Light weakly coupled axial forces: models, constraints, and projections

DOE PAGES

Kahn, Yonatan; Krnjaic, Gordan; Mishra-Sharma, Siddharth; ...

2017-05-01

Here, we investigate the landscape of constraints on MeV-GeV scale, hidden U(1) forces with nonzero axial-vector couplings to Standard Model fermions. While the purely vector-coupled dark photon, which may arise from kinetic mixing, is a well-motivated scenario, several MeV-scale anomalies motivate a theory with axial couplings which can be UV-completed consistent with Standard Model gauge invariance. Moreover, existing constraints on dark photons depend on products of various combinations of axial and vector couplings, making it difficult to isolate the e ects of axial couplings for particular flavors of SM fermions. We present a representative renormalizable, UV-complete model of a darkmore » photon with adjustable axial and vector couplings, discuss its general features, and show how some UV constraints may be relaxed in a model with nonrenormalizable Yukawa couplings at the expense of fine-tuning. We survey the existing parameter space and the projected reach of planned experiments, brie y commenting on the relevance of the allowed parameter space to low-energy anomalies in π 0 and 8Be* decay.« less

Light weakly coupled axial forces: models, constraints, and projections

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

Kahn, Yonatan; Krnjaic, Gordan; Mishra-Sharma, Siddharth

Here, we investigate the landscape of constraints on MeV-GeV scale, hidden U(1) forces with nonzero axial-vector couplings to Standard Model fermions. While the purely vector-coupled dark photon, which may arise from kinetic mixing, is a well-motivated scenario, several MeV-scale anomalies motivate a theory with axial couplings which can be UV-completed consistent with Standard Model gauge invariance. Moreover, existing constraints on dark photons depend on products of various combinations of axial and vector couplings, making it difficult to isolate the e ects of axial couplings for particular flavors of SM fermions. We present a representative renormalizable, UV-complete model of a darkmore » photon with adjustable axial and vector couplings, discuss its general features, and show how some UV constraints may be relaxed in a model with nonrenormalizable Yukawa couplings at the expense of fine-tuning. We survey the existing parameter space and the projected reach of planned experiments, brie y commenting on the relevance of the allowed parameter space to low-energy anomalies in π 0 and 8Be* decay.« less

LS-APC v1.0: a tuning-free method for the linear inverse problem and its application to source-term determination

NASA Astrophysics Data System (ADS)

Tichý, Ondřej; Šmídl, Václav; Hofman, Radek; Stohl, Andreas

2016-11-01

Estimation of pollutant releases into the atmosphere is an important problem in the environmental sciences. It is typically formalized as an inverse problem using a linear model that can explain observable quantities (e.g., concentrations or deposition values) as a product of the source-receptor sensitivity (SRS) matrix obtained from an atmospheric transport model multiplied by the unknown source-term vector. Since this problem is typically ill-posed, current state-of-the-art methods are based on regularization of the problem and solution of a formulated optimization problem. This procedure depends on manual settings of uncertainties that are often very poorly quantified, effectively making them tuning parameters. We formulate a probabilistic model, that has the same maximum likelihood solution as the conventional method using pre-specified uncertainties. Replacement of the maximum likelihood solution by full Bayesian estimation also allows estimation of all tuning parameters from the measurements. The estimation procedure is based on the variational Bayes approximation which is evaluated by an iterative algorithm. The resulting method is thus very similar to the conventional approach, but with the possibility to also estimate all tuning parameters from the observations. The proposed algorithm is tested and compared with the standard methods on data from the European Tracer Experiment (ETEX) where advantages of the new method are demonstrated. A MATLAB implementation of the proposed algorithm is available for download.

Vectorization of optically sectioned brain microvasculature: learning aids completion of vascular graphs by connecting gaps and deleting open-ended segments.

PubMed

Kaufhold, John P; Tsai, Philbert S; Blinder, Pablo; Kleinfeld, David

2012-08-01

A graph of tissue vasculature is an essential requirement to model the exchange of gasses and nutriments between the blood and cells in the brain. Such a graph is derived from a vectorized representation of anatomical data, provides a map of all vessels as vertices and segments, and may include the location of nonvascular components, such as neuronal and glial somata. Yet vectorized data sets typically contain erroneous gaps, spurious endpoints, and spuriously merged strands. Current methods to correct such defects only address the issue of connecting gaps and further require manual tuning of parameters in a high dimensional algorithm. To address these shortcomings, we introduce a supervised machine learning method that (1) connects vessel gaps by "learned threshold relaxation"; (2) removes spurious segments by "learning to eliminate deletion candidate strands"; and (3) enforces consistency in the joint space of learned vascular graph corrections through "consistency learning." Human operators are only required to label individual objects they recognize in a training set and are not burdened with tuning parameters. The supervised learning procedure examines the geometry and topology of features in the neighborhood of each vessel segment under consideration. We demonstrate the effectiveness of these methods on four sets of microvascular data, each with >800(3) voxels, obtained with all optical histology of mouse tissue and vectorization by state-of-the-art techniques in image segmentation. Through statistically validated sampling and analysis in terms of precision recall curves, we find that learning with bagged boosted decision trees reduces equal-error error rates for threshold relaxation by 5-21% and strand elimination performance by 18-57%. We benchmark generalization performance across datasets; while improvements vary between data sets, learning always leads to a useful reduction in error rates. Overall, learning is shown to more than halve the total error rate, and therefore, human time spent manually correcting such vectorizations. Copyright © 2012 Elsevier B.V. All rights reserved.

Vectorization of optically sectioned brain microvasculature: Learning aids completion of vascular graphs by connecting gaps and deleting open-ended segments

PubMed Central

Kaufhold, John P.; Tsai, Philbert S.; Blinder, Pablo; Kleinfeld, David

2012-01-01

A graph of tissue vasculature is an essential requirement to model the exchange of gasses and nutriments between the blood and cells in the brain. Such a graph is derived from a vectorized representation of anatomical data, provides a map of all vessels as vertices and segments, and may include the location of nonvascular components, such as neuronal and glial somata. Yet vectorized data sets typically contain erroneous gaps, spurious endpoints, and spuriously merged strands. Current methods to correct such defects only address the issue of connecting gaps and further require manual tuning of parameters in a high dimensional algorithm. To address these shortcomings, we introduce a supervised machine learning method that (1) connects vessel gaps by “learned threshold relaxation”; (2) removes spurious segments by “learning to eliminate deletion candidate strands”; and (3) enforces consistency in the joint space of learned vascular graph corrections through “consistency learning.” Human operators are only required to label individual objects they recognize in a training set and are not burdened with tuning parameters. The supervised learning procedure examines the geometry and topology of features in the neighborhood of each vessel segment under consideration. We demonstrate the effectiveness of these methods on four sets of microvascular data, each with > 8003 voxels, obtained with all optical histology of mouse tissue and vectorization by state-of-the-art techniques in image segmentation. Through statistically validated sampling and analysis in terms of precision recall curves, we find that learning with bagged boosted decision trees reduces equal-error error rates for threshold relaxation by 5 to 21 % and strand elimination performance by 18 to 57 %. We benchmark generalization performance across datasets; while improvements vary between data sets, learning always leads to a useful reduction in error rates. Overall, learning is shown to more than halve the total error rate, and therefore, human time spent manually correcting such vectorizations. PMID:22854035

Adaptive track scheduling to optimize concurrency and vectorization in GeantV

DOE PAGES

Apostolakis, J.; Bandieramonte, M.; Bitzes, G.; ...

2015-05-22

The GeantV project is focused on the R&D of new particle transport techniques to maximize parallelism on multiple levels, profiting from the use of both SIMD instructions and co-processors for the CPU-intensive calculations specific to this type of applications. In our approach, vectors of tracks belonging to multiple events and matching different locality criteria must be gathered and dispatched to algorithms having vector signatures. While the transport propagates tracks and changes their individual states, data locality becomes harder to maintain. The scheduling policy has to be changed to maintain efficient vectors while keeping an optimal level of concurrency. The modelmore » has complex dynamics requiring tuning the thresholds to switch between the normal regime and special modes, i.e. prioritizing events to allow flushing memory, adding new events in the transport pipeline to boost locality, dynamically adjusting the particle vector size or switching between vector to single track mode when vectorization causes only overhead. Lastly, this work requires a comprehensive study for optimizing these parameters to make the behaviour of the scheduler self-adapting, presenting here its initial results.« less

Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2010-01-01

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy

Selection of optimum median-filter-based ambiguity removal algorithm parameters for NSCAT. [NASA scatterometer

NASA Technical Reports Server (NTRS)

Shaffer, Scott; Dunbar, R. Scott; Hsiao, S. Vincent; Long, David G.

1989-01-01

The NASA Scatterometer, NSCAT, is an active spaceborne radar designed to measure the normalized radar backscatter coefficient (sigma0) of the ocean surface. These measurements can, in turn, be used to infer the surface vector wind over the ocean using a geophysical model function. Several ambiguous wind vectors result because of the nature of the model function. A median-filter-based ambiguity removal algorithm will be used by the NSCAT ground data processor to select the best wind vector from the set of ambiguous wind vectors. This process is commonly known as dealiasing or ambiguity removal. The baseline NSCAT ambiguity removal algorithm and the method used to select the set of optimum parameter values are described. An extensive simulation of the NSCAT instrument and ground data processor provides a means of testing the resulting tuned algorithm. This simulation generates the ambiguous wind-field vectors expected from the instrument as it orbits over a set of realistic meoscale wind fields. The ambiguous wind field is then dealiased using the median-based ambiguity removal algorithm. Performance is measured by comparison of the unambiguous wind fields with the true wind fields. Results have shown that the median-filter-based ambiguity removal algorithm satisfies NSCAT mission requirements.

Support vector machines and generalisation in HEP

NASA Astrophysics Data System (ADS)

Bevan, Adrian; Gamboa Goñi, Rodrigo; Hays, Jon; Stevenson, Tom

2017-10-01

We review the concept of Support Vector Machines (SVMs) and discuss examples of their use in a number of scenarios. Several SVM implementations have been used in HEP and we exemplify this algorithm using the Toolkit for Multivariate Analysis (TMVA) implementation. We discuss examples relevant to HEP including background suppression for H → τ + τ - at the LHC with several different kernel functions. Performance benchmarking leads to the issue of generalisation of hyper-parameter selection. The avoidance of fine tuning (over training or over fitting) in MVA hyper-parameter optimisation, i.e. the ability to ensure generalised performance of an MVA that is independent of the training, validation and test samples, is of utmost importance. We discuss this issue and compare and contrast performance of hold-out and k-fold cross-validation. We have extended the SVM functionality and introduced tools to facilitate cross validation in TMVA and present results based on these improvements.

Reducing the Volume of NASA Earth-Science Data

NASA Technical Reports Server (NTRS)

Lee, Seungwon; Braverman, Amy J.; Guillaume, Alexandre

2010-01-01

A computer program reduces data generated by NASA Earth-science missions into representative clusters characterized by centroids and membership information, thereby reducing the large volume of data to a level more amenable to analysis. The program effects an autonomous data-reduction/clustering process to produce a representative distribution and joint relationships of the data, without assuming a specific type of distribution and relationship and without resorting to domain-specific knowledge about the data. The program implements a combination of a data-reduction algorithm known as the entropy-constrained vector quantization (ECVQ) and an optimization algorithm known as the differential evolution (DE). The combination of algorithms generates the Pareto front of clustering solutions that presents the compromise between the quality of the reduced data and the degree of reduction. Similar prior data-reduction computer programs utilize only a clustering algorithm, the parameters of which are tuned manually by users. In the present program, autonomous optimization of the parameters by means of the DE supplants the manual tuning of the parameters. Thus, the program determines the best set of clustering solutions without human intervention.

Vector curvaton with varying kinetic function

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

Dimopoulos, Konstantinos; Karciauskas, Mindaugas; Wagstaff, Jacques M.

2010-01-15

A new model realization of the vector curvaton paradigm is presented and analyzed. The model consists of a single massive Abelian vector field, with a Maxwell-type kinetic term. By assuming that the kinetic function and the mass of the vector field are appropriately varying during inflation, it is shown that a scale-invariant spectrum of superhorizon perturbations can be generated. These perturbations can contribute to the curvature perturbation of the Universe. If the vector field remains light at the end of inflation it is found that it can generate substantial statistical anisotropy in the spectrum and bispectrum of the curvature perturbation.more » In this case the non-Gaussianity in the curvature perturbation is predominantly anisotropic, which will be a testable prediction in the near future. If, on the other hand, the vector field is heavy at the end of inflation then it is demonstrated that particle production is approximately isotropic and the vector field alone can give rise to the curvature perturbation, without directly involving any fundamental scalar field. The parameter space for both possibilities is shown to be substantial. Finally, toy models are presented which show that the desired variation of the mass and kinetic function of the vector field can be realistically obtained, without unnatural tunings, in the context of supergravity or superstrings.« less

Regular and Chaotic Spatial Distribution of Bose-Einstein Condensed Atoms in a Ratchet Potential

NASA Astrophysics Data System (ADS)

Li, Fei; Xu, Lan; Li, Wenwu

2018-02-01

We study the regular and chaotic spatial distribution of Bose-Einstein condensed atoms with a space-dependent nonlinear interaction in a ratchet potential. There exists in the system a space-dependent atomic current that can be tuned via Feshbach resonance technique. In the presence of the space-dependent atomic current and a weak ratchet potential, the Smale-horseshoe chaos is studied and the Melnikov chaotic criterion is obtained. Numerical simulations show that the ratio between the intensities of optical potentials forming the ratchet potential, the wave vector of the laser producing the ratchet potential or the wave vector of the modulating laser can be chosen as the controlling parameters to result in or avoid chaotic spatial distributional states.

Wavelength tunable L Band polarization-locked vector soliton fiber laser based on SWCNT-SA and CFBG

NASA Astrophysics Data System (ADS)

Yan, Yaxi; Wang, Jiaqi; Wang, Liang; Cheng, Zhenzhou

2018-04-01

Wavelength tunable L-Band polarization-locked vector soliton fiber laser based on single-walled carbon nanotube saturable absorber (SWCNT-SA) and chirped fiber Bragg grating (CFBG) is presented for the first time. By inserting the SWCNT-SA into an all-fiber laser cavity, polarization-locked vector solitons (PLVS) are obtained. The CFBG glued on a plastic cantilever is used for wavelength tuning. By mechanically bending the cantilever, the center wavelength of the PLVS pulses can be continuously tuned from 1606.8 nm to 1614 nm, while the polarization-locked state is kept stable. The properties and dynamics of PLVSs are experimentally investigated and stable PLVS operation including high-order PLVSs is demonstrated. The pulse width and repetition rate are 7.06 ps and 11.9 MHz at a wavelength of 1611 nm, respectively. This work demonstrates the feasibility of using polarization-insensitive CFBG to realize wavelength tuning in PLVS fiber laser.

DsJ(2860) as the First Radial Excitation of Ds0*(2317)

NASA Astrophysics Data System (ADS)

van Beveren, Eef; Rupp, George

2006-11-01

A coupled-channel model previously employed to describe the narrow Ds0*(2317) and broad D0*(2400) charmed scalar mesons is generalized so as to include all ground-state pseudoscalar-pseudoscalar and vector-vector two-meson channels. All parameters are chosen fixed at published values, except for the overall coupling constant, which is fine-tuned to reproduce the Ds0*(2317) mass. Thus, the radial excitations Ds0*(2850) and D0*(2740) are predicted, both with a width of about 50 MeV. The former state appears to correspond to the new DsJ(2860) resonance decaying to DK announced by BABAR in the course of this work. Also, the D0*(2400) resonance is roughly reproduced, though perhaps with a somewhat too low central resonance peak.

Amplitude and dynamics of polarization-plane signaling in the central complex of the locust brain

PubMed Central

Bockhorst, Tobias

2015-01-01

The polarization pattern of skylight provides a compass cue that various insect species use for allocentric orientation. In the desert locust, Schistocerca gregaria, a network of neurons tuned to the electric field vector (E-vector) angle of polarized light is present in the central complex of the brain. Preferred E-vector angles vary along slices of neuropils in a compasslike fashion (polarotopy). We studied how the activity in this polarotopic population is modulated in ways suited to control compass-guided locomotion. To this end, we analyzed tuning profiles using measures of correlation between spike rate and E-vector angle and, furthermore, tested for adaptation to stationary angles. The results suggest that the polarotopy is stabilized by antagonistic integration across neurons with opponent tuning. Downstream to the input stage of the network, responses to stationary E-vector angles adapted quickly, which may correlate with a tendency to steer a steady course previously observed in tethered flying locusts. By contrast, rotating E-vectors corresponding to changes in heading direction under a natural sky elicited nonadapting responses. However, response amplitudes were particularly variable at the output stage, covarying with the level of ongoing activity. Moreover, the responses to rotating E-vector angles depended on the direction of rotation in an anticipatory manner. Our observations support a view of the central complex as a substrate of higher-stage processing that could assign contextual meaning to sensory input for motor control in goal-driven behaviors. Parallels to higher-stage processing of sensory information in vertebrates are discussed. PMID:25609107

Tuning to optimize SVM approach for assisting ovarian cancer diagnosis with photoacoustic imaging.

PubMed

Wang, Rui; Li, Rui; Lei, Yanyan; Zhu, Quing

2015-01-01

Support vector machine (SVM) is one of the most effective classification methods for cancer detection. The efficiency and quality of a SVM classifier depends strongly on several important features and a set of proper parameters. Here, a series of classification analyses, with one set of photoacoustic data from ovarian tissues ex vivo and a widely used breast cancer dataset- the Wisconsin Diagnostic Breast Cancer (WDBC), revealed the different accuracy of a SVM classification in terms of the number of features used and the parameters selected. A pattern recognition system is proposed by means of SVM-Recursive Feature Elimination (RFE) with the Radial Basis Function (RBF) kernel. To improve the effectiveness and robustness of the system, an optimized tuning ensemble algorithm called as SVM-RFE(C) with correlation filter was implemented to quantify feature and parameter information based on cross validation. The proposed algorithm is first demonstrated outperforming SVM-RFE on WDBC. Then the best accuracy of 94.643% and sensitivity of 94.595% were achieved when using SVM-RFE(C) to test 57 new PAT data from 19 patients. The experiment results show that the classifier constructed with SVM-RFE(C) algorithm is able to learn additional information from new data and has significant potential in ovarian cancer diagnosis.

Statistical Mechanical Analysis of Online Learning with Weight Normalization in Single Layer Perceptron

NASA Astrophysics Data System (ADS)

Yoshida, Yuki; Karakida, Ryo; Okada, Masato; Amari, Shun-ichi

2017-04-01

Weight normalization, a newly proposed optimization method for neural networks by Salimans and Kingma (2016), decomposes the weight vector of a neural network into a radial length and a direction vector, and the decomposed parameters follow their steepest descent update. They reported that learning with the weight normalization achieves better converging speed in several tasks including image recognition and reinforcement learning than learning with the conventional parameterization. However, it remains theoretically uncovered how the weight normalization improves the converging speed. In this study, we applied a statistical mechanical technique to analyze on-line learning in single layer linear and nonlinear perceptrons with weight normalization. By deriving order parameters of the learning dynamics, we confirmed quantitatively that weight normalization realizes fast converging speed by automatically tuning the effective learning rate, regardless of the nonlinearity of the neural network. This property is realized when the initial value of the radial length is near the global minimum; therefore, our theory suggests that it is important to choose the initial value of the radial length appropriately when using weight normalization.

SOLAR FLARE PREDICTION USING SDO/HMI VECTOR MAGNETIC FIELD DATA WITH A MACHINE-LEARNING ALGORITHM

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

Bobra, M. G.; Couvidat, S., E-mail: couvidat@stanford.edu

2015-01-10

We attempt to forecast M- and X-class solar flares using a machine-learning algorithm, called support vector machine (SVM), and four years of data from the Solar Dynamics Observatory's Helioseismic and Magnetic Imager, the first instrument to continuously map the full-disk photospheric vector magnetic field from space. Most flare forecasting efforts described in the literature use either line-of-sight magnetograms or a relatively small number of ground-based vector magnetograms. This is the first time a large data set of vector magnetograms has been used to forecast solar flares. We build a catalog of flaring and non-flaring active regions sampled from a databasemore » of 2071 active regions, comprised of 1.5 million active region patches of vector magnetic field data, and characterize each active region by 25 parameters. We then train and test the machine-learning algorithm and we estimate its performances using forecast verification metrics with an emphasis on the true skill statistic (TSS). We obtain relatively high TSS scores and overall predictive abilities. We surmise that this is partly due to fine-tuning the SVM for this purpose and also to an advantageous set of features that can only be calculated from vector magnetic field data. We also apply a feature selection algorithm to determine which of our 25 features are useful for discriminating between flaring and non-flaring active regions and conclude that only a handful are needed for good predictive abilities.« less

Kernel parameter variation-based selective ensemble support vector data description for oil spill detection on the ocean via hyperspectral imaging

NASA Astrophysics Data System (ADS)

Uslu, Faruk Sukru

2017-07-01

Oil spills on the ocean surface cause serious environmental, political, and economic problems. Therefore, these catastrophic threats to marine ecosystems require detection and monitoring. Hyperspectral sensors are powerful optical sensors used for oil spill detection with the help of detailed spectral information of materials. However, huge amounts of data in hyperspectral imaging (HSI) require fast and accurate computation methods for detection problems. Support vector data description (SVDD) is one of the most suitable methods for detection, especially for large data sets. Nevertheless, the selection of kernel parameters is one of the main problems in SVDD. This paper presents a method, inspired by ensemble learning, for improving performance of SVDD without tuning its kernel parameters. Additionally, a classifier selection technique is proposed to get more gain. The proposed approach also aims to solve the small sample size problem, which is very important for processing high-dimensional data in HSI. The algorithm is applied to two HSI data sets for detection problems. In the first HSI data set, various targets are detected; in the second HSI data set, oil spill detection in situ is realized. The experimental results demonstrate the feasibility and performance improvement of the proposed algorithm for oil spill detection problems.

Discriminative parameter estimation for random walks segmentation.

PubMed

Baudin, Pierre-Yves; Goodman, Danny; Kumrnar, Puneet; Azzabou, Noura; Carlier, Pierre G; Paragios, Nikos; Kumar, M Pawan

2013-01-01

The Random Walks (RW) algorithm is one of the most efficient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Specifically, they provide a hard segmentation of the images, instead of a probabilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach significantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.

Multiple-Point Temperature Gradient Algorithm for Ring Laser Gyroscope Bias Compensation

PubMed Central

Li, Geng; Zhang, Pengfei; Wei, Guo; Xie, Yuanping; Yu, Xudong; Long, Xingwu

2015-01-01

To further improve ring laser gyroscope (RLG) bias stability, a multiple-point temperature gradient algorithm is proposed for RLG bias compensation in this paper. Based on the multiple-point temperature measurement system, a complete thermo-image of the RLG block is developed. Combined with the multiple-point temperature gradients between different points of the RLG block, the particle swarm optimization algorithm is used to tune the support vector machine (SVM) parameters, and an optimized design for selecting the thermometer locations is also discussed. The experimental results validate the superiority of the introduced method and enhance the precision and generalizability in the RLG bias compensation model. PMID:26633401

Fidelity Study of Superconductivity in Extended Hubbard Models

NASA Astrophysics Data System (ADS)

Plonka, Nachum; Jia, Chunjing; Moritz, Brian; Wang, Yao; Devereaux, Thomas

2015-03-01

The role of strong electronic correlations on unconventional superconductivity remains an important open question. Here, we explore the influence of long-range Coulomb interactions, present in real material systems, through nearest and next-nearest neighbor extended Hubbard interactions in addition to the usual on-site terms. Utilizing large scale, numerical exact diagonalization, we analyze the signatures of superconductivity in the ground states through the fidelity metric of quantum information theory. We find that these extended interactions enhance charge fluctuations with various wave vectors. These suppress superconductivity in general, but in certain parameter regimes superconductivity is sustained. This has implications for tuning extended interactions in real materials.

In-situ characterization of highly reversible phase transformation by synchrotron X-ray Laue microdiffraction

DOE PAGES

Chen, Xian; Tamura, Nobumichi; MacDowell, Alastair; ...

2016-05-23

The alloy Cu 25 Au 30 Zn 45 undergoes a huge first-order phase transformation (6% strain) and shows a high reversibility under thermal cycling and an unusual martensitc microstructure in sharp contrast to its nearby compositions. We discovered this alloy by systematically tuning the composition so that its lattice parameters satisfy the cofactor conditions (i.e., the kinematic conditions of compatibility between phases). It was conjectured that satisfaction of these conditions is responsible for the enhanced reversibility as well as the observed unusual fluid-like microstructure during transformation, but so far, there has been no direct evidence confirming that these observed microstructuresmore » are those predicted by the cofactor conditions. In order to verify this hypothesis, we use synchrotron X-ray Laue microdiffraction to measure the orientations and structural parameters of variants and phases near the austenite/martensite interface. The areas consisting of both austenite and multi-variants of martensite are scanned by microLaue diffraction. The cofactor conditions have been examined from the kinematic relation of lattice vectors across the interface. The continuity condition of the interface is precisely verified from the correspondent lattice vectors between two phases.« less

Weight Vector Fluctuations in Adaptive Antenna Arrays Tuned Using the Least-Mean-Square Error Algorithm with Quadratic Constraint

NASA Astrophysics Data System (ADS)

Zimina, S. V.

2015-06-01

We present the results of statistical analysis of an adaptive antenna array tuned using the least-mean-square error algorithm with quadratic constraint on the useful-signal amplification with allowance for the weight-coefficient fluctuations. Using the perturbation theory, the expressions for the correlation function and power of the output signal of the adaptive antenna array, as well as the formula for the weight-vector covariance matrix are obtained in the first approximation. The fluctuations are shown to lead to the signal distortions at the antenna-array output. The weight-coefficient fluctuations result in the appearance of additional terms in the statistical characteristics of the antenna array. It is also shown that the weight-vector fluctuations are isotropic, i.e., identical in all directions of the weight-coefficient space.

Transition radiation on a superlattice in finite thickness plate generated by two acoustic waves

NASA Astrophysics Data System (ADS)

Mkrtchyan, A. R.; Parazian, V. V.; Saharian, A. A.

2018-01-01

Forward transition radiation from relativistic electrons is investigated in an ultrasonic superlattice excited in a finite thickness plate by two acoustic waves. In the quasi-classical approximation formulae are derived for the vector potential of the electromagnetic field and for the spectral-angular distribution of the radiation intensity. Zone structures appear in the plate, which makes it possible (by an appropriate choice of the frequencies of the two acoustic waves) to control the spectral-angular distribution of the radiation through changes in the parameters of the medium. The acoustic waves generate new resonance peaks in the spectral and angular distribution of the radiation intensity. The heights of the peaks can be tuned by choosing the parameters of the acoustic waves. Numerical examples are presented for a plate of fused quartz.

Applying spectral unmixing and support vector machine to airborne hyperspectral imagery for detecting giant reed

USDA-ARS?s Scientific Manuscript database

This study evaluated linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and support vector machine (SVM) techniques for detecting and mapping giant reed (Arundo donax L.), an invasive weed that presents a severe threat to agroecosystems and riparian areas throughout the southern ...

High-performance computing — an overview

NASA Astrophysics Data System (ADS)

Marksteiner, Peter

1996-08-01

An overview of high-performance computing (HPC) is given. Different types of computer architectures used in HPC are discussed: vector supercomputers, high-performance RISC processors, various parallel computers like symmetric multiprocessors, workstation clusters, massively parallel processors. Software tools and programming techniques used in HPC are reviewed: vectorizing compilers, optimization and vector tuning, optimization for RISC processors; parallel programming techniques like shared-memory parallelism, message passing and data parallelism; and numerical libraries.

Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time MIMO systems.

PubMed

Liu, Yan-Jun; Tang, Li; Tong, Shaocheng; Chen, C L Philip; Li, Dong-Juan

2015-01-01

Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete-time systems with unknown functions and disturbances. In the design procedure, two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function. An optimal control signal and adaptation laws can be generated based on two NNs. In the previous approaches, the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design. Consequently, compared with the existing results, the main contributions of this paper are: 1) only two parameters are needed to be adjusted, and thus the number of the adaptation laws is smaller than the previous results and 2) the updating parameters do not depend on the number of the subsystems for MIMO systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks. It is proven that the tracking errors, the adaptation laws, and the control inputs are uniformly bounded using Lyapunov analysis method. The simulation examples are employed to illustrate the effectiveness of the proposed algorithm.

Wave Phase-Sensitive Transformation of 3d-Straining of Mechanical Fields

NASA Astrophysics Data System (ADS)

Smirnov, I. N.; Speranskiy, A. A.

2015-11-01

It is the area of research of oscillatory processes in elastic mechanical systems. Technical result of innovation is creation of spectral set of multidimensional images which reflect time-correlated three-dimensional vector parameters of metrological, and\\or estimated, and\\or design parameters of oscillations in mechanical systems. Reconstructed images of different dimensionality integrated in various combinations depending on their objective function can be used as homeostatic profile or cybernetic image of oscillatory processes in mechanical systems for an objective estimation of current operational conditions in real time. The innovation can be widely used to enhance the efficiency of monitoring and research of oscillation processes in mechanical systems (objects) in construction, mechanical engineering, acoustics, etc. Concept method of vector vibrometry based on application of vector 3D phase- sensitive vibro-transducers permits unique evaluation of real stressed-strained states of power aggregates and loaded constructions and opens fundamental innovation opportunities: conduct of continuous (on-line regime) reliable monitoring of turboagregates of electrical machines, compressor installations, bases, supports, pipe-lines and other objects subjected to damaging effect of vibrations; control of operational safety of technical systems at all the stages of life cycle including design, test production, tuning, testing, operational use, repairs and resource enlargement; creation of vibro-diagnostic systems of authentic non-destructive control of anisotropic characteristics of materials resistance of power aggregates and loaded constructions under outer effects and operational flaws. The described technology is revolutionary, universal and common for all branches of engineering industry and construction building objects.

T-wave end detection using neural networks and Support Vector Machines.

PubMed

Suárez-León, Alexander Alexeis; Varon, Carolina; Willems, Rik; Van Huffel, Sabine; Vázquez-Seisdedos, Carlos Román

2018-05-01

In this paper we propose a new approach for detecting the end of the T-wave in the electrocardiogram (ECG) using Neural Networks and Support Vector Machines. Both, Multilayer Perceptron (MLP) neural networks and Fixed-Size Least-Squares Support Vector Machines (FS-LSSVM) were used as regression algorithms to determine the end of the T-wave. Different strategies for selecting the training set such as random selection, k-means, robust clustering and maximum quadratic (Rényi) entropy were evaluated. Individual parameters were tuned for each method during training and the results are given for the evaluation set. A comparison between MLP and FS-LSSVM approaches was performed. Finally, a fair comparison of the FS-LSSVM method with other state-of-the-art algorithms for detecting the end of the T-wave was included. The experimental results show that FS-LSSVM approaches are more suitable as regression algorithms than MLP neural networks. Despite the small training sets used, the FS-LSSVM methods outperformed the state-of-the-art techniques. FS-LSSVM can be successfully used as a T-wave end detection algorithm in ECG even with small training set sizes. Copyright © 2018 Elsevier Ltd. All rights reserved.

Constraints on muon-specific dark forces

NASA Astrophysics Data System (ADS)

Karshenboim, Savely G.; McKeen, David; Pospelov, Maxim

2014-10-01

The recent measurement of the Lamb shift in muonic hydrogen allows for the most precise extraction of the charge radius of the proton which is currently in conflict with other determinations based on e-p scattering and hydrogen spectroscopy. This discrepancy could be the result of some new muon-specific force with O(1-100) MeV force carrier—in this paper we concentrate on vector mediators. Such an explanation faces challenges from the constraints imposed by the g-2 of the muon and electron as well as precision spectroscopy of muonic atoms. In this work we complement the family of constraints by calculating the contribution of hypothetical forces to the muonium hyperfine structure. We also compute the two-loop contribution to the electron parity-violating amplitude due to a muon loop, which is sensitive to the muon axial-vector coupling. Overall, we find that the combination of low-energy constraints favors the mass of the mediator to be below 10 MeV and that a certain degree of tuning is required between vector and axial-vector couplings of new vector particles to muons in order to satisfy constraints from muon g-2. However, we also observe that in the absence of a consistent standard model embedding high-energy weak-charged processes accompanied by the emission of new vector particles are strongly enhanced by (E/mV)2, with E a characteristic energy scale and mV the mass of the mediator. In particular, leptonic W decays impose the strongest constraints on such models completely disfavoring the remainder of the parameter space.

Constraining ecosystem model with adaptive Metropolis algorithm using boreal forest site eddy covariance measurements

NASA Astrophysics Data System (ADS)

Mäkelä, Jarmo; Susiluoto, Jouni; Markkanen, Tiina; Aurela, Mika; Järvinen, Heikki; Mammarella, Ivan; Hagemann, Stefan; Aalto, Tuula

2016-12-01

We examined parameter optimisation in the JSBACH (Kaminski et al., 2013; Knorr and Kattge, 2005; Reick et al., 2013) ecosystem model, applied to two boreal forest sites (Hyytiälä and Sodankylä) in Finland. We identified and tested key parameters in soil hydrology and forest water and carbon-exchange-related formulations, and optimised them using the adaptive Metropolis (AM) algorithm for Hyytiälä with a 5-year calibration period (2000-2004) followed by a 4-year validation period (2005-2008). Sodankylä acted as an independent validation site, where optimisations were not made. The tuning provided estimates for full distribution of possible parameters, along with information about correlation, sensitivity and identifiability. Some parameters were correlated with each other due to a phenomenological connection between carbon uptake and water stress or other connections due to the set-up of the model formulations. The latter holds especially for vegetation phenology parameters. The least identifiable parameters include phenology parameters, parameters connecting relative humidity and soil dryness, and the field capacity of the skin reservoir. These soil parameters were masked by the large contribution from vegetation transpiration. In addition to leaf area index and the maximum carboxylation rate, the most effective parameters adjusting the gross primary production (GPP) and evapotranspiration (ET) fluxes in seasonal tuning were related to soil wilting point, drainage and moisture stress imposed on vegetation. For daily and half-hourly tunings the most important parameters were the ratio of leaf internal CO2 concentration to external CO2 and the parameter connecting relative humidity and soil dryness. Effectively the seasonal tuning transferred water from soil moisture into ET, and daily and half-hourly tunings reversed this process. The seasonal tuning improved the month-to-month development of GPP and ET, and produced the most stable estimates of water use efficiency. When compared to the seasonal tuning, the daily tuning is worse on the seasonal scale. However, daily parametrisation reproduced the observations for average diurnal cycle best, except for the GPP for Sodankylä validation period, where half-hourly tuned parameters were better. In general, the daily tuning provided the largest reduction in model-data mismatch. The models response to drought was unaffected by our parametrisations and further studies are needed into enhancing the dry response in JSBACH.

Ensemble Kalman filter inference of spatially-varying Manning's n coefficients in the coastal ocean

NASA Astrophysics Data System (ADS)

Siripatana, Adil; Mayo, Talea; Knio, Omar; Dawson, Clint; Maître, Olivier Le; Hoteit, Ibrahim

2018-07-01

Ensemble Kalman (EnKF) filtering is an established framework for large scale state estimation problems. EnKFs can also be used for state-parameter estimation, using the so-called "Joint-EnKF" approach. The idea is simply to augment the state vector with the parameters to be estimated and assign invariant dynamics for the time evolution of the parameters. In this contribution, we investigate the efficiency of the Joint-EnKF for estimating spatially-varying Manning's n coefficients used to define the bottom roughness in the Shallow Water Equations (SWEs) of a coastal ocean model. Observation System Simulation Experiments (OSSEs) are conducted using the ADvanced CIRCulation (ADCIRC) model, which solves a modified form of the Shallow Water Equations. A deterministic EnKF, the Singular Evolutive Interpolated Kalman (SEIK) filter, is used to estimate a vector of Manning's n coefficients defined at the model nodal points by assimilating synthetic water elevation data. It is found that with reasonable ensemble size (O (10)) , the filter's estimate converges to the reference Manning's field. To enhance performance, we have further reduced the dimension of the parameter search space through a Karhunen-Loéve (KL) expansion. We have also iterated on the filter update step to better account for the nonlinearity of the parameter estimation problem. We study the sensitivity of the system to the ensemble size, localization scale, dimension of retained KL modes, and number of iterations. The performance of the proposed framework in term of estimation accuracy suggests that a well-tuned Joint-EnKF provides a promising robust approach to infer spatially varying seabed roughness parameters in the context of coastal ocean modeling.

Effectiveness and limitations of parameter tuning in reducing biases of top-of-atmosphere radiation and clouds in MIROC version 5

NASA Astrophysics Data System (ADS)

Ogura, Tomoo; Shiogama, Hideo; Watanabe, Masahiro; Yoshimori, Masakazu; Yokohata, Tokuta; Annan, James D.; Hargreaves, Julia C.; Ushigami, Naoto; Hirota, Kazuya; Someya, Yu; Kamae, Youichi; Tatebe, Hiroaki; Kimoto, Masahide

2017-12-01

This study discusses how much of the biases in top-of-atmosphere (TOA) radiation and clouds can be removed by parameter tuning in the present-day simulation of a climate model in the Coupled Model Inter-comparison Project phase 5 (CMIP5) generation. We used output of a perturbed parameter ensemble (PPE) experiment conducted with an atmosphere-ocean general circulation model (AOGCM) without flux adjustment. The Model for Interdisciplinary Research on Climate version 5 (MIROC5) was used for the PPE experiment. Output of the PPE was compared with satellite observation data to evaluate the model biases and the parametric uncertainty of the biases with respect to TOA radiation and clouds. The results indicate that removing or changing the sign of the biases by parameter tuning alone is difficult. In particular, the cooling bias of the shortwave cloud radiative effect at low latitudes could not be removed, neither in the zonal mean nor at each latitude-longitude grid point. The bias was related to the overestimation of both cloud amount and cloud optical thickness, which could not be removed by the parameter tuning either. However, they could be alleviated by tuning parameters such as the maximum cumulus updraft velocity at the cloud base. On the other hand, the bias of the shortwave cloud radiative effect in the Arctic was sensitive to parameter tuning. It could be removed by tuning such parameters as albedo of ice and snow both in the zonal mean and at each grid point. The obtained results illustrate the benefit of PPE experiments which provide useful information regarding effectiveness and limitations of parameter tuning. Implementing a shallow convection parameterization is suggested as a potential measure to alleviate the biases in radiation and clouds.

An Adaptive Kalman Filter using a Simple Residual Tuning Method

NASA Technical Reports Server (NTRS)

Harman, Richard R.

1999-01-01

One difficulty in using Kalman filters in real world situations is the selection of the correct process noise, measurement noise, and initial state estimate and covariance. These parameters are commonly referred to as tuning parameters. Multiple methods have been developed to estimate these parameters. Most of those methods such as maximum likelihood, subspace, and observer Kalman Identification require extensive offline processing and are not suitable for real time processing. One technique, which is suitable for real time processing, is the residual tuning method. Any mismodeling of the filter tuning parameters will result in a non-white sequence for the filter measurement residuals. The residual tuning technique uses this information to estimate corrections to those tuning parameters. The actual implementation results in a set of sequential equations that run in parallel with the Kalman filter. Equations for the estimation of the measurement noise have also been developed. These algorithms are used to estimate the process noise and measurement noise for the Wide Field Infrared Explorer star tracker and gyro.

Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning

PubMed Central

Kok, Kai Yit; Rajendran, Parvathy

2016-01-01

The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost. PMID:26943630

Controller design for a class of nonlinear MIMO coupled system using multiple models and second level adaptation.

PubMed

Pandey, Vinay Kumar; Kar, Indrani; Mahanta, Chitralekha

2017-07-01

In this paper, an adaptive control method using multiple models with second level adaptation is proposed for a class of nonlinear multi-input multi-output (MIMO) coupled systems. Multiple estimation models are used to tune the unknown parameters at the first level. The second level adaptation provides a single parameter vector for the controller. A feedback linearization technique is used to design a state feedback control. The efficacy of the designed controller is validated by conducting real time experiment on a laboratory setup of twin rotor MIMO system (TRMS). The TRMS setup is discussed in detail and the experiments were performed for regulation and tracking problem for pitch and yaw control using different reference signals. An Extended Kalman Filter (EKF) has been used to observe the unavailable states of the TRMS. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Emergence of topological semimetals in gap closing in semiconductors without inversion symmetry.

PubMed

Murakami, Shuichi; Hirayama, Motoaki; Okugawa, Ryo; Miyake, Takashi

2017-05-01

A band gap for electronic states in crystals governs various properties of solids, such as transport, optical, and magnetic properties. Its estimation and control have been an important issue in solid-state physics. The band gap can be controlled externally by various parameters, such as pressure, atomic compositions, and external field. Sometimes, the gap even collapses by tuning some parameter. In the field of topological insulators, this closing of the gap at a time-reversal invariant momentum indicates a band inversion, that is, it leads to a topological phase transition from a normal insulator to a topological insulator. We show, through an exhaustive study on possible space groups, that the gap closing in inversion-asymmetric crystals is universal, in the sense that the gap closing always leads either to a Weyl semimetal or to a nodal-line semimetal. We consider three-dimensional spinful systems with time-reversal symmetry. The space group of the system and the wave vector at the gap closing uniquely determine which possibility occurs and where the gap-closing points or lines lie in the wave vector space after the closing of the gap. In particular, we show that an insulator-to-insulator transition never happens, which is in sharp contrast to inversion-symmetric systems.

Infrared modified gravity with propagating torsion: Instability of torsionfull de Sitter-like solutions

NASA Astrophysics Data System (ADS)

Nikiforova, Vasilisa; Damour, Thibault

2018-06-01

We continue the exploration of the consistency of a modified-gravity theory that generalizes general relativity by including a dynamical torsion in addition to the dynamical metric. The six-parameter theory we consider was found to be consistent around arbitrary torsionless Einstein backgrounds, in spite of its containing a (notoriously delicate) massive spin-2 excitation. At zero bare cosmological constant, this theory was found to admit a self-accelerating solution whose exponential expansion is sustained by a nonzero torsion background. The scalar-type perturbations of the latter torsionfull self-accelerating solution were recently studied and were found to preserve the number of propagating scalar degrees of freedom, but to exhibit, for some values of the torsion background, some exponential instabilities (of a rather mild type). Here, we study the tensor-type and vector-type perturbations of the torsionfull self-accelerating solution, and of its deformation by a nonzero bare cosmological constant. We find strong, "gradient" instabilities in the vector sector. No tuning of the parameters of the theory can kill these instabilities without creating instabilities in the other sectors. Further work is needed to see whether generic torsionfull backgrounds are prone to containing gradient instabilities, or if the instabilities we found are mainly due to the (generalized) self-accelerating nature of the special de Sitter backgrounds we considered.

Merged or monolithic? Using machine-learning to reconstruct the dynamical history of simulated star clusters

NASA Astrophysics Data System (ADS)

Pasquato, Mario; Chung, Chul

2016-05-01

Context. Machine-learning (ML) solves problems by learning patterns from data with limited or no human guidance. In astronomy, ML is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims: We apply ML to gravitational N-body simulations of star clusters that are either formed by merging two progenitors or evolved in isolation, planning to later identify globular clusters (GCs) that may have a history of merging from observational data. Methods: We create mock-observations from simulated GCs, from which we measure a set of parameters (also called features in the machine-learning field). After carrying out dimensionality reduction on the feature space, the resulting datapoints are fed in to various classification algorithms. Using repeated random subsampling validation, we check whether the groups identified by the algorithms correspond to the underlying physical distinction between mergers and monolithically evolved simulations. Results: The three algorithms we considered (C5.0 trees, k-nearest neighbour, and support-vector machines) all achieve a test misclassification rate of about 10% without parameter tuning, with support-vector machines slightly outperforming the others. The first principal component of feature space correlates with cluster concentration. If we exclude it from the regression, the performance of the algorithms is only slightly reduced.

Spectrum-doubled heavy vector bosons at the LHC

DOE PAGES

Appelquist, Thomas; Bai, Yang; Ingoldby, James; ...

2016-01-19

We study a simple effective field theory incorporating six heavy vector bosons together with the standard-model field content. The new particles preserve custodial symmetry as well as an approximate left-right parity symmetry. The enhanced symmetry of the model allows it to satisfy precision electroweak constraints and bounds from Higgs physics in a regime where all the couplings are perturbative and where the amount of fine-tuning is comparable to that in the standard model itself. We find that the model could explain the recently observed excesses in di-boson processes at invariant mass close to 2TeV from LHC Run 1 for amore » range of allowed parameter space. The masses of all the particles differ by no more than roughly 10%. In a portion of the allowed parameter space only one of the new particles has a production cross section large enough to be detectable with the energy and luminosity of Run 1, both via its decay to WZ and to Wh, while the others have suppressed production rates. Furthermore, the model can be tested at the higher-energy and higher-luminosity run of the LHC even for an overall scale of the new particles higher than 3TeV.« less

Extraction of Built-Up Areas Using Convolutional Neural Networks and Transfer Learning from SENTINEL-2 Satellite Images

NASA Astrophysics Data System (ADS)

Bramhe, V. S.; Ghosh, S. K.; Garg, P. K.

2018-04-01

With rapid globalization, the extent of built-up areas is continuously increasing. Extraction of features for classifying built-up areas that are more robust and abstract is a leading research topic from past many years. Although, various studies have been carried out where spatial information along with spectral features has been utilized to enhance the accuracy of classification. Still, these feature extraction techniques require a large number of user-specific parameters and generally application specific. On the other hand, recently introduced Deep Learning (DL) techniques requires less number of parameters to represent more abstract aspects of the data without any manual effort. Since, it is difficult to acquire high-resolution datasets for applications that require large scale monitoring of areas. Therefore, in this study Sentinel-2 image has been used for built-up areas extraction. In this work, pre-trained Convolutional Neural Networks (ConvNets) i.e. Inception v3 and VGGNet are employed for transfer learning. Since these networks are trained on generic images of ImageNet dataset which are having very different characteristics from satellite images. Therefore, weights of networks are fine-tuned using data derived from Sentinel-2 images. To compare the accuracies with existing shallow networks, two state of art classifiers i.e. Gaussian Support Vector Machine (SVM) and Back-Propagation Neural Network (BP-NN) are also implemented. Both SVM and BP-NN gives 84.31 % and 82.86 % overall accuracies respectively. Inception-v3 and VGGNet gives 89.43 % of overall accuracy using fine-tuned VGGNet and 92.10 % when using Inception-v3. The results indicate high accuracy of proposed fine-tuned ConvNets on a 4-channel Sentinel-2 dataset for built-up area extraction.

Tuning Parameters in Heuristics by Using Design of Experiments Methods

NASA Technical Reports Server (NTRS)

Arin, Arif; Rabadi, Ghaith; Unal, Resit

2010-01-01

With the growing complexity of today's large scale problems, it has become more difficult to find optimal solutions by using exact mathematical methods. The need to find near-optimal solutions in an acceptable time frame requires heuristic approaches. In many cases, however, most heuristics have several parameters that need to be "tuned" before they can reach good results. The problem then turns into "finding best parameter setting" for the heuristics to solve the problems efficiently and timely. One-Factor-At-a-Time (OFAT) approach for parameter tuning neglects the interactions between parameters. Design of Experiments (DOE) tools can be instead employed to tune the parameters more effectively. In this paper, we seek the best parameter setting for a Genetic Algorithm (GA) to solve the single machine total weighted tardiness problem in which n jobs must be scheduled on a single machine without preemption, and the objective is to minimize the total weighted tardiness. Benchmark instances for the problem are available in the literature. To fine tune the GA parameters in the most efficient way, we compare multiple DOE models including 2-level (2k ) full factorial design, orthogonal array design, central composite design, D-optimal design and signal-to-noise (SIN) ratios. In each DOE method, a mathematical model is created using regression analysis, and solved to obtain the best parameter setting. After verification runs using the tuned parameter setting, the preliminary results for optimal solutions of multiple instances were found efficiently.

An Adaptive Kalman Filter Using a Simple Residual Tuning Method

NASA Technical Reports Server (NTRS)

Harman, Richard R.

1999-01-01

One difficulty in using Kalman filters in real world situations is the selection of the correct process noise, measurement noise, and initial state estimate and covariance. These parameters are commonly referred to as tuning parameters. Multiple methods have been developed to estimate these parameters. Most of those methods such as maximum likelihood, subspace, and observer Kalman Identification require extensive offline processing and are not suitable for real time processing. One technique, which is suitable for real time processing, is the residual tuning method. Any mismodeling of the filter tuning parameters will result in a non-white sequence for the filter measurement residuals. The residual tuning technique uses this information to estimate corrections to those tuning parameters. The actual implementation results in a set of sequential equations that run in parallel with the Kalman filter. A. H. Jazwinski developed a specialized version of this technique for estimation of process noise. Equations for the estimation of the measurement noise have also been developed. These algorithms are used to estimate the process noise and measurement noise for the Wide Field Infrared Explorer star tracker and gyro.

Utilization of Short-Simulations for Tuning High-Resolution Climate Model

NASA Astrophysics Data System (ADS)

Lin, W.; Xie, S.; Ma, P. L.; Rasch, P. J.; Qian, Y.; Wan, H.; Ma, H. Y.; Klein, S. A.

2016-12-01

Many physical parameterizations in atmospheric models are sensitive to resolution. Tuning the models that involve a multitude of parameters at high resolution is computationally expensive, particularly when relying primarily on multi-year simulations. This work describes a complementary set of strategies for tuning high-resolution atmospheric models, using ensembles of short simulations to reduce the computational cost and elapsed time. Specifically, we utilize the hindcast approach developed through the DOE Cloud Associated Parameterization Testbed (CAPT) project for high-resolution model tuning, which is guided by a combination of short (< 10 days ) and longer ( 1 year) Perturbed Parameters Ensemble (PPE) simulations at low resolution to identify model feature sensitivity to parameter changes. The CAPT tests have been found to be effective in numerous previous studies in identifying model biases due to parameterized fast physics, and we demonstrate that it is also useful for tuning. After the most egregious errors are addressed through an initial "rough" tuning phase, longer simulations are performed to "hone in" on model features that evolve over longer timescales. We explore these strategies to tune the DOE ACME (Accelerated Climate Modeling for Energy) model. For the ACME model at 0.25° resolution, it is confirmed that, given the same parameters, major biases in global mean statistics and many spatial features are consistent between Atmospheric Model Intercomparison Project (AMIP)-type simulations and CAPT-type hindcasts, with just a small number of short-term simulations for the latter over the corresponding season. The use of CAPT hindcasts to find parameter choice for the reduction of large model biases dramatically improves the turnaround time for the tuning at high resolution. Improvement seen in CAPT hindcasts generally translates to improved AMIP-type simulations. An iterative CAPT-AMIP tuning approach is therefore adopted during each major tuning cycle, with the former to survey the likely responses and narrow the parameter space, and the latter to verify the results in climate context along with assessment in greater detail once an educated set of parameter choice is selected. Limitations on using short-term simulations for tuning climate model are also discussed.

Tuning the cache memory usage in tomographic reconstruction on standard computers with Advanced Vector eXtensions (AVX)

PubMed Central

Agulleiro, Jose-Ignacio; Fernandez, Jose-Jesus

2015-01-01

Cache blocking is a technique widely used in scientific computing to minimize the exchange of information with main memory by reusing the data kept in cache memory. In tomographic reconstruction on standard computers using vector instructions, cache blocking turns out to be central to optimize performance. To this end, sinograms of the tilt-series and slices of the volumes to be reconstructed have to be divided into small blocks that fit into the different levels of cache memory. The code is then reorganized so as to operate with a block as much as possible before proceeding with another one. This data article is related to the research article titled Tomo3D 2.0 – Exploitation of Advanced Vector eXtensions (AVX) for 3D reconstruction (Agulleiro and Fernandez, 2015) [1]. Here we present data of a thorough study of the performance of tomographic reconstruction by varying cache block sizes, which allows derivation of expressions for their automatic quasi-optimal tuning. PMID:26217710

Tuning the cache memory usage in tomographic reconstruction on standard computers with Advanced Vector eXtensions (AVX).

PubMed

Agulleiro, Jose-Ignacio; Fernandez, Jose-Jesus

2015-06-01

Cache blocking is a technique widely used in scientific computing to minimize the exchange of information with main memory by reusing the data kept in cache memory. In tomographic reconstruction on standard computers using vector instructions, cache blocking turns out to be central to optimize performance. To this end, sinograms of the tilt-series and slices of the volumes to be reconstructed have to be divided into small blocks that fit into the different levels of cache memory. The code is then reorganized so as to operate with a block as much as possible before proceeding with another one. This data article is related to the research article titled Tomo3D 2.0 - Exploitation of Advanced Vector eXtensions (AVX) for 3D reconstruction (Agulleiro and Fernandez, 2015) [1]. Here we present data of a thorough study of the performance of tomographic reconstruction by varying cache block sizes, which allows derivation of expressions for their automatic quasi-optimal tuning.

Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques

NASA Astrophysics Data System (ADS)

Sun, Ziheng; Fang, Hui; Di, Liping; Yue, Peng

2016-09-01

It was an untouchable dream for remote sensing experts to realize total automatic image classification without inputting any parameter values. Experts usually spend hours and hours on tuning the input parameters of classification algorithms in order to obtain the best results. With the rapid development of knowledge engineering and cyberinfrastructure, a lot of data processing and knowledge reasoning capabilities become online accessible, shareable and interoperable. Based on these recent improvements, this paper presents an idea of parameterless automatic classification which only requires an image and automatically outputs a labeled vector. No parameters and operations are needed from endpoint consumers. An approach is proposed to realize the idea. It adopts an ontology database to store the experiences of tuning values for classifiers. A sample database is used to record training samples of image segments. Geoprocessing Web services are used as functionality blocks to finish basic classification steps. Workflow technology is involved to turn the overall image classification into a total automatic process. A Web-based prototypical system named PACS (Parameterless Automatic Classification System) is implemented. A number of images are fed into the system for evaluation purposes. The results show that the approach could automatically classify remote sensing images and have a fairly good average accuracy. It is indicated that the classified results will be more accurate if the two databases have higher quality. Once the experiences and samples in the databases are accumulated as many as an expert has, the approach should be able to get the results with similar quality to that a human expert can get. Since the approach is total automatic and parameterless, it can not only relieve remote sensing workers from the heavy and time-consuming parameter tuning work, but also significantly shorten the waiting time for consumers and facilitate them to engage in image classification activities. Currently, the approach is used only on high resolution optical three-band remote sensing imagery. The feasibility using the approach on other kinds of remote sensing images or involving additional bands in classification will be studied in future.

Bias in error estimation when using cross-validation for model selection.

PubMed

Varma, Sudhir; Simon, Richard

2006-02-23

Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers by choosing classifier parameter values that minimize the CV error estimate. We have evaluated the validity of using the CV error estimate of the optimized classifier as an estimate of the true error expected on independent data. We used CV to optimize the classification parameters for two kinds of classifiers; Shrunken Centroids and Support Vector Machines (SVM). Random training datasets were created, with no difference in the distribution of the features between the two classes. Using these "null" datasets, we selected classifier parameter values that minimized the CV error estimate. 10-fold CV was used for Shrunken Centroids while Leave-One-Out-CV (LOOCV) was used for the SVM. Independent test data was created to estimate the true error. With "null" and "non null" (with differential expression between the classes) data, we also tested a nested CV procedure, where an inner CV loop is used to perform the tuning of the parameters while an outer CV is used to compute an estimate of the error. The CV error estimate for the classifier with the optimal parameters was found to be a substantially biased estimate of the true error that the classifier would incur on independent data. Even though there is no real difference between the two classes for the "null" datasets, the CV error estimate for the Shrunken Centroid with the optimal parameters was less than 30% on 18.5% of simulated training data-sets. For SVM with optimal parameters the estimated error rate was less than 30% on 38% of "null" data-sets. Performance of the optimized classifiers on the independent test set was no better than chance. The nested CV procedure reduces the bias considerably and gives an estimate of the error that is very close to that obtained on the independent testing set for both Shrunken Centroids and SVM classifiers for "null" and "non-null" data distributions. We show that using CV to compute an error estimate for a classifier that has itself been tuned using CV gives a significantly biased estimate of the true error. Proper use of CV for estimating true error of a classifier developed using a well defined algorithm requires that all steps of the algorithm, including classifier parameter tuning, be repeated in each CV loop. A nested CV procedure provides an almost unbiased estimate of the true error.

Design and parametric study on energy harvesting from bridge vibration using tuned dual-mass damper systems

NASA Astrophysics Data System (ADS)

Takeya, Kouichi; Sasaki, Eiichi; Kobayashi, Yusuke

2016-01-01

A bridge vibration energy harvester has been proposed in this paper using a tuned dual-mass damper system, named hereafter Tuned Mass Generator (TMG). A linear electromagnetic transducer has been applied to harvest and make use of the unused reserve of energy the aforementioned damper system absorbs. The benefits of using dual-mass systems over single-mass systems for power generation have been clarified according to the theory of vibrations. TMG parameters have been determined considering multi-domain parameters, and TMG has been tuned using a newly proposed parameter design method. Theoretical analysis results have shown that for effective energy harvesting, it is essential that TMG has robustness against uncertainties in bridge vibrations and tuning errors, and the proposed parameter design method for TMG has demonstrated this feature.

Transverse spin in the scattering of focused radially and azimuthally polarized vector beams

NASA Astrophysics Data System (ADS)

Singh, Ankit Kumar; Saha, Sudipta; Gupta, Subhasish Dutta; Ghosh, Nirmalya

2018-04-01

We study the effect of focusing of the radially and azimuthally polarized vector beams on the spin angular momentum (SAM) density and Poynting vector of scattered waves from a Mie particle. Remarkably, the study reveals that the SAM density of the scattered field is solely transverse in nature for radially and azimuthally polarized incident vector beams; however, the Poynting vector shows the usual longitudinal character. We also demonstrate that the transverse SAM density can further be tuned with wavelength and focusing of the incident beam by exploiting the interference of different scattering modes. These results may stimulate further experimental techniques to detect the transverse spin and Belinfante's spin-momentum densities.

Improved model reduction and tuning of fractional-order PI(λ)D(μ) controllers for analytical rule extraction with genetic programming.

PubMed

Das, Saptarshi; Pan, Indranil; Das, Shantanu; Gupta, Amitava

2012-03-01

Genetic algorithm (GA) has been used in this study for a new approach of suboptimal model reduction in the Nyquist plane and optimal time domain tuning of proportional-integral-derivative (PID) and fractional-order (FO) PI(λ)D(μ) controllers. Simulation studies show that the new Nyquist-based model reduction technique outperforms the conventional H(2)-norm-based reduced parameter modeling technique. With the tuned controller parameters and reduced-order model parameter dataset, optimum tuning rules have been developed with a test-bench of higher-order processes via genetic programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by a weighted sum of error index and controller effort. From the reported Pareto optimal front of the GP-based optimal rule extraction technique, a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP-based tuning rules has been compared with the original GA-based control performance for the PID and PI(λ)D(μ) controllers, handling four different classes of representative higher-order processes. These rules are very useful for process control engineers, as they inherit the power of the GA-based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three-dimensional plots of the required variation in PID/fractional-order PID (FOPID) controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP-based tuning rules has also been shown with credible simulation examples. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Inelastic X-ray Scattering Studies of Plasmons in Carbon Nanotubes

NASA Astrophysics Data System (ADS)

Upton, M. H.; Klie, R. F.; Hill, J. P.; Gog, T.; Casa, D.; Ku, W.; Zhu, Y.; Sfeir, M. Y.; Misewich, J.; Eres, G.; Lowndes, D.

2007-03-01

We investigate the physical parameters controlling the low energy screening in carbon nanotubes via electron energy loss spectroscopy and inelastic x-ray scattering. Two plasmon-like features are observed, one near 9 eV (the so- called π plasmon) and one near 20 eV (the so-called π+σ plasmon). At large nanotube diameters, the π+σ plasmon energies depend exclusively on the number of walls and not on the radius or chiral vector. This shift indicates a change of strength of screening and the effective interaction at inter-atomic distance, and thus suggests an alternative mechanism of tuning the properties of the nanotube in addition to the well-known control provided by chirality and tube diameter.

Investigation on the properties of omnidirectional photonic band gaps in two-dimensional plasma photonic crystals

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

Zhang, Hai-Feng, E-mail: hanlor@163.com; Nanjing Artillery Academy, Nanjing 211132; Liu, Shao-Bin

2016-01-15

The properties of omnidirectional photonic band gaps (OBGs) in two-dimensional plasma photonic crystals (2D PPCs) are theoretically investigated by the modified plane wave expansion method. In the simulation, we consider the off-plane incident wave vector. The configuration of 2D PPCs is the triangular lattices filled with the nonmagnetized plasma cylinders in the homogeneous and isotropic dielectric background. The calculated results show that the proposed 2D PPCs possess a flatbands region and the OBGs. Compared with the OBGs in the conventional 2D dielectric-air PCs, it can be obtained more easily and enlarged in the 2D PPCs with a similar structure. Themore » effects of configurational parameters of the PPCs on the OBGs also are studied. The simulated results demonstrate that the locations of OBGs can be tuned easily by manipulating those parameters except for changing plasma collision frequency. The achieved OBGs can be enlarged by optimizations. The OBGs of two novel configurations of PPCs with different cross sections are computed for a comparison. Both configurations have the advantages of obtaining the larger OBGs compared with the conventional configuration, since the symmetry of 2D PPCs is broken by different sizes of periodically inserted plasma cylinders or connected by the embedded plasma cylinders with thin veins. The analysis of the results shows that the bandwidths of OBGs can be tuned by changing geometric and physical parameters of such two PPCs structures. The theoretical results may open a new scope for designing the omnidirectional reflectors or mirrors based on the 2D PPCs.« less

An automatic and effective parameter optimization method for model tuning

NASA Astrophysics Data System (ADS)

Zhang, T.; Li, L.; Lin, Y.; Xue, W.; Xie, F.; Xu, H.; Huang, X.

2015-05-01

Physical parameterizations in General Circulation Models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determines parameter sensitivity and the other chooses the optimum initial value of sensitive parameters, are introduced before the downhill simplex method to reduce the computational cost and improve the tuning performance. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9%. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameters tuning during the model development stage.

Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Azmi, Nur Iffah Mohamed; Arifin Mat Piah, Kamal; Yusoff, Wan Azhar Wan; Romlay, Fadhlur Rahman Mohd

2018-03-01

Controller that uses PID parameters requires a good tuning method in order to improve the control system performance. Tuning PID control method is divided into two namely the classical methods and the methods of artificial intelligence. Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. Previously, researchers had integrated PSO algorithms in the PID parameter tuning process. This research aims to improve the PSO-PID tuning algorithms by integrating the tuning process with the Variable Weight Grey- Taguchi Design of Experiment (DOE) method. This is done by conducting the DOE on the two PSO optimizing parameters: the particle velocity limit and the weight distribution factor. Computer simulations and physical experiments were conducted by using the proposed PSO- PID with the Variable Weight Grey-Taguchi DOE and the classical Ziegler-Nichols methods. They are implemented on the hydraulic positioning system. Simulation results show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE has reduced the rise time by 48.13% and settling time by 48.57% compared to the Ziegler-Nichols method. Furthermore, the physical experiment results also show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE tuning method responds better than Ziegler-Nichols tuning. In conclusion, this research has improved the PSO-PID parameter by applying the PSO-PID algorithm together with the Variable Weight Grey-Taguchi DOE method as a tuning method in the hydraulic positioning system.

Parameters-tuning of PID controller for automatic voltage regulators using the African buffalo optimization.

PubMed

Odili, Julius Beneoluchi; Mohmad Kahar, Mohd Nizam; Noraziah, A

2017-01-01

In this paper, an attempt is made to apply the African Buffalo Optimization (ABO) to tune the parameters of a PID controller for an effective Automatic Voltage Regulator (AVR). Existing metaheuristic tuning methods have been proven to be quite successful but there were observable areas that need improvements especially in terms of the system's gain overshoot and steady steady state errors. Using the ABO algorithm where each buffalo location in the herd is a candidate solution to the Proportional-Integral-Derivative parameters was very helpful in addressing these two areas of concern. The encouraging results obtained from the simulation of the PID Controller parameters-tuning using the ABO when compared with the performance of Genetic Algorithm PID (GA-PID), Particle-Swarm Optimization PID (PSO-PID), Ant Colony Optimization PID (ACO-PID), PID, Bacteria-Foraging Optimization PID (BFO-PID) etc makes ABO-PID a good addition to solving PID Controller tuning problems using metaheuristics.

Tuning of active vibration controllers for ACTEX by genetic algorithm

NASA Astrophysics Data System (ADS)

Kwak, Moon K.; Denoyer, Keith K.

1999-06-01

This paper is concerned with the optimal tuning of digitally programmable analog controllers on the ACTEX-1 smart structures flight experiment. The programmable controllers for each channel include a third order Strain Rate Feedback (SRF) controller, a fifth order SRF controller, a second order Positive Position Feedback (PPF) controller, and a fourth order PPF controller. Optimal manual tuning of several control parameters can be a difficult task even though the closed-loop control characteristics of each controller are well known. Hence, the automatic tuning of individual control parameters using Genetic Algorithms is proposed in this paper. The optimal control parameters of each control law are obtained by imposing a constraint on the closed-loop frequency response functions using the ACTEX mathematical model. The tuned control parameters are then uploaded to the ACTEX electronic control electronics and experiments on the active vibration control are carried out in space. The experimental results on ACTEX will be presented.

A tuning algorithm for model predictive controllers based on genetic algorithms and fuzzy decision making.

PubMed

van der Lee, J H; Svrcek, W Y; Young, B R

2008-01-01

Model Predictive Control is a valuable tool for the process control engineer in a wide variety of applications. Because of this the structure of an MPC can vary dramatically from application to application. There have been a number of works dedicated to MPC tuning for specific cases. Since MPCs can differ significantly, this means that these tuning methods become inapplicable and a trial and error tuning approach must be used. This can be quite time consuming and can result in non-optimum tuning. In an attempt to resolve this, a generalized automated tuning algorithm for MPCs was developed. This approach is numerically based and combines a genetic algorithm with multi-objective fuzzy decision-making. The key advantages to this approach are that genetic algorithms are not problem specific and only need to be adapted to account for the number and ranges of tuning parameters for a given MPC. As well, multi-objective fuzzy decision-making can handle qualitative statements of what optimum control is, in addition to being able to use multiple inputs to determine tuning parameters that best match the desired results. This is particularly useful for multi-input, multi-output (MIMO) cases where the definition of "optimum" control is subject to the opinion of the control engineer tuning the system. A case study will be presented in order to illustrate the use of the tuning algorithm. This will include how different definitions of "optimum" control can arise, and how they are accounted for in the multi-objective decision making algorithm. The resulting tuning parameters from each of the definition sets will be compared, and in doing so show that the tuning parameters vary in order to meet each definition of optimum control, thus showing the generalized automated tuning algorithm approach for tuning MPCs is feasible.

Effect of handling characteristics on minimum time cornering with torque vectoring

NASA Astrophysics Data System (ADS)

Smith, E. N.; Velenis, E.; Tavernini, D.; Cao, D.

2018-02-01

In this paper, the effect of both passive and actively-modified vehicle handling characteristics on minimum time manoeuvring for vehicles with 4-wheel torque vectoring (TV) capability is studied. First, a baseline optimal TV strategy is sought, independent of any causal control law. An optimal control problem (OCP) is initially formulated considering 4 independent wheel torque inputs, together with the steering angle rate, as the control variables. Using this formulation, the performance benefit using TV against an electric drive train with a fixed torque distribution, is demonstrated. The sensitivity of TV-controlled manoeuvre time to the passive understeer gradient of the vehicle is then studied. A second formulation of the OCP is introduced where a closed-loop TV controller is incorporated into the system dynamics of the OCP. This formulation allows the effect of actively modifying a vehicle's handling characteristic via TV on its minimum time cornering performance of the vehicle to be assessed. In particular, the effect of the target understeer gradient as the key tuning parameter of the literature-standard steady-state linear single-track model yaw rate reference is analysed.

Hidden and antiferromagnetic order as a rank-5 superspin in URu2Si2

NASA Astrophysics Data System (ADS)

Rau, Jeffrey G.; Kee, Hae-Young

2012-06-01

We propose a candidate for the hidden order in URu2Si2: a rank-5 E type spin-density wave between uranium 5f crystal-field doublets Γ7(1) and Γ7(2), breaking time-reversal and lattice tetragonal symmetry in a manner consistent with recent torque measurements [Okazaki , ScienceSCIEAS0036-807510.1126/science.1197358 331, 439 (2011)]. We argue that coupling of this order parameter to magnetic probes can be hidden by crystal-field effects, while still having significant effects on transport, thermodynamics, and magnetic susceptibilities. In a simple tight-binding model for the heavy quasiparticles, we show the connection between the hidden order and antiferromagnetic phases arises since they form different components of this single rank-5 pseudospin vector. Using a phenomenological theory, we show that the experimental pressure-temperature phase diagram can be qualitatively reproduced by tuning terms which break pseudospin rotational symmetry. As a test of our proposal, we predict the presence of small magnetic moments in the basal plane oriented in the [110] direction ordered at the wave vector (0,0,1).

A practical iterative PID tuning method for mechanical systems using parameter chart

NASA Astrophysics Data System (ADS)

Kang, M.; Cheong, J.; Do, H. M.; Son, Y.; Niculescu, S.-I.

2017-10-01

In this paper, we propose a method of iterative proportional-integral-derivative parameter tuning for mechanical systems that possibly possess hidden mechanical resonances, using a parameter chart which visualises the closed-loop characteristics in a 2D parameter space. We employ a hypothetical assumption that the considered mechanical systems have their upper limit of the derivative feedback gain, from which the feasible region in the parameter chart becomes fairly reduced and thus the gain selection can be extremely simplified. Then, a two-directional parameter search is carried out within the feasible region in order to find the best set of parameters. Experimental results show the validity of the assumption used and the proposed parameter tuning method.

An automatic and effective parameter optimization method for model tuning

NASA Astrophysics Data System (ADS)

Zhang, T.; Li, L.; Lin, Y.; Xue, W.; Xie, F.; Xu, H.; Huang, X.

2015-11-01

Physical parameterizations in general circulation models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time-consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determining the model's sensitivity to the parameters and the other choosing the optimum initial value for those sensitive parameters, are introduced before the downhill simplex method. This new method reduces the number of parameters to be tuned and accelerates the convergence of the downhill simplex method. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.

Intelligent tuning method of PID parameters based on iterative learning control for atomic force microscopy.

PubMed

Liu, Hui; Li, Yingzi; Zhang, Yingxu; Chen, Yifu; Song, Zihang; Wang, Zhenyu; Zhang, Suoxin; Qian, Jianqiang

2018-01-01

Proportional-integral-derivative (PID) parameters play a vital role in the imaging process of an atomic force microscope (AFM). Traditional parameter tuning methods require a lot of manpower and it is difficult to set PID parameters in unattended working environments. In this manuscript, an intelligent tuning method of PID parameters based on iterative learning control is proposed to self-adjust PID parameters of the AFM according to the sample topography. This method gets enough information about the output signals of PID controller and tracking error, which will be used to calculate the proper PID parameters, by repeated line scanning until convergence before normal scanning to learn the topography. Subsequently, the appropriate PID parameters are obtained by fitting method and then applied to the normal scanning process. The feasibility of the method is demonstrated by the convergence analysis. Simulations and experimental results indicate that the proposed method can intelligently tune PID parameters of the AFM for imaging different topographies and thus achieve good tracking performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Adaptive Self-Tuning Networks

NASA Astrophysics Data System (ADS)

Knox, H. A.; Draelos, T.; Young, C. J.; Lawry, B.; Chael, E. P.; Faust, A.; Peterson, M. G.

2015-12-01

The quality of automatic detections from seismic sensor networks depends on a large number of data processing parameters that interact in complex ways. The largely manual process of identifying effective parameters is painstaking and does not guarantee that the resulting controls are the optimal configuration settings. Yet, achieving superior automatic detection of seismic events is closely related to these parameters. We present an automated sensor tuning (AST) system that learns near-optimal parameter settings for each event type using neuro-dynamic programming (reinforcement learning) trained with historic data. AST learns to test the raw signal against all event-settings and automatically self-tunes to an emerging event in real-time. The overall goal is to reduce the number of missed legitimate event detections and the number of false event detections. Reducing false alarms early in the seismic pipeline processing will have a significant impact on this goal. Applicable both for existing sensor performance boosting and new sensor deployment, this system provides an important new method to automatically tune complex remote sensing systems. Systems tuned in this way will achieve better performance than is currently possible by manual tuning, and with much less time and effort devoted to the tuning process. With ground truth on detections in seismic waveforms from a network of stations, we show that AST increases the probability of detection while decreasing false alarms.

Novel Observer Scheme of Fuzzy-MRAS Sensorless Speed Control of Induction Motor Drive

NASA Astrophysics Data System (ADS)

Chekroun, S.; Zerikat, M.; Mechernene, A.; Benharir, N.

2017-01-01

This paper presents a novel approach Fuzzy-MRAS conception for robust accurate tracking of induction motor drive operating in a high-performance drives environment. Of the different methods for sensorless control of induction motor drive the model reference adaptive system (MRAS) finds lot of attention due to its good performance. The analysis of the sensorless vector control system using MRAS is presented and the resistance parameters variations and speed observer using new Fuzzy Self-Tuning adaptive IP Controller is proposed. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. The present approach helps to achieve a good dynamic response, disturbance rejection and low to plant parameter variations of the induction motor. In order to verify the performances of the proposed observer and control algorithms and to test behaviour of the controlled system, numerical simulation is achieved. Simulation results are presented and discussed to shown the validity and the performance of the proposed observer.

Longitudinal control of aircraft dynamics based on optimization of PID parameters

NASA Astrophysics Data System (ADS)

Deepa, S. N.; Sudha, G.

2016-03-01

Recent years many flight control systems and industries are employing PID controllers to improve the dynamic behavior of the characteristics. In this paper, PID controller is developed to improve the stability and performance of general aviation aircraft system. Designing the optimum PID controller parameters for a pitch control aircraft is important in expanding the flight safety envelope. Mathematical model is developed to describe the longitudinal pitch control of an aircraft. The PID controller is designed based on the dynamic modeling of an aircraft system. Different tuning methods namely Zeigler-Nichols method (ZN), Modified Zeigler-Nichols method, Tyreus-Luyben tuning, Astrom-Hagglund tuning methods are employed. The time domain specifications of different tuning methods are compared to obtain the optimum parameters value. The results prove that PID controller tuned by Zeigler-Nichols for aircraft pitch control dynamics is better in stability and performance in all conditions. Future research work of obtaining optimum PID controller parameters using artificial intelligence techniques should be carried out.

The Art and Science of Climate Model Tuning

DOE PAGES

Hourdin, Frederic; Mauritsen, Thorsten; Gettelman, Andrew; ...

2017-03-31

The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling withmore » its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. Here, we discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.« less

The Art and Science of Climate Model Tuning

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

Hourdin, Frederic; Mauritsen, Thorsten; Gettelman, Andrew

The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling withmore » its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. Here, we discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.« less

Extending the Peak Bandwidth of Parameters for Softmax Selection in Reinforcement Learning.

PubMed

Iwata, Kazunori

2016-05-11

Softmax selection is one of the most popular methods for action selection in reinforcement learning. Although various recently proposed methods may be more effective with full parameter tuning, implementing a complicated method that requires the tuning of many parameters can be difficult. Thus, softmax selection is still worth revisiting, considering the cost savings of its implementation and tuning. In fact, this method works adequately in practice with only one parameter appropriately set for the environment. The aim of this paper is to improve the variable setting of this method to extend the bandwidth of good parameters, thereby reducing the cost of implementation and parameter tuning. To achieve this, we take advantage of the asymptotic equipartition property in a Markov decision process to extend the peak bandwidth of softmax selection. Using a variety of episodic tasks, we show that our setting is effective in extending the bandwidth and that it yields a better policy in terms of stability. The bandwidth is quantitatively assessed in a series of statistical tests.

Modeling Dengue vector population using remotely sensed data and machine learning.

PubMed

Scavuzzo, Juan M; Trucco, Francisco; Espinosa, Manuel; Tauro, Carolina B; Abril, Marcelo; Scavuzzo, Carlos M; Frery, Alejandro C

2018-05-16

Mosquitoes are vectors of many human diseases. In particular, Aedes ægypti (Linnaeus) is the main vector for Chikungunya, Dengue, and Zika viruses in Latin America and it represents a global threat. Public health policies that aim at combating this vector require dependable and timely information, which is usually expensive to obtain with field campaigns. For this reason, several efforts have been done to use remote sensing due to its reduced cost. The present work includes the temporal modeling of the oviposition activity (measured weekly on 50 ovitraps in a north Argentinean city) of Aedes ægypti (Linnaeus), based on time series of data extracted from operational earth observation satellite images. We use are NDVI, NDWI, LST night, LST day and TRMM-GPM rain from 2012 to 2016 as predictive variables. In contrast to previous works which use linear models, we employ Machine Learning techniques using completely accessible open source toolkits. These models have the advantages of being non-parametric and capable of describing nonlinear relationships between variables. Specifically, in addition to two linear approaches, we assess a support vector machine, an artificial neural networks, a K-nearest neighbors and a decision tree regressor. Considerations are made on parameter tuning and the validation and training approach. The results are compared to linear models used in previous works with similar data sets for generating temporal predictive models. These new tools perform better than linear approaches, in particular nearest neighbor regression (KNNR) performs the best. These results provide better alternatives to be implemented operatively on the Argentine geospatial risk system that is running since 2012. Copyright © 2018 Elsevier B.V. All rights reserved.

Auto-tuning for NMR probe using LabVIEW

NASA Astrophysics Data System (ADS)

Quen, Carmen; Pham, Stephanie; Bernal, Oscar

2014-03-01

Typical manual NMR-tuning method is not suitable for broadband spectra spanning several megahertz linewidths. Among the main problems encountered during manual tuning are pulse-power reproducibility, baselines, and transmission line reflections, to name a few. We present a design of an auto-tuning system using graphic programming language, LabVIEW, to minimize these problems. The program uses a simplified model of the NMR probe conditions near perfect tuning to mimic the tuning process and predict the position of the capacitor shafts needed to achieve the desirable impedance. The tuning capacitors of the probe are controlled by stepper motors through a LabVIEW/computer interface. Our program calculates the effective capacitance needed to tune the probe and provides controlling parameters to advance the motors in the right direction. The impedance reading of a network analyzer can be used to correct the model parameters in real time for feedback control.

Meta-Learning Approach for Automatic Parameter Tuning: A Case Study with Educational Datasets

ERIC Educational Resources Information Center

Molina, M. M.; Luna, J. M.; Romero, C.; Ventura, S.

2012-01-01

This paper proposes to the use of a meta-learning approach for automatic parameter tuning of a well-known decision tree algorithm by using past information about algorithm executions. Fourteen educational datasets were analysed using various combinations of parameter values to examine the effects of the parameter values on accuracy classification.…

Tuning a climate model using nudging to reanalysis.

NASA Astrophysics Data System (ADS)

Cheedela, S. K.; Mapes, B. E.

2014-12-01

Tuning a atmospheric general circulation model involves a daunting task of adjusting non-observable parameters to adjust the mean climate. These parameters arise from necessity to describe unresolved flow through parametrizations. Tuning a climate model is often done with certain set of priorities, such as global mean temperature, net top of the atmosphere radiation. These priorities are hard enough to reach let alone reducing systematic biases in the models. The goal of currently study is to explore alternate ways to tune a climate model to reduce some systematic biases that can be used in synergy with existing efforts. Nudging a climate model to a known state is a poor man's inverse of tuning process described above. Our approach involves nudging the atmospheric model to state of art reanalysis fields thereby providing a balanced state with respect to the global mean temperature and winds. The tendencies derived from nudging are negative of errors from physical parametrizations as the errors from dynamical core would be small. Patterns of nudging are compared to the patterns of different physical parametrizations to decipher the cause for certain biases in relation to tuning parameters. This approach might also help in understanding certain compensating errors that arise from tuning process. ECHAM6 is a comprehensive general model, also used in recent Coupled Model Intercomparision Project(CMIP5). The approach used to tune it and effect of certain parameters that effect its mean climate are reported clearly, hence it serves as a benchmark for our approach. Our planned experiments include nudging ECHAM6 atmospheric model to European Center Reanalysis (ERA-Interim) and reanalysis from National Center for Environmental Prediction (NCEP) and decipher choice of certain parameters that lead to systematic biases in its simulations. Of particular interest are reducing long standing biases related to simulation of Asian summer monsoon.

Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data.

PubMed

Becker, Natalia; Toedt, Grischa; Lichter, Peter; Benner, Axel

2011-05-09

Classification and variable selection play an important role in knowledge discovery in high-dimensional data. Although Support Vector Machine (SVM) algorithms are among the most powerful classification and prediction methods with a wide range of scientific applications, the SVM does not include automatic feature selection and therefore a number of feature selection procedures have been developed. Regularisation approaches extend SVM to a feature selection method in a flexible way using penalty functions like LASSO, SCAD and Elastic Net.We propose a novel penalty function for SVM classification tasks, Elastic SCAD, a combination of SCAD and ridge penalties which overcomes the limitations of each penalty alone.Since SVM models are extremely sensitive to the choice of tuning parameters, we adopted an interval search algorithm, which in comparison to a fixed grid search finds rapidly and more precisely a global optimal solution. Feature selection methods with combined penalties (Elastic Net and Elastic SCAD SVMs) are more robust to a change of the model complexity than methods using single penalties. Our simulation study showed that Elastic SCAD SVM outperformed LASSO (L1) and SCAD SVMs. Moreover, Elastic SCAD SVM provided sparser classifiers in terms of median number of features selected than Elastic Net SVM and often better predicted than Elastic Net in terms of misclassification error.Finally, we applied the penalization methods described above on four publicly available breast cancer data sets. Elastic SCAD SVM was the only method providing robust classifiers in sparse and non-sparse situations. The proposed Elastic SCAD SVM algorithm provides the advantages of the SCAD penalty and at the same time avoids sparsity limitations for non-sparse data. We were first to demonstrate that the integration of the interval search algorithm and penalized SVM classification techniques provides fast solutions on the optimization of tuning parameters.The penalized SVM classification algorithms as well as fixed grid and interval search for finding appropriate tuning parameters were implemented in our freely available R package 'penalizedSVM'.We conclude that the Elastic SCAD SVM is a flexible and robust tool for classification and feature selection tasks for high-dimensional data such as microarray data sets.

Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data

PubMed Central

2011-01-01

Background Classification and variable selection play an important role in knowledge discovery in high-dimensional data. Although Support Vector Machine (SVM) algorithms are among the most powerful classification and prediction methods with a wide range of scientific applications, the SVM does not include automatic feature selection and therefore a number of feature selection procedures have been developed. Regularisation approaches extend SVM to a feature selection method in a flexible way using penalty functions like LASSO, SCAD and Elastic Net. We propose a novel penalty function for SVM classification tasks, Elastic SCAD, a combination of SCAD and ridge penalties which overcomes the limitations of each penalty alone. Since SVM models are extremely sensitive to the choice of tuning parameters, we adopted an interval search algorithm, which in comparison to a fixed grid search finds rapidly and more precisely a global optimal solution. Results Feature selection methods with combined penalties (Elastic Net and Elastic SCAD SVMs) are more robust to a change of the model complexity than methods using single penalties. Our simulation study showed that Elastic SCAD SVM outperformed LASSO (L1) and SCAD SVMs. Moreover, Elastic SCAD SVM provided sparser classifiers in terms of median number of features selected than Elastic Net SVM and often better predicted than Elastic Net in terms of misclassification error. Finally, we applied the penalization methods described above on four publicly available breast cancer data sets. Elastic SCAD SVM was the only method providing robust classifiers in sparse and non-sparse situations. Conclusions The proposed Elastic SCAD SVM algorithm provides the advantages of the SCAD penalty and at the same time avoids sparsity limitations for non-sparse data. We were first to demonstrate that the integration of the interval search algorithm and penalized SVM classification techniques provides fast solutions on the optimization of tuning parameters. The penalized SVM classification algorithms as well as fixed grid and interval search for finding appropriate tuning parameters were implemented in our freely available R package 'penalizedSVM'. We conclude that the Elastic SCAD SVM is a flexible and robust tool for classification and feature selection tasks for high-dimensional data such as microarray data sets. PMID:21554689

A simple attitude control of quadrotor helicopter based on Ziegler-Nichols rules for tuning PD parameters.

PubMed

He, ZeFang; Zhao, Long

2014-01-01

An attitude control strategy based on Ziegler-Nichols rules for tuning PD (proportional-derivative) parameters of quadrotor helicopters is presented to solve the problem that quadrotor tends to be instable. This problem is caused by the narrow definition domain of attitude angles of quadrotor helicopters. The proposed controller is nonlinear and consists of a linear part and a nonlinear part. The linear part is a PD controller with PD parameters tuned by Ziegler-Nichols rules and acts on the quadrotor decoupled linear system after feedback linearization; the nonlinear part is a feedback linearization item which converts a nonlinear system into a linear system. It can be seen from the simulation results that the attitude controller proposed in this paper is highly robust, and its control effect is better than the other two nonlinear controllers. The nonlinear parts of the other two nonlinear controllers are the same as the attitude controller proposed in this paper. The linear part involves a PID (proportional-integral-derivative) controller with the PID controller parameters tuned by Ziegler-Nichols rules and a PD controller with the PD controller parameters tuned by GA (genetic algorithms). Moreover, this attitude controller is simple and easy to implement.

Anytime query-tuned kernel machine classifiers via Cholesky factorization

NASA Technical Reports Server (NTRS)

DeCoste, D.

2002-01-01

We recently demonstrated 2 to 64-fold query-time speedups of Support Vector Machine and Kernel Fisher classifiers via a new computational geometry method for anytime output bounds (DeCoste,2002). This new paper refines our approach in two key ways. First, we introduce a simple linear algebra formulation based on Cholesky factorization, yielding simpler equations and lower computational overhead. Second, this new formulation suggests new methods for achieving additional speedups, including tuning on query samples. We demonstrate effectiveness on benchmark datasets.

Self-tuning regulator for an interacting CSTR process

NASA Astrophysics Data System (ADS)

Rajendra Mungale, Niraj; Upadhyay, Akshay; Jaganatha Pandian, B.

2017-11-01

In the paper we have laid emphasis on STR that is Self Tuning Regulator and its application for an interacting process. CSTR has a great importance in Chemical Process when we deal with controlling different parameters of a process using CSTR. Basically CSTR is used to maintain a constant liquid temperature in the process. The proposed method called self-tuning regulator, is a different scheme where process parameters are updated and the controller parameters are obtained from the solution of a design problem. The paper deals with STR and methods associated with it.

Kinematic sensitivity of robot manipulators

NASA Technical Reports Server (NTRS)

Vuskovic, Marko I.

1989-01-01

Kinematic sensitivity vectors and matrices for open-loop, n degrees-of-freedom manipulators are derived. First-order sensitivity vectors are defined as partial derivatives of the manipulator's position and orientation with respect to its geometrical parameters. The four-parameter kinematic model is considered, as well as the five-parameter model in case of nominally parallel joint axes. Sensitivity vectors are expressed in terms of coordinate axes of manipulator frames. Second-order sensitivity vectors, the partial derivatives of first-order sensitivity vectors, are also considered. It is shown that second-order sensitivity vectors can be expressed as vector products of the first-order sensitivity vectors.

Tuning a Tetrahertz Wire Laser

NASA Technical Reports Server (NTRS)

Qin, Qi; Williams, Benjamin S.; Kumar, Sushil; Reno, John L.; Hu, Qing

2009-01-01

Tunable terahertz lasers are desirable in applications in sensing and spectroscopy because many biochemical species have strong spectral fingerprints at terahertz frequencies. Conventionally, the frequency of a laser is tuned in a similar manner to a stringed musical instrument, in which pitch is varied by changing the length of the string (the longitudinal component of the wave vector) and/ or its tension (the refractive index). However, such methods are difficult to implement in terahertz semiconductor lasers because of their poor outcoupling efficiencies. Here, we demonstrate a novel tuning mechanism based on a unique 'wire laser' device for which the transverse dimension w is much much less than lambda. Placing a movable object close to the wire laser manipulates a large fraction of the waveguided mode propagating outside the cavity, thereby tuning its resonant frequency. Continuous single-mode redshift and blueshift tuning is demonstrated for the same device by using either a dielectric or metallic movable object. In combination, this enables a frequency tuning of approximately equal to 137 GHz (3.6%) from a single laser device at approximately equal to 3.8 THz.

Natural implementation of neutralino dark matter

NASA Astrophysics Data System (ADS)

King, Steve F.; Roberts, Jonathan P.

2006-09-01

The prediction of neutralino dark matter is generally regarded as one of the successes of the Minimal Supersymmetric Standard Model (MSSM). However the successful regions of parameter space allowed by WMAP and collider constraints are quite restricted. We discuss fine-tuning with respect to both dark matter and Electroweak Symmetry Breaking (EWSB) and explore regions of MSSM parameter space with non-universal gaugino and third family scalar masses in which neutralino dark matter may be implemented naturally. In particular allowing non-universal gauginos opens up the bulk region that allows Bino annihilation via t-channel slepton exchange, leading to ``supernatural dark matter'' corresponding to no fine-tuning at all with respect to dark matter. By contrast we find that the recently proposed ``well tempered neutralino'' regions involve substantial fine-tuning of MSSM parameters in order to satisfy the dark matter constraints, although the fine tuning may be ameliorated if several annihilation channels act simultaneously. Although we have identified regions of ``supernatural dark matter'' in which there is no fine tuning to achieve successful dark matter, the usual MSSM fine tuning to achieve EWSB always remains.

PID Tuning Using Extremum Seeking

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

Killingsworth, N; Krstic, M

2005-11-15

Although proportional-integral-derivative (PID) controllers are widely used in the process industry, their effectiveness is often limited due to poor tuning. Manual tuning of PID controllers, which requires optimization of three parameters, is a time-consuming task. To remedy this difficulty, much effort has been invested in developing systematic tuning methods. Many of these methods rely on knowledge of the plant model or require special experiments to identify a suitable plant model. Reviews of these methods are given in [1] and the survey paper [2]. However, in many situations a plant model is not known, and it is not desirable to openmore » the process loop for system identification. Thus a method for tuning PID parameters within a closed-loop setting is advantageous. In relay feedback tuning [3]-[5], the feedback controller is temporarily replaced by a relay. Relay feedback causes most systems to oscillate, thus determining one point on the Nyquist diagram. Based on the location of this point, PID parameters can be chosen to give the closed-loop system a desired phase and gain margin. An alternative tuning method, which does not require either a modification of the system or a system model, is unfalsified control [6], [7]. This method uses input-output data to determine whether a set of PID parameters meets performance specifications. An adaptive algorithm is used to update the PID controller based on whether or not the controller falsifies a given criterion. The method requires a finite set of candidate PID controllers that must be initially specified [6]. Unfalsified control for an infinite set of PID controllers has been developed in [7]; this approach requires a carefully chosen input signal [8]. Yet another model-free PID tuning method that does not require opening of the loop is iterative feedback tuning (IFT). IFT iteratively optimizes the controller parameters with respect to a cost function derived from the output signal of the closed-loop system, see [9]. This method is based on the performance of the closed-loop system during a step response experiment [10], [11]. In this article we present a method for optimizing the step response of a closed-loop system consisting of a PID controller and an unknown plant with a discrete version of extremum seeking (ES). Specifically, ES is used to minimize a cost function similar to that used in [10], [11], which quantifies the performance of the PID controller. ES, a non-model-based method, iteratively modifies the arguments (in this application the PID parameters) of a cost function so that the output of the cost function reaches a local minimum or local maximum. In the next section we apply ES to PID controller tuning. We illustrate this technique through simulations comparing the effectiveness of ES to other PID tuning methods. Next, we address the importance of the choice of cost function and consider the effect of controller saturation. Furthermore, we discuss the choice of ES tuning parameters. Finally, we offer some conclusions.« less

The Magnetically-Tuned Transition-Edge Sensor

NASA Technical Reports Server (NTRS)

Sadleir, John E.; Lee, Sang-Jun; Smith, Stephen J.; Busch, Sarah E.; Bandler, Simon R.; Adams, Joseph S.; Eckart, Megan E.; Chevenak, James A.; Kelley, Richard L.; Kilbourne, Caroline A.;

Generation of vector dissipative and conventional solitons in large normal dispersion regime.

PubMed

Yun, Ling

2017-08-07

We report the generation of both polarization-locked vector dissipative soliton and group velocity-locked vector conventional soliton in a nanotube-mode-locked fiber ring laser with large normal dispersion, for the first time to our best knowledge. Depending on the polarization-depended extinction ratio of the fiber-based Lyot filter, the two types of vector solitons can be switched by simply tuning the polarization controller. In the case of low filter extinction ratio, the output vector dissipative soliton exhibits steep spectral edges and strong frequency chirp, which presents a typical pulse duration of ~23.4 ps, and can be further compressed to ~0.9 ps. In the contrastive case of high filter extinction ratio, the vector conventional soliton has clear Kelly sidebands with transform-limited pulse duration of ~1.8 ps. Our study provides a new and simple method to achieve two different vector soliton sources, which is attractive for potential applications requiring different pulse profiles.

Post-LHC7 fine-tuning in the minimal supergravity/CMSSM model with a 125 GeV Higgs boson

NASA Astrophysics Data System (ADS)

Baer, Howard; Barger, Vernon; Huang, Peisi; Mickelson, Dan; Mustafayev, Azar; Tata, Xerxes

2013-02-01

The recent discovery of a 125 GeV Higgs-like resonance at LHC, coupled with the lack of evidence for weak scale supersymmetry (SUSY), has severely constrained SUSY models such as minimal supergravity (mSUGRA)/CMSSM. As LHC probes deeper into SUSY model parameter space, the little hierarchy problem—how to reconcile the Z and Higgs boson mass scale with the scale of SUSY breaking—will become increasingly exacerbated unless a sparticle signal is found. We evaluate two different measures of fine-tuning in the mSUGRA/CMSSM model. The more stringent of these, ΔHS, includes effects that arise from the high-scale origin of the mSUGRA parameters while the second measure, ΔEW, is determined only by weak scale parameters: hence, it is universal to any model with the same particle spectrum and couplings. Our results incorporate the latest constraints from LHC7 sparticle searches, LHCb limits from Bs→μ+μ- and also require a light Higgs scalar with mh˜123-127GeV. We present fine-tuning contours in the m0 vs m1/2 plane for several sets of A0 and tan⁡β values. We also present results for ΔHS and ΔEW from a scan over the entire viable model parameter space. We find a ΔHS≳103, or at best 0.1%, fine-tuning. For the less stringent electroweak fine-tuning, we find ΔEW≳102, or at best 1%, fine-tuning. Two benchmark points are presented that have the lowest values of ΔHS and ΔEW. Our results provide a quantitative measure for ascertaining whether or not the remaining mSUGRA/CMSSM model parameter space is excessively fine-tuned and so could provide impetus for considering alternative SUSY models.

Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia.

PubMed

Morabito, Francesco Carlo; Campolo, Maurizio; Mammone, Nadia; Versaci, Mario; Franceschetti, Silvana; Tagliavini, Fabrizio; Sofia, Vito; Fatuzzo, Daniela; Gambardella, Antonio; Labate, Angelo; Mumoli, Laura; Tripodi, Giovanbattista Gaspare; Gasparini, Sara; Cianci, Vittoria; Sueri, Chiara; Ferlazzo, Edoardo; Aguglia, Umberto

2017-03-01

A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt-Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features from the time-frequency representation of the EEG signals through continuous wavelet transform (CWT). An average measure of complexity of the EEG signal obtained by permutation entropy (PE) is also included. The dimensionality of the feature space is reduced through a multilayer processing system based on the recently emerged deep learning (DL) concept. The DL processor includes a stacked auto-encoder, trained by unsupervised learning techniques, and a classifier whose parameters are determined in a supervised way by associating the known category labels to the reduced vector of high-level features generated by the previous processing blocks. The supervised learning step is carried out by using either support vector machines (SVM) or multilayer neural networks (MLP-NN). A subset of EEG from patients suffering from Alzheimer's Disease (AD) and healthy controls (HC) is considered for differentiating CJD patients. When fine-tuning the parameters of the global processing system by a supervised learning procedure, the proposed system is able to achieve an average accuracy of 89%, an average sensitivity of 92%, and an average specificity of 89% in differentiating CJD from RPD. Similar results are obtained for CJD versus AD and CJD versus HC.

An implicit iterative algorithm with a tuning parameter for Itô Lyapunov matrix equations

NASA Astrophysics Data System (ADS)

Zhang, Ying; Wu, Ai-Guo; Sun, Hui-Jie

2018-01-01

In this paper, an implicit iterative algorithm is proposed for solving a class of Lyapunov matrix equations arising in Itô stochastic linear systems. A tuning parameter is introduced in this algorithm, and thus the convergence rate of the algorithm can be changed. Some conditions are presented such that the developed algorithm is convergent. In addition, an explicit expression is also derived for the optimal tuning parameter, which guarantees that the obtained algorithm achieves its fastest convergence rate. Finally, numerical examples are employed to illustrate the effectiveness of the given algorithm.

A Simple Attitude Control of Quadrotor Helicopter Based on Ziegler-Nichols Rules for Tuning PD Parameters

PubMed Central

He, ZeFang

2014-01-01

An attitude control strategy based on Ziegler-Nichols rules for tuning PD (proportional-derivative) parameters of quadrotor helicopters is presented to solve the problem that quadrotor tends to be instable. This problem is caused by the narrow definition domain of attitude angles of quadrotor helicopters. The proposed controller is nonlinear and consists of a linear part and a nonlinear part. The linear part is a PD controller with PD parameters tuned by Ziegler-Nichols rules and acts on the quadrotor decoupled linear system after feedback linearization; the nonlinear part is a feedback linearization item which converts a nonlinear system into a linear system. It can be seen from the simulation results that the attitude controller proposed in this paper is highly robust, and its control effect is better than the other two nonlinear controllers. The nonlinear parts of the other two nonlinear controllers are the same as the attitude controller proposed in this paper. The linear part involves a PID (proportional-integral-derivative) controller with the PID controller parameters tuned by Ziegler-Nichols rules and a PD controller with the PD controller parameters tuned by GA (genetic algorithms). Moreover, this attitude controller is simple and easy to implement. PMID:25614879

Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines

PubMed Central

Kurç, Tahsin M.; Taveira, Luís F. R.; Melo, Alba C. M. A.; Gao, Yi; Kong, Jun; Saltz, Joel H.

2017-01-01

Abstract Motivation: Sensitivity analysis and parameter tuning are important processes in large-scale image analysis. They are very costly because the image analysis workflows are required to be executed several times to systematically correlate output variations with parameter changes or to tune parameters. An integrated solution with minimum user interaction that uses effective methodologies and high performance computing is required to scale these studies to large imaging datasets and expensive analysis workflows. Results: The experiments with two segmentation workflows show that the proposed approach can (i) quickly identify and prune parameters that are non-influential; (ii) search a small fraction (about 100 points) of the parameter search space with billions to trillions of points and improve the quality of segmentation results (Dice and Jaccard metrics) by as much as 1.42× compared to the results from the default parameters; (iii) attain good scalability on a high performance cluster with several effective optimizations. Conclusions: Our work demonstrates the feasibility of performing sensitivity analyses, parameter studies and auto-tuning with large datasets. The proposed framework can enable the quantification of error estimations and output variations in image segmentation pipelines. Availability and Implementation: Source code: https://github.com/SBU-BMI/region-templates/. Contact: teodoro@unb.br Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28062445

Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines.

PubMed

Teodoro, George; Kurç, Tahsin M; Taveira, Luís F R; Melo, Alba C M A; Gao, Yi; Kong, Jun; Saltz, Joel H

2017-04-01

Sensitivity analysis and parameter tuning are important processes in large-scale image analysis. They are very costly because the image analysis workflows are required to be executed several times to systematically correlate output variations with parameter changes or to tune parameters. An integrated solution with minimum user interaction that uses effective methodologies and high performance computing is required to scale these studies to large imaging datasets and expensive analysis workflows. The experiments with two segmentation workflows show that the proposed approach can (i) quickly identify and prune parameters that are non-influential; (ii) search a small fraction (about 100 points) of the parameter search space with billions to trillions of points and improve the quality of segmentation results (Dice and Jaccard metrics) by as much as 1.42× compared to the results from the default parameters; (iii) attain good scalability on a high performance cluster with several effective optimizations. Our work demonstrates the feasibility of performing sensitivity analyses, parameter studies and auto-tuning with large datasets. The proposed framework can enable the quantification of error estimations and output variations in image segmentation pipelines. Source code: https://github.com/SBU-BMI/region-templates/ . teodoro@unb.br. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Fault diagnosis for analog circuits utilizing time-frequency features and improved VVRKFA

NASA Astrophysics Data System (ADS)

He, Wei; He, Yigang; Luo, Qiwu; Zhang, Chaolong

2018-04-01

This paper proposes a novel scheme for analog circuit fault diagnosis utilizing features extracted from the time-frequency representations of signals and an improved vector-valued regularized kernel function approximation (VVRKFA). First, the cross-wavelet transform is employed to yield the energy-phase distribution of the fault signals over the time and frequency domain. Since the distribution is high-dimensional, a supervised dimensionality reduction technique—the bilateral 2D linear discriminant analysis—is applied to build a concise feature set from the distributions. Finally, VVRKFA is utilized to locate the fault. In order to improve the classification performance, the quantum-behaved particle swarm optimization technique is employed to gradually tune the learning parameter of the VVRKFA classifier. The experimental results for the analog circuit faults classification have demonstrated that the proposed diagnosis scheme has an advantage over other approaches.

Domain engineering of the metastable domains in the 4f-uniaxial-ferromagnet CeRu2Ga2B

NASA Astrophysics Data System (ADS)

Wulferding, D.; Kim, H.; Yang, I.; Jeong, J.; Barros, K.; Kato, Y.; Martin, I.; Ayala-Valenzuela, O. E.; Lee, M.; Choi, H. C.; Ronning, F.; Civale, L.; Baumbach, R. E.; Bauer, E. D.; Thompson, J. D.; Movshovich, R.; Kim, Jeehoon

2017-04-01

In search of novel, improved materials for magnetic data storage and spintronic devices, compounds that allow a tailoring of magnetic domain shapes and sizes are essential. Good candidates are materials with intrinsic anisotropies or competing interactions, as they are prone to host various domain phases that can be easily and precisely selected by external tuning parameters such as temperature and magnetic field. Here, we utilize vector magnetic fields to visualize directly the magnetic anisotropy in the uniaxial ferromagnet CeRu2Ga2B. We demonstrate a feasible control both globally and locally of domain shapes and sizes by the external field as well as a smooth transition from single stripe to bubble domains, which opens the door to future applications based on magnetic domain tailoring.

Domain engineering of the metastable domains in the 4f-uniaxial-ferromagnet CeRu 2Ga 2B

DOE PAGES

Wulferding, Dirk; Kim, Hoon; Yang, Ilkyu; ...

2017-04-10

In search of novel, improved materials for magnetic data storage and spintronic devices, compounds that allow a tailoring of magnetic domain shapes and sizes are essential. Good candidates are materials with intrinsic anisotropies or competing interactions, as they are prone to host various domain phases that can be easily and precisely selected by external tuning parameters such as temperature and magnetic field. Here, we utilize vector magnetic fields to visualize directly the magnetic anisotropy in the uniaxial ferromagnet CeRu 2Ga 2B. We demonstrate a feasible control both globally and locally of domain shapes and sizes by the external field asmore » well as a smooth transition from single stripe to bubble domains, which opens the door to future applications based on magnetic domain tailoring.« less

Neutrino mass implications for muon decay parameters

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

Erwin, Rebecca J.; Kile, Jennifer; Ramsey-Musolf, Michael J.

2007-02-01

We use the scale of neutrino mass and naturalness considerations to obtain model-independent expectations for the magnitude of possible contributions to muon decay Michel parameters from new physics above the electroweak symmetry-breaking scale. Focusing on Dirac neutrinos, we obtain a complete basis of dimension four and dimension six effective operators that are invariant under the gauge symmetry of the standard model and that contribute to both muon decay and neutrino mass. We show that - in the absence of fine tuning - the most stringent neutrino-mass naturalness bounds on chirality-changing vector operators relevant to muon decay arise from one-loop operatormore » mixing. The bounds we obtain on their contributions to the Michel parameters are 2 orders of magnitude stronger than bounds previously obtained in the literature. In addition, we analyze the implications of one-loop matching considerations and find that the expectations for the size of various scalar and tensor contributions to the Michel parameters are considerably smaller than derived from previous estimates of two-loop operator mixing. We also show, however, that there exist gauge-invariant operators that generate scalar and tensor contributions to muon decay but whose flavor structure allows them to evade neutrino-mass naturalness bounds. We discuss the implications of our analysis for the interpretation of muon-decay experiments.« less

A parametric model and estimation techniques for the inharmonicity and tuning of the piano.

PubMed

Rigaud, François; David, Bertrand; Daudet, Laurent

2013-05-01

Inharmonicity of piano tones is an essential property of their timbre that strongly influences the tuning, leading to the so-called octave stretching. It is proposed in this paper to jointly model the inharmonicity and tuning of pianos on the whole compass. While using a small number of parameters, these models are able to reflect both the specificities of instrument design and tuner's practice. An estimation algorithm is derived that can run either on a set of isolated note recordings, but also on chord recordings, assuming that the played notes are known. It is applied to extract parameters highlighting some tuner's choices on different piano types and to propose tuning curves for out-of-tune pianos or piano synthesizers.

A semisupervised support vector regression method to estimate biophysical parameters from remotely sensed images

NASA Astrophysics Data System (ADS)

Castelletti, Davide; Demir, Begüm; Bruzzone, Lorenzo

2014-10-01

This paper presents a novel semisupervised learning (SSL) technique defined in the context of ɛ-insensitive support vector regression (SVR) to estimate biophysical parameters from remotely sensed images. The proposed SSL method aims to mitigate the problems of small-sized biased training sets without collecting any additional samples with reference measures. This is achieved on the basis of two consecutive steps. The first step is devoted to inject additional priors information in the learning phase of the SVR in order to adapt the importance of each training sample according to distribution of the unlabeled samples. To this end, a weight is initially associated to each training sample based on a novel strategy that defines higher weights for the samples located in the high density regions of the feature space while giving reduced weights to those that fall into the low density regions of the feature space. Then, in order to exploit different weights for training samples in the learning phase of the SVR, we introduce a weighted SVR (WSVR) algorithm. The second step is devoted to jointly exploit labeled and informative unlabeled samples for further improving the definition of the WSVR learning function. To this end, the most informative unlabeled samples that have an expected accurate target values are initially selected according to a novel strategy that relies on the distribution of the unlabeled samples in the feature space and on the WSVR function estimated at the first step. Then, we introduce a restructured WSVR algorithm that jointly uses labeled and unlabeled samples in the learning phase of the WSVR algorithm and tunes their importance by different values of regularization parameters. Experimental results obtained for the estimation of single-tree stem volume show the effectiveness of the proposed SSL method.

A novel auto-tuning PID control mechanism for nonlinear systems.

PubMed

Cetin, Meric; Iplikci, Serdar

2015-09-01

In this paper, a novel Runge-Kutta (RK) discretization-based model-predictive auto-tuning proportional-integral-derivative controller (RK-PID) is introduced for the control of continuous-time nonlinear systems. The parameters of the PID controller are tuned using RK model of the system through prediction error-square minimization where the predicted information of tracking error provides an enhanced tuning of the parameters. Based on the model-predictive control (MPC) approach, the proposed mechanism provides necessary PID parameter adaptations while generating additive correction terms to assist the initially inadequate PID controller. Efficiency of the proposed mechanism has been tested on two experimental real-time systems: an unstable single-input single-output (SISO) nonlinear magnetic-levitation system and a nonlinear multi-input multi-output (MIMO) liquid-level system. RK-PID has been compared to standard PID, standard nonlinear MPC (NMPC), RK-MPC and conventional sliding-mode control (SMC) methods in terms of control performance, robustness, computational complexity and design issue. The proposed mechanism exhibits acceptable tuning and control performance with very small steady-state tracking errors, and provides very short settling time for parameter convergence. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Design of an iterative auto-tuning algorithm for a fuzzy PID controller

NASA Astrophysics Data System (ADS)

Saeed, Bakhtiar I.; Mehrdadi, B.

2012-05-01

Since the first application of fuzzy logic in the field of control engineering, it has been extensively employed in controlling a wide range of applications. The human knowledge on controlling complex and non-linear processes can be incorporated into a controller in the form of linguistic terms. However, with the lack of analytical design study it is becoming more difficult to auto-tune controller parameters. Fuzzy logic controller has several parameters that can be adjusted, such as: membership functions, rule-base and scaling gains. Furthermore, it is not always easy to find the relation between the type of membership functions or rule-base and the controller performance. This study proposes a new systematic auto-tuning algorithm to fine tune fuzzy logic controller gains. A fuzzy PID controller is proposed and applied to several second order systems. The relationship between the closed-loop response and the controller parameters is analysed to devise an auto-tuning method. The results show that the proposed method is highly effective and produces zero overshoot with enhanced transient response. In addition, the robustness of the controller is investigated in the case of parameter changes and the results show a satisfactory performance.

Benchmarking GPU and CPU codes for Heisenberg spin glass over-relaxation

NASA Astrophysics Data System (ADS)

Bernaschi, M.; Parisi, G.; Parisi, L.

2011-06-01

We present a set of possible implementations for Graphics Processing Units (GPU) of the Over-relaxation technique applied to the 3D Heisenberg spin glass model. The results show that a carefully tuned code can achieve more than 100 GFlops/s of sustained performance and update a single spin in about 0.6 nanoseconds. A multi-hit technique that exploits the GPU shared memory further reduces this time. Such results are compared with those obtained by means of a highly-tuned vector-parallel code on latest generation multi-core CPUs.

Auditory steady-state evoked potentials vs. compound action potentials for the measurement of suppression tuning curves in the sedated dog puppy.

PubMed

Markessis, Emily; Poncelet, Luc; Colin, Cécile; Hoonhorst, Ingrid; Collet, Grégory; Deltenre, Paul; Moore, Brian C J

2010-06-01

Auditory steady-state evoked potential (ASSEP) tuning curves were compared to compound action potential (CAP) tuning curves, both measured at 2 Hz, using sedated beagle puppies. The effect of two types of masker (narrowband noise and sinusoidal) on the tuning curve parameters was assessed. Whatever the masker type, CAP tuning curve parameters were qualitatively and quantitatively similar to the ASSEP ones, with a similar inter-subject variability, but with a greater incidence of upward tip displacement. Whatever the procedure, sinusoidal maskers produced sharper tuning curves than narrow-band maskers. Although these differences are not likely to have significant implications for clinical work, from a fundamental point of view, their origin requires further investigations. The same amount of time was needed to record a CAP and an ASSEP 13-point tuning curve. The data further validate the ASSEP technique, which has the advantages of having a smaller tendency to produce upward tip shifts than the CAP technique. Moreover, being non invasive, ASSEP tuning curves can be easily repeated over time in the same subject for clinical and research purposes.

Polarization ellipse and Stokes parameters in geometric algebra.

PubMed

Santos, Adler G; Sugon, Quirino M; McNamara, Daniel J

2012-01-01

In this paper, we use geometric algebra to describe the polarization ellipse and Stokes parameters. We show that a solution to Maxwell's equation is a product of a complex basis vector in Jackson and a linear combination of plane wave functions. We convert both the amplitudes and the wave function arguments from complex scalars to complex vectors. This conversion allows us to separate the electric field vector and the imaginary magnetic field vector, because exponentials of imaginary scalars convert vectors to imaginary vectors and vice versa, while exponentials of imaginary vectors only rotate the vector or imaginary vector they are multiplied to. We convert this expression for polarized light into two other representations: the Cartesian representation and the rotated ellipse representation. We compute the conversion relations among the representation parameters and their corresponding Stokes parameters. And finally, we propose a set of geometric relations between the electric and magnetic fields that satisfy an equation similar to the Poincaré sphere equation.

Applying six classifiers to airborne hyperspectral imagery for detecting giant reed

USDA-ARS?s Scientific Manuscript database

This study evaluated and compared six different image classifiers, including minimum distance (MD), Mahalanobis distance (MAHD), maximum likelihood (ML), spectral angle mapper (SAM), mixture tuned matched filtering (MTMF) and support vector machine (SVM), for detecting and mapping giant reed (Arundo...

The minimal SUSY B - L model: from the unification scale to the LHC

DOE PAGES

Ovrut, Burt A.; Purves, Austin; Spinner, Sogee

2015-06-26

Here, this paper introduces a random statistical scan over the high-energy initial parameter space of the minimal SUSY B - L model — denoted as the B - L MSSM. Each initial set of points is renormalization group evolved to the electroweak scale — being subjected, sequentially, to the requirement of radiative B - L and electroweak symmetry breaking, the present experimental lower bounds on the B - L vector boson and sparticle masses, as well as the lightest neutral Higgs mass of ~125 GeV. The subspace of initial parameters that satisfies all such constraints is presented, shown to bemore » robust and to contain a wide range of different configurations of soft supersymmetry breaking masses. The low-energy predictions of each such “valid” point — such as the sparticle mass spectrum and, in particular, the LSP — are computed and then statistically analyzed over the full subspace of valid points. Finally, the amount of fine-tuning required is quantified and compared to the MSSM computed using an identical random scan. The B - L MSSM is shown to generically require less fine-tuninng.« less

Dynamics of entanglement of a three-level atom in motion interacting with two coupled modes including parametric down conversion

NASA Astrophysics Data System (ADS)

Faghihi, M. J.; Tavassoly, M. K.; Hatami, M.

In this paper, a model by which we study the interaction between a motional three-level atom and two-mode field injected simultaneously in a bichromatic cavity is considered; the three-level atom is assumed to be in a Λ-type configuration. As a result, the atom-field and the field-field interaction (parametric down conversion) will be appeared. It is shown that, by applying a canonical transformation, the introduced model can be reduced to a well-known form of the generalized Jaynes-Cummings model. Under particular initial conditions, which may be prepared for the atom and the field, the time evolution of state vector of the entire system is analytically evaluated. Then, the dynamics of atom by considering ‘atomic population inversion’ and two different measures of entanglement, i.e., ‘von Neumann entropy’ and ‘idempotency defect’ is discussed, in detail. It is deduced from the numerical results that, the duration and the maximum amount of the considered physical quantities can be suitably tuned by selecting the proper field-mode structure parameter p and the detuning parameters.

Entanglement Criteria of Two Two-Level Atoms Interacting with Two Coupled Modes

NASA Astrophysics Data System (ADS)

Baghshahi, Hamid Reza; Tavassoly, Mohammad Kazem; Faghihi, Mohammad Javad

2015-08-01

In this paper, we study the interaction between two two-level atoms and two coupled modes of a quantized radiation field in the form of parametric frequency converter injecting within an optical cavity enclosed by a medium with Kerr nonlinearity. It is demonstrated that, by applying the Bogoliubov-Valatin canonical transformation, the introduced model is reduced to a well-known form of the generalized Jaynes-Cummings model. Then, under particular initial conditions for the atoms (in a coherent superposition of its ground and upper states) and the fields (in a standard coherent state) which may be prepared, the time evolution of state vector of the entire system is analytically evaluated. In order to understand the degree of entanglement between subsystems (atom-field and atom-atom), the dynamics of entanglement through different measures, namely, von Neumann reduced entropy, concurrence and negativity is evaluated. In each case, the effects of Kerr nonlinearity and detuning parameter on the above measures are numerically analyzed, in detail. It is illustrated that the amount of entanglement can be tuned by choosing the evolved parameters, appropriately.

Transcranial magnetic stimulation changes response selectivity of neurons in the visual cortex

PubMed Central

Kim, Taekjun; Allen, Elena A.; Pasley, Brian N.; Freeman, Ralph D.

2015-01-01

Background Transcranial magnetic stimulation (TMS) is used to selectively alter neuronal activity of specific regions in the cerebral cortex. TMS is reported to induce either transient disruption or enhancement of different neural functions. However, its effects on tuning properties of sensory neurons have not been studied quantitatively. Objective/Hypothesis Here, we use specific TMS application parameters to determine how they may alter tuning characteristics (orientation, spatial frequency, and contrast sensitivity) of single neurons in the cat’s visual cortex. Methods Single unit spikes were recorded with tungsten microelectrodes from the visual cortex of anesthetized and paralyzed cats (12 males). Repetitive TMS (4Hz, 4sec) was delivered with a 70mm figure-8 coil. We quantified basic tuning parameters of individual neurons for each pre- and post-TMS condition. The statistical significance of changes for each tuning parameter between the two conditions was evaluated with a Wilcoxon signed-rank test. Results We generally find long-lasting suppression which persists well beyond the stimulation period. Pre- and post-TMS orientation tuning curves show constant peak values. However, strong suppression at non-preferred orientations tends to narrow the widths of tuning curves. Spatial frequency tuning exhibits an asymmetric change in overall shape, which results in an emphasis on higher frequencies. Contrast tuning curves show nonlinear changes consistent with a gain control mechanism. Conclusions These findings suggest that TMS causes extended interruption of the balance between sub-cortical and intra-cortical inputs. PMID:25862599

A Parameter Tuning Scheme of Sea-ice Model Based on Automatic Differentiation Technique

NASA Astrophysics Data System (ADS)

Kim, J. G.; Hovland, P. D.

2001-05-01

Automatic diferentiation (AD) technique was used to illustrate a new approach for parameter tuning scheme of an uncoupled sea-ice model. Atmospheric forcing field of 1992 obtained from NCEP data was used as enforcing variables in the study. The simulation results were compared with the observed ice movement provided by the International Arctic Buoy Programme (IABP). All of the numerical experiments were based on a widely used dynamic and thermodynamic model for simulating the seasonal sea-ice chnage of the main Arctic ocean. We selected five dynamic and thermodynamic parameters for the tuning process in which the cost function defined by the norm of the difference between observed and simulated ice drift locations was minimized. The selected parameters are the air and ocean drag coefficients, the ice strength constant, the turning angle at ice-air/ocean interface, and the bulk sensible heat transfer coefficient. The drag coefficients were the major parameters to control sea-ice movement and extent. The result of the study shows that more realistic simulations of ice thickness distribution was produced by tuning the simulated ice drift trajectories. In the tuning process, the L-BFCGS-B minimization algorithm of a quasi-Newton method was used. The derivative information required in the minimization iterations was provided by the AD processed Fortran code. Compared with a conventional approach, AD generated derivative code provided fast and robust computations of derivative information.

Decoding the non-stationary neuron spike trains by dual Monte Carlo point process estimation in motor Brain Machine Interfaces.

PubMed

Liao, Yuxi; Li, Hongbao; Zhang, Qiaosheng; Fan, Gong; Wang, Yiwen; Zheng, Xiaoxiang

2014-01-01

Decoding algorithm in motor Brain Machine Interfaces translates the neural signals to movement parameters. They usually assume the connection between the neural firings and movements to be stationary, which is not true according to the recent studies that observe the time-varying neuron tuning property. This property results from the neural plasticity and motor learning etc., which leads to the degeneration of the decoding performance when the model is fixed. To track the non-stationary neuron tuning during decoding, we propose a dual model approach based on Monte Carlo point process filtering method that enables the estimation also on the dynamic tuning parameters. When applied on both simulated neural signal and in vivo BMI data, the proposed adaptive method performs better than the one with static tuning parameters, which raises a promising way to design a long-term-performing model for Brain Machine Interfaces decoder.

Taming parallel I/O complexity with auto-tuning

DOE PAGES

Behzad, Babak; Luu, Huong Vu Thanh; Huchette, Joseph; ...

2013-11-17

We present an auto-tuning system for optimizing I/O performance of HDF5 applications and demonstrate its value across platforms, applications, and at scale. The system uses a genetic algorithm to search a large space of tunable parameters and to identify effective settings at all layers of the parallel I/O stack. The parameter settings are applied transparently by the auto-tuning system via dynamically intercepted HDF5 calls. To validate our auto-tuning system, we applied it to three I/O benchmarks (VPIC, VORPAL, and GCRM) that replicate the I/O activity of their respective applications. We tested the system with different weak-scaling configurations (128, 2048, andmore » 4096 CPU cores) that generate 30 GB to 1 TB of data, and executed these configurations on diverse HPC platforms (Cray XE6, IBM BG/P, and Dell Cluster). In all cases, the auto-tuning framework identified tunable parameters that substantially improved write performance over default system settings. In conclusion, we consistently demonstrate I/O write speedups between 2x and 100x for test configurations.« less

An algorithm for the design and tuning of RF accelerating structures with variable cell lengths

NASA Astrophysics Data System (ADS)

Lal, Shankar; Pant, K. K.

2018-05-01

An algorithm is proposed for the design of a π mode standing wave buncher structure with variable cell lengths. It employs a two-parameter, multi-step approach for the design of the structure with desired resonant frequency and field flatness. The algorithm, along with analytical scaling laws for the design of the RF power coupling slot, makes it possible to accurately design the structure employing a freely available electromagnetic code like SUPERFISH. To compensate for machining errors, a tuning method has been devised to achieve desired RF parameters for the structure, which has been qualified by the successful tuning of a 7-cell buncher to π mode frequency of 2856 MHz with field flatness <3% and RF coupling coefficient close to unity. The proposed design algorithm and tuning method have demonstrated the feasibility of developing an S-band accelerating structure for desired RF parameters with a relatively relaxed machining tolerance of ∼ 25 μm. This paper discusses the algorithm for the design and tuning of an RF accelerating structure with variable cell lengths.

Continuous Firefly Algorithm for Optimal Tuning of Pid Controller in Avr System

NASA Astrophysics Data System (ADS)

Bendjeghaba, Omar

2014-01-01

This paper presents a tuning approach based on Continuous firefly algorithm (CFA) to obtain the proportional-integral- derivative (PID) controller parameters in Automatic Voltage Regulator system (AVR). In the tuning processes the CFA is iterated to reach the optimal or the near optimal of PID controller parameters when the main goal is to improve the AVR step response characteristics. Conducted simulations show the effectiveness and the efficiency of the proposed approach. Furthermore the proposed approach can improve the dynamic of the AVR system. Compared with particle swarm optimization (PSO), the new CFA tuning method has better control system performance in terms of time domain specifications and set-point tracking.

Online automatic tuning and control for fed-batch cultivation

PubMed Central

van Straten, Gerrit; van der Pol, Leo A.; van Boxtel, Anton J. B.

2007-01-01

Performance of controllers applied in biotechnological production is often below expectation. Online automatic tuning has the capability to improve control performance by adjusting control parameters. This work presents automatic tuning approaches for model reference specific growth rate control during fed-batch cultivation. The approaches are direct methods that use the error between observed specific growth rate and its set point; systematic perturbations of the cultivation are not necessary. Two automatic tuning methods proved to be efficient, in which the adaptation rate is based on a combination of the error, squared error and integral error. These methods are relatively simple and robust against disturbances, parameter uncertainties, and initialization errors. Application of the specific growth rate controller yields a stable system. The controller and automatic tuning methods are qualified by simulations and laboratory experiments with Bordetella pertussis. PMID:18157554

Polarization-sensitive descending neurons in the locust: connecting the brain to thoracic ganglia.

PubMed

Träger, Ulrike; Homberg, Uwe

2011-02-09

Many animal species, in particular insects, exploit the E-vector pattern of the blue sky for sun compass navigation. Like other insects, locusts detect dorsal polarized light via photoreceptors in a specialized dorsal rim area of the compound eye. Polarized light information is transmitted through several processing stages to the central complex, a brain area involved in the control of goal-directed orientation behavior. To investigate how polarized light information is transmitted to thoracic motor circuits, we studied the responses of locust descending neurons to polarized light. Three sets of polarization-sensitive descending neurons were characterized through intracellular recordings from axonal fibers in the neck connectives combined with single-cell dye injections. Two descending neurons from the brain, one with ipsilaterally and the second with contralaterally descending axon, are likely to bridge the gap between polarization-sensitive neurons in the brain and thoracic motor centers. In both neurons, E-vector tuning changed linearly with daytime, suggesting that they signal time-compensated spatial directions, an important prerequisite for navigation using celestial signals. The third type connects the suboesophageal ganglion with the prothoracic ganglion. It showed no evidence for time compensation in E-vector tuning and might play a role in flight stabilization and control of head movements.

Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model

NASA Astrophysics Data System (ADS)

Williamson, Daniel B.; Blaker, Adam T.; Sinha, Bablu

2017-04-01

In this paper we discuss climate model tuning and present an iterative automatic tuning method from the statistical science literature. The method, which we refer to here as iterative refocussing (though also known as history matching), avoids many of the common pitfalls of automatic tuning procedures that are based on optimisation of a cost function, principally the over-tuning of a climate model due to using only partial observations. This avoidance comes by seeking to rule out parameter choices that we are confident could not reproduce the observations, rather than seeking the model that is closest to them (a procedure that risks over-tuning). We comment on the state of climate model tuning and illustrate our approach through three waves of iterative refocussing of the NEMO (Nucleus for European Modelling of the Ocean) ORCA2 global ocean model run at 2° resolution. We show how at certain depths the anomalies of global mean temperature and salinity in a standard configuration of the model exceeds 10 standard deviations away from observations and show the extent to which this can be alleviated by iterative refocussing without compromising model performance spatially. We show how model improvements can be achieved by simultaneously perturbing multiple parameters, and illustrate the potential of using low-resolution ensembles to tune NEMO ORCA configurations at higher resolutions.

Self-tuning bistable parametric feedback oscillator: Near-optimal amplitude maximization without model information

NASA Astrophysics Data System (ADS)

Braun, David J.; Sutas, Andrius; Vijayakumar, Sethu

2017-01-01

Theory predicts that parametrically excited oscillators, tuned to operate under resonant condition, are capable of large-amplitude oscillation useful in diverse applications, such as signal amplification, communication, and analog computation. However, due to amplitude saturation caused by nonlinearity, lack of robustness to model uncertainty, and limited sensitivity to parameter modulation, these oscillators require fine-tuning and strong modulation to generate robust large-amplitude oscillation. Here we present a principle of self-tuning parametric feedback excitation that alleviates the above-mentioned limitations. This is achieved using a minimalistic control implementation that performs (i) self-tuning (slow parameter adaptation) and (ii) feedback pumping (fast parameter modulation), without sophisticated signal processing past observations. The proposed approach provides near-optimal amplitude maximization without requiring model-based control computation, previously perceived inevitable to implement optimal control principles in practical application. Experimental implementation of the theory shows that the oscillator self-tunes itself near to the onset of dynamic bifurcation to achieve extreme sensitivity to small resonant parametric perturbations. As a result, it achieves large-amplitude oscillations by capitalizing on the effect of nonlinearity, despite substantial model uncertainties and strong unforeseen external perturbations. We envision the present finding to provide an effective and robust approach to parametric excitation when it comes to real-world application.

High speed flux feedback for tuning a universal field oriented controller capable of operating in direct and indirect field orientation modes

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

De Doncker, Rik W. A. A.

The direct (d) and quadrature (q) components of flux, as sensed by flux sensors or determined from voltage and current measurements in a direct field orientation scheme, are processed rapidly and accurately to provide flux amplitude and angular position values for use by the vector rotator of a universal field-oriented (UFO) controller. Flux amplitude (linear or squared) is provided as feedback to tune the UFO controller for operation in direct and indirect field orientation modes and enables smooth transitions from one mode to the other.

High speed flux feedback for tuning a universal field oriented controller capable of operating in direct and indirect field orientation modes

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

De Doncker, R.W.A.A.

The direct (d) and quadrature (q) components of flux, as sensed by flux sensors or determined from voltage and current measurements in a direct field orientation scheme, are processed rapidly and accurately to provide flux amplitude and angular position values for use by the vector rotator of a universal field-oriented (UFO) controller. Flux amplitude (linear or squared) is provided as feedback to tune the UFO controller for operation in direct and indirect field orientation modes and enables smooth transitions from one mode to the other. 3 figs.

High speed flux feedback for tuning a universal field oriented controller capable of operating in direct and indirect field orientation modes

DOEpatents

De Doncker, R.W.A.A.

1992-09-01

The direct (d) and quadrature (q) components of flux, as sensed by flux sensors or determined from voltage and current measurements in a direct field orientation scheme, are processed rapidly and accurately to provide flux amplitude and angular position values for use by the vector rotator of a universal field-oriented (UFO) controller. Flux amplitude (linear or squared) is provided as feedback to tune the UFO controller for operation in direct and indirect field orientation modes and enables smooth transitions from one mode to the other. 3 figs.

On the effect of response transformations in sequential parameter optimization.

PubMed

Wagner, Tobias; Wessing, Simon

2012-01-01

Parameter tuning of evolutionary algorithms (EAs) is attracting more and more interest. In particular, the sequential parameter optimization (SPO) framework for the model-assisted tuning of stochastic optimizers has resulted in established parameter tuning algorithms. In this paper, we enhance the SPO framework by introducing transformation steps before the response aggregation and before the actual modeling. Based on design-of-experiments techniques, we empirically analyze the effect of integrating different transformations. We show that in particular, a rank transformation of the responses provides significant improvements. A deeper analysis of the resulting models and additional experiments with adaptive procedures indicates that the rank and the Box-Cox transformation are able to improve the properties of the resultant distributions with respect to symmetry and normality of the residuals. Moreover, model-based effect plots document a higher discriminatory power obtained by the rank transformation.

Heavy and Light Quarks with Lattice Chiral Fermions

NASA Astrophysics Data System (ADS)

Liu, K. F.; Dong, S. J.

The feasibility of using lattice chiral fermions which are free of O(a) errors for both the heavy and light quarks is examined. The fact that the effective quark propagators in these fermions have the same form as that in the continuum with the quark mass being only an additive parameter to a chirally symmetric anti-Hermitian Dirac operator is highlighted. This implies that there is no distinction between the heavy and light quarks and no mass dependent tuning of the action or operators as long as the discretization error O(m2a2) is negligible. Using the overlap fermion, we find that the O(m2a2) (and O(ma2)) errors in the dispersion relations of the pseudoscalar and vector mesons and the renormalization of the axial-vector current and scalar density are small. This suggests that the applicable range of ma may be extended to ~0.56 with only 5% error, which is a factor of ~2.4 larger than the corresponding range of the improved Wilson action. We show that the generalized Gell-Mann-Oakes-Renner relation with unequal masses can be utilized to determine the finite ma corrections in the renormalization of the matrix elements for the heavy-light decay constants and semileptonic decay constants of the B/D meson.

Optimizing antibody expression: The nuts and bolts.

PubMed

Ayyar, B Vijayalakshmi; Arora, Sushrut; Ravi, Shiva Shankar

2017-03-01

Antibodies are extensively utilized entities in biomedical research, and in the development of diagnostics and therapeutics. Many of these applications require high amounts of antibodies. However, meeting this ever-increasing demand of antibodies in the global market is one of the outstanding challenges. The need to maintain a balance between demand and supply of antibodies has led the researchers to discover better means and methods for optimizing their expression. These strategies aim to increase the volumetric productivity of the antibodies along with the reduction of associated manufacturing costs. Recent years have witnessed major advances in recombinant protein technology, owing to the introduction of novel cloning strategies, gene manipulation techniques, and an array of cell and vector engineering techniques, together with the progress in fermentation technologies. These innovations were also highly beneficial for antibody expression. Antibody expression depends upon the complex interplay of multiple factors that may require fine tuning at diverse levels to achieve maximum yields. However, each antibody is unique and requires individual consideration and customization for optimizing the associated expression parameters. This review provides a comprehensive overview of several state-of-the-art approaches, such as host selection, strain engineering, codon optimization, gene optimization, vector modification and process optimization that are deemed suitable for enhancing antibody expression. Copyright © 2017 Elsevier Inc. All rights reserved.

Klein tunneling in Weyl semimetals under the influence of magnetic field.

PubMed

Yesilyurt, Can; Tan, Seng Ghee; Liang, Gengchiau; Jalil, Mansoor B A

2016-12-12

Klein tunneling refers to the absence of normal backscattering of electrons even under the case of high potential barriers. At the barrier interface, the perfect matching of electron and hole wavefunctions enables a unit transmission probability for normally incident electrons. It is theoretically and experimentally well understood in two-dimensional relativistic materials such as graphene. Here we investigate the Klein tunneling effect in Weyl semimetals under the influence of magnetic field induced by ferromagnetic stripes placed at barrier boundaries. Our results show that the resonance of Fermi wave vector at specific barrier lengths gives rise to perfect transmission rings, i.e., three-dimensional analogue of the so-called magic transmission angles in two-dimensional Dirac semimetals. Besides, the transmission profile can be shifted by application of magnetic field in the central region, a property which may be utilized in electro-optic applications. When the applied potential is close to the Fermi level, a particular incident vector can be selected by tuning the magnetic field, thus enabling highly selective transmission of electrons in the bulk of Weyl semimetals. Our analytical and numerical calculations obtained by considering Dirac electrons in three regions and using experimentally feasible parameters can pave the way for relativistic tunneling applications in Weyl semimetals.

Fuzzy logic controllers for electrotechnical devices - On-site tuning approach

NASA Astrophysics Data System (ADS)

Hissel, D.; Maussion, P.; Faucher, J.

2001-12-01

Fuzzy logic offers nowadays an interesting alternative to the designers of non linear control laws for electrical or electromechanical systems. However, due to the huge number of tuning parameters, this kind of control is only used in a few industrial applications. This paper proposes a new, very simple, on-site tuning strategy for a PID-like fuzzy logic controller. Thanks to the experimental designs methodology, we will propose sets of optimized pre-established settings for this kind of fuzzy controllers. The proposed parameters are only depending on one on-site open-loop identification test. In this way, this on-site tuning methodology has to be compared to the Ziegler-Nichols one's for conventional controllers. Experimental results (on a permanent magnets synchronous motor and on a DC/DC converter) will underline all the efficiency of this tuning methodology. Finally, the field of validity of the proposed pre-established settings will be given.

PID controller tuning using metaheuristic optimization algorithms for benchmark problems

NASA Astrophysics Data System (ADS)

Gholap, Vishal; Naik Dessai, Chaitali; Bagyaveereswaran, V.

2017-11-01

This paper contributes to find the optimal PID controller parameters using particle swarm optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm. The algorithms were developed through simulation of chemical process and electrical system and the PID controller is tuned. Here, two different fitness functions such as Integral Time Absolute Error and Time domain Specifications were chosen and applied on PSO, GA and SA while tuning the controller. The proposed Algorithms are implemented on two benchmark problems of coupled tank system and DC motor. Finally, comparative study has been done with different algorithms based on best cost, number of iterations and different objective functions. The closed loop process response for each set of tuned parameters is plotted for each system with each fitness function.

Tripartite entanglement dynamics and entropic squeezing of a three-level atom interacting with a bimodal cavity field

NASA Astrophysics Data System (ADS)

Faghihi, M. J.; Tavassoly, M. K.; Bagheri Harouni, M.

2014-04-01

In this paper, we study the interaction between a Λ-type three-level atom and two quantized electromagnetic fields which are simultaneously injected in a bichromatic cavity surrounded by a Kerr medium in the presence of field-field interaction (parametric down conversion) and detuning parameters. By applying a canonical transformation, the introduced model is reduced to a well-known form of the generalized Jaynes-Cummings model. Under particular initial conditions which may be prepared for the atom and the field, the time evolution of the state vector of the entire system is analytically evaluated. Then, the dynamics of the atom is studied through the evolution of the atomic population inversion. In addition, two different measures of entanglement between the tripartite system (three entities make the system: two field modes and one atom), i.e., von Neumann and linear entropy are investigated. Also, two kinds of entropic uncertainty relations, from which entropy squeezing can be obtained, are discussed. In each case, the influences of the detuning parameters and Kerr medium on the above nonclassicality features are analyzed in detail via numerical results. It is illustrated that the amount of the above-mentioned physical phenomena can be tuned by choosing the evolved parameters, appropriately.

Adjoint-Based Climate Model Tuning: Application to the Planet Simulator

NASA Astrophysics Data System (ADS)

Lyu, Guokun; Köhl, Armin; Matei, Ion; Stammer, Detlef

2018-01-01

The adjoint method is used to calibrate the medium complexity climate model "Planet Simulator" through parameter estimation. Identical twin experiments demonstrate that this method can retrieve default values of the control parameters when using a long assimilation window of the order of 2 months. Chaos synchronization through nudging, required to overcome limits in the temporal assimilation window in the adjoint method, is employed successfully to reach this assimilation window length. When assimilating ERA-Interim reanalysis data, the observations of air temperature and the radiative fluxes are the most important data for adjusting the control parameters. The global mean net longwave fluxes at the surface and at the top of the atmosphere are significantly improved by tuning two model parameters controlling the absorption of clouds and water vapor. The global mean net shortwave radiation at the surface is improved by optimizing three model parameters controlling cloud optical properties. The optimized parameters improve the free model (without nudging terms) simulation in a way similar to that in the assimilation experiments. Results suggest a promising way for tuning uncertain parameters in nonlinear coupled climate models.

Numerical weather prediction model tuning via ensemble prediction system

NASA Astrophysics Data System (ADS)

Jarvinen, H.; Laine, M.; Ollinaho, P.; Solonen, A.; Haario, H.

2011-12-01

This paper discusses a novel approach to tune predictive skill of numerical weather prediction (NWP) models. NWP models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. Currently, numerical values of these parameters are specified manually. In a recent dual manuscript (QJRMS, revised) we developed a new concept and method for on-line estimation of the NWP model parameters. The EPPES ("Ensemble prediction and parameter estimation system") method requires only minimal changes to the existing operational ensemble prediction infra-structure and it seems very cost-effective because practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating each member of the ensemble of predictions using different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In the presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an atmospheric general circulation model based ensemble prediction system show that the NWP model tuning capacity of EPPES scales up to realistic models and ensemble prediction systems. Finally, a global top-end NWP model tuning exercise with preliminary results is published.

Large-area settlement pattern recognition from Landsat-8 data

NASA Astrophysics Data System (ADS)

Wieland, Marc; Pittore, Massimiliano

2016-09-01

The study presents an image processing and analysis pipeline that combines object-based image analysis with a Support Vector Machine to derive a multi-layered settlement product from Landsat-8 data over large areas. 43 image scenes are processed over large parts of Central Asia (Southern Kazakhstan, Kyrgyzstan, Tajikistan and Eastern Uzbekistan). The main tasks tackled by this work include built-up area identification, settlement type classification and urban structure types pattern recognition. Besides commonly used accuracy assessments of the resulting map products, thorough performance evaluations are carried out under varying conditions to tune algorithm parameters and assess their applicability for the given tasks. As part of this, several research questions are being addressed. In particular the influence of the improved spatial and spectral resolution of Landsat-8 on the SVM performance to identify built-up areas and urban structure types are evaluated. Also the influence of an extended feature space including digital elevation model features is tested for mountainous regions. Moreover, the spatial distribution of classification uncertainties is analyzed and compared to the heterogeneity of the building stock within the computational unit of the segments. The study concludes that the information content of Landsat-8 images is sufficient for the tested classification tasks and even detailed urban structures could be extracted with satisfying accuracy. Freely available ancillary settlement point location data could further improve the built-up area classification. Digital elevation features and pan-sharpening could, however, not significantly improve the classification results. The study highlights the importance of dynamically tuned classifier parameters, and underlines the use of Shannon entropy computed from the soft answers of the SVM as a valid measure of the spatial distribution of classification uncertainties.

New vector-like fermions and flavor physics

DOE PAGES

Ishiwata, Koji; Ligeti, Zoltan; Wise, Mark B.

2015-10-06

We study renormalizable extensions of the standard model that contain vector-like fermions in a (single) complex representation of the standard model gauge group. There are 11 models where the vector-like fermions Yukawa couple to the standard model fermions via the Higgs field. These models do not introduce additional fine-tunings. They can lead to, and are constrained by, a number of different flavor-changing processes involving leptons and quarks, as well as direct searches. An interesting feature of the models with strongly interacting vector-like fermions is that constraints from neutral meson mixings (apart from CP violation inmore » $$ {K}^0-{\\overline{K}}^0 $$ mixing) are not sensitive to higher scales than other flavor-changing neutral-current processes. We identify order 1/(4πM) 2 (where M is the vector-like fermion mass) one-loop contributions to the coefficients of the four-quark operators for meson mixing, that are not suppressed by standard model quark masses and/or mixing angles.« less

Valence electronic structure of cobalt phthalocyanine from an optimally tuned range-separated hybrid functional.

PubMed

Brumboiu, Iulia Emilia; Prokopiou, Georgia; Kronik, Leeor; Brena, Barbara

2017-07-28

We analyse the valence electronic structure of cobalt phthalocyanine (CoPc) by means of optimally tuning a range-separated hybrid functional. The tuning is performed by modifying both the amount of short-range exact exchange (α) included in the hybrid functional and the range-separation parameter (γ), with two strategies employed for finding the optimal γ for each α. The influence of these two parameters on the structural, electronic, and magnetic properties of CoPc is thoroughly investigated. The electronic structure is found to be very sensitive to the amount and range in which the exact exchange is included. The electronic structure obtained using the optimal parameters is compared to gas-phase photo-electron data and GW calculations, with the unoccupied states additionally compared with inverse photo-electron spectroscopy measurements. The calculated spectrum with tuned γ, determined for the optimal value of α = 0.1, yields a very good agreement with both experimental results and with GW calculations that well-reproduce the experimental data.

Parametrization of Combined Quantum Mechanical and Molecular Mechanical Methods: Bond-Tuned Link Atoms.

PubMed

Wu, Xin-Ping; Gagliardi, Laura; Truhlar, Donald G

2018-05-30

Combined quantum mechanical and molecular mechanical (QM/MM) methods are the most powerful available methods for high-level treatments of subsystems of very large systems. The treatment of the QM-MM boundary strongly affects the accuracy of QM/MM calculations. For QM/MM calculations having covalent bonds cut by the QM-MM boundary, it has been proposed previously to use a scheme with system-specific tuned fluorine link atoms. Here, we propose a broadly parametrized scheme where the parameters of the tuned F link atoms depend only on the type of bond being cut. In the proposed new scheme, the F link atom is tuned for systems with a certain type of cut bond at the QM-MM boundary instead of for a specific target system, and the resulting link atoms are call bond-tuned link atoms. In principle, the bond-tuned link atoms can be as convenient as the popular H link atoms, and they are especially well adapted for high-throughput and accurate QM/MM calculations. Here, we present the parameters for several kinds of cut bonds along with a set of validation calculations that confirm that the proposed bond-tuned link-atom scheme can be as accurate as the system-specific tuned F link-atom scheme.

Experiments on Adaptive Self-Tuning of Seismic Signal Detector Parameters

NASA Astrophysics Data System (ADS)

Knox, H. A.; Draelos, T.; Young, C. J.; Chael, E. P.; Peterson, M. G.; Lawry, B.; Phillips-Alonge, K. E.; Balch, R. S.; Ziegler, A.

2016-12-01

Scientific applications, including underground nuclear test monitoring and microseismic monitoring can benefit enormously from data-driven dynamic algorithms for tuning seismic and infrasound signal detection parameters since continuous streams are producing waveform archives on the order of 1TB per month. Tuning is a challenge because there are a large number of data processing parameters that interact in complex ways, and because the underlying populating of true signal detections is generally unknown. The largely manual process of identifying effective parameters, often performed only over a subset of stations over a short time period, is painstaking and does not guarantee that the resulting controls are the optimal configuration settings. We present improvements to an Adaptive Self-Tuning algorithm for continuously adjusting detection parameters based on consistency with neighboring sensors. Results are shown for 1) data from a very dense network ( 120 stations, 10 km radius) deployed during 2008 on Erebus Volcano, Antarctica, and 2) data from a continuous downhole seismic array in the Farnsworth Field, an oil field in Northern Texas that hosts an ongoing carbon capture, utilization, and storage project. Performance is assessed in terms of missed detections and false detections relative to human analyst detections, simulated waveforms where ground-truth detections exist and visual inspection.

Inverse optimal self-tuning PID control design for an autonomous underwater vehicle

NASA Astrophysics Data System (ADS)

Rout, Raja; Subudhi, Bidyadhar

2017-01-01

This paper presents a new approach to path following control design for an autonomous underwater vehicle (AUV). A NARMAX model of the AUV is derived first and then its parameters are adapted online using the recursive extended least square algorithm. An adaptive Propotional-Integral-Derivative (PID) controller is developed using the derived parameters to accomplish the path following task of an AUV. The gain parameters of the PID controller are tuned using an inverse optimal control technique, which alleviates the problem of solving Hamilton-Jacobian equation and also satisfies an error cost function. Simulation studies were pursued to verify the efficacy of the proposed control algorithm. From the obtained results, it is envisaged that the proposed NARMAX model-based self-tuning adaptive PID control provides good path following performance even in the presence of uncertainty arising due to ocean current or hydrodynamic parameter.

Optimum Parameters of a Tuned Liquid Column Damper in a Wind Turbine Subject to Stochastic Load

NASA Astrophysics Data System (ADS)

Alkmim, M. H.; de Morais, M. V. G.; Fabro, A. T.

2017-12-01

Parameter optimization for tuned liquid column dampers (TLCD), a class of passive structural control, have been previously proposed in the literature for reducing vibration in wind turbines, and several other applications. However, most of the available work consider the wind excitation as either a deterministic harmonic load or random load with white noise spectra. In this paper, a global direct search optimization algorithm to reduce vibration of a tuned liquid column damper (TLCD), a class of passive structural control device, is presented. The objective is to find optimized parameters for the TLCD under stochastic load from different wind power spectral density. A verification is made considering the analytical solution of undamped primary system under white noise excitation by comparing with result from the literature. Finally, it is shown that different wind profiles can significantly affect the optimum TLCD parameters.

High-volume image quality assessment systems: tuning performance with an interactive data visualization tool

NASA Astrophysics Data System (ADS)

Bresnahan, Patricia A.; Pukinskis, Madeleine; Wiggins, Michael

1999-03-01

Image quality assessment systems differ greatly with respect to the number and types of mags they need to evaluate, and their overall architectures. Managers of these systems, however, all need to be able to tune and evaluate system performance, requirements often overlooked or under-designed during project planning. Performance tuning tools allow users to define acceptable quality standards for image features and attributes by adjusting parameter settings. Performance analysis tools allow users to evaluate and/or predict how well a system performs in a given parameter state. While image assessment algorithms are becoming quite sophisticated, duplicating or surpassing the human decision making process in their speed and reliability, they often require a greater investment in 'training' or fine tuning of parameters in order to achieve optimum performance. This process may involve the analysis of hundreds or thousands of images, generating a large database of files and statistics that can be difficult to sort through and interpret. Compounding the difficulty is the fact that personnel charged with tuning and maintaining the production system may not have the statistical or analytical background required for the task. Meanwhile, hardware innovations have greatly increased the volume of images that can be handled in a given time frame, magnifying the consequences of running a production site with an inadequately tuned system. In this paper, some general requirements for a performance evaluation and tuning data visualization system are discussed. A custom engineered solution to the tuning and evaluation problem is then presented, developed within the context of a high volume image quality assessment, data entry, OCR, and image archival system. A key factor influencing the design of the system was the context-dependent definition of image quality, as perceived by a human interpreter. This led to the development of a five-level, hierarchical approach to image quality evaluation. Lower-level pass-fail conditions and decision rules were coded into the system. Higher-level image quality states were defined by allowing the users to interactively adjust the system's sensitivity to various image attributes by manipulating graphical controls. Results were presented in easily interpreted bar graphs. These graphs were mouse- sensitive, allowing the user to more fully explore the subsets of data indicated by various color blocks. In order to simplify the performance evaluation and tuning process, users could choose to view the results of (1) the existing system parameter state, (2) the results of any arbitrary parameter values they chose, or (3) the results of a quasi-optimum parameter state, derived by applying a decision rule to a large set of possible parameter states. Giving managers easy- to-use tools for defining the more subjective aspects of quality resulted in a system that responded to contextual cues that are difficult to hard-code. It had the additional advantage of allowing the definition of quality to evolve over time, as users became more knowledgeable as to the strengths and limitations of an automated quality inspection system.

Parameterization of an interfacial force field for accurate representation of peptide adsorption free energy on high-density polyethylene

PubMed Central

Abramyan, Tigran M.; Snyder, James A.; Yancey, Jeremy A.; Thyparambil, Aby A.; Wei, Yang; Stuart, Steven J.; Latour, Robert A.

2015-01-01

Interfacial force field (IFF) parameters for use with the CHARMM force field have been developed for interactions between peptides and high-density polyethylene (HDPE). Parameterization of the IFF was performed to achieve agreement between experimental and calculated adsorption free energies of small TGTG–X–GTGT host–guest peptides (T = threonine, G = glycine, and X = variable amino-acid residue) on HDPE, with ±0.5 kcal/mol agreement. This IFF parameter set consists of tuned nonbonded parameters (i.e., partial charges and Lennard–Jones parameters) for use with an in-house-modified CHARMM molecular dynamic program that enables the use of an independent set of force field parameters to control molecular behavior at a solid–liquid interface. The R correlation coefficient between the simulated and experimental peptide adsorption free energies increased from 0.00 for the standard CHARMM force field parameters to 0.88 for the tuned IFF parameters. Subsequent studies are planned to apply the tuned IFF parameter set for the simulation of protein adsorption behavior on an HDPE surface for comparison with experimental values of adsorbed protein orientation and conformation. PMID:25818122

Vector magnetic fields in sunspots. I - Stokes profile analysis using the Marshall Space Flight Center magnetograph

NASA Technical Reports Server (NTRS)

Balasubramaniam, K. S.; West, E. A.

1991-01-01

The Marshall Space Flight Center (MSFC) vector magnetograph is a tunable filter magnetograph with a bandpass of 125 mA. Results are presented of the inversion of Stokes polarization profiles observed with the MSFC vector magnetograph centered on a sunspot to recover the vector magnetic field parameters and thermodynamic parameters of the spectral line forming region using the Fe I 5250.2 A spectral line using a nonlinear least-squares fitting technique. As a preliminary investigation, it is also shown that the recovered thermodynamic parameters could be better understood if the fitted parameters like Doppler width, opacity ratio, and damping constant were broken down into more basic quantities like temperature, microturbulent velocity, or density parameter.

Elements of the quality management in the materials' industry

NASA Astrophysics Data System (ADS)

Ioana, Adrian; Semenescu, Augustin; Costoiu, Mihnea; Marcu, Dragoş

2017-12-01

The criteria function concept consists of transforming the criteria function (CF) in a quality-economical matrix math MQE. The levels of prescribing the criteria function was obtained by using a composition algorithm for three vectors: T¯ vector - technical parameters' vector (ti); Ē vector - economical parameters' vector (ej) and P¯ vector - weight vector (p1). For each product or service, the area of the circle represents the value of its sales. The BCG Matrix thus offers a very useful map of the organization's service strengths and weaknesses, at least in terms of current profitability, as well as the likely cash flows.

The use of Argo for validation and tuning of mixed layer models

NASA Astrophysics Data System (ADS)

Acreman, D. M.; Jeffery, C. D.

We present results from validation and tuning of 1-D ocean mixed layer models using data from Argo floats and data from Ocean Weather Station Papa (145°W, 50°N). Model tests at Ocean Weather Station Papa showed that a bulk model could perform well provided it was tuned correctly. The Large et al. [Large, W.G., McWilliams, J.C., Doney, S.C., 1994. Oceanic vertical mixing: a review and a model with a nonlocal boundary layer parameterisation. Rev. Geophys. 32 (Novermber), 363-403] K-profile parameterisation (KPP) model also gave a good representation of mixed layer depth provided the vertical resolution was sufficiently high. Model tests using data from a single Argo float indicated a tendency for the KPP model to deepen insufficiently over an annual cycle, whereas the tuned bulk model and general ocean turbulence model (GOTM) gave a better representation of mixed layer depth. The bulk model was then tuned using data from a sample of Argo floats and a set of optimum parameters was found; these optimum parameters were consistent with the tuning at OWS Papa.

Word-level recognition of multifont Arabic text using a feature vector matching approach

NASA Astrophysics Data System (ADS)

Erlandson, Erik J.; Trenkle, John M.; Vogt, Robert C., III

1996-03-01

Many text recognition systems recognize text imagery at the character level and assemble words from the recognized characters. An alternative approach is to recognize text imagery at the word level, without analyzing individual characters. This approach avoids the problem of individual character segmentation, and can overcome local errors in character recognition. A word-level recognition system for machine-printed Arabic text has been implemented. Arabic is a script language, and is therefore difficult to segment at the character level. Character segmentation has been avoided by recognizing text imagery of complete words. The Arabic recognition system computes a vector of image-morphological features on a query word image. This vector is matched against a precomputed database of vectors from a lexicon of Arabic words. Vectors from the database with the highest match score are returned as hypotheses for the unknown image. Several feature vectors may be stored for each word in the database. Database feature vectors generated using multiple fonts and noise models allow the system to be tuned to its input stream. Used in conjunction with database pruning techniques, this Arabic recognition system has obtained promising word recognition rates on low-quality multifont text imagery.

Self-tuning control algorithm design for vehicle adaptive cruise control system through real-time estimation of vehicle parameters and road grade

NASA Astrophysics Data System (ADS)

Marzbanrad, Javad; Tahbaz-zadeh Moghaddam, Iman

2016-09-01

The main purpose of this paper is to design a self-tuning control algorithm for an adaptive cruise control (ACC) system that can adapt its behaviour to variations of vehicle dynamics and uncertain road grade. To this aim, short-time linear quadratic form (STLQF) estimation technique is developed so as to track simultaneously the trend of the time-varying parameters of vehicle longitudinal dynamics with a small delay. These parameters are vehicle mass, road grade and aerodynamic drag-area coefficient. Next, the values of estimated parameters are used to tune the throttle and brake control inputs and to regulate the throttle/brake switching logic that governs the throttle and brake switching. The performance of the designed STLQF-based self-tuning control (STLQF-STC) algorithm for ACC system is compared with the conventional method based on fixed control structure regarding the speed/distance tracking control modes. Simulation results show that the proposed control algorithm improves the performance of throttle and brake controllers, providing more comfort while travelling, enhancing driving safety and giving a satisfactory performance in the presence of different payloads and road grade variations.

Parameter meta-optimization of metaheuristics of solving specific NP-hard facility location problem

NASA Astrophysics Data System (ADS)

Skakov, E. S.; Malysh, V. N.

2018-03-01

The aim of the work is to create an evolutionary method for optimizing the values of the control parameters of metaheuristics of solving the NP-hard facility location problem. A system analysis of the tuning process of optimization algorithms parameters is carried out. The problem of finding the parameters of a metaheuristic algorithm is formulated as a meta-optimization problem. Evolutionary metaheuristic has been chosen to perform the task of meta-optimization. Thus, the approach proposed in this work can be called “meta-metaheuristic”. Computational experiment proving the effectiveness of the procedure of tuning the control parameters of metaheuristics has been performed.

Vectoring of parallel synthetic jets: A parametric study

NASA Astrophysics Data System (ADS)

Berk, Tim; Gomit, Guillaume; Ganapathisubramani, Bharathram

2016-11-01

The vectoring of a pair of parallel synthetic jets can be described using five dimensionless parameters: the aspect ratio of the slots, the Strouhal number, the Reynolds number, the phase difference between the jets and the spacing between the slots. In the present study, the influence of the latter four on the vectoring behaviour of the jets is examined experimentally using particle image velocimetry. Time-averaged velocity maps are used to study the variations in vectoring behaviour for a parametric sweep of each of the four parameters independently. A topological map is constructed for the full four-dimensional parameter space. The vectoring behaviour is described both qualitatively and quantitatively. A vectoring mechanism is proposed, based on measured vortex positions. We acknowledge the financial support from the European Research Council (ERC Grant Agreement No. 277472).

AOF LTAO mode: reconstruction strategy and first test results

NASA Astrophysics Data System (ADS)

Oberti, Sylvain; Kolb, Johann; Le Louarn, Miska; La Penna, Paolo; Madec, Pierre-Yves; Neichel, Benoit; Sauvage, Jean-François; Fusco, Thierry; Donaldson, Robert; Soenke, Christian; Suárez Valles, Marcos; Arsenault, Robin

2016-07-01

GALACSI is the Adaptive Optics (AO) system serving the instrument MUSE in the framework of the Adaptive Optics Facility (AOF) project. Its Narrow Field Mode (NFM) is a Laser Tomography AO (LTAO) mode delivering high resolution in the visible across a small Field of View (FoV) of 7.5" diameter around the optical axis. From a reconstruction standpoint, GALACSI NFM intends to optimize the correction on axis by estimating the turbulence in volume via a tomographic process, then projecting the turbulence profile onto one single Deformable Mirror (DM) located in the pupil, close to the ground. In this paper, the laser tomographic reconstruction process is described. Several methods (virtual DM, virtual layer projection) are studied, under the constraint of a single matrix vector multiplication. The pseudo-synthetic interaction matrix model and the LTAO reconstructor design are analysed. Moreover, the reconstruction parameter space is explored, in particular the regularization terms. Furthermore, we present here the strategy to define the modal control basis and split the reconstruction between the Low Order (LO) loop and the High Order (HO) loop. Finally, closed loop performance obtained with a 3D turbulence generator will be analysed with respect to the most relevant system parameters to be tuned.

Nonlinear adaptive control of grid-connected three-phase inverters for renewable energy applications

NASA Astrophysics Data System (ADS)

Mahdian-Dehkordi, N.; Namvar, M.; Karimi, H.; Piya, P.; Karimi-Ghartemani, M.

2017-01-01

Distributed generation (DG) units are often interfaced to the main grid using power electronic converters including voltage-source converters (VSCs). A VSC offers dc/ac power conversion, high controllability, and fast dynamic response. Because of nonlinearities, uncertainties, and system parameters' changes involved in the nature of a grid-connected renewable DG system, conventional linear control methods cannot completely and efficiently address all control objectives. In this paper, a nonlinear adaptive control scheme based on adaptive backstepping strategy is presented to control the operation of a grid-connected renewable DG unit. As compared to the popular vector control technique, the proposed controller offers smoother transient responses, and lower level of current distortions. The Lyapunov approach is used to establish global asymptotic stability of the proposed control system. Linearisation technique is employed to develop guidelines for parameters tuning of the controller. Extensive time-domain digital simulations are performed and presented to verify the performance of the proposed controller when employed in a VSC to control the operation of a two-stage DG unit and also that of a single-stage solar photovoltaic system. Desirable and superior performance of the proposed controller is observed.

Multi-Objective Control Optimization for Greenhouse Environment Using Evolutionary Algorithms

PubMed Central

Hu, Haigen; Xu, Lihong; Wei, Ruihua; Zhu, Bingkun

2011-01-01

This paper investigates the issue of tuning the Proportional Integral and Derivative (PID) controller parameters for a greenhouse climate control system using an Evolutionary Algorithm (EA) based on multiple performance measures such as good static-dynamic performance specifications and the smooth process of control. A model of nonlinear thermodynamic laws between numerous system variables affecting the greenhouse climate is formulated. The proposed tuning scheme is tested for greenhouse climate control by minimizing the integrated time square error (ITSE) and the control increment or rate in a simulation experiment. The results show that by tuning the gain parameters the controllers can achieve good control performance through step responses such as small overshoot, fast settling time, and less rise time and steady state error. Besides, it can be applied to tuning the system with different properties, such as strong interactions among variables, nonlinearities and conflicting performance criteria. The results implicate that it is a quite effective and promising tuning method using multi-objective optimization algorithms in the complex greenhouse production. PMID:22163927

Stochastic control system parameter identifiability

NASA Technical Reports Server (NTRS)

Lee, C. H.; Herget, C. J.

1975-01-01

The parameter identification problem of general discrete time, nonlinear, multiple input/multiple output dynamic systems with Gaussian white distributed measurement errors is considered. The knowledge of the system parameterization was assumed to be known. Concepts of local parameter identifiability and local constrained maximum likelihood parameter identifiability were established. A set of sufficient conditions for the existence of a region of parameter identifiability was derived. A computation procedure employing interval arithmetic was provided for finding the regions of parameter identifiability. If the vector of the true parameters is locally constrained maximum likelihood (CML) identifiable, then with probability one, the vector of true parameters is a unique maximal point of the maximum likelihood function in the region of parameter identifiability and the constrained maximum likelihood estimation sequence will converge to the vector of true parameters.

Parameter tuning method for dither compensation of a pneumatic proportional valve with friction

NASA Astrophysics Data System (ADS)

Wang, Tao; Song, Yang; Huang, Leisheng; Fan, Wei

2016-05-01

In the practical application of pneumatic control devices, the nonlinearity of a pneumatic control valve become the main factor affecting the control effect, which comes mainly from the dynamic friction force. The dynamic friction inside the valve may cause hysteresis and a dead zone. In this paper, a dither compensation mechanism is proposed to reduce negative effects on the basis of analyzing the mechanism of friction force. The specific dither signal (using a sinusoidal signal) was superimposed on the control signal of the valve. Based on the relationship between the parameters of the dither signal and the inherent characteristics of the proportional servo valve, a parameter tuning method was proposed, which uses a displacement sensor to measure the maximum static friction inside the valve. According to the experimental results, the proper amplitude ranges are determined for different pressures. In order to get the optimal parameters of the dither signal, some dither compensation experiments have been carried out on different signal amplitude and gas pressure conditions. Optimal parameters are determined under two kinds of pressure conditions. Using tuning parameters the valve spool displacement experiment has been taken. From the experiment results, hysteresis of the proportional servo valve is significantly reduced. And through simulation and experiments, the cut-off frequency of the proportional valve has also been widened. Therefore after adding the dither signal, the static and dynamic characteristics of the proportional valve are both improved to a certain degree. This research proposes a parameter tuning method of dither signal, and the validity of the method is verified experimentally.

Dual Extended Kalman Filter for the Identification of Time-Varying Human Manual Control Behavior

NASA Technical Reports Server (NTRS)

Popovici, Alexandru; Zaal, Peter M. T.; Pool, Daan M.

2017-01-01

A Dual Extended Kalman Filter was implemented for the identification of time-varying human manual control behavior. Two filters that run concurrently were used, a state filter that estimates the equalization dynamics, and a parameter filter that estimates the neuromuscular parameters and time delay. Time-varying parameters were modeled as a random walk. The filter successfully estimated time-varying human control behavior in both simulated and experimental data. Simple guidelines are proposed for the tuning of the process and measurement covariance matrices and the initial parameter estimates. The tuning was performed on simulation data, and when applied on experimental data, only an increase in measurement process noise power was required in order for the filter to converge and estimate all parameters. A sensitivity analysis to initial parameter estimates showed that the filter is more sensitive to poor initial choices of neuromuscular parameters than equalization parameters, and bad choices for initial parameters can result in divergence, slow convergence, or parameter estimates that do not have a real physical interpretation. The promising results when applied to experimental data, together with its simple tuning and low dimension of the state-space, make the use of the Dual Extended Kalman Filter a viable option for identifying time-varying human control parameters in manual tracking tasks, which could be used in real-time human state monitoring and adaptive human-vehicle haptic interfaces.

[Spectral Study on the Effects of Angle-Tuned Filter Wedge Angle Parameter to Reflecting Characteristics].

PubMed

Yu, Kan; Huang, De-xiu; Yin, Juan-juan; Bao, Jia-qi

2015-08-01

Three-port tunable optical filter is a key device in the all-optic intelligent switching network and dense wavelength division multiplexing system. The characteristics of the reflecting spectrum, especially the reflectivity and the isolation degree are very important to the three-port filter. Angle-tuned thin film filter is widely used as a three-port tunable filter for its high rectangular degree and good temperature stability. The characteristics of the reflecting spectrum are greatly influenced not only by the incident angle, but also by the wedge angle parameter of the non-paralleled wedge thin film filter. In the present paper, the influences of the wedge angle parameter to the reflectivity and the half bandwidth are analyzed, and the reflecting spectrum characterstics are simulationed in different wedge angle parameter and polarity. The wedge angle-tuned thin film filter with 0.8° wedge angle parameter is fabricated. The experimental results show that keeping the wedge angle the same orientation to the incident angle will worsen the reflectivity and the rectangular degree of the reflecting spectrum. However, keeping the wedge angle orientation reverse to the incident angle will enhance the reflectivity and decrease the bandwidth, which will give higher reflectivity and isolation degree to the three-port filter than that of high parallel degree angle-tuned thin film filter.

Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar.

PubMed

Lomp, Oliver; Richter, Mathis; Zibner, Stephan K U; Schöner, Gregor

2016-01-01

Embodied artificial cognitive systems, such as autonomous robots or intelligent observers, connect cognitive processes to sensory and effector systems in real time. Prime candidates for such embodied intelligence are neurally inspired architectures. While components such as forward neural networks are well established, designing pervasively autonomous neural architectures remains a challenge. This includes the problem of tuning the parameters of such architectures so that they deliver specified functionality under variable environmental conditions and retain these functions as the architectures are expanded. The scaling and autonomy problems are solved, in part, by dynamic field theory (DFT), a theoretical framework for the neural grounding of sensorimotor and cognitive processes. In this paper, we address how to efficiently build DFT architectures that control embodied agents and how to tune their parameters so that the desired cognitive functions emerge while such agents are situated in real environments. In DFT architectures, dynamic neural fields or nodes are assigned dynamic regimes, that is, attractor states and their instabilities, from which cognitive function emerges. Tuning thus amounts to determining values of the dynamic parameters for which the components of a DFT architecture are in the specified dynamic regime under the appropriate environmental conditions. The process of tuning is facilitated by the software framework cedar , which provides a graphical interface to build and execute DFT architectures. It enables to change dynamic parameters online and visualize the activation states of any component while the agent is receiving sensory inputs in real time. Using a simple example, we take the reader through the workflow of conceiving of DFT architectures, implementing them on embodied agents, tuning their parameters, and assessing performance while the system is coupled to real sensory inputs.

Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar

PubMed Central

Lomp, Oliver; Richter, Mathis; Zibner, Stephan K. U.; Schöner, Gregor

2016-01-01

Embodied artificial cognitive systems, such as autonomous robots or intelligent observers, connect cognitive processes to sensory and effector systems in real time. Prime candidates for such embodied intelligence are neurally inspired architectures. While components such as forward neural networks are well established, designing pervasively autonomous neural architectures remains a challenge. This includes the problem of tuning the parameters of such architectures so that they deliver specified functionality under variable environmental conditions and retain these functions as the architectures are expanded. The scaling and autonomy problems are solved, in part, by dynamic field theory (DFT), a theoretical framework for the neural grounding of sensorimotor and cognitive processes. In this paper, we address how to efficiently build DFT architectures that control embodied agents and how to tune their parameters so that the desired cognitive functions emerge while such agents are situated in real environments. In DFT architectures, dynamic neural fields or nodes are assigned dynamic regimes, that is, attractor states and their instabilities, from which cognitive function emerges. Tuning thus amounts to determining values of the dynamic parameters for which the components of a DFT architecture are in the specified dynamic regime under the appropriate environmental conditions. The process of tuning is facilitated by the software framework cedar, which provides a graphical interface to build and execute DFT architectures. It enables to change dynamic parameters online and visualize the activation states of any component while the agent is receiving sensory inputs in real time. Using a simple example, we take the reader through the workflow of conceiving of DFT architectures, implementing them on embodied agents, tuning their parameters, and assessing performance while the system is coupled to real sensory inputs. PMID:27853431

PERI - Auto-tuning Memory Intensive Kernels for Multicore

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

Bailey, David H; Williams, Samuel; Datta, Kaushik

2008-06-24

We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to Sparse Matrix Vector Multiplication (SpMV), the explicit heat equation PDE on a regular grid (Stencil), and a lattice Boltzmann application (LBMHD). We explore one of the broadest sets of multicore architectures in the HPC literature, including the Intel Xeon Clovertown, AMD Opteron Barcelona, Sun Victoria Falls, and the Sony-Toshiba-IBM (STI) Cell. Rather than hand-tuning each kernel for each system, we developmore » a code generator for each kernel that allows us to identify a highly optimized version for each platform, while amortizing the human programming effort. Results show that our auto-tuned kernel applications often achieve a better than 4X improvement compared with the original code. Additionally, we analyze a Roofline performance model for each platform to reveal hardware bottlenecks and software challenges for future multicore systems and applications.« less

Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

PubMed

Hussain, Lal

2018-06-01

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

Application of genetic algorithms to tuning fuzzy control systems

NASA Technical Reports Server (NTRS)

Espy, Todd; Vombrack, Endre; Aldridge, Jack

1993-01-01

Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.

PSO algorithm enhanced with Lozi Chaotic Map - Tuning experiment

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

Pluhacek, Michal; Senkerik, Roman; Zelinka, Ivan

2015-03-10

In this paper it is investigated the effect of tuning of control parameters of the Lozi Chaotic Map employed as a chaotic pseudo-random number generator for the particle swarm optimization algorithm. Three different benchmark functions are selected from the IEEE CEC 2013 competition benchmark set. The Lozi map is extensively tuned and the performance of PSO is evaluated.

A case study of tuning MapReduce for efficient Bioinformatics in the cloud

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

Shi, Lizhen; Wang, Zhong; Yu, Weikuan

The combination of the Hadoop MapReduce programming model and cloud computing allows biological scientists to analyze next-generation sequencing (NGS) data in a timely and cost-effective manner. Cloud computing platforms remove the burden of IT facility procurement and management from end users and provide ease of access to Hadoop clusters. However, biological scientists are still expected to choose appropriate Hadoop parameters for running their jobs. More importantly, the available Hadoop tuning guidelines are either obsolete or too general to capture the particular characteristics of bioinformatics applications. In this paper, we aim to minimize the cloud computing cost spent on bioinformatics datamore » analysis by optimizing the extracted significant Hadoop parameters. When using MapReduce-based bioinformatics tools in the cloud, the default settings often lead to resource underutilization and wasteful expenses. We choose k-mer counting, a representative application used in a large number of NGS data analysis tools, as our study case. Experimental results show that, with the fine-tuned parameters, we achieve a total of 4× speedup compared with the original performance (using the default settings). Finally, this paper presents an exemplary case for tuning MapReduce-based bioinformatics applications in the cloud, and documents the key parameters that could lead to significant performance benefits.« less

Compute Server Performance Results

NASA Technical Reports Server (NTRS)

Stockdale, I. E.; Barton, John; Woodrow, Thomas (Technical Monitor)

1994-01-01

Parallel-vector supercomputers have been the workhorses of high performance computing. As expectations of future computing needs have risen faster than projected vector supercomputer performance, much work has been done investigating the feasibility of using Massively Parallel Processor systems as supercomputers. An even more recent development is the availability of high performance workstations which have the potential, when clustered together, to replace parallel-vector systems. We present a systematic comparison of floating point performance and price-performance for various compute server systems. A suite of highly vectorized programs was run on systems including traditional vector systems such as the Cray C90, and RISC workstations such as the IBM RS/6000 590 and the SGI R8000. The C90 system delivers 460 million floating point operations per second (FLOPS), the highest single processor rate of any vendor. However, if the price-performance ration (PPR) is considered to be most important, then the IBM and SGI processors are superior to the C90 processors. Even without code tuning, the IBM and SGI PPR's of 260 and 220 FLOPS per dollar exceed the C90 PPR of 160 FLOPS per dollar when running our highly vectorized suite,

Widely Tunable Mode-Hop-Free External-Cavity Quantum Cascade Laser

NASA Technical Reports Server (NTRS)

Wysocki, Gerard; Curl, Robert F.; Tittel, Frank K.

2010-01-01

The external-cavity quantum cascade laser (EC-QCL) system is based on an optical configuration of the Littrow type. It is a room-temperature, continuous wave, widely tunable, mode-hop-free, mid-infrared, EC-QCL spectroscopic source. It has a single-mode tuning range of 155 cm(exp -1) (approximately equal to 8% of the center wavelength) with a maximum power of 11.1 mW and 182 cm(exp -1) (approximately equal to 15% of the center wavelength), and a maximum power of 50 mW as demonstrated for 5.3 micron and 8.4 micron EC-QCLs, respectively. This technology is particularly suitable for high-resolution spectroscopic applications, multi-species tracegas detection, and spectroscopic measurements of broadband absorbers. Wavelength tuning of EC-QCL spectroscopic source can be implemented by varying three independent parameters of the laser: (1) the optical length of the gain medium (which, in this case, is equivalent to QCL injection current modulation), (2) the length of the EC (which can be independently varied in the Rice EC-QCL setup), and (3) the angle of beam incidence at the diffraction grating (frequency tuning related directly to angular dispersion of the grating). All three mechanisms of frequency tuning have been demonstrated and are required to obtain a true mode-hop-free laser frequency tuning. The precise frequency tuning characteristics of the EC-QCL output have been characterized using a variety of diagnostic tools available at Rice University (e.g., a monochromator, FTIR spectrometer, and a Fabry-Perot spectrometer). Spectroscopic results were compared with available databases (such as HITRAN, PNNL, EPA, and NIST). These enable precision verification of complete spectral parameters of the EC-QCL, such as wavelength, tuning range, tuning characteristics, and line width. The output power of the EC-QCL is determined by the performance of the QC laser chip, its operating conditions, and parameters of the QC laser cavity such as mirror reflectivity or intracavity losses. In order to maximize the output power, an analysis and optimization of the EC laser parameters has been performed. The parameters of the beam emitted from the gain medium, such as divergence angle, beam profile, and astigmatism, have been investigated. The gain medium has been fully characterized before and after each stage of modification. The main modification steps are coating one facet of the gain chip with a high reflectivity mirror and the other facet with an anti-reflection layer. Then the chip is mounted in the EC-QCL. The optomechanical design has been reviewed and improved to provide for precise collimation of the strongly divergent beam of the QCL and the tuning diffraction grating.

Gene/protein name recognition based on support vector machine using dictionary as features.

PubMed

Mitsumori, Tomohiro; Fation, Sevrani; Murata, Masaki; Doi, Kouichi; Doi, Hirohumi

2005-01-01

Automated information extraction from biomedical literature is important because a vast amount of biomedical literature has been published. Recognition of the biomedical named entities is the first step in information extraction. We developed an automated recognition system based on the SVM algorithm and evaluated it in Task 1.A of BioCreAtIvE, a competition for automated gene/protein name recognition. In the work presented here, our recognition system uses the feature set of the word, the part-of-speech (POS), the orthography, the prefix, the suffix, and the preceding class. We call these features "internal resource features", i.e., features that can be found in the training data. Additionally, we consider the features of matching against dictionaries to be external resource features. We investigated and evaluated the effect of these features as well as the effect of tuning the parameters of the SVM algorithm. We found that the dictionary matching features contributed slightly to the improvement in the performance of the f-score. We attribute this to the possibility that the dictionary matching features might overlap with other features in the current multiple feature setting. During SVM learning, each feature alone had a marginally positive effect on system performance. This supports the fact that the SVM algorithm is robust on the high dimensionality of the feature vector space and means that feature selection is not required.

Tuning Features of Chinese Folk Song Singing: A Case Study of Hua'er Music.

PubMed

Yang, Yang; Welch, Graham; Sundberg, Johan; Himonides, Evangelos

2015-07-01

The learning and teaching of different singing styles, such as operatic and Chinese folk singing, was often found to be very challenging in professional music education because of the complexity of varied musical properties and vocalizations. By studying the acoustical and musical parameters of the singing voice, this study identified distinctive tuning characteristics of a particular folk music in China-Hua'er music-to inform the ineffective folk singing practices, which were hampered by the neglect of inherent tuning issues in music. Thirteen unaccompanied folk song examples from four folk singers were digitally audio recorded in a sound studio. Using an analyzing toolkit consisting of Praat, PeakFit, and MS Excel, the fundamental frequencies (F0) of these song examples were extracted into sets of "anchor pitches" mostly used, which were further divided into 253 F0 clusters. The interval structures of anchor pitches within each song were analyzed and then compared across 13 examples providing parameters that indicate the tuning preference of this particular singing style. The data analyses demonstrated that all singers used a tuning pattern consisting of five major anchor pitches suggesting a nonequal-tempered bias in singing. This partly verified the pentatonic scale proposed in previous empirical research but also argued a potential misunderstanding of the studied folk music scale that failed to take intrinsic tuning issues into consideration. This study suggests that, in professional music training, any tuning strategy should be considered in terms of the reference pitch and likely tuning systems. Any accompanying instruments would need to be tuned to match the underlying tuning bias. Copyright © 2015 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Vector dark matter detection using the quantum jump of atoms

NASA Astrophysics Data System (ADS)

Yang, Qiaoli; Di, Haoran

2018-05-01

The hidden sector U(1) vector bosons created from inflationary fluctuations can be a substantial fraction of dark matter if their mass is around 10-5 eV. The creation mechanism makes the vector bosons' energy spectral density ρcdm / ΔE very high. Therefore, the dark electric dipole transition rate in atoms is boosted if the energy gap between atomic states equals the mass of the vector bosons. By using the Zeeman effect, the energy gap between the 2S state and the 2P state in hydrogen atoms or hydrogen like ions can be tuned. The 2S state can be populated with electrons due to its relatively long life, which is about 1/7 s. When the energy gap between the semi-ground 2S state and the 2P state matches the mass of the cosmic vector bosons, induced transitions occur and the 2P state subsequently decays into the 1S state. The 2 P → 1 S decay emitted Lyman-α photons can then be registered. The choices of target atoms depend on the experimental facilities and the mass ranges of the vector bosons. Because the mass of the vector boson is connected to the inflation scale, the proposed experiment may provide a probe to inflation.

Reducing variable frequency vibrations in a powertrain system with an adaptive tuned vibration absorber group

NASA Astrophysics Data System (ADS)

Gao, Pu; Xiang, Changle; Liu, Hui; Zhou, Han

2018-07-01

Based on a multiple degrees of freedom dynamic model of a vehicle powertrain system, natural vibration analyses and sensitivity analyses of the eigenvalues are performed to determine the key inertia for each natural vibration of a powertrain system. Then, the results are used to optimize the installation position of each adaptive tuned vibration absorber. According to the relationship between the variable frequency torque excitation and the natural vibration of a powertrain system, the entire vibration frequency band is divided into segments, and the auxiliary vibration absorber and dominant vibration absorber are determined for each sensitive frequency band. The optimum parameters of the auxiliary vibration absorber are calculated based on the optimal frequency ratio and the optimal damping ratio of the passive vibration absorber. The instantaneous change state of the natural vibrations of a powertrain system with adaptive tuned vibration absorbers is studied, and the optimized start and stop tuning frequencies of the adaptive tuned vibration absorber are obtained. These frequencies can be translated into the optimum parameters of the dominant vibration absorber. Finally, the optimal tuning scheme for the adaptive tuned vibration absorber group, which can be used to reduce the variable frequency vibrations of a powertrain system, is proposed, and corresponding numerical simulations are performed. The simulation time history signals are transformed into three-dimensional information related to time, frequency and vibration energy via the Hilbert-Huang transform (HHT). A comprehensive time-frequency analysis is then conducted to verify that the optimal tuning scheme for the adaptive tuned vibration absorber group can significantly reduce the variable frequency vibrations of a powertrain system.

A model of anuran auditory periphery reveals frequency-dependent adaptation to be a contributing mechanism for two-tone suppression and amplitude modulation coding.

PubMed

Wotton, J M; Ferragamo, M J

2011-10-01

Anuran auditory nerve fibers (ANF) tuned to low frequencies display unusual frequency-dependent adaptation which results in a more phasic response to signals above best frequency (BF) and a more tonic response to signals below. A network model of the first two layers of the anuran auditory system was used to test the contribution of this dynamic peripheral adaptation on two-tone suppression and amplitude modulation (AM) tuning. The model included a peripheral sandwich component, leaky-integrate-and-fire cells and adaptation was implemented by means of a non-linear increase in threshold weighted by the signal frequency. The results of simulations showed that frequency-dependent adaptation was both necessary and sufficient to produce high-frequency-side two-tone suppression for the ANF and cells of the dorsal medullary nucleus (DMN). It seems likely that both suppression and this dynamic adaptation share a common mechanism. The response of ANFs to AM signals was influenced by adaptation and carrier frequency. Vector strength synchronization to an AM signal improved with increased adaptation. The spike rate response to a carrier at BF was the expected flat function with AM rate. However, for non-BF carrier frequencies the response showed a weak band-pass pattern due to the influence of signal sidebands and adaptation. The DMN received inputs from three ANFs and when the frequency tuning of inputs was near the carrier, then the rate response was a low-pass or all-pass shape. When most of the inputs were biased above or below the carrier, then band-pass responses were observed. Frequency-dependent adaptation enhanced the band-pass tuning for AM rate, particularly when the response of the inputs was predominantly phasic for a given carrier. Different combinations of inputs can therefore bias a DMN cell to be especially well suited to detect specific ranges of AM rates for a particular carrier frequency. Such selection of inputs would clearly be advantageous to the frog in recognizing distinct spectral and temporal parameters in communication calls. Copyright © 2011 Elsevier B.V. All rights reserved.

Research on intrusion detection based on Kohonen network and support vector machine

NASA Astrophysics Data System (ADS)

Shuai, Chunyan; Yang, Hengcheng; Gong, Zeweiyi

2018-05-01

In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time. a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. The experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate.

Achievement of needle-like focus by engineering radial-variant vector fields.

PubMed

Gu, Bing; Wu, Jia-Lu; Pan, Yang; Cui, Yiping

2013-12-16

We present and demonstrate a novel method for engineering the radial-variant polarization on the incident field to achieve a needle of transversally polarized field without any pupil filters. We generate a new kind of localized linearly-polarized vector fields with distributions of states of polarization (SoPs) describing by the radius to the power p and explore its tight focusing, nonparaxial focusing, and paraxial focusing properties. By tuning the power p, we obtain the needle-like focal field with hybrid SoPs and give the formula for describing the length of the needle. Experimentally, we systematically investigate both the intensity distributions and the polarization evolution of the optical needle by paraxial focusing the generated vector field. Such an optical needle, which enhances the light-matter interaction, has intriguing applications in optical microma-chining and nonlinear optics.

Model Adaptation for Prognostics in a Particle Filtering Framework

NASA Technical Reports Server (NTRS)

Saha, Bhaskar; Goebel, Kai Frank

2011-01-01

One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.

Multichannel calculation of the very narrow Ds0 *(2317) and the very broad D0 *(2300-2400)

NASA Astrophysics Data System (ADS)

Rupp, G.; van Beveren, E.

2007-03-01

The narrow D s0 * (2317) and broad D 0 * (2300-2400) charmed scalar mesons and their radial excitations are described in a coupled-channel quark model that also reproduces the properties of the light scalar nonet. All two-meson channels containing ground-state pseudoscalars and vectors are included. The parameters are chosen fixed at published values, except for the overall coupling constant λ, which is fine-tuned to reproduce the D s0 * (2317) mass, and a damping constant α for subthreshold contributions. Variations of λ and D 0 * (2300-2400) pole postions are studied for different α values. Calculated cross-sections for S-wave DK and Dπ scattering, as well as resonance pole positions, are given for the value of α that fits the light scalars. The thus predicted radially excited state D s0 *‧(2850), with a width of about 50MeV, seems to have been observed already.

Damage identification via asymmetric active magnetic bearing acceleration feedback control

NASA Astrophysics Data System (ADS)

Zhao, Jie; DeSmidt, Hans; Yao, Wei

2015-04-01

A Floquet-based damage detection methodology for cracked rotor systems is developed and demonstrated on a shaft-disk system. This approach utilizes measured changes in the system natural frequencies to estimate the severity and location of shaft structural cracks during operation. The damage detection algorithms are developed with the initial guess solved by least square method and iterative damage parameter vector by updating the eigenvector updating. Active Magnetic Bearing is introduced to break the symmetric structure of rotor system and the tuning range of proper stiffness/virtual mass gains is studied. The system model is built based on energy method and the equations of motion are derived by applying assumed modes method and Lagrange Principle. In addition, the crack model is based on the Strain Energy Release Rate (SERR) concept in fracture mechanics. Finally, the method is synthesized via harmonic balance and numerical examples for a shaft/disk system demonstrate the effectiveness in detecting both location and severity of the structural damage.

Self-tuning wireless power transmission scheme based on on-line scattering parameters measurement and two-side power matching.

PubMed

Luo, Yanting; Yang, Yongmin; Chen, Zhongsheng

2014-04-10

Sub-resonances often happen in wireless power transmission (WPT) systems using coupled magnetic resonances (CMR) due to environmental changes, coil movements or component degradations, which is a serious challenge for high efficiency power transmission. Thus self-tuning is very significant to keep WPT systems following strongly magnetic resonant conditions in practice. Traditional coupled-mode ways is difficult to solve this problem. In this paper a two-port power wave model is presented, where power matching and the overall systemic power transmission efficiency are firstly defined by scattering (S) parameters. Then we propose a novel self-tuning scheme based on on-line S parameters measurements and two-side power matching. Experimental results testify the feasibility of the proposed method. These findings suggest that the proposed method is much potential to develop strongly self-adaptive WPT systems with CMR.

Tuning magnetofluidic spreading in microchannels

NASA Astrophysics Data System (ADS)

Wang, Zhaomeng; Varma, V. B.; Wang, Z. P.; Ramanujan, R. V.

2015-12-01

Magnetofluidic spreading (MFS) is a phenomenon in which a uniform magnetic field is used to induce spreading of a ferrofluid core cladded by diamagnetic fluidic streams in a three-stream channel. Applications of MFS include micromixing, cell sorting and novel microfluidic lab-on-a-chip design. However, the relative importance of the parameters which govern MFS is still unclear, leading to non-optimal control of MFS. Hence, in this work, the effect of various key parameters on MFS was experimentally and numerically studied. Our multi-physics model, which combines magnetic and fluidic analysis, showed excellent agreement between theory and experiment. It was found that spreading was mainly due to cross-sectional convection induced by magnetic forces, and can be enhanced by tuning various parameters. Smaller flow rate ratio, higher magnetic field, higher core stream or lower cladding stream dynamic viscosity, and larger magnetic particle size can increase MFS. These results can be used to tune magnetofluidic spreading in microchannels.

Vector adaptive predictive coder for speech and audio

NASA Technical Reports Server (NTRS)

Chen, Juin-Hwey (Inventor); Gersho, Allen (Inventor)

1990-01-01

A real-time vector adaptive predictive coder which approximates each vector of K speech samples by using each of M fixed vectors in a first codebook to excite a time-varying synthesis filter and picking the vector that minimizes distortion. Predictive analysis for each frame determines parameters used for computing from vectors in the first codebook zero-state response vectors that are stored at the same address (index) in a second codebook. Encoding of input speech vectors s.sub.n is then carried out using the second codebook. When the vector that minimizes distortion is found, its index is transmitted to a decoder which has a codebook identical to the first codebook of the decoder. There the index is used to read out a vector that is used to synthesize an output speech vector s.sub.n. The parameters used in the encoder are quantized, for example by using a table, and the indices are transmitted to the decoder where they are decoded to specify transfer characteristics of filters used in producing the vector s.sub.n from the receiver codebook vector selected by the vector index transmitted.

Kernel learning at the first level of inference.

PubMed

Cawley, Gavin C; Talbot, Nicola L C

2014-05-01

Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-fitting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation. The kernel parameters and the coefficients of the kernel expansion are jointly optimised at the first level of inference, minimising a training criterion with an additional regularisation term acting on the kernel parameters. The key advantage of this approach is that the values of only two regularisation parameters need be determined in model selection, substantially alleviating the problem of over-fitting the model selection criterion. The benefits of this approach are demonstrated using a suite of synthetic and real-world binary classification benchmark problems, where kernel learning at the first level of inference is shown to be statistically superior to the conventional approach, improves on our previous work (Cawley and Talbot, 2007) and is competitive with Multiple Kernel Learning approaches, but with reduced computational expense. Copyright © 2014 Elsevier Ltd. All rights reserved.

Filter parameter tuning analysis for operational orbit determination support

NASA Technical Reports Server (NTRS)

Dunham, J.; Cox, C.; Niklewski, D.; Mistretta, G.; Hart, R.

1994-01-01

The use of an extended Kalman filter (EKF) for operational orbit determination support is being considered by the Goddard Space Flight Center (GSFC) Flight Dynamics Division (FDD). To support that investigation, analysis was performed to determine how an EKF can be tuned for operational support of a set of earth-orbiting spacecraft. The objectives of this analysis were to design and test a general purpose scheme for filter tuning, evaluate the solution accuracies, and develop practical methods to test the consistency of the EKF solutions in an operational environment. The filter was found to be easily tuned to produce estimates that were consistent, agreed with results from batch estimation, and compared well among the common parameters estimated for several spacecraft. The analysis indicates that there is not a sharply defined 'best' tunable parameter set, especially when considering only the position estimates over the data arc. The comparison of the EKF estimates for the user spacecraft showed that the filter is capable of high-accuracy results and can easily meet the current accuracy requirements for the spacecraft included in the investigation. The conclusion is that the EKF is a viable option for FDD operational support.

Design and Theoretical Analysis of a Resonant Sensor for Liquid Density Measurement

PubMed Central

Zheng, Dezhi; Shi, Jiying; Fan, Shangchun

2012-01-01

In order to increase the accuracy of on-line liquid density measurements, a sensor equipped with a tuning fork as the resonant sensitive component is designed in this paper. It is a quasi-digital sensor with simple structure and high precision. The sensor is based on resonance theory and composed of a sensitive unit and a closed-loop control unit, where the sensitive unit consists of the actuator, the resonant tuning fork and the detector and the closed-loop control unit comprises precondition circuit, digital signal processing and control unit, analog-to-digital converter and digital-to-analog converter. An approximate parameters model of the tuning fork is established and the impact of liquid density, position of the tuning fork, temperature and structural parameters on the natural frequency of the tuning fork are also analyzed. On this basis, a tuning fork liquid density measurement sensor is developed. In addition, experimental testing on the sensor has been carried out on standard calibration facilities under constant 20 °C, and the sensor coefficients are calibrated. The experimental results show that the repeatability error is about 0.03% and the accuracy is about 0.4 kg/m3. The results also confirm that the method to increase the accuracy of liquid density measurement is feasible. PMID:22969378

Design and theoretical analysis of a resonant sensor for liquid density measurement.

PubMed

Zheng, Dezhi; Shi, Jiying; Fan, Shangchun

2012-01-01

In order to increase the accuracy of on-line liquid density measurements, a sensor equipped with a tuning fork as the resonant sensitive component is designed in this paper. It is a quasi-digital sensor with simple structure and high precision. The sensor is based on resonance theory and composed of a sensitive unit and a closed-loop control unit, where the sensitive unit consists of the actuator, the resonant tuning fork and the detector and the closed-loop control unit comprises precondition circuit, digital signal processing and control unit, analog-to-digital converter and digital-to-analog converter. An approximate parameters model of the tuning fork is established and the impact of liquid density, position of the tuning fork, temperature and structural parameters on the natural frequency of the tuning fork are also analyzed. On this basis, a tuning fork liquid density measurement sensor is developed. In addition, experimental testing on the sensor has been carried out on standard calibration facilities under constant 20 °C, and the sensor coefficients are calibrated. The experimental results show that the repeatability error is about 0.03% and the accuracy is about 0.4 kg/m(3). The results also confirm that the method to increase the accuracy of liquid density measurement is feasible.

Efficient and robust pupil size and blink estimation from near-field video sequences for human-machine interaction.

PubMed

Chen, Siyuan; Epps, Julien

2014-12-01

Monitoring pupil and blink dynamics has applications in cognitive load measurement during human-machine interaction. However, accurate, efficient, and robust pupil size and blink estimation pose significant challenges to the efficacy of real-time applications due to the variability of eye images, hence to date, require manual intervention for fine tuning of parameters. In this paper, a novel self-tuning threshold method, which is applicable to any infrared-illuminated eye images without a tuning parameter, is proposed for segmenting the pupil from the background images recorded by a low cost webcam placed near the eye. A convex hull and a dual-ellipse fitting method are also proposed to select pupil boundary points and to detect the eyelid occlusion state. Experimental results on a realistic video dataset show that the measurement accuracy using the proposed methods is higher than that of widely used manually tuned parameter methods or fixed parameter methods. Importantly, it demonstrates convenience and robustness for an accurate and fast estimate of eye activity in the presence of variations due to different users, task types, load, and environments. Cognitive load measurement in human-machine interaction can benefit from this computationally efficient implementation without requiring a threshold calibration beforehand. Thus, one can envisage a mini IR camera embedded in a lightweight glasses frame, like Google Glass, for convenient applications of real-time adaptive aiding and task management in the future.

Fault Detection of Bearing Systems through EEMD and Optimization Algorithm

PubMed Central

Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan

2017-01-01

This study proposes a fault detection and diagnosis method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate vibration signals from damaged bearing components, such as the inner-race, outer-race, and rolling elements. The process of decomposing vibration signals into intrinsic mode functions (IMFs) and extracting statistical features is introduced to develop a damage-sensitive parameter vector. Finally, PCA and Isomap algorithm are used to classify and visualize this parameter vector, to separate damage characteristics from healthy bearing components. Moreover, the PSO-based optimization algorithm improves the classification performance by selecting proper weightings for the parameter vector, to maximize the visualization effect of separating and grouping of parameter vectors in three-dimensional space. PMID:29143772

Regularized estimation of Euler pole parameters

NASA Astrophysics Data System (ADS)

Aktuğ, Bahadir; Yildirim, Ömer

2013-07-01

Euler vectors provide a unified framework to quantify the relative or absolute motions of tectonic plates through various geodetic and geophysical observations. With the advent of space geodesy, Euler parameters of several relatively small plates have been determined through the velocities derived from the space geodesy observations. However, the available data are usually insufficient in number and quality to estimate both the Euler vector components and the Euler pole parameters reliably. Since Euler vectors are defined globally in an Earth-centered Cartesian frame, estimation with the limited geographic coverage of the local/regional geodetic networks usually results in highly correlated vector components. In the case of estimating the Euler pole parameters directly, the situation is even worse, and the position of the Euler pole is nearly collinear with the magnitude of the rotation rate. In this study, a new method, which consists of an analytical derivation of the covariance matrix of the Euler vector in an ideal network configuration, is introduced and a regularized estimation method specifically tailored for estimating the Euler vector is presented. The results show that the proposed method outperforms the least squares estimation in terms of the mean squared error.

Tuning maps for setpoint changes and load disturbance upsets in a three capacity process under multivariable control

NASA Technical Reports Server (NTRS)

Litt, Jonathan S.; Smith, Ira C.

1991-01-01

Tuning maps are an aid in the controller tuning process because they provide a convenient way for the plant operator to determine the consequences of adjusting different controller parameters. In this application the maps provide a graphical representation of the effect of varying the gains in the state feedback matrix on startup and load disturbance transients for a three capacity process. Nominally, the three tank system, represented in diagonal form, has a Proportional-Integral control on each loop. Cross coupling is then introduced between the loops by using non-zero off-diagonal proportional parameters. Changes in transient behavior due to setpoint and load changes are examined by varying the gains of the cross coupling terms.

Robust support vector regression networks for function approximation with outliers.

PubMed

Chuang, Chen-Chia; Su, Shun-Feng; Jeng, Jin-Tsong; Hsiao, Chih-Ching

2002-01-01

Support vector regression (SVR) employs the support vector machine (SVM) to tackle problems of function approximation and regression estimation. SVR has been shown to have good robust properties against noise. When the parameters used in SVR are improperly selected, overfitting phenomena may still occur. However, the selection of various parameters is not straightforward. Besides, in SVR, outliers may also possibly be taken as support vectors. Such an inclusion of outliers in support vectors may lead to seriously overfitting phenomena. In this paper, a novel regression approach, termed as the robust support vector regression (RSVR) network, is proposed to enhance the robust capability of SVR. In the approach, traditional robust learning approaches are employed to improve the learning performance for any selected parameters. From the simulation results, our RSVR can always improve the performance of the learned systems for all cases. Besides, it can be found that even the training lasted for a long period, the testing errors would not go up. In other words, the overfitting phenomenon is indeed suppressed.

Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data.

PubMed

Koutsoukas, Alexios; Monaghan, Keith J; Li, Xiaoli; Huan, Jun

2017-06-28

In recent years, research in artificial neural networks has resurged, now under the deep-learning umbrella, and grown extremely popular. Recently reported success of DL techniques in crowd-sourced QSAR and predictive toxicology competitions has showcased these methods as powerful tools in drug-discovery and toxicology research. The aim of this work was dual, first large number of hyper-parameter configurations were explored to investigate how they affect the performance of DNNs and could act as starting points when tuning DNNs and second their performance was compared to popular methods widely employed in the field of cheminformatics namely Naïve Bayes, k-nearest neighbor, random forest and support vector machines. Moreover, robustness of machine learning methods to different levels of artificially introduced noise was assessed. The open-source Caffe deep-learning framework and modern NVidia GPU units were utilized to carry out this study, allowing large number of DNN configurations to be explored. We show that feed-forward deep neural networks are capable of achieving strong classification performance and outperform shallow methods across diverse activity classes when optimized. Hyper-parameters that were found to play critical role are the activation function, dropout regularization, number hidden layers and number of neurons. When compared to the rest methods, tuned DNNs were found to statistically outperform, with p value <0.01 based on Wilcoxon statistical test. DNN achieved on average MCC units of 0.149 higher than NB, 0.092 than kNN, 0.052 than SVM with linear kernel, 0.021 than RF and finally 0.009 higher than SVM with radial basis function kernel. When exploring robustness to noise, non-linear methods were found to perform well when dealing with low levels of noise, lower than or equal to 20%, however when dealing with higher levels of noise, higher than 30%, the Naïve Bayes method was found to perform well and even outperform at the highest level of noise 50% more sophisticated methods across several datasets.

Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model

NASA Astrophysics Data System (ADS)

Yang, B.; Qian, Y.; Lin, G.; Leung, R.; Zhang, Y.

2011-12-01

The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. While the latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic important-sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e., the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

Uncertainty Quantification and Parameter Tuning: A Case Study of Convective Parameterization Scheme in the WRF Regional Climate Model

NASA Astrophysics Data System (ADS)

Qian, Y.; Yang, B.; Lin, G.; Leung, R.; Zhang, Y.

2012-04-01

The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. The latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic important-sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e., the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model

NASA Astrophysics Data System (ADS)

Yang, B.; Qian, Y.; Lin, G.; Leung, R.; Zhang, Y.

2012-03-01

The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. While the latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic importance sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e. the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

Human pose tracking from monocular video by traversing an image motion mapped body pose manifold

NASA Astrophysics Data System (ADS)

Basu, Saurav; Poulin, Joshua; Acton, Scott T.

2010-01-01

Tracking human pose from monocular video sequences is a challenging problem due to the large number of independent parameters affecting image appearance and nonlinear relationships between generating parameters and the resultant images. Unlike the current practice of fitting interpolation functions to point correspondences between underlying pose parameters and image appearance, we exploit the relationship between pose parameters and image motion flow vectors in a physically meaningful way. Change in image appearance due to pose change is realized as navigating a low dimensional submanifold of the infinite dimensional Lie group of diffeomorphisms of the two dimensional sphere S2. For small changes in pose, image motion flow vectors lie on the tangent space of the submanifold. Any observed image motion flow vector field is decomposed into the basis motion vector flow fields on the tangent space and combination weights are used to update corresponding pose changes in the different dimensions of the pose parameter space. Image motion flow vectors are largely invariant to style changes in experiments with synthetic and real data where the subjects exhibit variation in appearance and clothing. The experiments demonstrate the robustness of our method (within +/-4° of ground truth) to style variance.

Spatiotemporal source tuning filter bank for multiclass EEG based brain computer interfaces.

PubMed

Acharya, Soumyadipta; Mollazadeh, Moshen; Murari, Kartikeya; Thakor, Nitish

2006-01-01

Non invasive brain-computer interfaces (BCI) allow people to communicate by modulating features of their electroencephalogram (EEG). Spatiotemporal filtering has a vital role in multi-class, EEG based BCI. In this study, we used a novel combination of principle component analysis, independent component analysis and dipole source localization to design a spatiotemporal multiple source tuning (SPAMSORT) filter bank, each channel of which was tuned to the activity of an underlying dipole source. Changes in the event-related spectral perturbation (ERSP) were measured and used to train a linear support vector machine to classify between four classes of motor imagery tasks (left hand, right hand, foot and tongue) for one subject. ERSP values were significantly (p<0.01) different across tasks and better (p<0.01) than conventional spatial filtering methods (large Laplacian and common average reference). Classification resulted in an average accuracy of 82.5%. This approach could lead to promising BCI applications such as control of a prosthesis with multiple degrees of freedom.

Parameter optimization of an inerter-based isolator for passive vibration control of Michelangelo's Rondanini Pietà

NASA Astrophysics Data System (ADS)

Siami, A.; Karimi, H. R.; Cigada, A.; Zappa, E.; Sabbioni, E.

2018-01-01

Preserving cultural heritage against earthquake and ambient vibrations can be an attractive topic in the field of vibration control. This paper proposes a passive vibration isolator methodology based on inerters for improving the performance of the isolation system of the famous statue of Michelangelo Buonarroti Pietà Rondanini. More specifically, a five-degree-of-freedom (5DOF) model of the statue and the anti-seismic and anti-vibration base is presented and experimentally validated. The parameters of this model are tuned according to the experimental tests performed on the assembly of the isolator and the structure. Then, the developed model is used to investigate the impact of actuation devices such as tuned mass-damper (TMD) and tuned mass-damper-inerter (TMDI) in vibration reduction of the structure. The effect of implementation of TMDI on the 5DOF model is shown based on physical limitations of the system parameters. Simulation results are provided to illustrate effectiveness of the passive element of TMDI in reduction of the vibration transmitted to the statue in vertical direction. Moreover, the optimal design parameters of the passive system such as frequency and damping coefficient will be calculated using two different performance indexes. The obtained optimal parameters have been evaluated by using two different optimization algorithms: the sequential quadratic programming method and the Firefly algorithm. The results prove significant reduction in the transmitted vibration to the structure in the presence of the proposed tuned TMDI, without imposing a large amount of mass or modification to the structure of the isolator.

Hydrogen maser frequency standard computer model for automatic cavity tuning servo simulations

NASA Technical Reports Server (NTRS)

Potter, P. D.; Finnie, C.

1978-01-01

A computer model of the JPL hydrogen maser frequency standard was developed. This model allows frequency stability data to be generated, as a function of various maser parameters, many orders of magnitude faster than these data can be obtained by experimental test. In particular, the maser performance as a function of the various automatic tuning servo parameters may be readily determined. Areas of discussion include noise sources, first-order autotuner loop, second-order autotuner loop, and a comparison of the loops.

Analysis Test of Understanding of Vectors with the Three-Parameter Logistic Model of Item Response Theory and Item Response Curves Technique

ERIC Educational Resources Information Center

Rakkapao, Suttida; Prasitpong, Singha; Arayathanitkul, Kwan

2016-01-01

This study investigated the multiple-choice test of understanding of vectors (TUV), by applying item response theory (IRT). The difficulty, discriminatory, and guessing parameters of the TUV items were fit with the three-parameter logistic model of IRT, using the parscale program. The TUV ability is an ability parameter, here estimated assuming…

A Tuned Single Parameter for Representing Conjunction Risk

NASA Technical Reports Server (NTRS)

Plakaloic, D.; Hejduk, M. D.; Frigm, R. C.; Newman, L. K.

2011-01-01

Satellite conjunction assessment risk analysis is a subjective enterprise that can benefit from quantitative aids and, to this end, NASA/GSFC has developed a fuzzy logic construct - called the F-value - to attempt to provide a statement of conjunction risk that amalgamates multiple indices and yields a more stable intra-event assessment. This construct has now sustained an extended tuning procedure against heuristic analyst assessment of event risk. The tuning effort has resulted in modifications to the calculation procedure and the adjustment of tuning coefficients, producing a construct with both more predictive force and a better statement of its error.

Systematic characterization of a 1550 nm microelectromechanical (MEMS)-tunable vertical-cavity surface-emitting laser (VCSEL) with 7.92 THz tuning range for terahertz photomixing systems

NASA Astrophysics Data System (ADS)

Haidar, M. T.; Preu, S.; Cesar, J.; Paul, S.; Hajo, A. S.; Neumeyr, C.; Maune, H.; Küppers, F.

2018-01-01

Continuous-wave (CW) terahertz (THz) photomixing requires compact, widely tunable, mode-hop-free driving lasers. We present a single-mode microelectromechanical system (MEMS)-tunable vertical-cavity surface-emitting laser (VCSEL) featuring an electrothermal tuning range of 64 nm (7.92 THz) that exceeds the tuning range of commercially available distributed-feedback laser (DFB) diodes (˜4.8 nm) by a factor of about 13. We first review the underlying theory and perform a systematic characterization of the MEMS-VCSEL, with particular focus on the parameters relevant for THz photomixing. These parameters include mode-hop-free CW tuning with a side-mode-suppression-ratio >50 dB, a linewidth as narrow as 46.1 MHz, and wavelength and polarization stability. We conclude with a demonstration of a CW THz photomixing setup by subjecting the MEMS-VCSEL to optical beating with a DFB diode driving commercial photomixers. The achievable THz bandwidth is limited only by the employed photomixers. Once improved photomixers become available, electrothermally actuated MEMS-VCSELs should allow for a tuning range covering almost the whole THz domain with a single system.

Multiresolution Wavelet Based Adaptive Numerical Dissipation Control for Shock-Turbulence Computations

NASA Technical Reports Server (NTRS)

Sjoegreen, B.; Yee, H. C.

2001-01-01

The recently developed essentially fourth-order or higher low dissipative shock-capturing scheme of Yee, Sandham and Djomehri (1999) aimed at minimizing nu- merical dissipations for high speed compressible viscous flows containing shocks, shears and turbulence. To detect non smooth behavior and control the amount of numerical dissipation to be added, Yee et al. employed an artificial compression method (ACM) of Harten (1978) but utilize it in an entirely different context than Harten originally intended. The ACM sensor consists of two tuning parameters and is highly physical problem dependent. To minimize the tuning of parameters and physical problem dependence, new sensors with improved detection properties are proposed. The new sensors are derived from utilizing appropriate non-orthogonal wavelet basis functions and they can be used to completely switch to the extra numerical dissipation outside shock layers. The non-dissipative spatial base scheme of arbitrarily high order of accuracy can be maintained without compromising its stability at all parts of the domain where the solution is smooth. Two types of redundant non-orthogonal wavelet basis functions are considered. One is the B-spline wavelet (Mallat & Zhong 1992) used by Gerritsen and Olsson (1996) in an adaptive mesh refinement method, to determine regions where re nement should be done. The other is the modification of the multiresolution method of Harten (1995) by converting it to a new, redundant, non-orthogonal wavelet. The wavelet sensor is then obtained by computing the estimated Lipschitz exponent of a chosen physical quantity (or vector) to be sensed on a chosen wavelet basis function. Both wavelet sensors can be viewed as dual purpose adaptive methods leading to dynamic numerical dissipation control and improved grid adaptation indicators. Consequently, they are useful not only for shock-turbulence computations but also for computational aeroacoustics and numerical combustion. In addition, these sensors are scheme independent and can be stand alone options for numerical algorithm other than the Yee et al. scheme.

Event generator tunes obtained from underlying event and multiparton scattering measurements.

PubMed

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Ortona, G; Paganini, P; Pigard, P; Regnard, S; Salerno, R; Sauvan, J B; Sirois, Y; Strebler, T; Yilmaz, Y; Zabi, A; Agram, J-L; Andrea, J; Aubin, A; Bloch, D; Brom, J-M; Buttignol, M; Chabert, E C; Chanon, N; Collard, C; Conte, E; Coubez, X; Fontaine, J-C; Gelé, D; Goerlach, U; Goetzmann, C; Le Bihan, A-C; Merlin, J A; Skovpen, K; Van Hove, P; Gadrat, S; Beauceron, S; Bernet, C; Boudoul, G; Bouvier, E; Carrillo Montoya, C A; Chierici, R; Contardo, D; Courbon, B; Depasse, P; El Mamouni, H; Fan, J; Fay, J; Gascon, S; Gouzevitch, M; Ille, B; Lagarde, F; Laktineh, I B; Lethuillier, M; Mirabito, L; Pequegnot, A L; Perries, S; Ruiz Alvarez, J D; Sabes, D; Sgandurra, L; Sordini, V; Vander Donckt, M; Verdier, P; Viret, S; Toriashvili, T; Lomidze, D; Autermann, C; Beranek, S; Edelhoff, M; Feld, L; Heister, A; Kiesel, M K; Klein, K; Lipinski, M; Ostapchuk, A; Preuten, M; Raupach, F; Schael, S; Schulte, J F; Verlage, T; Weber, H; Wittmer, B; Zhukov, V; Ata, M; Brodski, M; Dietz-Laursonn, E; Duchardt, D; Endres, M; Erdmann, M; Erdweg, S; Esch, T; Fischer, R; Güth, A; Hebbeker, T; Heidemann, C; Hoepfner, K; Knutzen, S; Kreuzer, P; Merschmeyer, M; Meyer, A; Millet, P; Olschewski, M; Padeken, K; Papacz, P; Pook, T; Radziej, M; Reithler, H; Rieger, M; Scheuch, F; Sonnenschein, L; Teyssier, D; Thüer, S; Cherepanov, V; Erdogan, Y; Flügge, G; Geenen, H; Geisler, M; Hoehle, F; Kargoll, B; Kress, T; Kuessel, Y; Künsken, A; Lingemann, J; Nehrkorn, A; Nowack, A; Nugent, I M; Pistone, C; Pooth, O; Stahl, A; Aldaya Martin, M; Asin, I; Bartosik, N; Behnke, O; Behrens, U; Bell, A J; Borras, K; Burgmeier, A; Campbell, A; Choudhury, S; Costanza, F; Diez Pardos, C; Dolinska, G; Dooling, S; Dorland, T; Eckerlin, G; Eckstein, D; Eichhorn, T; Flucke, G; Gallo, E; Garcia, J Garay; Geiser, A; Gizhko, A; Gunnellini, P; Hauk, J; Hempel, M; Jung, H; Kalogeropoulos, A; Karacheban, O; Kasemann, M; Katsas, P; Kieseler, J; Kleinwort, C; Korol, I; Lange, W; Leonard, J; Lipka, K; Lobanov, A; Lohmann, W; Mankel, R; Marfin, I; Melzer-Pellmann, I-A; Meyer, A B; Mittag, G; Mnich, J; Mussgiller, A; Naumann-Emme, S; Nayak, A; Ntomari, E; Perrey, H; Pitzl, D; Placakyte, R; Raspereza, A; Roland, B; Sahin, M Ö; Saxena, P; Schoerner-Sadenius, T; Schröder, M; Seitz, C; Spannagel, S; Trippkewitz, K D; Walsh, R; Wissing, C; Blobel, V; Centis Vignali, M; Draeger, A R; Erfle, J; Garutti, E; Goebel, K; Gonzalez, D; Görner, M; Haller, J; Hoffmann, M; Höing, R S; Junkes, A; Klanner, R; Kogler, R; Kovalchuk, N; Lapsien, T; Lenz, T; Marchesini, I; Marconi, D; Meyer, M; Nowatschin, D; Ott, J; Pantaleo, F; Peiffer, T; Perieanu, A; Pietsch, N; Poehlsen, J; Rathjens, D; Sander, C; Scharf, C; Schettler, H; Schleper, P; Schlieckau, E; Schmidt, A; Schwandt, J; Sola, V; Stadie, H; Steinbrück, G; Tholen, H; Troendle, D; Usai, E; Vanelderen, L; Vanhoefer, A; Vormwald, B; Barth, C; Baus, C; Berger, J; Böser, C; Butz, E; Chwalek, T; Colombo, F; De Boer, W; Descroix, A; Dierlamm, A; Fink, S; Frensch, F; Friese, R; Giffels, M; Gilbert, A; Haitz, D; Hartmann, F; Heindl, S M; Husemann, U; Katkov, I; Kornmayer, A; Lobelle Pardo, P; Maier, B; Mildner, H; Mozer, M U; Müller, T; Müller, Th; Plagge, M; Quast, G; Rabbertz, K; Röcker, S; Roscher, F; Sieber, G; Simonis, H J; Stober, F M; Ulrich, R; Wagner-Kuhr, J; Wayand, S; Weber, M; Weiler, T; Williamson, S; Wöhrmann, C; Wolf, R; Anagnostou, G; Daskalakis, G; Geralis, T; Giakoumopoulou, V A; Kyriakis, A; Loukas, D; Psallidas, A; Topsis-Giotis, I; Agapitos, A; Kesisoglou, S; Panagiotou, A; Saoulidou, N; Tziaferi, E; Evangelou, I; Flouris, G; Foudas, C; Kokkas, P; Loukas, N; Manthos, N; Papadopoulos, I; Paradas, E; Strologas, J; Bencze, G; Hajdu, C; Hazi, A; Hidas, P; Horvath, D; Sikler, F; Veszpremi, V; Vesztergombi, G; Zsigmond, A J; Beni, N; Czellar, S; Karancsi, J; Molnar, J; Szillasi, Z; Bartók, M; Makovec, A; Raics, P; Trocsanyi, Z L; Ujvari, B; Mal, P; Mandal, K; Sahoo, D K; Sahoo, N; Swain, S K; Bansal, S; Beri, S B; Bhatnagar, V; Chawla, R; Gupta, R; Bhawandeep, U; Kalsi, A K; Kaur, A; Kaur, M; Kumar, R; Mehta, A; Mittal, M; Singh, J B; Walia, G; Kumar, Ashok; Bhardwaj, A; Choudhary, B C; Garg, R B; Kumar, A; Malhotra, S; Naimuddin, M; Nishu, N; Ranjan, K; Sharma, R; Sharma, V; Bhattacharya, S; Chatterjee, K; Dey, S; Dutta, S; Jain, Sa; Majumdar, N; Modak, A; Mondal, K; Mukherjee, S; Mukhopadhyay, S; Roy, A; Roy, D; Roy Chowdhury, S; Sarkar, S; Sharan, M; Abdulsalam, A; Chudasama, R; Dutta, D; Jha, V; Kumar, V; Mohanty, A K; Pant, L M; Shukla, P; Topkar, A; Aziz, T; Banerjee, S; Bhowmik, S; Chatterjee, R M; Dewanjee, R K; Dugad, S; Ganguly, S; Ghosh, S; Guchait, M; Gurtu, A; Kole, G; Kumar, S; Mahakud, B; Maity, M; Majumder, G; Mazumdar, K; Mitra, S; Mohanty, G B; Parida, B; Sarkar, T; Sur, N; Sutar, B; Wickramage, N; Chauhan, S; Dube, S; Kapoor, A; Kothekar, K; Sharma, S; Bakhshiansohi, H; Behnamian, H; Etesami, S M; Fahim, A; Goldouzian, R; Khakzad, M; Mohammadi Najafabadi, M; Naseri, M; Paktinat Mehdiabadi, S; Rezaei Hosseinabadi, F; Safarzadeh, B; Zeinali, M; 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Dev, N; Hildreth, M; Jessop, C; Karmgard, D J; Kellams, N; Lannon, K; Marinelli, N; Meng, F; Mueller, C; Musienko, Y; Planer, M; Reinsvold, A; Ruchti, R; Smith, G; Taroni, S; Valls, N; Wayne, M; Wolf, M; Woodard, A; Antonelli, L; Brinson, J; Bylsma, B; Durkin, L S; Flowers, S; Hart, A; Hill, C; Hughes, R; Ji, W; Ling, T Y; Liu, B; Luo, W; Puigh, D; Rodenburg, M; Winer, B L; Wulsin, H W; Driga, O; Elmer, P; Hardenbrook, J; Hebda, P; Koay, S A; Lujan, P; Marlow, D; Medvedeva, T; Mooney, M; Olsen, J; Palmer, C; Piroué, P; Saka, H; Stickland, D; Tully, C; Zuranski, A; Malik, S; Barnes, V E; Benedetti, D; Bortoletto, D; Gutay, L; Jha, M K; Jones, M; Jung, K; Miller, D H; Neumeister, N; Primavera, F; Radburn-Smith, B C; Shi, X; Shipsey, I; Silvers, D; Sun, J; Svyatkovskiy, A; Wang, F; Xie, W; Xu, L; Parashar, N; Stupak, J; Adair, A; Akgun, B; Chen, Z; Ecklund, K M; Geurts, F J M; Guilbaud, M; Li, W; Michlin, B; Northup, M; Padley, B P; Redjimi, R; Roberts, J; Rorie, J; Tu, Z; Zabel, J; Betchart, B; Bodek, A; de Barbaro, P; Demina, R; Eshaq, Y; Ferbel, T; Galanti, M; Galanti, M; Garcia-Bellido, A; Han, J; Harel, A; Hindrichs, O; Hindrichs, O; Khukhunaishvili, A; Petrillo, G; Tan, P; Verzetti, M; Arora, S; Barker, A; Chou, J P; Contreras-Campana, C; Contreras-Campana, E; Ferencek, D; Gershtein, Y; Gray, R; Halkiadakis, E; Hidas, D; Hughes, E; Kaplan, S; Kunnawalkam Elayavalli, R; Lath, A; Nash, K; Panwalkar, S; Park, M; Salur, S; Schnetzer, S; Sheffield, D; Somalwar, S; Stone, R; Thomas, S; Thomassen, P; Walker, M; Foerster, M; Riley, G; Rose, K; Spanier, S; York, A; Bouhali, O; Castaneda Hernandez, A; Celik, A; Dalchenko, M; De Mattia, M; Delgado, A; Dildick, S; Dildick, S; Eusebi, R; Gilmore, J; Huang, T; Kamon, T; Krutelyov, V; Krutelyov, V; Mueller, R; Osipenkov, I; Pakhotin, Y; Patel, R; Patel, R; Perloff, A; Rose, A; Safonov, A; Tatarinov, A; Ulmer, K A; Akchurin, N; Cowden, C; Damgov, J; Dragoiu, C; Dudero, P R; Faulkner, J; Kunori, S; Lamichhane, K; Lee, S W; Libeiro, T; Undleeb, S; Volobouev, I; Appelt, E; Delannoy, A G; Greene, S; Gurrola, A; Janjam, R; Johns, W; Maguire, C; Mao, Y; Melo, A; Ni, H; Sheldon, P; Snook, B; Tuo, S; Velkovska, J; Xu, Q; Arenton, M W; Cox, B; Francis, B; Goodell, J; Hirosky, R; Ledovskoy, A; Li, H; Lin, C; Neu, C; Sinthuprasith, T; Sun, X; Wang, Y; Wolfe, E; Wood, J; Xia, F; Clarke, C; Harr, R; Karchin, P E; Kottachchi Kankanamge Don, C; Lamichhane, P; Sturdy, J; Belknap, D A; Carlsmith, D; Cepeda, M; Dasu, S; Dodd, L; Duric, S; Gomber, B; Grothe, M; Hall-Wilton, R; Herndon, M; Hervé, A; Klabbers, P; Lanaro, A; Levine, A; Long, K; Loveless, R; Mohapatra, A; Ojalvo, I; Perry, T; Pierro, G A; Polese, G; Ruggles, T; Sarangi, T; Savin, A; Sharma, A; Smith, N; Smith, W H; Taylor, D; Woods, N

New sets of parameters ("tunes") for the underlying-event (UE) modelling of the pythia8, pythia6 and herwig++ Monte Carlo event generators are constructed using different parton distribution functions. Combined fits to CMS UE proton-proton ([Formula: see text]) data at [Formula: see text] and to UE proton-antiproton ([Formula: see text]) data from the CDF experiment at lower [Formula: see text], are used to study the UE models and constrain their parameters, providing thereby improved predictions for proton-proton collisions at 13[Formula: see text]. In addition, it is investigated whether the values of the parameters obtained from fits to UE observables are consistent with the values determined from fitting observables sensitive to double-parton scattering processes. Finally, comparisons are presented of the UE tunes to "minimum bias" (MB) events, multijet, and Drell-Yan ([Formula: see text] lepton-antilepton+jets) observables at 7 and 8[Formula: see text], as well as predictions for MB and UE observables at 13[Formula: see text].

Analytical design of modified Smith predictor for unstable second-order processes with time delay

NASA Astrophysics Data System (ADS)

Ajmeri, Moina; Ali, Ahmad

2017-06-01

In this paper, a modified Smith predictor using three controllers, namely, stabilising (Gc), set-point tracking (Gc1), and load disturbance rejection (Gc2) controllers is proposed for second-order unstable processes with time delay. Controllers of the proposed structure are tuned using direct synthesis approach as this method enables the user to achieve a trade-off between the performance and robustness by adjusting a single design parameter. Furthermore, suitable values of the tuning parameters are recommended after studying their effect on the closed-loop performance and robustness. This is the main advantage of the proposed work over other recently published manuscripts, where authors provide only suitable ranges for the tuning parameters in spite of giving their suitable values. Simulation studies show that the proposed method results in satisfactory performance and improved robustness as compared to the recently reported control schemes. It is observed that the proposed scheme is able to work in the noisy environment also.

Two-dimensional spatiotemporal coding of linear acceleration in vestibular nuclei neurons

NASA Technical Reports Server (NTRS)

Angelaki, D. E.; Bush, G. A.; Perachio, A. A.

1993-01-01

Response properties of vertical (VC) and horizontal (HC) canal/otolith-convergent vestibular nuclei neurons were studied in decerebrate rats during stimulation with sinusoidal linear accelerations (0.2-1.4 Hz) along different directions in the head horizontal plane. A novel characteristic of the majority of tested neurons was the nonzero response often elicited during stimulation along the "null" direction (i.e., the direction perpendicular to the maximum sensitivity vector, Smax). The tuning ratio (Smin gain/Smax gain), a measure of the two-dimensional spatial sensitivity, depended on stimulus frequency. For most vestibular nuclei neurons, the tuning ratio was small at the lowest stimulus frequencies and progressively increased with frequency. Specifically, HC neurons were characterized by a flat Smax gain and an approximately 10-fold increase of Smin gain per frequency decade. Thus, these neurons encode linear acceleration when stimulated along their maximum sensitivity direction, and the rate of change of linear acceleration (jerk) when stimulated along their minimum sensitivity direction. While the Smax vectors were distributed throughout the horizontal plane, the Smin vectors were concentrated mainly ipsilaterally with respect to head acceleration and clustered around the naso-occipital head axis. The properties of VC neurons were distinctly different from those of HC cells. The majority of VC cells showed decreasing Smax gains and small, relatively flat, Smin gains as a function of frequency. The Smax vectors were distributed ipsilaterally relative to the induced (apparent) head tilt. In type I anterior or posterior VC neurons, Smax vectors were clustered around the projection of the respective ipsilateral canal plane onto the horizontal head plane. These distinct spatial and temporal properties of HC and VC neurons during linear acceleration are compatible with the spatiotemporal organization of the horizontal and the vertical/torsional ocular responses, respectively, elicited in the rat during linear translation in the horizontal head plane. In addition, the data suggest a spatially and temporally specific and selective otolith/canal convergence. We propose that the central otolith system is organized in canal coordinates such that there is a close alignment between the plane of angular acceleration (canal) sensitivity and the plane of linear acceleration (otolith) sensitivity in otolith/canal-convergent vestibular nuclei neurons.

A fine-tuned vector-parasite dialogue in tsetse's cardia determines peritrophic matrix integrity and trypanosome transmission success

PubMed Central

Aksoy, Emre; Weiss, Brian L.; Zhao, Xin; Awuoche, Erick O.; Wu, Yineng; Aksoy, Serap

2018-01-01

Arthropod vectors have multiple physical and immunological barriers that impede the development and transmission of parasites to new vertebrate hosts. These barriers include the peritrophic matrix (PM), a chitinous barrier that separates the blood bolus from the midgut epithelia and modulates vector-pathogens interactions. In tsetse flies, a sleeve-like PM is continuously produced by the cardia organ located at the fore- and midgut junction. African trypanosomes, Trypanosoma brucei, must bypass the PM twice; first to colonize the midgut and secondly to reach the salivary glands (SG), to complete their transmission cycle in tsetse. However, not all flies with midgut infections develop mammalian transmissible SG infections—the reasons for which are unclear. Here, we used transcriptomics, microscopy and functional genomics analyses to understand the factors that regulate parasite migration from midgut to SG. In flies with midgut infections only, parasites fail to cross the PM as they are eliminated from the cardia by reactive oxygen intermediates (ROIs)—albeit at the expense of collateral cytotoxic damage to the cardia. In flies with midgut and SG infections, expression of genes encoding components of the PM is reduced in the cardia, and structural integrity of the PM barrier is compromised. Under these circumstances trypanosomes traverse through the newly secreted and compromised PM. The process of PM attrition that enables the parasites to re-enter into the midgut lumen is apparently mediated by components of the parasites residing in the cardia. Thus, a fine-tuned dialogue between tsetse and trypanosomes at the cardia determines the outcome of PM integrity and trypanosome transmission success. PMID:29614112

Electronic Tuning In The Hidden Order Compound URu2Si 2 Through Si → P substitution

NASA Astrophysics Data System (ADS)

Gallagher, Andrew

Crystalline materials that include 4f- and 5 f-electron elements frequently exhibit a variety of intriguing phenomena including spin and charge orderings, spin and valence fluctuations, heavy fermion behavior, breakdown of Fermi liquid behavior, and unconventional superconductivity. [5, 6, 7, 8, 9, 10, 11, 12, 13] Amongst such materials, the Kondo lattice system URu2Si2 stands out as being particularly unusual. [14, 15, 16] While at high temperature it exhibits behavior that is typical for an f-electron lattice immersed in a sea of conduction electrons, at T0 = 17:5 K there is a second order phase transition that is followed by unconventional superconductivity near Tc ≈ 1:5 K. [15] Despite three decades of work, the order parameter for the transition at T0 remains unknown and hence, it has been named "hidden order". There have been a multitude of experimental attempts to unravel hidden order, mainly through tuning of the electronic state via pressure, applied magnetic field, and chemical substitution. [17, 18] While these strategies reveal interesting phase diagrams, a longstanding challenge is that any such approach explores the phase space along an unknown vector: i.e., many different factors are affected. To address this issue, we developed a new organizational map for the U-based ThCr2Si2-type compounds that are related to URu2Si2 and thus guided, we explored a new chemical tuning axis: Si -> P. Our studies were enabled by the development of a new molten metal crystal growth method for URu2Si2 which produces high quality single crystals and allows us to introduce high vapor pressure elements, such as phosphorous. [19, 20] Si → P tuning reveals that while the high temperature Kondo lattice behavior is robust, the low temperature phenomena are remarkably sensitive to electronic tuning. [21, 22] In the URu2Si2-xPx phase diagram we find that while hidden order is monotonically suppressed and destroyed for x < 0.035, the superconducting strength evolves non-monotonically with a maximum near x = 0.01 and that superconductivity is destroyed near x ≈ 0.028. For 0.03 < x < 0.26 there is a region with Kondo coherence but no ordered state. Antiferromagnetism abruptly appears for x = 0.26. This phase diagram differs significantly from those produced by most other tuning strategies in URu2Si2, including applied pressure, and isoelectronic chemical substitution (i.e. Ru→Fe and Os), where hidden order and magnetism share a common phase boundary. [2, 23, 24] We discuss implications for understanding hidden order, its relationship to magnetism, and prospects for uncovering novel sibling electronic states.

An adaptive control scheme for a flexible manipulator

NASA Technical Reports Server (NTRS)

Yang, T. C.; Yang, J. C. S.; Kudva, P.

1987-01-01

The problem of controlling a single link flexible manipulator is considered. A self-tuning adaptive control scheme is proposed which consists of a least squares on-line parameter identification of an equivalent linear model followed by a tuning of the gains of a pole placement controller using the parameter estimates. Since the initial parameter values for this model are assumed unknown, the use of arbitrarily chosen initial parameter estimates in the adaptive controller would result in undesirable transient effects. Hence, the initial stage control is carried out with a PID controller. Once the identified parameters have converged, control is transferred to the adaptive controller. Naturally, the relevant issues in this scheme are tests for parameter convergence and minimization of overshoots during control switch-over. To demonstrate the effectiveness of the proposed scheme, simulation results are presented with an analytical nonlinear dynamic model of a single link flexible manipulator.

Generalized dark-bright vector soliton solution to the mixed coupled nonlinear Schrödinger equations.

PubMed

Manikandan, N; Radhakrishnan, R; Aravinthan, K

2014-08-01

We have constructed a dark-bright N-soliton solution with 4N+3 real parameters for the physically interesting system of mixed coupled nonlinear Schrödinger equations. Using this as well as an asymptotic analysis we have investigated the interaction between dark-bright vector solitons. Each colliding dark-bright one-soliton at the asymptotic limits includes more coupling parameters not only in the polarization vector but also in the amplitude part. Our present solution generalizes the dark-bright soliton in the literature with parametric constraints. By exploiting the role of such coupling parameters we are able to control certain interaction effects, namely beating, breathing, bouncing, attraction, jumping, etc., without affecting other soliton parameters. Particularly, the results of the interactions between the bound state dark-bright vector solitons reveal oscillations in their amplitudes under certain parametric choices. A similar kind of effect was also observed experimentally in the BECs. We have also characterized the solutions with complicated structure and nonobvious wrinkle to define polarization vector, envelope speed, envelope width, envelope amplitude, grayness, and complex modulation. It is interesting to identify that the polarization vector of the dark-bright one-soliton evolves on a spherical surface instead of a hyperboloid surface as in the bright-bright case of the mixed coupled nonlinear Schrödinger equations.

Analyzing neural responses with vector fields.

PubMed

Buneo, Christopher A

2011-04-15

Analyzing changes in the shape and scale of single cell response fields is a key component of many neurophysiological studies. Typical analyses of shape change involve correlating firing rates between experimental conditions or "cross-correlating" single cell tuning curves by shifting them with respect to one another and correlating the overlapping data. Such shifting results in a loss of data, making interpretation of the resulting correlation coefficients problematic. The problem is particularly acute for two dimensional response fields, which require shifting along two axes. Here, an alternative method for quantifying response field shape and scale based on correlation of vector field representations is introduced. The merits and limitations of the methods are illustrated using both simulated and experimental data. It is shown that vector correlation provides more information on response field changes than scalar correlation without requiring field shifting and concomitant data loss. An extension of this vector field approach is also demonstrated which can be used to identify the manner in which experimental variables are encoded in studies of neural reference frames. Copyright © 2011 Elsevier B.V. All rights reserved.

TCP performance in ATM networks: ABR parameter tuning and ABR/UBR comparisons

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

Chien Fang; Lin, A.

1996-02-27

This paper explores two issues on TOP performance over ATM networks: ABR parameter tuning and performance comparison of binary mode ABR with enhanced UBR services. Of the fifteen parameters defined for ABR, two parameters dominate binary mode ABR performance: Rate Increase Factor (RIF) and Rate Decrease Factor (RDF). Using simulations, we study the effects of these two parameters on TOP over ABR performance. We compare TOP performance with different ABR parameter settings in terms of through-puts and fairness. The effects of different buffer sizes and LAN/WAN distances are also examined. We then compare TOP performance with the best ABR parametermore » setting with corresponding UBR service enhanced with Early Packet Discard and also with a fair buffer allocation scheme. The results show that TOP performance over binary mode ABR is very sensitive to parameter value settings, and that a poor choice of parameters can result in ABR performance worse than that of the much less expensive UBR-EPD scheme.« less

Fine-tuning free paradigm of two-measures theory: k-essence, absence of initial singularity of the curvature, and inflation with graceful exit to the zero cosmological constant state

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

Guendelman, E. I.; Kaganovich, A. B.

2007-04-15

The dilaton-gravity sector of the two-measures field theory (TMT) is explored in detail in the context of spatially flat Friedman-Robertson-Walker (FRW) cosmology. The model possesses scale invariance which is spontaneously broken due to the intrinsic features of the TMT dynamics. The dilaton {phi} dependence of the effective Lagrangian appears only as a result of the spontaneous breakdown of the scale invariance. If no fine-tuning is made, the effective {phi}-Lagrangian p({phi},X) depends quadratically upon the kinetic term X. Hence TMT represents an explicit example of the effective k-essence resulting from first principles without any exotic term in the underlying action intendedmore » for obtaining this result. Depending of the choice of regions in the parameter space (but without fine-tuning), TMT exhibits different possible outputs for cosmological dynamics: (a) Absence of initial singularity of the curvature while its time derivative is singular. This is a sort of sudden singularities studied by Barrow on purely kinematic grounds. (b) Power law inflation in the subsequent stage of evolution. Depending on the region in the parameter space the inflation ends with a graceful exit either into the state with zero cosmological constant (CC) or into the state driven by both a small CC and the field {phi} with a quintessencelike potential. (c) Possibility of resolution of the old CC problem. From the point of view of TMT, it becomes clear why the old CC problem cannot be solved (without fine-tuning) in conventional field theories. (d) TMT enables two ways for achieving small CC without fine-tuning of dimensionful parameters: either by a seesaw type mechanism or due to a correspondence principle between TMT and conventional field theories (i.e. theories with only the measure of integration {radical}(-g) in the action). (e) There is a wide range of the parameters such that in the late time universe: the equation of state w=p/{rho}<-1; w asymptotically (as t{yields}{infinity}) approaches -1 from below; {rho} approaches a constant, the smallness of which does not require fine-tuning of dimensionful parameters.« less

Eddy current characterization of small cracks using least square support vector machine

NASA Astrophysics Data System (ADS)

Chelabi, M.; Hacib, T.; Le Bihan, Y.; Ikhlef, N.; Boughedda, H.; Mekideche, M. R.

2016-04-01

Eddy current (EC) sensors are used for non-destructive testing since they are able to probe conductive materials. Despite being a conventional technique for defect detection and localization, the main weakness of this technique is that defect characterization, of the exact determination of the shape and dimension, is still a question to be answered. In this work, we demonstrate the capability of small crack sizing using signals acquired from an EC sensor. We report our effort to develop a systematic approach to estimate the size of rectangular and thin defects (length and depth) in a conductive plate. The achieved approach by the novel combination of a finite element method (FEM) with a statistical learning method is called least square support vector machines (LS-SVM). First, we use the FEM to design the forward problem. Next, an algorithm is used to find an adaptive database. Finally, the LS-SVM is used to solve the inverse problems, creating polynomial functions able to approximate the correlation between the crack dimension and the signal picked up from the EC sensor. Several methods are used to find the parameters of the LS-SVM. In this study, the particle swarm optimization (PSO) and genetic algorithm (GA) are proposed for tuning the LS-SVM. The results of the design and the inversions were compared to both simulated and experimental data, with accuracy experimentally verified. These suggested results prove the applicability of the presented approach.

Transverse fields to tune an Ising-nematic quantum phase transition [Transverse fields to tune an Ising-nematic quantum critical transition

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

Maharaj, Akash V.; Rosenberg, Elliott W.; Hristov, Alexander T.

Here, the paradigmatic example of a continuous quantum phase transition is the transverse field Ising ferromagnet. In contrast to classical critical systems, whose properties depend only on symmetry and the dimension of space, the nature of a quantum phase transition also depends on the dynamics. In the transverse field Ising model, the order parameter is not conserved, and increasing the transverse field enhances quantum fluctuations until they become strong enough to restore the symmetry of the ground state. Ising pseudospins can represent the order parameter of any system with a twofold degenerate broken-symmetry phase, including electronic nematic order associated withmore » spontaneous point-group symmetry breaking. Here, we show for the representative example of orbital-nematic ordering of a non-Kramers doublet that an orthogonal strain or a perpendicular magnetic field plays the role of the transverse field, thereby providing a practical route for tuning appropriate materials to a quantum critical point. While the transverse fields are conjugate to seemingly unrelated order parameters, their nontrivial commutation relations with the nematic order parameter, which can be represented by a Berry-phase term in an effective field theory, intrinsically intertwine the different order parameters.« less

Transverse fields to tune an Ising-nematic quantum phase transition [Transverse fields to tune an Ising-nematic quantum critical transition

DOE PAGES

Maharaj, Akash V.; Rosenberg, Elliott W.; Hristov, Alexander T.; ...

2017-12-05

Here, the paradigmatic example of a continuous quantum phase transition is the transverse field Ising ferromagnet. In contrast to classical critical systems, whose properties depend only on symmetry and the dimension of space, the nature of a quantum phase transition also depends on the dynamics. In the transverse field Ising model, the order parameter is not conserved, and increasing the transverse field enhances quantum fluctuations until they become strong enough to restore the symmetry of the ground state. Ising pseudospins can represent the order parameter of any system with a twofold degenerate broken-symmetry phase, including electronic nematic order associated withmore » spontaneous point-group symmetry breaking. Here, we show for the representative example of orbital-nematic ordering of a non-Kramers doublet that an orthogonal strain or a perpendicular magnetic field plays the role of the transverse field, thereby providing a practical route for tuning appropriate materials to a quantum critical point. While the transverse fields are conjugate to seemingly unrelated order parameters, their nontrivial commutation relations with the nematic order parameter, which can be represented by a Berry-phase term in an effective field theory, intrinsically intertwine the different order parameters.« less

Training in cortical control of neuroprosthetic devices improves signal extraction from small neuronal ensembles.

PubMed

Helms Tillery, S I; Taylor, D M; Schwartz, A B

2003-01-01

We have recently developed a closed-loop environment in which we can test the ability of primates to control the motion of a virtual device using ensembles of simultaneously recorded neurons /29/. Here we use a maximum likelihood method to assess the information about task performance contained in the neuronal ensemble. We trained two animals to control the motion of a computer cursor in three dimensions. Initially the animals controlled cursor motion using arm movements, but eventually they learned to drive the cursor directly from cortical activity. Using a population vector (PV) based upon the relation between cortical activity and arm motion, the animals were able to control the cursor directly from the brain in a closed-loop environment, but with difficulty. We added a supervised learning method that modified the parameters of the PV according to task performance (adaptive PV), and found that animals were able to exert much finer control over the cursor motion from brain signals. Here we describe a maximum likelihood method (ML) to assess the information about target contained in neuronal ensemble activity. Using this method, we compared the information about target contained in the ensemble during arm control, during brain control early in the adaptive PV, and during brain control after the adaptive PV had settled and the animal could drive the cursor reliably and with fine gradations. During the arm-control task, the ML was able to determine the target of the movement in as few as 10% of the trials, and as many as 75% of the trials, with an average of 65%. This average dropped when the animals used a population vector to control motion of the cursor. On average we could determine the target in around 35% of the trials. This low percentage was also reflected in poor control of the cursor, so that the animal was unable to reach the target in a large percentage of trials. Supervised adjustment of the population vector parameters produced new weighting coefficients and directional tuning parameters for many neurons. This produced a much better performance of the brain-controlled cursor motion. It was also reflected in the maximum likelihood measure of cell activity, producing the correct target based only on neuronal activity in over 80% of the trials on average. The changes in maximum likelihood estimates of target location based on ensemble firing show that an animal's ability to regulate the motion of a cortically controlled device is not crucially dependent on the experimenter's ability to estimate intention from neuronal activity.

Person Authentication Using Learned Parameters of Lifting Wavelet Filters

NASA Astrophysics Data System (ADS)

Niijima, Koichi

2006-10-01

This paper proposes a method for identifying persons by the use of the lifting wavelet parameters learned by kurtosis-minimization. Our learning method uses desirable properties of kurtosis and wavelet coefficients of a facial image. Exploiting these properties, the lifting parameters are trained so as to minimize the kurtosis of lifting wavelet coefficients computed for the facial image. Since this minimization problem is an ill-posed problem, it is solved by the aid of Tikhonov's regularization method. Our learning algorithm is applied to each of the faces to be identified to generate its feature vector whose components consist of the learned parameters. The constructed feature vectors are memorized together with the corresponding faces in a feature vectors database. Person authentication is performed by comparing the feature vector of a query face with those stored in the database. In numerical experiments, the lifting parameters are trained for each of the neutral faces of 132 persons (74 males and 58 females) in the AR face database. Person authentication is executed by using the smile and anger faces of the same persons in the database.

Multivariable PID controller design tuning using bat algorithm for activated sludge process

NASA Astrophysics Data System (ADS)

Atikah Nor’Azlan, Nur; Asmiza Selamat, Nur; Mat Yahya, Nafrizuan

2018-04-01

The designing of a multivariable PID control for multi input multi output is being concerned with this project by applying four multivariable PID control tuning which is Davison, Penttinen-Koivo, Maciejowski and Proposed Combined method. The determination of this study is to investigate the performance of selected optimization technique to tune the parameter of MPID controller. The selected optimization technique is Bat Algorithm (BA). All the MPID-BA tuning result will be compared and analyzed. Later, the best MPID-BA will be chosen in order to determine which techniques are better based on the system performances in terms of transient response.

ANSYS simulation of the capacitance coupling of quartz tuning fork gyroscope

NASA Astrophysics Data System (ADS)

Zhang, Qing; Feng, Lihui; Zhao, Ke; Cui, Fang; Sun, Yu-nan

2013-12-01

Coupling error is one of the main error sources of the quartz tuning fork gyroscope. The mechanism of capacitance coupling error is analyzed in this article. Finite Element Method (FEM) is used to simulate the structure of the quartz tuning fork by ANSYS software. The voltage output induced by the capacitance coupling is simulated with the harmonic analysis and characteristics of electrical and mechanical parameters influenced by the capacitance coupling between drive electrodes and sense electrodes are discussed with the transient analysis.

Optimizing of a high-order digital filter using PSO algorithm

NASA Astrophysics Data System (ADS)

Xu, Fuchun

2018-04-01

A self-adaptive high-order digital filter, which offers opportunity to simplify the process of tuning parameters and further improve the noise performance, is presented in this paper. The parameters of traditional digital filter are mainly tuned by complex calculation, whereas this paper presents a 5th order digital filter to obtain outstanding performance and the parameters of the proposed filter are optimized by swarm intelligent algorithm. Simulation results with respect to the proposed 5th order digital filter, SNR>122dB and the noise floor under -170dB are obtained in frequency range of [5-150Hz]. In further simulation, the robustness of the proposed 5th order digital is analyzed.

Simultaneous feature selection and parameter optimisation using an artificial ant colony: case study of melting point prediction.

PubMed

O'Boyle, Noel M; Palmer, David S; Nigsch, Florian; Mitchell, John Bo

2008-10-29

We present a novel feature selection algorithm, Winnowing Artificial Ant Colony (WAAC), that performs simultaneous feature selection and model parameter optimisation for the development of predictive quantitative structure-property relationship (QSPR) models. The WAAC algorithm is an extension of the modified ant colony algorithm of Shen et al. (J Chem Inf Model 2005, 45: 1024-1029). We test the ability of the algorithm to develop a predictive partial least squares model for the Karthikeyan dataset (J Chem Inf Model 2005, 45: 581-590) of melting point values. We also test its ability to perform feature selection on a support vector machine model for the same dataset. Starting from an initial set of 203 descriptors, the WAAC algorithm selected a PLS model with 68 descriptors which has an RMSE on an external test set of 46.6 degrees C and R2 of 0.51. The number of components chosen for the model was 49, which was close to optimal for this feature selection. The selected SVM model has 28 descriptors (cost of 5, epsilon of 0.21) and an RMSE of 45.1 degrees C and R2 of 0.54. This model outperforms a kNN model (RMSE of 48.3 degrees C, R2 of 0.47) for the same data and has similar performance to a Random Forest model (RMSE of 44.5 degrees C, R2 of 0.55). However it is much less prone to bias at the extremes of the range of melting points as shown by the slope of the line through the residuals: -0.43 for WAAC/SVM, -0.53 for Random Forest. With a careful choice of objective function, the WAAC algorithm can be used to optimise machine learning and regression models that suffer from overfitting. Where model parameters also need to be tuned, as is the case with support vector machine and partial least squares models, it can optimise these simultaneously. The moving probabilities used by the algorithm are easily interpreted in terms of the best and current models of the ants, and the winnowing procedure promotes the removal of irrelevant descriptors.

Assessing first-order emulator inference for physical parameters in nonlinear mechanistic models

USGS Publications Warehouse

Hooten, Mevin B.; Leeds, William B.; Fiechter, Jerome; Wikle, Christopher K.

2011-01-01

We present an approach for estimating physical parameters in nonlinear models that relies on an approximation to the mechanistic model itself for computational efficiency. The proposed methodology is validated and applied in two different modeling scenarios: (a) Simulation and (b) lower trophic level ocean ecosystem model. The approach we develop relies on the ability to predict right singular vectors (resulting from a decomposition of computer model experimental output) based on the computer model input and an experimental set of parameters. Critically, we model the right singular vectors in terms of the model parameters via a nonlinear statistical model. Specifically, we focus our attention on first-order models of these right singular vectors rather than the second-order (covariance) structure.

Implications for New Physics from Fine-Tuning Arguments: II. Little Higgs Models

NASA Astrophysics Data System (ADS)

Casas, J. A.; Espinosa, J. R.; Hidalgo, I.

2005-03-01

We examine the fine-tuning associated to electroweak breaking in Little Higgs scenarios and find it to be always substantial and, generically, much higher than suggested by the rough estimates usually made. This is due to implicit tunings between parameters that can be overlooked at first glance but show up in a more systematic analysis. Focusing on four popular and representative Little Higgs scenarios, we find that the fine-tuning is essentially comparable to that of the Little Hierarchy problem of the Standard Model (which these scenarios attempt to solve) and higher than in supersymmetric models. This does not demonstrate that all Little Higgs models are fine-tuned, but stresses the need of a careful analysis of this issue in model-building before claiming that a particular model is not fine-tuned. In this respect we identify the main sources of potential fine-tuning that should be watched out for, in order to construct a successful Little Higgs model, which seems to be a non-trivial goal.

Development and application of the modal space self-tuning regulator

NASA Astrophysics Data System (ADS)

Schultze, John Francis

The control and reduction of vibration of flexible structures is currently an area of much research and concern in the aerospace and automotive industries. Often these systems are idealized as discrete systems with a finite number of degrees of freedom. Traditional active control approaches have attempted either to identify the complete system and design an appropriate controller or; use an ad-hoc set of single degree of freedom controllers. Both methods have limitations. The former requires great computational and control design effort. This approach also attempts to reduce the vibration across the complete spectrum as opposed to applying control effort only to the problematic mode(s). The latter method is often limited by its inability to address the structural coupling inherent in these systems. The Modal Space Self Tuning Regulator (MSSTR) method proposed in this research addresses both of these problems as well as changes in the structural properties of a system. The control problem is approached in a two stage effort, decoupling and adaptive control. The structure's motion is decoupled through the Modified Reciprocal Modal Vector method. The control is then implemented in modal space as a new acceleration feedback based, single degree of freedom, form of the Self Tuning Regulator. The range of application of this controller in terms of maximum additive damping, actuator location sensitivity, and discrete and continuous system mass changes are investigated. Also, the behavior of the internal controller parameters are studied for the extension of this method to system monitoring and damage detection. Proof of the numeric stability of the controller in the ideal case is presented as well as its practical implementation issues. This control approach was shown to be effective for the cases of specified damping increases up to 10 dB, several actuator locations, three discrete mass perturbations and several continuous mass change cases. There appears to be little dependence on the actuator position until the additive damping limit is reached. The discrete mass change tests investigate both increases and reductions in the effective moving mass of the system. The controller performed well in all cases investigated achieving a minimum of 7 dB and up to 15 dB of attenuation. The continuous mass change cases, modeling tool-wear, fuel consumption, or other time varying phenomena, show good convergence behavior of the system model and the accompanying regulator law parameters. This validates the controller for its implementation in a rapidly changing system. The MSSTR performed well in several varied test cases, showing both insensitivity to actuator location and resilience to changing system parameters. Extensions to multi-input, multi-mode control appears within ready grasp.

Learning to Estimate Dynamical State with Probabilistic Population Codes.

PubMed

Makin, Joseph G; Dichter, Benjamin K; Sabes, Philip N

2015-11-01

Tracking moving objects, including one's own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, "probabilistic population codes." We show that a recurrent neural network-a modified form of an exponential family harmonium (EFH)-that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.

Learning to Estimate Dynamical State with Probabilistic Population Codes

PubMed Central

Sabes, Philip N.

2015-01-01

Tracking moving objects, including one’s own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, “probabilistic population codes.” We show that a recurrent neural network—a modified form of an exponential family harmonium (EFH)—that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states. PMID:26540152

Method for the reduction of image content redundancy in large image databases

DOEpatents

Tobin, Kenneth William; Karnowski, Thomas P.

2010-03-02

A method of increasing information content for content-based image retrieval (CBIR) systems includes the steps of providing a CBIR database, the database having an index for a plurality of stored digital images using a plurality of feature vectors, the feature vectors corresponding to distinct descriptive characteristics of the images. A visual similarity parameter value is calculated based on a degree of visual similarity between features vectors of an incoming image being considered for entry into the database and feature vectors associated with a most similar of the stored images. Based on said visual similarity parameter value it is determined whether to store or how long to store the feature vectors associated with the incoming image in the database.

Stabilization and analytical tuning rule of double-loop control scheme for unstable dead-time process

NASA Astrophysics Data System (ADS)

Ugon, B.; Nandong, J.; Zang, Z.

2017-06-01

The presence of unstable dead-time systems in process plants often leads to a daunting challenge in the design of standard PID controllers, which are not only intended to provide close-loop stability but also to give good performance-robustness overall. In this paper, we conduct stability analysis on a double-loop control scheme based on the Routh-Hurwitz stability criteria. We propose to use this unstable double-loop control scheme which employs two P/PID controllers to control first-order or second-order unstable dead-time processes typically found in process industries. Based on the Routh-Hurwitz stability necessary and sufficient criteria, we establish several stability regions which enclose within them the P/PID parameter values that guarantee close-loop stability of the double-loop control scheme. A systematic tuning rule is developed for the purpose of obtaining the optimal P/PID parameter values within the established regions. The effectiveness of the proposed tuning rule is demonstrated using several numerical examples and the result are compared with some well-established tuning methods reported in the literature.

A universal multilingual weightless neural network tagger via quantitative linguistics.

PubMed

Carneiro, Hugo C C; Pedreira, Carlos E; França, Felipe M G; Lima, Priscila M V

2017-07-01

In the last decade, given the availability of corpora in several distinct languages, research on multilingual part-of-speech tagging started to grow. Amongst the novelties there is mWANN-Tagger (multilingual weightless artificial neural network tagger), a weightless neural part-of-speech tagger capable of being used for mostly-suffix-oriented languages. The tagger was subjected to corpora in eight languages of quite distinct natures and had a remarkable accuracy with very low sample deviation in every one of them, indicating the robustness of weightless neural systems for part-of-speech tagging tasks. However, mWANN-Tagger needed to be tuned for every new corpus, since each one required a different parameter configuration. For mWANN-Tagger to be truly multilingual, it should be usable for any new language with no need of parameter tuning. This article proposes a study that aims to find a relation between the lexical diversity of a language and the parameter configuration that would produce the best performing mWANN-Tagger instance. Preliminary analyses suggested that a single parameter configuration may be applied to the eight aforementioned languages. The mWANN-Tagger instance produced by this configuration was as accurate as the language-dependent ones obtained through tuning. Afterwards, the weightless neural tagger was further subjected to new corpora in languages that range from very isolating to polysynthetic ones. The best performing instances of mWANN-Tagger are again the ones produced by the universal parameter configuration. Hence, mWANN-Tagger can be applied to new corpora with no need of parameter tuning, making it a universal multilingual part-of-speech tagger. Further experiments with Universal Dependencies treebanks reveal that mWANN-Tagger may be extended and that it has potential to outperform most state-of-the-art part-of-speech taggers if better word representations are provided. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sample Skewness as a Statistical Measurement of Neuronal Tuning Sharpness

PubMed Central

Samonds, Jason M.; Potetz, Brian R.; Lee, Tai Sing

2014-01-01

We propose using the statistical measurement of the sample skewness of the distribution of mean firing rates of a tuning curve to quantify sharpness of tuning. For some features, like binocular disparity, tuning curves are best described by relatively complex and sometimes diverse functions, making it difficult to quantify sharpness with a single function and parameter. Skewness provides a robust nonparametric measure of tuning curve sharpness that is invariant with respect to the mean and variance of the tuning curve and is straightforward to apply to a wide range of tuning, including simple orientation tuning curves and complex object tuning curves that often cannot even be described parametrically. Because skewness does not depend on a specific model or function of tuning, it is especially appealing to cases of sharpening where recurrent interactions among neurons produce sharper tuning curves that deviate in a complex manner from the feedforward function of tuning. Since tuning curves for all neurons are not typically well described by a single parametric function, this model independence additionally allows skewness to be applied to all recorded neurons, maximizing the statistical power of a set of data. We also compare skewness with other nonparametric measures of tuning curve sharpness and selectivity. Compared to these other nonparametric measures tested, skewness is best used for capturing the sharpness of multimodal tuning curves defined by narrow peaks (maximum) and broad valleys (minima). Finally, we provide a more formal definition of sharpness using a shape-based information gain measure and derive and show that skewness is correlated with this definition. PMID:24555451

Adaptive distance metric learning for diffusion tensor image segmentation.

PubMed

Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C N; Chu, Winnie C W

2014-01-01

High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

Adaptive Distance Metric Learning for Diffusion Tensor Image Segmentation

PubMed Central

Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C. N.; Chu, Winnie C. W.

2014-01-01

High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework. PMID:24651858

Output Feedback-Based Boundary Control of Uncertain Coupled Semilinear Parabolic PDE Using Neurodynamic Programming.

PubMed

Talaei, Behzad; Jagannathan, Sarangapani; Singler, John

2018-04-01

In this paper, neurodynamic programming-based output feedback boundary control of distributed parameter systems governed by uncertain coupled semilinear parabolic partial differential equations (PDEs) under Neumann or Dirichlet boundary control conditions is introduced. First, Hamilton-Jacobi-Bellman (HJB) equation is formulated in the original PDE domain and the optimal control policy is derived using the value functional as the solution of the HJB equation. Subsequently, a novel observer is developed to estimate the system states given the uncertain nonlinearity in PDE dynamics and measured outputs. Consequently, the suboptimal boundary control policy is obtained by forward-in-time estimation of the value functional using a neural network (NN)-based online approximator and estimated state vector obtained from the NN observer. Novel adaptive tuning laws in continuous time are proposed for learning the value functional online to satisfy the HJB equation along system trajectories while ensuring the closed-loop stability. Local uniformly ultimate boundedness of the closed-loop system is verified by using Lyapunov theory. The performance of the proposed controller is verified via simulation on an unstable coupled diffusion reaction process.

Speaker gender identification based on majority vote classifiers

NASA Astrophysics Data System (ADS)

Mezghani, Eya; Charfeddine, Maha; Nicolas, Henri; Ben Amar, Chokri

2017-03-01

Speaker gender identification is considered among the most important tools in several multimedia applications namely in automatic speech recognition, interactive voice response systems and audio browsing systems. Gender identification systems performance is closely linked to the selected feature set and the employed classification model. Typical techniques are based on selecting the best performing classification method or searching optimum tuning of one classifier parameters through experimentation. In this paper, we consider a relevant and rich set of features involving pitch, MFCCs as well as other temporal and frequency-domain descriptors. Five classification models including decision tree, discriminant analysis, nave Bayes, support vector machine and k-nearest neighbor was experimented. The three best perming classifiers among the five ones will contribute by majority voting between their scores. Experimentations were performed on three different datasets spoken in three languages: English, German and Arabic in order to validate language independency of the proposed scheme. Results confirm that the presented system has reached a satisfying accuracy rate and promising classification performance thanks to the discriminating abilities and diversity of the used features combined with mid-level statistics.

Markov-random-field-based super-resolution mapping for identification of urban trees in VHR images

NASA Astrophysics Data System (ADS)

Ardila, Juan P.; Tolpekin, Valentyn A.; Bijker, Wietske; Stein, Alfred

2011-11-01

Identification of tree crowns from remote sensing requires detailed spectral information and submeter spatial resolution imagery. Traditional pixel-based classification techniques do not fully exploit the spatial and spectral characteristics of remote sensing datasets. We propose a contextual and probabilistic method for detection of tree crowns in urban areas using a Markov random field based super resolution mapping (SRM) approach in very high resolution images. Our method defines an objective energy function in terms of the conditional probabilities of panchromatic and multispectral images and it locally optimizes the labeling of tree crown pixels. Energy and model parameter values are estimated from multiple implementations of SRM in tuning areas and the method is applied in QuickBird images to produce a 0.6 m tree crown map in a city of The Netherlands. The SRM output shows an identification rate of 66% and commission and omission errors in small trees and shrub areas. The method outperforms tree crown identification results obtained with maximum likelihood, support vector machines and SRM at nominal resolution (2.4 m) approaches.

Vector-algebra approach to extract Denavit-Hartenberg parameters of assembled robot arms

NASA Technical Reports Server (NTRS)

Barker, L. K.

1983-01-01

The Denavit-Hartenberg parameters characterize the joint axis systems in a robot arm and, naturally, appear in the transformation matrices from one joint axis system to another. These parameters are needed in the control of robot arms and in the passage of sensor information along the arm. This paper presents a vector algebra method to determine these parameters for any assembled robot arm. The idea is to measure the location of the robot hand (or extension) for different joint angles and then use these measurements to calculate the parameters.

Spin-orbit coupling effects in indium antimonide quantum well structures

NASA Astrophysics Data System (ADS)

Dedigama, Aruna Ruwan

Indium antimonide (InSb) is a narrow band gap material which has the smallest electron effective mass (0.014m0) and the largest electron Lande g-facture (-51) of all the III-V semiconductors. Spin-orbit effects of III-V semiconductor heterostructures arise from two different inversion asymmetries namely bulk inversion asymmetry (BIA) and structural inversion asymmetry (SIA). BIA is due to the zinc-blende nature of this material which leads to the Dresselhaus spin splitting consisting of both linear and cubic in-plane wave vector terms. As its name implies SIA arises due to the asymmetry of the quantum well structure, this leads to the Rashba spin splitting term which is linear in wave vector. Although InSb has theoretically predicted large Dresselhaus (760 eVA3) and Rashba (523 eA 2) coefficients there has been relatively little experimental investigation of spin-orbit coefficients. Spin-orbit coefficients can be extracted from the beating patterns of Shubnikov--de Haas oscillations (SdH), for material like InSb it is hard to use this method due to the existence of large electron Lande g-facture. Therefore it is essential to use a low field magnetotransport technique such as weak antilocalization to extract spin-orbit parameters for InSb. The main focus of this thesis is to experimentally determine the spin-orbit parameters for both symmetrically and asymmetrically doped InSb/InxAl 1-xSb heterostructures. During this study attempts have been made to tune the Rashba spin-orbit coupling coefficient by using a back gate to change the carrier density of the samples. Dominant phase breaking mechanisms for InSb/InxAl1-xSb heterostructures have been identified by analyzing the temperature dependence of the phase breaking field from weak antilocalization measurements. Finally the strong spin-orbit effects on InSb/InxAl1-xSb heterostructures have been demonstrated with ballistic spin focusing devices.

The feasibility of manual parameter tuning for deformable breast MR image registration from a multi-objective optimization perspective.

PubMed

Pirpinia, Kleopatra; Bosman, Peter A N; Loo, Claudette E; Winter-Warnars, Gonneke; Janssen, Natasja N Y; Scholten, Astrid N; Sonke, Jan-Jakob; van Herk, Marcel; Alderliesten, Tanja

2017-06-23

Deformable image registration is typically formulated as an optimization problem involving a linearly weighted combination of terms that correspond to objectives of interest (e.g. similarity, deformation magnitude). The weights, along with multiple other parameters, need to be manually tuned for each application, a task currently addressed mainly via trial-and-error approaches. Such approaches can only be successful if there is a sensible interplay between parameters, objectives, and desired registration outcome. This, however, is not well established. To study this interplay, we use multi-objective optimization, where multiple solutions exist that represent the optimal trade-offs between the objectives, forming a so-called Pareto front. Here, we focus on weight tuning. To study the space a user has to navigate during manual weight tuning, we randomly sample multiple linear combinations. To understand how these combinations relate to desirability of registration outcome, we associate with each outcome a mean target registration error (TRE) based on expert-defined anatomical landmarks. Further, we employ a multi-objective evolutionary algorithm that optimizes the weight combinations, yielding a Pareto front of solutions, which can be directly navigated by the user. To study how the complexity of manual weight tuning changes depending on the registration problem, we consider an easy problem, prone-to-prone breast MR image registration, and a hard problem, prone-to-supine breast MR image registration. Lastly, we investigate how guidance information as an additional objective influences the prone-to-supine registration outcome. Results show that the interplay between weights, objectives, and registration outcome makes manual weight tuning feasible for the prone-to-prone problem, but very challenging for the harder prone-to-supine problem. Here, patient-specific, multi-objective weight optimization is needed, obtaining a mean TRE of 13.6 mm without guidance information reduced to 7.3 mm with guidance information, but also providing a Pareto front that exhibits an intuitively sensible interplay between weights, objectives, and registration outcome, allowing outcome selection.

The feasibility of manual parameter tuning for deformable breast MR image registration from a multi-objective optimization perspective

NASA Astrophysics Data System (ADS)

Pirpinia, Kleopatra; Bosman, Peter A. N.; E Loo, Claudette; Winter-Warnars, Gonneke; Y Janssen, Natasja N.; Scholten, Astrid N.; Sonke, Jan-Jakob; van Herk, Marcel; Alderliesten, Tanja

2017-07-01

Deformable image registration is typically formulated as an optimization problem involving a linearly weighted combination of terms that correspond to objectives of interest (e.g. similarity, deformation magnitude). The weights, along with multiple other parameters, need to be manually tuned for each application, a task currently addressed mainly via trial-and-error approaches. Such approaches can only be successful if there is a sensible interplay between parameters, objectives, and desired registration outcome. This, however, is not well established. To study this interplay, we use multi-objective optimization, where multiple solutions exist that represent the optimal trade-offs between the objectives, forming a so-called Pareto front. Here, we focus on weight tuning. To study the space a user has to navigate during manual weight tuning, we randomly sample multiple linear combinations. To understand how these combinations relate to desirability of registration outcome, we associate with each outcome a mean target registration error (TRE) based on expert-defined anatomical landmarks. Further, we employ a multi-objective evolutionary algorithm that optimizes the weight combinations, yielding a Pareto front of solutions, which can be directly navigated by the user. To study how the complexity of manual weight tuning changes depending on the registration problem, we consider an easy problem, prone-to-prone breast MR image registration, and a hard problem, prone-to-supine breast MR image registration. Lastly, we investigate how guidance information as an additional objective influences the prone-to-supine registration outcome. Results show that the interplay between weights, objectives, and registration outcome makes manual weight tuning feasible for the prone-to-prone problem, but very challenging for the harder prone-to-supine problem. Here, patient-specific, multi-objective weight optimization is needed, obtaining a mean TRE of 13.6 mm without guidance information reduced to 7.3 mm with guidance information, but also providing a Pareto front that exhibits an intuitively sensible interplay between weights, objectives, and registration outcome, allowing outcome selection.

Comparison of scoliosis measurements based on three-dimensional vertebra vectors and conventional two-dimensional measurements: advantages in evaluation of prognosis and surgical results.

PubMed

Illés, Tamás; Somoskeöy, Szabolcs

2013-06-01

A new concept of vertebra vectors based on spinal three-dimensional (3D) reconstructions of images from the EOS system, a new low-dose X-ray imaging device, was recently proposed to facilitate interpretation of EOS 3D data, especially with regard to horizontal plane images. This retrospective study was aimed at the evaluation of the spinal layout visualized by EOS 3D and vertebra vectors before and after surgical correction, the comparison of scoliotic spine measurement values based on 3D vertebra vectors with measurements using conventional two-dimensional (2D) methods, and an evaluation of horizontal plane vector parameters for their relationship with the magnitude of scoliotic deformity. 95 patients with adolescent idiopathic scoliosis operated according to the Cotrel-Dubousset principle were subjected to EOS X-ray examinations pre- and postoperatively, followed by 3D reconstructions and generation of vertebra vectors in a calibrated coordinate system to calculate vector coordinates and parameters, as published earlier. Differences in values of conventional 2D Cobb methods and methods based on vertebra vectors were evaluated by means comparison T test and relationship of corresponding parameters was analysed by bivariate correlation. Relationship of horizontal plane vector parameters with the magnitude of scoliotic deformities and results of surgical correction were analysed by Pearson correlation and linear regression. In comparison to manual 2D methods, a very close relationship was detectable in vertebra vector-based curvature data for coronal curves (preop r 0.950, postop r 0.935) and thoracic kyphosis (preop r 0.893, postop r 0.896), while the found small difference in L1-L5 lordosis values (preop r 0.763, postop r 0.809) was shown to be strongly related to the magnitude of corresponding L5 wedge. The correlation analysis results revealed strong correlation between the magnitude of scoliosis and the lateral translation of apical vertebra in horizontal plane. The horizontal plane coordinates of the terminal and initial points of apical vertebra vectors represent this (r 0.701; r 0.667). Less strong correlation was detected in the axial rotation of apical vertebras and the magnitudes of the frontal curves (r 0.459). Vertebra vectors provide a key opportunity to visualize spinal deformities in all three planes simultaneously. Measurement methods based on vertebral vectors proved to be just as accurate and reliable as conventional measurement methods for coronal and sagittal plane parameters. In addition, the horizontal plane display of the curves can be studied using the same vertebra vectors. Based on the vertebra vectors data, during the surgical treatment of spinal deformities, the diminution of the lateral translation of the vertebras seems to be more important in the results of the surgical correction than the correction of the axial rotation.

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

Liu, Shengqiang; Li, Jie; Yu, Junsheng, E-mail: jsyu@uestc.edu.cn

A color tuning index (I{sub CT}) parameter for evaluating the color change capability of color-tunable organic light-emitting diodes (CT-OLEDs) was proposed and formulated. And a series of CT-OLEDs, consisting of five different carrier/exciton adjusting interlayers (C/EALs) inserted between two complementary emitting layers, were fabricated and applied to disclose the relationship between I{sub CT} and C/EALs. The result showed that the trend of electroluminescence spectra behavior in CT-OLEDs has good accordance with I{sub CT} values, indicating that the I{sub CT} parameter is feasible for the evaluation of color variation. Meanwhile, by changing energy level and C/EAL thickness, the optimized device withmore » the widest color tuning range was based on N,N′-dicarbazolyl-3,5-benzene C/EAL, exhibiting the highest I{sub CT} value of 41.2%. Based on carrier quadratic hopping theory and exciton transfer model, two fitting I{sub CT} formulas derived from the highest occupied molecular orbital (HOMO) energy level and triplet energy level were simulated. Finally, a color tuning prediction (CTP) model was developed to deduce the I{sub CT} via C/EAL HOMO and triplet energy levels, and verified by the fabricated OLEDs with five different C/EALs. We believe that the CTP model assisted with I{sub CT} parameter will be helpful for fabricating high performance CT-OLEDs with a broad range of color tuning.« less

Adjudicating between face-coding models with individual-face fMRI responses

PubMed Central

Kriegeskorte, Nikolaus

2017-01-01

The perceptual representation of individual faces is often explained with reference to a norm-based face space. In such spaces, individuals are encoded as vectors where identity is primarily conveyed by direction and distinctiveness by eccentricity. Here we measured human fMRI responses and psychophysical similarity judgments of individual face exemplars, which were generated as realistic 3D animations using a computer-graphics model. We developed and evaluated multiple neurobiologically plausible computational models, each of which predicts a representational distance matrix and a regional-mean activation profile for 24 face stimuli. In the fusiform face area, a face-space coding model with sigmoidal ramp tuning provided a better account of the data than one based on exemplar tuning. However, an image-processing model with weighted banks of Gabor filters performed similarly. Accounting for the data required the inclusion of a measurement-level population averaging mechanism that approximates how fMRI voxels locally average distinct neuronal tunings. Our study demonstrates the importance of comparing multiple models and of modeling the measurement process in computational neuroimaging. PMID:28746335

Integration of celestial compass cues in the central complex of the locust brain.

PubMed

Pegel, Uta; Pfeiffer, Keram; Homberg, Uwe

2018-01-29

Many insects rely on celestial compass cues such as the polarization pattern of the sky for spatial orientation. In the desert locust, the central complex (CX) houses multiple sets of neurons, sensitive to the oscillation plane of polarized light and thus probably acts as an internal polarization compass. We investigated whether other sky compass cues like direct sunlight or the chromatic gradient of the sky might contribute to this compass. We recorded from polarization-sensitive CX neurons while an unpolarized green or ultraviolet light spot was moved around the head of the animal. All types of neuron that were sensitive to the plane of polarization ( E -vector) above the animal also responded to the unpolarized light spots in an azimuth-dependent way. The tuning to the unpolarized light spots was independent of wavelength, suggesting that the neurons encode solar azimuth based on direct sunlight and not on the sky chromatic gradient. Two cell types represented the natural 90 deg relationship between solar azimuth and zenithal E -vector orientation, providing evidence to suggest that solar azimuth information supports the internal polarization compass. Most neurons showed advances in their tuning to the E -vector and the unpolarized light spots dependent on rotation direction, consistent with anticipatory signaling. The amplitude of responses and its variability were dependent on the level of background firing, possibly indicating different internal states. The integration of polarization and solar azimuth information strongly suggests that besides the polarization pattern of the sky, direct sunlight might be an important cue for sky compass navigation in the locust. © 2018. Published by The Company of Biologists Ltd.

Bifilar analysis study, volume 1

NASA Technical Reports Server (NTRS)

Miao, W.; Mouzakis, T.

1980-01-01

A coupled rotor/bifilar/airframe analysis was developed and utilized to study the dynamic characteristics of the centrifugally tuned, rotor-hub-mounted, bifilar vibration absorber. The analysis contains the major components that impact the bifilar absorber performance, namely, an elastic rotor with hover aerodynamics, a flexible fuselage, and nonlinear individual degrees of freedom for each bifilar mass. Airspeed, rotor speed, bifilar mass and tuning variations are considered. The performance of the bifilar absorber is shown to be a function of its basic parameters: dynamic mass, damping and tuning, as well as the impedance of the rotor hub. The effect of the dissimilar responses of the individual bifilar masses which are caused by tolerance induced mass, damping and tuning variations is also examined.

Parameters Estimation For A Patellofemoral Joint Of A Human Knee Using A Vector Method

NASA Astrophysics Data System (ADS)

Ciszkiewicz, A.; Knapczyk, J.

2015-08-01

Position and displacement analysis of a spherical model of a human knee joint using the vector method was presented. Sensitivity analysis and parameter estimation were performed using the evolutionary algorithm method. Computer simulations for the mechanism with estimated parameters proved the effectiveness of the prepared software. The method itself can be useful when solving problems concerning the displacement and loads analysis in the knee joint.

Rank-Optimized Logistic Matrix Regression toward Improved Matrix Data Classification.

PubMed

Zhang, Jianguang; Jiang, Jianmin

2018-02-01

While existing logistic regression suffers from overfitting and often fails in considering structural information, we propose a novel matrix-based logistic regression to overcome the weakness. In the proposed method, 2D matrices are directly used to learn two groups of parameter vectors along each dimension without vectorization, which allows the proposed method to fully exploit the underlying structural information embedded inside the 2D matrices. Further, we add a joint [Formula: see text]-norm on two parameter matrices, which are organized by aligning each group of parameter vectors in columns. This added co-regularization term has two roles-enhancing the effect of regularization and optimizing the rank during the learning process. With our proposed fast iterative solution, we carried out extensive experiments. The results show that in comparison to both the traditional tensor-based methods and the vector-based regression methods, our proposed solution achieves better performance for matrix data classifications.

Optical frequency up-conversion by supercontinuum-free widely-tunable fiber-optic Cherenkov radiation

PubMed Central

Tu, Haohua; Boppart, Stephen A.

2010-01-01

Spectrally-isolated narrowband Cherenkov radiation from commercial nonlinear photonic crystal fibers is demonstrated as an ultrafast optical source with a visible tuning range of 485–690 nm, which complementarily extends the near-infrared tuning range of 690–1020 nm from the corresponding femtosecond Ti:sapphire pump laser. Pump-to-signal conversion efficiency routinely surpasses 10%, enabling multimilliwatt visible output across the entire tuning range. Appropriate selection of fiber parameters and pumping conditions efficiently suppresses the supercontinuum generation typically associated with Cherenkov radiation. PMID:19506636

Controllability of the Coulomb charging energy in close-packed nanoparticle arrays.

PubMed

Duan, Chao; Wang, Ying; Sun, Jinling; Guan, Changrong; Grunder, Sergio; Mayor, Marcel; Peng, Lianmao; Liao, Jianhui

2013-11-07

We studied the electronic transport properties of metal nanoparticle arrays, particularly focused on the Coulomb charging energy. By comparison, we confirmed that it is more reasonable to estimate the Coulomb charging energy using the activation energy from the temperature-dependent zero-voltage conductance. Based on this, we systematically and comprehensively investigated the parameters that could be used to tune the Coulomb charging energy in nanoparticle arrays. We found that four parameters, including the particle core size, the inter-particle distance, the nearest neighboring number, and the dielectric constant of ligand molecules, could significantly tune the Coulomb charging energy.

Dirac gauginos, R symmetry and the 125 GeV Higgs

DOE PAGES

Bertuzzo, Enrico; Frugiuele, Claudia; Gregoire, Thomas; ...

2015-04-20

We study a supersymmetric scenario with a quasi exact R-symmetry in light of the discovery of a Higgs resonance with a mass of 125 GeV. In such a framework, the additional adjoint superfields, needed to give Dirac masses to the gauginos, contribute both to the Higgs mass and to electroweak precision observables. We then analyze the interplay between the two aspects, finding regions in parameter space in which the contributions to the precision observables are under control and a 125 GeV Higgs boson can be accommodated. Furthermore, we estimate the fine-tuning of the model finding regions of the parameter spacemore » still unexplored by the LHC with a fine-tuning considerably improved with respect to the minimal supersymmetric scenario. In particular, sizable non-holomorphic (non-supersoft) adjoints masses are required to reduce the fine-tuning.« less

Modelling and tuning for a time-delayed vibration absorber with friction

NASA Astrophysics Data System (ADS)

Zhang, Xiaoxu; Xu, Jian; Ji, Jinchen

2018-06-01

This paper presents an integrated analytical and experimental study to the modelling and tuning of a time-delayed vibration absorber (TDVA) with friction. In system modelling, this paper firstly applies the method of averaging to obtain the frequency response function (FRF), and then uses the derived FRF to evaluate the fitness of different friction models. After the determination of the system model, this paper employs the obtained FRF to evaluate the vibration absorption performance with respect to tunable parameters. A significant feature of the TDVA with friction is that its stability is dependent on the excitation parameters. To ensure the stability of the time-delayed control, this paper defines a sufficient condition for stability estimation. Experimental measurements show that the dynamic response of the TDVA with friction can be accurately predicted and the time-delayed control can be precisely achieved by using the modelling and tuning technique provided in this paper.

Fibre-coupled red diode-pumped Alexandrite TEM00 laser with single and double-pass end-pumping

NASA Astrophysics Data System (ADS)

Arbabzadah, E. A.; Damzen, M. J.

2016-06-01

We report the investigation of an Alexandrite laser end-pumped by a fibre-coupled red diode laser module. Power, efficiency, spatial, spectral, and wavelength tuning performance are studied as a function of pump and laser cavity parameters. It is the first demonstration, to our knowledge, of greater than 1 W power and also highest laser slope efficiency (44.2%) in a diode-pumped Alexandrite laser with diffraction-limited TEM00 mode operation. Spatial quality was excellent with beam propagation parameter M 2 ~ 1.05. Wavelength tuning from 737-796 nm was demonstrated using an intracavity birefringent tuning filter. Using a novel double pass end-pumping scheme to get efficient absorption of both polarisation states of the scrambled fibre-delivered diode pump, a total output coupled power of 1.66 W is produced in TEM00 mode with 40% slope efficiency.

Nonlinear Adaptive PID Control for Greenhouse Environment Based on RBF Network

PubMed Central

Zeng, Songwei; Hu, Haigen; Xu, Lihong; Li, Guanghui

2012-01-01

This paper presents a hybrid control strategy, combining Radial Basis Function (RBF) network with conventional proportional, integral, and derivative (PID) controllers, for the greenhouse climate control. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is formulated. RBF network is used to tune and identify all PID gain parameters online and adaptively. The presented Neuro-PID control scheme is validated through simulations of set-point tracking and disturbance rejection. We compare the proposed adaptive online tuning method with the offline tuning scheme that employs Genetic Algorithm (GA) to search the optimal gain parameters. The results show that the proposed strategy has good adaptability, strong robustness and real-time performance while achieving satisfactory control performance for the complex and nonlinear greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production. PMID:22778587

Difference between BPM reading one bunch and the average of multi-bunch in Booster

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

Xi Yang

2004-08-18

Differences caused by BPM reading one bunch and multi-bunch average need to be well understood before the beam parameters, such as the synchrotron tune, betatron tune, and chromaticity, are extracted from those BPM data. It is easy to perform such a study using numerical simulation other than modifying the BPM electronics.

Note: a transimpedance amplifier for remotely located quartz tuning forks.

PubMed

Kleinbaum, Ethan; Csáthy, Gábor A

2012-12-01

The cable capacitance in cryogenic and high vacuum applications of quartz tuning forks imposes severe constraints on the bandwidth and noise performance of the measurement. We present a single stage low noise transimpedance amplifier with a bandwidth exceeding 1 MHz and provide an in-depth analysis of the dependence of the amplifier parameters on the cable capacitance.

Algebraic and radical potential fields. Stability domains in coordinate and parametric space

NASA Astrophysics Data System (ADS)

Uteshev, Alexei Yu.

2018-05-01

A dynamical system d X/d t = F(X; A) is treated where F(X; A) is a polynomial (or some general type of radical contained) function in the vectors of state variables X ∈ ℝn and parameters A ∈ ℝm. We are looking for stability domains in both spaces, i.e. (a) domain ℙ ⊂ ℝm such that for any parameter vector specialization A ∈ ℙ, there exists a stable equilibrium for the dynamical system, and (b) domain 𝕊 ⊂ ℝn such that any point X* ∈ 𝕊 could be made a stable equilibrium by a suitable specialization of the parameter vector A.

Fourier transform inequalities for phylogenetic trees.

PubMed

Matsen, Frederick A

2009-01-01

Phylogenetic invariants are not the only constraints on site-pattern frequency vectors for phylogenetic trees. A mutation matrix, by its definition, is the exponential of a matrix with non-negative off-diagonal entries; this positivity requirement implies non-trivial constraints on the site-pattern frequency vectors. We call these additional constraints "edge-parameter inequalities". In this paper, we first motivate the edge-parameter inequalities by considering a pathological site-pattern frequency vector corresponding to a quartet tree with a negative internal edge. This site-pattern frequency vector nevertheless satisfies all of the constraints described up to now in the literature. We next describe two complete sets of edge-parameter inequalities for the group-based models; these constraints are square-free monomial inequalities in the Fourier transformed coordinates. These inequalities, along with the phylogenetic invariants, form a complete description of the set of site-pattern frequency vectors corresponding to bona fide trees. Said in mathematical language, this paper explicitly presents two finite lists of inequalities in Fourier coordinates of the form "monomial < or = 1", each list characterizing the phylogenetically relevant semialgebraic subsets of the phylogenetic varieties.

Computing the Sensitivity Kernels for 2.5-D Seismic Waveform Inversion in Heterogeneous, Anisotropic Media

NASA Astrophysics Data System (ADS)

Zhou, Bing; Greenhalgh, S. A.

2011-10-01

2.5-D modeling and inversion techniques are much closer to reality than the simple and traditional 2-D seismic wave modeling and inversion. The sensitivity kernels required in full waveform seismic tomographic inversion are the Fréchet derivatives of the displacement vector with respect to the independent anisotropic model parameters of the subsurface. They give the sensitivity of the seismograms to changes in the model parameters. This paper applies two methods, called `the perturbation method' and `the matrix method', to derive the sensitivity kernels for 2.5-D seismic waveform inversion. We show that the two methods yield the same explicit expressions for the Fréchet derivatives using a constant-block model parameterization, and are available for both the line-source (2-D) and the point-source (2.5-D) cases. The method involves two Green's function vectors and their gradients, as well as the derivatives of the elastic modulus tensor with respect to the independent model parameters. The two Green's function vectors are the responses of the displacement vector to the two directed unit vectors located at the source and geophone positions, respectively; they can be generally obtained by numerical methods. The gradients of the Green's function vectors may be approximated in the same manner as the differential computations in the forward modeling. The derivatives of the elastic modulus tensor with respect to the independent model parameters can be obtained analytically, dependent on the class of medium anisotropy. Explicit expressions are given for two special cases—isotropic and tilted transversely isotropic (TTI) media. Numerical examples are given for the latter case, which involves five independent elastic moduli (or Thomsen parameters) plus one angle defining the symmetry axis.

Ellipsoidal fuzzy learning for smart car platoons

NASA Astrophysics Data System (ADS)

Dickerson, Julie A.; Kosko, Bart

1993-12-01

A neural-fuzzy system combined supervised and unsupervised learning to find and tune the fuzzy-rules. An additive fuzzy system approximates a function by covering its graph with fuzzy rules. A fuzzy rule patch can take the form of an ellipsoid in the input-output space. Unsupervised competitive learning found the statistics of data clusters. The covariance matrix of each synaptic quantization vector defined on ellipsoid centered at the centroid of the data cluster. Tightly clustered data gave smaller ellipsoids or more certain rules. Sparse data gave larger ellipsoids or less certain rules. Supervised learning tuned the ellipsoids to improve the approximation. The supervised neural system used gradient descent to find the ellipsoidal fuzzy patches. It locally minimized the mean-squared error of the fuzzy approximation. Hybrid ellipsoidal learning estimated the control surface for a smart car controller.

Light trapping and circularly polarization at a Dirac point in 2D plasma photonic crystals

NASA Astrophysics Data System (ADS)

Li, Qian; Hu, Lei; Mao, Qiuping; Jiang, Haiming; Hu, Zhijia; Xie, Kang; Wei, Zhang

2018-03-01

Light trapping at the Dirac point in 2D plasma photonic crystal has been obtained. The new localized mode, Dirac mode, is attributable to neither photonic bandgap nor total internal reflection. It exhibits a unique algebraic profile and possesses a high-Q factor resonator of about 105. The Dirac point could be modulated by tuning the filling factor, plasma frequency and plasma cyclotron frequency, respectively. When a magnetic field parallel to the wave vector is applied, Dirac modes for right circularly polarized and left circularly polarized waves could be obtained at different frequencies, and the Q factor could be tuned. This property will add more controllability and flexibility to the design and modulation of novel photonic devices. It is also valuable for the possibilities of Dirac modes in photonic crystal containing other kinds of metamaterials.

Item Vector Plots for the Multidimensional Three-Parameter Logistic Model

ERIC Educational Resources Information Center

Bryant, Damon; Davis, Larry

2011-01-01

This brief technical note describes how to construct item vector plots for dichotomously scored items fitting the multidimensional three-parameter logistic model (M3PLM). As multidimensional item response theory (MIRT) shows promise of being a very useful framework in the test development life cycle, graphical tools that facilitate understanding…

Output-only modal parameter estimator of linear time-varying structural systems based on vector TAR model and least squares support vector machine

NASA Astrophysics Data System (ADS)

Zhou, Si-Da; Ma, Yuan-Chen; Liu, Li; Kang, Jie; Ma, Zhi-Sai; Yu, Lei

2018-01-01

Identification of time-varying modal parameters contributes to the structural health monitoring, fault detection, vibration control, etc. of the operational time-varying structural systems. However, it is a challenging task because there is not more information for the identification of the time-varying systems than that of the time-invariant systems. This paper presents a vector time-dependent autoregressive model and least squares support vector machine based modal parameter estimator for linear time-varying structural systems in case of output-only measurements. To reduce the computational cost, a Wendland's compactly supported radial basis function is used to achieve the sparsity of the Gram matrix. A Gamma-test-based non-parametric approach of selecting the regularization factor is adapted for the proposed estimator to replace the time-consuming n-fold cross validation. A series of numerical examples have illustrated the advantages of the proposed modal parameter estimator on the suppression of the overestimate and the short data. A laboratory experiment has further validated the proposed estimator.

Effects of timbre and tempo change on memory for music.

PubMed

Halpern, Andrea R; Müllensiefen, Daniel

2008-09-01

We investigated the effects of different encoding tasks and of manipulations of two supposedly surface parameters of music on implicit and explicit memory for tunes. In two experiments, participants were first asked to either categorize instrument or judge familiarity of 40 unfamiliar short tunes. Subsequently, participants were asked to give explicit and implicit memory ratings for a list of 80 tunes, which included 40 previously heard. Half of the 40 previously heard tunes differed in timbre (Experiment 1) or tempo (Experiment 2) in comparison with the first exposure. A third experiment compared similarity ratings of the tunes that varied in timbre or tempo. Analysis of variance (ANOVA) results suggest first that the encoding task made no difference for either memory mode. Secondly, timbre and tempo change both impaired explicit memory, whereas tempo change additionally made implicit tune recognition worse. Results are discussed in the context of implicit memory for nonsemantic materials and the possible differences in timbre and tempo in musical representations.

Efficient receiver tuning using differential evolution strategies

NASA Astrophysics Data System (ADS)

Wheeler, Caleb H.; Toland, Trevor G.

2016-08-01

Differential evolution (DE) is a powerful and computationally inexpensive optimization strategy that can be used to search an entire parameter space or to converge quickly on a solution. The Kilopixel Array Pathfinder Project (KAPPa) is a heterodyne receiver system delivering 5 GHz of instantaneous bandwidth in the tuning range of 645-695 GHz. The fully automated KAPPa receiver test system finds optimal receiver tuning using performance feedback and DE. We present an adaptation of DE for use in rapid receiver characterization. The KAPPa DE algorithm is written in Python 2.7 and is fully integrated with the KAPPa instrument control, data processing, and visualization code. KAPPa develops the technologies needed to realize heterodyne focal plane arrays containing 1000 pixels. Finding optimal receiver tuning by investigating large parameter spaces is one of many challenges facing the characterization phase of KAPPa. This is a difficult task via by-hand techniques. Characterizing or tuning in an automated fashion without need for human intervention is desirable for future large scale arrays. While many optimization strategies exist, DE is ideal for time and performance constraints because it can be set to converge to a solution rapidly with minimal computational overhead. We discuss how DE is utilized in the KAPPa system and discuss its performance and look toward the future of 1000 pixel array receivers and consider how the KAPPa DE system might be applied.

Polarized skylight navigation in insects: model and electrophysiology of e-vector coding by neurons in the central complex.

PubMed

Sakura, Midori; Lambrinos, Dimitrios; Labhart, Thomas

2008-02-01

Many insects exploit skylight polarization for visual compass orientation or course control. As found in crickets, the peripheral visual system (optic lobe) contains three types of polarization-sensitive neurons (POL neurons), which are tuned to different ( approximately 60 degrees diverging) e-vector orientations. Thus each e-vector orientation elicits a specific combination of activities among the POL neurons coding any e-vector orientation by just three neural signals. In this study, we hypothesize that in the presumed orientation center of the brain (central complex) e-vector orientation is population-coded by a set of "compass neurons." Using computer modeling, we present a neural network model transforming the signal triplet provided by the POL neurons to compass neuron activities coding e-vector orientation by a population code. Using intracellular electrophysiology and cell marking, we present evidence that neurons with the response profile of the presumed compass neurons do indeed exist in the insect brain: each of these compass neuron-like (CNL) cells is activated by a specific e-vector orientation only and otherwise remains silent. Morphologically, CNL cells are tangential neurons extending from the lateral accessory lobe to the lower division of the central body. Surpassing the modeled compass neurons in performance, CNL cells are insensitive to the degree of polarization of the stimulus between 99% and at least down to 18% polarization and thus largely disregard variations of skylight polarization due to changing solar elevations or atmospheric conditions. This suggests that the polarization vision system includes a gain control circuit keeping the output activity at a constant level.

MIC-SVM: Designing A Highly Efficient Support Vector Machine For Advanced Modern Multi-Core and Many-Core Architectures

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

You, Yang; Song, Shuaiwen; Fu, Haohuan

2014-08-16

Support Vector Machine (SVM) has been widely used in data-mining and Big Data applications as modern commercial databases start to attach an increasing importance to the analytic capabilities. In recent years, SVM was adapted to the field of High Performance Computing for power/performance prediction, auto-tuning, and runtime scheduling. However, even at the risk of losing prediction accuracy due to insufficient runtime information, researchers can only afford to apply offline model training to avoid significant runtime training overhead. To address the challenges above, we designed and implemented MICSVM, a highly efficient parallel SVM for x86 based multi-core and many core architectures,more » such as the Intel Ivy Bridge CPUs and Intel Xeon Phi coprocessor (MIC).« less

Adaptive GSA-based optimal tuning of PI controlled servo systems with reduced process parametric sensitivity, robust stability and controller robustness.

PubMed

Precup, Radu-Emil; David, Radu-Codrut; Petriu, Emil M; Radac, Mircea-Bogdan; Preitl, Stefan

2014-11-01

This paper suggests a new generation of optimal PI controllers for a class of servo systems characterized by saturation and dead zone static nonlinearities and second-order models with an integral component. The objective functions are expressed as the integral of time multiplied by absolute error plus the weighted sum of the integrals of output sensitivity functions of the state sensitivity models with respect to two process parametric variations. The PI controller tuning conditions applied to a simplified linear process model involve a single design parameter specific to the extended symmetrical optimum (ESO) method which offers the desired tradeoff to several control system performance indices. An original back-calculation and tracking anti-windup scheme is proposed in order to prevent the integrator wind-up and to compensate for the dead zone nonlinearity of the process. The minimization of the objective functions is carried out in the framework of optimization problems with inequality constraints which guarantee the robust stability with respect to the process parametric variations and the controller robustness. An adaptive gravitational search algorithm (GSA) solves the optimization problems focused on the optimal tuning of the design parameter specific to the ESO method and of the anti-windup tracking gain. A tuning method for PI controllers is proposed as an efficient approach to the design of resilient control systems. The tuning method and the PI controllers are experimentally validated by the adaptive GSA-based tuning of PI controllers for the angular position control of a laboratory servo system.

Can invertebrates see the e-vector of polarization as a separate modality of light?

PubMed

Labhart, Thomas

2016-12-15

The visual world is rich in linearly polarized light stimuli, which are hidden from the human eye. But many invertebrate species make use of polarized light as a source of valuable visual information. However, exploiting light polarization does not necessarily imply that the electric (e)-vector orientation of polarized light can be perceived as a separate modality of light. In this Review, I address the question of whether invertebrates can detect specific e-vector orientations in a manner similar to that of humans perceiving spectral stimuli as specific hues. To analyze e-vector orientation, the signals of at least three polarization-sensitive sensors (analyzer channels) with different e-vector tuning axes must be compared. The object-based, imaging polarization vision systems of cephalopods and crustaceans, as well as the water-surface detectors of flying backswimmers, use just two analyzer channels. Although this excludes the perception of specific e-vector orientations, a two-channel system does provide a coarse, categoric analysis of polarized light stimuli, comparable to the limited color sense of dichromatic, 'color-blind' humans. The celestial compass of insects employs three or more analyzer channels. However, that compass is multimodal, i.e. e-vector information merges with directional information from other celestial cues, such as the solar azimuth and the spectral gradient in the sky, masking e-vector information. It seems that invertebrate organisms take no interest in the polarization details of visual stimuli, but polarization vision grants more practical benefits, such as improved object detection and visual communication for cephalopods and crustaceans, compass readings to traveling insects, or the alert 'water below!' to water-seeking bugs. © 2016. Published by The Company of Biologists Ltd.

Can invertebrates see the e-vector of polarization as a separate modality of light?

PubMed Central

2016-01-01

ABSTRACT The visual world is rich in linearly polarized light stimuli, which are hidden from the human eye. But many invertebrate species make use of polarized light as a source of valuable visual information. However, exploiting light polarization does not necessarily imply that the electric (e)-vector orientation of polarized light can be perceived as a separate modality of light. In this Review, I address the question of whether invertebrates can detect specific e-vector orientations in a manner similar to that of humans perceiving spectral stimuli as specific hues. To analyze e-vector orientation, the signals of at least three polarization-sensitive sensors (analyzer channels) with different e-vector tuning axes must be compared. The object-based, imaging polarization vision systems of cephalopods and crustaceans, as well as the water-surface detectors of flying backswimmers, use just two analyzer channels. Although this excludes the perception of specific e-vector orientations, a two-channel system does provide a coarse, categoric analysis of polarized light stimuli, comparable to the limited color sense of dichromatic, ‘color-blind’ humans. The celestial compass of insects employs three or more analyzer channels. However, that compass is multimodal, i.e. e-vector information merges with directional information from other celestial cues, such as the solar azimuth and the spectral gradient in the sky, masking e-vector information. It seems that invertebrate organisms take no interest in the polarization details of visual stimuli, but polarization vision grants more practical benefits, such as improved object detection and visual communication for cephalopods and crustaceans, compass readings to traveling insects, or the alert ‘water below!’ to water-seeking bugs. PMID:27974532

Bayesian LASSO, scale space and decision making in association genetics.

PubMed

Pasanen, Leena; Holmström, Lasse; Sillanpää, Mikko J

2015-01-01

LASSO is a penalized regression method that facilitates model fitting in situations where there are as many, or even more explanatory variables than observations, and only a few variables are relevant in explaining the data. We focus on the Bayesian version of LASSO and consider four problems that need special attention: (i) controlling false positives, (ii) multiple comparisons, (iii) collinearity among explanatory variables, and (iv) the choice of the tuning parameter that controls the amount of shrinkage and the sparsity of the estimates. The particular application considered is association genetics, where LASSO regression can be used to find links between chromosome locations and phenotypic traits in a biological organism. However, the proposed techniques are relevant also in other contexts where LASSO is used for variable selection. We separate the true associations from false positives using the posterior distribution of the effects (regression coefficients) provided by Bayesian LASSO. We propose to solve the multiple comparisons problem by using simultaneous inference based on the joint posterior distribution of the effects. Bayesian LASSO also tends to distribute an effect among collinear variables, making detection of an association difficult. We propose to solve this problem by considering not only individual effects but also their functionals (i.e. sums and differences). Finally, whereas in Bayesian LASSO the tuning parameter is often regarded as a random variable, we adopt a scale space view and consider a whole range of fixed tuning parameters, instead. The effect estimates and the associated inference are considered for all tuning parameters in the selected range and the results are visualized with color maps that provide useful insights into data and the association problem considered. The methods are illustrated using two sets of artificial data and one real data set, all representing typical settings in association genetics.

Modal analysis using a Fourier analyzer, curve-fitting, and modal tuning

NASA Technical Reports Server (NTRS)

Craig, R. R., Jr.; Chung, Y. T.

1981-01-01

The proposed modal test program differs from single-input methods in that preliminary data may be acquired using multiple inputs, and modal tuning procedures may be employed to define closely spaced frquency modes more accurately or to make use of frequency response functions (FRF's) which are based on several input locations. In some respects the proposed modal test proram resembles earlier sine-sweep and sine-dwell testing in that broadband FRF's are acquired using several input locations, and tuning is employed to refine the modal parameter estimates. The major tasks performed in the proposed modal test program are outlined. Data acquisition and FFT processing, curve fitting, and modal tuning phases are described and examples are given to illustrate and evaluate them.

Tuning and Robustness Analysis for the Orion Absolute Navigation System

NASA Technical Reports Server (NTRS)

Holt, Greg N.; Zanetti, Renato; D'Souza, Christopher

2013-01-01

The Orion Multi-Purpose Crew Vehicle (MPCV) is currently under development as NASA's next-generation spacecraft for exploration missions beyond Low Earth Orbit. The MPCV is set to perform an orbital test flight, termed Exploration Flight Test 1 (EFT-1), some time in late 2014. The navigation system for the Orion spacecraft is being designed in a Multi-Organizational Design Environment (MODE) team including contractor and NASA personnel. The system uses an Extended Kalman Filter to process measurements and determine the state. The design of the navigation system has undergone several iterations and modifications since its inception, and continues as a work-in-progress. This paper seeks to show the efforts made to-date in tuning the filter for the EFT-1 mission and instilling appropriate robustness into the system to meet the requirements of manned space ight. Filter performance is affected by many factors: data rates, sensor measurement errors, tuning, and others. This paper focuses mainly on the error characterization and tuning portion. Traditional efforts at tuning a navigation filter have centered around the observation/measurement noise and Gaussian process noise of the Extended Kalman Filter. While the Orion MODE team must certainly address those factors, the team is also looking at residual edit thresholds and measurement underweighting as tuning tools. Tuning analysis is presented with open loop Monte-Carlo simulation results showing statistical errors bounded by the 3-sigma filter uncertainty covariance. The Orion filter design uses 24 Exponentially Correlated Random Variable (ECRV) parameters to estimate the accel/gyro misalignment and nonorthogonality. By design, the time constant and noise terms of these ECRV parameters were set to manufacturer specifications and not used as tuning parameters. They are included in the filter as a more analytically correct method of modeling uncertainties than ad-hoc tuning of the process noise. Tuning is explored for the powered-flight ascent phase, where measurements are scarce and unmodelled vehicle accelerations dominate. On orbit, there are important trade-off cases between process and measurement noise. On entry, there are considerations about trading performance accuracy for robustness. Process Noise is divided into powered flight and coasting ight and can be adjusted for each phase and mode of the Orion EFT-1 mission. Measurement noise is used for the integrated velocity measurements during pad alignment. It is also used for Global Positioning System (GPS) pseudorange and delta- range measurements during the rest of the flight. The robustness effort has been focused on maintaining filter convergence and performance in the presence of unmodeled error sources. These include unmodeled forces on the vehicle and uncorrected errors on the sensor measurements. Orion uses a single-frequency, non-keyed GPS receiver, so the effects due to signal distortion in Earth's ionosphere and troposphere are present in the raw measurements. Results are presented showing the efforts to compensate for these errors as well as characterize the residual effect for measurement noise tuning. Another robustness tool in use is tuning the residual edit thresholds. The trade-off between noise tuning and edit thresholds is explored in the context of robustness to errors in dynamics models and sensor measurements. Measurement underweighting is also presented as a method of additional robustness when processing highly accurate measurements in the presence of large filter uncertainties.

Adaptive road crack detection system by pavement classification.

PubMed

Gavilán, Miguel; Balcones, David; Marcos, Oscar; Llorca, David F; Sotelo, Miguel A; Parra, Ignacio; Ocaña, Manuel; Aliseda, Pedro; Yarza, Pedro; Amírola, Alejandro

2011-01-01

This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement.

Adaptive Road Crack Detection System by Pavement Classification

PubMed Central

Gavilán, Miguel; Balcones, David; Marcos, Oscar; Llorca, David F.; Sotelo, Miguel A.; Parra, Ignacio; Ocaña, Manuel; Aliseda, Pedro; Yarza, Pedro; Amírola, Alejandro

2011-01-01

This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement. PMID:22163717

A self-tuning automatic voltage regulator designed for an industrial environment

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

Flynn, D.; Hogg, B.W.; Swidenbank, E.

Examination of the performance of fixed parameter controllers has resulted in the development of self-tuning strategies for excitation control of turbogenerator systems. In conjunction with the advanced control algorithms, sophisticated measurement techniques have previously been adopted on micromachine systems to provide generator terminal quantities. In power stations, however, a minimalist hardware arrangement would be selected leading to relatively simple measurement techniques. The performance of a range of self-tuning schemes is investigated on an industrial test-bed, employing a typical industrial hardware measurement system. Individual controllers are implemented on a standard digital automatic voltage regulator, as installed in power stations. This employsmore » a VME platform, and the self-tuning algorithms are introduced by linking to a transputer network. The AVR includes all normal features, such as field forcing, VAR limiting and overflux protection. Self-tuning controller performance is compared with that of a fixed gain digital AVR.« less

Supernatural MSSM

NASA Astrophysics Data System (ADS)

Du, Guangle; Li, Tianjun; Nanopoulos, D. V.; Raza, Shabbar

2015-07-01

We point out that the electroweak fine-tuning problem in the supersymmetric standard models (SSMs) is mainly due to the high energy definition of the fine-tuning measure. We propose supernatural supersymmetry which has an order one high energy fine-tuning measure automatically. The key point is that all the mass parameters in the SSMs arise from a single supersymmetry breaking parameter. In this paper, we show that there is no supersymmetry electroweak fine-tuning problem explicitly in the minimal SSM (MSSM) with no-scale supergravity and Giudice-Masiero mechanism. We demonstrate that the Z -boson mass, the supersymmetric Higgs mixing parameter μ at the unification scale, and the sparticle spectrum can be given as functions of the universal gaugino mass M1 /2. Because the light stau is the lightest supersymmetric particle (LSP) in the no-scale MSSM, to preserve R parity, we introduce a non-thermally generated axino as the LSP dark matter candidate. We estimate the lifetime of the light stau by calculating its two-body and three-body decays to the LSP axino for several values of axion decay constant fa, and find that the light stau has a lifetime ττ ˜1 in [10-4,100 ] s for an fa range [109,1012] GeV . We show that our next to the LSP stau solutions are consistent with all the current experimental constraints, including the sparticle mass bounds, B-physics bounds, Higgs mass, cosmological bounds, and the bounds on long-lived charged particles at the LHC.

A genetic algorithms approach for altering the membership functions in fuzzy logic controllers

NASA Technical Reports Server (NTRS)

Shehadeh, Hana; Lea, Robert N.

1992-01-01

Through previous work, a fuzzy control system was developed to perform translational and rotational control of a space vehicle. This problem was then re-examined to determine the effectiveness of genetic algorithms on fine tuning the controller. This paper explains the problems associated with the design of this fuzzy controller and offers a technique for tuning fuzzy logic controllers. A fuzzy logic controller is a rule-based system that uses fuzzy linguistic variables to model human rule-of-thumb approaches to control actions within a given system. This 'fuzzy expert system' features rules that direct the decision process and membership functions that convert the linguistic variables into the precise numeric values used for system control. Defining the fuzzy membership functions is the most time consuming aspect of the controller design. One single change in the membership functions could significantly alter the performance of the controller. This membership function definition can be accomplished by using a trial and error technique to alter the membership functions creating a highly tuned controller. This approach can be time consuming and requires a great deal of knowledge from human experts. In order to shorten development time, an iterative procedure for altering the membership functions to create a tuned set that used a minimal amount of fuel for velocity vector approach and station-keep maneuvers was developed. Genetic algorithms, search techniques used for optimization, were utilized to solve this problem.

Automation of extrusion of porous cable products based on a digital controller

NASA Astrophysics Data System (ADS)

Chostkovskii, B. K.; Mitroshin, V. N.

2017-07-01

This paper presents a new approach to designing an automated system for monitoring and controlling the process of applying porous insulation material on a conductive cable core, which is based on using structurally and parametrically optimized digital controllers of an arbitrary order instead of calculating typical PID controllers using known methods. The digital controller is clocked by signals from the clock length sensor of a measuring wheel, instead of a timer signal, and this provides the robust properties of the system with respect to the changing insulation speed. Digital controller parameters are tuned to provide the operating parameters of the manufactured cable using a simulation model of stochastic extrusion and are minimized by moving a regular simplex in the parameter space of the tuned controller.

High-Resolution Large-Field-of-View Ultrasound Breast Imager

DTIC Science & Technology

2012-06-01

plane waves all having the same wave vector magnitude 0k but propagating in different directions . This observation forms the mathematical basis of the...origin of the object Fourier space and is oriented opposite the propagation direction of the probing plane wave field. Moreover, the 43 radius of...in water. Each element was electrically tuned to match to the 50-Ohm impedance of an RF Amplifier powered by a 4.0 MHz electrical signal from a

Direct and indirect signals of natural composite Higgs models

NASA Astrophysics Data System (ADS)

Niehoff, Christoph; Stangl, Peter; Straub, David M.

2016-01-01

We present a comprehensive numerical analysis of a four-dimensional model with the Higgs as a composite pseudo-Nambu-Goldstone boson that features a calculable Higgs potential and protective custodial and flavour symmetries to reduce electroweak fine-tuning. We employ a novel numerical technique that allows us for the first time to study constraints from radiative electroweak symmetry breaking, Higgs physics, electroweak precision tests, flavour physics, and direct LHC bounds on fermion and vector boson resonances in a single framework. We consider four different flavour symmetries in the composite sector, one of which we show to not be viable anymore in view of strong precision constraints. In the other cases, all constraints can be passed with a sub-percent electroweak fine-tuning. The models can explain the excesses recently observed in WW, WZ, Wh and ℓ + ℓ - resonance searches by ATLAS and CMS and the anomalies in angular observables and branching ratios of rare semi-leptonic B decays observed by LHCb. Solving the B physics anomalies predicts the presence of a dijet or toverline{t} resonance around 1 TeV just below the sensitivity of LHC run 1. We discuss the prospects to probe the models at run 2 of the LHC. As a side product, we identify several gaps in the searches for vector-like quarks at hadron colliders, that could be closed by reanalyzing existing LHC data.

Custodial vector model

NASA Astrophysics Data System (ADS)

Becciolini, Diego; Franzosi, Diogo Buarque; Foadi, Roshan; Frandsen, Mads T.; Hapola, Tuomas; Sannino, Francesco

2015-07-01

We analyze the Large Hadron Collider (LHC) phenomenology of heavy vector resonances with a S U (2 )L×S U (2 )R spectral global symmetry. This symmetry partially protects the electroweak S parameter from large contributions of the vector resonances. The resulting custodial vector model spectrum and interactions with the standard model fields lead to distinct signatures at the LHC in the diboson, dilepton, and associated Higgs channels.

Fast temporal neural learning using teacher forcing

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad (Inventor); Bahren, Jacob (Inventor)

1992-01-01

A neural network is trained to output a time dependent target vector defined over a predetermined time interval in response to a time dependent input vector defined over the same time interval by applying corresponding elements of the error vector, or difference between the target vector and the actual neuron output vector, to the inputs of corresponding output neurons of the network as corrective feedback. This feedback decreases the error and quickens the learning process, so that a much smaller number of training cycles are required to complete the learning process. A conventional gradient descent algorithm is employed to update the neural network parameters at the end of the predetermined time interval. The foregoing process is repeated in repetitive cycles until the actual output vector corresponds to the target vector. In the preferred embodiment, as the overall error of the neural network output decreasing during successive training cycles, the portion of the error fed back to the output neurons is decreased accordingly, allowing the network to learn with greater freedom from teacher forcing as the network parameters converge to their optimum values. The invention may also be used to train a neural network with stationary training and target vectors.

Fast temporal neural learning using teacher forcing

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad (Inventor); Bahren, Jacob (Inventor)

1995-01-01

A neural network is trained to output a time dependent target vector defined over a predetermined time interval in response to a time dependent input vector defined over the same time interval by applying corresponding elements of the error vector, or difference between the target vector and the actual neuron output vector, to the inputs of corresponding output neurons of the network as corrective feedback. This feedback decreases the error and quickens the learning process, so that a much smaller number of training cycles are required to complete the learning process. A conventional gradient descent algorithm is employed to update the neural network parameters at the end of the predetermined time interval. The foregoing process is repeated in repetitive cycles until the actual output vector corresponds to the target vector. In the preferred embodiment, as the overall error of the neural network output decreasing during successive training cycles, the portion of the error fed back to the output neurons is decreased accordingly, allowing the network to learn with greater freedom from teacher forcing as the network parameters converge to their optimum values. The invention may also be used to train a neural network with stationary training and target vectors.

Backward and forward Monte Carlo method for vector radiative transfer in a two-dimensional graded index medium

NASA Astrophysics Data System (ADS)

Qian, Lin-Feng; Shi, Guo-Dong; Huang, Yong; Xing, Yu-Ming

2017-10-01

In vector radiative transfer, backward ray tracing is seldom used. We present a backward and forward Monte Carlo method to simulate vector radiative transfer in a two-dimensional graded index medium, which is new and different from the conventional Monte Carlo method. The backward and forward Monte Carlo method involves dividing the ray tracing into two processes backward tracing and forward tracing. In multidimensional graded index media, the trajectory of a ray is usually a three-dimensional curve. During the transport of a polarization ellipse, the curved ray trajectory will induce geometrical effects and cause Stokes parameters to continuously change. The solution processes for a non-scattering medium and an anisotropic scattering medium are analysed. We also analyse some parameters that influence the Stokes vector in two-dimensional graded index media. The research shows that the Q component of the Stokes vector cannot be ignored. However, the U and V components of the Stokes vector are very small.

Development of a tuned interfacial force field parameter set for the simulation of protein adsorption to silica glass.

PubMed

Snyder, James A; Abramyan, Tigran; Yancey, Jeremy A; Thyparambil, Aby A; Wei, Yang; Stuart, Steven J; Latour, Robert A

2012-12-01

Adsorption free energies for eight host-guest peptides (TGTG-X-GTGT, with X = N, D, G, K, F, T, W, and V) on two different silica surfaces [quartz (100) and silica glass] were calculated using umbrella sampling and replica exchange molecular dynamics and compared with experimental values determined by atomic force microscopy. Using the CHARMM force field, adsorption free energies were found to be overestimated (i.e., too strongly adsorbing) by about 5-9 kcal/mol compared to the experimental data for both types of silica surfaces. Peptide adsorption behavior for the silica glass surface was then adjusted using a modified version of the CHARMM program, which we call dual force-field CHARMM, which allows separate sets of nonbonded parameters (i.e., partial charge and Lennard-Jones parameters) to be used to represent intra-phase and inter-phase interactions within a given molecular system. Using this program, interfacial force field (IFF) parameters for the peptide-silica glass systems were corrected to obtain adsorption free energies within about 0.5 kcal/mol of their respective experimental values, while IFF tuning for the quartz (100) surface remains for future work. The tuned IFF parameter set for silica glass will subsequently be used for simulations of protein adsorption behavior on silica glass with greater confidence in the balance between relative adsorption affinities of amino acid residues and the aqueous solution for the silica glass surface.

Development of a Tuned Interfacial Force Field Parameter Set for the Simulation of Protein Adsorption to Silica Glass

PubMed Central

Snyder, James A.; Abramyan, Tigran; Yancey, Jeremy A.; Thyparambil, Aby A.; Wei, Yang; Stuart, Steven J.; Latour, Robert A.

2012-01-01

Adsorption free energies for eight host–guest peptides (TGTG-X-GTGT, with X = N, D, G, K, F, T, W, and V) on two different silica surfaces [quartz (100) and silica glass] were calculated using umbrella sampling and replica exchange molecular dynamics and compared with experimental values determined by atomic force microscopy. Using the CHARMM force field, adsorption free energies were found to be overestimated (i.e., too strongly adsorbing) by about 5–9 kcal/mol compared to the experimental data for both types of silica surfaces. Peptide adsorption behavior for the silica glass surface was then adjusted using a modified version of the CHARMM program, which we call dual force-field CHARMM, which allows separate sets of nonbonded parameters (i.e., partial charge and Lennard-Jones parameters) to be used to represent intra-phase and inter-phase interactions within a given molecular system. Using this program, interfacial force field (IFF) parameters for the peptide-silica glass systems were corrected to obtain adsorption free energies within about 0.5 kcal/mol of their respective experimental values, while IFF tuning for the quartz (100) surface remains for future work. The tuned IFF parameter set for silica glass will subsequently be used for simulations of protein adsorption behavior on silica glass with greater confidence in the balance between relative adsorption affinities of amino acid residues and the aqueous solution for the silica glass surface. PMID:22941539

Fast ultrasonic wavelength tuning in X-ray experiment

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

Blagov, A. E., E-mail: blagov-ae@mail.ru; Pisarevskii, Yu. V.; Koval’chuk, M. V.

2016-03-15

A method of tuning (scanning) X-ray beam wavelength based on modulation of the lattice parameter of X-ray optical crystal by an ultrasonic standing wave excited in it has been proposed and experimentally implemented. The double-crystal antiparallel scheme of X-ray diffraction, in which an ultrasonic wave is excited in the second crystal, is used in the experiment. The profile of characteristic line k{sub α1} of an X-ray tube with a molybdenum anode is recorded using both the proposed tuning scheme and conventional mechanical rotation of crystal. The results obtained by both techniques are in good agreement.

Radical kinetics in sub- and supercritical carbon dioxide: thermodynamic rate tuning.

PubMed

Ghandi, Khashayar; McFadden, Ryan M L; Cormier, Philip J; Satija, Paras; Smith, Marisa

2012-06-28

We report rate constants for muonium addition to 1,1-difluoroethylene (vinylidene fluoride) in CO2 at 290-530 K, 40-360 bar, and 0.05-0.90 g cm(-3). Rate constants are mapped against their thermodynamic conditions, demonstrating the kinetic tuning ability of the solvent. The reaction exhibits critical slowing near conditions of maximum solvent isothermal compressibility, where activation volumes of unprecedentedly large magnitudes on the order of ±10(6) cm(3) mol(-1) are observed. Such values are suggestive of pressure being a significant parameter for tuning fluorolkene reactivity.

Deformation-Aware Log-Linear Models

NASA Astrophysics Data System (ADS)

Gass, Tobias; Deselaers, Thomas; Ney, Hermann

In this paper, we present a novel deformation-aware discriminative model for handwritten digit recognition. Unlike previous approaches our model directly considers image deformations and allows discriminative training of all parameters, including those accounting for non-linear transformations of the image. This is achieved by extending a log-linear framework to incorporate a latent deformation variable. The resulting model has an order of magnitude less parameters than competing approaches to handling image deformations. We tune and evaluate our approach on the USPS task and show its generalization capabilities by applying the tuned model to the MNIST task. We gain interesting insights and achieve highly competitive results on both tasks.

Hairy black holes in scalar extended massive gravity

NASA Astrophysics Data System (ADS)

Tolley, Andrew J.; Wu, De-Jun; Zhou, Shuang-Yong

2015-12-01

We construct static, spherically symmetric black hole solutions in scalar extended ghost-free massive gravity and show the existence of hairy black holes in this class of extension. While the existence seems to be a generic feature, we focus on the simplest models of this extension and find that asymptotically flat hairy black holes can exist without fine-tuning the theory parameters, unlike the bi-gravity extension, where asymptotical flatness requires fine-tuning in the parameter space. Like the bi-gravity extension, we are unable to obtain asymptotically dS regular black holes in the simplest models considered, but it is possible to obtain asymptotically AdS black holes.

On the fusion of tuning parameters of fuzzy rules and neural network

NASA Astrophysics Data System (ADS)

Mamuda, Mamman; Sathasivam, Saratha

2017-08-01

Learning fuzzy rule-based system with neural network can lead to a precise valuable empathy of several problems. Fuzzy logic offers a simple way to reach at a definite conclusion based upon its vague, ambiguous, imprecise, noisy or missing input information. Conventional learning algorithm for tuning parameters of fuzzy rules using training input-output data usually end in a weak firing state, this certainly powers the fuzzy rule and makes it insecure for a multiple-input fuzzy system. In this paper, we introduce a new learning algorithm for tuning the parameters of the fuzzy rules alongside with radial basis function neural network (RBFNN) in training input-output data based on the gradient descent method. By the new learning algorithm, the problem of weak firing using the conventional method was addressed. We illustrated the efficiency of our new learning algorithm by means of numerical examples. MATLAB R2014(a) software was used in simulating our result The result shows that the new learning method has the best advantage of training the fuzzy rules without tempering with the fuzzy rule table which allowed a membership function of the rule to be used more than one time in the fuzzy rule base.

New bandwidth selection criterion for Kernel PCA: approach to dimensionality reduction and classification problems.

PubMed

Thomas, Minta; De Brabanter, Kris; De Moor, Bart

2014-05-10

DNA microarrays are potentially powerful technology for improving diagnostic classification, treatment selection, and prognostic assessment. The use of this technology to predict cancer outcome has a history of almost a decade. Disease class predictors can be designed for known disease cases and provide diagnostic confirmation or clarify abnormal cases. The main input to this class predictors are high dimensional data with many variables and few observations. Dimensionality reduction of these features set significantly speeds up the prediction task. Feature selection and feature transformation methods are well known preprocessing steps in the field of bioinformatics. Several prediction tools are available based on these techniques. Studies show that a well tuned Kernel PCA (KPCA) is an efficient preprocessing step for dimensionality reduction, but the available bandwidth selection method for KPCA was computationally expensive. In this paper, we propose a new data-driven bandwidth selection criterion for KPCA, which is related to least squares cross-validation for kernel density estimation. We propose a new prediction model with a well tuned KPCA and Least Squares Support Vector Machine (LS-SVM). We estimate the accuracy of the newly proposed model based on 9 case studies. Then, we compare its performances (in terms of test set Area Under the ROC Curve (AUC) and computational time) with other well known techniques such as whole data set + LS-SVM, PCA + LS-SVM, t-test + LS-SVM, Prediction Analysis of Microarrays (PAM) and Least Absolute Shrinkage and Selection Operator (Lasso). Finally, we assess the performance of the proposed strategy with an existing KPCA parameter tuning algorithm by means of two additional case studies. We propose, evaluate, and compare several mathematical/statistical techniques, which apply feature transformation/selection for subsequent classification, and consider its application in medical diagnostics. Both feature selection and feature transformation perform well on classification tasks. Due to the dynamic selection property of feature selection, it is hard to define significant features for the classifier, which predicts classes of future samples. Moreover, the proposed strategy enjoys a distinctive advantage with its relatively lesser time complexity.

Selection and parameterization of cortical neurons for neuroprosthetic control.

PubMed

Wahnoun, Remy; He, Jiping; Helms Tillery, Stephen I

2006-06-01

When designing neuroprosthetic interfaces for motor function, it is crucial to have a system that can extract reliable information from available neural signals and produce an output suitable for real life applications. Systems designed to date have relied on establishing a relationship between neural discharge patterns in motor cortical areas and limb movement, an approach not suitable for patients who require such implants but who are unable to provide proper motor behavior to initially tune the system. We describe here a method that allows rapid tuning of a population vector-based system for neural control without arm movements. We trained highly motivated primates to observe a 3D center-out task as the computer played it very slowly. Based on only 10-12 s of neuronal activity observed in M1 and PMd, we generated an initial mapping between neural activity and device motion that the animal could successfully use for neuroprosthetic control. Subsequent tunings of the parameters led to improvements in control, but the initial selection of neurons and estimated preferred direction for those cells remained stable throughout the remainder of the day. Using this system, we have observed that the contribution of individual neurons to the overall control of the system is very heterogeneous. We thus derived a novel measure of unit quality and an indexing scheme that allowed us to rate each neuron's contribution to the overall control. In offline tests, we found that fewer than half of the units made positive contributions to the performance. We tested this experimentally by having the animals control the neuroprosthetic system using only the 20 best neurons. We found that performance in this case was better than when the entire set of available neurons was used. Based on these results, we believe that, with careful task design, it is feasible to parameterize control systems without any overt behaviors and that subsequent control system design will be enhanced with cautious unit selection. These improvements can lead to systems demanding lower bandwidth and computational power, and will pave the way for more feasible clinical systems.

Accurate Magnetometer/Gyroscope Attitudes Using a Filter with Correlated Sensor Noise

NASA Technical Reports Server (NTRS)

Sedlak, J.; Hashmall, J.

1997-01-01

Magnetometers and gyroscopes have been shown to provide very accurate attitudes for a variety of spacecraft. These results have been obtained, however, using a batch-least-squares algorithm and long periods of data. For use in onboard applications, attitudes are best determined using sequential estimators such as the Kalman filter. When a filter is used to determine attitudes using magnetometer and gyroscope data for input, the resulting accuracy is limited by both the sensor accuracies and errors inherent in the Earth magnetic field model. The Kalman filter accounts for the random component by modeling the magnetometer and gyroscope errors as white noise processes. However, even when these tuning parameters are physically realistic, the rate biases (included in the state vector) have been found to show systematic oscillations. These are attributed to the field model errors. If the gyroscope noise is sufficiently small, the tuned filter 'memory' will be long compared to the orbital period. In this case, the variations in the rate bias induced by field model errors are substantially reduced. Mistuning the filter to have a short memory time leads to strongly oscillating rate biases and increased attitude errors. To reduce the effect of the magnetic field model errors, these errors are estimated within the filter and used to correct the reference model. An exponentially-correlated noise model is used to represent the filter estimate of the systematic error. Results from several test cases using in-flight data from the Compton Gamma Ray Observatory are presented. These tests emphasize magnetometer errors, but the method is generally applicable to any sensor subject to a combination of random and systematic noise.

The Optimization of Trained and Untrained Image Classification Algorithms for Use on Large Spatial Datasets

NASA Technical Reports Server (NTRS)

Kocurek, Michael J.

2005-01-01

The HARVIST project seeks to automatically provide an accurate, interactive interface to predict crop yield over the entire United States. In order to accomplish this goal, large images must be quickly and automatically classified by crop type. Current trained and untrained classification algorithms, while accurate, are highly inefficient when operating on large datasets. This project sought to develop new variants of two standard trained and untrained classification algorithms that are optimized to take advantage of the spatial nature of image data. The first algorithm, harvist-cluster, utilizes divide-and-conquer techniques to precluster an image in the hopes of increasing overall clustering speed. The second algorithm, harvistSVM, utilizes support vector machines (SVMs), a type of trained classifier. It seeks to increase classification speed by applying a "meta-SVM" to a quick (but inaccurate) SVM to approximate a slower, yet more accurate, SVM. Speedups were achieved by tuning the algorithm to quickly identify when the quick SVM was incorrect, and then reclassifying low-confidence pixels as necessary. Comparing the classification speeds of both algorithms to known baselines showed a slight speedup for large values of k (the number of clusters) for harvist-cluster, and a significant speedup for harvistSVM. Future work aims to automate the parameter tuning process required for harvistSVM, and further improve classification accuracy and speed. Additionally, this research will move documents created in Canvas into ArcGIS. The launch of the Mars Reconnaissance Orbiter (MRO) will provide a wealth of image data such as global maps of Martian weather and high resolution global images of Mars. The ability to store this new data in a georeferenced format will support future Mars missions by providing data for landing site selection and the search for water on Mars.

Combined fast multipole-QR compression technique for solving electrically small to large structures for broadband applications

NASA Technical Reports Server (NTRS)

Jandhyala, Vikram (Inventor); Chowdhury, Indranil (Inventor)

2011-01-01

An approach that efficiently solves for a desired parameter of a system or device that can include both electrically large fast multipole method (FMM) elements, and electrically small QR elements. The system or device is setup as an oct-tree structure that can include regions of both the FMM type and the QR type. An iterative solver is then used to determine a first matrix vector product for any electrically large elements, and a second matrix vector product for any electrically small elements that are included in the structure. These matrix vector products for the electrically large elements and the electrically small elements are combined, and a net delta for a combination of the matrix vector products is determined. The iteration continues until a net delta is obtained that is within predefined limits. The matrix vector products that were last obtained are used to solve for the desired parameter.

Nonparaxial propagation and focusing properties of azimuthal-variant vector fields diffracted by an annular aperture.

PubMed

Gu, Bing; Xu, Danfeng; Pan, Yang; Cui, Yiping

2014-07-01

Based on the vectorial Rayleigh-Sommerfeld integrals, the analytical expressions for azimuthal-variant vector fields diffracted by an annular aperture are presented. This helps us to investigate the propagation behaviors and the focusing properties of apertured azimuthal-variant vector fields under nonparaxial and paraxial approximations. The diffraction by a circular aperture, a circular disk, or propagation in free space can be treated as special cases of this general result. Simulation results show that the transverse intensity, longitudinal intensity, and far-field divergence angle of nonparaxially apertured azimuthal-variant vector fields depend strongly on the azimuthal index, the outer truncation parameter and the inner truncation parameter of the annular aperture, as well as the ratio of the waist width to the wavelength. Moreover, the multiple-ring-structured intensity pattern of the focused azimuthal-variant vector field, which originates from the diffraction effect caused by an annular aperture, is experimentally demonstrated.

THE EFFECT OF GRAVITATION ON THE POLARIZATION STATE OF A LIGHT RAY

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

Ghosh, Tanay; Sen, A. K.

In the present work, detailed calculations have been carried out on the rotation of the polarization vector of an electromagnetic wave due to the presence of a gravitational field of a rotating body. This has been done using the general expression of Maxwell’s equation in curved spacetime. Considering the far-field approximation (i.e., the impact parameter is greater than the Schwarzschild radius and rotation parameter), the amount of rotation of the polarization vector as a function of impact parameter has been obtained for a rotating body (considering Kerr geometry). The present work shows that the rotation of the polarization vector cannotmore » be observed in the case of Schwarzschild geometry. This work also calculates the rotational effect when considering prograde and retrograde orbits for the light ray. Although the present work demonstrates the effect of rotation of the polarization vector, it confirms that there would be no net polarization of an electromagnetic wave due to the curved spacetime geometry in a Kerr field.« less

The ecological foundations of transmission potential and vector-borne disease in urban landscapes.

PubMed

LaDeau, Shannon L; Allan, Brian F; Leisnham, Paul T; Levy, Michael Z

2015-07-01

Urban transmission of arthropod-vectored disease has increased in recent decades. Understanding and managing transmission potential in urban landscapes requires integration of sociological and ecological processes that regulate vector population dynamics, feeding behavior, and vector-pathogen interactions in these unique ecosystems. Vectorial capacity is a key metric for generating predictive understanding about transmission potential in systems with obligate vector transmission. This review evaluates how urban conditions, specifically habitat suitability and local temperature regimes, and the heterogeneity of urban landscapes can influence the biologically-relevant parameters that define vectorial capacity: vector density, survivorship, biting rate, extrinsic incubation period, and vector competence.Urban landscapes represent unique mosaics of habitat. Incidence of vector-borne disease in urban host populations is rarely, if ever, evenly distributed across an urban area. The persistence and quality of vector habitat can vary significantly across socio-economic boundaries to influence vector species composition and abundance, often generating socio-economically distinct gradients of transmission potential across neighborhoods.Urban regions often experience unique temperature regimes, broadly termed urban heat islands (UHI). Arthropod vectors are ectothermic organisms and their growth, survival, and behavior are highly sensitive to environmental temperatures. Vector response to UHI conditions is dependent on regional temperature profiles relative to the vector's thermal performance range. In temperate climates UHI can facilitate increased vector development rates while having countervailing influence on survival and feeding behavior. Understanding how urban heat island (UHI) conditions alter thermal and moisture constraints across the vector life cycle to influence transmission processes is an important direction for both empirical and modeling research.There remain persistent gaps in understanding of vital rates and drivers in mosquito-vectored disease systems, and vast holes in understanding for other arthropod vectored diseases. Empirical studies are needed to better understand the physiological constraints and socio-ecological processes that generate heterogeneity in critical transmission parameters, including vector survival and fitness. Likewise, laboratory experiments and transmission models must evaluate vector response to realistic field conditions, including variability in sociological and environmental conditions.

Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance

Treesearch

E. Freeman; G. Moisen; J. Coulston; B. Wilson

2014-01-01

Random forests (RF) and stochastic gradient boosting (SGB), both involving an ensemble of classification and regression trees, are compared for modeling tree canopy cover for the 2011 National Land Cover Database (NLCD). The objectives of this study were twofold. First, sensitivity of RF and SGB to choices in tuning parameters was explored. Second, performance of the...

Tuning of Schottky barrier height of Al/n-Si by electron beam irradiation

NASA Astrophysics Data System (ADS)

Vali, Indudhar Panduranga; Shetty, Pramoda Kumara; Mahesha, M. G.; Petwal, V. C.; Dwivedi, Jishnu; Choudhary, R. J.

2017-06-01

The effect of electron beam irradiation (EBI) on Al/n-Si Schottky diode has been studied by I-V characterization at room temperature. The behavior of the metal-semiconductor (MS) interface is analyzed by means of variations in the MS contact parameters such as, Schottky barrier height (ΦB), ideality factor (n) and series resistance (Rs). These parameters were found to depend on the EBI dose having a fixed incident beam of energy 7.5 MeV. At different doses (500, 1000, 1500 kGy) of EBI, the Schottky contacts were prepared and extracted their contact parameters by applying thermionic emission and Cheung models. Remarkably, the tuning of ΦB was observed as a function of EBI dose. The improved n with increased ΦB is seen for all the EBI doses. As a consequence of which the thermionic emission is more favored. However, the competing transport mechanisms such as space charge limited emission, tunneling and tunneling through the trap states were ascribed due to n > 1. The analysis of XPS spectra have shown the presence of native oxide and increased radiation induced defect states. The thickness variation in the MS interface contributing to Schottky contact behavior is discussed. This study explains a new technique to tune Schottky contact parameters by metal deposition on the electron beam irradiated n-Si wafers.

McMillan Lens in a System with Space Charge

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

Lobach, I.; Nagaitsev, S.; Stern, E.

Space charge (SC) in a circulating beam in a ring produces both betatron tune shift and betatron tune spread. These effects make some particles move on to a machine resonance and become unstable. Linear elements of beam optics cannot reduce the tune spread induced by SC because of its intrinsic nonlinear nature. We investigate the possibility to mitigate it by a thin McMillan lens providing a nonlinear axially symmetric kick, which is qualitatively opposite to the accumulated kick by SC. Experimentally, the proposed concept can be tested in Fermilab's IOTA ring. A thin McMillan lens can be implemented by amore » short (70 cm) insertion of an electron beam with specifically chosen density distribution in transverse directions. In this article, to see if McMillan lenses reduce the tune spread induced by SC, we make several simulations with particle tracking code Synergia. We choose such beam and lattice parameters that tune spread is roughly 0.5 and a beam instability due to the half-integer resonance 0.5 is observed. Then, we try to reduce emittance growth by shifting betatron tunes by adjusting quadrupoles and reducing the tune spread by McMillan lenses.« less

Wind speed vector restoration algorithm

NASA Astrophysics Data System (ADS)

Baranov, Nikolay; Petrov, Gleb; Shiriaev, Ilia

2018-04-01

Impulse wind lidar (IWL) signal processing software developed by JSC «BANS» recovers full wind speed vector by radial projections and provides wind parameters information up to 2 km distance. Increasing accuracy and speed of wind parameters calculation signal processing technics have been studied in this research. Measurements results of IWL and continuous scanning lidar were compared. Also, IWL data processing modeling results have been analyzed.

Energy-exchange collisions of dark-bright-bright vector solitons.

PubMed

Radhakrishnan, R; Manikandan, N; Aravinthan, K

2015-12-01

We find a dark component guiding the practically interesting bright-bright vector one-soliton to two different parametric domains giving rise to different physical situations by constructing a more general form of three-component dark-bright-bright mixed vector one-soliton solution of the generalized Manakov model with nine free real parameters. Moreover our main investigation of the collision dynamics of such mixed vector solitons by constructing the multisoliton solution of the generalized Manakov model with the help of Hirota technique reveals that the dark-bright-bright vector two-soliton supports energy-exchange collision dynamics. In particular the dark component preserves its initial form and the energy-exchange collision property of the bright-bright vector two-soliton solution of the Manakov model during collision. In addition the interactions between bound state dark-bright-bright vector solitons reveal oscillations in their amplitudes. A similar kind of breathing effect was also experimentally observed in the Bose-Einstein condensates. Some possible ways are theoretically suggested not only to control this breathing effect but also to manage the beating, bouncing, jumping, and attraction effects in the collision dynamics of dark-bright-bright vector solitons. The role of multiple free parameters in our solution is examined to define polarization vector, envelope speed, envelope width, envelope amplitude, grayness, and complex modulation of our solution. It is interesting to note that the polarization vector of our mixed vector one-soliton evolves in sphere or hyperboloid depending upon the initial parametric choices.

Radio frequency measurements and tuning of the China Material Irradiation Facility RFQ

NASA Astrophysics Data System (ADS)

Li, Chenxing; He, Yuan; Wang, Fengfeng; Yu, Peiyan; Yang, Lei; Li, Chunlong; Wang, Wenbin; Xu, Xianbo; Shi, Longbo; Ma, Wei; Sun, Liepeng; Lu, Liang; Wang, Zhijun; Shi, Aimin; Wang, Tieshan

2018-05-01

The full assembly and alignment of the China Material Irradiation Facility RFQ have been completed. Before the completion, the assembly and braze of single segments had been done. Radio frequency measurements of each module with dummy extension undercuts were performed before and after braze. The results reveal that there is no unexpected deformation after braze. After the full assembly, RF measurements and tuning have been performed in order to compensate the errors originated from the fabrication, braze and assembly. The impact of these errors on the field distribution is depressed to a level that is restricted by beam dynamics simulation. In this paper, the procedure of radio frequency measurement and tuning will be expatiated and the ultimate RF parameters of the cavity after tuning will be presented.

Dynamic response mitigation of floating wind turbine platforms using tuned liquid column dampers.

PubMed

Jaksic, V; Wright, C S; Murphy, J; Afeef, C; Ali, S F; Mandic, D P; Pakrashi, V

2015-02-28

In this paper, we experimentally study and compare the effects of three combinations of multiple tuned liquid column dampers (MTLCDs) on the dynamic performance of a model floating tension-leg platform (TLP) structure in a wave basin. The structural stability and safety of the floating structure during operation and maintenance is of concern for the performance of a renewable energy device that it might be supporting. The dynamic responses of the structure should thus be limited for these renewable energy devices to perform as intended. This issue is particularly important during the operation of a TLP in extreme weather conditions. Tuned liquid column dampers (TLCDs) can use the power of sloshing water to reduce surge motions of a floating TLP exposed to wind and waves. This paper demonstrates the potential of MTLCDs in reducing dynamic responses of a scaled TLP model through an experimental study. The potential of using output-only statistical markers for monitoring changes in structural conditions is also investigated through the application of a delay vector variance (DVV) marker for different conditions of control for the experiments. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Tuning of optical mode magnetic resonance in CoZr/Ru/CoZr synthetic antiferromagnetic trilayers by oblique sputtering

NASA Astrophysics Data System (ADS)

Wang, Wenqiang; Wang, Fenglong; Cao, Cuimei; Li, Pingping; Yao, Jinli; Jiang, Changjun

2018-04-01

CoZr/Ru/CoZr synthetic antiferromagnetic trilayers with strong antiferromagnetic interlayer coupling were fabricated by an oblique sputtering method that induced in-plane uniaxial magnetic anisotropy. A microstrip method using a vector network analyzer was applied to investigate the magnetic resonance modes of the trilayers, including the acoustic modes (AMs) and the optical modes (OMs). At zero magnetic field, the CoZr/Ru/CoZr trilayers showed OMs with resonance frequencies of up to 7.1 GHz. By increasing the applied external magnetic field, the magnetic resonance mode can be tuned to various OMs, mixed modes, and AMs. Additionally, the magnetic resonance mode showed an angular dependence between the magnetization and the microwave field, which showed similar switching of the magnetic modes with variation of the angle. Our results provide important information that will be helpful in the design of multifunctional microwave devices.

Mosquitoes on the Wing ``Tune In'' to Acoustic Distortion

NASA Astrophysics Data System (ADS)

Warren, Ben; Russell, Ian

2011-11-01

Our current understanding of the mating game for many mosquito species is that males aggregate in noisy mating swarms and listen with their Johnston's organs (JOs) for the deeper flight tones of approaching females, to which they are attracted. As has been demonstrated, at least for the most intensely studied vector species, the mechanical resonance of the flagellum and the frequency range of the female's JO is far below that of the male's flight tones. Therefore, it has been assumed that females do not use hearing to detect the presence of males. Here we reveal that this may not be the case, and that the JOs of female Culex quinquefasciatus are exquisitely tuned to low frequency distortion products in the vibrations of the antenna due to a nonlinear interaction between her own flight tones and those of a nearby male. She can hear male flight tones by virtue of, and not despite, hearing her own flight tones.

Topological RPdBi half-Heusler semimetals: A new family of noncentrosymmetric magnetic superconductors.

PubMed

Nakajima, Yasuyuki; Hu, Rongwei; Kirshenbaum, Kevin; Hughes, Alex; Syers, Paul; Wang, Xiangfeng; Wang, Kefeng; Wang, Renxiong; Saha, Shanta R; Pratt, Daniel; Lynn, Jeffrey W; Paglione, Johnpierre

2015-06-01

We report superconductivity and magnetism in a new family of topological semimetals, the ternary half-Heusler compound RPdBi (R: rare earth). In this series, tuning of the rare earth f-electron component allows for simultaneous control of both lattice density via lanthanide contraction and the strength of magnetic interaction via de Gennes scaling, allowing for a unique tuning of the normal-state band inversion strength, superconducting pairing, and magnetically ordered ground states. Antiferromagnetism with ordering vector (½,½,½) occurs below a Néel temperature that scales with de Gennes factor dG, whereas a superconducting transition is simultaneously supressed with increasing dG. With superconductivity appearing in a system with noncentrosymmetric crystallographic symmetry, the possibility of spin-triplet Cooper pairing with nontrivial topology analogous to that predicted for the normal-state electronic structure provides a unique and rich opportunity to realize both predicted and new exotic excitations in topological materials.

Loss-induced super scattering and gain-induced absorption.

PubMed

Feng, Simin

2016-01-25

Giant transmission and reflection of a finite bandwidth are demonstrated at the same wavelength when the electromagnetic wave is incident on a subwavelength array of parity-time (PT) symmetric dimers embedded in a metallic film. Remarkably, this phenomenon vanishes if the metallic substrate is lossless while keeping other parameters unchanged. Moreover super scattering can also occur when increasing the loss of the dimers while keeping the gain unchanged. When the metafilm is adjusted to the vicinity of an exceptional point, tuning either the substrate dissipation or the loss of the dimers can lead to supper scattering in stark contrast to what would be expected in conventional systems. In addition, increasing the gain of the dimers can increase the absorption near the exceptional point. These phenomena indicate that the PT-synthetic plasmonic metafilm can function as a thinfilm PT-plasmonic laser or absorber depending on the tuning parameter. One implication is that super radiation is possible from a cavity by tuning cavity dissipation or lossy element inside the cavity.

The Parametric Study and Fine-Tuning of Bow-Tie Slot Antenna with Loaded Stub

PubMed Central

2017-01-01

A printed Bow-Tie slot antenna with loaded stub is proposed and the effects of changing the dimensions of the slot area, the stub and load sizes are considered in this paper. These parameters have a considerable effect on the antenna characteristics as well as its performance. An in-depth parametric study of these dimensions is presented. This paper proposes the necessary conditions for initial approximation of dimensions needed to design this antenna. In order to achieve the desired performance of the antenna fine tuning of all sizes of these parameters is required. The parametric studies used in this paper provide proper trends for initiation and tuning the design. A prototype of the antenna for 1.7GHz to 2.6GHz band is fabricated. Measurements conducted verify that the designed antenna has wideband characteristics with 50% bandwidth around the center frequency of 2.1GHz. Conducted measurements for reflection coefficient (S11) and radiation pattern also validate our simulation results. PMID:28114354

The Parametric Study and Fine-Tuning of Bow-Tie Slot Antenna with Loaded Stub.

PubMed

Shafiei, M M; Moghavvemi, Mahmoud; Wan Mahadi, Wan Nor Liza

2017-01-01

A printed Bow-Tie slot antenna with loaded stub is proposed and the effects of changing the dimensions of the slot area, the stub and load sizes are considered in this paper. These parameters have a considerable effect on the antenna characteristics as well as its performance. An in-depth parametric study of these dimensions is presented. This paper proposes the necessary conditions for initial approximation of dimensions needed to design this antenna. In order to achieve the desired performance of the antenna fine tuning of all sizes of these parameters is required. The parametric studies used in this paper provide proper trends for initiation and tuning the design. A prototype of the antenna for 1.7GHz to 2.6GHz band is fabricated. Measurements conducted verify that the designed antenna has wideband characteristics with 50% bandwidth around the center frequency of 2.1GHz. Conducted measurements for reflection coefficient (S11) and radiation pattern also validate our simulation results.

Receptor heteromeric assembly-how it works and why it matters: the case of ionotropic glutamate receptors.

PubMed

Herguedas, Beatriz; Krieger, James; Greger, Ingo H

2013-01-01

The composition and spatial arrangement of subunits in ion channels are essential for their function. Diverse stoichiometries are possible in a multitude of channels. These depend upon cell type-specific subunit expression, which can be tuned in a developmentally regulated manner and in response to activity, on subunit stability in the endoplasmic reticulum, intersubunit affinities, and potentially subunit diffusion within the ER membrane. In concert, these parameters shape channel biogenesis and ultimately tune cellular response properties. The complexity of this assembly process is particularly well illustrated by the ionotropic glutamate receptors, the main mediators of excitatory neurotransmission. These tetrameric cation channels predominantly assemble into heteromers, which is "obligatory" for some iGluR subfamilies but "preferential" for others. Here, we discuss recent insights into the rules underlying these two pathways, the role of individual domains based on an ever increasing list of crystal structures, and how these assembly parameters tune assembly across diverse receptor oligomers. Copyright © 2013 Elsevier Inc. All rights reserved.

Computational model of a vector-mediated epidemic

NASA Astrophysics Data System (ADS)

Dickman, Adriana Gomes; Dickman, Ronald

2015-05-01

We discuss a lattice model of vector-mediated transmission of a disease to illustrate how simulations can be applied in epidemiology. The population consists of two species, human hosts and vectors, which contract the disease from one another. Hosts are sedentary, while vectors (mosquitoes) diffuse in space. Examples of such diseases are malaria, dengue fever, and Pierce's disease in vineyards. The model exhibits a phase transition between an absorbing (infection free) phase and an active one as parameters such as infection rates and vector density are varied.

Vector breather-to-soliton transitions and nonlinear wave interactions induced by higher-order effects in an erbium-doped fiber

NASA Astrophysics Data System (ADS)

Sun, Wen-Rong; Wang, Lei; Xie, Xi-Yang

2018-06-01

Vector breather-to-soliton transitions for the higher-order nonlinear Schrödinger-Maxwell-Bloch (NLS-MB) system with sextic terms are investigated. The Lax pair and Darboux transformation (DT) of such system are constructed. With the DT, analytic vector breather solutions up to the second order are obtained. With appropriate choices of the spectra parameters, vector breather-to-soliton transitions happen. Interaction mechanisms of vector nonlinear waves (breather-soliton or soliton-soliton interactions) are displayed.

Machine Learning Feature Selection for Tuning Memory Page Swapping

DTIC Science & Technology

2013-09-01

environments we set up. 13 Figure 4.1 Updated Feature Vector List. Features we added to the kernel are anno - tated with “(MLVM...Feb. 1966. [2] P. J . Denning, “The working set model for program behavior,” Communications of the ACM, vol. 11, no. 5, pp. 323–333, May 1968. [3] L. A...8] R. W. Cart and J . L. Hennessy, “WSClock — A simple and effective algorithm for virtual memory management,” M.S. thesis, Dept. Computer Science

Inertial navigation system using three TDF gyroscopic sensors not jointly mounted on a stable platform

NASA Technical Reports Server (NTRS)

Stieler, B.

1971-01-01

An inertial navigation system is described and analyzed based on two two-degree-of-freedom Schuler-gyropendulums and one two-degree-of-freedom azimuth gyro. The three sensors, each base motion isolated about its two input axes, are mounted on a common base, strapped down to the vehicle. The up and down pointing spin vectors of the two properly tuned gyropendulums track the vertical and indicate physically their velocity with respect to inertial space. The spin vector of the azimuth gyro is pointing northerly parallel to the earth axis. The system can be made self-aligning on a stationary base. If external measurements for the north direction and the vertical are available, initial disturbance torques can be measured and easily biased out. The error analysis shows that the system is practicable with today's technology.

Electric-field control of spin waves in multiferroic BiFeO3: Theory

NASA Astrophysics Data System (ADS)

de Sousa, Rogério; Rovillain, P.; Gallais, Y.; Sacuto, A.; Méasson, M. A.; Colson, D.; Forget, A.; Bibes, M.; Barthélémy, A.; Cazayous, M.

2011-03-01

Our recent experiment demonstrated gigantic (30%) electric-field tuning of magnon frequencies in multiferroic BiFeO3. We demonstrate that the origin of this effect is related to two linear magnetoelectric interactions that couple the component of electric field perpendicular to the ferroelectric vector to a quadratic form of the Néel vector. We calculate the magnon spectra due to each of these interactions and show that only one of them is consistent with experimental data. At high electric fields, this interaction induces a phase transition to a homogeneous state, and the multi-magnon spectra will fuse into two magnon frequencies. We discuss the possible microscopic mechanisms responsible for this novel interaction and the prospect for applications in magnonics. We acknowledge support from NSERC-Discovery (Canada) and the Agence Nationale pour la Recherche (France).

An actively Q-switched fiber laser with cylindrical vector beam generation

NASA Astrophysics Data System (ADS)

Zhang, Jiaojiao; Zhang, Zuxing; Cai, Yu; Wan, Hongdan; Wang, Zhiqiang; Zhang, Lin

2018-03-01

We demonstrate an actively Q-switched fiber laser with cylindrical vector beam (CVB) emission using a few-mode fiber Bragg grating as the mode selection component and an acousto-optic modulator to achieve Q-switching. To the best of our knowledge, this is the first such demonstration. Using a linear cavity configuration, an actively Q-switched CVB with a pulse width of about 64 ns, a pulse energy of 4.25 µJ and a repetition rate of 20 kHz has been obtained. Moreover, by tuning the polarization controllers radially and azimuthally, polarized Q-switched beams can be excited separately with a polarization purity of  >94.5%. This compact Q-switched fiber laser with ns CVB pulse output could find potential applications in the field of material processing, nonlinear optics and so on.

Heading-vector navigation based on head-direction cells and path integration.

PubMed

Kubie, John L; Fenton, André A

2009-05-01

Insect navigation is guided by heading vectors that are computed by path integration. Mammalian navigation models, on the other hand, are typically based on map-like place representations provided by hippocampal place cells. Such models compute optimal routes as a continuous series of locations that connect the current location to a goal. We propose a "heading-vector" model in which head-direction cells or their derivatives serve both as key elements in constructing the optimal route and as the straight-line guidance during route execution. The model is based on a memory structure termed the "shortcut matrix," which is constructed during the initial exploration of an environment when a set of shortcut vectors between sequential pairs of visited waypoint locations is stored. A mechanism is proposed for calculating and storing these vectors that relies on a hypothesized cell type termed an "accumulating head-direction cell." Following exploration, shortcut vectors connecting all pairs of waypoint locations are computed by vector arithmetic and stored in the shortcut matrix. On re-entry, when local view or place representations query the shortcut matrix with a current waypoint and goal, a shortcut trajectory is retrieved. Since the trajectory direction is in head-direction compass coordinates, navigation is accomplished by tracking the firing of head-direction cells that are tuned to the heading angle. Section 1 of the manuscript describes the properties of accumulating head-direction cells. It then shows how accumulating head-direction cells can store local vectors and perform vector arithmetic to perform path-integration-based homing. Section 2 describes the construction and use of the shortcut matrix for computing direct paths between any pair of locations that have been registered in the shortcut matrix. In the discussion, we analyze the advantages of heading-based navigation over map-based navigation. Finally, we survey behavioral evidence that nonhippocampal, heading-based navigation is used in small mammals and humans. Copyright 2008 Wiley-Liss, Inc.

Parameter Estimation of a Spiking Silicon Neuron

PubMed Central

Russell, Alexander; Mazurek, Kevin; Mihalaş, Stefan; Niebur, Ernst; Etienne-Cummings, Ralph

2012-01-01

Spiking neuron models are used in a multitude of tasks ranging from understanding neural behavior at its most basic level to neuroprosthetics. Parameter estimation of a single neuron model, such that the model’s output matches that of a biological neuron is an extremely important task. Hand tuning of parameters to obtain such behaviors is a difficult and time consuming process. This is further complicated when the neuron is instantiated in silicon (an attractive medium in which to implement these models) as fabrication imperfections make the task of parameter configuration more complex. In this paper we show two methods to automate the configuration of a silicon (hardware) neuron’s parameters. First, we show how a Maximum Likelihood method can be applied to a leaky integrate and fire silicon neuron with spike induced currents to fit the neuron’s output to desired spike times. We then show how a distance based method which approximates the negative log likelihood of the lognormal distribution can also be used to tune the neuron’s parameters. We conclude that the distance based method is better suited for parameter configuration of silicon neurons due to its superior optimization speed. PMID:23852978

Bayesian data assimilation provides rapid decision support for vector-borne diseases.

PubMed

Jewell, Chris P; Brown, Richard G

2015-07-06

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host-vector-pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

The influence of probe level on the tuning of stimulus frequency otoacoustic emissions and behavioral test in human.

PubMed

Wang, Yao; Gong, Qin; Zhang, Tao

2016-05-10

Frequency selectivity (FS) of the auditory system is established at the level of the cochlea and it is important for the perception of complex sounds. Although direct measurements of cochlear FS require surgical preparation, it can also be estimated with the measurements of otoacoustic emissions or behavioral tests, including stimulus frequency otoacoustic emission suppression tuning curves (SFOAE STCs) or psychophysical tuning curves (PTCs). These two methods result in similar estimates of FS at low probe levels. As the compressive nonlinearity of cochlea is strongly dependent on the stimulus intensity, the sharpness of tuning curves which is relevant to the cochlear nonlinearity will change as a function of probe level. The present study aims to investigate the influence of different probe levels on the relationship between SFOAE STCs and PTCs. The study included 15 young subjects with normal hearing. SFOAE STCs and PTCs were recorded at low and moderate probe levels for frequencies centred at 1, 2, and 4 kHz. The ratio or the difference of the characteristic parameters between the two methods was calculated at each probe level. The effect of probe level on the ratio or the difference between the parameters of SFOAE STCs and PTCs was then statistically analysed. The tuning of SFOAE STCs was significantly positively correlated with the tuning of the PTCs at both low and moderate probe levels; yet, at the moderate probe level, the SFOAE STCs were consistently broader than the PTCs. The mean ratio of sharpness of tuning at low probe levels was constantly around 1 while around 1.5 at moderate probe levels. Probe level had a significant effect on the sharpness of tuning between the two methods of estimating FS. SFOAE STC seems a good alternative measurement of PTC for FS assessment at low probe levels. At moderate probe levels, SFOAE STC and PTC were not equivalent measures of the FS in terms of their bandwidths. Because SFOAE STCs are not biased by higher levels auditory processing, they may represent cochlear FS better than PTCs.

Bayesian data assimilation provides rapid decision support for vector-borne diseases

PubMed Central

Jewell, Chris P.; Brown, Richard G.

2015-01-01

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host–vector–pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks. PMID:26136225

Explicit analytical tuning rules for digital PID controllers via the magnitude optimum criterion.

PubMed

Papadopoulos, Konstantinos G; Yadav, Praveen K; Margaris, Nikolaos I

2017-09-01

Analytical tuning rules for digital PID type-I controllers are presented regardless of the process complexity. This explicit solution allows control engineers 1) to make an accurate examination of the effect of the controller's sampling time to the control loop's performance both in the time and frequency domain 2) to decide when the control has to be I, PI and when the derivative, D, term has to be added or omitted 3) apply this control action to a series of stable benchmark processes regardless of their complexity. The former advantages are considered critical in industry applications, since 1) most of the times the choice of the digital controller's sampling time is based on heuristics and past criteria, 2) there is little a-priori knowledge of the controlled process making the choice of the type of the controller a trial and error exercise 3) model parameters change often depending on the control loop's operating point making in this way, the problem of retuning the controller's parameter a much challenging issue. Basis of the proposed control law is the principle of the PID tuning via the Magnitude Optimum criterion. The final control law involves the controller's sampling time T s within the explicit solution of the controller's parameters. Finally, the potential of the proposed method is justified by comparing its performance with the conventional PID tuning when controlling the same process. Further investigation regarding the choice of the controller's sampling time T s is also presented and useful conclusions for control engineers are derived. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Guiding automated left ventricular chamber segmentation in cardiac imaging using the concept of conserved myocardial volume.

PubMed

Garson, Christopher D; Li, Bing; Acton, Scott T; Hossack, John A

2008-06-01

The active surface technique using gradient vector flow allows semi-automated segmentation of ventricular borders. The accuracy of the algorithm depends on the optimal selection of several key parameters. We investigated the use of conservation of myocardial volume for quantitative assessment of each of these parameters using synthetic and in vivo data. We predicted that for a given set of model parameters, strong conservation of volume would correlate with accurate segmentation. The metric was most useful when applied to the gradient vector field weighting and temporal step-size parameters, but less effective in guiding an optimal choice of the active surface tension and rigidity parameters.

Anisotropic power spectrum and bispectrum in the f(Φ)F² mechanism

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

Bartolo, Nicola; Matarrese, Sabino; Peloso, Marco

2013-01-04

A suitable coupling of the inflaton φ to a vector kinetic term F² gives frozen and scale invariant vector perturbations. We compute the cosmological perturbations ζ that result from such coupling by taking into account the classical vector field that unavoidably gets generated at large scales during inflation. This generically results in a too-anisotropic power spectrum of ζ. Specifically, the anisotropy exceeds the 1% level (10% level) if inflation lasts ~5 e-folds (~50 e-folds) more than the minimal amount required to produce the cosmic microwave background modes. This conclusion applies, among others, to the application of this mechanism for magnetogenesis,more » for anisotropic inflation, and for the generation of anisotropic perturbations at the end of inflation through a waterfall field coupled to the vector (in this case, the unavoidable contribution that we obtain is effective all throughout inflation, and it is independent of the waterfall field). For a tuned duration of inflation, a 1% (10%) anisotropy in the power spectrum corresponds to an anisotropic bispectrum which is enhanced like the local one in the squeezed limit, and with an effective local f NL~3(~30). More in general, a significant anisotropy of the perturbations may be a natural outcome of all models that sustain higher than 0 spin fields during inflation.« less

Anisotropic power spectrum and bispectrum in the f(ϕ)F2 mechanism

NASA Astrophysics Data System (ADS)

Bartolo, Nicola; Matarrese, Sabino; Peloso, Marco; Ricciardone, Angelo

2013-01-01

A suitable coupling of the inflaton φ to a vector kinetic term F2 gives frozen and scale invariant vector perturbations. We compute the cosmological perturbations ζ that result from such coupling by taking into account the classical vector field that unavoidably gets generated at large scales during inflation. This generically results in a too-anisotropic power spectrum of ζ. Specifically, the anisotropy exceeds the 1% level (10% level) if inflation lasts ˜5 e-folds (˜50 e-folds) more than the minimal amount required to produce the cosmic microwave background modes. This conclusion applies, among others, to the application of this mechanism for magnetogenesis, for anisotropic inflation, and for the generation of anisotropic perturbations at the end of inflation through a waterfall field coupled to the vector (in this case, the unavoidable contribution that we obtain is effective all throughout inflation, and it is independent of the waterfall field). For a tuned duration of inflation, a 1% (10%) anisotropy in the power spectrum corresponds to an anisotropic bispectrum which is enhanced like the local one in the squeezed limit, and with an effective local fNL˜3(˜30). More in general, a significant anisotropy of the perturbations may be a natural outcome of all models that sustain higher than 0 spin fields during inflation.

Analysis of filter tuning techniques for sequential orbit determination

NASA Technical Reports Server (NTRS)

Lee, T.; Yee, C.; Oza, D.

1995-01-01

This paper examines filter tuning techniques for a sequential orbit determination (OD) covariance analysis. Recently, there has been a renewed interest in sequential OD, primarily due to the successful flight qualification of the Tracking and Data Relay Satellite System (TDRSS) Onboard Navigation System (TONS) using Doppler data extracted onboard the Extreme Ultraviolet Explorer (EUVE) spacecraft. TONS computes highly accurate orbit solutions onboard the spacecraft in realtime using a sequential filter. As the result of the successful TONS-EUVE flight qualification experiment, the Earth Observing System (EOS) AM-1 Project has selected TONS as the prime navigation system. In addition, sequential OD methods can be used successfully for ground OD. Whether data are processed onboard or on the ground, a sequential OD procedure is generally favored over a batch technique when a realtime automated OD system is desired. Recently, OD covariance analyses were performed for the TONS-EUVE and TONS-EOS missions using the sequential processing options of the Orbit Determination Error Analysis System (ODEAS). ODEAS is the primary covariance analysis system used by the Goddard Space Flight Center (GSFC) Flight Dynamics Division (FDD). The results of these analyses revealed a high sensitivity of the OD solutions to the state process noise filter tuning parameters. The covariance analysis results show that the state estimate error contributions from measurement-related error sources, especially those due to the random noise and satellite-to-satellite ionospheric refraction correction errors, increase rapidly as the state process noise increases. These results prompted an in-depth investigation of the role of the filter tuning parameters in sequential OD covariance analysis. This paper analyzes how the spacecraft state estimate errors due to dynamic and measurement-related error sources are affected by the process noise level used. This information is then used to establish guidelines for determining optimal filter tuning parameters in a given sequential OD scenario for both covariance analysis and actual OD. Comparisons are also made with corresponding definitive OD results available from the TONS-EUVE analysis.

Adaptive Estimation and Heuristic Optimization of Nonlinear Spacecraft Attitude Dynamics

DTIC Science & Technology

2016-09-15

Algorithm GPS Global Positioning System HOUF Higher Order Unscented Filter IC initial conditions IMM Interacting Multiple Model IMU Inertial Measurement Unit ...sources ranging from inertial measurement units to star sensors are used to construct observations for attitude estimation algorithms. The sensor...parameters. A single vector measurement will provide two independent parameters, as a unit vector constraint removes a DOF making the problem underdetermined

An Analytical Investigation of the Robustness and Power of ANCOVA with the Presence of Heterogeneous Regression Slopes.

ERIC Educational Resources Information Center

Hollingsworth, Holly H.

This study shows that the test statistic for Analysis of Covariance (ANCOVA) has a noncentral F-districution with noncentrality parameter equal to zero if and only if the regression planes are homogeneous and/or the vector of overall covariate means is the null vector. The effect of heterogeneous regression slope parameters is to either increase…

Angular motion estimation using dynamic models in a gyro-free inertial measurement unit.

PubMed

Edwan, Ezzaldeen; Knedlik, Stefan; Loffeld, Otmar

2012-01-01

In this paper, we summarize the results of using dynamic models borrowed from tracking theory in describing the time evolution of the state vector to have an estimate of the angular motion in a gyro-free inertial measurement unit (GF-IMU). The GF-IMU is a special type inertial measurement unit (IMU) that uses only a set of accelerometers in inferring the angular motion. Using distributed accelerometers, we get an angular information vector (AIV) composed of angular acceleration and quadratic angular velocity terms. We use a Kalman filter approach to estimate the angular velocity vector since it is not expressed explicitly within the AIV. The bias parameters inherent in the accelerometers measurements' produce a biased AIV and hence the AIV bias parameters are estimated within an augmented state vector. Using dynamic models, the appended bias parameters of the AIV become observable and hence we can have unbiased angular motion estimate. Moreover, a good model is required to extract the maximum amount of information from the observation. Observability analysis is done to determine the conditions for having an observable state space model. For higher grades of accelerometers and under relatively higher sampling frequency, the error of accelerometer measurements is dominated by the noise error. Consequently, simulations are conducted on two models, one has bias parameters appended in the state space model and the other is a reduced model without bias parameters.

Angular Motion Estimation Using Dynamic Models in a Gyro-Free Inertial Measurement Unit

PubMed Central

Edwan, Ezzaldeen; Knedlik, Stefan; Loffeld, Otmar

2012-01-01

In this paper, we summarize the results of using dynamic models borrowed from tracking theory in describing the time evolution of the state vector to have an estimate of the angular motion in a gyro-free inertial measurement unit (GF-IMU). The GF-IMU is a special type inertial measurement unit (IMU) that uses only a set of accelerometers in inferring the angular motion. Using distributed accelerometers, we get an angular information vector (AIV) composed of angular acceleration and quadratic angular velocity terms. We use a Kalman filter approach to estimate the angular velocity vector since it is not expressed explicitly within the AIV. The bias parameters inherent in the accelerometers measurements' produce a biased AIV and hence the AIV bias parameters are estimated within an augmented state vector. Using dynamic models, the appended bias parameters of the AIV become observable and hence we can have unbiased angular motion estimate. Moreover, a good model is required to extract the maximum amount of information from the observation. Observability analysis is done to determine the conditions for having an observable state space model. For higher grades of accelerometers and under relatively higher sampling frequency, the error of accelerometer measurements is dominated by the noise error. Consequently, simulations are conducted on two models, one has bias parameters appended in the state space model and the other is a reduced model without bias parameters. PMID:22778586

JetWeb: A WWW interface and database for Monte Carlo tuning and validation

NASA Astrophysics Data System (ADS)

Butterworth, J. M.; Butterworth, S.

2003-06-01

A World Wide Web interface to a Monte Carlo tuning facility is described. The aim of the package is to allow rapid and reproducible comparisons to be made between detailed measurements at high-energy physics colliders and general physics simulation packages. The package includes a relational database, a Java servlet query and display facility, and clean interfaces to simulation packages and their parameters.

Electrical birefringence tuning of VCSELs

NASA Astrophysics Data System (ADS)

Pusch, Tobias; Lindemann, Markus; Gerhardt, Nils C.; Hofmann, Martin R.; Michalzik, Rainer

2018-02-01

The birefringence splitting B, which is the frequency difference between the two fundamental linear polarization modes in vertical-cavity surface-emitting lasers (VCSELs), is the key parameter determining the polarization dynamics of spin-VCSELs that can be much faster than the intensity dynamics. For easy handling and control, electrical tuning of B is favored. This was realized in an integrated chip by thermally induced strain via asymmetric heating with a birefringence tuning range of 45 GHz. In this paper we present our work on VCSEL structures mounted on piezoelectric transducers for strain generation. Furthermore we show a combination of both techniques, namely VCSELs with piezo-thermal birefringence tunability.

Capsule implosion optimization during the indirect-drive National Ignition Campaign

NASA Astrophysics Data System (ADS)

Landen, O. L.; Edwards, J.; Haan, S. W.; Robey, H. F.; Milovich, J.; Spears, B. K.; Weber, S. V.; Clark, D. S.; Lindl, J. D.; MacGowan, B. J.; Moses, E. I.; Atherton, J.; Amendt, P. A.; Boehly, T. R.; Bradley, D. K.; Braun, D. G.; Callahan, D. A.; Celliers, P. M.; Collins, G. W.; Dewald, E. L.; Divol, L.; Frenje, J. A.; Glenzer, S. H.; Hamza, A.; Hammel, B. A.; Hicks, D. G.; Hoffman, N.; Izumi, N.; Jones, O. S.; Kilkenny, J. D.; Kirkwood, R. K.; Kline, J. L.; Kyrala, G. A.; Marinak, M. M.; Meezan, N.; Meyerhofer, D. D.; Michel, P.; Munro, D. H.; Olson, R. E.; Nikroo, A.; Regan, S. P.; Suter, L. J.; Thomas, C. A.; Wilson, D. C.

2011-05-01

Capsule performance optimization campaigns will be conducted at the National Ignition Facility [G. H. Miller, E. I. Moses, and C. R. Wuest, Nucl. Fusion 44, 228 (2004)] to substantially increase the probability of ignition. The campaigns will experimentally correct for residual uncertainties in the implosion and hohlraum physics used in our radiation-hydrodynamic computational models using a variety of ignition capsule surrogates before proceeding to cryogenic-layered implosions and ignition experiments. The quantitative goals and technique options and down selections for the tuning campaigns are first explained. The computationally derived sensitivities to key laser and target parameters are compared to simple analytic models to gain further insight into the physics of the tuning techniques. The results of the validation of the tuning techniques at the OMEGA facility [J. M. Soures et al., Phys. Plasmas 3, 2108 (1996)] under scaled hohlraum and capsule conditions relevant to the ignition design are shown to meet the required sensitivity and accuracy. A roll-up of all expected random and systematic uncertainties in setting the key ignition laser and target parameters due to residual measurement, calibration, cross-coupling, surrogacy, and scale-up errors has been derived that meets the required budget. Finally, we show how the tuning precision will be improved after a number of shots and iterations to meet an acceptable level of residual uncertainty.

An express method for optimally tuning an analog controller with respect to integral quality criteria

NASA Astrophysics Data System (ADS)

Golinko, I. M.; Kovrigo, Yu. M.; Kubrak, A. I.

2014-03-01

An express method for optimally tuning analog PI and PID controllers is considered. An integral quality criterion with minimizing the control output is proposed for optimizing control systems. The suggested criterion differs from existing ones in that the control output applied to the technological process is taken into account in a correct manner, due to which it becomes possible to maximally reduce the expenditure of material and/or energy resources in performing control of industrial equipment sets. With control organized in such manner, smaller wear and longer service life of control devices are achieved. A unimodal nature of the proposed criterion for optimally tuning a controller is numerically demonstrated using the methods of optimization theory. A functional interrelation between the optimal controller parameters and dynamic properties of a controlled plant is numerically determined for a single-loop control system. The results obtained from simulation of transients in a control system carried out using the proposed and existing functional dependences are compared with each other. The proposed calculation formulas differ from the existing ones by a simple structure and highly accurate search for the optimal controller tuning parameters. The obtained calculation formulas are recommended for being used by specialists in automation for design and optimization of control systems.

Analysis and design of negative resistance oscillators using surface transverse wave-based single port resonators.

PubMed

Avramov, Ivan D

2003-03-01

This practically oriented paper presents the fundamentals for analysis, optimization, and design of negative resistance oscillators (NRO) stabilized with surface transverse wave (STW)-based single-port resonators (SPR). Data on a variety of high-Q, low-loss SPR devices in the 900- to 2000-MHz range, suitable for NRO applications, are presented, and a simple method for SPR parameter extraction through Pi-circuit measurements is outlined. Negative resistance analysis, based on S-parameter data of the active device, is performed on a tuned-base, grounded collector transistor NRO, known for its good stability and tuning at microwave frequencies. By adding a SPR in the emitter network, the static transducer capacitance is absorbed by the circuit and is used to generate negative resistance only over the narrow bandwidth of the acoustic device, eliminating the risk of spurious oscillations. The analysis allows exact prediction of the oscillation frequency, tuning range, loaded Q, and excess gain. Simulation and experimental data on a 915-MHz fixed-frequency NRO and a wide tuning range, voltage-controlled STW oscillator, built and tested experimentally, are presented. Practical design aspects including the choice of transistor, negative feedback circuits, load coupling, and operation at the highest phase slope for minimum phase noise are discussed.

Effects of OCR Errors on Ranking and Feedback Using the Vector Space Model.

ERIC Educational Resources Information Center

Taghva, Kazem; And Others

1996-01-01

Reports on the performance of the vector space model in the presence of OCR (optical character recognition) errors in information retrieval. Highlights include precision and recall, a full-text test collection, smart vector representation, impact of weighting parameters, ranking variability, and the effect of relevance feedback. (Author/LRW)

Simultaneous feature selection and parameter optimisation using an artificial ant colony: case study of melting point prediction

PubMed Central

O'Boyle, Noel M; Palmer, David S; Nigsch, Florian; Mitchell, John BO

2008-01-01

Background We present a novel feature selection algorithm, Winnowing Artificial Ant Colony (WAAC), that performs simultaneous feature selection and model parameter optimisation for the development of predictive quantitative structure-property relationship (QSPR) models. The WAAC algorithm is an extension of the modified ant colony algorithm of Shen et al. (J Chem Inf Model 2005, 45: 1024–1029). We test the ability of the algorithm to develop a predictive partial least squares model for the Karthikeyan dataset (J Chem Inf Model 2005, 45: 581–590) of melting point values. We also test its ability to perform feature selection on a support vector machine model for the same dataset. Results Starting from an initial set of 203 descriptors, the WAAC algorithm selected a PLS model with 68 descriptors which has an RMSE on an external test set of 46.6°C and R2 of 0.51. The number of components chosen for the model was 49, which was close to optimal for this feature selection. The selected SVM model has 28 descriptors (cost of 5, ε of 0.21) and an RMSE of 45.1°C and R2 of 0.54. This model outperforms a kNN model (RMSE of 48.3°C, R2 of 0.47) for the same data and has similar performance to a Random Forest model (RMSE of 44.5°C, R2 of 0.55). However it is much less prone to bias at the extremes of the range of melting points as shown by the slope of the line through the residuals: -0.43 for WAAC/SVM, -0.53 for Random Forest. Conclusion With a careful choice of objective function, the WAAC algorithm can be used to optimise machine learning and regression models that suffer from overfitting. Where model parameters also need to be tuned, as is the case with support vector machine and partial least squares models, it can optimise these simultaneously. The moving probabilities used by the algorithm are easily interpreted in terms of the best and current models of the ants, and the winnowing procedure promotes the removal of irrelevant descriptors. PMID:18959785

Artificial Vector Calibration Method for Differencing Magnetic Gradient Tensor Systems

PubMed Central

Li, Zhining; Zhang, Yingtang; Yin, Gang

2018-01-01

The measurement error of the differencing (i.e., using two homogenous field sensors at a known baseline distance) magnetic gradient tensor system includes the biases, scale factors, nonorthogonality of the single magnetic sensor, and the misalignment error between the sensor arrays, all of which can severely affect the measurement accuracy. In this paper, we propose a low-cost artificial vector calibration method for the tensor system. Firstly, the error parameter linear equations are constructed based on the single-sensor’s system error model to obtain the artificial ideal vector output of the platform, with the total magnetic intensity (TMI) scalar as a reference by two nonlinear conversions, without any mathematical simplification. Secondly, the Levenberg–Marquardt algorithm is used to compute the integrated model of the 12 error parameters by nonlinear least-squares fitting method with the artificial vector output as a reference, and a total of 48 parameters of the system is estimated simultaneously. The calibrated system outputs along the reference platform-orthogonal coordinate system. The analysis results show that the artificial vector calibrated output can track the orientation fluctuations of TMI accurately, effectively avoiding the “overcalibration” problem. The accuracy of the error parameters’ estimation in the simulation is close to 100%. The experimental root-mean-square error (RMSE) of the TMI and tensor components is less than 3 nT and 20 nT/m, respectively, and the estimation of the parameters is highly robust. PMID:29373544

Adaptive Control of Synchronization in Delay-Coupled Heterogeneous Networks of FitzHugh-Nagumo Nodes

NASA Astrophysics Data System (ADS)

Plotnikov, S. A.; Lehnert, J.; Fradkov, A. L.; Schöll, E.

We study synchronization in delay-coupled neural networks of heterogeneous nodes. It is well known that heterogeneities in the nodes hinder synchronization when becoming too large. We show that an adaptive tuning of the overall coupling strength can be used to counteract the effect of the heterogeneity. Our adaptive controller is demonstrated on ring networks of FitzHugh-Nagumo systems which are paradigmatic for excitable dynamics but can also — depending on the system parameters — exhibit self-sustained periodic firing. We show that the adaptively tuned time-delayed coupling enables synchronization even if parameter heterogeneities are so large that excitable nodes coexist with oscillatory ones.

Investigation of earthquake factor for optimum tuned mass dampers

NASA Astrophysics Data System (ADS)

Nigdeli, Sinan Melih; Bekdaş, Gebrail

2012-09-01

In this study the optimum parameters of tuned mass dampers (TMD) are investigated under earthquake excitations. An optimization strategy was carried out by using the Harmony Search (HS) algorithm. HS is a metaheuristic method which is inspired from the nature of musical performances. In addition to the HS algorithm, the results of the optimization objective are compared with the results of the other documented method and the corresponding results are eliminated. In that case, the best optimum results are obtained. During the optimization, the optimum TMD parameters were searched for single degree of freedom (SDOF) structure models with different periods. The optimization was done for different earthquakes separately and the results were compared.

Observation of High-Order Polarization-Locked Vector Solitons in a Fiber Laser

NASA Astrophysics Data System (ADS)

Tang, D. Y.; Zhang, H.; Zhao, L. M.; Wu, X.

2008-10-01

We report on the experimental observation of a new type of polarization-locked vector soliton in a passively mode-locked fiber laser. The vector soliton is characterized by the fact that not only are the two orthogonally polarized soliton components phase-locked, but also one of the components has a double-humped intensity profile. Multiple phase-locked high-order vector solitons with identical soliton parameters and harmonic mode locking of the vector solitons were also obtained in the laser. Numerical simulations confirmed the existence of stable high-order vector solitons in the fiber laser.

Observation of high-order polarization-locked vector solitons in a fiber laser.

PubMed

Tang, D Y; Zhang, H; Zhao, L M; Wu, X

2008-10-10

We report on the experimental observation of a new type of polarization-locked vector soliton in a passively mode-locked fiber laser. The vector soliton is characterized by the fact that not only are the two orthogonally polarized soliton components phase-locked, but also one of the components has a double-humped intensity profile. Multiple phase-locked high-order vector solitons with identical soliton parameters and harmonic mode locking of the vector solitons were also obtained in the laser. Numerical simulations confirmed the existence of stable high-order vector solitons in the fiber laser.

Fine-tuning gene networks using simple sequence repeats

PubMed Central

Egbert, Robert G.; Klavins, Eric

2012-01-01

The parameters in a complex synthetic gene network must be extensively tuned before the network functions as designed. Here, we introduce a simple and general approach to rapidly tune gene networks in Escherichia coli using hypermutable simple sequence repeats embedded in the spacer region of the ribosome binding site. By varying repeat length, we generated expression libraries that incrementally and predictably sample gene expression levels over a 1,000-fold range. We demonstrate the utility of the approach by creating a bistable switch library that programmatically samples the expression space to balance the two states of the switch, and we illustrate the need for tuning by showing that the switch’s behavior is sensitive to host context. Further, we show that mutation rates of the repeats are controllable in vivo for stability or for targeted mutagenesis—suggesting a new approach to optimizing gene networks via directed evolution. This tuning methodology should accelerate the process of engineering functionally complex gene networks. PMID:22927382

Practice and philosophy of climate model tuning across six US modeling centers

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

Schmidt, Gavin A.; Bader, David; Donner, Leo J.

Model calibration (or tuning) is a necessary part of developing and testing coupled ocean–atmosphere climate models regardless of their main scientific purpose. There is an increasing recognition that this process needs to become more transparent for both users of climate model output and other developers. Knowing how and why climate models are tuned and which targets are used is essential to avoiding possible misattributions of skillful predictions to data accommodation and vice versa. This paper describes the approach and practice of model tuning for the six major US climate modeling centers. While details differ among groups in terms of scientificmore » missions, tuning targets, and tunable parameters, there is a core commonality of approaches. Furthermore, practices differ significantly on some key aspects, in particular, in the use of initialized forecast analyses as a tool, the explicit use of the historical transient record, and the use of the present-day radiative imbalance vs. the implied balance in the preindustrial era as a target.« less

Practice and philosophy of climate model tuning across six US modeling centers

NASA Astrophysics Data System (ADS)

Schmidt, Gavin A.; Bader, David; Donner, Leo J.; Elsaesser, Gregory S.; Golaz, Jean-Christophe; Hannay, Cecile; Molod, Andrea; Neale, Richard B.; Saha, Suranjana

2017-09-01

Model calibration (or tuning) is a necessary part of developing and testing coupled ocean-atmosphere climate models regardless of their main scientific purpose. There is an increasing recognition that this process needs to become more transparent for both users of climate model output and other developers. Knowing how and why climate models are tuned and which targets are used is essential to avoiding possible misattributions of skillful predictions to data accommodation and vice versa. This paper describes the approach and practice of model tuning for the six major US climate modeling centers. While details differ among groups in terms of scientific missions, tuning targets, and tunable parameters, there is a core commonality of approaches. However, practices differ significantly on some key aspects, in particular, in the use of initialized forecast analyses as a tool, the explicit use of the historical transient record, and the use of the present-day radiative imbalance vs. the implied balance in the preindustrial era as a target.

Practice and philosophy of climate model tuning across six US modeling centers

DOE PAGES

Schmidt, Gavin A.; Bader, David; Donner, Leo J.; ...

2017-09-01

Model calibration (or tuning) is a necessary part of developing and testing coupled ocean–atmosphere climate models regardless of their main scientific purpose. There is an increasing recognition that this process needs to become more transparent for both users of climate model output and other developers. Knowing how and why climate models are tuned and which targets are used is essential to avoiding possible misattributions of skillful predictions to data accommodation and vice versa. This paper describes the approach and practice of model tuning for the six major US climate modeling centers. While details differ among groups in terms of scientificmore » missions, tuning targets, and tunable parameters, there is a core commonality of approaches. Furthermore, practices differ significantly on some key aspects, in particular, in the use of initialized forecast analyses as a tool, the explicit use of the historical transient record, and the use of the present-day radiative imbalance vs. the implied balance in the preindustrial era as a target.« less

Towards a Negative Refractive Index in an Atomic System

NASA Astrophysics Data System (ADS)

Simmons, Zach; Brewer, Nick; Yavuz, Deniz

2014-05-01

The goal of our experiments is to obtain a negative index of refraction in the optical region of the spectrum using an atomic system. The concept of negative refraction, which was first predicted by Veselago more than four decades ago, has recently emerged as a very exciting field of science. Negative index materials exhibit many seemingly strange properties such as electromagnetic vectors forming a left-handed triad. A key potential application for these materials was discovered in 2000 when Pendry predicted that a slab with a negative refractive index can image objects with a resolution far better than the diffraction limit. Thus far, research in negative index materials has primarily focused on meta-materials. The fixed response and often large absorption of these engineered materials motivates our efforts to work in an atomic system. An atomic media offers the potential to be actively modified, for example by changing laser parameters, and can be tuned to cancel absorption. A doped crystal allows for high atomic densities compared to other atomic systems. So far we have identified a transition in such a material, Eu:YSO, as a candidate for these experiments and are performing spectroscopy on this material.

Vector-Borne Pathogen and Host Evolution in a Structured Immuno-Epidemiological System.

PubMed

Gulbudak, Hayriye; Cannataro, Vincent L; Tuncer, Necibe; Martcheva, Maia

2017-02-01

Vector-borne disease transmission is a common dissemination mode used by many pathogens to spread in a host population. Similar to directly transmitted diseases, the within-host interaction of a vector-borne pathogen and a host's immune system influences the pathogen's transmission potential between hosts via vectors. Yet there are few theoretical studies on virulence-transmission trade-offs and evolution in vector-borne pathogen-host systems. Here, we consider an immuno-epidemiological model that links the within-host dynamics to between-host circulation of a vector-borne disease. On the immunological scale, the model mimics antibody-pathogen dynamics for arbovirus diseases, such as Rift Valley fever and West Nile virus. The within-host dynamics govern transmission and host mortality and recovery in an age-since-infection structured host-vector-borne pathogen epidemic model. By considering multiple pathogen strains and multiple competing host populations differing in their within-host replication rate and immune response parameters, respectively, we derive evolutionary optimization principles for both pathogen and host. Invasion analysis shows that the [Formula: see text] maximization principle holds for the vector-borne pathogen. For the host, we prove that evolution favors minimizing case fatality ratio (CFR). These results are utilized to compute host and pathogen evolutionary trajectories and to determine how model parameters affect evolution outcomes. We find that increasing the vector inoculum size increases the pathogen [Formula: see text], but can either increase or decrease the pathogen virulence (the host CFR), suggesting that vector inoculum size can contribute to virulence of vector-borne diseases in distinct ways.

Fault tolerant control of multivariable processes using auto-tuning PID controller.

PubMed

Yu, Ding-Li; Chang, T K; Yu, Ding-Wen

2005-02-01

Fault tolerant control of dynamic processes is investigated in this paper using an auto-tuning PID controller. A fault tolerant control scheme is proposed composing an auto-tuning PID controller based on an adaptive neural network model. The model is trained online using the extended Kalman filter (EKF) algorithm to learn system post-fault dynamics. Based on this model, the PID controller adjusts its parameters to compensate the effects of the faults, so that the control performance is recovered from degradation. The auto-tuning algorithm for the PID controller is derived with the Lyapunov method and therefore, the model predicted tracking error is guaranteed to converge asymptotically. The method is applied to a simulated two-input two-output continuous stirred tank reactor (CSTR) with various faults, which demonstrate the applicability of the developed scheme to industrial processes.

Monitoring of the Conformational Space of Dipeptides by Generative Topographic Mapping.

PubMed

Horvath, Dragos; Marcou, Gilles; Varnek, Alexandre

2018-01-01

This work describes a procedure to build generative topographic maps (GTM) as 2D representation of the conformational space (CS) of dipeptides. GTMs with excellent propensities to support highly predictive landscapes of various conformational properties were reported for three dipeptides (AA, KE and KR). CS monitoring via GTMproceeds through the projection of conformer ensembles on the map, producing cumulated responsibility (CR) vectors characteristic of the CS areas covered by the ensemble. Overlap of the CS areas visited by two distinct simulations can be expressed by the Tanimoto coefficient Tc of the associated CRs. This idea was used to monitor the reproducibility of the stochastic evolutionary conformer generation process implemented in S4MPLE. It could be shown that conformers produced by <500 S4MPLE runs reproducibly cover the relevant CS zone at given setup of the driving force field. The propensity of a simulation to visit the native CS zone can thus be quantitatively estimated, as the Tc score with respect to the "native" CR, as defined by the ensemble of dipeptide geometries extracted from PDB proteins. It could be shown that low-energy CS regions were indeed found to fall within the native zone. The Tc overlap score behaved as a smooth function of force field parameters. This opens the perspective of a novel force field parameter tuning procedure, bound to simultaneously optimize the behavior of the in Silico simulations for every possible dipeptide. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Intelligent earthquake data processing for global adjoint tomography

NASA Astrophysics Data System (ADS)

Chen, Y.; Hill, J.; Li, T.; Lei, W.; Ruan, Y.; Lefebvre, M. P.; Tromp, J.

2016-12-01

Due to the increased computational capability afforded by modern and future computing architectures, the seismology community is demanding a more comprehensive understanding of the full waveform information from the recorded earthquake seismograms. Global waveform tomography is a complex workflow that matches observed seismic data with synthesized seismograms by iteratively updating the earth model parameters based on the adjoint state method. This methodology allows us to compute a very accurate model of the earth's interior. The synthetic data is simulated by solving the wave equation in the entire globe using a spectral-element method. In order to ensure the inversion accuracy and stability, both the synthesized and observed seismograms must be carefully pre-processed. Because the scale of the inversion problem is extremely large and there is a very large volume of data to both be read and written, an efficient and reliable pre-processing workflow must be developed. We are investigating intelligent algorithms based on a machine-learning (ML) framework that will automatically tune parameters for the data processing chain. One straightforward application of ML in data processing is to classify all possible misfit calculation windows into usable and unusable ones, based on some intelligent ML models such as neural network, support vector machine or principle component analysis. The intelligent earthquake data processing framework will enable the seismology community to compute the global waveform tomography using seismic data from an arbitrarily large number of earthquake events in the fastest, most efficient way.

Novel technique for ST-T interval characterization in patients with acute myocardial ischemia.

PubMed

Correa, Raúl; Arini, Pedro David; Correa, Lorena Sabrina; Valentinuzzi, Max; Laciar, Eric

2014-07-01

The novel signal processing techniques have allowed and improved the use of vectorcardiography (VCG) to diagnose and characterize myocardial ischemia. Herein, we studied vectorcardiographic dynamic changes of ventricular repolarization in 80 patients before (control) and during Percutaneous Transluminal Coronary Angioplasty (PTCA). We propose four vectorcardiographic ST-T parameters, i.e., (a) ST Vector Magnitude Area (aSTVM); (b) T-wave Vector Magnitude Area (aTVM); (c) ST-T Vector Magnitude Difference (ST-TVD), and (d) T-wave Vector Magnitude Difference (TVD). For comparison, the conventional ST-Change Vector Magnitude (STCVM) and Spatial Ventricular Gradient (SVG) were also calculated. Our results indicate that several vectorcardiographic parameters show significant differences (p-value<0.05) before starting and during PTCA. Statistical minute-by-minute PTCA comparison against the control situation showed that ischemic monitoring reached a sensitivity=90.5% and a specificity=92.6% at the 5th minute of the PTCA, when aSTVM and ST-TVD were used as classifiers. We conclude that the sensitivity and specificity for acute ischemia monitoring could be increased with the use of only two vectorcardiographic parameters. Hence, the proposed technique based on vectorcardiography could be used in addition to the conventional ST-T analysis for better monitoring of ischemic patients. Copyright © 2014 Elsevier Ltd. All rights reserved.

Universal Parameter Measurement and Sensorless Vector Control of Induction and Permanent Magnet Synchronous Motors

NASA Astrophysics Data System (ADS)

Yamamoto, Shu; Ara, Takahiro

Recently, induction motors (IMs) and permanent-magnet synchronous motors (PMSMs) have been used in various industrial drive systems. The features of the hardware device used for controlling the adjustable-speed drive in these motors are almost identical. Despite this, different techniques are generally used for parameter measurement and speed-sensorless control of these motors. If the same technique can be used for parameter measurement and sensorless control, a highly versatile adjustable-speed-drive system can be realized. In this paper, the authors describe a new universal sensorless control technique for both IMs and PMSMs (including salient pole and nonsalient pole machines). A mathematical model applicable for IMs and PMSMs is discussed. Using this model, the authors derive the proposed universal sensorless vector control algorithm on the basis of estimation of the stator flux linkage vector. All the electrical motor parameters are determined by a unified test procedure. The proposed method is implemented on three test machines. The actual driving test results demonstrate the validity of the proposed method.

Two particle model for studying the effects of space-charge force on strong head-tail instabilities

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

Chin, Yong Ho; Chao, Alexander Wu; Blaskiewicz, Michael M.

In this paper, we present a new two particle model for studying the strong head-tail instabilities in the presence of the space-charge force. It is a simple expansion of the well-known two particle model for strong head-tail instability and is still analytically solvable. No chromaticity effect is included. It leads to a formula for the growth rate as a function of the two dimensionless parameters: the space-charge tune shift parameter (normalized by the synchrotron tune) and the wakefield strength, Upsilon. The three-dimensional contour plot of the growth rate as a function of those two dimensionless parameters reveals stopband structures. Manymore » simulation results generally indicate that a strong head-tail instability can be damped by a weak space-charge force, but the beam becomes unstable again when the space-charge force is further increased. The new two particle model indicates a similar behavior. In weak space-charge regions, additional tune shifts by the space-charge force dissolve the mode coupling. As the space-charge force is increased, they conversely restore the mode coupling, but then a further increase of the space-charge force decouples the modes again. Lastly, this mode coupling/decoupling behavior creates the stopband structures.« less

Two particle model for studying the effects of space-charge force on strong head-tail instabilities

DOE PAGES

Chin, Yong Ho; Chao, Alexander Wu; Blaskiewicz, Michael M.

2016-01-19

In this paper, we present a new two particle model for studying the strong head-tail instabilities in the presence of the space-charge force. It is a simple expansion of the well-known two particle model for strong head-tail instability and is still analytically solvable. No chromaticity effect is included. It leads to a formula for the growth rate as a function of the two dimensionless parameters: the space-charge tune shift parameter (normalized by the synchrotron tune) and the wakefield strength, Upsilon. The three-dimensional contour plot of the growth rate as a function of those two dimensionless parameters reveals stopband structures. Manymore » simulation results generally indicate that a strong head-tail instability can be damped by a weak space-charge force, but the beam becomes unstable again when the space-charge force is further increased. The new two particle model indicates a similar behavior. In weak space-charge regions, additional tune shifts by the space-charge force dissolve the mode coupling. As the space-charge force is increased, they conversely restore the mode coupling, but then a further increase of the space-charge force decouples the modes again. Lastly, this mode coupling/decoupling behavior creates the stopband structures.« less

Optimal placement of tuning masses for vibration reduction in helicopter rotor blades

NASA Technical Reports Server (NTRS)

Pritchard, Jocelyn I.; Adelman, Howard M.

1988-01-01

Described are methods for reducing vibration in helicopter rotor blades by determining optimum sizes and locations of tuning masses through formal mathematical optimization techniques. An optimization procedure is developed which employs the tuning masses and corresponding locations as design variables which are systematically changed to achieve low values of shear without a large mass penalty. The finite-element structural analysis of the blade and the optimization formulation require development of discretized expressions for two performance parameters: modal shaping parameter and modal shear amplitude. Matrix expressions for both quantities and their sensitivity derivatives are developed. Three optimization strategies are developed and tested. The first is based on minimizing the modal shaping parameter which indirectly reduces the modal shear amplitudes corresponding to each harmonic of airload. The second strategy reduces these amplitudes directly, and the third strategy reduces the shear as a function of time during a revolution of the blade. The first strategy works well for reducing the shear for one mode responding to a single harmonic of the airload, but has been found in some cases to be ineffective for more than one mode. The second and third strategies give similar results and show excellent reduction of the shear with a low mass penalty.

The cross-validated AUC for MCP-logistic regression with high-dimensional data.

PubMed

Jiang, Dingfeng; Huang, Jian; Zhang, Ying

2013-10-01

We propose a cross-validated area under the receiving operator characteristic (ROC) curve (CV-AUC) criterion for tuning parameter selection for penalized methods in sparse, high-dimensional logistic regression models. We use this criterion in combination with the minimax concave penalty (MCP) method for variable selection. The CV-AUC criterion is specifically designed for optimizing the classification performance for binary outcome data. To implement the proposed approach, we derive an efficient coordinate descent algorithm to compute the MCP-logistic regression solution surface. Simulation studies are conducted to evaluate the finite sample performance of the proposed method and its comparison with the existing methods including the Akaike information criterion (AIC), Bayesian information criterion (BIC) or Extended BIC (EBIC). The model selected based on the CV-AUC criterion tends to have a larger predictive AUC and smaller classification error than those with tuning parameters selected using the AIC, BIC or EBIC. We illustrate the application of the MCP-logistic regression with the CV-AUC criterion on three microarray datasets from the studies that attempt to identify genes related to cancers. Our simulation studies and data examples demonstrate that the CV-AUC is an attractive method for tuning parameter selection for penalized methods in high-dimensional logistic regression models.

Simple graph models of information spread in finite populations

PubMed Central

Voorhees, Burton; Ryder, Bergerud

2015-01-01

We consider several classes of simple graphs as potential models for information diffusion in a structured population. These include biases cycles, dual circular flows, partial bipartite graphs and what we call ‘single-link’ graphs. In addition to fixation probabilities, we study structure parameters for these graphs, including eigenvalues of the Laplacian, conductances, communicability and expected hitting times. In several cases, values of these parameters are related, most strongly so for partial bipartite graphs. A measure of directional bias in cycles and circular flows arises from the non-zero eigenvalues of the antisymmetric part of the Laplacian and another measure is found for cycles as the value of the transition probability for which hitting times going in either direction of the cycle are equal. A generalization of circular flow graphs is used to illustrate the possibility of tuning edge weights to match pre-specified values for graph parameters; in particular, we show that generalizations of circular flows can be tuned to have fixation probabilities equal to the Moran probability for a complete graph by tuning vertex temperature profiles. Finally, single-link graphs are introduced as an example of a graph involving a bottleneck in the connection between two components and these are compared to the partial bipartite graphs. PMID:26064661

Design and construction of a novel 1H/19F double-tuned coil system using PIN-diode switches at 9.4T.

PubMed

Choi, Chang-Hoon; Hong, Suk-Min; Ha, YongHyun; Shah, N Jon

2017-06-01

A double-tuned 1 H/ 19 F coil using PIN-diode switches was developed and its performance evaluated. The is a key difference from the previous developments being that this design used a PIN-diode switch in series with an additionally inserted inductor in parallel to one of the capacitors on the loop. The probe was adjusted to 19 F when the reverse bias voltage was applied (PIN-diode OFF), whilst it was switched to 1 H when forward current was flowing (PIN-diode ON). S-parameters and Q-factors of single- and double-tuned coils were examined and compared with/without a phantom on the bench. Imaging experiments were carried out on a 9.4T preclinical scanner. All coils were tuned at resonance frequencies and matched well. It is shown that the Q-ratio and SNR of double-tuned coil at 19 F frequency are nearly as good as those of a single-tuned coil. Since the operating frequency was tuned to 19 F when the PIN-diodes were turned off, losses due to PIN-diodes were substantially lower resulting in the provision of excellent image quality of X-nuclei. Copyright © 2017 Elsevier Inc. All rights reserved.

Heart Electrical Actions as Biometric Indicia

NASA Technical Reports Server (NTRS)

Schipper, John F. (Inventor); Dusan, Sorin V. (Inventor); Jorgensen, Charles C. (Inventor); Belousof, Eugene (Inventor)

2013-01-01

A method and associated system for use of statistical parameters based on peak amplitudes and/or time interval lengths and/or depolarization-repolarization vector angles and/or depolarization-repolarization vector lengths for PQRST electrical signals associated with heart waves, to identify a person. The statistical parameters, estimated to be at least 192, serve as biometric indicia, to authenticate, or to decline to authenticate, an asserted identity of a candidate person.

A Study on Aircraft Engine Control Systems for Integrated Flight and Propulsion Control

NASA Astrophysics Data System (ADS)

Yamane, Hideaki; Matsunaga, Yasushi; Kusakawa, Takeshi; Yasui, Hisako

The Integrated Flight and Propulsion Control (IFPC) for a highly maneuverable aircraft and a fighter-class engine with pitch/yaw thrust vectoring is described. Of the two IFPC functions the aircraft maneuver control utilizes the thrust vectoring based on aerodynamic control surfaces/thrust vectoring control allocation specified by the Integrated Control Unit (ICU) of a FADEC (Full Authority Digital Electronic Control) system. On the other hand in the Performance Seeking Control (PSC) the ICU identifies engine's various characteristic changes, optimizes manipulated variables and finally adjusts engine control parameters in cooperation with the Engine Control Unit (ECU). It is shown by hardware-in-the-loop simulation that the thrust vectoring can enhance aircraft maneuverability/agility and that the PSC can improve engine performance parameters such as SFC (specific fuel consumption), thrust and gas temperature.

Using the basic reproduction number to assess the effects of climate change in the risk of Chagas disease transmission in Colombia.

PubMed

Cordovez, Juan M; Rendon, Lina Maria; Gonzalez, Camila; Guhl, Felipe

2014-01-01

The dynamics of vector-borne diseases has often been linked to climate change. However the commonly complex dynamics of vector-borne diseases make it very difficult to predict risk based on vector or host distributions. The basic reproduction number (R0) integrates all factors that determine whether a pathogen can establish or not. To obtain R0 for complex vector-borne diseases one can use the next-generation matrix (NGM) approach. We used the NGM to compute R0 for Chagas disease in Colombia incorporating the effect of temperature in some of the transmission routes of Trypanosoma cruzi. We used R0 to generate a risk map of present conditions and a forecast risk map at 20 years from now based on mean annual temperature (data obtained from Worldclim). In addition we used the model to compute elasticity and sensitivity indexes on all model parameters and routes of transmission. We present this work as an approach to indicate which transmission pathways are more critical for disease transmission but acknowledge the fact that results and projections strongly depend on better knowledge of entomological parameters and transmission routes. We concluded that the highest contribution to R0 comes from transmission of the parasites from humans to vectors, which is a surprising result. In addition,parameters related to contacts between human and vectors and the efficiency of parasite transmission between them also show a prominent effect on R0.

Quantification of AAV particle titers by infrared fluorescence scanning of coomassie-stained sodium dodecyl sulfate-polyacrylamide gels.

PubMed

Kohlbrenner, Erik; Henckaerts, Els; Rapti, Kleopatra; Gordon, Ronald E; Linden, R Michael; Hajjar, Roger J; Weber, Thomas

2012-06-01

Adeno-associated virus (AAV)-based vectors have gained increasing attention as gene delivery vehicles in basic and preclinical studies as well as in human gene therapy trials. Especially for the latter two-for both safety and therapeutic efficacy reasons-a detailed characterization of all relevant parameters of the vector preparation is essential. Two important parameters that are routinely used to analyze recombinant AAV vectors are (1) the titer of viral particles containing a (recombinant) viral genome and (2) the purity of the vector preparation, most commonly assessed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) followed by silver staining. An important, third parameter, the titer of total viral particles, that is, the combined titer of both genome-containing and empty viral capsids, is rarely determined. Here, we describe a simple and inexpensive method that allows the simultaneous assessment of both vector purity and the determination of the total viral particle titer. This method, which was validated by comparison with established methods to determine viral particle titers, is based on the fact that Coomassie Brilliant Blue, when bound to proteins, fluoresces in the infrared spectrum. Viral samples are separated by SDS-PAGE followed by Coomassie Brilliant Blue staining and gel analysis with an infrared laser-scanning device. In combination with a protein standard, our method allows the rapid and accurate determination of viral particle titers simultaneously with the assessment of vector purity.

An automated method of tuning an attitude estimator

NASA Technical Reports Server (NTRS)

Mason, Paul A. C.; Mook, D. Joseph

1995-01-01

Attitude determination is a major element of the operation and maintenance of a spacecraft. There are several existing methods of determining the attitude of a spacecraft. One of the most commonly used methods utilizes the Kalman filter to estimate the attitude of the spacecraft. Given an accurate model of a system and adequate observations, a Kalman filter can produce accurate estimates of the attitude. If the system model, filter parameters, or observations are inaccurate, the attitude estimates may be degraded. Therefore, it is advantageous to develop a method of automatically tuning the Kalman filter to produce the accurate estimates. In this paper, a three-axis attitude determination Kalman filter, which uses only magnetometer measurements, is developed and tested using real data. The appropriate filter parameters are found via the Process Noise Covariance Estimator (PNCE). The PNCE provides an optimal criterion for determining the best filter parameters.

Fractional order sliding-mode control based on parameters auto-tuning for velocity control of permanent magnet synchronous motor.

PubMed

Zhang, BiTao; Pi, YouGuo; Luo, Ying

2012-09-01

A fractional order sliding mode control (FROSMC) scheme based on parameters auto-tuning for the velocity control of permanent magnet synchronous motor (PMSM) is proposed in this paper. The control law of the proposed F(R)OSMC scheme is designed according to Lyapunov stability theorem. Based on the property of transferring energy with adjustable type in F(R)OSMC, this paper analyzes the chattering phenomenon in classic sliding mode control (SMC) is attenuated with F(R)OSMC system. A fuzzy logic inference scheme (FLIS) is utilized to obtain the gain of switching control. Simulations and experiments demonstrate that the proposed FROSMC not only achieve better control performance with smaller chatting than that with integer order sliding mode control, but also is robust to external load disturbance and parameter variations. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Basic electronic properties of iron selenide under variation of structural parameters

NASA Astrophysics Data System (ADS)

Guterding, Daniel; Jeschke, Harald O.; Valentí, Roser

2017-09-01

Since the discovery of high-temperature superconductivity in the thin-film FeSe /SrTiO3 system, iron selenide and its derivates have been intensively scrutinized. Using ab initio density functional theory calculations we review the electronic structures that could be realized in iron selenide if the structural parameters could be tuned at liberty. We calculate the momentum dependence of the susceptibility and investigate the symmetry of electron pairing within the random phase approximation. Both the susceptibility and the symmetry of electron pairing depend on the structural parameters in a nontrivial way. These results are consistent with the known experimental behavior of binary iron chalcogenides and, at the same time, reveal two promising ways of tuning superconducting transition temperatures in these materials: on one hand by expanding the iron lattice of FeSe at constant iron-selenium distance and, on the other hand, by increasing the iron-selenium distance with unchanged iron lattice.

Vector nature of multi-soliton patterns in a passively mode-locked figure-eight fiber laser.

PubMed

Ning, Qiu-Yi; Liu, Hao; Zheng, Xu-Wu; Yu, Wei; Luo, Ai-Ping; Huang, Xu-Guang; Luo, Zhi-Chao; Xu, Wen-Cheng; Xu, Shan-Hui; Yang, Zhong-Min

2014-05-19

The vector nature of multi-soliton dynamic patterns was investigated in a passively mode-locked figure-eight fiber laser based on the nonlinear amplifying loop mirror (NALM). By properly adjusting the cavity parameters such as the pump power level and intra-cavity polarization controllers (PCs), in addition to the fundamental vector soliton, various vector multi-soliton regimes were observed, such as the random static distribution of vector multiple solitons, vector soliton cluster, vector soliton flow, and the state of vector multiple solitons occupying the whole cavity. Both the polarization-locked vector solitons (PLVSs) and the polarization-rotating vector solitons (PRVSs) were observed for fundamental soliton and each type of multi-soliton patterns. The obtained results further reveal the fundamental physics of multi-soliton patterns and demonstrate that the figure-eight fiber lasers are indeed a good platform for investigating the vector nature of different soliton types.

A New Feedback-Based Method for Parameter Adaptation in Image Processing Routines.

PubMed

Khan, Arif Ul Maula; Mikut, Ralf; Reischl, Markus

2016-01-01

The parametrization of automatic image processing routines is time-consuming if a lot of image processing parameters are involved. An expert can tune parameters sequentially to get desired results. This may not be productive for applications with difficult image analysis tasks, e.g. when high noise and shading levels in an image are present or images vary in their characteristics due to different acquisition conditions. Parameters are required to be tuned simultaneously. We propose a framework to improve standard image segmentation methods by using feedback-based automatic parameter adaptation. Moreover, we compare algorithms by implementing them in a feedforward fashion and then adapting their parameters. This comparison is proposed to be evaluated by a benchmark data set that contains challenging image distortions in an increasing fashion. This promptly enables us to compare different standard image segmentation algorithms in a feedback vs. feedforward implementation by evaluating their segmentation quality and robustness. We also propose an efficient way of performing automatic image analysis when only abstract ground truth is present. Such a framework evaluates robustness of different image processing pipelines using a graded data set. This is useful for both end-users and experts.

A New Feedback-Based Method for Parameter Adaptation in Image Processing Routines

PubMed Central

Mikut, Ralf; Reischl, Markus

2016-01-01

The parametrization of automatic image processing routines is time-consuming if a lot of image processing parameters are involved. An expert can tune parameters sequentially to get desired results. This may not be productive for applications with difficult image analysis tasks, e.g. when high noise and shading levels in an image are present or images vary in their characteristics due to different acquisition conditions. Parameters are required to be tuned simultaneously. We propose a framework to improve standard image segmentation methods by using feedback-based automatic parameter adaptation. Moreover, we compare algorithms by implementing them in a feedforward fashion and then adapting their parameters. This comparison is proposed to be evaluated by a benchmark data set that contains challenging image distortions in an increasing fashion. This promptly enables us to compare different standard image segmentation algorithms in a feedback vs. feedforward implementation by evaluating their segmentation quality and robustness. We also propose an efficient way of performing automatic image analysis when only abstract ground truth is present. Such a framework evaluates robustness of different image processing pipelines using a graded data set. This is useful for both end-users and experts. PMID:27764213

TU-AB-BRC-05: Creation of a Monte Carlo TrueBeam Model by Reproducing Varian Phase Space Data

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

O’Grady, K; Davis, S; Seuntjens, J

Purpose: To create a Varian TrueBeam 6 MV FFF Monte Carlo model using BEAMnrc/EGSnrc that accurately reproduces the Varian representative dataset, followed by tuning the model’s source parameters to accurately reproduce in-house measurements. Methods: A BEAMnrc TrueBeam model for 6 MV FFF has been created by modifying a validated 6 MV Varian CL21EX model. Geometric dimensions and materials were adjusted in a trial and error approach to match the fluence and spectra of TrueBeam phase spaces output by the Varian VirtuaLinac. Once the model’s phase space matched Varian’s counterpart using the default source parameters, it was validated to match 10more » × 10 cm{sup 2} Varian representative data obtained with the IBA CC13. The source parameters were then tuned to match in-house 5 × 5 cm{sup 2} PTW microDiamond measurements. All dose to water simulations included detector models to include the effects of volume averaging and the non-water equivalence of the chamber materials, allowing for more accurate source parameter selection. Results: The Varian phase space spectra and fluence were matched with excellent agreement. The in-house model’s PDD agreement with CC13 TrueBeam representative data was within 0.9% local percent difference beyond the first 3 mm. Profile agreement at 10 cm depth was within 0.9% local percent difference and 1.3 mm distance-to-agreement in the central axis and penumbra regions, respectively. Once the source parameters were tuned, PDD agreement with microDiamond measurements was within 0.9% local percent difference beyond 2 mm. The microDiamond profile agreement at 10 cm depth was within 0.6% local percent difference and 0.4 mm distance-to-agreement in the central axis and penumbra regions, respectively. Conclusion: An accurate in-house Monte Carlo model of the Varian TrueBeam was achieved independently of the Varian phase space solution and was tuned to in-house measurements. KO acknowledges partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290).« less

Error vector magnitude based parameter estimation for digital filter back-propagation mitigating SOA distortions in 16-QAM.

PubMed

Amiralizadeh, Siamak; Nguyen, An T; Rusch, Leslie A

2013-08-26

We investigate the performance of digital filter back-propagation (DFBP) using coarse parameter estimation for mitigating SOA nonlinearity in coherent communication systems. We introduce a simple, low overhead method for parameter estimation for DFBP based on error vector magnitude (EVM) as a figure of merit. The bit error rate (BER) penalty achieved with this method has negligible penalty as compared to DFBP with fine parameter estimation. We examine different bias currents for two commercial SOAs used as booster amplifiers in our experiments to find optimum operating points and experimentally validate our method. The coarse parameter DFBP efficiently compensates SOA-induced nonlinearity for both SOA types in 80 km propagation of 16-QAM signal at 22 Gbaud.

Estimating normal mixture parameters from the distribution of a reduced feature vector

NASA Technical Reports Server (NTRS)

Guseman, L. F.; Peters, B. C., Jr.; Swasdee, M.

1976-01-01

A FORTRAN computer program was written and tested. The measurements consisted of 1000 randomly chosen vectors representing 1, 2, 3, 7, and 10 subclasses in equal portions. In the first experiment, the vectors are computed from the input means and covariances. In the second experiment, the vectors are 16 channel measurements. The starting covariances were constructed as if there were no correlation between separate passes. The biases obtained from each run are listed.

Workshop focuses on study of climate's effects on health

NASA Astrophysics Data System (ADS)

Diaz, Henry F.; Epstein, Paul R.; Aron, Joan L.; Confalonieri, Ulisses E. C.

Changes in temperature, precipitation, humidity, and storm patterns influence upsurges of waterborne diseases such as hepatitis, shigella dysentery, typhoid, and cholera as well as vector-borne pathogens such as malaria, dengue, yellow fever, encephalitis, schistosomiasis, plague, and hantavirus. Cycles of flooding and drought directly affect factors such as the multiplication rates of disease vectors, the biting rate of vectors, and the amount of host-vector interaction. Indirectly, climate influences parameters important to vector spread or survival such as agricultural practices, the disruption of ecosystems, or changes in social systems and practices, which in turn change the relationship between the parasite, the vector, its predators, and the host.

Manipulation of radial-variant polarization for creating tunable bifocusing spots.

PubMed

Gu, Bing; Pan, Yang; Wu, Jia-Lu; Cui, Yiping

2014-02-01

We propose and generate a new radial-variant vector field (RV-VF) with a distribution of states of polarization described by the square of the radius and exploit its focusing property. Theoretically, we present the analytical expressions for the three-dimensional electric field of the vector field focused by a thin lens under the nonparaxial and paraxial approximations based on the vectorial Rayleigh-Sommerfeld formulas. Numerical simulations indicate that this focused field exhibits bifocusing spots along the optical axis. The underlying mechanism for generating the bifocusing property is analyzed in detail. We give the analytical formula for the interval between two foci. Experimentally, we generate the RV-VFs with alterable topological charge and demonstrate that the interval between two foci is controllable by tuning the radial topological charge. This particular focal field has specific applications for biparticle trapping, manipulating, alignment, transportation, and accelerating along the optical axis.

Simultaneously frequency down-conversion, independent multichannel phase shifting and zero-IF receiving using a phase modulator in a sagnac loop and balanced detection

NASA Astrophysics Data System (ADS)

Zhu, Zihang; Zhao, Shanghong; Li, Xuan; Lin, Tao; Hu, Dapeng

2018-03-01

Photonic microwave frequency down-conversion with independent multichannel phase shifting and zero-intermediate frequency (IF) receiving is proposed and demonstrated by simulation. By combined use of a phase modulator (PM) in a sagnac loop and an optical bandpass filter (OBPF), orthogonal polarized carrier suppression single sideband (CS-SSB) signals are obtained. By adjusting the polarization controllers (PCs) to introduce the phase difference in the optical domain and using balanced detection to eliminate the direct current components, the phase of the generated IF signal can be arbitrarily tuned. Besides, the radio frequency (RF) vector signal can be also frequency down-converted to baseband directly by choosing two quadrature channels. In the simulation, high gain and continuously tunable phase shifts over the 360 degree range are verified. Furthermore, 2.5 Gbit/s RF vector signals centered at 10 GHz with different modulation formats are successfully demodulated.

MISR Level 3 Cloud Motion Vector Versioning

Atmospheric Science Data Center

2016-11-04

... Versioning   Cloud Motion Vector Product (CMV) - Monthly, Quarterly, Yearly products Processing Status ... MI3MCMVN, MI3QCMVN, MI3YCMVN MISR_AM1_CMV Stage 1 Validated:  All parameters MISR maturity ...

Rutherford's Scattering Formula via the Runge-Lenz Vector.

ERIC Educational Resources Information Center

Basano, L.; Bianchi, A.

1980-01-01

Discusses how the Runge-Lenz vector provides a way to derive the relation between deflection angle and impact parameter for Coulomb- and Kepler-fields in a very simple and a straightforward way. (Author/HM)

Wavelength tuning of multimode interference bandpass filters by mechanical bending: experiment and theory in comparison

NASA Astrophysics Data System (ADS)

Walbaum, T.; Fallnich, C.

2012-07-01

We present the tuning of multimode interference bandpass filters made of standard fibers by mechanical bending. Our setup allows continuous adjustment of the bending radius from infinity down to about 5 cm. The impact of bending on the transmission spectrum and on polarization is investigated experimentally, and a filter with a continuous tuning range of 13.6 nm and 86 % peak transmission was realized. By use of numerical simulations employing a semi-analytical mode expansion approach, we obtain quantitative understanding of the underlying physics. Further breakdown of the governing equations enables us to identify the fiber parameters that are relevant for the design of customized filters.

Calibration of sea ice dynamic parameters in an ocean-sea ice model using an ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Massonnet, F.; Goosse, H.; Fichefet, T.; Counillon, F.

2014-07-01

The choice of parameter values is crucial in the course of sea ice model development, since parameters largely affect the modeled mean sea ice state. Manual tuning of parameters will soon become impractical, as sea ice models will likely include more parameters to calibrate, leading to an exponential increase of the number of possible combinations to test. Objective and automatic methods for parameter calibration are thus progressively called on to replace the traditional heuristic, "trial-and-error" recipes. Here a method for calibration of parameters based on the ensemble Kalman filter is implemented, tested and validated in the ocean-sea ice model NEMO-LIM3. Three dynamic parameters are calibrated: the ice strength parameter P*, the ocean-sea ice drag parameter Cw, and the atmosphere-sea ice drag parameter Ca. In twin, perfect-model experiments, the default parameter values are retrieved within 1 year of simulation. Using 2007-2012 real sea ice drift data, the calibration of the ice strength parameter P* and the oceanic drag parameter Cw improves clearly the Arctic sea ice drift properties. It is found that the estimation of the atmospheric drag Ca is not necessary if P* and Cw are already estimated. The large reduction in the sea ice speed bias with calibrated parameters comes with a slight overestimation of the winter sea ice areal export through Fram Strait and a slight improvement in the sea ice thickness distribution. Overall, the estimation of parameters with the ensemble Kalman filter represents an encouraging alternative to manual tuning for ocean-sea ice models.

Tuned range separated hybrid functionals for solvated low bandgap oligomers

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

Queiroz, Thiago B. de, E-mail: thiago.branquinho-de-queiroz@uni-bayreuth.de; Kümmel, Stephan

2015-07-21

The description of charge transfer excitations has long been a challenge to time dependent density functional theory. The recently developed concept of “optimally tuned range separated hybrid (OT-RSH) functionals” has proven to describe charge transfer excitations accurately in many cases. However, describing solvated or embedded systems is yet a challenge. This challenge is not only computational but also conceptual, because the tuning requires identifying a specific orbital, typically the highest occupied one of the molecule under study. For solvated molecules, this orbital may be delocalized over the solvent. We here demonstrate that one way of overcoming this problem is tomore » use a locally projected self-consistent field diagonalization on an absolutely localized molecular orbital expansion. We employ this approach to determine ionization energies and the optical gap of solvated oligothiophenes, i.e., paradigm low gap systems that are of relevance in organic electronics. Dioxane solvent molecules are explicitly represented in our calculations, and the ambiguities of straightforward parameter tuning in solution are elucidated. We show that a consistent estimate of the optimal range separated parameter (ω) at the limit of bulk solvation can be obtained by gradually extending the solvated system. In particular, ω is influenced by the solvent beyond the first coordination sphere. For determining ionization energies, a considerable number of solvent molecules on the first solvation shell must be taken into account. We demonstrate that accurately calculating optical gaps of solvated systems using OT-RSH can be done in three steps: (i) including the chemical environment when determining the range-separation parameter, (ii) taking into account the screening due to the solvent, and (iii) using realistic molecular geometries.« less

Frequency pulling in a low-voltage medium-power gyrotron

NASA Astrophysics Data System (ADS)

Luo, Li; Du, Chao-Hai; Huang, Ming-Guang; Liu, Pu-Kun

2018-04-01

Many recent biomedical applications use medium-power frequency-tunable terahertz (THz) sources, such as sensitivity-enhanced nuclear magnetic resonance, THz imaging, and biomedical treatment. As a promising candidate, a low-voltage gyrotron can generate watt-level, continuous THz-wave radiation. In particular, the frequency-pulling effect in a gyrotron, namely, the effect of the electron beam parameters on the oscillation frequency, can be used to tune the operating frequency. Most previous investigations used complicated and time-consuming gyrotron nonlinear theory to study the influence of many beam parameters on the interaction performance. While gyrotron linear theory investigation demonstrates the advantages of rapidly and clearly revealing the physical influence of individual key beam parameters on the overall system performance, this paper demonstrates systematically the use of gyrotron linear theory to study the frequency-pulling effect in a low-voltage gyrotron with either a Gaussian or a sinusoidal axial-field profile. Furthermore, simulations of a gyrotron operating in the first axial mode are carried out in the framework of nonlinear theory as a contrast. Close agreement is achieved between the two theories. Besides, some interesting results are obtained. In a low-current sinusoidal-profile cavity, the ranges of frequency variation for different axial modes are isolated from each other, and the frequency tuning bandwidth for each axial mode increases by increasing either the beam voltage or pitch factor. Lowering the voltage, the total tuning ranges are squeezed and become concentrated. However, the isolated frequency regions of each axial mode cannot be linked up unless the beam current is increased, meaning that higher current operation is the key to achieving a wider and continuous tuning frequency range. The results presented in this paper can provide a reference for designing a broadband low-voltage gyrotron.

The feature-weighted receptive field: an interpretable encoding model for complex feature spaces.

PubMed

St-Yves, Ghislain; Naselaris, Thomas

2017-06-20

We introduce the feature-weighted receptive field (fwRF), an encoding model designed to balance expressiveness, interpretability and scalability. The fwRF is organized around the notion of a feature map-a transformation of visual stimuli into visual features that preserves the topology of visual space (but not necessarily the native resolution of the stimulus). The key assumption of the fwRF model is that activity in each voxel encodes variation in a spatially localized region across multiple feature maps. This region is fixed for all feature maps; however, the contribution of each feature map to voxel activity is weighted. Thus, the model has two separable sets of parameters: "where" parameters that characterize the location and extent of pooling over visual features, and "what" parameters that characterize tuning to visual features. The "where" parameters are analogous to classical receptive fields, while "what" parameters are analogous to classical tuning functions. By treating these as separable parameters, the fwRF model complexity is independent of the resolution of the underlying feature maps. This makes it possible to estimate models with thousands of high-resolution feature maps from relatively small amounts of data. Once a fwRF model has been estimated from data, spatial pooling and feature tuning can be read-off directly with no (or very little) additional post-processing or in-silico experimentation. We describe an optimization algorithm for estimating fwRF models from data acquired during standard visual neuroimaging experiments. We then demonstrate the model's application to two distinct sets of features: Gabor wavelets and features supplied by a deep convolutional neural network. We show that when Gabor feature maps are used, the fwRF model recovers receptive fields and spatial frequency tuning functions consistent with known organizational principles of the visual cortex. We also show that a fwRF model can be used to regress entire deep convolutional networks against brain activity. The ability to use whole networks in a single encoding model yields state-of-the-art prediction accuracy. Our results suggest a wide variety of uses for the feature-weighted receptive field model, from retinotopic mapping with natural scenes, to regressing the activities of whole deep neural networks onto measured brain activity. Copyright © 2017. Published by Elsevier Inc.

Interplanetary medium data book, appendix

NASA Technical Reports Server (NTRS)

King, J. H.

1977-01-01

Computer generated listings of hourly average interplanetary plasma and magnetic field parameters are given. Parameters include proton temperature, proton density, bulk speed, an identifier of the source of the plasma data for the hour, average magnetic field magnitude and cartesian components of the magnetic field. Also included are longitude and latitude angles of the vector made up of the average field components, a vector standard deviation, and an identifier of the source of magnetic field data.

Oscillon in Einstein-scalar system with double well potential and its properties.

NASA Astrophysics Data System (ADS)

Ikeda, Taishi; Yoo, Chul-Moon; Cardoso, Vitor

2018-01-01

The dynamical evolution of self-interacting scalar field has many nontrivial behaviors, which tell us many lessons in a nonlinear dynamics. On Minkowski spacetime, the scalar field with double well potential has localized, non-singular, time-dependent, long-lived solutions, which are called oscillons. The lifetime of the oscillon depends on the initial conditions. Furthermore, when the initial parameter is fine-tuned, oscillons can be infinitely, and type I critical behavior is observed. Here, we investigate the Einstein-scalar system with double well potential. We show that oscillons exist in this system, and discuss the behavior when the initial parameter is fine-tuned. Our results suggests that a new type of critical behavior appears in this theory.

Many-body localization in a long range XXZ model with random-field

NASA Astrophysics Data System (ADS)

Li, Bo

2016-12-01

Many-body localization (MBL) in a long range interaction XXZ model with random field are investigated. Using the exact diagonal method, the MBL phase diagram with different tuning parameters and interaction range is obtained. It is found that the phase diagram of finite size results supplies strong evidence to confirm that the threshold interaction exponent α = 2. The tuning parameter Δ can efficiently change the MBL edge in high energy density stats, thus the system can be controlled to transfer from thermal phase to MBL phase by changing Δ. The energy level statistics data are consistent with result of the MBL phase diagram. However energy level statistics data cannot detect the thermal phase correctly in extreme long range case.

Tuning of PID controller using optimization techniques for a MIMO process

NASA Astrophysics Data System (ADS)

Thulasi dharan, S.; Kavyarasan, K.; Bagyaveereswaran, V.

2017-11-01

In this paper, two processes were considered one is Quadruple tank process and the other is CSTR (Continuous Stirred Tank Reactor) process. These are majorly used in many industrial applications for various domains, especially, CSTR in chemical plants.At first mathematical model of both the process is to be done followed by linearization of the system due to MIMO process and controllers are the major part to control the whole process to our desired point as per the applications so the tuning of the controller plays a major role among the whole process. For tuning of parameters we use two optimizations techniques like Particle Swarm Optimization, Genetic Algorithm. The above techniques are majorly used in different applications to obtain which gives the best among all, we use these techniques to obtain the best tuned values among many. Finally, we will compare the performance of the each process with both the techniques.

Model-Based Self-Tuning Multiscale Method for Combustion Control

NASA Technical Reports Server (NTRS)

Le, Dzu, K.; DeLaat, John C.; Chang, Clarence T.; Vrnak, Daniel R.

2006-01-01

A multi-scale representation of the combustor dynamics was used to create a self-tuning, scalable controller to suppress multiple instability modes in a liquid-fueled aero engine-derived combustor operating at engine-like conditions. Its self-tuning features designed to handle the uncertainties in the combustor dynamics and time-delays are essential for control performance and robustness. The controller was implemented to modulate a high-frequency fuel valve with feedback from dynamic pressure sensors. This scalable algorithm suppressed pressure oscillations of different instability modes by as much as 90 percent without the peak-splitting effect. The self-tuning logic guided the adjustment of controller parameters and converged quickly toward phase-lock for optimal suppression of the instabilities. The forced-response characteristics of the control model compare well with those of the test rig on both the frequency-domain and the time-domain.

Dynamical tuning for MPC using population games: A water supply network application.

PubMed

Barreiro-Gomez, Julian; Ocampo-Martinez, Carlos; Quijano, Nicanor

2017-07-01

Model predictive control (MPC) is a suitable strategy for the control of large-scale systems that have multiple design requirements, e.g., multiple physical and operational constraints. Besides, an MPC controller is able to deal with multiple control objectives considering them within the cost function, which implies to determine a proper prioritization for each of the objectives. Furthermore, when the system has time-varying parameters and/or disturbances, the appropriate prioritization might vary along the time as well. This situation leads to the need of a dynamical tuning methodology. This paper addresses the dynamical tuning issue by using evolutionary game theory. The advantages of the proposed method are highlighted and tested over a large-scale water supply network with periodic time-varying disturbances. Finally, results are analyzed with respect to a multi-objective MPC controller that uses static tuning. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Electronic frequency tuning of the acousto-optic mode-locking device of a laser

NASA Astrophysics Data System (ADS)

Magdich, L. N.; Balakshy, V. I.; Mantsevich, S. N.

2017-11-01

The effect of the electronic tuning of the acoustic resonances in an acousto-optic mode-locking device of a laser is investigated theoretically and experimentally. The problem of the excitation of a Fabry-Perot acoustic resonator by a plate-like piezoelectric transducer (PET) is solved in the approximation of plane acoustic waves taking into consideration the actual parameters of an RF generator and the elements for matching the PET to the generator. Resonances are tuned by changing the matching inductance that was connected in parallel to the transducer of the acousto-optic cell. The cell used in the experiment was manufactured from fused silica and included a lithium niobate PET. Changes in the matching inductance in the range of 0.025 to 0.2 μH provided the acoustic-resonance frequency tuning by 0.19 MHz, which exceeds the acoustic- resonance half-width.

Graphene Dirac point tuned by ferroelectric polarization field

NASA Astrophysics Data System (ADS)

Wang, Xudong; Chen, Yan; Wu, Guangjian; Wang, Jianlu; Tian, Bobo; Sun, Shuo; Shen, Hong; Lin, Tie; Hu, Weida; Kang, Tingting; Tang, Minghua; Xiao, Yongguang; Sun, Jinglan; Meng, Xiangjian; Chu, Junhao

2018-04-01

Graphene has received numerous attention for future nanoelectronics and optoelectronics. The Dirac point is a key parameter of graphene that provides information about its carrier properties. There are lots of methods to tune the Dirac point of graphene, such as chemical doping, impurities, defects, and disorder. In this study, we report a different approach to tune the Dirac point of graphene using a ferroelectric polarization field. The Dirac point can be adjusted to near the ferroelectric coercive voltage regardless its original position. We have ensured this phenomenon by temperature-dependent experiments, and analyzed its mechanism with the theory of impurity correlation in graphene. Additionally, with the modulation of ferroelectric polymer, the current on/off ratio and mobility of graphene transistor both have been improved. This work provides an effective method to tune the Dirac point of graphene, which can be readily used to configure functional devices such as p-n junctions and inverters.

Seismic design of passive tuned mass damper parameters using active control algorithm

NASA Astrophysics Data System (ADS)

Chang, Chia-Ming; Shia, Syuan; Lai, Yong-An

2018-07-01

Tuned mass dampers are a widely-accepted control method to effectively reduce the vibrations of tall buildings. A tuned mass damper employs a damped harmonic oscillator with specific dynamic characteristics, thus the response of structures can be regulated by the additive dynamics. The additive dynamics are, however, similar to the feedback control system in active control. Therefore, the objective of this study is to develop a new tuned mass damper design procedure based on the active control algorithm, i.e., the H2/LQG control. This design facilitates the similarity of feedback control in the active control algorithm to determine the spring and damper in a tuned mass damper. Given a mass ratio between the damper and structure, the stiffness and damping coefficient of the tuned mass damper are derived by minimizing the response objective function of the primary structure, where the structural properties are known. Varying a single weighting in this objective function yields the optimal TMD design when the minimum peak in the displacement transfer function of the structure with the TMD is met. This study examines various objective functions as well as derives the associated equations to compute the stiffness and damping coefficient. The relationship between the primary structure and optimal tuned mass damper is parametrically studied. Performance is evaluated by exploring the h2-and h∞-norms of displacements and accelerations of the primary structure. In time-domain analysis, the damping effectiveness of the tune mass damper controlled structures is investigated under impulse excitation. Structures with the optimal tuned mass dampers are also assessed under seismic excitation. As a result, the proposed design procedure produces an effective tuned mass damper to be employed in a structure against earthquakes.

Lopsided gauge mediation

NASA Astrophysics Data System (ADS)

de Simone, Andrea; Franceschini, Roberto; Giudice, Gian Francesco; Pappadopulo, Duccio; Rattazzi, Riccardo

2011-05-01

It has been recently pointed out that the unavoidable tuning among supersymmetric parameters required to raise the Higgs boson mass beyond its experimental limit opens up new avenues for dealing with the so called μ- B μ problem of gauge mediation. In fact, it allows for accommodating, with no further parameter tuning, large values of B μ and of the other Higgs-sector soft masses, as predicted in models where both μ and B μ are generated at one-loop order. This class of models, called Lopsided Gauge Mediation, offers an interesting alternative to conventional gauge mediation and is characterized by a strikingly different phenomenology, with light higgsinos, very large Higgs pseudoscalar mass, and moderately light sleptons. We discuss general parametric relations involving the fine-tuning of the model and various observables such as the chargino mass and the value of tan β. We build an explicit model and we study the constraints coming from LEP and Tevatron. We show that in spite of new interactions between the Higgs and the messenger superfields, the theory can remain perturbative up to very large scales, thus retaining gauge coupling unification.

Intelligent Systems for Stabilizing Mode-Locked Lasers and Frequency Combs: Machine Learning and Equation-Free Control Paradigms for Self-Tuning Optics

NASA Astrophysics Data System (ADS)

Kutz, J. Nathan; Brunton, Steven L.

2015-12-01

We demonstrate that a software architecture using innovations in machine learning and adaptive control provides an ideal integration platform for self-tuning optics. For mode-locked lasers, commercially available optical telecom components can be integrated with servocontrollers to enact a training and execution software module capable of self-tuning the laser cavity even in the presence of mechanical and/or environmental perturbations, thus potentially stabilizing a frequency comb. The algorithm training stage uses an exhaustive search of parameter space to discover best regions of performance for one or more objective functions of interest. The execution stage first uses a sparse sensing procedure to recognize the parameter space before quickly moving to the near optimal solution and maintaining it using the extremum seeking control protocol. The method is robust and equationfree, thus requiring no detailed or quantitatively accurate model of the physics. It can also be executed on a broad range of problems provided only that suitable objective functions can be found and experimentally measured.

A Coarse Alignment Method Based on Digital Filters and Reconstructed Observation Vectors

PubMed Central

Xu, Xiang; Xu, Xiaosu; Zhang, Tao; Li, Yao; Wang, Zhicheng

2017-01-01

In this paper, a coarse alignment method based on apparent gravitational motion is proposed. Due to the interference of the complex situations, the true observation vectors, which are calculated by the apparent gravity, are contaminated. The sources of the interference are analyzed in detail, and then a low-pass digital filter is designed in this paper for eliminating the high-frequency noise of the measurement observation vectors. To extract the effective observation vectors from the inertial sensors’ outputs, a parameter recognition and vector reconstruction method are designed, where an adaptive Kalman filter is employed to estimate the unknown parameters. Furthermore, a robust filter, which is based on Huber’s M-estimation theory, is developed for addressing the outliers of the measurement observation vectors due to the maneuver of the vehicle. A comprehensive experiment, which contains a simulation test and physical test, is designed to verify the performance of the proposed method, and the results show that the proposed method is equivalent to the popular apparent velocity method in swaying mode, but it is superior to the current methods while in moving mode when the strapdown inertial navigation system (SINS) is under entirely self-contained conditions. PMID:28353682

A Caveat Note on Tuning in the Development of Coupled Climate Models

NASA Astrophysics Data System (ADS)

Dommenget, Dietmar; Rezny, Michael

2018-01-01

State-of-the-art coupled general circulation models (CGCMs) have substantial errors in their simulations of climate. In particular, these errors can lead to large uncertainties in the simulated climate response (both globally and regionally) to a doubling of CO2. Currently, tuning of the parameterization schemes in CGCMs is a significant part of the developed. It is not clear whether such tuning actually improves models. The tuning process is (in general) neither documented, nor reproducible. Alternative methods such as flux correcting are not used nor is it clear if such methods would perform better. In this study, ensembles of perturbed physics experiments are performed with the Globally Resolved Energy Balance (GREB) model to test the impact of tuning. The work illustrates that tuning has, in average, limited skill given the complexity of the system, the limited computing resources, and the limited observations to optimize parameters. While tuning may improve model performance (such as reproducing observed past climate), it will not get closer to the "true" physics nor will it significantly improve future climate change projections. Tuning will introduce artificial compensating error interactions between submodels that will hamper further model development. In turn, flux corrections do perform well in most, but not all aspects. A main advantage of flux correction is that it is much cheaper, simpler, more transparent, and it does not introduce artificial error interactions between submodels. These GREB model experiments should be considered as a pilot study to motivate further CGCM studies that address the issues of model tuning.

Low-mass neutralino dark matter in supergravity scenarios: phenomenology and naturalness

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

Peiró, M.; Robles, S., E-mail: mpeirogarcia@gmail.com, E-mail: sandra.robles@uam.es

2017-05-01

The latest experimental results from the LHC and dark matter (DM) searches suggest that the parameter space allowed in supersymmetric theories is subject to strong reductions. These bounds are especially constraining for scenarios entailing light DM particles. Previous studies have shown that light neutralino DM in the Minimal Supersymmetric Standard Model (MSSM), with parameters defined at the electroweak scale, is still viable when the low energy spectrum of the model features light sleptons, in which case, the relic density constraint can be fulfilled. In view of this, we have investigated the viability of light neutralinos as DM candidates in themore » MSSM, with parameters defined at the grand unification scale. We have analysed the optimal choices of non-universalities in the soft supersymmetry-breaking parameters for both, gauginos and scalars, in order to avoid the stringent experimental constraints. We show that light neutralinos, with a mass as low as 25 GeV, are viable in supergravity scenarios if the gaugino mass parameters at high energy are very non universal, while the scalar masses can remain of the same order. These scenarios typically predict a very small cross section of neutralinos off protons and neutrons, thereby being very challenging for direct detection experiments. However, a potential detection of smuons and selectrons at the LHC, together with a hypothetical discovery of a gamma-ray signal from neutralino annihilations in dwarf spheroidal galaxies could shed light on this kind of solutions. Finally, we have investigated the naturalness of these scenarios, taking into account all the potential sources of tuning. Besides the electroweak fine-tuning, we have found that the tuning to reproduce the correct DM relic abundance and that to match the measured Higgs mass can also be important when estimating the total degree of naturalness.« less

Low-mass neutralino dark matter in supergravity scenarios: phenomenology and naturalness

NASA Astrophysics Data System (ADS)

Peiró, M.; Robles, S.

2017-05-01

The latest experimental results from the LHC and dark matter (DM) searches suggest that the parameter space allowed in supersymmetric theories is subject to strong reductions. These bounds are especially constraining for scenarios entailing light DM particles. Previous studies have shown that light neutralino DM in the Minimal Supersymmetric Standard Model (MSSM), with parameters defined at the electroweak scale, is still viable when the low energy spectrum of the model features light sleptons, in which case, the relic density constraint can be fulfilled. In view of this, we have investigated the viability of light neutralinos as DM candidates in the MSSM, with parameters defined at the grand unification scale. We have analysed the optimal choices of non-universalities in the soft supersymmetry-breaking parameters for both, gauginos and scalars, in order to avoid the stringent experimental constraints. We show that light neutralinos, with a mass as low as 25 GeV, are viable in supergravity scenarios if the gaugino mass parameters at high energy are very non universal, while the scalar masses can remain of the same order. These scenarios typically predict a very small cross section of neutralinos off protons and neutrons, thereby being very challenging for direct detection experiments. However, a potential detection of smuons and selectrons at the LHC, together with a hypothetical discovery of a gamma-ray signal from neutralino annihilations in dwarf spheroidal galaxies could shed light on this kind of solutions. Finally, we have investigated the naturalness of these scenarios, taking into account all the potential sources of tuning. Besides the electroweak fine-tuning, we have found that the tuning to reproduce the correct DM relic abundance and that to match the measured Higgs mass can also be important when estimating the total degree of naturalness.

Prediction and assimilation of surf-zone processes using a Bayesian network: Part II: Inverse models

USGS Publications Warehouse

Plant, Nathaniel G.; Holland, K. Todd

2011-01-01

A Bayesian network model has been developed to simulate a relatively simple problem of wave propagation in the surf zone (detailed in Part I). Here, we demonstrate that this Bayesian model can provide both inverse modeling and data-assimilation solutions for predicting offshore wave heights and depth estimates given limited wave-height and depth information from an onshore location. The inverse method is extended to allow data assimilation using observational inputs that are not compatible with deterministic solutions of the problem. These inputs include sand bar positions (instead of bathymetry) and estimates of the intensity of wave breaking (instead of wave-height observations). Our results indicate that wave breaking information is essential to reduce prediction errors. In many practical situations, this information could be provided from a shore-based observer or from remote-sensing systems. We show that various combinations of the assimilated inputs significantly reduce the uncertainty in the estimates of water depths and wave heights in the model domain. Application of the Bayesian network model to new field data demonstrated significant predictive skill (R2 = 0.7) for the inverse estimate of a month-long time series of offshore wave heights. The Bayesian inverse results include uncertainty estimates that were shown to be most accurate when given uncertainty in the inputs (e.g., depth and tuning parameters). Furthermore, the inverse modeling was extended to directly estimate tuning parameters associated with the underlying wave-process model. The inverse estimates of the model parameters not only showed an offshore wave height dependence consistent with results of previous studies but the uncertainty estimates of the tuning parameters also explain previously reported variations in the model parameters.

Mixture-Tuned, Clutter Matched Filter for Remote Detection of Subpixel Spectral Signals

NASA Technical Reports Server (NTRS)

Thompson, David R.; Mandrake, Lukas; Green, Robert O.

2013-01-01

Mapping localized spectral features in large images demands sensitive and robust detection algorithms. Two aspects of large images that can harm matched-filter detection performance are addressed simultaneously. First, multimodal backgrounds may thwart the typical Gaussian model. Second, outlier features can trigger false detections from large projections onto the target vector. Two state-of-the-art approaches are combined that independently address outlier false positives and multimodal backgrounds. The background clustering models multimodal backgrounds, and the mixture tuned matched filter (MT-MF) addresses outliers. Combining the two methods captures significant additional performance benefits. The resulting mixture tuned clutter matched filter (MT-CMF) shows effective performance on simulated and airborne datasets. The classical MNF transform was applied, followed by k-means clustering. Then, each cluster s mean, covariance, and the corresponding eigenvalues were estimated. This yields a cluster-specific matched filter estimate as well as a cluster- specific feasibility score to flag outlier false positives. The technology described is a proof of concept that may be employed in future target detection and mapping applications for remote imaging spectrometers. It is of most direct relevance to JPL proposals for airborne and orbital hyperspectral instruments. Applications include subpixel target detection in hyperspectral scenes for military surveillance. Earth science applications include mineralogical mapping, species discrimination for ecosystem health monitoring, and land use classification.

Internal performance of two nozzles utilizing gimbal concepts for thrust vectoring

NASA Technical Reports Server (NTRS)

Berrier, Bobby L.; Taylor, John G.

1990-01-01

The internal performance of an axisymmetric convergent-divergent nozzle and a nonaxisymmetric convergent-divergent nozzle, both of which utilized a gimbal type mechanism for thrust vectoring was evaluated in the Static Test Facility of the Langley 16-Foot Transonic Tunnel. The nonaxisymmetric nozzle used the gimbal concept for yaw thrust vectoring only; pitch thrust vectoring was accomplished by simultaneous deflection of the upper and lower divergent flaps. The model geometric parameters investigated were pitch vector angle for the axisymmetric nozzle and pitch vector angle, yaw vector angle, nozzle throat aspect ratio, and nozzle expansion ratio for the nonaxisymmetric nozzle. All tests were conducted with no external flow, and nozzle pressure ratio was varied from 2.0 to approximately 12.0.

Spectroscopic Investigation of TW Dra: Improved Stellar and System Parameters

NASA Astrophysics Data System (ADS)

Tkachenko, A.; Lehmann, H.; Mkrtichian, D.

2010-12-01

We investigate the Algol-type system TW Dra by means of the new computer program Shellspec07_inverse which is specially designed for the fine-tuning of stellar and system parameters of eclipsing binaries. We derive precise atmospheric and system parameters of TW Dra with an accuracy comparable to that expected from photometric data, and give a short comparison of our results with previous determinations.

From micro-correlations to macro-correlations

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

Eliazar, Iddo, E-mail: iddo.eliazar@intel.com

2016-11-15

Random vectors with a symmetric correlation structure share a common value of pair-wise correlation between their different components. The symmetric correlation structure appears in a multitude of settings, e.g. mixture models. In a mixture model the components of the random vector are drawn independently from a general probability distribution that is determined by an underlying parameter, and the parameter itself is randomized. In this paper we study the overall correlation of high-dimensional random vectors with a symmetric correlation structure. Considering such a random vector, and terming its pair-wise correlation “micro-correlation”, we use an asymptotic analysis to derive the random vector’smore » “macro-correlation” : a score that takes values in the unit interval, and that quantifies the random vector’s overall correlation. The method of obtaining macro-correlations from micro-correlations is then applied to a diverse collection of frameworks that demonstrate the method’s wide applicability.« less

User's Guide for Monthly Vector Wind Profile Model

NASA Technical Reports Server (NTRS)

Adelfang, S. I.

1999-01-01

The background, theoretical concepts, and methodology for construction of vector wind profiles based on a statistical model are presented. The derived monthly vector wind profiles are to be applied by the launch vehicle design community for establishing realistic estimates of critical vehicle design parameter dispersions related to wind profile dispersions. During initial studies a number of months are used to establish the model profiles that produce the largest monthly dispersions of ascent vehicle aerodynamic load indicators. The largest monthly dispersions for wind, which occur during the winter high-wind months, are used for establishing the design reference dispersions for the aerodynamic load indicators. This document includes a description of the computational process for the vector wind model including specification of input data, parameter settings, and output data formats. Sample output data listings are provided to aid the user in the verification of test output.

Capsule implosion optimization during the indirect-drive National Ignition Campaign

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

Landen, O. L.; Edwards, J.; Haan, S. W.

2011-05-15

Capsule performance optimization campaigns will be conducted at the National Ignition Facility [G. H. Miller, E. I. Moses, and C. R. Wuest, Nucl. Fusion 44, 228 (2004)] to substantially increase the probability of ignition. The campaigns will experimentally correct for residual uncertainties in the implosion and hohlraum physics used in our radiation-hydrodynamic computational models using a variety of ignition capsule surrogates before proceeding to cryogenic-layered implosions and ignition experiments. The quantitative goals and technique options and down selections for the tuning campaigns are first explained. The computationally derived sensitivities to key laser and target parameters are compared to simple analyticmore » models to gain further insight into the physics of the tuning techniques. The results of the validation of the tuning techniques at the OMEGA facility [J. M. Soures et al., Phys. Plasmas 3, 2108 (1996)] under scaled hohlraum and capsule conditions relevant to the ignition design are shown to meet the required sensitivity and accuracy. A roll-up of all expected random and systematic uncertainties in setting the key ignition laser and target parameters due to residual measurement, calibration, cross-coupling, surrogacy, and scale-up errors has been derived that meets the required budget. Finally, we show how the tuning precision will be improved after a number of shots and iterations to meet an acceptable level of residual uncertainty.« less

OpenSQUID: A Flexible Open-Source Software Framework for the Control of SQUID Electronics

DOE PAGES

Jaeckel, Felix T.; Lafler, Randy J.; Boyd, S. T. P.

2013-02-06

We report commercially available computer-controlled SQUID electronics are usually delivered with software providing a basic user interface for adjustment of SQUID tuning parameters, such as bias current, flux offset, and feedback loop settings. However, in a research context it would often be useful to be able to modify this code and/or to have full control over all these parameters from researcher-written software. In the case of the STAR Cryoelectronics PCI/PFL family of SQUID control electronics, the supplied software contains modules for automatic tuning and noise characterization, but does not provide an interface for user code. On the other hand, themore » Magnicon SQUIDViewer software package includes a public application programming interface (API), but lacks auto-tuning and noise characterization features. To overcome these and other limitations, we are developing an "open-source" framework for controlling SQUID electronics which should provide maximal interoperability with user software, a unified user interface for electronics from different manufacturers, and a flexible platform for the rapid development of customized SQUID auto-tuning and other advanced features. Finally, we have completed a first implementation for the STAR Cryoelectronics hardware and have made the source code for this ongoing project available to the research community on SourceForge (http://opensquid.sourceforge.net) under the GNU public license.« less

Aviation Trainer Technology Test Plan. Volume II. Software Development

DTIC Science & Technology

1991-11-25

feild values in new node *Ieg>X=x newg->X = Y newg->Len =len; newg->Help =help; newg->Ignore = ignore; newg->Format = format; newg->Validation - NULL;I... vector : North long varVFE; /* F-16A velocity vector : East long varVFU; /* F-16A velocity vector : Up */ long varH; /* plane heading */ long varC; /* plane...31\\\\ETH523.sys" parmsdr.args=getds(); parmsdr.non7=OxOO; /*save interrupt vector for future restoration */ cSavvecso; rc=getdso; rc=cInitParameters

Segmentation of magnetic resonance images using fuzzy algorithms for learning vector quantization.

PubMed

Karayiannis, N B; Pai, P I

1999-02-01

This paper evaluates a segmentation technique for magnetic resonance (MR) images of the brain based on fuzzy algorithms for learning vector quantization (FALVQ). These algorithms perform vector quantization by updating all prototypes of a competitive network through an unsupervised learning process. Segmentation of MR images is formulated as an unsupervised vector quantization process, where the local values of different relaxation parameters form the feature vectors which are represented by a relatively small set of prototypes. The experiments evaluate a variety of FALVQ algorithms in terms of their ability to identify different tissues and discriminate between normal tissues and abnormalities.

Orbifold Schur index and IR formula

NASA Astrophysics Data System (ADS)

Imamura, Yosuke

2018-04-01

We discuss an orbifold version of the Schur index defined as the supersymmetric partition function in S^3/{Z}_n×{S}^1. We first give a general formula for Lagrangian theories obtained by the localization technique, and then suggest a generalization of the Cordova and Shao IR formula. We confirm that the generalized IR formula gives the correct answer for systems with free hypermultiplets if we tune the background fields so that they are invariant under the orbifold action. Unfortunately, we find disagreement for theories with dynamical vector multiplets.

Field by field hybrid upwind splitting methods

NASA Technical Reports Server (NTRS)

Coquel, Frederic; Liou, Meng-Sing

1993-01-01

A new and general approach to upwind splitting is presented. The design principle combines the robustness of flux vector splitting schemes in the capture of nonlinear waves and the accuracy of some flux difference splitting schemes in the resolution of linear waves. The new schemes are derived following a general hybridization technique performed directly at the basic level of the field by field decomposition involved in FDS methods. The scheme does not use a spatial switch to be tuned up according to the local smoothness of the approximate solution.

A Compact 600 GHz Electronically Tunable Vector Measurement System for Submillimeter Wave Imaging

NASA Technical Reports Server (NTRS)

Dengler, Robert J.; Maiwald, Frank; Siegel, Peter H.

2006-01-01

A compact submillimeter wave transmission / reflection measurement system has been demonstrated at 560-635 GHz, with electronic tuning over the entire band. Maximum dynamic range measured at a single frequency is 90 dB (60 dB typical), and phase noise is less than +/- 2(deg). By using a frequency steerable lens at the source output and mixer input, the frequency agility of the system can be used to scan the source and receive beams, resulting in near real-time imaging capability using only a single pixel.

LRS Bianchi type-I cosmological model with constant deceleration parameter in f(R,T) gravity

NASA Astrophysics Data System (ADS)

Bishi, Binaya K.; Pacif, S. K. J.; Sahoo, P. K.; Singh, G. P.

A spatially homogeneous anisotropic LRS Bianchi type-I cosmological model is studied in f(R,T) gravity with a special form of Hubble's parameter, which leads to constant deceleration parameter. The parameters involved in the considered form of Hubble parameter can be tuned to match, our models with the ΛCDM model. With the present observed value of the deceleration parameter, we have discussed physical and kinematical properties of a specific model. Moreover, we have discussed the cosmological distances for our model.

Online tuning of impedance matching circuit for long pulse inductively coupled plasma source operation—An alternate approach

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

Sudhir, Dass; Bandyopadhyay, M., E-mail: mainak@ter-india.org; Chakraborty, A.

2014-01-15

Impedance matching circuit between radio frequency (RF) generator and the plasma load, placed between them, determines the RF power transfer from RF generator to the plasma load. The impedance of plasma load depends on the plasma parameters through skin depth and plasma conductivity or resistivity. Therefore, for long pulse operation of inductively coupled plasmas, particularly for high power (∼100 kW or more) where plasma load condition may vary due to different reasons (e.g., pressure, power, and thermal), online tuning of impedance matching circuit is necessary through feedback. In fusion grade ion source operation, such online methodology through feedback is notmore » present but offline remote tuning by adjusting the matching circuit capacitors and tuning the driving frequency of the RF generator between the ion source operation pulses is envisaged. The present model is an approach for remote impedance tuning methodology for long pulse operation and corresponding online impedance matching algorithm based on RF coil antenna current measurement or coil antenna calorimetric measurement may be useful in this regard.« less

Adaptive control with self-tuning for non-invasive beat-by-beat blood pressure measurement.

PubMed

Nogawa, Masamichi; Ogawa, Mitsuhiro; Yamakoshi, Takehiro; Tanaka, Shinobu; Yamakoshi, Ken-ichi

2011-01-01

Up to now, we have successfully carried out the non-invasive beat-by-beat measurement of blood pressure (BP) in the root of finger, superficial temporal and radial artery based on the volume-compensation technique with reasonable accuracy. The present study concerns with improvement of control method for this beat-by-beat BP measurement. The measurement system mainly consists of a partial pressurization cuff with a pair of LED and photo-diode for the detection of arterial blood volume, and a digital self-tuning control method. Using healthy subjects, the performance and accuracy of this system were evaluated through comparison experiments with the system using a conventional empirically tuned PID controller. The significant differences of BP measured in finger artery were not showed in systolic (SBP), p=0.52, and diastolic BP (DBP), p=0.35. With the advantage of the adaptive control with self-tuning method, which can tune the control parameters without disturbing the control system, the application area of the non-invasive beat-by-beat measurement method will be broadened.

An adjustable RF tuning element for microwave, millimeter wave, and submillimeter wave integrated circuits

NASA Technical Reports Server (NTRS)

Lubecke, Victor M.; Mcgrath, William R.; Rutledge, David B.

1991-01-01

Planar RF circuits are used in a wide range of applications from 1 GHz to 300 GHz, including radar, communications, commercial RF test instruments, and remote sensing radiometers. These circuits, however, provide only fixed tuning elements. This lack of adjustability puts severe demands on circuit design procedures and materials parameters. We have developed a novel tuning element which can be incorporated into the design of a planar circuit in order to allow active, post-fabrication tuning by varying the electrical length of a coplanar strip transmission line. It consists of a series of thin plates which can slide in unison along the transmission line, and the size and spacing of the plates are designed to provide a large reflection of RF power over a useful frequency bandwidth. Tests of this structure at 1 GHz to 3 Ghz showed that it produced a reflection coefficient greater than 0.90 over a 20 percent bandwidth. A 2 GHz circuit incorporating this tuning element was also tested to demonstrate practical tuning ranges. This structure can be fabricated for frequencies as high as 1000 GHz using existing micromachining techniques. Many commercial applications can benefit from this micromechanical RF tuning element, as it will aid in extending microwave integrated circuit technology into the high millimeter wave and submillimeter wave bands by easing constraints on circuit technology.

Application of Vector Spherical Harmonics and Kernel Regression to the Computations of OMM Parameters

NASA Astrophysics Data System (ADS)

Marco, F. J.; Martínez, M. J.; López, J. A.

2015-04-01

The high quality of Hipparcos data in position, proper motion, and parallax has allowed for studies about stellar kinematics with the aim of achieving a better physical understanding of our galaxy, based on accurate calculus of the Ogorodnikov-Milne model (OMM) parameters. The use of discrete least squares is the most common adjustment method, but it may lead to errors mainly because of the inhomogeneous spatial distribution of the data. We present an example of the instability of this method using the case of a function given by a linear combination of Legendre polynomials. These polynomials are basic in the use of vector spherical harmonics, which have been used to compute the OMM parameters by several authors, such as Makarov & Murphy, Mignard & Klioner, and Vityazev & Tsvetkov. To overcome the former problem, we propose the use of a mixed method (see Marco et al.) that includes the extension of the functions of residuals to any point on the celestial sphere. The goal is to be able to work with continuous variables in the calculation of the coefficients of the vector spherical harmonic developments with stability and efficiency. We apply this mixed procedure to the study of the kinematics of the stars in our Galaxy, employing the Hipparcos velocity field data to obtain the OMM parameters. Previously, we tested the method by perturbing the Vectorial Spherical Harmonics model as well as the velocity vector field.

Differential Resonant Ring YIG Tuned Oscillator

NASA Technical Reports Server (NTRS)

Parrott, Ronald A.

2010-01-01

A differential SiGe oscillator circuit uses a resonant ring-oscillator topology in order to electronically tune the oscillator over multi-octave bandwidths. The oscillator s tuning is extremely linear, because the oscillator s frequency depends on the magnetic tuning of a YIG sphere, whose resonant frequency is equal to a fundamental constant times the DC magnetic field. This extremely simple circuit topology uses two coupling loops connecting a differential pair of SiGe bipolar transistors into a feedback configuration using a YIG tuned filter creating a closed-loop ring oscillator. SiGe device technology is used for this oscillator in order to keep the transistor s 1/f noise to an absolute minimum in order to achieve minimum RF phase noise. The single-end resonant ring oscillator currently has an advantage in fewer parts, but when the oscillation frequency is greater than 16 GHz, the package s parasitic behavior couples energy to the sphere and causes holes and poor phase noise performance. This is because the coupling to the YIG is extremely low, so that the oscillator operates at near the unloaded Q. With the differential resonant ring oscillator, the oscillation currents are just in the YIG coupling mechanisms. The phase noise is even better, and the physical size can be reduced to permit monolithic microwave integrated circuit oscillators. This invention is a YIG tuned oscillator circuit making use of a differential topology to simultaneously achieve an extremely broadband electronic tuning range and ultra-low phase noise. As a natural result of its differential circuit topology, all reactive elements, such as tuning stubs, which limit tuning bandwidth by contributing excessive open loop phase shift, have been eliminated. The differential oscillator s open-loop phase shift is associated with completely non-dispersive circuit elements such as the physical angle of the coupling loops, a differential loop crossover, and the high-frequency phase shift of the n-p-n transistors. At the input of the oscillator s feedback loop is a pair of differentially connected n-p-n SiGe transistors that provides extremely high gain, and because they are bulk-effect devices, extremely low 1/f noise (leading to ultralow RF phase noise). The 1/f corner frequency for n-p-n SiGe transistors is approximately 500 Hz. The RF energy from the transistor s collector output is connected directly to the top-coupling loop (the excitation loop) of a single-sphere YIG tuned filter. A uniform magnetic field to bias the YIG must be at a right angle to any vector associated with an RF current in a coupling loop in order for the precession to interact with the RF currents.

Mannosylated poly(beta-amino esters) for targeted antigen presenting cell immune modulation

PubMed Central

Jones, Charles H.; Chen, Mingfu; Ravikrishnan, Anitha; Reddinger, Ryan; Zhang, Guojian; Hakansson, Anders P.; Pfeifer, Blaine A.

2014-01-01

Given the rise of antibiotic resistance and other difficult-to-treat diseases, genetic vaccination is a promising preventative approach that can be tailored and scaled according to the vector chosen for gene delivery. However, most vectors currently utilized rely on ubiquitous delivery mechanisms that ineffectively target important immune effectors such as antigen presenting cells (APCs). As such, APC targeting allows the option for tuning the direction (humoral vs cell-mediated) and strength of the resulting immune responses. In this work, we present the development and assessment of a library of mannosylated poly(beta-amino esters) (PBAEs) that represent a new class of easily synthesized APC-targeting cationic polymers. Polymeric characterization and assessment methodologies were designed to provide a more realistic physiochemical profile prior to in vivo evaluation. Gene delivery assessment in vitro showed significant improvement upon PBAE mannosylation and suggested that mannose-mediated uptake and processing influence the magnitude of gene delivery. Furthermore, mannosylated PBAEs demonstrated a strong, efficient, and safe in vivo humoral immune response without use of adjuvants when compared to genetic and protein control antigens. In summary, the gene delivery effectiveness provided by mannosylated PBAE vectors offers specificity and potency in directing APC activation and subsequent immune responses. PMID:25453962

A new method for distortion magnetic field compensation of a geomagnetic vector measurement system

NASA Astrophysics Data System (ADS)

Liu, Zhongyan; Pan, Mengchun; Tang, Ying; Zhang, Qi; Geng, Yunling; Wan, Chengbiao; Chen, Dixiang; Tian, Wugang

2016-12-01

The geomagnetic vector measurement system mainly consists of three-axis magnetometer and an INS (inertial navigation system), which have many ferromagnetic parts on them. The magnetometer is always distorted by ferromagnetic parts and other electric equipments such as INS and power circuit module within the system, which can lead to geomagnetic vector measurement error of thousands of nT. Thus, the geomagnetic vector measurement system has to be compensated in order to guarantee the measurement accuracy. In this paper, a new distortion magnetic field compensation method is proposed, in which a permanent magnet with different relative positions is used to change the ambient magnetic field to construct equations of the error model parameters, and the parameters can be accurately estimated by solving linear equations. In order to verify effectiveness of the proposed method, the experiment is conducted, and the results demonstrate that, after compensation, the components errors of measured geomagnetic field are reduced significantly. It demonstrates that the proposed method can effectively improve the accuracy of the geomagnetic vector measurement system.

Tuning of Kalman filter parameters via genetic algorithm for state-of-charge estimation in battery management system.

PubMed

Ting, T O; Man, Ka Lok; Lim, Eng Gee; Leach, Mark

2014-01-01

In this work, a state-space battery model is derived mathematically to estimate the state-of-charge (SoC) of a battery system. Subsequently, Kalman filter (KF) is applied to predict the dynamical behavior of the battery model. Results show an accurate prediction as the accumulated error, in terms of root-mean-square (RMS), is a very small value. From this work, it is found that different sets of Q and R values (KF's parameters) can be applied for better performance and hence lower RMS error. This is the motivation for the application of a metaheuristic algorithm. Hence, the result is further improved by applying a genetic algorithm (GA) to tune Q and R parameters of the KF. In an online application, a GA can be applied to obtain the optimal parameters of the KF before its application to a real plant (system). This simply means that the instantaneous response of the KF is not affected by the time consuming GA as this approach is applied only once to obtain the optimal parameters. The relevant workable MATLAB source codes are given in the appendix to ease future work and analysis in this area.

Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System

PubMed Central

Ting, T. O.; Lim, Eng Gee

2014-01-01

In this work, a state-space battery model is derived mathematically to estimate the state-of-charge (SoC) of a battery system. Subsequently, Kalman filter (KF) is applied to predict the dynamical behavior of the battery model. Results show an accurate prediction as the accumulated error, in terms of root-mean-square (RMS), is a very small value. From this work, it is found that different sets of Q and R values (KF's parameters) can be applied for better performance and hence lower RMS error. This is the motivation for the application of a metaheuristic algorithm. Hence, the result is further improved by applying a genetic algorithm (GA) to tune Q and R parameters of the KF. In an online application, a GA can be applied to obtain the optimal parameters of the KF before its application to a real plant (system). This simply means that the instantaneous response of the KF is not affected by the time consuming GA as this approach is applied only once to obtain the optimal parameters. The relevant workable MATLAB source codes are given in the appendix to ease future work and analysis in this area. PMID:25162041

Transverse fields to tune an Ising-nematic quantum phase transition

NASA Astrophysics Data System (ADS)

Maharaj, Akash V.; Rosenberg, Elliott W.; Hristov, Alexander T.; Berg, Erez; Fernandes, Rafael M.; Fisher, Ian R.; Kivelson, Steven A.

2017-12-01

The paradigmatic example of a continuous quantum phase transition is the transverse field Ising ferromagnet. In contrast to classical critical systems, whose properties depend only on symmetry and the dimension of space, the nature of a quantum phase transition also depends on the dynamics. In the transverse field Ising model, the order parameter is not conserved, and increasing the transverse field enhances quantum fluctuations until they become strong enough to restore the symmetry of the ground state. Ising pseudospins can represent the order parameter of any system with a twofold degenerate broken-symmetry phase, including electronic nematic order associated with spontaneous point-group symmetry breaking. Here, we show for the representative example of orbital-nematic ordering of a non-Kramers doublet that an orthogonal strain or a perpendicular magnetic field plays the role of the transverse field, thereby providing a practical route for tuning appropriate materials to a quantum critical point. While the transverse fields are conjugate to seemingly unrelated order parameters, their nontrivial commutation relations with the nematic order parameter, which can be represented by a Berry-phase term in an effective field theory, intrinsically intertwine the different order parameters.

Optimized pulsed write schemes improve linearity and write speed for low-power organic neuromorphic devices

NASA Astrophysics Data System (ADS)

Keene, Scott T.; Melianas, Armantas; Fuller, Elliot J.; van de Burgt, Yoeri; Talin, A. Alec; Salleo, Alberto

2018-06-01

Neuromorphic devices are becoming increasingly appealing as efficient emulators of neural networks used to model real world problems. However, no hardware to date has demonstrated the necessary high accuracy and energy efficiency gain over CMOS in both (1) training via backpropagation and (2) in read via vector matrix multiplication. Such shortcomings are due to device non-idealities, particularly asymmetric conductance tuning in response to uniform voltage pulse inputs. Here, by formulating a general circuit model for capacitive ion-exchange neuromorphic devices, we show that asymmetric nonlinearity in organic electrochemical neuromorphic devices (ENODes) can be suppressed by an appropriately chosen write scheme. Simulations based upon our model suggest that a nonlinear write-selector could reduce the switching voltage and energy, enabling analog tuning via a continuous set of resistance states (100 states) with extremely low switching energy (~170 fJ · µm‑2). This work clarifies the pathway to neural algorithm accelerators capable of parallelism during both read and write operations.

Hands-on parameter search for neural simulations by a MIDI-controller.

PubMed

Eichner, Hubert; Borst, Alexander

2011-01-01

Computational neuroscientists frequently encounter the challenge of parameter fitting--exploring a usually high dimensional variable space to find a parameter set that reproduces an experimental data set. One common approach is using automated search algorithms such as gradient descent or genetic algorithms. However, these approaches suffer several shortcomings related to their lack of understanding the underlying question, such as defining a suitable error function or getting stuck in local minima. Another widespread approach is manual parameter fitting using a keyboard or a mouse, evaluating different parameter sets following the users intuition. However, this process is often cumbersome and time-intensive. Here, we present a new method for manual parameter fitting. A MIDI controller provides input to the simulation software, where model parameters are then tuned according to the knob and slider positions on the device. The model is immediately updated on every parameter change, continuously plotting the latest results. Given reasonably short simulation times of less than one second, we find this method to be highly efficient in quickly determining good parameter sets. Our approach bears a close resemblance to tuning the sound of an analog synthesizer, giving the user a very good intuition of the problem at hand, such as immediate feedback if and how results are affected by specific parameter changes. In addition to be used in research, our approach should be an ideal teaching tool, allowing students to interactively explore complex models such as Hodgkin-Huxley or dynamical systems.

Hands-On Parameter Search for Neural Simulations by a MIDI-Controller

PubMed Central

Eichner, Hubert; Borst, Alexander

2011-01-01

Computational neuroscientists frequently encounter the challenge of parameter fitting – exploring a usually high dimensional variable space to find a parameter set that reproduces an experimental data set. One common approach is using automated search algorithms such as gradient descent or genetic algorithms. However, these approaches suffer several shortcomings related to their lack of understanding the underlying question, such as defining a suitable error function or getting stuck in local minima. Another widespread approach is manual parameter fitting using a keyboard or a mouse, evaluating different parameter sets following the users intuition. However, this process is often cumbersome and time-intensive. Here, we present a new method for manual parameter fitting. A MIDI controller provides input to the simulation software, where model parameters are then tuned according to the knob and slider positions on the device. The model is immediately updated on every parameter change, continuously plotting the latest results. Given reasonably short simulation times of less than one second, we find this method to be highly efficient in quickly determining good parameter sets. Our approach bears a close resemblance to tuning the sound of an analog synthesizer, giving the user a very good intuition of the problem at hand, such as immediate feedback if and how results are affected by specific parameter changes. In addition to be used in research, our approach should be an ideal teaching tool, allowing students to interactively explore complex models such as Hodgkin-Huxley or dynamical systems. PMID:22066027

Determine Neuronal Tuning Curves by Exploring Optimum Firing Rate Distribution for Information Efficiency

PubMed Central

Han, Fang; Wang, Zhijie; Fan, Hong

2017-01-01

This paper proposed a new method to determine the neuronal tuning curves for maximum information efficiency by computing the optimum firing rate distribution. Firstly, we proposed a general definition for the information efficiency, which is relevant to mutual information and neuronal energy consumption. The energy consumption is composed of two parts: neuronal basic energy consumption and neuronal spike emission energy consumption. A parameter to model the relative importance of energy consumption is introduced in the definition of the information efficiency. Then, we designed a combination of exponential functions to describe the optimum firing rate distribution based on the analysis of the dependency of the mutual information and the energy consumption on the shape of the functions of the firing rate distributions. Furthermore, we developed a rapid algorithm to search the parameter values of the optimum firing rate distribution function. Finally, we found with the rapid algorithm that a combination of two different exponential functions with two free parameters can describe the optimum firing rate distribution accurately. We also found that if the energy consumption is relatively unimportant (important) compared to the mutual information or the neuronal basic energy consumption is relatively large (small), the curve of the optimum firing rate distribution will be relatively flat (steep), and the corresponding optimum tuning curve exhibits a form of sigmoid if the stimuli distribution is normal. PMID:28270760

Self-tuning pressure-feedback control by pole placement for vibration reduction of excavator with independent metering fluid power system

NASA Astrophysics Data System (ADS)

Ding, Ruqi; Xu, Bing; Zhang, Junhui; Cheng, Min

2017-08-01

Independent metering control systems are promising fluid power technologies compared with traditional valve controlled systems. By breaking the mechanical coupling between the inlet and outlet, the meter-out valve can open as large as possible to reduce energy consumptions. However, the lack of damping in outlet causes stronger vibrations. To address the problem, the paper designs a hybrid control method combining dynamic pressure-feedback and active damping control. The innovation resides in the optimization of damping by introducing pressure feedback to make trade-offs between high stability and fast response. To achieve this goal, the dynamic response pertaining to the control parameters consisting of feedback gain and cut-off frequency, are analyzed via pole-zero locations. Accordingly, these parameters are tuned online in terms of guaranteed dominant pole placement such that the optimal damping can be accurately captured under a considerable variation of operating conditions. The experiment is deployed in a mini-excavator. The results pertaining to different control parameters confirm the theoretical expectations via pole-zero locations. By using proposed self-tuning controller, the vibrations are almost eliminated after only one overshoot for different operation conditions. The overshoots are also reduced with less decrease of the response time. In addition, the energy-saving capability of independent metering system is still not affected by the improvement of controllability.

Tuning a physically-based model of the air-sea gas transfer velocity

NASA Astrophysics Data System (ADS)

Jeffery, C. D.; Robinson, I. S.; Woolf, D. K.

Air-sea gas transfer velocities are estimated for one year using a 1-D upper-ocean model (GOTM) and a modified version of the NOAA-COARE transfer velocity parameterization. Tuning parameters are evaluated with the aim of bringing the physically based NOAA-COARE parameterization in line with current estimates, based on simple wind-speed dependent models derived from bomb-radiocarbon inventories and deliberate tracer release experiments. We suggest that A = 1.3 and B = 1.0, for the sub-layer scaling parameter and the bubble mediated exchange, respectively, are consistent with the global average CO 2 transfer velocity k. Using these parameters and a simple 2nd order polynomial approximation, with respect to wind speed, we estimate a global annual average k for CO 2 of 16.4 ± 5.6 cm h -1 when using global mean winds of 6.89 m s -1 from the NCEP/NCAR Reanalysis 1 1954-2000. The tuned model can be used to predict the transfer velocity of any gas, with appropriate treatment of the dependence on molecular properties including the strong solubility dependence of bubble-mediated transfer. For example, an initial estimate of the global average transfer velocity of DMS (a relatively soluble gas) is only 11.9 cm h -1 whilst for less soluble methane the estimate is 18.0 cm h -1.

Research on bearing fault diagnosis of large machinery based on mathematical morphology

NASA Astrophysics Data System (ADS)

Wang, Yu

2018-04-01

To study the automatic diagnosis of large machinery fault based on support vector machine, combining the four common faults of the large machinery, the support vector machine is used to classify and identify the fault. The extracted feature vectors are entered. The feature vector is trained and identified by multi - classification method. The optimal parameters of the support vector machine are searched by trial and error method and cross validation method. Then, the support vector machine is compared with BP neural network. The results show that the support vector machines are short in time and high in classification accuracy. It is more suitable for the research of fault diagnosis in large machinery. Therefore, it can be concluded that the training speed of support vector machines (SVM) is fast and the performance is good.

Interface-based two-way tuning of the in-plane thermal transport in nanofilms

NASA Astrophysics Data System (ADS)

Hua, Yu-Chao; Cao, Bing-Yang

2018-03-01

Here, the two-way tuning of in-plane thermal transport is obtained in the bi-layer nanofilms with an interfacial effect by using the Boltzmann transport equation (BTE) and the phonon Monte Carlo (MC) technique. A thermal conductivity model was derived from the BTE and verified by the MC simulations. Both the model and the MC simulations indicate that the tuning of the thermal transport can be bidirectional (reduced or enhanced), depending on the interface conditions (i.e., roughness and adhesion energy) and the phonon property dissimilarity at the interface. For the identical-material interface, the emergence of thermal conductivity variation requires two conditions: (a) the interface is not completely specular and (b) the transmission specularity parameter differs from the reflection specularity parameter at the interface. When the transmission specularity parameter is larger than the reflection specularity parameter at the interface, the thermal conductivity improvement effect emerges, whereas the thermal conductivity reduction effect occurs. For the disparate-material interface, the phonon property perturbation near the interface causes the thermal conductivity variation, even when neither the above two conditions are satisfied. The mean free path ratio (γ) between the disparate materials was defined to characterize the phonon property dissimilarity. γ > 1 can lead to the thermal conductivity improvement effect, while γ < 1 corresponds to the thermal conductivity reduction effect. Our work provides a more in-depth understanding of the interfacial effect on the nanoscale thermal transport, with an applicable predictive model, which can be helpful for predicting and manipulating phonon transport in nanofilms.

Convolutional Dictionary Learning: Acceleration and Convergence

NASA Astrophysics Data System (ADS)

Chun, Il Yong; Fessler, Jeffrey A.

2018-04-01

Convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image processing and computer vision. There has been growing interest in developing efficient algorithms for CDL, mostly relying on the augmented Lagrangian (AL) method or the variant alternating direction method of multipliers (ADMM). When their parameters are properly tuned, AL methods have shown fast convergence in CDL. However, the parameter tuning process is not trivial due to its data dependence and, in practice, the convergence of AL methods depends on the AL parameters for nonconvex CDL problems. To moderate these problems, this paper proposes a new practically feasible and convergent Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The BPG-M-based CDL is investigated with different block updating schemes and majorization matrix designs, and further accelerated by incorporating some momentum coefficient formulas and restarting techniques. All of the methods investigated incorporate a boundary artifacts removal (or, more generally, sampling) operator in the learning model. Numerical experiments show that, without needing any parameter tuning process, the proposed BPG-M approach converges more stably to desirable solutions of lower objective values than the existing state-of-the-art ADMM algorithm and its memory-efficient variant do. Compared to the ADMM approaches, the BPG-M method using a multi-block updating scheme is particularly useful in single-threaded CDL algorithm handling large datasets, due to its lower memory requirement and no polynomial computational complexity. Image denoising experiments show that, for relatively strong additive white Gaussian noise, the filters learned by BPG-M-based CDL outperform those trained by the ADMM approach.

Support-vector-machines-based multidimensional signal classification for fetal activity characterization

NASA Astrophysics Data System (ADS)

Ribes, S.; Voicu, I.; Girault, J. M.; Fournier, M.; Perrotin, F.; Tranquart, F.; Kouamé, D.

2011-03-01

Electronic fetal monitoring may be required during the whole pregnancy to closely monitor specific fetal and maternal disorders. Currently used methods suffer from many limitations and are not sufficient to evaluate fetal asphyxia. Fetal activity parameters such as movements, heart rate and associated parameters are essential indicators of the fetus well being, and no current device gives a simultaneous and sufficient estimation of all these parameters to evaluate the fetus well-being. We built for this purpose, a multi-transducer-multi-gate Doppler system and developed dedicated signal processing techniques for fetal activity parameter extraction in order to investigate fetus's asphyxia or well-being through fetal activity parameters. To reach this goal, this paper shows preliminary feasibility of separating normal and compromised fetuses using our system. To do so, data set consisting of two groups of fetal signals (normal and compromised) has been established and provided by physicians. From estimated parameters an instantaneous Manning-like score, referred to as ultrasonic score was introduced and was used together with movements, heart rate and associated parameters in a classification process using Support Vector Machines (SVM) method. The influence of the fetal activity parameters and the performance of the SVM were evaluated using the computation of sensibility, specificity, percentage of support vectors and total classification accuracy. We showed our ability to separate the data into two sets : normal fetuses and compromised fetuses and obtained an excellent matching with the clinical classification performed by physician.

Optimal Trajectories Generation in Robotic Fiber Placement Systems

NASA Astrophysics Data System (ADS)

Gao, Jiuchun; Pashkevich, Anatol; Caro, Stéphane

2017-06-01

The paper proposes a methodology for optimal trajectories generation in robotic fiber placement systems. A strategy to tune the parameters of the optimization algorithm at hand is also introduced. The presented technique transforms the original continuous problem into a discrete one where the time-optimal motions are generated by using dynamic programming. The developed strategy for the optimization algorithm tuning allows essentially reducing the computing time and obtaining trajectories satisfying industrial constraints. Feasibilities and advantages of the proposed methodology are confirmed by an application example.

Control of Crazyflie nano quadcopter using Simulink

NASA Astrophysics Data System (ADS)

Gopabhat Madhusudhan, Meghana

This thesis focuses on developing a mathematical model in Simulink to Crazyflie, an open source platform. Attitude, altitude and position controllers of a Crazyflie are designed in the mathematical model. The mathematical model is developed based on the quadcopter system dynamics using a non-linear approach. The parameters of translational and rotational dynamics of the quadcopter system are linearized and tuned individually. The tuned attitude and altitude controllers from the mathematical model are implemented on real time Crazyflie Simulink model to achieve autonomous and controlled flight.

ATLAS I/O performance optimization in as-deployed environments

NASA Astrophysics Data System (ADS)

Maier, T.; Benjamin, D.; Bhimji, W.; Elmsheuser, J.; van Gemmeren, P.; Malon, D.; Krumnack, N.

2015-12-01

This paper provides an overview of an integrated program of work underway within the ATLAS experiment to optimise I/O performance for large-scale physics data analysis in a range of deployment environments. It proceeds to examine in greater detail one component of that work, the tuning of job-level I/O parameters in response to changes to the ATLAS event data model, and considers the implications of such tuning for a number of measures of I/O performance.

An ℋ∞ full information approach for the feedforward controller design of a large blended wing body flexible aircraft

NASA Astrophysics Data System (ADS)

Westermayer, C.; Schirrer, A.; Hemedi, M.; Kozek, M.

2013-12-01

An ℋ∞ full information feedforward design approach for longitudinal motion prefilter design of a large flexible blended wing body (BWB) aircraft is presented. An existing onset is extended such that specifications concerning command tracking, limited control energy, and manoeuvre load reduction can be addressed simultaneously. Therefore, the utilized design architecture is provided and manual tuning aspects are considered. In order to increase controller tuning efficiency, an automated tuning process based on several optimization criteria is proposed. Moreover, two design methodologies for the parameter-varying design case are investigated. The obtained controller is validated on a high-order nonlinear model, indicating the high potential of the presented approach for flexible aircraft control.

On the Possibility of Using Nonlinear Elements for Landau Damping in High-Intensity Beams

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

Alexahin, Y.; Gianfelice-Wendt, E.; Lebedev, V.

2016-09-30

Direct space-charge force shifts incoherent tunes downwards from the coherent ones breaking the Landau mechanism of coherent oscillations damping at high beam intensity. To restore it nonlinear elements can be employed which move back tunes of large amplitude particles. In the present report we consider the possibility of creating a “nonlinear integrable optics” insertion in the Fermilab Recycler to host either octupoles or hollow electron lens for this purpose. For comparison we also consider the classic scheme with distributed octupole families. It is shown that for the Proton Improvement Plan II (PIP II) parameters the required nonlinear tune shift canmore » be created without destroying the dynamic aperture.« less

Prediction of biomechanical parameters of the proximal femur using statistical appearance models and support vector regression.

PubMed

Fritscher, Karl; Schuler, Benedikt; Link, Thomas; Eckstein, Felix; Suhm, Norbert; Hänni, Markus; Hengg, Clemens; Schubert, Rainer

2008-01-01

Fractures of the proximal femur are one of the principal causes of mortality among elderly persons. Traditional methods for the determination of femoral fracture risk use methods for measuring bone mineral density. However, BMD alone is not sufficient to predict bone failure load for an individual patient and additional parameters have to be determined for this purpose. In this work an approach that uses statistical models of appearance to identify relevant regions and parameters for the prediction of biomechanical properties of the proximal femur will be presented. By using Support Vector Regression the proposed model based approach is capable of predicting two different biomechanical parameters accurately and fully automatically in two different testing scenarios.

Analysis of the sensitivity properties of a model of vector-borne bubonic plague.

PubMed

Buzby, Megan; Neckels, David; Antolin, Michael F; Estep, Donald

2008-09-06

Model sensitivity is a key to evaluation of mathematical models in ecology and evolution, especially in complex models with numerous parameters. In this paper, we use some recently developed methods for sensitivity analysis to study the parameter sensitivity of a model of vector-borne bubonic plague in a rodent population proposed by Keeling & Gilligan. The new sensitivity tools are based on a variational analysis involving the adjoint equation. The new approach provides a relatively inexpensive way to obtain derivative information about model output with respect to parameters. We use this approach to determine the sensitivity of a quantity of interest (the force of infection from rats and their fleas to humans) to various model parameters, determine a region over which linearization at a specific parameter reference point is valid, develop a global picture of the output surface, and search for maxima and minima in a given region in the parameter space.

Assessing the applicability of WRF optimal parameters under the different precipitation simulations in the Greater Beijing Area

NASA Astrophysics Data System (ADS)

Di, Zhenhua; Duan, Qingyun; Wang, Chen; Ye, Aizhong; Miao, Chiyuan; Gong, Wei

2018-03-01

Forecasting skills of the complex weather and climate models have been improved by tuning the sensitive parameters that exert the greatest impact on simulated results based on more effective optimization methods. However, whether the optimal parameter values are still work when the model simulation conditions vary, which is a scientific problem deserving of study. In this study, a highly-effective optimization method, adaptive surrogate model-based optimization (ASMO), was firstly used to tune nine sensitive parameters from four physical parameterization schemes of the Weather Research and Forecasting (WRF) model to obtain better summer precipitation forecasting over the Greater Beijing Area in China. Then, to assess the applicability of the optimal parameter values, simulation results from the WRF model with default and optimal parameter values were compared across precipitation events, boundary conditions, spatial scales, and physical processes in the Greater Beijing Area. The summer precipitation events from 6 years were used to calibrate and evaluate the optimal parameter values of WRF model. Three boundary data and two spatial resolutions were adopted to evaluate the superiority of the calibrated optimal parameters to default parameters under the WRF simulations with different boundary conditions and spatial resolutions, respectively. Physical interpretations of the optimal parameters indicating how to improve precipitation simulation results were also examined. All the results showed that the optimal parameters obtained by ASMO are superior to the default parameters for WRF simulations for predicting summer precipitation in the Greater Beijing Area because the optimal parameters are not constrained by specific precipitation events, boundary conditions, and spatial resolutions. The optimal values of the nine parameters were determined from 127 parameter samples using the ASMO method, which showed that the ASMO method is very highly-efficient for optimizing WRF model parameters.

Spectral embedding finds meaningful (relevant) structure in image and microarray data

PubMed Central

Higgs, Brandon W; Weller, Jennifer; Solka, Jeffrey L

2006-01-01

Background Accurate methods for extraction of meaningful patterns in high dimensional data have become increasingly important with the recent generation of data types containing measurements across thousands of variables. Principal components analysis (PCA) is a linear dimensionality reduction (DR) method that is unsupervised in that it relies only on the data; projections are calculated in Euclidean or a similar linear space and do not use tuning parameters for optimizing the fit to the data. However, relationships within sets of nonlinear data types, such as biological networks or images, are frequently mis-rendered into a low dimensional space by linear methods. Nonlinear methods, in contrast, attempt to model important aspects of the underlying data structure, often requiring parameter(s) fitting to the data type of interest. In many cases, the optimal parameter values vary when different classification algorithms are applied on the same rendered subspace, making the results of such methods highly dependent upon the type of classifier implemented. Results We present the results of applying the spectral method of Lafon, a nonlinear DR method based on the weighted graph Laplacian, that minimizes the requirements for such parameter optimization for two biological data types. We demonstrate that it is successful in determining implicit ordering of brain slice image data and in classifying separate species in microarray data, as compared to two conventional linear methods and three nonlinear methods (one of which is an alternative spectral method). This spectral implementation is shown to provide more meaningful information, by preserving important relationships, than the methods of DR presented for comparison. Tuning parameter fitting is simple and is a general, rather than data type or experiment specific approach, for the two datasets analyzed here. Tuning parameter optimization is minimized in the DR step to each subsequent classification method, enabling the possibility of valid cross-experiment comparisons. Conclusion Results from the spectral method presented here exhibit the desirable properties of preserving meaningful nonlinear relationships in lower dimensional space and requiring minimal parameter fitting, providing a useful algorithm for purposes of visualization and classification across diverse datasets, a common challenge in systems biology. PMID:16483359

Objective calibration of regional climate models

NASA Astrophysics Data System (ADS)

Bellprat, O.; Kotlarski, S.; Lüthi, D.; SchäR, C.

2012-12-01

Climate models are subject to high parametric uncertainty induced by poorly confined model parameters of parameterized physical processes. Uncertain model parameters are typically calibrated in order to increase the agreement of the model with available observations. The common practice is to adjust uncertain model parameters manually, often referred to as expert tuning, which lacks objectivity and transparency in the use of observations. These shortcomings often haze model inter-comparisons and hinder the implementation of new model parameterizations. Methods which would allow to systematically calibrate model parameters are unfortunately often not applicable to state-of-the-art climate models, due to computational constraints facing the high dimensionality and non-linearity of the problem. Here we present an approach to objectively calibrate a regional climate model, using reanalysis driven simulations and building upon a quadratic metamodel presented by Neelin et al. (2010) that serves as a computationally cheap surrogate of the model. Five model parameters originating from different parameterizations are selected for the optimization according to their influence on the model performance. The metamodel accurately estimates spatial averages of 2 m temperature, precipitation and total cloud cover, with an uncertainty of similar magnitude as the internal variability of the regional climate model. The non-linearities of the parameter perturbations are well captured, such that only a limited number of 20-50 simulations are needed to estimate optimal parameter settings. Parameter interactions are small, which allows to further reduce the number of simulations. In comparison to an ensemble of the same model which has undergone expert tuning, the calibration yields similar optimal model configurations, but leading to an additional reduction of the model error. The performance range captured is much wider than sampled with the expert-tuned ensemble and the presented methodology is effective and objective. It is argued that objective calibration is an attractive tool and could become standard procedure after introducing new model implementations, or after a spatial transfer of a regional climate model. Objective calibration of parameterizations with regional models could also serve as a strategy toward improving parameterization packages of global climate models.

A Comparative Study of Distribution System Parameter Estimation Methods

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

Sun, Yannan; Williams, Tess L.; Gourisetti, Sri Nikhil Gup

2016-07-17

In this paper, we compare two parameter estimation methods for distribution systems: residual sensitivity analysis and state-vector augmentation with a Kalman filter. These two methods were originally proposed for transmission systems, and are still the most commonly used methods for parameter estimation. Distribution systems have much lower measurement redundancy than transmission systems. Therefore, estimating parameters is much more difficult. To increase the robustness of parameter estimation, the two methods are applied with combined measurement snapshots (measurement sets taken at different points in time), so that the redundancy for computing the parameter values is increased. The advantages and disadvantages of bothmore » methods are discussed. The results of this paper show that state-vector augmentation is a better approach for parameter estimation in distribution systems. Simulation studies are done on a modified version of IEEE 13-Node Test Feeder with varying levels of measurement noise and non-zero error in the other system model parameters.« less

Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection.

PubMed

Nuryani, Nuryani; Ling, Steve S H; Nguyen, H T

2012-04-01

Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity.

Sensitivity analysis of the space shuttle to ascent wind profiles

NASA Technical Reports Server (NTRS)

Smith, O. E.; Austin, L. D., Jr.

1982-01-01

A parametric sensitivity analysis of the space shuttle ascent flight to the wind profile is presented. Engineering systems parameters are obtained by flight simulations using wind profile models and samples of detailed (Jimsphere) wind profile measurements. The wind models used are the synthetic vector wind model, with and without the design gust, and a model of the vector wind change with respect to time. From these comparison analyses an insight is gained on the contribution of winds to ascent subsystems flight parameters.

Propagation of partially coherent vector anomalous vortex beam in turbulent atmosphere

NASA Astrophysics Data System (ADS)

Zhang, Xu; Wang, Haiyan; Tang, Lei

2018-01-01

A theoretical model is proposed to describe a partially coherent vector anomalous vortex(AV) beam. Based on the extended Huygens-Fresnel principle, analytical propagation formula for the proposed beams in turbulent atmosphere is derived. The spectral properties of the partially coherent vector AV beam are explored by using the unified theory of coherence and polarization in detail. It is interesting to find that the turbulence of atmosphere and the source parameter of the partially coherent vector AV beam( order, topological charge, coherence length, beam waist size etc) have significantly impacted the propagation properties of the partially coherent vector AV beam in turbulent atmosphere.

Effects of Cavity on the Performance of Dual Throat Nozzle During the Thrust-Vectoring Starting Transient Process.

PubMed

Gu, Rui; Xu, Jinglei

2014-01-01

The dual throat nozzle (DTN) technique is capable to achieve higher thrust-vectoring efficiencies than other fluidic techniques, without compromising thrust efficiency significantly during vectoring operation. The excellent performance of the DTN is mainly due to the concaved cavity. In this paper, two DTNs of different scales have been investigated by unsteady numerical simulations to compare the parameter variations and study the effects of cavity during the vector starting process. The results remind us that during the vector starting process, dynamic loads may be generated, which is a potentially challenging problem for the aircraft trim and control.

Analysis of ground-motion simulation big data

NASA Astrophysics Data System (ADS)

Maeda, T.; Fujiwara, H.

2016-12-01

We developed a parallel distributed processing system which applies a big data analysis to the large-scale ground motion simulation data. The system uses ground-motion index values and earthquake scenario parameters as input. We used peak ground velocity value and velocity response spectra as the ground-motion index. The ground-motion index values are calculated from our simulation data. We used simulated long-period ground motion waveforms at about 80,000 meshes calculated by a three dimensional finite difference method based on 369 earthquake scenarios of a great earthquake in the Nankai Trough. These scenarios were constructed by considering the uncertainty of source model parameters such as source area, rupture starting point, asperity location, rupture velocity, fmax and slip function. We used these parameters as the earthquake scenario parameter. The system firstly carries out the clustering of the earthquake scenario in each mesh by the k-means method. The number of clusters is determined in advance using a hierarchical clustering by the Ward's method. The scenario clustering results are converted to the 1-D feature vector. The dimension of the feature vector is the number of scenario combination. If two scenarios belong to the same cluster the component of the feature vector is 1, and otherwise the component is 0. The feature vector shows a `response' of mesh to the assumed earthquake scenario group. Next, the system performs the clustering of the mesh by k-means method using the feature vector of each mesh previously obtained. Here the number of clusters is arbitrarily given. The clustering of scenarios and meshes are performed by parallel distributed processing with Hadoop and Spark, respectively. In this study, we divided the meshes into 20 clusters. The meshes in each cluster are geometrically concentrated. Thus this system can extract regions, in which the meshes have similar `response', as clusters. For each cluster, it is possible to determine particular scenario parameters which characterize the cluster. In other word, by utilizing this system, we can obtain critical scenario parameters of the ground-motion simulation for each evaluation point objectively. This research was supported by CREST, JST.

Intelligent Machine Learning Approaches for Aerospace Applications

NASA Astrophysics Data System (ADS)

Sathyan, Anoop

Machine Learning is a type of artificial intelligence that provides machines or networks the ability to learn from data without the need to explicitly program them. There are different kinds of machine learning techniques. This thesis discusses the applications of two of these approaches: Genetic Fuzzy Logic and Convolutional Neural Networks (CNN). Fuzzy Logic System (FLS) is a powerful tool that can be used for a wide variety of applications. FLS is a universal approximator that reduces the need for complex mathematics and replaces it with expert knowledge of the system to produce an input-output mapping using If-Then rules. The expert knowledge of a system can help in obtaining the parameters for small-scale FLSs, but for larger networks we will need to use sophisticated approaches that can automatically train the network to meet the design requirements. This is where Genetic Algorithms (GA) and EVE come into the picture. Both GA and EVE can tune the FLS parameters to minimize a cost function that is designed to meet the requirements of the specific problem. EVE is an artificial intelligence developed by Psibernetix that is trained to tune large scale FLSs. The parameters of an FLS can include the membership functions and rulebase of the inherent Fuzzy Inference Systems (FISs). The main issue with using the GFS is that the number of parameters in a FIS increase exponentially with the number of inputs thus making it increasingly harder to tune them. To reduce this issue, the FLSs discussed in this thesis consist of 2-input-1-output FISs in cascade (Chapter 4) or as a layer of parallel FISs (Chapter 7). We have obtained extremely good results using GFS for different applications at a reduced computational cost compared to other algorithms that are commonly used to solve the corresponding problems. In this thesis, GFSs have been designed for controlling an inverted double pendulum, a task allocation problem of clustering targets amongst a set of UAVs, a fire detection problem and the aircraft conflict resolution problem. During the last decade, CNNs have become increasingly popular in the domain of image and speech processing. CNNs have a lot more parameters compared to GFSs that are tuned using the back-propagation algorithm. CNNs typically have hundreds of thousands or maybe millions of parameters that are tuned using common cost functions such as integral squared error, softmax loss etc. Chapter 5 discusses a classification problem to classify images as humans or not and Chapter 6 discusses a regression task using CNN for producing an approximate near-optimal route for the Traveling Salesman Problem (TSP) which is regarded as one of the most complicated decision making problem. Both the GFS and CNN are used to develop intelligent systems specific to the application providing them computational efficiency, robustness in the face of uncertainties and scalability.

Modeling spatial tuning of adaptation of the angular vestibulo-ocular reflex

PubMed Central

Yakushin, Sergei B.

2012-01-01

Gain adaptation of the yaw angular vestibular ocular reflex (aVOR) induced in side-down positions has gravity-independent (global) and -dependent (localized) components. When the head oscillation angles are small during adaptation, localized gain changes are maximal in the approximate position of adaptation. Concurrently, polarization vectors of canal–otolith vestibular neurons adapt their orientations during these small-angle adaptation paradigms. Whether there is orientation adaptation with large amplitude head oscillations, when the head is not localized to a specific position, is unknown. Yaw aVOR gains were decreased by oscillating monkeys about a yaw axis in a side-down position in a subject–stationary visual surround for 2 h. Amplitudes of head oscillation ranged from 15° to 180°. The yaw aVOR gain was tested in darkness at 0.5 Hz, with small angles of oscillation (±15°) while upright and in tilted positions. The peak value of the gain change was highly tuned for small angular oscillations during adaptation and significantly broadened with larger oscillation angles during adaptation. When the orientation of the polarization vectors associated with the gravity-dependent component of the neural network model was adapted toward the direction of gravity, it predicted the localized learning for small angles and the broadening when the orientation adaptation was diminished. The model-based analysis suggests that the otolith orientation adaptation plays an important role in the localized behavior of aVOR as a function of gravity and in regulating the relationship between global and localized adaptation. PMID:22660376

Cereal transformation through particle bombardment

NASA Technical Reports Server (NTRS)

Casas, A. M.; Kononowicz, A. K.; Bressan, R. A.; Hasegawa, P. M.; Mitchell, C. A. (Principal Investigator)

1995-01-01

The review focuses on experiments that lead to stable transformation in cereals using microprojectile bombardment. The discussion of biological factors that affect transformation examines target tissues and vector systems for gene transfer. The vector systems include reporter genes, selectable markers, genes of agronomic interest, and vector constructions. Other topics include physical parameters that affect DNA delivery, selection of stably transformed cells and plant regeneration, and analysis of gene expression and transmission to the progeny.

Sparse Method for Direction of Arrival Estimation Using Denoised Fourth-Order Cumulants Vector.

PubMed

Fan, Yangyu; Wang, Jianshu; Du, Rui; Lv, Guoyun

2018-06-04

Fourth-order cumulants (FOCs) vector-based direction of arrival (DOA) estimation methods of non-Gaussian sources may suffer from poor performance for limited snapshots or difficulty in setting parameters. In this paper, a novel FOCs vector-based sparse DOA estimation method is proposed. Firstly, by utilizing the concept of a fourth-order difference co-array (FODCA), an advanced FOCs vector denoising or dimension reduction procedure is presented for arbitrary array geometries. Then, a novel single measurement vector (SMV) model is established by the denoised FOCs vector, and efficiently solved by an off-grid sparse Bayesian inference (OGSBI) method. The estimation errors of FOCs are integrated in the SMV model, and are approximately estimated in a simple way. A necessary condition regarding the number of identifiable sources of our method is presented that, in order to uniquely identify all sources, the number of sources K must fulfill K ≤ ( M 4 - 2 M 3 + 7 M 2 - 6 M ) / 8 . The proposed method suits any geometry, does not need prior knowledge of the number of sources, is insensitive to associated parameters, and has maximum identifiability O ( M 4 ) , where M is the number of sensors in the array. Numerical simulations illustrate the superior performance of the proposed method.

750 GeV diphotons: implications for supersymmetric unification II

DOE PAGES

Hall, Lawrence J.; Harigaya, Keisuke; Nomura, Yasunori

2016-07-29

Perturbative supersymmetric gauge coupling unification is possible in six theories where complete SU (5) TeV-scale multiplets of vector matter account for the size of the reported 750 GeV diphoton resonance, interpreted as a singlet multiplet S=(s+ia)/√2. One of these has a full generation of vector matter and a unified gauge coupling αG ~ 1. The diphoton signal rate is enhanced by loops of vector squarks and sleptons, especially when the trilinear A couplings are large. If the SH uH d coupling is absent, both s and a can contribute to the resonance, which may then have a large apparent widthmore » if the mass splitting from s and a arises from loops of vector matter. The width depends sensitively on A parameters and phases of the vector squark and slepton masses. Vector quarks and/or squarks are expected to be in reach of the LHC. If the SH uH d coupling is present, a leads to a narrow diphoton resonance, while a second resonance with decays s → hh, W +W – , ZZ is likely to be discovered at future LHC runs. In some of the theories a non-standard origin or running of the soft parameters is required, for example involving conformal hidden sector interactions.« less

Model predictive control of attitude maneuver of a geostationary flexible satellite based on genetic algorithm

NASA Astrophysics Data System (ADS)

TayyebTaher, M.; Esmaeilzadeh, S. Majid

2017-07-01

This article presents an application of Model Predictive Controller (MPC) to the attitude control of a geostationary flexible satellite. SIMO model has been used for the geostationary satellite, using the Lagrange equations. Flexibility is also included in the modelling equations. The state space equations are expressed in order to simplify the controller. Naturally there is no specific tuning rule to find the best parameters of an MPC controller which fits the desired controller. Being an intelligence method for optimizing problem, Genetic Algorithm has been used for optimizing the performance of MPC controller by tuning the controller parameter due to minimum rise time, settling time, overshoot of the target point of the flexible structure and its mode shape amplitudes to make large attitude maneuvers possible. The model included geosynchronous orbit environment and geostationary satellite parameters. The simulation results of the flexible satellite with attitude maneuver shows the efficiency of proposed optimization method in comparison with LQR optimal controller.

Tunable non-interacting free-energy functionals: development and applications to low-density aluminum

NASA Astrophysics Data System (ADS)

Trickey, Samuel; Karasiev, Valentin

We introduce the concept of tunable orbital-free non-interacting free-energy density functionals and present a generalized gradient approximation (GGA) with a subset of parameters defined from constraints and a few free parameters. Those free parameters are tuned to reproduce reference Kohn-Sham (KS) static-lattice pressures for Al at T=8 kK for bulk densities between 0.6 and 2 g/cm3. The tuned functional then is used in OF molecular dynamics (MD) simulations for Al with densities between 0.1 and 2 g/cm3 and T between 6 and 50 kK to calculate the equation of state and generate configurations for electrical conductivity calculations. The tunable functional produces accurate results. Computationally it is very effective especially at elevated temperature. Kohn-Shiam calculations for such low densities are affordable only up to T=10 kK, while other OF approximations, including two-point functionals, fail badly in that regime. Work supported by US DoE Grant DE-SC0002139.

Distributed and Dynamic Neural Encoding of Multiple Motion Directions of Transparently Moving Stimuli in Cortical Area MT

PubMed Central

Xiao, Jianbo

2015-01-01

Segmenting visual scenes into distinct objects and surfaces is a fundamental visual function. To better understand the underlying neural mechanism, we investigated how neurons in the middle temporal cortex (MT) of macaque monkeys represent overlapping random-dot stimuli moving transparently in slightly different directions. It has been shown that the neuronal response elicited by two stimuli approximately follows the average of the responses elicited by the constituent stimulus components presented alone. In this scheme of response pooling, the ability to segment two simultaneously presented motion directions is limited by the width of the tuning curve to motion in a single direction. We found that, although the population-averaged neuronal tuning showed response averaging, subgroups of neurons showed distinct patterns of response tuning and were capable of representing component directions that were separated by a small angle—less than the tuning width to unidirectional stimuli. One group of neurons preferentially represented the component direction at a specific side of the bidirectional stimuli, weighting one stimulus component more strongly than the other. Another group of neurons pooled the component responses nonlinearly and showed two separate peaks in their tuning curves even when the average of the component responses was unimodal. We also show for the first time that the direction tuning of MT neurons evolved from initially representing the vector-averaged direction of slightly different stimuli to gradually representing the component directions. Our results reveal important neural processes underlying image segmentation and suggest that information about slightly different stimulus components is computed dynamically and distributed across neurons. SIGNIFICANCE STATEMENT Natural scenes often contain multiple entities. The ability to segment visual scenes into distinct objects and surfaces is fundamental to sensory processing and is crucial for generating the perception of our environment. Because cortical neurons are broadly tuned to a given visual feature, segmenting two stimuli that differ only slightly is a challenge for the visual system. In this study, we discovered that many neurons in the visual cortex are capable of representing individual components of slightly different stimuli by selectively and nonlinearly pooling the responses elicited by the stimulus components. We also show for the first time that the neural representation of individual stimulus components developed over a period of ∼70–100 ms, revealing a dynamic process of image segmentation. PMID:26658869

A simplified model for predicting malaria entomologic inoculation rates based on entomologic and parasitologic parameters relevant to control.

PubMed

Killeen, G F; McKenzie, F E; Foy, B D; Schieffelin, C; Billingsley, P F; Beier, J C

2000-05-01

Malaria transmission intensity is modeled from the starting perspective of individual vector mosquitoes and is expressed directly as the entomologic inoculation rate (EIR). The potential of individual mosquitoes to transmit malaria during their lifetime is presented graphically as a function of their feeding cycle length and survival, human biting preferences, and the parasite sporogonic incubation period. The EIR is then calculated as the product of 1) the potential of individual vectors to transmit malaria during their lifetime, 2) vector emergence rate relative to human population size, and 3) the infectiousness of the human population to vectors. Thus, impacts on more than one of these parameters will amplify each other's effects. The EIRs transmitted by the dominant vector species at four malaria-endemic sites from Papua New Guinea, Tanzania, and Nigeria were predicted using field measurements of these characteristics together with human biting rate and human reservoir infectiousness. This model predicted EIRs (+/- SD) that are 1.13 +/- 0.37 (range = 0.84-1.59) times those measured in the field. For these four sites, mosquito emergence rate and lifetime transmission potential were more important determinants of the EIR than human reservoir infectiousness. This model and the input parameters from the four sites allow the potential impacts of various control measures on malaria transmission intensity to be tested under a range of endemic conditions. The model has potential applications for the development and implementation of transmission control measures and for public health education.

Design of analytical failure detection using secondary observers

NASA Technical Reports Server (NTRS)

Sisar, M.

1982-01-01

The problem of designing analytical failure-detection systems (FDS) for sensors and actuators, using observers, is addressed. The use of observers in FDS is related to the examination of the n-dimensional observer error vector which carries the necessary information on possible failures. The problem is that in practical systems, in which only some of the components of the state vector are measured, one has access only to the m-dimensional observer-output error vector, with m or = to n. In order to cope with these cases, a secondary observer is synthesized to reconstruct the entire observer-error vector from the observer output error vector. This approach leads toward the design of highly sensitive and reliable FDS, with the possibility of obtaining a unique fingerprint for every possible failure. In order to keep the observer's (or Kalman filter) false-alarm rate under a certain specified value, it is necessary to have an acceptable matching between the observer (or Kalman filter) models and the system parameters. A previously developed adaptive observer algorithm is used to maintain the desired system-observer model matching, despite initial mismatching or system parameter variations. Conditions for convergence for the adaptive process are obtained, leading to a simple adaptive law (algorithm) with the possibility of an a priori choice of fixed adaptive gains. Simulation results show good tracking performance with small observer output errors, while accurate and fast parameter identification, in both deterministic and stochastic cases, is obtained.

A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

NASA Astrophysics Data System (ADS)

Khawaja, Taimoor Saleem

A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior and any abnormal or novel data during real-time operation. The results of the scheme are interpreted as a posterior probability of health (1 - probability of fault). As shown through two case studies in Chapter 3, the scheme is well suited for diagnosing imminent faults in dynamical non-linear systems. Finally, the failure prognosis scheme is based on an incremental weighted Bayesian LS-SVR machine. It is particularly suited for online deployment given the incremental nature of the algorithm and the quick optimization problem solved in the LS-SVR algorithm. By way of kernelization and a Gaussian Mixture Modeling (GMM) scheme, the algorithm can estimate "possibly" non-Gaussian posterior distributions for complex non-linear systems. An efficient regression scheme associated with the more rigorous core algorithm allows for long-term predictions, fault growth estimation with confidence bounds and remaining useful life (RUL) estimation after a fault is detected. The leading contributions of this thesis are (a) the development of a novel Bayesian Anomaly Detector for efficient and reliable Fault Detection and Identification (FDI) based on Least Squares Support Vector Machines, (b) the development of a data-driven real-time architecture for long-term Failure Prognosis using Least Squares Support Vector Machines, (c) Uncertainty representation and management using Bayesian Inference for posterior distribution estimation and hyper-parameter tuning, and finally (d) the statistical characterization of the performance of diagnosis and prognosis algorithms in order to relate the efficiency and reliability of the proposed schemes.

Multi-model analysis of terrestrial carbon cycles in Japan: limitations and implications of model calibration using eddy flux observations

NASA Astrophysics Data System (ADS)

Ichii, K.; Suzuki, T.; Kato, T.; Ito, A.; Hajima, T.; Ueyama, M.; Sasai, T.; Hirata, R.; Saigusa, N.; Ohtani, Y.; Takagi, K.

2010-07-01

Terrestrial biosphere models show large differences when simulating carbon and water cycles, and reducing these differences is a priority for developing more accurate estimates of the condition of terrestrial ecosystems and future climate change. To reduce uncertainties and improve the understanding of their carbon budgets, we investigated the utility of the eddy flux datasets to improve model simulations and reduce variabilities among multi-model outputs of terrestrial biosphere models in Japan. Using 9 terrestrial biosphere models (Support Vector Machine - based regressions, TOPS, CASA, VISIT, Biome-BGC, DAYCENT, SEIB, LPJ, and TRIFFID), we conducted two simulations: (1) point simulations at four eddy flux sites in Japan and (2) spatial simulations for Japan with a default model (based on original settings) and a modified model (based on model parameter tuning using eddy flux data). Generally, models using default model settings showed large deviations in model outputs from observation with large model-by-model variability. However, after we calibrated the model parameters using eddy flux data (GPP, RE and NEP), most models successfully simulated seasonal variations in the carbon cycle, with less variability among models. We also found that interannual variations in the carbon cycle are mostly consistent among models and observations. Spatial analysis also showed a large reduction in the variability among model outputs. This study demonstrated that careful validation and calibration of models with available eddy flux data reduced model-by-model differences. Yet, site history, analysis of model structure changes, and more objective procedure of model calibration should be included in the further analysis.

A Novel Degradation Identification Method for Wind Turbine Pitch System

NASA Astrophysics Data System (ADS)

Guo, Hui-Dong

2018-04-01

It’s difficult for traditional threshold value method to identify degradation of operating equipment accurately. An novel degradation evaluation method suitable for wind turbine condition maintenance strategy implementation was proposed in this paper. Based on the analysis of typical variable-speed pitch-to-feather control principle and monitoring parameters for pitch system, a multi input multi output (MIMO) regression model was applied to pitch system, where wind speed, power generation regarding as input parameters, wheel rotation speed, pitch angle and motor driving currency for three blades as output parameters. Then, the difference between the on-line measurement and the calculated value from the MIMO regression model applying least square support vector machines (LSSVM) method was defined as the Observed Vector of the system. The Gaussian mixture model (GMM) was applied to fitting the distribution of the multi dimension Observed Vectors. Applying the model established, the Degradation Index was calculated using the SCADA data of a wind turbine damaged its pitch bearing retainer and rolling body, which illustrated the feasibility of the provided method.

The combined geodetic network adjusted on the reference ellipsoid - a comparison of three functional models for GNSS observations

NASA Astrophysics Data System (ADS)

Kadaj, Roman

2016-12-01

The adjustment problem of the so-called combined (hybrid, integrated) network created with GNSS vectors and terrestrial observations has been the subject of many theoretical and applied works. The network adjustment in various mathematical spaces was considered: in the Cartesian geocentric system on a reference ellipsoid and on a mapping plane. For practical reasons, it often takes a geodetic coordinate system associated with the reference ellipsoid. In this case, the Cartesian GNSS vectors are converted, for example, into geodesic parameters (azimuth and length) on the ellipsoid, but the simple form of converted pseudo-observations are the direct differences of the geodetic coordinates. Unfortunately, such an approach may be essentially distorted by a systematic error resulting from the position error of the GNSS vector, before its projection on the ellipsoid surface. In this paper, an analysis of the impact of this error on the determined measures of geometric ellipsoid elements, including the differences of geodetic coordinates or geodesic parameters is presented. Assuming that the adjustment of a combined network on the ellipsoid shows that the optimal functional approach in relation to the satellite observation, is to create the observational equations directly for the original GNSS Cartesian vector components, writing them directly as a function of the geodetic coordinates (in numerical applications, we use the linearized forms of observational equations with explicitly specified coefficients). While retaining the original character of the Cartesian vector, one avoids any systematic errors that may occur in the conversion of the original GNSS vectors to ellipsoid elements, for example the vector of the geodesic parameters. The problem is theoretically developed and numerically tested. An example of the adjustment of a subnet loaded from the database of reference stations of the ASG-EUPOS system was considered for the preferred functional model of the GNSS observations.

Robot tracking system improvements and visual calibration of orbiter position for radiator inspection

NASA Technical Reports Server (NTRS)

Tonkay, Gregory

1990-01-01

The following separate topics are addressed: (1) improving a robotic tracking system; and (2) providing insights into orbiter position calibration for radiator inspection. The objective of the tracking system project was to provide the capability to track moving targets more accurately by adjusting parameters in the control system and implementing a predictive algorithm. A computer model was developed to emulate the tracking system. Using this model as a test bed, a self-tuning algorithm was developed to tune the system gains. The model yielded important findings concerning factors that affect the gains. The self-tuning algorithms will provide the concepts to write a program to automatically tune the gains in the real system. The section concerning orbiter position calibration provides a comparison to previous work that had been performed for plant growth. It provided the conceptualized routines required to visually determine the orbiter position and orientation. Furthermore, it identified the types of information which are required to flow between the robot controller and the vision system.

Impact of tuning CO 2-philicity in polydimethylsiloxane-based membranes for carbon dioxide separation

DOE PAGES

Hong, Tao; Chatterjee, Sabornie; Mahurin, Shannon M.; ...

2017-02-22

Amidoxime-functionalized polydimethylsiloxane (AO-PDMSPNB) membranes with various amidoxime compositions were synthesized via ring-opening metathesis polymerization followed by post-polymerization modification. Compared to other previously reported PDMS-based membranes, the amidoxime-functionalized membranes show enhanced CO 2 permeability and CO 2/N 2 selectivity. The overall gas separation performance (CO 2 permeability 6800 Barrer; CO 2/N 2 selectivity 19) of the highest performing membrane exceeds the Robeson upper bound line, and the excellent permeability of the copolymer itself provides great potential for real world applications where huge volumes of gases are separated. This study details how tuning the CO 2-philicity within rubbery polymer matrices influences gasmore » transport properties. Key parameters for tuning gas transport properties are discussed, and the experimental results show good consistency with theoretical calculations. Finally, this study provides a roadmap to enhancing gas separation performance in rubbery polymers by tuning gas solubility selectivity.« less

Learning place cells, grid cells and invariances with excitatory and inhibitory plasticity

PubMed Central

2018-01-01

Neurons in the hippocampus and adjacent brain areas show a large diversity in their tuning to location and head direction, and the underlying circuit mechanisms are not yet resolved. In particular, it is unclear why certain cell types are selective to one spatial variable, but invariant to another. For example, place cells are typically invariant to head direction. We propose that all observed spatial tuning patterns – in both their selectivity and their invariance – arise from the same mechanism: Excitatory and inhibitory synaptic plasticity driven by the spatial tuning statistics of synaptic inputs. Using simulations and a mathematical analysis, we show that combined excitatory and inhibitory plasticity can lead to localized, grid-like or invariant activity. Combinations of different input statistics along different spatial dimensions reproduce all major spatial tuning patterns observed in rodents. Our proposed model is robust to changes in parameters, develops patterns on behavioral timescales and makes distinctive experimental predictions. PMID:29465399

Dynamically controlled electromagnetically induced transparency in terahertz graphene metamaterial for modulation and slow light applications

NASA Astrophysics Data System (ADS)

He, Xunjun; Yao, Yuan; Yang, Xingyu; Lu, Guangjun; Yang, Wenlong; Yang, Yuqiang; Wu, Fengmin; Yu, Zhigang; Jiang, Jiuxing

2018-03-01

By patterning two graphene resonators on a SiO2/Si substrate, a dynamically controlled electromagnetically induced transparency (EIT) in the terahertz graphene metamaterial was numerically studied through tuning the structural parameter and Fermi energy of graphene. The calculated surface current distributions demonstrate that the distinct EIT window in the graphene metamaterial results from the near-field coupling of two graphene resonators. Moreover, the EIT window can be actively controlled by tuning Fermi energy combined states of two resonators. When the Fermi energy combined state of two resonators changes from (0.21 and 0.16 eV) to (0.4 and 0.11 eV), the amplitude modulation depth of the EIT peak is 97.8% at 0.45 THz, and the corresponding enhanced factor of group delay with 6 times is obtained. This study offers an alternative tuning method to existing optical, thermal, and relative distance tuning, delivering a promising potential for designing active and miniaturized THz devices.

Model-independent particle accelerator tuning

DOE PAGES

Scheinker, Alexander; Pang, Xiaoying; Rybarcyk, Larry

2013-10-21

We present a new model-independent dynamic feedback technique, rotation rate tuning, for automatically and simultaneously tuning coupled components of uncertain, complex systems. The main advantages of the method are: 1) It has the ability to handle unknown, time-varying systems, 2) It gives known bounds on parameter update rates, 3) We give an analytic proof of its convergence and its stability, and 4) It has a simple digital implementation through a control system such as the Experimental Physics and Industrial Control System (EPICS). Because this technique is model independent it may be useful as a real-time, in-hardware, feedback-based optimization scheme formore » uncertain and time-varying systems. In particular, it is robust enough to handle uncertainty due to coupling, thermal cycling, misalignments, and manufacturing imperfections. As a result, it may be used as a fine-tuning supplement for existing accelerator tuning/control schemes. We present multi-particle simulation results demonstrating the scheme’s ability to simultaneously adaptively adjust the set points of twenty two quadrupole magnets and two RF buncher cavities in the Los Alamos Neutron Science Center Linear Accelerator’s transport region, while the beam properties and RF phase shift are continuously varying. The tuning is based only on beam current readings, without knowledge of particle dynamics. We also present an outline of how to implement this general scheme in software for optimization, and in hardware for feedback-based control/tuning, for a wide range of systems.« less

Type-II domains in ferroelectric gadolinium molybdate (in German)

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

Bohm, J.; Kuersten, H.D.

Etching (001)-faces of gadolinium molybdate (GMO) reveals new kinds of domains. They are created by a translation, that leaves the spontaneous polarization and the transition parameter invariant. The translation vector is a part of a lattice vector, similar to stacking faults. (auth)

Static internal performance of single expansion-ramp nozzles with thrust vectoring and reversing

NASA Technical Reports Server (NTRS)

Re, R. J.; Berrier, B. L.

1982-01-01

The effects of geometric design parameters on the internal performance of nonaxisymmetric single expansion-ramp nozzles were investigated at nozzle pressure ratios up to approximately 10. Forward-flight (cruise), vectored-thrust, and reversed-thrust nozzle operating modes were investigated.

Fine tuning of transmission features in nanoporous anodic alumina distributed Bragg reflectors

NASA Astrophysics Data System (ADS)

Lim, Siew Yee; Law, Cheryl Suwen; Santos, Abel

2018-01-01

This study introduces an innovative apodisation strategy to tune the filtering features of distributed Bragg reflectors based on nanoporous anodic alumina (NAA-DBRs). The effective medium of NAA-DBRs, which is modulated in a stepwise fashion by a pulse-like anodisation approach, is apodised following a logarithmic negative function to engineer the transmission features of NAA-DBRs. We investigate the effect of various apodisation parameters such as apodisation amplitude difference, anodisation period, current density offset and pore widening time, to tune and optimise the optical properties of NAA-DBRs in terms of central wavelength position, full width at half maximum and quality of photonic stop band. The transmission features of NAA-DBRs are shown to be fully controllable with precision across the spectral regions by means of the apodisation parameters. Our study demonstrates that an apodisation strategy can significantly narrow the width and enhance the quality of the characteristic photonic stop band of NAA-DBRs. This rationally designed anodisation approach based on the combination of apodisation and stepwise pulse anodisation enables the development of optical filters with tuneable filtering features to be integrated into optical technologies acting as essential photonic elements in devices such as optical sensors and biosensors.

Tuning the dielectric properties of metallic-nanoparticle/elastomer composites by strain.

PubMed

Gaiser, Patrick; Binz, Jonas; Gompf, Bruno; Berrier, Audrey; Dressel, Martin

2015-03-14

Tunable metal/dielectric composites are promising candidates for a large number of potential applications in electronics, sensor technologies and optical devices. Here we systematically investigate the dielectric properties of Ag-nanoparticles embedded in the highly flexible elastomer poly-dimethylsiloxane (PDMS). As tuning parameter we use uniaxial and biaxial strain applied to the composite. We demonstrate that both static variations of the filling factor and applied strain lead to the same behavior, i.e., the filling factor of the composite can be tuned by application of strain. In this way the effective static permittivity εeff of the composite can be varied over a very large range. Once the Poisson's ratio of the composite is known, the strain dependent dielectric constant can be accurately described by effective medium theory without any additional free fit parameter up to metal filling factors close to the percolation threshold. It is demonstrated that, starting above the percolation threshold in the metallic phase, applying strain provides the possibility to cross the percolation threshold into the insulating region. The change of regime from conductive phase down to insulating follows the description given by percolation theory and can be actively controlled.

Optical properties and magnetic flux-induced electronic band tuning of a T-graphene sheet and nanoribbon.

PubMed

Bandyopadhyay, Arka; Nandy, Atanu; Chakrabarti, Arunava; Jana, Debnarayan

2017-08-16

Tetragonal graphene (T-graphene) is a theoretically proposed dynamically stable, metallic allotrope of graphene. In this theoretical investigation, a tight binding (TB) model is used to unravel the metal to semiconductor transition of this 2D sheet under the influence of an external magnetic flux. In addition, the environment under which the sheet exposes an appreciable direct band gap of 1.41 ± 0.01 eV is examined. Similarly, the electronic band structure of the narrowest armchair T-graphene nanoribbon (NATGNR) also gets modified with different combinations of magnetic fluxes through the elementary rings. The band tuning parameters are critically identified for both systems. It is observed that the induced band gaps vary remarkably with the tuning parameters. We have also introduced an exact analytical approach to address the band structure of the NATGNR in the absence of any magnetic flux. Finally, the optical properties of the sheet and NATGNR are also critically analysed for both parallel and perpendicular polarizations with the help of density functional theory (DFT). Our study predicts that this material and its nanoribbons can be used in optoelectronic devices.

Modeling Enclosure Design in Above-Grade Walls

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

Lstiburek, J.; Ueno, K.; Musunuru, S.

2016-03-01

This report describes the modeling of typical wall assemblies that have performed well historically in various climate zones. The WUFI (Warme und Feuchte instationar) software (Version 5.3) model was used. A library of input data and results are provided. The provided information can be generalized for application to a broad population of houses, within the limits of existing experience. The WUFI software model was calibrated or tuned using wall assemblies with historically successful performance. The primary performance criteria or failure criteria establishing historic performance was moisture content of the exterior sheathing. The primary tuning parameters (simulation inputs) were airflow andmore » specifying appropriate material properties. Rational hygric loads were established based on experience - specifically rain wetting and interior moisture (RH levels). The tuning parameters were limited or bounded by published data or experience. The WUFI templates provided with this report supply useful information resources to new or less-experienced users. The files present various custom settings that will help avoid results that will require overly conservative enclosure assemblies. Overall, better material data, consistent initial assumptions, and consistent inputs among practitioners will improve the quality of WUFI modeling, and improve the level of sophistication in the field.« less

Viability of strongly coupled scenarios with a light Higgs-like boson.

PubMed

Pich, Antonio; Rosell, Ignasi; Sanz-Cillero, Juan José

2013-05-03

We present a one-loop calculation of the oblique S and T parameters within strongly coupled models of electroweak symmetry breaking with a light Higgs-like boson. We use a general effective Lagrangian, implementing the chiral symmetry breaking SU(2)(L) [Symbol: see text]SU(2)(R) → SU(2)(L+R) with Goldstone bosons, gauge bosons, the Higgs-like scalar, and one multiplet of vector and axial-vector massive resonance states. Using a dispersive representation and imposing a proper ultraviolet behavior, we obtain S and T at the next-to-leading order in terms of a few resonance parameters. The experimentally allowed range forces the vector and axial-vector states to be heavy, with masses above the TeV scale, and suggests that the Higgs-like scalar should have a WW coupling close to the standard model one. Our conclusions are generic and apply to more specific scenarios such as the minimal SO(5)/SO(4) composite Higgs model.

Magnetic field vector and electron density diagnostics from linear polarization measurements in 14 solar prominences

NASA Technical Reports Server (NTRS)

Bommier, V.

1986-01-01

The Hanle effect is the modification of the linear polarization parameters of a spectral line due to the effect of the magnetic field. It has been successfully applied to the magnetic field vector diagnostic in solar prominences. The magnetic field vector is determined by comparing the measured polarization to the polarization computed, taking into account all the polarizing and depolarizing processes in line formation and the depolarizing effect of the magnetic field. The method was applied to simultaneous polarization measurements in the Helium D3 line and in the hydrogen beta line in 14 prominences. Four polarization parameters are measured, which lead to the determination of the three coordinates of the magnetic field vector and the electron density, owing to the sensitivity of the hydrogen beta line to the non-negligible effect of depolarizing collisions with electrons and protons of the medium. A mean value of 1.3 x 10 to the 10th power cu. cm. is derived in 14 prominences.

Antigen expression level threshold tunes the fate of CD8 T cells during primary hepatic immune responses.

PubMed

Tay, Szun Szun; Wong, Yik Chun; McDonald, David M; Wood, Nicole A W; Roediger, Ben; Sierro, Frederic; Mcguffog, Claire; Alexander, Ian E; Bishop, G Alex; Gamble, Jennifer R; Weninger, Wolfgang; McCaughan, Geoffrey W; Bertolino, Patrick; Bowen, David G

2014-06-24

CD8 T-cell responses to liver-expressed antigens range from deletional tolerance to full effector differentiation resulting in overt hepatotoxicity. The reasons for these heterogeneous outcomes are not well understood. To identify factors that govern the fate of CD8 T cells activated by hepatocyte-expressed antigen, we exploited recombinant adenoassociated viral vectors that enabled us to vary potential parameters determining these outcomes in vivo. Our findings reveal a threshold of antigen expression within the liver as the dominant factor determining T-cell fate, irrespective of T-cell receptor affinity or antigen cross-presentation. Thus, when a low percentage of hepatocytes expressed cognate antigen, high-affinity T cells developed and maintained effector function, whereas, at a high percentage, they became functionally exhausted and silenced. Exhaustion was not irreversibly determined by initial activation, but was maintained by high intrahepatic antigen load during the early phase of the response; cytolytic function was restored when T cells primed under high antigen load conditions were transferred into an environment of low-level antigen expression. Our study reveals a hierarchy of factors dictating the fate of CD8 T cells during hepatic immune responses, and provides an explanation for the different immune outcomes observed in a variety of immune-mediated liver pathologic conditions.

GI-SVM: A sensitive method for predicting genomic islands based on unannotated sequence of a single genome.

PubMed

Lu, Bingxin; Leong, Hon Wai

2016-02-01

Genomic islands (GIs) are clusters of functionally related genes acquired by lateral genetic transfer (LGT), and they are present in many bacterial genomes. GIs are extremely important for bacterial research, because they not only promote genome evolution but also contain genes that enhance adaption and enable antibiotic resistance. Many methods have been proposed to predict GI. But most of them rely on either annotations or comparisons with other closely related genomes. Hence these methods cannot be easily applied to new genomes. As the number of newly sequenced bacterial genomes rapidly increases, there is a need for methods to detect GI based solely on sequences of a single genome. In this paper, we propose a novel method, GI-SVM, to predict GIs given only the unannotated genome sequence. GI-SVM is based on one-class support vector machine (SVM), utilizing composition bias in terms of k-mer content. From our evaluations on three real genomes, GI-SVM can achieve higher recall compared with current methods, without much loss of precision. Besides, GI-SVM allows flexible parameter tuning to get optimal results for each genome. In short, GI-SVM provides a more sensitive method for researchers interested in a first-pass detection of GI in newly sequenced genomes.

Predominance of Movement Speed Over Direction in Neuronal Population Signals of Motor Cortex: Intracranial EEG Data and A Simple Explanatory Model

PubMed Central

Hammer, Jiří; Pistohl, Tobias; Fischer, Jörg; Kršek, Pavel; Tomášek, Martin; Marusič, Petr; Schulze-Bonhage, Andreas; Aertsen, Ad; Ball, Tonio

2016-01-01

How neuronal activity of motor cortex is related to movement is a central topic in motor neuroscience. Motor-cortical single neurons are more closely related to hand movement velocity than speed, that is, the magnitude of the (directional) velocity vector. Recently, there is also increasing interest in the representation of movement parameters in neuronal population activity, such as reflected in the intracranial EEG (iEEG). We show that in iEEG, contrasting to what has been previously found on the single neuron level, speed predominates over velocity. The predominant speed representation was present in nearly all iEEG signal features, up to the 600–1000 Hz range. Using a model of motor-cortical signals arising from neuronal populations with realistic single neuron tuning properties, we show how this reversal can be understood as a consequence of increasing population size. Our findings demonstrate that the information profile in large population signals may systematically differ from the single neuron level, a principle that may be helpful in the interpretation of neuronal population signals in general, including, for example, EEG and functional magnetic resonance imaging. Taking advantage of the robust speed population signal may help in developing brain–machine interfaces exploiting population signals. PMID:26984895

Evaluation of image features and classification methods for Barrett's cancer detection using VLE imaging

NASA Astrophysics Data System (ADS)

Klomp, Sander; van der Sommen, Fons; Swager, Anne-Fré; Zinger, Svitlana; Schoon, Erik J.; Curvers, Wouter L.; Bergman, Jacques J.; de With, Peter H. N.

2017-03-01

Volumetric Laser Endomicroscopy (VLE) is a promising technique for the detection of early neoplasia in Barrett's Esophagus (BE). VLE generates hundreds of high resolution, grayscale, cross-sectional images of the esophagus. However, at present, classifying these images is a time consuming and cumbersome effort performed by an expert using a clinical prediction model. This paper explores the feasibility of using computer vision techniques to accurately predict the presence of dysplastic tissue in VLE BE images. Our contribution is threefold. First, a benchmarking is performed for widely applied machine learning techniques and feature extraction methods. Second, three new features based on the clinical detection model are proposed, having superior classification accuracy and speed, compared to earlier work. Third, we evaluate automated parameter tuning by applying simple grid search and feature selection methods. The results are evaluated on a clinically validated dataset of 30 dysplastic and 30 non-dysplastic VLE images. Optimal classification accuracy is obtained by applying a support vector machine and using our modified Haralick features and optimal image cropping, obtaining an area under the receiver operating characteristic of 0.95 compared to the clinical prediction model at 0.81. Optimal execution time is achieved using a proposed mean and median feature, which is extracted at least factor 2.5 faster than alternative features with comparable performance.

Differentially Private Empirical Risk Minimization

PubMed Central

Chaudhuri, Kamalika; Monteleoni, Claire; Sarwate, Anand D.

2011-01-01

Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the ε-differential privacy definition due to Dwork et al. (2006). First we apply the output perturbation ideas of Dwork et al. (2006), to ERM classification. Then we propose a new method, objective perturbation, for privacy-preserving machine learning algorithm design. This method entails perturbing the objective function before optimizing over classifiers. If the loss and regularizer satisfy certain convexity and differentiability criteria, we prove theoretical results showing that our algorithms preserve privacy, and provide generalization bounds for linear and nonlinear kernels. We further present a privacy-preserving technique for tuning the parameters in general machine learning algorithms, thereby providing end-to-end privacy guarantees for the training process. We apply these results to produce privacy-preserving analogues of regularized logistic regression and support vector machines. We obtain encouraging results from evaluating their performance on real demographic and benchmark data sets. Our results show that both theoretically and empirically, objective perturbation is superior to the previous state-of-the-art, output perturbation, in managing the inherent tradeoff between privacy and learning performance. PMID:21892342

Impeded Dark Matter

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

Kopp, Joachim; Liu, Jia; Slatyer, Tracy

Here, we consider dark matter models in which the mass splitting between the dark matter particles and their annihilation products is tiny. Compared to the previously proposed Forbidden Dark Matter scenario, the mass splittings we consider are much smaller, and are allowed to be either positive or negative. To emphasize this modification, we dub our scenario \\Impeded Dark Matter". We also demonstrate that Impeded Dark Matter can be easily realized without requiring tuning of model parameters. For negative mass splitting, we demonstrate that the annihilation cross-section for Impeded Dark Matter depends linearly on the dark matter velocity or may evenmore » be kinematically forbidden, making this scenario almost insensitive to constraints from the cosmic microwave background and from observations of dwarf galaxies. Accordingly, it may be possible for Impeded Dark Matter to yield observable signals in clusters or the Galactic center, with no corresponding signal in dwarfs. Furthermore, for positive mass splitting, we show that the annihilation cross-section is suppressed by the small mass splitting, which helps light dark matter to survive increasingly stringent constraints from indirect searches. As specific realizations for Impeded Dark Matter, we introduce a model of vector dark matter from a hidden SU(2) sector, and a composite dark matter scenario based on a QCD-like dark sector.« less

Impeded Dark Matter

DOE PAGES

Kopp, Joachim; Liu, Jia; Slatyer, Tracy; ...

2016-12-12

Here, we consider dark matter models in which the mass splitting between the dark matter particles and their annihilation products is tiny. Compared to the previously proposed Forbidden Dark Matter scenario, the mass splittings we consider are much smaller, and are allowed to be either positive or negative. To emphasize this modification, we dub our scenario \\Impeded Dark Matter". We also demonstrate that Impeded Dark Matter can be easily realized without requiring tuning of model parameters. For negative mass splitting, we demonstrate that the annihilation cross-section for Impeded Dark Matter depends linearly on the dark matter velocity or may evenmore » be kinematically forbidden, making this scenario almost insensitive to constraints from the cosmic microwave background and from observations of dwarf galaxies. Accordingly, it may be possible for Impeded Dark Matter to yield observable signals in clusters or the Galactic center, with no corresponding signal in dwarfs. Furthermore, for positive mass splitting, we show that the annihilation cross-section is suppressed by the small mass splitting, which helps light dark matter to survive increasingly stringent constraints from indirect searches. As specific realizations for Impeded Dark Matter, we introduce a model of vector dark matter from a hidden SU(2) sector, and a composite dark matter scenario based on a QCD-like dark sector.« less

Gradient Evolution-based Support Vector Machine Algorithm for Classification

NASA Astrophysics Data System (ADS)

Zulvia, Ferani E.; Kuo, R. J.

2018-03-01

This paper proposes a classification algorithm based on a support vector machine (SVM) and gradient evolution (GE) algorithms. SVM algorithm has been widely used in classification. However, its result is significantly influenced by the parameters. Therefore, this paper aims to propose an improvement of SVM algorithm which can find the best SVMs’ parameters automatically. The proposed algorithm employs a GE algorithm to automatically determine the SVMs’ parameters. The GE algorithm takes a role as a global optimizer in finding the best parameter which will be used by SVM algorithm. The proposed GE-SVM algorithm is verified using some benchmark datasets and compared with other metaheuristic-based SVM algorithms. The experimental results show that the proposed GE-SVM algorithm obtains better results than other algorithms tested in this paper.

Generation of arbitrary vector fields based on a pair of orthogonal elliptically polarized base vectors.

PubMed

Xu, Danfeng; Gu, Bing; Rui, Guanghao; Zhan, Qiwen; Cui, Yiping

2016-02-22

We present an arbitrary vector field with hybrid polarization based on the combination of a pair of orthogonal elliptically polarized base vectors on the Poincaré sphere. It is shown that the created vector field is only dependent on the latitude angle 2χ but is independent on the longitude angle 2ψ on the Poincaré sphere. By adjusting the latitude angle 2χ, which is related to two identical waveplates in a common path interferometric arrangement, one could obtain arbitrary type of vector fields. Experimentally, we demonstrate the generation of such kind of vector fields and confirm the distribution of state of polarization by the measurement of Stokes parameters. Besides, we investigate the tight focusing properties of these vector fields. It is found that the additional degree of freedom 2χ provided by arbitrary vector field with hybrid polarization allows one to control the spatial structure of polarization and to engineer the focusing field.

A low cost implementation of multi-parameter patient monitor using intersection kernel support vector machine classifier

NASA Astrophysics Data System (ADS)

Mohan, Dhanya; Kumar, C. Santhosh

2016-03-01

Predicting the physiological condition (normal/abnormal) of a patient is highly desirable to enhance the quality of health care. Multi-parameter patient monitors (MPMs) using heart rate, arterial blood pressure, respiration rate and oxygen saturation (S pO2) as input parameters were developed to monitor the condition of patients, with minimum human resource utilization. The Support vector machine (SVM), an advanced machine learning approach popularly used for classification and regression is used for the realization of MPMs. For making MPMs cost effective, we experiment on the hardware implementation of the MPM using support vector machine classifier. The training of the system is done using the matlab environment and the detection of the alarm/noalarm condition is implemented in hardware. We used different kernels for SVM classification and note that the best performance was obtained using intersection kernel SVM (IKSVM). The intersection kernel support vector machine classifier MPM has outperformed the best known MPM using radial basis function kernel by an absoute improvement of 2.74% in accuracy, 1.86% in sensitivity and 3.01% in specificity. The hardware model was developed based on the improved performance system using Verilog Hardware Description Language and was implemented on Altera cyclone-II development board.

Patient-specific parameter estimation in single-ventricle lumped circulation models under uncertainty

PubMed Central

Schiavazzi, Daniele E.; Baretta, Alessia; Pennati, Giancarlo; Hsia, Tain-Yen; Marsden, Alison L.

2017-01-01

Summary Computational models of cardiovascular physiology can inform clinical decision-making, providing a physically consistent framework to assess vascular pressures and flow distributions, and aiding in treatment planning. In particular, lumped parameter network (LPN) models that make an analogy to electrical circuits offer a fast and surprisingly realistic method to reproduce the circulatory physiology. The complexity of LPN models can vary significantly to account, for example, for cardiac and valve function, respiration, autoregulation, and time-dependent hemodynamics. More complex models provide insight into detailed physiological mechanisms, but their utility is maximized if one can quickly identify patient specific parameters. The clinical utility of LPN models with many parameters will be greatly enhanced by automated parameter identification, particularly if parameter tuning can match non-invasively obtained clinical data. We present a framework for automated tuning of 0D lumped model parameters to match clinical data. We demonstrate the utility of this framework through application to single ventricle pediatric patients with Norwood physiology. Through a combination of local identifiability, Bayesian estimation and maximum a posteriori simplex optimization, we show the ability to automatically determine physiologically consistent point estimates of the parameters and to quantify uncertainty induced by errors and assumptions in the collected clinical data. We show that multi-level estimation, that is, updating the parameter prior information through sub-model analysis, can lead to a significant reduction in the parameter marginal posterior variance. We first consider virtual patient conditions, with clinical targets generated through model solutions, and second application to a cohort of four single-ventricle patients with Norwood physiology. PMID:27155892

Impact of Climate Variability on Maize Production in Pakistan using Remote Sensing and Machine Learning

NASA Astrophysics Data System (ADS)

Richetti, J.; Ahmad, I.; Aristizabal, F.; Judge, J.

2017-12-01

Determining maize agricultural production under climate variability is valuable to policy makers in Pakistan since maize is the third most produced crop by area after wheat and rice. This study aims to predict the maize production under climate variability. Two-hundred ground truth points of both maize and non-maize land covers were collected from the Faisalabad district during the growing seasons of 2015 and 2016. Landsat-8 images taken in second week of May which correspond spatially and temporally to the local, peak growing season for maize were gathered. For classifying the region training data was constructed for a variety of machine learning algorithms by sampling the second, third, and fourth bands of the Landsat-8 imagery at these reference locations. Cross validation was used for parameter tuning as well as estimating the generalized performances. All the classifiers resulted in overall accuracies of greater than 90% for both years and a support vector machine with a radial basis kernel recorded the maximum accuracy of 97%. The tuned models were used to determine the spatial distribution of maize fields for both growing seasons in the Faisalabad district using parallel processing to improve computation time. The overall classified maize growing area represented 12% difference than that reported by the Crop Reporting Service (CRS) of Punjab Pakistan for both 2015 and 2016. For the agricultural production normalized difference vegetation index from Landsat-8 and climate indicators from ground stations will be used as inputs in a variety of machine learning regression algorithms. The expected results will be compared to actual yield from 64 commercial farms. To verify the impact of climate variability in the maize agricultural production historical climate data from previous 30 years will be used in the developed model to asses the impact of climate variability on the maize production.