Accounting for large deformations in real-time simulations of soft tissues based on reduced-order models.

PubMed

Niroomandi, S; Alfaro, I; Cueto, E; Chinesta, F

2012-01-01

Model reduction techniques have shown to constitute a valuable tool for real-time simulation in surgical environments and other fields. However, some limitations, imposed by real-time constraints, have not yet been overcome. One of such limitations is the severe limitation in time (established in 500Hz of frequency for the resolution) that precludes the employ of Newton-like schemes for solving non-linear models as the ones usually employed for modeling biological tissues. In this work we present a technique able to deal with geometrically non-linear models, based on the employ of model reduction techniques, together with an efficient non-linear solver. Examples of the performance of the technique over some examples will be given. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

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.

Comparison of lung tumor motion measured using a model-based 4DCT technique and a commercial protocol.

PubMed

O'Connell, Dylan; Shaverdian, Narek; Kishan, Amar U; Thomas, David H; Dou, Tai H; Lewis, John H; Lamb, James M; Cao, Minsong; Tenn, Stephen; Percy, Lee P; Low, Daniel A

To compare lung tumor motion measured with a model-based technique to commercial 4-dimensional computed tomography (4DCT) scans and describe a workflow for using model-based 4DCT as a clinical simulation protocol. Twenty patients were imaged using a model-based technique and commercial 4DCT. Tumor motion was measured on each commercial 4DCT dataset and was calculated on model-based datasets for 3 breathing amplitude percentile intervals: 5th to 85th, 5th to 95th, and 0th to 100th. Internal target volumes (ITVs) were defined on the 4DCT and 5th to 85th interval datasets and compared using Dice similarity. Images were evaluated for noise and rated by 2 radiation oncologists for artifacts. Mean differences in tumor motion magnitude between commercial and model-based images were 0.47 ± 3.0, 1.63 ± 3.17, and 5.16 ± 4.90 mm for the 5th to 85th, 5th to 95th, and 0th to 100th amplitude intervals, respectively. Dice coefficients between ITVs defined on commercial and 5th to 85th model-based images had a mean value of 0.77 ± 0.09. Single standard deviation image noise was 11.6 ± 9.6 HU in the liver and 6.8 ± 4.7 HU in the aorta for the model-based images compared with 57.7 ± 30 and 33.7 ± 15.4 for commercial 4DCT. Mean model error within the ITV regions was 1.71 ± 0.81 mm. Model-based images exhibited reduced presence of artifacts at the tumor compared with commercial images. Tumor motion measured with the model-based technique using the 5th to 85th percentile breathing amplitude interval corresponded more closely to commercial 4DCT than the 5th to 95th or 0th to 100th intervals, which showed greater motion on average. The model-based technique tended to display increased tumor motion when breathing amplitude intervals wider than 5th to 85th were used because of the influence of unusually deep inhalations. These results suggest that care must be taken in selecting the appropriate interval during image generation when using model-based 4DCT methods. Copyright © 2017 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

Prediction of In Vivo Knee Joint Kinematics Using a Combined Dual Fluoroscopy Imaging and Statistical Shape Modeling Technique

PubMed Central

Li, Jing-Sheng; Tsai, Tsung-Yuan; Wang, Shaobai; Li, Pingyue; Kwon, Young-Min; Freiberg, Andrew; Rubash, Harry E.; Li, Guoan

2014-01-01

Using computed tomography (CT) or magnetic resonance (MR) images to construct 3D knee models has been widely used in biomedical engineering research. Statistical shape modeling (SSM) method is an alternative way to provide a fast, cost-efficient, and subject-specific knee modeling technique. This study was aimed to evaluate the feasibility of using a combined dual-fluoroscopic imaging system (DFIS) and SSM method to investigate in vivo knee kinematics. Three subjects were studied during a treadmill walking. The data were compared with the kinematics obtained using a CT-based modeling technique. Geometric root-mean-square (RMS) errors between the knee models constructed using the SSM and CT-based modeling techniques were 1.16 mm and 1.40 mm for the femur and tibia, respectively. For the kinematics of the knee during the treadmill gait, the SSM model can predict the knee kinematics with RMS errors within 3.3 deg for rotation and within 2.4 mm for translation throughout the stance phase of the gait cycle compared with those obtained using the CT-based knee models. The data indicated that the combined DFIS and SSM technique could be used for quick evaluation of knee joint kinematics. PMID:25320846

An Approach to the Evaluation of Hypermedia.

ERIC Educational Resources Information Center

Knussen, Christina; And Others

1991-01-01

Discusses methods that may be applied to the evaluation of hypermedia, based on six models described by Lawton. Techniques described include observation, self-report measures, interviews, automated measures, psychometric tests, checklists and criterion-based techniques, process models, Experimentally Measuring Usability (EMU), and a naturalistic…

Numerical Modeling of Nonlinear Thermodynamics in SMA Wires

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

Reynolds, D R; Kloucek, P

We present a mathematical model describing the thermodynamic behavior of shape memory alloy wires, as well as a computational technique to solve the resulting system of partial differential equations. The model consists of conservation equations based on a new Helmholtz free energy potential. The computational technique introduces a viscosity-based continuation method, which allows the model to handle dynamic applications where the temporally local behavior of solutions is desired. Computational experiments document that this combination of modeling and solution techniques appropriately predicts the thermally- and stress-induced martensitic phase transitions, as well as the hysteretic behavior and production of latent heat associatedmore » with such materials.« less

System equivalent model mixing

NASA Astrophysics Data System (ADS)

Klaassen, Steven W. B.; van der Seijs, Maarten V.; de Klerk, Dennis

2018-05-01

This paper introduces SEMM: a method based on Frequency Based Substructuring (FBS) techniques that enables the construction of hybrid dynamic models. With System Equivalent Model Mixing (SEMM) frequency based models, either of numerical or experimental nature, can be mixed to form a hybrid model. This model follows the dynamic behaviour of a predefined weighted master model. A large variety of applications can be thought of, such as the DoF-space expansion of relatively small experimental models using numerical models, or the blending of different models in the frequency spectrum. SEMM is outlined, both mathematically and conceptually, based on a notation commonly used in FBS. A critical physical interpretation of the theory is provided next, along with a comparison to similar techniques; namely DoF expansion techniques. SEMM's concept is further illustrated by means of a numerical example. It will become apparent that the basic method of SEMM has some shortcomings which warrant a few extensions to the method. One of the main applications is tested in a practical case, performed on a validated benchmark structure; it will emphasize the practicality of the method.

A Biomechanical Modeling Guided CBCT Estimation Technique

PubMed Central

Zhang, You; Tehrani, Joubin Nasehi; Wang, Jing

2017-01-01

Two-dimensional-to-three-dimensional (2D-3D) deformation has emerged as a new technique to estimate cone-beam computed tomography (CBCT) images. The technique is based on deforming a prior high-quality 3D CT/CBCT image to form a new CBCT image, guided by limited-view 2D projections. The accuracy of this intensity-based technique, however, is often limited in low-contrast image regions with subtle intensity differences. The solved deformation vector fields (DVFs) can also be biomechanically unrealistic. To address these problems, we have developed a biomechanical modeling guided CBCT estimation technique (Bio-CBCT-est) by combining 2D-3D deformation with finite element analysis (FEA)-based biomechanical modeling of anatomical structures. Specifically, Bio-CBCT-est first extracts the 2D-3D deformation-generated displacement vectors at the high-contrast anatomical structure boundaries. The extracted surface deformation fields are subsequently used as the boundary conditions to drive structure-based FEA to correct and fine-tune the overall deformation fields, especially those at low-contrast regions within the structure. The resulting FEA-corrected deformation fields are then fed back into 2D-3D deformation to form an iterative loop, combining the benefits of intensity-based deformation and biomechanical modeling for CBCT estimation. Using eleven lung cancer patient cases, the accuracy of the Bio-CBCT-est technique has been compared to that of the 2D-3D deformation technique and the traditional CBCT reconstruction techniques. The accuracy was evaluated in the image domain, and also in the DVF domain through clinician-tracked lung landmarks. PMID:27831866

Reduced-order modeling for hyperthermia: an extended balanced-realization-based approach.

PubMed

Mattingly, M; Bailey, E A; Dutton, A W; Roemer, R B; Devasia, S

1998-09-01

Accurate thermal models are needed in hyperthermia cancer treatments for such tasks as actuator and sensor placement design, parameter estimation, and feedback temperature control. The complexity of the human body produces full-order models which are too large for effective execution of these tasks, making use of reduced-order models necessary. However, standard balanced-realization (SBR)-based model reduction techniques require a priori knowledge of the particular placement of actuators and sensors for model reduction. Since placement design is intractable (computationally) on the full-order models, SBR techniques must use ad hoc placements. To alleviate this problem, an extended balanced-realization (EBR)-based model-order reduction approach is presented. The new technique allows model order reduction to be performed over all possible placement designs and does not require ad hoc placement designs. It is shown that models obtained using the EBR method are more robust to intratreatment changes in the placement of the applied power field than those models obtained using the SBR method.

Machine learning modelling for predicting soil liquefaction susceptibility

NASA Astrophysics Data System (ADS)

Samui, P.; Sitharam, T. G.

2011-01-01

This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT [(N1)60] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters [(N1)60 and peck ground acceleration (amax/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

Financial model calibration using consistency hints.

PubMed

Abu-Mostafa, Y S

2001-01-01

We introduce a technique for forcing the calibration of a financial model to produce valid parameters. The technique is based on learning from hints. It converts simple curve fitting into genuine calibration, where broad conclusions can be inferred from parameter values. The technique augments the error function of curve fitting with consistency hint error functions based on the Kullback-Leibler distance. We introduce an efficient EM-type optimization algorithm tailored to this technique. We also introduce other consistency hints, and balance their weights using canonical errors. We calibrate the correlated multifactor Vasicek model of interest rates, and apply it successfully to Japanese Yen swaps market and US dollar yield market.

Virtual 3d City Modeling: Techniques and Applications

NASA Astrophysics Data System (ADS)

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

2013-08-01

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. There are various terms used for 3D city models such as "Cybertown", "Cybercity", "Virtual City", or "Digital City". 3D city models are basically a computerized or digital model of a city contains the graphic representation of buildings and other objects in 2.5 or 3D. Generally three main Geomatics approach are using for Virtual 3-D City models generation, in first approach, researcher are using Conventional techniques such as Vector Map data, DEM, Aerial images, second approach are based on High resolution satellite images with LASER scanning, In third method, many researcher are using Terrestrial images by using Close Range Photogrammetry with DSM & Texture mapping. We start this paper from the introduction of various Geomatics techniques for 3D City modeling. These techniques divided in to two main categories: one is based on Automation (Automatic, Semi-automatic and Manual methods), and another is Based on Data input techniques (one is Photogrammetry, another is Laser Techniques). After details study of this, finally in short, we are trying to give the conclusions of this study. In the last, we are trying to give the conclusions of this research paper and also giving a short view for justification and analysis, and present trend for 3D City modeling. This paper gives an overview about the Techniques related with "Generation of Virtual 3-D City models using Geomatics Techniques" and the Applications of Virtual 3D City models. Photogrammetry, (Close range, Aerial, Satellite), Lasergrammetry, GPS, or combination of these modern Geomatics techniques play a major role to create a virtual 3-D City model. Each and every techniques and method has some advantages and some drawbacks. Point cloud model is a modern trend for virtual 3-D city model. Photo-realistic, Scalable, Geo-referenced virtual 3-D City model is a very useful for various kinds of applications such as for planning in Navigation, Tourism, Disasters Management, Transportations, Municipality, Urban Environmental Managements and Real-estate industry. So the Construction of Virtual 3-D city models is a most interesting research topic in recent years.

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

PubMed

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

2015-04-13

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

Agent-based modeling: Methods and techniques for simulating human systems

PubMed Central

Bonabeau, Eric

2002-01-01

Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. For each category, one or several business applications are described and analyzed. PMID:12011407

Application of Discrete Fracture Modeling and Upscaling Techniques to Complex Fractured Reservoirs

NASA Astrophysics Data System (ADS)

Karimi-Fard, M.; Lapene, A.; Pauget, L.

2012-12-01

During the last decade, an important effort has been made to improve data acquisition (seismic and borehole imaging) and workflow for reservoir characterization which has greatly benefited the description of fractured reservoirs. However, the geological models resulting from the interpretations need to be validated or calibrated against dynamic data. Flow modeling in fractured reservoirs remains a challenge due to the difficulty of representing mass transfers at different heterogeneity scales. The majority of the existing approaches are based on dual continuum representation where the fracture network and the matrix are represented separately and their interactions are modeled using transfer functions. These models are usually based on idealized representation of the fracture distribution which makes the integration of real data difficult. In recent years, due to increases in computer power, discrete fracture modeling techniques (DFM) are becoming popular. In these techniques the fractures are represented explicitly allowing the direct use of data. In this work we consider the DFM technique developed by Karimi-Fard et al. [1] which is based on an unstructured finite-volume discretization. The mass flux between two adjacent control-volumes is evaluated using an optimized two-point flux approximation. The result of the discretization is a list of control-volumes with the associated pore-volumes and positions, and a list of connections with the associated transmissibilities. Fracture intersections are simplified using a connectivity transformation which contributes considerably to the efficiency of the methodology. In addition, the method is designed for general purpose simulators and any connectivity based simulator can be used for flow simulations. The DFM technique is either used standalone or as part of an upscaling technique. The upscaling techniques are required for large reservoirs where the explicit representation of all fractures and faults is not possible. Karimi-Fard et al. [2] have developed an upscaling technique based on DFM representation. The original version of this technique was developed to construct a dual-porosity model from a discrete fracture description. This technique has been extended and generalized so it can be applied to a wide range of problems from reservoirs with a few or no fracture to highly fractured reservoirs. In this work, we present the application of these techniques to two three-dimensional fractured reservoirs constructed using real data. The first model contains more than 600 medium and large scale fractures. The fractures are not always connected which requires a general modeling technique. The reservoir has 50 wells (injectors and producers) and water flooding simulations are performed. The second test case is a larger reservoir with sparsely distributed faults. Single-phase simulations are performed with 5 producing wells. [1] Karimi-Fard M., Durlofsky L.J., and Aziz K. 2004. An efficient discrete-fracture model applicable for general-purpose reservoir simulators. SPE Journal, 9(2): 227-236. [2] Karimi-Fard M., Gong B., and Durlofsky L.J. 2006. Generation of coarse-scale continuum flow models from detailed fracture characterizations. Water Resources Research, 42(10): W10423.

RECURSIVE PROTEIN MODELING: A DIVIDE AND CONQUER STRATEGY FOR PROTEIN STRUCTURE PREDICTION AND ITS CASE STUDY IN CASP9

PubMed Central

CHENG, JIANLIN; EICKHOLT, JESSE; WANG, ZHENG; DENG, XIN

2013-01-01

After decades of research, protein structure prediction remains a very challenging problem. In order to address the different levels of complexity of structural modeling, two types of modeling techniques — template-based modeling and template-free modeling — have been developed. Template-based modeling can often generate a moderate- to high-resolution model when a similar, homologous template structure is found for a query protein but fails if no template or only incorrect templates are found. Template-free modeling, such as fragment-based assembly, may generate models of moderate resolution for small proteins of low topological complexity. Seldom have the two techniques been integrated together to improve protein modeling. Here we develop a recursive protein modeling approach to selectively and collaboratively apply template-based and template-free modeling methods to model template-covered (i.e. certain) and template-free (i.e. uncertain) regions of a protein. A preliminary implementation of the approach was tested on a number of hard modeling cases during the 9th Critical Assessment of Techniques for Protein Structure Prediction (CASP9) and successfully improved the quality of modeling in most of these cases. Recursive modeling can signicantly reduce the complexity of protein structure modeling and integrate template-based and template-free modeling to improve the quality and efficiency of protein structure prediction. PMID:22809379

Automation of energy demand forecasting

NASA Astrophysics Data System (ADS)

Siddique, Sanzad

Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy.

A systematic and critical review of model-based economic evaluations of pharmacotherapeutics in patients with bipolar disorder.

PubMed

Mohiuddin, Syed

2014-08-01

Bipolar disorder (BD) is a chronic and relapsing mental illness with a considerable health-related and economic burden. The primary goal of pharmacotherapeutics for BD is to improve patients' well-being. The use of decision-analytic models is key in assessing the added value of the pharmacotherapeutics aimed at treating the illness, but concerns have been expressed about the appropriateness of different modelling techniques and about the transparency in the reporting of economic evaluations. This paper aimed to identify and critically appraise published model-based economic evaluations of pharmacotherapeutics in BD patients. A systematic review combining common terms for BD and economic evaluation was conducted in MEDLINE, EMBASE, PSYCINFO and ECONLIT. Studies identified were summarised and critically appraised in terms of the use of modelling technique, model structure and data sources. Considering the prognosis and management of BD, the possible benefits and limitations of each modelling technique are discussed. Fourteen studies were identified using model-based economic evaluations of pharmacotherapeutics in BD patients. Of these 14 studies, nine used Markov, three used discrete-event simulation (DES) and two used decision-tree models. Most of the studies (n = 11) did not include the rationale for the choice of modelling technique undertaken. Half of the studies did not include the risk of mortality. Surprisingly, no study considered the risk of having a mixed bipolar episode. This review identified various modelling issues that could potentially reduce the comparability of one pharmacotherapeutic intervention with another. Better use and reporting of the modelling techniques in the future studies are essential. DES modelling appears to be a flexible and comprehensive technique for evaluating the comparability of BD treatment options because of its greater flexibility of depicting the disease progression over time. However, depending on the research question, modelling techniques other than DES might also be appropriate in some cases.

A dependency-based modelling mechanism for problem solving

NASA Technical Reports Server (NTRS)

London, P.

1978-01-01

The paper develops a technique of dependency net modeling which relies on an explicit representation of justifications for beliefs held by the problem solver. Using these justifications, the modeling mechanism is able to determine the relevant lines of inference to pursue during problem solving. Three particular problem-solving difficulties which may be handled by the dependency-based technique are discussed: (1) subgoal violation detection, (2) description binding, and (3) maintaining a consistent world model.

Agent-Based Modeling in Systems Pharmacology.

PubMed

Cosgrove, J; Butler, J; Alden, K; Read, M; Kumar, V; Cucurull-Sanchez, L; Timmis, J; Coles, M

2015-11-01

Modeling and simulation (M&S) techniques provide a platform for knowledge integration and hypothesis testing to gain insights into biological systems that would not be possible a priori. Agent-based modeling (ABM) is an M&S technique that focuses on describing individual components rather than homogenous populations. This tutorial introduces ABM to systems pharmacologists, using relevant case studies to highlight how ABM-specific strengths have yielded success in the area of preclinical mechanistic modeling.

Statistical shape model-based reconstruction of a scaled, patient-specific surface model of the pelvis from a single standard AP x-ray radiograph

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

Zheng Guoyan

2010-04-15

Purpose: The aim of this article is to investigate the feasibility of using a statistical shape model (SSM)-based reconstruction technique to derive a scaled, patient-specific surface model of the pelvis from a single standard anteroposterior (AP) x-ray radiograph and the feasibility of estimating the scale of the reconstructed surface model by performing a surface-based 3D/3D matching. Methods: Data sets of 14 pelvises (one plastic bone, 12 cadavers, and one patient) were used to validate the single-image based reconstruction technique. This reconstruction technique is based on a hybrid 2D/3D deformable registration process combining a landmark-to-ray registration with a SSM-based 2D/3D reconstruction.more » The landmark-to-ray registration was used to find an initial scale and an initial rigid transformation between the x-ray image and the SSM. The estimated scale and rigid transformation were used to initialize the SSM-based 2D/3D reconstruction. The optimal reconstruction was then achieved in three stages by iteratively matching the projections of the apparent contours extracted from a 3D model derived from the SSM to the image contours extracted from the x-ray radiograph: Iterative affine registration, statistical instantiation, and iterative regularized shape deformation. The image contours are first detected by using a semiautomatic segmentation tool based on the Livewire algorithm and then approximated by a set of sparse dominant points that are adaptively sampled from the detected contours. The unknown scales of the reconstructed models were estimated by performing a surface-based 3D/3D matching between the reconstructed models and the associated ground truth models that were derived from a CT-based reconstruction method. Such a matching also allowed for computing the errors between the reconstructed models and the associated ground truth models. Results: The technique could reconstruct the surface models of all 14 pelvises directly from the landmark-based initialization. Depending on the surface-based matching techniques, the reconstruction errors were slightly different. When a surface-based iterative affine registration was used, an average reconstruction error of 1.6 mm was observed. This error was increased to 1.9 mm, when a surface-based iterative scaled rigid registration was used. Conclusions: It is feasible to reconstruct a scaled, patient-specific surface model of the pelvis from single standard AP x-ray radiograph using the present approach. The unknown scale of the reconstructed model can be estimated by performing a surface-based 3D/3D matching.« less

Flexible system model reduction and control system design based upon actuator and sensor influence functions

NASA Technical Reports Server (NTRS)

Yam, Yeung; Johnson, Timothy L.; Lang, Jeffrey H.

1987-01-01

A model reduction technique based on aggregation with respect to sensor and actuator influence functions rather than modes is presented for large systems of coupled second-order differential equations. Perturbation expressions which can predict the effects of spillover on both the reduced-order plant model and the neglected plant model are derived. For the special case of collocated actuators and sensors, these expressions lead to the derivation of constraints on the controller gains that are, given the validity of the perturbation technique, sufficient to guarantee the stability of the closed-loop system. A case study demonstrates the derivation of stabilizing controllers based on the present technique. The use of control and observation synthesis in modifying the dimension of the reduced-order plant model is also discussed. A numerical example is provided for illustration.

Technique for Early Reliability Prediction of Software Components Using Behaviour Models

PubMed Central

Ali, Awad; N. A. Jawawi, Dayang; Adham Isa, Mohd; Imran Babar, Muhammad

2016-01-01

Behaviour models are the most commonly used input for predicting the reliability of a software system at the early design stage. A component behaviour model reveals the structure and behaviour of the component during the execution of system-level functionalities. There are various challenges related to component reliability prediction at the early design stage based on behaviour models. For example, most of the current reliability techniques do not provide fine-grained sequential behaviour models of individual components and fail to consider the loop entry and exit points in the reliability computation. Moreover, some of the current techniques do not tackle the problem of operational data unavailability and the lack of analysis results that can be valuable for software architects at the early design stage. This paper proposes a reliability prediction technique that, pragmatically, synthesizes system behaviour in the form of a state machine, given a set of scenarios and corresponding constraints as input. The state machine is utilized as a base for generating the component-relevant operational data. The state machine is also used as a source for identifying the nodes and edges of a component probabilistic dependency graph (CPDG). Based on the CPDG, a stack-based algorithm is used to compute the reliability. The proposed technique is evaluated by a comparison with existing techniques and the application of sensitivity analysis to a robotic wheelchair system as a case study. The results indicate that the proposed technique is more relevant at the early design stage compared to existing works, and can provide a more realistic and meaningful prediction. PMID:27668748

Finite volume model for two-dimensional shallow environmental flow

USGS Publications Warehouse

Simoes, F.J.M.

2011-01-01

This paper presents the development of a two-dimensional, depth integrated, unsteady, free-surface model based on the shallow water equations. The development was motivated by the desire of balancing computational efficiency and accuracy by selective and conjunctive use of different numerical techniques. The base framework of the discrete model uses Godunov methods on unstructured triangular grids, but the solution technique emphasizes the use of a high-resolution Riemann solver where needed, switching to a simpler and computationally more efficient upwind finite volume technique in the smooth regions of the flow. Explicit time marching is accomplished with strong stability preserving Runge-Kutta methods, with additional acceleration techniques for steady-state computations. A simplified mass-preserving algorithm is used to deal with wet/dry fronts. Application of the model is made to several benchmark cases that show the interplay of the diverse solution techniques.

A Flexible Hierarchical Bayesian Modeling Technique for Risk Analysis of Major Accidents.

PubMed

Yu, Hongyang; Khan, Faisal; Veitch, Brian

2017-09-01

Safety analysis of rare events with potentially catastrophic consequences is challenged by data scarcity and uncertainty. Traditional causation-based approaches, such as fault tree and event tree (used to model rare event), suffer from a number of weaknesses. These include the static structure of the event causation, lack of event occurrence data, and need for reliable prior information. In this study, a new hierarchical Bayesian modeling based technique is proposed to overcome these drawbacks. The proposed technique can be used as a flexible technique for risk analysis of major accidents. It enables both forward and backward analysis in quantitative reasoning and the treatment of interdependence among the model parameters. Source-to-source variability in data sources is also taken into account through a robust probabilistic safety analysis. The applicability of the proposed technique has been demonstrated through a case study in marine and offshore industry. © 2017 Society for Risk Analysis.

Acoustic classification of zooplankton

NASA Astrophysics Data System (ADS)

Martin Traykovski, Linda V.

1998-11-01

Work on the forward problem in zooplankton bioacoustics has resulted in the identification of three categories of acoustic scatterers: elastic-shelled (e.g. pteropods), fluid-like (e.g. euphausiids), and gas-bearing (e.g. siphonophores). The relationship between backscattered energy and animal biomass has been shown to vary by a factor of ~19,000 across these categories, so that to make accurate estimates of zooplankton biomass from acoustic backscatter measurements of the ocean, the acoustic characteristics of the species of interest must be well-understood. This thesis describes the development of both feature based and model based classification techniques to invert broadband acoustic echoes from individual zooplankton for scatterer type, as well as for particular parameters such as animal orientation. The feature based Empirical Orthogonal Function Classifier (EOFC) discriminates scatterer types by identifying characteristic modes of variability in the echo spectra, exploiting only the inherent characteristic structure of the acoustic signatures. The model based Model Parameterisation Classifier (MPC) classifies based on correlation of observed echo spectra with simplified parameterisations of theoretical scattering models for the three classes. The Covariance Mean Variance Classifiers (CMVC) are a set of advanced model based techniques which exploit the full complexity of the theoretical models by searching the entire physical model parameter space without employing simplifying parameterisations. Three different CMVC algorithms were developed: the Integrated Score Classifier (ISC), the Pairwise Score Classifier (PSC) and the Bayesian Probability Classifier (BPC); these classifiers assign observations to a class based on similarities in covariance, mean, and variance, while accounting for model ambiguity and validity. These feature based and model based inversion techniques were successfully applied to several thousand echoes acquired from broadband (~350 kHz-750 kHz) insonifications of live zooplankton collected on Georges Bank and the Gulf of Maine to determine scatterer class. CMVC techniques were also applied to echoes from fluid-like zooplankton (Antarctic krill) to invert for angle of orientation using generic and animal-specific theoretical and empirical models. Application of these inversion techniques in situ will allow correct apportionment of backscattered energy to animal biomass, significantly improving estimates of zooplankton biomass based on acoustic surveys. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)

Parametric Model Based On Imputations Techniques for Partly Interval Censored Data

NASA Astrophysics Data System (ADS)

Zyoud, Abdallah; Elfaki, F. A. M.; Hrairi, Meftah

2017-12-01

The term ‘survival analysis’ has been used in a broad sense to describe collection of statistical procedures for data analysis. In this case, outcome variable of interest is time until an event occurs where the time to failure of a specific experimental unit might be censored which can be right, left, interval, and Partly Interval Censored data (PIC). In this paper, analysis of this model was conducted based on parametric Cox model via PIC data. Moreover, several imputation techniques were used, which are: midpoint, left & right point, random, mean, and median. Maximum likelihood estimate was considered to obtain the estimated survival function. These estimations were then compared with the existing model, such as: Turnbull and Cox model based on clinical trial data (breast cancer data), for which it showed the validity of the proposed model. Result of data set indicated that the parametric of Cox model proved to be more superior in terms of estimation of survival functions, likelihood ratio tests, and their P-values. Moreover, based on imputation techniques; the midpoint, random, mean, and median showed better results with respect to the estimation of survival function.

Survey of in-situ and remote sensing methods for soil moisture determination

NASA Technical Reports Server (NTRS)

Schmugge, T. J.; Jackson, T. J.; Mckim, H. L.

1981-01-01

General methods for determining the moisture content in the surface layers of the soil based on in situ or point measurements, soil water models and remote sensing observations are surveyed. In situ methods described include gravimetric techniques, nuclear techniques based on neutron scattering or gamma-ray attenuation, electromagnetic techniques, tensiometric techniques and hygrometric techniques. Soil water models based on column mass balance treat soil moisture contents as a result of meteorological inputs (precipitation, runoff, subsurface flow) and demands (evaporation, transpiration, percolation). The remote sensing approaches are based on measurements of the diurnal range of surface temperature and the crop canopy temperature in the thermal infrared, measurements of the radar backscattering coefficient in the microwave region, and measurements of microwave emission or brightness temperature. Advantages and disadvantages of the various methods are pointed out, and it is concluded that a successful monitoring system must incorporate all of the approaches considered.

Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2016-02-01

Multiresolution analysis techniques including continuous wavelet transform, empirical mode decomposition, and variational mode decomposition are tested in the context of interest rate next-day variation prediction. In particular, multiresolution analysis techniques are used to decompose interest rate actual variation and feedforward neural network for training and prediction. Particle swarm optimization technique is adopted to optimize its initial weights. For comparison purpose, autoregressive moving average model, random walk process and the naive model are used as main reference models. In order to show the feasibility of the presented hybrid models that combine multiresolution analysis techniques and feedforward neural network optimized by particle swarm optimization, we used a set of six illustrative interest rates; including Moody's seasoned Aaa corporate bond yield, Moody's seasoned Baa corporate bond yield, 3-Month, 6-Month and 1-Year treasury bills, and effective federal fund rate. The forecasting results show that all multiresolution-based prediction systems outperform the conventional reference models on the criteria of mean absolute error, mean absolute deviation, and root mean-squared error. Therefore, it is advantageous to adopt hybrid multiresolution techniques and soft computing models to forecast interest rate daily variations as they provide good forecasting performance.

Modelling Technique for Demonstrating Gravity Collapse Structures in Jointed Rock.

ERIC Educational Resources Information Center

Stimpson, B.

1979-01-01

Described is a base-friction modeling technique for studying the development of collapse structures in jointed rocks. A moving belt beneath weak material is designed to simulate gravity. A description is given of the model frame construction. (Author/SA)

Large Terrain Modeling and Visualization for Planets

NASA Technical Reports Server (NTRS)

Myint, Steven; Jain, Abhinandan; Cameron, Jonathan; Lim, Christopher

2011-01-01

Physics-based simulations are actively used in the design, testing, and operations phases of surface and near-surface planetary space missions. One of the challenges in realtime simulations is the ability to handle large multi-resolution terrain data sets within models as well as for visualization. In this paper, we describe special techniques that we have developed for visualization, paging, and data storage for dealing with these large data sets. The visualization technique uses a real-time GPU-based continuous level-of-detail technique that delivers multiple frames a second performance even for planetary scale terrain model sizes.

Two degree of freedom internal model control-PID design for LFC of power systems via logarithmic approximations.

PubMed

Singh, Jay; Chattterjee, Kalyan; Vishwakarma, C B

2018-01-01

Load frequency controller has been designed for reduced order model of single area and two-area reheat hydro-thermal power system through internal model control - proportional integral derivative (IMC-PID) control techniques. The controller design method is based on two degree of freedom (2DOF) internal model control which combines with model order reduction technique. Here, in spite of taking full order system model a reduced order model has been considered for 2DOF-IMC-PID design and the designed controller is directly applied to full order system model. The Logarithmic based model order reduction technique is proposed to reduce the single and two-area high order power systems for the application of controller design.The proposed IMC-PID design of reduced order model achieves good dynamic response and robustness against load disturbance with the original high order system. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

A Search Technique for Weak and Long-Duration Gamma-Ray Bursts from Background Model Residuals

NASA Technical Reports Server (NTRS)

Skelton, R. T.; Mahoney, W. A.

1993-01-01

We report on a planned search technique for Gamma-Ray Bursts too weak to trigger the on-board threshold. The technique is to search residuals from a physically based background model used for analysis of point sources by the Earth occultation method.

Confidence Intervals from Realizations of Simulated Nuclear Data

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

Younes, W.; Ratkiewicz, A.; Ressler, J. J.

2017-09-28

Various statistical techniques are discussed that can be used to assign a level of confidence in the prediction of models that depend on input data with known uncertainties and correlations. The particular techniques reviewed in this paper are: 1) random realizations of the input data using Monte-Carlo methods, 2) the construction of confidence intervals to assess the reliability of model predictions, and 3) resampling techniques to impose statistical constraints on the input data based on additional information. These techniques are illustrated with a calculation of the keff value, based on the 235U(n, f) and 239Pu (n, f) cross sections.

Reachability analysis of real-time systems using time Petri nets.

PubMed

Wang, J; Deng, Y; Xu, G

2000-01-01

Time Petri nets (TPNs) are a popular Petri net model for specification and verification of real-time systems. A fundamental and most widely applied method for analyzing Petri nets is reachability analysis. The existing technique for reachability analysis of TPNs, however, is not suitable for timing property verification because one cannot derive end-to-end delay in task execution, an important issue for time-critical systems, from the reachability tree constructed using the technique. In this paper, we present a new reachability based analysis technique for TPNs for timing property analysis and verification that effectively addresses the problem. Our technique is based on a concept called clock-stamped state class (CS-class). With the reachability tree generated based on CS-classes, we can directly compute the end-to-end time delay in task execution. Moreover, a CS-class can be uniquely mapped to a traditional state class based on which the conventional reachability tree is constructed. Therefore, our CS-class-based analysis technique is more general than the existing technique. We show how to apply this technique to timing property verification of the TPN model of a command and control (C2) system.

A spline-based parameter and state estimation technique for static models of elastic surfaces

NASA Technical Reports Server (NTRS)

Banks, H. T.; Daniel, P. L.; Armstrong, E. S.

1983-01-01

Parameter and state estimation techniques for an elliptic system arising in a developmental model for the antenna surface in the Maypole Hoop/Column antenna are discussed. A computational algorithm based on spline approximations for the state and elastic parameters is given and numerical results obtained using this algorithm are summarized.

Imputatoin and Model-Based Updating Technique for Annual Forest Inventories

Treesearch

Ronald E. McRoberts

2001-01-01

The USDA Forest Service is developing an annual inventory system to establish the capability of producing annual estimates of timber volume and related variables. The inventory system features measurement of an annual sample of field plots with options for updating data for plots measured in previous years. One imputation and two model-based updating techniques are...

Nonlinear ultrasonics for material state awareness

NASA Astrophysics Data System (ADS)

Jacobs, L. J.

2014-02-01

Predictive health monitoring of structural components will require the development of advanced sensing techniques capable of providing quantitative information on the damage state of structural materials. By focusing on nonlinear acoustic techniques, it is possible to measure absolute, strength based material parameters that can then be coupled with uncertainty models to enable accurate and quantitative life prediction. Starting at the material level, this review will present current research that involves a combination of sensing techniques and physics-based models to characterize damage in metallic materials. In metals, these nonlinear ultrasonic measurements can sense material state, before the formation of micro- and macro-cracks. Typically, cracks of a measurable size appear quite late in a component's total life, while the material's integrity in terms of toughness and strength gradually decreases due to the microplasticity (dislocations) and associated change in the material's microstructure. This review focuses on second harmonic generation techniques. Since these nonlinear acoustic techniques are acoustic wave based, component interrogation can be performed with bulk, surface and guided waves using the same underlying material physics; these nonlinear ultrasonic techniques provide results which are independent of the wave type used. Recent physics-based models consider the evolution of damage due to dislocations, slip bands, interstitials, and precipitates in the lattice structure, which can lead to localized damage.

Application of neural network technique to determine a corrector surface for global geopotential model using GPS/levelling measurements in Egypt

NASA Astrophysics Data System (ADS)

Elshambaky, Hossam Talaat

2018-01-01

Owing to the appearance of many global geopotential models, it is necessary to determine the most appropriate model for use in Egyptian territory. In this study, we aim to investigate three global models, namely EGM2008, EIGEN-6c4, and GECO. We use five mathematical transformation techniques, i.e., polynomial expression, exponential regression, least-squares collocation, multilayer feed forward neural network, and radial basis neural networks to make the conversion from regional geometrical geoid to global geoid models and vice versa. From a statistical comparison study based on quality indexes between previous transformation techniques, we confirm that the multilayer feed forward neural network with two neurons is the most accurate of the examined transformation technique, and based on the mean tide condition, EGM2008 represents the most suitable global geopotential model for use in Egyptian territory to date. The final product gained from this study was the corrector surface that was used to facilitate the transformation process between regional geometrical geoid model and the global geoid model.

Automated Predictive Big Data Analytics Using Ontology Based Semantics.

PubMed

Nural, Mustafa V; Cotterell, Michael E; Peng, Hao; Xie, Rui; Ma, Ping; Miller, John A

2015-10-01

Predictive analytics in the big data era is taking on an ever increasingly important role. Issues related to choice on modeling technique, estimation procedure (or algorithm) and efficient execution can present significant challenges. For example, selection of appropriate and optimal models for big data analytics often requires careful investigation and considerable expertise which might not always be readily available. In this paper, we propose to use semantic technology to assist data analysts and data scientists in selecting appropriate modeling techniques and building specific models as well as the rationale for the techniques and models selected. To formally describe the modeling techniques, models and results, we developed the Analytics Ontology that supports inferencing for semi-automated model selection. The SCALATION framework, which currently supports over thirty modeling techniques for predictive big data analytics is used as a testbed for evaluating the use of semantic technology.

Automated Predictive Big Data Analytics Using Ontology Based Semantics

PubMed Central

Nural, Mustafa V.; Cotterell, Michael E.; Peng, Hao; Xie, Rui; Ma, Ping; Miller, John A.

2017-01-01

Predictive analytics in the big data era is taking on an ever increasingly important role. Issues related to choice on modeling technique, estimation procedure (or algorithm) and efficient execution can present significant challenges. For example, selection of appropriate and optimal models for big data analytics often requires careful investigation and considerable expertise which might not always be readily available. In this paper, we propose to use semantic technology to assist data analysts and data scientists in selecting appropriate modeling techniques and building specific models as well as the rationale for the techniques and models selected. To formally describe the modeling techniques, models and results, we developed the Analytics Ontology that supports inferencing for semi-automated model selection. The SCALATION framework, which currently supports over thirty modeling techniques for predictive big data analytics is used as a testbed for evaluating the use of semantic technology. PMID:29657954

Videogrammetric Model Deformation Measurement Technique

NASA Technical Reports Server (NTRS)

Burner, A. W.; Liu, Tian-Shu

2001-01-01

The theory, methods, and applications of the videogrammetric model deformation (VMD) measurement technique used at NASA for wind tunnel testing are presented. The VMD technique, based on non-topographic photogrammetry, can determine static and dynamic aeroelastic deformation and attitude of a wind-tunnel model. Hardware of the system includes a video-rate CCD camera, a computer with an image acquisition frame grabber board, illumination lights, and retroreflective or painted targets on a wind tunnel model. Custom software includes routines for image acquisition, target-tracking/identification, target centroid calculation, camera calibration, and deformation calculations. Applications of the VMD technique at five large NASA wind tunnels are discussed.

The relevance of Newton's laws and selected principles of physics to dance techniques: Theory and application

NASA Astrophysics Data System (ADS)

Lei, Li

1999-07-01

In this study the researcher develops and presents a new model, founded on the laws of physics, for analyzing dance technique. Based on a pilot study of four advanced dance techniques, she creates a new model for diagnosing, analyzing and describing basic, intermediate and advanced dance techniques. The name for this model is ``PED,'' which stands for Physics of Expressive Dance. The research design consists of five phases: (1) Conduct a pilot study to analyze several advanced dance techniques chosen from Chinese dance, modem dance, and ballet; (2) Based on learning obtained from the pilot study, create the PED Model for analyzing dance technique; (3) Apply this model to eight categories of dance technique; (4) Select two advanced dance techniques from each category and analyze these sample techniques to demonstrate how the model works; (5) Develop an evaluation framework and use it to evaluate the effectiveness of the model, taking into account both scientific and artistic aspects of dance training. In this study the researcher presents new solutions to three problems highly relevant to dance education: (1) Dancers attempting to learn difficult movements often fail because they are unaware of physics laws; (2) Even those who do master difficult movements can suffer injury due to incorrect training methods; (3) Even the best dancers can waste time learning by trial and error, without scientific instruction. In addition, the researcher discusses how the application of the PED model can benefit dancers, allowing them to avoid inefficient and ineffective movements and freeing them to focus on the artistic expression of dance performance. This study is unique, presenting the first comprehensive system for analyzing dance techniques in terms of physics laws. The results of this study are useful, allowing a new level of awareness about dance techniques that dance professionals can utilize for more effective and efficient teaching and learning. The approach utilized in this study is universal, and can be applied to any dance movement and to any dance style.

FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model.

PubMed

Yaghini Bonabi, Safa; Asgharian, Hassan; Safari, Saeed; Nili Ahmadabadi, Majid

2014-01-01

A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well.

Constraint-based component-modeling for knowledge-based design

NASA Technical Reports Server (NTRS)

Kolb, Mark A.

1992-01-01

The paper describes the application of various advanced programming techniques derived from artificial intelligence research to the development of flexible design tools for conceptual design. Special attention is given to two techniques which appear to be readily applicable to such design tools: the constraint propagation technique and the object-oriented programming. The implementation of these techniques in a prototype computer tool, Rubber Airplane, is described.

Using semantic data modeling techniques to organize an object-oriented database for extending the mass storage model

NASA Technical Reports Server (NTRS)

Campbell, William J.; Short, Nicholas M., Jr.; Roelofs, Larry H.; Dorfman, Erik

1991-01-01

A methodology for optimizing organization of data obtained by NASA earth and space missions is discussed. The methodology uses a concept based on semantic data modeling techniques implemented in a hierarchical storage model. The modeling is used to organize objects in mass storage devices, relational database systems, and object-oriented databases. The semantic data modeling at the metadata record level is examined, including the simulation of a knowledge base and semantic metadata storage issues. The semantic data model hierarchy and its application for efficient data storage is addressed, as is the mapping of the application structure to the mass storage.

Automatic welding detection by an intelligent tool pipe inspection

NASA Astrophysics Data System (ADS)

Arizmendi, C. J.; Garcia, W. L.; Quintero, M. A.

2015-07-01

This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called “smart pig” in Oil and Gas pipelines. The model uses a signal noise reduction phase by means of pre-processing algorithms and attribute-selection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent.

Comparison between two meshless methods based on collocation technique for the numerical solution of four-species tumor growth model

NASA Astrophysics Data System (ADS)

Dehghan, Mehdi; Mohammadi, Vahid

2017-03-01

As is said in [27], the tumor-growth model is the incorporation of nutrient within the mixture as opposed to being modeled with an auxiliary reaction-diffusion equation. The formulation involves systems of highly nonlinear partial differential equations of surface effects through diffuse-interface models [27]. Simulations of this practical model using numerical methods can be applied for evaluating it. The present paper investigates the solution of the tumor growth model with meshless techniques. Meshless methods are applied based on the collocation technique which employ multiquadrics (MQ) radial basis function (RBFs) and generalized moving least squares (GMLS) procedures. The main advantages of these choices come back to the natural behavior of meshless approaches. As well as, a method based on meshless approach can be applied easily for finding the solution of partial differential equations in high-dimension using any distributions of points on regular and irregular domains. The present paper involves a time-dependent system of partial differential equations that describes four-species tumor growth model. To overcome the time variable, two procedures will be used. One of them is a semi-implicit finite difference method based on Crank-Nicolson scheme and another one is based on explicit Runge-Kutta time integration. The first case gives a linear system of algebraic equations which will be solved at each time-step. The second case will be efficient but conditionally stable. The obtained numerical results are reported to confirm the ability of these techniques for solving the two and three-dimensional tumor-growth equations.

Modelling and Simulation for Requirements Engineering and Options Analysis

DTIC Science & Technology

2010-05-01

should be performed to work successfully in the domain; and process-based techniques model the processes that occur in the work domain. There is a crisp ...acad/sed/sedres/ dm /erg/cwa. DRDC Toronto CR 2010-049 39 23. Can the current technique for developing simulation models for assessments

Derivative Trade Optimizing Model Utilizing GP Based on Behavioral Finance Theory

NASA Astrophysics Data System (ADS)

Matsumura, Koki; Kawamoto, Masaru

This paper proposed a new technique which makes the strategy trees for the derivative (option) trading investment decision based on the behavioral finance theory and optimizes it using evolutionary computation, in order to achieve high profitability. The strategy tree uses a technical analysis based on a statistical, experienced technique for the investment decision. The trading model is represented by various technical indexes, and the strategy tree is optimized by the genetic programming(GP) which is one of the evolutionary computations. Moreover, this paper proposed a method using the prospect theory based on the behavioral finance theory to set psychological bias for profit and deficit and attempted to select the appropriate strike price of option for the higher investment efficiency. As a result, this technique produced a good result and found the effectiveness of this trading model by the optimized dealings strategy.

B-tree search reinforcement learning for model based intelligent agent

NASA Astrophysics Data System (ADS)

Bhuvaneswari, S.; Vignashwaran, R.

2013-03-01

Agents trained by learning techniques provide a powerful approximation of active solutions for naive approaches. In this study using B - Trees implying reinforced learning the data search for information retrieval is moderated to achieve accuracy with minimum search time. The impact of variables and tactics applied in training are determined using reinforcement learning. Agents based on these techniques perform satisfactory baseline and act as finite agents based on the predetermined model against competitors from the course.

Space-Time Fluid-Structure Interaction Computation of Flapping-Wing Aerodynamics

DTIC Science & Technology

2013-12-01

SST-VMST." The structural mechanics computations are based on the Kirchhoff -Love shell model. We use a sequential coupling technique, which is...mechanics computations are based on the Kirchhoff -Love shell model. We use a sequential coupling technique, which is ap- plicable to some classes of FSI...we use the ST-VMS method in combination with the ST-SUPS method. The structural mechanics computations are mostly based on the Kirchhoff –Love shell

Statistics and Machine Learning based Outlier Detection Techniques for Exoplanets

NASA Astrophysics Data System (ADS)

Goel, Amit; Montgomery, Michele

2015-08-01

Architectures of planetary systems are observable snapshots in time that can indicate formation and dynamic evolution of planets. The observable key parameters that we consider are planetary mass and orbital period. If planet masses are significantly less than their host star masses, then Keplerian Motion is defined as P^2 = a^3 where P is the orbital period in units of years and a is the orbital period in units of Astronomical Units (AU). Keplerian motion works on small scales such as the size of the Solar System but not on large scales such as the size of the Milky Way Galaxy. In this work, for confirmed exoplanets of known stellar mass, planetary mass, orbital period, and stellar age, we analyze Keplerian motion of systems based on stellar age to seek if Keplerian motion has an age dependency and to identify outliers. For detecting outliers, we apply several techniques based on statistical and machine learning methods such as probabilistic, linear, and proximity based models. In probabilistic and statistical models of outliers, the parameters of a closed form probability distributions are learned in order to detect the outliers. Linear models use regression analysis based techniques for detecting outliers. Proximity based models use distance based algorithms such as k-nearest neighbour, clustering algorithms such as k-means, or density based algorithms such as kernel density estimation. In this work, we will use unsupervised learning algorithms with only the proximity based models. In addition, we explore the relative strengths and weaknesses of the various techniques by validating the outliers. The validation criteria for the outliers is if the ratio of planetary mass to stellar mass is less than 0.001. In this work, we present our statistical analysis of the outliers thus detected.

Real-Time Onboard Global Nonlinear Aerodynamic Modeling from Flight Data

NASA Technical Reports Server (NTRS)

Brandon, Jay M.; Morelli, Eugene A.

2014-01-01

Flight test and modeling techniques were developed to accurately identify global nonlinear aerodynamic models onboard an aircraft. The techniques were developed and demonstrated during piloted flight testing of an Aermacchi MB-326M Impala jet aircraft. Advanced piloting techniques and nonlinear modeling techniques based on fuzzy logic and multivariate orthogonal function methods were implemented with efficient onboard calculations and flight operations to achieve real-time maneuver monitoring and analysis, and near-real-time global nonlinear aerodynamic modeling and prediction validation testing in flight. Results demonstrated that global nonlinear aerodynamic models for a large portion of the flight envelope were identified rapidly and accurately using piloted flight test maneuvers during a single flight, with the final identified and validated models available before the aircraft landed.

Model-Based Economic Evaluation of Treatments for Depression: A Systematic Literature Review.

PubMed

Kolovos, Spyros; Bosmans, Judith E; Riper, Heleen; Chevreul, Karine; Coupé, Veerle M H; van Tulder, Maurits W

2017-09-01

An increasing number of model-based studies that evaluate the cost effectiveness of treatments for depression are being published. These studies have different characteristics and use different simulation methods. We aimed to systematically review model-based studies evaluating the cost effectiveness of treatments for depression and examine which modelling technique is most appropriate for simulating the natural course of depression. The literature search was conducted in the databases PubMed, EMBASE and PsycInfo between 1 January 2002 and 1 October 2016. Studies were eligible if they used a health economic model with quality-adjusted life-years or disability-adjusted life-years as an outcome measure. Data related to various methodological characteristics were extracted from the included studies. The available modelling techniques were evaluated based on 11 predefined criteria. This methodological review included 41 model-based studies, of which 21 used decision trees (DTs), 15 used cohort-based state-transition Markov models (CMMs), two used individual-based state-transition models (ISMs), and three used discrete-event simulation (DES) models. Just over half of the studies (54%) evaluated antidepressants compared with a control condition. The data sources, time horizons, cycle lengths, perspectives adopted and number of health states/events all varied widely between the included studies. DTs scored positively in four of the 11 criteria, CMMs in five, ISMs in six, and DES models in seven. There were substantial methodological differences between the studies. Since the individual history of each patient is important for the prognosis of depression, DES and ISM simulation methods may be more appropriate than the others for a pragmatic representation of the course of depression. However, direct comparisons between the available modelling techniques are necessary to yield firm conclusions.

Parallel State Space Construction for a Model Checking Based on Maximality Semantics

NASA Astrophysics Data System (ADS)

El Abidine Bouneb, Zine; Saīdouni, Djamel Eddine

2009-03-01

The main limiting factor of the model checker integrated in the concurrency verification environment FOCOVE [1, 2], which use the maximality based labeled transition system (noted MLTS) as a true concurrency model[3, 4], is currently the amount of available physical memory. Many techniques have been developed to reduce the size of a state space. An interesting technique among them is the alpha equivalence reduction. Distributed memory execution environment offers yet another choice. The main contribution of the paper is to show that the parallel state space construction algorithm proposed in [5], which is based on interleaving semantics using LTS as semantic model, may be adapted easily to the distributed implementation of the alpha equivalence reduction for the maximality based labeled transition systems.

Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals.

PubMed

Batres-Mendoza, Patricia; Montoro-Sanjose, Carlos R; Guerra-Hernandez, Erick I; Almanza-Ojeda, Dora L; Rostro-Gonzalez, Horacio; Romero-Troncoso, Rene J; Ibarra-Manzano, Mario A

2016-03-05

Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states.

Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals

PubMed Central

Batres-Mendoza, Patricia; Montoro-Sanjose, Carlos R.; Guerra-Hernandez, Erick I.; Almanza-Ojeda, Dora L.; Rostro-Gonzalez, Horacio; Romero-Troncoso, Rene J.; Ibarra-Manzano, Mario A.

2016-01-01

Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states. PMID:26959029

Novel SVM-based technique to improve rainfall estimation over the Mediterranean region (north of Algeria) using the multispectral MSG SEVIRI imagery

NASA Astrophysics Data System (ADS)

Sehad, Mounir; Lazri, Mourad; Ameur, Soltane

2017-03-01

In this work, a new rainfall estimation technique based on the high spatial and temporal resolution of the Spinning Enhanced Visible and Infra Red Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) is presented. This work proposes efficient scheme rainfall estimation based on two multiclass support vector machine (SVM) algorithms: SVM_D for daytime and SVM_N for night time rainfall estimations. Both SVM models are trained using relevant rainfall parameters based on optical, microphysical and textural cloud proprieties. The cloud parameters are derived from the Spectral channels of the SEVIRI MSG radiometer. The 3-hourly and daily accumulated rainfall are derived from the 15 min-rainfall estimation given by the SVM classifiers for each MSG observation image pixel. The SVMs were trained with ground meteorological radar precipitation scenes recorded from November 2006 to March 2007 over the north of Algeria located in the Mediterranean region. Further, the SVM_D and SVM_N models were used to estimate 3-hourly and daily rainfall using data set gathered from November 2010 to March 2011 over north Algeria. The results were validated against collocated rainfall observed by rain gauge network. Indeed, the statistical scores given by correlation coefficient, bias, root mean square error and mean absolute error, showed good accuracy of rainfall estimates by the present technique. Moreover, rainfall estimates of our technique were compared with two high accuracy rainfall estimates methods based on MSG SEVIRI imagery namely: random forests (RF) based approach and an artificial neural network (ANN) based technique. The findings of the present technique indicate higher correlation coefficient (3-hourly: 0.78; daily: 0.94), and lower mean absolute error and root mean square error values. The results show that the new technique assign 3-hourly and daily rainfall with good and better accuracy than ANN technique and (RF) model.

Upper atmosphere research: Reaction rate and optical measurements

NASA Technical Reports Server (NTRS)

Stief, L. J.; Allen, J. E., Jr.; Nava, D. F.; Payne, W. A., Jr.

1990-01-01

The objective is to provide photochemical, kinetic, and spectroscopic information necessary for photochemical models of the Earth's upper atmosphere and to examine reactions or reactants not presently in the models to either confirm the correctness of their exclusion or provide evidence to justify future inclusion in the models. New initiatives are being taken in technique development (many of them laser based) and in the application of established techniques to address gaps in the photochemical/kinetic data base, as well as to provide increasingly reliable information.

Underwater 3D Surface Measurement Using Fringe Projection Based Scanning Devices

PubMed Central

Bräuer-Burchardt, Christian; Heinze, Matthias; Schmidt, Ingo; Kühmstedt, Peter; Notni, Gunther

2015-01-01

In this work we show the principle of optical 3D surface measurements based on the fringe projection technique for underwater applications. The challenges of underwater use of this technique are shown and discussed in comparison with the classical application. We describe an extended camera model which takes refraction effects into account as well as a proposal of an effective, low-effort calibration procedure for underwater optical stereo scanners. This calibration technique combines a classical air calibration based on the pinhole model with ray-based modeling and requires only a few underwater recordings of an object of known length and a planar surface. We demonstrate a new underwater 3D scanning device based on the fringe projection technique. It has a weight of about 10 kg and the maximal water depth for application of the scanner is 40 m. It covers an underwater measurement volume of 250 mm × 200 mm × 120 mm. The surface of the measurement objects is captured with a lateral resolution of 150 μm in a third of a second. Calibration evaluation results are presented and examples of first underwater measurements are given. PMID:26703624

Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

NASA Astrophysics Data System (ADS)

Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

2018-04-01

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

Terminology model discovery using natural language processing and visualization techniques.

PubMed

Zhou, Li; Tao, Ying; Cimino, James J; Chen, Elizabeth S; Liu, Hongfang; Lussier, Yves A; Hripcsak, George; Friedman, Carol

2006-12-01

Medical terminologies are important for unambiguous encoding and exchange of clinical information. The traditional manual method of developing terminology models is time-consuming and limited in the number of phrases that a human developer can examine. In this paper, we present an automated method for developing medical terminology models based on natural language processing (NLP) and information visualization techniques. Surgical pathology reports were selected as the testing corpus for developing a pathology procedure terminology model. The use of a general NLP processor for the medical domain, MedLEE, provides an automated method for acquiring semantic structures from a free text corpus and sheds light on a new high-throughput method of medical terminology model development. The use of an information visualization technique supports the summarization and visualization of the large quantity of semantic structures generated from medical documents. We believe that a general method based on NLP and information visualization will facilitate the modeling of medical terminologies.

Systems modeling and simulation applications for critical care medicine

PubMed Central

2012-01-01

Critical care delivery is a complex, expensive, error prone, medical specialty and remains the focal point of major improvement efforts in healthcare delivery. Various modeling and simulation techniques offer unique opportunities to better understand the interactions between clinical physiology and care delivery. The novel insights gained from the systems perspective can then be used to develop and test new treatment strategies and make critical care delivery more efficient and effective. However, modeling and simulation applications in critical care remain underutilized. This article provides an overview of major computer-based simulation techniques as applied to critical care medicine. We provide three application examples of different simulation techniques, including a) pathophysiological model of acute lung injury, b) process modeling of critical care delivery, and c) an agent-based model to study interaction between pathophysiology and healthcare delivery. Finally, we identify certain challenges to, and opportunities for, future research in the area. PMID:22703718

A novel technique for presurgical nasoalveolar molding using computer-aided reverse engineering and rapid prototyping.

PubMed

Yu, Quan; Gong, Xin; Wang, Guo-Min; Yu, Zhe-Yuan; Qian, Yu-Fen; Shen, Gang

2011-01-01

To establish a new method of presurgical nasoalveolar molding (NAM) using computer-aided reverse engineering and rapid prototyping technique in infants with unilateral cleft lip and palate (UCLP). Five infants (2 males and 3 females with mean age of 1.2 w) with complete UCLP were recruited. All patients were subjected to NAM before the cleft lip repair. The upper denture casts were recorded using a three-dimensional laser scanner within 2 weeks after birth in UCLP infants. A digital model was constructed and analyzed to simulate the NAM procedure with reverse engineering software. The digital geometrical data were exported to print the solid model with rapid prototyping system. The whole set of appliances was fabricated based on these solid models. Laser scanning and digital model construction simplified the NAM procedure and estimated the treatment objective. The appliances were fabricated based on the rapid prototyping technique, and for each patient, the complete set of appliances could be obtained at one time. By the end of presurgical NAM treatment, the cleft was narrowed, and the malformation of nasoalveolar segments was aligned normally. We have developed a novel technique of presurgical NAM based on a computer-aided design. The accurate digital denture model of UCLP infants could be obtained with laser scanning. The treatment design and appliance fabrication could be simplified with a computer-aided reverse engineering and rapid prototyping technique.

Comparison of different estimation techniques for biomass concentration in large scale yeast fermentation.

PubMed

Hocalar, A; Türker, M; Karakuzu, C; Yüzgeç, U

2011-04-01

In this study, previously developed five different state estimation methods are examined and compared for estimation of biomass concentrations at a production scale fed-batch bioprocess. These methods are i. estimation based on kinetic model of overflow metabolism; ii. estimation based on metabolic black-box model; iii. estimation based on observer; iv. estimation based on artificial neural network; v. estimation based on differential evaluation. Biomass concentrations are estimated from available measurements and compared with experimental data obtained from large scale fermentations. The advantages and disadvantages of the presented techniques are discussed with regard to accuracy, reproducibility, number of primary measurements required and adaptation to different working conditions. Among the various techniques, the metabolic black-box method seems to have advantages although the number of measurements required is more than that for the other methods. However, the required extra measurements are based on commonly employed instruments in an industrial environment. This method is used for developing a model based control of fed-batch yeast fermentations. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Global Aerodynamic Modeling for Stall/Upset Recovery Training Using Efficient Piloted Flight Test Techniques

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; Cunningham, Kevin; Hill, Melissa A.

2013-01-01

Flight test and modeling techniques were developed for efficiently identifying global aerodynamic models that can be used to accurately simulate stall, upset, and recovery on large transport airplanes. The techniques were developed and validated in a high-fidelity fixed-base flight simulator using a wind-tunnel aerodynamic database, realistic sensor characteristics, and a realistic flight deck representative of a large transport aircraft. Results demonstrated that aerodynamic models for stall, upset, and recovery can be identified rapidly and accurately using relatively simple piloted flight test maneuvers. Stall maneuver predictions and comparisons of identified aerodynamic models with data from the underlying simulation aerodynamic database were used to validate the techniques.

Weighted least squares techniques for improved received signal strength based localization.

PubMed

Tarrío, Paula; Bernardos, Ana M; Casar, José R

2011-01-01

The practical deployment of wireless positioning systems requires minimizing the calibration procedures while improving the location estimation accuracy. Received Signal Strength localization techniques using propagation channel models are the simplest alternative, but they are usually designed under the assumption that the radio propagation model is to be perfectly characterized a priori. In practice, this assumption does not hold and the localization results are affected by the inaccuracies of the theoretical, roughly calibrated or just imperfect channel models used to compute location. In this paper, we propose the use of weighted multilateration techniques to gain robustness with respect to these inaccuracies, reducing the dependency of having an optimal channel model. In particular, we propose two weighted least squares techniques based on the standard hyperbolic and circular positioning algorithms that specifically consider the accuracies of the different measurements to obtain a better estimation of the position. These techniques are compared to the standard hyperbolic and circular positioning techniques through both numerical simulations and an exhaustive set of real experiments on different types of wireless networks (a wireless sensor network, a WiFi network and a Bluetooth network). The algorithms not only produce better localization results with a very limited overhead in terms of computational cost but also achieve a greater robustness to inaccuracies in channel modeling.

Weighted Least Squares Techniques for Improved Received Signal Strength Based Localization

PubMed Central

Tarrío, Paula; Bernardos, Ana M.; Casar, José R.

2011-01-01

The practical deployment of wireless positioning systems requires minimizing the calibration procedures while improving the location estimation accuracy. Received Signal Strength localization techniques using propagation channel models are the simplest alternative, but they are usually designed under the assumption that the radio propagation model is to be perfectly characterized a priori. In practice, this assumption does not hold and the localization results are affected by the inaccuracies of the theoretical, roughly calibrated or just imperfect channel models used to compute location. In this paper, we propose the use of weighted multilateration techniques to gain robustness with respect to these inaccuracies, reducing the dependency of having an optimal channel model. In particular, we propose two weighted least squares techniques based on the standard hyperbolic and circular positioning algorithms that specifically consider the accuracies of the different measurements to obtain a better estimation of the position. These techniques are compared to the standard hyperbolic and circular positioning techniques through both numerical simulations and an exhaustive set of real experiments on different types of wireless networks (a wireless sensor network, a WiFi network and a Bluetooth network). The algorithms not only produce better localization results with a very limited overhead in terms of computational cost but also achieve a greater robustness to inaccuracies in channel modeling. PMID:22164092

An Object-Based Requirements Modeling Method.

ERIC Educational Resources Information Center

Cordes, David W.; Carver, Doris L.

1992-01-01

Discusses system modeling and specification as it relates to object-based information systems development and software development. An automated system model based on the objects in the initial requirements document is described, the requirements document translator is explained, and a sample application of the technique is provided. (12…

Verification Techniques for Parameter Selection and Bayesian Model Calibration Presented for an HIV Model

NASA Astrophysics Data System (ADS)

Wentworth, Mami Tonoe

Uncertainty quantification plays an important role when making predictive estimates of model responses. In this context, uncertainty quantification is defined as quantifying and reducing uncertainties, and the objective is to quantify uncertainties in parameter, model and measurements, and propagate the uncertainties through the model, so that one can make a predictive estimate with quantified uncertainties. Two of the aspects of uncertainty quantification that must be performed prior to propagating uncertainties are model calibration and parameter selection. There are several efficient techniques for these processes; however, the accuracy of these methods are often not verified. This is the motivation for our work, and in this dissertation, we present and illustrate verification frameworks for model calibration and parameter selection in the context of biological and physical models. First, HIV models, developed and improved by [2, 3, 8], describe the viral infection dynamics of an HIV disease. These are also used to make predictive estimates of viral loads and T-cell counts and to construct an optimal control for drug therapy. Estimating input parameters is an essential step prior to uncertainty quantification. However, not all the parameters are identifiable, implying that they cannot be uniquely determined by the observations. These unidentifiable parameters can be partially removed by performing parameter selection, a process in which parameters that have minimal impacts on the model response are determined. We provide verification techniques for Bayesian model calibration and parameter selection for an HIV model. As an example of a physical model, we employ a heat model with experimental measurements presented in [10]. A steady-state heat model represents a prototypical behavior for heat conduction and diffusion process involved in a thermal-hydraulic model, which is a part of nuclear reactor models. We employ this simple heat model to illustrate verification techniques for model calibration. For Bayesian model calibration, we employ adaptive Metropolis algorithms to construct densities for input parameters in the heat model and the HIV model. To quantify the uncertainty in the parameters, we employ two MCMC algorithms: Delayed Rejection Adaptive Metropolis (DRAM) [33] and Differential Evolution Adaptive Metropolis (DREAM) [66, 68]. The densities obtained using these methods are compared to those obtained through the direct numerical evaluation of the Bayes' formula. We also combine uncertainties in input parameters and measurement errors to construct predictive estimates for a model response. A significant emphasis is on the development and illustration of techniques to verify the accuracy of sampling-based Metropolis algorithms. We verify the accuracy of DRAM and DREAM by comparing chains, densities and correlations obtained using DRAM, DREAM and the direct evaluation of Bayes formula. We also perform similar analysis for credible and prediction intervals for responses. Once the parameters are estimated, we employ energy statistics test [63, 64] to compare the densities obtained by different methods for the HIV model. The energy statistics are used to test the equality of distributions. We also consider parameter selection and verification techniques for models having one or more parameters that are noninfluential in the sense that they minimally impact model outputs. We illustrate these techniques for a dynamic HIV model but note that the parameter selection and verification framework is applicable to a wide range of biological and physical models. To accommodate the nonlinear input to output relations, which are typical for such models, we focus on global sensitivity analysis techniques, including those based on partial correlations, Sobol indices based on second-order model representations, and Morris indices, as well as a parameter selection technique based on standard errors. A significant objective is to provide verification strategies to assess the accuracy of those techniques, which we illustrate in the context of the HIV model. Finally, we examine active subspace methods as an alternative to parameter subset selection techniques. The objective of active subspace methods is to determine the subspace of inputs that most strongly affect the model response, and to reduce the dimension of the input space. The major difference between active subspace methods and parameter selection techniques is that parameter selection identifies influential parameters whereas subspace selection identifies a linear combination of parameters that impacts the model responses significantly. We employ active subspace methods discussed in [22] for the HIV model and present a verification that the active subspace successfully reduces the input dimensions.

FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model

PubMed Central

Yaghini Bonabi, Safa; Asgharian, Hassan; Safari, Saeed; Nili Ahmadabadi, Majid

2014-01-01

A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well. PMID:25484854

Stochastic Game Analysis and Latency Awareness for Self-Adaptation

DTIC Science & Technology

2014-01-01

this paper, we introduce a formal analysis technique based on model checking of stochastic multiplayer games (SMGs) that enables us to quantify the...Additional Key Words and Phrases: Proactive adaptation, Stochastic multiplayer games , Latency 1. INTRODUCTION When planning how to adapt, self-adaptive...contribution of this paper is twofold: (1) A novel analysis technique based on model checking of stochastic multiplayer games (SMGs) that enables us to

A technique for measuring vertically and horizontally polarized microwave brightness temperatures using electronic polarization-basis rotation

NASA Technical Reports Server (NTRS)

Gasiewski, Albin J.

1992-01-01

This technique for electronically rotating the polarization basis of an orthogonal-linear polarization radiometer is based on the measurement of the first three feedhorn Stokes parameters, along with the subsequent transformation of this measured Stokes vector into a rotated coordinate frame. The technique requires an accurate measurement of the cross-correlation between the two orthogonal feedhorn modes, for which an innovative polarized calibration load was developed. The experimental portion of this investigation consisted of a proof of concept demonstration of the technique of electronic polarization basis rotation (EPBR) using a ground based 90-GHz dual orthogonal-linear polarization radiometer. Practical calibration algorithms for ground-, aircraft-, and space-based instruments were identified and tested. The theoretical effort consisted of radiative transfer modeling using the planar-stratified numerical model described in Gasiewski and Staelin (1990).

Application of Multiregressive Linear Models, Dynamic Kriging Models and Neural Network Models to Predictive Maintenance of Hydroelectric Power Systems

NASA Astrophysics Data System (ADS)

Lucifredi, A.; Mazzieri, C.; Rossi, M.

2000-05-01

Since the operational conditions of a hydroelectric unit can vary within a wide range, the monitoring system must be able to distinguish between the variations of the monitored variable caused by variations of the operation conditions and those due to arising and progressing of failures and misoperations. The paper aims to identify the best technique to be adopted for the monitoring system. Three different methods have been implemented and compared. Two of them use statistical techniques: the first, the linear multiple regression, expresses the monitored variable as a linear function of the process parameters (independent variables), while the second, the dynamic kriging technique, is a modified technique of multiple linear regression representing the monitored variable as a linear combination of the process variables in such a way as to minimize the variance of the estimate error. The third is based on neural networks. Tests have shown that the monitoring system based on the kriging technique is not affected by some problems common to the other two models e.g. the requirement of a large amount of data for their tuning, both for training the neural network and defining the optimum plane for the multiple regression, not only in the system starting phase but also after a trivial operation of maintenance involving the substitution of machinery components having a direct impact on the observed variable. Or, in addition, the necessity of different models to describe in a satisfactory way the different ranges of operation of the plant. The monitoring system based on the kriging statistical technique overrides the previous difficulties: it does not require a large amount of data to be tuned and is immediately operational: given two points, the third can be immediately estimated; in addition the model follows the system without adapting itself to it. The results of the experimentation performed seem to indicate that a model based on a neural network or on a linear multiple regression is not optimal, and that a different approach is necessary to reduce the amount of work during the learning phase using, when available, all the information stored during the initial phase of the plant to build the reference baseline, elaborating, if it is the case, the raw information available. A mixed approach using the kriging statistical technique and neural network techniques could optimise the result.

Characterization of Model-Based Reasoning Strategies for Use in IVHM Architectures

NASA Technical Reports Server (NTRS)

Poll, Scott; Iverson, David; Patterson-Hine, Ann

2003-01-01

Open architectures are gaining popularity for Integrated Vehicle Health Management (IVHM) applications due to the diversity of subsystem health monitoring strategies in use and the need to integrate a variety of techniques at the system health management level. The basic concept of an open architecture suggests that whatever monitoring or reasoning strategy a subsystem wishes to deploy, the system architecture will support the needs of that subsystem and will be capable of transmitting subsystem health status across subsystem boundaries and up to the system level for system-wide fault identification and diagnosis. There is a need to understand the capabilities of various reasoning engines and how they, coupled with intelligent monitoring techniques, can support fault detection and system level fault management. Researchers in IVHM at NASA Ames Research Center are supporting the development of an IVHM system for liquefying-fuel hybrid rockets. In the initial stage of this project, a few readily available reasoning engines were studied to assess candidate technologies for application in next generation launch systems. Three tools representing the spectrum of model-based reasoning approaches, from a quantitative simulation based approach to a graph-based fault propagation technique, were applied to model the behavior of the Hybrid Combustion Facility testbed at Ames. This paper summarizes the characterization of the modeling process for each of the techniques.

Dense mesh sampling for video-based facial animation

NASA Astrophysics Data System (ADS)

Peszor, Damian; Wojciechowska, Marzena

2016-06-01

The paper describes an approach for selection of feature points on three-dimensional, triangle mesh obtained using various techniques from several video footages. This approach has a dual purpose. First, it allows to minimize the data stored for the purpose of facial animation, so that instead of storing position of each vertex in each frame, one could store only a small subset of vertices for each frame and calculate positions of others based on the subset. Second purpose is to select feature points that could be used for anthropometry-based retargeting of recorded mimicry to another model, with sampling density beyond that which can be achieved using marker-based performance capture techniques. Developed approach was successfully tested on artificial models, models constructed using structured light scanner, and models constructed from video footages using stereophotogrammetry.

Properties of hypothesis testing techniques and (Bayesian) model selection for exploration-based and theory-based (order-restricted) hypotheses.

PubMed

Kuiper, Rebecca M; Nederhoff, Tim; Klugkist, Irene

2015-05-01

In this paper, the performance of six types of techniques for comparisons of means is examined. These six emerge from the distinction between the method employed (hypothesis testing, model selection using information criteria, or Bayesian model selection) and the set of hypotheses that is investigated (a classical, exploration-based set of hypotheses containing equality constraints on the means, or a theory-based limited set of hypotheses with equality and/or order restrictions). A simulation study is conducted to examine the performance of these techniques. We demonstrate that, if one has specific, a priori specified hypotheses, confirmation (i.e., investigating theory-based hypotheses) has advantages over exploration (i.e., examining all possible equality-constrained hypotheses). Furthermore, examining reasonable order-restricted hypotheses has more power to detect the true effect/non-null hypothesis than evaluating only equality restrictions. Additionally, when investigating more than one theory-based hypothesis, model selection is preferred over hypothesis testing. Because of the first two results, we further examine the techniques that are able to evaluate order restrictions in a confirmatory fashion by examining their performance when the homogeneity of variance assumption is violated. Results show that the techniques are robust to heterogeneity when the sample sizes are equal. When the sample sizes are unequal, the performance is affected by heterogeneity. The size and direction of the deviations from the baseline, where there is no heterogeneity, depend on the effect size (of the means) and on the trend in the group variances with respect to the ordering of the group sizes. Importantly, the deviations are less pronounced when the group variances and sizes exhibit the same trend (e.g., are both increasing with group number). © 2014 The British Psychological Society.

Cardiovascular oscillations: in search of a nonlinear parametric model

NASA Astrophysics Data System (ADS)

Bandrivskyy, Andriy; Luchinsky, Dmitry; McClintock, Peter V.; Smelyanskiy, Vadim; Stefanovska, Aneta; Timucin, Dogan

2003-05-01

We suggest a fresh approach to the modeling of the human cardiovascular system. Taking advantage of a new Bayesian inference technique, able to deal with stochastic nonlinear systems, we show that one can estimate parameters for models of the cardiovascular system directly from measured time series. We present preliminary results of inference of parameters of a model of coupled oscillators from measured cardiovascular data addressing cardiorespiratory interaction. We argue that the inference technique offers a very promising tool for the modeling, able to contribute significantly towards the solution of a long standing challenge -- development of new diagnostic techniques based on noninvasive measurements.

Reduced-Order Models Based on Linear and Nonlinear Aerodynamic Impulse Responses

NASA Technical Reports Server (NTRS)

Silva, Walter A.

1999-01-01

This paper discusses a method for the identification and application of reduced-order models based on linear and nonlinear aerodynamic impulse responses. The Volterra theory of nonlinear systems and an appropriate kernel identification technique are described. Insight into the nature of kernels is provided by applying the method to the nonlinear Riccati equation in a non-aerodynamic application. The method is then applied to a nonlinear aerodynamic model of RAE 2822 supercritical airfoil undergoing plunge motions using the CFL3D Navier-Stokes flow solver with the Spalart-Allmaras turbulence model. Results demonstrate the computational efficiency of the technique.

Reduced Order Models Based on Linear and Nonlinear Aerodynamic Impulse Responses

NASA Technical Reports Server (NTRS)

Silva, Walter A.

1999-01-01

This paper discusses a method for the identification and application of reduced-order models based on linear and nonlinear aerodynamic impulse responses. The Volterra theory of nonlinear systems and an appropriate kernel identification technique are described. Insight into the nature of kernels is provided by applying the method to the nonlinear Riccati equation in a non-aerodynamic application. The method is then applied to a nonlinear aerodynamic model of an RAE 2822 supercritical airfoil undergoing plunge motions using the CFL3D Navier-Stokes flow solver with the Spalart-Allmaras turbulence model. Results demonstrate the computational efficiency of the technique.

A performance model for GPUs with caches

DOE PAGES

Dao, Thanh Tuan; Kim, Jungwon; Seo, Sangmin; ...

2014-06-24

To exploit the abundant computational power of the world's fastest supercomputers, an even workload distribution to the typically heterogeneous compute devices is necessary. While relatively accurate performance models exist for conventional CPUs, accurate performance estimation models for modern GPUs do not exist. This paper presents two accurate models for modern GPUs: a sampling-based linear model, and a model based on machine-learning (ML) techniques which improves the accuracy of the linear model and is applicable to modern GPUs with and without caches. We first construct the sampling-based linear model to predict the runtime of an arbitrary OpenCL kernel. Based on anmore » analysis of NVIDIA GPUs' scheduling policies we determine the earliest sampling points that allow an accurate estimation. The linear model cannot capture well the significant effects that memory coalescing or caching as implemented in modern GPUs have on performance. We therefore propose a model based on ML techniques that takes several compiler-generated statistics about the kernel as well as the GPU's hardware performance counters as additional inputs to obtain a more accurate runtime performance estimation for modern GPUs. We demonstrate the effectiveness and broad applicability of the model by applying it to three different NVIDIA GPU architectures and one AMD GPU architecture. On an extensive set of OpenCL benchmarks, on average, the proposed model estimates the runtime performance with less than 7 percent error for a second-generation GTX 280 with no on-chip caches and less than 5 percent for the Fermi-based GTX 580 with hardware caches. On the Kepler-based GTX 680, the linear model has an error of less than 10 percent. On an AMD GPU architecture, Radeon HD 6970, the model estimates with 8 percent of error rates. As a result, the proposed technique outperforms existing models by a factor of 5 to 6 in terms of accuracy.« less

Development and evaluation of an innovative model of inter-professional education focused on asthma medication use

PubMed Central

2014-01-01

Background Inter-professional learning has been promoted as the solution to many clinical management issues. One such issue is the correct use of asthma inhaler devices. Up to 80% of people with asthma use their inhaler device incorrectly. The implications of this are poor asthma control and quality of life. Correct inhaler technique can be taught, however these educational instructions need to be repeated if correct technique is to be maintained. It is important to maximise the opportunities to deliver this education in primary care. In light of this, it is important to explore how health care providers, in particular pharmacists and general medical practitioners, can work together in delivering inhaler technique education to patients, over time. Therefore, there is a need to develop and evaluate effective inter-professional education, which will address the need to educate patients in the correct use of their inhalers as well as equip health care professionals with skills to engage in collaborative relationships with each other. Methods This mixed methods study involves the development and evaluation of three modules of continuing education, Model 1, Model 2 and Model 3. A fourth group, Model 4, acting as a control. Model 1 consists of face-to-face continuing professional education on asthma inhaler technique, aimed at pharmacists, general medical practitioners and their practice nurses. Model 2 is an electronic online continuing education module based on Model 1 principles. Model 3 is also based on asthma inhaler technique education but employs a learning intervention targeting health care professional relationships and is based on sociocultural theory. This study took the form of a parallel group, repeated measure design. Following the completion of continuing professional education, health care professionals recruited people with asthma and followed them up for 6 months. During this period, inhaler device technique training was delivered and data on patient inhaler technique, clinical and humanistic outcomes were collected. Outcomes related to professional collaborative relationships were also measured. Discussion Challenges presented included the requirement of significant financial resources for development of study materials and limited availability of validated tools to measure health care professional collaboration over time. PMID:24708800

Development and evaluation of an innovative model of inter-professional education focused on asthma medication use.

PubMed

Bosnic-Anticevich, Sinthia Z; Stuart, Meg; Mackson, Judith; Cvetkovski, Biljana; Sainsbury, Erica; Armour, Carol; Mavritsakis, Sofia; Mendrela, Gosia; Travers-Mason, Pippa; Williamson, Margaret

2014-04-07

Inter-professional learning has been promoted as the solution to many clinical management issues. One such issue is the correct use of asthma inhaler devices. Up to 80% of people with asthma use their inhaler device incorrectly. The implications of this are poor asthma control and quality of life. Correct inhaler technique can be taught, however these educational instructions need to be repeated if correct technique is to be maintained. It is important to maximise the opportunities to deliver this education in primary care. In light of this, it is important to explore how health care providers, in particular pharmacists and general medical practitioners, can work together in delivering inhaler technique education to patients, over time. Therefore, there is a need to develop and evaluate effective inter-professional education, which will address the need to educate patients in the correct use of their inhalers as well as equip health care professionals with skills to engage in collaborative relationships with each other. This mixed methods study involves the development and evaluation of three modules of continuing education, Model 1, Model 2 and Model 3. A fourth group, Model 4, acting as a control.Model 1 consists of face-to-face continuing professional education on asthma inhaler technique, aimed at pharmacists, general medical practitioners and their practice nurses.Model 2 is an electronic online continuing education module based on Model 1 principles.Model 3 is also based on asthma inhaler technique education but employs a learning intervention targeting health care professional relationships and is based on sociocultural theory.This study took the form of a parallel group, repeated measure design. Following the completion of continuing professional education, health care professionals recruited people with asthma and followed them up for 6 months. During this period, inhaler device technique training was delivered and data on patient inhaler technique, clinical and humanistic outcomes were collected. Outcomes related to professional collaborative relationships were also measured. Challenges presented included the requirement of significant financial resources for development of study materials and limited availability of validated tools to measure health care professional collaboration over time.

Agent-based modeling: case study in cleavage furrow models.

PubMed

Mogilner, Alex; Manhart, Angelika

2016-11-07

The number of studies in cell biology in which quantitative models accompany experiments has been growing steadily. Roughly, mathematical and computational techniques of these models can be classified as "differential equation based" (DE) or "agent based" (AB). Recently AB models have started to outnumber DE models, but understanding of AB philosophy and methodology is much less widespread than familiarity with DE techniques. Here we use the history of modeling a fundamental biological problem-positioning of the cleavage furrow in dividing cells-to explain how and why DE and AB models are used. We discuss differences, advantages, and shortcomings of these two approaches. © 2016 Mogilner and Manhart. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Model-based segmentation in orbital volume measurement with cone beam computed tomography and evaluation against current concepts.

PubMed

Wagner, Maximilian E H; Gellrich, Nils-Claudius; Friese, Karl-Ingo; Becker, Matthias; Wolter, Franz-Erich; Lichtenstein, Juergen T; Stoetzer, Marcus; Rana, Majeed; Essig, Harald

2016-01-01

Objective determination of the orbital volume is important in the diagnostic process and in evaluating the efficacy of medical and/or surgical treatment of orbital diseases. Tools designed to measure orbital volume with computed tomography (CT) often cannot be used with cone beam CT (CBCT) because of inferior tissue representation, although CBCT has the benefit of greater availability and lower patient radiation exposure. Therefore, a model-based segmentation technique is presented as a new method for measuring orbital volume and compared to alternative techniques. Both eyes from thirty subjects with no known orbital pathology who had undergone CBCT as a part of routine care were evaluated (n = 60 eyes). Orbital volume was measured with manual, atlas-based, and model-based segmentation methods. Volume measurements, volume determination time, and usability were compared between the three methods. Differences in means were tested for statistical significance using two-tailed Student's t tests. Neither atlas-based (26.63 ± 3.15 mm(3)) nor model-based (26.87 ± 2.99 mm(3)) measurements were significantly different from manual volume measurements (26.65 ± 4.0 mm(3)). However, the time required to determine orbital volume was significantly longer for manual measurements (10.24 ± 1.21 min) than for atlas-based (6.96 ± 2.62 min, p < 0.001) or model-based (5.73 ± 1.12 min, p < 0.001) measurements. All three orbital volume measurement methods examined can accurately measure orbital volume, although atlas-based and model-based methods seem to be more user-friendly and less time-consuming. The new model-based technique achieves fully automated segmentation results, whereas all atlas-based segmentations at least required manipulations to the anterior closing. Additionally, model-based segmentation can provide reliable orbital volume measurements when CT image quality is poor.

Advantage of the modified Lunn-McNeil technique over Kalbfleisch-Prentice technique in competing risks

NASA Astrophysics Data System (ADS)

Lukman, Iing; Ibrahim, Noor A.; Daud, Isa B.; Maarof, Fauziah; Hassan, Mohd N.

2002-03-01

Survival analysis algorithm is often applied in the data mining process. Cox regression is one of the survival analysis tools that has been used in many areas, and it can be used to analyze the failure times of aircraft crashed. Another survival analysis tool is the competing risks where we have more than one cause of failure acting simultaneously. Lunn-McNeil analyzed the competing risks in the survival model using Cox regression with censored data. The modified Lunn-McNeil technique is a simplify of the Lunn-McNeil technique. The Kalbfleisch-Prentice technique is involving fitting models separately from each type of failure, treating other failure types as censored. To compare the two techniques, (the modified Lunn-McNeil and Kalbfleisch-Prentice) a simulation study was performed. Samples with various sizes and censoring percentages were generated and fitted using both techniques. The study was conducted by comparing the inference of models, using Root Mean Square Error (RMSE), the power tests, and the Schoenfeld residual analysis. The power tests in this study were likelihood ratio test, Rao-score test, and Wald statistics. The Schoenfeld residual analysis was conducted to check the proportionality of the model through its covariates. The estimated parameters were computed for the cause-specific hazard situation. Results showed that the modified Lunn-McNeil technique was better than the Kalbfleisch-Prentice technique based on the RMSE measurement and Schoenfeld residual analysis. However, the Kalbfleisch-Prentice technique was better than the modified Lunn-McNeil technique based on power tests measurement.

Improving wave forecasting by integrating ensemble modelling and machine learning

NASA Astrophysics Data System (ADS)

O'Donncha, F.; Zhang, Y.; James, S. C.

2017-12-01

Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.

Predicting School Enrollments Using the Modified Regression Technique.

ERIC Educational Resources Information Center

Grip, Richard S.; Young, John W.

This report is based on a study in which a regression model was constructed to increase accuracy in enrollment predictions. A model, known as the Modified Regression Technique (MRT), was used to examine K-12 enrollment over the past 20 years in 2 New Jersey school districts of similar size and ethnicity. To test the model's accuracy, MRT was…

Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier.

PubMed

Pandey, Daya Shankar; Pan, Indranil; Das, Saptarshi; Leahy, James J; Kwapinski, Witold

2015-03-01

A multi-gene genetic programming technique is proposed as a new method to predict syngas yield production and the lower heating value for municipal solid waste gasification in a fluidized bed gasifier. The study shows that the predicted outputs of the municipal solid waste gasification process are in good agreement with the experimental dataset and also generalise well to validation (untrained) data. Published experimental datasets are used for model training and validation purposes. The results show the effectiveness of the genetic programming technique for solving complex nonlinear regression problems. The multi-gene genetic programming are also compared with a single-gene genetic programming model to show the relative merits and demerits of the technique. This study demonstrates that the genetic programming based data-driven modelling strategy can be a good candidate for developing models for other types of fuels as well. Copyright © 2014 Elsevier Ltd. All rights reserved.

Spatial modeling of environmental vulnerability of marine finfish aquaculture using GIS-based neuro-fuzzy techniques.

PubMed

Navas, Juan Moreno; Telfer, Trevor C; Ross, Lindsay G

2011-08-01

Combining GIS with neuro-fuzzy modeling has the advantage that expert scientific knowledge in coastal aquaculture activities can be incorporated into a geospatial model to classify areas particularly vulnerable to pollutants. Data on the physical environment and its suitability for aquaculture in an Irish fjard, which is host to a number of different aquaculture activities, were derived from a three-dimensional hydrodynamic and GIS models. Subsequent incorporation into environmental vulnerability models, based on neuro-fuzzy techniques, highlighted localities particularly vulnerable to aquaculture development. The models produced an overall classification accuracy of 85.71%, with a Kappa coefficient of agreement of 81%, and were sensitive to different input parameters. A statistical comparison between vulnerability scores and nitrogen concentrations in sediment associated with salmon cages showed good correlation. Neuro-fuzzy techniques within GIS modeling classify vulnerability of coastal regions appropriately and have a role in policy decisions for aquaculture site selection. Copyright © 2011 Elsevier Ltd. All rights reserved.

A comparison of linear and nonlinear statistical techniques in performance attribution.

PubMed

Chan, N H; Genovese, C R

2001-01-01

Performance attribution is usually conducted under the linear framework of multifactor models. Although commonly used by practitioners in finance, linear multifactor models are known to be less than satisfactory in many situations. After a brief survey of nonlinear methods, nonlinear statistical techniques are applied to performance attribution of a portfolio constructed from a fixed universe of stocks using factors derived from some commonly used cross sectional linear multifactor models. By rebalancing this portfolio monthly, the cumulative returns for procedures based on standard linear multifactor model and three nonlinear techniques-model selection, additive models, and neural networks-are calculated and compared. It is found that the first two nonlinear techniques, especially in combination, outperform the standard linear model. The results in the neural-network case are inconclusive because of the great variety of possible models. Although these methods are more complicated and may require some tuning, toolboxes are developed and suggestions on calibration are proposed. This paper demonstrates the usefulness of modern nonlinear statistical techniques in performance attribution.

A High-Level Language for Modeling Algorithms and Their Properties

NASA Astrophysics Data System (ADS)

Akhtar, Sabina; Merz, Stephan; Quinson, Martin

Designers of concurrent and distributed algorithms usually express them using pseudo-code. In contrast, most verification techniques are based on more mathematically-oriented formalisms such as state transition systems. This conceptual gap contributes to hinder the use of formal verification techniques. Leslie Lamport introduced PlusCal, a high-level algorithmic language that has the "look and feel" of pseudo-code, but is equipped with a precise semantics and includes a high-level expression language based on set theory. PlusCal models can be compiled to TLA + and verified using the model checker tlc.

Flight test derived heating math models for critical locations on the orbiter during reentry

NASA Technical Reports Server (NTRS)

Hertzler, E. K.; Phillips, P. W.

1983-01-01

An analysis technique was developed for expanding the aerothermodynamic envelope of the Space Shuttle without subjecting the vehicle to sustained flight at more stressing heating conditions. A transient analysis program was developed to take advantage of the transient maneuvers that were flown as part of this analysis technique. Heat rates were derived from flight test data for various locations on the orbiter. The flight derived heat rates were used to update heating models based on predicted data. Future missions were then analyzed based on these flight adjusted models. A technique for comparing flight and predicted heating rate data and the extrapolation of the data to predict the aerothermodynamic environment of future missions is presented.

Chronology of DIC technique based on the fundamental mathematical modeling and dehydration impact.

PubMed

Alias, Norma; Saipol, Hafizah Farhah Saipan; Ghani, Asnida Che Abd

2014-12-01

A chronology of mathematical models for heat and mass transfer equation is proposed for the prediction of moisture and temperature behavior during drying using DIC (Détente Instantanée Contrôlée) or instant controlled pressure drop technique. DIC technique has the potential as most commonly used dehydration method for high impact food value including the nutrition maintenance and the best possible quality for food storage. The model is governed by the regression model, followed by 2D Fick's and Fourier's parabolic equation and 2D elliptic-parabolic equation in a rectangular slice. The models neglect the effect of shrinkage and radiation effects. The simulations of heat and mass transfer equations with parabolic and elliptic-parabolic types through some numerical methods based on finite difference method (FDM) have been illustrated. Intel®Core™2Duo processors with Linux operating system and C programming language have been considered as a computational platform for the simulation. Qualitative and quantitative differences between DIC technique and the conventional drying methods have been shown as a comparative.

Graph-based real-time fault diagnostics

NASA Technical Reports Server (NTRS)

Padalkar, S.; Karsai, G.; Sztipanovits, J.

1988-01-01

A real-time fault detection and diagnosis capability is absolutely crucial in the design of large-scale space systems. Some of the existing AI-based fault diagnostic techniques like expert systems and qualitative modelling are frequently ill-suited for this purpose. Expert systems are often inadequately structured, difficult to validate and suffer from knowledge acquisition bottlenecks. Qualitative modelling techniques sometimes generate a large number of failure source alternatives, thus hampering speedy diagnosis. In this paper we present a graph-based technique which is well suited for real-time fault diagnosis, structured knowledge representation and acquisition and testing and validation. A Hierarchical Fault Model of the system to be diagnosed is developed. At each level of hierarchy, there exist fault propagation digraphs denoting causal relations between failure modes of subsystems. The edges of such a digraph are weighted with fault propagation time intervals. Efficient and restartable graph algorithms are used for on-line speedy identification of failure source components.

Obesity prevention: Comparison of techniques and potential solution

NASA Astrophysics Data System (ADS)

Zulkepli, Jafri; Abidin, Norhaslinda Zainal; Zaibidi, Nerda Zura

2014-12-01

Over the years, obesity prevention has been a broadly studied subject by both academicians and practitioners. It is one of the most serious public health issue as it can cause numerous chronic health and psychosocial problems. Research is needed to suggest a population-based strategy for obesity prevention. In the academic environment, the importance of obesity prevention has triggered various problem solving approaches. A good obesity prevention model, should comprehend and cater all complex and dynamics issues. Hence, the main purpose of this paper is to discuss the qualitative and quantitative approaches on obesity prevention study and to provide an extensive literature review on various recent modelling techniques for obesity prevention. Based on these literatures, the comparison of both quantitative and qualitative approahes are highlighted and the justification on the used of system dynamics technique to solve the population of obesity is discussed. Lastly, a potential framework solution based on system dynamics modelling is proposed.

Quantitative model analysis with diverse biological data: applications in developmental pattern formation.

PubMed

Pargett, Michael; Umulis, David M

2013-07-15

Mathematical modeling of transcription factor and signaling networks is widely used to understand if and how a mechanism works, and to infer regulatory interactions that produce a model consistent with the observed data. Both of these approaches to modeling are informed by experimental data, however, much of the data available or even acquirable are not quantitative. Data that is not strictly quantitative cannot be used by classical, quantitative, model-based analyses that measure a difference between the measured observation and the model prediction for that observation. To bridge the model-to-data gap, a variety of techniques have been developed to measure model "fitness" and provide numerical values that can subsequently be used in model optimization or model inference studies. Here, we discuss a selection of traditional and novel techniques to transform data of varied quality and enable quantitative comparison with mathematical models. This review is intended to both inform the use of these model analysis methods, focused on parameter estimation, and to help guide the choice of method to use for a given study based on the type of data available. Applying techniques such as normalization or optimal scaling may significantly improve the utility of current biological data in model-based study and allow greater integration between disparate types of data. Copyright © 2013 Elsevier Inc. All rights reserved.

Effective wind speed estimation: Comparison between Kalman Filter and Takagi-Sugeno observer techniques.

PubMed

Gauterin, Eckhard; Kammerer, Philipp; Kühn, Martin; Schulte, Horst

2016-05-01

Advanced model-based control of wind turbines requires knowledge of the states and the wind speed. This paper benchmarks a nonlinear Takagi-Sugeno observer for wind speed estimation with enhanced Kalman Filter techniques: The performance and robustness towards model-structure uncertainties of the Takagi-Sugeno observer, a Linear, Extended and Unscented Kalman Filter are assessed. Hence the Takagi-Sugeno observer and enhanced Kalman Filter techniques are compared based on reduced-order models of a reference wind turbine with different modelling details. The objective is the systematic comparison with different design assumptions and requirements and the numerical evaluation of the reconstruction quality of the wind speed. Exemplified by a feedforward loop employing the reconstructed wind speed, the benefit of wind speed estimation within wind turbine control is illustrated. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Innovative application of virtual display technique in virtual museum

NASA Astrophysics Data System (ADS)

Zhang, Jiankang

2017-09-01

Virtual museum refers to display and simulate the functions of real museum on the Internet in the form of 3 Dimensions virtual reality by applying interactive programs. Based on Virtual Reality Modeling Language, virtual museum building and its effective interaction with the offline museum lie in making full use of 3 Dimensions panorama technique, virtual reality technique and augmented reality technique, and innovatively taking advantages of dynamic environment modeling technique, real-time 3 Dimensions graphics generating technique, system integration technique and other key virtual reality techniques to make sure the overall design of virtual museum.3 Dimensions panorama technique, also known as panoramic photography or virtual reality, is a technique based on static images of the reality. Virtual reality technique is a kind of computer simulation system which can create and experience the interactive 3 Dimensions dynamic visual world. Augmented reality, also known as mixed reality, is a technique which simulates and mixes the information (visual, sound, taste, touch, etc.) that is difficult for human to experience in reality. These technologies make virtual museum come true. It will not only bring better experience and convenience to the public, but also be conducive to improve the influence and cultural functions of the real museum.

Aviation Safety: Modeling and Analyzing Complex Interactions between Humans and Automated Systems

NASA Technical Reports Server (NTRS)

Rungta, Neha; Brat, Guillaume; Clancey, William J.; Linde, Charlotte; Raimondi, Franco; Seah, Chin; Shafto, Michael

2013-01-01

The on-going transformation from the current US Air Traffic System (ATS) to the Next Generation Air Traffic System (NextGen) will force the introduction of new automated systems and most likely will cause automation to migrate from ground to air. This will yield new function allocations between humans and automation and therefore change the roles and responsibilities in the ATS. Yet, safety in NextGen is required to be at least as good as in the current system. We therefore need techniques to evaluate the safety of the interactions between humans and automation. We think that current human factor studies and simulation-based techniques will fall short in front of the ATS complexity, and that we need to add more automated techniques to simulations, such as model checking, which offers exhaustive coverage of the non-deterministic behaviors in nominal and off-nominal scenarios. In this work, we present a verification approach based both on simulations and on model checking for evaluating the roles and responsibilities of humans and automation. Models are created using Brahms (a multi-agent framework) and we show that the traditional Brahms simulations can be integrated with automated exploration techniques based on model checking, thus offering a complete exploration of the behavioral space of the scenario. Our formal analysis supports the notion of beliefs and probabilities to reason about human behavior. We demonstrate the technique with the Ueberligen accident since it exemplifies authority problems when receiving conflicting advices from human and automated systems.

Support vector methods for survival analysis: a comparison between ranking and regression approaches.

PubMed

Van Belle, Vanya; Pelckmans, Kristiaan; Van Huffel, Sabine; Suykens, Johan A K

2011-10-01

To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data. The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a problem which can be solved efficiently in a context of structural risk minimization and convex optimization techniques. In a second approach, one uses a regression approach, dealing with censoring by means of inequality constraints. The goal of this paper is then twofold: (i) introducing a new model combining the ranking and regression strategy, which retains the link with existing survival models such as the proportional hazards model via transformation models; and (ii) comparison of the three techniques on 6 clinical and 3 high-dimensional datasets and discussing the relevance of these techniques over classical approaches fur survival data. We compare svm-based survival models based on ranking constraints, based on regression constraints and models based on both ranking and regression constraints. The performance of the models is compared by means of three different measures: (i) the concordance index, measuring the model's discriminating ability; (ii) the logrank test statistic, indicating whether patients with a prognostic index lower than the median prognostic index have a significant different survival than patients with a prognostic index higher than the median; and (iii) the hazard ratio after normalization to restrict the prognostic index between 0 and 1. Our results indicate a significantly better performance for models including regression constraints above models only based on ranking constraints. This work gives empirical evidence that svm-based models using regression constraints perform significantly better than svm-based models based on ranking constraints. Our experiments show a comparable performance for methods including only regression or both regression and ranking constraints on clinical data. On high dimensional data, the former model performs better. However, this approach does not have a theoretical link with standard statistical models for survival data. This link can be made by means of transformation models when ranking constraints are included. Copyright © 2011 Elsevier B.V. All rights reserved.

Post-processing techniques to enhance reliability of assignment algorithm based performance measures : [technical summary].

DOT National Transportation Integrated Search

2011-01-01

Travel demand modeling plays a key role in the transportation system planning and evaluation process. The four-step sequential travel demand model is the most widely used technique in practice. Traffic assignment is the key step in the conventional f...

Steady-state, lumped-parameter model for capacitor-run, single-phase induction motors

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

Umans, S.D.

1996-01-01

This paper documents a technique for deriving a steady-state, lumped-parameter model for capacitor-run, single-phase induction motors. The objective of this model is to predict motor performance parameters such as torque, loss distribution, and efficiency as a function of applied voltage and motor speed as well as the temperatures of the stator windings and of the rotor. The model includes representations of both the main and auxiliary windings (including arbitrary external impedances) and also the effects of core and rotational losses. The technique can be easily implemented and the resultant model can be used in a wide variety of analyses tomore » investigate motor performance as a function of load, speed, and winding and rotor temperatures. The technique is based upon a coupled-circuit representation of the induction motor. A notable feature of the model is the technique used for representing core loss. In equivalent-circuit representations of transformers and induction motors, core loss is typically represented by a core-loss resistance in shunt with the magnetizing inductance. In order to maintain the coupled-circuit viewpoint adopted in this paper, this technique was modified slightly; core loss is represented by a set of core-loss resistances connected to the ``secondaries`` of a set of windings which perfectly couple to the air-gap flux of the motor. An example of the technique is presented based upon a 3.5 kW, single-phase, capacitor-run motor and the validity of the technique is demonstrated by comparing predicted and measured motor performance.« less

How Many Wolves (Canis lupus) Fit into Germany? The Role of Assumptions in Predictive Rule-Based Habitat Models for Habitat Generalists

PubMed Central

Fechter, Dominik; Storch, Ilse

2014-01-01

Due to legislative protection, many species, including large carnivores, are currently recolonizing Europe. To address the impending human-wildlife conflicts in advance, predictive habitat models can be used to determine potentially suitable habitat and areas likely to be recolonized. As field data are often limited, quantitative rule based models or the extrapolation of results from other studies are often the techniques of choice. Using the wolf (Canis lupus) in Germany as a model for habitat generalists, we developed a habitat model based on the location and extent of twelve existing wolf home ranges in Eastern Germany, current knowledge on wolf biology, different habitat modeling techniques and various input data to analyze ten different input parameter sets and address the following questions: (1) How do a priori assumptions and different input data or habitat modeling techniques affect the abundance and distribution of potentially suitable wolf habitat and the number of wolf packs in Germany? (2) In a synthesis across input parameter sets, what areas are predicted to be most suitable? (3) Are existing wolf pack home ranges in Eastern Germany consistent with current knowledge on wolf biology and habitat relationships? Our results indicate that depending on which assumptions on habitat relationships are applied in the model and which modeling techniques are chosen, the amount of potentially suitable habitat estimated varies greatly. Depending on a priori assumptions, Germany could accommodate between 154 and 1769 wolf packs. The locations of the existing wolf pack home ranges in Eastern Germany indicate that wolves are able to adapt to areas densely populated by humans, but are limited to areas with low road densities. Our analysis suggests that predictive habitat maps in general, should be interpreted with caution and illustrates the risk for habitat modelers to concentrate on only one selection of habitat factors or modeling technique. PMID:25029506

On prognostic models, artificial intelligence and censored observations.

PubMed

Anand, S S; Hamilton, P W; Hughes, J G; Bell, D A

2001-03-01

The development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On the other hand, new modelling paradigms are being proposed continuously within the machine learning and statistical community and claims, often based on inadequate evaluation, being made on their superiority over traditional modelling methods. We believe that for new modelling approaches to deliver true net benefits over traditional techniques, an evaluation centric approach to their development is essential. In this paper we present such an evaluation centric approach to developing extensions to the basic k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance the distance metric used and a framework based on evidence theory to obtain a prediction for the target example from the outcome of the retrieved exemplars. We refer to this new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing a means for handling censored observations within k-NN.

Introduction to Information Visualization (InfoVis) Techniques for Model-Based Systems Engineering

NASA Technical Reports Server (NTRS)

Sindiy, Oleg; Litomisky, Krystof; Davidoff, Scott; Dekens, Frank

2013-01-01

This paper presents insights that conform to numerous system modeling languages/representation standards. The insights are drawn from best practices of Information Visualization as applied to aerospace-based applications.

iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space.

PubMed

Akbar, Shahid; Hayat, Maqsood; Iqbal, Muhammad; Jan, Mian Ahmad

2017-06-01

Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to perilous impact of cancer, the development of an accurate and highly efficient intelligent computational model is desirable for identification of anticancer peptides. In this paper, evolutionary intelligent genetic algorithm-based ensemble model, 'iACP-GAEnsC', is proposed for the identification of anticancer peptides. In this model, the protein sequences are formulated, using three different discrete feature representation methods, i.e., amphiphilic Pseudo amino acid composition, g-Gap dipeptide composition, and Reduce amino acid alphabet composition. The performance of the extracted feature spaces are investigated separately and then merged to exhibit the significance of hybridization. In addition, the predicted results of individual classifiers are combined together, using optimized genetic algorithm and simple majority technique in order to enhance the true classification rate. It is observed that genetic algorithm-based ensemble classification outperforms than individual classifiers as well as simple majority voting base ensemble. The performance of genetic algorithm-based ensemble classification is highly reported on hybrid feature space, with an accuracy of 96.45%. In comparison to the existing techniques, 'iACP-GAEnsC' model has achieved remarkable improvement in terms of various performance metrics. Based on the simulation results, it is observed that 'iACP-GAEnsC' model might be a leading tool in the field of drug design and proteomics for researchers. Copyright © 2017 Elsevier B.V. All rights reserved.

Search-based model identification of smart-structure damage

NASA Technical Reports Server (NTRS)

Glass, B. J.; Macalou, A.

1991-01-01

This paper describes the use of a combined model and parameter identification approach, based on modal analysis and artificial intelligence (AI) techniques, for identifying damage or flaws in a rotating truss structure incorporating embedded piezoceramic sensors. This smart structure example is representative of a class of structures commonly found in aerospace systems and next generation space structures. Artificial intelligence techniques of classification, heuristic search, and an object-oriented knowledge base are used in an AI-based model identification approach. A finite model space is classified into a search tree, over which a variant of best-first search is used to identify the model whose stored response most closely matches that of the input. Newly-encountered models can be incorporated into the model space. This adaptativeness demonstrates the potential for learning control. Following this output-error model identification, numerical parameter identification is used to further refine the identified model. Given the rotating truss example in this paper, noisy data corresponding to various damage configurations are input to both this approach and a conventional parameter identification method. The combination of the AI-based model identification with parameter identification is shown to lead to smaller parameter corrections than required by the use of parameter identification alone.

Modeling and prototyping of biometric systems using dataflow programming

NASA Astrophysics Data System (ADS)

Minakova, N.; Petrov, I.

2018-01-01

The development of biometric systems is one of the labor-intensive processes. Therefore, the creation and analysis of approaches and techniques is an urgent task at present. This article presents a technique of modeling and prototyping biometric systems based on dataflow programming. The technique includes three main stages: the development of functional blocks, the creation of a dataflow graph and the generation of a prototype. A specially developed software modeling environment that implements this technique is described. As an example of the use of this technique, an example of the implementation of the iris localization subsystem is demonstrated. A variant of modification of dataflow programming is suggested to solve the problem related to the undefined order of block activation. The main advantage of the presented technique is the ability to visually display and design the model of the biometric system, the rapid creation of a working prototype and the reuse of the previously developed functional blocks.

A Micro-Level Data-Calibrated Agent-Based Model: The Synergy between Microsimulation and Agent-Based Modeling.

PubMed

Singh, Karandeep; Ahn, Chang-Won; Paik, Euihyun; Bae, Jang Won; Lee, Chun-Hee

2018-01-01

Artificial life (ALife) examines systems related to natural life, its processes, and its evolution, using simulations with computer models, robotics, and biochemistry. In this article, we focus on the computer modeling, or "soft," aspects of ALife and prepare a framework for scientists and modelers to be able to support such experiments. The framework is designed and built to be a parallel as well as distributed agent-based modeling environment, and does not require end users to have expertise in parallel or distributed computing. Furthermore, we use this framework to implement a hybrid model using microsimulation and agent-based modeling techniques to generate an artificial society. We leverage this artificial society to simulate and analyze population dynamics using Korean population census data. The agents in this model derive their decisional behaviors from real data (microsimulation feature) and interact among themselves (agent-based modeling feature) to proceed in the simulation. The behaviors, interactions, and social scenarios of the agents are varied to perform an analysis of population dynamics. We also estimate the future cost of pension policies based on the future population structure of the artificial society. The proposed framework and model demonstrates how ALife techniques can be used by researchers in relation to social issues and policies.

A technique for evaluating the application of the pin-level stuck-at fault model to VLSI circuits

NASA Technical Reports Server (NTRS)

Palumbo, Daniel L.; Finelli, George B.

1987-01-01

Accurate fault models are required to conduct the experiments defined in validation methodologies for highly reliable fault-tolerant computers (e.g., computers with a probability of failure of 10 to the -9 for a 10-hour mission). Described is a technique by which a researcher can evaluate the capability of the pin-level stuck-at fault model to simulate true error behavior symptoms in very large scale integrated (VLSI) digital circuits. The technique is based on a statistical comparison of the error behavior resulting from faults applied at the pin-level of and internal to a VLSI circuit. As an example of an application of the technique, the error behavior of a microprocessor simulation subjected to internal stuck-at faults is compared with the error behavior which results from pin-level stuck-at faults. The error behavior is characterized by the time between errors and the duration of errors. Based on this example data, the pin-level stuck-at fault model is found to deliver less than ideal performance. However, with respect to the class of faults which cause a system crash, the pin-level, stuck-at fault model is found to provide a good modeling capability.

Correcting for deformation in skin-based marker systems.

PubMed

Alexander, E J; Andriacchi, T P

2001-03-01

A new technique is described that reduces error due to skin movement artifact in the opto-electronic measurement of in vivo skeletal motion. This work builds on a previously described point cluster technique marker set and estimation algorithm by extending the transformation equations to the general deformation case using a set of activity-dependent deformation models. Skin deformation during activities of daily living are modeled as consisting of a functional form defined over the observation interval (the deformation model) plus additive noise (modeling error). The method is described as an interval deformation technique. The method was tested using simulation trials with systematic and random components of deformation error introduced into marker position vectors. The technique was found to substantially outperform methods that require rigid-body assumptions. The method was tested in vivo on a patient fitted with an external fixation device (Ilizarov). Simultaneous measurements from markers placed on the Ilizarov device (fixed to bone) were compared to measurements derived from skin-based markers. The interval deformation technique reduced the errors in limb segment pose estimate by 33 and 25% compared to the classic rigid-body technique for position and orientation, respectively. This newly developed method has demonstrated that by accounting for the changing shape of the limb segment, a substantial improvement in the estimates of in vivo skeletal movement can be achieved.

Multivariate Bias Correction Procedures for Improving Water Quality Predictions from the SWAT Model

NASA Astrophysics Data System (ADS)

Arumugam, S.; Libera, D.

2017-12-01

Water quality observations are usually not available on a continuous basis for longer than 1-2 years at a time over a decadal period given the labor requirements making calibrating and validating mechanistic models difficult. Further, any physical model predictions inherently have bias (i.e., under/over estimation) and require post-simulation techniques to preserve the long-term mean monthly attributes. This study suggests a multivariate bias-correction technique and compares to a common technique in improving the performance of the SWAT model in predicting daily streamflow and TN loads across the southeast based on split-sample validation. The approach is a dimension reduction technique, canonical correlation analysis (CCA) that regresses the observed multivariate attributes with the SWAT model simulated values. The common approach is a regression based technique that uses an ordinary least squares regression to adjust model values. The observed cross-correlation between loadings and streamflow is better preserved when using canonical correlation while simultaneously reducing individual biases. Additionally, canonical correlation analysis does a better job in preserving the observed joint likelihood of observed streamflow and loadings. These procedures were applied to 3 watersheds chosen from the Water Quality Network in the Southeast Region; specifically, watersheds with sufficiently large drainage areas and number of observed data points. The performance of these two approaches are compared for the observed period and over a multi-decadal period using loading estimates from the USGS LOADEST model. Lastly, the CCA technique is applied in a forecasting sense by using 1-month ahead forecasts of P & T from ECHAM4.5 as forcings in the SWAT model. Skill in using the SWAT model for forecasting loadings and streamflow at the monthly and seasonal timescale is also discussed.

Aerodynamic force measurement on a large-scale model in a short duration test facility

NASA Astrophysics Data System (ADS)

Tanno, H.; Kodera, M.; Komuro, T.; Sato, K.; Takahasi, M.; Itoh, K.

2005-03-01

A force measurement technique has been developed for large-scale aerodynamic models with a short test time. The technique is based on direct acceleration measurements, with miniature accelerometers mounted on a test model suspended by wires. Measuring acceleration at two different locations, the technique can eliminate oscillations from natural vibration of the model. The technique was used for drag force measurements on a 3m long supersonic combustor model in the HIEST free-piston driven shock tunnel. A time resolution of 350μs is guaranteed during measurements, whose resolution is enough for ms order test time in HIEST. To evaluate measurement reliability and accuracy, measured values were compared with results from a three-dimensional Navier-Stokes numerical simulation. The difference between measured values and numerical simulation values was less than 5%. We conclude that this measurement technique is sufficiently reliable for measuring aerodynamic force within test durations of 1ms.

Teaching, Learning and Evaluation Techniques in the Engineering Courses.

ERIC Educational Resources Information Center

Vermaas, Luiz Lenarth G.; Crepaldi, Paulo Cesar; Fowler, Fabio Roberto

This article presents some techniques of professional formation from the Petra Model that can be applied in Engineering Programs. It shows its philosophy, teaching methods for listening, making abstracts, studying, researching, team working and problem solving. Some questions regarding planning and evaluation, based in the model are, as well,…

Discrete-time modelling of musical instruments

NASA Astrophysics Data System (ADS)

Välimäki, Vesa; Pakarinen, Jyri; Erkut, Cumhur; Karjalainen, Matti

2006-01-01

This article describes physical modelling techniques that can be used for simulating musical instruments. The methods are closely related to digital signal processing. They discretize the system with respect to time, because the aim is to run the simulation using a computer. The physics-based modelling methods can be classified as mass-spring, modal, wave digital, finite difference, digital waveguide and source-filter models. We present the basic theory and a discussion on possible extensions for each modelling technique. For some methods, a simple model example is chosen from the existing literature demonstrating a typical use of the method. For instance, in the case of the digital waveguide modelling technique a vibrating string model is discussed, and in the case of the wave digital filter technique we present a classical piano hammer model. We tackle some nonlinear and time-varying models and include new results on the digital waveguide modelling of a nonlinear string. Current trends and future directions in physical modelling of musical instruments are discussed.

Human tracking over camera networks: a review

NASA Astrophysics Data System (ADS)

Hou, Li; Wan, Wanggen; Hwang, Jenq-Neng; Muhammad, Rizwan; Yang, Mingyang; Han, Kang

2017-12-01

In recent years, automated human tracking over camera networks is getting essential for video surveillance. The tasks of tracking human over camera networks are not only inherently challenging due to changing human appearance, but also have enormous potentials for a wide range of practical applications, ranging from security surveillance to retail and health care. This review paper surveys the most widely used techniques and recent advances for human tracking over camera networks. Two important functional modules for the human tracking over camera networks are addressed, including human tracking within a camera and human tracking across non-overlapping cameras. The core techniques of human tracking within a camera are discussed based on two aspects, i.e., generative trackers and discriminative trackers. The core techniques of human tracking across non-overlapping cameras are then discussed based on the aspects of human re-identification, camera-link model-based tracking and graph model-based tracking. Our survey aims to address existing problems, challenges, and future research directions based on the analyses of the current progress made toward human tracking techniques over camera networks.

A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles.

PubMed

Omar, Hani; Hoang, Van Hai; Liu, Duen-Ren

2016-01-01

Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words.

A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles

PubMed Central

Omar, Hani; Hoang, Van Hai; Liu, Duen-Ren

2016-01-01

Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words. PMID:27313605

Protein Modelling: What Happened to the “Protein Structure Gap”?

PubMed Central

Schwede, Torsten

2013-01-01

Computational modeling and prediction of three-dimensional macromolecular structures and complexes from their sequence has been a long standing vision in structural biology as it holds the promise to bypass part of the laborious process of experimental structure solution. Over the last two decades, a paradigm shift has occurred: starting from a situation where the “structure knowledge gap” between the huge number of protein sequences and small number of known structures has hampered the widespread use of structure-based approaches in life science research, today some form of structural information – either experimental or computational – is available for the majority of amino acids encoded by common model organism genomes. Template based homology modeling techniques have matured to a point where they are now routinely used to complement experimental techniques. With the scientific focus of interest moving towards larger macromolecular complexes and dynamic networks of interactions, the integration of computational modeling methods with low-resolution experimental techniques allows studying large and complex molecular machines. Computational modeling and prediction techniques are still facing a number of challenges which hamper the more widespread use by the non-expert scientist. For example, it is often difficult to convey the underlying assumptions of a computational technique, as well as the expected accuracy and structural variability of a specific model. However, these aspects are crucial to understand the limitations of a model, and to decide which interpretations and conclusions can be supported. PMID:24010712

Rocket engine diagnostics using qualitative modeling techniques

NASA Technical Reports Server (NTRS)

Binder, Michael; Maul, William; Meyer, Claudia; Sovie, Amy

1992-01-01

Researchers at NASA Lewis Research Center are presently developing qualitative modeling techniques for automated rocket engine diagnostics. A qualitative model of a turbopump interpropellant seal system has been created. The qualitative model describes the effects of seal failures on the system steady-state behavior. This model is able to diagnose the failure of particular seals in the system based on anomalous temperature and pressure values. The anomalous values input to the qualitative model are generated using numerical simulations. Diagnostic test cases include both single and multiple seal failures.

Rocket engine diagnostics using qualitative modeling techniques

NASA Technical Reports Server (NTRS)

Binder, Michael; Maul, William; Meyer, Claudia; Sovie, Amy

1992-01-01

Researchers at NASA Lewis Research Center are presently developing qualitative modeling techniques for automated rocket engine diagnostics. A qualitative model of a turbopump interpropellant seal system was created. The qualitative model describes the effects of seal failures on the system steady state behavior. This model is able to diagnose the failure of particular seals in the system based on anomalous temperature and pressure values. The anomalous values input to the qualitative model are generated using numerical simulations. Diagnostic test cases include both single and multiple seal failures.

On the comparison of stochastic model predictive control strategies applied to a hydrogen-based microgrid

NASA Astrophysics Data System (ADS)

Velarde, P.; Valverde, L.; Maestre, J. M.; Ocampo-Martinez, C.; Bordons, C.

2017-03-01

In this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control is presented. To this end, three predictive controllers have been designed and implemented in a real renewable-hydrogen-based microgrid. The experimental set-up includes a PEM electrolyzer, lead-acid batteries, and a PEM fuel cell as main equipment. The real experimental results show significant differences from the plant components, mainly in terms of use of energy, for each implemented technique. Effectiveness, performance, advantages, and disadvantages of these techniques are extensively discussed and analyzed to give some valid criteria when selecting an appropriate stochastic predictive controller.

Model Based Document and Report Generation for Systems Engineering

NASA Technical Reports Server (NTRS)

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

2013-01-01

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

Model based document and report generation for systems engineering

NASA Astrophysics Data System (ADS)

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

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

Load Modeling – A Review

DOE PAGES

Arif, Anmar; Wang, Zhaoyu; Wang, Jianhui; ...

2017-05-02

Load modeling has significant impact on power system studies. This paper presents a review on load modeling and identification techniques. Load models can be classified into two broad categories: static and dynamic models, while there are two types of approaches to identify model parameters: measurement-based and component-based. Load modeling has received more attention in recent years because of the renewable integration, demand-side management, and smart metering devices. However, the commonly used load models are outdated, and cannot represent emerging loads. There is a need to systematically review existing load modeling techniques and suggest future research directions to meet the increasingmore » interests from industry and academia. In this study, we provide a thorough survey on the academic research progress and industry practices, and highlight existing issues and new trends in load modeling.« less

Load Modeling – A Review

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

Arif, Anmar; Wang, Zhaoyu; Wang, Jianhui

Load modeling has significant impact on power system studies. This paper presents a review on load modeling and identification techniques. Load models can be classified into two broad categories: static and dynamic models, while there are two types of approaches to identify model parameters: measurement-based and component-based. Load modeling has received more attention in recent years because of the renewable integration, demand-side management, and smart metering devices. However, the commonly used load models are outdated, and cannot represent emerging loads. There is a need to systematically review existing load modeling techniques and suggest future research directions to meet the increasingmore » interests from industry and academia. In this study, we provide a thorough survey on the academic research progress and industry practices, and highlight existing issues and new trends in load modeling.« less

A modified active appearance model based on an adaptive artificial bee colony.

PubMed

Abdulameer, Mohammed Hasan; Sheikh Abdullah, Siti Norul Huda; Othman, Zulaiha Ali

2014-01-01

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.

Improved aortic enhancement in CT angiography using slope-based triggering with table speed optimization: a pilot study.

PubMed

Bashir, Mustafa R; Weber, Paul W; Husarik, Daniela B; Howle, Laurens E; Nelson, Rendon C

2012-08-01

To assess whether a scan triggering technique based on the slope of the time-attenuation curve combined with table speed optimization may improve arterial enhancement in aortic CT angiography compared to conventional threshold-based triggering techniques. Measurements of arterial enhancement were performed in a physiologic flow phantom over a range of simulated cardiac outputs (2.2-8.1 L/min) using contrast media boluses of 80 and 150 mL injected at 4 mL/s. These measurements were used to construct computer models of aortic attenuation in CT angiography, using cardiac output, aortic diameter, and CT table speed as input parameters. In-plane enhancement was calculated for normal and aneurysmal aortic diameters. Calculated arterial enhancement was poor (<150 HU) along most of the scan length using the threshold-based triggering technique for low cardiac outputs and the aneurysmal aorta model. Implementation of the slope-based triggering technique with table speed optimization improved enhancement in all scenarios and yielded good- (>200 HU; 13/16 scenarios) to excellent-quality (>300 HU; 3/16 scenarios) enhancement in all cases. Slope-based triggering with table speed optimization may improve the technical quality of aortic CT angiography over conventional threshold-based techniques, and may reduce technical failures related to low cardiac output and slow flow through an aneurysmal aorta.

Real-time simulation of biological soft tissues: a PGD approach.

PubMed

Niroomandi, S; González, D; Alfaro, I; Bordeu, F; Leygue, A; Cueto, E; Chinesta, F

2013-05-01

We introduce here a novel approach for the numerical simulation of nonlinear, hyperelastic soft tissues at kilohertz feedback rates necessary for haptic rendering. This approach is based upon the use of proper generalized decomposition techniques, a generalization of PODs. Proper generalized decomposition techniques can be considered as a means of a priori model order reduction and provides a physics-based meta-model without the need for prior computer experiments. The suggested strategy is thus composed of an offline phase, in which a general meta-model is computed, and an online evaluation phase in which the results are obtained at real time. Results are provided that show the potential of the proposed technique, together with some benchmark test that shows the accuracy of the method. Copyright © 2013 John Wiley & Sons, Ltd.

A new data assimilation engine for physics-based thermospheric density models

NASA Astrophysics Data System (ADS)

Sutton, E. K.; Henney, C. J.; Hock-Mysliwiec, R.

2017-12-01

The successful assimilation of data into physics-based coupled Ionosphere-Thermosphere models requires rethinking the filtering techniques currently employed in fields such as tropospheric weather modeling. In the realm of Ionospheric-Thermospheric modeling, the estimation of system drivers is a critical component of any reliable data assimilation technique. How to best estimate and apply these drivers, however, remains an open question and active area of research. The recently developed method of Iterative Re-Initialization, Driver Estimation and Assimilation (IRIDEA) accounts for the driver/response time-delay characteristics of the Ionosphere-Thermosphere system relative to satellite accelerometer observations. Results from two near year-long simulations are shown: (1) from a period of elevated solar and geomagnetic activity during 2003, and (2) from a solar minimum period during 2007. This talk will highlight the challenges and successes of implementing a technique suited for both solar min and max, as well as expectations for improving neutral density forecasts.

Plasticity models of material variability based on uncertainty quantification techniques

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

Jones, Reese E.; Rizzi, Francesco; Boyce, Brad

The advent of fabrication techniques like additive manufacturing has focused attention on the considerable variability of material response due to defects and other micro-structural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. Lastly, we demonstrate that the new method provides predictive realizations that are superior to more traditional ones, and how these UQmore » techniques can be used in model selection and assessing the quality of calibrated physical parameters.« less

A Comparison between Physics-based and Polytropic MHD Models for Stellar Coronae and Stellar Winds of Solar Analogs

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

Cohen, O.

The development of the Zeeman–Doppler Imaging (ZDI) technique has provided synoptic observations of surface magnetic fields of low-mass stars. This led the stellar astrophysics community to adopt modeling techniques that have been used in solar physics using solar magnetograms. However, many of these techniques have been neglected by the solar community due to their failure to reproduce solar observations. Nevertheless, some of these techniques are still used to simulate the coronae and winds of solar analogs. Here we present a comparative study between two MHD models for the solar corona and solar wind. The first type of model is amore » polytropic wind model, and the second is the physics-based AWSOM model. We show that while the AWSOM model consistently reproduces many solar observations, the polytropic model fails to reproduce many of them, and in the cases where it does, its solutions are unphysical. Our recommendation is that polytropic models, which are used to estimate mass-loss rates and other parameters of solar analogs, must first be calibrated with solar observations. Alternatively, these models can be calibrated with models that capture more detailed physics of the solar corona (such as the AWSOM model) and that can reproduce solar observations in a consistent manner. Without such a calibration, the results of the polytropic models cannot be validated, but they can be wrongly used by others.« less

Autonomous selection of PDE inpainting techniques vs. exemplar inpainting techniques for void fill of high resolution digital surface models

NASA Astrophysics Data System (ADS)

Rahmes, Mark; Yates, J. Harlan; Allen, Josef DeVaughn; Kelley, Patrick

2007-04-01

High resolution Digital Surface Models (DSMs) may contain voids (missing data) due to the data collection process used to obtain the DSM, inclement weather conditions, low returns, system errors/malfunctions for various collection platforms, and other factors. DSM voids are also created during bare earth processing where culture and vegetation features have been extracted. The Harris LiteSite TM Toolkit handles these void regions in DSMs via two novel techniques. We use both partial differential equations (PDEs) and exemplar based inpainting techniques to accurately fill voids. The PDE technique has its origin in fluid dynamics and heat equations (a particular subset of partial differential equations). The exemplar technique has its origin in texture analysis and image processing. Each technique is optimally suited for different input conditions. The PDE technique works better where the area to be void filled does not have disproportionately high frequency data in the neighborhood of the boundary of the void. Conversely, the exemplar based technique is better suited for high frequency areas. Both are autonomous with respect to detecting and repairing void regions. We describe a cohesive autonomous solution that dynamically selects the best technique as each void is being repaired.

Comparative evaluation of polarimetric and bi-spectral cloud microphysics retrievals: Retrieval closure experiments and comparisons based on idealized and LES case studies

NASA Astrophysics Data System (ADS)

Miller, D. J.; Zhang, Z.; Ackerman, A. S.; Platnick, S. E.; Cornet, C.

2016-12-01

A remote sensing cloud retrieval simulator, created by coupling an LES cloud model with vector radiative transfer (RT) models is the ideal framework for assessing cloud remote sensing techniques. This simulator serves as a tool for understanding bi-spectral and polarimetric retrievals by comparing them directly to LES cloud properties (retrieval closure comparison) and for comparing the retrieval techniques to one another. Our simulator utilizes the DHARMA LES [Ackerman et al., 2004] with cloud properties based on marine boundary layer (MBL) clouds observed during the DYCOMS-II and ATEX field campaigns. The cloud reflectances are produced by the vectorized RT models based on polarized doubling adding and monte carlo techniques (PDA, MCPOL). Retrievals are performed utilizing techniques as similar as possible to those implemented on their corresponding well known instruments; polarimetric retrievals are based on techniques implemented for polarimeters (POLDER, AirMSPI, and RSP) and bi-spectral retrievals are performed using the Nakajima-King LUT method utilized on a number of spectral instruments (MODIS and VIIRS). Retrieval comparisons focus on cloud droplet effective radius (re), effective variance (ve), and cloud optical thickness (τ). This work explores the sensitivities of these two retrieval techniques to various observation limitations, such as spatial resolution/cloud inhomogeneity, impact of 3D radiative effects, and angular resolution requirements. With future remote sensing missions like NASA's Aerosols/Clouds/Ecosystems (ACE) planning to feature advanced polarimetric instruments it is important to understand how these retrieval techniques compare to one another. The cloud retrieval simulator we've developed allows us to probe these important questions in a realistically relevant test bed.

Acoustic thermometry for detecting quenches in superconducting coils and conductor stacks

NASA Astrophysics Data System (ADS)

Marchevsky, M.; Gourlay, S. A.

2017-01-01

Quench detection capability is essential for reliable operation and protection of superconducting magnets, coils, cables, and machinery. We propose a quench detection technique based on sensing local temperature variations in the bulk of a superconducting winding by monitoring its transient acoustic response. Our approach is primarily aimed at coils and devices built with high-temperature superconductor materials where quench detection using standard voltage-based techniques may be inefficient due to the slow velocity of quench propagation. The acoustic sensing technique is non-invasive, fast, and capable of detecting temperature variations of less than 1 K in the interior of the superconductor cable stack in a 77 K cryogenic environment. We show results of finite element modeling and experiments conducted on a model superconductor stack demonstrating viability of the technique for practical quench detection, discuss sensitivity limits of the technique, and its various applications.

From guideline modeling to guideline execution: defining guideline-based decision-support services.

PubMed Central

Tu, S. W.; Musen, M. A.

2000-01-01

We describe our task-based approach to defining the guideline-based decision-support services that the EON system provides. We categorize uses of guidelines in patient-specific decision support into a set of generic tasks--making of decisions, specification of work to be performed, interpretation of data, setting of goals, and issuance of alert and reminders--that can be solved using various techniques. Our model includes constructs required for representing the knowledge used by these techniques. These constructs form a toolkit from which developers can select modeling solutions for guideline task. Based on the tasks and the guideline model, we define a guideline-execution architecture and a model of interactions between a decision-support server and clients that invoke services provided by the server. These services use generic interfaces derived from guideline tasks and their associated modeling constructs. We describe two implementations of these decision-support services and discuss how this work can be generalized. We argue that a well-defined specification of guideline-based decision-support services will facilitate sharing of tools that implement computable clinical guidelines. PMID:11080007

Conditional Monte Carlo randomization tests for regression models.

PubMed

Parhat, Parwen; Rosenberger, William F; Diao, Guoqing

2014-08-15

We discuss the computation of randomization tests for clinical trials of two treatments when the primary outcome is based on a regression model. We begin by revisiting the seminal paper of Gail, Tan, and Piantadosi (1988), and then describe a method based on Monte Carlo generation of randomization sequences. The tests based on this Monte Carlo procedure are design based, in that they incorporate the particular randomization procedure used. We discuss permuted block designs, complete randomization, and biased coin designs. We also use a new technique by Plamadeala and Rosenberger (2012) for simple computation of conditional randomization tests. Like Gail, Tan, and Piantadosi, we focus on residuals from generalized linear models and martingale residuals from survival models. Such techniques do not apply to longitudinal data analysis, and we introduce a method for computation of randomization tests based on the predicted rate of change from a generalized linear mixed model when outcomes are longitudinal. We show, by simulation, that these randomization tests preserve the size and power well under model misspecification. Copyright © 2014 John Wiley & Sons, Ltd.

3D micro-crack propagation simulation at enamel/adhesive interface using FE submodeling and element death techniques.

PubMed

Liu, Heng-Liang; Lin, Chun-Li; Sun, Ming-Tsung; Chang, Yen-Hsiang

2010-06-01

This study investigates micro-crack propagation at the enamel/adhesive interface using finite element (FE) submodeling and element death techniques. A three-dimensional (3D) FE macro-model of the enamel/adhesive/ceramic subjected to shear bond testing was generated and analyzed. A 3D micro-model with interfacial bonding structure was constructed at the upper enamel/adhesive interface where the stress concentration was found from the macro-model results. The morphology of this interfacial bonding structure (i.e., resin tag) was assigned based on resin tag geometry and enamel rod arrangement from a scanning electron microscopy micrograph. The boundary conditions for the micro-model were determined from the macro-model results. A custom iterative code combined with the element death technique was used to calculate the micro-crack propagation. Parallel experiments were performed to validate this FE simulation. The stress concentration within the adhesive occurred mainly at the upper corner near the enamel/adhesive interface and the resin tag base. A simulated fracture path was found at the resin tag base along the enamel/adhesive interface. A morphological observation of the fracture patterns obtained from in vitro testing corresponded with the simulation results. This study shows that the FE submodeling and element death techniques could be used to simulate the 3D micro-stress pattern and the crack propagation noted at the enamel/adhesive interface.

Image-Based Modeling Techniques for Architectural Heritage 3d Digitalization: Limits and Potentialities

NASA Astrophysics Data System (ADS)

Santagati, C.; Inzerillo, L.; Di Paola, F.

2013-07-01

3D reconstruction from images has undergone a revolution in the last few years. Computer vision techniques use photographs from data set collection to rapidly build detailed 3D models. The simultaneous applications of different algorithms (MVS), the different techniques of image matching, feature extracting and mesh optimization are inside an active field of research in computer vision. The results are promising: the obtained models are beginning to challenge the precision of laser-based reconstructions. Among all the possibilities we can mainly distinguish desktop and web-based packages. Those last ones offer the opportunity to exploit the power of cloud computing in order to carry out a semi-automatic data processing, thus allowing the user to fulfill other tasks on its computer; whereas desktop systems employ too much processing time and hard heavy approaches. Computer vision researchers have explored many applications to verify the visual accuracy of 3D model but the approaches to verify metric accuracy are few and no one is on Autodesk 123D Catch applied on Architectural Heritage Documentation. Our approach to this challenging problem is to compare the 3Dmodels by Autodesk 123D Catch and 3D models by terrestrial LIDAR considering different object size, from the detail (capitals, moldings, bases) to large scale buildings for practitioner purpose.

Comparing the landcapes of common retroviral insertion sites across tumor models

NASA Astrophysics Data System (ADS)

Weishaupt, Holger; Čančer, Matko; Engström, Cristopher; Silvestrov, Sergei; Swartling, Fredrik J.

2017-01-01

Retroviral tagging represents an important technique, which allows researchers to screen for candidate cancer genes. The technique is based on the integration of retroviral sequences into the genome of a host organism, which might then lead to the artificial inhibition or expression of proximal genetic elements. The identification of potential cancer genes in this framework involves the detection of genomic regions (common insertion sites; CIS) which contain a number of such viral integration sites that is greater than expected by chance. During the last two decades, a number of different methods have been discussed for the identification of such loci and the respective techniques have been applied to a variety of different retroviruses and/or tumor models. We have previously established a retrovirus driven brain tumor model and reported the CISs which were found based on a Monte Carlo statistics derived detection paradigm. In this study, we consider a recently proposed alternative graph theory based method for identifying CISs and compare the resulting CIS landscape in our brain tumor dataset to those obtained when using the Monte Carlo approach. Finally, we also employ the graph-based method to compare the CIS landscape in our brain tumor model with those of other published retroviral tumor models.

Virtual reality system for treatment of the fear of public speaking using image-based rendering and moving pictures.

PubMed

Lee, Jae M; Ku, Jeong H; Jang, Dong P; Kim, Dong H; Choi, Young H; Kim, In Y; Kim, Sun I

2002-06-01

The fear of speaking is often cited as the world's most common social phobia. The rapid growth of computer technology enabled us to use virtual reality (VR) for the treatment of the fear of public speaking. There have been two techniques used to construct a virtual environment for the treatment of the fear of public speaking: model-based and movie-based. Virtual audiences and virtual environments made by model-based technique are unrealistic and unnatural. The movie-based technique has a disadvantage in that each virtual audience cannot be controlled respectively, because all virtual audiences are included in one moving picture file. To address this disadvantage, this paper presents a virtual environment made by using image-based rendering (IBR) and chroma keying simultaneously. IBR enables us to make the virtual environment realistic because the images are stitched panoramically with the photos taken from a digital camera. And the use of chroma keying allows a virtual audience to be controlled individually. In addition, a real-time capture technique was applied in constructing the virtual environment to give the subjects more interaction, in that they can talk with a therapist or another subject.

A proposed technique for vehicle tracking, direction, and speed determination

NASA Astrophysics Data System (ADS)

Fisher, Paul S.; Angaye, Cleopas O.; Fisher, Howard P.

2004-12-01

A technique for recognition of vehicles in terms of direction, distance, and rate of change is presented. This represents very early work on this problem with significant hurdles still to be addressed. These are discussed in the paper. However, preliminary results also show promise for this technique for use in security and defense environments where the penetration of a perimeter is of concern. The material described herein indicates a process whereby the protection of a barrier could be augmented by computers and installed cameras assisting the individuals charged with this responsibility. The technique we employ is called Finite Inductive Sequences (FI) and is proposed as a means for eliminating data requiring storage and recognition where conventional mathematical models don"t eliminate enough and statistical models eliminate too much. FI is a simple idea and is based upon a symbol push-out technique that allows the order (inductive base) of the model to be set to an a priori value for all derived rules. The rules are obtained from exemplar data sets, and are derived by a technique called Factoring, yielding a table of rules called a Ruling. These rules can then be used in pattern recognition applications such as described in this paper.

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.

Aircraft applications of fault detection and isolation techniques

NASA Astrophysics Data System (ADS)

Marcos Esteban, Andres

In this thesis the problems of fault detection & isolation and fault tolerant systems are studied from the perspective of LTI frequency-domain, model-based techniques. Emphasis is placed on the applicability of these LTI techniques to nonlinear models, especially to aerospace systems. Two applications of Hinfinity LTI fault diagnosis are given using an open-loop (no controller) design approach: one for the longitudinal motion of a Boeing 747-100/200 aircraft, the other for a turbofan jet engine. An algorithm formalizing a robust identification approach based on model validation ideas is also given and applied to the previous jet engine. A general linear fractional transformation formulation is given in terms of the Youla and Dual Youla parameterizations for the integrated (control and diagnosis filter) approach. This formulation provides better insight into the trade-off between the control and the diagnosis objectives. It also provides the basic groundwork towards the development of nested schemes for the integrated approach. These nested structures allow iterative improvements on the control/filter Youla parameters based on successive identification of the system uncertainty (as given by the Dual Youla parameter). The thesis concludes with an application of Hinfinity LTI techniques to the integrated design for the longitudinal motion of the previous Boeing 747-100/200 model.

Modeling 3-D objects with planar surfaces for prediction of electromagnetic scattering

NASA Technical Reports Server (NTRS)

Koch, M. B.; Beck, F. B.; Cockrell, C. R.

1992-01-01

Electromagnetic scattering analysis of objects at resonance is difficult because low frequency techniques are slow and computer intensive, and high frequency techniques may not be reliable. A new technique for predicting the electromagnetic backscatter from electrically conducting objects at resonance is studied. This technique is based on modeling three dimensional objects as a combination of flat plates where some of the plates are blocking the scattering from others. A cube is analyzed as a simple example. The preliminary results compare well with the Geometrical Theory of Diffraction and with measured data.

On the usage of ultrasound computational models for decision making under ambiguity

NASA Astrophysics Data System (ADS)

Dib, Gerges; Sexton, Samuel; Prowant, Matthew; Crawford, Susan; Diaz, Aaron

2018-04-01

Computer modeling and simulation is becoming pervasive within the non-destructive evaluation (NDE) industry as a convenient tool for designing and assessing inspection techniques. This raises a pressing need for developing quantitative techniques for demonstrating the validity and applicability of the computational models. Computational models provide deterministic results based on deterministic and well-defined input, or stochastic results based on inputs defined by probability distributions. However, computational models cannot account for the effects of personnel, procedures, and equipment, resulting in ambiguity about the efficacy of inspections based on guidance from computational models only. In addition, ambiguity arises when model inputs, such as the representation of realistic cracks, cannot be defined deterministically, probabilistically, or by intervals. In this work, Pacific Northwest National Laboratory demonstrates the ability of computational models to represent field measurements under known variabilities, and quantify the differences using maximum amplitude and power spectrum density metrics. Sensitivity studies are also conducted to quantify the effects of different input parameters on the simulation results.

A model-based 3D template matching technique for pose acquisition of an uncooperative space object.

PubMed

Opromolla, Roberto; Fasano, Giancarmine; Rufino, Giancarlo; Grassi, Michele

2015-03-16

This paper presents a customized three-dimensional template matching technique for autonomous pose determination of uncooperative targets. This topic is relevant to advanced space applications, like active debris removal and on-orbit servicing. The proposed technique is model-based and produces estimates of the target pose without any prior pose information, by processing three-dimensional point clouds provided by a LIDAR. These estimates are then used to initialize a pose tracking algorithm. Peculiar features of the proposed approach are the use of a reduced number of templates and the idea of building the database of templates on-line, thus significantly reducing the amount of on-board stored data with respect to traditional techniques. An algorithm variant is also introduced aimed at further accelerating the pose acquisition time and reducing the computational cost. Technique performance is investigated within a realistic numerical simulation environment comprising a target model, LIDAR operation and various target-chaser relative dynamics scenarios, relevant to close-proximity flight operations. Specifically, the capability of the proposed techniques to provide a pose solution suitable to initialize the tracking algorithm is demonstrated, as well as their robustness against highly variable pose conditions determined by the relative dynamics. Finally, a criterion for autonomous failure detection of the presented techniques is presented.

Application of Petri net based analysis techniques to signal transduction pathways.

PubMed

Sackmann, Andrea; Heiner, Monika; Koch, Ina

2006-11-02

Signal transduction pathways are usually modelled using classical quantitative methods, which are based on ordinary differential equations (ODEs). However, some difficulties are inherent in this approach. On the one hand, the kinetic parameters involved are often unknown and have to be estimated. With increasing size and complexity of signal transduction pathways, the estimation of missing kinetic data is not possible. On the other hand, ODEs based models do not support any explicit insights into possible (signal-) flows within the network. Moreover, a huge amount of qualitative data is available due to high-throughput techniques. In order to get information on the systems behaviour, qualitative analysis techniques have been developed. Applications of the known qualitative analysis methods concern mainly metabolic networks. Petri net theory provides a variety of established analysis techniques, which are also applicable to signal transduction models. In this context special properties have to be considered and new dedicated techniques have to be designed. We apply Petri net theory to model and analyse signal transduction pathways first qualitatively before continuing with quantitative analyses. This paper demonstrates how to build systematically a discrete model, which reflects provably the qualitative biological behaviour without any knowledge of kinetic parameters. The mating pheromone response pathway in Saccharomyces cerevisiae serves as case study. We propose an approach for model validation of signal transduction pathways based on the network structure only. For this purpose, we introduce the new notion of feasible t-invariants, which represent minimal self-contained subnets being active under a given input situation. Each of these subnets stands for a signal flow in the system. We define maximal common transition sets (MCT-sets), which can be used for t-invariant examination and net decomposition into smallest biologically meaningful functional units. The paper demonstrates how Petri net analysis techniques can promote a deeper understanding of signal transduction pathways. The new concepts of feasible t-invariants and MCT-sets have been proven to be useful for model validation and the interpretation of the biological system behaviour. Whereas MCT-sets provide a decomposition of the net into disjunctive subnets, feasible t-invariants describe subnets, which generally overlap. This work contributes to qualitative modelling and to the analysis of large biological networks by their fully automatic decomposition into biologically meaningful modules.

Application of Petri net based analysis techniques to signal transduction pathways

PubMed Central

Sackmann, Andrea; Heiner, Monika; Koch, Ina

2006-01-01

Background Signal transduction pathways are usually modelled using classical quantitative methods, which are based on ordinary differential equations (ODEs). However, some difficulties are inherent in this approach. On the one hand, the kinetic parameters involved are often unknown and have to be estimated. With increasing size and complexity of signal transduction pathways, the estimation of missing kinetic data is not possible. On the other hand, ODEs based models do not support any explicit insights into possible (signal-) flows within the network. Moreover, a huge amount of qualitative data is available due to high-throughput techniques. In order to get information on the systems behaviour, qualitative analysis techniques have been developed. Applications of the known qualitative analysis methods concern mainly metabolic networks. Petri net theory provides a variety of established analysis techniques, which are also applicable to signal transduction models. In this context special properties have to be considered and new dedicated techniques have to be designed. Methods We apply Petri net theory to model and analyse signal transduction pathways first qualitatively before continuing with quantitative analyses. This paper demonstrates how to build systematically a discrete model, which reflects provably the qualitative biological behaviour without any knowledge of kinetic parameters. The mating pheromone response pathway in Saccharomyces cerevisiae serves as case study. Results We propose an approach for model validation of signal transduction pathways based on the network structure only. For this purpose, we introduce the new notion of feasible t-invariants, which represent minimal self-contained subnets being active under a given input situation. Each of these subnets stands for a signal flow in the system. We define maximal common transition sets (MCT-sets), which can be used for t-invariant examination and net decomposition into smallest biologically meaningful functional units. Conclusion The paper demonstrates how Petri net analysis techniques can promote a deeper understanding of signal transduction pathways. The new concepts of feasible t-invariants and MCT-sets have been proven to be useful for model validation and the interpretation of the biological system behaviour. Whereas MCT-sets provide a decomposition of the net into disjunctive subnets, feasible t-invariants describe subnets, which generally overlap. This work contributes to qualitative modelling and to the analysis of large biological networks by their fully automatic decomposition into biologically meaningful modules. PMID:17081284

4D computerized ionospheric tomography by using GPS measurements and IRI-Plas model

NASA Astrophysics Data System (ADS)

Tuna, Hakan; Arikan, Feza; Arikan, Orhan

2016-07-01

Ionospheric imaging is an important subject in ionospheric studies. GPS based TEC measurements provide very accurate information about the electron density values in the ionosphere. However, since the measurements are generally very sparse and non-uniformly distributed, computation of 3D electron density estimation from measurements alone is an ill-defined problem. Model based 3D electron density estimations provide physically feasible distributions. However, they are not generally compliant with the TEC measurements obtained from GPS receivers. In this study, GPS based TEC measurements and an ionosphere model known as International Reference Ionosphere Extended to Plasmasphere (IRI-Plas) are employed together in order to obtain a physically accurate 3D electron density distribution which is compliant with the real measurements obtained from a GPS satellite - receiver network. Ionospheric parameters input to the IRI-Plas model are perturbed in the region of interest by using parametric perturbation models such that the synthetic TEC measurements calculated from the resultant 3D electron density distribution fit to the real TEC measurements. The problem is considered as an optimization problem where the optimization parameters are the parameters of the parametric perturbation models. Proposed technique is applied over Turkey, on both calm and storm days of the ionosphere. Results show that the proposed technique produces 3D electron density distributions which are compliant with IRI-Plas model, GPS TEC measurements and ionosonde measurements. The effect of the GPS receiver station number on the performance of the proposed technique is investigated. Results showed that 7 GPS receiver stations in a region as large as Turkey is sufficient for both calm and storm days of the ionosphere. Since the ionization levels in the ionosphere are highly correlated in time, the proposed technique is extended to the time domain by applying Kalman based tracking and smoothing approaches onto the obtained results. Combining Kalman methods with the proposed 3D CIT technique creates a robust 4D ionospheric electron density estimation model, and has the advantage of decreasing the computational cost of the proposed method. Results applied on both calm and storm days of the ionosphere show that, new technique produces more robust solutions especially when the number of GPS receiver stations in the region is small. This study is supported by TUBITAK 114E541, 115E915 and Joint TUBITAK 114E092 and AS CR 14/001 projects.

User modeling techniques for enhanced usability of OPSMODEL operations simulation software

NASA Technical Reports Server (NTRS)

Davis, William T.

1991-01-01

The PC based OPSMODEL operations software for modeling and simulation of space station crew activities supports engineering and cost analyses and operations planning. Using top-down modeling, the level of detail required in the data base can be limited to being commensurate with the results required of any particular analysis. To perform a simulation, a resource environment consisting of locations, crew definition, equipment, and consumables is first defined. Activities to be simulated are then defined as operations and scheduled as desired. These operations are defined within a 1000 level priority structure. The simulation on OPSMODEL, then, consists of the following: user defined, user scheduled operations executing within an environment of user defined resource and priority constraints. Techniques for prioritizing operations to realistically model a representative daily scenario of on-orbit space station crew activities are discussed. The large number of priority levels allows priorities to be assigned commensurate with the detail necessary for a given simulation. Several techniques for realistic modeling of day-to-day work carryover are also addressed.

Analytical transmissibility based transfer path analysis for multi-energy-domain systems using four-pole parameter theory

NASA Astrophysics Data System (ADS)

Mashayekhi, Mohammad Jalali; Behdinan, Kamran

2017-10-01

The increasing demand to minimize undesired vibration and noise levels in several high-tech industries has generated a renewed interest in vibration transfer path analysis. Analyzing vibration transfer paths within a system is of crucial importance in designing an effective vibration isolation strategy. Most of the existing vibration transfer path analysis techniques are empirical which are suitable for diagnosis and troubleshooting purpose. The lack of an analytical transfer path analysis to be used in the design stage is the main motivation behind this research. In this paper an analytical transfer path analysis based on the four-pole theory is proposed for multi-energy-domain systems. Bond graph modeling technique which is an effective approach to model multi-energy-domain systems is used to develop the system model. In this paper an electro-mechanical system is used as a benchmark example to elucidate the effectiveness of the proposed technique. An algorithm to obtain the equivalent four-pole representation of a dynamical systems based on the corresponding bond graph model is also presented in this paper.

Modeling corneal surfaces with rational functions for high-speed videokeratoscopy data compression.

PubMed

Schneider, Martin; Iskander, D Robert; Collins, Michael J

2009-02-01

High-speed videokeratoscopy is an emerging technique that enables study of the corneal surface and tear-film dynamics. Unlike its static predecessor, this new technique results in a very large amount of digital data for which storage needs become significant. We aimed to design a compression technique that would use mathematical functions to parsimoniously fit corneal surface data with a minimum number of coefficients. Since the Zernike polynomial functions that have been traditionally used for modeling corneal surfaces may not necessarily correctly represent given corneal surface data in terms of its optical performance, we introduced the concept of Zernike polynomial-based rational functions. Modeling optimality criteria were employed in terms of both the rms surface error as well as the point spread function cross-correlation. The parameters of approximations were estimated using a nonlinear least-squares procedure based on the Levenberg-Marquardt algorithm. A large number of retrospective videokeratoscopic measurements were used to evaluate the performance of the proposed rational-function-based modeling approach. The results indicate that the rational functions almost always outperform the traditional Zernike polynomial approximations with the same number of coefficients.

Very high resolution surface mass balance over Greenland modeled by the regional climate model MAR with a downscaling technique

NASA Astrophysics Data System (ADS)

Kittel, Christoph; Lang, Charlotte; Agosta, Cécile; Prignon, Maxime; Fettweis, Xavier; Erpicum, Michel

2016-04-01

This study presents surface mass balance (SMB) results at 5 km resolution with the regional climate MAR model over the Greenland ice sheet. Here, we use the last MAR version (v3.6) where the land-ice module (SISVAT) using a high resolution grid (5km) for surface variables is fully coupled while the MAR atmospheric module running at a lower resolution of 10km. This online downscaling technique enables to correct near-surface temperature and humidity from MAR by a gradient based on elevation before forcing SISVAT. The 10 km precipitation is not corrected. Corrections are stronger over the ablation zone where topography presents more variations. The model has been force by ERA-Interim between 1979 and 2014. We will show the advantages of using an online SMB downscaling technique in respect to an offline downscaling extrapolation based on local SMB vertical gradients. Results at 5 km show a better agreement with the PROMICE surface mass balance data base than the extrapolated 10 km MAR SMB results.

Evaluating sustainable energy harvesting systems for human implantable sensors

NASA Astrophysics Data System (ADS)

AL-Oqla, Faris M.; Omar, Amjad A.; Fares, Osama

2018-03-01

Achieving most appropriate energy-harvesting technique for human implantable sensors is still challenging for the industry where keen decisions have to be performed. Moreover, the available polymeric-based composite materials are offering plentiful renewable applications that can help sustainable development as being useful for the energy-harvesting systems such as photovoltaic, piezoelectric, thermoelectric devices as well as other energy storage systems. This work presents an expert-based model capable of better evaluating and examining various available renewable energy-harvesting techniques in urban surroundings subject to various technical and economic, often conflicting, criteria. Wide evaluation criteria have been adopted in the proposed model after examining their suitability as well as ensuring the expediency and reliability of the model by worldwide experts' feedback. The model includes establishing an analytic hierarchy structure with simultaneous 12 conflicting factors to establish a systematic road map for designers to better assess such techniques for human implantable medical sensors. The energy-harvesting techniques considered were limited to Wireless, Thermoelectric, Infrared Radiator, Piezoelectric, Magnetic Induction and Electrostatic Energy Harvesters. Results have demonstrated that the best decision was in favour of wireless-harvesting technology for the medical sensors as it is preferable by most of the considered evaluation criteria in the model.

MO-C-18A-01: Advances in Model-Based 3D Image Reconstruction

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

Chen, G; Pan, X; Stayman, J

2014-06-15

Recent years have seen the emergence of CT image reconstruction techniques that exploit physical models of the imaging system, photon statistics, and even the patient to achieve improved 3D image quality and/or reduction of radiation dose. With numerous advantages in comparison to conventional 3D filtered backprojection, such techniques bring a variety of challenges as well, including: a demanding computational load associated with sophisticated forward models and iterative optimization methods; nonlinearity and nonstationarity in image quality characteristics; a complex dependency on multiple free parameters; and the need to understand how best to incorporate prior information (including patient-specific prior images) within themore » reconstruction process. The advantages, however, are even greater – for example: improved image quality; reduced dose; robustness to noise and artifacts; task-specific reconstruction protocols; suitability to novel CT imaging platforms and noncircular orbits; and incorporation of known characteristics of the imager and patient that are conventionally discarded. This symposium features experts in 3D image reconstruction, image quality assessment, and the translation of such methods to emerging clinical applications. Dr. Chen will address novel methods for the incorporation of prior information in 3D and 4D CT reconstruction techniques. Dr. Pan will show recent advances in optimization-based reconstruction that enable potential reduction of dose and sampling requirements. Dr. Stayman will describe a “task-based imaging” approach that leverages models of the imaging system and patient in combination with a specification of the imaging task to optimize both the acquisition and reconstruction process. Dr. Samei will describe the development of methods for image quality assessment in such nonlinear reconstruction techniques and the use of these methods to characterize and optimize image quality and dose in a spectrum of clinical applications. Learning Objectives: Learn the general methodologies associated with model-based 3D image reconstruction. Learn the potential advantages in image quality and dose associated with model-based image reconstruction. Learn the challenges associated with computational load and image quality assessment for such reconstruction methods. Learn how imaging task can be incorporated as a means to drive optimal image acquisition and reconstruction techniques. Learn how model-based reconstruction methods can incorporate prior information to improve image quality, ease sampling requirements, and reduce dose.« less

Determination of a Limited Scope Network's Lightning Detection Efficiency

NASA Technical Reports Server (NTRS)

Rompala, John T.; Blakeslee, R.

2008-01-01

This paper outlines a modeling technique to map lightning detection efficiency variations over a region surveyed by a sparse array of ground based detectors. A reliable flash peak current distribution (PCD) for the region serves as the technique's base. This distribution is recast as an event probability distribution function. The technique then uses the PCD together with information regarding: site signal detection thresholds, type of solution algorithm used, and range attenuation; to formulate the probability that a flash at a specified location will yield a solution. Applying this technique to the full region produces detection efficiency contour maps specific to the parameters employed. These contours facilitate a comparative analysis of each parameter's effect on the network's detection efficiency. In an alternate application, this modeling technique gives an estimate of the number, strength, and distribution of events going undetected. This approach leads to a variety of event density contour maps. This application is also illustrated. The technique's base PCD can be empirical or analytical. A process for formulating an empirical PCD specific to the region and network being studied is presented. A new method for producing an analytical representation of the empirical PCD is also introduced.

Unified multiphase modeling for evolving, acoustically coupled systems consisting of acoustic, elastic, poroelastic media and septa

NASA Astrophysics Data System (ADS)

Lee, Joong Seok; Kang, Yeon June; Kim, Yoon Young

2012-12-01

This paper presents a new modeling technique that can represent acoustically coupled systems in a unified manner. The proposed unified multiphase (UMP) modeling technique uses Biot's equations that are originally derived for poroelastic media to represent not only poroelastic media but also non-poroelastic ones ranging from acoustic and elastic media to septa. To recover the original vibro-acoustic behaviors of non-poroelastic media, material parameters of a base poroelastic medium are adjusted depending on the target media. The real virtue of this UMP technique is that interface coupling conditions between any media can be automatically satisfied, so no medium-dependent interface condition needs to be imposed explicitly. Thereby, the proposed technique can effectively model any acoustically coupled system having locally varying medium phases and evolving interfaces. A typical situation can occur in an iterative design process. Because the proposed UMP modeling technique needs theoretical justifications for further development, this work is mainly focused on how the technique recovers the governing equations of non-poroelastic media and expresses their interface conditions. We also address how to describe various boundary conditions of the media in the technique. Some numerical studies are carried out to demonstrate the validity of the proposed modeling technique.

Fusing modeling techniques to support domain analysis for reuse opportunities identification

NASA Technical Reports Server (NTRS)

Hall, Susan Main; Mcguire, Eileen

1993-01-01

Functional modeling techniques or object-oriented graphical representations, which are more useful to someone trying to understand the general design or high level requirements of a system? For a recent domain analysis effort, the answer was a fusion of popular modeling techniques of both types. By using both functional and object-oriented techniques, the analysts involved were able to lean on their experience in function oriented software development, while taking advantage of the descriptive power available in object oriented models. In addition, a base of familiar modeling methods permitted the group of mostly new domain analysts to learn the details of the domain analysis process while producing a quality product. This paper describes the background of this project and then provides a high level definition of domain analysis. The majority of this paper focuses on the modeling method developed and utilized during this analysis effort.

The Living Cell as a Multi-agent Organisation: A Compositional Organisation Model of Intracellular Dynamics

NASA Astrophysics Data System (ADS)

Jonker, C. M.; Snoep, J. L.; Treur, J.; Westerhoff, H. V.; Wijngaards, W. C. A.

Within the areas of Computational Organisation Theory and Artificial Intelligence, techniques have been developed to simulate and analyse dynamics within organisations in society. Usually these modelling techniques are applied to factories and to the internal organisation of their process flows, thus obtaining models of complex organisations at various levels of aggregation. The dynamics in living cells are often interpreted in terms of well-organised processes, a bacterium being considered a (micro)factory. This suggests that organisation modelling techniques may also benefit their analysis. Using the example of Escherichia coli it is shown how indeed agent-based organisational modelling techniques can be used to simulate and analyse E.coli's intracellular dynamics. Exploiting the abstraction levels entailed by this perspective, a concise model is obtained that is readily simulated and analysed at the various levels of aggregation, yet shows the cell's essential dynamic patterns.

Prediction of slope stability based on numerical modeling of stress–strain state of rocks

NASA Astrophysics Data System (ADS)

Kozhogulov Nifadyev, KCh, VI; Usmanov, SF

2018-03-01

The paper presents the developed technique for the estimation of rock mass stability based on the finite element modeling of stress–strain state of rocks. The modeling results on the pit wall landslide as a flow of particles along a sloped surface are described.

Predicting Plywood Properties with Wood-based Composite Models

Treesearch

Christopher Adam Senalik; Robert J. Ross

2015-01-01

Previous research revealed that stress wave nondestructive testing techniques could be used to evaluate the tensile and flexural properties of wood-based composite materials. Regression models were developed that related stress wave transmission characteristics (velocity and attenuation) to modulus of elasticity and strength. The developed regression models accounted...

A Nonlinear Regression Model Estimating Single Source Concentrations of Primary and Secondarily Formed 2.5

EPA Science Inventory

Various approaches and tools exist to estimate local and regional PM_2.5 impacts from a single emissions source, ranging from simple screening techniques to Gaussian based dispersion models and complex grid-based Eulerian photochemical transport models. These approache...

Visual Modelling of Data Warehousing Flows with UML Profiles

NASA Astrophysics Data System (ADS)

Pardillo, Jesús; Golfarelli, Matteo; Rizzi, Stefano; Trujillo, Juan

Data warehousing involves complex processes that transform source data through several stages to deliver suitable information ready to be analysed. Though many techniques for visual modelling of data warehouses from the static point of view have been devised, only few attempts have been made to model the data flows involved in a data warehousing process. Besides, each attempt was mainly aimed at a specific application, such as ETL, OLAP, what-if analysis, data mining. Data flows are typically very complex in this domain; for this reason, we argue, designers would greatly benefit from a technique for uniformly modelling data warehousing flows for all applications. In this paper, we propose an integrated visual modelling technique for data cubes and data flows. This technique is based on UML profiling; its feasibility is evaluated by means of a prototype implementation.

Requirements analysis, domain knowledge, and design

NASA Technical Reports Server (NTRS)

Potts, Colin

1988-01-01

Two improvements to current requirements analysis practices are suggested: domain modeling, and the systematic application of analysis heuristics. Domain modeling is the representation of relevant application knowledge prior to requirements specification. Artificial intelligence techniques may eventually be applicable for domain modeling. In the short term, however, restricted domain modeling techniques, such as that in JSD, will still be of practical benefit. Analysis heuristics are standard patterns of reasoning about the requirements. They usually generate questions of clarification or issues relating to completeness. Analysis heuristics can be represented and therefore systematically applied in an issue-based framework. This is illustrated by an issue-based analysis of JSD's domain modeling and functional specification heuristics. They are discussed in the context of the preliminary design of simple embedded systems.

Reducing the Complexity of an Agent-Based Local Heroin Market Model

PubMed Central

Heard, Daniel; Bobashev, Georgiy V.; Morris, Robert J.

2014-01-01

This project explores techniques for reducing the complexity of an agent-based model (ABM). The analysis involved a model developed from the ethnographic research of Dr. Lee Hoffer in the Larimer area heroin market, which involved drug users, drug sellers, homeless individuals and police. The authors used statistical techniques to create a reduced version of the original model which maintained simulation fidelity while reducing computational complexity. This involved identifying key summary quantities of individual customer behavior as well as overall market activity and replacing some agents with probability distributions and regressions. The model was then extended to allow external market interventions in the form of police busts. Extensions of this research perspective, as well as its strengths and limitations, are discussed. PMID:25025132

Model and controller reduction of large-scale structures based on projection methods

NASA Astrophysics Data System (ADS)

Gildin, Eduardo

The design of low-order controllers for high-order plants is a challenging problem theoretically as well as from a computational point of view. Frequently, robust controller design techniques result in high-order controllers. It is then interesting to achieve reduced-order models and controllers while maintaining robustness properties. Controller designed for large structures based on models obtained by finite element techniques yield large state-space dimensions. In this case, problems related to storage, accuracy and computational speed may arise. Thus, model reduction methods capable of addressing controller reduction problems are of primary importance to allow the practical applicability of advanced controller design methods for high-order systems. A challenging large-scale control problem that has emerged recently is the protection of civil structures, such as high-rise buildings and long-span bridges, from dynamic loadings such as earthquakes, high wind, heavy traffic, and deliberate attacks. Even though significant effort has been spent in the application of control theory to the design of civil structures in order increase their safety and reliability, several challenging issues are open problems for real-time implementation. This dissertation addresses with the development of methodologies for controller reduction for real-time implementation in seismic protection of civil structures using projection methods. Three classes of schemes are analyzed for model and controller reduction: nodal truncation, singular value decomposition methods and Krylov-based methods. A family of benchmark problems for structural control are used as a framework for a comparative study of model and controller reduction techniques. It is shown that classical model and controller reduction techniques, such as balanced truncation, modal truncation and moment matching by Krylov techniques, yield reduced-order controllers that do not guarantee stability of the closed-loop system, that is, the reduced-order controller implemented with the full-order plant. A controller reduction approach is proposed such that to guarantee closed-loop stability. It is based on the concept of dissipativity (or positivity) of linear dynamical systems. Utilizing passivity preserving model reduction together with dissipative-LQG controllers, effective low-order optimal controllers are obtained. Results are shown through simulations.

Modeling susceptibility difference artifacts produced by metallic implants in magnetic resonance imaging with point-based thin-plate spline image registration.

PubMed

Pauchard, Y; Smith, M; Mintchev, M

2004-01-01

Magnetic resonance imaging (MRI) suffers from geometric distortions arising from various sources. One such source are the non-linearities associated with the presence of metallic implants, which can profoundly distort the obtained images. These non-linearities result in pixel shifts and intensity changes in the vicinity of the implant, often precluding any meaningful assessment of the entire image. This paper presents a method for correcting these distortions based on non-rigid image registration techniques. Two images from a modelled three-dimensional (3D) grid phantom were subjected to point-based thin-plate spline registration. The reference image (without distortions) was obtained from a grid model including a spherical implant, and the corresponding test image containing the distortions was obtained using previously reported technique for spatial modelling of magnetic susceptibility artifacts. After identifying the nonrecoverable area in the distorted image, the calculated spline model was able to quantitatively account for the distortions, thus facilitating their compensation. Upon the completion of the compensation procedure, the non-recoverable area was removed from the reference image and the latter was compared to the compensated image. Quantitative assessment of the goodness of the proposed compensation technique is presented.

The effect of various parameters of large scale radio propagation models on improving performance mobile communications

NASA Astrophysics Data System (ADS)

Pinem, M.; Fauzi, R.

2018-02-01

One technique for ensuring continuity of wireless communication services and keeping a smooth transition on mobile communication networks is the soft handover technique. In the Soft Handover (SHO) technique the inclusion and reduction of Base Station from the set of active sets is determined by initiation triggers. One of the initiation triggers is based on the strong reception signal. In this paper we observed the influence of parameters of large-scale radio propagation models to improve the performance of mobile communications. The observation parameters for characterizing the performance of the specified mobile system are Drop Call, Radio Link Degradation Rate and Average Size of Active Set (AS). The simulated results show that the increase in altitude of Base Station (BS) Antenna and Mobile Station (MS) Antenna contributes to the improvement of signal power reception level so as to improve Radio Link quality and increase the average size of Active Set and reduce the average Drop Call rate. It was also found that Hata’s propagation model contributed significantly to improvements in system performance parameters compared to Okumura’s propagation model and Lee’s propagation model.

Prostate Cancer Probability Prediction By Machine Learning Technique.

PubMed

Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

2017-11-26

The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

Theoretical and experimental study of a wireless power supply system for moving low power devices in ferromagnetic and conductive medium

NASA Astrophysics Data System (ADS)

Safour, Salaheddine; Bernard, Yves

2017-10-01

This paper focuses on the design of a wireless power supply system for low power devices (e.g. sensors) located in harsh electromagnetic environment with ferromagnetic and conductive materials. Such particular environment could be found in linear and rotating actuators. The studied power transfer system is based on the resonant magnetic coupling between a fixed transmitter coil and a moving receiver coil. The technique was utilized successfully for rotary machines. The aim of this paper is to extend the technique to linear actuators. A modeling approach based on 2D Axisymmetric Finite Element model and an electrical lumped model based on the two-port network theory is introduced. The study shows the limitation of the technique to transfer the required power in the presence of ferromagnetic and conductive materials. Parametric and circuit analysis were conducted in order to design a resonant magnetic coupler that ensures good power transfer capability and efficiency. A design methodology is proposed based on this study. Measurements on the prototype show efficiency up to 75% at a linear distance of 20 mm.

Generalized image contrast enhancement technique based on the Heinemann contrast discrimination model

NASA Astrophysics Data System (ADS)

Liu, Hong; Nodine, Calvin F.

1996-07-01

This paper presents a generalized image contrast enhancement technique, which equalizes the perceived brightness distribution based on the Heinemann contrast discrimination model. It is based on the mathematically proven existence of a unique solution to a nonlinear equation, and is formulated with easily tunable parameters. The model uses a two-step log-log representation of luminance contrast between targets and surround in a luminous background setting. The algorithm consists of two nonlinear gray scale mapping functions that have seven parameters, two of which are adjustable Heinemann constants. Another parameter is the background gray level. The remaining four parameters are nonlinear functions of the gray-level distribution of the given image, and can be uniquely determined once the previous three are set. Tests have been carried out to demonstrate the effectiveness of the algorithm for increasing the overall contrast of radiology images. The traditional histogram equalization can be reinterpreted as an image enhancement technique based on the knowledge of human contrast perception. In fact, it is a special case of the proposed algorithm.

Parameterizing unresolved obstacles with source terms in wave modeling: A real-world application

NASA Astrophysics Data System (ADS)

Mentaschi, Lorenzo; Kakoulaki, Georgia; Vousdoukas, Michalis; Voukouvalas, Evangelos; Feyen, Luc; Besio, Giovanni

2018-06-01

Parameterizing the dissipative effects of small, unresolved coastal features, is fundamental to improve the skills of wave models. The established technique to deal with this problem consists in reducing the amount of energy advected within the propagation scheme, and is currently available only for regular grids. To find a more general approach, Mentaschi et al., 2015b formulated a technique based on source terms, and validated it on synthetic case studies. This technique separates the parameterization of the unresolved features from the energy advection, and can therefore be applied to any numerical scheme and to any type of mesh. Here we developed an open-source library for the estimation of the transparency coefficients needed by this approach, from bathymetric data and for any type of mesh. The spectral wave model WAVEWATCH III was used to show that in a real-world domain, such as the Caribbean Sea, the proposed approach has skills comparable and sometimes better than the established propagation-based technique.

Ground Vibration Test Planning and Pre-Test Analysis for the X-33 Vehicle

NASA Technical Reports Server (NTRS)

Bedrossian, Herand; Tinker, Michael L.; Hidalgo, Homero

2000-01-01

This paper describes the results of the modal test planning and the pre-test analysis for the X-33 vehicle. The pre-test analysis included the selection of the target modes, selection of the sensor and shaker locations and the development of an accurate Test Analysis Model (TAM). For target mode selection, four techniques were considered, one based on the Modal Cost technique, one based on Balanced Singular Value technique, a technique known as the Root Sum Squared (RSS) method, and a Modal Kinetic Energy (MKE) approach. For selecting sensor locations, four techniques were also considered; one based on the Weighted Average Kinetic Energy (WAKE), one based on Guyan Reduction (GR), one emphasizing engineering judgment, and one based on an optimum sensor selection technique using Genetic Algorithm (GA) search technique combined with a criteria based on Hankel Singular Values (HSV's). For selecting shaker locations, four techniques were also considered; one based on the Weighted Average Driving Point Residue (WADPR), one based on engineering judgment and accessibility considerations, a frequency response method, and an optimum shaker location selection based on a GA search technique combined with a criteria based on HSV's. To evaluate the effectiveness of the proposed sensor and shaker locations for exciting the target modes, extensive numerical simulations were performed. Multivariate Mode Indicator Function (MMIF) was used to evaluate the effectiveness of each sensor & shaker set with respect to modal parameter identification. Several TAM reduction techniques were considered including, Guyan, IRS, Modal, and Hybrid. Based on a pre-test cross-orthogonality checks using various reduction techniques, a Hybrid TAM reduction technique was selected and was used for all three vehicle fuel level configurations.

INDUCTIVE SYSTEM HEALTH MONITORING WITH STATISTICAL METRICS

NASA Technical Reports Server (NTRS)

Iverson, David L.

2005-01-01

Model-based reasoning is a powerful method for performing system monitoring and diagnosis. Building models for model-based reasoning is often a difficult and time consuming process. The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring. IMS processes nominal data sets collected either directly from the system or from simulations to build a knowledge base that can be used to detect anomalous behavior in the system. Machine learning and data mining techniques are used to characterize typical system behavior by extracting general classes of nominal data from archived data sets. In particular, a clustering algorithm forms groups of nominal values for sets of related parameters. This establishes constraints on those parameter values that should hold during nominal operation. During monitoring, IMS provides a statistically weighted measure of the deviation of current system behavior from the established normal baseline. If the deviation increases beyond the expected level, an anomaly is suspected, prompting further investigation by an operator or automated system. IMS has shown potential to be an effective, low cost technique to produce system monitoring capability for a variety of applications. We describe the training and system health monitoring techniques of IMS. We also present the application of IMS to a data set from the Space Shuttle Columbia STS-107 flight. IMS was able to detect an anomaly in the launch telemetry shortly after a foam impact damaged Columbia's thermal protection system.

Model authoring system for fail safe analysis

NASA Technical Reports Server (NTRS)

Sikora, Scott E.

1990-01-01

The Model Authoring System is a prototype software application for generating fault tree analyses and failure mode and effects analyses for circuit designs. Utilizing established artificial intelligence and expert system techniques, the circuits are modeled as a frame-based knowledge base in an expert system shell, which allows the use of object oriented programming and an inference engine. The behavior of the circuit is then captured through IF-THEN rules, which then are searched to generate either a graphical fault tree analysis or failure modes and effects analysis. Sophisticated authoring techniques allow the circuit to be easily modeled, permit its behavior to be quickly defined, and provide abstraction features to deal with complexity.

Teaching Floating and Sinking Concepts with Different Methods and Techniques Based on the 5E Instructional Model

ERIC Educational Resources Information Center

Cepni, Salih; Sahin, Cigdem; Ipek, Hava

2010-01-01

The purpose of this study was to test the influences of prepared instructional material based on the 5E instructional model combined with CCT, CC, animations, worksheets and POE on conceptual changes about floating and sinking concepts. The experimental group was taught with teaching material based on the 5E instructional model enriched with…

Agent-based modeling: a new approach for theory building in social psychology.

PubMed

Smith, Eliot R; Conrey, Frederica R

2007-02-01

Most social and psychological phenomena occur not as the result of isolated decisions by individuals but rather as the result of repeated interactions between multiple individuals over time. Yet the theory-building and modeling techniques most commonly used in social psychology are less than ideal for understanding such dynamic and interactive processes. This article describes an alternative approach to theory building, agent-based modeling (ABM), which involves simulation of large numbers of autonomous agents that interact with each other and with a simulated environment and the observation of emergent patterns from their interactions. The authors believe that the ABM approach is better able than prevailing approaches in the field, variable-based modeling (VBM) techniques such as causal modeling, to capture types of complex, dynamic, interactive processes so important in the social world. The article elaborates several important contrasts between ABM and VBM and offers specific recommendations for learning more and applying the ABM approach.

Model-Based Safety Analysis

NASA Technical Reports Server (NTRS)

Joshi, Anjali; Heimdahl, Mats P. E.; Miller, Steven P.; Whalen, Mike W.

2006-01-01

System safety analysis techniques are well established and are used extensively during the design of safety-critical systems. Despite this, most of the techniques are highly subjective and dependent on the skill of the practitioner. Since these analyses are usually based on an informal system model, it is unlikely that they will be complete, consistent, and error free. In fact, the lack of precise models of the system architecture and its failure modes often forces the safety analysts to devote much of their effort to gathering architectural details about the system behavior from several sources and embedding this information in the safety artifacts such as the fault trees. This report describes Model-Based Safety Analysis, an approach in which the system and safety engineers share a common system model created using a model-based development process. By extending the system model with a fault model as well as relevant portions of the physical system to be controlled, automated support can be provided for much of the safety analysis. We believe that by using a common model for both system and safety engineering and automating parts of the safety analysis, we can both reduce the cost and improve the quality of the safety analysis. Here we present our vision of model-based safety analysis and discuss the advantages and challenges in making this approach practical.

Rewriting Modulo SMT

NASA Technical Reports Server (NTRS)

Rocha, Camilo; Meseguer, Jose; Munoz, Cesar A.

2013-01-01

Combining symbolic techniques such as: (i) SMT solving, (ii) rewriting modulo theories, and (iii) model checking can enable the analysis of infinite-state systems outside the scope of each such technique. This paper proposes rewriting modulo SMT as a new technique combining the powers of (i)-(iii) and ideally suited to model and analyze infinite-state open systems; that is, systems that interact with a non-deterministic environment. Such systems exhibit both internal non-determinism due to the system, and external non-determinism due to the environment. They are not amenable to finite-state model checking analysis because they typically are infinite-state. By being reducible to standard rewriting using reflective techniques, rewriting modulo SMT can both naturally model and analyze open systems without requiring any changes to rewriting-based reachability analysis techniques for closed systems. This is illustrated by the analysis of a real-time system beyond the scope of timed automata methods.

Problem based learning with scaffolding technique on geometry

NASA Astrophysics Data System (ADS)

Bayuningsih, A. S.; Usodo, B.; Subanti, S.

2018-05-01

Geometry as one of the branches of mathematics has an important role in the study of mathematics. This research aims to explore the effectiveness of Problem Based Learning (PBL) with scaffolding technique viewed from self-regulation learning toward students’ achievement learning in mathematics. The research data obtained through mathematics learning achievement test and self-regulated learning (SRL) questionnaire. This research employed quasi-experimental research. The subjects of this research are students of the junior high school in Banyumas Central Java. The result of the research showed that problem-based learning model with scaffolding technique is more effective to generate students’ mathematics learning achievement than direct learning (DL). This is because in PBL model students are more able to think actively and creatively. The high SRL category student has better mathematic learning achievement than middle and low SRL categories, and then the middle SRL category has better than low SRL category. So, there are interactions between learning model with self-regulated learning in increasing mathematic learning achievement.

Coupling discrete and continuum concentration particle models for multiscale and hybrid molecular-continuum simulations

NASA Astrophysics Data System (ADS)

Petsev, Nikolai D.; Leal, L. Gary; Shell, M. Scott

2017-12-01

Hybrid molecular-continuum simulation techniques afford a number of advantages for problems in the rapidly burgeoning area of nanoscale engineering and technology, though they are typically quite complex to implement and limited to single-component fluid systems. We describe an approach for modeling multicomponent hydrodynamic problems spanning multiple length scales when using particle-based descriptions for both the finely resolved (e.g., molecular dynamics) and coarse-grained (e.g., continuum) subregions within an overall simulation domain. This technique is based on the multiscale methodology previously developed for mesoscale binary fluids [N. D. Petsev, L. G. Leal, and M. S. Shell, J. Chem. Phys. 144, 084115 (2016)], simulated using a particle-based continuum method known as smoothed dissipative particle dynamics. An important application of this approach is the ability to perform coupled molecular dynamics (MD) and continuum modeling of molecularly miscible binary mixtures. In order to validate this technique, we investigate multicomponent hybrid MD-continuum simulations at equilibrium, as well as non-equilibrium cases featuring concentration gradients.

Statistical Techniques to Explore the Quality of Constraints in Constraint-Based Modeling Environments

ERIC Educational Resources Information Center

Gálvez, Jaime; Conejo, Ricardo; Guzmán, Eduardo

2013-01-01

One of the most popular student modeling approaches is Constraint-Based Modeling (CBM). It is an efficient approach that can be easily applied inside an Intelligent Tutoring System (ITS). Even with these characteristics, building new ITSs requires carefully designing the domain model to be taught because different sources of errors could affect…

Control Theory Perspective of Effects-Based Thinking and Operations: Modelling Operations as a Feedback Control System

DTIC Science & Technology

2007-11-01

Control Theory Perspective of Effects-Based Thinking and Operations Modelling “Operations” as a Feedback Control System Philip S. E... Theory Perspective of Effects-Based Thinking and Operations Modelling “Operations” as a Feedback Control System Philip S. E. Farrell...Abstract This paper explores operations that involve effects-based thinking (EBT) using Control Theory techniques in order to highlight the concept’s

Speaker-independent phoneme recognition with a binaural auditory image model

NASA Astrophysics Data System (ADS)

Francis, Keith Ivan

1997-09-01

This dissertation presents phoneme recognition techniques based on a binaural fusion of outputs of the auditory image model and subsequent azimuth-selective phoneme recognition in a noisy environment. Background information concerning speech variations, phoneme recognition, current binaural fusion techniques and auditory modeling issues is explained. The research is constrained to sources in the frontal azimuthal plane of a simulated listener. A new method based on coincidence detection of neural activity patterns from the auditory image model of Patterson is used for azimuth-selective phoneme recognition. The method is tested in various levels of noise and the results are reported in contrast to binaural fusion methods based on various forms of correlation to demonstrate the potential of coincidence- based binaural phoneme recognition. This method overcomes smearing of fine speech detail typical of correlation based methods. Nevertheless, coincidence is able to measure similarity of left and right inputs and fuse them into useful feature vectors for phoneme recognition in noise.

2D Flood Modelling Using Advanced Terrain Analysis Techniques And A Fully Continuous DEM-Based Rainfall-Runoff Algorithm

NASA Astrophysics Data System (ADS)

Nardi, F.; Grimaldi, S.; Petroselli, A.

2012-12-01

Remotely sensed Digital Elevation Models (DEMs), largely available at high resolution, and advanced terrain analysis techniques built in Geographic Information Systems (GIS), provide unique opportunities for DEM-based hydrologic and hydraulic modelling in data-scarce river basins paving the way for flood mapping at the global scale. This research is based on the implementation of a fully continuous hydrologic-hydraulic modelling optimized for ungauged basins with limited river flow measurements. The proposed procedure is characterized by a rainfall generator that feeds a continuous rainfall-runoff model producing flow time series that are routed along the channel using a bidimensional hydraulic model for the detailed representation of the inundation process. The main advantage of the proposed approach is the characterization of the entire physical process during hydrologic extreme events of channel runoff generation, propagation, and overland flow within the floodplain domain. This physically-based model neglects the need for synthetic design hyetograph and hydrograph estimation that constitute the main source of subjective analysis and uncertainty of standard methods for flood mapping. Selected case studies show results and performances of the proposed procedure as respect to standard event-based approaches.

Rapid prototyping model for percutaneous nephrolithotomy training.

PubMed

Bruyère, Franck; Leroux, Cecile; Brunereau, Laurent; Lermusiaux, Patrick

2008-01-01

Rapid prototyping is a technique used for creating computer images in three dimensions more efficiently than classic techniques. Percutaneous nephrolithotomy (PCNL) is a popular method to remove kidney stones; however, broader use by the urologic community has been hampered by the morbidity associated with needle puncture to gain access to the renal calix (bleeding, pneumothorax, hydrothorax, inadvertent colon injury). A training model to improve technique and understanding of renal anatomy could improve complications related to renal puncture; however, no model currently exists for resident training. We created a training model using the rapid prototyping technique based on abdominal CT images of a patient scheduled to undergo PCNL. This allowed our staff and residents to train on the model before performing the operation. This model allowed anticipation of particular difficulties inherent to the patient's anatomy. After training, the procedure proceeded without complication, and the patient was discharged at postoperative day 1 without problems. We hypothesize that rapid prototyping could be useful for resident education, allowing the creation of numerous models for research and surgical training. In addition, we anticipate that experienced urologists could find this technique helpful in preparation for difficult PCNL operations.

3D surface pressure measurement with single light-field camera and pressure-sensitive paint

NASA Astrophysics Data System (ADS)

Shi, Shengxian; Xu, Shengming; Zhao, Zhou; Niu, Xiaofu; Quinn, Mark Kenneth

2018-05-01

A novel technique that simultaneously measures three-dimensional model geometry, as well as surface pressure distribution, with single camera is demonstrated in this study. The technique takes the advantage of light-field photography which can capture three-dimensional information with single light-field camera, and combines it with the intensity-based pressure-sensitive paint method. The proposed single camera light-field three-dimensional pressure measurement technique (LF-3DPSP) utilises a similar hardware setup to the traditional two-dimensional pressure measurement technique, with exception that the wind-on, wind-off and model geometry images are captured via an in-house-constructed light-field camera. The proposed LF-3DPSP technique was validated with a Mach 5 flared cone model test. Results show that the technique is capable of measuring three-dimensional geometry with high accuracy for relatively large curvature models, and the pressure results compare well with the Schlieren tests, analytical calculations, and numerical simulations.

Noninvasive in vivo glucose sensing using an iris based technique

NASA Astrophysics Data System (ADS)

Webb, Anthony J.; Cameron, Brent D.

2011-03-01

Physiological glucose monitoring is important aspect in the treatment of individuals afflicted with diabetes mellitus. Although invasive techniques for glucose monitoring are widely available, it would be very beneficial to make such measurements in a noninvasive manner. In this study, a New Zealand White (NZW) rabbit animal model was utilized to evaluate a developed iris-based imaging technique for the in vivo measurement of physiological glucose concentration. The animals were anesthetized with isoflurane and an insulin/dextrose protocol was used to control blood glucose concentration. To further help restrict eye movement, a developed ocular fixation device was used. During the experimental time frame, near infrared illuminated iris images were acquired along with corresponding discrete blood glucose measurements taken with a handheld glucometer. Calibration was performed using an image based Partial Least Squares (PLS) technique. Independent validation was also performed to assess model performance along with Clarke Error Grid Analysis (CEGA). Initial validation results were promising and show that a high percentage of the predicted glucose concentrations are within 20% of the reference values.

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

Andronov, V.A.; Zhidov, I.G.; Meskov, E.E.

The report presents the basic results of some calculations, theoretical and experimental efforts in the study of Rayleigh-Taylor, Kelvin-Helmholtz, Richtmyer-Meshkov instabilities and the turbulent mixing which is caused by their evolution. Since the late forties the VNIIEF has been conducting these investigations. This report is based on the data which were published in different times in Russian and foreign journals. The first part of the report deals with calculations an theoretical techniques for the description of hydrodynamic instabilities applied currently, as well as with the results of several individual problems and their comparison with the experiment. These methods can bemore » divided into two types: direct numerical simulation methods and phenomenological methods. The first type includes the regular 2D and 3D gasdynamical techniques as well as the techniques based on small perturbation approximation and on incompressible liquid approximation. The second type comprises the techniques based on various phenomenological turbulence models. The second part of the report describes the experimental methods and cites the experimental results of Rayleigh-Taylor and Richtmyer-Meskov instability studies as well as of turbulent mixing. The applied methods were based on thin-film gaseous models, on jelly models and liquid layer models. The research was done for plane and cylindrical geometries. As drivers, the shock tubes of different designs were used as well as gaseous explosive mixtures, compressed air and electric wire explosions. The experimental results were applied in calculational-theoretical technique calibrations. The authors did not aim at covering all VNIIEF research done in this field of science. To a great extent the choice of the material depended on the personal contribution of the author in these studies.« less

Hybrid modeling method for a DEP based particle manipulation.

PubMed

Miled, Mohamed Amine; Gagne, Antoine; Sawan, Mohamad

2013-01-30

In this paper, a new modeling approach for Dielectrophoresis (DEP) based particle manipulation is presented. The proposed method fulfills missing links in finite element modeling between the multiphysic simulation and the biological behavior. This technique is amongst the first steps to develop a more complex platform covering several types of manipulations such as magnetophoresis and optics. The modeling approach is based on a hybrid interface using both ANSYS and MATLAB to link the propagation of the electrical field in the micro-channel to the particle motion. ANSYS is used to simulate the electrical propagation while MATLAB interprets the results to calculate cell displacement and send the new information to ANSYS for another turn. The beta version of the proposed technique takes into account particle shape, weight and its electrical properties. First obtained results are coherent with experimental results.

Hybrid Modeling Method for a DEP Based Particle Manipulation

PubMed Central

Miled, Mohamed Amine; Gagne, Antoine; Sawan, Mohamad

2013-01-01

In this paper, a new modeling approach for Dielectrophoresis (DEP) based particle manipulation is presented. The proposed method fulfills missing links in finite element modeling between the multiphysic simulation and the biological behavior. This technique is amongst the first steps to develop a more complex platform covering several types of manipulations such as magnetophoresis and optics. The modeling approach is based on a hybrid interface using both ANSYS and MATLAB to link the propagation of the electrical field in the micro-channel to the particle motion. ANSYS is used to simulate the electrical propagation while MATLAB interprets the results to calculate cell displacement and send the new information to ANSYS for another turn. The beta version of the proposed technique takes into account particle shape, weight and its electrical properties. First obtained results are coherent with experimental results. PMID:23364197

Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer

PubMed Central

Kim, Jong-Myon

2018-01-01

An effective bearing fault detection and diagnosis (FDD) model is important for ensuring the normal and safe operation of machines. This paper presents a reliable model-reference observer technique for FDD based on modeling of a bearing’s vibration data by analyzing the dynamic properties of the bearing and a higher-order super-twisting sliding mode observation (HOSTSMO) technique for making diagnostic decisions using these data models. The HOSTSMO technique can adaptively improve the performance of estimating nonlinear failures in rolling element bearings (REBs) over a linear approach by modeling 5 degrees of freedom under normal and faulty conditions. The effectiveness of the proposed technique is evaluated using a vibration dataset provided by Case Western Reserve University, which consists of vibration acceleration signals recorded for REBs with inner, outer, ball, and no faults, i.e., normal. Experimental results indicate that the proposed technique outperforms the ARX-Laguerre proportional integral observation (ALPIO) technique, yielding 18.82%, 16.825%, and 17.44% performance improvements for three levels of crack severity of 0.007, 0.014, and 0.021 inches, respectively. PMID:29642459

Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer.

PubMed

Piltan, Farzin; Kim, Jong-Myon

2018-04-07

An effective bearing fault detection and diagnosis (FDD) model is important for ensuring the normal and safe operation of machines. This paper presents a reliable model-reference observer technique for FDD based on modeling of a bearing's vibration data by analyzing the dynamic properties of the bearing and a higher-order super-twisting sliding mode observation (HOSTSMO) technique for making diagnostic decisions using these data models. The HOSTSMO technique can adaptively improve the performance of estimating nonlinear failures in rolling element bearings (REBs) over a linear approach by modeling 5 degrees of freedom under normal and faulty conditions. The effectiveness of the proposed technique is evaluated using a vibration dataset provided by Case Western Reserve University, which consists of vibration acceleration signals recorded for REBs with inner, outer, ball, and no faults, i.e., normal. Experimental results indicate that the proposed technique outperforms the ARX-Laguerre proportional integral observation (ALPIO) technique, yielding 18.82%, 16.825%, and 17.44% performance improvements for three levels of crack severity of 0.007, 0.014, and 0.021 inches, respectively.

Modelling Subjectivity in Visual Perception of Orientation for Image Retrieval.

ERIC Educational Resources Information Center

Sanchez, D.; Chamorro-Martinez, J.; Vila, M. A.

2003-01-01

Discussion of multimedia libraries and the need for storage, indexing, and retrieval techniques focuses on the combination of computer vision and data mining techniques to model high-level concepts for image retrieval based on perceptual features of the human visual system. Uses fuzzy set theory to measure users' assessments and to capture users'…

Fourier Descriptor Analysis and Unification of Voice Range Profile Contours: Method and Applications

ERIC Educational Resources Information Center

Pabon, Peter; Ternstrom, Sten; Lamarche, Anick

2011-01-01

Purpose: To describe a method for unified description, statistical modeling, and comparison of voice range profile (VRP) contours, even from diverse sources. Method: A morphologic modeling technique, which is based on Fourier descriptors (FDs), is applied to the VRP contour. The technique, which essentially involves resampling of the curve of the…

A near-optimal low complexity sensor fusion technique for accurate indoor localization based on ultrasound time of arrival measurements from low-quality sensors

NASA Astrophysics Data System (ADS)

Mitilineos, Stelios A.; Argyreas, Nick D.; Thomopoulos, Stelios C. A.

2009-05-01

A fusion-based localization technique for location-based services in indoor environments is introduced herein, based on ultrasound time-of-arrival measurements from multiple off-the-shelf range estimating sensors which are used in a market-available localization system. In-situ field measurements results indicated that the respective off-the-shelf system was unable to estimate position in most of the cases, while the underlying sensors are of low-quality and yield highly inaccurate range and position estimates. An extensive analysis is performed and a model of the sensor-performance characteristics is established. A low-complexity but accurate sensor fusion and localization technique is then developed, which consists inof evaluating multiple sensor measurements and selecting the one that is considered most-accurate based on the underlying sensor model. Optimality, in the sense of a genie selecting the optimum sensor, is subsequently evaluated and compared to the proposed technique. The experimental results indicate that the proposed fusion method exhibits near-optimal performance and, albeit being theoretically suboptimal, it largely overcomes most flaws of the underlying single-sensor system resulting in a localization system of increased accuracy, robustness and availability.

Photogrammetry-Based Head Digitization for Rapid and Accurate Localization of EEG Electrodes and MEG Fiducial Markers Using a Single Digital SLR Camera.

PubMed

Clausner, Tommy; Dalal, Sarang S; Crespo-García, Maité

2017-01-01

The performance of EEG source reconstruction has benefited from the increasing use of advanced head modeling techniques that take advantage of MRI together with the precise positions of the recording electrodes. The prevailing technique for registering EEG electrode coordinates involves electromagnetic digitization. However, the procedure adds several minutes to experiment preparation and typical digitizers may not be accurate enough for optimal source reconstruction performance (Dalal et al., 2014). Here, we present a rapid, accurate, and cost-effective alternative method to register EEG electrode positions, using a single digital SLR camera, photogrammetry software, and computer vision techniques implemented in our open-source toolbox, janus3D . Our approach uses photogrammetry to construct 3D models from multiple photographs of the participant's head wearing the EEG electrode cap. Electrodes are detected automatically or semi-automatically using a template. The rigid facial features from these photo-based models are then surface-matched to MRI-based head reconstructions to facilitate coregistration to MRI space. This method yields a final electrode coregistration error of 0.8 mm, while a standard technique using an electromagnetic digitizer yielded an error of 6.1 mm. The technique furthermore reduces preparation time, and could be extended to a multi-camera array, which would make the procedure virtually instantaneous. In addition to EEG, the technique could likewise capture the position of the fiducial markers used in magnetoencephalography systems to register head position.

Photogrammetry-Based Head Digitization for Rapid and Accurate Localization of EEG Electrodes and MEG Fiducial Markers Using a Single Digital SLR Camera

PubMed Central

Clausner, Tommy; Dalal, Sarang S.; Crespo-García, Maité

2017-01-01

The performance of EEG source reconstruction has benefited from the increasing use of advanced head modeling techniques that take advantage of MRI together with the precise positions of the recording electrodes. The prevailing technique for registering EEG electrode coordinates involves electromagnetic digitization. However, the procedure adds several minutes to experiment preparation and typical digitizers may not be accurate enough for optimal source reconstruction performance (Dalal et al., 2014). Here, we present a rapid, accurate, and cost-effective alternative method to register EEG electrode positions, using a single digital SLR camera, photogrammetry software, and computer vision techniques implemented in our open-source toolbox, janus3D. Our approach uses photogrammetry to construct 3D models from multiple photographs of the participant's head wearing the EEG electrode cap. Electrodes are detected automatically or semi-automatically using a template. The rigid facial features from these photo-based models are then surface-matched to MRI-based head reconstructions to facilitate coregistration to MRI space. This method yields a final electrode coregistration error of 0.8 mm, while a standard technique using an electromagnetic digitizer yielded an error of 6.1 mm. The technique furthermore reduces preparation time, and could be extended to a multi-camera array, which would make the procedure virtually instantaneous. In addition to EEG, the technique could likewise capture the position of the fiducial markers used in magnetoencephalography systems to register head position. PMID:28559791

Least-squares model-based halftoning

NASA Astrophysics Data System (ADS)

Pappas, Thrasyvoulos N.; Neuhoff, David L.

1992-08-01

A least-squares model-based approach to digital halftoning is proposed. It exploits both a printer model and a model for visual perception. It attempts to produce an 'optimal' halftoned reproduction, by minimizing the squared error between the response of the cascade of the printer and visual models to the binary image and the response of the visual model to the original gray-scale image. Conventional methods, such as clustered ordered dither, use the properties of the eye only implicitly, and resist printer distortions at the expense of spatial and gray-scale resolution. In previous work we showed that our printer model can be used to modify error diffusion to account for printer distortions. The modified error diffusion algorithm has better spatial and gray-scale resolution than conventional techniques, but produces some well known artifacts and asymmetries because it does not make use of an explicit eye model. Least-squares model-based halftoning uses explicit eye models and relies on printer models that predict distortions and exploit them to increase, rather than decrease, both spatial and gray-scale resolution. We have shown that the one-dimensional least-squares problem, in which each row or column of the image is halftoned independently, can be implemented with the Viterbi's algorithm. Unfortunately, no closed form solution can be found in two dimensions. The two-dimensional least squares solution is obtained by iterative techniques. Experiments show that least-squares model-based halftoning produces more gray levels and better spatial resolution than conventional techniques. We also show that the least- squares approach eliminates the problems associated with error diffusion. Model-based halftoning can be especially useful in transmission of high quality documents using high fidelity gray-scale image encoders. As we have shown, in such cases halftoning can be performed at the receiver, just before printing. Apart from coding efficiency, this approach permits the halftoner to be tuned to the individual printer, whose characteristics may vary considerably from those of other printers, for example, write-black vs. write-white laser printers.

Getting expert systems off the ground: Lessons learned from integrating model-based diagnostics with prototype flight hardware

NASA Technical Reports Server (NTRS)

Stephan, Amy; Erikson, Carol A.

1991-01-01

As an initial attempt to introduce expert system technology into an onboard environment, a model based diagnostic system using the TRW MARPLE software tool was integrated with prototype flight hardware and its corresponding control software. Because this experiment was designed primarily to test the effectiveness of the model based reasoning technique used, the expert system ran on a separate hardware platform, and interactions between the control software and the model based diagnostics were limited. While this project met its objective of showing that model based reasoning can effectively isolate failures in flight hardware, it also identified the need for an integrated development path for expert system and control software for onboard applications. In developing expert systems that are ready for flight, artificial intelligence techniques must be evaluated to determine whether they offer a real advantage onboard, identify which diagnostic functions should be performed by the expert systems and which are better left to the procedural software, and work closely with both the hardware and the software developers from the beginning of a project to produce a well designed and thoroughly integrated application.

Collisional-radiative switching - A powerful technique for converging non-LTE calculations

NASA Technical Reports Server (NTRS)

Hummer, D. G.; Voels, S. A.

1988-01-01

A very simple technique has been developed to converge statistical equilibrium and model atmospheric calculations in extreme non-LTE conditions when the usual iterative methods fail to converge from an LTE starting model. The proposed technique is based on a smooth transition from a collision-dominated LTE situation to the desired non-LTE conditions in which radiation dominates, at least in the most important transitions. The proposed approach was used to successfully compute stellar models with He abundances of 0.20, 0.30, and 0.50; Teff = 30,000 K, and log g = 2.9.

A novel method for predicting kidney stone type using ensemble learning.

PubMed

Kazemi, Yassaman; Mirroshandel, Seyed Abolghasem

2018-01-01

The high morbidity rate associated with kidney stone disease, which is a silent killer, is one of the main concerns in healthcare systems all over the world. Advanced data mining techniques such as classification can help in the early prediction of this disease and reduce its incidence and associated costs. The objective of the present study is to derive a model for the early detection of the type of kidney stone and the most influential parameters with the aim of providing a decision-support system. Information was collected from 936 patients with nephrolithiasis at the kidney center of the Razi Hospital in Rasht from 2012 through 2016. The prepared dataset included 42 features. Data pre-processing was the first step toward extracting the relevant features. The collected data was analyzed with Weka software, and various data mining models were used to prepare a predictive model. Various data mining algorithms such as the Bayesian model, different types of Decision Trees, Artificial Neural Networks, and Rule-based classifiers were used in these models. We also proposed four models based on ensemble learning to improve the accuracy of each learning algorithm. In addition, a novel technique for combining individual classifiers in ensemble learning was proposed. In this technique, for each individual classifier, a weight is assigned based on our proposed genetic algorithm based method. The generated knowledge was evaluated using a 10-fold cross-validation technique based on standard measures. However, the assessment of each feature for building a predictive model was another significant challenge. The predictive strength of each feature for creating a reproducible outcome was also investigated. Regarding the applied models, parameters such as sex, acid uric condition, calcium level, hypertension, diabetes, nausea and vomiting, flank pain, and urinary tract infection (UTI) were the most vital parameters for predicting the chance of nephrolithiasis. The final ensemble-based model (with an accuracy of 97.1%) was a robust one and could be safely applied to future studies to predict the chances of developing nephrolithiasis. This model provides a novel way to study stone disease by deciphering the complex interaction among different biological variables, thus helping in an early identification and reduction in diagnosis time. Copyright © 2017 Elsevier B.V. All rights reserved.

Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints.

PubMed

van der Ploeg, Tjeerd; Austin, Peter C; Steyerberg, Ewout W

2014-12-22

Modern modelling techniques may potentially provide more accurate predictions of binary outcomes than classical techniques. We aimed to study the predictive performance of different modelling techniques in relation to the effective sample size ("data hungriness"). We performed simulation studies based on three clinical cohorts: 1282 patients with head and neck cancer (with 46.9% 5 year survival), 1731 patients with traumatic brain injury (22.3% 6 month mortality) and 3181 patients with minor head injury (7.6% with CT scan abnormalities). We compared three relatively modern modelling techniques: support vector machines (SVM), neural nets (NN), and random forests (RF) and two classical techniques: logistic regression (LR) and classification and regression trees (CART). We created three large artificial databases with 20 fold, 10 fold and 6 fold replication of subjects, where we generated dichotomous outcomes according to different underlying models. We applied each modelling technique to increasingly larger development parts (100 repetitions). The area under the ROC-curve (AUC) indicated the performance of each model in the development part and in an independent validation part. Data hungriness was defined by plateauing of AUC and small optimism (difference between the mean apparent AUC and the mean validated AUC <0.01). We found that a stable AUC was reached by LR at approximately 20 to 50 events per variable, followed by CART, SVM, NN and RF models. Optimism decreased with increasing sample sizes and the same ranking of techniques. The RF, SVM and NN models showed instability and a high optimism even with >200 events per variable. Modern modelling techniques such as SVM, NN and RF may need over 10 times as many events per variable to achieve a stable AUC and a small optimism than classical modelling techniques such as LR. This implies that such modern techniques should only be used in medical prediction problems if very large data sets are available.

Molecular-Based Optical Measurement Techniques for Transition and Turbulence in High-Speed Flow

NASA Technical Reports Server (NTRS)

Bathel, Brett F.; Danehy, Paul M.; Cutler, Andrew D.

2013-01-01

High-speed laminar-to-turbulent transition and turbulence affect the control of flight vehicles, the heat transfer rate to a flight vehicle's surface, the material selected to protect such vehicles from high heating loads, the ultimate weight of a flight vehicle due to the presence of thermal protection systems, the efficiency of fuel-air mixing processes in high-speed combustion applications, etc. Gaining a fundamental understanding of the physical mechanisms involved in the transition process will lead to the development of predictive capabilities that can identify transition location and its impact on parameters like surface heating. Currently, there is no general theory that can completely describe the transition-to-turbulence process. However, transition research has led to the identification of the predominant pathways by which this process occurs. For a truly physics-based model of transition to be developed, the individual stages in the paths leading to the onset of fully turbulent flow must be well understood. This requires that each pathway be computationally modeled and experimentally characterized and validated. This may also lead to the discovery of new physical pathways. This document is intended to describe molecular based measurement techniques that have been developed, addressing the needs of the high-speed transition-to-turbulence and high-speed turbulence research fields. In particular, we focus on techniques that have either been used to study high speed transition and turbulence or techniques that show promise for studying these flows. This review is not exhaustive. In addition to the probe-based techniques described in the previous paragraph, several other classes of measurement techniques that are, or could be, used to study high speed transition and turbulence are excluded from this manuscript. For example, surface measurement techniques such as pressure and temperature paint, phosphor thermography, skin friction measurements and photogrammetry (for model attitude and deformation measurement) are excluded to limit the scope of this report. Other physical probes such as heat flux gauges, total temperature probes are also excluded. We further exclude measurement techniques that require particle seeding though particle based methods may still be useful in many high speed flow applications. This manuscript details some of the more widely used molecular-based measurement techniques for studying transition and turbulence: laser-induced fluorescence (LIF), Rayleigh and Raman Scattering and coherent anti-Stokes Raman scattering (CARS). These techniques are emphasized, in part, because of the prior experience of the authors. Additional molecular based techniques are described, albeit in less detail. Where possible, an effort is made to compare the relative advantages and disadvantages of the various measurement techniques, although these comparisons can be subjective views of the authors. Finally, the manuscript concludes by evaluating the different measurement techniques in view of the precision requirements described in this chapter. Additional requirements and considerations are discussed to assist with choosing an optical measurement technique for a given application.

A Secret 3D Model Sharing Scheme with Reversible Data Hiding Based on Space Subdivision

NASA Astrophysics Data System (ADS)

Tsai, Yuan-Yu

2016-03-01

Secret sharing is a highly relevant research field, and its application to 2D images has been thoroughly studied. However, secret sharing schemes have not kept pace with the advances of 3D models. With the rapid development of 3D multimedia techniques, extending the application of secret sharing schemes to 3D models has become necessary. In this study, an innovative secret 3D model sharing scheme for point geometries based on space subdivision is proposed. Each point in the secret point geometry is first encoded into a series of integer values that fall within [0, p - 1], where p is a predefined prime number. The share values are derived by substituting the specified integer values for all coefficients of the sharing polynomial. The surface reconstruction and the sampling concepts are then integrated to derive a cover model with sufficient model complexity for each participant. Finally, each participant has a separate 3D stego model with embedded share values. Experimental results show that the proposed technique supports reversible data hiding and the share values have higher levels of privacy and improved robustness. This technique is simple and has proven to be a feasible secret 3D model sharing scheme.

Model-checking techniques based on cumulative residuals.

PubMed

Lin, D Y; Wei, L J; Ying, Z

2002-03-01

Residuals have long been used for graphical and numerical examinations of the adequacy of regression models. Conventional residual analysis based on the plots of raw residuals or their smoothed curves is highly subjective, whereas most numerical goodness-of-fit tests provide little information about the nature of model misspecification. In this paper, we develop objective and informative model-checking techniques by taking the cumulative sums of residuals over certain coordinates (e.g., covariates or fitted values) or by considering some related aggregates of residuals, such as moving sums and moving averages. For a variety of statistical models and data structures, including generalized linear models with independent or dependent observations, the distributions of these stochastic processes tinder the assumed model can be approximated by the distributions of certain zero-mean Gaussian processes whose realizations can be easily generated by computer simulation. Each observed process can then be compared, both graphically and numerically, with a number of realizations from the Gaussian process. Such comparisons enable one to assess objectively whether a trend seen in a residual plot reflects model misspecification or natural variation. The proposed techniques are particularly useful in checking the functional form of a covariate and the link function. Illustrations with several medical studies are provided.

Extending the Distributed Lag Model framework to handle chemical mixtures.

PubMed

Bello, Ghalib A; Arora, Manish; Austin, Christine; Horton, Megan K; Wright, Robert O; Gennings, Chris

2017-07-01

Distributed Lag Models (DLMs) are used in environmental health studies to analyze the time-delayed effect of an exposure on an outcome of interest. Given the increasing need for analytical tools for evaluation of the effects of exposure to multi-pollutant mixtures, this study attempts to extend the classical DLM framework to accommodate and evaluate multiple longitudinally observed exposures. We introduce 2 techniques for quantifying the time-varying mixture effect of multiple exposures on an outcome of interest. Lagged WQS, the first technique, is based on Weighted Quantile Sum (WQS) regression, a penalized regression method that estimates mixture effects using a weighted index. We also introduce Tree-based DLMs, a nonparametric alternative for assessment of lagged mixture effects. This technique is based on the Random Forest (RF) algorithm, a nonparametric, tree-based estimation technique that has shown excellent performance in a wide variety of domains. In a simulation study, we tested the feasibility of these techniques and evaluated their performance in comparison to standard methodology. Both methods exhibited relatively robust performance, accurately capturing pre-defined non-linear functional relationships in different simulation settings. Further, we applied these techniques to data on perinatal exposure to environmental metal toxicants, with the goal of evaluating the effects of exposure on neurodevelopment. Our methods identified critical neurodevelopmental windows showing significant sensitivity to metal mixtures. Copyright © 2017 Elsevier Inc. All rights reserved.

A reduced order, test verified component mode synthesis approach for system modeling applications

NASA Astrophysics Data System (ADS)

Butland, Adam; Avitabile, Peter

2010-05-01

Component mode synthesis (CMS) is a very common approach used for the generation of large system models. In general, these modeling techniques can be separated into two categories: those utilizing a combination of constraint modes and fixed interface normal modes and those based on a combination of free interface normal modes and residual flexibility terms. The major limitation of the methods utilizing constraint modes and fixed interface normal modes is the inability to easily obtain the required information from testing; the result of this limitation is that constraint mode-based techniques are primarily used with numerical models. An alternate approach is proposed which utilizes frequency and shape information acquired from modal testing to update reduced order finite element models using exact analytical model improvement techniques. The connection degrees of freedom are then rigidly constrained in the test verified, reduced order model to provide the boundary conditions necessary for constraint modes and fixed interface normal modes. The CMS approach is then used with this test verified, reduced order model to generate the system model for further analysis. A laboratory structure is used to show the application of the technique with both numerical and simulated experimental components to describe the system and validate the proposed approach. Actual test data is then used in the approach proposed. Due to typical measurement data contaminants that are always included in any test, the measured data is further processed to remove contaminants and is then used in the proposed approach. The final case using improved data with the reduced order, test verified components is shown to produce very acceptable results from the Craig-Bampton component mode synthesis approach. Use of the technique with its strengths and weaknesses are discussed.

Wildlife tradeoffs based on landscape models of habitat preference

USGS Publications Warehouse

Loehle, C.; Mitchell, M.S.; White, M.

2000-01-01

Wildlife tradeoffs based on landscape models of habitat preference were presented. Multiscale logistic regression models were used and based on these models a spatial optimization technique was utilized to generate optimal maps. The tradeoffs were analyzed by gradually increasing the weighting on a single species in the objective function over a series of simulations. Results indicated that efficiency of habitat management for species diversity could be maximized for small landscapes by incorporating spatial context.

New Flutter Analysis Technique for Time-Domain Computational Aeroelasticity

NASA Technical Reports Server (NTRS)

Pak, Chan-Gi; Lung, Shun-Fat

2017-01-01

A new time-domain approach for computing flutter speed is presented. Based on the time-history result of aeroelastic simulation, the unknown unsteady aerodynamics model is estimated using a system identification technique. The full aeroelastic model is generated via coupling the estimated unsteady aerodynamic model with the known linear structure model. The critical dynamic pressure is computed and used in the subsequent simulation until the convergence of the critical dynamic pressure is achieved. The proposed method is applied to a benchmark cantilevered rectangular wing.

Machine learning models in breast cancer survival prediction.

PubMed

Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin

2016-01-01

Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of accuracy. Therefore, this model is recommended as a useful tool for breast cancer survival prediction as well as medical decision making.

Intelligent evaluation of color sensory quality of black tea by visible-near infrared spectroscopy technology: A comparison of spectra and color data information

NASA Astrophysics Data System (ADS)

Ouyang, Qin; Liu, Yan; Chen, Quansheng; Zhang, Zhengzhu; Zhao, Jiewen; Guo, Zhiming; Gu, Hang

2017-06-01

Instrumental test of black tea samples instead of human panel test is attracting massive attention recently. This study focused on an investigation of the feasibility for estimation of the color sensory quality of black tea samples using the VIS-NIR spectroscopy technique, comparing the performances of models based on the spectra and color information. In model calibration, the variables were first selected by genetic algorithm (GA); then the nonlinear back propagation-artificial neural network (BPANN) models were established based on the optimal variables. In comparison with the other models, GA-BPANN models from spectra data information showed the best performance, with the correlation coefficient of 0.8935, and the root mean square error of 0.392 in the prediction set. In addition, models based on the spectra information provided better performance than that based on the color parameters. Therefore, the VIS-NIR spectroscopy technique is a promising tool for rapid and accurate evaluation of the sensory quality of black tea samples.

Intelligent evaluation of color sensory quality of black tea by visible-near infrared spectroscopy technology: A comparison of spectra and color data information.

PubMed

Ouyang, Qin; Liu, Yan; Chen, Quansheng; Zhang, Zhengzhu; Zhao, Jiewen; Guo, Zhiming; Gu, Hang

2017-06-05

Instrumental test of black tea samples instead of human panel test is attracting massive attention recently. This study focused on an investigation of the feasibility for estimation of the color sensory quality of black tea samples using the VIS-NIR spectroscopy technique, comparing the performances of models based on the spectra and color information. In model calibration, the variables were first selected by genetic algorithm (GA); then the nonlinear back propagation-artificial neural network (BPANN) models were established based on the optimal variables. In comparison with the other models, GA-BPANN models from spectra data information showed the best performance, with the correlation coefficient of 0.8935, and the root mean square error of 0.392 in the prediction set. In addition, models based on the spectra information provided better performance than that based on the color parameters. Therefore, the VIS-NIR spectroscopy technique is a promising tool for rapid and accurate evaluation of the sensory quality of black tea samples. Copyright © 2017 Elsevier B.V. All rights reserved.

A system identification technique based on the random decrement signatures. Part 2: Experimental results

NASA Technical Reports Server (NTRS)

Bedewi, Nabih E.; Yang, Jackson C. S.

1987-01-01

Identification of the system parameters of a randomly excited structure may be treated using a variety of statistical techniques. Of all these techniques, the Random Decrement is unique in that it provides the homogeneous component of the system response. Using this quality, a system identification technique was developed based on a least-squares fit of the signatures to estimate the mass, damping, and stiffness matrices of a linear randomly excited system. The results of an experiment conducted on an offshore platform scale model to verify the validity of the technique and to demonstrate its application in damage detection are presented.

A Model Independent S/W Framework for Search-Based Software Testing

PubMed Central

Baik, Jongmoon

2014-01-01

In Model-Based Testing (MBT) area, Search-Based Software Testing (SBST) has been employed to generate test cases from the model of a system under test. However, many types of models have been used in MBT. If the type of a model has changed from one to another, all functions of a search technique must be reimplemented because the types of models are different even if the same search technique has been applied. It requires too much time and effort to implement the same algorithm over and over again. We propose a model-independent software framework for SBST, which can reduce redundant works. The framework provides a reusable common software platform to reduce time and effort. The software framework not only presents design patterns to find test cases for a target model but also reduces development time by using common functions provided in the framework. We show the effectiveness and efficiency of the proposed framework with two case studies. The framework improves the productivity by about 50% when changing the type of a model. PMID:25302314

Solving large mixed linear models using preconditioned conjugate gradient iteration.

PubMed

Strandén, I; Lidauer, M

1999-12-01

Continuous evaluation of dairy cattle with a random regression test-day model requires a fast solving method and algorithm. A new computing technique feasible in Jacobi and conjugate gradient based iterative methods using iteration on data is presented. In the new computing technique, the calculations in multiplication of a vector by a matrix were recorded to three steps instead of the commonly used two steps. The three-step method was implemented in a general mixed linear model program that used preconditioned conjugate gradient iteration. Performance of this program in comparison to other general solving programs was assessed via estimation of breeding values using univariate, multivariate, and random regression test-day models. Central processing unit time per iteration with the new three-step technique was, at best, one-third that needed with the old technique. Performance was best with the test-day model, which was the largest and most complex model used. The new program did well in comparison to other general software. Programs keeping the mixed model equations in random access memory required at least 20 and 435% more time to solve the univariate and multivariate animal models, respectively. Computations of the second best iteration on data took approximately three and five times longer for the animal and test-day models, respectively, than did the new program. Good performance was due to fast computing time per iteration and quick convergence to the final solutions. Use of preconditioned conjugate gradient based methods in solving large breeding value problems is supported by our findings.

A computer graphics based model for scattering from objects of arbitrary shapes in the optical region

NASA Technical Reports Server (NTRS)

Goel, Narendra S.; Rozehnal, Ivan; Thompson, Richard L.

1991-01-01

A computer-graphics-based model, named DIANA, is presented for generation of objects of arbitrary shape and for calculating bidirectional reflectances and scattering from them, in the visible and infrared region. The computer generation is based on a modified Lindenmayer system approach which makes it possible to generate objects of arbitrary shapes and to simulate their growth, dynamics, and movement. Rendering techniques are used to display an object on a computer screen with appropriate shading and shadowing and to calculate the scattering and reflectance from the object. The technique is illustrated with scattering from canopies of simulated corn plants.

A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony

PubMed Central

Othman, Zulaiha Ali

2014-01-01

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition. PMID:25165748

Uncertain viscoelastic models with fractional order: A new spectral tau method to study the numerical simulations of the solution

NASA Astrophysics Data System (ADS)

Ahmadian, A.; Ismail, F.; Salahshour, S.; Baleanu, D.; Ghaemi, F.

2017-12-01

The analysis of the behaviors of physical phenomena is important to discover significant features of the character and the structure of mathematical models. Frequently the unknown parameters involve in the models are assumed to be unvarying over time. In reality, some of them are uncertain and implicitly depend on several factors. In this study, to consider such uncertainty in variables of the models, they are characterized based on the fuzzy notion. We propose here a new model based on fractional calculus to deal with the Kelvin-Voigt (KV) equation and non-Newtonian fluid behavior model with fuzzy parameters. A new and accurate numerical algorithm using a spectral tau technique based on the generalized fractional Legendre polynomials (GFLPs) is developed to solve those problems under uncertainty. Numerical simulations are carried out and the analysis of the results highlights the significant features of the new technique in comparison with the previous findings. A detailed error analysis is also carried out and discussed.

Modeling and prediction of extraction profile for microwave-assisted extraction based on absorbed microwave energy.

PubMed

Chan, Chung-Hung; Yusoff, Rozita; Ngoh, Gek-Cheng

2013-09-01

A modeling technique based on absorbed microwave energy was proposed to model microwave-assisted extraction (MAE) of antioxidant compounds from cocoa (Theobroma cacao L.) leaves. By adapting suitable extraction model at the basis of microwave energy absorbed during extraction, the model can be developed to predict extraction profile of MAE at various microwave irradiation power (100-600 W) and solvent loading (100-300 ml). Verification with experimental data confirmed that the prediction was accurate in capturing the extraction profile of MAE (R-square value greater than 0.87). Besides, the predicted yields from the model showed good agreement with the experimental results with less than 10% deviation observed. Furthermore, suitable extraction times to ensure high extraction yield at various MAE conditions can be estimated based on absorbed microwave energy. The estimation is feasible as more than 85% of active compounds can be extracted when compared with the conventional extraction technique. Copyright © 2013 Elsevier Ltd. All rights reserved.

Cost and schedule analytical techniques development

NASA Technical Reports Server (NTRS)

1994-01-01

This contract provided technical services and products to the Marshall Space Flight Center's Engineering Cost Office (PP03) and the Program Plans and Requirements Office (PP02) for the period of 3 Aug. 1991 - 30 Nov. 1994. Accomplishments summarized cover the REDSTAR data base, NASCOM hard copy data base, NASCOM automated data base, NASCOM cost model, complexity generators, program planning, schedules, NASA computer connectivity, other analytical techniques, and special project support.

Phase demodulation from a single fringe pattern based on a correlation technique.

PubMed

Robin, Eric; Valle, Valéry

2004-08-01

We present a method for determining the demodulated phase from a single fringe pattern. This method, based on a correlation technique, searches in a zone of interest for the degree of similarity between a real fringe pattern and a mathematical model. This method, named modulated phase correlation, is tested with different examples.

Compact lumped circuit model of discharges in DC accelerator using partial element equivalent circuit

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

Banerjee, Srutarshi; Rajan, Rehim N.; Singh, Sandeep K.

2014-07-01

DC Accelerators undergoes different types of discharges during its operation. A model depicting the discharges has been simulated to study the different transient conditions. The paper presents a Physics based approach of developing a compact circuit model of the DC Accelerator using Partial Element Equivalent Circuit (PEEC) technique. The equivalent RLC model aids in analyzing the transient behavior of the system and predicting anomalies in the system. The electrical discharges and its properties prevailing in the accelerator can be evaluated by this equivalent model. A parallel coupled voltage multiplier structure is simulated in small scale using few stages of coronamore » guards and the theoretical and practical results are compared. The PEEC technique leads to a simple model for studying the fault conditions in accelerator systems. Compared to the Finite Element Techniques, this technique gives the circuital representation. The lumped components of the PEEC are used to obtain the input impedance and the result is also compared to that of the FEM technique for a frequency range of (0-200) MHz. (author)« less

Single particle analysis based on Zernike phase contrast transmission electron microscopy.

PubMed

Danev, Radostin; Nagayama, Kuniaki

2008-02-01

We present the first application of Zernike phase-contrast transmission electron microscopy to single-particle 3D reconstruction of a protein, using GroEL chaperonin as the test specimen. We evaluated the performance of the technique by comparing 3D models derived from Zernike phase contrast imaging, with models from conventional underfocus phase contrast imaging. The same resolution, about 12A, was achieved by both imaging methods. The reconstruction based on Zernike phase contrast data required about 30% fewer particles. The advantages and prospects of each technique are discussed.

Application of decision rules for empowering of Indonesian telematics services SMEs

NASA Astrophysics Data System (ADS)

Tosida, E. T.; Hairlangga, O.; Amirudin, F.; Ridwanah, M.

2018-03-01

The independence of the field of telematics became one of Indonesia's vision in 2024. One effort to achieve it can be done by empowering SMEs in the field of telematics. Empowerment carried out need a practical mechanism by utilizing data centered, including through the National Economic Census database (Susenas). Based on the Susenas can be formulated the decision rules of determining the provision of assistance for SMEs in the field of telematics. The way it did by generating the rule base through the classification technique. The CART algorithm-based decision rule model performs better than C45 and ID3 models. The high level of performance model is also in line with the regulations applied by the government. This becomes one of the strengths of research, because the resulting model is consistent with the existing conditions in Indonesia. The rules base generated from the three classification techniques show different rules. The CART technique has pattern matching with the realization of activities in The Ministry of Cooperatives and SMEs. So far, the government has difficulty in referring data related to the empowerment of SMEs telematics services. Therefore, the findings resulting from this research can be used as an alternative decision support system related to the program of empowerment of SMEs in telematics.

Mitigating randomness of consumer preferences under certain conditional choices

NASA Astrophysics Data System (ADS)

Bothos, John M. A.; Thanos, Konstantinos-Georgios; Papadopoulou, Eirini; Daveas, Stelios; Thomopoulos, Stelios C. A.

2017-05-01

Agent-based crowd behaviour consists a significant field of research that has drawn a lot of attention in recent years. Agent-based crowd simulation techniques have been used excessively to forecast the behaviour of larger or smaller crowds in terms of certain given conditions influenced by specific cognition models and behavioural rules and norms, imposed from the beginning. Our research employs conditional event algebra, statistical methodology and agent-based crowd simulation techniques in developing a behavioural econometric model about the selection of certain economic behaviour by a consumer that faces a spectre of potential choices when moving and acting in a multiplex mall. More specifically we try to analyse the influence of demographic, economic, social and cultural factors on the economic behaviour of a certain individual and then we try to link its behaviour with the general behaviour of the crowds of consumers in multiplex malls using agent-based crowd simulation techniques. We then run our model using Generalized Least Squares and Maximum Likelihood methods to come up with the most probable forecast estimations, regarding the agent's behaviour. Our model is indicative about the formation of consumers' spectre of choices in multiplex malls under the condition of predefined preferences and can be used as a guide for further research in this area.

Analysis of creative mathematic thinking ability in problem based learning model based on self-regulation learning

NASA Astrophysics Data System (ADS)

Munahefi, D. N.; Waluya, S. B.; Rochmad

2018-03-01

The purpose of this research identified the effectiveness of Problem Based Learning (PBL) models based on Self Regulation Leaning (SRL) on the ability of mathematical creative thinking and analyzed the ability of mathematical creative thinking of high school students in solving mathematical problems. The population of this study was students of grade X SMA N 3 Klaten. The research method used in this research was sequential explanatory. Quantitative stages with simple random sampling technique, where two classes were selected randomly as experimental class was taught with the PBL model based on SRL and control class was taught with expository model. The selection of samples at the qualitative stage was non-probability sampling technique in which each selected 3 students were high, medium, and low academic levels. PBL model with SRL approach effectived to students’ mathematical creative thinking ability. The ability of mathematical creative thinking of low academic level students with PBL model approach of SRL were achieving the aspect of fluency and flexibility. Students of academic level were achieving fluency and flexibility aspects well. But the originality of students at the academic level was not yet well structured. Students of high academic level could reach the aspect of originality.

Computational method for analysis of polyethylene biodegradation

NASA Astrophysics Data System (ADS)

Watanabe, Masaji; Kawai, Fusako; Shibata, Masaru; Yokoyama, Shigeo; Sudate, Yasuhiro

2003-12-01

In a previous study concerning the biodegradation of polyethylene, we proposed a mathematical model based on two primary factors: the direct consumption or absorption of small molecules and the successive weight loss of large molecules due to β-oxidation. Our model is an initial value problem consisting of a differential equation whose independent variable is time. Its unknown variable represents the total weight of all the polyethylene molecules that belong to a molecular-weight class specified by a parameter. In this paper, we describe a numerical technique to introduce experimental results into analysis of our model. We first establish its mathematical foundation in order to guarantee its validity, by showing that the initial value problem associated with the differential equation has a unique solution. Our computational technique is based on a linear system of differential equations derived from the original problem. We introduce some numerical results to illustrate our technique as a practical application of the linear approximation. In particular, we show how to solve the inverse problem to determine the consumption rate and the β-oxidation rate numerically, and illustrate our numerical technique by analyzing the GPC patterns of polyethylene wax obtained before and after 5 weeks cultivation of a fungus, Aspergillus sp. AK-3. A numerical simulation based on these degradation rates confirms that the primary factors of the polyethylene biodegradation posed in modeling are indeed appropriate.

EXPERIMENTAL MODELLING OF AORTIC ANEURYSMS

PubMed Central

Doyle, Barry J; Corbett, Timothy J; Cloonan, Aidan J; O’Donnell, Michael R; Walsh, Michael T; Vorp, David A; McGloughlin, Timothy M

2009-01-01

A range of silicone rubbers were created based on existing commercially available materials. These silicones were designed to be visually different from one another and have distinct material properties, in particular, ultimate tensile strengths and tear strengths. In total, eleven silicone rubbers were manufactured, with the materials designed to have a range of increasing tensile strengths from approximately 2-4MPa, and increasing tear strengths from approximately 0.45-0.7N/mm. The variations in silicones were detected using a standard colour analysis technique. Calibration curves were then created relating colour intensity to individual material properties. All eleven materials were characterised and a 1st order Ogden strain energy function applied. Material coefficients were determined and examined for effectiveness. Six idealised abdominal aortic aneurysm models were also created using the two base materials of the study, with a further model created using a new mixing technique to create a rubber model with randomly assigned material properties. These models were then examined using videoextensometry and compared to numerical results. Colour analysis revealed a statistically significant linear relationship (p<0.0009) with both tensile strength and tear strength, allowing material strength to be determined using a non-destructive experimental technique. The effectiveness of this technique was assessed by comparing predicted material properties to experimentally measured methods, with good agreement in the results. Videoextensometry and numerical modelling revealed minor percentage differences, with all results achieving significance (p<0.0009). This study has successfully designed and developed a range of silicone rubbers that have unique colour intensities and material strengths. Strengths can be readily determined using a non-destructive analysis technique with proven effectiveness. These silicones may further aid towards an improved understanding of the biomechanical behaviour of aneurysms using experimental techniques. PMID:19595622

Maternal, Infant Characteristics, Breastfeeding Techniques, and Initiation: Structural Equation Modeling Approaches

PubMed Central

Htun, Tha Pyai; Lim, Peng Im; Ho-Lim, Sarah

2015-01-01

Objectives The aim of this study was to examine the relationships among maternal and infant characteristics, breastfeeding techniques, and exclusive breastfeeding initiation in different modes of birth using structural equation modeling approaches. Methods We examined a hypothetical model based on integrating concepts of a breastfeeding decision-making model, a breastfeeding initiation model, and a social cognitive theory among 952 mother-infant dyads. The LATCH breastfeeding assessment tool was used to evaluate breastfeeding techniques and two infant feeding categories were used (exclusive and non-exclusive breastfeeding). Results Structural equation models (SEM) showed that multiparity was significantly positively associated with breastfeeding techniques and the jaundice of an infant was significantly negatively related to exclusive breastfeeding initiation. A multigroup analysis in the SEM showed no difference between the caesarean section and vaginal delivery groups estimates of breastfeeding techniques on exclusive breastfeeding initiation. Breastfeeding techniques were significantly positively associated with exclusive breastfeeding initiation in the entire sample and in the vaginal deliveries group. However, breastfeeding techniques were not significantly associated with exclusive breastfeeding initiation in the cesarean section group. Maternal age, maternal race, gestations, birth weight of infant, and postnatal complications had no significant impacts on breastfeeding techniques or exclusive breastfeeding initiation in our study. Overall, the models fitted the data satisfactorily (GFI = 0.979–0.987; AGFI = 0.951–0.962; IFI = 0.958–0.962; CFI = 0.955–0.960, and RMSEA = 0.029–0.034). Conclusions Multiparity and jaundice of an infant were found to affect breastfeeding technique and exclusive breastfeeding initiation respectively. Breastfeeding technique was related to exclusive breastfeeding initiation according to the mode of birth. This relationship implies the importance of early effective interventions among first-time mothers with jaundice infants in improving breastfeeding techniques and promoting exclusive breastfeeding initiation. PMID:26566028

Analysis of the dynamic behavior of structures using the high-rate GNSS-PPP method combined with a wavelet-neural model: Numerical simulation and experimental tests

NASA Astrophysics Data System (ADS)

Kaloop, Mosbeh R.; Yigit, Cemal O.; Hu, Jong W.

2018-03-01

Recently, the high rate global navigation satellite system-precise point positioning (GNSS-PPP) technique has been used to detect the dynamic behavior of structures. This study aimed to increase the accuracy of the extraction oscillation properties of structural movements based on the high-rate (10 Hz) GNSS-PPP monitoring technique. A developmental model based on the combination of wavelet package transformation (WPT) de-noising and neural network prediction (NN) was proposed to improve the dynamic behavior of structures for GNSS-PPP method. A complicated numerical simulation involving highly noisy data and 13 experimental cases with different loads were utilized to confirm the efficiency of the proposed model design and the monitoring technique in detecting the dynamic behavior of structures. The results revealed that, when combined with the proposed model, GNSS-PPP method can be used to accurately detect the dynamic behavior of engineering structures as an alternative to relative GNSS method.

Analytical model for real time, noninvasive estimation of blood glucose level.

PubMed

Adhyapak, Anoop; Sidley, Matthew; Venkataraman, Jayanti

2014-01-01

The paper presents an analytical model to estimate blood glucose level from measurements made non-invasively and in real time by an antenna strapped to a patient's wrist. Some promising success has been shown by the RIT ETA Lab research group that an antenna's resonant frequency can track, in real time, changes in glucose concentration. Based on an in-vitro study of blood samples of diabetic patients, the paper presents a modified Cole-Cole model that incorporates a factor to represent the change in glucose level. A calibration technique using the input impedance technique is discussed and the results show a good estimation as compared to the glucose meter readings. An alternate calibration methodology has been developed that is based on the shift in the antenna resonant frequency using an equivalent circuit model containing a shunt capacitor to represent the shift in resonant frequency with changing glucose levels. Work under progress is the optimization of the technique with a larger sample of patients.

An analysis of supersonic flows with low-Reynolds number compressible two-equation turbulence models using LU finite volume implicit numerical techniques

NASA Technical Reports Server (NTRS)

Lee, J.

1994-01-01

A generalized flow solver using an implicit Lower-upper (LU) diagonal decomposition based numerical technique has been coupled with three low-Reynolds number kappa-epsilon models for analysis of problems with engineering applications. The feasibility of using the LU technique to obtain efficient solutions to supersonic problems using the kappa-epsilon model has been demonstrated. The flow solver is then used to explore limitations and convergence characteristics of several popular two equation turbulence models. Several changes to the LU solver have been made to improve the efficiency of turbulent flow predictions. In general, the low-Reynolds number kappa-epsilon models are easier to implement than the models with wall-functions, but require much finer near-wall grid to accurately resolve the physics. The three kappa-epsilon models use different approaches to characterize the near wall regions of the flow. Therefore, the limitations imposed by the near wall characteristics have been carefully resolved. The convergence characteristics of a particular model using a given numerical technique are also an important, but most often overlooked, aspect of turbulence model predictions. It is found that some convergence characteristics could be sacrificed for more accurate near-wall prediction. However, even this gain in accuracy is not sufficient to model the effects of an external pressure gradient imposed by a shock-wave/ boundary-layer interaction. Additional work on turbulence models, especially for compressibility, is required since the solutions obtained with base line turbulence are in only reasonable agreement with the experimental data for the viscous interaction problems.

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.

Propulsion Health Monitoring for Enhanced Safety

NASA Technical Reports Server (NTRS)

Butz, Mark G.; Rodriguez, Hector M.

2003-01-01

This report presents the results of the NASA contract Propulsion System Health Management for Enhanced Safety performed by General Electric Aircraft Engines (GE AE), General Electric Global Research (GE GR), and Pennsylvania State University Applied Research Laboratory (PSU ARL) under the NASA Aviation Safety Program. This activity supports the overall goal of enhanced civil aviation safety through a reduction in the occurrence of safety-significant propulsion system malfunctions. Specific objectives are to develop and demonstrate vibration diagnostics techniques for the on-line detection of turbine rotor disk cracks, and model-based fault tolerant control techniques for the prevention and mitigation of in-flight engine shutdown, surge/stall, and flameout events. The disk crack detection work was performed by GE GR which focused on a radial-mode vibration monitoring technique, and PSU ARL which focused on a torsional-mode vibration monitoring technique. GE AE performed the Model-Based Fault Tolerant Control work which focused on the development of analytical techniques for detecting, isolating, and accommodating gas-path faults.

An Examination of Exposure Control and Content Balancing Restrictions on Item Selection in CATs Using the Partial Credit Model.

ERIC Educational Resources Information Center

Davis, Laurie Laughlin; Pastor, Dena A.; Dodd, Barbara G.; Chiang, Claire; Fitzpatrick, Steven J.

2003-01-01

Examined the effectiveness of the Sympson-Hetter technique and rotated content balancing relative to no exposure control and no content rotation conditions in a computerized adaptive testing system based on the partial credit model. Simulation results show the Sympson-Hetter technique can be used with minimal impact on measurement precision,…

AGENT-BASED MODELS IN EMPIRICAL SOCIAL RESEARCH*

PubMed Central

Bruch, Elizabeth; Atwell, Jon

2014-01-01

Agent-based modeling has become increasingly popular in recent years, but there is still no codified set of recommendations or practices for how to use these models within a program of empirical research. This article provides ideas and practical guidelines drawn from sociology, biology, computer science, epidemiology, and statistics. We first discuss the motivations for using agent-based models in both basic science and policy-oriented social research. Next, we provide an overview of methods and strategies for incorporating data on behavior and populations into agent-based models, and review techniques for validating and testing the sensitivity of agent-based models. We close with suggested directions for future research. PMID:25983351

UTOPIAN: user-driven topic modeling based on interactive nonnegative matrix factorization.

PubMed

Choo, Jaegul; Lee, Changhyun; Reddy, Chandan K; Park, Haesun

2013-12-01

Topic modeling has been widely used for analyzing text document collections. Recently, there have been significant advancements in various topic modeling techniques, particularly in the form of probabilistic graphical modeling. State-of-the-art techniques such as Latent Dirichlet Allocation (LDA) have been successfully applied in visual text analytics. However, most of the widely-used methods based on probabilistic modeling have drawbacks in terms of consistency from multiple runs and empirical convergence. Furthermore, due to the complicatedness in the formulation and the algorithm, LDA cannot easily incorporate various types of user feedback. To tackle this problem, we propose a reliable and flexible visual analytics system for topic modeling called UTOPIAN (User-driven Topic modeling based on Interactive Nonnegative Matrix Factorization). Centered around its semi-supervised formulation, UTOPIAN enables users to interact with the topic modeling method and steer the result in a user-driven manner. We demonstrate the capability of UTOPIAN via several usage scenarios with real-world document corpuses such as InfoVis/VAST paper data set and product review data sets.

Conservative strategy-based ensemble surrogate model for optimal groundwater remediation design at DNAPLs-contaminated sites

NASA Astrophysics Data System (ADS)

Ouyang, Qi; Lu, Wenxi; Lin, Jin; Deng, Wenbing; Cheng, Weiguo

2017-08-01

The surrogate-based simulation-optimization techniques are frequently used for optimal groundwater remediation design. When this technique is used, surrogate errors caused by surrogate-modeling uncertainty may lead to generation of infeasible designs. In this paper, a conservative strategy that pushes the optimal design into the feasible region was used to address surrogate-modeling uncertainty. In addition, chance-constrained programming (CCP) was adopted to compare with the conservative strategy in addressing this uncertainty. Three methods, multi-gene genetic programming (MGGP), Kriging (KRG) and support vector regression (SVR), were used to construct surrogate models for a time-consuming multi-phase flow model. To improve the performance of the surrogate model, ensemble surrogates were constructed based on combinations of different stand-alone surrogate models. The results show that: (1) the surrogate-modeling uncertainty was successfully addressed by the conservative strategy, which means that this method is promising for addressing surrogate-modeling uncertainty. (2) The ensemble surrogate model that combines MGGP with KRG showed the most favorable performance, which indicates that this ensemble surrogate can utilize both stand-alone surrogate models to improve the performance of the surrogate model.

Evaluating the role of evapotranspiration remote sensing data in improving hydrological modeling predictability

NASA Astrophysics Data System (ADS)

Herman, Matthew R.; Nejadhashemi, A. Pouyan; Abouali, Mohammad; Hernandez-Suarez, Juan Sebastian; Daneshvar, Fariborz; Zhang, Zhen; Anderson, Martha C.; Sadeghi, Ali M.; Hain, Christopher R.; Sharifi, Amirreza

2018-01-01

As the global demands for the use of freshwater resources continues to rise, it has become increasingly important to insure the sustainability of this resources. This is accomplished through the use of management strategies that often utilize monitoring and the use of hydrological models. However, monitoring at large scales is not feasible and therefore model applications are becoming challenging, especially when spatially distributed datasets, such as evapotranspiration, are needed to understand the model performances. Due to these limitations, most of the hydrological models are only calibrated for data obtained from site/point observations, such as streamflow. Therefore, the main focus of this paper is to examine whether the incorporation of remotely sensed and spatially distributed datasets can improve the overall performance of the model. In this study, actual evapotranspiration (ETa) data was obtained from the two different sets of satellite based remote sensing data. One dataset estimates ETa based on the Simplified Surface Energy Balance (SSEBop) model while the other one estimates ETa based on the Atmosphere-Land Exchange Inverse (ALEXI) model. The hydrological model used in this study is the Soil and Water Assessment Tool (SWAT), which was calibrated against spatially distributed ETa and single point streamflow records for the Honeyoey Creek-Pine Creek Watershed, located in Michigan, USA. Two different techniques, multi-variable and genetic algorithm, were used to calibrate the SWAT model. Using the aforementioned datasets, the performance of the hydrological model in estimating ETa was improved using both calibration techniques by achieving Nash-Sutcliffe efficiency (NSE) values >0.5 (0.73-0.85), percent bias (PBIAS) values within ±25% (±21.73%), and root mean squared error - observations standard deviation ratio (RSR) values <0.7 (0.39-0.52). However, the genetic algorithm technique was more effective with the ETa calibration while significantly reducing the model performance for estimating the streamflow (NSE: 0.32-0.52, PBIAS: ±32.73%, and RSR: 0.63-0.82). Meanwhile, using the multi-variable technique, the model performance for estimating the streamflow was maintained with a high level of accuracy (NSE: 0.59-0.61, PBIAS: ±13.70%, and RSR: 0.63-0.64) while the evapotranspiration estimations were improved. Results from this assessment shows that incorporation of remotely sensed and spatially distributed data can improve the hydrological model performance if it is coupled with a right calibration technique.

Assessing Requirements Quality through Requirements Coverage

NASA Technical Reports Server (NTRS)

Rajan, Ajitha; Heimdahl, Mats; Woodham, Kurt

2008-01-01

In model-based development, the development effort is centered around a formal description of the proposed software system the model. This model is derived from some high-level requirements describing the expected behavior of the software. For validation and verification purposes, this model can then be subjected to various types of analysis, for example, completeness and consistency analysis [6], model checking [3], theorem proving [1], and test-case generation [4, 7]. This development paradigm is making rapid inroads in certain industries, e.g., automotive, avionics, space applications, and medical technology. This shift towards model-based development naturally leads to changes in the verification and validation (V&V) process. The model validation problem determining that the model accurately captures the customer's high-level requirements has received little attention and the sufficiency of the validation activities has been largely determined through ad-hoc methods. Since the model serves as the central artifact, its correctness with respect to the users needs is absolutely crucial. In our investigation, we attempt to answer the following two questions with respect to validation (1) Are the requirements sufficiently defined for the system? and (2) How well does the model implement the behaviors specified by the requirements? The second question can be addressed using formal verification. Nevertheless, the size and complexity of many industrial systems make formal verification infeasible even if we have a formal model and formalized requirements. Thus, presently, there is no objective way of answering these two questions. To this end, we propose an approach based on testing that, when given a set of formal requirements, explores the relationship between requirements-based structural test-adequacy coverage and model-based structural test-adequacy coverage. The proposed technique uses requirements coverage metrics defined in [9] on formal high-level software requirements and existing model coverage metrics such as the Modified Condition and Decision Coverage (MC/DC) used when testing highly critical software in the avionics industry [8]. Our work is related to Chockler et al. [2], but we base our work on traditional testing techniques as opposed to verification techniques.

Frequency Response Function Expansion for Unmeasured Translation and Rotation Dofs for Impedance Modelling Applications

NASA Astrophysics Data System (ADS)

Avitabile, P.; O'Callahan, J.

2003-07-01

Inclusion of rotational effects is critical for the accuracy of the predicted system characteristics, in almost all system modelling studies. However, experimentally derived information for the description of one or more of the components for the system will generally not have any rotational effects included in the description of the component. The lack of rotational effects has long affected the results from any system model development whether using a modal-based approach or an impedance-based approach. Several new expansion processes are described herein for the development of FRFs needed for impedance-based system models. These techniques expand experimentally derived mode shapes, residual modes from the modal parameter estimation process and FRFs directly to allow for the inclusion of the necessary rotational dof. The FRFs involving translational to rotational dofs are developed as well as the rotational to rotational dof. Examples are provided to show the use of these techniques.

20180312 - Structure-based QSAR Models to Predict Systemic Toxicity Points of Departure (SOT)

EPA Science Inventory

Human health risk assessment associated with environmental chemical exposure is limited by the tens of thousands of chemicals with little or no experimental in vivo toxicity data. Data gap filling techniques, such as quantitative structure activity relationship (QSAR) models base...

Verification of Autonomous Systems for Space Applications

NASA Technical Reports Server (NTRS)

Brat, G.; Denney, E.; Giannakopoulou, D.; Frank, J.; Jonsson, A.

2006-01-01

Autonomous software, especially if it is based on model, can play an important role in future space applications. For example, it can help streamline ground operations, or, assist in autonomous rendezvous and docking operations, or even, help recover from problems (e.g., planners can be used to explore the space of recovery actions for a power subsystem and implement a solution without (or with minimal) human intervention). In general, the exploration capabilities of model-based systems give them great flexibility. Unfortunately, it also makes them unpredictable to our human eyes, both in terms of their execution and their verification. The traditional verification techniques are inadequate for these systems since they are mostly based on testing, which implies a very limited exploration of their behavioral space. In our work, we explore how advanced V&V techniques, such as static analysis, model checking, and compositional verification, can be used to gain trust in model-based systems. We also describe how synthesis can be used in the context of system reconfiguration and in the context of verification.

Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer

NASA Astrophysics Data System (ADS)

Zhang, Yucheng; Oikonomou, Anastasia; Wong, Alexander; Haider, Masoom A.; Khalvati, Farzad

2017-04-01

Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative features from radiological images. Radiomic features have been shown to provide prognostic value in predicting clinical outcomes in several studies. However, several challenges including feature redundancy, unbalanced data, and small sample sizes have led to relatively low predictive accuracy. In this study, we explore different strategies for overcoming these challenges and improving predictive performance of radiomics-based prognosis for non-small cell lung cancer (NSCLC). CT images of 112 patients (mean age 75 years) with NSCLC who underwent stereotactic body radiotherapy were used to predict recurrence, death, and recurrence-free survival using a comprehensive radiomics analysis. Different feature selection and predictive modeling techniques were used to determine the optimal configuration of prognosis analysis. To address feature redundancy, comprehensive analysis indicated that Random Forest models and Principal Component Analysis were optimum predictive modeling and feature selection methods, respectively, for achieving high prognosis performance. To address unbalanced data, Synthetic Minority Over-sampling technique was found to significantly increase predictive accuracy. A full analysis of variance showed that data endpoints, feature selection techniques, and classifiers were significant factors in affecting predictive accuracy, suggesting that these factors must be investigated when building radiomics-based predictive models for cancer prognosis.

Comparison of System Identification Techniques for the Hydraulic Manipulator Test Bed (HMTB)

NASA Technical Reports Server (NTRS)

Morris, A. Terry

1996-01-01

In this thesis linear, dynamic, multivariable state-space models for three joints of the ground-based Hydraulic Manipulator Test Bed (HMTB) are identified. HMTB, housed at the NASA Langley Research Center, is a ground-based version of the Dexterous Orbital Servicing System (DOSS), a representative space station manipulator. The dynamic models of the HMTB manipulator will first be estimated by applying nonparametric identification methods to determine each joint's response characteristics using various input excitations. These excitations include sum of sinusoids, pseudorandom binary sequences (PRBS), bipolar ramping pulses, and chirp input signals. Next, two different parametric system identification techniques will be applied to identify the best dynamical description of the joints. The manipulator is localized about a representative space station orbital replacement unit (ORU) task allowing the use of linear system identification methods. Comparisons, observations, and results of both parametric system identification techniques are discussed. The thesis concludes by proposing a model reference control system to aid in astronaut ground tests. This approach would allow the identified models to mimic on-orbit dynamic characteristics of the actual flight manipulator thus providing astronauts with realistic on-orbit responses to perform space station tasks in a ground-based environment.

Application of inorganic element ratios to chemometrics for determination of the geographic origin of welsh onions.

PubMed

Ariyama, Kaoru; Horita, Hiroshi; Yasui, Akemi

2004-09-22

The composition of concentration ratios of 19 inorganic elements to Mg (hereinafter referred to as 19-element/Mg composition) was applied to chemometric techniques to determine the geographic origin (Japan or China) of Welsh onions (Allium fistulosum L.). Using a composition of element ratios has the advantage of simplified sample preparation, and it was possible to determine the geographic origin of a Welsh onion within 2 days. The classical technique based on 20 element concentrations was also used along with the new simpler one based on 19 elements/Mg in order to validate the new technique. Twenty elements, Na, P, K, Ca, Mg, Mn, Fe, Cu, Zn, Sr, Ba, Co, Ni, Rb, Mo, Cd, Cs, La, Ce, and Tl, in 244 Welsh onion samples were analyzed by flame atomic absorption spectroscopy, inductively coupled plasma atomic emission spectrometry, and inductively coupled plasma mass spectrometry. Linear discriminant analysis (LDA) on 20-element concentrations and 19-element/Mg composition was applied to these analytical data, and soft independent modeling of class analogy (SIMCA) on 19-element/Mg composition was applied to these analytical data. The results showed that techniques based on 19-element/Mg composition were effective. LDA, based on 19-element/Mg composition for classification of samples from Japan and from Shandong, Shanghai, and Fujian in China, classified 101 samples used for modeling 97% correctly and predicted another 119 samples excluding 24 nonauthentic samples 93% correctly. In discriminations by 10 times of SIMCA based on 19-element/Mg composition modeled using 101 samples, 220 samples from known production areas including samples used for modeling and excluding 24 nonauthentic samples were predicted 92% correctly.

Comparing terrestrial laser scanning with ground and UAV-based imaging for national-level assessment of upland soil erosion

NASA Astrophysics Data System (ADS)

McShane, Gareth; Farrow, Luke; Morgan, David; Glendell, Miriam; James, Mike; Quinton, John; Evans, Martin; Anderson, Karen; Rawlins, Barry; Quine, Timothy; Debell, Leon; Benaud, Pia; Jones, Lee; Kirkham, Matthew; Lark, Murray; Rickson, Jane; Brazier, Richard

2015-04-01

Quantifying soil loss through erosion processes at a high resolution can be a time consuming and costly undertaking. In this pilot study 'a cost effective framework for monitoring soil erosion in England and Wales', funded by the UK Department for Environment, Food and Rural Affairs (Defra), we compare methods for collecting suitable topographic measurements via remote sensing. The aim is to enable efficient but detailed site-scale studies of erosion forms in inaccessible UK upland environments, to quantify dynamic processes, such as erosion and mass movement. The techniques assessed are terrestrial laser scanning (TLS), and unmanned aerial vehicle (UAV) photography and ground-based photography, both processed using structure-from-motion (SfM) 3D reconstruction software. Compared to other established techniques, such as expensive TLS, SfM offers a potentially low-cost alternative for the reconstruction of 3D high-resolution micro-topographic models from photographs taken with consumer grade cameras. However, whilst an increasing number of research papers examine the relative merits of these novel versus more established survey techniques, no study to date has compared both ground-based and aerial SfM photogrammetry with TLS scanning across a range of scales (from m2 to 16ha). The evaluation of these novel low cost techniques is particularly relevant in upland landscapes, where the remoteness and inaccessibility of field sites may render some of the more established survey techniques impractical. Volumetric estimates of soil loss are quantified using the digital surface models (DSMs) derived from the data from each technique and subtracted from a modelled pre-erosion surface. The results from each technique are compared. The UAV was able to capture information over a wide area, a range of altitudes and angles over the study area. Combined with automated SfM-based processing, this technique was able to produce rapid orthophotos to support ground-based data acquisition, as well as a DSM for volume loss measurement in larger features. However, the DSM of erosion features lacked the detail of those captured using the ground-based methods. Terrestrial laser scanning provided detailed, accurate, high density measurements of the ground surface over long (100s m) distances, but size and weight of the instrument made it difficult to use in mountainous environments. In addition, deriving a reliable bare-earth digital terrain model (DTM) from TLS was at times problematic due to the presence of tall shrubby vegetation. Ground-based photography produced comparable data sets to terrestrial laser scanning and was the most useful for characterising small and difficult to view features. The relative advantages, limitations and cost-effectiveness of each approach at 5 upland sites across the UK are discussed.

Image resolution enhancement via image restoration using neural network

NASA Astrophysics Data System (ADS)

Zhang, Shuangteng; Lu, Yihong

2011-04-01

Image super-resolution aims to obtain a high-quality image at a resolution that is higher than that of the original coarse one. This paper presents a new neural network-based method for image super-resolution. In this technique, the super-resolution is considered as an inverse problem. An observation model that closely follows the physical image acquisition process is established to solve the problem. Based on this model, a cost function is created and minimized by a Hopfield neural network to produce high-resolution images from the corresponding low-resolution ones. Not like some other single frame super-resolution techniques, this technique takes into consideration point spread function blurring as well as additive noise and therefore generates high-resolution images with more preserved or restored image details. Experimental results demonstrate that the high-resolution images obtained by this technique have a very high quality in terms of PSNR and visually look more pleasant.

Agent-based modeling: case study in cleavage furrow models

PubMed Central

Mogilner, Alex; Manhart, Angelika

2016-01-01

The number of studies in cell biology in which quantitative models accompany experiments has been growing steadily. Roughly, mathematical and computational techniques of these models can be classified as “differential equation based” (DE) or “agent based” (AB). Recently AB models have started to outnumber DE models, but understanding of AB philosophy and methodology is much less widespread than familiarity with DE techniques. Here we use the history of modeling a fundamental biological problem—positioning of the cleavage furrow in dividing cells—to explain how and why DE and AB models are used. We discuss differences, advantages, and shortcomings of these two approaches. PMID:27811328

Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures

ERIC Educational Resources Information Center

Steinley, Douglas; Brusco, Michael J.

2008-01-01

Eight different variable selection techniques for model-based and non-model-based clustering are evaluated across a wide range of cluster structures. It is shown that several methods have difficulties when non-informative variables (i.e., random noise) are included in the model. Furthermore, the distribution of the random noise greatly impacts the…

Medial-based deformable models in nonconvex shape-spaces for medical image segmentation.

PubMed

McIntosh, Chris; Hamarneh, Ghassan

2012-01-01

We explore the application of genetic algorithms (GA) to deformable models through the proposition of a novel method for medical image segmentation that combines GA with nonconvex, localized, medial-based shape statistics. We replace the more typical gradient descent optimizer used in deformable models with GA, and the convex, implicit, global shape statistics with nonconvex, explicit, localized ones. Specifically, we propose GA to reduce typical deformable model weaknesses pertaining to model initialization, pose estimation and local minima, through the simultaneous evolution of a large number of models. Furthermore, we constrain the evolution, and thus reduce the size of the search-space, by using statistically-based deformable models whose deformations are intuitive (stretch, bulge, bend) and are driven in terms of localized principal modes of variation, instead of modes of variation across the entire shape that often fail to capture localized shape changes. Although GA are not guaranteed to achieve the global optima, our method compares favorably to the prevalent optimization techniques, convex/nonconvex gradient-based optimizers and to globally optimal graph-theoretic combinatorial optimization techniques, when applied to the task of corpus callosum segmentation in 50 mid-sagittal brain magnetic resonance images.

Singularity-sensitive gauge-based radar rainfall adjustment methods for urban hydrological applications

NASA Astrophysics Data System (ADS)

Wang, L.-P.; Ochoa-Rodríguez, S.; Onof, C.; Willems, P.

2015-09-01

Gauge-based radar rainfall adjustment techniques have been widely used to improve the applicability of radar rainfall estimates to large-scale hydrological modelling. However, their use for urban hydrological applications is limited as they were mostly developed based upon Gaussian approximations and therefore tend to smooth off so-called "singularities" (features of a non-Gaussian field) that can be observed in the fine-scale rainfall structure. Overlooking the singularities could be critical, given that their distribution is highly consistent with that of local extreme magnitudes. This deficiency may cause large errors in the subsequent urban hydrological modelling. To address this limitation and improve the applicability of adjustment techniques at urban scales, a method is proposed herein which incorporates a local singularity analysis into existing adjustment techniques and allows the preservation of the singularity structures throughout the adjustment process. In this paper the proposed singularity analysis is incorporated into the Bayesian merging technique and the performance of the resulting singularity-sensitive method is compared with that of the original Bayesian (non singularity-sensitive) technique and the commonly used mean field bias adjustment. This test is conducted using as case study four storm events observed in the Portobello catchment (53 km2) (Edinburgh, UK) during 2011 and for which radar estimates, dense rain gauge and sewer flow records, as well as a recently calibrated urban drainage model were available. The results suggest that, in general, the proposed singularity-sensitive method can effectively preserve the non-normality in local rainfall structure, while retaining the ability of the original adjustment techniques to generate nearly unbiased estimates. Moreover, the ability of the singularity-sensitive technique to preserve the non-normality in rainfall estimates often leads to better reproduction of the urban drainage system's dynamics, particularly of peak runoff flows.

Moving alcohol prevention research forward-Part II: new directions grounded in community-based system dynamics modeling.

PubMed

Apostolopoulos, Yorghos; Lemke, Michael K; Barry, Adam E; Lich, Kristen Hassmiller

2018-02-01

Given the complexity of factors contributing to alcohol misuse, appropriate epistemologies and methodologies are needed to understand and intervene meaningfully. We aimed to (1) provide an overview of computational modeling methodologies, with an emphasis on system dynamics modeling; (2) explain how community-based system dynamics modeling can forge new directions in alcohol prevention research; and (3) present a primer on how to build alcohol misuse simulation models using system dynamics modeling, with an emphasis on stakeholder involvement, data sources and model validation. Throughout, we use alcohol misuse among college students in the United States as a heuristic example for demonstrating these methodologies. System dynamics modeling employs a top-down aggregate approach to understanding dynamically complex problems. Its three foundational properties-stocks, flows and feedbacks-capture non-linearity, time-delayed effects and other system characteristics. As a methodological choice, system dynamics modeling is amenable to participatory approaches; in particular, community-based system dynamics modeling has been used to build impactful models for addressing dynamically complex problems. The process of community-based system dynamics modeling consists of numerous stages: (1) creating model boundary charts, behavior-over-time-graphs and preliminary system dynamics models using group model-building techniques; (2) model formulation; (3) model calibration; (4) model testing and validation; and (5) model simulation using learning-laboratory techniques. Community-based system dynamics modeling can provide powerful tools for policy and intervention decisions that can result ultimately in sustainable changes in research and action in alcohol misuse prevention. © 2017 Society for the Study of Addiction.

A burnout prediction model based around char morphology

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

Tao Wu; Edward Lester; Michael Cloke

Several combustion models have been developed that can make predictions about coal burnout and burnout potential. Most of these kinetic models require standard parameters such as volatile content and particle size to make a burnout prediction. This article presents a new model called the char burnout (ChB) model, which also uses detailed information about char morphology in its prediction. The input data to the model is based on information derived from two different image analysis techniques. One technique generates characterization data from real char samples, and the other predicts char types based on characterization data from image analysis of coalmore » particles. The pyrolyzed chars in this study were created in a drop tube furnace operating at 1300{sup o}C, 200 ms, and 1% oxygen. Modeling results were compared with a different carbon burnout kinetic model as well as the actual burnout data from refiring the same chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen, and residence times of 200, 400, and 600 ms. A good agreement between ChB model and experimental data indicates that the inclusion of char morphology in combustion models could well improve model predictions. 38 refs., 5 figs., 6 tabs.« less

Speech enhancement using the modified phase-opponency model.

PubMed

Deshmukh, Om D; Espy-Wilson, Carol Y; Carney, Laurel H

2007-06-01

In this paper we present a model called the Modified Phase-Opponency (MPO) model for single-channel speech enhancement when the speech is corrupted by additive noise. The MPO model is based on the auditory PO model, proposed for detection of tones in noise. The PO model includes a physiologically realistic mechanism for processing the information in neural discharge times and exploits the frequency-dependent phase properties of the tuned filters in the auditory periphery by using a cross-auditory-nerve-fiber coincidence detection for extracting temporal cues. The MPO model alters the components of the PO model such that the basic functionality of the PO model is maintained but the properties of the model can be analyzed and modified independently. The MPO-based speech enhancement scheme does not need to estimate the noise characteristics nor does it assume that the noise satisfies any statistical model. The MPO technique leads to the lowest value of the LPC-based objective measures and the highest value of the perceptual evaluation of speech quality measure compared to other methods when the speech signals are corrupted by fluctuating noise. Combining the MPO speech enhancement technique with our aperiodicity, periodicity, and pitch detector further improves its performance.

A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users.

PubMed

Ravi, Logesh; Vairavasundaram, Subramaniyaswamy

2016-01-01

Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented.

Order reduction for a model of marine bacteriophage evolution

NASA Astrophysics Data System (ADS)

Pagliarini, Silvia; Korobeinikov, Andrei

2017-02-01

A typical mechanistic model of viral evolution necessary includes several time scales which can differ by orders of magnitude. Such a diversity of time scales makes analysis of these models difficult. Reducing the order of a model is highly desirable when handling such a model. A typical approach applied to such slow-fast (or singularly perturbed) systems is the time scales separation technique. Constructing the so-called quasi-steady-state approximation is the usual first step in applying the technique. While this technique is commonly applied, in some cases its straightforward application can lead to unsatisfactory results. In this paper we construct the quasi-steady-state approximation for a model of evolution of marine bacteriophages based on the Beretta-Kuang model. We show that for this particular model the quasi-steady-state approximation is able to produce only qualitative but not quantitative fit.

Extending enterprise architecture modelling with business goals and requirements

NASA Astrophysics Data System (ADS)

Engelsman, Wilco; Quartel, Dick; Jonkers, Henk; van Sinderen, Marten

2011-02-01

The methods for enterprise architecture (EA), such as The Open Group Architecture Framework, acknowledge the importance of requirements modelling in the development of EAs. Modelling support is needed to specify, document, communicate and reason about goals and requirements. The current modelling techniques for EA focus on the products, services, processes and applications of an enterprise. In addition, techniques may be provided to describe structured requirements lists and use cases. Little support is available however for modelling the underlying motivation of EAs in terms of stakeholder concerns and the high-level goals that address these concerns. This article describes a language that supports the modelling of this motivation. The definition of the language is based on existing work on high-level goal and requirements modelling and is aligned with an existing standard for enterprise modelling: the ArchiMate language. Furthermore, the article illustrates how EA can benefit from analysis techniques from the requirements engineering domain.

Nonlinear dynamic macromodeling techniques for audio systems

NASA Astrophysics Data System (ADS)

Ogrodzki, Jan; Bieńkowski, Piotr

2015-09-01

This paper develops a modelling method and a models identification technique for the nonlinear dynamic audio systems. Identification is performed by means of a behavioral approach based on a polynomial approximation. This approach makes use of Discrete Fourier Transform and Harmonic Balance Method. A model of an audio system is first created and identified and then it is simulated in real time using an algorithm of low computational complexity. The algorithm consists in real time emulation of the system response rather than in simulation of the system itself. The proposed software is written in Python language using object oriented programming techniques. The code is optimized for a multithreads environment.

Wab-InSAR: a new wavelet based InSAR time series technique applied to volcanic and tectonic areas

NASA Astrophysics Data System (ADS)

Walter, T. R.; Shirzaei, M.; Nankali, H.; Roustaei, M.

2009-12-01

Modern geodetic techniques such as InSAR and GPS provide valuable observations of the deformation field. Because of the variety of environmental interferences (e.g., atmosphere, topography distortion) and incompleteness of the models (assumption of the linear model for deformation), those observations are usually tainted by various systematic and random errors. Therefore we develop and test new methods to identify and filter unwanted periodic or episodic artifacts to obtain accurate and precise deformation measurements. Here we present and implement a new wavelet based InSAR (Wab-InSAR) time series approach. Because wavelets are excellent tools for identifying hidden patterns and capturing transient signals, we utilize wavelet functions for reducing the effect of atmospheric delay and digital elevation model inaccuracies. Wab-InSAR is a model free technique, reducing digital elevation model errors in individual interferograms using a 2D spatial Legendre polynomial wavelet filter. Atmospheric delays are reduced using a 3D spatio-temporal wavelet transform algorithm and a novel technique for pixel selection. We apply Wab-InSAR to several targets, including volcano deformation processes at Hawaii Island, and mountain building processes in Iran. Both targets are chosen to investigate large and small amplitude signals, variable and complex topography and atmospheric effects. In this presentation we explain different steps of the technique, validate the results by comparison to other high resolution processing methods (GPS, PS-InSAR, SBAS) and discuss the geophysical results.

Overcoming limitations of model-based diagnostic reasoning systems

NASA Technical Reports Server (NTRS)

Holtzblatt, Lester J.; Marcotte, Richard A.; Piazza, Richard L.

1989-01-01

The development of a model-based diagnostic system to overcome the limitations of model-based reasoning systems is discussed. It is noted that model-based reasoning techniques can be used to analyze the failure behavior and diagnosability of system and circuit designs as part of the system process itself. One goal of current research is the development of a diagnostic algorithm which can reason efficiently about large numbers of diagnostic suspects and can handle both combinational and sequential circuits. A second goal is to address the model-creation problem by developing an approach for using design models to construct the GMODS model in an automated fashion.

Bolometric Luminosities of Peculiar Type II-P Supernovae: Observational and Theoretical Approaches

NASA Astrophysics Data System (ADS)

Lusk, Jeremy Alexander

2018-01-01

In the three decades since the explosion of SN 1987A, only a handful of other supernovae have been detected which are also thought to originate from blue supergiant progenitors. In this study, we use the five best observed of these supernovae (SNe 1998A, 2000cb, 2006V, 2006au, and 2009E) to examine the bolometric properties of the class through observations and theoretical models. Several techniques for taking photometric observations and inferring bolometric luminosities have been used in the literature. Our newly-improved python package SuperBoL implements many of these techniques. The challenge remains that the true bolometric luminosity of the supernova cannot be directly observed. We must turn to theoretical models in order to examine the validity of the different observationally-based techniques. In this work, we make use of the NLTE generalized atmosphere code PHOENIX to produce synthetic spectra of known luminosity which match the observed supernova spectra. Synthetic photometry of these models is then used as input to SuperBoL to test the different observationally-based bolometric luminosity techniques.

Spindle speed variation technique in turning operations: Modeling and real implementation

NASA Astrophysics Data System (ADS)

Urbikain, G.; Olvera, D.; de Lacalle, L. N. López; Elías-Zúñiga, A.

2016-11-01

Chatter is still one of the most challenging problems in machining vibrations. Researchers have focused their efforts to prevent, avoid or reduce chatter vibrations by introducing more accurate predictive physical methods. Among them, the techniques based on varying the rotational speed of the spindle (or SSV, Spindle Speed ​​Variation) have gained great relevance. However, several problems need to be addressed due to technical and practical reasons. On one hand, they can generate harmful overheating of the spindle especially at high speeds. On the other hand, the machine may be unable to perform the interpolation properly. Moreover, it is not trivial to select the most appropriate tuning parameters. This paper conducts a study of the real implementation of the SSV technique in turning systems. First, a stability model based on perturbation theory was developed for simulation purposes. Secondly, the procedure to realistically implement the technique in a conventional turning center was tested and developed. The balance between the improved stability margins and acceptable behavior of the spindle is ensured by energy consumption measurements. Mathematical model shows good agreement with experimental cutting tests.

X-ray Phase Contrast Allows Three Dimensional, Quantitative Imaging of Hydrogel Implants

PubMed Central

Appel, Alyssa A.; Larson, Jeffery C.; Jiang, Bin; Zhong, Zhong; Anastasio, Mark A.; Brey, Eric M.

2015-01-01

Three dimensional imaging techniques are needed for the evaluation and assessment of biomaterials used for tissue engineering and drug delivery applications. Hydrogels are a particularly popular class of materials for medical applications but are difficult to image in tissue using most available imaging modalities. Imaging techniques based on X-ray Phase Contrast (XPC) have shown promise for tissue engineering applications due to their ability to provide image contrast based on multiple X-ray properties. In this manuscript, we investigate the use of XPC for imaging a model hydrogel and soft tissue structure. Porous fibrin loaded poly(ethylene glycol) hydrogels were synthesized and implanted in a rodent subcutaneous model. Samples were explanted and imaged with an analyzer-based XPC technique and processed and stained for histology for comparison. Both hydrogel and soft tissues structures could be identified in XPC images. Structure in skeletal muscle adjacent could be visualized and invading fibrovascular tissue could be quantified. There were no differences between invading tissue measurements from XPC and the gold-standard histology. These results provide evidence of the significant potential of techniques based on XPC for 3D imaging of hydrogel structure and local tissue response. PMID:26487123

Justification of Fuzzy Declustering Vector Quantization Modeling in Classification of Genotype-Image Phenotypes

NASA Astrophysics Data System (ADS)

Ng, Theam Foo; Pham, Tuan D.; Zhou, Xiaobo

2010-01-01

With the fast development of multi-dimensional data compression and pattern classification techniques, vector quantization (VQ) has become a system that allows large reduction of data storage and computational effort. One of the most recent VQ techniques that handle the poor estimation of vector centroids due to biased data from undersampling is to use fuzzy declustering-based vector quantization (FDVQ) technique. Therefore, in this paper, we are motivated to propose a justification of FDVQ based hidden Markov model (HMM) for investigating its effectiveness and efficiency in classification of genotype-image phenotypes. The performance evaluation and comparison of the recognition accuracy between a proposed FDVQ based HMM (FDVQ-HMM) and a well-known LBG (Linde, Buzo, Gray) vector quantization based HMM (LBG-HMM) will be carried out. The experimental results show that the performances of both FDVQ-HMM and LBG-HMM are almost similar. Finally, we have justified the competitiveness of FDVQ-HMM in classification of cellular phenotype image database by using hypotheses t-test. As a result, we have validated that the FDVQ algorithm is a robust and an efficient classification technique in the application of RNAi genome-wide screening image data.

X-ray Phase Contrast Allows Three Dimensional, Quantitative Imaging of Hydrogel Implants

DOE PAGES

Appel, Alyssa A.; Larson, Jeffrey C.; Jiang, Bin; ...

2015-10-20

Three dimensional imaging techniques are needed for the evaluation and assessment of biomaterials used for tissue engineering and drug delivery applications. Hydrogels are a particularly popular class of materials for medical applications but are difficult to image in tissue using most available imaging modalities. Imaging techniques based on X-ray Phase Contrast (XPC) have shown promise for tissue engineering applications due to their ability to provide image contrast based on multiple X-ray properties. In this manuscript we describe results using XPC to image a model hydrogel and soft tissue structure. Porous fibrin loaded poly(ethylene glycol) hydrogels were synthesized and implanted inmore » a rodent subcutaneous model. Samples were explanted and imaged with an analyzer-based XPC technique and processed and stained for histology for comparison. Both hydrogel and soft tissues structures could be identified in XPC images. Structure in skeletal muscle adjacent could be visualized and invading fibrovascular tissue could be quantified. In quantitative results, there were no differences between XPC and the gold-standard histological measurements. These results provide evidence of the significant potential of techniques based on XPC for 3D imaging of hydrogel structure and local tissue response.« less

Application of Large-Scale Database-Based Online Modeling to Plant State Long-Term Estimation

NASA Astrophysics Data System (ADS)

Ogawa, Masatoshi; Ogai, Harutoshi

Recently, attention has been drawn to the local modeling techniques of a new idea called “Just-In-Time (JIT) modeling”. To apply “JIT modeling” to a large amount of database online, “Large-scale database-based Online Modeling (LOM)” has been proposed. LOM is a technique that makes the retrieval of neighboring data more efficient by using both “stepwise selection” and quantization. In order to predict the long-term state of the plant without using future data of manipulated variables, an Extended Sequential Prediction method of LOM (ESP-LOM) has been proposed. In this paper, the LOM and the ESP-LOM are introduced.

Quantum Approximate Methods for the Atomistic Modeling of Multicomponent Alloys. Chapter 7

NASA Technical Reports Server (NTRS)

Bozzolo, Guillermo; Garces, Jorge; Mosca, Hugo; Gargano, pablo; Noebe, Ronald D.; Abel, Phillip

2007-01-01

This chapter describes the role of quantum approximate methods in the understanding of complex multicomponent alloys at the atomic level. The need to accelerate materials design programs based on economical and efficient modeling techniques provides the framework for the introduction of approximations and simplifications in otherwise rigorous theoretical schemes. As a promising example of the role that such approximate methods might have in the development of complex systems, the BFS method for alloys is presented and applied to Ru-rich Ni-base superalloys and also to the NiAI(Ti,Cu) system, highlighting the benefits that can be obtained from introducing simple modeling techniques to the investigation of such complex systems.

Verification and Validation of Autonomy Software at NASA

NASA Technical Reports Server (NTRS)

Pecheur, Charles

2000-01-01

Autonomous software holds the promise of new operation possibilities, easier design and development and lower operating costs. However, as those system close control loops and arbitrate resources on board with specialized reasoning, the range of possible situations becomes very large and uncontrollable from the outside, making conventional scenario-based testing very inefficient. Analytic verification and validation (V&V) techniques, and model checking in particular, can provide significant help for designing autonomous systems in a more efficient and reliable manner, by providing a better coverage and allowing early error detection. This article discusses the general issue of V&V of autonomy software, with an emphasis towards model-based autonomy, model-checking techniques and concrete experiments at NASA.

Verification and Validation of Autonomy Software at NASA

NASA Technical Reports Server (NTRS)

Pecheur, Charles

2000-01-01

Autonomous software holds the promise of new operation possibilities, easier design and development, and lower operating costs. However, as those system close control loops and arbitrate resources on-board with specialized reasoning, the range of possible situations becomes very large and uncontrollable from the outside, making conventional scenario-based testing very inefficient. Analytic verification and validation (V&V) techniques, and model checking in particular, can provide significant help for designing autonomous systems in a more efficient and reliable manner, by providing a better coverage and allowing early error detection. This article discusses the general issue of V&V of autonomy software, with an emphasis towards model-based autonomy, model-checking techniques, and concrete experiments at NASA.

Approaches to Classroom-Based Computational Science.

ERIC Educational Resources Information Center

Guzdial, Mark

Computational science includes the use of computer-based modeling and simulation to define and test theories about scientific phenomena. The challenge for educators is to develop techniques for implementing computational science in the classroom. This paper reviews some previous work on the use of simulation alone (without modeling), modeling…

Querying and Ranking XML Documents.

ERIC Educational Resources Information Center

Schlieder, Torsten; Meuss, Holger

2002-01-01

Discussion of XML, information retrieval, precision, and recall focuses on a retrieval technique that adopts the similarity measure of the vector space model, incorporates the document structure, and supports structured queries. Topics include a query model based on tree matching; structured queries and term-based ranking; and term frequency and…

Image-based 3D reconstruction and virtual environmental walk-through

NASA Astrophysics Data System (ADS)

Sun, Jifeng; Fang, Lixiong; Luo, Ying

2001-09-01

We present a 3D reconstruction method, which combines geometry-based modeling, image-based modeling and rendering techniques. The first component is an interactive geometry modeling method which recovery of the basic geometry of the photographed scene. The second component is model-based stereo algorithm. We discus the image processing problems and algorithms of walking through in virtual space, then designs and implement a high performance multi-thread wandering algorithm. The applications range from architectural planning and archaeological reconstruction to virtual environments and cinematic special effects.

The accuracy comparison between ARFIMA and singular spectrum analysis for forecasting the sales volume of motorcycle in Indonesia

NASA Astrophysics Data System (ADS)

Sitohang, Yosep Oktavianus; Darmawan, Gumgum

2017-08-01

This research attempts to compare between two forecasting models in time series analysis for predicting the sales volume of motorcycle in Indonesia. The first forecasting model used in this paper is Autoregressive Fractionally Integrated Moving Average (ARFIMA). ARFIMA can handle non-stationary data and has a better performance than ARIMA in forecasting accuracy on long memory data. This is because the fractional difference parameter can explain correlation structure in data that has short memory, long memory, and even both structures simultaneously. The second forecasting model is Singular spectrum analysis (SSA). The advantage of the technique is that it is able to decompose time series data into the classic components i.e. trend, cyclical, seasonal and noise components. This makes the forecasting accuracy of this technique significantly better. Furthermore, SSA is a model-free technique, so it is likely to have a very wide range in its application. Selection of the best model is based on the value of the lowest MAPE. Based on the calculation, it is obtained the best model for ARFIMA is ARFIMA (3, d = 0, 63, 0) with MAPE value of 22.95 percent. For SSA with a window length of 53 and 4 group of reconstructed data, resulting MAPE value of 13.57 percent. Based on these results it is concluded that SSA produces better forecasting accuracy.

Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson's Disease.

PubMed

Gao, Chao; Sun, Hanbo; Wang, Tuo; Tang, Ming; Bohnen, Nicolaas I; Müller, Martijn L T M; Herman, Talia; Giladi, Nir; Kalinin, Alexandr; Spino, Cathie; Dauer, William; Hausdorff, Jeffrey M; Dinov, Ivo D

2018-05-08

In this study, we apply a multidisciplinary approach to investigate falls in PD patients using clinical, demographic and neuroimaging data from two independent initiatives (University of Michigan and Tel Aviv Sourasky Medical Center). Using machine learning techniques, we construct predictive models to discriminate fallers and non-fallers. Through controlled feature selection, we identified the most salient predictors of patient falls including gait speed, Hoehn and Yahr stage, postural instability and gait difficulty-related measurements. The model-based and model-free analytical methods we employed included logistic regression, random forests, support vector machines, and XGboost. The reliability of the forecasts was assessed by internal statistical (5-fold) cross validation as well as by external out-of-bag validation. Four specific challenges were addressed in the study: Challenge 1, develop a protocol for harmonizing and aggregating complex, multisource, and multi-site Parkinson's disease data; Challenge 2, identify salient predictive features associated with specific clinical traits, e.g., patient falls; Challenge 3, forecast patient falls and evaluate the classification performance; and Challenge 4, predict tremor dominance (TD) vs. posture instability and gait difficulty (PIGD). Our findings suggest that, compared to other approaches, model-free machine learning based techniques provide a more reliable clinical outcome forecasting of falls in Parkinson's patients, for example, with a classification accuracy of about 70-80%.

Modelling hourly dissolved oxygen concentration (DO) using dynamic evolving neural-fuzzy inference system (DENFIS)-based approach: case study of Klamath River at Miller Island Boat Ramp, OR, USA.

PubMed

Heddam, Salim

2014-01-01

In this study, we present application of an artificial intelligence (AI) technique model called dynamic evolving neural-fuzzy inference system (DENFIS) based on an evolving clustering method (ECM), for modelling dissolved oxygen concentration in a river. To demonstrate the forecasting capability of DENFIS, a one year period from 1 January 2009 to 30 December 2009, of hourly experimental water quality data collected by the United States Geological Survey (USGS Station No: 420853121505500) station at Klamath River at Miller Island Boat Ramp, OR, USA, were used for model development. Two DENFIS-based models are presented and compared. The two DENFIS systems are: (1) offline-based system named DENFIS-OF, and (2) online-based system, named DENFIS-ON. The input variables used for the two models are water pH, temperature, specific conductance, and sensor depth. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE), Willmott index of agreement (d) and correlation coefficient (CC) statistics. The lowest root mean square error and highest correlation coefficient values were obtained with the DENFIS-ON method. The results obtained with DENFIS models are compared with linear (multiple linear regression, MLR) and nonlinear (multi-layer perceptron neural networks, MLPNN) methods. This study demonstrates that DENFIS-ON investigated herein outperforms all the proposed techniques for DO modelling.

Segment-based acoustic models for continuous speech recognition

NASA Astrophysics Data System (ADS)

Ostendorf, Mari; Rohlicek, J. R.

1993-07-01

This research aims to develop new and more accurate stochastic models for speaker-independent continuous speech recognition, by extending previous work in segment-based modeling and by introducing a new hierarchical approach to representing intra-utterance statistical dependencies. These techniques, which are more costly than traditional approaches because of the large search space associated with higher order models, are made feasible through rescoring a set of HMM-generated N-best sentence hypotheses. We expect these different modeling techniques to result in improved recognition performance over that achieved by current systems, which handle only frame-based observations and assume that these observations are independent given an underlying state sequence. In the fourth quarter of the project, we have completed the following: (1) ported our recognition system to the Wall Street Journal task, a standard task in the ARPA community; (2) developed an initial dependency-tree model of intra-utterance observation correlation; and (3) implemented baseline language model estimation software. Our initial results on the Wall Street Journal task are quite good and represent significantly improved performance over most HMM systems reporting on the Nov. 1992 5k vocabulary test set.

Coupling discrete and continuum concentration particle models for multiscale and hybrid molecular-continuum simulations

DOE PAGES

Petsev, Nikolai Dimitrov; Leal, L. Gary; Shell, M. Scott

2017-12-21

Hybrid molecular-continuum simulation techniques afford a number of advantages for problems in the rapidly burgeoning area of nanoscale engineering and technology, though they are typically quite complex to implement and limited to single-component fluid systems. We describe an approach for modeling multicomponent hydrodynamic problems spanning multiple length scales when using particle-based descriptions for both the finely-resolved (e.g. molecular dynamics) and coarse-grained (e.g. continuum) subregions within an overall simulation domain. This technique is based on the multiscale methodology previously developed for mesoscale binary fluids [N. D. Petsev, L. G. Leal, and M. S. Shell, J. Chem. Phys. 144, 84115 (2016)], simulatedmore » using a particle-based continuum method known as smoothed dissipative particle dynamics (SDPD). An important application of this approach is the ability to perform coupled molecular dynamics (MD) and continuum modeling of molecularly miscible binary mixtures. In order to validate this technique, we investigate multicomponent hybrid MD-continuum simulations at equilibrium, as well as non-equilibrium cases featuring concentration gradients.« less

Coupling discrete and continuum concentration particle models for multiscale and hybrid molecular-continuum simulations

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

Petsev, Nikolai Dimitrov; Leal, L. Gary; Shell, M. Scott

Hybrid molecular-continuum simulation techniques afford a number of advantages for problems in the rapidly burgeoning area of nanoscale engineering and technology, though they are typically quite complex to implement and limited to single-component fluid systems. We describe an approach for modeling multicomponent hydrodynamic problems spanning multiple length scales when using particle-based descriptions for both the finely-resolved (e.g. molecular dynamics) and coarse-grained (e.g. continuum) subregions within an overall simulation domain. This technique is based on the multiscale methodology previously developed for mesoscale binary fluids [N. D. Petsev, L. G. Leal, and M. S. Shell, J. Chem. Phys. 144, 84115 (2016)], simulatedmore » using a particle-based continuum method known as smoothed dissipative particle dynamics (SDPD). An important application of this approach is the ability to perform coupled molecular dynamics (MD) and continuum modeling of molecularly miscible binary mixtures. In order to validate this technique, we investigate multicomponent hybrid MD-continuum simulations at equilibrium, as well as non-equilibrium cases featuring concentration gradients.« less

A cavitation model based on Eulerian stochastic fields

NASA Astrophysics Data System (ADS)

Magagnato, F.; Dumond, J.

2013-12-01

Non-linear phenomena can often be described using probability density functions (pdf) and pdf transport models. Traditionally the simulation of pdf transport requires Monte-Carlo codes based on Lagrangian "particles" or prescribed pdf assumptions including binning techniques. Recently, in the field of combustion, a novel formulation called the stochastic-field method solving pdf transport based on Eulerian fields has been proposed which eliminates the necessity to mix Eulerian and Lagrangian techniques or prescribed pdf assumptions. In the present work, for the first time the stochastic-field method is applied to multi-phase flow and in particular to cavitating flow. To validate the proposed stochastic-field cavitation model, two applications are considered. Firstly, sheet cavitation is simulated in a Venturi-type nozzle. The second application is an innovative fluidic diode which exhibits coolant flashing. Agreement with experimental results is obtained for both applications with a fixed set of model constants. The stochastic-field cavitation model captures the wide range of pdf shapes present at different locations.

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.

Using Technology to Facilitate and Enhance Project-based Learning in Mathematical Physics

NASA Astrophysics Data System (ADS)

Duda, Gintaras

2011-04-01

Problem-based and project-based learning are two pedagogical techniques that have several clear advantages over traditional instructional methods: 1) both techniques are active and student centered, 2) students confront real-world and/or highly complex problems, and 3) such exercises model the way science and engineering are done professionally. This talk will present an experiment in project/problem-based learning in a mathematical physics course. The group project in the course involved modeling a zombie outbreak of the type seen in AMC's ``The Walking Dead.'' Students researched, devised, and solved their mathematical models for the spread of zombie-like infection. Students used technology in all stages; in fact, since analytical solutions to the models were often impossible, technology was a necessary and critical component of the challenge. This talk will explore the use of technology in general in problem and project-based learning and will detail some specific examples of how technology was used to enhance student learning in this course. A larger issue of how students use the Internet to learn will also be explored.

A comparison of two approaches to modelling snow cover dynamics at the Polish Polar Station at Hornsund

NASA Astrophysics Data System (ADS)

Luks, B.; Osuch, M.; Romanowicz, R. J.

2012-04-01

We compare two approaches to modelling snow cover dynamics at the Polish Polar Station at Hornsund. In the first approach we apply physically-based Utah Energy Balance Snow Accumulation and Melt Model (UEB) (Tarboton et al., 1995; Tarboton and Luce, 1996). The model uses a lumped representation of the snowpack with two primary state variables: snow water equivalence and energy. Its main driving inputs are: air temperature, precipitation, wind speed, humidity and radiation (estimated from the diurnal temperature range). Those variables are used for physically-based calculations of radiative, sensible, latent and advective heat exchanges with a 3 hours time step. The second method is an application of a statistically efficient lumped parameter time series approach to modelling the dynamics of snow cover , based on daily meteorological measurements from the same area. A dynamic Stochastic Transfer Function model is developed that follows the Data Based Mechanistic approach, where a stochastic data-based identification of model structure and an estimation of its parameters are followed by a physical interpretation. We focus on the analysis of uncertainty of both model outputs. In the time series approach, the applied techniques also provide estimates of the modeling errors and the uncertainty of the model parameters. In the first, physically-based approach the applied UEB model is deterministic. It assumes that the observations are without errors and that the model structure perfectly describes the processes within the snowpack. To take into account the model and observation errors, we applied a version of the Generalized Likelihood Uncertainty Estimation technique (GLUE). This technique also provide estimates of the modelling errors and the uncertainty of the model parameters. The observed snowpack water equivalent values are compared with those simulated with 95% confidence bounds. This work was supported by National Science Centre of Poland (grant no. 7879/B/P01/2011/40). Tarboton, D. G., T. G. Chowdhury and T. H. Jackson, 1995. A Spatially Distributed Energy Balance Snowmelt Model. In K. A. Tonnessen, M. W. Williams and M. Tranter (Ed.), Proceedings of a Boulder Symposium, July 3-14, IAHS Publ. no. 228, pp. 141-155. Tarboton, D. G. and C. H. Luce, 1996. Utah Energy Balance Snow Accumulation and Melt Model (UEB). Computer model technical description and users guide, Utah Water Research Laboratory and USDA Forest Service Intermountain Research Station (http://www.engineering.usu.edu/dtarb/). 64 pp.

Accurate lithography simulation model based on convolutional neural networks

NASA Astrophysics Data System (ADS)

Watanabe, Yuki; Kimura, Taiki; Matsunawa, Tetsuaki; Nojima, Shigeki

2017-07-01

Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.

GRAVTool, a Package to Compute Geoid Model by Remove-Compute-Restore Technique

NASA Astrophysics Data System (ADS)

Marotta, G. S.; Blitzkow, D.; Vidotti, R. M.

2015-12-01

Currently, there are several methods to determine geoid models. They can be based on terrestrial gravity data, geopotential coefficients, astro-geodetic data or a combination of them. Among the techniques to compute a precise geoid model, the Remove-Compute-Restore (RCR) has been widely applied. It considers short, medium and long wavelengths derived from altitude data provided by Digital Terrain Models (DTM), terrestrial gravity data and global geopotential coefficients, respectively. In order to apply this technique, it is necessary to create procedures that compute gravity anomalies and geoid models, by the integration of different wavelengths, and that adjust these models to one local vertical datum. This research presents a developed package called GRAVTool based on MATLAB software to compute local geoid models by RCR technique and its application in a study area. The studied area comprehends the federal district of Brazil, with ~6000 km², wavy relief, heights varying from 600 m to 1340 m, located between the coordinates 48.25ºW, 15.45ºS and 47.33ºW, 16.06ºS. The results of the numerical example on the studied area show the local geoid model computed by the GRAVTool package (Figure), using 1377 terrestrial gravity data, SRTM data with 3 arc second of resolution, and geopotential coefficients of the EIGEN-6C4 model to degree 360. The accuracy of the computed model (σ = ± 0.071 m, RMS = 0.069 m, maximum = 0.178 m and minimum = -0.123 m) matches the uncertainty (σ =± 0.073) of 21 points randomly spaced where the geoid was computed by geometrical leveling technique supported by positioning GNSS. The results were also better than those achieved by Brazilian official regional geoid model (σ = ± 0.099 m, RMS = 0.208 m, maximum = 0.419 m and minimum = -0.040 m).

Active cleaning technique device

NASA Technical Reports Server (NTRS)

Shannon, R. L.; Gillette, R. B.

1973-01-01

The objective of this program was to develop a laboratory demonstration model of an active cleaning technique (ACT) device. The principle of this device is based primarily on the technique for removing contaminants from optical surfaces. This active cleaning technique involves exposing contaminated surfaces to a plasma containing atomic oxygen or combinations of other reactive gases. The ACT device laboratory demonstration model incorporates, in addition to plasma cleaning, the means to operate the device as an ion source for sputtering experiments. The overall ACT device includes a plasma generation tube, an ion accelerator, a gas supply system, a RF power supply and a high voltage dc power supply.

Model-based Acceleration Control of Turbofan Engines with a Hammerstein-Wiener Representation

NASA Astrophysics Data System (ADS)

Wang, Jiqiang; Ye, Zhifeng; Hu, Zhongzhi; Wu, Xin; Dimirovsky, Georgi; Yue, Hong

2017-05-01

Acceleration control of turbofan engines is conventionally designed through either schedule-based or acceleration-based approach. With the widespread acceptance of model-based design in aviation industry, it becomes necessary to investigate the issues associated with model-based design for acceleration control. In this paper, the challenges for implementing model-based acceleration control are explained; a novel Hammerstein-Wiener representation of engine models is introduced; based on the Hammerstein-Wiener model, a nonlinear generalized minimum variance type of optimal control law is derived; the feature of the proposed approach is that it does not require the inversion operation that usually upsets those nonlinear control techniques. The effectiveness of the proposed control design method is validated through a detailed numerical study.

A VAS-numerical model impact study using the Gal-Chen variational approach

NASA Technical Reports Server (NTRS)

Aune, Robert M.; Tuccillo, James J.; Uccellini, Louis W.; Petersen, Ralph A.

1987-01-01

A numerical study based on the use of a variational assimilation technique of Gal-Chen (1983, 1986) was conducted to assess the impact of incorporating temperature data from the VISSR Atmospheric Sounder (VAS) into a regional-scale numerical model. A comparison with the results of a control forecast using only conventional data indicated that the assimilation technique successfully combines actual VAS temperature observations with the dynamically balanced model fields without destabilizing the model during the assimilation cycle. Moreover, increasing the temporal frequency of VAS temperature insertions during the assimilation cycle was shown to enhance the impact on the model forecast through successively longer forecast periods. The incorporation of a nudging technique, whereby the model temperature field is constrained toward the VAS 'updated' values during the assimilation cycle, further enhances the impact of the VAS temperature data.

Building Thermal Models

NASA Technical Reports Server (NTRS)

Peabody, Hume L.

2017-01-01

This presentation is meant to be an overview of the model building process It is based on typical techniques (Monte Carlo Ray Tracing for radiation exchange, Lumped Parameter, Finite Difference for thermal solution) used by the aerospace industry This is not intended to be a "How to Use ThermalDesktop" course. It is intended to be a "How to Build Thermal Models" course and the techniques will be demonstrated using the capabilities of ThermalDesktop (TD). Other codes may or may not have similar capabilities. The General Model Building Process can be broken into four top level steps: 1. Build Model; 2. Check Model; 3. Execute Model; 4. Verify Results.

Integrating Intracellular Dynamics Using CompuCell3D and Bionetsolver: Applications to Multiscale Modelling of Cancer Cell Growth and Invasion

PubMed Central

Andasari, Vivi; Roper, Ryan T.; Swat, Maciej H.; Chaplain, Mark A. J.

2012-01-01

In this paper we present a multiscale, individual-based simulation environment that integrates CompuCell3D for lattice-based modelling on the cellular level and Bionetsolver for intracellular modelling. CompuCell3D or CC3D provides an implementation of the lattice-based Cellular Potts Model or CPM (also known as the Glazier-Graner-Hogeweg or GGH model) and a Monte Carlo method based on the metropolis algorithm for system evolution. The integration of CC3D for cellular systems with Bionetsolver for subcellular systems enables us to develop a multiscale mathematical model and to study the evolution of cell behaviour due to the dynamics inside of the cells, capturing aspects of cell behaviour and interaction that is not possible using continuum approaches. We then apply this multiscale modelling technique to a model of cancer growth and invasion, based on a previously published model of Ramis-Conde et al. (2008) where individual cell behaviour is driven by a molecular network describing the dynamics of E-cadherin and -catenin. In this model, which we refer to as the centre-based model, an alternative individual-based modelling technique was used, namely, a lattice-free approach. In many respects, the GGH or CPM methodology and the approach of the centre-based model have the same overall goal, that is to mimic behaviours and interactions of biological cells. Although the mathematical foundations and computational implementations of the two approaches are very different, the results of the presented simulations are compatible with each other, suggesting that by using individual-based approaches we can formulate a natural way of describing complex multi-cell, multiscale models. The ability to easily reproduce results of one modelling approach using an alternative approach is also essential from a model cross-validation standpoint and also helps to identify any modelling artefacts specific to a given computational approach. PMID:22461894

A new multiscale air quality transport model (Fluidity, 4.1.9) using fully unstructured anisotropic adaptive mesh technology

NASA Astrophysics Data System (ADS)

Zheng, J.; Zhu, J.; Wang, Z.; Fang, F.; Pain, C. C.; Xiang, J.

2015-06-01

A new anisotropic hr-adaptive mesh technique has been applied to modelling of multiscale transport phenomena, which is based on a discontinuous Galerkin/control volume discretization on unstructured meshes. Over existing air quality models typically based on static-structured grids using a locally nesting technique, the advantage of the anisotropic hr-adaptive model has the ability to adapt the mesh according to the evolving pollutant distribution and flow features. That is, the mesh resolution can be adjusted dynamically to simulate the pollutant transport process accurately and effectively. To illustrate the capability of the anisotropic adaptive unstructured mesh model, three benchmark numerical experiments have been setup for two-dimensional (2-D) transport phenomena. Comparisons have been made between the results obtained using uniform resolution meshes and anisotropic adaptive resolution meshes.

Integrative Approach for Computationally Inferring Interactions between the Alpha and Beta Subunits of the Calcium-Activated Potassium Channel (BK): a Docking Study

PubMed Central

González, Janneth; Gálvez, Angela; Morales, Ludis; Barreto, George E.; Capani, Francisco; Sierra, Omar; Torres, Yolima

2013-01-01

Three-dimensional models of the alpha- and beta-1 subunits of the calcium-activated potassium channel (BK) were predicted by threading modeling. A recursive approach comprising of sequence alignment and model building based on three templates was used to build these models, with the refinement of non-conserved regions carried out using threading techniques. The complex formed by the subunits was studied by means of docking techniques, using 3D models of the two subunits, and an approach based on rigid-body structures. Structural effects of the complex were analyzed with respect to hydrogen-bond interactions and binding-energy calculations. Potential interaction sites of the complex were determined by referencing a study of the difference accessible surface area (DASA) of the protein subunits in the complex. PMID:23492851

Research on ionospheric tomography based on variable pixel height

NASA Astrophysics Data System (ADS)

Zheng, Dunyong; Li, Peiqing; He, Jie; Hu, Wusheng; Li, Chaokui

2016-05-01

A novel ionospheric tomography technique based on variable pixel height was developed for the tomographic reconstruction of the ionospheric electron density distribution. The method considers the height of each pixel as an unknown variable, which is retrieved during the inversion process together with the electron density values. In contrast to conventional computerized ionospheric tomography (CIT), which parameterizes the model with a fixed pixel height, the variable-pixel-height computerized ionospheric tomography (VHCIT) model applies a disturbance to the height of each pixel. In comparison with conventional CIT models, the VHCIT technique achieved superior results in a numerical simulation. A careful validation of the reliability and superiority of VHCIT was performed. According to the results of the statistical analysis of the average root mean square errors, the proposed model offers an improvement by 15% compared with conventional CIT models.

Flexible multibody simulation of automotive systems with non-modal model reduction techniques

NASA Astrophysics Data System (ADS)

Shiiba, Taichi; Fehr, Jörg; Eberhard, Peter

2012-12-01

The stiffness of the body structure of an automobile has a strong relationship with its noise, vibration, and harshness (NVH) characteristics. In this paper, the effect of the stiffness of the body structure upon ride quality is discussed with flexible multibody dynamics. In flexible multibody simulation, the local elastic deformation of the vehicle has been described traditionally with modal shape functions. Recently, linear model reduction techniques from system dynamics and mathematics came into the focus to find more sophisticated elastic shape functions. In this work, the NVH-relevant states of a racing kart are simulated, whereas the elastic shape functions are calculated with modern model reduction techniques like moment matching by projection on Krylov-subspaces, singular value decomposition-based reduction techniques, and combinations of those. The whole elastic multibody vehicle model consisting of tyres, steering, axle, etc. is considered, and an excitation with a vibration characteristics in a wide frequency range is evaluated in this paper. The accuracy and the calculation performance of those modern model reduction techniques is investigated including a comparison of the modal reduction approach.

Floating Node Method and Virtual Crack Closure Technique for Modeling Matrix Cracking-Delamination Interaction

NASA Technical Reports Server (NTRS)

DeCarvalho, N. V.; Chen, B. Y.; Pinho, S. T.; Baiz, P. M.; Ratcliffe, J. G.; Tay, T. E.

2013-01-01

A novel approach is proposed for high-fidelity modeling of progressive damage and failure in composite materials that combines the Floating Node Method (FNM) and the Virtual Crack Closure Technique (VCCT) to represent multiple interacting failure mechanisms in a mesh-independent fashion. In this study, the approach is applied to the modeling of delamination migration in cross-ply tape laminates. Delamination, matrix cracking, and migration are all modeled using fracture mechanics based failure and migration criteria. The methodology proposed shows very good qualitative and quantitative agreement with experiments.

Floating Node Method and Virtual Crack Closure Technique for Modeling Matrix Cracking-Delamination Migration

NASA Technical Reports Server (NTRS)

DeCarvalho, Nelson V.; Chen, B. Y.; Pinho, Silvestre T.; Baiz, P. M.; Ratcliffe, James G.; Tay, T. E.

2013-01-01

A novel approach is proposed for high-fidelity modeling of progressive damage and failure in composite materials that combines the Floating Node Method (FNM) and the Virtual Crack Closure Technique (VCCT) to represent multiple interacting failure mechanisms in a mesh-independent fashion. In this study, the approach is applied to the modeling of delamination migration in cross-ply tape laminates. Delamination, matrix cracking, and migration are all modeled using fracture mechanics based failure and migration criteria. The methodology proposed shows very good qualitative and quantitative agreement with experiments.

Intelligence Fusion Modeling. A Proposed Approach.

DTIC Science & Technology

1983-09-16

based techniques developed by artificial intelligence researchers. This paper describes the application of these techniques in the modeling of an... intelligence requirements, although the methods presented are applicable . We treat PIR/IR as given. -7- -- -W V"W v* 1.- . :71.,v It k*~ ~-- Movement...items from the PIR/IR/HVT decomposition are received from the CMDS. Formatted tactical intelligence reports are received from sensors of like types

Quality assurance paradigms for artificial intelligence in modelling and simulation

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

Oren, T.I.

1987-04-01

New classes of quality assurance concepts and techniques are required for the advanced knowledge-processing paradigms (such as artificial intelligence, expert systems, or knowledge-based systems) and the complex problems that only simulative systems can cope with. A systematization of quality assurance problems as well as examples are given to traditional and cognizant quality assurance techniques in traditional and cognizant modelling and simulation.

The Use of a Context-Based Information Retrieval Technique

DTIC Science & Technology

2009-07-01

provided in context. Latent Semantic Analysis (LSA) is a statistical technique for inferring contextual and structural information, and previous studies...WAIS). 10 DSTO-TR-2322 1.4.4 Latent Semantic Analysis LSA, which is also known as latent semantic indexing (LSI), uses a statistical and...1.4.6 Language Models In contrast, natural language models apply algorithms that combine statistical information with semantic information. Semantic

A new class of compact high sensitive tiltmeter based on the UNISA folded pendulum mechanical architecture

NASA Astrophysics Data System (ADS)

Barone, Fabrizio; Giordano, Gerardo

2018-02-01

We present the Extended Folded Pendulum Model (EFPM), a model developed for a quantitative description of the dynamical behavior of a folded pendulum generically oriented in space. This model, based on the Tait-Bryan angular reference system, highlights the relationship between the folded pendulum orientation in the gravitational field and its natural resonance frequency. Tis model validated by tests performed with a monolithic UNISA Folded Pendulum, highlights a new technique of implementation of folded pendulum based tiltmeters.

Image processing, geometric modeling and data management for development of a virtual bone surgery system.

PubMed

Niu, Qiang; Chi, Xiaoyi; Leu, Ming C; Ochoa, Jorge

2008-01-01

This paper describes image processing, geometric modeling and data management techniques for the development of a virtual bone surgery system. Image segmentation is used to divide CT scan data into different segments representing various regions of the bone. A region-growing algorithm is used to extract cortical bone and trabecular bone structures systematically and efficiently. Volume modeling is then used to represent the bone geometry based on the CT scan data. Material removal simulation is achieved by continuously performing Boolean subtraction of the surgical tool model from the bone model. A quadtree-based adaptive subdivision technique is developed to handle the large set of data in order to achieve the real-time simulation and visualization required for virtual bone surgery. A Marching Cubes algorithm is used to generate polygonal faces from the volumetric data. Rendering of the generated polygons is performed with the publicly available VTK (Visualization Tool Kit) software. Implementation of the developed techniques consists of developing a virtual bone-drilling software program, which allows the user to manipulate a virtual drill to make holes with the use of a PHANToM device on a bone model derived from real CT scan data.

Interactive, graphical processing unitbased evaluation of evacuation scenarios at the state scale

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

Perumalla, Kalyan S; Aaby, Brandon G; Yoginath, Srikanth B

2011-01-01

In large-scale scenarios, transportation modeling and simulation is severely constrained by simulation time. For example, few real- time simulators scale to evacuation traffic scenarios at the level of an entire state, such as Louisiana (approximately 1 million links) or Florida (2.5 million links). New simulation approaches are needed to overcome severe computational demands of conventional (microscopic or mesoscopic) modeling techniques. Here, a new modeling and execution methodology is explored that holds the potential to provide a tradeoff among the level of behavioral detail, the scale of transportation network, and real-time execution capabilities. A novel, field-based modeling technique and its implementationmore » on graphical processing units are presented. Although additional research with input from domain experts is needed for refining and validating the models, the techniques reported here afford interactive experience at very large scales of multi-million road segments. Illustrative experiments on a few state-scale net- works are described based on an implementation of this approach in a software system called GARFIELD. Current modeling cap- abilities and implementation limitations are described, along with possible use cases and future research.« less

Deformation-based augmented reality for hepatic surgery.

PubMed

Haouchine, Nazim; Dequidt, Jérémie; Berger, Marie-Odile; Cotin, Stéphane

2013-01-01

In this paper we introduce a method for augmenting the laparoscopic view during hepatic tumor resection. Using augmented reality techniques, vessels, tumors and cutting planes computed from pre-operative data can be overlaid onto the laparoscopic video. Compared to current techniques, which are limited to a rigid registration of the pre-operative liver anatomy with the intra-operative image, we propose a real-time, physics-based, non-rigid registration. The main strength of our approach is that the deformable model can also be used to regularize the data extracted from the computer vision algorithms. We show preliminary results on a video sequence which clearly highlights the interest of using physics-based model for elastic registration.

An assessment of finite-element modeling techniques for thick-solid/thin-shell joints analysis

NASA Technical Reports Server (NTRS)

Min, J. B.; Androlake, S. G.

1993-01-01

The subject of finite-element modeling has long been of critical importance to the practicing designer/analyst who is often faced with obtaining an accurate and cost-effective structural analysis of a particular design. Typically, these two goals are in conflict. The purpose is to discuss the topic of finite-element modeling for solid/shell connections (joints) which are significant for the practicing modeler. Several approaches are currently in use, but frequently various assumptions restrict their use. Such techniques currently used in practical applications were tested, especially to see which technique is the most ideally suited for the computer aided design (CAD) environment. Some basic thoughts regarding each technique are also discussed. As a consequence, some suggestions based on the results are given to lead reliable results in geometrically complex joints where the deformation and stress behavior are complicated.

Design and Evaluation of Perceptual-based Object Group Selection Techniques

NASA Astrophysics Data System (ADS)

Dehmeshki, Hoda

Selecting groups of objects is a frequent task in graphical user interfaces. It is required prior to many standard operations such as deletion, movement, or modification. Conventional selection techniques are lasso, rectangle selection, and the selection and de-selection of items through the use of modifier keys. These techniques may become time-consuming and error-prone when target objects are densely distributed or when the distances between target objects are large. Perceptual-based selection techniques can considerably improve selection tasks when targets have a perceptual structure, for example when arranged along a line. Current methods to detect such groups use ad hoc grouping algorithms that are not based on results from perception science. Moreover, these techniques do not allow selecting groups with arbitrary arrangements or permit modifying a selection. This dissertation presents two domain-independent perceptual-based systems that address these issues. Based on established group detection models from perception research, the proposed systems detect perceptual groups formed by the Gestalt principles of good continuation and proximity. The new systems provide gesture-based or click-based interaction techniques for selecting groups with curvilinear or arbitrary structures as well as clusters. Moreover, the gesture-based system is adapted for the graph domain to facilitate path selection. This dissertation includes several user studies that show the proposed systems outperform conventional selection techniques when targets form salient perceptual groups and are still competitive when targets are semi-structured.

An efficient interpolation technique for jump proposals in reversible-jump Markov chain Monte Carlo calculations

PubMed Central

Farr, W. M.; Mandel, I.; Stevens, D.

2015-01-01

Selection among alternative theoretical models given an observed dataset is an important challenge in many areas of physics and astronomy. Reversible-jump Markov chain Monte Carlo (RJMCMC) is an extremely powerful technique for performing Bayesian model selection, but it suffers from a fundamental difficulty and it requires jumps between model parameter spaces, but cannot efficiently explore both parameter spaces at once. Thus, a naive jump between parameter spaces is unlikely to be accepted in the Markov chain Monte Carlo (MCMC) algorithm and convergence is correspondingly slow. Here, we demonstrate an interpolation technique that uses samples from single-model MCMCs to propose intermodel jumps from an approximation to the single-model posterior of the target parameter space. The interpolation technique, based on a kD-tree data structure, is adaptive and efficient in modest dimensionality. We show that our technique leads to improved convergence over naive jumps in an RJMCMC, and compare it to other proposals in the literature to improve the convergence of RJMCMCs. We also demonstrate the use of the same interpolation technique as a way to construct efficient ‘global’ proposal distributions for single-model MCMCs without prior knowledge of the structure of the posterior distribution, and discuss improvements that permit the method to be used in higher dimensional spaces efficiently. PMID:26543580

Fuzzy logic application for modeling man-in-the-loop space shuttle proximity operations. M.S. Thesis - MIT

NASA Technical Reports Server (NTRS)

Brown, Robert B.

1994-01-01

A software pilot model for Space Shuttle proximity operations is developed, utilizing fuzzy logic. The model is designed to emulate a human pilot during the terminal phase of a Space Shuttle approach to the Space Station. The model uses the same sensory information available to a human pilot and is based upon existing piloting rules and techniques determined from analysis of human pilot performance. Such a model is needed to generate numerous rendezvous simulations to various Space Station assembly stages for analysis of current NASA procedures and plume impingement loads on the Space Station. The advantages of a fuzzy logic pilot model are demonstrated by comparing its performance with NASA's man-in-the-loop simulations and with a similar model based upon traditional Boolean logic. The fuzzy model is shown to respond well from a number of initial conditions, with results typical of an average human. In addition, the ability to model different individual piloting techniques and new piloting rules is demonstrated.

Response Surface Modeling Using Multivariate Orthogonal Functions

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; DeLoach, Richard

2001-01-01

A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.

A New Femtosecond Laser-Based Three-Dimensional Tomography Technique

NASA Astrophysics Data System (ADS)

Echlin, McLean P.

2011-12-01

Tomographic imaging has dramatically changed science, most notably in the fields of medicine and biology, by producing 3D views of structures which are too complex to understand in any other way. Current tomographic techniques require extensive time both for post-processing and data collection. Femtosecond laser based tomographic techniques have been developed in both standard atmosphere (femtosecond laser-based serial sectioning technique - FSLSS) and in vacuum (Tri-Beam System) for the fast collection (10 5mum3/s) of mm3 sized 3D datasets. Both techniques use femtosecond laser pulses to selectively remove layer-by-layer areas of material with low collateral damage and a negligible heat affected zone. To the authors knowledge, femtosecond lasers have never been used to serial section and these techniques have been entirely and uniquely developed by the author and his collaborators at the University of Michigan and University of California Santa Barbara. The FSLSS was applied to measure the 3D distribution of TiN particles in a 4330 steel. Single pulse ablation morphologies and rates were measured and collected from literature. Simultaneous two-phase ablation of TiN and steel matrix was shown to occur at fluences of 0.9-2 J/cm2. Laser scanning protocols were developed minimizing surface roughness to 0.1-0.4 mum for laser-based sectioning. The FSLSS technique was used to section and 3D reconstruct titanium nitride (TiN) containing 4330 steel. Statistical analysis of 3D TiN particle sizes, distribution parameters, and particle density were measured. A methodology was developed to use the 3D datasets to produce statistical volume elements (SVEs) for toughness modeling. Six FSLSS TiN datasets were sub-sampled into 48 SVEs for statistical analysis and toughness modeling using the Rice-Tracey and Garrison-Moody models. A two-parameter Weibull analysis was performed and variability in the toughness data agreed well with Ruggieri et al. bulk toughness measurements. The Tri-Beam system combines the benefits of laser based material removal (speed, low-damage, automated) with detectors that collect chemical, structural, and topological information. Multi-modal sectioning information was collected after many laser scanning passes demonstrating the capability of the Tri-Beam system.

Verifying Multi-Agent Systems via Unbounded Model Checking

NASA Technical Reports Server (NTRS)

Kacprzak, M.; Lomuscio, A.; Lasica, T.; Penczek, W.; Szreter, M.

2004-01-01

We present an approach to the problem of verification of epistemic properties in multi-agent systems by means of symbolic model checking. In particular, it is shown how to extend the technique of unbounded model checking from a purely temporal setting to a temporal-epistemic one. In order to achieve this, we base our discussion on interpreted systems semantics, a popular semantics used in multi-agent systems literature. We give details of the technique and show how it can be applied to the well known train, gate and controller problem. Keywords: model checking, unbounded model checking, multi-agent systems

Agent based simulations in disease modeling Comment on "Towards a unified approach in the modeling of fibrosis: A review with research perspectives" by Martine Ben Amar and Carlo Bianca

NASA Astrophysics Data System (ADS)

Pappalardo, Francesco; Pennisi, Marzio

2016-07-01

Fibrosis represents a process where an excessive tissue formation in an organ follows the failure of a physiological reparative or reactive process. Mathematical and computational techniques may be used to improve the understanding of the mechanisms that lead to the disease and to test potential new treatments that may directly or indirectly have positive effects against fibrosis [1]. In this scenario, Ben Amar and Bianca [2] give us a broad picture of the existing mathematical and computational tools that have been used to model fibrotic processes at the molecular, cellular, and tissue levels. Among such techniques, agent based models (ABM) can give a valuable contribution in the understanding and better management of fibrotic diseases.

A dynamic model-based approach to motion and deformation tracking of prosthetic valves from biplane x-ray images.

PubMed

Wagner, Martin G; Hatt, Charles R; Dunkerley, David A P; Bodart, Lindsay E; Raval, Amish N; Speidel, Michael A

2018-04-16

Transcatheter aortic valve replacement (TAVR) is a minimally invasive procedure in which a prosthetic heart valve is placed and expanded within a defective aortic valve. The device placement is commonly performed using two-dimensional (2D) fluoroscopic imaging. Within this work, we propose a novel technique to track the motion and deformation of the prosthetic valve in three dimensions based on biplane fluoroscopic image sequences. The tracking approach uses a parameterized point cloud model of the valve stent which can undergo rigid three-dimensional (3D) transformation and different modes of expansion. Rigid elements of the model are individually rotated and translated in three dimensions to approximate the motions of the stent. Tracking is performed using an iterative 2D-3D registration procedure which estimates the model parameters by minimizing the mean-squared image values at the positions of the forward-projected model points. Additionally, an initialization technique is proposed, which locates clusters of salient features to determine the initial position and orientation of the model. The proposed algorithms were evaluated based on simulations using a digital 4D CT phantom as well as experimentally acquired images of a prosthetic valve inside a chest phantom with anatomical background features. The target registration error was 0.12 ± 0.04 mm in the simulations and 0.64 ± 0.09 mm in the experimental data. The proposed algorithm could be used to generate 3D visualization of the prosthetic valve from two projections. In combination with soft-tissue sensitive-imaging techniques like transesophageal echocardiography, this technique could enable 3D image guidance during TAVR procedures. © 2018 American Association of Physicists in Medicine.

Mathematical models used in segmentation and fractal methods of 2-D ultrasound images

NASA Astrophysics Data System (ADS)

Moldovanu, Simona; Moraru, Luminita; Bibicu, Dorin

2012-11-01

Mathematical models are widely used in biomedical computing. The extracted data from images using the mathematical techniques are the "pillar" achieving scientific progress in experimental, clinical, biomedical, and behavioural researches. This article deals with the representation of 2-D images and highlights the mathematical support for the segmentation operation and fractal analysis in ultrasound images. A large number of mathematical techniques are suitable to be applied during the image processing stage. The addressed topics cover the edge-based segmentation, more precisely the gradient-based edge detection and active contour model, and the region-based segmentation namely Otsu method. Another interesting mathematical approach consists of analyzing the images using the Box Counting Method (BCM) to compute the fractal dimension. The results of the paper provide explicit samples performed by various combination of methods.

Evidence-based hypnotherapy for depression.

PubMed

Alladin, Assen

2010-04-01

Cognitive hypnotherapy (CH) is a comprehensive evidence-based hypnotherapy for clinical depression. This article describes the major components of CH, which integrate hypnosis with cognitive-behavior therapy as the latter provides an effective host theory for the assimilation of empirically supported treatment techniques derived from various theoretical models of psychotherapy and psychopathology. CH meets criteria for an assimilative model of psychotherapy, which is considered to be an efficacious model of psychotherapy integration. The major components of CH for depression are described in sufficient detail to allow replication, verification, and validation of the techniques delineated. CH for depression provides a template that clinicians and investigators can utilize to study the additive effects of hypnosis in the management of other psychological or medical disorders. Evidence-based hypnotherapy and research are encouraged; such a movement is necessary if clinical hypnosis is to integrate into mainstream psychotherapy.

Three Dimensional Reconstruction Workflows for Lost Cultural Heritage Monuments Exploiting Public Domain and Professional Photogrammetric Imagery

NASA Astrophysics Data System (ADS)

Wahbeh, W.; Nebiker, S.

2017-08-01

In our paper, we document experiments and results of image-based 3d reconstructions of famous heritage monuments which were recently damaged or completely destroyed by the so-called Islamic state in Syria and Iraq. The specific focus of our research is on the combined use of professional photogrammetric imagery and of publicly available imagery from the web for optimally 3d reconstructing those monuments. The investigated photogrammetric reconstruction techniques include automated bundle adjustment and dense multi-view 3d reconstruction using public domain and professional imagery on the one hand and an interactive polygonal modelling based on projected panoramas on the other. Our investigations show that the combination of these two image-based modelling techniques delivers better results in terms of model completeness, level of detail and appearance.

A Method for Generating Reduced Order Linear Models of Supersonic Inlets

NASA Technical Reports Server (NTRS)

Chicatelli, Amy; Hartley, Tom T.

1997-01-01

For the modeling of high speed propulsion systems, there are at least two major categories of models. One is based on computational fluid dynamics (CFD), and the other is based on design and analysis of control systems. CFD is accurate and gives a complete view of the internal flow field, but it typically has many states and runs much slower dm real-time. Models based on control design typically run near real-time but do not always capture the fundamental dynamics. To provide improved control models, methods are needed that are based on CFD techniques but yield models that are small enough for control analysis and design.

Application of a Tenax Model to Assess Bioavailability of Polychlorinated Biphenyls in Field Sediments

EPA Science Inventory

Recent literature has shown that bioavailability-based techniques, such as Tenax extraction, can estimate sediment exposure to benthos. In a previous study by the authors,Tenax extraction was used to create and validate a literature-based Tenax model to predict oligochaete bioac...

Color regeneration from reflective color sensor using an artificial intelligent technique.

PubMed

Saracoglu, Ömer Galip; Altural, Hayriye

2010-01-01

A low-cost optical sensor based on reflective color sensing is presented. Artificial neural network models are used to improve the color regeneration from the sensor signals. Analog voltages of the sensor are successfully converted to RGB colors. The artificial intelligent models presented in this work enable color regeneration from analog outputs of the color sensor. Besides, inverse modeling supported by an intelligent technique enables the sensor probe for use of a colorimetric sensor that relates color changes to analog voltages.

Ionospheric propagation correction modeling for satellite altimeters

NASA Technical Reports Server (NTRS)

Nesterczuk, G.

1981-01-01

The theoretical basis and avaliable accuracy verifications were reviewed and compared for ionospheric correction procedures based on a global ionsopheric model driven by solar flux, and a technique in which measured electron content (using Faraday rotation measurements) for one path is mapped into corrections for a hemisphere. For these two techniques, RMS errors for correcting satellite altimeters data (at 14 GHz) are estimated to be 12 cm and 3 cm, respectively. On the basis of global accuracy and reliability after implementation, the solar flux model is recommended.

Analytical and numerical techniques for predicting the interfacial stresses of wavy carbon nanotube/polymer composites

NASA Astrophysics Data System (ADS)

Yazdchi, K.; Salehi, M.; Shokrieh, M. M.

2009-03-01

By introducing a new simplified 3D representative volume element for wavy carbon nanotubes, an analytical model is developed to study the stress transfer in single-walled carbon nanotube-reinforced polymer composites. Based on the pull-out modeling technique, the effects of waviness, aspect ratio, and Poisson ratio on the axial and interfacial shear stresses are analyzed in detail. The results of the present analytical model are in a good agreement with corresponding results for straight nanotubes.

Estimation of Human Body Volume (BV) from Anthropometric Measurements Based on Three-Dimensional (3D) Scan Technique.

PubMed

Liu, Xingguo; Niu, Jianwei; Ran, Linghua; Liu, Taijie

2017-08-01

This study aimed to develop estimation formulae for the total human body volume (BV) of adult males using anthropometric measurements based on a three-dimensional (3D) scanning technique. Noninvasive and reliable methods to predict the total BV from anthropometric measurements based on a 3D scan technique were addressed in detail. A regression analysis of BV based on four key measurements was conducted for approximately 160 adult male subjects. Eight total models of human BV show that the predicted results fitted by the regression models were highly correlated with the actual BV (p < 0.001). Two metrics, the mean value of the absolute difference between the actual and predicted BV (V error ) and the mean value of the ratio between V error and actual BV (RV error ), were calculated. The linear model based on human weight was recommended as the most optimal due to its simplicity and high efficiency. The proposed estimation formulae are valuable for estimating total body volume in circumstances in which traditional underwater weighing or air displacement plethysmography is not applicable or accessible. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Comparing and combining terrestrial laser scanning with ground-and UAV-based imaging for national-level assessment of soil erosion

NASA Astrophysics Data System (ADS)

McShane, Gareth; James, Mike R.; Quinton, John; Anderson, Karen; DeBell, Leon; Evans, Martin; Farrow, Luke; Glendell, Miriam; Jones, Lee; Kirkham, Matthew; Lark, Murray; Rawlins, Barry; Rickson, Jane; Quine, Tim; Wetherelt, Andy; Brazier, Richard

2014-05-01

3D topographic or surface models are increasingly being utilised for a wide range of applications and are established tools in geomorphological research. In this pilot study 'a cost effective framework for monitoring soil erosion in England and Wales', funded by the UK Department for Environment, Food and Rural Affairs (Defra), we compare methods of collecting topographic measurements via remote sensing for detailed studies of dynamic processes such as erosion and mass movement. The techniques assessed are terrestrial laser scanning (TLS), and unmanned aerial vehicle (UAV) photography and ground-based photography, processed using structure-from-motion (SfM) 3D reconstruction software. The methods will be applied in regions of different land use, including arable and horticultural, upland and semi natural habitats, and grassland, to quantify visible erosion pathways at the site scale. Volumetric estimates of soil loss will be quantified using the digital surface models (DSMs) provided by each technique and a modelled pre-erosion surface. Visible erosion and severity will be independently established through each technique, with their results compared and combined effectiveness assessed. A fixed delta-wing UAV (QuestUAV, http://www.questuav.com/) captures photos from a range of altitudes and angles over the study area, with automated SfM-based processing enabling rapid orthophoto production to support ground-based data acquisition. At sites with suitable scale erosion features, UAV data will also provide a DSM for volume loss measurement. Terrestrial laser scanning will provide detailed, accurate, high density measurements of the ground surface over long (100s m) distances. Ground-based photography is anticipated to be most useful for characterising small and difficult to view features. By using a consumer-grade digital camera and an SfM-based approach (using Agisoft Photoscan version 1.0.0, http://www.agisoft.ru/products/photoscan/), less expertise and fewer control measurements are required compared with traditional photogrammetry, and image processing is automated. Differential GPS data will be used to geo-reference the models to facilitate comparison. The relative advantages, limitations and cost-effectiveness of each approach will be established, and which technique, or combination of techniques, is most appropriate to monitor, model and estimate soil erosion at the national scale, determined.

What Do the Numbers Say? Clarifying Our Interpretation of Carbon Use Efficiency Data from Soil.

NASA Astrophysics Data System (ADS)

Geyer, K.; Dijkstra, P.; Sinsabaugh, R. L.; Frey, S. D.

2017-12-01

Carbon use efficiency (CUE) is the proportion of carbon resources that a microorganism commits towards cellular growth and thus affects the dynamics of soil organic matter pools. While numerous approaches exist for estimating CUE, no attempts have been made to simultaneously compare methods and reconcile their inherent biases. Such work is necessary to partition the observed variation in CUE estimates (commonly between 0.3 - 0.7) as biological or technical in origin. Here we review our results from experimental work aimed at comparing both traditional and emerging CUE techniques. Soil from the Harvard Forest Long Term Ecological Research site in Massachusetts, USA, was amended with 13C-glucose and 18O-water in laboratory mesocosms and monitored for changing rates of soil dissolved organic carbon (DOC) uptake, respiration (R), microbial biomass (MBC) production, DNA synthesis, and heat (Q) flux over 72 hrs. Three CUE estimates were generated from this data: 1) Δ13MBC/(Δ13MBC+ 13R), 2) Δ18DNA/(Δ18DNA + R), 3) Q/R. CUE was also measured via two indirect techniques: metabolic flux modeling and stoichiometric modeling. Results indicate that the 18O technique is able to discern gross growth of soil microbes while the 13C technique indicates net growth. As a result, 18O-based CUE remains unchanged ( 0.45) for the incubation duration at low amendment rates (0.0 and 0.05 mg glucose-C/g) while 13C-based CUE declines with time (0.75 to 0.5). The 13C technique likely overestimates CUE because the numerator (Δ13MBC) integrates any label (whether or not destined for growth) residing within the cell. High amendment rates (2.0 mg glucose-C/g) cause dramatic declines in 18O- and 13C-based CUE for the first 24 hr of incubation, but neither modeling approach was able to detect these dynamics. In summary, our results suggest that 13C-based estimates of CUE are best interpreted as net changes in the residence of labeled substrate within MBC pools while 18O-based estimates directly capture gross growth dynamics. Metabolic flux modeling and stoichiometric modeling appear to be suited for conditions of steady-state MBC only, where they approximate the CUE magnitude of the 13C- and 18O-based approaches, respectively.

New Geophysical Technique for Mineral Exploration and Mineral Discrimination Based on Electromagnetic Methods

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

Michael S. Zhdanov

2005-03-09

The research during the first year of the project was focused on developing the foundations of a new geophysical technique for mineral exploration and mineral discrimination, based on electromagnetic (EM) methods. The proposed new technique is based on examining the spectral induced polarization effects in electromagnetic data using modern distributed acquisition systems and advanced methods of 3-D inversion. The analysis of IP phenomena is usually based on models with frequency dependent complex conductivity distribution. One of the most popular is the Cole-Cole relaxation model. In this progress report we have constructed and analyzed a different physical and mathematical model ofmore » the IP effect based on the effective-medium theory. We have developed a rigorous mathematical model of multi-phase conductive media, which can provide a quantitative tool for evaluation of the type of mineralization, using the conductivity relaxation model parameters. The parameters of the new conductivity relaxation model can be used for discrimination of the different types of rock formations, which is an important goal in mineral exploration. The solution of this problem requires development of an effective numerical method for EM forward modeling in 3-D inhomogeneous media. During the first year of the project we have developed a prototype 3-D IP modeling algorithm using the integral equation (IP) method. Our IE forward modeling code INTEM3DIP is based on the contraction IE method, which improves the convergence rate of the iterative solvers. This code can handle various types of sources and receivers to compute the effect of a complex resistivity model. We have tested the working version of the INTEM3DIP code for computer simulation of the IP data for several models including a southwest US porphyry model and a Kambalda-style nickel sulfide deposit. The numerical modeling study clearly demonstrates how the various complex resistivity models manifest differently in the observed EM data. These modeling studies lay a background for future development of the IP inversion method, directed at determining the electrical conductivity and the intrinsic chargeability distributions, as well as the other parameters of the relaxation model simultaneously. The new technology envisioned in this proposal, will be used for the discrimination of different rocks, and in this way will provide an ability to distinguish between uneconomic mineral deposits and the location of zones of economic mineralization and geothermal resources.« less

Retrieval of Dry Snow Parameters from Radiometric Data Using a Dense Medium Model and Genetic Algorithms

NASA Technical Reports Server (NTRS)

Tedesco, Marco; Kim, Edward J.

2005-01-01

In this paper, GA-based techniques are used to invert the equations of an electromagnetic model based on Dense Medium Radiative Transfer Theory (DMRT) under the Quasi Crystalline Approximation with Coherent Potential to retrieve snow depth, mean grain size and fractional volume from microwave brightness temperatures. The technique is initially tested on both noisy and not-noisy simulated data. During this phase, different configurations of genetic algorithm parameters are considered to quantify how their change can affect the algorithm performance. A configuration of GA parameters is then selected and the algorithm is applied to experimental data acquired during the NASA Cold Land Process Experiment. Snow parameters retrieved with the GA-DMRT technique are then compared with snow parameters measured on field.

Automated extraction of knowledge for model-based diagnostics

NASA Technical Reports Server (NTRS)

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

1990-01-01

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

Thoracic respiratory motion estimation from MRI using a statistical model and a 2-D image navigator.

PubMed

King, A P; Buerger, C; Tsoumpas, C; Marsden, P K; Schaeffter, T

2012-01-01

Respiratory motion models have potential application for estimating and correcting the effects of motion in a wide range of applications, for example in PET-MR imaging. Given that motion cycles caused by breathing are only approximately repeatable, an important quality of such models is their ability to capture and estimate the intra- and inter-cycle variability of the motion. In this paper we propose and describe a technique for free-form nonrigid respiratory motion correction in the thorax. Our model is based on a principal component analysis of the motion states encountered during different breathing patterns, and is formed from motion estimates made from dynamic 3-D MRI data. We apply our model using a data-driven technique based on a 2-D MRI image navigator. Unlike most previously reported work in the literature, our approach is able to capture both intra- and inter-cycle motion variability. In addition, the 2-D image navigator can be used to estimate how applicable the current motion model is, and hence report when more imaging data is required to update the model. We also use the motion model to decide on the best positioning for the image navigator. We validate our approach using MRI data acquired from 10 volunteers and demonstrate improvements of up to 40.5% over other reported motion modelling approaches, which corresponds to 61% of the overall respiratory motion present. Finally we demonstrate one potential application of our technique: MRI-based motion correction of real-time PET data for simultaneous PET-MRI acquisition. Copyright © 2011 Elsevier B.V. All rights reserved.

Model-Based Trade Space Exploration for Near-Earth Space Missions

NASA Technical Reports Server (NTRS)

Cohen, Ronald H.; Boncyk, Wayne; Brutocao, James; Beveridge, Iain

2005-01-01

We developed a capability for model-based trade space exploration to be used in the conceptual design of Earth-orbiting space missions. We have created a set of reusable software components to model various subsystems and aspects of space missions. Several example mission models were created to test the tools and process. This technique and toolset has demonstrated itself to be valuable for space mission architectural design.

A spline-based parameter estimation technique for static models of elastic structures

NASA Technical Reports Server (NTRS)

Dutt, P.; Taasan, S.

1986-01-01

The problem of identifying the spatially varying coefficient of elasticity using an observed solution to the forward problem is considered. Under appropriate conditions this problem can be treated as a first order hyperbolic equation in the unknown coefficient. Some continuous dependence results are developed for this problem and a spline-based technique is proposed for approximating the unknown coefficient, based on these results. The convergence of the numerical scheme is established and error estimates obtained.

Modeling of Tool-Tissue Interactions for Computer-Based Surgical Simulation: A Literature Review

PubMed Central

Misra, Sarthak; Ramesh, K. T.; Okamura, Allison M.

2009-01-01

Surgical simulators present a safe and potentially effective method for surgical training, and can also be used in robot-assisted surgery for pre- and intra-operative planning. Accurate modeling of the interaction between surgical instruments and organs has been recognized as a key requirement in the development of high-fidelity surgical simulators. Researchers have attempted to model tool-tissue interactions in a wide variety of ways, which can be broadly classified as (1) linear elasticity-based, (2) nonlinear (hyperelastic) elasticity-based finite element (FE) methods, and (3) other techniques that not based on FE methods or continuum mechanics. Realistic modeling of organ deformation requires populating the model with real tissue data (which are difficult to acquire in vivo) and simulating organ response in real time (which is computationally expensive). Further, it is challenging to account for connective tissue supporting the organ, friction, and topological changes resulting from tool-tissue interactions during invasive surgical procedures. Overcoming such obstacles will not only help us to model tool-tissue interactions in real time, but also enable realistic force feedback to the user during surgical simulation. This review paper classifies the existing research on tool-tissue interactions for surgical simulators specifically based on the modeling techniques employed and the kind of surgical operation being simulated, in order to inform and motivate future research on improved tool-tissue interaction models. PMID:20119508

Improvement of Storm Forecasts Using Gridded Bayesian Linear Regression for Northeast United States

NASA Astrophysics Data System (ADS)

Yang, J.; Astitha, M.; Schwartz, C. S.

2017-12-01

Bayesian linear regression (BLR) is a post-processing technique in which regression coefficients are derived and used to correct raw forecasts based on pairs of observation-model values. This study presents the development and application of a gridded Bayesian linear regression (GBLR) as a new post-processing technique to improve numerical weather prediction (NWP) of rain and wind storm forecasts over northeast United States. Ten controlled variables produced from ten ensemble members of the National Center for Atmospheric Research (NCAR) real-time prediction system are used for a GBLR model. In the GBLR framework, leave-one-storm-out cross-validation is utilized to study the performances of the post-processing technique in a database composed of 92 storms. To estimate the regression coefficients of the GBLR, optimization procedures that minimize the systematic and random error of predicted atmospheric variables (wind speed, precipitation, etc.) are implemented for the modeled-observed pairs of training storms. The regression coefficients calculated for meteorological stations of the National Weather Service are interpolated back to the model domain. An analysis of forecast improvements based on error reductions during the storms will demonstrate the value of GBLR approach. This presentation will also illustrate how the variances are optimized for the training partition in GBLR and discuss the verification strategy for grid points where no observations are available. The new post-processing technique is successful in improving wind speed and precipitation storm forecasts using past event-based data and has the potential to be implemented in real-time.

Endoscopic skull base training using 3D printed models with pre-existing pathology.

PubMed

Narayanan, Vairavan; Narayanan, Prepageran; Rajagopalan, Raman; Karuppiah, Ravindran; Rahman, Zainal Ariff Abdul; Wormald, Peter-John; Van Hasselt, Charles Andrew; Waran, Vicknes

2015-03-01

Endoscopic base of skull surgery has been growing in acceptance in the recent past due to improvements in visualisation and micro instrumentation as well as the surgical maturing of early endoscopic skull base practitioners. Unfortunately, these demanding procedures have a steep learning curve. A physical simulation that is able to reproduce the complex anatomy of the anterior skull base provides very useful means of learning the necessary skills in a safe and effective environment. This paper aims to assess the ease of learning endoscopic skull base exposure and drilling techniques using an anatomically accurate physical model with a pre-existing pathology (i.e., basilar invagination) created from actual patient data. Five models of a patient with platy-basia and basilar invagination were created from the original MRI and CT imaging data of a patient. The models were used as part of a training workshop for ENT surgeons with varying degrees of experience in endoscopic base of skull surgery, from trainees to experienced consultants. The surgeons were given a list of key steps to achieve in exposing and drilling the skull base using the simulation model. They were then asked to list the level of difficulty of learning these steps using the model. The participants found the models suitable for learning registration, navigation and skull base drilling techniques. All participants also found the deep structures to be accurately represented spatially as confirmed by the navigation system. These models allow structured simulation to be conducted in a workshop environment where surgeons and trainees can practice to perform complex procedures in a controlled fashion under the supervision of experts.

Automatic control of the NMB level in general anaesthesia with a switching total system mass control strategy.

PubMed

Teixeira, Miguel; Mendonça, Teresa; Rocha, Paula; Rabiço, Rui

2014-12-01

This paper presents a model based switching control strategy to drive the neuromuscular blockade (NMB) level of patients undergoing general anesthesia to a predefined reference. A single-input single-output Wiener system with only two parameters is used to model the effect of two different muscle relaxants, atracurium and rocuronium, and a switching controller is designed based on a bank of total system mass control laws. Each of such laws is tuned for an individual model from a bank chosen to represent the behavior of the whole population. The control law to be applied at each instant corresponds to the model whose NMB response is closer to the patient's response. Moreover a scheme to improve the reference tracking quality based on the analysis of the patient's response, as well as, a comparison between the switching strategy and the Extended Kalman Kilter (EKF) technique are presented. The results are illustrated by means of several simulations, where switching shows to provide good results, both in theory and in practice, with a desirable reference tracking. The reference tracking improvement technique is able to produce a better reference tracking. Also, this technique showed a better performance than the (EKF). Based on these results, the switching control strategy with a bank of total system mass control laws proved to be robust enough to be used as an automatic control system for the NMB level.

Simulations of multi-contrast x-ray imaging using near-field speckles

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

Zdora, Marie-Christine; Diamond Light Source, Harwell Science and Innovation Campus, Didcot, Oxfordshire, OX11 0DE, United Kingdom and Department of Physics & Astronomy, University College London, London, WC1E 6BT; Thibault, Pierre

2016-01-28

X-ray dark-field and phase-contrast imaging using near-field speckles is a novel technique that overcomes limitations inherent in conventional absorption x-ray imaging, i.e. poor contrast for features with similar density. Speckle-based imaging yields a wealth of information with a simple setup tolerant to polychromatic and divergent beams, and simple data acquisition and analysis procedures. Here, we present a simulation software used to model the image formation with the speckle-based technique, and we compare simulated results on a phantom sample with experimental synchrotron data. Thorough simulation of a speckle-based imaging experiment will help for better understanding and optimising the technique itself.

Remote sensing of high-latitude ionization profiles by ground-based and spaceborne instrumentation

NASA Technical Reports Server (NTRS)

Vondrak, R. R.

1981-01-01

Ionospheric specification and modeling are now largely based on data provided by active remote sensing with radiowave techniques (ionosondes, incoherent-scatter radars, and satellite beacons). More recently, passive remote sensing techniques have been developed that can be used to monitor quantitatively the spatial distribution of high-latitude E-region ionization. These passive methods depend on the measurement, or inference, of the energy distribution of precipitating kilovolt electrons, the principal source of the nighttime E-region at high latitudes. To validate these techniques, coordinated measurements of the auroral ionosphere have been made with the Chatanika incoherent-scatter radar and a variety of ground-based and spaceborne sensors

Loss-compensation technique using a split-spectrum approach for optical fiber air-gap intensity-based sensors

NASA Astrophysics Data System (ADS)

Wang, Anbo; Miller, Mark S.; Gunther, Michael F.; Murphy, Kent A.; Claus, Richard O.

1993-03-01

A self-referencing technique compensating for fiber losses and source fluctuations in air-gap intensity-based optical fiber sensors is described and demonstrated. A resolution of 0.007 micron has been obtained over a measurement range of 0-250 microns for an intensity-based displacement sensor using this referencing technique. The sensor is shown to have minimal sensitivity to fiber bending losses and variations in the LED input power. A theoretical model for evaluation of step-index multimode optical fiber splice is proposed. The performance of the sensor as a displacement sensor agrees well with the theoretical analysis.

Obstacle Avoidance On Roadways Using Range Data

NASA Astrophysics Data System (ADS)

Dunlay, R. Terry; Morgenthaler, David G.

1987-02-01

This report describes range data based obstacle avoidance techniques developed for use on an autonomous road-following robot vehicle. The purpose of these techniques is to detect and locate obstacles present in a road environment for navigation of a robot vehicle equipped with an active laser-based range sensor. Techniques are presented for obstacle detection, obstacle location, and coordinate transformations needed in the construction of Scene Models (symbolic structures representing the 3-D obstacle boundaries used by the vehicle's Navigator for path planning). These techniques have been successfully tested on an outdoor robotic vehicle, the Autonomous Land Vehicle (ALV), at speeds up to 3.5 km/hour.

Model based Computerized Ionospheric Tomography in space and time

NASA Astrophysics Data System (ADS)

Tuna, Hakan; Arikan, Orhan; Arikan, Feza

2018-04-01

Reconstruction of the ionospheric electron density distribution in space and time not only provide basis for better understanding the physical nature of the ionosphere, but also provide improvements in various applications including HF communication. Recently developed IONOLAB-CIT technique provides physically admissible 3D model of the ionosphere by using both Slant Total Electron Content (STEC) measurements obtained from a GPS satellite - receiver network and IRI-Plas model. IONOLAB-CIT technique optimizes IRI-Plas model parameters in the region of interest such that the synthetic STEC computations obtained from the IRI-Plas model are in accordance with the actual STEC measurements. In this work, the IONOLAB-CIT technique is extended to provide reconstructions both in space and time. This extension exploits the temporal continuity of the ionosphere to provide more reliable reconstructions with a reduced computational load. The proposed 4D-IONOLAB-CIT technique is validated on real measurement data obtained from TNPGN-Active GPS receiver network in Turkey.

ICTNET at Microblog Track TREC 2012

DTIC Science & Technology

2012-11-01

Weight(T) = 1 _(()) ∗ ∑ () ∗ () ∑ ()∈ () In external expansion, we use Google ...based on language model, we choose stupid backoff as the smoothing technique and “queue” as the history retention technique[7].In the filter based on...is used in ICTWDSERUN2. In both ICTWDSERUN3 and ICTWDSERUN4, we use google search results as query expansion. RankSVMmethod is used in both

Sparse Matrix Motivated Reconstruction of Far-Field Radiation Patterns

DTIC Science & Technology

2015-03-01

method for base - station antenna radiation patterns. IEEE Antennas Propagation Magazine. 2001;43(2):132. 4. Vasiliadis TG, Dimitriou D, Sergiadis JD...algorithm based on sparse representations of radiation patterns using the inverse Discrete Fourier Transform (DFT) and the inverse Discrete Cosine...patterns using a Model- Based Parameter Estimation (MBPE) technique that reduces the computational time required to model radiation patterns. Another

Reconstruction of dynamic image series from undersampled MRI data using data-driven model consistency condition (MOCCO).

PubMed

Velikina, Julia V; Samsonov, Alexey A

2015-11-01

To accelerate dynamic MR imaging through development of a novel image reconstruction technique using low-rank temporal signal models preestimated from training data. We introduce the model consistency condition (MOCCO) technique, which utilizes temporal models to regularize reconstruction without constraining the solution to be low-rank, as is performed in related techniques. This is achieved by using a data-driven model to design a transform for compressed sensing-type regularization. The enforcement of general compliance with the model without excessively penalizing deviating signal allows recovery of a full-rank solution. Our method was compared with a standard low-rank approach utilizing model-based dimensionality reduction in phantoms and patient examinations for time-resolved contrast-enhanced angiography (CE-MRA) and cardiac CINE imaging. We studied the sensitivity of all methods to rank reduction and temporal subspace modeling errors. MOCCO demonstrated reduced sensitivity to modeling errors compared with the standard approach. Full-rank MOCCO solutions showed significantly improved preservation of temporal fidelity and aliasing/noise suppression in highly accelerated CE-MRA (acceleration up to 27) and cardiac CINE (acceleration up to 15) data. MOCCO overcomes several important deficiencies of previously proposed methods based on pre-estimated temporal models and allows high quality image restoration from highly undersampled CE-MRA and cardiac CINE data. © 2014 Wiley Periodicals, Inc.

RECONSTRUCTION OF DYNAMIC IMAGE SERIES FROM UNDERSAMPLED MRI DATA USING DATA-DRIVEN MODEL CONSISTENCY CONDITION (MOCCO)

PubMed Central

Velikina, Julia V.; Samsonov, Alexey A.

2014-01-01

Purpose To accelerate dynamic MR imaging through development of a novel image reconstruction technique using low-rank temporal signal models pre-estimated from training data. Theory We introduce the MOdel Consistency COndition (MOCCO) technique that utilizes temporal models to regularize the reconstruction without constraining the solution to be low-rank as performed in related techniques. This is achieved by using a data-driven model to design a transform for compressed sensing-type regularization. The enforcement of general compliance with the model without excessively penalizing deviating signal allows recovery of a full-rank solution. Methods Our method was compared to standard low-rank approach utilizing model-based dimensionality reduction in phantoms and patient examinations for time-resolved contrast-enhanced angiography (CE MRA) and cardiac CINE imaging. We studied sensitivity of all methods to rank-reduction and temporal subspace modeling errors. Results MOCCO demonstrated reduced sensitivity to modeling errors compared to the standard approach. Full-rank MOCCO solutions showed significantly improved preservation of temporal fidelity and aliasing/noise suppression in highly accelerated CE MRA (acceleration up to 27) and cardiac CINE (acceleration up to 15) data. Conclusions MOCCO overcomes several important deficiencies of previously proposed methods based on pre-estimated temporal models and allows high quality image restoration from highly undersampled CE-MRA and cardiac CINE data. PMID:25399724

From laptop to benchtop to bedside: Structure-based Drug Design on Protein Targets

PubMed Central

Chen, Lu; Morrow, John K.; Tran, Hoang T.; Phatak, Sharangdhar S.; Du-Cuny, Lei; Zhang, Shuxing

2013-01-01

As an important aspect of computer-aided drug design, structure-based drug design brought a new horizon to pharmaceutical development. This in silico method permeates all aspects of drug discovery today, including lead identification, lead optimization, ADMET prediction and drug repurposing. Structure-based drug design has resulted in fruitful successes drug discovery targeting protein-ligand and protein-protein interactions. Meanwhile, challenges, noted by low accuracy and combinatoric issues, may also cause failures. In this review, state-of-the-art techniques for protein modeling (e.g. structure prediction, modeling protein flexibility, etc.), hit identification/optimization (e.g. molecular docking, focused library design, fragment-based design, molecular dynamic, etc.), and polypharmacology design will be discussed. We will explore how structure-based techniques can facilitate the drug discovery process and interplay with other experimental approaches. PMID:22316152

Learning outcomes and student-perceived value of clay modeling and cat dissection in undergraduate human anatomy and physiology.

PubMed

DeHoff, Mary Ellen; Clark, Krista L; Meganathan, Karthikeyan

2011-03-01

Alternatives and/or supplements to animal dissection are being explored by educators of human anatomy at different academic levels. Clay modeling is one such alternative that provides a kinesthetic, three-dimensional, constructive, and sensory approach to learning human anatomy. The present study compared two laboratory techniques, clay modeling of human anatomy and dissection of preserved cat specimens, in the instruction of muscles, peripheral nerves, and blood vessels. Specifically, we examined the effect of each technique on student performance on low-order and high-order questions related to each body system as well as the student-perceived value of each technique. Students who modeled anatomic structures in clay scored significantly higher on low-order questions related to peripheral nerves; scores were comparable between groups for high-order questions on peripheral nerves and for questions on muscles and blood vessels. Likert-scale surveys were used to measure student responses to statements about each laboratory technique. A significantly greater percentage of students in the clay modeling group "agreed" or "strongly agreed" with positive statements about their respective technique. These results indicate that clay modeling and cat dissection are equally effective in achieving student learning outcomes for certain systems in undergraduate human anatomy. Furthermore, clay modeling appears to be the preferred technique based on students' subjective perceptions of value to their learning experience.

New approaches in agent-based modeling of complex financial systems

NASA Astrophysics Data System (ADS)

Chen, Ting-Ting; Zheng, Bo; Li, Yan; Jiang, Xiong-Fei

2017-12-01

Agent-based modeling is a powerful simulation technique to understand the collective behavior and microscopic interaction in complex financial systems. Recently, the concept for determining the key parameters of agent-based models from empirical data instead of setting them artificially was suggested. We first review several agent-based models and the new approaches to determine the key model parameters from historical market data. Based on the agents' behaviors with heterogeneous personal preferences and interactions, these models are successful in explaining the microscopic origination of the temporal and spatial correlations of financial markets. We then present a novel paradigm combining big-data analysis with agent-based modeling. Specifically, from internet query and stock market data, we extract the information driving forces and develop an agent-based model to simulate the dynamic behaviors of complex financial systems.

Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.

PubMed

Attallah, Omneya; Karthikesalingam, Alan; Holt, Peter J E; Thompson, Matthew M; Sayers, Rob; Bown, Matthew J; Choke, Eddie C; Ma, Xianghong

2017-08-03

Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox's proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Techniques that have been proposed to adopt MLT to perform FS with survival data cannot be used with the high level of censoring. The researcher's previous publications proposed a technique to deal with the high level of censoring. It also used existing FS techniques to reduce dataset dimension. However, in this paper a new FS technique was proposed and combined with feature transformation and the proposed uncensoring approaches to select a reduced set of features and produce a stable predictive model. In this paper, a FS technique based on artificial neural network (ANN) MLT is proposed to deal with highly censored Endovascular Aortic Repair (EVAR). Survival data EVAR datasets were collected during 2004 to 2010 from two vascular centers in order to produce a final stable model. They contain almost 91% of censored patients. The proposed approach used a wrapper FS method with ANN to select a reduced subset of features that predict the risk of EVAR re-intervention after 5 years to patients from two different centers located in the United Kingdom, to allow it to be potentially applied to cross-centers predictions. The proposed model is compared with the two popular FS techniques; Akaike and Bayesian information criteria (AIC, BIC) that are used with Cox's model. The final model outperforms other methods in distinguishing the high and low risk groups; as they both have concordance index and estimated AUC better than the Cox's model based on AIC, BIC, Lasso, and SCAD approaches. These models have p-values lower than 0.05, meaning that patients with different risk groups can be separated significantly and those who would need re-intervention can be correctly predicted. The proposed approach will save time and effort made by physicians to collect unnecessary variables. The final reduced model was able to predict the long-term risk of aortic complications after EVAR. This predictive model can help clinicians decide patients' future observation plan.

Evaluating Mixture Modeling for Clustering: Recommendations and Cautions

ERIC Educational Resources Information Center

Steinley, Douglas; Brusco, Michael J.

2011-01-01

This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison,…

Review of Modelling Techniques for In Vivo Muscle Force Estimation in the Lower Extremities during Strength Training

PubMed Central

Schellenberg, Florian; Oberhofer, Katja; Taylor, William R.

2015-01-01

Background. Knowledge of the musculoskeletal loading conditions during strength training is essential for performance monitoring, injury prevention, rehabilitation, and training design. However, measuring muscle forces during exercise performance as a primary determinant of training efficacy and safety has remained challenging. Methods. In this paper we review existing computational techniques to determine muscle forces in the lower limbs during strength exercises in vivo and discuss their potential for uptake into sports training and rehabilitation. Results. Muscle forces during exercise performance have almost exclusively been analysed using so-called forward dynamics simulations, inverse dynamics techniques, or alternative methods. Musculoskeletal models based on forward dynamics analyses have led to considerable new insights into muscular coordination, strength, and power during dynamic ballistic movement activities, resulting in, for example, improved techniques for optimal performance of the squat jump, while quasi-static inverse dynamics optimisation and EMG-driven modelling have helped to provide an understanding of low-speed exercises. Conclusion. The present review introduces the different computational techniques and outlines their advantages and disadvantages for the informed usage by nonexperts. With sufficient validation and widespread application, muscle force calculations during strength exercises in vivo are expected to provide biomechanically based evidence for clinicians and therapists to evaluate and improve training guidelines. PMID:26417378

Review of Modelling Techniques for In Vivo Muscle Force Estimation in the Lower Extremities during Strength Training.

PubMed

Schellenberg, Florian; Oberhofer, Katja; Taylor, William R; Lorenzetti, Silvio

2015-01-01

Knowledge of the musculoskeletal loading conditions during strength training is essential for performance monitoring, injury prevention, rehabilitation, and training design. However, measuring muscle forces during exercise performance as a primary determinant of training efficacy and safety has remained challenging. In this paper we review existing computational techniques to determine muscle forces in the lower limbs during strength exercises in vivo and discuss their potential for uptake into sports training and rehabilitation. Muscle forces during exercise performance have almost exclusively been analysed using so-called forward dynamics simulations, inverse dynamics techniques, or alternative methods. Musculoskeletal models based on forward dynamics analyses have led to considerable new insights into muscular coordination, strength, and power during dynamic ballistic movement activities, resulting in, for example, improved techniques for optimal performance of the squat jump, while quasi-static inverse dynamics optimisation and EMG-driven modelling have helped to provide an understanding of low-speed exercises. The present review introduces the different computational techniques and outlines their advantages and disadvantages for the informed usage by nonexperts. With sufficient validation and widespread application, muscle force calculations during strength exercises in vivo are expected to provide biomechanically based evidence for clinicians and therapists to evaluate and improve training guidelines.

ENSURF: multi-model sea level forecast - implementation and validation results for the IBIROOS and Western Mediterranean regions

NASA Astrophysics Data System (ADS)

Pérez, B.; Brouwer, R.; Beckers, J.; Paradis, D.; Balseiro, C.; Lyons, K.; Cure, M.; Sotillo, M. G.; Hackett, B.; Verlaan, M.; Fanjul, E. A.

2012-03-01

ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast that makes use of several storm surge or circulation models and near-real time tide gauge data in the region, with the following main goals: 1. providing easy access to existing forecasts, as well as to its performance and model validation, by means of an adequate visualization tool; 2. generation of better forecasts of sea level, including confidence intervals, by means of the Bayesian Model Average technique (BMA). The Bayesian Model Average technique generates an overall forecast probability density function (PDF) by making a weighted average of the individual forecasts PDF's; the weights represent the Bayesian likelihood that a model will give the correct forecast and are continuously updated based on the performance of the models during a recent training period. This implies the technique needs the availability of sea level data from tide gauges in near-real time. The system was implemented for the European Atlantic facade (IBIROOS region) and Western Mediterranean coast based on the MATROOS visualization tool developed by Deltares. Results of validation of the different models and BMA implementation for the main harbours are presented for these regions where this kind of activity is performed for the first time. The system is currently operational at Puertos del Estado and has proved to be useful in the detection of calibration problems in some of the circulation models, in the identification of the systematic differences between baroclinic and barotropic models for sea level forecasts and to demonstrate the feasibility of providing an overall probabilistic forecast, based on the BMA method.

Completing and Adapting Models of Biological Processes

NASA Technical Reports Server (NTRS)

Margaria, Tiziana; Hinchey, Michael G.; Raffelt, Harald; Rash, James L.; Rouff, Christopher A.; Steffen, Bernhard

2006-01-01

We present a learning-based method for model completion and adaptation, which is based on the combination of two approaches: 1) R2D2C, a technique for mechanically transforming system requirements via provably equivalent models to running code, and 2) automata learning-based model extrapolation. The intended impact of this new combination is to make model completion and adaptation accessible to experts of the field, like biologists or engineers. The principle is briefly illustrated by generating models of biological procedures concerning gene activities in the production of proteins, although the main application is going to concern autonomic systems for space exploration.

Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques.

PubMed

Guo, Doudou; Juan, Jiaxiang; Chang, Liying; Zhang, Jingjin; Huang, Danfeng

2017-08-15

Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.

A singular value decomposition linear programming (SVDLP) optimization technique for circular cone based robotic radiotherapy.

PubMed

Liang, Bin; Li, Yongbao; Wei, Ran; Guo, Bin; Xu, Xuang; Liu, Bo; Li, Jiafeng; Wu, Qiuwen; Zhou, Fugen

2018-01-05

With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendor-supplied treatment planning system (MultiPlan) cannot fully explore the increased beam direction search space. Besides, a sparse treatment plan (using fewer beams) is desired to improve treatment efficiency. This study proposes a singular value decomposition linear programming (SVDLP) optimization technique for circular collimator based robotic radiotherapy. The SVDLP approach initializes the input beams by simulating the process of covering the entire target volume with equivalent beam tapers. The requirements on dosimetry distribution are modeled as hard and soft constraints, and the sparsity of the treatment plan is achieved by compressive sensing. The proposed linear programming (LP) model optimizes beam weights by minimizing the deviation of soft constraints subject to hard constraints, with a constraint on the l 1 norm of the beam weight. A singular value decomposition (SVD) based acceleration technique was developed for the LP model. Based on the degeneracy of the influence matrix, the model is first compressed into lower dimension for optimization, and then back-projected to reconstruct the beam weight. After beam weight optimization, the number of beams is reduced by removing the beams with low weight, and optimizing the weights of the remaining beams using the same model. This beam reduction technique is further validated by a mixed integer programming (MIP) model. The SVDLP approach was tested on a lung case. The results demonstrate that the SVD acceleration technique speeds up the optimization by a factor of 4.8. Furthermore, the beam reduction achieves a similar plan quality to the globally optimal plan obtained by the MIP model, but is one to two orders of magnitude faster. Furthermore, the SVDLP approach is tested and compared with MultiPlan on three clinical cases of varying complexities. In general, the plans generated by the SVDLP achieve steeper dose gradient, better conformity and less damage to normal tissues. In conclusion, the SVDLP approach effectively improves the quality of treatment plan due to the use of the complete beam search space. This challenging optimization problem with the complete beam search space is effectively handled by the proposed SVD acceleration.

A singular value decomposition linear programming (SVDLP) optimization technique for circular cone based robotic radiotherapy

NASA Astrophysics Data System (ADS)

Liang, Bin; Li, Yongbao; Wei, Ran; Guo, Bin; Xu, Xuang; Liu, Bo; Li, Jiafeng; Wu, Qiuwen; Zhou, Fugen

2018-01-01

With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendor-supplied treatment planning system (MultiPlan) cannot fully explore the increased beam direction search space. Besides, a sparse treatment plan (using fewer beams) is desired to improve treatment efficiency. This study proposes a singular value decomposition linear programming (SVDLP) optimization technique for circular collimator based robotic radiotherapy. The SVDLP approach initializes the input beams by simulating the process of covering the entire target volume with equivalent beam tapers. The requirements on dosimetry distribution are modeled as hard and soft constraints, and the sparsity of the treatment plan is achieved by compressive sensing. The proposed linear programming (LP) model optimizes beam weights by minimizing the deviation of soft constraints subject to hard constraints, with a constraint on the l 1 norm of the beam weight. A singular value decomposition (SVD) based acceleration technique was developed for the LP model. Based on the degeneracy of the influence matrix, the model is first compressed into lower dimension for optimization, and then back-projected to reconstruct the beam weight. After beam weight optimization, the number of beams is reduced by removing the beams with low weight, and optimizing the weights of the remaining beams using the same model. This beam reduction technique is further validated by a mixed integer programming (MIP) model. The SVDLP approach was tested on a lung case. The results demonstrate that the SVD acceleration technique speeds up the optimization by a factor of 4.8. Furthermore, the beam reduction achieves a similar plan quality to the globally optimal plan obtained by the MIP model, but is one to two orders of magnitude faster. Furthermore, the SVDLP approach is tested and compared with MultiPlan on three clinical cases of varying complexities. In general, the plans generated by the SVDLP achieve steeper dose gradient, better conformity and less damage to normal tissues. In conclusion, the SVDLP approach effectively improves the quality of treatment plan due to the use of the complete beam search space. This challenging optimization problem with the complete beam search space is effectively handled by the proposed SVD acceleration.

A method for diagnosing time dependent faults using model-based reasoning systems

NASA Technical Reports Server (NTRS)

Goodrich, Charles H.

1995-01-01

This paper explores techniques to apply model-based reasoning to equipment and systems which exhibit dynamic behavior (that which changes as a function of time). The model-based system of interest is KATE-C (Knowledge based Autonomous Test Engineer) which is a C++ based system designed to perform monitoring and diagnosis of Space Shuttle electro-mechanical systems. Methods of model-based monitoring and diagnosis are well known and have been thoroughly explored by others. A short example is given which illustrates the principle of model-based reasoning and reveals some limitations of static, non-time-dependent simulation. This example is then extended to demonstrate representation of time-dependent behavior and testing of fault hypotheses in that environment.

Layout Slam with Model Based Loop Closure for 3d Indoor Corridor Reconstruction

NASA Astrophysics Data System (ADS)

Baligh Jahromi, A.; Sohn, G.; Jung, J.; Shahbazi, M.; Kang, J.

2018-05-01

In this paper, we extend a recently proposed visual Simultaneous Localization and Mapping (SLAM) techniques, known as Layout SLAM, to make it robust against error accumulations, abrupt changes of camera orientation and miss-association of newly visited parts of the scene to the previously visited landmarks. To do so, we present a novel technique of loop closing based on layout model matching; i.e., both model information (topology and geometry of reconstructed models) and image information (photometric features) are used to address a loop-closure detection. The advantages of using the layout-related information in the proposed loop-closing technique are twofold. First, it imposes a metric constraint on the global map consistency and, thus, adjusts the mapping scale drifts. Second, it can reduce matching ambiguity in the context of indoor corridors, where the scene is homogenously textured and extracting sufficient amount of distinguishable point features is a challenging task. To test the impact of the proposed technique on the performance of Layout SLAM, we have performed the experiments on wide-angle videos captured by a handheld camera. This dataset was collected from the indoor corridors of a building at York University. The obtained results demonstrate that the proposed method successfully detects the instances of loops while producing very limited trajectory errors.

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

Demeure, I.M.

The research presented here is concerned with representation techniques and tools to support the design, prototyping, simulation, and evaluation of message-based parallel, distributed computations. The author describes ParaDiGM-Parallel, Distributed computation Graph Model-a visual representation technique for parallel, message-based distributed computations. ParaDiGM provides several views of a computation depending on the aspect of concern. It is made of two complementary submodels, the DCPG-Distributed Computing Precedence Graph-model, and the PAM-Process Architecture Model-model. DCPGs are precedence graphs used to express the functionality of a computation in terms of tasks, message-passing, and data. PAM graphs are used to represent the partitioning of a computationmore » into schedulable units or processes, and the pattern of communication among those units. There is a natural mapping between the two models. He illustrates the utility of ParaDiGM as a representation technique by applying it to various computations (e.g., an adaptive global optimization algorithm, the client-server model). ParaDiGM representations are concise. They can be used in documenting the design and the implementation of parallel, distributed computations, in describing such computations to colleagues, and in comparing and contrasting various implementations of the same computation. He then describes VISA-VISual Assistant, a software tool to support the design, prototyping, and simulation of message-based parallel, distributed computations. VISA is based on the ParaDiGM model. In particular, it supports the editing of ParaDiGM graphs to describe the computations of interest, and the animation of these graphs to provide visual feedback during simulations. The graphs are supplemented with various attributes, simulation parameters, and interpretations which are procedures that can be executed by VISA.« less

Basic study on a lower-energy defibrillation method using computer simulation and cultured myocardial cell models.

PubMed

Yaguchi, A; Nagase, K; Ishikawa, M; Iwasaka, T; Odagaki, M; Hosaka, H

2006-01-01

Computer simulation and myocardial cell models were used to evaluate a low-energy defibrillation technique. A generated spiral wave, considered to be a mechanism of fibrillation, and fibrillation were investigated using two myocardial sheet models: a two-dimensional computer simulation model and a two-dimensional experimental model. A new defibrillation technique that has few side effects, which are induced by the current passing into the patient's body, on cardiac muscle is desired. The purpose of the present study is to conduct a basic investigation into an efficient defibrillation method. In order to evaluate the defibrillation method, the propagation of excitation in the myocardial sheet is measured during the normal state and during fibrillation, respectively. The advantages of the low-energy defibrillation technique are then discussed based on the stimulation timing.

A high-frequency warm shallow water acoustic communications channel model and measurements.

PubMed

Chitre, Mandar

2007-11-01

Underwater acoustic communication is a core enabling technology with applications in ocean monitoring using remote sensors and autonomous underwater vehicles. One of the more challenging underwater acoustic communication channels is the medium-range very shallow warm-water channel, common in tropical coastal regions. This channel exhibits two key features-extensive time-varying multipath and high levels of non-Gaussian ambient noise due to snapping shrimp-both of which limit the performance of traditional communication techniques. A good understanding of the communications channel is key to the design of communication systems. It aids in the development of signal processing techniques as well as in the testing of the techniques via simulation. In this article, a physics-based channel model for the very shallow warm-water acoustic channel at high frequencies is developed, which are of interest to medium-range communication system developers. The model is based on ray acoustics and includes time-varying statistical effects as well as non-Gaussian ambient noise statistics observed during channel studies. The model is calibrated and its accuracy validated using measurements made at sea.

Developing Formal Correctness Properties from Natural Language Requirements

NASA Technical Reports Server (NTRS)

Nikora, Allen P.

2006-01-01

This viewgraph presentation reviews the rationale of the program to transform natural language specifications into formal notation.Specifically, automate generation of Linear Temporal Logic (LTL)correctness properties from natural language temporal specifications. There are several reasons for this approach (1) Model-based techniques becoming more widely accepted, (2) Analytical verification techniques (e.g., model checking, theorem proving) significantly more effective at detecting types of specification design errors (e.g., race conditions, deadlock) than manual inspection, (3) Many requirements still written in natural language, which results in a high learning curve for specification languages, associated tools and increased schedule and budget pressure on projects reduce training opportunities for engineers, and (4) Formulation of correctness properties for system models can be a difficult problem. This has relevance to NASA in that it would simplify development of formal correctness properties, lead to more widespread use of model-based specification, design techniques, assist in earlier identification of defects and reduce residual defect content for space mission software systems. The presentation also discusses: potential applications, accomplishments and/or technological transfer potential and the next steps.

Investigation and Development of Control Laws for the NASA Langley Research Center Cockpit Motion Facility

NASA Technical Reports Server (NTRS)

Coon, Craig R.; Cardullo, Frank M.; Zaychik, Kirill B.

2014-01-01

The ability to develop highly advanced simulators is a critical need that has the ability to significantly impact the aerospace industry. The aerospace industry is advancing at an ever increasing pace and flight simulators must match this development with ever increasing urgency. In order to address both current problems and potential advancements with flight simulator techniques, several aspects of current control law technology of the National Aeronautics and Space Administration (NASA) Langley Research Center's Cockpit Motion Facility (CMF) motion base simulator were examined. Preliminary investigation of linear models based upon hardware data were examined to ensure that the most accurate models are used. This research identified both system improvements in the bandwidth and more reliable linear models. Advancements in the compensator design were developed and verified through multiple techniques. The position error rate feedback, the acceleration feedback and the force feedback were all analyzed in the heave direction using the nonlinear model of the hardware. Improvements were made using the position error rate feedback technique. The acceleration feedback compensator also provided noteworthy improvement, while attempts at implementing a force feedback compensator proved unsuccessful.

New paradigms in internal architecture design and freeform fabrication of tissue engineering porous scaffolds.

PubMed

Yoo, Dongjin

2012-07-01

Advanced additive manufacture (AM) techniques are now being developed to fabricate scaffolds with controlled internal pore architectures in the field of tissue engineering. In general, these techniques use a hybrid method which combines computer-aided design (CAD) with computer-aided manufacturing (CAM) tools to design and fabricate complicated three-dimensional (3D) scaffold models. The mathematical descriptions of micro-architectures along with the macro-structures of the 3D scaffold models are limited by current CAD technologies as well as by the difficulty of transferring the designed digital models to standard formats for fabrication. To overcome these difficulties, we have developed an efficient internal pore architecture design system based on triply periodic minimal surface (TPMS) unit cell libraries and associated computational methods to assemble TPMS unit cells into an entire scaffold model. In addition, we have developed a process planning technique based on TPMS internal architecture pattern of unit cells to generate tool paths for freeform fabrication of tissue engineering porous scaffolds. Copyright © 2012 IPEM. Published by Elsevier Ltd. All rights reserved.

Correlation techniques to determine model form in robust nonlinear system realization/identification

NASA Technical Reports Server (NTRS)

Stry, Greselda I.; Mook, D. Joseph

1991-01-01

The fundamental challenge in identification of nonlinear dynamic systems is determining the appropriate form of the model. A robust technique is presented which essentially eliminates this problem for many applications. The technique is based on the Minimum Model Error (MME) optimal estimation approach. A detailed literature review is included in which fundamental differences between the current approach and previous work is described. The most significant feature is the ability to identify nonlinear dynamic systems without prior assumption regarding the form of the nonlinearities, in contrast to existing nonlinear identification approaches which usually require detailed assumptions of the nonlinearities. Model form is determined via statistical correlation of the MME optimal state estimates with the MME optimal model error estimates. The example illustrations indicate that the method is robust with respect to prior ignorance of the model, and with respect to measurement noise, measurement frequency, and measurement record length.

Biological applications of confocal fluorescence polarization microscopy

NASA Astrophysics Data System (ADS)

Bigelow, Chad E.

Fluorescence polarization microscopy is a powerful modality capable of sensing changes in the physical properties and local environment of fluorophores. In this thesis we present new applications for the technique in cancer diagnosis and treatment and explore the limits of the modality in scattering media. We describe modifications to our custom-built confocal fluorescence microscope that enable dual-color imaging, optical fiber-based confocal spectroscopy and fluorescence polarization imaging. Experiments are presented that indicate the performance of the instrument for all three modalities. The limits of confocal fluorescence polarization imaging in scattering media are explored and the microscope parameters necessary for accurate polarization images in this regime are determined. A Monte Carlo routine is developed to model the effect of scattering on images. Included in it are routines to track the polarization state of light using the Mueller-Stokes formalism and a model for fluorescence generation that includes sampling the excitation light polarization ellipse, Brownian motion of excited-state fluorophores in solution, and dipole fluorophore emission. Results from this model are compared to experiments performed on a fluorophore-embedded polymer rod in a turbid medium consisting of polystyrene microspheres in aqueous suspension. We demonstrate the utility of the fluorescence polarization imaging technique for removal of contaminating autofluorescence and for imaging photodynamic therapy drugs in cell monolayers. Images of cells expressing green fluorescent protein are extracted from contaminating fluorescein emission. The distribution of meta-tetrahydroxypheny1chlorin in an EMT6 cell monolayer is also presented. A new technique for imaging enzyme activity is presented that is based on observing changes in the anisotropy of fluorescently-labeled substrates. Proof-of-principle studies are performed in a model system consisting of fluorescently labeled bovine serum albumin attached to sepharose beads. The action of trypsin and proteinase K on the albumin is monitored to demonstrate validity of the technique. Images of the processing of the albumin in J774 murine macrophages are also presented indicating large intercellular differences in enzyme activity. Future directions for the technique are also presented, including the design of enzyme probes specific for prostate specific antigen based on fluorescently-labeled dendrimers. A technique for enzyme imaging based on extracellular autofluorescence is also proposed.

Software Safety Analysis of a Flight Guidance System

NASA Technical Reports Server (NTRS)

Butler, Ricky W. (Technical Monitor); Tribble, Alan C.; Miller, Steven P.; Lempia, David L.

2004-01-01

This document summarizes the safety analysis performed on a Flight Guidance System (FGS) requirements model. In particular, the safety properties desired of the FGS model are identified and the presence of the safety properties in the model is formally verified. Chapter 1 provides an introduction to the entire project, while Chapter 2 gives a brief overview of the problem domain, the nature of accidents, model based development, and the four-variable model. Chapter 3 outlines the approach. Chapter 4 presents the results of the traditional safety analysis techniques and illustrates how the hazardous conditions associated with the system trace into specific safety properties. Chapter 5 presents the results of the formal methods analysis technique model checking that was used to verify the presence of the safety properties in the requirements model. Finally, Chapter 6 summarizes the main conclusions of the study, first and foremost that model checking is a very effective verification technique to use on discrete models with reasonable state spaces. Additional supporting details are provided in the appendices.

Efficient Testing Combining Design of Experiment and Learn-to-Fly Strategies

NASA Technical Reports Server (NTRS)

Murphy, Patrick C.; Brandon, Jay M.

2017-01-01

Rapid modeling and efficient testing methods are important in a number of aerospace applications. In this study efficient testing strategies were evaluated in a wind tunnel test environment and combined to suggest a promising approach for both ground-based and flight-based experiments. Benefits of using Design of Experiment techniques, well established in scientific, military, and manufacturing applications are evaluated in combination with newly developing methods for global nonlinear modeling. The nonlinear modeling methods, referred to as Learn-to-Fly methods, utilize fuzzy logic and multivariate orthogonal function techniques that have been successfully demonstrated in flight test. The blended approach presented has a focus on experiment design and identifies a sequential testing process with clearly defined completion metrics that produce increased testing efficiency.

Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction models.

PubMed

Hristov, A N; Kebreab, E; Niu, M; Oh, J; Bannink, A; Bayat, A R; Boland, T B; Brito, A F; Casper, D P; Crompton, L A; Dijkstra, J; Eugène, M; Garnsworthy, P C; Haque, N; Hellwing, A L F; Huhtanen, P; Kreuzer, M; Kuhla, B; Lund, P; Madsen, J; Martin, C; Moate, P J; Muetzel, S; Muñoz, C; Peiren, N; Powell, J M; Reynolds, C K; Schwarm, A; Shingfield, K J; Storlien, T M; Weisbjerg, M R; Yáñez-Ruiz, D R; Yu, Z

2018-04-18

Ruminant production systems are important contributors to anthropogenic methane (CH 4 ) emissions, but there are large uncertainties in national and global livestock CH 4 inventories. Sources of uncertainty in enteric CH 4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH 4 emission factors. There is also significant uncertainty associated with enteric CH 4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF 6 ) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH 4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH 4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH 4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH 4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH 4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH 4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes. The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

A novel technique for real-time estimation of edge pedestal density gradients via reflectometer time delay data

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

Zeng, L., E-mail: zeng@fusion.gat.com; Doyle, E. J.; Rhodes, T. L.

2016-11-15

A new model-based technique for fast estimation of the pedestal electron density gradient has been developed. The technique uses ordinary mode polarization profile reflectometer time delay data and does not require direct profile inversion. Because of its simple data processing, the technique can be readily implemented via a Field-Programmable Gate Array, so as to provide a real-time density gradient estimate, suitable for use in plasma control systems such as envisioned for ITER, and possibly for DIII-D and Experimental Advanced Superconducting Tokamak. The method is based on a simple edge plasma model with a linear pedestal density gradient and low scrape-off-layermore » density. By measuring reflectometer time delays for three adjacent frequencies, the pedestal density gradient can be estimated analytically via the new approach. Using existing DIII-D profile reflectometer data, the estimated density gradients obtained from the new technique are found to be in good agreement with the actual density gradients for a number of dynamic DIII-D plasma conditions.« less

Modeling the Performance of Direct-Detection Doppler Lidar Systems in Real Atmospheres

NASA Technical Reports Server (NTRS)

McGill, Matthew J.; Hart, William D.; McKay, Jack A.; Spinhirne, James D.

1999-01-01

Previous modeling of the performance of spaceborne direct-detection Doppler lidar systems has assumed extremely idealized atmospheric models. Here we develop a technique for modeling the performance of these systems in a more realistic atmosphere, based on actual airborne lidar observations. The resulting atmospheric model contains cloud and aerosol variability that is absent in other simulations of spaceborne Doppler lidar instruments. To produce a realistic simulation of daytime performance, we include solar radiance values that are based on actual measurements and are allowed to vary as the viewing scene changes. Simulations are performed for two types of direct-detection Doppler lidar systems: the double-edge and the multi-channel techniques. Both systems were optimized to measure winds from Rayleigh backscatter at 355 nm. Simulations show that the measurement uncertainty during daytime is degraded by only about 10-20% compared to nighttime performance, provided a proper solar filter is included in the instrument design.

Modeling the performance of direct-detection Doppler lidar systems including cloud and solar background variability.

PubMed

McGill, M J; Hart, W D; McKay, J A; Spinhirne, J D

1999-10-20

Previous modeling of the performance of spaceborne direct-detection Doppler lidar systems assumed extremely idealized atmospheric models. Here we develop a technique for modeling the performance of these systems in a more realistic atmosphere, based on actual airborne lidar observations. The resulting atmospheric model contains cloud and aerosol variability that is absent in other simulations of spaceborne Doppler lidar instruments. To produce a realistic simulation of daytime performance, we include solar radiance values that are based on actual measurements and are allowed to vary as the viewing scene changes. Simulations are performed for two types of direct-detection Doppler lidar system: the double-edge and the multichannel techniques. Both systems were optimized to measure winds from Rayleigh backscatter at 355 nm. Simulations show that the measurement uncertainty during daytime is degraded by only approximately 10-20% compared with nighttime performance, provided that a proper solar filter is included in the instrument design.

Probabilistic topic modeling for the analysis and classification of genomic sequences

PubMed Central

2015-01-01

Background Studies on genomic sequences for classification and taxonomic identification have a leading role in the biomedical field and in the analysis of biodiversity. These studies are focusing on the so-called barcode genes, representing a well defined region of the whole genome. Recently, alignment-free techniques are gaining more importance because they are able to overcome the drawbacks of sequence alignment techniques. In this paper a new alignment-free method for DNA sequences clustering and classification is proposed. The method is based on k-mers representation and text mining techniques. Methods The presented method is based on Probabilistic Topic Modeling, a statistical technique originally proposed for text documents. Probabilistic topic models are able to find in a document corpus the topics (recurrent themes) characterizing classes of documents. This technique, applied on DNA sequences representing the documents, exploits the frequency of fixed-length k-mers and builds a generative model for a training group of sequences. This generative model, obtained through the Latent Dirichlet Allocation (LDA) algorithm, is then used to classify a large set of genomic sequences. Results and conclusions We performed classification of over 7000 16S DNA barcode sequences taken from Ribosomal Database Project (RDP) repository, training probabilistic topic models. The proposed method is compared to the RDP tool and Support Vector Machine (SVM) classification algorithm in a extensive set of trials using both complete sequences and short sequence snippets (from 400 bp to 25 bp). Our method reaches very similar results to RDP classifier and SVM for complete sequences. The most interesting results are obtained when short sequence snippets are considered. In these conditions the proposed method outperforms RDP and SVM with ultra short sequences and it exhibits a smooth decrease of performance, at every taxonomic level, when the sequence length is decreased. PMID:25916734

Cost-sensitive AdaBoost algorithm for ordinal regression based on extreme learning machine.

PubMed

Riccardi, Annalisa; Fernández-Navarro, Francisco; Carloni, Sante

2014-10-01

In this paper, the well known stagewise additive modeling using a multiclass exponential (SAMME) boosting algorithm is extended to address problems where there exists a natural order in the targets using a cost-sensitive approach. The proposed ensemble model uses an extreme learning machine (ELM) model as a base classifier (with the Gaussian kernel and the additional regularization parameter). The closed form of the derived weighted least squares problem is provided, and it is employed to estimate analytically the parameters connecting the hidden layer to the output layer at each iteration of the boosting algorithm. Compared to the state-of-the-art boosting algorithms, in particular those using ELM as base classifier, the suggested technique does not require the generation of a new training dataset at each iteration. The adoption of the weighted least squares formulation of the problem has been presented as an unbiased and alternative approach to the already existing ELM boosting techniques. Moreover, the addition of a cost model for weighting the patterns, according to the order of the targets, enables the classifier to tackle ordinal regression problems further. The proposed method has been validated by an experimental study by comparing it with already existing ensemble methods and ELM techniques for ordinal regression, showing competitive results.

Mathematical model of cycad cones' thermogenic temperature responses: inverse calorimetry to estimate metabolic heating rates.

PubMed

Roemer, R B; Booth, D; Bhavsar, A A; Walter, G H; Terry, L I

2012-12-21

A mathematical model based on conservation of energy has been developed and used to simulate the temperature responses of cones of the Australian cycads Macrozamia lucida and Macrozamia. macleayi during their daily thermogenic cycle. These cones generate diel midday thermogenic temperature increases as large as 12 °C above ambient during their approximately two week pollination period. The cone temperature response model is shown to accurately predict the cones' temperatures over multiple days as based on simulations of experimental results from 28 thermogenic events from 3 different cones, each simulated for either 9 or 10 sequential days. The verified model is then used as the foundation of a new, parameter estimation based technique (termed inverse calorimetry) that estimates the cones' daily metabolic heating rates from temperature measurements alone. The inverse calorimetry technique's predictions of the major features of the cones' thermogenic metabolism compare favorably with the estimates from conventional respirometry (indirect calorimetry). Because the new technique uses only temperature measurements, and does not require measurements of oxygen consumption, it provides a simple, inexpensive and portable complement to conventional respirometry for estimating metabolic heating rates. It thus provides an additional tool to facilitate field and laboratory investigations of the bio-physics of thermogenic plants. Copyright © 2012 Elsevier Ltd. All rights reserved.

MCAT to XCAT: The Evolution of 4-D Computerized Phantoms for Imaging Research: Computer models that take account of body movements promise to provide evaluation and improvement of medical imaging devices and technology.

PubMed

Paul Segars, W; Tsui, Benjamin M W

2009-12-01

Recent work in the development of computerized phantoms has focused on the creation of ideal "hybrid" models that seek to combine the realism of a patient-based voxelized phantom with the flexibility of a mathematical or stylized phantom. We have been leading the development of such computerized phantoms for use in medical imaging research. This paper will summarize our developments dating from the original four-dimensional (4-D) Mathematical Cardiac-Torso (MCAT) phantom, a stylized model based on geometric primitives, to the current 4-D extended Cardiac-Torso (XCAT) and Mouse Whole-Body (MOBY) phantoms, hybrid models of the human and laboratory mouse based on state-of-the-art computer graphics techniques. This paper illustrates the evolution of computerized phantoms toward more accurate models of anatomy and physiology. This evolution was catalyzed through the introduction of nonuniform rational b-spline (NURBS) and subdivision (SD) surfaces, tools widely used in computer graphics, as modeling primitives to define a more ideal hybrid phantom. With NURBS and SD surfaces as a basis, we progressed from a simple geometrically based model of the male torso (MCAT) containing only a handful of structures to detailed, whole-body models of the male and female (XCAT) anatomies (at different ages from newborn to adult), each containing more than 9000 structures. The techniques we applied for modeling the human body were similarly used in the creation of the 4-D MOBY phantom, a whole-body model for the mouse designed for small animal imaging research. From our work, we have found the NURBS and SD surface modeling techniques to be an efficient and flexible way to describe the anatomy and physiology for realistic phantoms. Based on imaging data, the surfaces can accurately model the complex organs and structures in the body, providing a level of realism comparable to that of a voxelized phantom. In addition, they are very flexible. Like stylized models, they can easily be manipulated to model anatomical variations and patient motion. With the vast improvement in realism, the phantoms developed in our lab can be combined with accurate models of the imaging process (SPECT, PET, CT, magnetic resonance imaging, and ultrasound) to generate simulated imaging data close to that from actual human or animal subjects. As such, they can provide vital tools to generate predictive imaging data from many different subjects under various scanning parameters from which to quantitatively evaluate and improve imaging devices and techniques. From the MCAT to XCAT, we will demonstrate how NURBS and SD surface modeling have resulted in a major evolutionary advance in the development of computerized phantoms for imaging research.

Simulation of a Petri net-based model of the terpenoid biosynthesis pathway.

PubMed

Hawari, Aliah Hazmah; Mohamed-Hussein, Zeti-Azura

2010-02-09

The development and simulation of dynamic models of terpenoid biosynthesis has yielded a systems perspective that provides new insights into how the structure of this biochemical pathway affects compound synthesis. These insights may eventually help identify reactions that could be experimentally manipulated to amplify terpenoid production. In this study, a dynamic model of the terpenoid biosynthesis pathway was constructed based on the Hybrid Functional Petri Net (HFPN) technique. This technique is a fusion of three other extended Petri net techniques, namely Hybrid Petri Net (HPN), Dynamic Petri Net (HDN) and Functional Petri Net (FPN). The biological data needed to construct the terpenoid metabolic model were gathered from the literature and from biological databases. These data were used as building blocks to create an HFPNe model and to generate parameters that govern the global behaviour of the model. The dynamic model was simulated and validated against known experimental data obtained from extensive literature searches. The model successfully simulated metabolite concentration changes over time (pt) and the observations correlated with known data. Interactions between the intermediates that affect the production of terpenes could be observed through the introduction of inhibitors that established feedback loops within and crosstalk between the pathways. Although this metabolic model is only preliminary, it will provide a platform for analysing various high-throughput data, and it should lead to a more holistic understanding of terpenoid biosynthesis.

Mixture Modeling: Applications in Educational Psychology

ERIC Educational Resources Information Center

Harring, Jeffrey R.; Hodis, Flaviu A.

2016-01-01

Model-based clustering methods, commonly referred to as finite mixture modeling, have been applied to a wide variety of cross-sectional and longitudinal data to account for heterogeneity in population characteristics. In this article, we elucidate 2 such approaches: growth mixture modeling and latent profile analysis. Both techniques are…

An improved simulation based biomechanical model to estimate static muscle loadings

NASA Technical Reports Server (NTRS)

Rajulu, Sudhakar L.; Marras, William S.; Woolford, Barbara

1991-01-01

The objectives of this study are to show that the characteristics of an intact muscle are different from those of an isolated muscle and to describe a simulation based model. This model, unlike the optimization based models, accounts for the redundancy in the musculoskeletal system in predicting the amount of forces generated within a muscle. The results of this study show that the loading of the primary muscle is increased by the presence of other muscle activities. Hence, the previous models based on optimization techniques may underestimate the severity of the muscle and joint loadings which occur during manual material handling tasks.

A numerical projection technique for large-scale eigenvalue problems

NASA Astrophysics Data System (ADS)

Gamillscheg, Ralf; Haase, Gundolf; von der Linden, Wolfgang

2011-10-01

We present a new numerical technique to solve large-scale eigenvalue problems. It is based on the projection technique, used in strongly correlated quantum many-body systems, where first an effective approximate model of smaller complexity is constructed by projecting out high energy degrees of freedom and in turn solving the resulting model by some standard eigenvalue solver. Here we introduce a generalization of this idea, where both steps are performed numerically and which in contrast to the standard projection technique converges in principle to the exact eigenvalues. This approach is not just applicable to eigenvalue problems encountered in many-body systems but also in other areas of research that result in large-scale eigenvalue problems for matrices which have, roughly speaking, mostly a pronounced dominant diagonal part. We will present detailed studies of the approach guided by two many-body models.

Overview of existing algorithms for emotion classification. Uncertainties in evaluations of accuracies.

NASA Astrophysics Data System (ADS)

Avetisyan, H.; Bruna, O.; Holub, J.

2016-11-01

A numerous techniques and algorithms are dedicated to extract emotions from input data. In our investigation it was stated that emotion-detection approaches can be classified into 3 following types: Keyword based / lexical-based, learning based, and hybrid. The most commonly used techniques, such as keyword-spotting method, Support Vector Machines, Naïve Bayes Classifier, Hidden Markov Model and hybrid algorithms, have impressive results in this sphere and can reach more than 90% determining accuracy.

A fluctuation-induced plasma transport diagnostic based upon fast-Fourier transform spectral analysis

NASA Technical Reports Server (NTRS)

Powers, E. J.; Kim, Y. C.; Hong, J. Y.; Roth, J. R.; Krawczonek, W. M.

1978-01-01

A diagnostic, based on fast Fourier-transform spectral analysis techniques, that provides experimental insight into the relationship between the experimentally observable spectral characteristics of the fluctuations and the fluctuation-induced plasma transport is described. The model upon which the diagnostic technique is based and its experimental implementation is discussed. Some characteristic results obtained during the course of an experimental study of fluctuation-induced transport in the electric field dominated NASA Lewis bumpy torus plasma are presented.

Towards Symbolic Model Checking for Multi-Agent Systems via OBDDs

NASA Technical Reports Server (NTRS)

Raimondi, Franco; Lomunscio, Alessio

2004-01-01

We present an algorithm for model checking temporal-epistemic properties of multi-agent systems, expressed in the formalism of interpreted systems. We first introduce a technique for the translation of interpreted systems into boolean formulae, and then present a model-checking algorithm based on this translation. The algorithm is based on OBDD's, as they offer a compact and efficient representation for boolean formulae.

A Multiagent Based Model for Tactical Planning

DTIC Science & Technology

2002-10-01

Pub. Co. 1985. [10] Castillo, J.M. Aproximación mediante procedimientos de Inteligencia Artificial al planeamiento táctico. Doctoral Thesis...been developed under the same conceptual model and using similar Artificial Intelligence Tools. We use four different stimulus/response agents in...The conceptual model is built on base of the Agents theory. To implement the different agents we have used Artificial Intelligence techniques such

Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer.

PubMed

Hoogendoorn, Mark; Szolovits, Peter; Moons, Leon M G; Numans, Mattijs E

2016-05-01

Machine learning techniques can be used to extract predictive models for diseases from electronic medical records (EMRs). However, the nature of EMRs makes it difficult to apply off-the-shelf machine learning techniques while still exploiting the rich content of the EMRs. In this paper, we explore the usage of a range of natural language processing (NLP) techniques to extract valuable predictors from uncoded consultation notes and study whether they can help to improve predictive performance. We study a number of existing techniques for the extraction of predictors from the consultation notes, namely a bag of words based approach and topic modeling. In addition, we develop a dedicated technique to match the uncoded consultation notes with a medical ontology. We apply these techniques as an extension to an existing pipeline to extract predictors from EMRs. We evaluate them in the context of predictive modeling for colorectal cancer (CRC), a disease known to be difficult to diagnose before performing an endoscopy. Our results show that we are able to extract useful information from the consultation notes. The predictive performance of the ontology-based extraction method moves significantly beyond the benchmark of age and gender alone (area under the receiver operating characteristic curve (AUC) of 0.870 versus 0.831). We also observe more accurate predictive models by adding features derived from processing the consultation notes compared to solely using coded data (AUC of 0.896 versus 0.882) although the difference is not significant. The extracted features from the notes are shown be equally predictive (i.e. there is no significant difference in performance) compared to the coded data of the consultations. It is possible to extract useful predictors from uncoded consultation notes that improve predictive performance. Techniques linking text to concepts in medical ontologies to derive these predictors are shown to perform best for predicting CRC in our EMR dataset. Copyright © 2016 Elsevier B.V. All rights reserved.

Identifying and prioritizing the tools/techniques of knowledge management based on the Asian Productivity Organization Model (APO) to use in hospitals.

PubMed

Khajouei, Hamid; Khajouei, Reza

2017-12-01

Appropriate knowledge, correct information, and relevant data are vital in medical diagnosis and treatment systems. Knowledge Management (KM) through its tools/techniques provides a pertinent framework for decision-making in healthcare systems. The objective of this study was to identify and prioritize the KM tools/techniques that apply to hospital setting. This is a descriptive-survey study. Data were collected using a -researcher-made questionnaire that was developed based on experts' opinions to select the appropriate tools/techniques from 26 tools/techniques of the Asian Productivity Organization (APO) model. Questions were categorized into five steps of KM (identifying, creating, storing, sharing, and applying the knowledge) according to this model. The study population consisted of middle and senior managers of hospitals and managing directors of Vice-Chancellor for Curative Affairs in Kerman University of Medical Sciences in Kerman, Iran. The data were analyzed in SPSS v.19 using one-sample t-test. Twelve out of 26 tools/techniques of the APO model were identified as the tools applicable in hospitals. "Knowledge café" and "APO knowledge management assessment tool" with respective means of 4.23 and 3.7 were the most and the least applicable tools in the knowledge identification step. "Mentor-mentee scheme", as well as "voice and Voice over Internet Protocol (VOIP)" with respective means of 4.20 and 3.52 were the most and the least applicable tools/techniques in the knowledge creation step. "Knowledge café" and "voice and VOIP" with respective means of 3.85 and 3.42 were the most and the least applicable tools/techniques in the knowledge storage step. "Peer assist and 'voice and VOIP' with respective means of 4.14 and 3.38 were the most and the least applicable tools/techniques in the knowledge sharing step. Finally, "knowledge worker competency plan" and "knowledge portal" with respective means of 4.38 and 3.85 were the most and the least applicable tools/techniques in the knowledge application step. The results showed that 12 out of 26 tools in the APO model are appropriate for hospitals of which 11 are significantly applicable, and "storytelling" is marginally applicable. In this study, the preferred tools/techniques for implementation of each of the five KM steps in hospitals are introduced. Copyright © 2017 Elsevier B.V. All rights reserved.

Simulation and optimization of pressure swing adsorption systmes using reduced-order modeling

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

Agarwal, A.; Biegler, L.; Zitney, S.

2009-01-01

Over the past three decades, pressure swing adsorption (PSA) processes have been widely used as energyefficient gas separation techniques, especially for high purity hydrogen purification from refinery gases. Models for PSA processes are multiple instances of partial differential equations (PDEs) in time and space with periodic boundary conditions that link the processing steps together. The solution of this coupled stiff PDE system is governed by steep fronts moving with time. As a result, the optimization of such systems represents a significant computational challenge to current differential algebraic equation (DAE) optimization techniques and nonlinear programming algorithms. Model reduction is one approachmore » to generate cost-efficient low-order models which can be used as surrogate models in the optimization problems. This study develops a reducedorder model (ROM) based on proper orthogonal decomposition (POD), which is a low-dimensional approximation to a dynamic PDE-based model. The proposed method leads to a DAE system of significantly lower order, thus replacing the one obtained from spatial discretization and making the optimization problem computationally efficient. The method has been applied to the dynamic coupled PDE-based model of a twobed four-step PSA process for separation of hydrogen from methane. Separate ROMs have been developed for each operating step with different POD modes for each of them. A significant reduction in the order of the number of states has been achieved. The reduced-order model has been successfully used to maximize hydrogen recovery by manipulating operating pressures, step times and feed and regeneration velocities, while meeting product purity and tight bounds on these parameters. Current results indicate the proposed ROM methodology as a promising surrogate modeling technique for cost-effective optimization purposes.« less

Personalized models of bones based on radiographic photogrammetry.

PubMed

Berthonnaud, E; Hilmi, R; Dimnet, J

2009-07-01

The radiographic photogrammetry is applied, for locating anatomical landmarks in space, from their two projected images. The goal of this paper is to define a personalized geometric model of bones, based uniquely on photogrammetric reconstructions. The personalized models of bones are obtained from two successive steps: their functional frameworks are first determined experimentally, then, the 3D bone representation results from modeling techniques. Each bone functional framework is issued from direct measurements upon two radiographic images. These images may be obtained using either perpendicular (spine and sacrum) or oblique incidences (pelvis and lower limb). Frameworks link together their functional axes and punctual landmarks. Each global bone volume is decomposed in several elementary components. Each volumic component is represented by simple geometric shapes. Volumic shapes are articulated to the patient's bone structure. The volumic personalization is obtained by best fitting the geometric model projections to their real images, using adjustable articulations. Examples are presented to illustrating the technique of personalization of bone volumes, directly issued from the treatment of only two radiographic images. The chosen techniques for treating data are then discussed. The 3D representation of bones completes, for clinical users, the information brought by radiographic images.

Reduced-Order Modeling and Wavelet Analysis of Turbofan Engine Structural Response Due to Foreign Object Damage (FOD) Events

NASA Technical Reports Server (NTRS)

Turso, James; Lawrence, Charles; Litt, Jonathan

2004-01-01

The development of a wavelet-based feature extraction technique specifically targeting FOD-event induced vibration signal changes in gas turbine engines is described. The technique performs wavelet analysis of accelerometer signals from specified locations on the engine and is shown to be robust in the presence of significant process and sensor noise. It is envisioned that the technique will be combined with Kalman filter thermal/health parameter estimation for FOD-event detection via information fusion from these (and perhaps other) sources. Due to the lack of high-frequency FOD-event test data in the open literature, a reduced-order turbofan structural model (ROM) was synthesized from a finite element model modal analysis to support the investigation. In addition to providing test data for algorithm development, the ROM is used to determine the optimal sensor location for FOD-event detection. In the presence of significant noise, precise location of the FOD event in time was obtained using the developed wavelet-based feature.

Reduced-Order Modeling and Wavelet Analysis of Turbofan Engine Structural Response Due to Foreign Object Damage "FOD" Events

NASA Technical Reports Server (NTRS)

Turso, James A.; Lawrence, Charles; Litt, Jonathan S.

2007-01-01

The development of a wavelet-based feature extraction technique specifically targeting FOD-event induced vibration signal changes in gas turbine engines is described. The technique performs wavelet analysis of accelerometer signals from specified locations on the engine and is shown to be robust in the presence of significant process and sensor noise. It is envisioned that the technique will be combined with Kalman filter thermal/ health parameter estimation for FOD-event detection via information fusion from these (and perhaps other) sources. Due to the lack of high-frequency FOD-event test data in the open literature, a reduced-order turbofan structural model (ROM) was synthesized from a finite-element model modal analysis to support the investigation. In addition to providing test data for algorithm development, the ROM is used to determine the optimal sensor location for FOD-event detection. In the presence of significant noise, precise location of the FOD event in time was obtained using the developed wavelet-based feature.

Generalized image contrast enhancement technique based on Heinemann contrast discrimination model

NASA Astrophysics Data System (ADS)

Liu, Hong; Nodine, Calvin F.

1994-03-01

This paper presents a generalized image contrast enhancement technique which equalizes perceived brightness based on the Heinemann contrast discrimination model. This is a modified algorithm which presents an improvement over the previous study by Mokrane in its mathematically proven existence of a unique solution and in its easily tunable parameterization. The model uses a log-log representation of contrast luminosity between targets and the surround in a fixed luminosity background setting. The algorithm consists of two nonlinear gray-scale mapping functions which have seven parameters, two of which are adjustable Heinemann constants. Another parameter is the background gray level. The remaining four parameters are nonlinear functions of gray scale distribution of the image, and can be uniquely determined once the previous three are given. Tests have been carried out to examine the effectiveness of the algorithm for increasing the overall contrast of images. It can be demonstrated that the generalized algorithm provides better contrast enhancement than histogram equalization. In fact, the histogram equalization technique is a special case of the proposed mapping.

Interpolative modeling of GaAs FET S-parameter data bases for use in Monte Carlo simulations

NASA Technical Reports Server (NTRS)

Campbell, L.; Purviance, J.

1992-01-01

A statistical interpolation technique is presented for modeling GaAs FET S-parameter measurements for use in the statistical analysis and design of circuits. This is accomplished by interpolating among the measurements in a GaAs FET S-parameter data base in a statistically valid manner.

Investigation of Propagation in Foliage Using Simulation Techniques

DTIC Science & Technology

2011-12-01

simulation models provide a rough approximation to radiowave propagation in an actual rainforest environment. Based on the simulated results, the...simulation models provide a rough approximation to radiowave propagation in an actual rainforest environment. Based on the simulated results, the path... Rainforest ...............................2 2. Electrical Properties of a Forest .........................................................3 B. OBJECTIVES OF

Relationships Between the External and Internal Training Load in Professional Soccer: What Can We Learn From Machine Learning?

PubMed

Jaspers, Arne; De Beéck, Tim Op; Brink, Michel S; Frencken, Wouter G P; Staes, Filip; Davis, Jesse J; Helsen, Werner F

2018-05-01

Machine learning may contribute to understanding the relationship between the external load and internal load in professional soccer. Therefore, the relationship between external load indicators (ELIs) and the rating of perceived exertion (RPE) was examined using machine learning techniques on a group and individual level. Training data were collected from 38 professional soccer players over 2 seasons. The external load was measured using global positioning system technology and accelerometry. The internal load was obtained using the RPE. Predictive models were constructed using 2 machine learning techniques, artificial neural networks and least absolute shrinkage and selection operator (LASSO) models, and 1 naive baseline method. The predictions were based on a large set of ELIs. Using each technique, 1 group model involving all players and 1 individual model for each player were constructed. These models' performance on predicting the reported RPE values for future training sessions was compared with the naive baseline's performance. Both the artificial neural network and LASSO models outperformed the baseline. In addition, the LASSO model made more accurate predictions for the RPE than did the artificial neural network model. Furthermore, decelerations were identified as important ELIs. Regardless of the applied machine learning technique, the group models resulted in equivalent or better predictions for the reported RPE values than the individual models. Machine learning techniques may have added value in predicting RPE for future sessions to optimize training design and evaluation. These techniques may also be used in conjunction with expert knowledge to select key ELIs for load monitoring.

Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework.

PubMed

El-Assady, Mennatallah; Sevastjanova, Rita; Sperrle, Fabian; Keim, Daniel; Collins, Christopher

2018-01-01

Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but remain difficult to interpret and adjust. Addressing these limitations, we present a modular visual analytics framework, tackling the understandability and adaptability of topic models through a user-driven reinforcement learning process which does not require a deep understanding of the underlying topic modeling algorithms. Given a document corpus, our approach initializes two algorithm configurations based on a parameter space analysis that enhances document separability. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models, investigating topic summaries, analyzing parameter distributions, and reviewing documents. The main contribution of our work is an iterative decision-making technique in which users provide a document-based relevance feedback that allows the framework to converge to a user-endorsed topic distribution. We also report feedback from a two-stage study which shows that our technique results in topic model quality improvements on two independent measures.

Robust Approach for Nonuniformity Correction in Infrared Focal Plane Array.

PubMed

Boutemedjet, Ayoub; Deng, Chenwei; Zhao, Baojun

2016-11-10

In this paper, we propose a new scene-based nonuniformity correction technique for infrared focal plane arrays. Our work is based on the use of two well-known scene-based methods, namely, adaptive and interframe registration-based exploiting pure translation motion model between frames. The two approaches have their benefits and drawbacks, which make them extremely effective in certain conditions and not adapted for others. Following on that, we developed a method robust to various conditions, which may slow or affect the correction process by elaborating a decision criterion that adapts the process to the most effective technique to ensure fast and reliable correction. In addition to that, problems such as bad pixels and ghosting artifacts are also dealt with to enhance the overall quality of the correction. The performance of the proposed technique is investigated and compared to the two state-of-the-art techniques cited above.

Robust Approach for Nonuniformity Correction in Infrared Focal Plane Array

PubMed Central

Boutemedjet, Ayoub; Deng, Chenwei; Zhao, Baojun

2016-01-01

In this paper, we propose a new scene-based nonuniformity correction technique for infrared focal plane arrays. Our work is based on the use of two well-known scene-based methods, namely, adaptive and interframe registration-based exploiting pure translation motion model between frames. The two approaches have their benefits and drawbacks, which make them extremely effective in certain conditions and not adapted for others. Following on that, we developed a method robust to various conditions, which may slow or affect the correction process by elaborating a decision criterion that adapts the process to the most effective technique to ensure fast and reliable correction. In addition to that, problems such as bad pixels and ghosting artifacts are also dealt with to enhance the overall quality of the correction. The performance of the proposed technique is investigated and compared to the two state-of-the-art techniques cited above. PMID:27834893

A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users

PubMed Central

Ravi, Logesh; Vairavasundaram, Subramaniyaswamy

2016-01-01

Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented. PMID:27069468

Combining Computational Fluid Dynamics and Agent-Based Modeling: A New Approach to Evacuation Planning

PubMed Central

Epstein, Joshua M.; Pankajakshan, Ramesh; Hammond, Ross A.

2011-01-01

We introduce a novel hybrid of two fields—Computational Fluid Dynamics (CFD) and Agent-Based Modeling (ABM)—as a powerful new technique for urban evacuation planning. CFD is a predominant technique for modeling airborne transport of contaminants, while ABM is a powerful approach for modeling social dynamics in populations of adaptive individuals. The hybrid CFD-ABM method is capable of simulating how large, spatially-distributed populations might respond to a physically realistic contaminant plume. We demonstrate the overall feasibility of CFD-ABM evacuation design, using the case of a hypothetical aerosol release in Los Angeles to explore potential effectiveness of various policy regimes. We conclude by arguing that this new approach can be powerfully applied to arbitrary population centers, offering an unprecedented preparedness and catastrophic event response tool. PMID:21687788

Structural characterisation of medically relevant protein assemblies by integrating mass spectrometry with computational modelling.

PubMed

Politis, Argyris; Schmidt, Carla

2018-03-20

Structural mass spectrometry with its various techniques is a powerful tool for the structural elucidation of medically relevant protein assemblies. It delivers information on the composition, stoichiometries, interactions and topologies of these assemblies. Most importantly it can deal with heterogeneous mixtures and assemblies which makes it universal among the conventional structural techniques. In this review we summarise recent advances and challenges in structural mass spectrometric techniques. We describe how the combination of the different mass spectrometry-based methods with computational strategies enable structural models at molecular levels of resolution. These models hold significant potential for helping us in characterizing the function of protein assemblies related to human health and disease. In this review we summarise the techniques of structural mass spectrometry often applied when studying protein-ligand complexes. We exemplify these techniques through recent examples from literature that helped in the understanding of medically relevant protein assemblies. We further provide a detailed introduction into various computational approaches that can be integrated with these mass spectrometric techniques. Last but not least we discuss case studies that integrated mass spectrometry and computational modelling approaches and yielded models of medically important protein assembly states such as fibrils and amyloids. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Navigating complex decision spaces: Problems and paradigms in sequential choice

PubMed Central

Walsh, Matthew M.; Anderson, John R.

2015-01-01

To behave adaptively, we must learn from the consequences of our actions. Doing so is difficult when the consequences of an action follow a delay. This introduces the problem of temporal credit assignment. When feedback follows a sequence of decisions, how should the individual assign credit to the intermediate actions that comprise the sequence? Research in reinforcement learning provides two general solutions to this problem: model-free reinforcement learning and model-based reinforcement learning. In this review, we examine connections between stimulus-response and cognitive learning theories, habitual and goal-directed control, and model-free and model-based reinforcement learning. We then consider a range of problems related to temporal credit assignment. These include second-order conditioning and secondary reinforcers, latent learning and detour behavior, partially observable Markov decision processes, actions with distributed outcomes, and hierarchical learning. We ask whether humans and animals, when faced with these problems, behave in a manner consistent with reinforcement learning techniques. Throughout, we seek to identify neural substrates of model-free and model-based reinforcement learning. The former class of techniques is understood in terms of the neurotransmitter dopamine and its effects in the basal ganglia. The latter is understood in terms of a distributed network of regions including the prefrontal cortex, medial temporal lobes cerebellum, and basal ganglia. Not only do reinforcement learning techniques have a natural interpretation in terms of human and animal behavior, but they also provide a useful framework for understanding neural reward valuation and action selection. PMID:23834192

Communication system modeling

NASA Technical Reports Server (NTRS)

Holland, L. D.; Walsh, J. R., Jr.; Wetherington, R. D.

1971-01-01

This report presents the results of work on communications systems modeling and covers three different areas of modeling. The first of these deals with the modeling of signals in communication systems in the frequency domain and the calculation of spectra for various modulations. These techniques are applied in determining the frequency spectra produced by a unified carrier system, the down-link portion of the Command and Communications System (CCS). The second modeling area covers the modeling of portions of a communication system on a block basis. A detailed analysis and modeling effort based on control theory is presented along with its application to modeling of the automatic frequency control system of an FM transmitter. A third topic discussed is a method for approximate modeling of stiff systems using state variable techniques.

Study of fault tolerant software technology for dynamic systems

NASA Technical Reports Server (NTRS)

Caglayan, A. K.; Zacharias, G. L.

1985-01-01

The major aim of this study is to investigate the feasibility of using systems-based failure detection isolation and compensation (FDIC) techniques in building fault-tolerant software and extending them, whenever possible, to the domain of software fault tolerance. First, it is shown that systems-based FDIC methods can be extended to develop software error detection techniques by using system models for software modules. In particular, it is demonstrated that systems-based FDIC techniques can yield consistency checks that are easier to implement than acceptance tests based on software specifications. Next, it is shown that systems-based failure compensation techniques can be generalized to the domain of software fault tolerance in developing software error recovery procedures. Finally, the feasibility of using fault-tolerant software in flight software is investigated. In particular, possible system and version instabilities, and functional performance degradation that may occur in N-Version programming applications to flight software are illustrated. Finally, a comparative analysis of N-Version and recovery block techniques in the context of generic blocks in flight software is presented.

[Research progress of three-dimensional digital model for repair and reconstruction of knee joint].

PubMed

Tong, Lu; Li, Yanlin; Hu, Meng

2013-01-01

To review recent advance in the application and research of three-dimensional digital knee model. The recent original articles about three-dimensional digital knee model were extensively reviewed and analyzed. The digital three-dimensional knee model can simulate the knee complex anatomical structure very well. Based on this, there are some developments of new software and techniques, and good clinical results are achieved. With the development of computer techniques and software, the knee repair and reconstruction procedure has been improved, the operation will be more simple and its accuracy will be further improved.

MCAT to XCAT: The Evolution of 4-D Computerized Phantoms for Imaging Research

PubMed Central

Paul Segars, W.; Tsui, Benjamin M. W.

2012-01-01

Recent work in the development of computerized phantoms has focused on the creation of ideal “hybrid” models that seek to combine the realism of a patient-based voxelized phantom with the flexibility of a mathematical or stylized phantom. We have been leading the development of such computerized phantoms for use in medical imaging research. This paper will summarize our developments dating from the original four-dimensional (4-D) Mathematical Cardiac-Torso (MCAT) phantom, a stylized model based on geometric primitives, to the current 4-D extended Cardiac-Torso (XCAT) and Mouse Whole-Body (MOBY) phantoms, hybrid models of the human and laboratory mouse based on state-of-the-art computer graphics techniques. This paper illustrates the evolution of computerized phantoms toward more accurate models of anatomy and physiology. This evolution was catalyzed through the introduction of nonuniform rational b-spline (NURBS) and subdivision (SD) surfaces, tools widely used in computer graphics, as modeling primitives to define a more ideal hybrid phantom. With NURBS and SD surfaces as a basis, we progressed from a simple geometrically based model of the male torso (MCAT) containing only a handful of structures to detailed, whole-body models of the male and female (XCAT) anatomies (at different ages from newborn to adult), each containing more than 9000 structures. The techniques we applied for modeling the human body were similarly used in the creation of the 4-D MOBY phantom, a whole-body model for the mouse designed for small animal imaging research. From our work, we have found the NURBS and SD surface modeling techniques to be an efficient and flexible way to describe the anatomy and physiology for realistic phantoms. Based on imaging data, the surfaces can accurately model the complex organs and structures in the body, providing a level of realism comparable to that of a voxelized phantom. In addition, they are very flexible. Like stylized models, they can easily be manipulated to model anatomical variations and patient motion. With the vast improvement in realism, the phantoms developed in our lab can be combined with accurate models of the imaging process (SPECT, PET, CT, magnetic resonance imaging, and ultrasound) to generate simulated imaging data close to that from actual human or animal subjects. As such, they can provide vital tools to generate predictive imaging data from many different subjects under various scanning parameters from which to quantitatively evaluate and improve imaging devices and techniques. From the MCAT to XCAT, we will demonstrate how NURBS and SD surface modeling have resulted in a major evolutionary advance in the development of computerized phantoms for imaging research. PMID:26472880

Development and validation of a sensor-based health monitoring model for the Parkview Bridge deck.

DOT National Transportation Integrated Search

2012-01-31

Accelerated bridge construction (ABC) using full-depth precast deck panels is an innovative technique that brings all : the benefits listed under ABC to full fruition. However, this technique needs to be evaluated and the performance of : the bridge ...

FDTD subcell graphene model beyond the thin-film approximation

NASA Astrophysics Data System (ADS)

Valuev, Ilya; Belousov, Sergei; Bogdanova, Maria; Kotov, Oleg; Lozovik, Yurii

2017-01-01

A subcell technique for calculation of optical properties of graphene with the finite-difference time-domain (FDTD) method is presented. The technique takes into account the surface conductivity of graphene which allows the correct calculation of its dispersive response for arbitrarily polarized incident waves interacting with the graphene. The developed technique is verified for a planar graphene sheet configuration against the exact analytical solution. Based on the same test case scenario, we also show that the subcell technique demonstrates a superior accuracy and numerical efficiency with respect to the widely used thin-film FDTD approach for modeling graphene. We further apply our technique to the simulations of a graphene metamaterial containing periodically spaced graphene strips (graphene strip-grating) and demonstrate good agreement with the available theoretical results.

Investigation of advanced phase-shifting projected fringe profilometry techniques

NASA Astrophysics Data System (ADS)

Liu, Hongyu

1999-11-01

The phase-shifting projected fringe profilometry (PSPFP) technique is a powerful tool in the profile measurements of rough engineering surfaces. Compared with other competing techniques, this technique is notable for its full-field measurement capacity, system simplicity, high measurement speed, and low environmental vulnerability. The main purpose of this dissertation is to tackle three important problems, which severely limit the capability and the accuracy of the PSPFP technique, with some new approaches. Chapter 1 provides some background information of the PSPFP technique including the measurement principles, basic features, and related techniques is briefly introduced. The objectives and organization of the thesis are also outlined. Chapter 2 gives a theoretical treatment to the absolute PSPFP measurement. The mathematical formulations and basic requirements of the absolute PSPFP measurement and its supporting techniques are discussed in detail. Chapter 3 introduces the experimental verification of the proposed absolute PSPFP technique. Some design details of a prototype system are discussed as supplements to the previous theoretical analysis. Various fundamental experiments performed for concept verification and accuracy evaluation are introduced together with some brief comments. Chapter 4 presents the theoretical study of speckle- induced phase measurement errors. In this analysis, the expression for speckle-induced phase errors is first derived based on the multiplicative noise model of image- plane speckles. The statistics and the system dependence of speckle-induced phase errors are then thoroughly studied through numerical simulations and analytical derivations. Based on the analysis, some suggestions on the system design are given to improve measurement accuracy. Chapter 5 discusses a new technique combating surface reflectivity variations. The formula used for error compensation is first derived based on a simplified model of the detection process. The techniques coping with two major effects of surface reflectivity variations are then introduced. Some fundamental problems in the proposed technique are studied through simulations. Chapter 6 briefly summarizes the major contributions of the current work and provides some suggestions for the future research.

A novel fast and flexible technique of radical kinetic behaviour investigation based on pallet for plasma evaluation structure and numerical analysis

NASA Astrophysics Data System (ADS)

Malinowski, Arkadiusz; Takeuchi, Takuya; Chen, Shang; Suzuki, Toshiya; Ishikawa, Kenji; Sekine, Makoto; Hori, Masaru; Lukasiak, Lidia; Jakubowski, Andrzej

2013-07-01

This paper describes a new, fast, and case-independent technique for sticking coefficient (SC) estimation based on pallet for plasma evaluation (PAPE) structure and numerical analysis. Our approach does not require complicated structure, apparatus, or time-consuming measurements but offers high reliability of data and high flexibility. Thermal analysis is also possible. This technique has been successfully applied to estimation of very low value of SC of hydrogen radicals on chemically amplified ArF 193 nm photoresist (the main goal of this study). Upper bound of our technique has been determined by investigation of SC of fluorine radical on polysilicon (in elevated temperature). Sources of estimation error and ways of its reduction have been also discussed. Results of this study give an insight into the process kinetics, and not only they are helpful in better process understanding but additionally they may serve as parameters in a phenomenological model development for predictive modelling of etching for ultimate CMOS topography simulation.

Joint use of over- and under-sampling techniques and cross-validation for the development and assessment of prediction models.

PubMed

Blagus, Rok; Lusa, Lara

2015-11-04

Prediction models are used in clinical research to develop rules that can be used to accurately predict the outcome of the patients based on some of their characteristics. They represent a valuable tool in the decision making process of clinicians and health policy makers, as they enable them to estimate the probability that patients have or will develop a disease, will respond to a treatment, or that their disease will recur. The interest devoted to prediction models in the biomedical community has been growing in the last few years. Often the data used to develop the prediction models are class-imbalanced as only few patients experience the event (and therefore belong to minority class). Prediction models developed using class-imbalanced data tend to achieve sub-optimal predictive accuracy in the minority class. This problem can be diminished by using sampling techniques aimed at balancing the class distribution. These techniques include under- and oversampling, where a fraction of the majority class samples are retained in the analysis or new samples from the minority class are generated. The correct assessment of how the prediction model is likely to perform on independent data is of crucial importance; in the absence of an independent data set, cross-validation is normally used. While the importance of correct cross-validation is well documented in the biomedical literature, the challenges posed by the joint use of sampling techniques and cross-validation have not been addressed. We show that care must be taken to ensure that cross-validation is performed correctly on sampled data, and that the risk of overestimating the predictive accuracy is greater when oversampling techniques are used. Examples based on the re-analysis of real datasets and simulation studies are provided. We identify some results from the biomedical literature where the incorrect cross-validation was performed, where we expect that the performance of oversampling techniques was heavily overestimated.

Instantiating the art of war for effects-based operations

NASA Astrophysics Data System (ADS)

Burns, Carla L.

2002-07-01

Effects-Based Operations (EBO) is a mindset, a philosophy and an approach for planning, executing and assessing military operations for the effects they produce rather than the targets or even objectives they deal with. An EBO approach strives to provide economy of force, dynamic tasking, and reduced collateral damage. The notion of EBO is not new. Military Commanders certainly have desired effects in mind when conducting military operations. However, to date EBO has been an art of war that lacks automated techniques and tools that enable effects-based analysis and assessment. Modeling and simulation is at the heart of this challenge. The Air Force Research Laboratory (AFRL) EBO Program is developing modeling techniques and corresponding tool capabilities that can be brought to bear against the challenges presented by effects-based analysis and assessment. Effects-based course-of-action development, center of gravity/target system analysis, and wargaming capabilities are being developed and integrated to help give Commanders the information decision support required to achieve desired national security objectives. This paper presents an introduction to effects-based operations, discusses the benefits of an EBO approach, and focuses on modeling and analysis for effects-based strategy development. An overview of modeling and simulation challenges for EBO is presented, setting the stage for the detailed technical papers in the subject session.

Model Reduction for Control System Design

NASA Technical Reports Server (NTRS)

Enns, D. F.

1985-01-01

An approach and a technique for effectively obtaining reduced order mathematical models of a given large order model for the purposes of synthesis, analysis and implementation of control systems is developed. This approach involves the use of an error criterion which is the H-infinity norm of a frequency weighted error between the full and reduced order models. The weightings are chosen to take into account the purpose for which the reduced order model is intended. A previously unknown error bound in the H-infinity norm for reduced order models obtained from internally balanced realizations was obtained. This motivated further development of the balancing technique to include the frequency dependent weightings. This resulted in the frequency weighted balanced realization and a new model reduction technique. Two approaches to designing reduced order controllers were developed. The first involves reducing the order of a high order controller with an appropriate weighting. The second involves linear quadratic Gaussian synthesis based on a reduced order model obtained with an appropriate weighting.

Distillation Designs for the Lunar Surface

NASA Technical Reports Server (NTRS)

Boul, Peter J.; Lange,Kevin E.; Conger, Bruce; Anderson, Molly

2010-01-01

Gravity-based distillation methods may be applied to the purification of wastewater on the lunar base. These solutions to water processing are robust physical separation techniques, which may be more advantageous than many other techniques for their simplicity in design and operation. The two techniques can be used in conjunction with each other to obtain high purity water. The components and feed compositions for modeling waste water streams are presented in conjunction with the Aspen property system for traditional stage distillation. While the individual components for each of the waste streams will vary naturally within certain bounds, an analog model for waste water processing is suggested based on typical concentration ranges for these components. Target purity levels for recycled water are determined for each individual component based on NASA s required maximum contaminant levels for potable water Optimum parameters such as reflux ratio, feed stage location, and processing rates are determined with respect to the power consumption of the process. Multistage distillation is evaluated for components in wastewater to determine the minimum number of stages necessary for each of 65 components in humidity condensate and urine wastewater mixed streams.

Model-based cell number quantification using online single-oxygen sensor data for tissue engineering perfusion bioreactors.

PubMed

Lambrechts, T; Papantoniou, I; Sonnaert, M; Schrooten, J; Aerts, J-M

2014-10-01

Online and non-invasive quantification of critical tissue engineering (TE) construct quality attributes in TE bioreactors is indispensable for the cost-effective up-scaling and automation of cellular construct manufacturing. However, appropriate monitoring techniques for cellular constructs in bioreactors are still lacking. This study presents a generic and robust approach to determine cell number and metabolic activity of cell-based TE constructs in perfusion bioreactors based on single oxygen sensor data in dynamic perfusion conditions. A data-based mechanistic modeling technique was used that is able to correlate the number of cells within the scaffold (R(2)  = 0.80) and the metabolic activity of the cells (R(2)  = 0.82) to the dynamics of the oxygen response to step changes in the perfusion rate. This generic non-destructive measurement technique is effective for a large range of cells, from as low as 1.0 × 10(5) cells to potentially multiple millions of cells, and can open-up new possibilities for effective bioprocess monitoring. © 2014 Wiley Periodicals, Inc.

Practical quantum mechanics-based fragment methods for predicting molecular crystal properties.

PubMed

Wen, Shuhao; Nanda, Kaushik; Huang, Yuanhang; Beran, Gregory J O

2012-06-07

Significant advances in fragment-based electronic structure methods have created a real alternative to force-field and density functional techniques in condensed-phase problems such as molecular crystals. This perspective article highlights some of the important challenges in modeling molecular crystals and discusses techniques for addressing them. First, we survey recent developments in fragment-based methods for molecular crystals. Second, we use examples from our own recent research on a fragment-based QM/MM method, the hybrid many-body interaction (HMBI) model, to analyze the physical requirements for a practical and effective molecular crystal model chemistry. We demonstrate that it is possible to predict molecular crystal lattice energies to within a couple kJ mol(-1) and lattice parameters to within a few percent in small-molecule crystals. Fragment methods provide a systematically improvable approach to making predictions in the condensed phase, which is critical to making robust predictions regarding the subtle energy differences found in molecular crystals.

Reduced-order modeling of piezoelectric energy harvesters with nonlinear circuits under complex conditions

NASA Astrophysics Data System (ADS)

Xiang, Hong-Jun; Zhang, Zhi-Wei; Shi, Zhi-Fei; Li, Hong

2018-04-01

A fully coupled modeling approach is developed for piezoelectric energy harvesters in this work based on the use of available robust finite element packages and efficient reducing order modeling techniques. At first, the harvester is modeled using finite element packages. The dynamic equilibrium equations of harvesters are rebuilt by extracting system matrices from the finite element model using built-in commands without any additional tools. A Krylov subspace-based scheme is then applied to obtain a reduced-order model for improving simulation efficiency but preserving the key features of harvesters. Co-simulation of the reduced-order model with nonlinear energy harvesting circuits is achieved in a system level. Several examples in both cases of harmonic response and transient response analysis are conducted to validate the present approach. The proposed approach allows to improve the simulation efficiency by several orders of magnitude. Moreover, the parameters used in the equivalent circuit model can be conveniently obtained by the proposed eigenvector-based model order reduction technique. More importantly, this work establishes a methodology for modeling of piezoelectric energy harvesters with any complicated mechanical geometries and nonlinear circuits. The input load may be more complex also. The method can be employed by harvester designers to optimal mechanical structures or by circuit designers to develop novel energy harvesting circuits.

Contributions of computational chemistry and biophysical techniques to fragment-based drug discovery.

PubMed

Gozalbes, Rafael; Carbajo, Rodrigo J; Pineda-Lucena, Antonio

2010-01-01

In the last decade, fragment-based drug discovery (FBDD) has evolved from a novel approach in the search of new hits to a valuable alternative to the high-throughput screening (HTS) campaigns of many pharmaceutical companies. The increasing relevance of FBDD in the drug discovery universe has been concomitant with an implementation of the biophysical techniques used for the detection of weak inhibitors, e.g. NMR, X-ray crystallography or surface plasmon resonance (SPR). At the same time, computational approaches have also been progressively incorporated into the FBDD process and nowadays several computational tools are available. These stretch from the filtering of huge chemical databases in order to build fragment-focused libraries comprising compounds with adequate physicochemical properties, to more evolved models based on different in silico methods such as docking, pharmacophore modelling, QSAR and virtual screening. In this paper we will review the parallel evolution and complementarities of biophysical techniques and computational methods, providing some representative examples of drug discovery success stories by using FBDD.

Satellite and Ground-based Radiometers Reveal Much Lower Dust Absorption of Sunlight than Used in Climate Models

NASA Technical Reports Server (NTRS)

Kaufman, Y. J.; Tanre, D.; Dubovik, O.; Karnieli, A.; Remer, L. A.; Einaudi, Franco (Technical Monitor)

2000-01-01

The ability of dust to absorb solar radiation and heat the atmosphere is one of the main uncertainties in climate modeling and the prediction of climate change. Dust absorption is not well known due to limitations of in situ measurements. New techniques to measure dust absorption are needed in order to assess the impact of dust on climate. Here we report two new independent remote sensing techniques that provide sensitive measurements of dust absorption. Both are based on remote sensing. One uses satellite spectral measurements, the second uses ground based sky measurements from the AERONET network. Both techniques demonstrate that Saharan dust absorption of solar radiation is several times smaller than the current international standards. Dust cooling of the earth system in the solar spectrum is therefore significantly stronger than recent calculations indicate. We shall also address the issue of the effects of dust non-sphericity on the aerosol optical properties.

In Vitro Measurement of Tissue Integrity during Saccular Aneurysm Embolizations for Simulator-Based Training

PubMed Central

Tercero, C.; Ikeda, S.; Ooe, K.; Fukuda, T.; Arai, F.; Negoro, M.; Takahashi, I.; Kwon, G.

2012-01-01

Summary In the domain of endovascular neurosurgery, the measurement of tissue integrity is needed for simulator-based training and for the development of new intravascular instruments and treatment techniques. In vitro evaluation of tissue manipulation can be achieved using photoelastic stress analysis and vasculature modeling with photoelastic materials. In this research we constructed two types of vasculature models of saccular aneurysms for differentiation of embolization techniques according to the respect for tissue integrity measurements based on the stress within the blood vessel model wall. In an aneurysm model with 5 mm dome diameter, embolization using MicroPlex 10 (Complex 1D, with 4 mm diameter loops), a maximum area of 3.97 mm2 with stress above 1 kPa was measured. This area increased to 5.50 mm2 when the dome was touched deliberately with the release mechanism of the coil, and to 4.87 mm2 for an embolization using Micrusphere, (Spherical 18 Platinum Coil). In a similar way trans-cell stent-assisted coil embolization was also compared to human blood pressure simulation using a model of a wide-necked saccular aneurysm with 7 mm diameter. The area with stress above 1kPa was below 1 mm2 for the pressure simulation and maximized at 3.79 mm2 during the trans-cell insertion of the micro-catheter and at 8.92 mm2 during the embolization. The presented results show that this measurement system is useful for identifying techniques compromising tissue integrity, comparing and studying coils and embolization techniques for a specific vasculature morphology and comparing their natural stress variations such as that produced by blood pressure. PMID:23217635

Test Platforms for Model-Based Flight Research

NASA Astrophysics Data System (ADS)

Dorobantu, Andrei

Demonstrating the reliability of flight control algorithms is critical to integrating unmanned aircraft systems into the civilian airspace. For many potential applications, design and certification of these algorithms will rely heavily on mathematical models of the aircraft dynamics. Therefore, the aerospace community must develop flight test platforms to support the advancement of model-based techniques. The University of Minnesota has developed a test platform dedicated to model-based flight research for unmanned aircraft systems. This thesis provides an overview of the test platform and its research activities in the areas of system identification, model validation, and closed-loop control for small unmanned aircraft.

Dynamic Chest Image Analysis: Evaluation of Model-Based Pulmonary Perfusion Analysis With Pyramid Images

DTIC Science & Technology

2001-10-25

Image Analysis aims to develop model-based computer analysis and visualization methods for showing focal and general abnormalities of lung ventilation and perfusion based on a sequence of digital chest fluoroscopy frames collected with the Dynamic Pulmonary Imaging technique 18,5,17,6. We have proposed and evaluated a multiresolutional method with an explicit ventilation model based on pyramid images for ventilation analysis. We have further extended the method for ventilation analysis to pulmonary perfusion. This paper focuses on the clinical evaluation of our method for

On the development of a new methodology in sub-surface parameterisation on the calibration of groundwater models

NASA Astrophysics Data System (ADS)

Klaas, D. K. S. Y.; Imteaz, M. A.; Sudiayem, I.; Klaas, E. M. E.; Klaas, E. C. M.

2017-10-01

In groundwater modelling, robust parameterisation of sub-surface parameters is crucial towards obtaining an agreeable model performance. Pilot point is an alternative in parameterisation step to correctly configure the distribution of parameters into a model. However, the methodology given by the current studies are considered less practical to be applied on real catchment conditions. In this study, a practical approach of using geometric features of pilot point and distribution of hydraulic gradient over the catchment area is proposed to efficiently configure pilot point distribution in the calibration step of a groundwater model. A development of new pilot point distribution, Head Zonation-based (HZB) technique, which is based on the hydraulic gradient distribution of groundwater flow, is presented. Seven models of seven zone ratios (1, 5, 10, 15, 20, 25 and 30) using HZB technique were constructed on an eogenetic karst catchment in Rote Island, Indonesia and their performances were assessed. This study also concludes some insights into the trade-off between restricting and maximising the number of pilot points and offers a new methodology for selecting pilot point properties and distribution method in the development of a physically-based groundwater model.

Stochastic modelling of temperatures affecting the in situ performance of a solar-assisted heat pump: The multivariate approach and physical interpretation

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

Loveday, D.L.; Craggs, C.

Box-Jenkins-based multivariate stochastic modeling is carried out using data recorded from a domestic heating system. The system comprises an air-source heat pump sited in the roof space of a house, solar assistance being provided by the conventional tile roof acting as a radiation absorber. Multivariate models are presented which illustrate the time-dependent relationships between three air temperatures - at external ambient, at entry to, and at exit from, the heat pump evaporator. Using a deterministic modeling approach, physical interpretations are placed on the results of the multivariate technique. It is concluded that the multivariate Box-Jenkins approach is a suitable techniquemore » for building thermal analysis. Application to multivariate Box-Jenkins approach is a suitable technique for building thermal analysis. Application to multivariate model-based control is discussed, with particular reference to building energy management systems. It is further concluded that stochastic modeling of data drawn from a short monitoring period offers a means of retrofitting an advanced model-based control system in existing buildings, which could be used to optimize energy savings. An approach to system simulation is suggested.« less

Next generation initiation techniques

NASA Technical Reports Server (NTRS)

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

1993-01-01

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

Modular techniques for dynamic fault-tree analysis

NASA Technical Reports Server (NTRS)

Patterson-Hine, F. A.; Dugan, Joanne B.

1992-01-01

It is noted that current approaches used to assess the dependability of complex systems such as Space Station Freedom and the Air Traffic Control System are incapable of handling the size and complexity of these highly integrated designs. A novel technique for modeling such systems which is built upon current techniques in Markov theory and combinatorial analysis is described. It enables the development of a hierarchical representation of system behavior which is more flexible than either technique alone. A solution strategy which is based on an object-oriented approach to model representation and evaluation is discussed. The technique is virtually transparent to the user since the fault tree models can be built graphically and the objects defined automatically. The tree modularization procedure allows the two model types, Markov and combinatoric, to coexist and does not require that the entire fault tree be translated to a Markov chain for evaluation. This effectively reduces the size of the Markov chain required and enables solutions with less truncation, making analysis of longer mission times possible. Using the fault-tolerant parallel processor as an example, a model is built and solved for a specific mission scenario and the solution approach is illustrated in detail.

Modeling paradigms for medical diagnostic decision support: a survey and future directions.

PubMed

Wagholikar, Kavishwar B; Sundararajan, Vijayraghavan; Deshpande, Ashok W

2012-10-01

Use of computer based decision tools to aid clinical decision making, has been a primary goal of research in biomedical informatics. Research in the last five decades has led to the development of Medical Decision Support (MDS) applications using a variety of modeling techniques, for a diverse range of medical decision problems. This paper surveys literature on modeling techniques for diagnostic decision support, with a focus on decision accuracy. Trends and shortcomings of research in this area are discussed and future directions are provided. The authors suggest that-(i) Improvement in the accuracy of MDS application may be possible by modeling of vague and temporal data, research on inference algorithms, integration of patient information from diverse sources and improvement in gene profiling algorithms; (ii) MDS research would be facilitated by public release of de-identified medical datasets, and development of opensource data-mining tool kits; (iii) Comparative evaluations of different modeling techniques are required to understand characteristics of the techniques, which can guide developers in choice of technique for a particular medical decision problem; and (iv) Evaluations of MDS applications in clinical setting are necessary to foster physicians' utilization of these decision aids.

Three-dimensional dominant frequency mapping using autoregressive spectral analysis of atrial electrograms of patients in persistent atrial fibrillation.

PubMed

Salinet, João L; Masca, Nicholas; Stafford, Peter J; Ng, G André; Schlindwein, Fernando S

2016-03-08

Areas with high frequency activity within the atrium are thought to be 'drivers' of the rhythm in patients with atrial fibrillation (AF) and ablation of these areas seems to be an effective therapy in eliminating DF gradient and restoring sinus rhythm. Clinical groups have applied the traditional FFT-based approach to generate the three-dimensional dominant frequency (3D DF) maps during electrophysiology (EP) procedures but literature is restricted on using alternative spectral estimation techniques that can have a better frequency resolution that FFT-based spectral estimation. Autoregressive (AR) model-based spectral estimation techniques, with emphasis on selection of appropriate sampling rate and AR model order, were implemented to generate high-density 3D DF maps of atrial electrograms (AEGs) in persistent atrial fibrillation (persAF). For each patient, 2048 simultaneous AEGs were recorded for 20.478 s-long segments in the left atrium (LA) and exported for analysis, together with their anatomical locations. After the DFs were identified using AR-based spectral estimation, they were colour coded to produce sequential 3D DF maps. These maps were systematically compared with maps found using the Fourier-based approach. 3D DF maps can be obtained using AR-based spectral estimation after AEGs downsampling (DS) and the resulting maps are very similar to those obtained using FFT-based spectral estimation (mean 90.23 %). There were no significant differences between AR techniques (p = 0.62). The processing time for AR-based approach was considerably shorter (from 5.44 to 5.05 s) when lower sampling frequencies and model order values were used. Higher levels of DS presented higher rates of DF agreement (sampling frequency of 37.5 Hz). We have demonstrated the feasibility of using AR spectral estimation methods for producing 3D DF maps and characterised their differences to the maps produced using the FFT technique, offering an alternative approach for 3D DF computation in human persAF studies.

Generation of the first structure-based pharmacophore model containing a selective "zinc binding group" feature to identify potential glyoxalase-1 inhibitors.

PubMed

Al-Balas, Qosay; Hassan, Mohammad; Al-Oudat, Buthina; Alzoubi, Hassan; Mhaidat, Nizar; Almaaytah, Ammar

2012-11-22

Within this study, a unique 3D structure-based pharmacophore model of the enzyme glyoxalase-1 (Glo-1) has been revealed. Glo-1 is considered a zinc metalloenzyme in which the inhibitor binding with zinc atom at the active site is crucial. To our knowledge, this is the first pharmacophore model that has a selective feature for a "zinc binding group" which has been customized within the structure-based pharmacophore model of Glo-1 to extract ligands that possess functional groups able to bind zinc atom solely from database screening. In addition, an extensive 2D similarity search using three diverse similarity techniques (Tanimoto, Dice, Cosine) has been performed over the commercially available "Zinc Clean Drug-Like Database" that contains around 10 million compounds to help find suitable inhibitors for this enzyme based on known inhibitors from the literature. The resultant hits were mapped over the structure based pharmacophore and the successful hits were further docked using three docking programs with different pose fitting and scoring techniques (GOLD, LibDock, CDOCKER). Nine candidates were suggested to be novel Glo-1 inhibitors containing the "zinc binding group" with the highest consensus scoring from docking.

Model-based monitoring and diagnosis of a satellite-based instrument

NASA Technical Reports Server (NTRS)

Bos, Andre; Callies, Jorg; Lefebvre, Alain

1995-01-01

For about a decade model-based reasoning has been propounded by a number of researchers. Maybe one of the most convincing arguments in favor of this kind of reasoning has been given by Davis in his paper on diagnosis from first principles (Davis 1984). Following their guidelines we have developed a system to verify the behavior of a satellite-based instrument GOME (which will be measuring Ozone concentrations in the near future (1995)). We start by giving a description of model-based monitoring. Besides recognizing that something is wrong, we also like to find the cause for misbehaving automatically. Therefore, we show how the monitoring technique can be extended to model-based diagnosis.

Model-based monitoring and diagnosis of a satellite-based instrument

NASA Astrophysics Data System (ADS)

Bos, Andre; Callies, Jorg; Lefebvre, Alain

1995-05-01

For about a decade model-based reasoning has been propounded by a number of researchers. Maybe one of the most convincing arguments in favor of this kind of reasoning has been given by Davis in his paper on diagnosis from first principles (Davis 1984). Following their guidelines we have developed a system to verify the behavior of a satellite-based instrument GOME (which will be measuring Ozone concentrations in the near future (1995)). We start by giving a description of model-based monitoring. Besides recognizing that something is wrong, we also like to find the cause for misbehaving automatically. Therefore, we show how the monitoring technique can be extended to model-based diagnosis.

Visualization of 3D CT-based anatomical models

NASA Astrophysics Data System (ADS)

Alaytsev, Innokentiy K.; Danilova, Tatyana V.; Manturov, Alexey O.; Mareev, Gleb O.; Mareev, Oleg V.

2018-04-01

Biomedical volumetric data visualization techniques for the exploration purposes are well developed. Most of the known methods are inappropriate for surgery simulation systems due to lack of realism. A segmented data visualization is a well-known approach for the visualization of the structured volumetric data. The research is focused on improvement of the segmented data visualization technique by the aliasing problems resolution and the use of material transparency modeling for better semitransparent structures rendering.

Validation of a satellite-based cyclogenesis technique over the North Indian Ocean

NASA Astrophysics Data System (ADS)

Goyal, Suman; Mohapatra, M.; Kumar, Ashish; Dube, S. K.; Rajendra, Kushagra; Goswami, P.

2016-10-01

Indian region is severely affected by the tropical cyclones (TCs) due to the long coast line of about 7500 km. Hence, whenever any low level circulation (LLC) forms over the Indian Seas, the prediction of its intensification into a TC is very essential for the management of TC disaster. Satellite Application Centre (SAC) of Indian Space Research Organization (ISRO), Ahmedabad, has developed a technique to predict TCs based on scatterometer-derived winds from the polar orbiting satellite, QuikSCAT and Oceansat-II. The India Meteorological Department (IMD) has acquired the technique and verified it for the years 2010-2013 for operational use. The model is based on the concept of analogs of the sea surface wind distribution at the stage of LLC or vortex (T1.0) as per Dvorak's classifications, which eventually leads to cyclogenesis (T2.5). The results indicate that the developed model could predict cyclogenesis with a probability of detection of 61% and critical success index of 0.29. However, it shows high over-prediction of the model is better over the Bay of Bengal than over Arabian Sea and during post-monsoon season (September-December) than in pre-monsoon season (March-June).

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 data compression technique for synthetic aperture radar images

NASA Technical Reports Server (NTRS)

Frost, V. S.; Minden, G. J.

1986-01-01

A data compression technique is developed for synthetic aperture radar (SAR) imagery. The technique is based on an SAR image model and is designed to preserve the local statistics in the image by an adaptive variable rate modification of block truncation coding (BTC). A data rate of approximately 1.6 bit/pixel is achieved with the technique while maintaining the image quality and cultural (pointlike) targets. The algorithm requires no large data storage and is computationally simple.

Overland Flow Analysis Using Time Series of Suas-Derived Elevation Models

NASA Astrophysics Data System (ADS)

Jeziorska, J.; Mitasova, H.; Petrasova, A.; Petras, V.; Divakaran, D.; Zajkowski, T.

2016-06-01

With the advent of the innovative techniques for generating high temporal and spatial resolution terrain models from Unmanned Aerial Systems (UAS) imagery, it has become possible to precisely map overland flow patterns. Furthermore, the process has become more affordable and efficient through the coupling of small UAS (sUAS) that are easily deployed with Structure from Motion (SfM) algorithms that can efficiently derive 3D data from RGB imagery captured with consumer grade cameras. We propose applying the robust overland flow algorithm based on the path sampling technique for mapping flow paths in the arable land on a small test site in Raleigh, North Carolina. By comparing a time series of five flights in 2015 with the results of a simulation based on the most recent lidar derived DEM (2013), we show that the sUAS based data is suitable for overland flow predictions and has several advantages over the lidar data. The sUAS based data captures preferential flow along tillage and more accurately represents gullies. Furthermore the simulated water flow patterns over the sUAS based terrain models are consistent throughout the year. When terrain models are reconstructed only from sUAS captured RGB imagery, however, water flow modeling is only appropriate in areas with sparse or no vegetation cover.

High-resolution regional gravity field modelling in a mountainous area from terrestrial gravity data

NASA Astrophysics Data System (ADS)

Bucha, Blažej; Janák, Juraj; Papčo, Juraj; Bezděk, Aleš

2016-11-01

We develop a high-resolution regional gravity field model by a combination of spherical harmonics, band-limited spherical radial basis functions (SRBFs) and the residual terrain model (RTM) technique. As the main input data set, we employ a dense terrestrial gravity database (3-6 stations km-2), which enables gravity field modelling up to very short spatial scales. The approach is based on the remove-compute-restore methodology in which all the parts of the signal that can be modelled are removed prior to the least-squares adjustment in order to smooth the input gravity data. To this end, we utilize degree-2159 spherical harmonic models and the RTM technique using topographic models at 2 arcsec resolution. The residual short-scale gravity signal is modelled via the band-limited Shannon SRBF expanded up to degree 21 600, which corresponds to a spatial resolution of 30 arcsec. The combined model is validated against GNSS/levelling-based height anomalies, independent surface gravity data, deflections of the vertical and terrestrial vertical gravity gradients achieving an accuracy of 2.7 cm, 0.53 mGal, 0.39 arcsec and 279 E in terms of the RMS error, respectively. A key aspect of the combined approach, especially in mountainous areas, is the quality of the RTM. We therefore compare the performance of two RTM techniques within the innermost zone, the tesseroids and the polyhedron. It is shown that the polyhedron-based approach should be preferred in rugged terrain if a high-quality RTM is required. In addition, we deal with the RTM computations at points located below the reference surface of the residual terrain which is known to be a rather delicate issue.

Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Modeling.

PubMed

Nguyen, Hien D; Ullmann, Jeremy F P; McLachlan, Geoffrey J; Voleti, Venkatakaushik; Li, Wenze; Hillman, Elizabeth M C; Reutens, David C; Janke, Andrew L

2018-02-01

Calcium is a ubiquitous messenger in neural signaling events. An increasing number of techniques are enabling visualization of neurological activity in animal models via luminescent proteins that bind to calcium ions. These techniques generate large volumes of spatially correlated time series. A model-based functional data analysis methodology via Gaussian mixtures is suggested for the clustering of data from such visualizations is proposed. The methodology is theoretically justified and a computationally efficient approach to estimation is suggested. An example analysis of a zebrafish imaging experiment is presented.

Assessment of traffic noise levels in urban areas using different soft computing techniques.

PubMed

Tomić, J; Bogojević, N; Pljakić, M; Šumarac-Pavlović, D

2016-10-01

Available traffic noise prediction models are usually based on regression analysis of experimental data, and this paper presents the application of soft computing techniques in traffic noise prediction. Two mathematical models are proposed and their predictions are compared to data collected by traffic noise monitoring in urban areas, as well as to predictions of commonly used traffic noise models. The results show that application of evolutionary algorithms and neural networks may improve process of development, as well as accuracy of traffic noise prediction.

Skin fluorescence model based on the Monte Carlo technique

NASA Astrophysics Data System (ADS)

Churmakov, Dmitry Y.; Meglinski, Igor V.; Piletsky, Sergey A.; Greenhalgh, Douglas A.

2003-10-01

The novel Monte Carlo technique of simulation of spatial fluorescence distribution within the human skin is presented. The computational model of skin takes into account spatial distribution of fluorophores following the collagen fibers packing, whereas in epidermis and stratum corneum the distribution of fluorophores assumed to be homogeneous. The results of simulation suggest that distribution of auto-fluorescence is significantly suppressed in the NIR spectral region, while fluorescence of sensor layer embedded in epidermis is localized at the adjusted depth. The model is also able to simulate the skin fluorescence spectra.

Moderating Factors of Video-Modeling with Other as Model: A Meta-Analysis of Single-Case Studies

ERIC Educational Resources Information Center

Mason, Rose A.; Ganz, Jennifer B.; Parker, Richard I.; Burke, Mack D.; Camargo, Siglia P.

2012-01-01

Video modeling with other as model (VMO) is a more practical method for implementing video-based modeling techniques, such as video self-modeling, which requires significantly more editing. Despite this, identification of contextual factors such as participant characteristics and targeted outcomes that moderate the effectiveness of VMO has not…

Short Duration Base Heating Test Improvements

NASA Technical Reports Server (NTRS)

Bender, Robert L.; Dagostino, Mark G.; Engel, Bradley A.; Engel, Carl D.

1999-01-01

Significant improvements have been made to a short duration space launch vehicle base heating test technique. This technique was first developed during the 1960's to investigate launch vehicle plume induced convective environments. Recent improvements include the use of coiled nitrogen buffer gas lines upstream of the hydrogen / oxygen propellant charge tubes, fast acting solenoid valves, stand alone gas delivery and data acquisition systems, and an integrated model design code. Technique improvements were successfully demonstrated during a 2.25% scale X-33 base heating test conducted in the NASA/MSFC Nozzle Test Facility in early 1999. Cost savings of approximately an order of magnitude over previous tests were realized due in large part to these improvements.

Image-based modeling of tumor shrinkage in head and neck radiation therapy1

PubMed Central

Chao, Ming; Xie, Yaoqin; Moros, Eduardo G.; Le, Quynh-Thu; Xing, Lei

2010-01-01

Purpose: Understanding the kinetics of tumor growth∕shrinkage represents a critical step in quantitative assessment of therapeutics and realization of adaptive radiation therapy. This article presents a novel framework for image-based modeling of tumor change and demonstrates its performance with synthetic images and clinical cases. Methods: Due to significant tumor tissue content changes, similarity-based models are not suitable for describing the process of tumor volume changes. Under the hypothesis that tissue features in a tumor volume or at the boundary region are partially preserved, the kinetic change was modeled in two steps: (1) Autodetection of homologous tissue features shared by two input images using the scale invariance feature transformation (SIFT) method; and (2) establishment of a voxel-to-voxel correspondence between the images for the remaining spatial points by interpolation. The correctness of the tissue feature correspondence was assured by a bidirectional association procedure, where SIFT features were mapped from template to target images and reversely. A series of digital phantom experiments and five head and neck clinical cases were used to assess the performance of the proposed technique. Results: The proposed technique can faithfully identify the known changes introduced when constructing the digital phantoms. The subsequent feature-guided thin plate spline calculation reproduced the “ground truth” with accuracy better than 1.5 mm. For the clinical cases, the new algorithm worked reliably for a volume change as large as 30%. Conclusions: An image-based tumor kinetic algorithm was developed to model the tumor response to radiation therapy. The technique provides a practical framework for future application in adaptive radiation therapy. PMID:20527569

Prediction of aircraft handling qualities using analytical models of the human pilot

NASA Technical Reports Server (NTRS)

Hess, R. A.

1982-01-01

The optimal control model (OCM) of the human pilot is applied to the study of aircraft handling qualities. Attention is focused primarily on longitudinal tasks. The modeling technique differs from previous applications of the OCM in that considerable effort is expended in simplifying the pilot/vehicle analysis. After briefly reviewing the OCM, a technique for modeling the pilot controlling higher order systems is introduced. Following this, a simple criterion for determining the susceptibility of an aircraft to pilot-induced oscillations (PIO) is formulated. Finally, a model-based metric for pilot rating prediction is discussed. The resulting modeling procedure provides a relatively simple, yet unified approach to the study of a variety of handling qualities problems.

Prediction of aircraft handling qualities using analytical models of the human pilot

NASA Technical Reports Server (NTRS)

Hess, R. A.

1982-01-01

The optimal control model (OCM) of the human pilot is applied to the study of aircraft handling qualities. Attention is focused primarily on longitudinal tasks. The modeling technique differs from previous applications of the OCM in that considerable effort is expended in simplifying the pilot/vehicle analysis. After briefly reviewing the OCM, a technique for modeling the pilot controlling higher order systems is introduced. Following this, a simple criterion for determining the susceptibility of an aircraft to pilot induced oscillations is formulated. Finally, a model based metric for pilot rating prediction is discussed. The resulting modeling procedure provides a relatively simple, yet unified approach to the study of a variety of handling qualities problems.

Human exposure assessment in the near field of GSM base-station antennas using a hybrid finite element/method of moments technique.

PubMed

Meyer, Frans J C; Davidson, David B; Jakobus, Ulrich; Stuchly, Maria A

2003-02-01

A hybrid finite-element method (FEM)/method of moments (MoM) technique is employed for specific absorption rate (SAR) calculations in a human phantom in the near field of a typical group special mobile (GSM) base-station antenna. The MoM is used to model the metallic surfaces and wires of the base-station antenna, and the FEM is used to model the heterogeneous human phantom. The advantages of each of these frequency domain techniques are, thus, exploited, leading to a highly efficient and robust numerical method for addressing this type of bioelectromagnetic problem. The basic mathematical formulation of the hybrid technique is presented. This is followed by a discussion of important implementation details-in particular, the linear algebra routines for sparse, complex FEM matrices combined with dense MoM matrices. The implementation is validated by comparing results to MoM (surface equivalence principle implementation) and finite-difference time-domain (FDTD) solutions of human exposure problems. A comparison of the computational efficiency of the different techniques is presented. The FEM/MoM implementation is then used for whole-body and critical-organ SAR calculations in a phantom at different positions in the near field of a base-station antenna. This problem cannot, in general, be solved using the MoM or FDTD due to computational limitations. This paper shows that the specific hybrid FEM/MoM implementation is an efficient numerical tool for accurate assessment of human exposure in the near field of base-station antennas.

A comparative study of multi-sensor data fusion methods for highly accurate assessment of manufactured parts

NASA Astrophysics Data System (ADS)

Hannachi, Ammar; Kohler, Sophie; Lallement, Alex; Hirsch, Ernest

2015-04-01

3D modeling of scene contents takes an increasing importance for many computer vision based applications. In particular, industrial applications of computer vision require efficient tools for the computation of this 3D information. Routinely, stereo-vision is a powerful technique to obtain the 3D outline of imaged objects from the corresponding 2D images. As a consequence, this approach provides only a poor and partial description of the scene contents. On another hand, for structured light based reconstruction techniques, 3D surfaces of imaged objects can often be computed with high accuracy. However, the resulting active range data in this case lacks to provide data enabling to characterize the object edges. Thus, in order to benefit from the positive points of various acquisition techniques, we introduce in this paper promising approaches, enabling to compute complete 3D reconstruction based on the cooperation of two complementary acquisition and processing techniques, in our case stereoscopic and structured light based methods, providing two 3D data sets describing respectively the outlines and surfaces of the imaged objects. We present, accordingly, the principles of three fusion techniques and their comparison based on evaluation criterions related to the nature of the workpiece and also the type of the tackled application. The proposed fusion methods are relying on geometric characteristics of the workpiece, which favour the quality of the registration. Further, the results obtained demonstrate that the developed approaches are well adapted for 3D modeling of manufactured parts including free-form surfaces and, consequently quality control applications using these 3D reconstructions.

Shock and vibration technology with applications to electrical systems

NASA Technical Reports Server (NTRS)

Eshleman, R. L.

1972-01-01

A survey is presented of shock and vibration technology for electrical systems developed by the aerospace programs. The shock environment is surveyed along with new techniques for modeling, computer simulation, damping, and response analysis. Design techniques based on the use of analog computers, shock spectra, optimization, and nonlinear isolation are discussed. Shock mounting of rotors for performance and survival, and vibration isolation techniques are reviewed.

An evaluation of the carbon balance technique for estimating emission factors and fuel consumption in forest fires

Treesearch

Nelson, Jr. Ralph M.

1982-01-01

Eighteen experimental fires were used to compare measured and calculated values for emission factors and fuel consumption to evaluate the carbon balance technique. The technique is based on a model for the emission factor of carbon dioxide, corrected for the production of other emissions, and which requires measurements of effluent concentrations and air volume in the...

Development and validation of a sensor-based health monitoring model for the Parkview Bridge deck : [appendices].

DOT National Transportation Integrated Search

2012-01-31

Accelerated bridge construction (ABC) using full-depth precast deck panels is an innovative technique that brings all the benefits listed under ABC to full fruition. However, this technique needs to be evaluated and the performance of the bridge need...

Fourier descriptor analysis and unification of voice range profile contours: method and applications.

PubMed

Pabon, Peter; Ternström, Sten; Lamarche, Anick

2011-06-01

To describe a method for unified description, statistical modeling, and comparison of voice range profile (VRP) contours, even from diverse sources. A morphologic modeling technique, which is based on Fourier descriptors (FDs), is applied to the VRP contour. The technique, which essentially involves resampling of the curve of the contour, is assessed and also is compared to density-based VRP averaging methods that use the overlap count. VRP contours can be usefully described and compared using FDs. The method also permits the visualization of the local covariation along the contour average. For example, the FD-based analysis shows that the population variance for ensembles of VRP contours is usually smallest at the upper left part of the VRP. To illustrate the method's advantages and possible further application, graphs are given that compare the averaged contours from different authors and recording devices--for normal, trained, and untrained male and female voices as well as for child voices. The proposed technique allows any VRP shape to be brought to the same uniform base. On this uniform base, VRP contours or contour elements coming from a variety of sources may be placed within the same graph for comparison and for statistical analysis.

Surface and Flow Field Measurements on the FAITH Hill Model

NASA Technical Reports Server (NTRS)

Bell, James H.; Heineck, James T.; Zilliac, Gregory; Mehta, Rabindra D.; Long, Kurtis R.

2012-01-01

A series of experimental tests, using both qualitative and quantitative techniques, were conducted to characterize both surface and off-surface flow characteristics of an axisymmetric, modified-cosine-shaped, wall-mounted hill named "FAITH" (Fundamental Aero Investigates The Hill). Two separate models were employed: a 6" high, 18" base diameter machined aluminum model that was used for wind tunnel tests and a smaller scale (2" high, 6" base diameter) sintered nylon version that was used in the water channel facility. Wind tunnel and water channel tests were conducted at mean test section speeds of 165 fps (Reynolds Number based on height = 500,000) and 0.1 fps (Reynolds Number of 1000), respectively. The ratio of model height to boundary later height was approximately 3 for both tests. Qualitative techniques that were employed to characterize the complex flow included surface oil flow visualization for the wind tunnel tests, and dye injection for the water channel tests. Quantitative techniques that were employed to characterize the flow included Cobra Probe to determine point-wise steady and unsteady 3D velocities, Particle Image Velocimetry (PIV) to determine 3D velocities and turbulence statistics along specified planes, Pressure Sensitive Paint (PSP) to determine mean surface pressures, and Fringe Imaging Skin Friction (FISF) to determine surface skin friction (magnitude and direction). This initial report summarizes the experimental set-up, techniques used, data acquired and describes some details of the dataset that is being constructed for use by other researchers, especially the CFD community. Subsequent reports will discuss the data and their interpretation in more detail

The GOSSIP on the MCV V347 Pavonis

NASA Astrophysics Data System (ADS)

Potter, S. B.; Cropper, Mark; Hakala, P. J.

Modelling of the polarized cyclotron emission from magnetic cataclysmic variables (MCVs) has been a powerful technique for determining the structure of the accretion zones on the white dwarf. Until now, this has been achieved by constructing emission regions (for example arcs and spots) put in by hand, in order to recover the polarized emission. These models were all inferred indirectly from arguments based on polarization and X-ray light curves. Potter, Hakala & Cropper (1998) presented a technique (Stokes imaging) which objectively and analytically models the polarized emission to recover the structure of the cyclotron emission region(s) in MCVs. We demonstrate this technique with the aid of a test case, then we apply the technique to polarimetric observations of the AM Her system V347 Pav. As the system parameters of V347 Pav (for example its inclination) have not been well determined, we describe an extension to the Stokes imaging technique which also searches the system parameter space (GOSSIP).

Daily pan evaporation modelling using a neuro-fuzzy computing technique

NASA Astrophysics Data System (ADS)

Kişi, Özgür

2006-10-01

SummaryEvaporation, as a major component of the hydrologic cycle, is important in water resources development and management. This paper investigates the abilities of neuro-fuzzy (NF) technique to improve the accuracy of daily evaporation estimation. Five different NF models comprising various combinations of daily climatic variables, that is, air temperature, solar radiation, wind speed, pressure and humidity are developed to evaluate degree of effect of each of these variables on evaporation. A comparison is made between the estimates provided by the NF model and the artificial neural networks (ANNs). The Stephens-Stewart (SS) method is also considered for the comparison. Various statistic measures are used to evaluate the performance of the models. Based on the comparisons, it was found that the NF computing technique could be employed successfully in modelling evaporation process from the available climatic data. The ANN also found to perform better than the SS method.

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.

Towards Artificial Speech Therapy: A Neural System for Impaired Speech Segmentation.

PubMed

Iliya, Sunday; Neri, Ferrante

2016-09-01

This paper presents a neural system-based technique for segmenting short impaired speech utterances into silent, unvoiced, and voiced sections. Moreover, the proposed technique identifies those points of the (voiced) speech where the spectrum becomes steady. The resulting technique thus aims at detecting that limited section of the speech which contains the information about the potential impairment of the speech. This section is of interest to the speech therapist as it corresponds to the possibly incorrect movements of speech organs (lower lip and tongue with respect to the vocal tract). Two segmentation models to detect and identify the various sections of the disordered (impaired) speech signals have been developed and compared. The first makes use of a combination of four artificial neural networks. The second is based on a support vector machine (SVM). The SVM has been trained by means of an ad hoc nested algorithm whose outer layer is a metaheuristic while the inner layer is a convex optimization algorithm. Several metaheuristics have been tested and compared leading to the conclusion that some variants of the compact differential evolution (CDE) algorithm appears to be well-suited to address this problem. Numerical results show that the SVM model with a radial basis function is capable of effective detection of the portion of speech that is of interest to a therapist. The best performance has been achieved when the system is trained by the nested algorithm whose outer layer is hybrid-population-based/CDE. A population-based approach displays the best performance for the isolation of silence/noise sections, and the detection of unvoiced sections. On the other hand, a compact approach appears to be clearly well-suited to detect the beginning of the steady state of the voiced signal. Both the proposed segmentation models display outperformed two modern segmentation techniques based on Gaussian mixture model and deep learning.

Towards Effective Clustering Techniques for the Analysis of Electric Power Grids

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

Hogan, Emilie A.; Cotilla Sanchez, Jose E.; Halappanavar, Mahantesh

2013-11-30

Clustering is an important data analysis technique with numerous applications in the analysis of electric power grids. Standard clustering techniques are oblivious to the rich structural and dynamic information available for power grids. Therefore, by exploiting the inherent topological and electrical structure in the power grid data, we propose new methods for clustering with applications to model reduction, locational marginal pricing, phasor measurement unit (PMU or synchrophasor) placement, and power system protection. We focus our attention on model reduction for analysis based on time-series information from synchrophasor measurement devices, and spectral techniques for clustering. By comparing different clustering techniques onmore » two instances of realistic power grids we show that the solutions are related and therefore one could leverage that relationship for a computational advantage. Thus, by contrasting different clustering techniques we make a case for exploiting structure inherent in the data with implications for several domains including power systems.« less

Quantitative computed tomography-based predictions of vertebral strength in anterior bending.

PubMed

Buckley, Jenni M; Cheng, Liu; Loo, Kenneth; Slyfield, Craig; Xu, Zheng

2007-04-20

This study examined the ability of QCT-based structural assessment techniques to predict vertebral strength in anterior bending. The purpose of this study was to compare the abilities of QCT-based bone mineral density (BMD), mechanics of solids models (MOS), e.g., bending rigidity, and finite element analyses (FE) to predict the strength of isolated vertebral bodies under anterior bending boundary conditions. Although the relative performance of QCT-based structural measures is well established for uniform compression, the ability of these techniques to predict vertebral strength under nonuniform loading conditions has not yet been established. Thirty human thoracic vertebrae from 30 donors (T9-T10, 20 female, 10 male; 87 +/- 5 years of age) were QCT scanned and destructively tested in anterior bending using an industrial robot arm. The QCT scans were processed to generate specimen-specific FE models as well as trabecular bone mineral density (tBMD), integral bone mineral density (iBMD), and MOS measures, such as axial and bending rigidities. Vertebral strength in anterior bending was poorly to moderately predicted by QCT-based BMD and MOS measures (R2 = 0.14-0.22). QCT-based FE models were better strength predictors (R2 = 0.34-0.40); however, their predictive performance was not statistically different from MOS bending rigidity (P > 0.05). Our results suggest that the poor clinical performance of noninvasive structural measures may be due to their inability to predict vertebral strength under bending loads. While their performance was not statistically better than MOS bending rigidities, QCT-based FE models were moderate predictors of both compressive and bending loads at failure, suggesting that this technique has the potential for strength prediction under nonuniform loads. The current FE modeling strategy is insufficient, however, and significant modifications must be made to better mimic whole bone elastic and inelastic material behavior.

Improved model reduction and tuning of fractional-order PI(λ)D(μ) controllers for analytical rule extraction with genetic programming.

PubMed

Das, Saptarshi; Pan, Indranil; Das, Shantanu; Gupta, Amitava

2012-03-01

Genetic algorithm (GA) has been used in this study for a new approach of suboptimal model reduction in the Nyquist plane and optimal time domain tuning of proportional-integral-derivative (PID) and fractional-order (FO) PI(λ)D(μ) controllers. Simulation studies show that the new Nyquist-based model reduction technique outperforms the conventional H(2)-norm-based reduced parameter modeling technique. With the tuned controller parameters and reduced-order model parameter dataset, optimum tuning rules have been developed with a test-bench of higher-order processes via genetic programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by a weighted sum of error index and controller effort. From the reported Pareto optimal front of the GP-based optimal rule extraction technique, a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP-based tuning rules has been compared with the original GA-based control performance for the PID and PI(λ)D(μ) controllers, handling four different classes of representative higher-order processes. These rules are very useful for process control engineers, as they inherit the power of the GA-based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three-dimensional plots of the required variation in PID/fractional-order PID (FOPID) controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP-based tuning rules has also been shown with credible simulation examples. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

A three-dimensional muscle activity imaging technique for assessing pelvic muscle function

NASA Astrophysics Data System (ADS)

Zhang, Yingchun; Wang, Dan; Timm, Gerald W.

2010-11-01

A novel multi-channel surface electromyography (EMG)-based three-dimensional muscle activity imaging (MAI) technique has been developed by combining the bioelectrical source reconstruction approach and subject-specific finite element modeling approach. Internal muscle activities are modeled by a current density distribution and estimated from the intra-vaginal surface EMG signals with the aid of a weighted minimum norm estimation algorithm. The MAI technique was employed to minimally invasively reconstruct electrical activity in the pelvic floor muscles and urethral sphincter from multi-channel intra-vaginal surface EMG recordings. A series of computer simulations were conducted to evaluate the performance of the present MAI technique. With appropriate numerical modeling and inverse estimation techniques, we have demonstrated the capability of the MAI technique to accurately reconstruct internal muscle activities from surface EMG recordings. This MAI technique combined with traditional EMG signal analysis techniques is being used to study etiologic factors associated with stress urinary incontinence in women by correlating functional status of muscles characterized from the intra-vaginal surface EMG measurements with the specific pelvic muscle groups that generated these signals. The developed MAI technique described herein holds promise for eliminating the need to place needle electrodes into muscles to obtain accurate EMG recordings in some clinical applications.

Modeling and Model Identification of Autonomous Underwater Vehicles

DTIC Science & Technology

2015-06-01

setup, based on a quadrifilar pendulum , is developed to measure the moments of inertia of the vehicle. System identification techniques, based on...parametric models of the platforms: an individual channel excitation approach and a free decay pendulum test. The former is applied to THAUS, which can...excite the system in individual channels in four degrees of freedom. These results are verified in the free decay pendulum setup, which has the

SCI model structure determination program (OSR) user's guide. [optimal subset regression

NASA Technical Reports Server (NTRS)

1979-01-01

The computer program, OSR (Optimal Subset Regression) which estimates models for rotorcraft body and rotor force and moment coefficients is described. The technique used is based on the subset regression algorithm. Given time histories of aerodynamic coefficients, aerodynamic variables, and control inputs, the program computes correlation between various time histories. The model structure determination is based on these correlations. Inputs and outputs of the program are given.

Approximation techniques for parameter estimation and feedback control for distributed models of large flexible structures

NASA Technical Reports Server (NTRS)

Banks, H. T.; Rosen, I. G.

1984-01-01

Approximation ideas are discussed that can be used in parameter estimation and feedback control for Euler-Bernoulli models of elastic systems. Focusing on parameter estimation problems, ways by which one can obtain convergence results for cubic spline based schemes for hybrid models involving an elastic cantilevered beam with tip mass and base acceleration are outlined. Sample numerical findings are also presented.

Comparison of TG-43 and TG-186 in breast irradiation using a low energy electronic brachytherapy source.

PubMed

White, Shane A; Landry, Guillaume; Fonseca, Gabriel Paiva; Holt, Randy; Rusch, Thomas; Beaulieu, Luc; Verhaegen, Frank; Reniers, Brigitte

2014-06-01

The recently updated guidelines for dosimetry in brachytherapy in TG-186 have recommended the use of model-based dosimetry calculations as a replacement for TG-43. TG-186 highlights shortcomings in the water-based approach in TG-43, particularly for low energy brachytherapy sources. The Xoft Axxent is a low energy (<50 kV) brachytherapy system used in accelerated partial breast irradiation (APBI). Breast tissue is a heterogeneous tissue in terms of density and composition. Dosimetric calculations of seven APBI patients treated with Axxent were made using a model-based Monte Carlo platform for a number of tissue models and dose reporting methods and compared to TG-43 based plans. A model of the Axxent source, the S700, was created and validated against experimental data. CT scans of the patients were used to create realistic multi-tissue/heterogeneous models with breast tissue segmented using a published technique. Alternative water models were used to isolate the influence of tissue heterogeneity and backscatter on the dose distribution. Dose calculations were performed using Geant4 according to the original treatment parameters. The effect of the Axxent balloon applicator used in APBI which could not be modeled in the CT-based model, was modeled using a novel technique that utilizes CAD-based geometries. These techniques were validated experimentally. Results were calculated using two dose reporting methods, dose to water (Dw,m) and dose to medium (Dm,m), for the heterogeneous simulations. All results were compared against TG-43-based dose distributions and evaluated using dose ratio maps and DVH metrics. Changes in skin and PTV dose were highlighted. All simulated heterogeneous models showed a reduced dose to the DVH metrics that is dependent on the method of dose reporting and patient geometry. Based on a prescription dose of 34 Gy, the average D90 to PTV was reduced by between ~4% and ~40%, depending on the scoring method, compared to the TG-43 result. Peak skin dose is also reduced by 10%-15% due to the absence of backscatter not accounted for in TG-43. The balloon applicator also contributed to the reduced dose. Other ROIs showed a difference depending on the method of dose reporting. TG-186-based calculations produce results that are different from TG-43 for the Axxent source. The differences depend strongly on the method of dose reporting. This study highlights the importance of backscatter to peak skin dose. Tissue heterogeneities, applicator, and patient geometries demonstrate the need for a more robust dose calculation method for low energy brachytherapy sources.

Ab Initio Studies of Shock-Induced Chemical Reactions of Inter-Metallics

NASA Astrophysics Data System (ADS)

Zaharieva, Roussislava; Hanagud, Sathya

2009-06-01

Shock-induced and shock assisted chemical reactions of intermetallic mixtures are studied by many researchers, using both experimental and theoretical techniques. The theoretical studies are primarily at continuum scales. The model frameworks include mixture theories and meso-scale models of grains of porous mixtures. The reaction models vary from equilibrium thermodynamic model to several non-equilibrium thermodynamic models. The shock-effects are primarily studied using appropriate conservation equations and numerical techniques to integrate the equations. All these models require material constants from experiments and estimates of transition states. Thus, the objective of this paper is to present studies based on ab initio techniques. The ab inito studies, to date, use ab inito molecular dynamics. This paper presents a study that uses shock pressures, and associated temperatures as starting variables. Then intermetallic mixtures are modeled as slabs. The required shock stresses are created by straining the lattice. Then, ab initio binding energy calculations are used to examine the stability of the reactions. Binding energies are obtained for different strain components super imposed on uniform compression and finite temperatures. Then, vibrational frequencies and nudge elastic band techniques are used to study reactivity and transition states. Examples include Ni and Al.

Socrates Meets the 21st Century

ERIC Educational Resources Information Center

Lege, Jerry

2005-01-01

A inquiry-based approach called the "modelling discussion" is introduced for structuring beginning modelling activity, teaching new mathematics from examining its applications in contextual situations, and as a general classroom management technique when students are engaged in mathematical modelling. An example which illustrates the style and…

Concept for a Satellite-Based Advanced Air Traffic Management System : Volume 9. System and Subsystem Performance Models.

DOT National Transportation Integrated Search

1973-02-01

The volume presents the models used to analyze basic features of the system, establish feasibility of techniques, and evaluate system performance. The models use analytical expressions and computer simulations to represent the relationship between sy...

Commercial Demand Module - NEMS Documentation

EIA Publications

2017-01-01

Documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Commercial Sector Demand Module. The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated through the synthesis and scenario development based on these components.

A fluidized bed technique for estimating soil critical shear stress

USDA-ARS?s Scientific Manuscript database

Soil erosion models, depending on how they are formulated, always have erodibilitiy parameters in the erosion equations. For a process-based model like the Water Erosion Prediction Project (WEPP) model, the erodibility parameters include rill and interrill erodibility and critical shear stress. Thes...

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

One technique for refining the global Earth gravity models

NASA Astrophysics Data System (ADS)

Koneshov, V. N.; Nepoklonov, V. B.; Polovnev, O. V.

2017-01-01

The results of the theoretical and experimental research on the technique for refining the global Earth geopotential models such as EGM2008 in the continental regions are presented. The discussed technique is based on the high-resolution satellite data for the Earth's surface topography which enables the allowance for the fine structure of the Earth's gravitational field without the additional gravimetry data. The experimental studies are conducted by the example of the new GGMplus global gravity model of the Earth with a resolution about 0.5 km, which is obtained by expanding the EGM2008 model to degree 2190 with the corrections for the topograohy calculated from the SRTM data. The GGMplus and EGM2008 models are compared with the regional geoid models in 21 regions of North America, Australia, Africa, and Europe. The obtained estimates largely support the possibility of refining the global geopotential models such as EGM2008 by the procedure implemented in GGMplus, particularly in the regions with relatively high elevation difference.

Use of Model-Based Design Methods for Enhancing Resiliency Analysis of Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Knox, Lenora A.

The most common traditional non-functional requirement analysis is reliability. With systems becoming more complex, networked, and adaptive to environmental uncertainties, system resiliency has recently become the non-functional requirement analysis of choice. Analysis of system resiliency has challenges; which include, defining resilience for domain areas, identifying resilience metrics, determining resilience modeling strategies, and understanding how to best integrate the concepts of risk and reliability into resiliency. Formal methods that integrate all of these concepts do not currently exist in specific domain areas. Leveraging RAMSoS, a model-based reliability analysis methodology for Systems of Systems (SoS), we propose an extension that accounts for resiliency analysis through evaluation of mission performance, risk, and cost using multi-criteria decision-making (MCDM) modeling and design trade study variability modeling evaluation techniques. This proposed methodology, coined RAMSoS-RESIL, is applied to a case study in the multi-agent unmanned aerial vehicle (UAV) domain to investigate the potential benefits of a mission architecture where functionality to complete a mission is disseminated across multiple UAVs (distributed) opposed to being contained in a single UAV (monolithic). The case study based research demonstrates proof of concept for the proposed model-based technique and provides sufficient preliminary evidence to conclude which architectural design (distributed vs. monolithic) is most resilient based on insight into mission resilience performance, risk, and cost in addition to the traditional analysis of reliability.

Gaussian process regression for tool wear prediction

NASA Astrophysics Data System (ADS)

Kong, Dongdong; Chen, Yongjie; Li, Ning

2018-05-01

To realize and accelerate the pace of intelligent manufacturing, this paper presents a novel tool wear assessment technique based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and Gaussian process regression (GPR) for real-timely and accurately monitoring the in-process tool wear parameters (flank wear width). The KPCA_IRBF is a kind of new nonlinear dimension-increment technique and firstly proposed for feature fusion. The tool wear predictive value and the corresponding confidence interval are both provided by utilizing the GPR model. Besides, GPR performs better than artificial neural networks (ANN) and support vector machines (SVM) in prediction accuracy since the Gaussian noises can be modeled quantitatively in the GPR model. However, the existence of noises will affect the stability of the confidence interval seriously. In this work, the proposed KPCA_IRBF technique helps to remove the noises and weaken its negative effects so as to make the confidence interval compressed greatly and more smoothed, which is conducive for monitoring the tool wear accurately. Moreover, the selection of kernel parameter in KPCA_IRBF can be easily carried out in a much larger selectable region in comparison with the conventional KPCA_RBF technique, which helps to improve the efficiency of model construction. Ten sets of cutting tests are conducted to validate the effectiveness of the presented tool wear assessment technique. The experimental results show that the in-process flank wear width of tool inserts can be monitored accurately by utilizing the presented tool wear assessment technique which is robust under a variety of cutting conditions. This study lays the foundation for tool wear monitoring in real industrial settings.

The Objective Borderline Method: A Probabilistic Method for Standard Setting

ERIC Educational Resources Information Center

Shulruf, Boaz; Poole, Phillippa; Jones, Philip; Wilkinson, Tim

2015-01-01

A new probability-based standard setting technique, the Objective Borderline Method (OBM), was introduced recently. This was based on a mathematical model of how test scores relate to student ability. The present study refined the model and tested it using 2500 simulated data-sets. The OBM was feasible to use. On average, the OBM performed well…

Detection of abrupt changes in dynamic systems

NASA Technical Reports Server (NTRS)

Willsky, A. S.

1984-01-01

Some of the basic ideas associated with the detection of abrupt changes in dynamic systems are presented. Multiple filter-based techniques and residual-based method and the multiple model and generalized likelihood ratio methods are considered. Issues such as the effect of unknown onset time on algorithm complexity and structure and robustness to model uncertainty are discussed.

R symmetries and a heterotic MSSM

NASA Astrophysics Data System (ADS)

Kappl, Rolf; Nilles, Hans Peter; Schmitz, Matthias

2015-02-01

We employ powerful techniques based on Hilbert and Gröbner bases to analyze particle physics models derived from string theory. Individual models are shown to have a huge landscape of vacua that differ in their phenomenological properties. We explore the (discrete) symmetries of these vacua, the new R symmetry selection rules and their consequences for moduli stabilization.

Evaluating model accuracy for model-based reasoning

NASA Technical Reports Server (NTRS)

Chien, Steve; Roden, Joseph

1992-01-01

Described here is an approach to automatically assessing the accuracy of various components of a model. In this approach, actual data from the operation of a target system is used to drive statistical measures to evaluate the prediction accuracy of various portions of the model. We describe how these statistical measures of model accuracy can be used in model-based reasoning for monitoring and design. We then describe the application of these techniques to the monitoring and design of the water recovery system of the Environmental Control and Life Support System (ECLSS) of Space Station Freedom.

A Bibliography of Externally Published Works by the SEI Engineering Techniques Program

DTIC Science & Technology

1992-08-01

media, and virtual reality * model- based engineering * programming languages * reuse * software architectures * software engineering as a discipline...Knowledge- Based Engineering Environments." IEEE Expert 3, 2 (May 1988): 18-23, 26-32. Audience: Practitioner [Klein89b] Klein, D.V. "Comparison of...Terms with Software Reuse Terminology: A Model- Based Approach." ACM SIGSOFT Software Engineering Notes 16, 2 (April 1991): 45-51. Audience: Practitioner

Properties of a Formal Method to Model Emergence in Swarm-Based Systems

NASA Technical Reports Server (NTRS)

Rouff, Christopher; Vanderbilt, Amy; Truszkowski, Walt; Rash, James; Hinchey, Mike

2004-01-01

Future space missions will require cooperation between multiple satellites and/or rovers. Developers are proposing intelligent autonomous swarms for these missions, but swarm-based systems are difficult or impossible to test with current techniques. This viewgraph presentation examines the use of formal methods in testing swarm-based systems. The potential usefulness of formal methods in modeling the ANTS asteroid encounter mission is also examined.