An Extension of SIC Predictions to the Wiener Coactive Model

PubMed Central

Houpt, Joseph W.; Townsend, James T.

2011-01-01

The survivor interaction contrasts (SIC) is a powerful measure for distinguishing among candidate models of human information processing. One class of models to which SIC analysis can apply are the coactive, or channel summation, models of human information processing. In general, parametric forms of coactive models assume that responses are made based on the first passage time across a fixed threshold of a sum of stochastic processes. Previous work has shown that that the SIC for a coactive model based on the sum of Poisson processes has a distinctive down-up-down form, with an early negative region that is smaller than the later positive region. In this note, we demonstrate that a coactive process based on the sum of two Wiener processes has the same SIC form. PMID:21822333

An Extension of SIC Predictions to the Wiener Coactive Model.

PubMed

Houpt, Joseph W; Townsend, James T

2011-06-01

The survivor interaction contrasts (SIC) is a powerful measure for distinguishing among candidate models of human information processing. One class of models to which SIC analysis can apply are the coactive, or channel summation, models of human information processing. In general, parametric forms of coactive models assume that responses are made based on the first passage time across a fixed threshold of a sum of stochastic processes. Previous work has shown that that the SIC for a coactive model based on the sum of Poisson processes has a distinctive down-up-down form, with an early negative region that is smaller than the later positive region. In this note, we demonstrate that a coactive process based on the sum of two Wiener processes has the same SIC form.

Process-based models are required to manage ecological systems in a changing world

Treesearch

K. Cuddington; M.-J. Fortin; L.R. Gerber; A. Hastings; A. Liebhold; M. OConnor; C. Ray

2013-01-01

Several modeling approaches can be used to guide management decisions. However, some approaches are better fitted than others to address the problem of prediction under global change. Process-based models, which are based on a theoretical understanding of relevant ecological processes, provide a useful framework to incorporate specific responses to altered...

Simplified process model discovery based on role-oriented genetic mining.

PubMed

Zhao, Weidong; Liu, Xi; Dai, Weihui

2014-01-01

Process mining is automated acquisition of process models from event logs. Although many process mining techniques have been developed, most of them are based on control flow. Meanwhile, the existing role-oriented process mining methods focus on correctness and integrity of roles while ignoring role complexity of the process model, which directly impacts understandability and quality of the model. To address these problems, we propose a genetic programming approach to mine the simplified process model. Using a new metric of process complexity in terms of roles as the fitness function, we can find simpler process models. The new role complexity metric of process models is designed from role cohesion and coupling, and applied to discover roles in process models. Moreover, the higher fitness derived from role complexity metric also provides a guideline for redesigning process models. Finally, we conduct case study and experiments to show that the proposed method is more effective for streamlining the process by comparing with related studies.

Process Correlation Analysis Model for Process Improvement Identification

PubMed Central

Park, Sooyong

2014-01-01

Software process improvement aims at improving the development process of software systems. It is initiated by process assessment identifying strengths and weaknesses and based on the findings, improvement plans are developed. In general, a process reference model (e.g., CMMI) is used throughout the process of software process improvement as the base. CMMI defines a set of process areas involved in software development and what to be carried out in process areas in terms of goals and practices. Process areas and their elements (goals and practices) are often correlated due to the iterative nature of software development process. However, in the current practice, correlations of process elements are often overlooked in the development of an improvement plan, which diminishes the efficiency of the plan. This is mainly attributed to significant efforts and the lack of required expertise. In this paper, we present a process correlation analysis model that helps identify correlations of process elements from the results of process assessment. This model is defined based on CMMI and empirical data of improvement practices. We evaluate the model using industrial data. PMID:24977170

Process correlation analysis model for process improvement identification.

PubMed

Choi, Su-jin; Kim, Dae-Kyoo; Park, Sooyong

2014-01-01

Software process improvement aims at improving the development process of software systems. It is initiated by process assessment identifying strengths and weaknesses and based on the findings, improvement plans are developed. In general, a process reference model (e.g., CMMI) is used throughout the process of software process improvement as the base. CMMI defines a set of process areas involved in software development and what to be carried out in process areas in terms of goals and practices. Process areas and their elements (goals and practices) are often correlated due to the iterative nature of software development process. However, in the current practice, correlations of process elements are often overlooked in the development of an improvement plan, which diminishes the efficiency of the plan. This is mainly attributed to significant efforts and the lack of required expertise. In this paper, we present a process correlation analysis model that helps identify correlations of process elements from the results of process assessment. This model is defined based on CMMI and empirical data of improvement practices. We evaluate the model using industrial data.

Validating the ACE Model for Evaluating Student Performance Using a Teaching-Learning Process Based on Computational Modeling Systems

ERIC Educational Resources Information Center

Louzada, Alexandre Neves; Elia, Marcos da Fonseca; Sampaio, Fábio Ferrentini; Vidal, Andre Luiz Pestana

2014-01-01

The aim of this work is to adapt and test, in a Brazilian public school, the ACE model proposed by Borkulo for evaluating student performance as a teaching-learning process based on computational modeling systems. The ACE model is based on different types of reasoning involving three dimensions. In addition to adapting the model and introducing…

Gaze data reveal distinct choice processes underlying model-based and model-free reinforcement learning

PubMed Central

Konovalov, Arkady; Krajbich, Ian

2016-01-01

Organisms appear to learn and make decisions using different strategies known as model-free and model-based learning; the former is mere reinforcement of previously rewarded actions and the latter is a forward-looking strategy that involves evaluation of action-state transition probabilities. Prior work has used neural data to argue that both model-based and model-free learners implement a value comparison process at trial onset, but model-based learners assign more weight to forward-looking computations. Here using eye-tracking, we report evidence for a different interpretation of prior results: model-based subjects make their choices prior to trial onset. In contrast, model-free subjects tend to ignore model-based aspects of the task and instead seem to treat the decision problem as a simple comparison process between two differentially valued items, consistent with previous work on sequential-sampling models of decision making. These findings illustrate a problem with assuming that experimental subjects make their decisions at the same prescribed time. PMID:27511383

Stochastic simulation by image quilting of process-based geological models

NASA Astrophysics Data System (ADS)

Hoffimann, Júlio; Scheidt, Céline; Barfod, Adrian; Caers, Jef

2017-09-01

Process-based modeling offers a way to represent realistic geological heterogeneity in subsurface models. The main limitation lies in conditioning such models to data. Multiple-point geostatistics can use these process-based models as training images and address the data conditioning problem. In this work, we further develop image quilting as a method for 3D stochastic simulation capable of mimicking the realism of process-based geological models with minimal modeling effort (i.e. parameter tuning) and at the same time condition them to a variety of data. In particular, we develop a new probabilistic data aggregation method for image quilting that bypasses traditional ad-hoc weighting of auxiliary variables. In addition, we propose a novel criterion for template design in image quilting that generalizes the entropy plot for continuous training images. The criterion is based on the new concept of voxel reuse-a stochastic and quilting-aware function of the training image. We compare our proposed method with other established simulation methods on a set of process-based training images of varying complexity, including a real-case example of stochastic simulation of the buried-valley groundwater system in Denmark.

Development and evaluation of spatial point process models for epidermal nerve fibers.

PubMed

Olsbo, Viktor; Myllymäki, Mari; Waller, Lance A; Särkkä, Aila

2013-06-01

We propose two spatial point process models for the spatial structure of epidermal nerve fibers (ENFs) across human skin. The models derive from two point processes, Φb and Φe, describing the locations of the base and end points of the fibers. Each point of Φe (the end point process) is connected to a unique point in Φb (the base point process). In the first model, both Φe and Φb are Poisson processes, yielding a null model of uniform coverage of the skin by end points and general baseline results and reference values for moments of key physiologic indicators. The second model provides a mechanistic model to generate end points for each base, and we model the branching structure more directly by defining Φe as a cluster process conditioned on the realization of Φb as its parent points. In both cases, we derive distributional properties for observable quantities of direct interest to neurologists such as the number of fibers per base, and the direction and range of fibers on the skin. We contrast both models by fitting them to data from skin blister biopsy images of ENFs and provide inference regarding physiological properties of ENFs. Copyright © 2013 Elsevier Inc. All rights reserved.

Process-based Cost Estimation for Ramjet/Scramjet Engines

NASA Technical Reports Server (NTRS)

Singh, Brijendra; Torres, Felix; Nesman, Miles; Reynolds, John

2003-01-01

Process-based cost estimation plays a key role in effecting cultural change that integrates distributed science, technology and engineering teams to rapidly create innovative and affordable products. Working together, NASA Glenn Research Center and Boeing Canoga Park have developed a methodology of process-based cost estimation bridging the methodologies of high-level parametric models and detailed bottoms-up estimation. The NASA GRC/Boeing CP process-based cost model provides a probabilistic structure of layered cost drivers. High-level inputs characterize mission requirements, system performance, and relevant economic factors. Design alternatives are extracted from a standard, product-specific work breakdown structure to pre-load lower-level cost driver inputs and generate the cost-risk analysis. As product design progresses and matures the lower level more detailed cost drivers can be re-accessed and the projected variation of input values narrowed, thereby generating a progressively more accurate estimate of cost-risk. Incorporated into the process-based cost model are techniques for decision analysis, specifically, the analytic hierarchy process (AHP) and functional utility analysis. Design alternatives may then be evaluated not just on cost-risk, but also user defined performance and schedule criteria. This implementation of full-trade study support contributes significantly to the realization of the integrated development environment. The process-based cost estimation model generates development and manufacturing cost estimates. The development team plans to expand the manufacturing process base from approximately 80 manufacturing processes to over 250 processes. Operation and support cost modeling is also envisioned. Process-based estimation considers the materials, resources, and processes in establishing cost-risk and rather depending on weight as an input, actually estimates weight along with cost and schedule.

Hydrological modelling in forested systems | Science ...

EPA Pesticide Factsheets

This chapter provides a brief overview of forest hydrology modelling approaches for answering important global research and management questions. Many hundreds of hydrological models have been applied globally across multiple decades to represent and predict forest hydrological processes. The focus of this chapter is on process-based models and approaches, specifically 'forest hydrology models'; that is, physically based simulation tools that quantify compartments of the forest hydrological cycle. Physically based models can be considered those that describe the conservation of mass, momentum and/or energy. The purpose of this chapter is to provide a brief overview of forest hydrology modeling approaches for answering important global research and management questions. The focus of this chapter is on process-based models and approaches, specifically “forest hydrology models”, i.e., physically-based simulation tools that quantify compartments of the forest hydrological cycle.

A dual-process perspective on fluency-based aesthetics: the pleasure-interest model of aesthetic liking.

PubMed

Graf, Laura K M; Landwehr, Jan R

2015-11-01

In this article, we develop an account of how aesthetic preferences can be formed as a result of two hierarchical, fluency-based processes. Our model suggests that processing performed immediately upon encountering an aesthetic object is stimulus driven, and aesthetic preferences that accrue from this processing reflect aesthetic evaluations of pleasure or displeasure. When sufficient processing motivation is provided by a perceiver's need for cognitive enrichment and/or the stimulus' processing affordance, elaborate perceiver-driven processing can emerge, which gives rise to fluency-based aesthetic evaluations of interest, boredom, or confusion. Because the positive outcomes in our model are pleasure and interest, we call it the Pleasure-Interest Model of Aesthetic Liking (PIA Model). Theoretically, this model integrates a dual-process perspective and ideas from lay epistemology into processing fluency theory, and it provides a parsimonious framework to embed and unite a wealth of aesthetic phenomena, including contradictory preference patterns for easy versus difficult-to-process aesthetic stimuli. © 2015 by the Society for Personality and Social Psychology, Inc.

Fecal indicator organism modeling and microbial source tracking in environmental waters: Chapter 3.4.6

USGS Publications Warehouse

Nevers, Meredith; Byappanahalli, Muruleedhara; Phanikumar, Mantha S.; Whitman, Richard L.

2016-01-01

Mathematical models have been widely applied to surface waters to estimate rates of settling, resuspension, flow, dispersion, and advection in order to calculate movement of particles that influence water quality. Of particular interest are the movement, survival, and persistence of microbial pathogens or their surrogates, which may contaminate recreational water, drinking water, or shellfish. Most models devoted to microbial water quality have been focused on fecal indicator organisms (FIO), which act as a surrogate for pathogens and viruses. Process-based modeling and statistical modeling have been used to track contamination events to source and to predict future events. The use of these two types of models require different levels of expertise and input; process-based models rely on theoretical physical constructs to explain present conditions and biological distribution while data-based, statistical models use extant paired data to do the same. The selection of the appropriate model and interpretation of results is critical to proper use of these tools in microbial source tracking. Integration of the modeling approaches could provide insight for tracking and predicting contamination events in real time. A review of modeling efforts reveals that process-based modeling has great promise for microbial source tracking efforts; further, combining the understanding of physical processes influencing FIO contamination developed with process-based models and molecular characterization of the population by gene-based (i.e., biological) or chemical markers may be an effective approach for locating sources and remediating contamination in order to protect human health better.

The Iterative Research Cycle: Process-Based Model Evaluation

NASA Astrophysics Data System (ADS)

Vrugt, J. A.

2014-12-01

The ever increasing pace of computational power, along with continued advances in measurement technologies and improvements in process understanding has stimulated the development of increasingly complex physics based models that simulate a myriad of processes at different spatial and temporal scales. Reconciling these high-order system models with perpetually larger volumes of field data is becoming more and more difficult, particularly because classical likelihood-based fitting methods lack the power to detect and pinpoint deficiencies in the model structure. In this talk I will give an overview of our latest research on process-based model calibration and evaluation. This approach, rooted in Bayesian theory, uses summary metrics of the calibration data rather than the data itself to help detect which component(s) of the model is (are) malfunctioning and in need of improvement. A few case studies involving hydrologic and geophysical models will be used to demonstrate the proposed methodology.

Design and Implementation of Hydrologic Process Knowledge-base Ontology: A case study for the Infiltration Process

NASA Astrophysics Data System (ADS)

Elag, M.; Goodall, J. L.

2013-12-01

Hydrologic modeling often requires the re-use and integration of models from different disciplines to simulate complex environmental systems. Component-based modeling introduces a flexible approach for integrating physical-based processes across disciplinary boundaries. Several hydrologic-related modeling communities have adopted the component-based approach for simulating complex physical systems by integrating model components across disciplinary boundaries in a workflow. However, it is not always straightforward to create these interdisciplinary models due to the lack of sufficient knowledge about a hydrologic process. This shortcoming is a result of using informal methods for organizing and sharing information about a hydrologic process. A knowledge-based ontology provides such standards and is considered the ideal approach for overcoming this challenge. The aims of this research are to present the methodology used in analyzing the basic hydrologic domain in order to identify hydrologic processes, the ontology itself, and how the proposed ontology is integrated with the Water Resources Component (WRC) ontology. The proposed ontology standardizes the definitions of a hydrologic process, the relationships between hydrologic processes, and their associated scientific equations. The objective of the proposed Hydrologic Process (HP) Ontology is to advance the idea of creating a unified knowledge framework for components' metadata by introducing a domain-level ontology for hydrologic processes. The HP ontology is a step toward an explicit and robust domain knowledge framework that can be evolved through the contribution of domain users. Analysis of the hydrologic domain is accomplished using the Formal Concept Approach (FCA), in which the infiltration process, an important hydrologic process, is examined. Two infiltration methods, the Green-Ampt and Philip's methods, were used to demonstrate the implementation of information in the HP ontology. Furthermore, a SPARQL service is provided for semantic-based querying of the ontology.

Patch-based models and algorithms for image processing: a review of the basic principles and methods, and their application in computed tomography.

PubMed

Karimi, Davood; Ward, Rabab K

2016-10-01

Image models are central to all image processing tasks. The great advancements in digital image processing would not have been made possible without powerful models which, themselves, have evolved over time. In the past decade, "patch-based" models have emerged as one of the most effective models for natural images. Patch-based methods have outperformed other competing methods in many image processing tasks. These developments have come at a time when greater availability of powerful computational resources and growing concerns over the health risks of the ionizing radiation encourage research on image processing algorithms for computed tomography (CT). The goal of this paper is to explain the principles of patch-based methods and to review some of their recent applications in CT. We first review the central concepts in patch-based image processing and explain some of the state-of-the-art algorithms, with a focus on aspects that are more relevant to CT. Then, we review some of the recent application of patch-based methods in CT. Patch-based methods have already transformed the field of image processing, leading to state-of-the-art results in many applications. More recently, several studies have proposed patch-based algorithms for various image processing tasks in CT, from denoising and restoration to iterative reconstruction. Although these studies have reported good results, the true potential of patch-based methods for CT has not been yet appreciated. Patch-based methods can play a central role in image reconstruction and processing for CT. They have the potential to lead to substantial improvements in the current state of the art.

Application of agent-based system for bioprocess description and process improvement.

PubMed

Gao, Ying; Kipling, Katie; Glassey, Jarka; Willis, Mark; Montague, Gary; Zhou, Yuhong; Titchener-Hooker, Nigel J

2010-01-01

Modeling plays an important role in bioprocess development for design and scale-up. Predictive models can also be used in biopharmaceutical manufacturing to assist decision-making either to maintain process consistency or to identify optimal operating conditions. To predict the whole bioprocess performance, the strong interactions present in a processing sequence must be adequately modeled. Traditionally, bioprocess modeling considers process units separately, which makes it difficult to capture the interactions between units. In this work, a systematic framework is developed to analyze the bioprocesses based on a whole process understanding and considering the interactions between process operations. An agent-based approach is adopted to provide a flexible infrastructure for the necessary integration of process models. This enables the prediction of overall process behavior, which can then be applied during process development or once manufacturing has commenced, in both cases leading to the capacity for fast evaluation of process improvement options. The multi-agent system comprises a process knowledge base, process models, and a group of functional agents. In this system, agent components co-operate with each other in performing their tasks. These include the description of the whole process behavior, evaluating process operating conditions, monitoring of the operating processes, predicting critical process performance, and providing guidance to decision-making when coping with process deviations. During process development, the system can be used to evaluate the design space for process operation. During manufacture, the system can be applied to identify abnormal process operation events and then to provide suggestions as to how best to cope with the deviations. In all cases, the function of the system is to ensure an efficient manufacturing process. The implementation of the agent-based approach is illustrated via selected application scenarios, which demonstrate how such a framework may enable the better integration of process operations by providing a plant-wide process description to facilitate process improvement. Copyright 2009 American Institute of Chemical Engineers

Reduced order model based on principal component analysis for process simulation and optimization

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

Lang, Y.; Malacina, A.; Biegler, L.

2009-01-01

It is well-known that distributed parameter computational fluid dynamics (CFD) models provide more accurate results than conventional, lumped-parameter unit operation models used in process simulation. Consequently, the use of CFD models in process/equipment co-simulation offers the potential to optimize overall plant performance with respect to complex thermal and fluid flow phenomena. Because solving CFD models is time-consuming compared to the overall process simulation, we consider the development of fast reduced order models (ROMs) based on CFD results to closely approximate the high-fidelity equipment models in the co-simulation. By considering process equipment items with complicated geometries and detailed thermodynamic property models,more » this study proposes a strategy to develop ROMs based on principal component analysis (PCA). Taking advantage of commercial process simulation and CFD software (for example, Aspen Plus and FLUENT), we are able to develop systematic CFD-based ROMs for equipment models in an efficient manner. In particular, we show that the validity of the ROM is more robust within well-sampled input domain and the CPU time is significantly reduced. Typically, it takes at most several CPU seconds to evaluate the ROM compared to several CPU hours or more to solve the CFD model. Two case studies, involving two power plant equipment examples, are described and demonstrate the benefits of using our proposed ROM methodology for process simulation and optimization.« less

Creating "Intelligent" Ensemble Averages Using a Process-Based Framework

NASA Astrophysics Data System (ADS)

Baker, Noel; Taylor, Patrick

2014-05-01

The CMIP5 archive contains future climate projections from over 50 models provided by dozens of modeling centers from around the world. Individual model projections, however, are subject to biases created by structural model uncertainties. As a result, ensemble averaging of multiple models is used to add value to individual model projections and construct a consensus projection. Previous reports for the IPCC establish climate change projections based on an equal-weighted average of all model projections. However, individual models reproduce certain climate processes better than other models. Should models be weighted based on performance? Unequal ensemble averages have previously been constructed using a variety of mean state metrics. What metrics are most relevant for constraining future climate projections? This project develops a framework for systematically testing metrics in models to identify optimal metrics for unequal weighting multi-model ensembles. The intention is to produce improved ("intelligent") unequal-weight ensemble averages. A unique aspect of this project is the construction and testing of climate process-based model evaluation metrics. A climate process-based metric is defined as a metric based on the relationship between two physically related climate variables—e.g., outgoing longwave radiation and surface temperature. Several climate process metrics are constructed using high-quality Earth radiation budget data from NASA's Clouds and Earth's Radiant Energy System (CERES) instrument in combination with surface temperature data sets. It is found that regional values of tested quantities can vary significantly when comparing the equal-weighted ensemble average and an ensemble weighted using the process-based metric. Additionally, this study investigates the dependence of the metric weighting scheme on the climate state using a combination of model simulations including a non-forced preindustrial control experiment, historical simulations, and several radiative forcing Representative Concentration Pathway (RCP) scenarios. Ultimately, the goal of the framework is to advise better methods for ensemble averaging models and create better climate predictions.

Soft sensor modeling based on variable partition ensemble method for nonlinear batch processes

NASA Astrophysics Data System (ADS)

Wang, Li; Chen, Xiangguang; Yang, Kai; Jin, Huaiping

2017-01-01

Batch processes are always characterized by nonlinear and system uncertain properties, therefore, the conventional single model may be ill-suited. A local learning strategy soft sensor based on variable partition ensemble method is developed for the quality prediction of nonlinear and non-Gaussian batch processes. A set of input variable sets are obtained by bootstrapping and PMI criterion. Then, multiple local GPR models are developed based on each local input variable set. When a new test data is coming, the posterior probability of each best performance local model is estimated based on Bayesian inference and used to combine these local GPR models to get the final prediction result. The proposed soft sensor is demonstrated by applying to an industrial fed-batch chlortetracycline fermentation process.

Applying Constructivist and Objectivist Learning Theories in the Design of a Web-based Course: Implications for Practice.

ERIC Educational Resources Information Center

Moallem, Mahnaz

2001-01-01

Provides an overview of the process of designing and developing a Web-based course using instructional design principles and models, including constructivist and objectivist theories. Explains the process of implementing an instructional design model in designing a Web-based undergraduate course and evaluates the model based on course evaluations.…

A Methodology and Software Environment for Testing Process Model’s Sequential Predictions with Protocols

DTIC Science & Technology

1992-12-21

in preparation). Foundations of artificial intelligence. Cambridge, MA: MIT Press. O’Reilly, R. C. (1991). X3DNet: An X- Based Neural Network ...2.2.3 Trace based protocol analysis 19 2.2A Summary of important data features 21 2.3 Tools related to process model testing 23 2.3.1 Tools for building...algorithm 57 3. Requirements for testing process models using trace based protocol 59 analysis 3.1 Definition of trace based protocol analysis (TBPA) 59

Integration of a three-dimensional process-based hydrological model into the Object Modeling System

USDA-ARS?s Scientific Manuscript database

The integration of a spatial process model into an environmental modelling framework can enhance the model’s capabilities. We present the integration of the GEOtop model into the Object Modeling System (OMS) version 3.0 and illustrate its application in a small watershed. GEOtop is a physically base...

Pre- and Post-Processing Tools to Create and Characterize Particle-Based Composite Model Structures

DTIC Science & Technology

2017-11-01

ARL-TR-8213 ● NOV 2017 US Army Research Laboratory Pre- and Post -Processing Tools to Create and Characterize Particle-Based...ARL-TR-8213 ● NOV 2017 US Army Research Laboratory Pre- and Post -Processing Tools to Create and Characterize Particle-Based Composite...AND SUBTITLE Pre- and Post -Processing Tools to Create and Characterize Particle-Based Composite Model Structures 5a. CONTRACT NUMBER 5b. GRANT

Improved workflow modelling using role activity diagram-based modelling with application to a radiology service case study.

PubMed

Shukla, Nagesh; Keast, John E; Ceglarek, Darek

2014-10-01

The modelling of complex workflows is an important problem-solving technique within healthcare settings. However, currently most of the workflow models use a simplified flow chart of patient flow obtained using on-site observations, group-based debates and brainstorming sessions, together with historic patient data. This paper presents a systematic and semi-automatic methodology for knowledge acquisition with detailed process representation using sequential interviews of people in the key roles involved in the service delivery process. The proposed methodology allows the modelling of roles, interactions, actions, and decisions involved in the service delivery process. This approach is based on protocol generation and analysis techniques such as: (i) initial protocol generation based on qualitative interviews of radiology staff, (ii) extraction of key features of the service delivery process, (iii) discovering the relationships among the key features extracted, and, (iv) a graphical representation of the final structured model of the service delivery process. The methodology is demonstrated through a case study of a magnetic resonance (MR) scanning service-delivery process in the radiology department of a large hospital. A set of guidelines is also presented in this paper to visually analyze the resulting process model for identifying process vulnerabilities. A comparative analysis of different workflow models is also conducted. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Service-based analysis of biological pathways

PubMed Central

Zheng, George; Bouguettaya, Athman

2009-01-01

Background Computer-based pathway discovery is concerned with two important objectives: pathway identification and analysis. Conventional mining and modeling approaches aimed at pathway discovery are often effective at achieving either objective, but not both. Such limitations can be effectively tackled leveraging a Web service-based modeling and mining approach. Results Inspired by molecular recognitions and drug discovery processes, we developed a Web service mining tool, named PathExplorer, to discover potentially interesting biological pathways linking service models of biological processes. The tool uses an innovative approach to identify useful pathways based on graph-based hints and service-based simulation verifying user's hypotheses. Conclusion Web service modeling of biological processes allows the easy access and invocation of these processes on the Web. Web service mining techniques described in this paper enable the discovery of biological pathways linking these process service models. Algorithms presented in this paper for automatically highlighting interesting subgraph within an identified pathway network enable the user to formulate hypothesis, which can be tested out using our simulation algorithm that are also described in this paper. PMID:19796403

Technical Note: Approximate Bayesian parameterization of a process-based tropical forest model

NASA Astrophysics Data System (ADS)

Hartig, F.; Dislich, C.; Wiegand, T.; Huth, A.

2014-02-01

Inverse parameter estimation of process-based models is a long-standing problem in many scientific disciplines. A key question for inverse parameter estimation is how to define the metric that quantifies how well model predictions fit to the data. This metric can be expressed by general cost or objective functions, but statistical inversion methods require a particular metric, the probability of observing the data given the model parameters, known as the likelihood. For technical and computational reasons, likelihoods for process-based stochastic models are usually based on general assumptions about variability in the observed data, and not on the stochasticity generated by the model. Only in recent years have new methods become available that allow the generation of likelihoods directly from stochastic simulations. Previous applications of these approximate Bayesian methods have concentrated on relatively simple models. Here, we report on the application of a simulation-based likelihood approximation for FORMIND, a parameter-rich individual-based model of tropical forest dynamics. We show that approximate Bayesian inference, based on a parametric likelihood approximation placed in a conventional Markov chain Monte Carlo (MCMC) sampler, performs well in retrieving known parameter values from virtual inventory data generated by the forest model. We analyze the results of the parameter estimation, examine its sensitivity to the choice and aggregation of model outputs and observed data (summary statistics), and demonstrate the application of this method by fitting the FORMIND model to field data from an Ecuadorian tropical forest. Finally, we discuss how this approach differs from approximate Bayesian computation (ABC), another method commonly used to generate simulation-based likelihood approximations. Our results demonstrate that simulation-based inference, which offers considerable conceptual advantages over more traditional methods for inverse parameter estimation, can be successfully applied to process-based models of high complexity. The methodology is particularly suitable for heterogeneous and complex data structures and can easily be adjusted to other model types, including most stochastic population and individual-based models. Our study therefore provides a blueprint for a fairly general approach to parameter estimation of stochastic process-based models.

Striatal and Hippocampal Entropy and Recognition Signals in Category Learning: Simultaneous Processes Revealed by Model-Based fMRI

PubMed Central

Davis, Tyler; Love, Bradley C.; Preston, Alison R.

2012-01-01

Category learning is a complex phenomenon that engages multiple cognitive processes, many of which occur simultaneously and unfold dynamically over time. For example, as people encounter objects in the world, they simultaneously engage processes to determine their fit with current knowledge structures, gather new information about the objects, and adjust their representations to support behavior in future encounters. Many techniques that are available to understand the neural basis of category learning assume that the multiple processes that subserve it can be neatly separated between different trials of an experiment. Model-based functional magnetic resonance imaging offers a promising tool to separate multiple, simultaneously occurring processes and bring the analysis of neuroimaging data more in line with category learning’s dynamic and multifaceted nature. We use model-based imaging to explore the neural basis of recognition and entropy signals in the medial temporal lobe and striatum that are engaged while participants learn to categorize novel stimuli. Consistent with theories suggesting a role for the anterior hippocampus and ventral striatum in motivated learning in response to uncertainty, we find that activation in both regions correlates with a model-based measure of entropy. Simultaneously, separate subregions of the hippocampus and striatum exhibit activation correlated with a model-based recognition strength measure. Our results suggest that model-based analyses are exceptionally useful for extracting information about cognitive processes from neuroimaging data. Models provide a basis for identifying the multiple neural processes that contribute to behavior, and neuroimaging data can provide a powerful test bed for constraining and testing model predictions. PMID:22746951

Vibration and acoustic frequency spectra for industrial process modeling using selective fusion multi-condition samples and multi-source features

NASA Astrophysics Data System (ADS)

Tang, Jian; Qiao, Junfei; Wu, ZhiWei; Chai, Tianyou; Zhang, Jian; Yu, Wen

2018-01-01

Frequency spectral data of mechanical vibration and acoustic signals relate to difficult-to-measure production quality and quantity parameters of complex industrial processes. A selective ensemble (SEN) algorithm can be used to build a soft sensor model of these process parameters by fusing valued information selectively from different perspectives. However, a combination of several optimized ensemble sub-models with SEN cannot guarantee the best prediction model. In this study, we use several techniques to construct mechanical vibration and acoustic frequency spectra of a data-driven industrial process parameter model based on selective fusion multi-condition samples and multi-source features. Multi-layer SEN (MLSEN) strategy is used to simulate the domain expert cognitive process. Genetic algorithm and kernel partial least squares are used to construct the inside-layer SEN sub-model based on each mechanical vibration and acoustic frequency spectral feature subset. Branch-and-bound and adaptive weighted fusion algorithms are integrated to select and combine outputs of the inside-layer SEN sub-models. Then, the outside-layer SEN is constructed. Thus, "sub-sampling training examples"-based and "manipulating input features"-based ensemble construction methods are integrated, thereby realizing the selective information fusion process based on multi-condition history samples and multi-source input features. This novel approach is applied to a laboratory-scale ball mill grinding process. A comparison with other methods indicates that the proposed MLSEN approach effectively models mechanical vibration and acoustic signals.

Process-based modeling of temperature and water profiles in the seedling recruitment zone: Part II. Seedling emergence timing

USDA-ARS?s Scientific Manuscript database

Predictions of seedling emergence timing for spring wheat are facilitated by process-based modeling of the microsite environment in the shallow seedling recruitment zone. Hourly temperature and water profiles within the recruitment zone for 60 days after planting were simulated from the process-base...

An analysis of the Petri net based model of the human body iron homeostasis process.

PubMed

Sackmann, Andrea; Formanowicz, Dorota; Formanowicz, Piotr; Koch, Ina; Blazewicz, Jacek

2007-02-01

In the paper a Petri net based model of the human body iron homeostasis is presented and analyzed. The body iron homeostasis is an important but not fully understood complex process. The modeling of the process presented in the paper is expressed in the language of Petri net theory. An application of this theory to the description of biological processes allows for very precise analysis of the resulting models. Here, such an analysis of the body iron homeostasis model from a mathematical point of view is given.

Models of recognition: a review of arguments in favor of a dual-process account.

PubMed

Diana, Rachel A; Reder, Lynne M; Arndt, Jason; Park, Heekyeong

2006-02-01

The majority of computationally specified models of recognition memory have been based on a single-process interpretation, claiming that familiarity is the only influence on recognition. There is increasing evidence that recognition is, in fact, based on two processes: recollection and familiarity. This article reviews the current state of the evidence for dual-process models, including the usefulness of the remember/know paradigm, and interprets the relevant results in terms of the source of activation confusion (SAC) model of memory. We argue that the evidence from each of the areas we discuss, when combined, presents a strong case that inclusion of a recollection process is necessary. Given this conclusion, we also argue that the dual-process claim that the recollection process is always available is, in fact, more parsimonious than the single-process claim that the recollection process is used only in certain paradigms. The value of a well-specified process model such as the SAC model is discussed with regard to other types of dual-process models.

Cognitive components underpinning the development of model-based learning.

PubMed

Potter, Tracey C S; Bryce, Nessa V; Hartley, Catherine A

2017-06-01

Reinforcement learning theory distinguishes "model-free" learning, which fosters reflexive repetition of previously rewarded actions, from "model-based" learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9-25, we examined whether the abilities to infer sequential regularities in the environment ("statistical learning"), maintain information in an active state ("working memory") and integrate distant concepts to solve problems ("fluid reasoning") predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

A neuroanatomical model of space-based and object-centered processing in spatial neglect.

PubMed

Pedrazzini, Elena; Schnider, Armin; Ptak, Radek

2017-11-01

Visual attention can be deployed in space-based or object-centered reference frames. Right-hemisphere damage may lead to distinct deficits of space- or object-based processing, and such dissociations are thought to underlie the heterogeneous nature of spatial neglect. Previous studies have suggested that object-centered processing deficits (such as in copying, reading or line bisection) result from damage to retro-rolandic regions while impaired spatial exploration reflects damage to more anterior regions. However, this evidence is based on small samples and heterogeneous tasks. Here, we tested a theoretical model of neglect that takes in account the space- and object-based processing and relates them to neuroanatomical predictors. One hundred and one right-hemisphere-damaged patients were examined with classic neuropsychological tests and structural brain imaging. Relations between neglect measures and damage to the temporal-parietal junction, intraparietal cortex, insula and middle frontal gyrus were examined with two structural equation models by assuming that object-centered processing (involved in line bisection and single-word reading) and space-based processing (involved in cancelation tasks) either represented a unique latent variable or two distinct variables. Of these two models the latter had better explanatory power. Damage to the intraparietal sulcus was a significant predictor of object-centered, but not space-based processing, while damage to the temporal-parietal junction predicted space-based, but not object-centered processing. Space-based processing and object-centered processing were strongly intercorrelated, indicating that they rely on similar, albeit partly dissociated processes. These findings indicate that object-centered and space-based deficits in neglect are partly independent and result from superior parietal and inferior parietal damage, respectively.

A Mixed Kijima Model Using the Weibull-Based Generalized Renewal Processes

PubMed Central

2015-01-01

Generalized Renewal Processes are useful for approaching the rejuvenation of dynamical systems resulting from planned or unplanned interventions. We present new perspectives for the Generalized Renewal Processes in general and for the Weibull-based Generalized Renewal Processes in particular. Disregarding from literature, we present a mixed Generalized Renewal Processes approach involving Kijima Type I and II models, allowing one to infer the impact of distinct interventions on the performance of the system under study. The first and second theoretical moments of this model are introduced as well as its maximum likelihood estimation and random sampling approaches. In order to illustrate the usefulness of the proposed Weibull-based Generalized Renewal Processes model, some real data sets involving improving, stable, and deteriorating systems are used. PMID:26197222

Examination of Modeling Languages to Allow Quantitative Analysis for Model-Based Systems Engineering

DTIC Science & Technology

2014-06-01

x THIS PAGE INTENTIONALLY LEFT BLANK xi LIST OF ACRONYMS AND ABBREVIATIONS BOM Base Object Model BPMN Business Process Model & Notation DOD...SysML. There are many variants such as the Unified Profile for DODAF/MODAF (UPDM) and Business Process Model & Notation ( BPMN ) that have origins in

Extracting business vocabularies from business process models: SBVR and BPMN standards-based approach

NASA Astrophysics Data System (ADS)

Skersys, Tomas; Butleris, Rimantas; Kapocius, Kestutis

2013-10-01

Approaches for the analysis and specification of business vocabularies and rules are very relevant topics in both Business Process Management and Information Systems Development disciplines. However, in common practice of Information Systems Development, the Business modeling activities still are of mostly empiric nature. In this paper, basic aspects of the approach for business vocabularies' semi-automated extraction from business process models are presented. The approach is based on novel business modeling-level OMG standards "Business Process Model and Notation" (BPMN) and "Semantics for Business Vocabularies and Business Rules" (SBVR), thus contributing to OMG's vision about Model-Driven Architecture (MDA) and to model-driven development in general.

Agent-Based Modeling of Growth Processes

ERIC Educational Resources Information Center

Abraham, Ralph

2014-01-01

Growth processes abound in nature, and are frequently the target of modeling exercises in the sciences. In this article we illustrate an agent-based approach to modeling, in the case of a single example from the social sciences: bullying.

TOWARDS A MULTI-SCALE AGENT-BASED PROGRAMMING LANGUAGE METHODOLOGY

PubMed Central

Somogyi, Endre; Hagar, Amit; Glazier, James A.

2017-01-01

Living tissues are dynamic, heterogeneous compositions of objects, including molecules, cells and extra-cellular materials, which interact via chemical, mechanical and electrical process and reorganize via transformation, birth, death and migration processes. Current programming language have difficulty describing the dynamics of tissues because: 1: Dynamic sets of objects participate simultaneously in multiple processes, 2: Processes may be either continuous or discrete, and their activity may be conditional, 3: Objects and processes form complex, heterogeneous relationships and structures, 4: Objects and processes may be hierarchically composed, 5: Processes may create, destroy and transform objects and processes. Some modeling languages support these concepts, but most cannot translate models into executable simulations. We present a new hybrid executable modeling language paradigm, the Continuous Concurrent Object Process Methodology (CCOPM) which naturally expresses tissue models, enabling users to visually create agent-based models of tissues, and also allows computer simulation of these models. PMID:29282379

TOWARDS A MULTI-SCALE AGENT-BASED PROGRAMMING LANGUAGE METHODOLOGY.

PubMed

Somogyi, Endre; Hagar, Amit; Glazier, James A

2016-12-01

Living tissues are dynamic, heterogeneous compositions of objects , including molecules, cells and extra-cellular materials, which interact via chemical, mechanical and electrical process and reorganize via transformation, birth, death and migration processes . Current programming language have difficulty describing the dynamics of tissues because: 1: Dynamic sets of objects participate simultaneously in multiple processes, 2: Processes may be either continuous or discrete, and their activity may be conditional, 3: Objects and processes form complex, heterogeneous relationships and structures, 4: Objects and processes may be hierarchically composed, 5: Processes may create, destroy and transform objects and processes. Some modeling languages support these concepts, but most cannot translate models into executable simulations. We present a new hybrid executable modeling language paradigm, the Continuous Concurrent Object Process Methodology ( CCOPM ) which naturally expresses tissue models, enabling users to visually create agent-based models of tissues, and also allows computer simulation of these models.

Model-Based Verification and Validation of the SMAP Uplink Processes

NASA Technical Reports Server (NTRS)

Khan, M. Omair; Dubos, Gregory F.; Tirona, Joseph; Standley, Shaun

2013-01-01

This case study stands as an example of how a project can validate a system-level design earlier in the project life cycle than traditional V&V processes by using simulation on a system model. Specifically, this paper describes how simulation was added to a system model of the Soil Moisture Active-Passive (SMAP) mission's uplink process.Also discussed are the advantages and disadvantages of the methods employed and the lessons learned; which are intended to benefit future model-based and simulation-based V&V development efforts.

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

Simpson, L.; Britt, J.; Birkmire, R.

ITN Energy Systems, Inc., and Global Solar Energy, Inc., assisted by NREL's PV Manufacturing R&D program, have continued to advance CIGS production technology by developing trajectory-oriented predictive/control models, fault-tolerance control, control platform development, in-situ sensors, and process improvements. Modeling activities included developing physics-based and empirical models for CIGS and sputter-deposition processing, implementing model-based control, and applying predictive models to the construction of new evaporation sources and for control. Model-based control is enabled by implementing reduced or empirical models into a control platform. Reliability improvement activities include implementing preventive maintenance schedules; detecting failed sensors/equipment and reconfiguring to tinue processing; and systematicmore » development of fault prevention and reconfiguration strategies for the full range of CIGS PV production deposition processes. In-situ sensor development activities have resulted in improved control and indicated the potential for enhanced process status monitoring and control of the deposition processes. Substantial process improvements have been made, including significant improvement in CIGS uniformity, thickness control, efficiency, yield, and throughput. In large measure, these gains have been driven by process optimization, which in turn have been enabled by control and reliability improvements due to this PV Manufacturing R&D program.« less

The Gravitational Process Path (GPP) model (v1.0) - a GIS-based simulation framework for gravitational processes

NASA Astrophysics Data System (ADS)

Wichmann, Volker

2017-09-01

The Gravitational Process Path (GPP) model can be used to simulate the process path and run-out area of gravitational processes based on a digital terrain model (DTM). The conceptual model combines several components (process path, run-out length, sink filling and material deposition) to simulate the movement of a mass point from an initiation site to the deposition area. For each component several modeling approaches are provided, which makes the tool configurable for different processes such as rockfall, debris flows or snow avalanches. The tool can be applied to regional-scale studies such as natural hazard susceptibility mapping but also contains components for scenario-based modeling of single events. Both the modeling approaches and precursor implementations of the tool have proven their applicability in numerous studies, also including geomorphological research questions such as the delineation of sediment cascades or the study of process connectivity. This is the first open-source implementation, completely re-written, extended and improved in many ways. The tool has been committed to the main repository of the System for Automated Geoscientific Analyses (SAGA) and thus will be available with every SAGA release.

Cognitive Components Underpinning the Development of Model-Based Learning

PubMed Central

Potter, Tracey C.S.; Bryce, Nessa V.; Hartley, Catherine A.

2016-01-01

Reinforcement learning theory distinguishes “model-free” learning, which fosters reflexive repetition of previously rewarded actions, from “model-based” learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9–25, we examined whether the abilities to infer sequential regularities in the environment (“statistical learning”), maintain information in an active state (“working memory”) and integrate distant concepts to solve problems (“fluid reasoning”) predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. PMID:27825732

The human body metabolism process mathematical simulation based on Lotka-Volterra model

NASA Astrophysics Data System (ADS)

Oliynyk, Andriy; Oliynyk, Eugene; Pyptiuk, Olexandr; DzierŻak, RóŻa; Szatkowska, Małgorzata; Uvaysova, Svetlana; Kozbekova, Ainur

2017-08-01

The mathematical model of metabolism process in human organism based on Lotka-Volterra model has beeng proposed, considering healing regime, nutrition system, features of insulin and sugar fragmentation process in the organism. The numerical algorithm of the model using IV-order Runge-Kutta method has been realized. After the result of calculations the conclusions have been made, recommendations about using the modeling results have been showed, the vectors of the following researches are defined.

Kinetic Modeling of Microbiological Processes

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

Liu, Chongxuan; Fang, Yilin

Kinetic description of microbiological processes is vital for the design and control of microbe-based biotechnologies such as waste water treatment, petroleum oil recovery, and contaminant attenuation and remediation. Various models have been proposed to describe microbiological processes. This editorial article discusses the advantages and limiation of these modeling approaches in cluding tranditional, Monod-type models and derivatives, and recently developed constraint-based approaches. The article also offers the future direction of modeling researches that best suit for petroleum and environmental biotechnologies.

An Extended Petri-Net Based Approach for Supply Chain Process Enactment in Resource-Centric Web Service Environment

NASA Astrophysics Data System (ADS)

Wang, Xiaodong; Zhang, Xiaoyu; Cai, Hongming; Xu, Boyi

Enacting a supply-chain process involves variant partners and different IT systems. REST receives increasing attention for distributed systems with loosely coupled resources. Nevertheless, resource model incompatibilities and conflicts prevent effective process modeling and deployment in resource-centric Web service environment. In this paper, a Petri-net based framework for supply-chain process integration is proposed. A resource meta-model is constructed to represent the basic information of resources. Then based on resource meta-model, XML schemas and documents are derived, which represent resources and their states in Petri-net. Thereafter, XML-net, a high level Petri-net, is employed for modeling control and data flow of process. From process model in XML-net, RESTful services and choreography descriptions are deduced. Therefore, unified resource representation and RESTful services description are proposed for cross-system integration in a more effective way. A case study is given to illustrate the approach and the desirable features of the approach are discussed.

Modelling and simulating decision processes of linked lives: An approach based on concurrent processes and stochastic race.

PubMed

Warnke, Tom; Reinhardt, Oliver; Klabunde, Anna; Willekens, Frans; Uhrmacher, Adelinde M

2017-10-01

Individuals' decision processes play a central role in understanding modern migration phenomena and other demographic processes. Their integration into agent-based computational demography depends largely on suitable support by a modelling language. We are developing the Modelling Language for Linked Lives (ML3) to describe the diverse decision processes of linked lives succinctly in continuous time. The context of individuals is modelled by networks the individual is part of, such as family ties and other social networks. Central concepts, such as behaviour conditional on agent attributes, age-dependent behaviour, and stochastic waiting times, are tightly integrated in the language. Thereby, alternative decisions are modelled by concurrent processes that compete by stochastic race. Using a migration model, we demonstrate how this allows for compact description of complex decisions, here based on the Theory of Planned Behaviour. We describe the challenges for the simulation algorithm posed by stochastic race between multiple concurrent complex decisions.

Integration agent-based models and GIS as a virtual urban dynamic laboratory

NASA Astrophysics Data System (ADS)

Chen, Peng; Liu, Miaolong

2007-06-01

Based on the Agent-based Model and spatial data model, a tight-coupling integrating method of GIS and Agent-based Model (ABM) is to be discussed in this paper. The use of object-orientation for both spatial data and spatial process models facilitates their integration, which can allow exploration and explanation of spatial-temporal phenomena such as urban dynamic. In order to better understand how tight coupling might proceed and to evaluate the possible functional and efficiency gains from such a tight coupling, the agent-based model and spatial data model are discussed, and then the relationships affecting spatial data model and agent-based process models interaction. After that, a realistic crowd flow simulation experiment is presented. Using some tools provided by general GIS systems and a few specific programming languages, a new software system integrating GIS and MAS as a virtual laboratory applicable for simulating pedestrian flows in a crowd activity centre has been developed successfully. Under the environment supported by the software system, as an applicable case, a dynamic evolution process of the pedestrian's flows (dispersed process for the spectators) in a crowds' activity center - The Shanghai Stadium has been simulated successfully. At the end of the paper, some new research problems have been pointed out for the future.

Processing speed enhances model-based over model-free reinforcement learning in the presence of high working memory functioning

PubMed Central

Schad, Daniel J.; Jünger, Elisabeth; Sebold, Miriam; Garbusow, Maria; Bernhardt, Nadine; Javadi, Amir-Homayoun; Zimmermann, Ulrich S.; Smolka, Michael N.; Heinz, Andreas; Rapp, Michael A.; Huys, Quentin J. M.

2014-01-01

Theories of decision-making and its neural substrates have long assumed the existence of two distinct and competing valuation systems, variously described as goal-directed vs. habitual, or, more recently and based on statistical arguments, as model-free vs. model-based reinforcement-learning. Though both have been shown to control choices, the cognitive abilities associated with these systems are under ongoing investigation. Here we examine the link to cognitive abilities, and find that individual differences in processing speed covary with a shift from model-free to model-based choice control in the presence of above-average working memory function. This suggests shared cognitive and neural processes; provides a bridge between literatures on intelligence and valuation; and may guide the development of process models of different valuation components. Furthermore, it provides a rationale for individual differences in the tendency to deploy valuation systems, which may be important for understanding the manifold neuropsychiatric diseases associated with malfunctions of valuation. PMID:25566131

Mathematization Competencies of Pre-Service Elementary Mathematics Teachers in the Mathematical Modelling Process

ERIC Educational Resources Information Center

Yilmaz, Suha; Tekin-Dede, Ayse

2016-01-01

Mathematization competency is considered in the field as the focus of modelling process. Considering the various definitions, the components of the mathematization competency are determined as identifying assumptions, identifying variables based on the assumptions and constructing mathematical model/s based on the relations among identified…

Parameter prediction based on Improved Process neural network and ARMA error compensation in Evaporation Process

NASA Astrophysics Data System (ADS)

Qian, Xiaoshan

2018-01-01

The traditional model of evaporation process parameters have continuity and cumulative characteristics of the prediction error larger issues, based on the basis of the process proposed an adaptive particle swarm neural network forecasting method parameters established on the autoregressive moving average (ARMA) error correction procedure compensated prediction model to predict the results of the neural network to improve prediction accuracy. Taking a alumina plant evaporation process to analyze production data validation, and compared with the traditional model, the new model prediction accuracy greatly improved, can be used to predict the dynamic process of evaporation of sodium aluminate solution components.

Modeling formalisms in Systems Biology

PubMed Central

2011-01-01

Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future. PMID:22141422

Formal Specification of Information Systems Requirements.

ERIC Educational Resources Information Center

Kampfner, Roberto R.

1985-01-01

Presents a formal model for specification of logical requirements of computer-based information systems that incorporates structural and dynamic aspects based on two separate models: the Logical Information Processing Structure and the Logical Information Processing Network. The model's role in systems development is discussed. (MBR)

A new approach towards image based virtual 3D city modeling by using close range photogrammetry

NASA Astrophysics Data System (ADS)

Singh, S. P.; Jain, K.; Mandla, V. R.

2014-05-01

3D city model is a digital representation of the Earth's surface and it's related objects such as building, tree, vegetation, and some manmade feature belonging to urban area. The demand of 3D city modeling is increasing day to day for various engineering and non-engineering applications. Generally three main image based approaches are using for virtual 3D city models generation. In first approach, researchers used Sketch based modeling, second method is Procedural grammar based modeling and third approach is Close range photogrammetry based modeling. Literature study shows that till date, there is no complete solution available to create complete 3D city model by using images. These image based methods also have limitations This paper gives a new approach towards image based virtual 3D city modeling by using close range photogrammetry. This approach is divided into three sections. First, data acquisition process, second is 3D data processing, and third is data combination process. In data acquisition process, a multi-camera setup developed and used for video recording of an area. Image frames created from video data. Minimum required and suitable video image frame selected for 3D processing. In second section, based on close range photogrammetric principles and computer vision techniques, 3D model of area created. In third section, this 3D model exported to adding and merging of other pieces of large area. Scaling and alignment of 3D model was done. After applying the texturing and rendering on this model, a final photo-realistic textured 3D model created. This 3D model transferred into walk-through model or in movie form. Most of the processing steps are automatic. So this method is cost effective and less laborious. Accuracy of this model is good. For this research work, study area is the campus of department of civil engineering, Indian Institute of Technology, Roorkee. This campus acts as a prototype for city. Aerial photography is restricted in many country and high resolution satellite images are costly. In this study, proposed method is based on only simple video recording of area. Thus this proposed method is suitable for 3D city modeling. Photo-realistic, scalable, geo-referenced virtual 3D city model is useful for various kinds of applications such as for planning in navigation, tourism, disasters management, transportations, municipality, urban and environmental managements, real-estate industry. Thus this study will provide a good roadmap for geomatics community to create photo-realistic virtual 3D city model by using close range photogrammetry.

Devil is in the details: Using logic models to investigate program process.

PubMed

Peyton, David J; Scicchitano, Michael

2017-12-01

Theory-based logic models are commonly developed as part of requirements for grant funding. As a tool to communicate complex social programs, theory based logic models are an effective visual communication. However, after initial development, theory based logic models are often abandoned and remain in their initial form despite changes in the program process. This paper examines the potential benefits of committing time and resources to revising the initial theory driven logic model and developing detailed logic models that describe key activities to accurately reflect the program and assist in effective program management. The authors use a funded special education teacher preparation program to exemplify the utility of drill down logic models. The paper concludes with lessons learned from the iterative revision process and suggests how the process can lead to more flexible and calibrated program management. Copyright © 2017 Elsevier Ltd. All rights reserved.

Models of recognition: A review of arguments in favor of a dual-process account

PubMed Central

DIANA, RACHEL A.; REDER, LYNNE M.; ARNDT, JASON; PARK, HEEKYEONG

2008-01-01

The majority of computationally specified models of recognition memory have been based on a single-process interpretation, claiming that familiarity is the only influence on recognition. There is increasing evidence that recognition is, in fact, based on two processes: recollection and familiarity. This article reviews the current state of the evidence for dual-process models, including the usefulness of the remember/know paradigm, and interprets the relevant results in terms of the source of activation confusion (SAC) model of memory. We argue that the evidence from each of the areas we discuss, when combined, presents a strong case that inclusion of a recollection process is necessary. Given this conclusion, we also argue that the dual-process claim that the recollection process is always available is, in fact, more parsimonious than the single-process claim that the recollection process is used only in certain paradigms. The value of a well-specified process model such as the SAC model is discussed with regard to other types of dual-process models. PMID:16724763

Mathematical modelling of disintegration-limited co-digestion of OFMSW and sewage sludge.

PubMed

Esposito, G; Frunzo, L; Panico, A; d'Antonio, G

2008-01-01

This paper presents a mathematical model able to simulate under dynamic conditions the physical, chemical and biological processes prevailing in a OFMSW and sewage sludge anaerobic digestion system. The model proposed is based on differential mass balance equations for substrates, products and bacterial groups involved in the co-digestion process and includes the biochemical reactions of the substrate conversion and the kinetics of microbial growth and decay. The main peculiarity of the model is the surface based kinetic description of the OFMSW disintegration process, whereas the pH determination is based on a nine-order polynomial equation derived by acid-base equilibria. The model can be applied to simulate the co-digestion process for several purposes, such as the evaluation of the optimal process conditions in terms of OFMSW/sewage sludge ratio, temperature, OFMSW particle size, solid mixture retention time, reactor stirring rate, etc. Biogas production and composition can also be evaluated to estimate the potential energy production under different process conditions. In particular, model simulations reported in this paper show the model capability to predict the OFMSW amount which can be treated in the digester of an existing MWWTP and to assess the OFMSW particle size diminution pre-treatment required to increase the rate of the disintegration process, which otherwise can highly limit the co-digestion system. Copyright IWA Publishing 2008.

Global Sensitivity Analysis for Process Identification under Model Uncertainty

NASA Astrophysics Data System (ADS)

Ye, M.; Dai, H.; Walker, A. P.; Shi, L.; Yang, J.

2015-12-01

The environmental system consists of various physical, chemical, and biological processes, and environmental models are always built to simulate these processes and their interactions. For model building, improvement, and validation, it is necessary to identify important processes so that limited resources can be used to better characterize the processes. While global sensitivity analysis has been widely used to identify important processes, the process identification is always based on deterministic process conceptualization that uses a single model for representing a process. However, environmental systems are complex, and it happens often that a single process may be simulated by multiple alternative models. Ignoring the model uncertainty in process identification may lead to biased identification in that identified important processes may not be so in the real world. This study addresses this problem by developing a new method of global sensitivity analysis for process identification. The new method is based on the concept of Sobol sensitivity analysis and model averaging. Similar to the Sobol sensitivity analysis to identify important parameters, our new method evaluates variance change when a process is fixed at its different conceptualizations. The variance considers both parametric and model uncertainty using the method of model averaging. The method is demonstrated using a synthetic study of groundwater modeling that considers recharge process and parameterization process. Each process has two alternative models. Important processes of groundwater flow and transport are evaluated using our new method. The method is mathematically general, and can be applied to a wide range of environmental problems.

Business Process Design Method Based on Business Event Model for Enterprise Information System Integration

NASA Astrophysics Data System (ADS)

Kobayashi, Takashi; Komoda, Norihisa

The traditional business process design methods, in which the usecase is the most typical, have no useful framework to design the activity sequence with. Therefore, the design efficiency and quality vary widely according to the designer’s experience and skill. In this paper, to solve this problem, we propose the business events and their state transition model (a basic business event model) based on the language/action perspective, which is the result in the cognitive science domain. In the business process design, using this model, we decide event occurrence conditions so that every event synchronizes with each other. We also propose the design pattern to decide the event occurrence condition (a business event improvement strategy). Lastly, we apply the business process design method based on the business event model and the business event improvement strategy to the credit card issue process and estimate its effect.

Pursuing the method of multiple working hypotheses to understand differences in process-based snow models

NASA Astrophysics Data System (ADS)

Clark, Martyn; Essery, Richard

2017-04-01

When faced with the complex and interdisciplinary challenge of building process-based land models, different modelers make different decisions at different points in the model development process. These modeling decisions are generally based on several considerations, including fidelity (e.g., what approaches faithfully simulate observed processes), complexity (e.g., which processes should be represented explicitly), practicality (e.g., what is the computational cost of the model simulations; are there sufficient resources to implement the desired modeling concepts), and data availability (e.g., is there sufficient data to force and evaluate models). Consequently the research community, comprising modelers of diverse background, experience, and modeling philosophy, has amassed a wide range of models, which differ in almost every aspect of their conceptualization and implementation. Model comparison studies have been undertaken to explore model differences, but have not been able to meaningfully attribute inter-model differences in predictive ability to individual model components because there are often too many structural and implementation differences among the different models considered. As a consequence, model comparison studies to date have provided limited insight into the causes of differences in model behavior, and model development has often relied on the inspiration and experience of individual modelers rather than on a systematic analysis of model shortcomings. This presentation will summarize the use of "multiple-hypothesis" modeling frameworks to understand differences in process-based snow models. Multiple-hypothesis frameworks define a master modeling template, and include a a wide variety of process parameterizations and spatial configurations that are used in existing models. Such frameworks provide the capability to decompose complex models into the individual decisions that are made as part of model development, and evaluate each decision in isolation. It is hence possible to attribute differences in system-scale model predictions to individual modeling decisions, providing scope to mimic the behavior of existing models, understand why models differ, characterize model uncertainty, and identify productive pathways to model improvement. Results will be presented applying multiple hypothesis frameworks to snow model comparison projects, including PILPS, SnowMIP, and the upcoming ESM-SnowMIP project.

Using artificial neural networks to model aluminium based sheet forming processes and tools details

NASA Astrophysics Data System (ADS)

Mekras, N.

2017-09-01

In this paper, a methodology and a software system will be presented concerning the use of Artificial Neural Networks (ANNs) for modeling aluminium based sheet forming processes. ANNs models’ creation is based on the training of the ANNs using experimental, trial and historical data records of processes’ inputs and outputs. ANNs models are useful in cases that processes’ mathematical models are not accurate enough, are not well defined or are missing e.g. in cases of complex product shapes, new material alloys, new process requirements, micro-scale products, etc. Usually, after the design and modeling of the forming tools (die, punch, etc.) and before mass production, a set of trials takes place at the shop floor for finalizing processes and tools details concerning e.g. tools’ minimum radii, die/punch clearance, press speed, process temperature, etc. and in relation with the material type, the sheet thickness and the quality achieved from the trials. Using data from the shop floor trials and forming theory data, ANNs models can be trained and created, and can be used to estimate processes and tools final details, hence supporting efficient set-up of processes and tools before mass production starts. The proposed ANNs methodology and the respective software system are implemented within the EU H2020 project LoCoMaTech for the aluminium-based sheet forming process HFQ (solution Heat treatment, cold die Forming and Quenching).

Two Undergraduate Process Modeling Courses Taught Using Inductive Learning Methods

ERIC Educational Resources Information Center

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…

Social Models: Blueprints or Processes?

ERIC Educational Resources Information Center

Little, Graham R.

1981-01-01

Discusses the nature and implications of two different models for societal planning: (1) the problem-solving process approach based on Karl Popper; and (2) the goal-setting "blueprint" approach based on Karl Marx. (DC)

Laser welding of polymers: phenomenological model for a quick and reliable process quality estimation considering beam shape influences

NASA Astrophysics Data System (ADS)

Timpe, Nathalie F.; Stuch, Julia; Scholl, Marcus; Russek, Ulrich A.

2016-03-01

This contribution presents a phenomenological, analytical model for laser welding of polymers which is suited for a quick process quality estimation for the practitioner. Besides material properties of the polymer and processing parameters like welding pressure, feed rate and laser power the model is based on a simple few parameter description of the size and shape of the laser power density distribution (PDD) in the processing zone. The model allows an estimation of the weld seam tensile strength. It is based on energy balance considerations within a thin sheet with the thickness of the optical penetration depth on the surface of the absorbing welding partner. The joining process itself is modelled by a phenomenological approach. The model reproduces the experimentally known process windows for the main process parameters correctly. Using the parameters describing the shape of the laser PDD the critical dependence of the process windows on the PDD shape will be predicted and compared with experiments. The adaption of the model to other laser manufacturing processes where the PDD influence can be modelled comparably will be discussed.

Thorough specification of the neurophysiologic processes underlying behavior and of their manifestation in EEG - demonstration with the go/no-go task.

PubMed

Shahaf, Goded; Pratt, Hillel

2013-01-01

In this work we demonstrate the principles of a systematic modeling approach of the neurophysiologic processes underlying a behavioral function. The modeling is based upon a flexible simulation tool, which enables parametric specification of the underlying neurophysiologic characteristics. While the impact of selecting specific parameters is of interest, in this work we focus on the insights, which emerge from rather accepted assumptions regarding neuronal representation. We show that harnessing of even such simple assumptions enables the derivation of significant insights regarding the nature of the neurophysiologic processes underlying behavior. We demonstrate our approach in some detail by modeling the behavioral go/no-go task. We further demonstrate the practical significance of this simplified modeling approach in interpreting experimental data - the manifestation of these processes in the EEG and ERP literature of normal and abnormal (ADHD) function, as well as with comprehensive relevant ERP data analysis. In-fact we show that from the model-based spatiotemporal segregation of the processes, it is possible to derive simple and yet effective and theory-based EEG markers differentiating normal and ADHD subjects. We summarize by claiming that the neurophysiologic processes modeled for the go/no-go task are part of a limited set of neurophysiologic processes which underlie, in a variety of combinations, any behavioral function with measurable operational definition. Such neurophysiologic processes could be sampled directly from EEG on the basis of model-based spatiotemporal segregation.

DEVS Unified Process for Web-Centric Development and Testing of System of Systems

DTIC Science & Technology

2008-05-20

gathering from the user. Further, methodologies have been developed to generate DEVS models from BPMN /BPEL-based and message-based requirement specifications...27] 3. BPMN /BPEL based system specifications: Business Process Modeling Notation ( BPMN ) [bpm] or Business Process Execution Language (BPEL) provide a...information is stored in .wsdl and .bpel files for BPEL but in proprietary format for BPMN . 4. DoDAF-based requirement specifications: Department of

Aligning observed and modelled behaviour based on workflow decomposition

NASA Astrophysics Data System (ADS)

Wang, Lu; Du, YuYue; Liu, Wei

2017-09-01

When business processes are mostly supported by information systems, the availability of event logs generated from these systems, as well as the requirement of appropriate process models are increasing. Business processes can be discovered, monitored and enhanced by extracting process-related information. However, some events cannot be correctly identified because of the explosion of the amount of event logs. Therefore, a new process mining technique is proposed based on a workflow decomposition method in this paper. Petri nets (PNs) are used to describe business processes, and then conformance checking of event logs and process models is investigated. A decomposition approach is proposed to divide large process models and event logs into several separate parts that can be analysed independently; while an alignment approach based on a state equation method in PN theory enhances the performance of conformance checking. Both approaches are implemented in programmable read-only memory (ProM). The correctness and effectiveness of the proposed methods are illustrated through experiments.

Wheat stress indicator model, Crop Condition Assessment Division (CCAD) data base interface driver, user's manual

NASA Technical Reports Server (NTRS)

Hansen, R. F. (Principal Investigator)

1981-01-01

The use of the wheat stress indicator model CCAD data base interface driver is described. The purpose of this system is to interface the wheat stress indicator model with the CCAD operational data base. The interface driver routine decides what meteorological stations should be processed and calls the proper subroutines to process the stations.

Lebedev acceleration and comparison of different photometric models in the inversion of lightcurves for asteroids

NASA Astrophysics Data System (ADS)

Lu, Xiao-Ping; Huang, Xiang-Jie; Ip, Wing-Huen; Hsia, Chi-Hao

2018-04-01

In the lightcurve inversion process where asteroid's physical parameters such as rotational period, pole orientation and overall shape are searched, the numerical calculations of the synthetic photometric brightness based on different shape models are frequently implemented. Lebedev quadrature is an efficient method to numerically calculate the surface integral on the unit sphere. By transforming the surface integral on the Cellinoid shape model to that on the unit sphere, the lightcurve inversion process based on the Cellinoid shape model can be remarkably accelerated. Furthermore, Matlab codes of the lightcurve inversion process based on the Cellinoid shape model are available on Github for free downloading. The photometric models, i.e., the scattering laws, also play an important role in the lightcurve inversion process, although the shape variations of asteroids dominate the morphologies of the lightcurves. Derived from the radiative transfer theory, the Hapke model can describe the light reflectance behaviors from the viewpoint of physics, while there are also many empirical models in numerical applications. Numerical simulations are implemented for the comparison of the Hapke model with the other three numerical models, including the Lommel-Seeliger, Minnaert, and Kaasalainen models. The results show that the numerical models with simple function expressions can fit well with the synthetic lightcurves generated based on the Hapke model; this good fit implies that they can be adopted in the lightcurve inversion process for asteroids to improve the numerical efficiency and derive similar results to those of the Hapke model.

Customer-centered careflow modeling based on guidelines.

PubMed

Huang, Biqing; Zhu, Peng; Wu, Cheng

2012-10-01

In contemporary society, customer-centered health care, which stresses customer participation and long-term tailored care, is inevitably becoming a trend. Compared with the hospital or physician-centered healthcare process, the customer-centered healthcare process requires more knowledge and modeling such a process is extremely complex. Thus, building a care process model for a special customer is cost prohibitive. In addition, during the execution of a care process model, the information system should have flexibility to modify the model so that it adapts to changes in the healthcare process. Therefore, supporting the process in a flexible, cost-effective way is a key challenge for information technology. To meet this challenge, first, we analyze various kinds of knowledge used in process modeling, illustrate their characteristics, and detail their roles and effects in careflow modeling. Secondly, we propose a methodology to manage a lifecycle of the healthcare process modeling, with which models could be built gradually with convenience and efficiency. In this lifecycle, different levels of process models are established based on the kinds of knowledge involved, and the diffusion strategy of these process models is designed. Thirdly, architecture and prototype of the system supporting the process modeling and its lifecycle are given. This careflow system also considers the compatibility of legacy systems and authority problems. Finally, an example is provided to demonstrate implementation of the careflow system.

A CSP-Based Agent Modeling Framework for the Cougaar Agent-Based Architecture

NASA Technical Reports Server (NTRS)

Gracanin, Denis; Singh, H. Lally; Eltoweissy, Mohamed; Hinchey, Michael G.; Bohner, Shawn A.

2005-01-01

Cognitive Agent Architecture (Cougaar) is a Java-based architecture for large-scale distributed agent-based applications. A Cougaar agent is an autonomous software entity with behaviors that represent a real-world entity (e.g., a business process). A Cougaar-based Model Driven Architecture approach, currently under development, uses a description of system's functionality (requirements) to automatically implement the system in Cougaar. The Communicating Sequential Processes (CSP) formalism is used for the formal validation of the generated system. Two main agent components, a blackboard and a plugin, are modeled as CSP processes. A set of channels represents communications between the blackboard and individual plugins. The blackboard is represented as a CSP process that communicates with every agent in the collection. The developed CSP-based Cougaar modeling framework provides a starting point for a more complete formal verification of the automatically generated Cougaar code. Currently it is used to verify the behavior of an individual agent in terms of CSP properties and to analyze the corresponding Cougaar society.

Modelling of the mercury loss in fluorescent lamps under the influence of metal oxide coatings

NASA Astrophysics Data System (ADS)

Santos Abreu, A.; Mayer, J.; Lenk, D.; Horn, S.; Konrad, A.; Tidecks, R.

2016-11-01

The mercury transport and loss mechanisms in the metal oxide coatings of mercury low pressure discharge fluorescent lamps have been investigated. An existing model based on a ballistic process is discussed in the context of experimental mercury loss data. Two different approaches to the modeling of the mercury loss have been developed. The first one is based on mercury transition rates between the plasma, the coating, and the glass without specifying the underlying physical processes. The second one is based on a transport process driven by diffusion and a binding process of mercury reacting to mercury oxide inside the layers. Moreover, we extended the diffusion based model to handle multi-component coatings. All approaches are applied to describe mercury loss experiments under the influence of an Al 2 O 3 coating.

Modeling and Simulation of Metallurgical Process Based on Hybrid Petri Net

NASA Astrophysics Data System (ADS)

Ren, Yujuan; Bao, Hong

2016-11-01

In order to achieve the goals of energy saving and emission reduction of iron and steel enterprises, an increasing number of modeling and simulation technologies are used to research and analyse metallurgical production process. In this paper, the basic principle of Hybrid Petri net is used to model and analyse the Metallurgical Process. Firstly, the definition of Hybrid Petri Net System of Metallurgical Process (MPHPNS) and its modeling theory are proposed. Secondly, the model of MPHPNS based on material flow is constructed. The dynamic flow of materials and the real-time change of each technological state in metallurgical process are simulated vividly by using this model. The simulation process can implement interaction between the continuous event dynamic system and the discrete event dynamic system at the same level, and play a positive role in the production decision.

Process Modeling and Dynamic Simulation for EAST Helium Refrigerator

NASA Astrophysics Data System (ADS)

Lu, Xiaofei; Fu, Peng; Zhuang, Ming; Qiu, Lilong; Hu, Liangbing

2016-06-01

In this paper, the process modeling and dynamic simulation for the EAST helium refrigerator has been completed. The cryogenic process model is described and the main components are customized in detail. The process model is controlled by the PLC simulator, and the realtime communication between the process model and the controllers is achieved by a customized interface. Validation of the process model has been confirmed based on EAST experimental data during the cool down process of 300-80 K. Simulation results indicate that this process simulator is able to reproduce dynamic behaviors of the EAST helium refrigerator very well for the operation of long pulsed plasma discharge. The cryogenic process simulator based on control architecture is available for operation optimization and control design of EAST cryogenic systems to cope with the long pulsed heat loads in the future. supported by National Natural Science Foundation of China (No. 51306195) and Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, CAS (No. CRYO201408)

The research on construction and application of machining process knowledge base

NASA Astrophysics Data System (ADS)

Zhao, Tan; Qiao, Lihong; Qie, Yifan; Guo, Kai

2018-03-01

In order to realize the application of knowledge in machining process design, from the perspective of knowledge in the application of computer aided process planning(CAPP), a hierarchical structure of knowledge classification is established according to the characteristics of mechanical engineering field. The expression of machining process knowledge is structured by means of production rules and the object-oriented methods. Three kinds of knowledge base models are constructed according to the representation of machining process knowledge. In this paper, the definition and classification of machining process knowledge, knowledge model, and the application flow of the process design based on the knowledge base are given, and the main steps of the design decision of the machine tool are carried out as an application by using the knowledge base.

SLS Navigation Model-Based Design Approach

NASA Technical Reports Server (NTRS)

Oliver, T. Emerson; Anzalone, Evan; Geohagan, Kevin; Bernard, Bill; Park, Thomas

2018-01-01

The SLS Program chose to implement a Model-based Design and Model-based Requirements approach for managing component design information and system requirements. This approach differs from previous large-scale design efforts at Marshall Space Flight Center where design documentation alone conveyed information required for vehicle design and analysis and where extensive requirements sets were used to scope and constrain the design. The SLS Navigation Team has been responsible for the Program-controlled Design Math Models (DMMs) which describe and represent the performance of the Inertial Navigation System (INS) and the Rate Gyro Assemblies (RGAs) used by Guidance, Navigation, and Controls (GN&C). The SLS Navigation Team is also responsible for the navigation algorithms. The navigation algorithms are delivered for implementation on the flight hardware as a DMM. For the SLS Block 1-B design, the additional GPS Receiver hardware is managed as a DMM at the vehicle design level. This paper provides a discussion of the processes and methods used to engineer, design, and coordinate engineering trades and performance assessments using SLS practices as applied to the GN&C system, with a particular focus on the Navigation components. These include composing system requirements, requirements verification, model development, model verification and validation, and modeling and analysis approaches. The Model-based Design and Requirements approach does not reduce the effort associated with the design process versus previous processes used at Marshall Space Flight Center. Instead, the approach takes advantage of overlap between the requirements development and management process, and the design and analysis process by efficiently combining the control (i.e. the requirement) and the design mechanisms. The design mechanism is the representation of the component behavior and performance in design and analysis tools. The focus in the early design process shifts from the development and management of design requirements to the development of usable models, model requirements, and model verification and validation efforts. The models themselves are represented in C/C++ code and accompanying data files. Under the idealized process, potential ambiguity in specification is reduced because the model must be implementable versus a requirement which is not necessarily subject to this constraint. Further, the models are shown to emulate the hardware during validation. For models developed by the Navigation Team, a common interface/standalone environment was developed. The common environment allows for easy implementation in design and analysis tools. Mechanisms such as unit test cases ensure implementation as the developer intended. The model verification and validation process provides a very high level of component design insight. The origin and implementation of the SLS variant of Model-based Design is described from the perspective of the SLS Navigation Team. The format of the models and the requirements are described. The Model-based Design approach has many benefits but is not without potential complications. Key lessons learned associated with the implementation of the Model Based Design approach and process from infancy to verification and certification are discussed

Comparing statistical and process-based flow duration curve models in ungauged basins and changing rain regimes

NASA Astrophysics Data System (ADS)

Müller, M. F.; Thompson, S. E.

2016-02-01

The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drivers of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by frequent wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are favored over statistical models.

Cascade process modeling with mechanism-based hierarchical neural networks.

PubMed

Cong, Qiumei; Yu, Wen; Chai, Tianyou

2010-02-01

Cascade process, such as wastewater treatment plant, includes many nonlinear sub-systems and many variables. When the number of sub-systems is big, the input-output relation in the first block and the last block cannot represent the whole process. In this paper we use two techniques to overcome the above problem. Firstly we propose a new neural model: hierarchical neural networks to identify the cascade process; then we use serial structural mechanism model based on the physical equations to connect with neural model. A stable learning algorithm and theoretical analysis are given. Finally, this method is used to model a wastewater treatment plant. Real operational data of wastewater treatment plant is applied to illustrate the modeling approach.

EVALUATING REGIONAL PREDICTIVE CAPACITY OF A PROCESS-BASED MERCURY EXPOSURE MODEL, REGIONAL-MERCURY CYCLING MODEL (R-MCM), APPLIED TO 91 VERMONT AND NEW HAMPSHIRE LAKES AND PONDS, USA

EPA Science Inventory

Regulatory agencies must develop fish consumption advisories for many lakes and rivers with limited resources. Process-based mathematical models are potentially valuable tools for developing regional fish advisories. The Regional Mercury Cycling model (R-MCM) was specifically d...

Automated workflows for modelling chemical fate, kinetics and toxicity.

PubMed

Sala Benito, J V; Paini, Alicia; Richarz, Andrea-Nicole; Meinl, Thorsten; Berthold, Michael R; Cronin, Mark T D; Worth, Andrew P

2017-12-01

Automation is universal in today's society, from operating equipment such as machinery, in factory processes, to self-parking automobile systems. While these examples show the efficiency and effectiveness of automated mechanical processes, automated procedures that support the chemical risk assessment process are still in their infancy. Future human safety assessments will rely increasingly on the use of automated models, such as physiologically based kinetic (PBK) and dynamic models and the virtual cell based assay (VCBA). These biologically-based models will be coupled with chemistry-based prediction models that also automate the generation of key input parameters such as physicochemical properties. The development of automated software tools is an important step in harmonising and expediting the chemical safety assessment process. In this study, we illustrate how the KNIME Analytics Platform can be used to provide a user-friendly graphical interface for these biokinetic models, such as PBK models and VCBA, which simulates the fate of chemicals in vivo within the body and in vitro test systems respectively. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

A Model-based B2B (Batch to Batch) Control for An Industrial Batch Polymerization Process

NASA Astrophysics Data System (ADS)

Ogawa, Morimasa

This paper describes overview of a model-based B2B (batch to batch) control for an industrial batch polymerization process. In order to control the reaction temperature precisely, several methods based on the rigorous process dynamics model are employed at all design stage of the B2B control, such as modeling and parameter estimation of the reaction kinetics which is one of the important part of the process dynamics model. The designed B2B control consists of the gain scheduled I-PD/II2-PD control (I-PD with double integral control), the feed-forward compensation at the batch start time, and the model adaptation utilizing the results of the last batch operation. Throughout the actual batch operations, the B2B control provides superior control performance compared with that of conventional control methods.

A systems-based approach for integrated design of materials, products and design process chains

NASA Astrophysics Data System (ADS)

Panchal, Jitesh H.; Choi, Hae-Jin; Allen, Janet K.; McDowell, David L.; Mistree, Farrokh

2007-12-01

The concurrent design of materials and products provides designers with flexibility to achieve design objectives that were not previously accessible. However, the improved flexibility comes at a cost of increased complexity of the design process chains and the materials simulation models used for executing the design chains. Efforts to reduce the complexity generally result in increased uncertainty. We contend that a systems based approach is essential for managing both the complexity and the uncertainty in design process chains and simulation models in concurrent material and product design. Our approach is based on simplifying the design process chains systematically such that the resulting uncertainty does not significantly affect the overall system performance. Similarly, instead of striving for accurate models for multiscale systems (that are inherently complex), we rely on making design decisions that are robust to uncertainties in the models. Accordingly, we pursue hierarchical modeling in the context of design of multiscale systems. In this paper our focus is on design process chains. We present a systems based approach, premised on the assumption that complex systems can be designed efficiently by managing the complexity of design process chains. The approach relies on (a) the use of reusable interaction patterns to model design process chains, and (b) consideration of design process decisions using value-of-information based metrics. The approach is illustrated using a Multifunctional Energetic Structural Material (MESM) design example. Energetic materials store considerable energy which can be released through shock-induced detonation; conventionally, they are not engineered for strength properties. The design objectives for the MESM in this paper include both sufficient strength and energy release characteristics. The design is carried out by using models at different length and time scales that simulate different aspects of the system. Finally, by applying the method to the MESM design problem, we show that the integrated design of materials and products can be carried out more efficiently by explicitly accounting for design process decisions with the hierarchy of models.

42 CFR § 484.330 - Process for determining and applying the value-based payment adjustment under the Home Health...

Code of Federal Regulations, 2010 CFR

2016-10-01

...-based payment adjustment under the Home Health Value-Based Purchasing (HHVBP) Model. § 484.330 Section... (HHVBP) Model Components for Competing Home Health Agencies Within State Boundaries § 484.330 Process for determining and applying the value-based payment adjustment under the Home Health Value-Based Purchasing...

42 CFR § 484.330 - Process for determining and applying the value-based payment adjustment under the Home Health...

Code of Federal Regulations, 2010 CFR

2017-10-01

...-based payment adjustment under the Home Health Value-Based Purchasing (HHVBP) Model. § 484.330 Section... (HHVBP) Model Components for Competing Home Health Agencies Within State Boundaries § 484.330 Process for determining and applying the value-based payment adjustment under the Home Health Value-Based Purchasing...

Using lab notebooks to examine students' engagement in modeling in an upper-division electronics lab course

NASA Astrophysics Data System (ADS)

Stanley, Jacob T.; Su, Weifeng; Lewandowski, H. J.

2017-12-01

We demonstrate how students' use of modeling can be examined and assessed using student notebooks collected from an upper-division electronics lab course. The use of models is a ubiquitous practice in undergraduate physics education, but the process of constructing, testing, and refining these models is much less common. We focus our attention on a lab course that has been transformed to engage students in this modeling process during lab activities. The design of the lab activities was guided by a framework that captures the different components of model-based reasoning, called the Modeling Framework for Experimental Physics. We demonstrate how this framework can be used to assess students' written work and to identify how students' model-based reasoning differed from activity to activity. Broadly speaking, we were able to identify the different steps of students' model-based reasoning and assess the completeness of their reasoning. Varying degrees of scaffolding present across the activities had an impact on how thoroughly students would engage in the full modeling process, with more scaffolded activities resulting in more thorough engagement with the process. Finally, we identified that the step in the process with which students had the most difficulty was the comparison between their interpreted data and their model prediction. Students did not use sufficiently sophisticated criteria in evaluating such comparisons, which had the effect of halting the modeling process. This may indicate that in order to engage students further in using model-based reasoning during lab activities, the instructor needs to provide further scaffolding for how students make these types of experimental comparisons. This is an important design consideration for other such courses attempting to incorporate modeling as a learning goal.

Self-optimisation and model-based design of experiments for developing a C-H activation flow process.

PubMed

Echtermeyer, Alexander; Amar, Yehia; Zakrzewski, Jacek; Lapkin, Alexei

2017-01-01

A recently described C(sp 3 )-H activation reaction to synthesise aziridines was used as a model reaction to demonstrate the methodology of developing a process model using model-based design of experiments (MBDoE) and self-optimisation approaches in flow. The two approaches are compared in terms of experimental efficiency. The self-optimisation approach required the least number of experiments to reach the specified objectives of cost and product yield, whereas the MBDoE approach enabled a rapid generation of a process model.

Expert models and modeling processes associated with a computer-modeling tool

NASA Astrophysics Data System (ADS)

Zhang, Baohui; Liu, Xiufeng; Krajcik, Joseph S.

2006-07-01

Holding the premise that the development of expertise is a continuous process, this study concerns expert models and modeling processes associated with a modeling tool called Model-It. Five advanced Ph.D. students in environmental engineering and public health used Model-It to create and test models of water quality. Using think aloud technique and video recording, we captured their computer screen modeling activities and thinking processes. We also interviewed them the day following their modeling sessions to further probe the rationale of their modeling practices. We analyzed both the audio-video transcripts and the experts' models. We found the experts' modeling processes followed the linear sequence built in the modeling program with few instances of moving back and forth. They specified their goals up front and spent a long time thinking through an entire model before acting. They specified relationships with accurate and convincing evidence. Factors (i.e., variables) in expert models were clustered, and represented by specialized technical terms. Based on the above findings, we made suggestions for improving model-based science teaching and learning using Model-It.

Unifying Model-Based and Reactive Programming within a Model-Based Executive

NASA Technical Reports Server (NTRS)

Williams, Brian C.; Gupta, Vineet; Norvig, Peter (Technical Monitor)

1999-01-01

Real-time, model-based, deduction has recently emerged as a vital component in AI's tool box for developing highly autonomous reactive systems. Yet one of the current hurdles towards developing model-based reactive systems is the number of methods simultaneously employed, and their corresponding melange of programming and modeling languages. This paper offers an important step towards unification. We introduce RMPL, a rich modeling language that combines probabilistic, constraint-based modeling with reactive programming constructs, while offering a simple semantics in terms of hidden state Markov processes. We introduce probabilistic, hierarchical constraint automata (PHCA), which allow Markov processes to be expressed in a compact representation that preserves the modularity of RMPL programs. Finally, a model-based executive, called Reactive Burton is described that exploits this compact encoding to perform efficIent simulation, belief state update and control sequence generation.

Process-based modeling of temperature and water profiles in the seedling recruitment zone: Part I. Model validation

USDA-ARS?s Scientific Manuscript database

Process-based modeling provides detailed spatial and temporal information of the soil environment in the shallow seedling recruitment zone across field topography where measurements of soil temperature and water may not sufficiently describe the zone. Hourly temperature and water profiles within the...

Basin infilling of a schematic 1D estuary using two different approaches: an aggregate diffusive type model and a processed based model.

NASA Astrophysics Data System (ADS)

Laginha Silva, Patricia; Martins, Flávio A.; Boski, Tomász; Sampath, Dissanayake M. R.

2010-05-01

Fluvial sediment transport creates great challenges for river scientists and engineers. The interaction between the fluid (water) and the solid (dispersed sediment particles) phases is crucial in morphodynamics. The process of sediment transport and the resulting morphological evolution of rivers get more complex with the exposure of the fluvial systems to the natural and variable environment (climatic, geological, ecological and social, etc.). The earlier efforts in mathematical river modelling were almost exclusively built on traditional fluvial hydraulics. The last half century has seen more and more developments and applications of mathematical models for fluvial flow, sediment transport and morphological evolution. The first attempts for a quantitative description and simulation of basin filling in geological time scales started in the late 60´s of the last century (eg. Schwarzacher, 1966; Briggs & Pollack, 1967). However, the quality of this modelling practice has emerged as a crucial issue for concern, which is widely viewed as the key that could unlock the full potential of computational fluvial hydraulics. Most of the models presently used to study fluvial basin filling are of the "diffusion type" (Flemmings and Jordan, 1989). It must be noted that this type of models do not assume that the sediment transport is performed by a physical diffusive process. Rather they are synthetic models based on mass conservation. In the "synthesist" viewpoint (Tipper, 1992; Goldenfeld & Kadanoff, 1999; Werner, 1999 in Paola, 2000) the dynamics of complex systems may occur on many levels (time or space scales) and the dynamics of higher levels may be more or less independent of that at lower levels. In this type of models the low frequency dynamics is controlled by only a few important processes and the high frequency processes are not included. In opposition to this is the "reductionist" viewpoint that states that there is no objective reason to discard high frequency processes. In this viewpoint the system is broken down into its fundamental components and processes and the model is build up by selecting the important processes regardless of its time and space scale. This viewpoint was only possible to pursue in the recent years due to improvement in system knowledge and computer power (Paola, 2000). The primary aim of this paper is to demonstrate that it is possible to simulate the evolution of the sediment river bed, traditionally studied with synthetic models, with a process-based hydrodynamic, sediment transport and morphodynamic model, solving explicitly the mass and momentum conservation equations. With this objective, a comparison between two mathematical models for alluvial rivers is made to simulate the evolution of the sediment river bed of a conceptual 1D embayment for periods in the order of a thousand years: the traditional synthetic basin infilling aggregate diffusive type model based on the diffusion equation (Paola, 2000), used in the "synthesist" viewpoint and the process-based model MOHID (Miranda et al., 2000). The simulation of the sediment river bed evolution achieved by the process-based model MOHID is very similar to those obtained by the diffusive type model, but more complete due to the complexity of the process-based model. In the MOHID results it is possible to observe a more comprehensive and realistic results because this type of model include processes that is impossible to a synthetic model to describe. At last the combined effect of tide, sea level rise and river discharges was investigated in the process based model. These effects cannot be simulated using the diffusive type model. The results demonstrate the feasibility of using process based models to perform studies in scales of 10000 years. This is an advance relative to the use of synthetic models, enabling the use of variable forcing. REFERENCES • Briggs, L.I. and Pollack, H.N., 1967. Digital model of evaporate sedimentation. Science, 155, 453-456. • Flemmings, P.B. and Jordan, T.E., 1989. A synthetic stratigraphic model of foreland basin development. J. Geophys. Res., 94, 3851-3866. • Miranda, R., Braunschweig, F., Leitão, P., Neves, R., Martins, F. & Santos A., 2000. MOHID 2000 - A coastal integrated object oriented model. Proc. Hydraulic Engineering Software VIII, Lisbon, 2000, 393-401, Ed. W.R. Blain & C.A. Brebbia, WITpress. • Paola, C., 2000. Quantitative models of sedimentary basin filing. Sedimentology, 47, 121-178. • Schwarzacher, W., 1966. Sedimentation in a subsiding basin. Nature, 5043, 1349-1350. ACKNOWLEDGMENTS This work was supported by the EVEDUS PTDC/CLI/68488/2006 Research Project

The design of multi-core DSP parallel model based on message passing and multi-level pipeline

NASA Astrophysics Data System (ADS)

Niu, Jingyu; Hu, Jian; He, Wenjing; Meng, Fanrong; Li, Chuanrong

2017-10-01

Currently, the design of embedded signal processing system is often based on a specific application, but this idea is not conducive to the rapid development of signal processing technology. In this paper, a parallel processing model architecture based on multi-core DSP platform is designed, and it is mainly suitable for the complex algorithms which are composed of different modules. This model combines the ideas of multi-level pipeline parallelism and message passing, and summarizes the advantages of the mainstream model of multi-core DSP (the Master-Slave model and the Data Flow model), so that it has better performance. This paper uses three-dimensional image generation algorithm to validate the efficiency of the proposed model by comparing with the effectiveness of the Master-Slave and the Data Flow model.

An overview of current applications, challenges, and future trends in distributed process-based models in hydrology

USGS Publications Warehouse

Fatichi, Simone; Vivoni, Enrique R.; Odgen, Fred L; Ivanov, Valeriy Y; Mirus, Benjamin B.; Gochis, David; Downer, Charles W; Camporese, Matteo; Davison, Jason H; Ebel, Brian A.; Jones, Norm; Kim, Jongho; Mascaro, Giuseppe; Niswonger, Richard G.; Restrepo, Pedro; Rigon, Riccardo; Shen, Chaopeng; Sulis, Mauro; Tarboton, David

2016-01-01

Process-based hydrological models have a long history dating back to the 1960s. Criticized by some as over-parameterized, overly complex, and difficult to use, a more nuanced view is that these tools are necessary in many situations and, in a certain class of problems, they are the most appropriate type of hydrological model. This is especially the case in situations where knowledge of flow paths or distributed state variables and/or preservation of physical constraints is important. Examples of this include: spatiotemporal variability of soil moisture, groundwater flow and runoff generation, sediment and contaminant transport, or when feedbacks among various Earth’s system processes or understanding the impacts of climate non-stationarity are of primary concern. These are situations where process-based models excel and other models are unverifiable. This article presents this pragmatic view in the context of existing literature to justify the approach where applicable and necessary. We review how improvements in data availability, computational resources and algorithms have made detailed hydrological simulations a reality. Avenues for the future of process-based hydrological models are presented suggesting their use as virtual laboratories, for design purposes, and with a powerful treatment of uncertainty.

Representing Micro-Macro Linkages by Actor-Based Dynamic Network Models

ERIC Educational Resources Information Center

Snijders, Tom A. B.; Steglich, Christian E. G.

2015-01-01

Stochastic actor-based models for network dynamics have the primary aim of statistical inference about processes of network change, but may be regarded as a kind of agent-based models. Similar to many other agent-based models, they are based on local rules for actor behavior. Different from many other agent-based models, by including elements of…

Generic Raman-based calibration models enabling real-time monitoring of cell culture bioreactors.

PubMed

Mehdizadeh, Hamidreza; Lauri, David; Karry, Krizia M; Moshgbar, Mojgan; Procopio-Melino, Renee; Drapeau, Denis

2015-01-01

Raman-based multivariate calibration models have been developed for real-time in situ monitoring of multiple process parameters within cell culture bioreactors. Developed models are generic, in the sense that they are applicable to various products, media, and cell lines based on Chinese Hamster Ovarian (CHO) host cells, and are scalable to large pilot and manufacturing scales. Several batches using different CHO-based cell lines and corresponding proprietary media and process conditions have been used to generate calibration datasets, and models have been validated using independent datasets from separate batch runs. All models have been validated to be generic and capable of predicting process parameters with acceptable accuracy. The developed models allow monitoring multiple key bioprocess metabolic variables, and hence can be utilized as an important enabling tool for Quality by Design approaches which are strongly supported by the U.S. Food and Drug Administration. © 2015 American Institute of Chemical Engineers.

Towards a model-based cognitive neuroscience of stopping - a neuroimaging perspective.

PubMed

Sebastian, Alexandra; Forstmann, Birte U; Matzke, Dora

2018-07-01

Our understanding of the neural correlates of response inhibition has greatly advanced over the last decade. Nevertheless the specific function of regions within this stopping network remains controversial. The traditional neuroimaging approach cannot capture many processes affecting stopping performance. Despite the shortcomings of the traditional neuroimaging approach and a great progress in mathematical and computational models of stopping, model-based cognitive neuroscience approaches in human neuroimaging studies are largely lacking. To foster model-based approaches to ultimately gain a deeper understanding of the neural signature of stopping, we outline the most prominent models of response inhibition and recent advances in the field. We highlight how a model-based approach in clinical samples has improved our understanding of altered cognitive functions in these disorders. Moreover, we show how linking evidence-accumulation models and neuroimaging data improves the identification of neural pathways involved in the stopping process and helps to delineate these from neural networks of related but distinct functions. In conclusion, adopting a model-based approach is indispensable to identifying the actual neural processes underlying stopping. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

A simple computational algorithm of model-based choice preference.

PubMed

Toyama, Asako; Katahira, Kentaro; Ohira, Hideki

2017-08-01

A broadly used computational framework posits that two learning systems operate in parallel during the learning of choice preferences-namely, the model-free and model-based reinforcement-learning systems. In this study, we examined another possibility, through which model-free learning is the basic system and model-based information is its modulator. Accordingly, we proposed several modified versions of a temporal-difference learning model to explain the choice-learning process. Using the two-stage decision task developed by Daw, Gershman, Seymour, Dayan, and Dolan (2011), we compared their original computational model, which assumes a parallel learning process, and our proposed models, which assume a sequential learning process. Choice data from 23 participants showed a better fit with the proposed models. More specifically, the proposed eligibility adjustment model, which assumes that the environmental model can weight the degree of the eligibility trace, can explain choices better under both model-free and model-based controls and has a simpler computational algorithm than the original model. In addition, the forgetting learning model and its variation, which assume changes in the values of unchosen actions, substantially improved the fits to the data. Overall, we show that a hybrid computational model best fits the data. The parameters used in this model succeed in capturing individual tendencies with respect to both model use in learning and exploration behavior. This computational model provides novel insights into learning with interacting model-free and model-based components.

Scalable approximate policies for Markov decision process models of hospital elective admissions.

PubMed

Zhu, George; Lizotte, Dan; Hoey, Jesse

2014-05-01

To demonstrate the feasibility of using stochastic simulation methods for the solution of a large-scale Markov decision process model of on-line patient admissions scheduling. The problem of admissions scheduling is modeled as a Markov decision process in which the states represent numbers of patients using each of a number of resources. We investigate current state-of-the-art real time planning methods to compute solutions to this Markov decision process. Due to the complexity of the model, traditional model-based planners are limited in scalability since they require an explicit enumeration of the model dynamics. To overcome this challenge, we apply sample-based planners along with efficient simulation techniques that given an initial start state, generate an action on-demand while avoiding portions of the model that are irrelevant to the start state. We also propose a novel variant of a popular sample-based planner that is particularly well suited to the elective admissions problem. Results show that the stochastic simulation methods allow for the problem size to be scaled by a factor of almost 10 in the action space, and exponentially in the state space. We have demonstrated our approach on a problem with 81 actions, four specialities and four treatment patterns, and shown that we can generate solutions that are near-optimal in about 100s. Sample-based planners are a viable alternative to state-based planners for large Markov decision process models of elective admissions scheduling. Copyright © 2014 Elsevier B.V. All rights reserved.

Computational modeling of residual stress formation during the electron beam melting process for Inconel 718

DOE PAGES

Prabhakar, P.; Sames, William J.; Dehoff, Ryan R.; ...

2015-03-28

Here, a computational modeling approach to simulate residual stress formation during the electron beam melting (EBM) process within the additive manufacturing (AM) technologies for Inconel 718 is presented in this paper. The EBM process has demonstrated a high potential to fabricate components with complex geometries, but the resulting components are influenced by the thermal cycles observed during the manufacturing process. When processing nickel based superalloys, very high temperatures (approx. 1000 °C) are observed in the powder bed, base plate, and build. These high temperatures, when combined with substrate adherence, can result in warping of the base plate and affect themore » final component by causing defects. It is important to have an understanding of the thermo-mechanical response of the entire system, that is, its mechanical behavior towards thermal loading occurring during the EBM process prior to manufacturing a component. Therefore, computational models to predict the response of the system during the EBM process will aid in eliminating the undesired process conditions, a priori, in order to fabricate the optimum component. Such a comprehensive computational modeling approach is demonstrated to analyze warping of the base plate, stress and plastic strain accumulation within the material, and thermal cycles in the system during different stages of the EBM process.« less

Model-Based Reasoning in Humans Becomes Automatic with Training.

PubMed

Economides, Marcos; Kurth-Nelson, Zeb; Lübbert, Annika; Guitart-Masip, Marc; Dolan, Raymond J

2015-09-01

Model-based and model-free reinforcement learning (RL) have been suggested as algorithmic realizations of goal-directed and habitual action strategies. Model-based RL is more flexible than model-free but requires sophisticated calculations using a learnt model of the world. This has led model-based RL to be identified with slow, deliberative processing, and model-free RL with fast, automatic processing. In support of this distinction, it has recently been shown that model-based reasoning is impaired by placing subjects under cognitive load--a hallmark of non-automaticity. Here, using the same task, we show that cognitive load does not impair model-based reasoning if subjects receive prior training on the task. This finding is replicated across two studies and a variety of analysis methods. Thus, task familiarity permits use of model-based reasoning in parallel with other cognitive demands. The ability to deploy model-based reasoning in an automatic, parallelizable fashion has widespread theoretical implications, particularly for the learning and execution of complex behaviors. It also suggests a range of important failure modes in psychiatric disorders.

Model and system learners, optimal process constructors and kinetic theory-based goal-oriented design: A new paradigm in materials and processes informatics

NASA Astrophysics Data System (ADS)

Abisset-Chavanne, Emmanuelle; Duval, Jean Louis; Cueto, Elias; Chinesta, Francisco

2018-05-01

Traditionally, Simulation-Based Engineering Sciences (SBES) has relied on the use of static data inputs (model parameters, initial or boundary conditions, … obtained from adequate experiments) to perform simulations. A new paradigm in the field of Applied Sciences and Engineering has emerged in the last decade. Dynamic Data-Driven Application Systems [9, 10, 11, 12, 22] allow the linkage of simulation tools with measurement devices for real-time control of simulations and applications, entailing the ability to dynamically incorporate additional data into an executing application, and in reverse, the ability of an application to dynamically steer the measurement process. It is in that context that traditional "digital-twins" are giving raise to a new generation of goal-oriented data-driven application systems, also known as "hybrid-twins", embracing models based on physics and models exclusively based on data adequately collected and assimilated for filling the gap between usual model predictions and measurements. Within this framework new methodologies based on model learners, machine learning and kinetic goal-oriented design are defining a new paradigm in materials, processes and systems engineering.

Automated method for the systematic interpretation of resonance peaks in spectrum data

DOEpatents

Damiano, B.; Wood, R.T.

1997-04-22

A method is described for spectral signature interpretation. The method includes the creation of a mathematical model of a system or process. A neural network training set is then developed based upon the mathematical model. The neural network training set is developed by using the mathematical model to generate measurable phenomena of the system or process based upon model input parameter that correspond to the physical condition of the system or process. The neural network training set is then used to adjust internal parameters of a neural network. The physical condition of an actual system or process represented by the mathematical model is then monitored by extracting spectral features from measured spectra of the actual process or system. The spectral features are then input into said neural network to determine the physical condition of the system or process represented by the mathematical model. More specifically, the neural network correlates the spectral features (i.e. measurable phenomena) of the actual process or system with the corresponding model input parameters. The model input parameters relate to specific components of the system or process, and, consequently, correspond to the physical condition of the process or system. 1 fig.

Model-based pH monitor for sensor assessment.

PubMed

van Schagen, Kim; Rietveld, Luuk; Veersma, Alex; Babuska, Robert

2009-01-01

Owing to the nature of the treatment processes, monitoring the processes based on individual online measurements is difficult or even impossible. However, the measurements (online and laboratory) can be combined with a priori process knowledge, using mathematical models, to objectively monitor the treatment processes and measurement devices. The pH measurement is a commonly used measurement at different stages in the drinking water treatment plant, although it is a unreliable instrument, requiring significant maintenance. It is shown that, using a grey-box model, it is possible to assess the measurement devices effectively, even if detailed information of the specific processes is unknown.

Petri net based model of the body iron homeostasis.

PubMed

Formanowicz, Dorota; Sackmann, Andrea; Formanowicz, Piotr; Błazewicz, Jacek

2007-10-01

The body iron homeostasis is a not fully understood complex process. Despite the fact that some components of this process have been described in the literature, the complete model of the whole process has not been proposed. In this paper a Petri net based model of the body iron homeostasis is presented. Recently, Petri nets have been used for describing and analyzing various biological processes since they allow modeling the system under consideration very precisely. The main result presented in the paper is twofold, i.e., an informal description of the main part of the whole iron homeostasis process is described, and then it is also formulated in the formal language of Petri net theory. This model allows for a possible simulation of the process, since Petri net theory provides a lot of established analysis techniques.

Model-based software process improvement

NASA Technical Reports Server (NTRS)

Zettervall, Brenda T.

1994-01-01

The activities of a field test site for the Software Engineering Institute's software process definition project are discussed. Products tested included the improvement model itself, descriptive modeling techniques, the CMM level 2 framework document, and the use of process definition guidelines and templates. The software process improvement model represents a five stage cyclic approach for organizational process improvement. The cycles consist of the initiating, diagnosing, establishing, acting, and leveraging phases.

Gradient-based model calibration with proxy-model assistance

NASA Astrophysics Data System (ADS)

Burrows, Wesley; Doherty, John

2016-02-01

Use of a proxy model in gradient-based calibration and uncertainty analysis of a complex groundwater model with large run times and problematic numerical behaviour is described. The methodology is general, and can be used with models of all types. The proxy model is based on a series of analytical functions that link all model outputs used in the calibration process to all parameters requiring estimation. In enforcing history-matching constraints during the calibration and post-calibration uncertainty analysis processes, the proxy model is run for the purposes of populating the Jacobian matrix, while the original model is run when testing parameter upgrades; the latter process is readily parallelized. Use of a proxy model in this fashion dramatically reduces the computational burden of complex model calibration and uncertainty analysis. At the same time, the effect of model numerical misbehaviour on calculation of local gradients is mitigated, this allowing access to the benefits of gradient-based analysis where lack of integrity in finite-difference derivatives calculation would otherwise have impeded such access. Construction of a proxy model, and its subsequent use in calibration of a complex model, and in analysing the uncertainties of predictions made by that model, is implemented in the PEST suite.

Upscaling from research watersheds: an essential stage of trustworthy general-purpose hydrologic model building

NASA Astrophysics Data System (ADS)

McNamara, J. P.; Semenova, O.; Restrepo, P. J.

2011-12-01

Highly instrumented research watersheds provide excellent opportunities for investigating hydrologic processes. A danger, however, is that the processes observed at a particular research watershed are too specific to the watershed and not representative even of the larger scale watershed that contains that particular research watershed. Thus, models developed based on those partial observations may not be suitable for general hydrologic use. Therefore demonstrating the upscaling of hydrologic process from research watersheds to larger watersheds is essential to validate concepts and test model structure. The Hydrograph model has been developed as a general-purpose process-based hydrologic distributed system. In its applications and further development we evaluate the scaling of model concepts and parameters in a wide range of hydrologic landscapes. All models, either lumped or distributed, are based on a discretization concept. It is common practice that watersheds are discretized into so called hydrologic units or hydrologic landscapes possessing assumed homogeneous hydrologic functioning. If a model structure is fixed, the difference in hydrologic functioning (difference in hydrologic landscapes) should be reflected by a specific set of model parameters. Research watersheds provide the possibility for reasonable detailed combining of processes into some typical hydrologic concept such as hydrologic units, hydrologic forms, and runoff formation complexes in the Hydrograph model. And here by upscaling we imply not the upscaling of a single process but upscaling of such unified hydrologic functioning. The simulation of runoff processes for the Dry Creek research watershed, Idaho, USA (27 km2) was undertaken using the Hydrograph model. The information on the watershed was provided by Boise State University and included a GIS database of watershed characteristics and a detailed hydrometeorological observational dataset. The model provided good simulation results in terms of runoff and variable states of soil and snow over a simulation period 2000 - 2009. The parameters of the model were hand-adjusted based on rational sense, observational data and available understanding of underlying processes. For the first run some processes as riparian vegetation impact on runoff and streamflow/groundwater interaction were handled in a conceptual way. It was shown that the use of Hydrograph model which requires modest amount of parameter calibration may serve also as a quality control for observations. Based on the obtained parameters values and process understanding at the research watershed the model was applied to the larger scale watersheds located in similar environment - the Boise River at South Fork (1660 km2) and Twin Springs (2155 km2). The evaluation of the results of such upscaling will be presented.

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

Simpson, L.

ITN Energy Systems, Inc., and Global Solar Energy, Inc., with the assistance of NREL's PV Manufacturing R&D program, have continued the advancement of CIGS production technology through the development of trajectory-oriented predictive/control models, fault-tolerance control, control-platform development, in-situ sensors, and process improvements. Modeling activities to date include the development of physics-based and empirical models for CIGS and sputter-deposition processing, implementation of model-based control, and application of predictive models to the construction of new evaporation sources and for control. Model-based control is enabled through implementation of reduced or empirical models into a control platform. Reliability improvement activities include implementation of preventivemore » maintenance schedules; detection of failed sensors/equipment and reconfiguration to continue processing; and systematic development of fault prevention and reconfiguration strategies for the full range of CIGS PV production deposition processes. In-situ sensor development activities have resulted in improved control and indicated the potential for enhanced process status monitoring and control of the deposition processes. Substantial process improvements have been made, including significant improvement in CIGS uniformity, thickness control, efficiency, yield, and throughput. In large measure, these gains have been driven by process optimization, which, in turn, have been enabled by control and reliability improvements due to this PV Manufacturing R&D program. This has resulted in substantial improvements of flexible CIGS PV module performance and efficiency.« less

Design Of Computer Based Test Using The Unified Modeling Language

NASA Astrophysics Data System (ADS)

Tedyyana, Agus; Danuri; Lidyawati

2017-12-01

The Admission selection of Politeknik Negeri Bengkalis through interest and talent search (PMDK), Joint Selection of admission test for state Polytechnics (SB-UMPN) and Independent (UM-Polbeng) were conducted by using paper-based Test (PBT). Paper Based Test model has some weaknesses. They are wasting too much paper, the leaking of the questios to the public, and data manipulation of the test result. This reasearch was Aimed to create a Computer-based Test (CBT) models by using Unified Modeling Language (UML) the which consists of Use Case diagrams, Activity diagram and sequence diagrams. During the designing process of the application, it is important to pay attention on the process of giving the password for the test questions before they were shown through encryption and description process. RSA cryptography algorithm was used in this process. Then, the questions shown in the questions banks were randomized by using the Fisher-Yates Shuffle method. The network architecture used in Computer Based test application was a client-server network models and Local Area Network (LAN). The result of the design was the Computer Based Test application for admission to the selection of Politeknik Negeri Bengkalis.

A Multinomial Model of Event-Based Prospective Memory

ERIC Educational Resources Information Center

Smith, Rebekah E.; Bayen, Ute J.

2004-01-01

Prospective memory is remembering to perform an action in the future. The authors introduce the 1st formal model of event-based prospective memory, namely, a multinomial model that includes 2 separate parameters related to prospective memory processes. The 1st measures preparatory attentional processes, and the 2nd measures retrospective memory…

The Use of Modeling-Based Text to Improve Students' Modeling Competencies

ERIC Educational Resources Information Center

Jong, Jing-Ping; Chiu, Mei-Hung; Chung, Shiao-Lan

2015-01-01

This study investigated the effects of a modeling-based text on 10th graders' modeling competencies. Fifteen 10th graders read a researcher-developed modeling-based science text on the ideal gas law that included explicit descriptions and representations of modeling processes (i.e., model selection, model construction, model validation, model…

Identification of Biokinetic Models Using the Concept of Extents.

PubMed

Mašić, Alma; Srinivasan, Sriniketh; Billeter, Julien; Bonvin, Dominique; Villez, Kris

2017-07-05

The development of a wide array of process technologies to enable the shift from conventional biological wastewater treatment processes to resource recovery systems is matched by an increasing demand for predictive capabilities. Mathematical models are excellent tools to meet this demand. However, obtaining reliable and fit-for-purpose models remains a cumbersome task due to the inherent complexity of biological wastewater treatment processes. In this work, we present a first study in the context of environmental biotechnology that adopts and explores the use of extents as a way to simplify and streamline the dynamic process modeling task. In addition, the extent-based modeling strategy is enhanced by optimal accounting for nonlinear algebraic equilibria and nonlinear measurement equations. Finally, a thorough discussion of our results explains the benefits of extent-based modeling and its potential to turn environmental process modeling into a highly automated task.

Knowledge-based modularization and global optimization of artificial neural network models in hydrological forecasting.

PubMed

Corzo, Gerald; Solomatine, Dimitri

2007-05-01

Natural phenomena are multistationary and are composed of a number of interacting processes, so one single model handling all processes often suffers from inaccuracies. A solution is to partition data in relation to such processes using the available domain knowledge or expert judgment, to train separate models for each of the processes, and to merge them in a modular model (committee). In this paper a problem of water flow forecast in watershed hydrology is considered where the flow process can be presented as consisting of two subprocesses -- base flow and excess flow, so that these two processes can be separated. Several approaches to data separation techniques are studied. Two case studies with different forecast horizons are considered. Parameters of the algorithms responsible for data partitioning are optimized using genetic algorithms and global pattern search. It was found that modularization of ANN models using domain knowledge makes models more accurate, if compared with a global model trained on the whole data set, especially when forecast horizon (and hence the complexity of the modelled processes) is increased.

Application of simulation models for the optimization of business processes

NASA Astrophysics Data System (ADS)

Jašek, Roman; Sedláček, Michal; Chramcov, Bronislav; Dvořák, Jiří

2016-06-01

The paper deals with the applications of modeling and simulation tools in the optimization of business processes, especially in solving an optimization of signal flow in security company. As a modeling tool was selected Simul8 software that is used to process modeling based on discrete event simulation and which enables the creation of a visual model of production and distribution processes.

Modeling and managing risk early in software development

NASA Technical Reports Server (NTRS)

Briand, Lionel C.; Thomas, William M.; Hetmanski, Christopher J.

1993-01-01

In order to improve the quality of the software development process, we need to be able to build empirical multivariate models based on data collectable early in the software process. These models need to be both useful for prediction and easy to interpret, so that remedial actions may be taken in order to control and optimize the development process. We present an automated modeling technique which can be used as an alternative to regression techniques. We show how it can be used to facilitate the identification and aid the interpretation of the significant trends which characterize 'high risk' components in several Ada systems. Finally, we evaluate the effectiveness of our technique based on a comparison with logistic regression based models.

Transforming Collaborative Process Models into Interface Process Models by Applying an MDA Approach

NASA Astrophysics Data System (ADS)

Lazarte, Ivanna M.; Chiotti, Omar; Villarreal, Pablo D.

Collaborative business models among enterprises require defining collaborative business processes. Enterprises implement B2B collaborations to execute these processes. In B2B collaborations the integration and interoperability of processes and systems of the enterprises are required to support the execution of collaborative processes. From a collaborative process model, which describes the global view of the enterprise interactions, each enterprise must define the interface process that represents the role it performs in the collaborative process in order to implement the process in a Business Process Management System. Hence, in this work we propose a method for the automatic generation of the interface process model of each enterprise from a collaborative process model. This method is based on a Model-Driven Architecture to transform collaborative process models into interface process models. By applying this method, interface processes are guaranteed to be interoperable and defined according to a collaborative process.

From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining

PubMed Central

Truccolo, Wilson

2017-01-01

This review presents a perspective on capturing collective dynamics in recorded neuronal ensembles based on multivariate point process models, inference of low-dimensional dynamics and coarse graining of spatiotemporal measurements. A general probabilistic framework for continuous time point processes reviewed, with an emphasis on multivariate nonlinear Hawkes processes with exogenous inputs. A point process generalized linear model (PP-GLM) framework for the estimation of discrete time multivariate nonlinear Hawkes processes is described. The approach is illustrated with the modeling of collective dynamics in neocortical neuronal ensembles recorded in human and non-human primates, and prediction of single-neuron spiking. A complementary approach to capture collective dynamics based on low-dimensional dynamics (“order parameters”) inferred via latent state-space models with point process observations is presented. The approach is illustrated by inferring and decoding low-dimensional dynamics in primate motor cortex during naturalistic reach and grasp movements. Finally, we briefly review hypothesis tests based on conditional inference and spatiotemporal coarse graining for assessing collective dynamics in recorded neuronal ensembles. PMID:28336305

From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining.

PubMed

Truccolo, Wilson

2016-11-01

This review presents a perspective on capturing collective dynamics in recorded neuronal ensembles based on multivariate point process models, inference of low-dimensional dynamics and coarse graining of spatiotemporal measurements. A general probabilistic framework for continuous time point processes reviewed, with an emphasis on multivariate nonlinear Hawkes processes with exogenous inputs. A point process generalized linear model (PP-GLM) framework for the estimation of discrete time multivariate nonlinear Hawkes processes is described. The approach is illustrated with the modeling of collective dynamics in neocortical neuronal ensembles recorded in human and non-human primates, and prediction of single-neuron spiking. A complementary approach to capture collective dynamics based on low-dimensional dynamics ("order parameters") inferred via latent state-space models with point process observations is presented. The approach is illustrated by inferring and decoding low-dimensional dynamics in primate motor cortex during naturalistic reach and grasp movements. Finally, we briefly review hypothesis tests based on conditional inference and spatiotemporal coarse graining for assessing collective dynamics in recorded neuronal ensembles. Published by Elsevier Ltd.

Technical Note: Approximate Bayesian parameterization of a complex tropical forest model

NASA Astrophysics Data System (ADS)

Hartig, F.; Dislich, C.; Wiegand, T.; Huth, A.

2013-08-01

Inverse parameter estimation of process-based models is a long-standing problem in ecology and evolution. A key problem of inverse parameter estimation is to define a metric that quantifies how well model predictions fit to the data. Such a metric can be expressed by general cost or objective functions, but statistical inversion approaches are based on a particular metric, the probability of observing the data given the model, known as the likelihood. Deriving likelihoods for dynamic models requires making assumptions about the probability for observations to deviate from mean model predictions. For technical reasons, these assumptions are usually derived without explicit consideration of the processes in the simulation. Only in recent years have new methods become available that allow generating likelihoods directly from stochastic simulations. Previous applications of these approximate Bayesian methods have concentrated on relatively simple models. Here, we report on the application of a simulation-based likelihood approximation for FORMIND, a parameter-rich individual-based model of tropical forest dynamics. We show that approximate Bayesian inference, based on a parametric likelihood approximation placed in a conventional MCMC, performs well in retrieving known parameter values from virtual field data generated by the forest model. We analyze the results of the parameter estimation, examine the sensitivity towards the choice and aggregation of model outputs and observed data (summary statistics), and show results from using this method to fit the FORMIND model to field data from an Ecuadorian tropical forest. Finally, we discuss differences of this approach to Approximate Bayesian Computing (ABC), another commonly used method to generate simulation-based likelihood approximations. Our results demonstrate that simulation-based inference, which offers considerable conceptual advantages over more traditional methods for inverse parameter estimation, can successfully be applied to process-based models of high complexity. The methodology is particularly suited to heterogeneous and complex data structures and can easily be adjusted to other model types, including most stochastic population and individual-based models. Our study therefore provides a blueprint for a fairly general approach to parameter estimation of stochastic process-based models in ecology and evolution.

Model-based verification and validation of the SMAP uplink processes

NASA Astrophysics Data System (ADS)

Khan, M. O.; Dubos, G. F.; Tirona, J.; Standley, S.

Model-Based Systems Engineering (MBSE) is being used increasingly within the spacecraft design community because of its benefits when compared to document-based approaches. As the complexity of projects expands dramatically with continually increasing computational power and technology infusion, the time and effort needed for verification and validation (V& V) increases geometrically. Using simulation to perform design validation with system-level models earlier in the life cycle stands to bridge the gap between design of the system (based on system-level requirements) and verifying those requirements/validating the system as a whole. This case study stands as an example of how a project can validate a system-level design earlier in the project life cycle than traditional V& V processes by using simulation on a system model. Specifically, this paper describes how simulation was added to a system model of the Soil Moisture Active-Passive (SMAP) mission's uplink process. Also discussed are the advantages and disadvantages of the methods employed and the lessons learned; which are intended to benefit future model-based and simulation-based development efforts.

Quality prediction modeling for sintered ores based on mechanism models of sintering and extreme learning machine based error compensation

NASA Astrophysics Data System (ADS)

Tiebin, Wu; Yunlian, Liu; Xinjun, Li; Yi, Yu; Bin, Zhang

2018-06-01

Aiming at the difficulty in quality prediction of sintered ores, a hybrid prediction model is established based on mechanism models of sintering and time-weighted error compensation on the basis of the extreme learning machine (ELM). At first, mechanism models of drum index, total iron, and alkalinity are constructed according to the chemical reaction mechanism and conservation of matter in the sintering process. As the process is simplified in the mechanism models, these models are not able to describe high nonlinearity. Therefore, errors are inevitable. For this reason, the time-weighted ELM based error compensation model is established. Simulation results verify that the hybrid model has a high accuracy and can meet the requirement for industrial applications.

Using Quality Management Methods in Knowledge-Based Organizations. An Approach to the Application of the Taguchi Method to the Process of Pressing Tappets into Anchors

NASA Astrophysics Data System (ADS)

Ţîţu, M. A.; Pop, A. B.; Ţîţu, Ș

2017-06-01

This paper presents a study on the modelling and optimization of certain variables by using the Taguchi Method with a view to modelling and optimizing the process of pressing tappets into anchors, process conducted in an organization that promotes knowledge-based management. The paper promotes practical concepts of the Taguchi Method and describes the way in which the objective functions are obtained and used during the modelling and optimization of the process of pressing tappets into the anchors.

Modeling of 2D diffusion processes based on microscopy data: parameter estimation and practical identifiability analysis.

PubMed

Hock, Sabrina; Hasenauer, Jan; Theis, Fabian J

2013-01-01

Diffusion is a key component of many biological processes such as chemotaxis, developmental differentiation and tissue morphogenesis. Since recently, the spatial gradients caused by diffusion can be assessed in-vitro and in-vivo using microscopy based imaging techniques. The resulting time-series of two dimensional, high-resolutions images in combination with mechanistic models enable the quantitative analysis of the underlying mechanisms. However, such a model-based analysis is still challenging due to measurement noise and sparse observations, which result in uncertainties of the model parameters. We introduce a likelihood function for image-based measurements with log-normal distributed noise. Based upon this likelihood function we formulate the maximum likelihood estimation problem, which is solved using PDE-constrained optimization methods. To assess the uncertainty and practical identifiability of the parameters we introduce profile likelihoods for diffusion processes. As proof of concept, we model certain aspects of the guidance of dendritic cells towards lymphatic vessels, an example for haptotaxis. Using a realistic set of artificial measurement data, we estimate the five kinetic parameters of this model and compute profile likelihoods. Our novel approach for the estimation of model parameters from image data as well as the proposed identifiability analysis approach is widely applicable to diffusion processes. The profile likelihood based method provides more rigorous uncertainty bounds in contrast to local approximation methods.

Through-process modelling of texture and anisotropy in AA5182

NASA Astrophysics Data System (ADS)

Crumbach, M.; Neumann, L.; Goerdeler, M.; Aretz, H.; Gottstein, G.; Kopp, R.

2006-07-01

A through-process texture and anisotropy prediction for AA5182 sheet production from hot rolling through cold rolling and annealing is reported. Thermo-mechanical process data predicted by the finite element method (FEM) package T-Pack based on the software LARSTRAN were fed into a combination of physics based microstructure models for deformation texture (GIA), work hardening (3IVM), nucleation texture (ReNuc), and recrystallization texture (StaRT). The final simulated sheet texture was fed into a FEM simulation of cup drawing employing a new concept of interactively updated texture based yield locus predictions. The modelling results of texture development and anisotropy were compared to experimental data. The applicability to other alloys and processes is discussed.

Research on manufacturing service behavior modeling based on block chain theory

NASA Astrophysics Data System (ADS)

Zhao, Gang; Zhang, Guangli; Liu, Ming; Yu, Shuqin; Liu, Yali; Zhang, Xu

2018-04-01

According to the attribute characteristics of processing craft, the manufacturing service behavior is divided into service attribute, basic attribute, process attribute, resource attribute. The attribute information model of manufacturing service is established. The manufacturing service behavior information is successfully divided into public and private domain. Additionally, the block chain technology is introduced, and the information model of manufacturing service based on block chain principle is established, which solves the problem of sharing and secreting information of processing behavior, and ensures that data is not tampered with. Based on the key pairing verification relationship, the selective publishing mechanism for manufacturing information is established, achieving the traceability of product data, guarantying the quality of processing quality.

Mathematical modeling and characteristic analysis for over-under turbine based combined cycle engine

NASA Astrophysics Data System (ADS)

Ma, Jingxue; Chang, Juntao; Ma, Jicheng; Bao, Wen; Yu, Daren

2018-07-01

The turbine based combined cycle engine has become the most promising hypersonic airbreathing propulsion system for its superiority of ground self-starting, wide flight envelop and reusability. The simulation model of the turbine based combined cycle engine plays an important role in the research of performance analysis and control system design. In this paper, a turbine based combined cycle engine mathematical model is built on the Simulink platform, including a dual-channel air intake system, a turbojet engine and a ramjet. It should be noted that the model of the air intake system is built based on computational fluid dynamics calculation, which provides valuable raw data for modeling of the turbine based combined cycle engine. The aerodynamic characteristics of turbine based combined cycle engine in turbojet mode, ramjet mode and mode transition process are studied by the mathematical model, and the influence of dominant variables on performance and safety of the turbine based combined cycle engine is analyzed. According to the stability requirement of thrust output and the safety in the working process of turbine based combined cycle engine, a control law is proposed that could guarantee the steady output of thrust by controlling the control variables of the turbine based combined cycle engine in the whole working process.

Extending rule-based methods to model molecular geometry and 3D model resolution.

PubMed

Hoard, Brittany; Jacobson, Bruna; Manavi, Kasra; Tapia, Lydia

2016-08-01

Computational modeling is an important tool for the study of complex biochemical processes associated with cell signaling networks. However, it is challenging to simulate processes that involve hundreds of large molecules due to the high computational cost of such simulations. Rule-based modeling is a method that can be used to simulate these processes with reasonably low computational cost, but traditional rule-based modeling approaches do not include details of molecular geometry. The incorporation of geometry into biochemical models can more accurately capture details of these processes, and may lead to insights into how geometry affects the products that form. Furthermore, geometric rule-based modeling can be used to complement other computational methods that explicitly represent molecular geometry in order to quantify binding site accessibility and steric effects. We propose a novel implementation of rule-based modeling that encodes details of molecular geometry into the rules and binding rates. We demonstrate how rules are constructed according to the molecular curvature. We then perform a study of antigen-antibody aggregation using our proposed method. We simulate the binding of antibody complexes to binding regions of the shrimp allergen Pen a 1 using a previously developed 3D rigid-body Monte Carlo simulation, and we analyze the aggregate sizes. Then, using our novel approach, we optimize a rule-based model according to the geometry of the Pen a 1 molecule and the data from the Monte Carlo simulation. We use the distances between the binding regions of Pen a 1 to optimize the rules and binding rates. We perform this procedure for multiple conformations of Pen a 1 and analyze the impact of conformation and resolution on the optimal rule-based model. We find that the optimized rule-based models provide information about the average steric hindrance between binding regions and the probability that antibodies will bind to these regions. These optimized models quantify the variation in aggregate size that results from differences in molecular geometry and from model resolution.

Comparing niche- and process-based models to reduce prediction uncertainty in species range shifts under climate change.

PubMed

Morin, Xavier; Thuiller, Wilfried

2009-05-01

Obtaining reliable predictions of species range shifts under climate change is a crucial challenge for ecologists and stakeholders. At the continental scale, niche-based models have been widely used in the last 10 years to predict the potential impacts of climate change on species distributions all over the world, although these models do not include any mechanistic relationships. In contrast, species-specific, process-based predictions remain scarce at the continental scale. This is regrettable because to secure relevant and accurate predictions it is always desirable to compare predictions derived from different kinds of models applied independently to the same set of species and using the same raw data. Here we compare predictions of range shifts under climate change scenarios for 2100 derived from niche-based models with those of a process-based model for 15 North American boreal and temperate tree species. A general pattern emerged from our comparisons: niche-based models tend to predict a stronger level of extinction and a greater proportion of colonization than the process-based model. This result likely arises because niche-based models do not take phenotypic plasticity and local adaptation into account. Nevertheless, as the two kinds of models rely on different assumptions, their complementarity is revealed by common findings. Both modeling approaches highlight a major potential limitation on species tracking their climatic niche because of migration constraints and identify similar zones where species extirpation is likely. Such convergent predictions from models built on very different principles provide a useful way to offset uncertainties at the continental scale. This study shows that the use in concert of both approaches with their own caveats and advantages is crucial to obtain more robust results and that comparisons among models are needed in the near future to gain accuracy regarding predictions of range shifts under climate change.

Modeling erosion and sedimentation coupled with hydrological and overland flow processes at the watershed scale

NASA Astrophysics Data System (ADS)

Kim, Jongho; Ivanov, Valeriy Y.; Katopodes, Nikolaos D.

2013-09-01

A novel two-dimensional, physically based model of soil erosion and sediment transport coupled to models of hydrological and overland flow processes has been developed. The Hairsine-Rose formulation of erosion and deposition processes is used to account for size-selective sediment transport and differentiate bed material into original and deposited soil layers. The formulation is integrated within the framework of the hydrologic and hydrodynamic model tRIBS-OFM, Triangulated irregular network-based, Real-time Integrated Basin Simulator-Overland Flow Model. The integrated model explicitly couples the hydrodynamic formulation with the advection-dominated transport equations for sediment of multiple particle sizes. To solve the system of equations including both the Saint-Venant and the Hairsine-Rose equations, the finite volume method is employed based on Roe's approximate Riemann solver on an unstructured grid. The formulation yields space-time dynamics of flow, erosion, and sediment transport at fine scale. The integrated model has been successfully verified with analytical solutions and empirical data for two benchmark cases. Sensitivity tests to grid resolution and the number of used particle sizes have been carried out. The model has been validated at the catchment scale for the Lucky Hills watershed located in southeastern Arizona, USA, using 10 events for which catchment-scale streamflow and sediment yield data were available. Since the model is based on physical laws and explicitly uses multiple types of watershed information, satisfactory results were obtained. The spatial output has been analyzed and the driving role of topography in erosion processes has been discussed. It is expected that the integrated formulation of the model has the promise to reduce uncertainties associated with typical parameterizations of flow and erosion processes. A potential for more credible modeling of earth-surface processes is thus anticipated.

Switching and optimizing control for coal flotation process based on a hybrid model

PubMed Central

Dong, Zhiyong; Wang, Ranfeng; Fan, Minqiang; Fu, Xiang

2017-01-01

Flotation is an important part of coal preparation, and the flotation column is widely applied as efficient flotation equipment. This process is complex and affected by many factors, with the froth depth and reagent dosage being two of the most important and frequently manipulated variables. This paper proposes a new method of switching and optimizing control for the coal flotation process. A hybrid model is built and evaluated using industrial data. First, wavelet analysis and principal component analysis (PCA) are applied for signal pre-processing. Second, a control model for optimizing the set point of the froth depth is constructed based on fuzzy control, and a control model is designed to optimize the reagent dosages based on expert system. Finally, the least squares-support vector machine (LS-SVM) is used to identify the operating conditions of the flotation process and to select one of the two models (froth depth or reagent dosage) for subsequent operation according to the condition parameters. The hybrid model is developed and evaluated on an industrial coal flotation column and exhibits satisfactory performance. PMID:29040305

Motivation and Performance within a Collaborative Computer-Based Modeling Task: Relations between Students' Achievement Goal Orientation, Self-Efficacy, Cognitive Processing, and Achievement

ERIC Educational Resources Information Center

Sins, Patrick H. M.; van Joolingen, Wouter R.; Savelsbergh, Elwin R.; van Hout-Wolters, Bernadette

2008-01-01

Purpose of the present study was to test a conceptual model of relations among achievement goal orientation, self-efficacy, cognitive processing, and achievement of students working within a particular collaborative task context. The task involved a collaborative computer-based modeling task. In order to test the model, group measures of…

Model-assisted template extraction SRAF application to contact holes patterns in high-end flash memory device fabrication

NASA Astrophysics Data System (ADS)

Seoud, Ahmed; Kim, Juhwan; Ma, Yuansheng; Jayaram, Srividya; Hong, Le; Chae, Gyu-Yeol; Lee, Jeong-Woo; Park, Dae-Jin; Yune, Hyoung-Soon; Oh, Se-Young; Park, Chan-Ha

2018-03-01

Sub-resolution assist feature (SRAF) insertion techniques have been effectively used for a long time now to increase process latitude in the lithography patterning process. Rule-based SRAF and model-based SRAF are complementary solutions, and each has its own benefits, depending on the objectives of applications and the criticality of the impact on manufacturing yield, efficiency, and productivity. Rule-based SRAF provides superior geometric output consistency and faster runtime performance, but the associated recipe development time can be of concern. Model-based SRAF provides better coverage for more complicated pattern structures in terms of shapes and sizes, with considerably less time required for recipe development, although consistency and performance may be impacted. In this paper, we introduce a new model-assisted template extraction (MATE) SRAF solution, which employs decision tree learning in a model-based solution to provide the benefits of both rule-based and model-based SRAF insertion approaches. The MATE solution is designed to automate the creation of rules/templates for SRAF insertion, and is based on the SRAF placement predicted by model-based solutions. The MATE SRAF recipe provides optimum lithographic quality in relation to various manufacturing aspects in a very short time, compared to traditional methods of rule optimization. Experiments were done using memory device pattern layouts to compare the MATE solution to existing model-based SRAF and pixelated SRAF approaches, based on lithographic process window quality, runtime performance, and geometric output consistency.

Utilizing DMAIC six sigma and evidence-based medicine to streamline diagnosis in chest pain.

PubMed

Kumar, Sameer; Thomas, Kory M

2010-01-01

The purpose of this study was to quantify the difference between the current process flow model for a typical patient workup for chest pain and development of a new process flow model that incorporates DMAIC (define, measure, analyze, improve, control) Six Sigma and evidence-based medicine in a best practices model for diagnosis and treatment. The first stage, DMAIC Six Sigma, is used to highlight areas of variability and unnecessary tests in the current process flow for a patient presenting to the emergency department or physician's clinic with chest pain (also known as angina). The next stage, patient process flow, utilizes DMAIC results in the development of a simulated model that represents real-world variability in the diagnosis and treatment of a patient presenting with angina. The third and final stage is used to analyze the evidence-based output and quantify the factors that drive physician diagnosis accuracy and treatment, as well as review the potential for a broad national evidence-based database. Because of the collective expertise captured within the computer-oriented evidence-based model, the study has introduced an innovative approach to health care delivery by bringing expert-level care to any physician triaging a patient for chest pain anywhere in the world. Similar models can be created for other ailments as well, such as headache, gastrointestinal upset, and back pain. This updated way of looking at diagnosing patients stemming from an evidence-based best practice decision support model may improve workflow processes and cost savings across the health care continuum.

Efficient SRAM yield optimization with mixture surrogate modeling

NASA Astrophysics Data System (ADS)

Zhongjian, Jiang; Zuochang, Ye; Yan, Wang

2016-12-01

Largely repeated cells such as SRAM cells usually require extremely low failure-rate to ensure a moderate chi yield. Though fast Monte Carlo methods such as importance sampling and its variants can be used for yield estimation, they are still very expensive if one needs to perform optimization based on such estimations. Typically the process of yield calculation requires a lot of SPICE simulation. The circuit SPICE simulation analysis accounted for the largest proportion of time in the process yield calculation. In the paper, a new method is proposed to address this issue. The key idea is to establish an efficient mixture surrogate model. The surrogate model is based on the design variables and process variables. This model construction method is based on the SPICE simulation to get a certain amount of sample points, these points are trained for mixture surrogate model by the lasso algorithm. Experimental results show that the proposed model is able to calculate accurate yield successfully and it brings significant speed ups to the calculation of failure rate. Based on the model, we made a further accelerated algorithm to further enhance the speed of the yield calculation. It is suitable for high-dimensional process variables and multi-performance applications.

System Identification of a Heaving Point Absorber: Design of Experiment and Device Modeling

DOE PAGES

Bacelli, Giorgio; Coe, Ryan; Patterson, David; ...

2017-04-01

Empirically based modeling is an essential aspect of design for a wave energy converter. These models are used in structural, mechanical and control design processes, as well as for performance prediction. The design of experiments and methods used to produce models from collected data have a strong impact on the quality of the model. This study considers the system identification and model validation process based on data collected from a wave tank test of a model-scale wave energy converter. Experimental design and data processing techniques based on general system identification procedures are discussed and compared with the practices often followedmore » for wave tank testing. The general system identification processes are shown to have a number of advantages. The experimental data is then used to produce multiple models for the dynamics of the device. These models are validated and their performance is compared against one and other. Furthermore, while most models of wave energy converters use a formulation with wave elevation as an input, this study shows that a model using a hull pressure sensor to incorporate the wave excitation phenomenon has better accuracy.« less

System Identification of a Heaving Point Absorber: Design of Experiment and Device Modeling

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

Bacelli, Giorgio; Coe, Ryan; Patterson, David

Empirically based modeling is an essential aspect of design for a wave energy converter. These models are used in structural, mechanical and control design processes, as well as for performance prediction. The design of experiments and methods used to produce models from collected data have a strong impact on the quality of the model. This study considers the system identification and model validation process based on data collected from a wave tank test of a model-scale wave energy converter. Experimental design and data processing techniques based on general system identification procedures are discussed and compared with the practices often followedmore » for wave tank testing. The general system identification processes are shown to have a number of advantages. The experimental data is then used to produce multiple models for the dynamics of the device. These models are validated and their performance is compared against one and other. Furthermore, while most models of wave energy converters use a formulation with wave elevation as an input, this study shows that a model using a hull pressure sensor to incorporate the wave excitation phenomenon has better accuracy.« less

Modelling surface water-groundwater interaction with a conceptual approach: model development and application in New Zealand

NASA Astrophysics Data System (ADS)

Yang, J.; Zammit, C.; McMillan, H. K.

2016-12-01

As in most countries worldwide, water management in lowland areas is a big concern for New Zealand due to its economic importance for water related human activities. As a result, the estimation of available water resources in these areas (e.g., for irrigation and water supply purpose) is crucial and often requires an understanding of complex hydrological processes, which are often characterized by strong interactions between surface water and groundwater (usually expressed as losing and gaining rivers). These processes are often represented and simulated using integrated physically based hydrological models. However models with physically based groundwater modules typically require large amount of non-readily available geologic and aquifer information and are computationally intensive. Instead, this paper presents a conceptual groundwater model that is fully integrated into New Zealand's national hydrological model TopNet based on TopModel concepts (Beven, 1992). Within this conceptual framework, the integrated model can simulate not only surface processes, but also groundwater processes and surface water-groundwater interaction processes (including groundwater flow, river-groundwater interaction, and groundwater interaction with external watersheds). The developed model was applied to two New Zealand catchments with different hydro-geological and climate characteristics (Pareora catchment in the Canterbury Plains and Grey catchment on the West Coast). Previous studies have documented strong interactions between the river and groundwater, based on the analysis of a large number of concurrent flow measurements and associated information along the river main stem. Application of the integrated hydrological model indicates flow simulation (compared to the original hydrological model conceptualisation) during low flow conditions are significantly improved and further insights on local river dynamics are gained. Due to its conceptual characteristics and low level of data requirement, the integrated model could be used at local and national scales to improve the simulation of hydrological processes in non-topographically driven areas (where groundwater processes are important), and to assess impact of climate change on the integrated hydrological cycle in these areas.

An Ontology-Based Conceptual Model For Accumulating And Reusing Knowledge In A DMAIC Process

NASA Astrophysics Data System (ADS)

Nguyen, ThanhDat; Kifor, Claudiu Vasile

2015-09-01

DMAIC (Define, Measure, Analyze, Improve, and Control) is an important process used to enhance quality of processes basing on knowledge. However, it is difficult to access DMAIC knowledge. Conventional approaches meet a problem arising from structuring and reusing DMAIC knowledge. The main reason is that DMAIC knowledge is not represented and organized systematically. In this article, we overcome the problem basing on a conceptual model that is a combination of DMAIC process, knowledge management, and Ontology engineering. The main idea of our model is to utilizing Ontologies to represent knowledge generated by each of DMAIC phases. We build five different knowledge bases for storing all knowledge of DMAIC phases with the support of necessary tools and appropriate techniques in Information Technology area. Consequently, these knowledge bases provide knowledge available to experts, managers, and web users during or after DMAIC execution in order to share and reuse existing knowledge.

Implicit Schemata and Categories in Memory-Based Language Processing

ERIC Educational Resources Information Center

van den Bosch, Antal; Daelemans, Walter

2013-01-01

Memory-based language processing (MBLP) is an approach to language processing based on exemplar storage during learning and analogical reasoning during processing. From a cognitive perspective, the approach is attractive as a model for human language processing because it does not make any assumptions about the way abstractions are shaped, nor any…

Process modeling for carbon-phenolic nozzle materials

NASA Technical Reports Server (NTRS)

Letson, Mischell A.; Bunker, Robert C.; Remus, Walter M., III; Clinton, R. G.

1989-01-01

A thermochemical model based on the SINDA heat transfer program is developed for carbon-phenolic nozzle material processes. The model can be used to optimize cure cycles and to predict material properties based on the types of materials and the process by which these materials are used to make nozzle components. Chemical kinetic constants for Fiberite MX4926 were determined so that optimization of cure cycles for the current Space Shuttle Solid Rocket Motor nozzle rings can be determined.

Automated method for the systematic interpretation of resonance peaks in spectrum data

DOEpatents

Damiano, Brian; Wood, Richard T.

1997-01-01

A method for spectral signature interpretation. The method includes the creation of a mathematical model of a system or process. A neural network training set is then developed based upon the mathematical model. The neural network training set is developed by using the mathematical model to generate measurable phenomena of the system or process based upon model input parameter that correspond to the physical condition of the system or process. The neural network training set is then used to adjust internal parameters of a neural network. The physical condition of an actual system or process represented by the mathematical model is then monitored by extracting spectral features from measured spectra of the actual process or system. The spectral features are then input into said neural network to determine the physical condition of the system or process represented by the mathematical. More specifically, the neural network correlates the spectral features (i.e. measurable phenomena) of the actual process or system with the corresponding model input parameters. The model input parameters relate to specific components of the system or process, and, consequently, correspond to the physical condition of the process or system.

SEIPS-based process modeling in primary care.

PubMed

Wooldridge, Abigail R; Carayon, Pascale; Hundt, Ann Schoofs; Hoonakker, Peter L T

2017-04-01

Process mapping, often used as part of the human factors and systems engineering approach to improve care delivery and outcomes, should be expanded to represent the complex, interconnected sociotechnical aspects of health care. Here, we propose a new sociotechnical process modeling method to describe and evaluate processes, using the SEIPS model as the conceptual framework. The method produces a process map and supplementary table, which identify work system barriers and facilitators. In this paper, we present a case study applying this method to three primary care processes. We used purposeful sampling to select staff (care managers, providers, nurses, administrators and patient access representatives) from two clinics to observe and interview. We show the proposed method can be used to understand and analyze healthcare processes systematically and identify specific areas of improvement. Future work is needed to assess usability and usefulness of the SEIPS-based process modeling method and further refine it. Copyright © 2016 Elsevier Ltd. All rights reserved.

SEIPS-Based Process Modeling in Primary Care

PubMed Central

Wooldridge, Abigail R.; Carayon, Pascale; Hundt, Ann Schoofs; Hoonakker, Peter

2016-01-01

Process mapping, often used as part of the human factors and systems engineering approach to improve care delivery and outcomes, should be expanded to represent the complex, interconnected sociotechnical aspects of health care. Here, we propose a new sociotechnical process modeling method to describe and evaluate processes, using the SEIPS model as the conceptual framework. The method produces a process map and supplementary table, which identify work system barriers and facilitators. In this paper, we present a case study applying this method to three primary care processes. We used purposeful sampling to select staff (care managers, providers, nurses, administrators and patient access representatives) from two clinics to observe and interview. We show the proposed method can be used to understand and analyze healthcare processes systematically and identify specific areas of improvement. Future work is needed to assess usability and usefulness of the SEIPS-based process modeling method and further refine it. PMID:28166883

A 2-D process-based model for suspended sediment dynamics: A first step towards ecological modeling

USGS Publications Warehouse

Achete, F. M.; van der Wegen, M.; Roelvink, D.; Jaffe, B.

2015-01-01

In estuaries suspended sediment concentration (SSC) is one of the most important contributors to turbidity, which influences habitat conditions and ecological functions of the system. Sediment dynamics differs depending on sediment supply and hydrodynamic forcing conditions that vary over space and over time. A robust sediment transport model is a first step in developing a chain of models enabling simulations of contaminants, phytoplankton and habitat conditions. This works aims to determine turbidity levels in the complex-geometry delta of the San Francisco estuary using a process-based approach (Delft3D Flexible Mesh software). Our approach includes a detailed calibration against measured SSC levels, a sensitivity analysis on model parameters and the determination of a yearly sediment budget as well as an assessment of model results in terms of turbidity levels for a single year, water year (WY) 2011. Model results show that our process-based approach is a valuable tool in assessing sediment dynamics and their related ecological parameters over a range of spatial and temporal scales. The model may act as the base model for a chain of ecological models assessing the impact of climate change and management scenarios. Here we present a modeling approach that, with limited data, produces reliable predictions and can be useful for estuaries without a large amount of processes data.

A 2-D process-based model for suspended sediment dynamics: a first step towards ecological modeling

NASA Astrophysics Data System (ADS)

Achete, F. M.; van der Wegen, M.; Roelvink, D.; Jaffe, B.

2015-06-01

In estuaries suspended sediment concentration (SSC) is one of the most important contributors to turbidity, which influences habitat conditions and ecological functions of the system. Sediment dynamics differs depending on sediment supply and hydrodynamic forcing conditions that vary over space and over time. A robust sediment transport model is a first step in developing a chain of models enabling simulations of contaminants, phytoplankton and habitat conditions. This works aims to determine turbidity levels in the complex-geometry delta of the San Francisco estuary using a process-based approach (Delft3D Flexible Mesh software). Our approach includes a detailed calibration against measured SSC levels, a sensitivity analysis on model parameters and the determination of a yearly sediment budget as well as an assessment of model results in terms of turbidity levels for a single year, water year (WY) 2011. Model results show that our process-based approach is a valuable tool in assessing sediment dynamics and their related ecological parameters over a range of spatial and temporal scales. The model may act as the base model for a chain of ecological models assessing the impact of climate change and management scenarios. Here we present a modeling approach that, with limited data, produces reliable predictions and can be useful for estuaries without a large amount of processes data.

CATS - A process-based model for turbulent turbidite systems at the reservoir scale

NASA Astrophysics Data System (ADS)

Teles, Vanessa; Chauveau, Benoît; Joseph, Philippe; Weill, Pierre; Maktouf, Fakher

2016-09-01

The Cellular Automata for Turbidite systems (CATS) model is intended to simulate the fine architecture and facies distribution of turbidite reservoirs with a multi-event and process-based approach. The main processes of low-density turbulent turbidity flow are modeled: downslope sediment-laden flow, entrainment of ambient water, erosion and deposition of several distinct lithologies. This numerical model, derived from (Salles, 2006; Salles et al., 2007), proposes a new approach based on the Rouse concentration profile to consider the flow capacity to carry the sediment load in suspension. In CATS, the flow distribution on a given topography is modeled with local rules between neighboring cells (cellular automata) based on potential and kinetic energy balance and diffusion concepts. Input parameters are the initial flow parameters and a 3D topography at depositional time. An overview of CATS capabilities in different contexts is presented and discussed.

EVALUATING THE REGIONAL PREDICTIVE CAPACITY OF A PROCESS-BASED MERCURY EXPOSURE MODEL (R-MCM) FOR LAKES ACROSS VERMONT AND NEW HAMPSHIRE, USA

EPA Science Inventory

Regulatory agencies are confronted with a daunting task of developing fish consumption advisories for a large number of lakes and rivers with little resources. A feasible mechanism to develop region-wide fish advisories is by using a process-based mathematical model. One model of...

Evaluating crown fire rate of spread predictions from physics-based models

Treesearch

C. M. Hoffman; J. Ziegler; J. Canfield; R. R. Linn; W. Mell; C. H. Sieg; F. Pimont

2015-01-01

Modeling the behavior of crown fires is challenging due to the complex set of coupled processes that drive the characteristics of a spreading wildfire and the large range of spatial and temporal scales over which these processes occur. Detailed physics-based modeling approaches such as FIRETEC and the Wildland Urban Interface Fire Dynamics Simulator (WFDS) simulate...

Use of high resolution remotely sensed evapotranspiration retrievals for calibration of a process-based hydrologic model in data-poor basins

USDA-ARS?s Scientific Manuscript database

Calibration of process-based hydrologic models is a challenging task in data-poor basins, where monitored hydrologic data are scarce. In this study, we present a novel approach that benefits from remotely sensed evapotranspiration (ET) data to calibrate a complex watershed model, namely the Soil and...

The Process Communication Model: Understanding Ourselves and Others.

ERIC Educational Resources Information Center

Gilbert, Michael

1996-01-01

The Process Communication Model is based on personality types (reactors, persisters, workaholics, dreamers, rebels, and promoters) denoting different sets of behaviors, perceptions, and motivators that influence individual learning and teaching styles. The model is comprehensive and process-oriented, covering interaction styles, communication…

DEVELOPMENT OF CAPE-OPEN COMPLIANT PROCESS MODELING COMPONENTS IN MICROSOFT .NET

EPA Science Inventory

The CAPE-OPEN middleware standards were created to allow process modeling components (PMCs) developed by third parties to be used in any process modeling environment (PME) utilizing these standards. The CAPE-OPEN middleware specifications were based upon both Microsoft's Compone...

The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0)

NASA Astrophysics Data System (ADS)

Caldararu, Silvia; Purves, Drew W.; Smith, Matthew J.

2017-04-01

Improving international food security under a changing climate and increasing human population will be greatly aided by improving our ability to modify, understand and predict crop growth. What we predominantly have at our disposal are either process-based models of crop physiology or statistical analyses of yield datasets, both of which suffer from various sources of error. In this paper, we present a generic process-based crop model (PeakN-crop v1.0) which we parametrise using a Bayesian model-fitting algorithm to three different sources: data-space-based vegetation indices, eddy covariance productivity measurements and regional crop yields. We show that the model parametrised without data, based on prior knowledge of the parameters, can largely capture the observed behaviour but the data-constrained model greatly improves both the model fit and reduces prediction uncertainty. We investigate the extent to which each dataset contributes to the model performance and show that while all data improve on the prior model fit, the satellite-based data and crop yield estimates are particularly important for reducing model error and uncertainty. Despite these improvements, we conclude that there are still significant knowledge gaps, in terms of available data for model parametrisation, but our study can help indicate the necessary data collection to improve our predictions of crop yields and crop responses to environmental changes.

Statistical and engineering methods for model enhancement

NASA Astrophysics Data System (ADS)

Chang, Chia-Jung

Models which describe the performance of physical process are essential for quality prediction, experimental planning, process control and optimization. Engineering models developed based on the underlying physics/mechanics of the process such as analytic models or finite element models are widely used to capture the deterministic trend of the process. However, there usually exists stochastic randomness in the system which may introduce the discrepancy between physics-based model predictions and observations in reality. Alternatively, statistical models can be used to develop models to obtain predictions purely based on the data generated from the process. However, such models tend to perform poorly when predictions are made away from the observed data points. This dissertation contributes to model enhancement research by integrating physics-based model and statistical model to mitigate the individual drawbacks and provide models with better accuracy by combining the strengths of both models. The proposed model enhancement methodologies including the following two streams: (1) data-driven enhancement approach and (2) engineering-driven enhancement approach. Through these efforts, more adequate models are obtained, which leads to better performance in system forecasting, process monitoring and decision optimization. Among different data-driven enhancement approaches, Gaussian Process (GP) model provides a powerful methodology for calibrating a physical model in the presence of model uncertainties. However, if the data contain systematic experimental errors, the GP model can lead to an unnecessarily complex adjustment of the physical model. In Chapter 2, we proposed a novel enhancement procedure, named as “Minimal Adjustment”, which brings the physical model closer to the data by making minimal changes to it. This is achieved by approximating the GP model by a linear regression model and then applying a simultaneous variable selection of the model and experimental bias terms. Two real examples and simulations are presented to demonstrate the advantages of the proposed approach. Different from enhancing the model based on data-driven perspective, an alternative approach is to focus on adjusting the model by incorporating the additional domain or engineering knowledge when available. This often leads to models that are very simple and easy to interpret. The concepts of engineering-driven enhancement are carried out through two applications to demonstrate the proposed methodologies. In the first application where polymer composite quality is focused, nanoparticle dispersion has been identified as a crucial factor affecting the mechanical properties. Transmission Electron Microscopy (TEM) images are commonly used to represent nanoparticle dispersion without further quantifications on its characteristics. In Chapter 3, we developed the engineering-driven nonhomogeneous Poisson random field modeling strategy to characterize nanoparticle dispersion status of nanocomposite polymer, which quantitatively represents the nanomaterial quality presented through image data. The model parameters are estimated through the Bayesian MCMC technique to overcome the challenge of limited amount of accessible data due to the time consuming sampling schemes. The second application is to calibrate the engineering-driven force models of laser-assisted micro milling (LAMM) process statistically, which facilitates a systematic understanding and optimization of targeted processes. In Chapter 4, the force prediction interval has been derived by incorporating the variability in the runout parameters as well as the variability in the measured cutting forces. The experimental results indicate that the model predicts the cutting force profile with good accuracy using a 95% confidence interval. To conclude, this dissertation is the research drawing attention to model enhancement, which has considerable impacts on modeling, design, and optimization of various processes and systems. The fundamental methodologies of model enhancement are developed and further applied to various applications. These research activities developed engineering compliant models for adequate system predictions based on observational data with complex variable relationships and uncertainty, which facilitate process planning, monitoring, and real-time control.

How can model comparison help improving species distribution models?

PubMed

Gritti, Emmanuel Stephan; Gaucherel, Cédric; Crespo-Perez, Maria-Veronica; Chuine, Isabelle

2013-01-01

Today, more than ever, robust projections of potential species range shifts are needed to anticipate and mitigate the impacts of climate change on biodiversity and ecosystem services. Such projections are so far provided almost exclusively by correlative species distribution models (correlative SDMs). However, concerns regarding the reliability of their predictive power are growing and several authors call for the development of process-based SDMs. Still, each of these methods presents strengths and weakness which have to be estimated if they are to be reliably used by decision makers. In this study we compare projections of three different SDMs (STASH, LPJ and PHENOFIT) that lie in the continuum between correlative models and process-based models for the current distribution of three major European tree species, Fagussylvatica L., Quercusrobur L. and Pinussylvestris L. We compare the consistency of the model simulations using an innovative comparison map profile method, integrating local and multi-scale comparisons. The three models simulate relatively accurately the current distribution of the three species. The process-based model performs almost as well as the correlative model, although parameters of the former are not fitted to the observed species distributions. According to our simulations, species range limits are triggered, at the European scale, by establishment and survival through processes primarily related to phenology and resistance to abiotic stress rather than to growth efficiency. The accuracy of projections of the hybrid and process-based model could however be improved by integrating a more realistic representation of the species resistance to water stress for instance, advocating for pursuing efforts to understand and formulate explicitly the impact of climatic conditions and variations on these processes.

How Can Model Comparison Help Improving Species Distribution Models?

PubMed Central

Gritti, Emmanuel Stephan; Gaucherel, Cédric; Crespo-Perez, Maria-Veronica; Chuine, Isabelle

2013-01-01

Today, more than ever, robust projections of potential species range shifts are needed to anticipate and mitigate the impacts of climate change on biodiversity and ecosystem services. Such projections are so far provided almost exclusively by correlative species distribution models (correlative SDMs). However, concerns regarding the reliability of their predictive power are growing and several authors call for the development of process-based SDMs. Still, each of these methods presents strengths and weakness which have to be estimated if they are to be reliably used by decision makers. In this study we compare projections of three different SDMs (STASH, LPJ and PHENOFIT) that lie in the continuum between correlative models and process-based models for the current distribution of three major European tree species, Fagus sylvatica L., Quercus robur L. and Pinus sylvestris L. We compare the consistency of the model simulations using an innovative comparison map profile method, integrating local and multi-scale comparisons. The three models simulate relatively accurately the current distribution of the three species. The process-based model performs almost as well as the correlative model, although parameters of the former are not fitted to the observed species distributions. According to our simulations, species range limits are triggered, at the European scale, by establishment and survival through processes primarily related to phenology and resistance to abiotic stress rather than to growth efficiency. The accuracy of projections of the hybrid and process-based model could however be improved by integrating a more realistic representation of the species resistance to water stress for instance, advocating for pursuing efforts to understand and formulate explicitly the impact of climatic conditions and variations on these processes. PMID:23874779

Modeling Global Biogenic Emission of Isoprene: Exploration of Model Drivers

NASA Technical Reports Server (NTRS)

Alexander, Susan E.; Potter, Christopher S.; Coughlan, Joseph C.; Klooster, Steven A.; Lerdau, Manuel T.; Chatfield, Robert B.; Peterson, David L. (Technical Monitor)

1996-01-01

Vegetation provides the major source of isoprene emission to the atmosphere. We present a modeling approach to estimate global biogenic isoprene emission. The isoprene flux model is linked to a process-based computer simulation model of biogenic trace-gas fluxes that operates on scales that link regional and global data sets and ecosystem nutrient transformations Isoprene emission estimates are determined from estimates of ecosystem specific biomass, emission factors, and algorithms based on light and temperature. Our approach differs from an existing modeling framework by including the process-based global model for terrestrial ecosystem production, satellite derived ecosystem classification, and isoprene emission measurements from a tropical deciduous forest. We explore the sensitivity of model estimates to input parameters. The resulting emission products from the global 1 degree x 1 degree coverage provided by the satellite datasets and the process model allow flux estimations across large spatial scales and enable direct linkage to atmospheric models of trace-gas transport and transformation.

Degradation data analysis based on a generalized Wiener process subject to measurement error

NASA Astrophysics Data System (ADS)

Li, Junxing; Wang, Zhihua; Zhang, Yongbo; Fu, Huimin; Liu, Chengrui; Krishnaswamy, Sridhar

2017-09-01

Wiener processes have received considerable attention in degradation modeling over the last two decades. In this paper, we propose a generalized Wiener process degradation model that takes unit-to-unit variation, time-correlated structure and measurement error into considerations simultaneously. The constructed methodology subsumes a series of models studied in the literature as limiting cases. A simple method is given to determine the transformed time scale forms of the Wiener process degradation model. Then model parameters can be estimated based on a maximum likelihood estimation (MLE) method. The cumulative distribution function (CDF) and the probability distribution function (PDF) of the Wiener process with measurement errors are given based on the concept of the first hitting time (FHT). The percentiles of performance degradation (PD) and failure time distribution (FTD) are also obtained. Finally, a comprehensive simulation study is accomplished to demonstrate the necessity of incorporating measurement errors in the degradation model and the efficiency of the proposed model. Two illustrative real applications involving the degradation of carbon-film resistors and the wear of sliding metal are given. The comparative results show that the constructed approach can derive a reasonable result and an enhanced inference precision.

Integrated Main Propulsion System Performance Reconstruction Process/Models

NASA Technical Reports Server (NTRS)

Lopez, Eduardo; Elliott, Katie; Snell, Steven; Evans, Michael

2013-01-01

The Integrated Main Propulsion System (MPS) Performance Reconstruction process provides the MPS post-flight data files needed for postflight reporting to the project integration management and key customers to verify flight performance. This process/model was used as the baseline for the currently ongoing Space Launch System (SLS) work. The process utilizes several methodologies, including multiple software programs, to model integrated propulsion system performance through space shuttle ascent. It is used to evaluate integrated propulsion systems, including propellant tanks, feed systems, rocket engine, and pressurization systems performance throughout ascent based on flight pressure and temperature data. The latest revision incorporates new methods based on main engine power balance model updates to model higher mixture ratio operation at lower engine power levels.

Model of Values-Based Management Process in Schools: A Mixed Design Study

ERIC Educational Resources Information Center

Dogan, Soner

2016-01-01

The aim of this paper is to evaluate the school administrators' values-based management behaviours according to the teachers' perceptions and opinions and, accordingly, to build a model of values-based management process in schools. The study was conducted using explanatory design which is inclusive of both quantitative and qualitative methods.…

Designing a Web-Based Science Learning Environment for Model-Based Collaborative Inquiry

ERIC Educational Resources Information Center

Sun, Daner; Looi, Chee-Kit

2013-01-01

The paper traces a research process in the design and development of a science learning environment called WiMVT (web-based inquirer with modeling and visualization technology). The WiMVT system is designed to help secondary school students build a sophisticated understanding of scientific conceptions, and the science inquiry process, as well as…

The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism

NASA Astrophysics Data System (ADS)

Clark, Martyn P.; Bierkens, Marc F. P.; Samaniego, Luis; Woods, Ross A.; Uijlenhoet, Remko; Bennett, Katrina E.; Pauwels, Valentijn R. N.; Cai, Xitian; Wood, Andrew W.; Peters-Lidard, Christa D.

2017-07-01

The diversity in hydrologic models has historically led to great controversy on the correct approach to process-based hydrologic modeling, with debates centered on the adequacy of process parameterizations, data limitations and uncertainty, and computational constraints on model analysis. In this paper, we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, provide examples of modeling advances that address these challenges, and define outstanding research needs. We illustrate how modeling advances have been made by groups using models of different type and complexity, and we argue for the need to more effectively use our diversity of modeling approaches in order to advance our collective quest for physically realistic hydrologic models.

The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism

NASA Astrophysics Data System (ADS)

Clark, M. P.; Nijssen, B.; Wood, A.; Mizukami, N.; Newman, A. J.

2017-12-01

The diversity in hydrologic models has historically led to great controversy on the "correct" approach to process-based hydrologic modeling, with debates centered on the adequacy of process parameterizations, data limitations and uncertainty, and computational constraints on model analysis. In this paper, we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, provide examples of modeling advances that address these challenges, and define outstanding research needs. We illustrate how modeling advances have been made by groups using models of different type and complexity, and we argue for the need to more effectively use our diversity of modeling approaches in order to advance our collective quest for physically realistic hydrologic models.

Agent-Based Computing in Distributed Adversarial Planning

DTIC Science & Technology

2010-08-09

plans. An agent is expected to agree to deviate from its optimal uncoordinated plan only if it improves its position. - process models for opponent...Game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2 Improvements ...plan only if it improves its position. – process models for opponent modeling – We have analyzed the suitability of business process models for creating

Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting.

PubMed

Yu, Wenxi; Liu, Yang; Ma, Zongwei; Bi, Jun

2017-08-01

Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM 2.5 is a promising way to fill the areas that are not covered by ground PM 2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM 2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM 2.5 concentrations together with other factors, such as AOD, spatial and non-spatial random effects. We evaluate the results of our model and compare them with those of other, conventional statistical models (GWR and LME) by within-sample model fitting and out-of-sample validation (cross validation, CV). The results show that our model possesses a CV result (R 2  = 0.81) that reflects higher accuracy than that of GWR and LME (0.74 and 0.48, respectively). Our results indicate that Gaussian process models have the potential to improve the accuracy of satellite-based PM 2.5 estimates.

The Monash University Interactive Simple Climate Model

NASA Astrophysics Data System (ADS)

Dommenget, D.

2013-12-01

The Monash university interactive simple climate model is a web-based interface that allows students and the general public to explore the physical simulation of the climate system with a real global climate model. It is based on the Globally Resolved Energy Balance (GREB) model, which is a climate model published by Dommenget and Floeter [2011] in the international peer review science journal Climate Dynamics. The model simulates most of the main physical processes in the climate system in a very simplistic way and therefore allows very fast and simple climate model simulations on a normal PC computer. Despite its simplicity the model simulates the climate response to external forcings, such as doubling of the CO2 concentrations very realistically (similar to state of the art climate models). The Monash simple climate model web-interface allows you to study the results of more than a 2000 different model experiments in an interactive way and it allows you to study a number of tutorials on the interactions of physical processes in the climate system and solve some puzzles. By switching OFF/ON physical processes you can deconstruct the climate and learn how all the different processes interact to generate the observed climate and how the processes interact to generate the IPCC predicted climate change for anthropogenic CO2 increase. The presentation will illustrate how this web-base tool works and what are the possibilities in teaching students with this tool are.

PID-based error signal modeling

NASA Astrophysics Data System (ADS)

Yohannes, Tesfay

1997-10-01

This paper introduces a PID based signal error modeling. The error modeling is based on the betterment process. The resulting iterative learning algorithm is introduced and a detailed proof is provided for both linear and nonlinear systems.

Enhancing Users' Participation in Business Process Modeling through Ontology-Based Training

NASA Astrophysics Data System (ADS)

Macris, A.; Malamateniou, F.; Vassilacopoulos, G.

Successful business process design requires active participation of users who are familiar with organizational activities and business process modelling concepts. Hence, there is a need to provide users with reusable, flexible, agile and adaptable training material in order to enable them instil their knowledge and expertise in business process design and automation activities. Knowledge reusability is of paramount importance in designing training material on process modelling since it enables users participate actively in process design/redesign activities stimulated by the changing business environment. This paper presents a prototype approach for the design and use of training material that provides significant advantages to both the designer (knowledge - content reusability and semantic web enabling) and the user (semantic search, knowledge navigation and knowledge dissemination). The approach is based on externalizing domain knowledge in the form of ontology-based knowledge networks (i.e. training scenarios serving specific training needs) so that it is made reusable.

Design-based research in designing the model for educating simulation facilitators.

PubMed

Koivisto, Jaana-Maija; Hannula, Leena; Bøje, Rikke Buus; Prescott, Stephen; Bland, Andrew; Rekola, Leena; Haho, Päivi

2018-03-01

The purpose of this article is to introduce the concept of design-based research, its appropriateness in creating education-based models, and to describe the process of developing such a model. The model was designed as part of the Nurse Educator Simulation based learning project, funded by the EU's Lifelong Learning program (2013-1-DK1-LEO05-07053). The project partners were VIA University College, Denmark, the University of Huddersfield, UK and Metropolia University of Applied Sciences, Finland. As an outcome of the development process, "the NESTLED model for educating simulation facilitators" (NESTLED model) was generated. This article also illustrates five design principles that could be applied to other pedagogies. Copyright © 2018 Elsevier Ltd. All rights reserved.

Model-Based Reasoning in Upper-division Lab Courses

NASA Astrophysics Data System (ADS)

Lewandowski, Heather

2015-05-01

Modeling, which includes developing, testing, and refining models, is a central activity in physics. Well-known examples from AMO physics include everything from the Bohr model of the hydrogen atom to the Bose-Hubbard model of interacting bosons in a lattice. Modeling, while typically considered a theoretical activity, is most fully represented in the laboratory where measurements of real phenomena intersect with theoretical models, leading to refinement of models and experimental apparatus. However, experimental physicists use models in complex ways and the process is often not made explicit in physics laboratory courses. We have developed a framework to describe the modeling process in physics laboratory activities. The framework attempts to abstract and simplify the complex modeling process undertaken by expert experimentalists. The framework can be applied to understand typical processes such the modeling of the measurement tools, modeling ``black boxes,'' and signal processing. We demonstrate that the framework captures several important features of model-based reasoning in a way that can reveal common student difficulties in the lab and guide the development of curricula that emphasize modeling in the laboratory. We also use the framework to examine troubleshooting in the lab and guide students to effective methods and strategies.

Simulation-based MDP verification for leading-edge masks

NASA Astrophysics Data System (ADS)

Su, Bo; Syrel, Oleg; Pomerantsev, Michael; Hagiwara, Kazuyuki; Pearman, Ryan; Pang, Leo; Fujimara, Aki

2017-07-01

For IC design starts below the 20nm technology node, the assist features on photomasks shrink well below 60nm and the printed patterns of those features on masks written by VSB eBeam writers start to show a large deviation from the mask designs. Traditional geometry-based fracturing starts to show large errors for those small features. As a result, other mask data preparation (MDP) methods have become available and adopted, such as rule-based Mask Process Correction (MPC), model-based MPC and eventually model-based MDP. The new MDP methods may place shot edges slightly differently from target to compensate for mask process effects, so that the final patterns on a mask are much closer to the design (which can be viewed as the ideal mask), especially for those assist features. Such an alteration generally produces better masks that are closer to the intended mask design. Traditional XOR-based MDP verification cannot detect problems caused by eBeam effects. Much like model-based OPC verification which became a necessity for OPC a decade ago, we see the same trend in MDP today. Simulation-based MDP verification solution requires a GPU-accelerated computational geometry engine with simulation capabilities. To have a meaningful simulation-based mask check, a good mask process model is needed. The TrueModel® system is a field tested physical mask model developed by D2S. The GPU-accelerated D2S Computational Design Platform (CDP) is used to run simulation-based mask check, as well as model-based MDP. In addition to simulation-based checks such as mask EPE or dose margin, geometry-based rules are also available to detect quality issues such as slivers or CD splits. Dose margin related hotspots can also be detected by setting a correct detection threshold. In this paper, we will demonstrate GPU-acceleration for geometry processing, and give examples of mask check results and performance data. GPU-acceleration is necessary to make simulation-based mask MDP verification acceptable.

A Java-based fMRI processing pipeline evaluation system for assessment of univariate general linear model and multivariate canonical variate analysis-based pipelines.

PubMed

Zhang, Jing; Liang, Lichen; Anderson, Jon R; Gatewood, Lael; Rottenberg, David A; Strother, Stephen C

2008-01-01

As functional magnetic resonance imaging (fMRI) becomes widely used, the demands for evaluation of fMRI processing pipelines and validation of fMRI analysis results is increasing rapidly. The current NPAIRS package, an IDL-based fMRI processing pipeline evaluation framework, lacks system interoperability and the ability to evaluate general linear model (GLM)-based pipelines using prediction metrics. Thus, it can not fully evaluate fMRI analytical software modules such as FSL.FEAT and NPAIRS.GLM. In order to overcome these limitations, a Java-based fMRI processing pipeline evaluation system was developed. It integrated YALE (a machine learning environment) into Fiswidgets (a fMRI software environment) to obtain system interoperability and applied an algorithm to measure GLM prediction accuracy. The results demonstrated that the system can evaluate fMRI processing pipelines with univariate GLM and multivariate canonical variates analysis (CVA)-based models on real fMRI data based on prediction accuracy (classification accuracy) and statistical parametric image (SPI) reproducibility. In addition, a preliminary study was performed where four fMRI processing pipelines with GLM and CVA modules such as FSL.FEAT and NPAIRS.CVA were evaluated with the system. The results indicated that (1) the system can compare different fMRI processing pipelines with heterogeneous models (NPAIRS.GLM, NPAIRS.CVA and FSL.FEAT) and rank their performance by automatic performance scoring, and (2) the rank of pipeline performance is highly dependent on the preprocessing operations. These results suggest that the system will be of value for the comparison, validation, standardization and optimization of functional neuroimaging software packages and fMRI processing pipelines.

Roi-Orientated Sensor Correction Based on Virtual Steady Reimaging Model for Wide Swath High Resolution Optical Satellite Imagery

NASA Astrophysics Data System (ADS)

Zhu, Y.; Jin, S.; Tian, Y.; Wang, M.

2017-09-01

To meet the requirement of high accuracy and high speed processing for wide swath high resolution optical satellite imagery under emergency situation in both ground processing system and on-board processing system. This paper proposed a ROI-orientated sensor correction algorithm based on virtual steady reimaging model for wide swath high resolution optical satellite imagery. Firstly, the imaging time and spatial window of the ROI is determined by a dynamic search method. Then, the dynamic ROI sensor correction model based on virtual steady reimaging model is constructed. Finally, the corrected image corresponding to the ROI is generated based on the coordinates mapping relationship which is established by the dynamic sensor correction model for corrected image and rigours imaging model for original image. Two experimental results show that the image registration between panchromatic and multispectral images can be well achieved and the image distortion caused by satellite jitter can be also corrected efficiently.

Modelling and control for laser based welding processes: modern methods of process control to improve quality of laser-based joining methods

NASA Astrophysics Data System (ADS)

Zäh, Ralf-Kilian; Mosbach, Benedikt; Hollwich, Jan; Faupel, Benedikt

2017-02-01

To ensure the competitiveness of manufacturing companies it is indispensable to optimize their manufacturing processes. Slight variations of process parameters and machine settings have only marginally effects on the product quality. Therefore, the largest possible editing window is required. Such parameters are, for example, the movement of the laser beam across the component for the laser keyhole welding. That`s why it is necessary to keep the formation of welding seams within specified limits. Therefore, the quality of laser welding processes is ensured, by using post-process methods, like ultrasonic inspection, or special in-process methods. These in-process systems only achieve a simple evaluation which shows whether the weld seam is acceptable or not. Furthermore, in-process systems use no feedback for changing the control variables such as speed of the laser or adjustment of laser power. In this paper the research group presents current results of the research field of Online Monitoring, Online Controlling and Model predictive controlling in laser welding processes to increase the product quality. To record the characteristics of the welding process, tested online methods are used during the process. Based on the measurement data, a state space model is ascertained, which includes all the control variables of the system. Depending on simulation tools the model predictive controller (MPC) is designed for the model and integrated into an NI-Real-Time-System.

Additive Manufacturing of IN100 Superalloy Through Scanning Laser Epitaxy for Turbine Engine Hot-Section Component Repair: Process Development, Modeling, Microstructural Characterization, and Process Control

NASA Astrophysics Data System (ADS)

Acharya, Ranadip; Das, Suman

2015-09-01

This article describes additive manufacturing (AM) of IN100, a high gamma-prime nickel-based superalloy, through scanning laser epitaxy (SLE), aimed at the creation of thick deposits onto like-chemistry substrates for enabling repair of turbine engine hot-section components. SLE is a metal powder bed-based laser AM technology developed for nickel-base superalloys with equiaxed, directionally solidified, and single-crystal microstructural morphologies. Here, we combine process modeling, statistical design-of-experiments (DoE), and microstructural characterization to demonstrate fully metallurgically bonded, crack-free and dense deposits exceeding 1000 μm of SLE-processed IN100 powder onto IN100 cast substrates produced in a single pass. A combined thermal-fluid flow-solidification model of the SLE process compliments DoE-based process development. A customized quantitative metallography technique analyzes digital cross-sectional micrographs and extracts various microstructural parameters, enabling process model validation and process parameter optimization. Microindentation measurements show an increase in the hardness by 10 pct in the deposit region compared to the cast substrate due to microstructural refinement. The results illustrate one of the very few successes reported for the crack-free deposition of IN100, a notoriously "non-weldable" hot-section alloy, thus establishing the potential of SLE as an AM method suitable for hot-section component repair and for future new-make components in high gamma-prime containing crack-prone nickel-based superalloys.

Computer Models of Personality: Implications for Measurement

ERIC Educational Resources Information Center

Cranton, P. A.

1976-01-01

Current research on computer models of personality is reviewed and categorized under five headings: (1) models of belief systems; (2) models of interpersonal behavior; (3) models of decision-making processes; (4) prediction models; and (5) theory-based simulations of specific processes. The use of computer models in personality measurement is…

MIQSTURE: An Experimental Online Language for Army Tactical Intelligence Information Processing

DTIC Science & Technology

1978-07-01

algorithms. The most critical component of an active information processing model for Army tactical intelligence is the user interface, which must be based on...1976)** defined some preliminary notions of an active information model centered around a data base that can introspect about its contents and...34An Introspective Data Base for an Active Information Model." OSI Technical Note N76-017, 17 November 1976 1-4 L4 beyond optimistic expectations and

Is Dysfunctional Use of the Mobile Phone a Behavioural Addiction? Confronting Symptom-Based Versus Process-Based Approaches.

PubMed

Billieux, Joël; Philippot, Pierre; Schmid, Cécile; Maurage, Pierre; De Mol, Jan; Van der Linden, Martial

2015-01-01

Dysfunctional use of the mobile phone has often been conceptualized as a 'behavioural addiction' that shares most features with drug addictions. In the current article, we challenge the clinical utility of the addiction model as applied to mobile phone overuse. We describe the case of a woman who overuses her mobile phone from two distinct approaches: (1) a symptom-based categorical approach inspired from the addiction model of dysfunctional mobile phone use and (2) a process-based approach resulting from an idiosyncratic clinical case conceptualization. In the case depicted here, the addiction model was shown to lead to standardized and non-relevant treatment, whereas the clinical case conceptualization allowed identification of specific psychological processes that can be targeted with specific, empirically based psychological interventions. This finding highlights that conceptualizing excessive behaviours (e.g., gambling and sex) within the addiction model can be a simplification of an individual's psychological functioning, offering only limited clinical relevance. The addiction model, applied to excessive behaviours (e.g., gambling, sex and Internet-related activities) may lead to non-relevant standardized treatments. Clinical case conceptualization allowed identification of specific psychological processes that can be targeted with specific empirically based psychological interventions. The biomedical model might lead to the simplification of an individual's psychological functioning with limited clinical relevance. Copyright © 2014 John Wiley & Sons, Ltd.

Stimulating Scientific Reasoning with Drawing-Based Modeling

ERIC Educational Resources Information Center

Heijnes, Dewi; van Joolingen, Wouter; Leenaars, Frank

2018-01-01

We investigate the way students' reasoning about evolution can be supported by drawing-based modeling. We modified the drawing-based modeling tool SimSketch to allow for modeling evolutionary processes. In three iterations of development and testing, students in lower secondary education worked on creating an evolutionary model. After each…

Learning of Chemical Equilibrium through Modelling-Based Teaching

ERIC Educational Resources Information Center

Maia, Poliana Flavia; Justi, Rosaria

2009-01-01

This paper presents and discusses students' learning process of chemical equilibrium from a modelling-based approach developed from the use of the "Model of Modelling" diagram. The investigation was conducted in a regular classroom (students 14-15 years old) and aimed at discussing how modelling-based teaching can contribute to students…

BioNetSim: a Petri net-based modeling tool for simulations of biochemical processes.

PubMed

Gao, Junhui; Li, Li; Wu, Xiaolin; Wei, Dong-Qing

2012-03-01

BioNetSim, a Petri net-based software for modeling and simulating biochemistry processes, is developed, whose design and implement are presented in this paper, including logic construction, real-time access to KEGG (Kyoto Encyclopedia of Genes and Genomes), and BioModel database. Furthermore, glycolysis is simulated as an example of its application. BioNetSim is a helpful tool for researchers to download data, model biological network, and simulate complicated biochemistry processes. Gene regulatory networks, metabolic pathways, signaling pathways, and kinetics of cell interaction are all available in BioNetSim, which makes modeling more efficient and effective. Similar to other Petri net-based softwares, BioNetSim does well in graphic application and mathematic construction. Moreover, it shows several powerful predominances. (1) It creates models in database. (2) It realizes the real-time access to KEGG and BioModel and transfers data to Petri net. (3) It provides qualitative analysis, such as computation of constants. (4) It generates graphs for tracing the concentration of every molecule during the simulation processes.

MSEE: Stochastic Cognitive Linguistic Behavior Models for Semantic Sensing

DTIC Science & Technology

2013-09-01

recognition, a Gaussian Process Dynamic Model with Social Network Analysis (GPDM-SNA) for a small human group action recognition, an extended GPDM-SNA...44  3.2. Small Human Group Activity Modeling Based on Gaussian Process Dynamic Model and Social Network Analysis (SN-GPDM...51  Approved for public release; distribution unlimited. 3 3.2.3. Gaussian Process Dynamical Model and

Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

DOE PAGES

Tapia, Gustavo; Khairallah, Saad A.; Matthews, Manyalibo J.; ...

2017-09-22

Here, Laser Powder-Bed Fusion (L-PBF) metal-based additive manufacturing (AM) is complex and not fully understood. Successful processing for one material, might not necessarily apply to a different material. This paper describes a workflow process that aims at creating a material data sheet standard that describes regimes where the process can be expected to be robust. The procedure consists of building a Gaussian process-based surrogate model of the L-PBF process that predicts melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination. The predictions are then mapped onto a power versus scan speed diagrammore » delimiting the conduction from the keyhole melting controlled regimes. This statistical framework is shown to be robust even for cases where experimental training data might be suboptimal in quality, if appropriate physics-based filters are applied. Additionally, it is demonstrated that a high-fidelity simulation model of L-PBF can equally be successfully used for building a surrogate model, which is beneficial since simulations are getting more efficient and are more practical to study the response of different materials, than to re-tool an AM machine for new material powder.« less

Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

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

Tapia, Gustavo; Khairallah, Saad A.; Matthews, Manyalibo J.

Here, Laser Powder-Bed Fusion (L-PBF) metal-based additive manufacturing (AM) is complex and not fully understood. Successful processing for one material, might not necessarily apply to a different material. This paper describes a workflow process that aims at creating a material data sheet standard that describes regimes where the process can be expected to be robust. The procedure consists of building a Gaussian process-based surrogate model of the L-PBF process that predicts melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination. The predictions are then mapped onto a power versus scan speed diagrammore » delimiting the conduction from the keyhole melting controlled regimes. This statistical framework is shown to be robust even for cases where experimental training data might be suboptimal in quality, if appropriate physics-based filters are applied. Additionally, it is demonstrated that a high-fidelity simulation model of L-PBF can equally be successfully used for building a surrogate model, which is beneficial since simulations are getting more efficient and are more practical to study the response of different materials, than to re-tool an AM machine for new material powder.« less

Integrating physically based simulators with Event Detection Systems: Multi-site detection approach.

PubMed

Housh, Mashor; Ohar, Ziv

2017-03-01

The Fault Detection (FD) Problem in control theory concerns of monitoring a system to identify when a fault has occurred. Two approaches can be distinguished for the FD: Signal processing based FD and Model-based FD. The former concerns of developing algorithms to directly infer faults from sensors' readings, while the latter uses a simulation model of the real-system to analyze the discrepancy between sensors' readings and expected values from the simulation model. Most contamination Event Detection Systems (EDSs) for water distribution systems have followed the signal processing based FD, which relies on analyzing the signals from monitoring stations independently of each other, rather than evaluating all stations simultaneously within an integrated network. In this study, we show that a model-based EDS which utilizes a physically based water quality and hydraulics simulation models, can outperform the signal processing based EDS. We also show that the model-based EDS can facilitate the development of a Multi-Site EDS (MSEDS), which analyzes the data from all the monitoring stations simultaneously within an integrated network. The advantage of the joint analysis in the MSEDS is expressed by increased detection accuracy (higher true positive alarms and fewer false alarms) and shorter detection time. Copyright © 2016 Elsevier Ltd. All rights reserved.

Analytical and regression models of glass rod drawing process

NASA Astrophysics Data System (ADS)

Alekseeva, L. B.

2018-03-01

The process of drawing glass rods (light guides) is being studied. The parameters of the process affecting the quality of the light guide have been determined. To solve the problem, mathematical models based on general equations of continuum mechanics are used. The conditions for the stable flow of the drawing process have been found, which are determined by the stability of the motion of the glass mass in the formation zone to small uncontrolled perturbations. The sensitivity of the formation zone to perturbations of the drawing speed and viscosity is estimated. Experimental models of the drawing process, based on the regression analysis methods, have been obtained. These models make it possible to customize a specific production process to obtain light guides of the required quality. They allow one to find the optimum combination of process parameters in the chosen area and to determine the required accuracy of maintaining them at a specified level.

Model-Based GN and C Simulation and Flight Software Development for Orion Missions beyond LEO

NASA Technical Reports Server (NTRS)

Odegard, Ryan; Milenkovic, Zoran; Henry, Joel; Buttacoli, Michael

2014-01-01

For Orion missions beyond low Earth orbit (LEO), the Guidance, Navigation, and Control (GN&C) system is being developed using a model-based approach for simulation and flight software. Lessons learned from the development of GN&C algorithms and flight software for the Orion Exploration Flight Test One (EFT-1) vehicle have been applied to the development of further capabilities for Orion GN&C beyond EFT-1. Continuing the use of a Model-Based Development (MBD) approach with the Matlab®/Simulink® tool suite, the process for GN&C development and analysis has been largely improved. Furthermore, a model-based simulation environment in Simulink, rather than an external C-based simulation, greatly eases the process for development of flight algorithms. The benefits seen by employing lessons learned from EFT-1 are described, as well as the approach for implementing additional MBD techniques. Also detailed are the key enablers for improvements to the MBD process, including enhanced configuration management techniques for model-based software systems, automated code and artifact generation, and automated testing and integration.

Modelling Of Flotation Processes By Classical Mathematical Methods - A Review

NASA Astrophysics Data System (ADS)

Jovanović, Ivana; Miljanović, Igor

2015-12-01

Flotation process modelling is not a simple task, mostly because of the process complexity, i.e. the presence of a large number of variables that (to a lesser or a greater extent) affect the final outcome of the mineral particles separation based on the differences in their surface properties. The attempts toward the development of the quantitative predictive model that would fully describe the operation of an industrial flotation plant started in the middle of past century and it lasts to this day. This paper gives a review of published research activities directed toward the development of flotation models based on the classical mathematical rules. The description and systematization of classical flotation models were performed according to the available references, with emphasize exclusively given to the flotation process modelling, regardless of the model application in a certain control system. In accordance with the contemporary considerations, models were classified as the empirical, probabilistic, kinetic and population balance types. Each model type is presented through the aspects of flotation modelling at the macro and micro process levels.

Simulation and sensitivity analysis of carbon storage and fluxes in the New Jersey Pinelands

Treesearch

Zewei Miao; Richard G. Lathrop; Ming Xu; Inga P. La Puma; Kenneth L. Clark; John Hom; Nicholas Skowronski; Steve Van Tuyl

2011-01-01

A major challenge in modeling the carbon dynamics of vegetation communities is the proper parameterization and calibration of eco-physiological variables that are critical determinants of the ecosystem process-based model behavior. In this study, we improved and calibrated a biochemical process-based WxBGC model by using in situ AmeriFlux eddy covariance tower...

Physically based modeling of bedrock incision by abrasion, plucking, and macroabrasion

NASA Astrophysics Data System (ADS)

Chatanantavet, Phairot; Parker, Gary

2009-11-01

Many important insights into the dynamic coupling among climate, erosion, and tectonics in mountain areas have derived from several numerical models of the past few decades which include descriptions of bedrock incision. However, many questions regarding incision processes and morphology of bedrock streams still remain unanswered. A more mechanistically based incision model is needed as a component to study landscape evolution. Major bedrock incision processes include (among other mechanisms) abrasion by bed load, plucking, and macroabrasion (a process of fracturing of the bedrock into pluckable sizes mediated by particle impacts). The purpose of this paper is to develop a physically based model of bedrock incision that includes all three processes mentioned above. To build the model, we start by developing a theory of abrasion, plucking, and macroabrasion mechanisms. We then incorporate hydrology, the evaluation of boundary shear stress, capacity transport, an entrainment relation for pluckable particles, a routing model linking in-stream sediment and hillslopes, a formulation for alluvial channel coverage, a channel width relation, Hack's law, and Exner equation into the model so that we can simulate the evolution of bedrock channels. The model successfully simulates various features of bed elevation profiles of natural bedrock rivers under a variety of input or boundary conditions. The results also illustrate that knickpoints found in bedrock rivers may be autogenic in addition to being driven by base level fall and lithologic changes. This supports the concept that bedrock incision by knickpoint migration may be an integral part of normal incision processes. The model is expected to improve the current understanding of the linkage among physically meaningful input parameters, the physics of incision process, and morphological changes in bedrock streams.

On the upscaling of process-based models in deltaic applications

NASA Astrophysics Data System (ADS)

Li, L.; Storms, J. E. A.; Walstra, D. J. R.

2018-03-01

Process-based numerical models are increasingly used to study the evolution of marine and terrestrial depositional environments. Whilst a detailed description of small-scale processes provides an accurate representation of reality, application on geological timescales is restrained by the associated increase in computational time. In order to reduce the computational time, a number of acceleration methods are combined and evaluated for a schematic supply-driven delta (static base level) and an accommodation-driven delta (variable base level). The performance of the combined acceleration methods is evaluated by comparing the morphological indicators such as distributary channel networking and delta volumes derived from the model predictions for various levels of acceleration. The results of the accelerated models are compared to the outcomes from a series of simulations to capture autogenic variability. Autogenic variability is quantified by re-running identical models on an initial bathymetry with 1 cm added noise. The overall results show that the variability of the accelerated models fall within the autogenic variability range, suggesting that the application of acceleration methods does not significantly affect the simulated delta evolution. The Time-scale compression method (the acceleration method introduced in this paper) results in an increased computational efficiency of 75% without adversely affecting the simulated delta evolution compared to a base case. The combination of the Time-scale compression method with the existing acceleration methods has the potential to extend the application range of process-based models towards geologic timescales.

Optimal filtering and Bayesian detection for friction-based diagnostics in machines.

PubMed

Ray, L R; Townsend, J R; Ramasubramanian, A

2001-01-01

Non-model-based diagnostic methods typically rely on measured signals that must be empirically related to process behavior or incipient faults. The difficulty in interpreting a signal that is indirectly related to the fundamental process behavior is significant. This paper presents an integrated non-model and model-based approach to detecting when process behavior varies from a proposed model. The method, which is based on nonlinear filtering combined with maximum likelihood hypothesis testing, is applicable to dynamic systems whose constitutive model is well known, and whose process inputs are poorly known. Here, the method is applied to friction estimation and diagnosis during motion control in a rotating machine. A nonlinear observer estimates friction torque in a machine from shaft angular position measurements and the known input voltage to the motor. The resulting friction torque estimate can be analyzed directly for statistical abnormalities, or it can be directly compared to friction torque outputs of an applicable friction process model in order to diagnose faults or model variations. Nonlinear estimation of friction torque provides a variable on which to apply diagnostic methods that is directly related to model variations or faults. The method is evaluated experimentally by its ability to detect normal load variations in a closed-loop controlled motor driven inertia with bearing friction and an artificially-induced external line contact. Results show an ability to detect statistically significant changes in friction characteristics induced by normal load variations over a wide range of underlying friction behaviors.

A conceptual prediction model for seasonal drought processes using atmospheric and oceanic standardized anomalies: application to regional drought processes in China

NASA Astrophysics Data System (ADS)

Liu, Zhenchen; Lu, Guihua; He, Hai; Wu, Zhiyong; He, Jian

2018-01-01

Reliable drought prediction is fundamental for water resource managers to develop and implement drought mitigation measures. Considering that drought development is closely related to the spatial-temporal evolution of large-scale circulation patterns, we developed a conceptual prediction model of seasonal drought processes based on atmospheric and oceanic standardized anomalies (SAs). Empirical orthogonal function (EOF) analysis is first applied to drought-related SAs at 200 and 500 hPa geopotential height (HGT) and sea surface temperature (SST). Subsequently, SA-based predictors are built based on the spatial pattern of the first EOF modes. This drought prediction model is essentially the synchronous statistical relationship between 90-day-accumulated atmospheric-oceanic SA-based predictors and SPI3 (3-month standardized precipitation index), calibrated using a simple stepwise regression method. Predictor computation is based on forecast atmospheric-oceanic products retrieved from the NCEP Climate Forecast System Version 2 (CFSv2), indicating the lead time of the model depends on that of CFSv2. The model can make seamless drought predictions for operational use after a year-to-year calibration. Model application to four recent severe regional drought processes in China indicates its good performance in predicting seasonal drought development, despite its weakness in predicting drought severity. Overall, the model can be a worthy reference for seasonal water resource management in China.

An accurate nonlinear stochastic model for MEMS-based inertial sensor error with wavelet networks

NASA Astrophysics Data System (ADS)

El-Diasty, Mohammed; El-Rabbany, Ahmed; Pagiatakis, Spiros

2007-12-01

The integration of Global Positioning System (GPS) with Inertial Navigation System (INS) has been widely used in many applications for positioning and orientation purposes. Traditionally, random walk (RW), Gauss-Markov (GM), and autoregressive (AR) processes have been used to develop the stochastic model in classical Kalman filters. The main disadvantage of classical Kalman filter is the potentially unstable linearization of the nonlinear dynamic system. Consequently, a nonlinear stochastic model is not optimal in derivative-based filters due to the expected linearization error. With a derivativeless-based filter such as the unscented Kalman filter or the divided difference filter, the filtering process of a complicated highly nonlinear dynamic system is possible without linearization error. This paper develops a novel nonlinear stochastic model for inertial sensor error using a wavelet network (WN). A wavelet network is a highly nonlinear model, which has recently been introduced as a powerful tool for modelling and prediction. Static and kinematic data sets are collected using a MEMS-based IMU (DQI-100) to develop the stochastic model in the static mode and then implement it in the kinematic mode. The derivativeless-based filtering method using GM, AR, and the proposed WN-based processes are used to validate the new model. It is shown that the first-order WN-based nonlinear stochastic model gives superior positioning results to the first-order GM and AR models with an overall improvement of 30% when 30 and 60 seconds GPS outages are introduced.

Dominant root locus in state estimator design for material flow processes: A case study of hot strip rolling.

PubMed

Fišer, Jaromír; Zítek, Pavel; Skopec, Pavel; Knobloch, Jan; Vyhlídal, Tomáš

2017-05-01

The purpose of the paper is to achieve a constrained estimation of process state variables using the anisochronic state observer tuned by the dominant root locus technique. The anisochronic state observer is based on the state-space time delay model of the process. Moreover the process model is identified not only as delayed but also as non-linear. This model is developed to describe a material flow process. The root locus technique combined with the magnitude optimum method is utilized to investigate the estimation process. Resulting dominant roots location serves as a measure of estimation process performance. The higher the dominant (natural) frequency in the leftmost position of the complex plane the more enhanced performance with good robustness is achieved. Also the model based observer control methodology for material flow processes is provided by means of the separation principle. For demonstration purposes, the computer-based anisochronic state observer is applied to the strip temperatures estimation in the hot strip finishing mill composed of seven stands. This application was the original motivation to the presented research. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Conceptual Model-Based Systems Biology: Mapping Knowledge and Discovering Gaps in the mRNA Transcription Cycle

PubMed Central

Somekh, Judith; Choder, Mordechai; Dori, Dov

2012-01-01

We propose a Conceptual Model-based Systems Biology framework for qualitative modeling, executing, and eliciting knowledge gaps in molecular biology systems. The framework is an adaptation of Object-Process Methodology (OPM), a graphical and textual executable modeling language. OPM enables concurrent representation of the system's structure—the objects that comprise the system, and behavior—how processes transform objects over time. Applying a top-down approach of recursively zooming into processes, we model a case in point—the mRNA transcription cycle. Starting with this high level cell function, we model increasingly detailed processes along with participating objects. Our modeling approach is capable of modeling molecular processes such as complex formation, localization and trafficking, molecular binding, enzymatic stimulation, and environmental intervention. At the lowest level, similar to the Gene Ontology, all biological processes boil down to three basic molecular functions: catalysis, binding/dissociation, and transporting. During modeling and execution of the mRNA transcription model, we discovered knowledge gaps, which we present and classify into various types. We also show how model execution enhances a coherent model construction. Identification and pinpointing knowledge gaps is an important feature of the framework, as it suggests where research should focus and whether conjectures about uncertain mechanisms fit into the already verified model. PMID:23308089

An assessment of the carbon balance of arctic tundra: comparisons among observations, process models, and atmospheric inversions

USGS Publications Warehouse

McGuire, A.D.; Christensen, T.R.; Hayes, D.; Heroult, A.; Euskirchen, E.; Yi, Y.; Kimball, J.S.; Koven, C.; Lafleur, P.; Miller, P.A.; Oechel, W.; Peylin, P.; Williams, M.

2012-01-01

Although arctic tundra has been estimated to cover only 8% of the global land surface, the large and potentially labile carbon pools currently stored in tundra soils have the potential for large emissions of carbon (C) under a warming climate. These emissions as radiatively active greenhouse gases in the form of both CO2 and CH4 could amplify global warming. Given the potential sensitivity of these ecosystems to climate change and the expectation that the Arctic will experience appreciable warming over the next century, it is important to assess whether responses of C exchange in tundra regions are likely to enhance or mitigate warming. In this study we compared analyses of C exchange of Arctic tundra between 1990–1999 and 2000–2006 among observations, regional and global applications of process-based terrestrial biosphere models, and atmospheric inversion models. Syntheses of the compilation of flux observations and of inversion model results indicate that the annual exchange of CO2 between arctic tundra and the atmosphere has large uncertainties that cannot be distinguished from neutral balance. The mean estimate from an ensemble of process-based model simulations suggests that arctic tundra acted as a sink for atmospheric CO2 in recent decades, but based on the uncertainty estimates it cannot be determined with confidence whether these ecosystems represent a weak or a strong sink. Tundra was 0.6 °C warmer in the 2000s compared to the 1990s. The central estimates of the observations, process-based models, and inversion models each identify stronger sinks in the 2000s compared with the 1990s. Similarly, the observations and the applications of regional process-based models suggest that CH4 emissions from arctic tundra have increased from the 1990s to 2000s. Based on our analyses of the estimates from observations, process-based models, and inversion models, we estimate that arctic tundra was a sink for atmospheric CO2 of 110 Tg C yr-1 (uncertainty between a sink of 291 Tg C yr-1 and a source of 80 Tg C yr-1) and a source of CH4 to the atmosphere of 19 Tg C yr-1 (uncertainty between sources of 8 and 29 Tg C yr-1). The suite of analyses conducted in this study indicate that it is clearly important to reduce uncertainties in the observations, process-based models, and inversions in order to better understand the degree to which Arctic tundra is influencing atmospheric CO2 and CH4 concentrations. The reduction of uncertainties can be accomplished through (1) the strategic placement of more CO2 and CH4 monitoring stations to reduce uncertainties in inversions, (2) improved observation networks of ground-based measurements of CO2 and CH4 exchange to understand exchange in response to disturbance and across gradients of hydrological variability, and (3) the effective transfer of information from enhanced observation networks into process-based models to improve the simulation of CO2 and CH4 exchange from arctic tundra to the atmosphere.

Quantifying the Global Nitrous Oxide Emissions Using a Trait-based Biogeochemistry Model

NASA Astrophysics Data System (ADS)

Zhuang, Q.; Yu, T.

2017-12-01

Nitrogen is an essential element for the global biogeochemical cycle. It is a key nutrient for organisms and N compounds including nitrous oxide significantly influence the global climate. The activities of bacteria and archaea are responsible for the nitrification and denitrification in a wide variety of environments, so microbes play an important role in the nitrogen cycle in soils. To date, most existing process-based models treated nitrification and denitrification as chemical reactions driven by soil physical variables including soil temperature and moisture. In general, the effect of microbes on N cycling has not been modeled in sufficient details. Soil organic carbon also affects the N cycle because it supplies energy to microbes. In my study, a trait-based biogeochemistry model quantifying N2O emissions from the terrestrial ecosystems is developed based on an extant process-based model TEM (Terrestrial Ecosystem Model). Specifically, the improvement to TEM includes: 1) Incorporating the N fixation process to account for the inflow of N from the atmosphere to biosphere; 2) Implementing the effects of microbial dynamics on nitrification process; 3) fully considering the effects of carbon cycling on N nitrogen cycling following the principles of stoichiometry of carbon and nitrogen in soils, plants, and microbes. The difference between simulations with and without the consideration of bacterial activity lies between 5% 25% based on climate conditions and vegetation types. The trait based module allows a more detailed estimation of global N2O emissions.

Improving the Aircraft Design Process Using Web-Based Modeling and Simulation

NASA Technical Reports Server (NTRS)

Reed, John A.; Follen, Gregory J.; Afjeh, Abdollah A.; Follen, Gregory J. (Technical Monitor)

2000-01-01

Designing and developing new aircraft systems is time-consuming and expensive. Computational simulation is a promising means for reducing design cycle times, but requires a flexible software environment capable of integrating advanced multidisciplinary and multifidelity analysis methods, dynamically managing data across heterogeneous computing platforms, and distributing computationally complex tasks. Web-based simulation, with its emphasis on collaborative composition of simulation models, distributed heterogeneous execution, and dynamic multimedia documentation, has the potential to meet these requirements. This paper outlines the current aircraft design process, highlighting its problems and complexities, and presents our vision of an aircraft design process using Web-based modeling and simulation.

Improving the Aircraft Design Process Using Web-based Modeling and Simulation

NASA Technical Reports Server (NTRS)

Reed, John A.; Follen, Gregory J.; Afjeh, Abdollah A.

2003-01-01

Designing and developing new aircraft systems is time-consuming and expensive. Computational simulation is a promising means for reducing design cycle times, but requires a flexible software environment capable of integrating advanced multidisciplinary and muitifidelity analysis methods, dynamically managing data across heterogeneous computing platforms, and distributing computationally complex tasks. Web-based simulation, with its emphasis on collaborative composition of simulation models, distributed heterogeneous execution, and dynamic multimedia documentation, has the potential to meet these requirements. This paper outlines the current aircraft design process, highlighting its problems and complexities, and presents our vision of an aircraft design process using Web-based modeling and simulation.

Chang'E-3 data pre-processing system based on scientific workflow

NASA Astrophysics Data System (ADS)

tan, xu; liu, jianjun; wang, yuanyuan; yan, wei; zhang, xiaoxia; li, chunlai

2016-04-01

The Chang'E-3(CE3) mission have obtained a huge amount of lunar scientific data. Data pre-processing is an important segment of CE3 ground research and application system. With a dramatic increase in the demand of data research and application, Chang'E-3 data pre-processing system(CEDPS) based on scientific workflow is proposed for the purpose of making scientists more flexible and productive by automating data-driven. The system should allow the planning, conduct and control of the data processing procedure with the following possibilities: • describe a data processing task, include:1)define input data/output data, 2)define the data relationship, 3)define the sequence of tasks,4)define the communication between tasks,5)define mathematical formula, 6)define the relationship between task and data. • automatic processing of tasks. Accordingly, Describing a task is the key point whether the system is flexible. We design a workflow designer which is a visual environment for capturing processes as workflows, the three-level model for the workflow designer is discussed:1) The data relationship is established through product tree.2)The process model is constructed based on directed acyclic graph(DAG). Especially, a set of process workflow constructs, including Sequence, Loop, Merge, Fork are compositional one with another.3)To reduce the modeling complexity of the mathematical formulas using DAG, semantic modeling based on MathML is approached. On top of that, we will present how processed the CE3 data with CEDPS.

Process-Based Governance in Public Administrations Using Activity-Based Costing

NASA Astrophysics Data System (ADS)

Becker, Jörg; Bergener, Philipp; Räckers, Michael

Decision- and policy-makers in public administrations currently lack on missing relevant information for sufficient governance. In Germany the introduction of New Public Management and double-entry accounting enable public administrations to get the opportunity to use cost-centered accounting mechanisms to establish new governance mechanisms. Process modelling in this case can be a useful instrument to help the public administrations decision- and policy-makers to structure their activities and capture relevant information. In combination with approaches like Activity-Based Costing, higher management level can be supported with a reasonable data base for fruitful and reasonable governance approaches. Therefore, the aim of this article is combining the public sector domain specific process modelling method PICTURE and concept of activity-based costing for supporting Public Administrations in process-based Governance.

A Multi-layer Dynamic Model for Coordination Based Group Decision Making in Water Resource Allocation and Scheduling

NASA Astrophysics Data System (ADS)

Huang, Wei; Zhang, Xingnan; Li, Chenming; Wang, Jianying

Management of group decision-making is an important issue in water source management development. In order to overcome the defects in lacking of effective communication and cooperation in the existing decision-making models, this paper proposes a multi-layer dynamic model for coordination in water resource allocation and scheduling based group decision making. By introducing the scheme-recognized cooperative satisfaction index and scheme-adjusted rationality index, the proposed model can solve the problem of poor convergence of multi-round decision-making process in water resource allocation and scheduling. Furthermore, the problem about coordination of limited resources-based group decision-making process can be solved based on the effectiveness of distance-based group of conflict resolution. The simulation results show that the proposed model has better convergence than the existing models.

Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion

PubMed Central

Wang, Jie-sheng; Han, Shuang; Shen, Na-na

2014-01-01

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, an echo state network (ESN) based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO) algorithm is proposed. Firstly, the color feature (saturation and brightness) and texture features (angular second moment, sum entropy, inertia moment, etc.) based on grey-level co-occurrence matrix (GLCM) are adopted to describe the visual characteristics of the flotation froth image. Then the kernel principal component analysis (KPCA) method is used to reduce the dimensionality of the high-dimensional input vector composed by the flotation froth image characteristics and process datum and extracts the nonlinear principal components in order to reduce the ESN dimension and network complex. The ESN soft-sensor model of flotation process is optimized by the GSO algorithm with congestion factor. Simulation results show that the model has better generalization and prediction accuracy to meet the online soft-sensor requirements of the real-time control in the flotation process. PMID:24982935

A Team-Based Process for Designing Comprehensive, Integrated, Three-Tiered (CI3T) Models of Prevention: How Does My School-Site Leadership Team Design a CI3T Model?

ERIC Educational Resources Information Center

Lane, Kathleen Lynne; Oakes, Wendy Peia; Jenkins, Abbie; Menzies, Holly Mariah; Kalberg, Jemma Robertson

2014-01-01

Comprehensive, integrated, three-tiered models are context specific and developed by school-site teams according to the core values held by the school community. In this article, the authors provide a step-by-step, team-based process for designing comprehensive, integrated, three-tiered models of prevention that integrate academic, behavioral, and…

Pavement maintenance optimization model using Markov Decision Processes

NASA Astrophysics Data System (ADS)

Mandiartha, P.; Duffield, C. F.; Razelan, I. S. b. M.; Ismail, A. b. H.

2017-09-01

This paper presents an optimization model for selection of pavement maintenance intervention using a theory of Markov Decision Processes (MDP). There are some particular characteristics of the MDP developed in this paper which distinguish it from other similar studies or optimization models intended for pavement maintenance policy development. These unique characteristics include a direct inclusion of constraints into the formulation of MDP, the use of an average cost method of MDP, and the policy development process based on the dual linear programming solution. The limited information or discussions that are available on these matters in terms of stochastic based optimization model in road network management motivates this study. This paper uses a data set acquired from road authorities of state of Victoria, Australia, to test the model and recommends steps in the computation of MDP based stochastic optimization model, leading to the development of optimum pavement maintenance policy.

Alterations in choice behavior by manipulations of world model.

PubMed

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

2010-09-14

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

Alterations in choice behavior by manipulations of world model

PubMed Central

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

2010-01-01

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

Simulating soil moisture change in a semiarid rangeland watershed with a process-based water-balance model

Treesearch

Howard Evan Canfield; Vicente L. Lopes

2000-01-01

A process-based, simulation model for evaporation, soil water and streamflow (BROOK903) was used to estimate soil moisture change on a semiarid rangeland watershed in southeastern Arizona. A sensitivity analysis was performed to select parameters affecting ET and soil moisture for calibration. Automatic parameter calibration was performed using a procedure based on a...

Integrated urban systems model with multiple transportation supply agents.

DOT National Transportation Integrated Search

2012-10-01

This project demonstrates the feasibility of developing quantitative models that can forecast future networks under : current and alternative transportation planning processes. The current transportation planning process is modeled : based on empiric...

Bidirectional optimization of the melting spinning process.

PubMed

Liang, Xiao; Ding, Yongsheng; Wang, Zidong; Hao, Kuangrong; Hone, Kate; Wang, Huaping

2014-02-01

A bidirectional optimizing approach for the melting spinning process based on an immune-enhanced neural network is proposed. The proposed bidirectional model can not only reveal the internal nonlinear relationship between the process configuration and the quality indices of the fibers as final product, but also provide a tool for engineers to develop new fiber products with expected quality specifications. A neural network is taken as the basis for the bidirectional model, and an immune component is introduced to enlarge the searching scope of the solution field so that the neural network has a larger possibility to find the appropriate and reasonable solution, and the error of prediction can therefore be eliminated. The proposed intelligent model can also help to determine what kind of process configuration should be made in order to produce satisfactory fiber products. To make the proposed model practical to the manufacturing, a software platform is developed. Simulation results show that the proposed model can eliminate the approximation error raised by the neural network-based optimizing model, which is due to the extension of focusing scope by the artificial immune mechanism. Meanwhile, the proposed model with the corresponding software can conduct optimization in two directions, namely, the process optimization and category development, and the corresponding results outperform those with an ordinary neural network-based intelligent model. It is also proved that the proposed model has the potential to act as a valuable tool from which the engineers and decision makers of the spinning process could benefit.

Developing Emotion-Based Case Formulations: A Research-Informed Method.

PubMed

Pascual-Leone, Antonio; Kramer, Ueli

2017-01-01

New research-informed methods for case conceptualization that cut across traditional therapy approaches are increasingly popular. This paper presents a trans-theoretical approach to case formulation based on the research observations of emotion. The sequential model of emotional processing (Pascual-Leone & Greenberg, 2007) is a process research model that provides concrete markers for therapists to observe the emerging emotional development of their clients. We illustrate how this model can be used by clinicians to track change and provides a 'clinical map,' by which therapist may orient themselves in-session and plan treatment interventions. Emotional processing offers as a trans-theoretical framework for therapists who wish to conduct emotion-based case formulations. First, we present criteria for why this research model translates well into practice. Second, two contrasting case studies are presented to demonstrate the method. The model bridges research with practice by using client emotion as an axis of integration. Key Practitioner Message Process research on emotion can offer a template for therapists to make case formulations while using a range of treatment approaches. The sequential model of emotional processing provides a 'process map' of concrete markers for therapists to (1) observe the emerging emotional development of their clients, and (2) help therapists develop a treatment plan. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Applying STAMP in Accident Analysis

NASA Technical Reports Server (NTRS)

Leveson, Nancy; Daouk, Mirna; Dulac, Nicolas; Marais, Karen

2003-01-01

Accident models play a critical role in accident investigation and analysis. Most traditional models are based on an underlying chain of events. These models, however, have serious limitations when used for complex, socio-technical systems. Previously, Leveson proposed a new accident model (STAMP) based on system theory. In STAMP, the basic concept is not an event but a constraint. This paper shows how STAMP can be applied to accident analysis using three different views or models of the accident process and proposes a notation for describing this process.

The Difficult Process of Scientific Modelling: An Analysis Of Novices' Reasoning During Computer-Based Modelling

ERIC Educational Resources Information Center

Sins, Patrick H. M.; Savelsbergh, Elwin R.; van Joolingen, Wouter R.

2005-01-01

Although computer modelling is widely advocated as a way to offer students a deeper understanding of complex phenomena, the process of modelling is rather complex itself and needs scaffolding. In order to offer adequate support, a thorough understanding of the reasoning processes students employ and of difficulties they encounter during a…

An overview of TOUGH-based geomechanics models

DOE PAGES

Rutqvist, Jonny

2016-09-22

After the initial development of the first TOUGH-based geomechanics model 15 years ago based on linking TOUGH2 multiphase flow simulator to the FLAC3D geomechanics simulator, at least 15 additional TOUGH-based geomechanics models have appeared in the literature. This development has been fueled by a growing demand and interest for modeling coupled multiphase flow and geomechanical processes related to a number of geoengineering applications, such as in geologic CO 2 sequestration, enhanced geothermal systems, unconventional hydrocarbon production, and most recently, related to reservoir stimulation and injection-induced seismicity. This paper provides a short overview of these TOUGH-based geomechanics models, focusing on somemore » of the most frequently applied to a diverse set of problems associated with geomechanics and its couplings to hydraulic, thermal and chemical processes.« less

Generalized free-space diffuse photon transport model based on the influence analysis of a camera lens diaphragm.

PubMed

Chen, Xueli; Gao, Xinbo; Qu, Xiaochao; Chen, Duofang; Ma, Xiaopeng; Liang, Jimin; Tian, Jie

2010-10-10

The camera lens diaphragm is an important component in a noncontact optical imaging system and has a crucial influence on the images registered on the CCD camera. However, this influence has not been taken into account in the existing free-space photon transport models. To model the photon transport process more accurately, a generalized free-space photon transport model is proposed. It combines Lambertian source theory with analysis of the influence of the camera lens diaphragm to simulate photon transport process in free space. In addition, the radiance theorem is also adopted to establish the energy relationship between the virtual detector and the CCD camera. The accuracy and feasibility of the proposed model is validated with a Monte-Carlo-based free-space photon transport model and physical phantom experiment. A comparison study with our previous hybrid radiosity-radiance theorem based model demonstrates the improvement performance and potential of the proposed model for simulating photon transport process in free space.

The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism

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

Clark, Martyn P.; Bierkens, Marc F. P.; Samaniego, Luis

The diversity in hydrologic models has historically led to great controversy on the correct approach to process-based hydrologic modeling, with debates centered on the adequacy of process parameterizations, data limitations and uncertainty, and computational constraints on model analysis. Here, we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, provide examples of modeling advances that address these challenges, and define outstanding research needs. We also illustrate how modeling advances have been made by groups using models of different type and complexity,more » and we argue for the need to more effectively use our diversity of modeling approaches in order to advance our collective quest for physically realistic hydrologic models.« less

The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism

DOE PAGES

Clark, Martyn P.; Bierkens, Marc F. P.; Samaniego, Luis; ...

2017-07-11

The diversity in hydrologic models has historically led to great controversy on the correct approach to process-based hydrologic modeling, with debates centered on the adequacy of process parameterizations, data limitations and uncertainty, and computational constraints on model analysis. Here, we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, provide examples of modeling advances that address these challenges, and define outstanding research needs. We also illustrate how modeling advances have been made by groups using models of different type and complexity,more » and we argue for the need to more effectively use our diversity of modeling approaches in order to advance our collective quest for physically realistic hydrologic models.« less

Chronic Motivational State Interacts with Task Reward Structure in Dynamic Decision-Making

PubMed Central

Cooper, Jessica A.; Worthy, Darrell A.; Maddox, W. Todd

2015-01-01

Research distinguishes between a habitual, model-free system motivated toward immediately rewarding actions, and a goal-directed, model-based system motivated toward actions that improve future state. We examined the balance of processing in these two systems during state-based decision-making. We tested a regulatory fit hypothesis (Maddox & Markman, 2010) that predicts that global trait motivation affects the balance of habitual- vs. goal-directed processing but only through its interaction with the task framing as gain-maximization or loss-minimization. We found support for the hypothesis that a match between an individual’s chronic motivational state and the task framing enhances goal-directed processing, and thus state-based decision-making. Specifically, chronic promotion-focused individuals under gain-maximization and chronic prevention-focused individuals under loss-minimization both showed enhanced state-based decision-making. Computational modeling indicates that individuals in a match between global chronic motivational state and local task reward structure engaged more goal-directed processing, whereas those in a mismatch engaged more habitual processing. PMID:26520256

Model-Based PAT for Quality Management in Pharmaceuticals Freeze-Drying: State of the Art

PubMed Central

Fissore, Davide

2017-01-01

Model-based process analytical technologies can be used for the in-line control and optimization of a pharmaceuticals freeze-drying process, as well as for the off-line design of the process, i.e., the identification of the optimal operating conditions. This paper aims at presenting the state of the art in this field, focusing, particularly, on three groups of systems, namely, those based on the temperature measurement (i.e., the soft sensor), on the chamber pressure measurement (i.e., the systems based on the test of pressure rise and of pressure decrease), and on the sublimation flux estimate (i.e., the tunable diode laser absorption spectroscopy and the valveless monitoring system). The application of these systems for in-line process optimization (e.g., using a model predictive control algorithm) and to get a true quality by design (e.g., through the off-line calculation of the design space of the process) is presented and discussed. PMID:28224123

Neuro-estimator based GMC control of a batch reactive distillation.

PubMed

Prakash, K J Jithin; Patle, Dipesh S; Jana, Amiya K

2011-07-01

In this paper, an artificial neural network (ANN)-based nonlinear control algorithm is proposed for a simulated batch reactive distillation (RD) column. In the homogeneously catalyzed reactive process, an esterification reaction takes place for the production of ethyl acetate. The fundamental model has been derived incorporating the reaction term in the model structure of the nonreactive distillation process. The process operation is simulated at the startup phase under total reflux conditions. The open-loop process dynamics is also addressed running the batch process at the production phase under partial reflux conditions. In this study, a neuro-estimator based generic model controller (GMC), which consists of an ANN-based state predictor and the GMC law, has been synthesized. Finally, this proposed control law has been tested on the representative batch reactive distillation comparing with a gain-scheduled proportional integral (GSPI) controller and with its ideal performance (ideal GMC). Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Towards a voxel-based geographic automata for the simulation of geospatial processes

NASA Astrophysics Data System (ADS)

Jjumba, Anthony; Dragićević, Suzana

2016-07-01

Many geographic processes evolve in a three dimensional space and time continuum. However, when they are represented with the aid of geographic information systems (GIS) or geosimulation models they are modelled in a framework of two-dimensional space with an added temporal component. The objective of this study is to propose the design and implementation of voxel-based automata as a methodological approach for representing spatial processes evolving in the four-dimensional (4D) space-time domain. Similar to geographic automata models which are developed to capture and forecast geospatial processes that change in a two-dimensional spatial framework using cells (raster geospatial data), voxel automata rely on the automata theory and use three-dimensional volumetric units (voxels). Transition rules have been developed to represent various spatial processes which range from the movement of an object in 3D to the diffusion of airborne particles and landslide simulation. In addition, the proposed 4D models demonstrate that complex processes can be readily reproduced from simple transition functions without complex methodological approaches. The voxel-based automata approach provides a unique basis to model geospatial processes in 4D for the purpose of improving representation, analysis and understanding their spatiotemporal dynamics. This study contributes to the advancement of the concepts and framework of 4D GIS.

Fuzzy control of burnout of multilayer ceramic actuators

NASA Astrophysics Data System (ADS)

Ling, Alice V.; Voss, David; Christodoulou, Leo

1996-08-01

To improve the yield and repeatability of the burnout process of multilayer ceramic actuators (MCAs), an intelligent processing of materials (IPM-based) control system has been developed for the manufacture of MCAs. IPM involves the active (ultimately adaptive) control of a material process using empirical or analytical models and in situ sensing of critical process states (part features and process parameters) to modify the processing conditions in real time to achieve predefined product goals. Thus, the three enabling technologies for the IPM burnout control system are process modeling, in situ sensing and intelligent control. This paper presents the design of an IPM-based control strategy for the burnout process of MCAs.

Stochastic or statistic? Comparing flow duration curve models in ungauged basins and changing climates

NASA Astrophysics Data System (ADS)

Müller, M. F.; Thompson, S. E.

2015-09-01

The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash-Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drives of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by a strong wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are strongly favored over statistical models.

Model-Based Thermal System Design Optimization for the James Webb Space Telescope

NASA Technical Reports Server (NTRS)

Cataldo, Giuseppe; Niedner, Malcolm B.; Fixsen, Dale J.; Moseley, Samuel H.

2017-01-01

Spacecraft thermal model validation is normally performed by comparing model predictions with thermal test data and reducing their discrepancies to meet the mission requirements. Based on thermal engineering expertise, the model input parameters are adjusted to tune the model output response to the test data. The end result is not guaranteed to be the best solution in terms of reduced discrepancy and the process requires months to complete. A model-based methodology was developed to perform the validation process in a fully automated fashion and provide mathematical bases to the search for the optimal parameter set that minimizes the discrepancies between model and data. The methodology was successfully applied to several thermal subsystems of the James Webb Space Telescope (JWST). Global or quasiglobal optimal solutions were found and the total execution time of the model validation process was reduced to about two weeks. The model sensitivities to the parameters, which are required to solve the optimization problem, can be calculated automatically before the test begins and provide a library for sensitivity studies. This methodology represents a crucial commodity when testing complex, large-scale systems under time and budget constraints. Here, results for the JWST Core thermal system will be presented in detail.

Model-based thermal system design optimization for the James Webb Space Telescope

NASA Astrophysics Data System (ADS)

Cataldo, Giuseppe; Niedner, Malcolm B.; Fixsen, Dale J.; Moseley, Samuel H.

2017-10-01

Spacecraft thermal model validation is normally performed by comparing model predictions with thermal test data and reducing their discrepancies to meet the mission requirements. Based on thermal engineering expertise, the model input parameters are adjusted to tune the model output response to the test data. The end result is not guaranteed to be the best solution in terms of reduced discrepancy and the process requires months to complete. A model-based methodology was developed to perform the validation process in a fully automated fashion and provide mathematical bases to the search for the optimal parameter set that minimizes the discrepancies between model and data. The methodology was successfully applied to several thermal subsystems of the James Webb Space Telescope (JWST). Global or quasiglobal optimal solutions were found and the total execution time of the model validation process was reduced to about two weeks. The model sensitivities to the parameters, which are required to solve the optimization problem, can be calculated automatically before the test begins and provide a library for sensitivity studies. This methodology represents a crucial commodity when testing complex, large-scale systems under time and budget constraints. Here, results for the JWST Core thermal system will be presented in detail.

A process-based emission model for volatile organic compounds from silage sources on farms

USDA-ARS?s Scientific Manuscript database

Silage on dairy farms can emit large amounts of volatile organic compounds (VOCs), a precursor in the formation of tropospheric ozone. Because of the challenges associated with direct measurements, process-based modeling is another approach for estimating emissions of air pollutants from sources suc...

Generating structure from experience: A retrieval-based model of language processing.

PubMed

Johns, Brendan T; Jones, Michael N

2015-09-01

Standard theories of language generally assume that some abstraction of linguistic input is necessary to create higher level representations of linguistic structures (e.g., a grammar). However, the importance of individual experiences with language has recently been emphasized by both usage-based theories (Tomasello, 2003) and grounded and situated theories (e.g., Zwaan & Madden, 2005). Following the usage-based approach, we present a formal exemplar model that stores instances of sentences across a natural language corpus, applying recent advances from models of semantic memory. In this model, an exemplar memory is used to generate expectations about the future structure of sentences, using a mechanism for prediction in language processing (Altmann & Mirković, 2009). The model successfully captures a broad range of behavioral effects-reduced relative clause processing (Reali & Christiansen, 2007), the role of contextual constraint (Rayner & Well, 1996), and event knowledge activation (Ferretti, Kutas, & McRae, 2007), among others. We further demonstrate how perceptual knowledge could be integrated into this exemplar-based framework, with the goal of grounding language processing in perception. Finally, we illustrate how an exemplar memory system could have been used in the cultural evolution of language. The model provides evidence that an impressive amount of language processing may be bottom-up in nature, built on the storage and retrieval of individual linguistic experiences. (c) 2015 APA, all rights reserved).

Combining the Old and the New: Designing a Curriculum Based on the Taba Model and the Global Scale of English

ERIC Educational Resources Information Center

Aydin, Belgin; Unver, Meral Melek; Alan, Bülent; Saglam, Sercan

2017-01-01

This paper explains the process of designing a curriculum based on the Taba Model and the Global Scale of English (GSE) in an intensive language education program. The Taba Model emphasizing the involvement of the teachers and the learners in the curriculum development process was combined with the GSE, a psychometric tool measuring language…

Observer-based perturbation extremum seeking control with input constraints for direct-contact membrane distillation process

NASA Astrophysics Data System (ADS)

Eleiwi, Fadi; Laleg-Kirati, Taous Meriem

2018-06-01

An observer-based perturbation extremum seeking control is proposed for a direct-contact membrane distillation (DCMD) process. The process is described with a dynamic model that is based on a 2D advection-diffusion equation model which has pump flow rates as process inputs. The objective of the controller is to optimise the trade-off between the permeate mass flux and the energy consumption by the pumps inside the process. Cases of single and multiple control inputs are considered through the use of only the feed pump flow rate or both the feed and the permeate pump flow rates. A nonlinear Lyapunov-based observer is designed to provide an estimation for the temperature distribution all over the designated domain of the DCMD process. Moreover, control inputs are constrained with an anti-windup technique to be within feasible and physical ranges. Performance of the proposed structure is analysed, and simulations based on real DCMD process parameters for each control input are provided.

Transactions in domain-specific information systems

NASA Astrophysics Data System (ADS)

Zacek, Jaroslav

2017-07-01

Substantial number of the current information system (IS) implementations is based on transaction approach. In addition, most of the implementations are domain-specific (e.g. accounting IS, resource planning IS). Therefore, we have to have a generic transaction model to build and verify domain-specific IS. The paper proposes a new transaction model for domain-specific ontologies. This model is based on value oriented business process modelling technique. The transaction model is formalized by the Petri Net theory. First part of the paper presents common business processes and analyses related to business process modeling. Second part defines the transactional model delimited by REA enterprise ontology paradigm and introduces states of the generic transaction model. The generic model proposal is defined and visualized by the Petri Net modelling tool. Third part shows application of the generic transaction model. Last part of the paper concludes results and discusses a practical usability of the generic transaction model.

Guideline validation in multiple trauma care through business process modeling.

PubMed

Stausberg, Jürgen; Bilir, Hüseyin; Waydhas, Christian; Ruchholtz, Steffen

2003-07-01

Clinical guidelines can improve the quality of care in multiple trauma. In our Department of Trauma Surgery a specific guideline is available paper-based as a set of flowcharts. This format is appropriate for the use by experienced physicians but insufficient for electronic support of learning, workflow and process optimization. A formal and logically consistent version represented with a standardized meta-model is necessary for automatic processing. In our project we transferred the paper-based into an electronic format and analyzed the structure with respect to formal errors. Several errors were detected in seven error categories. The errors were corrected to reach a formally and logically consistent process model. In a second step the clinical content of the guideline was revised interactively using a process-modeling tool. Our study reveals that guideline development should be assisted by process modeling tools, which check the content in comparison to a meta-model. The meta-model itself could support the domain experts in formulating their knowledge systematically. To assure sustainability of guideline development a representation independent of specific applications or specific provider is necessary. Then, clinical guidelines could be used for eLearning, process optimization and workflow management additionally.

LANDPLANER (LANDscape, Plants, LANdslide and ERosion): a model to describe the dynamic response of slopes (or basins) under different changing scenarios

NASA Astrophysics Data System (ADS)

Rossi, Mauro; Torri, Dino; Santi, Elisa; Bacaro, Giovanni; Marchesini, Ivan

2014-05-01

Landslide phenomena and erosion processes are widespread and cause every year extensive damages to the environment and sensible reduction of ecosystem services. These processes are in competition among them, and their complex interaction control the landscapes evolution. Landslide phenomena and erosion processes can be strongly influenced by land use, vegetation, soil characteristics and anthropic actions. Such type of phenomena are mainly model separately using empirical and physically based approaches. The former rely upon the identification of simple empirical laws correlating/relating the occurrence of instability processes to some of their potential causes. The latter are based on physical descriptions of the processes, and depending on the degree of complexity they can integrate different variables characterizing the process and their trigger. Those model often couple an hydrological model with an erosion or a landslide model. The spatial modeling schemas are heterogeneous, but mostly the raster (i.e. matrices of data) or the conceptual (i.e. cascading planes and channels) description of the terrain are used. The two model types are generally designed and applied at different scales. Empirical models, less demanding in terms of input data cannot consider explicitly the real process triggering mechanisms and commonly they are exploited to assess the potential occurrence of instability phenomena over large areas (small scale assessment). Physically-based models are high-demanding in term of input data, difficult to obtain over large areas if not with large uncertainty, and their applicability is often limited to small catchments or single slopes (large scale assessment). More those models, even if physically-based, are simplified description of the instability processes and can neglect significant issues of the real triggering mechanisms. For instance the influence of vegetation has been considered just partially. Although in the literature a variety of model approaches have been proposed to model separately landslide and erosion processes, only few attempts were made to model both jointly, mostly integrating pre-existing models. To overcome this limitation we develop a new model called LANDPLANER (LANDscape, Plants, LANdslide and ERosion), specifically design to describe the dynamic response of slopes (or basins) under different changing scenarios including: (i) changes of meteorological factors, (ii) changes of vegetation or land-use, (iii) and changes of slope morphology. The was applied in different study area in order to check its basic assumptions, and to test its general operability and applicability. Results show a reasonable model behaviors and confirm its easy applicability in real cases.

Embedding Task-Based Neural Models into a Connectome-Based Model of the Cerebral Cortex.

PubMed

Ulloa, Antonio; Horwitz, Barry

2016-01-01

A number of recent efforts have used large-scale, biologically realistic, neural models to help understand the neural basis for the patterns of activity observed in both resting state and task-related functional neural imaging data. An example of the former is The Virtual Brain (TVB) software platform, which allows one to apply large-scale neural modeling in a whole brain framework. TVB provides a set of structural connectomes of the human cerebral cortex, a collection of neural processing units for each connectome node, and various forward models that can convert simulated neural activity into a variety of functional brain imaging signals. In this paper, we demonstrate how to embed a previously or newly constructed task-based large-scale neural model into the TVB platform. We tested our method on a previously constructed large-scale neural model (LSNM) of visual object processing that consisted of interconnected neural populations that represent, primary and secondary visual, inferotemporal, and prefrontal cortex. Some neural elements in the original model were "non-task-specific" (NS) neurons that served as noise generators to "task-specific" neurons that processed shapes during a delayed match-to-sample (DMS) task. We replaced the NS neurons with an anatomical TVB connectome model of the cerebral cortex comprising 998 regions of interest interconnected by white matter fiber tract weights. We embedded our LSNM of visual object processing into corresponding nodes within the TVB connectome. Reciprocal connections between TVB nodes and our task-based modules were included in this framework. We ran visual object processing simulations and showed that the TVB simulator successfully replaced the noise generation originally provided by NS neurons; i.e., the DMS tasks performed with the hybrid LSNM/TVB simulator generated equivalent neural and fMRI activity to that of the original task-based models. Additionally, we found partial agreement between the functional connectivities using the hybrid LSNM/TVB model and the original LSNM. Our framework thus presents a way to embed task-based neural models into the TVB platform, enabling a better comparison between empirical and computational data, which in turn can lead to a better understanding of how interacting neural populations give rise to human cognitive behaviors.

Translating building information modeling to building energy modeling using model view definition.

PubMed

Jeong, WoonSeong; Kim, Jong Bum; Clayton, Mark J; Haberl, Jeff S; Yan, Wei

2014-01-01

This paper presents a new approach to translate between Building Information Modeling (BIM) and Building Energy Modeling (BEM) that uses Modelica, an object-oriented declarative, equation-based simulation environment. The approach (BIM2BEM) has been developed using a data modeling method to enable seamless model translations of building geometry, materials, and topology. Using data modeling, we created a Model View Definition (MVD) consisting of a process model and a class diagram. The process model demonstrates object-mapping between BIM and Modelica-based BEM (ModelicaBEM) and facilitates the definition of required information during model translations. The class diagram represents the information and object relationships to produce a class package intermediate between the BIM and BEM. The implementation of the intermediate class package enables system interface (Revit2Modelica) development for automatic BIM data translation into ModelicaBEM. In order to demonstrate and validate our approach, simulation result comparisons have been conducted via three test cases using (1) the BIM-based Modelica models generated from Revit2Modelica and (2) BEM models manually created using LBNL Modelica Buildings library. Our implementation shows that BIM2BEM (1) enables BIM models to be translated into ModelicaBEM models, (2) enables system interface development based on the MVD for thermal simulation, and (3) facilitates the reuse of original BIM data into building energy simulation without an import/export process.

Translating Building Information Modeling to Building Energy Modeling Using Model View Definition

PubMed Central

Kim, Jong Bum; Clayton, Mark J.; Haberl, Jeff S.

2014-01-01

This paper presents a new approach to translate between Building Information Modeling (BIM) and Building Energy Modeling (BEM) that uses Modelica, an object-oriented declarative, equation-based simulation environment. The approach (BIM2BEM) has been developed using a data modeling method to enable seamless model translations of building geometry, materials, and topology. Using data modeling, we created a Model View Definition (MVD) consisting of a process model and a class diagram. The process model demonstrates object-mapping between BIM and Modelica-based BEM (ModelicaBEM) and facilitates the definition of required information during model translations. The class diagram represents the information and object relationships to produce a class package intermediate between the BIM and BEM. The implementation of the intermediate class package enables system interface (Revit2Modelica) development for automatic BIM data translation into ModelicaBEM. In order to demonstrate and validate our approach, simulation result comparisons have been conducted via three test cases using (1) the BIM-based Modelica models generated from Revit2Modelica and (2) BEM models manually created using LBNL Modelica Buildings library. Our implementation shows that BIM2BEM (1) enables BIM models to be translated into ModelicaBEM models, (2) enables system interface development based on the MVD for thermal simulation, and (3) facilitates the reuse of original BIM data into building energy simulation without an import/export process. PMID:25309954

A web-based portfolio model as the students' final assignment: Dealing with the development of higher education trend

NASA Astrophysics Data System (ADS)

Utanto, Yuli; Widhanarto, Ghanis Putra; Maretta, Yoris Adi

2017-03-01

This study aims to develop a web-based portfolio model. The model developed in this study could reveal the effectiveness of the new model in experiments conducted at research respondents in the department of curriculum and educational technology FIP Unnes. In particular, the further research objectives to be achieved through this development of research, namely: (1) Describing the process of implementing a portfolio in a web-based model; (2) Assessing the effectiveness of web-based portfolio model for the final task, especially in Web-Based Learning courses. This type of research is the development of research Borg and Gall (2008: 589) says "educational research and development (R & D) is a process used to develop and validate educational production". The series of research and development carried out starting with exploration and conceptual studies, followed by testing and evaluation, and also implementation. For the data analysis, the technique used is simple descriptive analysis, analysis of learning completeness, which then followed by prerequisite test for normality and homogeneity to do T - test. Based on the data analysis, it was concluded that: (1) a web-based portfolio model can be applied to learning process in higher education; (2) The effectiveness of web-based portfolio model with field data from the respondents of large group trial participants (field trial), the number of respondents who reached mastery learning (a score of 60 and above) were 24 people (92.3%) in which it indicates that the web-based portfolio model is effective. The conclusion of this study is that a web-based portfolio model is effective. The implications of the research development of this model, the next researcher is expected to be able to use the guideline of the development model based on the research that has already been conducted to be developed on other subjects.

[Monitoring method for macroporous resin column chromatography process of salvianolic acids based on near infrared spectroscopy].

PubMed

Hou, Xiang-Mei; Zhang, Lei; Yue, Hong-Shui; Ju, Ai-Chun; Ye, Zheng-Liang

2016-07-01

To study and establish a monitoring method for macroporous resin column chromatography process of salvianolic acids by using near infrared spectroscopy (NIR) as a process analytical technology (PAT).The multivariate statistical process control (MSPC) model was developed based on 7 normal operation batches, and 2 test batches (including one normal operation batch and one abnormal operation batch) were used to verify the monitoring performance of this model. The results showed that MSPC model had a good monitoring ability for the column chromatography process. Meanwhile, NIR quantitative calibration model was established for three key quality indexes (rosmarinic acid, lithospermic acid and salvianolic acid B) by using partial least squares (PLS) algorithm. The verification results demonstrated that this model had satisfactory prediction performance. The combined application of the above two models could effectively achieve real-time monitoring for macroporous resin column chromatography process of salvianolic acids, and can be used to conduct on-line analysis of key quality indexes. This established process monitoring method could provide reference for the development of process analytical technology for traditional Chinese medicines manufacturing. Copyright© by the Chinese Pharmaceutical Association.

A Comparative of business process modelling techniques

NASA Astrophysics Data System (ADS)

Tangkawarow, I. R. H. T.; Waworuntu, J.

2016-04-01

In this era, there is a lot of business process modeling techniques. This article is the research about differences of business process modeling techniques. For each technique will explain about the definition and the structure. This paper presents a comparative analysis of some popular business process modelling techniques. The comparative framework is based on 2 criteria: notation and how it works when implemented in Somerleyton Animal Park. Each technique will end with the advantages and disadvantages. The final conclusion will give recommend of business process modeling techniques that easy to use and serve the basis for evaluating further modelling techniques.

Modeling marine oily wastewater treatment by a probabilistic agent-based approach.

PubMed

Jing, Liang; Chen, Bing; Zhang, Baiyu; Ye, Xudong

2018-02-01

This study developed a novel probabilistic agent-based approach for modeling of marine oily wastewater treatment processes. It begins first by constructing a probability-based agent simulation model, followed by a global sensitivity analysis and a genetic algorithm-based calibration. The proposed modeling approach was tested through a case study of the removal of naphthalene from marine oily wastewater using UV irradiation. The removal of naphthalene was described by an agent-based simulation model using 8 types of agents and 11 reactions. Each reaction was governed by a probability parameter to determine its occurrence. The modeling results showed that the root mean square errors between modeled and observed removal rates were 8.73 and 11.03% for calibration and validation runs, respectively. Reaction competition was analyzed by comparing agent-based reaction probabilities, while agents' heterogeneity was visualized by plotting their real-time spatial distribution, showing a strong potential for reactor design and process optimization. Copyright © 2017 Elsevier Ltd. All rights reserved.

LISP based simulation generators for modeling complex space processes

NASA Technical Reports Server (NTRS)

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

1987-01-01

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

Fault detection of Tennessee Eastman process based on topological features and SVM

NASA Astrophysics Data System (ADS)

Zhao, Huiyang; Hu, Yanzhu; Ai, Xinbo; Hu, Yu; Meng, Zhen

2018-03-01

Fault detection in industrial process is a popular research topic. Although the distributed control system(DCS) has been introduced to monitor the state of industrial process, it still cannot satisfy all the requirements for fault detection of all the industrial systems. In this paper, we proposed a novel method based on topological features and support vector machine(SVM), for fault detection of industrial process. The proposed method takes global information of measured variables into account by complex network model and predicts whether a system has generated some faults or not by SVM. The proposed method can be divided into four steps, i.e. network construction, network analysis, model training and model testing respectively. Finally, we apply the model to Tennessee Eastman process(TEP). The results show that this method works well and can be a useful supplement for fault detection of industrial process.

Creating "Intelligent" Climate Model Ensemble Averages Using a Process-Based Framework

NASA Astrophysics Data System (ADS)

Baker, N. C.; Taylor, P. C.

2014-12-01

The CMIP5 archive contains future climate projections from over 50 models provided by dozens of modeling centers from around the world. Individual model projections, however, are subject to biases created by structural model uncertainties. As a result, ensemble averaging of multiple models is often used to add value to model projections: consensus projections have been shown to consistently outperform individual models. Previous reports for the IPCC establish climate change projections based on an equal-weighted average of all model projections. However, certain models reproduce climate processes better than other models. Should models be weighted based on performance? Unequal ensemble averages have previously been constructed using a variety of mean state metrics. What metrics are most relevant for constraining future climate projections? This project develops a framework for systematically testing metrics in models to identify optimal metrics for unequal weighting multi-model ensembles. A unique aspect of this project is the construction and testing of climate process-based model evaluation metrics. A climate process-based metric is defined as a metric based on the relationship between two physically related climate variables—e.g., outgoing longwave radiation and surface temperature. Metrics are constructed using high-quality Earth radiation budget data from NASA's Clouds and Earth's Radiant Energy System (CERES) instrument and surface temperature data sets. It is found that regional values of tested quantities can vary significantly when comparing weighted and unweighted model ensembles. For example, one tested metric weights the ensemble by how well models reproduce the time-series probability distribution of the cloud forcing component of reflected shortwave radiation. The weighted ensemble for this metric indicates lower simulated precipitation (up to .7 mm/day) in tropical regions than the unweighted ensemble: since CMIP5 models have been shown to overproduce precipitation, this result could indicate that the metric is effective in identifying models which simulate more realistic precipitation. Ultimately, the goal of the framework is to identify performance metrics for advising better methods for ensemble averaging models and create better climate predictions.

NHPP-Based Software Reliability Models Using Equilibrium Distribution

NASA Astrophysics Data System (ADS)

Xiao, Xiao; Okamura, Hiroyuki; Dohi, Tadashi

Non-homogeneous Poisson processes (NHPPs) have gained much popularity in actual software testing phases to estimate the software reliability, the number of remaining faults in software and the software release timing. In this paper, we propose a new modeling approach for the NHPP-based software reliability models (SRMs) to describe the stochastic behavior of software fault-detection processes. The fundamental idea is to apply the equilibrium distribution to the fault-detection time distribution in NHPP-based modeling. We also develop efficient parameter estimation procedures for the proposed NHPP-based SRMs. Through numerical experiments, it can be concluded that the proposed NHPP-based SRMs outperform the existing ones in many data sets from the perspective of goodness-of-fit and prediction performance.

Analysis of exhaled breath by laser detection

NASA Astrophysics Data System (ADS)

Thrall, Karla D.; Toth, James J.; Sharpe, Steven W.

1996-04-01

The goal of our work is two fold: (1) to develop a portable rapid laser based breath analyzer for monitoring metabolic processes, and (2) predict these metabolic processes through physiologically based pharmacokinetic (PBPK) modeling. Small infrared active molecules such as ammonia, carbon monoxide, carbon dioxide, methane and ethane are present in exhaled breath and can be readily detected by laser absorption spectroscopy. In addition, many of the stable isotopomers of these molecules can be accurately detected, making it possible to follow specific metabolic processes. Potential areas of applications for this technology include the diagnosis of certain pathologies (e.g. Helicobacter Pylori infection), detection of trauma due to either physical or chemical causes and monitoring nutrient uptake (i.e., malnutrition). In order to understand the origin and elucidate the metabolic processes associated with these small molecules, we are employing physiologically based pharmacokinetic (PBPK) models. A PBPK model is founded on known physiological processes (i.e., blood flow rates, tissue volumes, breathing rate, etc.), chemical-specific processes (i.e., tissue solubility coefficients, molecular weight, chemical density, etc.), and on metabolic processes (tissue site and rate of metabolic biotransformation). Since many of these processes are well understood, a PBPK model can be developed and validated against the more readily available experimental animal data, and then by extrapolating the parameters to apply to man, the model can predict chemical behavior in humans.

Fault detection and diagnosis using neural network approaches

NASA Technical Reports Server (NTRS)

Kramer, Mark A.

1992-01-01

Neural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify faults. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.

Mathematical modeling of PDC bit drilling process based on a single-cutter mechanics

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

Wojtanowicz, A.K.; Kuru, E.

1993-12-01

An analytical development of a new mechanistic drilling model for polycrystalline diamond compact (PDC) bits is presented. The derivation accounts for static balance of forces acting on a single PDC cutter and is based on assumed similarity between bit and cutter. The model is fully explicit with physical meanings given to all constants and functions. Three equations constitute the mathematical model: torque, drilling rate, and bit life. The equations comprise cutter`s geometry, rock properties drilling parameters, and four empirical constants. The constants are used to match the model to a PDC drilling process. Also presented are qualitative and predictive verificationsmore » of the model. Qualitative verification shows that the model`s response to drilling process variables is similar to the behavior of full-size PDC bits. However, accuracy of the model`s predictions of PDC bit performance is limited primarily by imprecision of bit-dull evaluation. The verification study is based upon the reported laboratory drilling and field drilling tests as well as field data collected by the authors.« less

Different modelling approaches to evaluate nitrogen transport and turnover at the watershed scale

NASA Astrophysics Data System (ADS)

Epelde, Ane Miren; Antiguedad, Iñaki; Brito, David; Jauch, Eduardo; Neves, Ramiro; Garneau, Cyril; Sauvage, Sabine; Sánchez-Pérez, José Miguel

2016-08-01

This study presents the simulation of hydrological processes and nutrient transport and turnover processes using two integrated numerical models: Soil and Water Assessment Tool (SWAT) (Arnold et al., 1998), an empirical and semi-distributed numerical model; and Modelo Hidrodinâmico (MOHID) (Neves, 1985), a physics-based and fully distributed numerical model. This work shows that both models reproduce satisfactorily water and nitrate exportation at the watershed scale at annual and daily basis, MOHID providing slightly better results. At the watershed scale, both SWAT and MOHID simulated similarly and satisfactorily the denitrification amount. However, as MOHID numerical model was the only one able to reproduce adequately the spatial variation of the soil hydrological conditions and water table level fluctuation, it proved to be the only model able of reproducing the spatial variation of the nutrient cycling processes that are dependent to the soil hydrological conditions such as the denitrification process. This evidences the strength of the fully distributed and physics-based models to simulate the spatial variability of nutrient cycling processes that are dependent to the hydrological conditions of the soils.

A Novel BA Complex Network Model on Color Template Matching

PubMed Central

Han, Risheng; Yue, Guangxue; Ding, Hui

2014-01-01

A novel BA complex network model of color space is proposed based on two fundamental rules of BA scale-free network model: growth and preferential attachment. The scale-free characteristic of color space is discovered by analyzing evolving process of template's color distribution. And then the template's BA complex network model can be used to select important color pixels which have much larger effects than other color pixels in matching process. The proposed BA complex network model of color space can be easily integrated into many traditional template matching algorithms, such as SSD based matching and SAD based matching. Experiments show the performance of color template matching results can be improved based on the proposed algorithm. To the best of our knowledge, this is the first study about how to model the color space of images using a proper complex network model and apply the complex network model to template matching. PMID:25243235

A novel BA complex network model on color template matching.

PubMed

Han, Risheng; Shen, Shigen; Yue, Guangxue; Ding, Hui

2014-01-01

A novel BA complex network model of color space is proposed based on two fundamental rules of BA scale-free network model: growth and preferential attachment. The scale-free characteristic of color space is discovered by analyzing evolving process of template's color distribution. And then the template's BA complex network model can be used to select important color pixels which have much larger effects than other color pixels in matching process. The proposed BA complex network model of color space can be easily integrated into many traditional template matching algorithms, such as SSD based matching and SAD based matching. Experiments show the performance of color template matching results can be improved based on the proposed algorithm. To the best of our knowledge, this is the first study about how to model the color space of images using a proper complex network model and apply the complex network model to template matching.

Experimental research on mathematical modelling and unconventional control of clinker kiln in cement plants

NASA Astrophysics Data System (ADS)

Rusu-Anghel, S.

2017-01-01

Analytical modeling of the flow of manufacturing process of the cement is difficult because of their complexity and has not resulted in sufficiently precise mathematical models. In this paper, based on a statistical model of the process and using the knowledge of human experts, was designed a fuzzy system for automatic control of clinkering process.

Multiphase porous media modelling: A novel approach to predicting food processing performance.

PubMed

Khan, Md Imran H; Joardder, M U H; Kumar, Chandan; Karim, M A

2018-03-04

The development of a physics-based model of food processing is essential to improve the quality of processed food and optimize energy consumption. Food materials, particularly plant-based food materials, are complex in nature as they are porous and have hygroscopic properties. A multiphase porous media model for simultaneous heat and mass transfer can provide a realistic understanding of transport processes and thus can help to optimize energy consumption and improve food quality. Although the development of a multiphase porous media model for food processing is a challenging task because of its complexity, many researchers have attempted it. The primary aim of this paper is to present a comprehensive review of the multiphase models available in the literature for different methods of food processing, such as drying, frying, cooking, baking, heating, and roasting. A critical review of the parameters that should be considered for multiphase modelling is presented which includes input parameters, material properties, simulation techniques and the hypotheses. A discussion on the general trends in outcomes, such as moisture saturation, temperature profile, pressure variation, and evaporation patterns, is also presented. The paper concludes by considering key issues in the existing multiphase models and future directions for development of multiphase models.

Equivalent model construction for a non-linear dynamic system based on an element-wise stiffness evaluation procedure and reduced analysis of the equivalent system

NASA Astrophysics Data System (ADS)

Kim, Euiyoung; Cho, Maenghyo

2017-11-01

In most non-linear analyses, the construction of a system matrix uses a large amount of computation time, comparable to the computation time required by the solving process. If the process for computing non-linear internal force matrices is substituted with an effective equivalent model that enables the bypass of numerical integrations and assembly processes used in matrix construction, efficiency can be greatly enhanced. A stiffness evaluation procedure (STEP) establishes non-linear internal force models using polynomial formulations of displacements. To efficiently identify an equivalent model, the method has evolved such that it is based on a reduced-order system. The reduction process, however, makes the equivalent model difficult to parameterize, which significantly affects the efficiency of the optimization process. In this paper, therefore, a new STEP, E-STEP, is proposed. Based on the element-wise nature of the finite element model, the stiffness evaluation is carried out element-by-element in the full domain. Since the unit of computation for the stiffness evaluation is restricted by element size, and since the computation is independent, the equivalent model can be constructed efficiently in parallel, even in the full domain. Due to the element-wise nature of the construction procedure, the equivalent E-STEP model is easily characterized by design parameters. Various reduced-order modeling techniques can be applied to the equivalent system in a manner similar to how they are applied in the original system. The reduced-order model based on E-STEP is successfully demonstrated for the dynamic analyses of non-linear structural finite element systems under varying design parameters.

Electrophysiological correlates reflect the integration of model-based and model-free decision information.

PubMed

Eppinger, Ben; Walter, Maik; Li, Shu-Chen

2017-04-01

In this study, we investigated the interplay of habitual (model-free) and goal-directed (model-based) decision processes by using a two-stage Markov decision task in combination with event-related potentials (ERPs) and computational modeling. To manipulate the demands on model-based decision making, we applied two experimental conditions with different probabilities of transitioning from the first to the second stage of the task. As we expected, when the stage transitions were more predictable, participants showed greater model-based (planning) behavior. Consistent with this result, we found that stimulus-evoked parietal (P300) activity at the second stage of the task increased with the predictability of the state transitions. However, the parietal activity also reflected model-free information about the expected values of the stimuli, indicating that at this stage of the task both types of information are integrated to guide decision making. Outcome-related ERP components only reflected reward-related processes: Specifically, a medial prefrontal ERP component (the feedback-related negativity) was sensitive to negative outcomes, whereas a component that is elicited by reward (the feedback-related positivity) increased as a function of positive prediction errors. Taken together, our data indicate that stimulus-locked parietal activity reflects the integration of model-based and model-free information during decision making, whereas feedback-related medial prefrontal signals primarily reflect reward-related decision processes.

A process-based model for cattle manure compost windrows: Model performance and application

USDA-ARS?s Scientific Manuscript database

A model was developed and incorporated in the Integrated Farm System Model (IFSM, v.4.3) that simulates important processes occurring during windrow composting of manure. The model, documented in an accompanying paper, predicts changes in windrow properties and conditions and the resulting emissions...

Competition-Based Learning: A Model for the Integration of Competitions with Project-Based Learning Using Open Source LMS

ERIC Educational Resources Information Center

Issa, Ghassan; Hussain, Shakir M.; Al-Bahadili, Hussein

2014-01-01

In an effort to enhance the learning process in higher education, a new model for Competition-Based Learning (CBL) is presented. The new model utilizes two well-known learning models, namely, the Project-Based Learning (PBL) and competitions. The new model is also applied in a networked environment with emphasis on collective learning as well as…

Model-based adaptive 3D sonar reconstruction in reverberating environments.

PubMed

Saucan, Augustin-Alexandru; Sintes, Christophe; Chonavel, Thierry; Caillec, Jean-Marc Le

2015-10-01

In this paper, we propose a novel model-based approach for 3D underwater scene reconstruction, i.e., bathymetry, for side scan sonar arrays in complex and highly reverberating environments like shallow water areas. The presence of multipath echoes and volume reverberation generates false depth estimates. To improve the resulting bathymetry, this paper proposes and develops an adaptive filter, based on several original geometrical models. This multimodel approach makes it possible to track and separate the direction of arrival trajectories of multiple echoes impinging the array. Echo tracking is perceived as a model-based processing stage, incorporating prior information on the temporal evolution of echoes in order to reject cluttered observations generated by interfering echoes. The results of the proposed filter on simulated and real sonar data showcase the clutter-free and regularized bathymetric reconstruction. Model validation is carried out with goodness of fit tests, and demonstrates the importance of model-based processing for bathymetry reconstruction.

HESS Opinions: The need for process-based evaluation of large-domain hyper-resolution models

NASA Astrophysics Data System (ADS)

Melsen, Lieke A.; Teuling, Adriaan J.; Torfs, Paul J. J. F.; Uijlenhoet, Remko; Mizukami, Naoki; Clark, Martyn P.

2016-03-01

A meta-analysis on 192 peer-reviewed articles reporting on applications of the variable infiltration capacity (VIC) model in a distributed way reveals that the spatial resolution at which the model is applied has increased over the years, while the calibration and validation time interval has remained unchanged. We argue that the calibration and validation time interval should keep pace with the increase in spatial resolution in order to resolve the processes that are relevant at the applied spatial resolution. We identified six time concepts in hydrological models, which all impact the model results and conclusions. Process-based model evaluation is particularly relevant when models are applied at hyper-resolution, where stakeholders expect credible results both at a high spatial and temporal resolution.

HESS Opinions: The need for process-based evaluation of large-domain hyper-resolution models

NASA Astrophysics Data System (ADS)

Melsen, L. A.; Teuling, A. J.; Torfs, P. J. J. F.; Uijlenhoet, R.; Mizukami, N.; Clark, M. P.

2015-12-01

A meta-analysis on 192 peer-reviewed articles reporting applications of the Variable Infiltration Capacity (VIC) model in a distributed way reveals that the spatial resolution at which the model is applied has increased over the years, while the calibration and validation time interval has remained unchanged. We argue that the calibration and validation time interval should keep pace with the increase in spatial resolution in order to resolve the processes that are relevant at the applied spatial resolution. We identified six time concepts in hydrological models, which all impact the model results and conclusions. Process-based model evaluation is particularly relevant when models are applied at hyper-resolution, where stakeholders expect credible results both at a high spatial and temporal resolution.

Argumentation in Science Education: A Model-based Framework

NASA Astrophysics Data System (ADS)

Böttcher, Florian; Meisert, Anke

2011-02-01

The goal of this article is threefold: First, the theoretical background for a model-based framework of argumentation to describe and evaluate argumentative processes in science education is presented. Based on the general model-based perspective in cognitive science and the philosophy of science, it is proposed to understand arguments as reasons for the appropriateness of a theoretical model which explains a certain phenomenon. Argumentation is considered to be the process of the critical evaluation of such a model if necessary in relation to alternative models. Secondly, some methodological details are exemplified for the use of a model-based analysis in the concrete classroom context. Third, the application of the approach in comparison with other analytical models will be presented to demonstrate the explicatory power and depth of the model-based perspective. Primarily, the framework of Toulmin to structurally analyse arguments is contrasted with the approach presented here. It will be demonstrated how common methodological and theoretical problems in the context of Toulmin's framework can be overcome through a model-based perspective. Additionally, a second more complex argumentative sequence will also be analysed according to the invented analytical scheme to give a broader impression of its potential in practical use.

Comparison of Modeling Approaches for Carbon Partitioning: Impact on Estimates of Global Net Primary Production and Equilibrium Biomass of Woody Vegetation from MODIS GPP

NASA Astrophysics Data System (ADS)

Ise, T.; Litton, C. M.; Giardina, C. P.; Ito, A.

2009-12-01

Plant partitioning of carbon (C) to above- vs. belowground, to growth vs. respiration, and to short vs. long lived tissues exerts a large influence on ecosystem structure and function with implications for the global C budget. Importantly, outcomes of process-based terrestrial vegetation models are likely to vary substantially with different C partitioning algorithms. However, controls on C partitioning patterns remain poorly quantified, and studies have yielded variable, and at times contradictory, results. A recent meta-analysis of forest studies suggests that the ratio of net primary production (NPP) and gross primary production (GPP) is fairly conservative across large scales. To illustrate the effect of this unique meta-analysis-based partitioning scheme (MPS), we compared an application of MPS to a terrestrial satellite-based (MODIS) GPP to estimate NPP vs. two global process-based vegetation models (Biome-BGC and VISIT) to examine the influence of C partitioning on C budgets of woody plants. Due to the temperature dependence of maintenance respiration, NPP/GPP predicted by the process-based models increased with latitude while the ratio remained constant with MPS. Overall, global NPP estimated with MPS was 17 and 27% lower than the process-based models for temperate and boreal biomes, respectively, with smaller differences in the tropics. Global equilibrium biomass of woody plants was then calculated from the NPP estimates and tissue turnover rates from VISIT. Since turnover rates differed greatly across tissue types (i.e., metabolically active vs. structural), global equilibrium biomass estimates were sensitive to the partitioning scheme employed. The MPS estimate of global woody biomass was 7-21% lower than that of the process-based models. In summary, we found that model output for NPP and equilibrium biomass was quite sensitive to the choice of C partitioning schemes. Carbon use efficiency (CUE; NPP/GPP) by forest biome and the globe. Values are means for 2001-2006.

Identifying Model-Based Reconfiguration Goals through Functional Deficiencies

NASA Technical Reports Server (NTRS)

Benazera, Emmanuel; Trave-Massuyes, Louise

2004-01-01

Model-based diagnosis is now advanced to the point autonomous systems face some uncertain and faulty situations with success. The next step toward more autonomy is to have the system recovering itself after faults occur, a process known as model-based reconfiguration. After faults occur, given a prediction of the nominal behavior of the system and the result of the diagnosis operation, this paper details how to automatically determine the functional deficiencies of the system. These deficiencies are characterized in the case of uncertain state estimates. A methodology is then presented to determine the reconfiguration goals based on the deficiencies. Finally, a recovery process interleaves planning and model predictive control to restore the functionalities in prioritized order.

A Model-Based Approach to Developing Your Mission Operations System

NASA Technical Reports Server (NTRS)

Smith, Robert R.; Schimmels, Kathryn A.; Lock, Patricia D; Valerio, Charlene P.

2014-01-01

Model-Based System Engineering (MBSE) is an increasingly popular methodology for designing complex engineering systems. As the use of MBSE has grown, it has begun to be applied to systems that are less hardware-based and more people- and process-based. We describe our approach to incorporating MBSE as a way to streamline development, and how to build a model consisting of core resources, such as requirements and interfaces, that can be adapted and used by new and upcoming projects. By comparing traditional Mission Operations System (MOS) system engineering with an MOS designed via a model, we will demonstrate the benefits to be obtained by incorporating MBSE in system engineering design processes.

A Collective Case Study of Secondary Students' Model-Based Inquiry on Natural Selection through Programming in an Agent-Based Modeling Environment

ERIC Educational Resources Information Center

Xiang, Lin

2011-01-01

This is a collective case study seeking to develop detailed descriptions of how programming an agent-based simulation influences a group of 8th grade students' model-based inquiry (MBI) by examining students' agent-based programmable modeling (ABPM) processes and the learning outcomes. The context of the present study was a biology unit on…

Plasma Processing of Model Residential Solid Waste

NASA Astrophysics Data System (ADS)

Messerle, V. E.; Mossé, A. L.; Nikonchuk, A. N.; Ustimenko, A. B.; Baimuldin, R. V.

2017-09-01

The authors have tested the technology of processing of model residential solid waste. They have developed and created a pilot plasma unit based on a plasma chamber incinerator. The waste processing technology has been tested and prepared for commercialization.

Integrating Mathematical Modeling for Undergraduate Pre-Service Science Education Learning and Instruction in Middle School Classrooms

ERIC Educational Resources Information Center

Carrejo, David; Robertson, William H.

2011-01-01

Computer-based mathematical modeling in physics is a process of constructing models of concepts and the relationships between them in the scientific characteristics of work. In this manner, computer-based modeling integrates the interactions of natural phenomenon through the use of models, which provide structure for theories and a base for…

Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks

PubMed Central

Walpole, J.; Chappell, J.C.; Cluceru, J.G.; Mac Gabhann, F.; Bautch, V.L.; Peirce, S. M.

2015-01-01

Many biological processes are controlled by both deterministic and stochastic influences. However, efforts to model these systems often rely on either purely stochastic or purely rule-based methods. To better understand the balance between stochasticity and determinism in biological processes a computational approach that incorporates both influences may afford additional insight into underlying biological mechanisms that give rise to emergent system properties. We apply a combined approach to the simulation and study of angiogenesis, the growth of new blood vessels from existing networks. This complex multicellular process begins with selection of an initiating endothelial cell, or tip cell, which sprouts from the parent vessels in response to stimulation by exogenous cues. We have constructed an agent-based model of sprouting angiogenesis to evaluate endothelial cell sprout initiation frequency and location, and we have experimentally validated it using high-resolution time-lapse confocal microscopy. ABM simulations were then compared to a Monte Carlo model, revealing that purely stochastic simulations could not generate sprout locations as accurately as the rule-informed agent-based model. These findings support the use of rule-based approaches for modeling the complex mechanisms underlying sprouting angiogenesis over purely stochastic methods. PMID:26158406

Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks.

PubMed

Walpole, J; Chappell, J C; Cluceru, J G; Mac Gabhann, F; Bautch, V L; Peirce, S M

2015-09-01

Many biological processes are controlled by both deterministic and stochastic influences. However, efforts to model these systems often rely on either purely stochastic or purely rule-based methods. To better understand the balance between stochasticity and determinism in biological processes a computational approach that incorporates both influences may afford additional insight into underlying biological mechanisms that give rise to emergent system properties. We apply a combined approach to the simulation and study of angiogenesis, the growth of new blood vessels from existing networks. This complex multicellular process begins with selection of an initiating endothelial cell, or tip cell, which sprouts from the parent vessels in response to stimulation by exogenous cues. We have constructed an agent-based model of sprouting angiogenesis to evaluate endothelial cell sprout initiation frequency and location, and we have experimentally validated it using high-resolution time-lapse confocal microscopy. ABM simulations were then compared to a Monte Carlo model, revealing that purely stochastic simulations could not generate sprout locations as accurately as the rule-informed agent-based model. These findings support the use of rule-based approaches for modeling the complex mechanisms underlying sprouting angiogenesis over purely stochastic methods.

Developing a semantic web model for medical differential diagnosis recommendation.

PubMed

Mohammed, Osama; Benlamri, Rachid

2014-10-01

In this paper we describe a novel model for differential diagnosis designed to make recommendations by utilizing semantic web technologies. The model is a response to a number of requirements, ranging from incorporating essential clinical diagnostic semantics to the integration of data mining for the process of identifying candidate diseases that best explain a set of clinical features. We introduce two major components, which we find essential to the construction of an integral differential diagnosis recommendation model: the evidence-based recommender component and the proximity-based recommender component. Both approaches are driven by disease diagnosis ontologies designed specifically to enable the process of generating diagnostic recommendations. These ontologies are the disease symptom ontology and the patient ontology. The evidence-based diagnosis process develops dynamic rules based on standardized clinical pathways. The proximity-based component employs data mining to provide clinicians with diagnosis predictions, as well as generates new diagnosis rules from provided training datasets. This article describes the integration between these two components along with the developed diagnosis ontologies to form a novel medical differential diagnosis recommendation model. This article also provides test cases from the implementation of the overall model, which shows quite promising diagnostic recommendation results.

Stochastic, compartmental, and dynamic modeling of cross-contamination during mechanical smearing of cheeses.

PubMed

Aziza, Fanny; Mettler, Eric; Daudin, Jean-Jacques; Sanaa, Moez

2006-06-01

Cheese smearing is a complex process and the potential for cross-contamination with pathogenic or undesirable microorganisms is critical. During ripening, cheeses are salted and washed with brine to develop flavor and remove molds that could develop on the surfaces. Considering the potential for cross-contamination of this process in quantitative risk assessments could contribute to a better understanding of this phenomenon and, eventually, improve its control. The purpose of this article is to model the cross-contamination of smear-ripened cheeses due to the smearing operation under industrial conditions. A compartmental, dynamic, and stochastic model is proposed for mechanical brush smearing. This model has been developed to describe the exchange of microorganisms between compartments. Based on the analytical solution of the model equations and on experimental data collected with an industrial smearing machine, we assessed the values of the transfer parameters of the model. Monte Carlo simulations, using the distributions of transfer parameters, provide the final number of contaminated products in a batch and their final level of contamination for a given scenario taking into account the initial number of contaminated cheeses of the batch and their contaminant load. Based on analytical results, the model provides indicators for smearing efficiency and propensity of the process for cross-contamination. Unlike traditional approaches in mechanistic models, our approach captures the variability and uncertainty inherent in the process and the experimental data. More generally, this model could represent a generic base to use in modeling similar processes prone to cross-contamination.

Weight and the Future of Space Flight Hardware Cost Modeling

NASA Technical Reports Server (NTRS)

Prince, Frank A.

2003-01-01

Weight has been used as the primary input variable for cost estimating almost as long as there have been parametric cost models. While there are good reasons for using weight, serious limitations exist. These limitations have been addressed by multi-variable equations and trend analysis in models such as NAFCOM, PRICE, and SEER; however, these models have not be able to address the significant time lags that can occur between the development of similar space flight hardware systems. These time lags make the cost analyst's job difficult because insufficient data exists to perform trend analysis, and the current set of parametric models are not well suited to accommodating process improvements in space flight hardware design, development, build and test. As a result, people of good faith can have serious disagreement over the cost for new systems. To address these shortcomings, new cost modeling approaches are needed. The most promising approach is process based (sometimes called activity) costing. Developing process based models will require a detailed understanding of the functions required to produce space flight hardware combined with innovative approaches to estimating the necessary resources. Particularly challenging will be the lack of data at the process level. One method for developing a model is to combine notional algorithms with a discrete event simulation and model changes to the total cost as perturbations to the program are introduced. Despite these challenges, the potential benefits are such that efforts should be focused on developing process based cost models.

A new process sensitivity index to identify important system processes under process model and parametric uncertainty

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

Dai, Heng; Ye, Ming; Walker, Anthony P.

Hydrological models are always composed of multiple components that represent processes key to intended model applications. When a process can be simulated by multiple conceptual-mathematical models (process models), model uncertainty in representing the process arises. While global sensitivity analysis methods have been widely used for identifying important processes in hydrologic modeling, the existing methods consider only parametric uncertainty but ignore the model uncertainty for process representation. To address this problem, this study develops a new method to probe multimodel process sensitivity by integrating the model averaging methods into the framework of variance-based global sensitivity analysis, given that the model averagingmore » methods quantify both parametric and model uncertainty. A new process sensitivity index is derived as a metric of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and model parameters. For demonstration, the new index is used to evaluate the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that converting precipitation to recharge, and the geology process is also simulated by two models of different parameterizations of hydraulic conductivity; each process model has its own random parameters. The new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.« less

Event-based hydrological modeling for detecting dominant hydrological process and suitable model strategy for semi-arid catchments

NASA Astrophysics Data System (ADS)

Huang, Pengnian; Li, Zhijia; Chen, Ji; Li, Qiaoling; Yao, Cheng

2016-11-01

To simulate the hydrological processes in semi-arid areas properly is still challenging. This study assesses the impact of different modeling strategies on simulating flood processes in semi-arid catchments. Four classic hydrological models, TOPMODEL, XINANJIANG (XAJ), SAC-SMA and TANK, were selected and applied to three semi-arid catchments in North China. Based on analysis and comparison of the simulation results of these classic models, four new flexible models were constructed and used to further investigate the suitability of various modeling strategies for semi-arid environments. Numerical experiments were also designed to examine the performances of the models. The results show that in semi-arid catchments a suitable model needs to include at least one nonlinear component to simulate the main process of surface runoff generation. If there are more than two nonlinear components in the hydrological model, they should be arranged in parallel, rather than in series. In addition, the results show that the parallel nonlinear components should be combined by multiplication rather than addition. Moreover, this study reveals that the key hydrological process over semi-arid catchments is the infiltration excess surface runoff, a non-linear component.

Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions.

PubMed

Krajbich, Ian; Rangel, Antonio

2011-08-16

How do we make decisions when confronted with several alternatives (e.g., on a supermarket shelf)? Previous work has shown that accumulator models, such as the drift-diffusion model, can provide accurate descriptions of the psychometric data for binary value-based choices, and that the choice process is guided by visual attention. However, the computational processes used to make choices in more complicated situations involving three or more options are unknown. We propose a model of trinary value-based choice that generalizes what is known about binary choice, and test it using an eye-tracking experiment. We find that the model provides a quantitatively accurate description of the relationship between choice, reaction time, and visual fixation data using the same parameters that were estimated in previous work on binary choice. Our findings suggest that the brain uses similar computational processes to make binary and trinary choices.

Using fuzzy rule-based knowledge model for optimum plating conditions search

NASA Astrophysics Data System (ADS)

Solovjev, D. S.; Solovjeva, I. A.; Litovka, Yu V.; Arzamastsev, A. A.; Glazkov, V. P.; L’vov, A. A.

2018-03-01

The paper discusses existing approaches to plating process modeling in order to decrease the distribution thickness of plating surface cover. However, these approaches do not take into account the experience, knowledge, and intuition of the decision-makers when searching the optimal conditions of electroplating technological process. The original approach to optimal conditions search for applying the electroplating coatings, which uses the rule-based model of knowledge and allows one to reduce the uneven product thickness distribution, is proposed. The block diagrams of a conventional control system of a galvanic process as well as the system based on the production model of knowledge are considered. It is shown that the fuzzy production model of knowledge in the control system makes it possible to obtain galvanic coatings of a given thickness unevenness with a high degree of adequacy to the experimental data. The described experimental results confirm the theoretical conclusions.

Modeling of yield and environmental impact categories in tea processing units based on artificial neural networks.

PubMed

Khanali, Majid; Mobli, Hossein; Hosseinzadeh-Bandbafha, Homa

2017-12-01

In this study, an artificial neural network (ANN) model was developed for predicting the yield and life cycle environmental impacts based on energy inputs required in processing of black tea, green tea, and oolong tea in Guilan province of Iran. A life cycle assessment (LCA) approach was used to investigate the environmental impact categories of processed tea based on the cradle to gate approach, i.e., from production of input materials using raw materials to the gate of tea processing units, i.e., packaged tea. Thus, all the tea processing operations such as withering, rolling, fermentation, drying, and packaging were considered in the analysis. The initial data were obtained from tea processing units while the required data about the background system was extracted from the EcoInvent 2.2 database. LCA results indicated that diesel fuel and corrugated paper box used in drying and packaging operations, respectively, were the main hotspots. Black tea processing unit caused the highest pollution among the three processing units. Three feed-forward back-propagation ANN models based on Levenberg-Marquardt training algorithm with two hidden layers accompanied by sigmoid activation functions and a linear transfer function in output layer, were applied for three types of processed tea. The neural networks were developed based on energy equivalents of eight different input parameters (energy equivalents of fresh tea leaves, human labor, diesel fuel, electricity, adhesive, carton, corrugated paper box, and transportation) and 11 output parameters (yield, global warming, abiotic depletion, acidification, eutrophication, ozone layer depletion, human toxicity, freshwater aquatic ecotoxicity, marine aquatic ecotoxicity, terrestrial ecotoxicity, and photochemical oxidation). The results showed that the developed ANN models with R 2 values in the range of 0.878 to 0.990 had excellent performance in predicting all the output variables based on inputs. Energy consumption for processing of green tea, oolong tea, and black tea were calculated as 58,182, 60,947, and 66,301 MJ per ton of dry tea, respectively.

Rapid response tools and datasets for post-fire modeling: Linking Earth Observations and process-based hydrological models to support post-fire remediation

Treesearch

M. E. Miller; M. Billmire; W. J. Elliot; K. A. Endsley; P. R. Robichaud

2015-01-01

Preparation is key to utilizing Earth Observations and process-based models to support post-wildfire mitigation. Post-fire flooding and erosion can pose a serious threat to life, property and municipal water supplies. Increased runoff and sediment delivery due to the loss of surface cover and fire-induced changes in soil properties are of great concern. Remediation...

Moment-Based Physical Models of Broadband Clutter due to Aggregations of Fish

DTIC Science & Technology

2013-09-30

statistical models for signal-processing algorithm development. These in turn will help to develop a capability to statistically forecast the impact of...aggregations of fish based on higher-order statistical measures describable in terms of physical and system parameters. Environmentally , these models...processing. In this experiment, we had good ground truth on (1) and (2), and had control over (3) and (4) except for environmentally -imposed restrictions

Applying 3-PG, a simple process-based model designed to produce practical results, to data from loblolly pine experiments

Treesearch

Joe J. Landsberg; Kurt H. Johnsen; Timothy J. Albaugh; H. Lee Allen; Steven E. McKeand

2001-01-01

3-PG is a simple process-based model that requires few parameter values and only readily available input data. We tested the structure of the model by calibrating it against loblolly pine data from the control treatment of the SETRES experiment in Scotland County, NC, then altered the fertility rating to simulate the effects of fertilization. There was excellent...

a Marker-Based Eulerian-Lagrangian Method for Multiphase Flow with Supersonic Combustion Applications

NASA Astrophysics Data System (ADS)

Fan, Xiaofeng; Wang, Jiangfeng

2016-06-01

The atomization of liquid fuel is a kind of intricate dynamic process from continuous phase to discrete phase. Procedures of fuel spray in supersonic flow are modeled with an Eulerian-Lagrangian computational fluid dynamics methodology. The method combines two distinct techniques and develops an integrated numerical simulation method to simulate the atomization processes. The traditional finite volume method based on stationary (Eulerian) Cartesian grid is used to resolve the flow field, and multi-component Navier-Stokes equations are adopted in present work, with accounting for the mass exchange and heat transfer occupied by vaporization process. The marker-based moving (Lagrangian) grid is utilized to depict the behavior of atomized liquid sprays injected into a gaseous environment, and discrete droplet model 13 is adopted. To verify the current approach, the proposed method is applied to simulate processes of liquid atomization in supersonic cross flow. Three classic breakup models, TAB model, wave model and K-H/R-T hybrid model, are discussed. The numerical results are compared with multiple perspectives quantitatively, including spray penetration height and droplet size distribution. In addition, the complex flow field structures induced by the presence of liquid spray are illustrated and discussed. It is validated that the maker-based Eulerian-Lagrangian method is effective and reliable.

Identification of AR(I)MA processes for modelling temporal correlations of GPS observations

NASA Astrophysics Data System (ADS)

Luo, X.; Mayer, M.; Heck, B.

2009-04-01

In many geodetic applications observations of the Global Positioning System (GPS) are routinely processed by means of the least-squares method. However, this algorithm delivers reliable estimates of unknown parameters und realistic accuracy measures only if both the functional and stochastic models are appropriately defined within GPS data processing. One deficiency of the stochastic model used in many GPS software products consists in neglecting temporal correlations of GPS observations. In practice the knowledge of the temporal stochastic behaviour of GPS observations can be improved by analysing time series of residuals resulting from the least-squares evaluation. This paper presents an approach based on the theory of autoregressive (integrated) moving average (AR(I)MA) processes to model temporal correlations of GPS observations using time series of observation residuals. A practicable integration of AR(I)MA models in GPS data processing requires the determination of the order parameters of AR(I)MA processes at first. In case of GPS, the identification of AR(I)MA processes could be affected by various factors impacting GPS positioning results, e.g. baseline length, multipath effects, observation weighting, or weather variations. The influences of these factors on AR(I)MA identification are empirically analysed based on a large amount of representative residual time series resulting from differential GPS post-processing using 1-Hz observation data collected within the permanent SAPOS® (Satellite Positioning Service of the German State Survey) network. Both short and long time series are modelled by means of AR(I)MA processes. The final order parameters are determined based on the whole residual database; the corresponding empirical distribution functions illustrate that multipath and weather variations seem to affect the identification of AR(I)MA processes much more significantly than baseline length and observation weighting. Additionally, the modelling results of temporal correlations using high-order AR(I)MA processes are compared with those by means of first order autoregressive (AR(1)) processes and empirically estimated autocorrelation functions.

Mining manufacturing data for discovery of high productivity process characteristics.

PubMed

Charaniya, Salim; Le, Huong; Rangwala, Huzefa; Mills, Keri; Johnson, Kevin; Karypis, George; Hu, Wei-Shou

2010-06-01

Modern manufacturing facilities for bioproducts are highly automated with advanced process monitoring and data archiving systems. The time dynamics of hundreds of process parameters and outcome variables over a large number of production runs are archived in the data warehouse. This vast amount of data is a vital resource to comprehend the complex characteristics of bioprocesses and enhance production robustness. Cell culture process data from 108 'trains' comprising production as well as inoculum bioreactors from Genentech's manufacturing facility were investigated. Each run constitutes over one-hundred on-line and off-line temporal parameters. A kernel-based approach combined with a maximum margin-based support vector regression algorithm was used to integrate all the process parameters and develop predictive models for a key cell culture performance parameter. The model was also used to identify and rank process parameters according to their relevance in predicting process outcome. Evaluation of cell culture stage-specific models indicates that production performance can be reliably predicted days prior to harvest. Strong associations between several temporal parameters at various manufacturing stages and final process outcome were uncovered. This model-based data mining represents an important step forward in establishing a process data-driven knowledge discovery in bioprocesses. Implementation of this methodology on the manufacturing floor can facilitate a real-time decision making process and thereby improve the robustness of large scale bioprocesses. 2010 Elsevier B.V. All rights reserved.

ATMOSPHERIC AMMONIA EMISSIONS FROM THE LIVESTOCK SECTOR: DEVELOPMENT AND EVALUATION OF A PROCESS-BASED MODELING APPROACH

EPA Science Inventory

We propose multi-faceted research to enhance our understanding of NH3 emissions from livestock feeding operations. A process-based emissions modeling approach will be used, and we will investigate ammonia emissions from the scale of the individual farm out to impacts on region...

A Test of Two Alternative Cognitive Processing Models: Learning Styles and Dual Coding

ERIC Educational Resources Information Center

Cuevas, Joshua; Dawson, Bryan L.

2018-01-01

This study tested two cognitive models, learning styles and dual coding, which make contradictory predictions about how learners process and retain visual and auditory information. Learning styles-based instructional practices are common in educational environments despite a questionable research base, while the use of dual coding is less…

School Processes Mediate School Compositional Effects: Model Specification and Estimation

ERIC Educational Resources Information Center

Liu, Hongqiang; Van Damme, Jan; Gielen, Sarah; Van Den Noortgate, Wim

2015-01-01

School composition effects have been consistently verified, but few studies ever attempted to study how school composition affects school achievement. Based on prior research findings, we employed multilevel mediation modeling to examine whether school processes mediate the effect of school composition upon school outcomes based on the data of 28…

Research on application of intelligent computation based LUCC model in urbanization process

NASA Astrophysics Data System (ADS)

Chen, Zemin

2007-06-01

Global change study is an interdisciplinary and comprehensive research activity with international cooperation, arising in 1980s, with the largest scopes. The interaction between land use and cover change, as a research field with the crossing of natural science and social science, has become one of core subjects of global change study as well as the front edge and hot point of it. It is necessary to develop research on land use and cover change in urbanization process and build an analog model of urbanization to carry out description, simulation and analysis on dynamic behaviors in urban development change as well as to understand basic characteristics and rules of urbanization process. This has positive practical and theoretical significance for formulating urban and regional sustainable development strategy. The effect of urbanization on land use and cover change is mainly embodied in the change of quantity structure and space structure of urban space, and LUCC model in urbanization process has been an important research subject of urban geography and urban planning. In this paper, based upon previous research achievements, the writer systematically analyzes the research on land use/cover change in urbanization process with the theories of complexity science research and intelligent computation; builds a model for simulating and forecasting dynamic evolution of urban land use and cover change, on the basis of cellular automation model of complexity science research method and multi-agent theory; expands Markov model, traditional CA model and Agent model, introduces complexity science research theory and intelligent computation theory into LUCC research model to build intelligent computation-based LUCC model for analog research on land use and cover change in urbanization research, and performs case research. The concrete contents are as follows: 1. Complexity of LUCC research in urbanization process. Analyze urbanization process in combination with the contents of complexity science research and the conception of complexity feature to reveal the complexity features of LUCC research in urbanization process. Urban space system is a complex economic and cultural phenomenon as well as a social process, is the comprehensive characterization of urban society, economy and culture, and is a complex space system formed by society, economy and nature. It has dissipative structure characteristics, such as opening, dynamics, self-organization, non-balance etc. Traditional model cannot simulate these social, economic and natural driving forces of LUCC including main feedback relation from LUCC to driving force. 2. Establishment of Markov extended model of LUCC analog research in urbanization process. Firstly, use traditional LUCC research model to compute change speed of regional land use through calculating dynamic degree, exploitation degree and consumption degree of land use; use the theory of fuzzy set to rewrite the traditional Markov model, establish structure transfer matrix of land use, forecast and analyze dynamic change and development trend of land use, and present noticeable problems and corresponding measures in urbanization process according to research results. 3. Application of intelligent computation research and complexity science research method in LUCC analog model in urbanization process. On the basis of detailed elaboration of the theory and the model of LUCC research in urbanization process, analyze the problems of existing model used in LUCC research (namely, difficult to resolve many complexity phenomena in complex urban space system), discuss possible structure realization forms of LUCC analog research in combination with the theories of intelligent computation and complexity science research. Perform application analysis on BP artificial neural network and genetic algorithms of intelligent computation and CA model and MAS technology of complexity science research, discuss their theoretical origins and their own characteristics in detail, elaborate the feasibility of them in LUCC analog research, and bring forward improvement methods and measures on existing problems of this kind of model. 4. Establishment of LUCC analog model in urbanization process based on theories of intelligent computation and complexity science. Based on the research on abovementioned BP artificial neural network, genetic algorithms, CA model and multi-agent technology, put forward improvement methods and application assumption towards their expansion on geography, build LUCC analog model in urbanization process based on CA model and Agent model, realize the combination of learning mechanism of BP artificial neural network and fuzzy logic reasoning, express the regulation with explicit formula, and amend the initial regulation through self study; optimize network structure of LUCC analog model and methods and procedures of model parameters with genetic algorithms. In this paper, I introduce research theory and methods of complexity science into LUCC analog research and presents LUCC analog model based upon CA model and MAS theory. Meanwhile, I carry out corresponding expansion on traditional Markov model and introduce the theory of fuzzy set into data screening and parameter amendment of improved model to improve the accuracy and feasibility of Markov model in the research on land use/cover change.

Authoring and verification of clinical guidelines: a model driven approach.

PubMed

Pérez, Beatriz; Porres, Ivan

2010-08-01

The goal of this research is to provide a framework to enable authoring and verification of clinical guidelines. The framework is part of a larger research project aimed at improving the representation, quality and application of clinical guidelines in daily clinical practice. The verification process of a guideline is based on (1) model checking techniques to verify guidelines against semantic errors and inconsistencies in their definition, (2) combined with Model Driven Development (MDD) techniques, which enable us to automatically process manually created guideline specifications and temporal-logic statements to be checked and verified regarding these specifications, making the verification process faster and cost-effective. Particularly, we use UML statecharts to represent the dynamics of guidelines and, based on this manually defined guideline specifications, we use a MDD-based tool chain to automatically process them to generate the input model of a model checker. The model checker takes the resulted model together with the specific guideline requirements, and verifies whether the guideline fulfils such properties. The overall framework has been implemented as an Eclipse plug-in named GBDSSGenerator which, particularly, starting from the UML statechart representing a guideline, allows the verification of the guideline against specific requirements. Additionally, we have established a pattern-based approach for defining commonly occurring types of requirements in guidelines. We have successfully validated our overall approach by verifying properties in different clinical guidelines resulting in the detection of some inconsistencies in their definition. The proposed framework allows (1) the authoring and (2) the verification of clinical guidelines against specific requirements defined based on a set of property specification patterns, enabling non-experts to easily write formal specifications and thus easing the verification process. Copyright 2010 Elsevier Inc. All rights reserved.

A Bayesian Network Based Global Sensitivity Analysis Method for Identifying Dominant Processes in a Multi-physics Model

NASA Astrophysics Data System (ADS)

Dai, H.; Chen, X.; Ye, M.; Song, X.; Zachara, J. M.

2016-12-01

Sensitivity analysis has been an important tool in groundwater modeling to identify the influential parameters. Among various sensitivity analysis methods, the variance-based global sensitivity analysis has gained popularity for its model independence characteristic and capability of providing accurate sensitivity measurements. However, the conventional variance-based method only considers uncertainty contribution of single model parameters. In this research, we extended the variance-based method to consider more uncertainty sources and developed a new framework to allow flexible combinations of different uncertainty components. We decompose the uncertainty sources into a hierarchical three-layer structure: scenario, model and parametric. Furthermore, each layer of uncertainty source is capable of containing multiple components. An uncertainty and sensitivity analysis framework was then constructed following this three-layer structure using Bayesian network. Different uncertainty components are represented as uncertain nodes in this network. Through the framework, variance-based sensitivity analysis can be implemented with great flexibility of using different grouping strategies for uncertainty components. The variance-based sensitivity analysis thus is improved to be able to investigate the importance of an extended range of uncertainty sources: scenario, model, and other different combinations of uncertainty components which can represent certain key model system processes (e.g., groundwater recharge process, flow reactive transport process). For test and demonstration purposes, the developed methodology was implemented into a test case of real-world groundwater reactive transport modeling with various uncertainty sources. The results demonstrate that the new sensitivity analysis method is able to estimate accurate importance measurements for any uncertainty sources which were formed by different combinations of uncertainty components. The new methodology can provide useful information for environmental management and decision-makers to formulate policies and strategies.

The Algebra of Sleepiness: Investigating the Interaction of Homeostatic (S) and Circadian (C) Processes in Sleepiness Using Linear Metrics"

ERIC Educational Resources Information Center

Mairesse, Olivier; Hofmans, Joeri; Neu, Daniel; Dinis Monica de Oliveira, Armando Luis; Cluydts, Raymond; Theuns, Peter

2010-01-01

The present studies were conducted to contribute to the debate on the interaction between circadian (C) and homeostatic (S) processes in models of sleep regulation. The Two-Process Model of Sleep Regulation assumes a linear relationship between processes S and C. However, recent elaborations of the model, based on data from forced desynchrony…

Operator Performance Measures for Assessing Voice Communication Effectiveness

DTIC Science & Technology

1989-07-01

performance and work- load assessment techniques have been based.I Broadbent (1958) described a limited capacity filter model of human information...INFORMATION PROCESSING 20 3.1.1. Auditory Attention 20 3.1.2. Auditory Memory 24 3.2. MODELS OF INFORMATION PROCESSING 24 3.2.1. Capacity Theories 25...Learning 0 Attention * Language Specialization • Decision Making• Problem Solving Auditory Information Processing Models of Processing Ooemtor

Developing a CD-CBM Anticipatory Approach for Cavitation - Defining a Model Descriptor Consistent Between Processes

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

Allgood, G.O.; Dress, W.B.; Kercel, S.W.

1999-05-10

A major problem with cavitation in pumps and other hydraulic devices is that there is no effective method for detecting or predicting its inception. The traditional approach is to declare the pump in cavitation when the total head pressure drops by some arbitrary value (typically 3o/0) in response to a reduction in pump inlet pressure. However, the pump is already cavitating at this point. A method is needed in which cavitation events are captured as they occur and characterized by their process dynamics. The object of this research was to identify specific features of cavitation that could be used asmore » a model-based descriptor in a context-dependent condition-based maintenance (CD-CBM) anticipatory prognostic and health assessment model. This descriptor was based on the physics of the phenomena, capturing the salient features of the process dynamics. An important element of this concept is the development and formulation of the extended process feature vector @) or model vector. Thk model-based descriptor encodes the specific information that describes the phenomena and its dynamics and is formulated as a data structure consisting of several elements. The first is a descriptive model abstracting the phenomena. The second is the parameter list associated with the functional model. The third is a figure of merit, a single number between [0,1] representing a confidence factor that the functional model and parameter list actually describes the observed data. Using this as a basis and applying it to the cavitation problem, any given location in a flow loop will have this data structure, differing in value but not content. The extended process feature vector is formulated as follows: E`> [ , {parameter Iist}, confidence factor]. (1) For this study, the model that characterized cavitation was a chirped-exponentially decaying sinusoid. Using the parameters defined by this model, the parameter list included frequency, decay, and chirp rate. Based on this, the process feature vector has the form: @=> [, {01 = a, ~= b, ~ = c}, cf = 0.80]. (2) In this experiment a reversible catastrophe was examined. The reason for this is that the same catastrophe could be repeated to ensure the statistical significance of the data.« less

Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems

NASA Astrophysics Data System (ADS)

Hunter, Jason M.; Maier, Holger R.; Gibbs, Matthew S.; Foale, Eloise R.; Grosvenor, Naomi A.; Harders, Nathan P.; Kikuchi-Miller, Tahali C.

2018-05-01

Salinity modelling in river systems is complicated by a number of processes, including in-stream salt transport and various mechanisms of saline accession that vary dynamically as a function of water level and flow, often at different temporal scales. Traditionally, salinity models in rivers have either been process- or data-driven. The primary problem with process-based models is that in many instances, not all of the underlying processes are fully understood or able to be represented mathematically. There are also often insufficient historical data to support model development. The major limitation of data-driven models, such as artificial neural networks (ANNs) in comparison, is that they provide limited system understanding and are generally not able to be used to inform management decisions targeting specific processes, as different processes are generally modelled implicitly. In order to overcome these limitations, a generic framework for developing hybrid process and data-driven models of salinity in river systems is introduced and applied in this paper. As part of the approach, the most suitable sub-models are developed for each sub-process affecting salinity at the location of interest based on consideration of model purpose, the degree of process understanding and data availability, which are then combined to form the hybrid model. The approach is applied to a 46 km reach of the Murray River in South Australia, which is affected by high levels of salinity. In this reach, the major processes affecting salinity include in-stream salt transport, accession of saline groundwater along the length of the reach and the flushing of three waterbodies in the floodplain during overbank flows of various magnitudes. Based on trade-offs between the degree of process understanding and data availability, a process-driven model is developed for in-stream salt transport, an ANN model is used to model saline groundwater accession and three linear regression models are used to account for the flushing of the different floodplain storages. The resulting hybrid model performs very well on approximately 3 years of daily validation data, with a Nash-Sutcliffe efficiency (NSE) of 0.89 and a root mean squared error (RMSE) of 12.62 mg L-1 (over a range from approximately 50 to 250 mg L-1). Each component of the hybrid model results in noticeable improvements in model performance corresponding to the range of flows for which they are developed. The predictive performance of the hybrid model is significantly better than that of a benchmark process-driven model (NSE = -0.14, RMSE = 41.10 mg L-1, Gbench index = 0.90) and slightly better than that of a benchmark data-driven (ANN) model (NSE = 0.83, RMSE = 15.93 mg L-1, Gbench index = 0.36). Apart from improved predictive performance, the hybrid model also has advantages over the ANN benchmark model in terms of increased capacity for improving system understanding and greater ability to support management decisions.

Optimal Estimation with Two Process Models and No Measurements

DTIC Science & Technology

2015-08-01

models will be lost if either of the models includes deterministic modeling errors. 12 5. References and Notes 1. Brown RG, Hwang PYC. Introduction to...independent process models when no measurements are present. The observer follows a derivation similar to that of the discrete time Kalman filter. A simulation...discrete time Kalman filter. A simulation example is provided in which a process model based on the dynamics of a ballistic projectile is blended with an

A Co-modeling Method Based on Component Features for Mechatronic Devices in Aero-engines

NASA Astrophysics Data System (ADS)

Wang, Bin; Zhao, Haocen; Ye, Zhifeng

2017-08-01

Data-fused and user-friendly design of aero-engine accessories is required because of their structural complexity and stringent reliability. This paper gives an overview of a typical aero-engine control system and the development process of key mechatronic devices used. Several essential aspects of modeling and simulation in the process are investigated. Considering the limitations of a single theoretic model, feature-based co-modeling methodology is suggested to satisfy the design requirements and compensate for diversity of component sub-models for these devices. As an example, a stepper motor controlled Fuel Metering Unit (FMU) is modeled in view of the component physical features using two different software tools. An interface is suggested to integrate the single discipline models into the synthesized one. Performance simulation of this device using the co-model and parameter optimization for its key components are discussed. Comparison between delivery testing and the simulation shows that the co-model for the FMU has a high accuracy and the absolute superiority over a single model. Together with its compatible interface with the engine mathematical model, the feature-based co-modeling methodology is proven to be an effective technical measure in the development process of the device.

Finding Groups Using Model-Based Cluster Analysis: Heterogeneous Emotional Self-Regulatory Processes and Heavy Alcohol Use Risk

ERIC Educational Resources Information Center

Mun, Eun Young; von Eye, Alexander; Bates, Marsha E.; Vaschillo, Evgeny G.

2008-01-01

Model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities. It is an inferentially based, statistically principled procedure that allows comparison of nonnested models using the Bayesian information criterion to compare multiple models and identify the…

[Cognitive Functions in the Prefrontal Association Cortex; Transitive Inference and the Lateral Prefrontal Cortex].

PubMed

Tanaka, Shingo; Oguchi, Mineki; Sakagami, Masamichi

2016-11-01

To behave appropriately in a complex and uncertain world, the brain makes use of several distinct learning systems. One such system is called the "model-free process", via which conditioning allows the association between a stimulus or response and a given reward to be learned. Another system is called the "model-based process". Via this process, the state transition between a stimulus and a response is learned so that the brain is able to plan actions prior to their execution. Several studies have tried to relate the difference between model-based and model-free processes to the difference in functions of the lateral prefrontal cortex (LPFC) and the striatum. Here, we describe a series of studies that demonstrate the ability of LPFC neurons to categorize visual stimuli by their associated behavioral responses and to generate abstract information. If LPFC neurons utilize abstract code to associate a stimulus with a reward, they should be able to infer similar relationships between other stimuli of the same category and their rewards without direct experience of these stimulus-reward contingencies. We propose that this ability of LPFC neurons to utilize abstract information can contribute to the model-based learning process.

Mammalian cell culture process for monoclonal antibody production: nonlinear modelling and parameter estimation.

PubMed

Selişteanu, Dan; Șendrescu, Dorin; Georgeanu, Vlad; Roman, Monica

2015-01-01

Monoclonal antibodies (mAbs) are at present one of the fastest growing products of pharmaceutical industry, with widespread applications in biochemistry, biology, and medicine. The operation of mAbs production processes is predominantly based on empirical knowledge, the improvements being achieved by using trial-and-error experiments and precedent practices. The nonlinearity of these processes and the absence of suitable instrumentation require an enhanced modelling effort and modern kinetic parameter estimation strategies. The present work is dedicated to nonlinear dynamic modelling and parameter estimation for a mammalian cell culture process used for mAb production. By using a dynamical model of such kind of processes, an optimization-based technique for estimation of kinetic parameters in the model of mammalian cell culture process is developed. The estimation is achieved as a result of minimizing an error function by a particle swarm optimization (PSO) algorithm. The proposed estimation approach is analyzed in this work by using a particular model of mammalian cell culture, as a case study, but is generic for this class of bioprocesses. The presented case study shows that the proposed parameter estimation technique provides a more accurate simulation of the experimentally observed process behaviour than reported in previous studies.

Mammalian Cell Culture Process for Monoclonal Antibody Production: Nonlinear Modelling and Parameter Estimation

PubMed Central

Selişteanu, Dan; Șendrescu, Dorin; Georgeanu, Vlad

2015-01-01

Monoclonal antibodies (mAbs) are at present one of the fastest growing products of pharmaceutical industry, with widespread applications in biochemistry, biology, and medicine. The operation of mAbs production processes is predominantly based on empirical knowledge, the improvements being achieved by using trial-and-error experiments and precedent practices. The nonlinearity of these processes and the absence of suitable instrumentation require an enhanced modelling effort and modern kinetic parameter estimation strategies. The present work is dedicated to nonlinear dynamic modelling and parameter estimation for a mammalian cell culture process used for mAb production. By using a dynamical model of such kind of processes, an optimization-based technique for estimation of kinetic parameters in the model of mammalian cell culture process is developed. The estimation is achieved as a result of minimizing an error function by a particle swarm optimization (PSO) algorithm. The proposed estimation approach is analyzed in this work by using a particular model of mammalian cell culture, as a case study, but is generic for this class of bioprocesses. The presented case study shows that the proposed parameter estimation technique provides a more accurate simulation of the experimentally observed process behaviour than reported in previous studies. PMID:25685797

[Monitoring method of extraction process for Schisandrae Chinensis Fructus based on near infrared spectroscopy and multivariate statistical process control].

PubMed

Xu, Min; Zhang, Lei; Yue, Hong-Shui; Pang, Hong-Wei; Ye, Zheng-Liang; Ding, Li

2017-10-01

To establish an on-line monitoring method for extraction process of Schisandrae Chinensis Fructus, the formula medicinal material of Yiqi Fumai lyophilized injection by combining near infrared spectroscopy with multi-variable data analysis technology. The multivariate statistical process control (MSPC) model was established based on 5 normal batches in production and 2 test batches were monitored by PC scores, DModX and Hotelling T2 control charts. The results showed that MSPC model had a good monitoring ability for the extraction process. The application of the MSPC model to actual production process could effectively achieve on-line monitoring for extraction process of Schisandrae Chinensis Fructus, and can reflect the change of material properties in the production process in real time. This established process monitoring method could provide reference for the application of process analysis technology in the process quality control of traditional Chinese medicine injections. Copyright© by the Chinese Pharmaceutical Association.

Model-based evaluation of two BNR processes--UCT and A2N.

PubMed

Hao, X; Van Loosdrecht, M C; Meijer, S C; Qian, Y

2001-08-01

The activity of denitrifying P-accumulating bacteria (DPB) has been verified to exist in most WWTPs with biological nutrient removal (BNR). The modified UCT process has a high content of DPB. A new BNR process with a two-sludge system named A2N was especially developed to exploit denitrifying dephosphatation. With the identical inflow and effluent standards, an existing full-scale UCT-type WWTP and a designed A2N process were evaluated by simulation. The used model is based on the Delft metabolical model for bio-P removal and ASM2d model for COD and N removal. Both processes accommodate denitrifying dephosphatation, but the A2N process has a more stable performance in N removal. Although excess sludge is increased by 6%, the A2N process leads to savings of 35, 85 and 30% in aeration energy, mixed liquor internal recirculation and land occupation respectively, as compared to the UCT process. Low temperature has a negative effect on growth of poly-P bacteria, which becomes to especially appear in the A2N process.

Edgar Schein's Process versus Content Consultation Models.

ERIC Educational Resources Information Center

Rockwood, Gary F.

1993-01-01

Describes Schein's three models of consultation based on assumptions inherent in different helping styles: purchase of expertise and doctor-patient models, which focus on content of organization problems; and process consultation model, which focuses on how organizational problems are solved. Notes that Schein has suggested that consultants begin…

Memory-Based Simple Heuristics as Attribute Substitution: Competitive Tests of Binary Choice Inference Models.

PubMed

Honda, Hidehito; Matsuka, Toshihiko; Ueda, Kazuhiro

2017-05-01

Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in terms of attribute substitution in heuristic use (Kahneman & Frederick, 2005). In this framework, it is predicted that people will rely on heuristic or knowledge-based inference depending on the subjective difficulty of the inference task. We conducted competitive tests of binary choice inference models representing simple heuristics (fluency and familiarity heuristics) and knowledge-based inference models. We found that a simple heuristic model (especially a familiarity heuristic model) explained inference patterns for subjectively difficult inference tasks, and that a knowledge-based inference model explained subjectively easy inference tasks. These results were consistent with the predictions of the attribute substitution framework. Issues on usage of simple heuristics and psychological processes are discussed. Copyright © 2016 Cognitive Science Society, Inc.

A dual-process account of auditory change detection.

PubMed

McAnally, Ken I; Martin, Russell L; Eramudugolla, Ranmalee; Stuart, Geoffrey W; Irvine, Dexter R F; Mattingley, Jason B

2010-08-01

Listeners can be "deaf" to a substantial change in a scene comprising multiple auditory objects unless their attention has been directed to the changed object. It is unclear whether auditory change detection relies on identification of the objects in pre- and post-change scenes. We compared the rates at which listeners correctly identify changed objects with those predicted by change-detection models based on signal detection theory (SDT) and high-threshold theory (HTT). Detected changes were not identified as accurately as predicted by models based on either theory, suggesting that some changes are detected by a process that does not support change identification. Undetected changes were identified as accurately as predicted by the HTT model but much less accurately than predicted by the SDT models. The process underlying change detection was investigated further by determining receiver-operating characteristics (ROCs). ROCs did not conform to those predicted by either a SDT or a HTT model but were well modeled by a dual-process that incorporated HTT and SDT components. The dual-process model also accurately predicted the rates at which detected and undetected changes were correctly identified.

Model-Based Diagnostics for Propellant Loading Systems

NASA Technical Reports Server (NTRS)

Daigle, Matthew John; Foygel, Michael; Smelyanskiy, Vadim N.

2011-01-01

The loading of spacecraft propellants is a complex, risky operation. Therefore, diagnostic solutions are necessary to quickly identify when a fault occurs, so that recovery actions can be taken or an abort procedure can be initiated. Model-based diagnosis solutions, established using an in-depth analysis and understanding of the underlying physical processes, offer the advanced capability to quickly detect and isolate faults, identify their severity, and predict their effects on system performance. We develop a physics-based model of a cryogenic propellant loading system, which describes the complex dynamics of liquid hydrogen filling from a storage tank to an external vehicle tank, as well as the influence of different faults on this process. The model takes into account the main physical processes such as highly nonequilibrium condensation and evaporation of the hydrogen vapor, pressurization, and also the dynamics of liquid hydrogen and vapor flows inside the system in the presence of helium gas. Since the model incorporates multiple faults in the system, it provides a suitable framework for model-based diagnostics and prognostics algorithms. Using this model, we analyze the effects of faults on the system, derive symbolic fault signatures for the purposes of fault isolation, and perform fault identification using a particle filter approach. We demonstrate the detection, isolation, and identification of a number of faults using simulation-based experiments.

Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models.

PubMed

Yu, Kezi; Quirk, J Gerald; Djurić, Petar M

2017-01-01

In this paper, we propose an application of non-parametric Bayesian (NPB) models for classification of fetal heart rate (FHR) recordings. More specifically, we propose models that are used to differentiate between FHR recordings that are from fetuses with or without adverse outcomes. In our work, we rely on models based on hierarchical Dirichlet processes (HDP) and the Chinese restaurant process with finite capacity (CRFC). Two mixture models were inferred from real recordings, one that represents healthy and another, non-healthy fetuses. The models were then used to classify new recordings and provide the probability of the fetus being healthy. First, we compared the classification performance of the HDP models with that of support vector machines on real data and concluded that the HDP models achieved better performance. Then we demonstrated the use of mixture models based on CRFC for dynamic classification of the performance of (FHR) recordings in a real-time setting.

Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models

PubMed Central

Yu, Kezi; Quirk, J. Gerald

2017-01-01

In this paper, we propose an application of non-parametric Bayesian (NPB) models for classification of fetal heart rate (FHR) recordings. More specifically, we propose models that are used to differentiate between FHR recordings that are from fetuses with or without adverse outcomes. In our work, we rely on models based on hierarchical Dirichlet processes (HDP) and the Chinese restaurant process with finite capacity (CRFC). Two mixture models were inferred from real recordings, one that represents healthy and another, non-healthy fetuses. The models were then used to classify new recordings and provide the probability of the fetus being healthy. First, we compared the classification performance of the HDP models with that of support vector machines on real data and concluded that the HDP models achieved better performance. Then we demonstrated the use of mixture models based on CRFC for dynamic classification of the performance of (FHR) recordings in a real-time setting. PMID:28953927

Argumentation in Science Education: A Model-Based Framework

ERIC Educational Resources Information Center

Bottcher, Florian; Meisert, Anke

2011-01-01

The goal of this article is threefold: First, the theoretical background for a model-based framework of argumentation to describe and evaluate argumentative processes in science education is presented. Based on the general model-based perspective in cognitive science and the philosophy of science, it is proposed to understand arguments as reasons…

Model-Based Fatigue Prognosis of Fiber-Reinforced Laminates Exhibiting Concurrent Damage Mechanisms

NASA Technical Reports Server (NTRS)

Corbetta, M.; Sbarufatti, C.; Saxena, A.; Giglio, M.; Goebel, K.

2016-01-01

Prognostics of large composite structures is a topic of increasing interest in the field of structural health monitoring for aerospace, civil, and mechanical systems. Along with recent advancements in real-time structural health data acquisition and processing for damage detection and characterization, model-based stochastic methods for life prediction are showing promising results in the literature. Among various model-based approaches, particle-filtering algorithms are particularly capable in coping with uncertainties associated with the process. These include uncertainties about information on the damage extent and the inherent uncertainties of the damage propagation process. Some efforts have shown successful applications of particle filtering-based frameworks for predicting the matrix crack evolution and structural stiffness degradation caused by repetitive fatigue loads. Effects of other damage modes such as delamination, however, are not incorporated in these works. It is well established that delamination and matrix cracks not only co-exist in most laminate structures during the fatigue degradation process but also affect each other's progression. Furthermore, delamination significantly alters the stress-state in the laminates and accelerates the material degradation leading to catastrophic failure. Therefore, the work presented herein proposes a particle filtering-based framework for predicting a structure's remaining useful life with consideration of multiple co-existing damage-mechanisms. The framework uses an energy-based model from the composite modeling literature. The multiple damage-mode model has been shown to suitably estimate the energy release rate of cross-ply laminates as affected by matrix cracks and delamination modes. The model is also able to estimate the reduction in stiffness of the damaged laminate. This information is then used in the algorithms for life prediction capabilities. First, a brief summary of the energy-based damage model is provided. Then, the paper describes how the model is embedded within the prognostic framework and how the prognostics performance is assessed using observations from run-to-failure experiments

Reason and reaction: the utility of a dual-focus, dual-processing perspective on promotion and prevention of adolescent health risk behaviour.

PubMed

Gibbons, Frederick X; Houlihan, Amy E; Gerrard, Meg

2009-05-01

A brief overview of theories of health behaviour that are based on the expectancy-value perspective is presented. This approach maintains that health behaviours are the result of a deliberative decision-making process that involves consideration of behavioural options along with anticipated outcomes associated with those options. It is argued that this perspective is effective at explaining and predicting many types of health behaviour, including health-promoting actions (e.g. UV protection, condom use, smoking cessation), but less effective at predicting risky health behaviours, such as unprotected, casual sex, drunk driving or binge drinking. These are behaviours that are less reasoned or premeditated - especially among adolescents. An argument is made for incorporating elements of dual-processing theories in an effort to improve the 'utility' of these models. Specifically, it is suggested that adolescent health behaviour involves both analytic and heuristic processing. Both types of processing are incorporated in the prototype-willingness (prototype) model, which is described in some detail. Studies of health behaviour based on the expectancy-value perspective (e.g. theory of reasoned action) are reviewed, along with studies based on the prototype model. These two sets of studies together suggest that the dual-processing perspective, in general, and the prototype model, in particular, add to the predictive validity of expectancy-value models for predicting adolescent health behaviour. Research and interventions that incorporate elements of dual-processing and elements of expectancy-value are more effective at explaining and changing adolescent health behaviour than are those based on expectancy-value theories alone.

Social network supported process recommender system.

PubMed

Ye, Yanming; Yin, Jianwei; Xu, Yueshen

2014-01-01

Process recommendation technologies have gained more and more attention in the field of intelligent business process modeling to assist the process modeling. However, most of the existing technologies only use the process structure analysis and do not take the social features of processes into account, while the process modeling is complex and comprehensive in most situations. This paper studies the feasibility of social network research technologies on process recommendation and builds a social network system of processes based on the features similarities. Then, three process matching degree measurements are presented and the system implementation is discussed subsequently. Finally, experimental evaluations and future works are introduced.

The Scenario-Based Engineering Process (SEP): a user-centered approach for the development of health care systems.

PubMed

Harbison, K; Kelly, J; Burnell, L; Silva, J

1995-01-01

The Scenario-based Engineering Process (SEP) is a user-focused methodology for large and complex system design. This process supports new application development from requirements analysis with domain models to component selection, design and modification, implementation, integration, and archival placement. It is built upon object-oriented methodologies, domain modeling strategies, and scenario-based techniques to provide an analysis process for mapping application requirements to available components. We are using SEP in the health care applications that we are developing. The process has already achieved success in the manufacturing and military domains and is being adopted by many organizations. SEP should prove viable in any domain containing scenarios that can be decomposed into tasks.

A UML-based metamodel for software evolution process

NASA Astrophysics Data System (ADS)

Jiang, Zuo; Zhou, Wei-Hong; Fu, Zhi-Tao; Xiong, Shun-Qing

2014-04-01

A software evolution process is a set of interrelated software processes under which the corresponding software is evolving. An object-oriented software evolution process meta-model (OO-EPMM), abstract syntax and formal OCL constraint of meta-model are presented in this paper. OO-EPMM can not only represent software development process, but also represent software evolution.

Momentum Concept in the Process of Knowledge Construction

ERIC Educational Resources Information Center

Ergul, N. Remziye

2013-01-01

Abstraction is one of the methods for learning knowledge with using mental processes that cannot be obtained through experiment and observation. RBC model that is based on abstraction in the process of creating knowledge is directly related to mental processes. In this study, the RBC model is used for the high school students' processes of…

A model of the hierarchy of behaviour, cognition, and consciousness.

PubMed

Toates, Frederick

2006-03-01

Processes comparable in important respects to those underlying human conscious and non-conscious processing can be identified in a range of species and it is argued that these reflect evolutionary precursors of the human processes. A distinction is drawn between two types of processing: (1) stimulus-based and (2) higher-order. For 'higher-order,' in humans the operations of processing are themselves associated with conscious awareness. Conscious awareness sets the context for stimulus-based processing and its end-point is accessible to conscious awareness. However, the mechanics of the translation between stimulus and response proceeds without conscious control. The paper argues that higher-order processing is an evolutionary addition to stimulus-based processing. The model's value is shown for gaining insight into a range of phenomena and their link with consciousness. These include brain damage, learning, memory, development, vision, emotion, motor control, reasoning, the voluntary versus involuntary debate, and mental disorder.

Model-based query language for analyzing clinical processes.

PubMed

Barzdins, Janis; Barzdins, Juris; Rencis, Edgars; Sostaks, Agris

2013-01-01

Nowadays large databases of clinical process data exist in hospitals. However, these data are rarely used in full scope. In order to perform queries on hospital processes, one must either choose from the predefined queries or develop queries using MS Excel-type software system, which is not always a trivial task. In this paper we propose a new query language for analyzing clinical processes that is easily perceptible also by non-IT professionals. We develop this language based on a process modeling language which is also described in this paper. Prototypes of both languages have already been verified using real examples from hospitals.

SIG-VISA: Signal-based Vertically Integrated Seismic Monitoring

NASA Astrophysics Data System (ADS)

Moore, D.; Mayeda, K. M.; Myers, S. C.; Russell, S.

2013-12-01

Traditional seismic monitoring systems rely on discrete detections produced by station processing software; however, while such detections may constitute a useful summary of station activity, they discard large amounts of information present in the original recorded signal. We present SIG-VISA (Signal-based Vertically Integrated Seismic Analysis), a system for seismic monitoring through Bayesian inference on seismic signals. By directly modeling the recorded signal, our approach incorporates additional information unavailable to detection-based methods, enabling higher sensitivity and more accurate localization using techniques such as waveform matching. SIG-VISA's Bayesian forward model of seismic signal envelopes includes physically-derived models of travel times and source characteristics as well as Gaussian process (kriging) statistical models of signal properties that combine interpolation of historical data with extrapolation of learned physical trends. Applying Bayesian inference, we evaluate the model on earthquakes as well as the 2009 DPRK test event, demonstrating a waveform matching effect as part of the probabilistic inference, along with results on event localization and sensitivity. In particular, we demonstrate increased sensitivity from signal-based modeling, in which the SIGVISA signal model finds statistical evidence for arrivals even at stations for which the IMS station processing failed to register any detection.

Investigation of model-based physical design restrictions (Invited Paper)

NASA Astrophysics Data System (ADS)

Lucas, Kevin; Baron, Stanislas; Belledent, Jerome; Boone, Robert; Borjon, Amandine; Couderc, Christophe; Patterson, Kyle; Riviere-Cazaux, Lionel; Rody, Yves; Sundermann, Frank; Toublan, Olivier; Trouiller, Yorick; Urbani, Jean-Christophe; Wimmer, Karl

2005-05-01

As lithography and other patterning processes become more complex and more non-linear with each generation, the task of physical design rules necessarily increases in complexity also. The goal of the physical design rules is to define the boundary between the physical layout structures which will yield well from those which will not. This is essentially a rule-based pre-silicon guarantee of layout correctness. However the rapid increase in design rule requirement complexity has created logistical problems for both the design and process functions. Therefore, similar to the semiconductor industry's transition from rule-based to model-based optical proximity correction (OPC) due to increased patterning complexity, opportunities for improving physical design restrictions by implementing model-based physical design methods are evident. In this paper we analyze the possible need and applications for model-based physical design restrictions (MBPDR). We first analyze the traditional design rule evolution, development and usage methodologies for semiconductor manufacturers. Next we discuss examples of specific design rule challenges requiring new solution methods in the patterning regime of low K1 lithography and highly complex RET. We then evaluate possible working strategies for MBPDR in the process development and product design flows, including examples of recent model-based pre-silicon verification techniques. Finally we summarize with a proposed flow and key considerations for MBPDR implementation.

New Vistas in Chemical Product and Process Design.

PubMed

Zhang, Lei; Babi, Deenesh K; Gani, Rafiqul

2016-06-07

Design of chemicals-based products is broadly classified into those that are process centered and those that are product centered. In this article, the designs of both classes of products are reviewed from a process systems point of view; developments related to the design of the chemical product, its corresponding process, and its integration are highlighted. Although significant advances have been made in the development of systematic model-based techniques for process design (also for optimization, operation, and control), much work is needed to reach the same level for product design. Timeline diagrams illustrating key contributions in product design, process design, and integrated product-process design are presented. The search for novel, innovative, and sustainable solutions must be matched by consideration of issues related to the multidisciplinary nature of problems, the lack of data needed for model development, solution strategies that incorporate multiscale options, and reliability versus predictive power. The need for an integrated model-experiment-based design approach is discussed together with benefits of employing a systematic computer-aided framework with built-in design templates.

Development of optimization model for sputtering process parameter based on gravitational search algorithm

NASA Astrophysics Data System (ADS)

Norlina, M. S.; Diyana, M. S. Nor; Mazidah, P.; Rusop, M.

2016-07-01

In the RF magnetron sputtering process, the desirable layer properties are largely influenced by the process parameters and conditions. If the quality of the thin film has not reached up to its intended level, the experiments have to be repeated until the desirable quality has been met. This research is proposing Gravitational Search Algorithm (GSA) as the optimization model to reduce the time and cost to be spent in the thin film fabrication. The optimization model's engine has been developed using Java. The model is developed based on GSA concept, which is inspired by the Newtonian laws of gravity and motion. In this research, the model is expected to optimize four deposition parameters which are RF power, deposition time, oxygen flow rate and substrate temperature. The results have turned out to be promising and it could be concluded that the performance of the model is satisfying in this parameter optimization problem. Future work could compare GSA with other nature based algorithms and test them with various set of data.

Space-based infrared scanning sensor LOS determination and calibration using star observation

NASA Astrophysics Data System (ADS)

Chen, Jun; Xu, Zhan; An, Wei; Deng, Xin-Pu; Yang, Jun-Gang

2015-10-01

This paper provides a novel methodology for removing sensor bias from a space based infrared (IR) system (SBIRS) through the use of stars detected in the background field of the sensor. Space based IR system uses the LOS (line of sight) of target for target location. LOS determination and calibration is the key precondition of accurate location and tracking of targets in Space based IR system and the LOS calibration of scanning sensor is one of the difficulties. The subsequent changes of sensor bias are not been taking into account in the conventional LOS determination and calibration process. Based on the analysis of the imaging process of scanning sensor, a theoretical model based on the estimation of bias angles using star observation is proposed. By establishing the process model of the bias angles and the observation model of stars, using an extended Kalman filter (EKF) to estimate the bias angles, and then calibrating the sensor LOS. Time domain simulations results indicate that the proposed method has a high precision and smooth performance for sensor LOS determination and calibration. The timeliness and precision of target tracking process in the space based infrared (IR) tracking system could be met with the proposed algorithm.

Evaluating a process-based model for use in streambank stabilization and stream restoration: insights on the bank stability and toe erosion model (BSTEM)

USDA-ARS?s Scientific Manuscript database

Streambank retreat is a complex cyclical process involving subaerial processes, fluvial erosion, seepage erosion, and geotechnical failures and is driven by several soil properties that themselves are temporally and spatially variable. Therefore, it can be extremely challenging to predict and model ...

Networks for image acquisition, processing and display

NASA Technical Reports Server (NTRS)

Ahumada, Albert J., Jr.

1990-01-01

The human visual system comprises layers of networks which sample, process, and code images. Understanding these networks is a valuable means of understanding human vision and of designing autonomous vision systems based on network processing. Ames Research Center has an ongoing program to develop computational models of such networks. The models predict human performance in detection of targets and in discrimination of displayed information. In addition, the models are artificial vision systems sharing properties with biological vision that has been tuned by evolution for high performance. Properties include variable density sampling, noise immunity, multi-resolution coding, and fault-tolerance. The research stresses analysis of noise in visual networks, including sampling, photon, and processing unit noises. Specific accomplishments include: models of sampling array growth with variable density and irregularity comparable to that of the retinal cone mosaic; noise models of networks with signal-dependent and independent noise; models of network connection development for preserving spatial registration and interpolation; multi-resolution encoding models based on hexagonal arrays (HOP transform); and mathematical procedures for simplifying analysis of large networks.

PREDICTING SUBSURFACE CONTAMINANT TRANSPORT AND TRANSFORMATION: CONSIDERATIONS FOR MODEL SELECTION AND FIELD VALIDATION

EPA Science Inventory

Predicting subsurface contaminant transport and transformation requires mathematical models based on a variety of physical, chemical, and biological processes. The mathematical model is an attempt to quantitatively describe observed processes in order to permit systematic forecas...

Animated-simulation modeling facilitates clinical-process costing.

PubMed

Zelman, W N; Glick, N D; Blackmore, C C

2001-09-01

Traditionally, the finance department has assumed responsibility for assessing process costs in healthcare organizations. To enhance process-improvement efforts, however, many healthcare providers need to include clinical staff in process cost analysis. Although clinical staff often use electronic spreadsheets to model the cost of specific processes, PC-based animated-simulation tools offer two major advantages over spreadsheets: they allow clinicians to interact more easily with the costing model so that it more closely represents the process being modeled, and they represent cost output as a cost range rather than as a single cost estimate, thereby providing more useful information for decision making.

Results of research on development of an intellectual information system of bankruptcy risk assessment of the enterprise

NASA Astrophysics Data System (ADS)

Telipenko, E.; Chernysheva, T.; Zakharova, A.; Dumchev, A.

2015-10-01

The article represents research results about the knowledge base development for the intellectual information system for the bankruptcy risk assessment of the enterprise. It is described the process analysis of the knowledge base development; the main process stages, some problems and their solutions are given. The article introduces the connectionist model for the bankruptcy risk assessment based on the analysis of industrial enterprise financial accounting. The basis for this connectionist model is a three-layer perceptron with the back propagation of error algorithm. The knowledge base for the intellectual information system consists of processed information and the processing operation method represented as the connectionist model. The article represents the structure of the intellectual information system, the knowledge base, and the information processing algorithm for neural network training. The paper shows mean values of 10 indexes for industrial enterprises; with the help of them it is possible to carry out a financial analysis of industrial enterprises and identify correctly the current situation for well-timed managerial decisions. Results are given about neural network testing on the data of both bankrupt and financially strong enterprises, which were not included into training and test sets.

A stochastic hybrid systems based framework for modeling dependent failure processes

PubMed Central

Fan, Mengfei; Zeng, Zhiguo; Zio, Enrico; Kang, Rui; Chen, Ying

2017-01-01

In this paper, we develop a framework to model and analyze systems that are subject to dependent, competing degradation processes and random shocks. The degradation processes are described by stochastic differential equations, whereas transitions between the system discrete states are triggered by random shocks. The modeling is, then, based on Stochastic Hybrid Systems (SHS), whose state space is comprised of a continuous state determined by stochastic differential equations and a discrete state driven by stochastic transitions and reset maps. A set of differential equations are derived to characterize the conditional moments of the state variables. System reliability and its lower bounds are estimated from these conditional moments, using the First Order Second Moment (FOSM) method and Markov inequality, respectively. The developed framework is applied to model three dependent failure processes from literature and a comparison is made to Monte Carlo simulations. The results demonstrate that the developed framework is able to yield an accurate estimation of reliability with less computational costs compared to traditional Monte Carlo-based methods. PMID:28231313

A stochastic hybrid systems based framework for modeling dependent failure processes.

PubMed

Fan, Mengfei; Zeng, Zhiguo; Zio, Enrico; Kang, Rui; Chen, Ying

2017-01-01

In this paper, we develop a framework to model and analyze systems that are subject to dependent, competing degradation processes and random shocks. The degradation processes are described by stochastic differential equations, whereas transitions between the system discrete states are triggered by random shocks. The modeling is, then, based on Stochastic Hybrid Systems (SHS), whose state space is comprised of a continuous state determined by stochastic differential equations and a discrete state driven by stochastic transitions and reset maps. A set of differential equations are derived to characterize the conditional moments of the state variables. System reliability and its lower bounds are estimated from these conditional moments, using the First Order Second Moment (FOSM) method and Markov inequality, respectively. The developed framework is applied to model three dependent failure processes from literature and a comparison is made to Monte Carlo simulations. The results demonstrate that the developed framework is able to yield an accurate estimation of reliability with less computational costs compared to traditional Monte Carlo-based methods.

Chronic motivational state interacts with task reward structure in dynamic decision-making.

PubMed

Cooper, Jessica A; Worthy, Darrell A; Maddox, W Todd

2015-12-01

Research distinguishes between a habitual, model-free system motivated toward immediately rewarding actions, and a goal-directed, model-based system motivated toward actions that improve future state. We examined the balance of processing in these two systems during state-based decision-making. We tested a regulatory fit hypothesis (Maddox & Markman, 2010) that predicts that global trait motivation affects the balance of habitual- vs. goal-directed processing but only through its interaction with the task framing as gain-maximization or loss-minimization. We found support for the hypothesis that a match between an individual's chronic motivational state and the task framing enhances goal-directed processing, and thus state-based decision-making. Specifically, chronic promotion-focused individuals under gain-maximization and chronic prevention-focused individuals under loss-minimization both showed enhanced state-based decision-making. Computational modeling indicates that individuals in a match between global chronic motivational state and local task reward structure engaged more goal-directed processing, whereas those in a mismatch engaged more habitual processing. Copyright © 2015 Elsevier Inc. All rights reserved.

On-Ground Processing of Yaogan-24 Remote Sensing Satellite Attitude Data and Verification Using Geometric Field Calibration

PubMed Central

Wang, Mi; Fan, Chengcheng; Yang, Bo; Jin, Shuying; Pan, Jun

2016-01-01

Satellite attitude accuracy is an important factor affecting the geometric processing accuracy of high-resolution optical satellite imagery. To address the problem whereby the accuracy of the Yaogan-24 remote sensing satellite’s on-board attitude data processing is not high enough and thus cannot meet its image geometry processing requirements, we developed an approach involving on-ground attitude data processing and digital orthophoto (DOM) and the digital elevation model (DEM) verification of a geometric calibration field. The approach focuses on three modules: on-ground processing based on bidirectional filter, overall weighted smoothing and fitting, and evaluation in the geometric calibration field. Our experimental results demonstrate that the proposed on-ground processing method is both robust and feasible, which ensures the reliability of the observation data quality, convergence and stability of the parameter estimation model. In addition, both the Euler angle and quaternion could be used to build a mathematical fitting model, while the orthogonal polynomial fitting model is more suitable for modeling the attitude parameter. Furthermore, compared to the image geometric processing results based on on-board attitude data, the image uncontrolled and relative geometric positioning result accuracy can be increased by about 50%. PMID:27483287

One-dimension modeling on the parallel-plate ion extraction process based on a non-electron-equilibrium fluid model

NASA Astrophysics Data System (ADS)

Li, He-Ping; Chen, Jian; Guo, Heng; Jiang, Dong-Jun; Zhou, Ming-Sheng; Department of Engineering Physics Team

2017-10-01

Ion extraction from a plasma under an externally applied electric field involve multi-particle and multi-field interactions, and has wide applications in the fields of materials processing, etching, chemical analysis, etc. In order to develop the high-efficiency ion extraction methods, it is indispensable to establish a feasible model to understand the non-equilibrium transportation processes of the charged particles and the evolutions of the space charge sheath during the extraction process. Most of the previous studies on the ion extraction process are mainly based on the electron-equilibrium fluid model, which assumed that the electrons are in the thermodynamic equilibrium state. However, it may lead to some confusions with neglecting the electron movement during the sheath formation process. In this study, a non-electron-equilibrium model is established to describe the transportation of the charged particles in a parallel-plate ion extraction process. The numerical results show that the formation of the Child-Langmuir sheath is mainly caused by the charge separation. And thus, the sheath shielding effect will be significantly weakened if the charge separation is suppressed during the extraction process of the charged particles.

Quantum-like model of processing of information in the brain based on classical electromagnetic field.

PubMed

Khrennikov, Andrei

2011-09-01

We propose a model of quantum-like (QL) processing of mental information. This model is based on quantum information theory. However, in contrast to models of "quantum physical brain" reducing mental activity (at least at the highest level) to quantum physical phenomena in the brain, our model matches well with the basic neuronal paradigm of the cognitive science. QL information processing is based (surprisingly) on classical electromagnetic signals induced by joint activity of neurons. This novel approach to quantum information is based on representation of quantum mechanics as a version of classical signal theory which was recently elaborated by the author. The brain uses the QL representation (QLR) for working with abstract concepts; concrete images are described by classical information theory. Two processes, classical and QL, are performed parallely. Moreover, information is actively transmitted from one representation to another. A QL concept given in our model by a density operator can generate a variety of concrete images given by temporal realizations of the corresponding (Gaussian) random signal. This signal has the covariance operator coinciding with the density operator encoding the abstract concept under consideration. The presence of various temporal scales in the brain plays the crucial role in creation of QLR in the brain. Moreover, in our model electromagnetic noise produced by neurons is a source of superstrong QL correlations between processes in different spatial domains in the brain; the binding problem is solved on the QL level, but with the aid of the classical background fluctuations. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Airport security inspection process model and optimization based on GSPN

NASA Astrophysics Data System (ADS)

Mao, Shuainan

2018-04-01

Aiming at the efficiency of airport security inspection process, Generalized Stochastic Petri Net is used to establish the security inspection process model. The model is used to analyze the bottleneck problem of airport security inspection process. The solution to the bottleneck is given, which can significantly improve the efficiency and reduce the waiting time by adding the place for people to remove their clothes and the X-ray detector.

A simulation framework for mapping risks in clinical processes: the case of in-patient transfers.

PubMed

Dunn, Adam G; Ong, Mei-Sing; Westbrook, Johanna I; Magrabi, Farah; Coiera, Enrico; Wobcke, Wayne

2011-05-01

To model how individual violations in routine clinical processes cumulatively contribute to the risk of adverse events in hospital using an agent-based simulation framework. An agent-based simulation was designed to model the cascade of common violations that contribute to the risk of adverse events in routine clinical processes. Clinicians and the information systems that support them were represented as a group of interacting agents using data from direct observations. The model was calibrated using data from 101 patient transfers observed in a hospital and results were validated for one of two scenarios (a misidentification scenario and an infection control scenario). Repeated simulations using the calibrated model were undertaken to create a distribution of possible process outcomes. The likelihood of end-of-chain risk is the main outcome measure, reported for each of the two scenarios. The simulations demonstrate end-of-chain risks of 8% and 24% for the misidentification and infection control scenarios, respectively. Over 95% of the simulations in both scenarios are unique, indicating that the in-patient transfer process diverges from prescribed work practices in a variety of ways. The simulation allowed us to model the risk of adverse events in a clinical process, by generating the variety of possible work subject to violations, a novel prospective risk analysis method. The in-patient transfer process has a high proportion of unique trajectories, implying that risk mitigation may benefit from focusing on reducing complexity rather than augmenting the process with further rule-based protocols.

Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection.

PubMed

Hu, Weiming; Gao, Jun; Wang, Yanguo; Wu, Ou; Maybank, Stephen

2014-01-01

Current network intrusion detection systems lack adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two online Adaboost-based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online Gaussian mixture models (GMMs) are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines. The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process that uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO, and SVM-based algorithm effectively combines the local detection models into the global model in each node; the global model in a node can handle the intrusion types that are found in other nodes, without sharing the samples of these intrusion types.

A meta-model based approach for rapid formability estimation of continuous fibre reinforced components

NASA Astrophysics Data System (ADS)

Zimmerling, Clemens; Dörr, Dominik; Henning, Frank; Kärger, Luise

2018-05-01

Due to their high mechanical performance, continuous fibre reinforced plastics (CoFRP) become increasingly important for load bearing structures. In many cases, manufacturing CoFRPs comprises a forming process of textiles. To predict and optimise the forming behaviour of a component, numerical simulations are applied. However, for maximum part quality, both the geometry and the process parameters must match in mutual regard, which in turn requires numerous numerically expensive optimisation iterations. In both textile and metal forming, a lot of research has focused on determining optimum process parameters, whilst regarding the geometry as invariable. In this work, a meta-model based approach on component level is proposed, that provides a rapid estimation of the formability for variable geometries based on pre-sampled, physics-based draping data. Initially, a geometry recognition algorithm scans the geometry and extracts a set of doubly-curved regions with relevant geometry parameters. If the relevant parameter space is not part of an underlying data base, additional samples via Finite-Element draping simulations are drawn according to a suitable design-table for computer experiments. Time saving parallel runs of the physical simulations accelerate the data acquisition. Ultimately, a Gaussian Regression meta-model is built from the data base. The method is demonstrated on a box-shaped generic structure. The predicted results are in good agreement with physics-based draping simulations. Since evaluations of the established meta-model are numerically inexpensive, any further design exploration (e.g. robustness analysis or design optimisation) can be performed in short time. It is expected that the proposed method also offers great potential for future applications along virtual process chains: For each process step along the chain, a meta-model can be set-up to predict the impact of design variations on manufacturability and part performance. Thus, the method is considered to facilitate a lean and economic part and process design under consideration of manufacturing effects.

Using the Logic Model to Plan Extension and Outreach Program Development and Scholarship

ERIC Educational Resources Information Center

Corbin, Marilyn; Kiernan, Nancy Ellen; Koble, Margaret A.; Watson, Jack; Jackson, Daney

2004-01-01

In searching for a process to help program teams of campus-based faculty and field-based educators develop five-year and annual statewide program plans, cooperative extension administrators and specialists in Penn State's College of Agricultural Sciences discovered that the use of the logic model process can influence the successful design of…

A Rule-Based Modeling for the Description of Flexible and Self-healing Business Processes

NASA Astrophysics Data System (ADS)

Boukhebouze, Mohamed; Amghar, Youssef; Benharkat, Aïcha-Nabila; Maamar, Zakaria

In this paper we discuss the importance of ensuring that business processes are label robust and agile at the same time robust and agile. To this end, we consider reviewing the way business processes are managed. For instance we consider offering a flexible way to model processes so that changes in regulations are handled through some self-healing mechanisms. These changes may raise exceptions at run-time if not properly reflected on these processes. To this end we propose a new rule based model that adopts the ECA rules and is built upon formal tools. The business logic of a process can be summarized with a set of rules that implement an organization’s policies. Each business rule is formalized using our ECAPE formalism (Event-Condition-Action-Post condition- post Event). This formalism allows translating a process into a graph of rules that is analyzed in terms of reliably and flexibility.

Economic assessment of single-walled carbon nanotube processes

NASA Astrophysics Data System (ADS)

Isaacs, J. A.; Tanwani, A.; Healy, M. L.; Dahlben, L. J.

2010-02-01

The carbon nanotube market is steadily growing and projected to reach 1.9 billion by 2010. This study examines the economics of manufacturing single-walled carbon nanotubes (SWNT) using process-based cost models developed for arc, CVD, and HiPco processes. Using assumed input parameters, manufacturing costs are calculated for 1 g SWNT for arc, CVD, and HiPco, totaling 1,906, 1,706, and 485, respectively. For each SWNT process, the synthesis and filtration steps showed the highest costs, with direct labor as a primary cost driver. Reductions in production costs are calculated for increased working hours per day and for increased synthesis reaction yield (SRY) in each process. The process-based cost models offer a means for exploring opportunities for cost reductions, and provide a structured system for comparisons among alternative SWNT manufacturing processes. Further, the models can be used to comprehensively evaluate additional scenarios on the economics of environmental, health, and safety best manufacturing practices.

Unraveling the sub-processes of selective attention: insights from dynamic modeling and continuous behavior.

PubMed

Frisch, Simon; Dshemuchadse, Maja; Görner, Max; Goschke, Thomas; Scherbaum, Stefan

2015-11-01

Selective attention biases information processing toward stimuli that are relevant for achieving our goals. However, the nature of this bias is under debate: Does it solely rely on the amplification of goal-relevant information or is there a need for additional inhibitory processes that selectively suppress currently distracting information? Here, we explored the processes underlying selective attention with a dynamic, modeling-based approach that focuses on the continuous evolution of behavior over time. We present two dynamic neural field models incorporating the diverging theoretical assumptions. Simulations with both models showed that they make similar predictions with regard to response times but differ markedly with regard to their continuous behavior. Human data observed via mouse tracking as a continuous measure of performance revealed evidence for the model solely based on amplification but no indication of persisting selective distracter inhibition.

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

NASA Astrophysics Data System (ADS)

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

2015-10-01

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

A template-based approach for responsibility management in executable business processes

NASA Astrophysics Data System (ADS)

Cabanillas, Cristina; Resinas, Manuel; Ruiz-Cortés, Antonio

2018-05-01

Process-oriented organisations need to manage the different types of responsibilities their employees may have w.r.t. the activities involved in their business processes. Despite several approaches provide support for responsibility modelling, in current Business Process Management Systems (BPMS) the only responsibility considered at runtime is the one related to performing the work required for activity completion. Others like accountability or consultation must be implemented by manually adding activities in the executable process model, which is time-consuming and error-prone. In this paper, we address this limitation by enabling current BPMS to execute processes in which people with different responsibilities interact to complete the activities. We introduce a metamodel based on Responsibility Assignment Matrices (RAM) to model the responsibility assignment for each activity, and a flexible template-based mechanism that automatically transforms such information into BPMN elements, which can be interpreted and executed by a BPMS. Thus, our approach does not enforce any specific behaviour for the different responsibilities but new templates can be modelled to specify the interaction that best suits the activity requirements. Furthermore, libraries of templates can be created and reused in different processes. We provide a reference implementation and build a library of templates for a well-known set of responsibilities.

Predictive Models for Semiconductor Device Design and Processing

NASA Technical Reports Server (NTRS)

Meyyappan, Meyya; Arnold, James O. (Technical Monitor)

1998-01-01

The device feature size continues to be on a downward trend with a simultaneous upward trend in wafer size to 300 mm. Predictive models are needed more than ever before for this reason. At NASA Ames, a Device and Process Modeling effort has been initiated recently with a view to address these issues. Our activities cover sub-micron device physics, process and equipment modeling, computational chemistry and material science. This talk would outline these efforts and emphasize the interaction among various components. The device physics component is largely based on integrating quantum effects into device simulators. We have two parallel efforts, one based on a quantum mechanics approach and the second, a semiclassical hydrodynamics approach with quantum correction terms. Under the first approach, three different quantum simulators are being developed and compared: a nonequlibrium Green's function (NEGF) approach, Wigner function approach, and a density matrix approach. In this talk, results using various codes will be presented. Our process modeling work focuses primarily on epitaxy and etching using first-principles models coupling reactor level and wafer level features. For the latter, we are using a novel approach based on Level Set theory. Sample results from this effort will also be presented.

Genetic Programming for Automatic Hydrological Modelling

NASA Astrophysics Data System (ADS)

Chadalawada, Jayashree; Babovic, Vladan

2017-04-01

One of the recent challenges for the hydrologic research community is the need for the development of coupled systems that involves the integration of hydrologic, atmospheric and socio-economic relationships. This poses a requirement for novel modelling frameworks that can accurately represent complex systems, given, the limited understanding of underlying processes, increasing volume of data and high levels of uncertainity. Each of the existing hydrological models vary in terms of conceptualization and process representation and is the best suited to capture the environmental dynamics of a particular hydrological system. Data driven approaches can be used in the integration of alternative process hypotheses in order to achieve a unified theory at catchment scale. The key steps in the implementation of integrated modelling framework that is influenced by prior understanding and data, include, choice of the technique for the induction of knowledge from data, identification of alternative structural hypotheses, definition of rules, constraints for meaningful, intelligent combination of model component hypotheses and definition of evaluation metrics. This study aims at defining a Genetic Programming based modelling framework that test different conceptual model constructs based on wide range of objective functions and evolves accurate and parsimonious models that capture dominant hydrological processes at catchment scale. In this paper, GP initializes the evolutionary process using the modelling decisions inspired from the Superflex framework [Fenicia et al., 2011] and automatically combines them into model structures that are scrutinized against observed data using statistical, hydrological and flow duration curve based performance metrics. The collaboration between data driven and physical, conceptual modelling paradigms improves the ability to model and manage hydrologic systems. Fenicia, F., D. Kavetski, and H. H. Savenije (2011), Elements of a flexible approach for conceptual hydrological modeling: 1. Motivation and theoretical development, Water Resources Research, 47(11).

Pedestrians' intention to jaywalk: Automatic or planned? A study based on a dual-process model in China.

PubMed

Xu, Yaoshan; Li, Yongjuan; Zhang, Feng

2013-01-01

The present study investigates the determining factors of Chinese pedestrians' intention to violate traffic laws using a dual-process model. This model divides the cognitive processes of intention formation into controlled analytical processes and automatic associative processes. Specifically, the process explained by the augmented theory of planned behavior (TPB) is controlled, whereas the process based on past behavior is automatic. The results of a survey conducted on 323 adult pedestrian respondents showed that the two added TPB variables had different effects on the intention to violate, i.e., personal norms were significantly related to traffic violation intention, whereas descriptive norms were non-significant predictors. Past behavior significantly but uniquely predicted the intention to violate: the results of the relative weight analysis indicated that the largest percentage of variance in pedestrians' intention to violate was explained by past behavior (42%). According to the dual-process model, therefore, pedestrians' intention formation relies more on habit than on cognitive TPB components and social norms. The implications of these findings for the development of intervention programs are discussed. Copyright © 2012 Elsevier Ltd. All rights reserved.

Excellent approach to modeling urban expansion by fuzzy cellular automata: agent base model

NASA Astrophysics Data System (ADS)

Khajavigodellou, Yousef; Alesheikh, Ali A.; Mohammed, Abdulrazak A. S.; Chapi, Kamran

2014-09-01

Recently, the interaction between humans and their environment is the one of important challenges in the world. Landuse/ cover change (LUCC) is a complex process that includes actors and factors at different social and spatial levels. The complexity and dynamics of urban systems make the applicable practice of urban modeling very difficult. With the increased computational power and the greater availability of spatial data, micro-simulation such as the agent based and cellular automata simulation methods, has been developed by geographers, planners, and scholars, and it has shown great potential for representing and simulating the complexity of the dynamic processes involved in urban growth and land use change. This paper presents Fuzzy Cellular Automata in Geospatial Information System and remote Sensing to simulated and predicted urban expansion pattern. These FCA-based dynamic spatial urban models provide an improved ability to forecast and assess future urban growth and to create planning scenarios, allowing us to explore the potential impacts of simulations that correspond to urban planning and management policies. A fuzzy inference guided cellular automata approach. Semantic or linguistic knowledge on Land use change is expressed as fuzzy rules, based on which fuzzy inference is applied to determine the urban development potential for each pixel. The model integrates an ABM (agent-based model) and FCA (Fuzzy Cellular Automata) to investigate a complex decision-making process and future urban dynamic processes. Based on this model rapid development and green land protection under the influences of the behaviors and decision modes of regional authority agents, real estate developer agents, resident agents and non- resident agents and their interactions have been applied to predict the future development patterns of the Erbil metropolitan region.

An automated process for building reliable and optimal in vitro/in vivo correlation models based on Monte Carlo simulations.

PubMed

Sutton, Steven C; Hu, Mingxiu

2006-05-05

Many mathematical models have been proposed for establishing an in vitro/in vivo correlation (IVIVC). The traditional IVIVC model building process consists of 5 steps: deconvolution, model fitting, convolution, prediction error evaluation, and cross-validation. This is a time-consuming process and typically a few models at most are tested for any given data set. The objectives of this work were to (1) propose a statistical tool to screen models for further development of an IVIVC, (2) evaluate the performance of each model under different circumstances, and (3) investigate the effectiveness of common statistical model selection criteria for choosing IVIVC models. A computer program was developed to explore which model(s) would be most likely to work well with a random variation from the original formulation. The process used Monte Carlo simulation techniques to build IVIVC models. Data-based model selection criteria (Akaike Information Criteria [AIC], R2) and the probability of passing the Food and Drug Administration "prediction error" requirement was calculated. To illustrate this approach, several real data sets representing a broad range of release profiles are used to illustrate the process and to demonstrate the advantages of this automated process over the traditional approach. The Hixson-Crowell and Weibull models were often preferred over the linear. When evaluating whether a Level A IVIVC model was possible, the model selection criteria AIC generally selected the best model. We believe that the approach we proposed may be a rapid tool to determine which IVIVC model (if any) is the most applicable.

A new process sensitivity index to identify important system processes under process model and parametric uncertainty

DOE PAGES

Dai, Heng; Ye, Ming; Walker, Anthony P.; ...

2017-03-28

A hydrological model consists of multiple process level submodels, and each submodel represents a process key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this study develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods withmore » variance-based global sensitivity analysis. A process sensitivity index is derived as a single summary measure of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and their parameters. Here, for demonstration, the new index is used to assign importance to the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that convert precipitation to recharge, and the geology process is simulated by two models of hydraulic conductivity. Each process model has its own random parameters. Finally, the new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.« less

A new process sensitivity index to identify important system processes under process model and parametric uncertainty

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

Dai, Heng; Ye, Ming; Walker, Anthony P.

A hydrological model consists of multiple process level submodels, and each submodel represents a process key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this study develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods withmore » variance-based global sensitivity analysis. A process sensitivity index is derived as a single summary measure of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and their parameters. Here, for demonstration, the new index is used to assign importance to the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that convert precipitation to recharge, and the geology process is simulated by two models of hydraulic conductivity. Each process model has its own random parameters. Finally, the new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.« less

Influence of branding on preference-based decision making.

PubMed

Philiastides, Marios G; Ratcliff, Roger

2013-07-01

Branding has become one of the most important determinants of consumer choices. Intriguingly, the psychological mechanisms of how branding influences decision making remain elusive. In the research reported here, we used a preference-based decision-making task and computational modeling to identify which internal components of processing are affected by branding. We found that a process of noisy temporal integration of subjective value information can model preference-based choices reliably and that branding biases are explained by changes in the rate of the integration process itself. This result suggests that branding information and subjective preference are integrated into a single source of evidence in the decision-making process, thereby altering choice behavior.

Activated sludge model (ASM) based modelling of membrane bioreactor (MBR) processes: a critical review with special regard to MBR specificities.

PubMed

Fenu, A; Guglielmi, G; Jimenez, J; Spèrandio, M; Saroj, D; Lesjean, B; Brepols, C; Thoeye, C; Nopens, I

2010-08-01

Membrane bioreactors (MBRs) have been increasingly employed for municipal and industrial wastewater treatment in the last decade. The efforts for modelling of such wastewater treatment systems have always targeted either the biological processes (treatment quality target) as well as the various aspects of engineering (cost effective design and operation). The development of Activated Sludge Models (ASM) was an important evolution in the modelling of Conventional Activated Sludge (CAS) processes and their use is now very well established. However, although they were initially developed to describe CAS processes, they have simply been transferred and applied to MBR processes. Recent studies on MBR biological processes have reported several crucial specificities: medium to very high sludge retention times, high mixed liquor concentration, accumulation of soluble microbial products (SMP) rejected by the membrane filtration step, and high aeration rates for scouring purposes. These aspects raise the question as to what extent the ASM framework is applicable to MBR processes. Several studies highlighting some of the aforementioned issues are scattered through the literature. Hence, through a concise and structured overview of the past developments and current state-of-the-art in biological modelling of MBR, this review explores ASM-based modelling applied to MBR processes. The work aims to synthesize previous studies and differentiates between unmodified and modified applications of ASM to MBR. Particular emphasis is placed on influent fractionation, biokinetics, and soluble microbial products (SMPs)/exo-polymeric substances (EPS) modelling, and suggestions are put forward as to good modelling practice with regard to MBR modelling both for end-users and academia. A last section highlights shortcomings and future needs for improved biological modelling of MBR processes. (c) 2010 Elsevier Ltd. All rights reserved.

Comparing an annual and daily time-step model for predicting field-scale phosphorus loss

USDA-ARS?s Scientific Manuscript database

Numerous models exist for describing phosphorus (P) losses from agricultural fields. The complexity of these models varies considerably ranging from simple empirically-based annual time-step models to more complex process-based daily time step models. While better accuracy is often assumed with more...

0-6759 : developing a business process and logical model to support a tour-based travel demand model design for TxDOT.

DOT National Transportation Integrated Search

2013-08-01

The Texas Department of Transportation : (TxDOT) created a standardized trip-based : modeling approach for travel demand modeling : called the Texas Package Suite of Travel Demand : Models (referred to as the Texas Package) to : oversee the travel de...

The Process Model of Group-Based Emotion: Integrating Intergroup Emotion and Emotion Regulation Perspectives.

PubMed

Goldenberg, Amit; Halperin, Eran; van Zomeren, Martijn; Gross, James J

2016-05-01

Scholars interested in emotion regulation have documented the different goals and strategies individuals have for regulating their emotions. However, little attention has been paid to the regulation of group-based emotions, which are based on individuals' self-categorization as a group member and occur in response to situations perceived as relevant for that group. We propose a model for examining group-based emotion regulation that integrates intergroup emotions theory and the process model of emotion regulation. This synergy expands intergroup emotion theory by facilitating further investigation of different goals (i.e., hedonic or instrumental) and strategies (e.g., situation selection and modification strategies) used to regulate group-based emotions. It also expands emotion regulation research by emphasizing the role of self-categorization (e.g., as an individual or a group member) in the emotional process. Finally, we discuss the promise of this theoretical synergy and suggest several directions for future research on group-based emotion regulation. © 2015 by the Society for Personality and Social Psychology, Inc.

Finding shared decisions in stakeholder networks: An agent-based approach

NASA Astrophysics Data System (ADS)

Le Pira, Michela; Inturri, Giuseppe; Ignaccolo, Matteo; Pluchino, Alessandro; Rapisarda, Andrea

2017-01-01

We address the problem of a participatory decision-making process where a shared priority list of alternatives has to be obtained while avoiding inconsistent decisions. An agent-based model (ABM) is proposed to mimic this process in different social networks of stakeholders who interact according to an opinion dynamics model. Simulations' results show the efficacy of interaction in finding a transitive and, above all, shared decision. These findings are in agreement with real participation experiences regarding transport planning decisions and can give useful suggestions on how to plan an effective participation process for sustainable policy-making based on opinion consensus.

Process-based, morphodynamic hindcast of decadal deposition patterns in San Pablo Bay, California, 1856-1887

USGS Publications Warehouse

van der Wegen, M.; Jaffe, B.E.; Roelvink, J.A.

2011-01-01

This study investigates the possibility of hindcasting-observed decadal-scale morphologic change in San Pablo Bay, a subembayment of the San Francisco Estuary, California, USA, by means of a 3-D numerical model (Delft3D). The hindcast period, 1856-1887, is characterized by upstream hydraulic mining that resulted in a high sediment input to the estuary. The model includes wind waves, salt water and fresh water interactions, and graded sediment transport, among others. Simplified initial conditions and hydrodynamic forcing were necessary because detailed historic descriptions were lacking. Model results show significant skill. The river discharge and sediment concentration have a strong positive influence on deposition volumes. Waves decrease deposition rates and have, together with tidal movement, the greatest effect on sediment distribution within San Pablo Bay. The applied process-based (or reductionist) modeling approach is valuable once reasonable values for model parameters and hydrodynamic forcing are obtained. Sensitivity analysis reveals the dominant forcing of the system and suggests that the model planform plays a dominant role in the morphodynamic development. A detailed physical explanation of the model outcomes is difficult because of the high nonlinearity of the processes. Process formulation refinement, a more detailed description of the forcing, or further model parameter variations may lead to an enhanced model performance, albeit to a limited extent. The approach potentially provides a sound basis for prediction of future developments. Parallel use of highly schematized box models and a process-based approach as described in the present work is probably the most valuable method to assess decadal morphodynamic development. Copyright ?? 2011 by the American Geophysical Union.

Dynamic Emulation Modelling (DEMo) of large physically-based environmental models

NASA Astrophysics Data System (ADS)

Galelli, S.; Castelletti, A.

2012-12-01

In environmental modelling large, spatially-distributed, physically-based models are widely adopted to describe the dynamics of physical, social and economic processes. Such an accurate process characterization comes, however, to a price: the computational requirements of these models are considerably high and prevent their use in any problem requiring hundreds or thousands of model runs to be satisfactory solved. Typical examples include optimal planning and management, data assimilation, inverse modelling and sensitivity analysis. An effective approach to overcome this limitation is to perform a top-down reduction of the physically-based model by identifying a simplified, computationally efficient emulator, constructed from and then used in place of the original model in highly resource-demanding tasks. The underlying idea is that not all the process details in the original model are equally important and relevant to the dynamics of the outputs of interest for the type of problem considered. Emulation modelling has been successfully applied in many environmental applications, however most of the literature considers non-dynamic emulators (e.g. metamodels, response surfaces and surrogate models), where the original dynamical model is reduced to a static map between input and the output of interest. In this study we focus on Dynamic Emulation Modelling (DEMo), a methodological approach that preserves the dynamic nature of the original physically-based model, with consequent advantages in a wide variety of problem areas. In particular, we propose a new data-driven DEMo approach that combines the many advantages of data-driven modelling in representing complex, non-linear relationships, but preserves the state-space representation typical of process-based models, which is both particularly effective in some applications (e.g. optimal management and data assimilation) and facilitates the ex-post physical interpretation of the emulator structure, thus enhancing the credibility of the model to stakeholders and decision-makers. Numerical results from the application of the approach to the reduction of 3D coupled hydrodynamic-ecological models in several real world case studies, including Marina Reservoir (Singapore) and Googong Reservoir (Australia), are illustrated.

Adaptive Filtering Using Recurrent Neural Networks

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Menon, Sunil K.; Atiya, Amir F.

2005-01-01

A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.

Quantifying the sensitivity of feedstock properties and process conditions on hydrochar yield, carbon content, and energy content.

PubMed

Li, Liang; Wang, Yiying; Xu, Jiting; Flora, Joseph R V; Hoque, Shamia; Berge, Nicole D

2018-08-01

Hydrothermal carbonization (HTC) is a wet, low temperature thermal conversion process that continues to gain attention for the generation of hydrochar. The importance of specific process conditions and feedstock properties on hydrochar characteristics is not well understood. To evaluate this, linear and non-linear models were developed to describe hydrochar characteristics based on data collected from HTC-related literature. A Sobol analysis was subsequently conducted to identify parameters that most influence hydrochar characteristics. Results from this analysis indicate that for each investigated hydrochar property, the model fit and predictive capability associated with the random forest models is superior to both the linear and regression tree models. Based on results from the Sobol analysis, the feedstock properties and process conditions most influential on hydrochar yield, carbon content, and energy content were identified. In addition, a variational process parameter sensitivity analysis was conducted to determine how feedstock property importance changes with process conditions. Copyright © 2018 Elsevier Ltd. All rights reserved.

A General Accelerated Degradation Model Based on the Wiener Process.

PubMed

Liu, Le; Li, Xiaoyang; Sun, Fuqiang; Wang, Ning

2016-12-06

Accelerated degradation testing (ADT) is an efficient tool to conduct material service reliability and safety evaluations by analyzing performance degradation data. Traditional stochastic process models are mainly for linear or linearization degradation paths. However, those methods are not applicable for the situations where the degradation processes cannot be linearized. Hence, in this paper, a general ADT model based on the Wiener process is proposed to solve the problem for accelerated degradation data analysis. The general model can consider the unit-to-unit variation and temporal variation of the degradation process, and is suitable for both linear and nonlinear ADT analyses with single or multiple acceleration variables. The statistical inference is given to estimate the unknown parameters in both constant stress and step stress ADT. The simulation example and two real applications demonstrate that the proposed method can yield reliable lifetime evaluation results compared with the existing linear and time-scale transformation Wiener processes in both linear and nonlinear ADT analyses.

A General Accelerated Degradation Model Based on the Wiener Process

PubMed Central

Liu, Le; Li, Xiaoyang; Sun, Fuqiang; Wang, Ning

2016-01-01

Accelerated degradation testing (ADT) is an efficient tool to conduct material service reliability and safety evaluations by analyzing performance degradation data. Traditional stochastic process models are mainly for linear or linearization degradation paths. However, those methods are not applicable for the situations where the degradation processes cannot be linearized. Hence, in this paper, a general ADT model based on the Wiener process is proposed to solve the problem for accelerated degradation data analysis. The general model can consider the unit-to-unit variation and temporal variation of the degradation process, and is suitable for both linear and nonlinear ADT analyses with single or multiple acceleration variables. The statistical inference is given to estimate the unknown parameters in both constant stress and step stress ADT. The simulation example and two real applications demonstrate that the proposed method can yield reliable lifetime evaluation results compared with the existing linear and time-scale transformation Wiener processes in both linear and nonlinear ADT analyses. PMID:28774107

The Answering Process for Multiple-Choice Questions in Collaborative Learning: A Mathematical Learning Model Analysis

ERIC Educational Resources Information Center

Nakamura, Yasuyuki; Nishi, Shinnosuke; Muramatsu, Yuta; Yasutake, Koichi; Yamakawa, Osamu; Tagawa, Takahiro

2014-01-01

In this paper, we introduce a mathematical model for collaborative learning and the answering process for multiple-choice questions. The collaborative learning model is inspired by the Ising spin model and the model for answering multiple-choice questions is based on their difficulty level. An intensive simulation study predicts the possibility of…

GPU based 3D feature profile simulation of high-aspect ratio contact hole etch process under fluorocarbon plasmas

NASA Astrophysics Data System (ADS)

Chun, Poo-Reum; Lee, Se-Ah; Yook, Yeong-Geun; Choi, Kwang-Sung; Cho, Deog-Geun; Yu, Dong-Hun; Chang, Won-Seok; Kwon, Deuk-Chul; Im, Yeon-Ho

2013-09-01

Although plasma etch profile simulation has been attracted much interest for developing reliable plasma etching, there still exist big gaps between current research status and predictable modeling due to the inherent complexity of plasma process. As an effort to address this issue, we present 3D feature profile simulation coupled with well-defined plasma-surface kinetic model for silicon dioxide etching process under fluorocarbon plasmas. To capture the realistic plasma surface reaction behaviors, a polymer layer based surface kinetic model was proposed to consider the simultaneous polymer deposition and oxide etching. Finally, the realistic plasma surface model was used for calculation of speed function for 3D topology simulation, which consists of multiple level set based moving algorithm, and ballistic transport module. In addition, the time consumable computations in the ballistic transport calculation were improved drastically by GPU based numerical computation, leading to the real time computation. Finally, we demonstrated that the surface kinetic model could be coupled successfully for 3D etch profile simulations in high-aspect ratio contact hole plasma etching.

Monitoring autocorrelated process: A geometric Brownian motion process approach

NASA Astrophysics Data System (ADS)

Li, Lee Siaw; Djauhari, Maman A.

2013-09-01

Autocorrelated process control is common in today's modern industrial process control practice. The current practice of autocorrelated process control is to eliminate the autocorrelation by using an appropriate model such as Box-Jenkins models or other models and then to conduct process control operation based on the residuals. In this paper we show that many time series are governed by a geometric Brownian motion (GBM) process. Therefore, in this case, by using the properties of a GBM process, we only need an appropriate transformation and model the transformed data to come up with the condition needs in traditional process control. An industrial example of cocoa powder production process in a Malaysian company will be presented and discussed to illustrate the advantages of the GBM approach.

Embedding Task-Based Neural Models into a Connectome-Based Model of the Cerebral Cortex

PubMed Central

Ulloa, Antonio; Horwitz, Barry

2016-01-01

A number of recent efforts have used large-scale, biologically realistic, neural models to help understand the neural basis for the patterns of activity observed in both resting state and task-related functional neural imaging data. An example of the former is The Virtual Brain (TVB) software platform, which allows one to apply large-scale neural modeling in a whole brain framework. TVB provides a set of structural connectomes of the human cerebral cortex, a collection of neural processing units for each connectome node, and various forward models that can convert simulated neural activity into a variety of functional brain imaging signals. In this paper, we demonstrate how to embed a previously or newly constructed task-based large-scale neural model into the TVB platform. We tested our method on a previously constructed large-scale neural model (LSNM) of visual object processing that consisted of interconnected neural populations that represent, primary and secondary visual, inferotemporal, and prefrontal cortex. Some neural elements in the original model were “non-task-specific” (NS) neurons that served as noise generators to “task-specific” neurons that processed shapes during a delayed match-to-sample (DMS) task. We replaced the NS neurons with an anatomical TVB connectome model of the cerebral cortex comprising 998 regions of interest interconnected by white matter fiber tract weights. We embedded our LSNM of visual object processing into corresponding nodes within the TVB connectome. Reciprocal connections between TVB nodes and our task-based modules were included in this framework. We ran visual object processing simulations and showed that the TVB simulator successfully replaced the noise generation originally provided by NS neurons; i.e., the DMS tasks performed with the hybrid LSNM/TVB simulator generated equivalent neural and fMRI activity to that of the original task-based models. Additionally, we found partial agreement between the functional connectivities using the hybrid LSNM/TVB model and the original LSNM. Our framework thus presents a way to embed task-based neural models into the TVB platform, enabling a better comparison between empirical and computational data, which in turn can lead to a better understanding of how interacting neural populations give rise to human cognitive behaviors. PMID:27536235

Kernel Regression Estimation of Fiber Orientation Mixtures in Diffusion MRI

PubMed Central

Cabeen, Ryan P.; Bastin, Mark E.; Laidlaw, David H.

2016-01-01

We present and evaluate a method for kernel regression estimation of fiber orientations and associated volume fractions for diffusion MR tractography and population-based atlas construction in clinical imaging studies of brain white matter. This is a model-based image processing technique in which representative fiber models are estimated from collections of component fiber models in model-valued image data. This extends prior work in nonparametric image processing and multi-compartment processing to provide computational tools for image interpolation, smoothing, and fusion with fiber orientation mixtures. In contrast to related work on multi-compartment processing, this approach is based on directional measures of divergence and includes data-adaptive extensions for model selection and bilateral filtering. This is useful for reconstructing complex anatomical features in clinical datasets analyzed with the ball-and-sticks model, and our framework’s data-adaptive extensions are potentially useful for general multi-compartment image processing. We experimentally evaluate our approach with both synthetic data from computational phantoms and in vivo clinical data from human subjects. With synthetic data experiments, we evaluate performance based on errors in fiber orientation, volume fraction, compartment count, and tractography-based connectivity. With in vivo data experiments, we first show improved scan-rescan reproducibility and reliability of quantitative fiber bundle metrics, including mean length, volume, streamline count, and mean volume fraction. We then demonstrate the creation of a multi-fiber tractography atlas from a population of 80 human subjects. In comparison to single tensor atlasing, our multi-fiber atlas shows more complete features of known fiber bundles and includes reconstructions of the lateral projections of the corpus callosum and complex fronto-parietal connections of the superior longitudinal fasciculus I, II, and III. PMID:26691524

Physiologically based pharmacokinetic modeling of deltamethrin: Development of a rat and human diffusion-limited model

EPA Science Inventory

Mirfazaelian et al. (2006) developed a physiologically based pharmacokinetic (PBPK) model for the pyrethroid pesticide deltamethrin in the rat. This model describes gastrointestinal tract absorption as a saturable process mediated by phase III efflux transporters which pump delta...

A model for process representation and synthesis. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Thomas, R. H.

1971-01-01

The problem of representing groups of loosely connected processes is investigated, and a model for process representation useful for synthesizing complex patterns of process behavior is developed. There are three parts, the first part isolates the concepts which form the basis for the process representation model by focusing on questions such as: What is a process; What is an event; Should one process be able to restrict the capabilities of another? The second part develops a model for process representation which captures the concepts and intuitions developed in the first part. The model presented is able to describe both the internal structure of individual processes and the interface structure between interacting processes. Much of the model's descriptive power derives from its use of the notion of process state as a vehicle for relating the internal and external aspects of process behavior. The third part demonstrates by example that the model for process representation is a useful one for synthesizing process behavior patterns. In it the model is used to define a variety of interesting process behavior patterns. The dissertation closes by suggesting how the model could be used as a semantic base for a very potent language extension facility.

Application of process mining to assess the data quality of routinely collected time-based performance data sourced from electronic health records by validating process conformance.

PubMed

Perimal-Lewis, Lua; Teubner, David; Hakendorf, Paul; Horwood, Chris

2016-12-01

Effective and accurate use of routinely collected health data to produce Key Performance Indicator reporting is dependent on the underlying data quality. In this research, Process Mining methodology and tools were leveraged to assess the data quality of time-based Emergency Department data sourced from electronic health records. This research was done working closely with the domain experts to validate the process models. The hospital patient journey model was used to assess flow abnormalities which resulted from incorrect timestamp data used in time-based performance metrics. The research demonstrated process mining as a feasible methodology to assess data quality of time-based hospital performance metrics. The insight gained from this research enabled appropriate corrective actions to be put in place to address the data quality issues. © The Author(s) 2015.

A Method for Combining Experimentation and Molecular Dynamics Simulation to Improve Cohesive Zone Models for Metallic Microstructures

NASA Technical Reports Server (NTRS)

Hochhalter, J. D.; Glaessgen, E. H.; Ingraffea, A. R.; Aquino, W. A.

2009-01-01

Fracture processes within a material begin at the nanometer length scale at which the formation, propagation, and interaction of fundamental damage mechanisms occur. Physics-based modeling of these atomic processes quickly becomes computationally intractable as the system size increases. Thus, a multiscale modeling method, based on the aggregation of fundamental damage processes occurring at the nanoscale within a cohesive zone model, is under development and will enable computationally feasible and physically meaningful microscale fracture simulation in polycrystalline metals. This method employs atomistic simulation to provide an optimization loop with an initial prediction of a cohesive zone model (CZM). This initial CZM is then applied at the crack front region within a finite element model. The optimization procedure iterates upon the CZM until the finite element model acceptably reproduces the near-crack-front displacement fields obtained from experimental observation. With this approach, a comparison can be made between the original CZM predicted by atomistic simulation and the converged CZM that is based on experimental observation. Comparison of the two CZMs gives insight into how atomistic simulation scales.

A Harris-Todaro Agent-Based Model to Rural-Urban Migration

NASA Astrophysics Data System (ADS)

Espíndola, Aquino L.; Silveira, Jaylson J.; Penna, T. J. P.

2006-09-01

The Harris-Todaro model of the rural-urban migration process is revisited under an agent-based approach. The migration of the workers is interpreted as a process of social learning by imitation, formalized by a computational model. By simulating this model, we observe a transitional dynamics with continuous growth of the urban fraction of overall population toward an equilibrium. Such an equilibrium is characterized by stabilization of rural-urban expected wages differential (generalized Harris-Todaro equilibrium condition), urban concentration and urban unemployment. These classic results obtained originally by Harris and Todaro are emergent properties of our model.

Word of Mouth : An Agent-based Approach to Predictability of Stock Prices

NASA Astrophysics Data System (ADS)

Shimokawa, Tetsuya; Misawa, Tadanobu; Watanabe, Kyoko

This paper addresses how communication processes among investors affect stock prices formation, especially emerging predictability of stock prices, in financial markets. An agent based model, called the word of mouth model, is introduced for analyzing the problem. This model provides a simple, but sufficiently versatile, description of informational diffusion process and is successful in making lucidly explanation for the predictability of small sized stocks, which is a stylized fact in financial markets but difficult to resolve by traditional models. Our model also provides a rigorous examination of the under reaction hypothesis to informational shocks.

Modelling the Cast Component Weight in Hot Chamber Die Casting using Combined Taguchi and Buckingham's π Approach

NASA Astrophysics Data System (ADS)

Singh, Rupinder

2018-02-01

Hot chamber (HC) die casting process is one of the most widely used commercial processes for the casting of low temperature metals and alloys. This process gives near-net shape product with high dimensional accuracy. However in actual field environment the best settings of input parameters is often conflicting as the shape and size of the casting changes and one have to trade off among various output parameters like hardness, dimensional accuracy, casting defects, microstructure etc. So for online inspection of the cast components properties (without affecting the production line) the weight measurement has been established as one of the cost effective method (as the difference in weight of sound and unsound casting reflects the possible casting defects) in field environment. In the present work at first stage the effect of three input process parameters (namely: pressure at 2nd phase in HC die casting; metal pouring temperature and die opening time) has been studied for optimizing the cast component weight `W' as output parameter in form of macro model based upon Taguchi L9 OA. After this Buckingham's π approach has been applied on Taguchi based macro model for the development of micro model. This study highlights the Taguchi-Buckingham based combined approach as a case study (for conversion of macro model into micro model) by identification of optimum levels of input parameters (based on Taguchi approach) and development of mathematical model (based on Buckingham's π approach). Finally developed mathematical model can be used for predicting W in HC die casting process with more flexibility. The results of study highlights second degree polynomial equation for predicting cast component weight in HC die casting and suggest that pressure at 2nd stage is one of the most contributing factors for controlling the casting defect/weight of casting.

A framework for modeling scenario-based barrier island storm impacts

USGS Publications Warehouse

Mickey, Rangley; Long, Joseph W.; Dalyander, P. Soupy; Plant, Nathaniel G.; Thompson, David M.

2018-01-01

Methods for investigating the vulnerability of existing or proposed coastal features to storm impacts often rely on simplified parametric models or one-dimensional process-based modeling studies that focus on changes to a profile across a dune or barrier island. These simple studies tend to neglect the impacts to curvilinear or alongshore varying island planforms, influence of non-uniform nearshore hydrodynamics and sediment transport, irregular morphology of the offshore bathymetry, and impacts from low magnitude wave events (e.g. cold fronts). Presented here is a framework for simulating regionally specific, low and high magnitude scenario-based storm impacts to assess the alongshore variable vulnerabilities of a coastal feature. Storm scenarios based on historic hydrodynamic conditions were derived and simulated using the process-based morphologic evolution model XBeach. Model results show that the scenarios predicted similar patterns of erosion and overwash when compared to observed qualitative morphologic changes from recent storm events that were not included in the dataset used to build the scenarios. The framework model simulations were capable of predicting specific areas of vulnerability in the existing feature and the results illustrate how this storm vulnerability simulation framework could be used as a tool to help inform the decision-making process for scientists, engineers, and stakeholders involved in coastal zone management or restoration projects.

Modeling winter hydrological processes under differing climatic conditions: Modifying WEPP

NASA Astrophysics Data System (ADS)

Dun, Shuhui

Water erosion is a serious and continuous environmental problem worldwide. In cold regions, soil freeze and thaw has great impacts on infiltration and erosion. Rain or snowmelt on a thawing soil can cause severe water erosion. Of equal importance is snow accumulation and snowmelt, which can be the predominant hydrological process in areas of mid- to high latitudes and forested watersheds. Modelers must properly simulate winter processes to adequately represent the overall hydrological outcome and sediment and chemical transport in these areas. Modeling winter hydrology is presently lacking in water erosion models. Most of these models are based on the functional Universal Soil Loss Equation (USLE) or its revised forms, e.g., Revised USLE (RUSLE). In RUSLE a seasonally variable soil erodibility factor (K) was used to account for the effects of frozen and thawing soil. Yet the use of this factor requires observation data for calibration, and such a simplified approach cannot represent the complicated transient freeze-thaw processes and their impacts on surface runoff and erosion. The Water Erosion Prediction Project (WEPP) watershed model, a physically-based erosion prediction software developed by the USDA-ARS, has seen numerous applications within and outside the US. WEPP simulates winter processes, including snow accumulation, snowmelt, and soil freeze-thaw, using an approach based on mass and energy conservation. However, previous studies showed the inadequacy of the winter routines in the WEPP model. Therefore, the objectives of this study were: (1) To adapt a modeling approach for winter hydrology based on mass and energy conservation, and to implement this approach into a physically-oriented hydrological model, such as WEPP; and (2) To assess this modeling approach through case applications to different geographic conditions. A new winter routine was developed and its performance was evaluated by incorporating it into WEPP (v2008.9) and then applying WEPP to four study sites at different spatial scales under different climatic conditions, including experimental plots in Pullman, WA and Morris, MN, two agricultural drainages in Pendleton, OR, and a forest watershed in Mica Creek, ID. The model applications showed promising results, indicating adequacy of the mass- and energy-balance-based approach for winter hydrology simulation.

Study of the 5E Instructional Model to Improve the Instructional Design Process of Novice Teachers

ERIC Educational Resources Information Center

Hu, Jiuhua; Gao, Chong; Liu, Yang

2017-01-01

This study investigated the effects of 5E instructional model on the teaching processes of novice teachers. First, we conducted a teaching design training project based on the 5E model for 40 novice teachers, and compared pre-texts of the teachers' teaching process from before the training with post-texts obtained immediately following the…

Using a Systematic Conceptual Model for a Process Evaluation of a Middle School Obesity Risk-Reduction Nutrition Curriculum Intervention: "Choice, Control & Change"

ERIC Educational Resources Information Center

Lee, Heewon; Contento, Isobel R.; Koch, Pamela

2013-01-01

Objective: To use and review a conceptual model of process evaluation and to examine the implementation of a nutrition education curriculum, "Choice, Control & Change", designed to promote dietary and physical activity behaviors that reduce obesity risk. Design: A process evaluation study based on a systematic conceptual model. Setting: Five…

An incremental DPMM-based method for trajectory clustering, modeling, and retrieval.

PubMed

Hu, Weiming; Li, Xi; Tian, Guodong; Maybank, Stephen; Zhang, Zhongfei

2013-05-01

Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.

Modeling of the pyruvate production with Escherichia coli: comparison of mechanistic and neural networks-based models.

PubMed

Zelić, B; Bolf, N; Vasić-Racki, D

2006-06-01

Three different models: the unstructured mechanistic black-box model, the input-output neural network-based model and the externally recurrent neural network model were used to describe the pyruvate production process from glucose and acetate using the genetically modified Escherichia coli YYC202 ldhA::Kan strain. The experimental data were used from the recently described batch and fed-batch experiments [ Zelić B, Study of the process development for Escherichia coli-based pyruvate production. PhD Thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, Zagreb, Croatia, July 2003. (In English); Zelić et al. Bioproc Biosyst Eng 26:249-258 (2004); Zelić et al. Eng Life Sci 3:299-305 (2003); Zelić et al Biotechnol Bioeng 85:638-646 (2004)]. The neural networks were built out of the experimental data obtained in the fed-batch pyruvate production experiments with the constant glucose feed rate. The model validation was performed using the experimental results obtained from the batch and fed-batch pyruvate production experiments with the constant acetate feed rate. Dynamics of the substrate and product concentration changes was estimated using two neural network-based models for biomass and pyruvate. It was shown that neural networks could be used for the modeling of complex microbial fermentation processes, even in conditions in which mechanistic unstructured models cannot be applied.

Development and testing of a fast conceptual river water quality model.

PubMed

Keupers, Ingrid; Willems, Patrick

2017-04-15

Modern, model based river quality management strongly relies on river water quality models to simulate the temporal and spatial evolution of pollutant concentrations in the water body. Such models are typically constructed by extending detailed hydrodynamic models with a component describing the advection-diffusion and water quality transformation processes in a detailed, physically based way. This approach is too computational time demanding, especially when simulating long time periods that are needed for statistical analysis of the results or when model sensitivity analysis, calibration and validation require a large number of model runs. To overcome this problem, a structure identification method to set up a conceptual river water quality model has been developed. Instead of calculating the water quality concentrations at each water level and discharge node, the river branch is divided into conceptual reservoirs based on user information such as location of interest and boundary inputs. These reservoirs are modelled as Plug Flow Reactor (PFR) and Continuously Stirred Tank Reactor (CSTR) to describe advection and diffusion processes. The same water quality transformation processes as in the detailed models are considered but with adjusted residence times based on the hydrodynamic simulation results and calibrated to the detailed water quality simulation results. The developed approach allows for a much faster calculation time (factor 10 5 ) without significant loss of accuracy, making it feasible to perform time demanding scenario runs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Physics-based signal processing algorithms for micromachined cantilever arrays

DOEpatents

Candy, James V; Clague, David S; Lee, Christopher L; Rudd, Robert E; Burnham, Alan K; Tringe, Joseph W

2013-11-19

A method of using physics-based signal processing algorithms for micromachined cantilever arrays. The methods utilize deflection of a micromachined cantilever that represents the chemical, biological, or physical element being detected. One embodiment of the method comprises the steps of modeling the deflection of the micromachined cantilever producing a deflection model, sensing the deflection of the micromachined cantilever and producing a signal representing the deflection, and comparing the signal representing the deflection with the deflection model.

A Bayesian alternative for multi-objective ecohydrological model specification

NASA Astrophysics Data System (ADS)

Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori

2018-01-01

Recent studies have identified the importance of vegetation processes in terrestrial hydrologic systems. Process-based ecohydrological models combine hydrological, physical, biochemical and ecological processes of the catchments, and as such are generally more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov chain Monte Carlo (MCMC) techniques. The Bayesian approach offers an appealing alternative to traditional multi-objective hydrologic model calibrations by defining proper prior distributions that can be considered analogous to the ad-hoc weighting often prescribed in multi-objective calibration. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique. In our study, a Pareto-based multi-objective optimization and a formal Bayesian framework are implemented in a conceptual ecohydrological model that combines a hydrological model (HYMOD) and a modified Bucket Grassland Model (BGM). Simulations focused on one objective (streamflow/LAI) and multiple objectives (streamflow and LAI) with different emphasis defined via the prior distribution of the model error parameters. Results show more reliable outputs for both predicted streamflow and LAI using Bayesian multi-objective calibration with specified prior distributions for error parameters based on results from the Pareto front in the ecohydrological modeling. The methodology implemented here provides insight into the usefulness of multiobjective Bayesian calibration for ecohydrologic systems and the importance of appropriate prior distributions in such approaches.

A technology path to tactical agent-based modeling

NASA Astrophysics Data System (ADS)

James, Alex; Hanratty, Timothy P.

2017-05-01

Wargaming is a process of thinking through and visualizing events that could occur during a possible course of action. Over the past 200 years, wargaming has matured into a set of formalized processes. One area of growing interest is the application of agent-based modeling. Agent-based modeling and its additional supporting technologies has potential to introduce a third-generation wargaming capability to the Army, creating a positive overmatch decision-making capability. In its simplest form, agent-based modeling is a computational technique that helps the modeler understand and simulate how the "whole of a system" responds to change over time. It provides a decentralized method of looking at situations where individual agents are instantiated within an environment, interact with each other, and empowered to make their own decisions. However, this technology is not without its own risks and limitations. This paper explores a technology roadmap, identifying research topics that could realize agent-based modeling within a tactical wargaming context.

Ocean Acidification Portends Acute Habitat Compression for Atlantic Cod (Gadus morhua) in a Physiologically-informed Metabolic Rate Model

NASA Astrophysics Data System (ADS)

Del Raye, G.; Weng, K.

2011-12-01

Ocean acidification affects organisms on a biochemical scale, yet its societal impacts manifest from changes that propagate through entire populations. Successful forecasting of the effects of ocean acidification therefore depends on at least two steps: (1) deducing systemic physiology based on subcellular stresses and (2) scaling individual physiology up to ecosystem processes. Predictions that are based on known biological processes (process-based models) may fare better than purely statistical models in both these steps because the latter are less robust to novel environmental conditions. Here we present a process-based model that uses temperature, pO2, and pCO2 to predict maximal aerobic scope in Atlantic cod. Using this model, we show that (i) experimentally-derived physiological parameters are sufficient to capture the response of cod aerobic scope to temperature and oxygen, and (ii) subcellular pH effects can be used to predict the systemic physiological response of cod to an acidified ocean. We predict that acute pH stress (on a scale of hours) could limit the mobility of Atlantic cod during diel vertical migration across a pCO2 gradient, promoting habitat compression. Finally, we use a global sensitivity analysis to identify opportunities for the improvement of model uncertainty as well as some physiological adaptations that could mitigate climate stresses on cod in the future.

Gas leak detection in infrared video with background modeling

NASA Astrophysics Data System (ADS)

Zeng, Xiaoxia; Huang, Likun

2018-03-01

Background modeling plays an important role in the task of gas detection based on infrared video. VIBE algorithm is a widely used background modeling algorithm in recent years. However, the processing speed of the VIBE algorithm sometimes cannot meet the requirements of some real time detection applications. Therefore, based on the traditional VIBE algorithm, we propose a fast prospect model and optimize the results by combining the connected domain algorithm and the nine-spaces algorithm in the following processing steps. Experiments show the effectiveness of the proposed method.

Social Network Supported Process Recommender System

PubMed Central

Ye, Yanming; Yin, Jianwei; Xu, Yueshen

2014-01-01

Process recommendation technologies have gained more and more attention in the field of intelligent business process modeling to assist the process modeling. However, most of the existing technologies only use the process structure analysis and do not take the social features of processes into account, while the process modeling is complex and comprehensive in most situations. This paper studies the feasibility of social network research technologies on process recommendation and builds a social network system of processes based on the features similarities. Then, three process matching degree measurements are presented and the system implementation is discussed subsequently. Finally, experimental evaluations and future works are introduced. PMID:24672309

Coupling System Dynamics and Physically-based Models for Participatory Water Management - A Methodological Framework, with Two Case Studies: Water Quality in Quebec, and Soil Salinity in Pakistan

NASA Astrophysics Data System (ADS)

Boisvert-Chouinard, J.; Halbe, J.; Baig, A. I.; Adamowski, J. F.

2014-12-01

The principles of Integrated Water Resource Management outline the importance of stakeholder participation in water management processes, but in practice, there is a lack of meaningful engagement in water planning and implementation, and participation is often limited to public consultation and education. When models are used to support water planning, stakeholders are usually not involved in their development and use, and the models commonly fail to represent important feedbacks between socio-economic and physical processes. This paper presents the development of holistic models of the Du Chêne basin in Quebec, and the Rechna Doab basin in Pakistan, that simulate socio-economic and physical processes related to, respectively, water quality management, and soil salinity management. The models each consists of two sub-components: a System Dynamics (SD) model, and a physically based model. The SD component was developed in collaboration with key stakeholders in the basins. The Du Chêne SD model was coupled with a Soil and Water Assessment Tool (SWAT) model, while the Rechna Doab SD model was coupled with SahysMod, a soil salinity model. The coupled models were used to assess the environmental and socio-economic impacts of different management scenarios proposed by stakeholders. Results indicate that coupled SD - physically-based models can be used as effective tools for participatory water planning and implementation. The participatory modeling process provides a structure for meaningful stakeholder engagement, and the models themselves can be used to transparently and coherently assess and compare different management options.

Query Language for Location-Based Services: A Model Checking Approach

NASA Astrophysics Data System (ADS)

Hoareau, Christian; Satoh, Ichiro

We present a model checking approach to the rationale, implementation, and applications of a query language for location-based services. Such query mechanisms are necessary so that users, objects, and/or services can effectively benefit from the location-awareness of their surrounding environment. The underlying data model is founded on a symbolic model of space organized in a tree structure. Once extended to a semantic model for modal logic, we regard location query processing as a model checking problem, and thus define location queries as hybrid logicbased formulas. Our approach is unique to existing research because it explores the connection between location models and query processing in ubiquitous computing systems, relies on a sound theoretical basis, and provides modal logic-based query mechanisms for expressive searches over a decentralized data structure. A prototype implementation is also presented and will be discussed.

SEIR Model of Rumor Spreading in Online Social Network with Varying Total Population Size

NASA Astrophysics Data System (ADS)

Dong, Suyalatu; Deng, Yan-Bin; Huang, Yong-Chang

2017-10-01

Based on the infectious disease model with disease latency, this paper proposes a new model for the rumor spreading process in online social network. In this paper what we establish an SEIR rumor spreading model to describe the online social network with varying total number of users and user deactivation rate. We calculate the exact equilibrium points and reproduction number for this model. Furthermore, we perform the rumor spreading process in the online social network with increasing population size based on the original real world Facebook network. The simulation results indicate that the SEIR model of rumor spreading in online social network with changing total number of users can accurately reveal the inherent characteristics of rumor spreading process in online social network. Supported by National Natural Science Foundation of China under Grant Nos. 11275017 and 11173028

A Simulation Study Comparing Incineration and Composting in a Mars-Based Advanced Life Support System

NASA Technical Reports Server (NTRS)

Hogan, John; Kang, Sukwon; Cavazzoni, Jim; Levri, Julie; Finn, Cory; Luna, Bernadette (Technical Monitor)

2000-01-01

The objective of this study is to compare incineration and composting in a Mars-based advanced life support (ALS) system. The variables explored include waste pre-processing requirements, reactor sizing and buffer capacities. The study incorporates detailed mathematical models of biomass production and waste processing into an existing dynamic ALS system model. The ALS system and incineration models (written in MATLAB/SIMULINK(c)) were developed at the NASA Ames Research Center. The composting process is modeled using first order kinetics, with different degradation rates for individual waste components (carbohydrates, proteins, fats, cellulose and lignin). The biomass waste streams are generated using modified "Eneray Cascade" crop models, which use light- and dark-cycle temperatures, irradiance, photoperiod, [CO2], planting density, and relative humidity as model inputs. The study also includes an evaluation of equivalent system mass (ESM).

Process Analytical Technology for Advanced Process Control in Biologics Manufacturing with the Aid of Macroscopic Kinetic Modeling.

PubMed

Kornecki, Martin; Strube, Jochen

2018-03-16

Productivity improvements of mammalian cell culture in the production of recombinant proteins have been made by optimizing cell lines, media, and process operation. This led to enhanced titers and process robustness without increasing the cost of the upstream processing (USP); however, a downstream bottleneck remains. In terms of process control improvement, the process analytical technology (PAT) initiative, initiated by the American Food and Drug Administration (FDA), aims to measure, analyze, monitor, and ultimately control all important attributes of a bioprocess. Especially, spectroscopic methods such as Raman or near-infrared spectroscopy enable one to meet these analytical requirements, preferably in-situ. In combination with chemometric techniques like partial least square (PLS) or principal component analysis (PCA), it is possible to generate soft sensors, which estimate process variables based on process and measurement models for the enhanced control of bioprocesses. Macroscopic kinetic models can be used to simulate cell metabolism. These models are able to enhance the process understanding by predicting the dynamic of cells during cultivation. In this article, in-situ turbidity (transmission, 880 nm) and ex-situ Raman spectroscopy (785 nm) measurements are combined with an offline macroscopic Monod kinetic model in order to predict substrate concentrations. Experimental data of Chinese hamster ovary cultivations in bioreactors show a sufficiently linear correlation (R² ≥ 0.97) between turbidity and total cell concentration. PLS regression of Raman spectra generates a prediction model, which was validated via offline viable cell concentration measurement (RMSE ≤ 13.82, R² ≥ 0.92). Based on these measurements, the macroscopic Monod model can be used to determine different process attributes, e.g., glucose concentration. In consequence, it is possible to approximately calculate (R² ≥ 0.96) glucose concentration based on online cell concentration measurements using turbidity or Raman spectroscopy. Future approaches will use these online substrate concentration measurements with turbidity and Raman measurements, in combination with the kinetic model, in order to control the bioprocess in terms of feeding strategies, by employing an open platform communication (OPC) network-either in fed-batch or perfusion mode, integrated into a continuous operation of upstream and downstream.

Process Analytical Technology for Advanced Process Control in Biologics Manufacturing with the Aid of Macroscopic Kinetic Modeling

PubMed Central

Kornecki, Martin; Strube, Jochen

2018-01-01

Productivity improvements of mammalian cell culture in the production of recombinant proteins have been made by optimizing cell lines, media, and process operation. This led to enhanced titers and process robustness without increasing the cost of the upstream processing (USP); however, a downstream bottleneck remains. In terms of process control improvement, the process analytical technology (PAT) initiative, initiated by the American Food and Drug Administration (FDA), aims to measure, analyze, monitor, and ultimately control all important attributes of a bioprocess. Especially, spectroscopic methods such as Raman or near-infrared spectroscopy enable one to meet these analytical requirements, preferably in-situ. In combination with chemometric techniques like partial least square (PLS) or principal component analysis (PCA), it is possible to generate soft sensors, which estimate process variables based on process and measurement models for the enhanced control of bioprocesses. Macroscopic kinetic models can be used to simulate cell metabolism. These models are able to enhance the process understanding by predicting the dynamic of cells during cultivation. In this article, in-situ turbidity (transmission, 880 nm) and ex-situ Raman spectroscopy (785 nm) measurements are combined with an offline macroscopic Monod kinetic model in order to predict substrate concentrations. Experimental data of Chinese hamster ovary cultivations in bioreactors show a sufficiently linear correlation (R2 ≥ 0.97) between turbidity and total cell concentration. PLS regression of Raman spectra generates a prediction model, which was validated via offline viable cell concentration measurement (RMSE ≤ 13.82, R2 ≥ 0.92). Based on these measurements, the macroscopic Monod model can be used to determine different process attributes, e.g., glucose concentration. In consequence, it is possible to approximately calculate (R2 ≥ 0.96) glucose concentration based on online cell concentration measurements using turbidity or Raman spectroscopy. Future approaches will use these online substrate concentration measurements with turbidity and Raman measurements, in combination with the kinetic model, in order to control the bioprocess in terms of feeding strategies, by employing an open platform communication (OPC) network—either in fed-batch or perfusion mode, integrated into a continuous operation of upstream and downstream. PMID:29547557

Global sensitivity analysis of DRAINMOD-FOREST, an integrated forest ecosystem model

Treesearch

Shiying Tian; Mohamed A. Youssef; Devendra M. Amatya; Eric D. Vance

2014-01-01

Global sensitivity analysis is a useful tool to understand process-based ecosystem models by identifying key parameters and processes controlling model predictions. This study reported a comprehensive global sensitivity analysis for DRAINMOD-FOREST, an integrated model for simulating water, carbon (C), and nitrogen (N) cycles and plant growth in lowland forests. The...

A Neuroconstructivist Model of Past Tense Development and Processing

ERIC Educational Resources Information Center

Westermann, Gert; Ruh, Nicolas

2012-01-01

We present a neural network model of learning and processing the English past tense that is based on the notion that experience-dependent cortical development is a core aspect of cognitive development. During learning the model adds and removes units and connections to develop a task-specific final architecture. The model provides an integrated…

Ensuring congruency in multiscale modeling: towards linking agent based and continuum biomechanical models of arterial adaptation.

PubMed

Hayenga, Heather N; Thorne, Bryan C; Peirce, Shayn M; Humphrey, Jay D

2011-11-01

There is a need to develop multiscale models of vascular adaptations to understand tissue-level manifestations of cellular level mechanisms. Continuum-based biomechanical models are well suited for relating blood pressures and flows to stress-mediated changes in geometry and properties, but less so for describing underlying mechanobiological processes. Discrete stochastic agent-based models are well suited for representing biological processes at a cellular level, but not for describing tissue-level mechanical changes. We present here a conceptually new approach to facilitate the coupling of continuum and agent-based models. Because of ubiquitous limitations in both the tissue- and cell-level data from which one derives constitutive relations for continuum models and rule-sets for agent-based models, we suggest that model verification should enforce congruency across scales. That is, multiscale model parameters initially determined from data sets representing different scales should be refined, when possible, to ensure that common outputs are consistent. Potential advantages of this approach are illustrated by comparing simulated aortic responses to a sustained increase in blood pressure predicted by continuum and agent-based models both before and after instituting a genetic algorithm to refine 16 objectively bounded model parameters. We show that congruency-based parameter refinement not only yielded increased consistency across scales, it also yielded predictions that are closer to in vivo observations.

Mechanisms and Model Diversity of Trade-Wind Shallow Cumulus Cloud Feedbacks: A Review.

PubMed

Vial, Jessica; Bony, Sandrine; Stevens, Bjorn; Vogel, Raphaela

2017-01-01

Shallow cumulus clouds in the trade-wind regions are at the heart of the long standing uncertainty in climate sensitivity estimates. In current climate models, cloud feedbacks are strongly influenced by cloud-base cloud amount in the trades. Therefore, understanding the key factors controlling cloudiness near cloud-base in shallow convective regimes has emerged as an important topic of investigation. We review physical understanding of these key controlling factors and discuss the value of the different approaches that have been developed so far, based on global and high-resolution model experimentations and process-oriented analyses across a range of models and for observations. The trade-wind cloud feedbacks appear to depend on two important aspects: (1) how cloudiness near cloud-base is controlled by the local interplay between turbulent, convective and radiative processes; (2) how these processes interact with their surrounding environment and are influenced by mesoscale organization. Our synthesis of studies that have explored these aspects suggests that the large diversity of model responses is related to fundamental differences in how the processes controlling trade cumulus operate in models, notably, whether they are parameterized or resolved. In models with parameterized convection, cloudiness near cloud-base is very sensitive to the vigor of convective mixing in response to changes in environmental conditions. This is in contrast with results from high-resolution models, which suggest that cloudiness near cloud-base is nearly invariant with warming and independent of large-scale environmental changes. Uncertainties are difficult to narrow using current observations, as the trade cumulus variability and its relation to large-scale environmental factors strongly depend on the time and/or spatial scales at which the mechanisms are evaluated. New opportunities for testing physical understanding of the factors controlling shallow cumulus cloud responses using observations and high-resolution modeling on large domains are discussed.

Mechanisms and Model Diversity of Trade-Wind Shallow Cumulus Cloud Feedbacks: A Review

NASA Astrophysics Data System (ADS)

Vial, Jessica; Bony, Sandrine; Stevens, Bjorn; Vogel, Raphaela

2017-11-01

Shallow cumulus clouds in the trade-wind regions are at the heart of the long standing uncertainty in climate sensitivity estimates. In current climate models, cloud feedbacks are strongly influenced by cloud-base cloud amount in the trades. Therefore, understanding the key factors controlling cloudiness near cloud-base in shallow convective regimes has emerged as an important topic of investigation. We review physical understanding of these key controlling factors and discuss the value of the different approaches that have been developed so far, based on global and high-resolution model experimentations and process-oriented analyses across a range of models and for observations. The trade-wind cloud feedbacks appear to depend on two important aspects: (1) how cloudiness near cloud-base is controlled by the local interplay between turbulent, convective and radiative processes; (2) how these processes interact with their surrounding environment and are influenced by mesoscale organization. Our synthesis of studies that have explored these aspects suggests that the large diversity of model responses is related to fundamental differences in how the processes controlling trade cumulus operate in models, notably, whether they are parameterized or resolved. In models with parameterized convection, cloudiness near cloud-base is very sensitive to the vigor of convective mixing in response to changes in environmental conditions. This is in contrast with results from high-resolution models, which suggest that cloudiness near cloud-base is nearly invariant with warming and independent of large-scale environmental changes. Uncertainties are difficult to narrow using current observations, as the trade cumulus variability and its relation to large-scale environmental factors strongly depend on the time and/or spatial scales at which the mechanisms are evaluated. New opportunities for testing physical understanding of the factors controlling shallow cumulus cloud responses using observations and high-resolution modeling on large domains are discussed.

Mechanisms and Model Diversity of Trade-Wind Shallow Cumulus Cloud Feedbacks: A Review

NASA Astrophysics Data System (ADS)

Vial, Jessica; Bony, Sandrine; Stevens, Bjorn; Vogel, Raphaela

Shallow cumulus clouds in the trade-wind regions are at the heart of the long standing uncertainty in climate sensitivity estimates. In current climate models, cloud feedbacks are strongly influenced by cloud-base cloud amount in the trades. Therefore, understanding the key factors controlling cloudiness near cloud-base in shallow convective regimes has emerged as an important topic of investigation. We review physical understanding of these key controlling factors and discuss the value of the different approaches that have been developed so far, based on global and high-resolution model experimentations and process-oriented analyses across a range of models and for observations. The trade-wind cloud feedbacks appear to depend on two important aspects: (1) how cloudiness near cloud-base is controlled by the local interplay between turbulent, convective and radiative processes; (2) how these processes interact with their surrounding environment and are influenced by mesoscale organization. Our synthesis of studies that have explored these aspects suggests that the large diversity of model responses is related to fundamental differences in how the processes controlling trade cumulus operate in models, notably, whether they are parameterized or resolved. In models with parameterized convection, cloudiness near cloud-base is very sensitive to the vigor of convective mixing in response to changes in environmental conditions. This is in contrast with results from high-resolution models, which suggest that cloudiness near cloud-base is nearly invariant with warming and independent of large-scale environmental changes. Uncertainties are difficult to narrow using current observations, as the trade cumulus variability and its relation to large-scale environmental factors strongly depend on the time and/or spatial scales at which the mechanisms are evaluated. New opportunities for testing physical understanding of the factors controlling shallow cumulus cloud responses using observations and highresolution modeling on large domains are discussed.

Clinical, information and business process modeling to promote development of safe and flexible software.

PubMed

Liaw, Siaw-Teng; Deveny, Elizabeth; Morrison, Iain; Lewis, Bryn

2006-09-01

Using a factorial vignette survey and modeling methodology, we developed clinical and information models - incorporating evidence base, key concepts, relevant terms, decision-making and workflow needed to practice safely and effectively - to guide the development of an integrated rule-based knowledge module to support prescribing decisions in asthma. We identified workflows, decision-making factors, factor use, and clinician information requirements. The Unified Modeling Language (UML) and public domain software and knowledge engineering tools (e.g. Protégé) were used, with the Australian GP Data Model as the starting point for expressing information needs. A Web Services service-oriented architecture approach was adopted within which to express functional needs, and clinical processes and workflows were expressed in the Business Process Execution Language (BPEL). This formal analysis and modeling methodology to define and capture the process and logic of prescribing best practice in a reference implementation is fundamental to tackling deficiencies in prescribing decision support software.

Urban Expansion Modeling Approach Based on Multi-Agent System and Cellular Automata

NASA Astrophysics Data System (ADS)

Zeng, Y. N.; Yu, M. M.; Li, S. N.

2018-04-01

Urban expansion is a land-use change process that transforms non-urban land into urban land. This process results in the loss of natural vegetation and increase in impervious surfaces. Urban expansion also alters the hydrologic cycling, atmospheric circulation, and nutrient cycling processes and generates enormous environmental and social impacts. Urban expansion monitoring and modeling are crucial to understanding urban expansion process, mechanism, and its environmental impacts, and predicting urban expansion in future scenarios. Therefore, it is important to study urban expansion monitoring and modeling approaches. We proposed to simulate urban expansion by combining CA and MAS model. The proposed urban expansion model based on MSA and CA was applied to a case study area of Changsha-Zhuzhou-Xiangtan urban agglomeration, China. The results show that this model can capture urban expansion with good adaptability. The Kappa coefficient of the simulation results is 0.75, which indicated that the combination of MAS and CA offered the better simulation result.

A work-centered cognitively based architecture for decision support: the work-centered infomediary layer (WIL) model

NASA Astrophysics Data System (ADS)

Zachary, Wayne; Eggleston, Robert; Donmoyer, Jason; Schremmer, Serge

2003-09-01

Decision-making is strongly shaped and influenced by the work context in which decisions are embedded. This suggests that decision support needs to be anchored by a model (implicit or explicit) of the work process, in contrast to traditional approaches that anchor decision support to either context free decision models (e.g., utility theory) or to detailed models of the external (e.g., battlespace) environment. An architecture for cognitively-based, work centered decision support called the Work-centered Informediary Layer (WIL) is presented. WIL separates decision support into three overall processes that build and dynamically maintain an explicit context model, use the context model to identify opportunities for decision support and tailor generic decision-support strategies to the current context and offer them to the system-user/decision-maker. The generic decision support strategies include such things as activity/attention aiding, decision process structuring, work performance support (selective, contextual automation), explanation/ elaboration, infosphere data retrieval, and what if/action-projection and visualization. A WIL-based application is a work-centered decision support layer that provides active support without intent inferencing, and that is cognitively based without requiring classical cognitive task analyses. Example WIL applications are detailed and discussed.

Inverse Modeling of Tropospheric Methane Constrained by 13C Isotope in Methane

NASA Astrophysics Data System (ADS)

Mikaloff Fletcher, S. E.; Tans, P. P.; Bruhwiler, L. M.

2001-12-01

Understanding the budget of methane is crucial to predicting climate change and managing earth's carbon reservoirs. Methane is responsible for approximately 15% of the anthropogenic greenhouse forcing and has a large impact on the oxidative capacity of Earth's atmosphere due to its reaction with hydroxyl radical. At present, many of the sources and sinks of methane are poorly understood, due in part to the large spatial and temporal variability of the methane flux. Model calculations of methane mixing ratios using most process-based source estimates typically over-predict the inter-hemispheric gradient of atmospheric methane. Inverse models, which estimate trace gas budgets by using observations of atmospheric mixing ratios and transport models to estimate sources and sinks, have been used to incorporate features of the atmospheric observations into methane budgets. While inverse models of methane generally tend to find a decrease in northern hemisphere sources and an increase in southern hemisphere sources relative to process-based estimates,no inverse study has definitively associated the inter-hemispheric gradient difference with a specific source process or group of processes. In this presentation, observations of isotopic ratios of 13C in methane and isotopic signatures of methane source processes are used in conjunction with an inverse model of methane to further constrain the source estimates of methane. In order to investigate the advantages of incorporating 13C, the TM3 three-dimensional transport model was used. The methane and carbon dioxide measurements used are from a cooperative international effort, the Cooperative Air Sampling Network, lead by the Climate Monitoring Diagnostics Laboratory (CMDL) at the National Oceanic and Atmospheric Administration (NOAA). Experiments using model calculations based on process-based source estimates show that the inter-hemispheric gradient of δ 13CH4 is not reproduced by these source estimates, showing that the addition of observations of δ 13CH4 should provide unique insight into the methane problem.

Upgrade to Ion Exchange Modeling for Removal of Technetium from Hanford Waste Using SuperLig® 639 Resin

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

Hamm, L.; Smith, F.; Aleman, S.

2013-05-16

This report documents the development and application of computer models to describe the sorption of pertechnetate [TcO₄⁻], and its surrogate perrhenate [ReO₄⁻], on SuperLig® 639 resin. Two models have been developed: 1) A thermodynamic isotherm model, based on experimental data, that predicts [TcO₄⁻] and [ReO₄⁻] sorption as a function of solution composition and temperature and 2) A column model that uses the isotherm calculated by the first model to simulate the performance of a full-scale sorption process. The isotherm model provides a synthesis of experimental data collected from many different sources to give a best estimate prediction of the behaviormore » of the pertechnetate-SuperLig® 639 system and an estimate of the uncertainty in this prediction. The column model provides a prediction of the expected performance of the plant process by determining the volume of waste solution that can be processed based on process design parameters such as column size, flow rate and resin physical properties.« less

Storage and growth of denitrifiers in aerobic granules: part I. model development.

PubMed

Ni, Bing-Jie; Yu, Han-Qing

2008-02-01

A mathematical model, based on the Activated Sludge Model No.3 (ASM3), is developed to describe the storage and growth activities of denitrifiers in aerobic granules under anoxic conditions. In this model, mass transfer, hydrolysis, simultaneous anoxic storage and growth, anoxic maintenance, and endogenous decay are all taken into account. The model established is implemented in the well-established AQUASIM simulation software. A combination of completely mixed reactor and biofilm reactor compartments provided by AQUASIM is used to simulate the mass transport and conversion processes occurring in both bulk liquid and granules. The modeling results explicitly show that the external substrate is immediately utilized for storage and growth at feast phase. More external substrates are diverted to storage process than the primary biomass production process. The model simulation indicates that the nitrate utilization rate (NUR) of granules-based denitrification process includes four linear phases of nitrate reduction. Furthermore, the methodology for determining the most important parameter in this model, that is, anoxic reduction factor, is established. (c) 2007 Wiley Periodicals, Inc.

Functional Enzyme-Based Approach for Linking Microbial Community Functions with Biogeochemical Process Kinetics

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

Li, Minjing; Qian, Wei-jun; Gao, Yuqian

The kinetics of biogeochemical processes in natural and engineered environmental systems are typically described using Monod-type or modified Monod-type models. These models rely on biomass as surrogates for functional enzymes in microbial community that catalyze biogeochemical reactions. A major challenge to apply such models is the difficulty to quantitatively measure functional biomass for constraining and validating the models. On the other hand, omics-based approaches have been increasingly used to characterize microbial community structure, functions, and metabolites. Here we proposed an enzyme-based model that can incorporate omics-data to link microbial community functions with biogeochemical process kinetics. The model treats enzymes asmore » time-variable catalysts for biogeochemical reactions and applies biogeochemical reaction network to incorporate intermediate metabolites. The sequences of genes and proteins from metagenomes, as well as those from the UniProt database, were used for targeted enzyme quantification and to provide insights into the dynamic linkage among functional genes, enzymes, and metabolites that are necessary to be incorporated in the model. The application of the model was demonstrated using denitrification as an example by comparing model-simulated with measured functional enzymes, genes, denitrification substrates and intermediates« less

3D model of filler melting with micro-beam plasma arc based on additive manufacturing technology

NASA Astrophysics Data System (ADS)

Chen, Weilin; Yang, Tao; Yang, Ruixin

2017-07-01

Additive manufacturing technology is a systematic process based on discrete-accumulation principle, which is derived by the dimension of parts. Aiming at the dimension mathematical model and slicing problems in additive manufacturing process, the constitutive relations between micro-beam plasma welding parameters and the dimension of part were investigated. The slicing algorithm and slicing were also studied based on the dimension characteristics. By using the direct slicing algorithm according to the geometric characteristics of model, a hollow thin-wall spherical part was fabricated by 3D additive manufacturing technology using micro-beam plasma.

Simulating Runoff from a Grid Based Mercury Model: Flow Comparisons

EPA Science Inventory

Several mercury cycling models, including general mass balance approaches, mixed-batch reactors in streams or lakes, or regional process-based models, exist to assess the ecological exposure risks associated with anthropogenically increased atmospheric mercury (Hg) deposition, so...

Modeling High Rate Phosphorus and Nitrogen Removal in a Vertical Flow Alum Sludge based Constructed Wetlands

NASA Astrophysics Data System (ADS)

Jeyakumar, Lordwin; Zhao, Yaqian

2014-05-01

Increased awareness of the impacts of diffuse pollution and their intensification has pushed forward the need for the development of low-cost wastewater treatment techniques. One of such efforts is the use of novel DASC (Dewatered Alum Sludge Cakes) based constructed wetlands (CWs) for removing nutrients, organics, trace elements and other pollutants from wastewater. Understanding of the processes in CWs requires a numerical model that describes the biochemical transformation and degradation processes in subsurface vertical flow (VF) CWs. Therefore, this research focuses on the development of a process-based model for phosphorus (P) and nitrogen (N) removal to achieve a stable performance by using DASC as a substrate in CWs treatment system. An object-oriented modelling tool known as "STELLA" which works based on the principle of system dynamics is used for the development of P and N model. The core objective of the modelling work is oriented towards understanding the process in DASC-based CWs and optimizes design criteria. The P and N dynamic model is developed for DASC-based CWs. The P model developed exclusively for DASC-based CW was able to simulate the effluent P concentration leaving the system satisfactorily. Moreover, the developed P dynamic model has identified the major P pathways as adsorption (72%) followed by plant uptake (20%) and microbial uptake (7%) in single-stage laboratory scale DASC-based CW. Similarly, P dynamic simulation model was developed to simulate the four-stage laboratory scale DASC-based CWs. It was found that simulated and observed values of P removal were in good agreement. The fate of P in all the four stages clearly shows that adsorption played a pivotal role in each stage of the system due to the use of the DASC as a substrate. P adsorption by wetland substrate/DASC represents 59-75% of total P reduction. Subsequently, plant uptake and microbial uptake have lesser role regarding P removal (as compared to adsorption).With regard to N, DASC-based CWs dynamic model was developed and was run for 18 months from Feb 2009 to May 2010. The results reveal that the simulated effluent DN, NH4-N, NO3-N and TN had a considerably good agreement with the observed results. The TN removal was found to be 52% in the DASC-based CW. Interestingly, NIT is the main agent (65.60%) for the removal followed by ad (11.90%), AMM (8.90%), NH4-N (P) (5.90%), and NO3-N (P) (4.40%). DeN did not result in any significant removal (2.90%) in DASC-based CW which may be due to lack of anaerobic condition and absence of carbon sources. The N model also attempted to simulate the internal process behaviour of the system which provided a useful tool for gaining insight into the N dynamics of VFCWs. The results obtained for both N and P models can be used to improve the design of the newly developed DASC-based CWs to increase the efficiency of nutrient removal by CWs.

Infrared small target detection based on Danger Theory

NASA Astrophysics Data System (ADS)

Lan, Jinhui; Yang, Xiao

2009-11-01

To solve the problem that traditional method can't detect the small objects whose local SNR is less than 2 in IR images, a Danger Theory-based model to detect infrared small target is presented in this paper. First, on the analog with immunology, the definition is given, in this paper, to such terms as dangerous signal, antigens, APC, antibodies. Besides, matching rule between antigen and antibody is improved. Prior to training the detection model and detecting the targets, the IR images are processed utilizing adaptive smooth filter to decrease the stochastic noise. Then at the training process, deleting rule, generating rule, crossover rule and the mutation rule are established after a large number of experiments in order to realize immediate convergence and obtain good antibodies. The Danger Theory-based model is built after the training process, and this model can detect the target whose local SNR is only 1.5.

Advances in soil erosion research: processes, measurement, and modeling

USDA-ARS?s Scientific Manuscript database

Soil erosion by the environmental agents of water and wind is a continuing global menace that threatens the agricultural base that sustains our civilization. Members of ASABE have been at the forefront of research to understand erosion processes, measure erosion and related processes, and model very...

Chemical vapor deposition modeling for high temperature materials

NASA Technical Reports Server (NTRS)

Gokoglu, Suleyman A.

1992-01-01

The formalism for the accurate modeling of chemical vapor deposition (CVD) processes has matured based on the well established principles of transport phenomena and chemical kinetics in the gas phase and on surfaces. The utility and limitations of such models are discussed in practical applications for high temperature structural materials. Attention is drawn to the complexities and uncertainties in chemical kinetics. Traditional approaches based on only equilibrium thermochemistry and/or transport phenomena are defended as useful tools, within their validity, for engineering purposes. The role of modeling is discussed within the context of establishing the link between CVD process parameters and material microstructures/properties. It is argued that CVD modeling is an essential part of designing CVD equipment and controlling/optimizing CVD processes for the production and/or coating of high performance structural materials.

The value of oxygen-isotope data and multiple discharge records in calibrating a fully-distributed, physically-based rainfall-runoff model (CRUM3) to improve predictive capability

NASA Astrophysics Data System (ADS)

Neill, Aaron; Reaney, Sim

2015-04-01

Fully-distributed, physically-based rainfall-runoff models attempt to capture some of the complexity of the runoff processes that operate within a catchment, and have been used to address a variety of issues including water quality and the effect of climate change on flood frequency. Two key issues are prevalent, however, which call into question the predictive capability of such models. The first is the issue of parameter equifinality which can be responsible for large amounts of uncertainty. The second is whether such models make the right predictions for the right reasons - are the processes operating within a catchment correctly represented, or do the predictive abilities of these models result only from the calibration process? The use of additional data sources, such as environmental tracers, has been shown to help address both of these issues, by allowing for multi-criteria model calibration to be undertaken, and by permitting a greater understanding of the processes operating in a catchment and hence a more thorough evaluation of how well catchment processes are represented in a model. Using discharge and oxygen-18 data sets, the ability of the fully-distributed, physically-based CRUM3 model to represent the runoff processes in three sub-catchments in Cumbria, NW England has been evaluated. These catchments (Morland, Dacre and Pow) are part of the of the River Eden demonstration test catchment project. The oxygen-18 data set was firstly used to derive transit-time distributions and mean residence times of water for each of the catchments to gain an integrated overview of the types of processes that were operating. A generalised likelihood uncertainty estimation procedure was then used to calibrate the CRUM3 model for each catchment based on a single discharge data set from each catchment. Transit-time distributions and mean residence times of water obtained from the model using the top 100 behavioural parameter sets for each catchment were then compared to those derived from the oxygen-18 data to see how well the model captured catchment dynamics. The value of incorporating the oxygen-18 data set, as well as discharge data sets from multiple as opposed to single gauging stations in each catchment, in the calibration process to improve the predictive capability of the model was then investigated. This was achieved by assessing by how much the identifiability of the model parameters and the ability of the model to represent the runoff processes operating in each catchment improved with the inclusion of the additional data sets with respect to the likely costs that would be incurred in obtaining the data sets themselves.

Problem solving based learning model with multiple representations to improve student's mental modelling ability on physics

NASA Astrophysics Data System (ADS)

Haili, Hasnawati; Maknun, Johar; Siahaan, Parsaoran

2017-08-01

Physics is a lessons that related to students' daily experience. Therefore, before the students studying in class formally, actually they have already have a visualization and prior knowledge about natural phenomenon and could wide it themselves. The learning process in class should be aimed to detect, process, construct, and use students' mental model. So, students' mental model agree with and builds in the right concept. The previous study held in MAN 1 Muna informs that in learning process the teacher did not pay attention students' mental model. As a consequence, the learning process has not tried to build students' mental modelling ability (MMA). The purpose of this study is to describe the improvement of students' MMA as a effect of problem solving based learning model with multiple representations approach. This study is pre experimental design with one group pre post. It is conducted in XI IPA MAN 1 Muna 2016/2017. Data collection uses problem solving test concept the kinetic theory of gasses and interview to get students' MMA. The result of this study is clarification students' MMA which is categorized in 3 category; High Mental Modelling Ability (H-MMA) for 7

Development of a Model for Measuring Scientific Processing Skills Based on Brain-Imaging Technology: Focused on the Experimental Design Process

ERIC Educational Resources Information Center

Lee, Il-Sun; Byeon, Jung-Ho; Kim, Young-shin; Kwon, Yong-Ju

2014-01-01

The purpose of this study was to develop a model for measuring experimental design ability based on functional magnetic resonance imaging (fMRI) during biological inquiry. More specifically, the researchers developed an experimental design task that measures experimental design ability. Using the developed experimental design task, they measured…

A structurally based analytic model for estimation of biomass and fuel loads of woodland trees

Treesearch

Robin J. Tausch

2009-01-01

Allometric/structural relationships in tree crowns are a consequence of the physical, physiological, and fluid conduction processes of trees, which control the distribution, efficient support, and growth of foliage in the crown. The structural consequences of these processes are used to develop an analytic model based on the concept of branch orders. A set of...

Determining the Roles of Mentors in the Teachers' Use of Technology: Implementation of Systems-Based Mentoring Model

ERIC Educational Resources Information Center

Gökoglu, Seyfullah; Çakiroglu, Ünal

2017-01-01

The aim of this case study is to evaluate the effect of mentors on teachers' technology integration process into their classrooms. In integration process, interactions between the mentors and the teachers are implemented in terms of Systems-Based Mentoring Model (SBMM). Mentors' leadership roles were determined and changes in teachers' technology…

The choices, choosing model of quality of life: description and rationale.

PubMed

Gurland, Barry J; Gurland, Roni V

2009-01-01

This introductory paper offers a critical review of current models and measures of quality of life, and describes a choices and choosing (c-c) process as a new model of quality of life. Criteria are proposed for judging the relative merits of models of quality of life with preference being given to explicit mechanisms, linkages to a science base, a means of identifying deficits amenable to rational restorative interventions, and with embedded values of the whole person. A conjectured model, based on the processes of gaining access to choices and choosing among them, matches the proposed criteria. The c-c process is an evolved adaptive mechanism dedicated to the pursuit of quality of life, driven by specific biological and psychological systems, and influenced by social and environmental forces. This model strengthens the science base for the field of quality of life, unifies approaches to concept and measurement, and guides the evaluation of impairments of quality of life. Corresponding interventions can be aimed at relieving restrictions or distortions of the c-c process; thus helping people to preserve and improve their quality of life. RELATED WORK: Companion papers detail relevant aspects of the science base, present methods of identifying deficits and distortions of the c-c model so as to open opportunities for rational restorative interventions, and explore empirical analyses of the relationship between health imposed restrictions of c-c and conventional indicators of diminished quality of life. [corrected] (c) 2008 John Wiley & Sons, Ltd.

A Classification Model and an Open E-Learning System Based on Intuitionistic Fuzzy Sets for Instructional Design Concepts

ERIC Educational Resources Information Center

Güyer, Tolga; Aydogdu, Seyhmus

2016-01-01

This study suggests a classification model and an e-learning system based on this model for all instructional theories, approaches, models, strategies, methods, and technics being used in the process of instructional design that constitutes a direct or indirect resource for educational technology based on the theory of intuitionistic fuzzy sets…

A Model of the Creative Process Based on Quantum Physics and Vedic Science.

ERIC Educational Resources Information Center

Rose, Laura Hall

1988-01-01

Using tenets from Vedic science and quantum physics, this model of the creative process suggests that the unified field of creation is pure consciousness, and that the development of the creative process within individuals mirrors the creative process within the universe. Rational and supra-rational creative thinking techniques are also described.…

Wave processes in the human cardiovascular system: The measuring complex, computing models, and diagnostic analysis

NASA Astrophysics Data System (ADS)

Ganiev, R. F.; Reviznikov, D. L.; Rogoza, A. N.; Slastushenskiy, Yu. V.; Ukrainskiy, L. E.

2017-03-01

A description of a complex approach to investigation of nonlinear wave processes in the human cardiovascular system based on a combination of high-precision methods of measuring a pulse wave, mathematical methods of processing the empirical data, and methods of direct numerical modeling of hemodynamic processes in an arterial tree is given.

Modeling of thermal processes arising during shaping gears with internal non-involute teeth

NASA Astrophysics Data System (ADS)

Kanatnikov, N. V.; Kanatnikova, P. A.; Vlasov, V. V.; Pashmentova, A. S.

2018-03-01

The paper presents a model for predicting the thermal processes arising during shaping gears with internal non-involute teeth. The kinematics of cutting is modeled due to the analytical model. Chipping is modeled using the finite element method. The experiment is based on the method of infrared photography of the cutting process. The simulation results showed that the maximum temperatures and heat flows in the tool vary by more than 10% when the rake and clearance angels of the cutting are changed.

Soil Erosion as a stochastic process

NASA Astrophysics Data System (ADS)

Casper, Markus C.

2015-04-01

The main tools to provide estimations concerning risk and amount of erosion are different types of soil erosion models: on the one hand, there are empirically based model concepts on the other hand there are more physically based or process based models. However, both types of models have substantial weak points. All empirical model concepts are only capable of providing rough estimates over larger temporal and spatial scales, they do not account for many driving factors that are in the scope of scenario related analysis. In addition, the physically based models contain important empirical parts and hence, the demand for universality and transferability is not given. As a common feature, we find, that all models rely on parameters and input variables, which are to certain, extend spatially and temporally averaged. A central question is whether the apparent heterogeneity of soil properties or the random nature of driving forces needs to be better considered in our modelling concepts. Traditionally, researchers have attempted to remove spatial and temporal variability through homogenization. However, homogenization has been achieved through physical manipulation of the system, or by statistical averaging procedures. The price for obtaining this homogenized (average) model concepts of soils and soil related processes has often been a failure to recognize the profound importance of heterogeneity in many of the properties and processes that we study. Especially soil infiltrability and the resistance (also called "critical shear stress" or "critical stream power") are the most important empirical factors of physically based erosion models. The erosion resistance is theoretically a substrate specific parameter, but in reality, the threshold where soil erosion begins is determined experimentally. The soil infiltrability is often calculated with empirical relationships (e.g. based on grain size distribution). Consequently, to better fit reality, this value needs to be corrected experimentally. To overcome this disadvantage of our actual models, soil erosion models are needed that are able to use stochastic directly variables and parameter distributions. There are only some minor approaches in this direction. The most advanced is the model "STOSEM" proposed by Sidorchuk in 2005. In this model, only a small part of the soil erosion processes is described, the aggregate detachment and the aggregate transport by flowing water. The concept is highly simplified, for example, many parameters are temporally invariant. Nevertheless, the main problem is that our existing measurements and experiments are not geared to provide stochastic parameters (e.g. as probability density functions); in the best case they deliver a statistical validation of the mean values. Again, we get effective parameters, spatially and temporally averaged. There is an urgent need for laboratory and field experiments on overland flow structure, raindrop effects and erosion rate, which deliver information on spatial and temporal structure of soil and surface properties and processes.

Examining the effect of down regulation under high [CO2] on the growth of soybean assimilating a semi process-based model and FACE data

NASA Astrophysics Data System (ADS)

Sakurai, G.; Iizumi, T.; Yokozawa, M.

2011-12-01

The actual impact of elevated [CO2] with the interaction of the other climatic factors on the crop growth is still debated. In many process-based crop models, the response of photosynthesis per single leaf to environmental factors is basically described using the biochemical model of Farquhar et al. (1980). However, the decline in photosynthetic enhancement known as down regulation has not been taken into account. On the other hand, the mechanisms causing photosynthetic down regulation is still unknown, which makes it difficult to include the effect of down regulation into process-based crop models. The current results of Free-air CO2 enrichment (FACE) experiments have reported the effect of down regulation under actual environments. One of the effective approaches to involve these results into future crop yield prediction is developing a semi process-based crop growth model, which includes the effect of photosynthetic down regulation as a statistical model, and assimilating the data obtained in FACE experiments. In this study, we statistically estimated the parameters of a semi process-based model for soybean growth ('SPM-soybean') using a hierarchical Baysian method with the FACE data on soybeans (Morgan et al. 2005). We also evaluated the effect of down regulation on the soybean yield in future climatic conditions. The model selection analysis showed that the effective correction to the overestimation of the Farquhar's biochemical C3 model was to reduce the maximum rate of carboxylation (Vcmax) under elevated [CO2]. However, interestingly, the difference in the estimated final crop yields between the corrected model and the non-corrected model was very slight (Fig.1a) for future projection under climate change scenario (Miroc-ESM). This was due to that the reduction in Vcmax also brought about the reduction of the base dark respiration rate of leaves. Because the dark respiration rate exponentially increases with temperature, the slight difference in base respiration rate becomes a large difference under high temperature under the future climate scenarios. In other words, if the temperature rise is very small or zero under elevated [CO2] condition, the effect of down regulation significantly appears (Fig.1b). This result suggest that further experimental data that considering high CO2 effect and high temperature effect in field conditions should be important and elaborate the model projection of the future crop yield through data assimilation method.

Modeling and Simulation of Voids in Composite Tape Winding Process Based on Domain Superposition Technique

NASA Astrophysics Data System (ADS)

Deng, Bo; Shi, Yaoyao

2017-11-01

The tape winding technology is an effective way to fabricate rotationally composite materials. Nevertheless, some inevitable defects will seriously influence the performance of winding products. One of the crucial ways to identify the quality of fiber-reinforced composite material products is examining its void content. Significant improvement in products' mechanical properties can be achieved by minimizing the void defect. Two methods were applied in this study, finite element analysis and experimental testing, respectively, to investigate the mechanism of how void forming in composite tape winding processing. Based on the theories of interlayer intimate contact and Domain Superposition Technique (DST), a three-dimensional model of prepreg tape void with SolidWorks has been modeled in this paper. Whereafter, ABAQUS simulation software was used to simulate the void content change with pressure and temperature. Finally, a series of experiments were performed to determine the accuracy of the model-based predictions. The results showed that the model is effective for predicting the void content in the composite tape winding process.

Comparison of a Conceptual Groundwater Model and Physically Based Groundwater Mode

NASA Astrophysics Data System (ADS)

Yang, J.; Zammit, C.; Griffiths, J.; Moore, C.; Woods, R. A.

2017-12-01

Groundwater is a vital resource for human activities including agricultural practice and urban water demand. Hydrologic modelling is an important way to study groundwater recharge, movement and discharge, and its response to both human activity and climate change. To understand the groundwater hydrologic processes nationally in New Zealand, we have developed a conceptually based groundwater flow model, which is fully integrated into a national surface-water model (TopNet), and able to simulate groundwater recharge, movement, and interaction with surface water. To demonstrate the capability of this groundwater model (TopNet-GW), we applied the model to an irrigated area with water shortage and pollution problems in the upper Ruamahanga catchment in Great Wellington Region, New Zealand, and compared its performance with a physically-based groundwater model (MODFLOW). The comparison includes river flow at flow gauging sites, and interaction between groundwater and river. Results showed that the TopNet-GW produced similar flow and groundwater interaction patterns as the MODFLOW model, but took less computation time. This shows the conceptually-based groundwater model has the potential to simulate national groundwater process, and could be used as a surrogate for the more physically based model.

Modeling Healthcare Processes Using Commitments: An Empirical Evaluation.

PubMed

Telang, Pankaj R; Kalia, Anup K; Singh, Munindar P

2015-01-01

The two primary objectives of this paper are: (a) to demonstrate how Comma, a business modeling methodology based on commitments, can be applied in healthcare process modeling, and (b) to evaluate the effectiveness of such an approach in producing healthcare process models. We apply the Comma approach on a breast cancer diagnosis process adapted from an HHS committee report, and presents the results of an empirical study that compares Comma with a traditional approach based on the HL7 Messaging Standard (Traditional-HL7). Our empirical study involved 47 subjects, and two phases. In the first phase, we partitioned the subjects into two approximately equal groups. We gave each group the same requirements based on a process scenario for breast cancer diagnosis. Members of one group first applied Traditional-HL7 and then Comma whereas members of the second group first applied Comma and then Traditional-HL7-each on the above-mentioned requirements. Thus, each subject produced two models, each model being a set of UML Sequence Diagrams. In the second phase, we repartitioned the subjects into two groups with approximately equal distributions from both original groups. We developed exemplar Traditional-HL7 and Comma models; we gave one repartitioned group our Traditional-HL7 model and the other repartitioned group our Comma model. We provided the same changed set of requirements to all subjects and asked them to modify the provided exemplar model to satisfy the new requirements. We assessed solutions produced by subjects in both phases with respect to measures of flexibility, time, difficulty, objective quality, and subjective quality. Our study found that Comma is superior to Traditional-HL7 in flexibility and objective quality as validated via Student's t-test to the 10% level of significance. Comma is a promising new approach for modeling healthcare processes. Further gains could be made through improved tooling and enhanced training of modeling personnel.

Modeling Healthcare Processes Using Commitments: An Empirical Evaluation

PubMed Central

2015-01-01

The two primary objectives of this paper are: (a) to demonstrate how Comma, a business modeling methodology based on commitments, can be applied in healthcare process modeling, and (b) to evaluate the effectiveness of such an approach in producing healthcare process models. We apply the Comma approach on a breast cancer diagnosis process adapted from an HHS committee report, and presents the results of an empirical study that compares Comma with a traditional approach based on the HL7 Messaging Standard (Traditional-HL7). Our empirical study involved 47 subjects, and two phases. In the first phase, we partitioned the subjects into two approximately equal groups. We gave each group the same requirements based on a process scenario for breast cancer diagnosis. Members of one group first applied Traditional-HL7 and then Comma whereas members of the second group first applied Comma and then Traditional-HL7—each on the above-mentioned requirements. Thus, each subject produced two models, each model being a set of UML Sequence Diagrams. In the second phase, we repartitioned the subjects into two groups with approximately equal distributions from both original groups. We developed exemplar Traditional-HL7 and Comma models; we gave one repartitioned group our Traditional-HL7 model and the other repartitioned group our Comma model. We provided the same changed set of requirements to all subjects and asked them to modify the provided exemplar model to satisfy the new requirements. We assessed solutions produced by subjects in both phases with respect to measures of flexibility, time, difficulty, objective quality, and subjective quality. Our study found that Comma is superior to Traditional-HL7 in flexibility and objective quality as validated via Student’s t-test to the 10% level of significance. Comma is a promising new approach for modeling healthcare processes. Further gains could be made through improved tooling and enhanced training of modeling personnel. PMID:26539985

Virtual Sensor Web Architecture

NASA Astrophysics Data System (ADS)

Bose, P.; Zimdars, A.; Hurlburt, N.; Doug, S.

2006-12-01

NASA envisions the development of smart sensor webs, intelligent and integrated observation network that harness distributed sensing assets, their associated continuous and complex data sets, and predictive observation processing mechanisms for timely, collaborative hazard mitigation and enhanced science productivity and reliability. This paper presents Virtual Sensor Web Infrastructure for Collaborative Science (VSICS) Architecture for sustained coordination of (numerical and distributed) model-based processing, closed-loop resource allocation, and observation planning. VSICS's key ideas include i) rich descriptions of sensors as services based on semantic markup languages like OWL and SensorML; ii) service-oriented workflow composition and repair for simple and ensemble models; event-driven workflow execution based on event-based and distributed workflow management mechanisms; and iii) development of autonomous model interaction management capabilities providing closed-loop control of collection resources driven by competing targeted observation needs. We present results from initial work on collaborative science processing involving distributed services (COSEC framework) that is being extended to create VSICS.

Mashup Model and Verification Using Mashup Processing Network

NASA Astrophysics Data System (ADS)

Zahoor, Ehtesham; Perrin, Olivier; Godart, Claude

Mashups are defined to be lightweight Web applications aggregating data from different Web services, built using ad-hoc composition and being not concerned with long term stability and robustness. In this paper we present a pattern based approach, called Mashup Processing Network (MPN). The idea is based on Event Processing Network and is supposed to facilitate the creation, modeling and the verification of mashups. MPN provides a view of how different actors interact for the mashup development namely the producer, consumer, mashup processing agent and the communication channels. It also supports modeling transformations and validations of data and offers validation of both functional and non-functional requirements, such as reliable messaging and security, that are key issues within the enterprise context. We have enriched the model with a set of processing operations and categorize them into data composition, transformation and validation categories. These processing operations can be seen as a set of patterns for facilitating the mashup development process. MPN also paves a way for realizing Mashup Oriented Architecture where mashups along with services are used as building blocks for application development.

Modeling Dynamic Food Choice Processes to Understand Dietary Intervention Effects.

PubMed

Marcum, Christopher Steven; Goldring, Megan R; McBride, Colleen M; Persky, Susan

2018-02-17

Meal construction is largely governed by nonconscious and habit-based processes that can be represented as a collection of in dividual, micro-level food choices that eventually give rise to a final plate. Despite this, dietary behavior intervention research rarely captures these micro-level food choice processes, instead measuring outcomes at aggregated levels. This is due in part to a dearth of analytic techniques to model these dynamic time-series events. The current article addresses this limitation by applying a generalization of the relational event framework to model micro-level food choice behavior following an educational intervention. Relational event modeling was used to model the food choices that 221 mothers made for their child following receipt of an information-based intervention. Participants were randomized to receive either (a) control information; (b) childhood obesity risk information; (c) childhood obesity risk information plus a personalized family history-based risk estimate for their child. Participants then made food choices for their child in a virtual reality-based food buffet simulation. Micro-level aspects of the built environment, such as the ordering of each food in the buffet, were influential. Other dynamic processes such as choice inertia also influenced food selection. Among participants receiving the strongest intervention condition, choice inertia decreased and the overall rate of food selection increased. Modeling food selection processes can elucidate the points at which interventions exert their influence. Researchers can leverage these findings to gain insight into nonconscious and uncontrollable aspects of food selection that influence dietary outcomes, which can ultimately improve the design of dietary interventions.

A model to describe the surface gradient-nanograin formation and property of friction stir processed laser Co-Cr-Ni-Mo alloy

NASA Astrophysics Data System (ADS)

Li, Ruidi; Yuan, Tiechui; Qiu, Zili

2014-07-01

A gradient-nanograin surface layer of Co-base alloy was prepared by friction stir processing (FSP) of laser-clad coating in this work. However, it is lack of a quantitatively function relationship between grain refinement and FSP conditions. Based on this, an analytic model is derived for the correlations between carbide size, hardness and rotary speed, layer depth during in-situ FSP of laser-clad Co-Cr-Ni-Mo alloy. The model is based on the principle of typical plastic flow in friction welding and dynamic recrystallization. The FSP experiment for modification of laser-clad Co-based alloy was conducted and its gradient nanograin and hardness were characterized. It shows that the model is consistent with experimental results.

Virtual Observation System for Earth System Model: An Application to ACME Land Model Simulations

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

Wang, Dali; Yuan, Fengming; Hernandez, Benjamin

Investigating and evaluating physical-chemical-biological processes within an Earth system model (EMS) can be very challenging due to the complexity of both model design and software implementation. A virtual observation system (VOS) is presented to enable interactive observation of these processes during system simulation. Based on advance computing technologies, such as compiler-based software analysis, automatic code instrumentation, and high-performance data transport, the VOS provides run-time observation capability, in-situ data analytics for Earth system model simulation, model behavior adjustment opportunities through simulation steering. A VOS for a terrestrial land model simulation within the Accelerated Climate Modeling for Energy model is also presentedmore » to demonstrate the implementation details and system innovations.« less

Virtual Observation System for Earth System Model: An Application to ACME Land Model Simulations

DOE PAGES

Wang, Dali; Yuan, Fengming; Hernandez, Benjamin; ...

2017-01-01

Investigating and evaluating physical-chemical-biological processes within an Earth system model (EMS) can be very challenging due to the complexity of both model design and software implementation. A virtual observation system (VOS) is presented to enable interactive observation of these processes during system simulation. Based on advance computing technologies, such as compiler-based software analysis, automatic code instrumentation, and high-performance data transport, the VOS provides run-time observation capability, in-situ data analytics for Earth system model simulation, model behavior adjustment opportunities through simulation steering. A VOS for a terrestrial land model simulation within the Accelerated Climate Modeling for Energy model is also presentedmore » to demonstrate the implementation details and system innovations.« less

Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating.

PubMed

Lu, Xiaoman; Zheng, Guang; Miller, Colton; Alvarado, Ernesto

2017-09-08

Monitoring and understanding the spatio-temporal variations of forest aboveground biomass (AGB) is a key basis to quantitatively assess the carbon sequestration capacity of a forest ecosystem. To map and update forest AGB in the Greater Khingan Mountains (GKM) of China, this work proposes a physical-based approach. Based on the baseline forest AGB from Landsat Enhanced Thematic Mapper Plus (ETM+) images in 2008, we dynamically updated the annual forest AGB from 2009 to 2012 by adding the annual AGB increment (ABI) obtained from the simulated daily and annual net primary productivity (NPP) using the Boreal Ecosystem Productivity Simulator (BEPS) model. The 2012 result was validated by both field- and aerial laser scanning (ALS)-based AGBs. The predicted forest AGB for 2012 estimated from the process-based model can explain 31% ( n = 35, p < 0.05, RMSE = 2.20 kg/m²) and 85% ( n = 100, p < 0.01, RMSE = 1.71 kg/m²) of variation in field- and ALS-based forest AGBs, respectively. However, due to the saturation of optical remote sensing-based spectral signals and contribution of understory vegetation, the BEPS-based AGB tended to underestimate/overestimate the AGB for dense/sparse forests. Generally, our results showed that the remotely sensed forest AGB estimates could serve as the initial carbon pool to parameterize the process-based model for NPP simulation, and the combination of the baseline forest AGB and BEPS model could effectively update the spatiotemporal distribution of forest AGB.

Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating

PubMed Central

Lu, Xiaoman; Zheng, Guang; Miller, Colton

2017-01-01

Monitoring and understanding the spatio-temporal variations of forest aboveground biomass (AGB) is a key basis to quantitatively assess the carbon sequestration capacity of a forest ecosystem. To map and update forest AGB in the Greater Khingan Mountains (GKM) of China, this work proposes a physical-based approach. Based on the baseline forest AGB from Landsat Enhanced Thematic Mapper Plus (ETM+) images in 2008, we dynamically updated the annual forest AGB from 2009 to 2012 by adding the annual AGB increment (ABI) obtained from the simulated daily and annual net primary productivity (NPP) using the Boreal Ecosystem Productivity Simulator (BEPS) model. The 2012 result was validated by both field- and aerial laser scanning (ALS)-based AGBs. The predicted forest AGB for 2012 estimated from the process-based model can explain 31% (n = 35, p < 0.05, RMSE = 2.20 kg/m2) and 85% (n = 100, p < 0.01, RMSE = 1.71 kg/m2) of variation in field- and ALS-based forest AGBs, respectively. However, due to the saturation of optical remote sensing-based spectral signals and contribution of understory vegetation, the BEPS-based AGB tended to underestimate/overestimate the AGB for dense/sparse forests. Generally, our results showed that the remotely sensed forest AGB estimates could serve as the initial carbon pool to parameterize the process-based model for NPP simulation, and the combination of the baseline forest AGB and BEPS model could effectively update the spatiotemporal distribution of forest AGB. PMID:28885556

Offline modeling for product quality prediction of mineral processing using modeling error PDF shaping and entropy minimization.

PubMed

Ding, Jinliang; Chai, Tianyou; Wang, Hong

2011-03-01

This paper presents a novel offline modeling for product quality prediction of mineral processing which consists of a number of unit processes in series. The prediction of the product quality of the whole mineral process (i.e., the mixed concentrate grade) plays an important role and the establishment of its predictive model is a key issue for the plantwide optimization. For this purpose, a hybrid modeling approach of the mixed concentrate grade prediction is proposed, which consists of a linear model and a nonlinear model. The least-squares support vector machine is adopted to establish the nonlinear model. The inputs of the predictive model are the performance indices of each unit process, while the output is the mixed concentrate grade. In this paper, the model parameter selection is transformed into the shape control of the probability density function (PDF) of the modeling error. In this context, both the PDF-control-based and minimum-entropy-based model parameter selection approaches are proposed. Indeed, this is the first time that the PDF shape control idea is used to deal with system modeling, where the key idea is to turn model parameters so that either the modeling error PDF is controlled to follow a target PDF or the modeling error entropy is minimized. The experimental results using the real plant data and the comparison of the two approaches are discussed. The results show the effectiveness of the proposed approaches.

Model-based derivation, analysis and control of unstable microaerobic steady-states--considering Rhodospirillum rubrum as an example.

PubMed

Carius, Lisa; Rumschinski, Philipp; Faulwasser, Timm; Flockerzi, Dietrich; Grammel, Hartmut; Findeisen, Rolf

2014-04-01

Microaerobic (oxygen-limited) conditions are critical for inducing many important microbial processes in industrial or environmental applications. At very low oxygen concentrations, however, the process performance often suffers from technical limitations. Available dissolved oxygen measurement techniques are not sensitive enough and thus control techniques, that can reliable handle these conditions, are lacking. Recently, we proposed a microaerobic process control strategy, which overcomes these restrictions and allows to assess different degrees of oxygen limitation in bioreactor batch cultivations. Here, we focus on the design of a control strategy for the automation of oxygen-limited continuous cultures using the microaerobic formation of photosynthetic membranes (PM) in Rhodospirillum rubrum as model phenomenon. We draw upon R. rubrum since the considered phenomenon depends on the optimal availability of mixed-carbon sources, hence on boundary conditions which make the process performance challenging. Empirically assessing these specific microaerobic conditions is scarcely practicable as such a process reacts highly sensitive to changes in the substrate composition and the oxygen availability in the culture broth. Therefore, we propose a model-based process control strategy which allows to stabilize steady-states of cultures grown under these conditions. As designing the appropriate strategy requires a detailed knowledge of the system behavior, we begin by deriving and validating an unstructured process model. This model is used to optimize the experimental conditions, and identify properties of the system which are critical for process performance. The derived model facilitates the good process performance via the proposed optimal control strategy. In summary the presented model-based control strategy allows to access and maintain microaerobic steady-states of interest and to precisely and efficiently transfer the culture from one stable microaerobic steady-state into another. Therefore, the presented approach is a valuable tool to study regulatory mechanisms of microaerobic phenomena in response to oxygen limitation alone. Biotechnol. Bioeng. 2014;111: 734-747. © 2013 Wiley Periodicals, Inc. © 2013 Wiley Periodicals, Inc.

When family enters the picture: the model of cultural negotiation and gendered experiences of Japanese academic sojourners in the United States.

PubMed

Sakamoto, Izumi

2006-07-01

A grounded-theory study aimed at reconceptualizing cultural adaptation processes from gender role and family/couple perspectives while critically drawing from acculturation and culture and self literatures. In-depth interviews with 34 Japanese academic sojourners (international students, scholars) and their spouses (a total of 50 interviews with select longitudinal interviews) were conducted. The author earlier developed the Model of Cultural Negotiation (2001; 2006) capturing uneven and cyclical processes of dealing with multiple cultural contexts. The current study further develops more tailored versions of this model, Family-Based (Couple-Based) Cultural Negotiation and Individual-Based Cultural Negotiation, highlighting the impacts of family/couple and gender roles, especially for female spouses. These conceptualizations afford a sophisticated understanding of the processes of culture.

Model-based tomographic reconstruction

DOEpatents

Chambers, David H; Lehman, Sean K; Goodman, Dennis M

2012-06-26

A model-based approach to estimating wall positions for a building is developed and tested using simulated data. It borrows two techniques from geophysical inversion problems, layer stripping and stacking, and combines them with a model-based estimation algorithm that minimizes the mean-square error between the predicted signal and the data. The technique is designed to process multiple looks from an ultra wideband radar array. The processed signal is time-gated and each section processed to detect the presence of a wall and estimate its position, thickness, and material parameters. The floor plan of a building is determined by moving the array around the outside of the building. In this paper we describe how the stacking and layer stripping algorithms are combined and show the results from a simple numerical example of three parallel walls.

Modelling of Two-Stage Methane Digestion With Pretreatment of Biomass

NASA Astrophysics Data System (ADS)

Dychko, A.; Remez, N.; Opolinskyi, I.; Kraychuk, S.; Ostapchuk, N.; Yevtieieva, L.

2018-04-01

Systems of anaerobic digestion should be used for processing of organic waste. Managing the process of anaerobic recycling of organic waste requires reliable predicting of biogas production. Development of mathematical model of process of organic waste digestion allows determining the rate of biogas output at the two-stage process of anaerobic digestion considering the first stage. Verification of Konto's model, based on the studied anaerobic processing of organic waste, is implemented. The dependencies of biogas output and its rate from time are set and may be used to predict the process of anaerobic processing of organic waste.

Advances in modeling soil erosion after disturbance on rangelands

USDA-ARS?s Scientific Manuscript database

Research has been undertaken to develop process based models that predict soil erosion rate after disturbance on rangelands. In these models soil detachment is predicted as a combination of multiple erosion processes, rain splash and thin sheet flow (splash and sheet) detachment and concentrated flo...

Process-Based Development of Competence Models to Computer Science Education

ERIC Educational Resources Information Center

Zendler, Andreas; Seitz, Cornelia; Klaudt, Dieter

2016-01-01

A process model ("cpm.4.CSE") is introduced that allows the development of competence models in computer science education related to curricular requirements. It includes eight subprocesses: (a) determine competence concept, (b) determine competence areas, (c) identify computer science concepts, (d) assign competence dimensions to…

Statistical properties of several models of fractional random point processes

NASA Astrophysics Data System (ADS)

Bendjaballah, C.

2011-08-01

Statistical properties of several models of fractional random point processes have been analyzed from the counting and time interval statistics points of view. Based on the criterion of the reduced variance, it is seen that such processes exhibit nonclassical properties. The conditions for these processes to be treated as conditional Poisson processes are examined. Numerical simulations illustrate part of the theoretical calculations.

Meta-control of combustion performance with a data mining approach

NASA Astrophysics Data System (ADS)

Song, Zhe

Large scale combustion process is complex and proposes challenges of optimizing its performance. Traditional approaches based on thermal dynamics have limitations on finding optimal operational regions due to time-shift nature of the process. Recent advances in information technology enable people collect large volumes of process data easily and continuously. The collected process data contains rich information about the process and, to some extent, represents a digital copy of the process over time. Although large volumes of data exist in industrial combustion processes, they are not fully utilized to the level where the process can be optimized. Data mining is an emerging science which finds patterns or models from large data sets. It has found many successful applications in business marketing, medical and manufacturing domains The focus of this dissertation is on applying data mining to industrial combustion processes, and ultimately optimizing the combustion performance. However the philosophy, methods and frameworks discussed in this research can also be applied to other industrial processes. Optimizing an industrial combustion process has two major challenges. One is the underlying process model changes over time and obtaining an accurate process model is nontrivial. The other is that a process model with high fidelity is usually highly nonlinear, solving the optimization problem needs efficient heuristics. This dissertation is set to solve these two major challenges. The major contribution of this 4-year research is the data-driven solution to optimize the combustion process, where process model or knowledge is identified based on the process data, then optimization is executed by evolutionary algorithms to search for optimal operating regions.

.NET INTEROPERABILITY GUIDELINES

EPA Science Inventory

The CAPE-OPEN middleware standards were created to allow process modelling components (PMCs) developed by third parties to be used in any process modelling environment (PME) utilizing these standards. The CAPE-OPEN middleware specifications were based upon both Microsoft's Compo...

USING ECO-EVOLUTIONARY INDIVIDUAL-BASED MODELS TO INVESTIGATE SPATIALLY-DEPENDENT PROCESSES IN CONSERVATION GENETICS

EPA Science Inventory

Eco-evolutionary population simulation models are powerful new forecasting tools for exploring management strategies for climate change and other dynamic disturbance regimes. Additionally, eco-evo individual-based models (IBMs) are useful for investigating theoretical feedbacks ...

Innovative model of business process reengineering at machine building enterprises

NASA Astrophysics Data System (ADS)

Nekrasov, R. Yu; Tempel, Yu A.; Tempel, O. A.

2017-10-01

The paper provides consideration of business process reengineering viewed as amanagerial innovation accepted by present day machine building enterprises, as well as waysto improve its procedure. A developed innovative model of reengineering measures isdescribed and is based on the process approach and other principles of company management.

Semantic Service Design for Collaborative Business Processes in Internetworked Enterprises

NASA Astrophysics Data System (ADS)

Bianchini, Devis; Cappiello, Cinzia; de Antonellis, Valeria; Pernici, Barbara

Modern collaborating enterprises can be seen as borderless organizations whose processes are dynamically transformed and integrated with the ones of their partners (Internetworked Enterprises, IE), thus enabling the design of collaborative business processes. The adoption of Semantic Web and service-oriented technologies for implementing collaboration in such distributed and heterogeneous environments promises significant benefits. IE can model their own processes independently by using the Software as a Service paradigm (SaaS). Each enterprise maintains a catalog of available services and these can be shared across IE and reused to build up complex collaborative processes. Moreover, each enterprise can adopt its own terminology and concepts to describe business processes and component services. This brings requirements to manage semantic heterogeneity in process descriptions which are distributed across different enterprise systems. To enable effective service-based collaboration, IEs have to standardize their process descriptions and model them through component services using the same approach and principles. For enabling collaborative business processes across IE, services should be designed following an homogeneous approach, possibly maintaining a uniform level of granularity. In the paper we propose an ontology-based semantic modeling approach apt to enrich and reconcile semantics of process descriptions to facilitate process knowledge management and to enable semantic service design (by discovery, reuse and integration of process elements/constructs). The approach brings together Semantic Web technologies, techniques in process modeling, ontology building and semantic matching in order to provide a comprehensive semantic modeling framework.

Comparison of Fuzzy-Based Models in Landslide Hazard Mapping

NASA Astrophysics Data System (ADS)

Mijani, N.; Neysani Samani, N.

2017-09-01

Landslide is one of the main geomorphic processes which effects on the development of prospect in mountainous areas and causes disastrous accidents. Landslide is an event which has different uncertain criteria such as altitude, slope, aspect, land use, vegetation density, precipitation, distance from the river and distance from the road network. This research aims to compare and evaluate different fuzzy-based models including Fuzzy Analytic Hierarchy Process (Fuzzy-AHP), Fuzzy Gamma and Fuzzy-OR. The main contribution of this paper reveals to the comprehensive criteria causing landslide hazard considering their uncertainties and comparison of different fuzzy-based models. The quantify of evaluation process are calculated by Density Ratio (DR) and Quality Sum (QS). The proposed methodology implemented in Sari, one of the city of Iran which has faced multiple landslide accidents in recent years due to the particular environmental conditions. The achieved results of accuracy assessment based on the quantifier strated that Fuzzy-AHP model has higher accuracy compared to other two models in landslide hazard zonation. Accuracy of zoning obtained from Fuzzy-AHP model is respectively 0.92 and 0.45 based on method Precision (P) and QS indicators. Based on obtained landslide hazard maps, Fuzzy-AHP, Fuzzy Gamma and Fuzzy-OR respectively cover 13, 26 and 35 percent of the study area with a very high risk level. Based on these findings, fuzzy-AHP model has been selected as the most appropriate method of zoning landslide in the city of Sari and the Fuzzy-gamma method with a minor difference is in the second order.

A first packet processing subdomain cluster model based on SDN

NASA Astrophysics Data System (ADS)

Chen, Mingyong; Wu, Weimin

2017-08-01

For the current controller cluster packet processing performance bottlenecks and controller downtime problems. An SDN controller is proposed to allocate the priority of each device in the SDN (Software Defined Network) network, and the domain contains several network devices and Controller, the controller is responsible for managing the network equipment within the domain, the switch performs data delivery based on the load of the controller, processing network equipment data. The experimental results show that the model can effectively solve the risk of single point failure of the controller, and can solve the performance bottleneck of the first packet processing.

RockFall analyst: A GIS extension for three-dimensional and spatially distributed rockfall hazard modeling

NASA Astrophysics Data System (ADS)

Lan, Hengxing; Derek Martin, C.; Lim, C. H.

2007-02-01

Geographic information system (GIS) modeling is used in combination with three-dimensional (3D) rockfall process modeling to assess rockfall hazards. A GIS extension, RockFall Analyst (RA), which is capable of effectively handling large amounts of geospatial information relative to rockfall behaviors, has been developed in ArcGIS using ArcObjects and C#. The 3D rockfall model considers dynamic processes on a cell plane basis. It uses inputs of distributed parameters in terms of raster and polygon features created in GIS. Two major components are included in RA: particle-based rockfall process modeling and geostatistics-based rockfall raster modeling. Rockfall process simulation results, 3D rockfall trajectories and their velocity features either for point seeders or polyline seeders are stored in 3D shape files. Distributed raster modeling, based on 3D rockfall trajectories and a spatial geostatistical technique, represents the distribution of spatial frequency, the flying and/or bouncing height, and the kinetic energy of falling rocks. A distribution of rockfall hazard can be created by taking these rockfall characteristics into account. A barrier analysis tool is also provided in RA to aid barrier design. An application of these modeling techniques to a case study is provided. The RA has been tested in ArcGIS 8.2, 8.3, 9.0 and 9.1.

The Importance of Uncertainty and Sensitivity Analysis in Process-based Models of Carbon and Nitrogen Cycling in Terrestrial Ecosystems with Particular Emphasis on Forest Ecosystems — Selected Papers from a Workshop Organized by the International Society for Ecological Modelling (ISEM) at the Third Biennal Meeting of the International Environmental Modelling and Software Society (IEMSS) in Burlington, Vermont, USA, August 9-13, 2006

USGS Publications Warehouse

Larocque, Guy R.; Bhatti, Jagtar S.; Liu, Jinxun; Ascough, James C.; Gordon, Andrew M.

2008-01-01

Many process-based models of carbon (C) and nitrogen (N) cycles have been developed for terrestrial ecosystems, including forest ecosystems. They address many basic issues of ecosystems structure and functioning, such as the role of internal feedback in ecosystem dynamics. The critical factor in these phenomena is scale, as these processes operate at scales from the minute (e.g. particulate pollution impacts on trees and other organisms) to the global (e.g. climate change). Research efforts remain important to improve the capability of such models to better represent the dynamics of terrestrial ecosystems, including the C, nutrient, (e.g. N) and water cycles. Existing models are sufficiently well advanced to help decision makers develop sustainable management policies and planning of terrestrial ecosystems, as they make realistic predictions when used appropriately. However, decision makers must be aware of their limitations by having the opportunity to evaluate the uncertainty associated with process-based models (Smith and Heath, 2001 and Allen et al., 2004). The variation in scale of issues currently being addressed by modelling efforts makes the evaluation of uncertainty a daunting task.

Junior high school students' cognitive process in solving the developed algebraic problems based on information processing taxonomy model

NASA Astrophysics Data System (ADS)

Purwoko, Saad, Noor Shah; Tajudin, Nor'ain Mohd

2017-05-01

This study aims to: i) develop problem solving questions of Linear Equations System of Two Variables (LESTV) based on levels of IPT Model, ii) explain the level of students' skill of information processing in solving LESTV problems; iii) explain students' skill in information processing in solving LESTV problems; and iv) explain students' cognitive process in solving LESTV problems. This study involves three phases: i) development of LESTV problem questions based on Tessmer Model; ii) quantitative survey method on analyzing students' skill level of information processing; and iii) qualitative case study method on analyzing students' cognitive process. The population of the study was 545 eighth grade students represented by a sample of 170 students of five Junior High Schools in Hilir Barat Zone, Palembang (Indonesia) that were chosen using cluster sampling. Fifteen students among them were drawn as a sample for the interview session with saturated information obtained. The data were collected using the LESTV problem solving test and the interview protocol. The quantitative data were analyzed using descriptive statistics, while the qualitative data were analyzed using the content analysis. The finding of this study indicated that students' cognitive process was just at the step of indentifying external source and doing algorithm in short-term memory fluently. Only 15.29% students could retrieve type A information and 5.88% students could retrieve type B information from long-term memory. The implication was the development problems of LESTV had validated IPT Model in modelling students' assessment by different level of hierarchy.

JAMS - a software platform for modular hydrological modelling

NASA Astrophysics Data System (ADS)

Kralisch, Sven; Fischer, Christian

2015-04-01

Current challenges of understanding and assessing the impacts of climate and land use changes on environmental systems demand for an ever-increasing integration of data and process knowledge in corresponding simulation models. Software frameworks that allow for a seamless creation of integrated models based on less complex components (domain models, process simulation routines) have therefore gained increasing attention during the last decade. JAMS is an Open-Source software framework that has been especially designed to cope with the challenges of eco-hydrological modelling. This is reflected by (i) its flexible approach for representing time and space, (ii) a strong separation of process simulation components from the declarative description of more complex models using domain specific XML, (iii) powerful analysis and visualization functions for spatial and temporal input and output data, and (iv) parameter optimization and uncertainty analysis functions commonly used in environmental modelling. Based on JAMS, different hydrological and nutrient-transport simulation models were implemented and successfully applied during the last years. We will present the JAMS core concepts and give an overview of models, simulation components and support tools available for that framework. Sample applications will be used to underline the advantages of component-based model designs and to show how JAMS can be used to address the challenges of integrated hydrological modelling.

New figuring model based on surface slope profile for grazing-incidence reflective optics

DOE PAGES

Zhou, Lin; Huang, Lei; Bouet, Nathalie; ...

2016-08-09

Surface slope profile is widely used in the metrology of grazing-incidence reflective optics instead of surface height profile. Nevertheless, the theoretical and experimental model currently used in deterministic optical figuring processes is based on surface height, not on surface slope. This means that the raw slope profile data from metrology need to be converted to height profile to perform the current height-based figuring processes. The inevitable measurement noise in the raw slope data will introduce significant cumulative error in the resultant height profiles. As a consequence, this conversion will degrade the determinism of the figuring processes, and will have anmore » impact on the ultimate surface figuring results. To overcome this problem, an innovative figuring model is proposed, which directly uses the raw slope profile data instead of the usual height data as input for the deterministic process. In this article, first the influence of the measurement noise on the resultant height profile is analyzed, and then a new model is presented; finally a demonstration experiment is carried out using a one-dimensional ion beam figuring process to demonstrate the validity of our approach.« less

How Does Higher Frequency Monitoring Data Affect the Calibration of a Process-Based Water Quality Model?

NASA Astrophysics Data System (ADS)

Jackson-Blake, L.

2014-12-01

Process-based catchment water quality models are increasingly used as tools to inform land management. However, for such models to be reliable they need to be well calibrated and shown to reproduce key catchment processes. Calibration can be challenging for process-based models, which tend to be complex and highly parameterised. Calibrating a large number of parameters generally requires a large amount of monitoring data, but even in well-studied catchments, streams are often only sampled at a fortnightly or monthly frequency. The primary aim of this study was therefore to investigate how the quality and uncertainty of model simulations produced by one process-based catchment model, INCA-P (the INtegrated CAtchment model of Phosphorus dynamics), were improved by calibration to higher frequency water chemistry data. Two model calibrations were carried out for a small rural Scottish catchment: one using 18 months of daily total dissolved phosphorus (TDP) concentration data, another using a fortnightly dataset derived from the daily data. To aid comparability, calibrations were carried out automatically using the MCMC-DREAM algorithm. Using daily rather than fortnightly data resulted in improved simulation of the magnitude of peak TDP concentrations, in turn resulting in improved model performance statistics. Marginal posteriors were better constrained by the higher frequency data, resulting in a large reduction in parameter-related uncertainty in simulated TDP (the 95% credible interval decreased from 26 to 6 μg/l). The number of parameters that could be reliably auto-calibrated was lower for the fortnightly data, leading to the recommendation that parameters should not be varied spatially for models such as INCA-P unless there is solid evidence that this is appropriate, or there is a real need to do so for the model to fulfil its purpose. Secondary study aims were to highlight the subjective elements involved in auto-calibration and suggest practical improvements that could make models such as INCA-P more suited to auto-calibration and uncertainty analyses. Two key improvements include model simplification, so that all model parameters can be included in an analysis of this kind, and better documenting of recommended ranges for each parameter, to help in choosing sensible priors.

[Advance in researches on the effect of forest on hydrological process].

PubMed

Zhang, Zhiqiang; Yu, Xinxiao; Zhao, Yutao; Qin, Yongsheng

2003-01-01

According to the effects of forest on hydrological process, forest hydrology can be divided into three related aspects: experimental research on the effects of forest changing on hydrological process quantity and water quality; mechanism study on the effects of forest changing on hydrological cycle, and establishing and exploitating physical-based distributed forest hydrological model for resource management and engineering construction. Orientation experiment research can not only support the first-hand data for forest hydrological model, but also make clear the precipitation-runoff mechanisms. Research on runoff mechanisms can be valuable for the exploitation and improvement of physical based hydrological models. Moreover, the model can also improve the experimental and runoff mechanism researches. A review of above three aspects are summarized in this paper.

Cortical processing of pitch: Model-based encoding and decoding of auditory fMRI responses to real-life sounds.

PubMed

De Angelis, Vittoria; De Martino, Federico; Moerel, Michelle; Santoro, Roberta; Hausfeld, Lars; Formisano, Elia

2017-11-13

Pitch is a perceptual attribute related to the fundamental frequency (or periodicity) of a sound. So far, the cortical processing of pitch has been investigated mostly using synthetic sounds. However, the complex harmonic structure of natural sounds may require different mechanisms for the extraction and analysis of pitch. This study investigated the neural representation of pitch in human auditory cortex using model-based encoding and decoding analyses of high field (7 T) functional magnetic resonance imaging (fMRI) data collected while participants listened to a wide range of real-life sounds. Specifically, we modeled the fMRI responses as a function of the sounds' perceived pitch height and salience (related to the fundamental frequency and the harmonic structure respectively), which we estimated with a computational algorithm of pitch extraction (de Cheveigné and Kawahara, 2002). First, using single-voxel fMRI encoding, we identified a pitch-coding region in the antero-lateral Heschl's gyrus (HG) and adjacent superior temporal gyrus (STG). In these regions, the pitch representation model combining height and salience predicted the fMRI responses comparatively better than other models of acoustic processing and, in the right hemisphere, better than pitch representations based on height/salience alone. Second, we assessed with model-based decoding that multi-voxel response patterns of the identified regions are more informative of perceived pitch than the remainder of the auditory cortex. Further multivariate analyses showed that complementing a multi-resolution spectro-temporal sound representation with pitch produces a small but significant improvement to the decoding of complex sounds from fMRI response patterns. In sum, this work extends model-based fMRI encoding and decoding methods - previously employed to examine the representation and processing of acoustic sound features in the human auditory system - to the representation and processing of a relevant perceptual attribute such as pitch. Taken together, the results of our model-based encoding and decoding analyses indicated that the pitch of complex real life sounds is extracted and processed in lateral HG/STG regions, at locations consistent with those indicated in several previous fMRI studies using synthetic sounds. Within these regions, pitch-related sound representations reflect the modulatory combination of height and the salience of the pitch percept. Copyright © 2017 Elsevier Inc. All rights reserved.

Collective learning modeling based on the kinetic theory of active particles

NASA Astrophysics Data System (ADS)

Burini, D.; De Lillo, S.; Gibelli, L.

2016-03-01

This paper proposes a systems approach to the theory of perception and learning in populations composed of many living entities. Starting from a phenomenological description of these processes, a mathematical structure is derived which is deemed to incorporate their complexity features. The modeling is based on a generalization of kinetic theory methods where interactions are described by theoretical tools of game theory. As an application, the proposed approach is used to model the learning processes that take place in a classroom.

Modeling of Bulk Evaporation and Condensation

NASA Technical Reports Server (NTRS)

Anghaie, S.; Ding, Z.

1996-01-01

This report describes the modeling and mathematical formulation of the bulk evaporation and condensation involved in liquid-vapor phase change processes. An internal energy formulation, for these phase change processes that occur under the constraint of constant volume, was studied. Compared to the enthalpy formulation, the internal energy formulation has a more concise and compact form. The velocity and time scales of the interface movement were obtained through scaling analysis and verified by performing detailed numerical experiments. The convection effect induced by the density change was analyzed and found to be negligible compared to the conduction effect. Two iterative methods for updating the value of the vapor phase fraction, the energy based (E-based) and temperature based (T-based) methods, were investigated. Numerical experiments revealed that for the evaporation and condensation problems the E-based method is superior to the T-based method in terms of computational efficiency. The internal energy formulation and the E-based method were used to compute the bulk evaporation and condensation processes under different conditions. The evolution of the phase change processes was investigated. This work provided a basis for the modeling of thermal performance of multi-phase nuclear fuel elements under variable gravity conditions, in which the buoyancy convection due to gravity effects and internal heating are involved.

Numerical modeling of heat and mass transport processes in an evaporative thermal protection system

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

Bobrov, I.N.; Kuryachii, A.P.

1992-08-01

We propose a mathematical model of heat and mass transport processes in a moist, porous material subject to capillary action. The material is in contact with a heated surface, and the processes take place while the liquid is evaporating in a cavity with a drainage hole. A sample calculation based on the model is presented. 45 refs., 4 figs.

Study on process evaluation model of students' learning in practical course

NASA Astrophysics Data System (ADS)

Huang, Jie; Liang, Pei; Shen, Wei-min; Ye, Youxiang

2017-08-01

In practical course teaching based on project object method, the traditional evaluation methods include class attendance, assignments and exams fails to give incentives to undergraduate students to learn innovatively and autonomously. In this paper, the element such as creative innovation, teamwork, document and reporting were put into process evaluation methods, and a process evaluation model was set up. Educational practice shows that the evaluation model makes process evaluation of students' learning more comprehensive, accurate, and fairly.

DoD Lead System Integrator (LSI) Transformation - Creating a Model Based Acquisition Framework (MBAF)

DTIC Science & Technology

2014-04-30

cost to acquire systems as design maturity could be verified incrementally as the system was developed vice waiting for specific large “ big bang ...Framework (MBAF) be applied to simulate or optimize process variations on programs? LSI Roles and Responsibilities A review of the roles and...the model/process optimization process. It is the current intent that NAVAIR will use the model to run simulations on process changes in an attempt to

Cognitive control predicts use of model-based reinforcement learning.

PubMed

Otto, A Ross; Skatova, Anya; Madlon-Kay, Seth; Daw, Nathaniel D

2015-02-01

Accounts of decision-making and its neural substrates have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental work suggest that this classic distinction between behaviorally and neurally dissociable systems for habitual and goal-directed (or more generally, automatic and controlled) choice may arise from two computational strategies for reinforcement learning (RL), called model-free and model-based RL, but the cognitive or computational processes by which one system may dominate over the other in the control of behavior is a matter of ongoing investigation. To elucidate this question, we leverage the theoretical framework of cognitive control, demonstrating that individual differences in utilization of goal-related contextual information--in the service of overcoming habitual, stimulus-driven responses--in established cognitive control paradigms predict model-based behavior in a separate, sequential choice task. The behavioral correspondence between cognitive control and model-based RL compellingly suggests that a common set of processes may underpin the two behaviors. In particular, computational mechanisms originally proposed to underlie controlled behavior may be applicable to understanding the interactions between model-based and model-free choice behavior.

Discrete post-processing of total cloud cover ensemble forecasts

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Haiden, Thomas; Pappenberger, Florian

2017-04-01

This contribution presents an approach to post-process ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical post-processing of ensemble predictions are tested. The first approach is based on multinomial logistic regression, the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on station-wise post-processing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model. Reference Hemri, S., Haiden, T., & Pappenberger, F. (2016). Discrete post-processing of total cloud cover ensemble forecasts. Monthly Weather Review 144, 2565-2577.

Markov Chain-Like Quantum Biological Modeling of Mutations, Aging, and Evolution.

PubMed

Djordjevic, Ivan B

2015-08-24

Recent evidence suggests that quantum mechanics is relevant in photosynthesis, magnetoreception, enzymatic catalytic reactions, olfactory reception, photoreception, genetics, electron-transfer in proteins, and evolution; to mention few. In our recent paper published in Life, we have derived the operator-sum representation of a biological channel based on codon basekets, and determined the quantum channel model suitable for study of the quantum biological channel capacity. However, this model is essentially memoryless and it is not able to properly model the propagation of mutation errors in time, the process of aging, and evolution of genetic information through generations. To solve for these problems, we propose novel quantum mechanical models to accurately describe the process of creation spontaneous, induced, and adaptive mutations and their propagation in time. Different biological channel models with memory, proposed in this paper, include: (i) Markovian classical model, (ii) Markovian-like quantum model, and (iii) hybrid quantum-classical model. We then apply these models in a study of aging and evolution of quantum biological channel capacity through generations. We also discuss key differences of these models with respect to a multilevel symmetric channel-based Markovian model and a Kimura model-based Markovian process. These models are quite general and applicable to many open problems in biology, not only biological channel capacity, which is the main focus of the paper. We will show that the famous quantum Master equation approach, commonly used to describe different biological processes, is just the first-order approximation of the proposed quantum Markov chain-like model, when the observation interval tends to zero. One of the important implications of this model is that the aging phenotype becomes determined by different underlying transition probabilities in both programmed and random (damage) Markov chain-like models of aging, which are mutually coupled.

Markov Chain-Like Quantum Biological Modeling of Mutations, Aging, and Evolution

PubMed Central

Djordjevic, Ivan B.

2015-01-01

Recent evidence suggests that quantum mechanics is relevant in photosynthesis, magnetoreception, enzymatic catalytic reactions, olfactory reception, photoreception, genetics, electron-transfer in proteins, and evolution; to mention few. In our recent paper published in Life, we have derived the operator-sum representation of a biological channel based on codon basekets, and determined the quantum channel model suitable for study of the quantum biological channel capacity. However, this model is essentially memoryless and it is not able to properly model the propagation of mutation errors in time, the process of aging, and evolution of genetic information through generations. To solve for these problems, we propose novel quantum mechanical models to accurately describe the process of creation spontaneous, induced, and adaptive mutations and their propagation in time. Different biological channel models with memory, proposed in this paper, include: (i) Markovian classical model, (ii) Markovian-like quantum model, and (iii) hybrid quantum-classical model. We then apply these models in a study of aging and evolution of quantum biological channel capacity through generations. We also discuss key differences of these models with respect to a multilevel symmetric channel-based Markovian model and a Kimura model-based Markovian process. These models are quite general and applicable to many open problems in biology, not only biological channel capacity, which is the main focus of the paper. We will show that the famous quantum Master equation approach, commonly used to describe different biological processes, is just the first-order approximation of the proposed quantum Markov chain-like model, when the observation interval tends to zero. One of the important implications of this model is that the aging phenotype becomes determined by different underlying transition probabilities in both programmed and random (damage) Markov chain-like models of aging, which are mutually coupled. PMID:26305258

Advanced process control framework initiative

NASA Astrophysics Data System (ADS)

Hill, Tom; Nettles, Steve

1997-01-01

The semiconductor industry, one the world's most fiercely competitive industries, is driven by increasingly complex process technologies and global competition to improve cycle time, quality, and process flexibility. Due to the complexity of these problems, current process control techniques are generally nonautomated, time-consuming, reactive, nonadaptive, and focused on individual fabrication tools and processes. As the semiconductor industry moves into higher density processes, radical new approaches are required. To address the need for advanced factory-level process control in this environment, Honeywell, Advanced Micro Devices (AMD), and SEMATECH formed the Advanced Process Control Framework Initiative (APCFI) joint research project. The project defines and demonstrates an Advanced Process Control (APC) approach based on SEMATECH's Computer Integrated Manufacturing (CIM) Framework. Its scope includes the coordination of Manufacturing Execution Systems, process control tools, and wafer fabrication equipment to provide necessary process control capabilities. Moreover, it takes advantage of the CIM Framework to integrate and coordinate applications from other suppliers that provide services necessary for the overall system to function. This presentation discusses the key concept of model-based process control that differentiates the APC Framework. This major improvement over current methods enables new systematic process control by linking the knowledge of key process settings to desired product characteristics that reside in models created with commercial model development tools The unique framework-based approach facilitates integration of commercial tools and reuse of their data by tying them together in an object-based structure. The presentation also explores the perspective of each organization's involvement in the APCFI project. Each has complementary goals and expertise to contribute; Honeywell represents the supplier viewpoint, AMD represents the user with 'real customer requirements', and SEMATECH provides a consensus-building organization that widely disseminates technology to suppliers and users in the semiconductor industry that face similar equipment and factory control systems challenges.

Development and verification of an agent-based model of opinion leadership.

PubMed

Anderson, Christine A; Titler, Marita G

2014-09-27

The use of opinion leaders is a strategy used to speed the process of translating research into practice. Much is still unknown about opinion leader attributes and activities and the context in which they are most effective. Agent-based modeling is a methodological tool that enables demonstration of the interactive and dynamic effects of individuals and their behaviors on other individuals in the environment. The purpose of this study was to develop and test an agent-based model of opinion leadership. The details of the design and verification of the model are presented. The agent-based model was developed by using a software development platform to translate an underlying conceptual model of opinion leadership into a computer model. Individual agent attributes (for example, motives and credibility) and behaviors (seeking or providing an opinion) were specified as variables in the model in the context of a fictitious patient care unit. The verification process was designed to test whether or not the agent-based model was capable of reproducing the conditions of the preliminary conceptual model. The verification methods included iterative programmatic testing ('debugging') and exploratory analysis of simulated data obtained from execution of the model. The simulation tests included a parameter sweep, in which the model input variables were adjusted systematically followed by an individual time series experiment. Statistical analysis of model output for the 288 possible simulation scenarios in the parameter sweep revealed that the agent-based model was performing, consistent with the posited relationships in the underlying model. Nurse opinion leaders act on the strength of their beliefs and as a result, become an opinion resource for their uncertain colleagues, depending on their perceived credibility. Over time, some nurses consistently act as this type of resource and have the potential to emerge as opinion leaders in a context where uncertainty exists. The development and testing of agent-based models is an iterative process. The opinion leader model presented here provides a basic structure for continued model development, ongoing verification, and the establishment of validation procedures, including empirical data collection.

Testing process predictions of models of risky choice: a quantitative model comparison approach

PubMed Central

Pachur, Thorsten; Hertwig, Ralph; Gigerenzer, Gerd; Brandstätter, Eduard

2013-01-01

This article presents a quantitative model comparison contrasting the process predictions of two prominent views on risky choice. One view assumes a trade-off between probabilities and outcomes (or non-linear functions thereof) and the separate evaluation of risky options (expectation models). Another view assumes that risky choice is based on comparative evaluation, limited search, aspiration levels, and the forgoing of trade-offs (heuristic models). We derived quantitative process predictions for a generic expectation model and for a specific heuristic model, namely the priority heuristic (Brandstätter et al., 2006), and tested them in two experiments. The focus was on two key features of the cognitive process: acquisition frequencies (i.e., how frequently individual reasons are looked up) and direction of search (i.e., gamble-wise vs. reason-wise). In Experiment 1, the priority heuristic predicted direction of search better than the expectation model (although neither model predicted the acquisition process perfectly); acquisition frequencies, however, were inconsistent with both models. Additional analyses revealed that these frequencies were primarily a function of what Rubinstein (1988) called “similarity.” In Experiment 2, the quantitative model comparison approach showed that people seemed to rely more on the priority heuristic in difficult problems, but to make more trade-offs in easy problems. This finding suggests that risky choice may be based on a mental toolbox of strategies. PMID:24151472

Integrated controls design optimization

DOEpatents

Lou, Xinsheng; Neuschaefer, Carl H.

2015-09-01

A control system (207) for optimizing a chemical looping process of a power plant includes an optimizer (420), an income algorithm (230) and a cost algorithm (225) and a chemical looping process models. The process models are used to predict the process outputs from process input variables. Some of the process in puts and output variables are related to the income of the plant; and some others are related to the cost of the plant operations. The income algorithm (230) provides an income input to the optimizer (420) based on a plurality of input parameters (215) of the power plant. The cost algorithm (225) provides a cost input to the optimizer (420) based on a plurality of output parameters (220) of the power plant. The optimizer (420) determines an optimized operating parameter solution based on at least one of the income input and the cost input, and supplies the optimized operating parameter solution to the power plant.

A Correlation-Based Transition Model using Local Variables. Part 1; Model Formation

NASA Technical Reports Server (NTRS)

Menter, F. R.; Langtry, R. B.; Likki, S. R.; Suzen, Y. B.; Huang, P. G.; Volker, S.

2006-01-01

A new correlation-based transition model has been developed, which is based strictly on local variables. As a result, the transition model is compatible with modern computational fluid dynamics (CFD) approaches, such as unstructured grids and massive parallel execution. The model is based on two transport equations, one for intermittency and one for the transition onset criteria in terms of momentum thickness Reynolds number. The proposed transport equations do not attempt to model the physics of the transition process (unlike, e.g., turbulence models) but from a framework for the implementation of correlation-based models into general-purpose CFD methods.

LinkEHR-Ed: a multi-reference model archetype editor based on formal semantics.

PubMed

Maldonado, José A; Moner, David; Boscá, Diego; Fernández-Breis, Jesualdo T; Angulo, Carlos; Robles, Montserrat

2009-08-01

To develop a powerful archetype editing framework capable of handling multiple reference models and oriented towards the semantic description and standardization of legacy data. The main prerequisite for implementing tools providing enhanced support for archetypes is the clear specification of archetype semantics. We propose a formalization of the definition section of archetypes based on types over tree-structured data. It covers the specialization of archetypes, the relationship between reference models and archetypes and conformance of data instances to archetypes. LinkEHR-Ed, a visual archetype editor based on the former formalization with advanced processing capabilities that supports multiple reference models, the editing and semantic validation of archetypes, the specification of mappings to data sources, and the automatic generation of data transformation scripts, is developed. LinkEHR-Ed is a useful tool for building, processing and validating archetypes based on any reference model.

Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm

PubMed Central

Wang, Jie-sheng; Han, Shuang; Shen, Na-na; Li, Shu-xia

2014-01-01

For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy. PMID:25133210

Linear Elastic and Cohesive Fracture Analysis to Model Hydraulic Fracture in Brittle and Ductile Rocks

NASA Astrophysics Data System (ADS)

Yao, Yao

2012-05-01

Hydraulic fracturing technology is being widely used within the oil and gas industry for both waste injection and unconventional gas production wells. It is essential to predict the behavior of hydraulic fractures accurately based on understanding the fundamental mechanism(s). The prevailing approach for hydraulic fracture modeling continues to rely on computational methods based on Linear Elastic Fracture Mechanics (LEFM). Generally, these methods give reasonable predictions for hard rock hydraulic fracture processes, but still have inherent limitations, especially when fluid injection is performed in soft rock/sand or other non-conventional formations. These methods typically give very conservative predictions on fracture geometry and inaccurate estimation of required fracture pressure. One of the reasons the LEFM-based methods fail to give accurate predictions for these materials is that the fracture process zone ahead of the crack tip and softening effect should not be neglected in ductile rock fracture analysis. A 3D pore pressure cohesive zone model has been developed and applied to predict hydraulic fracturing under fluid injection. The cohesive zone method is a numerical tool developed to model crack initiation and growth in quasi-brittle materials considering the material softening effect. The pore pressure cohesive zone model has been applied to investigate the hydraulic fracture with different rock properties. The hydraulic fracture predictions of a three-layer water injection case have been compared using the pore pressure cohesive zone model with revised parameters, LEFM-based pseudo 3D model, a Perkins-Kern-Nordgren (PKN) model, and an analytical solution. Based on the size of the fracture process zone and its effect on crack extension in ductile rock, the fundamental mechanical difference of LEFM and cohesive fracture mechanics-based methods is discussed. An effective fracture toughness method has been proposed to consider the fracture process zone effect on the ductile rock fracture.

Verification of ARMA identification for modelling temporal correlation of GPS observations using the toolbox ARMASA

NASA Astrophysics Data System (ADS)

Luo, Xiaoguang; Mayer, Michael; Heck, Bernhard

2010-05-01

One essential deficiency of the stochastic model used in many GNSS (Global Navigation Satellite Systems) software products consists in neglecting temporal correlation of GNSS observations. Analysing appropriately detrended time series of observation residuals resulting from GPS (Global Positioning System) data processing, the temporal correlation behaviour of GPS observations can be sufficiently described by means of so-called autoregressive moving average (ARMA) processes. Using the toolbox ARMASA which is available free of charge in MATLAB® Central (open exchange platform for the MATLAB® and SIMULINK® user community), a well-fitting time series model can be identified automatically in three steps. Firstly, AR, MA, and ARMA models are computed up to some user-specified maximum order. Subsequently, for each model type, the best-fitting model is selected using the combined (for AR processes) resp. generalised (for MA and ARMA processes) information criterion. The final model identification among the best-fitting AR, MA, and ARMA models is performed based on the minimum prediction error characterising the discrepancies between the given data and the fitted model. The ARMA coefficients are computed using Burg's maximum entropy algorithm (for AR processes), Durbin's first (for MA processes) and second (for ARMA processes) methods, respectively. This paper verifies the performance of the automated ARMA identification using the toolbox ARMASA. For this purpose, a representative data base is generated by means of ARMA simulation with respect to sample size, correlation level, and model complexity. The model error defined as a transform of the prediction error is used as measure for the deviation between the true and the estimated model. The results of the study show that the recognition rates of underlying true processes increase with increasing sample sizes and decrease with rising model complexity. Considering large sample sizes, the true underlying processes can be correctly recognised for nearly 80% of the analysed data sets. Additionally, the model errors of first-order AR resp. MA processes converge clearly more rapidly to the corresponding asymptotical values than those of high-order ARMA processes.

Comparison of Centralized-Manual, Centralized-Computerized, and Decentralized-Computerized Order and Management Information Models for the Turkish Air Force Logistics System.

DTIC Science & Technology

1986-09-01

differentiation between the systems. This study will investigate an appropriate Order Processing and Management Information System (OP&MIS) to link base-level...methodology: 1. Reviewed the current order processing and information model of the TUAF Logistics System. (centralized-manual model) 2. Described the...RDS program’s order processing and information system. (centralized-computerized model) 3. Described the order irocessing and information system of

Multibody dynamical modeling for spacecraft docking process with spring-damper buffering device: A new validation approach

NASA Astrophysics Data System (ADS)

Daneshjou, Kamran; Alibakhshi, Reza

2018-01-01

In the current manuscript, the process of spacecraft docking, as one of the main risky operations in an on-orbit servicing mission, is modeled based on unconstrained multibody dynamics. The spring-damper buffering device is utilized here in the docking probe-cone system for micro-satellites. Owing to the impact occurs inevitably during docking process and the motion characteristics of multibody systems are remarkably affected by this phenomenon, a continuous contact force model needs to be considered. Spring-damper buffering device, keeping the spacecraft stable in an orbit when impact occurs, connects a base (cylinder) inserted in the chaser satellite and the end of docking probe. Furthermore, by considering a revolute joint equipped with torsional shock absorber, between base and chaser satellite, the docking probe can experience both translational and rotational motions simultaneously. Although spacecraft docking process accompanied by the buffering mechanisms may be modeled by constrained multibody dynamics, this paper deals with a simple and efficient formulation to eliminate the surplus generalized coordinates and solve the impact docking problem based on unconstrained Lagrangian mechanics. By an example problem, first, model verification is accomplished by comparing the computed results with those recently reported in the literature. Second, according to a new alternative validation approach, which is based on constrained multibody problem, the accuracy of presented model can be also evaluated. This proposed verification approach can be applied to indirectly solve the constrained multibody problems by minimum required effort. The time history of impact force, the influence of system flexibility and physical interaction between shock absorber and penetration depth caused by impact are the issues followed in this paper. Third, the MATLAB/SIMULINK multibody dynamic analysis software will be applied to build impact docking model to validate computed results and then, investigate the trajectories of both satellites to take place the successful capture process.

Demonstration of emulator-based Bayesian calibration of safety analysis codes: Theory and formulation

DOE PAGES

Yurko, Joseph P.; Buongiorno, Jacopo; Youngblood, Robert

2015-05-28

System codes for simulation of safety performance of nuclear plants may contain parameters whose values are not known very accurately. New information from tests or operating experience is incorporated into safety codes by a process known as calibration, which reduces uncertainty in the output of the code and thereby improves its support for decision-making. The work reported here implements several improvements on classic calibration techniques afforded by modern analysis techniques. The key innovation has come from development of code surrogate model (or code emulator) construction and prediction algorithms. Use of a fast emulator makes the calibration processes used here withmore » Markov Chain Monte Carlo (MCMC) sampling feasible. This study uses Gaussian Process (GP) based emulators, which have been used previously to emulate computer codes in the nuclear field. The present work describes the formulation of an emulator that incorporates GPs into a factor analysis-type or pattern recognition-type model. This “function factorization” Gaussian Process (FFGP) model allows overcoming limitations present in standard GP emulators, thereby improving both accuracy and speed of the emulator-based calibration process. Calibration of a friction-factor example using a Method of Manufactured Solution is performed to illustrate key properties of the FFGP based process.« less

Modeling urbanized watershed flood response changes with distributed hydrological model: key hydrological processes, parameterization and case studies

NASA Astrophysics Data System (ADS)

Chen, Y.

2017-12-01

Urbanization is the world development trend for the past century, and the developing countries have been experiencing much rapider urbanization in the past decades. Urbanization brings many benefits to human beings, but also causes negative impacts, such as increasing flood risk. Impact of urbanization on flood response has long been observed, but quantitatively studying this effect still faces great challenges. For example, setting up an appropriate hydrological model representing the changed flood responses and determining accurate model parameters are very difficult in the urbanized or urbanizing watershed. In the Pearl River Delta area, rapidest urbanization has been observed in China for the past decades, and dozens of highly urbanized watersheds have been appeared. In this study, a physically based distributed watershed hydrological model, the Liuxihe model is employed and revised to simulate the hydrological processes of the highly urbanized watershed flood in the Pearl River Delta area. A virtual soil type is then defined in the terrain properties dataset, and its runoff production and routing algorithms are added to the Liuxihe model. Based on a parameter sensitive analysis, the key hydrological processes of a highly urbanized watershed is proposed, that provides insight into the hydrological processes and for parameter optimization. Based on the above analysis, the model is set up in the Songmushan watershed where there is hydrological data observation. A model parameter optimization and updating strategy is proposed based on the remotely sensed LUC types, which optimizes model parameters with PSO algorithm and updates them based on the changed LUC types. The model parameters in Songmushan watershed are regionalized at the Pearl River Delta area watersheds based on the LUC types of the other watersheds. A dozen watersheds in the highly urbanized area of Dongguan City in the Pearl River Delta area were studied for the flood response changes due to urbanization, and the results show urbanization has big impact on the watershed flood responses. The peak flow increased a few times after urbanization which is much higher than previous reports.

Going beyond the unitary curve: incorporating richer cognition into agent-based water resources models

NASA Astrophysics Data System (ADS)

Kock, B. E.

2008-12-01

The increased availability and understanding of agent-based modeling technology and techniques provides a unique opportunity for water resources modelers, allowing them to go beyond traditional behavioral approaches from neoclassical economics, and add rich cognition to social-hydrological models. Agent-based models provide for an individual focus, and the easier and more realistic incorporation of learning, memory and other mechanisms for increased cognitive sophistication. We are in an age of global change impacting complex water resources systems, and social responses are increasingly recognized as fundamentally adaptive and emergent. In consideration of this, water resources models and modelers need to better address social dynamics in a manner beyond the capabilities of neoclassical economics theory and practice. However, going beyond the unitary curve requires unique levels of engagement with stakeholders, both to elicit the richer knowledge necessary for structuring and parameterizing agent-based models, but also to make sure such models are appropriately used. With the aim of encouraging epistemological and methodological convergence in the agent-based modeling of water resources, we have developed a water resources-specific cognitive model and an associated collaborative modeling process. Our cognitive model emphasizes efficiency in architecture and operation, and capacity to adapt to different application contexts. We describe a current application of this cognitive model and modeling process in the Arkansas Basin of Colorado. In particular, we highlight the potential benefits of, and challenges to, using more sophisticated cognitive models in agent-based water resources models.