Joint Use of PROSAIL and DART for Fast LUT Building: Application to Gap Fraction and Leaf Biochemistry Estimations over Sparse Oak Stands

Gap Fraction, leaf pigment contents (content of chlorophylls a and b (Cab) and carotenoids content (Car)), Leaf Mass per Area (LMA), and Equivalent Water Thickness (EWT) are considered relevant indicators of forests’ health status, influencing many biological and physical processes. Various methods exist to estimate these variables, often relying on the extensive use of Radiation Transfer Models (RTMs). While 3D RTMs are more realistic to model open canopies, their complexity leads to important computation times that limit the number of simulations that can be considered; 1D RTMs, although less realistic, are also less computationally expensive. We investigated the possibility to approximate the outputs of a 3D RTM (DART) from a 1D RTM (PROSAIL) to generate in very short time numerous extensive Look-Up Tables (LUTs). The intrinsic error of the approximation model was evaluated through comparison with DART reference values. The model was then used to generate LUTs used to estimate Gap Fraction, Cab, Car, EWT, and LMA of Blue Oak-dominant stands in a woodland savanna from AVIRIS-C data. Performances of the approximation model for estimation purposes compared to DART were evaluated using Wilmott’s index of agreement (dr), and estimation accuracy was measured with coefficients of determination (R2) and Root Mean Squared Error (RMSE). The low approximation error of the proposed model demonstrated that the model could be considered for canopy covers as low as 30%. Gap Fraction estimations presented similar performances with either DART or the approximation (dr 0.78 and 0.77, respectively), while Cab and Car showed improved performances (dr increasing from 0.65 to 0.77 and 0.34 to 0.65, respectively). No satisfying estimation methods were found for LMA and EWT using either models, probably due to the high sensitivity of the scene’s reflectance to Gap Fraction and soil modeling at such low LAI. Overall, estimations using the approximated reflectances presented either similar or improved accuracy. Our findings show that it is possible to approximate DART reflectances from PROSAIL using a minimal number of DART outputs for calibration purposes, drastically reducing computation times to generate reflectance databases: 300,000 entries could be generated in 1.5 h, compared to the 12,666 total CPU hours necessary to generate the 21,840 calibration entries with DART.

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Evaluation for estimating of the PDF and the CDF of Generalized Inverted Exponential Distribution with Application in Industry

Advances in Mathematics: Scientific Journal ◽

10.37418/amsj.9.1.39 ◽

2020 ◽

pp. 507-522

Author(s):

Parisa Torkaman

Keyword(s):

Least Squares ◽

Exponential Distribution ◽

Mean Squared Error ◽

Weighted Least Squares ◽

Real Data ◽

Minimum Variance ◽

Cumulative Distribution ◽

Estimation Methods ◽

Data Set ◽

Better Than

The generalized inverted exponential distribution is introduced as a lifetime model with good statistical properties. This paper, the estimation of the probability density function and the cumulative distribution function of with five different estimation methods: uniformly minimum variance unbiased(UMVU), maximum likelihood(ML), least squares(LS), weighted least squares (WLS) and percentile(PC) estimators are considered. The performance of these estimation procedures, based on the mean squared error (MSE) by numerical simulations are compared. Simulation studies express that the UMVU estimator performs better than others and when the sample size is large enough the ML and UMVU estimators are almost equivalent and efficient than LS, WLS and PC. Finally, the result using a real data set are analyzed.

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Development of a Deep Neural Network Model for Estimating Joint Location of Occupant Indoor Activities for Providing Thermal Comfort

Energies ◽

10.3390/en14030696 ◽

2021 ◽

Vol 14 (3) ◽

pp. 696

Author(s):

Eun Ji Choi ◽

Jin Woo Moon ◽

Ji-hoon Han ◽

Yongseok Yoo

Keyword(s):

Neural Network ◽

Thermal Comfort ◽

Deep Neural Network ◽

Mean Squared Error ◽

Thermal Environment ◽

The Body ◽

Estimation Accuracy ◽

Accurate Estimation ◽

Body Parts ◽

Joint Location

The type of occupant activities is a significantly important factor to determine indoor thermal comfort; thus, an accurate method to estimate occupant activity needs to be developed. The purpose of this study was to develop a deep neural network (DNN) model for estimating the joint location of diverse human activities, which will be used to provide a comfortable thermal environment. The DNN model was trained with images to estimate 14 joints of a person performing 10 common indoor activities. The DNN contained numerous shortcut connections for efficient training and had two stages of sequential and parallel layers for accurate joint localization. Estimation accuracy was quantified using the mean squared error (MSE) for the estimated joints and the percentage of correct parts (PCP) for the body parts. The results show that the joint MSEs for the head and neck were lowest, and the PCP was highest for the torso. The PCP for individual activities ranged from 0.71 to 0.92, while typing and standing in a relaxed manner were the activities with the highest PCP. Estimation accuracy was higher for relatively still activities and lower for activities involving wide-ranging arm or leg motion. This study thus highlights the potential for the accurate estimation of occupant indoor activities by proposing a novel DNN model. This approach holds significant promise for finding the actual type of occupant activities and for use in target indoor applications related to thermal comfort in buildings.

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Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data

Econometrics ◽

10.3390/econometrics8040040 ◽

2020 ◽

Vol 8 (4) ◽

pp. 40

Author(s):

Erhard Reschenhofer ◽

Manveer K. Mangat

Keyword(s):

Simulation Study ◽

High Frequency ◽

Mean Squared Error ◽

Estimation Methods ◽

High Frequency Data ◽

Frequency Data ◽

Periodic Patterns ◽

Typical Sample ◽

Squared Error ◽

Proper Interpretation

For typical sample sizes occurring in economic and financial applications, the squared bias of estimators for the memory parameter is small relative to the variance. Smoothing is therefore a suitable way to improve the performance in terms of the mean squared error. However, in an analysis of financial high-frequency data, where the estimates are obtained separately for each day and then combined by averaging, the variance decreases with the sample size but the bias remains fixed. This paper proposes a method of smoothing that does not entail an increase in the bias. This method is based on the simultaneous examination of different partitions of the data. An extensive simulation study is carried out to compare it with conventional estimation methods. In this study, the new method outperforms its unsmoothed competitors with respect to the variance and its smoothed competitors with respect to the bias. Using the results of the simulation study for the proper interpretation of the empirical results obtained from a financial high-frequency dataset, we conclude that significant long-range dependencies are present only in the intraday volatility but not in the intraday returns. Finally, the robustness of these findings against daily and weekly periodic patterns is established.

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Use of simulation–extrapolation estimation in catch–effort analyses

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/f99-052 ◽

1999 ◽

Vol 56 (7) ◽

pp. 1234-1240

Author(s):

W R Gould ◽

L A Stefanski ◽

K H Pollock

Keyword(s):

Measurement Error ◽

Measurement Errors ◽

Mean Squared Error ◽

Estimation Procedure ◽

Bias Reduction ◽

Estimation Methods ◽

Effort Estimation ◽

Stat Assoc ◽

General Terms ◽

Naive Estimator

All catch-effort estimation methods implicitly assume catch and effort are known quantities, whereas in many cases, they have been estimated and are subject to error. We evaluate the application of a simulation-based estimation procedure for measurement error models (J.R. Cook and L.A. Stefanski. 1994. J. Am. Stat. Assoc. 89: 1314-1328) in catch-effort studies. The technique involves a simulation component and an extrapolation step, hence the name SIMEX estimation. We describe SIMEX estimation in general terms and illustrate its use with applications to real and simulated catch and effort data. Correcting for measurement error with SIMEX estimation resulted in population size and catchability coefficient estimates that were substantially less than naive estimates, which ignored measurement errors in some cases. In a simulation of the procedure, we compared estimators from SIMEX with "naive" estimators that ignore measurement errors in catch and effort to determine the ability of SIMEX to produce bias-corrected estimates. The SIMEX estimators were less biased than the naive estimators but in some cases were also more variable. Despite the bias reduction, the SIMEX estimator had a larger mean squared error than the naive estimator for one of two artificial populations studied. However, our results suggest the SIMEX estimator may outperform the naive estimator in terms of bias and precision for larger populations.

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Location-Aided Uplink Transmission for User-Centric Cell-Free Massive MIMO Systems: A Fairness Priority Perspective

10.21203/rs.3.rs-1202385/v1 ◽

2022 ◽

Author(s):

Chen Wei ◽

Kui Xu ◽

Zhexian Shen ◽

Xiaochen Xia ◽

Wei Xie ◽

...

Keyword(s):

Large Scale ◽

Mean Squared Error ◽

Mimo Systems ◽

Multiple Input Multiple Output ◽

Access Point ◽

Estimation Accuracy ◽

Minimum Mean Squared Error ◽

Input Multiple Output ◽

User Centric ◽

Assignment Scheme

Abstract In this paper, we investigate the uplink transmission for user-centric cell-free massive multiple-input multiple-output (MIMO) systems. The largest-large-scale-fading-based access point (AP) selection method is adopted to achieve a user-centric operation. Under this user-centric framework, we propose a novel inter-cluster interference-based (IC-IB) pilot assignment scheme to alleviate pilot contamination. Considering the local characteristics of channel estimates and statistics, we propose a location-aided distributed uplink combining scheme based on a novel proposed metric representing inter-user interference to balance the relationship among the spectral efficiency (SE), user equipment (UE) fairness and complexity, in which the normalized local partial minimum mean-squared error (LP-MMSE) combining is adopted for some APs, while the normalized maximum ratio (MR) combining is adopted for the remaining APs. A new closed-form SE expression using the normalized MR combining is derived and a novel metric to indicate the UE fairness is also proposed. Moreover, the max-min fairness (MMF) power control algorithm is utilized to further ensure uniformly good service to the UEs. Simulation results demonstrate that the channel estimation accuracy of our proposed IC-IB pilot assignment scheme outperforms that of the conventional pilot assignment schemes. Furthermore, although the proposed location-aided uplink combining scheme is not always the best in terms of the per-UE SE, it can provide the more fairness among UEs and can achieve a good trade-off between the average SE and computational complexity.

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Understanding the Impact of Machine Learning Models on the Performance of Different Flexible Strain Sensor Modalities

Frontiers in Materials ◽

10.3389/fmats.2021.639823 ◽

2021 ◽

Vol 8 ◽

Author(s):

Brett C. Hannigan ◽

Tyler J. Cuthbert ◽

Wanhaoyi Geng ◽

Mohammad Tavassolian ◽

Carlo Menon

Keyword(s):

Machine Learning ◽

Mean Squared Error ◽

Strain Sensor ◽

High Sensitivity ◽

Signal Amplitude ◽

Electrical Signal ◽

Gauge Factor ◽

Piezoresistive Sensor ◽

Fibre Strain ◽

The Impact

Fibre strain sensors commonly use three major modalities to transduce strain—piezoresistance, capacitance, and inductance. The electrical signal in response to strain differs between these sensing technologies, having varying sensitivity, maximum measurable loading rate, and susceptibility to deleterious effects like hysteresis and drift. The wide variety of sensor materials and strain tracking applications makes it difficult to choose the best sensor modality for a wearable device when considering signal quality, cost, and difficulty of manufacture. Fibre strain sensor samples employing the three sensing mechanisms are fabricated and subjected to strain using a tensile tester. Their mechanical and electrical properties are measured in response to strain profiles designed to exhibit particular shortcomings of sensor behaviour. Using these data, the sensors are compared to identify materials and sensing technologies well suited for different textile motion tracking applications. Several regression models are trained and validated on random strain pattern data, providing guidance for pairing each sensor with a model architecture that compensates for non-ideal effects. A thermoplastic elastomer-core piezoresistive sensor had the highest sensitivity (average gauge factor: 12.6) and a piezoresistive sensor of similar construction with a polyether urethane-urea core had the largest bandwidth, capable of resolving strain rates above 300% s−1 with 36% signal amplitude attenuation. However, both piezoresistve sensors suffered from larger hysteresis and drift than a coaxial polymer sensor using the capacitive strain sensing mechanism. Machine learning improved the piezoresistive sensors’ root-mean-squared error when tracking a random strain signal by up to 58% while maintaining their high sensitivity, bandwidth, and ease of interfacing electronically.

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SAR Observation Error Estimation Based on Maximum Relative Projection Matching

International Journal of Antennas and Propagation ◽

10.1155/2020/3517834 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Y. Zhang ◽

B. P. Wang ◽

Y. Fang ◽

Z. X. Song

Keyword(s):

Error Estimation ◽

Estimation Method ◽

Estimation Methods ◽

Estimation Accuracy ◽

Reconstruction Method ◽

Observation Error ◽

Estimation Model ◽

Reconstruction Process ◽

Single Observation ◽

Imaging Results

The existing sparse imaging observation error estimation methods are to usually estimate the error of each observation position by substituting the error parameters into the iterative reconstruction process, which has a huge calculation cost. In this paper, by analysing the relationship between imaging results of single-observation sampling data and error parameters, a SAR observation error estimation method based on maximum relative projection matching is proposed. First, the method estimates the precise position parameters of the reference position by the sparse reconstruction method of joint error parameters. Second, a relative error estimation model is constructed based on the maximum correlation of base-space projection. Finally, the accurate error parameters are estimated by the Broyden–Fletcher–Goldfarb–Shanno method. Simulation and measured data of microwave anechoic chambers show that, compared to the existing methods, the proposed method has higher estimation accuracy, lower noise sensitivity, and higher computational efficiency.

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Leaf Photosynthetic Capacity of Sunlit and Shaded Mature Leaves in a Deciduous Forest

Forests ◽

10.3390/f11030318 ◽

2020 ◽

Vol 11 (3) ◽

pp. 318

Author(s):

Guangman Song ◽

Quan Wang ◽

Jia Jin

Keyword(s):

Close Relationships ◽

Deciduous Forest ◽

Photosynthetic Capacity ◽

Leaf Gas Exchange ◽

Nitrogen Concentration ◽

Leaf Traits ◽

Leaf Nitrogen ◽

Leaf Mass Per Area ◽

Clear Understanding ◽

Equivalent Water Thickness

A clear understanding of the dynamics of photosynthetic capacity is crucial for accurate modeling of ecosystem carbon uptake. However, such dynamical information is hardly available and has dramatically impeded our understanding of carbon cycles. Although tremendous efforts have been made in coupling the dynamic information of photosynthetic capacity into models, using “proxies” rooted from the close relationships between photosynthetic capacity and other available leaf parameters remains the popular selection. Unfortunately, no consensus has yet been reached on such “proxies”, leading them only applicable to limited cases. In this study, we aim to identify if there are close relationships between the photosynthetic capacity (represented by the maximum carboxylation rate, Vcmax) and leaf traits for mature broadleaves within a cold temperature deciduous forest. This is based on a long-term in situ dataset including leaf chlorophyll content (Chl), leaf nitrogen concentration (Narea, Nmass), leaf carbon concentration (Carea, Cmass), equivalent water thickness (EWT), leaf mass per area (LMA), and leaf gas exchange measurements from which Vcmax was derived, for both sunlit and shaded leaves during leaf mature periods from 2014 to 2019. The results show that the Vcmax values of sunlit and shaded leaves were relatively stable during these periods, and no statistically significant interannual variations occurred (p > 0.05). However, this is not applicable to specific species. Path analysis revealed that Narea was the major contributor to Vcmax for sunlit leaves (0.502), while LMA had the greatest direct relationship with Vcmax for shaded leaves (0.625). The LMA has further been confirmed as a primary proxy if no leaf type information is available. These findings provide a promising way to better understand photosynthesis and to predict carbon and water cycles in temperate deciduous forests.

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Estimation Methods for Viscosity, Flow Rate and Pressure from Pump-Motor Assembly Parameters

Sensors ◽

10.3390/s20051451 ◽

2020 ◽

Vol 20 (5) ◽

pp. 1451

Author(s):

Martin Elenkov ◽

Paul Ecker ◽

Benjamin Lukitsch ◽

Christoph Janeczek ◽

Michael Harasek ◽

...

Keyword(s):

Blood Flow ◽

Flow Rate ◽

Pressure Difference ◽

Mean Squared Error ◽

Blood Flow Rate ◽

Estimation Methods ◽

In Vitro Test ◽

Motor Speed ◽

Term Operation

Blood pumps have found applications in heart support devices, oxygenators, and dialysis systems, among others. Often, there is no room for sensors, or the sensors are simply unreliable when long-term operation is required. However, control systems rely on those hard-to-measure parameters, such as blood flow rate and pressure difference, thus their estimation takes a central role in the development process of such medical devices. The viscosity of the blood not only influences the estimation of those parameters but is often a parameter that is of great interest to both doctors and engineers. In this work, estimation methods for blood flow rate, pressure difference, and viscosity are presented using Gaussian process regression models. Different water–glycerol mixtures were used to model blood. Data was collected from a custom-built blood pump, designed for intracorporeal oxygenators in an in vitro test circuit. The estimation was performed from motor current and motor speed measurements and its accuracy was measured for: blood flow rate r2 = 0.98, root mean squared error (RMSE) = 46 mL.min−1; pressure difference r2 = 0.98, RMSE = 8.7 mmHg; and viscosity r2 = 0.98, RMSE = 0.049 mPa.s. The results suggest that the presented methods can be used to accurately predict blood flow rate, pressure, and viscosity online.

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Precipitation Estimation Methods in Continuous, Distributed Urban Hydrologic Modeling

Water ◽

10.3390/w11071340 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1340

Author(s):

Woodson ◽

Adams ◽

Dymond

Keyword(s):

Hydrological Modeling ◽

Mean Squared Error ◽

Uniform Estimate ◽

Mean Field ◽

Radar Data ◽

Rain Gauge ◽

Hydrologic Model ◽

Estimation Methods ◽

Urban Watersheds ◽

Precipitation Estimation

Quantitative precipitation estimation (QPE) remains a key area of uncertainty in hydrological modeling and prediction, particularly in small, urban watersheds, which respond rapidly to precipitation and can experience significant spatial variability in rainfall fields. Few studies have compared QPE methods in small, urban watersheds, and studies that have examined this topic only compared model results on an event basis using a small number of storms. This study sought to compare the efficacy of multiple QPE methods when simulating discharge in a small, urban watershed on a continuous basis using an operational hydrologic model and QPE forcings. The research distributed hydrologic model (RDHM) was used to model a basin in Roanoke, Virginia, USA, forced with QPEs from four methods: mean field bias (MFB) correction of radar data, kriging of rain gauge data, uncorrected radar data, and a basin-uniform estimate from a single gauge inside the watershed. Based on comparisons between simulated and observed discharge at the basin outlet for a six-month period in 2018, simulations forced with the uncorrected radar QPE had the highest accuracy, as measured by root mean squared error (RMSE) and peak flow relative error, despite systematic underprediction of the mean areal precipitation (MAP). Simulations forced with MFB-corrected radar data consistently and significantly overpredicted discharge, but had the highest accuracy in predicting the timing of peak flows.

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