Estimating unknown input parameters when implementing the NGA ground-motion prediction equations in engineering practice

Science.gov (United States)

Kaklamanos, James; Baise, Laurie G.; Boore, David M.

2011-01-01

The ground-motion prediction equations (GMPEs) developed as part of the Next Generation Attenuation of Ground Motions (NGA-West) project in 2008 are becoming widely used in seismic hazard analyses. However, these new models are considerably more complicated than previous GMPEs, and they require several more input parameters. When employing the NGA models, users routinely face situations in which some of the required input parameters are unknown. In this paper, we present a framework for estimating the unknown source, path, and site parameters when implementing the NGA models in engineering practice, and we derive geometrically-based equations relating the three distance measures found in the NGA models. Our intent is for the content of this paper not only to make the NGA models more accessible, but also to help with the implementation of other present or future GMPEs.

Reconstruction of signals with unknown spectra in information field theory with parameter uncertainty

International Nuclear Information System (INIS)

Ensslin, Torsten A.; Frommert, Mona

2011-01-01

The optimal reconstruction of cosmic metric perturbations and other signals requires knowledge of their power spectra and other parameters. If these are not known a priori, they have to be measured simultaneously from the same data used for the signal reconstruction. We formulate the general problem of signal inference in the presence of unknown parameters within the framework of information field theory. To solve this, we develop a generic parameter-uncertainty renormalized estimation (PURE) technique. As a concrete application, we address the problem of reconstructing Gaussian signals with unknown power-spectrum with five different approaches: (i) separate maximum-a-posteriori power-spectrum measurement and subsequent reconstruction, (ii) maximum-a-posteriori reconstruction with marginalized power-spectrum, (iii) maximizing the joint posterior of signal and spectrum, (iv) guessing the spectrum from the variance in the Wiener-filter map, and (v) renormalization flow analysis of the field-theoretical problem providing the PURE filter. In all cases, the reconstruction can be described or approximated as Wiener-filter operations with assumed signal spectra derived from the data according to the same recipe, but with differing coefficients. All of these filters, except the renormalized one, exhibit a perception threshold in case of a Jeffreys prior for the unknown spectrum. Data modes with variance below this threshold do not affect the signal reconstruction at all. Filter (iv) seems to be similar to the so-called Karhune-Loeve and Feldman-Kaiser-Peacock estimators for galaxy power spectra used in cosmology, which therefore should also exhibit a marginal perception threshold if correctly implemented. We present statistical performance tests and show that the PURE filter is superior to the others, especially if the post-Wiener-filter corrections are included or in case an additional scale-independent spectral smoothness prior can be adopted.

Chaos anti-synchronization of two non-identical chaotic systems with known or fully unknown parameters

International Nuclear Information System (INIS)

Al-Sawalha, Ayman

2009-01-01

This work is devoted to investigating the anti-synchronization between two novel different chaotic systems. Two different anti-synchronization methods are proposed. Active control is applied when system parameters are known and adaptive control is employed when system parameters are uncertain or unknown. Controllers and update laws of parameters are designed based on Lyapunov stability theory. In both cases, sufficient conditions for the anti-synchronization are obtained analytically. Finally, a numerical simulations is presented to show the effectiveness of the proposed chaos anti-synchronization schemes.

Synchronization of coupled different chaotic FitzHugh-Nagumo neurons with unknown parameters under communication-direction-dependent coupling.

Science.gov (United States)

Iqbal, Muhammad; Rehan, Muhammad; Khaliq, Abdul; Saeed-ur-Rehman; Hong, Keum-Shik

2014-01-01

This paper investigates the chaotic behavior and synchronization of two different coupled chaotic FitzHugh-Nagumo (FHN) neurons with unknown parameters under external electrical stimulation (EES). The coupled FHN neurons of different parameters admit unidirectional and bidirectional gap junctions in the medium between them. Dynamical properties, such as the increase in synchronization error as a consequence of the deviation of neuronal parameters for unlike neurons, the effect of difference in coupling strengths caused by the unidirectional gap junctions, and the impact of large time-delay due to separation of neurons, are studied in exploring the behavior of the coupled system. A novel integral-based nonlinear adaptive control scheme, to cope with the infeasibility of the recovery variable, for synchronization of two coupled delayed chaotic FHN neurons of different and unknown parameters under uncertain EES is derived. Further, to guarantee robust synchronization of different neurons against disturbances, the proposed control methodology is modified to achieve the uniformly ultimately bounded synchronization. The parametric estimation errors can be reduced by selecting suitable control parameters. The effectiveness of the proposed control scheme is illustrated via numerical simulations.

On synchronisation of a class of complex chaotic systems with complex unknown parameters via integral sliding mode control

Science.gov (United States)

Tirandaz, Hamed; Karami-Mollaee, Ali

2018-06-01

Chaotic systems demonstrate complex behaviour in their state variables and their parameters, which generate some challenges and consequences. This paper presents a new synchronisation scheme based on integral sliding mode control (ISMC) method on a class of complex chaotic systems with complex unknown parameters. Synchronisation between corresponding states of a class of complex chaotic systems and also convergence of the errors of the system parameters to zero point are studied. The designed feedback control vector and complex unknown parameter vector are analytically achieved based on the Lyapunov stability theory. Moreover, the effectiveness of the proposed methodology is verified by synchronisation of the Chen complex system and the Lorenz complex systems as the leader and the follower chaotic systems, respectively. In conclusion, some numerical simulations related to the synchronisation methodology is given to illustrate the effectiveness of the theoretical discussions.

Exploiting intrinsic fluctuations to identify model parameters.

Science.gov (United States)

Zimmer, Christoph; Sahle, Sven; Pahle, Jürgen

2015-04-01

Parameterisation of kinetic models plays a central role in computational systems biology. Besides the lack of experimental data of high enough quality, some of the biggest challenges here are identification issues. Model parameters can be structurally non-identifiable because of functional relationships. Noise in measured data is usually considered to be a nuisance for parameter estimation. However, it turns out that intrinsic fluctuations in particle numbers can make parameters identifiable that were previously non-identifiable. The authors present a method to identify model parameters that are structurally non-identifiable in a deterministic framework. The method takes time course recordings of biochemical systems in steady state or transient state as input. Often a functional relationship between parameters presents itself by a one-dimensional manifold in parameter space containing parameter sets of optimal goodness. Although the system's behaviour cannot be distinguished on this manifold in a deterministic framework it might be distinguishable in a stochastic modelling framework. Their method exploits this by using an objective function that includes a measure for fluctuations in particle numbers. They show on three example models, immigration-death, gene expression and Epo-EpoReceptor interaction, that this resolves the non-identifiability even in the case of measurement noise with known amplitude. The method is applied to partially observed recordings of biochemical systems with measurement noise. It is simple to implement and it is usually very fast to compute. This optimisation can be realised in a classical or Bayesian fashion.

Synchronization of Coupled Different Chaotic FitzHugh-Nagumo Neurons with Unknown Parameters under Communication-Direction-Dependent Coupling

Directory of Open Access Journals (Sweden)

Muhammad Iqbal

2014-01-01

Full Text Available This paper investigates the chaotic behavior and synchronization of two different coupled chaotic FitzHugh-Nagumo (FHN neurons with unknown parameters under external electrical stimulation (EES. The coupled FHN neurons of different parameters admit unidirectional and bidirectional gap junctions in the medium between them. Dynamical properties, such as the increase in synchronization error as a consequence of the deviation of neuronal parameters for unlike neurons, the effect of difference in coupling strengths caused by the unidirectional gap junctions, and the impact of large time-delay due to separation of neurons, are studied in exploring the behavior of the coupled system. A novel integral-based nonlinear adaptive control scheme, to cope with the infeasibility of the recovery variable, for synchronization of two coupled delayed chaotic FHN neurons of different and unknown parameters under uncertain EES is derived. Further, to guarantee robust synchronization of different neurons against disturbances, the proposed control methodology is modified to achieve the uniformly ultimately bounded synchronization. The parametric estimation errors can be reduced by selecting suitable control parameters. The effectiveness of the proposed control scheme is illustrated via numerical simulations.

Adaptive Synchronization for Two Different Stochastic Chaotic Systems with Unknown Parameters via a Sliding Mode Controller

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Zengyun Wang

2013-01-01

Full Text Available This paper investigates the problem of synchronization for two different stochastic chaotic systems with unknown parameters and uncertain terms. The main work of this paper consists of the following aspects. Firstly, based on the Lyapunov theory in stochastic differential equations and the theory of sliding mode control, we propose a simple sliding surface and discuss the occurrence of the sliding motion. Secondly, we design an adaptive sliding mode controller to realize the asymptotical synchronization in mean squares. Thirdly, we design an adaptive sliding mode controller to realize the almost surely synchronization. Finally, the designed adaptive sliding mode controllers are used to achieve synchronization between two pairs of different stochastic chaos systems (Lorenz-Chen and Chen-Lu in the presence of the uncertainties and unknown parameters. Numerical simulations are given to demonstrate the robustness and efficiency of the proposed robust adaptive sliding mode controller.

Numerical identifiability of the parameters of induction machines

Energy Technology Data Exchange (ETDEWEB)

Corcoles, F.; Pedra, J.; Salichs, M. [Dep. d' Eng. Electrica ETSEIB. UPC, Barcelona (Spain)

2000-08-01

This paper analyses the numerical identifiability of the electrical parameters of induction machines. Relations between parameters and the impossibility to estimate all of them - when only external measures are used: voltage, current, speed and torque - are shown. Formulations of the single and double-cage induction machine, with and without core losses in both models, are developed. The proposed solution is the formulation of machine equations by using the minimum number of parameters (which are identifiable parameters). As an application example, the parameters of a double-cage induction machine are identified using steady-state measurements corresponding to different angular speeds. (orig.)

Structural parameter identifiability analysis for dynamic reaction networks

DEFF Research Database (Denmark)

Davidescu, Florin Paul; Jørgensen, Sten Bay

2008-01-01

method based on Lie derivatives. The proposed systematic two phase methodology is illustrated on a mass action based model for an enzymatically catalyzed reaction pathway network where only a limited set of variables is measured. The methodology clearly pinpoints the structurally identifiable parameters...... where for a given set of measured variables it is desirable to investigate which parameters may be estimated prior to spending computational effort on the actual estimation. This contribution addresses the structural parameter identifiability problem for the typical case of reaction network models....... The proposed analysis is performed in two phases. The first phase determines the structurally identifiable reaction rates based on reaction network stoichiometry. The second phase assesses the structural parameter identifiability of the specific kinetic rate expressions using a generating series expansion...

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.

Identifying an unknown function in a parabolic equation with overspecified data via He's variational iteration method

International Nuclear Information System (INIS)

Dehghan, Mehdi; Tatari, Mehdi

2008-01-01

In this research, the He's variational iteration technique is used for computing an unknown time-dependent parameter in an inverse quasilinear parabolic partial differential equation. Parabolic partial differential equations with overspecified data play a crucial role in applied mathematics and physics, as they appear in various engineering models. The He's variational iteration method is an analytical procedure for finding solutions of differential equations, is based on the use of Lagrange multipliers for identification of an optimal value of a parameter in a functional. To show the efficiency of the new approach, several test problems are presented for one-, two- and three-dimensional cases

MoCha: Molecular Characterization of Unknown Pathways.

Science.gov (United States)

Lobo, Daniel; Hammelman, Jennifer; Levin, Michael

2016-04-01

Automated methods for the reverse-engineering of complex regulatory networks are paving the way for the inference of mechanistic comprehensive models directly from experimental data. These novel methods can infer not only the relations and parameters of the known molecules defined in their input datasets, but also unknown components and pathways identified as necessary by the automated algorithms. Identifying the molecular nature of these unknown components is a crucial step for making testable predictions and experimentally validating the models, yet no specific and efficient tools exist to aid in this process. To this end, we present here MoCha (Molecular Characterization), a tool optimized for the search of unknown proteins and their pathways from a given set of known interacting proteins. MoCha uses the comprehensive dataset of protein-protein interactions provided by the STRING database, which currently includes more than a billion interactions from over 2,000 organisms. MoCha is highly optimized, performing typical searches within seconds. We demonstrate the use of MoCha with the characterization of unknown components from reverse-engineered models from the literature. MoCha is useful for working on network models by hand or as a downstream step of a model inference engine workflow and represents a valuable and efficient tool for the characterization of unknown pathways using known data from thousands of organisms. MoCha and its source code are freely available online under the GPLv3 license.

IDENTIFICATION OF MODAL PARAMETERS OF VIBRATING STRUCTURES WITH UNKNOWN ORSTOCHASTIC EXCITATION

OpenAIRE

Amaro Baldeón, Roberto; Gardel Kurka, Paulo

2014-01-01

The Vector Autoregressive Moving Average (VARMA) Model is used to identify dynamical characteristics of a structural system in the presence of noise. In order to estimate the parameters of the VARMA Model, the Spliid’s fast algorithm is used. To determine the modal parameters the companion matrix is built with the autoregressive part of the VARMA Model. The performance of this method here discussed is presented by means of simulations, using three degrees of freedom mass-dampingstiffness vibr...

Two statistics for evaluating parameter identifiability and error reduction

Science.gov (United States)

Doherty, John; Hunt, Randall J.

2009-01-01

Two statistics are presented that can be used to rank input parameters utilized by a model in terms of their relative identifiability based on a given or possible future calibration dataset. Identifiability is defined here as the capability of model calibration to constrain parameters used by a model. Both statistics require that the sensitivity of each model parameter be calculated for each model output for which there are actual or presumed field measurements. Singular value decomposition (SVD) of the weighted sensitivity matrix is then undertaken to quantify the relation between the parameters and observations that, in turn, allows selection of calibration solution and null spaces spanned by unit orthogonal vectors. The first statistic presented, "parameter identifiability", is quantitatively defined as the direction cosine between a parameter and its projection onto the calibration solution space. This varies between zero and one, with zero indicating complete non-identifiability and one indicating complete identifiability. The second statistic, "relative error reduction", indicates the extent to which the calibration process reduces error in estimation of a parameter from its pre-calibration level where its value must be assigned purely on the basis of prior expert knowledge. This is more sophisticated than identifiability, in that it takes greater account of the noise associated with the calibration dataset. Like identifiability, it has a maximum value of one (which can only be achieved if there is no measurement noise). Conceptually it can fall to zero; and even below zero if a calibration problem is poorly posed. An example, based on a coupled groundwater/surface-water model, is included that demonstrates the utility of the statistics. ?? 2009 Elsevier B.V.

Adaptive Backstepping Controller Design for the Anti-Synchronization of Identical WINDMI Chaotic Systems with Unknown Parameters and its SPICE Implementation

Directory of Open Access Journals (Sweden)

S. Vaidyanathan

2014-11-01

Full Text Available This paper derives new results for the adaptive backstepping controller design for the anti-synchronization of identical WINDMI systems (Wind-Magnetosphere-Ionosphere models with unknown parameters and also details the SPICE implementation of the proposed adaptive backstepping controller. In the anti-synchronization of chaotic systems, the sum of the outputs of master and slave systems is made to converge asymptotically to zero with time. The adaptive controller design for the anti-synchronization of identical WINDMI systems with unknown parameters has been established by applying Lyapunov stability theory. MATLAB simulations have been shown for the illustration of the adaptive anti-synchronizing backstepping controller for identical WINDMI chaotic systems. Finally, the proposed controller has been implemented using SPICE and circuit simulation results have been detailed.

Parameter identification of thermophilic anaerobic degradation of valerate

DEFF Research Database (Denmark)

Flotats, X; Ahring, Birgitte Kiær; Angelidaki, Irini

2002-01-01

Mathematical model of the decomposition of valerate presents 3 unknown kinetic parameters, 2 unknown stoichiometric coefficients and 3 unknown initial concentrations for biomass. Applying a structural identifiability study, it is concluded that it is necessary to perform simultaneous batch experi...

Convergence monitoring of Markov chains generated for inverse tracking of unknown model parameters in atmospheric dispersion

International Nuclear Information System (INIS)

Kim, Joo Yeon; Ryu, Hyung Joon; Jung, Gyu Hwan; Lee, Jai Ki

2011-01-01

The dependency within the sequential realizations in the generated Markov chains and their reliabilities are monitored by introducing the autocorrelation and the potential scale reduction factor (PSRF) by model parameters in the atmospheric dispersion. These two diagnostics have been applied for the posterior quantities of the release point and the release rate inferred through the inverse tracking of unknown model parameters for the Yonggwang atmospheric tracer experiment in Korea. The autocorrelations of model parameters are decreasing to low values approaching to zero with increase of lag, resulted in decrease of the dependencies within the two sequential realizations. Their PSRFs are reduced to within 1.2 and the adequate simulation number recognized from these results. From these two convergence diagnostics, the validation of Markov chains generated have been ensured and PSRF then is especially suggested as the efficient tool for convergence monitoring for the source reconstruction in atmospheric dispersion. (author)

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction.

Science.gov (United States)

Boiteau, Rene M; Hoyt, David W; Nicora, Carrie D; Kinmonth-Schultz, Hannah A; Ward, Joy K; Bingol, Kerem

2018-01-17

We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS²), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter out the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture, and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana . The NMR/MS² approach is well suited to the discovery of new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.

Identifiability of altimetry-based rating curve parameters in function of river morphological parameters

Science.gov (United States)

Paris, Adrien; André Garambois, Pierre; Calmant, Stéphane; Paiva, Rodrigo; Walter, Collischonn; Santos da Silva, Joecila; Medeiros Moreira, Daniel; Bonnet, Marie-Paule; Seyler, Frédérique; Monnier, Jérôme

2016-04-01

Estimating river discharge for ungauged river reaches from satellite measurements is not straightforward given the nonlinearity of flow behavior with respect to measurable and non measurable hydraulic parameters. As a matter of facts, current satellite datasets do not give access to key parameters such as river bed topography and roughness. A unique set of almost one thousand altimetry-based rating curves was built by fit of ENVISAT and Jason-2 water stages with discharges obtained from the MGB-IPH rainfall-runoff model in the Amazon basin. These rated discharges were successfully validated towards simulated discharges (Ens = 0.70) and in-situ discharges (Ens = 0.71) and are not mission-dependent. The rating curve writes Q = a(Z-Z0)b*sqrt(S), with Z the water surface elevation and S its slope gained from satellite altimetry, a and b power law coefficient and exponent and Z0 the river bed elevation such as Q(Z0) = 0. For several river reaches in the Amazon basin where ADCP measurements are available, the Z0 values are fairly well validated with a relative error lower than 10%. The present contribution aims at relating the identifiability and the physical meaning of a, b and Z0given various hydraulic and geomorphologic conditions. Synthetic river bathymetries sampling a wide range of rivers and inflow discharges are used to perform twin experiments. A shallow water model is run for generating synthetic satellite observations, and then rating curve parameters are determined for each river section thanks to a MCMC algorithm. Thanks to twin experiments, it is shown that rating curve formulation with water surface slope, i.e. closer from Manning equation form, improves parameter identifiability. The compensation between parameters is limited, especially for reaches with little water surface variability. Rating curve parameters are analyzed for riffle and pools for small to large rivers, different river slopes and cross section shapes. It is shown that the river bed

Identifying crucial parameter correlations maintaining bursting activity.

Directory of Open Access Journals (Sweden)

Anca Doloc-Mihu

2014-06-01

Full Text Available Recent experimental and computational studies suggest that linearly correlated sets of parameters (intrinsic and synaptic properties of neurons allow central pattern-generating networks to produce and maintain their rhythmic activity regardless of changing internal and external conditions. To determine the role of correlated conductances in the robust maintenance of functional bursting activity, we used our existing database of half-center oscillator (HCO model instances of the leech heartbeat CPG. From the database, we identified functional activity groups of burster (isolated neuron and half-center oscillator model instances and realistic subgroups of each that showed burst characteristics (principally period and spike frequency similar to the animal. To find linear correlations among the conductance parameters maintaining functional leech bursting activity, we applied Principal Component Analysis (PCA to each of these four groups. PCA identified a set of three maximal conductances (leak current, [Formula: see text]Leak; a persistent K current, [Formula: see text]K2; and of a persistent Na+ current, [Formula: see text]P that correlate linearly for the two groups of burster instances but not for the HCO groups. Visualizations of HCO instances in a reduced space suggested that there might be non-linear relationships between these parameters for these instances. Experimental studies have shown that period is a key attribute influenced by modulatory inputs and temperature variations in heart interneurons. Thus, we explored the sensitivity of period to changes in maximal conductances of [Formula: see text]Leak, [Formula: see text]K2, and [Formula: see text]P, and we found that for our realistic bursters the effect of these parameters on period could not be assessed because when varied individually bursting activity was not maintained.

Global identifiability of linear compartmental models--a computer algebra algorithm.

Science.gov (United States)

Audoly, S; D'Angiò, L; Saccomani, M P; Cobelli, C

1998-01-01

A priori global identifiability deals with the uniqueness of the solution for the unknown parameters of a model and is, thus, a prerequisite for parameter estimation of biological dynamic models. Global identifiability is however difficult to test, since it requires solving a system of algebraic nonlinear equations which increases both in nonlinearity degree and number of terms and unknowns with increasing model order. In this paper, a computer algebra tool, GLOBI (GLOBal Identifiability) is presented, which combines the topological transfer function method with the Buchberger algorithm, to test global identifiability of linear compartmental models. GLOBI allows for the automatic testing of a priori global identifiability of general structure compartmental models from general multi input-multi output experiments. Examples of usage of GLOBI to analyze a priori global identifiability of some complex biological compartmental models are provided.

Robust Adaptive Sliding Mode Control for Generalized Function Projective Synchronization of Different Chaotic Systems with Unknown Parameters

Directory of Open Access Journals (Sweden)

Xiuchun Li

2013-01-01

Full Text Available When the parameters of both drive and response systems are all unknown, an adaptive sliding mode controller, strongly robust to exotic perturbations, is designed for realizing generalized function projective synchronization. Sliding mode surface is given and the controlled system is asymptotically stable on this surface with the passage of time. Based on the adaptation laws and Lyapunov stability theory, an adaptive sliding controller is designed to ensure the occurrence of the sliding motion. Finally, numerical simulations are presented to verify the effectiveness and robustness of the proposed method even when both drive and response systems are perturbed with external disturbances.

Identifying the connective strength between model parameters and performance criteria

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B. Guse

2017-11-01

Full Text Available In hydrological models, parameters are used to represent the time-invariant characteristics of catchments and to capture different aspects of hydrological response. Hence, model parameters need to be identified based on their role in controlling the hydrological behaviour. For the identification of meaningful parameter values, multiple and complementary performance criteria are used that compare modelled and measured discharge time series. The reliability of the identification of hydrologically meaningful model parameter values depends on how distinctly a model parameter can be assigned to one of the performance criteria. To investigate this, we introduce the new concept of connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the interrelationship between model parameters and performance criteria in a bijective way. In our analysis of connective strength, model simulations are carried out based on a latin hypercube sampling. Ten performance criteria including Nash–Sutcliffe efficiency (NSE, Kling–Gupta efficiency (KGE and its three components (alpha, beta and r as well as RSR (the ratio of the root mean square error to the standard deviation for different segments of the flow duration curve (FDC are calculated. With a joint analysis of two regression tree (RT approaches, we derive how a model parameter is connected to different performance criteria. At first, RTs are constructed using each performance criterion as the target variable to detect the most relevant model parameters for each performance criterion. Secondly, RTs are constructed using each parameter as the target variable to detect which performance criteria are impacted by changes in the values of one distinct model parameter. Based on this, appropriate performance criteria are identified for each model parameter. In this study, a high bijective connective strength between model parameters and performance criteria

Parameter Identification and Synchronization of Uncertain Chaotic Systems Based on Sliding Mode Observer

Directory of Open Access Journals (Sweden)

Li-lian Huang

2013-01-01

Full Text Available The synchronization of nonlinear uncertain chaotic systems is investigated. We propose a sliding mode state observer scheme which combines the sliding mode control with observer theory and apply it into the uncertain chaotic system with unknown parameters and bounded interference. Based on Lyapunov stability theory, the constraints of synchronization and proof are given. This method not only can realize the synchronization of chaotic systems, but also identify the unknown parameters and obtain the correct parameter estimation. Otherwise, the synchronization of chaotic systems with unknown parameters and bounded external disturbances is robust by the design of the sliding surface. Finally, numerical simulations on Liu chaotic system with unknown parameters and disturbances are carried out. Simulation results show that this synchronization and parameter identification has been totally achieved and the effectiveness is verified very well.

Adaptive lag synchronization and parameters adaptive lag identification of chaotic systems

Energy Technology Data Exchange (ETDEWEB)

Xu Yuhua, E-mail: yuhuaxu2004@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China) and Department of Mathematics, Yunyang Teachers' College, Hubei, Shiyan 442000 (China); Zhou Wuneng, E-mail: wnzhou@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China) and Key Laboratory of Wireless Sensor Network and Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050 (China); Fang Jian' an, E-mail: jafang@dhu.edu.c [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Sun Wen, E-mail: sunwen_2201@163.co [School of Mathematics and Information, Yangtze University, Hubei, Jingzhou 434023 (China)

2010-07-26

This Letter investigates the problem of adaptive lag synchronization and parameters adaptive lag identification of chaotic systems. In comparison with those of existing parameters identification schemes, the unknown parameters are identified by adaptive lag laws, and the delay time is also identified in this Letter. Numerical simulations are also given to show the effectiveness of the proposed method.

Information sensitivity functions to assess parameter information gain and identifiability of dynamical systems.

Science.gov (United States)

Pant, Sanjay

2018-05-01

A new class of functions, called the 'information sensitivity functions' (ISFs), which quantify the information gain about the parameters through the measurements/observables of a dynamical system are presented. These functions can be easily computed through classical sensitivity functions alone and are based on Bayesian and information-theoretic approaches. While marginal information gain is quantified by decrease in differential entropy, correlations between arbitrary sets of parameters are assessed through mutual information. For individual parameters, these information gains are also presented as marginal posterior variances, and, to assess the effect of correlations, as conditional variances when other parameters are given. The easy to interpret ISFs can be used to (a) identify time intervals or regions in dynamical system behaviour where information about the parameters is concentrated; (b) assess the effect of measurement noise on the information gain for the parameters; (c) assess whether sufficient information in an experimental protocol (input, measurements and their frequency) is available to identify the parameters; (d) assess correlation in the posterior distribution of the parameters to identify the sets of parameters that are likely to be indistinguishable; and (e) assess identifiability problems for particular sets of parameters. © 2018 The Authors.

Identification of fractional-order systems with unknown initial values and structure

Energy Technology Data Exchange (ETDEWEB)

Du, Wei, E-mail: duwei0203@gmail.com [Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237 (China); Miao, Qingying, E-mail: qymiao@sjtu.edu.cn [School of Continuing Education, Shanghai Jiao Tong University, Shanghai 200030 (China); Tong, Le, E-mail: tongle0328@gmail.com [Faculty of Applied Science and Textiles, The Hong Kong Polytechnic University, Hong Kong (China); Tang, Yang [Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237 (China)

2017-06-21

In this paper, the identification problem of fractional-order chaotic systems is proposed and investigated via an evolutionary optimization approach. Different with other studies to date, this research focuses on the identification of fractional-order chaotic systems with not only unknown orders and parameters, but also unknown initial values and structure. A group of fractional-order chaotic systems, i.e., Lorenz, Lü, Chen, Rössler, Arneodo and Volta chaotic systems, are set as the system candidate pool. The identification problem of fractional-order chaotic systems in this research belongs to mixed integer nonlinear optimization in essence. A powerful evolutionary algorithm called composite differential evolution (CoDE) is introduced for the identification problem presented in this paper. Extensive experiments are carried out to show that the fractional-order chaotic systems with unknown initial values and structure can be successfully identified by means of CoDE. - Highlights: • Unknown initial values and structure are introduced in the identification of fractional-order chaotic systems; • Only a series of output is utilized in the identification of fractional-order chaotic systems; • CoDE is used for the identification problem and the results are satisfactory when compared with other DE variants.

Identifiability and error minimization of receptor model parameters with PET

International Nuclear Information System (INIS)

Delforge, J.; Syrota, A.; Mazoyer, B.M.

1989-01-01

The identifiability problem and the general framework for experimental design optimization are presented. The methodology is applied to the problem of the receptor-ligand model parameter estimation with dynamic positron emission tomography data. The first attempts to identify the model parameters from data obtained with a single tracer injection led to disappointing numerical results. The possibility of improving parameter estimation using a new experimental design combining an injection of the labelled ligand and an injection of the cold ligand (displacement experiment) has been investigated. However, this second protocol led to two very different numerical solutions and it was necessary to demonstrate which solution was biologically valid. This has been possible by using a third protocol including both a displacement and a co-injection experiment. (authors). 16 refs.; 14 figs

Identifying tectonic parameters that influence tsunamigenesis

Science.gov (United States)

van Zelst, Iris; Brizzi, Silvia; van Dinther, Ylona; Heuret, Arnauld; Funiciello, Francesca

2017-04-01

The role of tectonics in tsunami generation is at present poorly understood. However, the fact that some regions produce more tsunamis than others indicates that tectonics could influence tsunamigenesis. Here, we complement a global earthquake database that contains geometrical, mechanical, and seismicity parameters of subduction zones with tsunami data. We statistically analyse the database to identify the tectonic parameters that affect tsunamigenesis. The Pearson's product-moment correlation coefficients reveal high positive correlations of 0.65 between, amongst others, the maximum water height of tsunamis and the seismic coupling in a subduction zone. However, these correlations are mainly caused by outliers. The Spearman's rank correlation coefficient results in more robust correlations of 0.60 between the number of tsunamis in a subduction zone and subduction velocity (positive correlation) and the sediment thickness at the trench (negative correlation). Interestingly, there is a positive correlation between the latter and tsunami magnitude. In an effort towards multivariate statistics, a binary decision tree analysis is conducted with one variable. However, this shows that the amount of data is too scarce. To complement this limited amount of data and to assess physical causality of the tectonic parameters with regard to tsunamigenesis, we conduct a numerical study of the most promising parameters using a geodynamic seismic cycle model. We show that an increase in sediment thickness on the subducting plate results in a shift in seismic activity from outerrise normal faults to splay faults. We also show that the splay fault is the preferred rupture path for a strongly velocity strengthening friction regime in the shallow part of the subduction zone, which increases the tsunamigenic potential. A larger updip limit of the seismogenic zone results in larger vertical surface displacement.

Dynamic parameter identification of robot arms with servo-controlled electrical motors

Science.gov (United States)

Jiang, Zhao-Hui; Senda, Hiroshi

2005-12-01

This paper addresses the issue of dynamic parameter identification of the robot manipulator with servo-controlled electrical motors. An assumption is made that all kinematical parameters, such as link lengths, are known, and only dynamic parameters containing mass, moment of inertia, and their functions need to be identified. First, we derive dynamics of the robot arm with a linear form of the unknown dynamic parameters by taking dynamic characteristics of the motor and servo unit into consideration. Then, we implement the parameter identification approach to identify the unknown parameters with respect to individual link separately. A pseudo-inverse matrix is used for formulation of the parameter identification. The optimal solution is guaranteed in a sense of least-squares of the mean errors. A Direct Drive (DD) SCARA type industrial robot arm AdeptOne is used as an application example of the parameter identification. Simulations and experiments for both open loop and close loop controls are carried out. Comparison of the results confirms the correctness and usefulness of the parameter identification and the derived dynamic model.

Structural identifiability of polynomial and rational systems

NARCIS (Netherlands)

J. Nemcová (Jana)

2010-01-01

htmlabstractSince analysis and simulation of biological phenomena require the availability of their fully specified models, one needs to be able to estimate unknown parameter values of the models. In this paper we deal with identifiability of parametrizations which is the property of one-to-one

M-MRAC Backstepping for Systems with Unknown Virtual Control Coefficients

Science.gov (United States)

Stepanyan, Vahram; Krishnakumar, Kalmanje

2015-01-01

The paper presents an over-parametrization free certainty equivalence state feedback backstepping adaptive control design method for systems of any relative degree with unmatched uncertainties and unknown virtual control coefficients. It uses a fast prediction model to estimate the unknown parameters, which is independent of the control design. It is shown that the system's input and output tracking errors can be systematically decreased by the proper choice of the design parameters. The benefits of the approach are demonstrated in numerical simulations.

Allocating monitoring effort in the face of unknown unknowns

Science.gov (United States)

Wintle, B.A.; Runge, M.C.; Bekessy, S.A.

2010-01-01

There is a growing view that to make efficient use of resources, ecological monitoring should be hypothesis-driven and targeted to address specific management questions. 'Targeted' monitoring has been contrasted with other approaches in which a range of quantities are monitored in case they exhibit an alarming trend or provide ad hoc ecological insights. The second form of monitoring, described as surveillance, has been criticized because it does not usually aim to discern between competing hypotheses, and its benefits are harder to identify a priori. The alternative view is that the existence of surveillance data may enable rapid corroboration of emerging hypotheses or help to detect important 'unknown unknowns' that, if undetected, could lead to catastrophic outcomes or missed opportunities. We derive a model to evaluate and compare the efficiency of investments in surveillance and targeted monitoring. We find that a decision to invest in surveillance monitoring may be defensible if: (1) the surveillance design is more likely to discover or corroborate previously unknown phenomena than a targeted design and (2) the expected benefits (or avoided costs) arising from discovery are substantially higher than those arising from a well-planned targeted design. Our examination highlights the importance of being explicit about the objectives, costs and expected benefits of monitoring in a decision analytic framework. ?? 2010 Blackwell Publishing Ltd/CNRS.

A simple method for identifying parameter correlations in partially observed linear dynamic models.

Science.gov (United States)

Li, Pu; Vu, Quoc Dong

2015-12-14

Parameter estimation represents one of the most significant challenges in systems biology. This is because biological models commonly contain a large number of parameters among which there may be functional interrelationships, thus leading to the problem of non-identifiability. Although identifiability analysis has been extensively studied by analytical as well as numerical approaches, systematic methods for remedying practically non-identifiable models have rarely been investigated. We propose a simple method for identifying pairwise correlations and higher order interrelationships of parameters in partially observed linear dynamic models. This is made by derivation of the output sensitivity matrix and analysis of the linear dependencies of its columns. Consequently, analytical relations between the identifiability of the model parameters and the initial conditions as well as the input functions can be achieved. In the case of structural non-identifiability, identifiable combinations can be obtained by solving the resulting homogenous linear equations. In the case of practical non-identifiability, experiment conditions (i.e. initial condition and constant control signals) can be provided which are necessary for remedying the non-identifiability and unique parameter estimation. It is noted that the approach does not consider noisy data. In this way, the practical non-identifiability issue, which is popular for linear biological models, can be remedied. Several linear compartment models including an insulin receptor dynamics model are taken to illustrate the application of the proposed approach. Both structural and practical identifiability of partially observed linear dynamic models can be clarified by the proposed method. The result of this method provides important information for experimental design to remedy the practical non-identifiability if applicable. The derivation of the method is straightforward and thus the algorithm can be easily implemented into a

Adaptive Control for Revolute Joints Robot Manipulator with Uncertain/Unknown Dynamic Parameters and in Presence of Disturbance in Control Input

DEFF Research Database (Denmark)

Seyed Sakha, Masoud; Shaker, Hamid Reza

2017-01-01

This paper presents an effective adaptive controller for revolute joints robot manipulator where the control input is accompanied with a random disturbance (with unknown PSD). It is clear that, disturbance can compromise the overall performance of the system. To cope with this problem, a control...... technique is proposed which uses the concept of exponential practical stability. Unlike other counterparts, the proposed method does not need information such as the physical parameters of robot and gravitational acceleration. The results show that the proposed controller achieves an excellent performance...

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models.

Directory of Open Access Journals (Sweden)

Jonathan R Karr

2015-05-01

Full Text Available Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.

Approximate effect of parameter pseudonoise intensity on rate of convergence for EKF parameter estimators. [Extended Kalman Filter

Science.gov (United States)

Hill, Bryon K.; Walker, Bruce K.

1991-01-01

When using parameter estimation methods based on extended Kalman filter (EKF) theory, it is common practice to assume that the unknown parameter values behave like a random process, such as a random walk, in order to guarantee their identifiability by the filter. The present work is the result of an ongoing effort to quantitatively describe the effect that the assumption of a fictitious noise (called pseudonoise) driving the unknown parameter values has on the parameter estimate convergence rate in filter-based parameter estimators. The initial approach is to examine a first-order system described by one state variable with one parameter to be estimated. The intent is to derive analytical results for this simple system that might offer insight into the effect of the pseudonoise assumption for more complex systems. Such results would make it possible to predict the estimator error convergence behavior as a function of the assumed pseudonoise intensity, and this leads to the natural application of the results to the design of filter-based parameter estimators. The results obtained show that the analytical description of the convergence behavior is very difficult.

Characterizing unknown systematics in large scale structure surveys

International Nuclear Information System (INIS)

Agarwal, Nishant; Ho, Shirley; Myers, Adam D.; Seo, Hee-Jong; Ross, Ashley J.; Bahcall, Neta; Brinkmann, Jonathan; Eisenstein, Daniel J.; Muna, Demitri; Palanque-Delabrouille, Nathalie; Yèche, Christophe; Pâris, Isabelle; Petitjean, Patrick; Schneider, Donald P.; Streblyanska, Alina; Weaver, Benjamin A.

2014-01-01

Photometric large scale structure (LSS) surveys probe the largest volumes in the Universe, but are inevitably limited by systematic uncertainties. Imperfect photometric calibration leads to biases in our measurements of the density fields of LSS tracers such as galaxies and quasars, and as a result in cosmological parameter estimation. Earlier studies have proposed using cross-correlations between different redshift slices or cross-correlations between different surveys to reduce the effects of such systematics. In this paper we develop a method to characterize unknown systematics. We demonstrate that while we do not have sufficient information to correct for unknown systematics in the data, we can obtain an estimate of their magnitude. We define a parameter to estimate contamination from unknown systematics using cross-correlations between different redshift slices and propose discarding bins in the angular power spectrum that lie outside a certain contamination tolerance level. We show that this method improves estimates of the bias using simulated data and further apply it to photometric luminous red galaxies in the Sloan Digital Sky Survey as a case study

Characterizing unknown systematics in large scale structure surveys

Energy Technology Data Exchange (ETDEWEB)

Agarwal, Nishant; Ho, Shirley [McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15213 (United States); Myers, Adam D. [Department of Physics and Astronomy, University of Wyoming, Laramie, WY 82071 (United States); Seo, Hee-Jong [Berkeley Center for Cosmological Physics, LBL and Department of Physics, University of California, Berkeley, CA 94720 (United States); Ross, Ashley J. [Institute of Cosmology and Gravitation, University of Portsmouth, Portsmouth, PO1 3FX (United Kingdom); Bahcall, Neta [Princeton University Observatory, Peyton Hall, Princeton, NJ 08544 (United States); Brinkmann, Jonathan [Apache Point Observatory, P.O. Box 59, Sunspot, NM 88349 (United States); Eisenstein, Daniel J. [Harvard-Smithsonian Center for Astrophysics, 60 Garden St., Cambridge, MA 02138 (United States); Muna, Demitri [Department of Astronomy, Ohio State University, Columbus, OH 43210 (United States); Palanque-Delabrouille, Nathalie; Yèche, Christophe [CEA, Centre de Saclay, Irfu/SPP, F-91191 Gif-sur-Yvette (France); Pâris, Isabelle [Departamento de Astronomía, Universidad de Chile, Casilla 36-D, Santiago (Chile); Petitjean, Patrick [Université Paris 6 et CNRS, Institut d' Astrophysique de Paris, 98bis blvd. Arago, 75014 Paris (France); Schneider, Donald P. [Department of Astronomy and Astrophysics, Pennsylvania State University, University Park, PA 16802 (United States); Streblyanska, Alina [Instituto de Astrofisica de Canarias (IAC), E-38200 La Laguna, Tenerife (Spain); Weaver, Benjamin A., E-mail: nishanta@andrew.cmu.edu [Center for Cosmology and Particle Physics, New York University, New York, NY 10003 (United States)

2014-04-01

Photometric large scale structure (LSS) surveys probe the largest volumes in the Universe, but are inevitably limited by systematic uncertainties. Imperfect photometric calibration leads to biases in our measurements of the density fields of LSS tracers such as galaxies and quasars, and as a result in cosmological parameter estimation. Earlier studies have proposed using cross-correlations between different redshift slices or cross-correlations between different surveys to reduce the effects of such systematics. In this paper we develop a method to characterize unknown systematics. We demonstrate that while we do not have sufficient information to correct for unknown systematics in the data, we can obtain an estimate of their magnitude. We define a parameter to estimate contamination from unknown systematics using cross-correlations between different redshift slices and propose discarding bins in the angular power spectrum that lie outside a certain contamination tolerance level. We show that this method improves estimates of the bias using simulated data and further apply it to photometric luminous red galaxies in the Sloan Digital Sky Survey as a case study.

Structural identifiability of systems biology models: a critical comparison of methods.

Directory of Open Access Journals (Sweden)

Oana-Teodora Chis

Full Text Available Analysing the properties of a biological system through in silico experimentation requires a satisfactory mathematical representation of the system including accurate values of the model parameters. Fortunately, modern experimental techniques allow obtaining time-series data of appropriate quality which may then be used to estimate unknown parameters. However, in many cases, a subset of those parameters may not be uniquely estimated, independently of the experimental data available or the numerical techniques used for estimation. This lack of identifiability is related to the structure of the model, i.e. the system dynamics plus the observation function. Despite the interest in knowing a priori whether there is any chance of uniquely estimating all model unknown parameters, the structural identifiability analysis for general non-linear dynamic models is still an open question. There is no method amenable to every model, thus at some point we have to face the selection of one of the possibilities. This work presents a critical comparison of the currently available techniques. To this end, we perform the structural identifiability analysis of a collection of biological models. The results reveal that the generating series approach, in combination with identifiability tableaus, offers the most advantageous compromise among range of applicability, computational complexity and information provided.

On finding and using identifiable parameter combinations in nonlinear dynamic systems biology models and COMBOS: a novel web implementation.

Science.gov (United States)

Meshkat, Nicolette; Kuo, Christine Er-zhen; DiStefano, Joseph

2014-01-01

Parameter identifiability problems can plague biomodelers when they reach the quantification stage of development, even for relatively simple models. Structural identifiability (SI) is the primary question, usually understood as knowing which of P unknown biomodel parameters p1,…, pi,…, pP are-and which are not-quantifiable in principle from particular input-output (I-O) biodata. It is not widely appreciated that the same database also can provide quantitative information about the structurally unidentifiable (not quantifiable) subset, in the form of explicit algebraic relationships among unidentifiable pi. Importantly, this is a first step toward finding what else is needed to quantify particular unidentifiable parameters of interest from new I-O experiments. We further develop, implement and exemplify novel algorithms that address and solve the SI problem for a practical class of ordinary differential equation (ODE) systems biology models, as a user-friendly and universally-accessible web application (app)-COMBOS. Users provide the structural ODE and output measurement models in one of two standard forms to a remote server via their web browser. COMBOS provides a list of uniquely and non-uniquely SI model parameters, and-importantly-the combinations of parameters not individually SI. If non-uniquely SI, it also provides the maximum number of different solutions, with important practical implications. The behind-the-scenes symbolic differential algebra algorithms are based on computing Gröbner bases of model attributes established after some algebraic transformations, using the computer-algebra system Maxima. COMBOS was developed for facile instructional and research use as well as modeling. We use it in the classroom to illustrate SI analysis; and have simplified complex models of tumor suppressor p53 and hormone regulation, based on explicit computation of parameter combinations. It's illustrated and validated here for models of moderate complexity, with

Parameter trajectory analysis to identify treatment effects of pharmacological interventions.

Directory of Open Access Journals (Sweden)

Christian A Tiemann

Full Text Available The field of medical systems biology aims to advance understanding of molecular mechanisms that drive disease progression and to translate this knowledge into therapies to effectively treat diseases. A challenging task is the investigation of long-term effects of a (pharmacological treatment, to establish its applicability and to identify potential side effects. We present a new modeling approach, called Analysis of Dynamic Adaptations in Parameter Trajectories (ADAPT, to analyze the long-term effects of a pharmacological intervention. A concept of time-dependent evolution of model parameters is introduced to study the dynamics of molecular adaptations. The progression of these adaptations is predicted by identifying necessary dynamic changes in the model parameters to describe the transition between experimental data obtained during different stages of the treatment. The trajectories provide insight in the affected underlying biological systems and identify the molecular events that should be studied in more detail to unravel the mechanistic basis of treatment outcome. Modulating effects caused by interactions with the proteome and transcriptome levels, which are often less well understood, can be captured by the time-dependent descriptions of the parameters. ADAPT was employed to identify metabolic adaptations induced upon pharmacological activation of the liver X receptor (LXR, a potential drug target to treat or prevent atherosclerosis. The trajectories were investigated to study the cascade of adaptations. This provided a counter-intuitive insight concerning the function of scavenger receptor class B1 (SR-B1, a receptor that facilitates the hepatic uptake of cholesterol. Although activation of LXR promotes cholesterol efflux and -excretion, our computational analysis showed that the hepatic capacity to clear cholesterol was reduced upon prolonged treatment. This prediction was confirmed experimentally by immunoblotting measurements of SR-B1

Learning Unknown Structure in CRFs via Adaptive Gradient Projection Method

Directory of Open Access Journals (Sweden)

Wei Xue

2016-08-01

Full Text Available We study the problem of fitting probabilistic graphical models to the given data when the structure is not known. More specifically, we focus on learning unknown structure in conditional random fields, especially learning both the structure and parameters of a conditional random field model simultaneously. To do this, we first formulate the learning problem as a convex minimization problem by adding an l_2-regularization to the node parameters and a group l_1-regularization to the edge parameters, and then a gradient-based projection method is proposed to solve it which combines an adaptive stepsize selection strategy with a nonmonotone line search. Extensive simulation experiments are presented to show the performance of our approach in solving unknown structure learning problems.

Known knowns, known unknowns and unknown unknowns in prokaryotic transposition.

Science.gov (United States)

Siguier, Patricia; Gourbeyre, Edith; Chandler, Michael

2017-08-01

Although the phenomenon of transposition has been known for over 60 years, its overarching importance in modifying and streamlining genomes took some time to recognize. In spite of a robust understanding of transposition of some TE, there remain a number of important TE groups with potential high genome impact and unknown transposition mechanisms and yet others, only recently identified by bioinformatics, yet to be formally confirmed as mobile. Here, we point to some areas of limited understanding concerning well established important TE groups with DDE Tpases, to address central gaps in our knowledge of characterised Tn with other types of Tpases and finally, to highlight new potentially mobile DNA species. It is not exhaustive. Examples have been chosen to provide encouragement in the continued exploration of the considerable prokaryotic mobilome especially in light of the current threat to public health posed by the spread of multiple Ab R . Copyright © 2017 Elsevier Ltd. All rights reserved.

Back Analysis of Geomechanical Parameters in Underground Engineering Using Artificial Bee Colony

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Changxing Zhu

2014-01-01

Full Text Available Accurate geomechanical parameters are critical in tunneling excavation, design, and supporting. In this paper, a displacements back analysis based on artificial bee colony (ABC algorithm is proposed to identify geomechanical parameters from monitored displacements. ABC was used as global optimal algorithm to search the unknown geomechanical parameters for the problem with analytical solution. To the problem without analytical solution, optimal back analysis is time-consuming, and least square support vector machine (LSSVM was used to build the relationship between unknown geomechanical parameters and displacement and improve the efficiency of back analysis. The proposed method was applied to a tunnel with analytical solution and a tunnel without analytical solution. The results show the proposed method is feasible.

An approach of parameter estimation for non-synchronous systems

International Nuclear Information System (INIS)

Xu Daolin; Lu Fangfang

2005-01-01

Synchronization-based parameter estimation is simple and effective but only available to synchronous systems. To come over this limitation, we propose a technique that the parameters of an unknown physical process (possibly a non-synchronous system) can be identified from a time series via a minimization procedure based on a synchronization control. The feasibility of this approach is illustrated in several chaotic systems

Nonlinear adaptive control system design with asymptotically stable parameter estimation error

Science.gov (United States)

Mishkov, Rumen; Darmonski, Stanislav

2018-01-01

The paper presents a new general method for nonlinear adaptive system design with asymptotic stability of the parameter estimation error. The advantages of the approach include asymptotic unknown parameter estimation without persistent excitation and capability to directly control the estimates transient response time. The method proposed modifies the basic parameter estimation dynamics designed via a known nonlinear adaptive control approach. The modification is based on the generalised prediction error, a priori constraints with a hierarchical parameter projection algorithm, and the stable data accumulation concepts. The data accumulation principle is the main tool for achieving asymptotic unknown parameter estimation. It relies on the parametric identifiability system property introduced. Necessary and sufficient conditions for exponential stability of the data accumulation dynamics are derived. The approach is applied in a nonlinear adaptive speed tracking vector control of a three-phase induction motor.

Non-identifier based adaptive control in mechatronics theory and application

CERN Document Server

Hackl, Christoph M

2017-01-01

This book introduces non-identifier-based adaptive control (with and without internal model) and its application to the current, speed and position control of mechatronic systems such as electrical synchronous machines, wind turbine systems, industrial servo systems, and rigid-link, revolute-joint robots. In mechatronics, there is often only rough knowledge of the system. Due to parameter uncertainties, nonlinearities and unknown disturbances, model-based control strategies can reach their performance or stability limits without iterative controller design and performance evaluation, or system identification and parameter estimation. The non-identifier-based adaptive control presented is an alternative that neither identifies the system nor estimates its parameters but ensures stability. The adaptive controllers are easy to implement, compensate for disturbances and are inherently robust to parameter uncertainties and nonlinearities. For controller implementation only structural system knowledge (like relativ...

Synthesis of vibration control and health monitoring of building structures under unknown excitation

International Nuclear Information System (INIS)

He, Jia; Huang, Qin; Xu, You-Lin

2014-01-01

The vibration control and health monitoring of building structures have been actively investigated in recent years but often treated separately according to the primary objective pursued. In this study, a time-domain integrated vibration control and health monitoring approach is proposed based on the extended Kalman filter (EKF) for identifying the physical parameters of the controlled building structures without the knowledge of the external excitation. The physical parameters and state vectors of the building structure are then estimated and used for the determination of the control force for the purpose of the vibration attenuation. The interaction between the health monitoring and vibration control is revealed and assessed. The feasibility and reliability of the proposed approach is numerically demonstrated via a five-story shear building structure equipped with magneto-rheological (MR) dampers. Two types of excitations are considered: (1) the EI-Centro ground excitation underneath of the building and (2) a swept-frequency excitation applied on the top floor of the building. Results show that the structural parameters as well as the unknown dynamic loadings could be identified accurately; and, at the same time, the structural vibration is significantly reduced in the building structure. (paper)

On the identifiability of inertia parameters of planar Multi-Body Space Systems

Science.gov (United States)

Nabavi-Chashmi, Seyed Yaser; Malaek, Seyed Mohammad-Bagher

2018-04-01

This work describes a new formulation to study the identifiability characteristics of Serially Linked Multi-body Space Systems (SLMBSS). The process exploits the so called "Lagrange Formulation" to develop a linear form of Equations of Motion w.r.t the system Inertia Parameters (IPs). Having developed a specific form of regressor matrix, we aim to expedite the identification process. The new approach allows analytical as well as numerical identification and identifiability analysis for different SLMBSSs' configurations. Moreover, the explicit forms of SLMBSSs identifiable parameters are derived by analyzing the identifiability characteristics of the robot. We further show that any SLMBSS designed with Variable Configurations Joint allows all IPs to be identifiable through comparing two successive identification outcomes. This feature paves the way to design new class of SLMBSS for which accurate identification of all IPs is at hand. Different case studies reveal that proposed formulation provides fast and accurate results, as required by the space applications. Further studies might be necessary for cases where planar-body assumption becomes inaccurate.

Three-dimensional cinematography with control object of unknown shape.

Science.gov (United States)

Dapena, J; Harman, E A; Miller, J A

1982-01-01

A technique for reconstruction of three-dimensional (3D) motion which involves a simple filming procedure but allows the deduction of coordinates in large object volumes was developed. Internal camera parameters are calculated from measurements of the film images of two calibrated crosses while external camera parameters are calculated from the film images of points in a control object of unknown shape but at least one known length. The control object, which includes the volume in which the activity is to take place, is formed by a series of poles placed at unknown locations, each carrying two targets. From the internal and external camera parameters, and from locations of the images of point in the films of the two cameras, 3D coordinates of the point can be calculated. Root mean square errors of the three coordinates of points in a large object volume (5m x 5m x 1.5m) were 15 mm, 13 mm, 13 mm and 6 mm, and relative errors in lengths averaged 0.5%, 0.7% and 0.5%, respectively.

Estimating unknown parameters in haemophilia using expert judgement elicitation.

Science.gov (United States)

Fischer, K; Lewandowski, D; Janssen, M P

2013-09-01

The increasing attention to healthcare costs and treatment efficiency has led to an increasing demand for quantitative data concerning patient and treatment characteristics in haemophilia. However, most of these data are difficult to obtain. The aim of this study was to use expert judgement elicitation (EJE) to estimate currently unavailable key parameters for treatment models in severe haemophilia A. Using a formal expert elicitation procedure, 19 international experts provided information on (i) natural bleeding frequency according to age and onset of bleeding, (ii) treatment of bleeds, (iii) time needed to control bleeding after starting secondary prophylaxis, (iv) dose requirements for secondary prophylaxis according to onset of bleeding, and (v) life-expectancy. For each parameter experts provided their quantitative estimates (median, P10, P90), which were combined using a graphical method. In addition, information was obtained concerning key decision parameters of haemophilia treatment. There was most agreement between experts regarding bleeding frequencies for patients treated on demand with an average onset of joint bleeding (1.7 years): median 12 joint bleeds per year (95% confidence interval 0.9-36) for patients ≤ 18, and 11 (0.8-61) for adult patients. Less agreement was observed concerning estimated effective dose for secondary prophylaxis in adults: median 2000 IU every other day The majority (63%) of experts expected that a single minor joint bleed could cause irreversible damage, and would accept up to three minor joint bleeds or one trauma related joint bleed annually on prophylaxis. Expert judgement elicitation allowed structured capturing of quantitative expert estimates. It generated novel data to be used in computer modelling, clinical care, and trial design. © 2013 John Wiley & Sons Ltd.

Finding identifiable parameter combinations in nonlinear ODE models and the rational reparameterization of their input-output equations.

Science.gov (United States)

Meshkat, Nicolette; Anderson, Chris; Distefano, Joseph J

2011-09-01

When examining the structural identifiability properties of dynamic system models, some parameters can take on an infinite number of values and yet yield identical input-output data. These parameters and the model are then said to be unidentifiable. Finding identifiable combinations of parameters with which to reparameterize the model provides a means for quantitatively analyzing the model and computing solutions in terms of the combinations. In this paper, we revisit and explore the properties of an algorithm for finding identifiable parameter combinations using Gröbner Bases and prove useful theoretical properties of these parameter combinations. We prove a set of M algebraically independent identifiable parameter combinations can be found using this algorithm and that there exists a unique rational reparameterization of the input-output equations over these parameter combinations. We also demonstrate application of the procedure to a nonlinear biomodel. Copyright © 2011 Elsevier Inc. All rights reserved.

Adaptive Tracking and Obstacle Avoidance Control for Mobile Robots with Unknown Sliding

Directory of Open Access Journals (Sweden)

Mingyue Cui

2012-11-01

Full Text Available An adaptive control approach is proposed for trajectory tracking and obstacle avoidance for mobile robots with consideration given to unknown sliding. A kinematic model of mobile robots is established in this paper, in which both longitudinal and lateral sliding are considered and processed as three time-varying parameters. A sliding model observer is introduced to estimate the sliding parameters online. A stable tracking control law for this nonholonomic system is proposed to compensate the unknown sliding effect. From Lyapunov-stability analysis, it is proved, regardless of unknown sliding, that tracking errors of the controlled closed-loop system are asymptotically stable, the tracking errors converge to zero outside the obstacle detection region and obstacle avoidance is guaranteed inside the obstacle detection region. The efficiency and robustness of the proposed control system are verified by simulation results.

Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters

Czech Academy of Sciences Publication Activity Database

Ökzan, E.; Šmídl, Václav; Saha, S.; Lundquist, C.; Gustafsson, F.

2013-01-01

Roč. 49, č. 6 (2013), s. 1566-1575 ISSN 0005-1098 R&D Projects: GA ČR(CZ) GAP102/11/0437 Keywords : Unknown Noise Statistics * Adaptive Filtering * Marginalized Particle Filter * Bayesian Conjugate prior Subject RIV: BC - Control Systems Theory Impact factor: 3.132, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/smidl-0393047.pdf

Structural Identifiability of Dynamic Systems Biology Models.

Science.gov (United States)

Villaverde, Alejandro F; Barreiro, Antonio; Papachristodoulou, Antonis

2016-10-01

A powerful way of gaining insight into biological systems is by creating a nonlinear differential equation model, which usually contains many unknown parameters. Such a model is called structurally identifiable if it is possible to determine the values of its parameters from measurements of the model outputs. Structural identifiability is a prerequisite for parameter estimation, and should be assessed before exploiting a model. However, this analysis is seldom performed due to the high computational cost involved in the necessary symbolic calculations, which quickly becomes prohibitive as the problem size increases. In this paper we show how to analyse the structural identifiability of a very general class of nonlinear models by extending methods originally developed for studying observability. We present results about models whose identifiability had not been previously determined, report unidentifiabilities that had not been found before, and show how to modify those unidentifiable models to make them identifiable. This method helps prevent problems caused by lack of identifiability analysis, which can compromise the success of tasks such as experiment design, parameter estimation, and model-based optimization. The procedure is called STRIKE-GOLDD (STRuctural Identifiability taKen as Extended-Generalized Observability with Lie Derivatives and Decomposition), and it is implemented in a MATLAB toolbox which is available as open source software. The broad applicability of this approach facilitates the analysis of the increasingly complex models used in systems biology and other areas.

Bayesian source term determination with unknown covariance of measurements

Science.gov (United States)

Belal, Alkomiet; Tichý, Ondřej; Šmídl, Václav

2017-04-01

Determination of a source term of release of a hazardous material into the atmosphere is a very important task for emergency response. We are concerned with the problem of estimation of the source term in the conventional linear inverse problem, y = Mx, where the relationship between the vector of observations y is described using the source-receptor-sensitivity (SRS) matrix M and the unknown source term x. Since the system is typically ill-conditioned, the problem is recast as an optimization problem minR,B(y - Mx)TR-1(y - Mx) + xTB-1x. The first term minimizes the error of the measurements with covariance matrix R, and the second term is a regularization of the source term. There are different types of regularization arising for different choices of matrices R and B, for example, Tikhonov regularization assumes covariance matrix B as the identity matrix multiplied by scalar parameter. In this contribution, we adopt a Bayesian approach to make inference on the unknown source term x as well as unknown R and B. We assume prior on x to be a Gaussian with zero mean and unknown diagonal covariance matrix B. The covariance matrix of the likelihood R is also unknown. We consider two potential choices of the structure of the matrix R. First is the diagonal matrix and the second is a locally correlated structure using information on topology of the measuring network. Since the inference of the model is intractable, iterative variational Bayes algorithm is used for simultaneous estimation of all model parameters. The practical usefulness of our contribution is demonstrated on an application of the resulting algorithm to real data from the European Tracer Experiment (ETEX). This research is supported by EEA/Norwegian Financial Mechanism under project MSMT-28477/2014 Source-Term Determination of Radionuclide Releases by Inverse Atmospheric Dispersion Modelling (STRADI).

Function analysis of unknown genes

DEFF Research Database (Denmark)

Rogowska-Wrzesinska, A.

2002-01-01

  This thesis entitled "Function analysis of unknown genes" presents the use of proteome analysis for the characterisation of yeast (Saccharomyces cerevisiae) genes and their products (proteins especially those of unknown function). This study illustrates that proteome analysis can be used...... to describe different aspects of molecular biology of the cell, to study changes that occur in the cell due to overexpression or deletion of a gene and to identify various protein modifications. The biological questions and the results of the described studies show the diversity of the information that can...... genes and proteins. It reports the first global proteome database collecting 36 yeast single gene deletion mutants and selecting over 650 differences between analysed mutants and the wild type strain. The obtained results show that two-dimensional gel electrophoresis and mass spectrometry based proteome...

A highly precise frequency-based method for estimating the tension of an inclined cable with unknown boundary conditions

Science.gov (United States)

Ma, Lin

2017-11-01

This paper develops a method for precisely determining the tension of an inclined cable with unknown boundary conditions. First, the nonlinear motion equation of an inclined cable is derived, and a numerical model of the motion of the cable is proposed using the finite difference method. The proposed numerical model includes the sag-extensibility, flexural stiffness, inclination angle and rotational stiffness at two ends of the cable. Second, the influence of the dynamic parameters of the cable on its frequencies is discussed in detail, and a method for precisely determining the tension of an inclined cable is proposed based on the derivatives of the eigenvalues of the matrices. Finally, a multiparameter identification method is developed that can simultaneously identify multiple parameters, including the rotational stiffness at two ends. This scheme is applicable to inclined cables with varying sag, varying flexural stiffness and unknown boundary conditions. Numerical examples indicate that the method provides good precision. Because the parameters of cables other than tension (e.g., the flexural stiffness and rotational stiffness at the ends) are not accurately known in practical engineering, the multiparameter identification method could further improve the accuracy of cable tension measurements.

Parameter estimation for chaotic systems with a Drift Particle Swarm Optimization method

International Nuclear Information System (INIS)

Sun Jun; Zhao Ji; Wu Xiaojun; Fang Wei; Cai Yujie; Xu Wenbo

2010-01-01

Inspired by the motion of electrons in metal conductors in an electric field, we propose a variant of Particle Swarm Optimization (PSO), called Drift Particle Swarm Optimization (DPSO) algorithm, and apply it in estimating the unknown parameters of chaotic dynamic systems. The principle and procedure of DPSO are presented, and the algorithm is used to identify Lorenz system and Chen system. The experiment results show that for the given parameter configurations, DPSO can identify the parameters of the systems accurately and effectively, and it may be a promising tool for chaotic system identification as well as other numerical optimization problems in physics.

Multidimensional procurement auctions with unknown weights

DEFF Research Database (Denmark)

Greve, Thomas

This paper studies the consequences of holding a procurement auction when the principal chooses not to show its preferences. My paper extends the procurement auction model of Che (1993) to a situation where both the principal and the agents have private information. Thus, unknown parameters of bo...... gives rise to an analysis of a principal that can not fully commit to the outcome induced by the scoring rule. Therefore, my result apply to contract theory and it’s problems with imperfect commitment....

Bayesian parameter inference from continuously monitored quantum systems

DEFF Research Database (Denmark)

Gammelmark, Søren; Mølmer, Klaus

2013-01-01

We review the introduction of likelihood functions and Fisher information in classical estimation theory, and we show how they can be defined in a very similar manner within quantum measurement theory. We show that the stochastic master equations describing the dynamics of a quantum system subject...... to a definite set of measurements provides likelihood functions for unknown parameters in the system dynamics, and we show that the estimation error, given by the Fisher information, can be identified by stochastic master equation simulations. For large parameter spaces we describe and illustrate the efficient...

Angiogenesis in cancer of unknown primary: clinicopathological study of CD34, VEGF and TSP-1

International Nuclear Information System (INIS)

Karavasilis, Vasilis; Malamou-Mitsi, Vasiliki; Briasoulis, Evangelos; Tsanou, Elena; Kitsou, Evangelia; Kalofonos, Haralambos; Fountzilas, George; Fotsis, Theodore; Pavlidis, Nicholas

2005-01-01

Cancer of unknown primary remains a mallignancy of elusive biology and grim prognosis that lacks effective therapeutic options. We investigated angiogenesis in cancer of unknown primary to expand our knowledge on the biology of these tumors and identify potential therapeutic targets. Paraffin embedded archival material from 81 patients diagnosed with CUP was used. Tumor histology was adenocarcinoma (77%), undifferentiated carcinoma (18%) and squamous cell carcinoma (5%). The tissue expression of CD34, VEGF and TSP-1 was assessed immunohistochemically by use of specific monoclonal antibodies and was analyzed against clinicopathological data. VEGF expression was detected in all cases and was strong in 83%. Stromal expression of TSP-1 was seen in 80% of cases and was strong in 20%. The expression of both proteins was not associated with any clinical or pathological parameters. Tumor MVD was higher in tumors classified as unfavorable compared to more favorable and was positively associated with VEGF and negatively with TSP-1. Angiogenesis is very active and expression of VEGF is almost universal in cancers of unknown primary. These findings support the clinical investigation of VEGF targeted therapy in this clinical setting

Identifiability Results for Several Classes of Linear Compartment Models.

Science.gov (United States)

Meshkat, Nicolette; Sullivant, Seth; Eisenberg, Marisa

2015-08-01

Identifiability concerns finding which unknown parameters of a model can be estimated, uniquely or otherwise, from given input-output data. If some subset of the parameters of a model cannot be determined given input-output data, then we say the model is unidentifiable. In this work, we study linear compartment models, which are a class of biological models commonly used in pharmacokinetics, physiology, and ecology. In past work, we used commutative algebra and graph theory to identify a class of linear compartment models that we call identifiable cycle models, which are unidentifiable but have the simplest possible identifiable functions (so-called monomial cycles). Here we show how to modify identifiable cycle models by adding inputs, adding outputs, or removing leaks, in such a way that we obtain an identifiable model. We also prove a constructive result on how to combine identifiable models, each corresponding to strongly connected graphs, into a larger identifiable model. We apply these theoretical results to several real-world biological models from physiology, cell biology, and ecology.

Using systematic and comparative GC/MS and GC/FID data to identify the source of an unknown oil on contaminated birds

International Nuclear Information System (INIS)

Wang, Z.; Fingas, M.; Landriault, M.; Sigouin, L.; Feng, Y.

1996-01-01

A method to identify and differentiate spilled oil and petroleum products was developed. In January 1996, four birds covered with an unknown oil were found near Larchipel-de-Mingan National Park in Quebec. Environment Canada wanted to know if the oil came from a leak in a barge which was grounded on Anticosti Island. To do so, it was necessary to determine the nature of the oil, the type of petroleum hydrocarbons, the age, the weathering and degradation extent of the spilled oil, and changes in oil character since the occurrence of any possible spill. The analytical approach to determine the source of the unknown oil was described. The analysis of individual aliphatic, aromatic, and biomarker hydrocarbons were made with the use of gas chromatography/mass spectroscopy (GC/MS), and gas chromatography/flame ionization detector (GC/FID). Pattern recognition plot analysis was also used in determining the source of the oil. It was concluded that the residual oil on the birds was not from the suspected barge oil, and was most probably old, highly weathered, somewhat biodegraded bunker type oil. 23 refs., 5 tabs., 7 figs

Identifying the effects of parameter uncertainty on the reliability of riverbank stability modelling

Science.gov (United States)

Samadi, A.; Amiri-Tokaldany, E.; Darby, S. E.

2009-05-01

Bank retreat is a key process in fluvial dynamics affecting a wide range of physical, ecological and socioeconomic issues in the fluvial environment. To predict the undesirable effects of bank retreat and to inform effective measures to prevent it, a wide range of bank stability models have been presented in the literature. These models typically express bank stability by defining a factor of safety as the ratio of driving and resisting forces acting on the incipient failure block. These forces are affected by a range of controlling factors that include such aspects as the bank profile (bank height and angle), the geotechnical properties of the bank materials, as well as the hydrological status of the riverbanks. In this paper we evaluate the extent to which uncertainties in the parameterization of these controlling factors feed through to influence the reliability of the resulting bank stability estimate. This is achieved by employing a simple model of riverbank stability with respect to planar failure (which is the most common type of bank stability model) in a series of sensitivity tests and Monte Carlo analyses to identify, for each model parameter, the range of values that induce significant changes in the simulated factor of safety. These identified parameter value ranges are compared to empirically derived parameter uncertainties to determine whether they are likely to confound the reliability of the resulting bank stability calculations. Our results show that parameter uncertainties are typically high enough that the likelihood of generating unreliable predictions is typically very high (> ˜ 80% for predictions requiring a precision of < ± 15%). Because parameter uncertainties are derived primarily from the natural variability of the parameters, rather than measurement errors, much more careful attention should be paid to field sampling strategies, such that the parameter uncertainties and consequent prediction unreliabilities can be quantified more

Accelerated maximum likelihood parameter estimation for stochastic biochemical systems

Directory of Open Access Journals (Sweden)

Daigle Bernie J

2012-05-01

Full Text Available Abstract Background A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data is still a bottleneck for obtaining accurate simulation results. Many methods exist for parameter estimation in deterministic biochemical systems; methods for discrete stochastic systems are less well developed. Given the probabilistic nature of stochastic biochemical models, a natural approach is to choose parameter values that maximize the probability of the observed data with respect to the unknown parameters, a.k.a. the maximum likelihood parameter estimates (MLEs. MLE computation for all but the simplest models requires the simulation of many system trajectories that are consistent with experimental data. For models with unknown parameters, this presents a computational challenge, as the generation of consistent trajectories can be an extremely rare occurrence. Results We have developed Monte Carlo Expectation-Maximization with Modified Cross-Entropy Method (MCEM2: an accelerated method for calculating MLEs that combines advances in rare event simulation with a computationally efficient version of the Monte Carlo expectation-maximization (MCEM algorithm. Our method requires no prior knowledge regarding parameter values, and it automatically provides a multivariate parameter uncertainty estimate. We applied the method to five stochastic systems of increasing complexity, progressing from an analytically tractable pure-birth model to a computationally demanding model of yeast-polarization. Our results demonstrate that MCEM2 substantially accelerates MLE computation on all tested models when compared to a stand-alone version of MCEM. Additionally, we show how our method identifies parameter values for certain classes of models more accurately than two recently proposed computationally efficient methods

Distributed Optimization Design of Continuous-Time Multiagent Systems With Unknown-Frequency Disturbances.

Science.gov (United States)

Wang, Xinghu; Hong, Yiguang; Yi, Peng; Ji, Haibo; Kang, Yu

2017-05-24

In this paper, a distributed optimization problem is studied for continuous-time multiagent systems with unknown-frequency disturbances. A distributed gradient-based control is proposed for the agents to achieve the optimal consensus with estimating unknown frequencies and rejecting the bounded disturbance in the semi-global sense. Based on convex optimization analysis and adaptive internal model approach, the exact optimization solution can be obtained for the multiagent system disturbed by exogenous disturbances with uncertain parameters.

Identifying parameter regions for multistationarity

DEFF Research Database (Denmark)

Conradi, Carsten; Feliu, Elisenda; Mincheva, Maya

2017-01-01

is the avoidance of numerical analysis and parameter sampling. The procedure consists of a number of steps. Each of these steps might be addressed algorithmically using various computer programs and available software, or manually. We demonstrate our procedure on several models of gene transcription and cell...

Biased sampling, over-identified parameter problems and beyond

CERN Document Server

Qin, Jing

2017-01-01

This book is devoted to biased sampling problems (also called choice-based sampling in Econometrics parlance) and over-identified parameter estimation problems. Biased sampling problems appear in many areas of research, including Medicine, Epidemiology and Public Health, the Social Sciences and Economics. The book addresses a range of important topics, including case and control studies, causal inference, missing data problems, meta-analysis, renewal process and length biased sampling problems, capture and recapture problems, case cohort studies, exponential tilting genetic mixture models etc. The goal of this book is to make it easier for Ph. D students and new researchers to get started in this research area. It will be of interest to all those who work in the health, biological, social and physical sciences, as well as those who are interested in survey methodology and other areas of statistical science, among others. .

[On the value of tattoos for identifying unknown bodies - a retrospective study of forensic autopsy cases from Giessen, Germany].

Science.gov (United States)

Birngruber, Christoph G; Görner, Nicole; Ramsthaler, H Frank

2016-01-01

The number of tattooed people in Germany has constantly grown over the past few years. The present study deals with the question if this social trend can be seen in foren- sic autopsy cases as well. In a retrospective study, forensic autopsy cases of two periods (1990-1994 and 2010-2014) have been reviewed and statistically analyzed. Comparison of the two periods revealed a significant increase in tattooed individuals, especially in the female subgroup. Between 2010 and 2014, 14.2 % of the deceased showed tattoos. There are significant differences in the frequency and localization of tattoos dependent on age and sex. About 50 % of the tattooed deceased showed tattoos on body sites that are visible for other persons in everyday life. The resulting value of tattoos for the purpose of identifying unknown bodies is discussed and illustrated.

Rendezvous with connectivity preservation for multi-robot systems with an unknown leader

Science.gov (United States)

Dong, Yi

2018-02-01

This paper studies the leader-following rendezvous problem with connectivity preservation for multi-agent systems composed of uncertain multi-robot systems subject to external disturbances and an unknown leader, both of which are generated by a so-called exosystem with parametric uncertainty. By combining internal model design, potential function technique and adaptive control, two distributed control strategies are proposed to maintain the connectivity of the communication network, to achieve the asymptotic tracking of all the followers to the output of the unknown leader system, as well as to reject unknown external disturbances. It is also worth to mention that the uncertain parameters in the multi-robot systems and exosystem are further allowed to belong to unknown and unbounded sets when applying the second fully distributed control law containing a dynamic gain inspired by high-gain adaptive control or self-tuning regulator.

Parameter identification technique for uncertain chaotic systems using state feedback and steady-state analysis.

Science.gov (United States)

Zaher, Ashraf A

2008-03-01

A technique is introduced for identifying uncertain and/or unknown parameters of chaotic dynamical systems via using simple state feedback. The proposed technique is based on bringing the system into a stable steady state and then solving for the unknown parameters using a simple algebraic method that requires access to the complete or partial states of the system depending on the dynamical model of the chaotic system. The choice of the state feedback is optimized in terms of practicality and causality via employing a single feedback signal and tuning the feedback gain to ensure both stability and identifiability. The case when only a single scalar time series of one of the states is available is also considered and it is demonstrated that a synchronization-based state observer can be augmented to the state feedback to address this problem. A detailed case study using the Lorenz system is used to exemplify the suggested technique. In addition, both the Rössler and Chua systems are examined as possible candidates for utilizing the proposed methodology when partial identification of the unknown parameters is considered. Finally, the dependence of the proposed technique on the structure of the chaotic dynamical model and the operating conditions is discussed and its advantages and limitations are highlighted via comparing it with other methods reported in the literature.

The meaning and classification of tattoos in the context of their suitability to identify corpses of unknown identity.

Science.gov (United States)

Sadowski, Wojciech A; Borowska-Solonynko, Aleksandra B

2017-01-01

To present the most popular types of tattoos, their meaning and classification, and to assess the suitability of different forms of tattoos in the process of identification of corpses of unknown identity. The tattoos found on 729 cadavers who underwent post mortem examinations at the Department of Forensic Medicine in Warsaw in the years 2012-2015 were analyzed. The tattoos were photographed and identified in terms of their meaning domain and classified into groups. Tattoos belonging to all groups were found, according to the most popular tattoo classification, which is based on their nature and includes: criminal and prison tattoos (defining the prison hierarchy, criminal profession as well as intentions and goals, erotic tattoos, environmental, penitentiary), military and artistic. The novel classification, focusing on the utility of certain kinds of tattoos for identyfying corpses of unknown identity, was also developed. According to the above mentioned classification the following kinds of tattoos are distinguished: individual (artistic), group (e.g. penitentiary - indicating the fact of being imprisoned in a penitentiary institution or belonging to a "prison kites" subculture, or presenting criminal profession; group confined tattoo (indicating a staying in a specific penitentiary institution), group tattoo with individual data (indicating the fact of staying in a penitentiary institution as well as dates of imprisonment), and others (e.g. names of relatives, military tattoo, etc.). Analysis of individual types of tattoo can accelerate the process of identification of the cadavers. The proposed classification allows to quickly determine whether a particular tattoo can be helpful in initial individual identification (in the case of individual tattoos) or whether it can be used to reduce the group of people considered (in cases of different types of group tattoos).

Unknown loads affect force production capacity in early phases of bench press throws.

Science.gov (United States)

Hernández Davó, J L; Sabido Solana, R; Sarabia Marínm, J M; Sánchez Martos, Á; Moya Ramón, M

2015-10-01

Explosive strength training aims to improve force generation in early phases of movement due to its importance in sport performance. The present study examined the influence of lack of knowledge about the load lifted in explosive parameters during bench press throws. Thirteen healthy young men (22.8±2.0 years) participated in the study. Participants performed bench press throws with three different loads (30, 50 and 70% of 1 repetition maximum) in two different conditions (known and unknown loads). In unknown condition, loads were changed within sets in each repetition and participants did not know the load, whereas in known condition the load did not change within sets and participants had knowledge about the load lifted. Results of repeated-measures ANOVA revealed that unknown conditions involves higher power in the first 30, 50, 100 and 150 ms with the three loads, higher values of ratio of force development in those first instants, and differences in time to reach maximal rate of force development with 50 and 70% of 1 repetition maximum. This study showed that unknown conditions elicit higher values of explosive parameters in early phases of bench press throws, thereby this kind of methodology could be considered in explosive strength training.

Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics

Science.gov (United States)

Lv, Yongfeng; Na, Jing; Yang, Qinmin; Wu, Xing; Guo, Yu

2016-01-01

An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which is achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm with a dual neural network (NN) approximation structure. First, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is employed to approximate the optimal value function. Then, the optimal control law is computed based on the information from the identifier NN and the critic NN, so that the actor NN is not needed. In particular, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simultaneously, which converge to small neighbourhoods around their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.

Methodology for identifying parameters for the TRNSYS model Type 210 - wood pellet stoves and boilers

Energy Technology Data Exchange (ETDEWEB)

Persson, Tomas; Fiedler, Frank; Nordlander, Svante

2006-05-15

This report describes a method how to perform measurements on boilers and stoves and how to identify parameters from the measurements for the boiler/stove-model TRNSYS Type 210. The model can be used for detailed annual system simulations using TRNSYS. Experience from measurements on three different pellet stoves and four boilers were used to develop this methodology. Recommendations for the set up of measurements are given and the required combustion theory for the data evaluation and data preparation are given. The data evaluation showed that the uncertainties are quite large for the measured flue gas flow rate and for boilers and stoves with high fraction of energy going to the water jacket also the calculated heat rate to the room may have large uncertainties. A methodology for the parameter identification process and identified parameters for two different stoves and three boilers are given. Finally the identified models are compared with measured data showing that the model generally agreed well with measured data during both stationary and dynamic conditions.

Pre-Analytical Parameters Affecting Vascular Endothelial Growth Factor Measurement in Plasma: Identifying Confounders.

Science.gov (United States)

Walz, Johanna M; Boehringer, Daniel; Deissler, Heidrun L; Faerber, Lothar; Goepfert, Jens C; Heiduschka, Peter; Kleeberger, Susannah M; Klettner, Alexa; Krohne, Tim U; Schneiderhan-Marra, Nicole; Ziemssen, Focke; Stahl, Andreas

2016-01-01

Vascular endothelial growth factor-A (VEGF-A) is intensively investigated in various medical fields. However, comparing VEGF-A measurements is difficult because sample acquisition and pre-analytic procedures differ between studies. We therefore investigated which variables act as confounders of VEGF-A measurements. Following a standardized protocol, blood was taken at three clinical sites from six healthy participants (one male and one female participant at each center) twice one week apart. The following pre-analytical parameters were varied in order to analyze their impact on VEGF-A measurements: analyzing center, anticoagulant (EDTA vs. PECT / CTAD), cannula (butterfly vs. neonatal), type of centrifuge (swing-out vs. fixed-angle), time before and after centrifugation, filling level (completely filled vs. half-filled tubes) and analyzing method (ELISA vs. multiplex bead array). Additionally, intrapersonal variations over time and sex differences were explored. Statistical analysis was performed using a linear regression model. The following parameters were identified as statistically significant independent confounders of VEGF-A measurements: analyzing center, anticoagulant, centrifuge, analyzing method and sex of the proband. The following parameters were no significant confounders in our data set: intrapersonal variation over one week, cannula, time before and after centrifugation and filling level of collection tubes. VEGF-A measurement results can be affected significantly by the identified pre-analytical parameters. We recommend the use of CTAD anticoagulant, a standardized type of centrifuge and one central laboratory using the same analyzing method for all samples.

Pre-Analytical Parameters Affecting Vascular Endothelial Growth Factor Measurement in Plasma: Identifying Confounders.

Directory of Open Access Journals (Sweden)

Johanna M Walz

Full Text Available Vascular endothelial growth factor-A (VEGF-A is intensively investigated in various medical fields. However, comparing VEGF-A measurements is difficult because sample acquisition and pre-analytic procedures differ between studies. We therefore investigated which variables act as confounders of VEGF-A measurements.Following a standardized protocol, blood was taken at three clinical sites from six healthy participants (one male and one female participant at each center twice one week apart. The following pre-analytical parameters were varied in order to analyze their impact on VEGF-A measurements: analyzing center, anticoagulant (EDTA vs. PECT / CTAD, cannula (butterfly vs. neonatal, type of centrifuge (swing-out vs. fixed-angle, time before and after centrifugation, filling level (completely filled vs. half-filled tubes and analyzing method (ELISA vs. multiplex bead array. Additionally, intrapersonal variations over time and sex differences were explored. Statistical analysis was performed using a linear regression model.The following parameters were identified as statistically significant independent confounders of VEGF-A measurements: analyzing center, anticoagulant, centrifuge, analyzing method and sex of the proband. The following parameters were no significant confounders in our data set: intrapersonal variation over one week, cannula, time before and after centrifugation and filling level of collection tubes.VEGF-A measurement results can be affected significantly by the identified pre-analytical parameters. We recommend the use of CTAD anticoagulant, a standardized type of centrifuge and one central laboratory using the same analyzing method for all samples.

THE ANALYSIS OF PHYSICO-CHEMICAL PROPERTIES OF TWO UNKNOWN FILTER MATERIALS

Directory of Open Access Journals (Sweden)

Iwona Skoczko

2016-07-01

Full Text Available One of the most important technological processes of water treatment is the process of filtration. Scientists and producers keep on searching new filtration materials which allow for better water purification, are simple in exploitation and do not add chemical substances to the treated water. Therefore, the aim of the present study was to analyze physical and chemical parameters of two unknown porous masses X1 and X2. Such physical parameters as color, granulation, bulk density, the equivalent diameter, the coefficient of uniformity and the porosity of the material were measured and determined. Additionally, the possibility of water treatment was studied during the filtration process in the laboratory tests. Chemical parameters were examined in the water flowing through the mass, such as pH, conductivity and COD-Mn as a general indicator of the content of organic substances in the water. Both studied porous masses were characterized by uniform size of particles. But they were not efficient enough in satisfactory reduction of oxygen consumption. Mass X2 slightly better adsorbed organic substances. It was found that the tested unknown mass filter slightly increase the pH of the filtered water.

Parameter sensitivity and identifiability for a biogeochemical model of hypoxia in the northern Gulf of Mexico

Science.gov (United States)

Local sensitivity analyses and identifiable parameter subsets were used to describe numerical constraints of a hypoxia model for bottom waters of the northern Gulf of Mexico. The sensitivity of state variables differed considerably with parameter changes, although most variables ...

MUSIC-type imaging of small perfectly conducting cracks with an unknown frequency

International Nuclear Information System (INIS)

Park, Won-Kwang

2015-01-01

MUltiple SIgnal Classification (MUSIC) is a famous non-iterative detection algorithm in inverse scattering problems. However, when the applied frequency is unknown, inaccurate locations are identified via MUSIC. This fact has been confirmed through numerical simulations. However, the reason behind this phenomenon has not been investigated theoretically. Motivated by this fact, we identify the structure of MUSIC-type imaging functionals with unknown frequency, by establishing a relationship with Bessel functions of order zero of the first kind. Through this, we can explain why inaccurate results appear. (paper)

MUSIC-type imaging of small perfectly conducting cracks with an unknown frequency

Science.gov (United States)

Park, Won-Kwang

2015-09-01

MUltiple SIgnal Classification (MUSIC) is a famous non-iterative detection algorithm in inverse scattering problems. However, when the applied frequency is unknown, inaccurate locations are identified via MUSIC. This fact has been confirmed through numerical simulations. However, the reason behind this phenomenon has not been investigated theoretically. Motivated by this fact, we identify the structure of MUSIC-type imaging functionals with unknown frequency, by establishing a relationship with Bessel functions of order zero of the first kind. Through this, we can explain why inaccurate results appear.

Structural identifiability analyses of candidate models for in vitro Pitavastatin hepatic uptake.

Science.gov (United States)

Grandjean, Thomas R B; Chappell, Michael J; Yates, James W T; Evans, Neil D

2014-05-01

In this paper a review of the application of four different techniques (a version of the similarity transformation approach for autonomous uncontrolled systems, a non-differential input/output observable normal form approach, the characteristic set differential algebra and a recent algebraic input/output relationship approach) to determine the structural identifiability of certain in vitro nonlinear pharmacokinetic models is provided. The Organic Anion Transporting Polypeptide (OATP) substrate, Pitavastatin, is used as a probe on freshly isolated animal and human hepatocytes. Candidate pharmacokinetic non-linear compartmental models have been derived to characterise the uptake process of Pitavastatin. As a prerequisite to parameter estimation, structural identifiability analyses are performed to establish that all unknown parameters can be identified from the experimental observations available. Copyright © 2013. Published by Elsevier Ireland Ltd.

Safe Exploration for Identifying Linear Systems via Robust Optimization

OpenAIRE

Lu, Tyler; Zinkevich, Martin; Boutilier, Craig; Roy, Binz; Schuurmans, Dale

2017-01-01

Safely exploring an unknown dynamical system is critical to the deployment of reinforcement learning (RL) in physical systems where failures may have catastrophic consequences. In scenarios where one knows little about the dynamics, diverse transition data covering relevant regions of state-action space is needed to apply either model-based or model-free RL. Motivated by the cooling of Google's data centers, we study how one can safely identify the parameters of a system model with a desired ...

Lag synchronization of unknown chaotic delayed Yang-Yang-type fuzzy neural networks with noise perturbation based on adaptive control and parameter identification.

Science.gov (United States)

Xia, Yonghui; Yang, Zijiang; Han, Maoan

2009-07-01

This paper considers the lag synchronization (LS) issue of unknown coupled chaotic delayed Yang-Yang-type fuzzy neural networks (YYFCNN) with noise perturbation. Separate research work has been published on the stability of fuzzy neural network and LS issue of unknown coupled chaotic neural networks, as well as its application in secure communication. However, there have not been any studies that integrate the two. Motivated by the achievements from both fields, we explored the benefits of integrating fuzzy logic theories into the study of LS problems and applied the findings to secure communication. Based on adaptive feedback control techniques and suitable parameter identification, several sufficient conditions are developed to guarantee the LS of coupled chaotic delayed YYFCNN with or without noise perturbation. The problem studied in this paper is more general in many aspects. Various problems studied extensively in the literature can be treated as special cases of the findings of this paper, such as complete synchronization (CS), effect of fuzzy logic, and noise perturbation. This paper presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed adaptive scheme. This research also demonstrates the effectiveness of application of the proposed adaptive feedback scheme in secure communication by comparing chaotic masking with fuzziness with some previous studies. Chaotic signal with fuzziness is more complex, which makes unmasking more difficult due to the added fuzzy logic.

A sensitivity analysis approach to control of manipulators with unknown load

International Nuclear Information System (INIS)

Tzes, A.; Yurkovich, S.

1987-01-01

This paper presents a straightforward control strategy applied to an N-link manipulator holding an unknown load and driving its end effector along a prespecified trajectory. The control is constituted into two primary components. The non-adaptive component is derived from the inverse problem technique while the adaptive component is computed via the application of sensitivity analysis applied to the complete, centralized dynamic model of the manipulator. The result is a robust adaptive controller which tunes its parameters at specified time instants and can withstand all expected variations of the payload. The control synthesis is illustrated by simulations in a 2-link planar manipulator holding an unknown load

Parameter Optimization for Feature and Hit Generation in a General Unknown Screening Method-Proof of Concept Study Using a Design of Experiment Approach for a High Resolution Mass Spectrometry Procedure after Data Independent Acquisition.

Science.gov (United States)

Elmiger, Marco P; Poetzsch, Michael; Steuer, Andrea E; Kraemer, Thomas

2018-03-06

High resolution mass spectrometry and modern data independent acquisition (DIA) methods enable the creation of general unknown screening (GUS) procedures. However, even when DIA is used, its potential is far from being exploited, because often, the untargeted acquisition is followed by a targeted search. Applying an actual GUS (including untargeted screening) produces an immense amount of data that must be dealt with. An optimization of the parameters regulating the feature detection and hit generation algorithms of the data processing software could significantly reduce the amount of unnecessary data and thereby the workload. Design of experiment (DoE) approaches allow a simultaneous optimization of multiple parameters. In a first step, parameters are evaluated (crucial or noncrucial). Second, crucial parameters are optimized. The aim in this study was to reduce the number of hits, without missing analytes. The obtained parameter settings from the optimization were compared to the standard settings by analyzing a test set of blood samples spiked with 22 relevant analytes as well as 62 authentic forensic cases. The optimization lead to a marked reduction of workload (12.3 to 1.1% and 3.8 to 1.1% hits for the test set and the authentic cases, respectively) while simultaneously increasing the identification rate (68.2 to 86.4% and 68.8 to 88.1%, respectively). This proof of concept study emphasizes the great potential of DoE approaches to master the data overload resulting from modern data independent acquisition methods used for general unknown screening procedures by optimizing software parameters.

Knowns and unknowns in metabolomics identified by multidimensional NMR and hybrid MS/NMR methods

Energy Technology Data Exchange (ETDEWEB)

Bingol, Kerem; Brüschweiler, Rafael

2017-02-01

Metabolomics continues to make rapid progress through the development of new and better methods and their applications to gain insight into the metabolism of a wide range of different biological systems from a systems biology perspective. Customization of NMR databases and search tools allows the faster and more accurate identification of known metabolites, whereas the identification of unknowns, without a need for extensive purification, requires new strategies to integrate NMR with mass spectrometry, cheminformatics, and computational methods. For some applications, the use of covalent and non-covalent attachments in the form of labeled tags or nanoparticles can significantly reduce the complexity of these tasks.

Determination of power system component parameters using nonlinear dead beat estimation method

Science.gov (United States)

Kolluru, Lakshmi

Power systems are considered the most complex man-made wonders in existence today. In order to effectively supply the ever increasing demands of the consumers, power systems are required to remain stable at all times. Stability and monitoring of these complex systems are achieved by strategically placed computerized control centers. State and parameter estimation is an integral part of these facilities, as they deal with identifying the unknown states and/or parameters of the systems. Advancements in measurement technologies and the introduction of phasor measurement units (PMU) provide detailed and dynamic information of all measurements. Accurate availability of dynamic measurements provides engineers the opportunity to expand and explore various possibilities in power system dynamic analysis/control. This thesis discusses the development of a parameter determination algorithm for nonlinear power systems, using dynamic data obtained from local measurements. The proposed algorithm was developed by observing the dead beat estimator used in state space estimation of linear systems. The dead beat estimator is considered to be very effective as it is capable of obtaining the required results in a fixed number of steps. The number of steps required is related to the order of the system and the number of parameters to be estimated. The proposed algorithm uses the idea of dead beat estimator and nonlinear finite difference methods to create an algorithm which is user friendly and can determine the parameters fairly accurately and effectively. The proposed algorithm is based on a deterministic approach, which uses dynamic data and mathematical models of power system components to determine the unknown parameters. The effectiveness of the algorithm is tested by implementing it to identify the unknown parameters of a synchronous machine. MATLAB environment is used to create three test cases for dynamic analysis of the system with assumed known parameters. Faults are

Design of a DNA chip for detection of unknown genetically modified organisms (GMOs).

Science.gov (United States)

Nesvold, Håvard; Kristoffersen, Anja Bråthen; Holst-Jensen, Arne; Berdal, Knut G

2005-05-01

Unknown genetically modified organisms (GMOs) have not undergone a risk evaluation, and hence might pose a danger to health and environment. There are, today, no methods for detecting unknown GMOs. In this paper we propose a novel method intended as a first step in an approach for detecting unknown genetically modified (GM) material in a single plant. A model is designed where biological and combinatorial reduction rules are applied to a set of DNA chip probes containing all possible sequences of uniform length n, creating probes capable of detecting unknown GMOs. The model is theoretically tested for Arabidopsis thaliana Columbia, and the probabilities for detecting inserts and receiving false positives are assessed for various parameters for this organism. From a theoretical standpoint, the model looks very promising but should be tested further in the laboratory. The model and algorithms will be available upon request to the corresponding author.

Identification of Parameters in Active Magnetic Bearing Systems

DEFF Research Database (Denmark)

Lauridsen, Jonas Skjødt; Voigt, Andreas Jauernik; Mandrup-Poulsen, Christian

2016-01-01

A method for identifying uncertain parameters in Active Magnetic Bearing (AMB) based rotordynamic systems is introduced and adapted for experimental application. The Closed Loop Identification (CLI) method is utilised to estimate the current/force factors Ki and the displacement/force factors Ks...... as well as a time constant Τe for a first order approxima-tion of unknown actuator dynamics. To assess the precision with which CLI method can be employed to estimate AMBparameters the factors Ki, estimated using the CLI method, is compared to Ki factors attained through a Static Loading(SL) method....... The CLI method and SL method produce similar results, indicating that the CLI method is able to performclosed loop identification of uncertain AMB parameters....

Adaptive observer for the joint estimation of parameters and input for a coupled wave PDE and infinite dimensional ODE system

KAUST Repository

Belkhatir, Zehor

2016-08-05

This paper deals with joint parameters and input estimation for coupled PDE-ODE system. The system consists of a damped wave equation and an infinite dimensional ODE. This model describes the spatiotemporal hemodynamic response in the brain and the objective is to characterize brain regions using functional Magnetic Resonance Imaging (fMRI) data. For this reason, we propose an adaptive estimator and prove the asymptotic convergence of the state, the unknown input and the unknown parameters. The proof is based on a Lyapunov approach combined with a priori identifiability assumptions. The performance of the proposed observer is illustrated through some simulation results.

Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics.

Science.gov (United States)

Vaniya, Arpana; Fiehn, Oliver

2015-06-01

Identification of unknown metabolites is the bottleneck in advancing metabolomics, leaving interpretation of metabolomics results ambiguous. The chemical diversity of metabolism is vast, making structure identification arduous and time consuming. Currently, comprehensive analysis of mass spectra in metabolomics is limited to library matching, but tandem mass spectral libraries are small compared to the large number of compounds found in the biosphere, including xenobiotics. Resolving this bottleneck requires richer data acquisition and better computational tools. Multi-stage mass spectrometry (MSn) trees show promise to aid in this regard. Fragmentation trees explore the fragmentation process, generate fragmentation rules and aid in sub-structure identification, while mass spectral trees delineate the dependencies in multi-stage MS of collision-induced dissociations. This review covers advancements over the past 10 years as a tool for metabolite identification, including algorithms, software and databases used to build and to implement fragmentation trees and mass spectral annotations.

Impacts of Different Types of Measurements on Estimating Unsaturatedflow Parameters

Science.gov (United States)

Shi, L.

2015-12-01

This study evaluates the value of different types of measurements for estimating soil hydraulic parameters. A numerical method based on ensemble Kalman filter (EnKF) is presented to solely or jointly assimilate point-scale soil water head data, point-scale soil water content data, surface soil water content data and groundwater level data. This study investigates the performance of EnKF under different types of data, the potential worth contained in these data, and the factors that may affect estimation accuracy. Results show that for all types of data, smaller measurements errors lead to faster convergence to the true values. Higher accuracy measurements are required to improve the parameter estimation if a large number of unknown parameters need to be identified simultaneously. The data worth implied by the surface soil water content data and groundwater level data is prone to corruption by a deviated initial guess. Surface soil moisture data are capable of identifying soil hydraulic parameters for the top layers, but exert less or no influence on deeper layers especially when estimating multiple parameters simultaneously. Groundwater level is one type of valuable information to infer the soil hydraulic parameters. However, based on the approach used in this study, the estimates from groundwater level data may suffer severe degradation if a large number of parameters must be identified. Combined use of two or more types of data is helpful to improve the parameter estimation.

Parameter identification based synchronization for a class of chaotic systems with offset vectors

International Nuclear Information System (INIS)

Chen Cailian; Feng Gang; Guan Xinping

2004-01-01

Based on a parameter identification scheme, a novel synchronization method is presented for a class of chaotic systems with offset vectors which can be represented by the so-called T-S fuzzy model. It is shown that the slave system can synchronize the master system and the unknown parameters of the master system can be identified simultaneously. The delayed feedback technique is also developed in order to reduce the energy and time required for the identification and synchronization. Numerical simulations demonstrate the effectiveness of the proposed method

Analysis and Adaptive Synchronization of Two Novel Chaotic Systems with Hyperbolic Sinusoidal and Cosinusoidal Nonlinearity and Unknown Parameters

Directory of Open Access Journals (Sweden)

S. Vaidyanathan

2013-09-01

Full Text Available This research work describes the modelling of two novel 3-D chaotic systems, the first with a hyperbolic sinusoidal nonlinearity and two quadratic nonlinearities (denoted as system (A and the second with a hyperbolic cosinusoidal nonlinearity and two quadratic nonlinearities (denoted as system (B. In this work, a detailed qualitative analysis of the novel chaotic systems (A and (B has been presented, and the Lyapunov exponents and Kaplan-Yorke dimension of these chaotic systems have been obtained. It is found that the maximal Lyapunov exponent (MLE for the novel chaotic systems (A and (B has a large value, viz. for the system (A and for the system (B. Thus, both the novel chaotic systems (A and (B display strong chaotic behaviour. This research work also discusses the problem of finding adaptive controllers for the global chaos synchronization of identical chaotic systems (A, identical chaotic systems (B and nonidentical chaotic systems (A and (B with unknown system parameters. The adaptive controllers for achieving global chaos synchronization of the novel chaotic systems (A and (B have been derived using adaptive control theory and Lyapunov stability theory. MATLAB simulations have been shown to illustrate the novel chaotic systems (A and (B, and also the adaptive synchronization results derived for the novel chaotic systems (A and (B.

Comment on “Two statistics for evaluating parameter identifiability and error reduction” by John Doherty and Randall J. Hunt

Science.gov (United States)

Hill, Mary C.

2010-01-01

Doherty and Hunt (2009) present important ideas for first-order-second moment sensitivity analysis, but five issues are discussed in this comment. First, considering the composite-scaled sensitivity (CSS) jointly with parameter correlation coefficients (PCC) in a CSS/PCC analysis addresses the difficulties with CSS mentioned in the introduction. Second, their new parameter identifiability statistic actually is likely to do a poor job of parameter identifiability in common situations. The statistic instead performs the very useful role of showing how model parameters are included in the estimated singular value decomposition (SVD) parameters. Its close relation to CSS is shown. Third, the idea from p. 125 that a suitable truncation point for SVD parameters can be identified using the prediction variance is challenged using results from Moore and Doherty (2005). Fourth, the relative error reduction statistic of Doherty and Hunt is shown to belong to an emerging set of statistics here named perturbed calculated variance statistics. Finally, the perturbed calculated variance statistics OPR and PPR mentioned on p. 121 are shown to explicitly include the parameter null-space component of uncertainty. Indeed, OPR and PPR results that account for null-space uncertainty have appeared in the literature since 2000.

Identifying elementary iterated systems through algorithmic inference: The Cantor set example

Energy Technology Data Exchange (ETDEWEB)

Apolloni, Bruno [Dipartimento di Scienze dell' Informazione, Universita degli Studi di Milano, Via Comelico 39/41, 20135 Milan (Italy)]. E-mail: apolloni@dsi.unimi.it; Bassis, Simone [Dipartimento di Scienze dell' Informazione, Universita degli Studi di Milano, Via Comelico 39/41, 20135 Milan (Italy)]. E-mail: bassis@dsi.unimi.it

2006-10-15

We come back to the old problem of fractal identification within the new framework of algorithmic Inference. The key points are: (i) to identify sufficient statistics to be put in connection with the unknown values of the fractal parameters, and (ii) to manage the timing of the iterated process through spatial statistics. We fill these tasks successfully with the Cantor sets. We are able to compute confidence intervals for both the scaling parameter {theta} and the iteration number n at which we are observing a set. We both check numerically the coverage of these intervals and delineate a general strategy for affording more complex iterated systems.

Digital Modulation Identification Model Using Wavelet Transform and Statistical Parameters

Directory of Open Access Journals (Sweden)

P. Prakasam

2008-01-01

Full Text Available A generalized modulation identification scheme is developed and presented. With the help of this scheme, the automatic modulation classification and recognition of wireless communication signals with a priori unknown parameters are possible effectively. The special features of the procedure are the possibility to adapt it dynamically to nearly all modulation types, and the capability to identify. The developed scheme based on wavelet transform and statistical parameters has been used to identify M-ary PSK, M-ary QAM, GMSK, and M-ary FSK modulations. The simulated results show that the correct modulation identification is possible to a lower bound of 5 dB. The identification percentage has been analyzed based on the confusion matrix. When SNR is above 5 dB, the probability of detection of the proposed system is more than 0.968. The performance of the proposed scheme has been compared with existing methods and found it will identify all digital modulation schemes with low SNR.

A method for model identification and parameter estimation

International Nuclear Information System (INIS)

Bambach, M; Heinkenschloss, M; Herty, M

2013-01-01

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

Financial Development and Economic Growth: Known Knowns, Known Unknowns, and Unknown Unknowns

OpenAIRE

Ugo Panizza

2014-01-01

This paper summarizes the main findings of the literature on the relationship between financial and economic development (the known knowns), points to directions for future research (the known unknowns), and then speculates on the third Rumsfeldian category. The known knowns section organizes the empirical literature on finance and growth into three strands: (i) the traditional literature which established the link between finance and growth; (ii) the new literature which qualified some of th...

Estimating model parameters in nonautonomous chaotic systems using synchronization

International Nuclear Information System (INIS)

Yang, Xiaoli; Xu, Wei; Sun, Zhongkui

2007-01-01

In this Letter, a technique is addressed for estimating unknown model parameters of multivariate, in particular, nonautonomous chaotic systems from time series of state variables. This technique uses an adaptive strategy for tracking unknown parameters in addition to a linear feedback coupling for synchronizing systems, and then some general conditions, by means of the periodic version of the LaSalle invariance principle for differential equations, are analytically derived to ensure precise evaluation of unknown parameters and identical synchronization between the concerned experimental system and its corresponding receiver one. Exemplifies are presented by employing a parametrically excited 4D new oscillator and an additionally excited Ueda oscillator. The results of computer simulations reveal that the technique not only can quickly track the desired parameter values but also can rapidly respond to changes in operating parameters. In addition, the technique can be favorably robust against the effect of noise when the experimental system is corrupted by bounded disturbance and the normalized absolute error of parameter estimation grows almost linearly with the cutoff value of noise strength in simulation

Mean--variance portfolio optimization when means and covariances are unknown

OpenAIRE

Tze Leung Lai; Haipeng Xing; Zehao Chen

2011-01-01

Markowitz's celebrated mean--variance portfolio optimization theory assumes that the means and covariances of the underlying asset returns are known. In practice, they are unknown and have to be estimated from historical data. Plugging the estimates into the efficient frontier that assumes known parameters has led to portfolios that may perform poorly and have counter-intuitive asset allocation weights; this has been referred to as the "Markowitz optimization enigma." After reviewing differen...

A dynamical approach in exploring the unknown mass in the Solar system using pulsar timing arrays

Science.gov (United States)

Guo, Y. J.; Lee, K. J.; Caballero, R. N.

2018-04-01

The error in the Solar system ephemeris will lead to dipolar correlations in the residuals of pulsar timing array for widely separated pulsars. In this paper, we utilize such correlated signals, and construct a Bayesian data-analysis framework to detect the unknown mass in the Solar system and to measure the orbital parameters. The algorithm is designed to calculate the waveform of the induced pulsar-timing residuals due to the unmodelled objects following the Keplerian orbits in the Solar system. The algorithm incorporates a Bayesian-analysis suit used to simultaneously analyse the pulsar-timing data of multiple pulsars to search for coherent waveforms, evaluate the detection significance of unknown objects, and to measure their parameters. When the object is not detectable, our algorithm can be used to place upper limits on the mass. The algorithm is verified using simulated data sets, and cross-checked with analytical calculations. We also investigate the capability of future pulsar-timing-array experiments in detecting the unknown objects. We expect that the future pulsar-timing data can limit the unknown massive objects in the Solar system to be lighter than 10-11-10-12 M⊙, or measure the mass of Jovian system to a fractional precision of 10-8-10-9.

Identifying Generalizable Image Segmentation Parameters for Urban Land Cover Mapping through Meta-Analysis and Regression Tree Modeling

Directory of Open Access Journals (Sweden)

Brian A. Johnson

2018-01-01

Full Text Available The advent of very high resolution (VHR satellite imagery and the development of Geographic Object-Based Image Analysis (GEOBIA have led to many new opportunities for fine-scale land cover mapping, especially in urban areas. Image segmentation is an important step in the GEOBIA framework, so great time/effort is often spent to ensure that computer-generated image segments closely match real-world objects of interest. In the remote sensing community, segmentation is frequently performed using the multiresolution segmentation (MRS algorithm, which is tuned through three user-defined parameters (the scale, shape/color, and compactness/smoothness parameters. The scale parameter (SP is the most important parameter and governs the average size of generated image segments. Existing automatic methods to determine suitable SPs for segmentation are scene-specific and often computationally intensive, so an approach to estimating appropriate SPs that is generalizable (i.e., not scene-specific could speed up the GEOBIA workflow considerably. In this study, we attempted to identify generalizable SPs for five common urban land cover types (buildings, vegetation, roads, bare soil, and water through meta-analysis and nonlinear regression tree (RT modeling. First, we performed a literature search of recent studies that employed GEOBIA for urban land cover mapping and extracted the MRS parameters used, the image properties (i.e., spatial and radiometric resolutions, and the land cover classes mapped. Using this data extracted from the literature, we constructed RT models for each land cover class to predict suitable SP values based on the: image spatial resolution, image radiometric resolution, shape/color parameter, and compactness/smoothness parameter. Based on a visual and quantitative analysis of results, we found that for all land cover classes except water, relatively accurate SPs could be identified using our RT modeling results. The main advantage of our

Neurological Autoantibody Prevalence in Epilepsy of Unknown Etiology.

Science.gov (United States)

Dubey, Divyanshu; Alqallaf, Abdulradha; Hays, Ryan; Freeman, Matthew; Chen, Kevin; Ding, Kan; Agostini, Mark; Vernino, Steven

2017-04-01

Autoimmune epilepsy is an underrecognized condition, and its true incidence is unknown. Identifying patients with an underlying autoimmune origin is critical because these patients' condition may remain refractory to conventional antiseizure medications but may respond to immunotherapy. To determine the prevalence of neurological autoantibodies (Abs) among adult patients with epilepsy of unknown etiology. Consecutive patients presenting to neurology services with new-onset epilepsy or established epilepsy of unknown etiology were identified. Serum samples were tested for autoimmune encephalitis Abs as well as thyroperoxidase (TPO) and glutamic acid decarboxylase 65 (GAD65) Abs. An antibody prevalence in epilepsy (APE) score based on clinical characteristics was assigned prospectively. Data were collected from June 1, 2015, to June 1, 2016. Presence of neurological Abs. A score based on clinical characteristics was assigned to estimate the probability of seropositivity prior to antibody test results. Good seizure outcome was estimated on the basis of significant reduction of seizure frequency at the first follow-up or seizure freedom. Of the 127 patients (68 males and 59 females) enrolled in the study, 15 were subsequently excluded after identification of an alternative diagnosis. Serum Abs suggesting a potential autoimmune etiology were detected in 39 (34.8%) cases. More than 1 Ab was detected in 7 patients (6.3%): 3 (2.7%) had TPO-Ab and voltage-gated potassium channel complex (VGKCc) Ab, 2 (1.8%) had GAD65-Ab and VGKCc-Ab, 1 had TPO-Ab and GAD65-Ab, and 1 had anti-Hu Ab and GAD65-Ab. Thirty-two patients (28.6%) had a single Ab marker. Among 112 patients included in the study, 15 (13.4%) had TPO-Ab, 14 (12.5%) had GAD65-Ab, 12 (10.7%) had VGKCc (4 of whom were positive for leucine-rich glioma-inactivated protein 1 [LGI1] Ab), and 4 (3.6%) had N-methyl-D-aspartate receptor (NMDAR) Ab. Even after excluding TPO-Ab and low-titer GAD65-Ab, Abs strongly suggesting an

Identifying known unknowns using the US EPA's CompTox Chemistry Dashboard

Science.gov (United States)

Chemical features observed using high-resolution mass spectrometry can be tentatively identified using online chemical reference databases by searching molecular formulae and monoisotopic masses and then rank-ordering of the hits using appropriate relevance criteria. The most li...

Designing towards the unknown

DEFF Research Database (Denmark)

Wilde, Danielle; Underwood, Jenny

2018-01-01

the research potential to far-ranging possibilities. In this article we unpack the motivations driving the PKI project. We present our mixed-methodology, which entangles textile crafts, design interactions and materiality to shape an embodied enquiry. Our research outcomes are procedural and methodological......New materials with new capabilities demand new ways of approaching design. Destabilising existing methods is crucial to develop new methods. Yet, radical destabilisation—where outcomes remain unknown long enough that new discoveries become possible—is not easy in technology design where complex......, to design towards unknown outcomes, using unknown materials. The impossibility of this task is proving as useful as it is disruptive. At its most potent, it is destabilising expectations, aesthetics and processes. Keeping the researchers, collaborators and participants in a state of unknowing, is opening...

Recension: Mao - The Unknown Story

DEFF Research Database (Denmark)

Clausen, Søren

2005-01-01

Anmeldelse - kritisk! - til Sveriges førende Kinatidsskrift af Jung Chang & Jon Halliday's sensationelle "Mao - the Unknown Story".......Anmeldelse - kritisk! - til Sveriges førende Kinatidsskrift af Jung Chang & Jon Halliday's sensationelle "Mao - the Unknown Story"....

Parameter estimation and determinability analysis applied to Drosophila gap gene circuits

Directory of Open Access Journals (Sweden)

Jaeger Johannes

2008-09-01

Full Text Available Abstract Background Mathematical modeling of real-life processes often requires the estimation of unknown parameters. Once the parameters are found by means of optimization, it is important to assess the quality of the parameter estimates, especially if parameter values are used to draw biological conclusions from the model. Results In this paper we describe how the quality of parameter estimates can be analyzed. We apply our methodology to assess parameter determinability for gene circuit models of the gap gene network in early Drosophila embryos. Conclusion Our analysis shows that none of the parameters of the considered model can be determined individually with reasonable accuracy due to correlations between parameters. Therefore, the model cannot be used as a tool to infer quantitative regulatory weights. On the other hand, our results show that it is still possible to draw reliable qualitative conclusions on the regulatory topology of the gene network. Moreover, it improves previous analyses of the same model by allowing us to identify those interactions for which qualitative conclusions are reliable, and those for which they are ambiguous.

The unknown-unknowns: Revealing the hidden insights in massive biomedical data using combined artificial intelligence and knowledge networks

Directory of Open Access Journals (Sweden)

Chris Yoo

2017-12-01

Full Text Available Genomic data is estimated to be doubling every seven months with over 2 trillion bases from whole genome sequence studies deposited in Genbank in just the last 15 years alone. Recent advances in compute and storage have enabled the use of artificial intelligence techniques in areas such as feature recognition in digital pathology and chemical synthesis for drug development. To apply A.I. productively to multidimensional data such as cellular processes and their dysregulation, the data must be transformed into a structured format, using prior knowledge to create contextual relationships and hierarchies upon which computational analysis can be performed. Here we present the organization of complex data into hypergraphs that facilitate the application of A.I. We provide an example use case of a hypergraph containing hundreds of biological data values and the results of several classes of A.I. algorithms applied in a popular compute cloud. While multiple, biologically insightful correlations between disease states, behavior, and molecular features were identified, the insights of scientific import were revealed only when exploration of the data included visualization of subgraphs of represented knowledge. The results suggest that while machine learning can identify known correlations and suggest testable ones, the greater probability of discovering unexpected relationships between seemingly independent variables (unknown-unknowns requires a context-aware system – hypergraphs that impart biological meaning in nodes and edges. We discuss the implications of a combined hypergraph-A.I. analysis approach to multidimensional data and the pre-processing requirements for such a system.

Structural identifiability of cyclic graphical models of biological networks with latent variables.

Science.gov (United States)

Wang, Yulin; Lu, Na; Miao, Hongyu

2016-06-13

Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and

Metastatic meningioma presenting as cancer of unknown primary

Directory of Open Access Journals (Sweden)

Vinay Gupta

2013-12-01

Full Text Available We describe a case of anaplastic meningioma presenting in an extracranial osseous location, initially diagnosed as cancer of unknown primary. Although anaplastic meningioma comprise 3% of all meningiomas, this subtype is more likely to be associated with metastases. The increased degree of dedifferentiation in anaplastic meningioma makes diagnosis difficult, especially if characteristic imaging findings of meningioma are not identified. Adequate tissue for diagnostic purposes and appropriate imaging studies may help in establishing a definitive diagnosis.

ℋ- adaptive observer design and parameter identification for a class of nonlinear fractional-order systems

KAUST Repository

Ndoye, Ibrahima

2014-12-01

In this paper, an adaptive observer design with parameter identification for a nonlinear system with external perturbations and unknown parameters is proposed. The states of the nonlinear system are estimated by a nonlinear observer and the unknown parameters are also adapted to their values. Sufficient conditions for the stability of the adaptive observer error dynamics are derived in terms of linear matrix inequalities. Simulation results for chaotic Lorenz systems with unknown parameters in the presence of external perturbations are given to illustrate the effectiveness of our proposed approach. © 2014 IEEE.

A Novel Coupled State/Input/Parameter Identification Method for Linear Structural Systems

Directory of Open Access Journals (Sweden)

Zhimin Wan

2018-01-01

Full Text Available In many engineering applications, unknown states, inputs, and parameters exist in the structures. However, most methods require one or two of these variables to be known in order to identify the other(s. Recently, the authors have proposed a method called EGDF for coupled state/input/parameter identification for nonlinear system in state space. However, the EGDF method based solely on acceleration measurements is found to be unstable, which can cause the drift of the identified inputs and displacements. Although some regularization methods can be adopted for solving the problem, they are not suitable for joint input-state identification in real time. In this paper, a strategy of data fusion of displacement and acceleration measurements is used to avoid the low-frequency drift in the identified inputs and structural displacements for linear structural systems. Two numerical examples about a plane truss and a single-stage isolation system are conducted to verify the effectiveness of the proposed modified EGDF algorithm.

Active fault tolerance control of a wind turbine system using an unknown input observer with an actuator fault

Directory of Open Access Journals (Sweden)

Li Shanzhi

2018-03-01

Full Text Available This paper proposes a fault tolerant control scheme based on an unknown input observer for a wind turbine system subject to an actuator fault and disturbance. Firstly, an unknown input observer for state estimation and fault detection using a linear parameter varying model is developed. By solving linear matrix inequalities (LMIs and linear matrix equalities (LMEs, the gains of the unknown input observer are obtained. The convergence of the unknown input observer is also analysed with Lyapunov theory. Secondly, using fault estimation, an active fault tolerant controller is applied to a wind turbine system. Finally, a simulation of a wind turbine benchmark with an actuator fault is tested for the proposed method. The simulation results indicate that the proposed FTC scheme is efficient.

F-18-FDG PET/CT in inflammation of unknown origin : a cost-effectiveness pilot-study

NARCIS (Netherlands)

Balink, H.; Tan, S. S.; Veeger, N. J. G. M.; Holleman, F.; van Eck-Smit, B. L. F.; Bennink, R. J.; Verberne, H. J.

Purpose Patients with increased inflammatory parameters, nonspecific signs and symptoms without fever and without a diagnosis after a variety of diagnostic procedures are a diagnostic dilemma and are referred to as having inflammation of unknown origin (IUO). The objective of this pilot study was to

Reducing uncertainty at minimal cost: a method to identify important input parameters and prioritize data collection

NARCIS (Netherlands)

Uwizeye, U.A.; Groen, E.A.; Gerber, P.J.; Schulte, Rogier P.O.; Boer, de I.J.M.

2016-01-01

The study aims to illustrate a method to identify important input parameters that explain most of the output variance ofenvironmental assessment models. The method is tested for the computation of life-cycle nitrogen (N) use efficiencyindicators among mixed dairy production systems in Rwanda. We

Comparison of methods for the detection of gravitational waves from unknown neutron stars

Science.gov (United States)

Walsh, S.; Pitkin, M.; Oliver, M.; D'Antonio, S.; Dergachev, V.; Królak, A.; Astone, P.; Bejger, M.; Di Giovanni, M.; Dorosh, O.; Frasca, S.; Leaci, P.; Mastrogiovanni, S.; Miller, A.; Palomba, C.; Papa, M. A.; Piccinni, O. J.; Riles, K.; Sauter, O.; Sintes, A. M.

2016-12-01

Rapidly rotating neutron stars are promising sources of continuous gravitational wave radiation for the LIGO and Virgo interferometers. The majority of neutron stars in our galaxy have not been identified with electromagnetic observations. All-sky searches for isolated neutron stars offer the potential to detect gravitational waves from these unidentified sources. The parameter space of these blind all-sky searches, which also cover a large range of frequencies and frequency derivatives, presents a significant computational challenge. Different methods have been designed to perform these searches within acceptable computational limits. Here we describe the first benchmark in a project to compare the search methods currently available for the detection of unknown isolated neutron stars. The five methods compared here are individually referred to as the PowerFlux, sky Hough, frequency Hough, Einstein@Home, and time domain F -statistic methods. We employ a mock data challenge to compare the ability of each search method to recover signals simulated assuming a standard signal model. We find similar performance among the four quick-look search methods, while the more computationally intensive search method, Einstein@Home, achieves up to a factor of two higher sensitivity. We find that the absence of a second derivative frequency in the search parameter space does not degrade search sensitivity for signals with physically plausible second derivative frequencies. We also report on the parameter estimation accuracy of each search method, and the stability of the sensitivity in frequency and frequency derivative and in the presence of detector noise.

Optimizing incomplete sample designs for item response model parameters

NARCIS (Netherlands)

van der Linden, Willem J.

Several models for optimizing incomplete sample designs with respect to information on the item parameters are presented. The following cases are considered: (1) known ability parameters; (2) unknown ability parameters; (3) item sets with multiple ability scales; and (4) response models with

Neurological autoantibodies in drug-resistant epilepsy of unknown cause.

Science.gov (United States)

Tecellioglu, Mehmet; Kamisli, Ozden; Kamisli, Suat; Yucel, Fatma Ebru; Ozcan, Cemal

2018-03-09

Autoimmune epilepsy is a rarely diagnosed condition. Recognition of the underlying autoimmune condition is important, as these patients can be resistant to antiepileptic drugs. To determine the autoimmune and oncological antibodies in adult drug-resistant epilepsy of unknown cause and identify the clinical, radiological, and EEG findings associated with these antibodies according to data in the literature. Eighty-two patients with drug-resistant epilepsy of unknown cause were prospectively identified. Clinical features were recorded. The levels of anti-voltage-gated potassium channel complex (anti-VGKCc), anti-thyroid peroxidase (anti-TPO), anti-nuclear antibody (ANA), anti-glutamic acid decarboxylase (anti-GAD), anti-phospholipid IgG and IgM, anti-cardiolipin IgG and IgM, and onconeural antibodies were determined. Serum antibody positivity suggesting the potential role of autoimmunity in the aetiology was present in 17 patients with resistant epilepsy (22.0%). Multiple antibodies were found in two patients (2.6%). One of these patients (1.3%) had anti-VGKCc and ANA, whereas another (1.3%) had anti-VGKCc and anti-TPO. A single antibody was present in 15 patients (19.5%). Of the 77 patients finally included in the study, 4 had anti-TPO (5.2%), 1 had anti-GAD (1.3%), 4 had anti-VGKCc (5.2%) 8 had ANA (10.3%), and 2 had onconeural antibodies (2.6%) (1 patient had anti-Yo and 1 had anti-MA2/TA). The other antibodies investigated were not detected. EEG abnormality (focal), focal seizure incidence, and frequent seizures were more common in antibody-positive patients. Autoimmune factors may be aetiologically relevant in patients with drug-resistant epilepsy of unknown cause, especially if focal seizures are present together with focal EEG abnormality and frequent seizures.

Histogram analysis parameters identify multiple associations between DWI and DCE MRI in head and neck squamous cell carcinoma.

Science.gov (United States)

Meyer, Hans Jonas; Leifels, Leonard; Schob, Stefan; Garnov, Nikita; Surov, Alexey

2018-01-01

Nowadays, multiparametric investigations of head and neck squamous cell carcinoma (HNSCC) are established. These approaches can better characterize tumor biology and behavior. Diffusion weighted imaging (DWI) can by means of apparent diffusion coefficient (ADC) quantitatively characterize different tissue compartments. Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) reflects perfusion and vascularization of tissues. Recently, a novel approach of data acquisition, namely histogram analysis of different images is a novel diagnostic approach, which can provide more information of tissue heterogeneity. The purpose of this study was to analyze possible associations between DWI, and DCE parameters derived from histogram analysis in patients with HNSCC. Overall, 34 patients, 9 women and 25 men, mean age, 56.7±10.2years, with different HNSCC were involved in the study. DWI was obtained by using of an axial echo planar imaging sequence with b-values of 0 and 800s/mm 2 . Dynamic T1w DCE sequence after intravenous application of contrast medium was performed for estimation of the following perfusion parameters: volume transfer constant (K trans ), volume of the extravascular extracellular leakage space (Ve), and diffusion of contrast medium from the extravascular extracellular leakage space back to the plasma (Kep). Both ADC and perfusion parameters maps were processed offline in DICOM format with custom-made Matlab-based application. Thereafter, polygonal ROIs were manually drawn on the transferred maps on each slice. For every parameter, mean, maximal, minimal, and median values, as well percentiles 10th, 25th, 75th, 90th, kurtosis, skewness, and entropy were estimated. Сorrelation analysis identified multiple statistically significant correlations between the investigated parameters. Ve related parameters correlated well with different ADC values. Especially, percentiles 10 and 75, mode, and median values showed stronger correlations in comparison to other

ℋ- adaptive observer design and parameter identification for a class of nonlinear fractional-order systems

KAUST Repository

Ndoye, Ibrahima; Voos, Holger; Laleg-Kirati, Taous-Meriem; Darouach, Mohamed

2014-01-01

In this paper, an adaptive observer design with parameter identification for a nonlinear system with external perturbations and unknown parameters is proposed. The states of the nonlinear system are estimated by a nonlinear observer and the unknown

Using an epiphytic moss to identify previously unknown sources of atmospheric cadmium pollution

Science.gov (United States)

Geoffrey H. Donovan; Sarah E. Jovan; Demetrios Gatziolis; Igor Burstyn; Yvonne L. Michael; Michael C. Amacher; Vicente J. Monleon

2016-01-01

Urban networks of air-quality monitors are often too widely spaced to identify sources of air pollutants, especially if they do not disperse far from emission sources. The objectives of this study were to test the use of moss bio-indicators to develop a fine-scale map of atmospherically-derived cadmium and to identify the sources of cadmium in a complex urban setting....

Prevalence and Impact of Unknown Diabetes in the ICU.

Science.gov (United States)

Carpenter, David L; Gregg, Sara R; Xu, Kejun; Buchman, Timothy G; Coopersmith, Craig M

2015-12-01

Many patients with diabetes and their care providers are unaware of the presence of the disease. Dysglycemia encompassing hyperglycemia, hypoglycemia, and glucose variability is common in the ICU in patients with and without diabetes. The purpose of this study was to determine the impact of unknown diabetes on glycemic control in the ICU. Prospective observational study. Nine ICUs in an academic, tertiary hospital and a hybrid academic/community hospital. Hemoglobin A1c levels were ordered at all ICU admissions from March 1, 2011 to September 30, 2013. Electronic medical records were examined for a history of antihyperglycemic medications or International Classification of Diseases, 9th Edition diagnosis of diabetes. Patients were categorized as having unknown diabetes (hemoglobin A1c > 6.5%, without history of diabetes), no diabetes (hemoglobin A1c 6.5%, with documented history of diabetes). None. A total of 15,737 patients had an hemoglobin A1c and medical record evaluable for the history of diabetes, and 5,635 patients had diabetes diagnosed by either medical history or an elevated hemoglobin A1c in the ICU. Of these, 1,460 patients had unknown diabetes, accounting for 26.0% of all patients with diabetes. This represented 41.0% of patients with an hemoglobin A1c > 6.5% and 9.3% of all ICU patients. Compared with patients without diabetes, patients with unknown diabetes had a higher likelihood of requiring an insulin infusion (44.3% vs 29.3%; p 180 mg/dL; p < 0.0001) and hypoglycemia (8.9% vs 2.5%; blood glucose < 70 mg/dL; p < 0.0001), higher glycemic variability (55.6 vs 28.8, average of patient SD of glucose; p < 0.0001), and increased mortality (13.8% vs 11.4%; p = 0.01). Patients with unknown diabetes represent a significant percentage of ICU admissions. Measurement of hemoglobin A1c at admission can prospectively identify a population that are not known to have diabetes but have significant challenges in glycemic control in the ICU.

NIH Researchers Identify OCD Risk Gene

Science.gov (United States)

... News From NIH NIH Researchers Identify OCD Risk Gene Past Issues / Summer 2006 Table of Contents For ... and Alcoholism (NIAAA) have identified a previously unknown gene variant that doubles an individual's risk for obsessive- ...

Identifying quantum phase transitions with adversarial neural networks

Science.gov (United States)

Huembeli, Patrick; Dauphin, Alexandre; Wittek, Peter

2018-04-01

The identification of phases of matter is a challenging task, especially in quantum mechanics, where the complexity of the ground state appears to grow exponentially with the size of the system. Traditionally, physicists have to identify the relevant order parameters for the classification of the different phases. We here follow a radically different approach: we address this problem with a state-of-the-art deep learning technique, adversarial domain adaptation. We derive the phase diagram of the whole parameter space starting from a fixed and known subspace using unsupervised learning. This method has the advantage that the input of the algorithm can be directly the ground state without any ad hoc feature engineering. Furthermore, the dimension of the parameter space is unrestricted. More specifically, the input data set contains both labeled and unlabeled data instances. The first kind is a system that admits an accurate analytical or numerical solution, and one can recover its phase diagram. The second type is the physical system with an unknown phase diagram. Adversarial domain adaptation uses both types of data to create invariant feature extracting layers in a deep learning architecture. Once these layers are trained, we can attach an unsupervised learner to the network to find phase transitions. We show the success of this technique by applying it on several paradigmatic models: the Ising model with different temperatures, the Bose-Hubbard model, and the Su-Schrieffer-Heeger model with disorder. The method finds unknown transitions successfully and predicts transition points in close agreement with standard methods. This study opens the door to the classification of physical systems where the phase boundaries are complex such as the many-body localization problem or the Bose glass phase.

Quantum circuits cannot control unknown operations

International Nuclear Information System (INIS)

Araújo, Mateus; Feix, Adrien; Costa, Fabio; Brukner, Časlav

2014-01-01

One of the essential building blocks of classical computer programs is the ‘if’ clause, which executes a subroutine depending on the value of a control variable. Similarly, several quantum algorithms rely on applying a unitary operation conditioned on the state of a control system. Here we show that this control cannot be performed by a quantum circuit if the unitary is completely unknown. The task remains impossible even if we allow the control to be done modulo a global phase. However, this no-go theorem does not prevent implementing quantum control of unknown unitaries in practice, as any physical implementation of an unknown unitary provides additional information that makes the control possible. We then argue that one should extend the quantum circuit formalism to capture this possibility in a straightforward way. This is done by allowing unknown unitaries to be applied to subspaces and not only to subsystems. (paper)

Cost-effectiveness of using a gene expression profiling test to aid in identifying the primary tumour in patients with cancer of unknown primary.

Science.gov (United States)

Hannouf, M B; Winquist, E; Mahmud, S M; Brackstone, M; Sarma, S; Rodrigues, G; Rogan, P; Hoch, J S; Zaric, G S

2017-06-01

We aimed to investigate the cost-effectiveness of a 2000-gene-expression profiling (GEP) test to help identify the primary tumor site when clinicopathological diagnostic evaluation was inconclusive in patients with cancer of unknown primary (CUP). We built a decision-analytic-model to project the lifetime clinical and economic consequences of different clinical management strategies for CUP. The model was parameterized using follow-up data from the Manitoba Cancer Registry, cost data from Manitoba Health administrative databases and secondary sources. The 2000-GEP-based strategy compared to current clinical practice resulted in an incremental cost-effectiveness ratio (ICER) of $44,151 per quality-adjusted life years (QALY) gained. The total annual-budget impact was $36.2 million per year. A value-of-information analysis revealed that the expected value of perfect information about the test's clinical impact was $4.2 million per year. The 2000-GEP test should be considered for adoption in CUP. Field evaluations of the test are associated with a large societal benefit.

Optimal Design of Shock Tube Experiments for Parameter Inference

KAUST Repository

Bisetti, Fabrizio; Knio, Omar

2014-01-01

We develop a Bayesian framework for the optimal experimental design of the shock tube experiments which are being carried out at the KAUST Clean Combustion Research Center. The unknown parameters are the pre-exponential parameters and the activation

The energy equation with three unknowns

International Nuclear Information System (INIS)

Schifano, Fabio; Moriconi, Daniele

2008-01-01

This article discusses the alarming situation of energy in Italy as this country depends at 82 per cent on its imports (oil, natural gas and electricity), a dependence which could even increase. The authors first propose overviews of the situation regarding oil, natural gas and electric power (origins of imports, role of Italian companies, status of infrastructures), and also briefly of renewable energies. They recall the history of the use of nuclear energy: Italy has been one of the first country to use nuclear energy to produce electric power, but a referendum organised after Chernobyl resulted in phasing out nuclear. Then, the authors discuss perspectives associated with three main strategic unknowns: an increase of energy dependence with respect to hydrocarbons and to foreign nuclear power, a supply insecurity due to a dependence concentrated on a small number of countries (notably as far as natural gas is concerned), and an increasing interdependence between economic growth and sustainable development (the reduction of greenhouse emissions is a prevailing parameter for future energetic choices)

Volume-based predictive biomarkers of sequential FDG-PET/CT for sunitinib in cancer of unknown primary: identification of the best benefited patients

International Nuclear Information System (INIS)

Ma, Yifei; Xu, Wei; Xiao, Jianru; Bai, Ruojing; Li, Yiming; Yu, Hongyu; Yang, Chunshan; Shi, Huazheng; Zhang, Jian; Li, Jidong; Wang, Chenguang

2017-01-01

To test the performance of sequential "1"8F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) in predicting survival after sunitinib therapies in patients with cancer of unknown primary (CUP). CUP patients were enrolled for sequential PET/CT scanning for sunitinib and a control group. Univariate and multivariate analysis were applied to test the efficacy of sunitinib therapy in CUP patients. Next, sequential analyses involving PET/CT parameters were performed to identify and validate sensitive PET/CT biomarkers for sunitinib therapy. Finally, time-dependent receiver operating characteristic (TDROC) analyses were performed to compare the predictive accuracy. Multivariate analysis proved that sunitinib group had significantly improved survival (p < 0.01) as compared to control group. After cycle 2 of therapy, multivariate analysis identified volume-based PET/CT parameters as sensitive biomarkers for sunitinib (p < 0.01). TDROC curves demonstrated whole-body total lesion glycolysis reduction (Δ WTLG) and follow-up WTLG to have good accuracy for efficacy prediction. This evidence was validated after cycle 4 of therapy with the same method. Sunitinib therapy proved effective in treatment of CUP. PET/CT volume-based parameters may help predict outcome of sunitinib therapy, in which Δ WTLG and follow-up WTLG seem to be sensitive biomarkers for sunitinib efficacy. Patients with greater reduction and lower WTLG at follow-up seem to have better survival outcome. (orig.)

Volume-based predictive biomarkers of sequential FDG-PET/CT for sunitinib in cancer of unknown primary: identification of the best benefited patients

Energy Technology Data Exchange (ETDEWEB)

Ma, Yifei [Second Military Medical University, Department of Orthorpedic Oncology, Changzheng Hospital, Shanghai (China); Second Military Medical University, Department of Pathology, Changzheng Hospital, Shanghai (China); Xu, Wei; Xiao, Jianru [Second Military Medical University, Department of Orthorpedic Oncology, Changzheng Hospital, Shanghai (China); Bai, Ruojing [Geriatrics Institute, Department of Geriatrics, Tianjin Medical University General Hospital, Laboratory of Neuro-Trauma and Neurodegenerative Disorder, Tianjin (China); Li, Yiming [Neurosurgery Institute, Department of Neuro-oncology, Beijing (China); Yu, Hongyu [Second Military Medical University, Department of Pathology, Changzheng Hospital, Shanghai (China); Yang, Chunshan [Panorama Medical Imaging Center, Department of PET/CT Radiology, Shanghai (China); Department of PET/CT Radiology Center, Shanghai (China); Shi, Huazheng; Zhang, Jian [Department of PET/CT Radiology Center, Shanghai (China); Li, Jidong [The First People' s Hospital of Shangqiu, Department of Stomatology, Shangqiu, Henan Province (China); Wang, Chenguang [Second Military Medical University, Department of Radiology, Changzheng Hospital, Shanghai (China)

2017-02-15

To test the performance of sequential {sup 18}F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) in predicting survival after sunitinib therapies in patients with cancer of unknown primary (CUP). CUP patients were enrolled for sequential PET/CT scanning for sunitinib and a control group. Univariate and multivariate analysis were applied to test the efficacy of sunitinib therapy in CUP patients. Next, sequential analyses involving PET/CT parameters were performed to identify and validate sensitive PET/CT biomarkers for sunitinib therapy. Finally, time-dependent receiver operating characteristic (TDROC) analyses were performed to compare the predictive accuracy. Multivariate analysis proved that sunitinib group had significantly improved survival (p < 0.01) as compared to control group. After cycle 2 of therapy, multivariate analysis identified volume-based PET/CT parameters as sensitive biomarkers for sunitinib (p < 0.01). TDROC curves demonstrated whole-body total lesion glycolysis reduction (Δ WTLG) and follow-up WTLG to have good accuracy for efficacy prediction. This evidence was validated after cycle 4 of therapy with the same method. Sunitinib therapy proved effective in treatment of CUP. PET/CT volume-based parameters may help predict outcome of sunitinib therapy, in which Δ WTLG and follow-up WTLG seem to be sensitive biomarkers for sunitinib efficacy. Patients with greater reduction and lower WTLG at follow-up seem to have better survival outcome. (orig.)

Application of Novel Lateral Tire Force Sensors to Vehicle Parameter Estimation of Electric Vehicles.

Science.gov (United States)

Nam, Kanghyun

2015-11-11

This article presents methods for estimating lateral vehicle velocity and tire cornering stiffness, which are key parameters in vehicle dynamics control, using lateral tire force measurements. Lateral tire forces acting on each tire are directly measured by load-sensing hub bearings that were invented and further developed by NSK Ltd. For estimating the lateral vehicle velocity, tire force models considering lateral load transfer effects are used, and a recursive least square algorithm is adapted to identify the lateral vehicle velocity as an unknown parameter. Using the estimated lateral vehicle velocity, tire cornering stiffness, which is an important tire parameter dominating the vehicle's cornering responses, is estimated. For the practical implementation, the cornering stiffness estimation algorithm based on a simple bicycle model is developed and discussed. Finally, proposed estimation algorithms were evaluated using experimental test data.

Previously unknown species of Aspergillus.

Science.gov (United States)

Gautier, M; Normand, A-C; Ranque, S

2016-08-01

The use of multi-locus DNA sequence analysis has led to the description of previously unknown 'cryptic' Aspergillus species, whereas classical morphology-based identification of Aspergillus remains limited to the section or species-complex level. The current literature highlights two main features concerning these 'cryptic' Aspergillus species. First, the prevalence of such species in clinical samples is relatively high compared with emergent filamentous fungal taxa such as Mucorales, Scedosporium or Fusarium. Second, it is clearly important to identify these species in the clinical laboratory because of the high frequency of antifungal drug-resistant isolates of such Aspergillus species. Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) has recently been shown to enable the identification of filamentous fungi with an accuracy similar to that of DNA sequence-based methods. As MALDI-TOF MS is well suited to the routine clinical laboratory workflow, it facilitates the identification of these 'cryptic' Aspergillus species at the routine mycology bench. The rapid establishment of enhanced filamentous fungi identification facilities will lead to a better understanding of the epidemiology and clinical importance of these emerging Aspergillus species. Based on routine MALDI-TOF MS-based identification results, we provide original insights into the key interpretation issues of a positive Aspergillus culture from a clinical sample. Which ubiquitous species that are frequently isolated from air samples are rarely involved in human invasive disease? Can both the species and the type of biological sample indicate Aspergillus carriage, colonization or infection in a patient? Highly accurate routine filamentous fungi identification is central to enhance the understanding of these previously unknown Aspergillus species, with a vital impact on further improved patient care. Copyright © 2016 European Society of Clinical Microbiology and

Estimation of Parameters in Mean-Reverting Stochastic Systems

Directory of Open Access Journals (Sweden)

Tianhai Tian

2014-01-01

Full Text Available Stochastic differential equation (SDE is a very important mathematical tool to describe complex systems in which noise plays an important role. SDE models have been widely used to study the dynamic properties of various nonlinear systems in biology, engineering, finance, and economics, as well as physical sciences. Since a SDE can generate unlimited numbers of trajectories, it is difficult to estimate model parameters based on experimental observations which may represent only one trajectory of the stochastic model. Although substantial research efforts have been made to develop effective methods, it is still a challenge to infer unknown parameters in SDE models from observations that may have large variations. Using an interest rate model as a test problem, in this work we use the Bayesian inference and Markov Chain Monte Carlo method to estimate unknown parameters in SDE models.

Identification of metabolic system parameters using global optimization methods

Directory of Open Access Journals (Sweden)

Gatzke Edward P

2006-01-01

Full Text Available Abstract Background The problem of estimating the parameters of dynamic models of complex biological systems from time series data is becoming increasingly important. Methods and results Particular consideration is given to metabolic systems that are formulated as Generalized Mass Action (GMA models. The estimation problem is posed as a global optimization task, for which novel techniques can be applied to determine the best set of parameter values given the measured responses of the biological system. The challenge is that this task is nonconvex. Nonetheless, deterministic optimization techniques can be used to find a global solution that best reconciles the model parameters and measurements. Specifically, the paper employs branch-and-bound principles to identify the best set of model parameters from observed time course data and illustrates this method with an existing model of the fermentation pathway in Saccharomyces cerevisiae. This is a relatively simple yet representative system with five dependent states and a total of 19 unknown parameters of which the values are to be determined. Conclusion The efficacy of the branch-and-reduce algorithm is illustrated by the S. cerevisiae example. The method described in this paper is likely to be widely applicable in the dynamic modeling of metabolic networks.

Chinese Unknown Word Recognition for PCFG-LA Parsing

Directory of Open Access Journals (Sweden)

Qiuping Huang

2014-01-01

Full Text Available This paper investigates the recognition of unknown words in Chinese parsing. Two methods are proposed to handle this problem. One is the modification of a character-based model. We model the emission probability of an unknown word using the first and last characters in the word. It aims to reduce the POS tag ambiguities of unknown words to improve the parsing performance. In addition, a novel method, using graph-based semisupervised learning (SSL, is proposed to improve the syntax parsing of unknown words. Its goal is to discover additional lexical knowledge from a large amount of unlabeled data to help the syntax parsing. The method is mainly to propagate lexical emission probabilities to unknown words by building the similarity graphs over the words of labeled and unlabeled data. The derived distributions are incorporated into the parsing process. The proposed methods are effective in dealing with the unknown words to improve the parsing. Empirical results for Penn Chinese Treebank and TCT Treebank revealed its effectiveness.

DAISY: a new software tool to test global identifiability of biological and physiological systems.

Science.gov (United States)

Bellu, Giuseppina; Saccomani, Maria Pia; Audoly, Stefania; D'Angiò, Leontina

2007-10-01

A priori global identifiability is a structural property of biological and physiological models. It is considered a prerequisite for well-posed estimation, since it concerns the possibility of recovering uniquely the unknown model parameters from measured input-output data, under ideal conditions (noise-free observations and error-free model structure). Of course, determining if the parameters can be uniquely recovered from observed data is essential before investing resources, time and effort in performing actual biomedical experiments. Many interesting biological models are nonlinear but identifiability analysis for nonlinear system turns out to be a difficult mathematical problem. Different methods have been proposed in the literature to test identifiability of nonlinear models but, to the best of our knowledge, so far no software tools have been proposed for automatically checking identifiability of nonlinear models. In this paper, we describe a software tool implementing a differential algebra algorithm to perform parameter identifiability analysis for (linear and) nonlinear dynamic models described by polynomial or rational equations. Our goal is to provide the biological investigator a completely automatized software, requiring minimum prior knowledge of mathematical modelling and no in-depth understanding of the mathematical tools. The DAISY (Differential Algebra for Identifiability of SYstems) software will potentially be useful in biological modelling studies, especially in physiology and clinical medicine, where research experiments are particularly expensive and/or difficult to perform. Practical examples of use of the software tool DAISY are presented. DAISY is available at the web site http://www.dei.unipd.it/~pia/.

Application of Novel Lateral Tire Force Sensors to Vehicle Parameter Estimation of Electric Vehicles

Directory of Open Access Journals (Sweden)

Kanghyun Nam

2015-11-01

Full Text Available This article presents methods for estimating lateral vehicle velocity and tire cornering stiffness, which are key parameters in vehicle dynamics control, using lateral tire force measurements. Lateral tire forces acting on each tire are directly measured by load-sensing hub bearings that were invented and further developed by NSK Ltd. For estimating the lateral vehicle velocity, tire force models considering lateral load transfer effects are used, and a recursive least square algorithm is adapted to identify the lateral vehicle velocity as an unknown parameter. Using the estimated lateral vehicle velocity, tire cornering stiffness, which is an important tire parameter dominating the vehicle’s cornering responses, is estimated. For the practical implementation, the cornering stiffness estimation algorithm based on a simple bicycle model is developed and discussed. Finally, proposed estimation algorithms were evaluated using experimental test data.

RSS-based localization of isotropically decaying source with unknown power and pathloss factor

International Nuclear Information System (INIS)

Sun, Shunyuan; Sun, Li; Ding, Zhiguo

2016-01-01

This paper addresses the localization of an isotropically decaying source based on the received signal strength (RSS) measurements that are collected from nearby active sensors that are position-known and wirelessly connected, and it propose a novel iterative algorithm for RSS-based source localization in order to improve the location accuracy and realize real-time location and automatic monitoring for hospital patients and medical equipment in the smart hospital. In particular, we consider the general case where the source power and pathloss factor are both unknown. For such a source localization problem, we propose an iterative algorithm, in which the unknown source position and two other unknown parameters (i.e. the source power and pathloss factor) are estimated in an alternating way based on each other, with our proposed sub-optimum initial estimate on source position obtained based on the RSS measurements that are collected from a few (closest) active sensors with largest RSS values. Analysis and simulation study show that our proposed iterative algorithm guarantees globally convergence to the least-squares (LS) solution, where for our suitably assumed independent and identically distributed (i.i.d.) zero-mean Gaussian RSS measurement errors the converged localization performance achieves the optimum that corresponds to the Cramer–Rao lower bound (CRLB).

Known Unknowns in Judgment and Choice

OpenAIRE

Walters, Daniel

2017-01-01

This dissertation investigates how people make inferences about missing information. Whereas most prior literature focuses on how people process known information, I show that the extent to which people make inferences about missing information impacts judgments and choices. Specifically, I investigate how (1) awareness of known unknowns affects overconfidence in judgment in Chapter 1, (2) beliefs about the knowability of unknowns impacts investment strategies in Chapter 2, and (3) inferences...

Multi-objective optimization in quantum parameter estimation

Science.gov (United States)

Gong, BeiLi; Cui, Wei

2018-04-01

We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the parameter estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ɛ-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.

Identification of a Discontinuous Parameter in Stochastic Parabolic Systems

International Nuclear Information System (INIS)

Aihara, S. I.

1998-01-01

The purpose of this paper is to study the identification problem for a spatially varying discontinuous parameter in stochastic diffusion equations. The consistency property of the maximum likelihood estimate (M.L.E.) and a generating algorithm for M.L.E. have been explored under the condition that the unknown parameter is in a sufficiently regular space with respect to spatial variables. In order to prove the consistency property of the M.L.E. for a discontinuous diffusion coefficient, we use the method of sieves, i.e., first the admissible class of unknown parameters is projected into a finite-dimensional space and next the convergence of the derived finite-dimensional M.L.E. to the infinite-dimensional M.L.E. is justified under some conditions. An iterative algorithm for generating the M.L.E. is also proposed with two numerical examples

Distributed Synchronization Control of Multiagent Systems With Unknown Nonlinearities.

Science.gov (United States)

Su, Shize; Lin, Zongli; Garcia, Alfredo

2016-01-01

This paper revisits the distributed adaptive control problem for synchronization of multiagent systems where the dynamics of the agents are nonlinear, nonidentical, unknown, and subject to external disturbances. Two communication topologies, represented, respectively, by a fixed strongly-connected directed graph and by a switching connected undirected graph, are considered. Under both of these communication topologies, we use distributed neural networks to approximate the uncertain dynamics. Decentralized adaptive control protocols are then constructed to solve the cooperative tracker problem, the problem of synchronization of all follower agents to a leader agent. In particular, we show that, under the proposed decentralized control protocols, the synchronization errors are ultimately bounded, and their ultimate bounds can be reduced arbitrarily by choosing the control parameter appropriately. Simulation study verifies the effectiveness of our proposed protocols.

Analysing Trust Transitivity and The Effects of Unknown Dependence

Directory of Open Access Journals (Sweden)

Touhid Bhuiyan

2010-03-01

Full Text Available Trust can be used to improve online automated recommendation within a given domain. Trust transitivity is used to make it successful. But trust transitivity has different interpretations. Trust and trust transitivity; both are the human mental phenomenon and for this reason, there is no such thing as objective transitivity. Trust transitivity and trust fusion both are important elements in computational trust. This paper analyses the parameter dependence problem in trust transitivity and proposes some definitions considering the effects of base rate. In addition, it also proposes belief functions based on subjective logic to analyse trust transitivity of three specified cases with sensitive and insensitive based rate. Then it presents a quantitative analysis of the effects of unknown dependence problem in an interconnected network environment; such Internet.

Measurement-based perturbation theory and differential equation parameter estimation with applications to satellite gravimetry

Science.gov (United States)

Xu, Peiliang

2018-06-01

The numerical integration method has been routinely used by major institutions worldwide, for example, NASA Goddard Space Flight Center and German Research Center for Geosciences (GFZ), to produce global gravitational models from satellite tracking measurements of CHAMP and/or GRACE types. Such Earth's gravitational products have found widest possible multidisciplinary applications in Earth Sciences. The method is essentially implemented by solving the differential equations of the partial derivatives of the orbit of a satellite with respect to the unknown harmonic coefficients under the conditions of zero initial values. From the mathematical and statistical point of view, satellite gravimetry from satellite tracking is essentially the problem of estimating unknown parameters in the Newton's nonlinear differential equations from satellite tracking measurements. We prove that zero initial values for the partial derivatives are incorrect mathematically and not permitted physically. The numerical integration method, as currently implemented and used in mathematics and statistics, chemistry and physics, and satellite gravimetry, is groundless, mathematically and physically. Given the Newton's nonlinear governing differential equations of satellite motion with unknown equation parameters and unknown initial conditions, we develop three methods to derive new local solutions around a nominal reference orbit, which are linked to measurements to estimate the unknown corrections to approximate values of the unknown parameters and the unknown initial conditions. Bearing in mind that satellite orbits can now be tracked almost continuously at unprecedented accuracy, we propose the measurement-based perturbation theory and derive global uniformly convergent solutions to the Newton's nonlinear governing differential equations of satellite motion for the next generation of global gravitational models. Since the solutions are global uniformly convergent, theoretically speaking

Mobile assistant for unknown caller identification

OpenAIRE

Hribernik, Andraž

2012-01-01

The main motivation of this diploma thesis is a development of Android application, which helps user of application to find out who is the owner of unknown phone number. Data source for finding unknown phone number are free available web sources. Through the development of prototype, data from different web sources were integrated. Result of this integration is shown in Android application. Data integration includes access to semi-structured data on web portal of Phone Directory of Slovenia, ...

Global Learning Spectral Archive- A new Way to deal with Unknown Urban Spectra -

Science.gov (United States)

Jilge, M.; Heiden, U.; Habermeyer, M.; Jürgens, C.

2015-12-01

Rapid urbanization processes and the need of identifying urban materials demand urban planners and the remote sensing community since years. Urban planners cannot overcome the issue of up-to-date information of urban materials due to time-intensive fieldwork. Hyperspectral remote sensing can facilitate this issue by interpreting spectral signals to provide information of occurring materials. However, the complexity of urban areas and the occurrence of diverse urban materials vary due to regional and cultural aspects as well as the size of a city, which makes identification of surface materials a challenging analysis task. For the various surface material identification approaches, spectral libraries containing pure material spectra are commonly used, which are derived from field, laboratory or the hyperspectral image itself. One of the requirements for successful image analysis is that all spectrally different surface materials are represented by the library. Currently, a universal library, applicable in every urban area worldwide and taking each spectral variability into account, is and will not be existent. In this study, the issue of unknown surface material spectra and the demand of an urban site-specific spectral library is tackled by the development of a learning spectral archive tool. Starting with an incomplete library of labelled image spectra from several German cities, surface materials of pure image pixels will be identified in a hyperspectral image based on a similarity measure (e.g. SID-SAM). Additionally, unknown image spectra of urban objects are identified based on an object- and spectral-based-rule set. The detected unknown surface material spectra are entered with additional metadata, such as regional occurrence into the existing spectral library and thus, are reusable for further studies. Our approach is suitable for pure surface material detection of urban hyperspectral images that is globally applicable by taking incompleteness into account

[Reconstructive investigations and identification measures in unknown soldiers of the Second World War].

Science.gov (United States)

Jopp-van Well, Eilin; Gehl, Axel; Säring, Dennis; Amling, Michael; Hahn, Michael; Sperhake, Jan; Augustin, Christa; Krebs, Oliver; Püschel, Klaus

2016-01-01

The article reports on the exhumation and identification of unknown soldiers from the Second World War. With the help of medicolegal investigation and reconstruction methods an American pilot presumably murdered by a shot to the head (lynch law) and an interned Italian soldier could be identified after about 70 years and brought back home.

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.

Navigation through unknown and dynamic open spaces using topological notions

Science.gov (United States)

Miguel-Tomé, Sergio

2018-04-01

Until now, most algorithms used for navigation have had the purpose of directing system towards one point in space. However, humans communicate tasks by specifying spatial relations among elements or places. In addition, the environments in which humans develop their activities are extremely dynamic. The only option that allows for successful navigation in dynamic and unknown environments is making real-time decisions. Therefore, robots capable of collaborating closely with human beings must be able to make decisions based on the local information registered by the sensors and interpret and express spatial relations. Furthermore, when one person is asked to perform a task in an environment, this task is communicated given a category of goals so the person does not need to be supervised. Thus, two problems appear when one wants to create multifunctional robots: how to navigate in dynamic and unknown environments using spatial relations and how to accomplish this without supervision. In this article, a new architecture to address the two cited problems is presented, called the topological qualitative navigation architecture. In previous works, a qualitative heuristic called the heuristic of topological qualitative semantics (HTQS) has been developed to establish and identify spatial relations. However, that heuristic only allows for establishing one spatial relation with a specific object. In contrast, navigation requires a temporal sequence of goals with different objects. The new architecture attains continuous generation of goals and resolves them using HTQS. Thus, the new architecture achieves autonomous navigation in dynamic or unknown open environments.

Random neural Q-learning for obstacle avoidance of a mobile robot in unknown environments

Directory of Open Access Journals (Sweden)

Jing Yang

2016-07-01

Full Text Available The article presents a random neural Q-learning strategy for the obstacle avoidance problem of an autonomous mobile robot in unknown environments. In the proposed strategy, two independent modules, namely, avoidance without considering the target and goal-seeking without considering obstacles, are first trained using the proposed random neural Q-learning algorithm to obtain their best control policies. Then, the two trained modules are combined based on a switching function to realize the obstacle avoidance in unknown environments. For the proposed random neural Q-learning algorithm, a single-hidden layer feedforward network is used to approximate the Q-function to estimate the Q-value. The parameters of the single-hidden layer feedforward network are modified using the recently proposed neural algorithm named the online sequential version of extreme learning machine, where the parameters of the hidden nodes are assigned randomly and the sample data can come one by one. However, different from the original online sequential version of extreme learning machine algorithm, the initial output weights are estimated subjected to quadratic inequality constraint to improve the convergence speed. Finally, the simulation results demonstrate that the proposed random neural Q-learning strategy can successfully solve the obstacle avoidance problem. Also, the higher learning efficiency and better generalization ability are achieved by the proposed random neural Q-learning algorithm compared with the Q-learning based on the back-propagation method.

The Early Eocene equable climate problem: can perturbations of climate model parameters identify possible solutions?

Science.gov (United States)

Sagoo, Navjit; Valdes, Paul; Flecker, Rachel; Gregoire, Lauren J

2013-10-28

Geological data for the Early Eocene (56-47.8 Ma) indicate extensive global warming, with very warm temperatures at both poles. However, despite numerous attempts to simulate this warmth, there are remarkable data-model differences in the prediction of these polar surface temperatures, resulting in the so-called 'equable climate problem'. In this paper, for the first time an ensemble with a perturbed climate-sensitive model parameters approach has been applied to modelling the Early Eocene climate. We performed more than 100 simulations with perturbed physics parameters, and identified two simulations that have an optimal fit with the proxy data. We have simulated the warmth of the Early Eocene at 560 ppmv CO2, which is a much lower CO2 level than many other models. We investigate the changes in atmospheric circulation, cloud properties and ocean circulation that are common to these simulations and how they differ from the remaining simulations in order to understand what mechanisms contribute to the polar warming. The parameter set from one of the optimal Early Eocene simulations also produces a favourable fit for the last glacial maximum boundary climate and outperforms the control parameter set for the present day. Although this does not 'prove' that this model is correct, it is very encouraging that there is a parameter set that creates a climate model able to simulate well very different palaeoclimates and the present-day climate. Interestingly, to achieve the great warmth of the Early Eocene this version of the model does not have a strong future climate change Charney climate sensitivity. It produces a Charney climate sensitivity of 2.7(°)C, whereas the mean value of the 18 models in the IPCC Fourth Assessment Report (AR4) is 3.26(°)C±0.69(°)C. Thus, this value is within the range and below the mean of the models included in the AR4.

Using an epiphytic moss to identify previously unknown sources of atmospheric cadmium pollution

International Nuclear Information System (INIS)

Donovan, Geoffrey H.; Jovan, Sarah E.; Gatziolis, Demetrios; Burstyn, Igor; Michael, Yvonne L.; Amacher, Michael C.; Monleon, Vicente J.

2016-01-01

Urban networks of air-quality monitors are often too widely spaced to identify sources of air pollutants, especially if they do not disperse far from emission sources. The objectives of this study were to test the use of moss bio-indicators to develop a fine-scale map of atmospherically-derived cadmium and to identify the sources of cadmium in a complex urban setting. We collected 346 samples of the moss Orthotrichum lyellii from deciduous trees in December, 2013 using a modified randomized grid-based sampling strategy across Portland, Oregon. We estimated a spatial linear model of moss cadmium levels and predicted cadmium on a 50 m grid across the city. Cadmium levels in moss were positively correlated with proximity to two stained-glass manufacturers, proximity to the Oregon–Washington border, and percent industrial land in a 500 m buffer, and negatively correlated with percent residential land in a 500 m buffer. The maps showed very high concentrations of cadmium around the two stained-glass manufacturers, neither of which were known to environmental regulators as cadmium emitters. In addition, in response to our findings, the Oregon Department of Environmental Quality placed an instrumental monitor 120 m from the larger stained-glass manufacturer in October, 2015. The monthly average atmospheric cadmium concentration was 29.4 ng/m"3, which is 49 times higher than Oregon's benchmark of 0.6 ng/m"3, and high enough to pose a health risk from even short-term exposure. Both stained-glass manufacturers voluntarily stopped using cadmium after the monitoring results were made public, and the monthly average cadmium levels precipitously dropped to 1.1 ng/m"3 for stained-glass manufacturer #1 and 0.67 ng/m"3 for stained-glass manufacturer #2. - Highlights: • Bio-indicators are a valid method for measuring atmospheric pollutants • We used moss to map atmospheric cadmium in Portland, Oregon • Using a spatial linear model, we identified two stained

Parameter identification based on modified simulated annealing differential evolution algorithm for giant magnetostrictive actuator

Science.gov (United States)

Gao, Xiaohui; Liu, Yongguang

2018-01-01

There is a serious nonlinear relationship between input and output in the giant magnetostrictive actuator (GMA) and how to establish mathematical model and identify its parameters is very important to study characteristics and improve control accuracy. The current-displacement model is firstly built based on Jiles-Atherton (J-A) model theory, Ampere loop theorem and stress-magnetism coupling model. And then laws between unknown parameters and hysteresis loops are studied to determine the data-taking scope. The modified simulated annealing differential evolution algorithm (MSADEA) is proposed by taking full advantage of differential evolution algorithm's fast convergence and simulated annealing algorithm's jumping property to enhance the convergence speed and performance. Simulation and experiment results shows that this algorithm is not only simple and efficient, but also has fast convergence speed and high identification accuracy.

Determination of the origin of unknown irradiated nuclear fuel.

Science.gov (United States)

Nicolaou, G

2006-01-01

An isotopic fingerprinting method is presented to determine the origin of unknown nuclear material with forensic importance. Spent nuclear fuel of known origin has been considered as the 'unknown' nuclear material in order to demonstrate the method and verify its prediction capabilities. The method compares, using factor analysis, the measured U, Pu isotopic compositions of the 'unknown' material with U, Pu isotopic compositions simulating well known spent fuels from a range of commercial nuclear power stations. Then, the 'unknown' fuel has the same origin as the commercial fuel with which it exhibits the highest similarity in U, Pu compositions.

Determination of the origin of unknown irradiated nuclear fuel

International Nuclear Information System (INIS)

Nicolaou, G.

2006-01-01

An isotopic fingerprinting method is presented to determine the origin of unknown nuclear material with forensic importance. Spent nuclear fuel of known origin has been considered as the 'unknown' nuclear material in order to demonstrate the method and verify its prediction capabilities. The method compares, using factor analysis, the measured U, Pu isotopic compositions of the 'unknown' material with U, Pu isotopic compositions simulating well known spent fuels from a range of commercial nuclear power stations. Then, the 'unknown' fuel has the same origin as the commercial fuel with which it exhibits the highest similarity in U, Pu compositions

Determination of supersymmetric parameters with neural networks at the large hadron collider

International Nuclear Information System (INIS)

Bornhauser, Nicki

2013-12-01

The LHC is running and in the near future potentially some signs of new physics are measured. In this thesis it is assumed that the underlying theory of such a signal would be identified and that it is some kind of minimal supersymmetric extension of the Standard Model. Generally, the mapping from the measurable observables onto the parameter values of the supersymmetric theory is unknown. Instead, only the opposite direction is known, i.e. for fixed parameters the measurable observables can be computed with some uncertainties. In this thesis, the ability of artifical neural networks to determine this unknown function is demonstrated. At the end of a training process, the created networks are capable to calculate the parameter values with errors for an existing measurement. To do so, at first a set of mostly counting observables is introduced. In the following, the usefulness of these observables for the determination of supersymmetric parameters is checked. This is done by applying them on 283 pairs of parameter sets of a MSSM with 15 parameters. These pairs were found to be indistinguishable at the LHC by another study, even without the consideration of SM background. It can be shown that 260 of these pairs can be discriminated using the introduced observables. Without systematic errors even all pairs can be distinguished. Also with the consideration of SM background still most pairs can be disentangled (282 without and 237 with systematic errors). This result indicates the usefulness of the observables for the direct parameter determination. The performance of neural networks is investigated for four different parameter regions of the CMSSM. With the right set of observables, the neural network approach generally could also be used for any other (non-supersymmetric) theory. In each region, a reference point with around 1,000 events after cuts should be determined in the context of a LHC with a center of mass energy of 14 TeV and an integrated luminosity of 10 fb

Analytic continuation by duality estimation of the S parameter

International Nuclear Information System (INIS)

Ignjatovic, S. R.; Wijewardhana, L. C. R.; Takeuchi, T.

2000-01-01

We investigate the reliability of the analytic continuation by duality (ACD) technique in estimating the electroweak S parameter for technicolor theories. The ACD technique, which is an application of finite energy sum rules, relates the S parameter for theories with unknown particle spectra to known OPE coefficients. We identify the sources of error inherent in the technique and evaluate them for several toy models to see if they can be controlled. The evaluation of errors is done analytically and all relevant formulas are provided in appendixes including analytical formulas for approximating the function 1/s with a polynomial in s. The use of analytical formulas protects us from introducing additional errors due to numerical integration. We find that it is very difficult to control the errors even when the momentum dependence of the OPE coefficients is known exactly. In realistic cases in which the momentum dependence of the OPE coefficients is only known perturbatively, it is impossible to obtain a reliable estimate. (c) 2000 The American Physical Society

Identifying Known Unknowns Using the USEPA CompTox Chemistry Dashboard AnalytBioanlytChem Data

Data.gov (United States)

U.S. Environmental Protection Agency — In this research, the performance of the Dashboard for identifying “known unknowns” was evaluated against that of the online ChemSpider database, one of the primary...

Screening for significant chronic liver disease by using three simple ultrasound parameters

International Nuclear Information System (INIS)

Lignon, Grégoire; Boursier, Jérome; Delumeau, Stéphanie; Michalak-Provost, Sophie; Lebigot, Jérome; Oberti, Frederic

2015-01-01

Highlights: • Three US parameters have diagnosis accuracy for the diagnosis of severe fibrosis equal to 66%. • These three signs detect unidentified fibrosis with a predictive positive value of 32%. • It would be an easy way to detect patients with silent chronic liver diseases. - Abstract: Objectives: Chronic liver diseases remain asymptomatic for many years. Consequently, patients are diagnosed belatedly, when cirrhosis is unmasked by lifethreatening complications. We aimed to identify simple ultrasound parameters for the screening of patients with unknown significant chronic liver disease. Methods: Three hundred and twenty seven patients with chronic liver disease, liver biopsy, and ultrasound examination were included in the derivation set. 283 consecutive patients referred for ultrasound examination were included in the validation set; those selected according to the ultrasound parameters identified in the derivation set were then referred for specialized consultation including non-invasive fibrosis tests and ultimately liver biopsy if liver fibrosis was suspected. Results: In the derivation set, three ultrasound parameters were independent predictors of severe fibrosis: liver surface irregularity, spleen length (>110 mm), and demodulation of hepatic veins. The association of ≥2 of the three above parameters provided 49.1% sensitivity and 86.9% specificity. In the validation set, at ≥2 of the three parameters were present in 23 (8%) of the patients. Among these patients, 8 had liver fibrosis (F ≥ 1), 5 had significant fibrosis (F ≥2) and two cirrhosis. Conclusion: The generalized search of three simple ultrasound signs in patients referred for abdominal ultrasound examination may be an easy way to detect those with silent but significant chronic liver disease

Distributed parameter estimation in unreliable sensor networks via broadcast gossip algorithms.

Science.gov (United States)

Wang, Huiwei; Liao, Xiaofeng; Wang, Zidong; Huang, Tingwen; Chen, Guo

2016-01-01

In this paper, we present an asynchronous algorithm to estimate the unknown parameter under an unreliable network which allows new sensors to join and old sensors to leave, and can tolerate link failures. Each sensor has access to partially informative measurements when it is awakened. In addition, the proposed algorithm can avoid the interference among messages and effectively reduce the accumulated measurement and quantization errors. Based on the theory of stochastic approximation, we prove that our proposed algorithm almost surely converges to the unknown parameter. Finally, we present a numerical example to assess the performance and the communication cost of the algorithm. Copyright © 2015 Elsevier Ltd. All rights reserved.

Experience Replay for Optimal Control of Nonzero-Sum Game Systems With Unknown Dynamics.

Science.gov (United States)

Zhao, Dongbin; Zhang, Qichao; Wang, Ding; Zhu, Yuanheng

2016-03-01

In this paper, an approximate online equilibrium solution is developed for an N -player nonzero-sum (NZS) game systems with completely unknown dynamics. First, a model identifier based on a three-layer neural network (NN) is established to reconstruct the unknown NZS games systems. Moreover, the identifier weight vector is updated based on experience replay technique which can relax the traditional persistence of excitation condition to a simplified condition on recorded data. Then, the single-network adaptive dynamic programming (ADP) with experience replay algorithm is proposed for each player to solve the coupled nonlinear Hamilton- (HJ) equations, where only the critic NN weight vectors are required to tune for each player. The feedback Nash equilibrium is provided by the solution of the coupled HJ equations. Based on the experience replay technique, a novel critic NN weights tuning law is proposed to guarantee the stability of the closed-loop system and the convergence of the value functions. Furthermore, a Lyapunov-based stability analysis shows that the uniform ultimate boundedness of the closed-loop system is achieved. Finally, two simulation examples are given to verify the effectiveness of the proposed control scheme.

Twelve previously unknown phage genera are ubiquitous in global oceans.

Science.gov (United States)

Holmfeldt, Karin; Solonenko, Natalie; Shah, Manesh; Corrier, Kristen; Riemann, Lasse; Verberkmoes, Nathan C; Sullivan, Matthew B

2013-07-30

Viruses are fundamental to ecosystems ranging from oceans to humans, yet our ability to study them is bottlenecked by the lack of ecologically relevant isolates, resulting in "unknowns" dominating culture-independent surveys. Here we present genomes from 31 phages infecting multiple strains of the aquatic bacterium Cellulophaga baltica (Bacteroidetes) to provide data for an underrepresented and environmentally abundant bacterial lineage. Comparative genomics delineated 12 phage groups that (i) each represent a new genus, and (ii) represent one novel and four well-known viral families. This diversity contrasts the few well-studied marine phage systems, but parallels the diversity of phages infecting human-associated bacteria. Although all 12 Cellulophaga phages represent new genera, the podoviruses and icosahedral, nontailed ssDNA phages were exceptional, with genomes up to twice as large as those previously observed for each phage type. Structural novelty was also substantial, requiring experimental phage proteomics to identify 83% of the structural proteins. The presence of uncommon nucleotide metabolism genes in four genera likely underscores the importance of scavenging nutrient-rich molecules as previously seen for phages in marine environments. Metagenomic recruitment analyses suggest that these particular Cellulophaga phages are rare and may represent a first glimpse into the phage side of the rare biosphere. However, these analyses also revealed that these phage genera are widespread, occurring in 94% of 137 investigated metagenomes. Together, this diverse and novel collection of phages identifies a small but ubiquitous fraction of unknown marine viral diversity and provides numerous environmentally relevant phage-host systems for experimental hypothesis testing.

Using an epiphytic moss to identify previously unknown sources of atmospheric cadmium pollution

Energy Technology Data Exchange (ETDEWEB)

Donovan, Geoffrey H., E-mail: gdonovan@fs.fed.us [USDA Forest Service, PNW Research Station, 620 SW Main, Suite 400, Portland, OR 97205 (United States); Jovan, Sarah E., E-mail: sjovan@fs.fed.us [USDA Forest Service, PNW Research Station, 620 SW Main, Suite 400, Portland, OR 97205 (United States); Gatziolis, Demetrios, E-mail: dgatziolis@fs.fed.us [USDA Forest Service, PNW Research Station, 620 SW Main, Suite 400, Portland, OR 97205 (United States); Burstyn, Igor, E-mail: igor.burstyn@drexel.edu [Dornsife School of Public Health, Drexel University, Nesbitt Hall, 3215 Market St, Philadelphia, PA 19104 (United States); Michael, Yvonne L., E-mail: ylm23@drexel.edu [Dornsife School of Public Health, Drexel University, Nesbitt Hall, 3215 Market St, Philadelphia, PA 19104 (United States); Amacher, Michael C., E-mail: mcamacher1@outlook.com [USDA Forest Service, Logan Forest Sciences Laboratory, 860 North 1200 East, Logan, UT 84321 (United States); Monleon, Vicente J., E-mail: vjmonleon@fs.fed.us [USDA Forest Service, PNW Research Station, 3200 SW Jefferson Way, Corvallis, OR 97331 (United States)

2016-07-15

Urban networks of air-quality monitors are often too widely spaced to identify sources of air pollutants, especially if they do not disperse far from emission sources. The objectives of this study were to test the use of moss bio-indicators to develop a fine-scale map of atmospherically-derived cadmium and to identify the sources of cadmium in a complex urban setting. We collected 346 samples of the moss Orthotrichum lyellii from deciduous trees in December, 2013 using a modified randomized grid-based sampling strategy across Portland, Oregon. We estimated a spatial linear model of moss cadmium levels and predicted cadmium on a 50 m grid across the city. Cadmium levels in moss were positively correlated with proximity to two stained-glass manufacturers, proximity to the Oregon–Washington border, and percent industrial land in a 500 m buffer, and negatively correlated with percent residential land in a 500 m buffer. The maps showed very high concentrations of cadmium around the two stained-glass manufacturers, neither of which were known to environmental regulators as cadmium emitters. In addition, in response to our findings, the Oregon Department of Environmental Quality placed an instrumental monitor 120 m from the larger stained-glass manufacturer in October, 2015. The monthly average atmospheric cadmium concentration was 29.4 ng/m{sup 3}, which is 49 times higher than Oregon's benchmark of 0.6 ng/m{sup 3}, and high enough to pose a health risk from even short-term exposure. Both stained-glass manufacturers voluntarily stopped using cadmium after the monitoring results were made public, and the monthly average cadmium levels precipitously dropped to 1.1 ng/m{sup 3} for stained-glass manufacturer #1 and 0.67 ng/m{sup 3} for stained-glass manufacturer #2. - Highlights: • Bio-indicators are a valid method for measuring atmospheric pollutants • We used moss to map atmospheric cadmium in Portland, Oregon • Using a spatial linear model, we identified two

Global sensitivity analysis for identifying important parameters of nitrogen nitrification and denitrification under model uncertainty and scenario uncertainty

Science.gov (United States)

Chen, Zhuowei; Shi, Liangsheng; Ye, Ming; Zhu, Yan; Yang, Jinzhong

2018-06-01

Nitrogen reactive transport modeling is subject to uncertainty in model parameters, structures, and scenarios. By using a new variance-based global sensitivity analysis method, this paper identifies important parameters for nitrogen reactive transport with simultaneous consideration of these three uncertainties. A combination of three scenarios of soil temperature and two scenarios of soil moisture creates a total of six scenarios. Four alternative models describing the effect of soil temperature and moisture content are used to evaluate the reduction functions used for calculating actual reaction rates. The results show that for nitrogen reactive transport problem, parameter importance varies substantially among different models and scenarios. Denitrification and nitrification process is sensitive to soil moisture content status rather than to the moisture function parameter. Nitrification process becomes more important at low moisture content and low temperature. However, the changing importance of nitrification activity with respect to temperature change highly relies on the selected model. Model-averaging is suggested to assess the nitrification (or denitrification) contribution by reducing the possible model error. Despite the introduction of biochemical heterogeneity or not, fairly consistent parameter importance rank is obtained in this study: optimal denitrification rate (Kden) is the most important parameter; reference temperature (Tr) is more important than temperature coefficient (Q10); empirical constant in moisture response function (m) is the least important one. Vertical distribution of soil moisture but not temperature plays predominant role controlling nitrogen reaction. This study provides insight into the nitrogen reactive transport modeling and demonstrates an effective strategy of selecting the important parameters when future temperature and soil moisture carry uncertainties or when modelers face with multiple ways of establishing nitrogen

Decentralized adaptive neural control for high-order interconnected stochastic nonlinear time-delay systems with unknown system dynamics.

Science.gov (United States)

Si, Wenjie; Dong, Xunde; Yang, Feifei

2018-03-01

This paper is concerned with the problem of decentralized adaptive backstepping state-feedback control for uncertain high-order large-scale stochastic nonlinear time-delay systems. For the control design of high-order large-scale nonlinear systems, only one adaptive parameter is constructed to overcome the over-parameterization, and neural networks are employed to cope with the difficulties raised by completely unknown system dynamics and stochastic disturbances. And then, the appropriate Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions of high-order large-scale systems for the first time. At last, on the basis of Lyapunov stability theory, the decentralized adaptive neural controller was developed, and it decreases the number of learning parameters. The actual controller can be designed so as to ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges in the small neighborhood of zero. The simulation example is used to further show the validity of the design method. Copyright © 2018 Elsevier Ltd. All rights reserved.

Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics.

Science.gov (United States)

Tong, Shaocheng; Wang, Tong; Li, Yongming; Zhang, Huaguang

2014-06-01

This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

Organizational Conditions for Dealing with The Unknown Unknown Illustrated by how a Dutch water management authority is preparing for climate change

NARCIS (Netherlands)

Termeer, Catrien J. A. M.; van den Brink, Margo A.

2013-01-01

The central question of this article is the extent to which organizations, governmental authorities in particular, are able to deal with the unknown unknown. Drawing on Weick's work on sensemaking, we introduce seven organizational conditions that can facilitate organizations to be reliable under

Unknown foundation determination for scour.

Science.gov (United States)

2012-04-01

Unknown foundations affect about 9,000 bridges in Texas. For bridges over rivers, this creates a problem : regarding scour decisions as the calculated scour depth cannot be compared to the foundation depth, and a : very conservative costly approach m...

The role of PET in initial work-up and evaluation after therapy in patients with carcinoma of unknown primary

Energy Technology Data Exchange (ETDEWEB)

Ryoo, Baek Yeol; Kang, Yoon Koo

1998-12-01

The carcinoma of unknown primary occupied 5 - 10 % of all malignancies. It is heterogenous in origin and has poor prognosis. The indentification of primary site and definition of involved area are more helpful in the management. The efficacy of positron emission tomography (PET) with fluorine-18- fluorodeoxyglucose (F18-FDG) positron emission tomography (PET) with fluorine-18-fluorodeoxyglucose (F18-FDG) was evaluated in several tumors such as breast, pancreas and head and neck cancers. In carcinoma of unknown primary, it was reported that the concentration of FDG was increased in tumor tissues, and that PET with F18-FDG may be much helpful in identifying primary site and defining involved area. The authors evaluated the usefulness of PET with F18-FDG in initial work-up and in evaluation after radical therapy for the patients with carcinoma of unknown primary. The visual analysis of FDG-PET would be helpful in identifying primary site and defining involved area. In detecting recurrent of residual lesions, FDG-PET seemed to be less helpful than conventional diagnostic work-up. But more studies with larger number of cases and longer follow-up were required. The results of this study can be bases for the direction of future studies for the usefulness of PET in carcinoma of unknown primary.

The role of PET in initial work-up and evaluation after therapy in patients with carcinoma of unknown primary

International Nuclear Information System (INIS)

Ryoo, Baek Yeol; Kang, Yoon Koo

1998-12-01

The carcinoma of unknown primary occupied 5 - 10 % of all malignancies. It is heterogenous in origin and has poor prognosis. The indentification of primary site and definition of involved area are more helpful in the management. The efficacy of positron emission tomography (PET) with fluorine-18- fluorodeoxyglucose (F18-FDG) positron emission tomography (PET) with fluorine-18-fluorodeoxyglucose (F18-FDG) was evaluated in several tumors such as breast, pancreas and head and neck cancers. In carcinoma of unknown primary, it was reported that the concentration of FDG was increased in tumor tissues, and that PET with F18-FDG may be much helpful in identifying primary site and defining involved area. The authors evaluated the usefulness of PET with F18-FDG in initial work-up and in evaluation after radical therapy for the patients with carcinoma of unknown primary. The visual analysis of FDG-PET would be helpful in identifying primary site and defining involved area. In detecting recurrent of residual lesions, FDG-PET seemed to be less helpful than conventional diagnostic work-up. But more studies with larger number of cases and longer follow-up were required. The results of this study can be bases for the direction of future studies for the usefulness of PET in carcinoma of unknown primary

Organizational conditions for dealing with the unknown unknown : illustrated by how a Dutch water management authority is preparing for climate change

NARCIS (Netherlands)

Termeer, C.J.A.M.; Brink, van den M.A.

2013-01-01

The central question of this article is the extent to which organizations, governmental authorities in particular, are able to deal with the unknown unknown. Drawing on Weick’s work on sensemaking, we introduce seven organizational conditions that can facilitate organizations to be reliable under

Estimation of Poisson-Dirichlet Parameters with Monotone Missing Data

Directory of Open Access Journals (Sweden)

Xueqin Zhou

2017-01-01

Full Text Available This article considers the estimation of the unknown numerical parameters and the density of the base measure in a Poisson-Dirichlet process prior with grouped monotone missing data. The numerical parameters are estimated by the method of maximum likelihood estimates and the density function is estimated by kernel method. A set of simulations was conducted, which shows that the estimates perform well.

Post-operative therapy following transoral robotic surgery for unknown primary cancers of the head and neck.

Science.gov (United States)

Patel, Sapna A; Parvathaneni, Aarthi; Parvathaneni, Upendra; Houlton, Jeffrey J; Karni, Ron J; Liao, Jay J; Futran, Neal D; Méndez, Eduardo

2017-09-01

Our primary objective is to describe the post- operative management in patients with an unknown primary squamous cell carcinoma of the head and neck (HNSCC) treated with trans-oral robotic surgery (TORS). We conducted a retrospective multi-institutional case series including all patients diagnosed with an unknown primary HNSCC who underwent TORS to identify the primary site from January 1, 2010 to June 30, 2016. We excluded those with recurrent disease, ≤6months of follow up from TORS, previous history of radiation therapy (RT) to the head and neck, or evidence of primary tumor site based on previous biopsies. Our main outcome measure was receipt of post-operative therapy. The tumor was identified in 26/35 (74.3%) subjects. Post-TORS, 2 subjects did not receive adjuvant therapy due to favorable pathology. Volume reduction of RT mucosal site coverage was achieved in 12/26 (46.1%) subjects who had lateralizing tumors, ie. those confined to the palatine tonsil or glossotonsillar sulcus. In addition, for 8/26 (30.1%), the contralateral neck RT was also avoided. In 9 subjects, no primary was identified (pT0); four of these received RT to the involved ipsilateral neck nodal basin only without pharyngeal mucosal irradiation. Surgical management of an unknown primary with TORS can lead to deintensification of adjuvant therapy including avoidance of chemotherapy and reduction in RT doses and volume. There was no increase in short term treatment failures. Treatment after TORS can vary significantly, thus we advocate adherence to NCCN guideline therapy post-TORS to avoid treatment-associated variability. Published by Elsevier Ltd.

Non-destructive identification of unknown minor phases in polycrystalline bulk alloys using three-dimensional X-ray diffraction

Energy Technology Data Exchange (ETDEWEB)

Yang, Yiming, E-mail: yangyiming1988@outlook.com [Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800 (China); University of Chinese Academy of Sciences, Beijing 100049 (China); Xu, Liang [Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800 (China); University of Chinese Academy of Sciences, Beijing 100049 (China); Wang, Yudan; Du, Guohao [Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800 (China); Yang, Sam [Commonwealth Scientific and Industrial Research Organization, Melbourne, VIC 3168 (Australia); Xiao, Tiqiao, E-mail: tqxiao@sinap.ac.cn [Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800 (China); University of Chinese Academy of Sciences, Beijing 100049 (China)

2017-02-15

Minor phases make considerable contributions to the mechanical and physical properties of metals and alloys. Unfortunately, it is difficult to identify unknown minor phases in a bulk polycrystalline material using conventional metallographic methods. Here, a non-destructive method based on three-dimensional X-ray diffraction (3DXRD) is developed to solve this problem. Simulation results demonstrate that this method is simultaneously able to identify minor phase grains and reveal their positions, orientations and sizes within bulk alloys. According to systematic simulations, the 3DXRD method is practicable for an extensive sample set, including polycrystalline alloys with hexagonal, orthorhombic and cubic minor phases. Experiments were also conducted to confirm the simulation results. The results for a bulk sample of aluminum alloy AA6061 show that the crystal grains of an unexpected γ-Fe (austenite) phase can be identified, three-dimensionally and nondestructively. Therefore, we conclude that the 3DXRD method is a powerful tool for the identification of unknown minor phases in bulk alloys belonging to a variety of crystal systems. This method also has the potential to be used for in situ observations of the effects of minor phases on the crystallographic behaviors of alloys. - Highlights: •A method based on 3DXRD is developed for identification of unknown minor phase. •Grain position, orientation and size, is simultaneously acquired. •A systematic simulation demonstrated the applicability of the proposed method. •Experimental results on a AA6061 sample confirmed the practicability of the method.

Rational Design of Molecular Gelator - Solvent Systems Guided by Solubility Parameters

Science.gov (United States)

Lan, Yaqi

Self-assembled architectures, such as molecular gels, have attracted wide interest among chemists, physicists and engineers during the past decade. However, the mechanism behind self-assembly remains largely unknown and no capability exists to predict a priori whether a small molecule will gelate a specific solvent or not. The process of self-assembly, in molecular gels, is intricate and must balance parameters influencing solubility and those contrasting forces that govern epitaxial growth into axially symmetric elongated aggregates. Although the gelator-gelator interactions are of paramount importance in understanding gelation, the solvent-gelator specific (i.e., H-bonding) and nonspecific (dipole-dipole, dipole-induced and instantaneous dipole induced forces) intermolecular interactions are equally important. Solvent properties mediate the self-assembly of molecular gelators into their self-assembled fibrillar networks. Herein, solubility parameters of solvents, ranging from partition coefficients (logP), to Henry's law constants (HLC), to solvatochromic ET(30) parameters, to Kamlet-Taft parameters (beta, alpha and pi), to Hansen solubility parameters (deltap, deltad, deltah), etc., are correlated with the gelation ability of numerous classes of molecular gelators. Advanced solvent clustering techniques have led to the development of a priori tools that can identify the solvents that will be gelled and not gelled by molecular gelators. These tools will greatly aid in the development of novel gelators without solely relying on serendipitous discoveries.

Chronic kidney disease of unknown etiology in Sri Lanka.

Science.gov (United States)

Rajapakse, Senaka; Shivanthan, Mitrakrishnan Chrishan; Selvarajah, Mathu

2016-07-01

In the last two decades, chronic kidney disease of unknown etiology (CKDu) has emerged as a significant contributor to the burden of chronic kidney disease (CKD) in rural Sri Lanka. It is characterized by the absence of identified causes for CKD. The prevalence of CKDu is 15.1-22.9% in some Sri Lankan districts, and previous research has found an association with farming occupations. A systematic literature review in Pubmed, Embase, Scopus, and Lilacs databases identified 46 eligible peer-reviewed articles and one conference abstract. Geographical mapping indicates a relationship between CKDu and agricultural irrigation water sources. Health mapping studies, human biological studies, and environment-based studies have explored possible causative agents. Most studies focused on likely causative agents related to agricultural practices, geographical distribution based on the prevalence and incidence of CKDu, and contaminants identified in drinking water. Nonetheless, the link between agrochemicals or heavy metals and CKDu remains to be established. No definitive cause for CKDu has been identified. Evidence to date suggests that the disease is related to one or more environmental agents, however pinpointing a definite cause for CKDu is challenging. It is plausible that CKDu is multifactorial. No specific guidelines or recommendations exist for treatment of CKDu, and standard management protocols for CKD apply. Changes in agricultural practices, provision of safe drinking water, and occupational safety precautions are recommended by the World Health Organization.

Automated pathway and reaction prediction facilitates in silico identification of unknown metabolites in human cohort studies.

Science.gov (United States)

Quell, Jan D; Römisch-Margl, Werner; Colombo, Marco; Krumsiek, Jan; Evans, Anne M; Mohney, Robert; Salomaa, Veikko; de Faire, Ulf; Groop, Leif C; Agakov, Felix; Looker, Helen C; McKeigue, Paul; Colhoun, Helen M; Kastenmüller, Gabi

2017-12-15

Identification of metabolites in non-targeted metabolomics continues to be a bottleneck in metabolomics studies in large human cohorts. Unidentified metabolites frequently emerge in the results of association studies linking metabolite levels to, for example, clinical phenotypes. For further analyses these unknown metabolites must be identified. Current approaches utilize chemical information, such as spectral details and fragmentation characteristics to determine components of unknown metabolites. Here, we propose a systems biology model exploiting the internal correlation structure of metabolite levels in combination with existing biochemical and genetic information to characterize properties of unknown molecules. Levels of 758 metabolites (439 known, 319 unknown) in human blood samples of 2279 subjects were measured using a non-targeted metabolomics platform (LC-MS and GC-MS). We reconstructed the structure of biochemical pathways that are imprinted in these metabolomics data by building an empirical network model based on 1040 significant partial correlations between metabolites. We further added associations of these metabolites to 134 genes from genome-wide association studies as well as reactions and functional relations to genes from the public database Recon 2 to the network model. From the local neighborhood in the network, we were able to predict the pathway annotation of 180 unknown metabolites. Furthermore, we classified 100 pairs of known and unknown and 45 pairs of unknown metabolites to 21 types of reactions based on their mass differences. As a proof of concept, we then looked further into the special case of predicted dehydrogenation reactions leading us to the selection of 39 candidate molecules for 5 unknown metabolites. Finally, we could verify 2 of those candidates by applying LC-MS analyses of commercially available candidate substances. The formerly unknown metabolites X-13891 and X-13069 were shown to be 2-dodecendioic acid and 9

Non-uniform Mutation Rates for Problems with Unknown Solution Lengths

DEFF Research Database (Denmark)

Cathabard, Stephan; Lehre, Per Kristian; Yao, Xin

2011-01-01

Many practical optimisation problems allow candidate solu- tions of varying lengths, and where the length of the opti- mal solution is thereby a priori unknown. We suggest that non-uniform mutation rates can be beneficial when solving such problems. In particular, we consider a mutation oper- ator...... that flips each bit with a probability that is inversely proportional to the bit position, rather than the bitstring length. The runtime of the (1+1) EA using this mutation operator is analysed rigorously on standard example func- tions. Furthermore, the behaviour of the new mutation op- erator...... distribution, and show that the new operator can yield exponentially faster runtimes for some parameters of this distribution. The experimental results show that the new mutation operator leads to dramatically shorter runtimes on a class of instances of the software engi- neering problem that is conjectured...

A firefly algorithm approach for determining the parameters characteristics of solar cell

Directory of Open Access Journals (Sweden)

Mohamed LOUZAZNI

2017-12-01

Full Text Available A metaheuristic algorithm is proposed to describe the characteristics of solar cell. The I-V characteristics of solar cell present double nonlinearity in the presence of exponential and in the five parameters. Since, these parameters are unknown, it is important to predict these parameters for accurate modelling of I-V and P-V curves of solar cell. Moreover, firefly algorithm has attracted the intention to optimize the non-linear and complex systems, based on the flashing patterns and behaviour of firefly’s swarm. Besides, the proposed constrained objective function is derived from the current-voltage curve. Using the experimental current and voltage of commercial RTC France Company mono-crystalline silicon solar cell single diode at 33°C and 1000W/m² to predict the unknown parameters. The statistical errors are calculated to verify the accuracy of the results. The obtained results are compared with experimental data and other reported meta-heuristic optimization algorithms. In the end, the theoretical results confirm the validity and reliability of firefly algorithm in estimation the optimal parameters of the solar cell.

Dynamic Contrast-Enhanced MRI of Cervical Cancers: Temporal Percentile Screening of Contrast Enhancement Identifies Parameters for Prediction of Chemoradioresistance

International Nuclear Information System (INIS)

Andersen, Erlend K.F.; Hole, Knut Håkon; Lund, Kjersti V.; Sundfør, Kolbein; Kristensen, Gunnar B.; Lyng, Heidi; Malinen, Eirik

2012-01-01

Purpose: To systematically screen the tumor contrast enhancement of locally advanced cervical cancers to assess the prognostic value of two descriptive parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods and Materials: This study included a prospectively collected cohort of 81 patients who underwent DCE-MRI with gadopentetate dimeglumine before chemoradiotherapy. The following descriptive DCE-MRI parameters were extracted voxel by voxel and presented as histograms for each time point in the dynamic series: normalized relative signal increase (nRSI) and normalized area under the curve (nAUC). The first to 100th percentiles of the histograms were included in a log-rank survival test, resulting in p value and relative risk maps of all percentile–time intervals for each DCE-MRI parameter. The maps were used to evaluate the robustness of the individual percentile–time pairs and to construct prognostic parameters. Clinical endpoints were locoregional control and progression-free survival. The study was approved by the institutional ethics committee. Results: The p value maps of nRSI and nAUC showed a large continuous region of percentile–time pairs that were significantly associated with locoregional control (p < 0.05). These parameters had prognostic impact independent of tumor stage, volume, and lymph node status on multivariate analysis. Only a small percentile–time interval of nRSI was associated with progression-free survival. Conclusions: The percentile–time screening identified DCE-MRI parameters that predict long-term locoregional control after chemoradiotherapy of cervical cancer.

Analysis of Milk Production Traits in Early Lactation Using a Reaction Norm Model with Unknown Covariates

DEFF Research Database (Denmark)

Mahdi Shariati, Mohammad; Su, Guosheng; Madsen, Per

2007-01-01

The reaction norm model is becoming a popular approach to study genotype x environment interaction (GxE), especially when there is a continuum of environmental effects. These effects are typically unknown, and an approximation that is used in the literature is to replace them by the phenotypic...... means of each environment. It has been shown that this method results in poor inferences and that a more satisfactory alternative is to infer environmental effects jointly with the other parameters of the model. Such a reaction norm model with unknown covariates and heterogeneous residual variances...... across herds was fitted to milk, protein, and fat yield of first-lactation Danish Holstein cows to investigate the presence of GxE. Data included 188,502 first test-day records from 299 herds and 3,775 herd-years in a time period ranging from 1991 to 2003. Variance components and breeding values were...

Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression.

Science.gov (United States)

Ding, A Adam; Wu, Hulin

2014-10-01

We propose a new method to use a constrained local polynomial regression to estimate the unknown parameters in ordinary differential equation models with a goal of improving the smoothing-based two-stage pseudo-least squares estimate. The equation constraints are derived from the differential equation model and are incorporated into the local polynomial regression in order to estimate the unknown parameters in the differential equation model. We also derive the asymptotic bias and variance of the proposed estimator. Our simulation studies show that our new estimator is clearly better than the pseudo-least squares estimator in estimation accuracy with a small price of computational cost. An application example on immune cell kinetics and trafficking for influenza infection further illustrates the benefits of the proposed new method.

Using an epiphytic moss to identify previously unknown sources of atmospheric cadmium pollution.

Science.gov (United States)

Donovan, Geoffrey H; Jovan, Sarah E; Gatziolis, Demetrios; Burstyn, Igor; Michael, Yvonne L; Amacher, Michael C; Monleon, Vicente J

2016-07-15

Urban networks of air-quality monitors are often too widely spaced to identify sources of air pollutants, especially if they do not disperse far from emission sources. The objectives of this study were to test the use of moss bio-indicators to develop a fine-scale map of atmospherically-derived cadmium and to identify the sources of cadmium in a complex urban setting. We collected 346 samples of the moss Orthotrichum lyellii from deciduous trees in December, 2013 using a modified randomized grid-based sampling strategy across Portland, Oregon. We estimated a spatial linear model of moss cadmium levels and predicted cadmium on a 50m grid across the city. Cadmium levels in moss were positively correlated with proximity to two stained-glass manufacturers, proximity to the Oregon-Washington border, and percent industrial land in a 500m buffer, and negatively correlated with percent residential land in a 500m buffer. The maps showed very high concentrations of cadmium around the two stained-glass manufacturers, neither of which were known to environmental regulators as cadmium emitters. In addition, in response to our findings, the Oregon Department of Environmental Quality placed an instrumental monitor 120m from the larger stained-glass manufacturer in October, 2015. The monthly average atmospheric cadmium concentration was 29.4ng/m(3), which is 49 times higher than Oregon's benchmark of 0.6ng/m(3), and high enough to pose a health risk from even short-term exposure. Both stained-glass manufacturers voluntarily stopped using cadmium after the monitoring results were made public, and the monthly average cadmium levels precipitously dropped to 1.1ng/m(3) for stained-glass manufacturer #1 and 0.67ng/m(3) for stained-glass manufacturer #2. Published by Elsevier B.V.

Using Conductivity Measurements to Determine the Identities and Concentrations of Unknown Acids: An Inquiry Laboratory Experiment

Science.gov (United States)

Smith, K. Christopher; Garza, Ariana

2015-01-01

This paper describes a student designed experiment using titrations involving conductivity measurements to identify unknown acids as being either HCl or H[subscript 2]SO[subscript 4], and to determine the concentrations of the acids, thereby improving the utility of standard acid-base titrations. Using an inquiry context, students gain experience…

Determination Of Adaptive Control Parameter Using Fuzzy Logic Controller

Directory of Open Access Journals (Sweden)

Omur Can Ozguney

2017-08-01

Full Text Available The robot industry has developed along with the increasing the use of robots in industry. This has led to increase the studies on robots. The most important part of these studies is that the robots must be work with minimum tracking trajectory error. But it is not easy for robots to track the desired trajectory because of the external disturbances and parametric uncertainty. Therefore adaptive and robust controllers are used to decrease tracking error. The aim of this study is to increase the tracking performance of the robot and minimize the trajectory tracking error. For this purpose adaptive control law for robot manipulator is identified and fuzzy logic controller is applied to find the accurate values for adaptive control parameter. Based on the Lyapunov theory stability of the uncertain system is guaranteed. In this study robot parameters are assumed to be unknown. This controller is applied to a robot model and the results of simulations are given. Controller with fuzzy logic and without fuzzy logic are compared with each other. Simulation results show that the fuzzy logic controller has improved the results.

Classification of Unknown Thermocouple Types Using Similarity Factor Measurement

Directory of Open Access Journals (Sweden)

Seshu K. DAMARLA

2011-01-01

Full Text Available In contrast to classification using PCA method, a new methodology is proposed for type identification of unknown thermocouple. The new methodology is based on calculating the degree of similarity between two multivariate datasets using two types of similarity factors. One similarity factor is based on principle component analysis and the angles between the principle component subspaces while the other is based on the Mahalanobis distance between the datasets. Datasets containing thermo-emfs against given temperature ranges are formed for each type of thermocouple (e.g. J, K, S, T, R, E, B and N type by experimentation are considered as reference datasets. Datasets corresponding to unknown type are captured. Similarity factor between the datasets one of which being the unknown type and the other being each known type are compared. When maximum similarity factor occurs, then the class of unknown type is allocated to that of known type.

Characterization of unknown genetic modifications using high throughput sequencing and computational subtraction

Directory of Open Access Journals (Sweden)

Butenko Melinka A

2009-10-01

Full Text Available Abstract Background When generating a genetically modified organism (GMO, the primary goal is to give a target organism one or several novel traits by using biotechnology techniques. A GMO will differ from its parental strain in that its pool of transcripts will be altered. Currently, there are no methods that are reliably able to determine if an organism has been genetically altered if the nature of the modification is unknown. Results We show that the concept of computational subtraction can be used to identify transgenic cDNA sequences from genetically modified plants. Our datasets include 454-type sequences from a transgenic line of Arabidopsis thaliana and published EST datasets from commercially relevant species (rice and papaya. Conclusion We believe that computational subtraction represents a powerful new strategy for determining if an organism has been genetically modified as well as to define the nature of the modification. Fewer assumptions have to be made compared to methods currently in use and this is an advantage particularly when working with unknown GMOs.

A Sparse Bayesian Learning Algorithm With Dictionary Parameter Estimation

DEFF Research Database (Denmark)

Hansen, Thomas Lundgaard; Badiu, Mihai Alin; Fleury, Bernard Henri

2014-01-01

This paper concerns sparse decomposition of a noisy signal into atoms which are specified by unknown continuous-valued parameters. An example could be estimation of the model order, frequencies and amplitudes of a superposition of complex sinusoids. The common approach is to reduce the continuous...

Unifying parameter estimation and the Deutsch-Jozsa algorithm for continuous variables

International Nuclear Information System (INIS)

Zwierz, Marcin; Perez-Delgado, Carlos A.; Kok, Pieter

2010-01-01

We reveal a close relationship between quantum metrology and the Deutsch-Jozsa algorithm on continuous-variable quantum systems. We develop a general procedure, characterized by two parameters, that unifies parameter estimation and the Deutsch-Jozsa algorithm. Depending on which parameter we keep constant, the procedure implements either the parameter-estimation protocol or the Deutsch-Jozsa algorithm. The parameter-estimation part of the procedure attains the Heisenberg limit and is therefore optimal. Due to the use of approximate normalizable continuous-variable eigenstates, the Deutsch-Jozsa algorithm is probabilistic. The procedure estimates a value of an unknown parameter and solves the Deutsch-Jozsa problem without the use of any entanglement.

Surgeon Reported Outcome Measure for Spine Trauma an International Expert Survey Identifying Parameters Relevant for The Outcome of Subaxial Cervical Spine Injuries

NARCIS (Netherlands)

Sadiqi, Said; Verlaan, Jorrit Jan; Lehr, A. M.; Dvorak, Marcel F.; Kandziora, Frank; Rajasekaran, S.; Schnake, Klaus J.; Vaccaro, Alexander R.; Oner, F. C.

2016-01-01

STUDY DESIGN.: International web-based survey OBJECTIVE.: To identify clinical and radiological parameters that spine surgeons consider most relevant when evaluating clinical and functional outcomes of subaxial cervical spine trauma patients. SUMMARY OF BACKGROUND DATA.: While an outcome instrument

Hyperfamiliarity for unknown faces after left lateral temporo-occipital venous infarction: a double dissociation with prosopagnosia.

Science.gov (United States)

Vuilleumier, Patrik; Mohr, Christine; Valenza, Nathalie; Wetzel, Corinne; Landis, Theodor

2003-04-01

Right hemisphere dominance in face processing is well established and unilateral right inferior temporo-occipital damage can result in prosopagnosia. Here, we describe a 21-year-old right-handed woman with acute impairment in face recognition that selectively concerned unfamiliar faces, following a focal left lateral temporo-occipital venous infarct. She was severely impaired in discerning that unknown people seen in everyday life were unfamiliar, although she had no difficulty recognizing familiar people. Thus, she had no prosopagnosia, but abnormal 'hyperfamiliarity' for unknown faces. Her difficulty was not accompanied by delusions or deficits in discrimination, identification or memory for faces. Standard neuropsychological testing showed that her recognition of familiar faces was entirely normal. By contrast, her sense of personally knowing faces was severely impaired when unknown faces evoked weak signals of familiarity based on spurious cues, to the extent that she would misattribute fame to faces that were unknown but to which she had been incidentally exposed on a prior occasion. Priming experiments also revealed that, unlike normal subjects, she made familiarity judgements without accessing semantic identity representations. Moreover, in face recognition tests, she generally showed bias in that she relied more on right-hemisphere strategies to identify global traits and less on left-hemisphere processes compared with healthy subjects. This case provides novel evidence for a differential contribution of the two hemispheres to face recognition. Hyperfamiliarity for unknown faces might arise from an imbalance between reciprocal hemispheric functions in face recognition, with relative hypoactivation of left hemisphere processes but hyperactivation of right-hemisphere processes for retrieving stored associations about people, linking seen faces to representations of affective and personal relevance. Hence, abnormal bias in attributing some personal meaning to

Multifocal, chronic osteomyelitis of unknown etiology

International Nuclear Information System (INIS)

Kozlowski, K.; Beluffi, G.; Feltham, C.; James, M.; Nespoli, L.; Tamaela, L.; Pavia Univ.; Municipal Hospital, Nelson; Medical School, Jakarta

1985-01-01

Four cases of multifocal osteomyelitis of unknown origin in childhood are reported. The variable clinical and radiographic appearances of the disease are illustrated and the diagnostic difficulties in the early stages of the disease are stressed. (orig.) [de

Time-Varying FOPDT Modeling and On-line Parameter Identification

DEFF Research Database (Denmark)

Yang, Zhenyu; Sun, Zhen

2013-01-01

on the Mixed-Integer-Nonlinear Programming, Least-Mean-Square and sliding window techniques. The proposed approaches can simultaneously estimate the time-dependent system parameters, as well as the unknown disturbance input if it is the case, in an on-line manner. The proposed concepts and algorithms...

Genetic algorithm approach to thin film optical parameters determination

International Nuclear Information System (INIS)

Jurecka, S.; Jureckova, M.; Muellerova, J.

2003-01-01

Optical parameters of thin film are important for several optical and optoelectronic applications. In this work the genetic algorithm proposed to solve optical parameters of thin film values. The experimental reflectance is modelled by the Forouhi - Bloomer dispersion relations. The refractive index, the extinction coefficient and the film thickness are the unknown parameters in this model. Genetic algorithm use probabilistic examination of promissing areas of the parameter space. It creates a population of solutions based on the reflectance model and then operates on the population to evolve the best solution by using selection, crossover and mutation operators on the population individuals. The implementation of genetic algorithm method and the experimental results are described too (Authors)

A novel KFCM based fault diagnosis method for unknown faults in satellite reaction wheels.

Science.gov (United States)

Hu, Di; Sarosh, Ali; Dong, Yun-Feng

2012-03-01

Reaction wheels are one of the most critical components of the satellite attitude control system, therefore correct diagnosis of their faults is quintessential for efficient operation of these spacecraft. The known faults in any of the subsystems are often diagnosed by supervised learning algorithms, however, this method fails to work correctly when a new or unknown fault occurs. In such cases an unsupervised learning algorithm becomes essential for obtaining the correct diagnosis. Kernel Fuzzy C-Means (KFCM) is one of the unsupervised algorithms, although it has its own limitations; however in this paper a novel method has been proposed for conditioning of KFCM method (C-KFCM) so that it can be effectively used for fault diagnosis of both known and unknown faults as in satellite reaction wheels. The C-KFCM approach involves determination of exact class centers from the data of known faults, in this way discrete number of fault classes are determined at the start. Similarity parameters are derived and determined for each of the fault data point. Thereafter depending on the similarity threshold each data point is issued with a class label. The high similarity points fall into one of the 'known-fault' classes while the low similarity points are labeled as 'unknown-faults'. Simulation results show that as compared to the supervised algorithm such as neural network, the C-KFCM method can effectively cluster historical fault data (as in reaction wheels) and diagnose the faults to an accuracy of more than 91%. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Modulating Function-Based Method for Parameter and Source Estimation of Partial Differential Equations

KAUST Repository

Asiri, Sharefa M.

2017-10-08

Partial Differential Equations (PDEs) are commonly used to model complex systems that arise for example in biology, engineering, chemistry, and elsewhere. The parameters (or coefficients) and the source of PDE models are often unknown and are estimated from available measurements. Despite its importance, solving the estimation problem is mathematically and numerically challenging and especially when the measurements are corrupted by noise, which is often the case. Various methods have been proposed to solve estimation problems in PDEs which can be classified into optimization methods and recursive methods. The optimization methods are usually heavy computationally, especially when the number of unknowns is large. In addition, they are sensitive to the initial guess and stop condition, and they suffer from the lack of robustness to noise. Recursive methods, such as observer-based approaches, are limited by their dependence on some structural properties such as observability and identifiability which might be lost when approximating the PDE numerically. Moreover, most of these methods provide asymptotic estimates which might not be useful for control applications for example. An alternative non-asymptotic approach with less computational burden has been proposed in engineering fields based on the so-called modulating functions. In this dissertation, we propose to mathematically and numerically analyze the modulating functions based approaches. We also propose to extend these approaches to different situations. The contributions of this thesis are as follows. (i) Provide a mathematical analysis of the modulating function-based method (MFBM) which includes: its well-posedness, statistical properties, and estimation errors. (ii) Provide a numerical analysis of the MFBM through some estimation problems, and study the sensitivity of the method to the modulating functions\\' parameters. (iii) Propose an effective algorithm for selecting the method\\'s design parameters

Protocol for counterfactually transporting an unknown qubit

Directory of Open Access Journals (Sweden)

Hatim eSalih

2016-01-01

Full Text Available Quantum teleportation circumvents the uncertainty principle using dual channels: a quantum one consisting of previously-shared entanglement, and a classical one, together allowing the disembodied transport of an unknown quantum state over distance. It has recently been shown that a classical bit can be counterfactually communicated between two parties in empty space, Alice and Bob. Here, by using our dual version of the chained quantum Zeno effect to achieve a counterfactual CNOT gate, we propose a protocol for transporting an unknown qubit counterfactually, that is without any physical particles travelling between Alice and Bob—no classical channel and no previously-shared entanglement.

Challenges of the Unknown: Clinical Application of Microbial Metagenomics

Directory of Open Access Journals (Sweden)

Graham Rose

2015-01-01

Full Text Available Availability of fast, high throughput and low cost whole genome sequencing holds great promise within public health microbiology, with applications ranging from outbreak detection and tracking transmission events to understanding the role played by microbial communities in health and disease. Within clinical metagenomics, identifying microorganisms from a complex and host enriched background remains a central computational challenge. As proof of principle, we sequenced two metagenomic samples, a known viral mixture of 25 human pathogens and an unknown complex biological model using benchtop technology. The datasets were then analysed using a bioinformatic pipeline developed around recent fast classification methods. A targeted approach was able to detect 20 of the viruses against a background of host contamination from multiple sources and bacterial contamination. An alternative untargeted identification method was highly correlated with these classifications, and over 1,600 species were identified when applied to the complex biological model, including several species captured at over 50% genome coverage. In summary, this study demonstrates the great potential of applying metagenomics within the clinical laboratory setting and that this can be achieved using infrastructure available to nondedicated sequencing centres.

An Advanced Coupled Genetic Algorithm for Identifying Unknown Moving Loads on Bridge Decks

Directory of Open Access Journals (Sweden)

Sang-Youl Lee

2014-01-01

Full Text Available This study deals with an inverse method to identify moving loads on bridge decks using the finite element method (FEM and a coupled genetic algorithm (c-GA. We developed the inverse technique using a coupled genetic algorithm that can make global solution searches possible as opposed to classical gradient-based optimization techniques. The technique described in this paper allows us to not only detect the weight of moving vehicles but also find their moving velocities. To demonstrate the feasibility of the method, the algorithm is applied to a bridge deck model with beam elements. In addition, 1D and 3D finite element models are simulated to study the influence of measurement errors and model uncertainty between numerical and real structures. The results demonstrate the excellence of the method from the standpoints of computation efficiency and avoidance of premature convergence.

Fever of unknown origin

International Nuclear Information System (INIS)

Misaki, Takashi; Matsui, Akira; Tanaka, Fumiko; Okuno, Yoshishige; Mitsumori, Michihide; Torizuka, Tatsurou; Dokoh, Shigeharu; Hayakawa, Katsumi; Shimbo, Shin-ichirou

1990-01-01

Gallium-67 scintigraphy is a commonly performed imaging modality in deteting pyrogenic lesions in cases of long-standing inexplainable fever. To re-evaluate the significance of gallium imaging in such cases, a retrospective review was made of 56 scans performed in febrile patients in whom sufficient clinical and laboratory findings were obtained. Gallium scans were true positive in 30 patients, false positive in 3, true negative in 19, and false negative in 4. In the group of true positive, local inflammatory lesions were detected in 23 patients with a final diagnosis of lung tuberculosis, urinary tract infection, and inflammatory joint disease. Abnormal gallium accumulation, as shown in the other 7 patients, provided clues to the diagnosis of generalized disorders, such as hematological malignancies (n=3), systemic autoimmune diseases (n=3), and severe infectious mononucleosis (n=one). In the group of false positive, gallium imaging revealed intestinal excretion of gallium in 2 patients and physiological pulmonary hilar accumulation in one. In the true negative group of 19 patients, fever of unknown origin was resolved spontaneously in 12 patients, and with antibiotics and corticosteroids in 2 and 5 patients, respectively. Four patients having false negative scans were finally diagnosed as having urinary tract infection (n=2), bacterial meningitis (n=one), and polyarteritis (n=one). Gallium imaging would remain the technique of choice in searching for origin of unknown fever. It may also be useful for early diagnosis of systemic disease, as well as focal inflammation. (N.K.)

N-grams Based Supervised Machine Learning Model for Mobile Agent Platform Protection against Unknown Malicious Mobile Agents

Directory of Open Access Journals (Sweden)

Pallavi Bagga

2017-12-01

Full Text Available From many past years, the detection of unknown malicious mobile agents before they invade the Mobile Agent Platform has been the subject of much challenging activity. The ever-growing threat of malicious agents calls for techniques for automated malicious agent detection. In this context, the machine learning (ML methods are acknowledged more effective than the Signature-based and Behavior-based detection methods. Therefore, in this paper, the prime contribution has been made to detect the unknown malicious mobile agents based on n-gram features and supervised ML approach, which has not been done so far in the sphere of the Mobile Agents System (MAS security. To carry out the study, the n-grams ranging from 3 to 9 are extracted from a dataset containing 40 malicious and 40 non-malicious mobile agents. Subsequently, the classification is performed using different classifiers. A nested 5-fold cross validation scheme is employed in order to avoid the biasing in the selection of optimal parameters of classifier. The observations of extensive experiments demonstrate that the work done in this paper is suitable for the task of unknown malicious mobile agent detection in a Mobile Agent Environment, and also adds the ML in the interest list of researchers dealing with MAS security.

Cancer of unknown primary origin: a case report

Directory of Open Access Journals (Sweden)

Elisa De Carlo

2013-03-01

Full Text Available Carcinoma of unknown primary origin (CUP accounts for 2-10% of all malignancies. The apparent absence of the primary tumour, the development of early, uncommon systemic metastases and the resistance to therapy and poor prognosis are hallmarks of this heterogeneous clinical entity and are a challenge for physicians. The diagnostic workup of patients with CUP includes a large amount of histopathological examination, as well as the use of imaging techniques that often fail to identify the primary tumour. Therefore, the optimal workup and treatment for these patients remains to be determined. Molecular diagnostic tools, such as DNA microarray analysis, could help in the search for "lost" CUP origin and guide the further treatment approach. We report the case of a 66-year-old man, with mediastinal lymph nodes metastasis of carcinoma and neurological syndrome with diplopia and balance disorders, in which many exams have been performed without finding the primary tumour.

State and parameter estimation in nonlinear systems as an optimal tracking problem

International Nuclear Information System (INIS)

Creveling, Daniel R.; Gill, Philip E.; Abarbanel, Henry D.I.

2008-01-01

In verifying and validating models of nonlinear processes it is important to incorporate information from observations in an efficient manner. Using the idea of synchronization of nonlinear dynamical systems, we present a framework for connecting a data signal with a model in a way that minimizes the required coupling yet allows the estimation of unknown parameters in the model. The need to evaluate unknown parameters in models of nonlinear physical, biophysical, and engineering systems occurs throughout the development of phenomenological or reduced models of dynamics. Our approach builds on existing work that uses synchronization as a tool for parameter estimation. We address some of the critical issues in that work and provide a practical framework for finding an accurate solution. In particular, we show the equivalence of this problem to that of tracking within an optimal control framework. This equivalence allows the application of powerful numerical methods that provide robust practical tools for model development and validation

The Roles of Feedback and Feedforward as Humans Learn to Control Unknown Dynamic Systems.

Science.gov (United States)

Zhang, Xingye; Wang, Shaoqian; Hoagg, Jesse B; Seigler, T Michael

2018-02-01

We present results from an experiment in which human subjects interact with an unknown dynamic system 40 times during a two-week period. During each interaction, subjects are asked to perform a command-following (i.e., pursuit tracking) task. Each subject's performance at that task improves from the first trial to the last trial. For each trial, we use subsystem identification to estimate each subject's feedforward (or anticipatory) control, feedback (or reactive) control, and feedback time delay. Over the 40 trials, the magnitudes of the identified feedback controllers and the identified feedback time delays do not change significantly. In contrast, the identified feedforward controllers do change significantly. By the last trial, the average identified feedforward controller approximates the inverse of the dynamic system. This observation provides evidence that a fundamental component of human learning is updating the anticipatory control until it models the inverse dynamics.

Whole body 18F-deoxyglucose positron emission tomography in detecting the primary focus of metastatic cancer with an unknown primary

International Nuclear Information System (INIS)

Chen Yingrui; Li Weixiong; Gu Meixin; Zhan Zhiguang; Zeng Zijun

2002-01-01

Objective: To evaluate the value of 18 F-deoxyglucose (FDG) positron emission tomography (PET) in detecting the primary focus of metastatic cancer with an unknown primary. Methods: Twenty-nine patients with various histological types of metastases from an unknown primary after extensive conventional diagnostic work-up were studied. After intravenous 370 MBq FDG, whole body scan was made 50 minutes after injection. The results of FDG PET were compared with those of CT or MRI. Results: With FDG PET, the primary tumors were identified in 13 patients and confirmed by pathology. The corresponding detection rate was 44.8% (13/29) as compared with 27.6% (8/29) by CT or MRI. In addition, 26 metastases were discovered by FDG PET whole body imaging but only 13 were found by CT or MRI. During 2-13 months' follow-up, the mortality rates were 15.4%(2/13) and 42.9%(6/14) for patients with the primary tumor identified or unidentified. Conclusions: FDG PET is valuable in staging, selecting appropriate treatment protocol and predicting prognosis for patients suffering from metastatic cancers with an unknown primary

Typical parameters of the plasma chemical similarity in non-isothermal reactive plasmas

International Nuclear Information System (INIS)

Gundermann, S.; Jacobs, H.; Miethke, F.; Rutsher, A.; Wagner, H.E.

1996-01-01

The substance of physical similarity principles is contained in parameters which govern the comparison of different realizations of a model device. Because similarity parameters for non-isothermal plasma chemical reactors are unknown to a great extent, an analysis of relevant equations is given together with some experimental results. Modelling of the reactor and experimental results for the ozone synthesis are presented

A Multidisciplinary Investigation of a Polycythemia Vera Cancer Cluster of Unknown Origin

Science.gov (United States)

Seaman, Vincent; Dearwent, Steve M; Gable, Debra; Lewis, Brian; Metcalf, Susan; Orloff, Ken; Tierney, Bruce; Zhu, Jane; Logue, James; Marchetto, David; Ostroff, Stephen; Hoffman, Ronald; Xu, Mingjiang; Carey, David; Erlich, Porat; Gerhard, Glenn; Roda, Paul; Iannuzzo, Joseph; Lewis, Robert; Mellow, John; Mulvihill, Linda; Myles, Zachary; Wu, Manxia; Frank, Arthur; Gross-Davis, Carol Ann; Klotz, Judith; Lynch, Adam; Weissfeld, Joel; Weinberg, Rona; Cole, Henry

2010-01-01

Cancer cluster investigations rarely receive significant public health resource allocations due to numerous inherent challenges and the limited success of past efforts. In 2008, a cluster of polycythemia vera, a rare blood cancer with unknown etiology, was identified in northeast Pennsylvania. A multidisciplinary group of federal and state agencies, academic institutions, and local healthcare providers subsequently developed a multifaceted research portfolio designed to better understand the cause of the cluster. This research agenda represents a unique and important opportunity to demonstrate that cancer cluster investigations can produce desirable public health and scientific outcomes when necessary resources are available. PMID:20617023

Quantum jointly assisted cloning of an unknown three-dimensional equatorial state

Science.gov (United States)

Ma, Peng-Cheng; Chen, Gui-Bin; Li, Xiao-Wei; Zhan, You-Bang

2018-02-01

We present two schemes for perfectly cloning an unknown single-qutrit equatorial state with assistance from two and N state preparers, respectively. In the first scheme, the sender wishes to teleport an unknown single-qutrit equatorial state from two state preparers to a remote receiver, and then to create a perfect copy of the unknown state at her location. The scheme consists of two stages. The first stage of the scheme requires the usual teleportation. In the second stage, to help the sender realize the quantum cloning, two state preparers perform single-qutrit projective measurements on their own qutrits from the sender, then the sender can acquire a perfect copy of the unknown state. It is shown that, only if the two state preparers collaborate with each other, the sender can create a copy of the unknown state by means of some appropriate unitary operations. In the second scheme, we generalized the jointly assisted cloning in the first scheme to the case of N state prepares. In the present schemes, the total probability of success for assisted cloning of a perfect copy of the unknown state can reach 1.

Parameter identification of piezoelectric hysteresis model based on improved artificial bee colony algorithm

Science.gov (United States)

Wang, Geng; Zhou, Kexin; Zhang, Yeming

2018-04-01

The widely used Bouc-Wen hysteresis model can be utilized to accurately simulate the voltage-displacement curves of piezoelectric actuators. In order to identify the unknown parameters of the Bouc-Wen model, an improved artificial bee colony (IABC) algorithm is proposed in this paper. A guiding strategy for searching the current optimal position of the food source is proposed in the method, which can help balance the local search ability and global exploitation capability. And the formula for the scout bees to search for the food source is modified to increase the convergence speed. Some experiments were conducted to verify the effectiveness of the IABC algorithm. The results show that the identified hysteresis model agreed well with the actual actuator response. Moreover, the identification results were compared with the standard particle swarm optimization (PSO) method, and it can be seen that the search performance in convergence rate of the IABC algorithm is better than that of the standard PSO method.

Application of Parallel Hierarchical Matrices in Spatial Statistics and Parameter Identification

KAUST Repository

Litvinenko, Alexander

2018-04-20

Parallel H-matrices in spatial statistics 1. Motivation: improve statistical model 2. Tools: Hierarchical matrices [Hackbusch 1999] 3. Matern covariance function and joint Gaussian likelihood 4. Identification of unknown parameters via maximizing Gaussian log-likelihood 5. Implementation with HLIBPro

A distributed approach for parameters estimation in System Biology models

International Nuclear Information System (INIS)

Mosca, E.; Merelli, I.; Alfieri, R.; Milanesi, L.

2009-01-01

Due to the lack of experimental measurements, biological variability and experimental errors, the value of many parameters of the systems biology mathematical models is yet unknown or uncertain. A possible computational solution is the parameter estimation, that is the identification of the parameter values that determine the best model fitting respect to experimental data. We have developed an environment to distribute each run of the parameter estimation algorithm on a different computational resource. The key feature of the implementation is a relational database that allows the user to swap the candidate solutions among the working nodes during the computations. The comparison of the distributed implementation with the parallel one showed that the presented approach enables a faster and better parameter estimation of systems biology models.

Quasi-Maximum Likelihood Estimation and Bootstrap Inference in Fractional Time Series Models with Heteroskedasticity of Unknown Form

DEFF Research Database (Denmark)

Cavaliere, Giuseppe; Nielsen, Morten Ørregaard; Taylor, Robert

We consider the problem of conducting estimation and inference on the parameters of univariate heteroskedastic fractionally integrated time series models. We first extend existing results in the literature, developed for conditional sum-of squares estimators in the context of parametric fractional...... time series models driven by conditionally homoskedastic shocks, to allow for conditional and unconditional heteroskedasticity both of a quite general and unknown form. Global consistency and asymptotic normality are shown to still obtain; however, the covariance matrix of the limiting distribution...... of the estimator now depends on nuisance parameters derived both from the weak dependence and heteroskedasticity present in the shocks. We then investigate classical methods of inference based on the Wald, likelihood ratio and Lagrange multiplier tests for linear hypotheses on either or both of the long and short...

Iterative Selection of Unknown Weights in Direct Weight Optimization Identification

Directory of Open Access Journals (Sweden)

Xiao Xuan

2014-01-01

Full Text Available To the direct weight optimization identification of the nonlinear system, we add some linear terms about input sequences in the former linear affine function so as to approximate the nonlinear property. To choose the two classes of unknown weights in the more linear terms, this paper derives the detailed process on how to choose these unknown weights from theoretical analysis and engineering practice, respectively, and makes sure of their key roles between the unknown weights. From the theoretical analysis, the added unknown weights’ auxiliary role can be known in the whole process of approximating the nonlinear system. From the practical analysis, we learn how to transform one complex optimization problem to its corresponding common quadratic program problem. Then, the common quadratic program problem can be solved by the basic interior point method. Finally, the efficiency and possibility of the proposed strategies can be confirmed by the simulation results.

Effect of 4-nonylphenol on the sperm dynamic parameters ...

African Journals Online (AJOL)

4-Nonylphenol (NP) is a compound that causes endocrine disruption and affects sperm quality of mammals and fish. However, the effects of NP on the sperm and fertilization rate of amphibians remain unknown. This study investigates the in vivo and in vitro effects of NP on the sperm dynamic parameters and fertilization ...

Parameters estimation online for Lorenz system by a novel quantum-behaved particle swarm optimization

International Nuclear Information System (INIS)

Gao Fei; Tong Hengqing; Li Zhuoqiu

2008-01-01

This paper proposes a novel quantum-behaved particle swarm optimization (NQPSO) for the estimation of chaos' unknown parameters by transforming them into nonlinear functions' optimization. By means of the techniques in the following three aspects: contracting the searching space self-adaptively; boundaries restriction strategy; substituting the particles' convex combination for their centre of mass, this paper achieves a quite effective search mechanism with fine equilibrium between exploitation and exploration. Details of applying the proposed method and other methods into Lorenz systems are given, and experiments done show that NQPSO has better adaptability, dependability and robustness. It is a successful approach in unknown parameter estimation online especially in the cases with white noises

Large-scale parameter extraction in electrocardiology models through Born approximation

KAUST Repository

He, Yuan

2012-12-04

One of the main objectives in electrocardiology is to extract physical properties of cardiac tissues from measured information on electrical activity of the heart. Mathematically, this is an inverse problem for reconstructing coefficients in electrocardiology models from partial knowledge of the solutions of the models. In this work, we consider such parameter extraction problems for two well-studied electrocardiology models: the bidomain model and the FitzHugh-Nagumo model. We propose a systematic reconstruction method based on the Born approximation of the original nonlinear inverse problem. We describe a two-step procedure that allows us to reconstruct not only perturbations of the unknowns, but also the backgrounds around which the linearization is performed. We show some numerical simulations under various conditions to demonstrate the performance of our method. We also introduce a parameterization strategy using eigenfunctions of the Laplacian operator to reduce the number of unknowns in the parameter extraction problem. © 2013 IOP Publishing Ltd.

Chaos Synchronization Based on Unknown Input Proportional Multiple-Integral Fuzzy Observer

Directory of Open Access Journals (Sweden)

T. Youssef

2013-01-01

Full Text Available This paper presents an unknown input Proportional Multiple-Integral Observer (PIO for synchronization of chaotic systems based on Takagi-Sugeno (TS fuzzy chaotic models subject to unmeasurable decision variables and unknown input. In a secure communication configuration, this unknown input is regarded as a message encoded in the chaotic system and recovered by the proposed PIO. Both states and outputs of the fuzzy chaotic models are subject to polynomial unknown input with kth derivative zero. Using Lyapunov stability theory, sufficient design conditions for synchronization are proposed. The PIO gains matrices are obtained by resolving linear matrix inequalities (LMIs constraints. Simulation results show through two TS fuzzy chaotic models the validity of the proposed method.

Outpatient endometrial aspiration: an alternative to methotrexate for pregnancy of unknown location.

Science.gov (United States)

Insogna, Iris G; Farland, Leslie V; Missmer, Stacey A; Ginsburg, Elizabeth S; Brady, Paula C

2017-08-01

Pregnancies of unknown location with abnormal beta-human chorionic gonadotropin trends are frequently treated as presumed ectopic pregnancies with methotrexate. Preliminary data suggest that outpatient endometrial aspiration may be an effective tool to diagnose pregnancy location, while also sparing women exposure to methotrexate. The purpose of this study was to evaluate the utility of an endometrial sampling protocol for the diagnosis of pregnancies of unknown location after in vitro fertilization. A retrospective cohort study of 14,505 autologous fresh and frozen in vitro fertilization cycles from October 2007 to September 2015 was performed; 110 patients were diagnosed with pregnancy of unknown location, defined as a positive beta-human chorionic gonadotropin without ultrasound evidence of intrauterine or ectopic pregnancy and an abnormal beta-human chorionic gonadotropin trend (location, failed intrauterine pregnancy was diagnosed in 46 patients (42%), and ectopic pregnancy was diagnosed in 64 patients (58%). Clinical variables that included fresh or frozen embryo transfer, day of embryo transfer, serum beta-human chorionic gonadotropin at the time of sampling, endometrial thickness, and presence of an adnexal mass were not significantly different between patients with failed intrauterine pregnancy or ectopic pregnancy. In patients with failed intrauterine pregnancy, 100% demonstrated adequate postsampling beta-human chorionic gonadotropin declines; villi were identified in just 46% (n=21 patients). Patients with failed intrauterine pregnancy had significantly shorter time to resolution (negative serum beta-human chorionic gonadotropin) after sampling compared with patients with ectopic pregnancy (12.6 vs 26.3 days; Plocation are spared methotrexate, with a shorter time to pregnancy resolution than those who receive methotrexate. Copyright © 2017 Elsevier Inc. All rights reserved.

Metastatic cancer of unknown primary in 21 dogs.

Science.gov (United States)

Rossi, F; Aresu, L; Vignoli, M; Buracco, P; Bettini, G; Ferro, S; Gattino, F; Ghiani, F; Costantino, R; Ressel, L; Bellei, E; Marconato, L

2015-03-01

The aim of this retrospective study was to describe clinical features, treatment and outcome of 21 dogs with metastatic cancer of unknown primary (MCUP), a biopsy-proven malignancy being diagnosed at a metastatic stage, in which the anatomical origin of the primary tumour cannot be detected. All dogs underwent total-body computed tomography. Signalment, type and duration of clinical signs, metastasis site, pathology results, treatment and outcome were recorded. Carcinoma was the most common diagnosis (57.1%), followed by sarcoma, melanoma and mast cell tumour. The median number of disease sites per dog was 2, with bones, lymph nodes, lungs and spleen being the most frequent metastatic locations. The median survival for all dogs was 30 days. Overall, a primary site was not identified in 20 (95.2%) dogs. MCUP encompasses a variety of different pathologic entities and harbours a poor prognosis. © 2013 Blackwell Publishing Ltd.

Joint estimation of the fractional differentiation orders and the unknown input for linear fractional non-commensurate system

KAUST Repository

Belkhatir, Zehor

2015-11-05

This paper deals with the joint estimation of the unknown input and the fractional differentiation orders of a linear fractional order system. A two-stage algorithm combining the modulating functions with a first-order Newton method is applied to solve this estimation problem. First, the modulating functions approach is used to estimate the unknown input for a given fractional differentiation orders. Then, the method is combined with a first-order Newton technique to identify the fractional orders jointly with the input. To show the efficiency of the proposed method, numerical examples illustrating the estimation of the neural activity, considered as input of a fractional model of the neurovascular coupling, along with the fractional differentiation orders are presented in both noise-free and noisy cases.

Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

Science.gov (United States)

Yang, Xiong; Liu, Derong; Wang, Ding

2014-03-01

In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

Morgellons disease: Analysis of a population with clinically confirmed microscopic subcutaneous fibers of unknown etiology

OpenAIRE

Savely, Virginia R; Stricker, Raphael B

2010-01-01

Virginia R Savely1, Raphael B Stricker21TBD Medical Associates, San Francisco, CA, USA; 2International Lyme and Associated Diseases Society, Bethesda, MD, USABackground: Morgellons disease is a controversial illness in which patients complain of stinging, burning, and biting sensations under the skin. Unusual subcutaneous fibers are the unique objective finding. The etiology of Morgellons disease is unknown, and diagnostic criteria have yet to be established. Our goal was to identify prevalen...

Statistical inference involving binomial and negative binomial parameters.

Science.gov (United States)

García-Pérez, Miguel A; Núñez-Antón, Vicente

2009-05-01

Statistical inference about two binomial parameters implies that they are both estimated by binomial sampling. There are occasions in which one aims at testing the equality of two binomial parameters before and after the occurrence of the first success along a sequence of Bernoulli trials. In these cases, the binomial parameter before the first success is estimated by negative binomial sampling whereas that after the first success is estimated by binomial sampling, and both estimates are related. This paper derives statistical tools to test two hypotheses, namely, that both binomial parameters equal some specified value and that both parameters are equal though unknown. Simulation studies are used to show that in small samples both tests are accurate in keeping the nominal Type-I error rates, and also to determine sample size requirements to detect large, medium, and small effects with adequate power. Additional simulations also show that the tests are sufficiently robust to certain violations of their assumptions.

Estimation of time- and state-dependent delays and other parameters in functional differential equations

Science.gov (United States)

Murphy, K. A.

1990-01-01

A parameter estimation algorithm is developed which can be used to estimate unknown time- or state-dependent delays and other parameters (e.g., initial condition) appearing within a nonlinear nonautonomous functional differential equation. The original infinite dimensional differential equation is approximated using linear splines, which are allowed to move with the variable delay. The variable delays are approximated using linear splines as well. The approximation scheme produces a system of ordinary differential equations with nice computational properties. The unknown parameters are estimated within the approximating systems by minimizing a least-squares fit-to-data criterion. Convergence theorems are proved for time-dependent delays and state-dependent delays within two classes, which say essentially that fitting the data by using approximations will, in the limit, provide a fit to the data using the original system. Numerical test examples are presented which illustrate the method for all types of delay.

Robust Fault Detection for Switched Fuzzy Systems With Unknown Input.

Science.gov (United States)

Han, Jian; Zhang, Huaguang; Wang, Yingchun; Sun, Xun

2017-10-03

This paper investigates the fault detection problem for a class of switched nonlinear systems in the T-S fuzzy framework. The unknown input is considered in the systems. A novel fault detection unknown input observer design method is proposed. Based on the proposed observer, the unknown input can be removed from the fault detection residual. The weighted H∞ performance level is considered to ensure the robustness. In addition, the weighted H₋ performance level is introduced, which can increase the sensibility of the proposed detection method. To verify the proposed scheme, a numerical simulation example and an electromechanical system simulation example are provided at the end of this paper.

Off-Policy Actor-Critic Structure for Optimal Control of Unknown Systems With Disturbances.

Science.gov (United States)

Song, Ruizhuo; Lewis, Frank L; Wei, Qinglai; Zhang, Huaguang

2016-05-01

An optimal control method is developed for unknown continuous-time systems with unknown disturbances in this paper. The integral reinforcement learning (IRL) algorithm is presented to obtain the iterative control. Off-policy learning is used to allow the dynamics to be completely unknown. Neural networks are used to construct critic and action networks. It is shown that if there are unknown disturbances, off-policy IRL may not converge or may be biased. For reducing the influence of unknown disturbances, a disturbances compensation controller is added. It is proven that the weight errors are uniformly ultimately bounded based on Lyapunov techniques. Convergence of the Hamiltonian function is also proven. The simulation study demonstrates the effectiveness of the proposed optimal control method for unknown systems with disturbances.

Identifying the optimal HVOF spray parameters to attain minimum porosity and maximum hardness in iron based amorphous metallic coatings

Directory of Open Access Journals (Sweden)

S. Vignesh

2017-04-01

Full Text Available Flow based Erosion – corrosion problems are very common in fluid handling equipments such as propellers, impellers, pumps in warships, submarine. Though there are many coating materials available to combat erosion–corrosion damage in the above components, iron based amorphous coatings are considered to be more effective to combat erosion–corrosion problems. High velocity oxy-fuel (HVOF spray process is considered to be a better process to coat the iron based amorphous powders. In this investigation, iron based amorphous metallic coating was developed on 316 stainless steel substrate using HVOF spray technique. Empirical relationships were developed to predict the porosity and micro hardness of iron based amorphous coating incorporating HVOF spray parameters such as oxygen flow rate, fuel flow rate, powder feed rate, carrier gas flow rate, and spray distance. Response surface methodology (RSM was used to identify the optimal HVOF spray parameters to attain coating with minimum porosity and maximum hardness.

Adaptively locating unknown steady states: Formalism and basin of attraction

International Nuclear Information System (INIS)

Wu, Yu; Lin, Wei

2011-01-01

The adaptive technique, which includes both dynamical estimators and coupling gains, has been recently verified to be practical for locating the unknown steady states numerically. This Letter, in the light of the center manifold theory for dynamical systems and the matrix spectrum principle, establishes an analytical formalism of this adaptive technique and reveals a connection between this technique and the original adaptive controller which includes only the dynamical estimator. More interestingly, in study of the well-known Lorenz system, the selections of the estimator parameters and initial values are found to be crucial to the successful application of the adaptive technique. Some Milnor-like basins of attraction with fractal structures are found quantitatively. All the results obtained in the Letter can be further extended to more general dynamical systems of higher dimensions. -- Highlights: → Establishing a new and rigorous formalism for the adaptive stabilization technique. → Showing a close connection between the adaptive technique and the original controller. → Providing feasible algorithms for simultaneous stabilization of multiple steady states. → Finding Milnor-like basins of attraction with fractal structures in adaptive control.

Modelling intelligence-led policing to identify its potential

NARCIS (Netherlands)

Hengst-Bruggeling, M. den; Graaf, H.A.L.M. de; Scheepstal, P.G.M. van

2014-01-01

lntelligence-led policing is a concept of policing that has been applied throughout the world. Despite some encouraging reports, the effect of intelligence-led policing is largely unknown. This paper presents a method with which it is possible to identify intelligence-led policing's potential to

Minimal-Approximation-Based Distributed Consensus Tracking of a Class of Uncertain Nonlinear Multiagent Systems With Unknown Control Directions.

Science.gov (United States)

Choi, Yun Ho; Yoo, Sung Jin

2017-03-28

A minimal-approximation-based distributed adaptive consensus tracking approach is presented for strict-feedback multiagent systems with unknown heterogeneous nonlinearities and control directions under a directed network. Existing approximation-based consensus results for uncertain nonlinear multiagent systems in lower-triangular form have used multiple function approximators in each local controller to approximate unmatched nonlinearities of each follower. Thus, as the follower's order increases, the number of the approximators used in its local controller increases. However, the proposed approach employs only one function approximator to construct the local controller of each follower regardless of the order of the follower. The recursive design methodology using a new error transformation is derived for the proposed minimal-approximation-based design. Furthermore, a bounding lemma on parameters of Nussbaum functions is presented to handle the unknown control direction problem in the minimal-approximation-based distributed consensus tracking framework and the stability of the overall closed-loop system is rigorously analyzed in the Lyapunov sense.

[Badminton--unknown sport].

Science.gov (United States)

Zekan-Petrinović, Lidija

2007-01-01

For a long time, badminton was considered to be only a slow and light game for children, a game that is played outdoors and is structurally undemanding.Today, it is not an unknown and unrecognised sport, especially after it was included into the Olympics Games in 1992. Badminton is one of the oldest sports in the world. It is suitable for all ages (for children and elderly equally), women and men and even handicapped persons. Beginners can start playing badminton matches early because the basics are learned quickly. As a recreational activity, badminton is very popular in Zagreb. In the last 10 years, a number of halls specialized for badminton or offering badminton as one of available sports activities have been opened in Zagreb. At present, there are over 70 professional playgrounds for training of top contestants but also for the citizens who can play recreational badminton.

Detection of viral sequence fragments of HIV-1 subfamilies yet unknown

Directory of Open Access Journals (Sweden)

Stanke Mario

2011-04-01

Full Text Available Abstract Background Methods of determining whether or not any particular HIV-1 sequence stems - completely or in part - from some unknown HIV-1 subtype are important for the design of vaccines and molecular detection systems, as well as for epidemiological monitoring. Nevertheless, a single algorithm only, the Branching Index (BI, has been developed for this task so far. Moving along the genome of a query sequence in a sliding window, the BI computes a ratio quantifying how closely the query sequence clusters with a subtype clade. In its current version, however, the BI does not provide predicted boundaries of unknown fragments. Results We have developed Unknown Subtype Finder (USF, an algorithm based on a probabilistic model, which automatically determines which parts of an input sequence originate from a subtype yet unknown. The underlying model is based on a simple profile hidden Markov model (pHMM for each known subtype and an additional pHMM for an unknown subtype. The emission probabilities of the latter are estimated using the emission frequencies of the known subtypes by means of a (position-wise probabilistic model for the emergence of new subtypes. We have applied USF to SIV and HIV-1 sequences formerly classified as having emerged from an unknown subtype. Moreover, we have evaluated its performance on artificial HIV-1 recombinants and non-recombinant HIV-1 sequences. The results have been compared with the corresponding results of the BI. Conclusions Our results demonstrate that USF is suitable for detecting segments in HIV-1 sequences stemming from yet unknown subtypes. Comparing USF with the BI shows that our algorithm performs as good as the BI or better.

Modulating Function-Based Method for Parameter and Source Estimation of Partial Differential Equations

KAUST Repository

Asiri, Sharefa M.

2017-01-01

Partial Differential Equations (PDEs) are commonly used to model complex systems that arise for example in biology, engineering, chemistry, and elsewhere. The parameters (or coefficients) and the source of PDE models are often unknown

Psychological profile: the problem of modeling the unknown criminal personality

Directory of Open Access Journals (Sweden)

Г. М. Гетьман

2013-10-01

Full Text Available The article investigates the problem of modeling an unknown person in the preparation of criminal psychological profile. Some approaches to the concept of "psychological profile" and "psychological portrait", in particular the proposed delineation of these terms. We consider the system steps in the development of the psychological profile of an unknown perpetrator.

Multifocal chronic osteomyelitis of unknown etiology

International Nuclear Information System (INIS)

Kozlowski, K.; Masel, J.; Harbison, S.; Yu, J.; Royal Brisbane Children Hospital; Regional Hospital Bowral

1983-01-01

Five cases of chronic, inflammatory, multifocal bone lesions of unknown etiology are reported. Although bone biopsy confirmed osteomyelitis in each case in none of them were organisms found inspite of an extensive work up. Different clinical course of the disease reflects different aetiology in respective cases. These cases present changing aspects of osteomyelitis emerging since introduction of antibiotics. (orig.)

Probabilistic and Nonprobabilistic Sensitivity Analyses of Uncertain Parameters

Directory of Open Access Journals (Sweden)

Sheng-En Fang

2014-01-01

Full Text Available Parameter sensitivity analyses have been widely applied to industrial problems for evaluating parameter significance, effects on responses, uncertainty influence, and so forth. In the interest of simple implementation and computational efficiency, this study has developed two sensitivity analysis methods corresponding to the situations with or without sufficient probability information. The probabilistic method is established with the aid of the stochastic response surface and the mathematical derivation proves that the coefficients of first-order items embody the parameter main effects on the response. Simultaneously, a nonprobabilistic interval analysis based method is brought forward for the circumstance when the parameter probability distributions are unknown. The two methods have been verified against a numerical beam example with their accuracy compared to that of a traditional variance-based method. The analysis results have demonstrated the reliability and accuracy of the developed methods. And their suitability for different situations has also been discussed.

Bayesian uncertainty analysis for complex systems biology models: emulation, global parameter searches and evaluation of gene functions.

Science.gov (United States)

Vernon, Ian; Liu, Junli; Goldstein, Michael; Rowe, James; Topping, Jen; Lindsey, Keith

2018-01-02

Many mathematical models have now been employed across every area of systems biology. These models increasingly involve large numbers of unknown parameters, have complex structure which can result in substantial evaluation time relative to the needs of the analysis, and need to be compared to observed data of various forms. The correct analysis of such models usually requires a global parameter search, over a high dimensional parameter space, that incorporates and respects the most important sources of uncertainty. This can be an extremely difficult task, but it is essential for any meaningful inference or prediction to be made about any biological system. It hence represents a fundamental challenge for the whole of systems biology. Bayesian statistical methodology for the uncertainty analysis of complex models is introduced, which is designed to address the high dimensional global parameter search problem. Bayesian emulators that mimic the systems biology model but which are extremely fast to evaluate are embeded within an iterative history match: an efficient method to search high dimensional spaces within a more formal statistical setting, while incorporating major sources of uncertainty. The approach is demonstrated via application to a model of hormonal crosstalk in Arabidopsis root development, which has 32 rate parameters, for which we identify the sets of rate parameter values that lead to acceptable matches between model output and observed trend data. The multiple insights into the model's structure that this analysis provides are discussed. The methodology is applied to a second related model, and the biological consequences of the resulting comparison, including the evaluation of gene functions, are described. Bayesian uncertainty analysis for complex models using both emulators and history matching is shown to be a powerful technique that can greatly aid the study of a large class of systems biology models. It both provides insight into model behaviour

Celiac Disease Presenting as Fever of Unknown Origin

Directory of Open Access Journals (Sweden)

Megan J. Cooney

2013-01-01

Full Text Available Celiac disease (CD is a common autoimmune enteropathy that occurs, in affected individuals, with exposure to gluten in the diet and improves with removal of dietary gluten. Although CD is readily considered in patients with classical presentations of the disease, atypical manifestations may be the only presenting symptoms. We present a case of CD in a 16-year-old female presenting as fever of unknown origin, which has not been reported previously. The postulated mechanism for fever in CD and the importance of clinicians having a low threshold for considering CD in the differential diagnosis of fever of unknown origin and other enigmatic clinical presentations is discussed.

Parameter identification of an electrically actuated imperfect microbeam

KAUST Repository

Ruzziconi, Laura

2013-12-01

In this study we consider a microelectromechanical system (MEMS) and focus on extracting analytically the model parameters that describe its non-linear dynamic features accurately. The device consists of a clamped-clamped polysilicon microbeam electrostatically and electrodynamically actuated. The microbeam has imperfections in the geometry, which are related to the microfabrication process, resulting in many unknown and uncertain parameters of the device. The objective of the present paper is to introduce a simple but appropriate model which, despite the inevitable approximations, is able to describe and predict the most relevant aspects of the experimental response in a neighborhood of the first symmetric resonance. The modeling includes the main imperfections in the microstructure. The unknown parameters are settled via parametric identification. The approach is developed in the frequency domain and is based on matching both the frequency values and, remarkably, the frequency response curves, which are considered as the most salient features of the device response. Non-linearities and imperfections considerably complicate the identification process. Via the combined use of linear analysis and non-linear dynamic simulations, a single first symmetric mode reduced-order model is derived. Extensive numerical simulations are performed at increasing values of electrodynamic excitation. Comparison with experimental data shows a satisfactory concurrence of results not only at low electrodynamic voltage, but also at higher ones. This validates the proposed theoretical approach. We highlight its applicability, both in similar case-studies and, more in general, in systems. © 2013 Elsevier Ltd.

Fast grasping of unknown objects using principal component analysis

Science.gov (United States)

Lei, Qujiang; Chen, Guangming; Wisse, Martijn

2017-09-01

Fast grasping of unknown objects has crucial impact on the efficiency of robot manipulation especially subjected to unfamiliar environments. In order to accelerate grasping speed of unknown objects, principal component analysis is utilized to direct the grasping process. In particular, a single-view partial point cloud is constructed and grasp candidates are allocated along the principal axis. Force balance optimization is employed to analyze possible graspable areas. The obtained graspable area with the minimal resultant force is the best zone for the final grasping execution. It is shown that an unknown object can be more quickly grasped provided that the component analysis principle axis is determined using single-view partial point cloud. To cope with the grasp uncertainty, robot motion is assisted to obtain a new viewpoint. Virtual exploration and experimental tests are carried out to verify this fast gasping algorithm. Both simulation and experimental tests demonstrated excellent performances based on the results of grasping a series of unknown objects. To minimize the grasping uncertainty, the merits of the robot hardware with two 3D cameras can be utilized to suffice the partial point cloud. As a result of utilizing the robot hardware, the grasping reliance is highly enhanced. Therefore, this research demonstrates practical significance for increasing grasping speed and thus increasing robot efficiency under unpredictable environments.

Parameter identification of Rossler's chaotic system by an evolutionary algorithm

Energy Technology Data Exchange (ETDEWEB)

Chang, W.-D. [Department of Computer and Communication, Shu-Te University, Kaohsiung 824, Taiwan (China)]. E-mail: wdchang@mail.stu.edu.tw

2006-09-15

In this paper, a differential evolution (DE) algorithm is applied to parameter identification of Rossler's chaotic system. The differential evolution has been shown to possess a powerful searching capability for finding the solutions for a given optimization problem, and it allows for parameter solution to appear directly in the form of floating point without further numerical coding or decoding. Three unknown parameters of Rossler's Chaotic system are optimally estimated by using the DE algorithm. Finally, a numerical example is given to verify the effectiveness of the proposed method.

Two similar cases of elderly women with moderate abdominal pain and pneumoperitoneum of unknown origin: a surgeon's successful conservative management.

Science.gov (United States)

Vinzens, Fabrizio; Zumstein, Valentin; Bieg, Christian; Ackermann, Christoph

2016-05-26

Patients presenting with abdominal pain and pneumoperitoneum in radiological examination usually require emergency explorative laparoscopy or laparotomy. Pneumoperitoneum mostly associates with gastrointestinal perforation. There are very few cases where surgery can be avoided. We present 2 cases of pneumoperitoneum with unknown origin and successful conservative treatment. Both patients were elderly women presenting to our emergency unit, with moderate abdominal pain. There was neither medical intervention nor trauma in their medical history. Physical examination revealed mild abdominal tenderness, but no clinical sign of peritonitis. Cardiopulmonary examination remained unremarkable. Blood studies showed only slight abnormalities, in particular, inflammation parameters were not significantly increased. Finally, obtained CTs showed free abdominal gas of unknown origin in both cases. We performed conservative management with nil per os, nasogastric tube, total parenteral nutrition and prophylactic antibiotics. After 2 weeks, both were discharged home. 2016 BMJ Publishing Group Ltd.

An eigenvalue approach for the automatic scaling of unknowns in model-based reconstructions: Application to real-time phase-contrast flow MRI.

Science.gov (United States)

Tan, Zhengguo; Hohage, Thorsten; Kalentev, Oleksandr; Joseph, Arun A; Wang, Xiaoqing; Voit, Dirk; Merboldt, K Dietmar; Frahm, Jens

2017-12-01

The purpose of this work is to develop an automatic method for the scaling of unknowns in model-based nonlinear inverse reconstructions and to evaluate its application to real-time phase-contrast (RT-PC) flow magnetic resonance imaging (MRI). Model-based MRI reconstructions of parametric maps which describe a physical or physiological function require the solution of a nonlinear inverse problem, because the list of unknowns in the extended MRI signal equation comprises multiple functional parameters and all coil sensitivity profiles. Iterative solutions therefore rely on an appropriate scaling of unknowns to numerically balance partial derivatives and regularization terms. The scaling of unknowns emerges as a self-adjoint and positive-definite matrix which is expressible by its maximal eigenvalue and solved by power iterations. The proposed method is applied to RT-PC flow MRI based on highly undersampled acquisitions. Experimental validations include numerical phantoms providing ground truth and a wide range of human studies in the ascending aorta, carotid arteries, deep veins during muscular exercise and cerebrospinal fluid during deep respiration. For RT-PC flow MRI, model-based reconstructions with automatic scaling not only offer velocity maps with high spatiotemporal acuity and much reduced phase noise, but also ensure fast convergence as well as accurate and precise velocities for all conditions tested, i.e. for different velocity ranges, vessel sizes and the simultaneous presence of signals with velocity aliasing. In summary, the proposed automatic scaling of unknowns in model-based MRI reconstructions yields quantitatively reliable velocities for RT-PC flow MRI in various experimental scenarios. Copyright © 2017 John Wiley & Sons, Ltd.

Quantum key distribution with an unknown and untrusted source

Science.gov (United States)

Zhao, Yi; Qi, Bing; Lo, Hoi-Kwong

2009-03-01

The security of a standard bi-directional ``plug & play'' quantum key distribution (QKD) system has been an open question for a long time. This is mainly because its source is equivalently controlled by an eavesdropper, which means the source is unknown and untrusted. Qualitative discussion on this subject has been made previously. In this paper, we present the first quantitative security analysis on a general class of QKD protocols whose sources are unknown and untrusted. The securities of standard BB84 protocol, weak+vacuum decoy state protocol, and one-decoy decoy state protocol, with unknown and untrusted sources are rigorously proved. We derive rigorous lower bounds to the secure key generation rates of the above three protocols. Our numerical simulation results show that QKD with an untrusted source gives a key generation rate that is close to that with a trusted source. Our work is published in [1]. [4pt] [1] Y. Zhao, B. Qi, and H.-K. Lo, Phys. Rev. A, 77:052327 (2008).

Grasping Unknown Objects in an Early Cognitive Vision System

DEFF Research Database (Denmark)

Popovic, Mila

2011-01-01

Grasping of unknown objects presents an important and challenging part of robot manipulation. The growing area of service robotics depends upon the ability of robots to autonomously grasp and manipulate a wide range of objects in everyday environments. Simple, non task-specific grasps of unknown ...... and comparing vision-based grasping methods, and the creation of algorithms for bootstrapping a process of acquiring world understanding for artificial cognitive agents....... presents a system for robotic grasping of unknown objects us- ing stereo vision. Grasps are defined based on contour and surface information provided by the Early Cognitive Vision System, that organizes visual informa- tion into a biologically motivated hierarchical representation. The contributions...... of the thesis are: the extension of the Early Cognitive Vision representation with a new type of feature hierarchy in the texture domain, the definition and evaluation of contour based grasping methods, the definition and evaluation of surface based grasping methods, the definition of a benchmark for testing...

Inverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land Model

Science.gov (United States)

Sun, Y.; Hou, Z.; Huang, M.; Tian, F.; Leung, L. Ruby

2013-12-01

This study demonstrates the possibility of inverting hydrologic parameters using surface flux and runoff observations in version 4 of the Community Land Model (CLM4). Previous studies showed that surface flux and runoff calculations are sensitive to major hydrologic parameters in CLM4 over different watersheds, and illustrated the necessity and possibility of parameter calibration. Both deterministic least-square fitting and stochastic Markov-chain Monte Carlo (MCMC)-Bayesian inversion approaches are evaluated by applying them to CLM4 at selected sites with different climate and soil conditions. The unknowns to be estimated include surface and subsurface runoff generation parameters and vadose zone soil water parameters. We find that using model parameters calibrated by the sampling-based stochastic inversion approaches provides significant improvements in the model simulations compared to using default CLM4 parameter values, and that as more information comes in, the predictive intervals (ranges of posterior distributions) of the calibrated parameters become narrower. In general, parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or runoff observations. Temporal resolution of observations has larger impacts on the results of inverse modeling using heat flux data than runoff data. Soil and vegetation cover have important impacts on parameter sensitivities, leading to different patterns of posterior distributions of parameters at different sites. Overall, the MCMC-Bayesian inversion approach effectively and reliably improves the simulation of CLM under different climates and environmental conditions. Bayesian model averaging of the posterior estimates with different reference acceptance probabilities can smooth the posterior distribution and provide more reliable parameter estimates, but at the expense of wider uncertainty bounds.

No novel, high penetrant gene might remain to be found in Japanese patients with unknown MODY.

Science.gov (United States)

Horikawa, Yukio; Hosomichi, Kazuyoshi; Enya, Mayumi; Ishiura, Hiroyuki; Suzuki, Yutaka; Tsuji, Shoji; Sugano, Sumio; Inoue, Ituro; Takeda, Jun

2018-07-01

MODY 5 and 6 have been shown to be low-penetrant MODYs. As the genetic background of unknown MODY is assumed to be similar, a new analytical strategy is applied here to elucidate genetic predispositions to unknown MODY. We examined to find whether there are major MODY gene loci remaining to be identified using SNP linkage analysis in Japanese. Whole-exome sequencing was performed with seven families with typical MODY. Candidates for novel MODY genes were examined combined with in silico network analysis. Some peaks were found only in either parametric or non-parametric analysis; however, none of these peaks showed a LOD score greater than 3.7, which is approved to be the significance threshold of evidence for linkage. Exome sequencing revealed that three mutated genes were common among 3 families and 42 mutated genes were common in two families. Only one of these genes, MYO5A, having rare amino acid mutations p.R849Q and p.V1601G, was involved in the biological network of known MODY genes through the intermediary of the INS. Although only one promising candidate gene, MYO5A, was identified, no novel, high penetrant MODY genes might remain to be found in Japanese MODY.

18F-FDG-PET/CT in fever of unknown origin

DEFF Research Database (Denmark)

Middelbo Buch-Olsen, Karen; Andersen, Rikke V; Hess, Søren

2014-01-01

OBJECTIVE: Fever of unknown origin continues to be a diagnostic challenge for clinicians. The aim of this study was to confirm whether (18)F-fluorodeoxyglucose ((18)F-FDG)-PET/computed tomography (CT) is a helpful tool in patients suffering from this condition. PATIENTS AND METHODS: Fifty......-seven patients with fever of unknown origin were examined with (18)F-FDG-PET/CT as part of their diagnostic workup at the clinicians' discretion. The medical records were read retrospectively to establish the final diagnosis and evaluate the degree to which PET/CT contributed to the diagnosis. RESULTS......-FDG-PET/CT is a useful tool in the investigation of fever of unknown origin; it can reduce patient inconvenience and possibly costs to society if used earlier in the diagnostic process....

Iterative methods for distributed parameter estimation in parabolic PDE

Energy Technology Data Exchange (ETDEWEB)

Vogel, C.R. [Montana State Univ., Bozeman, MT (United States); Wade, J.G. [Bowling Green State Univ., OH (United States)

1994-12-31

The goal of the work presented is the development of effective iterative techniques for large-scale inverse or parameter estimation problems. In this extended abstract, a detailed description of the mathematical framework in which the authors view these problem is presented, followed by an outline of the ideas and algorithms developed. Distributed parameter estimation problems often arise in mathematical modeling with partial differential equations. They can be viewed as inverse problems; the `forward problem` is that of using the fully specified model to predict the behavior of the system. The inverse or parameter estimation problem is: given the form of the model and some observed data from the system being modeled, determine the unknown parameters of the model. These problems are of great practical and mathematical interest, and the development of efficient computational algorithms is an active area of study.

Parameter Identification for Salinity in a Quasilinear Thermodynamic System of Sea Ice

OpenAIRE

Wei Lv; Xiaojiao Li; Enmin Feng

2014-01-01

This study is intended to provide a parameter identification method to determine salinity of sea ice by temperature and salinity observations. A quasilinear thermodynamic system of sea ice with unknown salinity is described and its property is proved. Then, a parameter identification model is established and the existence of its optimal solution is discussed. The salinity profile is calculated by the temperature and salinity data, which were measured at Nella Fjord around Zhongshan Station, A...

Data Series Subtraction with Unknown and Unmodeled Background Noise

Science.gov (United States)

Vitale, Stefano; Congedo, Giuseppe; Dolesi, Rita; Ferroni, Valerio; Hueller, Mauro; Vetrugno, Daniele; Weber, William Joseph; Audley, Heather; Danzmann, Karsten; Diepholz, Ingo;

Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks.

Science.gov (United States)

Modares, Hamidreza; Lewis, Frank L; Naghibi-Sistani, Mohammad-Bagher

2013-10-01

This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example.

Intraabdominal abscessus of unknown etiology

Directory of Open Access Journals (Sweden)

Čolović Radoje

2012-01-01

Full Text Available Introduction. Intraabdominal abscesses are in 98-99% cases the result of secondary and only in 1-2% of primary peritonitis. They are easy and successfully diagnosed. Abdominal abscesses of unknown cause are extremely rare. Case Outline. The authors present a 68-year-old man, without significant data in past history, who suddenly developed epigastric pain, nausea, vomiting and leukocytosis which was treated with antibiotics resulting in the alleviation of complaints and reduction of white blood cells count. After five days ultrasonography showed incapsulated collection of dense fluid in the epigastrium confirmed by CT scan two days later. Upper endoscopy excluded ulcer and/or perforation of the stomach and duodenum. Under local anesthesia, through the upper part of the left rectal muscle, puncture followed by incision was done, and about 50 ml of dense pus was removed. Finger exploration of the cavity showed no foreign body within the cavity. Using drainage, the recovery was quick and uneventful. By preoperative and postoperative abdominal investigations no cause of the abscess was found. Two and a half years after surgery the patient remained symptom-free with normal clinical, laboratory and ultrasonographic findings. Conclusion. The authors presented an intraabdominal abscess of unknown cause that was successfully treated with antibiotics, percutaneous puncture and drainage under local anaesthesia. In spite of all diagnostic methods the cause of the abscess could not be found. Thus, such a possibility, although being rare, should be taken into account.

Joint Model and Parameter Dimension Reduction for Bayesian Inversion Applied to an Ice Sheet Flow Problem

Science.gov (United States)

Ghattas, O.; Petra, N.; Cui, T.; Marzouk, Y.; Benjamin, P.; Willcox, K.

2016-12-01

Model-based projections of the dynamics of the polar ice sheets play a central role in anticipating future sea level rise. However, a number of mathematical and computational challenges place significant barriers on improving predictability of these models. One such challenge is caused by the unknown model parameters (e.g., in the basal boundary conditions) that must be inferred from heterogeneous observational data, leading to an ill-posed inverse problem and the need to quantify uncertainties in its solution. In this talk we discuss the problem of estimating the uncertainty in the solution of (large-scale) ice sheet inverse problems within the framework of Bayesian inference. Computing the general solution of the inverse problem--i.e., the posterior probability density--is intractable with current methods on today's computers, due to the expense of solving the forward model (3D full Stokes flow with nonlinear rheology) and the high dimensionality of the uncertain parameters (which are discretizations of the basal sliding coefficient field). To overcome these twin computational challenges, it is essential to exploit problem structure (e.g., sensitivity of the data to parameters, the smoothing property of the forward model, and correlations in the prior). To this end, we present a data-informed approach that identifies low-dimensional structure in both parameter space and the forward model state space. This approach exploits the fact that the observations inform only a low-dimensional parameter space and allows us to construct a parameter-reduced posterior. Sampling this parameter-reduced posterior still requires multiple evaluations of the forward problem, therefore we also aim to identify a low dimensional state space to reduce the computational cost. To this end, we apply a proper orthogonal decomposition (POD) approach to approximate the state using a low-dimensional manifold constructed using ``snapshots'' from the parameter reduced posterior, and the discrete

Scheme for teleportation of unknown states of trapped ion

Institute of Scientific and Technical Information of China (English)

Chen Mei-Feng; Ma Song-She

2008-01-01

A scheme is presented for teleporting an unknown state in a trapped ion system.The scheme only requires a single laser beam.It allows the trap to be in any state with a few phonons,e.g.a thermal motion.Furthermore,it works in the regime,where the Rabi frequency of the laser is on the order of the trap frequency.Thus,the teleportation speed is greatly increased,which is important for decreasing the decoherence effect.This idea can also be used to teleport an unknown ionic entangled state.

InSourcerer: a high-throughput method to search for unknown metabolite modifications by mass spectrometry.

Science.gov (United States)

Mrzic, Aida; Lermyte, Frederik; Vu, Trung Nghia; Valkenborg, Dirk; Laukens, Kris

2017-09-15

Using mass spectrometry, the analysis of known metabolite structures has become feasible in a systematic high-throughput fashion. Nevertheless, the identification of previously unknown structures remains challenging, partially because many unidentified variants originate from known molecules that underwent unexpected modifications. Here, we present a method for the discovery of unknown metabolite modifications and conjugate metabolite isoforms in a high-throughput fashion. The method is based on user-controlled in-source fragmentation which is used to induce loss of weakly bound modifications. This is followed by the comparison of product ions from in-source fragmentation and collision-induced dissociation (CID). Diagonal MS 2 -MS 3 matching allows the detection of unknown metabolite modifications, as well as substructure similarities. As the method relies heavily on the advantages of in-source fragmentation and its ability to 'magically' elucidate unknown modification, we have named it inSourcerer as a portmanteau of in-source and sorcerer. The method was evaluated using a set of 15 different cytokinin standards. Product ions from in-source fragmentation and CID were compared. Hierarchical clustering revealed that good matches are due to the presence of common substructures. Plant leaf extract, spiked with a mix of all 15 standards, was used to demonstrate the method's ability to detect these standards in a complex mixture, as well as confidently identify compounds already present in the plant material. Here we present a method that incorporates a classic liquid chromatography/mass spectrometry (LC/MS) workflow with fragmentation models and computational algorithms. The assumptions upon which the concept of the method was built were shown to be valid and the method showed that in-source fragmentation can be used to pinpoint structural similarities and indicate the occurrence of a modification. Copyright © 2017 John Wiley & Sons, Ltd.

Parameter Estimation of Partial Differential Equation Models.

Science.gov (United States)

Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Carroll, Raymond J; Maity, Arnab

2013-01-01

Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown, and need to be estimated from the measurements of the dynamic system in the present of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE, and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from LIDAR data.

On piecewise constant level-set (PCLS) methods for the identification of discontinuous parameters in ill-posed problems

International Nuclear Information System (INIS)

De Cezaro, A; Leitão, A; Tai, X-C

2013-01-01

We investigate level-set-type methods for solving ill-posed problems with discontinuous (piecewise constant) coefficients. The goal is to identify the level sets as well as the level values of an unknown parameter function on a model described by a nonlinear ill-posed operator equation. The PCLS approach is used here to parametrize the solution of a given operator equation in terms of a L 2 level-set function, i.e. the level-set function itself is assumed to be a piecewise constant function. Two distinct methods are proposed for computing stable solutions of the resulting ill-posed problem: the first is based on Tikhonov regularization, while the second is based on the augmented Lagrangian approach with total variation penalization. Classical regularization results (Engl H W et al 1996 Mathematics and its Applications (Dordrecht: Kluwer)) are derived for the Tikhonov method. On the other hand, for the augmented Lagrangian method, we succeed in proving the existence of (generalized) Lagrangian multipliers in the sense of (Rockafellar R T and Wets R J-B 1998 Grundlehren der Mathematischen Wissenschaften (Berlin: Springer)). Numerical experiments are performed for a 2D inverse potential problem (Hettlich F and Rundell W 1996 Inverse Problems 12 251–66), demonstrating the capabilities of both methods for solving this ill-posed problem in a stable way (complicated inclusions are recovered without any a priori geometrical information on the unknown parameter). (paper)

Markov Chain Monte Carlo (MCMC) methods for parameter estimation of a novel hybrid redundant robot

International Nuclear Information System (INIS)

Wang Yongbo; Wu Huapeng; Handroos, Heikki

2011-01-01

This paper presents a statistical method for the calibration of a redundantly actuated hybrid serial-parallel robot IWR (Intersector Welding Robot). The robot under study will be used to carry out welding, machining, and remote handing for the assembly of vacuum vessel of International Thermonuclear Experimental Reactor (ITER). The robot has ten degrees of freedom (DOF), among which six DOF are contributed by the parallel mechanism and the rest are from the serial mechanism. In this paper, a kinematic error model which involves 54 unknown geometrical error parameters is developed for the proposed robot. Based on this error model, the mean values of the unknown parameters are statistically analyzed and estimated by means of Markov Chain Monte Carlo (MCMC) approach. The computer simulation is conducted by introducing random geometric errors and measurement poses which represent the corresponding real physical behaviors. The simulation results of the marginal posterior distributions of the estimated model parameters indicate that our method is reliable and robust.

ENU Mutagenesis in Mice Identifies Candidate Genes For Hypogonadism

Science.gov (United States)

Weiss, Jeffrey; Hurley, Lisa A.; Harris, Rebecca M.; Finlayson, Courtney; Tong, Minghan; Fisher, Lisa A.; Moran, Jennifer L.; Beier, David R.; Mason, Christopher; Jameson, J. Larry

2012-01-01

Genome-wide mutagenesis was performed in mice to identify candidate genes for male infertility, for which the predominant causes remain idiopathic. Mice were mutagenized using N-ethyl-N-nitrosourea (ENU), bred, and screened for phenotypes associated with the male urogenital system. Fifteen heritable lines were isolated and chromosomal loci were assigned using low density genome-wide SNP arrays. Ten of the fifteen lines were pursued further using higher resolution SNP analysis to narrow the candidate gene regions. Exon sequencing of candidate genes identified mutations in mice with cystic kidneys (Bicc1), cryptorchidism (Rxfp2), restricted germ cell deficiency (Plk4), and severe germ cell deficiency (Prdm9). In two other lines with severe hypogonadism candidate sequencing failed to identify mutations, suggesting defects in genes with previously undocumented roles in gonadal function. These genomic intervals were sequenced in their entirety and a candidate mutation was identified in SnrpE in one of the two lines. The line harboring the SnrpE variant retains substantial spermatogenesis despite small testis size, an unusual phenotype. In addition to the reproductive defects, heritable phenotypes were observed in mice with ataxia (Myo5a), tremors (Pmp22), growth retardation (unknown gene), and hydrocephalus (unknown gene). These results demonstrate that the ENU screen is an effective tool for identifying potential causes of male infertility. PMID:22258617

Decentralised output feedback control of Markovian jump interconnected systems with unknown interconnections

Science.gov (United States)

Li, Li-Wei; Yang, Guang-Hong

2017-07-01

The problem of decentralised output feedback control is addressed for Markovian jump interconnected systems with unknown interconnections and general transition rates (TRs) allowed to be unknown or known with uncertainties. A class of decentralised dynamic output feedback controllers are constructed, and a cyclic-small-gain condition is exploited to dispose the unknown interconnections so that the resultant closed-loop system is stochastically stable and satisfies an H∞ performance. With slack matrices to cope with the nonlinearities incurred by unknown and uncertain TRs in control synthesis, a novel controller design condition is developed in linear matrix inequality formalism. Compared with the existing works, the proposed approach leads to less conservatism. Finally, two examples are used to illustrate the effectiveness of the new results.

Renal disease masquerading as pyrexia of unknown origin

Directory of Open Access Journals (Sweden)

D Korivi

2013-01-01

Full Text Available Pyrexia of unknown origin is a challenging clinical problem. Infections, malignancies, and connective tissue diseases form the major etiologies for this condition. We report a case of a 57-year-old diabetic male who presented with fever of unknown origin for several months. The course of investigations led to a kidney biopsy which clinched the cause of his fever as well as the underlying diagnosis. The light microscopy findings of expansile storiform fibrosis with a dense inflammatory infiltrate suggested the diagnosis which was confirmed by positive staining of Immunoglobulin G4, the dense lympho-plasmacytic infiltrate and elevated serum IgG4 concentrations. A course of steroids followed by mycophenolate mofetil as maintenance immunosuppression rendered the patient afebrile with improvement of renal function.

[Standard of care of carcinomas on cancer of unknown primary site in 2016].

Science.gov (United States)

Benderra, Marc-Antoine; Ilié, Marius; Hofman, Paul; Massard, Christophe

2016-01-01

Patients with Cancer of unknown primary (cup) represent 2-10%, and have disseminated cancers for which we cannot find the primary site despite the clinical, pathological and radiological exams at our disposal. Diagnosis is based on a thorough clinical and histopathologic examination as well as new imaging techniques. Several clinicopathologic entities requiring specific treatment can be identified. Genome sequencing and liquid biopsy (circulating tumor cells and tumor free DNA) could allow further advances in the diagnosis. Therapeutically, in addition to surgery, radiotherapy and chemotherapy, precision medicine provides new therapeutic approaches. Copyright © 2016 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

Bio-media Citizenship and Chronic Kidney Disease of Unknown Etiology in Sri Lanka.

Science.gov (United States)

de Silva, M W Amarasiri

2018-04-01

In this article, I examine the crucial role of the biomedical industry, epidemiological and biomedical research, and the media in forming attitudes to and the understanding of chronic kidney disease of unknown etiology (CKDu) in Sri Lanka. Local conceptions of CKDu have been shaped by the circulation in the media of epidemiological research findings pertaining to the disease, biomedical interventions in the management of the disease in hospitals and clinics, community programs involving mass blood surveys and the testing of well water, and local food and health education programs carried out through village health committees. This process of circulation I identify as bio-media citizenship.

Parameter estimation in stochastic differential equations

CERN Document Server

Bishwal, Jaya P N

2008-01-01

Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modelling complex phenomena and making beautiful decisions. The subject has attracted researchers from several areas of mathematics and other related fields like economics and finance. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods. Useful because of the current availability of high frequency data is the study of refined asymptotic properties of several estimators when the observation time length is large and the observation time interval is small. Also space time white noise driven models, useful for spatial data, and more sophisticated non-Markovian and non-semimartingale models like fractional diffusions that model the long memory phenomena are examined in this volume.

Kalman filters for assimilating near-surface observations in the Richards equation - Part 2: A dual filter approach for simultaneous retrieval of states and parameters

Science.gov (United States)

Medina, H.; Romano, N.; Chirico, G. B.

2012-12-01

We present a dual Kalman Filter (KF) approach for retrieving states and parameters controlling soil water dynamics in a homogenous soil column by using near-surface state observations. The dual Kalman filter couples a standard KF algorithm for retrieving the states and an unscented KF algorithm for retrieving the parameters. We examine the performance of the dual Kalman Filter applied to two alternative state-space formulations of the Richards equation, respectively differentiated by the type of variable employed for representing the states: either the soil water content (θ) or the soil matric pressure head (h). We use a synthetic time-series series of true states and noise corrupted observations and a synthetic time-series of meteorological forcing. The performance analyses account for the effect of the input parameters, the observation depth and the assimilation frequency as well as the relationship between the retrieved states and the assimilated variables. We show that the identifiability of the parameters is strongly conditioned by several factors, such as the initial guess of the unknown parameters, the wet or dry range of the retrieved states, the boundary conditions, as well as the form (h-based or θ-based) of the state-space formulation. State identifiability is instead efficient even with a relatively coarse time-resolution of the assimilated observation. The accuracy of the retrieved states exhibits limited sensitivity to the observation depth and the assimilation frequency.

Autonomous Flight in Unknown Indoor Environments

OpenAIRE

Bachrach, Abraham Galton; He, Ruijie; Roy, Nicholas

2009-01-01

This paper presents our solution for enabling a quadrotor helicopter, equipped with a laser rangefinder sensor, to autonomously explore and map unstructured and unknown indoor environments. While these capabilities are already commodities on ground vehicles, air vehicles seeking the same performance face unique challenges. In this paper, we describe the difficulties in achieving fully autonomous helicopter flight, highlighting the differences between ground and helicopter robots that make it ...

Root-MUSIC Based Angle Estimation for MIMO Radar with Unknown Mutual Coupling

Directory of Open Access Journals (Sweden)

Jianfeng Li

2014-01-01

Full Text Available Direction of arrival (DOA estimation problem for multiple-input multiple-output (MIMO radar with unknown mutual coupling is studied, and an algorithm for the DOA estimation based on root multiple signal classification (MUSIC is proposed. Firstly, according to the Toeplitz structure of the mutual coupling matrix, output data of some specified sensors are selected to eliminate the influence of the mutual coupling. Then the reduced-dimension transformation is applied to make the computation burden lower as well as obtain a Vandermonde structure of the direction matrix. Finally, Root-MUSIC can be adopted for the angle estimation. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT-like algorithm and MUSIC-like algorithm. Furthermore, the proposed algorithm has lower complexity than them. The simulation results verify the effectiveness of the algorithm, and the theoretical estimation error of the algorithm is also derived.

Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone Nonlinearities

Directory of Open Access Journals (Sweden)

J. Humberto Pérez-Cruz

2014-01-01

Full Text Available The trajectory tracking for a class of uncertain nonlinear systems in which the number of possible states is equal to the number of inputs and each input is preceded by an unknown symmetric deadzone is considered. The unknown dynamics is identified by means of a continuous time recurrent neural network in which the control singularity is conveniently avoided by guaranteeing the invertibility of the coupling matrix. Given this neural network-based mathematical model of the uncertain system, a singularity-free feedback linearization control law is developed in order to compel the system state to follow a reference trajectory. By means of Lyapunov-like analysis, the exponential convergence of the tracking error to a bounded zone can be proven. Likewise, the boundedness of all closed-loop signals can be guaranteed.

Parameter discovery in stochastic biological models using simulated annealing and statistical model checking.

Science.gov (United States)

Hussain, Faraz; Jha, Sumit K; Jha, Susmit; Langmead, Christopher J

2014-01-01

Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.

Designing towards the Unknown: Engaging with Material and Aesthetic Uncertainty

Directory of Open Access Journals (Sweden)

Danielle Wilde

2017-12-01

Full Text Available New materials with new capabilities demand new ways of approaching design. Destabilising existing methods is crucial to develop new methods. Yet, radical destabilisation—where outcomes remain unknown long enough that new discoveries become possible—is not easy in technology design where complex interdisciplinary teams with time and resource constraints need to deliver concrete outcomes on schedule. The Poetic Kinaesthetic Interface project (PKI engages with this problematic directly. In PKI we use unfolding processes—informed by participatory, speculative and critical design—in emergent actions, to design towards unknown outcomes, using unknown materials. The impossibility of this task is proving as useful as it is disruptive. At its most potent, it is destabilising expectations, aesthetics and processes. Keeping the researchers, collaborators and participants in a state of unknowing, is opening the research potential to far-ranging possibilities. In this article we unpack the motivations driving the PKI project. We present our mixed-methodology, which entangles textile crafts, design interactions and materiality to shape an embodied enquiry. Our research outcomes are procedural and methodological. PKI brings together diverse human, non-human, known and unknown actors to discover where the emergent assemblages might lead. Our approach is re-invigorating—as it demands re-envisioning of—the design process.

Value of Bone marrow Examination in Pyrexia of unknown origin

Directory of Open Access Journals (Sweden)

A Jha

2013-10-01

Full Text Available Background: Pyrexia of unknown origin is a common diagnostic dilemma. Series of diagnostic modalities are required to arrive at diagnosis. Bone marrow examination is one of the common tests implicated in the diagnosis in combination with other diagnostic modalities. Present study has attempted to explore the causes of pyrexia of unknown origin based on bone marrow morphological study. Materials and Methods: In a one year prospective study conducted at Manipal Teaching Hospital, Pokhara, Nepal; bone marrow aspiration and biopsy was performed and evaluated morphologically, in 57 patients fulfilling the criteria of classic pyrexia of unknown origin. Results: In 42% cases; specific diagnosis could be made and hematological neoplasm was the most common finding followed by megaloblastic anemia, hypoplastic anemia and one case each of hemophagocytosis, malaria and tuberculosis. Acute leukemia was the most frequently encountered hematological malignancy followed by multiple myeloma, chronic myeloid leukemia, essential thrombocythemia and myelodysplastic syndrome. Conclusion: Morphological examination of bone marrow has important role in diagnosis of pyrexia of unknown origin. However, yield of diagnosis can be increased if it is combined with other diagnostic modalities including radiological, microbiological and serological tests. DOI: http://dx.doi.org/10.3126/jpn.v3i6.8991 Journal of Pathology of Nepal (2013 Vol. 3, 447-451

Application of decomposition method and inverse prediction of parameters in a moving fin

International Nuclear Information System (INIS)

Singla, Rohit K.; Das, Ranjan

2014-01-01

Highlights: • Adomian decomposition is used to study a moving fin. • Effects of different parameters on the temperature and efficiency are studied. • Binary-coded GA is used to solve an inverse problem. • Sensitivity analyses of important parameters are carried out. • Measurement error up to 8% is found to be tolerable. - Abstract: The application of the Adomian decomposition method (ADM) is extended to study a conductive–convective and radiating moving fin having variable thermal conductivity. Next, through an inverse approach, ADM in conjunction with a binary-coded genetic algorithm (GA) is also applied for estimation of unknown properties in order to satisfy a given temperature distribution. ADM being one of the widely-used numerical methods for solving non-linear equations, the required temperature field has been obtained using a forward method involving ADM. In the forward problem, the temperature field and efficiency are investigated for various parameters such as convection–conduction parameter, radiation–conduction parameter, Peclet number, convection sink temperature, radiation sink temperature, and dimensionless thermal conductivity. Additionally, in the inverse problem, the effect of random measurement errors, iterative variation of parameters, sensitivity coefficients of unknown parameters are investigated. The performance of GA is compared with few other optimization methods as well as with different temperature measurement points. It is found from the present study that the results obtained from ADM are in good agreement with the results of the differential transformation method available in the literature. It is also observed that for satisfactory reconstruction of the temperature field, the measurement error should be within 8% and the temperature field is strongly dependent on the speed than thermal parameters of the moving fin

Modulating functions-based method for parameters and source estimation in one-dimensional partial differential equations

KAUST Repository

Asiri, Sharefa M.

2016-10-20

In this paper, modulating functions-based method is proposed for estimating space–time-dependent unknowns in one-dimensional partial differential equations. The proposed method simplifies the problem into a system of algebraic equations linear in unknown parameters. The well-posedness of the modulating functions-based solution is proved. The wave and the fifth-order KdV equations are used as examples to show the effectiveness of the proposed method in both noise-free and noisy cases.

Robot navigation in unknown terrains: Introductory survey of non-heuristic algorithms

Energy Technology Data Exchange (ETDEWEB)

Rao, N.S.V. [Oak Ridge National Lab., TN (US); Kareti, S.; Shi, Weimin [Old Dominion Univ., Norfolk, VA (US). Dept. of Computer Science; Iyengar, S.S. [Louisiana State Univ., Baton Rouge, LA (US). Dept. of Computer Science

1993-07-01

A formal framework for navigating a robot in a geometric terrain by an unknown set of obstacles is considered. Here the terrain model is not a priori known, but the robot is equipped with a sensor system (vision or touch) employed for the purpose of navigation. The focus is restricted to the non-heuristic algorithms which can be theoretically shown to be correct within a given framework of models for the robot, terrain and sensor system. These formulations, although abstract and simplified compared to real-life scenarios, provide foundations for practical systems by highlighting the underlying critical issues. First, the authors consider the algorithms that are shown to navigate correctly without much consideration given to the performance parameters such as distance traversed, etc. Second, they consider non-heuristic algorithms that guarantee bounds on the distance traversed or the ratio of the distance traversed to the shortest path length (computed if the terrain model is known). Then they consider the navigation of robots with very limited computational capabilities such as finite automata, etc.

An effective automatic procedure for testing parameter identifiability of HIV/AIDS models.

Science.gov (United States)

Saccomani, Maria Pia

2011-08-01

Realistic HIV models tend to be rather complex and many recent models proposed in the literature could not yet be analyzed by traditional identifiability testing techniques. In this paper, we check a priori global identifiability of some of these nonlinear HIV models taken from the recent literature, by using a differential algebra algorithm based on previous work of the author. The algorithm is implemented in a software tool, called DAISY (Differential Algebra for Identifiability of SYstems), which has been recently released (DAISY is freely available on the web site http://www.dei.unipd.it/~pia/ ). The software can be used to automatically check global identifiability of (linear and) nonlinear models described by polynomial or rational differential equations, thus providing a general and reliable tool to test global identifiability of several HIV models proposed in the literature. It can be used by researchers with a minimum of mathematical background.

Identifying bioaccumulative halogenated organic compounds using a nontargeted analytical approach: seabirds as sentinels.

Directory of Open Access Journals (Sweden)

Christopher J Millow

Full Text Available Persistent organic pollutants (POPs are typically monitored via targeted mass spectrometry, which potentially identifies only a fraction of the contaminants actually present in environmental samples. With new anthropogenic compounds continuously introduced to the environment, novel and proactive approaches that provide a comprehensive alternative to targeted methods are needed in order to more completely characterize the diversity of known and unknown compounds likely to cause adverse effects. Nontargeted mass spectrometry attempts to extensively screen for compounds, providing a feasible approach for identifying contaminants that warrant future monitoring. We employed a nontargeted analytical method using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC/TOF-MS to characterize halogenated organic compounds (HOCs in California Black skimmer (Rynchops niger eggs. Our study identified 111 HOCs; 84 of these compounds were regularly detected via targeted approaches, while 27 were classified as typically unmonitored or unknown. Typically unmonitored compounds of note in bird eggs included tris(4-chlorophenylmethane (TCPM, tris(4-chlorophenylmethanol (TCPMOH, triclosan, permethrin, heptachloro-1'-methyl-1,2'-bipyrrole (MBP, as well as four halogenated unknown compounds that could not be identified through database searching or the literature. The presence of these compounds in Black skimmer eggs suggests they are persistent, bioaccumulative, potentially biomagnifying, and maternally transferring. Our results highlight the utility and importance of employing nontargeted analytical tools to assess true contaminant burdens in organisms, as well as to demonstrate the value in using environmental sentinels to proactively identify novel contaminants.

Towards high-speed autonomous navigation of unknown environments

Science.gov (United States)

Richter, Charles; Roy, Nicholas

2015-05-01

In this paper, we summarize recent research enabling high-speed navigation in unknown environments for dynamic robots that perceive the world through onboard sensors. Many existing solutions to this problem guarantee safety by making the conservative assumption that any unknown portion of the map may contain an obstacle, and therefore constrain planned motions to lie entirely within known free space. In this work, we observe that safety constraints may significantly limit performance and that faster navigation is possible if the planner reasons about collision with unobserved obstacles probabilistically. Our overall approach is to use machine learning to approximate the expected costs of collision using the current state of the map and the planned trajectory. Our contribution is to demonstrate fast but safe planning using a learned function to predict future collision probabilities.

Capecitabine and oxaliplatin as second-line treatment in patients with carcinoma of unknown primary site

DEFF Research Database (Denmark)

Møller, Anne Kirstine Hundahl; Pedersen, Karen Damgaard; Abildgaard, Julie Rafn

2010-01-01

tumours may be overrepresented. These patients could be candidates for GI tract-directed therapy. We here report the results obtained with oxaliplatin and capecitabine as second-line therapy in 25 recurrent/refractory CUP patients following first-line treatment with paclitaxel, cisplatin and gemcitabine.......Treatment of patients with carcinoma of unknown primary site (CUP) remains a challenge, and no effective second-line treatment has been identified. In CUP patients who are non-responsive or relapse early after first-line platinum/taxane-based regimens, it is likely that gastrointestinal (GI) tract...

Identifying the role of initial wave parameters on tsunami focusing

Science.gov (United States)

Aydın, Baran

2018-04-01

Unexpected local tsunami amplification, which is referred to as tsunami focusing, is attributed to two different mechanisms: bathymetric features of the ocean bottom such as underwater ridges and dipolar shape of the initial wave itself. In this study, we characterize the latter; that is, we explore how amplitude and location of the focusing point vary with certain geometric parameters of the initial wave such as its steepness and crest length. Our results reveal two important features of tsunami focusing: for mild waves maximum wave amplitude increases significantly with transverse length of wave crest, while location of the focusing point is almost invariant. For steep waves, on the other hand, increasing crest length dislocates focusing point significantly, while it causes a rather small increase in wave maximum.

Adaptive function project synchronization of Roessler hyperchaotic system with uncertain parameters

International Nuclear Information System (INIS)

Luo Runzi

2008-01-01

This Letter addresses the function project synchronization problem of two Roessler hyperchaotic in the presence of unknown system parameters. Based on Lyapunov stability theory an adaptive control law is proposed to make the states of two identical Roessler hyperchaotic systems asymptotically synchronized. Numerical simulations are presented to show the effectiveness of the proposed schemes

Relationships between lifestyle patterns and cardio-renal-metabolic parameters in patients with type 2 diabetes mellitus: A cross-sectional study.

Directory of Open Access Journals (Sweden)

Takeshi Ogihara

Full Text Available While individuals tend to show accumulation of certain lifestyle patterns, the effect of such patterns in real daily life on cardio-renal-metabolic parameters remains largely unknown. This study aimed to assess clustering of lifestyle patterns and investigate the relationships between such patterns and cardio-renal-metabolic parameters.The study participants were 726 Japanese type 2 diabetes mellitus (T2DM outpatients free of history of cardiovascular diseases. The relationship between lifestyle patterns and cardio-renal-metabolic parameters was investigated by linear and logistic regression analyses.Factor analysis identified three lifestyle patterns. Subjects characterized by evening type, poor sleep quality and depressive status (type 1 pattern had high levels of HbA1c, alanine aminotransferase and albuminuria. Subjects characterized by high consumption of food, alcohol and cigarettes (type 2 pattern had high levels of γ-glutamyl transpeptidase, triglycerides, HDL-cholesterol, blood pressure, and brachial-ankle pulse wave velocity. Subjects characterized by high physical activity (type 3 pattern had low uric acid and mild elevation of alanine aminotransferase and aspartate aminotransferase. In multivariate regression analysis adjusted by age, gender and BMI, type 1 pattern was associated with higher HbA1c levels, systolic BP and brachial-ankle pulse wave velocity. Type 2 pattern was associated with higher HDL-cholesterol levels, triglycerides, aspartate aminotransferase, ɤ- glutamyl transpeptidase levels, and diastolic BP.The study identified three lifestyle patterns that were associated with distinct cardio-metabolic-renal parameters in T2DM patients.UMIN000010932.

Relationships between lifestyle patterns and cardio-renal-metabolic parameters in patients with type 2 diabetes mellitus: A cross-sectional study.

Science.gov (United States)

Ogihara, Takeshi; Mita, Tomoya; Osonoi, Yusuke; Osonoi, Takeshi; Saito, Miyoko; Tamasawa, Atsuko; Nakayama, Shiho; Someya, Yuki; Ishida, Hidenori; Gosho, Masahiko; Kanazawa, Akio; Watada, Hirotaka

2017-01-01

While individuals tend to show accumulation of certain lifestyle patterns, the effect of such patterns in real daily life on cardio-renal-metabolic parameters remains largely unknown. This study aimed to assess clustering of lifestyle patterns and investigate the relationships between such patterns and cardio-renal-metabolic parameters. The study participants were 726 Japanese type 2 diabetes mellitus (T2DM) outpatients free of history of cardiovascular diseases. The relationship between lifestyle patterns and cardio-renal-metabolic parameters was investigated by linear and logistic regression analyses. Factor analysis identified three lifestyle patterns. Subjects characterized by evening type, poor sleep quality and depressive status (type 1 pattern) had high levels of HbA1c, alanine aminotransferase and albuminuria. Subjects characterized by high consumption of food, alcohol and cigarettes (type 2 pattern) had high levels of γ-glutamyl transpeptidase, triglycerides, HDL-cholesterol, blood pressure, and brachial-ankle pulse wave velocity. Subjects characterized by high physical activity (type 3 pattern) had low uric acid and mild elevation of alanine aminotransferase and aspartate aminotransferase. In multivariate regression analysis adjusted by age, gender and BMI, type 1 pattern was associated with higher HbA1c levels, systolic BP and brachial-ankle pulse wave velocity. Type 2 pattern was associated with higher HDL-cholesterol levels, triglycerides, aspartate aminotransferase, ɤ- glutamyl transpeptidase levels, and diastolic BP. The study identified three lifestyle patterns that were associated with distinct cardio-metabolic-renal parameters in T2DM patients. UMIN000010932.

Villitis of unknown aetiology: correlation of recurrence with clinical outcome.

LENUS (Irish Health Repository)

Feeley, L

2010-01-01

Villitis of unknown aetiology (VUA) is associated with adverse pregnancy outcome. Consequently, an ability to predict recurrence could be clinically relevant. We examined placentas where villitis was diagnosed in a previous pregnancy to establish the risk of recurrence and outcome. A total of 304 cases of VUA were diagnosed in our laboratory over a 4-year period. Subsequently, 19 of this cohort had a second placenta examined histologically. Recurrence and clinical outcome were recorded. Villitis recurred in 7 of 19 cases (37%). There was a high level of adverse pregnancy outcome in this cohort overall, characterised by small for gestational age infants and stillbirth, particularly in cases with high-grade villitis. We identified recurrent villitis more frequently than previously reported. Our findings confirm an association between high-grade villitis and poor outcome. Adequately powered prospective studies are required to determine if enhanced surveillance of subsequent pregnancies is indicated following a diagnosis of villitis.

Parameter and state estimation in a Neisseria meningitidis model: A study case of Niger

Science.gov (United States)

Bowong, S.; Mountaga, L.; Bah, A.; Tewa, J. J.; Kurths, J.

2016-12-01

Neisseria meningitidis (Nm) is a major cause of bacterial meningitidis outbreaks in Africa and the Middle East. The availability of yearly reported meningitis cases in the African meningitis belt offers the opportunity to analyze the transmission dynamics and the impact of control strategies. In this paper, we propose a method for the estimation of state variables that are not accessible to measurements and an unknown parameter in a Nm model. We suppose that the yearly number of Nm induced mortality and the total population are known inputs, which can be obtained from data, and the yearly number of new Nm cases is the model output. We also suppose that the Nm transmission rate is an unknown parameter. We first show how the recruitment rate into the population can be estimated using real data of the total population and Nm induced mortality. Then, we use an auxiliary system called observer whose solutions converge exponentially to those of the original model. This observer does not use the unknown infection transmission rate but only uses the known inputs and the model output. This allows us to estimate unmeasured state variables such as the number of carriers that play an important role in the transmission of the infection and the total number of infected individuals within a human community. Finally, we also provide a simple method to estimate the unknown Nm transmission rate. In order to validate the estimation results, numerical simulations are conducted using real data of Niger.

Lod score curves for phase-unknown matings.

Science.gov (United States)

Hulbert-Shearon, T; Boehnke, M; Lange, K

1996-01-01

For a phase-unknown nuclear family, we show that the likelihood and lod score are unimodal, and we describe conditions under which the maximum occurs at recombination fraction theta = 0, theta = 1/2, and 0 < theta < 1/2. These simply stated necessary and sufficient conditions seem to have escaped the notice of previous statistical geneticists.

Parameter extraction of different fuel cell models with transferred adaptive differential evolution

International Nuclear Information System (INIS)

Gong, Wenyin; Yan, Xuesong; Liu, Xiaobo; Cai, Zhihua

2015-01-01

To improve the design and control of FC (fuel cell) models, it is important to extract their unknown parameters. Generally, the parameter extraction problems of FC models can be transformed as nonlinear and multi-variable optimization problems. To extract the parameters of different FC models exactly and fast, in this paper, we propose a transferred adaptive DE (differential evolution) framework, in which the successful parameters of the adaptive DE solving previous problems are properly transferred to solve new optimization problems in the similar problem-domains. Based on this framework, an improved adaptive DE method (TRADE, in short) is presented as an illustration. To verify the performance of our proposal, TRADE is used to extract the unknown parameters of two types of fuel cell models, i.e., PEMFC (proton exchange membrane fuel cell) and SOFC (solid oxide fuel cell). The results of TRADE are also compared with those of other state-of-the-art EAs (evolutionary algorithms). Even though the modification is very simple, the results indicate that TRADE can extract the parameters of both PEMFC and SOFC models exactly and fast. Moreover, the V–I characteristics obtained by TRADE agree well with the simulated and experimental data in all cases for both types of fuel cell models. Also, it improves the performance of the original adaptive DE significantly in terms of both the quality of final solutions and the convergence speed in all cases. Additionally, TRADE is able to provide better results compared with other EAs. - Highlights: • A framework of transferred adaptive differential evolution is proposed. • Based on the framework, an improved differential evolution (TRADE) is presented. • TRADE obtains very promising results to extract the parameters of PEMFC and SOFC models

Finite-time sliding surface constrained control for a robot manipulator with an unknown deadzone and disturbance.

Science.gov (United States)

Ik Han, Seong; Lee, Jangmyung

2016-11-01

This paper presents finite-time sliding mode control (FSMC) with predefined constraints for the tracking error and sliding surface in order to obtain robust positioning of a robot manipulator with input nonlinearity due to an unknown deadzone and external disturbance. An assumed model feedforward FSMC was designed to avoid tedious identification procedures for the manipulator parameters and to obtain a fast response time. Two constraint switching control functions based on the tracking error and finite-time sliding surface were added to the FSMC to guarantee the predefined tracking performance despite the presence of an unknown deadzone and disturbance. The tracking error due to the deadzone and disturbance can be suppressed within the predefined error boundary simply by tuning the gain value of the constraint switching function and without the addition of an extra compensator. Therefore, the designed constraint controller has a simpler structure than conventional transformed error constraint methods and the sliding surface constraint scheme can also indirectly guarantee the tracking error constraint while being more stable than the tracking error constraint control. A simulation and experiment were performed on an articulated robot manipulator to validate the proposed control schemes. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive Incentive Controls for Stackelberg Games with Unknown Cost Functionals.

Science.gov (United States)

1984-01-01

APR EZT:: F I AN 73S e OsL:-: UNCLASSI?:-- Q4~.’~- .A.., 6, *~*i i~~*~~*.- U ADAPTIVE INCENTIVE CONTROLS FOR STACKELBERG GAMES WITH UNKNOWN COST...AD-A161 885 ADAPTIVE INCENTIVE CONTROLS FOR STACKELBERG GAMES WITH i/1 UNKNOWN COST FUNCTIONALSCU) ILLINOIS UNIV AT URBANA DECISION AND CONTROL LAB T...ORGANIZATION 6b. OFFICE SYMBOL 7.. NAME OF MONITORING ORGANIZATION CoriaeLcenef~pda~ Joint Services Electronics Program Laboratory, Univ. of Illinois N/A

Optimal Design of Shock Tube Experiments for Parameter Inference

KAUST Repository

Bisetti, Fabrizio

2014-01-06

We develop a Bayesian framework for the optimal experimental design of the shock tube experiments which are being carried out at the KAUST Clean Combustion Research Center. The unknown parameters are the pre-exponential parameters and the activation energies in the reaction rate expressions. The control parameters are the initial mixture composition and the temperature. The approach is based on first building a polynomial based surrogate model for the observables relevant to the shock tube experiments. Based on these surrogates, a novel MAP based approach is used to estimate the expected information gain in the proposed experiments, and to select the best experimental set-ups yielding the optimal expected information gains. The validity of the approach is tested using synthetic data generated by sampling the PC surrogate. We finally outline a methodology for validation using actual laboratory experiments, and extending experimental design methodology to the cases where the control parameters are noisy.

Method for Determining the Time Parameter

Directory of Open Access Journals (Sweden)

K. P. Baslyk

2014-01-01

Full Text Available This article proposes a method for calculating one of the characteristics that represents the flight program of the first stage of ballistic rocket i.e. time parameter of the program of attack angle.In simulation of placing the payload for the first stage, a program of flight is used which consists of three segments, namely a vertical climb of the rocket, a segment of programmed reversal by attack angle, and a segment of gravitational reversal with zero angle of attack.The programed reversal by attack angle is simulated as a rapidly decreasing and increasing function. This function depends on the attack angle amplitude, time and time parameter.If the projected and ballistic parameters and the amplitude of attack angle were determined this coefficient is calculated based the constraint that the rocket velocity is equal to 0.8 from the sound velocity (0,264 km/sec when the angle of attack becomes equal to zero. Such constraint is transformed to the nonlinear equation, which can be solved using a Newton method.The attack angle amplitude value is unknown for the design analysis. Exceeding some maximum admissible value for this parameter may lead to excessive trajectory collapsing (foreshortening, which can be identified as an arising negative trajectory angle.Consequently, therefore it is necessary to compute the maximum value of the attack angle amplitude with the following constraints: a trajectory angle is positive during the entire first stage flight and the rocket velocity is equal to 0,264 km/sec by the end of program of angle attack. The problem can be formulated as a task of the nonlinear programming, minimization of the modified Lagrange function, which is solved using the multipliers method.If multipliers and penalty parameter are constant the optimization problem without constraints takes place. Using the determined coordinate descent method allows solving the problem of modified Lagrange function of unconstrained minimization with fixed

On Drift Parameter Estimation in Models with Fractional Brownian Motion by Discrete Observations

Directory of Open Access Journals (Sweden)

Yuliya Mishura

2014-06-01

Full Text Available We study a problem of an unknown drift parameter estimation in a stochastic differen- tial equation driven by fractional Brownian motion. We represent the likelihood ratio as a function of the observable process. The form of this representation is in general rather complicated. However, in the simplest case it can be simplified and we can discretize it to establish the a. s. convergence of the discretized version of maximum likelihood estimator to the true value of parameter. We also investigate a non-standard estimator of the drift parameter showing further its strong consistency. 

Speeding up transmissions of unknown quantum information along Ising-type quantum channels

International Nuclear Information System (INIS)

Guo W J; Wei L F

2017-01-01

Quantum teleportation with entanglement channels and a series of two-qubit SWAP gates between the nearest-neighbor qubits are usually utilized to achieve the transfers of unknown quantum state from the sender to the distant receiver. In this paper, by simplifying the usual SWAP gates we propose an approach to speed up the transmissions of unknown quantum information, specifically including the single-qubit unknown state and two-qubit unknown entangled ones, by a series of entangling and disentangling operations between the remote qubits with distant interactions. The generic proposal is demonstrated specifically with experimentally-existing Ising-type quantum channels without transverse interaction; liquid NMR-molecules driven by global radio frequency electromagnetic pulses and capacitively-coupled Josephson circuits driven by local microwave pulses. The proposal should be particularly useful to set up the connections between the distant qubits in a chip of quantum computing. (paper)

Robust Control for the Segway with Unknown Control Coefficient and Model Uncertainties

Directory of Open Access Journals (Sweden)

Byung Woo Kim

2016-06-01

Full Text Available The Segway, which is a popular vehicle nowadays, is an uncertain nonlinear system and has an unknown time-varying control coefficient. Thus, we should consider the unknown time-varying control coefficient and model uncertainties to design the controller. Motivated by this observation, we propose a robust control for the Segway with unknown control coefficient and model uncertainties. To deal with the time-varying unknown control coefficient, we employ the Nussbaum gain technique. We introduce an auxiliary variable to solve the underactuated problem. Due to the prescribed performance control technique, the proposed controller does not require the adaptive technique, neural network, and fuzzy logic to compensate the uncertainties. Therefore, it can be simple. From the Lyapunov stability theory, we prove that all signals in the closed-loop system are bounded. Finally, we provide the simulation results to demonstrate the effectiveness of the proposed control scheme.

Kidnapping Detection and Recognition in Previous Unknown Environment

Directory of Open Access Journals (Sweden)

Yang Tian

2017-01-01

Full Text Available An unaware event referred to as kidnapping makes the estimation result of localization incorrect. In a previous unknown environment, incorrect localization result causes incorrect mapping result in Simultaneous Localization and Mapping (SLAM by kidnapping. In this situation, the explored area and unexplored area are divided to make the kidnapping recovery difficult. To provide sufficient information on kidnapping, a framework to judge whether kidnapping has occurred and to identify the type of kidnapping with filter-based SLAM is proposed. The framework is called double kidnapping detection and recognition (DKDR by performing two checks before and after the “update” process with different metrics in real time. To explain one of the principles of DKDR, we describe a property of filter-based SLAM that corrects the mapping result of the environment using the current observations after the “update” process. Two classical filter-based SLAM algorithms, Extend Kalman Filter (EKF SLAM and Particle Filter (PF SLAM, are modified to show that DKDR can be simply and widely applied in existing filter-based SLAM algorithms. Furthermore, a technique to determine the adapted thresholds of metrics in real time without previous data is presented. Both simulated and experimental results demonstrate the validity and accuracy of the proposed method.

Cellular signaling identifiability analysis: a case study.

Science.gov (United States)

Roper, Ryan T; Pia Saccomani, Maria; Vicini, Paolo

2010-05-21

Two primary purposes for mathematical modeling in cell biology are (1) simulation for making predictions of experimental outcomes and (2) parameter estimation for drawing inferences from experimental data about unobserved aspects of biological systems. While the former purpose has become common in the biological sciences, the latter is less common, particularly when studying cellular and subcellular phenomena such as signaling-the focus of the current study. Data are difficult to obtain at this level. Therefore, even models of only modest complexity can contain parameters for which the available data are insufficient for estimation. In the present study, we use a set of published cellular signaling models to address issues related to global parameter identifiability. That is, we address the following question: assuming known time courses for some model variables, which parameters is it theoretically impossible to estimate, even with continuous, noise-free data? Following an introduction to this problem and its relevance, we perform a full identifiability analysis on a set of cellular signaling models using DAISY (Differential Algebra for the Identifiability of SYstems). We use our analysis to bring to light important issues related to parameter identifiability in ordinary differential equation (ODE) models. We contend that this is, as of yet, an under-appreciated issue in biological modeling and, more particularly, cell biology. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Indistinguishability and identifiability of kinetic models for the MurC reaction in peptidoglycan biosynthesis.

Science.gov (United States)

Hattersley, J G; Pérez-Velázquez, J; Chappell, M J; Bearup, D; Roper, D; Dowson, C; Bugg, T; Evans, N D

2011-11-01

An important question in Systems Biology is the design of experiments that enable discrimination between two (or more) competing chemical pathway models or biological mechanisms. In this paper analysis is performed between two different models describing the kinetic mechanism of a three-substrate three-product reaction, namely the MurC reaction in the cytoplasmic phase of peptidoglycan biosynthesis. One model involves ordered substrate binding and ordered release of the three products; the competing model also assumes ordered substrate binding, but with fast release of the three products. The two versions are shown to be distinguishable; however, if standard quasi-steady-state assumptions are made distinguishability cannot be determined. Once model structure uniqueness is ensured the experimenter must determine if it is possible to successfully recover rate constant values given the experiment observations, a process known as structural identifiability. Structural identifiability analysis is carried out for both models to determine which of the unknown reaction parameters can be determined uniquely, or otherwise, from the ideal system outputs. This structural analysis forms an integrated step towards the modelling of the full pathway of the cytoplasmic phase of peptidoglycan biosynthesis. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Parameter estimation for stiff deterministic dynamical systems via ensemble Kalman filter

International Nuclear Information System (INIS)

Arnold, Andrea; Calvetti, Daniela; Somersalo, Erkki

2014-01-01

A commonly encountered problem in numerous areas of applications is to estimate the unknown coefficients of a dynamical system from direct or indirect observations at discrete times of some of the components of the state vector. A related problem is to estimate unobserved components of the state. An egregious example of such a problem is provided by metabolic models, in which the numerous model parameters and the concentrations of the metabolites in tissue are to be estimated from concentration data in the blood. A popular method for addressing similar questions in stochastic and turbulent dynamics is the ensemble Kalman filter (EnKF), a particle-based filtering method that generalizes classical Kalman filtering. In this work, we adapt the EnKF algorithm for deterministic systems in which the numerical approximation error is interpreted as a stochastic drift with variance based on classical error estimates of numerical integrators. This approach, which is particularly suitable for stiff systems where the stiffness may depend on the parameters, allows us to effectively exploit the parallel nature of particle methods. Moreover, we demonstrate how spatial prior information about the state vector, which helps the stability of the computed solution, can be incorporated into the filter. The viability of the approach is shown by computed examples, including a metabolic system modeling an ischemic episode in skeletal muscle, with a high number of unknown parameters. (paper)

Clostridium difficile: A healthcare-associated infection of unknown ...

African Journals Online (AJOL)

Clostridium difficile: A healthcare-associated infection of unknown significance in adults in sub-Saharan Africa. ... Abstract. Background: Clostridium difficile infection (CDI) causes a high burden of disease in high-resource healthcare systems, with significant morbidity, mortality, and financial implications. CDI is a ...

Severe scratcher-reaction: an unknown health hazard?

Directory of Open Access Journals (Sweden)

Carsten Sauer Mikkelsen

2015-03-01

Full Text Available Tattoos are well known to cause skin problems and the number of reported adverse reactions after tattooing has increased. Illegally imported tattoo ink is unrestrained and can contain unknown ingredients and contamination thereby posing a serious health hazard. We present a case illustrating the risk of pronounced phototoxic allergic reaction and other severe complications after using home kit tattoo ink.

Uncovering the unknown: A grounded theory study exploring the impact of self-awareness on the culture of feedback in residency education.

Science.gov (United States)

Ramani, Subha; Könings, Karen; Mann, Karen V; van der Vleuten, Cees

2017-10-01

Self-assessment and reflection are essential for meaningful feedback. We aimed to explore whether the well-known Johari window model of self-awareness could guide feedback conversations between faculty and residents and enhance the institutional feedback culture. We had previously explored perceptions of residents and faculty regarding sociocultural factors impacting feedback. We re-analyzed data targeting themes related to self-assessment, reflection, feedback seeking and acceptance, aiming to generate individual and institutional feedback strategies applicable to each quadrant of the window. We identified the following themes for each quadrant: (1) Behaviors known to self and others - Validating the known; (2) Behaviors unknown to self but known to others - Accepting the blind; (3) Behaviors known to self and unknown to others - Disclosure of hidden; and (4) Behaviors unknown to self and others - Uncovering the unknown. Normalizing self-disclosure of limitations, encouraging feedback seeking, training in nonjudgmental feedback and providing opportunities for longitudinal relationships could promote self-awareness, ultimately expanding the "open" quadrant of the Johari window. The Johari window, a model of self-awareness in interpersonal communications, could provide a robust framework for individuals to improve their feedback conversations and institutions to design feedback initiatives that enhance its quality and impact.

Part 2. Development of Enhanced Statistical Methods for Assessing Health Effects Associated with an Unknown Number of Major Sources of Multiple Air Pollutants.

Science.gov (United States)

Park, Eun Sug; Symanski, Elaine; Han, Daikwon; Spiegelman, Clifford

2015-06-01

A major difficulty with assessing source-specific health effects is that source-specific exposures cannot be measured directly; rather, they need to be estimated by a source-apportionment method such as multivariate receptor modeling. The uncertainty in source apportionment (uncertainty in source-specific exposure estimates and model uncertainty due to the unknown number of sources and identifiability conditions) has been largely ignored in previous studies. Also, spatial dependence of multipollutant data collected from multiple monitoring sites has not yet been incorporated into multivariate receptor modeling. The objectives of this project are (1) to develop a multipollutant approach that incorporates both sources of uncertainty in source-apportionment into the assessment of source-specific health effects and (2) to develop enhanced multivariate receptor models that can account for spatial correlations in the multipollutant data collected from multiple sites. We employed a Bayesian hierarchical modeling framework consisting of multivariate receptor models, health-effects models, and a hierarchical model on latent source contributions. For the health model, we focused on the time-series design in this project. Each combination of number of sources and identifiability conditions (additional constraints on model parameters) defines a different model. We built a set of plausible models with extensive exploratory data analyses and with information from previous studies, and then computed posterior model probability to estimate model uncertainty. Parameter estimation and model uncertainty estimation were implemented simultaneously by Markov chain Monte Carlo (MCMC*) methods. We validated the methods using simulated data. We illustrated the methods using PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter) speciation data and mortality data from Phoenix, Arizona, and Houston, Texas. The Phoenix data included counts of cardiovascular deaths and daily PM2

Parameter and state estimation in nonlinear dynamical systems

Science.gov (United States)

Creveling, Daniel R.

This thesis is concerned with the problem of state and parameter estimation in nonlinear systems. The need to evaluate unknown parameters in models of nonlinear physical, biophysical and engineering systems occurs throughout the development of phenomenological or reduced models of dynamics. When verifying and validating these models, it is important to incorporate information from observations in an efficient manner. Using the idea of synchronization of nonlinear dynamical systems, this thesis develops a framework for presenting data to a candidate model of a physical process in a way that makes efficient use of the measured data while allowing for estimation of the unknown parameters in the model. The approach presented here builds on existing work that uses synchronization as a tool for parameter estimation. Some critical issues of stability in that work are addressed and a practical framework is developed for overcoming these difficulties. The central issue is the choice of coupling strength between the model and data. If the coupling is too strong, the model will reproduce the measured data regardless of the adequacy of the model or correctness of the parameters. If the coupling is too weak, nonlinearities in the dynamics could lead to complex dynamics rendering any cost function comparing the model to the data inadequate for the determination of model parameters. Two methods are introduced which seek to balance the need for coupling with the desire to allow the model to evolve in its natural manner without coupling. One method, 'balanced' synchronization, adds to the synchronization cost function a requirement that the conditional Lyapunov exponents of the model system, conditioned on being driven by the data, remain negative but small in magnitude. Another method allows the coupling between the data and the model to vary in time according to a specific form of differential equation. The coupling dynamics is damped to allow for a tendency toward zero coupling

Offline analysis in SNLS: measurement of type-Ia supernovae explosion rate and cosmological parameters

International Nuclear Information System (INIS)

Lusset, Vincent

2006-01-01

The Supernova Legacy Survey is a second generation experiment for the measurement of cosmological parameters using type-la supernovae. Il follows the discovery of the acceleration of the expansion of the Universe, attributed to an unknown 'dark energy'. This thesis presents a type-la supernovae search using an offline analysis of SNLS data. It makes it possible to detect the supernovae that were missed online and to study possible selection biases. One of its principal characteristics is that it uses entirely automatic selection criteria. This type of automated offline analysis had never been carried out before for data reaching this redshift. This analysis enabled us to discover 73 additional SNIa candidates compared to those identified in the real time analysis on the same data, representing an increase of more than 50% of the number of supernovae. The final Hubble diagram contains 262 SNIa which gives us, for a flat ACDM model, the following values for the cosmological parameters: Ω_M = 0,31 ± 0,028 (stat) ± 0,036 (syst) et Ω_A = 0,69. This offline analysis of SNLS data opens new horizons, both by checking for possible biases in current measurements of cosmological parameters by supernovae experiments and by preparing the third generation experiments, on the ground or in space, which will detect thousands of SNIa. (author) [fr

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

Fast estimation of space-robots inertia parameters: A modular mathematical formulation

Science.gov (United States)

Nabavi Chashmi, Seyed Yaser; Malaek, Seyed Mohammad-Bagher

2016-10-01

This work aims to propose a new technique that considerably helps enhance time and precision needed to identify ;Inertia Parameters (IPs); of a typical Autonomous Space-Robot (ASR). Operations might include, capturing an unknown Target Space-Object (TSO), ;active space-debris removal; or ;automated in-orbit assemblies;. In these operations generating precise successive commands are essential to the success of the mission. We show how a generalized, repeatable estimation-process could play an effective role to manage the operation. With the help of the well-known Force-Based approach, a new ;modular formulation; has been developed to simultaneously identify IPs of an ASR while it captures a TSO. The idea is to reorganize the equations with associated IPs with a ;Modular Set; of matrices instead of a single matrix representing the overall system dynamics. The devised Modular Matrix Set will then facilitate the estimation process. It provides a conjugate linear model in mass and inertia terms. The new formulation is, therefore, well-suited for ;simultaneous estimation processes; using recursive algorithms like RLS. Further enhancements would be needed for cases the effect of center of mass location becomes important. Extensive case studies reveal that estimation time is drastically reduced which in-turn paves the way to acquire better results.

Row Reduced Echelon Form for Solving Fully Fuzzy System with Unknown Coefficients

Directory of Open Access Journals (Sweden)

Ghassan Malkawi

2014-08-01

Full Text Available This study proposes a new method for finding a feasible fuzzy solution in positive Fully Fuzzy Linear System (FFLS, where the coefficients are unknown. The fully fuzzy system is transferred to linear system in order to obtain the solution using row reduced echelon form, thereafter; the crisp solution is restricted in obtaining the positive fuzzy solution. The fuzzy solution of FFLS is included crisp intervals, to assign alternative values of unknown entries of fuzzy numbers. To illustrate the proposed method, numerical examples are solved, where the entries of coefficients are unknown in right or left hand side, to demonstrate the contributions in this study.

Vision-based autonomous grasping of unknown piled objects

International Nuclear Information System (INIS)

Johnson, R.K.

1994-01-01

Computer vision techniques have been used to develop a vision-based grasping capability for autonomously picking and placing unknown piled objects. This work is currently being applied to the problem of hazardous waste sorting in support of the Department of Energy's Mixed Waste Operations Program

48 CFR 52.222-49 - Service Contract Act-Place of Performance Unknown.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 2 2010-10-01 2010-10-01 false Service Contract Act-Place... Provisions and Clauses 52.222-49 Service Contract Act—Place of Performance Unknown. As prescribed in 22.1006(f), insert the following clause: Service Contract Act—Place of Performance Unknown (MAY 1989) (a...

Adaptive Algorithm For Identification Of The Environment Parameters In Contact Tasks

Energy Technology Data Exchange (ETDEWEB)

Tuneski, Atanasko; Babunski, Darko [Faculty of Mechanical Engineering, ' St. Cyril and Methodius' University, Skopje (Macedonia, The Former Yugoslav Republic of)

2003-07-01

An adaptive algorithm for identification of the unknown parameters of the dynamic environment in contact tasks is proposed in this paper using the augmented least square estimation method. An approximate environment digital simulator for the continuous environment dynamics is derived, i.e. a discrete transfer function which has the approximately the same characteristics as the continuous environment dynamics is found. For solving this task a method named hold equivalence is used. The general model of the environment dynamics is given and the case when the environment dynamics is represented by second order models with parameter uncertainties is considered. (Author)

Parameter Estimation for Partial Differential Equations by Collage-Based Numerical Approximation

Directory of Open Access Journals (Sweden)

Xiaoyan Deng

2009-01-01

into a minimization problem of a function of several variables after the partial differential equation is approximated by a differential dynamical system. Then numerical schemes for solving this minimization problem are proposed, including grid approximation and ant colony optimization. The proposed schemes are applied to a parameter estimation problem for the Belousov-Zhabotinskii equation, and the results show that the proposed approximation method is efficient for both linear and nonlinear partial differential equations with respect to unknown parameters. At worst, the presented method provides an excellent starting point for traditional inversion methods that must first select a good starting point.

Adaptive Algorithm For Identification Of The Environment Parameters In Contact Tasks

International Nuclear Information System (INIS)

Tuneski, Atanasko; Babunski, Darko

2003-01-01

An adaptive algorithm for identification of the unknown parameters of the dynamic environment in contact tasks is proposed in this paper using the augmented least square estimation method. An approximate environment digital simulator for the continuous environment dynamics is derived, i.e. a discrete transfer function which has the approximately the same characteristics as the continuous environment dynamics is found. For solving this task a method named hold equivalence is used. The general model of the environment dynamics is given and the case when the environment dynamics is represented by second order models with parameter uncertainties is considered. (Author)

Parameter Estimation of Partial Differential Equation Models

KAUST Repository

Xun, Xiaolei

2013-09-01

Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown and need to be estimated from the measurements of the dynamic system in the presence of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from long-range infrared light detection and ranging data. Supplementary materials for this article are available online. © 2013 American Statistical Association.

A simulation-based approach to capturing auto-correlated demand parameter uncertainty in inventory management

NARCIS (Netherlands)

Akçay, A.E.; Biller, B.; Tayur, S.

2012-01-01

We consider a repeated newsvendor setting where the parameters of the demand distribution are unknown, and we study the problem of setting inventory targets using only a limited amount of historical demand data. We assume that the demand process is autocorrelated and represented by an

Comparison of Parameter Identification Techniques

Directory of Open Access Journals (Sweden)

Eder Rafael

2016-01-01

Full Text Available Model-based control of mechatronic systems requires excellent knowledge about the physical behavior of each component. For several types of components of a system, e.g. mechanical or electrical ones, the dynamic behavior can be described by means of a mathematic model consisting of a set of differential equations, difference equations and/or algebraic constraint equations. The knowledge of a realistic mathematic model and its parameter values is essential to represent the behaviour of a mechatronic system. Frequently it is hard or impossible to obtain all required values of the model parameters from the producer, so an appropriate parameter estimation technique is required to compute missing parameters. A manifold of parameter identification techniques can be found in the literature, but their suitability depends on the mathematic model. Previous work dealt with the automatic assembly of mathematical models of serial and parallel robots with drives and controllers within the dynamic multibody simulation code HOTINT as fully-fledged mechatronic simulation. Several parameters of such robot models were identified successfully by our embedded algorithm. The present work proposes an improved version of the identification algorithm with higher performance. The quality of the identified parameter values and the computation effort are compared with another standard technique.

Inferior petrosal sinus sampling in the diagnosis of adrenocorticotropin dependent Cushing syndrome with unknown origin

International Nuclear Information System (INIS)

Shen Xuefeng; Yuan Dequan; Yue Ming; Feng Juanjuan

2011-01-01

Objective: To evaluate the value of inferior petrosal sinus sampling (IPSS) in the diagnosis of adrenocorticotropic hormone (ACTH) dependent Cushing syndrome (CS) with unknown origin. Methods: IPSS was carried out for the diagnosis of 16 cases with ACTH dependent CS who had not been identified after a series of dexamethasone suppression tests and radiological examinations. The ratio of inferior petrosal sinus/peripheral ACTH was assayed. The sensitivity and specificity of diagnosis of the Cushing disease were estimated. Results: The inferior petrosal sinus/peripheral ACTH ratio was over 2.0 in 13 cases. Twelve cases underwent surgery with pathological diagnosis of pituitary ACTH adenoma, 1 patient relieved after γ knife treatment. The ratio was < 2.0 in 3 cases including 2 pulmonary carcinoid and one pituitary ACTH adenoma. The sensitivity and specify of IPSS for the diagnosis of Cushing disease were 13/14 and 2/2 respectively. Conclusion: IPSS was a safe technique with high sensitivity, specify and infrequent complications in the diagnosis of ACTH dependent Cushing disease. It had great clinical value in the differential diagnosis of ACTH dependent Cushing disease with unknown origin. (authors)

Carcinoma of Unknown Primary Treatment (PDQ®)—Patient Version

Science.gov (United States)

Carcinoma of unknown primary (CUP), treatment can include surgery, radiation therapy, chemotherapy, or hormone therapy. Get detailed information about the diagnosis and treatment of CUP in this expert-reviewed summary.

Melanoma of unknown origin: a case series.

LENUS (Irish Health Repository)

Kelly, J

2010-12-01

The natural history of metastatic melanoma involving lymph nodes, in the absence of a known primary site (cutaneous, ocular or mucosal) has, to date, been poorly defined; and the optimal management of this rare subtype of disease is therefore unclear. Melanomas of unknown primary site (MUP) are estimated to comprise between 3.7 and 6% of all melanomas (Anbari et al. in Cancer 79:1861-1821, 1997).

Parameter identification of civil engineering structures

Science.gov (United States)

Juang, J. N.; Sun, C. T.

1980-01-01

This paper concerns the development of an identification method required in determining structural parameter variations for systems subjected to an extended exposure to the environment. The concept of structural identifiability of a large scale structural system in the absence of damping is presented. Three criteria are established indicating that a large number of system parameters (the coefficient parameters of the differential equations) can be identified by a few actuators and sensors. An eight-bay-fifteen-story frame structure is used as example. A simple model is employed for analyzing the dynamic response of the frame structure.

Performance Analysis of Blind Subspace-Based Signature Estimation Algorithms for DS-CDMA Systems with Unknown Correlated Noise

Science.gov (United States)

Zarifi, Keyvan; Gershman, Alex B.

2006-12-01

We analyze the performance of two popular blind subspace-based signature waveform estimation techniques proposed by Wang and Poor and Buzzi and Poor for direct-sequence code division multiple-access (DS-CDMA) systems with unknown correlated noise. Using the first-order perturbation theory, analytical expressions for the mean-square error (MSE) of these algorithms are derived. We also obtain simple high SNR approximations of the MSE expressions which explicitly clarify how the performance of these techniques depends on the environmental parameters and how it is related to that of the conventional techniques that are based on the standard white noise assumption. Numerical examples further verify the consistency of the obtained analytical results with simulation results.

Atmospheric turbulence profiling with unknown power spectral density

Science.gov (United States)

Helin, Tapio; Kindermann, Stefan; Lehtonen, Jonatan; Ramlau, Ronny

2018-04-01

Adaptive optics (AO) is a technology in modern ground-based optical telescopes to compensate for the wavefront distortions caused by atmospheric turbulence. One method that allows to retrieve information about the atmosphere from telescope data is so-called SLODAR, where the atmospheric turbulence profile is estimated based on correlation data of Shack-Hartmann wavefront measurements. This approach relies on a layered Kolmogorov turbulence model. In this article, we propose a novel extension of the SLODAR concept by including a general non-Kolmogorov turbulence layer close to the ground with an unknown power spectral density. We prove that the joint estimation problem of the turbulence profile above ground simultaneously with the unknown power spectral density at the ground is ill-posed and propose three numerical reconstruction methods. We demonstrate by numerical simulations that our methods lead to substantial improvements in the turbulence profile reconstruction compared to the standard SLODAR-type approach. Also, our methods can accurately locate local perturbations in non-Kolmogorov power spectral densities.

Computational Approach to Annotating Variants of Unknown Significance in Clinical Next Generation Sequencing.

Science.gov (United States)

Schulz, Wade L; Tormey, Christopher A; Torres, Richard

2015-01-01

Next generation sequencing (NGS) has become a common technology in the clinical laboratory, particularly for the analysis of malignant neoplasms. However, most mutations identified by NGS are variants of unknown clinical significance (VOUS). Although the approach to define these variants differs by institution, software algorithms that predict variant effect on protein function may be used. However, these algorithms commonly generate conflicting results, potentially adding uncertainty to interpretation. In this review, we examine several computational tools used to predict whether a variant has clinical significance. In addition to describing the role of these tools in clinical diagnostics, we assess their efficacy in analyzing known pathogenic and benign variants in hematologic malignancies. Copyright© by the American Society for Clinical Pathology (ASCP).

Spatio-temporal modeling of nonlinear distributed parameter systems

CERN Document Server

Li, Han-Xiong

2011-01-01

The purpose of this volume is to provide a brief review of the previous work on model reduction and identifi cation of distributed parameter systems (DPS), and develop new spatio-temporal models and their relevant identifi cation approaches. In this book, a systematic overview and classifi cation on the modeling of DPS is presented fi rst, which includes model reduction, parameter estimation and system identifi cation. Next, a class of block-oriented nonlinear systems in traditional lumped parameter systems (LPS) is extended to DPS, which results in the spatio-temporal Wiener and Hammerstein s

Identifying dominant controls on hydrologic parameter transfer from gauged to ungauged catchments: a comparative hydrology approach

Science.gov (United States)

Singh, R.; Archfield, S.A.; Wagener, T.

2014-01-01

Daily streamflow information is critical for solving various hydrologic problems, though observations of continuous streamflow for model calibration are available at only a small fraction of the world’s rivers. One approach to estimate daily streamflow at an ungauged location is to transfer rainfall–runoff model parameters calibrated at a gauged (donor) catchment to an ungauged (receiver) catchment of interest. Central to this approach is the selection of a hydrologically similar donor. No single metric or set of metrics of hydrologic similarity have been demonstrated to consistently select a suitable donor catchment. We design an experiment to diagnose the dominant controls on successful hydrologic model parameter transfer. We calibrate a lumped rainfall–runoff model to 83 stream gauges across the United States. All locations are USGS reference gauges with minimal human influence. Parameter sets from the calibrated models are then transferred to each of the other catchments and the performance of the transferred parameters is assessed. This transfer experiment is carried out both at the scale of the entire US and then for six geographic regions. We use classification and regression tree (CART) analysis to determine the relationship between catchment similarity and performance of transferred parameters. Similarity is defined using physical/climatic catchment characteristics, as well as streamflow response characteristics (signatures such as baseflow index and runoff ratio). Across the entire US, successful parameter transfer is governed by similarity in elevation and climate, and high similarity in streamflow signatures. Controls vary for different geographic regions though. Geology followed by drainage, topography and climate constitute the dominant similarity metrics in forested eastern mountains and plateaus, whereas agricultural land use relates most strongly with successful parameter transfer in the humid plains.

Quantitative comparative linguistics based on tiny corpora: N-gram language identification of wordlists of known and unknown languages from Amazonia and beyond

NARCIS (Netherlands)

Seifart, F.; Mundry, R.

2015-01-01

Can an unknown Amazonian language be identified by statistical procedures based on n-gram frequencies if only a short list of words is available and at the same time, the available data of the potential candidate languages are also limited to relatively short wordlists? In this paper we show that

A hybrid search algorithm for swarm robots searching in an unknown environment.

Science.gov (United States)

Li, Shoutao; Li, Lina; Lee, Gordon; Zhang, Hao

2014-01-01

This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency.

Adaptive synchronization of fractional Lorenz systems using a reduced number of control signals and parameters

International Nuclear Information System (INIS)

Aguila-Camacho, Norelys; Duarte-Mermoud, Manuel A.; Delgado-Aguilera, Efredy

2016-01-01

This paper analyzes the synchronization of two fractional Lorenz systems in two cases: the first one considering fractional Lorenz systems with unknown parameters, and the second one considering known upper bounds on some of the fractional Lorenz systems parameters. The proposed control strategies use a reduced number of control signals and control parameters, employing mild assumptions. The stability of the synchronization errors is analytically demonstrated in all cases, and the convergence to zero of the synchronization errors is analytically proved in the case when the upper bounds on some system parameters are assumed to be known. Simulation studies are presented, which allows verifying the effectiveness of the proposed control strategies.

Parameter-space metric of semicoherent searches for continuous gravitational waves

International Nuclear Information System (INIS)

Pletsch, Holger J.

2010-01-01

Continuous gravitational-wave (CW) signals such as emitted by spinning neutron stars are an important target class for current detectors. However, the enormous computational demand prohibits fully coherent broadband all-sky searches for prior unknown CW sources over wide ranges of parameter space and for yearlong observation times. More efficient hierarchical ''semicoherent'' search strategies divide the data into segments much shorter than one year, which are analyzed coherently; then detection statistics from different segments are combined incoherently. To optimally perform the incoherent combination, understanding of the underlying parameter-space structure is requisite. This problem is addressed here by using new coordinates on the parameter space, which yield the first analytical parameter-space metric for the incoherent combination step. This semicoherent metric applies to broadband all-sky surveys (also embedding directed searches at fixed sky position) for isolated CW sources. Furthermore, the additional metric resolution attained through the combination of segments is studied. From the search parameters (sky position, frequency, and frequency derivatives), solely the metric resolution in the frequency derivatives is found to significantly increase with the number of segments.

Genetic parameters for reproductive traits in female Nile tilapia (Oreochromis niloticus): II. Fecundity and fertility

NARCIS (Netherlands)

Trong, T.Q.; Arendonk, van J.A.M.; Komen, J.

2013-01-01

Harvest weight is the main trait in Nile tilapia (Oreochromis niloticus) breeding programmes. The effects of selection for harvest weight on female reproductive traits are unknown. In this paper we estimate genetic parameters for reproductive traits and their correlation with harvest weight using

How to know unknown fungi: the role of a herbarium.

Science.gov (United States)

Brock, Patrick M; Döring, Heidi; Bidartondo, Martin I

2009-01-01

The development of a universal approach to the identification of fungi from the environment is impeded by the limited number and narrow phylogenetic range of the named internal transcribed spacer DNA sequences available on GenBank. The goal here was to assess the potential impact of systematic DNA sequencing from a fungal herbarium collection. DNA sequences were generated from a diverse set of 279 specimens deposited at the fungal herbarium of the Royal Botanic Gardens at Kew (UK) and bioinformatic analyses were used to study their overlap with the public database. It is estimated that c. 70% of the herbarium taxonomic diversity is not yet represented in GenBank and that a further c. 10% of our sequences match solely to 'environmental samples' or fungi otherwise unidentified. Here it is shown that the unsampled diversity residing in fungal herbaria can substantially enlarge the coverage of GenBank's fully identified sequence pool to ameliorate the problem of environmental unknowns and to aid in the detection of truly novel fungi by molecular data.

Dynamic Parameter Identification of Hydrodynamic Bearing-Rotor System

Directory of Open Access Journals (Sweden)

Zhiqiang Song

2015-01-01

Full Text Available A new method called modal parameter genetic time domain identification was employed to study the characteristics of the bearing-rotor system. A multifrequency signal decomposition technology to identify the main components of the measured signal and reject the image mode produced by noise has been used. The first- and second-order natural frequency and damping ratios of the shaft system are identified. Furthermore, because of the deficiency of the traditional least square method, a new genetic identification method to identify the bearing dynamic characteristic parameters has been proposed. The method has been effective albeit with few testing points and operation cases. The derivation of oil-film dynamic coefficients could also provide a basis for shaft system natural vibration characteristic and vibration response analysis. Using the identified dynamic coefficients as the supporting condition, the shaft system modal characteristics were studied. The calculated first- and second-order natural frequencies match quite well those obtained from the modal parameter identification. It was proved that the modal parameter and physical parameter identification methods utilized in this paper are reasonable.

Uncertainty of Modal Parameters Estimated by ARMA Models

DEFF Research Database (Denmark)

Jensen, Jakob Laigaard; Brincker, Rune; Rytter, Anders

In this paper the uncertainties of identified modal parameters such as eigenfrequencies and damping ratios are assessed. From the measured response of dynamic excited structures the modal parameters may be identified and provide important structural knowledge. However the uncertainty of the param...

Adaptive Parameter Estimation of Person Recognition Model in a Stochastic Human Tracking Process

Science.gov (United States)

Nakanishi, W.; Fuse, T.; Ishikawa, T.

2015-05-01

This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation using general state space model. Firstly we explain the way to formulate human tracking in general state space model with their components. Then referring to previous researches, we use Bhattacharyya coefficient to formulate observation model of general state space model, which is corresponding to person recognition model. The observation model in this paper is a function of Bhattacharyya coefficient with one unknown parameter. At last we sequentially estimate this parameter in real dataset with some settings. Results showed that sequential parameter estimation was succeeded and were consistent with observation situations such as occlusions.

Previously Unidentified Single Nucleotide Polymorphisms in HIV/AIDS Cases Associate with Clinical Parameters and Disease Progression

Directory of Open Access Journals (Sweden)

Vladimir V. Anokhin

2016-01-01

Full Text Available The genetic background of an individual plays an important role in the progression of HIV infection to AIDS. Identifying previously unknown or uncharacterized single nucleotide polymorphisms (SNPs that associate with disease progression may reveal important therapeutic targets and provide a greater understanding of disease pathogenesis. In the present study, we employed ultra-high multiplex PCR on an Ion Torrent next-generation sequencing platform to sequence 23 innate immune genes from 94 individuals with HIV/AIDS. This data was used to identify potential associations of SNPs with clinical parameters and disease progression. SNPs that associated with an increased viral load were identified in the genes for the interleukin 15 receptor (IL15RA, toll-like receptor 7 (TLR7, tripartite motif-containing protein 5 (TRIM5, and two killer-cell immunoglobulin-like receptors (KIR2DL1 and KIR2DL3. Additionally, SNPs that associated with progression from HIV infection to AIDS were identified in two 2′-5′-oligoadenylate synthetase genes (OAS2 and OAS3. In contrast, other SNPs identified in OAS2 and OAS3 genes, as well as in the TRIM5 and KIR2DS4 genes, were associated with a slower progression of disease. Taken together, our data demonstrates the utility of ultra-high multiplex PCR in identifying polymorphisms of potential clinical significance and further,identifies SNPs that may play a role in HIV pathogenesis.

The Effect of Known-and-Unknown Word Combinations on Intentional Vocabulary Learning

Science.gov (United States)

Kasahara, Kiwamu

2011-01-01

The purpose of this study is to examine whether learning a known-and-unknown word combination is superior in terms of retention and retrieval of meaning to learning a single unknown word. The term "combination" in this study means a two-word collocation of a familiar word and a word that is new to the participants. Following the results of…

Estimations of parameters in Pareto reliability model in the presence of masked data

International Nuclear Information System (INIS)

Sarhan, Ammar M.

2003-01-01

Estimations of parameters included in the individual distributions of the life times of system components in a series system are considered in this paper based on masked system life test data. We consider a series system of two independent components each has a Pareto distributed lifetime. The maximum likelihood and Bayes estimators for the parameters and the values of the reliability of the system's components at a specific time are obtained. Symmetrical triangular prior distributions are assumed for the unknown parameters to be estimated in obtaining the Bayes estimators of these parameters. Large simulation studies are done in order: (i) explain how one can utilize the theoretical results obtained; (ii) compare the maximum likelihood and Bayes estimates obtained of the underlying parameters; and (iii) study the influence of the masking level and the sample size on the accuracy of the estimates obtained

Zoonotic and vector borne agents causing disease in adult patients hospitalized due to fever of unknown origin in Thailand

Directory of Open Access Journals (Sweden)

Soawapak Hinjoy

2017-10-01

Full Text Available Objective: To determine the etiologic agents of fever of unknown origin among populations in agricultural communities and to assess the possible risk factors for zoonotic infections. Methods: Hospitalized patients with fever of unknown origin under physician care were asked to participate and provide blood samples for laboratory tests and screening for endemic diseases at the hospitals. Samples were stored at –80 °C until they were tested at Chulalongkorn University to identify additional pathogens. Results: We were able to identify the etiologic agents in 24.6% of the 463 enrolled patients. Zoonotic and vector borne agents were confirmed in 59 cases (12.7%. Dengue virus (7.3% was the most frequently detected disease followed by scrub typhus (3.2%. There were two cases of comorbidities of scrub typhus and dengue fever. The other six cases of zoonoses were leptospirosis, melioidosis, and Streptococcus suis infections. Patients with zoonotic/vector borne agents noticed rats in their houses and reported having contact with livestock feces more frequently than those patients without zoonotic/vector borne agents. Conclusions: Dengue virus and scrub typhus were mostly detected in the rainy season. During this specific season, clinicians should raise awareness of those diseases when any patients are admitted to the hospital with fever of an unidentified source.

Exome Sequencing Fails to Identify the Genetic Cause of Aicardi Syndrome

DEFF Research Database (Denmark)

Lund, Caroline; Striano, Pasquale; Sorte, Hanne Sørmo

2016-01-01

Aicardi syndrome (AS) is a well-characterized neurodevelopmental disorder with an unknown etiology. In this study, we performed whole-exome sequencing in 11 female patients with the diagnosis of AS, in order to identify the disease-causing gene. In particular, we focused on detecting variants in ...

Type Ia Supernova Intrinsic Magnitude Dispersion and the Fitting of Cosmological Parameters

Science.gov (United States)

Kim, A. G.

2011-02-01

I present an analysis for fitting cosmological parameters from a Hubble diagram of a standard candle with unknown intrinsic magnitude dispersion. The dispersion is determined from the data, simultaneously with the cosmological parameters. This contrasts with the strategies used to date. The advantages of the presented analysis are that it is done in a single fit (it is not iterative), it provides a statistically founded and unbiased estimate of the intrinsic dispersion, and its cosmological-parameter uncertainties account for the intrinsic-dispersion uncertainty. Applied to Type Ia supernovae, my strategy provides a statistical measure to test for subtypes and assess the significance of any magnitude corrections applied to the calibrated candle. Parameter bias and differences between likelihood distributions produced by the presented and currently used fitters are negligibly small for existing and projected supernova data sets.

Squamous cell carcinoma presenting with trigeminal anesthesia: An uncommon presentation of head & neck cancer with unknown primary.

Science.gov (United States)

Shah, Ameer T; Dagher, Walid I; O'Leary, Miriam A; Wein, Richard O

The differential diagnosis of facial anesthesia is vast. This may be secondary to trauma, neoplasm, both intracranial and extracranial, infection, and neurologic disease. When evaluating a patient with isolated facial anesthesia, the head and neck surgeon often thinks of adenoid cystic carcinoma, which has a propensity for perineural invasion and spread. When one thinks of head and neck squamous cell carcinoma with or without unknown primary, the typical presentation involves dysphagia, odynophagia, weight loss, hoarseness, or more commonly, a neck mass. Squamous cell carcinoma presenting as facial anesthesia and perineural spread, with no primary site is quite rare. Case presentations and review of the literature. Trigeminal anesthesia is an uncommon presentation of head and neck squamous cell carcinoma with unknown primary. We present two interesting cases of invasive squamous cell carcinoma of the trigeminal nerve, with no primary site identified. We will also review the literature of head and neck malignancies with perineural spread and the management techniques for the two different cases presented. Copyright © 2016 Elsevier Inc. All rights reserved.

Online Solution of Two-Player Zero-Sum Games for Continuous-Time Nonlinear Systems With Completely Unknown Dynamics.

Science.gov (United States)

Fu, Yue; Chai, Tianyou

2016-12-01

Regarding two-player zero-sum games of continuous-time nonlinear systems with completely unknown dynamics, this paper presents an online adaptive algorithm for learning the Nash equilibrium solution, i.e., the optimal policy pair. First, for known systems, the simultaneous policy updating algorithm (SPUA) is reviewed. A new analytical method to prove the convergence is presented. Then, based on the SPUA, without using a priori knowledge of any system dynamics, an online algorithm is proposed to simultaneously learn in real time either the minimal nonnegative solution of the Hamilton-Jacobi-Isaacs (HJI) equation or the generalized algebraic Riccati equation for linear systems as a special case, along with the optimal policy pair. The approximate solution to the HJI equation and the admissible policy pair is reexpressed by the approximation theorem. The unknown constants or weights of each are identified simultaneously by resorting to the recursive least square method. The convergence of the online algorithm to the optimal solutions is provided. A practical online algorithm is also developed. Simulation results illustrate the effectiveness of the proposed method.

IDENTIFIABILITY VERSUS HETEROGENEITY IN GROUNDWATER MODELING SYSTEMS

Directory of Open Access Journals (Sweden)

A M BENALI

2003-06-01

Full Text Available Review of history matching of reservoirs parameters in groundwater flow raises the problem of identifiability of aquifer systems. Lack of identifiability means that there exists parameters to which the heads are insensitive. From the guidelines of the study of the homogeneous case, we inspect the identifiability of the distributed transmissivity field of heterogeneous groundwater aquifers. These are derived from multiple realizations of a random function Y = log T  whose probability distribution function is normal. We follow the identifiability of the autocorrelated block transmissivities through the measure of the sensitivity of the local derivatives DTh = (∂hi  ∕ ∂Tj computed for each sample of a population N (0; σY, αY. Results obtained from an analysis of Monte Carlo type suggest that the more a system is heterogeneous, the less it is identifiable.

Outbreaks of Acute Gastroenteritis Transmitted by Person-to-Person Contact, Environmental Contamination, and Unknown Modes of Transmission--United States, 2009-2013.

Science.gov (United States)

Wikswo, Mary E; Kambhampati, Anita; Shioda, Kayoko; Walsh, Kelly A; Bowen, Anna; Hall, Aron J

2015-12-11

-term-care facilities (n = 4,894). In contrast, 59% (n = 143) of shigellosis outbreaks, 36% (n = 30) of salmonellosis outbreaks, and 32% (n = 84) of other or multiple etiology outbreaks were identified in child care facilities. NORS is the first U.S. surveillance system that provides national data on AGE outbreaks spread through person-to-person contact, environmental contamination, and unknown modes of transmission. The increase in reporting rates during 2009-2013 indicates that reporting to NORS improved notably in the 5 years since its inception. Norovirus is the most commonly reported cause of these outbreaks and, on the basis of epidemiologic data, might account for a substantial proportion of outbreaks without a reported etiology. During 2009-2013, norovirus accounted for most deaths and health care visits in AGE outbreaks spread through person-to-person contact, environmental contamination, and unknown modes of transmission. Recommendations for prevention and control of AGE outbreaks transmitted through person-to-person contact, environmental contamination, and unknown modes of transmission depend primarily on appropriate hand hygiene, environmental disinfection, and isolation of ill persons. NORS surveillance data can help identify priority targets for the development of future control strategies, including hygiene interventions and vaccines, and help monitor the frequency and severity of AGE outbreaks in the United States. Ongoing study of these AGE outbreaks can provide a better understanding of certain pathogens and their modes of transmission. For example, certain reported outbreak etiologies (e.g., Salmonella) are considered primarily foodborne pathogens but can be transmitted through multiple routes. Similarly, further examination of outbreaks of unknown etiology could help identify barriers to making an etiologic determination, to analyze clinical and epidemiologic clues suggestive of a probable etiology, and to discover new and emerging etiologic agents. Outbreak

Unknown quantum states: The quantum de Finetti representation

International Nuclear Information System (INIS)

Caves, Carlton M.; Fuchs, Christopher A.; Schack, Ruediger

2002-01-01

We present an elementary proof of the quantum de Finetti representation theorem, a quantum analog of de Finetti's classical theorem on exchangeable probability assignments. This contrasts with the original proof of Hudson and Moody [Z. Wahrschein. verw. Geb. 33, 343 (1976)], which relies on advanced mathematics and does not share the same potential for generalization. The classical de Finetti theorem provides an operational definition of the concept of an unknown probability in Bayesian probability theory, where probabilities are taken to be degrees of belief instead of objective states of nature. The quantum de Finetti theorem, in a closely analogous fashion, deals with exchangeable density-operator assignments and provides an operational definition of the concept of an ''unknown quantum state'' in quantum-state tomography. This result is especially important for information-based interpretations of quantum mechanics, where quantum states, like probabilities, are taken to be states of knowledge rather than states of nature. We further demonstrate that the theorem fails for real Hilbert spaces and discuss the significance of this point

Intrathecal immunoglobulin synthesis in patients with symptomatic epilepsy and epilepsy of unknown etiology ('cryptogenic').

Science.gov (United States)

Fauser, S; Soellner, C; Bien, C G; Tumani, H

2017-09-01

To compare the frequency of intrathecal immunoglobulin (Ig) synthesis in patients with symptomatic epilepsy and epilepsy of unknown etiology ('cryptogenic'). Patients with epileptic (n = 301) and non-epileptic (n = 10) seizures were retrospectively screened for autochthonous intrathecal Ig synthesis and oligoclonal bands (OCBs) in the cerebrospinal fluid. Intrathecal IgG/OCBs were detected in 8% of patients with epilepsies of unknown etiology, 5% of patients with first seizures of unknown cause and 0-4% of patients with epilepsy due to brain tumors, cerebrovascular disease or other etiologies. Intrathecal IgG/OCBs were not seen in patients with psychogenic seizures. Identical OCBs in serum and cerebrospinal fluid were more common in all patient groups (10-40% depending on underlying etiology). Intrathecal IgG synthesis/OCBs were observed slightly more frequently in patients with 'cryptogenic' epilepsy and with first seizures of unknown etiology than in other patient groups. However, this remained an infrequent finding and thus we could not confirm humoral immunity as a leading disease mechanism in patients with epilepsy in general or with unknown etiology in particular. © 2017 EAN.

RBF neural network based H∞ synchronization for unknown chaotic ...

Indian Academy of Sciences (India)

, 172 ... the effect of disturbance to an H∞ norm constraint. It is shown that ... unknown chaotic systems; linear matrix inequality (LMI); learning law. 1. Introduction .... (9) is RBFNN H∞ synchronized if the synchronization error e(t) satisfies. ∫ ∞.

Distributed Time-Varying Formation Robust Tracking for General Linear Multiagent Systems With Parameter Uncertainties and External Disturbances.

Science.gov (United States)

Hua, Yongzhao; Dong, Xiwang; Li, Qingdong; Ren, Zhang

2017-05-18

This paper investigates the time-varying formation robust tracking problems for high-order linear multiagent systems with a leader of unknown control input in the presence of heterogeneous parameter uncertainties and external disturbances. The followers need to accomplish an expected time-varying formation in the state space and track the state trajectory produced by the leader simultaneously. First, a time-varying formation robust tracking protocol with a totally distributed form is proposed utilizing the neighborhood state information. With the adaptive updating mechanism, neither any global knowledge about the communication topology nor the upper bounds of the parameter uncertainties, external disturbances and leader's unknown input are required in the proposed protocol. Then, in order to determine the control parameters, an algorithm with four steps is presented, where feasible conditions for the followers to accomplish the expected time-varying formation tracking are provided. Furthermore, based on the Lyapunov-like analysis theory, it is proved that the formation tracking error can converge to zero asymptotically. Finally, the effectiveness of the theoretical results is verified by simulation examples.

Surface ocean metabarcoding confirms limited diversity in planktonic foraminifera but reveals unknown hyper-abundant lineages.

Science.gov (United States)

Morard, Raphaël; Garet-Delmas, Marie-José; Mahé, Frédéric; Romac, Sarah; Poulain, Julie; Kucera, Michal; de Vargas, Colomban

2018-02-07

Since the advent of DNA metabarcoding surveys, the planktonic realm is considered a treasure trove of diversity, inhabited by a small number of abundant taxa, and a hugely diverse and taxonomically uncharacterized consortium of rare species. Here we assess if the apparent underestimation of plankton diversity applies universally. We target planktonic foraminifera, a group of protists whose known morphological diversity is limited, taxonomically resolved and linked to ribosomal DNA barcodes. We generated a pyrosequencing dataset of ~100,000 partial 18S rRNA foraminiferal sequences from 32 size fractioned photic-zone plankton samples collected at 8 stations in the Indian and Atlantic Oceans during the Tara Oceans expedition (2009-2012). We identified 69 genetic types belonging to 41 morphotaxa in our metabarcoding dataset. The diversity saturated at local and regional scale as well as in the three size fractions and the two depths sampled indicating that the diversity of foraminifera is modest and finite. The large majority of the newly discovered lineages occur in the small size fraction, neglected by classical taxonomy. These unknown lineages dominate the bulk [>0.8 µm] size fraction, implying that a considerable part of the planktonic foraminifera community biomass has its origin in unknown lineages.

Parameter Estimation of a Closed Loop Coupled Tank Time Varying System using Recursive Methods

International Nuclear Information System (INIS)

Basir, Siti Nora; Yussof, Hanafiah; Shamsuddin, Syamimi; Selamat, Hazlina; Zahari, Nur Ismarrubie

2013-01-01

This project investigates the direct identification of closed loop plant using discrete-time approach. The uses of Recursive Least Squares (RLS), Recursive Instrumental Variable (RIV) and Recursive Instrumental Variable with Centre-Of-Triangle (RIV + COT) in the parameter estimation of closed loop time varying system have been considered. The algorithms were applied in a coupled tank system that employs covariance resetting technique where the time of parameter changes occur is unknown. The performances of all the parameter estimation methods, RLS, RIV and RIV + COT were compared. The estimation of the system whose output was corrupted with white and coloured noises were investigated. Covariance resetting technique successfully executed when the parameters change. RIV + COT gives better estimates than RLS and RIV in terms of convergence and maximum overshoot

HLIBCov: Parallel Hierarchical Matrix Approximation of Large Covariance Matrices and Likelihoods with Applications in Parameter Identification

KAUST Repository

Litvinenko, Alexander

2017-01-01

and maximizing likelihood functions. We show that an approximate Cholesky factorization of a dense matrix of size $2M\\times 2M$ can be computed on a modern multi-core desktop in few minutes. Further, HLIBCov is used for estimating the unknown parameters

Structure identification and adaptive synchronization of uncertain general complex dynamical networks

International Nuclear Information System (INIS)

Xu Yuhua; Zhou Wuneng; Fang Jian'an; Lu Hongqian

2009-01-01

This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.

Structure identification and adaptive synchronization of uncertain general complex dynamical networks

Energy Technology Data Exchange (ETDEWEB)

Xu Yuhua, E-mail: yuhuaxu2004@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China) and Department of Maths, Yunyang Teacher' s College, Hubei 442000 (China); Zhou Wuneng, E-mail: wnzhou@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Fang Jian' an [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Lu Hongqian [Shandong Institute of Light Industry, Shandong Jinan 250353 (China)

2009-12-28

This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.

Content-Based Multimedia Retrieval in the Presence of Unknown User Preferences

DEFF Research Database (Denmark)

Beecks, Christian; Assent, Ira; Seidl, Thomas

2011-01-01

Content-based multimedia retrieval requires an appropriate similarity model which reflects user preferences. When these preferences are unknown or when the structure of the data collection is unclear, retrieving the most preferable objects the user has in mind is challenging, as the notion...... address the problem of content-based multimedia retrieval in the presence of unknown user preferences. Our idea consists in performing content-based retrieval by considering all possibilities in a family of similarity models simultaneously. To this end, we propose a novel content-based retrieval approach...

Liability for Unknown Risks: A Law and Economics Perspective

NARCIS (Netherlands)

M.G. Faure (Michael); L.T. Visscher (Louis); F. Weber (Franziska)

2017-01-01

textabstractIn the law and economics literature liability is generally regarded as an instrument which provides potential tortfeasors with incentives for optimal care taking. The question, however, arises whether liability can still provide those incentives when risks are unknown. That is the

Teleportation of Unknown Superpositions of Collective Atomic Coherent States

Institute of Scientific and Technical Information of China (English)

ZHENG ShiBiao

2001-01-01

We propose a scheme to teleport an unknown superposition of two atomic coherent states with different phases. Our scheme is based on resonant and dispersive atom-field interaction. Our scheme provides a possibility of teleporting macroscopic superposition states of many atoms first time.``

Near Identifiability of Dynamical Systems

Science.gov (United States)

Hadaegh, F. Y.; Bekey, G. A.

1987-01-01

Concepts regarding approximate mathematical models treated rigorously. Paper presents new results in analysis of structural identifiability, equivalence, and near equivalence between mathematical models and physical processes they represent. Helps establish rigorous mathematical basis for concepts related to structural identifiability and equivalence revealing fundamental requirements, tacit assumptions, and sources of error. "Structural identifiability," as used by workers in this field, loosely translates as meaning ability to specify unique mathematical model and set of model parameters that accurately predict behavior of corresponding physical system.

Adresse inconnue / Address unknown / Suchwiin Bulmyeong

OpenAIRE

Serge Gruzinski

2005-01-01

Tous les films asiatiques parlent de métissage, même ceux qui se présentent comme de vastes fresques historiques perdues dans le temps. Les emprunts aux traditions hollywoodiennes et européennes n'ont cessé d'enrichir une cinématographie aussi ancienne que celle du monde occidental. Dans Adresse inconnue (Address unknown) le cinéaste coréen Kim Ki-duk explore l'expérience du métissage et le corps du métis à la frontière entre Corée du Nord et Corée du sud. Fils d'un GI américain et noir et d'...

A neutron balance approach in critical parameter determination

International Nuclear Information System (INIS)

Dall'Osso, Aldo

2008-01-01

The determination of a critical parameter, process known also as criticality or eigenvalue search, is one of the major functionalities in neutronics codes. The determination of the critical boron concentration or the critical control rod position are two examples. Classical procedures used to solve this problem are based on the iterative Newton-Raphson method where the value of the parameter is changed until the eigenvalue matches the target. We present here a different approach where an equation, derived from the neutron balance, is set and the critical parameter is the unknown. Solving this equation is equivalent to solve an eigenvalue problem where the critical parameter is the eigenvalue. It is also shown that this approach can be seen as an application of inverse perturbation theory. This method reduces considerably the computation time in situations where changes on the critical parameter make a high distortion on the flux distribution, as it is the case of the control rods. Some numerical examples illustrate the performances and the gain in stability in cases of simultaneous control of criticality and axial offset of the power distribution. The application to the determination of the critical uranium enrichment in a transport code is also presented. The simplicity of the method makes its implementation in fuel bundle lattice and reactor codes very easy

Application of Parallel Hierarchical Matrices and Low-Rank Tensors in Spatial Statistics and Parameter Identification

KAUST Repository

Litvinenko, Alexander

2018-03-12

Part 1: Parallel H-matrices in spatial statistics 1. Motivation: improve statistical model 2. Tools: Hierarchical matrices 3. Matern covariance function and joint Gaussian likelihood 4. Identification of unknown parameters via maximizing Gaussian log-likelihood 5. Implementation with HLIBPro. Part 2: Low-rank Tucker tensor methods in spatial statistics

Multiple analysis of an unknown optical multilayer coating

International Nuclear Information System (INIS)

Dobrowolski, J.A.; Ho, F.C.; Waldorf, A.

1985-01-01

Results are given of the analysis at five different laboratories of an unknown optical multilayer coating. In all, eleven different analytical and laboratory techniques were applied to the problem. The multilayer nominally consisted of three dielectric and two metallic layers. It was demonstrated convincingly that with present day techniques it is possible to determine the basic structure of such a coating

Estimation of the false discovery proportion with unknown dependence.

Science.gov (United States)

Fan, Jianqing; Han, Xu

2017-09-01

Large-scale multiple testing with correlated test statistics arises frequently in many scientific research. Incorporating correlation information in approximating false discovery proportion has attracted increasing attention in recent years. When the covariance matrix of test statistics is known, Fan, Han & Gu (2012) provided an accurate approximation of False Discovery Proportion (FDP) under arbitrary dependence structure and some sparsity assumption. However, the covariance matrix is often unknown in many applications and such dependence information has to be estimated before approximating FDP. The estimation accuracy can greatly affect FDP approximation. In the current paper, we aim to theoretically study the impact of unknown dependence on the testing procedure and establish a general framework such that FDP can be well approximated. The impacts of unknown dependence on approximating FDP are in the following two major aspects: through estimating eigenvalues/eigenvectors and through estimating marginal variances. To address the challenges in these two aspects, we firstly develop general requirements on estimates of eigenvalues and eigenvectors for a good approximation of FDP. We then give conditions on the structures of covariance matrices that satisfy such requirements. Such dependence structures include banded/sparse covariance matrices and (conditional) sparse precision matrices. Within this framework, we also consider a special example to illustrate our method where data are sampled from an approximate factor model, which encompasses most practical situations. We provide a good approximation of FDP via exploiting this specific dependence structure. The results are further generalized to the situation where the multivariate normality assumption is relaxed. Our results are demonstrated by simulation studies and some real data applications.

Inadmissibility of Usual and Mixed Estimators of Two Ordered Gamma Scale Parameters Under Reflected Gamma Loss Function

OpenAIRE

Z. Meghnatisi; N. Nematollahi

2009-01-01

Let Xi1, · · · , Xini be a random sample from a gamma distribution with known shape parameter νi > 0 and unknown scale parameter βi > 0, i = 1, 2, satisfying 0 < β1 6 β2. We consider the class of mixed estimators for estimation of β1 and β2 under reflected gamma loss function. It has been shown that the minimum risk equivariant estimator of βi, i = 1, 2, which is admissible when no information on the ordering of parameters are given, is inadmissible and dominated by a cla...

Numerical method of identification of an unknown source term in a heat equation

Directory of Open Access Journals (Sweden)

Fatullayev Afet Golayo?lu

2002-01-01

Full Text Available A numerical procedure for an inverse problem of identification of an unknown source in a heat equation is presented. Approach of proposed method is to approximate unknown function by polygons linear pieces which are determined consecutively from the solution of minimization problem based on the overspecified data. Numerical examples are presented.

Finite-Time Adaptive Synchronization of a New Hyperchaotic System with Uncertain Parameters

Directory of Open Access Journals (Sweden)

Ma Yongguang

2014-01-01

Full Text Available This paper presents a finite-time adaptive synchronization strategy for a class of new hyperchaotic systems with unknown slave system’s parameters. Based on the finite-time stability theory, an adaptive control law is derived to make the states of the new hyperchaotic systems synchronized in finite-time. Numerical simulations are presented to show the effectiveness of the proposed finite time synchronization scheme.

Simulation-based Extraction of Key Material Parameters from Atomic Force Microscopy

Science.gov (United States)

Alsafi, Huseen; Peninngton, Gray

Models for the atomic force microscopy (AFM) tip and sample interaction contain numerous material parameters that are often poorly known. This is especially true when dealing with novel material systems or when imaging samples that are exposed to complicated interactions with the local environment. In this work we use Monte Carlo methods to extract sample material parameters from the experimental AFM analysis of a test sample. The parameterized theoretical model that we use is based on the Virtual Environment for Dynamic AFM (VEDA) [1]. The extracted material parameters are then compared with the accepted values for our test sample. Using this procedure, we suggest a method that can be used to successfully determine unknown material properties in novel and complicated material systems. We acknowledge Fisher Endowment Grant support from the Jess and Mildred Fisher College of Science and Mathematics,Towson University.

Cancer of unknown primitive metastatic. About two clinical cases

International Nuclear Information System (INIS)

Cawen, L; Cordoba, A.

2010-01-01

This work is about the two clinical cases about the unknown primitive metastatic cancer. The main techniques used for the diagnosis, treatment and monitoring of different s carcinomas are: Electronic microscope, molecular biology and genetics, especially histopathological study, topographic survey, ultrasound, radiography, chemotherapy, radiotherapy

High-order sliding mode observer for fractional commensurate linear systems with unknown input

KAUST Repository

Belkhatir, Zehor; Laleg-Kirati, Taous-Meriem

2017-01-01

In this paper, a high-order sliding mode observer (HOSMO) is proposed for the joint estimation of the pseudo-state and the unknown input of fractional commensurate linear systems with single unknown input and a single output. The convergence of the proposed observer is proved using a Lyapunov-based approach. In addition, an enhanced variant of the proposed fractional-HOSMO is introduced to avoid the peaking phenomenon and thus to improve the estimation results in the transient phase. Simulation results are provided to illustrate the performance of the proposed fractional observer in both noise-free and noisy cases. The effect of the observer’s gains on the estimated pseudo-state and unknown input is also discussed.

High-order sliding mode observer for fractional commensurate linear systems with unknown input

KAUST Repository

Belkhatir, Zehor

2017-05-20

In this paper, a high-order sliding mode observer (HOSMO) is proposed for the joint estimation of the pseudo-state and the unknown input of fractional commensurate linear systems with single unknown input and a single output. The convergence of the proposed observer is proved using a Lyapunov-based approach. In addition, an enhanced variant of the proposed fractional-HOSMO is introduced to avoid the peaking phenomenon and thus to improve the estimation results in the transient phase. Simulation results are provided to illustrate the performance of the proposed fractional observer in both noise-free and noisy cases. The effect of the observer’s gains on the estimated pseudo-state and unknown input is also discussed.

Identifying criteria and establishing parameters for forest-based ecotourism in Northern Ontario, Canada

Science.gov (United States)

Stephen W. Boyd; Richard W. Butler; Wolfgang Haider

1995-01-01

This paper identifies the following criteria as indicators for ecotourism suitability within a Northern Ontario context: naturalness, wildlife, cultural heritage, landscape and community. A methodology is proposed which uses Geographical Information Systems (GIS) to identify ecotourism sites by linking criteria deemed important with actual landscape characteristics of...

On reconstruction of an unknown polygonal cavity in a linearized elasticity with one measurement

International Nuclear Information System (INIS)

Ikehata, M; Itou, H

2011-01-01

In this paper we consider a reconstruction problem of an unknown polygonal cavity in a linearized elastic body. For this problem, an extraction formula of the convex hull of the unknown polygonal cavity is established by means of the enclosure method introduced by Ikehata. The advantages of our method are that it needs only a single set of boundary data and we do not require any a priori assumptions for the unknown polygonal cavity and any constraints on boundary data. The theoretical formula may have possibility of application in nondestructive evaluation.

Dead or Alive? Dealing with Unknown Eligibility in Longitudinal Surveys

Directory of Open Access Journals (Sweden)

Watson Nicole

2016-12-01

Full Text Available Longitudinal surveys follow people over time and some of these people will die during the life of the panel. Through fieldwork effort, some deaths will be reported or known, but others will be unobserved due to sample members no longer being issued to field or having inconclusive fieldwork outcomes (such as a noncontact that is not followed by a contact at a later wave. The coverage of deaths identified among sample members has flow-on implications to nonresponse correction. Using the Household, Income and Labour Dynamics in Australia (HILDA Survey, four methods are used to examine the extent of missing death reports. The first method matches the sample to the national death register. The second method uses life-expectancy tables to extrapolate the expected number of deaths among the sample with unknown eligibility. The third method is similar but models deaths from data internal to the survey. The fourth method models deaths as part of the attrition process of a longitudinal survey. The last three methods are compared to the first method and the implications for the construction of balanced panel weights and subsequent population inference are explored.

Failure probability under parameter uncertainty.

Science.gov (United States)

Gerrard, R; Tsanakas, A

2011-05-01

In many problems of risk analysis, failure is equivalent to the event of a random risk factor exceeding a given threshold. Failure probabilities can be controlled if a decisionmaker is able to set the threshold at an appropriate level. This abstract situation applies, for example, to environmental risks with infrastructure controls; to supply chain risks with inventory controls; and to insurance solvency risks with capital controls. However, uncertainty around the distribution of the risk factor implies that parameter error will be present and the measures taken to control failure probabilities may not be effective. We show that parameter uncertainty increases the probability (understood as expected frequency) of failures. For a large class of loss distributions, arising from increasing transformations of location-scale families (including the log-normal, Weibull, and Pareto distributions), the article shows that failure probabilities can be exactly calculated, as they are independent of the true (but unknown) parameters. Hence it is possible to obtain an explicit measure of the effect of parameter uncertainty on failure probability. Failure probability can be controlled in two different ways: (1) by reducing the nominal required failure probability, depending on the size of the available data set, and (2) by modifying of the distribution itself that is used to calculate the risk control. Approach (1) corresponds to a frequentist/regulatory view of probability, while approach (2) is consistent with a Bayesian/personalistic view. We furthermore show that the two approaches are consistent in achieving the required failure probability. Finally, we briefly discuss the effects of data pooling and its systemic risk implications. © 2010 Society for Risk Analysis.

Novel prescribed performance neural control of a flexible air-breathing hypersonic vehicle with unknown initial errors.

Science.gov (United States)

Bu, Xiangwei; Wu, Xiaoyan; Zhu, Fujing; Huang, Jiaqi; Ma, Zhen; Zhang, Rui

2015-11-01

A novel prescribed performance neural controller with unknown initial errors is addressed for the longitudinal dynamic model of a flexible air-breathing hypersonic vehicle (FAHV) subject to parametric uncertainties. Different from traditional prescribed performance control (PPC) requiring that the initial errors have to be known accurately, this paper investigates the tracking control without accurate initial errors via exploiting a new performance function. A combined neural back-stepping and minimal learning parameter (MLP) technology is employed for exploring a prescribed performance controller that provides robust tracking of velocity and altitude reference trajectories. The highlight is that the transient performance of velocity and altitude tracking errors is satisfactory and the computational load of neural approximation is low. Finally, numerical simulation results from a nonlinear FAHV model demonstrate the efficacy of the proposed strategy. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Identifiability of Additive Actuator and Sensor Faults by State Augmentation

Science.gov (United States)

Joshi, Suresh; Gonzalez, Oscar R.; Upchurch, Jason M.

2014-01-01

A class of fault detection and identification (FDI) methods for bias-type actuator and sensor faults is explored in detail from the point of view of fault identifiability. The methods use state augmentation along with banks of Kalman-Bucy filters for fault detection, fault pattern determination, and fault value estimation. A complete characterization of conditions for identifiability of bias-type actuator faults, sensor faults, and simultaneous actuator and sensor faults is presented. It is shown that FDI of simultaneous actuator and sensor faults is not possible using these methods when all sensors have unknown biases. The fault identifiability conditions are demonstrated via numerical examples. The analytical and numerical results indicate that caution must be exercised to ensure fault identifiability for different fault patterns when using such methods.

Extensive screening for primary tumor is redundant in melanoma of unknown primary

DEFF Research Database (Denmark)

Tos, Tina; Klyver, Helle; Drzewiecki, Krzysztof T

2011-01-01

For decades, patients in our institution with metastastic melanoma of unknown primary have been subjected to extensive examinations in search of the primary tumor. This retrospective study questions the results, and thus the feasibility of these examinations. Of 103 patients diagnosed with unknow......, for patients referred with metastastic melanoma of unknown primary, we recommend that a detailed history is obtained, and a standard physical examination performed, in addition to a histopathological review and CT/PET for staging....

Brain nonoxidative carbohydrate consumption is not explained by export of an unknown carbon source: evaluation of the arterial and jugular venous metabolome

DEFF Research Database (Denmark)

Rasmussen, Peter; Nyberg, Nils; Jaroszewski, Jerzy W.

2010-01-01

uptake is unknown, but it may be that brain metabolism is balanced by a yet-unidentified substance(s). This study used a nuclear magnetic resonance-based metabolomics approach to plasma samples obtained from the brachial artery and the right internal jugular vein in 16 healthy young males to identify...... be accounted for by changes in the NMR-derived plasma metabolome across the brain....

Optimization of multilayer neural network parameters for speaker recognition

Science.gov (United States)

Tovarek, Jaromir; Partila, Pavol; Rozhon, Jan; Voznak, Miroslav; Skapa, Jan; Uhrin, Dominik; Chmelikova, Zdenka

2016-05-01

This article discusses the impact of multilayer neural network parameters for speaker identification. The main task of speaker identification is to find a specific person in the known set of speakers. It means that the voice of an unknown speaker (wanted person) belongs to a group of reference speakers from the voice database. One of the requests was to develop the text-independent system, which means to classify wanted person regardless of content and language. Multilayer neural network has been used for speaker identification in this research. Artificial neural network (ANN) needs to set parameters like activation function of neurons, steepness of activation functions, learning rate, the maximum number of iterations and a number of neurons in the hidden and output layers. ANN accuracy and validation time are directly influenced by the parameter settings. Different roles require different settings. Identification accuracy and ANN validation time were evaluated with the same input data but different parameter settings. The goal was to find parameters for the neural network with the highest precision and shortest validation time. Input data of neural networks are a Mel-frequency cepstral coefficients (MFCC). These parameters describe the properties of the vocal tract. Audio samples were recorded for all speakers in a laboratory environment. Training, testing and validation data set were split into 70, 15 and 15 %. The result of the research described in this article is different parameter setting for the multilayer neural network for four speakers.

Teleportation of an Unknown Atomic State via Adiabatic Passage

Institute of Scientific and Technical Information of China (English)

无

2007-01-01

We propose a scheme for teleporting an unknown atomic state via adiabatic passage. Taking advantage of adiabatic passage, the atom has no probability of being excited and thus the atomic spontaneous emission is suppressed.We also show that the fidelity can reach 1 under certain condition.

Metastasis to neck from unknown primary tumor

International Nuclear Information System (INIS)

Jose, B.; Bosch, A.; Caldwell, W.L.; Frias, Z.

1979-01-01

The records of 54 consecutive patients who were irradiated for metastatic disease in the neck from an unknown primary tumor were reviewed. The overall survival results are comparable to those of other reported series. Patients with high or posterior cervical lymph node involvement were irradiated with fields including the nasopharynx and oropharynx. Patients with high neck nodes had a better survival rate than those with low neck nodes. The size of the neck tumors and the local control after treatment also have prognostic significance. (Auth.)

Quadrotor Control in the Presence of Unknown Mass Properties

Science.gov (United States)

Duivenvoorden, Rikky Ricardo Petrus Rufino

Quadrotor UAVs are popular due to their mechanical simplicity, as well as their capability to hover and vertically take-off and land. As applications diversify, quadrotors are increasingly required to operate under unknown mass properties, for example as a multirole sensor platform or for package delivery operations. The work presented here consists of the derivation of a generalized quadrotor dynamic model without the typical simplifying assumptions on the first and second moments of mass. The maximum payload capacity of a quadrotor in hover, and the observability of the unknown mass properties are discussed. A brief introduction of L1 adaptive control is provided, and three different L 1 adaptive controllers were designed for the Parrot AR.Drone quadrotor. Their tracking and disturbance rejection performance was compared to the baseline nonlinear controller in experiments. Finally, the results of the combination of L1 adaptive control with iterative learning control are presented, showing high performance trajectory tracking under uncertainty.

Matrix- and tensor-based recommender systems for the discovery of currently unknown inorganic compounds

Science.gov (United States)

Seko, Atsuto; Hayashi, Hiroyuki; Kashima, Hisashi; Tanaka, Isao

2018-01-01

Chemically relevant compositions (CRCs) and atomic arrangements of inorganic compounds have been collected as inorganic crystal structure databases. Machine learning is a unique approach to search for currently unknown CRCs from vast candidates. Herein we propose matrix- and tensor-based recommender system approaches to predict currently unknown CRCs from database entries of CRCs. Firstly, the performance of the recommender system approaches to discover currently unknown CRCs is examined. A Tucker decomposition recommender system shows the best discovery rate of CRCs as the majority of the top 100 recommended ternary and quaternary compositions correspond to CRCs. Secondly, systematic density functional theory (DFT) calculations are performed to investigate the phase stability of the recommended compositions. The phase stability of the 27 compositions reveals that 23 currently unknown compounds are newly found to be stable. These results indicate that the recommender system has great potential to accelerate the discovery of new compounds.

Kalman filter estimation of RLC parameters for UMP transmission line

Directory of Open Access Journals (Sweden)

Mohd Amin Siti Nur Aishah

2018-01-01

Full Text Available This paper present the development of Kalman filter that allows evaluation in the estimation of resistance (R, inductance (L, and capacitance (C values for Universiti Malaysia Pahang (UMP short transmission line. To overcome the weaknesses of existing system such as power losses in the transmission line, Kalman Filter can be a better solution to estimate the parameters. The aim of this paper is to estimate RLC values by using Kalman filter that in the end can increase the system efficiency in UMP. In this research, matlab simulink model is developed to analyse the UMP short transmission line by considering different noise conditions to reprint certain unknown parameters which are difficult to predict. The data is then used for comparison purposes between calculated and estimated values. The results have illustrated that the Kalman Filter estimate accurately the RLC parameters with less error. The comparison of accuracy between Kalman Filter and Least Square method is also presented to evaluate their performances.

Adresse inconnue / Address unknown / Suchwiin Bulmyeong

Directory of Open Access Journals (Sweden)

Serge Gruzinski

2005-03-01

Full Text Available Tous les films asiatiques parlent de métissage, même ceux qui se présentent comme de vastes fresques historiques perdues dans le temps. Les emprunts aux traditions hollywoodiennes et européennes n'ont cessé d'enrichir une cinématographie aussi ancienne que celle du monde occidental. Dans Adresse inconnue (Address unknown le cinéaste coréen Kim Ki-duk explore l'expérience du métissage et le corps du métis à la frontière entre Corée du Nord et Corée du sud. Fils d'un GI américain et noir et d...

Automated parameter estimation for biological models using Bayesian statistical model checking.

Science.gov (United States)

Hussain, Faraz; Langmead, Christopher J; Mi, Qi; Dutta-Moscato, Joyeeta; Vodovotz, Yoram; Jha, Sumit K

2015-01-01

Probabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems. Such models are developed using existing knowledge of the structure and dynamics of the system, experimental observations, and inferences drawn from statistical analysis of empirical data. A key bottleneck in building such models is that some system variables cannot be measured experimentally. These variables are incorporated into the model as numerical parameters. Determining values of these parameters that justify existing experiments and provide reliable predictions when model simulations are performed is a key research problem. Using an agent-based model of the dynamics of acute inflammation, we demonstrate a novel parameter estimation algorithm by discovering the amount and schedule of doses of bacterial lipopolysaccharide that guarantee a set of observed clinical outcomes with high probability. We synthesized values of twenty-eight unknown parameters such that the parameterized model instantiated with these parameter values satisfies four specifications describing the dynamic behavior of the model. We have developed a new algorithmic technique for discovering parameters in complex stochastic models of biological systems given behavioral specifications written in a formal mathematical logic. Our algorithm uses Bayesian model checking, sequential hypothesis testing, and stochastic optimization to automatically synthesize parameters of probabilistic biological models.

Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm

Directory of Open Access Journals (Sweden)

Anish Pandey

2017-02-01

Full Text Available This article introduces a singleton type-1 fuzzy logic system (T1-SFLS controller and Fuzzy-WDO hybrid for the autonomous mobile robot navigation and collision avoidance in an unknown static and dynamic environment. The WDO (Wind Driven Optimization algorithm is used to optimize and tune the input/output membership function parameters of the fuzzy controller. The WDO algorithm is working based on the atmospheric motion of infinitesimal small air parcels navigates over an N-dimensional search domain. The performance of this proposed technique has compared through many computer simulations and real-time experiments by using Khepera-III mobile robot. As compared to the T1-SFLS controller the Fuzzy-WDO algorithm is found good agreement for mobile robot navigation.

Optimal conclusive teleportation of a d-dimensional two-particle unknown quantum state

Institute of Scientific and Technical Information of China (English)

Yang Yu-Guang; Wen Qiao-Yan; Zhu Fu-Chen

2006-01-01

A conclusive teleportation protocol of a d-dimensional two-particle unknown quantum state using three ddimensional particles in an arbitrary pure state is proposed. A sender teleports the unknown state conclusively to a receiver by using the positive operator valued measure(POVM) and introducing an ancillary qudit to perform the generalized Bell basis measurement. We calculate the optimal teleportation fidelity. We also discuss and analyse the reason why the information on the teleported state is lost in the course of the protocol.

Histogram Analysis of Apparent Diffusion Coefficients for Occult Tonsil Cancer in Patients with Cervical Nodal Metastasis from an Unknown Primary Site at Presentation.

Science.gov (United States)

Choi, Young Jun; Lee, Jeong Hyun; Kim, Hye Ok; Kim, Dae Yoon; Yoon, Ra Gyoung; Cho, So Hyun; Koh, Myeong Ju; Kim, Namkug; Kim, Sang Yoon; Baek, Jung Hwan

2016-01-01

To explore the added value of histogram analysis of apparent diffusion coefficient (ADC) values over magnetic resonance (MR) imaging and fluorine 18 ((18)F) fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) for the detection of occult palatine tonsil squamous cell carcinoma (SCC) in patients with cervical nodal metastasis from a cancer of an unknown primary site. The institutional review board approved this retrospective study, and the requirement for informed consent was waived. Differences in the bimodal histogram parameters of the ADC values were assessed among occult palatine tonsil SCC (n = 19), overt palatine tonsil SCC (n = 20), and normal palatine tonsils (n = 20). One-way analysis of variance was used to analyze differences among the three groups. Receiver operating characteristic curve analysis was used to determine the best differentiating parameters. The increased sensitivity of histogram analysis over MR imaging and (18)F-FDG PET/CT for the detection of occult palatine tonsil SCC was evaluated as added value. Histogram analysis showed statistically significant differences in the mean, standard deviation, and 50th and 90th percentile ADC values among the three groups (P histogram analysis was 52.6% over MR imaging alone and 15.8% over combined conventional MR imaging and (18)F-FDG PET/CT. Adding ADC histogram analysis to conventional MR imaging can improve the detection sensitivity for occult palatine tonsil SCC in patients with a cervical nodal metastasis originating from a cancer of an unknown primary site. © RSNA, 2015.

Effectiveness of functional training on cardiorespiratory parameters: a systematic review and meta-analysis of randomized controlled trials.

Science.gov (United States)

Rezende Barbosa, Marianne Penachini da Costa de; Oliveira, Vinicius Cunha; Silva, Anne Kastelianne França da; Pérez-Riera, Andrés Ricardo; Vanderlei, Luiz Carlos

2017-07-28

Functional training is a new training vision that was prepared from the gesture imitation of daily activities. Although your use has become popular in clinical practice, the influence of the several cardiorespiratory adjustments performed during the functional training in different populations and conditions is unknown. So, the aim of this systematic review was to gather information in the literature regarding the influence of functional training on cardiorespiratory parameters. We conducted search strategies on MEDLINE, PEDro, EMBASE, SportDiscus and Cochrane to identify randomized controlled trials investigating the effects of functional training on cardiorespiratory parameters. Methodological quality of the included studies was assessed using the PEDro scale. Grading of Recommendations Assessment, Development and Evaluation (GRADE) summarized the evidence. Five original studies were included. Effects favoured functional training on oxygen consumption (VO 2 ) at intermediate-term follow-up: weighted mean difference -1·0 (95% CI: 5·4-3·3), P = 0·642, and a small and not clinically important effect observed on VO 2 favouring control at intermediate-term follow-up (i.e. mean difference of 1·30 (95% CI 1·07-1·53), Pfunctional training is better than other interventions to improve cardiovascular parameters. This result encourages new searches about the theme. © 2017 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.

Mesoscopic modeling and parameter estimation of a lithium-ion battery based on LiFePO4/graphite

Science.gov (United States)

Jokar, Ali; Désilets, Martin; Lacroix, Marcel; Zaghib, Karim

2018-03-01

A novel numerical model for simulating the behavior of lithium-ion batteries based on LiFePO4(LFP)/graphite is presented. The model is based on the modified Single Particle Model (SPM) coupled to a mesoscopic approach for the LFP electrode. The model comprises one representative spherical particle as the graphite electrode, and N LFP units as the positive electrode. All the SPM equations are retained to model the negative electrode performance. The mesoscopic model rests on non-equilibrium thermodynamic conditions and uses a non-monotonic open circuit potential for each unit. A parameter estimation study is also carried out to identify all the parameters needed for the model. The unknown parameters are the solid diffusion coefficient of the negative electrode (Ds,n), reaction-rate constant of the negative electrode (Kn), negative and positive electrode porosity (εn&εn), initial State-Of-Charge of the negative electrode (SOCn,0), initial partial composition of the LFP units (yk,0), minimum and maximum resistance of the LFP units (Rmin&Rmax), and solution resistance (Rcell). The results show that the mesoscopic model can simulate successfully the electrochemical behavior of lithium-ion batteries at low and high charge/discharge rates. The model also describes adequately the lithiation/delithiation of the LFP particles, however, it is computationally expensive compared to macro-based models.

Methodology for Evaluating Safety System Operability using Virtual Parameter Network

International Nuclear Information System (INIS)

Park, Sukyoung; Heo, Gyunyoung; Kim, Jung Taek; Kim, Tae Wan

2014-01-01

KAERI (Korea Atomic Energy Research Institute) and UTK (University of Tennessee Knoxville) are working on the I-NERI project to suggest complement of this problem. This research propose the methodology which provide the alternative signal in case of unable guaranteed reliability of some instrumentation with KAERI. Proposed methodology is assumed that several instrumentations are working normally under the power supply condition because we do not consider the instrumentation survivability itself. Thus, concept of the Virtual Parameter Network (VPN) is used to identify the associations between plant parameters. This paper is extended version of the paper which was submitted last KNS meeting by changing the methodology and adding the result of the case study. In previous research, we used Artificial Neural Network (ANN) inferential technique for estimation model but every time this model showed different estimate value due to random bias each time. Therefore Auto-Associative Kernel Regression (AAKR) model which have same number of inputs and outputs is used to estimate. Also the importance measures in the previous method depend on estimation model but importance measure of improved method independent on estimation model. Also importance index of previous method depended on estimation model but importance index of improved method is independent on estimation model. In this study, we proposed the methodology to identify the internal state of power plant when severe accident happens also it has been validated through case study. SBLOCA which has large contribution to severe accident is considered as initiating event and relationship amongst parameter has been identified. VPN has ability to identify that which parameter has to be observed and which parameter can be alternative to the missing parameter when some instruments are failed in severe accident. In this study we have identified through results that commonly number 2, 3, 4 parameter has high connectivity while

Methodology for Evaluating Safety System Operability using Virtual Parameter Network

Energy Technology Data Exchange (ETDEWEB)

Park, Sukyoung; Heo, Gyunyoung [Kyung Hee Univ., Yongin (Korea, Republic of); Kim, Jung Taek [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Kim, Tae Wan [Kepco International Nuclear Graduate School, Ulsan (Korea, Republic of)

2014-05-15

KAERI (Korea Atomic Energy Research Institute) and UTK (University of Tennessee Knoxville) are working on the I-NERI project to suggest complement of this problem. This research propose the methodology which provide the alternative signal in case of unable guaranteed reliability of some instrumentation with KAERI. Proposed methodology is assumed that several instrumentations are working normally under the power supply condition because we do not consider the instrumentation survivability itself. Thus, concept of the Virtual Parameter Network (VPN) is used to identify the associations between plant parameters. This paper is extended version of the paper which was submitted last KNS meeting by changing the methodology and adding the result of the case study. In previous research, we used Artificial Neural Network (ANN) inferential technique for estimation model but every time this model showed different estimate value due to random bias each time. Therefore Auto-Associative Kernel Regression (AAKR) model which have same number of inputs and outputs is used to estimate. Also the importance measures in the previous method depend on estimation model but importance measure of improved method independent on estimation model. Also importance index of previous method depended on estimation model but importance index of improved method is independent on estimation model. In this study, we proposed the methodology to identify the internal state of power plant when severe accident happens also it has been validated through case study. SBLOCA which has large contribution to severe accident is considered as initiating event and relationship amongst parameter has been identified. VPN has ability to identify that which parameter has to be observed and which parameter can be alternative to the missing parameter when some instruments are failed in severe accident. In this study we have identified through results that commonly number 2, 3, 4 parameter has high connectivity while

Blind Identification of FIR Channels in the Presence of Unknown Noise

Directory of Open Access Journals (Sweden)

Kon Max Wong

2007-01-01

Full Text Available Blind channel identification techniques based on second-order statistics (SOS of the received data have been a topic of active research in recent years. Among the most popular is the subspace method (SS proposed by Moulines et al. (1995. It has good performance when the channel output is corrupted by white noise. However, when the channel noise is correlated and unknown as is often encountered in practice, the performance of the SS method degrades severely. In this paper, we address the problem of estimating FIR channels in the presence of arbitrarily correlated noise whose covariance matrix is unknown. We propose several algorithms according to the different available system resources: (1 when only one receiving antenna is available, by upsampling the output, we develop the maximum a posteriori (MAP algorithm for which a simple criterion is obtained and an efficient implementation algorithm is developed; (2 when two receiving antennae are available, by upsampling both the outputs and utilizing canonical correlation decomposition (CCD to obtain the subspaces, we present two algorithms (CCD-SS and CCD-ML to blindly estimate the channels. Our algorithms perform well in unknown noise environment and outperform existing methods proposed for similar scenarios.

Children with hypercholesterolemia of unknown cause: Value of genetic risk scores.

Science.gov (United States)

Sjouke, Barbara; Tanck, Michael W T; Fouchier, Sigrid W; Defesche, Joep C; Hutten, Barbara A; Wiegman, Albert; Kastelein, John J P; Hovingh, G Kees

2016-01-01

Familial hypercholesterolemia (FH) is caused by mutations in LDLR, APOB, or PCSK9, and in a previous study, we identified a causative mutation in these FH genes in 95% (255 of 269) of children with the FH phenotype. It has been hypothesized that a polygenic form of hypercholesterolemia is present in FH patients in whom no mutation is identified in the 3 FH genes. To address whether a polygenic form of hypercholesterolemia, defined as high-weighted effect of low-density lipoprotein cholesterol (LDL-C) raising SNPs expressed as the genetic risk score (GRS), is present in the remaining 14 children. On reassessment of the molecular diagnosis and clinical phenotype, 8 FH kindreds met the criteria for hypercholesterolemia of unknown cause and were included in this study. We calculated a weighted GRS comprising 10 established LDL-C-associated SNPs and the APOE genotype in these index cases and evaluated whether the index cases were characterized by an increased GRS compared to 26 first-degree relatives. Phenotypically affected and unaffected individuals could not be distinguished based on any of the risk scores. In this and our previous study, we show that a causal mutation in LDLR, APOB, and PCSK9 can be identified in almost all children with a definite clinical diagnosis of FH. In the small group of patients without a mutation, we did not observe a higher GRS compared with unaffected relatives, which suggests that the FH phenotype is not caused by the aggregate of LDL-C increasing SNPs. Our data imply that application of the GRS is not instrumental as a diagnostic tool to individually define clinically diagnosed FH patients with polygenic hypercholesterolemia in our study population. Copyright © 2016 National Lipid Association. Published by Elsevier Inc. All rights reserved.

A novel algorithm for fast grasping of unknown objects using C-shape configuration

Science.gov (United States)

Lei, Qujiang; Chen, Guangming; Meijer, Jonathan; Wisse, Martijn

2018-02-01

Increasing grasping efficiency is very important for the robots to grasp unknown objects especially subjected to unfamiliar environments. To achieve this, a new algorithm is proposed based on the C-shape configuration. Specifically, the geometric model of the used under-actuated gripper is approximated as a C-shape. To obtain an appropriate graspable position, this C-shape configuration is applied to fit geometric model of an unknown object. The geometric model of unknown object is constructed by using a single-view partial point cloud. To examine the algorithm using simulations, a comparison of the commonly used motion planners is made. The motion planner with the highest number of solved runs, lowest computing time and the shortest path length is chosen to execute grasps found by this grasping algorithm. The simulation results demonstrate that excellent grasping efficiency is achieved by adopting our algorithm. To validate this algorithm, experiment tests are carried out using a UR5 robot arm and an under-actuated gripper. The experimental results show that steady grasping actions are obtained. Hence, this research provides a novel algorithm for fast grasping of unknown objects.

Design parameters and source terms: Volume 1, Design parameters: Revision 0

International Nuclear Information System (INIS)

1987-10-01

The Design Parameters and Source Terms Document was prepared in accordance with DOE request and to provide data for the environmental impact study to be performed in the future for the Deaf Smith County, Texas site for a nuclear waste repository in salt. This document updates a previous unpublished report by Stearns Catalytic Corporation (SCC), entitled ''Design Parameters and Source Terms for a Two-Phase Repository in Salt,'' 1985, to the level of the Site Characterization Plan - Conceptual Design Report. The previous unpublished SCC Study identifies the data needs for the Environmental Assessment effort for seven possible Salt Repository sites

Using multiple criteria for fingerprinting unknown oil samples having very similar chemical composition

International Nuclear Information System (INIS)

Wang, Z.; Fingas, M.F.; Sigouin, L.

2002-01-01

A study was conducted in which 3 mystery oil samples from Quebec were fingerprinted using a multi-criterion approach. The three objectives of the study were to determine the nature and the type of product, to obtain the detailed hydrocarbon composition of the samples, and to determine if the samples came from the same source. The product type was first determined by identifying the hydrocarbon distribution patterns. Polycyclic aromatic hydrocarbon (PAH) profiles were then compared and then the conclusions were verified by quantifying biomarkers and by determining several diagnostic ratios of source-specific marker compounds. Additives in the oil were also identified. The samples were analyzed using gas chromatography combined with flame ionization detection (GC-FID), and by gas chromatography-mass spectrometry (GC-MS). It was determined that the 3 oils were probably hydraulic-fluid type oil. They were very pure, and composed mostly of saturated hydrocarbons with the total aromatics being 4 to 10 per cent of the total petroleum hydrocarbon. Although it was determined that the oils were mixtures of 2 different hydraulic fluids, there was no clear indication if they had been weathered. The PAH concentration was very low, while the biomarker concentration was very high. Three unknown compounds (antioxidants) were positively identified. Two of the samples came from the same source. One of the samples had similar group hydrocarbon composition but it was not identical in chemical composition and did not come from the same source. 34 refs., 3 tabs., 6 figs

Information and treatment of unknown correlations in the combination of measurements using the BLUE method

CERN Document Server

Valassi, A

2014-01-01

We discuss the effect of large positive correlations in the combinations of several measurements of a single physical quantity using the Best Linear Unbiased Estimate (BLUE) method. We suggest a new approach for comparing the relative weights of the different measurements in their contributions to the combined knowledge about the unknown parameter, using the well-established concept of Fisher information. We argue, in particular, that one contribution to information comes from the collective interplay of the measurements through their correlations and that this contribution cannot be attributed to any of the individual measurements alone. We show that negative coefficients in the BLUE weighted average invariably indicate the presence of a regime of high correlations, where the effect of further increasing some of these correlations is that of reducing the error on the combined estimate. In these regimes, we stress that the correlations provided as input to BLUE combinations need to be assessed with extreme ca...

Unique genetic loci identified for emotional behavior in control and chronic stress conditions

OpenAIRE

Carhuatanta, Kimberly A. K.; Shea, Chloe J. A.; Herman, James P.; Jankord, Ryan

2014-01-01

An individual's genetic background affects their emotional behavior and response to stress. Although studies have been conducted to identify genetic predictors for emotional behavior or stress response, it remains unknown how prior stress history alters the interaction between an individual's genome and their emotional behavior. Therefore, the purpose of this study is to identify chromosomal regions that affect emotional behavior and are sensitive to stress exposure. We utilized the BXD behav...

PARTICLE FILTERING WITH SEQUENTIAL PARAMETER LEARNING FOR NONLINEAR BOLD fMRI SIGNALS.

Science.gov (United States)

Xia, Jing; Wang, Michelle Yongmei

Analyzing the blood oxygenation level dependent (BOLD) effect in the functional magnetic resonance imaging (fMRI) is typically based on recent ground-breaking time series analysis techniques. This work represents a significant improvement over existing approaches to system identification using nonlinear hemodynamic models. It is important for three reasons. First, instead of using linearized approximations of the dynamics, we present a nonlinear filtering based on the sequential Monte Carlo method to capture the inherent nonlinearities in the physiological system. Second, we simultaneously estimate the hidden physiological states and the system parameters through particle filtering with sequential parameter learning to fully take advantage of the dynamic information of the BOLD signals. Third, during the unknown static parameter learning, we employ the low-dimensional sufficient statistics for efficiency and avoiding potential degeneration of the parameters. The performance of the proposed method is validated using both the simulated data and real BOLD fMRI data.

Magnetic resonance appearance of monoclonal gammopathies of unknown significance and multiple myeloma. The GRI Study Group.

Science.gov (United States)

Bellaïche, L; Laredo, J D; Lioté, F; Koeger, A C; Hamze, B; Ziza, J M; Pertuiset, E; Bardin, T; Tubiana, J M

1997-11-01

A prospective multicenter study. To evaluate the use of magnetic resonance imaging, in the differentiation between monoclonal gammopathies of unknown significance and multiple myeloma. Although multiple myeloma has been studied extensively with magnetic resonance imaging, to the authors' knowledge, no study has evaluated the clinical interest of magnetic resonance imaging in the differentiation between monoclonal gammopathies of unknown significance and multiple myeloma. The magnetic resonance examinations of the thoracolumbar spine in 24 patients with newly diagnosed monoclonal gammopathies of unknown significance were compared with those performed in 44 patients with newly diagnosed nontreated multiple myeloma. All findings on magnetic resonance examination performed in patients with monoclonal gammopathies of unknown significance were normal, whereas findings on 38 (86%) of the 44 magnetic resonance examinations performed in patients with multiple myeloma were abnormal. Magnetic resonance imaging can be considered as an additional diagnostic tool in differentiating between monoclonal gammopathies of unknown significance and multiple myeloma, which may be helpful when routine criteria are not sufficient. An abnormal finding on magnetic resonance examination in a patient with monoclonal gammopathies of unknown significance should suggest the diagnosis of multiple myeloma after other causes of marrow signal abnormalities are excluded. Magnetic resonance imaging also may be proposed in the long-term follow-up of monoclonal gammopathies of unknown significance when a new biologic or clinical event suggests the diagnosis of malignant monoclonal gammopathy.

A genome-wide association study of COPD identifies a susceptibility locus on chromosome 19q13

DEFF Research Database (Denmark)

Cho, Michael H; Castaldi, Peter J; Wan, Emily S

2012-01-01

The genetic risk factors for chronic obstructive pulmonary disease (COPD) are still largely unknown. To date, genome-wide association studies (GWASs) of limited size have identified several novel risk loci for COPD at CHRNA3/CHRNA5/IREB2, HHIP and FAM13A; additional loci may be identified through...

A Next-Generation Sequencing Data Analysis Pipeline for Detecting Unknown Pathogens from Mixed Clinical Samples and Revealing Their Genetic Diversity.

Directory of Open Access Journals (Sweden)

Yu-Nong Gong

Full Text Available Forty-two cytopathic effect (CPE-positive isolates were collected from 2008 to 2012. All isolates could not be identified for known viral pathogens by routine diagnostic assays. They were pooled into 8 groups of 5-6 isolates to reduce the sequencing cost. Next-generation sequencing (NGS was conducted for each group of mixed samples, and the proposed data analysis pipeline was used to identify viral pathogens in these mixed samples. Polymerase chain reaction (PCR or enzyme-linked immunosorbent assay (ELISA was individually conducted for each of these 42 isolates depending on the predicted viral types in each group. Two isolates remained unknown after these tests. Moreover, iteration mapping was implemented for each of these 2 isolates, and predicted human parechovirus (HPeV in both. In summary, our NGS pipeline detected the following viruses among the 42 isolates: 29 human rhinoviruses (HRVs, 10 HPeVs, 1 human adenovirus (HAdV, 1 echovirus and 1 rotavirus. We then focused on the 10 identified Taiwanese HPeVs because of their reported clinical significance over HRVs. Their genomes were assembled and their genetic diversity was explored. One novel 6-bp deletion was found in one HPeV-1 virus. In terms of nucleotide heterogeneity, 64 genetic variants were detected from these HPeVs using the mapped NGS reads. Most importantly, a recombination event was found between our HPeV-3 and a known HPeV-4 strain in the database. Similar event was detected in the other HPeV-3 strains in the same clade of the phylogenetic tree. These findings demonstrated that the proposed NGS data analysis pipeline identified unknown viruses from the mixed clinical samples, revealed their genetic identity and variants, and characterized their genetic features in terms of viral evolution.

Editoria: EBOLA: Fear of the unknown | Comoro | Tanzania Journal ...

African Journals Online (AJOL)

Tanzania Journal of Health Research. Journal Home · ABOUT THIS JOURNAL · Advanced Search · Current Issue · Archives · Journal Home > Vol 3, No 2 (2001) >. Log in or Register to get access to full text downloads. Username, Password, Remember me, or Register. Editoria: EBOLA: Fear of the unknown. C. Comoro, J.

[Focal myositis: An unknown disease].

Science.gov (United States)

Gallay, L; Streichenberger, N; Benveniste, O; Allenbach, Y

2017-10-01

Focal myositis are inflammatory muscle diseases of unknown origin. At the opposite from the other idiopathic inflammatory myopathies, they are restricted to a single muscle or to a muscle group. They are not associated with extramuscular manifestations, and they have a good prognosis without any treatment. They are characterized by a localized swelling affecting mostly lower limbs. The pseudo-tumor can be painful, but is not associated with a muscle weakness. Creatine kinase level is normal. Muscle MRI shows an inflammation restricted to a muscle or a muscle group. Muscle biopsy and pathological analysis remain necessary for the diagnosis, showing inflammatory infiltrates composed by macrophages and lymphocytes without any specific distribution within the muscle. Focal overexpression of HLA-1 by the muscle fibers is frequently observed. The muscle biopsy permits to rule out differential diagnosis such a malignancy (sarcoma). Spontaneous remission occurs within weeks or months after the first symptoms, relapse is unusual. Copyright © 2017. Published by Elsevier SAS.

Structural identifiability analysis of a cardiovascular system model.

Science.gov (United States)

Pironet, Antoine; Dauby, Pierre C; Chase, J Geoffrey; Docherty, Paul D; Revie, James A; Desaive, Thomas

2016-05-01

The six-chamber cardiovascular system model of Burkhoff and Tyberg has been used in several theoretical and experimental studies. However, this cardiovascular system model (and others derived from it) are not identifiable from any output set. In this work, two such cases of structural non-identifiability are first presented. These cases occur when the model output set only contains a single type of information (pressure or volume). A specific output set is thus chosen, mixing pressure and volume information and containing only a limited number of clinically available measurements. Then, by manipulating the model equations involving these outputs, it is demonstrated that the six-chamber cardiovascular system model is structurally globally identifiable. A further simplification is made, assuming known cardiac valve resistances. Because of the poor practical identifiability of these four parameters, this assumption is usual. Under this hypothesis, the six-chamber cardiovascular system model is structurally identifiable from an even smaller dataset. As a consequence, parameter values computed from limited but well-chosen datasets are theoretically unique. This means that the parameter identification procedure can safely be performed on the model from such a well-chosen dataset. Thus, the model may be considered suitable for use in diagnosis. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

On selecting a prior for the precision parameter of Dirichlet process mixture models

Science.gov (United States)

Dorazio, R.M.

2009-01-01

In hierarchical mixture models the Dirichlet process is used to specify latent patterns of heterogeneity, particularly when the distribution of latent parameters is thought to be clustered (multimodal). The parameters of a Dirichlet process include a precision parameter ?? and a base probability measure G0. In problems where ?? is unknown and must be estimated, inferences about the level of clustering can be sensitive to the choice of prior assumed for ??. In this paper an approach is developed for computing a prior for the precision parameter ?? that can be used in the presence or absence of prior information about the level of clustering. This approach is illustrated in an analysis of counts of stream fishes. The results of this fully Bayesian analysis are compared with an empirical Bayes analysis of the same data and with a Bayesian analysis based on an alternative commonly used prior.

Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies

Science.gov (United States)

Zhang, Shujun

2018-01-01

Genome-wide association studies (GWASs) have identified many disease associated loci, the majority of which have unknown biological functions. Understanding the mechanism underlying trait associations requires identifying trait-relevant tissues and investigating associations in a trait-specific fashion. Here, we extend the widely used linear mixed model to incorporate multiple SNP functional annotations from omics studies with GWAS summary statistics to facilitate the identification of trait-relevant tissues, with which to further construct powerful association tests. Specifically, we rely on a generalized estimating equation based algorithm for parameter inference, a mixture modeling framework for trait-tissue relevance classification, and a weighted sequence kernel association test constructed based on the identified trait-relevant tissues for powerful association analysis. We refer to our analytic procedure as the Scalable Multiple Annotation integration for trait-Relevant Tissue identification and usage (SMART). With extensive simulations, we show how our method can make use of multiple complementary annotations to improve the accuracy for identifying trait-relevant tissues. In addition, our procedure allows us to make use of the inferred trait-relevant tissues, for the first time, to construct more powerful SNP set tests. We apply our method for an in-depth analysis of 43 traits from 28 GWASs using tissue-specific annotations in 105 tissues derived from ENCODE and Roadmap. Our results reveal new trait-tissue relevance, pinpoint important annotations that are informative of trait-tissue relationship, and illustrate how we can use the inferred trait-relevant tissues to construct more powerful association tests in the Wellcome trust case control consortium study. PMID:29377896

Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies.

Directory of Open Access Journals (Sweden)

Xingjie Hao

2018-01-01

Full Text Available Genome-wide association studies (GWASs have identified many disease associated loci, the majority of which have unknown biological functions. Understanding the mechanism underlying trait associations requires identifying trait-relevant tissues and investigating associations in a trait-specific fashion. Here, we extend the widely used linear mixed model to incorporate multiple SNP functional annotations from omics studies with GWAS summary statistics to facilitate the identification of trait-relevant tissues, with which to further construct powerful association tests. Specifically, we rely on a generalized estimating equation based algorithm for parameter inference, a mixture modeling framework for trait-tissue relevance classification, and a weighted sequence kernel association test constructed based on the identified trait-relevant tissues for powerful association analysis. We refer to our analytic procedure as the Scalable Multiple Annotation integration for trait-Relevant Tissue identification and usage (SMART. With extensive simulations, we show how our method can make use of multiple complementary annotations to improve the accuracy for identifying trait-relevant tissues. In addition, our procedure allows us to make use of the inferred trait-relevant tissues, for the first time, to construct more powerful SNP set tests. We apply our method for an in-depth analysis of 43 traits from 28 GWASs using tissue-specific annotations in 105 tissues derived from ENCODE and Roadmap. Our results reveal new trait-tissue relevance, pinpoint important annotations that are informative of trait-tissue relationship, and illustrate how we can use the inferred trait-relevant tissues to construct more powerful association tests in the Wellcome trust case control consortium study.

Carcinomatous Meningitis from Unknown Primary Carcinoma

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L. Favier

2009-10-01

Full Text Available Carcinomatous meningitis (CM occurs in 3 to 8% of cancer patients. Patients present with a focal symptom, and multifocal signs are often found following neurological examination. The gold standard for diagnosis remains the demonstration of carcinomatous cells in the cerebrospinal fluid on cytopathological examination. Despite the poor prognosis, palliative treatment could improve quality of life and, in some cases, overall survival. We report on a patient who presented with vertigo, tinnitus and left-sided hearing loss followed by progressive diffuse facial nerve paralysis. Lumbar cerebrospinal fluid confirmed the diagnosis of CM. However, no primary tumor was discovered, even after multiple invasive investigations. This is the first reported case in the English-language medical literature of CM resulting from a carcinoma of unknown primary origin.

Calculation of the neutron activation parameters from recently evaluated nuclear data

International Nuclear Information System (INIS)

Lopez Aldama, Daniel; Diaz Martinez, Nereida C.

1999-01-01

Neutron Activation Analysis (NAA) requires the values for nuclear data such as the 2200 m/s cross section so, the resonance integral I0, the parameter Q0 and the well-known Westcott factors. The availability of recently evaluated nuclear data libraries as the ENDF/B-VI Rev. 5, JEF 2.2, CENDL-2.1 and JENDL-3.2, makes possible to derive the above quantities from the basic nuclear data. It could be very helpful for those NAA parameters, which are unknown or difficult to measure accurately. The procedure to compute the NAA parameters includes the processing of the evaluated nuclear data and the calculation of each parameter directly from its definition. The evaluated nuclear data libraries ENDF/B-VI Rev. 5 and JENDL 3.2 were selected as the main sources of basic nuclear data. The ENDF pre-processing codes were used for processing the source evaluated data and a modified version of the INTER code was applied to calculate the required NAA integrals. The NAA parameters were computed for more than 30 important isotopes. The obtained results were compared with experimental values whenever possible

Synchronization and parameter identification of one class of realistic chaotic circuit

International Nuclear Information System (INIS)

Chun-Ni, Wang; Jun, Ma; Run-Tong, Chu; Shi-Rong, Li

2009-01-01

In this paper, the synchronization and the parameter identification of the chaotic Pikovsky–Rabinovich (PR) circuits are investigated. The linear error of the second corresponding variables is used to change the driven chaotic PR circuit, and the complete synchronization of the two identical chaotic PR circuits is realized with feedback intensity k increasing to a certain threshold. The Lyapunov exponents of the chaotic PR circuits are calculated by using different feedback intensities and our results are confirmed. The case where the two chaotic PR circuits are not identical is also investigated. A general positive Lyapunov function V, which consists of all the errors of the corresponding variables and parameters and changeable gain coefficient, is constructed by using the Lyapunov stability theory to study the parameter identification and complete synchronization of two non-identical chaotic circuits. The controllers and the parameter observers could be obtained analytically only by simplifying the criterion dV/dt < 0 (differential coefficient of Lyapunov function V with respect to time is negative). It is confirmed that the two non-identical chaotic PR circuits could still reach complete synchronization and all the unknown parameters in the drive system are estimated exactly within a short transient period

ESTIMATION OF CONSTANT AND TIME-VARYING DYNAMIC PARAMETERS OF HIV INFECTION IN A NONLINEAR DIFFERENTIAL EQUATION MODEL.

Science.gov (United States)

Liang, Hua; Miao, Hongyu; Wu, Hulin

2010-03-01

Modeling viral dynamics in HIV/AIDS studies has resulted in deep understanding of pathogenesis of HIV infection from which novel antiviral treatment guidance and strategies have been derived. Viral dynamics models based on nonlinear differential equations have been proposed and well developed over the past few decades. However, it is quite challenging to use experimental or clinical data to estimate the unknown parameters (both constant and time-varying parameters) in complex nonlinear differential equation models. Therefore, investigators usually fix some parameter values, from the literature or by experience, to obtain only parameter estimates of interest from clinical or experimental data. However, when such prior information is not available, it is desirable to determine all the parameter estimates from data. In this paper, we intend to combine the newly developed approaches, a multi-stage smoothing-based (MSSB) method and the spline-enhanced nonlinear least squares (SNLS) approach, to estimate all HIV viral dynamic parameters in a nonlinear differential equation model. In particular, to the best of our knowledge, this is the first attempt to propose a comparatively thorough procedure, accounting for both efficiency and accuracy, to rigorously estimate all key kinetic parameters in a nonlinear differential equation model of HIV dynamics from clinical data. These parameters include the proliferation rate and death rate of uninfected HIV-targeted cells, the average number of virions produced by an infected cell, and the infection rate which is related to the antiviral treatment effect and is time-varying. To validate the estimation methods, we verified the identifiability of the HIV viral dynamic model and performed simulation studies. We applied the proposed techniques to estimate the key HIV viral dynamic parameters for two individual AIDS patients treated with antiretroviral therapies. We demonstrate that HIV viral dynamics can be well characterized and

Joint state and parameter estimation for a class of cascade systems: Application to a hemodynamic model

KAUST Repository

Zayane, Chadia

2014-06-01

In this paper, we address a special case of state and parameter estimation, where the system can be put on a cascade form allowing to estimate the state components and the set of unknown parameters separately. Inspired by the nonlinear Balloon hemodynamic model for functional Magnetic Resonance Imaging problem, we propose a hierarchical approach. The system is divided into two subsystems in cascade. The state and input are first estimated from a noisy measured signal using an adaptive observer. The obtained input is then used to estimate the parameters of a linear system using the modulating functions method. Some numerical results are presented to illustrate the efficiency of the proposed method.

Gene expression profile and immunological evaluation of unique hypothetical unknown proteins of Mycobacterium leprae by using quantitative real-time PCR.

Science.gov (United States)

Kim, Hee Jin; Prithiviraj, Kalyani; Groathouse, Nathan; Brennan, Patrick J; Spencer, John S

2013-02-01

The cell-mediated immunity (CMI)-based in vitro gamma interferon release assay (IGRA) of Mycobacterium leprae-specific antigens has potential as a promising diagnostic means to detect those individuals in the early stages of M. leprae infection. Diagnosis of leprosy is a major obstacle toward ultimate disease control and has been compromised in the past by the lack of specific markers. Comparative bioinformatic analysis among mycobacterial genomes identified potential M. leprae-specific proteins called "hypothetical unknowns." Due to massive gene decay and the prevalence of pseudogenes, it is unclear whether any of these proteins are expressed or are immunologically relevant. In this study, we performed cDNA-based quantitative real-time PCR to investigate the expression status of 131 putative open reading frames (ORFs) encoding hypothetical unknowns. Twenty-six of the M. leprae-specific antigen candidates showed significant levels of gene expression compared to that of ESAT-6 (ML0049), which is an important T cell antigen of low abundance in M. leprae. Fifteen of 26 selected antigen candidates were expressed and purified in Escherichia coli. The seroreactivity to these proteins of pooled sera from lepromatous leprosy patients and cavitary tuberculosis patients revealed that 9 of 15 recombinant hypothetical unknowns elicited M. leprae-specific immune responses. These nine proteins may be good diagnostic reagents to improve both the sensitivity and specificity of detection of individuals with asymptomatic leprosy.

Chronic kidney disease of unknown aetiology in Sri Lanka: is cadmium a likely cause?

Directory of Open Access Journals (Sweden)

Peiris-John Roshini J

2011-07-01

Full Text Available Abstract Background The rising prevalence of chronic kidney disease (CKD and subsequent end stage renal failure necessitating renal replacement therapy has profound consequences for affected individuals and health care resources. This community based study was conducted to identify potential predictors of microalbuminuria in a randomly selected sample of adults from the North Central Province (NCP of Sri Lanka, where the burden of CKD is pronounced and the underlying cause still unknown. Methods Exposures to possible risk factors were determined in randomly recruited subjects (425 females and 461 males from selected areas of the NCP of Sri Lanka using an interviewer administered questionnaire. Sulphosalicylic acid and the Light Dependent Resister microalbumin gel filtration method was used for initial screening for microalbuminuria and reconfirmed by the Micral strip test. Results Microalbumnuria was detected in 6.1% of the females and 8.5% of the males. Smoking (p Conclusions Hypertension, diabetes mellitus, UTI, and smoking are known risk factors for microalbuminuria. The association between microalbuminuria and consumption of well water suggests an environmental aetiology to CKD in NCP. The causative agent is yet to be identified. Investigations for cadmium as a potential causative agent needs to be initiated.

Monitoring early hydration of reinforced concrete structures using structural parameters identified by piezo sensors via electromechanical impedance technique

Science.gov (United States)

Talakokula, Visalakshi; Bhalla, Suresh; Gupta, Ashok

2018-01-01

Concrete is the most widely used material in civil engineering construction. Its life begins when the hydration process is activated after mixing the cement granulates with water. In this paper, a non-dimensional hydration parameter, obtained from piezoelectric ceramic (PZT) patches bonded to rebars embedded inside concrete, is employed to monitor the early age hydration of concrete. The non-dimensional hydration parameter is derived from the equivalent stiffness determined from the piezo-impedance transducers using the electro-mechanical impedance (EMI) technique. The focus of the study is to monitor the hydration process of cementitious materials commencing from the early hours and continue till 28 days using single non-dimensional parameter. The experimental results show that the proposed piezo-based non-dimensional hydration parameter is very effective in monitoring the early age hydration, as it has been derived from the refined structural impedance parameters, obtained by eliminating the PZT contribution, and using both the real and imaginary components of the admittance signature.

Assessing Reliability of Cellulose Hydrolysis Models to Support Biofuel Process Design – Identifiability and Uncertainty Analysis

DEFF Research Database (Denmark)

Sin, Gürkan; Meyer, Anne S.; Gernaey, Krist

2010-01-01

The reliability of cellulose hydrolysis models is studied using the NREL model. An identifiability analysis revealed that only 6 out of 26 parameters are identifiable from the available data (typical hydrolysis experiments). Attempting to identify a higher number of parameters (as done in the ori......The reliability of cellulose hydrolysis models is studied using the NREL model. An identifiability analysis revealed that only 6 out of 26 parameters are identifiable from the available data (typical hydrolysis experiments). Attempting to identify a higher number of parameters (as done...

Nonlinear parameter estimation in inviscid compressible flows in presence of uncertainties

International Nuclear Information System (INIS)

Jemcov, A.; Mathur, S.

2004-01-01

The focus of this paper is on the formulation and solution of inverse problems of parameter estimation using algorithmic differentiation. The inverse problem formulated here seeks to determine the input parameters that minimize a least squares functional with respect to certain target data. The formulation allows for uncertainty in the target data by considering the least squares functional in a stochastic basis described by the covariance of the target data. Furthermore, to allow for robust design, the formulation also accounts for uncertainties in the input parameters. This is achieved using the method of propagation of uncertainties using the directional derivatives of the output parameters with respect to unknown parameters. The required derivatives are calculated simultaneously with the solution using generic programming exploiting the template and operator overloading features of the C++ language. The methodology described here is general and applicable to any numerical solution procedure for any set of governing equations but for the purpose of this paper we consider a finite volume solution of the compressible Euler equations. In particular, we illustrate the method for the case of supersonic flow in a duct with a wedge. The parameter to be determined is the inlet Mach number and the target data is the axial component of velocity at the exit of the duct. (author)

ENVIRONMENTAL AND PROCESS PARAMETERS OF METHANE FERMENTATION IN CONTINUOSLY STIRRED TANK REACTOR (CSTR

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Kamil Kozłowski

2016-12-01

Full Text Available A key indicator of methane fermentation process which influences the cost-effectiveness of the biogas plant is efficient production of methane per 1 m3 of reactor. It depends on the proper selection of environmental and process parameters. This article present collected and analyzed the effect of the most important parameters of continuous methane fermentation (CSTR, which include temperature, pH, nutrient content and the C/N ratio in the feed medium, the presence of inhibitors, and the volume load of reactor, retention time and mixing of digestion reactor. Still, the impact of many factors remain unknown, hence there is a need for more comprehensive studies.

Complete synchronization of uncertain chaotic systems via a single proportional adaptive controller: A comparative study

Energy Technology Data Exchange (ETDEWEB)

Ahmad, Israr, E-mail: iak-2000plus@yahoo.com; Saaban, Azizan Bin, E-mail: azizan.s@uum.edu.my; Ibrahim, Adyda Binti, E-mail: adyda@uum.edu.my [School of Quantitative Sciences, College of Arts & Sciences, UUM (Malaysia); Shahzad, Mohammad, E-mail: dmsinfinite@gmail.com [College of Applied Sciences Nizwa, Ministry of Higher Education, Sultanate of Oman (Oman)

2015-12-11

This paper addresses a comparative computational study on the synchronization quality, cost and converging speed for two pairs of identical chaotic and hyperchaotic systems with unknown time-varying parameters. It is assumed that the unknown time-varying parameters are bounded. Based on the Lyapunov stability theory and using the adaptive control method, a single proportional controller is proposed to achieve the goal of complete synchronizations. Accordingly, appropriate adaptive laws are designed to identify the unknown time-varying parameters. The designed control strategy is easy to implement in practice. Numerical simulations results are provided to verify the effectiveness of the proposed synchronization scheme.

Zero Distribution of System with Unknown Random Variables Case Study: Avoiding Collision Path

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Parman Setyamartana

2014-07-01

Full Text Available This paper presents the stochastic analysis of finding the feasible trajectories of robotics arm motion at obstacle surrounding. Unknown variables are coefficients of polynomials joint angle so that the collision-free motion is achieved. ãk is matrix consisting of these unknown feasible polynomial coefficients. The pattern of feasible polynomial in the obstacle environment shows as random. This paper proposes to model the pattern of this randomness values using random polynomial with unknown variables as coefficients. The behavior of the system will be obtained from zero distribution as the characteristic of such random polynomial. Results show that the pattern of random polynomial of avoiding collision can be constructed from zero distribution. Zero distribution is like building block of the system with obstacles as uncertainty factor. By scale factor k, which has range, the random coefficient pattern can be predicted.

Carcinoma of Unknown Primary Treatment (PDQ®)—Health Professional Version

Science.gov (United States)

Carcinoma of unknown primary (CUP) treatment depends on the best determination of the primary site, if possible. Treatment options may include surgery, radiation therapy, and systemic treatment. Get detailed information about diagnosis and treatment of CUP in this summary for clinicians.

Running over unknown rough terrain with a one-legged planar robot

International Nuclear Information System (INIS)

Andrews, Ben; Miller, Bruce; Clark, Jonathan E; Schmitt, John

2011-01-01

The ability to traverse unknown, rough terrain is an advantage that legged locomoters have over their wheeled counterparts. However, due to the complexity of multi-legged systems, research in legged robotics has not yet been able to reproduce the agility found in the animal kingdom. In an effort to reduce the complexity of the problem, researchers have developed single-legged models to gain insight into the fundamental dynamics of legged running. Inspired by studies of animal locomotion, researchers have proposed numerous control strategies to achieve stable, one-legged running over unknown, rough terrain. One such control strategy incorporates energy variations into the system during the stance phase by changing the force-free leg length as a sinusoidal function of time. In this research, a one-legged planar robot capable of implementing this and other state-of-the-art control strategies was designed and built. Both simulated and experimental results were used to determine and compare the stability of the proposed controllers as the robot was subjected to unknown drop and raised step perturbations equal to 25% of the nominal leg length. This study illustrates the relative advantages of utilizing a minimal-sensing, active energy removal control scheme to stabilize running over rough terrain.

Iron overload patients with unknown etiology from national survey in Japan.

Science.gov (United States)

Ikuta, Katsuya; Hatayama, Mayumi; Addo, Lynda; Toki, Yasumichi; Sasaki, Katsunori; Tatsumi, Yasuaki; Hattori, Ai; Kato, Ayako; Kato, Koichi; Hayashi, Hisao; Suzuki, Takahiro; Kobune, Masayoshi; Tsutsui, Miyuki; Gotoh, Akihiko; Aota, Yasuo; Matsuura, Motoo; Hamada, Yuzuru; Tokuda, Takahiro; Komatsu, Norio; Kohgo, Yutaka

2017-03-01

Transfusion is believed to be the main cause of iron overload in Japan. A nationwide survey on post-transfusional iron overload subsequently led to the establishment of guidelines for iron chelation therapy in this country. To date, however, detailed clinical information on the entire iron overload population in Japan has not been fully investigated. In the present study, we obtained and studied detailed clinical information on the iron overload patient population in Japan. Of 1109 iron overload cases, 93.1% were considered to have occurred post-transfusion. There were, however, 76 cases of iron overload of unknown origin, which suggest that many clinicians in Japan may encounter some difficulty in correctly diagnosing and treating iron overload. Further clinical data were obtained for 32 cases of iron overload of unknown origin; median of serum ferritin was 1860.5 ng/mL. As occurs in post-transfusional iron overload, liver dysfunction was found to be as high as 95.7% when serum ferritin levels exceeded 1000 ng/mL in these patients. Gene mutation analysis of the iron metabolism-related genes in 27 cases of iron overload with unknown etiology revealed mutations in the gene coding hemojuvelin, transferrin receptor 2, and ferroportin; this indicates that although rare, hereditary hemochromatosis does occur in Japan.

Approximate and Conditional Teleportation of an Unknown Atomic-Entangled State Without Bell-State Measurement

Institute of Scientific and Technical Information of China (English)

CHEN Chang-Yong; LI Shao-Hua

2007-01-01

A scheme for approximately and conditionally teleporting an unknown atomic-entangled state in cavity QED is proposed.It is the novel extension of the scheme of [Phys.Rev.A 69 (2004) 064302],where the state to be teleported is an unknown atomic state and where only a time point of system evolution and the corresponding fidelity implementing the teleportation are given.In fact,there exists multi-time points and the corresponding fidclities,which are shown in this paper and then are used to realize the approximate and conditional teleportation of the unknown atomic-entangled state.Naturally,our scheme does not involve the Bell-state measurement or an additional atom,which is required in the Bell-state measurement,only requiring one single-mode cavity.The scheme may be generalized to not only the teleportation of the cavity-mode-entangled-state by means of a single atom but also the teleportation of the unknown trapped-ion-entangled-state in a linear ion trap and the teleportation of the multi-atomic entangled states included in generalized GHZ states.

Developing Probabilistic Safety Performance Margins for Unknown and Underappreciated Risks

Science.gov (United States)

Benjamin, Allan; Dezfuli, Homayoon; Everett, Chris

2015-01-01

Probabilistic safety requirements currently formulated or proposed for space systems, nuclear reactor systems, nuclear weapon systems, and other types of systems that have a low-probability potential for high-consequence accidents depend on showing that the probability of such accidents is below a specified safety threshold or goal. Verification of compliance depends heavily upon synthetic modeling techniques such as PRA. To determine whether or not a system meets its probabilistic requirements, it is necessary to consider whether there are significant risks that are not fully considered in the PRA either because they are not known at the time or because their importance is not fully understood. The ultimate objective is to establish a reasonable margin to account for the difference between known risks and actual risks in attempting to validate compliance with a probabilistic safety threshold or goal. In this paper, we examine data accumulated over the past 60 years from the space program, from nuclear reactor experience, from aircraft systems, and from human reliability experience to formulate guidelines for estimating probabilistic margins to account for risks that are initially unknown or underappreciated. The formulation includes a review of the safety literature to identify the principal causes of such risks.

Prosecutor: parameter-free inference of gene function for prokaryotes using DNA microarray data, genomic context and multiple gene annotation sources

Directory of Open Access Journals (Sweden)

van Hijum Sacha AFT

2008-10-01

Full Text Available Abstract Background Despite a plethora of functional genomic efforts, the function of many genes in sequenced genomes remains unknown. The increasing amount of microarray data for many species allows employing the guilt-by-association principle to predict function on a large scale: genes exhibiting similar expression patterns are more likely to participate in shared biological processes. Results We developed Prosecutor, an application that enables researchers to rapidly infer gene function based on available gene expression data and functional annotations. Our parameter-free functional prediction method uses a sensitive algorithm to achieve a high association rate of linking genes with unknown function to annotated genes. Furthermore, Prosecutor utilizes additional biological information such as genomic context and known regulatory mechanisms that are specific for prokaryotes. We analyzed publicly available transcriptome data sets and used literature sources to validate putative functions suggested by Prosecutor. We supply the complete results of our analysis for 11 prokaryotic organisms on a dedicated website. Conclusion The Prosecutor software and supplementary datasets available at http://www.prosecutor.nl allow researchers working on any of the analyzed organisms to quickly identify the putative functions of their genes of interest. A de novo analysis allows new organisms to be studied.

Rapid-throughput skeletal phenotyping of 100 knockout mice identifies 9 new genes that determine bone strength.

Directory of Open Access Journals (Sweden)

J H Duncan Bassett

Full Text Available Osteoporosis is a common polygenic disease and global healthcare priority but its genetic basis remains largely unknown. We report a high-throughput multi-parameter phenotype screen to identify functionally significant skeletal phenotypes in mice generated by the Wellcome Trust Sanger Institute Mouse Genetics Project and discover novel genes that may be involved in the pathogenesis of osteoporosis. The integrated use of primary phenotype data with quantitative x-ray microradiography, micro-computed tomography, statistical approaches and biomechanical testing in 100 unselected knockout mouse strains identified nine new genetic determinants of bone mass and strength. These nine new genes include five whose deletion results in low bone mass and four whose deletion results in high bone mass. None of the nine genes have been implicated previously in skeletal disorders and detailed analysis of the biomechanical consequences of their deletion revealed a novel functional classification of bone structure and strength. The organ-specific and disease-focused strategy described in this study can be applied to any biological system or tractable polygenic disease, thus providing a general basis to define gene function in a system-specific manner. Application of the approach to diseases affecting other physiological systems will help to realize the full potential of the International Mouse Phenotyping Consortium.

Diagnosis of the cancer of unknown primary origin

International Nuclear Information System (INIS)

Jurisova, S.; Poersoek, S.

2013-01-01

Cancer of unknown primary origin (CUP) is one of the ten most frequent cancers worldwide. It constitutes of 3-5% of all human malignancies. At time of diagnosis patients with CUP present with disseminated metastases without established primary origin. CUP manifests as heterogenous group of mainly epithelial cancers recognised by distinct clinico pathological entities. The diagnostic work-up includes extensive histopathology investigations and modern imaging technology. Nevertheless, the primary tumour remains undetected most of the time. (author)

Education Through Exploration: Evaluating the Unknown

Science.gov (United States)

Anbar, A. D.

2015-12-01

Mastery of the peculiar and powerful practices of science is increasingly important for the average citizen. With the rise of the Internet, most of human knowledge is at our fingertips. As content becomes a commodity, success and survival aren't about who knows the most, but who is better able to explore the unknown, actively applying and extending knowledge through critical thinking and hypothesis-driven problem-solving. This applies to the economic livelihoods of individuals and to society at large as we grapple with climate change and other science-infused challenges. Unfortunately, science is too often taught as an encyclopedic collection of settled facts to be mastered rather than as a process of exploration that embraces curiosity, inquiry, testing, and communication to reduce uncertainty about the unknown. This problem is exacerbated by the continued prevalence of teacher-centric pedagogy, which promotes learning-from-authority and passive learning. The initial wave of massively open online courses (MOOCs) generally mimic this teaching style in virtual form. It is hypothesized that emerging digital teaching technologies can help address this challenge at Internet scale in "next generation" MOOCs and flipped classroom experiences. Interactive simulations, immersive virtual field trips, gamified elements, rapid adaptive feedback, intelligent tutoring systems, and personalized pathways, should motivate and enhance learning. Through lab-like projects and tutorials, students should be able to construct knowledge from interactive experiences, modeling the authentic practice of science while mastering complex concepts. Freed from lecturing, teaching staff should be available for direct and intense student-teacher interactions. These claims are difficult to evaluate with traditional assessment instruments, but digital technologies provide powerful new ways to evaluate student learning and learn from student behaviors. We will describe ongoing experiences with such

Optimization of biomathematical model predictions for cognitive performance impairment in individuals: accounting for unknown traits and uncertain states in homeostatic and circadian processes.

Science.gov (United States)

Van Dongen, Hans P A; Mott, Christopher G; Huang, Jen-Kuang; Mollicone, Daniel J; McKenzie, Frederic D; Dinges, David F

2007-09-01

Current biomathematical models of fatigue and performance do not accurately predict cognitive performance for individuals with a priori unknown degrees of trait vulnerability to sleep loss, do not predict performance reliably when initial conditions are uncertain, and do not yield statistically valid estimates of prediction accuracy. These limitations diminish their usefulness for predicting the performance of individuals in operational environments. To overcome these 3 limitations, a novel modeling approach was developed, based on the expansion of a statistical technique called Bayesian forecasting. The expanded Bayesian forecasting procedure was implemented in the two-process model of sleep regulation, which has been used to predict performance on the basis of the combination of a sleep homeostatic process and a circadian process. Employing the two-process model with the Bayesian forecasting procedure to predict performance for individual subjects in the face of unknown traits and uncertain states entailed subject-specific optimization of 3 trait parameters (homeostatic build-up rate, circadian amplitude, and basal performance level) and 2 initial state parameters (initial homeostatic state and circadian phase angle). Prior information about the distribution of the trait parameters in the population at large was extracted from psychomotor vigilance test (PVT) performance measurements in 10 subjects who had participated in a laboratory experiment with 88 h of total sleep deprivation. The PVT performance data of 3 additional subjects in this experiment were set aside beforehand for use in prospective computer simulations. The simulations involved updating the subject-specific model parameters every time the next performance measurement became available, and then predicting performance 24 h ahead. Comparison of the predictions to the subjects' actual data revealed that as more data became available for the individuals at hand, the performance predictions became

Consistent Parameter and Transfer Function Estimation using Context Free Grammars

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Klotz, Daniel; Herrnegger, Mathew; Schulz, Karsten

2017-04-01

This contribution presents a method for the inference of transfer functions for rainfall-runoff models. Here, transfer functions are defined as parametrized (functional) relationships between a set of spatial predictors (e.g. elevation, slope or soil texture) and model parameters. They are ultimately used for estimation of consistent, spatially distributed model parameters from a limited amount of lumped global parameters. Additionally, they provide a straightforward method for parameter extrapolation from one set of basins to another and can even be used to derive parameterizations for multi-scale models [see: Samaniego et al., 2010]. Yet, currently an actual knowledge of the transfer functions is often implicitly assumed. As a matter of fact, for most cases these hypothesized transfer functions can rarely be measured and often remain unknown. Therefore, this contribution presents a general method for the concurrent estimation of the structure of transfer functions and their respective (global) parameters. Note, that by consequence an estimation of the distributed parameters of the rainfall-runoff model is also undertaken. The method combines two steps to achieve this. The first generates different possible transfer functions. The second then estimates the respective global transfer function parameters. The structural estimation of the transfer functions is based on the context free grammar concept. Chomsky first introduced context free grammars in linguistics [Chomsky, 1956]. Since then, they have been widely applied in computer science. But, to the knowledge of the authors, they have so far not been used in hydrology. Therefore, the contribution gives an introduction to context free grammars and shows how they can be constructed and used for the structural inference of transfer functions. This is enabled by new methods from evolutionary computation, such as grammatical evolution [O'Neill, 2001], which make it possible to exploit the constructed grammar as a

Sliding Mode Observer-Based Current Sensor Fault Reconstruction and Unknown Load Disturbance Estimation for PMSM Driven System.

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Zhao, Kaihui; Li, Peng; Zhang, Changfan; Li, Xiangfei; He, Jing; Lin, Yuliang

2017-12-06

This paper proposes a new scheme of reconstructing current sensor faults and estimating unknown load disturbance for a permanent magnet synchronous motor (PMSM)-driven system. First, the original PMSM system is transformed into two subsystems; the first subsystem has unknown system load disturbances, which are unrelated to sensor faults, and the second subsystem has sensor faults, but is free from unknown load disturbances. Introducing a new state variable, the augmented subsystem that has sensor faults can be transformed into having actuator faults. Second, two sliding mode observers (SMOs) are designed: the unknown load disturbance is estimated by the first SMO in the subsystem, which has unknown load disturbance, and the sensor faults can be reconstructed using the second SMO in the augmented subsystem, which has sensor faults. The gains of the proposed SMOs and their stability analysis are developed via the solution of linear matrix inequality (LMI). Finally, the effectiveness of the proposed scheme was verified by simulations and experiments. The results demonstrate that the proposed scheme can reconstruct current sensor faults and estimate unknown load disturbance for the PMSM-driven system.

On the necessity of identifying the true parameter in adaptive LQ control

NARCIS (Netherlands)

Polderman, Jan W.

1986-01-01

In adaptive control problems one may drop the requirement of identifying the true system in order to simplify the problem of control. It will be shown that in the adaptive LQ control problem this does not at all lead to an easier problem.

Accounting for unknown foster dams in the genetic evaluation of embryo transfer progeny.

Science.gov (United States)

Suárez, M J; Munilla, S; Cantet, R J C

2015-02-01

Animals born by embryo transfer (ET) are usually not included in the genetic evaluation of beef cattle for preweaning growth if the recipient dam is unknown. This is primarily to avoid potential bias in the estimation of the unknown age of dam. We present a method that allows including records of calves with unknown age of dam. Assumptions are as follows: (i) foster cows belong to the same breed being evaluated, (ii) there is no correlation between the breeding value (BV) of the calf and the maternal BV of the recipient cow, and (iii) cows of all ages are used as recipients. We examine the issue of bias for the fixed level of unknown age of dam (AOD) and propose an estimator of the effect based on classical measurement error theory (MEM) and a Bayesian approach. Using stochastic simulation under random mating or selection, the MEM estimating equations were compared with BLUP in two situations as follows: (i) full information (FI); (ii) missing AOD information on some dams. Predictions of breeding value (PBV) from the FI situation had the smallest empirical average bias followed by PBV obtained without taking measurement error into account. In turn, MEM displayed the highest bias, although the differences were small. On the other hand, MEM showed the smallest MSEP, for either random mating or selection, followed by FI, whereas ignoring measurement error produced the largest MSEP. As a consequence from the smallest MSEP with a relatively small bias, empirical accuracies of PBV were larger for MEM than those for full information, which in turn showed larger accuracies than the situation ignoring measurement error. It is concluded that MEM equations are a useful alternative for analysing weaning weight data when recipient cows are unknown, as it mitigates the effects of bias in AOD by decreasing MSEP. © 2014 Blackwell Verlag GmbH.

Investigation on Insar Time Series Deformation Model Considering Rheological Parameters for Soft Clay Subgrade Monitoring

Science.gov (United States)

Xing, X.; Yuan, Z.; Chen, L. F.; Yu, X. Y.; Xiao, L.

2018-04-01

The stability control is one of the major technical difficulties in the field of highway subgrade construction engineering. Building deformation model is a crucial step for InSAR time series deformation monitoring. Most of the InSAR deformation models for deformation monitoring are pure empirical mathematical models, without considering the physical mechanism of the monitored object. In this study, we take rheology into consideration, inducing rheological parameters into traditional InSAR deformation models. To assess the feasibility and accuracy for our new model, both simulation and real deformation data over Lungui highway (a typical highway built on soft clay subgrade in Guangdong province, China) are investigated with TerraSAR-X satellite imagery. In order to solve the unknows of the non-linear rheological model, three algorithms: Gauss-Newton (GN), Levenberg-Marquarat (LM), and Genetic Algorithm (GA), are utilized and compared to estimate the unknown parameters. Considering both the calculation efficiency and accuracy, GA is chosen as the final choice for the new model in our case study. Preliminary real data experiment is conducted with use of 17 TerraSAR-X Stripmap images (with a 3-m resolution). With the new deformation model and GA aforementioned, the unknown rheological parameters over all the high coherence points are obtained and the LOS deformation (the low-pass component) sequences are generated.

Stochastic Online Learning in Dynamic Networks under Unknown Models

Science.gov (United States)

2016-08-02

The key is to develop online learning strategies at each individual node. Specifically, through local information exchange with its neighbors, each...infinitely repeated game with incomplete information and developed a dynamic pricing strategy referred to as Competitive and Cooperative Demand Learning...Stochastic Online Learning in Dynamic Networks under Unknown Models This research aims to develop fundamental theories and practical algorithms for

Uncertainty of Modal Parameters Estimated by ARMA Models

DEFF Research Database (Denmark)

Jensen, Jacob Laigaard; Brincker, Rune; Rytter, Anders

1990-01-01

In this paper the uncertainties of identified modal parameters such as eidenfrequencies and damping ratios are assed. From the measured response of dynamic excited structures the modal parameters may be identified and provide important structural knowledge. However the uncertainty of the parameters...... by simulation study of a lightly damped single degree of freedom system. Identification by ARMA models has been choosen as system identification method. It is concluded that both the sampling interval and number of sampled points may play a significant role with respect to the statistical errors. Furthermore......, it is shown that the model errors may also contribute significantly to the uncertainty....

Coma of unknown origin in the emergency department: implementation of an in-house management routine.

Science.gov (United States)

Braun, Mischa; Schmidt, Wolf Ulrich; Möckel, Martin; Römer, Michael; Ploner, Christoph J; Lindner, Tobias

2016-04-27

Coma of unknown origin is an emergency caused by a variety of possibly life-threatening pathologies. Although lethality is high, there are currently no generally accepted management guidelines. We implemented a new interdisciplinary standard operating procedure (SOP) for patients presenting with non-traumatic coma of unknown origin. It includes a new in-house triage process, a new alert call, a new composition of the clinical response team and a new management algorithm (altogether termed "coma alarm"). It is triggered by two simple criteria to be checked with out-of-hospital emergency response teams before the patient arrives. A neurologist in collaboration with an internal specialist leads the in-hospital team. Collaboration with anaesthesiology, trauma surgery and neurosurgery is organised along structured pathways that include standardised laboratory tests and imaging. Patients were prospectively enrolled. We calculated response times as well as sensitivity and false positive rates, thus proportions of over- and undertriaged patients, as quality measures for the implementation in the SOP. During 24 months after implementation, we identified 325 eligible patients. Sensitivity was 60 % initially (months 1-4), then fluctuated between 84 and 94 % (months 5-24). Overtriage never exceeded 15 % and undertriage could be kept low at a maximum of 11 % after a learning period. We achieved a median door-to-CT time of 20 minutes. 85 % of patients needed subsequent ICU treatment, 40 % of which required specialised neuro-ICUs. Our results indicate that our new simple in-house triage criteria may be sufficient to identify eligible patients before arrival. We aimed at ensuring the fastest possible proceedings given high portions of underlying time-sensitive neurological and medical pathologies while using all available resources as purposefully as possible. Our SOP may provide an appropriate tool for efficient management of patients with non-traumatic coma. Our results

Quest to identify geochemical risk factors associated with chronic kidney disease of unknown etiology (CKDu) in an endemic region of Sri Lanka-a multimedia laboratory analysis of biological, food, and environmental samples.

Science.gov (United States)

Levine, Keith E; Redmon, Jennifer Hoponick; Elledge, Myles F; Wanigasuriya, Kamani P; Smith, Kristin; Munoz, Breda; Waduge, Vajira A; Periris-John, Roshini J; Sathiakumar, Nalini; Harrington, James M; Womack, Donna S; Wickremasinghe, Rajitha

2016-10-01

fluoride, iron, manganese, sodium, and lead exceeding applicable drinking water standards in some instances. Current literature suggests that the etiology of CKDu is likely multifactorial, with no single biological or hydrogeochemical parameter directly related to disease genesis and progression. This preliminary screening identified that specific constituents may be present above levels of concern, but does not compare results against specific kidney toxicity values or cumulative risk related to a multifactorial disease process. The data collected from this limited investigation are intended to be used in the subsequent study design of a comprehensive and multifactorial etiological study of CKDu risk factors that includes sample collection, individual surveys, and laboratory analyses to more fully evaluate the potential environmental, behavioral, genetic, and lifestyle risk factors associated with CKDu.

Optimal unambiguous comparison of two unknown squeezed vacua

International Nuclear Information System (INIS)

Olivares, Stefano; Paris, Matteo G. A.; Sedlak, Michal; Rapsan, Peter; Busek, Vladimir

2011-01-01

We propose a scheme for the unambiguous state comparison (USC) of two unknown squeezed vacuum states of the electromagnetic field. Our setup is based on linear optical elements and photon-number detectors, and it achieves optimal USC in an ideal case of unit quantum efficiency. In realistic conditions, i.e., for nonunit quantum efficiency of photodetectors, we evaluate the probability of getting an ambiguous result as well as the reliability of the scheme, thus showing its robustness in comparison to previous proposals.

Identifiability and Identification of Trace Continuous Pollutant Source

Directory of Open Access Journals (Sweden)

Hongquan Qu

2014-01-01

Full Text Available Accidental pollution events often threaten people’s health and lives, and a pollutant source is very necessary so that prompt remedial actions can be taken. In this paper, a trace continuous pollutant source identification method is developed to identify a sudden continuous emission pollutant source in an enclosed space. The location probability model is set up firstly, and then the identification method is realized by searching a global optimal objective value of the location probability. In order to discuss the identifiability performance of the presented method, a conception of a synergy degree of velocity fields is presented in order to quantitatively analyze the impact of velocity field on the identification performance. Based on this conception, some simulation cases were conducted. The application conditions of this method are obtained according to the simulation studies. In order to verify the presented method, we designed an experiment and identified an unknown source appearing in the experimental space. The result showed that the method can identify a sudden trace continuous source when the studied situation satisfies the application conditions.

Inverse identification of unknown finite-duration air pollutant release from a point source in urban environment

Science.gov (United States)

Kovalets, Ivan V.; Efthimiou, George C.; Andronopoulos, Spyros; Venetsanos, Alexander G.; Argyropoulos, Christos D.; Kakosimos, Konstantinos E.

2018-05-01

In this work, we present an inverse computational method for the identification of the location, start time, duration and quantity of emitted substance of an unknown air pollution source of finite time duration in an urban environment. We considered a problem of transient pollutant dispersion under stationary meteorological fields, which is a reasonable assumption for the assimilation of available concentration measurements within 1 h from the start of an incident. We optimized the calculation of the source-receptor function by developing a method which requires integrating as many backward adjoint equations as the available measurement stations. This resulted in high numerical efficiency of the method. The source parameters are computed by maximizing the correlation function of the simulated and observed concentrations. The method has been integrated into the CFD code ADREA-HF and it has been tested successfully by performing a series of source inversion runs using the data of 200 individual realizations of puff releases, previously generated in a wind tunnel experiment.

TOURISM PROMOTION FOR UNKNOWN AREAS IN ROMANIA

Directory of Open Access Journals (Sweden)

Fotache Lacramioara

2013-12-01

Full Text Available The paper is an effort to unknown areas identity affirmation, through collaborative development of advertising mix, with an emphasis on virtual platforms as admissible solution for increasing visibility. Based upon comparative effective analysis of categories of communication particularities, it is suggested a positioning strategic solution, via virtual advertising platform as unique, integrated, complex and very attractive tourism product promotion, fitted for the internal and international tourism circuit. The active promotion of the specified territorial identity will launch a brand with an impact among tourists by using marketing techniques and innovating technical means and prioritizing tourism as a principal vector of local and regional development.

Fever of Unknown Origin: the Value of FDG-PET/CT

NARCIS (Netherlands)

Kouijzer, I.J.E.; Mulders-Manders, C.M.; Bleeker-Rovers, C.P.; Oyen, W.J.G.

2018-01-01

Fever of unknown origin (FUO) is commonly defined as fever higher than 38.3 degrees C on several occasions during at least 3 weeks with uncertain diagnosis after a number of obligatory investigations. The differential diagnosis of FUO can be subdivided in four categories: infections, malignancies,

Unknown facets of Well-Known Scientists Series - Part II

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V S Dixit

2016-01-01

Full Text Available 1st in the series of articles on “Unknown Facets of well-known Scientists” was about Sir Frederick Grant Banting, co-discoverer of Insulin, who also researched in Aviation and Diving Medicines, results of which brought extraordinary benefits for Flight crew during the World War II. The article was published in the previous issue of the Journal Unknown facets could be celebrated attributes, talents or otherwise, but it is necessary that we get to know fully about the “great mind". THIS ARTICLE IS ABOUT DR WERNER THEODOR OTTO FORSSMANN, A CARDIOLOGIST, WHO BECAME A UROLOGIST! Does the name Dr Forssmann ring a bell? He shared the 1956 Nobel Prize in Physiology or Medicine with “Andre Cournand and Dickinson Richards". The trio was awarded for their “discoveries concerning heart catheterization and pathological changes in the circulatory system". Dr Forssmann was nominated for performing an experiment in which he introduced a catheter into a vein of his arm, further passing it onward into his heart It was risky. This was in the year 1929. Subject of this article is the self-experimentation he carried out and what happened later.

Comparison of various structural damage tracking techniques with unknown excitations based on experimental data

Science.gov (United States)

Huang, Hongwei; Yang, Jann N.; Zhou, Li

2009-03-01

An early detection of structural damages is critical for the decision making of repair and replacement maintenance in order to guarantee a specified structural reliability. Consequently, the structural damage detection, based on vibration data measured from the structural health monitoring (SHM) system, has received considerable attention recently. The traditional time-domain analysis techniques, such as the least square estimation (LSE) method and the extended Kalman filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for some SHM systems. Recently, these two approaches have been extended to cover the general case where some of the external excitations (inputs) are not measured, referred to as the LSE with unknown inputs (LSE-UI) and the EKF with unknown inputs (EKF-UI). Also, new analysis methods, referred to as the sequential non-linear least-square estimation with unknown inputs and unknown outputs (SNLSE-UI-UO) and the quadratic sum-square error with unknown inputs (QSSE-UI), have been proposed for the damage tracking of structures when some of the acceleration responses are not measured and the external excitations are not available. In this paper, these newly proposed analysis methods will be compared in terms of accuracy, convergence and efficiency, for damage identification of structures based on experimental data obtained through a series of experimental tests using a small-scale 3-story building model with white noise excitation. The capability of the LSE-UI, EKF-UI, SNLSE-UI-UO and QSSE-UI approaches in tracking the structural damages will be demonstrated.

ShinyKGode: an Interactive Application for ODE Parameter Inference Using Gradient Matching.

Science.gov (United States)

Wandy, Joe; Niu, Mu; Giurghita, Diana; Daly, Rónán; Rogers, Simon; Husmeier, Dirk

2018-02-27

Mathematical modelling based on ordinary differential equations (ODEs) is widely used to describe the dynamics of biological systems, particularly in systems and pathway biology. Often the kinetic parameters of these ODE systems are unknown and have to be inferred from the data. Approximate parameter inference methods based on gradient matching (which do not require performing computationally expensive numerical integration of the ODEs) have been getting popular in recent years, but many implementations are difficult to run without expert knowledge. Here we introduce ShinyKGode, an interactive web application to perform fast parameter inference on ODEs using gradient matching. ShinyKGode can be used to infer ODE parameters on simulated and observed data using gradient matching. Users can easily load their own models in Systems Biology Markup Language format, and a set of pre-defined ODE benchmark models are provided in the application. Inferred parameters are visualised alongside diagnostic plots to assess convergence. The R package for ShinyKGode can be installed through the Comprehensive R Archive Network (CRAN). Installation instructions, as well as tutorial videos and source code are available at https://joewandy.github.io/shinyKGode. dirk.husmeier@glasgow.ac.uk. None.

<<Unknown>> earthquakes: a growing contribution to the Catalogue of Strong Italian Earthquakes

Directory of Open Access Journals (Sweden)

E. Guidoboni

2000-06-01

Full Text Available The particular structure of the research into historical seismology found in this catalogue has allowed a lot of information about unknown seismic events to be traced. This new contribution to seismologic knowledge mainly consists in: i the retrieval and organisation within a coherent framework of documentary evidence of earthquakes that took place between the Middle Ages and the sixteenth century; ii the improved knowledge of seismic events, even destructive events, which in the past had been "obscured" by large earthquakes; iii the identification of earthquakes in "silent" seismic areas. The complex elements to be taken into account when dealing with unknown seismic events have been outlined; much "new" information often falls into one of the following categories: simple chronological errors relative to other well-known events; descriptions of other natural phenomena, though defined in texts as "earthquakes" (landslides, hurricanes, tornadoes, etc.; unknown tremors belonging to known seismic periods; tremors that may be connected with events which have been catalogued under incorrect dates and with very approximate estimates of location and intensity. This proves that this was not a real seismic "silence" but a research vacuum.

The dilemma in prioritizing chemicals for environmental analysis: known versus unknown hazards.

Science.gov (United States)

Anna, Sobek; Sofia, Bejgarn; Christina, Rudén; Magnus, Breitholtz

2016-08-10

A major challenge for society is to manage the risks posed by the many chemicals continuously emitted to the environment. All chemicals in production and use cannot be monitored and science-based strategies for prioritization are essential. In this study we review available data to investigate which substances are included in environmental monitoring programs and published research studies reporting analyses of chemicals in Baltic Sea fish between 2000 and 2012. Our aim is to contribute to the discussion of priority settings in environmental chemical monitoring and research, which is closely linked to chemical management. In total, 105 different substances or substance groups were analyzed in Baltic Sea fish. Polychlorinated dibenzo-p-dioxins, polychlorinated dibenzofurans (PCDD/Fs) and polychlorinated biphenyls (PCBs) were the most studied substances or substance groups. The majority, 87%, of all analyses comprised 20% of the substances or substance groups, whereas 46 substance groups (44%) were analyzed only once. Almost three quarters of all analyses regarded a POP-substance (persistent organic pollutant). These results demonstrate that the majority of analyses on environmental contaminants in Baltic Sea fish concern a small number of already regulated chemicals. Legacy pollutants such as POPs pose a high risk to the Baltic Sea due to their hazardous properties. Yet, there may be a risk that prioritizations for chemical analyses are biased based on the knowns of the past. Such biases may lead to society failing in identifying risks posed by yet unknown hazardous chemicals. Alternative and complementary ways to identify priority chemicals are needed. More transparent communication between risk assessments performed as part of the risk assessment process within REACH and monitoring programs, and information on chemicals contained in consumer articles, would offer ways to identify chemicals for environmental analysis.

Structural observability analysis and EKF based parameter estimation of building heating models

Directory of Open Access Journals (Sweden)

D.W.U. Perera

2016-07-01