for 10,000 times by varying the value of the parameters systematically. The results of the simulation are compared with other available weighting methods. The estimated complete rainfall data of the target station are compared and assessed with the observed data from the neighbouring station using mean, estimated bias (EB) and estimated root mean square error (ERMSE). The results show that ONRIDCWM is better than the other weighting methods for the correlation power of p at least four. For illustration of the weighting method, monthly rainfall data from Pahang is used to demonstrate the efficiency of the method using three error indices: S-Index, mean absolute error (MAE) and correlation, R.

Development and Evaluation of the High-Intensity Interval Training Self-Efficacy Questionnaire.

This study involved the design and evaluation of the High-Intensity Interval Training Self-Efficacy Questionnaire (HIIT-SQ). Phase 1: Questionnaire items were developed. Phase 2: Australian adolescents (N = 389, 16.0 ± 0.4 years, 41.10% female) completed the HIIT-SQ, and factorial validity of the measurement model was explored. Phase 3: Adolescents (N = 100, age 12-14years, 44% female) completed the HIIT-SQ twice (1 week apart) to evaluate test-retest reliability. Confirmatory factor analysis of the final six items (mean = 3.43-6.73, SD = 0.99-25.30) revealed adequate fit, χ2(21) = 21, p = .01, comparative fit index = .99, Tucker-Lewis index = .99, root mean square of approximation = .07, 90% confidence interval[.04, .11]. Factor loading estimates showed that all items were highly related to the factor (estimates range: 0.81-0.90). Intraclass coefficients and typical error values were .99 (95% confidence interval[.99, 1.00]) and .22, respectively. This study provides preliminary evidence for the validity and reliability of scores derived from the HIIT-SQ in adolescents.

A State of Charge Estimation Method of Lithium-Ion Battery Based on Fused Open Circuit Voltage Curve

The open circuit voltage (OCV) and model parameters are critical reference variables for a lithium-ion battery management system estimating the state of charge (SOC) accurately. However, the polarization effect reduces the accuracy of the OCV test, and the model parameters coupled to the polarization voltage increase the non-linearity of the cell model, all challenging SOC estimation. This paper presents an OCV curve fusion method based on the incremental and low-current test. Fusing the incremental test results without polarization effect and the low current test results with non-linear characteristics of electrodes, the fusion method improves the OCV curve’s accuracy. In addition, we design a state observer with model parameters and SOC, and the unscented Kalman filter (UKF) method is employed for co-estimation of SOC and model parameters to eliminate the drift noise effects. The SOC estimation root mean square error (RMSE) of the proposed method achieves 0.99% and 1.67% in the pulse constant current test and dynamic discharge test, respectively. Experimental results and comparisons with other methods highlight the SOC estimation accuracy and robustness of the proposed method.

Validation of the Portuguese Version of the Healthy Lifestyle Questionnaire.

The main objective of this study was the validation of the Healthy Lifestyle Questionnaire (EVS II), using a confirmatory factorial analysis of the measurement model, with veteran athletes. A total of 348 veteran Portuguese athletes of both genders, aged between 30 and 60 years (M = 41.64, SD = 9.83), of whom 200 were males and 148 were females, from several sports. The results of the confirmatory factor analysis demonstrate the adequacy of the adapted version of the EVS II, as the factorial structure (6 factors/24 items) has acceptable validity indexes: χ2 = 305.925, p = 0.000, df = 120.017, χ2/df = 2.549, NFI (Normed Fit Index) = 0.909, TLI (Tucker Lewis Index) = 0.918, CFI (Comparative Fit Index) = 0.944, GFI (Goodness of Fit Index) = 0.944, AGFI (Adjusted Goodness of Fit Index) = 0.909, SRMR (Standardized Root Mean Square Residual) = 0.048, RMSEA (Root Mean Square of Approximation) = 0.060, allowing evaluation of the dimensions of balanced diet, respect for mealtime, tobacco and alcohol consumption, other drugs consumption and resting habits. The adaptation to sport of the Portuguese version of EVS II can be used with reasonable confidence in the evaluation of healthy lifestyles in the context of sport.

Validation of the Quality of Life in Childhood Epilepsy Questionnaire (QOLCE-55 and QOLCE-16) for use by parents of young adults with childhood-onset epilepsy

ObjectiveThe objective of the study was to validate the parent-proxy reported Quality of Life in Childhood Epilepsy Questionnaire (QOLCE) in a sample of young adults with a history of childhood-onset epilepsy, allowing for the utilization of a consistent informant (the parent) across the youths' stages of development. The 55-item (QOLCE-55) and 16-item versions (QOLCE-16) were evaluated. MethodsData came from 134 young adults (aged 18.0 to 28.5 years) with childhood-onset epilepsy, recruited through community and tertiary care centers across Canada. Confirmatory factor analysis (CFA) was used to assess the higher-order factor structure of the QOLCE. Cronbach's alpha was used to evaluate internal consistency. Convergent validity was assessed by intraclass correlation coefficients (ICC) with the youth self-reported Quality of Life in Epilepsy Questionnaire (QOLIE-31-P). ResultsThe higher-order factor structure of the QOLCE-55 and QOLCE-16 demonstrated adequate fit: QOLCE-55 comparative fit index (CFI) = 0.968, Tucker–Lewis index (TLI) = 0.966; and root mean square of approximation (RMSEA) = 0.061; QOLCE-16 CFI = 0.966, TFI = 0.959, RMSEA = 0.141. Higher-order factor loadings were strong, ranging from 0.71 to 0.90. Internal consistency was excellent for the total score (αQOLCE-55 = 0.97; αQOLCE-16 = 0.93) and good-excellent for each subscale (α > 78). Convergent validity was moderate to good for the total score (ICC > 0.72) and each subscale (ICC > 0.51). SignificanceThese findings provide support for the use of the QOLCE-55 and QOLCE-16 among young adults with a history of childhood-onset epilepsy. Utilizing a consistent measure and informant across the stages of development is essential to reliably evaluate change over time.

Mining-Induced Time-Series Deformation Investigation Based on SBAS-InSAR Technique: A Case Study of Drilling Water Solution Rock Salt Mine.

Compared to traditional coal mines, the mining-induced dynamic deformation of drilling solution mining activities may result in even more serious damage to surface buildings and infrastructures due to the different exploitation mode. Therefore, long-term dynamic monitoring and analysis of rock salt mines is extremely important for preventing potential geological damages. In this work, the small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique with Sentinel−1A imagery is utilized to monitor the ground surface deformation of a rock salt mining area. The time-series analysis is carried out to obtain the spatial–temporal characteristics of land subsidence caused by drilling solution mining activities. A typical rock salt mine in Changde, China is selected as the test site. Twenty-four scenes of Sentinel−1A image data acquired from June 2015 to January 2017 are used to obtain the time-series subsidence of the test mine. The temporal–spatial evolution of the derived settlement funnels is revealed. The time-series deformation on typical feature points has been analyzed. Experimental results show that the obtained drilling solution mining-induced subsidence has a spatial characteristic of multiplied peaks along the transversal direction. Temporally, the large-scale surface settlement for the rock salt mine area begins to appear in September 2016, with a time lag of 8 months, and shows an obvious seasonal fluctuation. The maximum cumulative subsidence is detected up to 199 mm. These subsiding characteristics are consistent with the connected groove mining method used in drilling water solution mines. To evaluate the reliability of the results, the SBAS-derived results are compared with the field-leveling measurements. The estimated root mean square error (RMSE) of ±11 mm indicates a high consistency.

Estimation of soil surface water contents for intertidal mudflats using a near-infrared long-range terrestrial laser scanner

Estimations of the soil surface water contents and distributions play a key role in the ecological, environmental, and topographical investigations for intertidal mudflats. However, existing techniques have limitations. Long-range terrestrial laser scanners (TLSs) can record the co-located intensity value which refers to a measure of the backscattered laser from each scanned point. Most long-range TLSs emit near-infrared lasers that can be strongly absorbed by water. Thus, the intensity values can be used as proxies for water contents. In this study, the intensity data of long-range TLSs are corrected for the incidence angle and distance effects to quantitatively estimate the soil surface water contents of intertidal mudflats. A case study for a mudflat in Chongming Island, Shanghai, China, is conducted. Results indicate that compared with traditional techniques, the corrected intensity data of long-range TLSs are extremely effective data sources for a quick, accurate, and detailed estimation of water contents for large-area mudflats. The estimation root mean square error is approximately 3%. Furthermore, the 3D distributions of the water contents can be accurately mapped by combining the point cloud of the mudflats to potentially analyze the intrinsic association among water contents and topography, vegetation coverage, and habitation of creatures in mudflats.

Minimization of Current Ripple and Switching Losses in Double-Inverter Fed Wound Rotor Induction Machine Drive Using PWM Techniques

In the topology of double-inverter fed wound rotor induction machine (DI-WRIM) two voltage source inverters (VSIs) are connected one each on stator and rotor terminals. This leads to ripple in both stator and rotor currents. In this paper, these current ripples are analyzed in terms of the stator- and rotor-flux ripples, which are estimated from their respective terminals. To reduce the current ripples, the VSIs of the stator and rotor terminals are modulated with different pulsewidth modulation (PWM) sequences. The switching loss in the DI-WRIM is also analyzed for the proposed stator–rotor PWM sequence combinations, based on the flexibility of changing the power factor, by changing the flux component of the current fed from either side of a DI-WRIM drive. The proposed PWM combinations with the proposed flux component in currents are found to reduce the stator and rotor current ripples and, the switching losses. The analytically estimated root mean square current ripples for the proposed PWM combinations are validated using the simulation and the experimental results obtained from a 3-kW DI-WRIM drive. The analysis on switching losses is validated by measuring the temperature rise of heat sinks present in the individual stator and rotor side VSIs of the DI-WRIM drive.

Evaluation of the Simple Algorithm for Yield Estimate Model in Winter Wheat Simulation under Different Irrigation Scenarios

Winter wheat (Triticum aestivum L.) is one of the major grain crops grown in Guanzhong Plain, China. Rapid and accurate crop growth and yield estimation are important for agricultural monitoring and policy decision‐making. Crop model simulation is an effective way to provide objective growth and yield forecast. In this study, we investigated the applicability of the Simple Algorithm for Yield Estimate (SAFY) model for estimating winter wheat dry shoot biomass (MS,d) and grain yield, using two growing seasons field data from different irrigation scenarios. Results showed that the leaf area index (LAI) could be reasonably well simulated, with a minimum root mean square estimate (RMSE) of 0.11. The water stress intensity of each irrigation scenario was well accounted for through stress factor of effective radiation use efficiency. Good accuracy were achieved for MS,d simulation in both calibration (RMSE = 0.054–0.183 kg m−2) and validation (RMSE = 0.146 kg m−2) datasets, but showed a general overestimation during later growth stages. The grain yield was well estimated with a relative error of 1.9% to 16.7% in 2013 to 2014 and 0.1% to 16.7% in 2014 to 2015. The comparison between estimated and measured yield of all irrigation scenarios were robust in both seasons, with the minimum RMSE and MRE of 35.0 g m−2 and 4.7%, respectively. This work demonstrates the potential of a simple crop model for estimating biomass and yield only by calibrate the LAI without further in situ data, and prefigures assimilation application with remote sensing data in future.Core Ideas The SAFY model was evaluated in different irrigation scenarios. The variation of LAI was highly sensitive to leaf partition parameters. SAFY effectively simulated wheat biomass and yield under various irrigation scenarios. A general overestimation on biomass was noted during later growth stages.

Neighbourhood deprivation effects on body-mass index across the life course: structural equation modelling of data from two prospective British longitudinal birth cohorts

BackgroundLiving in a more deprived area is associated with worse health, at any stage of life. We aimed to improve understanding of how our health is related to our neighbourhood by using structural equation modelling to evaluate two conceptual frameworks: (1) the neighbourhood effect, in which area deprivation directly affects individuals’ health; and (2) health selection, in which people sort themselves into neighbourhoods over time by health. MethodsIn this analysis, we used data from the 1958 (ages 16, 23, 33, 42, and 55 years; n=18 555) and 1970 (ages 16, 24, 34, and 42 years; n=18 639) British Birth cohorts to investigate life-course relationships between body-mass index (BMI) and area deprivation; residential address at each age was linked to the closest Townsend deprivation index score taken from 1971–2011 census data, recalculated for 2011 lower-super-output boundaries. Model fit was compared between three models: area deprivation (in which pathways lead from Townsend score at one age to BMI at the next age); health selection (from BMI at one age to Townsend score at the next age); and both combined. In the best fitting model, we tested whether relationships differed for people who had moved house and those who had not (≥one vs no moves over each age interval), by using interaction terms fitted separately by pathway. FindingsModel fit statistics for all three models were almost identical (eg, root mean square of approximation was ~0·07 in the 1958 cohort ~0·07 and ~0·05 in the 1970 cohort), with associations seen for neighbourhood effect and health selection pathways, so the model with both frameworks was chosen for further analysis. For both cohorts, BMI and area deprivation strongly tracked through life, and area deprivation predicted BMI at the next interval (eg, a 1-unit increase in Townsend score at age 42 years led to a 0·09 [95% CI 0·05–0·12] increase in BMI at age 55 years), for all age intervals, except the 1958 cohort path of Townsend score at age 23 years to BMI at age 33 years. In contrast, health selection paths were weak and only occurred at four intervals: BMI at 23 years to Townsend score at 33 years, BMI at 33 years to Townsend score at 42 years, and BMI at 42 years to Townsend score at 55 years, all in the 1958 cohort, and BMI at age 34 years to Townsend score at 42 years in the 1970 cohort (eg a 1-unit higher BMI at age 23 years led to a 0·04 [95% CI 0·03–0·06] higher Townsend score at age 33 years). Moving status did not modify associations between any area deprivation effect or BMI health selection paths. InterpretationOn the basis of our prespecified models, selective sorting by BMI across adulthood is weak and does not explain life-course associations between area deprivation and BMI. Data on complete residential histories might have provided a more thorough test of the research hypothesis. Further research could also address the longitudinal study attrition to provide more robust findings. FundingLeverhulme Trust Research Project (RPG-2015-317).

Machine learning models accurately predict ozone exposure during wildfire events

Epidemiologists use prediction models to downscale (i.e., interpolate) air pollution exposure where monitoring data is insufficient. This study compares machine learning prediction models for ground-level ozone during wildfires, evaluating the predictive accuracy of ten algorithms on the daily 8-hour maximum average ozone during a 2008 wildfire event in northern California. Models were evaluated using a leave-one-location-out cross-validation (LOLO CV) procedure to account for the spatial and temporal dependence of the data and produce more realistic estimates of prediction error. LOLO CV avoids both the well-known overly optimistic bias of k-fold cross-validation on dependent data and the conservative bias of evaluating prediction error over a coarser spatial resolution via leave-k-locations-out CV. Gradient boosting was the most accurate of the ten machine learning algorithms with the lowest LOLO CV estimated root mean square error (0.228) and the highest LOLO CV Rˆ2 (0.677). Random forest was the second best performing algorithm with an LOLO CV Rˆ2 of 0.661. The LOLO CV estimates of predictive accuracy were less optimistic than 10-fold CV estimates for all ten models. The difference in estimated accuracy between the 10-fold CV and LOLO CV was greater for more flexible models like gradient boosting and random forest. The order of estimated model accuracy depended on the choice of evaluation metric, indicating that 10-fold CV and LOLO CV may select different models or sets of covariates as optimal, which calls into question the reliability of 10-fold CV for model (or variable) selection. These prediction models are designed for interpolating ozone exposure, and are not suited to inferring the effect of wildfires on ozone or extrapolating to predict ozone in other spatial or temporal domains. This is demonstrated by the inability of the best performing models to accurately predict ozone during 2007 southern California wildfires.

Confirming the factor structure of a generic quality of life instrument among pre-treatment substance use disorder patients

BackgroundQuality of life (QoL) is a patient-reported outcome of increasing importance in the substance use disorder (SUD) treatment field, and impaired QoL may be an important impetus for treatment uptake. Instruments and methodologies abound, precluding comparison, as does a dearth of population norms. The QOL10 is a generic, overall QoL tool containing ten items and with simple scoring procedures. It is therefore a potential alternative to the gold standard WHOQOL-BREF. This study aimed to assess the two-factor structure of the QOL10 that has been suggested by a previous exploratory factor analysis.MethodsAdults entering 21 participating inpatient or outpatient SUD treatment were recruited to join a national longitudinal cohort study. 531 completed the QOL10 at treatment entry and were included in the analysis. Structural equation modelling (SEM) was used to confirm the model fit of a two-factor structure, and the scaling qualities of the QOL10 were reported.ResultsAccording to the SEM analysis, the QOL10 was comprised of one latent variable measuring social QoL, and one measuring global QoL, and all ten items were retained. Goodness of fit tests included: root mean square of approximation = 0.063, 90% CI 0.050–0.076; normed-fit index = 0.919; and comparative fit index = 0.943.ConclusionsThe QOL10 should be considered when clinicians in the SUD treatment field need a short, valid instrument that measures both global QoL and social QoL, with minimum respondent and administrator burden. The social domain is of particular utility and may be used as a stand-alone instrument. Test-retest reliability should be established in future studies.

DOA estimation of LFM sources based on time-frequency interferometer in UCA

Based on the time-frequency (t-f) interferometer of the uniform circular array (UCA), a new direction of arrival (DOA) estimation algorithm of multiple linear frequency modulation (LFM) sources is proposed. Firstly, the cross smoothed pseudo Wigner-Ville distribution (SPWVD) of the elements’ output is calculated and the single source t-f points are selected by using a “noise threshold” procedure and cluster based algorithm. Then, by selecting different groups of sensor pairs, the proposed algorithm can solve the phase ambiguity of the t-f points by using a focusing based algorithm. And a lookup table based ambiguity resolution algorithm is also presented to reduce the computational complexity. The simulation results illustrate that the proposed t-f interferometer can be applied to multiple wideband LFM sources and it also can improve the DOA estimation precision compared with the traditional phase interferometer and sub-band interferometer. Especially in the low signal to noise ratio (SNR) and limited snapshots situations, the estimation root mean square errors (RMSEs) of the proposed algorithm are also within 3°.

Research on Simultaneous Location and Mapping Algorithm of Intelligent vehicle Based on Improved Particle Filter Resampling

For the estimation problem that the Simultaneous Location and Mapping algorithm used in Intelligent vehicle suffers from sample impoverishment, a Multi-threshold differential evolutionary particle resampling method is proposed. The method adopts different particle updating Strategys according to different thresholds of effective particle numbers. particle diversity will be rapidly improved by differential evolution when particle degradation is severe. When the particle degradation is light, the system resampling method is directly used to copy the large weight particles and eliminate the small weight particles. By adopting different particle regeneration strategies for particles with different degrees of degradation, the problem of particle degradation and depletion in the process of state estimation can be effectively solved. Simulation experiments show that the root mean square error of the improved algorithm pose is reduced by 2.592m compared with the traditional algorithm. The road sign estimation root mean square error is also reduced by 2.428m compared with the traditional algorithm. Experiments show that the multi-threshold differential particle filter resampling method can effectively improve the particle diversity and ensure the state estimation accuracy of the vehicle.

57 years (1960–2017) of snow and meteorological observations from a mid-altitude mountain site (Col de Porte, France, 1325 m of altitude)

Abstract. In this paper, we introduce and provide access to daily (1960–2017) and hourly (1993–2017) datasets of snow and meteorological data measured at the Col de Porte site, 1325 m a.s.l., Chartreuse, France. Site metadata and ancillary measurements such as soil properties and masks of the incident solar radiation are also provided. Weekly snow profiles are made available from September 1993 to March 2018. A detailed study of the uncertainties originating from both measurement errors and spatial variability within the measurement site is provided for several variables. We show that the estimates of the ratio of diffuse-to-total shortwave broadband irradiance is affected by an uncertainty of ±0.21 (no unit). The estimated root mean square deviation, which mainly represents spatial variability, is ±10 cm for snow depth, ±25 kg m−2 for the water equivalent of snow cover (SWE), and ±1 K for soil temperature (±0.4 K during the snow season). The daily dataset can be used to quantify the effect of climate change at this site, with a decrease of the mean snow depth (1 December to 30 April) of 39 cm from the 1960–1990 period to the 1990–2017 period (40 % of the mean snow depth for 1960–1990) and an increase in temperature of +0.90 K for the same periods. Finally, we show that the daily and hourly datasets are useful and appropriate for driving and evaluating a snowpack model over such a long period. The data are placed on the repository of the Observatoire des Sciences de l'Univers de Grenoble (OSUG) data centre: https://doi.org/10.17178/CRYOBSCLIM.CDP.2018.

PENGEMBANGAN SKALA SOCIAL DESIRABILITY

This research aims to develop a social desirability scale in Indonesian. The method used is mixed method. Participant in the exploration stage amounted to 400 subjects. Whereas, in the second stage, which purposed to identify a psychometric properties (reliability and validity), participant amounted to 501 subjects. Data analysis in the first stage was open coding. For the second stage data analysis consisted of reliability estimation using Cronbach’s Alpha and test of construct validity using factorial validity with Confirmatory Factor Analysis (CFA) and convergent validity. The Result shows that the scale has a high reliability coefficient (α = 0.76). Whereas, the test of model fit with CFA shows that the scale fits with the data with index of fit value of Comparative Fit Index (CFI) = 0.92, Goodness of Fit Index (GFI) = 0.93, Root Mean Square of Approximation (RMSEA) = 0.059, and Standardized Root Mean Residual (SRMR) = 0.052. A convergent validity also tested by correlating social desirability Indonesian scale with social desirability scale by Reynolds (1982).

Blind estimation of white Gaussian noise variance in highly textured images

In the paper, a new method of blind estimation of noise variance in a single highly textured image is proposed. An input image is divided into 8x8 blocks and discrete cosine transform (DCT) is performed for each block. A part of 64 DCT coefficients with lowest energy calculated through all blocks is selected for further analysis. For the DCT coefficients, a robust estimate of noise variance is calculated. Corresponding to the obtained estimate, a part of blocks having very large values of local variance calculated only for the selected DCT coefficients are excluded from the further analysis. These two steps (estimation of noise variance and exclusion of blocks) are iteratively repeated three times. For the verification of the proposed method, a new noise-free test image database TAMPERE17 consisting of many highly textured images is designed. It is shown for this database and different values of noise variance from the set {25, 49, 100, 225}, that the proposed method provides approximately two times lower estimation root mean square error than other methods.

Estimation of Concentration Parameter for Simultaneous Circular Functional Relationship Model Assuming Unequal Error Variance

In this study, we propose the estimation of the concentration parameter for simultaneous circular functional relationship model. In this case, the variances of the error term are not necessarily equal and the ratio of the concentration parameter λ = is not necessarily 1. The modified Bessel function was expended by using the asymptotic power series and it became a cubic equation of κ. From the cubic equation of κ, the roots were obtained by using the polyroot function in SPlus software. Simulation study was done to study the mean, estimated bias, absolute relative estimated bias, estimated standard error and estimated root mean square error of the estimation of the concentration parameter. From the simulation study, large concentration parameter and sample size show that the estimated concentration parameter has smaller bias. Also, an illustration to a real wind and wave data set is given to show its practical applicability.

High-resolution global grids of revised Priestley–Taylor and Hargreaves–Samani coefficients for assessing ASCE-standardized reference crop evapotranspiration and solar radiation

Abstract. The objective of the study is to provide global grids (0.5°) of revised annual coefficients for the Priestley–Taylor (P-T) and Hargreaves–Samani (H-S) evapotranspiration methods after calibration based on the ASCE (American Society of Civil Engineers)-standardized Penman–Monteith method (the ASCE method includes two reference crops: short-clipped grass and tall alfalfa). The analysis also includes the development of a global grid of revised annual coefficients for solar radiation (Rs) estimations using the respective Rs formula of H-S. The analysis was based on global gridded climatic data of the period 1950–2000. The method for deriving annual coefficients of the P-T and H-S methods was based on partial weighted averages (PWAs) of their mean monthly values. This method estimates the annual values considering the amplitude of the parameter under investigation (ETo and Rs) giving more weight to the monthly coefficients of the months with higher ETo values (or Rs values for the case of the H-S radiation formula). The method also eliminates the effect of unreasonably high or low monthly coefficients that may occur during periods where ETo and Rs fall below a specific threshold. The new coefficients were validated based on data from 140 stations located in various climatic zones of the USA and Australia with expanded observations up to 2016. The validation procedure for ETo estimations of the short reference crop showed that the P-T and H-S methods with the new revised coefficients outperformed the standard methods reducing the estimated root mean square error (RMSE) in ETo values by 40 and 25 %, respectively. The estimations of Rs using the H-S formula with revised coefficients reduced the RMSE by 28 % in comparison to the standard H-S formula. Finally, a raster database was built consisting of (a) global maps for the mean monthly ETo values estimated by ASCE-standardized method for both reference crops, (b) global maps for the revised annual coefficients of the P-T and H-S evapotranspiration methods for both reference crops and a global map for the revised annual coefficient of the H-S radiation formula and (c) global maps that indicate the optimum locations for using the standard P-T and H-S methods and their possible annual errors based on reference values. The database can support estimations of ETo and solar radiation for locations where climatic data are limited and it can support studies which require such estimations on larger scales (e.g. country, continent, world). The datasets produced in this study are archived in the PANGAEA database (https://doi.org/10.1594/PANGAEA.868808) and in the ESRN database (http://www.esrn-database.org or http://esrn-database.weebly.com).