Model Correction Factor Method

DEFF Research Database (Denmark)

Christensen, Claus; Randrup-Thomsen, Søren; Morsing Johannesen, Johannes

1997-01-01

The model correction factor method is proposed as an alternative to traditional polynomial based response surface techniques in structural reliability considering a computationally time consuming limit state procedure as a 'black box'. The class of polynomial functions is replaced by a limit...... of the model correction factor method, is that in simpler form not using gradient information on the original limit state function or only using this information once, a drastic reduction of the number of limit state evaluation is obtained together with good approximations on the reliability. Methods...

ADOxx Modelling Method Conceptualization Environment

Directory of Open Access Journals (Sweden)

Nesat Efendioglu

2017-04-01

Full Text Available The importance of Modelling Methods Engineering is equally rising with the importance of domain specific languages (DSL and individual modelling approaches. In order to capture the relevant semantic primitives for a particular domain, it is necessary to involve both, (a domain experts, who identify relevant concepts as well as (b method engineers who compose a valid and applicable modelling approach. This process consists of a conceptual design of formal or semi-formal of modelling method as well as a reliable, migratable, maintainable and user friendly software development of the resulting modelling tool. Modelling Method Engineering cycle is often under-estimated as both the conceptual architecture requires formal verification and the tool implementation requires practical usability, hence we propose a guideline and corresponding tools to support actors with different background along this complex engineering process. Based on practical experience in business, more than twenty research projects within the EU frame programmes and a number of bilateral research initiatives, this paper introduces the phases, corresponding a toolbox and lessons learned with the aim to support the engineering of a modelling method. ”The proposed approach is illustrated and validated within use cases from three different EU-funded research projects in the fields of (1 Industry 4.0, (2 e-learning and (3 cloud computing. The paper discusses the approach, the evaluation results and derived outlooks.

Mechatronic Systems Design Methods, Models, Concepts

CERN Document Server

Janschek, Klaus

2012-01-01

In this textbook, fundamental methods for model-based design of mechatronic systems are presented in a systematic, comprehensive form. The method framework presented here comprises domain-neutral methods for modeling and performance analysis: multi-domain modeling (energy/port/signal-based), simulation (ODE/DAE/hybrid systems), robust control methods, stochastic dynamic analysis, and quantitative evaluation of designs using system budgets. The model framework is composed of analytical dynamic models for important physical and technical domains of realization of mechatronic functions, such as multibody dynamics, digital information processing and electromechanical transducers. Building on the modeling concept of a technology-independent generic mechatronic transducer, concrete formulations for electrostatic, piezoelectric, electromagnetic, and electrodynamic transducers are presented. More than 50 fully worked out design examples clearly illustrate these methods and concepts and enable independent study of th...

A Pattern-Oriented Approach to a Methodical Evaluation of Modeling Methods

Directory of Open Access Journals (Sweden)

Michael Amberg

1996-11-01

Full Text Available The paper describes a pattern-oriented approach to evaluate modeling methods and to compare various methods with each other from a methodical viewpoint. A specific set of principles (the patterns is defined by investigating the notations and the documentation of comparable modeling methods. Each principle helps to examine some parts of the methods from a specific point of view. All principles together lead to an overall picture of the method under examination. First the core ("method neutral" meaning of each principle is described. Then the methods are examined regarding the principle. Afterwards the method specific interpretations are compared with each other and with the core meaning of the principle. By this procedure, the strengths and weaknesses of modeling methods regarding methodical aspects are identified. The principles are described uniformly using a principle description template according to descriptions of object oriented design patterns. The approach is demonstrated by evaluating a business process modeling method.

Graph modeling systems and methods

Science.gov (United States)

Neergaard, Mike

2015-10-13

An apparatus and a method for vulnerability and reliability modeling are provided. The method generally includes constructing a graph model of a physical network using a computer, the graph model including a plurality of terminating vertices to represent nodes in the physical network, a plurality of edges to represent transmission paths in the physical network, and a non-terminating vertex to represent a non-nodal vulnerability along a transmission path in the physical network. The method additionally includes evaluating the vulnerability and reliability of the physical network using the constructed graph model, wherein the vulnerability and reliability evaluation includes a determination of whether each terminating and non-terminating vertex represents a critical point of failure. The method can be utilized to evaluate wide variety of networks, including power grid infrastructures, communication network topologies, and fluid distribution systems.

Electromagnetic modeling method for eddy current signal analysis

International Nuclear Information System (INIS)

Lee, D. H.; Jung, H. K.; Cheong, Y. M.; Lee, Y. S.; Huh, H.; Yang, D. J.

2004-10-01

An electromagnetic modeling method for eddy current signal analysis is necessary before an experiment is performed. Electromagnetic modeling methods consists of the analytical method and the numerical method. Also, the numerical methods can be divided by Finite Element Method(FEM), Boundary Element Method(BEM) and Volume Integral Method(VIM). Each modeling method has some merits and demerits. Therefore, the suitable modeling method can be chosen by considering the characteristics of each modeling. This report explains the principle and application of each modeling method and shows the comparison modeling programs

Combining static and dynamic modelling methods: a comparison of four methods

NARCIS (Netherlands)

Wieringa, Roelf J.

1995-01-01

A conceptual model of a system is an explicit description of the behaviour required of the system. Methods for conceptual modelling include entity-relationship (ER) modelling, data flow modelling, Jackson System Development (JSD) and several object-oriented analysis method. Given the current

Methods for testing transport models

International Nuclear Information System (INIS)

Singer, C.; Cox, D.

1993-01-01

This report documents progress to date under a three-year contract for developing ''Methods for Testing Transport Models.'' The work described includes (1) choice of best methods for producing ''code emulators'' for analysis of very large global energy confinement databases, (2) recent applications of stratified regressions for treating individual measurement errors as well as calibration/modeling errors randomly distributed across various tokamaks, (3) Bayesian methods for utilizing prior information due to previous empirical and/or theoretical analyses, (4) extension of code emulator methodology to profile data, (5) application of nonlinear least squares estimators to simulation of profile data, (6) development of more sophisticated statistical methods for handling profile data, (7) acquisition of a much larger experimental database, and (8) extensive exploratory simulation work on a large variety of discharges using recently improved models for transport theories and boundary conditions. From all of this work, it has been possible to define a complete methodology for testing new sets of reference transport models against much larger multi-institutional databases

A Bayesian statistical method for quantifying model form uncertainty and two model combination methods

International Nuclear Information System (INIS)

Park, Inseok; Grandhi, Ramana V.

2014-01-01

Apart from parametric uncertainty, model form uncertainty as well as prediction error may be involved in the analysis of engineering system. Model form uncertainty, inherently existing in selecting the best approximation from a model set cannot be ignored, especially when the predictions by competing models show significant differences. In this research, a methodology based on maximum likelihood estimation is presented to quantify model form uncertainty using the measured differences of experimental and model outcomes, and is compared with a fully Bayesian estimation to demonstrate its effectiveness. While a method called the adjustment factor approach is utilized to propagate model form uncertainty alone into the prediction of a system response, a method called model averaging is utilized to incorporate both model form uncertainty and prediction error into it. A numerical problem of concrete creep is used to demonstrate the processes for quantifying model form uncertainty and implementing the adjustment factor approach and model averaging. Finally, the presented methodology is applied to characterize the engineering benefits of a laser peening process

Variational methods in molecular modeling

CERN Document Server

2017-01-01

This book presents tutorial overviews for many applications of variational methods to molecular modeling. Topics discussed include the Gibbs-Bogoliubov-Feynman variational principle, square-gradient models, classical density functional theories, self-consistent-field theories, phase-field methods, Ginzburg-Landau and Helfrich-type phenomenological models, dynamical density functional theory, and variational Monte Carlo methods. Illustrative examples are given to facilitate understanding of the basic concepts and quantitative prediction of the properties and rich behavior of diverse many-body systems ranging from inhomogeneous fluids, electrolytes and ionic liquids in micropores, colloidal dispersions, liquid crystals, polymer blends, lipid membranes, microemulsions, magnetic materials and high-temperature superconductors. All chapters are written by leading experts in the field and illustrated with tutorial examples for their practical applications to specific subjects. With emphasis placed on physical unders...

Tectonic implications of a paleomagnetic direction obtained from a Miocene dike swarm in central Honshu, Japan

Science.gov (United States)

Hoshi, H.; Sugisaki, Y.

2017-12-01

Central Honshu of Japan is an ideal field for the study of crustal deformation related to arc-arc collision. In this study we obtained rock magnetic and paleomagnetic results from early Miocene igneous rocks in central Honshu in order to examine rotational deformation caused by the collision of the Izu-Bonin-Mariana (IBM) arc with central Honshu. In Takane of the Hida region, gabbro intrusions and older sedimentary rocks are intruded by numerous andesitic dikes that comprise a parallel dike swarm. The dikes formed under two different normal-faulting paleostress conditions, which were suggested using a method of clustering dike orientations. Cross-cutting relationships indicate that the two paleostress conditions existed during the same period. More than 240 oriented cores were taken at 38 sites in two localities for magnetic study. The andesites and gabbros generally have magnetite, and some andesites also contain pyrrhotite. The magnetite records easterly deflected remanent magnetization directions of dual polarities that pass the reversals test. Positive baked contact tests at two sites demonstrate that the easterly deflected direction is a thermoremanent magnetization acquired at the time of intrusion. The overall in situ (i.e., in geographic coordinates) mean direction for andesitic dikes is judged to be highly reliable, although there are two possible scenarios for explaining the easterly deflection: (1) clockwise rotation and (2) tilting to the northwest. We prefer the former scenario and conclude that 45° clockwise rotation occurred in Takane with respect to the North China Block of the Asian continent. This rotation must represent the clockwise rotation of entire Southwest Japan during the opening period of the Japan Sea. Very little difference is observed between the amount of the easterly deflection in Takane and those in the Tokai and Hokuriku regions, indicating no significant relative rotation. Thus, the crust beneath Takane has not suffered rotation

Object Oriented Modeling : A method for combining model and software development

NARCIS (Netherlands)

Van Lelyveld, W.

2010-01-01

When requirements for a new model cannot be met by available modeling software, new software can be developed for a specific model. Methods for the development of both model and software exist, but a method for combined development has not been found. A compatible way of thinking is required to

Shell model Monte Carlo methods

International Nuclear Information System (INIS)

Koonin, S.E.

1996-01-01

We review quantum Monte Carlo methods for dealing with large shell model problems. These methods reduce the imaginary-time many-body evolution operator to a coherent superposition of one-body evolutions in fluctuating one-body fields; resultant path integral is evaluated stochastically. We first discuss the motivation, formalism, and implementation of such Shell Model Monte Carlo methods. There then follows a sampler of results and insights obtained from a number of applications. These include the ground state and thermal properties of pf-shell nuclei, thermal behavior of γ-soft nuclei, and calculation of double beta-decay matrix elements. Finally, prospects for further progress in such calculations are discussed. 87 refs

Multivariate analysis: models and method

International Nuclear Information System (INIS)

Sanz Perucha, J.

1990-01-01

Data treatment techniques are increasingly used since computer methods result of wider access. Multivariate analysis consists of a group of statistic methods that are applied to study objects or samples characterized by multiple values. A final goal is decision making. The paper describes the models and methods of multivariate analysis

Numerical methods and modelling for engineering

CERN Document Server

Khoury, Richard

2016-01-01

This textbook provides a step-by-step approach to numerical methods in engineering modelling. The authors provide a consistent treatment of the topic, from the ground up, to reinforce for students that numerical methods are a set of mathematical modelling tools which allow engineers to represent real-world systems and compute features of these systems with a predictable error rate. Each method presented addresses a specific type of problem, namely root-finding, optimization, integral, derivative, initial value problem, or boundary value problem, and each one encompasses a set of algorithms to solve the problem given some information and to a known error bound. The authors demonstrate that after developing a proper model and understanding of the engineering situation they are working on, engineers can break down a model into a set of specific mathematical problems, and then implement the appropriate numerical methods to solve these problems. Uses a “building-block” approach, starting with simpler mathemati...

Shell model Monte Carlo methods

International Nuclear Information System (INIS)

Koonin, S.E.; Dean, D.J.; Langanke, K.

1997-01-01

We review quantum Monte Carlo methods for dealing with large shell model problems. These methods reduce the imaginary-time many-body evolution operator to a coherent superposition of one-body evolutions in fluctuating one-body fields; the resultant path integral is evaluated stochastically. We first discuss the motivation, formalism, and implementation of such Shell Model Monte Carlo (SMMC) methods. There then follows a sampler of results and insights obtained from a number of applications. These include the ground state and thermal properties of pf-shell nuclei, the thermal and rotational behavior of rare-earth and γ-soft nuclei, and the calculation of double beta-decay matrix elements. Finally, prospects for further progress in such calculations are discussed. (orig.)

A Method of Upgrading a Hydrostatic Model to a Nonhydrostatic Model

Directory of Open Access Journals (Sweden)

Chi-Sann Liou

2009-01-01

Full Text Available As the sigma-p coordinate under hydrostatic approximation can be interpreted as the mass coordinate with out the hydro static approximation, we propose a method that up grades a hydro static model to a nonhydrostatic model with relatively less effort. The method adds to the primitive equations the extra terms omitted by the hydro static approximation and two prognostic equations for vertical speed w and nonhydrostatic part pres sure p'. With properly formulated governing equations, at each time step, the dynamic part of the model is first integrated as that for the original hydro static model and then nonhydrostatic contributions are added as corrections to the hydro static solutions. In applying physical parameterizations after the dynamic part integration, all physics pack ages of the original hydro static model can be directly used in the nonhydrostatic model, since the up graded nonhydrostatic model shares the same vertical coordinates with the original hydro static model. In this way, the majority codes of the nonhydrostatic model come from the original hydro static model. The extra codes are only needed for the calculation additional to the primitive equations. In order to handle sound waves, we use smaller time steps in the nonhydrostatic part dynamic time integration with a split-explicit scheme for horizontal momentum and temperature and a semi-implicit scheme for w and p'. Simulations of 2-dimensional mountain waves and density flows associated with a cold bubble have been used to test the method. The idealized case tests demonstrate that the pro posed method realistically simulates the nonhydrostatic effects on different atmospheric circulations that are revealed in the oretical solutions and simulations from other nonhydrostatic models. This method can be used in upgrading any global or mesoscale models from a hydrostatic to nonhydrostatic model.

Diverse methods for integrable models

NARCIS (Netherlands)

Fehér, G.

2017-01-01

This thesis is centered around three topics, sharing integrability as a common theme. This thesis explores different methods in the field of integrable models. The first two chapters are about integrable lattice models in statistical physics. The last chapter describes an integrable quantum chain.

CAD-based automatic modeling method for Geant4 geometry model through MCAM

International Nuclear Information System (INIS)

Wang, D.; Nie, F.; Wang, G.; Long, P.; LV, Z.

2013-01-01

The full text of publication follows. Geant4 is a widely used Monte Carlo transport simulation package. Before calculating using Geant4, the calculation model need be established which could be described by using Geometry Description Markup Language (GDML) or C++ language. However, it is time-consuming and error-prone to manually describe the models by GDML. Automatic modeling methods have been developed recently, but there are some problems that exist in most present modeling programs, specially some of them were not accurate or adapted to specifically CAD format. To convert the GDML format models to CAD format accurately, a Geant4 Computer Aided Design (CAD) based modeling method was developed for automatically converting complex CAD geometry model into GDML geometry model. The essence of this method was dealing with CAD model represented with boundary representation (B-REP) and GDML model represented with constructive solid geometry (CSG). At first, CAD model was decomposed to several simple solids which had only one close shell. And then the simple solid was decomposed to convex shell set. Then corresponding GDML convex basic solids were generated by the boundary surfaces getting from the topological characteristic of a convex shell. After the generation of these solids, GDML model was accomplished with series boolean operations. This method was adopted in CAD/Image-based Automatic Modeling Program for Neutronics and Radiation Transport (MCAM), and tested with several models including the examples in Geant4 install package. The results showed that this method could convert standard CAD model accurately, and can be used for Geant4 automatic modeling. (authors)

Near N-S paleo-extension in the western Deccan region, India: Does it link strike-slip tectonics with India-Seychelles rifting?

Science.gov (United States)

Misra, Achyuta Ayan; Bhattacharya, Gourab; Mukherjee, Soumyajit; Bose, Narayan

2014-09-01

This is the first detailed report and analyses of deformation from the W part of the Deccan large igneous province (DLIP), Maharashtra, India. This deformation, related to the India-Seychelles rifting during Late Cretaceous-Early Paleocene, was studied, and the paleostress tensors were deduced. Near N-S trending shear zones, lineaments, and faults were already reported without significant detail. An E-W extension was envisaged by the previous workers to explain the India-Seychelles rift at ~64 Ma. The direction of extension, however, does not match with their N-S brittle shear zones and also those faults (sub-vertical, ~NE-SW/~NW-SE, and few ~N-S) we report and emphasize in this work. Slickenside-bearing fault planes, brittle shear zones, and extension fractures in meso-scale enabled us to estimate the paleostress tensors (directions and relative magnitudes). The field study was complemented by remote sensing lineament analyses to map dykes and shear zones. Dykes emplaced along pre-existing ~N-S to ~NE-SW/~NW-SE shears/fractures. This information was used to derive regional paleostress trends. A ~NW-SE/NE-SW minimum compressive stress in the oldest Kalsubai Subgroup and a ~N-S direction for the younger Lonavala, Wai, and Salsette Subgroups were deciphered. Thus, a ~NW/NE to ~N-S extension is put forward that refutes the popular view of E-W India-Seychelles extension. Paleostress analyses indicate that this is an oblique rifted margin. Field criteria suggest only ~NE-SW and ~NW-SE, with some ~N-S strike-slip faults/brittle shear zones. We refer this deformation zone as the "Western Deccan Strike-slip Zone" (WDSZ). The observed deformation was matched with offshore tectonics deciphered mainly from faults interpreted on seismic profiles and from magnetic seafloor spreading anomalies. These geophysical findings too indicate oblique rifting in this part of the W Indian passive margin. We argue that the Seychelles microcontinent separated from India only after much of

A Systematic Identification Method for Thermodynamic Property Modelling

DEFF Research Database (Denmark)

Ana Perederic, Olivia; Cunico, Larissa; Sarup, Bent

2017-01-01

In this work, a systematic identification method for thermodynamic property modelling is proposed. The aim of the method is to improve the quality of phase equilibria prediction by group contribution based property prediction models. The method is applied to lipid systems where the Original UNIFAC...... model is used. Using the proposed method for estimating the interaction parameters using only VLE data, a better phase equilibria prediction for both VLE and SLE was obtained. The results were validated and compared with the original model performance...

A Method for Model Checking Feature Interactions

DEFF Research Database (Denmark)

Pedersen, Thomas; Le Guilly, Thibaut; Ravn, Anders Peter

2015-01-01

This paper presents a method to check for feature interactions in a system assembled from independently developed concurrent processes as found in many reactive systems. The method combines and refines existing definitions and adds a set of activities. The activities describe how to populate the ...... the definitions with models to ensure that all interactions are captured. The method is illustrated on a home automation example with model checking as analysis tool. In particular, the modelling formalism is timed automata and the analysis uses UPPAAL to find interactions....

Computer-Aided Modelling Methods and Tools

DEFF Research Database (Denmark)

Cameron, Ian; Gani, Rafiqul

2011-01-01

The development of models for a range of applications requires methods and tools. In many cases a reference model is required that allows the generation of application specific models that are fit for purpose. There are a range of computer aided modelling tools available that help to define the m...

TRAC methods and models

International Nuclear Information System (INIS)

Mahaffy, J.H.; Liles, D.R.; Bott, T.F.

1981-01-01

The numerical methods and physical models used in the Transient Reactor Analysis Code (TRAC) versions PD2 and PF1 are discussed. Particular emphasis is placed on TRAC-PF1, the version specifically designed to analyze small-break loss-of-coolant accidents

A business case method for business models

OpenAIRE

Meertens, Lucas Onno; Starreveld, E.; Iacob, Maria Eugenia; Nieuwenhuis, Lambertus Johannes Maria; Shishkov, Boris

2013-01-01

Intuitively, business cases and business models are closely connected. However, a thorough literature review revealed no research on the combination of them. Besides that, little is written on the evaluation of business models at all. This makes it difficult to compare different business model alternatives and choose the best one. In this article, we develop a business case method to objectively compare business models. It is an eight-step method, starting with business drivers and ending wit...

Model Uncertainty Quantification Methods In Data Assimilation

Science.gov (United States)

Pathiraja, S. D.; Marshall, L. A.; Sharma, A.; Moradkhani, H.

2017-12-01

Data Assimilation involves utilising observations to improve model predictions in a seamless and statistically optimal fashion. Its applications are wide-ranging; from improving weather forecasts to tracking targets such as in the Apollo 11 mission. The use of Data Assimilation methods in high dimensional complex geophysical systems is an active area of research, where there exists many opportunities to enhance existing methodologies. One of the central challenges is in model uncertainty quantification; the outcome of any Data Assimilation study is strongly dependent on the uncertainties assigned to both observations and models. I focus on developing improved model uncertainty quantification methods that are applicable to challenging real world scenarios. These include developing methods for cases where the system states are only partially observed, where there is little prior knowledge of the model errors, and where the model error statistics are likely to be highly non-Gaussian.

Analytical methods used at model facility

International Nuclear Information System (INIS)

Wing, N.S.

1984-01-01

A description of analytical methods used at the model LEU Fuel Fabrication Facility is presented. The methods include gravimetric uranium analysis, isotopic analysis, fluorimetric analysis, and emission spectroscopy

Projevy posunů po vrstevních plochách v křídových pískovcích Podbezdězí

Czech Academy of Sciences Publication Activity Database

Adamovič, Jiří; Coubal, Miroslav

2012-01-01

Roč. 2011, podzim (2012), s. 9-12 ISSN 0514-8057 Institutional research plan: CEZ:AV0Z30130516 Keywords : bedding -plane slip movements * paleostress analysis * Bohemian Cretaceous Basin Subject RIV: DB - Geology ; Mineralogy http://www.geology.cz/zpravy/obsah/2011/zpravy_2011-1.pdf

The results of borehole acoustic imaging from a granite in the Jihlava District, Czech Republic: implications for structural geological research

Czech Academy of Sciences Publication Activity Database

Nováková, Lucie; Novák, P.; Brož, Milan; Sosna, K.; Pitrák, K.; Kasíková, J.; Rukavičková, L.; Maňák, L.

2012-01-01

Roč. 4, č. 4 (2012), s. 92-101 ISSN 1916-9779 R&D Projects: GA MPO(CZ) FR-TI1/367 Institutional research plan: CEZ:AV0Z30460519 Keywords : acoustic borehole imaging * structural geology * paleostress analysis Subject RIV: DC - Siesmology, Volcanology, Earth Structure

Accuracy evaluation of dental models manufactured by CAD/CAM milling method and 3D printing method.

Science.gov (United States)

Jeong, Yoo-Geum; Lee, Wan-Sun; Lee, Kyu-Bok

2018-06-01

To evaluate the accuracy of a model made using the computer-aided design/computer-aided manufacture (CAD/CAM) milling method and 3D printing method and to confirm its applicability as a work model for dental prosthesis production. First, a natural tooth model (ANA-4, Frasaco, Germany) was scanned using an oral scanner. The obtained scan data were then used as a CAD reference model (CRM), to produce a total of 10 models each, either using the milling method or the 3D printing method. The 20 models were then scanned using a desktop scanner and the CAD test model was formed. The accuracy of the two groups was compared using dedicated software to calculate the root mean square (RMS) value after superimposing CRM and CAD test model (CTM). The RMS value (152±52 µm) of the model manufactured by the milling method was significantly higher than the RMS value (52±9 µm) of the model produced by the 3D printing method. The accuracy of the 3D printing method is superior to that of the milling method, but at present, both methods are limited in their application as a work model for prosthesis manufacture.

Extending product modeling methods for integrated product development

DEFF Research Database (Denmark)

Bonev, Martin; Wörösch, Michael; Hauksdóttir, Dagný

2013-01-01

Despite great efforts within the modeling domain, the majority of methods often address the uncommon design situation of an original product development. However, studies illustrate that development tasks are predominantly related to redesigning, improving, and extending already existing products...... and PVM methods, in a presented Product Requirement Development model some of the individual drawbacks of each method could be overcome. Based on the UML standard, the model enables the representation of complex hierarchical relationships in a generic product model. At the same time it uses matrix....... Updated design requirements have then to be made explicit and mapped against the existing product architecture. In this paper, existing methods are adapted and extended through linking updated requirements to suitable product models. By combining several established modeling techniques, such as the DSM...

Coherence method of identifying signal noise model

International Nuclear Information System (INIS)

Vavrin, J.

1981-01-01

The noise analysis method is discussed in identifying perturbance models and their parameters by a stochastic analysis of the noise model of variables measured on a reactor. The analysis of correlations is made in the frequency region using coherence analysis methods. In identifying an actual specific perturbance, its model should be determined and recognized in a compound model of the perturbance system using the results of observation. The determination of the optimum estimate of the perturbance system model is based on estimates of related spectral densities which are determined from the spectral density matrix of the measured variables. Partial and multiple coherence, partial transfers, the power spectral densities of the input and output variables of the noise model are determined from the related spectral densities. The possibilities of applying the coherence identification methods were tested on a simple case of a simulated stochastic system. Good agreement was found of the initial analytic frequency filters and the transfers identified. (B.S.)

Iterative method for Amado's model

International Nuclear Information System (INIS)

Tomio, L.

1980-01-01

A recently proposed iterative method for solving scattering integral equations is applied to the spin doublet and spin quartet neutron-deuteron scattering in the Amado model. The method is tested numerically in the calculation of scattering lengths and phase-shifts and results are found better than those obtained by using the conventional Pade technique. (Author) [pt

Methods for model selection in applied science and engineering.

Energy Technology Data Exchange (ETDEWEB)

Field, Richard V., Jr.

2004-10-01

Mathematical models are developed and used to study the properties of complex systems and/or modify these systems to satisfy some performance requirements in just about every area of applied science and engineering. A particular reason for developing a model, e.g., performance assessment or design, is referred to as the model use. Our objective is the development of a methodology for selecting a model that is sufficiently accurate for an intended use. Information on the system being modeled is, in general, incomplete, so that there may be two or more models consistent with the available information. The collection of these models is called the class of candidate models. Methods are developed for selecting the optimal member from a class of candidate models for the system. The optimal model depends on the available information, the selected class of candidate models, and the model use. Classical methods for model selection, including the method of maximum likelihood and Bayesian methods, as well as a method employing a decision-theoretic approach, are formulated to select the optimal model for numerous applications. There is no requirement that the candidate models be random. Classical methods for model selection ignore model use and require data to be available. Examples are used to show that these methods can be unreliable when data is limited. The decision-theoretic approach to model selection does not have these limitations, and model use is included through an appropriate utility function. This is especially important when modeling high risk systems, where the consequences of using an inappropriate model for the system can be disastrous. The decision-theoretic method for model selection is developed and applied for a series of complex and diverse applications. These include the selection of the: (1) optimal order of the polynomial chaos approximation for non-Gaussian random variables and stationary stochastic processes, (2) optimal pressure load model to be

Thermal Efficiency Degradation Diagnosis Method Using Regression Model

International Nuclear Information System (INIS)

Jee, Chang Hyun; Heo, Gyun Young; Jang, Seok Won; Lee, In Cheol

2011-01-01

This paper proposes an idea for thermal efficiency degradation diagnosis in turbine cycles, which is based on turbine cycle simulation under abnormal conditions and a linear regression model. The correlation between the inputs for representing degradation conditions (normally unmeasured but intrinsic states) and the simulation outputs (normally measured but superficial states) was analyzed with the linear regression model. The regression models can inversely response an associated intrinsic state for a superficial state observed from a power plant. The diagnosis method proposed herein is classified into three processes, 1) simulations for degradation conditions to get measured states (referred as what-if method), 2) development of the linear model correlating intrinsic and superficial states, and 3) determination of an intrinsic state using the superficial states of current plant and the linear regression model (referred as inverse what-if method). The what-if method is to generate the outputs for the inputs including various root causes and/or boundary conditions whereas the inverse what-if method is the process of calculating the inverse matrix with the given superficial states, that is, component degradation modes. The method suggested in this paper was validated using the turbine cycle model for an operating power plant

Advanced methods of solid oxide fuel cell modeling

CERN Document Server

Milewski, Jaroslaw; Santarelli, Massimo; Leone, Pierluigi

2011-01-01

Fuel cells are widely regarded as the future of the power and transportation industries. Intensive research in this area now requires new methods of fuel cell operation modeling and cell design. Typical mathematical models are based on the physical process description of fuel cells and require a detailed knowledge of the microscopic properties that govern both chemical and electrochemical reactions. ""Advanced Methods of Solid Oxide Fuel Cell Modeling"" proposes the alternative methodology of generalized artificial neural networks (ANN) solid oxide fuel cell (SOFC) modeling. ""Advanced Methods

SELECT NUMERICAL METHODS FOR MODELING THE DYNAMICS SYSTEMS

Directory of Open Access Journals (Sweden)

Tetiana D. Panchenko

2016-07-01

Full Text Available The article deals with the creation of methodical support for mathematical modeling of dynamic processes in elements of the systems and complexes. As mathematical models ordinary differential equations have been used. The coefficients of the equations of the models can be nonlinear functions of the process. The projection-grid method is used as the main tool. It has been described iterative method algorithms taking into account the approximate solution prior to the first iteration and proposed adaptive control computing process. The original method of estimation error in the calculation solutions as well as for a given level of error of the technique solutions purpose adaptive method for solving configuration parameters is offered. A method for setting an adaptive method for solving the settings for a given level of error is given. The proposed method can be used for distributed computing.

A catalog of automated analysis methods for enterprise models.

Science.gov (United States)

Florez, Hector; Sánchez, Mario; Villalobos, Jorge

2016-01-01

Enterprise models are created for documenting and communicating the structure and state of Business and Information Technologies elements of an enterprise. After models are completed, they are mainly used to support analysis. Model analysis is an activity typically based on human skills and due to the size and complexity of the models, this process can be complicated and omissions or miscalculations are very likely. This situation has fostered the research of automated analysis methods, for supporting analysts in enterprise analysis processes. By reviewing the literature, we found several analysis methods; nevertheless, they are based on specific situations and different metamodels; then, some analysis methods might not be applicable to all enterprise models. This paper presents the work of compilation (literature review), classification, structuring, and characterization of automated analysis methods for enterprise models, expressing them in a standardized modeling language. In addition, we have implemented the analysis methods in our modeling tool.

Accurate Modeling Method for Cu Interconnect

Science.gov (United States)

Yamada, Kenta; Kitahara, Hiroshi; Asai, Yoshihiko; Sakamoto, Hideo; Okada, Norio; Yasuda, Makoto; Oda, Noriaki; Sakurai, Michio; Hiroi, Masayuki; Takewaki, Toshiyuki; Ohnishi, Sadayuki; Iguchi, Manabu; Minda, Hiroyasu; Suzuki, Mieko

This paper proposes an accurate modeling method of the copper interconnect cross-section in which the width and thickness dependence on layout patterns and density caused by processes (CMP, etching, sputtering, lithography, and so on) are fully, incorporated and universally expressed. In addition, we have developed specific test patterns for the model parameters extraction, and an efficient extraction flow. We have extracted the model parameters for 0.15μm CMOS using this method and confirmed that 10%τpd error normally observed with conventional LPE (Layout Parameters Extraction) was completely dissolved. Moreover, it is verified that the model can be applied to more advanced technologies (90nm, 65nm and 55nm CMOS). Since the interconnect delay variations due to the processes constitute a significant part of what have conventionally been treated as random variations, use of the proposed model could enable one to greatly narrow the guardbands required to guarantee a desired yield, thereby facilitating design closure.

Structural equation modeling methods and applications

CERN Document Server

Wang, Jichuan

2012-01-01

A reference guide for applications of SEM using Mplus Structural Equation Modeling: Applications Using Mplus is intended as both a teaching resource and a reference guide. Written in non-mathematical terms, this book focuses on the conceptual and practical aspects of Structural Equation Modeling (SEM). Basic concepts and examples of various SEM models are demonstrated along with recently developed advanced methods, such as mixture modeling and model-based power analysis and sample size estimate for SEM. The statistical modeling program, Mplus, is also featured and provides researchers with a

Investigating the performance of directional boundary layer model through staged modeling method

Science.gov (United States)

Jeong, Moon-Gyu; Lee, Won-Chan; Yang, Seung-Hune; Jang, Sung-Hoon; Shim, Seong-Bo; Kim, Young-Chang; Suh, Chun-Suk; Choi, Seong-Woon; Kim, Young-Hee

2011-04-01

Generally speaking, the models used in the optical proximity effect correction (OPC) can be divided into three parts, mask part, optic part, and resist part. For the excellent quality of the OPC model, each part has to be described by the first principles. However, OPC model can't take the all of the principles since it should cover the full chip level calculation during the correction. Moreover, the calculation has to be done iteratively during the correction until the cost function we want to minimize converges. Normally the optic part in OPC model is described with the sum of coherent system (SOCS[1]) method. Thanks to this method we can calculate the aerial image so fast without the significant loss of accuracy. As for the resist part, the first principle is too complex to implement in detail, so it is normally expressed in a simple way, such as the approximation of the first principles, and the linear combinations of factors which is highly correlated with the chemistries in the resist. The quality of this kind of the resist model depends on how well we train the model through fitting to the empirical data. The most popular way of making the mask function is based on the Kirchhoff's thin mask approximation. This method works well when the feature size on the mask is sufficiently large, but as the line width of the semiconductor circuit becomes smaller, this method causes significant error due to the mask topography effect. To consider the mask topography effect accurately, we have to use rigorous methods of calculating the mask function, such as finite difference time domain (FDTD[2]) and rigorous coupled-wave analysis (RCWA[3]). But these methods are too time-consuming to be used as a part of the OPC model. Until now many alternatives have been suggested as the efficient way of considering the mask topography effect. Among them we focused on the boundary layer model (BLM) in this paper. We mainly investigated the way of optimization of the parameters for the

A Model-Driven Development Method for Management Information Systems

Science.gov (United States)

Mizuno, Tomoki; Matsumoto, Keinosuke; Mori, Naoki

Traditionally, a Management Information System (MIS) has been developed without using formal methods. By the informal methods, the MIS is developed on its lifecycle without having any models. It causes many problems such as lack of the reliability of system design specifications. In order to overcome these problems, a model theory approach was proposed. The approach is based on an idea that a system can be modeled by automata and set theory. However, it is very difficult to generate automata of the system to be developed right from the start. On the other hand, there is a model-driven development method that can flexibly correspond to changes of business logics or implementing technologies. In the model-driven development, a system is modeled using a modeling language such as UML. This paper proposes a new development method for management information systems applying the model-driven development method to a component of the model theory approach. The experiment has shown that a reduced amount of efforts is more than 30% of all the efforts.

How Qualitative Methods Can be Used to Inform Model Development.

Science.gov (United States)

Husbands, Samantha; Jowett, Susan; Barton, Pelham; Coast, Joanna

2017-06-01

Decision-analytic models play a key role in informing healthcare resource allocation decisions. However, there are ongoing concerns with the credibility of models. Modelling methods guidance can encourage good practice within model development, but its value is dependent on its ability to address the areas that modellers find most challenging. Further, it is important that modelling methods and related guidance are continually updated in light of any new approaches that could potentially enhance model credibility. The objective of this article was to highlight the ways in which qualitative methods have been used and recommended to inform decision-analytic model development and enhance modelling practices. With reference to the literature, the article discusses two key ways in which qualitative methods can be, and have been, applied. The first approach involves using qualitative methods to understand and inform general and future processes of model development, and the second, using qualitative techniques to directly inform the development of individual models. The literature suggests that qualitative methods can improve the validity and credibility of modelling processes by providing a means to understand existing modelling approaches that identifies where problems are occurring and further guidance is needed. It can also be applied within model development to facilitate the input of experts to structural development. We recommend that current and future model development would benefit from the greater integration of qualitative methods, specifically by studying 'real' modelling processes, and by developing recommendations around how qualitative methods can be adopted within everyday modelling practice.

Paleomagnetic and structural evidence for oblique slip in a fault-related fold, Grayback monocline, Colorado

Science.gov (United States)

Tetreault, J.; Jones, C.H.; Erslev, E.; Larson, S.; Hudson, M.; Holdaway, S.

2008-01-01

Significant fold-axis-parallel slip is accommodated in the folded strata of the Grayback monocline, northeastern Front Range, Colorado, without visible large strike-slip displacement on the fold surface. In many cases, oblique-slip deformation is partitioned; fold-axis-normal slip is accommodated within folds, and fold-axis-parallel slip is resolved onto adjacent strike-slip faults. Unlike partitioning strike-parallel slip onto adjacent strike-slip faults, fold-axis-parallel slip has deformed the forelimb of the Grayback monocline. Mean compressive paleostress orientations in the forelimb are deflected 15??-37?? clockwise from the regional paleostress orientation of the northeastern Front Range. Paleomagnetic directions from the Permian Ingleside Formation in the forelimb are rotated 16??-42?? clockwise about a bedding-normal axis relative to the North American Permian reference direction. The paleostress and paleomagnetic rotations increase with the bedding dip angle and decrease along strike toward the fold tip. These measurements allow for 50-120 m of fold-axis-parallel slip within the forelimb, depending on the kinematics of strike-slip shear. This resolved horizontal slip is nearly equal in magnitude to the ???180 m vertical throw across the fold. For 200 m of oblique-slip displacement (120 m of strike slip and 180 m of reverse slip), the true shortening direction across the fold is N90??E, indistinguishable from the regionally inferred direction of N90??E and quite different from the S53??E fold-normal direction. Recognition of this deformational style means that significant amounts of strike slip can be accommodated within folds without axis-parallel surficial faulting. ?? 2008 Geological Society of America.

Dynamic modeling method for infrared smoke based on enhanced discrete phase model

Science.gov (United States)

Zhang, Zhendong; Yang, Chunling; Zhang, Yan; Zhu, Hongbo

2018-03-01

The dynamic modeling of infrared (IR) smoke plays an important role in IR scene simulation systems and its accuracy directly influences the system veracity. However, current IR smoke models cannot provide high veracity, because certain physical characteristics are frequently ignored in fluid simulation; simplifying the discrete phase as a continuous phase and ignoring the IR decoy missile-body spinning. To address this defect, this paper proposes a dynamic modeling method for IR smoke, based on an enhanced discrete phase model (DPM). A mathematical simulation model based on an enhanced DPM is built and a dynamic computing fluid mesh is generated. The dynamic model of IR smoke is then established using an extended equivalent-blackbody-molecule model. Experiments demonstrate that this model realizes a dynamic method for modeling IR smoke with higher veracity.

A Comparison of Surface Acoustic Wave Modeling Methods

Science.gov (United States)

Wilson, W. c.; Atkinson, G. M.

2009-01-01

Surface Acoustic Wave (SAW) technology is low cost, rugged, lightweight, extremely low power and can be used to develop passive wireless sensors. For these reasons, NASA is investigating the use of SAW technology for Integrated Vehicle Health Monitoring (IVHM) of aerospace structures. To facilitate rapid prototyping of passive SAW sensors for aerospace applications, SAW models have been developed. This paper reports on the comparison of three methods of modeling SAWs. The three models are the Impulse Response Method a first order model, and two second order matrix methods; the conventional matrix approach, and a modified matrix approach that is extended to include internal finger reflections. The second order models are based upon matrices that were originally developed for analyzing microwave circuits using transmission line theory. Results from the models are presented with measured data from devices.

CAD-based Monte Carlo automatic modeling method based on primitive solid

International Nuclear Information System (INIS)

Wang, Dong; Song, Jing; Yu, Shengpeng; Long, Pengcheng; Wang, Yongliang

2016-01-01

Highlights: • We develop a method which bi-convert between CAD model and primitive solid. • This method was improved from convert method between CAD model and half space. • This method was test by ITER model and validated the correctness and efficiency. • This method was integrated in SuperMC which could model for SuperMC and Geant4. - Abstract: Monte Carlo method has been widely used in nuclear design and analysis, where geometries are described with primitive solids. However, it is time consuming and error prone to describe a primitive solid geometry, especially for a complicated model. To reuse the abundant existed CAD models and conveniently model with CAD modeling tools, an automatic modeling method for accurate prompt modeling between CAD model and primitive solid is needed. An automatic modeling method for Monte Carlo geometry described by primitive solid was developed which could bi-convert between CAD model and Monte Carlo geometry represented by primitive solids. While converting from CAD model to primitive solid model, the CAD model was decomposed into several convex solid sets, and then corresponding primitive solids were generated and exported. While converting from primitive solid model to the CAD model, the basic primitive solids were created and related operation was done. This method was integrated in the SuperMC and was benchmarked with ITER benchmark model. The correctness and efficiency of this method were demonstrated.

Modelling a coal subcrop using the impedance method

Energy Technology Data Exchange (ETDEWEB)

Wilson, G.A.; Thiel, D.V.; O' Keefe, S.G. [Griffith University, Nathan, Qld. (Australia). School of Microelectronic Engineering

2000-07-01

An impedance model was generated for two coal subcrops in the Biloela and Middlemount areas (Queensland, Australia). The model results were compared with actual surface impedance data. It was concluded that the impedance method satisfactorily modelled the surface response of the coal subcrops in two dimensions. There were some discrepancies between the field data and the model results, due to factors such as the method of discretization of the solution space in the impedance model and the lack of consideration of the three-dimensional nature of the coal outcrops. 10 refs., 8 figs.

Tectonic phase separation applied to the Sudetic Marginal Fault Zone (NE part of the Czech Republic)

Czech Academy of Sciences Publication Activity Database

Nováková, Lucie

2015-01-01

Roč. 12, č. 2 (2015), s. 251-267 ISSN 1672-6316 R&D Projects: GA ČR GA205/09/1244 Institutional support: RVO:67985891 Keywords : Sudetic Marginal Fault Zone * paleostress reconstruction * active tectonics * frequency analysis Subject RIV: DC - Siesmology, Volcanology, Earth Structure Impact factor: 1.017, year: 2015

Kink bands in thrust regime: Examples from Srinagar–Garhwal area ...

Indian Academy of Sciences (India)

Srinagar–Garhwal; kink bands; paleostress; shearing along kink planes; Himalaya; North Almora Thrust. J. Earth Syst. Sci. 120, No. 5, October 2011, pp. 939–948 ..... The α>β geometry observed in kink bands suggest towards shearing as a common phenomenon along the kink planes. (Anderson 1964). Since considerable ...

Reflexion on linear regression trip production modelling method for ensuring good model quality

Science.gov (United States)

Suprayitno, Hitapriya; Ratnasari, Vita

2017-11-01

Transport Modelling is important. For certain cases, the conventional model still has to be used, in which having a good trip production model is capital. A good model can only be obtained from a good sample. Two of the basic principles of a good sampling is having a sample capable to represent the population characteristics and capable to produce an acceptable error at a certain confidence level. It seems that this principle is not yet quite understood and used in trip production modeling. Therefore, investigating the Trip Production Modelling practice in Indonesia and try to formulate a better modeling method for ensuring the Model Quality is necessary. This research result is presented as follows. Statistics knows a method to calculate span of prediction value at a certain confidence level for linear regression, which is called Confidence Interval of Predicted Value. The common modeling practice uses R2 as the principal quality measure, the sampling practice varies and not always conform to the sampling principles. An experiment indicates that small sample is already capable to give excellent R2 value and sample composition can significantly change the model. Hence, good R2 value, in fact, does not always mean good model quality. These lead to three basic ideas for ensuring good model quality, i.e. reformulating quality measure, calculation procedure, and sampling method. A quality measure is defined as having a good R2 value and a good Confidence Interval of Predicted Value. Calculation procedure must incorporate statistical calculation method and appropriate statistical tests needed. A good sampling method must incorporate random well distributed stratified sampling with a certain minimum number of samples. These three ideas need to be more developed and tested.

Modeling shallow water flows using the discontinuous Galerkin method

CERN Document Server

Khan, Abdul A

2014-01-01

Replacing the Traditional Physical Model Approach Computational models offer promise in improving the modeling of shallow water flows. As new techniques are considered, the process continues to change and evolve. Modeling Shallow Water Flows Using the Discontinuous Galerkin Method examines a technique that focuses on hyperbolic conservation laws and includes one-dimensional and two-dimensional shallow water flows and pollutant transports. Combines the Advantages of Finite Volume and Finite Element Methods This book explores the discontinuous Galerkin (DG) method, also known as the discontinuous finite element method, in depth. It introduces the DG method and its application to shallow water flows, as well as background information for implementing and applying this method for natural rivers. It considers dam-break problems, shock wave problems, and flows in different regimes (subcritical, supercritical, and transcritical). Readily Adaptable to the Real World While the DG method has been widely used in the fie...

Structural modeling techniques by finite element method

International Nuclear Information System (INIS)

Kang, Yeong Jin; Kim, Geung Hwan; Ju, Gwan Jeong

1991-01-01

This book includes introduction table of contents chapter 1 finite element idealization introduction summary of the finite element method equilibrium and compatibility in the finite element solution degrees of freedom symmetry and anti symmetry modeling guidelines local analysis example references chapter 2 static analysis structural geometry finite element models analysis procedure modeling guidelines references chapter 3 dynamic analysis models for dynamic analysis dynamic analysis procedures modeling guidelines and modeling guidelines.

Model-Based Method for Sensor Validation

Science.gov (United States)

Vatan, Farrokh

2012-01-01

Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in situ platforms. One of NASA s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure. Sensor validation can be considered to be part of the larger effort of improving reliability and safety. The standard methods for solving the sensor validation problem are based on probabilistic analysis of the system, from which the method based on Bayesian networks is most popular. Therefore, these methods can only predict the most probable faulty sensors, which are subject to the initial probabilities defined for the failures. The method developed in this work is based on a model-based approach and provides the faulty sensors (if any), which can be logically inferred from the model of the system and the sensor readings (observations). The method is also more suitable for the systems when it is hard, or even impossible, to find the probability functions of the system. The method starts by a new mathematical description of the problem and develops a very efficient and systematic algorithm for its solution. The method builds on the concepts of analytical redundant relations (ARRs).

Estimation methods for nonlinear state-space models in ecology

DEFF Research Database (Denmark)

Pedersen, Martin Wæver; Berg, Casper Willestofte; Thygesen, Uffe Høgsbro

2011-01-01

The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta...... logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden...... Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance...

An Expectation-Maximization Method for Calibrating Synchronous Machine Models

Energy Technology Data Exchange (ETDEWEB)

Meng, Da; Zhou, Ning; Lu, Shuai; Lin, Guang

2013-07-21

The accuracy of a power system dynamic model is essential to its secure and efficient operation. Lower confidence in model accuracy usually leads to conservative operation and lowers asset usage. To improve model accuracy, this paper proposes an expectation-maximization (EM) method to calibrate the synchronous machine model using phasor measurement unit (PMU) data. First, an extended Kalman filter (EKF) is applied to estimate the dynamic states using measurement data. Then, the parameters are calculated based on the estimated states using maximum likelihood estimation (MLE) method. The EM method iterates over the preceding two steps to improve estimation accuracy. The proposed EM method’s performance is evaluated using a single-machine infinite bus system and compared with a method where both state and parameters are estimated using an EKF method. Sensitivity studies of the parameter calibration using EM method are also presented to show the robustness of the proposed method for different levels of measurement noise and initial parameter uncertainty.

Systems and methods for modeling and analyzing networks

Science.gov (United States)

Hill, Colin C; Church, Bruce W; McDonagh, Paul D; Khalil, Iya G; Neyarapally, Thomas A; Pitluk, Zachary W

2013-10-29

The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.

A Kriging Model Based Finite Element Model Updating Method for Damage Detection

Directory of Open Access Journals (Sweden)

Xiuming Yang

2017-10-01

Full Text Available Model updating is an effective means of damage identification and surrogate modeling has attracted considerable attention for saving computational cost in finite element (FE model updating, especially for large-scale structures. In this context, a surrogate model of frequency is normally constructed for damage identification, while the frequency response function (FRF is rarely used as it usually changes dramatically with updating parameters. This paper presents a new surrogate model based model updating method taking advantage of the measured FRFs. The Frequency Domain Assurance Criterion (FDAC is used to build the objective function, whose nonlinear response surface is constructed by the Kriging model. Then, the efficient global optimization (EGO algorithm is introduced to get the model updating results. The proposed method has good accuracy and robustness, which have been verified by a numerical simulation of a cantilever and experimental test data of a laboratory three-story structure.

Fault zone architecture within Miocene–Pliocene syn-rift sediments ...

Indian Academy of Sciences (India)

The present study focusses on field description of small normal fault zones in Upper Miocene–Pliocene sedimentary rocks on the northwestern side of the Red Sea, Egypt. The trend of these fault zones is mainly NW–SE. Paleostress analysis of 17 fault planes and slickenlines indicate that the tension direction is NE–SW.

Cache memory modelling method and system

OpenAIRE

Posadas Cobo, Héctor; Villar Bonet, Eugenio; Díaz Suárez, Luis

2011-01-01

The invention relates to a method for modelling a data cache memory of a destination processor, in order to simulate the behaviour of said data cache memory during the execution of a software code on a platform comprising said destination processor. According to the invention, the simulation is performed on a native platform having a processor different from the destination processor comprising the aforementioned data cache memory to be modelled, said modelling being performed by means of the...

SmartShadow models and methods for pervasive computing

CERN Document Server

Wu, Zhaohui

2013-01-01

SmartShadow: Models and Methods for Pervasive Computing offers a new perspective on pervasive computing with SmartShadow, which is designed to model a user as a personality ""shadow"" and to model pervasive computing environments as user-centric dynamic virtual personal spaces. Just like human beings' shadows in the physical world, it follows people wherever they go, providing them with pervasive services. The model, methods, and software infrastructure for SmartShadow are presented and an application for smart cars is also introduced.  The book can serve as a valuable reference work for resea

3D Face modeling using the multi-deformable method.

Science.gov (United States)

Hwang, Jinkyu; Yu, Sunjin; Kim, Joongrock; Lee, Sangyoun

2012-09-25

In this paper, we focus on the problem of the accuracy performance of 3D face modeling techniques using corresponding features in multiple views, which is quite sensitive to feature extraction errors. To solve the problem, we adopt a statistical model-based 3D face modeling approach in a mirror system consisting of two mirrors and a camera. The overall procedure of our 3D facial modeling method has two primary steps: 3D facial shape estimation using a multiple 3D face deformable model and texture mapping using seamless cloning that is a type of gradient-domain blending. To evaluate our method's performance, we generate 3D faces of 30 individuals and then carry out two tests: accuracy test and robustness test. Our method shows not only highly accurate 3D face shape results when compared with the ground truth, but also robustness to feature extraction errors. Moreover, 3D face rendering results intuitively show that our method is more robust to feature extraction errors than other 3D face modeling methods. An additional contribution of our method is that a wide range of face textures can be acquired by the mirror system. By using this texture map, we generate realistic 3D face for individuals at the end of the paper.

Design of nuclear power generation plants adopting model engineering method

International Nuclear Information System (INIS)

Waki, Masato

1983-01-01

The utilization of model engineering as the method of design has begun about ten years ago in nuclear power generation plants. By this method, the result of design can be confirmed three-dimensionally before actual production, and it is the quick and sure method to meet the various needs in design promptly. The adoption of models aims mainly at the improvement of the quality of design since the high safety is required for nuclear power plants in spite of the complex structure. The layout of nuclear power plants and piping design require the model engineering to arrange rationally enormous quantity of things in a limited period. As the method of model engineering, there are the use of check models and of design models, and recently, the latter method has been mainly taken. The procedure of manufacturing models and engineering is explained. After model engineering has been completed, the model information must be expressed in drawings, and the automation of this process has been attempted by various methods. The computer processing of design is in progress, and its role is explained (CAD system). (Kako, I.)

An Efficient Explicit-time Description Method for Timed Model Checking

Directory of Open Access Journals (Sweden)

Hao Wang

2009-12-01

Full Text Available Timed model checking, the method to formally verify real-time systems, is attracting increasing attention from both the model checking community and the real-time community. Explicit-time description methods verify real-time systems using general model constructs found in standard un-timed model checkers. Lamport proposed an explicit-time description method using a clock-ticking process (Tick to simulate the passage of time together with a group of global variables to model time requirements. Two methods, the Sync-based Explicit-time Description Method using rendezvous synchronization steps and the Semaphore-based Explicit-time Description Method using only one global variable were proposed; they both achieve better modularity than Lamport's method in modeling the real-time systems. In contrast to timed automata based model checkers like UPPAAL, explicit-time description methods can access and store the current time instant for future calculations necessary for many real-time systems, especially those with pre-emptive scheduling. However, the Tick process in the above three methods increments the time by one unit in each tick; the state spaces therefore grow relatively fast as the time parameters increase, a problem when the system's time period is relatively long. In this paper, we propose a more efficient method which enables the Tick process to leap multiple time units in one tick. Preliminary experimental results in a high performance computing environment show that this new method significantly reduces the state space and improves both the time and memory efficiency.

Analytical models approximating individual processes: a validation method.

Science.gov (United States)

Favier, C; Degallier, N; Menkès, C E

2010-12-01

Upscaling population models from fine to coarse resolutions, in space, time and/or level of description, allows the derivation of fast and tractable models based on a thorough knowledge of individual processes. The validity of such approximations is generally tested only on a limited range of parameter sets. A more general validation test, over a range of parameters, is proposed; this would estimate the error induced by the approximation, using the original model's stochastic variability as a reference. This method is illustrated by three examples taken from the field of epidemics transmitted by vectors that bite in a temporally cyclical pattern, that illustrate the use of the method: to estimate if an approximation over- or under-fits the original model; to invalidate an approximation; to rank possible approximations for their qualities. As a result, the application of the validation method to this field emphasizes the need to account for the vectors' biology in epidemic prediction models and to validate these against finer scale models. Copyright © 2010 Elsevier Inc. All rights reserved.

Modelling viscoacoustic wave propagation with the lattice Boltzmann method.

Science.gov (United States)

Xia, Muming; Wang, Shucheng; Zhou, Hui; Shan, Xiaowen; Chen, Hanming; Li, Qingqing; Zhang, Qingchen

2017-08-31

In this paper, the lattice Boltzmann method (LBM) is employed to simulate wave propagation in viscous media. LBM is a kind of microscopic method for modelling waves through tracking the evolution states of a large number of discrete particles. By choosing different relaxation times in LBM experiments and using spectrum ratio method, we can reveal the relationship between the quality factor Q and the parameter τ in LBM. A two-dimensional (2D) homogeneous model and a two-layered model are tested in the numerical experiments, and the LBM results are compared against the reference solution of the viscoacoustic equations based on the Kelvin-Voigt model calculated by finite difference method (FDM). The wavefields and amplitude spectra obtained by LBM coincide with those by FDM, which demonstrates the capability of the LBM with one relaxation time. The new scheme is relatively simple and efficient to implement compared with the traditional lattice methods. In addition, through a mass of experiments, we find that the relaxation time of LBM has a quantitative relationship with Q. Such a novel scheme offers an alternative forward modelling kernel for seismic inversion and a new model to describe the underground media.

New method dynamically models hydrocarbon fractionation

Energy Technology Data Exchange (ETDEWEB)

Kesler, M.G.; Weissbrod, J.M.; Sheth, B.V. [Kesler Engineering, East Brunswick, NJ (United States)

1995-10-01

A new method for calculating distillation column dynamics can be used to model time-dependent effects of independent disturbances for a range of hydrocarbon fractionation. It can model crude atmospheric and vacuum columns, with relatively few equilibrium stages and a large number of components, to C{sub 3} splitters, with few components and up to 300 equilibrium stages. Simulation results are useful for operations analysis, process-control applications and closed-loop control in petroleum, petrochemical and gas processing plants. The method is based on an implicit approach, where the time-dependent variations of inventory, temperatures, liquid and vapor flows and compositions are superimposed at each time step on the steady-state solution. Newton-Raphson (N-R) techniques are then used to simultaneously solve the resulting finite-difference equations of material, equilibrium and enthalpy balances that characterize distillation dynamics. The important innovation is component-aggregation and tray-aggregation to contract the equations without compromising accuracy. This contraction increases the N-R calculations` stability. It also significantly increases calculational speed, which is particularly important in dynamic simulations. This method provides a sound basis for closed-loop, supervisory control of distillation--directly or via multivariable controllers--based on a rigorous, phenomenological column model.

Global Optimization Ensemble Model for Classification Methods

Science.gov (United States)

Anwar, Hina; Qamar, Usman; Muzaffar Qureshi, Abdul Wahab

2014-01-01

Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity. PMID:24883382

Global Optimization Ensemble Model for Classification Methods

Directory of Open Access Journals (Sweden)

Hina Anwar

2014-01-01

Full Text Available Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity.

Diffuse interface methods for multiphase flow modeling

International Nuclear Information System (INIS)

Jamet, D.

2004-01-01

Full text of publication follows:Nuclear reactor safety programs need to get a better description of some stages of identified incident or accident scenarios. For some of them, such as the reflooding of the core or the dryout of fuel rods, the heat, momentum and mass transfers taking place at the scale of droplets or bubbles are part of the key physical phenomena for which a better description is needed. Experiments are difficult to perform at these very small scales and direct numerical simulations is viewed as a promising way to give new insight into these complex two-phase flows. This type of simulations requires numerical methods that are accurate, efficient and easy to run in three space dimensions and on parallel computers. Despite many years of development, direct numerical simulation of two-phase flows is still very challenging, mostly because it requires solving moving boundary problems. To avoid this major difficulty, a new class of numerical methods is arising, called diffuse interface methods. These methods are based on physical theories dating back to van der Waals and mostly used in materials science. In these methods, interfaces separating two phases are modeled as continuous transitions zones instead of surfaces of discontinuity. Since all the physical variables encounter possibly strong but nevertheless always continuous variations across the interfacial zones, these methods virtually eliminate the difficult moving boundary problem. We show that these methods lead to a single-phase like system of equations, which makes it easier to code in 3D and to make parallel compared to more classical methods. The first method presented is dedicated to liquid-vapor flows with phase-change. It is based on the van der Waals' theory of capillarity. This method has been used to study nucleate boiling of a pure fluid and of dilute binary mixtures. We discuss the importance of the choice and the meaning of the order parameter, i.e. a scalar which discriminates one

Two updating methods for dissipative models with non symmetric matrices

International Nuclear Information System (INIS)

Billet, L.; Moine, P.; Aubry, D.

1997-01-01

In this paper the feasibility of the extension of two updating methods to rotating machinery models is considered, the particularity of rotating machinery models is to use non-symmetric stiffness and damping matrices. It is shown that the two methods described here, the inverse Eigen-sensitivity method and the error in constitutive relation method can be adapted to such models given some modification.As far as inverse sensitivity method is concerned, an error function based on the difference between right hand calculated and measured Eigen mode shapes and calculated and measured Eigen values is used. Concerning the error in constitutive relation method, the equation which defines the error has to be modified due to the non definite positiveness of the stiffness matrix. The advantage of this modification is that, in some cases, it is possible to focus the updating process on some specific model parameters. Both methods were validated on a simple test model consisting in a two-bearing and disc rotor system. (author)

A service based estimation method for MPSoC performance modelling

DEFF Research Database (Denmark)

Tranberg-Hansen, Anders Sejer; Madsen, Jan; Jensen, Bjørn Sand

2008-01-01

This paper presents an abstract service based estimation method for MPSoC performance modelling which allows fast, cycle accurate design space exploration of complex architectures including multi processor configurations at a very early stage in the design phase. The modelling method uses a service...... oriented model of computation based on Hierarchical Colored Petri Nets and allows the modelling of both software and hardware in one unified model. To illustrate the potential of the method, a small MPSoC system, developed at Bang & Olufsen ICEpower a/s, is modelled and performance estimates are produced...

Estimating Model Probabilities using Thermodynamic Markov Chain Monte Carlo Methods

Science.gov (United States)

Ye, M.; Liu, P.; Beerli, P.; Lu, D.; Hill, M. C.

2014-12-01

Markov chain Monte Carlo (MCMC) methods are widely used to evaluate model probability for quantifying model uncertainty. In a general procedure, MCMC simulations are first conducted for each individual model, and MCMC parameter samples are then used to approximate marginal likelihood of the model by calculating the geometric mean of the joint likelihood of the model and its parameters. It has been found the method of evaluating geometric mean suffers from the numerical problem of low convergence rate. A simple test case shows that even millions of MCMC samples are insufficient to yield accurate estimation of the marginal likelihood. To resolve this problem, a thermodynamic method is used to have multiple MCMC runs with different values of a heating coefficient between zero and one. When the heating coefficient is zero, the MCMC run is equivalent to a random walk MC in the prior parameter space; when the heating coefficient is one, the MCMC run is the conventional one. For a simple case with analytical form of the marginal likelihood, the thermodynamic method yields more accurate estimate than the method of using geometric mean. This is also demonstrated for a case of groundwater modeling with consideration of four alternative models postulated based on different conceptualization of a confining layer. This groundwater example shows that model probabilities estimated using the thermodynamic method are more reasonable than those obtained using the geometric method. The thermodynamic method is general, and can be used for a wide range of environmental problem for model uncertainty quantification.

Multifunctional Collaborative Modeling and Analysis Methods in Engineering Science

Science.gov (United States)

Ransom, Jonathan B.; Broduer, Steve (Technical Monitor)

2001-01-01

Engineers are challenged to produce better designs in less time and for less cost. Hence, to investigate novel and revolutionary design concepts, accurate, high-fidelity results must be assimilated rapidly into the design, analysis, and simulation process. This assimilation should consider diverse mathematical modeling and multi-discipline interactions necessitated by concepts exploiting advanced materials and structures. Integrated high-fidelity methods with diverse engineering applications provide the enabling technologies to assimilate these high-fidelity, multi-disciplinary results rapidly at an early stage in the design. These integrated methods must be multifunctional, collaborative, and applicable to the general field of engineering science and mechanics. Multifunctional methodologies and analysis procedures are formulated for interfacing diverse subdomain idealizations including multi-fidelity modeling methods and multi-discipline analysis methods. These methods, based on the method of weighted residuals, ensure accurate compatibility of primary and secondary variables across the subdomain interfaces. Methods are developed using diverse mathematical modeling (i.e., finite difference and finite element methods) and multi-fidelity modeling among the subdomains. Several benchmark scalar-field and vector-field problems in engineering science are presented with extensions to multidisciplinary problems. Results for all problems presented are in overall good agreement with the exact analytical solution or the reference numerical solution. Based on the results, the integrated modeling approach using the finite element method for multi-fidelity discretization among the subdomains is identified as most robust. The multiple-method approach is advantageous when interfacing diverse disciplines in which each of the method's strengths are utilized. The multifunctional methodology presented provides an effective mechanism by which domains with diverse idealizations are

Rheology, strain localization and stress history in the limestones of Bure (Meuse, France)

International Nuclear Information System (INIS)

Rolland, A.

2013-01-01

In order to demonstrate the feasibility of a radioactive waste repository (HLW) in clay-stone formation, the french national radioactive waste management agency (Andra) started in 2000 to build an underground research laboratory (URL) at Bure in the south of the Meuse district. The target horizon for the laboratory is a 135 m thick layer of argillaceous rock (Callovo-Oxfordian clay-stone) that lies between about 420 and 555 meters below the surface at the URL site. The argillite layer (COX) is surrounded by limestones from the Dogger and the Oxfordian ages (respectively 164,7 to 175,6 Ma and 161,2 to 164,7 Ma). Numerous stylolites were found in these limestones. The aim of this work is to study the rheology of these stylolites-rich horizons and the stylolites as stress gauges in the Dogger and Oxfordian formations. In this work, a wide range of samples with and without stylolites were sampled in the Dogger and Oxfordian formations. Petrophysical measurements and microstructural studies showed that all these limestones have a microporous structure. We showed that the stylolites induced significant variations in some physical properties and in the rock strength. Based on an analytical model, presented here in details, linking a characteristic length associated to the stylolites morphology and the stress associated to the development of that stylolites, a method for the morphology analysis of stylolites is developed, using a Fourier power spectrum technique, by taking into account all the difficulties linked to the use of cores from deep boreholes. We apply this method on stylolites at various depths, starting from the Oxfordian formation at a depth of 158 meters to the Dogger formation at a depth of 800 meters. No stylolites are found in the intermediate Callovo-Oxfordian clay-stone formation. We analyze 1D profiles taken on the outer part of the cores. In agreement with theoretical predictions, we always observe two regimes for small and large scales separated by a

Monte Carlo methods and models in finance and insurance

CERN Document Server

Korn, Ralf; Kroisandt, Gerald

2010-01-01

Offering a unique balance between applications and calculations, Monte Carlo Methods and Models in Finance and Insurance incorporates the application background of finance and insurance with the theory and applications of Monte Carlo methods. It presents recent methods and algorithms, including the multilevel Monte Carlo method, the statistical Romberg method, and the Heath-Platen estimator, as well as recent financial and actuarial models, such as the Cheyette and dynamic mortality models. The authors separately discuss Monte Carlo techniques, stochastic process basics, and the theoretical background and intuition behind financial and actuarial mathematics, before bringing the topics together to apply the Monte Carlo methods to areas of finance and insurance. This allows for the easy identification of standard Monte Carlo tools and for a detailed focus on the main principles of financial and insurance mathematics. The book describes high-level Monte Carlo methods for standard simulation and the simulation of...

Methods of Model Reduction for Large-Scale Biological Systems: A Survey of Current Methods and Trends.

Science.gov (United States)

Snowden, Thomas J; van der Graaf, Piet H; Tindall, Marcus J

2017-07-01

Complex models of biochemical reaction systems have become increasingly common in the systems biology literature. The complexity of such models can present a number of obstacles for their practical use, often making problems difficult to intuit or computationally intractable. Methods of model reduction can be employed to alleviate the issue of complexity by seeking to eliminate those portions of a reaction network that have little or no effect upon the outcomes of interest, hence yielding simplified systems that retain an accurate predictive capacity. This review paper seeks to provide a brief overview of a range of such methods and their application in the context of biochemical reaction network models. To achieve this, we provide a brief mathematical account of the main methods including timescale exploitation approaches, reduction via sensitivity analysis, optimisation methods, lumping, and singular value decomposition-based approaches. Methods are reviewed in the context of large-scale systems biology type models, and future areas of research are briefly discussed.

Comparison of Model Reliabilities from Single-Step and Bivariate Blending Methods

DEFF Research Database (Denmark)

Taskinen, Matti; Mäntysaari, Esa; Lidauer, Martin

2013-01-01

Model based reliabilities in genetic evaluation are compared between three methods: animal model BLUP, single-step BLUP, and bivariate blending after genomic BLUP. The original bivariate blending is revised in this work to better account animal models. The study data is extracted from...... be calculated. Model reliabilities by the single-step and the bivariate blending methods were higher than by animal model due to genomic information. Compared to the single-step method, the bivariate blending method reliability estimates were, in general, lower. Computationally bivariate blending method was......, on the other hand, lighter than the single-step method....

Network modelling methods for FMRI.

Science.gov (United States)

Smith, Stephen M; Miller, Karla L; Salimi-Khorshidi, Gholamreza; Webster, Matthew; Beckmann, Christian F; Nichols, Thomas E; Ramsey, Joseph D; Woolrich, Mark W

2011-01-15

There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution. Copyright © 2010 Elsevier Inc. All rights reserved.

Numerical methods for modeling photonic-crystal VCSELs

DEFF Research Database (Denmark)

Dems, Maciej; Chung, Il-Sug; Nyakas, Peter

2010-01-01

We show comparison of four different numerical methods for simulating Photonic-Crystal (PC) VCSELs. We present the theoretical basis behind each method and analyze the differences by studying a benchmark VCSEL structure, where the PC structure penetrates all VCSEL layers, the entire top-mirror DBR...... to the effective index method. The simulation results elucidate the strength and weaknesses of the analyzed methods; and outline the limits of applicability of the different models....

Constructive Epistemic Modeling: A Hierarchical Bayesian Model Averaging Method

Science.gov (United States)

Tsai, F. T. C.; Elshall, A. S.

2014-12-01

Constructive epistemic modeling is the idea that our understanding of a natural system through a scientific model is a mental construct that continually develops through learning about and from the model. Using the hierarchical Bayesian model averaging (HBMA) method [1], this study shows that segregating different uncertain model components through a BMA tree of posterior model probabilities, model prediction, within-model variance, between-model variance and total model variance serves as a learning tool [2]. First, the BMA tree of posterior model probabilities permits the comparative evaluation of the candidate propositions of each uncertain model component. Second, systemic model dissection is imperative for understanding the individual contribution of each uncertain model component to the model prediction and variance. Third, the hierarchical representation of the between-model variance facilitates the prioritization of the contribution of each uncertain model component to the overall model uncertainty. We illustrate these concepts using the groundwater modeling of a siliciclastic aquifer-fault system. The sources of uncertainty considered are from geological architecture, formation dip, boundary conditions and model parameters. The study shows that the HBMA analysis helps in advancing knowledge about the model rather than forcing the model to fit a particularly understanding or merely averaging several candidate models. [1] Tsai, F. T.-C., and A. S. Elshall (2013), Hierarchical Bayesian model averaging for hydrostratigraphic modeling: Uncertainty segregation and comparative evaluation. Water Resources Research, 49, 5520-5536, doi:10.1002/wrcr.20428. [2] Elshall, A.S., and F. T.-C. Tsai (2014). Constructive epistemic modeling of groundwater flow with geological architecture and boundary condition uncertainty under Bayesian paradigm, Journal of Hydrology, 517, 105-119, doi: 10.1016/j.jhydrol.2014.05.027.

Modelling methods for milk intake measurements

International Nuclear Information System (INIS)

Coward, W.A.

1999-01-01

One component of the first Research Coordination Programme was a tutorial session on modelling in in-vivo tracer kinetic methods. This section describes the principles that are involved and how these can be translated into spreadsheets using Microsoft Excel and the SOLVER function to fit the model to the data. The purpose of this section is to describe the system developed within the RCM, and how it is used

A new method to determine the number of experimental data using statistical modeling methods

Energy Technology Data Exchange (ETDEWEB)

Jung, Jung-Ho; Kang, Young-Jin; Lim, O-Kaung; Noh, Yoojeong [Pusan National University, Busan (Korea, Republic of)

2017-06-15

For analyzing the statistical performance of physical systems, statistical characteristics of physical parameters such as material properties need to be estimated by collecting experimental data. For accurate statistical modeling, many such experiments may be required, but data are usually quite limited owing to the cost and time constraints of experiments. In this study, a new method for determining a rea- sonable number of experimental data is proposed using an area metric, after obtaining statistical models using the information on the underlying distribution, the Sequential statistical modeling (SSM) approach, and the Kernel density estimation (KDE) approach. The area metric is used as a convergence criterion to determine the necessary and sufficient number of experimental data to be acquired. The pro- posed method is validated in simulations, using different statistical modeling methods, different true models, and different convergence criteria. An example data set with 29 data describing the fatigue strength coefficient of SAE 950X is used for demonstrating the performance of the obtained statistical models that use a pre-determined number of experimental data in predicting the probability of failure for a target fatigue life.

Hydrological model uncertainty due to spatial evapotranspiration estimation methods

Science.gov (United States)

Yu, Xuan; Lamačová, Anna; Duffy, Christopher; Krám, Pavel; Hruška, Jakub

2016-05-01

Evapotranspiration (ET) continues to be a difficult process to estimate in seasonal and long-term water balances in catchment models. Approaches to estimate ET typically use vegetation parameters (e.g., leaf area index [LAI], interception capacity) obtained from field observation, remote sensing data, national or global land cover products, and/or simulated by ecosystem models. In this study we attempt to quantify the uncertainty that spatial evapotranspiration estimation introduces into hydrological simulations when the age of the forest is not precisely known. The Penn State Integrated Hydrologic Model (PIHM) was implemented for the Lysina headwater catchment, located 50°03‧N, 12°40‧E in the western part of the Czech Republic. The spatial forest patterns were digitized from forest age maps made available by the Czech Forest Administration. Two ET methods were implemented in the catchment model: the Biome-BGC forest growth sub-model (1-way coupled to PIHM) and with the fixed-seasonal LAI method. From these two approaches simulation scenarios were developed. We combined the estimated spatial forest age maps and two ET estimation methods to drive PIHM. A set of spatial hydrologic regime and streamflow regime indices were calculated from the modeling results for each method. Intercomparison of the hydrological responses to the spatial vegetation patterns suggested considerable variation in soil moisture and recharge and a small uncertainty in the groundwater table elevation and streamflow. The hydrologic modeling with ET estimated by Biome-BGC generated less uncertainty due to the plant physiology-based method. The implication of this research is that overall hydrologic variability induced by uncertain management practices was reduced by implementing vegetation models in the catchment models.

Comparison of Transmission Line Methods for Surface Acoustic Wave Modeling

Science.gov (United States)

Wilson, William; Atkinson, Gary

2009-01-01

Surface Acoustic Wave (SAW) technology is low cost, rugged, lightweight, extremely low power and can be used to develop passive wireless sensors. For these reasons, NASA is investigating the use of SAW technology for Integrated Vehicle Health Monitoring (IVHM) of aerospace structures. To facilitate rapid prototyping of passive SAW sensors for aerospace applications, SAW models have been developed. This paper reports on the comparison of three methods of modeling SAWs. The three models are the Impulse Response Method (a first order model), and two second order matrix methods; the conventional matrix approach, and a modified matrix approach that is extended to include internal finger reflections. The second order models are based upon matrices that were originally developed for analyzing microwave circuits using transmission line theory. Results from the models are presented with measured data from devices. Keywords: Surface Acoustic Wave, SAW, transmission line models, Impulse Response Method.

Model based methods and tools for process systems engineering

DEFF Research Database (Denmark)

Gani, Rafiqul

need to be integrated with work-flows and data-flows for specific product-process synthesis-design problems within a computer-aided framework. The framework therefore should be able to manage knowledge-data, models and the associated methods and tools needed by specific synthesis-design work...... of model based methods and tools within a computer aided framework for product-process synthesis-design will be highlighted.......Process systems engineering (PSE) provides means to solve a wide range of problems in a systematic and efficient manner. This presentation will give a perspective on model based methods and tools needed to solve a wide range of problems in product-process synthesis-design. These methods and tools...

Prospective Mathematics Teachers' Opinions about Mathematical Modeling Method and Applicability of This Method

Science.gov (United States)

Akgün, Levent

2015-01-01

The aim of this study is to identify prospective secondary mathematics teachers' opinions about the mathematical modeling method and the applicability of this method in high schools. The case study design, which is among the qualitative research methods, was used in the study. The study was conducted with six prospective secondary mathematics…

Geostatistical methods applied to field model residuals

DEFF Research Database (Denmark)

Maule, Fox; Mosegaard, K.; Olsen, Nils

consists of measurement errors and unmodelled signal), and is typically assumed to be uncorrelated and Gaussian distributed. We have applied geostatistical methods to analyse the residuals of the Oersted(09d/04) field model [http://www.dsri.dk/Oersted/Field_models/IGRF_2005_candidates/], which is based...

Multilevel method for modeling large-scale networks.

Energy Technology Data Exchange (ETDEWEB)

Safro, I. M. (Mathematics and Computer Science)

2012-02-24

Understanding the behavior of real complex networks is of great theoretical and practical significance. It includes developing accurate artificial models whose topological properties are similar to the real networks, generating the artificial networks at different scales under special conditions, investigating a network dynamics, reconstructing missing data, predicting network response, detecting anomalies and other tasks. Network generation, reconstruction, and prediction of its future topology are central issues of this field. In this project, we address the questions related to the understanding of the network modeling, investigating its structure and properties, and generating artificial networks. Most of the modern network generation methods are based either on various random graph models (reinforced by a set of properties such as power law distribution of node degrees, graph diameter, and number of triangles) or on the principle of replicating an existing model with elements of randomization such as R-MAT generator and Kronecker product modeling. Hierarchical models operate at different levels of network hierarchy but with the same finest elements of the network. However, in many cases the methods that include randomization and replication elements on the finest relationships between network nodes and modeling that addresses the problem of preserving a set of simplified properties do not fit accurately enough the real networks. Among the unsatisfactory features are numerically inadequate results, non-stability of algorithms on real (artificial) data, that have been tested on artificial (real) data, and incorrect behavior at different scales. One reason is that randomization and replication of existing structures can create conflicts between fine and coarse scales of the real network geometry. Moreover, the randomization and satisfying of some attribute at the same time can abolish those topological attributes that have been undefined or hidden from

Method of generating a computer readable model

DEFF Research Database (Denmark)

2008-01-01

A method of generating a computer readable model of a geometrical object constructed from a plurality of interconnectable construction elements, wherein each construction element has a number of connection elements for connecting the construction element with another construction element. The met......A method of generating a computer readable model of a geometrical object constructed from a plurality of interconnectable construction elements, wherein each construction element has a number of connection elements for connecting the construction element with another construction element....... The method comprises encoding a first and a second one of the construction elements as corresponding data structures, each representing the connection elements of the corresponding construction element, and each of the connection elements having associated with it a predetermined connection type. The method...... further comprises determining a first connection element of the first construction element and a second connection element of the second construction element located in a predetermined proximity of each other; and retrieving connectivity information of the corresponding connection types of the first...

Curve fitting methods for solar radiation data modeling

Energy Technology Data Exchange (ETDEWEB)

Karim, Samsul Ariffin Abdul, E-mail: samsul-ariffin@petronas.com.my, E-mail: balbir@petronas.com.my; Singh, Balbir Singh Mahinder, E-mail: samsul-ariffin@petronas.com.my, E-mail: balbir@petronas.com.my [Department of Fundamental and Applied Sciences, Faculty of Sciences and Information Technology, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750 Tronoh, Perak Darul Ridzuan (Malaysia)

2014-10-24

This paper studies the use of several type of curve fitting method to smooth the global solar radiation data. After the data have been fitted by using curve fitting method, the mathematical model of global solar radiation will be developed. The error measurement was calculated by using goodness-fit statistics such as root mean square error (RMSE) and the value of R{sup 2}. The best fitting methods will be used as a starting point for the construction of mathematical modeling of solar radiation received in Universiti Teknologi PETRONAS (UTP) Malaysia. Numerical results indicated that Gaussian fitting and sine fitting (both with two terms) gives better results as compare with the other fitting methods.

Curve fitting methods for solar radiation data modeling

Science.gov (United States)

Karim, Samsul Ariffin Abdul; Singh, Balbir Singh Mahinder

2014-10-01

This paper studies the use of several type of curve fitting method to smooth the global solar radiation data. After the data have been fitted by using curve fitting method, the mathematical model of global solar radiation will be developed. The error measurement was calculated by using goodness-fit statistics such as root mean square error (RMSE) and the value of R2. The best fitting methods will be used as a starting point for the construction of mathematical modeling of solar radiation received in Universiti Teknologi PETRONAS (UTP) Malaysia. Numerical results indicated that Gaussian fitting and sine fitting (both with two terms) gives better results as compare with the other fitting methods.

Curve fitting methods for solar radiation data modeling

International Nuclear Information System (INIS)

Karim, Samsul Ariffin Abdul; Singh, Balbir Singh Mahinder

2014-01-01

This paper studies the use of several type of curve fitting method to smooth the global solar radiation data. After the data have been fitted by using curve fitting method, the mathematical model of global solar radiation will be developed. The error measurement was calculated by using goodness-fit statistics such as root mean square error (RMSE) and the value of R 2 . The best fitting methods will be used as a starting point for the construction of mathematical modeling of solar radiation received in Universiti Teknologi PETRONAS (UTP) Malaysia. Numerical results indicated that Gaussian fitting and sine fitting (both with two terms) gives better results as compare with the other fitting methods

Annular dispersed flow analysis model by Lagrangian method and liquid film cell method

International Nuclear Information System (INIS)

Matsuura, K.; Kuchinishi, M.; Kataoka, I.; Serizawa, A.

2003-01-01

A new annular dispersed flow analysis model was developed. In this model, both droplet behavior and liquid film behavior were simultaneously analyzed. Droplet behavior in turbulent flow was analyzed by the Lagrangian method with refined stochastic model. On the other hand, liquid film behavior was simulated by the boundary condition of moving rough wall and liquid film cell model, which was used to estimate liquid film flow rate. The height of moving rough wall was estimated by disturbance wave height correlation. In each liquid film cell, liquid film flow rate was calculated by considering droplet deposition and entrainment flow rate. Droplet deposition flow rate was calculated by Lagrangian method and entrainment flow rate was calculated by entrainment correlation. For the verification of moving rough wall model, turbulent flow analysis results under the annular flow condition were compared with the experimental data. Agreement between analysis results and experimental results were fairly good. Furthermore annular dispersed flow experiments were analyzed, in order to verify droplet behavior model and the liquid film cell model. The experimental results of radial distribution of droplet mass flux were compared with analysis results. The agreement was good under low liquid flow rate condition and poor under high liquid flow rate condition. But by modifying entrainment rate correlation, the agreement become good even under high liquid flow rate. This means that basic analysis method of droplet and liquid film behavior was right. In future work, verification calculation should be carried out under different experimental condition and entrainment ratio correlation also should be corrected

Modeling complex work systems - method meets reality

NARCIS (Netherlands)

van der Veer, Gerrit C.; Hoeve, Machteld; Lenting, Bert

1996-01-01

Modeling an existing task situation is often a first phase in the (re)design of information systems. For complex systems design, this model should consider both the people and the organization involved, the work, and situational aspects. Groupware Task Analysis (GTA) as part of a method for the

Computational Methods for Modeling Aptamers and Designing Riboswitches

Directory of Open Access Journals (Sweden)

Sha Gong

2017-11-01

Full Text Available Riboswitches, which are located within certain noncoding RNA region perform functions as genetic “switches”, regulating when and where genes are expressed in response to certain ligands. Understanding the numerous functions of riboswitches requires computation models to predict structures and structural changes of the aptamer domains. Although aptamers often form a complex structure, computational approaches, such as RNAComposer and Rosetta, have already been applied to model the tertiary (three-dimensional (3D structure for several aptamers. As structural changes in aptamers must be achieved within the certain time window for effective regulation, kinetics is another key point for understanding aptamer function in riboswitch-mediated gene regulation. The coarse-grained self-organized polymer (SOP model using Langevin dynamics simulation has been successfully developed to investigate folding kinetics of aptamers, while their co-transcriptional folding kinetics can be modeled by the helix-based computational method and BarMap approach. Based on the known aptamers, the web server Riboswitch Calculator and other theoretical methods provide a new tool to design synthetic riboswitches. This review will represent an overview of these computational methods for modeling structure and kinetics of riboswitch aptamers and for designing riboswitches.

Review: Optimization methods for groundwater modeling and management

Science.gov (United States)

Yeh, William W.-G.

2015-09-01

Optimization methods have been used in groundwater modeling as well as for the planning and management of groundwater systems. This paper reviews and evaluates the various optimization methods that have been used for solving the inverse problem of parameter identification (estimation), experimental design, and groundwater planning and management. Various model selection criteria are discussed, as well as criteria used for model discrimination. The inverse problem of parameter identification concerns the optimal determination of model parameters using water-level observations. In general, the optimal experimental design seeks to find sampling strategies for the purpose of estimating the unknown model parameters. A typical objective of optimal conjunctive-use planning of surface water and groundwater is to minimize the operational costs of meeting water demand. The optimization methods include mathematical programming techniques such as linear programming, quadratic programming, dynamic programming, stochastic programming, nonlinear programming, and the global search algorithms such as genetic algorithms, simulated annealing, and tabu search. Emphasis is placed on groundwater flow problems as opposed to contaminant transport problems. A typical two-dimensional groundwater flow problem is used to explain the basic formulations and algorithms that have been used to solve the formulated optimization problems.

Alternative methods of modeling wind generation using production costing models

International Nuclear Information System (INIS)

Milligan, M.R.; Pang, C.K.

1996-08-01

This paper examines the methods of incorporating wind generation in two production costing models: one is a load duration curve (LDC) based model and the other is a chronological-based model. These two models were used to evaluate the impacts of wind generation on two utility systems using actual collected wind data at two locations with high potential for wind generation. The results are sensitive to the selected wind data and the level of benefits of wind generation is sensitive to the load forecast. The total production cost over a year obtained by the chronological approach does not differ significantly from that of the LDC approach, though the chronological commitment of units is more realistic and more accurate. Chronological models provide the capability of answering important questions about wind resources which are difficult or impossible to address with LDC models

Comparing model-based and model-free analysis methods for QUASAR arterial spin labeling perfusion quantification.

Science.gov (United States)

Chappell, Michael A; Woolrich, Mark W; Petersen, Esben T; Golay, Xavier; Payne, Stephen J

2013-05-01

Amongst the various implementations of arterial spin labeling MRI methods for quantifying cerebral perfusion, the QUASAR method is unique. By using a combination of labeling with and without flow suppression gradients, the QUASAR method offers the separation of macrovascular and tissue signals. This permits local arterial input functions to be defined and "model-free" analysis, using numerical deconvolution, to be used. However, it remains unclear whether arterial spin labeling data are best treated using model-free or model-based analysis. This work provides a critical comparison of these two approaches for QUASAR arterial spin labeling in the healthy brain. An existing two-component (arterial and tissue) model was extended to the mixed flow suppression scheme of QUASAR to provide an optimal model-based analysis. The model-based analysis was extended to incorporate dispersion of the labeled bolus, generally regarded as the major source of discrepancy between the two analysis approaches. Model-free and model-based analyses were compared for perfusion quantification including absolute measurements, uncertainty estimation, and spatial variation in cerebral blood flow estimates. Major sources of discrepancies between model-free and model-based analysis were attributed to the effects of dispersion and the degree to which the two methods can separate macrovascular and tissue signal. Copyright © 2012 Wiley Periodicals, Inc.

Modeling Nanoscale FinFET Performance by a Neural Network Method

Directory of Open Access Journals (Sweden)

Jin He

2017-07-01

Full Text Available This paper presents a neural network method to model nanometer FinFET performance. The principle of this method is firstly introduced and its application in modeling DC and conductance characteristics of nanoscale FinFET transistor is demonstrated in detail. It is shown that this method does not need parameter extraction routine while its prediction of the transistor performance has a small relative error within 1 % compared with measured data, thus this new method is as accurate as the physics based surface potential model.

Sensitivity analysis of infectious disease models: methods, advances and their application

Science.gov (United States)

Wu, Jianyong; Dhingra, Radhika; Gambhir, Manoj; Remais, Justin V.

2013-01-01

Sensitivity analysis (SA) can aid in identifying influential model parameters and optimizing model structure, yet infectious disease modelling has yet to adopt advanced SA techniques that are capable of providing considerable insights over traditional methods. We investigate five global SA methods—scatter plots, the Morris and Sobol’ methods, Latin hypercube sampling-partial rank correlation coefficient and the sensitivity heat map method—and detail their relative merits and pitfalls when applied to a microparasite (cholera) and macroparasite (schistosomaisis) transmission model. The methods investigated yielded similar results with respect to identifying influential parameters, but offered specific insights that vary by method. The classical methods differed in their ability to provide information on the quantitative relationship between parameters and model output, particularly over time. The heat map approach provides information about the group sensitivity of all model state variables, and the parameter sensitivity spectrum obtained using this method reveals the sensitivity of all state variables to each parameter over the course of the simulation period, especially valuable for expressing the dynamic sensitivity of a microparasite epidemic model to its parameters. A summary comparison is presented to aid infectious disease modellers in selecting appropriate methods, with the goal of improving model performance and design. PMID:23864497

Extension of local front reconstruction method with controlled coalescence model

Science.gov (United States)

Rajkotwala, A. H.; Mirsandi, H.; Peters, E. A. J. F.; Baltussen, M. W.; van der Geld, C. W. M.; Kuerten, J. G. M.; Kuipers, J. A. M.

2018-02-01

The physics of droplet collisions involves a wide range of length scales. This poses a challenge to accurately simulate such flows with standard fixed grid methods due to their inability to resolve all relevant scales with an affordable number of computational grid cells. A solution is to couple a fixed grid method with subgrid models that account for microscale effects. In this paper, we improved and extended the Local Front Reconstruction Method (LFRM) with a film drainage model of Zang and Law [Phys. Fluids 23, 042102 (2011)]. The new framework is first validated by (near) head-on collision of two equal tetradecane droplets using experimental film drainage times. When the experimental film drainage times are used, the LFRM method is better in predicting the droplet collisions, especially at high velocity in comparison with other fixed grid methods (i.e., the front tracking method and the coupled level set and volume of fluid method). When the film drainage model is invoked, the method shows a good qualitative match with experiments, but a quantitative correspondence of the predicted film drainage time with the experimental drainage time is not obtained indicating that further development of film drainage model is required. However, it can be safely concluded that the LFRM coupled with film drainage models is much better in predicting the collision dynamics than the traditional methods.

Applied systems ecology: models, data, and statistical methods

Energy Technology Data Exchange (ETDEWEB)

Eberhardt, L L

1976-01-01

In this report, systems ecology is largely equated to mathematical or computer simulation modelling. The need for models in ecology stems from the necessity to have an integrative device for the diversity of ecological data, much of which is observational, rather than experimental, as well as from the present lack of a theoretical structure for ecology. Different objectives in applied studies require specialized methods. The best predictive devices may be regression equations, often non-linear in form, extracted from much more detailed models. A variety of statistical aspects of modelling, including sampling, are discussed. Several aspects of population dynamics and food-chain kinetics are described, and it is suggested that the two presently separated approaches should be combined into a single theoretical framework. It is concluded that future efforts in systems ecology should emphasize actual data and statistical methods, as well as modelling.

Modeling of Landslides with the Material Point Method

DEFF Research Database (Denmark)

Andersen, Søren Mikkel; Andersen, Lars

2008-01-01

A numerical model for studying the dynamic evolution of landslides is presented. The numerical model is based on the Generalized Interpolation Material Point Method. A simplified slope with a house placed on top is analysed. An elasto-plastic material model based on the Mohr-Coulomb yield criterion...

Modelling of Landslides with the Material-point Method

DEFF Research Database (Denmark)

Andersen, Søren; Andersen, Lars

2009-01-01

A numerical model for studying the dynamic evolution of landslides is presented. The numerical model is based on the Generalized Interpolation Material Point Method. A simplified slope with a house placed on top is analysed. An elasto-plastic material model based on the Mohr-Coulomb yield criterion...

Modeling Methods

Science.gov (United States)

Healy, Richard W.; Scanlon, Bridget R.

2010-01-01

Simulation models are widely used in all types of hydrologic studies, and many of these models can be used to estimate recharge. Models can provide important insight into the functioning of hydrologic systems by identifying factors that influence recharge. The predictive capability of models can be used to evaluate how changes in climate, water use, land use, and other factors may affect recharge rates. Most hydrological simulation models, including watershed models and groundwater-flow models, are based on some form of water-budget equation, so the material in this chapter is closely linked to that in Chapter 2. Empirical models that are not based on a water-budget equation have also been used for estimating recharge; these models generally take the form of simple estimation equations that define annual recharge as a function of precipitation and possibly other climatic data or watershed characteristics.Model complexity varies greatly. Some models are simple accounting models; others attempt to accurately represent the physics of water movement through each compartment of the hydrologic system. Some models provide estimates of recharge explicitly; for example, a model based on the Richards equation can simulate water movement from the soil surface through the unsaturated zone to the water table. Recharge estimates can be obtained indirectly from other models. For example, recharge is a parameter in groundwater-flow models that solve for hydraulic head (i.e. groundwater level). Recharge estimates can be obtained through a model calibration process in which recharge and other model parameter values are adjusted so that simulated water levels agree with measured water levels. The simulation that provides the closest agreement is called the best fit, and the recharge value used in that simulation is the model-generated estimate of recharge.

A survey of real face modeling methods

Science.gov (United States)

Liu, Xiaoyue; Dai, Yugang; He, Xiangzhen; Wan, Fucheng

2017-09-01

The face model has always been a research challenge in computer graphics, which involves the coordination of multiple organs in faces. This article explained two kinds of face modeling method which is based on the data driven and based on parameter control, analyzed its content and background, summarized their advantages and disadvantages, and concluded muscle model which is based on the anatomy of the principle has higher veracity and easy to drive.

A sediment graph model based on SCS-CN method

Science.gov (United States)

Singh, P. K.; Bhunya, P. K.; Mishra, S. K.; Chaube, U. C.

2008-01-01

SummaryThis paper proposes new conceptual sediment graph models based on coupling of popular and extensively used methods, viz., Nash model based instantaneous unit sediment graph (IUSG), soil conservation service curve number (SCS-CN) method, and Power law. These models vary in their complexity and this paper tests their performance using data of the Nagwan watershed (area = 92.46 km 2) (India). The sensitivity of total sediment yield and peak sediment flow rate computations to model parameterisation is analysed. The exponent of the Power law, β, is more sensitive than other model parameters. The models are found to have substantial potential for computing sediment graphs (temporal sediment flow rate distribution) as well as total sediment yield.

Generalized framework for context-specific metabolic model extraction methods

Directory of Open Access Journals (Sweden)

Semidán eRobaina Estévez

2014-09-01

Full Text Available Genome-scale metabolic models are increasingly applied to investigate the physiology not only of simple prokaryotes, but also eukaryotes, such as plants, characterized with compartmentalized cells of multiple types. While genome-scale models aim at including the entirety of known metabolic reactions, mounting evidence has indicated that only a subset of these reactions is active in a given context, including: developmental stage, cell type, or environment. As a result, several methods have been proposed to reconstruct context-specific models from existing genome-scale models by integrating various types of high-throughput data. Here we present a mathematical framework that puts all existing methods under one umbrella and provides the means to better understand their functioning, highlight similarities and differences, and to help users in selecting a most suitable method for an application.

Advances in Applications of Hierarchical Bayesian Methods with Hydrological Models

Science.gov (United States)

Alexander, R. B.; Schwarz, G. E.; Boyer, E. W.

2017-12-01

Mechanistic and empirical watershed models are increasingly used to inform water resource decisions. Growing access to historical stream measurements and data from in-situ sensor technologies has increased the need for improved techniques for coupling models with hydrological measurements. Techniques that account for the intrinsic uncertainties of both models and measurements are especially needed. Hierarchical Bayesian methods provide an efficient modeling tool for quantifying model and prediction uncertainties, including those associated with measurements. Hierarchical methods can also be used to explore spatial and temporal variations in model parameters and uncertainties that are informed by hydrological measurements. We used hierarchical Bayesian methods to develop a hybrid (statistical-mechanistic) SPARROW (SPAtially Referenced Regression On Watershed attributes) model of long-term mean annual streamflow across diverse environmental and climatic drainages in 18 U.S. hydrological regions. Our application illustrates the use of a new generation of Bayesian methods that offer more advanced computational efficiencies than the prior generation. Evaluations of the effects of hierarchical (regional) variations in model coefficients and uncertainties on model accuracy indicates improved prediction accuracies (median of 10-50%) but primarily in humid eastern regions, where model uncertainties are one-third of those in arid western regions. Generally moderate regional variability is observed for most hierarchical coefficients. Accounting for measurement and structural uncertainties, using hierarchical state-space techniques, revealed the effects of spatially-heterogeneous, latent hydrological processes in the "localized" drainages between calibration sites; this improved model precision, with only minor changes in regional coefficients. Our study can inform advances in the use of hierarchical methods with hydrological models to improve their integration with stream

Robust modelling of solubility in supercritical carbon dioxide using Bayesian methods.

Science.gov (United States)

Tarasova, Anna; Burden, Frank; Gasteiger, Johann; Winkler, David A

2010-04-01

Two sparse Bayesian methods were used to derive predictive models of solubility of organic dyes and polycyclic aromatic compounds in supercritical carbon dioxide (scCO(2)), over a wide range of temperatures (285.9-423.2K) and pressures (60-1400 bar): a multiple linear regression employing an expectation maximization algorithm and a sparse prior (MLREM) method and a non-linear Bayesian Regularized Artificial Neural Network with a Laplacian Prior (BRANNLP). A randomly selected test set was used to estimate the predictive ability of the models. The MLREM method resulted in a model of similar predictivity to the less sparse MLR method, while the non-linear BRANNLP method created models of substantially better predictivity than either the MLREM or MLR based models. The BRANNLP method simultaneously generated context-relevant subsets of descriptors and a robust, non-linear quantitative structure-property relationship (QSPR) model for the compound solubility in scCO(2). The differences between linear and non-linear descriptor selection methods are discussed. (c) 2009 Elsevier Inc. All rights reserved.

Methods improvements incorporated into the SAPHIRE ASP models

International Nuclear Information System (INIS)

Sattison, M.B.; Blackman, H.S.; Novack, S.D.

1995-01-01

The Office for Analysis and Evaluation of Operational Data (AEOD) has sought the assistance of the Idaho National Engineering Laboratory (INEL) to make some significant enhancements to the SAPHIRE-based Accident Sequence Precursor (ASP) models recently developed by the INEL. The challenge of this project is to provide the features of a full-scale PRA within the framework of the simplified ASP models. Some of these features include: (1) uncertainty analysis addressing the standard PRA uncertainties and the uncertainties unique to the ASP models and methods, (2) incorporation and proper quantification of individual human actions and the interaction among human actions, (3) enhanced treatment of common cause failures, and (4) extension of the ASP models to more closely mimic full-scale PRAs (inclusion of more initiators, explicitly modeling support system failures, etc.). This paper provides an overview of the methods being used to make the above improvements

Model-based economic evaluation in Alzheimer's disease: a review of the methods available to model Alzheimer's disease progression.

Science.gov (United States)

Green, Colin; Shearer, James; Ritchie, Craig W; Zajicek, John P

2011-01-01

To consider the methods available to model Alzheimer's disease (AD) progression over time to inform on the structure and development of model-based evaluations, and the future direction of modelling methods in AD. A systematic search of the health care literature was undertaken to identify methods to model disease progression in AD. Modelling methods are presented in a descriptive review. The literature search identified 42 studies presenting methods or applications of methods to model AD progression over time. The review identified 10 general modelling frameworks available to empirically model the progression of AD as part of a model-based evaluation. Seven of these general models are statistical models predicting progression of AD using a measure of cognitive function. The main concerns with models are on model structure, around the limited characterization of disease progression, and on the use of a limited number of health states to capture events related to disease progression over time. None of the available models have been able to present a comprehensive model of the natural history of AD. Although helpful, there are serious limitations in the methods available to model progression of AD over time. Advances are needed to better model the progression of AD and the effects of the disease on peoples' lives. Recent evidence supports the need for a multivariable approach to the modelling of AD progression, and indicates that a latent variable analytic approach to characterising AD progression is a promising avenue for advances in the statistical development of modelling methods. Copyright © 2011 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Efficient model learning methods for actor-critic control.

Science.gov (United States)

Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

2012-06-01

We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.

Spatial autocorrelation method using AR model; Kukan jiko sokanho eno AR model no tekiyo

Energy Technology Data Exchange (ETDEWEB)

Yamamoto, H; Obuchi, T; Saito, T [Iwate University, Iwate (Japan). Faculty of Engineering

1996-05-01

Examination was made about the applicability of the AR model to the spatial autocorrelation (SAC) method, which analyzes the surface wave phase velocity in a microtremor, for the estimation of the underground structure. In this examination, microtremor data recorded in Morioka City, Iwate Prefecture, was used. In the SAC method, a spatial autocorrelation function with the frequency as a variable is determined from microtremor data observed by circular arrays. Then, the Bessel function is adapted to the spatial autocorrelation coefficient with the distance between seismographs as a variable for the determination of the phase velocity. The result of the AR model application in this study and the results of the conventional BPF and FFT method were compared. It was then found that the phase velocities obtained by the BPF and FFT methods were more dispersed than the same obtained by the AR model. The dispersion in the BPF method is attributed to the bandwidth used in the band-pass filter and, in the FFT method, to the impact of the bandwidth on the smoothing of the cross spectrum. 2 refs., 7 figs.

3D virtual human rapid modeling method based on top-down modeling mechanism

Directory of Open Access Journals (Sweden)

LI Taotao

2017-01-01

Full Text Available Aiming to satisfy the vast custom-made character demand of 3D virtual human and the rapid modeling in the field of 3D virtual reality, a new virtual human top-down rapid modeling method is put for-ward in this paper based on the systematic analysis of the current situation and shortage of the virtual hu-man modeling technology. After the top-level realization of virtual human hierarchical structure frame de-sign, modular expression of the virtual human and parameter design for each module is achieved gradu-al-level downwards. While the relationship of connectors and mapping restraints among different modules is established, the definition of the size and texture parameter is also completed. Standardized process is meanwhile produced to support and adapt the virtual human top-down rapid modeling practice operation. Finally, the modeling application, which takes a Chinese captain character as an example, is carried out to validate the virtual human rapid modeling method based on top-down modeling mechanism. The result demonstrates high modelling efficiency and provides one new concept for 3D virtual human geometric mod-eling and texture modeling.

Estimation of pump operational state with model-based methods

International Nuclear Information System (INIS)

Ahonen, Tero; Tamminen, Jussi; Ahola, Jero; Viholainen, Juha; Aranto, Niina; Kestilae, Juha

2010-01-01

Pumps are widely used in industry, and they account for 20% of the industrial electricity consumption. Since the speed variation is often the most energy-efficient method to control the head and flow rate of a centrifugal pump, frequency converters are used with induction motor-driven pumps. Although a frequency converter can estimate the operational state of an induction motor without external measurements, the state of a centrifugal pump or other load machine is not typically considered. The pump is, however, usually controlled on the basis of the required flow rate or output pressure. As the pump operational state can be estimated with a general model having adjustable parameters, external flow rate or pressure measurements are not necessary to determine the pump flow rate or output pressure. Hence, external measurements could be replaced with an adjustable model for the pump that uses estimates of the motor operational state. Besides control purposes, modelling the pump operation can provide useful information for energy auditing and optimization purposes. In this paper, two model-based methods for pump operation estimation are presented. Factors affecting the accuracy of the estimation methods are analyzed. The applicability of the methods is verified by laboratory measurements and tests in two pilot installations. Test results indicate that the estimation methods can be applied to the analysis and control of pump operation. The accuracy of the methods is sufficient for auditing purposes, and the methods can inform the user if the pump is driven inefficiently.

Quantitative Methods in Supply Chain Management Models and Algorithms

CERN Document Server

Christou, Ioannis T

2012-01-01

Quantitative Methods in Supply Chain Management presents some of the most important methods and tools available for modeling and solving problems arising in the context of supply chain management. In the context of this book, “solving problems” usually means designing efficient algorithms for obtaining high-quality solutions. The first chapter is an extensive optimization review covering continuous unconstrained and constrained linear and nonlinear optimization algorithms, as well as dynamic programming and discrete optimization exact methods and heuristics. The second chapter presents time-series forecasting methods together with prediction market techniques for demand forecasting of new products and services. The third chapter details models and algorithms for planning and scheduling with an emphasis on production planning and personnel scheduling. The fourth chapter presents deterministic and stochastic models for inventory control with a detailed analysis on periodic review systems and algorithmic dev...

Arctic curves in path models from the tangent method

Science.gov (United States)

Di Francesco, Philippe; Lapa, Matthew F.

2018-04-01

Recently, Colomo and Sportiello introduced a powerful method, known as the tangent method, for computing the arctic curve in statistical models which have a (non- or weakly-) intersecting lattice path formulation. We apply the tangent method to compute arctic curves in various models: the domino tiling of the Aztec diamond for which we recover the celebrated arctic circle; a model of Dyck paths equivalent to the rhombus tiling of a half-hexagon for which we find an arctic half-ellipse; another rhombus tiling model with an arctic parabola; the vertically symmetric alternating sign matrices, where we find the same arctic curve as for unconstrained alternating sign matrices. The latter case involves lattice paths that are non-intersecting but that are allowed to have osculating contact points, for which the tangent method was argued to still apply. For each problem we estimate the large size asymptotics of a certain one-point function using LU decomposition of the corresponding Gessel–Viennot matrices, and a reformulation of the result amenable to asymptotic analysis.

Application of homotopy-perturbation method to nonlinear population dynamics models

International Nuclear Information System (INIS)

Chowdhury, M.S.H.; Hashim, I.; Abdulaziz, O.

2007-01-01

In this Letter, the homotopy-perturbation method (HPM) is employed to derive approximate series solutions of nonlinear population dynamics models. The nonlinear models considered are the multispecies Lotka-Volterra equations. The accuracy of this method is examined by comparison with the available exact and the fourth-order Runge-Kutta method (RK4)

A meshless method for modeling convective heat transfer

Energy Technology Data Exchange (ETDEWEB)

Carrington, David B [Los Alamos National Laboratory

2010-01-01

A meshless method is used in a projection-based approach to solve the primitive equations for fluid flow with heat transfer. The method is easy to implement in a MATLAB format. Radial basis functions are used to solve two benchmark test cases: natural convection in a square enclosure and flow with forced convection over a backward facing step. The results are compared with two popular and widely used commercial codes: COMSOL, a finite element model, and FLUENT, a finite volume-based model.

The impact of Outer Western Carpathian nappe tectonics on the recent stress-strain state in the Upper Silesian Coal Basin (Moravosilesian Zone, Bohemian Massif)

Czech Academy of Sciences Publication Activity Database

Ptáček, Jiří; Grygar, R.; Koníček, Petr; Waclawik, P.

2012-01-01

Roč. 63, č. 1 (2012), s. 3-11 ISSN 1335-0552 R&D Projects: GA ČR GA105/08/1625 Institutional research plan: CEZ:AV0Z30860518 Keywords : Variscan orogeny * Upper Silesian Coal Basin * recent stress fields * Outer Western Carpathians * paleostress Subject RIV: DB - Geology ; Mineralogy Impact factor: 1.143, year: 2012 http://versita.metapress.com/content/0326174t34663755/

Mathematical methods and models in composites

CERN Document Server

Mantic, Vladislav

2014-01-01

This book provides a representative selection of the most relevant, innovative, and useful mathematical methods and models applied to the analysis and characterization of composites and their behaviour on micro-, meso-, and macroscale. It establishes the fundamentals for meaningful and accurate theoretical and computer modelling of these materials in the future. Although the book is primarily concerned with fibre-reinforced composites, which have ever-increasing applications in fields such as aerospace, many of the results presented can be applied to other kinds of composites. The topics cover

Kernel Method Based Human Model for Enhancing Interactive Evolutionary Optimization

Science.gov (United States)

Zhao, Qiangfu; Liu, Yong

2015-01-01

A fitness landscape presents the relationship between individual and its reproductive success in evolutionary computation (EC). However, discrete and approximate landscape in an original search space may not support enough and accurate information for EC search, especially in interactive EC (IEC). The fitness landscape of human subjective evaluation in IEC is very difficult and impossible to model, even with a hypothesis of what its definition might be. In this paper, we propose a method to establish a human model in projected high dimensional search space by kernel classification for enhancing IEC search. Because bivalent logic is a simplest perceptual paradigm, the human model is established by considering this paradigm principle. In feature space, we design a linear classifier as a human model to obtain user preference knowledge, which cannot be supported linearly in original discrete search space. The human model is established by this method for predicting potential perceptual knowledge of human. With the human model, we design an evolution control method to enhance IEC search. From experimental evaluation results with a pseudo-IEC user, our proposed model and method can enhance IEC search significantly. PMID:25879050

Efficient simulation and likelihood methods for non-neutral multi-allele models.

Science.gov (United States)

Joyce, Paul; Genz, Alan; Buzbas, Erkan Ozge

2012-06-01

Throughout the 1980s, Simon Tavaré made numerous significant contributions to population genetics theory. As genetic data, in particular DNA sequence, became more readily available, a need to connect population-genetic models to data became the central issue. The seminal work of Griffiths and Tavaré (1994a , 1994b , 1994c) was among the first to develop a likelihood method to estimate the population-genetic parameters using full DNA sequences. Now, we are in the genomics era where methods need to scale-up to handle massive data sets, and Tavaré has led the way to new approaches. However, performing statistical inference under non-neutral models has proved elusive. In tribute to Simon Tavaré, we present an article in spirit of his work that provides a computationally tractable method for simulating and analyzing data under a class of non-neutral population-genetic models. Computational methods for approximating likelihood functions and generating samples under a class of allele-frequency based non-neutral parent-independent mutation models were proposed by Donnelly, Nordborg, and Joyce (DNJ) (Donnelly et al., 2001). DNJ (2001) simulated samples of allele frequencies from non-neutral models using neutral models as auxiliary distribution in a rejection algorithm. However, patterns of allele frequencies produced by neutral models are dissimilar to patterns of allele frequencies produced by non-neutral models, making the rejection method inefficient. For example, in some cases the methods in DNJ (2001) require 10(9) rejections before a sample from the non-neutral model is accepted. Our method simulates samples directly from the distribution of non-neutral models, making simulation methods a practical tool to study the behavior of the likelihood and to perform inference on the strength of selection.

Spectral-element Method for 3D Marine Controlled-source EM Modeling

Science.gov (United States)

Liu, L.; Yin, C.; Zhang, B., Sr.; Liu, Y.; Qiu, C.; Huang, X.; Zhu, J.

2017-12-01

As one of the predrill reservoir appraisal methods, marine controlled-source EM (MCSEM) has been widely used in mapping oil reservoirs to reduce risk of deep water exploration. With the technical development of MCSEM, the need for improved forward modeling tools has become evident. We introduce in this paper spectral element method (SEM) for 3D MCSEM modeling. It combines the flexibility of finite-element and high accuracy of spectral method. We use Galerkin weighted residual method to discretize the vector Helmholtz equation, where the curl-conforming Gauss-Lobatto-Chebyshev (GLC) polynomials are chosen as vector basis functions. As a kind of high-order complete orthogonal polynomials, the GLC have the characteristic of exponential convergence. This helps derive the matrix elements analytically and improves the modeling accuracy. Numerical 1D models using SEM with different orders show that SEM method delivers accurate results. With increasing SEM orders, the modeling accuracy improves largely. Further we compare our SEM with finite-difference (FD) method for a 3D reservoir model (Figure 1). The results show that SEM method is more effective than FD method. Only when the mesh is fine enough, can FD achieve the same accuracy of SEM. Therefore, to obtain the same precision, SEM greatly reduces the degrees of freedom and cost. Numerical experiments with different models (not shown here) demonstrate that SEM is an efficient and effective tool for MSCEM modeling that has significant advantages over traditional numerical methods.This research is supported by Key Program of National Natural Science Foundation of China (41530320), China Natural Science Foundation for Young Scientists (41404093), and Key National Research Project of China (2016YFC0303100, 2017YFC0601900).

Engineering design of systems models and methods

CERN Document Server

Buede, Dennis M

2009-01-01

The ideal introduction to the engineering design of systems-now in a new edition. The Engineering Design of Systems, Second Edition compiles a wealth of information from diverse sources to provide a unique, one-stop reference to current methods for systems engineering. It takes a model-based approach to key systems engineering design activities and introduces methods and models used in the real world. Features new to this edition include: * The addition of Systems Modeling Language (SysML) to several of the chapters, as well as the introduction of new terminology * Additional material on partitioning functions and components * More descriptive material on usage scenarios based on literature from use case development * Updated homework assignments * The software product CORE (from Vitech Corporation) is used to generate the traditional SE figures and the software product MagicDraw UML with SysML plugins (from No Magic, Inc.) is used for the SysML figures This book is designed to be an introductory reference ...

A New Computationally Frugal Method For Sensitivity Analysis Of Environmental Models

Science.gov (United States)

Rakovec, O.; Hill, M. C.; Clark, M. P.; Weerts, A.; Teuling, R.; Borgonovo, E.; Uijlenhoet, R.

2013-12-01

Effective and efficient parameter sensitivity analysis methods are crucial to understand the behaviour of complex environmental models and use of models in risk assessment. This paper proposes a new computationally frugal method for analyzing parameter sensitivity: the Distributed Evaluation of Local Sensitivity Analysis (DELSA). The DELSA method can be considered a hybrid of local and global methods, and focuses explicitly on multiscale evaluation of parameter sensitivity across the parameter space. Results of the DELSA method are compared with the popular global, variance-based Sobol' method and the delta method. We assess the parameter sensitivity of both (1) a simple non-linear reservoir model with only two parameters, and (2) five different "bucket-style" hydrologic models applied to a medium-sized catchment (200 km2) in the Belgian Ardennes. Results show that in both the synthetic and real-world examples, the global Sobol' method and the DELSA method provide similar sensitivities, with the DELSA method providing more detailed insight at much lower computational cost. The ability to understand how sensitivity measures vary through parameter space with modest computational requirements provides exciting new opportunities.

Statistical models and methods for reliability and survival analysis

CERN Document Server

Couallier, Vincent; Huber-Carol, Catherine; Mesbah, Mounir; Huber -Carol, Catherine; Limnios, Nikolaos; Gerville-Reache, Leo

2013-01-01

Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts - Statistical Models and Methods, Statistical

Metamodel-based inverse method for parameter identification: elastic-plastic damage model

Science.gov (United States)

Huang, Changwu; El Hami, Abdelkhalak; Radi, Bouchaïb

2017-04-01

This article proposed a metamodel-based inverse method for material parameter identification and applies it to elastic-plastic damage model parameter identification. An elastic-plastic damage model is presented and implemented in numerical simulation. The metamodel-based inverse method is proposed in order to overcome the disadvantage in computational cost of the inverse method. In the metamodel-based inverse method, a Kriging metamodel is constructed based on the experimental design in order to model the relationship between material parameters and the objective function values in the inverse problem, and then the optimization procedure is executed by the use of a metamodel. The applications of the presented material model and proposed parameter identification method in the standard A 2017-T4 tensile test prove that the presented elastic-plastic damage model is adequate to describe the material's mechanical behaviour and that the proposed metamodel-based inverse method not only enhances the efficiency of parameter identification but also gives reliable results.

Application of blocking diagnosis methods to general circulation models. Part II: model simulations

Energy Technology Data Exchange (ETDEWEB)

Barriopedro, D.; Trigo, R.M. [Universidade de Lisboa, CGUL-IDL, Faculdade de Ciencias, Lisbon (Portugal); Garcia-Herrera, R.; Gonzalez-Rouco, J.F. [Universidad Complutense de Madrid, Departamento de Fisica de la Tierra II, Facultad de C.C. Fisicas, Madrid (Spain)

2010-12-15

A previously defined automatic method is applied to reanalysis and present-day (1950-1989) forced simulations of the ECHO-G model in order to assess its performance in reproducing atmospheric blocking in the Northern Hemisphere. Unlike previous methodologies, critical parameters and thresholds to estimate blocking occurrence in the model are not calibrated with an observed reference, but objectively derived from the simulated climatology. The choice of model dependent parameters allows for an objective definition of blocking and corrects for some intrinsic model bias, the difference between model and observed thresholds providing a measure of systematic errors in the model. The model captures reasonably the main blocking features (location, amplitude, annual cycle and persistence) found in observations, but reveals a relative southward shift of Eurasian blocks and an overall underestimation of blocking activity, especially over the Euro-Atlantic sector. Blocking underestimation mostly arises from the model inability to generate long persistent blocks with the observed frequency. This error is mainly attributed to a bias in the basic state. The bias pattern consists of excessive zonal winds over the Euro-Atlantic sector and a southward shift at the exit zone of the jet stream extending into in the Eurasian continent, that are more prominent in cold and warm seasons and account for much of Euro-Atlantic and Eurasian blocking errors, respectively. It is shown that other widely used blocking indices or empirical observational thresholds may not give a proper account of the lack of realism in the model as compared with the proposed method. This suggests that in addition to blocking changes that could be ascribed to natural variability processes or climate change signals in the simulated climate, attention should be paid to significant departures in the diagnosis of phenomena that can also arise from an inappropriate adaptation of detection methods to the climate of the

Moments Method for Shell-Model Level Density

International Nuclear Information System (INIS)

Zelevinsky, V; Horoi, M; Sen'kov, R A

2016-01-01

The modern form of the Moments Method applied to the calculation of the nuclear shell-model level density is explained and examples of the method at work are given. The calculated level density practically exactly coincides with the result of full diagonalization when the latter is feasible. The method provides the pure level density for given spin and parity with spurious center-of-mass excitations subtracted. The presence and interplay of all correlations leads to the results different from those obtained by the mean-field combinatorics. (paper)

A numerical method for a transient two-fluid model

International Nuclear Information System (INIS)

Le Coq, G.; Libmann, M.

1978-01-01

The transient boiling two-phase flow is studied. In nuclear reactors, the driving conditions for the transient boiling are a pump power decay or/and an increase in heating power. The physical model adopted for the two-phase flow is the two fluid model with the assumption that the vapor remains at saturation. The numerical method for solving the thermohydraulics problems is a shooting method, this method is highly implicit. A particular problem exists at the boiling and condensation front. A computer code using this numerical method allow the calculation of a transient boiling initiated by a steady state for a PWR or for a LMFBR

Evaluation of two updating methods for dissipative models on a real structure

International Nuclear Information System (INIS)

Moine, P.; Billet, L.

1996-01-01

Finite Element Models are widely used to predict the dynamic behaviour from structures. Frequently, the model does not represent the structure with all be expected accuracy i.e. the measurements realised on the structure differ from the data predicted by the model. It is therefore necessary to update the model. Although many modeling errors come from inadequate representation of the damping phenomena, most of the model updating techniques are up to now restricted to conservative models only. In this paper, we present two updating methods for dissipative models using Eigen mode shapes and Eigen values as behavioural information from the structure. The first method - the modal output error method - compares directly the experimental Eigen vectors and Eigen values to the model Eigen vectors and Eigen values whereas the second method - the error in constitutive relation method - uses an energy error derived from the equilibrium relation. The error function, in both cases, is minimized by a conjugate gradient algorithm and the gradient is calculated analytically. These two methods behave differently which can be evidenced by updating a real structure constituted from a piece of pipe mounted on two viscous elastic suspensions. The updating of the model validates an updating strategy consisting in realizing a preliminary updating with the error in constitutive relation method (a fast to converge but difficult to control method) and then to pursue the updating with the modal output error method (a slow to converge but reliable and easy to control method). Moreover the problems encountered during the updating process and their corresponding solutions are given. (authors)

Semi-Lagrangian methods in air pollution models

Directory of Open Access Journals (Sweden)

A. B. Hansen

2011-06-01

Full Text Available Various semi-Lagrangian methods are tested with respect to advection in air pollution modeling. The aim is to find a method fulfilling as many of the desirable properties by Rasch andWilliamson (1990 and Machenhauer et al. (2008 as possible. The focus in this study is on accuracy and local mass conservation.

The methods tested are, first, classical semi-Lagrangian cubic interpolation, see e.g. Durran (1999, second, semi-Lagrangian cubic cascade interpolation, by Nair et al. (2002, third, semi-Lagrangian cubic interpolation with the modified interpolation weights, Locally Mass Conserving Semi-Lagrangian (LMCSL, by Kaas (2008, and last, semi-Lagrangian cubic interpolation with a locally mass conserving monotonic filter by Kaas and Nielsen (2010.

Semi-Lagrangian (SL interpolation is a classical method for atmospheric modeling, cascade interpolation is more efficient computationally, modified interpolation weights assure mass conservation and the locally mass conserving monotonic filter imposes monotonicity.

All schemes are tested with advection alone or with advection and chemistry together under both typical rural and urban conditions using different temporal and spatial resolution. The methods are compared with a current state-of-the-art scheme, Accurate Space Derivatives (ASD, see Frohn et al. (2002, presently used at the National Environmental Research Institute (NERI in Denmark. To enable a consistent comparison only non-divergent flow configurations are tested.

The test cases are based either on the traditional slotted cylinder or the rotating cone, where the schemes' ability to model both steep gradients and slopes are challenged.

The tests showed that the locally mass conserving monotonic filter improved the results significantly for some of the test cases, however, not for all. It was found that the semi-Lagrangian schemes, in almost every case, were not able to outperform the current ASD scheme

On Angular Sampling Methods for 3-D Spatial Channel Models

DEFF Research Database (Denmark)

Fan, Wei; Jämsä, Tommi; Nielsen, Jesper Ødum

2015-01-01

This paper discusses generating three dimensional (3D) spatial channel models with emphasis on the angular sampling methods. Three angular sampling methods, i.e. modified uniform power sampling, modified uniform angular sampling, and random pairing methods are proposed and investigated in detail....... The random pairing method, which uses only twenty sinusoids in the ray-based model for generating the channels, presents good results if the spatial channel cluster is with a small elevation angle spread. For spatial clusters with large elevation angle spreads, however, the random pairing method would fail...... and the other two methods should be considered....

A longitudinal multilevel CFA-MTMM model for interchangeable and structurally different methods

Science.gov (United States)

Koch, Tobias; Schultze, Martin; Eid, Michael; Geiser, Christian

2014-01-01

One of the key interests in the social sciences is the investigation of change and stability of a given attribute. Although numerous models have been proposed in the past for analyzing longitudinal data including multilevel and/or latent variable modeling approaches, only few modeling approaches have been developed for studying the construct validity in longitudinal multitrait-multimethod (MTMM) measurement designs. The aim of the present study was to extend the spectrum of current longitudinal modeling approaches for MTMM analysis. Specifically, a new longitudinal multilevel CFA-MTMM model for measurement designs with structurally different and interchangeable methods (called Latent-State-Combination-Of-Methods model, LS-COM) is presented. Interchangeable methods are methods that are randomly sampled from a set of equivalent methods (e.g., multiple student ratings for teaching quality), whereas structurally different methods are methods that cannot be easily replaced by one another (e.g., teacher, self-ratings, principle ratings). Results of a simulation study indicate that the parameters and standard errors in the LS-COM model are well recovered even in conditions with only five observations per estimated model parameter. The advantages and limitations of the LS-COM model relative to other longitudinal MTMM modeling approaches are discussed. PMID:24860515

Combustion Model and Control Parameter Optimization Methods for Single Cylinder Diesel Engine

Directory of Open Access Journals (Sweden)

Bambang Wahono

2014-01-01

Full Text Available This research presents a method to construct a combustion model and a method to optimize some control parameters of diesel engine in order to develop a model-based control system. The construction purpose of the model is to appropriately manage some control parameters to obtain the values of fuel consumption and emission as the engine output objectives. Stepwise method considering multicollinearity was applied to construct combustion model with the polynomial model. Using the experimental data of a single cylinder diesel engine, the model of power, BSFC, NOx, and soot on multiple injection diesel engines was built. The proposed method succesfully developed the model that describes control parameters in relation to the engine outputs. Although many control devices can be mounted to diesel engine, optimization technique is required to utilize this method in finding optimal engine operating conditions efficiently beside the existing development of individual emission control methods. Particle swarm optimization (PSO was used to calculate control parameters to optimize fuel consumption and emission based on the model. The proposed method is able to calculate control parameters efficiently to optimize evaluation item based on the model. Finally, the model which added PSO then was compiled in a microcontroller.

Theoretical methods and models for mechanical properties of soft biomaterials

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Zhonggang Feng

2017-06-01

Full Text Available We review the most commonly used theoretical methods and models for the mechanical properties of soft biomaterials, which include phenomenological hyperelastic and viscoelastic models, structural biphasic and network models, and the structural alteration theory. We emphasize basic concepts and recent developments. In consideration of the current progress and needs of mechanobiology, we introduce methods and models for tackling micromechanical problems and their applications to cell biology. Finally, the challenges and perspectives in this field are discussed.

Extending existing structural identifiability analysis methods to mixed-effects models.

Science.gov (United States)

Janzén, David L I; Jirstrand, Mats; Chappell, Michael J; Evans, Neil D

2018-01-01

The concept of structural identifiability for state-space models is expanded to cover mixed-effects state-space models. Two methods applicable for the analytical study of the structural identifiability of mixed-effects models are presented. The two methods are based on previously established techniques for non-mixed-effects models; namely the Taylor series expansion and the input-output form approach. By generating an exhaustive summary, and by assuming an infinite number of subjects, functions of random variables can be derived which in turn determine the distribution of the system's observation function(s). By considering the uniqueness of the analytical statistical moments of the derived functions of the random variables, the structural identifiability of the corresponding mixed-effects model can be determined. The two methods are applied to a set of examples of mixed-effects models to illustrate how they work in practice. Copyright © 2017 Elsevier Inc. All rights reserved.

Mathematical Models and Methods for Living Systems

CERN Document Server

Chaplain, Mark; Pugliese, Andrea

2016-01-01

The aim of these lecture notes is to give an introduction to several mathematical models and methods that can be used to describe the behaviour of living systems. This emerging field of application intrinsically requires the handling of phenomena occurring at different spatial scales and hence the use of multiscale methods. Modelling and simulating the mechanisms that cells use to move, self-organise and develop in tissues is not only fundamental to an understanding of embryonic development, but is also relevant in tissue engineering and in other environmental and industrial processes involving the growth and homeostasis of biological systems. Growth and organization processes are also important in many tissue degeneration and regeneration processes, such as tumour growth, tissue vascularization, heart and muscle functionality, and cardio-vascular diseases.

New component-based normalization method to correct PET system models

International Nuclear Information System (INIS)

Kinouchi, Shoko; Miyoshi, Yuji; Suga, Mikio; Yamaya, Taiga; Yoshida, Eiji; Nishikido, Fumihiko; Tashima, Hideaki

2011-01-01

Normalization correction is necessary to obtain high-quality reconstructed images in positron emission tomography (PET). There are two basic types of normalization methods: the direct method and component-based methods. The former method suffers from the problem that a huge count number in the blank scan data is required. Therefore, the latter methods have been proposed to obtain high statistical accuracy normalization coefficients with a small count number in the blank scan data. In iterative image reconstruction methods, on the other hand, the quality of the obtained reconstructed images depends on the system modeling accuracy. Therefore, the normalization weighing approach, in which normalization coefficients are directly applied to the system matrix instead of a sinogram, has been proposed. In this paper, we propose a new component-based normalization method to correct system model accuracy. In the proposed method, two components are defined and are calculated iteratively in such a way as to minimize errors of system modeling. To compare the proposed method and the direct method, we applied both methods to our small OpenPET prototype system. We achieved acceptable statistical accuracy of normalization coefficients while reducing the count number of the blank scan data to one-fortieth that required in the direct method. (author)

Markov chain Monte Carlo methods in directed graphical models

DEFF Research Database (Denmark)

Højbjerre, Malene

Directed graphical models present data possessing a complex dependence structure, and MCMC methods are computer-intensive simulation techniques to approximate high-dimensional intractable integrals, which emerge in such models with incomplete data. MCMC computations in directed graphical models h...

Acceptance of Addiction Prevention Exiting Methods and Presentation of Appropriate Model

Directory of Open Access Journals (Sweden)

Ali Asghar Savad-Kouhi

2006-10-01

Full Text Available Objective: The aim of this study is assessment of acceptance of addiction prevention existing methods and design and present of appropriate model. Materials & Methods: This research has done by survey and desariptive method by using questionnaire we assessed knowledge and belief of people about suggesting and existing methods of addiction prevention and their acceptence and finally design new and appropriate model of addiction prevention. For designing questionnaire, first exports and professors were openly interviewed and according their views final questionnaire was planned. We used questionnaire with 2 open ended and 61 close-ended tests for gathering data. The subjects of research were 2500 persons 13-35 years old that were selected by randomized sampling from 15 provinces. Results: The findings showed that according to people who were studied, they have positive beliefs about prevention methods and their effectiveness. According to findings a good model is inclusive model that able to do in four level: knowledge, change believe and attitude, control and change behavior. Conclusion: The people of study belive that acceptance of suggesting and existing methods of addiction prevention are effective direct and indirect to others, and appropriate model is inclusive model.

Linear model correction: A method for transferring a near-infrared multivariate calibration model without standard samples

Science.gov (United States)

Liu, Yan; Cai, Wensheng; Shao, Xueguang

2016-12-01

Calibration transfer is essential for practical applications of near infrared (NIR) spectroscopy because the measurements of the spectra may be performed on different instruments and the difference between the instruments must be corrected. For most of calibration transfer methods, standard samples are necessary to construct the transfer model using the spectra of the samples measured on two instruments, named as master and slave instrument, respectively. In this work, a method named as linear model correction (LMC) is proposed for calibration transfer without standard samples. The method is based on the fact that, for the samples with similar physical and chemical properties, the spectra measured on different instruments are linearly correlated. The fact makes the coefficients of the linear models constructed by the spectra measured on different instruments are similar in profile. Therefore, by using the constrained optimization method, the coefficients of the master model can be transferred into that of the slave model with a few spectra measured on slave instrument. Two NIR datasets of corn and plant leaf samples measured with different instruments are used to test the performance of the method. The results show that, for both the datasets, the spectra can be correctly predicted using the transferred partial least squares (PLS) models. Because standard samples are not necessary in the method, it may be more useful in practical uses.

Research on Multi - Person Parallel Modeling Method Based on Integrated Model Persistent Storage

Science.gov (United States)

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

2018-03-01

This paper mainly studies the multi-person parallel modeling method based on the integrated model persistence storage. The integrated model refers to a set of MDDT modeling graphics system, which can carry out multi-angle, multi-level and multi-stage description of aerospace general embedded software. Persistent storage refers to converting the data model in memory into a storage model and converting the storage model into a data model in memory, where the data model refers to the object model and the storage model is a binary stream. And multi-person parallel modeling refers to the need for multi-person collaboration, the role of separation, and even real-time remote synchronization modeling.

OBJECT ORIENTED MODELLING, A MODELLING METHOD OF AN ECONOMIC ORGANIZATION ACTIVITY

Directory of Open Access Journals (Sweden)

TĂNĂSESCU ANA

2014-05-01

Full Text Available Now, most economic organizations use different information systems types in order to facilitate their activity. There are different methodologies, methods and techniques that can be used to design information systems. In this paper, I propose to present the advantages of using the object oriented modelling at the information system design of an economic organization. Thus, I have modelled the activity of a photo studio, using Visual Paradigm for UML as a modelling tool. For this purpose, I have identified the use cases for the analyzed system and I have presented the use case diagram. I have, also, realized the system static and dynamic modelling, through the most known UML diagrams.

Methods of mathematical modelling continuous systems and differential equations

CERN Document Server

Witelski, Thomas

2015-01-01

This book presents mathematical modelling and the integrated process of formulating sets of equations to describe real-world problems. It describes methods for obtaining solutions of challenging differential equations stemming from problems in areas such as chemical reactions, population dynamics, mechanical systems, and fluid mechanics. Chapters 1 to 4 cover essential topics in ordinary differential equations, transport equations and the calculus of variations that are important for formulating models. Chapters 5 to 11 then develop more advanced techniques including similarity solutions, matched asymptotic expansions, multiple scale analysis, long-wave models, and fast/slow dynamical systems. Methods of Mathematical Modelling will be useful for advanced undergraduate or beginning graduate students in applied mathematics, engineering and other applied sciences.

Nonstandard Finite Difference Method Applied to a Linear Pharmacokinetics Model

Directory of Open Access Journals (Sweden)

Oluwaseun Egbelowo

2017-05-01

Full Text Available We extend the nonstandard finite difference method of solution to the study of pharmacokinetic–pharmacodynamic models. Pharmacokinetic (PK models are commonly used to predict drug concentrations that drive controlled intravenous (I.V. transfers (or infusion and oral transfers while pharmacokinetic and pharmacodynamic (PD interaction models are used to provide predictions of drug concentrations affecting the response of these clinical drugs. We structure a nonstandard finite difference (NSFD scheme for the relevant system of equations which models this pharamcokinetic process. We compare the results obtained to standard methods. The scheme is dynamically consistent and reliable in replicating complex dynamic properties of the relevant continuous models for varying step sizes. This study provides assistance in understanding the long-term behavior of the drug in the system, and validation of the efficiency of the nonstandard finite difference scheme as the method of choice.

The Langevin method and Hubbard-like models

International Nuclear Information System (INIS)

Gross, M.; Hamber, H.

1989-01-01

The authors reexamine the difficulties associated with application of the Langevin method to numerical simulation of models with non-positive definite statistical weights, including the Hubbard model. They show how to avoid the violent crossing of the zeroes of the weight and how to move those nodes away from the real axis. However, it still appears necessary to keep track of the sign (or phase) of the weight

Methods for testing transport models

International Nuclear Information System (INIS)

Singer, C.; Cox, D.

1991-01-01

Substantial progress has been made over the past year on six aspects of the work supported by this grant. As a result, we have in hand for the first time a fairly complete set of transport models and improved statistical methods for testing them against large databases. We also have initial results of such tests. These results indicate that careful application of presently available transport theories can reasonably well produce a remarkably wide variety of tokamak data

A method to couple HEM and HRM two-phase flow models

Energy Technology Data Exchange (ETDEWEB)

Herard, J.M.; Hurisse, O. [Elect France, Div Rech and Dev, Dept Mecan Fluides Energies and Environm, F-78401 Chatou (France); Hurisse, O. [Univ Aix Marseille 1, Ctr Math and Informat, Lab Anal Topol and Probabil, CNRS, UMR 6632, F-13453 Marseille 13 (France); Ambroso, A. [CEA Saclay, DEN, DM2S, SFME, LETR, 91 - Gif sur Yvette (France)

2009-04-15

We present a method for the unsteady coupling of two distinct two-phase flow models (namely the Homogeneous Relaxation Model, and the Homogeneous Equilibrium Model) through a thin interface. The basic approach relies on recent works devoted to the interfacial coupling of CFD models, and thus requires to introduce an interface model. Many numerical test cases enable to investigate the stability of the coupling method. (authors)

A method to couple HEM and HRM two-phase flow models

International Nuclear Information System (INIS)

Herard, J.M.; Hurisse, O.; Hurisse, O.; Ambroso, A.

2009-01-01

We present a method for the unsteady coupling of two distinct two-phase flow models (namely the Homogeneous Relaxation Model, and the Homogeneous Equilibrium Model) through a thin interface. The basic approach relies on recent works devoted to the interfacial coupling of CFD models, and thus requires to introduce an interface model. Many numerical test cases enable to investigate the stability of the coupling method. (authors)

Comparative analysis of various methods for modelling permanent magnet machines

NARCIS (Netherlands)

Ramakrishnan, K.; Curti, M.; Zarko, D.; Mastinu, G.; Paulides, J.J.H.; Lomonova, E.A.

2017-01-01

In this paper, six different modelling methods for permanent magnet (PM) electric machines are compared in terms of their computational complexity and accuracy. The methods are based primarily on conformal mapping, mode matching, and harmonic modelling. In the case of conformal mapping, slotted air

An alternative method for centrifugal compressor loading factor modelling

Science.gov (United States)

Galerkin, Y.; Drozdov, A.; Rekstin, A.; Soldatova, K.

2017-08-01

The loading factor at design point is calculated by one or other empirical formula in classical design methods. Performance modelling as a whole is out of consideration. Test data of compressor stages demonstrates that loading factor versus flow coefficient at the impeller exit has a linear character independent of compressibility. Known Universal Modelling Method exploits this fact. Two points define the function - loading factor at design point and at zero flow rate. The proper formulae include empirical coefficients. A good modelling result is possible if the choice of coefficients is based on experience and close analogs. Earlier Y. Galerkin and K. Soldatova had proposed to define loading factor performance by the angle of its inclination to the ordinate axis and by the loading factor at zero flow rate. Simple and definite equations with four geometry parameters were proposed for loading factor performance calculated for inviscid flow. The authors of this publication have studied the test performance of thirteen stages of different types. The equations are proposed with universal empirical coefficients. The calculation error lies in the range of plus to minus 1,5%. The alternative model of a loading factor performance modelling is included in new versions of the Universal Modelling Method.

Methods for teaching geometric modelling and computer graphics

Energy Technology Data Exchange (ETDEWEB)

Rotkov, S.I.; Faitel`son, Yu. Ts.

1992-05-01

This paper considers methods for teaching the methods and algorithms of geometric modelling and computer graphics to programmers, designers and users of CAD and computer-aided research systems. There is a bibliography that can be used to prepare lectures and practical classes. 37 refs., 1 tab.

Method for modeling social care processes for national information exchange.

Science.gov (United States)

Miettinen, Aki; Mykkänen, Juha; Laaksonen, Maarit

2012-01-01

Finnish social services include 21 service commissions of social welfare including Adoption counselling, Income support, Child welfare, Services for immigrants and Substance abuse care. This paper describes the method used for process modeling in the National project for IT in Social Services in Finland (Tikesos). The process modeling in the project aimed to support common national target state processes from the perspective of national electronic archive, increased interoperability between systems and electronic client documents. The process steps and other aspects of the method are presented. The method was developed, used and refined during the three years of process modeling in the national project.

Solving the nuclear shell model with an algebraic method

International Nuclear Information System (INIS)

Feng, D.H.; Pan, X.W.; Guidry, M.

1997-01-01

We illustrate algebraic methods in the nuclear shell model through a concrete example, the fermion dynamical symmetry model (FDSM). We use this model to introduce important concepts such as dynamical symmetry, symmetry breaking, effective symmetry, and diagonalization within a higher-symmetry basis. (orig.)

A method for model identification and parameter estimation

International Nuclear Information System (INIS)

Bambach, M; Heinkenschloss, M; Herty, M

2013-01-01

We propose and analyze a new method for the identification of a parameter-dependent model that best describes a given system. This problem arises, for example, in the mathematical modeling of material behavior where several competing constitutive equations are available to describe a given material. In this case, the models are differential equations that arise from the different constitutive equations, and the unknown parameters are coefficients in the constitutive equations. One has to determine the best-suited constitutive equations for a given material and application from experiments. We assume that the true model is one of the N possible parameter-dependent models. To identify the correct model and the corresponding parameters, we can perform experiments, where for each experiment we prescribe an input to the system and observe a part of the system state. Our approach consists of two stages. In the first stage, for each pair of models we determine the experiment, i.e. system input and observation, that best differentiates between the two models, and measure the distance between the two models. Then we conduct N(N − 1) or, depending on the approach taken, N(N − 1)/2 experiments and use the result of the experiments as well as the previously computed model distances to determine the true model. We provide sufficient conditions on the model distances and measurement errors which guarantee that our approach identifies the correct model. Given the model, we identify the corresponding model parameters in the second stage. The problem in the second stage is a standard parameter estimation problem and we use a method suitable for the given application. We illustrate our approach on three examples, including one where the models are elliptic partial differential equations with different parameterized right-hand sides and an example where we identify the constitutive equation in a problem from computational viscoplasticity. (paper)

Numerical Modelling of the Special Light Source with Novel R-FEM Method

Directory of Open Access Journals (Sweden)

Pavel Fiala

2008-01-01

Full Text Available This paper presents information about new directions in the modelling of lighting systems, and an overview of methods for the modelling of lighting systems. The novel R-FEM method is described, which is a combination of the Radiosity method and the Finite Elements Method (FEM. The paper contains modelling results and their verification by experimental measurements and by the Matlab simulation for this R-FEM method.

Architecture oriented modeling and simulation method for combat mission profile

Directory of Open Access Journals (Sweden)

CHEN Xia

2017-05-01

Full Text Available In order to effectively analyze the system behavior and system performance of combat mission profile, an architecture-oriented modeling and simulation method is proposed. Starting from the architecture modeling,this paper describes the mission profile based on the definition from National Military Standard of China and the US Department of Defense Architecture Framework(DoDAFmodel, and constructs the architecture model of the mission profile. Then the transformation relationship between the architecture model and the agent simulation model is proposed to form the mission profile executable model. At last,taking the air-defense mission profile as an example,the agent simulation model is established based on the architecture model,and the input and output relations of the simulation model are analyzed. It provides method guidance for the combat mission profile design.

Method and apparatus for modeling, visualization and analysis of materials

KAUST Repository

Aboulhassan, Amal

2016-08-25

A method, apparatus, and computer readable medium are provided for modeling of materials and visualization of properties of the materials. An example method includes receiving data describing a set of properties of a material, and computing, by a processor and based on the received data, geometric features of the material. The example method further includes extracting, by the processor, particle paths within the material based on the computed geometric features, and geometrically modeling, by the processor, the material using the geometric features and the extracted particle paths. The example method further includes generating, by the processor and based on the geometric modeling of the material, one or more visualizations regarding the material, and causing display, by a user interface, of the one or more visualizations.

Easy handling of tectonic data: the programs TectonicVB for Mac and TectonicsFP for Windows™

Science.gov (United States)

Ortner, Hugo; Reiter, Franz; Acs, Peter

2002-12-01

TectonicVB for Macintosh and TectonicsFP for Windows TM operating systems are two menu-driven computer programs which allow the shared use of data on these environments. The programs can produce stereographic plots of orientation data (great circles, poles, lineations). Frequently used statistical procedures like calculation of eigenvalues and eigenvectors, calculation of mean vector with concentration parameters and confidence cone can be easily performed. Fault data can be plotted in stereographic projection (Angelier and Hoeppener plots). Sorting of datasets into homogeneous subsets and rotation of tectonic data can be performed in interactive two-diagram windows. The paleostress tensor can be calculated from fault data sets using graphical (calculation of kinematic axes and right dihedra method) or mathematical methods (direct inversion or numerical dynamical analysis). The calculations can be checked in dimensionless Mohr diagrams and fluctuation histograms.

Method for modeling post-mortem biometric 3D fingerprints

Science.gov (United States)

Rajeev, Srijith; Shreyas, Kamath K. M.; Agaian, Sos S.

2016-05-01

Despite the advancements of fingerprint recognition in 2-D and 3-D domain, authenticating deformed/post-mortem fingerprints continue to be an important challenge. Prior cleansing and reconditioning of the deceased finger is required before acquisition of the fingerprint. The victim's finger needs to be precisely and carefully operated by a medium to record the fingerprint impression. This process may damage the structure of the finger, which subsequently leads to higher false rejection rates. This paper proposes a non-invasive method to perform 3-D deformed/post-mortem finger modeling, which produces a 2-D rolled equivalent fingerprint for automated verification. The presented novel modeling method involves masking, filtering, and unrolling. Computer simulations were conducted on finger models with different depth variations obtained from Flashscan3D LLC. Results illustrate that the modeling scheme provides a viable 2-D fingerprint of deformed models for automated verification. The quality and adaptability of the obtained unrolled 2-D fingerprints were analyzed using NIST fingerprint software. Eventually, the presented method could be extended to other biometric traits such as palm, foot, tongue etc. for security and administrative applications.

Dynamic model based on Bayesian method for energy security assessment

International Nuclear Information System (INIS)

Augutis, Juozas; Krikštolaitis, Ričardas; Pečiulytė, Sigita; Žutautaitė, Inga

2015-01-01

Highlights: • Methodology for dynamic indicator model construction and forecasting of indicators. • Application of dynamic indicator model for energy system development scenarios. • Expert judgement involvement using Bayesian method. - Abstract: The methodology for the dynamic indicator model construction and forecasting of indicators for the assessment of energy security level is presented in this article. An indicator is a special index, which provides numerical values to important factors for the investigated area. In real life, models of different processes take into account various factors that are time-dependent and dependent on each other. Thus, it is advisable to construct a dynamic model in order to describe these dependences. The energy security indicators are used as factors in the dynamic model. Usually, the values of indicators are obtained from statistical data. The developed dynamic model enables to forecast indicators’ variation taking into account changes in system configuration. The energy system development is usually based on a new object construction. Since the parameters of changes of the new system are not exactly known, information about their influences on indicators could not be involved in the model by deterministic methods. Thus, dynamic indicators’ model based on historical data is adjusted by probabilistic model with the influence of new factors on indicators using the Bayesian method

Fuzzy cross-model cross-mode method and its application to update the finite element model of structures

International Nuclear Information System (INIS)

Liu Yang; Xu Dejian; Li Yan; Duan Zhongdong

2011-01-01

As a novel updating technique, cross-model cross-mode (CMCM) method possesses a high efficiency and capability of flexible selecting updating parameters. However, the success of this method depends on the accuracy of measured modal shapes. Usually, the measured modal shapes are inaccurate since many kinds of measured noises are inevitable. Furthermore, the complete testing modal shapes are required by CMCM method so that the calculating errors may be introduced into the measured modal shapes by conducting the modal expansion or model reduction technique. Therefore, this algorithm is faced with the challenge of updating the finite element (FE) model of practical complex structures. In this study, the fuzzy CMCM method is proposed in order to weaken the effect of errors of the measured modal shapes on the updated results. Then two simulated examples are applied to compare the performance of the fuzzy CMCM method with the CMCM method. The test results show that proposed method is more promising to update the FE model of practical structures than CMCM method.

A discontinuous Galerkin method on kinetic flocking models

OpenAIRE

Tan, Changhui

2014-01-01

We study kinetic representations of flocking models. They arise from agent-based models for self-organized dynamics, such as Cucker-Smale and Motsch-Tadmor models. We prove flocking behavior for the kinetic descriptions of flocking systems, which indicates a concentration in velocity variable in infinite time. We propose a discontinuous Galerkin method to treat the asymptotic $\\delta$-singularity, and construct high order positive preserving scheme to solve kinetic flocking systems.

Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

Science.gov (United States)

2016-01-05

SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model, is able to model the rate of occurrence of... kernel methods made use of: (i) the Bayesian property of improving predictive accuracy as data are dynamically obtained, and (ii) the kernel function

Seismic wavefield modeling based on time-domain symplectic and Fourier finite-difference method

Science.gov (United States)

Fang, Gang; Ba, Jing; Liu, Xin-xin; Zhu, Kun; Liu, Guo-Chang

2017-06-01

Seismic wavefield modeling is important for improving seismic data processing and interpretation. Calculations of wavefield propagation are sometimes not stable when forward modeling of seismic wave uses large time steps for long times. Based on the Hamiltonian expression of the acoustic wave equation, we propose a structure-preserving method for seismic wavefield modeling by applying the symplectic finite-difference method on time grids and the Fourier finite-difference method on space grids to solve the acoustic wave equation. The proposed method is called the symplectic Fourier finite-difference (symplectic FFD) method, and offers high computational accuracy and improves the computational stability. Using acoustic approximation, we extend the method to anisotropic media. We discuss the calculations in the symplectic FFD method for seismic wavefield modeling of isotropic and anisotropic media, and use the BP salt model and BP TTI model to test the proposed method. The numerical examples suggest that the proposed method can be used in seismic modeling of strongly variable velocities, offering high computational accuracy and low numerical dispersion. The symplectic FFD method overcomes the residual qSV wave of seismic modeling in anisotropic media and maintains the stability of the wavefield propagation for large time steps.

A heteroscedastic measurement error model for method comparison data with replicate measurements.

Science.gov (United States)

Nawarathna, Lakshika S; Choudhary, Pankaj K

2015-03-30

Measurement error models offer a flexible framework for modeling data collected in studies comparing methods of quantitative measurement. These models generally make two simplifying assumptions: (i) the measurements are homoscedastic, and (ii) the unobservable true values of the methods are linearly related. One or both of these assumptions may be violated in practice. In particular, error variabilities of the methods may depend on the magnitude of measurement, or the true values may be nonlinearly related. Data with these features call for a heteroscedastic measurement error model that allows nonlinear relationships in the true values. We present such a model for the case when the measurements are replicated, discuss its fitting, and explain how to evaluate similarity of measurement methods and agreement between them, which are two common goals of data analysis, under this model. Model fitting involves dealing with lack of a closed form for the likelihood function. We consider estimation methods that approximate either the likelihood or the model to yield approximate maximum likelihood estimates. The fitting methods are evaluated in a simulation study. The proposed methodology is used to analyze a cholesterol dataset. Copyright © 2015 John Wiley & Sons, Ltd.

Unsteady panel method for complex configurations including wake modeling

CSIR Research Space (South Africa)

Van Zyl, Lourens H

2008-01-01

Full Text Available implementations of the DLM are however not very versatile in terms of geometries that can be modeled. The ZONA6 code offers a versatile surface panel body model including a separated wake model, but uses a pressure panel method for lifting surfaces. This paper...

Soybean yield modeling using bootstrap methods for small samples

Energy Technology Data Exchange (ETDEWEB)

Dalposso, G.A.; Uribe-Opazo, M.A.; Johann, J.A.

2016-11-01

One of the problems that occur when working with regression models is regarding the sample size; once the statistical methods used in inferential analyzes are asymptotic if the sample is small the analysis may be compromised because the estimates will be biased. An alternative is to use the bootstrap methodology, which in its non-parametric version does not need to guess or know the probability distribution that generated the original sample. In this work we used a set of soybean yield data and physical and chemical soil properties formed with fewer samples to determine a multiple linear regression model. Bootstrap methods were used for variable selection, identification of influential points and for determination of confidence intervals of the model parameters. The results showed that the bootstrap methods enabled us to select the physical and chemical soil properties, which were significant in the construction of the soybean yield regression model, construct the confidence intervals of the parameters and identify the points that had great influence on the estimated parameters. (Author)

Acceleration methods and models in Sn calculations

International Nuclear Information System (INIS)

Sbaffoni, M.M.; Abbate, M.J.

1984-01-01

In some neutron transport problems solved by the discrete ordinate method, it is relatively common to observe some particularities as, for example, negative fluxes generation, slow and insecure convergences and solution instabilities. The commonly used models for neutron flux calculation and acceleration methods included in the most used codes were analyzed, in face of their use in problems characterized by a strong upscattering effect. Some special conclusions derived from this analysis are presented as well as a new method to perform the upscattering scaling for solving the before mentioned problems in this kind of cases. This method has been included in the DOT3.5 code (two dimensional discrete ordinates radiation transport code) generating a new version of wider application. (Author) [es

An Application of Taylor Models to the Nakao Method on ODEs

OpenAIRE

Yamamoto, Nobito; Komori, Takashi

2009-01-01

The authors give short survey on validated computaion of initial value problems for ODEs especially Taylor model methods. Then they propose an application of Taylor models to the Nakao method which has been developed for numerical verification methods on PDEs and apply it to initial value problems for ODEs with some numerical experiments.

Deformation data modeling through numerical models: an efficient method for tracking magma transport

Science.gov (United States)

Charco, M.; Gonzalez, P. J.; Galán del Sastre, P.

2017-12-01

Nowadays, multivariate collected data and robust physical models at volcano observatories are becoming crucial for providing effective volcano monitoring. Nevertheless, the forecast of volcanic eruption is notoriously difficult. Wthin this frame one of the most promising methods to evaluate the volcano hazard is the use of surface ground deformation and in the last decades many developments in the field of deformation modeling has been achieved. In particular, numerical modeling allows realistic media features such as topography and crustal heterogeneities to be included, although it is still very time cosuming to solve the inverse problem for near-real time interpretations. Here, we present a method that can be efficiently used to estimate the location and evolution of magmatic sources base on real-time surface deformation data and Finite Element (FE) models. Generally, the search for the best-fitting magmatic (point) source(s) is conducted for an array of 3-D locations extending below a predefined volume region and the Green functions for all the array components have to be precomputed. We propose a FE model for the pre-computation of Green functions in a mechanically heterogeneous domain which eventually will lead to a better description of the status of the volcanic area. The number of Green functions is reduced here to the number of observational points by using their reciprocity relationship. We present and test this methodology with an optimization method base on a Genetic Algorithm. Following synthetic and sensitivity test to estimate the uncertainty of the model parameters, we apply the tool for magma tracking during 2007 Kilauea volcano intrusion and eruption. We show how data inversion with numerical models can speed up the source parameters estimations for a given volcano showing signs of unrest.

Statistical learning modeling method for space debris photometric measurement

Science.gov (United States)

Sun, Wenjing; Sun, Jinqiu; Zhang, Yanning; Li, Haisen

2016-03-01

Photometric measurement is an important way to identify the space debris, but the present methods of photometric measurement have many constraints on star image and need complex image processing. Aiming at the problems, a statistical learning modeling method for space debris photometric measurement is proposed based on the global consistency of the star image, and the statistical information of star images is used to eliminate the measurement noises. First, the known stars on the star image are divided into training stars and testing stars. Then, the training stars are selected as the least squares fitting parameters to construct the photometric measurement model, and the testing stars are used to calculate the measurement accuracy of the photometric measurement model. Experimental results show that, the accuracy of the proposed photometric measurement model is about 0.1 magnitudes.

Quantitative sociodynamics stochastic methods and models of social interaction processes

CERN Document Server

Helbing, Dirk

1995-01-01

Quantitative Sociodynamics presents a general strategy for interdisciplinary model building and its application to a quantitative description of behavioural changes based on social interaction processes. Originally, the crucial methods for the modeling of complex systems (stochastic methods and nonlinear dynamics) were developed in physics but they have very often proved their explanatory power in chemistry, biology, economics and the social sciences. Quantitative Sociodynamics provides a unified and comprehensive overview of the different stochastic methods, their interrelations and properties. In addition, it introduces the most important concepts from nonlinear dynamics (synergetics, chaos theory). The applicability of these fascinating concepts to social phenomena is carefully discussed. By incorporating decision-theoretical approaches a very fundamental dynamic model is obtained which seems to open new perspectives in the social sciences. It includes many established models as special cases, e.g. the log...

Quantitative Sociodynamics Stochastic Methods and Models of Social Interaction Processes

CERN Document Server

Helbing, Dirk

2010-01-01

This new edition of Quantitative Sociodynamics presents a general strategy for interdisciplinary model building and its application to a quantitative description of behavioral changes based on social interaction processes. Originally, the crucial methods for the modeling of complex systems (stochastic methods and nonlinear dynamics) were developed in physics and mathematics, but they have very often proven their explanatory power in chemistry, biology, economics and the social sciences as well. Quantitative Sociodynamics provides a unified and comprehensive overview of the different stochastic methods, their interrelations and properties. In addition, it introduces important concepts from nonlinear dynamics (e.g. synergetics, chaos theory). The applicability of these fascinating concepts to social phenomena is carefully discussed. By incorporating decision-theoretical approaches, a fundamental dynamic model is obtained, which opens new perspectives in the social sciences. It includes many established models a...

A Novel Method for Decoding Any High-Order Hidden Markov Model

Directory of Open Access Journals (Sweden)

Fei Ye

2014-01-01

Full Text Available This paper proposes a novel method for decoding any high-order hidden Markov model. First, the high-order hidden Markov model is transformed into an equivalent first-order hidden Markov model by Hadar’s transformation. Next, the optimal state sequence of the equivalent first-order hidden Markov model is recognized by the existing Viterbi algorithm of the first-order hidden Markov model. Finally, the optimal state sequence of the high-order hidden Markov model is inferred from the optimal state sequence of the equivalent first-order hidden Markov model. This method provides a unified algorithm framework for decoding hidden Markov models including the first-order hidden Markov model and any high-order hidden Markov model.

Synthesis of Numerical Methods for Modeling Wave Energy Converter-Point Absorbers: Preprint

Energy Technology Data Exchange (ETDEWEB)

Li, Y.; Yu, Y. H.

2012-05-01

During the past few decades, wave energy has received significant attention among all ocean energy formats. Industry has proposed hundreds of prototypes such as an oscillating water column, a point absorber, an overtopping system, and a bottom-hinged system. In particular, many researchers have focused on modeling the floating-point absorber as the technology to extract wave energy. Several modeling methods have been used such as the analytical method, the boundary-integral equation method, the Navier-Stokes equations method, and the empirical method. However, no standardized method has been decided. To assist the development of wave energy conversion technologies, this report reviews the methods for modeling the floating-point absorber.

Reduction methods and uncertainty analysis: application to a Chemistry-Transport Model for modeling and simulation of impacts

International Nuclear Information System (INIS)

Boutahar, Jaouad

2004-01-01

In an integrated impact assessment, one has to test several scenarios of the model inputs or/and to identify the effects of model input uncertainties on the model outputs. In both cases, a large number of simulations of the model is necessary. That of course is not feasible with comprehensive Chemistry-Transport Model, due to the need for huge CPU times. Two approaches may be used in order to circumvent these difficulties: The first approach consists in reducing the computational cost of the original model by building a reduced model. Two reduction techniques are used: the first method, POD, is related to the statistical behaviour of the system and is based on a proper orthogonal decomposition of the solutions. The second method, is an efficient representation of the input/output behaviour through look-up tables. It describes the output model as an expansion of finite hierarchical correlated function in terms of the input variables. The second approach is based on reducing the number of models runs required by the standard Monte Carlo methods. It characterizes the probabilistic response of the uncertain model output as an expansion of orthogonal polynomials according to model inputs uncertainties. Then the classical Monte Carlo simulation can easily be used to compute the probability density of the uncertain output. Another key point in an integrated impact assessment is to develop strategies for the reduction of emissions by computing Source/Receptor matrices for several years of simulations. We proposed here an efficient method to calculate these matrices by using the adjoint model and in particular by defining the 'representative chemical day'. All of these methods are applied to POLAIR3D, a Chemistry-Transport model developed in this thesis. (author) [fr

Modeling dose-rate on/over the surface of cylindrical radio-models using Monte Carlo methods

International Nuclear Information System (INIS)

Xiao Xuefu; Ma Guoxue; Wen Fuping; Wang Zhongqi; Wang Chaohui; Zhang Jiyun; Huang Qingbo; Zhang Jiaqiu; Wang Xinxing; Wang Jun

2004-01-01

Objective: To determine the dose-rates on/over the surface of 10 cylindrical radio-models, which belong to the Metrology Station of Radio-Geological Survey of CNNC. Methods: The dose-rates on/over the surface of 10 cylindrical radio-models were modeled using the famous Monte Carlo code-MCNP. The dose-rates on/over the surface of 10 cylindrical radio-models were measured by a high gas pressurized ionization chamber dose-rate meter, respectively. The values of dose-rate modeled using MCNP code were compared with those obtained by authors in the present experimental measurement, and with those obtained by other workers previously. Some factors causing the discrepancy between the data obtained by authors using MCNP code and the data obtained using other methods are discussed in this paper. Results: The data of dose-rates on/over the surface of 10 cylindrical radio-models, obtained using MCNP code, were in good agreement with those obtained by other workers using the theoretical method. They were within the discrepancy of ±5% in general, and the maximum discrepancy was less than 10%. Conclusions: As if each factor needed for the Monte Carlo code is correct, the dose-rates on/over the surface of cylindrical radio-models modeled using the Monte Carlo code are correct with an uncertainty of 3%

Application of model-based and knowledge-based measuring methods as analytical redundancy

International Nuclear Information System (INIS)

Hampel, R.; Kaestner, W.; Chaker, N.; Vandreier, B.

1997-01-01

The safe operation of nuclear power plants requires the application of modern and intelligent methods of signal processing for the normal operation as well as for the management of accident conditions. Such modern and intelligent methods are model-based and knowledge-based ones being founded on analytical knowledge (mathematical models) as well as experiences (fuzzy information). In addition to the existing hardware redundancies analytical redundancies will be established with the help of these modern methods. These analytical redundancies support the operating staff during the decision-making. The design of a hybrid model-based and knowledge-based measuring method will be demonstrated by the example of a fuzzy-supported observer. Within the fuzzy-supported observer a classical linear observer is connected with a fuzzy-supported adaptation of the model matrices of the observer model. This application is realized for the estimation of the non-measurable variables as steam content and mixture level within pressure vessels with water-steam mixture during accidental depressurizations. For this example the existing non-linearities will be classified and the verification of the model will be explained. The advantages of the hybrid method in comparison to the classical model-based measuring methods will be demonstrated by the results of estimation. The consideration of the parameters which have an important influence on the non-linearities requires the inclusion of high-dimensional structures of fuzzy logic within the model-based measuring methods. Therefore methods will be presented which allow the conversion of these high-dimensional structures to two-dimensional structures of fuzzy logic. As an efficient solution of this problem a method based on cascaded fuzzy controllers will be presented. (author). 2 refs, 12 figs, 5 tabs

Improved Cell Culture Method for Growing Contracting Skeletal Muscle Models

Science.gov (United States)

Marquette, Michele L.; Sognier, Marguerite A.

2013-01-01

An improved method for culturing immature muscle cells (myoblasts) into a mature skeletal muscle overcomes some of the notable limitations of prior culture methods. The development of the method is a major advance in tissue engineering in that, for the first time, a cell-based model spontaneously fuses and differentiates into masses of highly aligned, contracting myotubes. This method enables (1) the construction of improved two-dimensional (monolayer) skeletal muscle test beds; (2) development of contracting three-dimensional tissue models; and (3) improved transplantable tissues for biomedical and regenerative medicine applications. With adaptation, this method also offers potential application for production of other tissue types (i.e., bone and cardiac) from corresponding precursor cells.

Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation

Directory of Open Access Journals (Sweden)

Y. Tang

2007-01-01

Full Text Available This study seeks to identify sensitivity tools that will advance our understanding of lumped hydrologic models for the purposes of model improvement, calibration efficiency and improved measurement schemes. Four sensitivity analysis methods were tested: (1 local analysis using parameter estimation software (PEST, (2 regional sensitivity analysis (RSA, (3 analysis of variance (ANOVA, and (4 Sobol's method. The methods' relative efficiencies and effectiveness have been analyzed and compared. These four sensitivity methods were applied to the lumped Sacramento soil moisture accounting model (SAC-SMA coupled with SNOW-17. Results from this study characterize model sensitivities for two medium sized watersheds within the Juniata River Basin in Pennsylvania, USA. Comparative results for the 4 sensitivity methods are presented for a 3-year time series with 1 h, 6 h, and 24 h time intervals. The results of this study show that model parameter sensitivities are heavily impacted by the choice of analysis method as well as the model time interval. Differences between the two adjacent watersheds also suggest strong influences of local physical characteristics on the sensitivity methods' results. This study also contributes a comprehensive assessment of the repeatability, robustness, efficiency, and ease-of-implementation of the four sensitivity methods. Overall ANOVA and Sobol's method were shown to be superior to RSA and PEST. Relative to one another, ANOVA has reduced computational requirements and Sobol's method yielded more robust sensitivity rankings.

Models and methods for hot spot safety work

DEFF Research Database (Denmark)

Vistisen, Dorte

2002-01-01

Despite the fact that millions DKK each year are spent on improving roadsafety in Denmark, funds for traffic safety are limited. It is therefore vital to spend the resources as effectively as possible. This thesis is concerned with the area of traffic safety denoted "hot spot safety work", which...... is the task of improving road safety through alterations of the geometrical and environmental characteristics of the existing road network. The presently applied models and methods in hot spot safety work on the Danish road network were developed about two decades ago, when data was more limited and software...... and statistical methods less developed. The purpose of this thesis is to contribute to improving "State of the art" in Denmark. Basis for the systematic hot spot safety work are the models describing the variation in accident counts on the road network. In the thesis hierarchical models disaggregated on time...

Multi-level decision making models, methods and applications

CERN Document Server

Zhang, Guangquan; Gao, Ya

2015-01-01

This monograph presents new developments in multi-level decision-making theory, technique and method in both modeling and solution issues. It especially presents how a decision support system can support managers in reaching a solution to a multi-level decision problem in practice. This monograph combines decision theories, methods, algorithms and applications effectively. It discusses in detail the models and solution algorithms of each issue of bi-level and tri-level decision-making, such as multi-leaders, multi-followers, multi-objectives, rule-set-based, and fuzzy parameters. Potential readers include organizational managers and practicing professionals, who can use the methods and software provided to solve their real decision problems; PhD students and researchers in the areas of bi-level and multi-level decision-making and decision support systems; students at an advanced undergraduate, master’s level in information systems, business administration, or the application of computer science.  

MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method.

Science.gov (United States)

Tuta, Jure; Juric, Matjaz B

2018-03-24

This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method), a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah) and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.). Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage.

MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method

Directory of Open Access Journals (Sweden)

Jure Tuta

2018-03-01

Full Text Available This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method, a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.. Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage.

Explicitly represented polygon wall boundary model for the explicit MPS method

Science.gov (United States)

Mitsume, Naoto; Yoshimura, Shinobu; Murotani, Kohei; Yamada, Tomonori

2015-05-01

This study presents an accurate and robust boundary model, the explicitly represented polygon (ERP) wall boundary model, to treat arbitrarily shaped wall boundaries in the explicit moving particle simulation (E-MPS) method, which is a mesh-free particle method for strong form partial differential equations. The ERP model expresses wall boundaries as polygons, which are explicitly represented without using the distance function. These are derived so that for viscous fluids, and with less computational cost, they satisfy the Neumann boundary condition for the pressure and the slip/no-slip condition on the wall surface. The proposed model is verified and validated by comparing computed results with the theoretical solution, results obtained by other models, and experimental results. Two simulations with complex boundary movements are conducted to demonstrate the applicability of the E-MPS method to the ERP model.

Evaluation of internal noise methods for Hotelling observer models

International Nuclear Information System (INIS)

Zhang Yani; Pham, Binh T.; Eckstein, Miguel P.

2007-01-01

The inclusion of internal noise in model observers is a common method to allow for quantitative comparisons between human and model observer performance in visual detection tasks. In this article, we studied two different strategies for inserting internal noise into Hotelling model observers. In the first strategy, internal noise was added to the output of individual channels: (a) Independent nonuniform channel noise, (b) independent uniform channel noise. In the second strategy, internal noise was added to the decision variable arising from the combination of channel responses. The standard deviation of the zero mean internal noise was either constant or proportional to: (a) the decision variable's standard deviation due to the external noise, (b) the decision variable's variance caused by the external noise, (c) the decision variable magnitude on a trial to trial basis. We tested three model observers: square window Hotelling observer (HO), channelized Hotelling observer (CHO), and Laguerre-Gauss Hotelling observer (LGHO) using a four alternative forced choice (4AFC) signal known exactly but variable task with a simulated signal embedded in real x-ray coronary angiogram backgrounds. The results showed that the internal noise method that led to the best prediction of human performance differed across the studied model observers. The CHO model best predicted human observer performance with the channel internal noise. The HO and LGHO best predicted human observer performance with the decision variable internal noise. The present results might guide researchers with the choice of methods to include internal noise into Hotelling model observers when evaluating and optimizing medical image quality

Developing a TQM quality management method model

NARCIS (Netherlands)

Zhang, Zhihai

1997-01-01

From an extensive review of total quality management literature, the external and internal environment affecting an organization's quality performance and the eleven primary elements of TQM are identified. Based on the primary TQM elements, a TQM quality management method model is developed. This

Impacts modeling using the SPH particulate method. Case study

International Nuclear Information System (INIS)

Debord, R.

1999-01-01

The aim of this study is the modeling of the impact of melted metal on the reactor vessel head in the case of a core-meltdown accident. Modeling using the classical finite-element method alone is not sufficient but requires a coupling with particulate methods in order to take into account the behaviour of the corium. After a general introduction about particulate methods, the Nabor and SPH (smoothed particle hydrodynamics) methods are described. Then, the theoretical and numerical reliability of the SPH method is determined using simple cases. In particular, the number of neighbours significantly influences the preciseness of calculations. Also, the mesh of the structure must be adapted to the mesh of the fluid in order to reduce the edge effects. Finally, this study has shown that the values of artificial velocity coefficients used in the simulation of the BERDA test performed by the FZK Karlsruhe (Germany) are not correct. The domain of use of these coefficients was precised during a low speed impact. (J.S.)

Stencil method: a Markov model for transport in porous media

Science.gov (United States)

Delgoshaie, A. H.; Tchelepi, H.; Jenny, P.

2016-12-01

In porous media the transport of fluid is dominated by flow-field heterogeneity resulting from the underlying transmissibility field. Since the transmissibility is highly uncertain, many realizations of a geological model are used to describe the statistics of the transport phenomena in a Monte Carlo framework. One possible way to avoid the high computational cost of physics-based Monte Carlo simulations is to model the velocity field as a Markov process and use Markov Chain Monte Carlo. In previous works multiple Markov models for discrete velocity processes have been proposed. These models can be divided into two general classes of Markov models in time and Markov models in space. Both of these choices have been shown to be effective to some extent. However some studies have suggested that the Markov property cannot be confirmed for a temporal Markov process; Therefore there is not a consensus about the validity and value of Markov models in time. Moreover, previous spacial Markov models have only been used for modeling transport on structured networks and can not be readily applied to model transport in unstructured networks. In this work we propose a novel approach for constructing a Markov model in time (stencil method) for a discrete velocity process. The results form the stencil method are compared to previously proposed spacial Markov models for structured networks. The stencil method is also applied to unstructured networks and can successfully describe the dispersion of particles in this setting. Our conclusion is that both temporal Markov models and spacial Markov models for discrete velocity processes can be valid for a range of model parameters. Moreover, we show that the stencil model can be more efficient in many practical settings and is suited to model dispersion both on structured and unstructured networks.

Physical Model Method for Seismic Study of Concrete Dams

Directory of Open Access Journals (Sweden)

Bogdan Roşca

2008-01-01

Full Text Available The study of the dynamic behaviour of concrete dams by means of the physical model method is very useful to understand the failure mechanism of these structures to action of the strong earthquakes. Physical model method consists in two main processes. Firstly, a study model must be designed by a physical modeling process using the dynamic modeling theory. The result is a equations system of dimensioning the physical model. After the construction and instrumentation of the scale physical model a structural analysis based on experimental means is performed. The experimental results are gathered and are available to be analysed. Depending on the aim of the research may be designed an elastic or a failure physical model. The requirements for the elastic model construction are easier to accomplish in contrast with those required for a failure model, but the obtained results provide narrow information. In order to study the behaviour of concrete dams to strong seismic action is required the employment of failure physical models able to simulate accurately the possible opening of joint, sliding between concrete blocks and the cracking of concrete. The design relations for both elastic and failure physical models are based on dimensional analysis and consist of similitude relations among the physical quantities involved in the phenomenon. The using of physical models of great or medium dimensions as well as its instrumentation creates great advantages, but this operation involves a large amount of financial, logistic and time resources.

Railway Track Allocation: Models and Methods

DEFF Research Database (Denmark)

Lusby, Richard Martin; Larsen, Jesper; Ehrgott, Matthias

2011-01-01

Efficiently coordinating the movement of trains on a railway network is a central part of the planning process for a railway company. This paper reviews models and methods that have been proposed in the literature to assist planners in finding train routes. Since the problem of routing trains......, and train routing problems, group them by railway network type, and discuss track allocation from a strategic, tactical, and operational level....... on a railway network entails allocating the track capacity of the network (or part thereof) over time in a conflict-free manner, all studies that model railway track allocation in some capacity are considered relevant. We hence survey work on the train timetabling, train dispatching, train platforming...

Railway Track Allocation: Models and Methods

DEFF Research Database (Denmark)

Lusby, Richard Martin; Larsen, Jesper; Ehrgott, Matthias

Eciently coordinating the movement of trains on a railway network is a central part of the planning process for a railway company. This paper reviews models and methods that have been proposed in the literature to assist planners in nding train routes. Since the problem of routing trains......, and train routing problems, group them by railway network type, and discuss track allocation from a strategic, tactical, and operational level....... on a railway network entails allocating the track capacity of the network (or part thereof) over time in a con ict-free manner, all studies that model railway track allocation in some capacity are considered relevant. We hence survey work on the train timetabling, train dispatching, train platforming...

Dynamic systems models new methods of parameter and state estimation

CERN Document Server

2016-01-01

This monograph is an exposition of a novel method for solving inverse problems, a method of parameter estimation for time series data collected from simulations of real experiments. These time series might be generated by measuring the dynamics of aircraft in flight, by the function of a hidden Markov model used in bioinformatics or speech recognition or when analyzing the dynamics of asset pricing provided by the nonlinear models of financial mathematics. Dynamic Systems Models demonstrates the use of algorithms based on polynomial approximation which have weaker requirements than already-popular iterative methods. Specifically, they do not require a first approximation of a root vector and they allow non-differentiable elements in the vector functions being approximated. The text covers all the points necessary for the understanding and use of polynomial approximation from the mathematical fundamentals, through algorithm development to the application of the method in, for instance, aeroplane flight dynamic...

Comparison of methods for the analysis of relatively simple mediation models.

Science.gov (United States)

Rijnhart, Judith J M; Twisk, Jos W R; Chinapaw, Mai J M; de Boer, Michiel R; Heymans, Martijn W

2017-09-01

Statistical mediation analysis is an often used method in trials, to unravel the pathways underlying the effect of an intervention on a particular outcome variable. Throughout the years, several methods have been proposed, such as ordinary least square (OLS) regression, structural equation modeling (SEM), and the potential outcomes framework. Most applied researchers do not know that these methods are mathematically equivalent when applied to mediation models with a continuous mediator and outcome variable. Therefore, the aim of this paper was to demonstrate the similarities between OLS regression, SEM, and the potential outcomes framework in three mediation models: 1) a crude model, 2) a confounder-adjusted model, and 3) a model with an interaction term for exposure-mediator interaction. Secondary data analysis of a randomized controlled trial that included 546 schoolchildren. In our data example, the mediator and outcome variable were both continuous. We compared the estimates of the total, direct and indirect effects, proportion mediated, and 95% confidence intervals (CIs) for the indirect effect across OLS regression, SEM, and the potential outcomes framework. OLS regression, SEM, and the potential outcomes framework yielded the same effect estimates in the crude mediation model, the confounder-adjusted mediation model, and the mediation model with an interaction term for exposure-mediator interaction. Since OLS regression, SEM, and the potential outcomes framework yield the same results in three mediation models with a continuous mediator and outcome variable, researchers can continue using the method that is most convenient to them.

Integral equation models for image restoration: high accuracy methods and fast algorithms

International Nuclear Information System (INIS)

Lu, Yao; Shen, Lixin; Xu, Yuesheng

2010-01-01

Discrete models are consistently used as practical models for image restoration. They are piecewise constant approximations of true physical (continuous) models, and hence, inevitably impose bottleneck model errors. We propose to work directly with continuous models for image restoration aiming at suppressing the model errors caused by the discrete models. A systematic study is conducted in this paper for the continuous out-of-focus image models which can be formulated as an integral equation of the first kind. The resulting integral equation is regularized by the Lavrentiev method and the Tikhonov method. We develop fast multiscale algorithms having high accuracy to solve the regularized integral equations of the second kind. Numerical experiments show that the methods based on the continuous model perform much better than those based on discrete models, in terms of PSNR values and visual quality of the reconstructed images

An efficient method for model refinement in diffuse optical tomography

Science.gov (United States)

Zirak, A. R.; Khademi, M.

2007-11-01

Diffuse optical tomography (DOT) is a non-linear, ill-posed, boundary value and optimization problem which necessitates regularization. Also, Bayesian methods are suitable owing to measurements data are sparse and correlated. In such problems which are solved with iterative methods, for stabilization and better convergence, the solution space must be small. These constraints subject to extensive and overdetermined system of equations which model retrieving criteria specially total least squares (TLS) must to refine model error. Using TLS is limited to linear systems which is not achievable when applying traditional Bayesian methods. This paper presents an efficient method for model refinement using regularized total least squares (RTLS) for treating on linearized DOT problem, having maximum a posteriori (MAP) estimator and Tikhonov regulator. This is done with combination Bayesian and regularization tools as preconditioner matrices, applying them to equations and then using RTLS to the resulting linear equations. The preconditioning matrixes are guided by patient specific information as well as a priori knowledge gained from the training set. Simulation results illustrate that proposed method improves the image reconstruction performance and localize the abnormally well.

"Method, system and storage medium for generating virtual brick models"

DEFF Research Database (Denmark)

2009-01-01

An exemplary embodiment is a method for generating a virtual brick model. The virtual brick models are generated by users and uploaded to a centralized host system. Users can build virtual models themselves or download and edit another user's virtual brick models while retaining the identity...

Arima model and exponential smoothing method: A comparison

Science.gov (United States)

Wan Ahmad, Wan Kamarul Ariffin; Ahmad, Sabri

2013-04-01

This study shows the comparison between Autoregressive Moving Average (ARIMA) model and Exponential Smoothing Method in making a prediction. The comparison is focused on the ability of both methods in making the forecasts with the different number of data sources and the different length of forecasting period. For this purpose, the data from The Price of Crude Palm Oil (RM/tonne), Exchange Rates of Ringgit Malaysia (RM) in comparison to Great Britain Pound (GBP) and also The Price of SMR 20 Rubber Type (cents/kg) with three different time series are used in the comparison process. Then, forecasting accuracy of each model is measured by examinethe prediction error that producedby using Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute deviation (MAD). The study shows that the ARIMA model can produce a better prediction for the long-term forecasting with limited data sources, butcannot produce a better prediction for time series with a narrow range of one point to another as in the time series for Exchange Rates. On the contrary, Exponential Smoothing Method can produce a better forecasting for Exchange Rates that has a narrow range of one point to another for its time series, while itcannot produce a better prediction for a longer forecasting period.

Development and comparison in uncertainty assessment based Bayesian modularization method in hydrological modeling

Science.gov (United States)

Li, Lu; Xu, Chong-Yu; Engeland, Kolbjørn

2013-04-01

SummaryWith respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches are used in hydrological modeling. A family of Bayesian methods, which incorporates different sources of information into a single analysis through Bayes' theorem, is widely used for uncertainty assessment. However, none of these approaches can well treat the impact of high flows in hydrological modeling. This study proposes a Bayesian modularization uncertainty assessment approach in which the highest streamflow observations are treated as suspect information that should not influence the inference of the main bulk of the model parameters. This study includes a comprehensive comparison and evaluation of uncertainty assessments by our new Bayesian modularization method and standard Bayesian methods using the Metropolis-Hastings (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions were used in combination with standard Bayesian method: the AR(1) plus Normal model independent of time (Model 1), the AR(1) plus Normal model dependent on time (Model 2) and the AR(1) plus Multi-normal model (Model 3). The results reveal that the Bayesian modularization method provides the most accurate streamflow estimates measured by the Nash-Sutcliffe efficiency and provide the best in uncertainty estimates for low, medium and entire flows compared to standard Bayesian methods. The study thus provides a new approach for reducing the impact of high flows on the discharge uncertainty assessment of hydrological models via Bayesian method.

Methods to model-check parallel systems software

International Nuclear Information System (INIS)

Matlin, O. S.; McCune, W.; Lusk, E.

2003-01-01

We report on an effort to develop methodologies for formal verification of parts of the Multi-Purpose Daemon (MPD) parallel process management system. MPD is a distributed collection of communicating processes. While the individual components of the collection execute simple algorithms, their interaction leads to unexpected errors that are difficult to uncover by conventional means. Two verification approaches are discussed here: the standard model checking approach using the software model checker SPIN and the nonstandard use of a general-purpose first-order resolution-style theorem prover OTTER to conduct the traditional state space exploration. We compare modeling methodology and analyze performance and scalability of the two methods with respect to verification of MPD

A modeling method of semiconductor fabrication flows with extended knowledge hybrid Petri nets

Institute of Scientific and Technical Information of China (English)

Zhou Binghai; Jiang Shuyu; Wang Shijin; Wu bin

2008-01-01

A modeling method of extended knowledge hybrid Petri nets (EKHPNs), incorporating object-oriented methods into hybrid Petri nets (HPNs), was presented and used for the representation and modeling of semiconductor wafer fabrication flows. To model the discrete and continuous parts of a complex semiconductor wafer fabrication flow, the HPNs were introduced into the EKHPNs. Object-oriented methods were combined into the EKHPNs for coping with the complexity of the fabrication flow. Knowledge annotations were introduced to solve input and output conflicts of the EKHPNs.Finally, to demonstrate the validity of the EKHPN method, a real semiconductor wafer fabrication case was used to illustrate the modeling procedure. The modeling results indicate that the proposed method can be used to model a complex semiconductor wafer fabrication flow expediently.

Development and comparison of Bayesian modularization method in uncertainty assessment of hydrological models

Science.gov (United States)

Li, L.; Xu, C.-Y.; Engeland, K.

2012-04-01

With respect to model calibration, parameter estimation and analysis of uncertainty sources, different approaches have been used in hydrological models. Bayesian method is one of the most widely used methods for uncertainty assessment of hydrological models, which incorporates different sources of information into a single analysis through Bayesian theorem. However, none of these applications can well treat the uncertainty in extreme flows of hydrological models' simulations. This study proposes a Bayesian modularization method approach in uncertainty assessment of conceptual hydrological models by considering the extreme flows. It includes a comprehensive comparison and evaluation of uncertainty assessments by a new Bayesian modularization method approach and traditional Bayesian models using the Metropolis Hasting (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions are used in combination with traditional Bayesian: the AR (1) plus Normal and time period independent model (Model 1), the AR (1) plus Normal and time period dependent model (Model 2) and the AR (1) plus multi-normal model (Model 3). The results reveal that (1) the simulations derived from Bayesian modularization method are more accurate with the highest Nash-Sutcliffe efficiency value, and (2) the Bayesian modularization method performs best in uncertainty estimates of entire flows and in terms of the application and computational efficiency. The study thus introduces a new approach for reducing the extreme flow's effect on the discharge uncertainty assessment of hydrological models via Bayesian. Keywords: extreme flow, uncertainty assessment, Bayesian modularization, hydrological model, WASMOD

A Multistep Extending Truncation Method towards Model Construction of Infinite-State Markov Chains

Directory of Open Access Journals (Sweden)

Kemin Wang

2014-01-01

Full Text Available The model checking of Infinite-State Continuous Time Markov Chains will inevitably encounter the state explosion problem when constructing the CTMCs model; our method is to get a truncated model of the infinite one; to get a sufficient truncated model to meet the model checking of Continuous Stochastic Logic based system properties, we propose a multistep extending advanced truncation method towards model construction of CTMCs and implement it in the INFAMY model checker; the experiment results show that our method is effective.

Alternative wind power modeling methods using chronological and load duration curve production cost models

Energy Technology Data Exchange (ETDEWEB)

Milligan, M R

1996-04-01

As an intermittent resource, capturing the temporal variation in windpower is an important issue in the context of utility production cost modeling. Many of the production cost models use a method that creates a cumulative probability distribution that is outside the time domain. The purpose of this report is to examine two production cost models that represent the two major model types: chronological and load duration cure models. This report is part of the ongoing research undertaken by the Wind Technology Division of the National Renewable Energy Laboratory in utility modeling and wind system integration.

Approximating methods for intractable probabilistic models: Applications in neuroscience

DEFF Research Database (Denmark)

Højen-Sørensen, Pedro

2002-01-01

This thesis investigates various methods for carrying out approximate inference in intractable probabilistic models. By capturing the relationships between random variables, the framework of graphical models hints at which sets of random variables pose a problem to the inferential step. The appro...

Development of a three dimensional circulation model based on fractional step method

Directory of Open Access Journals (Sweden)

Mazen Abualtayef

2010-03-01

Full Text Available A numerical model was developed for simulating a three-dimensional multilayer hydrodynamic and thermodynamic model in domains with irregular bottom topography. The model was designed for examining the interactions between flow and topography. The model was based on the three-dimensional Navier-Stokes equations and was solved using the fractional step method, which combines the finite difference method in the horizontal plane and the finite element method in the vertical plane. The numerical techniques were described and the model test and application were presented. For the model application to the northern part of Ariake Sea, the hydrodynamic and thermodynamic results were predicted. The numerically predicted amplitudes and phase angles were well consistent with the field observations.

A modeling method for hybrid energy behaviors in flexible machining systems

International Nuclear Information System (INIS)

Li, Yufeng; He, Yan; Wang, Yan; Wang, Yulin; Yan, Ping; Lin, Shenlong

2015-01-01

Increasingly environmental and economic pressures have led to great concerns regarding the energy consumption of machining systems. Understanding energy behaviors of flexible machining systems is a prerequisite for improving energy efficiency of these systems. This paper proposes a modeling method to predict energy behaviors in flexible machining systems. The hybrid energy behaviors not only depend on the technical specification related of machine tools and workpieces, but are significantly affected by individual production scenarios. In the method, hybrid energy behaviors are decomposed into Structure-related energy behaviors, State-related energy behaviors, Process-related energy behaviors and Assignment-related energy behaviors. The modeling method for the hybrid energy behaviors is proposed based on Colored Timed Object-oriented Petri Net (CTOPN). The former two types of energy behaviors are modeled by constructing the structure of CTOPN, whist the latter two types of behaviors are simulated by applying colored tokens and associated attributes. Machining on two workpieces in the experimental workshop were undertaken to verify the proposed modeling method. The results showed that the method can provide multi-perspective transparency on energy consumption related to machine tools, workpieces as well as production management, and is particularly suitable for flexible manufacturing system when frequent changes in machining systems are often encountered. - Highlights: • Energy behaviors in flexible machining systems are modeled in this paper. • Hybrid characteristics of energy behaviors are examined from multiple viewpoints. • Flexible modeling method CTOPN is used to predict the hybrid energy behaviors. • This work offers a multi-perspective transparency on energy consumption

Complex data modeling and computationally intensive methods for estimation and prediction

CERN Document Server

Secchi, Piercesare; Advances in Complex Data Modeling and Computational Methods in Statistics

2015-01-01

The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held...

Vortex Tube Modeling Using the System Identification Method

Energy Technology Data Exchange (ETDEWEB)

Han, Jaeyoung; Jeong, Jiwoong; Yu, Sangseok [Chungnam Nat’l Univ., Daejeon (Korea, Republic of); Im, Seokyeon [Tongmyong Univ., Busan (Korea, Republic of)

2017-05-15

In this study, vortex tube system model is developed to predict the temperature of the hot and the cold sides. The vortex tube model is developed based on the system identification method, and the model utilized in this work to design the vortex tube is ARX type (Auto-Regressive with eXtra inputs). The derived polynomial model is validated against experimental data to verify the overall model accuracy. It is also shown that the derived model passes the stability test. It is confirmed that the derived model closely mimics the physical behavior of the vortex tube from both the static and dynamic numerical experiments by changing the angles of the low-temperature side throttle valve, clearly showing temperature separation. These results imply that the system identification based modeling can be a promising approach for the prediction of complex physical systems, including the vortex tube.

Models, methods and software tools for building complex adaptive traffic systems

International Nuclear Information System (INIS)

Alyushin, S.A.

2011-01-01

The paper studies the modern methods and tools to simulate the behavior of complex adaptive systems (CAS), the existing systems of traffic modeling in simulators and their characteristics; proposes requirements for assessing the suitability of the system to simulate the CAS behavior in simulators. The author has developed a model of adaptive agent representation and its functioning environment to meet certain requirements set above, and has presented methods of agents' interactions and methods of conflict resolution in simulated traffic situations. A simulation system realizing computer modeling for simulating the behavior of CAS in traffic situations has been created [ru

A QFD-Based Evaluation Method for Business Models of Product Service Systems

Directory of Open Access Journals (Sweden)

Tianyang Li

2016-01-01

Full Text Available Organizations have been approaching Product Service Systems (PSS in unoptimized business model fashions. This is partially because there is ineffective evaluation of PSS business models. Therefore, a more sufficient evaluation method might advance the evaluation of PSS business models and assist organizations that are considering a servitisation strategy. In this paper, we develop a value oriented method by using the Quality Function Deployment (QFD technique to employ correlations derived from the design information of PSS business models to evaluate these PSS business models. We describe the method applying steps and its practical application in a real life case study. This method improves the formulation of an evaluation step within a design process of PSS business models based on correlations of different dimensions of the PSS value and PSS business models; it allows a balance between the customer value and organization value that are delivered by PSS business models to be made quantitatively. Finally, it fosters the effective utilization of the design information accumulated in the earlier part of the design process to quantitatively evaluate whether a PSS business model is optimized for providing appropriate values for customers and organizations.

Modelling across bioreactor scales: methods, challenges and limitations

DEFF Research Database (Denmark)

Gernaey, Krist

that it is challenging and expensive to acquire experimental data of good quality that can be used for characterizing gradients occurring inside a large industrial scale bioreactor. But which model building methods are available? And how can one ensure that the parameters in such a model are properly estimated? And what......Scale-up and scale-down of bioreactors are very important in industrial biotechnology, especially with the currently available knowledge on the occurrence of gradients in industrial-scale bioreactors. Moreover, it becomes increasingly appealing to model such industrial scale systems, considering...

Comparison of surrogate models with different methods in ...

Indian Academy of Sciences (India)

In this article, polynomial regression (PR), radial basis function artificial neural network (RBFANN), and kriging ..... 10 kriging models with different parameters were also obtained. ..... shapes using stochastic optimization methods and com-.

Do different methods of modeling statin treatment effectiveness influence the optimal decision?

NARCIS (Netherlands)

B.J.H. van Kempen (Bob); B.S. Ferket (Bart); A. Hofman (Albert); S. Spronk (Sandra); E.W. Steyerberg (Ewout); M.G.M. Hunink (Myriam)

2012-01-01

textabstractPurpose. Modeling studies that evaluate statin treatment for the prevention of cardiovascular disease (CVD) use different methods to model the effect of statins. The aim of this study was to evaluate the impact of using different modeling methods on the optimal decision found in such

New Ghost-node method for linking different models with varied grid refinement

International Nuclear Information System (INIS)

Mehl, Steffen W.; Hill, Mary Catherine; James, Scott Carlton; Leake, Stanley A.; Zyvoloski, George A.; Dickinson, Jesse E.; Eddebbarh, Al A.

2006-01-01

A flexible, robust method for linking grids of locally refined models constructed with different numerical methods is needed to address a variety of hydrologic problems. This work outlines and tests a new ghost-node model-linking method for a refined 'child' model that is contained within a larger and coarser 'parent' model that is based on the iterative method of Mehl and Hill (2002, 2004). The method is applicable to steady-state solutions for ground-water flow. Tests are presented for a homogeneous two-dimensional system that has either matching grids (parent cells border an integer number of child cells; Figure 2a) or non-matching grids (parent cells border a non-integer number of child cells; Figure 2b). The coupled grids are simulated using the finite-difference and finite-element models MODFLOW and FEHM, respectively. The simulations require no alteration of the MODFLOW or FEHM models and are executed using a batch file on Windows operating systems. Results indicate that when the grids are matched spatially so that nodes and child cell boundaries are aligned, the new coupling technique has error nearly equal to that when coupling two MODFLOW models (Mehl and Hill, 2002). When the grids are non-matching, model accuracy is slightly increased over matching-grid cases. Overall, results indicate that the ghost-node technique is a viable means to accurately couple distinct models because the overall error is less than if only the regional model was used to simulate flow in the child model's domain

New ghost-node method for linking different models with varied grid refinement

Science.gov (United States)

James, S.C.; Dickinson, J.E.; Mehl, S.W.; Hill, M.C.; Leake, S.A.; Zyvoloski, G.A.; Eddebbarh, A.-A.

2006-01-01

A flexible, robust method for linking grids of locally refined ground-water flow models constructed with different numerical methods is needed to address a variety of hydrologic problems. This work outlines and tests a new ghost-node model-linking method for a refined "child" model that is contained within a larger and coarser "parent" model that is based on the iterative method of Steffen W. Mehl and Mary C. Hill (2002, Advances in Water Res., 25, p. 497-511; 2004, Advances in Water Res., 27, p. 899-912). The method is applicable to steady-state solutions for ground-water flow. Tests are presented for a homogeneous two-dimensional system that has matching grids (parent cells border an integer number of child cells) or nonmatching grids. The coupled grids are simulated by using the finite-difference and finite-element models MODFLOW and FEHM, respectively. The simulations require no alteration of the MODFLOW or FEHM models and are executed using a batch file on Windows operating systems. Results indicate that when the grids are matched spatially so that nodes and child-cell boundaries are aligned, the new coupling technique has error nearly equal to that when coupling two MODFLOW models. When the grids are nonmatching, model accuracy is slightly increased compared to that for matching-grid cases. Overall, results indicate that the ghost-node technique is a viable means to couple distinct models because the overall head and flow errors relative to the analytical solution are less than if only the regional coarse-grid model was used to simulate flow in the child model's domain.

Modeling error distributions of growth curve models through Bayesian methods.

Science.gov (United States)

Zhang, Zhiyong

2016-06-01

Growth curve models are widely used in social and behavioral sciences. However, typical growth curve models often assume that the errors are normally distributed although non-normal data may be even more common than normal data. In order to avoid possible statistical inference problems in blindly assuming normality, a general Bayesian framework is proposed to flexibly model normal and non-normal data through the explicit specification of the error distributions. A simulation study shows when the distribution of the error is correctly specified, one can avoid the loss in the efficiency of standard error estimates. A real example on the analysis of mathematical ability growth data from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 is used to show the application of the proposed methods. Instructions and code on how to conduct growth curve analysis with both normal and non-normal error distributions using the the MCMC procedure of SAS are provided.

Solving Optimal Control Problem of Monodomain Model Using Hybrid Conjugate Gradient Methods

Directory of Open Access Journals (Sweden)

Kin Wei Ng

2012-01-01

Full Text Available We present the numerical solutions for the PDE-constrained optimization problem arising in cardiac electrophysiology, that is, the optimal control problem of monodomain model. The optimal control problem of monodomain model is a nonlinear optimization problem that is constrained by the monodomain model. The monodomain model consists of a parabolic partial differential equation coupled to a system of nonlinear ordinary differential equations, which has been widely used for simulating cardiac electrical activity. Our control objective is to dampen the excitation wavefront using optimal applied extracellular current. Two hybrid conjugate gradient methods are employed for computing the optimal applied extracellular current, namely, the Hestenes-Stiefel-Dai-Yuan (HS-DY method and the Liu-Storey-Conjugate-Descent (LS-CD method. Our experiment results show that the excitation wavefronts are successfully dampened out when these methods are used. Our experiment results also show that the hybrid conjugate gradient methods are superior to the classical conjugate gradient methods when Armijo line search is used.

Convex-based void filling method for CAD-based Monte Carlo geometry modeling

International Nuclear Information System (INIS)

Yu, Shengpeng; Cheng, Mengyun; Song, Jing; Long, Pengcheng; Hu, Liqin

2015-01-01

Highlights: • We present a new void filling method named CVF for CAD based MC geometry modeling. • We describe convex based void description based and quality-based space subdivision. • The results showed improvements provided by CVF for both modeling and MC calculation efficiency. - Abstract: CAD based automatic geometry modeling tools have been widely applied to generate Monte Carlo (MC) calculation geometry for complex systems according to CAD models. Automatic void filling is one of the main functions in the CAD based MC geometry modeling tools, because the void space between parts in CAD models is traditionally not modeled while MC codes such as MCNP need all the problem space to be described. A dedicated void filling method, named Convex-based Void Filling (CVF), is proposed in this study for efficient void filling and concise void descriptions. The method subdivides all the problem space into disjointed regions using Quality based Subdivision (QS) and describes the void space in each region with complementary descriptions of the convex volumes intersecting with that region. It has been implemented in SuperMC/MCAM, the Multiple-Physics Coupling Analysis Modeling Program, and tested on International Thermonuclear Experimental Reactor (ITER) Alite model. The results showed that the new method reduced both automatic modeling time and MC calculation time

Modelling of Airship Flight Mechanics by the Projection Equivalent Method

Directory of Open Access Journals (Sweden)

Frantisek Jelenciak

2015-12-01

Full Text Available This article describes the projection equivalent method (PEM as a specific and relatively simple approach for the modelling of aircraft dynamics. By the PEM it is possible to obtain a mathematic al model of the aerodynamic forces and momentums acting on different kinds of aircraft during flight. For the PEM, it is a characteristic of it that -in principle - it provides an acceptable regression model of aerodynamic forces and momentums which exhibits reasonable and plausible behaviour from a dynamics viewpoint. The principle of this method is based on applying Newton's mechanics, which are then combined with a specific form of the finite element method to cover additional effects. The main advantage of the PEM is that it is not necessary to carry out measurements in a wind tunnel for the identification of the model's parameters. The plausible dynamical behaviour of the model can be achieved by specific correction parameters, which can be determined on the basis of experimental data obtained during the flight of the aircraft. In this article, we present the PEM as applied to an airship as well as a comparison of the data calculated by the PEM and experimental flight data.

Automated Model Fit Method for Diesel Engine Control Development

NARCIS (Netherlands)

Seykens, X.; Willems, F.P.T.; Kuijpers, B.; Rietjens, C.

2014-01-01

This paper presents an automated fit for a control-oriented physics-based diesel engine combustion model. This method is based on the combination of a dedicated measurement procedure and structured approach to fit the required combustion model parameters. Only a data set is required that is

Automated model fit method for diesel engine control development

NARCIS (Netherlands)

Seykens, X.L.J.; Willems, F.P.T.; Kuijpers, B.; Rietjens, C.J.H.

2014-01-01

This paper presents an automated fit for a control-oriented physics-based diesel engine combustion model. This method is based on the combination of a dedicated measurement procedure and structured approach to fit the required combustion model parameters. Only a data set is required that is

Discrete gradient methods for solving variational image regularisation models

International Nuclear Information System (INIS)

Grimm, V; McLachlan, Robert I; McLaren, David I; Quispel, G R W; Schönlieb, C-B

2017-01-01

Discrete gradient methods are well-known methods of geometric numerical integration, which preserve the dissipation of gradient systems. In this paper we show that this property of discrete gradient methods can be interesting in the context of variational models for image processing, that is where the processed image is computed as a minimiser of an energy functional. Numerical schemes for computing minimisers of such energies are desired to inherit the dissipative property of the gradient system associated to the energy and consequently guarantee a monotonic decrease of the energy along iterations, avoiding situations in which more computational work might lead to less optimal solutions. Under appropriate smoothness assumptions on the energy functional we prove that discrete gradient methods guarantee a monotonic decrease of the energy towards stationary states, and we promote their use in image processing by exhibiting experiments with convex and non-convex variational models for image deblurring, denoising, and inpainting. (paper)

Bayesian risk-based decision method for model validation under uncertainty

International Nuclear Information System (INIS)

Jiang Xiaomo; Mahadevan, Sankaran

2007-01-01

This paper develops a decision-making methodology for computational model validation, considering the risk of using the current model, data support for the current model, and cost of acquiring new information to improve the model. A Bayesian decision theory-based method is developed for this purpose, using a likelihood ratio as the validation metric for model assessment. An expected risk or cost function is defined as a function of the decision costs, and the likelihood and prior of each hypothesis. The risk is minimized through correctly assigning experimental data to two decision regions based on the comparison of the likelihood ratio with a decision threshold. A Bayesian validation metric is derived based on the risk minimization criterion. Two types of validation tests are considered: pass/fail tests and system response value measurement tests. The methodology is illustrated for the validation of reliability prediction models in a tension bar and an engine blade subjected to high cycle fatigue. The proposed method can effectively integrate optimal experimental design into model validation to simultaneously reduce the cost and improve the accuracy of reliability model assessment

Computer modeling of oil spill trajectories with a high accuracy method

International Nuclear Information System (INIS)

Garcia-Martinez, Reinaldo; Flores-Tovar, Henry

1999-01-01

This paper proposes a high accuracy numerical method to model oil spill trajectories using a particle-tracking algorithm. The Euler method, used to calculate oil trajectories, can give adequate solutions in most open ocean applications. However, this method may not predict accurate particle trajectories in certain highly non-uniform velocity fields near coastal zones or in river problems. Simple numerical experiments show that the Euler method may also introduce artificial numerical dispersion that could lead to overestimation of spill areas. This article proposes a fourth-order Runge-Kutta method with fourth-order velocity interpolation to calculate oil trajectories that minimise these problems. The algorithm is implemented in the OilTrack model to predict oil trajectories following the 'Nissos Amorgos' oil spill accident that occurred in the Gulf of Venezuela in 1997. Despite lack of adequate field information, model results compare well with observations in the impacted area. (Author)

Deterministic Method for Obtaining Nominal and Uncertainty Models of CD Drives

DEFF Research Database (Denmark)

Vidal, Enrique Sanchez; Stoustrup, Jakob; Andersen, Palle

2002-01-01

In this paper a deterministic method for obtaining the nominal and uncertainty models of the focus loop in a CD-player is presented based on parameter identification and measurements in the focus loop of 12 actual CD drives that differ by having worst-case behaviors with respect to various...... properties. The method provides a systematic way to derive a nominal average model as well as a structures multiplicative input uncertainty model, and it is demonstrated how to apply mu-theory to design a controller based on the models obtained that meets certain robust performance criteria....

Modeling and simulation of ocean wave propagation using lattice Boltzmann method

Science.gov (United States)

Nuraiman, Dian

2017-10-01

In this paper, we present on modeling and simulation of ocean wave propagation from the deep sea to the shoreline. This requires high computational cost for simulation with large domain. We propose to couple a 1D shallow water equations (SWE) model with a 2D incompressible Navier-Stokes equations (NSE) model in order to reduce the computational cost. The coupled model is solved using the lattice Boltzmann method (LBM) with the lattice Bhatnagar-Gross-Krook (BGK) scheme. Additionally, a special method is implemented to treat the complex behavior of free surface close to the shoreline. The result shows the coupled model can reduce computational cost significantly compared to the full NSE model.

Large deformation analysis of adhesive by Eulerian method with new material model

International Nuclear Information System (INIS)

Maeda, K; Nishiguchi, K; Iwamoto, T; Okazawa, S

2010-01-01

The material model to describe large deformation of a pressure sensitive adhesive (PSA) is presented. A relationship between stress and strain of PSA includes viscoelasticity and rubber-elasticity. Therefore, we propose the material model for describing viscoelasticity and rubber-elasticity, and extend the presented material model to the rate form for three dimensional finite element analysis. After proposing the material model for PSA, we formulate the Eulerian method to simulate large deformation behavior. In the Eulerian calculation, the Piecewise Linear Interface Calculation (PLIC) method for capturing material surface is employed. By using PLIC method, we can impose dynamic and kinematic boundary conditions on captured material surface. The representative two computational examples are calculated to check validity of the present methods.

A Pansharpening Method Based on HCT and Joint Sparse Model

Directory of Open Access Journals (Sweden)

XU Ning

2016-04-01

Full Text Available A novel fusion method based on the hyperspherical color transformation (HCT and joint sparsity model is proposed for decreasing the spectral distortion of fused image further. In the method, an intensity component and angles of each band of the multispectral image is obtained by HCT firstly, and then the intensity component is fused with the panchromatic image through wavelet transform and joint sparsity model. In the joint sparsity model, the redundant and complement information of the different images can be efficiently extracted and employed to yield the high quality results. Finally, the fused multi spectral image is obtained by inverse transforms of wavelet and HCT on the new lower frequency image and the angle components, respectively. Experimental results on Pleiades-1 and WorldView-2 satellites indicate that the proposed method achieves remarkable results.

An evidence-based patient-centered method makes the biopsychosocial model scientific.

Science.gov (United States)

Smith, Robert C; Fortin, Auguste H; Dwamena, Francesca; Frankel, Richard M

2013-06-01

To review the scientific status of the biopsychosocial (BPS) model and to propose a way to improve it. Engel's BPS model added patients' psychological and social health concerns to the highly successful biomedical model. He proposed that the BPS model could make medicine more scientific, but its use in education, clinical care, and, especially, research remains minimal. Many aver correctly that the present model cannot be defined in a consistent way for the individual patient, making it untestable and non-scientific. This stems from not obtaining relevant BPS data systematically, where one interviewer obtains the same information another would. Recent research by two of the authors has produced similar patient-centered interviewing methods that are repeatable and elicit just the relevant patient information needed to define the model at each visit. We propose that the field adopt these evidence-based methods as the standard for identifying the BPS model. Identifying a scientific BPS model in each patient with an agreed-upon, evidence-based patient-centered interviewing method can produce a quantum leap ahead in both research and teaching. A scientific BPS model can give us more confidence in being humanistic. In research, we can conduct more rigorous studies to inform better practices. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Comparison of prosthetic models produced by traditional and additive manufacturing methods.

Science.gov (United States)

Park, Jin-Young; Kim, Hae-Young; Kim, Ji-Hwan; Kim, Jae-Hong; Kim, Woong-Chul

2015-08-01

The purpose of this study was to verify the clinical-feasibility of additive manufacturing by comparing the accuracy of four different manufacturing methods for metal coping: the conventional lost wax technique (CLWT); subtractive methods with wax blank milling (WBM); and two additive methods, multi jet modeling (MJM), and micro-stereolithography (Micro-SLA). Thirty study models were created using an acrylic model with the maxillary upper right canine, first premolar, and first molar teeth. Based on the scan files from a non-contact blue light scanner (Identica; Medit Co. Ltd., Seoul, Korea), thirty cores were produced using the WBM, MJM, and Micro-SLA methods, respectively, and another thirty frameworks were produced using the CLWT method. To measure the marginal and internal gap, the silicone replica method was adopted, and the silicone images obtained were evaluated using a digital microscope (KH-7700; Hirox, Tokyo, Japan) at 140X magnification. Analyses were performed using two-way analysis of variance (ANOVA) and Tukey post hoc test (α=.05). The mean marginal gaps and internal gaps showed significant differences according to tooth type (Pmanufacturing method (Pmanufacturing methods were within a clinically allowable range, and, thus, the clinical use of additive manufacturing methods is acceptable as an alternative to the traditional lost wax-technique and subtractive manufacturing.

Model Robust Calibration: Method and Application to Electronically-Scanned Pressure Transducers

Science.gov (United States)

Walker, Eric L.; Starnes, B. Alden; Birch, Jeffery B.; Mays, James E.

2010-01-01

This article presents the application of a recently developed statistical regression method to the controlled instrument calibration problem. The statistical method of Model Robust Regression (MRR), developed by Mays, Birch, and Starnes, is shown to improve instrument calibration by reducing the reliance of the calibration on a predetermined parametric (e.g. polynomial, exponential, logarithmic) model. This is accomplished by allowing fits from the predetermined parametric model to be augmented by a certain portion of a fit to the residuals from the initial regression using a nonparametric (locally parametric) regression technique. The method is demonstrated for the absolute scale calibration of silicon-based pressure transducers.

Statistical limitations in functional neuroimaging. I. Non-inferential methods and statistical models.

Science.gov (United States)

Petersson, K M; Nichols, T E; Poline, J B; Holmes, A P

1999-01-01

Functional neuroimaging (FNI) provides experimental access to the intact living brain making it possible to study higher cognitive functions in humans. In this review and in a companion paper in this issue, we discuss some common methods used to analyse FNI data. The emphasis in both papers is on assumptions and limitations of the methods reviewed. There are several methods available to analyse FNI data indicating that none is optimal for all purposes. In order to make optimal use of the methods available it is important to know the limits of applicability. For the interpretation of FNI results it is also important to take into account the assumptions, approximations and inherent limitations of the methods used. This paper gives a brief overview over some non-inferential descriptive methods and common statistical models used in FNI. Issues relating to the complex problem of model selection are discussed. In general, proper model selection is a necessary prerequisite for the validity of the subsequent statistical inference. The non-inferential section describes methods that, combined with inspection of parameter estimates and other simple measures, can aid in the process of model selection and verification of assumptions. The section on statistical models covers approaches to global normalization and some aspects of univariate, multivariate, and Bayesian models. Finally, approaches to functional connectivity and effective connectivity are discussed. In the companion paper we review issues related to signal detection and statistical inference. PMID:10466149

Monte Carlo based statistical power analysis for mediation models: methods and software.

Science.gov (United States)

Zhang, Zhiyong

2014-12-01

The existing literature on statistical power analysis for mediation models often assumes data normality and is based on a less powerful Sobel test instead of the more powerful bootstrap test. This study proposes to estimate statistical power to detect mediation effects on the basis of the bootstrap method through Monte Carlo simulation. Nonnormal data with excessive skewness and kurtosis are allowed in the proposed method. A free R package called bmem is developed to conduct the power analysis discussed in this study. Four examples, including a simple mediation model, a multiple-mediator model with a latent mediator, a multiple-group mediation model, and a longitudinal mediation model, are provided to illustrate the proposed method.

Effective method for construction of low-dimensional models for heat transfer process

Energy Technology Data Exchange (ETDEWEB)

Blinov, D.G.; Prokopov, V.G.; Sherenkovskii, Y.V.; Fialko, N.M.; Yurchuk, V.L. [National Academy of Sciences of Ukraine, Kiev (Ukraine). Inst. of Engineering Thermophysics

2004-12-01

A low-dimensional model based on the method of proper orthogonal decomposition (POD) and the method of polyargumental systems (MPS) for thermal conductivity problems with strongly localized source of heat has been presented. The key aspect of these methods is that they enable to avoid weak points of other projection methods, which consists in a priori choice of basis functions. It enables us to use the MPS method and the POD method as convenient means to construct low-dimensional models of heat and mass transfer problems. (Author)

Intelligent structural optimization: Concept, Model and Methods

International Nuclear Information System (INIS)

Lu, Dagang; Wang, Guangyuan; Peng, Zhang

2002-01-01

Structural optimization has many characteristics of Soft Design, and so, it is necessary to apply the experience of human experts to solving the uncertain and multidisciplinary optimization problems in large-scale and complex engineering systems. With the development of artificial intelligence (AI) and computational intelligence (CI), the theory of structural optimization is now developing into the direction of intelligent optimization. In this paper, a concept of Intelligent Structural Optimization (ISO) is proposed. And then, a design process model of ISO is put forward in which each design sub-process model are discussed. Finally, the design methods of ISO are presented

Construction of dynamic model of CANDU-SCWR using moving boundary method

International Nuclear Information System (INIS)

Sun Peiwei; Jiang Jin; Shan Jianqiang

2011-01-01

Highlights: → A dynamic model of a CANDU-SCWR is developed. → The advantages of the moving boundary method are demonstrated. → The dynamic behaviours of the CANDU-SCWR are obtained by simulation. → The model can predict the dynamic behaviours of the CANDU-SCWR. → Linear dynamic models for CANDU-SCWR are derived by system identification techniques. - Abstract: CANDU-SCWR (Supercritical Water-Cooled Reactor) is one type of Generation IV reactors being developed in Canada. Its dynamic characteristics are different from existing CANDU reactors due to the supercritical conditions of the coolant. To study the behaviours of such reactors under disturbances and to design adequate control systems, it is essential to have an accurate dynamic model to describe such a reactor. One dynamic model is developed for CANDU-SCWR in this paper. In the model construction process, three regions have been considered: Liquid Region I, Liquid Region II and Vapour Region, depending on bulk and wall temperatures being higher or lower the pseudo-critical temperature. A moving boundary method is used to describe the movement of boundaries across these regions. Some benefits of adopting moving boundary method are illustrated by comparing with the fixed boundary method. The results of the steady-state simulation based on the developed model agree well with the design parameters. The transient simulations demonstrate that the model can predict the dynamic behaviours of CANDU-SCWR. Furthermore, to investigate the responses of the reactor to small amplitude perturbations and to facilitate control system designs, a least-square based system identification technique is used to obtain a set of linear dynamic models around the design point. The responses based on the linear dynamic models are validated with simulation results from nonlinear CANDU-SCWR dynamic model.

A systematic composite service design modeling method using graph-based theory.

Science.gov (United States)

Elhag, Arafat Abdulgader Mohammed; Mohamad, Radziah; Aziz, Muhammad Waqar; Zeshan, Furkh

2015-01-01

The composite service design modeling is an essential process of the service-oriented software development life cycle, where the candidate services, composite services, operations and their dependencies are required to be identified and specified before their design. However, a systematic service-oriented design modeling method for composite services is still in its infancy as most of the existing approaches provide the modeling of atomic services only. For these reasons, a new method (ComSDM) is proposed in this work for modeling the concept of service-oriented design to increase the reusability and decrease the complexity of system while keeping the service composition considerations in mind. Furthermore, the ComSDM method provides the mathematical representation of the components of service-oriented design using the graph-based theoryto facilitate the design quality measurement. To demonstrate that the ComSDM method is also suitable for composite service design modeling of distributed embedded real-time systems along with enterprise software development, it is implemented in the case study of a smart home. The results of the case study not only check the applicability of ComSDM, but can also be used to validate the complexity and reusability of ComSDM. This also guides the future research towards the design quality measurement such as using the ComSDM method to measure the quality of composite service design in service-oriented software system.

Evaluation of Statistical Methods for Modeling Historical Resource Production and Forecasting

Science.gov (United States)

Nanzad, Bolorchimeg

This master's thesis project consists of two parts. Part I of the project compares modeling of historical resource production and forecasting of future production trends using the logit/probit transform advocated by Rutledge (2011) with conventional Hubbert curve fitting, using global coal production as a case study. The conventional Hubbert/Gaussian method fits a curve to historical production data whereas a logit/probit transform uses a linear fit to a subset of transformed production data. Within the errors and limitations inherent in this type of statistical modeling, these methods provide comparable results. That is, despite that apparent goodness-of-fit achievable using the Logit/Probit methodology, neither approach provides a significant advantage over the other in either explaining the observed data or in making future projections. For mature production regions, those that have already substantially passed peak production, results obtained by either method are closely comparable and reasonable, and estimates of ultimately recoverable resources obtained by either method are consistent with geologically estimated reserves. In contrast, for immature regions, estimates of ultimately recoverable resources generated by either of these alternative methods are unstable and thus, need to be used with caution. Although the logit/probit transform generates high quality-of-fit correspondence with historical production data, this approach provides no new information compared to conventional Gaussian or Hubbert-type models and may have the effect of masking the noise and/or instability in the data and the derived fits. In particular, production forecasts for immature or marginally mature production systems based on either method need to be regarded with considerable caution. Part II of the project investigates the utility of a novel alternative method for multicyclic Hubbert modeling tentatively termed "cycle-jumping" wherein overlap of multiple cycles is limited. The

A comparative study on effective dynamic modeling methods for flexible pipe

Energy Technology Data Exchange (ETDEWEB)

Lee, Chang Ho; Hong, Sup; Kim, Hyung Woo [Korea Research Institute of Ships and Ocean Engineering, Daejeon (Korea, Republic of); Kim, Sung Soo [Chungnam National University, Daejeon (Korea, Republic of)

2015-07-15

In this paper, in order to select a suitable method that is applicable to the large deflection with a small strain problem of pipe systems in the deep seabed mining system, the finite difference method with lumped mass from the field of cable dynamics and the substructure method from the field of flexible multibody dynamics were compared. Due to the difficulty of obtaining experimental results from an actual pipe system in the deep seabed mining system, a thin cantilever beam model with experimental results was employed for the comparative study. Accuracy of the methods was investigated by comparing the experimental results and simulation results from the cantilever beam model with different numbers of elements. Efficiency of the methods was also examined by comparing the operational counts required for solving equations of motion. Finally, this cantilever beam model with comparative study results can be promoted to be a benchmark problem for the flexible multibody dynamics.

ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.

Directory of Open Access Journals (Sweden)

Jan Hasenauer

2014-07-01

Full Text Available Functional cell-to-cell variability is ubiquitous in multicellular organisms as well as bacterial populations. Even genetically identical cells of the same cell type can respond differently to identical stimuli. Methods have been developed to analyse heterogeneous populations, e.g., mixture models and stochastic population models. The available methods are, however, either incapable of simultaneously analysing different experimental conditions or are computationally demanding and difficult to apply. Furthermore, they do not account for biological information available in the literature. To overcome disadvantages of existing methods, we combine mixture models and ordinary differential equation (ODE models. The ODE models provide a mechanistic description of the underlying processes while mixture models provide an easy way to capture variability. In a simulation study, we show that the class of ODE constrained mixture models can unravel the subpopulation structure and determine the sources of cell-to-cell variability. In addition, the method provides reliable estimates for kinetic rates and subpopulation characteristics. We use ODE constrained mixture modelling to study NGF-induced Erk1/2 phosphorylation in primary sensory neurones, a process relevant in inflammatory and neuropathic pain. We propose a mechanistic pathway model for this process and reconstructed static and dynamical subpopulation characteristics across experimental conditions. We validate the model predictions experimentally, which verifies the capabilities of ODE constrained mixture models. These results illustrate that ODE constrained mixture models can reveal novel mechanistic insights and possess a high sensitivity.

ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.

Science.gov (United States)

Hasenauer, Jan; Hasenauer, Christine; Hucho, Tim; Theis, Fabian J

2014-07-01

Functional cell-to-cell variability is ubiquitous in multicellular organisms as well as bacterial populations. Even genetically identical cells of the same cell type can respond differently to identical stimuli. Methods have been developed to analyse heterogeneous populations, e.g., mixture models and stochastic population models. The available methods are, however, either incapable of simultaneously analysing different experimental conditions or are computationally demanding and difficult to apply. Furthermore, they do not account for biological information available in the literature. To overcome disadvantages of existing methods, we combine mixture models and ordinary differential equation (ODE) models. The ODE models provide a mechanistic description of the underlying processes while mixture models provide an easy way to capture variability. In a simulation study, we show that the class of ODE constrained mixture models can unravel the subpopulation structure and determine the sources of cell-to-cell variability. In addition, the method provides reliable estimates for kinetic rates and subpopulation characteristics. We use ODE constrained mixture modelling to study NGF-induced Erk1/2 phosphorylation in primary sensory neurones, a process relevant in inflammatory and neuropathic pain. We propose a mechanistic pathway model for this process and reconstructed static and dynamical subpopulation characteristics across experimental conditions. We validate the model predictions experimentally, which verifies the capabilities of ODE constrained mixture models. These results illustrate that ODE constrained mixture models can reveal novel mechanistic insights and possess a high sensitivity.

Toric Lego: A method for modular model building

CERN Document Server

Balasubramanian, Vijay; García-Etxebarria, Iñaki

2010-01-01

Within the context of local type IIB models arising from branes at toric Calabi-Yau singularities, we present a systematic way of joining any number of desired sectors into a consistent theory. The different sectors interact via massive messengers with masses controlled by tunable parameters. We apply this method to a toy model of the minimal supersymmetric standard model (MSSM) interacting via gauge mediation with a metastable supersymmetry breaking sector and an interacting dark matter sector. We discuss how a mirror procedure can be applied in the type IIA case, allowing us to join certain intersecting brane configurations through massive mediators.

Two Undergraduate Process Modeling Courses Taught Using Inductive Learning Methods

Science.gov (United States)

Soroush, Masoud; Weinberger, Charles B.

2010-01-01

This manuscript presents a successful application of inductive learning in process modeling. It describes two process modeling courses that use inductive learning methods such as inquiry learning and problem-based learning, among others. The courses include a novel collection of multi-disciplinary complementary process modeling examples. They were…

Modeling Music Emotion Judgments Using Machine Learning Methods

Directory of Open Access Journals (Sweden)

Naresh N. Vempala

2018-01-01

Full Text Available Emotion judgments and five channels of physiological data were obtained from 60 participants listening to 60 music excerpts. Various machine learning (ML methods were used to model the emotion judgments inclusive of neural networks, linear regression, and random forests. Input for models of perceived emotion consisted of audio features extracted from the music recordings. Input for models of felt emotion consisted of physiological features extracted from the physiological recordings. Models were trained and interpreted with consideration of the classic debate in music emotion between cognitivists and emotivists. Our models supported a hybrid position wherein emotion judgments were influenced by a combination of perceived and felt emotions. In comparing the different ML approaches that were used for modeling, we conclude that neural networks were optimal, yielding models that were flexible as well as interpretable. Inspection of a committee machine, encompassing an ensemble of networks, revealed that arousal judgments were predominantly influenced by felt emotion, whereas valence judgments were predominantly influenced by perceived emotion.

A method to solve the aircraft magnetic field model basing on geomagnetic environment simulation

International Nuclear Information System (INIS)

Lin, Chunsheng; Zhou, Jian-jun; Yang, Zhen-yu

2015-01-01

In aeromagnetic survey, it is difficult to solve the aircraft magnetic field model by flying for some unman controlled or disposable aircrafts. So a model solving method on the ground is proposed. The method simulates the geomagnetic environment where the aircraft is flying and creates the background magnetic field samples which is the same as the magnetic field arose by aircraft’s maneuvering. Then the aircraft magnetic field model can be solved by collecting the magnetic field samples. The method to simulate the magnetic environment and the method to control the errors are presented as well. Finally, an experiment is done for verification. The result shows that the model solving precision and stability by the method is well. The calculated model parameters by the method in one district can be used in worldwide districts as well. - Highlights: • A method to solve the aircraft magnetic field model on the ground is proposed. • The method solves the model by simulating dynamic geomagnetic environment as in the real flying. • The way to control the error of the method was analyzed. • An experiment is done for verification

Three dimensional wavefield modeling using the pseudospectral method; Pseudospectral ho ni yoru sanjigen hadoba modeling

Energy Technology Data Exchange (ETDEWEB)

Sato, T; Matsuoka, T [Japan Petroleum Exploration Corp., Tokyo (Japan); Saeki, T [Japan National Oil Corp., Tokyo (Japan). Technology Research Center

1997-05-27

Discussed in this report is a wavefield simulation in the 3-dimensional seismic survey. With the level of the object of exploration growing deeper and the object more complicated in structure, the survey method is now turning 3-dimensional. There are several modelling methods for numerical calculation of 3-dimensional wavefields, such as the difference method, pseudospectral method, and the like, all of which demand an exorbitantly large memory and long calculation time, and are costly. Such methods have of late become feasible, however, thanks to the advent of the parallel computer. As compared with the difference method, the pseudospectral method requires a smaller computer memory and shorter computation time, and is more flexible in accepting models. It outputs the result in fullwave just like the difference method, and does not cause wavefield numerical variance. As the computation platform, the parallel computer nCUBE-2S is used. The object domain is divided into the number of the processors, and each of the processors takes care only of its share so that parallel computation as a whole may realize a very high-speed computation. By the use of the pseudospectral method, a 3-dimensional simulation is completed within a tolerable computation time length. 7 refs., 3 figs., 1 tab.

Protein structural model selection by combining consensus and single scoring methods.

Directory of Open Access Journals (Sweden)

Zhiquan He

Full Text Available Quality assessment (QA for predicted protein structural models is an important and challenging research problem in protein structure prediction. Consensus Global Distance Test (CGDT methods assess each decoy (predicted structural model based on its structural similarity to all others in a decoy set and has been proved to work well when good decoys are in a majority cluster. Scoring functions evaluate each single decoy based on its structural properties. Both methods have their merits and limitations. In this paper, we present a novel method called PWCom, which consists of two neural networks sequentially to combine CGDT and single model scoring methods such as RW, DDFire and OPUS-Ca. Specifically, for every pair of decoys, the difference of the corresponding feature vectors is input to the first neural network which enables one to predict whether the decoy-pair are significantly different in terms of their GDT scores to the native. If yes, the second neural network is used to decide which one of the two is closer to the native structure. The quality score for each decoy in the pool is based on the number of winning times during the pairwise comparisons. Test results on three benchmark datasets from different model generation methods showed that PWCom significantly improves over consensus GDT and single scoring methods. The QA server (MUFOLD-Server applying this method in CASP 10 QA category was ranked the second place in terms of Pearson and Spearman correlation performance.

A deep learning-based multi-model ensemble method for cancer prediction.

Science.gov (United States)

Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong

2018-01-01

Cancer is a complex worldwide health problem associated with high mortality. With the rapid development of the high-throughput sequencing technology and the application of various machine learning methods that have emerged in recent years, progress in cancer prediction has been increasingly made based on gene expression, providing insight into effective and accurate treatment decision making. Thus, developing machine learning methods, which can successfully distinguish cancer patients from healthy persons, is of great current interest. However, among the classification methods applied to cancer prediction so far, no one method outperforms all the others. In this paper, we demonstrate a new strategy, which applies deep learning to an ensemble approach that incorporates multiple different machine learning models. We supply informative gene data selected by differential gene expression analysis to five different classification models. Then, a deep learning method is employed to ensemble the outputs of the five classifiers. The proposed deep learning-based multi-model ensemble method was tested on three public RNA-seq data sets of three kinds of cancers, Lung Adenocarcinoma, Stomach Adenocarcinoma and Breast Invasive Carcinoma. The test results indicate that it increases the prediction accuracy of cancer for all the tested RNA-seq data sets as compared to using a single classifier or the majority voting algorithm. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for cancer prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

Geometry optimization method versus predictive ability in QSPR modeling for ionic liquids

Science.gov (United States)

Rybinska, Anna; Sosnowska, Anita; Barycki, Maciej; Puzyn, Tomasz

2016-02-01

Computational techniques, such as Quantitative Structure-Property Relationship (QSPR) modeling, are very useful in predicting physicochemical properties of various chemicals. Building QSPR models requires calculating molecular descriptors and the proper choice of the geometry optimization method, which will be dedicated to specific structure of tested compounds. Herein, we examine the influence of the ionic liquids' (ILs) geometry optimization methods on the predictive ability of QSPR models by comparing three models. The models were developed based on the same experimental data on density collected for 66 ionic liquids, but with employing molecular descriptors calculated from molecular geometries optimized at three different levels of the theory, namely: (1) semi-empirical (PM7), (2) ab initio (HF/6-311+G*) and (3) density functional theory (B3LYP/6-311+G*). The model in which the descriptors were calculated by using ab initio HF/6-311+G* method indicated the best predictivity capabilities ({{Q}}_{{EXT}}2 = 0.87). However, PM7-based model has comparable values of quality parameters ({{Q}}_{{EXT}}2 = 0.84). Obtained results indicate that semi-empirical methods (faster and less expensive regarding CPU time) can be successfully employed to geometry optimization in QSPR studies for ionic liquids.

Innovative Calibration Method for System Level Simulation Models of Internal Combustion Engines

Directory of Open Access Journals (Sweden)

Ivo Prah

2016-09-01

Full Text Available The paper outlines a procedure for the computer-controlled calibration of the combined zero-dimensional (0D and one-dimensional (1D thermodynamic simulation model of a turbocharged internal combustion engine (ICE. The main purpose of the calibration is to determine input parameters of the simulation model in such a way as to achieve the smallest difference between the results of the measurements and the results of the numerical simulations with minimum consumption of the computing time. An innovative calibration methodology is based on a novel interaction between optimization methods and physically based methods of the selected ICE sub-systems. Therein physically based methods were used for steering the division of the integral ICE to several sub-models and for determining parameters of selected components considering their governing equations. Innovative multistage interaction between optimization methods and physically based methods allows, unlike the use of well-established methods that rely only on the optimization techniques, for successful calibration of a large number of input parameters with low time consumption. Therefore, the proposed method is suitable for efficient calibration of simulation models of advanced ICEs.

Attitude Research in Science Education: Contemporary Models and Methods.

Science.gov (United States)

Crawley, Frank E.; Kobala, Thomas R., Jr.

1994-01-01

Presents a summary of models and methods of attitude research which are embedded in the theoretical tenets of social psychology and in the broader framework of constructivism. Focuses on the construction of social reality rather than the construction of physical reality. Models include theory of reasoned action, theory of planned behavior, and…

The n-component cubic model and flows: subgraph break-collapse method

International Nuclear Information System (INIS)

Essam, J.W.; Magalhaes, A.C.N. de.

1988-01-01

We generalise to the n-component cubic model the subgraph break-collapse method which we previously developed for the Potts model. The relations used are based on expressions which we recently derived for the Z(λ) model in terms of mod-λ flows. Our recursive algorithm is similar, for n = 2, to the break-collapse method for the Z(4) model proposed by Mariz and coworkers. It allows the exact calculation for the partition function and correlation functions for n-component cubic clusters with n as a variable, without the need to examine all of the spin configurations. (author) [pt

Large-signal modeling method for power FETs and diodes

Energy Technology Data Exchange (ETDEWEB)

Sun Lu; Wang Jiali; Wang Shan; Li Xuezheng; Shi Hui; Wang Na; Guo Shengping, E-mail: sunlu_1019@126.co [School of Electromechanical Engineering, Xidian University, Xi' an 710071 (China)

2009-06-01

Under a large signal drive level, a frequency domain black box model of the nonlinear scattering function is introduced into power FETs and diodes. A time domain measurement system and a calibration method based on a digital oscilloscope are designed to extract the nonlinear scattering function of semiconductor devices. The extracted models can reflect the real electrical performance of semiconductor devices and propose a new large-signal model to the design of microwave semiconductor circuits.

Large-signal modeling method for power FETs and diodes

International Nuclear Information System (INIS)

Sun Lu; Wang Jiali; Wang Shan; Li Xuezheng; Shi Hui; Wang Na; Guo Shengping

2009-01-01

Under a large signal drive level, a frequency domain black box model of the nonlinear scattering function is introduced into power FETs and diodes. A time domain measurement system and a calibration method based on a digital oscilloscope are designed to extract the nonlinear scattering function of semiconductor devices. The extracted models can reflect the real electrical performance of semiconductor devices and propose a new large-signal model to the design of microwave semiconductor circuits.

A novel method for right one-lung ventilation modeling in rabbits.

Science.gov (United States)

Xu, Ze-Ping; Gu, Lian-Bing; Bian, Qing-Ming; Li, Peng-Yi; Wang, Li-Jun; Chen, Xiao-Xiang; Zhang, Jing-Yuan

2016-08-01

There is no standard method by which to establish a right one-lung ventilation (OLV) model in rabbits. In the present study, a novel method is proposed to compare with two other methods. After 0.5 h of baseline two-lung ventilation (TLV), 40 rabbits were randomly divided into sham group (TLV for 3 h as a contrast) and three right-OLV groups (right OLV for 3 h with different methods): Deep intubation group, clamp group and blocker group (deeply intubate the self-made bronchial blocker into the left main bronchus, the novel method). These three methods were compared using a number of variables: Circulation by heart rate (HR), mean arterial pressure (MAP); oxygenation by arterial blood gas analysis; airway pressure; lung injury by histopathology; and time, blood loss, success rate of modeling. Following OLV, compared with the sham group, arterial partial pressure of oxygen and arterial hemoglobin oxygen saturation decreased, peak pressure increased and lung injury scores were higher in three OLV groups at 3 h of OLV. All these indexes showed no differences between the three OLV groups. During right-OLV modeling, less time was spent in the blocker group (6±2 min), compared with the other two OLV groups (13±4 min in deep intubation group, Pright-OLV model in rabbits.

Acoustic 3D modeling by the method of integral equations

Science.gov (United States)

Malovichko, M.; Khokhlov, N.; Yavich, N.; Zhdanov, M.

2018-02-01

This paper presents a parallel algorithm for frequency-domain acoustic modeling by the method of integral equations (IE). The algorithm is applied to seismic simulation. The IE method reduces the size of the problem but leads to a dense system matrix. A tolerable memory consumption and numerical complexity were achieved by applying an iterative solver, accompanied by an effective matrix-vector multiplication operation, based on the fast Fourier transform (FFT). We demonstrate that, the IE system matrix is better conditioned than that of the finite-difference (FD) method, and discuss its relation to a specially preconditioned FD matrix. We considered several methods of matrix-vector multiplication for the free-space and layered host models. The developed algorithm and computer code were benchmarked against the FD time-domain solution. It was demonstrated that, the method could accurately calculate the seismic field for the models with sharp material boundaries and a point source and receiver located close to the free surface. We used OpenMP to speed up the matrix-vector multiplication, while MPI was used to speed up the solution of the system equations, and also for parallelizing across multiple sources. The practical examples and efficiency tests are presented as well.

Deterministic methods for sensitivity and uncertainty analysis in large-scale computer models

International Nuclear Information System (INIS)

Worley, B.A.; Oblow, E.M.; Pin, F.G.; Maerker, R.E.; Horwedel, J.E.; Wright, R.Q.; Lucius, J.L.

1987-01-01

The fields of sensitivity and uncertainty analysis are dominated by statistical techniques when large-scale modeling codes are being analyzed. This paper reports on the development and availability of two systems, GRESS and ADGEN, that make use of computer calculus compilers to automate the implementation of deterministic sensitivity analysis capability into existing computer models. This automation removes the traditional limitation of deterministic sensitivity methods. The paper describes a deterministic uncertainty analysis method (DUA) that uses derivative information as a basis to propagate parameter probability distributions to obtain result probability distributions. The paper demonstrates the deterministic approach to sensitivity and uncertainty analysis as applied to a sample problem that models the flow of water through a borehole. The sample problem is used as a basis to compare the cumulative distribution function of the flow rate as calculated by the standard statistical methods and the DUA method. The DUA method gives a more accurate result based upon only two model executions compared to fifty executions in the statistical case

An adaptive sampling method for variable-fidelity surrogate models using improved hierarchical kriging

Science.gov (United States)

Hu, Jiexiang; Zhou, Qi; Jiang, Ping; Shao, Xinyu; Xie, Tingli

2018-01-01

Variable-fidelity (VF) modelling methods have been widely used in complex engineering system design to mitigate the computational burden. Building a VF model generally includes two parts: design of experiments and metamodel construction. In this article, an adaptive sampling method based on improved hierarchical kriging (ASM-IHK) is proposed to refine the improved VF model. First, an improved hierarchical kriging model is developed as the metamodel, in which the low-fidelity model is varied through a polynomial response surface function to capture the characteristics of a high-fidelity model. Secondly, to reduce local approximation errors, an active learning strategy based on a sequential sampling method is introduced to make full use of the already required information on the current sampling points and to guide the sampling process of the high-fidelity model. Finally, two numerical examples and the modelling of the aerodynamic coefficient for an aircraft are provided to demonstrate the approximation capability of the proposed approach, as well as three other metamodelling methods and two sequential sampling methods. The results show that ASM-IHK provides a more accurate metamodel at the same simulation cost, which is very important in metamodel-based engineering design problems.

Complex brittle deformation pattern along the Southern Patagonian Andes (Argentina)

Science.gov (United States)

Barberón, Vanesa; Sue, Christian; Ronda, Gonzalo; Ghiglione, Matías

2016-04-01

The Southern Patagonian Andes is located in the southern extreme of the Pacific subduction zone, where the Antartic oceanic plate sinks underneath South America. The history of the area begins with compression during Paleozoic, Jurassic extension associated to the rift and opening of the South Atlantic Ocean, then a sag stage in the Lower Cretaceous followed by a foreland phase as a result of plate tectonics (Ghiglione et al., 2016). The kinematic study is concentrated in the Argentinean foothills, between 46°40' and 48° SL. We measured around 800 fault planes and their striaes with the sense of movement in order to characterize the stress field. The software used to make the stress inversion were Tensor (Delvaux, 2011) and Multiple Inverse Method MIM (Yamaji et al., 2011). The stress field map was built with the results of the MIM. We present new data from 48 sites located in the northern sector of the Southern Patagonian Andes. The measurements were made in several rocks from Paleozoic to Lower Cretaceous, even though most were taken in pyroclastic jurassic rocks from El Quemado Complex. Paleostress tensors obtained are mostly strike-slip, although a 25% is normal and there are a few compresional. The pattern of faults found is complex. In some sites the tensor can be locally linked to satellite images and observations from the field or be related to a major thrust front. There is no clear correlation between the age and/or lithology with the tensor since the youngest rocks measured are Lower Cretaceous. Probably there are several generations of family faults connected to different and recent tectonic phases then the paleostress tensors might correspond to the latest tectonic events.

The conditional moment closure method for modeling lean premixed turbulent combustion

Science.gov (United States)

Martin, Scott Montgomery

Natural gas fired lean premixed gas turbines have become the method of choice for new power generation systems due to their high efficiency and low pollutant emissions. As emission regulations for these combustion systems become more stringent, the use of numerical modeling has become an important a priori tool in designing clean and efficient combustors. Here a new turbulent combustion model is developed in an attempt to improve the state of the art. The Conditional Moment Closure (CMC) method is a new theory that has been applied to non-premixed combustion with good success. The application of the CMC method to premixed systems has been proposed, but has not yet been done. The premixed CMC method replaces the species mass fractions as independent variables with the species mass fractions that are conditioned on a reaction progress variable (RPV). Conservation equations for these new variables are then derived and solved. The general idea behind the CMC method is that the behavior of the chemical species is closely coupled to the reaction progress variable. Thus, species conservation equations that are conditioned on the RPV will have terms involving the fluctuating quantities that are much more likely to be negligible. The CMC method accounts for the interaction between scalar dissipation (micromixing) and chemistry, while de-coupling the kinetics from the bulk flow (macromixing). Here the CMC method is combined with a commercial computational fluid dynamics program, which calculates the large-scale fluid motions. The CMC model is validated by comparison to 2-D reacting backward facing step data. Predicted species, temperature and velocity fields are compared to experimental data with good success. The CMC model is also validated against the University of Washington's 3-D jet stirred reactor (JSR) data, which is an idealized lean premixed combustor. The JSR results are encouraging, but not as good as the backward facing step. The largest source of error is from

Numerical methods for the Lévy LIBOR model

DEFF Research Database (Denmark)

Papapantoleon, Antonis; Skovmand, David

2010-01-01

but the methods are generally slow. We propose an alternative approximation scheme based on Picard iterations. Our approach is similar in accuracy to the full numerical solution, but with the feature that each rate is, unlike the standard method, evolved independently of the other rates in the term structure....... This enables simultaneous calculation of derivative prices of different maturities using parallel computing. We include numerical illustrations of the accuracy and speed of our method pricing caplets.......The aim of this work is to provide fast and accurate approximation schemes for the Monte-Carlo pricing of derivatives in the L\\'evy LIBOR model of Eberlein and \\"Ozkan (2005). Standard methods can be applied to solve the stochastic differential equations of the successive LIBOR rates...

Numerical Methods for the Lévy LIBOR Model

DEFF Research Database (Denmark)

Papapantoleon, Antonis; Skovmand, David

are generally slow. We propose an alternative approximation scheme based on Picard iterations. Our approach is similar in accuracy to the full numerical solution, but with the feature that each rate is, unlike the standard method, evolved independently of the other rates in the term structure. This enables...... simultaneous calculation of derivative prices of different maturities using parallel computing. We include numerical illustrations of the accuracy and speed of our method pricing caplets.......The aim of this work is to provide fast and accurate approximation schemes for the Monte-Carlo pricing of derivatives in the Lévy LIBOR model of Eberlein and Özkan (2005). Standard methods can be applied to solve the stochastic differential equations of the successive LIBOR rates but the methods...

Regression modeling methods, theory, and computation with SAS

CERN Document Server

Panik, Michael

2009-01-01

Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,

Optimisation-Based Solution Methods for Set Partitioning Models

DEFF Research Database (Denmark)

Rasmussen, Matias Sevel

The scheduling of crew, i.e. the construction of work schedules for crew members, is often not a trivial task, but a complex puzzle. The task is complicated by rules, restrictions, and preferences. Therefore, manual solutions as well as solutions from standard software packages are not always su......_cient with respect to solution quality and solution time. Enhancement of the overall solution quality as well as the solution time can be of vital importance to many organisations. The _elds of operations research and mathematical optimisation deal with mathematical modelling of di_cult scheduling problems (among...... other topics). The _elds also deal with the development of sophisticated solution methods for these mathematical models. This thesis describes the set partitioning model which has been widely used for modelling crew scheduling problems. Integer properties for the set partitioning model are shown...

IMAGE TO POINT CLOUD METHOD OF 3D-MODELING

Directory of Open Access Journals (Sweden)

A. G. Chibunichev

2012-07-01

Full Text Available This article describes the method of constructing 3D models of objects (buildings, monuments based on digital images and a point cloud obtained by terrestrial laser scanner. The first step is the automated determination of exterior orientation parameters of digital image. We have to find the corresponding points of the image and point cloud to provide this operation. Before the corresponding points searching quasi image of point cloud is generated. After that SIFT algorithm is applied to quasi image and real image. SIFT algorithm allows to find corresponding points. Exterior orientation parameters of image are calculated from corresponding points. The second step is construction of the vector object model. Vectorization is performed by operator of PC in an interactive mode using single image. Spatial coordinates of the model are calculated automatically by cloud points. In addition, there is automatic edge detection with interactive editing available. Edge detection is performed on point cloud and on image with subsequent identification of correct edges. Experimental studies of the method have demonstrated its efficiency in case of building facade modeling.

3-D Forward modeling of Induced Polarization Effects of Transient Electromagnetic Method

Science.gov (United States)

Wu, Y.; Ji, Y.; Guan, S.; Li, D.; Wang, A.

2017-12-01

In transient electromagnetic (TEM) detection, Induced polarization (IP) effects are so important that they cannot be ignored. The authors simulate the three-dimensional (3-D) induced polarization effects in time-domain directly by applying the finite-difference time-domain method (FDTD) based on Cole-Cole model. Due to the frequency dispersion characteristics of the electrical conductivity, the computations of convolution in the generalized Ohm's law of fractional order system makes the forward modeling particularly complicated. Firstly, we propose a method to approximate the fractional order function of Cole-Cole model using a lower order rational transfer function based on error minimum theory in the frequency domain. In this section, two auxiliary variables are introduced to transform nonlinear least square fitting problem of the fractional order system into a linear programming problem, thus avoiding having to solve a system of equations and nonlinear problems. Secondly, the time-domain expression of Cole-Cole model is obtained by using Inverse Laplace transform. Then, for the calculation of Ohm's law, we propose an e-index auxiliary equation of conductivity to transform the convolution to non-convolution integral; in this section, the trapezoid rule is applied to compute the integral. We then substitute the recursion equation into Maxwell's equations to derive the iterative equations of electromagnetic field using the FDTD method. Finally, we finish the stimulation of 3-D model and evaluate polarization parameters. The results are compared with those obtained from the digital filtering solution of the analytical equation in the homogeneous half space, as well as with the 3-D model results from the auxiliary ordinary differential equation method (ADE). Good agreements are obtained across the three methods. In terms of the 3-D model, the proposed method has higher efficiency and lower memory requirements as execution times and memory usage were reduced by 20

Huffman and linear scanning methods with statistical language models.

Science.gov (United States)

Roark, Brian; Fried-Oken, Melanie; Gibbons, Chris

2015-03-01

Current scanning access methods for text generation in AAC devices are limited to relatively few options, most notably row/column variations within a matrix. We present Huffman scanning, a new method for applying statistical language models to binary-switch, static-grid typing AAC interfaces, and compare it to other scanning options under a variety of conditions. We present results for 16 adults without disabilities and one 36-year-old man with locked-in syndrome who presents with complex communication needs and uses AAC scanning devices for writing. Huffman scanning with a statistical language model yielded significant typing speedups for the 16 participants without disabilities versus any of the other methods tested, including two row/column scanning methods. A similar pattern of results was found with the individual with locked-in syndrome. Interestingly, faster typing speeds were obtained with Huffman scanning using a more leisurely scan rate than relatively fast individually calibrated scan rates. Overall, the results reported here demonstrate great promise for the usability of Huffman scanning as a faster alternative to row/column scanning.

Modeling hemoglobin at optical frequency using the unconditionally stable fundamental ADI-FDTD method.

Science.gov (United States)

Heh, Ding Yu; Tan, Eng Leong

2011-04-12

This paper presents the modeling of hemoglobin at optical frequency (250 nm - 1000 nm) using the unconditionally stable fundamental alternating-direction-implicit finite-difference time-domain (FADI-FDTD) method. An accurate model based on complex conjugate pole-residue pairs is proposed to model the complex permittivity of hemoglobin at optical frequency. Two hemoglobin concentrations at 15 g/dL and 33 g/dL are considered. The model is then incorporated into the FADI-FDTD method for solving electromagnetic problems involving interaction of light with hemoglobin. The computation of transmission and reflection coefficients of a half space hemoglobin medium using the FADI-FDTD validates the accuracy of our model and method. The specific absorption rate (SAR) distribution of human capillary at optical frequency is also shown. While maintaining accuracy, the unconditionally stable FADI-FDTD method exhibits high efficiency in modeling hemoglobin.

Ratio-based vs. model-based methods to correct for urinary creatinine concentrations.

Science.gov (United States)

Jain, Ram B

2016-08-01

Creatinine-corrected urinary analyte concentration is usually computed as the ratio of the observed level of analyte concentration divided by the observed level of the urinary creatinine concentration (UCR). This ratio-based method is flawed since it implicitly assumes that hydration is the only factor that affects urinary creatinine concentrations. On the contrary, it has been shown in the literature, that age, gender, race/ethnicity, and other factors also affect UCR. Consequently, an optimal method to correct for UCR should correct for hydration as well as other factors like age, gender, and race/ethnicity that affect UCR. Model-based creatinine correction in which observed UCRs are used as an independent variable in regression models has been proposed. This study was conducted to evaluate the performance of ratio-based and model-based creatinine correction methods when the effects of gender, age, and race/ethnicity are evaluated one factor at a time for selected urinary analytes and metabolites. It was observed that ratio-based method leads to statistically significant pairwise differences, for example, between males and females or between non-Hispanic whites (NHW) and non-Hispanic blacks (NHB), more often than the model-based method. However, depending upon the analyte of interest, the reverse is also possible. The estimated ratios of geometric means (GM), for example, male to female or NHW to NHB, were also compared for the two methods. When estimated UCRs were higher for the group (for example, males) in the numerator of this ratio, these ratios were higher for the model-based method, for example, male to female ratio of GMs. When estimated UCR were lower for the group (for example, NHW) in the numerator of this ratio, these ratios were higher for the ratio-based method, for example, NHW to NHB ratio of GMs. Model-based method is the method of choice if all factors that affect UCR are to be accounted for.

Multicanonical simulation of the Domb-Joyce model and the Gō model: new enumeration methods for self-avoiding walks

International Nuclear Information System (INIS)

Shirai, Nobu C; Kikuchi, Macoto

2013-01-01

We develop statistical enumeration methods for self-avoiding walks using a powerful sampling technique called the multicanonical Monte Carlo method. Using these methods, we estimate the numbers of the two dimensional N-step self-avoiding walks up to N = 256 with statistical errors. The developed methods are based on statistical mechanical models of paths which include self-avoiding walks. The criterion for selecting a suitable model for enumerating self-avoiding walks is whether or not the configuration space of the model includes a set for which the number of the elements can be exactly counted. We call this set a scale fixing set. We selected the following two models which satisfy the criterion: the Gō model for lattice proteins and the Domb-Joyce model for generalized random walks. There is a contrast between these two models in the structures of the configuration space. The configuration space of the Gō model is defined as the universal set of self-avoiding walks, and the set of the ground state conformation provides a scale fixing set. On the other hand, the configuration space of the Domb-Joyce model is defined as the universal set of random walks which can be used as a scale fixing set, and the set of the ground state conformation is the same as the universal set of self-avoiding walks. From the perspective of enumeration performance, we conclude that the Domb-Joyce model is the better of the two. The reason for the performance difference is partly explained by the existence of the first-order phase transition of the Gō model

A comparison of uncertainty analysis methods using a groundwater flow model

International Nuclear Information System (INIS)

Doctor, P.G.; Jacobson, E.A.; Buchanan, J.A.

1988-06-01

This report evaluates three uncertainty analysis methods that are proposed for use in performances assessment activities within the OCRWM and Nuclear Regulatory Commission (NRC) communities. The three methods are Monte Carlo simulation with unconstrained sampling, Monte Carlo simulation with Latin Hypercube sampling, and first-order analysis. Monte Carlo simulation with unconstrained sampling is a generally accepted uncertainty analysis method, but it has the disadvantage of being costly and time consuming. Latin Hypercube sampling was proposed to make Monte Carlo simulation more efficient. However, although it was originally formulated for independent variables, which is a major drawback in performance assessment modeling, Latin Hypercube can be used to generate correlated samples. The first-order method is efficient to implement because it is based on the first-order Taylor series expansion; however, there is concern that it does not adequately describe the variability for complex models. These three uncertainty analysis methods were evaluated using a calibrated groundwater flow model of a unconfined aquifer in southern Arizona. The two simulation methods produced similar results, although the Latin Hypercube method tends to produce samples whose estimates of statistical parameters are closer to the desired parameters. The mean travel times for the first-order method does not agree with those of the simulations. In additions, the first-order method produces estimates of variance in travel times that are more variable than those produced by the simulation methods, resulting in nonconservative tolerance intervals. 13 refs., 33 figs

Probability of Detection (POD) as a statistical model for the validation of qualitative methods.

Science.gov (United States)

Wehling, Paul; LaBudde, Robert A; Brunelle, Sharon L; Nelson, Maria T

2011-01-01

A statistical model is presented for use in validation of qualitative methods. This model, termed Probability of Detection (POD), harmonizes the statistical concepts and parameters between quantitative and qualitative method validation. POD characterizes method response with respect to concentration as a continuous variable. The POD model provides a tool for graphical representation of response curves for qualitative methods. In addition, the model allows comparisons between candidate and reference methods, and provides calculations of repeatability, reproducibility, and laboratory effects from collaborative study data. Single laboratory study and collaborative study examples are given.

An automatic and effective parameter optimization method for model tuning

Directory of Open Access Journals (Sweden)

T. Zhang

2015-11-01

simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.

Methods and models used in comparative risk studies

International Nuclear Information System (INIS)

Devooght, J.

1983-01-01

Comparative risk studies make use of a large number of methods and models based upon a set of assumptions incompletely formulated or of value judgements. Owing to the multidimensionality of risks and benefits, the economic and social context may notably influence the final result. Five classes of models are briefly reviewed: accounting of fluxes of effluents, radiation and energy; transport models and health effects; systems reliability and bayesian analysis; economic analysis of reliability and cost-risk-benefit analysis; decision theory in presence of uncertainty and multiple objectives. Purpose and prospect of comparative studies are assessed in view of probable diminishing returns for large generic comparisons [fr

A diagnosis method for physical systems using a multi-modeling approach

International Nuclear Information System (INIS)

Thetiot, R.

2000-01-01

In this thesis we propose a method for diagnosis problem solving. This method is based on a multi-modeling approach describing both normal and abnormal behavior of a system. This modeling approach allows to represent a system at different abstraction levels (behavioral, functional and teleological. Fundamental knowledge is described according to a bond-graph representation. We show that bond-graph representation can be exploited in order to generate (completely or partially) the functional models. The different models of the multi-modeling approach allows to define the functional state of a system at different abstraction levels. We exploit this property to exonerate sub-systems for which the expected behavior is observed. The behavioral and functional descriptions of the remaining sub-systems are exploited hierarchically in a two steps process. In a first step, the abnormal behaviors explaining some observations are identified. In a second step, the remaining unexplained observations are used to generate conflict sets and thus the consistency based diagnoses. The modeling method and the diagnosis process have been applied to a Reactor Coolant Pump Sets. This application illustrates the concepts described in this thesis and shows its potentialities. (authors)

A hierarchical network modeling method for railway tunnels safety assessment

Science.gov (United States)

Zhou, Jin; Xu, Weixiang; Guo, Xin; Liu, Xumin

2017-02-01

Using network theory to model risk-related knowledge on accidents is regarded as potential very helpful in risk management. A large amount of defects detection data for railway tunnels is collected in autumn every year in China. It is extremely important to discover the regularities knowledge in database. In this paper, based on network theories and by using data mining techniques, a new method is proposed for mining risk-related regularities to support risk management in railway tunnel projects. A hierarchical network (HN) model which takes into account the tunnel structures, tunnel defects, potential failures and accidents is established. An improved Apriori algorithm is designed to rapidly and effectively mine correlations between tunnel structures and tunnel defects. Then an algorithm is presented in order to mine the risk-related regularities table (RRT) from the frequent patterns. At last, a safety assessment method is proposed by consideration of actual defects and possible risks of defects gained from the RRT. This method cannot only generate the quantitative risk results but also reveal the key defects and critical risks of defects. This paper is further development on accident causation network modeling methods which can provide guidance for specific maintenance measure.

Multinomial Response Models, for Modeling and Determining Important Factors in Different Contraceptive Methods in Women

Directory of Open Access Journals (Sweden)

E Haji Nejad

2001-06-01

Full Text Available Difference aspects of multinomial statistical modelings and its classifications has been studied so far. In these type of problems Y is the qualitative random variable with T possible states which are considered as classifications. The goal is prediction of Y based on a random Vector X ? IR^m. Many methods for analyzing these problems were considered. One of the modern and general method of classification is Classification and Regression Trees (CART. Another method is recursive partitioning techniques which has a strange relationship with nonparametric regression. Classical discriminant analysis is a standard method for analyzing these type of data. Flexible discriminant analysis method which is a combination of nonparametric regression and discriminant analysis and classification using spline that includes least square regression and additive cubic splines. Neural network is an advanced statistical method for analyzing these types of data. In this paper properties of multinomial logistics regression were investigated and this method was used for modeling effective factors in selecting contraceptive methods in Ghom province for married women age 15-49. The response variable has a tetranomial distibution. The levels of this variable are: nothing, pills, traditional and a collection of other contraceptive methods. A collection of significant independent variables were: place, age of women, education, history of pregnancy and family size. Menstruation age and age at marriage were not statistically significant.

Hybrid perturbation methods based on statistical time series models

Science.gov (United States)

San-Juan, Juan Félix; San-Martín, Montserrat; Pérez, Iván; López, Rosario

2016-04-01

In this work we present a new methodology for orbit propagation, the hybrid perturbation theory, based on the combination of an integration method and a prediction technique. The former, which can be a numerical, analytical or semianalytical theory, generates an initial approximation that contains some inaccuracies derived from the fact that, in order to simplify the expressions and subsequent computations, not all the involved forces are taken into account and only low-order terms are considered, not to mention the fact that mathematical models of perturbations not always reproduce physical phenomena with absolute precision. The prediction technique, which can be based on either statistical time series models or computational intelligence methods, is aimed at modelling and reproducing missing dynamics in the previously integrated approximation. This combination results in the precision improvement of conventional numerical, analytical and semianalytical theories for determining the position and velocity of any artificial satellite or space debris object. In order to validate this methodology, we present a family of three hybrid orbit propagators formed by the combination of three different orders of approximation of an analytical theory and a statistical time series model, and analyse their capability to process the effect produced by the flattening of the Earth. The three considered analytical components are the integration of the Kepler problem, a first-order and a second-order analytical theories, whereas the prediction technique is the same in the three cases, namely an additive Holt-Winters method.

Fuzzy decision-making: a new method in model selection via various validity criteria

International Nuclear Information System (INIS)

Shakouri Ganjavi, H.; Nikravesh, K.

2001-01-01

Modeling is considered as the first step in scientific investigations. Several alternative models may be candida ted to express a phenomenon. Scientists use various criteria to select one model between the competing models. Based on the solution of a Fuzzy Decision-Making problem, this paper proposes a new method in model selection. The method enables the scientist to apply all desired validity criteria, systematically by defining a proper Possibility Distribution Function due to each criterion. Finally, minimization of a utility function composed of the Possibility Distribution Functions will determine the best selection. The method is illustrated through a modeling example for the A verage Daily Time Duration of Electrical Energy Consumption in Iran

New method for model coupling using Stampi. Application to the coupling of atmosphere model (MM5) and land-surface model (SOLVEG)

International Nuclear Information System (INIS)

Nagai, Haruyasu

2003-12-01

A new method to couple atmosphere and land-surface models using the message passing interface (MPI) was proposed to develop an atmosphere-land model for studies on heat, water, and material exchanges around the land surface. A non-hydrostatic atmospheric dynamic model of Pennsylvania State University and National Center for Atmospheric Research (PUS/NCAR-MM5) and a detailed land surface model (SOLVEG) including the surface-layer atmosphere, soil, and vegetation developed at Japan Atomic Energy Research Institute (JAERI) are used as the atmosphere and land-surface models, respectively. Concerning the MPI, a message passing library named Stampi developed at JAERI that can be used between different parallel computers is used. The models are coupled by exchanging calculation results by using MPI on their independent parallel calculations. The modifications for this model coupling are easy, simply adding some modules for data exchanges to each model code without changing each model's original structure. Moreover, this coupling method is flexible and allows the use of independent time step and grid interval for each model. (author)

Finite-element method modeling of hyper-frequency structures

International Nuclear Information System (INIS)

Zhang, Min

1990-01-01

The modelization of microwave propagation problems, including Eigen-value problem and scattering problem, is accomplished by the finite element method with vector functional and scalar functional. For Eigen-value problem, propagation modes in waveguides and resonant modes in cavities can be calculated in a arbitrarily-shaped structure with inhomogeneous material. Several microwave structures are resolved in order to verify the program. One drawback associated with the vector functional is the appearance of spurious or non-physical solutions. A penalty function method has been introduced to reduce spurious' solutions. The adaptive charge method is originally proposed in this thesis to resolve waveguide scattering problem. This method, similar to VSWR measuring technique, is more efficient to obtain the reflection coefficient than the matrix method. Two waveguide discontinuity structures are calculated by the two methods and their results are compared. The adaptive charge method is also applied to a microwave plasma excitor. It allows us to understand the role of different physical parameters of excitor in the coupling of microwave energy to plasma mode and the mode without plasma. (author) [fr

Parallel shooting methods for finding steady state solutions to engine simulation models

DEFF Research Database (Denmark)

Andersen, Stig Kildegård; Thomsen, Per Grove; Carlsen, Henrik

2007-01-01

Parallel single- and multiple shooting methods were tested for finding periodic steady state solutions to a Stirling engine model. The model was used to illustrate features of the methods and possibilities for optimisations. Performance was measured using simulation of an experimental data set...

Methods improvements incorporated into the SAPHIRE ASP models

International Nuclear Information System (INIS)

Sattison, M.B.; Blackman, H.S.; Novack, S.D.; Smith, C.L.; Rasmuson, D.M.

1994-01-01

The Office for Analysis and Evaluation of Operational Data (AEOD) has sought the assistance of the Idaho National Engineering Laboratory (INEL) to make some significant enhancements to the SAPHIRE-based Accident Sequence Precursor (ASP) models recently developed by the INEL. The challenge of this project is to provide the features of a full-scale PRA within the framework of the simplified ASP models. Some of these features include: (1) uncertainty analysis addressing the standard PRA uncertainties and the uncertainties unique to the ASP models and methodology, (2) incorporation and proper quantification of individual human actions and the interaction among human actions, (3) enhanced treatment of common cause failures, and (4) extension of the ASP models to more closely mimic full-scale PRAs (inclusion of more initiators, explicitly modeling support system failures, etc.). This paper provides an overview of the methods being used to make the above improvements

A visual tracking method based on deep learning without online model updating

Science.gov (United States)

Tang, Cong; Wang, Yicheng; Feng, Yunsong; Zheng, Chao; Jin, Wei

2018-02-01

The paper proposes a visual tracking method based on deep learning without online model updating. In consideration of the advantages of deep learning in feature representation, deep model SSD (Single Shot Multibox Detector) is used as the object extractor in the tracking model. Simultaneously, the color histogram feature and HOG (Histogram of Oriented Gradient) feature are combined to select the tracking object. In the process of tracking, multi-scale object searching map is built to improve the detection performance of deep detection model and the tracking efficiency. In the experiment of eight respective tracking video sequences in the baseline dataset, compared with six state-of-the-art methods, the method in the paper has better robustness in the tracking challenging factors, such as deformation, scale variation, rotation variation, illumination variation, and background clutters, moreover, its general performance is better than other six tracking methods.

Hydrostratigraphic modelling using multiple-point statistics and airborne transient electromagnetic methods

DEFF Research Database (Denmark)

Barfod, Adrian; Straubhaar, Julien; Høyer, Anne-Sophie

2017-01-01

the incorporation of elaborate datasets and provides a framework for stochastic hydrostratigraphic modelling. This paper focuses on comparing three MPS methods: snesim, DS and iqsim. The MPS methods are tested and compared on a real-world hydrogeophysical survey from Kasted in Denmark, which covers an area of 45 km......2. The comparison of the stochastic hydrostratigraphic MPS models is carried out in an elaborate scheme of visual inspection, mathematical similarity and consistency with boreholes. Using the Kasted survey data, a practical example for modelling new survey areas is presented. A cognitive...

A Design Method of Robust Servo Internal Model Control with Control Input Saturation

OpenAIRE

山田, 功; 舩見, 洋祐

2001-01-01

In the present paper, we examine a design method of robust servo Internal Model Control with control input saturation. First of all, we clarify the condition that Internal Model Control has robust servo characteristics for the system with control input saturation. From this consideration, we propose new design method of Internal Model Control with robust servo characteristics. A numerical example to illustrate the effectiveness of the proposed method is shown.

Modeling Enzymatic Transition States by Force Field Methods

DEFF Research Database (Denmark)

Hansen, Mikkel Bo; Jensen, Hans Jørgen Aagaard; Jensen, Frank

2009-01-01

The SEAM method, which models a transition structure as a minimum on the seam of two diabatic surfaces represented by force field functions, has been used to generate 20 transition structures for the decarboxylation of orotidine by the orotidine-5'-monophosphate decarboxylase enzyme. The dependence...... of the TS geometry on the flexibility of the system has been probed by fixing layers of atoms around the active site and using increasingly larger nonbonded cutoffs. The variability over the 20 structures is found to decrease as the system is made more flexible. Relative energies have been calculated...... by various electronic structure methods, where part of the enzyme is represented by a force field description and the effects of the solvent are represented by a continuum model. The relative energies vary by several hundreds of kJ/mol between the transition structures, and tests showed that a large part...

Comparison of model reference and map based control method for vehicle stability enhancement

NARCIS (Netherlands)

Baek, S.; Son, M.; Song, J.; Boo, K.; Kim, H.

2012-01-01

A map based controller method to improve a vehicle lateral stability is proposed in this study and compared with the conventional method, a model referenced controller. A model referenced controller to determine compensated yaw moment uses the sliding mode method, but the proposed map based

A Robust Non-Gaussian Data Assimilation Method for Highly Non-Linear Models

Directory of Open Access Journals (Sweden)

Elias D. Nino-Ruiz

2018-03-01

Full Text Available In this paper, we propose an efficient EnKF implementation for non-Gaussian data assimilation based on Gaussian Mixture Models and Markov-Chain-Monte-Carlo (MCMC methods. The proposed method works as follows: based on an ensemble of model realizations, prior errors are estimated via a Gaussian Mixture density whose parameters are approximated by means of an Expectation Maximization method. Then, by using an iterative method, observation operators are linearized about current solutions and posterior modes are estimated via a MCMC implementation. The acceptance/rejection criterion is similar to that of the Metropolis-Hastings rule. Experimental tests are performed on the Lorenz 96 model. The results show that the proposed method can decrease prior errors by several order of magnitudes in a root-mean-square-error sense for nearly sparse or dense observational networks.

Multi-chain Markov chain Monte Carlo methods for computationally expensive models

Science.gov (United States)

Huang, M.; Ray, J.; Ren, H.; Hou, Z.; Bao, J.

2017-12-01

Markov chain Monte Carlo (MCMC) methods are used to infer model parameters from observational data. The parameters are inferred as probability densities, thus capturing estimation error due to sparsity of the data, and the shortcomings of the model. Multiple communicating chains executing the MCMC method have the potential to explore the parameter space better, and conceivably accelerate the convergence to the final distribution. We present results from tests conducted with the multi-chain method to show how the acceleration occurs i.e., for loose convergence tolerances, the multiple chains do not make much of a difference. The ensemble of chains also seems to have the ability to accelerate the convergence of a few chains that might start from suboptimal starting points. Finally, we show the performance of the chains in the estimation of O(10) parameters using computationally expensive forward models such as the Community Land Model, where the sampling burden is distributed over multiple chains.

Fuzzy Clustering Methods and their Application to Fuzzy Modeling

DEFF Research Database (Denmark)

Kroszynski, Uri; Zhou, Jianjun

1999-01-01

Fuzzy modeling techniques based upon the analysis of measured input/output data sets result in a set of rules that allow to predict system outputs from given inputs. Fuzzy clustering methods for system modeling and identification result in relatively small rule-bases, allowing fast, yet accurate....... An illustrative synthetic example is analyzed, and prediction accuracy measures are compared between the different variants...

Do mesoscale faults in a young fold belt indicate regional or local stress?

Science.gov (United States)

Kokado, Akihiro; Yamaji, Atsushi; Sato, Katsushi

2017-04-01

The result of paleostress analyses of mesoscale faults is usually thought of as evidence of a regional stress. On the other hand, the recent advancement of the trishear modeling has enabled us to predict the deformation field around fault-propagation folds without the difficulty of assuming paleo mechanical properties of rocks and sediments. We combined the analysis of observed mesoscale faults and the trishear modeling to understand the significance of regional and local stresses for the formation of mesoscale faults. To this end, we conducted the 2D trishear inverse modeling with a curved thrust fault to predict the subsurface structure and strain field of an anticline, which has a more or less horizontal axis and shows a map-scale plane strain perpendicular to the axis, in the active fold belt of Niigata region, central Japan. The anticline is thought to have been formed by fault-propagation folding under WNW-ESE regional compression. Based on the attitudes of strata and the positions of key tephra beds in Lower Pleistocene soft sediments cropping out at the surface, we obtained (1) a fault-propagation fold with the fault tip at a depth of ca. 4 km as the optimal subsurface structure, and (2) the temporal variation of deformation field during the folding. We assumed that mesoscale faults were activated along the direction of maximum shear strain on the faults to test whether the fault-slip data collected at the surface were consistent with the deformation in some stage(s) of folding. The Wallace-Bott hypothesis was used to estimate the consistence of faults with the regional stress. As a result, the folding and the regional stress explained 27 and 33 of 45 observed faults, respectively, with the 11 faults being consistent with the both. Both the folding and regional one were inconsistent with the remaining 17 faults, which could be explained by transfer faulting and/or the gravitational spreading of the growing anticline. The lesson we learnt from this work was

A RECREATION OPTIMIZATION MODEL BASED ON THE TRAVEL COST METHOD

OpenAIRE

Hof, John G.; Loomis, John B.

1983-01-01

A recreation allocation model is developed which efficiently selects recreation areas and degree of development from an array of proposed and existing sites. The model does this by maximizing the difference between gross recreation benefits and travel, investment, management, and site-opportunity costs. The model presented uses the Travel Cost Method for estimating recreation benefits within an operations research framework. The model is applied to selection of potential wilderness areas in C...

Developing energy forecasting model using hybrid artificial intelligence method

Institute of Scientific and Technical Information of China (English)

Shahram Mollaiy-Berneti

2015-01-01

An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation (BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand (gross domestic product (GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand (population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.

Method to determine the optimal constitutive model from spherical indentation tests

Science.gov (United States)

Zhang, Tairui; Wang, Shang; Wang, Weiqiang

2018-03-01

The limitation of current indentation theories was investigated and a method to determine the optimal constitutive model through spherical indentation tests was proposed. Two constitutive models, the Power-law and the Linear-law, were used in Finite Element (FE) calculations, and then a set of indentation governing equations was established for each model. The load-depth data from the normal indentation depth was used to fit the best parameters in each constitutive model while the data from the further loading part was compared with those from FE calculations, and the model that better predicted the further deformation was considered the optimal one. Moreover, a Yang's modulus calculation model which took the previous plastic deformation and the phenomenon of pile-up (or sink-in) into consideration was also proposed to revise the original Sneddon-Pharr-Oliver model. The indentation results on six materials, 304, 321, SA508, SA533, 15CrMoR, and Fv520B, were compared with tensile ones, which validated the reliability of the revised E calculation model and the optimal constitutive model determination method in this study.

Proposal for a Method for Business Model Performance Assessment: Toward an Experimentation Tool for Business Model Innovation

Directory of Open Access Journals (Sweden)

Antonio Batocchio

2017-04-01

Full Text Available The representation of business models has been recently widespread, especially in the pursuit of innovation. However, defining a company’s business model is sometimes limited to discussion and debates. This study observes the need for performance measurement so that business models can be data-driven. To meet this goal, the work proposed as a hypothesis the creation of a method that combines the practices of the Balanced Scorecard with a method of business models representation – the Business Model Canvas. Such a combination was based on study of conceptual adaptation, resulting in an application roadmap. A case study application was performed to check the functionality of the proposition, focusing on startup organizations. It was concluded that based on the performance assessment of the business model it is possible to propose the search for change through experimentation, a path that can lead to business model innovation.

A predictive estimation method for carbon dioxide transport by data-driven modeling with a physically-based data model

Science.gov (United States)

Jeong, Jina; Park, Eungyu; Han, Weon Shik; Kim, Kue-Young; Jun, Seong-Chun; Choung, Sungwook; Yun, Seong-Taek; Oh, Junho; Kim, Hyun-Jun

2017-11-01

In this study, a data-driven method for predicting CO2 leaks and associated concentrations from geological CO2 sequestration is developed. Several candidate models are compared based on their reproducibility and predictive capability for CO2 concentration measurements from the Environment Impact Evaluation Test (EIT) site in Korea. Based on the data mining results, a one-dimensional solution of the advective-dispersive equation for steady flow (i.e., Ogata-Banks solution) is found to be most representative for the test data, and this model is adopted as the data model for the developed method. In the validation step, the method is applied to estimate future CO2 concentrations with the reference estimation by the Ogata-Banks solution, where a part of earlier data is used as the training dataset. From the analysis, it is found that the ensemble mean of multiple estimations based on the developed method shows high prediction accuracy relative to the reference estimation. In addition, the majority of the data to be predicted are included in the proposed quantile interval, which suggests adequate representation of the uncertainty by the developed method. Therefore, the incorporation of a reasonable physically-based data model enhances the prediction capability of the data-driven model. The proposed method is not confined to estimations of CO2 concentration and may be applied to various real-time monitoring data from subsurface sites to develop automated control, management or decision-making systems.

A predictive estimation method for carbon dioxide transport by data-driven modeling with a physically-based data model.

Science.gov (United States)

Jeong, Jina; Park, Eungyu; Han, Weon Shik; Kim, Kue-Young; Jun, Seong-Chun; Choung, Sungwook; Yun, Seong-Taek; Oh, Junho; Kim, Hyun-Jun

2017-11-01

In this study, a data-driven method for predicting CO 2 leaks and associated concentrations from geological CO 2 sequestration is developed. Several candidate models are compared based on their reproducibility and predictive capability for CO 2 concentration measurements from the Environment Impact Evaluation Test (EIT) site in Korea. Based on the data mining results, a one-dimensional solution of the advective-dispersive equation for steady flow (i.e., Ogata-Banks solution) is found to be most representative for the test data, and this model is adopted as the data model for the developed method. In the validation step, the method is applied to estimate future CO 2 concentrations with the reference estimation by the Ogata-Banks solution, where a part of earlier data is used as the training dataset. From the analysis, it is found that the ensemble mean of multiple estimations based on the developed method shows high prediction accuracy relative to the reference estimation. In addition, the majority of the data to be predicted are included in the proposed quantile interval, which suggests adequate representation of the uncertainty by the developed method. Therefore, the incorporation of a reasonable physically-based data model enhances the prediction capability of the data-driven model. The proposed method is not confined to estimations of CO 2 concentration and may be applied to various real-time monitoring data from subsurface sites to develop automated control, management or decision-making systems. Copyright © 2017 Elsevier B.V. All rights reserved.

Methodes d'amas quantiques a temperature finie appliquees au modele de Hubbard

Science.gov (United States)

Plouffe, Dany

Depuis leur decouverte dans les annees 80, les supraconducteurs a haute temperature critique ont suscite beaucoup d'interet en physique du solide. Comprendre l'origine des phases observees dans ces materiaux, telle la supraconductivite, est l'un des grands defis de la physique theorique du solide des 25 dernieres annees. L'un des mecanismes pressentis pour expliquer ces phenomenes est la forte interaction electron-electron. Le modele de Hubbard est l'un des modeles les plus simples pour tenir compte de ces interactions. Malgre la simplicite apparente de ce modele, certaines de ses caracteristiques, dont son diagramme de phase, ne sont toujours pas bien etablies, et ce malgre plusieurs avancements theoriques dans les dernieres annees. Cette etude se consacre a faire une analyse de methodes numeriques permettant de calculer diverses proprietes du modele de Hubbard en fonction de la temperature. Nous decrivons des methodes (la VCA et la CPT) qui permettent de calculer approximativement la fonction de Green a temperature finie sur un systeme infini a partir de la fonction de Green calculee sur un amas de taille finie. Pour calculer ces fonctions de Green, nous allons utiliser des methodes permettant de reduire considerablement les efforts numeriques necessaires pour les calculs des moyennes thermodynamiques, en reduisant considerablement l'espace des etats a considerer dans ces moyennes. Bien que cette etude vise d'abord a developper des methodes d'amas pour resoudre le modele de Hubbard a temperature finie de facon generale ainsi qu'a etudier les proprietes de base de ce modele, nous allons l'appliquer a des conditions qui s'approchent de supraconducteurs a haute temperature critique. Les methodes presentees dans cette etude permettent de tracer un diagramme de phase pour l'antiferromagnetisme et la supraconductivite qui presentent plusieurs similarites avec celui des supraconducteurs a haute temperature. Mots-cles : modele de Hubbard, thermodynamique

A variation method in the optimization problem of the minority game model

International Nuclear Information System (INIS)

Blazhyijevs'kij, L.; Yanyishevs'kij, V.

2009-01-01

This article contains the results of applying a variation method in the investigation of the optimization problem in the minority game model. That suggested approach is shown to give relevant results about phase transition in the model. Other methods pertinent to the problem have also been assessed.

Experimental modeling methods in Industrial Engineering

Directory of Open Access Journals (Sweden)

Peter Trebuňa

2009-03-01

Full Text Available Dynamic approaches to a management system of the present industrial practice, forcing businesses to address management issues in-house continuous improvement of production and non-production processes. Experience has repeatedly demonstrated the need for a system approach not only in analysis but also in the planning and actual implementation of these processes. Therefore, the contribution is focused on the description of the modeling in industrial practice by a system approach, in order to avoid erroneous application of the decision to the implementation phase, and thus prevent any longer applying methods "attempt - fallacy".

Identification of a Multicriteria Decision-Making Model Using the Characteristic Objects Method

Directory of Open Access Journals (Sweden)

Andrzej Piegat

2014-01-01

Full Text Available This paper presents a new, nonlinear, multicriteria, decision-making method: the characteristic objects (COMET. This approach, which can be characterized as a fuzzy reference model, determines a measurement standard for decision-making problems. This model is distinguished by a constant set of specially chosen characteristic objects that are independent of the alternatives. After identifying a multicriteria model, this method can be used to compare any number of decisional objects (alternatives and select the best one. In the COMET, in contrast to other methods, the rank-reversal phenomenon is not observed. Rank-reversal is a paradoxical feature in the decision-making methods, which is caused by determining the absolute evaluations of considered alternatives on the basis of the alternatives themselves. In the Analytic Hierarchy Process (AHP method and similar methods, when a new alternative is added to the original alternative set, the evaluation base and the resulting evaluations of all objects change. A great advantage of the COMET is its ability to identify not only linear but also nonlinear multicriteria models of decision makers. This identification is based not on a ranking of component criteria of the multicriterion but on a ranking of a larger set of characteristic objects (characteristic alternatives that are independent of the small set of alternatives analyzed in a given problem. As a result, the COMET is free of the faults of other methods.

Character expansion methods for matrix models of dually weighted graphs

International Nuclear Information System (INIS)

Kazakov, V.A.; Staudacher, M.; Wynter, T.

1996-01-01

We consider generalized one-matrix models in which external fields allow control over the coordination numbers on both the original and dual lattices. We rederive in a simple fashion a character expansion formula for these models originally due to Itzykson and Di Francesco, and then demonstrate how to take the large N limit of this expansion. The relationship to the usual matrix model resolvent is elucidated. Our methods give as a by-product an extremely simple derivation of the Migdal integral equation describing the large N limit of the Itzykson-Zuber formula. We illustrate and check our methods by analysing a number of models solvable by traditional means. We then proceed to solve a new model: a sum over planar graphs possessing even coordination numbers on both the original and the dual lattice. We conclude by formulating equations for the case of arbitrary sets of even, self-dual coupling constants. This opens the way for studying the deep problem of phase transitions from random to flat lattices. (orig.). With 4 figs

A Method to Identify Flight Obstacles on Digital Surface Model

Institute of Scientific and Technical Information of China (English)

ZHAO Min; LIN Xinggang; SUN Shouyu; WANG Youzhi

2005-01-01

In modern low-altitude terrain-following guidance, a constructing method of the digital surface model (DSM) is presented in the paper to reduce the threat to flying vehicles of tall surface features for safe flight. The relationship between an isolated obstacle size and the intervals of vertical- and cross-section in the DSM model is established. The definition and classification of isolated obstacles are proposed, and a method for determining such isolated obstacles in the DSM model is given. The simulation of a typical urban district shows that when the vertical- and cross-section DSM intervals are between 3 m and 25 m, the threat to terrain-following flight at low-altitude is reduced greatly, and the amount of data required by the DSM model for monitoring in real time a flying vehicle is also smaller. Experiments show that the optimal results are for an interval of 12.5 m in the vertical- and cross-sections in the DSM model, with a 1:10 000 DSM scale grade.

Methods and models for accelerating dynamic simulation of fluid power circuits

Energy Technology Data Exchange (ETDEWEB)

Aaman, R.

2011-07-01

The objective of this dissertation is to improve the dynamic simulation of fluid power circuits. A fluid power circuit is a typical way to implement power transmission in mobile working machines, e.g. cranes, excavators etc. Dynamic simulation is an essential tool in developing controllability and energy-efficient solutions for mobile machines. Efficient dynamic simulation is the basic requirement for the real-time simulation. In the real-time simulation of fluid power circuits there exist numerical problems due to the software and methods used for modelling and integration. A simulation model of a fluid power circuit is typically created using differential and algebraic equations. Efficient numerical methods are required since differential equations must be solved in real time. Unfortunately, simulation software packages offer only a limited selection of numerical solvers. Numerical problems cause noise to the results, which in many cases leads the simulation run to fail. Mathematically the fluid power circuit models are stiff systems of ordinary differential equations. Numerical solution of the stiff systems can be improved by two alternative approaches. The first is to develop numerical solvers suitable for solving stiff systems. The second is to decrease the model stiffness itself by introducing models and algorithms that either decrease the highest eigenvalues or neglect them by introducing steady-state solutions of the stiff parts of the models. The thesis proposes novel methods using the latter approach. The study aims to develop practical methods usable in dynamic simulation of fluid power circuits using explicit fixed-step integration algorithms. In this thesis, two mechanisms which make the system stiff are studied. These are the pressure drop approaching zero in the turbulent orifice model and the volume approaching zero in the equation of pressure build-up. These are the critical areas to which alternative methods for modelling and numerical simulation

Continuum methods of physical modeling continuum mechanics, dimensional analysis, turbulence

CERN Document Server

Hutter, Kolumban

2004-01-01

The book unifies classical continuum mechanics and turbulence modeling, i.e. the same fundamental concepts are used to derive model equations for material behaviour and turbulence closure and complements these with methods of dimensional analysis. The intention is to equip the reader with the ability to understand the complex nonlinear modeling in material behaviour and turbulence closure as well as to derive or invent his own models. Examples are mostly taken from environmental physics and geophysics.

Modelling magnetic polarisation J 50 by different methods

International Nuclear Information System (INIS)

Yonamine, Taeko; Campos, Marcos F. de; Castro, Nicolau A.; Landgraf, Fernando J.G.

2006-01-01

Two different methods for modelling the angular behaviour of magnetic polarisation at 5000 A/m (J 50 ) of electrical steels were evaluated and compared. Both methods are based upon crystallographic texture data. The texture of non-oriented electrical steels with silicon content ranging from 0.11 to 3%Si was determined by X-ray diffraction. In the first method, J 50 was correlated to the calculated value of the average anisotropy energy in each direction, using texture data. In the second method, the first three coefficients of the spherical harmonic series of the ODF and two experimental points were used to estimate the angular variation of J 50 . The first method allows the estimation of J 50 for samples with different textures and Si contents using only the texture data, with no need of magnetic measurement, and this is advantageous, because texture data can be acquired with less than 2 g of material. The second method may give better adjust in some situations but besides the texture data, it requests magnetic measurements in at least two directions, for example, rolling and transverse directions

Model parameterization as method for data analysis in dendroecology

Science.gov (United States)

Tychkov, Ivan; Shishov, Vladimir; Popkova, Margarita

2017-04-01

There is no argue in usefulness of process-based models in ecological studies. Only limitations is how developed algorithm of model and how it will be applied for research. Simulation of tree-ring growth based on climate provides valuable information of tree-ring growth response on different environmental conditions, but also shares light on species-specifics of tree-ring growth process. Visual parameterization of the Vaganov-Shashkin model, allows to estimate non-linear response of tree-ring growth based on daily climate data: daily temperature, estimated day light and soil moisture. Previous using of the VS-Oscilloscope (a software tool of the visual parameterization) shows a good ability to recreate unique patterns of tree-ring growth for coniferous species in Siberian Russia, USA, China, Mediterranean Spain and Tunisia. But using of the models mostly is one-sided to better understand different tree growth processes, opposite to statistical methods of analysis (e.g. Generalized Linear Models, Mixed Models, Structural Equations.) which can be used for reconstruction and forecast. Usually the models are used either for checking of new hypothesis or quantitative assessment of physiological tree growth data to reveal a growth process mechanisms, while statistical methods used for data mining assessment and as a study tool itself. The high sensitivity of the model's VS-parameters reflects the ability of the model to simulate tree-ring growth and evaluates value of limiting growth climate factors. Precise parameterization of VS-Oscilloscope provides valuable information about growth processes of trees and under what conditions these processes occur (e.g. day of growth season onset, length of season, value of minimal/maximum temperature for tree-ring growth, formation of wide or narrow rings etc.). The work was supported by the Russian Science Foundation (RSF # 14-14-00219)

Modelling dynamic processes in a nuclear reactor by state change modal method

Science.gov (United States)

Avvakumov, A. V.; Strizhov, V. F.; Vabishchevich, P. N.; Vasilev, A. O.

2017-12-01

Modelling of dynamic processes in nuclear reactors is carried out, mainly, using the multigroup neutron diffusion approximation. The basic model includes a multidimensional set of coupled parabolic equations and ordinary differential equations. Dynamic processes are modelled by a successive change of the reactor states. It is considered that the transition from one state to another occurs promptly. In the modal method the approximate solution is represented as eigenfunction expansion. The numerical-analytical method is based on the use of dominant time-eigenvalues of a group diffusion model taking into account delayed neutrons.

METHOD OF DISPLAYING AN EXECUTABLE BUSINESS PROCESS MODELS INTO PETRI NETS

Directory of Open Access Journals (Sweden)

Igor G. Fedorov

2013-01-01

Full Text Available Executable business process models, as well as programs, require evidence of a defect-free finish. The methods based on the formalism of Petri nets are widely used. A business process is a network of dishes, and its properties are set by the analysis of the properties of the network. The aim is to study the methods of displaying an executable business process model in a Petri net. Analysis of the properties of the resulting model allows us to prove a number of important properties: it is a network of free choice and clean without looping.

Three-dimensional forward modeling of DC resistivity using the aggregation-based algebraic multigrid method

Science.gov (United States)

Chen, Hui; Deng, Ju-Zhi; Yin, Min; Yin, Chang-Chun; Tang, Wen-Wu

2017-03-01

To speed up three-dimensional (3D) DC resistivity modeling, we present a new multigrid method, the aggregation-based algebraic multigrid method (AGMG). We first discretize the differential equation of the secondary potential field with mixed boundary conditions by using a seven-point finite-difference method to obtain a large sparse system of linear equations. Then, we introduce the theory behind the pairwise aggregation algorithms for AGMG and use the conjugate-gradient method with the V-cycle AGMG preconditioner (AGMG-CG) to solve the linear equations. We use typical geoelectrical models to test the proposed AGMG-CG method and compare the results with analytical solutions and the 3DDCXH algorithm for 3D DC modeling (3DDCXH). In addition, we apply the AGMG-CG method to different grid sizes and geoelectrical models and compare it to different iterative methods, such as ILU-BICGSTAB, ILU-GCR, and SSOR-CG. The AGMG-CG method yields nearly linearly decreasing errors, whereas the number of iterations increases slowly with increasing grid size. The AGMG-CG method is precise and converges fast, and thus can improve the computational efficiency in forward modeling of three-dimensional DC resistivity.

A Proposed Model of Retransformed Qualitative Data within a Mixed Methods Research Design

Science.gov (United States)

Palladino, John M.

2009-01-01

Most models of mixed methods research design provide equal emphasis of qualitative and quantitative data analyses and interpretation. Other models stress one method more than the other. The present article is a discourse about the investigator's decision to employ a mixed method design to examine special education teachers' advocacy and…

Method to determine the optimal constitutive model from spherical indentation tests

Directory of Open Access Journals (Sweden)

Tairui Zhang

2018-03-01

Full Text Available The limitation of current indentation theories was investigated and a method to determine the optimal constitutive model through spherical indentation tests was proposed. Two constitutive models, the Power-law and the Linear-law, were used in Finite Element (FE calculations, and then a set of indentation governing equations was established for each model. The load-depth data from the normal indentation depth was used to fit the best parameters in each constitutive model while the data from the further loading part was compared with those from FE calculations, and the model that better predicted the further deformation was considered the optimal one. Moreover, a Yang’s modulus calculation model which took the previous plastic deformation and the phenomenon of pile-up (or sink-in into consideration was also proposed to revise the original Sneddon-Pharr-Oliver model. The indentation results on six materials, 304, 321, SA508, SA533, 15CrMoR, and Fv520B, were compared with tensile ones, which validated the reliability of the revised E calculation model and the optimal constitutive model determination method in this study. Keywords: Optimal constitutive model, Spherical indentation test, Finite Element calculations, Yang’s modulus

Complex Data Modeling and Computationally Intensive Statistical Methods

CERN Document Server

Mantovan, Pietro

2010-01-01

The last years have seen the advent and development of many devices able to record and store an always increasing amount of complex and high dimensional data; 3D images generated by medical scanners or satellite remote sensing, DNA microarrays, real time financial data, system control datasets. The analysis of this data poses new challenging problems and requires the development of novel statistical models and computational methods, fueling many fascinating and fast growing research areas of modern statistics. The book offers a wide variety of statistical methods and is addressed to statistici

[A new method of fabricating photoelastic model by rapid prototyping].

Science.gov (United States)

Fan, Li; Huang, Qing-feng; Zhang, Fu-qiang; Xia, Yin-pei

2011-10-01

To explore a novel method of fabricating the photoelastic model using rapid prototyping technique. A mandible model was made by rapid prototyping with computerized three-dimensional reconstruction, then the photoelastic model with teeth was fabricated by traditional impression duplicating and mould casting. The photoelastic model of mandible with teeth, which was fabricated indirectly by rapid prototyping, was very similar to the prototype in geometry and physical parameters. The model was of high optical sensibility and met the experimental requirements. Photoelastic model of mandible with teeth indirectly fabricated by rapid prototyping meets the photoelastic experimental requirements well.

Background field method for nonlinear σ-model in stochastic quantization

International Nuclear Information System (INIS)

Nakazawa, Naohito; Ennyu, Daiji

1988-01-01

We formulate the background field method for the nonlinear σ-model in stochastic quantization. We demonstrate a one-loop calculation for a two-dimensional non-linear σ-model on a general riemannian manifold based on our formulation. The formulation is consistent with the known results in ordinary quantization. As a simple application, we also analyse the multiplicative renormalization of the O(N) nonlinear σ-model. (orig.)

Novel extrapolation method in the Monte Carlo shell model

International Nuclear Information System (INIS)

Shimizu, Noritaka; Abe, Takashi; Utsuno, Yutaka; Mizusaki, Takahiro; Otsuka, Takaharu; Honma, Michio

2010-01-01

We propose an extrapolation method utilizing energy variance in the Monte Carlo shell model to estimate the energy eigenvalue and observables accurately. We derive a formula for the energy variance with deformed Slater determinants, which enables us to calculate the energy variance efficiently. The feasibility of the method is demonstrated for the full pf-shell calculation of 56 Ni, and the applicability of the method to a system beyond the current limit of exact diagonalization is shown for the pf+g 9/2 -shell calculation of 64 Ge.

Modelling of Conveyor Belt Passage by Driving Drum Using Finite Element Methods

Directory of Open Access Journals (Sweden)

Nikoleta Mikušová

2017-12-01

Full Text Available The finite element methods are used in many disciplines by the development of products, typically in mechanical engineering (for example in automotive industry, biomechanics, etc.. Some modern programs of the finite element's methods have specific tools (electromagnetic, fluid and structural simulations. The finite elements methods allow detailed presentation of structures by bending or torsion, complete design, testing and optimization before the prototype production. The aims of this paper were to the model of conveyor belt passage by driving drum. The model was created by the program Abaqus CAE. The created model presented data about forces, pressures, and deformation of the belt conveyor.

[Development method of healthcare information system integration based on business collaboration model].

Science.gov (United States)

Li, Shasha; Nie, Hongchao; Lu, Xudong; Duan, Huilong

2015-02-01

Integration of heterogeneous systems is the key to hospital information construction due to complexity of the healthcare environment. Currently, during the process of healthcare information system integration, people participating in integration project usually communicate by free-format document, which impairs the efficiency and adaptability of integration. A method utilizing business process model and notation (BPMN) to model integration requirement and automatically transforming it to executable integration configuration was proposed in this paper. Based on the method, a tool was developed to model integration requirement and transform it to integration configuration. In addition, an integration case in radiology scenario was used to verify the method.

Revisiting a model-independent dark energy reconstruction method

Energy Technology Data Exchange (ETDEWEB)

Lazkoz, Ruth; Salzano, Vincenzo; Sendra, Irene [Euskal Herriko Unibertsitatea, Fisika Teorikoaren eta Zientziaren Historia Saila, Zientzia eta Teknologia Fakultatea, Bilbao (Spain)

2012-09-15

In this work we offer new insights into the model-independent dark energy reconstruction method developed by Daly and Djorgovski (Astrophys. J. 597:9, 2003; Astrophys. J. 612:652, 2004; Astrophys. J. 677:1, 2008). Our results, using updated SNeIa and GRBs, allow to highlight some of the intrinsic weaknesses of the method. Conclusions on the main dark energy features as drawn from this method are intimately related to the features of the samples themselves, particularly for GRBs, which are poor performers in this context and cannot be used for cosmological purposes, that is, the state of the art does not allow to regard them on the same quality basis as SNeIa. We find there is a considerable sensitivity to some parameters (window width, overlap, selection criteria) affecting the results. Then, we try to establish what the current redshift range is for which one can make solid predictions on dark energy evolution. Finally, we strengthen the former view that this model is modest in the sense it provides only a picture of the global trend and has to be managed very carefully. But, on the other hand, we believe it offers an interesting complement to other approaches, given that it works on minimal assumptions. (orig.)

Statistical Method to Overcome Overfitting Issue in Rational Function Models

Science.gov (United States)

Alizadeh Moghaddam, S. H.; Mokhtarzade, M.; Alizadeh Naeini, A.; Alizadeh Moghaddam, S. A.

2017-09-01

Rational function models (RFMs) are known as one of the most appealing models which are extensively applied in geometric correction of satellite images and map production. Overfitting is a common issue, in the case of terrain dependent RFMs, that degrades the accuracy of RFMs-derived geospatial products. This issue, resulting from the high number of RFMs' parameters, leads to ill-posedness of the RFMs. To tackle this problem, in this study, a fast and robust statistical approach is proposed and compared to Tikhonov regularization (TR) method, as a frequently-used solution to RFMs' overfitting. In the proposed method, a statistical test, namely, significance test is applied to search for the RFMs' parameters that are resistant against overfitting issue. The performance of the proposed method was evaluated for two real data sets of Cartosat-1 satellite images. The obtained results demonstrate the efficiency of the proposed method in term of the achievable level of accuracy. This technique, indeed, shows an improvement of 50-80% over the TR.

Principles of the radiosity method versus radiative transfer for canopy reflectance modeling

Science.gov (United States)

Gerstl, Siegfried A. W.; Borel, Christoph C.

1992-01-01

The radiosity method is introduced to plant canopy reflectance modeling. We review the physics principles of the radiosity method which originates in thermal radiative transfer analyses when hot and cold surfaces are considered within a given enclosure. The radiosity equation, which is an energy balance equation for discrete surfaces, is described and contrasted with the radiative transfer equation, which is a volumetric energy balance equation. Comparing the strengths and weaknesses of the radiosity method and the radiative transfer method, we conclude that both methods are complementary to each other. Results of sample calculations are given for canopy models with up to 20,000 discrete leaves.

a Modeling Method of Fluttering Leaves Based on Point Cloud

Science.gov (United States)

Tang, J.; Wang, Y.; Zhao, Y.; Hao, W.; Ning, X.; Lv, K.; Shi, Z.; Zhao, M.

2017-09-01

Leaves falling gently or fluttering are common phenomenon in nature scenes. The authenticity of leaves falling plays an important part in the dynamic modeling of natural scenes. The leaves falling model has a widely applications in the field of animation and virtual reality. We propose a novel modeling method of fluttering leaves based on point cloud in this paper. According to the shape, the weight of leaves and the wind speed, three basic trajectories of leaves falling are defined, which are the rotation falling, the roll falling and the screw roll falling. At the same time, a parallel algorithm based on OpenMP is implemented to satisfy the needs of real-time in practical applications. Experimental results demonstrate that the proposed method is amenable to the incorporation of a variety of desirable effects.

A model of methods for influencing prescribing: Part I. A review of prescribing models, persuasion theories, and administrative and educational methods.

Science.gov (United States)

Raisch, D W

1990-04-01

The purpose of this literature review is to develop a model of methods to be used to influence prescribing. Four bodies of literature were identified as being important for developing the model: (1) Theoretical prescribing models furnish information concerning factors that affect prescribing and how prescribing decisions are made. (2) Theories of persuasion provide insight into important components of educational communications. (3) Research articles of programs to improve prescribing identify types of programs that have been found to be successful. (4) Theories of human inference describe how judgments are formulated and identify errors in judgment that can play a role in prescribing. This review is presented in two parts. This article reviews prescribing models, theories of persuasion, studies of administrative programs to control prescribing, and sub-optimally designed studies of educational efforts to influence drug prescribing.

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

Science.gov (United States)

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.

Adaptation to Climate Change: A Comparative Analysis of Modeling Methods for Heat-Related Mortality.

Science.gov (United States)

Gosling, Simon N; Hondula, David M; Bunker, Aditi; Ibarreta, Dolores; Liu, Junguo; Zhang, Xinxin; Sauerborn, Rainer

2017-08-16

Multiple methods are employed for modeling adaptation when projecting the impact of climate change on heat-related mortality. The sensitivity of impacts to each is unknown because they have never been systematically compared. In addition, little is known about the relative sensitivity of impacts to "adaptation uncertainty" (i.e., the inclusion/exclusion of adaptation modeling) relative to using multiple climate models and emissions scenarios. This study had three aims: a ) Compare the range in projected impacts that arises from using different adaptation modeling methods; b ) compare the range in impacts that arises from adaptation uncertainty with ranges from using multiple climate models and emissions scenarios; c ) recommend modeling method(s) to use in future impact assessments. We estimated impacts for 2070-2099 for 14 European cities, applying six different methods for modeling adaptation; we also estimated impacts with five climate models run under two emissions scenarios to explore the relative effects of climate modeling and emissions uncertainty. The range of the difference (percent) in impacts between including and excluding adaptation, irrespective of climate modeling and emissions uncertainty, can be as low as 28% with one method and up to 103% with another (mean across 14 cities). In 13 of 14 cities, the ranges in projected impacts due to adaptation uncertainty are larger than those associated with climate modeling and emissions uncertainty. Researchers should carefully consider how to model adaptation because it is a source of uncertainty that can be greater than the uncertainty in emissions and climate modeling. We recommend absolute threshold shifts and reductions in slope. https://doi.org/10.1289/EHP634.

A Mixed Method Research for Finding a Model of Administrative Decentralization

OpenAIRE

Tahereh Feizy; Alireza Moghali; Masuod Geramipoor; Reza Zare

2015-01-01

One of the critical issues of administrative decentralization in translating theory into practice is understanding its meaning. An important method to identify administrative decentralization is to address how it can be planned and implemented, and what are its implications, and how it would overcome challenges. The purpose of this study is finding a model for analyzing and evaluating administrative decentralization, so a mixed method research was used to explore and confirm the model of Admi...

3-D modeling of surface, borehole, and airborne EM methods; Chijo konai kuchu denjiho no sanjigen modeling

Energy Technology Data Exchange (ETDEWEB)

Sasaki, Y [Kyushu University, Fukuoka (Japan). Faculty of Engineering

1997-10-22

Three-dimensional modelling methods using the difference method and finite element method are applied to the simulations respectively of the surface electromagnetic method, borehole electromagnetic method, and airborne electromagnetic method, and they are compared with each other in point of accuracy and practicality. The object of calculation in this study is a 3-D model which is a semi-finite medium 100 ohm/m in resistivity that contains a rectangular parallelopiped 1 ohm/m in resistivity. A vertical magnetic dipole is installed on the surface in the surface electromagnetic method, providing a vertical magnetic field on the surface. In the borehole electromagnetic method, a vertical magnetic dipole is placed in a borehole and the resultant vertical magnetic field is measured at a station in another borehole. In the airborne electromagnetic method, the flight level is 20m high and the distance between the source and the receiving point is 10m. The results of calculation all agree well with the results of calculation previously made known. When the difference method and finite element method are compared, it is found that the finite element method requires calculation time and memory capacity two to three times more than the difference method. 5 refs., 9 figs.

Nuclear-fuel-cycle optimization: methods and modelling techniques

International Nuclear Information System (INIS)

Silvennoinen, P.

1982-01-01

This book present methods applicable to analyzing fuel-cycle logistics and optimization as well as in evaluating the economics of different reactor strategies. After an introduction to the phases of a fuel cycle, uranium cost trends are assessed in a global perspective. Subsequent chapters deal with the fuel-cycle problems faced by a power utility. The fuel-cycle models cover the entire cycle from the supply of uranium to the disposition of spent fuel. The chapter headings are: Nuclear Fuel Cycle, Uranium Supply and Demand, Basic Model of the LWR (light water reactor) Fuel Cycle, Resolution of Uncertainties, Assessment of Proliferation Risks, Multigoal Optimization, Generalized Fuel-Cycle Models, Reactor Strategy Calculations, and Interface with Energy Strategies. 47 references, 34 figures, 25 tables

Practical Validation of Economic Efficiency Modelling Method for Multi-Boiler Heating System

Directory of Open Access Journals (Sweden)

Aleksejs Jurenoks

2017-12-01

Full Text Available In up-to-date conditions information technology is frequently associated with the modelling process, using computer technology as well as information networks. Statistical modelling is one of the most widespread methods of research of economic systems. The selection of methods of modelling of the economic systems depends on a great number of conditions of the researched system. Modelling is frequently associated with the factor of uncertainty (or risk, who’s description goes outside the confines of the traditional statistical modelling, which, in its turn, complicates the modelling which, in its turn, complicates the modelling process. This article describes the modelling process of assessing the economic efficiency of a multi-boiler adaptive heating system in real-time systems which allows for dynamic change in the operation scenarios of system service installations while enhancing the economic efficiency of the system in consideration.

Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries

Directory of Open Access Journals (Sweden)

Zhongyue Zou

2014-08-01

Full Text Available Four model-based State of Charge (SOC estimation methods for lithium-ion (Li-ion batteries are studied and evaluated in this paper. Different from existing literatures, this work evaluates different aspects of the SOC estimation, such as the estimation error distribution, the estimation rise time, the estimation time consumption, etc. The equivalent model of the battery is introduced and the state function of the model is deduced. The four model-based SOC estimation methods are analyzed first. Simulations and experiments are then established to evaluate the four methods. The urban dynamometer driving schedule (UDDS current profiles are applied to simulate the drive situations of an electrified vehicle, and a genetic algorithm is utilized to identify the model parameters to find the optimal parameters of the model of the Li-ion battery. The simulations with and without disturbance are carried out and the results are analyzed. A battery test workbench is established and a Li-ion battery is applied to test the hardware in a loop experiment. Experimental results are plotted and analyzed according to the four aspects to evaluate the four model-based SOC estimation methods.

A graphical method for reducing and relating models in systems biology.

Science.gov (United States)

Gay, Steven; Soliman, Sylvain; Fages, François

2010-09-15

In Systems Biology, an increasing collection of models of various biological processes is currently developed and made available in publicly accessible repositories, such as biomodels.net for instance, through common exchange formats such as SBML. To date, however, there is no general method to relate different models to each other by abstraction or reduction relationships, and this task is left to the modeler for re-using and coupling models. In mathematical biology, model reduction techniques have been studied for a long time, mainly in the case where a model exhibits different time scales, or different spatial phases, which can be analyzed separately. These techniques are however far too restrictive to be applied on a large scale in systems biology, and do not take into account abstractions other than time or phase decompositions. Our purpose here is to propose a general computational method for relating models together, by considering primarily the structure of the interactions and abstracting from their dynamics in a first step. We present a graph-theoretic formalism with node merge and delete operations, in which model reductions can be studied as graph matching problems. From this setting, we derive an algorithm for deciding whether there exists a reduction from one model to another, and evaluate it on the computation of the reduction relations between all SBML models of the biomodels.net repository. In particular, in the case of the numerous models of MAPK signalling, and of the circadian clock, biologically meaningful mappings between models of each class are automatically inferred from the structure of the interactions. We conclude on the generality of our graphical method, on its limits with respect to the representation of the structure of the interactions in SBML, and on some perspectives for dealing with the dynamics. The algorithms described in this article are implemented in the open-source software modeling platform BIOCHAM available at http

Impact of Statistical Learning Methods on the Predictive Power of Multivariate Normal Tissue Complication Probability Models

Energy Technology Data Exchange (ETDEWEB)

Xu Chengjian, E-mail: c.j.xu@umcg.nl [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van' t [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands)

2012-03-15

Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

Impact of Statistical Learning Methods on the Predictive Power of Multivariate Normal Tissue Complication Probability Models

International Nuclear Information System (INIS)

Xu Chengjian; Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van’t

2012-01-01

Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

Comparison of two perturbation methods to estimate the land surface modeling uncertainty

Science.gov (United States)

Su, H.; Houser, P.; Tian, Y.; Kumar, S.; Geiger, J.; Belvedere, D.

2007-12-01

In land surface modeling, it is almost impossible to simulate the land surface processes without any error because the earth system is highly complex and the physics of the land processes has not yet been understood sufficiently. In most cases, people want to know not only the model output but also the uncertainty in the modeling, to estimate how reliable the modeling is. Ensemble perturbation is an effective way to estimate the uncertainty in land surface modeling, since land surface models are highly nonlinear which makes the analytical approach not applicable in this estimation. The ideal perturbation noise is zero mean Gaussian distribution, however, this requirement can't be satisfied if the perturbed variables in land surface model have physical boundaries because part of the perturbation noises has to be removed to feed the land surface models properly. Two different perturbation methods are employed in our study to investigate their impact on quantifying land surface modeling uncertainty base on the Land Information System (LIS) framework developed by NASA/GSFC land team. One perturbation method is the built-in algorithm named "STATIC" in LIS version 5; the other is a new perturbation algorithm which was recently developed to minimize the overall bias in the perturbation by incorporating additional information from the whole time series for the perturbed variable. The statistical properties of the perturbation noise generated by the two different algorithms are investigated thoroughly by using a large ensemble size on a NASA supercomputer and then the corresponding uncertainty estimates based on the two perturbation methods are compared. Their further impacts on data assimilation are also discussed. Finally, an optimal perturbation method is suggested.

Cellular Automaton Modeling of Dendritic Growth Using a Multi-grid Method

International Nuclear Information System (INIS)

Natsume, Y; Ohsasa, K

2015-01-01

A two-dimensional cellular automaton model with a multi-grid method was developed to simulate dendritic growth. In the present model, we used a triple-grid system for temperature, solute concentration and solid fraction fields as a new approach of the multi-grid method. In order to evaluate the validity of the present model, we carried out simulations of single dendritic growth, secondary dendrite arm growth, multi-columnar dendritic growth and multi-equiaxed dendritic growth. From the results of the grid dependency from the simulation of single dendritic growth, we confirmed that the larger grid can be used in the simulation and that the computational time can be reduced dramatically. In the simulation of secondary dendrite arm growth, the results from the present model were in good agreement with the experimental data and the simulated results from a phase-field model. Thus, the present model can quantitatively simulate dendritic growth. From the simulated results of multi-columnar and multi-equiaxed dendrites, we confirmed that the present model can perform simulations under practical solidification conditions. (paper)

Using the Scientific Method to Engage Mathematical Modeling: An Investigation of pi

Science.gov (United States)

Archer, Lester A. C.; Ng, Karen E.

2016-01-01

The purpose of this paper is to explain how to use the scientific method as the framework to introduce mathematical model. Two interdisciplinary activities, targeted for students in grade 6 or grade 7, are explained to show the application of the scientific method while building a mathematical model to investigate the relationship between the…

A simple flow-concentration modelling method for integrating water ...

African Journals Online (AJOL)

A simple flow-concentration modelling method for integrating water quality and ... flow requirements are assessed for maintenance low flow, drought low flow ... the instream concentrations of chemical constituents that will arise from different ...

Application of the conjugate-gradient method to ground-water models

Science.gov (United States)

Manteuffel, T.A.; Grove, D.B.; Konikow, Leonard F.

1984-01-01

The conjugate-gradient method can solve efficiently and accurately finite-difference approximations to the ground-water flow equation. An aquifer-simulation model using the conjugate-gradient method was applied to a problem of ground-water flow in an alluvial aquifer at the Rocky Mountain Arsenal, Denver, Colorado. For this application, the accuracy and efficiency of the conjugate-gradient method compared favorably with other available methods for steady-state flow. However, its efficiency relative to other available methods depends on the nature of the specific problem. The main advantage of the conjugate-gradient method is that it does not require the use of iteration parameters, thereby eliminating this partly subjective procedure. (USGS)

Comparison of PDF and Moment Closure Methods in the Modeling of Turbulent Reacting Flows

Science.gov (United States)

Norris, Andrew T.; Hsu, Andrew T.

1994-01-01

In modeling turbulent reactive flows, Probability Density Function (PDF) methods have an advantage over the more traditional moment closure schemes in that the PDF formulation treats the chemical reaction source terms exactly, while moment closure methods are required to model the mean reaction rate. The common model used is the laminar chemistry approximation, where the effects of turbulence on the reaction are assumed negligible. For flows with low turbulence levels and fast chemistry, the difference between the two methods can be expected to be small. However for flows with finite rate chemistry and high turbulence levels, significant errors can be expected in the moment closure method. In this paper, the ability of the PDF method and the moment closure scheme to accurately model a turbulent reacting flow is tested. To accomplish this, both schemes were used to model a CO/H2/N2- air piloted diffusion flame near extinction. Identical thermochemistry, turbulence models, initial conditions and boundary conditions are employed to ensure a consistent comparison can be made. The results of the two methods are compared to experimental data as well as to each other. The comparison reveals that the PDF method provides good agreement with the experimental data, while the moment closure scheme incorrectly shows a broad, laminar-like flame structure.

Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models.

Science.gov (United States)

Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A; van't Veld, Aart A

2012-03-15

To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended. Copyright © 2012 Elsevier Inc. All rights reserved.

Comparative Study of Three Data Assimilation Methods for Ice Sheet Model Initialisation

Science.gov (United States)

Mosbeux, Cyrille; Gillet-Chaulet, Fabien; Gagliardini, Olivier

2015-04-01

The current global warming has direct consequences on ice-sheet mass loss contributing to sea level rise. This loss is generally driven by an acceleration of some coastal outlet glaciers and reproducing these mechanisms is one of the major issues in ice-sheet and ice flow modelling. The construction of an initial state, as close as possible to current observations, is required as a prerequisite before producing any reliable projection of the evolution of ice-sheets. For this step, inverse methods are often used to infer badly known or unknown parameters. For instance, the adjoint inverse method has been implemented and applied with success by different authors in different ice flow models in order to infer the basal drag [ Schafer et al., 2012; Gillet-chauletet al., 2012; Morlighem et al., 2010]. Others data fields, such as ice surface and bedrock topography, are easily measurable with more or less uncertainty but only locally along tracks and interpolated on finer model grid. All these approximations lead to errors on the data elevation model and give rise to an ill-posed problem inducing non-physical anomalies in flux divergence [Seroussi et al, 2011]. A solution to dissipate these divergences of flux is to conduct a surface relaxation step at the expense of the accuracy of the modelled surface [Gillet-Chaulet et al., 2012]. Other solutions, based on the inversion of ice thickness and basal drag were proposed [Perego et al., 2014; Pralong & Gudmundsson, 2011]. In this study, we create a twin experiment to compare three different assimilation algorithms based on inverse methods and nudging to constrain the bedrock friction and the bedrock elevation: (i) cyclic inversion of friction parameter and bedrock topography using adjoint method, (ii) cycles coupling inversion of friction parameter using adjoint method and nudging of bedrock topography, (iii) one step inversion of both parameters with adjoint method. The three methods show a clear improvement in parameters

Modeling of radionuclide migration through porous material with meshless method

International Nuclear Information System (INIS)

Vrankar, L.; Turk, G.; Runovc, F.

2005-01-01

To assess the long term safety of a radioactive waste disposal system, mathematical models are used to describe groundwater flow, chemistry and potential radionuclide migration through geological formations. A number of processes need to be considered when predicting the movement of radionuclides through the geosphere. The most important input data are obtained from field measurements, which are not completely available for all regions of interest. For example, the hydraulic conductivity as an input parameter varies from place to place. In such cases geostatistical science offers a variety of spatial estimation procedures. Methods for solving the solute transport equation can also be classified as Eulerian, Lagrangian and mixed. The numerical solution of partial differential equations (PDE) is usually obtained by finite difference methods (FDM), finite element methods (FEM), or finite volume methods (FVM). Kansa introduced the concept of solving partial differential equations using radial basis functions (RBF) for hyperbolic, parabolic and elliptic PDEs. Our goal was to present a relatively new approach to the modelling of radionuclide migration through the geosphere using radial basis function methods in Eulerian and Lagrangian coordinates. Radionuclide concentrations will also be calculated in heterogeneous and partly heterogeneous 2D porous media. We compared the meshless method with the traditional finite difference scheme. (author)

New proposal of moderator temperature coefficient estimation method using gray-box model in NPP, (1)

International Nuclear Information System (INIS)