Method For Estimation Of Properties Research Articles

Hybrid solar irradiance nowcasting and forecasting with the SCOPE method and convolutional neural networks

We use a full year (2018) of GOES-R satellite data to produce 5-minute resolved information on cloud coverage for 7 Surface Radiation Budget Network (SURFRAD). The remote sensing images are then processed using the Spectral Cloud Optical Property Estimation (SCOPE) method in conjunction with Convolution Neural Networks (CNNs) for nowcasts and 1-hour ahead forecasts. We propose and compare two CNN based models for GHI now-casting with a 1-hour forecast horizon and a time resolution that processes data every 5 minutes: one enhanced with the SCOPE method for cloud estimation and another without it. The inclusion of SCOPE-derived information significantly improves model performance, yielding an average RMSE of 44.7 versus 68.9 W/m2 for the model without SCOPE information. The present work underscores the efficacy of even basic CNNs in interpreting satellite imagery combined with atmospheric models of the atmosphere to estimate ground irradiance accurately. The hybrid SCOPE-CNN model outperforms the basic CNN model that relies solely on 10 longwave channels, indicating the relevance of SCOPE’s physical features in enhancing predictions across various solar micro-climates. Further advancements using Convolutional Long Short-Time Memory (ConvLSTM) schemes lead to the development of a SCOPE-CNN-ConvLSTM model, showcasing significant enhancements over smart persistence across all conditions.

An Improved Machine Learning Model for Pure Component Property Estimation

Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product design. However, the lack of extensive data and high experimental costs hinder the development of prediction techniques for these properties. Moreover, accuracy and predictive capabilities still limit the scope and applicability of most property estimation methods. This paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach. A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different compounds. Prior selection techniques, including prior elicitation and prior predictive checking, are also applied during the building procedure to provide the model with more information from previous research findings. The framework is assessed using datasets of varying sizes for 20 pure component properties. For 18 out of the 20 pure component properties, the new models are found to give improved accuracy and predictive power in comparison with other published models, with and without machine learning.

An inverse method for the estimation of the vapor and liquid diffusivity coefficient of conventional and phase change material based hemp concrete

This study proposes a method for fast estimation of the hydric properties, including liquid and vapor diffusivities, of three distinct materials: a conventional biosourced insulating material (hemp concrete) and two innovative hybrid composites (hemp concrete with capric acid and hemp concrete with lauric acid). To estimate these parameters, an approach based on an inverse method has been developed. It involves minimizing the quadratic difference between numerical and experimental values of relative humidity, using the Nelder-Mead algorithm by assuming an exponential evolution of the liquid and vapor diffusivities. An unsteady-state experiment has been conducted in a climatic chamber during 18 days to provide experimental data for parameters’ estimation. The obtained maximum root mean square error (RMSE) for all the samples is 4.4% that is a satisfactory approximation given the model assumptions and the experimental uncertainties (±2%). The estimated parameters allow calculating all investigated materials’ liquid and vapor diffusivities. The validation of the approach is achieved by comparing its results with those from the existing literature.

PENDUGAAN TINGKAT KEMATANGAN BUAH JAMBU KRISTAL SECARA NON DESTRUKTIF DENGAN SPEKTROSKOPI FLUORESENSI

This research aims to explore the potential use of fluorescence spectroscopy combined with chemometrics for predicting the maturity level of crystal guava fruits non-destructively. The physicochemical properties including total soluble solids (TSS), water content, firmness, and pH were obtained from laboratory tests and used as a reference in developing the predictive model. The fluorescence emission spectra under excitation of 365 nm UV LED were used as predictors. The fluorescence spectra were acquired and recorded using a miniaturized UV-Vis spectrophotometer with an effective 340 – 800 nm range. In total, 240 sets of data from crystal guava fruit samples with varying ages of 90-106 DAA (days after anthesis) were used for calibration and validation. A multivariate analysis using Partial Least Squared Regression (PLSR) was used to develop the predictive models. Several data preprocessing methods were applied to enhance the information contained in the spectral data to find the best predictive model. Analysis results showed that the developed model could accurately predict water content with rcal=0.94; SEC=0.08%, rval=0.84; SEP=0.08%; RPD=2.59, followed by TSS with rcal =0.91; SEC = 0.47%Brix, rval =0.79; SEP=0.48%Brix; RPD=2.13. Although with lower accuracy, it also could predict firmness with rcal =0.86; SEC=0.43 kgf, rval =0.74; SEP=0.43 kgf; RPD=1.82 and pH with rcal =0.81; SEC=0.10, rval =0.79; SEP=0.09; RPD=1.76. The results indicate that fluorescence spectroscopy could be used as an alternative method for non-destructive estimation of physicochemical properties as indicators of the maturity level of crystal guava fruits.

Numerical analysis, experimental tests and non-linear analytical models in the study of bent timber beams reinforced with FRP strips

The paper presents selected methods for modelling bent timber beams strengthened with fibre-reinforced polymers (FRP). The assumptions are described, and the main analytical models representing the non-linear bending behaviour of wood are shown. Based on the models presented, analytical approaches considering reinforcement in the tension zone are proposed. The problem of determining the appropriate material parameters of the wood used in analytical calculations and numerical models is discussed. The main numerical methods used in timber analyses are presented. The Hill yield criterion and the generalised Hill yield criterion are discussed in more detail. A method for retrospective estimation of material properties of wood taking into account non-linear material models and fitting the generalised Hill yield criterion is described. Experimental bending tests of technical-scale beams reinforced with composites are presented. The numerical analysis was carried out and discussed. The generalised Hill yield criterion was applied to represent the behaviour of the wood, distinguishing the different yield stresses in compression and tension. The suitability of using non-linear models and advanced strength criteria in the numerical analysis of timber elements is discussed.

Measurements and estimation of frequency-dependent multi-scale viscoelastic damping properties of materials

The state-of-art viscoelastic property measurement and estimation methods at different frequencies/length scales inherently couple structural effects or sometimes lose accuracy for relevant parameters at smaller length scales in the material. We report a unified approach to measuring and estimating the frequency-dependent viscoelastic damping properties across different frequency scales independently of the specimen size effect and boundary conditions. The Dynamic Mechanical Analysis (DMA) method with three-point bending configuration is extended to higher-fidelity modeling in terms of time–frequency scales and material micro-mechanical scales together. The methodology demonstrates ways to identify the possible sources of undesired dynamics in the measurement process and eliminate those effects. It includes exciter tip contact dynamics in three-point bending configuration, anharmonic energy transfer to different frequencies (fractal bifurcation), and unmodelled artifacts or noise. Critically reviewing the standard DMA procedure with normal mode synthesis, this paper proposes a time–frequency Fourier spectral method that resolves a broader frequency range that extends to even acoustic/ultrasonic wave regimes of viscoelastic damping. An algorithm proposed in this paper with the advanced viscoelastic model with multi-parameters (standard linear solid model) estimates the fundamental material parameters at small length scales, which is more insightful to obtain a frequency-dependent correlation of material response to general dynamic loading. Stiffness and damping properties are estimated from measured data and validated for self-consistencies via different controls regarding static preload, specimen resonance, filtered noise, and model fidelity across the frequency range, and certain consistency and convergence properties of a series solution. The unified approach with a single test will be helpful in the design and evaluation of a wide variety of materials and their applications in vibration and acoustic problems. The proposed technique further demonstrates a novel way to extend low-frequency measurement to high-frequency response modeling.

Piezoelectric property optimization for piezoelectric single crystals using parametric estimation

ABSTRACT The piezoelectric properties of a piezoelectric single crystal are typically determined using a resonance method. However, it is challenging to evaluate these properties in single crystals owing to their high sensitivity and property variations. In this study, to accurately determine the piezoelectric properties, initial values were obtained using two types of samples (TE and TS modes). Impedance spectrum tendency analysis was performed according to the variables to develop a property estimation method. To determine the reliability of this method, the piezoelectric single crystal properties were evaluated and compared using the resonance method. Hence, it was confirmed that high-accuracy piezoelectric properties were obtained in a cost effective and timeous manner.

Inadequacy of current approaches for characterizing membrane transport properties at high salinities

Cost optimal design of osmotic membrane processes requires an accurate estimate of membrane transport parameters across their full operational range. However, standard approaches for estimating these parameters rely on empirical methods, the accuracy of which remains unquantified as a function of temperature, salinity, and measurement error. Herein, we present a systematic accuracy analysis of previously developed methods for estimation of membrane transport properties in reverse osmosis, high-pressure reverse osmosis, forward osmosis, pressure retarded reverse osmosis, and osmotically assisted reverse osmosis. We use a Monte Carlo approach to sample the full range of feasible membrane water permeabilities, salt permeabilities, structural parameters, and operating conditions for these processes. These material and process parameters are then incorporated into a physical transport model for each process. Our analysis shows that the statistical uncertainty of current empirical methods for estimating membrane parameters increases by 5 times from low-salinity to high-salinity conditions. The result of this work demonstrates that empirical methods are inadequate for precisely estimating membrane transport properties at high salinity and highlight a critical need for the development of statistically validated higher accuracy methods.

A Comparative Assessment of Regularized Regression Techniques for Modeling the Mechanical Properties of Rubberized Concrete

Background: Over the last few decades, many researchers have investigated the properties and behavior of concrete mixtures incorporating rubber-based solid wastes as a partial substitution of natural aggregates. Within this context, they have conducted experimental studies and developed numerical models that simulate the nature of rubberized concrete. Some of these mathematical simulations were intended to provide a rapid mixture of proportioning approaches and property estimation methods. Currently, it is believed that regression analysis provides an effective tool to simply construct a mathematical expression that models a set of data. For that reason, multiple linear regression was extensively utilized in predicting rubberized concrete properties in the literature. However, the performances of regularized regression analysis approaches were not evaluated even though they provide better alternatives to traditional regression methods in terms of controlling the overfitting issue. Objective: This study aims to assess the performance of Ridge, Lasso, and elastic net regression models in estimating the compressive and tensile strengths, and modulus of elasticity of rubberized concrete. Additionally, it intends to benchmark their capabilities against the traditional multiple linear regression method. Methods: Multiple linear regression, Ridge regression, Lasso regression, ElasticNet regression, Bayesian ridge regression, Stochastic gradient descent, Huber regression, and Quantile regression methods were used in the study. Result: In general, the research findings illustrated the superior performance of regression assessment in modeling the mechanical properties of rubberized concrete. Conclusion: Indeed rubberized concrete mechanical properties can be better modeled using regularized regression techniques, such as ElasticNet-based SGD compared to traditional methods, such as MLR.

Friction sensitivity of nitramine energetic materials: a prediction based on genetic function approximation

In this paper, we report a quantitative structure-property relationship (QSPR) model development for friction sensitivity prediction of nitramine energetic materials. The model is obtained by means of a sophisticated method, namely, genetic function approximation and consists of 10 descriptors. As a training set, we have applied 80 nitramine energetic materials of a few families, both molecular and salt-like. The corresponding test set included 30 compounds. As a result, we have obtained a good correlation with R2 = 0.90. Only two descriptors, the heat of formation and quadrupole components, need semi-empirical quantum-chemical calculations, while the rest ones are so-called “fast descriptors” meaning the relatively short time of the prediction. Using the obtained QSPR model along with our recent method for fast crude estimation of the detonation properties based on empirical formulas we have modified a few energetic salts in order to obtain materials with better friction sensitivity and detonation performance. This was mainly done by changing the constituting ions: thus, 10 nitramine energetic materials with improved characteristics were proposed.

Extraction of reflectance spectra features for estimation of surface, subsurface, and profile soil properties

• Accuracy of reflectance spectroscopy estimates of soil properties varied by depth. • Number and location of important wavebands varied by depth and by soil property. • Some calibrations could be applied successfully across multiple depth intervals. • Results could guide development of discrete-waveband soil property sensors. Diffuse reflectance spectroscopy in the visible and near-infrared wavelength ranges has potential to provide high-resolution, pollution-free, and nondestructive estimation of soil chemical and physical properties for use in precision agriculture. Practical implementation of this approach would be facilitated if soil property sensors using a limited number of reflectance bands could maintain accuracy similar to more expensive and complex full-spectrum sensors. Studies identifying such bands are limited, especially for subsurface soils. Thus, in this study, an existing spectral database of 697 soil samples was used to compare results for three soil categories (profile, surface, and subsurface) and multiple waveband selection methods. Soil cores were obtained to approximately 1.2 m depth from ten fields, two each in Missouri, Illinois, Michigan, South Dakota, and Iowa, USA, then sieved and air-dried. Laboratory soil spectra were obtained from 350 to 2500 nm using a commercial spectrometer and soil properties (total nitrogen, soil organic carbon, total carbon, magnesium, calcium, potassium, soil texture (clay, silt, and sand) fractions, cation exchange capacity, and pH) were measured using standard laboratory analyses. The ability of ten spectral preprocessing techniques to improve analysis results was investigated. Backward interval partial least squares was used to identify those spectral regions most predictive of soil properties. Alternatively, specific characteristic wavelengths were identified by a combination genetic algorithm (GA)-back propagation neural network (BPNN) approach. Results were compared for three soil property estimation methods: (1) partial least squares regression (PLSR) models based on the full spectrum, (2) PLSR models based on sensitive regions, and (3) BPNN models based on characteristic wavelengths. The best results for profile and subsurface soils were obtained with absorbance preprocessing, but for the surface soils, the standard normal variate transformation was best. For some soil properties, the prediction R 2 of the PLSR models based on sensitive regions was better than that of the PLSR models based on the full spectrum, demonstrating that retaining only sensitive wavebands could improve estimates. However, in some cases, the reduction in wavebands decreased accuracy. Differences in prediction accuracy across all calibration models over profile and subsurface soils were relatively small but were larger for surface soils. Furthermore, application of characteristic wavelength calibrations to other soil datasets resulted in a lower accuracy than with the full spectrum calibration developed for that dataset. In general, this study shows that there are measurable differences in prediction accuracy across all calibration models over the three soil depth categories. The experimental results of this study illustrate the potential for a set of wavelengths optimized for one depth category to still provide acceptable estimates for other depth categories. Overall, these results provide important guidance for the development of DRS soil sensors based on discrete wavebands to reduce cost and increase the speed of in-field data collection.

Machine Learning for Physicochemical Property Prediction of Complex Hydrocarbon Mixtures

Machine learning has proven effective for predicting properties of pure compounds from molecular structures, but properties of mixtures, in particular oil fractions, are rarely dealt with. At best, the bulk properties are estimated based on pure compound properties, linear mixing rules, and a reconstructed composition of the feedstock. As the detailed composition of such mixtures is rarely well determined and often approximated by lumps, the accuracy of the estimated bulk properties can be improved. In this work, we demonstrate for a naphtha case study our bulk property estimation method. First, a detailed PIONA composition is delumped into a molecule-level composition, and a machine learning-based approach is used to predict properties of those molecules, which are further combined in another deep neural network for the prediction of bulk properties. The latter machine learning models are trained on mixture properties using vectors that represent the mixture. The first vector is a linear combination of the molecular representation vectors and the representation of the molecular geometries that make up the mixture. The second vector applies linear mixing rules on boiling temperatures, critical temperatures, liquid densities, and vapor pressures that are predicted with machine learning. The last vector consists of a learned distillation curve. We show that an integrated machine learning approach that starts from the molecular structures in the mixture offers significant improvements in predicting mixture properties over existing approaches applied in industry and academia.

Analysis of the method for estimation of the lunar soil thermophysical properties under solar radiation heating

Представлен анализ способа определения теплофизических характеристик лунного грунта проникающим зондом при использовании в качестве нагревателя солнечного тепла. В качестве прототипа использовался зонд, применявшийся в миссиях «Аполлон».

Prediction of soil hydraulic properties using VIS-NIR spectral data in semi- arid region of Northern Karnataka Plateau

Field capacity (FC) and permanent wilting point (PWP) are the important soil hydraulic properties which determine the availability of water for plant growth. Conventional estimation of soil hydraulic properties is tedious and expensive which are normally derived from pedo-transfer functions. Visible and near-infrared (Vis-NIR) spectroscopy is a low-cost, non-destructive alternative method for the rapid estimation of soil properties. The present study was carried out to predict soil hydraulic properties using Vis-NIR spectral data in the semi-arid region of the Northern Karnataka plateau. Spectral data of 558 soil samples were acquired using Field Spec ASD Spectroradiometer and three models (support vector machine, random forest and partial least square regression) were evaluated for prediction of FC and PWP. The models were calibrated using 2/3rd of total observations and validated using 1/3rd of observations. The validation results showed that RF and SVM provided slightly higher performance compared to PLSR model. Permanent wilting point was predicted well (R2 = 0.70–0.74, RMSE = 5.44–5.74%) compared to field capacity (R2 = 0.66–0.69 and RMSE = 7.25–7.51%). Among the soil orders, FC and PWP of Vertisols were poorly predicted by Vis-NIR spectra (R2 = 0.34&0.42) and moderately predicted for Alfisols (0.44&0.52) and Inceptisols (0.55&0.65) while excellent prediction was recorded for Entisols (0.83&0.76). The present results showed that Vis-NIR spectroscopy is helpful for fast estimation of hydraulic properties in semi-arid regions of the country with high accuracy.

Neural net implementation of steam properties on FPGA

Real time applications like model predictive control, monitoring and data reconciliation of power plants and industrial processes employ nonlinear mathematical models and require thermodynamic properties and their derivatives of working fluids. Applications like super heater temperature control based on energy balance and real time data reconciliation, require an efficient and a compact method for simultaneous estimation of thermodynamic properties, and their partial derivatives suitable for implementation in field-programmable gate array (FPGA). However, the complex mathematical formulations of these properties prohibit direct implementations in FPGAs. Single artificial neural network (ANN) architecture is used to replace the entire code in higher level languages, running into a few thousand lines. FPGA implementation of a compact neural network for the entire range of thermodynamic properties is presented. Large arguments in sigmoid function are factored into a product of integer and a fractional part which is represented using series approximation with five terms only and the integers are represented in look up table (LUT). This ensures optimum storage and computational burden for the above applications. The ANN is implemented in IEEE 754 floating point with synthesis in Xilinx ISE design suite using Verilog HDL. The results are presented for a typical pressure versus saturation temperature.

Computer-aided design and solvent selection for organic paint and coating formulations

A methodology for product design of organic coatings is developed to help a formulator screen, modify, or design a paint formulation according to the desired functionality. The computer-aided product design (CAPD) method aims to improve and expand the current state of computer-aided coating formulation through a combination of databases and models capable of estimating various physicochemical properties. Additional paint-specific component interactions are considered, and new properties and estimation methods are included that better suit modern environmental, health, and safety-related considerations in the product design of paints. This work aims to provide a clear description of all property models, estimation methods, tools, and software used throughout the systematic framework. The solvent selection methodology is tested for several commercial paint formulations, using the original solvent mixtures to verify the results, while providing formulation alternatives to guide further experimental testing. For investigated case studies, the methodology provides both viable formulations and solvent selection alternatives. The suggested options can be used to retain key functionalities while lowering cost, substituting unwanted ingredients, or improving factors including health and safety, as indicated by lethal concentration, flash points, and biodegradability.

(Invited) Multi-Scale Analysis of Transport in Dry and Partially-Saturated Porous Media

Improvements in imaging techniques, such as micro- and nano-computer tomography (CT) and focus ion beam scanning electron microscopy (FIB-SEM), have enabled the reconstruction of complex porous media that can then be analyzed by computer simulation to estimate effective transport properties, e.g., ref. [1, 2]. Experimental setups have also been developed to measure dry and partially-saturated gas diffusivity and permeability of gas diffusion layers (GDLs) and catalyst layer (CLs), e.g., ref. [3, 4]. Based on the available literature, a comparison between micro-scale simulation results and experimental data must be performed to assess the accuracy of direct numerical simulation CT image analysis for dry and partially-saturated transport property estimation. In the later case, the validity of computational methods to predict water intrusion, such as full morphology/morphological image opening [1] and watershed segmentation [5], should also be assessed by comparing mercury intrusion experiments to numerical predictions, and by direct comparison of numerical results to CT images. Finally, these transport properties should be integrated into complete fuel cell models where volume-averaging techniques are used to properly estimate channel-porous media interactions. Such interactions however are seldom studied in literature.The proposed contribution aims at first studying the validity of two popular methods for numerical estimation of effective transport properties from three-dimensional reconstructed porous media, i.e., direct numerical simulation (DNS) and pore network modeling (PNM). Two fuel cell gas diffusion media and an electrolyzer porous transport layer are analyzed by CT and characterized by measuring mercury intrusion porosimetry (MIP), and dry permeability and diffusivity. A comparison of numerical and experimental results shows that DNS tools in OpenFCST [6] are capable of accurately predicting intrusion, and transport properties without using any fitting parameters. Accurate predictions are also achieved with the PNM implementation in OpenPNM [7] when the inscribed diameter method is used to estimate the pore size distribution, and the equivalent diameter is used to estimate pore transport properties. These tools open an avenue for computational design of porous materials.Effective transport properties must be integrated into volume-averaged fuel cell models where porous media-channel interactions are of paramount importance. These interactions however, as well as the method used for volume averaging fuel cell equations, are seldom studied in detail. Therefore, the second contribution of the talk aims at analyzing channel-porous media interactions by developing a compressible volume-averaged channel-porous media model in OpenFCST, and validating it with respect to permeability and diffusion bridge experiments in the literature. Comparison of numerical and experimental results show that, depending on the volume-averaging methodology used, experimentally obtained transport properties do not correspond to the input parameters needed in volume-averaged models. Finally, the numerical model is used to estimate flow by-pass in serpentine and inter-digitated channels, as well as compressibility effects [8].

From Multi-Modal Property Dataset to Robot-Centric Conceptual Knowledge About Household Objects.

Conceptual knowledge about objects is essential for humans, as well as for animals, to interact with their environment. On this basis, the objects can be understood as tools, a selection process can be implemented and their usage can be planned in order to achieve a specific goal. The conceptual knowledge, in this case, is primarily concerned about the physical properties and functional properties observed in the objects. Similarly tool-use applications in robotics require such conceptual knowledge about objects for substitute selection among other purposes. State-of-the-art methods employ a top-down approach where hand-crafted symbolic knowledge, which is defined from a human perspective, is grounded into sensory data afterwards. However, due to different sensing and acting capabilities of robots, a robot's conceptual understanding of objects (e.g., light/heavy) will vary and therefore should be generated from the robot's perspective entirely, which entails robot-centric conceptual knowledge about objects. A similar bottom-up argument has been put forth in cognitive science that humans and animals alike develop conceptual understanding of objects based on their own perceptual experiences with objects. With this goal in mind, we propose an extensible property estimation framework which consists of estimations methods to obtain the quantitative measurements of physical properties (rigidity, weight, etc.) and functional properties (containment, support, etc.) from household objects. This property estimation forms the basis for our second contribution: Generation of robot-centric conceptual knowledge. Our approach employs unsupervised clustering methods to transform numerical property data into symbols, and Bivariate Joint Frequency Distributions and Sample Proportion to generate conceptual knowledge about objects using the robot-centric symbols. A preliminary implementation of the proposed framework is employed to acquire a dataset comprising six physical and four functional properties of 110 household objects. This Robot-Centric dataSet (RoCS) is used to evaluate the framework regarding the property estimation methods and the semantics of the considered properties within the dataset. Furthermore, the dataset includes the derived robot-centric conceptual knowledge using the proposed framework. The application of the conceptual knowledge about objects is then evaluated by examining its usefulness in a tool substitution scenario.

Rubber Material Property Prediction Using Electron Microscope Images of Internal Structures Taken under Multiple Conditions.

A method for prediction of properties of rubber materials utilizing electron microscope images of internal structures taken under multiple conditions is presented in this paper. Electron microscope images of rubber materials are taken under several conditions, and effective conditions for the prediction of properties are different for each rubber material. Novel approaches for the selection and integration of reliable prediction results are used in the proposed method. The proposed method enables selection of reliable results based on prediction intervals that can be derived by the predictors that are each constructed from electron microscope images taken under each condition. By monitoring the relationship between prediction results and prediction intervals derived from the corresponding predictors, it can be determined whether the target prediction results are reliable. Furthermore, the proposed method integrates the selected reliable results based on Dempster–Shafer (DS) evidence theory, and this integration result is regarded as a final prediction result. The DS evidence theory enables integration of multiple prediction results, even if the results are obtained from different imaging conditions. This means that integration can even be realized if electron microscope images of each material are taken under different conditions and even if these conditions are different for target materials. This nonconventional approach is suitable for our application, i.e., property prediction. Experiments on rubber material data showed that the evaluation index mean absolute percent error (MAPE) was under 10% by the proposed method. The performance of the proposed method outperformed conventional comparative property estimation methods. Consequently, the proposed method can realize accurate prediction of the properties with consideration of the characteristic of electron microscope images described above.

A heterogenous, time harmonic, nearly incompressible transverse isotropic finite element brain simulation platform for MR elastography

In this study, we describe numerical implementation of a heterogenous, nearly incompressible, transverse isotropic (NITI) finite element (FE) model with key advantages for use in MR elastography of fibrous soft tissue. MR elastography (MRE) estimates heterogenous property distributions from MR-measured harmonic motion fields based on assumed mechanical models of tissue response. Current MRE property estimation methods usually assume isotropic properties, which cause inconsistencies arising from model-data mismatch when anisotropy is present. In this study, we use a NITI model parameterized by a base shear modulus, shear anisotropy, tensile anisotropy, and an isotropic bulk modulus, which describes the mechanical behavior of tissues with aligned fiber structures well. Property and fiber direction heterogeneity are implemented at the level of FE Gauss points, which allows high-resolution diffusion tensor imaging (DTI) data to be incorporated easily into the model. The resulting code was validated against analytical solutions and a commercial FEM package, and is suitable for incorporation into nonlinear inversion MRE algorithms. Simulations of MRE in brain tissue with heterogeneous properties and anisotropic fiber tracts, which produced wavefields similar to experimental MRE, were generated from anatomical, DTI and MRE image data, allowing investigation of MRE inversion performance in a realistic setting where the ground truth and underlying mechanical behavior are known. Two established isotropic inversion algorithms—nonlinear inversion (NLI) and local direct inversion (LDI)—were applied to simulated MRE data. Both algorithms performed well in simple isotropic homogenous cases; however, heterogeneity cased substantial artifacts in LDI arising from violation of local homogeneity assumptions. NLI was able to recover accurate heterogenous displacement fields in the presence of measurement noise. Isotropic NLI inversion of simulated anisotropic data (generated using the NITI model) produced maps of isotropic mechanical properties with undesirable dependence on the wavefield. Local anisotropy also caused wavefield-dependent errors of 7% in nearby isotropic structures, compared to 10% in the anisotropic structures.