Perceptions of Usefulness: Using the Holland Code Theory, Multiple Intelligences Theory, and Role Model Identification to Determine a Career Niche in the Fashion Industry for First-Quarter Fashion Students

Science.gov (United States)

Green, Crystal D.

2010-01-01

This action research study investigated the perceptions that student participants had on the development of a career exploration model and a career exploration project. The Holland code theory was the primary assessment used for this research study, in addition to the Multiple Intelligences theory and the identification of a role model for the…

Non-parametric identification of multivariable systems : a local rational modeling approach with application to a vibration isolation benchmark

NARCIS (Netherlands)

Voorhoeve, R.J.; van der Maas, A.; Oomen, T.A.J.

2018-01-01

Frequency response function (FRF) identification is often used as a basis for control systems design and as a starting point for subsequent parametric system identification. The aim of this paper is to develop a multiple-input multiple-output (MIMO) local parametric modeling approach for FRF

Metabolite identification through multiple kernel learning on fragmentation trees.

Science.gov (United States)

Shen, Huibin; Dührkop, Kai; Böcker, Sebastian; Rousu, Juho

2014-06-15

Metabolite identification from tandem mass spectrometric data is a key task in metabolomics. Various computational methods have been proposed for the identification of metabolites from tandem mass spectra. Fragmentation tree methods explore the space of possible ways in which the metabolite can fragment, and base the metabolite identification on scoring of these fragmentation trees. Machine learning methods have been used to map mass spectra to molecular fingerprints; predicted fingerprints, in turn, can be used to score candidate molecular structures. Here, we combine fragmentation tree computations with kernel-based machine learning to predict molecular fingerprints and identify molecular structures. We introduce a family of kernels capturing the similarity of fragmentation trees, and combine these kernels using recently proposed multiple kernel learning approaches. Experiments on two large reference datasets show that the new methods significantly improve molecular fingerprint prediction accuracy. These improvements result in better metabolite identification, doubling the number of metabolites ranked at the top position of the candidates list. © The Author 2014. Published by Oxford University Press.

Identification of Known and Novel Recurrent Viral Sequences in Data from Multiple Patients and Multiple Cancers

DEFF Research Database (Denmark)

Friis-Nielsen, Jens; Kjartansdóttir, Kristín Rós; Mollerup, Sarah

2016-01-01

have developed a species independent pipeline that utilises sequence clustering for the identification of nucleotide sequences that co-occur across multiple sequencing data instances. We applied the workflow to 686 sequencing libraries from 252 cancer samples of different cancer and tissue types, 32...

MSblender: A probabilistic approach for integrating peptide identifications from multiple database search engines.

Science.gov (United States)

Kwon, Taejoon; Choi, Hyungwon; Vogel, Christine; Nesvizhskii, Alexey I; Marcotte, Edward M

2011-07-01

Shotgun proteomics using mass spectrometry is a powerful method for protein identification but suffers limited sensitivity in complex samples. Integrating peptide identifications from multiple database search engines is a promising strategy to increase the number of peptide identifications and reduce the volume of unassigned tandem mass spectra. Existing methods pool statistical significance scores such as p-values or posterior probabilities of peptide-spectrum matches (PSMs) from multiple search engines after high scoring peptides have been assigned to spectra, but these methods lack reliable control of identification error rates as data are integrated from different search engines. We developed a statistically coherent method for integrative analysis, termed MSblender. MSblender converts raw search scores from search engines into a probability score for every possible PSM and properly accounts for the correlation between search scores. The method reliably estimates false discovery rates and identifies more PSMs than any single search engine at the same false discovery rate. Increased identifications increment spectral counts for most proteins and allow quantification of proteins that would not have been quantified by individual search engines. We also demonstrate that enhanced quantification contributes to improve sensitivity in differential expression analyses.

A feature point identification method for positron emission particle tracking with multiple tracers

Energy Technology Data Exchange (ETDEWEB)

Wiggins, Cody, E-mail: cwiggin2@vols.utk.edu [University of Tennessee-Knoxville, Department of Physics and Astronomy, 1408 Circle Drive, Knoxville, TN 37996 (United States); Santos, Roque [University of Tennessee-Knoxville, Department of Nuclear Engineering (United States); Escuela Politécnica Nacional, Departamento de Ciencias Nucleares (Ecuador); Ruggles, Arthur [University of Tennessee-Knoxville, Department of Nuclear Engineering (United States)

2017-01-21

A novel detection algorithm for Positron Emission Particle Tracking (PEPT) with multiple tracers based on optical feature point identification (FPI) methods is presented. This new method, the FPI method, is compared to a previous multiple PEPT method via analyses of experimental and simulated data. The FPI method outperforms the older method in cases of large particle numbers and fine time resolution. Simulated data show the FPI method to be capable of identifying 100 particles at 0.5 mm average spatial error. Detection error is seen to vary with the inverse square root of the number of lines of response (LORs) used for detection and increases as particle separation decreases. - Highlights: • A new approach to positron emission particle tracking is presented. • Using optical feature point identification analogs, multiple particle tracking is achieved. • Method is compared to previous multiple particle method. • Accuracy and applicability of method is explored.

Identification of physical models

DEFF Research Database (Denmark)

Melgaard, Henrik

1994-01-01

of the model with the available prior knowledge. The methods for identification of physical models have been applied in two different case studies. One case is the identification of thermal dynamics of building components. The work is related to a CEC research project called PASSYS (Passive Solar Components......The problem of identification of physical models is considered within the frame of stochastic differential equations. Methods for estimation of parameters of these continuous time models based on descrete time measurements are discussed. The important algorithms of a computer program for ML or MAP...... design of experiments, which is for instance the design of an input signal that are optimal according to a criterion based on the information provided by the experiment. Also model validation is discussed. An important verification of a physical model is to compare the physical characteristics...

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

Directory of Open Access Journals (Sweden)

Giorgio Bacelli

2017-04-01

Full Text Available Empirically based modeling is an essential aspect of design for a wave energy converter. Empirically based models are used in structural, mechanical and control design processes, as well as for performance prediction. Both the design of experiments and methods used in system identification have a strong impact on the quality of the resulting model. This study considers the system identification and model validation process based on data collected from a wave tank test of a model-scale wave energy converter. Experimental design and data processing techniques based on general system identification procedures are discussed and compared with the practices often followed for wave tank testing. The general system identification processes are shown to have a number of advantages, including an increased signal-to-noise ratio, reduced experimental time and higher frequency resolution. The experimental wave tank data is used to produce multiple models using different formulations to represent the dynamics of the wave energy converter. These models are validated and their performance is compared against one another. While most models of wave energy converters use a formulation with surface elevation as an input, this study shows that a model using a hull pressure measurement to incorporate the wave excitation phenomenon has better accuracy.

Maximizing the sensitivity and reliability of peptide identification in large-scale proteomic experiments by harnessing multiple search engines.

Science.gov (United States)

Yu, Wen; Taylor, J Alex; Davis, Michael T; Bonilla, Leo E; Lee, Kimberly A; Auger, Paul L; Farnsworth, Chris C; Welcher, Andrew A; Patterson, Scott D

2010-03-01

Despite recent advances in qualitative proteomics, the automatic identification of peptides with optimal sensitivity and accuracy remains a difficult goal. To address this deficiency, a novel algorithm, Multiple Search Engines, Normalization and Consensus is described. The method employs six search engines and a re-scoring engine to search MS/MS spectra against protein and decoy sequences. After the peptide hits from each engine are normalized to error rates estimated from the decoy hits, peptide assignments are then deduced using a minimum consensus model. These assignments are produced in a series of progressively relaxed false-discovery rates, thus enabling a comprehensive interpretation of the data set. Additionally, the estimated false-discovery rate was found to have good concordance with the observed false-positive rate calculated from known identities. Benchmarking against standard proteins data sets (ISBv1, sPRG2006) and their published analysis, demonstrated that the Multiple Search Engines, Normalization and Consensus algorithm consistently achieved significantly higher sensitivity in peptide identifications, which led to increased or more robust protein identifications in all data sets compared with prior methods. The sensitivity and the false-positive rate of peptide identification exhibit an inverse-proportional and linear relationship with the number of participating search engines.

Overall Memory Impairment Identification with Mathematical Modeling of the CVLT-II Learning Curve in Multiple Sclerosis

Directory of Open Access Journals (Sweden)

Igor I. Stepanov

2012-01-01

Full Text Available The CVLT-II provides standardized scores for each of the List A five learning trials, so that the clinician can compare the patient's raw trials 1–5 scores with standardized ones. However, frequently, a patient's raw scores fluctuate making a proper interpretation difficult. The CVLT-II does not offer any other methods for classifying a patient's learning and memory status on the background of the learning curve. The main objective of this research is to illustrate that discriminant analysis provides an accurate assessment of the learning curve, if suitable predictor variables are selected. Normal controls were ninety-eight healthy volunteers (78 females and 20 males. A group of MS patients included 365 patients (266 females and 99 males with clinically defined multiple sclerosis. We show that the best predictor variables are coefficients 3 and 4 of our mathematical model 3∗exp(−2∗(−1+4∗(1−exp(−2∗(−1 because discriminant functions, calculated separately for 3 and 4, allow nearly 100% correct classification. These predictors allow identification of separate impairment of readiness to learn or ability to learn, or both.

Cross-Identification of Astronomical Catalogs on Multiple GPUs

Science.gov (United States)

Lee, M. A.; Budavári, T.

2013-10-01

One of the most fundamental problems in observational astronomy is the cross-identification of sources. Observations are made in different wavelengths, at different times, and from different locations and instruments, resulting in a large set of independent observations. The scientific outcome is often limited by our ability to quickly perform meaningful associations between detections. The matching, however, is difficult scientifically, statistically, as well as computationally. The former two require detailed physical modeling and advanced probabilistic concepts; the latter is due to the large volumes of data and the problem's combinatorial nature. In order to tackle the computational challenge and to prepare for future surveys, whose measurements will be exponentially increasing in size past the scale of feasible CPU-based solutions, we developed a new implementation which addresses the issue by performing the associations on multiple Graphics Processing Units (GPUs). Our implementation utilizes up to 6 GPUs in combination with the Thrust library to achieve an over 40x speed up verses the previous best implementation running on a multi-CPU SQL Server.

On the identification of fractionally cointegrated VAR models with the F(d) condition

DEFF Research Database (Denmark)

Santucci de Magistris, Paolo; Carlini, Federico

for any choice of the lag-length when the true cointegration rank is known. The properties of these multiple non-identified models are studied and a necessary and sufficient condition for the identification of the fractional parameters of the system is provided. The condition is named F(d......). This is a generalization of the well-known I(1) condition to the fractional case. Imposing a proper restriction on the fractional integration parameter, d, is sufficient to guarantee identification of all model parameters and the validity of the F(d) condition. The paper also illustrates the indeterminacy between...

Identification of the timing-of-events model with multiple competing exit risks from single-spell data

DEFF Research Database (Denmark)

Drepper, Bettina; Effraimidis, G.

2016-01-01

The identification result of the timing-of-events model (Abbring and Van den Berg, 2003b) is extended to a model with several competing exit risk equations. This extension allows e.g. to simultaneously identify the different effects a benefit sanction has on the rate of finding work and leaving t...

‘Healthy’ identities? : Revisiting rejection-identification and rejection-disidentification models among voluntary and forced immigrants

NARCIS (Netherlands)

Bobowik, Magdalena; Martinovic, Borja; Basabe, Nekane; Barsties, Lisa S.; Wachter, Gusta

2017-01-01

Rejection-identification and rejection-disidentification models propose that low-status groups identify with their in-group and disidentify with a high-status out-group in response to rejection by the latter. Our research tests these two models simultaneously among multiple groups of foreign-born

A basket two-part model to analyze medical expenditure on interdependent multiple sectors.

Science.gov (United States)

Sugawara, Shinya; Wu, Tianyi; Yamanishi, Kenji

2018-05-01

This study proposes a novel statistical methodology to analyze expenditure on multiple medical sectors using consumer data. Conventionally, medical expenditure has been analyzed by two-part models, which separately consider purchase decision and amount of expenditure. We extend the traditional two-part models by adding the step of basket analysis for dimension reduction. This new step enables us to analyze complicated interdependence between multiple sectors without an identification problem. As an empirical application for the proposed method, we analyze data of 13 medical sectors from the Medical Expenditure Panel Survey. In comparison with the results of previous studies that analyzed the multiple sector independently, our method provides more detailed implications of the impacts of individual socioeconomic status on the composition of joint purchases from multiple medical sectors; our method has a better prediction performance.

Overall Memory Impairment Identification with Mathematical Modeling of the CVLT-II Learning Curve in Multiple Sclerosis

Science.gov (United States)

Stepanov, Igor I.; Abramson, Charles I.; Hoogs, Marietta; Benedict, Ralph H. B.

2012-01-01

The CVLT-II provides standardized scores for each of the List A five learning trials, so that the clinician can compare the patient's raw trials 1–5 scores with standardized ones. However, frequently, a patient's raw scores fluctuate making a proper interpretation difficult. The CVLT-II does not offer any other methods for classifying a patient's learning and memory status on the background of the learning curve. The main objective of this research is to illustrate that discriminant analysis provides an accurate assessment of the learning curve, if suitable predictor variables are selected. Normal controls were ninety-eight healthy volunteers (78 females and 20 males). A group of MS patients included 365 patients (266 females and 99 males) with clinically defined multiple sclerosis. We show that the best predictor variables are coefficients B3 and B4 of our mathematical model B3 ∗ exp(−B2  ∗  (X − 1)) + B4  ∗  (1 − exp(−B2  ∗  (X − 1))) because discriminant functions, calculated separately for B3 and B4, allow nearly 100% correct classification. These predictors allow identification of separate impairment of readiness to learn or ability to learn, or both. PMID:22745911

On the identification of fractionally cointegrated VAR models with the F(d) condition

DEFF Research Database (Denmark)

Carlini, Federico; Santucci de Magistris, Paolo

with different fractional integration and cointegration parameters. The properties of these multiple non-identified sub-models are studied and a necessary and sufficient condition for the identification of the fractional parameters of the system is provided. The condition is named F(d). The assessment of the F(d...

Are multiple-trial experiments appropriate for eyewitness identification studies? Accuracy, choosing, and confidence across trials.

Science.gov (United States)

Mansour, J K; Beaudry, J L; Lindsay, R C L

2017-12-01

Eyewitness identification experiments typically involve a single trial: A participant views an event and subsequently makes a lineup decision. As compared to this single-trial paradigm, multiple-trial designs are more efficient, but significantly reduce ecological validity and may affect the strategies that participants use to make lineup decisions. We examined the effects of a number of forensically relevant variables (i.e., memory strength, type of disguise, degree of disguise, and lineup type) on eyewitness accuracy, choosing, and confidence across 12 target-present and 12 target-absent lineup trials (N = 349; 8,376 lineup decisions). The rates of correct rejections and choosing (across both target-present and target-absent lineups) did not vary across the 24 trials, as reflected by main effects or interactions with trial number. Trial number had a significant but trivial quadratic effect on correct identifications (OR = 0.99) and interacted significantly, but again trivially, with disguise type (OR = 1.00). Trial number did not significantly influence participants' confidence in correct identifications, confidence in correct rejections, or confidence in target-absent selections. Thus, multiple-trial designs appear to have minimal effects on eyewitness accuracy, choosing, and confidence. Researchers should thus consider using multiple-trial designs for conducting eyewitness identification experiments.

MetaboSearch: tool for mass-based metabolite identification using multiple databases.

Directory of Open Access Journals (Sweden)

Bin Zhou

Full Text Available Searching metabolites against databases according to their masses is often the first step in metabolite identification for a mass spectrometry-based untargeted metabolomics study. Major metabolite databases include Human Metabolome DataBase (HMDB, Madison Metabolomics Consortium Database (MMCD, Metlin, and LIPID MAPS. Since each one of these databases covers only a fraction of the metabolome, integration of the search results from these databases is expected to yield a more comprehensive coverage. However, the manual combination of multiple search results is generally difficult when identification of hundreds of metabolites is desired. We have implemented a web-based software tool that enables simultaneous mass-based search against the four major databases, and the integration of the results. In addition, more complete chemical identifier information for the metabolites is retrieved by cross-referencing multiple databases. The search results are merged based on IUPAC International Chemical Identifier (InChI keys. Besides a simple list of m/z values, the software can accept the ion annotation information as input for enhanced metabolite identification. The performance of the software is demonstrated on mass spectrometry data acquired in both positive and negative ionization modes. Compared with search results from individual databases, MetaboSearch provides better coverage of the metabolome and more complete chemical identifier information.The software tool is available at http://omics.georgetown.edu/MetaboSearch.html.

Search-based model identification of smart-structure damage

Science.gov (United States)

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.

Pollutant source identification model for water pollution incidents in small straight rivers based on genetic algorithm

Science.gov (United States)

Zhang, Shou-ping; Xin, Xiao-kang

2017-07-01

Identification of pollutant sources for river pollution incidents is an important and difficult task in the emergency rescue, and an intelligent optimization method can effectively compensate for the weakness of traditional methods. An intelligent model for pollutant source identification has been established using the basic genetic algorithm (BGA) as an optimization search tool and applying an analytic solution formula of one-dimensional unsteady water quality equation to construct the objective function. Experimental tests show that the identification model is effective and efficient: the model can accurately figure out the pollutant amounts or positions no matter single pollution source or multiple sources. Especially when the population size of BGA is set as 10, the computing results are sound agree with analytic results for a single source amount and position identification, the relative errors are no more than 5 %. For cases of multi-point sources and multi-variable, there are some errors in computing results for the reasons that there exist many possible combinations of the pollution sources. But, with the help of previous experience to narrow the search scope, the relative errors of the identification results are less than 5 %, which proves the established source identification model can be used to direct emergency responses.

Identification of nonlinear anelastic models

International Nuclear Information System (INIS)

Draganescu, G E; Bereteu, L; Ercuta, A

2008-01-01

A useful nonlinear identification technique applied to the anelastic and rheologic models is presented in this paper. First introduced by Feldman, the method is based on the Hilbert transform, and is currently used for identification of the nonlinear vibrations

Nonlinear adaptive synchronization rule for identification of a large amount of parameters in dynamical models

International Nuclear Information System (INIS)

Ma Huanfei; Lin Wei

2009-01-01

The existing adaptive synchronization technique based on the stability theory and invariance principle of dynamical systems, though theoretically proved to be valid for parameters identification in specific models, is always showing slow convergence rate and even failed in practice when the number of parameters becomes large. Here, for parameters update, a novel nonlinear adaptive rule is proposed to accelerate the rate. Its feasibility is validated by analytical arguments as well as by specific parameters identification in the Lotka-Volterra model with multiple species. Two adjustable factors in this rule influence the identification accuracy, which means that a proper choice of these factors leads to an optimal performance of this rule. In addition, a feasible method for avoiding the occurrence of the approximate linear dependence among terms with parameters on the synchronized manifold is also proposed.

A neural network model of lateralization during letter identification.

Science.gov (United States)

Shevtsova, N; Reggia, J A

1999-03-01

The causes of cerebral lateralization of cognitive and other functions are currently not well understood. To investigate one aspect of function lateralization, a bihemispheric neural network model for a simple visual identification task was developed that has two parallel interacting paths of information processing. The model is based on commonly accepted concepts concerning neural connectivity, activity dynamics, and synaptic plasticity. A combination of both unsupervised (Hebbian) and supervised (Widrow-Hoff) learning rules is used to train the model to identify a small set of letters presented as input stimuli in the left visual hemifield, in the central position, and in the right visual hemifield. Each visual hemifield projects onto the contralateral hemisphere, and the two hemispheres interact via a simulated corpus callosum. The contribution of each individual hemisphere to the process of input stimuli identification was studied for a variety of underlying asymmetries. The results indicate that multiple asymmetries may cause lateralization. Lateralization occurred toward the side having larger size, higher excitability, or higher learning rate parameters. It appeared more intensively with strong inhibitory callosal connections, supporting the hypothesis that the corpus callosum plays a functionally inhibitory role. The model demonstrates clearly the dependence of lateralization on different hemisphere parameters and suggests that computational models can be useful in better understanding the mechanisms underlying emergence of lateralization.

The differential effects of transformational leadership on multiple identifications at work : A meta-analytic model

NARCIS (Netherlands)

Horstmeier, C.A.L.; Boer, D.; Homan, A.C.; Voelpel, S.C.

Employees’ identifications are a valuable asset for modern organizations, and identification research has stressed the necessity to distinguish identifications according to their focus (i.e. organizational, team, or leader identification). Interestingly, transformational leadership (TFL) has been

Quality of life and patient preferences: identification of subgroups of multiple sclerosis patients.

Science.gov (United States)

Rosato, Rosalba; Testa, Silvia; Oggero, Alessandra; Molinengo, Giorgia; Bertolotto, Antonio

2015-09-01

The aim of this study was to estimate preferences related to quality of life attributes in people with multiple sclerosis, by keeping heterogeneity of patient preference in mind, using the latent class approach. A discrete choice experiment survey was developed using the following attributes: activities of daily living, instrumental activities of daily living, pain/fatigue, anxiety/depression and attention/concentration. Choice sets were presented as pairs of hypothetical health status, based upon a fractional factorial design. The latent class logit model estimated on 152 patients identified three subpopulations, which, respectively, attached more importance to: (1) the physical dimension; (2) pain/fatigue and anxiety/depression; and (3) instrumental activities of daily living impairments, anxiety/depression and attention/concentration. A posterior analysis suggests that the latent class membership may be related to an individual's age to some extent, or to diagnosis and treatment, while apart from energy dimension, no significant difference exists between latent groups, with regard to Multiple Sclerosis Quality of Life-54 scales. A quality of life preference-based utility measure for people with multiple sclerosis was developed. These utility values allow identification of a hierarchic priority among different aspects of quality of life and may allow physicians to develop a care programme tailored to patient needs.

Optimization of inverse model identification for multi-axial test rig control

Directory of Open Access Journals (Sweden)

Müller Tino

2016-01-01

Full Text Available Laboratory testing of multi-axial fatigue situations improves repeatability and allows a time condensing of tests which can be carried out until component failure, compared to field testing. To achieve realistic and convincing durability results, precise load data reconstruction is necessary. Cross-talk and a high number of degrees of freedom negatively affect the control accuracy. Therefore a multiple input/multiple output (MIMO model of the system, capturing all inherent cross-couplings is identified. In a first step the model order is estimated based on the physical fundamentals of a one channel hydraulic-servo system. Subsequently, the structure of the MIMO model is optimized using correlation of the outputs, to increase control stability and reduce complexity of the parameter optimization. The identification process is successfully applied to the iterative control of a multi-axial suspension rig. The results show accurate control, with increased stability compared to control without structure optimization.

Iterative integral parameter identification of a respiratory mechanics model.

Science.gov (United States)

Schranz, Christoph; Docherty, Paul D; Chiew, Yeong Shiong; Möller, Knut; Chase, J Geoffrey

2012-07-18

Patient-specific respiratory mechanics models can support the evaluation of optimal lung protective ventilator settings during ventilation therapy. Clinical application requires that the individual's model parameter values must be identified with information available at the bedside. Multiple linear regression or gradient-based parameter identification methods are highly sensitive to noise and initial parameter estimates. Thus, they are difficult to apply at the bedside to support therapeutic decisions. An iterative integral parameter identification method is applied to a second order respiratory mechanics model. The method is compared to the commonly used regression methods and error-mapping approaches using simulated and clinical data. The clinical potential of the method was evaluated on data from 13 Acute Respiratory Distress Syndrome (ARDS) patients. The iterative integral method converged to error minima 350 times faster than the Simplex Search Method using simulation data sets and 50 times faster using clinical data sets. Established regression methods reported erroneous results due to sensitivity to noise. In contrast, the iterative integral method was effective independent of initial parameter estimations, and converged successfully in each case tested. These investigations reveal that the iterative integral method is beneficial with respect to computing time, operator independence and robustness, and thus applicable at the bedside for this clinical application.

Identification of potential biomarkers from microarray experiments using multiple criteria optimization

International Nuclear Information System (INIS)

Sánchez-Peña, Matilde L; Isaza, Clara E; Pérez-Morales, Jaileene; Rodríguez-Padilla, Cristina; Castro, José M; Cabrera-Ríos, Mauricio

2013-01-01

Microarray experiments are capable of determining the relative expression of tens of thousands of genes simultaneously, thus resulting in very large databases. The analysis of these databases and the extraction of biologically relevant knowledge from them are challenging tasks. The identification of potential cancer biomarker genes is one of the most important aims for microarray analysis and, as such, has been widely targeted in the literature. However, identifying a set of these genes consistently across different experiments, researches, microarray platforms, or cancer types is still an elusive endeavor. Besides the inherent difficulty of the large and nonconstant variability in these experiments and the incommensurability between different microarray technologies, there is the issue of the users having to adjust a series of parameters that significantly affect the outcome of the analyses and that do not have a biological or medical meaning. In this study, the identification of potential cancer biomarkers from microarray data is casted as a multiple criteria optimization (MCO) problem. The efficient solutions to this problem, found here through data envelopment analysis (DEA), are associated to genes that are proposed as potential cancer biomarkers. The method does not require any parameter adjustment by the user, and thus fosters repeatability. The approach also allows the analysis of different microarray experiments, microarray platforms, and cancer types simultaneously. The results include the analysis of three publicly available microarray databases related to cervix cancer. This study points to the feasibility of modeling the selection of potential cancer biomarkers from microarray data as an MCO problem and solve it using DEA. Using MCO entails a new optic to the identification of potential cancer biomarkers as it does not require the definition of a threshold value to establish significance for a particular gene and the selection of a normalization

Network-based identification of biomarkers coexpressed with multiple pathways.

Science.gov (United States)

Guo, Nancy Lan; Wan, Ying-Wooi

2014-01-01

Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson's correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson's correlation networks when evaluated with MSigDB database.

Mixed models, linear dependency, and identification in age-period-cohort models.

Science.gov (United States)

O'Brien, Robert M

2017-07-20

This paper examines the identification problem in age-period-cohort models that use either linear or categorically coded ages, periods, and cohorts or combinations of these parameterizations. These models are not identified using the traditional fixed effect regression model approach because of a linear dependency between the ages, periods, and cohorts. However, these models can be identified if the researcher introduces a single just identifying constraint on the model coefficients. The problem with such constraints is that the results can differ substantially depending on the constraint chosen. Somewhat surprisingly, age-period-cohort models that specify one or more of ages and/or periods and/or cohorts as random effects are identified. This is the case without introducing an additional constraint. I label this identification as statistical model identification and show how statistical model identification comes about in mixed models and why which effects are treated as fixed and which are treated as random can substantially change the estimates of the age, period, and cohort effects. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Dynamics of Practical Premixed Flames, Part I: Model Structure and Identification

Directory of Open Access Journals (Sweden)

A. Huber

2009-06-01

Full Text Available For the analysis of thermoacoustic instabilities it is most important to determine the dynamic flame response to acoustic disturbances. Premixed flames are often modelled as single-input single-output system, where the “output” (the overall rate of heat release responds to a single “input” variable (often the velocity at the exit of the burner nozzle. However, for practical premixed flames, where perturbations of pressure or velocity at the fuel injector will modulate the fuel equivalence ratio, the heat release rate will respond to fluctuations of equivalence ratio as well as nozzle mass flow rate. In this case, a multiple-input, single-output (MISO model structure for the flame is appropriate. Such a model structure is developed in the present paper. Staged fuel injection as well as fuel line impedances can be taken into account, the integration with low-order or finite-element based models for stability analysis is straightforward. In order to determine unit impulse and frequency response functions for such a model structure, an identification scheme based on unsteady CFD calculation with broadband excitation followed by correlation analysis is proposed and validated successfully. Identification of MISO model coefficients is a challenging task, especially in the presence of noise. Therefore criteria are introduced which allow to ascertain a posteriori how well the identified model represents the true system dynamics. Using these criteria, it is investigated how excitation signal type, time series length and signal-to-noise ratio influence the results of the identification process. Consequences for passive design strategies based on multi-stage fuel injection and experimental work on practical premixed flame dynamics are discussed.

Iterative integral parameter identification of a respiratory mechanics model

Directory of Open Access Journals (Sweden)

Schranz Christoph

2012-07-01

Full Text Available Abstract Background Patient-specific respiratory mechanics models can support the evaluation of optimal lung protective ventilator settings during ventilation therapy. Clinical application requires that the individual’s model parameter values must be identified with information available at the bedside. Multiple linear regression or gradient-based parameter identification methods are highly sensitive to noise and initial parameter estimates. Thus, they are difficult to apply at the bedside to support therapeutic decisions. Methods An iterative integral parameter identification method is applied to a second order respiratory mechanics model. The method is compared to the commonly used regression methods and error-mapping approaches using simulated and clinical data. The clinical potential of the method was evaluated on data from 13 Acute Respiratory Distress Syndrome (ARDS patients. Results The iterative integral method converged to error minima 350 times faster than the Simplex Search Method using simulation data sets and 50 times faster using clinical data sets. Established regression methods reported erroneous results due to sensitivity to noise. In contrast, the iterative integral method was effective independent of initial parameter estimations, and converged successfully in each case tested. Conclusion These investigations reveal that the iterative integral method is beneficial with respect to computing time, operator independence and robustness, and thus applicable at the bedside for this clinical application.

CEAI: CCM-based email authorship identification model

Directory of Open Access Journals (Sweden)

Sarwat Nizamani

2013-11-01

Full Text Available In this paper we present a model for email authorship identification (EAI by employing a Cluster-based Classification (CCM technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature set to include some more interesting and effective features for email authorship identification (e.g., the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell. We also included Info Gain feature selection based content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM-based models, as well as the models proposed by Iqbal et al. (2010, 2013 [1,2]. The proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5% accuracy has been achieved on authors’ constructed real email dataset. The results on Enron dataset have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1,2].

Design and selection of load control strategies using a multiple objective model and evolutionary algorithms

International Nuclear Information System (INIS)

Gomes, Alvaro; Antunes, Carlos Henggeler; Martins, Antonio Gomes

2005-01-01

This paper aims at presenting a multiple objective model to evaluate the attractiveness of the use of demand resources (through load management control actions) by different stakeholders and in diverse structure scenarios in electricity systems. For the sake of model flexibility, the multiple (and conflicting) objective functions of technical, economical and quality of service nature are able to capture distinct market scenarios and operating entities that may be interested in promoting load management activities. The computation of compromise solutions is made by resorting to evolutionary algorithms, which are well suited to tackle multiobjective problems of combinatorial nature herein involving the identification and selection of control actions to be applied to groups of loads. (Author)

CEAI: CCM based Email Authorship Identification Model

DEFF Research Database (Denmark)

Nizamani, Sarwat; Memon, Nasrullah

2013-01-01

In this paper we present a model for email authorship identification (EAI) by employing a Cluster-based Classification (CCM) technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature-set to include some...... more interesting and effective features for email authorship identification (e.g. the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell). We also included Info Gain feature selection based...... reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The proposed model attains an accuracy rate of 94% for 10...

A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model

Directory of Open Access Journals (Sweden)

Ozge Cagcag Yolcu

2013-01-01

Full Text Available Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.

Identification of Civil Engineering Structures using Vector ARMA Models

DEFF Research Database (Denmark)

Andersen, P.

The dissertation treats the matter of systems identification and modelling of load-bearing constructions using Auto-Regressive Moving Average Vector (ARMAV) models.......The dissertation treats the matter of systems identification and modelling of load-bearing constructions using Auto-Regressive Moving Average Vector (ARMAV) models....

Inverse mathematical modelling and identification in metal powder compaction process

International Nuclear Information System (INIS)

Gakwaya, A.; Hrairi, M.; Guillot, M.

2000-01-01

An online assessment of the quality of advanced integrated computer aided manufacturing systems require the knowledge of accurate and reliable non-linear constitutive material behavior. This paper is concerned with material parameter identification based on experimental data for which non uniform distribution of stresses and deformation within the volume of the specimen is considered. Both geometric and material non linearities as well interfacial frictional contact are taken into account during the simulation. Within the framework of finite deformation theory, a multisurface multiplicative plasticity model for metal powder compaction process is presented. The model is seen to involve several parameters which are not always activated by a single state variable even though it may be technologically important in assessing the final product quality and manufacturing performance. The resulting expressions are presented in spatial setting and gradient based descent method utilizing the modified Levenberg-Marquardt scheme is used for the minimization of least square functional so as to obtain the best agreement between relevant experimental data and simulated data in a specified energy norm. The identification of a subset of material parameters of the cap model for stainless steel powder compaction is performed. The obtained parameters are validated through a simulation of an industrial part manufacturing case. A very good agreement between simulated final density and measured density is obtained thus demonstrating the practical usefulness of the proposed approach. (author)

Detection and Identification of Multiple Stationary Human Targets Via Bio-Radar Based on the Cross-Correlation Method

Directory of Open Access Journals (Sweden)

Yang Zhang

2016-10-01

Full Text Available Ultra-wideband (UWB radar has been widely used for detecting human physiological signals (respiration, movement, etc. in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident, etc., the presence, number, and location of the trapped victims to be detected and rescued are the key issues of concern. Ample research has been done on the first issue, whereas the identification and localization of multi-targets remains a challenge. False positive and negative identification results are two common problems associated with the detection of multiple stationary human targets. This is mainly because the energy of the signal reflected from the target close to the receiving antenna is considerably stronger than those of the targets at further range, often leading to missing or false recognition if the identification method is based on the energy of the respiratory signal. Therefore, a novel method based on cross-correlation is proposed in this paper that is based on the relativity and periodicity of the signals, rather than on the energy. The validity of this method is confirmed through experiments using different scenarios; the results indicate a discernible improvement in the detection precision and identification of the multiple stationary targets.

Detection and Identification of Multiple Stationary Human Targets Via Bio-Radar Based on the Cross-Correlation Method.

Science.gov (United States)

Zhang, Yang; Chen, Fuming; Xue, Huijun; Li, Zhao; An, Qiang; Wang, Jianqi; Zhang, Yang

2016-10-27

Ultra-wideband (UWB) radar has been widely used for detecting human physiological signals (respiration, movement, etc.) in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident, etc.), the presence, number, and location of the trapped victims to be detected and rescued are the key issues of concern. Ample research has been done on the first issue, whereas the identification and localization of multi-targets remains a challenge. False positive and negative identification results are two common problems associated with the detection of multiple stationary human targets. This is mainly because the energy of the signal reflected from the target close to the receiving antenna is considerably stronger than those of the targets at further range, often leading to missing or false recognition if the identification method is based on the energy of the respiratory signal. Therefore, a novel method based on cross-correlation is proposed in this paper that is based on the relativity and periodicity of the signals, rather than on the energy. The validity of this method is confirmed through experiments using different scenarios; the results indicate a discernible improvement in the detection precision and identification of the multiple stationary targets.

A physiologically based nonhomogeneous Poisson counter model of visual identification

DEFF Research Database (Denmark)

Christensen, Jeppe H; Markussen, Bo; Bundesen, Claus

2018-01-01

A physiologically based nonhomogeneous Poisson counter model of visual identification is presented. The model was developed in the framework of a Theory of Visual Attention (Bundesen, 1990; Kyllingsbæk, Markussen, & Bundesen, 2012) and meant for modeling visual identification of objects that are ......A physiologically based nonhomogeneous Poisson counter model of visual identification is presented. The model was developed in the framework of a Theory of Visual Attention (Bundesen, 1990; Kyllingsbæk, Markussen, & Bundesen, 2012) and meant for modeling visual identification of objects...... that mimicked the dynamics of receptive field selectivity as found in neurophysiological studies. Furthermore, the initial sensory response yielded theoretical hazard rate functions that closely resembled empirically estimated ones. Finally, supplied with a Naka-Rushton type contrast gain control, the model...

Structural system identification: Structural dynamics model validation

Energy Technology Data Exchange (ETDEWEB)

Red-Horse, J.R.

1997-04-01

Structural system identification is concerned with the development of systematic procedures and tools for developing predictive analytical models based on a physical structure`s dynamic response characteristics. It is a multidisciplinary process that involves the ability (1) to define high fidelity physics-based analysis models, (2) to acquire accurate test-derived information for physical specimens using diagnostic experiments, (3) to validate the numerical simulation model by reconciling differences that inevitably exist between the analysis model and the experimental data, and (4) to quantify uncertainties in the final system models and subsequent numerical simulations. The goal of this project was to develop structural system identification techniques and software suitable for both research and production applications in code and model validation.

Quantitative Seq-LGS: Genome-Wide Identification of Genetic Drivers of Multiple Phenotypes in Malaria Parasites

KAUST Repository

Abkallo, Hussein M.

2016-10-01

Identifying the genetic determinants of phenotypes that impact on disease severity is of fundamental importance for the design of new interventions against malaria. Traditionally, such discovery has relied on labor-intensive approaches that require significant investments of time and resources. By combining Linkage Group Selection (LGS), quantitative whole genome population sequencing and a novel mathematical modeling approach (qSeq-LGS), we simultaneously identified multiple genes underlying two distinct phenotypes, identifying novel alleles for growth rate and strain specific immunity (SSI), while removing the need for traditionally required steps such as cloning, individual progeny phenotyping and marker generation. The detection of novel variants, verified by experimental phenotyping methods, demonstrates the remarkable potential of this approach for the identification of genes controlling selectable phenotypes in malaria and other apicomplexan parasites for which experimental genetic crosses are amenable.

LPV system identification using series expansion models

NARCIS (Netherlands)

Toth, R.; Heuberger, P.S.C.; Hof, Van den P.M.J.; Santos, dos P.L.; Perdicoúlis, T.P.A.; Novara, C.; Ramos, J.A.; Rivera, D.E.

2011-01-01

This review volume reports the state-of-the-art in Linear Parameter Varying (LPV) system identification. Written by world renowned researchers, the book contains twelve chapters, focusing on the most recent LPV identification methods for both discrete-time and continuous-time models, using different

On the identification of fractionally cointegrated VAR models with the F(d) condition

DEFF Research Database (Denmark)

Carlini, Federico; Santucci de Magistris, Paolo

for any choice of the lag length, also when the true cointegration rank is known. The properties of these multiple non-identified models are studied and a necessary and sufficient condition for the identification of the fractional parameters of the system is provided. The condition is named F(d......) and it is a generalization to the fractional case of the I(1) condition in the VECM model. The assessment of the F(d) condition in the empirical analysis is relevant for the determination of the fractional parameters as well as the number of lags. The paper also illustrates the indeterminacy between the cointegration rank...

Encryption and validation of multiple signals for optical identification systems

International Nuclear Information System (INIS)

Perez-Cabre, E; Millan, M S; Javidi, B

2007-01-01

Multifactor encryption-authentication technique reinforces optical security by allowing the simultaneous A N D-verification of more than one primary image. Instead of basing the identification on a unique signature or piece of information, our goal is to authenticate a given person, object, vehicle by the simultaneous recognition of several factors. Some of them are intrinsic to the person and object or vehicle under control. Other factors, act as keys of the authentication step. Such a system is proposed for situations such as the access control to restricted areas, where the demand of security is high. The multifactor identification method involves double random-phase encoding, fully phase-based encryption and a combined nonlinear joint transform correlator and a classical 4f-correlator for simultaneous recognition and authentication of multiple images. The encoded signal fulfils the general requirements of invisible content, extreme difficulty in counterfeiting and real-time automatic verification. Four reference double-phase encoded images are compared with the retrieved input images obtained in situ from the person or the vehicle whose authentication is wanted and from a database. A recognition step based on the correlation between the signatures and the stored references determines the authentication or rejection of the person and object under surveillance

Encryption and validation of multiple signals for optical identification systems

Energy Technology Data Exchange (ETDEWEB)

Perez-Cabre, E [Universitat PoliteGcnica de Catalunya, Department Optica i Optometria, Violinista Vellsola 37, 08222 Terrassa (Spain); Millan, M S [Universitat PoliteGcnica de Catalunya, Department Optica i Optometria, Violinista Vellsola 37, 08222 Terrassa (Spain); Javidi, B [University of Connecticut, Electrical and Computer Engineering Department, 371 Fairfield Road, CT 06269 Storrs (United States)

2007-07-15

Multifactor encryption-authentication technique reinforces optical security by allowing the simultaneous A N D-verification of more than one primary image. Instead of basing the identification on a unique signature or piece of information, our goal is to authenticate a given person, object, vehicle by the simultaneous recognition of several factors. Some of them are intrinsic to the person and object or vehicle under control. Other factors, act as keys of the authentication step. Such a system is proposed for situations such as the access control to restricted areas, where the demand of security is high. The multifactor identification method involves double random-phase encoding, fully phase-based encryption and a combined nonlinear joint transform correlator and a classical 4f-correlator for simultaneous recognition and authentication of multiple images. The encoded signal fulfils the general requirements of invisible content, extreme difficulty in counterfeiting and real-time automatic verification. Four reference double-phase encoded images are compared with the retrieved input images obtained in situ from the person or the vehicle whose authentication is wanted and from a database. A recognition step based on the correlation between the signatures and the stored references determines the authentication or rejection of the person and object under surveillance.

Identification of GMS friction model without friction force measurement

International Nuclear Information System (INIS)

Grami, Said; Aissaoui, Hicham

2011-01-01

This paper deals with an online identification of the Generalized Maxwell Slip (GMS) friction model for both presliding and sliding regime at the same time. This identification is based on robust adaptive observer without friction force measurement. To apply the observer, a new approach of calculating the filtered friction force from the measurable signals is introduced. Moreover, two approximations are proposed to get the friction model linear over the unknown parameters and an approach of suitable filtering is introduced to guarantee the continuity of the model. Simulation results are presented to prove the efficiency of the approach of identification.

Modeling emotional content of music using system identification.

Science.gov (United States)

Korhonen, Mark D; Clausi, David A; Jernigan, M Ed

2006-06-01

Research was conducted to develop a methodology to model the emotional content of music as a function of time and musical features. Emotion is quantified using the dimensions valence and arousal, and system-identification techniques are used to create the models. Results demonstrate that system identification provides a means to generalize the emotional content for a genre of music. The average R2 statistic of a valid linear model structure is 21.9% for valence and 78.4% for arousal. The proposed method of constructing models of emotional content generalizes previous time-series models and removes ambiguity from classifiers of emotion.

Predicting nurses' acceptance of radiofrequency identification technology.

Science.gov (United States)

Norten, Adam

2012-10-01

The technology of radiofrequency identification allows for the scanning of radiofrequency identification-tagged objects and individuals without line-of-sight requirements. Healthcare organizations use radiofrequency identification to ensure the health and safety of patients and medical personnel and to uncover inefficiencies. Although the successful implementation of a system incorporating radiofrequency identification technologies requires acceptance and use of the technology, some nurses using radiofrequency identification in hospitals feel like "Big Brother" is watching them. This predictive study used a theoretical model assessing the effect of five independent variables: privacy concerns, attitudes, subjective norms, controllability, and self-efficacy, on a dependent variable, nurses' behavioral intention to use radiofrequency identification. A Web-based questionnaire containing previously validated questions was answered by 106 US RNs. Multiple linear regression showed that all constructs together accounted for 60% of the variance in nurses' intention to use radiofrequency identification. Of the predictors in the model, attitudes provided the largest unique contribution when the other predictors in the model were held constant; subjective norms also provided a unique contribution. Privacy concerns, controllability, and self-efficacy did not provide a significant contribution to nurses' behavioral intention to use radiofrequency identification.

Multiple Indicator Stationary Time Series Models.

Science.gov (United States)

Sivo, Stephen A.

2001-01-01

Discusses the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. For time series data, the multiple indicator model specification improves on classical time series analysis. For panel data, the multiple indicator model…

Modeling and identification for robot motion control

NARCIS (Netherlands)

Kostic, D.; Jager, de A.G.; Steinbuch, M.; Kurfess, T.R.

2004-01-01

This chapter deals with the problems of robot modelling and identification for high-performance model-based motion control. A derivation of robot kinematic and dynamic models was explained. Modelling of friction effects was also discussed. Use of a writing task to establish correctness of the models

Modeling of Biometric Identification System Using the Colored Petri Nets

Science.gov (United States)

Petrosyan, G. R.; Ter-Vardanyan, L. A.; Gaboutchian, A. V.

2015-05-01

In this paper we present a model of biometric identification system transformed into Petri Nets. Petri Nets, as a graphical and mathematical tool, provide a uniform environment for modelling, formal analysis, and design of discrete event systems. The main objective of this paper is to introduce the fundamental concepts of Petri Nets to the researchers and practitioners, both from identification systems, who are involved in the work in the areas of modelling and analysis of biometric identification types of systems, as well as those who may potentially be involved in these areas. In addition, the paper introduces high-level Petri Nets, as Colored Petri Nets (CPN). In this paper the model of Colored Petri Net describes the identification process much simpler.

Practical Modeling and Comprehensive System Identification of a BLDC Motor

Directory of Open Access Journals (Sweden)

Changle Xiang

2015-01-01

Full Text Available The aim of this paper is to outline all the steps in a rigorous and simple procedure for system identification of BLDC motor. A practical mathematical model for identification is derived. Frequency domain identification techniques and time domain estimation method are combined to obtain the unknown parameters. The methods in time domain are founded on the least squares approximation method and a disturbance observer. Only the availability of experimental data for rotor speed and armature current are required for identification. The proposed identification method is systematically investigated, and the final identified model is validated by experimental results performed on a typical BLDC motor in UAV.

Experimental Damage Identification of a Model Reticulated Shell

Directory of Open Access Journals (Sweden)

Jing Xu

2017-04-01

Full Text Available The damage identification of a reticulated shell is a challenging task, facing various difficulties, such as the large number of degrees of freedom (DOFs, the phenomenon of modal localization and transition, and low modeling accuracy. Based on structural vibration responses, the damage identification of a reticulated shell was studied. At first, the auto-regressive (AR time series model was established based on the acceleration responses of the reticulated shell. According to the changes in the coefficients of the AR model between the damaged conditions and the undamaged condition, the damage of the reticulated shell can be detected. In addition, the damage sensitive factors were determined based on the coefficients of the AR model. With the damage sensitive factors as the inputs and the damage positions as the outputs, back-propagation neural networks (BPNNs were then established and were trained using the Levenberg–Marquardt algorithm (L–M algorithm. The locations of the damages can be predicted by the back-propagation neural networks. At last, according to the experimental scheme of single-point excitation and multi-point responses, the impact experiments on a K6 shell model with a scale of 1/10 were conducted. The experimental results verified the efficiency of the proposed damage identification method based on the AR time series model and back-propagation neural networks. The proposed damage identification method can ensure the safety of the practical engineering to some extent.

Identification of Known and Novel Recurrent Viral Sequences in Data from Multiple Patients and Multiple Cancers

DEFF Research Database (Denmark)

Friis-Nielsen, Jens; Kjartansdóttir, Kristín Rós; Mollerup, Sarah

2016-01-01

non-template controls, and 24 test samples. Recurrent sequences were statistically associated to biological, methodological or technical features with the aim to identify novel pathogens or plausible contaminants that may associate to a particular kit or method. We provide examples of identified......Virus discovery from high throughput sequencing data often follows a bottom-up approach where taxonomic annotation takes place prior to association to disease. Albeit effective in some cases, the approach fails to detect novel pathogens and remote variants not present in reference databases. We...... have developed a species independent pipeline that utilises sequence clustering for the identification of nucleotide sequences that co-occur across multiple sequencing data instances. We applied the workflow to 686 sequencing libraries from 252 cancer samples of different cancer and tissue types, 32...

Hazard identification based on plant functional modelling

International Nuclear Information System (INIS)

Rasmussen, B.; Whetton, C.

1993-10-01

A major objective of the present work is to provide means for representing a process plant as a socio-technical system, so as to allow hazard identification at a high level. The method includes technical, human and organisational aspects and is intended to be used for plant level hazard identification so as to identify critical areas and the need for further analysis using existing methods. The first part of the method is the preparation of a plant functional model where a set of plant functions link together hardware, software, operations, work organisation and other safety related aspects of the plant. The basic principle of the functional modelling is that any aspect of the plant can be represented by an object (in the sense that this term is used in computer science) based upon an Intent (or goal); associated with each Intent are Methods, by which the Intent is realized, and Constraints, which limit the Intent. The Methods and Constraints can themselves be treated as objects and decomposed into lower-level Intents (hence the procedure is known as functional decomposition) so giving rise to a hierarchical, object-oriented structure. The plant level hazard identification is carried out on the plant functional model using the Concept Hazard Analysis method. In this, the user will be supported by checklists and keywords and the analysis is structured by pre-defined worksheets. The preparation of the plant functional model and the performance of the hazard identification can be carried out manually or with computer support. (au) (4 tabs., 10 ills., 7 refs.)

Using Pareto points for model identification in predictive toxicology

Science.gov (United States)

2013-01-01

Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology. PMID:23517649

Correlations in multiple production on nuclei and Glauber model of multiple scattering

International Nuclear Information System (INIS)

Zoller, V.R.; Nikolaev, N.N.

1982-01-01

Critical analysis of possibility for describing correlation phenomena during multiple production on nuclei within the framework of the Glauber multiple seattering model generalized for particle production processes with Capella, Krziwinski and Shabelsky has been performed. It was mainly concluded that the suggested generalization of the Glauber model gives dependences on Ng(Np) (where Ng-the number of ''grey'' tracess, and Np-the number of protons flying out of nucleus) and, eventually, on #betta# (where #betta#-the number of intranuclear interactions) contradicting experience. Independent of choice of relation between #betta# and Ng(Np) in the model the rapidity corrletor Rsub(eta) is overstated in the central region and understated in the region of nucleus fragmentation. In mean multiplicities these two contradictions of experience are disguised with random compensation and agreement with experience in Nsub(S) (function of Ng) cannot be an argument in favour of the model. It is concluded that eiconal model doesn't permit to quantitatively describe correlation phenomena during the multiple production on nuclei

Integrated identification, modeling and control with applications

Science.gov (United States)

Shi, Guojun

This thesis deals with the integration of system design, identification, modeling and control. In particular, six interdisciplinary engineering problems are addressed and investigated. Theoretical results are established and applied to structural vibration reduction and engine control problems. First, the data-based LQG control problem is formulated and solved. It is shown that a state space model is not necessary to solve this problem; rather a finite sequence from the impulse response is the only model data required to synthesize an optimal controller. The new theory avoids unnecessary reliance on a model, required in the conventional design procedure. The infinite horizon model predictive control problem is addressed for multivariable systems. The basic properties of the receding horizon implementation strategy is investigated and the complete framework for solving the problem is established. The new theory allows the accommodation of hard input constraints and time delays. The developed control algorithms guarantee the closed loop stability. A closed loop identification and infinite horizon model predictive control design procedure is established for engine speed regulation. The developed algorithms are tested on the Cummins Engine Simulator and desired results are obtained. A finite signal-to-noise ratio model is considered for noise signals. An information quality index is introduced which measures the essential information precision required for stabilization. The problems of minimum variance control and covariance control are formulated and investigated. Convergent algorithms are developed for solving the problems of interest. The problem of the integrated passive and active control design is addressed in order to improve the overall system performance. A design algorithm is developed, which simultaneously finds: (i) the optimal values of the stiffness and damping ratios for the structure, and (ii) an optimal output variance constrained stabilizing

Study on validation method for femur finite element model under multiple loading conditions

Science.gov (United States)

Guan, Fengjiao; Zhang, Guanjun; Liu, Jie; Wang, Shujing; Luo, Xu

2018-03-01

Acquisition of accurate and reliable constitutive parameters related to bio-tissue materials was beneficial to improve biological fidelity of a Finite Element (FE) model and predict impact damages more effectively. In this paper, a femur FE model was established under multiple loading conditions with diverse impact positions. Then, based on sequential response surface method and genetic algorithms, the material parameters identification was transformed to a multi-response optimization problem. Finally, the simulation results successfully coincided with force-displacement curves obtained by numerous experiments. Thus, computational accuracy and efficiency of the entire inverse calculation process were enhanced. This method was able to effectively reduce the computation time in the inverse process of material parameters. Meanwhile, the material parameters obtained by the proposed method achieved higher accuracy.

A hybrid approach to parameter identification of linear delay differential equations involving multiple delays

Science.gov (United States)

Marzban, Hamid Reza

2018-05-01

In this paper, we are concerned with the parameter identification of linear time-invariant systems containing multiple delays. The approach is based upon a hybrid of block-pulse functions and Legendre's polynomials. The convergence of the proposed procedure is established and an upper error bound with respect to the L2-norm associated with the hybrid functions is derived. The problem under consideration is first transformed into a system of algebraic equations. The least squares technique is then employed for identification of the desired parameters. Several multi-delay systems of varying complexity are investigated to evaluate the performance and capability of the proposed approximation method. It is shown that the proposed approach is also applicable to a class of nonlinear multi-delay systems. It is demonstrated that the suggested procedure provides accurate results for the desired parameters.

Application of Metamodels to Identification of Metallic Materials Models

Directory of Open Access Journals (Sweden)

Maciej Pietrzyk

2016-01-01

Full Text Available Improvement of the efficiency of the inverse analysis (IA for various material tests was the objective of the paper. Flow stress models and microstructure evolution models of various complexity of mathematical formulation were considered. Different types of experiments were performed and the results were used for the identification of models. Sensitivity analysis was performed for all the models and the importance of parameters in these models was evaluated. Metamodels based on artificial neural network were proposed to simulate experiments in the inverse solution. Performed analysis has shown that significant decrease of the computing times could be achieved when metamodels substitute finite element model in the inverse analysis, which is the case in the identification of flow stress models. Application of metamodels gave good results for flow stress models based on closed form equations accounting for an influence of temperature, strain, and strain rate (4 coefficients and additionally for softening due to recrystallization (5 coefficients and for softening and saturation (7 coefficients. Good accuracy and high efficiency of the IA were confirmed. On the contrary, identification of microstructure evolution models, including phase transformation models, did not give noticeable reduction of the computing time.

Modeling and identification in structural dynamics

OpenAIRE

Jayakumar, Paramsothy

1987-01-01

Analytical modeling of structures subjected to ground motions is an important aspect of fully dynamic earthquake-resistant design. In general, linear models are only sufficient to represent structural responses resulting from earthquake motions of small amplitudes. However, the response of structures during strong ground motions is highly nonlinear and hysteretic. System identification is an effective tool for developing analytical models from experimental data. Testing of full-scale prot...

Influences of multiple low-n modes on n=1 resistive wall mode identification and feedback control

International Nuclear Information System (INIS)

In, Y.; Kim, J.; Kim, J. S.; Garofalo, A. M.; Jackson, G. L.; La Haye, R. J.; Strait, E. J.; Okabayashi, M.; Reimerdes, H.

2008-01-01

It is well known in theory that even after the n=1 resistive wall mode (RWM) is suppressed, the other low-n modes, such as n=2 or 3, can appear sequentially, as β increases. In recent DIII-D experiments [J. L. Luxon, Nucl. Fusion 42, 614 (2002)], we found such an example that supports the theoretical prediction: while the n=1 mode was suppressed, an n=3 mode grew dominant, leading to a β collapse. The n=1 RWM suppression was likely due to a combination of rotational stabilization and n=1 RWM feedback. The multiple RWM identification was performed using an expanded matched filter, where n=1 and n=3 RWM basis vectors are simultaneously considered. Taking advantage of the expanded matched filter, we found that an n=3 mode following an edge-localized-mode burst grew almost linearly for several milliseconds without being hindered. This n=3 mode appeared responsible for the β collapse (down to the n=3 no-wall limit), as well as for a drop in toroidal rotation. A preliminary analysis suggests that the identity of the n=3 mode could be related to the n=3 RWM (possibly the first observation in tokamak experiments), while the impact of the n=3 mode was not as destructive as that of n=1 RWM. A numerical postprocessing of Mirnov probes showed that the n=2 mode was also unstable, consistent with the theoretical prediction. In practice, since the presence of an n=3 mode can interfere with the existing n=1 RWM identification, multiple low-n mode identification is deemed essential not only to detect n>1 mode, but also to provide accurate n=1 RWM identification and feedback control.

Efficient multiple-trait association and estimation of genetic correlation using the matrix-variate linear mixed model.

Science.gov (United States)

Furlotte, Nicholas A; Eskin, Eleazar

2015-05-01

Multiple-trait association mapping, in which multiple traits are used simultaneously in the identification of genetic variants affecting those traits, has recently attracted interest. One class of approaches for this problem builds on classical variance component methodology, utilizing a multitrait version of a linear mixed model. These approaches both increase power and provide insights into the genetic architecture of multiple traits. In particular, it is possible to estimate the genetic correlation, which is a measure of the portion of the total correlation between traits that is due to additive genetic effects. Unfortunately, the practical utility of these methods is limited since they are computationally intractable for large sample sizes. In this article, we introduce a reformulation of the multiple-trait association mapping approach by defining the matrix-variate linear mixed model. Our approach reduces the computational time necessary to perform maximum-likelihood inference in a multiple-trait model by utilizing a data transformation. By utilizing a well-studied human cohort, we show that our approach provides more than a 10-fold speedup, making multiple-trait association feasible in a large population cohort on the genome-wide scale. We take advantage of the efficiency of our approach to analyze gene expression data. By decomposing gene coexpression into a genetic and environmental component, we show that our method provides fundamental insights into the nature of coexpressed genes. An implementation of this method is available at http://genetics.cs.ucla.edu/mvLMM. Copyright © 2015 by the Genetics Society of America.

Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach

Directory of Open Access Journals (Sweden)

Xiao-meng Song

2013-01-01

Full Text Available Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on (1 a screening method (Morris for qualitative ranking of parameters, and (2 a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model (RSMSobol. First, the Morris screening method was used to qualitatively identify the parameters' sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model (RSM were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.

Vortex Tube Modeling Using the System Identification Method

Energy Technology Data Exchange (ETDEWEB)

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

2017-05-15

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

Assimilation of concentration measurements for retrieving multiple point releases in atmosphere: A least-squares approach to inverse modelling

Science.gov (United States)

Singh, Sarvesh Kumar; Rani, Raj

2015-10-01

The study addresses the identification of multiple point sources, emitting the same tracer, from their limited set of merged concentration measurements. The identification, here, refers to the estimation of locations and strengths of a known number of simultaneous point releases. The source-receptor relationship is described in the framework of adjoint modelling by using an analytical Gaussian dispersion model. A least-squares minimization framework, free from an initialization of the release parameters (locations and strengths), is presented to estimate the release parameters. This utilizes the distributed source information observable from the given monitoring design and number of measurements. The technique leads to an exact retrieval of the true release parameters when measurements are noise free and exactly described by the dispersion model. The inversion algorithm is evaluated using the real data from multiple (two, three and four) releases conducted during Fusion Field Trials in September 2007 at Dugway Proving Ground, Utah. The release locations are retrieved, on average, within 25-45 m of the true sources with the distance from retrieved to true source ranging from 0 to 130 m. The release strengths are also estimated within a factor of three to the true release rates. The average deviations in retrieval of source locations are observed relatively large in two release trials in comparison to three and four release trials.

Testing for Nonuniform Differential Item Functioning with Multiple Indicator Multiple Cause Models

Science.gov (United States)

Woods, Carol M.; Grimm, Kevin J.

2011-01-01

In extant literature, multiple indicator multiple cause (MIMIC) models have been presented for identifying items that display uniform differential item functioning (DIF) only, not nonuniform DIF. This article addresses, for apparently the first time, the use of MIMIC models for testing both uniform and nonuniform DIF with categorical indicators. A…

Identification of multiple distinct Snf2 subfamilies with conserved structural motifs.

Science.gov (United States)

Flaus, Andrew; Martin, David M A; Barton, Geoffrey J; Owen-Hughes, Tom

2006-01-01

The Snf2 family of helicase-related proteins includes the catalytic subunits of ATP-dependent chromatin remodelling complexes found in all eukaryotes. These act to regulate the structure and dynamic properties of chromatin and so influence a broad range of nuclear processes. We have exploited progress in genome sequencing to assemble a comprehensive catalogue of over 1300 Snf2 family members. Multiple sequence alignment of the helicase-related regions enables 24 distinct subfamilies to be identified, a considerable expansion over earlier surveys. Where information is known, there is a good correlation between biological or biochemical function and these assignments, suggesting Snf2 family motor domains are tuned for specific tasks. Scanning of complete genomes reveals all eukaryotes contain members of multiple subfamilies, whereas they are less common and not ubiquitous in eubacteria or archaea. The large sample of Snf2 proteins enables additional distinguishing conserved sequence blocks within the helicase-like motor to be identified. The establishment of a phylogeny for Snf2 proteins provides an opportunity to make informed assignments of function, and the identification of conserved motifs provides a framework for understanding the mechanisms by which these proteins function.

Model Identification using Continuous Glucose Monitoring Data for Type 1 Diabetes

DEFF Research Database (Denmark)

Boiroux, Dimitri; Hagdrup, Morten; Mahmoudi, Zeinab

2016-01-01

This paper addresses model identification of continuous-discrete nonlinear models for people with type 1 diabetes using sampled data from a continuous glucose monitor (CGM). We compare five identification techniques: least squares, weighted least squares, Huber regression, maximum likelihood...... with extended Kalman filter and maximum likelihood with unscented Kalman filter. We perform the identification on a 24-hour simulation of a stochastic differential equation (SDE) version of the Medtronic Virtual Patient (MVP) model including process and output noise. We compare the fits with the actual CGM......, such as parameter tracking, population modeling and handling of outliers....

Efficient Parameterization for Grey-box Model Identification of Complex Physical Systems

DEFF Research Database (Denmark)

Blanke, Mogens; Knudsen, Morten Haack

2006-01-01

Grey box model identification preserves known physical structures in a model but with limits to the possible excitation, all parameters are rarely identifiable, and different parametrizations give significantly different model quality. Convenient methods to show which parameterizations are the be...... that need be constrained to achieve satisfactory convergence. Identification of nonlinear models for a ship illustrate the concept....

Parameter identification in ODE models with oscillatory dynamics: a Fourier regularization approach

Science.gov (United States)

Chiara D'Autilia, Maria; Sgura, Ivonne; Bozzini, Benedetto

2017-12-01

In this paper we consider a parameter identification problem (PIP) for data oscillating in time, that can be described in terms of the dynamics of some ordinary differential equation (ODE) model, resulting in an optimization problem constrained by the ODEs. In problems with this type of data structure, simple application of the direct method of control theory (discretize-then-optimize) yields a least-squares cost function exhibiting multiple ‘low’ minima. Since in this situation any optimization algorithm is liable to fail in the approximation of a good solution, here we propose a Fourier regularization approach that is able to identify an iso-frequency manifold {{ S}} of codimension-one in the parameter space \

Multiple model analysis with discriminatory data collection (MMA-DDC): A new method for improving measurement selection

Science.gov (United States)

Kikuchi, C.; Ferre, P. A.; Vrugt, J. A.

2011-12-01

Hydrologic models are developed, tested, and refined based on the ability of those models to explain available hydrologic data. The optimization of model performance based upon mismatch between model outputs and real world observations has been extensively studied. However, identification of plausible models is sensitive not only to the models themselves - including model structure and model parameters - but also to the location, timing, type, and number of observations used in model calibration. Therefore, careful selection of hydrologic observations has the potential to significantly improve the performance of hydrologic models. In this research, we seek to reduce prediction uncertainty through optimization of the data collection process. A new tool - multiple model analysis with discriminatory data collection (MMA-DDC) - was developed to address this challenge. In this approach, multiple hydrologic models are developed and treated as competing hypotheses. Potential new data are then evaluated on their ability to discriminate between competing hypotheses. MMA-DDC is well-suited for use in recursive mode, in which new observations are continuously used in the optimization of subsequent observations. This new approach was applied to a synthetic solute transport experiment, in which ranges of parameter values constitute the multiple hydrologic models, and model predictions are calculated using likelihood-weighted model averaging. MMA-DDC was used to determine the optimal location, timing, number, and type of new observations. From comparison with an exhaustive search of all possible observation sequences, we find that MMA-DDC consistently selects observations which lead to the highest reduction in model prediction uncertainty. We conclude that using MMA-DDC to evaluate potential observations may significantly improve the performance of hydrologic models while reducing the cost associated with collecting new data.

Overhead longwave infrared hyperspectral material identification using radiometric models

Energy Technology Data Exchange (ETDEWEB)

Zelinski, M. E. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2018-01-09

Material detection algorithms used in hyperspectral data processing are computationally efficient but can produce relatively high numbers of false positives. Material identification performed as a secondary processing step on detected pixels can help separate true and false positives. This paper presents a material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms. The algorithms utilize unwhitened radiance data and an iterative algorithm that determines the temperature, humidity, and ozone of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian Information Criteria for model selection. The resulting product includes an optimal atmospheric profile and full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing all model parameters to improve identification is also presented. This paper details the processing chain and provides justification for the algorithms used. Several examples are provided using modeled data at different noise levels.

Adding Personality to Gifted Identification: Relationships among Traditional and Personality-Based Constructs

Science.gov (United States)

Carman, Carol A.

2011-01-01

One of the underutilized tools in gifted identification is personality-based measures. A multiple confirmatory factor analysis was utilized to examine the relationships between traditional identification methods and personality-based measures. The pattern of correlations indicated this model could be measuring two constructs, one related to…

Using the domain identification model to study major and career decision-making processes

Science.gov (United States)

Tendhar, Chosang; Singh, Kusum; Jones, Brett D.

2018-03-01

The purpose of this study was to examine the extent to which (1) a domain identification model could be used to predict students' engineering major and career intentions and (2) the MUSIC Model of Motivation components could be used to predict domain identification. The data for this study were collected from first-year engineering students. We used a structural equation model to test the hypothesised relationship between variables in the partial domain identification model. The findings suggested that engineering identification significantly predicted engineering major intentions and career intentions and had the highest effect on those two variables compared to other motivational constructs. Furthermore, results suggested that success, interest, and caring are plausible contributors to students' engineering identification. Overall, there is strong evidence that the domain identification model can be used as a lens to study career decision-making processes in engineering, and potentially, in other fields as well.

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

Science.gov (United States)

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

2017-04-01

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

Hankel Matrix Correlation Function-Based Subspace Identification Method for UAV Servo System

Directory of Open Access Journals (Sweden)

Minghong She

2018-01-01

Full Text Available For the identification problem of closed-loop subspace model, we propose a zero space projection method based on the estimation of correlation function to fill the block Hankel matrix of identification model by combining the linear algebra with geometry. By using the same projection of related data in time offset set and LQ decomposition, the multiplication operation of projection is achieved and dynamics estimation of the unknown equipment system model is obtained. Consequently, we have solved the problem of biased estimation caused when the open-loop subspace identification algorithm is applied to the closed-loop identification. A simulation example is given to show the effectiveness of the proposed approach. In final, the practicability of the identification algorithm is verified by hardware test of UAV servo system in real environment.

Modeling and Analysis of Surgery Patient Identification Using RFID

OpenAIRE

Byungho Jeong; Chen-Yang Cheng; Vittal Prabhu

2009-01-01

This article proposes a workflow and reliability model for surgery patient identification using RFID (Radio Frequency Identification). Certain types of mistakes may be prevented by automatically identifying the patient before surgery. The proposed workflow is designed to ensure that both the correct site and patient are engaged in the surgical process. The reliability model can be used to assess improvements in patients’ safety during this process. A proof-of-concept system is developed to ...

Data-Driven Photovoltaic System Modeling Based on Nonlinear System Identification

Directory of Open Access Journals (Sweden)

Ayedh Alqahtani

2016-01-01

Full Text Available Solar photovoltaic (PV energy sources are rapidly gaining potential growth and popularity compared to conventional fossil fuel sources. As the merging of PV systems with existing power sources increases, reliable and accurate PV system identification is essential, to address the highly nonlinear change in PV system dynamic and operational characteristics. This paper deals with the identification of a PV system characteristic with a switch-mode power converter. Measured input-output data are collected from a real PV panel to be used for the identification. The data are divided into estimation and validation sets. The identification methodology is discussed. A Hammerstein-Wiener model is identified and selected due to its suitability to best capture the PV system dynamics, and results and discussion are provided to demonstrate the accuracy of the selected model structure.

Model Identification of Integrated ARMA Processes

Science.gov (United States)

Stadnytska, Tetiana; Braun, Simone; Werner, Joachim

2008-01-01

This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can…

Recursive Subspace Identification of AUV Dynamic Model under General Noise Assumption

Directory of Open Access Journals (Sweden)

Zheping Yan

2014-01-01

Full Text Available A recursive subspace identification algorithm for autonomous underwater vehicles (AUVs is proposed in this paper. Due to the advantages at handling nonlinearities and couplings, the AUV model investigated here is for the first time constructed as a Hammerstein model with nonlinear feedback in the linear part. To better take the environment and sensor noises into consideration, the identification problem is concerned as an errors-in-variables (EIV one which means that the identification procedure is under general noise assumption. In order to make the algorithm recursively, propagator method (PM based subspace approach is extended into EIV framework to form the recursive identification method called PM-EIV algorithm. With several identification experiments carried out by the AUV simulation platform, the proposed algorithm demonstrates its effectiveness and feasibility.

A physiologically based nonhomogeneous Poisson counter model of visual identification.

Science.gov (United States)

Christensen, Jeppe H; Markussen, Bo; Bundesen, Claus; Kyllingsbæk, Søren

2018-04-30

A physiologically based nonhomogeneous Poisson counter model of visual identification is presented. The model was developed in the framework of a Theory of Visual Attention (Bundesen, 1990; Kyllingsbæk, Markussen, & Bundesen, 2012) and meant for modeling visual identification of objects that are mutually confusable and hard to see. The model assumes that the visual system's initial sensory response consists in tentative visual categorizations, which are accumulated by leaky integration of both transient and sustained components comparable with those found in spike density patterns of early sensory neurons. The sensory response (tentative categorizations) feeds independent Poisson counters, each of which accumulates tentative object categorizations of a particular type to guide overt identification performance. We tested the model's ability to predict the effect of stimulus duration on observed distributions of responses in a nonspeeded (pure accuracy) identification task with eight response alternatives. The time courses of correct and erroneous categorizations were well accounted for when the event-rates of competing Poisson counters were allowed to vary independently over time in a way that mimicked the dynamics of receptive field selectivity as found in neurophysiological studies. Furthermore, the initial sensory response yielded theoretical hazard rate functions that closely resembled empirically estimated ones. Finally, supplied with a Naka-Rushton type contrast gain control, the model provided an explanation for Bloch's law. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

A comparative proteomics method for multiple samples based on a 18O-reference strategy and a quantitation and identification-decoupled strategy.

Science.gov (United States)

Wang, Hongbin; Zhang, Yongqian; Gui, Shuqi; Zhang, Yong; Lu, Fuping; Deng, Yulin

2017-08-15

Comparisons across large numbers of samples are frequently necessary in quantitative proteomics. Many quantitative methods used in proteomics are based on stable isotope labeling, but most of these are only useful for comparing two samples. For up to eight samples, the iTRAQ labeling technique can be used. For greater numbers of samples, the label-free method has been used, but this method was criticized for low reproducibility and accuracy. An ingenious strategy has been introduced, comparing each sample against a 18 O-labeled reference sample that was created by pooling equal amounts of all samples. However, it is necessary to use proportion-known protein mixtures to investigate and evaluate this new strategy. Another problem for comparative proteomics of multiple samples is the poor coincidence and reproducibility in protein identification results across samples. In present study, a method combining 18 O-reference strategy and a quantitation and identification-decoupled strategy was investigated with proportion-known protein mixtures. The results obviously demonstrated that the 18 O-reference strategy had greater accuracy and reliability than other previously used comparison methods based on transferring comparison or label-free strategies. By the decoupling strategy, the quantification data acquired by LC-MS and the identification data acquired by LC-MS/MS are matched and correlated to identify differential expressed proteins, according to retention time and accurate mass. This strategy made protein identification possible for all samples using a single pooled sample, and therefore gave a good reproducibility in protein identification across multiple samples, and allowed for optimizing peptide identification separately so as to identify more proteins. Copyright © 2017 Elsevier B.V. All rights reserved.

Modeling Rabbit Responses to Single and Multiple Aerosol ...

Science.gov (United States)

Journal Article Survival models are developed here to predict response and time-to-response for mortality in rabbits following exposures to single or multiple aerosol doses of Bacillus anthracis spores. Hazard function models were developed for a multiple dose dataset to predict the probability of death through specifying dose-response functions and the time between exposure and the time-to-death (TTD). Among the models developed, the best-fitting survival model (baseline model) has an exponential dose-response model with a Weibull TTD distribution. Alternative models assessed employ different underlying dose-response functions and use the assumption that, in a multiple dose scenario, earlier doses affect the hazard functions of each subsequent dose. In addition, published mechanistic models are analyzed and compared with models developed in this paper. None of the alternative models that were assessed provided a statistically significant improvement in fit over the baseline model. The general approach utilizes simple empirical data analysis to develop parsimonious models with limited reliance on mechanistic assumptions. The baseline model predicts TTDs consistent with reported results from three independent high-dose rabbit datasets. More accurate survival models depend upon future development of dose-response datasets specifically designed to assess potential multiple dose effects on response and time-to-response. The process used in this paper to dev

Nonlinear System Identification via Basis Functions Based Time Domain Volterra Model

Directory of Open Access Journals (Sweden)

Yazid Edwar

2014-07-01

Full Text Available This paper proposes basis functions based time domain Volterra model for nonlinear system identification. The Volterra kernels are expanded by using complex exponential basis functions and estimated via genetic algorithm (GA. The accuracy and practicability of the proposed method are then assessed experimentally from a scaled 1:100 model of a prototype truss spar platform. Identification results in time and frequency domain are presented and coherent functions are performed to check the quality of the identification results. It is shown that results between experimental data and proposed method are in good agreement.

Identification of Super Phenix steam generator by a simple polynomial model

International Nuclear Information System (INIS)

Rousseau, I.

1981-01-01

This note suggests a method of identification for the steam generator of the Super-Phenix fast neutron power plant for simple polynomial models. This approach is justified in the selection of the adaptive control. The identification algorithms presented will be applied to multivariable input-output behaviours. The results obtained with the representation in self-regressive form and by simple polynomial models will be compared and the effect of perturbations on the output signal will be tested, in order to select a good identification algorithm for multivariable adaptive regulation [fr

Identification of parameters of discrete-continuous models

International Nuclear Information System (INIS)

Cekus, Dawid; Warys, Pawel

2015-01-01

In the paper, the parameters of a discrete-continuous model have been identified on the basis of experimental investigations and formulation of optimization problem. The discrete-continuous model represents a cantilever stepped Timoshenko beam. The mathematical model has been formulated and solved according to the Lagrange multiplier formalism. Optimization has been based on the genetic algorithm. The presented proceeding’s stages make the identification of any parameters of discrete-continuous systems possible

Identification of parameters of discrete-continuous models

Energy Technology Data Exchange (ETDEWEB)

Cekus, Dawid, E-mail: cekus@imipkm.pcz.pl; Warys, Pawel, E-mail: warys@imipkm.pcz.pl [Institute of Mechanics and Machine Design Foundations, Czestochowa University of Technology, Dabrowskiego 73, 42-201 Czestochowa (Poland)

2015-03-10

In the paper, the parameters of a discrete-continuous model have been identified on the basis of experimental investigations and formulation of optimization problem. The discrete-continuous model represents a cantilever stepped Timoshenko beam. The mathematical model has been formulated and solved according to the Lagrange multiplier formalism. Optimization has been based on the genetic algorithm. The presented proceeding’s stages make the identification of any parameters of discrete-continuous systems possible.

Rapid Classification and Identification of Multiple Microorganisms with Accurate Statistical Significance via High-Resolution Tandem Mass Spectrometry.

Science.gov (United States)

Alves, Gelio; Wang, Guanghui; Ogurtsov, Aleksey Y; Drake, Steven K; Gucek, Marjan; Sacks, David B; Yu, Yi-Kuo

2018-06-05

Rapid and accurate identification and classification of microorganisms is of paramount importance to public health and safety. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is complicating correct microbial identification even in a simple sample due to the large number of candidates present. To properly untwine candidate microbes in samples containing one or more microbes, one needs to go beyond apparent morphology or simple "fingerprinting"; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptide-centric representations of microbes to better separate them and by augmenting our earlier analysis method that yields accurate statistical significance. Here, we present an updated analysis workflow that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using 226 MS/MS publicly available data files (each containing from 2500 to nearly 100,000 MS/MS spectra) and 4000 additional MS/MS data files, that the updated workflow can correctly identify multiple microbes at the genus and often the species level for samples containing more than one microbe. We have also shown that the proposed workflow computes accurate statistical significances, i.e., E values for identified peptides and unified E values for identified microbes. Our updated analysis workflow MiCId, a freely available software for Microorganism Classification and Identification, is available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html . Graphical Abstract ᅟ.

Model Updating Nonlinear System Identification Toolbox, Phase II

Data.gov (United States)

National Aeronautics and Space Administration — ZONA Technology (ZONA) proposes to develop an enhanced model updating nonlinear system identification (MUNSID) methodology that utilizes flight data with...

Modelling of Biometric Identification System with Given Parameters Using Colored Petri Nets

Science.gov (United States)

Petrosyan, G.; Ter-Vardanyan, L.; Gaboutchian, A.

2017-05-01

Biometric identification systems use given parameters and function on the basis of Colored Petri Nets as a modelling language developed for systems in which communication, synchronization and distributed resources play an important role. Colored Petri Nets combine the strengths of Classical Petri Nets with the power of a high-level programming language. Coloured Petri Nets have both, formal intuitive and graphical presentations. Graphical CPN model consists of a set of interacting modules which include a network of places, transitions and arcs. Mathematical representation has a well-defined syntax and semantics, as well as defines system behavioural properties. One of the best known features used in biometric is the human finger print pattern. During the last decade other human features have become of interest, such as iris-based or face recognition. The objective of this paper is to introduce the fundamental concepts of Petri Nets in relation to tooth shape analysis. Biometric identification systems functioning has two phases: data enrollment phase and identification phase. During the data enrollment phase images of teeth are added to database. This record contains enrollment data as a noisy version of the biometrical data corresponding to the individual. During the identification phase an unknown individual is observed again and is compared to the enrollment data in the database and then system estimates the individual. The purpose of modeling biometric identification system by means of Petri Nets is to reveal the following aspects of the functioning model: the efficiency of the model, behavior of the model, mistakes and accidents in the model, feasibility of the model simplification or substitution of its separate components for more effective components without interfering system functioning. The results of biometric identification system modeling and evaluating are presented and discussed.

Applying the Team Identification-Social Psychological Health Model to Older Sport Fans

Science.gov (United States)

Wann, Daniel L.; Rogers, Kelly; Dooley, Keith; Foley, Mary

2011-01-01

According to the Team Identification-Social Psychological Health Model (Wann, 2006b), team identification and social psychological health should be positively correlated because identification leads to important social connections which, in turn, facilitate well-being. Although past research substantiates the hypothesized positive relationship…

A comparative study of non-parametric models for identification of ...

African Journals Online (AJOL)

However, the frequency response method using random binary signals was good for unpredicted white noise characteristics and considered the best method for non-parametric system identifica-tion. The autoregressive external input (ARX) model was very useful for system identification, but on applicati-on, few input ...

Model identification methodology for fluid-based inerters

Science.gov (United States)

Liu, Xiaofu; Jiang, Jason Zheng; Titurus, Branislav; Harrison, Andrew

2018-06-01

Inerter is the mechanical dual of the capacitor via the force-current analogy. It has the property that the force across the terminals is proportional to their relative acceleration. Compared with flywheel-based inerters, fluid-based forms have advantages of improved durability, inherent damping and simplicity of design. In order to improve the understanding of the physical behaviour of this fluid-based device, especially caused by the hydraulic resistance and inertial effects in the external tube, this work proposes a comprehensive model identification methodology. Firstly, a modelling procedure is established, which allows the topological arrangement of the mechanical networks to be obtained by mapping the damping, inertance and stiffness effects directly to their respective hydraulic counterparts. Secondly, an experimental sequence is followed, which separates the identification of friction, stiffness and various damping effects. Furthermore, an experimental set-up is introduced, where two pressure gauges are used to accurately measure the pressure drop across the external tube. The theoretical models with improved confidence are obtained using the proposed methodology for a helical-tube fluid inerter prototype. The sources of remaining discrepancies are further analysed.

Talent identification in youth soccer.

Science.gov (United States)

Unnithan, Viswanath; White, Jordan; Georgiou, Andreas; Iga, John; Drust, Barry

2012-01-01

The purpose of this review article was firstly to evaluate the traditional approach to talent identification in youth soccer and secondly present pilot data on a more holistic method for talent identification. Research evidence exists to suggest that talent identification mechanisms that are predicated upon the physical (anthropometric) attributes of the early maturing individual only serve to identify current performance levels. Greater body mass and stature have both been related to faster ball shooting speed and vertical jump capacity respectively in elite youth soccer players. This approach, however, may prematurely exclude those late maturing individuals. Multiple physiological measures have also been used in an effort to determine key predictors of performance; with agility and sprint times, being identified as variables that could discriminate between elite and sub-elite groups of adolescent soccer players. Successful soccer performance is the product of multiple systems interacting with one another. Consequently, a more holistic approach to talent identification should be considered. Recent work, with elite youth soccer players, has considered whether multiple small-sided games could act as a talent identification tool in this population. The results demonstrated that there was a moderate agreement between the more technically gifted soccer player and success during multiple small-sided games.

Vibratory gyroscopes : identification of mathematical model from test data

CSIR Research Space (South Africa)

Shatalov, MY

2007-05-01

Full Text Available Simple mathematical model of vibratory gyroscopes imperfections is formulated, which includes anisotropic damping and variation of mass-stiffness parameters and their harmonics. The method of identification of parameters of the mathematical model...

Identification of Civil Engineering Structures using Multivariate ARMAV and RARMAV Models

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Andersen, P.; Brincker, Rune

This paper presents how to make system identification of civil engineering structures using multivariate auto-regressive moving-average vector (ARMAV) models. Further, the ARMAV technique is extended to a recursive technique (RARMAV). The ARMAV model is used to identify measured stationary data....... The results show the usefulness of the approaches for identification of civil engineering structures excited by natural excitation...

Structural model analysis of multiple quantitative traits.

Directory of Open Access Journals (Sweden)

Renhua Li

2006-07-01

Full Text Available We introduce a method for the analysis of multilocus, multitrait genetic data that provides an intuitive and precise characterization of genetic architecture. We show that it is possible to infer the magnitude and direction of causal relationships among multiple correlated phenotypes and illustrate the technique using body composition and bone density data from mouse intercross populations. Using these techniques we are able to distinguish genetic loci that affect adiposity from those that affect overall body size and thus reveal a shortcoming of standardized measures such as body mass index that are widely used in obesity research. The identification of causal networks sheds light on the nature of genetic heterogeneity and pleiotropy in complex genetic systems.

Model Updating Nonlinear System Identification Toolbox, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — ZONA Technology proposes to develop an enhanced model updating nonlinear system identification (MUNSID) methodology by adopting the flight data with state-of-the-art...

Geometrical model of multiple production

International Nuclear Information System (INIS)

Chikovani, Z.E.; Jenkovszky, L.L.; Kvaratshelia, T.M.; Struminskij, B.V.

1988-01-01

The relation between geometrical and KNO-scaling and their violation is studied in a geometrical model of multiple production of hadrons. Predictions concerning the behaviour of correlation coefficients at future accelerators are given

Parameter identification in the logistic STAR model

DEFF Research Database (Denmark)

Ekner, Line Elvstrøm; Nejstgaard, Emil

We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter is that th...

Identification of drought in Dhalai river watershed using MCDM and ANN models

Science.gov (United States)

Aher, Sainath; Shinde, Sambhaji; Guha, Shantamoy; Majumder, Mrinmoy

2017-03-01

An innovative approach for drought identification is developed using Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN) models from surveyed drought parameter data around the Dhalai river watershed in Tripura hinterlands, India. Total eight drought parameters, i.e., precipitation, soil moisture, evapotranspiration, vegetation canopy, cropping pattern, temperature, cultivated land, and groundwater level were obtained from expert, literature and cultivator survey. Then, the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) were used for weighting of parameters and Drought Index Identification (DII). Field data of weighted parameters in the meso scale Dhalai River watershed were collected and used to train the ANN model. The developed ANN model was used in the same watershed for identification of drought. Results indicate that the Limited-Memory Quasi-Newton algorithm was better than the commonly used training method. Results obtained from the ANN model shows the drought index developed from the study area ranges from 0.32 to 0.72. Overall analysis revealed that, with appropriate training, the ANN model can be used in the areas where the model is calibrated, or other areas where the range of input parameters is similar to the calibrated region for drought identification.

Multiplicity Control in Structural Equation Modeling

Science.gov (United States)

Cribbie, Robert A.

2007-01-01

Researchers conducting structural equation modeling analyses rarely, if ever, control for the inflated probability of Type I errors when evaluating the statistical significance of multiple parameters in a model. In this study, the Type I error control, power and true model rates of famsilywise and false discovery rate controlling procedures were…

Student Identification with Business Education Models: Measurement and Relationship to Educational Outcomes

Science.gov (United States)

Halbesleben, Jonathon R. B.; Wheeler, Anthony R.

2009-01-01

Although management scholars have provided a variety of metaphors to describe the role of students in management courses, researchers have yet to explore students' identification with the models and how they are linked to educational outcomes. This article develops a measurement tool for students' identification with business education models and…

Relative efficiency of joint-model and full-conditional-specification multiple imputation when conditional models are compatible: The general location model.

Science.gov (United States)

Seaman, Shaun R; Hughes, Rachael A

2018-06-01

Estimating the parameters of a regression model of interest is complicated by missing data on the variables in that model. Multiple imputation is commonly used to handle these missing data. Joint model multiple imputation and full-conditional specification multiple imputation are known to yield imputed data with the same asymptotic distribution when the conditional models of full-conditional specification are compatible with that joint model. We show that this asymptotic equivalence of imputation distributions does not imply that joint model multiple imputation and full-conditional specification multiple imputation will also yield asymptotically equally efficient inference about the parameters of the model of interest, nor that they will be equally robust to misspecification of the joint model. When the conditional models used by full-conditional specification multiple imputation are linear, logistic and multinomial regressions, these are compatible with a restricted general location joint model. We show that multiple imputation using the restricted general location joint model can be substantially more asymptotically efficient than full-conditional specification multiple imputation, but this typically requires very strong associations between variables. When associations are weaker, the efficiency gain is small. Moreover, full-conditional specification multiple imputation is shown to be potentially much more robust than joint model multiple imputation using the restricted general location model to mispecification of that model when there is substantial missingness in the outcome variable.

Nonlinear modeling and identification of a DC motor for bidirectional operation with real time experiments

International Nuclear Information System (INIS)

Kara, Tolgay; Eker, Ilyas

2004-01-01

Modeling and identification of mechanical systems constitute an essential stage in practical control design and applications. Controllers commanding systems that operate at varying conditions or require high precision operation raise the need for a nonlinear approach in modeling and identification. Most mechanical systems used in industry are composed of masses moving under the action of position and velocity dependent forces. These forces exhibit nonlinear behavior in certain regions of operation. For a multi-mass rotational system, the nonlinearities, like Coulomb friction and dead zone, significantly influence the system operation when the rotation changes direction. The paper presents nonlinear modeling and identification of a DC motor rotating in two directions together with real time experiments. Linear and nonlinear models for the system are obtained for identification purposes, and the major nonlinearities in the system, such as Coulomb friction and dead zone, are investigated and integrated in the nonlinear model. The Hammerstein nonlinear system approach is used for identification of the nonlinear system model. Online identification of the linear and nonlinear system models is performed using the recursive least squares method. Results of the real time experiments are graphically and numerically presented, and the advantages of the nonlinear identification approach are revealed

Bayesian models and meta analysis for multiple tissue gene expression data following corticosteroid administration

Directory of Open Access Journals (Sweden)

Kelemen Arpad

2008-08-01

Full Text Available Abstract Background This paper addresses key biological problems and statistical issues in the analysis of large gene expression data sets that describe systemic temporal response cascades to therapeutic doses in multiple tissues such as liver, skeletal muscle, and kidney from the same animals. Affymetrix time course gene expression data U34A are obtained from three different tissues including kidney, liver and muscle. Our goal is not only to find the concordance of gene in different tissues, identify the common differentially expressed genes over time and also examine the reproducibility of the findings by integrating the results through meta analysis from multiple tissues in order to gain a significant increase in the power of detecting differentially expressed genes over time and to find the differential differences of three tissues responding to the drug. Results and conclusion Bayesian categorical model for estimating the proportion of the 'call' are used for pre-screening genes. Hierarchical Bayesian Mixture Model is further developed for the identifications of differentially expressed genes across time and dynamic clusters. Deviance information criterion is applied to determine the number of components for model comparisons and selections. Bayesian mixture model produces the gene-specific posterior probability of differential/non-differential expression and the 95% credible interval, which is the basis for our further Bayesian meta-inference. Meta-analysis is performed in order to identify commonly expressed genes from multiple tissues that may serve as ideal targets for novel treatment strategies and to integrate the results across separate studies. We have found the common expressed genes in the three tissues. However, the up/down/no regulations of these common genes are different at different time points. Moreover, the most differentially expressed genes were found in the liver, then in kidney, and then in muscle.

Contribution to the modeling and the identification of haptic interfaces

International Nuclear Information System (INIS)

Janot, A.

2007-12-01

This thesis focuses on the modeling and the identification of haptic interfaces using cable drive. An haptic interface is a force feedback device, which enables its user to interact with a virtual world or a remote environment explored by a slave system. It aims at the matching between the forces and displacements given by the user and those applied to virtual world. Usually, haptic interfaces make use of a mechanical actuated structure whose distal link is equipped with a handle. When manipulating this handle to interact with explored world, the user feels the apparent mass, compliance and friction of the interface. This distortion introduced between the operator and the virtual world must be modeled and identified to enhance the design of the interface and develop appropriate control laws. The first approach has been to adapt the modeling and identification methods of rigid and localized flexibilities robots to haptic interfaces. The identification technique makes use of the inverse dynamic model and the linear least squares with the measurements of joint torques and positions. This approach is validated on a single degree of freedom and a three degree of freedom haptic devices. A new identification method needing only torque data is proposed. It is based on a closed loop simulation using the direct dynamic model. The optimal parameters minimize the 2 norms of the error between the actual torque and the simulated torque assuming the same control law and the same tracking trajectory. This non linear least squares problem dramatically is simplified using the inverse model to calculate the simulated torque. This method is validated on the single degree of freedom haptic device and the SCARA robot. (author)

Systematic identification of crystallization kinetics within a generic modelling framework

DEFF Research Database (Denmark)

Abdul Samad, Noor Asma Fazli Bin; Meisler, Kresten Troelstrup; Gernaey, Krist

2012-01-01

A systematic development of constitutive models within a generic modelling framework has been developed for use in design, analysis and simulation of crystallization operations. The framework contains a tool for model identification connected with a generic crystallizer modelling tool-box, a tool...

Reduced Complexity Volterra Models for Nonlinear System Identification

Directory of Open Access Journals (Sweden)

Hacıoğlu Rıfat

2001-01-01

Full Text Available A broad class of nonlinear systems and filters can be modeled by the Volterra series representation. However, its practical use in nonlinear system identification is sometimes limited due to the large number of parameters associated with the Volterra filter′s structure. The parametric complexity also complicates design procedures based upon such a model. This limitation for system identification is addressed in this paper using a Fixed Pole Expansion Technique (FPET within the Volterra model structure. The FPET approach employs orthonormal basis functions derived from fixed (real or complex pole locations to expand the Volterra kernels and reduce the number of estimated parameters. That the performance of FPET can considerably reduce the number of estimated parameters is demonstrated by a digital satellite channel example in which we use the proposed method to identify the channel dynamics. Furthermore, a gradient-descent procedure that adaptively selects the pole locations in the FPET structure is developed in the paper.

Integration of multiple, excess, backup, and expected covering models

OpenAIRE

M S Daskin; K Hogan; C ReVelle

1988-01-01

The concepts of multiple, excess, backup, and expected coverage are defined. Model formulations using these constructs are reviewed and contrasted to illustrate the relationships between them. Several new formulations are presented as is a new derivation of the expected covering model which indicates more clearly the relationship of the model to other multi-state covering models. An expected covering model with multiple time standards is also presented.

Evolving Four Part Harmony Using a Multiple Worlds Model

DEFF Research Database (Denmark)

Scirea, Marco; Brown, Joseph Alexander

2015-01-01

This application of the Multiple Worlds Model examines a collaborative fitness model for generating four part harmonies. In this model we have multiple populations and the fitness of the individuals is based on the ability of a member from each population to work with the members of other...

Simulacioni model višelamelastih frikcionih sklopova / Simulation model of multiple plate friction clutches and brakes

Directory of Open Access Journals (Sweden)

Aleksandar Grkić

2009-01-01

Full Text Available Višelamelasti frikcioni sklopovi koriste se za promenu stepena prenosa u planetarnim menjačkim prenosnicima motornih vozila. Razvijeni simulacioni model frikcione spojnice i kočnice omogućava simulaciju rada menjačkog prenosnika pri promeni stepena prenosa. Primenom razvijenog modela moguće je na bazi simulacije analizirati prelazni proces pri promeni stepena prenosa i obezbediti identifikaciju relevantnih parametara bez izrade većeg broja fizičkih prototipova. Na taj način obezbeđuje se smanjenje troškova i skraćenje procesa razvoja novih prenosnika snage, uz poboljšanje upotrebnog kvaliteta. Simulacioni model može da se koristi i pri razvoju upravljačkog sistema menjačkog prenosnika za definisanje potrebnih karakteristika njegovih komponenata. / Multiple plate friction clutches and brakes are used for gear shifting within planetary gear trains of motor vehicles. The developed simulation model of the friction clutch and brake enables the simulation and the analysis of the planetary gear train transitional processes during gear shifting and provides identification of relevant parameters without making numerous physical prototypes. Costs are thus reduced and time for developing new gear trains shortened, while the product quality is increased. The simulation model can be use additionally in developing steering systems of planetary gear trains for defining characteristics of their components.

Double-multiple streamtube model for Darrieus in turbines

Science.gov (United States)

Paraschivoiu, I.

1981-01-01

An analytical model is proposed for calculating the rotor performance and aerodynamic blade forces for Darrieus wind turbines with curved blades. The method of analysis uses a multiple-streamtube model, divided into two parts: one modeling the upstream half-cycle of the rotor and the other, the downstream half-cycle. The upwind and downwind components of the induced velocities at each level of the rotor were obtained using the principle of two actuator disks in tandem. Variation of the induced velocities in the two parts of the rotor produces larger forces in the upstream zone and smaller forces in the downstream zone. Comparisons of the overall rotor performance with previous methods and field test data show the important improvement obtained with the present model. The calculations were made using the computer code CARDAA developed at IREQ. The double-multiple streamtube model presented has two major advantages: it requires a much shorter computer time than the three-dimensional vortex model and is more accurate than multiple-streamtube model in predicting the aerodynamic blade loads.

Talent identification and development programmes in sport : current models and future directions.

Science.gov (United States)

Vaeyens, Roel; Lenoir, Matthieu; Williams, A Mark; Philippaerts, Renaat M

2008-01-01

Many children strive to attain excellence in sport. However, although talent identification and development programmes have gained popularity in recent decades, there remains a lack of consensus in relation to how talent should be defined or identified and there is no uniformly accepted theoretical framework to guide current practice. The success rates of talent identification and development programmes have rarely been assessed and the validity of the models applied remains highly debated. This article provides an overview of current knowledge in this area with special focus on problems associated with the identification of gifted adolescents. There is a growing agreement that traditional cross-sectional talent identification models are likely to exclude many, especially late maturing, 'promising' children from development programmes due to the dynamic and multidimensional nature of sport talent. A conceptual framework that acknowledges both genetic and environmental influences and considers the dynamic and multidimensional nature of sport talent is presented. The relevance of this model is highlighted and recommendations for future work provided. It is advocated that talent identification and development programmes should be dynamic and interconnected taking into consideration maturity status and the potential to develop rather than to exclude children at an early age. Finally, more representative real-world tasks should be developed and employed in a multidimensional design to increase the efficacy of talent identification and development programmes.

Metodology of identification parameters of models control objects of automatic trailing system

Directory of Open Access Journals (Sweden)

I.V. Zimchuk

2017-04-01

Full Text Available The determining factor for the successful solution of the problem of synthesis of optimal control systems of different processes are adequacy of mathematical model of control object. In practice, the options can differ from the objects taken priori, causing a need to clarification of them. In this context, the article presents the results of the development and application of methods parameters identification of mathematical models of control object of automatic trailing system. The stated problem in the article is solved provided that control object is fully controlled and observed, and a differential equation of control object is known a priori. The coefficients of this equation to be determined. Identifying quality criterion is to minimize the integral value of squared error of identification. The method is based on a description of the dynamics of the object in space state. Equation of identification synthesized using the vector-matrix representation of model. This equation describes the interconnection of coefficients of matrix state and control with inputs and outputs of object. The initial data for calculation are the results of experimental investigation of the reaction of phase coordinates of control object at a typical input signal. The process of calculating the model parameters is reduced to solving the system of equations of the first order each. Application the above approach is illustrated in the example identification of coefficients transfer function of control object first order. Results of digital simulation are presented, they are confirming the justice of set out mathematical calculations. The approach enables to do the identification of models of one-dimensional and multidimensional objects and does not require a large amount of calculation for its implementation. The order of identified model is limited capabilities of measurement phase coordinates of corresponding control object. The practical significance of the work is

A Fuzzy Logic Framework for Integrating Multiple Learned Models

Energy Technology Data Exchange (ETDEWEB)

Hartog, Bobi Kai Den [Univ. of Nebraska, Lincoln, NE (United States)

1999-03-01

The Artificial Intelligence field of Integrating Multiple Learned Models (IMLM) explores ways to combine results from sets of trained programs. Aroclor Interpretation is an ill-conditioned problem in which trained programs must operate in scenarios outside their training ranges because it is intractable to train them completely. Consequently, they fail in ways related to the scenarios. We developed a general-purpose IMLM solution, the Combiner, and applied it to Aroclor Interpretation. The Combiner's first step, Scenario Identification (M), learns rules from very sparse, synthetic training data consisting of results from a suite of trained programs called Methods. S1 produces fuzzy belief weights for each scenario by approximately matching the rules. The Combiner's second step, Aroclor Presence Detection (AP), classifies each of three Aroclors as present or absent in a sample. The third step, Aroclor Quantification (AQ), produces quantitative values for the concentration of each Aroclor in a sample. AP and AQ use automatically learned empirical biases for each of the Methods in each scenario. Through fuzzy logic, AP and AQ combine scenario weights, automatically learned biases for each of the Methods in each scenario, and Methods' results to determine results for a sample.

Probability of identification: a statistical model for the validation of qualitative botanical identification methods.

Science.gov (United States)

LaBudde, Robert A; Harnly, James M

2012-01-01

A qualitative botanical identification method (BIM) is an analytical procedure that returns a binary result (1 = Identified, 0 = Not Identified). A BIM may be used by a buyer, manufacturer, or regulator to determine whether a botanical material being tested is the same as the target (desired) material, or whether it contains excessive nontarget (undesirable) material. The report describes the development and validation of studies for a BIM based on the proportion of replicates identified, or probability of identification (POI), as the basic observed statistic. The statistical procedures proposed for data analysis follow closely those of the probability of detection, and harmonize the statistical concepts and parameters between quantitative and qualitative method validation. Use of POI statistics also harmonizes statistical concepts for botanical, microbiological, toxin, and other analyte identification methods that produce binary results. The POI statistical model provides a tool for graphical representation of response curves for qualitative methods, reporting of descriptive statistics, and application of performance requirements. Single collaborator and multicollaborative study examples are given.

Groundwater Pollution Source Identification using Linked ANN-Optimization Model

Science.gov (United States)

Ayaz, Md; Srivastava, Rajesh; Jain, Ashu

2014-05-01

Groundwater is the principal source of drinking water in several parts of the world. Contamination of groundwater has become a serious health and environmental problem today. Human activities including industrial and agricultural activities are generally responsible for this contamination. Identification of groundwater pollution source is a major step in groundwater pollution remediation. Complete knowledge of pollution source in terms of its source characteristics is essential to adopt an effective remediation strategy. Groundwater pollution source is said to be identified completely when the source characteristics - location, strength and release period - are known. Identification of unknown groundwater pollution source is an ill-posed inverse problem. It becomes more difficult for real field conditions, when the lag time between the first reading at observation well and the time at which the source becomes active is not known. We developed a linked ANN-Optimization model for complete identification of an unknown groundwater pollution source. The model comprises two parts- an optimization model and an ANN model. Decision variables of linked ANN-Optimization model contain source location and release period of pollution source. An objective function is formulated using the spatial and temporal data of observed and simulated concentrations, and then minimized to identify the pollution source parameters. In the formulation of the objective function, we require the lag time which is not known. An ANN model with one hidden layer is trained using Levenberg-Marquardt algorithm to find the lag time. Different combinations of source locations and release periods are used as inputs and lag time is obtained as the output. Performance of the proposed model is evaluated for two and three dimensional case with error-free and erroneous data. Erroneous data was generated by adding uniformly distributed random error (error level 0-10%) to the analytically computed concentration

A Systematic Identification Method for Thermodynamic Property Modelling

DEFF Research Database (Denmark)

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

2017-01-01

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

Identification of cracks in thick beams with a cracked beam element model

Science.gov (United States)

Hou, Chuanchuan; Lu, Yong

2016-12-01

The effect of a crack on the vibration of a beam is a classical problem, and various models have been proposed, ranging from the basic stiffness reduction method to the more sophisticated model involving formulation based on the additional flexibility due to a crack. However, in the damage identification or finite element model updating applications, it is still common practice to employ a simple stiffness reduction factor to represent a crack in the identification process, whereas the use of a more realistic crack model is rather limited. In this paper, the issues with the simple stiffness reduction method, particularly concerning thick beams, are highlighted along with a review of several other crack models. A robust finite element model updating procedure is then presented for the detection of cracks in beams. The description of the crack parameters is based on the cracked beam flexibility formulated by means of the fracture mechanics, and it takes into consideration of shear deformation and coupling between translational and longitudinal vibrations, and thus is particularly suitable for thick beams. The identification procedure employs a global searching technique using Genetic Algorithms, and there is no restriction on the location, severity and the number of cracks to be identified. The procedure is verified to yield satisfactory identification for practically any configurations of cracks in a beam.

Applying the Team Identification-Social Psychological Health Model to older sport fans.

Science.gov (United States)

Wann, Daniel L; Rogers, Kelly; Dooley, Keith; Foley, Mary

2011-01-01

According to the Team Identification-Social Psychological Health Model (Wann, 2006b), team identification and social psychological health should be positively correlated because identification leads to important social connections which, in turn, facilitate well-being. Although past research substantiates the hypothesized positive relationship between team identification and well-being, earlier studies focused solely on college student populations. The current study extended past work in this area by investigating the team identification/well-being relationship among older sport fans. A sample of older adults (N = 96; M age = 70.82) completed scales assessing demographics, identification with a local college basketball team, and measures of social psychological well-being. As hypothesized, team identification accounted for a significant proportion of unique variance in two measures of social psychological health (collective self-esteem and loneliness).

Application of Metamodels to Identification of Metallic Materials Models

OpenAIRE

Pietrzyk, Maciej; Kusiak, Jan; Szeliga, Danuta; Rauch, Łukasz; Sztangret, Łukasz; Górecki, Grzegorz

2016-01-01

Improvement of the efficiency of the inverse analysis (IA) for various material tests was the objective of the paper. Flow stress models and microstructure evolution models of various complexity of mathematical formulation were considered. Different types of experiments were performed and the results were used for the identification of models. Sensitivity analysis was performed for all the models and the importance of parameters in these models was evaluated. Metamodels based on artificial ne...

Transfer Function Identification Using Orthogonal Fourier Transform Modeling Functions

Science.gov (United States)

Morelli, Eugene A.

2013-01-01

A method for transfer function identification, including both model structure determination and parameter estimation, was developed and demonstrated. The approach uses orthogonal modeling functions generated from frequency domain data obtained by Fourier transformation of time series data. The method was applied to simulation data to identify continuous-time transfer function models and unsteady aerodynamic models. Model fit error, estimated model parameters, and the associated uncertainties were used to show the effectiveness of the method for identifying accurate transfer function models from noisy data.

Learning Data Set Influence on Identification Accuracy of Gas Turbine Neural Network Model

Science.gov (United States)

Kuznetsov, A. V.; Makaryants, G. M.

2018-01-01

There are many gas turbine engine identification researches via dynamic neural network models. It should minimize errors between model and real object during identification process. Questions about training data set processing of neural networks are usually missed. This article presents a study about influence of data set type on gas turbine neural network model accuracy. The identification object is thermodynamic model of micro gas turbine engine. The thermodynamic model input signal is the fuel consumption and output signal is the engine rotor rotation frequency. Four types input signals was used for creating training and testing data sets of dynamic neural network models - step, fast, slow and mixed. Four dynamic neural networks were created based on these types of training data sets. Each neural network was tested via four types test data sets. In the result 16 transition processes from four neural networks and four test data sets from analogous solving results of thermodynamic model were compared. The errors comparison was made between all neural network errors in each test data set. In the comparison result it was shown error value ranges of each test data set. It is shown that error values ranges is small therefore the influence of data set types on identification accuracy is low.

Multiple model cardinalized probability hypothesis density filter

Science.gov (United States)

Georgescu, Ramona; Willett, Peter

2011-09-01

The Probability Hypothesis Density (PHD) filter propagates the first-moment approximation to the multi-target Bayesian posterior distribution while the Cardinalized PHD (CPHD) filter propagates both the posterior likelihood of (an unlabeled) target state and the posterior probability mass function of the number of targets. Extensions of the PHD filter to the multiple model (MM) framework have been published and were implemented either with a Sequential Monte Carlo or a Gaussian Mixture approach. In this work, we introduce the multiple model version of the more elaborate CPHD filter. We present the derivation of the prediction and update steps of the MMCPHD particularized for the case of two target motion models and proceed to show that in the case of a single model, the new MMCPHD equations reduce to the original CPHD equations.

Time domain system identification of longitudinal dynamics of single rotor model helicopter using sidpac

International Nuclear Information System (INIS)

Khaizer, A.N.; Hussain, I.

2015-01-01

This paper presents a time-domain approach for identification of longitudinal dynamics of single rotor model helicopter. A frequency sweep excitation input signal is applied for hover flying mode widely used for space state linearized model. A fully automated programmed flight test method provides high quality flight data for system identification using the computer controlled flight simulator X-plane. The flight test data were recorded, analyzed and reduced using the SIDPAC (System Identification Programs for Air Craft) toolbox for MATLAB, resulting in an aerodynamic model of single rotor helicopter. Finally, the identified model of single rotor helicopter is validated on Raptor 30-class model helicopter at hover showing the reliability of proposed approach. (author)

The Impact of School Climate and School Identification on Academic Achievement: Multilevel Modeling with Student and Teacher Data.

Science.gov (United States)

Maxwell, Sophie; Reynolds, Katherine J; Lee, Eunro; Subasic, Emina; Bromhead, David

2017-01-01

School climate is a leading factor in explaining student learning and achievement. Less work has explored the impact of both staff and student perceptions of school climate raising interesting questions about whether staff school climate experiences can add "value" to students' achievement. In the current research, multiple sources were integrated into a multilevel model, including staff self-reports, student self-reports, objective school records of academic achievement, and socio-economic demographics. Achievement was assessed using a national literacy and numeracy tests ( N = 760 staff and 2,257 students from 17 secondary schools). In addition, guided by the "social identity approach," school identification is investigated as a possible psychological mechanism to explain the relationship between school climate and achievement. In line with predictions, results show that students' perceptions of school climate significantly explain writing and numeracy achievement and this effect is mediated by students' psychological identification with the school. Furthermore, staff perceptions of school climate explain students' achievement on numeracy, writing and reading tests (while accounting for students' responses). However, staff's school identification did not play a significant role. Implications of these findings for organizational, social, and educational research are discussed.

The Impact of School Climate and School Identification on Academic Achievement: Multilevel Modeling with Student and Teacher Data

Directory of Open Access Journals (Sweden)

Sophie Maxwell

2017-12-01

Full Text Available School climate is a leading factor in explaining student learning and achievement. Less work has explored the impact of both staff and student perceptions of school climate raising interesting questions about whether staff school climate experiences can add “value” to students' achievement. In the current research, multiple sources were integrated into a multilevel model, including staff self-reports, student self-reports, objective school records of academic achievement, and socio-economic demographics. Achievement was assessed using a national literacy and numeracy tests (N = 760 staff and 2,257 students from 17 secondary schools. In addition, guided by the “social identity approach,” school identification is investigated as a possible psychological mechanism to explain the relationship between school climate and achievement. In line with predictions, results show that students' perceptions of school climate significantly explain writing and numeracy achievement and this effect is mediated by students' psychological identification with the school. Furthermore, staff perceptions of school climate explain students' achievement on numeracy, writing and reading tests (while accounting for students' responses. However, staff's school identification did not play a significant role. Implications of these findings for organizational, social, and educational research are discussed.

Mean multiplicity in the Regge models with rising cross sections

International Nuclear Information System (INIS)

Chikovani, Z.E.; Kobylisky, N.A.; Martynov, E.S.

1979-01-01

Behaviour of the mean multiplicity and the total cross section σsub(t) of hadron-hadron interactions is considered in the framework of the Regge models at high energies. Generating function was plotted for models of dipole and froissaron, and the mean multiplicity and multiplicity moments were calculated. It is shown that approximately ln 2 S (energy square) in the dipole model, which is in good agreement with the experiment. It is also found that in various Regge models approximately σsub(t)lnS

Modeling and identification of induction micromachines in microelectromechanical systems applications

Energy Technology Data Exchange (ETDEWEB)

Lyshevski, S.E. [Purdue University at Indianapolis (United States). Dept. of Electrical and Computer Engineering

2002-11-01

Microelectromechanical systems (MEMS), which integrate motion microstructures, radiating energy microdevices, controlling and signal processing integrated circuits (ICs), are widely used. Rotational and translational electromagnetic based micromachines are used in MEMS as actuators and sensors. Brushless high performance micromachines are the preferable choice in different MEMS applications, and therefore, synchronous and induction micromachines are the best candidates. Affordability, good performance characteristics (efficiency, controllability, robustness, reliability, power and torque densities etc.) and expanded operating envelopes result in a strong interest in the application of induction micromachines. In addition, induction micromachines can be easily fabricated using surface micromachining and high aspect ratio fabrication technologies. Thus, it is anticipated that induction micromachines, controlled using different control algorithms implemented using ICs, will be widely used in MEMS. Controllers can be implemented using specifically designed ICs to attain superior performance, maximize efficiency and controllability, minimize losses and electromagnetic interference, reduce noise and vibration, etc. In order to design controllers, the induction micromachine must be modeled, and its mathematical model parameters must be identified. Using microelectromechanics, nonlinear mathematical models are derived. This paper illustrates the application of nonlinear identification methods as applied to identify the unknown parameters of three phase induction micromachines. Two identification methods are studied. In particular, nonlinear error mapping technique and least squares identification are researched. Analytical and numerical results, as well as practical capabilities and effectiveness, are illustrated, identifying the unknown parameters of a three phase brushless induction micromotor. Experimental results fully support the identification methods. (author)

Identification of a nuclear plant dynamics via ARMAX model

International Nuclear Information System (INIS)

Yamamoto, Shigeki; Otsuji, Tomoo; Muramatsu, Eiichi

2000-01-01

Dynamics of the reactor of nuclear ship 'Mutsu' is described by a linear time-invariant discrete-time model which is referred to as ARMAX (Auto-Regressive Moving Average eXogenious inputs) model. Applying system identification methods, parameters of the ARMAX model are determined from input-output data of the reactor. Accuracy of the model is examined in time and frequency domain. We show that the model can be a good approximation of the plant dynamics. (author)

Detection and Identification of Loss of Efficiency Faults of Flight Actuators

Directory of Open Access Journals (Sweden)

Ossmann Daniel

2015-03-01

Full Text Available We propose linear parameter-varying (LPV model-based approaches to the synthesis of robust fault detection and diagnosis (FDD systems for loss of efficiency (LOE faults of flight actuators. The proposed methods are applicable to several types of parametric (or multiplicative LOE faults such as actuator disconnection, surface damage, actuator power loss or stall loads. For the detection of these parametric faults, advanced LPV-model detection techniques are proposed, which implicitly provide fault identification information. Fast detection of intermittent stall loads (seen as nuisances, rather than faults is important in enhancing the performance of various fault detection schemes dealing with large input signals. For this case, a dedicated fast identification algorithm is devised. The developed FDD systems are tested on a nonlinear actuator model which is implemented in a full nonlinear aircraft simulation model. This enables the validation of the FDD system’s detection and identification characteristics under realistic conditions.

Identification and communication of uncertainties of phenomenological models in PSA

International Nuclear Information System (INIS)

Pulkkinen, U.; Simola, K.

2001-11-01

This report aims at presenting a view upon uncertainty analysis of phenomenological models with an emphasis on the identification and documentation of various types of uncertainties and assumptions in the modelling of the phenomena. In an uncertainty analysis, it is essential to include and document all unclear issues, in order to obtain a maximal coverage of unresolved issues. This holds independently on their nature or type of the issues. The classification of uncertainties is needed in the decomposition of the problem and it helps in the identification of means for uncertainty reduction. Further, an enhanced documentation serves to evaluate the applicability of the results to various risk-informed applications. (au)

Identification of mutated driver pathways in cancer using a multi-objective optimization model.

Science.gov (United States)

Zheng, Chun-Hou; Yang, Wu; Chong, Yan-Wen; Xia, Jun-Feng

2016-05-01

New-generation high-throughput technologies, including next-generation sequencing technology, have been extensively applied to solve biological problems. As a result, large cancer genomics projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium are producing large amount of rich and diverse data in multiple cancer types. The identification of mutated driver genes and driver pathways from these data is a significant challenge. Genome aberrations in cancer cells can be divided into two types: random 'passenger mutation' and functional 'driver mutation'. In this paper, we introduced a Multi-objective Optimization model based on a Genetic Algorithm (MOGA) to solve the maximum weight submatrix problem, which can be employed to identify driver genes and driver pathways promoting cancer proliferation. The maximum weight submatrix problem defined to find mutated driver pathways is based on two specific properties, i.e., high coverage and high exclusivity. The multi-objective optimization model can adjust the trade-off between high coverage and high exclusivity. We proposed an integrative model by combining gene expression data and mutation data to improve the performance of the MOGA algorithm in a biological context. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multi-Scale Parameter Identification of Lithium-Ion Battery Electric Models Using a PSO-LM Algorithm

Directory of Open Access Journals (Sweden)

Wen-Jing Shen

2017-03-01

Full Text Available This paper proposes a multi-scale parameter identification algorithm for the lithium-ion battery (LIB electric model by using a combination of particle swarm optimization (PSO and Levenberg-Marquardt (LM algorithms. Two-dimensional Poisson equations with unknown parameters are used to describe the potential and current density distribution (PDD of the positive and negative electrodes in the LIB electric model. The model parameters are difficult to determine in the simulation due to the nonlinear complexity of the model. In the proposed identification algorithm, PSO is used for the coarse-scale parameter identification and the LM algorithm is applied for the fine-scale parameter identification. The experiment results show that the multi-scale identification not only improves the convergence rate and effectively escapes from the stagnation of PSO, but also overcomes the local minimum entrapment drawback of the LM algorithm. The terminal voltage curves from the PDD model with the identified parameter values are in good agreement with those from the experiments at different discharge/charge rates.

Application of neural networks to multiple alarm processing and diagnosis in nuclear power plants

International Nuclear Information System (INIS)

Cheon, Se Woo; Chang Soon Heung; Chung, Hak Yeong

1992-01-01

This paper presents feasibility studies of multiple alarm processing and diagnosis using neural networks. The back-propagation neural network model is applied to the training of multiple alarm patterns for the identification of failure in a reactor coolant pump (RCP) system. The general mapping capability of the neural network enables to identify a fault easily. The case studies are performed with emphasis on the applicability of the neural network to pattern recognition problems. It is revealed that the neural network model can identify the cause of multiple alarms properly, even when untrained or sensor-failed alarm symptoms are given. It is also shown that multiple failures are easily identified using the symptoms of multiple alarms

Using the Domain Identification Model to Study Major and Career Decision-Making Processes

Science.gov (United States)

Tendhar, Chosang; Singh, Kusum; Jones, Brett D.

2018-01-01

The purpose of this study was to examine the extent to which (1) a domain identification model could be used to predict students' engineering major and career intentions and (2) the MUSIC Model of Motivation components could be used to predict domain identification. The data for this study were collected from first-year engineering students. We…

Robust model identification applied to type 1diabetes

DEFF Research Database (Denmark)

Finan, Daniel Aaron; Jørgensen, John Bagterp; Poulsen, Niels Kjølstad

2010-01-01

In many realistic applications, process noise is known to be neither white nor normally distributed. When identifying models in these cases, it may be more effective to minimize a different penalty function than the standard sum of squared errors (as in a least-squares identification method). Thi...

Multiple model adaptive control with mixing

Science.gov (United States)

Kuipers, Matthew

Despite the remarkable theoretical accomplishments and successful applications of adaptive control, the field is not sufficiently mature to solve challenging control problems requiring strict performance and safety guarantees. Towards addressing these issues, a novel deterministic multiple-model adaptive control approach called adaptive mixing control is proposed. In this approach, adaptation comes from a high-level system called the supervisor that mixes into feedback a number of candidate controllers, each finely-tuned to a subset of the parameter space. The mixing signal, the supervisor's output, is generated by estimating the unknown parameters and, at every instant of time, calculating the contribution level of each candidate controller based on certainty equivalence. The proposed architecture provides two characteristics relevant to solving stringent, performance-driven applications. First, the full-suite of linear time invariant control tools is available. A disadvantage of conventional adaptive control is its restriction to utilizing only those control laws whose solutions can be feasibly computed in real-time, such as model reference and pole-placement type controllers. Because its candidate controllers are computed off line, the proposed approach suffers no such restriction. Second, the supervisor's output is smooth and does not necessarily depend on explicit a priori knowledge of the disturbance model. These characteristics can lead to improved performance by avoiding the unnecessary switching and chattering behaviors associated with some other multiple adaptive control approaches. The stability and robustness properties of the adaptive scheme are analyzed. It is shown that the mean-square regulation error is of the order of the modeling error. And when the parameter estimate converges to its true value, which is guaranteed if a persistence of excitation condition is satisfied, the adaptive closed-loop system converges exponentially fast to a closed

Level-set techniques for facies identification in reservoir modeling

Science.gov (United States)

Iglesias, Marco A.; McLaughlin, Dennis

2011-03-01

In this paper we investigate the application of level-set techniques for facies identification in reservoir models. The identification of facies is a geometrical inverse ill-posed problem that we formulate in terms of shape optimization. The goal is to find a region (a geologic facies) that minimizes the misfit between predicted and measured data from an oil-water reservoir. In order to address the shape optimization problem, we present a novel application of the level-set iterative framework developed by Burger in (2002 Interfaces Free Bound. 5 301-29 2004 Inverse Problems 20 259-82) for inverse obstacle problems. The optimization is constrained by (the reservoir model) a nonlinear large-scale system of PDEs that describes the reservoir dynamics. We reformulate this reservoir model in a weak (integral) form whose shape derivative can be formally computed from standard results of shape calculus. At each iteration of the scheme, the current estimate of the shape derivative is utilized to define a velocity in the level-set equation. The proper selection of this velocity ensures that the new shape decreases the cost functional. We present results of facies identification where the velocity is computed with the gradient-based (GB) approach of Burger (2002) and the Levenberg-Marquardt (LM) technique of Burger (2004). While an adjoint formulation allows the straightforward application of the GB approach, the LM technique requires the computation of the large-scale Karush-Kuhn-Tucker system that arises at each iteration of the scheme. We efficiently solve this system by means of the representer method. We present some synthetic experiments to show and compare the capabilities and limitations of the proposed implementations of level-set techniques for the identification of geologic facies.

Level-set techniques for facies identification in reservoir modeling

International Nuclear Information System (INIS)

Iglesias, Marco A; McLaughlin, Dennis

2011-01-01

In this paper we investigate the application of level-set techniques for facies identification in reservoir models. The identification of facies is a geometrical inverse ill-posed problem that we formulate in terms of shape optimization. The goal is to find a region (a geologic facies) that minimizes the misfit between predicted and measured data from an oil–water reservoir. In order to address the shape optimization problem, we present a novel application of the level-set iterative framework developed by Burger in (2002 Interfaces Free Bound. 5 301–29; 2004 Inverse Problems 20 259–82) for inverse obstacle problems. The optimization is constrained by (the reservoir model) a nonlinear large-scale system of PDEs that describes the reservoir dynamics. We reformulate this reservoir model in a weak (integral) form whose shape derivative can be formally computed from standard results of shape calculus. At each iteration of the scheme, the current estimate of the shape derivative is utilized to define a velocity in the level-set equation. The proper selection of this velocity ensures that the new shape decreases the cost functional. We present results of facies identification where the velocity is computed with the gradient-based (GB) approach of Burger (2002) and the Levenberg–Marquardt (LM) technique of Burger (2004). While an adjoint formulation allows the straightforward application of the GB approach, the LM technique requires the computation of the large-scale Karush–Kuhn–Tucker system that arises at each iteration of the scheme. We efficiently solve this system by means of the representer method. We present some synthetic experiments to show and compare the capabilities and limitations of the proposed implementations of level-set techniques for the identification of geologic facies

System Identification, Environmental Modelling, and Control System Design

CERN Document Server

Garnier, Hugues

2012-01-01

System Identification, Environmetric Modelling, and Control Systems Design is dedicated to Professor Peter Young on the occasion of his seventieth birthday. Professor Young has been a pioneer in systems and control, and over the past 45 years he has influenced many developments in this field. This volume is comprised of a collection of contributions by leading experts in system identification, time-series analysis, environmetric modelling and control system design – modern research in topics that reflect important areas of interest in Professor Young’s research career. Recent theoretical developments in and relevant applications of these areas are explored treating the various subjects broadly and in depth. The authoritative and up-to-date research presented here will be of interest to academic researcher in control and disciplines related to environmental research, particularly those to with water systems. The tutorial style in which many of the contributions are composed also makes the book suitable as ...

Design of Xen Hybrid Multiple Police Model

Science.gov (United States)

Sun, Lei; Lin, Renhao; Zhu, Xianwei

2017-10-01

Virtualization Technology has attracted more and more attention. As a popular open-source virtualization tools, XEN is used more and more frequently. Xsm, XEN security model, has also been widespread concern. The safety status classification has not been established in the XSM, and it uses the virtual machine as a managed object to make Dom0 a unique administrative domain that does not meet the minimum privilege. According to these questions, we design a Hybrid multiple police model named SV_HMPMD that organically integrates multiple single security policy models include DTE,RBAC,BLP. It can fullfill the requirement of confidentiality and integrity for security model and use different particle size to different domain. In order to improve BLP’s practicability, the model introduce multi-level security labels. In order to divide the privilege in detail, we combine DTE with RBAC. In order to oversize privilege, we limit the privilege of domain0.

Recent developments in identification of kinetic and transport models from experimental data. Contributed Paper IT-08

International Nuclear Information System (INIS)

Bhatt, Nirav P.

2014-01-01

In this presentation, we will discuss recent developments in area of identification of kinetic and transport models from experimental data, and their importance in spent fuel reprocessing. The traditional kinetic modelling approaches, differentiation and integral methods, will be presented to set the stage. Then, two frameworks of identifying kinetic and transport models will be presented in details. These frameworks can be classified as follows: (i) simultaneous or global model identification (SMI), and (ii) incremental model identification (IMI). In the SMI framework, as name indicates, rate expressions of all reactions are integrated to predict concentrations that are fitted to measured values via a least-squares problem simultaneously. Alternatively, the identification task can be split into a sequence of sub-problems such as the identification of stoichiometry and rate expressions. For each subproblem, the number of model candidates can be kept small. In addition, the information available at a given step can be used to refine the model in subsequent steps. Further, the advantages and disadvantages of these frameworks will be presented

Identification of swine influenza virus epitopes and analysis of multiple specificities expressed by cytotoxic T cell subsets

DEFF Research Database (Denmark)

Pedersen, Lasse Eggers; Breum, Solvej Østergaard; Riber, Ulla

2014-01-01

Background: Major histocompatibility complex (MHC) class I peptide binding and presentation are essential for antigen-specific activation of cytotoxic T lymphocytes (CTLs) and swine MHC class I molecules, also termed swine leukocyte antigens (SLA), thus play a crucial role in the process that leads...... to elimination of viruses such as swine influenza virus (SwIV). This study describes the identification of SLA-presented peptide epitopes that are targets for a swine CTL response, and further analyses multiple specificities expressed by SwIV activated CTL subsets. Findings: Four SwIV derived peptides were...

Modeling and identification of centrifugal compressor dynamics with approximate realizations

NARCIS (Netherlands)

Helvoirt, van J.; Jager, de A.G.; Steinbuch, M.; Smeulers, J.P.M.

2005-01-01

This paper deals with the parameter identification of a model for the dynamic behavior of a large industrial centrifugal compression system. Experimental results are presented to evaluate a new approach for determining the parameters of the modified version of the well-known Greitzer model. This

Identification of Loss-of-Coolant Accidents in LWRs by Inverse Models

International Nuclear Information System (INIS)

Cholewa, Wojciech; Frid, Wiktor; Bednarski, Marcin

2004-01-01

This paper describes a novel diagnostic method based on inverse models that could be applied to identification of transients and accidents in nuclear power plants. In particular, it is shown that such models could be successfully applied to identification of loss-of-coolant accidents (LOCAs). This is demonstrated for LOCA scenarios for a boiling water reactor. Two classes of inverse models are discussed: local models valid only in a selected neighborhood of an unknown element in the data set, representing a state of a considered object, and global models, in the form of partially unilateral models, valid over the whole learning data set. An interesting and useful property of local inverse models is that they can be considered as example-based models, i.e., models that are spanned on particular sets of pattern data. It is concluded that the optimal diagnostic method should combine the advantages of both models, i.e., the high quality of results obtained from a local inverse model and the information about the confidence interval for the expected output provided by a partially unilateral model

Factors Linked to Identification with a Super-Ordinate Category in a ...

African Journals Online (AJOL)

Multiple regression analyses suggested that factors linked to identification with a super-ordinate category were related more to personal variables than to status differentiation and distribution of resources. These results are discussed with reference to the Common Ingroup Identity model (Gaertner et al, 1993), Breakwell's ...

Noisy: Identification of problematic columns in multiple sequence alignments

Directory of Open Access Journals (Sweden)

Grünewald Stefan

2008-06-01

Full Text Available Abstract Motivation Sequence-based methods for phylogenetic reconstruction from (nucleic acid sequence data are notoriously plagued by two effects: homoplasies and alignment errors. Large evolutionary distances imply a large number of homoplastic sites. As most protein-coding genes show dramatic variations in substitution rates that are not uncorrelated across the sequence, this often leads to a patchwork pattern of (i phylogenetically informative and (ii effectively randomized regions. In highly variable regions, furthermore, alignment errors accumulate resulting in sometimes misleading signals in phylogenetic reconstruction. Results We present here a method that, based on assessing the distribution of character states along a cyclic ordering of the taxa, allows the identification of phylogenetically uninformative homoplastic sites in a multiple sequence alignment. Removal of these sites appears to improve the performance of phylogenetic reconstruction algorithms as measured by various indices of "tree quality". In particular, we obtain more stable trees due to the exclusion of phylogenetically incompatible sites that most likely represent strongly randomized characters. Software The computer program noisy implements this approach. It can be employed to improving phylogenetic reconstruction capability with quite a considerable success rate whenever (1 the average bootstrap support obtained from the original alignment is low, and (2 there are sufficiently many taxa in the data set – at least, say, 12 to 15 taxa. The software can be obtained under the GNU Public License from http://www.bioinf.uni-leipzig.de/Software/noisy/.

Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme

Directory of Open Access Journals (Sweden)

Lan Wang

2017-01-01

Full Text Available Quasi-linear autoregressive with exogenous inputs (Quasi-ARX models have received considerable attention for their usefulness in nonlinear system identification and control. In this paper, identification methods of quasi-ARX type models are reviewed and categorized in three main groups, and a two-step learning approach is proposed as an extension of the parameter-classified methods to identify the quasi-ARX radial basis function network (RBFN model. Firstly, a clustering method is utilized to provide statistical properties of the dataset for determining the parameters nonlinear to the model, which are interpreted meaningfully in the sense of interpolation parameters of a local linear model. Secondly, support vector regression is used to estimate the parameters linear to the model; meanwhile, an explicit kernel mapping is given in terms of the nonlinear parameter identification procedure, in which the model is transformed from the nonlinear-in-nature to the linear-in-parameter. Numerical and real cases are carried out finally to demonstrate the effectiveness and generalization ability of the proposed method.

Supersymmetric U(1)' model with multiple dark matters

International Nuclear Information System (INIS)

Hur, Taeil; Lee, Hye-Sung; Nasri, Salah

2008-01-01

We consider a scenario where a supersymmetric model has multiple dark matter particles. Adding a U(1) ' gauge symmetry is a well-motivated extension of the minimal supersymmetric standard model (MSSM). It can cure the problems of the MSSM such as the μ problem or the proton decay problem with high-dimensional lepton number and baryon number violating operators which R parity allows. An extra parity (U parity) may arise as a residual discrete symmetry after U(1) ' gauge symmetry is spontaneously broken. The lightest U-parity particle (LUP) is stable under the new parity becoming a new dark matter candidate. Up to three massive particles can be stable in the presence of the R parity and the U parity. We numerically illustrate that multiple stable particles in our model can satisfy both constraints from the relic density and the direct detection, thus providing a specific scenario where a supersymmetric model has well-motivated multiple dark matters consistent with experimental constraints. The scenario provides new possibilities in the present and upcoming dark matter searches in the direct detection and collider experiments

Multiple Social Networks, Data Models and Measures for

DEFF Research Database (Denmark)

Magnani, Matteo; Rossi, Luca

2017-01-01

Multiple Social Network Analysis is a discipline defining models, measures, methodologies, and algorithms to study multiple social networks together as a single social system. It is particularly valuable when the networks are interconnected, e.g., the same actors are present in more than one...

Parameter identification in a nonlinear nuclear reactor model using quasilinearization

International Nuclear Information System (INIS)

Barreto, J.M.; Martins Neto, A.F.; Tanomaru, N.

1980-09-01

Parameter identification in a nonlinear, lumped parameter, nuclear reactor model is carried out using discrete output power measurements during the transient caused by an external reactivity change. In order to minimize the difference between the model and the reactor power responses, the parameter promt neutron generation time and a parameter in fuel temperature reactivity coefficient equation are adjusted using quasilinearization. The influences of the external reactivity disturbance, the number and frequency of measurements and the measurement noise level on the method accuracy and rate of convergence are analysed through simulation. Procedures for the design of the identification experiments are suggested. The method proved to be very effective for low level noise measurements. (Author) [pt

Identification of reduced-order model for an aeroelastic system from flutter test data

Directory of Open Access Journals (Sweden)

Wei Tang

2017-02-01

Full Text Available Recently, flutter active control using linear parameter varying (LPV framework has attracted a lot of attention. LPV control synthesis usually generates controllers that are at least of the same order as the aeroelastic models. Therefore, the reduced-order model is required by synthesis for avoidance of large computation cost and high-order controller. This paper proposes a new procedure for generation of accurate reduced-order linear time-invariant (LTI models by using system identification from flutter testing data. The proposed approach is in two steps. The well-known poly-reference least squares complex frequency (p-LSCF algorithm is firstly employed for modal parameter identification from frequency response measurement. After parameter identification, the dominant physical modes are determined by clear stabilization diagrams and clustering technique. In the second step, with prior knowledge of physical poles, the improved frequency-domain maximum likelihood (ML estimator is presented for building accurate reduced-order model. Before ML estimation, an improved subspace identification considering the poles constraint is also proposed for initializing the iterative procedure. Finally, the performance of the proposed procedure is validated by real flight flutter test data.

Rotor-System Log-Decrement Identification Using Short-Time Fourier-Transform Filter

Directory of Open Access Journals (Sweden)

Qihang Li

2015-01-01

Full Text Available With the increase of the centrifugal compressor capability, such as large scale LNG and CO2 reinjection, the stability margin evaluation is crucial to assure the compressor work in the designed operating conditions in field. Improving the precision of parameter identification of stability is essential and necessary as well. Based on the time-varying characteristics of response vibration during the sine-swept process, a short-time Fourier transform (STFT filter was introduced to increase the signal-noise ratio and improve the accuracy of the estimated stability parameters. A finite element model was established to simulate the sine-swept process, and the simulated vibration signals were used to study the filtering effect and demonstrate the feasibility to identify the stability parameters by using Multiple-Input and Multiple-Output system identification method that combines the prediction error method and instrumental variable method. Simulation results show that the identification method with STFT filter improves the estimated accuracy much well and makes the curves of frequency response function clearer. Experiment was carried out on a test rig as well, which indicates the identification method is feasible in stability identification, and the results of experiment indicate that STFT filter works very well.

Multiple Response Regression for Gaussian Mixture Models with Known Labels.

Science.gov (United States)

Lee, Wonyul; Du, Ying; Sun, Wei; Hayes, D Neil; Liu, Yufeng

2012-12-01

Multiple response regression is a useful regression technique to model multiple response variables using the same set of predictor variables. Most existing methods for multiple response regression are designed for modeling homogeneous data. In many applications, however, one may have heterogeneous data where the samples are divided into multiple groups. Our motivating example is a cancer dataset where the samples belong to multiple cancer subtypes. In this paper, we consider modeling the data coming from a mixture of several Gaussian distributions with known group labels. A naive approach is to split the data into several groups according to the labels and model each group separately. Although it is simple, this approach ignores potential common structures across different groups. We propose new penalized methods to model all groups jointly in which the common and unique structures can be identified. The proposed methods estimate the regression coefficient matrix, as well as the conditional inverse covariance matrix of response variables. Asymptotic properties of the proposed methods are explored. Through numerical examples, we demonstrate that both estimation and prediction can be improved by modeling all groups jointly using the proposed methods. An application to a glioblastoma cancer dataset reveals some interesting common and unique gene relationships across different cancer subtypes.

Identification of biased sectors in emission data using a combination of chemical transport model and receptor model

Science.gov (United States)

Uranishi, Katsushige; Ikemori, Fumikazu; Nakatsubo, Ryohei; Shimadera, Hikari; Kondo, Akira; Kikutani, Yuki; Asano, Katsuyoshi; Sugata, Seiji

2017-10-01

This study presented a comparison approach with multiple source apportionment methods to identify which sectors of emission data have large biases. The source apportionment methods for the comparison approach included both receptor and chemical transport models, which are widely used to quantify the impacts of emission sources on fine particulate matter of less than 2.5 μm in diameter (PM2.5). We used daily chemical component concentration data in the year 2013, including data for water-soluble ions, elements, and carbonaceous species of PM2.5 at 11 sites in the Kinki-Tokai district in Japan in order to apply the Positive Matrix Factorization (PMF) model for the source apportionment. Seven PMF factors of PM2.5 were identified with the temporal and spatial variation patterns and also retained features of the sites. These factors comprised two types of secondary sulfate, road transportation, heavy oil combustion by ships, biomass burning, secondary nitrate, and soil and industrial dust, accounting for 46%, 17%, 7%, 14%, 13%, and 3% of the PM2.5, respectively. The multiple-site data enabled a comprehensive identification of the PM2.5 sources. For the same period, source contributions were estimated by air quality simulations using the Community Multiscale Air Quality model (CMAQ) with the brute-force method (BFM) for four source categories. Both models provided consistent results for the following three of the four source categories: secondary sulfates, road transportation, and heavy oil combustion sources. For these three target categories, the models' agreement was supported by the small differences and high correlations between the CMAQ/BFM- and PMF-estimated source contributions to the concentrations of PM2.5, SO42-, and EC. In contrast, contributions of the biomass burning sources apportioned by CMAQ/BFM were much lower than and little correlated with those captured by the PMF model, indicating large uncertainties in the biomass burning emissions used in the

Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models?

Directory of Open Access Journals (Sweden)

Tabitha A Graves

Full Text Available Using multiple detection methods can increase the number, kind, and distribution of individuals sampled, which may increase accuracy and precision and reduce cost of population abundance estimates. However, when variables influencing abundance are of interest, if individuals detected via different methods are influenced by the landscape differently, separate analysis of multiple detection methods may be more appropriate. We evaluated the effects of combining two detection methods on the identification of variables important to local abundance using detections of grizzly bears with hair traps (systematic and bear rubs (opportunistic. We used hierarchical abundance models (N-mixture models with separate model components for each detection method. If both methods sample the same population, the use of either data set alone should (1 lead to the selection of the same variables as important and (2 provide similar estimates of relative local abundance. We hypothesized that the inclusion of 2 detection methods versus either method alone should (3 yield more support for variables identified in single method analyses (i.e. fewer variables and models with greater weight, and (4 improve precision of covariate estimates for variables selected in both separate and combined analyses because sample size is larger. As expected, joint analysis of both methods increased precision as well as certainty in variable and model selection. However, the single-method analyses identified different variables and the resulting predicted abundances had different spatial distributions. We recommend comparing single-method and jointly modeled results to identify the presence of individual heterogeneity between detection methods in N-mixture models, along with consideration of detection probabilities, correlations among variables, and tolerance to risk of failing to identify variables important to a subset of the population. The benefits of increased precision should be weighed

LEARNING VECTOR QUANTIZATION FOR ADAPTED GAUSSIAN MIXTURE MODELS IN AUTOMATIC SPEAKER IDENTIFICATION

Directory of Open Access Journals (Sweden)

IMEN TRABELSI

2017-05-01

Full Text Available Speaker Identification (SI aims at automatically identifying an individual by extracting and processing information from his/her voice. Speaker voice is a robust a biometric modality that has a strong impact in several application areas. In this study, a new combination learning scheme has been proposed based on Gaussian mixture model-universal background model (GMM-UBM and Learning vector quantization (LVQ for automatic text-independent speaker identification. Features vectors, constituted by the Mel Frequency Cepstral Coefficients (MFCC extracted from the speech signal are used to train the New England subset of the TIMIT database. The best results obtained (90% for gender- independent speaker identification, 97 % for male speakers and 93% for female speakers for test data using 36 MFCC features.

On Early Conflict Identification by Requirements Modeling of Energy System Control Structures

DEFF Research Database (Denmark)

Heussen, Kai; Gehrke, Oliver; Niemann, Hans Henrik

2015-01-01

issues early. For requirements formulation of control structures, cyber and physical aspects need to be jointly represented to express interdependencies, check for consistency and discover potentially conflicting requirements. Early identification of potential conflicts may prevent larger problems...... at later design stages. However, languages employed for requirements modeling today do not offer the expressiveness necessary to represent control purposes in relation to domain level interactions and therefore miss several types of interdependencies. This paper introduces the idea of control structure...... modeling for early requirements checking using a suitable modeling language, and illustrates how this approach enables the identification of several classes of controller conflict....

SDG and qualitative trend based model multiple scale validation

Science.gov (United States)

Gao, Dong; Xu, Xin; Yin, Jianjin; Zhang, Hongyu; Zhang, Beike

2017-09-01

Verification, Validation and Accreditation (VV&A) is key technology of simulation and modelling. For the traditional model validation methods, the completeness is weak; it is carried out in one scale; it depends on human experience. The SDG (Signed Directed Graph) and qualitative trend based multiple scale validation is proposed. First the SDG model is built and qualitative trends are added to the model. And then complete testing scenarios are produced by positive inference. The multiple scale validation is carried out by comparing the testing scenarios with outputs of simulation model in different scales. Finally, the effectiveness is proved by carrying out validation for a reactor model.

A Multiple Items EPQ/EOQ Model for a Vendor and Multiple Buyers System with Considering Continuous and Discrete Demand Simultaneously

Science.gov (United States)

Jonrinaldi; Rahman, T.; Henmaidi; Wirdianto, E.; Zhang, D. Z.

2018-03-01

This paper proposed a mathematical model for multiple items Economic Production and Order Quantity (EPQ/EOQ) with considering continuous and discrete demand simultaneously in a system consisting of a vendor and multiple buyers. This model is used to investigate the optimal production lot size of the vendor and the number of shipments policy of orders to multiple buyers. The model considers the multiple buyers’ holding cost as well as transportation cost, which minimize the total production and inventory costs of the system. The continuous demand from any other customers can be fulfilled anytime by the vendor while the discrete demand from multiple buyers can be fulfilled by the vendor using the multiple delivery policy with a number of shipments of items in the production cycle time. A mathematical model is developed to illustrate the system based on EPQ and EOQ model. Solution procedures are proposed to solve the model using a Mixed Integer Non Linear Programming (MINLP) and algorithm methods. Then, the numerical example is provided to illustrate the system and results are discussed.

Identification of cascade water tanks using a PWARX model

Science.gov (United States)

Mattsson, Per; Zachariah, Dave; Stoica, Petre

2018-06-01

In this paper we consider the identification of a discrete-time nonlinear dynamical model for a cascade water tank process. The proposed method starts with a nominal linear dynamical model of the system, and proceeds to model its prediction errors using a model that is piecewise affine in the data. As data is observed, the nominal model is refined into a piecewise ARX model which can capture a wide range of nonlinearities, such as the saturation in the cascade tanks. The proposed method uses a likelihood-based methodology which adaptively penalizes model complexity and directly leads to a computationally efficient implementation.

Encrypted data stream identification using randomness sparse representation and fuzzy Gaussian mixture model

Science.gov (United States)

Zhang, Hong; Hou, Rui; Yi, Lei; Meng, Juan; Pan, Zhisong; Zhou, Yuhuan

2016-07-01

The accurate identification of encrypted data stream helps to regulate illegal data, detect network attacks and protect users' information. In this paper, a novel encrypted data stream identification algorithm is introduced. The proposed method is based on randomness characteristics of encrypted data stream. We use a l1-norm regularized logistic regression to improve sparse representation of randomness features and Fuzzy Gaussian Mixture Model (FGMM) to improve identification accuracy. Experimental results demonstrate that the method can be adopted as an effective technique for encrypted data stream identification.

Dynamic Friction Parameter Identification Method with LuGre Model for Direct-Drive Rotary Torque Motor

Directory of Open Access Journals (Sweden)

Xingjian Wang

2016-01-01

Full Text Available Attainment of high-performance motion/velocity control objectives for the Direct-Drive Rotary (DDR torque motor should fully consider practical nonlinearities in controller design, such as dynamic friction. The LuGre model has been widely utilized to describe nonlinear friction behavior; however, parameter identification for the LuGre model remains a challenge. A new dynamic friction parameter identification method for LuGre model is proposed in this study. Static parameters are identified through a series of constant velocity experiments, while dynamic parameters are obtained through a presliding process. Novel evolutionary algorithm (NEA is utilized to increase identification accuracy. Experimental results gathered from the identification experiments conducted in the study for a practical DDR torque motor control system validate the effectiveness of the proposed method.

Model Identification for Control of Display Units in Supermarket Refrigeration Systems

DEFF Research Database (Denmark)

O'Connell, Niamh; Madsen, Henrik; Andersen, Philip Hvidthøft Delff

in a supermarket refrigeration system. The grey-box modelling approach is adopted, using stochastic differential equations to define the dynamics of the model, combining prior knowledge of the physical system with data-driven modelling. Model identification is performed using the forward selection method...... model can contribute to the extension of the control capabilities of the entire supermarket refrigeration system....

Diagnosis and Model Based Identification of a Coupling Misalignment

Directory of Open Access Journals (Sweden)

P. Pennacchi

2005-01-01

Full Text Available This paper is focused on the application of two different diagnostic techniques aimed to identify the most important faults in rotating machinery as well as on the simulation and prediction of the frequency response of rotating machines. The application of the two diagnostics techniques, the orbit shape analysis and the model based identification in the frequency domain, is described by means of an experimental case study that concerns a gas turbine-generator unit of a small power plant whose rotor-train was affected by an angular misalignment in a flexible coupling, caused by a wrong machine assembling. The fault type is identified by means of the orbit shape analysis, then the equivalent bending moments, which enable the shaft experimental vibrations to be simulated, have been identified using a model based identification method. These excitations have been used to predict the machine vibrations in a large rotating speed range inside which no monitoring data were available. To the best of the authors' knowledge, this is the first case of identification of coupling misalignment and prediction of the consequent machine behaviour in an actual size rotating machinery. The successful results obtained emphasise the usefulness of integrating common condition monitoring techniques with diagnostic strategies.

Multiple commodities in statistical microeconomics: Model and market

Science.gov (United States)

Baaquie, Belal E.; Yu, Miao; Du, Xin

2016-11-01

A statistical generalization of microeconomics has been made in Baaquie (2013). In Baaquie et al. (2015), the market behavior of single commodities was analyzed and it was shown that market data provides strong support for the statistical microeconomic description of commodity prices. The case of multiple commodities is studied and a parsimonious generalization of the single commodity model is made for the multiple commodities case. Market data shows that the generalization can accurately model the simultaneous correlation functions of up to four commodities. To accurately model five or more commodities, further terms have to be included in the model. This study shows that the statistical microeconomics approach is a comprehensive and complete formulation of microeconomics, and which is independent to the mainstream formulation of microeconomics.

Identification of linear error-models with projected dynamical systems

Czech Academy of Sciences Publication Activity Database

Krejčí, Pavel; Kuhnen, K.

2004-01-01

Roč. 10, č. 1 (2004), s. 59-91 ISSN 1387-3954 Keywords : identification * error models * projected dynamical systems Subject RIV: BA - General Mathematics Impact factor: 0.292, year: 2004 http://www.informaworld.com/smpp/content~db=all~content=a713682517

Analysis of Offshore Knuckle Boom Crane - Part One: Modeling and Parameter Identification

Directory of Open Access Journals (Sweden)

Morten K. Bak

2013-10-01

Full Text Available This paper presents an extensive model of a knuckle boom crane used for pipe handling on offshore drilling rigs. The mechanical system is modeled as a multi-body system and includes the structural flexibility and damping. The motion control system model includes the main components of the crane's electro-hydraulic actuation system. For this a novel black-box model for counterbalance valves is presented, which uses two different pressure ratios to compute the flow through the valve. Experimental data and parameter identification, based on both numerical optimization and manual tuning, are used to verify the crane model. The demonstrated modeling and parameter identification techniques target the system engineer and takes into account the limited access to component data normally encountered by engineers working with design of hydraulic systems.

AgMIP Training in Multiple Crop Models and Tools

Science.gov (United States)

Boote, Kenneth J.; Porter, Cheryl H.; Hargreaves, John; Hoogenboom, Gerrit; Thornburn, Peter; Mutter, Carolyn

2015-01-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has the goal of using multiple crop models to evaluate climate impacts on agricultural production and food security in developed and developing countries. There are several major limitations that must be overcome to achieve this goal, including the need to train AgMIP regional research team (RRT) crop modelers to use models other than the ones they are currently familiar with, plus the need to harmonize and interconvert the disparate input file formats used for the various models. Two activities were followed to address these shortcomings among AgMIP RRTs to enable them to use multiple models to evaluate climate impacts on crop production and food security. We designed and conducted courses in which participants trained on two different sets of crop models, with emphasis on the model of least experience. In a second activity, the AgMIP IT group created templates for inputting data on soils, management, weather, and crops into AgMIP harmonized databases, and developed translation tools for converting the harmonized data into files that are ready for multiple crop model simulations. The strategies for creating and conducting the multi-model course and developing entry and translation tools are reviewed in this chapter.

Group-level self-definition and self-investment: a hierarchical (multicomponent) model of in-group identification.

Science.gov (United States)

Leach, Colin Wayne; van Zomeren, Martijn; Zebel, Sven; Vliek, Michael L W; Pennekamp, Sjoerd F; Doosje, Bertjan; Ouwerkerk, Jaap W; Spears, Russell

2008-07-01

Recent research shows individuals' identification with in-groups to be psychologically important and socially consequential. However, there is little agreement about how identification should be conceptualized or measured. On the basis of previous work, the authors identified 5 specific components of in-group identification and offered a hierarchical 2-dimensional model within which these components are organized. Studies 1 and 2 used confirmatory factor analysis to validate the proposed model of self-definition (individual self-stereotyping, in-group homogeneity) and self-investment (solidarity, satisfaction, and centrality) dimensions, across 3 different group identities. Studies 3 and 4 demonstrated the construct validity of the 5 components by examining their (concurrent) correlations with established measures of in-group identification. Studies 5-7 demonstrated the predictive and discriminant validity of the 5 components by examining their (prospective) prediction of individuals' orientation to, and emotions about, real intergroup relations. Together, these studies illustrate the conceptual and empirical value of a hierarchical multicomponent model of in-group identification.

Efficient Adoption and Assessment of Multiple Process Improvement Reference Models

Directory of Open Access Journals (Sweden)

Simona Jeners

2013-06-01

Full Text Available A variety of reference models such as CMMI, COBIT or ITIL support IT organizations to improve their processes. These process improvement reference models (IRMs cover different domains such as IT development, IT Services or IT Governance but also share some similarities. As there are organizations that address multiple domains and need to coordinate their processes in their improvement we present MoSaIC, an approach to support organizations to efficiently adopt and conform to multiple IRMs. Our solution realizes a semantic integration of IRMs based on common meta-models. The resulting IRM integration model enables organizations to efficiently implement and asses multiple IRMs and to benefit from synergy effects.

Boosting healthy heart employer-sponsored health dissemination efforts: identification and information-sharing intentions.

Science.gov (United States)

Stephens, Keri K; Pastorek, Angie; Crook, Brittani; Mackert, Michael; Donovan, Erin E; Shalev, Heidi

2015-01-01

Health information dissemination options have expanded to include workplaces and employer-sponsored efforts. This study focuses on a core relational concept found in workplaces, organizational identification-the feeling of belongingness-and the impact of partnering with employers and health clinics in health information dissemination. We use social-identity theory and multiple identification to test our predictions from a sample of working adults representing more than 100 different employers. We found that when people strongly identify with their employer, they have increased health behavioral intentions and they intend to talk about the health information with coworkers. The significant models explain more than 50% and 30% of the variance in these two outcomes. The experimental results examining single and multiple organizational sources revealed no differences on any outcomes. These findings offer a contribution to health information dissemination research by articulating how identification with an employer functions to affect behavioral intentions.

MITIE: Simultaneous RNA-Seq-based transcript identification and quantification in multiple samples.

Science.gov (United States)

Behr, Jonas; Kahles, André; Zhong, Yi; Sreedharan, Vipin T; Drewe, Philipp; Rätsch, Gunnar

2013-10-15

High-throughput sequencing of mRNA (RNA-Seq) has led to tremendous improvements in the detection of expressed genes and reconstruction of RNA transcripts. However, the extensive dynamic range of gene expression, technical limitations and biases, as well as the observed complexity of the transcriptional landscape, pose profound computational challenges for transcriptome reconstruction. We present the novel framework MITIE (Mixed Integer Transcript IdEntification) for simultaneous transcript reconstruction and quantification. We define a likelihood function based on the negative binomial distribution, use a regularization approach to select a few transcripts collectively explaining the observed read data and show how to find the optimal solution using Mixed Integer Programming. MITIE can (i) take advantage of known transcripts, (ii) reconstruct and quantify transcripts simultaneously in multiple samples, and (iii) resolve the location of multi-mapping reads. It is designed for genome- and assembly-based transcriptome reconstruction. We present an extensive study based on realistic simulated RNA-Seq data. When compared with state-of-the-art approaches, MITIE proves to be significantly more sensitive and overall more accurate. Moreover, MITIE yields substantial performance gains when used with multiple samples. We applied our system to 38 Drosophila melanogaster modENCODE RNA-Seq libraries and estimated the sensitivity of reconstructing omitted transcript annotations and the specificity with respect to annotated transcripts. Our results corroborate that a well-motivated objective paired with appropriate optimization techniques lead to significant improvements over the state-of-the-art in transcriptome reconstruction. MITIE is implemented in C++ and is available from http://bioweb.me/mitie under the GPL license.

Systems identification: a theoretical method applied to tracer kinetics in aquatic microcosms

International Nuclear Information System (INIS)

Halfon, E.; Georgia Univ., Athens

1974-01-01

A mathematical model of radionuclide kinetics in a laboratory microcosm was built and the transfer parameters estimated by multiple regression and system identification techniques. Insight into the functioning of the system was obtained from analysis of the model. Methods employed have allowed movements of radioisotopes not directly observable in the experimental systems to be distinguished. Results are generalized to whole ecosystems

Contribution to the modeling and the identification of haptic interfaces; Contribution a la modelisation et a l'identification des interfaces haptiques

Energy Technology Data Exchange (ETDEWEB)

Janot, A

2007-12-15

This thesis focuses on the modeling and the identification of haptic interfaces using cable drive. An haptic interface is a force feedback device, which enables its user to interact with a virtual world or a remote environment explored by a slave system. It aims at the matching between the forces and displacements given by the user and those applied to virtual world. Usually, haptic interfaces make use of a mechanical actuated structure whose distal link is equipped with a handle. When manipulating this handle to interact with explored world, the user feels the apparent mass, compliance and friction of the interface. This distortion introduced between the operator and the virtual world must be modeled and identified to enhance the design of the interface and develop appropriate control laws. The first approach has been to adapt the modeling and identification methods of rigid and localized flexibilities robots to haptic interfaces. The identification technique makes use of the inverse dynamic model and the linear least squares with the measurements of joint torques and positions. This approach is validated on a single degree of freedom and a three degree of freedom haptic devices. A new identification method needing only torque data is proposed. It is based on a closed loop simulation using the direct dynamic model. The optimal parameters minimize the 2 norms of the error between the actual torque and the simulated torque assuming the same control law and the same tracking trajectory. This non linear least squares problem dramatically is simplified using the inverse model to calculate the simulated torque. This method is validated on the single degree of freedom haptic device and the SCARA robot. (author)

System identification and the modeling of sailing yachts

Science.gov (United States)

Legursky, Katrina

This research represents an exploration of sailing yacht dynamics with full-scale sailing motion data, physics-based models, and system identification techniques. The goal is to provide a method of obtaining and validating suitable physics-based dynamics models for use in control system design on autonomous sailing platforms, which have the capacity to serve as mobile, long range, high endurance autonomous ocean sensing platforms. The primary contributions of this study to the state-of-the-art are the formulation of a five degree-of-freedom (DOF) linear multi-input multi-output (MIMO) state space model of sailing yacht dynamics, the process for identification of this model from full-scale data, a description of the maneuvers performed during on-water tests, and an analysis method to validate estimated models. The techniques and results described herein can be directly applied to and tested on existing autonomous sailing platforms. A full-scale experiment on a 23ft monohull sailing yacht is developed to collect motion data for physics-based model identification. Measurements include 3 axes of accelerations, velocities, angular rates, and attitude angles in addition to apparent wind speed and direction. The sailing yacht herein is treated as a dynamic system with two control inputs, the rudder angle, deltaR, and the mainsail angle, delta B, which are also measured. Over 20 hours of full scale sailing motion data is collected, representing three sail configurations corresponding to a range of wind speeds: the Full Main and Genoa (abbrev. Genoa) for lower wind speeds, the Full Main and Jib (abbrev. Jib) for mid-range wind speeds, and the Reefed Main and Jib (abbrev. Reef) for the highest wind speeds. The data also covers true wind angles from upwind through a beam reach. A physics-based non-linear model to describe sailing yacht motion is outlined, including descriptions of methods to model the aerodynamics and hydrodynamics of a sailing yacht in surge, sway, roll, and

The theory of multiple intelligences in the identification of high-ability students

Directory of Open Access Journals (Sweden)

Daniel Hernández-Torrano

2014-01-01

Full Text Available This study provides a framework to implement the theory of multiple intelligences (MI in the identification of high-ability students in secondary education. The internal structure of three scales to assess students' MI (students, parents and teachers' ratings was analyzed in a sample of 566 students nominated as gifted by their teachers. Participants aged 11 to 16 years (M = 14.85, SD = 1.08. The results indicated differentiated intellectual profiles depending on the informant estimating students' MI. This study provided evidence for two components that allow us to analyze the cognitive competence of high-ability students beyond the areas commonly assessed at school: an academic component composed by the linguistic, logical-mathematical, naturalistic, and visual-spatial intelligences; and a non-academic component statistically loaded by the bodily-kinesthetic, musical and social intelligences. Convergence of the two components in the three scales was evidenced; and correlations between these components and students' objective performance on a psychometric intelligence test were found to be low. Finally, the utility of the MI scales to identify high-ability students in secondary education is discussed.

Integrating multiple distribution models to guide conservation efforts of an endangered toad

Science.gov (United States)

Treglia, Michael L.; Fisher, Robert N.; Fitzgerald, Lee A.

2015-01-01

Species distribution models are used for numerous purposes such as predicting changes in species’ ranges and identifying biodiversity hotspots. Although implications of distribution models for conservation are often implicit, few studies use these tools explicitly to inform conservation efforts. Herein, we illustrate how multiple distribution models developed using distinct sets of environmental variables can be integrated to aid in identification sites for use in conservation. We focus on the endangered arroyo toad (Anaxyrus californicus), which relies on open, sandy streams and surrounding floodplains in southern California, USA, and northern Baja California, Mexico. Declines of the species are largely attributed to habitat degradation associated with vegetation encroachment, invasive predators, and altered hydrologic regimes. We had three main goals: 1) develop a model of potential habitat for arroyo toads, based on long-term environmental variables and all available locality data; 2) develop a model of the species’ current habitat by incorporating recent remotely-sensed variables and only using recent locality data; and 3) integrate results of both models to identify sites that may be employed in conservation efforts. We used a machine learning technique, Random Forests, to develop the models, focused on riparian zones in southern California. We identified 14.37% and 10.50% of our study area as potential and current habitat for the arroyo toad, respectively. Generally, inclusion of remotely-sensed variables reduced modeled suitability of sites, thus many areas modeled as potential habitat were not modeled as current habitat. We propose such sites could be made suitable for arroyo toads through active management, increasing current habitat by up to 67.02%. Our general approach can be employed to guide conservation efforts of virtually any species with sufficient data necessary to develop appropriate distribution models.

Parameter identification of a BWR nuclear power plant model for use in optimal control

International Nuclear Information System (INIS)

Volf, K.

1976-02-01

The problem being considered is the modeling of a nuclear power plant for the development of an optimal control system of the plant. Current system identification concepts, combining input/output information with a-priori structural information are employed. Two of the known parameter identification methods i.e., a least squares method and a maximum likelihood technique, are studied as ways of parameter identification from measurement data. A low order state variable stochastic model of a BWR nuclear power plant is presented as an application of this approach. The model consists of a deterministic and a noise part. The deterministic part is formed by simplified modeling of the major plant dynamic phenomena. The moise part models the effects of input random disturbances to the deterministic part and additive measurement noise. Most of the model parameters are assumed to be initially unknown. They are identified using measurement data records. A detailed high order digital computer simulation is used to simulate plant dynamic behaviour since it is not conceivable for experimentation of this kind to be performed on the real nuclear power plant. The identification task consists in adapting the performance of the simple model to the data acquired from this plant simulation ensuring the applicability of the techniques to measurement data acquired directly from the plant. (orig.) [de

Identification of Influential Points in a Linear Regression Model

Directory of Open Access Journals (Sweden)

Jan Grosz

2011-03-01

Full Text Available The article deals with the detection and identification of influential points in the linear regression model. Three methods of detection of outliers and leverage points are described. These procedures can also be used for one-sample (independentdatasets. This paper briefly describes theoretical aspects of several robust methods as well. Robust statistics is a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. A simulation model of the simple linear regression is presented.

On multiple crack identification by ultrasonic scanning

Science.gov (United States)

Brigante, M.; Sumbatyan, M. A.

2018-04-01

The present work develops an approach which reduces operator equations arising in the engineering problems to the problem of minimizing the discrepancy functional. For this minimization, an algorithm of random global search is proposed, which is allied to some genetic algorithms. The efficiency of the method is demonstrated by the solving problem of simultaneous identification of several linear cracks forming an array in an elastic medium by using the circular Ultrasonic scanning.

Identification of Determinants of Sports Skill Level in Badminton Players Using the Multiple Regression Model

Directory of Open Access Journals (Sweden)

Jaworski Janusz

2016-03-01

Full Text Available Purpose. The aim of the study was to evaluate somatic and functional determinants of sports skill level in badminton players at three consecutive stages of training. Methods. The study examined 96 badminton players aged 11 to 19 years. The scope of the study included somatic characteristics, physical abilities and neurosensory abilities. Thirty nine variables were analysed in each athlete. Coefficients of multiple determination were used to evaluate the effect of structural and functional parameters on sports skill level in badminton players. Results. In the group of younger cadets, quality and effectiveness of playing were mostly determined by the level of physical abilities. In the group of cadets, the most important determinants were physical abilities, followed by somatic characteristics. In this group, coordination abilities were also important. In juniors, the most pronounced was a set of the variables that reflect physical abilities. Conclusions. Models of determination of sports skill level are most noticeable in the group of cadets. In all three groups of badminton players, the dominant effect on the quality of playing is due to a set of the variables that determine physical abilities.

Identification model of gifted students in secondary education

Directory of Open Access Journals (Sweden)

Carmen Ferrándiz

2010-04-01

Full Text Available The aim of this article is to describe the identification and assessment procedure to identify high ability secondary school students in the Spanish region of Murcia. In the screening process questionaires addressed to parents, teachers, and pupils nad based on the Multiple Intelligences Theory were used. In the identification process two other instruments were used: a the Differential Aptitude Test (DAT aimed to assess the following areas: reasoning, verbal abilities, numerical and abstract reasoning, spatial aptitude, mechanical reasoning, attention and perceptive aptitudes, and b the TTCT (Torrance Test of Creative Thinking in order to assess the main abilities of creativity (fluency, flexibility, originality and elaboration. These two assessment tools will allow us to distinguish gifted from talented (Castelló and Batlle, 1998. In a third stage, the socio-emotional characteristics of the identified students are analysed using: c the BFQ-NA whose aim is to assess the personality dimensions (openness, conscientiousness, extraversion; agreeableness and neuroticism, and d emotional intelligence questionnaires (EQ-i:YV and EQ-i:YV-O Barón and Parker, 2000. 565 took part in this research. The students were aged 11-18 (M= 14.6 and SD= 1.08 and attended high schools of Compulsory Secondary Education (ESO of the Murcia Region. The results showed different profiles of gifted and talented stduents. The cognitive-emotional complexity of these exceptional students is discussed.

Online Semiparametric Identification of Lithium-Ion Batteries Using the Wavelet-Based Partially Linear Battery Model

Directory of Open Access Journals (Sweden)

Caiping Zhang

2013-05-01

Full Text Available Battery model identification is very important for reliable battery management as well as for battery system design process. The common problem in identifying battery models is how to determine the most appropriate mathematical model structure and parameterized coefficients based on the measured terminal voltage and current. This paper proposes a novel semiparametric approach using the wavelet-based partially linear battery model (PLBM and a recursive penalized wavelet estimator for online battery model identification. Three main contributions are presented. First, the semiparametric PLBM is proposed to simulate the battery dynamics. Compared with conventional electrical models of a battery, the proposed PLBM is equipped with a semiparametric partially linear structure, which includes a parametric part (involving the linear equivalent circuit parameters and a nonparametric part [involving the open-circuit voltage (OCV]. Thus, even with little prior knowledge about the OCV, the PLBM can be identified using a semiparametric identification framework. Second, we model the nonparametric part of the PLBM using the truncated wavelet multiresolution analysis (MRA expansion, which leads to a parsimonious model structure that is highly desirable for model identification; using this model, the PLBM could be represented in a linear-in-parameter manner. Finally, to exploit the sparsity of the wavelet MRA representation and allow for online implementation, a penalized wavelet estimator that uses a modified online cyclic coordinate descent algorithm is proposed to identify the PLBM in a recursive fashion. The simulation and experimental results demonstrate that the proposed PLBM with the corresponding identification algorithm can accurately simulate the dynamic behavior of a lithium-ion battery in the Federal Urban Driving Schedule tests.

A genetic ensemble approach for gene-gene interaction identification

Directory of Open Access Journals (Sweden)

Ho Joshua WK

2010-10-01

Full Text Available Abstract Background It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging. Methods In this paper, we propose a new approach for identifying such gene-gene and gene-environment interactions underlying complex diseases. This is a hybrid algorithm and it combines genetic algorithm (GA and an ensemble of classifiers (called genetic ensemble. Using this approach, the original problem of SNP interaction identification is converted into a data mining problem of combinatorial feature selection. By collecting various single nucleotide polymorphisms (SNP subsets as well as environmental factors generated in multiple GA runs, patterns of gene-gene and gene-environment interactions can be extracted using a simple combinatorial ranking method. Also considered in this study is the idea of combining identification results obtained from multiple algorithms. A novel formula based on pairwise double fault is designed to quantify the degree of complementarity. Conclusions Our simulation study demonstrates that the proposed genetic ensemble algorithm has comparable identification power to Multifactor Dimensionality Reduction (MDR and is slightly better than Polymorphism Interaction Analysis (PIA, which are the two most popular methods for gene-gene interaction identification. More importantly, the identification results generated by using our genetic ensemble algorithm are highly complementary to those obtained by PIA and MDR. Experimental results from our simulation studies and real world data application also confirm the effectiveness of the proposed genetic ensemble algorithm, as well as the potential benefits of

Systematic approach for the identification of process reference models

CSIR Research Space (South Africa)

Van Der Merwe, A

2009-02-01

Full Text Available and make it economically viable. In the identification of core elements within the process reference model, the focus is often on the end-product and not on the procedure used to identify the elements. As often proved in development of projects, there is a...

Chaotic System Identification Based on a Fuzzy Wiener Model with Particle Swarm Optimization

International Nuclear Information System (INIS)

Yong, Li; Ying-Gan, Tang

2010-01-01

A fuzzy Wiener model is proposed to identify chaotic systems. The proposed fuzzy Wiener model consists of two parts, one is a linear dynamic subsystem and the other is a static nonlinear part, which is represented by the Takagi–Sugeno fuzzy model. Identification of chaotic systems is converted to find optimal parameters of the fuzzy Wiener model by minimizing the state error between the original chaotic system and the fuzzy Wiener model. Particle swarm optimization algorithm, a global optimizer, is used to search the optimal parameter of the fuzzy Wiener model. The proposed method can identify the parameters of the linear part and nonlinear part simultaneously. Numerical simulations for Henón and Lozi chaotic system identification show the effectiveness of the proposed method

A test for the parameters of multiple linear regression models ...

African Journals Online (AJOL)

A test for the parameters of multiple linear regression models is developed for conducting tests simultaneously on all the parameters of multiple linear regression models. The test is robust relative to the assumptions of homogeneity of variances and absence of serial correlation of the classical F-test. Under certain null and ...

Parametric modeling for damped sinusoids from multiple channels

DEFF Research Database (Denmark)

Zhou, Zhenhua; So, Hing Cheung; Christensen, Mads Græsbøll

2013-01-01

frequencies and damping factors are then computed with the multi-channel weighted linear prediction method. The estimated sinusoidal poles are then matched to each channel according to the extreme value theory of distribution of random fields. Simulations are performed to show the performance advantages......The problem of parametric modeling for noisy damped sinusoidal signals from multiple channels is addressed. Utilizing the shift invariance property of the signal subspace, the number of distinct sinusoidal poles in the multiple channels is first determined. With the estimated number, the distinct...... of the proposed multi-channel sinusoidal modeling methodology compared with existing methods....

The influence of talker and foreign-accent variability on spoken word identification.

Science.gov (United States)

Bent, Tessa; Holt, Rachael Frush

2013-03-01

In spoken word identification and memory tasks, stimulus variability from numerous sources impairs performance. In the current study, the influence of foreign-accent variability on spoken word identification was evaluated in two experiments. Experiment 1 used a between-subjects design to test word identification in noise in single-talker and two multiple-talker conditions: multiple talkers with the same accent and multiple talkers with different accents. Identification performance was highest in the single-talker condition, but there was no difference between the single-accent and multiple-accent conditions. Experiment 2 further explored word recognition for multiple talkers in single-accent versus multiple-accent conditions using a mixed design. A detriment to word recognition was observed in the multiple-accent condition compared to the single-accent condition, but the effect differed across the language backgrounds tested. These results demonstrate that the processing of foreign-accent variation may influence word recognition in ways similar to other sources of variability (e.g., speaking rate or style) in that the inclusion of multiple foreign accents can result in a small but significant performance decrement beyond the multiple-talker effect.

A model for diagnosing and explaining multiple disorders.

Science.gov (United States)

Jamieson, P W

1991-08-01

The ability to diagnose multiple interacting disorders and explain them in a coherent causal framework has only partially been achieved in medical expert systems. This paper proposes a causal model for diagnosing and explaining multiple disorders whose key elements are: physician-directed hypotheses generation, object-oriented knowledge representation, and novel explanation heuristics. The heuristics modify and link the explanations to make the physician aware of diagnostic complexities. A computer program incorporating the model currently is in use for diagnosing peripheral nerve and muscle disorders. The program successfully diagnoses and explains interactions between diseases in terms of underlying pathophysiologic concepts. The model offers a new architecture for medical domains where reasoning from first principles is difficult but explanation of disease interactions is crucial for the system's operation.

Grey-box state-space identification of nonlinear mechanical vibrations

Science.gov (United States)

Noël, J. P.; Schoukens, J.

2018-05-01

The present paper deals with the identification of nonlinear mechanical vibrations. A grey-box, or semi-physical, nonlinear state-space representation is introduced, expressing the nonlinear basis functions using a limited number of measured output variables. This representation assumes that the observed nonlinearities are localised in physical space, which is a generic case in mechanics. A two-step identification procedure is derived for the grey-box model parameters, integrating nonlinear subspace initialisation and weighted least-squares optimisation. The complete procedure is applied to an electrical circuit mimicking the behaviour of a single-input, single-output (SISO) nonlinear mechanical system and to a single-input, multiple-output (SIMO) geometrically nonlinear beam structure.

Reflector modelization for neutronic diffusion and parameters identification

International Nuclear Information System (INIS)

Argaud, J.P.

1993-04-01

Physical parameters of neutronic diffusion equations can be adjusted to decrease calculations-measurements errors. The reflector being always difficult to modelize, we choose to elaborate a new reflector model and to use the parameters of this model as adjustment coefficients in the identification procedure. Using theoretical results, and also the physical behaviour of neutronic flux solutions, the reflector model consists then in its replacement by boundary conditions for the diffusion equations on the core only. This theoretical result of non-local operator relations leads then to some discrete approximations by taking into account the multiscaled behaviour, on the core-reflector interface, of neutronic diffusion solutions. The resulting model of this approach is then compared with previous reflector modelizations, and first results indicate that this new model gives the same representation of reflector for the core than previous. (author). 12 refs

System Identification Based Proxy Model of a Reservoir under Water Injection

Directory of Open Access Journals (Sweden)

Berihun M. Negash

2017-01-01

Full Text Available Simulation of numerical reservoir models with thousands and millions of grid blocks may consume a significant amount of time and effort, even when high performance processors are used. In cases where the simulation runs are required for sensitivity analysis, dynamic control, and optimization, the act needs to be repeated several times by continuously changing parameters. This makes it even more time-consuming. Currently, proxy models that are based on response surface are being used to lessen the time required for running simulations during sensitivity analysis and optimization. Proxy models are lighter mathematical models that run faster and perform in place of heavier models that require large computations. Nevertheless, to acquire data for modeling and validation and develop the proxy model itself, hundreds of simulation runs are required. In this paper, a system identification based proxy model that requires only a single simulation run and a properly designed excitation signal was proposed and evaluated using a benchmark case study. The results show that, with proper design of excitation signal and proper selection of model structure, system identification based proxy models are found to be practical and efficient alternatives for mimicking the performance of numerical reservoir models. The resulting proxy models have potential applications for dynamic well control and optimization.

Identification techniques for phenomenological models of hysteresis based on the conjugate gradient method

International Nuclear Information System (INIS)

Andrei, Petru; Oniciuc, Liviu; Stancu, Alexandru; Stoleriu, Laurentiu

2007-01-01

An identification technique for the parameters of phenomenological models of hysteresis is presented. The basic idea of our technique is to set up a system of equations for the parameters of the model as a function of known quantities on the major or minor hysteresis loops (e.g. coercive force, susceptibilities at various points, remanence), or other magnetization curves. This system of equations can be either over or underspecified and is solved by using the conjugate gradient method. Numerical results related to the identification of parameters in the Energetic, Jiles-Atherton, and Preisach models are presented

One for All and All for One: Using Multiple Identification Theory Simulations to Build Cooperative Attitudes and Behaviors in a Middle Eastern Conflict Scenario

Science.gov (United States)

Williams, Robert Howard; Williams, Alexander Jonathan

2010-01-01

The authors previously developed multiple identification theory (MIT) as a system of simulation game design intended to promote attitude change. The present study further tests MIT's effectiveness. The authors created a game (CULTURE & CREED) via MIT as a complex simulation of Middle Eastern conflict resolution, designed to change attitudes…

Contribution to the modeling and the identification of haptic interfaces; Contribution a la modelisation et a l'identification des interfaces haptiques

Energy Technology Data Exchange (ETDEWEB)

Janot, A

2007-12-15

This thesis focuses on the modeling and the identification of haptic interfaces using cable drive. An haptic interface is a force feedback device, which enables its user to interact with a virtual world or a remote environment explored by a slave system. It aims at the matching between the forces and displacements given by the user and those applied to virtual world. Usually, haptic interfaces make use of a mechanical actuated structure whose distal link is equipped with a handle. When manipulating this handle to interact with explored world, the user feels the apparent mass, compliance and friction of the interface. This distortion introduced between the operator and the virtual world must be modeled and identified to enhance the design of the interface and develop appropriate control laws. The first approach has been to adapt the modeling and identification methods of rigid and localized flexibilities robots to haptic interfaces. The identification technique makes use of the inverse dynamic model and the linear least squares with the measurements of joint torques and positions. This approach is validated on a single degree of freedom and a three degree of freedom haptic devices. A new identification method needing only torque data is proposed. It is based on a closed loop simulation using the direct dynamic model. The optimal parameters minimize the 2 norms of the error between the actual torque and the simulated torque assuming the same control law and the same tracking trajectory. This non linear least squares problem dramatically is simplified using the inverse model to calculate the simulated torque. This method is validated on the single degree of freedom haptic device and the SCARA robot. (author)

Numerical study on identification of transfer functions in a feedback system and model reduction

International Nuclear Information System (INIS)

Kishida, Kuniharu

1997-01-01

Identification of transfer function matrices in a feedback system is discussed by using the singular value decomposition of Hankel matrix from the viewpoint of inverse problems. A method of model reduction is considered, and selection criteria are proposed for identification of them. Transformation formula between open loop and closed loop transfer function matrices are determined from the feedback loop structure, and they are needed for identification of open loop transfer function matrices under such a condition where the feedback system is in a minimum phase. Though the identifiability of open loop transfer function matrices can be examined in the framework of innovation model equivalent to the feedback system, there are pole-zero cancellations in the identification of them. The method to reduce a model order of an open loop transfer function is discussed by using the singular value decomposition of a gramian given by the open loop transfer function with higher degree. To check reliability of the present algorithm, a simulation study is performed for an example. (author)

A stochastic global identification framework for aerospace structures operating under varying flight states

Science.gov (United States)

Kopsaftopoulos, Fotis; Nardari, Raphael; Li, Yu-Hung; Chang, Fu-Kuo

2018-01-01

In this work, a novel data-based stochastic "global" identification framework is introduced for aerospace structures operating under varying flight states and uncertainty. In this context, the term "global" refers to the identification of a model that is capable of representing the structure under any admissible flight state based on data recorded from a sample of these states. The proposed framework is based on stochastic time-series models for representing the structural dynamics and aeroelastic response under multiple flight states, with each state characterized by several variables, such as the airspeed, angle of attack, altitude and temperature, forming a flight state vector. The method's cornerstone lies in the new class of Vector-dependent Functionally Pooled (VFP) models which allow the explicit analytical inclusion of the flight state vector into the model parameters and, hence, system dynamics. This is achieved via the use of functional data pooling techniques for optimally treating - as a single entity - the data records corresponding to the various flight states. In this proof-of-concept study the flight state vector is defined by two variables, namely the airspeed and angle of attack of the vehicle. The experimental evaluation and assessment is based on a prototype bio-inspired self-sensing composite wing that is subjected to a series of wind tunnel experiments under multiple flight states. Distributed micro-sensors in the form of stretchable sensor networks are embedded in the composite layup of the wing in order to provide the sensing capabilities. Experimental data collected from piezoelectric sensors are employed for the identification of a stochastic global VFP model via appropriate parameter estimation and model structure selection methods. The estimated VFP model parameters constitute two-dimensional functions of the flight state vector defined by the airspeed and angle of attack. The identified model is able to successfully represent the wing

Parameter Identification of Ship Maneuvering Models Using Recursive Least Square Method Based on Support Vector Machines

Directory of Open Access Journals (Sweden)

Man Zhu

2017-03-01

Full Text Available Determination of ship maneuvering models is a tough task of ship maneuverability prediction. Among several prime approaches of estimating ship maneuvering models, system identification combined with the full-scale or free- running model test is preferred. In this contribution, real-time system identification programs using recursive identification method, such as the recursive least square method (RLS, are exerted for on-line identification of ship maneuvering models. However, this method seriously depends on the objects of study and initial values of identified parameters. To overcome this, an intelligent technology, i.e., support vector machines (SVM, is firstly used to estimate initial values of the identified parameters with finite samples. As real measured motion data of the Mariner class ship always involve noise from sensors and external disturbances, the zigzag simulation test data include a substantial quantity of Gaussian white noise. Wavelet method and empirical mode decomposition (EMD are used to filter the data corrupted by noise, respectively. The choice of the sample number for SVM to decide initial values of identified parameters is extensively discussed and analyzed. With de-noised motion data as input-output training samples, parameters of ship maneuvering models are estimated using RLS and SVM-RLS, respectively. The comparison between identification results and true values of parameters demonstrates that both the identified ship maneuvering models from RLS and SVM-RLS have reasonable agreements with simulated motions of the ship, and the increment of the sample for SVM positively affects the identification results. Furthermore, SVM-RLS using data de-noised by EMD shows the highest accuracy and best convergence.

Application of Multiple Evaluation Models in Brazil

Directory of Open Access Journals (Sweden)

Rafael Victal Saliba

2008-07-01

Full Text Available Based on two different samples, this article tests the performance of a number of Value Drivers commonly used for evaluating companies by finance practitioners, through simple regression models of cross-section type which estimate the parameters associated to each Value Driver, denominated Market Multiples. We are able to diagnose the behavior of several multiples in the period 1994-2004, with an outlook also on the particularities of the economic activities performed by the sample companies (and their impacts on the performance through a subsequent analysis with segregation of companies in the sample by sectors. Extrapolating simple multiples evaluation standards from analysts of the main financial institutions in Brazil, we find that adjusting the ratio formulation to allow for an intercept does not provide satisfactory results in terms of pricing errors reduction. Results found, in spite of evidencing certain relative and absolute superiority among the multiples, may not be generically representative, given samples limitation.

Identification of Chemical Reactor Plant’s Mathematical Model

OpenAIRE

Pyakullya, Boris Ivanovich; Kladiev, Sergey Nikolaevich

2015-01-01

This work presents a solution of the identification problem of chemical reactor plant’s mathematical model. The main goal is to obtain a mathematical description of a chemical reactor plant from experimental data, which based on plant’s time response measurements. This data consists sequence of measurements for water jacket temperature and information about control input signal, which is used to govern plant’s behavior.

THE RELIABILITY OF IDENTIFICATION EVIDENCE WITH MULTIPLE LINEUPS

Directory of Open Access Journals (Sweden)

Nick J. Broers

2013-01-01

Full Text Available This study aimed to establish the diagnostic value of multiple lineup decisions made for portrait, body, and profile lineups, including multiple target/suspect choices, rejections, foil choices, and don’t know answers. A total of 192 participants identified a thief and a victim of theft from independent simultaneous target-present or target-absent 6-person portrait, body and profile lineups after watching one of two stimulus films. As hypothesized, multiple target/suspect choices had incriminating value whereas multiple rejections, foil choices, and don’t know answers had mostly exonerating value. For suspect choices, the combination of all three lineup modes consistently elicited high diagnosticities across targets. Combinations of non-suspect choices (rejections, foil choices, or don’t know answers were less successful and the different combinations showed less consistency in terms of diagnosticity. It was concluded that the use of multiple lineups, such as different facial poses and different aspects of a person should be particularly beneficial in three situations: if a witness only saw the perpetrator from a pose different than the frontal view normally used for lineups; if one or more witnesses saw the perpetrator from more than one perspective; and if different witnesses saw the perpetrator from different perspectives.

Tests of Multiplicative Models in Psychology: A Case Study Using the Unified Theory of Implicit Attitudes, Stereotypes, Self-Esteem, and Self-Concept

Science.gov (United States)

Blanton, Hart; Jaccard, James

2006-01-01

Theories that posit multiplicative relationships between variables are common in psychology. A. G. Greenwald et al. recently presented a theory that explicated relationships between group identification, group attitudes, and self-esteem. Their theory posits a multiplicative relationship between concepts when predicting a criterion variable.…

Deterministic integer multiple firing depending on initial state in Wang model

Energy Technology Data Exchange (ETDEWEB)

Xie Yong [Institute of Nonlinear Dynamics, MSSV, Department of Engineering Mechanics, Xi' an Jiaotong University, Xi' an 710049 (China)]. E-mail: yxie@mail.xjtu.edu.cn; Xu Jianxue [Institute of Nonlinear Dynamics, MSSV, Department of Engineering Mechanics, Xi' an Jiaotong University, Xi' an 710049 (China); Jiang Jun [Institute of Nonlinear Dynamics, MSSV, Department of Engineering Mechanics, Xi' an Jiaotong University, Xi' an 710049 (China)

2006-12-15

We investigate numerically dynamical behaviour of the Wang model, which describes the rhythmic activities of thalamic relay neurons. The model neuron exhibits Type I excitability from a global view, but Type II excitability from a local view. There exists a narrow range of bistability, in which a subthreshold oscillation and a suprathreshold firing behaviour coexist. A special firing pattern, integer multiple firing can be found in the certain part of the bistable range. The characteristic feature of such firing pattern is that the histogram of interspike intervals has a multipeaked structure, and the peaks are located at about integer multiples of a basic interspike interval. Since the Wang model is noise-free, the integer multiple firing is a deterministic firing pattern. The existence of bistability leads to the deterministic integer multiple firing depending on the initial state of the model neuron, i.e., the initial values of the state variables.

Deterministic integer multiple firing depending on initial state in Wang model

International Nuclear Information System (INIS)

Xie Yong; Xu Jianxue; Jiang Jun

2006-01-01

We investigate numerically dynamical behaviour of the Wang model, which describes the rhythmic activities of thalamic relay neurons. The model neuron exhibits Type I excitability from a global view, but Type II excitability from a local view. There exists a narrow range of bistability, in which a subthreshold oscillation and a suprathreshold firing behaviour coexist. A special firing pattern, integer multiple firing can be found in the certain part of the bistable range. The characteristic feature of such firing pattern is that the histogram of interspike intervals has a multipeaked structure, and the peaks are located at about integer multiples of a basic interspike interval. Since the Wang model is noise-free, the integer multiple firing is a deterministic firing pattern. The existence of bistability leads to the deterministic integer multiple firing depending on the initial state of the model neuron, i.e., the initial values of the state variables

Multiple regression and beyond an introduction to multiple regression and structural equation modeling

CERN Document Server

Keith, Timothy Z

2014-01-01

Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. Covers both MR and SEM, while explaining their relevance to one another Also includes path analysis, confirmatory factor analysis, and latent growth modeling Figures and tables throughout provide examples and illustrate key concepts and techniques For additional resources, please visit: http://tzkeith.com/.

System Identification for Nonlinear FOPDT Model with Input-Dependent Dead-Time

DEFF Research Database (Denmark)

Sun, Zhen; Yang, Zhenyu

2011-01-01

An on-line iterative method of system identification for a kind of nonlinear FOPDT system is proposed in the paper. The considered nonlinear FOPDT model is an extension of the standard FOPDT model by means that its dead time depends on the input signal and the other parameters are time dependent....

Customizable PCR-microplate array for differential identification of multiple pathogens.

Science.gov (United States)

Woubit, Abdela; Yehualaeshet, Teshome; Roberts, Sherrelle; Graham, Martha; Kim, Moonil; Samuel, Temesgen

2013-11-01

Customizable PCR-microplate arrays were developed for the rapid identification of Salmonella Typhimurium, Salmonella Saintpaul, Salmonella Typhi, Shigella dysenteriae, Escherichia coli O157:H7, Francisella tularensis subsp. tularensis, Francisella tularensis subsp. novicida, Vibrio cholerae, Vibrio parahaemolyticus, Yersinia pestis, and Yersinia pseudotuberculosis. Previously, we identified highly specific primers targeting each of these pathogens. Here, we report the development of customizable PCR-microplate arrays for simultaneous identification of the pathogens using the primers identified. A mixed aliquot of genomic DNA from 38 strains was used to validate three PCR-microplate array formats. Identical PCR conditions were used to run all the samples on the three formats. Specific amplifications were obtained on all three custom plates. In preliminary tests performed to evaluate the sensitivity of these assays in samples inoculated in the laboratory with Salmonella Typhimurium, amplifications were obtained from 1 g of beef hot dog inoculated at as low as 9 CFU/ml or from milk inoculated at as low as 78 CFU/ml. Such microplate arrays could be valuable tools for initial identification or secondary confirmation of contamination by these pathogens.

New Hybrid Variational Recovery Model for Blurred Images with Multiplicative Noise

DEFF Research Database (Denmark)

Dong, Yiqiu; Zeng, Tieyong

2013-01-01

A new hybrid variational model for recovering blurred images in the presence of multiplicative noise is proposed. Inspired by previous work on multiplicative noise removal, an I-divergence technique is used to build a strictly convex model under a condition that ensures the uniqueness...

A structured patient identification model for medication therapy management services in a community pharmacy.

Science.gov (United States)

Pagano, Gina M; Groves, Brigid K; Kuhn, Catherine H; Porter, Kyle; Mehta, Bella H

To describe the development and implementation of a structured patient identification model for medication therapy management (MTM) services within traditional dispensing activities of a community pharmacy to facilitate pharmacist-provided completion of MTM services. A daily clinical opportunity report was developed as a structured model to identify MTM opportunities daily for all MTM-eligible patients expecting to pick up a prescription. Pharmacy staff was trained and the standardized model was implemented at study sites. One hundred nineteen grocery store-based community pharmacies throughout Ohio, West Virginia, and Michigan. A structured patient identification model in a community pharmacy consists of reviewing a clinical opportunity report, identifying interventions for MTM-eligible patients, and possibly collaborating with an interdisciplinary team. This model allows pharmacists to increase MTM cases performed by providing a structured process for identifying MTM-eligible patients and completing MTM services. The development and implementation of a structured patient identification model in the community pharmacy was completed and consists of pharmacists reviewing a clinical opportunity report to identify MTM opportunities and perform clinical interventions for patients. In a 3-month pre- and post-implementation comparison, there was a 49% increase in the number of MTM services provided by pharmacists (P < 0.001). A structured patient identification model in the community pharmacy was associated with an increase in the amount of MTM services provided by pharmacists. This method could be a useful tool at a variety of community pharmacies to solve challenges associated with MTM completion. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Identification for Control

DEFF Research Database (Denmark)

Tøffner-Clausen, S.

1995-01-01

Identification of model error bounds for robust control design has recently achieved much attention.......Identification of model error bounds for robust control design has recently achieved much attention....

Identification of milling and baking quality QTL in multiple soft wheat mapping populations.

Science.gov (United States)

Cabrera, Antonio; Guttieri, Mary; Smith, Nathan; Souza, Edward; Sturbaum, Anne; Hua, Duc; Griffey, Carl; Barnett, Marla; Murphy, Paul; Ohm, Herb; Uphaus, Jim; Sorrells, Mark; Heffner, Elliot; Brown-Guedira, Gina; Van Sanford, David; Sneller, Clay

2015-11-01

Two mapping approaches were use to identify and validate milling and baking quality QTL in soft wheat. Two LG were consistently found important for multiple traits and we recommend the use marker-assisted selection on specific markers reported here. Wheat-derived food products require a range of characteristics. Identification and understanding of the genetic components controlling end-use quality of wheat is important for crop improvement. We assessed the underlying genetics controlling specific milling and baking quality parameters of soft wheat including flour yield, softness equivalent, flour protein, sucrose, sodium carbonate, water absorption and lactic acid, solvent retention capacities in a diversity panel and five bi-parental mapping populations. The populations were genotyped with SSR and DArT markers, with markers specific for the 1BL.1RS translocation and sucrose synthase gene. Association analysis and composite interval mapping were performed to identify quantitative trait loci (QTL). High heritability was observed for each of the traits evaluated, trait correlations were consistent over populations, and transgressive segregants were common in all bi-parental populations. A total of 26 regions were identified as potential QTL in the diversity panel and 74 QTL were identified across all five bi-parental mapping populations. Collinearity of QTL from chromosomes 1B and 2B was observed across mapping populations and was consistent with results from the association analysis in the diversity panel. Multiple regression analysis showed the importance of the two 1B and 2B regions and marker-assisted selection for the favorable alleles at these regions should improve quality.

Identification of Chemical Reactor Plant’s Mathematical Model

Directory of Open Access Journals (Sweden)

Pyakillya Boris

2015-01-01

Full Text Available This work presents a solution of the identification problem of chemical reactor plant’s mathematical model. The main goal is to obtain a mathematical description of a chemical reactor plant from experimental data, which based on plant’s time response measurements. This data consists sequence of measurements for water jacket temperature and information about control input signal, which is used to govern plant’s behavior.

Accelerating parameter identification of proton exchange membrane fuel cell model with ranking-based differential evolution

International Nuclear Information System (INIS)

Gong, Wenyin; Cai, Zhihua

2013-01-01

Parameter identification of PEM (proton exchange membrane) fuel cell model is a very active area of research. Generally, it can be treated as a numerical optimization problem with complex nonlinear and multi-variable features. DE (differential evolution), which has been successfully used in various fields, is a simple yet efficient evolutionary algorithm for global numerical optimization. In this paper, with the objective of accelerating the process of parameter identification of PEM fuel cell models and reducing the necessary computational efforts, we firstly present a generic and simple ranking-based mutation operator for the DE algorithm. Then, the ranking-based mutation operator is incorporated into five highly-competitive DE variants to solve the PEM fuel cell model parameter identification problems. The main contributions of this work are the proposed ranking-based DE variants and their application to the parameter identification problems of PEM fuel cell models. Experiments have been conducted by using both the simulated voltage–current data and the data obtained from the literature to validate the performance of our approach. The results indicate that the ranking-based DE methods provide better results with respect to the solution quality, the convergence rate, and the success rate compared with their corresponding original DE methods. In addition, the voltage–current characteristics obtained by our approach are in good agreement with the original voltage–current curves in all cases. - Highlights: • A simple and generic ranking-based mutation operator is presented in this paper. • Several DE (differential evolution) variants are used to solve the parameter identification of PEMFC (proton exchange membrane fuel cells) model. • Results show that our method accelerates the process of parameter identification. • The V–I characteristics are in very good agreement with experimental data

Multiplicity Analysis during Photon Interrogation of Fissionable Material

International Nuclear Information System (INIS)

Clarke, Shaun D.; Pozzi, Sara A.; Padovani, Enrico; Downar, Thomas J.

2007-01-01

Simulation of multiplicity distributions with the Monte Carlo method is difficult because each history is treated individually. In order to accurately model the multiplicity distribution, the intensity and time width of the interrogation pulse must be incorporated into the calculation. This behavior dictates how many photons arrive at the target essentially simultaneously. In order to model the pulse width correctly, a Monte Carlo code system consisting of modified versions of the codes MCNPX and MCNP-PoliMi has been developed in conjunction with a post-processing algorithm to operate on the MCNP-PoliMi output file. The purpose of this subroutine is to assemble the interactions into groups corresponding to the number of interactions which would occur during a given pulse. The resulting multiplicity distributions appear more realistic and capture the higher-order multiplets which are a product of multiple reactions occurring during a single accelerator pulse. Plans are underway to gather relevant experimental data to verify and validate the methodology developed and presented here. This capability will enable the simulation of a large number of materials and detector geometries. Analysis of this information will determine the feasibility of using multiplicity distributions as an identification tool for special nuclear material.

Mathematical Modeling of Loop Heat Pipes with Multiple Capillary Pumps and Multiple Condensers. Part 1; Stead State Stimulations

Science.gov (United States)

Hoang, Triem T.; OConnell, Tamara; Ku, Jentung

2004-01-01

Loop Heat Pipes (LHPs) have proven themselves as reliable and robust heat transport devices for spacecraft thermal control systems. So far, the LHPs in earth-orbit satellites perform very well as expected. Conventional LHPs usually consist of a single capillary pump for heat acquisition and a single condenser for heat rejection. Multiple pump/multiple condenser LHPs have shown to function very well in ground testing. Nevertheless, the test results of a dual pump/condenser LHP also revealed that the dual LHP behaved in a complicated manner due to the interaction between the pumps and condensers. Thus it is redundant to say that more research is needed before they are ready for 0-g deployment. One research area that perhaps compels immediate attention is the analytical modeling of LHPs, particularly the transient phenomena. Modeling a single pump/single condenser LHP is difficult enough. Only a handful of computer codes are available for both steady state and transient simulations of conventional LHPs. No previous effort was made to develop an analytical model (or even a complete theory) to predict the operational behavior of the multiple pump/multiple condenser LHP systems. The current research project offered a basic theory of the multiple pump/multiple condenser LHP operation. From it, a computer code was developed to predict the LHP saturation temperature in accordance with the system operating and environmental conditions.

Identification of risks stemming from new communication technologies

DEFF Research Database (Denmark)

Lessis, Vasileios; Taylor, J.R.; Kozin, Igor

Advanced distributed communication technologies play an important role today in the control and maintenance of safety -critical systems. However, the excessively optimistic reliance on the new technology without ecognizing the threats against its successful functioning, being able to maintain...... proved to be effective tools in developing more reliable and robust systems. As technology is developing fast though, a new need for an effective hazard identification methodology has emerged. To enhance the predictive performance of hazard identification in advanced distributed communication systems, we...... have envisioned and currently developing a multilevel-multidimensional HAZOP methodology. The methodology introduces a new creative thinking stimulation model to substitute the conventional guideword-based approaches that is based on a multiple level and dimension exploration of the system under...

A Bayesian Hierarchical Model for Relating Multiple SNPs within Multiple Genes to Disease Risk

Directory of Open Access Journals (Sweden)

Lewei Duan

2013-01-01

Full Text Available A variety of methods have been proposed for studying the association of multiple genes thought to be involved in a common pathway for a particular disease. Here, we present an extension of a Bayesian hierarchical modeling strategy that allows for multiple SNPs within each gene, with external prior information at either the SNP or gene level. The model involves variable selection at the SNP level through latent indicator variables and Bayesian shrinkage at the gene level towards a prior mean vector and covariance matrix that depend on external information. The entire model is fitted using Markov chain Monte Carlo methods. Simulation studies show that the approach is capable of recovering many of the truly causal SNPs and genes, depending upon their frequency and size of their effects. The method is applied to data on 504 SNPs in 38 candidate genes involved in DNA damage response in the WECARE study of second breast cancers in relation to radiotherapy exposure.

Predictive performance models and multiple task performance

Science.gov (United States)

Wickens, Christopher D.; Larish, Inge; Contorer, Aaron

1989-01-01

Five models that predict how performance of multiple tasks will interact in complex task scenarios are discussed. The models are shown in terms of the assumptions they make about human operator divided attention. The different assumptions about attention are then empirically validated in a multitask helicopter flight simulation. It is concluded from this simulation that the most important assumption relates to the coding of demand level of different component tasks.

Multiple Sclerosis Identification Based on Fractional Fourier Entropy and a Modified Jaya Algorithm

Directory of Open Access Journals (Sweden)

Shui-Hua Wang

2018-04-01

Full Text Available Aim: Currently, identifying multiple sclerosis (MS by human experts may come across the problem of “normal-appearing white matter”, which causes a low sensitivity. Methods: In this study, we presented a computer vision based approached to identify MS in an automatic way. This proposed method first extracted the fractional Fourier entropy map from a specified brain image. Afterwards, it sent the features to a multilayer perceptron trained by a proposed improved parameter-free Jaya algorithm. We used cost-sensitivity learning to handle the imbalanced data problem. Results: The 10 × 10-fold cross validation showed our method yielded a sensitivity of 97.40 ± 0.60%, a specificity of 97.39 ± 0.65%, and an accuracy of 97.39 ± 0.59%. Conclusions: We validated by experiments that the proposed improved Jaya performs better than plain Jaya algorithm and other latest bioinspired algorithms in terms of classification performance and training speed. In addition, our method is superior to four state-of-the-art MS identification approaches.

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

Science.gov (United States)

Tuta, Jure; Juric, Matjaz B

2018-03-24

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

MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method

Directory of Open Access Journals (Sweden)

Jure Tuta

2018-03-01

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

Pescara benchmark: overview of modelling, testing and identification

International Nuclear Information System (INIS)

Bellino, A; Garibaldi, L; Marchesiello, S; Brancaleoni, F; Gabriele, S; Spina, D; Bregant, L; Carminelli, A; Catania, G; Sorrentino, S; Di Evangelista, A; Valente, C; Zuccarino, L

2011-01-01

The 'Pescara benchmark' is part of the national research project 'BriViDi' (BRIdge VIbrations and DIagnosis) supported by the Italian Ministero dell'Universita e Ricerca. The project is aimed at developing an integrated methodology for the structural health evaluation of railway r/c, p/c bridges. The methodology should provide for applicability in operating conditions, easy data acquisition through common industrial instrumentation, robustness and reliability against structural and environmental uncertainties. The Pescara benchmark consisted in lab tests to get a consistent and large experimental data base and subsequent data processing. Special tests were devised to simulate the train transit effects in actual field conditions. Prestressed concrete beams of current industrial production both sound and damaged at various severity corrosion levels were tested. The results were collected either in a deterministic setting and in a form suitable to deal with experimental uncertainties. Damage identification was split in two approaches: with or without a reference model. In the first case f.e. models were used in conjunction with non conventional updating techniques. In the second case, specialized output-only identification techniques capable to deal with time-variant and possibly non linear systems were developed. The lab tests allowed validating the above approaches and the performances of classical modal based damage indicators.

Odor Emotional Quality Predicts Odor Identification.

Science.gov (United States)

Bestgen, Anne-Kathrin; Schulze, Patrick; Kuchinke, Lars

2015-09-01

It is commonly agreed upon a strong link between emotion and olfaction. Odor-evoked memories are experienced as more emotional compared with verbal, visual, and tactile stimuli. Moreover, the emotional quality of odor cues increases memory performance, but contrary to this, odors are poor retrieval cues for verbal labels. To examine the relation between the emotional quality of an odor and its likelihood of identification, this study evaluates how normative emotion ratings based on the 3-dimensional affective space model (that includes valence, arousal, and dominance), using the Self-Assessment Manikin by Bradley and Lang (Bradley MM, Lang PJ. 1994. Measuring emotion: the Self-Assessment Manikin and the Semantic Differential. J Behav Ther Exp Psychiatry. 25(1):49-59.) and the Positive and Negative Affect Schedule (Watson D, Clark LA, Tellegen A. 1988. Development and validation of brief measures of positive and negative affect: the PANAS scales. J Pers Soc Psychol. 54(6):1063-1070.) predict the identification of odors in a multiple choice condition. The best fitting logistic regression model includes squared valence and dominance and thus, points to a significant role of specific emotional features of odors as a main clue for odor identification. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Application of an improved model for the identification of material parameters

DEFF Research Database (Denmark)

Frederiksen, Per S.

1997-01-01

Elastic material constants of thick plates can be identified by combining a range of measured natural frequencies with an accurate numerical model for the theoretical predictions. To deal with thick plates, a model that takes transverse shear effects into account is necessary. Since modeling errors...... affect the estimates in a systematic way, an accurate numerical model is of primary importance. Compared to a model used previously, an improved more accurate plate model is studied here for the purpose of identification. This new advanced model is used to assess the systematic errors...

Race, Ethnicity and Ancestry in Unrelated Transplant Matching for the National Marrow Donor Program: A Comparison of Multiple Forms of Self-Identification with Genetics.

Directory of Open Access Journals (Sweden)

Jill A Hollenbach

Full Text Available We conducted a nationwide study comparing self-identification to genetic ancestry classifications in a large cohort (n = 1752 from the National Marrow Donor Program. We sought to determine how various measures of self-identification intersect with genetic ancestry, with the aim of improving matching algorithms for unrelated bone marrow transplant. Multiple dimensions of self-identification, including race/ethnicity and geographic ancestry were compared to classifications based on ancestry informative markers (AIMs, and the human leukocyte antigen (HLA genes, which are required for transplant matching. Nearly 20% of responses were inconsistent between reporting race/ethnicity versus geographic ancestry. Despite strong concordance between AIMs and HLA, no measure of self-identification shows complete correspondence with genetic ancestry. In certain cases geographic ancestry reporting matches genetic ancestry not reflected in race/ethnicity identification, but in other cases geographic ancestries show little correspondence to genetic measures, with important differences by gender. However, when respondents assign ancestry to grandparents, we observe sub-groups of individuals with well- defined genetic ancestries, including important differences in HLA frequencies, with implications for transplant matching. While we advocate for tailored questioning to improve accuracy of ancestry ascertainment, collection of donor grandparents' information will improve the chances of finding matches for many patients, particularly for mixed-ancestry individuals.

Race, Ethnicity and Ancestry in Unrelated Transplant Matching for the National Marrow Donor Program: A Comparison of Multiple Forms of Self-Identification with Genetics

Science.gov (United States)

Hollenbach, Jill A.; Saperstein, Aliya; Albrecht, Mark; Vierra-Green, Cynthia; Parham, Peter; Norman, Paul J.; Maiers, Martin

2015-01-01

We conducted a nationwide study comparing self-identification to genetic ancestry classifications in a large cohort (n = 1752) from the National Marrow Donor Program. We sought to determine how various measures of self-identification intersect with genetic ancestry, with the aim of improving matching algorithms for unrelated bone marrow transplant. Multiple dimensions of self-identification, including race/ethnicity and geographic ancestry were compared to classifications based on ancestry informative markers (AIMs), and the human leukocyte antigen (HLA) genes, which are required for transplant matching. Nearly 20% of responses were inconsistent between reporting race/ethnicity versus geographic ancestry. Despite strong concordance between AIMs and HLA, no measure of self-identification shows complete correspondence with genetic ancestry. In certain cases geographic ancestry reporting matches genetic ancestry not reflected in race/ethnicity identification, but in other cases geographic ancestries show little correspondence to genetic measures, with important differences by gender. However, when respondents assign ancestry to grandparents, we observe sub-groups of individuals with well- defined genetic ancestries, including important differences in HLA frequencies, with implications for transplant matching. While we advocate for tailored questioning to improve accuracy of ancestry ascertainment, collection of donor grandparents’ information will improve the chances of finding matches for many patients, particularly for mixed-ancestry individuals. PMID:26287376

Comparison of Multi-shot Models for Short-term Re-identification of People using RGB-D Sensors

DEFF Research Database (Denmark)

Møgelmose, Andreas; Bahnsen, Chris; Moeslund, Thomas B.

2015-01-01

This work explores different types of multi-shot descriptors for re-identification in an on-the-fly enrolled environment using RGB-D sensors. We present a full re-identification pipeline complete with detection, segmentation, feature extraction, and re-identification, which expands on previous work...... by using multi-shot descriptors modeling people over a full camera pass instead of single frames with no temporal linking. We compare two different multi-shot models; mean histogram and histogram series, and test them each in 3 different color spaces. Both histogram descriptors are assisted by a depth...

A Rapid Identification Method for Calamine Using Near-Infrared Spectroscopy Based on Multi-Reference Correlation Coefficient Method and Back Propagation Artificial Neural Network.

Science.gov (United States)

Sun, Yangbo; Chen, Long; Huang, Bisheng; Chen, Keli

2017-07-01

As a mineral, the traditional Chinese medicine calamine has a similar shape to many other minerals. Investigations of commercially available calamine samples have shown that there are many fake and inferior calamine goods sold on the market. The conventional identification method for calamine is complicated, therefore as a result of the large scale of calamine samples, a rapid identification method is needed. To establish a qualitative model using near-infrared (NIR) spectroscopy for rapid identification of various calamine samples, large quantities of calamine samples including crude products, counterfeits and processed products were collected and correctly identified using the physicochemical and powder X-ray diffraction method. The NIR spectroscopy method was used to analyze these samples by combining the multi-reference correlation coefficient (MRCC) method and the error back propagation artificial neural network algorithm (BP-ANN), so as to realize the qualitative identification of calamine samples. The accuracy rate of the model based on NIR and MRCC methods was 85%; in addition, the model, which took comprehensive multiple factors into consideration, can be used to identify crude calamine products, its counterfeits and processed products. Furthermore, by in-putting the correlation coefficients of multiple references as the spectral feature data of samples into BP-ANN, a BP-ANN model of qualitative identification was established, of which the accuracy rate was increased to 95%. The MRCC method can be used as a NIR-based method in the process of BP-ANN modeling.

Identification of control targets in Boolean molecular network models via computational algebra.

Science.gov (United States)

Murrugarra, David; Veliz-Cuba, Alan; Aguilar, Boris; Laubenbacher, Reinhard

2016-09-23

Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. This paper presents a method for the identification of potential intervention targets in Boolean molecular network models using algebraic techniques. The approach exploits an algebraic representation of Boolean networks to encode the control candidates in the network wiring diagram as the solutions of a system of polynomials equations, and then uses computational algebra techniques to find such controllers. The control methods in this paper are validated through the identification of combinatorial interventions in the signaling pathways of previously reported control targets in two well studied systems, a p53-mdm2 network and a blood T cell lymphocyte granular leukemia survival signaling network. Supplementary data is available online and our code in Macaulay2 and Matlab are available via http://www.ms.uky.edu/~dmu228/ControlAlg . This paper presents a novel method for the identification of intervention targets in Boolean network models. The results in this paper show that the proposed methods are useful and efficient for moderately large networks.

A goal-oriented field measurement filtering technique for the identification of material model parameters

KAUST Repository

Lubineau, Gilles

2009-05-16

The post-processing of experiments with nonuniform fields is still a challenge: the information is often much richer, but its interpretation for identification purposes is not straightforward. However, this is a very promising field of development because it would pave the way for the robust identification of multiple material parameters using only a small number of experiments. This paper presents a goal-oriented filtering technique in which data are combined into new output fields which are strongly correlated with specific quantities of interest (the material parameters to be identified). Thus, this combination, which is nonuniform in space, constitutes a filter of the experimental outputs, whose relevance is quantified by a quality function based on global variance analysis. Then, this filter is optimized using genetic algorithms. © 2009 Springer-Verlag.

A model structure for identification of linear models of the UH-60 helicopter in hover and forward flight

Science.gov (United States)

1995-08-01

A linear model structure applicable to identification of the UH-60 flight : dynamics in hover and forward flight without rotor-state data is developed. The : structure of the model is determined through consideration of the important : dynamic modes ...

A Survey of Modelling and Identification of Quadrotor Robot

Directory of Open Access Journals (Sweden)

Xiaodong Zhang

2014-01-01

Full Text Available A quadrotor is a rotorcraft capable of hover, forward flight, and VTOL and is emerging as a fundamental research and application platform at present with flexibility, adaptability, and ease of construction. Since a quadrotor is basically considered an unstable system with the characteristics of dynamics such as being intensively nonlinear, multivariable, strongly coupled, and underactuated, a precise and practical model is critical to control the vehicle which seems to be simple to operate. As a rotorcraft, the dynamics of a quadrotor is mainly dominated by the complicated aerodynamic effects of the rotors. This paper gives a tutorial of the platform configuration, methodology of modeling, comprehensive nonlinear model, the aerodynamic effects, and model identification for a quadrotor.

Identification of a parametric, discrete-time model of ankle stiffness.

Science.gov (United States)

Guarin, Diego L; Jalaleddini, Kian; Kearney, Robert E

2013-01-01

Dynamic ankle joint stiffness defines the relationship between the position of the ankle and the torque acting about it and can be separated into intrinsic and reflex components. Under stationary conditions, intrinsic stiffness can described by a linear second order system while reflex stiffness is described by Hammerstein system whose input is delayed velocity. Given that reflex and intrinsic torque cannot be measured separately, there has been much interest in the development of system identification techniques to separate them analytically. To date, most methods have been nonparametric and as a result there is no direct link between the estimated parameters and those of the stiffness model. This paper presents a novel algorithm for identification of a discrete-time model of ankle stiffness. Through simulations we show that the algorithm gives unbiased results even in the presence of large, non-white noise. Application of the method to experimental data demonstrates that it produces results consistent with previous findings.

Tip-tilt disturbance model identification based on non-linear least squares fitting for Linear Quadratic Gaussian control

Science.gov (United States)

Yang, Kangjian; Yang, Ping; Wang, Shuai; Dong, Lizhi; Xu, Bing

2018-05-01

We propose a method to identify tip-tilt disturbance model for Linear Quadratic Gaussian control. This identification method based on Levenberg-Marquardt method conducts with a little prior information and no auxiliary system and it is convenient to identify the tip-tilt disturbance model on-line for real-time control. This identification method makes it easy that Linear Quadratic Gaussian control runs efficiently in different adaptive optics systems for vibration mitigation. The validity of the Linear Quadratic Gaussian control associated with this tip-tilt disturbance model identification method is verified by experimental data, which is conducted in replay mode by simulation.

Improvement of and Parameter Identification for the Bimodal Time-Varying Modified Kanai-Tajimi Power Spectral Model

Directory of Open Access Journals (Sweden)

Huiguo Chen

2017-01-01

Full Text Available Based on the Kanai-Tajimi power spectrum filtering method proposed by Du Xiuli et al., a genetic algorithm and a quadratic optimization identification technique are employed to improve the bimodal time-varying modified Kanai-Tajimi power spectral model and the parameter identification method proposed by Vlachos et al. Additionally, a method for modeling time-varying power spectrum parameters for ground motion is proposed. The 8244 Orion and Chi-Chi earthquake accelerograms are selected as examples for time-varying power spectral model parameter identification and ground motion simulations to verify the feasibility and effectiveness of the improved bimodal time-varying modified Kanai-Tajimi power spectral model. The results of this study provide important references for designing ground motion inputs for seismic analyses of major engineering structures.

Multiple Scattering Model for Optical Coherence Tomography with Rytov Approximation

KAUST Repository

Li, Muxingzi

2017-01-01

of speckles due to multiple scatterers within the coherence length, and other random noise. Motivated by the above two challenges, a multiple scattering model based on Rytov approximation and Gaussian beam optics is proposed for the OCT setup. Some previous

MODEL PENSKORAN PARTIAL CREDIT PADA BUTIR MULTIPLE TRUE-FALSE BIDANG FISIKA

Directory of Open Access Journals (Sweden)

Wasis Wasis

2013-01-01

Full Text Available Tujuan penelitian ini menghasilkan model penskoran politomus untuk respons butir multiple true-false, sehingga dapat mengestimasi secara lebih akurat kemampuan di bidang fisika. Pengembangan penskoran menggunakan Four-D model dan diuji akurasinya melalui penelitian empiris dan simulasi. Penelitian empiris menggunakan 15 butir multiple true-false yang diambil dari soal UMPTN tahun 1996-2006 dan dikenakan pada 410 mahasiswa baru FMIPA Universitas Negeri Surabaya angkatan tahun 2007. Respons peserta tes diskor dengan tiga model partial credit (PCM I; II; dan III dan secara dikotomus. Hasil penskoran dianalisis dengan program Quest untuk mendapat-kan estimasi tingkat kesukaran butir (δ dan estimasi ke-mampuan peserta (θ untuk menentukan nilai fungsi informasi tes dan kesalahan baku estimasi. Penelitian simulasi mengguna-kan data bangkitan berdasarkan parameter empiris (δ dan θ memakai program statistik SAS dan akurasi estimasinya di-analisis dengan metode root mean squared error (RMSE. Hasil penelitian ini menunjukkan: (i Penskoran PCM dengan pem-bobotan mampu mengestimasi kemampuan lebih akurat di-bandingkan tanpa pembobotan maupun secara dikotomus; (ii Semakin banyak jumlah kategori dalam penskoran partial credit, semakin akurat. Kata kunci: model penskoran partial credit, butir multiple true-false ____________________________________________________________ THE PARTIAL CREDIT SCORING MODEL FOR THE MULTIPLE TRUE-FALSE BUTIRS IN PHYSICS Abstract This study is an attempt to overcome the weaknesses. This study aims to produce a polytomous scoring model for responses to multiple true-false butirs in order to get a more accurate estimation of abilities in physics. It adopts the Four-D model and its accuracy is assessed through empirical and simulation studies. The empirical study employed 15 multiple true-false butirs taken from the New Students Entrance Test of State University the year of 1996–2006. It administered to 410 new students enrolled

Are subject-specific musculoskeletal models robust to the uncertainties in parameter identification?

Directory of Open Access Journals (Sweden)

Giordano Valente

Full Text Available Subject-specific musculoskeletal modeling can be applied to study musculoskeletal disorders, allowing inclusion of personalized anatomy and properties. Independent of the tools used for model creation, there are unavoidable uncertainties associated with parameter identification, whose effect on model predictions is still not fully understood. The aim of the present study was to analyze the sensitivity of subject-specific model predictions (i.e., joint angles, joint moments, muscle and joint contact forces during walking to the uncertainties in the identification of body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. The uncertainties in input variables had a moderate effect on model predictions, as muscle and joint contact forces showed maximum standard deviation of 0.3 times body-weight and maximum range of 2.1 times body-weight. In addition, the output variables significantly correlated with few input variables (up to 7 out of 312 across the gait cycle, including the geometry definition of larger muscles and the maximum muscle tension in limited gait portions. Although we found subject-specific models not markedly sensitive to parameter identification, researchers should be aware of the model precision in relation to the intended application. In fact, force

Experimental parameter identification of a multi-scale musculoskeletal model controlled by electrical stimulation: application to patients with spinal cord injury.

Science.gov (United States)

Benoussaad, Mourad; Poignet, Philippe; Hayashibe, Mitsuhiro; Azevedo-Coste, Christine; Fattal, Charles; Guiraud, David

2013-06-01

We investigated the parameter identification of a multi-scale physiological model of skeletal muscle, based on Huxley's formulation. We focused particularly on the knee joint controlled by quadriceps muscles under electrical stimulation (ES) in subjects with a complete spinal cord injury. A noninvasive and in vivo identification protocol was thus applied through surface stimulation in nine subjects and through neural stimulation in one ES-implanted subject. The identification protocol included initial identification steps, which are adaptations of existing identification techniques to estimate most of the parameters of our model. Then we applied an original and safer identification protocol in dynamic conditions, which required resolution of a nonlinear programming (NLP) problem to identify the serial element stiffness of quadriceps. Each identification step and cross validation of the estimated model in dynamic condition were evaluated through a quadratic error criterion. The results highlighted good accuracy, the efficiency of the identification protocol and the ability of the estimated model to predict the subject-specific behavior of the musculoskeletal system. From the comparison of parameter values between subjects, we discussed and explored the inter-subject variability of parameters in order to select parameters that have to be identified in each patient.

The Interaction Network Ontology-supported modeling and mining of complex interactions represented with multiple keywords in biomedical literature.

Science.gov (United States)

Özgür, Arzucan; Hur, Junguk; He, Yongqun

2016-01-01

hierarchical display of these 34 interaction types and their ancestor terms in INO resulted in the identification of specific gene-gene interaction patterns from the LLL dataset. The phenomenon of having multi-keyword interaction types was also frequently observed in the vaccine dataset. By modeling and representing multiple textual keywords for interaction types, the extended INO enabled the identification of complex biological gene-gene interactions represented with multiple keywords.

A collaborative scheduling model for the supply-hub with multiple suppliers and multiple manufacturers.

Science.gov (United States)

Li, Guo; Lv, Fei; Guan, Xu

2014-01-01

This paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that suppliers and manufacturers make their decisions separately, and the other is that the Supply-Hub makes joint decisions with collaborative scheduling. The results show that our scheduling model with the Supply-Hub is a NP-complete problem, therefore, we propose an auto-adapted differential evolution algorithm to solve this problem. Moreover, we illustrate that the performance of collaborative scheduling by the Supply-Hub is superior to separate decision made by each manufacturer and supplier. Furthermore, we also show that the algorithm proposed has good convergence and reliability, which can be applicable to more complicated supply chain environment.

Concealed identification symbols and nondestructive determination of the identification symbols

Science.gov (United States)

Nance, Thomas A.; Gibbs, Kenneth M.

2014-09-16

The concealing of one or more identification symbols into a target object and the subsequent determination or reading of such symbols through non-destructive testing is described. The symbols can be concealed in a manner so that they are not visible to the human eye and/or cannot be readily revealed to the human eye without damage or destruction of the target object. The identification symbols can be determined after concealment by e.g., the compilation of multiple X-ray images. As such, the present invention can also provide e.g., a deterrent to theft and the recovery of lost or stolen objects.

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

Science.gov (United States)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

Identification of optimal strategies for energy management systems planning under multiple uncertainties

International Nuclear Information System (INIS)

Cai, Y.P.; Huang, G.H.; Yang, Z.F.; Tan, Q.

2009-01-01

Management of energy resources is crucial for many regions throughout the world. Many economic, environmental and political factors are having significant effects on energy management practices, leading to a variety of uncertainties in relevant decision making. The objective of this research is to identify optimal strategies in the planning of energy management systems under multiple uncertainties through the development of a fuzzy-random interval programming (FRIP) model. The method is based on an integration of the existing interval linear programming (ILP), superiority-inferiority-based fuzzy-stochastic programming (SI-FSP) and mixed integer linear programming (MILP). Such a FRIP model allows multiple uncertainties presented as interval values, possibilistic and probabilistic distributions, as well as their combinations within a general optimization framework. It can also be used for facilitating capacity-expansion planning of energy-production facilities within a multi-period and multi-option context. Complexities in energy management systems can be systematically reflected, thus applicability of the modeling process can be highly enhanced. The developed method has then been applied to a case of long-term energy management planning for a region with three cities. Useful solutions for the planning of energy management systems were generated. Interval solutions associated with different risk levels of constraint violation were obtained. They could be used for generating decision alternatives and thus help decision makers identify desired policies under various economic and system-reliability constraints. The solutions can also provide desired energy resource/service allocation and capacity-expansion plans with a minimized system cost, a maximized system reliability and a maximized energy security. Tradeoffs between system costs and constraint-violation risks could be successfully tackled, i.e., higher costs will increase system stability, while a desire for lower

Identification of the Skirt Piled Gullfaks C Gravity Platform using ARMAV Models

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Andersen, P.; Brincker, Rune

This paper presents the results from the system identification of the Gullfaks C gravity offshore platform excited by natural loads. The paper describes how modal parameters and mode shapes can be estimated by use of ARMAV models. The results estimated by an ARMAV model are compared with results...

Identification of the Skirt Piled Gullfaks C Gravity Platform using ARMAV Models

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Andersen, P.; Brincker, Rune

1996-01-01

This paper presents the results from the system identification of the Gullfaks C gravity offshore platform excited by natural loads. The paper describes how modal parameters and mode shapes can be estimated by use of ARMAV models. The results estimated by an ARMAV model are compared with results...

Takagi-Sugeno fuzzy model identification for turbofan aero-engines with guaranteed stability

Directory of Open Access Journals (Sweden)

Ruichao LI

2018-06-01

Full Text Available This paper is concerned with identifying a Takagi-Sugeno (TS fuzzy model for turbofan aero-engines working under the maximum power status (non-afterburning. To establish the fuzzy system, theoretical contributions are made as follows. First, by fixing antecedent parameters, the estimation of consequent parameters in state-space representations is formulated as minimizing a quadratic cost function. Second, to avoid obtaining unstable identified models, a new theorem is proposed to transform the prior-knowledge of stability into constraints. Then based on the aforementioned work, the identification problem is synthesized as a constrained quadratic optimization. By solving the constrained optimization, a TS fuzzy system is identified with guaranteed stability. Finally, the proposed method is applied to the turbofan aero-engine using simulation data generated from an aerothermodynamics component-level model. Results show the identified fuzzy model achieves a high fitting accuracy while stabilities of the overall fuzzy system and all its local models are also guaranteed. Keywords: Constrained optimization, Fuzzy system, Stability, System identification, Turbofan engine

Reduction of bias in neutron multiplicity assay using a weighted point model

Energy Technology Data Exchange (ETDEWEB)

Geist, W. H. (William H.); Krick, M. S. (Merlyn S.); Mayo, D. R. (Douglas R.)

2004-01-01

Accurate assay of most common plutonium samples was the development goal for the nondestructive assay technique of neutron multiplicity counting. Over the past 20 years the technique has been proven for relatively pure oxides and small metal items. Unfortunately, the technique results in large biases when assaying large metal items. Limiting assumptions, such as unifoh multiplication, in the point model used to derive the multiplicity equations causes these biases for large dense items. A weighted point model has been developed to overcome some of the limitations in the standard point model. Weighting factors are detemiined from Monte Carlo calculations using the MCNPX code. Monte Carlo calculations give the dependence of the weighting factors on sample mass and geometry, and simulated assays using Monte Carlo give the theoretical accuracy of the weighted-point-model assay. Measured multiplicity data evaluated with both the standard and weighted point models are compared to reference values to give the experimental accuracy of the assay. Initial results show significant promise for the weighted point model in reducing or eliminating biases in the neutron multiplicity assay of metal items. The negative biases observed in the assay of plutonium metal samples are caused by variations in the neutron multiplication for neutrons originating in various locations in the sample. The bias depends on the mass and shape of the sample and depends on the amount and energy distribution of the ({alpha},n) neutrons in the sample. When the standard point model is used, this variable-multiplication bias overestimates the multiplication and alpha values of the sample, and underestimates the plutonium mass. The weighted point model potentially can provide assay accuracy of {approx}2% (1 {sigma}) for cylindrical plutonium metal samples < 4 kg with {alpha} < 1 without knowing the exact shape of the samples, provided that the ({alpha},n) source is uniformly distributed throughout the

A review on modeling, identification and servo control of robotic ...

African Journals Online (AJOL)

user

This article reviews modeling, identification, and low level control of the robotic excavator. ... The oil viscosity, oil flow through the spool valves, and variable loading, ..... squares, to identify all the unknown individual parameters for a unmanned ..... Robust low level control of robotic excavation, PhD Thesis, The University of ...

Enhanced online model identification and state of charge estimation for lithium-ion battery with a FBCRLS based observer

International Nuclear Information System (INIS)

Wei, Zhongbao; Meng, Shujuan; Xiong, Binyu; Ji, Dongxu; Tseng, King Jet

2016-01-01

Highlights: • Integrated online model identification and SOC estimate is explored. • Noise variances are online estimated in a data-driven way. • Identification bias caused by noise corruption is attenuated. • SOC is online estimated with high accuracy and fast convergence. • Algorithm comparison shows the superiority of proposed method. - Abstract: State of charge (SOC) estimators with online identified battery model have proven to have high accuracy and better robustness due to the timely adaption of time varying model parameters. In this paper, we show that the common methods for model identification are intrinsically biased if both the current and voltage sensors are corrupted with noises. The uncertainties in battery model further degrade the accuracy and robustness of SOC estimate. To address this problem, this paper proposes a novel technique which integrates the Frisch scheme based bias compensating recursive least squares (FBCRLS) with a SOC observer for enhanced model identification and SOC estimate. The proposed method online estimates the noise statistics and compensates the noise effect so that the model parameters can be extracted without bias. The SOC is further estimated in real time with the online updated and unbiased battery model. Simulation and experimental studies show that the proposed FBCRLS based observer effectively attenuates the bias on model identification caused by noise contamination and as a consequence provides more reliable estimate on SOC. The proposed method is also compared with other existing methods to highlight its superiority in terms of accuracy and convergence speed.

A Universal Model of Giftedness--An Adaptation of the Munich Model

Science.gov (United States)

Jessurun, J. H.; Shearer, C. B.; Weggeman, M. C. D. P.

2016-01-01

The Munich Model of Giftedness (MMG) by Heller and his colleagues, developed for the identification of gifted children, is adapted and expanded, with the aim of making it more universally usable as a model for the pathway from talents to performance. On the side of the talent-factors, the concept of multiple intelligences is introduced, and the…

Infinite Multiple Membership Relational Modeling for Complex Networks

DEFF Research Database (Denmark)

Mørup, Morten; Schmidt, Mikkel Nørgaard; Hansen, Lars Kai

Learning latent structure in complex networks has become an important problem fueled by many types of networked data originating from practically all fields of science. In this paper, we propose a new non-parametric Bayesian multiplemembership latent feature model for networks. Contrary to existing...... multiplemembership models that scale quadratically in the number of vertices the proposedmodel scales linearly in the number of links admittingmultiple-membership analysis in large scale networks. We demonstrate a connection between the single membership relational model and multiple membership models and show...

Sparse modeling applied to patient identification for safety in medical physics applications

Science.gov (United States)

Lewkowitz, Stephanie

Every scheduled treatment at a radiation therapy clinic involves a series of safety protocol to ensure the utmost patient care. Despite safety protocol, on a rare occasion an entirely preventable medical event, an accident, may occur. Delivering a treatment plan to the wrong patient is preventable, yet still is a clinically documented error. This research describes a computational method to identify patients with a novel machine learning technique to combat misadministration. The patient identification program stores face and fingerprint data for each patient. New, unlabeled data from those patients are categorized according to the library. The categorization of data by this face-fingerprint detector is accomplished with new machine learning algorithms based on Sparse Modeling that have already begun transforming the foundation of Computer Vision. Previous patient recognition software required special subroutines for faces and different tailored subroutines for fingerprints. In this research, the same exact model is used for both fingerprints and faces, without any additional subroutines and even without adjusting the two hyperparameters. Sparse modeling is a powerful tool, already shown utility in the areas of super-resolution, denoising, inpainting, demosaicing, and sub-nyquist sampling, i.e. compressed sensing. Sparse Modeling is possible because natural images are inherently sparse in some bases, due to their inherent structure. This research chooses datasets of face and fingerprint images to test the patient identification model. The model stores the images of each dataset as a basis (library). One image at a time is removed from the library, and is classified by a sparse code in terms of the remaining library. The Locally Competitive Algorithm, a truly neural inspired Artificial Neural Network, solves the computationally difficult task of finding the sparse code for the test image. The components of the sparse representation vector are summed by ℓ1 pooling

Advances in Modelling, System Identification and Parameter ...

Indian Academy of Sciences (India)

Authors show, using numerical simulation for two system functions, the improvement in percentage normalized ... of nonlinear systems. The approach is to use multiple linearizing models fitted along the operating trajectories. ... over emphasized in the light of present day high level of research activity in the field of aerospace ...

Use of Multi-class Empirical Orthogonal Function for Identification of Hydrogeological Parameters and Spatiotemporal Pattern of Multiple Recharges in Groundwater Modeling

Science.gov (United States)

Huang, C. L.; Hsu, N. S.; Yeh, W. W. G.; Hsieh, I. H.

2017-12-01

This study develops an innovative calibration method for regional groundwater modeling by using multi-class empirical orthogonal functions (EOFs). The developed method is an iterative approach. Prior to carrying out the iterative procedures, the groundwater storage hydrographs associated with the observation wells are calculated. The combined multi-class EOF amplitudes and EOF expansion coefficients of the storage hydrographs are then used to compute the initial gauss of the temporal and spatial pattern of multiple recharges. The initial guess of the hydrogeological parameters are also assigned according to in-situ pumping experiment. The recharges include net rainfall recharge and boundary recharge, and the hydrogeological parameters are riverbed leakage conductivity, horizontal hydraulic conductivity, vertical hydraulic conductivity, storage coefficient, and specific yield. The first step of the iterative algorithm is to conduct the numerical model (i.e. MODFLOW) by the initial guess / adjusted values of the recharges and parameters. Second, in order to determine the best EOF combination of the error storage hydrographs for determining the correction vectors, the objective function is devised as minimizing the root mean square error (RMSE) of the simulated storage hydrographs. The error storage hydrograph are the differences between the storage hydrographs computed from observed and simulated groundwater level fluctuations. Third, adjust the values of recharges and parameters and repeat the iterative procedures until the stopping criterion is reached. The established methodology was applied to the groundwater system of Ming-Chu Basin, Taiwan. The study period is from January 1st to December 2ed in 2012. Results showed that the optimal EOF combination for the multiple recharges and hydrogeological parameters can decrease the RMSE of the simulated storage hydrographs dramatically within three calibration iterations. It represents that the iterative approach that

Modeling and identification of ARMG models for stochastic processes: application to on-line computation of the power spectral density

International Nuclear Information System (INIS)

Zwingelstein, Gilles; Thabet, Gabriel.

1977-01-01

Control algorithms for components of nuclear power plants are currently based on external diagnostic methods. Modeling and identification techniques for autoregressive moving average models (ARMA) for stochastic processes are described. The identified models provide a means of estimating the power spectral density with improved accuracy and computer time compared with the classical methods. They are particularly will suited for on-line estimation of the power spectral density. The observable stochastic process y (t) is modeled assuming that it is the output of a linear filter driven by Gaussian while noise w (t). Two identification schemes were tested to find the orders m and n of the ARMA (m,n) models and to estimate the parameters of the recursion equation relating the input and output signals. The first scheme consists in transforming the ARMA model to an autoregressive model. The parameters of this AR model are obtained using least squares estimation techniques. The second scheme consists in finding the parameters of the ARMA by nonlinear programming techniques. The power spectral density of y(t) is instantaneously deduced from these ARMA models [fr

Optimization of the Darrieus wind turbines with double-multiple-streamtube model

International Nuclear Information System (INIS)

Paraschivoiu, I.

1985-01-01

This paper discusses a new improvement of the double-multiple-stream tube model by considering the stream tube expansion effects on the Darrieus wind turbine. These effects, allowing a more realistic modeling of the upwind/downwind flow field asymmetries inherent in the Darrieus rotor, were calculated by using CARDAAX computer code. When the dynamic stall is introduced in the double-multiple-stream tube model, the aerodynamic loads and performance show significant changes in the range of low tip-speed ratio

Modeling, Identification, Estimation, and Simulation of Urban Traffic Flow in Jakarta and Bandung

Directory of Open Access Journals (Sweden)

Herman Y. Sutarto

2015-06-01

Full Text Available This paper presents an overview of urban traffic flow from the perspective of system theory and stochastic control. The topics of modeling, identification, estimation and simulation techniques are evaluated and validated using actual traffic flow data from the city of Jakarta and Bandung, Indonesia, and synthetic data generated from traffic micro-simulator VISSIM. The results on particle filter (PF based state estimation and Expectation-Maximization (EM based parameter estimation (identification confirm the proposed model gives satisfactory results that capture the variation of urban traffic flow. The combination of the technique and the simulator platform assembles possibility to develop a real-time traffic light controller.  

Application of multiple objective models to water resources planning and management

International Nuclear Information System (INIS)

North, R.M.

1993-01-01

Over the past 30 years, we have seen the birth and growth of multiple objective analysis from an idea without tools to one with useful applications. Models have been developed and applications have been researched to address the multiple purposes and objectives inherent in the development and management of water resources. A practical approach to multiple objective modelling incorporates macroeconomic-based policies and expectations in order to optimize the results from both engineering (structural) and management (non-structural) alternatives, while taking into account the economic and environmental trade-offs. (author). 27 refs, 4 figs, 3 tabs

Simultaneous identification of unknown groundwater pollution sources and estimation of aquifer parameters

Science.gov (United States)

Datta, Bithin; Chakrabarty, Dibakar; Dhar, Anirban

2009-09-01

Pollution source identification is a common problem encountered frequently. In absence of prior information about flow and transport parameters, the performance of source identification models depends on the accuracy in estimation of these parameters. A methodology is developed for simultaneous pollution source identification and parameter estimation in groundwater systems. The groundwater flow and transport simulator is linked to the nonlinear optimization model as an external module. The simulator defines the flow and transport processes, and serves as a binding equality constraint. The Jacobian matrix which determines the search direction in the nonlinear optimization model links the groundwater flow-transport simulator and the optimization method. Performance of the proposed methodology using spatiotemporal hydraulic head values and pollutant concentration measurements is evaluated by solving illustrative problems. Two different decision model formulations are developed. The computational efficiency of these models is compared using two nonlinear optimization algorithms. The proposed methodology addresses some of the computational limitations of using the embedded optimization technique which embeds the discretized flow and transport equations as equality constraints for optimization. Solution results obtained are also found to be better than those obtained using the embedded optimization technique. The performance evaluations reported here demonstrate the potential applicability of the developed methodology for a fairly large aquifer study area with multiple unknown pollution sources.

A Collaborative Scheduling Model for the Supply-Hub with Multiple Suppliers and Multiple Manufacturers

Directory of Open Access Journals (Sweden)

Guo Li

2014-01-01

Full Text Available This paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that suppliers and manufacturers make their decisions separately, and the other is that the Supply-Hub makes joint decisions with collaborative scheduling. The results show that our scheduling model with the Supply-Hub is a NP-complete problem, therefore, we propose an auto-adapted differential evolution algorithm to solve this problem. Moreover, we illustrate that the performance of collaborative scheduling by the Supply-Hub is superior to separate decision made by each manufacturer and supplier. Furthermore, we also show that the algorithm proposed has good convergence and reliability, which can be applicable to more complicated supply chain environment.

A Collaborative Scheduling Model for the Supply-Hub with Multiple Suppliers and Multiple Manufacturers

Science.gov (United States)

Lv, Fei; Guan, Xu

2014-01-01

This paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that suppliers and manufacturers make their decisions separately, and the other is that the Supply-Hub makes joint decisions with collaborative scheduling. The results show that our scheduling model with the Supply-Hub is a NP-complete problem, therefore, we propose an auto-adapted differential evolution algorithm to solve this problem. Moreover, we illustrate that the performance of collaborative scheduling by the Supply-Hub is superior to separate decision made by each manufacturer and supplier. Furthermore, we also show that the algorithm proposed has good convergence and reliability, which can be applicable to more complicated supply chain environment. PMID:24892104

Performance of svm, k-nn and nbc classifiers for text-independent speaker identification with and without modelling through merging models

Directory of Open Access Journals (Sweden)

Yussouf Nahayo

2016-04-01

Full Text Available This paper proposes some methods of robust text-independent speaker identification based on Gaussian Mixture Model (GMM. We implemented a combination of GMM model with a set of classifiers such as Support Vector Machine (SVM, K-Nearest Neighbour (K-NN, and Naive Bayes Classifier (NBC. In order to improve the identification rate, we developed a combination of hybrid systems by using validation technique. The experiments were performed on the dialect DR1 of the TIMIT corpus. The results have showed a better performance for the developed technique compared to the individual techniques.

Continuous-time interval model identification of blood glucose dynamics for type 1 diabetes

Science.gov (United States)

Kirchsteiger, Harald; Johansson, Rolf; Renard, Eric; del Re, Luigi

2014-07-01

While good physiological models of the glucose metabolism in type 1 diabetic patients are well known, their parameterisation is difficult. The high intra-patient variability observed is a further major obstacle. This holds for data-based models too, so that no good patient-specific models are available. Against this background, this paper proposes the use of interval models to cover the different metabolic conditions. The control-oriented models contain a carbohydrate and insulin sensitivity factor to be used for insulin bolus calculators directly. Available clinical measurements were sampled on an irregular schedule which prompts the use of continuous-time identification, also for the direct estimation of the clinically interpretable factors mentioned above. An identification method is derived and applied to real data from 28 diabetic patients. Model estimation was done on a clinical data-set, whereas validation results shown were done on an out-of-clinic, everyday life data-set. The results show that the interval model approach allows a much more regular estimation of the parameters and avoids physiologically incompatible parameter estimates.

A Nonlinear Ship Manoeuvering Model: Identification and adaptive control with experiments for a model ship

Directory of Open Access Journals (Sweden)

Roger Skjetne

2004-01-01

Full Text Available Complete nonlinear dynamic manoeuvering models of ships, with numerical values, are hard to find in the literature. This paper presents a modeling, identification, and control design where the objective is to manoeuver a ship along desired paths at different velocities. Material from a variety of references have been used to describe the ship model, its difficulties, limitations, and possible simplifications for the purpose of automatic control design. The numerical values of the parameters in the model is identified in towing tests and adaptive manoeuvering experiments for a small ship in a marine control laboratory.

Proceedings of the IASTED conference on modelling, identification and control : MIC 2010

Energy Technology Data Exchange (ETDEWEB)

Hangos, K. (ed.)

2010-03-09

This conference on energy and power systems provided a forum to discuss the latest research and innovative technologies related to power system modelling, identification and control. It was divided into the following tracks: applications in vehicle and transportation systems; economic, business and social applications; process and energy systems; mechanical and electrical applications; stability and controller design; mechatronics and robotics; identification, estimation and simulation; and control theory. The conference featured 64 presentations, of which 15 have been catalogued separately for inclusion in this database. refs., tabs., figs.

Multiple Time Series Ising Model for Financial Market Simulations

International Nuclear Information System (INIS)

Takaishi, Tetsuya

2015-01-01

In this paper we propose an Ising model which simulates multiple financial time series. Our model introduces the interaction which couples to spins of other systems. Simulations from our model show that time series exhibit the volatility clustering that is often observed in the real financial markets. Furthermore we also find non-zero cross correlations between the volatilities from our model. Thus our model can simulate stock markets where volatilities of stocks are mutually correlated

Efficient Blind System Identification of Non-Gaussian Auto-Regressive Models with HMM Modeling of the Excitation

DEFF Research Database (Denmark)

Li, Chunjian; Andersen, Søren Vang

2007-01-01

We propose two blind system identification methods that exploit the underlying dynamics of non-Gaussian signals. The two signal models to be identified are: an Auto-Regressive (AR) model driven by a discrete-state Hidden Markov process, and the same model whose output is perturbed by white Gaussi...... outputs. The signal models are general and suitable to numerous important signals, such as speech signals and base-band communication signals. Applications to speech analysis and blind channel equalization are given to exemplify the efficiency of the new methods....

Nonparametric Identification and Estimation of Finite Mixture Models of Dynamic Discrete Choices

OpenAIRE

Hiroyuki Kasahara; Katsumi Shimotsu

2006-01-01

In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an important issue, and finite mixture models provide flexible ways to account for unobserved heterogeneity. This paper studies nonparametric identifiability of type probabilities and type-specific component distributions in finite mixture models of dynamic discrete choices. We derive sufficient conditions for nonparametric identification for various finite mixture models of dynamic discrete choices used in appli...

Hysteresis modeling and identification of a dielectric electro-active polymer actuator using an APSO-based nonlinear Preisach NARX fuzzy model

International Nuclear Information System (INIS)

Truong, Bui Ngoc Minh; Nam, Doan Ngoc Chi; Ahn, Kyoung Kwan

2013-01-01

Dielectric electro-active polymer (DEAP) materials are attractive since they are low cost, lightweight and have a large deformation capability. They have no operating noise, very low electric power consumption and higher performance and efficiency than competing technologies. However, DEAP materials generally have strong hysteresis as well as uncertain and nonlinear characteristics. These disadvantages can limit the efficiency in the use of DEAP materials. To address these limitations, this research will present the combination of the Preisach model and the dynamic nonlinear autoregressive exogenous (NARX) fuzzy model-based adaptive particle swarm optimization (APSO) identification algorithm for modeling and identification of the nonlinear behavior of one typical type of DEAP actuator. Firstly, open loop input signals are applied to obtain nonlinear features and to investigate the responses of the DEAP actuator system. Then, a Preisach model can be combined with a dynamic NARX fuzzy structure to estimate the tip displacement of a DEAP actuator. To optimize all unknown parameters of the designed combination, an identification scheme based on a least squares method and an APSO algorithm is carried out. Finally, experimental validation research is carefully completed, and the effectiveness of the proposed model is evaluated by employing various input signals. (paper)

Alternative approaches to reliability modeling of a multiple engineered barrier system

International Nuclear Information System (INIS)

Ananda, M.M.A.; Singh, A.K.

1994-01-01

The lifetime of the engineered barrier system used for containment of high-level radioactive waste will significantly impact the total performance of a geological repository facility. Currently two types of designs are under consideration for an engineered barrier system, single engineered barrier system and multiple engineered barrier system. Multiple engineered barrier system consists of several metal barriers and the waste form (cladding). Some recent work show that a significant improvement of performance can be achieved by utilizing multiple engineered barrier systems. Considering sequential failures for each barrier, we model the reliability of the multiple engineered barrier system. Weibull and exponential lifetime distributions are used through out the analysis. Furthermore, the number of failed engineered barrier systems in a repository at a given time is modeled using a poisson approximation

Identification of grid model parameters using synchrophasor measurements

Energy Technology Data Exchange (ETDEWEB)

Boicea, Valentin; Albu, Mihaela [Politehnica University of Bucharest (Romania)

2012-07-01

Presently a critical element of the energy networks is represented by the active distribution grids, where generation intermittency and controllable loads contribute to a stochastic varability of the quantities characterizing the grid operation. The capability of controlling the electrical energy transfer is also limited by the incomplete knowledge of the detailed electrical model of each of the grid components. Asset management in distribution grids has to consider dynamic loads, while high loading of network sections might already have degraded some of the assets. Moreover, in case of functional microgrids, all elements need to be modelled accurately and an appropriate measurement layer enabling online control needs to be deployed. In this paper a method for online identification of the actual parameter values in grid electrical models is proposed. Laboratory results validating the proposed method are presented. (orig.)

Algorithm of Dynamic Model Structural Identification of the Multivariable Plant

Directory of Open Access Journals (Sweden)

Л.М. Блохін

2004-02-01

Full Text Available  The new algorithm of dynamic model structural identification of the multivariable stabilized plant with observable and unobservable disturbances in the regular operating  modes is offered in this paper. With the help of the offered algorithm it is possible to define the “perturbed” models of dynamics not only of the plant, but also the dynamics characteristics of observable and unobservable casual disturbances taking into account the absence of correlation between themselves and control inputs with the unobservable perturbations.

Feedback structure based entropy approach for multiple-model estimation

Institute of Scientific and Technical Information of China (English)

Shen-tu Han; Xue Anke; Guo Yunfei

2013-01-01

The variable-structure multiple-model (VSMM) approach, one of the multiple-model (MM) methods, is a popular and effective approach in handling problems with mode uncertainties. The model sequence set adaptation (MSA) is the key to design a better VSMM. However, MSA methods in the literature have big room to improve both theoretically and practically. To this end, we propose a feedback structure based entropy approach that could find the model sequence sets with the smallest size under certain conditions. The filtered data are fed back in real time and can be used by the minimum entropy (ME) based VSMM algorithms, i.e., MEVSMM. Firstly, the full Markov chains are used to achieve optimal solutions. Secondly, the myopic method together with particle filter (PF) and the challenge match algorithm are also used to achieve sub-optimal solutions, a trade-off between practicability and optimality. The numerical results show that the proposed algorithm provides not only refined model sets but also a good robustness margin and very high accuracy.

Feasibility Study on Tension Estimation Technique for Hanger Cables Using the FE Model-Based System Identification Method

Directory of Open Access Journals (Sweden)

Kyu-Sik Park

2015-01-01

Full Text Available Hanger cables in suspension bridges are partly constrained by horizontal clamps. So, existing tension estimation methods based on a single cable model are prone to higher errors as the cable gets shorter, making it more sensitive to flexural rigidity. Therefore, inverse analysis and system identification methods based on finite element models are suggested recently. In this paper, the applicability of system identification methods is investigated using the hanger cables of Gwang-An bridge. The test results show that the inverse analysis and systemic identification methods based on finite element models are more reliable than the existing string theory and linear regression method for calculating the tension in terms of natural frequency errors. However, the estimation error of tension can be varied according to the accuracy of finite element model in model based methods. In particular, the boundary conditions affect the results more profoundly when the cable gets shorter. Therefore, it is important to identify the boundary conditions through experiment if it is possible. The FE model-based tension estimation method using system identification method can take various boundary conditions into account. Also, since it is not sensitive to the number of natural frequency inputs, the availability of this system is high.

Analysis of blind identification methods for estimation of kinetic parameters in dynamic medical imaging

Science.gov (United States)

Riabkov, Dmitri

Compartment modeling of dynamic medical image data implies that the concentration of the tracer over time in a particular region of the organ of interest is well-modeled as a convolution of the tissue response with the tracer concentration in the blood stream. The tissue response is different for different tissues while the blood input is assumed to be the same for different tissues. The kinetic parameters characterizing the tissue responses can be estimated by blind identification methods. These algorithms use the simultaneous measurements of concentration in separate regions of the organ; if the regions have different responses, the measurement of the blood input function may not be required. In this work it is shown that the blind identification problem has a unique solution for two-compartment model tissue response. For two-compartment model tissue responses in dynamic cardiac MRI imaging conditions with gadolinium-DTPA contrast agent, three blind identification algorithms are analyzed here to assess their utility: Eigenvector-based Algorithm for Multichannel Blind Deconvolution (EVAM), Cross Relations (CR), and Iterative Quadratic Maximum Likelihood (IQML). Comparisons of accuracy with conventional (not blind) identification techniques where the blood input is known are made as well. The statistical accuracies of estimation for the three methods are evaluated and compared for multiple parameter sets. The results show that the IQML method gives more accurate estimates than the other two blind identification methods. A proof is presented here that three-compartment model blind identification is not unique in the case of only two regions. It is shown that it is likely unique for the case of more than two regions, but this has not been proved analytically. For the three-compartment model the tissue responses in dynamic FDG PET imaging conditions are analyzed with the blind identification algorithms EVAM and Separable variables Least Squares (SLS). A method of

Experimental identification of a comb-shaped chaotic region in multiple parameter spaces simulated by the Hindmarsh—Rose neuron model

Science.gov (United States)

Jia, Bing

2014-03-01

A comb-shaped chaotic region has been simulated in multiple two-dimensional parameter spaces using the Hindmarsh—Rose (HR) neuron model in many recent studies, which can interpret almost all of the previously simulated bifurcation processes with chaos in neural firing patterns. In the present paper, a comb-shaped chaotic region in a two-dimensional parameter space was reproduced, which presented different processes of period-adding bifurcations with chaos with changing one parameter and fixed the other parameter at different levels. In the biological experiments, different period-adding bifurcation scenarios with chaos by decreasing the extra-cellular calcium concentration were observed from some neural pacemakers at different levels of extra-cellular 4-aminopyridine concentration and from other pacemakers at different levels of extra-cellular caesium concentration. By using the nonlinear time series analysis method, the deterministic dynamics of the experimental chaotic firings were investigated. The period-adding bifurcations with chaos observed in the experiments resembled those simulated in the comb-shaped chaotic region using the HR model. The experimental results show that period-adding bifurcations with chaos are preserved in different two-dimensional parameter spaces, which provides evidence of the existence of the comb-shaped chaotic region and a demonstration of the simulation results in different two-dimensional parameter spaces in the HR neuron model. The results also present relationships between different firing patterns in two-dimensional parameter spaces.

Experimental identification of a comb-shaped chaotic region in multiple parameter spaces simulated by the Hindmarsh—Rose neuron model

International Nuclear Information System (INIS)

Jia Bing

2014-01-01

A comb-shaped chaotic region has been simulated in multiple two-dimensional parameter spaces using the Hindmarsh—Rose (HR) neuron model in many recent studies, which can interpret almost all of the previously simulated bifurcation processes with chaos in neural firing patterns. In the present paper, a comb-shaped chaotic region in a two-dimensional parameter space was reproduced, which presented different processes of period-adding bifurcations with chaos with changing one parameter and fixed the other parameter at different levels. In the biological experiments, different period-adding bifurcation scenarios with chaos by decreasing the extra-cellular calcium concentration were observed from some neural pacemakers at different levels of extra-cellular 4-aminopyridine concentration and from other pacemakers at different levels of extra-cellular caesium concentration. By using the nonlinear time series analysis method, the deterministic dynamics of the experimental chaotic firings were investigated. The period-adding bifurcations with chaos observed in the experiments resembled those simulated in the comb-shaped chaotic region using the HR model. The experimental results show that period-adding bifurcations with chaos are preserved in different two-dimensional parameter spaces, which provides evidence of the existence of the comb-shaped chaotic region and a demonstration of the simulation results in different two-dimensional parameter spaces in the HR neuron model. The results also present relationships between different firing patterns in two-dimensional parameter spaces

A PDP model of the simultaneous perception of multiple objects

Science.gov (United States)

Henderson, Cynthia M.; McClelland, James L.

2011-06-01

Illusory conjunctions in normal and simultanagnosic subjects are two instances where the visual features of multiple objects are incorrectly 'bound' together. A connectionist model explores how multiple objects could be perceived correctly in normal subjects given sufficient time, but could give rise to illusory conjunctions with damage or time pressure. In this model, perception of two objects benefits from lateral connections between hidden layers modelling aspects of the ventral and dorsal visual pathways. As with simultanagnosia, simulations of dorsal lesions impair multi-object recognition. In contrast, a large ventral lesion has minimal effect on dorsal functioning, akin to dissociations between simple object manipulation (retained in visual form agnosia and semantic dementia) and object discrimination (impaired in these disorders) [Hodges, J.R., Bozeat, S., Lambon Ralph, M.A., Patterson, K., and Spatt, J. (2000), 'The Role of Conceptual Knowledge: Evidence from Semantic Dementia', Brain, 123, 1913-1925; Milner, A.D., and Goodale, M.A. (2006), The Visual Brain in Action (2nd ed.), New York: Oxford]. It is hoped that the functioning of this model might suggest potential processes underlying dorsal and ventral contributions to the correct perception of multiple objects.

Objective ARX Model Order Selection for Multi-Channel Human Operator Identification

NARCIS (Netherlands)

Roggenkämper, N; Pool, D.M.; Drop, F.M.; van Paassen, M.M.; Mulder, M.

2016-01-01

In manual control, the human operator primarily responds to visual inputs but may elect to make use of other available feedback paths such as physical motion, adopting a multi-channel control strategy. Hu- man operator identification procedures generally require a priori selection of the model

MODEL PENSKORAN PARTIAL CREDIT PADA BUTIR MULTIPLE TRUE-FALSE BIDANG FISIKA

OpenAIRE

Wasis Wasis

2013-01-01

Tujuan penelitian ini menghasilkan model penskoran politomus untuk respons butir multiple true-false, sehingga dapat mengestimasi secara lebih akurat kemampuan di bidang fisika. Pengembangan penskoran menggunakan Four-D model dan diuji akurasinya melalui penelitian empiris dan simulasi. Penelitian empiris menggunakan 15 butir multiple true-false yang diambil dari soal UMPTN tahun 1996-2006 dan dikenakan pada 410 mahasiswa baru FMIPA Universitas Negeri Surabaya angkatan tahun 2007. Respons pes...

Parameter identification of PEMFC model based on hybrid adaptive differential evolution algorithm

International Nuclear Information System (INIS)

Sun, Zhe; Wang, Ning; Bi, Yunrui; Srinivasan, Dipti

2015-01-01

In this paper, a HADE (hybrid adaptive differential evolution) algorithm is proposed for the identification problem of PEMFC (proton exchange membrane fuel cell). Inspired by biological genetic strategy, a novel adaptive scaling factor and a dynamic crossover probability are presented to improve the adaptive and dynamic performance of differential evolution algorithm. Moreover, two kinds of neighborhood search operations based on the bee colony foraging mechanism are introduced for enhancing local search efficiency. Through testing the benchmark functions, the proposed algorithm exhibits better performance in convergent accuracy and speed. Finally, the HADE algorithm is applied to identify the nonlinear parameters of PEMFC stack model. Through experimental comparison with other identified methods, the PEMFC model based on the HADE algorithm shows better performance. - Highlights: • We propose a hybrid adaptive differential evolution algorithm (HADE). • The search efficiency is enhanced in low and high dimension search space. • The effectiveness is confirmed by testing benchmark functions. • The identification of the PEMFC model is conducted by adopting HADE.

Hybrid approaches for multiple-species stochastic reaction–diffusion models

International Nuclear Information System (INIS)

Spill, Fabian; Guerrero, Pilar; Alarcon, Tomas; Maini, Philip K.; Byrne, Helen

2015-01-01

Reaction–diffusion models are used to describe systems in fields as diverse as physics, chemistry, ecology and biology. The fundamental quantities in such models are individual entities such as atoms and molecules, bacteria, cells or animals, which move and/or react in a stochastic manner. If the number of entities is large, accounting for each individual is inefficient, and often partial differential equation (PDE) models are used in which the stochastic behaviour of individuals is replaced by a description of the averaged, or mean behaviour of the system. In some situations the number of individuals is large in certain regions and small in others. In such cases, a stochastic model may be inefficient in one region, and a PDE model inaccurate in another. To overcome this problem, we develop a scheme which couples a stochastic reaction–diffusion system in one part of the domain with its mean field analogue, i.e. a discretised PDE model, in the other part of the domain. The interface in between the two domains occupies exactly one lattice site and is chosen such that the mean field description is still accurate there. In this way errors due to the flux between the domains are small. Our scheme can account for multiple dynamic interfaces separating multiple stochastic and deterministic domains, and the coupling between the domains conserves the total number of particles. The method preserves stochastic features such as extinction not observable in the mean field description, and is significantly faster to simulate on a computer than the pure stochastic model. - Highlights: • A novel hybrid stochastic/deterministic reaction–diffusion simulation method is given. • Can massively speed up stochastic simulations while preserving stochastic effects. • Can handle multiple reacting species. • Can handle moving boundaries

Hybrid approaches for multiple-species stochastic reaction–diffusion models

Energy Technology Data Exchange (ETDEWEB)

Spill, Fabian, E-mail: fspill@bu.edu [Department of Biomedical Engineering, Boston University, 44 Cummington Street, Boston, MA 02215 (United States); Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 (United States); Guerrero, Pilar [Department of Mathematics, University College London, Gower Street, London WC1E 6BT (United Kingdom); Alarcon, Tomas [Centre de Recerca Matematica, Campus de Bellaterra, Edifici C, 08193 Bellaterra (Barcelona) (Spain); Departament de Matemàtiques, Universitat Atonòma de Barcelona, 08193 Bellaterra (Barcelona) (Spain); Maini, Philip K. [Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford OX2 6GG (United Kingdom); Byrne, Helen [Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford OX2 6GG (United Kingdom); Computational Biology Group, Department of Computer Science, University of Oxford, Oxford OX1 3QD (United Kingdom)

2015-10-15

Reaction–diffusion models are used to describe systems in fields as diverse as physics, chemistry, ecology and biology. The fundamental quantities in such models are individual entities such as atoms and molecules, bacteria, cells or animals, which move and/or react in a stochastic manner. If the number of entities is large, accounting for each individual is inefficient, and often partial differential equation (PDE) models are used in which the stochastic behaviour of individuals is replaced by a description of the averaged, or mean behaviour of the system. In some situations the number of individuals is large in certain regions and small in others. In such cases, a stochastic model may be inefficient in one region, and a PDE model inaccurate in another. To overcome this problem, we develop a scheme which couples a stochastic reaction–diffusion system in one part of the domain with its mean field analogue, i.e. a discretised PDE model, in the other part of the domain. The interface in between the two domains occupies exactly one lattice site and is chosen such that the mean field description is still accurate there. In this way errors due to the flux between the domains are small. Our scheme can account for multiple dynamic interfaces separating multiple stochastic and deterministic domains, and the coupling between the domains conserves the total number of particles. The method preserves stochastic features such as extinction not observable in the mean field description, and is significantly faster to simulate on a computer than the pure stochastic model. - Highlights: • A novel hybrid stochastic/deterministic reaction–diffusion simulation method is given. • Can massively speed up stochastic simulations while preserving stochastic effects. • Can handle multiple reacting species. • Can handle moving boundaries.

Modeling a Single SEP Event from Multiple Vantage Points Using the iPATH Model

Science.gov (United States)

Hu, Junxiang; Li, Gang; Fu, Shuai; Zank, Gary; Ao, Xianzhi

2018-02-01

Using the recently extended 2D improved Particle Acceleration and Transport in the Heliosphere (iPATH) model, we model an example gradual solar energetic particle event as observed at multiple locations. Protons and ions that are energized via the diffusive shock acceleration mechanism are followed at a 2D coronal mass ejection-driven shock where the shock geometry varies across the shock front. The subsequent transport of energetic particles, including cross-field diffusion, is modeled by a Monte Carlo code that is based on a stochastic differential equation method. Time intensity profiles and particle spectra at multiple locations and different radial distances, separated in longitudes, are presented. The results shown here are relevant to the upcoming Parker Solar Probe mission.

Model Predictive Control Based on System Re-Identification (MPC-SRI) to Control Bio-H2 Production from Biomass

Science.gov (United States)

Wahid, A.; Taqwallah, H. M. H.

2018-03-01

Compressors and a steam reformer are the important units in biohydrogen from biomass plant. The compressors are useful for achieving high-pressure operating conditions while the steam reformer is the main process to produce H2 gas. To control them, in this research used a model predictive control (MPC) expected to have better controller performance than conventional controllers. Because of the explicit model empowerment in MPC, obtaining a better model is the main objective before employing MPC. The common way to get the empirical model is through the identification system, so that obtained a first-order plus dead-time (FOPDT) model. This study has already improved that way since used the system re-identification (SRI) based on closed loop mode. Based on this method the results of the compressor pressure control and temperature control of steam reformer were that MPC based on system re-identification (MPC-SRI) has better performance than MPC without system re-identification (MPCWSRI) and the proportional-integral (PI) controller, by % improvement of 73% against MPCWSRI and 75% against the PI controller.

Validation of the measurement model concept for error structure identification

International Nuclear Information System (INIS)

Shukla, Pavan K.; Orazem, Mark E.; Crisalle, Oscar D.

2004-01-01

The development of different forms of measurement models for impedance has allowed examination of key assumptions on which the use of such models to assess error structure are based. The stochastic error structures obtained using the transfer-function and Voigt measurement models were identical, even when non-stationary phenomena caused some of the data to be inconsistent with the Kramers-Kronig relations. The suitability of the measurement model for assessment of consistency with the Kramers-Kronig relations, however, was found to be more sensitive to the confidence interval for the parameter estimates than to the number of parameters in the model. A tighter confidence interval was obtained for Voigt measurement model, which made the Voigt measurement model a more sensitive tool for identification of inconsistencies with the Kramers-Kronig relations

7 CFR 58.524 - Packaging and general identification.

Science.gov (United States)

2010-01-01

... 7 Agriculture 3 2010-01-01 2010-01-01 false Packaging and general identification. 58.524 Section... Service 1 Operations and Operating Procedures § 58.524 Packaging and general identification. (a) Containers. Containers used for packaging cottage cheese shall be any commercially acceptable multiple use or...

Research on marine and freshwater fish identification model based on hyper-spectral imaging technology

Science.gov (United States)

Fu, Yan; Guo, Pei-yuan; Xiang, Ling-zi; Bao, Man; Chen, Xing-hai

2013-08-01

With the gradually mature of hyper spectral image technology, the application of the meat nondestructive detection and recognition has become one of the current research focuses. This paper for the study of marine and freshwater fish by the pre-processing and feature extraction of the collected spectral curve data, combined with BP network structure and LVQ network structure, a predictive model of hyper spectral image data of marine and freshwater fish has been initially established and finally realized the qualitative analysis and identification of marine and freshwater fish quality. The results of this study show that hyper spectral imaging technology combined with the BP and LVQ Artificial Neural Network Model can be used for the identification of marine and freshwater fish detection. Hyper-spectral data acquisition can be carried out without any pretreatment of the samples, thus hyper-spectral imaging technique is the lossless, high- accuracy and rapid detection method for quality of fish. In this study, only 30 samples are used for the exploratory qualitative identification of research, although the ideal study results are achieved, we will further increase the sample capacity to take the analysis of quantitative identification and verify the feasibility of this theory.

Modeling and Parameter Identification Involving 3-Hydroxypropionaldehyde Inhibitory Effects in Glycerol Continuous Fermentation

Directory of Open Access Journals (Sweden)

Zhaohua Gong

2012-01-01

Full Text Available Mathematical modeling and parameter estimation are critical steps in the optimization of biotechnological processes. In the 1,3-propanediol (1,3-PD production by glycerol fermentation process under anaerobic conditions, 3-hydroxypropionaldehyde (3-HPA accumulation would arouse an irreversible cessation of the fermentation process. Considering 3-HPA inhibitions to cells growth and to activities of enzymes, we propose a novel mathematical model to describe glycerol continuous cultures. Some properties of the above model are discussed. On the basis of the concentrations of extracellular substances, a parameter identification model is established to determine the kinetic parameters in the presented system. Through the penalty function technique combined with an extension of the state space method, an improved genetic algorithm is then constructed to solve the parameter identification model. An illustrative numerical example shows the appropriateness of the proposed model and the validity of optimization algorithm. Since it is difficult to measure the concentrations of intracellular substances, a quantitative robustness analysis method is given to infer whether the model is plausible for the intracellular substances. Numerical results show that the proposed model is of good robustness.

Dealing with Multiple Solutions in Structural Vector Autoregressive Models.

Science.gov (United States)

Beltz, Adriene M; Molenaar, Peter C M

2016-01-01

Structural vector autoregressive models (VARs) hold great potential for psychological science, particularly for time series data analysis. They capture the magnitude, direction of influence, and temporal (lagged and contemporaneous) nature of relations among variables. Unified structural equation modeling (uSEM) is an optimal structural VAR instantiation, according to large-scale simulation studies, and it is implemented within an SEM framework. However, little is known about the uniqueness of uSEM results. Thus, the goal of this study was to investigate whether multiple solutions result from uSEM analysis and, if so, to demonstrate ways to select an optimal solution. This was accomplished with two simulated data sets, an empirical data set concerning children's dyadic play, and modifications to the group iterative multiple model estimation (GIMME) program, which implements uSEMs with group- and individual-level relations in a data-driven manner. Results revealed multiple solutions when there were large contemporaneous relations among variables. Results also verified several ways to select the correct solution when the complete solution set was generated, such as the use of cross-validation, maximum standardized residuals, and information criteria. This work has immediate and direct implications for the analysis of time series data and for the inferences drawn from those data concerning human behavior.

Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes.

Science.gov (United States)

Bai, Cong; Peng, Zhong-Ren; Lu, Qing-Chang; Sun, Jian

2015-01-01

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

Science.gov (United States)

Bai, Cong; Peng, Zhong-Ren; Lu, Qing-Chang; Sun, Jian

2015-01-01

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes. PMID:26294903

Green communication: The enabler to multiple business models

DEFF Research Database (Denmark)

Lindgren, Peter; Clemmensen, Suberia; Taran, Yariv

2010-01-01

Companies stand at the forefront of a new business model reality with new potentials - that will change their basic understanding and practice of running their business models radically. One of the drivers to this change is green communication, its strong relation to green business models and its...... possibility to enable lower energy consumption. This paper shows how green communication enables innovation of green business models and multiple business models running simultaneously in different markets to different customers.......Companies stand at the forefront of a new business model reality with new potentials - that will change their basic understanding and practice of running their business models radically. One of the drivers to this change is green communication, its strong relation to green business models and its...

Multiplication circuit for particle identification

International Nuclear Information System (INIS)

Gerlier, Jean

1962-01-01

After having commented some characteristics of the particles present in a cyclotron, and their interactions, this report addresses the development and the implementation of a method and a device for selecting and counting particles. The author presents the principle and existing techniques of selection. In comparison with an existing device, the proportional counter and the scintillator are replaced by junctions: a surface barrier type junction (a silicon N layer with a very thin oxygen layer playing the role of the P layer), and lithium-based junction (a silicon P type layer made intrinsic by migration of lithium). The author then describes the developed circuit and assembly (background of the choice of a multiplication circuit), and their operation. In the next part, he presents the performed tests and discuses the obtained results. He finally outlines the benefits of the herein presented circuit [fr

Closed-loop model identification of cooperative manipulators holding deformable objects

Science.gov (United States)

Alkathiri, A. A.; Akmeliawati, R.; Azlan, N. Z.

2017-11-01

This paper presents system identification to obtain the closed-loop models of a couple of cooperative manipulators in a system, which function to hold deformable objects. The system works using the master-slave principle. In other words, one of the manipulators is position-controlled through encoder feedback, while a force sensor gives feedback to the other force-controlled manipulator. Using the closed-loop input and output data, the closed-loop models, which are useful for model-based control design, are estimated. The criteria for model validation are a 95% fit between the measured and simulated output of the estimated models and residual analysis. The results show that for both position and force control respectively, the fits are 95.73% and 95.88%.

A parameter identification problem arising from a two-dimensional airfoil section model

International Nuclear Information System (INIS)

Cerezo, G.M.

1994-01-01

The development of state space models for aeroelastic systems, including unsteady aerodynamics, is particularly important for the design of highly maneuverable aircraft. In this work we present a state space formulation for a special class of singular neutral functional differential equations (SNFDE) with initial data in C(-1, 0). This work is motivated by the two-dimensional airfoil model presented by Burns, Cliff and Herdman in. In the same authors discuss the validity of the assumptions under which the model was formulated. They pay special attention to the derivation of the evolution equation for the circulation on the airfoil. This equation was coupled to the rigid-body dynamics of the airfoil in order to obtain a complete set of functional differential equations that describes the composite system. The resulting mathematical model for the aeroelastic system has a weakly singular component. In this work we consider a finite delay approximation to the model presented in. We work with a scalar model in which we consider the weak singularity appearing in the original problem. The main goal of this work is to develop numerical techniques for the identification of the parameters appearing in the kernel of the associated scalar integral equation. Clearly this is the first step in the study of parameter identification for the original model and the corresponding validation of this model for the aeroelastic system

Line impedance estimation using model based identification technique

DEFF Research Database (Denmark)

Ciobotaru, Mihai; Agelidis, Vassilios; Teodorescu, Remus

2011-01-01

The estimation of the line impedance can be used by the control of numerous grid-connected systems, such as active filters, islanding detection techniques, non-linear current controllers, detection of the on/off grid operation mode. Therefore, estimating the line impedance can add extra functions...... into the operation of the grid-connected power converters. This paper describes a quasi passive method for estimating the line impedance of the distribution electricity network. The method uses the model based identification technique to obtain the resistive and inductive parts of the line impedance. The quasi...

Identification of neutral biochemical network models from time series data.

Science.gov (United States)

Vilela, Marco; Vinga, Susana; Maia, Marco A Grivet Mattoso; Voit, Eberhard O; Almeida, Jonas S

2009-05-05

The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.

Role model identification by medical radiation science practitioners - a pilot study

International Nuclear Information System (INIS)

Lewis, S.J.; Robinson, J.W.

2003-01-01

Purpose: The objective of this study was to address the paucity of research in role modeling and to develop a greater understanding of role modeling within the occupations of diagnostic radiography and radiation therapy. It can be postulated that the shaping of professional growth may rest with the behavioural models accepted by the workplace or the individuals themselves; thus, role modeling is a vital ingredient in professionalization. The benefits of this research are to advance the attainment of professional development and awareness by diagnostic radiographers and radiation therapists through the processes of identification, classification and construction of generic role models. Method: A study was conducted with eight target centres representing diagnostic and therapy workplaces. The centres ranged from large teaching hospitals to small community hospitals with the inclusion of two large private practices; and geographically, these centres were all located within the Sydney Area Health Services. The research methodology consisted of a structured interview and a short written task. A hierarchical percentage of participants, ranging from chief/manager, senior/charge, junior to recently graduated diagnostic radiographers and radiation therapists were invited to participate in the study. Results and discussion: The results indicated that the generic role models were established for both diagnostic radiographers and radiation therapists despite their varying clinical roles. Also similar was the participants' identification of attributes while viewing themselves as suitable role models. The selection of choice of workplace role model was different for the two occupations. Interestingly, the results indicate a mismatch between the ideal characteristic composition of a role model and the self-perception of the participants as professional role models on the subject of ethical conduct

Role model identification by medical radiation science practitioners - a pilot study

Energy Technology Data Exchange (ETDEWEB)

Lewis, S.J. E-mail: s.lewis@cchs.usyd.edu.au; Robinson, J.W

2003-02-01

Purpose: The objective of this study was to address the paucity of research in role modeling and to develop a greater understanding of role modeling within the occupations of diagnostic radiography and radiation therapy. It can be postulated that the shaping of professional growth may rest with the behavioural models accepted by the workplace or the individuals themselves; thus, role modeling is a vital ingredient in professionalization. The benefits of this research are to advance the attainment of professional development and awareness by diagnostic radiographers and radiation therapists through the processes of identification, classification and construction of generic role models. Method: A study was conducted with eight target centres representing diagnostic and therapy workplaces. The centres ranged from large teaching hospitals to small community hospitals with the inclusion of two large private practices; and geographically, these centres were all located within the Sydney Area Health Services. The research methodology consisted of a structured interview and a short written task. A hierarchical percentage of participants, ranging from chief/manager, senior/charge, junior to recently graduated diagnostic radiographers and radiation therapists were invited to participate in the study. Results and discussion: The results indicated that the generic role models were established for both diagnostic radiographers and radiation therapists despite their varying clinical roles. Also similar was the participants' identification of attributes while viewing themselves as suitable role models. The selection of choice of workplace role model was different for the two occupations. Interestingly, the results indicate a mismatch between the ideal characteristic composition of a role model and the self-perception of the participants as professional role models on the subject of ethical conduct.

Dynamic biometric identification from multiple views using the GLBP-TOP method.

Science.gov (United States)

Wang, Yu; Shen, Xuanjing; Chen, Haipeng; Zhai, Yujie

2014-01-01

To realize effective and rapid dynamic biometric identification with low computational complexity, a video-based facial texture program that extracts local binary patterns from three orthogonal planes in the frequency domain of the Gabor transform (GLBP-TOP) was proposed. Firstly, each normalized face was transformed by Gabor wavelet to get the enhanced Gabor magnitude map, and then the LBP-TOP operator was applied to the maps to extract video texture. Finally, weighted Chi square statistics based on the Fisher Criterion were used to realize the identification. The proposed algorithm was proved effective through the biometric experiments using the Honda/UCSD database, and was robust against changes of illumination and expressions.

An extension of the multiple-trapping model

International Nuclear Information System (INIS)

Shkilev, V. P.

2012-01-01

The hopping charge transport in disordered semiconductors is considered. Using the concept of the transport energy level, macroscopic equations are derived that extend a multiple-trapping model to the case of semiconductors with both energy and spatial disorders. It is shown that, although both types of disorder can cause dispersive transport, the frequency dependence of conductivity is determined exclusively by the spatial disorder.

Measurement Uncertainty in Racial and Ethnic Identification among Adolescents of Mixed Ancestry: A Latent Variable Approach

Science.gov (United States)

Tracy, Allison J.; Erkut, Sumru; Porche, Michelle V.; Kim, Jo; Charmaraman, Linda; Grossman, Jennifer M.; Ceder, Ineke; Garcia, Heidie Vazquez

2010-01-01

In this article, we operationalize identification of mixed racial and ethnic ancestry among adolescents as a latent variable to (a) account for measurement uncertainty, and (b) compare alternative wording formats for racial and ethnic self-categorization in surveys. Two latent variable models were fit to multiple mixed-ancestry indicator data from…

The Blind Identification of Multi-Inputs and Multi-Outputs Shallow-Water Acoustic Channel

International Nuclear Information System (INIS)

Li, R Y; Zhou, J H; Wang, L

2006-01-01

Blind channel identification/estimation is very important for object detection, trace, localization in the ocean acoustics. Time domain blind identification algorithm requiring exact length of the channel being identification. Due to the characteristics of the shallow-water channel, the length of channel impulse response sequence is uncertain, Hence a frequency domain method for the blind MIMO (Multiple-Input Multiple-Output) underwater identification based on higher order statistics (HOS) is used to estimate the original acoustic channel from received signals on hydrophones only, with the low signal to noise ratio (SNR). The simulation results in the acoustic environment proved this work is effective and efficient for blind identification of the shallow-water acoustic channel

A Multiple Model Prediction Algorithm for CNC Machine Wear PHM

Directory of Open Access Journals (Sweden)

Huimin Chen

2011-01-01

Full Text Available The 2010 PHM data challenge focuses on the remaining useful life (RUL estimation for cutters of a high speed CNC milling machine using measurements from dynamometer, accelerometer, and acoustic emission sensors. We present a multiple model approach for wear depth estimation of milling machine cutters using the provided data. The feature selection, initial wear estimation and multiple model fusion components of the proposed algorithm are explained in details and compared with several alternative methods using the training data. The final submission ranked #2 among professional and student participants and the method is applicable to other data driven PHM problems.

Odour discrimination and identification are improved in early blindness.

Science.gov (United States)

Cuevas, Isabel; Plaza, Paula; Rombaux, Philippe; De Volder, Anne G; Renier, Laurent

2009-12-01

Previous studies showed that early blind humans develop superior abilities in the use of their remaining senses, hypothetically due to a functional reorganization of the deprived visual brain areas. While auditory and tactile functions have been investigated for long, little is known about the effects of early visual deprivation on olfactory processing. However, blind humans make an extensive use of olfactory information in their daily life. Here we investigated olfactory discrimination and identification abilities in early blind subjects and age-matched sighted controls. Three levels of cuing were used in the identification task, i.e., free-identification (no cue), categorization (semantic cues) and multiple choice (semantic and phonological cues). Early blind subjects significantly outperformed the controls in odour discrimination, free-identification and categorization. In addition, the larger group difference was observed in the free-identification as compared to the categorization and the multiple choice conditions. This indicated that a better access to the semantic information from odour perception accounted for part of the improved olfactory performances in odour identification in the blind. We concluded that early blind subjects have both improved perceptual abilities and a better access to the information stored in semantic memory than sighted subjects.

Trends and progress in system identification

CERN Document Server

Eykhoff, Pieter

1981-01-01

Trends and Progress in System Identification is a three-part book that focuses on model considerations, identification methods, and experimental conditions involved in system identification. Organized into 10 chapters, this book begins with a discussion of model method in system identification, citing four examples differing on the nature of the models involved, the nature of the fields, and their goals. Subsequent chapters describe the most important aspects of model theory; the """"classical"""" methods and time series estimation; application of least squares and related techniques for the e

Optimization of an individual re-identification modeling process using biometric features

Energy Technology Data Exchange (ETDEWEB)

Heredia-Langner, Alejandro; Amidan, Brett G.; Matzner, Shari; Jarman, Kristin H.

2014-09-24

We present results from the optimization of a re-identification process using two sets of biometric data obtained from the Civilian American and European Surface Anthropometry Resource Project (CAESAR) database. The datasets contain real measurements of features for 2378 individuals in a standing (43 features) and seated (16 features) position. A genetic algorithm (GA) was used to search a large combinatorial space where different features are available between the probe (seated) and gallery (standing) datasets. Results show that optimized model predictions obtained using less than half of the 43 gallery features and data from roughly 16% of the individuals available produce better re-identification rates than two other approaches that use all the information available.

FDRAnalysis: a tool for the integrated analysis of tandem mass spectrometry identification results from multiple search engines.

Science.gov (United States)

Wedge, David C; Krishna, Ritesh; Blackhurst, Paul; Siepen, Jennifer A; Jones, Andrew R; Hubbard, Simon J

2011-04-01

Confident identification of peptides via tandem mass spectrometry underpins modern high-throughput proteomics. This has motivated considerable recent interest in the postprocessing of search engine results to increase confidence and calculate robust statistical measures, for example through the use of decoy databases to calculate false discovery rates (FDR). FDR-based analyses allow for multiple testing and can assign a single confidence value for both sets and individual peptide spectrum matches (PSMs). We recently developed an algorithm for combining the results from multiple search engines, integrating FDRs for sets of PSMs made by different search engine combinations. Here we describe a web-server and a downloadable application that makes this routinely available to the proteomics community. The web server offers a range of outputs including informative graphics to assess the confidence of the PSMs and any potential biases. The underlying pipeline also provides a basic protein inference step, integrating PSMs into protein ambiguity groups where peptides can be matched to more than one protein. Importantly, we have also implemented full support for the mzIdentML data standard, recently released by the Proteomics Standards Initiative, providing users with the ability to convert native formats to mzIdentML files, which are available to download.

Identification of MIMO systems with sparse transfer function coefficients

Science.gov (United States)

Qiu, Wanzhi; Saleem, Syed Khusro; Skafidas, Efstratios

2012-12-01

We study the problem of estimating transfer functions of multivariable (multiple-input multiple-output--MIMO) systems with sparse coefficients. We note that subspace identification methods are powerful and convenient tools in dealing with MIMO systems since they neither require nonlinear optimization nor impose any canonical form on the systems. However, subspace-based methods are inefficient for systems with sparse transfer function coefficients since they work on state space models. We propose a two-step algorithm where the first step identifies the system order using the subspace principle in a state space format, while the second step estimates coefficients of the transfer functions via L1-norm convex optimization. The proposed algorithm retains good features of subspace methods with improved noise-robustness for sparse systems.

Selecting Tools to Model Integer and Binomial Multiplication

Science.gov (United States)

Pratt, Sarah Smitherman; Eddy, Colleen M.

2017-01-01

Mathematics teachers frequently provide concrete manipulatives to students during instruction; however, the rationale for using certain manipulatives in conjunction with concepts may not be explored. This article focuses on area models that are currently used in classrooms to provide concrete examples of integer and binomial multiplication. The…

TLM modeling and system identification of optimized antenna structures

Directory of Open Access Journals (Sweden)

N. Fichtner

2008-05-01

Full Text Available The transmission line matrix (TLM method in conjunction with the genetic algorithm (GA is presented for the bandwidth optimization of a low profile patch antenna. The optimization routine is supplemented by a system identification (SI procedure. By the SI the model parameters of the structure are estimated which is used for a reduction of the total TLM simulation time. The SI utilizes a new stability criterion of the physical poles for the parameter extraction.

Comparative study between a QCD inspired model and a multiple diffraction model

International Nuclear Information System (INIS)

Luna, E.G.S.; Martini, A.F.; Menon, M.J.

2003-01-01

A comparative study between a QCD Inspired Model (QCDIM) and a Multiple Diffraction Model (MDM) is presented, with focus on the results for pp differential cross section at √s = 52.8 GeV. It is shown that the MDM predictions are in agreement with experimental data, except for the dip region and that the QCDIM describes only the diffraction peak region. Interpretations in terms of the corresponding eikonals are also discussed. (author)

Experimental evaluation of a quasi-modal parameter based rotor foundation identification technique

Science.gov (United States)

Yu, Minli; Liu, Jike; Feng, Ningsheng; Hahn, Eric J.

2017-12-01

Correct modelling of the foundation of rotating machinery is an invaluable asset in model-based rotor dynamic study. One attractive approach for such purpose is to identify the relevant modal parameters of an equivalent foundation using the motion measurements of rotor and foundation at the bearing supports. Previous research showed that, a complex quasi-modal parameter based system identification technique could be feasible for this purpose; however, the technique was only validated by identifying simple structures under harmonic excitation. In this paper, such identification technique is further extended and evaluated by identifying the foundation of a numerical rotor-bearing-foundation system and an experimental rotor rig respectively. In the identification of rotor foundation with multiple bearing supports, all application points of excitation forces transmitted through bearings need to be included; however the assumed vibration modes far outside the rotor operating speed cannot or not necessary to be identified. The extended identification technique allows one to identify correctly an equivalent foundation with fewer modes than the assumed number of degrees of freedom, essentially by generalising the technique to be able to handle rectangular complex modal matrices. The extended technique is robust in numerical and experimental validation and is therefore likely to be applicable in the field.

Combining results of multiple search engines in proteomics.

Science.gov (United States)

Shteynberg, David; Nesvizhskii, Alexey I; Moritz, Robert L; Deutsch, Eric W

2013-09-01

A crucial component of the analysis of shotgun proteomics datasets is the search engine, an algorithm that attempts to identify the peptide sequence from the parent molecular ion that produced each fragment ion spectrum in the dataset. There are many different search engines, both commercial and open source, each employing a somewhat different technique for spectrum identification. The set of high-scoring peptide-spectrum matches for a defined set of input spectra differs markedly among the various search engine results; individual engines each provide unique correct identifications among a core set of correlative identifications. This has led to the approach of combining the results from multiple search engines to achieve improved analysis of each dataset. Here we review the techniques and available software for combining the results of multiple search engines and briefly compare the relative performance of these techniques.

Combining Results of Multiple Search Engines in Proteomics*

Science.gov (United States)

Shteynberg, David; Nesvizhskii, Alexey I.; Moritz, Robert L.; Deutsch, Eric W.

2013-01-01

A crucial component of the analysis of shotgun proteomics datasets is the search engine, an algorithm that attempts to identify the peptide sequence from the parent molecular ion that produced each fragment ion spectrum in the dataset. There are many different search engines, both commercial and open source, each employing a somewhat different technique for spectrum identification. The set of high-scoring peptide-spectrum matches for a defined set of input spectra differs markedly among the various search engine results; individual engines each provide unique correct identifications among a core set of correlative identifications. This has led to the approach of combining the results from multiple search engines to achieve improved analysis of each dataset. Here we review the techniques and available software for combining the results of multiple search engines and briefly compare the relative performance of these techniques. PMID:23720762

Spectroscopic pulsational frequency identification and mode determination of γ Doradus star HD 12901

Science.gov (United States)

Brunsden, E.; Pollard, K. R.; Cottrell, P. L.; Wright, D. J.; De Cat, P.

2012-12-01

Using multisite spectroscopic data collected from three sites, the frequencies and pulsational modes of the γ Doradus star HD 12901 were identified. A total of six frequencies in the range 1-2 d-1 were observed, their identifications supported by multiple line-profile measurement techniques and previously published photometry. Five frequencies were of sufficient signal-to-noise ratio for mode identification, and all five displayed similar three-bump standard deviation profiles which were fitted well with (l,m) = (1,1) modes. These fits had reduced χ2 values of less than 18. We propose that this star is an excellent candidate to test models of non-radially pulsating γ Doradus stars as a result of the presence of multiple (1,1) modes. This paper includes data taken at the Mount John University Observatory of the University of Canterbury (New Zealand), the McDonald Observatory of the University of Texas at Austin (Texas, USA) and the European Southern Observatory at La Silla (Chile).

REMI and ROUSE: Quantitative Models for Long-Term and Short-Term Priming in Perceptual Identification

NARCIS (Netherlands)

E.J. Wagenmakers (Eric-Jan); R. Zeelenberg (René); D.E. Huber (David); J.G.W. Raaijmakers (Jeroen)

2003-01-01

textabstractThe REM model originally developed for recognition memory (Shiffrin & Steyvers, 1997) has recently been extended to implicit memory phenomena observed during threshold identification of words. We discuss two REM models based on Bayesian principles: a model for long-term priming (REMI;

Identification of alternative splice variants in Aspergillus flavus through comparison of multiple tandem MS search algorithms

Directory of Open Access Journals (Sweden)

Chang Kung-Yen

2011-07-01

Full Text Available Abstract Background Database searching is the most frequently used approach for automated peptide assignment and protein inference of tandem mass spectra. The results, however, depend on the sequences in target databases and on search algorithms. Recently by using an alternative splicing database, we identified more proteins than with the annotated proteins in Aspergillus flavus. In this study, we aimed at finding a greater number of eligible splice variants based on newly available transcript sequences and the latest genome annotation. The improved database was then used to compare four search algorithms: Mascot, OMSSA, X! Tandem, and InsPecT. Results The updated alternative splicing database predicted 15833 putative protein variants, 61% more than the previous results. There was transcript evidence for 50% of the updated genes compared to the previous 35% coverage. Database searches were conducted using the same set of spectral data, search parameters, and protein database but with different algorithms. The false discovery rates of the peptide-spectrum matches were estimated Conclusions We were able to detect dozens of new peptides using the improved alternative splicing database with the recently updated annotation of the A. flavus genome. Unlike the identifications of the peptides and the RefSeq proteins, large variations existed between the putative splice variants identified by different algorithms. 12 candidates of putative isoforms were reported based on the consensus peptide-spectrum matches. This suggests that applications of multiple search engines effectively reduced the possible false positive results and validated the protein identifications from tandem mass spectra using an alternative splicing database.

On the Uncertainty of Identification of Civil Engineering Structures using ARMA Models

DEFF Research Database (Denmark)

Andersen, P.; Brincker, Rune; Kirkegaard, Poul Henning

In this paper the uncertainties of modal parameters estimated using ARMA models for identification of civil engineering structures are investigated. How to initialize the predictor part of a Gauss-Newton optimization algorithm is put in focus. A backward-forecasting procedure for initialization...

On the Uncertainty of Identification of Civil Engineering Structures Using ARMA Models

DEFF Research Database (Denmark)

Andersen, Palle; Brincker, Rune; Kirkegaard, Poul Henning

1995-01-01

In this paper the uncertainties of modal parameters estimated using ARMA models for identification of civil engineering structures are investigated. How to initialize the predictor part of a Gauss-Newton optimization algorithm is put in focus. A backward-forecasting procedure for initialization...

Implementation and identification of Preisach type hysteresis models with Everett Function in closed form

Energy Technology Data Exchange (ETDEWEB)

Szabó, Zsolt, E-mail: szabo@evt.bme.hu [Department of Broadband Infocommunications and Electromagnetic Theory, Budapest University of Technology and Economics, Budapest (Hungary); Füzi, János [Neutron Spectroscopy Department, Wigner Research Centre for Physics, Budapest (Hungary); Faculty of Engineering and Information Technology, University of Pécs (Hungary)

2016-05-15

The Preisach function is considered as a product of two special one dimensional functions, which allows the closed form evaluation of the Everett integral. The deduced closed form expressions are included in Preisach models, in particular in the static model, moving model and a rate dependent hysteresis model, which can simulate the frequency dependence of the magnetization process. The details of the freely available implementations, which are available online are presented. The identification of the model parameters and the accuracy to describe the magnetization process are discussed and demonstrated by fitting measured data. Transient electric circuit simulation with hysteresis demonstrates the applicability of the developed models. - Highlights: • Formulation of the Preisach model with Everett function in closed form. • Identification of the parameters: when the shape of the analytical Preisach function does not matches the ferromagnetic material the moving model can be applied to increase the accuracy. • Novel algorithm with Fixed Point iteration, which utilizes the closed formulation to simulate the frequency dependence of the magnetization process. • The developed hysteresis models are utilized in circuit simulation algorithm to determine the transient behavior of the current, which flows through a toroidal coil with ferromagnetic core.

Identification of neutral biochemical network models from time series data

Directory of Open Access Journals (Sweden)

Maia Marco

2009-05-01

Full Text Available Abstract Background The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. Results In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. Conclusion The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.

Study on Identification of Material Model Parameters from Compact Tension Test on Concrete Specimens

Science.gov (United States)

Hokes, Filip; Kral, Petr; Husek, Martin; Kala, Jiri

2017-10-01

Identification of a concrete material model parameters using optimization is based on a calculation of a difference between experimentally measured and numerically obtained data. Measure of the difference can be formulated via root mean squared error that is often used for determination of accuracy of a mathematical model in the field of meteorology or demography. The quality of the identified parameters is, however, determined not only by right choice of an objective function but also by the source experimental data. One of the possible way is to use load-displacement curves from three-point bending tests that were performed on concrete specimens. This option shows the significance of modulus of elasticity, tensile strength and specific fracture energy. Another possible option is to use experimental data from compact tension test. It is clear that the response in the second type of test is also dependent on the above mentioned material parameters. The question is whether the parameters identified within three-point bending test and within compact tension test will reach the same values. The presented article brings the numerical study of inverse identification of material model parameters from experimental data measured during compact tension tests. The article also presents utilization of the modified sensitivity analysis that calculates the sensitivity of the material model parameters for different parts of loading curve. The main goal of the article is to describe the process of inverse identification of parameters for plasticity-based material model of concrete and prepare data for future comparison with identified values of the material model parameters from different type of fracture tests.

Computerized nipple identification for multiple image analysis in computer-aided diagnosis

International Nuclear Information System (INIS)

Zhou Chuan; Chan Heangping; Paramagul, Chintana; Roubidoux, Marilyn A.; Sahiner, Berkman; Hadjiiski, Labomir M.; Petrick, Nicholas

2004-01-01

Correlation of information from multiple-view mammograms (e.g., MLO and CC views, bilateral views, or current and prior mammograms) can improve the performance of breast cancer diagnosis by radiologists or by computer. The nipple is a reliable and stable landmark on mammograms for the registration of multiple mammograms. However, accurate identification of nipple location on mammograms is challenging because of the variations in image quality and in the nipple projections, resulting in some nipples being nearly invisible on the mammograms. In this study, we developed a computerized method to automatically identify the nipple location on digitized mammograms. First, the breast boundary was obtained using a gradient-based boundary tracking algorithm, and then the gray level profiles along the inside and outside of the boundary were identified. A geometric convergence analysis was used to limit the nipple search to a region of the breast boundary. A two-stage nipple detection method was developed to identify the nipple location using the gray level information around the nipple, the geometric characteristics of nipple shapes, and the texture features of glandular tissue or ducts which converge toward the nipple. At the first stage, a rule-based method was designed to identify the nipple location by detecting significant changes of intensity along the gray level profiles inside and outside the breast boundary and the changes in the boundary direction. At the second stage, a texture orientation-field analysis was developed to estimate the nipple location based on the convergence of the texture pattern of glandular tissue or ducts towards the nipple. The nipple location was finally determined from the detected nipple candidates by a rule-based confidence analysis. In this study, 377 and 367 randomly selected digitized mammograms were used for training and testing the nipple detection algorithm, respectively. Two experienced radiologists identified the nipple locations

Modal Identification of A Tested Steel Frame using Linear ARX Model Structure

Directory of Open Access Journals (Sweden)

Yavuz Kaya

2009-07-01

Full Text Available This study contains the identification of modal dynamic properties of a 3-story large-scale steel test frame structure through shaking table measurements. Shaking table test is carried out to estimate the modal properties of the test frame such as natural frequencies, damping ratios and mode shapes. Among many different model structures, ARX (Auto Recursive Exogenous model structure is used for modal identification of the frame structure system. The unknown parameters in the obtained ARX model structure are estimated by Least-Square method by minimizing the AIC criteria with the help of a program coded in advanced computing software MATLAB®. The adopted model structure is then tested out in time domain to verify the validity of the model with the selected model parameters. Then the modal characteristics of test frame and the story stiffness are estimated using the white noise shakings. An attempt is done to determine the change of modal characteristics and the story stiffness of test frame according to the velocity, which the test frame structure experienced during the shaking schedule and also during the input shaking of El Centro 1940 NS. Results shows that there is an increase in damping ratio and a decrease in both story stiffness and natural frequency for all modes when the damage forms at cementitious device and the test frame structure itself during the shaking schedule.

Identification and Multiplicity of Double Vowels in Cochlear Implant Users

Science.gov (United States)

Kwon, Bomjun J.; Perry, Trevor T.

2014-01-01

Purpose: The present study examined cochlear implant (CI) users' perception of vowels presented concurrently (i.e., "double vowels") to further our understanding of auditory grouping in electric hearing. Method: Identification of double vowels and single vowels was measured with 10 CI subjects. Fundamental frequencies (F0s) of…

Towards Integration of CAx Systems and a Multiple-View Product Modeller in Mechanical Design

Directory of Open Access Journals (Sweden)

H. Song

2005-01-01

Full Text Available This paper deals with the development of an integration framework and its implementation for the connexion of CAx systems and multiple-view product modelling. The integration framework is presented regarding its conceptual level and the implementation level is described currently with the connexion of a functional modeller, a multiple-view product modeller, an optimisation module and a CAD system. The integration between the multiple-view product modeller and CATIA V5 based on the STEP standard is described in detail. Finally, the presented works are discussed and future research developments are suggested. 

Optimized production planning model for a multi-plant cultivation system under uncertainty

Science.gov (United States)

Ke, Shunkui; Guo, Doudou; Niu, Qingliang; Huang, Danfeng

2015-02-01

An inexact multi-constraint programming model under uncertainty was developed by incorporating a production plan algorithm into the crop production optimization framework under the multi-plant collaborative cultivation system. In the production plan, orders from the customers are assigned to a suitable plant under the constraints of plant capabilities and uncertainty parameters to maximize profit and achieve customer satisfaction. The developed model and solution method were applied to a case study of a multi-plant collaborative cultivation system to verify its applicability. As determined in the case analysis involving different orders from customers, the period of plant production planning and the interval between orders can significantly affect system benefits. Through the analysis of uncertain parameters, reliable and practical decisions can be generated using the suggested model of a multi-plant collaborative cultivation system.

A new modelling and identification scheme for time-delay systems with experimental investigation: a relay feedback approach

Science.gov (United States)

Pandey, Saurabh; Majhi, Somanath; Ghorai, Prasenjit

2017-07-01

In this paper, the conventional relay feedback test has been modified for modelling and identification of a class of real-time dynamical systems in terms of linear transfer function models with time-delay. An ideal relay and unknown systems are connected through a negative feedback loop to bring the sustained oscillatory output around the non-zero setpoint. Thereafter, the obtained limit cycle information is substituted in the derived mathematical equations for accurate identification of unknown plants in terms of overdamped, underdamped, critically damped second-order plus dead time and stable first-order plus dead time transfer function models. Typical examples from the literature are included for the validation of the proposed identification scheme through computer simulations. Subsequently, the comparisons between estimated model and true system are drawn through integral absolute error criterion and frequency response plots. Finally, the obtained output responses through simulations are verified experimentally on real-time liquid level control system using Yokogawa Distributed Control System CENTUM CS3000 set up.

Modeling misidentification errors in capture-recapture studies using photographic identification of evolving marks

Science.gov (United States)

Yoshizaki, J.; Pollock, K.H.; Brownie, C.; Webster, R.A.

2009-01-01

Misidentification of animals is potentially important when naturally existing features (natural tags) are used to identify individual animals in a capture-recapture study. Photographic identification (photoID) typically uses photographic images of animals' naturally existing features as tags (photographic tags) and is subject to two main causes of identification errors: those related to quality of photographs (non-evolving natural tags) and those related to changes in natural marks (evolving natural tags). The conventional methods for analysis of capture-recapture data do not account for identification errors, and to do so requires a detailed understanding of the misidentification mechanism. Focusing on the situation where errors are due to evolving natural tags, we propose a misidentification mechanism and outline a framework for modeling the effect of misidentification in closed population studies. We introduce methods for estimating population size based on this model. Using a simulation study, we show that conventional estimators can seriously overestimate population size when errors due to misidentification are ignored, and that, in comparison, our new estimators have better properties except in cases with low capture probabilities (<0.2) or low misidentification rates (<2.5%). ?? 2009 by the Ecological Society of America.

A multiple-dimension liquid chromatography coupled with mass spectrometry data strategy for the rapid discovery and identification of unknown compounds from a Chinese herbal formula (Er-xian decoction).

Science.gov (United States)

Wang, Caihong; Zhang, Jinlan; Wu, Caisheng; Wang, Zhe

2017-10-06

It is very important to rapidly discover and identify the multiple components of traditional Chinese medicine (TCM) formula. High performance liquid chromatography with high resolution tandem mass spectrometry (HPLC-HRMS/MS) has been widely used to analyze TCM formula and contains multiple-dimension data including retention time (RT), high resolution mass (HRMS), multiple-stage mass spectrometric (MS n ), and isotope intensity distribution (IID) data. So it is very necessary to exploit a useful strategy to utilize multiple-dimension data to rapidly probe structural information and identify chemical compounds. In this study, a new strategy to initiatively use the multiple-dimension LC-MS data has been developed to discover and identify unknown compounds of TCM in many styles. The strategy guarantees the fast discovery of candidate structural information and provides efficient structure clues for identification. The strategy contains four steps in sequence: (1) to discover potential compounds and obtain sub-structure information by the mass spectral tree similarity filter (MTSF) technique, based on HRMS and MS n data; (2) to classify potential compounds into known chemical classes by discriminant analysis (DA) on the basis of RT and HRMS data; (3) to hit the candidate structural information of compounds by intersection sub-structure between MTSF and DA (M,D-INSS); (4) to annotate and confirm candidate structures by IID data. This strategy allowed for the high exclusion efficiency (greater than 41%) of irrelevant ions in er-xian decoction (EXD) while providing accurate structural information of 553 potential compounds and identifying 66 candidates, therefore accelerating and simplifying the discovery and identification of unknown compounds in TCM formula. Copyright © 2017 Elsevier B.V. All rights reserved.

Parametric identification of the Nomoto generalized model using the apparatus of variational calculus

Directory of Open Access Journals (Sweden)

Agarkov S.A.

2015-03-01

Full Text Available A new approach to the identification of parameters of the Nomoto generalized vessel model has been proposed. The apparatus of classical calculus and the method of least squares have been used

The Development Of Mathematical Model For Automated Fingerprint Identification Systems Analysis

International Nuclear Information System (INIS)

Ardisasmita, M. Syamsa

2001-01-01

Fingerprint has a strong oriented and periodic structure composed of dark lines of raised skin (ridges) and clear lines of lowered skin (furrows)that twist to form a distinct pattern. Although the manner in which the ridges flow is distinctive, other characteristics of the fingerprint called m inutiae a re what are most unique to the individual. These features are particular patterns consisting of terminations or bifurcations of the ridges. To assert if two fingerprints are from the same finger or not, experts detect those minutiae. AFIS (Automated Fingerprint Identification Systems) extract and compare these features for determining a match. The classic methods of fingerprints recognition are not suitable for direct implementation in form of computer algorithms. The creation of a finger's model was however the necessity of development of new, better algorithms of analysis. This paper presents a new numerical methods of fingerprints' simulation based on mathematical model of arrangement of dermatoglyphics and creation of minutiae. This paper describes also the design and implementation of an automated fingerprint identification systems which operates in two stages: minutiae extraction and minutiae matching

Nonlinear State Space Modeling and System Identification for Electrohydraulic Control

Directory of Open Access Journals (Sweden)

Jun Yan

2013-01-01

Full Text Available The paper deals with nonlinear modeling and identification of an electrohydraulic control system for improving its tracking performance. We build the nonlinear state space model for analyzing the highly nonlinear system and then develop a Hammerstein-Wiener (H-W model which consists of a static input nonlinear block with two-segment polynomial nonlinearities, a linear time-invariant dynamic block, and a static output nonlinear block with single polynomial nonlinearity to describe it. We simplify the H-W model into a linear-in-parameters structure by using the key term separation principle and then use a modified recursive least square method with iterative estimation of internal variables to identify all the unknown parameters simultaneously. It is found that the proposed H-W model approximates the actual system better than the independent Hammerstein, Wiener, and ARX models. The prediction error of the H-W model is about 13%, 54%, and 58% less than the Hammerstein, Wiener, and ARX models, respectively.

Identification of Misconceptions through Multiple Choice Tasks at Municipal Chemistry Competition Test

Directory of Open Access Journals (Sweden)

Dušica D Milenković

2016-01-01

Full Text Available In this paper, the level of conceptual understanding of chemical contents among seventh grade students who participated in the municipal Chemistry competition in Novi Sad, Serbia, in 2013 have been examined. Tests for the municipal chemistry competition were used as a measuring instrument, wherein only multiple choice tasks were considered and analyzed. Determination of the level of conceptual understanding of the tested chemical contents was based on the calculation of the frequency of choosing the correct answers. Thereby, identification of areas of satisfactory conceptual understanding, areas of roughly adequate performance, areas of inadequate performance, and areas of quite inadequate performance have been conducted. On the other hand, the analysis of misconceptions was based on the analysis of distractors. The results showed that satisfactory level of conceptual understanding and roughly adequate performance characterize majority of contents, which was expected since only the best students who took part in the contest were surveyed. However, this analysis identified a large number of misunderstandings, as well. In most of the cases, these misconceptions were related to the inability to distinguish elements, compounds, homogeneous and heterogeneous mixtures. Besides, it is shown that students are not familiar with crystal structure of the diamond, and with metric prefixes. The obtained results indicate insufficient visualization of the submicroscopic level in school textbooks, the imprecise use of chemical language by teachers and imprecise use of language in chemistry textbooks.

Multiple organizational identification levels and the impact of perceived external prestige and communication climate

NARCIS (Netherlands)

Bartels, J.; Pruyn, A.T.H.; Jong, de M.D.T.; Joustra, I.

2007-01-01

Earlier studies have shown that perceived external prestige and communication climate influence organizational identification. In this paper we present the results of a study of the influence of communication climate and perceived external prestige on organizational identification at various

Modelling and Identification for Control of Gas Bearings

DEFF Research Database (Denmark)

Theisen, Lukas Roy Svane; Niemann, Hans Henrik; Santos, Ilmar

2015-01-01

Gas bearings are popular for their high speed capabilities, low friction and clean operation, but suffer from poor damping, which poses challenges for safe operation in presence of disturbances. Enhanced damping can be achieved through active lubrication techniques using feedback control laws....... Such control design requires models with low complexity, able to describe the dominant dynamics from actuator input to sensor output over the relevant range of operation. The mathematical models based on first principles are not easy to obtain, and in many cases, they cannot be directly used for control design...... to industrial rotating machinery with gas bearings and to allow for subsequent control design. The paper shows how piezoelectric actuators in a gas bearing are efficiently used to perturb the gas film for identification over relevant ranges of rotational speed and gas injection pressure. Parameter...

Multiple organizational identification levels and the impact of perceived external prestige and communication climate

NARCIS (Netherlands)

Bartels, J.; Pruyn, Adriaan T.H.; de Jong, Menno D.T.; Joustra, Inge

2007-01-01

sEarlier studies have shown that perceived external prestige and communication climate influence organizational identification. In this paper we present the results of a study of the influence of communication climate and perceived external prestige on organizational identification at various

Particle Identification in Jets and High-Multiplicity pp Events with the ALICE TPC

CERN Document Server

AUTHOR|(SzGeCERN)683272; Vogelsang, Werner

The spectra of identified particles in a collision experiment comprise crucial information about the underlying physical processes. The ALICE experiment has powerful Particle IDentification (PID) capabilities, which are unique at the Large Hadron Collider (LHC). In this thesis, a statistical PID method based on the specific energy loss d$E$/d$x$ in the ALICE Time Projection Chamber (TPC) is developed: the TPC Multi-Template Fit (MTF). The MTF allows for the extraction of identified charged particle spectra in a wide momentum range, which extends from about 150 MeV/$c$ to above 20 GeV/$c$. The TPC PID requires a detailed modelling of the TPC d$E$/d$x$ response for momenta above 2-3 GeV/$c$. A framework is developed that allows for the determination of the model parameters and for evaluating the PID information of charged particles. With the MTF, the transverse momentum $p_{\\mathrm{T}}$ spectra of charged pions, kaons and protons at mid-rapidity ($|\\eta| < 0.9$) are measured for pp collisions at $\\sqrt{s}$ ...

Model Optimization Identification Method Based on Closed-loop Operation Data and Process Characteristics Parameters

Directory of Open Access Journals (Sweden)

Zhiqiang GENG

2014-01-01

Full Text Available Output noise is strongly related to input in closed-loop control system, which makes model identification of closed-loop difficult, even unidentified in practice. The forward channel model is chosen to isolate disturbance from the output noise to input, and identified by optimization the dynamic characteristics of the process based on closed-loop operation data. The characteristics parameters of the process, such as dead time and time constant, are calculated and estimated based on the PI/PID controller parameters and closed-loop process input/output data. And those characteristics parameters are adopted to define the search space of the optimization identification algorithm. PSO-SQP optimization algorithm is applied to integrate the global search ability of PSO with the local search ability of SQP to identify the model parameters of forward channel. The validity of proposed method has been verified by the simulation. The practicability is checked with the PI/PID controller parameter turning based on identified forward channel model.

A study of a dual polarization laser backscatter system for remote identification and measurement of water pollution

Science.gov (United States)

Sheives, T. C.

1974-01-01

Remote identification and measurement of subsurface water turbidity and oil on water was accomplished with analytical models which describe the backscatter from smooth surface turbid water, including single scatter and multiple scatter effects. Lidar measurements from natural waterways are also presented and compared with ground observations of several physical water quality parameters.

Characteristic Model-Based Robust Model Predictive Control for Hypersonic Vehicles with Constraints

Directory of Open Access Journals (Sweden)

Jun Zhang

2017-06-01

Full Text Available Designing robust control for hypersonic vehicles in reentry is difficult, due to the features of the vehicles including strong coupling, non-linearity, and multiple constraints. This paper proposed a characteristic model-based robust model predictive control (MPC for hypersonic vehicles with reentry constraints. First, the hypersonic vehicle is modeled by a characteristic model composed of a linear time-varying system and a lumped disturbance. Then, the identification data are regenerated by the accumulative sum idea in the gray theory, which weakens effects of the random noises and strengthens regularity of the identification data. Based on the regenerated data, the time-varying parameters and the disturbance are online estimated according to the gray identification. At last, the mixed H2/H∞ robust predictive control law is proposed based on linear matrix inequalities (LMIs and receding horizon optimization techniques. Using active tackling system constraints of MPC, the input and state constraints are satisfied in the closed-loop control system. The validity of the proposed control is verified theoretically according to Lyapunov theory and illustrated by simulation results.

High-accuracy user identification using EEG biometrics.

Science.gov (United States)

Koike-Akino, Toshiaki; Mahajan, Ruhi; Marks, Tim K; Ye Wang; Watanabe, Shinji; Tuzel, Oncel; Orlik, Philip

2016-08-01

We analyze brain waves acquired through a consumer-grade EEG device to investigate its capabilities for user identification and authentication. First, we show the statistical significance of the P300 component in event-related potential (ERP) data from 14-channel EEGs across 25 subjects. We then apply a variety of machine learning techniques, comparing the user identification performance of various different combinations of a dimensionality reduction technique followed by a classification algorithm. Experimental results show that an identification accuracy of 72% can be achieved using only a single 800 ms ERP epoch. In addition, we demonstrate that the user identification accuracy can be significantly improved to more than 96.7% by joint classification of multiple epochs.

System identification methodology for grate modeling. Black- and grey-box models; Metodik foer modellering av foerbraenningsrost med systemidentifiering. Svart- och graalaademodeller

Energy Technology Data Exchange (ETDEWEB)

Lundgren, Astrid; Sjoeberg, Jonas; Ramstroem Erik; Sunnerstam, Fredrik

2004-10-01

The possibility to use system identification to model combustion on a grate was studied. The identification was based on collected data from the combustion unit, data which was used to determine the model parameters. A number of step response experiments have been performed, for instance with varying pusher speed and air supply. No clear response was seen and thus it is concluded that the system is poorly excited. The initial requirements on the input parameters were not met. For instance many of the input parameters are co-varying with each other which limits the possibilities to single out the influence from each parameter on the combustion process. This will obstruct the identification procedure. In an attempt to improve the model, and compensate for the poor data, theoretical insights, i.e. a mass- and heat balances, have been included. Two model approaches were suggested, one based on the measured grate temperature, and another based on the fuel bed extension on the grate (particularly the position of the burn-out of the fuel). The first approach was implemented in an existing grey-box identification software MoCaVa, but the model output was concluded to be in poor agreement with measured data. The second approach was never tested since it could not be implemented in the MoCaVa software due to a discontinuous optimisation criteria. Instead a linear model based on the grate temperature has been used for comparison. In this model, it was shown that the response time of the grate temperature signal is significantly shorter than the fuel transportation time on the grate, thus a change in grate temperature is not only a result of the fuel transport. Radiation and conduction of heat to the grate is influencing the grate temperature and needs to be included in future modeling work. A strategy in order to separate the response from each signal during normal operation have been suggested. In future work the model need to be identified by exciting the system further and

Optimization-Based Inverse Identification of the Parameters of a Concrete Cap Material Model

Science.gov (United States)

Král, Petr; Hokeš, Filip; Hušek, Martin; Kala, Jiří; Hradil, Petr

2017-10-01

Issues concerning the advanced numerical analysis of concrete building structures in sophisticated computing systems currently require the involvement of nonlinear mechanics tools. The efforts to design safer, more durable and mainly more economically efficient concrete structures are supported via the use of advanced nonlinear concrete material models and the geometrically nonlinear approach. The application of nonlinear mechanics tools undoubtedly presents another step towards the approximation of the real behaviour of concrete building structures within the framework of computer numerical simulations. However, the success rate of this application depends on having a perfect understanding of the behaviour of the concrete material models used and having a perfect understanding of the used material model parameters meaning. The effective application of nonlinear concrete material models within computer simulations often becomes very problematic because these material models very often contain parameters (material constants) whose values are difficult to obtain. However, getting of the correct values of material parameters is very important to ensure proper function of a concrete material model used. Today, one possibility, which permits successful solution of the mentioned problem, is the use of optimization algorithms for the purpose of the optimization-based inverse material parameter identification. Parameter identification goes hand in hand with experimental investigation while it trying to find parameter values of the used material model so that the resulting data obtained from the computer simulation will best approximate the experimental data. This paper is focused on the optimization-based inverse identification of the parameters of a concrete cap material model which is known under the name the Continuous Surface Cap Model. Within this paper, material parameters of the model are identified on the basis of interaction between nonlinear computer simulations

Conjugate heat transfer analysis of an energy conversion device with an updated numerical model obtained through inverse identification

International Nuclear Information System (INIS)

Hey, Jonathan; Malloy, Adam C.; Martinez-Botas, Ricardo; Lamperth, Michael

2015-01-01

Highlights: • Conjugate heat transfer analysis of an electric machine. • Inverse identification method for estimating the model parameters. • Experimentally determined thermal properties and electromagnetic losses. • Coupling of inverse identification method with a numerical model. • Improved modeling accuracy through introduction of interface material. - Abstract: Energy conversion devices undergo thermal loading during their operation as a result of inefficiencies in the energy conversion process. This will eventually lead to degradation and possible failure of the device if the heat generated is not properly managed. The ability to accurately predict the thermal behavior of such a device during the initial developmental stage is an important requirement. However, accurate predictions of critical temperature is challenging due to the variation of heat transfer parameters from one device to another. The ability to determine the model parameters is key to accurately representing the heat transfer in such a device. This paper presents the use of an inverse identification technique to estimate the model parameters of an energy conversion device designed for vehicular applications. To simulate the imperfect contact and the presence of insulating materials in the permanent magnet electric machine, thin material are introduced at the component interface of the numerical model. The proposed inverse identification method is used to estimate the equivalent thermal conductance of the thin material. In addition, the electromagnetic losses generated in the permanent magnet is also derived indirectly from the temperature measurement using the same method. With the thermal properties and input parameters of the numerical model obtained from the inverse identification method, the critical temperature of the device can be predicted more accurately. The deviation between the maximum measured and predicted winding temperature is less than 2.4%

Text-Independent Speaker Identification Using the Histogram Transform Model

DEFF Research Database (Denmark)

Ma, Zhanyu; Yu, Hong; Tan, Zheng-Hua

2016-01-01

In this paper, we propose a novel probabilistic method for the task of text-independent speaker identification (SI). In order to capture the dynamic information during SI, we design a super-MFCCs features by cascading three neighboring Mel-frequency Cepstral coefficients (MFCCs) frames together....... These super-MFCC vectors are utilized for probabilistic model training such that the speaker’s characteristics can be sufficiently captured. The probability density function (PDF) of the aforementioned super-MFCCs features is estimated by the recently proposed histogram transform (HT) method. To recedes...

Early identification of at-risk nursing students: a student support model.

Science.gov (United States)

Hopkins, T Hampton

2008-06-01

Due to the shortage of nurses in the health care industry, colleges offering associate-degree nursing programs are beginning to pay more attention to attrition and the factors contributing to success. Alogistic regression model was used to explain the cognitive and noncognitive variables that contribute to success in a nursing fundamentals course. Although much work is necessary to fully understand first-semester nursing students' retention and success, an early identification model is explored to better support students as they enter associate-degree nursing programs.

Automated identification of stream-channel geomorphic features from high‑resolution digital elevation models in West Tennessee watersheds

Science.gov (United States)

Cartwright, Jennifer M.; Diehl, Timothy H.

2017-01-17

High-resolution digital elevation models (DEMs) derived from light detection and ranging (lidar) enable investigations of stream-channel geomorphology with much greater precision than previously possible. The U.S. Geological Survey has developed the DEM Geomorphology Toolbox, containing seven tools to automate the identification of sites of geomorphic instability that may represent sediment sources and sinks in stream-channel networks. These tools can be used to modify input DEMs on the basis of known locations of stormwater infrastructure, derive flow networks at user-specified resolutions, and identify possible sites of geomorphic instability including steep banks, abrupt changes in channel slope, or areas of rough terrain. Field verification of tool outputs identified several tool limitations but also demonstrated their overall usefulness in highlighting likely sediment sources and sinks within channel networks. In particular, spatial clusters of outputs from multiple tools can be used to prioritize field efforts to assess and restore eroding stream reaches.

Multiple Imputation of Predictor Variables Using Generalized Additive Models

NARCIS (Netherlands)

de Jong, Roel; van Buuren, Stef; Spiess, Martin

2016-01-01

The sensitivity of multiple imputation methods to deviations from their distributional assumptions is investigated using simulations, where the parameters of scientific interest are the coefficients of a linear regression model, and values in predictor variables are missing at random. The

Model Seleksi Premi Asuransi Jiwa Dwiguna untuk Kasus Multiple Decrement

OpenAIRE

Cita, Devi Ramana; Pane, Rolan; ', Harison

2015-01-01

This article discusses a select survival model for the case of multiple decrements in evaluating endowment life insurance premium for person currently aged ( + ) years, who is selected at age with ℎ years selection period. The case of multiple decrements in this case is limited to two cases. The calculation of the annual premium is done by prior evaluating of the single premium, and the present value of annuity depends on theconstant force assumption.

Positive identification by a skull with multiple epigenetic traits and abnormal structure of the neurocranium, viscerocranium, and the skeleton.

Science.gov (United States)

Kuharić, Josip; Kovacic, Natasa; Marusic, Petar; Marusic, Ana; Petrovecki, Vedrana

2011-05-01

Wormian bones are small ossicles appearing within the cranial sutures in more than 40% of skulls, most commonly at the lambdoid suture and pterion. During the skeletal analysis of an unidentified male war victim, we observed multiple wormian bones and a patent metopic suture. Additionally, the right elbow was deformed, probably as a consequence of an old trauma. The skull was analyzed by cranial measurements and computerized tomography, revealing the presence of cranial deformities including hyperbrachicrania, localized reduction in hemispheral widths, increased cranial capacity, and sclerosis of the viscerocranium. Besides unique anatomical features and their anthropological value, such skeletal abnormalities also have a forensic value as the evidence to support the final identification of the victim. © 2011 American Academy of Forensic Sciences.

A multiple shock model for common cause failures using discrete Markov chain

International Nuclear Information System (INIS)

Chung, Dae Wook; Kang, Chang Soon

1992-01-01

The most widely used models in common cause analysis are (single) shock models such as the BFR, and the MFR. But, single shock model can not treat the individual common cause separately and has some irrational assumptions. Multiple shock model for common cause failures is developed using Markov chain theory. This model treats each common cause shock as separately and sequently occuring event to implicate the change in failure probability distribution due to each common cause shock. The final failure probability distribution is evaluated and compared with that from the BFR model. The results show that multiple shock model which minimizes the assumptions in the BFR model is more realistic and conservative than the BFR model. The further work for application is the estimations of parameters such as common cause shock rate and component failure probability given a shock,p, through the data analysis

Parameters identification of photovoltaic models using an improved JAYA optimization algorithm

International Nuclear Information System (INIS)

Yu, Kunjie; Liang, J.J.; Qu, B.Y.; Chen, Xu; Wang, Heshan

2017-01-01

Highlights: • IJAYA algorithm is proposed to identify the PV model parameters efficiently. • A self-adaptive weight is introduced to purposefully adjust the search process. • Experience-based learning strategy is developed to enhance the population diversity. • Chaotic learning method is proposed to refine the quality of the best solution. • IJAYA features the superior performance in identifying parameters of PV models. - Abstract: Parameters identification of photovoltaic (PV) models based on measured current-voltage characteristic curves is significant for the simulation, evaluation, and control of PV systems. To accurately and reliably identify the parameters of different PV models, an improved JAYA (IJAYA) optimization algorithm is proposed in the paper. In IJAYA, a self-adaptive weight is introduced to adjust the tendency of approaching the best solution and avoiding the worst solution at different search stages, which enables the algorithm to approach the promising area at the early stage and implement the local search at the later stage. Furthermore, an experience-based learning strategy is developed and employed randomly to maintain the population diversity and enhance the exploration ability. A chaotic elite learning method is proposed to refine the quality of the best solution in each generation. The proposed IJAYA is used to solve the parameters identification problems of different PV models, i.e., single diode, double diode, and PV module. Comprehensive experiment results and analyses indicate that IJAYA can obtain a highly competitive performance compared with other state-of-the-state algorithms, especially in terms of accuracy and reliability.

The Answering Process for Multiple-Choice Questions in Collaborative Learning: A Mathematical Learning Model Analysis

Science.gov (United States)

Nakamura, Yasuyuki; Nishi, Shinnosuke; Muramatsu, Yuta; Yasutake, Koichi; Yamakawa, Osamu; Tagawa, Takahiro

2014-01-01

In this paper, we introduce a mathematical model for collaborative learning and the answering process for multiple-choice questions. The collaborative learning model is inspired by the Ising spin model and the model for answering multiple-choice questions is based on their difficulty level. An intensive simulation study predicts the possibility of…

Multiple membranes in M-theory

Energy Technology Data Exchange (ETDEWEB)

Bagger, Jonathan, E-mail: bagger@jhu.edu [Department of Physics and Astronomy, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218 (United States); Lambert, Neil, E-mail: neil.lambert@cern.ch [Theory Division, CERN, 1211 Geneva 23 (Switzerland); Department of Mathematics, King’s College London, London WC2R 2LS (United Kingdom); Isaac Newton Institute for Mathematical Sciences, 20 Clarkson Road, Cambridge, CB3 OEH (United Kingdom); Mukhi, Sunil, E-mail: mukhi@tifr.res.in [Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai 400 005 (India); Isaac Newton Institute for Mathematical Sciences, 20 Clarkson Road, Cambridge, CB3 OEH (United Kingdom); Papageorgakis, Constantinos, E-mail: papageorgakis@physics.rutgers.edu [NHETC and Department of Physics and Astronomy, Rutgers University, 126 Frelinghuysen Road, Piscataway, NJ 08854-8019 (United States); Isaac Newton Institute for Mathematical Sciences, 20 Clarkson Road, Cambridge, CB3 OEH (United Kingdom)

2013-06-01

We review developments in the theory of multiple, parallel membranes in M-theory. After discussing the inherent difficulties with constructing a maximally supersymmetric lagrangian with the appropriate field content and symmetries, we introduce 3-algebras and show how they allow for such a description. Different choices of 3-algebras lead to distinct classes of 2+1 dimensional theories with varying degrees of supersymmetry. We then demonstrate that these theories are equivalent to conventional superconformal Chern–Simons gauge theories at level k, but with bifundamental matter. Analysing the physical properties of these theories leads to the identification of a certain subclass of models with configurations of M2-branes on Z{sub k} orbifolds. These models give rise to a whole new gauge/gravity duality in the form of an AdS{sub 4}/CFT{sub 3} correspondence. We also discuss mass deformations, higher derivative corrections, and the possibility of extracting information about M5-brane physics.

Multiple Membranes in M-theory

CERN Document Server

Bagger, Jonathan; Mukhi, Sunil; Papageorgakis, Constantinos

2013-01-01

We review developments in the theory of multiple, parallel membranes in M-theory. After discussing the inherent difficulties pertaining to a maximally supersymmetric lagrangian formulation with the appropriate field content and symmetries, we discuss how introducing the concept of 3-algebras allows for such a description. Different choices of 3-algebras lead to distinct classes of 2+1 dimensional theories with varying degrees of supersymmetry. We then describe how these are equivalent to a type of conventional superconformal Chern-Simons gauge theories at level k, coupled to bifundamental matter. Analysing the physical properties of these theories leads to the identification of a certain subclass of models with configurations of M2-branes in Z_k orbifolds of M-theory. In addition these models give rise to a whole new sector of the gauge/gravity duality in the form of an AdS_4/CFT_3 correspondence. We also discuss mass deformations, higher derivative corrections as well as the possibility of extracting informa...

Multiple membranes in M-theory

International Nuclear Information System (INIS)

Bagger, Jonathan; Lambert, Neil; Mukhi, Sunil; Papageorgakis, Constantinos

2013-01-01

We review developments in the theory of multiple, parallel membranes in M-theory. After discussing the inherent difficulties with constructing a maximally supersymmetric lagrangian with the appropriate field content and symmetries, we introduce 3-algebras and show how they allow for such a description. Different choices of 3-algebras lead to distinct classes of 2+1 dimensional theories with varying degrees of supersymmetry. We then demonstrate that these theories are equivalent to conventional superconformal Chern–Simons gauge theories at level k, but with bifundamental matter. Analysing the physical properties of these theories leads to the identification of a certain subclass of models with configurations of M2-branes on Z k orbifolds. These models give rise to a whole new gauge/gravity duality in the form of an AdS 4 /CFT 3 correspondence. We also discuss mass deformations, higher derivative corrections, and the possibility of extracting information about M5-brane physics

Protein structure modeling for CASP10 by multiple layers of global optimization.

Science.gov (United States)

Joo, Keehyoung; Lee, Juyong; Sim, Sangjin; Lee, Sun Young; Lee, Kiho; Heo, Seungryong; Lee, In-Ho; Lee, Sung Jong; Lee, Jooyoung

2014-02-01

In the template-based modeling (TBM) category of CASP10 experiment, we introduced a new protocol called protein modeling system (PMS) to generate accurate protein structures in terms of side-chains as well as backbone trace. In the new protocol, a global optimization algorithm, called conformational space annealing (CSA), is applied to the three layers of TBM procedure: multiple sequence-structure alignment, 3D chain building, and side-chain re-modeling. For 3D chain building, we developed a new energy function which includes new distance restraint terms of Lorentzian type (derived from multiple templates), and new energy terms that combine (physical) energy terms such as dynamic fragment assembly (DFA) energy, DFIRE statistical potential energy, hydrogen bonding term, etc. These physical energy terms are expected to guide the structure modeling especially for loop regions where no template structures are available. In addition, we developed a new quality assessment method based on random forest machine learning algorithm to screen templates, multiple alignments, and final models. For TBM targets of CASP10, we find that, due to the combination of three stages of CSA global optimizations and quality assessment, the modeling accuracy of PMS improves at each additional stage of the protocol. It is especially noteworthy that the side-chains of the final PMS models are far more accurate than the models in the intermediate steps. Copyright © 2013 Wiley Periodicals, Inc.

A frequency domain global parameter estimation method for multiple reference frequency response measurements

Science.gov (United States)

Shih, C. Y.; Tsuei, Y. G.; Allemang, R. J.; Brown, D. L.

1988-10-01

A method of using the matrix Auto-Regressive Moving Average (ARMA) model in the Laplace domain for multiple-reference global parameter identification is presented. This method is particularly applicable to the area of modal analysis where high modal density exists. The method is also applicable when multiple reference frequency response functions are used to characterise linear systems. In order to facilitate the mathematical solution, the Forsythe orthogonal polynomial is used to reduce the ill-conditioning of the formulated equations and to decouple the normal matrix into two reduced matrix blocks. A Complex Mode Indicator Function (CMIF) is introduced, which can be used to determine the proper order of the rational polynomials.

Interacting Multiple Model (IMM Fifth-Degree Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking

Directory of Open Access Journals (Sweden)

Hua Liu

2017-06-01

Full Text Available For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named the interacting multiple model fifth-degree spherical simplex-radial cubature Kalman filter (IMM5thSSRCKF is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM filter and the fifth-degree spherical simplex-radial cubature Kalman filter (5thSSRCKF. The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with the interacting multiple model unscented Kalman filter (IMMUKF, the interacting multiple model cubature Kalman filter (IMMCKF and the interacting multiple model fifth-degree cubature Kalman filter (IMM5thCKF.

Interacting Multiple Model (IMM) Fifth-Degree Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking.

Science.gov (United States)

Liu, Hua; Wu, Wen

2017-06-13

For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named the interacting multiple model fifth-degree spherical simplex-radial cubature Kalman filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and the fifth-degree spherical simplex-radial cubature Kalman filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with the interacting multiple model unscented Kalman filter (IMMUKF), the interacting multiple model cubature Kalman filter (IMMCKF) and the interacting multiple model fifth-degree cubature Kalman filter (IMM5thCKF).

Comparing Multiple-Group Multinomial Log-Linear Models for Multidimensional Skill Distributions in the General Diagnostic Model. Research Report. ETS RR-08-35

Science.gov (United States)

Xu, Xueli; von Davier, Matthias

2008-01-01

The general diagnostic model (GDM) utilizes located latent classes for modeling a multidimensional proficiency variable. In this paper, the GDM is extended by employing a log-linear model for multiple populations that assumes constraints on parameters across multiple groups. This constrained model is compared to log-linear models that assume…

Testing a Poisson counter model for visual identification of briefly presented, mutually confusable single stimuli in pure accuracy tasks.

Science.gov (United States)

Kyllingsbæk, Søren; Markussen, Bo; Bundesen, Claus

2012-06-01

The authors propose and test a simple model of the time course of visual identification of briefly presented, mutually confusable single stimuli in pure accuracy tasks. The model implies that during stimulus analysis, tentative categorizations that stimulus i belongs to category j are made at a constant Poisson rate, v(i, j). The analysis is continued until the stimulus disappears, and the overt response is based on the categorization made the greatest number of times. The model was evaluated by Monte Carlo tests of goodness of fit against observed probability distributions of responses in two extensive experiments and also by quantifications of the information loss of the model compared with the observed data by use of information theoretic measures. The model provided a close fit to individual data on identification of digits and an apparently perfect fit to data on identification of Landolt rings.

Model-based monitoring of rotors with multiple coexisting faults

International Nuclear Information System (INIS)

Rossner, Markus

2015-01-01

Monitoring systems are applied to many rotors, but only few monitoring systems can separate coexisting errors and identify their quantity. This research project solves this problem using a combination of signal-based and model-based monitoring. The signal-based part performs a pre-selection of possible errors; these errors are further separated with model-based methods. This approach is demonstrated for the errors unbalance, bow, stator-fixed misalignment, rotor-fixed misalignment and roundness errors. For the model-based part, unambiguous error definitions and models are set up. The Ritz approach reduces the model order and therefore speeds up the diagnosis. Identification algorithms are developed for the different rotor faults. Hereto, reliable damage indicators and proper sub steps of the diagnosis have to be defined. For several monitoring problems, measuring both deflection and bearing force is very useful. The monitoring system is verified by experiments on an academic rotor test rig. The interpretation of the measurements requires much knowledge concerning the dynamics of the rotor. Due to the model-based approach, the system can separate errors with similar signal patterns and identify bow and roundness error online at operation speed. [de

Linear systems with unstructured multiplicative uncertainty: Modeling and robust stability analysis.

Directory of Open Access Journals (Sweden)

Radek Matušů

Full Text Available This article deals with continuous-time Linear Time-Invariant (LTI Single-Input Single-Output (SISO systems affected by unstructured multiplicative uncertainty. More specifically, its aim is to present an approach to the construction of uncertain models based on the appropriate selection of a nominal system and a weight function and to apply the fundamentals of robust stability investigation for considered sort of systems. The initial theoretical parts are followed by three extensive illustrative examples in which the first order time-delay, second order and third order plants with parametric uncertainty are modeled as systems with unstructured multiplicative uncertainty and subsequently, the robust stability of selected feedback loops containing constructed models and chosen controllers is analyzed and obtained results are discussed.

Simplified Multimodal Biometric Identification

Directory of Open Access Journals (Sweden)

Abhijit Shete

2014-03-01

Full Text Available Multibiometric systems are expected to be more reliable than unimodal biometric systems for personal identification due to the presence of multiple, fairly independent pieces of evidence e.g. Unique Identification Project "Aadhaar" of Government of India. In this paper, we present a novel wavelet based technique to perform fusion at the feature level and score level by considering two biometric modalities, face and fingerprint. The results indicate that the proposed technique can lead to substantial improvement in multimodal matching performance. The proposed technique is simple because of no preprocessing of raw biometric traits as well as no feature and score normalization.

A Mini-Review of Track And Field’s Talent-Identification Models in Iran and Some Designated Countries

OpenAIRE

Ebrahim Ghasemzadeh Mirkolaee; Seyed Mohammad Hossein Razavi; Saeed Amirnejad

2013-01-01

Talent identification and training the athletes of the basic levels in track and field requires codifying a proper model like any other system so that any duplication is prevented as well as knowing the right path. The federation of track and field started to codify the national talent-identification scheme in track and field in 1385. Hence, the present studies track-and-field talent-identification patterns in some designated countries and compare them with the codified pattern in Iran. The r...

A latent class multiple constraint multiple discrete-continuous extreme value model of time use and goods consumption.

Science.gov (United States)

2016-06-01

This paper develops a microeconomic theory-based multiple discrete continuous choice model that considers: (a) that both goods consumption and time allocations (to work and non-work activities) enter separately as decision variables in the utility fu...

Hybrid approaches for multiple-species stochastic reaction-diffusion models

Science.gov (United States)

Spill, Fabian; Guerrero, Pilar; Alarcon, Tomas; Maini, Philip K.; Byrne, Helen

2015-10-01

Reaction-diffusion models are used to describe systems in fields as diverse as physics, chemistry, ecology and biology. The fundamental quantities in such models are individual entities such as atoms and molecules, bacteria, cells or animals, which move and/or react in a stochastic manner. If the number of entities is large, accounting for each individual is inefficient, and often partial differential equation (PDE) models are used in which the stochastic behaviour of individuals is replaced by a description of the averaged, or mean behaviour of the system. In some situations the number of individuals is large in certain regions and small in others. In such cases, a stochastic model may be inefficient in one region, and a PDE model inaccurate in another. To overcome this problem, we develop a scheme which couples a stochastic reaction-diffusion system in one part of the domain with its mean field analogue, i.e. a discretised PDE model, in the other part of the domain. The interface in between the two domains occupies exactly one lattice site and is chosen such that the mean field description is still accurate there. In this way errors due to the flux between the domains are small. Our scheme can account for multiple dynamic interfaces separating multiple stochastic and deterministic domains, and the coupling between the domains conserves the total number of particles. The method preserves stochastic features such as extinction not observable in the mean field description, and is significantly faster to simulate on a computer than the pure stochastic model.

Hybrid approaches for multiple-species stochastic reaction-diffusion models.

KAUST Repository

Spill, Fabian; Guerrero, Pilar; Alarcon, Tomas; Maini, Philip K; Byrne, Helen

2015-01-01

Reaction-diffusion models are used to describe systems in fields as diverse as physics, chemistry, ecology and biology. The fundamental quantities in such models are individual entities such as atoms and molecules, bacteria, cells or animals, which move and/or react in a stochastic manner. If the number of entities is large, accounting for each individual is inefficient, and often partial differential equation (PDE) models are used in which the stochastic behaviour of individuals is replaced by a description of the averaged, or mean behaviour of the system. In some situations the number of individuals is large in certain regions and small in others. In such cases, a stochastic model may be inefficient in one region, and a PDE model inaccurate in another. To overcome this problem, we develop a scheme which couples a stochastic reaction-diffusion system in one part of the domain with its mean field analogue, i.e. a discretised PDE model, in the other part of the domain. The interface in between the two domains occupies exactly one lattice site and is chosen such that the mean field description is still accurate there. In this way errors due to the flux between the domains are small. Our scheme can account for multiple dynamic interfaces separating multiple stochastic and deterministic domains, and the coupling between the domains conserves the total number of particles. The method preserves stochastic features such as extinction not observable in the mean field description, and is significantly faster to simulate on a computer than the pure stochastic model.

Hybrid approaches for multiple-species stochastic reaction-diffusion models.

KAUST Repository

Spill, Fabian

2015-10-01

Reaction-diffusion models are used to describe systems in fields as diverse as physics, chemistry, ecology and biology. The fundamental quantities in such models are individual entities such as atoms and molecules, bacteria, cells or animals, which move and/or react in a stochastic manner. If the number of entities is large, accounting for each individual is inefficient, and often partial differential equation (PDE) models are used in which the stochastic behaviour of individuals is replaced by a description of the averaged, or mean behaviour of the system. In some situations the number of individuals is large in certain regions and small in others. In such cases, a stochastic model may be inefficient in one region, and a PDE model inaccurate in another. To overcome this problem, we develop a scheme which couples a stochastic reaction-diffusion system in one part of the domain with its mean field analogue, i.e. a discretised PDE model, in the other part of the domain. The interface in between the two domains occupies exactly one lattice site and is chosen such that the mean field description is still accurate there. In this way errors due to the flux between the domains are small. Our scheme can account for multiple dynamic interfaces separating multiple stochastic and deterministic domains, and the coupling between the domains conserves the total number of particles. The method preserves stochastic features such as extinction not observable in the mean field description, and is significantly faster to simulate on a computer than the pure stochastic model.

Parameter Identification of the 2-Chlorophenol Oxidation Model Using Improved Differential Search Algorithm

Directory of Open Access Journals (Sweden)

Guang-zhou Chen

2015-01-01

Full Text Available Parameter identification plays a crucial role for simulating and using model. This paper firstly carried out the sensitivity analysis of the 2-chlorophenol oxidation model in supercritical water using the Monte Carlo method. Then, to address the nonlinearity of the model, two improved differential search (DS algorithms were proposed to carry out the parameter identification of the model. One strategy is to adopt the Latin hypercube sampling method to replace the uniform distribution of initial population; the other is to combine DS with simplex method. The results of sensitivity analysis reveal the sensitivity and the degree of difficulty identified for every model parameter. Furthermore, the posteriori probability distribution of parameters and the collaborative relationship between any two parameters can be obtained. To verify the effectiveness of the improved algorithms, the optimization performance of improved DS in kinetic parameter estimation is studied and compared with that of the basic DS algorithm, differential evolution, artificial bee colony optimization, and quantum-behaved particle swarm optimization. And the experimental results demonstrate that the DS with the Latin hypercube sampling method does not present better performance, while the hybrid methods have the advantages of strong global search ability and local search ability and are more effective than the other algorithms.

Risk Prediction Models for Other Cancers or Multiple Sites

Science.gov (United States)

Developing statistical models that estimate the probability of developing other multiple cancers over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

Exclusive description of multiple production on nuclei in the additive quark model. Multiplicity distributions in interactions with heavy nuclei

International Nuclear Information System (INIS)

Levchenko, B.B.; Nikolaev, N.N.

1985-01-01

In the framework of the additive quark model of multiple production on nuclei we calculate the multiplicity distributions of secondary particles and the correlations between secondary particles in πA and pA interactions with heavy nuclei. We show that intranuclear cascades are responsible for up to 50% of the nuclear increase of the multiplicity of fast particles. We analyze the sensitivity of the multiplicities and their correlations to the choice of the quark-hadronization function. We show that with good accuracy the yield of relativistic secondary particles from heavy and intermediate nuclei depends only on the number N/sub p/ of protons knocked out of the nucleus, and not on the mass number of the nucleus (N/sub p/ scaling)

Multiple attribute decision making model and application to food safety risk evaluation.

Science.gov (United States)

Ma, Lihua; Chen, Hong; Yan, Huizhe; Yang, Lifeng; Wu, Lifeng

2017-01-01

Decision making for supermarket food purchase decisions are characterized by network relationships. This paper analyzed factors that influence supermarket food selection and proposes a supplier evaluation index system based on the whole process of food production. The author established the intuitive interval value fuzzy set evaluation model based on characteristics of the network relationship among decision makers, and validated for a multiple attribute decision making case study. Thus, the proposed model provides a reliable, accurate method for multiple attribute decision making.

Identification of reverse logistics decision types from mathematical models

Directory of Open Access Journals (Sweden)

Pascual Cortés Pellicer

2018-04-01

Full Text Available Purpose: The increase in social awareness, politics and environmental regulation, the scarcity of raw materials and the desired “green” image, are some of the reasons that lead companies to decide for implement processes of Reverse Logistics (RL. At the time when incorporate new RL processes as key business processes, new and important decisions need to be made. Identification and knowledge of these decisions, including the information available and the implications for the company or supply chain, will be fundamental for decision-makers to achieve the best results. In the present work, the main types of RL decisions are identified. Design/methodology/approach: This paper is based on the analysis of mathematical models designed as tools to aid decision making in the field of RL. Once the types of interest work to be analyzed are defined, those studies that really deal about the object of study are searched and analyzed. The decision variables that are taken at work are identified and grouped according to the type of decision and, finally, are showed the main types of decisions used in mathematical models developed in the field of RL.     Findings: The principal conclusion of the research is that the most commonly addressed decisions with mathematical models in the field of RL are those related to the network’s configuration, followed by tactical/operative decisions such as the selections of product’s treatments to realize and the policy of returns or prices, among other decisions. Originality/value: The identification of the main decisions types of the reverse logistics will allow the managers of these processes to know and understand them better, while offer an integrated vision of them, favoring the achievement of better results.

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

Science.gov (United States)

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

2009-04-01

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

CVA identification of nonlinear systems with LPV state-space models of affine dependence

NARCIS (Netherlands)

Larimore, W.E.; Cox, P.B.; Toth, R.

2015-01-01

This paper discusses an improvement on the extension of linear subspace methods (originally developed in the Linear Time-Invariant (LTI) context) to the identification of Linear Parameter-Varying (LPV) and state-affine nonlinear system models. This includes the fitting of a special polynomial

Penerapan Model Pembelajaran Atraktif Berbasis Multiple Intelligences Tentang Pemantulan Cahaya pada Cermin

Directory of Open Access Journals (Sweden)

Intan Kusumawati

2016-03-01

Full Text Available Penelitian ini bertujuan untuk mengetahui efektivitas penerapan model pembelajaran atraktif berbasis multiple intelligences dalam meremediasi miskonsepsi siswa tentang pemantulan cahaya pada cermin. Pada penelitian ini digunakan bentuk pre-eksperimental design dengan rancangan one group pretest-post test design. Alat pengumpulan data berupa tes pilihan ganda dengan reasoning. Hasil validitas sebesar 4,08 dan reliabilitas 0,537. Siswa dibagi menjadi lima kelompok kecerdasan, yaitu kelompok linguistic intelligence, mathematical-logical intelligence, visual-spatial intelligence, bodily-khinestetic intelligence, dan musical intelligence. Siswa membahas konsep fisika sesuai kelompok kecerdasannya dalam bentuk pembuatan pantun-puisi, teka-teki silang, menggambar kreatif, drama, dan mengarang lirik lagu. Efektivitas penerapan model pembelajaran multiple intelligences menggunakan persamaan effect size. Ditemukan bahwa skor effect size masing-masing kelompok berkategori tinggi sebesar 5,76; 3,76; 4,60; 1,70; dan 1,34. Penerapan model pembelajaran atraktif berbasis multiple intelligences efektif dalam meremediasi miskonsepsi siswa. Penelitian ini diharapkan dapat digunakan pada materi fisika dan sekolah lainnya.

Analysis of Genome-Wide Association Studies with Multiple Outcomes Using Penalization

Science.gov (United States)