Mid-infrared rogue wave generation in chalcogenide fibers

NASA Astrophysics Data System (ADS)

Liu, Lai; Nagasaka, Kenshiro; Suzuki, Takenobu; Ohishi, Yasutake

2017-02-01

The supercontinuum generation and rogue wave generation in a step-index chalcogenide fiber are numerically investigated by solving the generalized nonlinear Schrödinger equation. Two noise models have been used to model the noise of the pump laser pulses to investigate the consistency of the noise modeling in rogue wave generation. First noise model is 0.1% amplitude noise which has been used in the report of rogue wave generation. Second noise model is the widely used one-photon-per-mode-noise and phase diffusion-noise. The results show that these two commonly used noise models have a good consistency in the simulations of rogue wave generation. The results also show that if the pump laser pulses carry more noise, the chance of a rogue wave with a high peak power becomes higher. This is harmful to the SC generation by using picosecond lasers in the chalcogenide fibers.

Developing models for the prediction of hospital healthcare waste generation rate.

PubMed

Tesfahun, Esubalew; Kumie, Abera; Beyene, Abebe

2016-01-01

An increase in the number of health institutions, along with frequent use of disposable medical products, has contributed to the increase of healthcare waste generation rate. For proper handling of healthcare waste, it is crucial to predict the amount of waste generation beforehand. Predictive models can help to optimise healthcare waste management systems, set guidelines and evaluate the prevailing strategies for healthcare waste handling and disposal. However, there is no mathematical model developed for Ethiopian hospitals to predict healthcare waste generation rate. Therefore, the objective of this research was to develop models for the prediction of a healthcare waste generation rate. A longitudinal study design was used to generate long-term data on solid healthcare waste composition, generation rate and develop predictive models. The results revealed that the healthcare waste generation rate has a strong linear correlation with the number of inpatients (R(2) = 0.965), and a weak one with the number of outpatients (R(2) = 0.424). Statistical analysis was carried out to develop models for the prediction of the quantity of waste generated at each hospital (public, teaching and private). In these models, the number of inpatients and outpatients were revealed to be significant factors on the quantity of waste generated. The influence of the number of inpatients and outpatients treated varies at different hospitals. Therefore, different models were developed based on the types of hospitals. © The Author(s) 2015.

Digital relief generation from 3D models

NASA Astrophysics Data System (ADS)

Wang, Meili; Sun, Yu; Zhang, Hongming; Qian, Kun; Chang, Jian; He, Dongjian

2016-09-01

It is difficult to extend image-based relief generation to high-relief generation, as the images contain insufficient height information. To generate reliefs from three-dimensional (3D) models, it is necessary to extract the height fields from the model, but this can only generate bas-reliefs. To overcome this problem, an efficient method is proposed to generate bas-reliefs and high-reliefs directly from 3D meshes. To produce relief features that are visually appropriate, the 3D meshes are first scaled. 3D unsharp masking is used to enhance the visual features in the 3D mesh, and average smoothing and Laplacian smoothing are implemented to achieve better smoothing results. A nonlinear variable scaling scheme is then employed to generate the final bas-reliefs and high-reliefs. Using the proposed method, relief models can be generated from arbitrary viewing positions with different gestures and combinations of multiple 3D models. The generated relief models can be printed by 3D printers. The proposed method provides a means of generating both high-reliefs and bas-reliefs in an efficient and effective way under the appropriate scaling factors.

Source Term Model for Vortex Generator Vanes in a Navier-Stokes Computer Code

NASA Technical Reports Server (NTRS)

Waithe, Kenrick A.

2004-01-01

A source term model for an array of vortex generators was implemented into a non-proprietary Navier-Stokes computer code, OVERFLOW. The source term models the side force created by a vortex generator vane. The model is obtained by introducing a side force to the momentum and energy equations that can adjust its strength automatically based on the local flow. The model was tested and calibrated by comparing data from numerical simulations and experiments of a single low profile vortex generator vane on a flat plate. In addition, the model was compared to experimental data of an S-duct with 22 co-rotating, low profile vortex generators. The source term model allowed a grid reduction of about seventy percent when compared with the numerical simulations performed on a fully gridded vortex generator on a flat plate without adversely affecting the development and capture of the vortex created. The source term model was able to predict the shape and size of the stream-wise vorticity and velocity contours very well when compared with both numerical simulations and experimental data. The peak vorticity and its location were also predicted very well when compared to numerical simulations and experimental data. The circulation predicted by the source term model matches the prediction of the numerical simulation. The source term model predicted the engine fan face distortion and total pressure recovery of the S-duct with 22 co-rotating vortex generators very well. The source term model allows a researcher to quickly investigate different locations of individual or a row of vortex generators. The researcher is able to conduct a preliminary investigation with minimal grid generation and computational time.

Exploring the Processes of Generating LOD (0-2) Citygml Models in Greater Municipality of Istanbul

NASA Astrophysics Data System (ADS)

Buyuksalih, I.; Isikdag, U.; Zlatanova, S.

2013-08-01

3D models of cities, visualised and exploded in 3D virtual environments have been available for several years. Currently a large number of impressive realistic 3D models have been regularly presented at scientific, professional and commercial events. One of the most promising developments is OGC standard CityGML. CityGML is object-oriented model that support 3D geometry and thematic semantics, attributes and relationships, and offers advanced options for realistic visualization. One of the very attractive characteristics of the model is the support of 5 levels of detail (LOD), starting from 2.5D less accurate model (LOD0) and ending with very detail indoor model (LOD4). Different local government offices and municipalities have different needs when utilizing the CityGML models, and the process of model generation depends on local and domain specific needs. Although the processes (i.e. the tasks and activities) for generating the models differs depending on its utilization purpose, there are also some common tasks (i.e. common denominator processes) in the model generation of City GML models. This paper focuses on defining the common tasks in generation of LOD (0-2) City GML models and representing them in a formal way with process modeling diagrams.

Generative models for network neuroscience: prospects and promise

PubMed Central

Betzel, Richard F.

2017-01-01

Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and identifying principles with which to understand them. Within this discipline, one particularly powerful approach is network generative modelling, in which wiring rules are algorithmically implemented to produce synthetic network architectures with the same properties as observed in empirical network data. Successful models can highlight the principles by which a network is organized and potentially uncover the mechanisms by which it grows and develops. Here, we review the prospects and promise of generative models for network neuroscience. We begin with a primer on network generative models, with a discussion of compressibility and predictability, and utility in intuiting mechanisms, followed by a short history on their use in network science, broadly. We then discuss generative models in practice and application, paying particular attention to the critical need for cross-validation. Next, we review generative models of biological neural networks, both at the cellular and large-scale level, and across a variety of species including Caenorhabditis elegans, Drosophila, mouse, rat, cat, macaque and human. We offer a careful treatment of a few relevant distinctions, including differences between generative models and null models, sufficiency and redundancy, inferring and claiming mechanism, and functional and structural connectivity. We close with a discussion of future directions, outlining exciting frontiers both in empirical data collection efforts as well as in method and theory development that, together, further the utility of the generative network modelling approach for network neuroscience. PMID:29187640

A universal algorithm for an improved finite element mesh generation Mesh quality assessment in comparison to former automated mesh-generators and an analytic model.

PubMed

Kaminsky, Jan; Rodt, Thomas; Gharabaghi, Alireza; Forster, Jan; Brand, Gerd; Samii, Madjid

2005-06-01

The FE-modeling of complex anatomical structures is not solved satisfyingly so far. Voxel-based as opposed to contour-based algorithms allow an automated mesh generation based on the image data. Nonetheless their geometric precision is limited. We developed an automated mesh-generator that combines the advantages of voxel-based generation with improved representation of the geometry by displacement of nodes on the object-surface. Models of an artificial 3D-pipe-section and a skullbase were generated with different mesh-densities using the newly developed geometric, unsmoothed and smoothed voxel generators. Compared to the analytic calculation of the 3D-pipe-section model the normalized RMS error of the surface stress was 0.173-0.647 for the unsmoothed voxel models, 0.111-0.616 for the smoothed voxel models with small volume error and 0.126-0.273 for the geometric models. The highest element-energy error as a criterion for the mesh quality was 2.61x10(-2) N mm, 2.46x10(-2) N mm and 1.81x10(-2) N mm for unsmoothed, smoothed and geometric voxel models, respectively. The geometric model of the 3D-skullbase resulted in the lowest element-energy error and volume error. This algorithm also allowed the best representation of anatomical details. The presented geometric mesh-generator is universally applicable and allows an automated and accurate modeling by combining the advantages of the voxel-technique and of improved surface-modeling.

Evaluation of Generation Alternation Models in Evolutionary Robotics

NASA Astrophysics Data System (ADS)

Oiso, Masashi; Matsumura, Yoshiyuki; Yasuda, Toshiyuki; Ohkura, Kazuhiro

For efficient implementation of Evolutionary Algorithms (EA) to a desktop grid computing environment, we propose a new generation alternation model called Grid-Oriented-Deletion (GOD) based on comparison with the conventional techniques. In previous research, generation alternation models are generally evaluated by using test functions. However, their exploration performance on the real problems such as Evolutionary Robotics (ER) has not been made very clear yet. Therefore we investigate the relationship between the exploration performance of EA on an ER problem and its generation alternation model. We applied four generation alternation models to the Evolutionary Multi-Robotics (EMR), which is the package-pushing problem to investigate their exploration performance. The results show that GOD is more effective than the other conventional models.

GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures.

PubMed

Urbanowicz, Ryan J; Kiralis, Jeff; Sinnott-Armstrong, Nicholas A; Heberling, Tamra; Fisher, Jonathan M; Moore, Jason H

2012-10-01

Geneticists who look beyond single locus disease associations require additional strategies for the detection of complex multi-locus effects. Epistasis, a multi-locus masking effect, presents a particular challenge, and has been the target of bioinformatic development. Thorough evaluation of new algorithms calls for simulation studies in which known disease models are sought. To date, the best methods for generating simulated multi-locus epistatic models rely on genetic algorithms. However, such methods are computationally expensive, difficult to adapt to multiple objectives, and unlikely to yield models with a precise form of epistasis which we refer to as pure and strict. Purely and strictly epistatic models constitute the worst-case in terms of detecting disease associations, since such associations may only be observed if all n-loci are included in the disease model. This makes them an attractive gold standard for simulation studies considering complex multi-locus effects. We introduce GAMETES, a user-friendly software package and algorithm which generates complex biallelic single nucleotide polymorphism (SNP) disease models for simulation studies. GAMETES rapidly and precisely generates random, pure, strict n-locus models with specified genetic constraints. These constraints include heritability, minor allele frequencies of the SNPs, and population prevalence. GAMETES also includes a simple dataset simulation strategy which may be utilized to rapidly generate an archive of simulated datasets for given genetic models. We highlight the utility and limitations of GAMETES with an example simulation study using MDR, an algorithm designed to detect epistasis. GAMETES is a fast, flexible, and precise tool for generating complex n-locus models with random architectures. While GAMETES has a limited ability to generate models with higher heritabilities, it is proficient at generating the lower heritability models typically used in simulation studies evaluating new algorithms. In addition, the GAMETES modeling strategy may be flexibly combined with any dataset simulation strategy. Beyond dataset simulation, GAMETES could be employed to pursue theoretical characterization of genetic models and epistasis.

Self-Generated Analogical Models of Respiratory Pathways

ERIC Educational Resources Information Center

Lee, Yeung Chung

2015-01-01

Self-generated analogical models have emerged recently as alternatives to teacher-supplied analogies and seem to have good potential to promote deep learning and scientific thinking. However, studies of the ways and contexts in which students generate these models are still too limited to allow a fuller appraisal of these models' effectiveness in…

Generative Modeling for Machine Learning on the D-Wave

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thulasidasan, Sunil

These are slides on Generative Modeling for Machine Learning on the D-Wave. The following topics are detailed: generative models; Boltzmann machines: a generative model; restricted Boltzmann machines; learning parameters: RBM training; practical ways to train RBM; D-Wave as a Boltzmann sampler; mapping RBM onto the D-Wave; Chimera restricted RBM; mapping binary RBM to Ising model; experiments; data; D-Wave effective temperature, parameters noise, etc.; experiments: contrastive divergence (CD) 1 step; after 50 steps of CD; after 100 steps of CD; D-Wave (experiments 1, 2, 3); D-Wave observations.

Modeling and Simulation of U-tube Steam Generator

NASA Astrophysics Data System (ADS)

Zhang, Mingming; Fu, Zhongguang; Li, Jinyao; Wang, Mingfei

2018-03-01

The U-tube natural circulation steam generator was mainly researched with modeling and simulation in this article. The research is based on simuworks system simulation software platform. By analyzing the structural characteristics and the operating principle of U-tube steam generator, there are 14 control volumes in the model, including primary side, secondary side, down channel and steam plenum, etc. The model depends completely on conservation laws, and it is applied to make some simulation tests. The results show that the model is capable of simulating properly the dynamic response of U-tube steam generator.

High Speed Civil Transport Aircraft Simulation: Reference-H Cycle 1, MATLAB Implementation

NASA Technical Reports Server (NTRS)

Sotack, Robert A.; Chowdhry, Rajiv S.; Buttrill, Carey S.

1999-01-01

The mathematical model and associated code to simulate a high speed civil transport aircraft - the Boeing Reference H configuration - are described. The simulation was constructed in support of advanced control law research. In addition to providing time histories of the dynamic response, the code includes the capabilities for calculating trim solutions and for generating linear models. The simulation relies on the nonlinear, six-degree-of-freedom equations which govern the motion of a rigid aircraft in atmospheric flight. The 1962 Standard Atmosphere Tables are used along with a turbulence model to simulate the Earth atmosphere. The aircraft model has three parts - an aerodynamic model, an engine model, and a mass model. These models use the data from the Boeing Reference H cycle 1 simulation data base. Models for the actuator dynamics, landing gear, and flight control system are not included in this aircraft model. Dynamic responses generated by the nonlinear simulation are presented and compared with results generated from alternate simulations at Boeing Commercial Aircraft Company and NASA Langley Research Center. Also, dynamic responses generated using linear models are presented and compared with dynamic responses generated using the nonlinear simulation.

Modeling SMAP Spacecraft Attitude Control Estimation Error Using Signal Generation Model

NASA Technical Reports Server (NTRS)

Rizvi, Farheen

2016-01-01

Two ground simulation software are used to model the SMAP spacecraft dynamics. The CAST software uses a higher fidelity model than the ADAMS software. The ADAMS software models the spacecraft plant, controller and actuator models, and assumes a perfect sensor and estimator model. In this simulation study, the spacecraft dynamics results from the ADAMS software are used as CAST software is unavailable. The main source of spacecraft dynamics error in the higher fidelity CAST software is due to the estimation error. A signal generation model is developed to capture the effect of this estimation error in the overall spacecraft dynamics. Then, this signal generation model is included in the ADAMS software spacecraft dynamics estimate such that the results are similar to CAST. This signal generation model has similar characteristics mean, variance and power spectral density as the true CAST estimation error. In this way, ADAMS software can still be used while capturing the higher fidelity spacecraft dynamics modeling from CAST software.

Automatic 3d Building Model Generations with Airborne LiDAR Data

NASA Astrophysics Data System (ADS)

Yastikli, N.; Cetin, Z.

2017-11-01

LiDAR systems become more and more popular because of the potential use for obtaining the point clouds of vegetation and man-made objects on the earth surface in an accurate and quick way. Nowadays, these airborne systems have been frequently used in wide range of applications such as DEM/DSM generation, topographic mapping, object extraction, vegetation mapping, 3 dimensional (3D) modelling and simulation, change detection, engineering works, revision of maps, coastal management and bathymetry. The 3D building model generation is the one of the most prominent applications of LiDAR system, which has the major importance for urban planning, illegal construction monitoring, 3D city modelling, environmental simulation, tourism, security, telecommunication and mobile navigation etc. The manual or semi-automatic 3D building model generation is costly and very time-consuming process for these applications. Thus, an approach for automatic 3D building model generation is needed in a simple and quick way for many studies which includes building modelling. In this study, automatic 3D building models generation is aimed with airborne LiDAR data. An approach is proposed for automatic 3D building models generation including the automatic point based classification of raw LiDAR point cloud. The proposed point based classification includes the hierarchical rules, for the automatic production of 3D building models. The detailed analyses for the parameters which used in hierarchical rules have been performed to improve classification results using different test areas identified in the study area. The proposed approach have been tested in the study area which has partly open areas, forest areas and many types of the buildings, in Zekeriyakoy, Istanbul using the TerraScan module of TerraSolid. The 3D building model was generated automatically using the results of the automatic point based classification. The obtained results of this research on study area verified that automatic 3D building models can be generated successfully using raw LiDAR point cloud data.

Simulating secondary organic aerosol in a regional air quality model using the statistical oxidation model - Part 1: Assessing the influence of constrained multi-generational ageing

NASA Astrophysics Data System (ADS)

Jathar, S. H.; Cappa, C. D.; Wexler, A. S.; Seinfeld, J. H.; Kleeman, M. J.

2015-09-01

Multi-generational oxidation of volatile organic compound (VOC) oxidation products can significantly alter the mass, chemical composition and properties of secondary organic aerosol (SOA) compared to calculations that consider only the first few generations of oxidation reactions. However, the most commonly used state-of-the-science schemes in 3-D regional or global models that account for multi-generational oxidation (1) consider only functionalization reactions but do not consider fragmentation reactions, (2) have not been constrained to experimental data; and (3) are added on top of existing parameterizations. The incomplete description of multi-generational oxidation in these models has the potential to bias source apportionment and control calculations for SOA. In this work, we used the Statistical Oxidation Model (SOM) of Cappa and Wilson (2012), constrained by experimental laboratory chamber data, to evaluate the regional implications of multi-generational oxidation considering both functionalization and fragmentation reactions. SOM was implemented into the regional UCD/CIT air quality model and applied to air quality episodes in California and the eastern US. The mass, composition and properties of SOA predicted using SOM are compared to SOA predictions generated by a traditional "two-product" model to fully investigate the impact of explicit and self-consistent accounting of multi-generational oxidation. Results show that SOA mass concentrations predicted by the UCD/CIT-SOM model are very similar to those predicted by a two-product model when both models use parameters that are derived from the same chamber data. Since the two-product model does not explicitly resolve multi-generational oxidation reactions, this finding suggests that the chamber data used to parameterize the models captures the majority of the SOA mass formation from multi-generational oxidation under the conditions tested. Consequently, the use of low and high NOx yields perturbs SOA concentrations by a factor of two and are probably a much stronger determinant in 3-D models than constrained multi-generational oxidation. While total predicted SOA mass is similar for the SOM and two-product models, the SOM model predicts increased SOA contributions from anthropogenic (alkane, aromatic) and sesquiterpenes and decreased SOA contributions from isoprene and monoterpene relative to the two-product model calculations. The SOA predicted by SOM has a much lower volatility than that predicted by the traditional model resulting in better qualitative agreement with volatility measurements of ambient OA. On account of its lower-volatility, the SOA mass produced by SOM does not appear to be as strongly influenced by the inclusion of oligomerization reactions, whereas the two-product model relies heavily on oligomerization to form low volatility SOA products. Finally, an unconstrained contemporary hybrid scheme to model multi-generational oxidation within the framework of a two-product model in which "ageing" reactions are added on top of the existing two-product parameterization is considered. This hybrid scheme formed at least three times more SOA than the SOM during regional simulations as a result of excessive transformation of semi-volatile vapors into lower volatility material that strongly partitions to the particle phase. This finding suggests that these "hybrid" multi-generational schemes should be used with great caution in regional models.

Simulating secondary organic aerosol in a regional air quality model using the statistical oxidation model - Part 1: Assessing the influence of constrained multi-generational ageing

NASA Astrophysics Data System (ADS)

Jathar, S. H.; Cappa, C. D.; Wexler, A. S.; Seinfeld, J. H.; Kleeman, M. J.

2016-02-01

Multi-generational oxidation of volatile organic compound (VOC) oxidation products can significantly alter the mass, chemical composition and properties of secondary organic aerosol (SOA) compared to calculations that consider only the first few generations of oxidation reactions. However, the most commonly used state-of-the-science schemes in 3-D regional or global models that account for multi-generational oxidation (1) consider only functionalization reactions but do not consider fragmentation reactions, (2) have not been constrained to experimental data and (3) are added on top of existing parameterizations. The incomplete description of multi-generational oxidation in these models has the potential to bias source apportionment and control calculations for SOA. In this work, we used the statistical oxidation model (SOM) of Cappa and Wilson (2012), constrained by experimental laboratory chamber data, to evaluate the regional implications of multi-generational oxidation considering both functionalization and fragmentation reactions. SOM was implemented into the regional University of California at Davis / California Institute of Technology (UCD/CIT) air quality model and applied to air quality episodes in California and the eastern USA. The mass, composition and properties of SOA predicted using SOM were compared to SOA predictions generated by a traditional two-product model to fully investigate the impact of explicit and self-consistent accounting of multi-generational oxidation.Results show that SOA mass concentrations predicted by the UCD/CIT-SOM model are very similar to those predicted by a two-product model when both models use parameters that are derived from the same chamber data. Since the two-product model does not explicitly resolve multi-generational oxidation reactions, this finding suggests that the chamber data used to parameterize the models captures the majority of the SOA mass formation from multi-generational oxidation under the conditions tested. Consequently, the use of low and high NOx yields perturbs SOA concentrations by a factor of two and are probably a much stronger determinant in 3-D models than multi-generational oxidation. While total predicted SOA mass is similar for the SOM and two-product models, the SOM model predicts increased SOA contributions from anthropogenic (alkane, aromatic) and sesquiterpenes and decreased SOA contributions from isoprene and monoterpene relative to the two-product model calculations. The SOA predicted by SOM has a much lower volatility than that predicted by the traditional model, resulting in better qualitative agreement with volatility measurements of ambient OA. On account of its lower-volatility, the SOA mass produced by SOM does not appear to be as strongly influenced by the inclusion of oligomerization reactions, whereas the two-product model relies heavily on oligomerization to form low-volatility SOA products. Finally, an unconstrained contemporary hybrid scheme to model multi-generational oxidation within the framework of a two-product model in which ageing reactions are added on top of the existing two-product parameterization is considered. This hybrid scheme formed at least 3 times more SOA than the SOM during regional simulations as a result of excessive transformation of semi-volatile vapors into lower volatility material that strongly partitions to the particle phase. This finding suggests that these hybrid multi-generational schemes should be used with great caution in regional models.

An Empirical Model for Vane-Type Vortex Generators in a Navier-Stokes Code

NASA Technical Reports Server (NTRS)

Dudek, Julianne C.

2005-01-01

An empirical model which simulates the effects of vane-type vortex generators in ducts was incorporated into the Wind-US Navier-Stokes computational fluid dynamics code. The model enables the effects of the vortex generators to be simulated without defining the details of the geometry within the grid, and makes it practical for researchers to evaluate multiple combinations of vortex generator arrangements. The model determines the strength of each vortex based on the generator geometry and the local flow conditions. Validation results are presented for flow in a straight pipe with a counter-rotating vortex generator arrangement, and the results are compared with experimental data and computational simulations using a gridded vane generator. Results are also presented for vortex generator arrays in two S-duct diffusers, along with accompanying experimental data. The effects of grid resolution and turbulence model are also examined.

Simulation for Wind Turbine Generators -- With FAST and MATLAB-Simulink Modules

DOE Office of Scientific and Technical Information (OSTI.GOV)

Singh, M.; Muljadi, E.; Jonkman, J.

This report presents the work done to develop generator and gearbox models in the Matrix Laboratory (MATLAB) environment and couple them to the National Renewable Energy Laboratory's Fatigue, Aerodynamics, Structures, and Turbulence (FAST) program. The goal of this project was to interface the superior aerodynamic and mechanical models of FAST to the excellent electrical generator models found in various Simulink libraries and applications. The scope was limited to Type 1, Type 2, and Type 3 generators and fairly basic gear-train models. Future work will include models of Type 4 generators and more-advanced gear-train models with increased degrees of freedom. Asmore » described in this study, implementation of the developed drivetrain model enables the software tool to be used in many ways. Several case studies are presented as examples of the many types of studies that can be performed using this tool.« less

Learning a generative model of images by factoring appearance and shape.

PubMed

Le Roux, Nicolas; Heess, Nicolas; Shotton, Jamie; Winn, John

2011-03-01

Computer vision has grown tremendously in the past two decades. Despite all efforts, existing attempts at matching parts of the human visual system's extraordinary ability to understand visual scenes lack either scope or power. By combining the advantages of general low-level generative models and powerful layer-based and hierarchical models, this work aims at being a first step toward richer, more flexible models of images. After comparing various types of restricted Boltzmann machines (RBMs) able to model continuous-valued data, we introduce our basic model, the masked RBM, which explicitly models occlusion boundaries in image patches by factoring the appearance of any patch region from its shape. We then propose a generative model of larger images using a field of such RBMs. Finally, we discuss how masked RBMs could be stacked to form a deep model able to generate more complicated structures and suitable for various tasks such as segmentation or object recognition.

High-Fidelity Roadway Modeling and Simulation

NASA Technical Reports Server (NTRS)

Wang, Jie; Papelis, Yiannis; Shen, Yuzhong; Unal, Ozhan; Cetin, Mecit

2010-01-01

Roads are an essential feature in our daily lives. With the advances in computing technologies, 2D and 3D road models are employed in many applications, such as computer games and virtual environments. Traditional road models were generated by professional artists manually using modeling software tools such as Maya and 3ds Max. This approach requires both highly specialized and sophisticated skills and massive manual labor. Automatic road generation based on procedural modeling can create road models using specially designed computer algorithms or procedures, reducing the tedious manual editing needed for road modeling dramatically. But most existing procedural modeling methods for road generation put emphasis on the visual effects of the generated roads, not the geometrical and architectural fidelity. This limitation seriously restricts the applicability of the generated road models. To address this problem, this paper proposes a high-fidelity roadway generation method that takes into account road design principles practiced by civil engineering professionals, and as a result, the generated roads can support not only general applications such as games and simulations in which roads are used as 3D assets, but also demanding civil engineering applications, which requires accurate geometrical models of roads. The inputs to the proposed method include road specifications, civil engineering road design rules, terrain information, and surrounding environment. Then the proposed method generates in real time 3D roads that have both high visual and geometrical fidelities. This paper discusses in details the procedures that convert 2D roads specified in shape files into 3D roads and civil engineering road design principles. The proposed method can be used in many applications that have stringent requirements on high precision 3D models, such as driving simulations and road design prototyping. Preliminary results demonstrate the effectiveness of the proposed method.

Program Helps Generate Boundary-Element Mathematical Models

NASA Technical Reports Server (NTRS)

Goldberg, R. K.

1995-01-01

Composite Model Generation-Boundary Element Method (COM-GEN-BEM) computer program significantly reduces time and effort needed to construct boundary-element mathematical models of continuous-fiber composite materials at micro-mechanical (constituent) scale. Generates boundary-element models compatible with BEST-CMS boundary-element code for anlaysis of micromechanics of composite material. Written in PATRAN Command Language (PCL).

Model misspecification detection by means of multiple generator errors, using the observed potential map.

PubMed

Zhang, Z; Jewett, D L

1994-01-01

Due to model misspecification, currently-used Dipole Source Localization (DSL) methods may contain Multiple-Generator Errors (MulGenErrs) when fitting simultaneously-active dipoles. The size of the MulGenErr is a function of both the model used, and the dipole parameters, including the dipoles' waveforms (time-varying magnitudes). For a given fitting model, by examining the variation of the MulGenErrs (or the fit parameters) under different waveforms for the same generating-dipoles, the accuracy of the fitting model for this set of dipoles can be determined. This method of testing model misspecification can be applied to evoked potential maps even when the parameters of the generating-dipoles are unknown. The dipole parameters fitted in a model should only be accepted if the model can be shown to be sufficiently accurate.

Carbon deposition model for oxygen-hydrocarbon combustion. Task 6: Data analysis and formulation of an empirical model

NASA Technical Reports Server (NTRS)

Makel, Darby B.; Rosenberg, Sanders D.

1990-01-01

The formation and deposition of carbon (soot) was studied in the Carbon Deposition Model for Oxygen-Hydrocarbon Combustion Program. An empirical, 1-D model for predicting soot formation and deposition in LO2/hydrocarbon gas generators/preburners was derived. The experimental data required to anchor the model were identified and a test program to obtain the data was defined. In support of the model development, cold flow mixing experiments using a high injection density injector were performed. The purpose of this investigation was to advance the state-of-the-art in LO2/hydrocarbon gas generator design by developing a reliable engineering model of gas generator operation. The model was formulated to account for the influences of fluid dynamics, chemical kinetics, and gas generator hardware design on soot formation and deposition.

A Point Rainfall Generator With Internal Storm Structure

NASA Astrophysics Data System (ADS)

Marien, J. L.; Vandewiele, G. L.

1986-04-01

A point rainfall generator is a probabilistic model for the time series of rainfall as observed in one geographical point. The main purpose of such a model is to generate long synthetic sequences of rainfall for simulation studies. The present generator is a continuous time model based on 13.5 years of 10-min point rainfalls observed in Belgium and digitized with a resolution of 0.1 mm. The present generator attempts to model all features of the rainfall time series which are important for flood studies as accurately as possible. The original aspects of the model are on the one hand the way in which storms are defined and on the other hand the theoretical model for the internal storm characteristics. The storm definition has the advantage that the important characteristics of successive storms are fully independent and very precisely modelled, even on time bases as small as 10 min. The model of the internal storm characteristics has a strong theoretical structure. This fact justifies better the extrapolation of this model to severe storms for which the data are very sparse. This can be important when using the model to simulate severe flood events.

Description of a computer program and numerical techniques for developing linear perturbation models from nonlinear systems simulations

NASA Technical Reports Server (NTRS)

Dieudonne, J. E.

1978-01-01

A numerical technique was developed which generates linear perturbation models from nonlinear aircraft vehicle simulations. The technique is very general and can be applied to simulations of any system that is described by nonlinear differential equations. The computer program used to generate these models is discussed, with emphasis placed on generation of the Jacobian matrices, calculation of the coefficients needed for solving the perturbation model, and generation of the solution of the linear differential equations. An example application of the technique to a nonlinear model of the NASA terminal configured vehicle is included.

Thermal Texture Generation and 3d Model Reconstruction Using SFM and Gan

NASA Astrophysics Data System (ADS)

Kniaz, V. V.; Mizginov, V. A.

2018-05-01

Realistic 3D models with textures representing thermal emission of the object are widely used in such fields as dynamic scene analysis, autonomous driving, and video surveillance. Structure from Motion (SfM) methods provide a robust approach for the generation of textured 3D models in the visible range. Still, automatic generation of 3D models from the infrared imagery is challenging due to an absence of the feature points and low sensor resolution. Recent advances in Generative Adversarial Networks (GAN) have proved that they can perform complex image-to-image transformations such as a transformation of day to night and generation of imagery in a different spectral range. In this paper, we propose a novel method for generation of realistic 3D models with thermal textures using the SfM pipeline and GAN. The proposed method uses visible range images as an input. The images are processed in two ways. Firstly, they are used for point matching and dense point cloud generation. Secondly, the images are fed into a GAN that performs the transformation from the visible range to the thermal range. We evaluate the proposed method using real infrared imagery captured with a FLIR ONE PRO camera. We generated a dataset with 2000 pairs of real images captured in thermal and visible range. The dataset is used to train the GAN network and to generate 3D models using SfM. The evaluation of the generated 3D models and infrared textures proved that they are similar to the ground truth model in both thermal emissivity and geometrical shape.

Modeling Imperfect Generator Behavior in Power System Operation Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krad, Ibrahim

A key component in power system operations is the use of computer models to quickly study and analyze different operating conditions and futures in an efficient manner. The output of these models are sensitive to the data used in them as well as the assumptions made during their execution. One typical assumption is that generators and load assets perfectly follow operator control signals. While this is a valid simulation assumption, generators may not always accurately follow control signals. This imperfect response of generators could impact cost and reliability metrics. This paper proposes a generator model that capture this imperfect behaviormore » and examines its impact on production costs and reliability metrics using a steady-state power system operations model. Preliminary analysis shows that while costs remain relatively unchanged, there could be significant impacts on reliability metrics.« less

Evaluation and application of site-specific data to revise the first-order decay model for estimating landfill gas generation and emissions at Danish landfills.

PubMed

Mou, Zishen; Scheutz, Charlotte; Kjeldsen, Peter

2015-06-01

Methane (CH₄) generated from low-organic waste degradation at four Danish landfills was estimated by three first-order decay (FOD) landfill gas (LFG) generation models (LandGEM, IPCC, and Afvalzorg). Actual waste data from Danish landfills were applied to fit model (IPCC and Afvalzorg) required categories. In general, the single-phase model, LandGEM, significantly overestimated CH₄generation, because it applied too high default values for key parameters to handle low-organic waste scenarios. The key parameters were biochemical CH₄potential (BMP) and CH₄generation rate constant (k-value). In comparison to the IPCC model, the Afvalzorg model was more suitable for estimating CH₄generation at Danish landfills, because it defined more proper waste categories rather than traditional municipal solid waste (MSW) fractions. Moreover, the Afvalzorg model could better show the influence of not only the total disposed waste amount, but also various waste categories. By using laboratory-determined BMPs and k-values for shredder, sludge, mixed bulky waste, and street-cleaning waste, the Afvalzorg model was revised. The revised model estimated smaller cumulative CH₄generation results at the four Danish landfills (from the start of disposal until 2020 and until 2100). Through a CH₄mass balance approach, fugitive CH₄emissions from whole sites and a specific cell for shredder waste were aggregated based on the revised Afvalzorg model outcomes. Aggregated results were in good agreement with field measurements, indicating that the revised Afvalzorg model could provide practical and accurate estimation for Danish LFG emissions. This study is valuable for both researchers and engineers aiming to predict, control, and mitigate fugitive CH₄emissions from landfills receiving low-organic waste. Landfill operators use the first-order decay (FOD) models to estimate methane (CH₄) generation. A single-phase model (LandGEM) and a traditional model (IPCC) could result in overestimation when handling a low-organic waste scenario. Site-specific data were important and capable of calibrating key parameter values in FOD models. The comparison study of the revised Afvalzorg model outcomes and field measurements at four Danish landfills provided a guideline for revising the Pollutants Release and Transfer Registers (PRTR) model, as well as indicating noteworthy waste fractions that could emit CH₄at modern landfills.

Interactive Model-Centric Systems Engineering (IMCSE) Phase 5

DTIC Science & Technology

2018-02-28

Conducting Program Team Launches ................................................................................................. 12 Informing Policy...research advances knowledge relevant to human interaction with models and model-generated information . Figure 1 highlights several questions the...stakeholders interact using models and model generated information ; facets of human interaction with visualizations and large data sets; and underlying

Observation and Modeling of Tsunami-Generated Gravity Waves in the Earth’s Upper Atmosphere

DTIC Science & Technology

2015-10-08

Observation and modeling of tsunami -generated gravity waves in the earth’s upper atmosphere 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6...ABSTRACT Build a compatible set of models which 1) calculate the spectrum of atmospheric GWs excited by a tsunami (using ocean model data as input...for public release; distribution is unlimited. Observation and modeling of tsunami -generated gravity waves in the earth’s upper atmosphere Sharon

Stochastic Generation of Monthly Rainfall Data

NASA Astrophysics Data System (ADS)

Srikanthan, R.

2009-03-01

Monthly rainfall data is generally needed in the simulation of water resources systems, and in the estimation of water yield from large catchments. Monthly streamflow data generation models are usually applied to generate monthly rainfall data, but this presents problems for most regions, which have significant months of no rainfall. In an earlier study, Srikanthan et al. (J. Hydrol. Eng., ASCE 11(3) (2006) 222-229) recommended the modified method of fragments to disaggregate the annual rainfall data generated by a first-order autoregressive model. The main drawback of this approach is the occurrence of similar patterns when only a short length of historic data is available. Porter and Pink (Hydrol. Water Res. Symp. (1991) 187-191) used synthetic fragments from a Thomas-Fiering monthly model to overcome this drawback. As an alternative, a new two-part monthly model is nested in an annual model to generate monthly rainfall data which preserves both the monthly and annual characteristics. This nested model was applied to generate rainfall data from seven rainfall stations located in eastern and southern parts of Australia, and the results showed that the model performed satisfactorily.

A neuronal network model with simplified tonotopicity for tinnitus generation and its relief by sound therapy.

PubMed

Nagashino, Hirofumi; Kinouchi, Yohsuke; Danesh, Ali A; Pandya, Abhijit S

2013-01-01

Tinnitus is the perception of sound in the ears or in the head where no external source is present. Sound therapy is one of the most effective techniques for tinnitus treatment that have been proposed. In order to investigate mechanisms of tinnitus generation and the clinical effects of sound therapy, we have proposed conceptual and computational models with plasticity using a neural oscillator or a neuronal network model. In the present paper, we propose a neuronal network model with simplified tonotopicity of the auditory system as more detailed structure. In this model an integrate-and-fire neuron model is employed and homeostatic plasticity is incorporated. The computer simulation results show that the present model can show the generation of oscillation and its cessation by external input. It suggests that the present framework is promising as a modeling for the tinnitus generation and the effects of sound therapy.

Generating target system specifications from a domain model using CLIPS

NASA Technical Reports Server (NTRS)

Sugumaran, Vijayan; Gomaa, Hassan; Kerschberg, Larry

1991-01-01

The quest for reuse in software engineering is still being pursued and researchers are actively investigating the domain modeling approach to software construction. There are several domain modeling efforts reported in the literature and they all agree that the components that are generated from domain modeling are more conducive to reuse. Once a domain model is created, several target systems can be generated by tailoring the domain model or by evolving the domain model and then tailoring it according to the specified requirements. This paper presents the Evolutionary Domain Life Cycle (EDLC) paradigm in which a domain model is created using multiple views, namely, aggregation hierarchy, generalization/specialization hierarchies, object communication diagrams and state transition diagrams. The architecture of the Knowledge Based Requirements Elicitation Tool (KBRET) which is used to generate target system specifications is also presented. The preliminary version of KBRET is implemented in the C Language Integrated Production System (CLIPS).

Forward modeling of gravity data using geostatistically generated subsurface density variations

USGS Publications Warehouse

Phelps, Geoffrey

2016-01-01

Using geostatistical models of density variations in the subsurface, constrained by geologic data, forward models of gravity anomalies can be generated by discretizing the subsurface and calculating the cumulative effect of each cell (pixel). The results of such stochastically generated forward gravity anomalies can be compared with the observed gravity anomalies to find density models that match the observed data. These models have an advantage over forward gravity anomalies generated using polygonal bodies of homogeneous density because generating numerous realizations explores a larger region of the solution space. The stochastic modeling can be thought of as dividing the forward model into two components: that due to the shape of each geologic unit and that due to the heterogeneous distribution of density within each geologic unit. The modeling demonstrates that the internally heterogeneous distribution of density within each geologic unit can contribute significantly to the resulting calculated forward gravity anomaly. Furthermore, the stochastic models match observed statistical properties of geologic units, the solution space is more broadly explored by producing a suite of successful models, and the likelihood of a particular conceptual geologic model can be compared. The Vaca Fault near Travis Air Force Base, California, can be successfully modeled as a normal or strike-slip fault, with the normal fault model being slightly more probable. It can also be modeled as a reverse fault, although this structural geologic configuration is highly unlikely given the realizations we explored.

An empirical model for prediction of household solid waste generation rate - A case study of Dhanbad, India.

PubMed

Kumar, Atul; Samadder, S R

2017-10-01

Accurate prediction of the quantity of household solid waste generation is very much essential for effective management of municipal solid waste (MSW). In actual practice, modelling methods are often found useful for precise prediction of MSW generation rate. In this study, two models have been proposed that established the relationships between the household solid waste generation rate and the socioeconomic parameters, such as household size, total family income, education, occupation and fuel used in the kitchen. Multiple linear regression technique was applied to develop the two models, one for the prediction of biodegradable MSW generation rate and the other for non-biodegradable MSW generation rate for individual households of the city Dhanbad, India. The results of the two models showed that the coefficient of determinations (R 2 ) were 0.782 for biodegradable waste generation rate and 0.676 for non-biodegradable waste generation rate using the selected independent variables. The accuracy tests of the developed models showed convincing results, as the predicted values were very close to the observed values. Validation of the developed models with a new set of data indicated a good fit for actual prediction purpose with predicted R 2 values of 0.76 and 0.64 for biodegradable and non-biodegradable MSW generation rate respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

Generative Models of Disfluency

ERIC Educational Resources Information Center

Miller, Timothy A.

2010-01-01

This thesis describes a generative model for representing disfluent phenomena in human speech. This model makes use of observed syntactic structure present in disfluent speech, and uses a right-corner transform on syntax trees to model this structure in a very natural way. Specifically, the phenomenon of speech repair is modeled by explicitly…

Generative electronic background music system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mazurowski, Lukasz

In this short paper-extended abstract the new approach to generation of electronic background music has been presented. The Generative Electronic Background Music System (GEBMS) has been located between other related approaches within the musical algorithm positioning framework proposed by Woller et al. The music composition process is performed by a number of mini-models parameterized by further described properties. The mini-models generate fragments of musical patterns used in output composition. Musical pattern and output generation are controlled by container for the mini-models - a host-model. General mechanism has been presented including the example of the synthesized output compositions.

A statistical approach for generating synthetic tip stress data from limited CPT soundings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Basalams, M.K.

CPT tip stress data obtained from a Uranium mill tailings impoundment are treated as time series. A statistical class of models that was developed to model time series is explored to investigate its applicability in modeling the tip stress series. These models were developed by Box and Jenkins (1970) and are known as Autoregressive Moving Average (ARMA) models. This research demonstrates how to apply the ARMA models to tip stress series. Generation of synthetic tip stress series that preserve the main statistical characteristics of the measured series is also investigated. Multiple regression analysis is used to model the regional variationmore » of the ARMA model parameters as well as the regional variation of the mean and the standard deviation of the measured tip stress series. The reliability of the generated series is investigated from a geotechnical point of view as well as from a statistical point of view. Estimation of the total settlement using the measured and the generated series subjected to the same loading condition are performed. The variation of friction angle with depth of the impoundment materials is also investigated. This research shows that these series can be modeled by the Box and Jenkins ARMA models. A third degree Autoregressive model AR(3) is selected to represent these series. A theoretical double exponential density function is fitted to the AR(3) model residuals. Synthetic tip stress series are generated at nearby locations. The generated series are shown to be reliable in estimating the total settlement and the friction angle variation with depth for this particular site.« less

Modeling long correlation times using additive binary Markov chains: Applications to wind generation time series.

PubMed

Weber, Juliane; Zachow, Christopher; Witthaut, Dirk

2018-03-01

Wind power generation exhibits a strong temporal variability, which is crucial for system integration in highly renewable power systems. Different methods exist to simulate wind power generation but they often cannot represent the crucial temporal fluctuations properly. We apply the concept of additive binary Markov chains to model a wind generation time series consisting of two states: periods of high and low wind generation. The only input parameter for this model is the empirical autocorrelation function. The two-state model is readily extended to stochastically reproduce the actual generation per period. To evaluate the additive binary Markov chain method, we introduce a coarse model of the electric power system to derive backup and storage needs. We find that the temporal correlations of wind power generation, the backup need as a function of the storage capacity, and the resting time distribution of high and low wind events for different shares of wind generation can be reconstructed.

Modeling long correlation times using additive binary Markov chains: Applications to wind generation time series

NASA Astrophysics Data System (ADS)

Weber, Juliane; Zachow, Christopher; Witthaut, Dirk

2018-03-01

Wind power generation exhibits a strong temporal variability, which is crucial for system integration in highly renewable power systems. Different methods exist to simulate wind power generation but they often cannot represent the crucial temporal fluctuations properly. We apply the concept of additive binary Markov chains to model a wind generation time series consisting of two states: periods of high and low wind generation. The only input parameter for this model is the empirical autocorrelation function. The two-state model is readily extended to stochastically reproduce the actual generation per period. To evaluate the additive binary Markov chain method, we introduce a coarse model of the electric power system to derive backup and storage needs. We find that the temporal correlations of wind power generation, the backup need as a function of the storage capacity, and the resting time distribution of high and low wind events for different shares of wind generation can be reconstructed.

The Model Averaging for Dichotomous Response Benchmark Dose (MADr-BMD) Tool

EPA Pesticide Factsheets

Providing quantal response models, which are also used in the U.S. EPA benchmark dose software suite, and generates a model-averaged dose response model to generate benchmark dose and benchmark dose lower bound estimates.

Controls/CFD Interdisciplinary Research Software Generates Low-Order Linear Models for Control Design From Steady-State CFD Results

NASA Technical Reports Server (NTRS)

Melcher, Kevin J.

1997-01-01

The NASA Lewis Research Center is developing analytical methods and software tools to create a bridge between the controls and computational fluid dynamics (CFD) disciplines. Traditionally, control design engineers have used coarse nonlinear simulations to generate information for the design of new propulsion system controls. However, such traditional methods are not adequate for modeling the propulsion systems of complex, high-speed vehicles like the High Speed Civil Transport. To properly model the relevant flow physics of high-speed propulsion systems, one must use simulations based on CFD methods. Such CFD simulations have become useful tools for engineers that are designing propulsion system components. The analysis techniques and software being developed as part of this effort are an attempt to evolve CFD into a useful tool for control design as well. One major aspect of this research is the generation of linear models from steady-state CFD results. CFD simulations, often used during the design of high-speed inlets, yield high resolution operating point data. Under a NASA grant, the University of Akron has developed analytical techniques and software tools that use these data to generate linear models for control design. The resulting linear models have the same number of states as the original CFD simulation, so they are still very large and computationally cumbersome. Model reduction techniques have been successfully applied to reduce these large linear models by several orders of magnitude without significantly changing the dynamic response. The result is an accurate, easy to use, low-order linear model that takes less time to generate than those generated by traditional means. The development of methods for generating low-order linear models from steady-state CFD is most complete at the one-dimensional level, where software is available to generate models with different kinds of input and output variables. One-dimensional methods have been extended somewhat so that linear models can also be generated from two- and three-dimensional steady-state results. Standard techniques are adequate for reducing the order of one-dimensional CFD-based linear models. However, reduction of linear models based on two- and three-dimensional CFD results is complicated by very sparse, ill-conditioned matrices. Some novel approaches are being investigated to solve this problem.

A step-by-step development of real-size chest model for simulation of thoracoscopic surgery.

PubMed

Morikawa, Toshiaki; Yamashita, Makoto; Odaka, Makoto; Tsukamoto, Yo; Shibasaki, Takamasa; Mori, Shohei; Asano, Hisatoshi; Akiba, Tadashi

2017-08-01

For the purpose of simulating thoracoscopic surgery, we have conducted stepwise development of a life-like chest model including thorax and intrathoracic organs. First, CT data of the human chest were obtained. First-generation model: based on the CT data, each component of the chest was made from a 3D printer. A hard resin was used for the bony thorax and a rubber-like resin for the vessels and bronchi. Lung parenchyma, muscles and skin were not created. Second-generation model: in addition to the 3D printer, a cast moulding method was used. Each part was casted using a 3D printed master and then assembled. The vasculature and bronchi were casted using silicon resin. The lung parenchyma and mediastinum organs were casted using urethane foam. Chest wall and bony thorax were also casted using a silicon resin. Third-generation model: foamed polyvinyl alcohol (PVA) was newly developed and casted onto the lung parenchyma. The vasculature and bronchi were developed using a soft resin. A PVA plate was made as the mediastinum, and all were combined. The first-generation model showed real distribution of the vasculature and bronchi; it enabled an understanding of the anatomy within the lung. The second-generation model is a total chest dry model, which enabled observation of the total anatomy of the organs and thorax. The third-generation model is a wet organ model. It allowed for realistic simulation of surgical procedures, such as cutting, suturing, stapling and energy device use. This single-use model achieved realistic simulation of thoracoscopic surgery. As the generation advances, the model provides a more realistic simulation of thoracoscopic surgery. Further improvement of the model is needed. © The Author 2017. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Overpressures in the Uinta Basin, Utah: Analysis using a three-dimensional basin evolution model

NASA Astrophysics Data System (ADS)

McPherson, Brian J. O. L.; Bredehoeft, John D.

2001-04-01

High pore fluid pressures, approaching lithostatic, are observed in the deepest sections of the Uinta basin, Utah. Geologic observations and previous modeling studies suggest that the most likely cause of observed overpressures is hydrocarbon generation. We studied Uinta overpressures by developing and applying a three-dimensional, numerical model of the evolution of the basin. The model was developed from a public domain computer code, with addition of a new mesh generator that builds the basin through time, coupling the structural, thermal, and hydrodynamic evolution. Also included in the model are in situ hydrocarbon generation and multiphase migration. The modeling study affirmed oil generation as an overpressure mechanism, but also elucidated the relative roles of multiphase fluid interaction, oil density and viscosity, and sedimentary compaction. An important result is that overpressures by oil generation create conditions for rock fracturing, and associated fracture permeability may regulate or control the propensity to maintain overpressures.

Graphical Modeling of Shipboard Electric Power Distribution Systems

DTIC Science & Technology

1993-12-01

examined. A means of modeling a load for a synchronous generator is then shown which accurately interrelates the loading of the generator and the...frequency and voltage output of the machine. This load is then connected to the synchronous generator and two different scenarios are examined including a...examined. A means of modeling a load for a synchronous generator is then shown which accurately interrelates the loading of the generator and tht

Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics

PubMed Central

Schwartz, Odelia; Sejnowski, Terrence J.; Dayan, Peter

2010-01-01

Gaussian scale mixture models offer a top-down description of signal generation that captures key bottom-up statistical characteristics of filter responses to images. However, the pattern of dependence among the filters for this class of models is prespecified. We propose a novel extension to the gaussian scale mixture model that learns the pattern of dependence from observed inputs and thereby induces a hierarchical representation of these inputs. Specifically, we propose that inputs are generated by gaussian variables (modeling local filter structure), multiplied by a mixer variable that is assigned probabilistically to each input from a set of possible mixers. We demonstrate inference of both components of the generative model, for synthesized data and for different classes of natural images, such as a generic ensemble and faces. For natural images, the mixer variable assignments show invariances resembling those of complex cells in visual cortex; the statistics of the gaussian components of the model are in accord with the outputs of divisive normalization models. We also show how our model helps interrelate a wide range of models of image statistics and cortical processing. PMID:16999575

Modeling Vortex Generators in a Navier-Stokes Code

NASA Technical Reports Server (NTRS)

Dudek, Julianne C.

2011-01-01

A source-term model that simulates the effects of vortex generators was implemented into the Wind-US Navier-Stokes code. The source term added to the Navier-Stokes equations simulates the lift force that would result from a vane-type vortex generator in the flowfield. The implementation is user-friendly, requiring the user to specify only three quantities for each desired vortex generator: the range of grid points over which the force is to be applied and the planform area and angle of incidence of the physical vane. The model behavior was evaluated for subsonic flow in a rectangular duct with a single vane vortex generator, subsonic flow in an S-duct with 22 corotating vortex generators, and supersonic flow in a rectangular duct with a counter-rotating vortex-generator pair. The model was also used to successfully simulate microramps in supersonic flow by treating each microramp as a pair of vanes with opposite angles of incidence. The validation results indicate that the source-term vortex-generator model provides a useful tool for screening vortex-generator configurations and gives comparable results to solutions computed using gridded vanes.

Testing Strategies for Model-Based Development

NASA Technical Reports Server (NTRS)

Heimdahl, Mats P. E.; Whalen, Mike; Rajan, Ajitha; Miller, Steven P.

2006-01-01

This report presents an approach for testing artifacts generated in a model-based development process. This approach divides the traditional testing process into two parts: requirements-based testing (validation testing) which determines whether the model implements the high-level requirements and model-based testing (conformance testing) which determines whether the code generated from a model is behaviorally equivalent to the model. The goals of the two processes differ significantly and this report explores suitable testing metrics and automation strategies for each. To support requirements-based testing, we define novel objective requirements coverage metrics similar to existing specification and code coverage metrics. For model-based testing, we briefly describe automation strategies and examine the fault-finding capability of different structural coverage metrics using tests automatically generated from the model.

Generating Variable Wind Profiles and Modeling Their Effects on Small-Arms Trajectories

DTIC Science & Technology

2016-04-01

ARL-TR-7642 ● APR 2016 US Army Research Laboratory Generating Variable Wind Profiles and Modeling Their Effects on Small-Arms... Wind Profiles and Modeling Their Effects on Small-Arms Trajectories by Timothy A Fargus Weapons and Materials Research Directorate, ARL...Generating Variable Wind Profiles and Modeling Their Effects on Small-Arms Trajectories 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM

Learning Orthographic Structure With Sequential Generative Neural Networks.

PubMed

Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

2016-04-01

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.

Comparison of the Battery Life of Nonrechargeable Generators for Deep Brain Stimulation.

PubMed

Helmers, Ann-Kristin; Lübbing, Isabel; Deuschl, Günther; Witt, Karsten; Synowitz, Michael; Mehdorn, Hubertus Maximilian; Falk, Daniela

2017-11-03

Nonrechargeable deep brain stimulation (DBS) generators must be replaced when the battery capacity is exhausted. Battery life depends on many factors and differs between generator models. A new nonrechargeable generator model replaced the previous model in 2008. Our clinical impression is that the earlier model had a longer battery life than the new one. We conducted this study to substantiate this. We determined the battery life of every DBS generator that had been implanted between 2005 and 2012 in our department for the treatment of Parkinson's disease, and compared the battery lives of the both devices. We calculated the current used by estimating the total electrical energy delivered (TEED) based on the stimulation parameters in use one year after electrode implantation. One hundred ninety-two patients were included in the study; 105 with the old and 86 with the new model generators. The mean battery life in the older model was significantly longer (5.44 ± 0.20 years) than that in the new model (4.44 ± 0.17 years) (p = 0.023). The mean TEED without impedance was 219.9 ± 121.5 mW * Ω in the older model and 145.1 ± 72.7 mW * Ω in the new one, which indicated significantly lower stimulation parameters in the new model (p = 0.00038). The battery life of the new model was significantly shorter than that of the previous model. A lower battery capacity is the most likely reason, since current consumption was similar in both groups. © 2017 International Neuromodulation Society.

Applying Hierarchical Model Calibration to Automatically Generated Items.

ERIC Educational Resources Information Center

Williamson, David M.; Johnson, Matthew S.; Sinharay, Sandip; Bejar, Isaac I.

This study explored the application of hierarchical model calibration as a means of reducing, if not eliminating, the need for pretesting of automatically generated items from a common item model prior to operational use. Ultimately the successful development of automatic item generation (AIG) systems capable of producing items with highly similar…

Multi-Scale Human Respiratory System Simulations to Study Health Effects of Aging, Disease, and Inhaled Substances

NASA Astrophysics Data System (ADS)

Kunz, Robert; Haworth, Daniel; Dogan, Gulkiz; Kriete, Andres

2006-11-01

Three-dimensional, unsteady simulations of multiphase flow, gas exchange, and particle/aerosol deposition in the human lung are reported. Surface data for human tracheo-bronchial trees are derived from CT scans, and are used to generate three- dimensional CFD meshes for the first several generations of branching. One-dimensional meshes for the remaining generations down to the respiratory units are generated using branching algorithms based on those that have been proposed in the literature, and a zero-dimensional respiratory unit (pulmonary acinus) model is attached at the end of each terminal bronchiole. The process is automated to facilitate rapid model generation. The model is exercised through multiple breathing cycles to compute the spatial and temporal variations in flow, gas exchange, and particle/aerosol deposition. The depth of the 3D/1D transition (at branching generation n) is a key parameter, and can be varied. High-fidelity models (large n) are run on massively parallel distributed-memory clusters, and are used to generate physical insight and to calibrate/validate the 1D and 0D models. Suitably validated lower-order models (small n) can be run on single-processor PC’s with run times that allow model-based clinical intervention for individual patients.

Comparison of Computational-Model and Experimental-Example Trained Neural Networks for Processing Speckled Fringe Patterns

NASA Technical Reports Server (NTRS)

Decker, A. J.; Fite, E. B.; Thorp, S. A.; Mehmed, O.

1998-01-01

The responses of artificial neural networks to experimental and model-generated inputs are compared for detection of damage in twisted fan blades using electronic holography. The training-set inputs, for this work, are experimentally generated characteristic patterns of the vibrating blades. The outputs are damage-flag indicators or second derivatives of the sensitivity-vector-projected displacement vectors from a finite element model. Artificial neural networks have been trained in the past with computational-model-generated training sets. This approach avoids the difficult inverse calculations traditionally used to compare interference fringes with the models. But the high modeling standards are hard to achieve, even with fan-blade finite-element models.

Comparison of Computational, Model and Experimental, Example Trained Neural Networks for Processing Speckled Fringe Patterns

NASA Technical Reports Server (NTRS)

Decker, A. J.; Fite, E. B.; Thorp, S. A.; Mehmed, O.

1998-01-01

The responses of artificial neural networks to experimental and model-generated inputs are compared for detection of damage in twisted fan blades using electronic holography. The training-set inputs, for this work, are experimentally generated characteristic patterns of the vibrating blades. The outputs are damage-flag indicators or second derivatives of the sensitivity-vector-projected displacement vectors from a finite element model. Artificial neural networks have been trained in the past with computational-model- generated training sets. This approach avoids the difficult inverse calculations traditionally used to compare interference fringes with the models. But the high modeling standards are hard to achieve, even with fan-blade finite-element models.

Model Checking Abstract PLEXIL Programs with SMART

NASA Technical Reports Server (NTRS)

Siminiceanu, Radu I.

2007-01-01

We describe a method to automatically generate discrete-state models of abstract Plan Execution Interchange Language (PLEXIL) programs that can be analyzed using model checking tools. Starting from a high-level description of a PLEXIL program or a family of programs with common characteristics, the generator lays the framework that models the principles of program execution. The concrete parts of the program are not automatically generated, but require the modeler to introduce them by hand. As a case study, we generate models to verify properties of the PLEXIL macro constructs that are introduced as shorthand notation. After an exhaustive analysis, we conclude that the macro definitions obey the intended semantics and behave as expected, but contingently on a few specific requirements on the timing semantics of micro-steps in the concrete executive implementation.

Generating Phenotypical Erroneous Human Behavior to Evaluate Human-automation Interaction Using Model Checking

PubMed Central

Bolton, Matthew L.; Bass, Ellen J.; Siminiceanu, Radu I.

2012-01-01

Breakdowns in complex systems often occur as a result of system elements interacting in unanticipated ways. In systems with human operators, human-automation interaction associated with both normative and erroneous human behavior can contribute to such failures. Model-driven design and analysis techniques provide engineers with formal methods tools and techniques capable of evaluating how human behavior can contribute to system failures. This paper presents a novel method for automatically generating task analytic models encompassing both normative and erroneous human behavior from normative task models. The generated erroneous behavior is capable of replicating Hollnagel’s zero-order phenotypes of erroneous action for omissions, jumps, repetitions, and intrusions. Multiple phenotypical acts can occur in sequence, thus allowing for the generation of higher order phenotypes. The task behavior model pattern capable of generating erroneous behavior can be integrated into a formal system model so that system safety properties can be formally verified with a model checker. This allows analysts to prove that a human-automation interactive system (as represented by the model) will or will not satisfy safety properties with both normative and generated erroneous human behavior. We present benchmarks related to the size of the statespace and verification time of models to show how the erroneous human behavior generation process scales. We demonstrate the method with a case study: the operation of a radiation therapy machine. A potential problem resulting from a generated erroneous human action is discovered. A design intervention is presented which prevents this problem from occurring. We discuss how our method could be used to evaluate larger applications and recommend future paths of development. PMID:23105914

Model-based software design

NASA Technical Reports Server (NTRS)

Iscoe, Neil; Liu, Zheng-Yang; Feng, Guohui; Yenne, Britt; Vansickle, Larry; Ballantyne, Michael

1992-01-01

Domain-specific knowledge is required to create specifications, generate code, and understand existing systems. Our approach to automating software design is based on instantiating an application domain model with industry-specific knowledge and then using that model to achieve the operational goals of specification elicitation and verification, reverse engineering, and code generation. Although many different specification models can be created from any particular domain model, each specification model is consistent and correct with respect to the domain model.

Spherical harmonic analysis of a synoptic climatology generated with a global general circulation model

NASA Technical Reports Server (NTRS)

Christidis, Z. D.; Spar, J.

1980-01-01

Spherical harmonic analysis was used to analyze the observed climatological (C) fields of temperature at 850 mb, geopotential height at 500 mb, and sea level pressure. The spherical harmonic method was also applied to the corresponding "model climatological" fields (M) generated by a general circulation model, the "GISS climate model." The climate model was initialized with observed data for the first of December 1976 at 00. GMT and allowed to generate five years of meteorological history. Monthly means of the above fields for the five years were computed and subjected to spherical harmonic analysis. It was found from the comparison of the spectral components of both sets, M and C, that the climate model generated reasonable 500 mb geopotential heights. The model temperature field at 850 mb exhibited a generally correct structure. However, the meridional temperature gradient was overestimated and overheating of the continents was observed in summer.

Pitching motion control of a butterfly-like 3D flapping wing-body model

NASA Astrophysics Data System (ADS)

Suzuki, Kosuke; Minami, Keisuke; Inamuro, Takaji

2014-11-01

Free flights and a pitching motion control of a butterfly-like flapping wing-body model are numerically investigated by using an immersed boundary-lattice Boltzmann method. The model flaps downward for generating the lift force and backward for generating the thrust force. Although the model can go upward against the gravity by the generated lift force, the model generates the nose-up torque, consequently gets off-balance. In this study, we discuss a way to control the pitching motion by flexing the body of the wing-body model like an actual butterfly. The body of the model is composed of two straight rigid rod connected by a rotary actuator. It is found that the pitching angle is suppressed in the range of +/-5° by using the proportional-plus-integral-plus-derivative (PID) control for the input torque of the rotary actuator.

Bootstrap data methodology for sequential hybrid model building

NASA Technical Reports Server (NTRS)

Volponi, Allan J. (Inventor); Brotherton, Thomas (Inventor)

2007-01-01

A method for modeling engine operation comprising the steps of: 1. collecting a first plurality of sensory data, 2. partitioning a flight envelope into a plurality of sub-regions, 3. assigning the first plurality of sensory data into the plurality of sub-regions, 4. generating an empirical model of at least one of the plurality of sub-regions, 5. generating a statistical summary model for at least one of the plurality of sub-regions, 6. collecting an additional plurality of sensory data, 7. partitioning the second plurality of sensory data into the plurality of sub-regions, 8. generating a plurality of pseudo-data using the empirical model, and 9. concatenating the plurality of pseudo-data and the additional plurality of sensory data to generate an updated empirical model and an updated statistical summary model for at least one of the plurality of sub-regions.

River Devices to Recover Energy with Advanced Materials (River DREAM)

DOE Office of Scientific and Technical Information (OSTI.GOV)

McMahon, Daniel P.

2013-07-03

The purpose of this project is to develop a generator called a Galloping Hydroelectric Energy Extraction Device (GHEED). It uses a galloping prism to convert water flow into linear motion. This motion is converted into electricity via a dielectric elastomer generator (DEG). The galloping mechanism and the DEG are combined to create a system to effectively generate electricity. This project has three research objectives: 1. Oscillator development and design a. Characterize galloping behavior, evaluate control surface shape change on oscillator performance and demonstrate shape change with water flow change. 2. Dielectric Energy Generator (DEG) characterization and modeling a. Characterize andmore » model the performance of the DEG based on oscillator design 3. Galloping Hydroelectric Energy Extraction Device (GHEED) system modeling and integration a. Create numerical models for construction of a system performance model and define operating capabilities for this approach Accomplishing these three objectives will result in the creation of a model that can be used to fully define the operating parameters and performance capabilities of a generator based on the GHEED design. This information will be used in the next phase of product development, the creation of an integrated laboratory scale generator to confirm model predictions.« less

Nodal network generator for CAVE3

NASA Technical Reports Server (NTRS)

Palmieri, J. V.; Rathjen, K. A.

1982-01-01

A new extension of CAVE3 code was developed that automates the creation of a finite difference math model in digital form ready for input to the CAVE3 code. The new software, Nodal Network Generator, is broken into two segments. One segment generates the model geometry using a Tektronix Tablet Digitizer and the other generates the actual finite difference model and allows for graphic verification using Tektronix 4014 Graphic Scope. Use of the Nodal Network Generator is described.

A nonlinear autoregressive Volterra model of the Hodgkin-Huxley equations.

PubMed

Eikenberry, Steffen E; Marmarelis, Vasilis Z

2013-02-01

We propose a new variant of Volterra-type model with a nonlinear auto-regressive (NAR) component that is a suitable framework for describing the process of AP generation by the neuron membrane potential, and we apply it to input-output data generated by the Hodgkin-Huxley (H-H) equations. Volterra models use a functional series expansion to describe the input-output relation for most nonlinear dynamic systems, and are applicable to a wide range of physiologic systems. It is difficult, however, to apply the Volterra methodology to the H-H model because is characterized by distinct subthreshold and suprathreshold dynamics. When threshold is crossed, an autonomous action potential (AP) is generated, the output becomes temporarily decoupled from the input, and the standard Volterra model fails. Therefore, in our framework, whenever membrane potential exceeds some threshold, it is taken as a second input to a dual-input Volterra model. This model correctly predicts membrane voltage deflection both within the subthreshold region and during APs. Moreover, the model naturally generates a post-AP afterpotential and refractory period. It is known that the H-H model converges to a limit cycle in response to a constant current injection. This behavior is correctly predicted by the proposed model, while the standard Volterra model is incapable of generating such limit cycle behavior. The inclusion of cross-kernels, which describe the nonlinear interactions between the exogenous and autoregressive inputs, is found to be absolutely necessary. The proposed model is general, non-parametric, and data-derived.

Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions

PubMed Central

Testolin, Alberto; Zorzi, Marco

2016-01-01

Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage. PMID:27468262

Koopman Operator Framework for Time Series Modeling and Analysis

NASA Astrophysics Data System (ADS)

Surana, Amit

2018-01-01

We propose an interdisciplinary framework for time series classification, forecasting, and anomaly detection by combining concepts from Koopman operator theory, machine learning, and linear systems and control theory. At the core of this framework is nonlinear dynamic generative modeling of time series using the Koopman operator which is an infinite-dimensional but linear operator. Rather than working with the underlying nonlinear model, we propose two simpler linear representations or model forms based on Koopman spectral properties. We show that these model forms are invariants of the generative model and can be readily identified directly from data using techniques for computing Koopman spectral properties without requiring the explicit knowledge of the generative model. We also introduce different notions of distances on the space of such model forms which is essential for model comparison/clustering. We employ the space of Koopman model forms equipped with distance in conjunction with classical machine learning techniques to develop a framework for automatic feature generation for time series classification. The forecasting/anomaly detection framework is based on using Koopman model forms along with classical linear systems and control approaches. We demonstrate the proposed framework for human activity classification, and for time series forecasting/anomaly detection in power grid application.

Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions.

PubMed

Testolin, Alberto; Zorzi, Marco

2016-01-01

Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage.

The new Kuznets cycle: a test of the Easterlin-Wachter-Wachter hypothesis.

PubMed

Ahlburg, D A

1982-01-01

The aim of this paper is to evaluate the Easterlin-Wachter-Wachter model of the effect of the size of one generation on the size of the succeeding generation. An attempt is made "to identify and test empirically each component of the Easterlin-Wachter-Wachter model..., to show how the components collapse to give a closed demographic model of generation size, and to investigate the impacts of relative cohort size on the economic performance of a cohort." The models derived are then used to generate forecasts of the U.S. birth rate to the year 2050. The results provide support for the major components of the original model. excerpt

Distributed Generation Market Demand Model | NREL

Science.gov Websites

Demand Model The Distributed Generation Market Demand (dGen) model simulates the potential adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the dGen model can help develop deployment forecasts for distributed resources, including sensitivity to

Aspects of Mathematical Modelling of Pressure Retarded Osmosis

PubMed Central

Anissimov, Yuri G.

2016-01-01

In power generating terms, a pressure retarded osmosis (PRO) energy generating plant, on a river entering a sea or ocean, is equivalent to a hydroelectric dam with a height of about 60 meters. Therefore, PRO can add significantly to existing renewable power generation capacity if economical constrains of the method are resolved. PRO energy generation relies on a semipermeable membrane that is permeable to water and impermeable to salt. Mathematical modelling plays an important part in understanding flows of water and salt near and across semipermeable membranes and helps to optimize PRO energy generation. Therefore, the modelling can help realizing PRO energy generation potential. In this work, a few aspects of mathematical modelling of the PRO process are reviewed and discussed. PMID:26848696

A CellML simulation compiler and code generator using ODE solving schemes

PubMed Central

2012-01-01

Models written in description languages such as CellML are becoming a popular solution to the handling of complex cellular physiological models in biological function simulations. However, in order to fully simulate a model, boundary conditions and ordinary differential equation (ODE) solving schemes have to be combined with it. Though boundary conditions can be described in CellML, it is difficult to explicitly specify ODE solving schemes using existing tools. In this study, we define an ODE solving scheme description language-based on XML and propose a code generation system for biological function simulations. In the proposed system, biological simulation programs using various ODE solving schemes can be easily generated. We designed a two-stage approach where the system generates the equation set associating the physiological model variable values at a certain time t with values at t + Δt in the first stage. The second stage generates the simulation code for the model. This approach enables the flexible construction of code generation modules that can support complex sets of formulas. We evaluate the relationship between models and their calculation accuracies by simulating complex biological models using various ODE solving schemes. Using the FHN model simulation, results showed good qualitative and quantitative correspondence with the theoretical predictions. Results for the Luo-Rudy 1991 model showed that only first order precision was achieved. In addition, running the generated code in parallel on a GPU made it possible to speed up the calculation time by a factor of 50. The CellML Compiler source code is available for download at http://sourceforge.net/projects/cellmlcompiler. PMID:23083065

Combined electrochemical, heat generation, and thermal model for large prismatic lithium-ion batteries in real-time applications

NASA Astrophysics Data System (ADS)

Farag, Mohammed; Sweity, Haitham; Fleckenstein, Matthias; Habibi, Saeid

2017-08-01

Real-time prediction of the battery's core temperature and terminal voltage is very crucial for an accurate battery management system. In this paper, a combined electrochemical, heat generation, and thermal model is developed for large prismatic cells. The proposed model consists of three sub-models, an electrochemical model, heat generation model, and thermal model which are coupled together in an iterative fashion through physicochemical temperature dependent parameters. The proposed parameterization cycles identify the sub-models' parameters separately by exciting the battery under isothermal and non-isothermal operating conditions. The proposed combined model structure shows accurate terminal voltage and core temperature prediction at various operating conditions while maintaining a simple mathematical structure, making it ideal for real-time BMS applications. Finally, the model is validated against both isothermal and non-isothermal drive cycles, covering a broad range of C-rates, and temperature ranges [-25 °C to 45 °C].

Application for managing model-based material properties for simulation-based engineering

DOEpatents

Hoffman, Edward L [Alameda, CA

2009-03-03

An application for generating a property set associated with a constitutive model of a material includes a first program module adapted to receive test data associated with the material and to extract loading conditions from the test data. A material model driver is adapted to receive the loading conditions and a property set and operable in response to the loading conditions and the property set to generate a model response for the material. A numerical optimization module is adapted to receive the test data and the model response and operable in response to the test data and the model response to generate the property set.

Method of performing computational aeroelastic analyses

NASA Technical Reports Server (NTRS)

Silva, Walter A. (Inventor)

2011-01-01

Computational aeroelastic analyses typically use a mathematical model for the structural modes of a flexible structure and a nonlinear aerodynamic model that can generate a plurality of unsteady aerodynamic responses based on the structural modes for conditions defining an aerodynamic condition of the flexible structure. In the present invention, a linear state-space model is generated using a single execution of the nonlinear aerodynamic model for all of the structural modes where a family of orthogonal functions is used as the inputs. Then, static and dynamic aeroelastic solutions are generated using computational interaction between the mathematical model and the linear state-space model for a plurality of periodic points in time.

A method to generate small-scale, high-resolution sedimentary bedform architecture models representing realistic geologic facies

DOE PAGES

Meckel, T. A.; Trevisan, L.; Krishnamurthy, P. G.

2017-08-23

Small-scale (mm to m) sedimentary structures (e.g. ripple lamination, cross-bedding) have received a great deal of attention in sedimentary geology. The influence of depositional heterogeneity on subsurface fluid flow is now widely recognized, but incorporating these features in physically-rational bedform models at various scales remains problematic. The current investigation expands the capability of an existing set of open-source codes, allowing generation of high-resolution 3D bedform architecture models. The implemented modifications enable the generation of 3D digital models consisting of laminae and matrix (binary field) with characteristic depositional architecture. The binary model is then populated with petrophysical properties using a texturalmore » approach for additional analysis such as statistical characterization, property upscaling, and single and multiphase fluid flow simulation. One example binary model with corresponding threshold capillary pressure field and the scripts used to generate them are provided, but the approach can be used to generate dozens of previously documented common facies models and a variety of property assignments. An application using the example model is presented simulating buoyant fluid (CO 2) migration and resulting saturation distribution.« less

A method to generate small-scale, high-resolution sedimentary bedform architecture models representing realistic geologic facies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meckel, T. A.; Trevisan, L.; Krishnamurthy, P. G.

Small-scale (mm to m) sedimentary structures (e.g. ripple lamination, cross-bedding) have received a great deal of attention in sedimentary geology. The influence of depositional heterogeneity on subsurface fluid flow is now widely recognized, but incorporating these features in physically-rational bedform models at various scales remains problematic. The current investigation expands the capability of an existing set of open-source codes, allowing generation of high-resolution 3D bedform architecture models. The implemented modifications enable the generation of 3D digital models consisting of laminae and matrix (binary field) with characteristic depositional architecture. The binary model is then populated with petrophysical properties using a texturalmore » approach for additional analysis such as statistical characterization, property upscaling, and single and multiphase fluid flow simulation. One example binary model with corresponding threshold capillary pressure field and the scripts used to generate them are provided, but the approach can be used to generate dozens of previously documented common facies models and a variety of property assignments. An application using the example model is presented simulating buoyant fluid (CO 2) migration and resulting saturation distribution.« less

Stochastic Hourly Weather Generator HOWGH: Validation and its Use in Pest Modelling under Present and Future Climates

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Hirschi, M.; Spirig, C.

2014-12-01

To quantify impact of the climate change on a specific pest (or any weather-dependent process) in a specific site, we may use a site-calibrated pest (or other) model and compare its outputs obtained with site-specific weather data representing present vs. perturbed climates. The input weather data may be produced by the stochastic weather generator. Apart from the quality of the pest model, the reliability of the results obtained in such experiment depend on an ability of the generator to represent the statistical structure of the real world weather series, and on the sensitivity of the pest model to possible imperfections of the generator. This contribution deals with the multivariate HOWGH weather generator, which is based on a combination of parametric and non-parametric statistical methods. Here, HOWGH is used to generate synthetic hourly series of three weather variables (solar radiation, temperature and precipitation) required by a dynamic pest model SOPRA to simulate the development of codling moth. The contribution presents results of the direct and indirect validation of HOWGH. In the direct validation, the synthetic series generated by HOWGH (various settings of its underlying model are assumed) are validated in terms of multiple climatic characteristics, focusing on the subdaily wet/dry and hot/cold spells. In the indirect validation, we assess the generator in terms of characteristics derived from the outputs of SOPRA model fed by the observed vs. synthetic series. The weather generator may be used to produce weather series representing present and future climates. In the latter case, the parameters of the generator may be modified by the climate change scenarios based on Global or Regional Climate Models. To demonstrate this feature, the results of codling moth simulations for future climate will be shown. Acknowledgements: The weather generator is developed and validated within the frame of projects WG4VALUE (project LD12029 sponsored by the Ministry of Education, Youth and Sports of CR), and VALUE (COST ES 1102 action).

Pharmacophore-Map-Pick: A Method to Generate Pharmacophore Models for All Human GPCRs.

PubMed

Dai, Shao-Xing; Li, Gong-Hua; Gao, Yue-Dong; Huang, Jing-Fei

2016-02-01

GPCR-based drug discovery is hindered by a lack of effective screening methods for most GPCRs that have neither ligands nor high-quality structures. With the aim to identify lead molecules for these GPCRs, we developed a new method called Pharmacophore-Map-Pick to generate pharmacophore models for all human GPCRs. The model of ADRB2 generated using this method not only predicts the binding mode of ADRB2-ligands correctly but also performs well in virtual screening. Findings also demonstrate that this method is powerful for generating high-quality pharmacophore models. The average enrichment for the pharmacophore models of the 15 targets in different GPCR families reached 15-fold at 0.5 % false-positive rate. Therefore, the pharmacophore models can be applied in virtual screening directly with no requirement for any ligand information or shape constraints. A total of 2386 pharmacophore models for 819 different GPCRs (99 % coverage (819/825)) were generated and are available at http://bsb.kiz.ac.cn/GPCRPMD. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Building energy analysis tool

DOEpatents

Brackney, Larry; Parker, Andrew; Long, Nicholas; Metzger, Ian; Dean, Jesse; Lisell, Lars

2016-04-12

A building energy analysis system includes a building component library configured to store a plurality of building components, a modeling tool configured to access the building component library and create a building model of a building under analysis using building spatial data and using selected building components of the plurality of building components stored in the building component library, a building analysis engine configured to operate the building model and generate a baseline energy model of the building under analysis and further configured to apply one or more energy conservation measures to the baseline energy model in order to generate one or more corresponding optimized energy models, and a recommendation tool configured to assess the one or more optimized energy models against the baseline energy model and generate recommendations for substitute building components or modifications.

Learning as a Generative Process

ERIC Educational Resources Information Center

Wittrock, M. C.

2010-01-01

A cognitive model of human learning with understanding is introduced. Empirical research supporting the model, which is called the generative model, is summarized. The model is used to suggest a way to integrate some of the research in cognitive development, human learning, human abilities, information processing, and aptitude-treatment…

Modeling Natural Selection

ERIC Educational Resources Information Center

Bogiages, Christopher A.; Lotter, Christine

2011-01-01

In their research, scientists generate, test, and modify scientific models. These models can be shared with others and demonstrate a scientist's understanding of how the natural world works. Similarly, students can generate and modify models to gain a better understanding of the content, process, and nature of science (Kenyon, Schwarz, and Hug…

Research on Generating Method of Embedded Software Test Document Based on Dynamic Model

NASA Astrophysics Data System (ADS)

Qu, MingCheng; Wu, XiangHu; Tao, YongChao; Liu, Ying

2018-03-01

This paper provides a dynamic model-based test document generation method for embedded software that provides automatic generation of two documents: test requirements specification documentation and configuration item test documentation. This method enables dynamic test requirements to be implemented in dynamic models, enabling dynamic test demand tracking to be easily generated; able to automatically generate standardized, standardized test requirements and test documentation, improved document-related content inconsistency and lack of integrity And other issues, improve the efficiency.

Simple Process-Based Simulators for Generating Spatial Patterns of Habitat Loss and Fragmentation: A Review and Introduction to the G-RaFFe Model

PubMed Central

Pe'er, Guy; Zurita, Gustavo A.; Schober, Lucia; Bellocq, Maria I.; Strer, Maximilian; Müller, Michael; Pütz, Sandro

2013-01-01

Landscape simulators are widely applied in landscape ecology for generating landscape patterns. These models can be divided into two categories: pattern-based models that generate spatial patterns irrespective of the processes that shape them, and process-based models that attempt to generate patterns based on the processes that shape them. The latter often tend toward complexity in an attempt to obtain high predictive precision, but are rarely used for generic or theoretical purposes. Here we show that a simple process-based simulator can generate a variety of spatial patterns including realistic ones, typifying landscapes fragmented by anthropogenic activities. The model “G-RaFFe” generates roads and fields to reproduce the processes in which forests are converted into arable lands. For a selected level of habitat cover, three factors dominate its outcomes: the number of roads (accessibility), maximum field size (accounting for land ownership patterns), and maximum field disconnection (which enables field to be detached from roads). We compared the performance of G-RaFFe to three other models: Simmap (neutral model), Qrule (fractal-based) and Dinamica EGO (with 4 model versions differing in complexity). A PCA-based analysis indicated G-RaFFe and Dinamica version 4 (most complex) to perform best in matching realistic spatial patterns, but an alternative analysis which considers model variability identified G-RaFFe and Qrule as performing best. We also found model performance to be affected by habitat cover and the actual land-uses, the latter reflecting on land ownership patterns. We suggest that simple process-based generators such as G-RaFFe can be used to generate spatial patterns as templates for theoretical analyses, as well as for gaining better understanding of the relation between spatial processes and patterns. We suggest caution in applying neutral or fractal-based approaches, since spatial patterns that typify anthropogenic landscapes are often non-fractal in nature. PMID:23724108

Simple process-based simulators for generating spatial patterns of habitat loss and fragmentation: a review and introduction to the G-RaFFe model.

PubMed

Pe'er, Guy; Zurita, Gustavo A; Schober, Lucia; Bellocq, Maria I; Strer, Maximilian; Müller, Michael; Pütz, Sandro

2013-01-01

Landscape simulators are widely applied in landscape ecology for generating landscape patterns. These models can be divided into two categories: pattern-based models that generate spatial patterns irrespective of the processes that shape them, and process-based models that attempt to generate patterns based on the processes that shape them. The latter often tend toward complexity in an attempt to obtain high predictive precision, but are rarely used for generic or theoretical purposes. Here we show that a simple process-based simulator can generate a variety of spatial patterns including realistic ones, typifying landscapes fragmented by anthropogenic activities. The model "G-RaFFe" generates roads and fields to reproduce the processes in which forests are converted into arable lands. For a selected level of habitat cover, three factors dominate its outcomes: the number of roads (accessibility), maximum field size (accounting for land ownership patterns), and maximum field disconnection (which enables field to be detached from roads). We compared the performance of G-RaFFe to three other models: Simmap (neutral model), Qrule (fractal-based) and Dinamica EGO (with 4 model versions differing in complexity). A PCA-based analysis indicated G-RaFFe and Dinamica version 4 (most complex) to perform best in matching realistic spatial patterns, but an alternative analysis which considers model variability identified G-RaFFe and Qrule as performing best. We also found model performance to be affected by habitat cover and the actual land-uses, the latter reflecting on land ownership patterns. We suggest that simple process-based generators such as G-RaFFe can be used to generate spatial patterns as templates for theoretical analyses, as well as for gaining better understanding of the relation between spatial processes and patterns. We suggest caution in applying neutral or fractal-based approaches, since spatial patterns that typify anthropogenic landscapes are often non-fractal in nature.

Model Based Document and Report Generation for Systems Engineering

NASA Technical Reports Server (NTRS)

Delp, Christopher; Lam, Doris; Fosse, Elyse; Lee, Cin-Young

2013-01-01

As Model Based Systems Engineering (MBSE) practices gain adoption, various approaches have been developed in order to simplify and automate the process of generating documents from models. Essentially, all of these techniques can be unified around the concept of producing different views of the model according to the needs of the intended audience. In this paper, we will describe a technique developed at JPL of applying SysML Viewpoints and Views to generate documents and reports. An architecture of model-based view and document generation will be presented, and the necessary extensions to SysML with associated rationale will be explained. A survey of examples will highlight a variety of views that can be generated, and will provide some insight into how collaboration and integration is enabled. We will also describe the basic architecture for the enterprise applications that support this approach.

Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

NASA Astrophysics Data System (ADS)

Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

2017-11-01

In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

Model based document and report generation for systems engineering

NASA Astrophysics Data System (ADS)

Delp, C.; Lam, D.; Fosse, E.; Lee, Cin-Young

As Model Based Systems Engineering (MBSE) practices gain adoption, various approaches have been developed in order to simplify and automate the process of generating documents from models. Essentially, all of these techniques can be unified around the concept of producing different views of the model according to the needs of the intended audience. In this paper, we will describe a technique developed at JPL of applying SysML Viewpoints and Views to generate documents and reports. An architecture of model-based view and document generation will be presented, and the necessary extensions to SysML with associated rationale will be explained. A survey of examples will highlight a variety of views that can be generated, and will provide some insight into how collaboration and integration is enabled. We will also describe the basic architecture for the enterprise applications that support this approach.

Supervised Learning Based Hypothesis Generation from Biomedical Literature.

PubMed

Sang, Shengtian; Yang, Zhihao; Li, Zongyao; Lin, Hongfei

2015-01-01

Nowadays, the amount of biomedical literatures is growing at an explosive speed, and there is much useful knowledge undiscovered in this literature. Researchers can form biomedical hypotheses through mining these works. In this paper, we propose a supervised learning based approach to generate hypotheses from biomedical literature. This approach splits the traditional processing of hypothesis generation with classic ABC model into AB model and BC model which are constructed with supervised learning method. Compared with the concept cooccurrence and grammar engineering-based approaches like SemRep, machine learning based models usually can achieve better performance in information extraction (IE) from texts. Then through combining the two models, the approach reconstructs the ABC model and generates biomedical hypotheses from literature. The experimental results on the three classic Swanson hypotheses show that our approach outperforms SemRep system.

Modeling Renewable Penertration Using a Network Economic Model

NASA Astrophysics Data System (ADS)

Lamont, A.

2001-03-01

This paper evaluates the accuracy of a network economic modeling approach in designing energy systems having renewable and conventional generators. The network approach models the system as a network of processes such as demands, generators, markets, and resources. The model reaches a solution by exchanging prices and quantity information between the nodes of the system. This formulation is very flexible and takes very little time to build and modify models. This paper reports an experiment designing a system with photovoltaic and base and peak fossil generators. The level of PV penetration as a function of its price and the capacities of the fossil generators were determined using the network approach and using an exact, analytic approach. It is found that the two methods agree very closely in terms of the optimal capacities and are nearly identical in terms of annual system costs.

Three-dimensional modeling of the cochlea by use of an arc fitting approach.

PubMed

Schurzig, Daniel; Lexow, G Jakob; Majdani, Omid; Lenarz, Thomas; Rau, Thomas S

2016-12-01

A cochlea modeling approach is presented allowing for a user defined degree of geometry simplification which automatically adjusts to the patient specific anatomy. Model generation can be performed in a straightforward manner due to error estimation prior to the actual generation, thus minimizing modeling time. Therefore, the presented technique is well suited for a wide range of applications including finite element analyses where geometrical simplifications are often inevitable. The method is presented for n=5 cochleae which were segmented using a custom software for increased accuracy. The linear basilar membrane cross sections are expanded to areas while the scalae contours are reconstructed by a predefined number of arc segments. Prior to model generation, geometrical errors are evaluated locally for each cross section as well as globally for the resulting models and their basal turn profiles. The final combination of all reconditioned features to a 3D volume is performed in Autodesk Inventor using the loft feature. Due to the volume generation based on cubic splines, low errors could be achieved even for low numbers of arc segments and provided cross sections, both of which correspond to a strong degree of model simplification. Model generation could be performed in a time efficient manner. The proposed simplification method was proven to be well suited for the helical cochlea geometry. The generated output data can be imported into commercial software tools for various analyses representing a time efficient way to create cochlea models optimally suited for the desired task.

A generative probabilistic model and discriminative extensions for brain lesion segmentation – with application to tumor and stroke

PubMed Central

Menze, Bjoern H.; Van Leemput, Koen; Lashkari, Danial; Riklin-Raviv, Tammy; Geremia, Ezequiel; Alberts, Esther; Gruber, Philipp; Wegener, Susanne; Weber, Marc-André; Székely, Gabor; Ayache, Nicholas; Golland, Polina

2016-01-01

We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM) to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as “tumor core” or “fluid-filled structure”, but without a one-to-one correspondence to the hypo-or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the generative-discriminative model to be one of the top ranking methods in the BRATS evaluation. PMID:26599702

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

PubMed Central

2017-01-01

In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active toward a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria), it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery. PMID:29392184

Validating EHR documents: automatic schematron generation using archetypes.

PubMed

Pfeiffer, Klaus; Duftschmid, Georg; Rinner, Christoph

2014-01-01

The goal of this study was to examine whether Schematron schemas can be generated from archetypes. The openEHR Java reference API was used to transform an archetype into an object model, which was then extended with context elements. The model was processed and the constraints were transformed into corresponding Schematron assertions. A prototype of the generator for the reference model HL7 v3 CDA R2 was developed and successfully tested. Preconditions for its reusability with other reference models were set. Our results indicate that an automated generation of Schematron schemas is possible with some limitations.

DOUBLE SHELL TANK (DST) HYDROXIDE DEPLETION MODEL FOR CARBON DIOXIDE ABSORPTION

DOE Office of Scientific and Technical Information (OSTI.GOV)

OGDEN DM; KIRCH NW

2007-10-31

This document generates a supernatant hydroxide ion depletion model based on mechanistic principles. The carbon dioxide absorption mechanistic model is developed in this report. The report also benchmarks the model against historical tank supernatant hydroxide data and vapor space carbon dioxide data. A comparison of the newly generated mechanistic model with previously applied empirical hydroxide depletion equations is also performed.

Time optimal control of a jet engine using a quasi-Hermite interpolation model. M.S. Thesis

NASA Technical Reports Server (NTRS)

Comiskey, J. G.

1979-01-01

This work made preliminary efforts to generate nonlinear numerical models of a two-spooled turbofan jet engine, and subject these models to a known method of generating global, nonlinear, time optimal control laws. The models were derived numerically, directly from empirical data, as a first step in developing an automatic modelling procedure.

Template-free modeling by LEE and LEER in CASP11.

PubMed

Joung, InSuk; Lee, Sun Young; Cheng, Qianyi; Kim, Jong Yun; Joo, Keehyoung; Lee, Sung Jong; Lee, Jooyoung

2016-09-01

For the template-free modeling of human targets of CASP11, we utilized two of our modeling protocols, LEE and LEER. The LEE protocol took CASP11-released server models as the input and used some of them as templates for 3D (three-dimensional) modeling. The template selection procedure was based on the clustering of the server models aided by a community detection method of a server-model network. Restraining energy terms generated from the selected templates together with physical and statistical energy terms were used to build 3D models. Side-chains of the 3D models were rebuilt using target-specific consensus side-chain library along with the SCWRL4 rotamer library, which completed the LEE protocol. The first success factor of the LEE protocol was due to efficient server model screening. The average backbone accuracy of selected server models was similar to that of top 30% server models. The second factor was that a proper energy function along with our optimization method guided us, so that we successfully generated better quality models than the input template models. In 10 out of 24 cases, better backbone structures than the best of input template structures were generated. LEE models were further refined by performing restrained molecular dynamics simulations to generate LEER models. CASP11 results indicate that LEE models were better than the average template models in terms of both backbone structures and side-chain orientations. LEER models were of improved physical realism and stereo-chemistry compared to LEE models, and they were comparable to LEE models in the backbone accuracy. Proteins 2016; 84(Suppl 1):118-130. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

A random spatial network model based on elementary postulates

USGS Publications Warehouse

Karlinger, Michael R.; Troutman, Brent M.

1989-01-01

A model for generating random spatial networks that is based on elementary postulates comparable to those of the random topology model is proposed. In contrast to the random topology model, this model ascribes a unique spatial specification to generated drainage networks, a distinguishing property of some network growth models. The simplicity of the postulates creates an opportunity for potential analytic investigations of the probabilistic structure of the drainage networks, while the spatial specification enables analyses of spatially dependent network properties. In the random topology model all drainage networks, conditioned on magnitude (number of first-order streams), are equally likely, whereas in this model all spanning trees of a grid, conditioned on area and drainage density, are equally likely. As a result, link lengths in the generated networks are not independent, as usually assumed in the random topology model. For a preliminary model evaluation, scale-dependent network characteristics, such as geometric diameter and link length properties, and topologic characteristics, such as bifurcation ratio, are computed for sets of drainage networks generated on square and rectangular grids. Statistics of the bifurcation and length ratios fall within the range of values reported for natural drainage networks, but geometric diameters tend to be relatively longer than those for natural networks.

Supplantation versus Generative Models: Implications for Designers of Instructional Text.

ERIC Educational Resources Information Center

Smith, Patricia L.

Two instructional design alternatives are described and discussed: (1) the supplantation model of Ausburn and Ausburn (1978), where learning strategies are built into the instructional materials; and (2) a generative design model, where strategies are "built" into the learner. These contrasting models are proposed as representing the…

SU-F-T-447: The Impact of Treatment Planning Methods On RapidPlan Modeling for Rectum Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, S; Peng, J; Li, K

2016-06-15

Purpose: To investigate the dose volume histogram (DVH) prediction varieties based on intensity modulate radiotherapy (IMRT) plan or volume arc modulate radiotherapy (VMAT) plan models on the RapidPlan. Methods: Two DVH prediction models were generated in this study, including an IMRT model trained from 83 IMRT rectum plans and a VMAT model trained from 60 VMAT rectum plans. In the internal validation, 20 plans from each training database were selected to verify the clinical feasibility of the model. Then, 10 IMRT plans (PIMRT-by-IMRT-model) generated from IMRT model and 10 IMRT plans generated from VMAT model (PIMRT-by-VMAT-model) were compared on themore » dose to organs at risk (OAR), which included bladder, left and right femoral heads. The similar comparison was also performed on the VMAT plans generated from IMRT model (PVMAT-by-IMRT-model) and VMAT plans generated from VMAT (PVMAT-by-VMAT-model) model. Results: For the internal validation, all plans from IMRT or VMAT model shows significantly improvement on OAR sparing compared with the corresponded clinical ones. Compared to the PIMRT-by-VMAT-model, the PIMRT-by-IMRT-model has a reduction of 6.90±3.87%(p<0.001) on V40 6.63±3.62%(p<0.001) on V45 and 4.74±2.26%(p<0.001) on V50 in bladder; and a mean dose reduction of 2.12±1.75Gy(p=0.004) and 2.84±1.53Gy(p<0.001) in right and left femoral head, respectively. There was no significant difference on OAR sparing between PVMAT-by-IMRT-model and PVMAT-by-VMAT-model. Conclusion: The IMRT model for the rectal cancer in the RapidPlan can be applied to for VMAT planning. However, the VMAT model is not suggested to use in the IMRT planning. Cautions should be taken that the planning model based on some technique may not feasible to other planning techniques.« less

Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhan, Yiduo; Zheng, Qipeng P.; Wang, Jianhui

Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined future uncertainties (i.e., fixed random outcomes with given probabilities). In several recent studies of generation assets' planning (e.g., thermal versus renewable), new findings show that the investment decisions could affect the future uncertainties as well. To this end, this paper proposes a multistage decision-dependent stochastic optimization model for long-term large-scale generation expansion planning, where large amounts of windmore » power are involved. In the decision-dependent model, the future uncertainties are not only affecting but also affected by the current decisions. In particular, the probability distribution function is determined by not only input parameters but also decision variables. To deal with the nonlinear constraints in our model, a quasi-exact solution approach is then introduced to reformulate the multistage stochastic investment model to a mixed-integer linear programming model. The wind penetration, investment decisions, and the optimality of the decision-dependent model are evaluated in a series of multistage case studies. The results show that the proposed decision-dependent model provides effective optimization solutions for long-term generation expansion planning.« less

Transient Control of Synchronous Machine Active and Reactive Power in Micro-grid Power Systems

NASA Astrophysics Data System (ADS)

Weber, Luke G.

There are two main topics associated with this dissertation. The first is to investigate phase-to-neutral fault current magnitude occurring in generators with multiple zero-sequence current sources. The second is to design, model, and tune a linear control system for operating a micro-grid in the event of a separation from the electric power system. In the former case, detailed generator, AC8B excitation system, and four-wire electric power system models are constructed. Where available, manufacturers data is used to validate the generator and exciter models. A gain-delay with frequency droop control is used to model an internal combustion engine and governor. The four wire system is connected through a transformer impedance to an infinite bus. Phase-to-neutral faults are imposed on the system, and fault magnitudes analyzed against three-phase faults to gauge their severity. In the latter case, a balanced three-phase system is assumed. The model structure from the former case - but using data for a different generator - is incorporated with a model for an energy storage device and a net load model to form a micro-grid. The primary control model for the energy storage device has a high level of detail, as does the energy storage device plant model in describing the LC filter and transformer. A gain-delay battery and inverter model is used at the front end. The net load model is intended to be the difference between renewable energy sources and load within a micro-grid system that has separated from the grid. Given the variability of both renewable generation and load, frequency and voltage stability are not guaranteed. This work is an attempt to model components of a proposed micro-grid system at the University of Wisconsin Milwaukee, and design, model, and tune a linear control system for operation in the event of a separation from the electric power system. The control module is responsible for management of frequency and active power, and voltage and reactive power. The scope of this work is to • develop a mathematical model for a salient pole, 2 damper winding synchronous generator with d axis saturation suitable for transient analysis, • develop a mathematical model for a voltage regulator and excitation system using the IEEE AC8B voltage regulator and excitation system template, • develop mathematical models for an energy storage primary control system, LC filter and transformer suitable for transient analysis, • combine the generator and energy storage models in a micro-grid context, • develop mathematical models for electric system components in the stationary abc frame and rotating dq reference frame, • develop a secondary control network for dispatch of micro-grid assets, • establish micro-grid limits of stable operation for step changes in load and power commands based on simulations of model data assuming net load on the micro-grid, and • use generator and electric system models to assess the generator current magnitude during phase-to-ground faults.

Implications of Higgs searches on the four-generation standard model.

PubMed

Kuflik, Eric; Nir, Yosef; Volansky, Tomer

2013-03-01

Within the four-generation standard model, the Higgs couplings to gluons and to photons deviate in a significant way from the predictions of the three-generation standard model. As a consequence, large departures in several Higgs production and decay channels are expected. Recent Higgs search results, presented by ATLAS, CMS, and CDF, hint on the existence of a Higgs boson with a mass around 125 GeV. Using these results and assuming such a Higgs boson, we derive exclusion limits on the four-generation standard model. For m(H)=125 GeV, the model is excluded above 99.95% confidence level. For 124.5 GeV≤m(H)≤127.5 GeV, an exclusion limit above 99% confidence level is found.

Charles Bonnet Syndrome: Evidence for a Generative Model in the Cortex?

PubMed Central

Reichert, David P.; Seriès, Peggy; Storkey, Amos J.

2013-01-01

Several theories propose that the cortex implements an internal model to explain, predict, and learn about sensory data, but the nature of this model is unclear. One condition that could be highly informative here is Charles Bonnet syndrome (CBS), where loss of vision leads to complex, vivid visual hallucinations of objects, people, and whole scenes. CBS could be taken as indication that there is a generative model in the brain, specifically one that can synthesise rich, consistent visual representations even in the absence of actual visual input. The processes that lead to CBS are poorly understood. Here, we argue that a model recently introduced in machine learning, the deep Boltzmann machine (DBM), could capture the relevant aspects of (hypothetical) generative processing in the cortex. The DBM carries both the semantics of a probabilistic generative model and of a neural network. The latter allows us to model a concrete neural mechanism that could underlie CBS, namely, homeostatic regulation of neuronal activity. We show that homeostatic plasticity could serve to make the learnt internal model robust against e.g. degradation of sensory input, but overcompensate in the case of CBS, leading to hallucinations. We demonstrate how a wide range of features of CBS can be explained in the model and suggest a potential role for the neuromodulator acetylcholine. This work constitutes the first concrete computational model of CBS and the first application of the DBM as a model in computational neuroscience. Our results lend further credence to the hypothesis of a generative model in the brain. PMID:23874177

Mind the Noise When Identifying Computational Models of Cognition from Brain Activity.

PubMed

Kolossa, Antonio; Kopp, Bruno

2016-01-01

The aim of this study was to analyze how measurement error affects the validity of modeling studies in computational neuroscience. A synthetic validity test was created using simulated P300 event-related potentials as an example. The model space comprised four computational models of single-trial P300 amplitude fluctuations which differed in terms of complexity and dependency. The single-trial fluctuation of simulated P300 amplitudes was computed on the basis of one of the models, at various levels of measurement error and at various numbers of data points. Bayesian model selection was performed based on exceedance probabilities. At very low numbers of data points, the least complex model generally outperformed the data-generating model. Invalid model identification also occurred at low levels of data quality and under low numbers of data points if the winning model's predictors were closely correlated with the predictors from the data-generating model. Given sufficient data quality and numbers of data points, the data-generating model could be correctly identified, even against models which were very similar to the data-generating model. Thus, a number of variables affects the validity of computational modeling studies, and data quality and numbers of data points are among the main factors relevant to the issue. Further, the nature of the model space (i.e., model complexity, model dependency) should not be neglected. This study provided quantitative results which show the importance of ensuring the validity of computational modeling via adequately prepared studies. The accomplishment of synthetic validity tests is recommended for future applications. Beyond that, we propose to render the demonstration of sufficient validity via adequate simulations mandatory to computational modeling studies.

An artificial EMG generation model based on signal-dependent noise and related application to motion classification

PubMed Central

Hayashi, Hideaki; Nakamura, Go; Chin, Takaaki; Tsuji, Toshio

2017-01-01

This paper proposes an artificial electromyogram (EMG) signal generation model based on signal-dependent noise, which has been ignored in existing methods, by introducing the stochastic construction of the EMG signals. In the proposed model, an EMG signal variance value is first generated from a probability distribution with a shape determined by a commanded muscle force and signal-dependent noise. Artificial EMG signals are then generated from the associated Gaussian distribution with a zero mean and the generated variance. This facilitates representation of artificial EMG signals with signal-dependent noise superimposed according to the muscle activation levels. The frequency characteristics of the EMG signals are also simulated via a shaping filter with parameters determined by an autoregressive model. An estimation method to determine EMG variance distribution using rectified and smoothed EMG signals, thereby allowing model parameter estimation with a small number of samples, is also incorporated in the proposed model. Moreover, the prediction of variance distribution with strong muscle contraction from EMG signals with low muscle contraction and related artificial EMG generation are also described. The results of experiments conducted, in which the reproduction capability of the proposed model was evaluated through comparison with measured EMG signals in terms of amplitude, frequency content, and EMG distribution demonstrate that the proposed model can reproduce the features of measured EMG signals. Further, utilizing the generated EMG signals as training data for a neural network resulted in the classification of upper limb motion with a higher precision than by learning from only measured EMG signals. This indicates that the proposed model is also applicable to motion classification. PMID:28640883

Petroleum generation and migration in the Mesopotamian Basin and Zagros fold belt of Iraq: Results from a basin-modeling study

USGS Publications Warehouse

Pitman, Janet K.; Steinshouer, D.; Lewan, M.D.

2004-01-01

A regional 3-D total petroleum-system model was developed to evaluate petroleum generation and migration histories in the Mesopotamian Basin and Zagros fold belt in Iraq. The modeling was undertaken in conjunction with Middle East petroleum assessment studies conducted by the USGS. Regional structure maps, isopach and facies maps, and thermal maturity data were used as input to the model. The oil-generation potential of Jurassic source-rocks, the principal known source of the petroleum in Jurassic, Cretaceous, and Tertiary reservoirs in these regions, was modeled using hydrous pyrolysis (Type II-S) kerogen kinetics. Results showed that oil generation in source rocks commenced in the Late Cretaceous in intrashelf basins, peak expulsion took place in the late Miocene and Pliocene when these depocenters had expanded along the Zagros foredeep trend, and generation ended in the Holocene when deposition in the foredeep ceased. The model indicates that, at present, the majority of Jurassic source rocks in Iraq have reached or exceeded peak oil generation and most rocks have completed oil generation and expulsion. Flow-path simulations demonstrate that virtually all oil and gas fields in the Mesopotamian Basin and Zagros fold belt overlie mature Jurassic source rocks (vertical migration dominated) and are situated on, or close to, modeled migration pathways. Fields closest to modeled pathways associated with source rocks in local intrashelf basins were charged earliest from Late Cretaceous through the middle Miocene, and other fields filled later when compression-related traps were being formed. Model results confirm petroleum migration along major, northwest-trending folds and faults, and oil migration loss at the surface.

Model and Scenario Variations in Predicted Number of Generations of Spodoptera litura Fab. on Peanut during Future Climate Change Scenario

PubMed Central

Srinivasa Rao, Mathukumalli; Swathi, Pettem; Rama Rao, Chitiprolu Anantha; Rao, K. V.; Raju, B. M. K.; Srinivas, Karlapudi; Manimanjari, Dammu; Maheswari, Mandapaka

2015-01-01

The present study features the estimation of number of generations of tobacco caterpillar, Spodoptera litura. Fab. on peanut crop at six locations in India using MarkSim, which provides General Circulation Model (GCM) of future data on daily maximum (T.max), minimum (T.min) air temperatures from six models viz., BCCR-BCM2.0, CNRM-CM3, CSIRO-Mk3.5, ECHams5, INCM-CM3.0 and MIROC3.2 along with an ensemble of the six from three emission scenarios (A2, A1B and B1). This data was used to predict the future pest scenarios following the growing degree days approach in four different climate periods viz., Baseline-1975, Near future (NF) -2020, Distant future (DF)-2050 and Very Distant future (VDF)—2080. It is predicted that more generations would occur during the three future climate periods with significant variation among scenarios and models. Among the seven models, 1–2 additional generations were predicted during DF and VDF due to higher future temperatures in CNRM-CM3, ECHams5 & CSIRO-Mk3.5 models. The temperature projections of these models indicated that the generation time would decrease by 18–22% over baseline. Analysis of variance (ANOVA) was used to partition the variation in the predicted number of generations and generation time of S. litura on peanut during crop season. Geographical location explained 34% of the total variation in number of generations, followed by time period (26%), model (1.74%) and scenario (0.74%). The remaining 14% of the variation was explained by interactions. Increased number of generations and reduction of generation time across the six peanut growing locations of India suggest that the incidence of S. litura may increase due to projected increase in temperatures in future climate change periods. PMID:25671564

Timescales and Mechanisms of Sigh-Like Bursting and Spiking in Models of Rhythmic Respiratory Neurons.

PubMed

Wang, Yangyang; Rubin, Jonathan E

2017-12-01

Neural networks generate a variety of rhythmic activity patterns, often involving different timescales. One example arises in the respiratory network in the pre-Bötzinger complex of the mammalian brainstem, which can generate the eupneic rhythm associated with normal respiration as well as recurrent low-frequency, large-amplitude bursts associated with sighing. Two competing hypotheses have been proposed to explain sigh generation: the recruitment of a neuronal population distinct from the eupneic rhythm-generating subpopulation or the reconfiguration of activity within a single population. Here, we consider two recent computational models, one of which represents each of the hypotheses. We use methods of dynamical systems theory, such as fast-slow decomposition, averaging, and bifurcation analysis, to understand the multiple-timescale mechanisms underlying sigh generation in each model. In the course of our analysis, we discover that a third timescale is required to generate sighs in both models. Furthermore, we identify the similarities of the underlying mechanisms in the two models and the aspects in which they differ.

Modeling Vortex Generators in the Wind-US Code

NASA Technical Reports Server (NTRS)

Dudek, Julianne C.

2010-01-01

A source term model which simulates the effects of vortex generators was implemented into the Wind-US Navier Stokes code. The source term added to the Navier-Stokes equations simulates the lift force which would result from a vane-type vortex generator in the flowfield. The implementation is user-friendly, requiring the user to specify only three quantities for each desired vortex generator: the range of grid points over which the force is to be applied and the planform area and angle of incidence of the physical vane. The model behavior was evaluated for subsonic flow in a rectangular duct with a single vane vortex generator, supersonic flow in a rectangular duct with a counterrotating vortex generator pair, and subsonic flow in an S-duct with 22 co-rotating vortex generators. The validation results indicate that the source term vortex generator model provides a useful tool for screening vortex generator configurations and gives comparable results to solutions computed using a gridded vane.

GeneratorSE: A Sizing Tool for Variable-Speed Wind Turbine Generators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sethuraman, Latha; Dykes, Katherine L

This report documents a set of analytical models employed by the optimization algorithms within the GeneratorSE framework. The initial values and boundary conditions employed for the generation of the various designs and initial estimates for basic design dimensions, masses, and efficiency for the four different models of generators are presented and compared with empirical data collected from previous studies and some existing commercial turbines. These models include designs applicable for variable-speed, high-torque application featuring direct-drive synchronous generators and low-torque application featuring induction generators. In all of the four models presented, the main focus of optimization is electromagnetic design with themore » exception of permanent-magnet and wire-wound synchronous generators, wherein the structural design is also optimized. Thermal design is accommodated in GeneratorSE as a secondary attribute by limiting the winding current densities to acceptable limits. A preliminary validation of electromagnetic design was carried out by comparing the optimized magnetic loading against those predicted by numerical simulation in FEMM4.2, a finite-element software for analyzing electromagnetic and thermal physics problems for electrical machines. For direct-drive synchronous generators, the analytical models for the structural design are validated by static structural analysis in ANSYS.« less

Schwarz-Christoffel Conformal Mapping based Grid Generation for Global Oceanic Circulation Models

NASA Astrophysics Data System (ADS)

Xu, Shiming

2015-04-01

We propose new grid generation algorithms for global ocean general circulation models (OGCMs). Contrary to conventional, analytical forms based dipolar or tripolar grids, the new algorithm are based on Schwarz-Christoffel (SC) conformal mapping with prescribed boundary information. While dealing with the conventional grid design problem of pole relocation, it also addresses more advanced issues of computational efficiency and the new requirements on OGCM grids arisen from the recent trend of high-resolution and multi-scale modeling. The proposed grid generation algorithm could potentially achieve the alignment of grid lines to coastlines, enhanced spatial resolution in coastal regions, and easier computational load balance. Since the generated grids are still orthogonal curvilinear, they can be readily 10 utilized in existing Bryan-Cox-Semtner type ocean models. The proposed methodology can also be applied to the grid generation task for regional ocean modeling when complex land-ocean distribution is present.

Dynamic Model and Control of a Photovoltaic Generation System using Energetic Macroscopic Representation

NASA Astrophysics Data System (ADS)

Solano, Javier; Duarte, José; Vargas, Erwin; Cabrera, Jhon; Jácome, Andrés; Botero, Mónica; Rey, Juan

2016-10-01

This paper addresses the Energetic Macroscopic Representation EMR, the modelling and the control of photovoltaic panel PVP generation systems for simulation purposes. The model of the PVP considers the variations on irradiance and temperature. A maximum power point tracking MPPT algorithm is considered to control the power converter. A novel EMR is proposed to consider the dynamic model of the PVP with variations in the irradiance and the temperature. The EMR is evaluated through simulations of a PVP generation system.

Evaluation of grid generation technologies from an applied perspective

NASA Technical Reports Server (NTRS)

Hufford, Gary S.; Harrand, Vincent J.; Patel, Bhavin C.; Mitchell, Curtis R.

1995-01-01

An analysis of the grid generation process from the point of view of an applied CFD engineer is given. Issues addressed include geometric modeling, structured grid generation, unstructured grid generation, hybrid grid generation and use of virtual parts libraries in large parametric analysis projects. The analysis is geared towards comparing the effective turn around time for specific grid generation and CFD projects. The conclusion was made that a single grid generation methodology is not universally suited for all CFD applications due to both limitations in grid generation and flow solver technology. A new geometric modeling and grid generation tool, CFD-GEOM, is introduced to effectively integrate the geometric modeling process to the various grid generation methodologies including structured, unstructured, and hybrid procedures. The full integration of the geometric modeling and grid generation allows implementation of extremely efficient updating procedures, a necessary requirement for large parametric analysis projects. The concept of using virtual parts libraries in conjunction with hybrid grids for large parametric analysis projects is also introduced to improve the efficiency of the applied CFD engineer.

Alterations in choice behavior by manipulations of world model.

PubMed

Green, C S; Benson, C; Kersten, D; Schrater, P

2010-09-14

How to compute initially unknown reward values makes up one of the key problems in reinforcement learning theory, with two basic approaches being used. Model-free algorithms rely on the accumulation of substantial amounts of experience to compute the value of actions, whereas in model-based learning, the agent seeks to learn the generative process for outcomes from which the value of actions can be predicted. Here we show that (i) "probability matching"-a consistent example of suboptimal choice behavior seen in humans-occurs in an optimal Bayesian model-based learner using a max decision rule that is initialized with ecologically plausible, but incorrect beliefs about the generative process for outcomes and (ii) human behavior can be strongly and predictably altered by the presence of cues suggestive of various generative processes, despite statistically identical outcome generation. These results suggest human decision making is rational and model based and not consistent with model-free learning.

Alterations in choice behavior by manipulations of world model

PubMed Central

Green, C. S.; Benson, C.; Kersten, D.; Schrater, P.

2010-01-01

How to compute initially unknown reward values makes up one of the key problems in reinforcement learning theory, with two basic approaches being used. Model-free algorithms rely on the accumulation of substantial amounts of experience to compute the value of actions, whereas in model-based learning, the agent seeks to learn the generative process for outcomes from which the value of actions can be predicted. Here we show that (i) “probability matching”—a consistent example of suboptimal choice behavior seen in humans—occurs in an optimal Bayesian model-based learner using a max decision rule that is initialized with ecologically plausible, but incorrect beliefs about the generative process for outcomes and (ii) human behavior can be strongly and predictably altered by the presence of cues suggestive of various generative processes, despite statistically identical outcome generation. These results suggest human decision making is rational and model based and not consistent with model-free learning. PMID:20805507

Developing Formal Object-oriented Requirements Specifications: A Model, Tool and Technique.

ERIC Educational Resources Information Center

Jackson, Robert B.; And Others

1995-01-01

Presents a formal object-oriented specification model (OSS) for computer software system development that is supported by a tool that automatically generates a prototype from an object-oriented analysis model (OSA) instance, lets the user examine the prototype, and permits the user to refine the OSA model instance to generate a requirements…

A MECHANISTIC MODEL FOR MERCURY CAPTURE WITH IN-SITU GENERATED TITANIA PARTICLES: ROLE OF WATER VAPOR

EPA Science Inventory

A mechanistic model to predict the capture of gas phase mercury species using in-situ generated titania nanosize particles activated by UV irradiation is developed. The model is an extension of a recently reported model1 for photochemical reactions that accounts for the rates of...

Generative Computer-Assisted Instruction and Artificial Intelligence. Report No. 5.

ERIC Educational Resources Information Center

Sinnott, Loraine T.

This paper reviews the state-of-the-art in generative computer-assisted instruction and artificial intelligence. It divides relevant research into three areas of instructional modeling: models of the subject matter; models of the learner's state of knowledge; and models of teaching strategies. Within these areas, work sponsored by Advanced…

Reliability models: the influence of model specification in generation expansion planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stremel, J.P.

1982-10-01

This paper is a critical evaluation of reliability methods used for generation expansion planning. It is shown that the methods for treating uncertainty are critical for determining the relative reliability value of expansion alternatives. It is also shown that the specification of the reliability model will not favor all expansion options equally. Consequently, the model is biased. In addition, reliability models should be augmented with an economic value of reliability (such as the cost of emergency procedures or energy not served). Generation expansion evaluations which ignore the economic value of excess reliability can be shown to be inconsistent. The conclusionsmore » are that, in general, a reliability model simplifies generation expansion planning evaluations. However, for a thorough analysis, the expansion options should be reviewed for candidates which may be unduly rejected because of the bias of the reliability model. And this implies that for a consistent formulation in an optimization framework, the reliability model should be replaced with a full economic optimization which includes the costs of emergency procedures and interruptions in the objective function.« less

A model of oil-generation in a waterlogged and closed system

NASA Astrophysics Data System (ADS)

Zhigao, He

This paper presents a new model on synthetic effects on oil-generation in a waterlogged and closed system. It is suggested based on information about oil in high pressure layers (including gas dissolved in oil), marsh gas and its fermentative solution, fermentation processes and mechanisms, gaseous hydrocarbons of carbonate rocks by acid treatment, oil-field water, recent and ancient sediments, and simulation experiments of artificial marsh gas and biological action. The model differs completely from the theory of oil-generation by thermal degradation of kerogen but stresses the synthetic effects of oil-generation in special waterlogged and closed geological systems, the importance of pressure in oil-forming processes, and direct oil generation by micro-organisms. Oil generated directly by micro-organisms is a particular biochemical reaction. Another feature of this model is that generation, migration and accumulation of petroleum are considered as a whole.

The Rack-Gear Tool Generation Modelling. Non-Analytical Method Developed in CATIA, Using the Relative Generating Trajectories Method

NASA Astrophysics Data System (ADS)

Teodor, V. G.; Baroiu, N.; Susac, F.; Oancea, N.

2016-11-01

The modelling of a curl of surfaces associated with a pair of rolling centrodes, when it is known the profile of the rack-gear's teeth profile, by direct measuring, as a coordinate matrix, has as goal the determining of the generating quality for an imposed kinematics of the relative motion of tool regarding the blank. In this way, it is possible to determine the generating geometrical error, as a base of the total error. The generation modelling allows highlighting the potential errors of the generating tool, in order to correct its profile, previously to use the tool in machining process. A method developed in CATIA is proposed, based on a new method, namely the method of “relative generating trajectories”. They are presented the analytical foundation, as so as some application for knows models of rack-gear type tools used on Maag teething machines.

Solid waste forecasting using modified ANFIS modeling.

PubMed

Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; K N A, Maulud

2015-10-01

Solid waste prediction is crucial for sustainable solid waste management. Usually, accurate waste generation record is challenge in developing countries which complicates the modelling process. Solid waste generation is related to demographic, economic, and social factors. However, these factors are highly varied due to population and economy growths. The objective of this research is to determine the most influencing demographic and economic factors that affect solid waste generation using systematic approach, and then develop a model to forecast solid waste generation using a modified Adaptive Neural Inference System (MANFIS). The model evaluation was performed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the coefficient of determination (R²). The results show that the best input variables are people age groups 0-14, 15-64, and people above 65 years, and the best model structure is 3 triangular fuzzy membership functions and 27 fuzzy rules. The model has been validated using testing data and the resulted training RMSE, MAE and R² were 0.2678, 0.045 and 0.99, respectively, while for testing phase RMSE =3.986, MAE = 0.673 and R² = 0.98. To date, a few attempts have been made to predict the annual solid waste generation in developing countries. This paper presents modeling of annual solid waste generation using Modified ANFIS, it is a systematic approach to search for the most influencing factors and then modify the ANFIS structure to simplify the model. The proposed method can be used to forecast the waste generation in such developing countries where accurate reliable data is not always available. Moreover, annual solid waste prediction is essential for sustainable planning.

Evaluation of Extratropical Cyclone Precipitation in the North Atlantic Basin: An analysis of ERA-Interim, WRF, and two CMIP5 models.

PubMed

Booth, James F; Naud, Catherine M; Willison, Jeff

2018-03-01

The representation of extratropical cyclones (ETCs) precipitation in general circulation models (GCMs) and a weather research and forecasting (WRF) model is analyzed. This work considers the link between ETC precipitation and dynamical strength and tests if parameterized convection affects this link for ETCs in the North Atlantic Basin. Lagrangian cyclone tracks of ETCs in ERA-Interim reanalysis (ERAI), the GISS and GFDL CMIP5 models, and WRF with two horizontal resolutions are utilized in a compositing analysis. The 20-km resolution WRF model generates stronger ETCs based on surface wind speed and cyclone precipitation. The GCMs and ERAI generate similar composite means and distributions for cyclone precipitation rates, but GCMs generate weaker cyclone surface winds than ERAI. The amount of cyclone precipitation generated by the convection scheme differs significantly across the datasets, with GISS generating the most, followed by ERAI and then GFDL. The models and reanalysis generate relatively more parameterized convective precipitation when the total cyclone-averaged precipitation is smaller. This is partially due to the contribution of parameterized convective precipitation occurring more often late in the ETC life cycle. For reanalysis and models, precipitation increases with both cyclone moisture and surface wind speed, and this is true if the contribution from the parameterized convection scheme is larger or not. This work shows that these different models generate similar total ETC precipitation despite large differences in the parameterized convection, and these differences do not cause unexpected behavior in ETC precipitation sensitivity to cyclone moisture or surface wind speed.

Technical note: A linear model for predicting δ13 Cprotein.

PubMed

Pestle, William J; Hubbe, Mark; Smith, Erin K; Stevenson, Joseph M

2015-08-01

Development of a model for the prediction of δ(13) Cprotein from δ(13) Ccollagen and Δ(13) Cap-co . Model-generated values could, in turn, serve as "consumer" inputs for multisource mixture modeling of paleodiet. Linear regression analysis of previously published controlled diet data facilitated the development of a mathematical model for predicting δ(13) Cprotein (and an experimentally generated error term) from isotopic data routinely generated during the analysis of osseous remains (δ(13) Cco and Δ(13) Cap-co ). Regression analysis resulted in a two-term linear model (δ(13) Cprotein (%) = (0.78 × δ(13) Cco ) - (0.58× Δ(13) Cap-co ) - 4.7), possessing a high R-value of 0.93 (r(2)  = 0.86, P < 0.01), and experimentally generated error terms of ±1.9% for any predicted individual value of δ(13) Cprotein . This model was tested using isotopic data from Formative Period individuals from northern Chile's Atacama Desert. The model presented here appears to hold significant potential for the prediction of the carbon isotope signature of dietary protein using only such data as is routinely generated in the course of stable isotope analysis of human osseous remains. These predicted values are ideal for use in multisource mixture modeling of dietary protein source contribution. © 2015 Wiley Periodicals, Inc.

Semi-Automatic Modelling of Building FAÇADES with Shape Grammars Using Historic Building Information Modelling

NASA Astrophysics Data System (ADS)

Dore, C.; Murphy, M.

2013-02-01

This paper outlines a new approach for generating digital heritage models from laser scan or photogrammetric data using Historic Building Information Modelling (HBIM). HBIM is a plug-in for Building Information Modelling (BIM) software that uses parametric library objects and procedural modelling techniques to automate the modelling stage. The HBIM process involves a reverse engineering solution whereby parametric interactive objects representing architectural elements are mapped onto laser scan or photogrammetric survey data. A library of parametric architectural objects has been designed from historic manuscripts and architectural pattern books. These parametric objects were built using an embedded programming language within the ArchiCAD BIM software called Geometric Description Language (GDL). Procedural modelling techniques have been implemented with the same language to create a parametric building façade which automatically combines library objects based on architectural rules and proportions. Different configurations of the façade are controlled by user parameter adjustment. The automatically positioned elements of the façade can be subsequently refined using graphical editing while overlaying the model with orthographic imagery. Along with this semi-automatic method for generating façade models, manual plotting of library objects can also be used to generate a BIM model from survey data. After the 3D model has been completed conservation documents such as plans, sections, elevations and 3D views can be automatically generated for conservation projects.

Capacity expansion model of wind power generation based on ELCC

NASA Astrophysics Data System (ADS)

Yuan, Bo; Zong, Jin; Wu, Shengyu

2018-02-01

Capacity expansion is an indispensable prerequisite for power system planning and construction. A reasonable, efficient and accurate capacity expansion model (CEM) is crucial to power system planning. In most current CEMs, the capacity of wind power generation is considered as boundary conditions instead of decision variables, which may lead to curtailment or over construction of flexible resource, especially at a high renewable energy penetration scenario. This paper proposed a wind power generation capacity value(CV) calculation method based on effective load-carrying capability, and a CEM that co-optimizes wind power generation and conventional power sources. Wind power generation is considered as decision variable in this model, and the model can accurately reflect the uncertainty nature of wind power.

Modeling Tsunami Wave Generation Using a Two-layer Granular Landslide Model

NASA Astrophysics Data System (ADS)

Ma, G.; Kirby, J. T., Jr.; Shi, F.; Grilli, S. T.; Hsu, T. J.

2016-12-01

Tsunamis can be generated by subaerial or submarine landslides in reservoirs, lakes, fjords, bays and oceans. Compared to seismogenic tsunamis, landslide or submarine mass failure (SMF) tsunamis are normally characterized by relatively shorter wave lengths and stronger wave dispersion, and potentially may generate large wave amplitudes locally and high run-up along adjacent coastlines. Due to a complex interplay between the landslide and tsunami waves, accurate simulation of landslide motion as well as tsunami generation is a challenging task. We develop and test a new two-layer model for granular landslide motion and tsunami wave generation. The landslide is described as a saturated granular flow, accounting for intergranular stresses governed by Coulomb friction. Tsunami wave generation is simulated by the three-dimensional non-hydrostatic wave model NHWAVE, which is capable of capturing wave dispersion efficiently using a small number of discretized vertical levels. Depth-averaged governing equations for the granular landslide are derived in a slope-oriented coordinate system, taking into account the dynamic interaction between the lower-layer granular landslide and upper-layer water motion. The model is tested against laboratory experiments on impulsive wave generation by subaerial granular landslides. Model results illustrate a complex interplay between the granular landslide and tsunami waves, and they reasonably predict not only the tsunami wave generation but also the granular landslide motion from initiation to deposition.

Accuracy of digital models generated by conventional impression/plaster-model methods and intraoral scanning.

PubMed

Tomita, Yuki; Uechi, Jun; Konno, Masahiro; Sasamoto, Saera; Iijima, Masahiro; Mizoguchi, Itaru

2018-04-17

We compared the accuracy of digital models generated by desktop-scanning of conventional impression/plaster models versus intraoral scanning. Eight ceramic spheres were attached to the buccal molar regions of dental epoxy models, and reference linear-distance measurement were determined using a contact-type coordinate measuring instrument. Alginate (AI group) and silicone (SI group) impressions were taken and converted into cast models using dental stone; the models were scanned using desktop scanner. As an alternative, intraoral scans were taken using an intraoral scanner, and digital models were generated from these scans (IOS group). Twelve linear-distance measurement combinations were calculated between different sphere-centers for all digital models. There were no significant differences among the three groups using total of six linear-distance measurements. When limited to five lineardistance measurement, the IOS group showed significantly higher accuracy compared to the AI and SI groups. Intraoral scans may be more accurate compared to scans of conventional impression/plaster models.

Sociality influences cultural complexity.

PubMed

Muthukrishna, Michael; Shulman, Ben W; Vasilescu, Vlad; Henrich, Joseph

2014-01-07

Archaeological and ethnohistorical evidence suggests a link between a population's size and structure, and the diversity or sophistication of its toolkits or technologies. Addressing these patterns, several evolutionary models predict that both the size and social interconnectedness of populations can contribute to the complexity of its cultural repertoire. Some models also predict that a sudden loss of sociality or of population will result in subsequent losses of useful skills/technologies. Here, we test these predictions with two experiments that permit learners to access either one or five models (teachers). Experiment 1 demonstrates that naive participants who could observe five models, integrate this information and generate increasingly effective skills (using an image editing tool) over 10 laboratory generations, whereas those with access to only one model show no improvement. Experiment 2, which began with a generation of trained experts, shows how learners with access to only one model lose skills (in knot-tying) more rapidly than those with access to five models. In the final generation of both experiments, all participants with access to five models demonstrate superior skills to those with access to only one model. These results support theoretical predictions linking sociality to cumulative cultural evolution.

Sociality influences cultural complexity

PubMed Central

Muthukrishna, Michael; Shulman, Ben W.; Vasilescu, Vlad; Henrich, Joseph

2014-01-01

Archaeological and ethnohistorical evidence suggests a link between a population's size and structure, and the diversity or sophistication of its toolkits or technologies. Addressing these patterns, several evolutionary models predict that both the size and social interconnectedness of populations can contribute to the complexity of its cultural repertoire. Some models also predict that a sudden loss of sociality or of population will result in subsequent losses of useful skills/technologies. Here, we test these predictions with two experiments that permit learners to access either one or five models (teachers). Experiment 1 demonstrates that naive participants who could observe five models, integrate this information and generate increasingly effective skills (using an image editing tool) over 10 laboratory generations, whereas those with access to only one model show no improvement. Experiment 2, which began with a generation of trained experts, shows how learners with access to only one model lose skills (in knot-tying) more rapidly than those with access to five models. In the final generation of both experiments, all participants with access to five models demonstrate superior skills to those with access to only one model. These results support theoretical predictions linking sociality to cumulative cultural evolution. PMID:24225461

A simple topography-driven, calibration-free runoff generation model

NASA Astrophysics Data System (ADS)

Gao, H.; Birkel, C.; Hrachowitz, M.; Tetzlaff, D.; Soulsby, C.; Savenije, H. H. G.

2017-12-01

Determining the amount of runoff generation from rainfall occupies a central place in rainfall-runoff modelling. Moreover, reading landscapes and developing calibration-free runoff generation models that adequately reflect land surface heterogeneities remains the focus of much hydrological research. In this study, we created a new method to estimate runoff generation - HAND-based Storage Capacity curve (HSC) which uses a topographic index (HAND, Height Above the Nearest Drainage) to identify hydrological similarity and partially the saturated areas of catchments. We then coupled the HSC model with the Mass Curve Technique (MCT) method to estimate root zone storage capacity (SuMax), and obtained the calibration-free runoff generation model HSC-MCT. Both the two models (HSC and HSC-MCT) allow us to estimate runoff generation and simultaneously visualize the spatial dynamic of saturated area. We tested the two models in the data-rich Bruntland Burn (BB) experimental catchment in Scotland with an unusual time series of the field-mapped saturation area extent. The models were subsequently tested in 323 MOPEX (Model Parameter Estimation Experiment) catchments in the United States. HBV and TOPMODEL were used as benchmarks. We found that the HSC performed better in reproducing the spatio-temporal pattern of the observed saturated areas in the BB catchment compared with TOPMODEL which is based on the topographic wetness index (TWI). The HSC also outperformed HBV and TOPMODEL in the MOPEX catchments for both calibration and validation. Despite having no calibrated parameters, the HSC-MCT model also performed comparably well with the calibrated HBV and TOPMODEL, highlighting the robustness of the HSC model to both describe the spatial distribution of the root zone storage capacity and the efficiency of the MCT method to estimate the SuMax. Moreover, the HSC-MCT model facilitated effective visualization of the saturated area, which has the potential to be used for broader geoscience studies beyond hydrology.

Predictive model for CO2 generation and decay in building envelopes

NASA Astrophysics Data System (ADS)

Aglan, Heshmat A.

2003-01-01

Understanding carbon dioxide generation and decay patterns in buildings with high occupancy levels is useful to identify their indoor air quality, air change rates, percent fresh air makeup, occupancy pattern, and how a variable air volume system to off-set undesirable CO2 level can be modulated. A mathematical model governing the generation and decay of CO2 in building envelopes with forced ventilation due to high occupancy is developed. The model has been verified experimentally in a newly constructed energy efficient healthy house. It was shown that the model accurately predicts the CO2 concentration at any time during the generation and decay processes.

Genome Editing in Rats Using TALE Nucleases.

PubMed

Tesson, Laurent; Remy, Séverine; Ménoret, Séverine; Usal, Claire; Thinard, Reynald; Savignard, Chloé; De Cian, Anne; Giovannangeli, Carine; Concordet, Jean-Paul; Anegon, Ignacio

2016-01-01

The rat is an important animal model to understand gene function and model human diseases. Since recent years, the development of gene-specific nucleases has become important for generating new rat models of human diseases, to analyze the role of genes and to generate human antibodies. Transcription activator-like (TALE) nucleases efficiently create gene-specific knockout rats and lead to the possibility of gene targeting by homology-directed recombination (HDR) and generating knock-in rats. We describe a detailed protocol for generating knockout and knock-in rats via microinjection of TALE nucleases into fertilized eggs. This technology is an efficient, cost- and time-effective method for creating new rat models.

Numerical modeling of particle generation from ozone reactions with human-worn clothing in indoor environments

NASA Astrophysics Data System (ADS)

Rai, Aakash C.; Lin, Chao-Hsin; Chen, Qingyan

2015-02-01

Ozone-terpene reactions are important sources of indoor ultrafine particles (UFPs), a potential health hazard for human beings. Humans themselves act as possible sites for ozone-initiated particle generation through reactions with squalene (a terpene) that is present in their skin, hair, and clothing. This investigation developed a numerical model to probe particle generation from ozone reactions with clothing worn by humans. The model was based on particle generation measured in an environmental chamber as well as physical formulations of particle nucleation, condensational growth, and deposition. In five out of the six test cases, the model was able to predict particle size distributions reasonably well. The failure in the remaining case demonstrated the fundamental limitations of nucleation models. The model that was developed was used to predict particle generation under various building and airliner cabin conditions. These predictions indicate that ozone reactions with human-worn clothing could be an important source of UFPs in densely occupied classrooms and airliner cabins. Those reactions could account for about 40% of the total UFPs measured on a Boeing 737-700 flight. The model predictions at this stage are indicative and should be improved further.

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-12-18

This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and comparesmore » the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

Stochastic Modeling based on Dictionary Approach for the Generation of Daily Precipitation Occurrences

NASA Astrophysics Data System (ADS)

Panu, U. S.; Ng, W.; Rasmussen, P. F.

2009-12-01

The modeling of weather states (i.e., precipitation occurrences) is critical when the historical data are not long enough for the desired analysis. Stochastic models (e.g., Markov Chain and Alternating Renewal Process (ARP)) of the precipitation occurrence processes generally assume the existence of short-term temporal-dependency between the neighboring states while implying the existence of long-term independency (randomness) of states in precipitation records. Existing temporal-dependent models for the generation of precipitation occurrences are restricted either by the fixed-length memory (e.g., the order of a Markov chain model), or by the reining states in segments (e.g., persistency of homogenous states within dry/wet-spell lengths of an ARP). The modeling of variable segment lengths and states could be an arduous task and a flexible modeling approach is required for the preservation of various segmented patterns of precipitation data series. An innovative Dictionary approach has been developed in the field of genome pattern recognition for the identification of frequently occurring genome segments in DNA sequences. The genome segments delineate the biologically meaningful ``words" (i.e., segments with a specific patterns in a series of discrete states) that can be jointly modeled with variable lengths and states. A meaningful “word”, in hydrology, can be referred to a segment of precipitation occurrence comprising of wet or dry states. Such flexibility would provide a unique advantage over the traditional stochastic models for the generation of precipitation occurrences. Three stochastic models, namely, the alternating renewal process using Geometric distribution, the second-order Markov chain model, and the Dictionary approach have been assessed to evaluate their efficacy for the generation of daily precipitation sequences. Comparisons involved three guiding principles namely (i) the ability of models to preserve the short-term temporal-dependency in data through the concepts of autocorrelation, average mutual information, and Hurst exponent, (ii) the ability of models to preserve the persistency within the homogenous dry/wet weather states through analysis of dry/wet-spell lengths between the observed and generated data, and (iii) the ability to assesses the goodness-of-fit of models through the likelihood estimates (i.e., AIC and BIC). Past 30 years of observed daily precipitation records from 10 Canadian meteorological stations were utilized for comparative analyses of the three models. In general, the Markov chain model performed well. The remainders of the models were found to be competitive from one another depending upon the scope and purpose of the comparison. Although the Markov chain model has a certain advantage in the generation of daily precipitation occurrences, the structural flexibility offered by the Dictionary approach in modeling the varied segment lengths of heterogeneous weather states provides a distinct and powerful advantage in the generation of precipitation sequences.

The S-curve for forecasting waste generation in construction projects.

PubMed

Lu, Weisheng; Peng, Yi; Chen, Xi; Skitmore, Martin; Zhang, Xiaoling

2016-10-01

Forecasting construction waste generation is the yardstick of any effort by policy-makers, researchers, practitioners and the like to manage construction and demolition (C&D) waste. This paper develops and tests an S-curve model to indicate accumulative waste generation as a project progresses. Using 37,148 disposal records generated from 138 building projects in Hong Kong in four consecutive years from January 2011 to June 2015, a wide range of potential S-curve models are examined, and as a result, the formula that best fits the historical data set is found. The S-curve model is then further linked to project characteristics using artificial neural networks (ANNs) so that it can be used to forecast waste generation in future construction projects. It was found that, among the S-curve models, cumulative logistic distribution is the best formula to fit the historical data. Meanwhile, contract sum, location, public-private nature, and duration can be used to forecast construction waste generation. The study provides contractors with not only an S-curve model to forecast overall waste generation before a project commences, but also with a detailed baseline to benchmark and manage waste during the course of construction. The major contribution of this paper is to the body of knowledge in the field of construction waste generation forecasting. By examining it with an S-curve model, the study elevates construction waste management to a level equivalent to project cost management where the model has already been readily accepted as a standard tool. Copyright © 2016 Elsevier Ltd. All rights reserved.

Coupling LaGrit unstructured mesh generation and model setup with TOUGH2 flow and transport: A case study

DOE PAGES

Sentis, Manuel Lorenzo; Gable, Carl W.

2017-06-15

Furthermore, there are many applications in science and engineering modeling where an accurate representation of a complex model geometry in the form of a mesh is important. In applications of flow and transport in subsurface porous media, this is manifest in models that must capture complex geologic stratigraphy, structure (faults, folds, erosion, deposition) and infrastructure (tunnels, boreholes, excavations). Model setup, defined as the activities of geometry definition, mesh generation (creation, optimization, modification, refine, de-refine, smooth), assigning material properties, initial conditions and boundary conditions requires specialized software tools to automate and streamline the process. In addition, some model setup tools willmore » provide more utility if they are designed to interface with and meet the needs of a particular flow and transport software suite. A control volume discretization that uses a two point flux approximation is for example most accurate when the underlying control volumes are 2D or 3D Voronoi tessellations. In this paper we will present the coupling of LaGriT, a mesh generation and model setup software suite and TOUGH2 to model subsurface flow problems and we show an example of how LaGriT can be used as a model setup tool for the generation of a Voronoi mesh for the simulation program TOUGH2. To generate the MESH file for TOUGH2 from the LaGriT output a standalone module Lagrit2Tough2 was developed, which is presented here and will be included in a future release of LaGriT. Here in this paper an alternative method to generate a Voronoi mesh for TOUGH2 with LaGriT is presented and thanks to the modular and command based structure of LaGriT this method is well suited to generating a mesh for complex models.« less

TH-CD-202-07: A Methodology for Generating Numerical Phantoms for Radiation Therapy Using Geometric Attribute Distribution Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dolly, S; Chen, H; Mutic, S

Purpose: A persistent challenge for the quality assessment of radiation therapy treatments (e.g. contouring accuracy) is the absence of the known, ground truth for patient data. Moreover, assessment results are often patient-dependent. Computer simulation studies utilizing numerical phantoms can be performed for quality assessment with a known ground truth. However, previously reported numerical phantoms do not include the statistical properties of inter-patient variations, as their models are based on only one patient. In addition, these models do not incorporate tumor data. In this study, a methodology was developed for generating numerical phantoms which encapsulate the statistical variations of patients withinmore » radiation therapy, including tumors. Methods: Based on previous work in contouring assessment, geometric attribute distribution (GAD) models were employed to model both the deterministic and stochastic properties of individual organs via principle component analysis. Using pre-existing radiation therapy contour data, the GAD models are trained to model the shape and centroid distributions of each organ. Then, organs with different shapes and positions can be generated by assigning statistically sound weights to the GAD model parameters. Organ contour data from 20 retrospective prostate patient cases were manually extracted and utilized to train the GAD models. As a demonstration, computer-simulated CT images of generated numerical phantoms were calculated and assessed subjectively and objectively for realism. Results: A cohort of numerical phantoms of the male human pelvis was generated. CT images were deemed realistic both subjectively and objectively in terms of image noise power spectrum. Conclusion: A methodology has been developed to generate realistic numerical anthropomorphic phantoms using pre-existing radiation therapy data. The GAD models guarantee that generated organs span the statistical distribution of observed radiation therapy patients, according to the training dataset. The methodology enables radiation therapy treatment assessment with multi-modality imaging and a known ground truth, and without patient-dependent bias.« less

Coupling LaGrit unstructured mesh generation and model setup with TOUGH2 flow and transport: A case study

NASA Astrophysics Data System (ADS)

Sentís, Manuel Lorenzo; Gable, Carl W.

2017-11-01

There are many applications in science and engineering modeling where an accurate representation of a complex model geometry in the form of a mesh is important. In applications of flow and transport in subsurface porous media, this is manifest in models that must capture complex geologic stratigraphy, structure (faults, folds, erosion, deposition) and infrastructure (tunnels, boreholes, excavations). Model setup, defined as the activities of geometry definition, mesh generation (creation, optimization, modification, refine, de-refine, smooth), assigning material properties, initial conditions and boundary conditions requires specialized software tools to automate and streamline the process. In addition, some model setup tools will provide more utility if they are designed to interface with and meet the needs of a particular flow and transport software suite. A control volume discretization that uses a two point flux approximation is for example most accurate when the underlying control volumes are 2D or 3D Voronoi tessellations. In this paper we will present the coupling of LaGriT, a mesh generation and model setup software suite and TOUGH2 (Pruess et al., 1999) to model subsurface flow problems and we show an example of how LaGriT can be used as a model setup tool for the generation of a Voronoi mesh for the simulation program TOUGH2. To generate the MESH file for TOUGH2 from the LaGriT output a standalone module Lagrit2Tough2 was developed, which is presented here and will be included in a future release of LaGriT. In this paper an alternative method to generate a Voronoi mesh for TOUGH2 with LaGriT is presented and thanks to the modular and command based structure of LaGriT this method is well suited to generating a mesh for complex models.

Integrated surface-subsurface model to investigate the role of groundwater in headwater catchment runoff generation: A minimalist approach to parameterisation

NASA Astrophysics Data System (ADS)

Ala-aho, Pertti; Soulsby, Chris; Wang, Hailong; Tetzlaff, Doerthe

2017-04-01

Understanding the role of groundwater for runoff generation in headwater catchments is a challenge in hydrology, particularly so in data-scarce areas. Fully-integrated surface-subsurface modelling has shown potential in increasing process understanding for runoff generation, but high data requirements and difficulties in model calibration are typically assumed to preclude their use in catchment-scale studies. We used a fully integrated surface-subsurface hydrological simulator to enhance groundwater-related process understanding in a headwater catchment with a rich background in empirical data. To set up the model we used minimal data that could be reasonably expected to exist for any experimental catchment. A novel aspect of our approach was in using simplified model parameterisation and including parameters from all model domains (surface, subsurface, evapotranspiration) in automated model calibration. Calibration aimed not only to improve model fit, but also to test the information content of the observations (streamflow, remotely sensed evapotranspiration, median groundwater level) used in calibration objective functions. We identified sensitive parameters in all model domains (subsurface, surface, evapotranspiration), demonstrating that model calibration should be inclusive of parameters from these different model domains. Incorporating groundwater data in calibration objectives improved the model fit for groundwater levels, but simulations did not reproduce well the remotely sensed evapotranspiration time series even after calibration. Spatially explicit model output improved our understanding of how groundwater functions in maintaining streamflow generation primarily via saturation excess overland flow. Steady groundwater inputs created saturated conditions in the valley bottom riparian peatlands, leading to overland flow even during dry periods. Groundwater on the hillslopes was more dynamic in its response to rainfall, acting to expand the saturated area extent and thereby promoting saturation excess overland flow during rainstorms. Our work shows the potential of using integrated surface-subsurface modelling alongside with rigorous model calibration to better understand and visualise the role of groundwater in runoff generation even with limited datasets.

Computed tomography landmark-based semi-automated mesh morphing and mapping techniques: generation of patient specific models of the human pelvis without segmentation.

PubMed

Salo, Zoryana; Beek, Maarten; Wright, David; Whyne, Cari Marisa

2015-04-13

Current methods for the development of pelvic finite element (FE) models generally are based upon specimen specific computed tomography (CT) data. This approach has traditionally required segmentation of CT data sets, which is time consuming and necessitates high levels of user intervention due to the complex pelvic anatomy. The purpose of this research was to develop and assess CT landmark-based semi-automated mesh morphing and mapping techniques to aid the generation and mechanical analysis of specimen-specific FE models of the pelvis without the need for segmentation. A specimen-specific pelvic FE model (source) was created using traditional segmentation methods and morphed onto a CT scan of a different (target) pelvis using a landmark-based method. The morphed model was then refined through mesh mapping by moving the nodes to the bone boundary. A second target model was created using traditional segmentation techniques. CT intensity based material properties were assigned to the morphed/mapped model and to the traditionally segmented target models. Models were analyzed to evaluate their geometric concurrency and strain patterns. Strains generated in a double-leg stance configuration were compared to experimental strain gauge data generated from the same target cadaver pelvis. CT landmark-based morphing and mapping techniques were efficiently applied to create a geometrically multifaceted specimen-specific pelvic FE model, which was similar to the traditionally segmented target model and better replicated the experimental strain results (R(2)=0.873). This study has shown that mesh morphing and mapping represents an efficient validated approach for pelvic FE model generation without the need for segmentation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Large-scale building scenes reconstruction from close-range images based on line and plane feature

NASA Astrophysics Data System (ADS)

Ding, Yi; Zhang, Jianqing

2007-11-01

Automatic generate 3D models of buildings and other man-made structures from images has become a topic of increasing importance, those models may be in applications such as virtual reality, entertainment industry and urban planning. In this paper we address the main problems and available solution for the generation of 3D models from terrestrial images. We first generate a coarse planar model of the principal scene planes and then reconstruct windows to refine the building models. There are several points of novelty: first we reconstruct the coarse wire frame model use the line segments matching with epipolar geometry constraint; Secondly, we detect the position of all windows in the image and reconstruct the windows by established corner points correspondences between images, then add the windows to the coarse model to refine the building models. The strategy is illustrated on image triple of college building.

A Protocol for Generating and Exchanging (Genome-Scale) Metabolic Resource Allocation Models.

PubMed

Reimers, Alexandra-M; Lindhorst, Henning; Waldherr, Steffen

2017-09-06

In this article, we present a protocol for generating a complete (genome-scale) metabolic resource allocation model, as well as a proposal for how to represent such models in the systems biology markup language (SBML). Such models are used to investigate enzyme levels and achievable growth rates in large-scale metabolic networks. Although the idea of metabolic resource allocation studies has been present in the field of systems biology for some years, no guidelines for generating such a model have been published up to now. This paper presents step-by-step instructions for building a (dynamic) resource allocation model, starting with prerequisites such as a genome-scale metabolic reconstruction, through building protein and noncatalytic biomass synthesis reactions and assigning turnover rates for each reaction. In addition, we explain how one can use SBML level 3 in combination with the flux balance constraints and our resource allocation modeling annotation to represent such models.

Learning and inference using complex generative models in a spatial localization task.

PubMed

Bejjanki, Vikranth R; Knill, David C; Aslin, Richard N

2016-01-01

A large body of research has established that, under relatively simple task conditions, human observers integrate uncertain sensory information with learned prior knowledge in an approximately Bayes-optimal manner. However, in many natural tasks, observers must perform this sensory-plus-prior integration when the underlying generative model of the environment consists of multiple causes. Here we ask if the Bayes-optimal integration seen with simple tasks also applies to such natural tasks when the generative model is more complex, or whether observers rely instead on a less efficient set of heuristics that approximate ideal performance. Participants localized a "hidden" target whose position on a touch screen was sampled from a location-contingent bimodal generative model with different variances around each mode. Over repeated exposure to this task, participants learned the a priori locations of the target (i.e., the bimodal generative model), and integrated this learned knowledge with uncertain sensory information on a trial-by-trial basis in a manner consistent with the predictions of Bayes-optimal behavior. In particular, participants rapidly learned the locations of the two modes of the generative model, but the relative variances of the modes were learned much more slowly. Taken together, our results suggest that human performance in a more complex localization task, which requires the integration of sensory information with learned knowledge of a bimodal generative model, is consistent with the predictions of Bayes-optimal behavior, but involves a much longer time-course than in simpler tasks.

Knowledge-based approach for generating target system specifications from a domain model

NASA Technical Reports Server (NTRS)

Gomaa, Hassan; Kerschberg, Larry; Sugumaran, Vijayan

1992-01-01

Several institutions in industry and academia are pursuing research efforts in domain modeling to address unresolved issues in software reuse. To demonstrate the concepts of domain modeling and software reuse, a prototype software engineering environment is being developed at George Mason University to support the creation of domain models and the generation of target system specifications. This prototype environment, which is application domain independent, consists of an integrated set of commercial off-the-shelf software tools and custom-developed software tools. This paper describes the knowledge-based tool that was developed as part of the environment to generate target system specifications from a domain model.

The influence of initial and surface boundary conditions on a model-generated January climatology

NASA Technical Reports Server (NTRS)

Wu, K. F.; Spar, J.

1981-01-01

The influence on a model-generated January climate of various surface boundary conditions, as well as initial conditions, was studied by using the GISS coarse-mesh climate model. Four experiments - two with water planets, one with flat continents, and one with mountains - were used to investigate the effects of initial conditions, and the thermal and dynamical effects of the surface on the model generated-climate. However, climatological mean zonal-symmetric sea surface temperature is used in all four runs over the model oceans. Moreover, zero ground wetness and uniform ground albedo except for snow are used in the last experiments.

NOAA's weather forecasts go hyper-local with next-generation weather

Science.gov Websites

model NOAA HOME WEATHER OCEANS FISHERIES CHARTING SATELLITES CLIMATE RESEARCH COASTS CAREERS with next-generation weather model New model will help forecasters predict a storm's path, timing and intensity better than ever September 30, 2014 This is a comparison of two weather forecast models looking

Implementing subgrid-scale cloudiness into the Model for Prediction Across Scales-Atmosphere (MPAS-A) for next generation global air quality modeling

EPA Science Inventory

A next generation air quality modeling system is being developed at the U.S. EPA to enable seamless modeling of air quality from global to regional to (eventually) local scales. State of the science chemistry and aerosol modules from the Community Multiscale Air Quality (CMAQ) mo...

View generated database

NASA Technical Reports Server (NTRS)

Downward, James G.

1992-01-01

This document represents the final report for the View Generated Database (VGD) project, NAS7-1066. It documents the work done on the project up to the point at which all project work was terminated due to lack of project funds. The VGD was to provide the capability to accurately represent any real-world object or scene as a computer model. Such models include both an accurate spatial/geometric representation of surfaces of the object or scene, as well as any surface detail present on the object. Applications of such models are numerous, including acquisition and maintenance of work models for tele-autonomous systems, generation of accurate 3-D geometric/photometric models for various 3-D vision systems, and graphical models for realistic rendering of 3-D scenes via computer graphics.

Turing instability in reaction-diffusion models on complex networks

NASA Astrophysics Data System (ADS)

Ide, Yusuke; Izuhara, Hirofumi; Machida, Takuya

2016-09-01

In this paper, the Turing instability in reaction-diffusion models defined on complex networks is studied. Here, we focus on three types of models which generate complex networks, i.e. the Erdős-Rényi, the Watts-Strogatz, and the threshold network models. From analysis of the Laplacian matrices of graphs generated by these models, we numerically reveal that stable and unstable regions of a homogeneous steady state on the parameter space of two diffusion coefficients completely differ, depending on the network architecture. In addition, we theoretically discuss the stable and unstable regions in the cases of regular enhanced ring lattices which include regular circles, and networks generated by the threshold network model when the number of vertices is large enough.

Loss of feed flow, steam generator tube rupture and steam line break thermohydraulic experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mendler, O J; Takeuchi, K; Young, M Y

1986-10-01

The Westinghouse Model Boiler No. 2 (MB-2) steam generator test model at the Engineering Test Facility in Tampa, Florida, was reinstrumented and modified for performing a series of tests simulating steam generator accident transients. The transients simulated were: loss of feed flow, steam generator tube rupture, and steam line break events. This document presents a description of (1) the model boiler and the associated test facility, (2) the tests performed, and (3) the analyses of the test results.

Multiple-generator errors are unavoidable under model misspecification.

PubMed

Jewett, D L; Zhang, Z

1995-08-01

Model misspecification poses a major problem for dipole source localization (DSL) because it causes insidious multiple-generator errors (MulGenErrs) to occur in the fitted dipole parameters. This paper describes how and why this occurs, based upon simple algebraic considerations. MulGenErrs must occur, to some degree, in any DSL analysis of real data because there is model misspecification and mathematically the equations used for the simultaneously active generators must be of a different form than the equations for each generator active alone.

The generation and use of numerical shape models for irregular Solar System objects

NASA Technical Reports Server (NTRS)

Simonelli, Damon P.; Thomas, Peter C.; Carcich, Brian T.; Veverka, Joseph

1993-01-01

We describe a procedure that allows the efficient generation of numerical shape models for irregular Solar System objects, where a numerical model is simply a table of evenly spaced body-centered latitudes and longitudes and their associated radii. This modeling technique uses a combination of data from limbs, terminators, and control points, and produces shape models that have some important advantages over analytical shape models. Accurate numerical shape models make it feasible to study irregular objects with a wide range of standard scientific analysis techniques. These applications include the determination of moments of inertia and surface gravity, the mapping of surface locations and structural orientations, photometric measurement and analysis, the reprojection and mosaicking of digital images, and the generation of albedo maps. The capabilities of our modeling procedure are illustrated through the development of an accurate numerical shape model for Phobos and the production of a global, high-resolution, high-pass-filtered digital image mosaic of this Martian moon. Other irregular objects that have been modeled, or are being modeled, include the asteroid Gaspra and the satellites Deimos, Amalthea, Epimetheus, Janus, Hyperion, and Proteus.

Object-Oriented Modeling of an Energy Harvesting System Based on Thermoelectric Generators

NASA Astrophysics Data System (ADS)

Nesarajah, Marco; Frey, Georg

This paper deals with the modeling of an energy harvesting system based on thermoelectric generators (TEG), and the validation of the model by means of a test bench. TEGs are capable to improve the overall energy efficiency of energy systems, e.g. combustion engines or heating systems, by using the remaining waste heat to generate electrical power. Previously, a component-oriented model of the TEG itself was developed in Modelica® language. With this model any TEG can be described and simulated given the material properties and the physical dimension. Now, this model was extended by the surrounding components to a complete model of a thermoelectric energy harvesting system. In addition to the TEG, the model contains the cooling system, the heat source, and the power electronics. To validate the simulation model, a test bench was built and installed on an oil-fired household heating system. The paper reports results of the measurements and discusses the validity of the developed simulation models. Furthermore, the efficiency of the proposed energy harvesting system is derived and possible improvements based on design variations tested in the simulation model are proposed.

Generation Of A Mouse Model For Schwannomatosis

DTIC Science & Technology

2010-09-01

TITLE: Generation of a Mouse Model for Schwannomatosis PRINCIPAL INVESTIGATOR: Long-Sheng Chang, Ph.D. CONTRACTING ORGANIZATION: The...Annual 3. DATES COVERED (From - To) 1 Sep 2009 - 31 Aug 2010 4. TITLE AND SUBTITLE Generation of a Mouse Model for Schwannomatosis 5a. CONTRACT...hypothesis involving inactivation of both the INI1/SNF5 and NF2 tumor suppressor genes in the formation of schwannomatosis -associated tumors. To

Leading Generative Groups: A Conceptual Model

ERIC Educational Resources Information Center

London, Manuel; Sobel-Lojeski, Karen A.; Reilly, Richard R.

2012-01-01

This article presents a conceptual model of leadership in generative groups. Generative groups have diverse team members who are expected to develop innovative solutions to complex, unstructured problems. The challenge for leaders of generative groups is to balance (a) establishing shared goals with recognizing members' vested interests, (b)…

Evaluating the Psychometric Characteristics of Generated Multiple-Choice Test Items

ERIC Educational Resources Information Center

Gierl, Mark J.; Lai, Hollis; Pugh, Debra; Touchie, Claire; Boulais, André-Philippe; De Champlain, André

2016-01-01

Item development is a time- and resource-intensive process. Automatic item generation integrates cognitive modeling with computer technology to systematically generate test items. To date, however, items generated using cognitive modeling procedures have received limited use in operational testing situations. As a result, the psychometric…

Aeon: Synthesizing Scheduling Algorithms from High-Level Models

NASA Astrophysics Data System (ADS)

Monette, Jean-Noël; Deville, Yves; van Hentenryck, Pascal

This paper describes the aeon system whose aim is to synthesize scheduling algorithms from high-level models. A eon, which is entirely written in comet, receives as input a high-level model for a scheduling application which is then analyzed to generate a dedicated scheduling algorithm exploiting the structure of the model. A eon provides a variety of synthesizers for generating complete or heuristic algorithms. Moreover, synthesizers are compositional, making it possible to generate complex hybrid algorithms naturally. Preliminary experimental results indicate that this approach may be competitive with state-of-the-art search algorithms.

Multi-objective optimization for generating a weighted multi-model ensemble

NASA Astrophysics Data System (ADS)

Lee, H.

2017-12-01

Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic ensemble mean and may provide reliable future projections.

A global reference for caesarean section rates (C-Model): a multicountry cross-sectional study.

PubMed

Souza, J P; Betran, A P; Dumont, A; de Mucio, B; Gibbs Pickens, C M; Deneux-Tharaux, C; Ortiz-Panozo, E; Sullivan, E; Ota, E; Togoobaatar, G; Carroli, G; Knight, H; Zhang, J; Cecatti, J G; Vogel, J P; Jayaratne, K; Leal, M C; Gissler, M; Morisaki, N; Lack, N; Oladapo, O T; Tunçalp, Ö; Lumbiganon, P; Mori, R; Quintana, S; Costa Passos, A D; Marcolin, A C; Zongo, A; Blondel, B; Hernández, B; Hogue, C J; Prunet, C; Landman, C; Ochir, C; Cuesta, C; Pileggi-Castro, C; Walker, D; Alves, D; Abalos, E; Moises, Ecd; Vieira, E M; Duarte, G; Perdona, G; Gurol-Urganci, I; Takahiko, K; Moscovici, L; Campodonico, L; Oliveira-Ciabati, L; Laopaiboon, M; Danansuriya, M; Nakamura-Pereira, M; Costa, M L; Torloni, M R; Kramer, M R; Borges, P; Olkhanud, P B; Pérez-Cuevas, R; Agampodi, S B; Mittal, S; Serruya, S; Bataglia, V; Li, Z; Temmerman, M; Gülmezoglu, A M

2016-02-01

To generate a global reference for caesarean section (CS) rates at health facilities. Cross-sectional study. Health facilities from 43 countries. Thirty eight thousand three hundred and twenty-four women giving birth from 22 countries for model building and 10,045,875 women giving birth from 43 countries for model testing. We hypothesised that mathematical models could determine the relationship between clinical-obstetric characteristics and CS. These models generated probabilities of CS that could be compared with the observed CS rates. We devised a three-step approach to generate the global benchmark of CS rates at health facilities: creation of a multi-country reference population, building mathematical models, and testing these models. Area under the ROC curves, diagnostic odds ratio, expected CS rate, observed CS rate. According to the different versions of the model, areas under the ROC curves suggested a good discriminatory capacity of C-Model, with summary estimates ranging from 0.832 to 0.844. The C-Model was able to generate expected CS rates adjusted for the case-mix of the obstetric population. We have also prepared an e-calculator to facilitate use of C-Model (www.who.int/reproductivehealth/publications/maternal_perinatal_health/c-model/en/). This article describes the development of a global reference for CS rates. Based on maternal characteristics, this tool was able to generate an individualised expected CS rate for health facilities or groups of health facilities. With C-Model, obstetric teams, health system managers, health facilities, health insurance companies, and governments can produce a customised reference CS rate for assessing use (and overuse) of CS. The C-Model provides a customized benchmark for caesarean section rates in health facilities and systems. © 2015 World Health Organization; licensed by John Wiley & Sons Ltd on behalf of Royal College of Obstetricians and Gynaecologists.

Study on generation investment decision-making considering multi-agent benefit for global energy internet

NASA Astrophysics Data System (ADS)

Li, Pai; Huang, Yuehui; Jia, Yanbing; Liu, Jichun; Niu, Yi

2018-02-01

Abstract . This article has studies on the generation investment decision in the background of global energy interconnection. Generation investment decision model considering the multiagent benefit is proposed. Under the back-ground of global energy Interconnection, generation investors in different clean energy base not only compete with other investors, but also facing being chosen by the power of the central area, therefor, constructing generation investment decision model considering multiagent benefit can be close to meet the interests demands. Using game theory, the complete information game model is adopted to solve the strategies of different subjects in equilibrium state.

A New Model that Generates Lotka's Law.

ERIC Educational Resources Information Center

Huber, John C.

2002-01-01

Develops a new model for a process that generates Lotka's Law. Topics include measuring scientific productivity through the number of publications; rate of production; career duration; randomness; Poisson distribution; computer simulations; goodness-of-fit; theoretical support for the model; and future research. (Author/LRW)

An evaluation of the transferability of cross classification trip generation models.

DOT National Transportation Integrated Search

1978-01-01

This report describes the results of the application in Virginia of the trip generation procedures described in the Federal Highway Administration report entitled Trip Generation Analysis and published in 1975. Cross classification models, disaggrega...

LISP based simulation generators for modeling complex space processes

NASA Technical Reports Server (NTRS)

Tseng, Fan T.; Schroer, Bernard J.; Dwan, Wen-Shing

1987-01-01

The development of a simulation assistant for modeling discrete event processes is presented. Included are an overview of the system, a description of the simulation generators, and a sample process generated using the simulation assistant.

Continuous data assimilation for downscaling large-footprint soil moisture retrievals

NASA Astrophysics Data System (ADS)

Altaf, Muhammad U.; Jana, Raghavendra B.; Hoteit, Ibrahim; McCabe, Matthew F.

2016-10-01

Soil moisture is a key component of the hydrologic cycle, influencing processes leading to runoff generation, infiltration and groundwater recharge, evaporation and transpiration. Generally, the measurement scale for soil moisture is found to be different from the modeling scales for these processes. Reducing this mismatch between observation and model scales in necessary for improved hydrological modeling. An innovative approach to downscaling coarse resolution soil moisture data by combining continuous data assimilation and physically based modeling is presented. In this approach, we exploit the features of Continuous Data Assimilation (CDA) which was initially designed for general dissipative dynamical systems and later tested numerically on the incompressible Navier-Stokes equation, and the Benard equation. A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model's large scale variability by available measurements. Soil moisture fields generated at a fine resolution by a physically-based vadose zone model (HYDRUS) are subjected to data assimilation conditioned upon coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. Results show that the approach is feasible to generate fine scale soil moisture fields across large extents, based on coarse scale observations. Application of this approach is likely in generating fine and intermediate resolution soil moisture fields conditioned on the radiometerbased, coarse resolution products from remote sensing satellites.

A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation--With Application to Tumor and Stroke.

PubMed

Menze, Bjoern H; Van Leemput, Koen; Lashkari, Danial; Riklin-Raviv, Tammy; Geremia, Ezequiel; Alberts, Esther; Gruber, Philipp; Wegener, Susanne; Weber, Marc-Andre; Szekely, Gabor; Ayache, Nicholas; Golland, Polina

2016-04-01

We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM), to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as "tumor core" or "fluid-filled structure", but without a one-to-one correspondence to the hypo- or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the extended discriminative -discriminative model to be one of the top ranking methods in the BRATS evaluation.

Analytical modelling of Halbach linear generator incorporating pole shifting and piece-wise spring for ocean wave energy harvesting

NASA Astrophysics Data System (ADS)

Tan, Yimin; Lin, Kejian; Zu, Jean W.

2018-05-01

Halbach permanent magnet (PM) array has attracted tremendous research attention in the development of electromagnetic generators for its unique properties. This paper has proposed a generalized analytical model for linear generators. The slotted stator pole-shifting and implementation of Halbach array have been combined for the first time. Initially, the magnetization components of the Halbach array have been determined using Fourier decomposition. Then, based on the magnetic scalar potential method, the magnetic field distribution has been derived employing specially treated boundary conditions. FEM analysis has been conducted to verify the analytical model. A slotted linear PM generator with Halbach PM has been constructed to validate the model and further improved using piece-wise springs to trigger full range reciprocating motion. A dynamic model has been developed to characterize the dynamic behavior of the slider. This analytical method provides an effective tool in development and optimization of Halbach PM generator. The experimental results indicate that piece-wise springs can be employed to improve generator performance under low excitation frequency.

A Structural Model Decomposition Framework for Hybrid Systems Diagnosis

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil

2015-01-01

Nowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study.

Models for nearly every occasion: Part I - One box models.

PubMed

Hewett, Paul; Ganser, Gary H

2017-01-01

The standard "well mixed room," "one box" model cannot be used to predict occupational exposures whenever the scenario involves the use of local controls. New "constant emission" one box models are proposed that permit either local exhaust or local exhaust with filtered return, coupled with general room ventilation or the recirculation of a portion of the general room exhaust. New "two box" models are presented in Part II of this series. Both steady state and transient models were developed. The steady state equation for each model, including the standard one box steady state model, is augmented with an additional factor reflecting the fraction of time the substance was generated during each task. This addition allows the easy calculation of the average exposure for cyclic and irregular emission patterns, provided the starting and ending concentrations are zero or near zero, or the cumulative time across all tasks is long (e.g., several tasks to a full shift). The new models introduce additional variables, such as the efficiency of the local exhaust to immediately capture freshly generated contaminant and the filtration efficiency whenever filtered exhaust is returned to the workspace. Many of the model variables are knowable (e.g., room volume and ventilation rate). A structured procedure for calibrating a model to a work scenario is introduced that can be applied to both continuous and cyclic processes. The "calibration" procedure generates estimates of the generation rate and all of remaining unknown model variables.

Generating Models of Surgical Procedures using UMLS Concepts and Multiple Sequence Alignment

PubMed Central

Meng, Frank; D’Avolio, Leonard W.; Chen, Andrew A.; Taira, Ricky K.; Kangarloo, Hooshang

2005-01-01

Surgical procedures can be viewed as a process composed of a sequence of steps performed on, by, or with the patient’s anatomy. This sequence is typically the pattern followed by surgeons when generating surgical report narratives for documenting surgical procedures. This paper describes a methodology for semi-automatically deriving a model of conducted surgeries, utilizing a sequence of derived Unified Medical Language System (UMLS) concepts for representing surgical procedures. A multiple sequence alignment was computed from a collection of such sequences and was used for generating the model. These models have the potential of being useful in a variety of informatics applications such as information retrieval and automatic document generation. PMID:16779094

Fermion hierarchy from sfermion anarchy

DOE PAGES

Altmannshofer, Wolfgang; Frugiuele, Claudia; Harnik, Roni

2014-12-31

We present a framework to generate the hierarchical flavor structure of Standard Model quarks and leptons from loops of superpartners. The simplest model consists of the minimal supersymmetric standard model with tree level Yukawa couplings for the third generation only and anarchic squark and slepton mass matrices. Agreement with constraints from low energy flavor observables, in particular Kaon mixing, is obtained for supersymmetric particles with masses at the PeV scale or above. In our framework both the second and the first generation fermion masses are generated at 1-loop. Despite this, a novel mechanism generates a hierarchy among the first andmore » second generations without imposing a symmetry or small parameters. A second-to-first generation mass ratio of order 100 is typical. The minimal supersymmetric standard model thus includes all the necessary ingredients to realize a fermion spectrum that is qualitatively similar to observation, with hierarchical masses and mixing. The minimal framework produces only a few quantitative discrepancies with observation, most notably the muon mass is too low. Furthermore, we discuss simple modifications which resolve this and also investigate the compatibility of our model with gauge and Yukawa coupling Unification.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sentis, Manuel Lorenzo; Gable, Carl W.

Furthermore, there are many applications in science and engineering modeling where an accurate representation of a complex model geometry in the form of a mesh is important. In applications of flow and transport in subsurface porous media, this is manifest in models that must capture complex geologic stratigraphy, structure (faults, folds, erosion, deposition) and infrastructure (tunnels, boreholes, excavations). Model setup, defined as the activities of geometry definition, mesh generation (creation, optimization, modification, refine, de-refine, smooth), assigning material properties, initial conditions and boundary conditions requires specialized software tools to automate and streamline the process. In addition, some model setup tools willmore » provide more utility if they are designed to interface with and meet the needs of a particular flow and transport software suite. A control volume discretization that uses a two point flux approximation is for example most accurate when the underlying control volumes are 2D or 3D Voronoi tessellations. In this paper we will present the coupling of LaGriT, a mesh generation and model setup software suite and TOUGH2 to model subsurface flow problems and we show an example of how LaGriT can be used as a model setup tool for the generation of a Voronoi mesh for the simulation program TOUGH2. To generate the MESH file for TOUGH2 from the LaGriT output a standalone module Lagrit2Tough2 was developed, which is presented here and will be included in a future release of LaGriT. Here in this paper an alternative method to generate a Voronoi mesh for TOUGH2 with LaGriT is presented and thanks to the modular and command based structure of LaGriT this method is well suited to generating a mesh for complex models.« less

Towards a Model of Human Resource Solutions for Achieving Intergenerational Interaction in Organisations

ERIC Educational Resources Information Center

McGuire, David; By, Rune Todnem; Hutchings, Kate

2007-01-01

Purpose: Achieving intergenerational interaction and avoiding conflict is becoming increasingly difficult in a workplace populated by three generations--Baby Boomers, Generation X-ers and Generation Y-ers. This paper presents a model and proposes HR solutions towards achieving co-operative generational interaction. Design/methodology/approach:…

The Role of Item Models in Automatic Item Generation

ERIC Educational Resources Information Center

Gierl, Mark J.; Lai, Hollis

2012-01-01

Automatic item generation represents a relatively new but rapidly evolving research area where cognitive and psychometric theories are used to produce tests that include items generated using computer technology. Automatic item generation requires two steps. First, test development specialists create item models, which are comparable to templates…

Can virtual reality improve anatomy education? A randomised controlled study of a computer-generated three-dimensional anatomical ear model.

PubMed

Nicholson, Daren T; Chalk, Colin; Funnell, W Robert J; Daniel, Sam J

2006-11-01

The use of computer-generated 3-dimensional (3-D) anatomical models to teach anatomy has proliferated. However, there is little evidence that these models are educationally effective. The purpose of this study was to test the educational effectiveness of a computer-generated 3-D model of the middle and inner ear. We reconstructed a fully interactive model of the middle and inner ear from a magnetic resonance imaging scan of a human cadaver ear. To test the model's educational usefulness, we conducted a randomised controlled study in which 28 medical students completed a Web-based tutorial on ear anatomy that included the interactive model, while a control group of 29 students took the tutorial without exposure to the model. At the end of the tutorials, both groups were asked a series of 15 quiz questions to evaluate their knowledge of 3-D relationships within the ear. The intervention group's mean score on the quiz was 83%, while that of the control group was 65%. This difference in means was highly significant (P < 0.001). Our findings stand in contrast to the handful of previous randomised controlled trials that evaluated the effects of computer-generated 3-D anatomical models on learning. The equivocal and negative results of these previous studies may be due to the limitations of these studies (such as small sample size) as well as the limitations of the models that were studied (such as a lack of full interactivity). Given our positive results, we believe that further research is warranted concerning the educational effectiveness of computer-generated anatomical models.

TLS for generating multi-LOD of 3D building model

NASA Astrophysics Data System (ADS)

Akmalia, R.; Setan, H.; Majid, Z.; Suwardhi, D.; Chong, A.

2014-02-01

The popularity of Terrestrial Laser Scanners (TLS) to capture three dimensional (3D) objects has been used widely for various applications. Development in 3D models has also led people to visualize the environment in 3D. Visualization of objects in a city environment in 3D can be useful for many applications. However, different applications require different kind of 3D models. Since a building is an important object, CityGML has defined a standard for 3D building models at four different levels of detail (LOD). In this research, the advantages of TLS for capturing buildings and the modelling process of the point cloud can be explored. TLS will be used to capture all the building details to generate multi-LOD. This task, in previous works, involves usually the integration of several sensors. However, in this research, point cloud from TLS will be processed to generate the LOD3 model. LOD2 and LOD1 will then be generalized from the resulting LOD3 model. Result from this research is a guiding process to generate the multi-LOD of 3D building starting from LOD3 using TLS. Lastly, the visualization for multi-LOD model will also be shown.

[An ADAA model and its analysis method for agronomic traits based on the double-cross mating design].

PubMed

Xu, Z C; Zhu, J

2000-01-01

According to the double-cross mating design and using principles of Cockerham's general genetic model, a genetic model with additive, dominance and epistatic effects (ADAA model) was proposed for the analysis of agronomic traits. Components of genetic effects were derived for different generations. Monte Carlo simulation was conducted for analyzing the ADAA model and its reduced AD model by using different generations. It was indicated that genetic variance components could be estimated without bias by MINQUE(1) method and genetic effects could be predicted effectively by AUP method; at least three generations (including parent, F1 of single cross and F1 of double-cross) were necessary for analyzing the ADAA model and only two generations (including parent and F1 of double-cross) were enough for the reduced AD model. When epistatic effects were taken into account, a new approach for predicting the heterosis of agronomic traits of double-crosses was given on the basis of unbiased prediction of genotypic merits of parents and their crosses. In addition, genotype x environment interaction effects and interaction heterosis due to G x E interaction were discussed briefly.

Performance Model and Sensitivity Analysis for a Solar Thermoelectric Generator

NASA Astrophysics Data System (ADS)

Rehman, Naveed Ur; Siddiqui, Mubashir Ali

2017-03-01

In this paper, a regression model for evaluating the performance of solar concentrated thermoelectric generators (SCTEGs) is established and the significance of contributing parameters is discussed in detail. The model is based on several natural, design and operational parameters of the system, including the thermoelectric generator (TEG) module and its intrinsic material properties, the connected electrical load, concentrator attributes, heat transfer coefficients, solar flux, and ambient temperature. The model is developed by fitting a response curve, using the least-squares method, to the results. The sample points for the model were obtained by simulating a thermodynamic model, also developed in this paper, over a range of values of input variables. These samples were generated employing the Latin hypercube sampling (LHS) technique using a realistic distribution of parameters. The coefficient of determination was found to be 99.2%. The proposed model is validated by comparing the predicted results with those in the published literature. In addition, based on the elasticity for parameters in the model, sensitivity analysis was performed and the effects of parameters on the performance of SCTEGs are discussed in detail. This research will contribute to the design and performance evaluation of any SCTEG system for a variety of applications.

Animal Models of Lymphangioleiomyomatosis (LAM) and Tuberous Sclerosis Complex (TSC)

PubMed Central

2010-01-01

Abstract Animal models of lymphangioleiomyomatosis (LAM) and tuberous sclerosis complex (TSC) are highly desired to enable detailed investigation of the pathogenesis of these diseases. Multiple rats and mice have been generated in which a mutation similar to that occurring in TSC patients is present in an allele of Tsc1 or Tsc2. Unfortunately, these mice do not develop pathologic lesions that match those seen in LAM or TSC. However, these Tsc rodent models have been useful in confirming the two-hit model of tumor development in TSC, and in providing systems in which therapeutic trials (e.g., rapamycin) can be performed. In addition, conditional alleles of both Tsc1 and Tsc2 have provided the opportunity to target loss of these genes to specific tissues and organs, to probe the in vivo function of these genes, and attempt to generate better models. Efforts to generate an authentic LAM model are impeded by a lack of understanding of the cell of origin of this process. However, ongoing studies provide hope that such a model will be generated in the coming years. PMID:20235887

Deformation analysis of rotary combustion engine housings

NASA Technical Reports Server (NTRS)

Vilmann, Carl

1991-01-01

This analysis of the deformation of rotary combustion engine housings targeted the following objectives: (1) the development and verification of a finite element model of the trochoid housing, (2) the prediction of the stress and deformation fields present within the trochoid housing during operating conditions, and (3) the development of a specialized preprocessor which would shorten the time necessary for mesh generation of a trochoid housing's FEM model from roughly one month to approximately two man hours. Executable finite element models were developed for both the Mazda and the Outboard Marine Corporation trochoid housings. It was also demonstrated that a preprocessor which would hasten the generation of finite element models of a rotary engine was possible to develop. The above objectives are treated in detail in the attached appendices. The first deals with finite element modeling of a Wankel engine center housing, and the second with the development of a preprocessor that generates finite element models of rotary combustion engine center housings. A computer program, designed to generate finite element models of user defined rotary combustion engine center housing geometries, is also included.

Impact of metal ionic characteristics on adsorption potential of Ficus carica leaves using QSPR modeling.

PubMed

Batool, Fozia; Iqbal, Shahid; Akbar, Jamshed

2018-04-03

The present study describes Quantitative Structure Property Relationship (QSPR) modeling to relate metal ions characteristics with adsorption potential of Ficus carica leaves for 13 selected metal ions (Ca +2 , Cr +3 , Co +2 , Cu +2 , Cd +2 , K +1 , Mg +2 , Mn +2 , Na +1 , Ni +2 , Pb +2 , Zn +2 , and Fe +2 ) to generate QSPR model. A set of 21 characteristic descriptors were selected and relationship of these metal characteristics with adsorptive behavior of metal ions was investigated. Stepwise Multiple Linear Regression (SMLR) analysis and Artificial Neural Network (ANN) were applied for descriptors selection and model generation. Langmuir and Freundlich isotherms were also applied on adsorption data to generate proper correlation for experimental findings. Model generated indicated covalent index as the most significant descriptor, which is responsible for more than 90% predictive adsorption (α = 0.05). Internal validation of model was performed by measuring [Formula: see text] (0.98). The results indicate that present model is a useful tool for prediction of adsorptive behavior of different metal ions based on their ionic characteristics.

QSPR models for predicting generator-column-derived octanol/water and octanol/air partition coefficients of polychlorinated biphenyls.

PubMed

Yuan, Jintao; Yu, Shuling; Zhang, Ting; Yuan, Xuejie; Cao, Yunyuan; Yu, Xingchen; Yang, Xuan; Yao, Wu

2016-06-01

Octanol/water (K(OW)) and octanol/air (K(OA)) partition coefficients are two important physicochemical properties of organic substances. In current practice, K(OW) and K(OA) values of some polychlorinated biphenyls (PCBs) are measured using generator column method. Quantitative structure-property relationship (QSPR) models can serve as a valuable alternative method of replacing or reducing experimental steps in the determination of K(OW) and K(OA). In this paper, two different methods, i.e., multiple linear regression based on dragon descriptors and hologram quantitative structure-activity relationship, were used to predict generator-column-derived log K(OW) and log K(OA) values of PCBs. The predictive ability of the developed models was validated using a test set, and the performances of all generated models were compared with those of three previously reported models. All results indicated that the proposed models were robust and satisfactory and can thus be used as alternative models for the rapid assessment of the K(OW) and K(OA) of PCBs. Copyright © 2016 Elsevier Inc. All rights reserved.

Modeling strategic competition in hydro-thermal electricity generation markets with cascaded reservoir-hydroelectric generation plants

NASA Astrophysics Data System (ADS)

Uluca, Basak

This dissertation aims to achieve two goals. The first is to model the strategic interactions of firms that own cascaded reservoir-hydro plants in oligopolistic and mixed oligopolistic hydrothermal electricity generation markets. Although competition in thermal generation has been extensively modeled since the beginning of deregulation, the literature on competition in hydro generation is still limited; in particular, equilibrium models of oligopoly that study the competitive behavior of firms that own reservoir-hydro plants along the same river in hydrothermal electricity generation markets are still under development. In competitive markets, when the reservoirs are located along the same river, the water released from an upstream reservoir for electricity generation becomes input to the immediate downstream reservoir, which may be owned by a competitor, for current or future use. To capture the strategic interactions among firms with cascaded reservoir-hydro plants, the Upstream-Conjecture approach is proposed. Under the Upstream-Conjecture approach, a firm with an upstream reservoir-hydro plant assumes that firms with downstream reservoir-hydro plants will respond to changes in the upstream firm's water release by adjusting their water release by the same amount. The results of the Upstream Conjecture experiments indicate that firms that own upstream reservoirs in a cascade may have incentive to withhold or limit hydro generation, forcing a reduction in the utilization of the downstream hydro generation plants that are owned by competitors. Introducing competition to hydroelectricity generation markets is challenging and ownership allocation of the previously state-owned cascaded reservoir-hydro plants through privatization can have significant impact on the competitiveness of the generation market. The second goal of the dissertation is to extract empirical guidance about best policy choices for the ownership of the state-owned generation plants, including the cascaded reservoir-hydro plants. Specifically, an equilibrium model of oligopoly, where only private firms compete for electricity supply is proposed. Since some electricity generation markets are better characterized as mixed oligopolies, where the public firm coexists with the private firms for electricity supply, and not as oligopolies, another equilibrium model of mixed oligopoly is proposed. The proposed mixed oligopoly equilibrium model is the first implementation of such market structure in electricity markets. The mathematical models developed in this research are applied to the simplified representation of the Turkish electricity generation market to investigate the impact of various ownership allocation scenarios that may result from the privatization of the state owned generation plants, including the cascaded reservoir-hydro plants, on the competitive market outcomes.

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system

PubMed Central

Jensen, Tue V.; Pinson, Pierre

2017-01-01

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation. PMID:29182600

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system.

PubMed

Jensen, Tue V; Pinson, Pierre

2017-11-28

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation.

Fast modeling of flux trapping cascaded explosively driven magnetic flux compression generators.

PubMed

Wang, Yuwei; Zhang, Jiande; Chen, Dongqun; Cao, Shengguang; Li, Da; Liu, Chebo

2013-01-01

To predict the performance of flux trapping cascaded flux compression generators, a calculation model based on an equivalent circuit is investigated. The system circuit is analyzed according to its operation characteristics in different steps. Flux conservation coefficients are added to the driving terms of circuit differential equations to account for intrinsic flux losses. To calculate the currents in the circuit by solving the circuit equations, a simple zero-dimensional model is used to calculate the time-varying inductance and dc resistance of the generator. Then a fast computer code is programmed based on this calculation model. As an example, a two-staged flux trapping generator is simulated by using this computer code. Good agreements are achieved by comparing the simulation results with the measurements. Furthermore, it is obvious that this fast calculation model can be easily applied to predict performances of other flux trapping cascaded flux compression generators with complex structures such as conical stator or conical armature sections and so on for design purpose.

The Oak Ridge Competitive Electricity Dispatch (ORCED) Model Version 9

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hadley, Stanton W.; Baek, Young Sun

The Oak Ridge Competitive Electricity Dispatch (ORCED) model dispatches power plants in a region to meet the electricity demands for any single given year up to 2030. It uses publicly available sources of data describing electric power units such as the National Energy Modeling System and hourly demands from utility submittals to the Federal Energy Regulatory Commission that are projected to a future year. The model simulates a single region of the country for a given year, matching generation to demands and predefined net exports from the region, assuming no transmission constraints within the region. ORCED can calculate a numbermore » of key financial and operating parameters for generating units and regional market outputs including average and marginal prices, air emissions, and generation adequacy. By running the model with and without changes such as generation plants, fuel prices, emission costs, plug-in hybrid electric vehicles, distributed generation, or demand response, the marginal impact of these changes can be found.« less

Medium term municipal solid waste generation prediction by autoregressive integrated moving average

DOE Office of Scientific and Technical Information (OSTI.GOV)

Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.

2014-09-12

Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressivemore » Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.« less

On the use of Schwarz-Christoffel conformal mappings to the grid generation for global ocean models

NASA Astrophysics Data System (ADS)

Xu, S.; Wang, B.; Liu, J.

2015-02-01

In this article we propose two conformal mapping based grid generation algorithms for global ocean general circulation models (OGCMs). Contrary to conventional, analytical forms based dipolar or tripolar grids, the new algorithms are based on Schwarz-Christoffel (SC) conformal mapping with prescribed boundary information. While dealing with the basic grid design problem of pole relocation, these new algorithms also address more advanced issues such as smoothed scaling factor, or the new requirements on OGCM grids arisen from the recent trend of high-resolution and multi-scale modeling. The proposed grid generation algorithm could potentially achieve the alignment of grid lines to coastlines, enhanced spatial resolution in coastal regions, and easier computational load balance. Since the generated grids are still orthogonal curvilinear, they can be readily utilized in existing Bryan-Cox-Semtner type ocean models. The proposed methodology can also be applied to the grid generation task for regional ocean modeling where complex land-ocean distribution is present.

Research on Operation Strategy for Bundled Wind-thermal Generation Power Systems Based on Two-Stage Optimization Model

NASA Astrophysics Data System (ADS)

Sun, Congcong; Wang, Zhijie; Liu, Sanming; Jiang, Xiuchen; Sheng, Gehao; Liu, Tianyu

2017-05-01

Wind power has the advantages of being clean and non-polluting and the development of bundled wind-thermal generation power systems (BWTGSs) is one of the important means to improve wind power accommodation rate and implement “clean alternative” on generation side. A two-stage optimization strategy for BWTGSs considering wind speed forecasting results and load characteristics is proposed. By taking short-term wind speed forecasting results of generation side and load characteristics of demand side into account, a two-stage optimization model for BWTGSs is formulated. By using the environmental benefit index of BWTGSs as the objective function, supply-demand balance and generator operation as the constraints, the first-stage optimization model is developed with the chance-constrained programming theory. By using the operation cost for BWTGSs as the objective function, the second-stage optimization model is developed with the greedy algorithm. The improved PSO algorithm is employed to solve the model and numerical test verifies the effectiveness of the proposed strategy.

Mechanism of the free charge carrier generation in the dielectric breakdown

NASA Astrophysics Data System (ADS)

Rahim, N. A. A.; Ranom, R.; Zainuddin, H.

2017-12-01

Many studies have been conducted to investigate the effect of environmental, mechanical and electrical stresses on insulator. However, studies on physical process of discharge phenomenon, leading to the breakdown of the insulator surface are lacking and difficult to comprehend. Therefore, this paper analysed charge carrier generation mechanism that can cause free charge carrier generation, leading toward surface discharge development. Besides, this paper developed a model of surface discharge based on the charge generation mechanism on the outdoor insulator. Nernst’s Planck theory was used in order to model the behaviour of the charge carriers while Poisson’s equation was used to determine the distribution of electric field on insulator surface. In the modelling of surface discharge on the outdoor insulator, electric field dependent molecular ionization was used as the charge generation mechanism. A mathematical model of the surface discharge was solved using method of line technique (MOL). The result from the mathematical model showed that the behaviour of net space charge density was correlated with the electric field distribution.

Medium term municipal solid waste generation prediction by autoregressive integrated moving average

NASA Astrophysics Data System (ADS)

Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan

2014-09-01

Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.

RE-Europe, a large-scale dataset for modeling a highly renewable European electricity system

NASA Astrophysics Data System (ADS)

Jensen, Tue V.; Pinson, Pierre

2017-11-01

Future highly renewable energy systems will couple to complex weather and climate dynamics. This coupling is generally not captured in detail by the open models developed in the power and energy system communities, where such open models exist. To enable modeling such a future energy system, we describe a dedicated large-scale dataset for a renewable electric power system. The dataset combines a transmission network model, as well as information for generation and demand. Generation includes conventional generators with their technical and economic characteristics, as well as weather-driven forecasts and corresponding realizations for renewable energy generation for a period of 3 years. These may be scaled according to the envisioned degrees of renewable penetration in a future European energy system. The spatial coverage, completeness and resolution of this dataset, open the door to the evaluation, scaling analysis and replicability check of a wealth of proposals in, e.g., market design, network actor coordination and forecasting of renewable power generation.

Generating Models of Infinite-State Communication Protocols Using Regular Inference with Abstraction

NASA Astrophysics Data System (ADS)

Aarts, Fides; Jonsson, Bengt; Uijen, Johan

In order to facilitate model-based verification and validation, effort is underway to develop techniques for generating models of communication system components from observations of their external behavior. Most previous such work has employed regular inference techniques which generate modest-size finite-state models. They typically suppress parameters of messages, although these have a significant impact on control flow in many communication protocols. We present a framework, which adapts regular inference to include data parameters in messages and states for generating components with large or infinite message alphabets. A main idea is to adapt the framework of predicate abstraction, successfully used in formal verification. Since we are in a black-box setting, the abstraction must be supplied externally, using information about how the component manages data parameters. We have implemented our techniques by connecting the LearnLib tool for regular inference with the protocol simulator ns-2, and generated a model of the SIP component as implemented in ns-2.

Democracy versus dictatorship in self-organized models of financial markets

NASA Astrophysics Data System (ADS)

D'Hulst, R.; Rodgers, G. J.

2000-06-01

Models to mimic the transmission of information in financial markets are introduced. As an attempt to generate the demand process, we distinguish between dictatorship associations, where groups of agents rely on one of them to make decision, and democratic associations, where each agent takes part in the group decision. In the dictatorship model, agents segregate into two distinct populations, while the democratic model is driven towards a critical state where groups of agents of all sizes exist. Hence, both models display a level of organization, but only the democratic model is self-organized. We show that the dictatorship model generates less-volatile markets than the democratic model.

Combining 3d Volume and Mesh Models for Representing Complicated Heritage Buildings

NASA Astrophysics Data System (ADS)

Tsai, F.; Chang, H.; Lin, Y.-W.

2017-08-01

This study developed a simple but effective strategy to combine 3D volume and mesh models for representing complicated heritage buildings and structures. The idea is to seamlessly integrate 3D parametric or polyhedral models and mesh-based digital surfaces to generate a hybrid 3D model that can take advantages of both modeling methods. The proposed hybrid model generation framework is separated into three phases. Firstly, after acquiring or generating 3D point clouds of the target, these 3D points are partitioned into different groups. Secondly, a parametric or polyhedral model of each group is generated based on plane and surface fitting algorithms to represent the basic structure of that region. A "bare-bones" model of the target can subsequently be constructed by connecting all 3D volume element models. In the third phase, the constructed bare-bones model is used as a mask to remove points enclosed by the bare-bones model from the original point clouds. The remaining points are then connected to form 3D surface mesh patches. The boundary points of each surface patch are identified and these boundary points are projected onto the surfaces of the bare-bones model. Finally, new meshes are created to connect the projected points and original mesh boundaries to integrate the mesh surfaces with the 3D volume model. The proposed method was applied to an open-source point cloud data set and point clouds of a local historical structure. Preliminary results indicated that the reconstructed hybrid models using the proposed method can retain both fundamental 3D volume characteristics and accurate geometric appearance with fine details. The reconstructed hybrid models can also be used to represent targets in different levels of detail according to user and system requirements in different applications.

Generate an Argument: An Instructional Model

ERIC Educational Resources Information Center

Sampson, Victor; Grooms, Jonathon

2010-01-01

The Generate an Argument instructional model was designed to engage students in scientific argumentation. By using this model, students develop complex reasoning and critical-thinking skills, understand the nature and development of scientific knowledge, and improve their communication skills (Duschl and Osborne 2002). This article describes the…

Generation of transgenic mouse model using PTTG as an oncogene.

PubMed

Kakar, Sham S; Kakar, Cohin

2015-01-01

The close physiological similarity between the mouse and human has provided tools to understanding the biological function of particular genes in vivo by introduction or deletion of a gene of interest. Using a mouse as a model has provided a wealth of resources, knowledge, and technology, helping scientists to understand the biological functions, translocation, trafficking, and interaction of a candidate gene with other intracellular molecules, transcriptional regulation, posttranslational modification, and discovery of novel signaling pathways for a particular gene. Most importantly, the generation of the mouse model for a specific human disease has provided a powerful tool to understand the etiology of a disease and discovery of novel therapeutics. This chapter describes in detail the step-by-step generation of the transgenic mouse model, which can be helpful in guiding new investigators in developing successful models. For practical purposes, we will describe the generation of a mouse model using pituitary tumor transforming gene (PTTG) as the candidate gene of interest.

Reduced order modeling of head related transfer functions for virtual acoustic displays

NASA Astrophysics Data System (ADS)

Willhite, Joel A.; Frampton, Kenneth D.; Grantham, D. Wesley

2003-04-01

The purpose of this work is to improve the computational efficiency in acoustic virtual applications by creating and testing reduced order models of the head related transfer functions used in localizing sound sources. State space models of varying order were generated from zero-elevation Head Related Impulse Responses (HRIRs) using Kungs Single Value Decomposition (SVD) technique. The inputs to the models are the desired azimuths of the virtual sound sources (from minus 90 deg to plus 90 deg, in 10 deg increments) and the outputs are the left and right ear impulse responses. Trials were conducted in an anechoic chamber in which subjects were exposed to real sounds that were emitted by individual speakers across a numbered speaker array, phantom sources generated from the original HRIRs, and phantom sound sources generated with the different reduced order state space models. The error in the perceived direction of the phantom sources generated from the reduced order models was compared to errors in localization using the original HRIRs.

Distributed state-space generation of discrete-state stochastic models

NASA Technical Reports Server (NTRS)

Ciardo, Gianfranco; Gluckman, Joshua; Nicol, David

1995-01-01

High-level formalisms such as stochastic Petri nets can be used to model complex systems. Analysis of logical and numerical properties of these models of ten requires the generation and storage of the entire underlying state space. This imposes practical limitations on the types of systems which can be modeled. Because of the vast amount of memory consumed, we investigate distributed algorithms for the generation of state space graphs. The distributed construction allows us to take advantage of the combined memory readily available on a network of workstations. The key technical problem is to find effective methods for on-the-fly partitioning, so that the state space is evenly distributed among processors. In this paper we report on the implementation of a distributed state-space generator that may be linked to a number of existing system modeling tools. We discuss partitioning strategies in the context of Petri net models, and report on performance observed on a network of workstations, as well as on a distributed memory multi-computer.

Point Cloud and Digital Surface Model Generation from High Resolution Multiple View Stereo Satellite Imagery

NASA Astrophysics Data System (ADS)

Gong, K.; Fritsch, D.

2018-05-01

Nowadays, multiple-view stereo satellite imagery has become a valuable data source for digital surface model generation and 3D reconstruction. In 2016, a well-organized multiple view stereo publicly benchmark for commercial satellite imagery has been released by the John Hopkins University Applied Physics Laboratory, USA. This benchmark motivates us to explore the method that can generate accurate digital surface models from a large number of high resolution satellite images. In this paper, we propose a pipeline for processing the benchmark data to digital surface models. As a pre-procedure, we filter all the possible image pairs according to the incidence angle and capture date. With the selected image pairs, the relative bias-compensated model is applied for relative orientation. After the epipolar image pairs' generation, dense image matching and triangulation, the 3D point clouds and DSMs are acquired. The DSMs are aligned to a quasi-ground plane by the relative bias-compensated model. We apply the median filter to generate the fused point cloud and DSM. By comparing with the reference LiDAR DSM, the accuracy, the completeness and the robustness are evaluated. The results show, that the point cloud reconstructs the surface with small structures and the fused DSM generated by our pipeline is accurate and robust.

Simulation of Ectopic Pacemakers in the Heart: Multiple Ectopic Beats Generated by Reentry inside Fibrotic Regions

PubMed Central

Gouvêa de Barros, Bruno; Weber dos Santos, Rodrigo; Alonso, Sergio

2015-01-01

The inclusion of nonconducting media, mimicking cardiac fibrosis, in two models of cardiac tissue produces the formation of ectopic beats. The fraction of nonconducting media in comparison with the fraction of healthy myocytes and the topological distribution of cells determines the probability of ectopic beat generation. First, a detailed subcellular microscopic model that accounts for the microstructure of the cardiac tissue is constructed and employed for the numerical simulation of action potential propagation. Next, an equivalent discrete model is implemented, which permits a faster integration of the equations. This discrete model is a simplified version of the microscopic model that maintains the distribution of connections between cells. Both models produce similar results when describing action potential propagation in homogeneous tissue; however, they slightly differ in the generation of ectopic beats in heterogeneous tissue. Nevertheless, both models present the generation of reentry inside fibrotic tissues. This kind of reentry restricted to microfibrosis regions can result in the formation of ectopic pacemakers, that is, regions that will generate a series of ectopic stimulus at a fast pacing rate. In turn, such activity has been related to trigger fibrillation in the atria and in the ventricles in clinical and animal studies. PMID:26583127

Coupling of electromagnetic and structural dynamics for a wind turbine generator

NASA Astrophysics Data System (ADS)

Matzke, D.; Rick, S.; Hollas, S.; Schelenz, R.; Jacobs, G.; Hameyer, K.

2016-09-01

This contribution presents a model interface of a wind turbine generator to represent the reciprocal effects between the mechanical and the electromagnetic system. Therefore, a multi-body-simulation (MBS) model in Simpack is set up and coupled with a quasi-static electromagnetic (EM) model of the generator in Matlab/Simulink via co-simulation. Due to lack of data regarding the structural properties of the generator the modal properties of the MBS model are fitted with respect to results of an experimental modal analysis (EMA) on the reference generator. The used method and the results of this approach are presented in this paper. The MB S model and the interface are set up in such a way that the EM forces can be applied to the structure and the response of the structure can be fed back to the EM model. The results of this cosimulation clearly show an influence of the feedback of the mechanical response which is mainly damping in the torsional degree of freedom and effects due to eccentricity in radial direction. The accuracy of these results will be validated via test bench measurements and presented in future work. Furthermore it is suggested that the EM model should be adjusted in future works so that transient effects are represented.

A Statistical Approach For Modeling Tropical Cyclones. Synthetic Hurricanes Generator Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pasqualini, Donatella

This manuscript brie y describes a statistical ap- proach to generate synthetic tropical cyclone tracks to be used in risk evaluations. The Synthetic Hur- ricane Generator (SynHurG) model allows model- ing hurricane risk in the United States supporting decision makers and implementations of adaptation strategies to extreme weather. In the literature there are mainly two approaches to model hurricane hazard for risk prediction: deterministic-statistical approaches, where the storm key physical parameters are calculated using physi- cal complex climate models and the tracks are usually determined statistically from historical data; and sta- tistical approaches, where both variables and tracks are estimatedmore » stochastically using historical records. SynHurG falls in the second category adopting a pure stochastic approach.« less

An ontology model for nursing narratives with natural language generation technology.

PubMed

Min, Yul Ha; Park, Hyeoun-Ae; Jeon, Eunjoo; Lee, Joo Yun; Jo, Soo Jung

2013-01-01

The purpose of this study was to develop an ontology model to generate nursing narratives as natural as human language from the entity-attribute-value triplets of a detailed clinical model using natural language generation technology. The model was based on the types of information and documentation time of the information along the nursing process. The typesof information are data characterizing the patient status, inferences made by the nurse from the patient data, and nursing actions selected by the nurse to change the patient status. This information was linked to the nursing process based on the time of documentation. We describe a case study illustrating the application of this model in an acute-care setting. The proposed model provides a strategy for designing an electronic nursing record system.

Maximum capacity model of grid-connected multi-wind farms considering static security constraints in electrical grids

NASA Astrophysics Data System (ADS)

Zhou, W.; Qiu, G. Y.; Oodo, S. O.; He, H.

2013-03-01

An increasing interest in wind energy and the advance of related technologies have increased the connection of wind power generation into electrical grids. This paper proposes an optimization model for determining the maximum capacity of wind farms in a power system. In this model, generator power output limits, voltage limits and thermal limits of branches in the grid system were considered in order to limit the steady-state security influence of wind generators on the power system. The optimization model was solved by a nonlinear primal-dual interior-point method. An IEEE-30 bus system with two wind farms was tested through simulation studies, plus an analysis conducted to verify the effectiveness of the proposed model. The results indicated that the model is efficient and reasonable.

The Collaborative Seismic Earth Model: Generation 1

NASA Astrophysics Data System (ADS)

Fichtner, Andreas; van Herwaarden, Dirk-Philip; Afanasiev, Michael; SimutÄ--, SaulÄ--; Krischer, Lion; ćubuk-Sabuncu, Yeşim; Taymaz, Tuncay; Colli, Lorenzo; Saygin, Erdinc; Villaseñor, Antonio; Trampert, Jeannot; Cupillard, Paul; Bunge, Hans-Peter; Igel, Heiner

2018-05-01

We present a general concept for evolutionary, collaborative, multiscale inversion of geophysical data, specifically applied to the construction of a first-generation Collaborative Seismic Earth Model. This is intended to address the limited resources of individual researchers and the often limited use of previously accumulated knowledge. Model evolution rests on a Bayesian updating scheme, simplified into a deterministic method that honors today's computational restrictions. The scheme is able to harness distributed human and computing power. It furthermore handles conflicting updates, as well as variable parameterizations of different model refinements or different inversion techniques. The first-generation Collaborative Seismic Earth Model comprises 12 refinements from full seismic waveform inversion, ranging from regional crustal- to continental-scale models. A global full-waveform inversion ensures that regional refinements translate into whole-Earth structure.

Comparison of L-system applications towards plant modelling, music rendering and score generation using visual language programming

NASA Astrophysics Data System (ADS)

Lim, Chen Kim; Tan, Kian Lam; Yusran, Hazwanni; Suppramaniam, Vicknesh

2017-10-01

Visual language or visual representation has been used in the past few years in order to express the knowledge in graphic. One of the important graphical elements is fractal and L-Systems is a mathematic-based grammatical model for modelling cell development and plant topology. From the plant model, L-Systems can be interpreted as music sound and score. In this paper, LSound which is a Visual Language Programming (VLP) framework has been developed to model plant to music sound and generate music score and vice versa. The objectives of this research has three folds: (i) To expand the grammar dictionary of L-Systems music based on visual programming, (ii) To design and produce a user-friendly and icon based visual language framework typically for L-Systems musical score generation which helps the basic learners in musical field and (iii) To generate music score from plant models and vice versa using L-Systems method. This research undergoes a four phases methodology where the plant is first modelled, then the music is interpreted, followed by the output of music sound through MIDI and finally score is generated. LSound is technically compared to other existing applications in the aspects of the capability of modelling the plant, rendering the music and generating the sound. It has been found that LSound is a flexible framework in which the plant can be easily altered through arrow-based programming and the music score can be altered through the music symbols and notes. This work encourages non-experts to understand L-Systems and music hand-in-hand.

A Knowledge Generation Model via the Hypernetwork

PubMed Central

Liu, Jian-Guo; Yang, Guang-Yong; Hu, Zhao-Long

2014-01-01

The influence of the statistical properties of the network on the knowledge diffusion has been extensively studied. However, the structure evolution and the knowledge generation processes are always integrated simultaneously. By introducing the Cobb-Douglas production function and treating the knowledge growth as a cooperative production of knowledge, in this paper, we present two knowledge-generation dynamic evolving models based on different evolving mechanisms. The first model, named “HDPH model,” adopts the hyperedge growth and the hyperdegree preferential attachment mechanisms. The second model, named “KSPH model,” adopts the hyperedge growth and the knowledge stock preferential attachment mechanisms. We investigate the effect of the parameters on the total knowledge stock of the two models. The hyperdegree distribution of the HDPH model can be theoretically analyzed by the mean-field theory. The analytic result indicates that the hyperdegree distribution of the HDPH model obeys the power-law distribution and the exponent is . Furthermore, we present the distributions of the knowledge stock for different parameters . The findings indicate that our proposed models could be helpful for deeply understanding the scientific research cooperation. PMID:24626143

A knowledge generation model via the hypernetwork.

PubMed

Liu, Jian-Guo; Yang, Guang-Yong; Hu, Zhao-Long

2014-01-01

The influence of the statistical properties of the network on the knowledge diffusion has been extensively studied. However, the structure evolution and the knowledge generation processes are always integrated simultaneously. By introducing the Cobb-Douglas production function and treating the knowledge growth as a cooperative production of knowledge, in this paper, we present two knowledge-generation dynamic evolving models based on different evolving mechanisms. The first model, named "HDPH model," adopts the hyperedge growth and the hyperdegree preferential attachment mechanisms. The second model, named "KSPH model," adopts the hyperedge growth and the knowledge stock preferential attachment mechanisms. We investigate the effect of the parameters (α,β) on the total knowledge stock of the two models. The hyperdegree distribution of the HDPH model can be theoretically analyzed by the mean-field theory. The analytic result indicates that the hyperdegree distribution of the HDPH model obeys the power-law distribution and the exponent is γ = 2 + 1/m. Furthermore, we present the distributions of the knowledge stock for different parameters (α,β). The findings indicate that our proposed models could be helpful for deeply understanding the scientific research cooperation.

A component modes projection and assembly model reduction methodology for articulated, multi-flexible body structures

NASA Technical Reports Server (NTRS)

Lee, Allan Y.; Tsuha, Walter S.

1993-01-01

A two-stage model reduction methodology, combining the classical Component Mode Synthesis (CMS) method and the newly developed Enhanced Projection and Assembly (EP&A) method, is proposed in this research. The first stage of this methodology, called the COmponent Modes Projection and Assembly model REduction (COMPARE) method, involves the generation of CMS mode sets, such as the MacNeal-Rubin mode sets. These mode sets are then used to reduce the order of each component model in the Rayleigh-Ritz sense. The resultant component models are then combined to generate reduced-order system models at various system configurations. A composite mode set which retains important system modes at all system configurations is then selected from these reduced-order system models. In the second stage, the EP&A model reduction method is employed to reduce further the order of the system model generated in the first stage. The effectiveness of the COMPARE methodology has been successfully demonstrated on a high-order, finite-element model of the cruise-configured Galileo spacecraft.

Complex networks generated by the Penna bit-string model: Emergence of small-world and assortative mixing

NASA Astrophysics Data System (ADS)

Li, Chunguang; Maini, Philip K.

2005-10-01

The Penna bit-string model successfully encompasses many phenomena of population evolution, including inheritance, mutation, evolution, and aging. If we consider social interactions among individuals in the Penna model, the population will form a complex network. In this paper, we first modify the Verhulst factor to control only the birth rate, and introduce activity-based preferential reproduction of offspring in the Penna model. The social interactions among individuals are generated by both inheritance and activity-based preferential increase. Then we study the properties of the complex network generated by the modified Penna model. We find that the resulting complex network has a small-world effect and the assortative mixing property.

Modeling of hydrogen/deuterium dynamics and heat generation on palladium nanoparticles for hydrogen storage and solid-state nuclear fusion.

PubMed

Tanabe, Katsuaki

2016-01-01

We modeled the dynamics of hydrogen and deuterium adsorbed on palladium nanoparticles including the heat generation induced by the chemical adsorption and desorption, as well as palladium-catalyzed reactions. Our calculations based on the proposed model reproduce the experimental time-evolution of pressure and temperature with a single set of fitting parameters for hydrogen and deuterium injection. The model we generated with a highly generalized set of formulations can be applied for any combination of a gas species and a catalytic adsorbent/absorbent. Our model can be used as a basis for future research into hydrogen storage and solid-state nuclear fusion technologies.

Automated extraction of knowledge for model-based diagnostics

NASA Technical Reports Server (NTRS)

Gonzalez, Avelino J.; Myler, Harley R.; Towhidnejad, Massood; Mckenzie, Frederic D.; Kladke, Robin R.

1990-01-01

The concept of accessing computer aided design (CAD) design databases and extracting a process model automatically is investigated as a possible source for the generation of knowledge bases for model-based reasoning systems. The resulting system, referred to as automated knowledge generation (AKG), uses an object-oriented programming structure and constraint techniques as well as internal database of component descriptions to generate a frame-based structure that describes the model. The procedure has been designed to be general enough to be easily coupled to CAD systems that feature a database capable of providing label and connectivity data from the drawn system. The AKG system is capable of defining knowledge bases in formats required by various model-based reasoning tools.

The application of a generativity model for older adults.

PubMed

Ehlman, Katie; Ligon, Mary

2012-01-01

Generativity is a concept first introduced by Erik Erikson as a part of his psychosocial theory which outlines eight stages of development in the human life. Generativity versus stagnation is the main developmental concern of middle adulthood; however, generativity is also recognized as an important theme in the lives of older adults. Building on the work of Erikson, McAdams and de St. Aubin (1992) developed a model explaining the generative process. The aims of this article are: (a) to explore the relationship between generativity and older adults as it appears in research literature; and (b) to examine McAdam's model and use it to explain the role of generativity in older adults who share life stories with gerontology students through an oral history project.

Single generation cycles and delayed feedback cycles are not separate phenomena.

PubMed

Pfaff, T; Brechtel, A; Drossel, B; Guill, C

2014-12-01

We study a simple model for generation cycles, which are oscillations with a period of one or a few generation times of the species. The model is formulated in terms of a single delay-differential equation for the population density of an adult stage, with recruitment to the adult stage depending on the intensity of competition during the juvenile phase. This model is a simplified version of a group of models proposed by Gurney and Nisbet, who were the first to distinguish between single-generation cycles and delayed-feedback cycles. According to these authors, the two oscillation types are caused by different mechanisms and have periods in different intervals, which are one to two generation times for single-generation cycles and two to four generation times for delayed-feedback cycles. By abolishing the strict coupling between the maturation time and the time delay between competition and its effect on the population dynamics, we find that single-generation cycles and delayed-feedback cycles occur in the same model version, with a gradual transition between the two as the model parameters are varied over a sufficiently large range. Furthermore, cycle periods are not bounded to lie within single octaves. This implies that a clear distinction between different types of generation cycles is not possible. Cycles of all periods and even chaos can be generated by varying the parameters that determine the time during which individuals from different cohorts compete with each other. This suggests that life-cycle features in the juvenile stage and during the transition to the adult stage are important determinants of the dynamics of density limited populations. Copyright © 2014 Elsevier Inc. All rights reserved.

A Probabilistic Model of Illegal Drug Trafficking Operations in the Eastern Pacific and Caribbean Sea

DTIC Science & Technology

2013-09-01

partner agencies and nations, detects, tracks, and interdicts illegal drug-trafficking in this region. In this thesis, we develop a probability model based...trafficking in this region. In this thesis, we develop a probability model based on intelligence inputs to generate a spatial temporal heat map specifying the...complement and vet such complicated simulation by developing more analytically tractable models. We develop probability models to generate a heat map

Automation on the generation of genome-scale metabolic models.

PubMed

Reyes, R; Gamermann, D; Montagud, A; Fuente, D; Triana, J; Urchueguía, J F; de Córdoba, P Fernández

2012-12-01

Nowadays, the reconstruction of genome-scale metabolic models is a nonautomatized and interactive process based on decision making. This lengthy process usually requires a full year of one person's work in order to satisfactory collect, analyze, and validate the list of all metabolic reactions present in a specific organism. In order to write this list, one manually has to go through a huge amount of genomic, metabolomic, and physiological information. Currently, there is no optimal algorithm that allows one to automatically go through all this information and generate the models taking into account probabilistic criteria of unicity and completeness that a biologist would consider. This work presents the automation of a methodology for the reconstruction of genome-scale metabolic models for any organism. The methodology that follows is the automatized version of the steps implemented manually for the reconstruction of the genome-scale metabolic model of a photosynthetic organism, Synechocystis sp. PCC6803. The steps for the reconstruction are implemented in a computational platform (COPABI) that generates the models from the probabilistic algorithms that have been developed. For validation of the developed algorithm robustness, the metabolic models of several organisms generated by the platform have been studied together with published models that have been manually curated. Network properties of the models, like connectivity and average shortest mean path of the different models, have been compared and analyzed.

Rethinking the Default Construction of Multimodel Climate Ensembles

DOE PAGES

Rauser, Florian; Gleckler, Peter; Marotzke, Jochem

2015-07-21

Here, we discuss the current code of practice in the climate sciences to routinely create climate model ensembles as ensembles of opportunity from the newest phase of the Coupled Model Intercomparison Project (CMIP). We give a two-step argument to rethink this process. First, the differences between generations of ensembles corresponding to different CMIP phases in key climate quantities are not large enough to warrant an automatic separation into generational ensembles for CMIP3 and CMIP5. Second, we suggest that climate model ensembles cannot continue to be mere ensembles of opportunity but should always be based on a transparent scientific decision process.more » If ensembles can be constrained by observation, then they should be constructed as target ensembles that are specifically tailored to a physical question. If model ensembles cannot be constrained by observation, then they should be constructed as cross-generational ensembles, including all available model data to enhance structural model diversity and to better sample the underlying uncertainties. To facilitate this, CMIP should guide the necessarily ongoing process of updating experimental protocols for the evaluation and documentation of coupled models. Finally, with an emphasis on easy access to model data and facilitating the filtering of climate model data across all CMIP generations and experiments, our community could return to the underlying idea of using model data ensembles to improve uncertainty quantification, evaluation, and cross-institutional exchange.« less

Parametric vs. non-parametric daily weather generator: validation and comparison

NASA Astrophysics Data System (ADS)

Dubrovsky, Martin

2016-04-01

As the climate models (GCMs and RCMs) fail to satisfactorily reproduce the real-world surface weather regime, various statistical methods are applied to downscale GCM/RCM outputs into site-specific weather series. The stochastic weather generators are among the most favourite downscaling methods capable to produce realistic (observed like) meteorological inputs for agrological, hydrological and other impact models used in assessing sensitivity of various ecosystems to climate change/variability. To name their advantages, the generators may (i) produce arbitrarily long multi-variate synthetic weather series representing both present and changed climates (in the latter case, the generators are commonly modified by GCM/RCM-based climate change scenarios), (ii) be run in various time steps and for multiple weather variables (the generators reproduce the correlations among variables), (iii) be interpolated (and run also for sites where no weather data are available to calibrate the generator). This contribution will compare two stochastic daily weather generators in terms of their ability to reproduce various features of the daily weather series. M&Rfi is a parametric generator: Markov chain model is used to model precipitation occurrence, precipitation amount is modelled by the Gamma distribution, and the 1st order autoregressive model is used to generate non-precipitation surface weather variables. The non-parametric GoMeZ generator is based on the nearest neighbours resampling technique making no assumption on the distribution of the variables being generated. Various settings of both weather generators will be assumed in the present validation tests. The generators will be validated in terms of (a) extreme temperature and precipitation characteristics (annual and 30 years extremes and maxima of duration of hot/cold/dry/wet spells); (b) selected validation statistics developed within the frame of VALUE project. The tests will be based on observational weather series from several European stations available from the ECA&D database.

A MATLAB based 3D modeling and inversion code for MT data

NASA Astrophysics Data System (ADS)

Singh, Arun; Dehiya, Rahul; Gupta, Pravin K.; Israil, M.

2017-07-01

The development of a MATLAB based computer code, AP3DMT, for modeling and inversion of 3D Magnetotelluric (MT) data is presented. The code comprises two independent components: grid generator code and modeling/inversion code. The grid generator code performs model discretization and acts as an interface by generating various I/O files. The inversion code performs core computations in modular form - forward modeling, data functionals, sensitivity computations and regularization. These modules can be readily extended to other similar inverse problems like Controlled-Source EM (CSEM). The modular structure of the code provides a framework useful for implementation of new applications and inversion algorithms. The use of MATLAB and its libraries makes it more compact and user friendly. The code has been validated on several published models. To demonstrate its versatility and capabilities the results of inversion for two complex models are presented.

Generating Performance Models for Irregular Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Friese, Ryan D.; Tallent, Nathan R.; Vishnu, Abhinav

2017-05-30

Many applications have irregular behavior --- non-uniform input data, input-dependent solvers, irregular memory accesses, unbiased branches --- that cannot be captured using today's automated performance modeling techniques. We describe new hierarchical critical path analyses for the \\Palm model generation tool. To create a model's structure, we capture tasks along representative MPI critical paths. We create a histogram of critical tasks with parameterized task arguments and instance counts. To model each task, we identify hot instruction-level sub-paths and model each sub-path based on data flow, instruction scheduling, and data locality. We describe application models that generate accurate predictions for strong scalingmore » when varying CPU speed, cache speed, memory speed, and architecture. We present results for the Sweep3D neutron transport benchmark; Page Rank on multiple graphs; Support Vector Machine with pruning; and PFLOTRAN's reactive flow/transport solver with domain-induced load imbalance.« less

Integrated Control Modeling for Propulsion Systems Using NPSS

NASA Technical Reports Server (NTRS)

Parker, Khary I.; Felder, James L.; Lavelle, Thomas M.; Withrow, Colleen A.; Yu, Albert Y.; Lehmann, William V. A.

2004-01-01

The Numerical Propulsion System Simulation (NPSS), an advanced engineering simulation environment used to design and analyze aircraft engines, has been enhanced by integrating control development tools into it. One of these tools is a generic controller interface that allows NPSS to communicate with control development software environments such as MATLAB and EASY5. The other tool is a linear model generator (LMG) that gives NPSS the ability to generate linear, time-invariant state-space models. Integrating these tools into NPSS enables it to be used for control system development. This paper will discuss the development and integration of these tools into NPSS. In addition, it will show a comparison of transient model results of a generic, dual-spool, military-type engine model that has been implemented in NPSS and Simulink. It will also show the linear model generator s ability to approximate the dynamics of a nonlinear NPSS engine model.

Selective interference with image retention and generation: evidence for the workspace model.

PubMed

van der Meulen, Marian; Logie, Robert H; Della Sala, Sergio

2009-08-01

We address three types of model of the relationship between working memory (WM) and long-term memory (LTM): (a) the gateway model, in which WM acts as a gateway between perceptual input and LTM; (b) the unitary model, in which WM is seen as the currently activated areas of LTM; and (c) the workspace model, in which perceptual input activates LTM, and WM acts as a separate workspace for processing and temporary retention of these activated traces. Predictions of these models were tested, focusing on visuospatial working memory and using dual-task methodology to combine two main tasks (visual short-term retention and image generation) with two interference tasks (irrelevant pictures and spatial tapping). The pictures selectively disrupted performance on the generation task, whereas the tapping selectively interfered with the retention task. Results are consistent with the predictions of the workspace model.

Advanced surface design for logistics analysis

NASA Astrophysics Data System (ADS)

Brown, Tim R.; Hansen, Scott D.

The development of anthropometric arm/hand and tool models and their manipulation in a large system model for maintenance simulation are discussed. The use of Advanced Surface Design and s-fig technology in anthropometrics, and three-dimensional graphics simulation tools, are found to achieve a good balance between model manipulation speed and model accuracy. The present second generation models are shown to be twice as fast to manipulate as the first generation b-surf models, to be easier to manipulate into various configurations, and to more closely approximate human contours.

Research on complex 3D tree modeling based on L-system

NASA Astrophysics Data System (ADS)

Gang, Chen; Bin, Chen; Yuming, Liu; Hui, Li

2018-03-01

L-system as a fractal iterative system could simulate complex geometric patterns. Based on the field observation data of trees and knowledge of forestry experts, this paper extracted modeling constraint rules and obtained an L-system rules set. Using the self-developed L-system modeling software the L-system rule set was parsed to generate complex tree 3d models.The results showed that the geometrical modeling method based on l-system could be used to describe the morphological structure of complex trees and generate 3D tree models.

Retrofitting and the mu Problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Green, Daniel; Weigand, Timo; /SLAC /Stanford U., Phys. Dept.

2010-08-26

One of the challenges of supersymmetry (SUSY) breaking and mediation is generating a {mu} term consistent with the requirements of electro-weak symmetry breaking. The most common approach to the problem is to generate the {mu} term through a SUSY breaking F-term. Often these models produce unacceptably large B{mu} terms as a result. We will present an alternate approach, where the {mu} term is generated directly by non-perturtative effects. The same non-perturbative effect will also retrofit the model of SUSY breaking in such a way that {mu} is at the same scale as masses of the Standard Model superpartners. Because themore » {mu} term is not directly generated by SUSY breaking effects, there is no associated B{mu} problem. These results are demonstrated in a toy model where a stringy instanton generates {mu}.« less

Time-series-based hybrid mathematical modelling method adapted to forecast automotive and medical waste generation: Case study of Lithuania.

PubMed

Karpušenkaitė, Aistė; Ruzgas, Tomas; Denafas, Gintaras

2018-05-01

The aim of the study was to create a hybrid forecasting method that could produce higher accuracy forecasts than previously used 'pure' time series methods. Mentioned methods were already tested with total automotive waste, hazardous automotive waste, and total medical waste generation, but demonstrated at least a 6% error rate in different cases and efforts were made to decrease it even more. Newly developed hybrid models used a random start generation method to incorporate different time-series advantages and it helped to increase the accuracy of forecasts by 3%-4% in hazardous automotive waste and total medical waste generation cases; the new model did not increase the accuracy of total automotive waste generation forecasts. Developed models' abilities to forecast short- and mid-term forecasts were tested using prediction horizon.

Progressively consolidating historical visual explorations for new discoveries

NASA Astrophysics Data System (ADS)

Zhao, Kaiyu; Ward, Matthew O.; Rundensteiner, Elke A.; Higgins, Huong N.

2013-12-01

A significant task within data mining is to identify data models of interest. While facilitating the exploration tasks, most visualization systems do not make use of all the data models that are generated during the exploration. In this paper, we introduce a system that allows the user to gain insights from the data space progressively by forming data models and consolidating the generated models on the fly. Each model can be a a computationally extracted or user-defined subset that contains a certain degree of interest and might lead to some discoveries. When the user generates more and more data models, the degree of interest of some portion of some models will either grow (indicating higher occurrence) or will fluctuate or decrease (corresponding to lower occurrence). Our system maintains a collection of such models and accumulates the interestingness of each model into a consolidated model. In order to consolidate the models, the system summarizes the associations between the models in the collection and identifies support (models reinforce each other), complementary (models complement each other), and overlap of the models. The accumulated interestingness keeps track of historical exploration and helps the user summarize their findings which can lead to new discoveries. This mechanism for integrating results from multiple models can be applied to a wide range of decision support systems. We demonstrate our system in a case study involving the financial status of US companies.

Learning Generation: Fostering Innovation with Tomorrow's Teachers and Technology

ERIC Educational Resources Information Center

Aust, Ronald; Newberry, Brian; O'Brien, Joseph; Thomas, Jennifer

2005-01-01

We discuss the context, conception, implementation, and research used to refine and evaluate a systemic model for fostering technology integration in teacher education. The Learning Generation model identifies conditions where innovations for using technology emerge in small group dialogues. The model uses a multifaceted implementation with…

10 CFR 490.307 - Option for Electric Utilities.

Code of Federal Regulations, 2010 CFR

2010-01-01

... affiliate, division, or business unit, whose principal business is generating, transmitting, importing, or... business unit, whose principal business is generating, transmitting, importing, or selling at wholesale or.... (2) 50 percent for model year 1999. (3) 70 percent for model year 2000. (4) 90 percent for model year...

Software Tools for Weed Seed Germination Modeling

USDA-ARS?s Scientific Manuscript database

The next generation of weed seed germination models will need to account for variable soil microclimate conditions. In order to predict this microclimate environment we have developed a suite of individual tools (models) that can be used in conjunction with the next generation of weed seed germinati...

Reliability model generator specification

NASA Technical Reports Server (NTRS)

Cohen, Gerald C.; Mccann, Catherine

1990-01-01

The Reliability Model Generator (RMG), a program which produces reliability models from block diagrams for ASSIST, the interface for the reliability evaluation tool SURE is described. An account is given of motivation for RMG and the implemented algorithms are discussed. The appendices contain the algorithms and two detailed traces of examples.

Development of the Next Generation Air Quality Modeling System

EPA Science Inventory

A next generation air quality modeling system is being developed at the U.S. EPA to enable modeling of air quality from global to regional to (eventually) local scales. We envision that the system will have three configurations: 1. Global meteorology with seamless mesh refinemen...

Development of Automated Procedures to Generate Reference Building Models for ASHRAE Standard 90.1 and India’s Building Energy Code and Implementation in OpenStudio

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parker, Andrew; Haves, Philip; Jegi, Subhash

This paper describes a software system for automatically generating a reference (baseline) building energy model from the proposed (as-designed) building energy model. This system is built using the OpenStudio Software Development Kit (SDK) and is designed to operate on building energy models in the OpenStudio file format.

Creating historical range of variation (HRV) time series using landscape modeling: Overview and issues [Chapter 8

Treesearch

Robert E. Keane

2012-01-01

Simulation modeling can be a powerful tool for generating information about historical range of variation (HRV) in landscape conditions. In this chapter, I will discuss several aspects of the use of simulation modeling to generate landscape HRV data, including (1) the advantages and disadvantages of using simulation, (2) a brief review of possible landscape models. and...

Testing the vulnerability and scar models of self-esteem and depressive symptoms from adolescence to middle adulthood and across generations.

PubMed

Steiger, Andrea E; Fend, Helmut A; Allemand, Mathias

2015-02-01

The vulnerability model states that low self-esteem functions as a predictor for the development of depressive symptoms whereas the scar model assumes that these symptoms leave scars in individuals resulting in lower self-esteem. Both models have received empirical support, however, they have only been tested within individuals and not across generations (i.e., between family members). Thus, we tested the scope of these competing models by (a) investigating whether the effects hold from adolescence to middle adulthood (long-term vulnerability and scar effects), (b) whether the effects hold across generations (intergenerational vulnerability and scar effects), and (c) whether intergenerational effects are mediated by parental self-esteem and depressive symptoms and parent-child discord. We used longitudinal data from adolescence to middle adulthood (N = 1,359) and from Generation 1 adolescents (G1) to Generation 2 adolescents (G2) (N = 572 parent-child pairs). Results from latent cross-lagged regression analyses demonstrated that both adolescent self-esteem and depressive symptoms were prospectively related to adult self-esteem and depressive symptoms 3 decades later. That is, both the vulnerability and scar models are valid over decades with stronger effects for the vulnerability model. Across generations, we found a substantial direct transmission effect from G1 to G2 adolescent depressive symptoms but no evidence for the proposed intergenerational vulnerability and scar effect or for any of the proposed mediating mechanisms. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Virtual reality and consciousness inference in dreaming

PubMed Central

Hobson, J. Allan; Hong, Charles C.-H.; Friston, Karl J.

2014-01-01

This article explores the notion that the brain is genetically endowed with an innate virtual reality generator that – through experience-dependent plasticity – becomes a generative or predictive model of the world. This model, which is most clearly revealed in rapid eye movement (REM) sleep dreaming, may provide the theater for conscious experience. Functional neuroimaging evidence for brain activations that are time-locked to rapid eye movements (REMs) endorses the view that waking consciousness emerges from REM sleep – and dreaming lays the foundations for waking perception. In this view, the brain is equipped with a virtual model of the world that generates predictions of its sensations. This model is continually updated and entrained by sensory prediction errors in wakefulness to ensure veridical perception, but not in dreaming. In contrast, dreaming plays an essential role in maintaining and enhancing the capacity to model the world by minimizing model complexity and thereby maximizing both statistical and thermodynamic efficiency. This perspective suggests that consciousness corresponds to the embodied process of inference, realized through the generation of virtual realities (in both sleep and wakefulness). In short, our premise or hypothesis is that the waking brain engages with the world to predict the causes of sensations, while in sleep the brain’s generative model is actively refined so that it generates more efficient predictions during waking. We review the evidence in support of this hypothesis – evidence that grounds consciousness in biophysical computations whose neuronal and neurochemical infrastructure has been disclosed by sleep research. PMID:25346710

Virtual reality and consciousness inference in dreaming.

PubMed

Hobson, J Allan; Hong, Charles C-H; Friston, Karl J

2014-01-01

This article explores the notion that the brain is genetically endowed with an innate virtual reality generator that - through experience-dependent plasticity - becomes a generative or predictive model of the world. This model, which is most clearly revealed in rapid eye movement (REM) sleep dreaming, may provide the theater for conscious experience. Functional neuroimaging evidence for brain activations that are time-locked to rapid eye movements (REMs) endorses the view that waking consciousness emerges from REM sleep - and dreaming lays the foundations for waking perception. In this view, the brain is equipped with a virtual model of the world that generates predictions of its sensations. This model is continually updated and entrained by sensory prediction errors in wakefulness to ensure veridical perception, but not in dreaming. In contrast, dreaming plays an essential role in maintaining and enhancing the capacity to model the world by minimizing model complexity and thereby maximizing both statistical and thermodynamic efficiency. This perspective suggests that consciousness corresponds to the embodied process of inference, realized through the generation of virtual realities (in both sleep and wakefulness). In short, our premise or hypothesis is that the waking brain engages with the world to predict the causes of sensations, while in sleep the brain's generative model is actively refined so that it generates more efficient predictions during waking. We review the evidence in support of this hypothesis - evidence that grounds consciousness in biophysical computations whose neuronal and neurochemical infrastructure has been disclosed by sleep research.

Temporal Cyber Attack Detection.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ingram, Joey Burton; Draelos, Timothy J.; Galiardi, Meghan

Rigorous characterization of the performance and generalization ability of cyber defense systems is extremely difficult, making it hard to gauge uncertainty, and thus, confidence. This difficulty largely stems from a lack of labeled attack data that fully explores the potential adversarial space. Currently, performance of cyber defense systems is typically evaluated in a qualitative manner by manually inspecting the results of the system on live data and adjusting as needed. Additionally, machine learning has shown promise in deriving models that automatically learn indicators of compromise that are more robust than analyst-derived detectors. However, to generate these models, most algorithms requiremore » large amounts of labeled data (i.e., examples of attacks). Algorithms that do not require annotated data to derive models are similarly at a disadvantage, because labeled data is still necessary when evaluating performance. In this work, we explore the use of temporal generative models to learn cyber attack graph representations and automatically generate data for experimentation and evaluation. Training and evaluating cyber systems and machine learning models requires significant, annotated data, which is typically collected and labeled by hand for one-off experiments. Automatically generating such data helps derive/evaluate detection models and ensures reproducibility of results. Experimentally, we demonstrate the efficacy of generative sequence analysis techniques on learning the structure of attack graphs, based on a realistic example. These derived models can then be used to generate more data. Additionally, we provide a roadmap for future research efforts in this area.« less

A program code generator for multiphysics biological simulation using markup languages.

PubMed

Amano, Akira; Kawabata, Masanari; Yamashita, Yoshiharu; Rusty Punzalan, Florencio; Shimayoshi, Takao; Kuwabara, Hiroaki; Kunieda, Yoshitoshi

2012-01-01

To cope with the complexity of the biological function simulation models, model representation with description language is becoming popular. However, simulation software itself becomes complex in these environment, thus, it is difficult to modify the simulation conditions, target computation resources or calculation methods. In the complex biological function simulation software, there are 1) model equations, 2) boundary conditions and 3) calculation schemes. Use of description model file is useful for first point and partly second point, however, third point is difficult to handle for various calculation schemes which is required for simulation models constructed from two or more elementary models. We introduce a simulation software generation system which use description language based description of coupling calculation scheme together with cell model description file. By using this software, we can easily generate biological simulation code with variety of coupling calculation schemes. To show the efficiency of our system, example of coupling calculation scheme with three elementary models are shown.

Simulation, Model Verification and Controls Development of Brayton Cycle PM Alternator: Testing and Simulation of 2 KW PM Generator with Diode Bridge Output

NASA Technical Reports Server (NTRS)

Stankovic, Ana V.

2003-01-01

Professor Stankovic will be developing and refining Simulink based models of the PM alternator and comparing the simulation results with experimental measurements taken from the unit. Her first task is to validate the models using the experimental data. Her next task is to develop alternative control techniques for the application of the Brayton Cycle PM Alternator in a nuclear electric propulsion vehicle. The control techniques will be first simulated using the validated models then tried experimentally with hardware available at NASA. Testing and simulation of a 2KW PM synchronous generator with diode bridge output is described. The parameters of a synchronous PM generator have been measured and used in simulation. Test procedures have been developed to verify the PM generator model with diode bridge output. Experimental and simulation results are in excellent agreement.

Evaluation of gravitational gradients generated by Earth's crustal structures

NASA Astrophysics Data System (ADS)

Novák, Pavel; Tenzer, Robert; Eshagh, Mehdi; Bagherbandi, Mohammad

2013-02-01

Spectral formulas for the evaluation of gravitational gradients generated by upper Earth's mass components are presented in the manuscript. The spectral approach allows for numerical evaluation of global gravitational gradient fields that can be used to constrain gravitational gradients either synthesised from global gravitational models or directly measured by the spaceborne gradiometer on board of the GOCE satellite mission. Gravitational gradients generated by static atmospheric, topographic and continental ice masses are evaluated numerically based on available global models of Earth's topography, bathymetry and continental ice sheets. CRUST2.0 data are then applied for the numerical evaluation of gravitational gradients generated by mass density contrasts within soft and hard sediments, upper, middle and lower crust layers. Combined gravitational gradients are compared to disturbing gravitational gradients derived from a global gravitational model and an idealised Earth's model represented by the geocentric homogeneous biaxial ellipsoid GRS80. The methodology could be used for improved modelling of the Earth's inner structure.

Integrated Mode Choice, Small Aircraft Demand, and Airport Operations Model User's Guide

NASA Technical Reports Server (NTRS)

Yackovetsky, Robert E. (Technical Monitor); Dollyhigh, Samuel M.

2004-01-01

A mode choice model that generates on-demand air travel forecasts at a set of GA airports based on changes in economic characteristics, vehicle performance characteristics such as speed and cost, and demographic trends has been integrated with a model to generate itinerate aircraft operations by airplane category at a set of 3227 airports. Numerous intermediate outputs can be generated, such as the number of additional trips diverted from automobiles and schedule air by the improved performance and cost of on-demand air vehicles. The total number of transported passenger miles that are diverted is also available. From these results the number of new aircraft to service the increased demand can be calculated. Output from the models discussed is in the format to generate the origin and destination traffic flow between the 3227 airports based on solutions to a gravity model.

Assessing CO2 Mitigation Options Utilizing Detailed Electricity Characteristics and Including Renewable Generation

NASA Astrophysics Data System (ADS)

Bensaida, K.; Alie, Colin; Elkamel, A.; Almansoori, A.

2017-08-01

This paper presents a novel techno-economic optimization model for assessing the effectiveness of CO2 mitigation options for the electricity generation sub-sector that includes renewable energy generation. The optimization problem was formulated as a MINLP model using the GAMS modeling system. The model seeks the minimization of the power generation costs under CO2 emission constraints by dispatching power from low CO2 emission-intensity units. The model considers the detailed operation of the electricity system to effectively assess the performance of GHG mitigation strategies and integrates load balancing, carbon capture and carbon taxes as methods for reducing CO2 emissions. Two case studies are discussed to analyze the benefits and challenges of the CO2 reduction methods in the electricity system. The proposed mitigations options would not only benefit the environment, but they will as well improve the marginal cost of producing energy which represents an advantage for stakeholders.

Positive and negative generation effects in source monitoring.

PubMed

Riefer, David M; Chien, Yuchin; Reimer, Jason F

2007-10-01

Research is mixed as to whether self-generation improves memory for the source of information. We propose the hypothesis that positive generation effects (better source memory for self-generated information) occur in reality-monitoring paradigms, while negative generation effects (better source memory for externally presented information) tend to occur in external source-monitoring paradigms. This hypothesis was tested in an experiment in which participants read or generated words, followed by a memory test for the source of each word (read or generated) and the word's colour. Meiser and Bröder's (2002) multinomial model for crossed source dimensions was used to analyse the data, showing that source memory for generation (reality monitoring) was superior for the generated words, while source memory for word colour (external source monitoring) was superior for the read words. The model also revealed the influence of strong response biases in the data, demonstrating the usefulness of formal modelling when examining generation effects in source monitoring.

Dark matter stability and one-loop neutrino mass generation based on Peccei-Quinn symmetry

NASA Astrophysics Data System (ADS)

Suematsu, Daijiro

2018-01-01

We propose a model which is a simple extension of the KSVZ invisible axion model with an inert doublet scalar. Peccei-Quinn symmetry forbids tree-level neutrino mass generation and its remnant Z_2 symmetry guarantees dark matter stability. The neutrino masses are generated by one-loop effects as a result of the breaking of Peccei-Quinn symmetry through a nonrenormalizable interaction. Although the low energy effective model coincides with an original scotogenic model which contains right-handed neutrinos with large masses, it is free from the strong CP problem.

Partial automation of database processing of simulation outputs from L-systems models of plant morphogenesis.

PubMed

Chen, Yi- Ping Phoebe; Hanan, Jim

2002-01-01

Models of plant architecture allow us to explore how genotype environment interactions effect the development of plant phenotypes. Such models generate masses of data organised in complex hierarchies. This paper presents a generic system for creating and automatically populating a relational database from data generated by the widely used L-system approach to modelling plant morphogenesis. Techniques from compiler technology are applied to generate attributes (new fields) in the database, to simplify query development for the recursively-structured branching relationship. Use of biological terminology in an interactive query builder contributes towards making the system biologist-friendly.

BOREAS TE-17 Production Efficiency Model Images

NASA Technical Reports Server (NTRS)

Hall, Forrest G.; Papagno, Andrea (Editor); Goetz, Scott J.; Goward, Samual N.; Prince, Stephen D.; Czajkowski, Kevin; Dubayah, Ralph O.

2000-01-01

A Boreal Ecosystem-Atmospheric Study (BOREAS) version of the Global Production Efficiency Model (http://www.inform.umd.edu/glopem/) was developed by TE-17 (Terrestrial Ecology) to generate maps of gross and net primary production, autotrophic respiration, and light use efficiency for the BOREAS region. This document provides basic information on the model and how the maps were generated. The data generated by the model are stored in binary image-format files. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

A cortical integrate-and-fire neural network model for blind decoding of visual prosthetic stimulation.

PubMed

Eiber, Calvin D; Morley, John W; Lovell, Nigel H; Suaning, Gregg J

2014-01-01

We present a computational model of the optic pathway which has been adapted to simulate cortical responses to visual-prosthetic stimulation. This model reproduces the statistically observed distributions of spikes for cortical recordings of sham and maximum-intensity stimuli, while simultaneously generating cellular receptive fields consistent with those observed using traditional visual neuroscience methods. By inverting this model to generate candidate phosphenes which could generate the responses observed to novel stimulation strategies, we hope to aid the development of said strategies in-vivo before being deployed in clinical settings.

A Small Aircraft Transportation System (SATS) Demand Model

NASA Technical Reports Server (NTRS)

Long, Dou; Lee, David; Johnson, Jesse; Kostiuk, Peter; Yackovetsky, Robert (Technical Monitor)

2001-01-01

The Small Aircraft Transportation System (SATS) demand modeling is a tool that will be useful for decision-makers to analyze SATS demands in both airport and airspace. We constructed a series of models following the general top-down, modular principles in systems engineering. There are three principal models, SATS Airport Demand Model (SATS-ADM), SATS Flight Demand Model (SATS-FDM), and LMINET-SATS. SATS-ADM models SATS operations, by aircraft type, from the forecasts in fleet, configuration and performance, utilization, and traffic mixture. Given the SATS airport operations such as the ones generated by SATS-ADM, SATS-FDM constructs the SATS origin and destination (O&D) traffic flow based on the solution of the gravity model, from which it then generates SATS flights using the Monte Carlo simulation based on the departure time-of-day profile. LMINET-SATS, an extension of LMINET, models SATS demands at airspace and airport by all aircraft operations in US The models use parameters to provide the user with flexibility and ease of use to generate SATS demand for different scenarios. Several case studies are included to illustrate the use of the models, which are useful to identify the need for a new air traffic management system to cope with SATS.

Model learning for robot control: a survey.

PubMed

Nguyen-Tuong, Duy; Peters, Jan

2011-11-01

Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot's own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.

Generating Three-Dimensional Surface Models of Solid Objects from Multiple Projections.

DTIC Science & Technology

1982-10-01

volume descriptions. The surface models are composed of curved, topologically rectangular, parametric patches. The data required to define these patches...geometry directly from image data .__ This method generates 3D surface descriptions of only those parts of the object that are illuminated by the pro- jected...objects. Generation of such models inherently requires the acquisition and analysis of 3D surface data . In this context, acquisition refers to the

The Sortie-Generation Model System. Volume 5. Maintenance Subsystem

DTIC Science & Technology

1981-09-01

Compuger RoanutI f and moidel 11, Computer operatinS system 17, Proorammino largualviso IS. Numlier of .ugic proltsm Hoewl -3 CSCobol 600 stuscomentm...THE SORTIE-GENERATION MODEL SYSTEM OC’ VOLUME V MAINTENANCE SUBSYSTEM September 1981 Robert S. Greenberg 05$ Prepared pursuant to Department of...Generation Model System Volume V Maintenance Subsystem 6. PERFORMING ORG. REPORT NUMBER LMI Task- L102 7. AUTHOR(a) 8. CONTRACT OR GRANT NUMBER(a

Test Generator for MATLAB Simulations

NASA Technical Reports Server (NTRS)

Henry, Joel

2011-01-01

MATLAB Automated Test Tool, version 3.0 (MATT 3.0) is a software package that provides automated tools that reduce the time needed for extensive testing of simulation models that have been constructed in the MATLAB programming language by use of the Simulink and Real-Time Workshop programs. MATT 3.0 runs on top of the MATLAB engine application-program interface to communicate with the Simulink engine. MATT 3.0 automatically generates source code from the models, generates custom input data for testing both the models and the source code, and generates graphs and other presentations that facilitate comparison of the outputs of the models and the source code for the same input data. Context-sensitive and fully searchable help is provided in HyperText Markup Language (HTML) format.

Veterans' informal caregivers in the "sandwich generation": a systematic review toward a resilience model.

PubMed

Smith-Osborne, Alexa; Felderhoff, Brandi

2014-01-01

Social work theory advanced the formulation of the construct of the sandwich generation to apply to the emerging generational cohort of caregivers, most often middle-aged women, who were caring for maturing children and aging parents simultaneously. This systematic review extends that focus by synthesizing the literature on sandwich generation caregivers for the general aging population with dementia and for veterans with dementia and polytrauma. It develops potential protective mechanisms based on empirical literature to support an intervention resilience model for social work practitioners. This theoretical model addresses adaptive coping of sandwich- generation families facing ongoing challenges related to caregiving demands.

An improved neutral landscape model for recreating real landscapes and generating landscape series for spatial ecological simulations.

PubMed

van Strien, Maarten J; Slager, Cornelis T J; de Vries, Bauke; Grêt-Regamey, Adrienne

2016-06-01

Many studies have assessed the effect of landscape patterns on spatial ecological processes by simulating these processes in computer-generated landscapes with varying composition and configuration. To generate such landscapes, various neutral landscape models have been developed. However, the limited set of landscape-level pattern variables included in these models is often inadequate to generate landscapes that reflect real landscapes. In order to achieve more flexibility and variability in the generated landscapes patterns, a more complete set of class- and patch-level pattern variables should be implemented in these models. These enhancements have been implemented in Landscape Generator (LG), which is a software that uses optimization algorithms to generate landscapes that match user-defined target values. Developed for participatory spatial planning at small scale, we enhanced the usability of LG and demonstrated how it can be used for larger scale ecological studies. First, we used LG to recreate landscape patterns from a real landscape (i.e., a mountainous region in Switzerland). Second, we generated landscape series with incrementally changing pattern variables, which could be used in ecological simulation studies. We found that LG was able to recreate landscape patterns that approximate those of real landscapes. Furthermore, we successfully generated landscape series that would not have been possible with traditional neutral landscape models. LG is a promising novel approach for generating neutral landscapes and enables testing of new hypotheses regarding the influence of landscape patterns on ecological processes. LG is freely available online.

Palm: Easing the Burden of Analytical Performance Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tallent, Nathan R.; Hoisie, Adolfy

2014-06-01

Analytical (predictive) application performance models are critical for diagnosing performance-limiting resources, optimizing systems, and designing machines. Creating models, however, is difficult because they must be both accurate and concise. To ease the burden of performance modeling, we developed Palm, a modeling tool that combines top-down (human-provided) semantic insight with bottom-up static and dynamic analysis. To express insight, Palm defines a source code modeling annotation language. By coordinating models and source code, Palm's models are `first-class' and reproducible. Unlike prior work, Palm formally links models, functions, and measurements. As a result, Palm (a) uses functions to either abstract or express complexitymore » (b) generates hierarchical models (representing an application's static and dynamic structure); and (c) automatically incorporates measurements to focus attention, represent constant behavior, and validate models. We discuss generating models for three different applications.« less

A geomorphic approach to 100-year floodplain mapping for the Conterminous United States

NASA Astrophysics Data System (ADS)

Jafarzadegan, Keighobad; Merwade, Venkatesh; Saksena, Siddharth

2018-06-01

Floodplain mapping using hydrodynamic models is difficult in data scarce regions. Additionally, using hydrodynamic models to map floodplain over large stream network can be computationally challenging. Some of these limitations of floodplain mapping using hydrodynamic modeling can be overcome by developing computationally efficient statistical methods to identify floodplains in large and ungauged watersheds using publicly available data. This paper proposes a geomorphic model to generate probabilistic 100-year floodplain maps for the Conterminous United States (CONUS). The proposed model first categorizes the watersheds in the CONUS into three classes based on the height of the water surface corresponding to the 100-year flood from the streambed. Next, the probability that any watershed in the CONUS belongs to one of these three classes is computed through supervised classification using watershed characteristics related to topography, hydrography, land use and climate. The result of this classification is then fed into a probabilistic threshold binary classifier (PTBC) to generate the probabilistic 100-year floodplain maps. The supervised classification algorithm is trained by using the 100-year Flood Insurance Rated Maps (FIRM) from the U.S. Federal Emergency Management Agency (FEMA). FEMA FIRMs are also used to validate the performance of the proposed model in areas not included in the training. Additionally, HEC-RAS model generated flood inundation extents are used to validate the model performance at fifteen sites that lack FEMA maps. Validation results show that the probabilistic 100-year floodplain maps, generated by proposed model, match well with both FEMA and HEC-RAS generated maps. On average, the error of predicted flood extents is around 14% across the CONUS. The high accuracy of the validation results shows the reliability of the geomorphic model as an alternative approach for fast and cost effective delineation of 100-year floodplains for the CONUS.

Integrating machine learning techniques into robust data enrichment approach and its application to gene expression data.

PubMed

Erdoğdu, Utku; Tan, Mehmet; Alhajj, Reda; Polat, Faruk; Rokne, Jon; Demetrick, Douglas

2013-01-01

The availability of enough samples for effective analysis and knowledge discovery has been a challenge in the research community, especially in the area of gene expression data analysis. Thus, the approaches being developed for data analysis have mostly suffered from the lack of enough data to train and test the constructed models. We argue that the process of sample generation could be successfully automated by employing some sophisticated machine learning techniques. An automated sample generation framework could successfully complement the actual sample generation from real cases. This argument is validated in this paper by describing a framework that integrates multiple models (perspectives) for sample generation. We illustrate its applicability for producing new gene expression data samples, a highly demanding area that has not received attention. The three perspectives employed in the process are based on models that are not closely related. The independence eliminates the bias of having the produced approach covering only certain characteristics of the domain and leading to samples skewed towards one direction. The first model is based on the Probabilistic Boolean Network (PBN) representation of the gene regulatory network underlying the given gene expression data. The second model integrates Hierarchical Markov Model (HIMM) and the third model employs a genetic algorithm in the process. Each model learns as much as possible characteristics of the domain being analysed and tries to incorporate the learned characteristics in generating new samples. In other words, the models base their analysis on domain knowledge implicitly present in the data itself. The developed framework has been extensively tested by checking how the new samples complement the original samples. The produced results are very promising in showing the effectiveness, usefulness and applicability of the proposed multi-model framework.

Next-Generation Lightweight Mirror Modeling Software

NASA Technical Reports Server (NTRS)

Arnold, William R., Sr.; Fitzgerald, Mathew; Rosa, Rubin Jaca; Stahl, Phil

2013-01-01

The advances in manufacturing techniques for lightweight mirrors, such as EXELSIS deep core low temperature fusion, Corning's continued improvements in the Frit bonding process and the ability to cast large complex designs, combined with water-jet and conventional diamond machining of glasses and ceramics has created the need for more efficient means of generating finite element models of these structures. Traditional methods of assembling 400,000 + element models can take weeks of effort, severely limiting the range of possible optimization variables. This paper will introduce model generation software developed under NASA sponsorship for the design of both terrestrial and space based mirrors. The software deals with any current mirror manufacturing technique, single substrates, multiple arrays of substrates, as well as the ability to merge submodels into a single large model. The modeler generates both mirror and suspension system elements, suspensions can be created either for each individual petal or the whole mirror. A typical model generation of 250,000 nodes and 450,000 elements only takes 5-10 minutes, much of that time being variable input time. The program can create input decks for ANSYS, ABAQUS and NASTRAN. An archive/retrieval system permits creation of complete trade studies, varying cell size, depth, and petal size, suspension geometry with the ability to recall a particular set of parameters and make small or large changes with ease. The input decks created by the modeler are text files which can be modified by any editor, all the key shell thickness parameters are accessible and comments in deck identify which groups of elements are associated with these parameters. This again makes optimization easier. With ANSYS decks, the nodes representing support attachments are grouped into components; in ABAQUS these are SETS and in NASTRAN as GRIDPOINT SETS, this make integration of these models into large telescope or satellite models possible

Next Generation Lightweight Mirror Modeling Software

NASA Technical Reports Server (NTRS)

Arnold, William; Fitzgerald, Matthew; Stahl, Philip

2013-01-01

The advances in manufacturing techniques for lightweight mirrors, such as EXELSIS deep core low temperature fusion, Corning's continued improvements in the Frit bonding process and the ability to cast large complex designs, combined with water-jet and conventional diamond machining of glasses and ceramics has created the need for more efficient means of generating finite element models of these structures. Traditional methods of assembling 400,000 + element models can take weeks of effort, severely limiting the range of possible optimization variables. This paper will introduce model generation software developed under NASA sponsorship for the design of both terrestrial and space based mirrors. The software deals with any current mirror manufacturing technique, single substrates, multiple arrays of substrates, as well as the ability to merge submodels into a single large model. The modeler generates both mirror and suspension system elements, suspensions can be created either for each individual petal or the whole mirror. A typical model generation of 250,000 nodes and 450,000 elements only takes 5-10 minutes, much of that time being variable input time. The program can create input decks for ANSYS, ABAQUS and NASTRAN. An archive/retrieval system permits creation of complete trade studies, varying cell size, depth, and petal size, suspension geometry with the ability to recall a particular set of parameters and make small or large changes with ease. The input decks created by the modeler are text files which can be modified by any editor, all the key shell thickness parameters are accessible and comments in deck identify which groups of elements are associated with these parameters. This again makes optimization easier. With ANSYS decks, the nodes representing support attachments are grouped into components; in ABAQUS these are SETS and in NASTRAN as GRIDPOINT SETS, this make integration of these models into large telescope or satellite models possible.

Next Generation Lightweight Mirror Modeling Software

NASA Technical Reports Server (NTRS)

Arnold, William R., Sr.; Fitzgerald, Mathew; Rosa, Rubin Jaca; Stahl, H. Philip

2013-01-01

The advances in manufacturing techniques for lightweight mirrors, such as EXELSIS deep core low temperature fusion, Corning's continued improvements in the Frit bonding process and the ability to cast large complex designs, combined with water-jet and conventional diamond machining of glasses and ceramics has created the need for more efficient means of generating finite element models of these structures. Traditional methods of assembling 400,000 + element models can take weeks of effort, severely limiting the range of possible optimization variables. This paper will introduce model generation software developed under NASA sponsorship for the design of both terrestrial and space based mirrors. The software deals with any current mirror manufacturing technique, single substrates, multiple arrays of substrates, as well as the ability to merge submodels into a single large model. The modeler generates both mirror and suspension system elements, suspensions can be created either for each individual petal or the whole mirror. A typical model generation of 250,000 nodes and 450,000 elements only takes 5-10 minutes, much of that time being variable input time. The program can create input decks for ANSYS, ABAQUS and NASTRAN. An archive/retrieval system permits creation of complete trade studies, varying cell size, depth, and petal size, suspension geometry with the ability to recall a particular set of parameters and make small or large changes with ease. The input decks created by the modeler are text files which can be modified by any editor, all the key shell thickness parameters are accessible and comments in deck identify which groups of elements are associated with these parameters. This again makes optimization easier. With ANSYS decks, the nodes representing support attachments are grouped into components; in ABAQUS these are SETS and in NASTRAN as GRIDPOINT SETS, this make integration of these models into large telescope or satellite models easier.

Progress on Implementing Additional Physics Schemes into MPAS-A v5.1 for Next Generation Air Quality Modeling

EPA Science Inventory

The U.S. Environmental Protection Agency (USEPA) has a team of scientists developing a next generation air quality modeling system employing the Model for Prediction Across Scales – Atmosphere (MPAS-A) as its meteorological foundation. Several preferred physics schemes and ...

An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation

PubMed Central

Zhang, Xiaoyan; Kim, Daeseung; Shen, Shunyao; Yuan, Peng; Liu, Siting; Tang, Zhen; Zhang, Guangming; Zhou, Xiaobo; Gateno, Jaime

2017-01-01

Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft tissue changes following osteotomy. This can only be achieved by using an anatomically detailed facial soft tissue model. The current state-of-the-art of model generation is not appropriate to clinical applications due to the time-intensive nature of manual segmentation and volumetric mesh generation. The conventional patient-specific finite element (FE) mesh generation methods are to deform a template FE mesh to match the shape of a patient based on registration. However, these methods commonly produce element distortion. Additionally, the mesh density for patients depends on that of the template model. It could not be adjusted to conduct mesh density sensitivity analysis. In this study, we propose a new framework of patient-specific facial soft tissue FE mesh generation. The goal of the developed method is to efficiently generate a high-quality patient-specific hexahedral FE mesh with adjustable mesh density while preserving the accuracy in anatomical structure correspondence. Our FE mesh is generated by eFace template deformation followed by volumetric parametrization. First, the patient-specific anatomically detailed facial soft tissue model (including skin, mucosa, and muscles) is generated by deforming an eFace template model. The adaptation of the eFace template model is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a thin-plate spline interpolation. Then, high-quality hexahedral mesh is constructed by using volumetric parameterization. The user can control the resolution of hexahedron mesh to best reflect clinicians’ need. Our approach was validated using 30 patient models and 4 visible human datasets. The generated patient-specific FE mesh showed high surface matching accuracy, element quality, and internal structure matching accuracy. They can be directly and effectively used for clinical simulation of facial soft tissue change. PMID:29027022

An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation.

PubMed

Zhang, Xiaoyan; Kim, Daeseung; Shen, Shunyao; Yuan, Peng; Liu, Siting; Tang, Zhen; Zhang, Guangming; Zhou, Xiaobo; Gateno, Jaime; Liebschner, Michael A K; Xia, James J

2018-04-01

Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft tissue changes following osteotomy. This can only be achieved by using an anatomically detailed facial soft tissue model. The current state-of-the-art of model generation is not appropriate to clinical applications due to the time-intensive nature of manual segmentation and volumetric mesh generation. The conventional patient-specific finite element (FE) mesh generation methods are to deform a template FE mesh to match the shape of a patient based on registration. However, these methods commonly produce element distortion. Additionally, the mesh density for patients depends on that of the template model. It could not be adjusted to conduct mesh density sensitivity analysis. In this study, we propose a new framework of patient-specific facial soft tissue FE mesh generation. The goal of the developed method is to efficiently generate a high-quality patient-specific hexahedral FE mesh with adjustable mesh density while preserving the accuracy in anatomical structure correspondence. Our FE mesh is generated by eFace template deformation followed by volumetric parametrization. First, the patient-specific anatomically detailed facial soft tissue model (including skin, mucosa, and muscles) is generated by deforming an eFace template model. The adaptation of the eFace template model is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a thin-plate spline interpolation. Then, high-quality hexahedral mesh is constructed by using volumetric parameterization. The user can control the resolution of hexahedron mesh to best reflect clinicians' need. Our approach was validated using 30 patient models and 4 visible human datasets. The generated patient-specific FE mesh showed high surface matching accuracy, element quality, and internal structure matching accuracy. They can be directly and effectively used for clinical simulation of facial soft tissue change.

3D molecular models of whole HIV-1 virions generated with cellPACK

PubMed Central

Goodsell, David S.; Autin, Ludovic; Forli, Stefano; Sanner, Michel F.; Olson, Arthur J.

2014-01-01

As knowledge of individual biological processes grows, it becomes increasingly useful to frame new findings within their larger biological contexts in order to generate new systems-scale hypotheses. This report highlights two major iterations of a whole virus model of HIV-1, generated with the cellPACK software. cellPACK integrates structural and systems biology data with packing algorithms to assemble comprehensive 3D models of cell-scale structures in molecular detail. This report describes the biological data, modeling parameters and cellPACK methods used to specify and construct editable models for HIV-1. Anticipating that cellPACK interfaces under development will enable researchers from diverse backgrounds to critique and improve the biological models, we discuss how cellPACK can be used as a framework to unify different types of data across all scales of biology. PMID:25253262

Methodologies for Development of Patient Specific Bone Models from Human Body CT Scans

NASA Astrophysics Data System (ADS)

Chougule, Vikas Narayan; Mulay, Arati Vinayak; Ahuja, Bharatkumar Bhagatraj

2016-06-01

This work deals with development of algorithm for physical replication of patient specific human bone and construction of corresponding implants/inserts RP models by using Reverse Engineering approach from non-invasive medical images for surgical purpose. In medical field, the volumetric data i.e. voxel and triangular facet based models are primarily used for bio-modelling and visualization, which requires huge memory space. On the other side, recent advances in Computer Aided Design (CAD) technology provides additional facilities/functions for design, prototyping and manufacturing of any object having freeform surfaces based on boundary representation techniques. This work presents a process to physical replication of 3D rapid prototyping (RP) physical models of human bone from various CAD modeling techniques developed by using 3D point cloud data which is obtained from non-invasive CT/MRI scans in DICOM 3.0 format. This point cloud data is used for construction of 3D CAD model by fitting B-spline curves through these points and then fitting surface between these curve networks by using swept blend techniques. This process also can be achieved by generating the triangular mesh directly from 3D point cloud data without developing any surface model using any commercial CAD software. The generated STL file from 3D point cloud data is used as a basic input for RP process. The Delaunay tetrahedralization approach is used to process the 3D point cloud data to obtain STL file. CT scan data of Metacarpus (human bone) is used as the case study for the generation of the 3D RP model. A 3D physical model of the human bone is generated on rapid prototyping machine and its virtual reality model is presented for visualization. The generated CAD model by different techniques is compared for the accuracy and reliability. The results of this research work are assessed for clinical reliability in replication of human bone in medical field.

Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI.

PubMed

Taghia, Jalil; Ryali, Srikanth; Chen, Tianwen; Supekar, Kaustubh; Cai, Weidong; Menon, Vinod

2017-07-15

There is growing interest in understanding the dynamical properties of functional interactions between distributed brain regions. However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas. Here, we develop a Bayesian generative model for fMRI time-series within the framework of hidden Markov models (HMMs). The model is a dynamic variant of the static factor analysis model (Ghahramani and Beal, 2000). We refer to this model as Bayesian switching factor analysis (BSFA) as it integrates factor analysis into a generative HMM in a unified Bayesian framework. In BSFA, brain dynamic functional networks are represented by latent states which are learnt from the data. Crucially, BSFA is a generative model which estimates the temporal evolution of brain states and transition probabilities between states as a function of time. An attractive feature of BSFA is the automatic determination of the number of latent states via Bayesian model selection arising from penalization of excessively complex models. Key features of BSFA are validated using extensive simulations on carefully designed synthetic data. We further validate BSFA using fingerprint analysis of multisession resting-state fMRI data from the Human Connectome Project (HCP). Our results show that modeling temporal dependencies in the generative model of BSFA results in improved fingerprinting of individual participants. Finally, we apply BSFA to elucidate the dynamic functional organization of the salience, central-executive, and default mode networks-three core neurocognitive systems with central role in cognitive and affective information processing (Menon, 2011). Across two HCP sessions, we demonstrate a high level of dynamic interactions between these networks and determine that the salience network has the highest temporal flexibility among the three networks. Our proposed methods provide a novel and powerful generative model for investigating dynamic brain connectivity. Copyright © 2017 Elsevier Inc. All rights reserved.

Multi Sensor Data Integration for AN Accurate 3d Model Generation

NASA Astrophysics Data System (ADS)

Chhatkuli, S.; Satoh, T.; Tachibana, K.

2015-05-01

The aim of this paper is to introduce a novel technique of data integration between two different data sets, i.e. laser scanned RGB point cloud and oblique imageries derived 3D model, to create a 3D model with more details and better accuracy. In general, aerial imageries are used to create a 3D city model. Aerial imageries produce an overall decent 3D city models and generally suit to generate 3D model of building roof and some non-complex terrain. However, the automatically generated 3D model, from aerial imageries, generally suffers from the lack of accuracy in deriving the 3D model of road under the bridges, details under tree canopy, isolated trees, etc. Moreover, the automatically generated 3D model from aerial imageries also suffers from undulated road surfaces, non-conforming building shapes, loss of minute details like street furniture, etc. in many cases. On the other hand, laser scanned data and images taken from mobile vehicle platform can produce more detailed 3D road model, street furniture model, 3D model of details under bridge, etc. However, laser scanned data and images from mobile vehicle are not suitable to acquire detailed 3D model of tall buildings, roof tops, and so forth. Our proposed approach to integrate multi sensor data compensated each other's weakness and helped to create a very detailed 3D model with better accuracy. Moreover, the additional details like isolated trees, street furniture, etc. which were missing in the original 3D model derived from aerial imageries could also be integrated in the final model automatically. During the process, the noise in the laser scanned data for example people, vehicles etc. on the road were also automatically removed. Hence, even though the two dataset were acquired in different time period the integrated data set or the final 3D model was generally noise free and without unnecessary details.

Universal Verification Methodology Based Register Test Automation Flow.

PubMed

Woo, Jae Hun; Cho, Yong Kwan; Park, Sun Kyu

2016-05-01

In today's SoC design, the number of registers has been increased along with complexity of hardware blocks. Register validation is a time-consuming and error-pron task. Therefore, we need an efficient way to perform verification with less effort in shorter time. In this work, we suggest register test automation flow based UVM (Universal Verification Methodology). UVM provides a standard methodology, called a register model, to facilitate stimulus generation and functional checking of registers. However, it is not easy for designers to create register models for their functional blocks or integrate models in test-bench environment because it requires knowledge of SystemVerilog and UVM libraries. For the creation of register models, many commercial tools support a register model generation from register specification described in IP-XACT, but it is time-consuming to describe register specification in IP-XACT format. For easy creation of register model, we propose spreadsheet-based register template which is translated to IP-XACT description, from which register models can be easily generated using commercial tools. On the other hand, we also automate all the steps involved integrating test-bench and generating test-cases, so that designers may use register model without detailed knowledge of UVM or SystemVerilog. This automation flow involves generating and connecting test-bench components (e.g., driver, checker, bus adaptor, etc.) and writing test sequence for each type of register test-case. With the proposed flow, designers can save considerable amount of time to verify functionality of registers.

The viscous lee wave problem and its implications for ocean modelling

NASA Astrophysics Data System (ADS)

Shakespeare, Callum J.; Hogg, Andrew McC.

2017-05-01

Ocean circulation models employ 'turbulent' viscosity and diffusivity to represent unresolved sub-gridscale processes such as breaking internal waves. Computational power has now advanced sufficiently to permit regional ocean circulation models to be run at sufficiently high (100 m-1 km) horizontal resolution to resolve a significant part of the internal wave spectrum. Here we develop theory for boundary generated internal waves in such models, and in particular, where the waves dissipate their energy. We focus specifically on the steady lee wave problem where stationary waves are generated by a large-scale flow acting across ocean bottom topography. We generalise the energy flux expressions of [Bell, T., 1975. Topographically generated internal waves in the open ocean. J. Geophys. Res. 80, 320-327] to include the effect of arbitrary viscosity and diffusivity. Applying these results for realistic parameter choices we show that in the present generation of models with O(1) m2s-1 horizontal viscosity/diffusivity boundary-generated waves will inevitably dissipate the majority of their energy within a few hundred metres of the boundary. This dissipation is a direct consequence of the artificially high viscosity/diffusivity, which is not always physically justified in numerical models. Hence, caution is necessary in comparing model results to ocean observations. Our theory further predicts that O(10-2) m2s-1 horizontal and O(10-4) m2s-1 vertical viscosity/diffusivity is required to achieve a qualitatively inviscid representation of internal wave dynamics in ocean models.

Modeling Long-term Creep Performance for Welded Nickel-base Superalloy Structures for Power Generation Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shen, Chen; Gupta, Vipul; Huang, Shenyan

The goal of this project is to model long-term creep performance for nickel-base superalloy weldments in high temperature power generation systems. The project uses physics-based modeling methodologies and algorithms for predicting alloy properties in heterogeneous material structures. The modeling methodology will be demonstrated on a gas turbine combustor liner weldment of Haynes 282 precipitate-strengthened nickel-base superalloy. The major developments are: (1) microstructure-property relationships under creep conditions and microstructure characterization (2) modeling inhomogeneous microstructure in superalloy weld (3) modeling mesoscale plastic deformation in superalloy weld and (4) a constitutive creep model that accounts for weld and base metal microstructure and theirmore » long term evolution. The developed modeling technology is aimed to provide a more efficient and accurate assessment of a material’s long-term performance compared with current testing and extrapolation methods. This modeling technology will also accelerate development and qualification of new materials in advanced power generation systems. This document is a final technical report for the project, covering efforts conducted from October 2014 to December 2016.« less

Modelling municipal solid waste generation: a review.

PubMed

Beigl, Peter; Lebersorger, Sandra; Salhofer, Stefan

2008-01-01

The objective of this paper is to review previously published models of municipal solid waste generation and to propose an implementation guideline which will provide a compromise between information gain and cost-efficient model development. The 45 modelling approaches identified in a systematic literature review aim at explaining or estimating the present or future waste generation using economic, socio-demographic or management-orientated data. A classification was developed in order to categorise these highly heterogeneous models according to the following criteria--the regional scale, the modelled waste streams, the hypothesised independent variables and the modelling method. A procedural practice guideline was derived from a discussion of the underlying models in order to propose beneficial design options concerning regional sampling (i.e., number and size of observed areas), waste stream definition and investigation, selection of independent variables and model validation procedures. The practical application of the findings was demonstrated with two case studies performed on different regional scales, i.e., on a household and on a city level. The findings of this review are finally summarised in the form of a relevance tree for methodology selection.

Deep Generative Models of Galaxy Images for the Calibration of the Next Generation of Weak Lensing Surveys

NASA Astrophysics Data System (ADS)

Lanusse, Francois; Ravanbakhsh, Siamak; Mandelbaum, Rachel; Schneider, Jeff; Poczos, Barnabas

2017-01-01

Weak gravitational lensing has long been identified as one of the most powerful probes to investigate the nature of dark energy. As such, weak lensing is at the heart of the next generation of cosmological surveys such as LSST, Euclid or WFIRST.One particularly crititcal source of systematic errors in these surveys comes from the shape measurement algorithms tasked with estimating galaxy shapes. GREAT3, the last community challenge to assess the quality of state-of-the-art shape measurement algorithms has in particular demonstrated that all current methods are biased to various degrees and, more importantly, that these biases depend on the details of the galaxy morphologies. These biases can be measured and calibrated by generating mock observations where a known lensing signal has been introduced and comparing the resulting measurements to the ground-truth. Producing these mock observations however requires input galaxy images of higher resolution and S/N than the simulated survey, which typically implies acquiring extremely expensive space-based observations.The goal of this work is to train a deep generative model on already available Hubble Space Telescope data which can then be used to sample new galaxy images conditioned on parameters such as magnitude, size or redshift and exhibiting complex morphologies. Such model can allow us to inexpensively produce large set of realistic realistic images for calibration purposes.We implement a conditional generative model based on state-of-the-art deep learning methods and fit it to deep galaxy images from the COSMOS survey. The quality of the model is assessed by computing an extensive set of galaxy morphology statistics on the generated images. Beyond simple second moment statistics such as size and ellipticity, we apply more complex statistics specifically designed to be sensitive to disturbed galaxy morphologies. We find excellent agreement between the morphologies of real and model generated galaxies.Our results suggest that such deep generative models represent a reliable alternative to the acquisition of expensive high quality observations for generating the calibration data needed by the next generation of weak lensing surveys.

Next generation initiation techniques

NASA Technical Reports Server (NTRS)

Warner, Tom; Derber, John; Zupanski, Milija; Cohn, Steve; Verlinde, Hans

1993-01-01

Four-dimensional data assimilation strategies can generally be classified as either current or next generation, depending upon whether they are used operationally or not. Current-generation data-assimilation techniques are those that are presently used routinely in operational-forecasting or research applications. They can be classified into the following categories: intermittent assimilation, Newtonian relaxation, and physical initialization. It should be noted that these techniques are the subject of continued research, and their improvement will parallel the development of next generation techniques described by the other speakers. Next generation assimilation techniques are those that are under development but are not yet used operationally. Most of these procedures are derived from control theory or variational methods and primarily represent continuous assimilation approaches, in which the data and model dynamics are 'fitted' to each other in an optimal way. Another 'next generation' category is the initialization of convective-scale models. Intermittent assimilation systems use an objective analysis to combine all observations within a time window that is centered on the analysis time. Continuous first-generation assimilation systems are usually based on the Newtonian-relaxation or 'nudging' techniques. Physical initialization procedures generally involve the use of standard or nonstandard data to force some physical process in the model during an assimilation period. Under the topic of next-generation assimilation techniques, variational approaches are currently being actively developed. Variational approaches seek to minimize a cost or penalty function which measures a model's fit to observations, background fields and other imposed constraints. Alternatively, the Kalman filter technique, which is also under investigation as a data assimilation procedure for numerical weather prediction, can yield acceptable initial conditions for mesoscale models. The third kind of next-generation technique involves strategies to initialize convective scale (non-hydrostatic) models.

Design and modeling of energy generated magneto rheological damper

NASA Astrophysics Data System (ADS)

Ahamed, Raju; Rashid, Muhammad Mahbubur; Ferdaus, Md Meftahul; Yusof, Hazlina Md.

2016-02-01

In this paper an energy generated mono tube MR damper model has been developed for vehicle suspension systems. A 3D model of energy generated MR damper is developed in Solid Works electromagnetic simulator (EMS) where it is analyzed extensively by finite element method. This dynamic simulation clearly illustrates the power generation ability of the damper. Two magnetic fields are induced inside this damper. One is in the outer coil of the power generator and another is in the piston head coils. The complete magnetic isolation between these two fields is accomplished here, which can be seen in the finite element analysis. The induced magnetic flux densities, magnetic field intensities of this damper are analyzed for characterizing the damper's power generation ability. Finally, the proposed MR damper's energy generation ability was studied experimentally.

Transient analysis of a superconducting AC generator using the compensated 2-D model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chun, Y.D.; Lee, H.W.; Lee, J.

1999-09-01

A SCG has many advantages over conventional generators, such as reduction in width and size, improvement in efficiency, and better steady-state stability. The paper presents a 2-D transient analysis of a superconducting AC generator (SCG) using the finite element method (FEM). The compensated 2-D model obtained by lengthening the airgap of the original 2-D model is proposed for the accurate and efficient transient analysis. The accuracy of the compensated 2-D model is verified by the small error 6.4% compared to experimental data. The transient characteristics of the 30 KVA SCG model have been investigated in detail and the damper performancemore » on various design parameters is examined.« less

Using Model-Based Systems Engineering To Provide Artifacts for NASA Project Life-Cycle and Technical Reviews

NASA Technical Reports Server (NTRS)

Parrott, Edith L.; Weiland, Karen J.

2017-01-01

The ability of systems engineers to use model-based systems engineering (MBSE) to generate self-consistent, up-to-date systems engineering products for project life-cycle and technical reviews is an important aspect for the continued and accelerated acceptance of MBSE. Currently, many review products are generated using labor-intensive, error-prone approaches based on documents, spreadsheets, and chart sets; a promised benefit of MBSE is that users will experience reductions in inconsistencies and errors. This work examines features of SysML that can be used to generate systems engineering products. Model elements, relationships, tables, and diagrams are identified for a large number of the typical systems engineering artifacts. A SysML system model can contain and generate most systems engineering products to a significant extent and this paper provides a guide on how to use MBSE to generate products for project life-cycle and technical reviews. The use of MBSE can reduce the schedule impact usually experienced for review preparation, as in many cases the review products can be auto-generated directly from the system model. These approaches are useful to systems engineers, project managers, review board members, and other key project stakeholders.

Variable Renewable Energy in Long-Term Planning Models: A Multi-Model Perspective

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cole, Wesley J.; Frew, Bethany A.; Mai, Trieu T.

Long-term capacity expansion models of the U.S. electricity sector have long been used to inform electric sector stakeholders and decision makers. With the recent surge in variable renewable energy (VRE) generators - primarily wind and solar photovoltaics - the need to appropriately represent VRE generators in these long-term models has increased. VRE generators are especially difficult to represent for a variety of reasons, including their variability, uncertainty, and spatial diversity. To assess current best practices, share methods and data, and identify future research needs for VRE representation in capacity expansion models, four capacity expansion modeling teams from the Electric Powermore » Research Institute, the U.S. Energy Information Administration, the U.S. Environmental Protection Agency, and the National Renewable Energy Laboratory conducted two workshops of VRE modeling for national-scale capacity expansion models. The workshops covered a wide range of VRE topics, including transmission and VRE resource data, VRE capacity value, dispatch and operational modeling, distributed generation, and temporal and spatial resolution. The objectives of the workshops were both to better understand these topics and to improve the representation of VRE across the suite of models. Given these goals, each team incorporated model updates and performed additional analyses between the first and second workshops. This report summarizes the analyses and model 'experiments' that were conducted as part of these workshops as well as the various methods for treating VRE among the four modeling teams. The report also reviews the findings and learnings from the two workshops. We emphasize the areas where there is still need for additional research and development on analysis tools to incorporate VRE into long-term planning and decision-making.« less

Evaluation of multiple hydraulic models in generating design/near-real time flood inundation extents under various geophysical settings

NASA Astrophysics Data System (ADS)

Liu, Z.; Rajib, M. A.; Jafarzadegan, K.; Merwade, V.

2015-12-01

Application of land surface/hydrologic models within an operational flood forecasting system can provide probable time of occurrence and magnitude of streamflow at specific locations along a stream. Creating time-varying spatial extent of flood inundation and depth requires the use of a hydraulic or hydrodynamic model. Models differ in representing river geometry and surface roughness which can lead to different output depending on the particular model being used. The result from a single hydraulic model provides just one possible realization of the flood extent without capturing the uncertainty associated with the input or the model parameters. The objective of this study is to compare multiple hydraulic models toward generating ensemble flood inundation extents. Specifically, relative performances of four hydraulic models, including AutoRoute, HEC-RAS, HEC-RAS 2D, and LISFLOOD are evaluated under different geophysical conditions in several locations across the United States. By using streamflow output from the same hydrologic model (SWAT in this case), hydraulic simulations are conducted for three configurations: (i) hindcasting mode by using past observed weather data at daily time scale in which models are being calibrated against USGS streamflow observations, (ii) validation mode using near real-time weather data at sub-daily time scale, and (iii) design mode with extreme streamflow data having specific return periods. Model generated inundation maps for observed flood events both from hindcasting and validation modes are compared with remotely sensed images, whereas the design mode outcomes are compared with corresponding FEMA generated flood hazard maps. The comparisons presented here will give insights on probable model-specific nature of biases and their relative advantages/disadvantages as components of an operational flood forecasting system.

Municipal solid waste generation in municipalities: Quantifying impacts of household structure, commercial waste and domestic fuel

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lebersorger, S.; Beigl, P., E-mail: peter.beigl@boku.ac.at

Waste management planning requires reliable data concerning waste generation, influencing factors on waste generation and forecasts of waste quantities based on facts. This paper aims at identifying and quantifying differences between different municipalities' municipal solid waste (MSW) collection quantities based on data from waste management and on socio-economic indicators. A large set of 116 indicators from 542 municipalities in the Province of Styria was investigated. The resulting regression model included municipal tax revenue per capita, household size and the percentage of buildings with solid fuel heating systems. The model explains 74.3% of the MSW variation and the model assumptions aremore » met. Other factors such as tourism, home composting or age distribution of the population did not significantly improve the model. According to the model, 21% of MSW collected in Styria was commercial waste and 18% of the generated MSW was burned in domestic heating systems. While the percentage of commercial waste is consistent with literature data, practically no literature data are available for the quantity of MSW burned, which seems to be overestimated by the model. The resulting regression model was used as basis for a waste prognosis model (Beigl and Lebersorger, in preparation).« less

Municipal solid waste generation in municipalities: quantifying impacts of household structure, commercial waste and domestic fuel.

PubMed

Lebersorger, S; Beigl, P

2011-01-01

Waste management planning requires reliable data concerning waste generation, influencing factors on waste generation and forecasts of waste quantities based on facts. This paper aims at identifying and quantifying differences between different municipalities' municipal solid waste (MSW) collection quantities based on data from waste management and on socio-economic indicators. A large set of 116 indicators from 542 municipalities in the Province of Styria was investigated. The resulting regression model included municipal tax revenue per capita, household size and the percentage of buildings with solid fuel heating systems. The model explains 74.3% of the MSW variation and the model assumptions are met. Other factors such as tourism, home composting or age distribution of the population did not significantly improve the model. According to the model, 21% of MSW collected in Styria was commercial waste and 18% of the generated MSW was burned in domestic heating systems. While the percentage of commercial waste is consistent with literature data, practically no literature data are available for the quantity of MSW burned, which seems to be overestimated by the model. The resulting regression model was used as basis for a waste prognosis model (Beigl and Lebersorger, in preparation). Copyright © 2011 Elsevier Ltd. All rights reserved.

Estimating methane emissions from landfills based on rainfall, ambient temperature, and waste composition: The CLEEN model.

PubMed

Karanjekar, Richa V; Bhatt, Arpita; Altouqui, Said; Jangikhatoonabad, Neda; Durai, Vennila; Sattler, Melanie L; Hossain, M D Sahadat; Chen, Victoria

2015-12-01

Accurately estimating landfill methane emissions is important for quantifying a landfill's greenhouse gas emissions and power generation potential. Current models, including LandGEM and IPCC, often greatly simplify treatment of factors like rainfall and ambient temperature, which can substantially impact gas production. The newly developed Capturing Landfill Emissions for Energy Needs (CLEEN) model aims to improve landfill methane generation estimates, but still require inputs that are fairly easy to obtain: waste composition, annual rainfall, and ambient temperature. To develop the model, methane generation was measured from 27 laboratory scale landfill reactors, with varying waste compositions (ranging from 0% to 100%); average rainfall rates of 2, 6, and 12 mm/day; and temperatures of 20, 30, and 37°C, according to a statistical experimental design. Refuse components considered were the major biodegradable wastes, food, paper, yard/wood, and textile, as well as inert inorganic waste. Based on the data collected, a multiple linear regression equation (R(2)=0.75) was developed to predict first-order methane generation rate constant values k as functions of waste composition, annual rainfall, and temperature. Because, laboratory methane generation rates exceed field rates, a second scale-up regression equation for k was developed using actual gas-recovery data from 11 landfills in high-income countries with conventional operation. The Capturing Landfill Emissions for Energy Needs (CLEEN) model was developed by incorporating both regression equations into the first-order decay based model for estimating methane generation rates from landfills. CLEEN model values were compared to actual field data from 6 US landfills, and to estimates from LandGEM and IPCC. For 4 of the 6 cases, CLEEN model estimates were the closest to actual. Copyright © 2015 Elsevier Ltd. All rights reserved.

Numerical Simulations of Vortex Generator Vanes and Jets on a Flat Plate

NASA Technical Reports Server (NTRS)

Allan, Brian G.; Yao, Chung-Sheng; Lin, John C.

2002-01-01

Numerical simulations of a single low-profile vortex generator vane, which is only a small fraction of the boundary-layer thickness, and a vortex generating jet have been performed for flows over a flat plate. The numerical simulations were computed by solving the steady-state solution to the Reynolds-averaged Navier-Stokes equations. The vortex generating vane results were evaluated by comparing the strength and trajectory of the streamwise vortex to experimental particle image velocimetry measurements. From the numerical simulations of the vane case, it was observed that the Shear-Stress Transport (SST) turbulence model resulted in a better prediction of the streamwise peak vorticity and trajectory when compared to the Spalart-Allmaras (SA) turbulence model. It is shown in this investigation that the estimation of the turbulent eddy viscosity near the vortex core, for both the vane and jet simulations, was higher for the SA model when compared to the SST model. Even though the numerical simulations of the vortex generating vane were able to predict the trajectory of the stream-wise vortex, the initial magnitude and decay of the peak streamwise vorticity were significantly under predicted. A comparison of the positive circulation associated with the streamwise vortex showed that while the numerical simulations produced a more diffused vortex, the vortex strength compared very well to the experimental observations. A grid resolution study for the vortex generating vane was also performed showing that the diffusion of the vortex was not a result of insufficient grid resolution. Comparisons were also made between a fully modeled trapezoidal vane with finite thickness to a simply modeled rectangular thin vane. The comparisons showed that the simply modeled rectangular vane produced a streamwise vortex which had a strength and trajectory very similar to the fully modeled trapezoidal vane.

An efficient and scalable graph modeling approach for capturing information at different levels in next generation sequencing reads

PubMed Central

2013-01-01

Background Next generation sequencing technologies have greatly advanced many research areas of the biomedical sciences through their capability to generate massive amounts of genetic information at unprecedented rates. The advent of next generation sequencing has led to the development of numerous computational tools to analyze and assemble the millions to billions of short sequencing reads produced by these technologies. While these tools filled an important gap, current approaches for storing, processing, and analyzing short read datasets generally have remained simple and lack the complexity needed to efficiently model the produced reads and assemble them correctly. Results Previously, we presented an overlap graph coarsening scheme for modeling read overlap relationships on multiple levels. Most current read assembly and analysis approaches use a single graph or set of clusters to represent the relationships among a read dataset. Instead, we use a series of graphs to represent the reads and their overlap relationships across a spectrum of information granularity. At each information level our algorithm is capable of generating clusters of reads from the reduced graph, forming an integrated graph modeling and clustering approach for read analysis and assembly. Previously we applied our algorithm to simulated and real 454 datasets to assess its ability to efficiently model and cluster next generation sequencing data. In this paper we extend our algorithm to large simulated and real Illumina datasets to demonstrate that our algorithm is practical for both sequencing technologies. Conclusions Our overlap graph theoretic algorithm is able to model next generation sequencing reads at various levels of granularity through the process of graph coarsening. Additionally, our model allows for efficient representation of the read overlap relationships, is scalable for large datasets, and is practical for both Illumina and 454 sequencing technologies. PMID:24564333

75 FR 39790 - Airworthiness Directives; Airbus Model A330-200 and -300 Airplanes and Model A340-200, -300, -500...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-07-13

..., requiring repetitive inspections of the APU generator Scavenge filter element and filter housing and of the.... The new requirements include inspecting the APU generator scavenge oil filter element for contamination, the APU generator drain plug for contamination, and the APU generator scavenge filter housing for...

Multilingual natural language generation as part of a medical terminology server.

PubMed

Wagner, J C; Solomon, W D; Michel, P A; Juge, C; Baud, R H; Rector, A L; Scherrer, J R

1995-01-01

Re-usable and sharable, and therefore language-independent concept models are of increasing importance in the medical domain. The GALEN project (Generalized Architecture for Languages Encyclopedias and Nomenclatures in Medicine) aims at developing language-independent concept representation systems as the foundations for the next generation of multilingual coding systems. For use within clinical applications, the content of the model has to be mapped to natural language. A so-called Multilingual Information Module (MM) establishes the link between the language-independent concept model and different natural languages. This text generation software must be versatile enough to cope at the same time with different languages and with different parts of a compositional model. It has to meet, on the one hand, the properties of the language as used in the medical domain and, on the other hand, the specific characteristics of the underlying model and its representation formalism. We propose a semantic-oriented approach to natural language generation that is based on linguistic annotations to a concept model. This approach is realized as an integral part of a Terminology Server, built around the concept model and offering different terminological services for clinical applications.

Spectral analysis of a two-species competition model: Determining the effects of extreme conditions on the color of noise generated from simulated time series

NASA Astrophysics Data System (ADS)

Golinski, M. R.

2006-07-01

Ecologists have observed that environmental noise affects population variance in the logistic equation for one-species growth. Interactions between deterministic and stochastic dynamics in a one-dimensional system result in increased variance in species population density over time. Since natural populations do not live in isolation, the present paper simulates a discrete-time two-species competition model with environmental noise to determine the type of colored population noise generated by extreme conditions in the long-term population dynamics of competing populations. Discrete Fourier analysis is applied to the simulation results and the calculated Hurst exponent ( H) is used to determine how the color of population noise for the two species corresponds to extreme conditions in population dynamics. To interpret the biological meaning of the color of noise generated by the two-species model, the paper determines the color of noise generated by three reference models: (1) A two-dimensional discrete-time white noise model (0⩽ H<1/2); (2) A two-dimensional fractional Brownian motion model (H=1/2); and (3) A two-dimensional discrete-time model with noise for unbounded growth of two uncoupled species (1/2< H⩽1).

Accuracy of latent-variable estimation in Bayesian semi-supervised learning.

PubMed

Yamazaki, Keisuke

2015-09-01

Hierarchical probabilistic models, such as Gaussian mixture models, are widely used for unsupervised learning tasks. These models consist of observable and latent variables, which represent the observable data and the underlying data-generation process, respectively. Unsupervised learning tasks, such as cluster analysis, are regarded as estimations of latent variables based on the observable ones. The estimation of latent variables in semi-supervised learning, where some labels are observed, will be more precise than that in unsupervised, and one of the concerns is to clarify the effect of the labeled data. However, there has not been sufficient theoretical analysis of the accuracy of the estimation of latent variables. In a previous study, a distribution-based error function was formulated, and its asymptotic form was calculated for unsupervised learning with generative models. It has been shown that, for the estimation of latent variables, the Bayes method is more accurate than the maximum-likelihood method. The present paper reveals the asymptotic forms of the error function in Bayesian semi-supervised learning for both discriminative and generative models. The results show that the generative model, which uses all of the given data, performs better when the model is well specified. Copyright © 2015 Elsevier Ltd. All rights reserved.

Rapid Crop Cover Mapping for the Conterminous United States.

PubMed

Dahal, Devendra; Wylie, Bruce; Howard, Danny

2018-06-05

Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing season. Through a process of incrementally removing weekly and monthly independent variables from the CCM and implementing a 'two model mapping' approach, we found it viable to generate conterminous United States-wide rapid crop cover maps at a resolution of 250 m for the current year by the month of September. In this approach, we divided the CCM model into one 'crop type model' to handle the classification of nine specific crops and a second, binary model to classify the presence or absence of 'other' crops. Under the two model mapping approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4%, respectively. With spatial mapping accuracies for annual maps reaching upwards of 70%, this approach demonstrated a strong potential for generating rapid crop cover maps by the 1 st of September.

Applying deep bidirectional LSTM and mixture density network for basketball trajectory prediction

NASA Astrophysics Data System (ADS)

Zhao, Yu; Yang, Rennong; Chevalier, Guillaume; Shah, Rajiv C.; Romijnders, Rob

2018-04-01

Data analytics helps basketball teams to create tactics. However, manual data collection and analytics are costly and ineffective. Therefore, we applied a deep bidirectional long short-term memory (BLSTM) and mixture density network (MDN) approach. This model is not only capable of predicting a basketball trajectory based on real data, but it also can generate new trajectory samples. It is an excellent application to help coaches and players decide when and where to shoot. Its structure is particularly suitable for dealing with time series problems. BLSTM receives forward and backward information at the same time, while stacking multiple BLSTMs further increases the learning ability of the model. Combined with BLSTMs, MDN is used to generate a multi-modal distribution of outputs. Thus, the proposed model can, in principle, represent arbitrary conditional probability distributions of output variables. We tested our model with two experiments on three-pointer datasets from NBA SportVu data. In the hit-or-miss classification experiment, the proposed model outperformed other models in terms of the convergence speed and accuracy. In the trajectory generation experiment, eight model-generated trajectories at a given time closely matched real trajectories.

Theoretical and simulation analysis of piezoelectric liquid resistance captor filled with pipeline

NASA Astrophysics Data System (ADS)

Zheng, Li; Zhigang, Yang; Junwu, Kan; Lisheng; Bo, Yan; Dan, Lu

2018-03-01

This paper designs a kind of Piezoelectric liquid resistance capture energy device, by using the superposition theory of the sheet deformation, the calculation model of the displacement curve of the circular piezoelectric vibrator and the power generation capacity under the concentrated load is established. The results show that the radius ratio, thickness ratio and Young’s modulus of the circular piezoelectric vibrator have greater influence on the power generation capacity. When the material of piezoelectric oscillator is determined, the best radius ratio and thickness ratio make the power generation capacity the largest. Excessive or small radius ratio and thickness ratio will reduce the generating capacity and even generate zero power. In addition, the electromechanical equivalent model is established. Equivalent analysis is made by changing the circuit impedance. The results are consistent with the theoretical simulation results, indicating that the established circuit model can truly reflect the characteristics of the theoretical model.

An empirical generative framework for computational modeling of language acquisition.

PubMed

Waterfall, Heidi R; Sandbank, Ben; Onnis, Luca; Edelman, Shimon

2010-06-01

This paper reports progress in developing a computer model of language acquisition in the form of (1) a generative grammar that is (2) algorithmically learnable from realistic corpus data, (3) viable in its large-scale quantitative performance and (4) psychologically real. First, we describe new algorithmic methods for unsupervised learning of generative grammars from raw CHILDES data and give an account of the generative performance of the acquired grammars. Next, we summarize findings from recent longitudinal and experimental work that suggests how certain statistically prominent structural properties of child-directed speech may facilitate language acquisition. We then present a series of new analyses of CHILDES data indicating that the desired properties are indeed present in realistic child-directed speech corpora. Finally, we suggest how our computational results, behavioral findings, and corpus-based insights can be integrated into a next-generation model aimed at meeting the four requirements of our modeling framework.

Structural Design Optimization of Doubly-Fed Induction Generators Using GeneratorSE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sethuraman, Latha; Fingersh, Lee J; Dykes, Katherine L

2017-11-13

A wind turbine with a larger rotor swept area can generate more electricity, however, this increases costs disproportionately for manufacturing, transportation, and installation. This poster presents analytical models for optimizing doubly-fed induction generators (DFIGs), with the objective of reducing the costs and mass of wind turbine drivetrains. The structural design for the induction machine includes models for the casing, stator, rotor, and high-speed shaft developed within the DFIG module in the National Renewable Energy Laboratory's wind turbine sizing tool, GeneratorSE. The mechanical integrity of the machine is verified by examining stresses, structural deflections, and modal properties. The optimization results aremore » then validated using finite element analysis (FEA). The results suggest that our analytical model correlates with the FEA in some areas, such as radial deflection, differing by less than 20 percent. But the analytical model requires further development for axial deflections, torsional deflections, and stress calculations.« less

Simulation for Grid Connected Wind Turbines with Fluctuating

NASA Astrophysics Data System (ADS)

Ye, Ying; Fu, Yang; Wei, Shurong

This paper establishes the whole dynamic model of wind turbine generator system which contains the wind speed model and DFIG wind turbines model .A simulation sample based on the mathematical models is built by using MATLAB in this paper. Research are did on the performance characteristics of doubly-fed wind generators (DFIG) which connected to power grid with three-phase ground fault and the disturbance by gust and mixed wind. The capacity of the wind farm is 9MW which consists of doubly-fed wind generators (DFIG). Simulation results demonstrate that the three-phase ground fault occurs on grid side runs less affected on the stability of doubly-fed wind generators. However, as a power source, fluctuations of the wind speed will run a large impact on stability of double-fed wind generators. The results also show that if the two disturbances occur in the meantime, the situation will be very serious.

Implementation of a next-generation electronic nursing records system based on detailed clinical models and integration of clinical practice guidelines.

PubMed

Min, Yul Ha; Park, Hyeoun-Ae; Chung, Eunja; Lee, Hyunsook

2013-12-01

The purpose of this paper is to describe the components of a next-generation electronic nursing records system ensuring full semantic interoperability and integrating evidence into the nursing records system. A next-generation electronic nursing records system based on detailed clinical models and clinical practice guidelines was developed at Seoul National University Bundang Hospital in 2013. This system has two components, a terminology server and a nursing documentation system. The terminology server manages nursing narratives generated from entity-attribute-value triplets of detailed clinical models using a natural language generation system. The nursing documentation system provides nurses with a set of nursing narratives arranged around the recommendations extracted from clinical practice guidelines. An electronic nursing records system based on detailed clinical models and clinical practice guidelines was successfully implemented in a hospital in Korea. The next-generation electronic nursing records system can support nursing practice and nursing documentation, which in turn will improve data quality.

Real-time simulation of a Doubly-Fed Induction Generator based wind power system on eMEGASimRTM Real-Time Digital Simulator

NASA Astrophysics Data System (ADS)

Boakye-Boateng, Nasir Abdulai

The growing demand for wind power integration into the generation mix prompts the need to subject these systems to stringent performance requirements. This study sought to identify the required tools and procedures needed to perform real-time simulation studies of Doubly-Fed Induction Generator (DFIG) based wind generation systems as basis for performing more practical tests of reliability and performance for both grid-connected and islanded wind generation systems. The author focused on developing a platform for wind generation studies and in addition, the author tested the performance of two DFIG models on the platform real-time simulation model; an average SimpowerSystemsRTM DFIG wind turbine, and a detailed DFIG based wind turbine using ARTEMiSRTM components. The platform model implemented here consists of a high voltage transmission system with four integrated wind farm models consisting in total of 65 DFIG based wind turbines and it was developed and tested on OPAL-RT's eMEGASimRTM Real-Time Digital Simulator.

Improvements to Fidelity, Generation and Implementation of Physics-Based Lithium-Ion Reduced-Order Models

NASA Astrophysics Data System (ADS)

Rodriguez Marco, Albert

Battery management systems (BMS) require computationally simple but highly accurate models of the battery cells they are monitoring and controlling. Historically, empirical equivalent-circuit models have been used, but increasingly researchers are focusing their attention on physics-based models due to their greater predictive capabilities. These models are of high intrinsic computational complexity and so must undergo some kind of order-reduction process to make their use by a BMS feasible: we favor methods based on a transfer-function approach of battery cell dynamics. In prior works, transfer functions have been found from full-order PDE models via two simplifying assumptions: (1) a linearization assumption--which is a fundamental necessity in order to make transfer functions--and (2) an assumption made out of expedience that decouples the electrolyte-potential and electrolyte-concentration PDEs in order to render an approach to solve for the transfer functions from the PDEs. This dissertation improves the fidelity of physics-based models by eliminating the need for the second assumption and, by linearizing nonlinear dynamics around different constant currents. Electrochemical transfer functions are infinite-order and cannot be expressed as a ratio of polynomials in the Laplace variable s. Thus, for practical use, these systems need to be approximated using reduced-order models that capture the most significant dynamics. This dissertation improves the generation of physics-based reduced-order models by introducing different realization algorithms, which produce a low-order model from the infinite-order electrochemical transfer functions. Physics-based reduced-order models are linear and describe cell dynamics if operated near the setpoint at which they have been generated. Hence, multiple physics-based reduced-order models need to be generated at different setpoints (i.e., state-of-charge, temperature and C-rate) in order to extend the cell operating range. This dissertation improves the implementation of physics-based reduced-order models by introducing different blending approaches that combine the pre-computed models generated (offline) at different setpoints in order to produce good electrochemical estimates (online) along the cell state-of-charge, temperature and C-rate range.

Study on optimization of the short-term operation of cascade hydropower stations by considering output error

NASA Astrophysics Data System (ADS)

Wang, Liping; Wang, Boquan; Zhang, Pu; Liu, Minghao; Li, Chuangang

2017-06-01

The study of reservoir deterministic optimal operation can improve the utilization rate of water resource and help the hydropower stations develop more reasonable power generation schedules. However, imprecise forecasting inflow may lead to output error and hinder implementation of power generation schedules. In this paper, output error generated by the uncertainty of the forecasting inflow was regarded as a variable to develop a short-term reservoir optimal operation model for reducing operation risk. To accomplish this, the concept of Value at Risk (VaR) was first applied to present the maximum possible loss of power generation schedules, and then an extreme value theory-genetic algorithm (EVT-GA) was proposed to solve the model. The cascade reservoirs of Yalong River Basin in China were selected as a case study to verify the model, according to the results, different assurance rates of schedules can be derived by the model which can present more flexible options for decision makers, and the highest assurance rate can reach 99%, which is much higher than that without considering output error, 48%. In addition, the model can greatly improve the power generation compared with the original reservoir operation scheme under the same confidence level and risk attitude. Therefore, the model proposed in this paper can significantly improve the effectiveness of power generation schedules and provide a more scientific reference for decision makers.

A statistical shape model of the human second cervical vertebra.

PubMed

Clogenson, Marine; Duff, John M; Luethi, Marcel; Levivier, Marc; Meuli, Reto; Baur, Charles; Henein, Simon

2015-07-01

Statistical shape and appearance models play an important role in reducing the segmentation processing time of a vertebra and in improving results for 3D model development. Here, we describe the different steps in generating a statistical shape model (SSM) of the second cervical vertebra (C2) and provide the shape model for general use by the scientific community. The main difficulties in its construction are the morphological complexity of the C2 and its variability in the population. The input dataset is composed of manually segmented anonymized patient computerized tomography (CT) scans. The alignment of the different datasets is done with the procrustes alignment on surface models, and then, the registration is cast as a model-fitting problem using a Gaussian process. A principal component analysis (PCA)-based model is generated which includes the variability of the C2. The SSM was generated using 92 CT scans. The resulting SSM was evaluated for specificity, compactness and generalization ability. The SSM of the C2 is freely available to the scientific community in Slicer (an open source software for image analysis and scientific visualization) with a module created to visualize the SSM using Statismo, a framework for statistical shape modeling. The SSM of the vertebra allows the shape variability of the C2 to be represented. Moreover, the SSM will enable semi-automatic segmentation and 3D model generation of the vertebra, which would greatly benefit surgery planning.

Exploring the Effect of Embedded Scaffolding Within Curricular Tasks on Third-Grade Students' Model-Based Explanations about Hydrologic Cycling

NASA Astrophysics Data System (ADS)

Zangori, Laura; Forbes, Cory T.; Schwarz, Christina V.

2015-10-01

Opportunities to generate model-based explanations are crucial for elementary students, yet are rarely foregrounded in elementary science learning environments despite evidence that early learners can reason from models when provided with scaffolding. We used a quasi-experimental research design to investigate the comparative impact of a scaffold test condition consisting of embedded physical scaffolds within a curricular modeling task on third-grade (age 8-9) students' formulation of model-based explanations for the water cycle. This condition was contrasted to the control condition where third-grade students used a curricular modeling task with no embedded physical scaffolds. Students from each condition ( n scaffold = 60; n unscaffold = 56) generated models of the water cycle before and after completion of a 10-week water unit. Results from quantitative analyses suggest that students in the scaffolded condition represented and linked more subsurface water process sequences with surface water process sequences than did students in the unscaffolded condition. However, results of qualitative analyses indicate that students in the scaffolded condition were less likely to build upon these process sequences to generate model-based explanations and experienced difficulties understanding their models as abstracted representations rather than recreations of real-world phenomena. We conclude that embedded curricular scaffolds may support students to consider non-observable components of the water cycle but, alone, may be insufficient for generation of model-based explanations about subsurface water movement.

The IDEA model: A single equation approach to the Ebola forecasting challenge.

PubMed

Tuite, Ashleigh R; Fisman, David N

2018-03-01

Mathematical modeling is increasingly accepted as a tool that can inform disease control policy in the face of emerging infectious diseases, such as the 2014-2015 West African Ebola epidemic, but little is known about the relative performance of alternate forecasting approaches. The RAPIDD Ebola Forecasting Challenge (REFC) tested the ability of eight mathematical models to generate useful forecasts in the face of simulated Ebola outbreaks. We used a simple, phenomenological single-equation model (the "IDEA" model), which relies only on case counts, in the REFC. Model fits were performed using a maximum likelihood approach. We found that the model performed reasonably well relative to other more complex approaches, with performance metrics ranked on average 4th or 5th among participating models. IDEA appeared better suited to long- than short-term forecasts, and could be fit using nothing but reported case counts. Several limitations were identified, including difficulty in identifying epidemic peak (even retrospectively), unrealistically precise confidence intervals, and difficulty interpolating daily case counts when using a model scaled to epidemic generation time. More realistic confidence intervals were generated when case counts were assumed to follow a negative binomial, rather than Poisson, distribution. Nonetheless, IDEA represents a simple phenomenological model, easily implemented in widely available software packages that could be used by frontline public health personnel to generate forecasts with accuracy that approximates that which is achieved using more complex methodologies. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.

Evaluation of Stochastic Rainfall Models in Capturing Climate Variability for Future Drought and Flood Risk Assessment

NASA Astrophysics Data System (ADS)

Chowdhury, A. F. M. K.; Lockart, N.; Willgoose, G. R.; Kuczera, G. A.; Kiem, A.; Nadeeka, P. M.

2016-12-01

One of the key objectives of stochastic rainfall modelling is to capture the full variability of climate system for future drought and flood risk assessment. However, it is not clear how well these models can capture the future climate variability when they are calibrated to Global/Regional Climate Model data (GCM/RCM) as these datasets are usually available for very short future period/s (e.g. 20 years). This study has assessed the ability of two stochastic daily rainfall models to capture climate variability by calibrating them to a dynamically downscaled RCM dataset in an east Australian catchment for 1990-2010, 2020-2040, and 2060-2080 epochs. The two stochastic models are: (1) a hierarchical Markov Chain (MC) model, which we developed in a previous study and (2) a semi-parametric MC model developed by Mehrotra and Sharma (2007). Our hierarchical model uses stochastic parameters of MC and Gamma distribution, while the semi-parametric model uses a modified MC process with memory of past periods and kernel density estimation. This study has generated multiple realizations of rainfall series by using parameters of each model calibrated to the RCM dataset for each epoch. The generated rainfall series are used to generate synthetic streamflow by using a SimHyd hydrology model. Assessing the synthetic rainfall and streamflow series, this study has found that both stochastic models can incorporate a range of variability in rainfall as well as streamflow generation for both current and future periods. However, the hierarchical model tends to overestimate the multiyear variability of wet spell lengths (therefore, is less likely to simulate long periods of drought and flood), while the semi-parametric model tends to overestimate the mean annual rainfall depths and streamflow volumes (hence, simulated droughts are likely to be less severe). Sensitivity of these limitations of both stochastic models in terms of future drought and flood risk assessment will be discussed.

A theoretical model of the application of RF energy to the airway wall and its experimental validation.

PubMed

Jarrard, Jerry; Wizeman, Bill; Brown, Robert H; Mitzner, Wayne

2010-11-27

Bronchial thermoplasty is a novel technique designed to reduce an airway's ability to contract by reducing the amount of airway smooth muscle through controlled heating of the airway wall. This method has been examined in animal models and as a treatment for asthma in human subjects. At the present time, there has been little research published about how radiofrequency (RF) energy and heat is transferred to the airways of the lung during bronchial thermoplasty procedures. In this manuscript we describe a computational, theoretical model of the delivery of RF energy to the airway wall. An electro-thermal finite-element-analysis model was designed to simulate the delivery of temperature controlled RF energy to airway walls of the in vivo lung. The model includes predictions of heat generation due to RF joule heating and transfer of heat within an airway wall due to thermal conduction. To implement the model, we use known physical characteristics and dimensions of the airway and lung tissues. The model predictions were tested with measurements of temperature, impedance, energy, and power in an experimental canine model. Model predictions of electrode temperature, voltage, and current, along with tissue impedance and delivered energy were compared to experiment measurements and were within ± 5% of experimental averages taken over 157 sample activations.The experimental results show remarkable agreement with the model predictions, and thus validate the use of this model to predict the heat generation and transfer within the airway wall following bronchial thermoplasty. The model also demonstrated the importance of evaporation as a loss term that affected both electrical measurements and heat distribution. The model predictions showed excellent agreement with the empirical results, and thus support using the model to develop the next generation of devices for bronchial thermoplasty. Our results suggest that comparing model results to RF generator electrical measurements may be a useful tool in the early evaluation of a model.

Generating High Resolution Climate Scenarios Through Regional Climate Modelling Over Southern Africa

NASA Astrophysics Data System (ADS)

Ndhlovu, G. Z.; Woyessa, Y. E.; Vijayaraghavan, S.

2017-12-01

limate change has impacted the global environment and the Continent of Africa, especially Southern Africa, regarded as one of the most vulnerable regions in Africa, has not been spared from these impacts. Global Climate Models (GCMs) with coarse horizontal resolutions of 150-300 km do not provide sufficient details at the local basin scale due to mismatch between the size of river basins and the grid cell of the GCM. This makes it difficult to apply the outputs of GCMs directly to impact studies such as hydrological modelling. This necessitates the use of regional climate modelling at high resolutions that provide detailed information at regional and local scales to study both climate change and its impacts. To this end, an experiment was set up and conducted with PRECIS, a regional climate model, to generate climate scenarios at a high resolution of 25km for the local region in Zambezi River basin of Southern Africa. The major input data used included lateral and surface boundary conditions based on the GCMs. The data is processed, analysed and compared with CORDEX climate change project data generated for Africa. This paper, highlights the major differences of the climate scenarios generated by PRECIS Model and CORDEX Project for Africa and further gives recommendations for further research on generation of climate scenarios. The climatic variables such as precipitation and temperatures have been analysed for flood and droughts in the region. The paper also describes the setting up and running of an experiment using a high-resolution PRECIS model. In addition, a description has been made in running the model and generating the output variables on a sub basin scale. Regional climate modelling which provides information on climate change impact may lead to enhanced understanding of adaptive water resources management. Understanding the regional climate modelling results on sub basin scale is the first step in analysing complex hydrological processes and a basis for designing of adaptation and mitigation strategies in the region. Key words: Climate change, regional climate modelling, hydrological processes, extremes, scenarios [1] Corresponding author: Email:gndhlovu@cut.ac.za Tel:+27 (0) 51 507 3072

Influence of Rainfall Product on Hydrological and Sediment Outputs when Calibrating the STREAP Rainfall Generator for the CAESAR-Lisflood Landscape Evolution Model

NASA Astrophysics Data System (ADS)

Skinner, Christopher; Peleg, Nadav; Quinn, Niall

2017-04-01

The use of Landscape Evolution Models often requires a timeseries of rainfall to drive the model. The spatial and temporal resolution of the driving data has an impact on several model outputs, including the shape of the landscape itself. Attempts to compensate for the spatiotemporal smoothing of local rainfall intensities are insufficient and may exacerbate these issues, meaning that to produce the best results the model needs to be run with data of highest spatial and temporal resolutions available. Some rainfall generators are able to produce timeseries with high spatial and temporal resolution. Observed data is used for the calibration of these generators. However, rainfall observations are highly uncertain and vary between different products (e.g. raingauges, weather radar) which may cascade through the Landscape Evolution Model. Here, we used the STREAP rainfall generator to produce high spatial (1km) and temporal (hourly) resolution ensembles of rainfall for a 50-year period, and used these to drive the CAESAR-Lisflood Landscape Evolution Model for a test catchment. Three different calibrations of STREAP were used against different products: gridded raingauge (TBR), weather radar (NIMROD), and a merged of the two. Analysis of the discharge and sediment yields from the model runs showed that the models run by STREAP calibrated by the different products were statistically significantly different, with the raingauge calibration producing 12.4 % more sediment on average over the 50-year period. The merged product produced results which were between the raingauge and radar products. The results demonstrate the importance of considering the selection of rainfall driving data on Landscape Evolution Modelling. Rainfall products are highly uncertain, different instruments will observe rainfall differently, and these uncertainties are clearly shown to cascade through the calibration of the rainfall generator and the Landscape Evolution Model. Merging raingauge and radar products is a common practise operationally, and by using features of both to calibrate the rainfall generator it is likely a more robust rainfall timeseries is produced.

Domain modeling and grid generation for multi-block structured grids with application to aerodynamic and hydrodynamic configurations

NASA Technical Reports Server (NTRS)

Spekreijse, S. P.; Boerstoel, J. W.; Vitagliano, P. L.; Kuyvenhoven, J. L.

1992-01-01

About five years ago, a joint development was started of a flow simulation system for engine-airframe integration studies on propeller as well as jet aircraft. The initial system was based on the Euler equations and made operational for industrial aerodynamic design work. The system consists of three major components: a domain modeller, for the graphical interactive subdivision of flow domains into an unstructured collection of blocks; a grid generator, for the graphical interactive computation of structured grids in blocks; and a flow solver, for the computation of flows on multi-block grids. The industrial partners of the collaboration and NLR have demonstrated that the domain modeller, grid generator and flow solver can be applied to simulate Euler flows around complete aircraft, including propulsion system simulation. Extension to Navier-Stokes flows is in progress. Delft Hydraulics has shown that both the domain modeller and grid generator can also be applied successfully for hydrodynamic configurations. An overview is given about the main aspects of both domain modelling and grid generation.

Network approaches for expert decisions in sports.

PubMed

Glöckner, Andreas; Heinen, Thomas; Johnson, Joseph G; Raab, Markus

2012-04-01

This paper focuses on a model comparison to explain choices based on gaze behavior via simulation procedures. We tested two classes of models, a parallel constraint satisfaction (PCS) artificial neuronal network model and an accumulator model in a handball decision-making task from a lab experiment. Both models predict action in an option-generation task in which options can be chosen from the perspective of a playmaker in handball (i.e., passing to another player or shooting at the goal). Model simulations are based on a dataset of generated options together with gaze behavior measurements from 74 expert handball players for 22 pieces of video footage. We implemented both classes of models as deterministic vs. probabilistic models including and excluding fitted parameters. Results indicated that both classes of models can fit and predict participants' initially generated options based on gaze behavior data, and that overall, the classes of models performed about equally well. Early fixations were thereby particularly predictive for choices. We conclude that the analyses of complex environments via network approaches can be successfully applied to the field of experts' decision making in sports and provide perspectives for further theoretical developments. Copyright © 2011 Elsevier B.V. All rights reserved.

Generation of topographic terrain models utilizing synthetic aperture radar and surface level data

NASA Technical Reports Server (NTRS)

Imhoff, Marc L. (Inventor)

1991-01-01

Topographical terrain models are generated by digitally delineating the boundary of the region under investigation from the data obtained from an airborne synthetic aperture radar image and surface elevation data concurrently acquired either from an airborne instrument or at ground level. A set of coregistered boundary maps thus generated are then digitally combined in three dimensional space with the acquired surface elevation data by means of image processing software stored in a digital computer. The method is particularly applicable for generating terrain models of flooded regions covered entirely or in part by foliage.

Modeling the Impacts of Solar Distributed Generation on U.S. Water Resources

DOE Office of Scientific and Technical Information (OSTI.GOV)

Amanda, Smith; Omitaomu, Olufemi A; Jaron, Peck

2015-01-01

Distributed electric power generation technologies typically use little or no water per unit of electrical energy produced; in particular, renewable energy sources such as solar PV systems do not require cooling systems and present an opportunity to reduce water usage for power generation. Within the US, the fuel mix used for power generation varies regionally, and certain areas use more water for power generation than others. The need to reduce water usage for power generation is even more urgent in view of climate change uncertainties. In this paper, we present an example case within the state of Tennessee, one ofmore » the top four states in water consumption for power generation and one of the states with little or no potential for developing centralized renewable energy generations. The potential for developing PV generation within Knox County, Tennessee, is studied, along with the potential for reducing water withdrawal and consumption within the Tennessee Valley stream region. Electric power generation plants in the region are quantified for their electricity production and expected water withdrawal and consumption over one year, where electrical generation data is provided over one year and water usage is modeled based on the cooling system(s) in use. Potential solar PV electrical production is modeled based on LiDAR data and weather data for the same year. Our proposed methodology can be summarized as follows: First, the potential solar generation is compared against the local grid demand. Next, electrical generation reductions are specified that would result in a given reduction in water withdrawal and a given reduction in water consumption, and compared with the current water withdrawal and consumption rates for the existing fuel mix. The increase in solar PV development that would produce an equivalent amount of power, is determined. In this way, we consider how targeted local actions may affect the larger stream region through thoughtful energy development. This model can be applied to other regions, other types of distributed generation, and used as a framework for modeling alternative growth scenarios in power production capacity in addition to modeling adjustments to existing capacity.« less

Validation of two (parametric vs non-parametric) daily weather generators

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Skalak, P.

2015-12-01

As the climate models (GCMs and RCMs) fail to satisfactorily reproduce the real-world surface weather regime, various statistical methods are applied to downscale GCM/RCM outputs into site-specific weather series. The stochastic weather generators are among the most favourite downscaling methods capable to produce realistic (observed-like) meteorological inputs for agrological, hydrological and other impact models used in assessing sensitivity of various ecosystems to climate change/variability. To name their advantages, the generators may (i) produce arbitrarily long multi-variate synthetic weather series representing both present and changed climates (in the latter case, the generators are commonly modified by GCM/RCM-based climate change scenarios), (ii) be run in various time steps and for multiple weather variables (the generators reproduce the correlations among variables), (iii) be interpolated (and run also for sites where no weather data are available to calibrate the generator). This contribution will compare two stochastic daily weather generators in terms of their ability to reproduce various features of the daily weather series. M&Rfi is a parametric generator: Markov chain model is used to model precipitation occurrence, precipitation amount is modelled by the Gamma distribution, and the 1st order autoregressive model is used to generate non-precipitation surface weather variables. The non-parametric GoMeZ generator is based on the nearest neighbours resampling technique making no assumption on the distribution of the variables being generated. Various settings of both weather generators will be assumed in the present validation tests. The generators will be validated in terms of (a) extreme temperature and precipitation characteristics (annual and 30-years extremes and maxima of duration of hot/cold/dry/wet spells); (b) selected validation statistics developed within the frame of VALUE project. The tests will be based on observational weather series from several European stations available from the ECA&D database. Acknowledgements: The weather generator is developed and validated within the frame of projects WG4VALUE (sponsored by the Ministry of Education, Youth and Sports of CR), and VALUE (COST ES 1102 action).

Generating Testable Questions in the Science Classroom: The BDC Model

ERIC Educational Resources Information Center

Tseng, ChingMei; Chen, Shu-Bi Shu-Bi; Chang, Wen-Hua

2015-01-01

Guiding students to generate testable scientific questions is essential in the inquiry classroom, but it is not easy. The purpose of the BDC ("Big Idea, Divergent Thinking, and Convergent Thinking") instructional model is to to scaffold students' inquiry learning. We illustrate the use of this model with an example lesson, designed…

How to Create a 3D Model from Scanned Data in 5 Easy Steps

NASA Technical Reports Server (NTRS)

Hagen, Richard

2017-01-01

Additive manufacturing is a cost effective way to generate copies of damaged parts for demonstrations. Integrating scanned data of a damaged area into an existing model may be challenging. However, using the relatively inexpensive Nettfab software (from one can generate a "watertight" model that is easy to print.

A Model for the Creation of Human-Generated Metadata within Communities

ERIC Educational Resources Information Center

Brasher, Andrew; McAndrew, Patrick

2005-01-01

This paper considers situations for which detailed metadata descriptions of learning resources are necessary, and focuses on human generation of such metadata. It describes a model which facilitates human production of good quality metadata by the development and use of structured vocabularies. Using examples, this model is applied to single and…

Introductory Biology Students' Conceptual Models and Explanations of the Origin of Variation

ERIC Educational Resources Information Center

Bray Speth, Elena; Shaw, Neil; Momsen, Jennifer; Reinagel, Adam; Le, Paul; Taqieddin, Ranya; Long, Tammy

2014-01-01

Mutation is the key molecular mechanism generating phenotypic variation, which is the basis for evolution. In an introductory biology course, we used a model-based pedagogy that enabled students to integrate their understanding of genetics and evolution within multiple case studies. We used student-generated conceptual models to assess…

Language Implications for Advertising in International Markets: A Model for Message Content and Message Execution.

ERIC Educational Resources Information Center

Beard, John; Yaprak, Attila

A content analysis model for assessing advertising themes and messages generated primarily for United States markets to overcome barriers in the cultural environment of international markets was developed and tested. The model is based on three primary categories for generating, evaluating, and executing advertisements: rational, emotional, and…

Monte Carlo simulation models of breeding-population advancement.

Treesearch

J.N. King; G.R. Johnson

1993-01-01

Five generations of population improvement were modeled using Monte Carlo simulations. The model was designed to address questions that are important to the development of an advanced generation breeding population. Specifically we addressed the effects on both gain and effective population size of different mating schemes when creating a recombinant population for...

Using Apex To Construct CPM-GOMS Models

NASA Technical Reports Server (NTRS)

John, Bonnie; Vera, Alonso; Matessa, Michael; Freed, Michael; Remington, Roger

2006-01-01

process for automatically generating computational models of human/computer interactions as well as graphical and textual representations of the models has been built on the conceptual foundation of a method known in the art as CPM-GOMS. This method is so named because it combines (1) the task decomposition of analysis according to an underlying method known in the art as the goals, operators, methods, and selection (GOMS) method with (2) a model of human resource usage at the level of cognitive, perceptual, and motor (CPM) operations. CPM-GOMS models have made accurate predictions about behaviors of skilled computer users in routine tasks, but heretofore, such models have been generated in a tedious, error-prone manual process. In the present process, CPM-GOMS models are generated automatically from a hierarchical task decomposition expressed by use of a computer program, known as Apex, designed previously to be used to model human behavior in complex, dynamic tasks. An inherent capability of Apex for scheduling of resources automates the difficult task of interleaving the cognitive, perceptual, and motor resources that underlie common task operators (e.g., move and click mouse). The user interface of Apex automatically generates Program Evaluation Review Technique (PERT) charts, which enable modelers to visualize the complex parallel behavior represented by a model. Because interleaving and the generation of displays to aid visualization are automated, it is now feasible to construct arbitrarily long sequences of behaviors. The process was tested by using Apex to create a CPM-GOMS model of a relatively simple human/computer-interaction task and comparing the time predictions of the model and measurements of the times taken by human users in performing the various steps of the task. The task was to withdraw $80 in cash from an automated teller machine (ATM). For the test, a Visual Basic mockup of an ATM was created, with a provision for input from (and measurement of the performance of) the user via a mouse. The times predicted by the automatically generated model turned out to approximate the measured times fairly well (see figure). While these results are promising, there is need for further development of the process. Moreover, it will also be necessary to test other, more complex models: The actions required of the user in the ATM task are too sequential to involve substantial parallelism and interleaving and, hence, do not serve as an adequate test of the unique strength of CPM-GOMS models to accommodate parallelism and interleaving.

Summary of development of 70 MW class model superconducting generator--research and development of superconducting for electric power application

NASA Astrophysics Data System (ADS)

Oishi, Ikuo; Nishijima, Kenichi

2002-03-01

A 70 MW class superconducting model generator was designed, manufactured, and tested from 1988 to 1999 as Phase I, which was Japan's national project on applications of superconducting technologies to electric power apparatuses that was commissioned by NEDO as part of New Sunshine Program of AIST and MITI. Phase II then is now being carried out by almost same organization as Phase I. With the development of the 70 MW class superconducting model generator, technologies for a 200 MW class pilot generator were established. The world's largest output (79 MW), world's longest continuous operation (1500 h), and other sufficient characteristics were achieved on the 70 MW class superconducting model generator, and key technologies of design and manufacture required for the 200 MW class pilot generator were established. This project contributed to progress of R&D of power apparatuses. Super-GM has started the next project (Phase II), which shall develop the key technologies for larger-capacity and more-compact machine and is scheduled from 2000 to 2003. Phase II shall be the first step for commercialization of superconducting generator.

An epidemiological modeling and data integration framework.

PubMed

Pfeifer, B; Wurz, M; Hanser, F; Seger, M; Netzer, M; Osl, M; Modre-Osprian, R; Schreier, G; Baumgartner, C

2010-01-01

In this work, a cellular automaton software package for simulating different infectious diseases, storing the simulation results in a data warehouse system and analyzing the obtained results to generate prediction models as well as contingency plans, is proposed. The Brisbane H3N2 flu virus, which has been spreading during the winter season 2009, was used for simulation in the federal state of Tyrol, Austria. The simulation-modeling framework consists of an underlying cellular automaton. The cellular automaton model is parameterized by known disease parameters and geographical as well as demographical conditions are included for simulating the spreading. The data generated by simulation are stored in the back room of the data warehouse using the Talend Open Studio software package, and subsequent statistical and data mining tasks are performed using the tool, termed Knowledge Discovery in Database Designer (KD3). The obtained simulation results were used for generating prediction models for all nine federal states of Austria. The proposed framework provides a powerful and easy to handle interface for parameterizing and simulating different infectious diseases in order to generate prediction models and improve contingency plans for future events.

Some unexamined aspects of analysis of covariance in pretest-posttest studies.

PubMed

Ganju, Jitendra

2004-09-01

The use of an analysis of covariance (ANCOVA) model in a pretest-posttest setting deserves to be studied separately from its use in other (non-pretest-posttest) settings. For pretest-posttest studies, the following points are made in this article: (a) If the familiar change from baseline model accurately describes the data-generating mechanism for a randomized study then it is impossible for unequal slopes to exist. Conversely, if unequal slopes exist, then it implies that the change from baseline model as a data-generating mechanism is inappropriate. An alternative data-generating model should be identified and the validity of the ANCOVA model should be demonstrated. (b) Under the usual assumptions of equal pretest and posttest within-subject error variances, the ratio of the standard error of a treatment contrast from a change from baseline analysis to that from ANCOVA is less than 2(1)/(2). (c) For an observational study it is possible for unequal slopes to exist even if the change from baseline model describes the data-generating mechanism. (d) Adjusting for the pretest variable in observational studies may actually introduce bias where none previously existed.

Modeling discrete and rhythmic movements through motor primitives: a review.

PubMed

Degallier, Sarah; Ijspeert, Auke

2010-10-01

Rhythmic and discrete movements are frequently considered separately in motor control, probably because different techniques are commonly used to study and model them. Yet the increasing interest in finding a comprehensive model for movement generation requires bridging the different perspectives arising from the study of those two types of movements. In this article, we consider discrete and rhythmic movements within the framework of motor primitives, i.e., of modular generation of movements. In this way we hope to gain an insight into the functional relationships between discrete and rhythmic movements and thus into a suitable representation for both of them. Within this framework we can define four possible categories of modeling for discrete and rhythmic movements depending on the required command signals and on the spinal processes involved in the generation of the movements. These categories are first discussed in terms of biological concepts such as force fields and central pattern generators and then illustrated by several mathematical models based on dynamical system theory. A discussion on the plausibility of theses models concludes the work.

Two general models that generate long range correlation

NASA Astrophysics Data System (ADS)

Gan, Xiaocong; Han, Zhangang

2012-06-01

In this paper we study two models that generate sequences with LRC (long range correlation). For the IFT (inverse Fourier transform) model, our conclusion is the low frequency part leads to LRC, while the high frequency part tends to eliminate it. Therefore, a typical method to generate a sequence with LRC is multiplying the spectrum of a white noise sequence by a decaying function. A special case is analyzed: the linear combination of a smooth curve and a white noise sequence, in which the DFA plot consists of two line segments. For the patch model, our conclusion is long subsequences leads to LRC, while short subsequences tend to eliminate it. Therefore, we can generate a sequence with LRC by using a fat-tailed PDF (probability distribution function) of the length of the subsequences. A special case is also analyzed: if a patch model with long subsequences is mixed with a white noise sequence, the DFA plot will consist of two line segments. We have checked known models and actual data, and found they are all consistent with this study.

Modelling and simulation of wood chip combustion in a hot air generator system.

PubMed

Rajika, J K A T; Narayana, Mahinsasa

2016-01-01

This study focuses on modelling and simulation of horizontal moving bed/grate wood chip combustor. A standalone finite volume based 2-D steady state Euler-Euler Computational Fluid Dynamics (CFD) model was developed for packed bed combustion. Packed bed combustion of a medium scale biomass combustor, which was retrofitted from wood log to wood chip feeding for Tea drying in Sri Lanka, was evaluated by a CFD simulation study. The model was validated by the experimental results of an industrial biomass combustor for a hot air generation system in tea industry. Open-source CFD tool; OpenFOAM was used to generate CFD model source code for the packed bed combustion and simulated along with an available solver for free board region modelling in the CFD tool. Height of the packed bed is about 20 cm and biomass particles are assumed to be spherical shape with constant surface area to volume ratio. Temperature measurements of the combustor are well agreed with simulation results while gas phase compositions have discrepancies. Combustion efficiency of the validated hot air generator is around 52.2 %.

On the next generation of reliability analysis tools

NASA Technical Reports Server (NTRS)

Babcock, Philip S., IV; Leong, Frank; Gai, Eli

1987-01-01

The current generation of reliability analysis tools concentrates on improving the efficiency of the description and solution of the fault-handling processes and providing a solution algorithm for the full system model. The tools have improved user efficiency in these areas to the extent that the problem of constructing the fault-occurrence model is now the major analysis bottleneck. For the next generation of reliability tools, it is proposed that techniques be developed to improve the efficiency of the fault-occurrence model generation and input. Further, the goal is to provide an environment permitting a user to provide a top-down design description of the system from which a Markov reliability model is automatically constructed. Thus, the user is relieved of the tedious and error-prone process of model construction, permitting an efficient exploration of the design space, and an independent validation of the system's operation is obtained. An additional benefit of automating the model construction process is the opportunity to reduce the specialized knowledge required. Hence, the user need only be an expert in the system he is analyzing; the expertise in reliability analysis techniques is supplied.

A coupled stochastic rainfall-evapotranspiration model for hydrological impact analysis

NASA Astrophysics Data System (ADS)

Pham, Minh Tu; Vernieuwe, Hilde; De Baets, Bernard; Verhoest, Niko E. C.

2018-02-01

A hydrological impact analysis concerns the study of the consequences of certain scenarios on one or more variables or fluxes in the hydrological cycle. In such an exercise, discharge is often considered, as floods originating from extremely high discharges often cause damage. Investigating the impact of extreme discharges generally requires long time series of precipitation and evapotranspiration to be used to force a rainfall-runoff model. However, such kinds of data may not be available and one should resort to stochastically generated time series, even though the impact of using such data on the overall discharge, and especially on the extreme discharge events, is not well studied. In this paper, stochastically generated rainfall and corresponding evapotranspiration time series, generated by means of vine copulas, are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically generated time series used. Notwithstanding this finding, it can be concluded that using a coupled stochastic rainfall-evapotranspiration model has great potential for hydrological impact analysis.

Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation.

PubMed

Oliveira, Roberta B; Pereira, Aledir S; Tavares, João Manuel R S

2017-10-01

The number of deaths worldwide due to melanoma has risen in recent times, in part because melanoma is the most aggressive type of skin cancer. Computational systems have been developed to assist dermatologists in early diagnosis of skin cancer, or even to monitor skin lesions. However, there still remains a challenge to improve classifiers for the diagnosis of such skin lesions. The main objective of this article is to evaluate different ensemble classification models based on input feature manipulation to diagnose skin lesions. Input feature manipulation processes are based on feature subset selections from shape properties, colour variation and texture analysis to generate diversity for the ensemble models. Three subset selection models are presented here: (1) a subset selection model based on specific feature groups, (2) a correlation-based subset selection model, and (3) a subset selection model based on feature selection algorithms. Each ensemble classification model is generated using an optimum-path forest classifier and integrated with a majority voting strategy. The proposed models were applied on a set of 1104 dermoscopic images using a cross-validation procedure. The best results were obtained by the first ensemble classification model that generates a feature subset ensemble based on specific feature groups. The skin lesion diagnosis computational system achieved 94.3% accuracy, 91.8% sensitivity and 96.7% specificity. The input feature manipulation process based on specific feature subsets generated the greatest diversity for the ensemble classification model with very promising results. Copyright © 2017 Elsevier B.V. All rights reserved.

Modeling complex aquifer systems: a case study in Baton Rouge, Louisiana (USA)

NASA Astrophysics Data System (ADS)

Pham, Hai V.; Tsai, Frank T.-C.

2017-05-01

This study targets two challenges in groundwater model development: grid generation and model calibration for aquifer systems that are fluvial in origin. Realistic hydrostratigraphy can be developed using a large quantity of well log data to capture the complexity of an aquifer system. However, generating valid groundwater model grids to be consistent with the complex hydrostratigraphy is non-trivial. Model calibration can also become intractable for groundwater models that intend to match the complex hydrostratigraphy. This study uses the Baton Rouge aquifer system, Louisiana (USA), to illustrate a technical need to cope with grid generation and model calibration issues. A grid generation technique is introduced based on indicator kriging to interpolate 583 wireline well logs in the Baton Rouge area to derive a hydrostratigraphic architecture with fine vertical discretization. Then, an upscaling procedure is developed to determine a groundwater model structure with 162 layers that captures facies geometry in the hydrostratigraphic architecture. To handle model calibration for such a large model, this study utilizes a derivative-free optimization method in parallel computing to complete parameter estimation in a few months. The constructed hydrostratigraphy indicates the Baton Rouge aquifer system is fluvial in origin. The calibration result indicates hydraulic conductivity for Miocene sands is higher than that for Pliocene to Holocene sands and indicates the Baton Rouge fault and the Denham Springs-Scotlandville fault to be low-permeability leaky aquifers. The modeling result shows significantly low groundwater level in the "2,000-foot" sand due to heavy pumping, indicating potential groundwater upward flow from the "2,400-foot" sand.

Generative Models of Segregation: Investigating Model-Generated Patterns of Residential Segregation by Ethnicity and Socioeconomic Status

PubMed Central

Fossett, Mark

2011-01-01

This paper considers the potential for using agent models to explore theories of residential segregation in urban areas. Results of generative experiments conducted using an agent-based simulation of segregation dynamics document that varying a small number of model parameters representing constructs from urban-ecological theories of segregation can generate a wide range of qualitatively distinct and substantively interesting segregation patterns. The results suggest how complex, macro-level patterns of residential segregation can arise from a small set of simple micro-level social dynamics operating within particular urban-demographic contexts. The promise and current limitations of agent simulation studies are noted and optimism is expressed regarding the potential for such studies to engage and contribute to the broader research literature on residential segregation. PMID:21379372

Next-generation genome-scale models for metabolic engineering.

PubMed

King, Zachary A; Lloyd, Colton J; Feist, Adam M; Palsson, Bernhard O

2015-12-01

Constraint-based reconstruction and analysis (COBRA) methods have become widely used tools for metabolic engineering in both academic and industrial laboratories. By employing a genome-scale in silico representation of the metabolic network of a host organism, COBRA methods can be used to predict optimal genetic modifications that improve the rate and yield of chemical production. A new generation of COBRA models and methods is now being developed--encompassing many biological processes and simulation strategies-and next-generation models enable new types of predictions. Here, three key examples of applying COBRA methods to strain optimization are presented and discussed. Then, an outlook is provided on the next generation of COBRA models and the new types of predictions they will enable for systems metabolic engineering. Copyright © 2014 Elsevier Ltd. All rights reserved.

Generating human-like movements on an anthropomorphic robot using an interior point method

NASA Astrophysics Data System (ADS)

Costa e Silva, E.; Araújo, J. P.; Machado, D.; Costa, M. F.; Erlhagen, W.; Bicho, E.

2013-10-01

In previous work we have presented a model for generating human-like arm and hand movements on an anthropomorphic robot involved in human-robot collaboration tasks. This model was inspired by the Posture-Based Motion-Planning Model of human movements. Numerical results and simulations for reach-to-grasp movements with two different grip types have been presented previously. In this paper we extend our model in order to address the generation of more complex movement sequences which are challenged by scenarios cluttered with obstacles. The numerical results were obtained using the IPOPT solver, which was integrated in our MATLAB simulator of an anthropomorphic robot.

The TimeGeo modeling framework for urban mobility without travel surveys

PubMed Central

Jiang, Shan; Yang, Yingxiang; Gupta, Siddharth; Veneziano, Daniele; Athavale, Shounak; González, Marta C.

2016-01-01

Well-established fine-scale urban mobility models today depend on detailed but cumbersome and expensive travel surveys for their calibration. Not much is known, however, about the set of mechanisms needed to generate complete mobility profiles if only using passive datasets with mostly sparse traces of individuals. In this study, we present a mechanistic modeling framework (TimeGeo) that effectively generates urban mobility patterns with resolution of 10 min and hundreds of meters. It ties together the inference of home and work activity locations from data, with the modeling of flexible activities (e.g., other) in space and time. The temporal choices are captured by only three features: the weekly home-based tour number, the dwell rate, and the burst rate. These combined generate for each individual: (i) stay duration of activities, (ii) number of visited locations per day, and (iii) daily mobility networks. These parameters capture how an individual deviates from the circadian rhythm of the population, and generate the wide spectrum of empirically observed mobility behaviors. The spatial choices of visited locations are modeled by a rank-based exploration and preferential return (r-EPR) mechanism that incorporates space in the EPR model. Finally, we show that a hierarchical multiplicative cascade method can measure the interaction between land use and generation of trips. In this way, urban structure is directly related to the observed distance of travels. This framework allows us to fully embrace the massive amount of individual data generated by information and communication technologies (ICTs) worldwide to comprehensively model urban mobility without travel surveys. PMID:27573826

Speedup computation of HD-sEMG signals using a motor unit-specific electrical source model.

PubMed

Carriou, Vincent; Boudaoud, Sofiane; Laforet, Jeremy

2018-01-23

Nowadays, bio-reliable modeling of muscle contraction is becoming more accurate and complex. This increasing complexity induces a significant increase in computation time which prevents the possibility of using this model in certain applications and studies. Accordingly, the aim of this work is to significantly reduce the computation time of high-density surface electromyogram (HD-sEMG) generation. This will be done through a new model of motor unit (MU)-specific electrical source based on the fibers composing the MU. In order to assess the efficiency of this approach, we computed the normalized root mean square error (NRMSE) between several simulations on single generated MU action potential (MUAP) using the usual fiber electrical sources and the MU-specific electrical source. This NRMSE was computed for five different simulation sets wherein hundreds of MUAPs are generated and summed into HD-sEMG signals. The obtained results display less than 2% error on the generated signals compared to the same signals generated with fiber electrical sources. Moreover, the computation time of the HD-sEMG signal generation model is reduced to about 90% compared to the fiber electrical source model. Using this model with MU electrical sources, we can simulate HD-sEMG signals of a physiological muscle (hundreds of MU) in less than an hour on a classical workstation. Graphical Abstract Overview of the simulation of HD-sEMG signals using the fiber scale and the MU scale. Upscaling the electrical source to the MU scale reduces the computation time by 90% inducing only small deviation of the same simulated HD-sEMG signals.

The TimeGeo modeling framework for urban motility without travel surveys.

PubMed

Jiang, Shan; Yang, Yingxiang; Gupta, Siddharth; Veneziano, Daniele; Athavale, Shounak; González, Marta C

2016-09-13

Well-established fine-scale urban mobility models today depend on detailed but cumbersome and expensive travel surveys for their calibration. Not much is known, however, about the set of mechanisms needed to generate complete mobility profiles if only using passive datasets with mostly sparse traces of individuals. In this study, we present a mechanistic modeling framework (TimeGeo) that effectively generates urban mobility patterns with resolution of 10 min and hundreds of meters. It ties together the inference of home and work activity locations from data, with the modeling of flexible activities (e.g., other) in space and time. The temporal choices are captured by only three features: the weekly home-based tour number, the dwell rate, and the burst rate. These combined generate for each individual: (i) stay duration of activities, (ii) number of visited locations per day, and (iii) daily mobility networks. These parameters capture how an individual deviates from the circadian rhythm of the population, and generate the wide spectrum of empirically observed mobility behaviors. The spatial choices of visited locations are modeled by a rank-based exploration and preferential return (r-EPR) mechanism that incorporates space in the EPR model. Finally, we show that a hierarchical multiplicative cascade method can measure the interaction between land use and generation of trips. In this way, urban structure is directly related to the observed distance of travels. This framework allows us to fully embrace the massive amount of individual data generated by information and communication technologies (ICTs) worldwide to comprehensively model urban mobility without travel surveys.

Electron beam generation in the turbulent plasma of Z-pinch discharges

NASA Astrophysics Data System (ADS)

Vikhrev, Victor V.; Baronova, Elena O.

1997-05-01

Numerical modeling of the process of electron beam generation in z-pinch discharges are presented. The proposed model represents the electron beam generation under turbulent plasma conditions. Strong current distribution inhomogeneity in the plasma column has been accounted for the adequate generation process investigation. Electron beam is generated near the maximum of compression due to run away mechanism and it is not related with the current break effect.

Price of Fairness in Kidney Exchange

DTIC Science & Technology

2014-05-01

solver uses branch-and-price, a technique that proves optimality by in- crementally generating only a small part of the model during tree search [8...factors like fail- ure probability and chain position, as in the probabilistic model ). We will use this multiplicative re-weighting in our experiments in...Table 2 gives the average loss in efficiency for each of these models over multiple generated pool sizes, with 40 runs per pool size per model , under

Accurate Modeling of Ionospheric Electromagnetic Fields Generated by a Low Altitude VLF Transmitter

DTIC Science & Technology

2009-03-31

AFRL-RV-HA-TR-2009-1055 Accurate Modeling of Ionospheric Electromagnetic Fields Generated by a Low Altitude VLF Transmitter ...m (or even 500 m) at mid to high latitudes . At low latitudes , the FDTD model exhibits variations that make it difficult to determine a reliable...Scientific, Final 3. DATES COVERED (From - To) 02-08-2006 – 31-12-2008 4. TITLE AND SUBTITLE Accurate Modeling of Ionospheric Electromagnetic Fields

Spherical harmonic analysis of a model-generated climatology

NASA Technical Reports Server (NTRS)

Christidis, Z. D.; Spar, J.

1981-01-01

Monthly mean fields of 850 mb temperature (T850), 500 mb geopotential height (G500) and sea level pressure (SLP) were generated in the course of a five-year climate simulation run with a global general circulation model. Both the model-generated climatology and an observed climatology were subjected to spherical harmonic analysis, with separate analyses of the globe and the Northern Hemisphere. Comparison of the dominant harmonics of the two climatologies indicates that more than 95% of the area-weighted spatial variance of G500 and more than 90% of that of T850 are explained by fewer than three components, and that the model adequately simulates these large-scale characteristics. On the other hand, as many as 25 harmonics are needed to explain 95% of the observed variance of SLP, and the model simulation of this field is much less satisfactory. The model climatology is also evaluated in terms of the annual cycles of the dominant harmonics.

A model for simulating random atmospheres as a function of latitude, season, and time

NASA Technical Reports Server (NTRS)

Campbell, J. W.

1977-01-01

An empirical stochastic computer model was developed with the capability of generating random thermodynamic profiles of the atmosphere below an altitude of 99 km which are characteristic of any given season, latitude, and time of day. Samples of temperature, density, and pressure profiles generated by the model are statistically similar to measured profiles in a data base of over 6000 rocket and high-altitude atmospheric soundings; that is, means and standard deviations of modeled profiles and their vertical gradients are in close agreement with data. Model-generated samples can be used for Monte Carlo simulations of aircraft or spacecraft trajectories to predict or account for the effects on a vehicle's performance of atmospheric variability. Other potential uses for the model are in simulating pollutant dispersion patterns, variations in sound propagation, and other phenomena which are dependent on atmospheric properties, and in developing data-reduction software for satellite monitoring systems.

Experience With Bayesian Image Based Surface Modeling

NASA Technical Reports Server (NTRS)

Stutz, John C.

2005-01-01

Bayesian surface modeling from images requires modeling both the surface and the image generation process, in order to optimize the models by comparing actual and generated images. Thus it differs greatly, both conceptually and in computational difficulty, from conventional stereo surface recovery techniques. But it offers the possibility of using any number of images, taken under quite different conditions, and by different instruments that provide independent and often complementary information, to generate a single surface model that fuses all available information. I describe an implemented system, with a brief introduction to the underlying mathematical models and the compromises made for computational efficiency. I describe successes and failures achieved on actual imagery, where we went wrong and what we did right, and how our approach could be improved. Lastly I discuss how the same approach can be extended to distinct types of instruments, to achieve true sensor fusion.

Dynamic Average-Value Modeling of Doubly-Fed Induction Generator Wind Energy Conversion Systems

NASA Astrophysics Data System (ADS)

Shahab, Azin

In a Doubly-fed Induction Generator (DFIG) wind energy conversion system, the rotor of a wound rotor induction generator is connected to the grid via a partial scale ac/ac power electronic converter which controls the rotor frequency and speed. In this research, detailed models of the DFIG wind energy conversion system with Sinusoidal Pulse-Width Modulation (SPWM) scheme and Optimal Pulse-Width Modulation (OPWM) scheme for the power electronic converter are developed in detail in PSCAD/EMTDC. As the computer simulation using the detailed models tends to be computationally extensive, time consuming and even sometimes not practical in terms of speed, two modified approaches (switching-function modeling and average-value modeling) are proposed to reduce the simulation execution time. The results demonstrate that the two proposed approaches reduce the simulation execution time while the simulation results remain close to those obtained using the detailed model simulation.

Fully automatic adjoints: a robust and efficient mechanism for generating adjoint ocean models

NASA Astrophysics Data System (ADS)

Ham, D. A.; Farrell, P. E.; Funke, S. W.; Rognes, M. E.

2012-04-01

The problem of generating and maintaining adjoint models is sufficiently difficult that typically only the most advanced and well-resourced community ocean models achieve it. There are two current technologies which each suffer from their own limitations. Algorithmic differentiation, also called automatic differentiation, is employed by models such as the MITGCM [2] and the Alfred Wegener Institute model FESOM [3]. This technique is very difficult to apply to existing code, and requires a major initial investment to prepare the code for automatic adjoint generation. AD tools may also have difficulty with code employing modern software constructs such as derived data types. An alternative is to formulate the adjoint differential equation and to discretise this separately. This approach, known as the continuous adjoint and employed in ROMS [4], has the disadvantage that two different model code bases must be maintained and manually kept synchronised as the model develops. The discretisation of the continuous adjoint is not automatically consistent with that of the forward model, producing an additional source of error. The alternative presented here is to formulate the flow model in the high level language UFL (Unified Form Language) and to automatically generate the model using the software of the FEniCS project. In this approach it is the high level code specification which is differentiated, a task very similar to the formulation of the continuous adjoint [5]. However since the forward and adjoint models are generated automatically, the difficulty of maintaining them vanishes and the software engineering process is therefore robust. The scheduling and execution of the adjoint model, including the application of an appropriate checkpointing strategy is managed by libadjoint [1]. In contrast to the conventional algorithmic differentiation description of a model as a series of primitive mathematical operations, libadjoint employs a new abstraction of the simulation process as a sequence of discrete equations which are assembled and solved. It is the coupling of the respective abstractions employed by libadjoint and the FEniCS project which produces the adjoint model automatically, without further intervention from the model developer. This presentation will demonstrate this new technology through linear and non-linear shallow water test cases. The exceptionally simple model syntax will be highlighted and the correctness of the resulting adjoint simulations will be demonstrated using rigorous convergence tests.

Brief history of agricultural systems modeling.

PubMed

Jones, James W; Antle, John M; Basso, Bruno; Boote, Kenneth J; Conant, Richard T; Foster, Ian; Godfray, H Charles J; Herrero, Mario; Howitt, Richard E; Janssen, Sander; Keating, Brian A; Munoz-Carpena, Rafael; Porter, Cheryl H; Rosenzweig, Cynthia; Wheeler, Tim R

2017-07-01

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the "next generation" models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

Brief history of agricultural systems modeling

DOE PAGES

Jones, James W.; Antle, John M.; Basso, Bruno; ...

2017-06-21

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the "next generation" models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of thismore » history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. Furthermore, the lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.« less

Brief history of agricultural systems modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jones, James W.; Antle, John M.; Basso, Bruno

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the "next generation" models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of thismore » history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. Furthermore, the lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.« less

Brief History of Agricultural Systems Modeling

NASA Technical Reports Server (NTRS)

Jones, James W.; Antle, John M.; Basso, Bruno O.; Boote, Kenneth J.; Conant, Richard T.; Foster, Ian; Godfray, H. Charles J.; Herrrero, Mario; Howitt, Richard E.; Janssen, Sandor;

Finite Element Analysis of Osteosynthesis Screw Fixation in the Bone Stock: An Appropriate Method for Automatic Screw Modelling

PubMed Central

Wieding, Jan; Souffrant, Robert; Fritsche, Andreas; Mittelmeier, Wolfram; Bader, Rainer

2012-01-01

The use of finite element analysis (FEA) has grown to a more and more important method in the field of biomedical engineering and biomechanics. Although increased computational performance allows new ways to generate more complex biomechanical models, in the area of orthopaedic surgery, solid modelling of screws and drill holes represent a limitation of their use for individual cases and an increase of computational costs. To cope with these requirements, different methods for numerical screw modelling have therefore been investigated to improve its application diversity. Exemplarily, fixation was performed for stabilization of a large segmental femoral bone defect by an osteosynthesis plate. Three different numerical modelling techniques for implant fixation were used in this study, i.e. without screw modelling, screws as solid elements as well as screws as structural elements. The latter one offers the possibility to implement automatically generated screws with variable geometry on arbitrary FE models. Structural screws were parametrically generated by a Python script for the automatic generation in the FE-software Abaqus/CAE on both a tetrahedral and a hexahedral meshed femur. Accuracy of the FE models was confirmed by experimental testing using a composite femur with a segmental defect and an identical osteosynthesis plate for primary stabilisation with titanium screws. Both deflection of the femoral head and the gap alteration were measured with an optical measuring system with an accuracy of approximately 3 µm. For both screw modelling techniques a sufficient correlation of approximately 95% between numerical and experimental analysis was found. Furthermore, using structural elements for screw modelling the computational time could be reduced by 85% using hexahedral elements instead of tetrahedral elements for femur meshing. The automatically generated screw modelling offers a realistic simulation of the osteosynthesis fixation with screws in the adjacent bone stock and can be used for further investigations. PMID:22470474

Modeling language and cognition with deep unsupervised learning: a tutorial overview

PubMed Central

Zorzi, Marco; Testolin, Alberto; Stoianov, Ivilin P.

2013-01-01

Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception problem of McClelland and Rumelhart (1981) is used as a tutorial example to illustrate how structured and abstract representations may emerge from deep generative learning. We argue that the focus on deep architectures and generative (rather than discriminative) learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as a way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition. PMID:23970869

Modeling language and cognition with deep unsupervised learning: a tutorial overview.

PubMed

Zorzi, Marco; Testolin, Alberto; Stoianov, Ivilin P

2013-01-01

Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception problem of McClelland and Rumelhart (1981) is used as a tutorial example to illustrate how structured and abstract representations may emerge from deep generative learning. We argue that the focus on deep architectures and generative (rather than discriminative) learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as a way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition.

Comparison of BrainTool to other UML modeling and model transformation tools

NASA Astrophysics Data System (ADS)

Nikiforova, Oksana; Gusarovs, Konstantins

2017-07-01

In the last 30 years there were numerous model generated software systems offered targeting problems with the development productivity and the resulting software quality. CASE tools developed due today's date are being advertised as having "complete code-generation capabilities". Nowadays the Object Management Group (OMG) is calling similar arguments in regards to the Unified Modeling Language (UML) models at different levels of abstraction. It is being said that software development automation using CASE tools enables significant level of automation. Actual today's CASE tools are usually offering a combination of several features starting with a model editor and a model repository for a traditional ones and ending with code generator (that could be using a scripting or domain-specific (DSL) language), transformation tool to produce the new artifacts from the manually created and transformation definition editor to define new transformations for the most advanced ones. Present paper contains the results of CASE tool (mainly UML editors) comparison against the level of the automation they are offering.

Functional model of biological neural networks.

PubMed

Lo, James Ting-Ho

2010-12-01

A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

The MOLGENIS toolkit: rapid prototyping of biosoftware at the push of a button.

PubMed

Swertz, Morris A; Dijkstra, Martijn; Adamusiak, Tomasz; van der Velde, Joeri K; Kanterakis, Alexandros; Roos, Erik T; Lops, Joris; Thorisson, Gudmundur A; Arends, Danny; Byelas, George; Muilu, Juha; Brookes, Anthony J; de Brock, Engbert O; Jansen, Ritsert C; Parkinson, Helen

2010-12-21

There is a huge demand on bioinformaticians to provide their biologists with user friendly and scalable software infrastructures to capture, exchange, and exploit the unprecedented amounts of new *omics data. We here present MOLGENIS, a generic, open source, software toolkit to quickly produce the bespoke MOLecular GENetics Information Systems needed. The MOLGENIS toolkit provides bioinformaticians with a simple language to model biological data structures and user interfaces. At the push of a button, MOLGENIS' generator suite automatically translates these models into a feature-rich, ready-to-use web application including database, user interfaces, exchange formats, and scriptable interfaces. Each generator is a template of SQL, JAVA, R, or HTML code that would require much effort to write by hand. This 'model-driven' method ensures reuse of best practices and improves quality because the modeling language and generators are shared between all MOLGENIS applications, so that errors are found quickly and improvements are shared easily by a re-generation. A plug-in mechanism ensures that both the generator suite and generated product can be customized just as much as hand-written software. In recent years we have successfully evaluated the MOLGENIS toolkit for the rapid prototyping of many types of biomedical applications, including next-generation sequencing, GWAS, QTL, proteomics and biobanking. Writing 500 lines of model XML typically replaces 15,000 lines of hand-written programming code, which allows for quick adaptation if the information system is not yet to the biologist's satisfaction. Each application generated with MOLGENIS comes with an optimized database back-end, user interfaces for biologists to manage and exploit their data, programming interfaces for bioinformaticians to script analysis tools in R, Java, SOAP, REST/JSON and RDF, a tab-delimited file format to ease upload and exchange of data, and detailed technical documentation. Existing databases can be quickly enhanced with MOLGENIS generated interfaces using the 'ExtractModel' procedure. The MOLGENIS toolkit provides bioinformaticians with a simple model to quickly generate flexible web platforms for all possible genomic, molecular and phenotypic experiments with a richness of interfaces not provided by other tools. All the software and manuals are available free as LGPLv3 open source at http://www.molgenis.org.

Coupling Poisson rectangular pulse and multiplicative microcanonical random cascade models to generate sub-daily precipitation timeseries

NASA Astrophysics Data System (ADS)

Pohle, Ina; Niebisch, Michael; Müller, Hannes; Schümberg, Sabine; Zha, Tingting; Maurer, Thomas; Hinz, Christoph

2018-07-01

To simulate the impacts of within-storm rainfall variabilities on fast hydrological processes, long precipitation time series with high temporal resolution are required. Due to limited availability of observed data such time series are typically obtained from stochastic models. However, most existing rainfall models are limited in their ability to conserve rainfall event statistics which are relevant for hydrological processes. Poisson rectangular pulse models are widely applied to generate long time series of alternating precipitation events durations and mean intensities as well as interstorm period durations. Multiplicative microcanonical random cascade (MRC) models are used to disaggregate precipitation time series from coarse to fine temporal resolution. To overcome the inconsistencies between the temporal structure of the Poisson rectangular pulse model and the MRC model, we developed a new coupling approach by introducing two modifications to the MRC model. These modifications comprise (a) a modified cascade model ("constrained cascade") which preserves the event durations generated by the Poisson rectangular model by constraining the first and last interval of a precipitation event to contain precipitation and (b) continuous sigmoid functions of the multiplicative weights to consider the scale-dependency in the disaggregation of precipitation events of different durations. The constrained cascade model was evaluated in its ability to disaggregate observed precipitation events in comparison to existing MRC models. For that, we used a 20-year record of hourly precipitation at six stations across Germany. The constrained cascade model showed a pronounced better agreement with the observed data in terms of both the temporal pattern of the precipitation time series (e.g. the dry and wet spell durations and autocorrelations) and event characteristics (e.g. intra-event intermittency and intensity fluctuation within events). The constrained cascade model also slightly outperformed the other MRC models with respect to the intensity-frequency relationship. To assess the performance of the coupled Poisson rectangular pulse and constrained cascade model, precipitation events were stochastically generated by the Poisson rectangular pulse model and then disaggregated by the constrained cascade model. We found that the coupled model performs satisfactorily in terms of the temporal pattern of the precipitation time series, event characteristics and the intensity-frequency relationship.

An advanced stochastic weather generator for simulating 2-D high-resolution climate variables

NASA Astrophysics Data System (ADS)

Peleg, Nadav; Fatichi, Simone; Paschalis, Athanasios; Molnar, Peter; Burlando, Paolo

2017-07-01

A new stochastic weather generator, Advanced WEather GENerator for a two-dimensional grid (AWE-GEN-2d) is presented. The model combines physical and stochastic approaches to simulate key meteorological variables at high spatial and temporal resolution: 2 km × 2 km and 5 min for precipitation and cloud cover and 100 m × 100 m and 1 h for near-surface air temperature, solar radiation, vapor pressure, atmospheric pressure, and near-surface wind. The model requires spatially distributed data for the calibration process, which can nowadays be obtained by remote sensing devices (weather radar and satellites), reanalysis data sets and ground stations. AWE-GEN-2d is parsimonious in terms of computational demand and therefore is particularly suitable for studies where exploring internal climatic variability at multiple spatial and temporal scales is fundamental. Applications of the model include models of environmental systems, such as hydrological and geomorphological models, where high-resolution spatial and temporal meteorological forcing is crucial. The weather generator was calibrated and validated for the Engelberg region, an area with complex topography in the Swiss Alps. Model test shows that the climate variables are generated by AWE-GEN-2d with a level of accuracy that is sufficient for many practical applications.

Modeling and simulation of pressure waves generated by nano-thermite reactions

NASA Astrophysics Data System (ADS)

Martirosyan, Karen S.; Zyskin, Maxim; Jenkins, Charles M.; (Yuki) Horie, Yasuyuki

2012-11-01

This paper reports the modeling of pressure waves from the explosive reaction of nano-thermites consisting of mixtures of nanosized aluminum and oxidizer granules. Such nanostructured thermites have higher energy density (up to 26 kJ/cm3) and can generate a transient pressure pulse four times larger than that from trinitrotoluene (TNT) based on volume equivalence. A plausible explanation for the high pressure generation is that the reaction times are much shorter than the time for a shock wave to propagate away from the reagents region so that all the reaction energy is dumped into the gaseous products almost instantaneously and thereby a strong shock wave is generated. The goal of the modeling is to characterize the gas dynamic behavior for thermite reactions in a cylindrical reaction chamber and to model the experimentally measured pressure histories. To simplify the details of the initial stage of the explosive reaction, it is assumed that the reaction generates a one dimensional shock wave into an air-filled cylinder and propagates down the tube in a self-similar mode. Experimental data for Al/Bi2O3 mixtures were used to validate the model with attention focused on the ratio of specific heats and the drag coefficient. Model predictions are in good agreement with the measured pressure histories.

Dynamic Models Applied to Landslides: Study Case Angangueo, MICHOACÁN, MÉXICO.

NASA Astrophysics Data System (ADS)

Torres Fernandez, L.; Hernández Madrigal, V. M., , Dr; Capra, L.; Domínguez Mota, F. J., , Dr

2017-12-01

Most existing models for landslide zonification are static type, do not consider the dynamic behavior of the trigger factor. This results in a limited representation of the actual zonation of slope instability, present a short-term validity, cańt be applied for the design of early warning systems, etc. Particularly in Mexico, these models are static because they do not consider triggering factor such as precipitation. In this work, we present a numerical evaluation to know the landslide susceptibility, based on probabilistic methods. Which are based on the generation of time series, which are generated from the meteorological stations, having limited information an interpolation is made to generate the simulation of the precipitation in the zone. The obtained information is integrated in PCRaster and in conjunction with the conditioning factors it is possible to generate a dynamic model. This model will be applied for landslide zoning in the municipality of Angangueo, characterized by frequent logging of debris and mud flow, translational and rotational landslides, detonated by atypical precipitations, such as those recorded in 2010. These caused economic losses and humans. With these models, it would be possible to generate probable scenarios that help the Angangueo's population to reduce the risks and to carry out actions of constant resilience activities.

A review of mechanisms and modelling procedures for landslide tsunamis

NASA Astrophysics Data System (ADS)

Løvholt, Finn; Harbitz, Carl B.; Glimsdal, Sylfest

2017-04-01

Landslides, including volcano flank collapses or volcanically induced flows, constitute the second-most important cause of tsunamis after earthquakes. Compared to earthquakes, landslides are more diverse with respect to how they generation tsunamis. Here, we give an overview over the main tsunami generation mechanisms for landslide tsunamis. In the presentation, a mix of results using analytical models, numerical models, laboratory experiments, and case studies are used to illustrate the diversity, but also to point out some common characteristics. Different numerical modelling techniques for the landslide evolution, and the tsunami generation and propagation, as well as the effect of frequency dispersion, are also briefly discussed. Basic tsunami generation mechanisms for different types of landslides, including large submarine translational landslide, to impulsive submarine slumps, and violent subaerial landslides and volcano flank collapses, are reviewed. The importance of the landslide kinematics is given attention, including the interplay between landslide acceleration, landslide velocity to depth ratio (Froude number) and dimensions. Using numerical simulations, we demonstrate how landslide deformation and retrogressive failure development influence tsunamigenesis. Generation mechanisms for subaerial landslides, are reviewed by means of scaling relations from laboratory experiments and numerical modelling. Finally, it is demonstrated how the different degree of complexity in the landslide tsunamigenesis needs to be reflected by increased sophistication in numerical models.

Event reweighting with the NuWro neutrino interaction generator

NASA Astrophysics Data System (ADS)

Pickering, Luke; Stowell, Patrick; Sobczyk, Jan

2017-09-01

Event reweighting has been implemented in the NuWro neutrino event generator for a number of free theory parameters in the interaction model. Event reweighting is a key analysis technique, used to efficiently study the effect of neutrino interaction model uncertainties. This opens up the possibility for NuWro to be used as a primary event generator by experimental analysis groups. A preliminary model tuning to ANL and BNL data of quasi-elastic and single pion production events was performed to validate the reweighting engine.

Tactical 3D Model Generation using Structure-From-Motion on Video from Unmanned Systems

DTIC Science & Technology

2015-04-01

available SfM application known as VisualSFM .6,7 VisualSFM is an end-user, “off-the-shelf” implementation of SfM that is easy to configure and used for...most 3D model generation applications from imagery. While the usual interface with VisualSFM is through their graphical user interface (GUI), we will be...of our system.5 There are two types of 3D model generation available within VisualSFM ; sparse and dense reconstruction. Sparse reconstruction begins

A Generative Angular Model of Protein Structure Evolution

PubMed Central

Golden, Michael; García-Portugués, Eduardo; Sørensen, Michael; Mardia, Kanti V.; Hamelryck, Thomas; Hein, Jotun

2017-01-01

Abstract Recently described stochastic models of protein evolution have demonstrated that the inclusion of structural information in addition to amino acid sequences leads to a more reliable estimation of evolutionary parameters. We present a generative, evolutionary model of protein structure and sequence that is valid on a local length scale. The model concerns the local dependencies between sequence and structure evolution in a pair of homologous proteins. The evolutionary trajectory between the two structures in the protein pair is treated as a random walk in dihedral angle space, which is modeled using a novel angular diffusion process on the two-dimensional torus. Coupling sequence and structure evolution in our model allows for modeling both “smooth” conformational changes and “catastrophic” conformational jumps, conditioned on the amino acid changes. The model has interpretable parameters and is comparatively more realistic than previous stochastic models, providing new insights into the relationship between sequence and structure evolution. For example, using the trained model we were able to identify an apparent sequence–structure evolutionary motif present in a large number of homologous protein pairs. The generative nature of our model enables us to evaluate its validity and its ability to simulate aspects of protein evolution conditioned on an amino acid sequence, a related amino acid sequence, a related structure or any combination thereof. PMID:28453724

Semantics-Based Composition of Integrated Cardiomyocyte Models Motivated by Real-World Use Cases.

PubMed

Neal, Maxwell L; Carlson, Brian E; Thompson, Christopher T; James, Ryan C; Kim, Karam G; Tran, Kenneth; Crampin, Edmund J; Cook, Daniel L; Gennari, John H

2015-01-01

Semantics-based model composition is an approach for generating complex biosimulation models from existing components that relies on capturing the biological meaning of model elements in a machine-readable fashion. This approach allows the user to work at the biological rather than computational level of abstraction and helps minimize the amount of manual effort required for model composition. To support this compositional approach, we have developed the SemGen software, and here report on SemGen's semantics-based merging capabilities using real-world modeling use cases. We successfully reproduced a large, manually-encoded, multi-model merge: the "Pandit-Hinch-Niederer" (PHN) cardiomyocyte excitation-contraction model, previously developed using CellML. We describe our approach for annotating the three component models used in the PHN composition and for merging them at the biological level of abstraction within SemGen. We demonstrate that we were able to reproduce the original PHN model results in a semi-automated, semantics-based fashion and also rapidly generate a second, novel cardiomyocyte model composed using an alternative, independently-developed tension generation component. We discuss the time-saving features of our compositional approach in the context of these merging exercises, the limitations we encountered, and potential solutions for enhancing the approach.

Semantics-Based Composition of Integrated Cardiomyocyte Models Motivated by Real-World Use Cases

PubMed Central

Neal, Maxwell L.; Carlson, Brian E.; Thompson, Christopher T.; James, Ryan C.; Kim, Karam G.; Tran, Kenneth; Crampin, Edmund J.; Cook, Daniel L.; Gennari, John H.

2015-01-01

Semantics-based model composition is an approach for generating complex biosimulation models from existing components that relies on capturing the biological meaning of model elements in a machine-readable fashion. This approach allows the user to work at the biological rather than computational level of abstraction and helps minimize the amount of manual effort required for model composition. To support this compositional approach, we have developed the SemGen software, and here report on SemGen’s semantics-based merging capabilities using real-world modeling use cases. We successfully reproduced a large, manually-encoded, multi-model merge: the “Pandit-Hinch-Niederer” (PHN) cardiomyocyte excitation-contraction model, previously developed using CellML. We describe our approach for annotating the three component models used in the PHN composition and for merging them at the biological level of abstraction within SemGen. We demonstrate that we were able to reproduce the original PHN model results in a semi-automated, semantics-based fashion and also rapidly generate a second, novel cardiomyocyte model composed using an alternative, independently-developed tension generation component. We discuss the time-saving features of our compositional approach in the context of these merging exercises, the limitations we encountered, and potential solutions for enhancing the approach. PMID:26716837

Multi-Topic Tracking Model for dynamic social network

NASA Astrophysics Data System (ADS)

Li, Yuhua; Liu, Changzheng; Zhao, Ming; Li, Ruixuan; Xiao, Hailing; Wang, Kai; Zhang, Jun

2016-07-01

The topic tracking problem has attracted much attention in the last decades. However, existing approaches rarely consider network structures and textual topics together. In this paper, we propose a novel statistical model based on dynamic bayesian network, namely Multi-Topic Tracking Model for Dynamic Social Network (MTTD). It takes influence phenomenon, selection phenomenon, document generative process and the evolution of textual topics into account. Specifically, in our MTTD model, Gibbs Random Field is defined to model the influence of historical status of users in the network and the interdependency between them in order to consider the influence phenomenon. To address the selection phenomenon, a stochastic block model is used to model the link generation process based on the users' interests to topics. Probabilistic Latent Semantic Analysis (PLSA) is used to describe the document generative process according to the users' interests. Finally, the dependence on the historical topic status is also considered to ensure the continuity of the topic itself in topic evolution model. Expectation Maximization (EM) algorithm is utilized to estimate parameters in the proposed MTTD model. Empirical experiments on real datasets show that the MTTD model performs better than Popular Event Tracking (PET) and Dynamic Topic Model (DTM) in generalization performance, topic interpretability performance, topic content evolution and topic popularity evolution performance.

Dynamic Modeling and Grid Interaction of a Tidal and River Generator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Muljadi, Eduard; Gevorgian, Vahan; Donegan, James

This presentation provides a high-level overview of the deployment of a river generator installed in a small system. The turbine dynamics of a river generator, electrical generator, and power converter are modeled in detail. Various simulations can be exercised, and the impact of different control algorithms, failures of power switches, and corresponding impacts can be examined.

A Comparison of Three Random Number Generators for Aircraft Dynamic Modeling Applications

NASA Technical Reports Server (NTRS)

Grauer, Jared A.

2017-01-01

Three random number generators, which produce Gaussian white noise sequences, were compared to assess their suitability in aircraft dynamic modeling applications. The first generator considered was the MATLAB (registered) implementation of the Mersenne-Twister algorithm. The second generator was a website called Random.org, which processes atmospheric noise measured using radios to create the random numbers. The third generator was based on synthesis of the Fourier series, where the random number sequences are constructed from prescribed amplitude and phase spectra. A total of 200 sequences, each having 601 random numbers, for each generator were collected and analyzed in terms of the mean, variance, normality, autocorrelation, and power spectral density. These sequences were then applied to two problems in aircraft dynamic modeling, namely estimating stability and control derivatives from simulated onboard sensor data, and simulating flight in atmospheric turbulence. In general, each random number generator had good performance and is well-suited for aircraft dynamic modeling applications. Specific strengths and weaknesses of each generator are discussed. For Monte Carlo simulation, the Fourier synthesis method is recommended because it most accurately and consistently approximated Gaussian white noise and can be implemented with reasonable computational effort.

Gray correlation analysis and prediction models of living refuse generation in Shanghai city.

PubMed

Liu, Gousheng; Yu, Jianguo

2007-01-01

A better understanding of the factors that affect the generation of municipal living refuse (MLF) and the accurate prediction of its generation are crucial for municipal planning projects and city management. Up to now, most of the design efforts have been based on a rough prediction of MLF without any actual support. In this paper, based on published data of socioeconomic variables and MLF generation from 1990 to 2003 in the city of Shanghai, the main factors that affect MLF generation have been quantitatively studied using the method of gray correlation coefficient. Several gray models, such as GM(1,1), GIM(1), GPPM(1) and GLPM(1), have been studied, and predicted results are verified with subsequent residual test. Results show that, among the selected seven factors, consumption of gas, water and electricity are the largest three factors affecting MLF generation, and GLPM(1) is the optimized model to predict MLF generation. Through this model, the predicted MLF generation in 2010 in Shanghai will be 7.65 million tons. The methods and results developed in this paper can provide valuable information for MLF management and related municipal planning projects.

A study of the kinetic energy generation with general circulation models

NASA Technical Reports Server (NTRS)

Chen, T.-C.; Lee, Y.-H.

1983-01-01

The history data of winter simulation by the GLAS climate model and the NCAR community climate model are used to examine the generation of atmospheric kinetic energy. The contrast between the geographic distributions of the generation of kinetic energy and divergence of kinetic energy flux shows that kinetic energy is generated in the upstream side of jets, transported to the downstream side and destroyed there. The contributions from the time-mean and transient modes to the counterbalance between generation of kinetic energy and divergence of kinetic energy flux are also investigated. It is observed that the kinetic energy generated by the time-mean mode is essentially redistributed by the time-mean flow, while that generated by the transient flow is mainly responsible for the maintenance of the kinetic energy of the entire atmospheric flow.

WE-D-303-02: Applications of Volumetric Images Generated with a Respiratory Motion Model Based On An External Surrogate Signal

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hurwitz, M; Williams, C; Dhou, S

Purpose: Respiratory motion can vary significantly over the course of simulation and treatment. Our goal is to use volumetric images generated with a respiratory motion model to improve the definition of the internal target volume (ITV) and the estimate of delivered dose. Methods: Ten irregular patient breathing patterns spanning 35 seconds each were incorporated into a digital phantom. Ten images over the first five seconds of breathing were used to emulate a 4DCT scan, build the ITV, and generate a patient-specific respiratory motion model which correlated the measured trajectories of markers placed on the patients’ chests with the motion ofmore » the internal anatomy. This model was used to generate volumetric images over the subsequent thirty seconds of breathing. The increase in the ITV taking into account the full 35 seconds of breathing was assessed with ground-truth and model-generated images. For one patient, a treatment plan based on the initial ITV was created and the delivered dose was estimated using images from the first five seconds as well as ground-truth and model-generated images from the next 30 seconds. Results: The increase in the ITV ranged from 0.2 cc to 6.9 cc for the ten patients based on ground-truth information. The model predicted this increase in the ITV with an average error of 0.8 cc. The delivered dose to the tumor (D95) changed significantly from 57 Gy to 41 Gy when estimated using 5 seconds and 30 seconds, respectively. The model captured this effect, giving an estimated D95 of 44 Gy. Conclusion: A respiratory motion model generating volumetric images of the internal patient anatomy could be useful in estimating the increase in the ITV due to irregular breathing during simulation and in assessing delivered dose during treatment. This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc. and Radiological Society of North America Research Scholar Grant #RSCH1206.« less

Generation capacity expansion planning in deregulated electricity markets

NASA Astrophysics Data System (ADS)

Sharma, Deepak

With increasing demand of electric power in the context of deregulated electricity markets, a good strategic planning for the growth of the power system is critical for our tomorrow. There is a need to build new resources in the form of generation plants and transmission lines while considering the effects of these new resources on power system operations, market economics and the long-term dynamics of the economy. In deregulation, the exercise of generation planning has undergone a paradigm shift. The first stage of generation planning is now undertaken by the individual investors. These investors see investments in generation capacity as an increasing business opportunity because of the increasing market prices. Therefore, the main objective of such a planning exercise, carried out by individual investors, is typically that of long-term profit maximization. This thesis presents some modeling frameworks for generation capacity expansion planning applicable to independent investor firms in the context of power industry deregulation. These modeling frameworks include various technical and financing issues within the process of power system planning. The proposed modeling frameworks consider the long-term decision making process of investor firms, the discrete nature of generation capacity addition and incorporates transmission network modeling. Studies have been carried out to examine the impact of the optimal investment plans on transmission network loadings in the long-run by integrating the generation capacity expansion planning framework within a modified IEEE 30-bus transmission system network. The work assesses the importance of arriving at an optimal IRR at which the firm's profit maximization objective attains an extremum value. The mathematical model is further improved to incorporate binary variables while considering discrete unit sizes, and subsequently to include the detailed transmission network representation. The proposed models are novel in the sense that the planning horizon is split into plan sub-periods so as to minimize the overall risks associated with long-term plan models, particularly in the context of deregulation.

Comparison of analytical models for zonal flow generation in ion-temperature-gradient mode turbulence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson, J.; Miki, K.; Uzawa, K.

2006-11-30

During the past years the understanding of the multi scale interaction problems have increased significantly. However, at present there exists a flora of different analytical models for investigating multi scale interactions and hardly any specific comparisons have been performed among these models. In this work two different models for the generation of zonal flows from ion-temperature-gradient (ITG) background turbulence are discussed and compared. The methods used are the coherent mode coupling model and the wave kinetic equation model (WKE). It is shown that the two models give qualitatively the same results even though the assumption on the spectral difference ismore » used in the (WKE) approach.« less

Accidental symmetries and massless quarks in the economical 3-3-1 model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Montero, J. C.; Sánchez–Vega, B. L.

In the framework of a 3-3-1 model with a minimal scalar sector, known as the economical 3-3-1 model, we study its capabilities of generating realistic quark masses. After a detailed study of the symmetries of the model, before and after the spontaneous symmetry breaking, we find a remaining axial symmetry that prevents some quarks from gaining mass at all orders in perturbation theory. Since this accidental symmetry is anomalous, we also consider briefly the possibility of generating their masses for nonperturbative effects. However, we find that nonperturbative effects are not enough to generate the measured masses for the three masslessmore » quarks. Hence, these results imply that the economical 3-3-1 model is not a realistic description of the electroweak interaction.« less

Formal methods for test case generation

NASA Technical Reports Server (NTRS)

Rushby, John (Inventor); De Moura, Leonardo Mendonga (Inventor); Hamon, Gregoire (Inventor)

2011-01-01

The invention relates to the use of model checkers to generate efficient test sets for hardware and software systems. The method provides for extending existing tests to reach new coverage targets; searching *to* some or all of the uncovered targets in parallel; searching in parallel *from* some or all of the states reached in previous tests; and slicing the model relative to the current set of coverage targets. The invention provides efficient test case generation and test set formation. Deep regions of the state space can be reached within allotted time and memory. The approach has been applied to use of the model checkers of SRI's SAL system and to model-based designs developed in Stateflow. Stateflow models achieving complete state and transition coverage in a single test case are reported.

Geometric Model for a Parametric Study of the Blended-Wing-Body Airplane

NASA Technical Reports Server (NTRS)

Mastin, C. Wayne; Smith, Robert E.; Sadrehaghighi, Ideen; Wiese, Micharl R.

1996-01-01

A parametric model is presented for the blended-wing-body airplane, one concept being proposed for the next generation of large subsonic transports. The model is defined in terms of a small set of parameters which facilitates analysis and optimization during the conceptual design process. The model is generated from a preliminary CAD geometry. From this geometry, airfoil cross sections are cut at selected locations and fitted with analytic curves. The airfoils are then used as boundaries for surfaces defined as the solution of partial differential equations. Both the airfoil curves and the surfaces are generated with free parameters selected to give a good representation of the original geometry. The original surface is compared with the parametric model, and solutions of the Euler equations for compressible flow are computed for both geometries. The parametric model is a good approximation of the CAD model and the computed solutions are qualitatively similar. An optimal NURBS approximation is constructed and can be used by a CAD model for further refinement or modification of the original geometry.

The Zebrafish Model Organism Database: new support for human disease models, mutation details, gene expression phenotypes and searching

PubMed Central

Howe, Douglas G.; Bradford, Yvonne M.; Eagle, Anne; Fashena, David; Frazer, Ken; Kalita, Patrick; Mani, Prita; Martin, Ryan; Moxon, Sierra Taylor; Paddock, Holly; Pich, Christian; Ramachandran, Sridhar; Ruzicka, Leyla; Schaper, Kevin; Shao, Xiang; Singer, Amy; Toro, Sabrina; Van Slyke, Ceri; Westerfield, Monte

2017-01-01

The Zebrafish Model Organism Database (ZFIN; http://zfin.org) is the central resource for zebrafish (Danio rerio) genetic, genomic, phenotypic and developmental data. ZFIN curators provide expert manual curation and integration of comprehensive data involving zebrafish genes, mutants, transgenic constructs and lines, phenotypes, genotypes, gene expressions, morpholinos, TALENs, CRISPRs, antibodies, anatomical structures, models of human disease and publications. We integrate curated, directly submitted, and collaboratively generated data, making these available to zebrafish research community. Among the vertebrate model organisms, zebrafish are superbly suited for rapid generation of sequence-targeted mutant lines, characterization of phenotypes including gene expression patterns, and generation of human disease models. The recent rapid adoption of zebrafish as human disease models is making management of these data particularly important to both the research and clinical communities. Here, we describe recent enhancements to ZFIN including use of the zebrafish experimental conditions ontology, ‘Fish’ records in the ZFIN database, support for gene expression phenotypes, models of human disease, mutation details at the DNA, RNA and protein levels, and updates to the ZFIN single box search. PMID:27899582

Simulation model of a variable-speed pumped-storage power plant in unstable operating conditions in pumping mode

NASA Astrophysics Data System (ADS)

Martínez-Lucas, G.; Pérez-Díaz, J. I.; Sarasúa, J. I.; Cavazzini, G.; Pavesi, G.; Ardizzon, G.

2017-04-01

This paper presents a dynamic simulation model of a laboratory-scale pumped-storage power plant (PSPP) operating in pumping mode with variable speed. The model considers the dynamic behavior of the conduits by means of an elastic water column approach, and synthetically generates both pressure and torque pulsations that reproduce the operation of the hydraulic machine in its instability region. The pressure and torque pulsations are generated each from a different set of sinusoidal functions. These functions were calibrated from the results of a CFD model, which was in turn validated from experimental data. Simulation model results match the numerical results of the CFD model with reasonable accuracy. The pump-turbine model (the functions used to generate pressure and torque pulsations inclusive) was up-scaled by hydraulic similarity according to the design parameters of a real PSPP and included in a dynamic simulation model of the said PSPP. Preliminary conclusions on the impact of unstable operation conditions on the penstock fatigue were obtained by means of a Monte Carlo simulation-based fatigue analysis.

Atmospheric turbulence simulation for Shuttle orbiter

NASA Technical Reports Server (NTRS)

Tatom, F. B.; Smith, S. R.

1979-01-01

An improved non-recursive model for atmospheric turbulence along the flight path of the Shuttle Orbiter is developed which provides for simulation of instantaneous vertical and horizontal gusts at the vehicle center-of-gravity, and also for simulation of instantaneous gust gradients. Based on this model the time series for both gusts and gust gradients are generated and stored on a series of magnetic tapes. Section 2 provides a description of the various technical considerations associated with the turbulence simulation model. Included in this section are descriptions of the digital filter simulation model, the von Karman spectra with finite upper limits, and the final non recursive turbulence simulation model which was used to generate the time series. Section 2 provides a description of the various technical considerations associated with the turbulence simulation model. Included in this section are descriptions of the digial filter simulation model, the von Karman spectra with finite upper limits, and the final non recursive turbulence simulation model which was used to generate the time series. Section 3 provides a description of the time series as currently recorded on magnetic tape. Conclusions and recommendations are presented in Section 4.

Holograms of a dynamical top quark

NASA Astrophysics Data System (ADS)

Clemens, Will; Evans, Nick; Scott, Marc

2017-09-01

We present holographic descriptions of dynamical electroweak symmetry breaking models that incorporate the top mass generation mechanism. The models allow computation of the spectrum in the presence of large anomalous dimensions due to walking and strong Nambu-Jona-Lasinio interactions. Technicolor and QCD dynamics are described by the bottom-up Dynamic AdS/QCD model for arbitrary gauge groups and numbers of quark flavors. An assumption about the running of the anomalous dimension of the quark bilinear operator is input, and the model then predicts the spectrum and decay constants for the mesons. We add Nambu-Jona-Lasinio interactions responsible for flavor physics from extended technicolor, top-color, etc., using Witten's multitrace prescription. We show the key behaviors of a top condensation model can be reproduced. We study generation of the top mass in (walking) one doublet and one family technicolor models and with strong extended technicolor interactions. The models clearly reveal the tensions between the large top mass and precision data for δ ρ . The necessary tunings needed to generate a model compatible with precision constraints are simply demonstrated.

Power Plant Model Validation Tool

DOE Office of Scientific and Technical Information (OSTI.GOV)

The PPMV is used to validate generator model using disturbance recordings. The PPMV tool contains a collection of power plant models and model validation studies, as well as disturbance recordings from a number of historic grid events. The user can import data from a new disturbance into the database, which converts PMU and SCADA data into GE PSLF format, and then run the tool to validate (or invalidate) the model for a specific power plant against its actual performance. The PNNL PPMV tool enables the automation of the process of power plant model validation using disturbance recordings. The tool usesmore » PMU and SCADA measurements as input information. The tool automatically adjusts all required EPCL scripts and interacts with GE PSLF in the batch mode. The main tool features includes: The tool interacts with GE PSLF; The tool uses GE PSLF Play-In Function for generator model validation; Database of projects (model validation studies); Database of the historic events; Database of the power plant; The tool has advanced visualization capabilities; and The tool automatically generates reports« less

78 FR 58492 - Generator Verification Reliability Standards

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-24

... Control Functions), MOD-027-1 (Verification of Models and Data for Turbine/Governor and Load Control or...), MOD-027-1 (Verification of Models and Data for Turbine/Governor and Load Control or Active Power... Category B and C contingencies, as required by wind generators in Order No. 661, or that those generators...

Dynamic Model Averaging in Large Model Spaces Using Dynamic Occam's Window.

PubMed

Onorante, Luca; Raftery, Adrian E

2016-01-01

Bayesian model averaging has become a widely used approach to accounting for uncertainty about the structural form of the model generating the data. When data arrive sequentially and the generating model can change over time, Dynamic Model Averaging (DMA) extends model averaging to deal with this situation. Often in macroeconomics, however, many candidate explanatory variables are available and the number of possible models becomes too large for DMA to be applied in its original form. We propose a new method for this situation which allows us to perform DMA without considering the whole model space, but using a subset of models and dynamically optimizing the choice of models at each point in time. This yields a dynamic form of Occam's window. We evaluate the method in the context of the problem of nowcasting GDP in the Euro area. We find that its forecasting performance compares well with that of other methods.

Dynamic Model Averaging in Large Model Spaces Using Dynamic Occam’s Window*

PubMed Central

Onorante, Luca; Raftery, Adrian E.

2015-01-01

Bayesian model averaging has become a widely used approach to accounting for uncertainty about the structural form of the model generating the data. When data arrive sequentially and the generating model can change over time, Dynamic Model Averaging (DMA) extends model averaging to deal with this situation. Often in macroeconomics, however, many candidate explanatory variables are available and the number of possible models becomes too large for DMA to be applied in its original form. We propose a new method for this situation which allows us to perform DMA without considering the whole model space, but using a subset of models and dynamically optimizing the choice of models at each point in time. This yields a dynamic form of Occam’s window. We evaluate the method in the context of the problem of nowcasting GDP in the Euro area. We find that its forecasting performance compares well with that of other methods. PMID:26917859

Second Generation Crop Yield Models Review

NASA Technical Reports Server (NTRS)

Hodges, T. (Principal Investigator)

1982-01-01

Second generation yield models, including crop growth simulation models and plant process models, may be suitable for large area crop yield forecasting in the yield model development project. Subjective and objective criteria for model selection are defined and models which might be selected are reviewed. Models may be selected to provide submodels as input to other models; for further development and testing; or for immediate testing as forecasting tools. A plant process model may range in complexity from several dozen submodels simulating (1) energy, carbohydrates, and minerals; (2) change in biomass of various organs; and (3) initiation and development of plant organs, to a few submodels simulating key physiological processes. The most complex models cannot be used directly in large area forecasting but may provide submodels which can be simplified for inclusion into simpler plant process models. Both published and unpublished models which may be used for development or testing are reviewed. Several other models, currently under development, may become available at a later date.

On models of the genetic code generated by binary dichotomic algorithms.

PubMed

Gumbel, Markus; Fimmel, Elena; Danielli, Alberto; Strüngmann, Lutz

2015-02-01

In this paper we introduce the concept of a BDA-generated model of the genetic code which is based on binary dichotomic algorithms (BDAs). A BDA-generated model is based on binary dichotomic algorithms (BDAs). Such a BDA partitions the set of 64 codons into two disjoint classes of size 32 each and provides a generalization of known partitions like the Rumer dichotomy. We investigate what partitions can be generated when a set of different BDAs is applied sequentially to the set of codons. The search revealed that these models are able to generate code tables with very different numbers of classes ranging from 2 to 64. We have analyzed whether there are models that map the codons to their amino acids. A perfect matching is not possible. However, we present models that describe the standard genetic code with only few errors. There are also models that map all 64 codons uniquely to 64 classes showing that BDAs can be used to identify codons precisely. This could serve as a basis for further mathematical analysis using coding theory, for example. The hypothesis that BDAs might reflect a molecular mechanism taking place in the decoding center of the ribosome is discussed. The scan demonstrated that binary dichotomic partitions are able to model different aspects of the genetic code very well. The search was performed with our tool Beady-A. This software is freely available at http://mi.informatik.hs-mannheim.de/beady-a. It requires a JVM version 6 or higher. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature

NASA Astrophysics Data System (ADS)

Hassan, Zulkarnain; Shamsudin, Supiah; Harun, Sobri

2014-04-01

Climate change is believed to have significant impacts on the water basin and region, such as in a runoff and hydrological system. However, impact studies on the water basin and region are difficult, since general circulation models (GCMs), which are widely used to simulate future climate scenarios, do not provide reliable hours of daily series rainfall and temperature for hydrological modeling. There is a technique named as "downscaling techniques", which can derive reliable hour of daily series rainfall and temperature due to climate scenarios from the GCMs output. In this study, statistical downscaling models are used to generate the possible future values of local meteorological variables such as rainfall and temperature in the selected stations in Peninsular of Malaysia. The models are: (1) statistical downscaling model (SDSM) that utilized the regression models and stochastic weather generators and (2) Long Ashton research station weather generator (LARS-WG) that only utilized the stochastic weather generators. The LARS-WG and SDSM models obviously are feasible methods to be used as tools in quantifying effects of climate change condition in a local scale. SDSM yields a better performance compared to LARS-WG, except SDSM is slightly underestimated for the wet and dry spell lengths. Although both models do not provide identical results, the time series generated by both methods indicate a general increasing trend in the mean daily temperature values. Meanwhile, the trend of the daily rainfall is not similar to each other, with SDSM giving a relatively higher change of annual rainfall compared to LARS-WG.

Deep Learning Fluid Mechanics

NASA Astrophysics Data System (ADS)

Barati Farimani, Amir; Gomes, Joseph; Pande, Vijay

2017-11-01

We have developed a new data-driven model paradigm for the rapid inference and solution of the constitutive equations of fluid mechanic by deep learning models. Using generative adversarial networks (GAN), we train models for the direct generation of solutions to steady state heat conduction and incompressible fluid flow without knowledge of the underlying governing equations. Rather than using artificial neural networks to approximate the solution of the constitutive equations, GANs can directly generate the solutions to these equations conditional upon an arbitrary set of boundary conditions. Both models predict temperature, velocity and pressure fields with great test accuracy (>99.5%). The application of our framework for inferring and generating the solutions of partial differential equations can be applied to any physical phenomena and can be used to learn directly from experiments where the underlying physical model is complex or unknown. We also have shown that our framework can be used to couple multiple physics simultaneously, making it amenable to tackle multi-physics problems.

Modal Survey of ETM-3, A 5-Segment Derivative of the Space Shuttle Solid Rocket Booster

NASA Technical Reports Server (NTRS)

Nielsen, D.; Townsend, J.; Kappus, K.; Driskill, T.; Torres, I.; Parks, R.

2005-01-01

The complex interactions between internal motor generated pressure oscillations and motor structural vibration modes associated with the static test configuration of a Reusable Solid Rocket Motor have potential to generate significant dynamic thrust loads in the 5-segment configuration (Engineering Test Motor 3). Finite element model load predictions for worst-case conditions were generated based on extrapolation of a previously correlated 4-segment motor model. A modal survey was performed on the largest rocket motor to date, Engineering Test Motor #3 (ETM-3), to provide data for finite element model correlation and validation of model generated design loads. The modal survey preparation included pretest analyses to determine an efficient analysis set selection using the Effective Independence Method and test simulations to assure critical test stand component loads did not exceed design limits. Historical Reusable Solid Rocket Motor modal testing, ETM-3 test analysis model development and pre-test loads analyses, as well as test execution, and a comparison of results to pre-test predictions are discussed.

The stability of a class of synchronous generator damping model

NASA Astrophysics Data System (ADS)

Liu, Jun

2018-03-01

Electricity is indispensable to modern society and the most convenient energy, it can be easily transformed into other forms of energy, has been widely used in engineering, transportation and so on, this paper studied the generator model with damping machine, using the Lyapunov function method, we obtain sufficient conditions for the asymptotic stability of the model.

The Knowledge Building Paradigm: A Model of Learning for Net Generation Students

ERIC Educational Resources Information Center

Philip, Donald

2005-01-01

In this article Donald Philip describes Knowledge Building, a pedagogy based on the way research organizations function. The global economy, Philip argues, is driving a shift from older, industrial models to the model of the business as a learning organization. The cognitive patterns of today's Net Generation students, formed by lifetime exposure…

Initial investigations into the damping characteristics of wire rope vibration isolators

NASA Technical Reports Server (NTRS)

Cutchins, M. A.; Cochran, J. E., Jr.; Kumar, K.; Fitz-Coy, N. G.; Tinker, M. L.

1987-01-01

Passive dampers composed of coils of multi-strand wire rope are investigated. Analytical results range from those produced by complex NASTRAN models to those of a Coulomb damping model with variable friction force. The latter agrees well with experiment. The Coulomb model is also utilized to generate hysteresis loops. Various other models related to early experimental investigations are described. Significant closed-form static solutions for physical properties of single-and multi-strand wire ropes are developed for certain specific geometries and loading conditions. NASTRAN models concentrate on model generation and mode shapes of 2-strand and 7-strand straight wire ropes with interfacial forces.

Numerical simulation of nonlinear feedback model of saccade generation circuit implemented in the LabView graphical programming language.

PubMed

Jackson, M E; Gnadt, J W

1999-03-01

The object-oriented graphical programming language LabView was used to implement the numerical solution to a computational model of saccade generation in primates. The computational model simulates the activity and connectivity of anatomical strictures known to be involved in saccadic eye movements. The LabView program provides a graphical user interface to the model that makes it easy to observe and modify the behavior of each element of the model. Essential elements of the source code of the LabView program are presented and explained. A copy of the model is available for download from the internet.

A model-based exploration of the role of pattern generating circuits during locomotor adaptation.

PubMed

Marjaninejad, Ali; Finley, James M

2016-08-01

In this study, we used a model-based approach to explore the potential contributions of central pattern generating circuits (CPGs) during adaptation to external perturbations during locomotion. We constructed a neuromechanical modeled of locomotion using a reduced-phase CPG controller and an inverted pendulum mechanical model. Two different forms of locomotor adaptation were examined in this study: split-belt treadmill adaptation and adaptation to a unilateral, elastic force field. For each simulation, we first examined the effects of phase resetting and varying the model's initial conditions on the resulting adaptation. After evaluating the effect of phase resetting on the adaptation of step length symmetry, we examined the extent to which the results from these simple models could explain previous experimental observations. We found that adaptation of step length symmetry during split-belt treadmill walking could be reproduced using our model, but this model failed to replicate patterns of adaptation observed in response to force field perturbations. Given that spinal animal models can adapt to both of these types of perturbations, our findings suggest that there may be distinct features of pattern generating circuits that mediate each form of adaptation.

Bas-Relief Modeling from Normal Images with Intuitive Styles.

PubMed

Ji, Zhongping; Ma, Weiyin; Sun, Xianfang

2014-05-01

Traditional 3D model-based bas-relief modeling methods are often limited to model-dependent and monotonic relief styles. This paper presents a novel method for digital bas-relief modeling with intuitive style control. Given a composite normal image, the problem discussed in this paper involves generating a discontinuity-free depth field with high compression of depth data while preserving or even enhancing fine details. In our framework, several layers of normal images are composed into a single normal image. The original normal image on each layer is usually generated from 3D models or through other techniques as described in this paper. The bas-relief style is controlled by choosing a parameter and setting a targeted height for them. Bas-relief modeling and stylization are achieved simultaneously by solving a sparse linear system. Different from previous work, our method can be used to freely design bas-reliefs in normal image space instead of in object space, which makes it possible to use any popular image editing tools for bas-relief modeling. Experiments with a wide range of 3D models and scenes show that our method can effectively generate digital bas-reliefs.

Sensitivities of Greenland ice sheet volume inferred from an ice sheet adjoint model

NASA Astrophysics Data System (ADS)

Heimbach, P.; Bugnion, V.

2009-04-01

We present a new and original approach to understanding the sensitivity of the Greenland ice sheet to key model parameters and environmental conditions. At the heart of this approach is the use of an adjoint ice sheet model. Since its introduction by MacAyeal (1992), the adjoint method has become widespread to fit ice stream models to the increasing number and diversity of satellite observations, and to estimate uncertain model parameters such as basal conditions. However, no attempt has been made to extend this method to comprehensive ice sheet models. As a first step toward the use of adjoints of comprehensive three-dimensional ice sheet models we have generated an adjoint of the ice sheet model SICOPOLIS of Greve (1997). The adjoint was generated by means of the automatic differentiation (AD) tool TAF. The AD tool generates exact source code representing the tangent linear and adjoint model of the nonlinear parent model provided. Model sensitivities are given by the partial derivatives of a scalar-valued model diagnostic with respect to the controls, and can be efficiently calculated via the adjoint. By way of example, we determine the sensitivity of the total Greenland ice volume to various control variables, such as spatial fields of basal flow parameters, surface and basal forcings, and initial conditions. Reliability of the adjoint was tested through finite-difference perturbation calculations for various control variables and perturbation regions. Besides confirming qualitative aspects of ice sheet sensitivities, such as expected regional variations, we detect regions where model sensitivities are seemingly unexpected or counter-intuitive, albeit ``real'' in the sense of actual model behavior. An example is inferred regions where sensitivities of ice sheet volume to basal sliding coefficient are positive, i.e. where a local increase in basal sliding parameter increases the ice sheet volume. Similarly, positive ice temperature sensitivities in certain parts of the ice sheet are found (in most regions it is negativ, i.e. an increase in temperature decreases ice sheet volume), the detection of which seems highly unlikely if only conventional perturbation experiments had been used. An effort to generate an efficient adjoint with the newly developed open-source AD tool OpenAD is also under way. Available adjoint code generation tools now open up a variety of novel model applications, notably with regard to sensitivity and uncertainty analyses and ice sheet state estimation or data assimilation.

A stochastical event-based continuous time step rainfall generator based on Poisson rectangular pulse and microcanonical random cascade models

NASA Astrophysics Data System (ADS)

Pohle, Ina; Niebisch, Michael; Zha, Tingting; Schümberg, Sabine; Müller, Hannes; Maurer, Thomas; Hinz, Christoph

2017-04-01

Rainfall variability within a storm is of major importance for fast hydrological processes, e.g. surface runoff, erosion and solute dissipation from surface soils. To investigate and simulate the impacts of within-storm variabilities on these processes, long time series of rainfall with high resolution are required. Yet, observed precipitation records of hourly or higher resolution are in most cases available only for a small number of stations and only for a few years. To obtain long time series of alternating rainfall events and interstorm periods while conserving the statistics of observed rainfall events, the Poisson model can be used. Multiplicative microcanonical random cascades have been widely applied to disaggregate rainfall time series from coarse to fine temporal resolution. We present a new coupling approach of the Poisson rectangular pulse model and the multiplicative microcanonical random cascade model that preserves the characteristics of rainfall events as well as inter-storm periods. In the first step, a Poisson rectangular pulse model is applied to generate discrete rainfall events (duration and mean intensity) and inter-storm periods (duration). The rainfall events are subsequently disaggregated to high-resolution time series (user-specified, e.g. 10 min resolution) by a multiplicative microcanonical random cascade model. One of the challenges of coupling these models is to parameterize the cascade model for the event durations generated by the Poisson model. In fact, the cascade model is best suited to downscale rainfall data with constant time step such as daily precipitation data. Without starting from a fixed time step duration (e.g. daily), the disaggregation of events requires some modifications of the multiplicative microcanonical random cascade model proposed by Olsson (1998): Firstly, the parameterization of the cascade model for events of different durations requires continuous functions for the probabilities of the multiplicative weights, which we implemented through sigmoid functions. Secondly, the branching of the first and last box is constrained to preserve the rainfall event durations generated by the Poisson rectangular pulse model. The event-based continuous time step rainfall generator has been developed and tested using 10 min and hourly rainfall data of four stations in North-Eastern Germany. The model performs well in comparison to observed rainfall in terms of event durations and mean event intensities as well as wet spell and dry spell durations. It is currently being tested using data from other stations across Germany and in different climate zones. Furthermore, the rainfall event generator is being applied in modelling approaches aimed at understanding the impact of rainfall variability on hydrological processes. Reference Olsson, J.: Evaluation of a scaling cascade model for temporal rainfall disaggregation, Hydrology and Earth System Sciences, 2, 19.30

Translational Control in Bone Marrow Failure

DTIC Science & Technology

2016-07-01

been made in using new tools to model granulopoiesis, including generation of patient-derived iPSC and CRISPR -Cas9 genome-editing technology to...in cell lines in patient-derived iPSC gene models, including using CRISPR genome editing, as overall described in Tidwell et al. 2014 and Nayak et...proxy for neutropenia in this cellular model system. 3h. Use patient-derived iPSC models and CRISPR /Cas9 genome-editing to generate a range of ELANE

Optimization and Validation of Rotating Current Excitation with GMR Array Sensors for Riveted

DTIC Science & Technology

2016-09-16

distribution. Simulation results, using both an optimized coil and a conventional coil, are generated using the finite element method (FEM) model...optimized coil and a conventional coil, are generated using the finite element method (FEM) model. The signal magnitude for an optimized coil is seen to be...optimized coil. 4. Model Based Performance Analysis A 3D finite element model (FEM) is used to analyze the performance of the optimized coil and

Next-generation concurrent engineering: developing models to complement point designs

NASA Technical Reports Server (NTRS)

Morse, Elizabeth; Leavens, Tracy; Cohanim, Barbak; Harmon, Corey; Mahr, Eric; Lewis, Brian

2006-01-01

Concurrent Engineering Design teams have made routine the rapid development of point designs for space missions. The Jet Propulsion Laboratory's Team X is now evolving into a next generation CED; nin addition to a point design, the team develops a model of the local trade space. The process is a balance between the power of model-developing tools and the creativity of human experts, enabling the development of a variety of trade models for any space mission.

Social cognitive predictors of first- and non-first-generation college students' academic and life satisfaction.

PubMed

Garriott, Patton O; Hudyma, Aaron; Keene, Chesleigh; Santiago, Dana

2015-04-01

The present study tested Lent's (2004) social-cognitive model of normative well-being in a sample (N = 414) of first- and non-first-generation college students. A model depicting relationships between: positive affect, environmental supports, college self-efficacy, college outcome expectations, academic progress, academic satisfaction, and life satisfaction was examined using structural equation modeling. The moderating roles of perceived importance of attending college and intrinsic goal motivation were also explored. Results suggested the hypothesized model provided an adequate fit to the data while hypothesized relationships in the model were partially supported. Environmental supports predicted college self-efficacy, college outcome expectations, and academic satisfaction. Furthermore, college self-efficacy predicted academic progress while college outcome expectations predicted academic satisfaction. Academic satisfaction, but not academic progress predicted life satisfaction. The structural model explained 44% of the variance in academic progress, 56% of the variance in academic satisfaction, and 28% of the variance in life satisfaction. Mediation analyses indicated several significant indirect effects between variables in the model while moderation analyses revealed a 3-way interaction between academic satisfaction, intrinsic motivation for attending college, and first-generation college student status on life satisfaction. Results are discussed in terms of applying the normative model of well-being to promote first- and non-first-generation college students' academic and life satisfaction. (c) 2015 APA, all rights reserved).

Generation of a three-dimensional ultrastructural model of human respiratory cilia.

PubMed

Burgoyne, Thomas; Dixon, Mellisa; Luther, Pradeep; Hogg, Claire; Shoemark, Amelia

2012-12-01

The ultrastructures of cilia and flagella are highly similar and well conserved through evolution. Consequently, Chlamydomonas is commonly used as a model organism for the study of human respiratory cilia. Since detailed models of Chlamydomonas axonemes were generated using cryoelectron tomography, disparities among some of the ultrastructural features have become apparent when compared with human cilia. Extrapolating information on human disease from the Chlamydomonas model may lead to discrepancies in translational research. This study aimed to establish the first three-dimensional ultrastructural model of human cilia. Tomograms of transverse sections (n = 6) and longitudinal sections (n = 9) of human nasal respiratory cilia were generated from three healthy volunteers. Key features of the cilium were resolved using subatomic averaging, and were measured. For validation of the method, a model of the well characterized structure of Chlamydomonas reinhardtii was simultaneously generated. Data were combined to create a fully quantified three-dimensional reconstruction of human nasal respiratory cilia. We highlight key differences in the axonemal sheath, microtubular doublets, radial spokes, and dynein arms between the two structures. We show a decreased axial periodicity of the radial spokes, inner dynein arms, and central pair protrusions in the human model. We propose that this first human model will provide a basis for research into the function and structure of human respiratory cilia in health and in disease.

Model-Driven Approach for Body Area Network Application Development.

PubMed

Venčkauskas, Algimantas; Štuikys, Vytautas; Jusas, Nerijus; Burbaitė, Renata

2016-05-12

This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN) applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level) and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS)). We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD) variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD)) and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor) is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application.

Model-Driven Approach for Body Area Network Application Development

PubMed Central

Venčkauskas, Algimantas; Štuikys, Vytautas; Jusas, Nerijus; Burbaitė, Renata

2016-01-01

This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN) applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level) and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS)). We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD) variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD)) and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor) is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application. PMID:27187394

Modeling of Thermoelectric Generator Power Characteristics for Motorcycle-Type Engines

NASA Astrophysics Data System (ADS)

Osipkov, Alexey; Poshekhonov, Roman; Arutyunyan, Georgy; Basov, Andrey; Safonov, Roman

2017-10-01

Thermoelectric generation in vehicles such as motorcycles, all-terrain vehicles, and snowmobiles opens the possibility of additional electrical energy generation by means of exhaust heat utilization. This is beneficial because replacing the mechanical generator used in such vehicles with a more powerful one in cases of electrical power deficiency is impossible. This paper proposes a calculation model for the thermoelectric generator (TEG) operational characteristics of the low-capacity internal combustion engines used in these vehicles. Two TEG structures are considered: (1) TEG with air cooling and (2) TEG with water cooling. Modeling consists of two calculation stages. In the first stage, the heat exchange coefficients of the hot and cold exchangers are determined using computational fluid dynamics. In the second stage, the TEG operational characteristics are modeled based on the nonlinear equations of the heat transfer and power balance. On the basis of the modeling results, the dependence of the TEG's major operating characteristics (such as the electrical power generated by the TEG and its efficiency and mass) on operating conditions or design parameters is determined. For example, the electrical power generated by a TEG for a Yamaha WR450F motorcycle engine with a volume of 0.449 × 10-3 m3 was calculated to be as much as 100 W. Use of the TEG arrangements proposed is justified by the additional electrical power generation for small capacity vehicles, without the need for internal combustion engine redesign.

The impacts of renewable energy policies on renewable energy sources for electricity generating capacity

NASA Astrophysics Data System (ADS)

Koo, Bryan Bonsuk

Electricity generation from non-hydro renewable sources has increased rapidly in the last decade. For example, Renewable Energy Sources for Electricity (RES-E) generating capacity in the U.S. almost doubled for the last three year from 2009 to 2012. Multiple papers point out that RES-E policies implemented by state governments play a crucial role in increasing RES-E generation or capacity. This study examines the effects of state RES-E policies on state RES-E generating capacity, using a fixed effects model. The research employs panel data from the 50 states and the District of Columbia, for the period 1990 to 2011, and uses a two-stage approach to control endogeneity embedded in the policies adopted by state governments, and a Prais-Winsten estimator to fix any autocorrelation in the panel data. The analysis finds that Renewable Portfolio Standards (RPS) and Net-metering are significantly and positively associated with RES-E generating capacity, but neither Public Benefit Funds nor the Mandatory Green Power Option has a statistically significant relation to RES-E generating capacity. Results of the two-stage model are quite different from models which do not employ predicted policy variables. Analysis using non-predicted variables finds that RPS and Net-metering policy are statistically insignificant and negatively associated with RES-E generating capacity. On the other hand, Green Energy Purchasing policy is insignificant in the two-stage model, but significant in the model without predicted values.

Gravity Waves Generated by Convection: A New Idealized Model Tool and Direct Validation with Satellite Observations

NASA Astrophysics Data System (ADS)

Alexander, M. Joan; Stephan, Claudia

2015-04-01

In climate models, gravity waves remain too poorly resolved to be directly modelled. Instead, simplified parameterizations are used to include gravity wave effects on model winds. A few climate models link some of the parameterized waves to convective sources, providing a mechanism for feedback between changes in convection and gravity wave-driven changes in circulation in the tropics and above high-latitude storms. These convective wave parameterizations are based on limited case studies with cloud-resolving models, but they are poorly constrained by observational validation, and tuning parameters have large uncertainties. Our new work distills results from complex, full-physics cloud-resolving model studies to essential variables for gravity wave generation. We use the Weather Research Forecast (WRF) model to study relationships between precipitation, latent heating/cooling and other cloud properties to the spectrum of gravity wave momentum flux above midlatitude storm systems. Results show the gravity wave spectrum is surprisingly insensitive to the representation of microphysics in WRF. This is good news for use of these models for gravity wave parameterization development since microphysical properties are a key uncertainty. We further use the full-physics cloud-resolving model as a tool to directly link observed precipitation variability to gravity wave generation. We show that waves in an idealized model forced with radar-observed precipitation can quantitatively reproduce instantaneous satellite-observed features of the gravity wave field above storms, which is a powerful validation of our understanding of waves generated by convection. The idealized model directly links observations of surface precipitation to observed waves in the stratosphere, and the simplicity of the model permits deep/large-area domains for studies of wave-mean flow interactions. This unique validated model tool permits quantitative studies of gravity wave driving of regional circulation and provides a new method for future development of realistic convective gravity wave parameterizations.

Use of a statistical model of the whole femur in a large scale, multi-model study of femoral neck fracture risk.

PubMed

Bryan, Rebecca; Nair, Prasanth B; Taylor, Mark

2009-09-18

Interpatient variability is often overlooked in orthopaedic computational studies due to the substantial challenges involved in sourcing and generating large numbers of bone models. A statistical model of the whole femur incorporating both geometric and material property variation was developed as a potential solution to this problem. The statistical model was constructed using principal component analysis, applied to 21 individual computer tomography scans. To test the ability of the statistical model to generate realistic, unique, finite element (FE) femur models it was used as a source of 1000 femurs to drive a study on femoral neck fracture risk. The study simulated the impact of an oblique fall to the side, a scenario known to account for a large proportion of hip fractures in the elderly and have a lower fracture load than alternative loading approaches. FE model generation, application of subject specific loading and boundary conditions, FE processing and post processing of the solutions were completed automatically. The generated models were within the bounds of the training data used to create the statistical model with a high mesh quality, able to be used directly by the FE solver without remeshing. The results indicated that 28 of the 1000 femurs were at highest risk of fracture. Closer analysis revealed the percentage of cortical bone in the proximal femur to be a crucial differentiator between the failed and non-failed groups. The likely fracture location was indicated to be intertrochantic. Comparison to previous computational, clinical and experimental work revealed support for these findings.

Modeling the topography of shallow braided rivers using Structure-from-Motion photogrammetry

NASA Astrophysics Data System (ADS)

Javernick, L.; Brasington, J.; Caruso, B.

2014-05-01

Recent advances in computer vision and image analysis have led to the development of a novel, fully automated photogrammetric method to generate dense 3d point cloud data. This approach, termed Structure-from-Motion or SfM, requires only limited ground-control and is ideally suited to imagery obtained from low-cost, non-metric cameras acquired either at close-range or using aerial platforms. Terrain models generated using SfM have begun to emerge recently and with a growing spectrum of software now available, there is an urgent need to provide a robust quality assessment of the data products generated using standard field and computational workflows. To address this demand, we present a detailed error analysis of sub-meter resolution terrain models of two contiguous reaches (1.6 and 1.7 km long) of the braided Ahuriri River, New Zealand, generated using SfM. A six stage methodology is described, involving: i) hand-held image acquisition from an aerial platform, ii) 3d point cloud extraction modeling using Agisoft PhotoScan, iii) georeferencing on a redundant network of GPS-surveyed ground-control points, iv) point cloud filtering to reduce computational demand as well as reduce vegetation noise, v) optical bathymetric modeling of inundated areas; and vi) data fusion and surface modeling to generate sub-meter raster terrain models. Bootstrapped geo-registration as well as extensive distributed GPS and sonar-based bathymetric check-data were used to quantify the quality of the models generated after each processing step. The results obtained provide the first quantified analysis of SfM applied to model the complex terrain of a braided river. Results indicate that geo-registration errors of 0.04 m (planar) and 0.10 m (elevation) and vertical surface errors of 0.10 m in non-vegetation areas can be achieved from a dataset of photographs taken at 600 m and 800 m above the ground level. These encouraging results suggest that this low-cost, logistically simple method can deliver high quality terrain datasets competitive with those obtained with significantly more expensive laser scanning, and suitable for geomorphic change detection and hydrodynamic modeling.

Using Model-Based Systems Engineering to Provide Artifacts for NASA Project Life-cycle and Technical Reviews

NASA Technical Reports Server (NTRS)

Parrott, Edith L.; Weiland, Karen J.

2017-01-01

This paper is for the AIAA Space Conference. The ability of systems engineers to use model-based systems engineering (MBSE) to generate self-consistent, up-to-date systems engineering products for project life-cycle and technical reviews is an important aspect for the continued and accelerated acceptance of MBSE. Currently, many review products are generated using labor-intensive, error-prone approaches based on documents, spreadsheets, and chart sets; a promised benefit of MBSE is that users will experience reductions in inconsistencies and errors. This work examines features of SysML that can be used to generate systems engineering products. Model elements, relationships, tables, and diagrams are identified for a large number of the typical systems engineering artifacts. A SysML system model can contain and generate most systems engineering products to a significant extent and this paper provides a guide on how to use MBSE to generate products for project life-cycle and technical reviews. The use of MBSE can reduce the schedule impact usually experienced for review preparation, as in many cases the review products can be auto-generated directly from the system model. These approaches are useful to systems engineers, project managers, review board members, and other key project stakeholders.

Patterns of waste generation: A gradient boosting model for short-term waste prediction in New York City.

PubMed

Johnson, Nicholas E; Ianiuk, Olga; Cazap, Daniel; Liu, Linglan; Starobin, Daniel; Dobler, Gregory; Ghandehari, Masoud

2017-04-01

Historical municipal solid waste (MSW) collection data supplied by the New York City Department of Sanitation (DSNY) was used in conjunction with other datasets related to New York City to forecast municipal solid waste generation across the city. Spatiotemporal tonnage data from the DSNY was combined with external data sets, including the Longitudinal Employer Household Dynamics data, the American Community Survey, the New York City Department of Finance's Primary Land Use and Tax Lot Output data, and historical weather data to build a Gradient Boosting Regression Model. The model was trained on historical data from 2005 to 2011 and validation was performed both temporally and spatially. With this model, we are able to accurately (R2>0.88) forecast weekly MSW generation tonnages for each of the 232 geographic sections in NYC across three waste streams of refuse, paper and metal/glass/plastic. Importantly, the model identifies regularity of urban waste generation and is also able to capture very short timescale fluctuations associated to holidays, special events, seasonal variations, and weather related events. This research shows New York City's waste generation trends and the importance of comprehensive data collection (especially weather patterns) in order to accurately predict waste generation. Copyright © 2017. Published by Elsevier Ltd.

Modelling ultrasound guided wave propagation for plate thickness measurement

NASA Astrophysics Data System (ADS)

Malladi, Rakesh; Dabak, Anand; Murthy, Nitish Krishna

2014-03-01

Structural Health monitoring refers to monitoring the health of plate-like walls of large reactors, pipelines and other structures in terms of corrosion detection and thickness estimation. The objective of this work is modeling the ultrasonic guided waves generated in a plate. The piezoelectric is excited by an input pulse to generate ultrasonic guided lamb waves in the plate that are received by another piezoelectric transducer. In contrast with existing methods, we develop a mathematical model of the direct component of the signal (DCS) recorded at the terminals of the piezoelectric transducer. The DCS model uses maximum likelihood technique to estimate the different parameters, namely the time delay of the signal due to the transducer delay and amplitude scaling of all the lamb wave modes due to attenuation, while taking into account the received signal spreading in time due to dispersion. The maximum likelihood estimate minimizes the energy difference between the experimental and the DCS model-generated signal. We demonstrate that the DCS model matches closely with experimentally recorded signals and show it can be used to estimate thickness of the plate. The main idea of the thickness estimation algorithm is to generate a bank of DCS model-generated signals, each corresponding to a different thickness of the plate and then find the closest match among these signals to the received signal, resulting in an estimate of the thickness of the plate. Therefore our approach provides a complementary suite of analytics to the existing thickness monitoring approaches.

Thermal modeling of the lithium/polymer battery

NASA Astrophysics Data System (ADS)

Pals, C. R.

1994-10-01

Research in the area of advanced batteries for electric-vehicle applications has increased steadily since the 1990 zero-emission-vehicle mandate of the California Air Resources Board. Due to their design flexibility and potentially high energy and power densities, lithium/polymer batteries are an emerging technology for electric-vehicle applications. Thermal modeling of lithium/polymer batteries is particularly important because the transport properties of the system depend exponentially on temperature. Two models have been presented for assessment of the thermal behavior of lithium/polymer batteries. The one-cell model predicts the cell potential, the concentration profiles, and the heat-generation rate during discharge. The cell-stack model predicts temperature profiles and heat transfer limitations of the battery. Due to the variation of ionic conductivity and salt diffusion coefficient with temperature, the performance of the lithium/polymer battery is greatly affected by temperature. Because of this variation, it is important to optimize the cell operating temperature and design a thermal management system for the battery. Since the thermal conductivity of the polymer electrolyte is very low, heat is not easily conducted in the direction perpendicular to cell layers. Temperature profiles in the cells are not as significant as expected because heat-generation rates in warmer areas of the cell stack are lower than heat-generation rates in cooler areas of the stack. This nonuniform heat-generation rate flattens the temperature profile. Temperature profiles as calculated by this model are not as steep as those calculated by previous models that assume a uniform heat-generation rate.

A strategy for the generation, characterization and distribution of animal models by The Michael J. Fox Foundation for Parkinson's Research.

PubMed

Baptista, Marco A S; Dave, Kuldip D; Sheth, Niketa P; De Silva, Shehan N; Carlson, Kirsten M; Aziz, Yasmin N; Fiske, Brian K; Sherer, Todd B; Frasier, Mark A

2013-11-01

Progress in Parkinson's disease (PD) research and therapeutic development is hindered by many challenges, including a need for robust preclinical animal models. Limited availability of these tools is due to technical hurdles, patent issues, licensing restrictions and the high costs associated with generating and distributing these animal models. Furthermore, the lack of standardization of phenotypic characterization and use of varying methodologies has made it difficult to compare outcome measures across laboratories. In response, The Michael J. Fox Foundation for Parkinson's Research (MJFF) is directly sponsoring the generation, characterization and distribution of preclinical rodent models, enabling increased access to these crucial tools in order to accelerate PD research. To date, MJFF has initiated and funded the generation of 30 different models, which include transgenic or knockout models of PD-relevant genes such as Park1 (also known as Park4 and SNCA), Park8 (LRRK2), Park7 (DJ-1), Park6 (PINK1), Park2 (Parkin), VPS35, EiF4G1 and GBA. The phenotypic characterization of these animals is performed in a uniform and streamlined manner at independent contract research organizations. Finally, MJFF created a central repository at The Jackson Laboratory (JAX) that houses both non-MJFF and MJFF-generated preclinical animal models. Funding from MJFF, which subsidizes the costs involved in transfer, rederivation and colony expansion, has directly resulted in over 2500 rodents being distributed to the PD community for research use.

Using Semantic Web technologies for the generation of domain-specific templates to support clinical study metadata standards.

PubMed

Jiang, Guoqian; Evans, Julie; Endle, Cory M; Solbrig, Harold R; Chute, Christopher G

2016-01-01

The Biomedical Research Integrated Domain Group (BRIDG) model is a formal domain analysis model for protocol-driven biomedical research, and serves as a semantic foundation for application and message development in the standards developing organizations (SDOs). The increasing sophistication and complexity of the BRIDG model requires new approaches to the management and utilization of the underlying semantics to harmonize domain-specific standards. The objective of this study is to develop and evaluate a Semantic Web-based approach that integrates the BRIDG model with ISO 21090 data types to generate domain-specific templates to support clinical study metadata standards development. We developed a template generation and visualization system based on an open source Resource Description Framework (RDF) store backend, a SmartGWT-based web user interface, and a "mind map" based tool for the visualization of generated domain-specific templates. We also developed a RESTful Web Service informed by the Clinical Information Modeling Initiative (CIMI) reference model for access to the generated domain-specific templates. A preliminary usability study is performed and all reviewers (n = 3) had very positive responses for the evaluation questions in terms of the usability and the capability of meeting the system requirements (with the average score of 4.6). Semantic Web technologies provide a scalable infrastructure and have great potential to enable computable semantic interoperability of models in the intersection of health care and clinical research.

Phenomenological model of photoluminescence degradation and photoinduced defect formation in silicon nanocrystal ensembles under singlet oxygen generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gongalsky, Maxim B., E-mail: mgongalsky@gmail.com; Timoshenko, Victor Yu.

2014-12-28

We propose a phenomenological model to explain photoluminescence degradation of silicon nanocrystals under singlet oxygen generation in gaseous and liquid systems. The model considers coupled rate equations, which take into account the exciton radiative recombination in silicon nanocrystals, photosensitization of singlet oxygen generation, defect formation on the surface of silicon nanocrystals as well as quenching processes for both excitons and singlet oxygen molecules. The model describes well the experimentally observed power law dependences of the photoluminescence intensity, singlet oxygen concentration, and lifetime versus photoexcitation time. The defect concentration in silicon nanocrystals increases by power law with a fractional exponent, whichmore » depends on the singlet oxygen concentration and ambient conditions. The obtained results are discussed in a view of optimization of the photosensitized singlet oxygen generation for biomedical applications.« less

Identification of influencing municipal characteristics regarding household waste generation and their forecasting ability in Biscay

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oribe-Garcia, Iraia, E-mail: iraia.oribe@deusto.es; Kamara-Esteban, Oihane; Martin, Cristina

Highlights: • We have modelled household waste generation in Biscay municipalities. • We have identified relevant characteristics regarding household waste generation. • Factor models are used in order to identify the best subset of explicative variables. • Biscay’s municipalities are grouped by means of hierarchical clustering. - Abstract: The planning of waste management strategies needs tools to support decisions at all stages of the process. Accurate quantification of the waste to be generated is essential for both the daily management (short-term) and proper design of facilities (long-term). Designing without rigorous knowledge may have serious economic and environmental consequences. The presentmore » works aims at identifying relevant socio-economic features of municipalities regarding Household Waste (HW) generation by means of factor models. Factor models face two main drawbacks, data collection and identifying relevant explanatory variables within a heterogeneous group. Grouping similar characteristics observations within a group may favour the deduction of more robust models. The methodology followed has been tested with Biscay Province because it stands out for having very different municipalities ranging from very rural to urban ones. Two main models are developed, one for the overall province and a second one after clustering the municipalities. The results prove that relating municipalities with specific characteristics, improves the results in a very heterogeneous situation. The methodology has identified urban morphology, tourism activity, level of education and economic situation as the most influencing characteristics in HW generation.« less

Quality Assessment and Comparison of Smartphone and Leica C10 Laser Scanner Based Point Clouds

NASA Astrophysics Data System (ADS)

Sirmacek, Beril; Lindenbergh, Roderik; Wang, Jinhu

2016-06-01

3D urban models are valuable for urban map generation, environment monitoring, safety planning and educational purposes. For 3D measurement of urban structures, generally airborne laser scanning sensors or multi-view satellite images are used as a data source. However, close-range sensors (such as terrestrial laser scanners) and low cost cameras (which can generate point clouds based on photogrammetry) can provide denser sampling of 3D surface geometry. Unfortunately, terrestrial laser scanning sensors are expensive and trained persons are needed to use them for point cloud acquisition. A potential effective 3D modelling can be generated based on a low cost smartphone sensor. Herein, we show examples of using smartphone camera images to generate 3D models of urban structures. We compare a smartphone based 3D model of an example structure with a terrestrial laser scanning point cloud of the structure. This comparison gives us opportunity to discuss the differences in terms of geometrical correctness, as well as the advantages, disadvantages and limitations in data acquisition and processing. We also discuss how smartphone based point clouds can help to solve further problems with 3D urban model generation in a practical way. We show that terrestrial laser scanning point clouds which do not have color information can be colored using smartphones. The experiments, discussions and scientific findings might be insightful for the future studies in fast, easy and low-cost 3D urban model generation field.

Learning a generative probabilistic grammar of experience: a process-level model of language acquisition.

PubMed

Kolodny, Oren; Lotem, Arnon; Edelman, Shimon

2015-03-01

We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in this manner takes the form of a directed weighted graph, whose nodes are recursively (hierarchically) defined patterns over the elements of the input stream. We evaluated the model in seventeen experiments, grouped into five studies, which examined, respectively, (a) the generative ability of grammar learned from a corpus of natural language, (b) the characteristics of the learned representation, (c) sequence segmentation and chunking, (d) artificial grammar learning, and (e) certain types of structure dependence. The model's performance largely vindicates our design choices, suggesting that progress in modeling language acquisition can be made on a broad front-ranging from issues of generativity to the replication of human experimental findings-by bringing biological and computational considerations, as well as lessons from prior efforts, to bear on the modeling approach. Copyright © 2014 Cognitive Science Society, Inc.

Global optimization framework for solar building design

NASA Astrophysics Data System (ADS)

Silva, N.; Alves, N.; Pascoal-Faria, P.

2017-07-01

The generative modeling paradigm is a shift from static models to flexible models. It describes a modeling process using functions, methods and operators. The result is an algorithmic description of the construction process. Each evaluation of such an algorithm creates a model instance, which depends on its input parameters (width, height, volume, roof angle, orientation, location). These values are normally chosen according to aesthetic aspects and style. In this study, the model's parameters are automatically generated according to an objective function. A generative model can be optimized according to its parameters, in this way, the best solution for a constrained problem is determined. Besides the establishment of an overall framework design, this work consists on the identification of different building shapes and their main parameters, the creation of an algorithmic description for these main shapes and the formulation of the objective function, respecting a building's energy consumption (solar energy, heating and insulation). Additionally, the conception of an optimization pipeline, combining an energy calculation tool with a geometric scripting engine is presented. The methods developed leads to an automated and optimized 3D shape generation for the projected building (based on the desired conditions and according to specific constrains). The approach proposed will help in the construction of real buildings that account for less energy consumption and for a more sustainable world.

Universities as Hubs for Next-Generation Networks: A Model for Universities to Spur 21st Century Internet Access and Innovation in Their Communities

ERIC Educational Resources Information Center

Lennett, Benjamin; Morris, Sarah J.; Byrum, Greta

2012-01-01

Based on a request for information (RFI) submitted to The University Community Next Generation Innovation Project (Gig.U), the paper describes a model for universities to develop next generation broadband infrastructure in their communities. In the our view universities can play a critical role in spurring next generation networks into their…

Full-Body Musculoskeletal Model for Muscle-Driven Simulation of Human Gait.

PubMed

Rajagopal, Apoorva; Dembia, Christopher L; DeMers, Matthew S; Delp, Denny D; Hicks, Jennifer L; Delp, Scott L

2016-10-01

Musculoskeletal models provide a non-invasive means to study human movement and predict the effects of interventions on gait. Our goal was to create an open-source 3-D musculoskeletal model with high-fidelity representations of the lower limb musculature of healthy young individuals that can be used to generate accurate simulations of gait. Our model includes bony geometry for the full body, 37 degrees of freedom to define joint kinematics, Hill-type models of 80 muscle-tendon units actuating the lower limbs, and 17 ideal torque actuators driving the upper body. The model's musculotendon parameters are derived from previous anatomical measurements of 21 cadaver specimens and magnetic resonance images of 24 young healthy subjects. We tested the model by evaluating its computational time and accuracy of simulations of healthy walking and running. Generating muscle-driven simulations of normal walking and running took approximately 10 minutes on a typical desktop computer. The differences between our muscle-generated and inverse dynamics joint moments were within 3% (RMSE) of the peak inverse dynamics joint moments in both walking and running, and our simulated muscle activity showed qualitative agreement with salient features from experimental electromyography data. These results suggest that our model is suitable for generating muscle-driven simulations of healthy gait. We encourage other researchers to further validate and apply the model to study other motions of the lower extremity. The model is implemented in the open-source software platform OpenSim. The model and data used to create and test the simulations are freely available at https://simtk.org/home/full_body/, allowing others to reproduce these results and create their own simulations.

Orbital and maxillofacial computer aided surgery: patient-specific finite element models to predict surgical outcomes.

PubMed

Luboz, Vincent; Chabanas, Matthieu; Swider, Pascal; Payan, Yohan

2005-08-01

This paper addresses an important issue raised for the clinical relevance of Computer-Assisted Surgical applications, namely the methodology used to automatically build patient-specific finite element (FE) models of anatomical structures. From this perspective, a method is proposed, based on a technique called the mesh-matching method, followed by a process that corrects mesh irregularities. The mesh-matching algorithm generates patient-specific volume meshes from an existing generic model. The mesh regularization process is based on the Jacobian matrix transform related to the FE reference element and the current element. This method for generating patient-specific FE models is first applied to computer-assisted maxillofacial surgery, and more precisely, to the FE elastic modelling of patient facial soft tissues. For each patient, the planned bone osteotomies (mandible, maxilla, chin) are used as boundary conditions to deform the FE face model, in order to predict the aesthetic outcome of the surgery. Seven FE patient-specific models were successfully generated by our method. For one patient, the prediction of the FE model is qualitatively compared with the patient's post-operative appearance, measured from a computer tomography scan. Then, our methodology is applied to computer-assisted orbital surgery. It is, therefore, evaluated for the generation of 11 patient-specific FE poroelastic models of the orbital soft tissues. These models are used to predict the consequences of the surgical decompression of the orbit. More precisely, an average law is extrapolated from the simulations carried out for each patient model. This law links the size of the osteotomy (i.e. the surgical gesture) and the backward displacement of the eyeball (the consequence of the surgical gesture).

EzGal: A Flexible Interface for Stellar Population Synthesis Models

NASA Astrophysics Data System (ADS)

Mancone, Conor L.; Gonzalez, Anthony H.

2012-06-01

We present EzGal, a flexible Python program designed to easily generate observable parameters (magnitudes, colors, and mass-to-light ratios) for arbitrary input stellar population synthesis (SPS) models. As has been demonstrated by various authors, for many applications the choice of input SPS models can be a significant source of systematic uncertainty. A key strength of EzGal is that it enables simple, direct comparison of different model sets so that the uncertainty introduced by choice of model set can be quantified. Its ability to work with new models will allow EzGal to remain useful as SPS modeling evolves to keep up with the latest research (such as varying IMFs). EzGal is also capable of generating composite stellar population models (CSPs) for arbitrary input star-formation histories and reddening laws, and it can be used to interpolate between metallicities for a given model set. To facilitate use, we have created an online interface to run EzGal and quickly generate magnitude and mass-to-light ratio predictions for a variety of star-formation histories and model sets. We make many commonly used SPS models available from the online interface, including the canonical Bruzual & Charlot models, an updated version of these models, the Maraston models, the BaSTI models, and the Flexible Stellar Population Synthesis (FSPS) models. We use EzGal to compare magnitude predictions for the model sets as a function of wavelength, age, metallicity, and star-formation history. From this comparison we quickly recover the well-known result that the models agree best in the optical for old solar-metallicity models, with differences at the level. Similarly, the most problematic regime for SPS modeling is for young ages (≲2 Gyr) and long wavelengths (λ ≳ 7500 Å), where thermally pulsating AGB stars are important and scatter between models can vary from 0.3 mag (Sloan i) to 0.7 mag (Ks). We find that these differences are not caused by one discrepant model set and should therefore be interpreted as general uncertainties in SPS modeling. Finally, we connect our results to a more physically motivated example by generating CSPs with a star-formation history matching the global star-formation history of the universe. We demonstrate that the wavelength and age dependence of SPS model uncertainty translates into a redshift-dependent model uncertainty, highlighting the importance of a quantitative understanding of model differences when comparing observations with models as a function of redshift.

Between the Rock and a Hard Place: The CCMC as a Transit Station Between Modelers and Forecasters

NASA Technical Reports Server (NTRS)

Hesse, Michael

2009-01-01

The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second CCMC activity is to support Space Weather forecasting at national Space Weather Forecasting Centers. This second activity involved model evaluations, model transitions to operations, and the development of draft Space Weather forecasting tools. This presentation will focus on the latter element. Specifically, we will discuss the process of transition research models, or information generated by research models, to Space Weather Forecasting organizations. We will analyze successes as well as obstacles to further progress, and we will suggest avenues for increased transitioning success.

Model-Drive Architecture for Agent-Based Systems

NASA Technical Reports Server (NTRS)

Gradanin, Denis; Singh, H. Lally; Bohner, Shawn A.; Hinchey, Michael G.

2004-01-01

The Model Driven Architecture (MDA) approach uses a platform-independent model to define system functionality, or requirements, using some specification language. The requirements are then translated to a platform-specific model for implementation. An agent architecture based on the human cognitive model of planning, the Cognitive Agent Architecture (Cougaar) is selected for the implementation platform. The resulting Cougaar MDA prescribes certain kinds of models to be used, how those models may be prepared and the relationships of the different kinds of models. Using the existing Cougaar architecture, the level of application composition is elevated from individual components to domain level model specifications in order to generate software artifacts. The software artifacts generation is based on a metamodel. Each component maps to a UML structured component which is then converted into multiple artifacts: Cougaar/Java code, documentation, and test cases.

System and method for anomaly detection

DOEpatents

Scherrer, Chad

2010-06-15

A system and method for detecting one or more anomalies in a plurality of observations is provided. In one illustrative embodiment, the observations are real-time network observations collected from a stream of network traffic. The method includes performing a discrete decomposition of the observations, and introducing derived variables to increase storage and query efficiencies. A mathematical model, such as a conditional independence model, is then generated from the formatted data. The formatted data is also used to construct frequency tables which maintain an accurate count of specific variable occurrence as indicated by the model generation process. The formatted data is then applied to the mathematical model to generate scored data. The scored data is then analyzed to detect anomalies.

Modeling and simulation research on electromagnetic and energy-recycled damper based on Adams

NASA Astrophysics Data System (ADS)

Zhou, C. F.; Zhang, K.; Zhang, Pengfei

2018-05-01

In order to study the voltage and power output characteristics of the electromagnetic and energy-recycled damper which consists of gear, rack and generator, the Adams model of this damper and the Simulink model of generator are established, and the co-simulation is accomplished with these two models. The output indexes such as the gear speed and power of generator are obtained by the simulation, and the simulation results demonstrate that the voltage peak of the damper is 25 V; the maximum output power of the damper is 8 W. The above research provides a basis for the prototype development of electromagnetic and energy-recycled damper with gear and rack.

A neural model of rule generation in inductive reasoning.

PubMed

Rasmussen, Daniel; Eliasmith, Chris

2011-01-01

Inductive reasoning is a fundamental and complex aspect of human intelligence. In particular, how do subjects, given a set of particular examples, generate general descriptions of the rules governing that set? We present a biologically plausible method for accomplishing this task and implement it in a spiking neuron model. We demonstrate the success of this model by applying it to the problem domain of Raven's Progressive Matrices, a widely used tool in the field of intelligence testing. The model is able to generate the rules necessary to correctly solve Raven's items, as well as recreate many of the experimental effects observed in human subjects. Copyright © 2011 Cognitive Science Society, Inc.

Transportation Sector Module - NEMS Documentation

EIA Publications

2017-01-01

Documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Transportation Model (TRAN). The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated by the model.

Model verification of large structural systems. [space shuttle model response

NASA Technical Reports Server (NTRS)

Lee, L. T.; Hasselman, T. K.

1978-01-01

A computer program for the application of parameter identification on the structural dynamic models of space shuttle and other large models with hundreds of degrees of freedom is described. Finite element, dynamic, analytic, and modal models are used to represent the structural system. The interface with math models is such that output from any structural analysis program applied to any structural configuration can be used directly. Processed data from either sine-sweep tests or resonant dwell tests are directly usable. The program uses measured modal data to condition the prior analystic model so as to improve the frequency match between model and test. A Bayesian estimator generates an improved analytical model and a linear estimator is used in an iterative fashion on highly nonlinear equations. Mass and stiffness scaling parameters are generated for an improved finite element model, and the optimum set of parameters is obtained in one step.

Modeling Images of Natural 3D Surfaces: Overview and Potential Applications

NASA Technical Reports Server (NTRS)

Jalobeanu, Andre; Kuehnel, Frank; Stutz, John

2004-01-01

Generative models of natural images have long been used in computer vision. However, since they only describe the of 2D scenes, they fail to capture all the properties of the underlying 3D world. Even though such models are sufficient for many vision tasks a 3D scene model is when it comes to inferring a 3D object or its characteristics. In this paper, we present such a generative model, incorporating both a multiscale surface prior model for surface geometry and reflectance, and an image formation process model based on realistic rendering, the computation of the posterior model parameter densities, and on the critical aspects of the rendering. We also how to efficiently invert the model within a Bayesian framework. We present a few potential applications, such as asteroid modeling and Planetary topography recovery, illustrated by promising results on real images.

Towards a New Generation of Agricultural System Data, Models and Knowledge Products: Design and Improvement

NASA Technical Reports Server (NTRS)

Antle, John M.; Basso, Bruno; Conant, Richard T.; Godfray, H. Charles J.; Jones, James W.; Herrero, Mario; Howitt, Richard E.; Keating, Brian A.; Munoz-Carpena, Rafael; Rosenzweig, Cynthia

2016-01-01

This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and global food security. We identify desirable features for models, and describe some of the potential advances that we envisage for model components and their integration. We propose an implementation strategy that would link a "pre-competitive" space for model development to a "competitive space" for knowledge product development and through private-public partnerships for new data infrastructure. Specific model improvements would be based on further testing and evaluation of existing models, the development and testing of modular model components and integration, and linkages of model integration platforms to new data management and visualization tools.

Towards a new generation of agricultural system data, models and knowledge products: Design and improvement.

PubMed

Antle, John M; Basso, Bruno; Conant, Richard T; Godfray, H Charles J; Jones, James W; Herrero, Mario; Howitt, Richard E; Keating, Brian A; Munoz-Carpena, Rafael; Rosenzweig, Cynthia; Tittonell, Pablo; Wheeler, Tim R

2017-07-01

This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and global food security. We identify desirable features for models, and describe some of the potential advances that we envisage for model components and their integration. We propose an implementation strategy that would link a "pre-competitive" space for model development to a "competitive space" for knowledge product development and through private-public partnerships for new data infrastructure. Specific model improvements would be based on further testing and evaluation of existing models, the development and testing of modular model components and integration, and linkages of model integration platforms to new data management and visualization tools.

Variable Renewable Energy in Long-Term Planning Models: A Multi-Model Perspective

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cole, Wesley; Frew, Bethany; Mai, Trieu

Long-term capacity expansion models of the U.S. electricity sector have long been used to inform electric sector stakeholders and decision-makers. With the recent surge in variable renewable energy (VRE) generators — primarily wind and solar photovoltaics — the need to appropriately represent VRE generators in these long-term models has increased. VRE generators are especially difficult to represent for a variety of reasons, including their variability, uncertainty, and spatial diversity. This report summarizes the analyses and model experiments that were conducted as part of two workshops on modeling VRE for national-scale capacity expansion models. It discusses the various methods for treatingmore » VRE among four modeling teams from the Electric Power Research Institute (EPRI), the U.S. Energy Information Administration (EIA), the U.S. Environmental Protection Agency (EPA), and the National Renewable Energy Laboratory (NREL). The report reviews the findings from the two workshops and emphasizes the areas where there is still need for additional research and development on analysis tools to incorporate VRE into long-term planning and decision-making. This research is intended to inform the energy modeling community on the modeling of variable renewable resources, and is not intended to advocate for or against any particular energy technologies, resources, or policies.« less

WWTP dynamic disturbance modelling--an essential module for long-term benchmarking development.

PubMed

Gernaey, K V; Rosen, C; Jeppsson, U

2006-01-01

Intensive use of the benchmark simulation model No. 1 (BSM1), a protocol for objective comparison of the effectiveness of control strategies in biological nitrogen removal activated sludge plants, has also revealed a number of limitations. Preliminary definitions of the long-term benchmark simulation model No. 1 (BSM1_LT) and the benchmark simulation model No. 2 (BSM2) have been made to extend BSM1 for evaluation of process monitoring methods and plant-wide control strategies, respectively. Influent-related disturbances for BSM1_LT/BSM2 are to be generated with a model, and this paper provides a general overview of the modelling methods used. Typical influent dynamic phenomena generated with the BSM1_LT/BSM2 influent disturbance model, including diurnal, weekend, seasonal and holiday effects, as well as rainfall, are illustrated with simulation results. As a result of the work described in this paper, a proposed influent model/file has been released to the benchmark developers for evaluation purposes. Pending this evaluation, a final BSM1_LT/BSM2 influent disturbance model definition is foreseen. Preliminary simulations with dynamic influent data generated by the influent disturbance model indicate that default BSM1 activated sludge plant control strategies will need extensions for BSM1_LT/BSM2 to efficiently handle 1 year of influent dynamics.

Applying ILAMB to data from several generations of the Community Land Model to assess the relative contribution of model improvements and forcing uncertainty to model-data agreement

NASA Astrophysics Data System (ADS)

Lawrence, D. M.; Fisher, R.; Koven, C.; Oleson, K. W.; Swenson, S. C.; Hoffman, F. M.; Randerson, J. T.; Collier, N.; Mu, M.

2017-12-01

The International Land Model Benchmarking (ILAMB) project is a model-data intercomparison and integration project designed to assess and help improve land models. The current package includes assessment of more than 25 land variables across more than 60 global, regional, and site-level (e.g., FLUXNET) datasets. ILAMB employs a broad range of metrics including RMSE, mean error, spatial distributions, interannual variability, and functional relationships. Here, we apply ILAMB for the purpose of assessment of several generations of the Community Land Model (CLM4, CLM4.5, and CLM5). Encouragingly, CLM5, which is the result of model development over the last several years by more than 50 researchers from 15 different institutions, shows broad improvements across many ILAMB metrics including LAI, GPP, vegetation carbon stocks, and the historical net ecosystem carbon balance among others. We will also show that considerable uncertainty arises from the historical climate forcing data used (GSWP3v1 and CRUNCEPv7). ILAMB score variations due to forcing data can be as large for many variables as that due to model structural differences. Strengths and weaknesses and persistent biases across model generations will also be presented.

A neural network model of metaphor understanding with dynamic interaction based on a statistical language analysis: targeting a human-like model.

PubMed

Terai, Asuka; Nakagawa, Masanori

2007-08-01

The purpose of this paper is to construct a model that represents the human process of understanding metaphors, focusing specifically on similes of the form an "A like B". Generally speaking, human beings are able to generate and understand many sorts of metaphors. This study constructs the model based on a probabilistic knowledge structure for concepts which is computed from a statistical analysis of a large-scale corpus. Consequently, this model is able to cover the many kinds of metaphors that human beings can generate. Moreover, the model implements the dynamic process of metaphor understanding by using a neural network with dynamic interactions. Finally, the validity of the model is confirmed by comparing model simulations with the results from a psychological experiment.

Preparing the Model for Prediction Across Scales (MPAS) for global retrospective air quality modeling

EPA Science Inventory

The US EPA has a plan to leverage recent advances in meteorological modeling to develop a "Next-Generation" air quality modeling system that will allow consistent modeling of problems from global to local scale. The meteorological model of choice is the Model for Predic...

A Framework of Operating Models for Interdisciplinary Research Programs in Clinical Service Organizations

ERIC Educational Resources Information Center

King, Gillian; Currie, Melissa; Smith, Linda; Servais, Michelle; McDougall, Janette

2008-01-01

A framework of operating models for interdisciplinary research programs in clinical service organizations is presented, consisting of a "clinician-researcher" skill development model, a program evaluation model, a researcher-led knowledge generation model, and a knowledge conduit model. Together, these models comprise a tailored, collaborative…

Influence of thermodynamic properties of a thermo-acoustic emitter on the efficiency of thermal airborne ultrasound generation.

PubMed

Daschewski, M; Kreutzbruck, M; Prager, J

2015-12-01

In this work we experimentally verify the theoretical prediction of the recently published Energy Density Fluctuation Model (EDF-model) of thermo-acoustic sound generation. Particularly, we investigate experimentally the influence of thermal inertia of an electrically conductive film on the efficiency of thermal airborne ultrasound generation predicted by the EDF-model. Unlike widely used theories, the EDF-model predicts that the thermal inertia of the electrically conductive film is a frequency-dependent parameter. Its influence grows non-linearly with the increase of excitation frequency and reduces the efficiency of the ultrasound generation. Thus, this parameter is the major limiting factor for the efficient thermal airborne ultrasound generation in the MHz-range. To verify this theoretical prediction experimentally, five thermo-acoustic emitter samples consisting of Indium-Tin-Oxide (ITO) coatings of different thicknesses (from 65 nm to 1.44 μm) on quartz glass substrates were tested for airborne ultrasound generation in a frequency range from 10 kHz to 800 kHz. For the measurement of thermally generated sound pressures a laser Doppler vibrometer combined with a 12 μm thin polyethylene foil was used as the sound pressure detector. All tested thermo-acoustic emitter samples showed a resonance-free frequency response in the entire tested frequency range. The thermal inertia of the heat producing film acts as a low-pass filter and reduces the generated sound pressure with the increasing excitation frequency and the ITO film thickness. The difference of generated sound pressure levels for samples with 65 nm and 1.44 μm thickness is in the order of about 6 dB at 50 kHz and of about 12 dB at 500 kHz. A comparison of sound pressure levels measured experimentally and those predicted by the EDF-model shows for all tested emitter samples a relative error of less than ±6%. Thus, experimental results confirm the prediction of the EDF-model and show that the model can be applied for design and optimization of thermo-acoustic airborne ultrasound emitters. Copyright © 2015 Elsevier B.V. All rights reserved.

Capacity withholding in wholesale electricity markets: The experience in England and Wales

NASA Astrophysics Data System (ADS)

Quinn, James Arnold

This thesis examines the incentives wholesale electricity generators face to withhold generating capacity from centralized electricity spot markets. The first chapter includes a brief history of electricity industry regulation in England and Wales and in the United States, including a description of key institutional features of England and Wales' restructured electricity market. The first chapter also includes a review of the literature on both bid price manipulation and capacity bid manipulation in centralized electricity markets. The second chapter details a theoretical model of wholesale generator behavior in a single price electricity market. A duopoly model is specified under the assumption that demand is non-stochastic. This model assumes that duopoly generators offer to sell electricity at their marginal cost, but can withhold a continuous segment of their capacity from the market. The Nash equilibrium withholding strategy of this model involves each duopoly generator withholding so that it produces the Cournot equilibrium output. A monopoly model along the lines of the duopoly model is specified and simulated under the assumption that demand is stochastic. The optimal strategy depends on the degree of demand uncertainty. When there is a moderate degree of demand uncertainty, the optimal withholding strategy involves production inefficiencies. When there is a high degree of demand uncertainty, the optimal monopoly quantity is greater than the optimal output level when demand is non-stochastic. The third chapter contains an empirical examination of the behavior of generators in the wholesale electricity market in England and Wales in the early 1990's. The wholesale market in England and Wales is analyzed because the industry structure in the early 1990's created a natural experiment, which is described in this chapter, whereby one of the two dominant generators had no incentive to behave non-competitively. This chapter develops a classification methodology consistent with the equilibrium identified in the second chapter. The availability of generating units owned by the two dominant generators is analyzed based on this classification system. This analysis includes the use of sample statistics as well as estimates from a dynamic random effects probit model. The analysis suggests a minimal degree of capacity withholding.

Next generation lightweight mirror modeling software

NASA Astrophysics Data System (ADS)

Arnold, William R.; Fitzgerald, Matthew; Rosa, Rubin Jaca; Stahl, H. Philip

2013-09-01

The advances in manufacturing techniques for lightweight mirrors, such as EXELSIS deep core low temperature fusion, Corning's continued improvements in the Frit bonding process and the ability to cast large complex designs, combined with water-jet and conventional diamond machining of glasses and ceramics has created the need for more efficient means of generating finite element models of these structures. Traditional methods of assembling 400,000 + element models can take weeks of effort, severely limiting the range of possible optimization variables. This paper will introduce model generation software developed under NASA sponsorship for the design of both terrestrial and space based mirrors. The software deals with any current mirror manufacturing technique, single substrates, multiple arrays of substrates, as well as the ability to merge submodels into a single large model. The modeler generates both mirror and suspension system elements, suspensions can be created either for each individual petal or the whole mirror. A typical model generation of 250,000 nodes and 450,000 elements only takes 3-5 minutes, much of that time being variable input time. The program can create input decks for ANSYS, ABAQUS and NASTRAN. An archive/retrieval system permits creation of complete trade studies, varying cell size, depth, and petal size, suspension geometry with the ability to recall a particular set of parameters and make small or large changes with ease. The input decks created by the modeler are text files which can be modified by any text editor, all the shell thickness parameters and suspension spring rates are accessible and comments in deck identify which groups of elements are associated with these parameters. This again makes optimization easier. With ANSYS decks, the nodes representing support attachments are grouped into components; in ABAQUS these are SETS and in NASTRAN as GRIDPOINT SETS, this make integration of these models into large telescope or satellite models easier.

Using Bond Graphs for Articulated, Flexible Multi-bodies, Sensors, Actuators, and Controllers with Application to the International Space Station

NASA Technical Reports Server (NTRS)

Montgomery, Raymond C.; Granda, Jose J.

2003-01-01

Conceptually, modeling of flexible, multi-body systems involves a formulation as a set of time-dependent partial differential equations. However, for practical, engineering purposes, this modeling is usually done using the method of Finite Elements, which approximates the set of partial differential equations, thus generalizing the approach to all continuous media. This research investigates the links between the Bond Graph method and the classical methods used to develop system models and advocates the Bond Graph Methodology and current bond graph tools as alternate approaches that will lead to a quick and precise understanding of a flexible multi-body system under automatic control. For long endurance, complex spacecraft, because of articulation and mission evolution the model of the physical system may change frequently. So a method of automatic generation and regeneration of system models that does not lead to implicit equations, as does the Lagrange equation approach, is desirable. The bond graph method has been shown to be amenable to automatic generation of equations with appropriate consideration of causality. Indeed human-interactive software now exists that automatically generates both symbolic and numeric system models and evaluates causality as the user develops the model, e.g. the CAMP-G software package. In this paper the CAMP-G package is used to generate a bond graph model of the International Space Station (ISS) at an early stage in its assembly, Zvezda. The ISS is an ideal example because it is a collection of bodies that are articulated, many of which are highly flexible. Also many reaction jets are used to control translation and attitude, and many electric motors are used to articulate appendages, which consist of photovoltaic arrays and composite assemblies. The Zvezda bond graph model is compared to an existing model, which was generated by the NASA Johnson Space Center during the Verification and Analysis Cycle of Zvezda.

Generation and comparison of CRISPR-Cas9 and Cre-mediated genetically engineered mouse models of sarcoma

PubMed Central

Huang, Jianguo; Chen, Mark; Whitley, Melodi Javid; Kuo, Hsuan-Cheng; Xu, Eric S.; Walens, Andrea; Mowery, Yvonne M.; Van Mater, David; Eward, William C.; Cardona, Diana M.; Luo, Lixia; Ma, Yan; Lopez, Omar M.; Nelson, Christopher E.; Robinson-Hamm, Jacqueline N.; Reddy, Anupama; Dave, Sandeep S.; Gersbach, Charles A.; Dodd, Rebecca D.; Kirsch, David G.

2017-01-01

Genetically engineered mouse models that employ site-specific recombinase technology are important tools for cancer research but can be costly and time-consuming. The CRISPR-Cas9 system has been adapted to generate autochthonous tumours in mice, but how these tumours compare to tumours generated by conventional recombinase technology remains to be fully explored. Here we use CRISPR-Cas9 to generate multiple subtypes of primary sarcomas efficiently in wild type and genetically engineered mice. These data demonstrate that CRISPR-Cas9 can be used to generate multiple subtypes of soft tissue sarcomas in mice. Primary sarcomas generated with CRISPR-Cas9 and Cre recombinase technology had similar histology, growth kinetics, copy number variation and mutational load as assessed by whole exome sequencing. These results show that sarcomas generated with CRISPR-Cas9 technology are similar to sarcomas generated with conventional modelling techniques and suggest that CRISPR-Cas9 can be used to more rapidly generate genotypically and phenotypically similar cancers. PMID:28691711

Surface Modeling and Grid Generation of Orbital Sciences X34 Vehicle. Phase 1

NASA Technical Reports Server (NTRS)

Alter, Stephen J.

1997-01-01

The surface modeling and grid generation requirements, motivations, and methods used to develop Computational Fluid Dynamic volume grids for the X34-Phase 1 are presented. The requirements set forth by the Aerothermodynamics Branch at the NASA Langley Research Center serve as the basis for the final techniques used in the construction of all volume grids, including grids for parametric studies of the X34. The Integrated Computer Engineering and Manufacturing code for Computational Fluid Dynamics (ICEM/CFD), the Grid Generation code (GRIDGEN), the Three-Dimensional Multi-block Advanced Grid Generation System (3DMAGGS) code, and Volume Grid Manipulator (VGM) code are used to enable the necessary surface modeling, surface grid generation, volume grid generation, and grid alterations, respectively. All volume grids generated for the X34, as outlined in this paper, were used for CFD simulations within the Aerothermodynamics Branch.

A simple stochastic weather generator for ecological modeling

Treesearch

A.G. Birt; M.R. Valdez-Vivas; R.M. Feldman; C.W. Lafon; D. Cairns; R.N. Coulson; M. Tchakerian; W. Xi; Jim Guldin

2010-01-01

Stochastic weather generators are useful tools for exploring the relationship between organisms and their environment. This paper describes a simple weather generator that can be used in ecological modeling projects. We provide a detailed description of methodology, and links to full C++ source code (http://weathergen.sourceforge.net) required to implement or modify...

Using the Kaleidoscope Career Model to Examine Generational Differences in Work Attitudes

ERIC Educational Resources Information Center

Sullivan, Sherry E.; Forret, Monica L.; Carraher, Shawn M.; Mainiero, Lisa A.

2009-01-01

Purpose: The purpose of this paper is to examine, utilising the Kaleidoscope Career Model, whether members of the Baby Boom generation and Generation X differ in their needs for authenticity, balance, and challenge. Design/methodology/approach: Survey data were obtained from 982 professionals located across the USA. Correlations, t-tests, and…

Reflections on Wittrock's Generative Model of Learning: A Motivation Perspective

ERIC Educational Resources Information Center

Anderman, Eric M.

2010-01-01

In this article, I examine developments in research on achievement motivation and comment on how those developments are reflected in Wittrock's generative model of learning. Specifically, I focus on the roles of prior knowledge, the generation of knowledge, and beliefs about ability. Examples from Wittrock's theory and from current motivational…

On the use of Schwarz-Christoffel conformal mappings to the grid generation for global ocean models

NASA Astrophysics Data System (ADS)

Xu, S.; Wang, B.; Liu, J.

2015-10-01

In this article we propose two grid generation methods for global ocean general circulation models. Contrary to conventional dipolar or tripolar grids, the proposed methods are based on Schwarz-Christoffel conformal mappings that map areas with user-prescribed, irregular boundaries to those with regular boundaries (i.e., disks, slits, etc.). The first method aims at improving existing dipolar grids. Compared with existing grids, the sample grid achieves a better trade-off between the enlargement of the latitudinal-longitudinal portion and the overall smooth grid cell size transition. The second method addresses more modern and advanced grid design requirements arising from high-resolution and multi-scale ocean modeling. The generated grids could potentially achieve the alignment of grid lines to the large-scale coastlines, enhanced spatial resolution in coastal regions, and easier computational load balance. Since the grids are orthogonal curvilinear, they can be easily utilized by the majority of ocean general circulation models that are based on finite difference and require grid orthogonality. The proposed grid generation algorithms can also be applied to the grid generation for regional ocean modeling where complex land-sea distribution is present.

Radiometric Block Adjusment and Digital Radiometric Model Generation

NASA Astrophysics Data System (ADS)

Pros, A.; Colomina, I.; Navarro, J. A.; Antequera, R.; Andrinal, P.

2013-05-01

In this paper we present a radiometric block adjustment method that is related to geometric block adjustment and to the concept of a terrain Digital Radiometric Model (DRM) as a complement to the terrain digital elevation and surface models. A DRM, in our concept, is a function that for each ground point returns a reflectance value and a Bidirectional Reflectance Distribution Function (BRDF). In a similar way to the terrain geometric reconstruction procedure, given an image block of some terrain area, we split the DRM generation in two phases: radiometric block adjustment and DRM generation. In the paper we concentrate on the radiometric block adjustment step, but we also describe a preliminary DRM generator. In the block adjustment step, after a radiometric pre-calibraton step, local atmosphere radiative transfer parameters, and ground reflectances and BRDFs at the radiometric tie points are estimated. This radiometric block adjustment is based on atmospheric radiative transfer (ART) models, pre-selected BRDF models and radiometric ground control points. The proposed concept is implemented and applied in an experimental campaign, and the obtained results are presented. The DRM and orthophoto mosaics are generated showing no radiometric differences at the seam lines.

Vulnerability of US thermoelectric power generation to climate change when incorporating state-level environmental regulations

NASA Astrophysics Data System (ADS)

Liu, Lu; Hejazi, Mohamad; Li, Hongyi; Forman, Barton; Zhang, Xiao

2017-08-01

Previous modelling studies suggest that thermoelectric power generation is vulnerable to climate change, whereas studies based on historical data suggest the impact will be less severe. Here we explore the vulnerability of thermoelectric power generation in the United States to climate change by coupling an Earth system model with a thermoelectric power generation model, including state-level representation of environmental regulations on thermal effluents. We find that the impact of climate change is lower than in previous modelling estimates due to an inclusion of a spatially disaggregated representation of environmental regulations and provisional variances that temporarily relieve power plants from permit requirements. More specifically, our results indicate that climate change alone may reduce average generating capacity by 2-3% by the 2060s, while reductions of up to 12% are expected if environmental requirements are enforced without waivers for thermal variation. Our work highlights the significance of accounting for legal constructs and underscores the effects of provisional variances in addition to environmental requirements.

A simple model for the generation of the vestibular evoked myogenic potential (VEMP).

PubMed

Wit, Hero P; Kingma, Charlotte M

2006-06-01

To describe the mechanism by which the vestibular evoked myogenic potential is generated. Vestibular evoked myogenic potential generation is modeled by adding a large number of muscle motor unit action potentials. These action potentials occur randomly in time along a 100 ms long time axis. But because between approximately 15 and 20 ms after a loud short sound stimulus (almost) no action potentials are generated during VEMP measurements in human subjects, no action potentials are present in the model during this time. The evoked potential is the result of the lack of amplitude cancellation in the averaged surface electromyogram at the edges of this 5 ms long time interval. The relatively simple model describes generation and some properties of the vestibular evoked myogenic potential very well. It is shown that, in contrast with other evoked potentials (BAEPs, VERs), the vestibular evoked myogenic potential is the result of an interruption of activity and not that of summed synchronized neural action potentials.

Regional stochastic generation of streamflows using an ARIMA (1,0,1) process and disaggregation

USGS Publications Warehouse

Armbruster, Jeffrey T.

1979-01-01

An ARIMA (1,0,1) model was calibrated and used to generate long annual flow sequences at three sites in the Juniata River basin, Pennsylvania. The model preserves the mean, variance, and cross correlations of the observed station data. In addition, it has a desirable blend of both high and low frequency characteristics and therefore is capable of preserving the Hurst coefficient, h. The generated annual flows are disaggregated into monthly sequences using a modification of the Valencia-Schaake model. The low-flow frequency and flow duration characteristics of the generated monthly flows, with length equal to the historical data, compare favorably with the historical data. Once the models were verified, 100-year sequences were generated and analyzed for their low flow characteristics. One-, three- and six- month low-flow frequencies at recurrence intervals greater than 10 years are generally found to be lower than flow computed from the historical flows. A method is proposed for synthesizing flows at ungaged sites. (Kosco-USGS)

Engine structures modeling software system: Computer code. User's manual

NASA Technical Reports Server (NTRS)

1992-01-01

ESMOSS is a specialized software system for the construction of geometric descriptive and discrete analytical models of engine parts, components and substructures which can be transferred to finite element analysis programs such as NASTRAN. The software architecture of ESMOSS is designed in modular form with a central executive module through which the user controls and directs the development of the analytical model. Modules consist of a geometric shape generator, a library of discretization procedures, interfacing modules to join both geometric and discrete models, a deck generator to produce input for NASTRAN and a 'recipe' processor which generates geometric models from parametric definitions. ESMOSS can be executed both in interactive and batch modes. Interactive mode is considered to be the default mode and that mode will be assumed in the discussion in this document unless stated otherwise.

Procedures for generation and reduction of linear models of a turbofan engine

NASA Technical Reports Server (NTRS)

Seldner, K.; Cwynar, D. S.

1978-01-01

A real time hybrid simulation of the Pratt & Whitney F100-PW-F100 turbofan engine was used for linear-model generation. The linear models were used to analyze the effect of disturbances about an operating point on the dynamic performance of the engine. A procedure that disturbs, samples, and records the state and control variables was developed. For large systems, such as the F100 engine, the state vector is large and may contain high-frequency information not required for control. This, reducing the full-state to a reduced-order model may be a practicable approach to simplifying the control design. A reduction technique was developed to generate reduced-order models. Selected linear and nonlinear output responses to exhaust-nozzle area and main-burner fuel flow disturbances are presented for comparison.

Free-piston engine linear generator for hybrid vehicles modeling study

NASA Astrophysics Data System (ADS)

Callahan, T. J.; Ingram, S. K.

1995-05-01

Development of a free piston engine linear generator was investigated for use as an auxiliary power unit for a hybrid electric vehicle. The main focus of the program was to develop an efficient linear generator concept to convert the piston motion directly into electrical power. Computer modeling techniques were used to evaluate five different designs for linear generators. These designs included permanent magnet generators, reluctance generators, linear DC generators, and two and three-coil induction generators. The efficiency of the linear generator was highly dependent on the design concept. The two-coil induction generator was determined to be the best design, with an efficiency of approximately 90 percent.

Generation of 1/f noise from a broken-symmetry model for the arbitrary absolute pitch of musical melodies.

PubMed

Grant, Martin; Faghihi, Niloufar

2017-11-01

A model is presented to generate power spectrum noise with intensity proportional to 1/f  as a function of frequency f. The model arises from a broken-symmetry variable, which corresponds to absolute pitch, where fluctuations occur in an attempt to restore that symmetry, influenced by interactions in the creation of musical melodies.

Development of the Next Generation Air Quality Modeling System (20th Joint Conference on the Applications of Air Pollution Meteorology with the A&WMA)

EPA Science Inventory

A next generation air quality modeling system is being developed at the U.S. EPA to enable modeling of air quality from global to regional to (eventually) local scales. We envision that the system will have three configurations: 1. Global meteorology with seamless mesh refinemen...

40 CFR 86.1861-17 - How do the NMOG+NOX and evaporative emission credit programs work?

Code of Federal Regulations, 2014 CFR

2014-07-01

... VEHICLES AND ENGINES General Compliance Provisions for Control of Air Pollution From New and In-Use Light...)(8), credits generated in model years 2017 through 2024 expire after eight years, or after model year... generating early Tier 3 credits under § 86.1811-17(b)(11) in model year 2017. (c) The credit-deficit...

Fusion of Hard and Soft Information in Nonparametric Density Estimation

DTIC Science & Technology

2015-06-10

and stochastic optimization models, in analysis of simulation output, and when instantiating probability models. We adopt a constrained maximum...particular, density estimation is needed for generation of input densities to simulation and stochastic optimization models, in analysis of simulation output...an essential step in simulation analysis and stochastic optimization is the generation of probability densities for input random variables; see for

An integer programming model for distal humerus fracture fixation planning.

PubMed

Maratt, Joseph D; Peaks, Ya-Sin A; Doro, Lisa Case; Karunakar, Madhav A; Hughes, Richard E

2008-05-01

To demonstrate the feasibility of an integer programming model to assist in pre-operative planning for open reduction and internal fixation of a distal humerus fracture. We describe an integer programming model based on the objective of maximizing the reward for screws placed while satisfying the requirements for sound internal fixation. The model maximizes the number of bicortical screws placed while avoiding screw collision and favoring screws of greater length that cross multiple fracture planes. The model was tested on three types of total articular fractures of the distal humerus. Solutions were generated using 5, 9, 21 and 33 possible screw orientations per hole. Solutions generated using 33 possible screw orientations per hole and five screw lengths resulted in the most clinically relevant fixation plan and required the calculation of 1,191,975 pairs of screws that resulted in collision. At this level of complexity, the pre-processor took 104 seconds to generate the constraints for the solver, and a solution was generated in under one minute in all three cases. Despite the large size of this problem, it can be solved in a reasonable amount of time, making use of the model practical in pre-surgical planning.

Bim Automation: Advanced Modeling Generative Process for Complex Structures

NASA Astrophysics Data System (ADS)

Banfi, F.; Fai, S.; Brumana, R.

2017-08-01

The new paradigm of the complexity of modern and historic structures, which are characterised by complex forms, morphological and typological variables, is one of the greatest challenges for building information modelling (BIM). Generation of complex parametric models needs new scientific knowledge concerning new digital technologies. These elements are helpful to store a vast quantity of information during the life cycle of buildings (LCB). The latest developments of parametric applications do not provide advanced tools, resulting in time-consuming work for the generation of models. This paper presents a method capable of processing and creating complex parametric Building Information Models (BIM) with Non-Uniform to NURBS) with multiple levels of details (Mixed and ReverseLoD) based on accurate 3D photogrammetric and laser scanning surveys. Complex 3D elements are converted into parametric BIM software and finite element applications (BIM to FEA) using specific exchange formats and new modelling tools. The proposed approach has been applied to different case studies: the BIM of modern structure for the courtyard of West Block on Parliament Hill in Ottawa (Ontario) and the BIM of Masegra Castel in Sondrio (Italy), encouraging the dissemination and interaction of scientific results without losing information during the generative process.

A software engineering perspective on environmental modeling framework design: The object modeling system

USDA-ARS?s Scientific Manuscript database

The environmental modeling community has historically been concerned with the proliferation of models and the effort associated with collective model development tasks (e.g., code generation, data provisioning and transformation, etc.). Environmental modeling frameworks (EMFs) have been developed to...

Experimental comparison of residual stresses for a thermomechanical model for the simulation of selective laser melting

DOE PAGES

Hodge, N. E.; Ferencz, R. M.; Vignes, R. M.

2016-05-30

Selective laser melting (SLM) is an additive manufacturing process in which multiple, successive layers of metal powders are heated via laser in order to build a part. Modeling of SLM requires consideration of the complex interaction between heat transfer and solid mechanics. Here, the present work describes the authors initial efforts to validate their first generation model. In particular, the comparison of model-generated solid mechanics results, including both deformation and stresses, is presented. Additionally, results of various perturbations of the process parameters and modeling strategies are discussed.

Next-generation concurrent engineering: developing models to complement point designs

NASA Technical Reports Server (NTRS)

Morse, Elizabeth; Leavens, Tracy; Cohanim, Babak; Harmon, Corey; Mahr, Eric; Lewis, Brian

2006-01-01

Concurrent Engineering Design (CED) teams have made routine the rapid development of point designs for space missions. The Jet Propulsion Laboratory's Team X is now evolving into a 'next-generation CED; in addition to a point design, the Team develops a model of the local trade space. The process is a balance between the power of a model developing tools and the creativity of humal experts, enabling the development of a variety of trade models for any space mission. This paper reviews the modeling method and its practical implementation in the ED environment. Example results illustrate the benefit of this approach.

Model reduction by trimming for a class of semi-Markov reliability models and the corresponding error bound

NASA Technical Reports Server (NTRS)

White, Allan L.; Palumbo, Daniel L.

1991-01-01

Semi-Markov processes have proved to be an effective and convenient tool to construct models of systems that achieve reliability by redundancy and reconfiguration. These models are able to depict complex system architectures and to capture the dynamics of fault arrival and system recovery. A disadvantage of this approach is that the models can be extremely large, which poses both a model and a computational problem. Techniques are needed to reduce the model size. Because these systems are used in critical applications where failure can be expensive, there must be an analytically derived bound for the error produced by the model reduction technique. A model reduction technique called trimming is presented that can be applied to a popular class of systems. Automatic model generation programs were written to help the reliability analyst produce models of complex systems. This method, trimming, is easy to implement and the error bound easy to compute. Hence, the method lends itself to inclusion in an automatic model generator.

Enabling full-field physics-based optical proximity correction via dynamic model generation

NASA Astrophysics Data System (ADS)

Lam, Michael; Clifford, Chris; Raghunathan, Ananthan; Fenger, Germain; Adam, Kostas

2017-07-01

As extreme ultraviolet lithography becomes closer to reality for high volume production, its peculiar modeling challenges related to both inter and intrafield effects have necessitated building an optical proximity correction (OPC) infrastructure that operates with field position dependency. Previous state-of-the-art approaches to modeling field dependency used piecewise constant models where static input models are assigned to specific x/y-positions within the field. OPC and simulation could assign the proper static model based on simulation-level placement. However, in the realm of 7 and 5 nm feature sizes, small discontinuities in OPC from piecewise constant model changes can cause unacceptable levels of edge placement errors. The introduction of dynamic model generation (DMG) can be shown to effectively avoid these dislocations by providing unique mask and optical models per simulation region, allowing a near continuum of models through the field. DMG allows unique models for electromagnetic field, apodization, aberrations, etc. to vary through the entire field and provides a capability to precisely and accurately model systematic field signatures.

TSARINA: A Computer Model for Assessing Conventional and Chemical Attacks on Airbases

DTIC Science & Technology

1990-09-01

IV, and has been updated to FORTRAN 77; it has been adapted to various computer systems, as was the widely used AIDA model and the previous versions of...conventional and chemical attacks on sortie generation. In the first version of TSARINA [1 2], several key additions were made to the AIDA model so that (1...various on-base resources, in addition to the estimates of hits and facility damage that are generated by the original AIDA model . The second version

Modeling the Webgraph: How Far We Are

NASA Astrophysics Data System (ADS)

Donato, Debora; Laura, Luigi; Leonardi, Stefano; Millozzi, Stefano

The following sections are included: * Introduction * Preliminaries * WebBase * In-degree and out-degree * PageRank * Bipartite cliques * Strongly connected components * Stochastic models of the webgraph * Models of the webgraph * A multi-layer model * Large scale simulation * Algorithmic techniques for generating and measuring webgraphs * Data representation and multifiles * Generating webgraphs * Traversal with two bits for each node * Semi-external breadth first search * Semi-external depth first search * Computation of the SCCs * Computation of the bow-tie regions * Disjoint bipartite cliques * PageRank * Summary and outlook

Thermosphere-Ionosphere-Mesosphere Modeling Using the TIE-GCM, TIME-GCM, and WACCM That Will Lead to the Development of a Seamless Model of the Whole Atmosphere

DTIC Science & Technology

2006-09-30

disturbances from the lower atmosphere and ocean affect the upper atmosphere and how this variability interacts with the variability generated by solar and...represents “ general circulation model.” Both models include self-consistent ionospheric electrodynamics, that is, a calculation of the electric fields...and currents generated by the ionospheric dynamo, and consideration of their effects on the neutral dynamics. The TIE-GCM is used for studies that

The SYSGEN user package

NASA Technical Reports Server (NTRS)

Carlson, C. R.

1981-01-01

The user documentation of the SYSGEN model and its links with other simulations is described. The SYSGEN is a production costing and reliability model of electric utility systems. Hydroelectric, storage, and time dependent generating units are modeled in addition to conventional generating plants. Input variables, modeling options, output variables, and reports formats are explained. SYSGEN also can be run interactively by using a program called FEPS (Front End Program for SYSGEN). A format for SYSGEN input variables which is designed for use with FEPS is presented.

Exactly solvable relativistic model with the anomalous interaction

NASA Astrophysics Data System (ADS)

Ferraro, Elena; Messina, Antonino; Nikitin, A. G.

2010-04-01

A special class of Dirac-Pauli equations with time-like vector potentials of an external field is investigated. An exactly solvable relativistic model describing the anomalous interaction of a neutral Dirac fermion with a cylindrically symmetric external electromagnetic field is presented. The related external field is a superposition of the electric field generated by a charged infinite filament and the magnetic field generated by a straight line current. In the nonrelativistic approximation the considered model is reduced to the integrable Pron’ko-Stroganov model.

Chain pooling to minimize prediction error in subset regression. [Monte Carlo studies using population models

NASA Technical Reports Server (NTRS)

Holms, A. G.

1974-01-01

Monte Carlo studies using population models intended to represent response surface applications are reported. Simulated experiments were generated by adding pseudo random normally distributed errors to population values to generate observations. Model equations were fitted to the observations and the decision procedure was used to delete terms. Comparison of values predicted by the reduced models with the true population values enabled the identification of deletion strategies that are approximately optimal for minimizing prediction errors.

Model for Increasing the Power Obtained from a Thermoelectric Generator Module

NASA Astrophysics Data System (ADS)

Huang, Gia-Yeh; Hsu, Cheng-Ting; Yao, Da-Jeng

2014-06-01

We have developed a model for finding the most efficient way of increasing the power obtained from a thermoelectric generator (TEG) module with a variety of operating conditions and limitations. The model is based on both thermoelectric principles and thermal resistance circuits, because a TEG converts heat into electricity consistent with these two theories. It is essential to take into account thermal contact resistance when estimating power generation. Thermal contact resistance causes overestimation of the measured temperature difference between the hot and cold sides of a TEG in calculation of the theoretical power generated, i.e. the theoretical power is larger than the experimental power. The ratio of the experimental open-loop voltage to the measured temperature difference, the effective Seebeck coefficient, can be used to estimate the thermal contact resistance in the model. The ratio of the effective Seebeck coefficient to the theoretical Seebeck coefficient, the Seebeck coefficient ratio, represents the contact conditions. From this ratio, a relationship between performance and different variables can be developed. The measured power generated by a TEG module (TMH400302055; Wise Life Technology, Taiwan) is consistent with the result obtained by use of the model; the relative deviation is 10%. Use of this model to evaluate the most efficient means of increasing the generated power reveals that the TEG module generates 0.14 W when the temperature difference is 25°C and the Seebeck coefficient ratio is 0.4. Several methods can be used triple the amount of power generated. For example, increasing the temperature difference to 43°C generates 0.41 W power; improving the Seebeck coefficient ratio to 0.65 increases the power to 0.39 W; simultaneously increasing the temperature difference to 34°C and improving the Seebeck coefficient ratio to 0.5 increases the power to 0.41 W. Choice of the appropriate method depends on the limitations of system, the cost, and the environment.

Several examples where turbulence models fail in inlet flow field analysis

NASA Technical Reports Server (NTRS)

Anderson, Bernhard H.

1993-01-01

Computational uncertainties in turbulence modeling for three dimensional inlet flow fields include flows approaching separation, strength of secondary flow field, three dimensional flow predictions of vortex liftoff, and influence of vortex-boundary layer interactions; computational uncertainties in vortex generator modeling include representation of generator vorticity field and the relationship between generator and vorticity field. The objectives of the inlet flow field studies presented in this document are to advance the understanding, prediction, and control of intake distortion and to study the basic interactions that influence this design problem.

Continuum modeling of large lattice structures: Status and projections

NASA Technical Reports Server (NTRS)

Noor, Ahmed K.; Mikulas, Martin M., Jr.

1988-01-01

The status and some recent developments of continuum modeling for large repetitive lattice structures are summarized. Discussion focuses on a number of aspects including definition of an effective substitute continuum; characterization of the continuum model; and the different approaches for generating the properties of the continuum, namely, the constitutive matrix, the matrix of mass densities, and the matrix of thermal coefficients. Also, a simple approach is presented for generating the continuum properties. The approach can be used to generate analytic and/or numerical values of the continuum properties.

Test-Case Generation using an Explicit State Model Checker Final Report

NASA Technical Reports Server (NTRS)

Heimdahl, Mats P. E.; Gao, Jimin

2003-01-01

In the project 'Test-Case Generation using an Explicit State Model Checker' we have extended an existing tools infrastructure for formal modeling to export Java code so that we can use the NASA Ames tool Java Pathfinder (JPF) for test case generation. We have completed a translator from our source language RSML(exp -e) to Java and conducted initial studies of how JPF can be used as a testing tool. In this final report, we provide a detailed description of the translation approach as implemented in our tools.

Multi-model analysis in hydrological prediction

NASA Astrophysics Data System (ADS)

Lanthier, M.; Arsenault, R.; Brissette, F.

2017-12-01

Hydrologic modelling, by nature, is a simplification of the real-world hydrologic system. Therefore ensemble hydrological predictions thus obtained do not present the full range of possible streamflow outcomes, thereby producing ensembles which demonstrate errors in variance such as under-dispersion. Past studies show that lumped models used in prediction mode can return satisfactory results, especially when there is not enough information available on the watershed to run a distributed model. But all lumped models greatly simplify the complex processes of the hydrologic cycle. To generate more spread in the hydrologic ensemble predictions, multi-model ensembles have been considered. In this study, the aim is to propose and analyse a method that gives an ensemble streamflow prediction that properly represents the forecast probabilities and reduced ensemble bias. To achieve this, three simple lumped models are used to generate an ensemble. These will also be combined using multi-model averaging techniques, which generally generate a more accurate hydrogram than the best of the individual models in simulation mode. This new predictive combined hydrogram is added to the ensemble, thus creating a large ensemble which may improve the variability while also improving the ensemble mean bias. The quality of the predictions is then assessed on different periods: 2 weeks, 1 month, 3 months and 6 months using a PIT Histogram of the percentiles of the real observation volumes with respect to the volumes of the ensemble members. Initially, the models were run using historical weather data to generate synthetic flows. This worked for individual models, but not for the multi-model and for the large ensemble. Consequently, by performing data assimilation at each prediction period and thus adjusting the initial states of the models, the PIT Histogram could be constructed using the observed flows while allowing the use of the multi-model predictions. The under-dispersion has been largely corrected on short-term predictions. For the longer term, the addition of the multi-model member has been beneficial to the quality of the predictions, although it is too early to determine whether the gain is related to the addition of a member or if multi-model member has plus-value itself.

Accuracy of DSM based on digital aerial image matching. (Polish Title: Dokładność NMPT tworzonego metodą automatycznego dopasowania cyfrowych zdjęć lotniczych)

NASA Astrophysics Data System (ADS)

Kubalska, J. L.; Preuss, R.

2013-12-01

Digital Surface Models (DSM) are used in GIS data bases as single product more often. They are also necessary to create other products such as3D city models, true-ortho and object-oriented classification. This article presents results of DSM generation for classification of vegetation in urban areas. Source data allowed producing DSM with using of image matching method and ALS data. The creation of DSM from digital images, obtained by Ultra Cam-D digital Vexcel camera, was carried out in Match-T by INPHO. This program optimizes the configuration of images matching process, which ensures high accuracy and minimize gap areas. The analysis of the accuracy of this process was made by comparison of DSM generated in Match-T with DSM generated from ALS data. Because of further purpose of generated DSM it was decided to create model in GRID structure with cell size of 1 m. With this parameter differential model from both DSMs was also built that allowed determining the relative accuracy of the compared models. The analysis indicates that the generation of DSM with multi-image matching method is competitive for the same surface model creation from ALS data. Thus, when digital images with high overlap are available, the additional registration of ALS data seems to be unnecessary.