scholarly journals A Seven-Parameter BRDF Model with Double-Peak Characteristic Suitable for Sandy Soil

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Yu-Feng Yang ◽  
Wen-Shuai Li ◽  
Han-Zhi Zhang
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Sandy Soil ◽  
Incident Angle ◽  
Mean Square ◽  
Double Peak ◽  
Practical Project ◽  
Parameter Values ◽  
Mean Square Errors ◽  
Combined Data

A seven-parameter BRDF model with double-peak characteristic was proposed in this paper, which can fit double-peak data. The global genetic algorithm was used to model the BRDF experimental data of sandy soil, and the parameter values and relative mean square error of the seven-parameter BRDF model were obtained. The results proved the correctness of the model, and the relative mean square errors of this model are, respectively, 0.30%, 0.22%, 0.26%, and 0.25% corresponding to the incident angles of 15°, 30°, 45°, and 60°. Additionally, we also combined data from four incident angles to derive seven parameters, which do not depend on incident angle, and the overall error is 1.79%. Finally, in order to intuitively show the BRDF of sandy soil, 3D BRDF graph of sandy soil with different incident angles is, respectively, given. It will be of great significance in practical project applications.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

scholarly journals Automatic modelling of human musculoskeletal ligaments – Framework overview and model quality evaluation

2021 ◽  
pp. 1-14
Author(s):  
Noura Hamze ◽  
Lukas Nocker ◽  
Nikolaus Rauch ◽  
Markus Walzthöni ◽  
Matthias Harders ◽  
...  
Keyword(s):  
Statistical Model ◽  
Soft Tissues ◽  
Automatic Generation ◽  
Cadaveric Study ◽  
Patient Specific ◽  
Mean Square ◽  
Shape Modelling ◽  
Morphological Studies ◽  
Insertion Sites ◽  
Mean Square Errors

BACKGROUND: Accurate segmentation of connective soft tissues in medical images is very challenging, hampering the generation of geometric models for bio-mechanical computations. Alternatively, one could predict ligament insertion sites and then approximate the shapes, based on anatomical knowledge and morphological studies. OBJECTIVE: In this work, we describe an integrated framework for automatic modelling of human musculoskeletal ligaments. METHOD: We combine statistical shape modelling with geometric algorithms to automatically identify insertion sites, based on which geometric surface/volume meshes are created. As clinical use case, the framework has been applied to generate models of the forearm interosseous membrane. Ligament insertion sites in the statistical model were defined according to anatomical predictions following a published approach. RESULTS: For evaluation we compared the generated sites, as well as the ligament shapes, to data obtained from a cadaveric study, involving five forearms with 15 ligaments. Our framework permitted the creation of models approximating ligaments’ shapes with good fidelity. However, we found that the statistical model trained with the state-of-the-art prediction of the insertion sites was not always reliable. Average mean square errors as well as Hausdorff distances of the meshes could increase by an order of magnitude, as compared to employing known insertion locations of the cadaveric study. Using those, an average mean square error of 0.59 mm and an average Hausdorff distance of less than 7 mm resulted, for all ligaments. CONCLUSIONS: The presented approach for automatic generation of ligament shapes from insertion points appears to be feasible but the detection of the insertion sites with a SSM is too inaccurate, thus making a patient-specific approach necessary.

scholarly journals Improving a real-time helicopter simulator model with linear input filters

2021 ◽  
Author(s):  
Pavle Šćepanović ◽  
Frederik A. Döring
Keyword(s):  
Feedback Control ◽  
Time Domain ◽  
Flight Test ◽  
Flight Mechanics ◽  
Mean Square ◽  
Qualification Test ◽  
Model Inversion ◽  
Helicopter Flight ◽  
The Time Domain ◽  
Mean Square Errors

AbstractFor a broad range of applications, flight mechanics simulator models have to accurately predict the aircraft dynamics. However, the development and improvement of such models is a difficult and time consuming process. This is especially true for helicopters. In this paper, two rapidly applicable and implementable methods to derive linear input filters that improve the simulator model are presented. The first method is based on model inversion, the second on feedback control. Both methods are evaluated in the time domain, compared to recorded helicopter flight test data, and assessed based on root mean square errors and the Qualification Test Guide bounds. The best results were achieved when using the first method.

MEAN SQUARE ERRORS OF POINT DETERMINATIONS

1944 ◽  
Vol 7 (53) ◽  
pp. 279-294
Author(s):  
G. H. Menzies
Keyword(s):  
Mean Square ◽  
Mean Square Errors

Larger-Eddy Simulation of Velocity Fluctuation in a Mixing T-Junction

2012 ◽  
Vol 152-154 ◽  
pp. 1313-1318
Author(s):  
Tao Lu ◽  
Su Mei Liu ◽  
Ping Wang ◽  
Wei Yyu Zhu
Keyword(s):  
Experimental Data ◽  
Velocity Fluctuation ◽  
Flow Model ◽  
Numerical Results ◽  
Mean Square ◽  
Main Duct ◽  
Velocity Fluctuations ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Les Model

Velocity fluctuations in a mixing T-junction were simulated in FLUENT using large-eddy simulation (LES) turbulent flow model with sub-grid scale (SGS) Smagorinsky–Lilly (SL) model. The normalized mean and root mean square velocities are used to describe the time-averaged velocities and the velocities fluctuation intensities. Comparison of the numerical results with experimental data shows that the LES model is valid for predicting the flow of mixing in a T-junction junction. The numerical results reveal the velocity distributions and fluctuations are basically symmetrical and the fluctuation at the upstream of the downstream of the main duct is stronger than that at the downstream of the downstream of the main duct.

scholarly journals CO2 Induced Foaming Behavior of Polystyrene near the Glass Transition

2017 ◽  
Vol 2017 ◽  
pp. 1-15
Author(s):  
Salah Al-Enezi
Keyword(s):  
Experimental Data ◽  
High Pressure ◽  
Glass Transition ◽  
Dissolved Gas ◽  
Foaming Process ◽  
Foaming Behavior ◽  
Potential Applications ◽  
Model C ◽  
Parameter Values ◽  
Good Agreement

This paper examines the effect of high-pressure carbon dioxide on the foaming process in polystyrene near the glass transition temperature and the foaming was studied using cylindrical high-pressure view cell with two optical windows. This technique has potential applications in the shape foaming of polymers at lower temperatures, dye impregnation, and the foaming of polystyrene. Three sets of experiments were carried out at operating temperatures of 50, 70, and 100°C, each over a range of pressures from 24 to 120 bar. Foaming was not observed when the polymer was initially at conditions below Tg but was observed above Tg. The nucleation appeared to occur randomly leading to subsequent bubble growth from these sites, with maximum radius of 0.02–0.83 mm. Three models were applied on the foaming experimental data. Variable diffusivity and viscosity model (Model C) was applied to assess the experimental data with the WLF equation. The model shows very good agreement by using realistic parameter values. The expansion occurs by diffusion of a dissolved gas from the supersaturated polymer envelope into the bubble.

Statistical Design of Fatigue Experiments

1952 ◽  
Vol 19 (1) ◽  
pp. 109-113
Author(s):  
Waloddi Weibull
Keyword(s):  
Experimental Data ◽  
Statistical Design ◽  
Testing Time ◽  
Optimum Distribution ◽  
Analytical Expression ◽  
Optimum Choice ◽  
Stress Levels ◽  
Parameter Values ◽  
Applied Stresses

Abstract An analytical expression connecting fatigue lives with applied stresses, and methods for computing the values of its parameters from experimental data are given. Formulas for estimating the uncertainty of computed parameter values, caused by scatter of loads and fatigue lives, for optimum distribution of specimens, and for optimum choice of stress levels, are deduced. Testing time and costs may be reduced by more than 40 per cent by using the formulas.

scholarly journals A Study on the Generalized Approximation Modeling Method Based on Fitting Sensitivity for Prediction of Engine Performance

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Lin Lin ◽  
Fang Wang ◽  
Shisheng Zhong
Keyword(s):  
Engine Performance ◽  
Grey Model ◽  
Model Parameters ◽  
Bayesian Regularization ◽  
Safety Engineering ◽  
The Novel ◽  
Mean Square ◽  
Approximation Model ◽  
Mean Square Errors ◽  
Fitting Accuracy

Prediction technology for aeroengine performance is significantly important in operational maintenance and safety engineering. In the prediction of engine performance, to address overfitting and underfitting problems with the approximation modeling technique, we derived a generalized approximation model that could be used to adjust fitting precision. Approximation precision was combined with fitting sensitivity to allow the model to obtain excellent fitting accuracy and generalization performance. Taking the Grey model (GM) as an example, we discussed the modeling approach of the novel GM based on fitting sensitivity, analyzed the setting methods and optimization range of model parameters, and solved the model by using a genetic algorithm. By investigating the effect of every model parameter on the prediction precision in experiments, we summarized the change regularities of the root-mean-square errors (RMSEs) varying with the model parameters in novel GM. Also, by analyzing the novel ANN and ANN with Bayesian regularization, it is concluded that the generalized approximation model based on fitting sensitivity can achieve a reasonable fitting degree and generalization ability.

Information content of data with respect to models

1983 ◽  
Vol 245 (5) ◽  
pp. R620-R623
Author(s):  
M. Berman ◽  
P. Van Eerdewegh
Keyword(s):  
Experimental Data ◽  
Data Collection ◽  
Information Content ◽  
Mathematical Framework ◽  
Statistical Measures ◽  
Parameter Values ◽  
Collection Strategies

A measure is proposed for the information content of data with respect to models. A model, defined by a set of parameter values in a mathematical framework, is considered a point in a hyperspace. The proposed measure expresses the information content of experimental data as the contribution they make, in units of information bits, in defining a model to within a desired region of the hyperspace. This measure is then normalized to conventional statistical measures of uncertainty. It is shown how the measure can be used to estimate the information of newly planned experiments and help in decisions on data collection strategies.

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