Bayesian inversion analysis of nonlinear dynamics in surface heterogeneous reactions.

PubMed

Omori, Toshiaki; Kuwatani, Tatsu; Okamoto, Atsushi; Hukushima, Koji

2016-09-01

It is essential to extract nonlinear dynamics from time-series data as an inverse problem in natural sciences. We propose a Bayesian statistical framework for extracting nonlinear dynamics of surface heterogeneous reactions from sparse and noisy observable data. Surface heterogeneous reactions are chemical reactions with conjugation of multiple phases, and they have the intrinsic nonlinearity of their dynamics caused by the effect of surface-area between different phases. We adapt a belief propagation method and an expectation-maximization (EM) algorithm to partial observation problem, in order to simultaneously estimate the time course of hidden variables and the kinetic parameters underlying dynamics. The proposed belief propagation method is performed by using sequential Monte Carlo algorithm in order to estimate nonlinear dynamical system. Using our proposed method, we show that the rate constants of dissolution and precipitation reactions, which are typical examples of surface heterogeneous reactions, as well as the temporal changes of solid reactants and products, were successfully estimated only from the observable temporal changes in the concentration of the dissolved intermediate product.

Normalized value coding explains dynamic adaptation in the human valuation process.

PubMed

Khaw, Mel W; Glimcher, Paul W; Louie, Kenway

2017-11-28

The notion of subjective value is central to choice theories in ecology, economics, and psychology, serving as an integrated decision variable by which options are compared. Subjective value is often assumed to be an absolute quantity, determined in a static manner by the properties of an individual option. Recent neurobiological studies, however, have shown that neural value coding dynamically adapts to the statistics of the recent reward environment, introducing an intrinsic temporal context dependence into the neural representation of value. Whether valuation exhibits this kind of dynamic adaptation at the behavioral level is unknown. Here, we show that the valuation process in human subjects adapts to the history of previous values, with current valuations varying inversely with the average value of recently observed items. The dynamics of this adaptive valuation are captured by divisive normalization, linking these temporal context effects to spatial context effects in decision making as well as spatial and temporal context effects in perception. These findings suggest that adaptation is a universal feature of neural information processing and offer a unifying explanation for contextual phenomena in fields ranging from visual psychophysics to economic choice.

Dynamic inverse models in human-cyber-physical systems

NASA Astrophysics Data System (ADS)

Robinson, Ryan M.; Scobee, Dexter R. R.; Burden, Samuel A.; Sastry, S. Shankar

2016-05-01

Human interaction with the physical world is increasingly mediated by automation. This interaction is characterized by dynamic coupling between robotic (i.e. cyber) and neuromechanical (i.e. human) decision-making agents. Guaranteeing performance of such human-cyber-physical systems will require predictive mathematical models of this dynamic coupling. Toward this end, we propose a rapprochement between robotics and neuromechanics premised on the existence of internal forward and inverse models in the human agent. We hypothesize that, in tele-robotic applications of interest, a human operator learns to invert automation dynamics, directly translating from desired task to required control input. By formulating the model inversion problem in the context of a tracking task for a nonlinear control system in control-a_ne form, we derive criteria for exponential tracking and show that the resulting dynamic inverse model generally renders a portion of the physical system state (i.e., the internal dynamics) unobservable from the human operator's perspective. Under stability conditions, we show that the human can achieve exponential tracking without formulating an estimate of the system's state so long as they possess an accurate model of the system's dynamics. These theoretical results are illustrated using a planar quadrotor example. We then demonstrate that the automation can intervene to improve performance of the tracking task by solving an optimal control problem. Performance is guaranteed to improve under the assumption that the human learns and inverts the dynamic model of the altered system. We conclude with a discussion of practical limitations that may hinder exact dynamic model inversion.

Adaptive dynamical networks

NASA Astrophysics Data System (ADS)

Maslennikov, O. V.; Nekorkin, V. I.

2017-10-01

Dynamical networks are systems of active elements (nodes) interacting with each other through links. Examples are power grids, neural structures, coupled chemical oscillators, and communications networks, all of which are characterized by a networked structure and intrinsic dynamics of their interacting components. If the coupling structure of a dynamical network can change over time due to nodal dynamics, then such a system is called an adaptive dynamical network. The term ‘adaptive’ implies that the coupling topology can be rewired; the term ‘dynamical’ implies the presence of internal node and link dynamics. The main results of research on adaptive dynamical networks are reviewed. Key notions and definitions of the theory of complex networks are given, and major collective effects that emerge in adaptive dynamical networks are described.

Effects of adaptive refinement on the inverse EEG solution

NASA Astrophysics Data System (ADS)

Weinstein, David M.; Johnson, Christopher R.; Schmidt, John A.

1995-10-01

One of the fundamental problems in electroencephalography can be characterized by an inverse problem. Given a subset of electrostatic potentials measured on the surface of the scalp and the geometry and conductivity properties within the head, calculate the current vectors and potential fields within the cerebrum. Mathematically the generalized EEG problem can be stated as solving Poisson's equation of electrical conduction for the primary current sources. The resulting problem is mathematically ill-posed i.e., the solution does not depend continuously on the data, such that small errors in the measurement of the voltages on the scalp can yield unbounded errors in the solution, and, for the general treatment of a solution of Poisson's equation, the solution is non-unique. However, if accurate solutions the general treatment of a solution of Poisson's equation, the solution is non-unique. However, if accurate solutions to such problems could be obtained, neurologists would gain noninvasive accesss to patient-specific cortical activity. Access to such data would ultimately increase the number of patients who could be effectively treated for pathological cortical conditions such as temporal lobe epilepsy. In this paper, we present the effects of spatial adaptive refinement on the inverse EEG problem and show that the use of adaptive methods allow for significantly better estimates of electric and potential fileds within the brain through an inverse procedure. To test these methods, we have constructed several finite element head models from magneteic resonance images of a patient. The finite element meshes ranged in size from 2724 nodes and 12,812 elements to 5224 nodes and 29,135 tetrahedral elements, depending on the level of discretization. We show that an adaptive meshing algorithm minimizes the error in the forward problem due to spatial discretization and thus increases the accuracy of the inverse solution.

Dynamic Inversion based Control of a Docking Mechanism

NASA Technical Reports Server (NTRS)

Kulkarni, Nilesh V.; Ippolito, Corey; Krishnakumar, Kalmanje

2006-01-01

The problem of position and attitude control of the Stewart platform based docking mechanism is considered motivated by its future application in space missions requiring the autonomous docking capability. The control design is initiated based on the framework of the intelligent flight control architecture being developed at NASA Ames Research Center. In this paper, the baseline position and attitude control system is designed using dynamic inversion with proportional-integral augmentation. The inverse dynamics uses a Newton-Euler formulation that includes the platform dynamics, the dynamics of the individual legs along with viscous friction in the joints. Simulation results are presented using forward dynamics simulated by a commercial physics engine that builds the system as individual elements with appropriate joints and uses constrained numerical integration,

Chromosomal Inversions, Natural Selection and Adaptation in the Malaria Vector Anopheles funestus

PubMed Central

Ayala, Diego; Fontaine, Michael C.; Cohuet, Anna; Fontenille, Didier; Vitalis, Renaud; Simard, Frédéric

2011-01-01

Chromosomal polymorphisms, such as inversions, are presumably involved in the rapid adaptation of populations to local environmental conditions. Reduced recombination between alternative arrangements in heterozygotes may protect sets of locally adapted genes, promoting ecological divergence and potentially leading to reproductive isolation and speciation. Through a comparative analysis of chromosomal inversions and microsatellite marker polymorphisms, we hereby present biological evidence that strengthens this view in the mosquito Anopheles funestus s.s, one of the most important and widespread malaria vectors in Africa. Specimens were collected across a wide range of geographical, ecological, and climatic conditions in Cameroon. We observed a sharp contrast between population structure measured at neutral microsatellite markers and at chromosomal inversions. Microsatellite data detected only a weak signal for population structuring among geographical zones (FST < 0.013, P < 0.01). By contrast, strong differentiation among ecological zones was revealed by chromosomal inversions (FST > 0.190, P < 0.01). Using standardized estimates of FST, we show that inversions behave at odds with neutral expectations strongly suggesting a role of environmental selection in shaping their distribution. We further demonstrate through canonical correspondence analysis that heterogeneity in eco-geographical variables measured at specimen sampling sites explained 89% of chromosomal variance in A. funestus. These results are in agreement with a role of chromosomal inversions in ecotypic adaptation in this species. We argue that this widespread mosquito represents an interesting model system for the study of chromosomal speciation mechanisms and should provide ample opportunity for comparative studies on the evolution of reproductive isolation and speciation in major human malaria vectors. PMID:20837604

Inversion for Refractivity Parameters Using a Dynamic Adaptive Cuckoo Search with Crossover Operator Algorithm

PubMed Central

Zhang, Zhihua; Sheng, Zheng; Shi, Hanqing; Fan, Zhiqiang

2016-01-01

Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter. PMID:27212938

An adaptive inverse kinematics algorithm for robot manipulators

NASA Technical Reports Server (NTRS)

Colbaugh, R.; Glass, K.; Seraji, H.

1990-01-01

An adaptive algorithm for solving the inverse kinematics problem for robot manipulators is presented. The algorithm is derived using model reference adaptive control (MRAC) theory and is computationally efficient for online applications. The scheme requires no a priori knowledge of the kinematics of the robot if Cartesian end-effector sensing is available, and it requires knowledge of only the forward kinematics if joint position sensing is used. Computer simulation results are given for the redundant seven-DOF robotics research arm, demonstrating that the proposed algorithm yields accurate joint angle trajectories for a given end-effector position/orientation trajectory.

Adaptive multi-step Full Waveform Inversion based on Waveform Mode Decomposition

NASA Astrophysics Data System (ADS)

Hu, Yong; Han, Liguo; Xu, Zhuo; Zhang, Fengjiao; Zeng, Jingwen

2017-04-01

Full Waveform Inversion (FWI) can be used to build high resolution velocity models, but there are still many challenges in seismic field data processing. The most difficult problem is about how to recover long-wavelength components of subsurface velocity models when seismic data is lacking of low frequency information and without long-offsets. To solve this problem, we propose to use Waveform Mode Decomposition (WMD) method to reconstruct low frequency information for FWI to obtain a smooth model, so that the initial model dependence of FWI can be reduced. In this paper, we use adjoint-state method to calculate the gradient for Waveform Mode Decomposition Full Waveform Inversion (WMDFWI). Through the illustrative numerical examples, we proved that the low frequency which is reconstructed by WMD method is very reliable. WMDFWI in combination with the adaptive multi-step inversion strategy can obtain more faithful and accurate final inversion results. Numerical examples show that even if the initial velocity model is far from the true model and lacking of low frequency information, we still can obtain good inversion results with WMD method. From numerical examples of anti-noise test, we see that the adaptive multi-step inversion strategy for WMDFWI has strong ability to resist Gaussian noise. WMD method is promising to be able to implement for the land seismic FWI, because it can reconstruct the low frequency information, lower the dominant frequency in the adjoint source, and has a strong ability to resist noise.

Adaptive divergence in the monkey flower Mimulus guttatus is maintained by a chromosomal inversion

PubMed Central

Twyford, Alex D.; Friedman, Jannice

2015-01-01

Organisms exhibit an incredible diversity of life history strategies as adaptive responses to environmental variation. The establishment of novel life history strategies involves multilocus polymorphisms, which will be challenging to establish in the face of gene flow and recombination. Theory predicts that adaptive allelic combinations may be maintained and spread if they occur in genomic regions of reduced recombination, such as chromosomal inversion polymorphisms, yet empirical support for this prediction is lacking. Here, we use genomic data to investigate the evolution of divergent adaptive ecotypes of the yellow monkey flower Mimulus guttatus. We show that a large chromosomal inversion polymorphism is the major region of divergence between geographically widespread annual and perennial ecotypes. In contrast, ∼40,000 single nucleotide polymorphisms in collinear regions of the genome show no signal of life history, revealing genomic patterns of diversity have been shaped by localized homogenizing gene flow and large‐scale Pleistocene range expansion. Our results provide evidence for an inversion capturing and protecting loci involved in local adaptation, while also explaining how adaptive divergence can occur with gene flow. PMID:25879251

The adaptive parallel UKF inversion method for the shape of space objects based on the ground-based photometric data

NASA Astrophysics Data System (ADS)

Du, Xiaoping; Wang, Yang; Liu, Hao

2018-04-01

The space object in highly elliptical orbit is always presented as an image point on the ground-based imaging equipment so that it is difficult to resolve and identify the shape and attitude directly. In this paper a novel algorithm is presented for the estimation of spacecraft shape. The apparent magnitude model suitable for the inversion of object information such as shape and attitude is established based on the analysis of photometric characteristics. A parallel adaptive shape inversion algorithm based on UKF was designed after the achievement of dynamic equation of the nonlinear, Gaussian system involved with the influence of various dragging forces. The result of a simulation study demonstrate the viability and robustness of the new filter and its fast convergence rate. It realizes the inversion of combination shape with high accuracy, especially for the bus of cube and cylinder. Even though with sparse photometric data, it still can maintain a higher success rate of inversion.

Predicting Adaptive Behavior in the Environment from Central Nervous System Dynamics

PubMed Central

Proekt, Alex; Wong, Jane; Zhurov, Yuriy; Kozlova, Nataliya; Weiss, Klaudiusz R.; Brezina, Vladimir

2008-01-01

To generate adaptive behavior, the nervous system is coupled to the environment. The coupling constrains the dynamical properties that the nervous system and the environment must have relative to each other if adaptive behavior is to be produced. In previous computational studies, such constraints have been used to evolve controllers or artificial agents to perform a behavioral task in a given environment. Often, however, we already know the controller, the real nervous system, and its dynamics. Here we propose that the constraints can also be used to solve the inverse problem—to predict from the dynamics of the nervous system the environment to which they are adapted, and so reconstruct the production of the adaptive behavior by the entire coupled system. We illustrate how this can be done in the feeding system of the sea slug Aplysia. At the core of this system is a central pattern generator (CPG) that, with dynamics on both fast and slow time scales, integrates incoming sensory stimuli to produce ingestive and egestive motor programs. We run models embodying these CPG dynamics—in effect, autonomous Aplysia agents—in various feeding environments and analyze the performance of the entire system in a realistic feeding task. We find that the dynamics of the system are tuned for optimal performance in a narrow range of environments that correspond well to those that Aplysia encounter in the wild. In these environments, the slow CPG dynamics implement efficient ingestion of edible seaweed strips with minimal sensory information about them. The fast dynamics then implement a switch to a different behavioral mode in which the system ignores the sensory information completely and follows an internal “goal,” emergent from the dynamics, to egest again a strip that proves to be inedible. Key predictions of this reconstruction are confirmed in real feeding animals. PMID:18989362

Modified Dynamic Inversion to Control Large Flexible Aircraft: What's Going On?

NASA Technical Reports Server (NTRS)

Gregory, Irene M.

1999-01-01

High performance aircraft of the future will be designed lighter, more maneuverable, and operate over an ever expanding flight envelope. One of the largest differences from the flight control perspective between current and future advanced aircraft is elasticity. Over the last decade, dynamic inversion methodology has gained considerable popularity in application to highly maneuverable fighter aircraft, which were treated as rigid vehicles. This paper explores dynamic inversion application to an advanced highly flexible aircraft. An initial application has been made to a large flexible supersonic aircraft. In the course of controller design for this advanced vehicle, modifications were made to the standard dynamic inversion methodology. The results of this application were deemed rather promising. An analytical study has been undertaken to better understand the nature of the made modifications and to determine its general applicability. This paper presents the results of this initial analytical look at the modifications to dynamic inversion to control large flexible aircraft.

Preliminary assessment of the robustness of dynamic inversion based flight control laws

NASA Technical Reports Server (NTRS)

Snell, S. A.

1992-01-01

Dynamic-inversion-based flight control laws present an attractive alternative to conventional gain-scheduled designs for high angle-of-attack maneuvering, where nonlinearities dominate the dynamics. Dynamic inversion is easily applied to the aircraft dynamics requiring a knowledge of the nonlinear equations of motion alone, rather than an extensive set of linearizations. However, the robustness properties of the dynamic inversion are questionable especially when considering the uncertainties involved with the aerodynamic database during post-stall flight. This paper presents a simple analysis and some preliminary results of simulations with a perturbed database. It is shown that incorporating integrators into the control loops helps to improve the performance in the presence of these perturbations.

Application of Adaptive Autopilot Designs for an Unmanned Aerial Vehicle

NASA Technical Reports Server (NTRS)

Shin, Yoonghyun; Calise, Anthony J.; Motter, Mark A.

2005-01-01

This paper summarizes the application of two adaptive approaches to autopilot design, and presents an evaluation and comparison of the two approaches in simulation for an unmanned aerial vehicle. One approach employs two-stage dynamic inversion and the other employs feedback dynamic inversions based on a command augmentation system. Both are augmented with neural network based adaptive elements. The approaches permit adaptation to both parametric uncertainty and unmodeled dynamics, and incorporate a method that permits adaptation during periods of control saturation. Simulation results for an FQM-117B radio controlled miniature aerial vehicle are presented to illustrate the performance of the neural network based adaptation.

Adaptive divergence in the monkey flower Mimulus guttatus is maintained by a chromosomal inversion.

PubMed

Twyford, Alex D; Friedman, Jannice

2015-06-01

Organisms exhibit an incredible diversity of life history strategies as adaptive responses to environmental variation. The establishment of novel life history strategies involves multilocus polymorphisms, which will be challenging to establish in the face of gene flow and recombination. Theory predicts that adaptive allelic combinations may be maintained and spread if they occur in genomic regions of reduced recombination, such as chromosomal inversion polymorphisms, yet empirical support for this prediction is lacking. Here, we use genomic data to investigate the evolution of divergent adaptive ecotypes of the yellow monkey flower Mimulus guttatus. We show that a large chromosomal inversion polymorphism is the major region of divergence between geographically widespread annual and perennial ecotypes. In contrast, ∼40,000 single nucleotide polymorphisms in collinear regions of the genome show no signal of life history, revealing genomic patterns of diversity have been shaped by localized homogenizing gene flow and large-scale Pleistocene range expansion. Our results provide evidence for an inversion capturing and protecting loci involved in local adaptation, while also explaining how adaptive divergence can occur with gene flow. © 2015 The Author(s). Evolution published by Wiley Periodicals, Inc. on behalf of The Society for the Study of Evolution.

SMI adaptive antenna arrays for weak interfering signals. [Sample Matrix Inversion

NASA Technical Reports Server (NTRS)

Gupta, Inder J.

1986-01-01

The performance of adaptive antenna arrays in the presence of weak interfering signals (below thermal noise) is studied. It is shown that a conventional adaptive antenna array sample matrix inversion (SMI) algorithm is unable to suppress such interfering signals. To overcome this problem, the SMI algorithm is modified. In the modified algorithm, the covariance matrix is redefined such that the effect of thermal noise on the weights of adaptive arrays is reduced. Thus, the weights are dictated by relatively weak signals. It is shown that the modified algorithm provides the desired interference protection.

An Innovations-Based Noise Cancelling Technique on Inverse Kepstrum Whitening Filter and Adaptive FIR Filter in Beamforming Structure

PubMed Central

Jeong, Jinsoo

2011-01-01

This paper presents an acoustic noise cancelling technique using an inverse kepstrum system as an innovations-based whitening application for an adaptive finite impulse response (FIR) filter in beamforming structure. The inverse kepstrum method uses an innovations-whitened form from one acoustic path transfer function between a reference microphone sensor and a noise source so that the rear-end reference signal will then be a whitened sequence to a cascaded adaptive FIR filter in the beamforming structure. By using an inverse kepstrum filter as a whitening filter with the use of a delay filter, the cascaded adaptive FIR filter estimates only the numerator of the polynomial part from the ratio of overall combined transfer functions. The test results have shown that the adaptive FIR filter is more effective in beamforming structure than an adaptive noise cancelling (ANC) structure in terms of signal distortion in the desired signal and noise reduction in noise with nonminimum phase components. In addition, the inverse kepstrum method shows almost the same convergence level in estimate of noise statistics with the use of a smaller amount of adaptive FIR filter weights than the kepstrum method, hence it could provide better computational simplicity in processing. Furthermore, the rear-end inverse kepstrum method in beamforming structure has shown less signal distortion in the desired signal than the front-end kepstrum method and the front-end inverse kepstrum method in beamforming structure. PMID:22163987

Using a pseudo-dynamic source inversion approach to improve earthquake source imaging

NASA Astrophysics Data System (ADS)

Zhang, Y.; Song, S. G.; Dalguer, L. A.; Clinton, J. F.

2014-12-01

Imaging a high-resolution spatio-temporal slip distribution of an earthquake rupture is a core research goal in seismology. In general we expect to obtain a higher quality source image by improving the observational input data (e.g. using more higher quality near-source stations). However, recent studies show that increasing the surface station density alone does not significantly improve source inversion results (Custodio et al. 2005; Zhang et al. 2014). We introduce correlation structures between the kinematic source parameters: slip, rupture velocity, and peak slip velocity (Song et al. 2009; Song and Dalguer 2013) in the non-linear source inversion. The correlation structures are physical constraints derived from rupture dynamics that effectively regularize the model space and may improve source imaging. We name this approach pseudo-dynamic source inversion. We investigate the effectiveness of this pseudo-dynamic source inversion method by inverting low frequency velocity waveforms from a synthetic dynamic rupture model of a buried vertical strike-slip event (Mw 6.5) in a homogeneous half space. In the inversion, we use a genetic algorithm in a Bayesian framework (Moneli et al. 2008), and a dynamically consistent regularized Yoffe function (Tinti, et al. 2005) was used for a single-window slip velocity function. We search for local rupture velocity directly in the inversion, and calculate the rupture time using a ray-tracing technique. We implement both auto- and cross-correlation of slip, rupture velocity, and peak slip velocity in the prior distribution. Our results suggest that kinematic source model estimates capture the major features of the target dynamic model. The estimated rupture velocity closely matches the target distribution from the dynamic rupture model, and the derived rupture time is smoother than the one we searched directly. By implementing both auto- and cross-correlation of kinematic source parameters, in comparison to traditional smoothing

Approximated Stable Inversion for Nonlinear Systems with Nonhyperbolic Internal Dynamics. Revised

NASA Technical Reports Server (NTRS)

Devasia, Santosh

1999-01-01

A technique to achieve output tracking for nonminimum phase nonlinear systems with non- hyperbolic internal dynamics is presented. The present paper integrates stable inversion techniques (that achieve exact-tracking) with approximation techniques (that modify the internal dynamics) to circumvent the nonhyperbolicity of the internal dynamics - this nonhyperbolicity is an obstruction to applying presently available stable inversion techniques. The theory is developed for nonlinear systems and the method is applied to a two-cart with inverted-pendulum example.

Large-scale inverse and forward modeling of adaptive resonance in the tinnitus decompensation.

PubMed

Low, Yin Fen; Trenado, Carlos; Delb, Wolfgang; D'Amelio, Roberto; Falkai, Peter; Strauss, Daniel J

2006-01-01

Neural correlates of psychophysiological tinnitus models in humans may be used for their neurophysiological validation as well as for their refinement and improvement to better understand the pathogenesis of the tinnitus decompensation and to develop new therapeutic approaches. In this paper we make use of neural correlates of top-down projections, particularly, a recently introduced synchronization stability measure, together with a multiscale evoked response potential (ERP) model in order to study and evaluate the tinnitus decompensation by using a hybrid inverse-forward mathematical methodology. The neural synchronization stability, which according to the underlying model is linked to the focus of attention on the tinnitus signal, follows the experimental and inverse way and allows to discriminate between a group of compensated and decompensated tinnitus patients. The multiscale ERP model, which works in the forward direction, is used to consolidate hypotheses which are derived from the experiments for a known neural source dynamics related to attention. It is concluded that both methodologies agree and support each other in the description of the discriminatory character of the neural correlate proposed, but also help to fill the gap between the top-down adaptive resonance theory and the Jastreboff model of tinnitus.

Method and system for training dynamic nonlinear adaptive filters which have embedded memory

NASA Technical Reports Server (NTRS)

Rabinowitz, Matthew (Inventor)

2002-01-01

Described herein is a method and system for training nonlinear adaptive filters (or neural networks) which have embedded memory. Such memory can arise in a multi-layer finite impulse response (FIR) architecture, or an infinite impulse response (IIR) architecture. We focus on filter architectures with separate linear dynamic components and static nonlinear components. Such filters can be structured so as to restrict their degrees of computational freedom based on a priori knowledge about the dynamic operation to be emulated. The method is detailed for an FIR architecture which consists of linear FIR filters together with nonlinear generalized single layer subnets. For the IIR case, we extend the methodology to a general nonlinear architecture which uses feedback. For these dynamic architectures, we describe how one can apply optimization techniques which make updates closer to the Newton direction than those of a steepest descent method, such as backpropagation. We detail a novel adaptive modified Gauss-Newton optimization technique, which uses an adaptive learning rate to determine both the magnitude and direction of update steps. For a wide range of adaptive filtering applications, the new training algorithm converges faster and to a smaller value of cost than both steepest-descent methods such as backpropagation-through-time, and standard quasi-Newton methods. We apply the algorithm to modeling the inverse of a nonlinear dynamic tracking system 5, as well as a nonlinear amplifier 6.

Comparison of dynamic treatment regimes via inverse probability weighting.

PubMed

Hernán, Miguel A; Lanoy, Emilie; Costagliola, Dominique; Robins, James M

2006-03-01

Appropriate analysis of observational data is our best chance to obtain answers to many questions that involve dynamic treatment regimes. This paper describes a simple method to compare dynamic treatment regimes by artificially censoring subjects and then using inverse probability weighting (IPW) to adjust for any selection bias introduced by the artificial censoring. The basic strategy can be summarized in four steps: 1) define two regimes of interest, 2) artificially censor individuals when they stop following one of the regimes of interest, 3) estimate inverse probability weights to adjust for the potential selection bias introduced by censoring in the previous step, 4) compare the survival of the uncensored individuals under each regime of interest by fitting an inverse probability weighted Cox proportional hazards model with the dichotomous regime indicator and the baseline confounders as covariates. In the absence of model misspecification, the method is valid provided data are available on all time-varying and baseline joint predictors of survival and regime discontinuation. We present an application of the method to compare the AIDS-free survival under two dynamic treatment regimes in a large prospective study of HIV-infected patients. The paper concludes by discussing the relative advantages and disadvantages of censoring/IPW versus g-estimation of nested structural models to compare dynamic regimes.

Dynamical Adaptation in Photoreceptors

PubMed Central

Clark, Damon A.; Benichou, Raphael; Meister, Markus; Azeredo da Silveira, Rava

2013-01-01

Adaptation is at the heart of sensation and nowhere is it more salient than in early visual processing. Light adaptation in photoreceptors is doubly dynamical: it depends upon the temporal structure of the input and it affects the temporal structure of the response. We introduce a non-linear dynamical adaptation model of photoreceptors. It is simple enough that it can be solved exactly and simulated with ease; analytical and numerical approaches combined provide both intuition on the behavior of dynamical adaptation and quantitative results to be compared with data. Yet the model is rich enough to capture intricate phenomenology. First, we show that it reproduces the known phenomenology of light response and short-term adaptation. Second, we present new recordings and demonstrate that the model reproduces cone response with great precision. Third, we derive a number of predictions on the response of photoreceptors to sophisticated stimuli such as periodic inputs, various forms of flickering inputs, and natural inputs. In particular, we demonstrate that photoreceptors undergo rapid adaptation of response gain and time scale, over ∼ 300 ms—i. e., over the time scale of the response itself—and we confirm this prediction with data. For natural inputs, this fast adaptation can modulate the response gain more than tenfold and is hence physiologically relevant. PMID:24244119

An adaptive coupling strategy for joint inversions that use petrophysical information as constraints

NASA Astrophysics Data System (ADS)

Heincke, Björn; Jegen, Marion; Moorkamp, Max; Hobbs, Richard W.; Chen, Jin

2017-01-01

Joint inversion strategies for geophysical data have become increasingly popular as they allow for the efficient combination of complementary information from different data sets. The algorithm used for the joint inversion needs to be flexible in its description of the subsurface so as to be able to handle the diverse nature of the data. Hence, joint inversion schemes are needed that 1) adequately balance data from the different methods, 2) have stable convergence behavior, 3) consider the different resolution power of the methods used and 4) link the parameter models in a way that they are suited for a wide range of applications. Here, we combine active source seismic P-wave tomography, gravity and magnetotelluric (MT) data in a petrophysical joint inversion that accounts for these issues. Data from the different methods are inverted separately but are linked through constraints accounting for parameter relationships. An advantage of performing the inversions separately is that no relative weighting between the data sets is required. To avoid perturbing the convergence behavior of the inversions by the coupling, the strengths of the constraints are readjusted at each iteration. The criterion we use to control the adaption of the coupling strengths is based on variations in the objective functions of the individual inversions from one to the next iteration. Adaption of the coupling strengths makes the joint inversion scheme also applicable to subsurface conditions, where assumed relationships are not valid everywhere, because the individual inversions decouple if it is not possible to reach adequately low data misfits for the made assumptions. In addition, the coupling constraints depend on the relative resolutions of the methods, which leads to an improved convergence behavior of the joint inversion. Another benefit of the proposed scheme is that structural information can easily be incorporated in the petrophysical joint inversion (no additional terms are added

A 3D generic inverse dynamic method using wrench notation and quaternion algebra.

PubMed

Dumas, R; Aissaoui, R; de Guise, J A

2004-06-01

In the literature, conventional 3D inverse dynamic models are limited in three aspects related to inverse dynamic notation, body segment parameters and kinematic formalism. First, conventional notation yields separate computations of the forces and moments with successive coordinate system transformations. Secondly, the way conventional body segment parameters are defined is based on the assumption that the inertia tensor is principal and the centre of mass is located between the proximal and distal ends. Thirdly, the conventional kinematic formalism uses Euler or Cardanic angles that are sequence-dependent and suffer from singularities. In order to overcome these limitations, this paper presents a new generic method for inverse dynamics. This generic method is based on wrench notation for inverse dynamics, a general definition of body segment parameters and quaternion algebra for the kinematic formalism.

Reproductive isolation and local adaptation quantified for a chromosome inversion in a malaria mosquito.

PubMed

Ayala, Diego; Guerrero, Rafael F; Kirkpatrick, Mark

2013-04-01

Chromosome inversions have long been thought to be involved in speciation and local adaptation. We have little quantitative information, however, about the effects that inversion polymorphisms have on reproductive isolation and viability. Here we provide the first estimates from any organism for the total amount of reproductive isolation associated with an inversion segregating in natural populations. We sampled chromosomes from 751 mosquitoes of the malaria vector Anopheles funestus along a 1421 km transect in Cameroon that traverses savannah, highland, and rainforest ecological zones. We then developed a series of population genetic models that account for selection, migration, and assortative mating, and fit the models to the data using likelihood. Results from the best-fit models suggest there is strong local adaptation, with relative viabilities of homozygotes ranging from 25% to 130% compared to heterozygotes. Viabilities vary qualitatively between regions: the inversion is underdominant in the savannah, whereas in the highlands it is overdominant. The inversion is also implicated in strong assortative mating. In the savannah, the two homozygote forms show 92% reproductive isolation, suggesting that this one inversion can generate most of the genetic barriers needed for speciation. © 2012 The Author(s). Evolution© 2012 The Society for the Study of Evolution.

3D CSEM inversion based on goal-oriented adaptive finite element method

NASA Astrophysics Data System (ADS)

Zhang, Y.; Key, K.

2016-12-01

We present a parallel 3D frequency domain controlled-source electromagnetic inversion code name MARE3DEM. Non-linear inversion of observed data is performed with the Occam variant of regularized Gauss-Newton optimization. The forward operator is based on the goal-oriented finite element method that efficiently calculates the responses and sensitivity kernels in parallel using a data decomposition scheme where independent modeling tasks contain different frequencies and subsets of the transmitters and receivers. To accommodate complex 3D conductivity variation with high flexibility and precision, we adopt the dual-grid approach where the forward mesh conforms to the inversion parameter grid and is adaptively refined until the forward solution converges to the desired accuracy. This dual-grid approach is memory efficient, since the inverse parameter grid remains independent from fine meshing generated around the transmitter and receivers by the adaptive finite element method. Besides, the unstructured inverse mesh efficiently handles multiple scale structures and allows for fine-scale model parameters within the region of interest. Our mesh generation engine keeps track of the refinement hierarchy so that the map of conductivity and sensitivity kernel between the forward and inverse mesh is retained. We employ the adjoint-reciprocity method to calculate the sensitivity kernels which establish a linear relationship between changes in the conductivity model and changes in the modeled responses. Our code uses a direcy solver for the linear systems, so the adjoint problem is efficiently computed by re-using the factorization from the primary problem. Further computational efficiency and scalability is obtained in the regularized Gauss-Newton portion of the inversion using parallel dense matrix-matrix multiplication and matrix factorization routines implemented with the ScaLAPACK library. We show the scalability, reliability and the potential of the algorithm to deal with

Genomic Evidence for Adaptive Inversion Clines in Drosophila melanogaster.

PubMed

Kapun, Martin; Fabian, Daniel K; Goudet, Jérôme; Flatt, Thomas

2016-05-01

Clines in chromosomal inversion polymorphisms-presumably driven by climatic gradients-are common but there is surprisingly little evidence for selection acting on them. Here we address this long-standing issue in Drosophila melanogaster by using diagnostic single nucleotide polymorphism (SNP) markers to estimate inversion frequencies from 28 whole-genome Pool-seq samples collected from 10 populations along the North American east coast. Inversions In(3L)P, In(3R)Mo, and In(3R)Payne showed clear latitudinal clines, and for In(2L)t, In(2R)NS, and In(3R)Payne the steepness of the clinal slopes changed between summer and fall. Consistent with an effect of seasonality on inversion frequencies, we detected small but stable seasonal fluctuations of In(2R)NS and In(3R)Payne in a temperate Pennsylvanian population over 4 years. In support of spatially varying selection, we observed that the cline in In(3R)Payne has remained stable for >40 years and that the frequencies of In(2L)t and In(3R)Payne are strongly correlated with climatic factors that vary latitudinally, independent of population structure. To test whether these patterns are adaptive, we compared the amount of genetic differentiation of inversions versus neutral SNPs and found that the clines in In(2L)t and In(3R)Payne are maintained nonneutrally and independent of admixture. We also identified numerous clinal inversion-associated SNPs, many of which exhibit parallel differentiation along the Australian cline and reside in genes known to affect fitness-related traits. Together, our results provide strong evidence that inversion clines are maintained by spatially-and perhaps also temporally-varying selection. We interpret our data in light of current hypotheses about how inversions are established and maintained. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Inferior olive mirrors joint dynamics to implement an inverse controller.

PubMed

Alvarez-Icaza, Rodrigo; Boahen, Kwabena

2012-10-01

To produce smooth and coordinated motion, our nervous systems need to generate precisely timed muscle activation patterns that, due to axonal conduction delay, must be generated in a predictive and feedforward manner. Kawato proposed that the cerebellum accomplishes this by acting as an inverse controller that modulates descending motor commands to predictively drive the spinal cord such that the musculoskeletal dynamics are canceled out. This and other cerebellar theories do not, however, account for the rich biophysical properties expressed by the olivocerebellar complex's various cell types, making these theories difficult to verify experimentally. Here we propose that a multizonal microcomplex's (MZMC) inferior olivary neurons use their subthreshold oscillations to mirror a musculoskeletal joint's underdamped dynamics, thereby achieving inverse control. We used control theory to map a joint's inverse model onto an MZMC's biophysics, and we used biophysical modeling to confirm that inferior olivary neurons can express the dynamics required to mirror biomechanical joints. We then combined both techniques to predict how experimentally injecting current into the inferior olive would affect overall motor output performance. We found that this experimental manipulation unmasked a joint's natural dynamics, as observed by motor output ringing at the joint's natural frequency, with amplitude proportional to the amount of current. These results support the proposal that the cerebellum-in particular an MZMC-is an inverse controller; the results also provide a biophysical implementation for this controller and allow one to make an experimentally testable prediction.

High effective inverse dynamics modelling for dual-arm robot

NASA Astrophysics Data System (ADS)

Shen, Haoyu; Liu, Yanli; Wu, Hongtao

2018-05-01

To deal with the problem of inverse dynamics modelling for dual arm robot, a recursive inverse dynamics modelling method based on decoupled natural orthogonal complement is presented. In this model, the concepts and methods of Decoupled Natural Orthogonal Complement matrices are used to eliminate the constraint forces in the Newton-Euler kinematic equations, and the screws is used to express the kinematic and dynamics variables. On this basis, the paper has developed a special simulation program with symbol software of Mathematica and conducted a simulation research on the a dual-arm robot. Simulation results show that the proposed method based on decoupled natural orthogonal complement can save an enormous amount of CPU time that was spent in computing compared with the recursive Newton-Euler kinematic equations and the results is correct and reasonable, which can verify the reliability and efficiency of the method.

An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions

DOE PAGES

Li, Weixuan; Lin, Guang

2015-03-21

Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes’ rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle thesemore » challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.« less

An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions

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

Li, Weixuan; Lin, Guang, E-mail: guanglin@purdue.edu

2015-08-01

Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes' rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle thesemore » challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.« less

An inverse dynamics approach to trajectory optimization and guidance for an aerospace plane

NASA Technical Reports Server (NTRS)

Lu, Ping

1992-01-01

The optimal ascent problem for an aerospace planes is formulated as an optimal inverse dynamic problem. Both minimum-fuel and minimax type of performance indices are considered. Some important features of the optimal trajectory and controls are used to construct a nonlinear feedback midcourse controller, which not only greatly simplifies the difficult constrained optimization problem and yields improved solutions, but is also suited for onboard implementation. Robust ascent guidance is obtained by using combination of feedback compensation and onboard generation of control through the inverse dynamics approach. Accurate orbital insertion can be achieved with near-optimal control of the rocket through inverse dynamics even in the presence of disturbances.

Non-recursive augmented Lagrangian algorithms for the forward and inverse dynamics of constrained flexible multibodies

NASA Technical Reports Server (NTRS)

Bayo, Eduardo; Ledesma, Ragnar

1993-01-01

A technique is presented for solving the inverse dynamics of flexible planar multibody systems. This technique yields the non-causal joint efforts (inverse dynamics) as well as the internal states (inverse kinematics) that produce a prescribed nominal trajectory of the end effector. A non-recursive global Lagrangian approach is used in formulating the equations for motion as well as in solving the inverse dynamics equations. Contrary to the recursive method previously presented, the proposed method solves the inverse problem in a systematic and direct manner for both open-chain as well as closed-chain configurations. Numerical simulation shows that the proposed procedure provides an excellent tracking of the desired end effector trajectory.

3-D inversion of airborne electromagnetic data parallelized and accelerated by local mesh and adaptive soundings

NASA Astrophysics Data System (ADS)

Yang, Dikun; Oldenburg, Douglas W.; Haber, Eldad

2014-03-01

Airborne electromagnetic (AEM) methods are highly efficient tools for assessing the Earth's conductivity structures in a large area at low cost. However, the configuration of AEM measurements, which typically have widely distributed transmitter-receiver pairs, makes the rigorous modelling and interpretation extremely time-consuming in 3-D. Excessive overcomputing can occur when working on a large mesh covering the entire survey area and inverting all soundings in the data set. We propose two improvements. The first is to use a locally optimized mesh for each AEM sounding for the forward modelling and calculation of sensitivity. This dedicated local mesh is small with fine cells near the sounding location and coarse cells far away in accordance with EM diffusion and the geometric decay of the signals. Once the forward problem is solved on the local meshes, the sensitivity for the inversion on the global mesh is available through quick interpolation. Using local meshes for AEM forward modelling avoids unnecessary computing on fine cells on a global mesh that are far away from the sounding location. Since local meshes are highly independent, the forward modelling can be efficiently parallelized over an array of processors. The second improvement is random and dynamic down-sampling of the soundings. Each inversion iteration only uses a random subset of the soundings, and the subset is reselected for every iteration. The number of soundings in the random subset, determined by an adaptive algorithm, is tied to the degree of model regularization. This minimizes the overcomputing caused by working with redundant soundings. Our methods are compared against conventional methods and tested with a synthetic example. We also invert a field data set that was previously considered to be too large to be practically inverted in 3-D. These examples show that our methodology can dramatically reduce the processing time of 3-D inversion to a practical level without losing resolution

Contributions of adaptation currents to dynamic spike threshold on slow timescales: Biophysical insights from conductance-based models

NASA Astrophysics Data System (ADS)

Yi, Guosheng; Wang, Jiang; Wei, Xile; Deng, Bin; Li, Huiyan; Che, Yanqiu

2017-06-01

Spike-frequency adaptation (SFA) mediated by various adaptation currents, such as voltage-gated K+ current (IM), Ca2+-gated K+ current (IAHP), or Na+-activated K+ current (IKNa), exists in many types of neurons, which has been shown to effectively shape their information transmission properties on slow timescales. Here we use conductance-based models to investigate how the activation of three adaptation currents regulates the threshold voltage for action potential (AP) initiation during the course of SFA. It is observed that the spike threshold gets depolarized and the rate of membrane depolarization (dV/dt) preceding AP is reduced as adaptation currents reduce firing rate. It is indicated that the presence of inhibitory adaptation currents enables the neuron to generate a dynamic threshold inversely correlated with preceding dV/dt on slower timescales than fast dynamics of AP generation. By analyzing the interactions of ionic currents at subthreshold potentials, we find that the activation of adaptation currents increase the outward level of net membrane current prior to AP initiation, which antagonizes inward Na+ to result in a depolarized threshold and lower dV/dt from one AP to the next. Our simulations demonstrate that the threshold dynamics on slow timescales is a secondary effect caused by the activation of adaptation currents. These findings have provided a biophysical interpretation of the relationship between adaptation currents and spike threshold.

Parallel three-dimensional magnetotelluric inversion using adaptive finite-element method. Part I: theory and synthetic study

NASA Astrophysics Data System (ADS)

Grayver, Alexander V.

2015-07-01

This paper presents a distributed magnetotelluric inversion scheme based on adaptive finite-element method (FEM). The key novel aspect of the introduced algorithm is the use of automatic mesh refinement techniques for both forward and inverse modelling. These techniques alleviate tedious and subjective procedure of choosing a suitable model parametrization. To avoid overparametrization, meshes for forward and inverse problems were decoupled. For calculation of accurate electromagnetic (EM) responses, automatic mesh refinement algorithm based on a goal-oriented error estimator has been adopted. For further efficiency gain, EM fields for each frequency were calculated using independent meshes in order to account for substantially different spatial behaviour of the fields over a wide range of frequencies. An automatic approach for efficient initial mesh design in inverse problems based on linearized model resolution matrix was developed. To make this algorithm suitable for large-scale problems, it was proposed to use a low-rank approximation of the linearized model resolution matrix. In order to fill a gap between initial and true model complexities and resolve emerging 3-D structures better, an algorithm for adaptive inverse mesh refinement was derived. Within this algorithm, spatial variations of the imaged parameter are calculated and mesh is refined in the neighborhoods of points with the largest variations. A series of numerical tests were performed to demonstrate the utility of the presented algorithms. Adaptive mesh refinement based on the model resolution estimates provides an efficient tool to derive initial meshes which account for arbitrary survey layouts, data types, frequency content and measurement uncertainties. Furthermore, the algorithm is capable to deliver meshes suitable to resolve features on multiple scales while keeping number of unknowns low. However, such meshes exhibit dependency on an initial model guess. Additionally, it is demonstrated

Adaptive control of robotic manipulators

NASA Technical Reports Server (NTRS)

Seraji, H.

1987-01-01

The author presents a novel approach to adaptive control of manipulators to achieve trajectory tracking by the joint angles. The central concept in this approach is the utilization of the manipulator inverse as a feedforward controller. The desired trajectory is applied as an input to the feedforward controller which behaves as the inverse of the manipulator at any operating point; the controller output is used as the driving torque for the manipulator. The controller gains are then updated by an adaptation algorithm derived from MRAC (model reference adaptive control) theory to cope with variations in the manipulator inverse due to changes of the operating point. An adaptive feedback controller and an auxiliary signal are also used to enhance closed-loop stability and to achieve faster adaptation. The proposed control scheme is computationally fast and does not require a priori knowledge of the complex dynamic model or the parameter values of the manipulator or the payload.

Inverse problem of HIV cell dynamics using Genetic Algorithms

NASA Astrophysics Data System (ADS)

González, J. A.; Guzmán, F. S.

2017-01-01

In order to describe the cell dynamics of T-cells in a patient infected with HIV, we use a flavour of Perelson's model. This is a non-linear system of Ordinary Differential Equations that describes the evolution of healthy, latently infected, infected T-cell concentrations and the free viral cells. Different parameters in the equations give different dynamics. Considering the concentration of these types of cells is known for a particular patient, the inverse problem consists in estimating the parameters in the model. We solve this inverse problem using a Genetic Algorithm (GA) that minimizes the error between the solutions of the model and the data from the patient. These errors depend on the parameters of the GA, like mutation rate and population, although a detailed analysis of this dependence will be described elsewhere.

Low-rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging

PubMed Central

Ravishankar, Saiprasad; Moore, Brian E.; Nadakuditi, Raj Rao; Fessler, Jeffrey A.

2017-01-01

Sparsity-based approaches have been popular in many applications in image processing and imaging. Compressed sensing exploits the sparsity of images in a transform domain or dictionary to improve image recovery from undersampled measurements. In the context of inverse problems in dynamic imaging, recent research has demonstrated the promise of sparsity and low-rank techniques. For example, the patches of the underlying data are modeled as sparse in an adaptive dictionary domain, and the resulting image and dictionary estimation from undersampled measurements is called dictionary-blind compressed sensing, or the dynamic image sequence is modeled as a sum of low-rank and sparse (in some transform domain) components (L+S model) that are estimated from limited measurements. In this work, we investigate a data-adaptive extension of the L+S model, dubbed LASSI, where the temporal image sequence is decomposed into a low-rank component and a component whose spatiotemporal (3D) patches are sparse in some adaptive dictionary domain. We investigate various formulations and efficient methods for jointly estimating the underlying dynamic signal components and the spatiotemporal dictionary from limited measurements. We also obtain efficient sparsity penalized dictionary-blind compressed sensing methods as special cases of our LASSI approaches. Our numerical experiments demonstrate the promising performance of LASSI schemes for dynamic magnetic resonance image reconstruction from limited k-t space data compared to recent methods such as k-t SLR and L+S, and compared to the proposed dictionary-blind compressed sensing method. PMID:28092528

Space-time adaptive solution of inverse problems with the discrete adjoint method

NASA Astrophysics Data System (ADS)

Alexe, Mihai; Sandu, Adrian

2014-08-01

This paper develops a framework for the construction and analysis of discrete adjoint sensitivities in the context of time dependent, adaptive grid, adaptive step models. Discrete adjoints are attractive in practice since they can be generated with low effort using automatic differentiation. However, this approach brings several important challenges. The space-time adjoint of the forward numerical scheme may be inconsistent with the continuous adjoint equations. A reduction in accuracy of the discrete adjoint sensitivities may appear due to the inter-grid transfer operators. Moreover, the optimization algorithm may need to accommodate state and gradient vectors whose dimensions change between iterations. This work shows that several of these potential issues can be avoided through a multi-level optimization strategy using discontinuous Galerkin (DG) hp-adaptive discretizations paired with Runge-Kutta (RK) time integration. We extend the concept of dual (adjoint) consistency to space-time RK-DG discretizations, which are then shown to be well suited for the adaptive solution of time-dependent inverse problems. Furthermore, we prove that DG mesh transfer operators on general meshes are also dual consistent. This allows the simultaneous derivation of the discrete adjoint for both the numerical solver and the mesh transfer logic with an automatic code generation mechanism such as algorithmic differentiation (AD), potentially speeding up development of large-scale simulation codes. The theoretical analysis is supported by numerical results reported for a two-dimensional non-stationary inverse problem.

Control of a high beta maneuvering reentry vehicle using dynamic inversion.

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

Watts, Alfred Chapman

2005-05-01

The design of flight control systems for high performance maneuvering reentry vehicles presents a significant challenge to the control systems designer. These vehicles typically have a much higher ballistic coefficient than crewed vehicles like as the Space Shuttle or proposed crew return vehicles such as the X-38. Moreover, the missions of high performance vehicles usually require a steeper reentry flight path angle, followed by a pull-out into level flight. These vehicles then must transit the entire atmosphere and robustly perform the maneuvers required for the mission. The vehicles must also be flown with small static margins in order to performmore » the required maneuvers, which can result in highly nonlinear aerodynamic characteristics that frequently transition from being aerodynamically stable to unstable as angle of attack increases. The control system design technique of dynamic inversion has been applied successfully to both high performance aircraft and low beta reentry vehicles. The objective of this study was to explore the application of this technique to high performance maneuvering reentry vehicles, including the basic derivation of the dynamic inversion technique, followed by the extension of that technique to the use of tabular trim aerodynamic models in the controller. The dynamic inversion equations are developed for high performance vehicles and augmented to allow the selection of a desired response for the control system. A six degree of freedom simulation is used to evaluate the performance of the dynamic inversion approach, and results for both nominal and off nominal aerodynamic characteristics are presented.« less

Dynamic optimization and adaptive controller design

NASA Astrophysics Data System (ADS)

Inamdar, S. R.

2010-10-01

In this work I present a new type of controller which is an adaptive tracking controller which employs dynamic optimization for optimizing current value of controller action for the temperature control of nonisothermal continuously stirred tank reactor (CSTR). We begin with a two-state model of nonisothermal CSTR which are mass and heat balance equations and then add cooling system dynamics to eliminate input multiplicity. The initial design value is obtained using local stability of steady states where approach temperature for cooling action is specified as a steady state and a design specification. Later we make a correction in the dynamics where material balance is manipulated to use feed concentration as a system parameter as an adaptive control measure in order to avoid actuator saturation for the main control loop. The analysis leading to design of dynamic optimization based parameter adaptive controller is presented. The important component of this mathematical framework is reference trajectory generation to form an adaptive control measure.

Force and Moment Approach for Achievable Dynamics Using Nonlinear Dynamic Inversion

NASA Technical Reports Server (NTRS)

Ostroff, Aaron J.; Bacon, Barton J.

1999-01-01

This paper describes a general form of nonlinear dynamic inversion control for use in a generic nonlinear simulation to evaluate candidate augmented aircraft dynamics. The implementation is specifically tailored to the task of quickly assessing an aircraft's control power requirements and defining the achievable dynamic set. The achievable set is evaluated while undergoing complex mission maneuvers, and perfect tracking will be accomplished when the desired dynamics are achievable. Variables are extracted directly from the simulation model each iteration, so robustness is not an issue. Included in this paper is a description of the implementation of the forces and moments from simulation variables, the calculation of control effectiveness coefficients, methods for implementing different types of aerodynamic and thrust vectoring controls, adjustments for control effector failures, and the allocation approach used. A few examples illustrate the perfect tracking results obtained.

Restricted Complexity Framework for Nonlinear Adaptive Control in Complex Systems

NASA Astrophysics Data System (ADS)

Williams, Rube B.

2004-02-01

Control law adaptation that includes implicit or explicit adaptive state estimation, can be a fundamental underpinning for the success of intelligent control in complex systems, particularly during subsystem failures, where vital system states and parameters can be impractical or impossible to measure directly. A practical algorithm is proposed for adaptive state filtering and control in nonlinear dynamic systems when the state equations are unknown or are too complex to model analytically. The state equations and inverse plant model are approximated by using neural networks. A framework for a neural network based nonlinear dynamic inversion control law is proposed, as an extrapolation of prior developed restricted complexity methodology used to formulate the adaptive state filter. Examples of adaptive filter performance are presented for an SSME simulation with high pressure turbine failure to support extrapolations to adaptive control problems.

Charge Inversion by Electrostatic Complexation: Molecular Dynamics Simulations

NASA Astrophysics Data System (ADS)

Faraudo, Jordi; Travesset, Alex

2007-03-01

Ions near interfaces play an important role in many biological and physico-chemical processes and exhibit a fascinating diverse range of phenomena. A relevant example is charge inversion, where interfacial charges attract counterions in excess of their own nominal charge, thus leading to an inversion of the sign of the interfacial charge. In this work, we argue that in the case of amphiphilic interfaces, charge inversion can be generated by complexation, that is, electrostatic complexes containing several counterions bound to amphiphilic molecules. The formation of these complexes require the presence at the interface of groups with conformational degrees of freedom with many electronegative atoms. We illustrate this mechanism by analyzing all atomic molecular dynamics simulations of a DMPA (Dimirystoil-Phosphatidic acid) phospholipid monolayer in contact with divalent counterions. The results are found to be in agreement with recent experimental results on Langmuir monolayers. We also discuss the implications for biological systems, as Phosphatidic acid is emerging as a key signaling phospholipid.

Self-organizing radial basis function networks for adaptive flight control and aircraft engine state estimation

NASA Astrophysics Data System (ADS)

Shankar, Praveen

The performance of nonlinear control algorithms such as feedback linearization and dynamic inversion is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the dynamics results in reduced performance and may lead to instability. Augmenting the baseline controller with approximators which utilize a parametrization structure that is adapted online reduces the effect of this error between the design model and actual dynamics. However, currently existing parameterizations employ a fixed set of basis functions that do not guarantee arbitrary tracking error performance. To address this problem, we develop a self-organizing parametrization structure that is proven to be stable and can guarantee arbitrary tracking error performance. The training algorithm to grow the network and adapt the parameters is derived from Lyapunov theory. In addition to growing the network of basis functions, a pruning strategy is incorporated to keep the size of the network as small as possible. This algorithm is implemented on a high performance flight vehicle such as F-15 military aircraft. The baseline dynamic inversion controller is augmented with a Self-Organizing Radial Basis Function Network (SORBFN) to minimize the effect of the inversion error which may occur due to imperfect modeling, approximate inversion or sudden changes in aircraft dynamics. The dynamic inversion controller is simulated for different situations including control surface failures, modeling errors and external disturbances with and without the adaptive network. A performance measure of maximum tracking error is specified for both the controllers a priori. Excellent tracking error minimization to a pre-specified level using the adaptive approximation based controller was achieved while the baseline dynamic inversion controller failed to meet this performance specification. The performance of the SORBFN based controller is also compared to a fixed RBF network

Adaptive Core Simulation Employing Discrete Inverse Theory - Part I: Theory

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

Abdel-Khalik, Hany S.; Turinsky, Paul J.

2005-07-15

Use of adaptive simulation is intended to improve the fidelity and robustness of important core attribute predictions such as core power distribution, thermal margins, and core reactivity. Adaptive simulation utilizes a selected set of past and current reactor measurements of reactor observables, i.e., in-core instrumentation readings, to adapt the simulation in a meaningful way. A meaningful adaption will result in high-fidelity and robust adapted core simulator models. To perform adaption, we propose an inverse theory approach in which the multitudes of input data to core simulators, i.e., reactor physics and thermal-hydraulic data, are to be adjusted to improve agreement withmore » measured observables while keeping core simulator models unadapted. At first glance, devising such adaption for typical core simulators with millions of input and observables data would spawn not only several prohibitive challenges but also numerous disparaging concerns. The challenges include the computational burdens of the sensitivity-type calculations required to construct Jacobian operators for the core simulator models. Also, the computational burdens of the uncertainty-type calculations required to estimate the uncertainty information of core simulator input data present a demanding challenge. The concerns however are mainly related to the reliability of the adjusted input data. The methodologies of adaptive simulation are well established in the literature of data adjustment. We adopt the same general framework for data adjustment; however, we refrain from solving the fundamental adjustment equations in a conventional manner. We demonstrate the use of our so-called Efficient Subspace Methods (ESMs) to overcome the computational and storage burdens associated with the core adaption problem. We illustrate the successful use of ESM-based adaptive techniques for a typical boiling water reactor core simulator adaption problem.« less

Adaptive Core Simulation Employing Discrete Inverse Theory - Part II: Numerical Experiments

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

Abdel-Khalik, Hany S.; Turinsky, Paul J.

2005-07-15

Use of adaptive simulation is intended to improve the fidelity and robustness of important core attribute predictions such as core power distribution, thermal margins, and core reactivity. Adaptive simulation utilizes a selected set of past and current reactor measurements of reactor observables, i.e., in-core instrumentation readings, to adapt the simulation in a meaningful way. The companion paper, ''Adaptive Core Simulation Employing Discrete Inverse Theory - Part I: Theory,'' describes in detail the theoretical background of the proposed adaptive techniques. This paper, Part II, demonstrates several computational experiments conducted to assess the fidelity and robustness of the proposed techniques. The intentmore » is to check the ability of the adapted core simulator model to predict future core observables that are not included in the adaption or core observables that are recorded at core conditions that differ from those at which adaption is completed. Also, this paper demonstrates successful utilization of an efficient sensitivity analysis approach to calculate the sensitivity information required to perform the adaption for millions of input core parameters. Finally, this paper illustrates a useful application for adaptive simulation - reducing the inconsistencies between two different core simulator code systems, where the multitudes of input data to one code are adjusted to enhance the agreement between both codes for important core attributes, i.e., core reactivity and power distribution. Also demonstrated is the robustness of such an application.« less

An Adaptive ANOVA-based PCKF for High-Dimensional Nonlinear Inverse Modeling

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

LI, Weixuan; Lin, Guang; Zhang, Dongxiao

2014-02-01

The probabilistic collocation-based Kalman filter (PCKF) is a recently developed approach for solving inverse problems. It resembles the ensemble Kalman filter (EnKF) in every aspect—except that it represents and propagates model uncertainty by polynomial chaos expansion (PCE) instead of an ensemble of model realizations. Previous studies have shown PCKF is a more efficient alternative to EnKF for many data assimilation problems. However, the accuracy and efficiency of PCKF depends on an appropriate truncation of the PCE series. Having more polynomial chaos bases in the expansion helps to capture uncertainty more accurately but increases computational cost. Bases selection is particularly importantmore » for high-dimensional stochastic problems because the number of polynomial chaos bases required to represent model uncertainty grows dramatically as the number of input parameters (random dimensions) increases. In classic PCKF algorithms, the PCE bases are pre-set based on users’ experience. Also, for sequential data assimilation problems, the bases kept in PCE expression remain unchanged in different Kalman filter loops, which could limit the accuracy and computational efficiency of classic PCKF algorithms. To address this issue, we present a new algorithm that adaptively selects PCE bases for different problems and automatically adjusts the number of bases in different Kalman filter loops. The algorithm is based on adaptive functional ANOVA (analysis of variance) decomposition, which approximates a high-dimensional function with the summation of a set of low-dimensional functions. Thus, instead of expanding the original model into PCE, we implement the PCE expansion on these low-dimensional functions, which is much less costly. We also propose a new adaptive criterion for ANOVA that is more suited for solving inverse problems. The new algorithm is tested with different examples and demonstrated great effectiveness in comparison with non-adaptive PCKF and En

An adaptive ANOVA-based PCKF for high-dimensional nonlinear inverse modeling

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

Li, Weixuan, E-mail: weixuan.li@usc.edu; Lin, Guang, E-mail: guang.lin@pnnl.gov; Zhang, Dongxiao, E-mail: dxz@pku.edu.cn

2014-02-01

The probabilistic collocation-based Kalman filter (PCKF) is a recently developed approach for solving inverse problems. It resembles the ensemble Kalman filter (EnKF) in every aspect—except that it represents and propagates model uncertainty by polynomial chaos expansion (PCE) instead of an ensemble of model realizations. Previous studies have shown PCKF is a more efficient alternative to EnKF for many data assimilation problems. However, the accuracy and efficiency of PCKF depends on an appropriate truncation of the PCE series. Having more polynomial chaos basis functions in the expansion helps to capture uncertainty more accurately but increases computational cost. Selection of basis functionsmore » is particularly important for high-dimensional stochastic problems because the number of polynomial chaos basis functions required to represent model uncertainty grows dramatically as the number of input parameters (random dimensions) increases. In classic PCKF algorithms, the PCE basis functions are pre-set based on users' experience. Also, for sequential data assimilation problems, the basis functions kept in PCE expression remain unchanged in different Kalman filter loops, which could limit the accuracy and computational efficiency of classic PCKF algorithms. To address this issue, we present a new algorithm that adaptively selects PCE basis functions for different problems and automatically adjusts the number of basis functions in different Kalman filter loops. The algorithm is based on adaptive functional ANOVA (analysis of variance) decomposition, which approximates a high-dimensional function with the summation of a set of low-dimensional functions. Thus, instead of expanding the original model into PCE, we implement the PCE expansion on these low-dimensional functions, which is much less costly. We also propose a new adaptive criterion for ANOVA that is more suited for solving inverse problems. The new algorithm was tested with different examples and

Adaptive critics for dynamic optimization.

PubMed

Kulkarni, Raghavendra V; Venayagamoorthy, Ganesh Kumar

2010-06-01

A novel action-dependent adaptive critic design (ACD) is developed for dynamic optimization. The proposed combination of a particle swarm optimization-based actor and a neural network critic is demonstrated through dynamic sleep scheduling of wireless sensor motes for wildlife monitoring. The objective of the sleep scheduler is to dynamically adapt the sleep duration to node's battery capacity and movement pattern of animals in its environment in order to obtain snapshots of the animal on its trajectory uniformly. Simulation results show that the sleep time of the node determined by the actor critic yields superior quality of sensory data acquisition and enhanced node longevity. Copyright 2010 Elsevier Ltd. All rights reserved.

A time domain inverse dynamic method for the end point tracking control of a flexible manipulator

NASA Technical Reports Server (NTRS)

Kwon, Dong-Soo; Book, Wayne J.

1991-01-01

The inverse dynamic equation of a flexible manipulator was solved in the time domain. By dividing the inverse system equation into the causal part and the anticausal part, we calculated the torque and the trajectories of all state variables for a given end point trajectory. The interpretation of this method in the frequency domain was explained in detail using the two-sided Laplace transform and the convolution integral. The open loop control of the inverse dynamic method shows an excellent result in simulation. For real applications, a practical control strategy is proposed by adding a feedback tracking control loop to the inverse dynamic feedforward control, and its good experimental performance is presented.

GNBP domain of Anopheles darlingi: are polymorphic inversions and gene variation related to adaptive evolution?

PubMed

Bridi, L C; Rafael, M S

2016-02-01

Anopheles darlingi is the main malaria vector in humans in South America. In the Amazon basin, it lives along the banks of rivers and lakes, which responds to the annual hydrological cycle (dry season and rainy season). In these breeding sites, the larvae of this mosquito feed on decomposing organic and microorganisms, which can be pathogenic and trigger the activation of innate immune system pathways, such as proteins Gram-negative binding protein (GNBP). Such environmental changes affect the occurrence of polymorphic inversions especially at the heterozygote frequency, which confer adaptative advantage compared to homozygous inversions. We mapped the GNBP probe to the An. darlingi 2Rd inversion by fluorescent in situ hybridization (FISH), which was a good indicator of the GNBP immune response related to the chromosomal polymorphic inversions and adaptative evolution. To better understand the evolutionary relations and time of divergence of the GNBP of An. darlingi, we compared it with nine other mosquito GNBPs. The results of the phylogenetic analysis of the GNBP sequence between the species of mosquitoes demonstrated three clades. Clade I and II included the GNBPB5 sequence, and clade III the sequence of GNBPB1. Most of these sequences of GNBP analyzed were homologous with that of subfamily B, including that of An. gambiae (87 %), therefore suggesting that GNBP of An. darling belongs to subfamily B. This work helps us understand the role of inversion polymorphism in evolution of An. darlingi.

Stability Result For Dynamic Inversion Devised to Control Large Flexible Aircraft

NASA Technical Reports Server (NTRS)

Gregory, Irene M.

2001-01-01

High performance aircraft of the future will be designed lighter, more maneuverable, and operate over an ever expanding flight envelope. One of the largest differences from the flight control perspective between current and future advanced aircraft is elasticity. Over the last decade, dynamic inversion methodology has gained considerable popularity in application to highly maneuverable fighter aircraft, which were treated as rigid vehicles. This paper is an initial attempt to establish global stability results for dynamic inversion methodology as applied to a large, flexible aircraft. This work builds on a previous result for rigid fighter aircraft and adds a new level of complexity that is the flexible aircraft dynamics, which cannot be ignored even in the most basic flight control. The results arise from observations of the control laws designed for a new generation of the High-Speed Civil Transport aircraft.

Low-Rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging.

PubMed

Ravishankar, Saiprasad; Moore, Brian E; Nadakuditi, Raj Rao; Fessler, Jeffrey A

2017-05-01

Sparsity-based approaches have been popular in many applications in image processing and imaging. Compressed sensing exploits the sparsity of images in a transform domain or dictionary to improve image recovery fromundersampledmeasurements. In the context of inverse problems in dynamic imaging, recent research has demonstrated the promise of sparsity and low-rank techniques. For example, the patches of the underlying data are modeled as sparse in an adaptive dictionary domain, and the resulting image and dictionary estimation from undersampled measurements is called dictionary-blind compressed sensing, or the dynamic image sequence is modeled as a sum of low-rank and sparse (in some transform domain) components (L+S model) that are estimated from limited measurements. In this work, we investigate a data-adaptive extension of the L+S model, dubbed LASSI, where the temporal image sequence is decomposed into a low-rank component and a component whose spatiotemporal (3D) patches are sparse in some adaptive dictionary domain. We investigate various formulations and efficient methods for jointly estimating the underlying dynamic signal components and the spatiotemporal dictionary from limited measurements. We also obtain efficient sparsity penalized dictionary-blind compressed sensing methods as special cases of our LASSI approaches. Our numerical experiments demonstrate the promising performance of LASSI schemes for dynamicmagnetic resonance image reconstruction from limited k-t space data compared to recent methods such as k-t SLR and L+S, and compared to the proposed dictionary-blind compressed sensing method.

Inverse dynamic substructuring using the direct hybrid assembly in the frequency domain

NASA Astrophysics Data System (ADS)

D'Ambrogio, Walter; Fregolent, Annalisa

2014-04-01

The paper deals with the identification of the dynamic behaviour of a structural subsystem, starting from the known dynamic behaviour of both the coupled system and the remaining part of the structural system (residual subsystem). This topic is also known as decoupling problem, subsystem subtraction or inverse dynamic substructuring. Whenever it is necessary to combine numerical models (e.g. FEM) and test models (e.g. FRFs), one speaks of experimental dynamic substructuring. Substructure decoupling techniques can be classified as inverse coupling or direct decoupling techniques. In inverse coupling, the equations describing the coupling problem are rearranged to isolate the unknown substructure instead of the coupled structure. On the contrary, direct decoupling consists in adding to the coupled system a fictitious subsystem that is the negative of the residual subsystem. Starting from a reduced version of the 3-field formulation (dynamic equilibrium using FRFs, compatibility and equilibrium of interface forces), a direct hybrid assembly is developed by requiring that both compatibility and equilibrium conditions are satisfied exactly, either at coupling DoFs only, or at additional internal DoFs of the residual subsystem. Equilibrium and compatibility DoFs might not be the same: this generates the so-called non-collocated approach. The technique is applied using experimental data from an assembled system made by a plate and a rigid mass.

Performance evaluation of the inverse dynamics method for optimal spacecraft reorientation

NASA Astrophysics Data System (ADS)

Ventura, Jacopo; Romano, Marcello; Walter, Ulrich

2015-05-01

This paper investigates the application of the inverse dynamics in the virtual domain method to Euler angles, quaternions, and modified Rodrigues parameters for rapid optimal attitude trajectory generation for spacecraft reorientation maneuvers. The impact of the virtual domain and attitude representation is numerically investigated for both minimum time and minimum energy problems. Owing to the nature of the inverse dynamics method, it yields sub-optimal solutions for minimum time problems. Furthermore, the virtual domain improves the optimality of the solution, but at the cost of more computational time. The attitude representation also affects solution quality and computational speed. For minimum energy problems, the optimal solution can be obtained without the virtual domain with any considered attitude representation.

Dynamic Information Encoding With Dynamic Synapses in Neural Adaptation

PubMed Central

Li, Luozheng; Mi, Yuanyuan; Zhang, Wenhao; Wang, Da-Hui; Wu, Si

2018-01-01

Adaptation refers to the general phenomenon that the neural system dynamically adjusts its response property according to the statistics of external inputs. In response to an invariant stimulation, neuronal firing rates first increase dramatically and then decrease gradually to a low level close to the background activity. This prompts a question: during the adaptation, how does the neural system encode the repeated stimulation with attenuated firing rates? It has been suggested that the neural system may employ a dynamical encoding strategy during the adaptation, the information of stimulus is mainly encoded by the strong independent spiking of neurons at the early stage of the adaptation; while the weak but synchronized activity of neurons encodes the stimulus information at the later stage of the adaptation. The previous study demonstrated that short-term facilitation (STF) of electrical synapses, which increases the synchronization between neurons, can provide a mechanism to realize dynamical encoding. In the present study, we further explore whether short-term plasticity (STP) of chemical synapses, an interaction form more common than electrical synapse in the cortex, can support dynamical encoding. We build a large-size network with chemical synapses between neurons. Notably, facilitation of chemical synapses only enhances pair-wise correlations between neurons mildly, but its effect on increasing synchronization of the network can be significant, and hence it can serve as a mechanism to convey the stimulus information. To read-out the stimulus information, we consider that a downstream neuron receives balanced excitatory and inhibitory inputs from the network, so that the downstream neuron only responds to synchronized firings of the network. Therefore, the response of the downstream neuron indicates the presence of the repeated stimulation. Overall, our study demonstrates that STP of chemical synapse can serve as a mechanism to realize dynamical neural

Dynamic accommodation responses following adaptation to defocus.

PubMed

Cufflin, Matthew P; Mallen, Edward A H

2008-10-01

Adaptation to defocus is known to influence the subjective sensitivity to blur in both emmetropes and myopes. Blur is a major contributing factor in the closed-loop dynamic accommodation response. Previous investigations have examined the magnitude of the accommodation response following blur adaptation. We have investigated whether a period of blur adaptation influences the dynamic accommodation response to step and sinusoidal changes in target vergence. Eighteen subjects (six emmetropes, six early onset myopes, and six late onset myopes) underwent 30 min of adaptation to 0.00 D (control), +1.00 D or +3.00 D myopic defocus. Following this adaptation period, accommodation responses to a 2.00 D step change and 2.00 D sinusoidal change (0.2 Hz) in target vergence were recorded continuously using an autorefractor. Adaptation to defocus failed to influence accommodation latency times, but did influence response times to a step change in target vergence. Adaptation to both +1.00 and +3.00 D induced significant increases in response times (p = 0.002 and p = 0.012, respectively) and adaptation to +3.00 D increased the change in accommodation response magnitude (p = 0.014) for a 2.00 D step change in demand. Blur adaptation also significantly increased the peak-to-peak phase lag for accommodation responses to a sinusoidally oscillating target, although failed to influence the accommodation gain. These changes in accommodative response were equivalent across all refractive groups. Adaptation to a degraded stimulus causes an increased level of accommodation for dynamic targets moving towards an observer and increases response times and phase lags. It is suggested that the contrast constancy theory may explain these changes in dynamic behavior.

Inverse dynamics of a 3 degree of freedom spatial flexible manipulator

NASA Technical Reports Server (NTRS)

Bayo, Eduardo; Serna, M.

1989-01-01

A technique is presented for solving the inverse dynamics and kinematics of 3 degree of freedom spatial flexible manipulator. The proposed method finds the joint torques necessary to produce a specified end effector motion. Since the inverse dynamic problem in elastic manipulators is closely coupled to the inverse kinematic problem, the solution of the first also renders the displacements and rotations at any point of the manipulator, including the joints. Furthermore the formulation is complete in the sense that it includes all the nonlinear terms due to the large rotation of the links. The Timoshenko beam theory is used to model the elastic characteristics, and the resulting equations of motion are discretized using the finite element method. An iterative solution scheme is proposed that relies on local linearization of the problem. The solution of each linearization is carried out in the frequency domain. The performance and capabilities of this technique are tested through simulation analysis. Results show the potential use of this method for the smooth motion control of space telerobots.

Improve earthquake hypocenter using adaptive simulated annealing inversion in regional tectonic, volcano tectonic, and geothermal observation

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

Ry, Rexha Verdhora, E-mail: rexha.vry@gmail.com; Nugraha, Andri Dian, E-mail: nugraha@gf.itb.ac.id

Observation of earthquakes is routinely used widely in tectonic activity observation, and also in local scale such as volcano tectonic and geothermal activity observation. It is necessary for determining the location of precise hypocenter which the process involves finding a hypocenter location that has minimum error between the observed and the calculated travel times. When solving this nonlinear inverse problem, simulated annealing inversion method can be applied to such global optimization problems, which the convergence of its solution is independent of the initial model. In this study, we developed own program codeby applying adaptive simulated annealing inversion in Matlab environment.more » We applied this method to determine earthquake hypocenter using several data cases which are regional tectonic, volcano tectonic, and geothermal field. The travel times were calculated using ray tracing shooting method. We then compared its results with the results using Geiger’s method to analyze its reliability. Our results show hypocenter location has smaller RMS error compared to the Geiger’s result that can be statistically associated with better solution. The hypocenter of earthquakes also well correlated with geological structure in the study area. Werecommend using adaptive simulated annealing inversion to relocate hypocenter location in purpose to get precise and accurate earthquake location.« less

An inverse dynamics approach to trajectory optimization for an aerospace plane

NASA Technical Reports Server (NTRS)

Lu, Ping

1992-01-01

An inverse dynamics approach for trajectory optimization is proposed. This technique can be useful in many difficult trajectory optimization and control problems. The application of the approach is exemplified by ascent trajectory optimization for an aerospace plane. Both minimum-fuel and minimax types of performance indices are considered. When rocket augmentation is available for ascent, it is shown that accurate orbital insertion can be achieved through the inverse control of the rocket in the presence of disturbances.

Gravity Wave Dynamics in a Mesospheric Inversion Layer: 1. Reflection, Trapping, and Instability Dynamics

PubMed Central

Laughman, Brian; Wang, Ling; Lund, Thomas S.; Collins, Richard L.

2018-01-01

Abstract An anelastic numerical model is employed to explore the dynamics of gravity waves (GWs) encountering a mesosphere inversion layer (MIL) having a moderate static stability enhancement and a layer of weaker static stability above. Instabilities occur within the MIL when the GW amplitude approaches that required for GW breaking due to compression of the vertical wavelength accompanying the increasing static stability. Thus, MILs can cause large‐amplitude GWs to yield instabilities and turbulence below the altitude where they would otherwise arise. Smaller‐amplitude GWs encountering a MIL do not lead to instability and turbulence but do exhibit partial reflection and transmission, and the transmission is a smaller fraction of the incident GW when instabilities and turbulence arise within the MIL. Additionally, greater GW transmission occurs for weaker MILs and for GWs having larger vertical wavelengths relative to the MIL depth and for lower GW intrinsic frequencies. These results imply similar dynamics for inversions due to other sources, including the tropopause inversion layer, the high stability capping the polar summer mesopause, and lower frequency GWs or tides having sufficient amplitudes to yield significant variations in stability at large and small vertical scales. MILs also imply much stronger reflections and less coherent GW propagation in environments having significant fine structure in the stability and velocity fields than in environments that are smoothly varying. PMID:29576994

Gravity Wave Dynamics in a Mesospheric Inversion Layer: 1. Reflection, Trapping, and Instability Dynamics

NASA Astrophysics Data System (ADS)

Fritts, David C.; Laughman, Brian; Wang, Ling; Lund, Thomas S.; Collins, Richard L.

2018-01-01

An anelastic numerical model is employed to explore the dynamics of gravity waves (GWs) encountering a mesosphere inversion layer (MIL) having a moderate static stability enhancement and a layer of weaker static stability above. Instabilities occur within the MIL when the GW amplitude approaches that required for GW breaking due to compression of the vertical wavelength accompanying the increasing static stability. Thus, MILs can cause large-amplitude GWs to yield instabilities and turbulence below the altitude where they would otherwise arise. Smaller-amplitude GWs encountering a MIL do not lead to instability and turbulence but do exhibit partial reflection and transmission, and the transmission is a smaller fraction of the incident GW when instabilities and turbulence arise within the MIL. Additionally, greater GW transmission occurs for weaker MILs and for GWs having larger vertical wavelengths relative to the MIL depth and for lower GW intrinsic frequencies. These results imply similar dynamics for inversions due to other sources, including the tropopause inversion layer, the high stability capping the polar summer mesopause, and lower frequency GWs or tides having sufficient amplitudes to yield significant variations in stability at large and small vertical scales. MILs also imply much stronger reflections and less coherent GW propagation in environments having significant fine structure in the stability and velocity fields than in environments that are smoothly varying.

Robust inverse kinematics using damped least squares with dynamic weighting

NASA Technical Reports Server (NTRS)

Schinstock, D. E.; Faddis, T. N.; Greenway, R. B.

1994-01-01

This paper presents a general method for calculating the inverse kinematics with singularity and joint limit robustness for both redundant and non-redundant serial-link manipulators. Damped least squares inverse of the Jacobian is used with dynamic weighting matrices in approximating the solution. This reduces specific joint differential vectors. The algorithm gives an exact solution away from the singularities and joint limits, and an approximate solution at or near the singularities and/or joint limits. The procedure is here implemented for a six d.o.f. teleoperator and a well behaved slave manipulator resulted under teleoperational control.

Short-Term Adaptive Modification of Dynamic Ocular Accommodation

PubMed Central

Bharadwaj, Shrikant R.; Vedamurthy, Indu; Schor, Clifton M.

2009-01-01

Purpose Indirect observations suggest that the neural control of accommodation may undergo adaptive recalibration in response to age-related biomechanical changes in the accommodative system. However, there has been no direct demonstration of such an adaptive capability. This investigation was conducted to demonstrate short-term adaptation of accommodative step response dynamics to optically induced changes in neuromuscular demands. Methods Repetitive changes in accommodative effort were induced in 15 subjects (18–34 years) with a double-step adaptation paradigm wherein an initial 2-D step change in blur was followed 350 ms later by either a 2-D step increase in blur (increasing-step paradigm) or a 1.75-D step decrease in blur (decreasing-step paradigm). Peak velocity, peak acceleration, and latency of 2-D single-step test responses were assessed before and after 1.5 hours of training with these paradigms. Results Peak velocity and peak acceleration of 2-D step responses increased after adaptation to the increasing-step paradigm (9/12 subjects), and they decreased after adaptation to the decreasing-step paradigm (4/9 subjects). Adaptive changes in peak velocity and peak acceleration generalized to responses that were smaller (1 D) and larger (3 D) than the 2-D adaptation stimulus. The magnitude of adaptation correlated poorly with the subject's age, but it was significantly negatively correlated with the preadaptation dynamics. Response latency decreased after adaptation, irrespective of the direction of adaptation. Conclusions Short-term adaptive changes in accommodative step response dynamics could be induced, at least in some of our subjects between 18 and 34 years, with a directional bias toward increasing rather than decreasing the dynamics. PMID:19255153

A robust bi-orthogonal/dynamically-orthogonal method using the covariance pseudo-inverse with application to stochastic flow problems

NASA Astrophysics Data System (ADS)

Babaee, Hessam; Choi, Minseok; Sapsis, Themistoklis P.; Karniadakis, George Em

2017-09-01

We develop a new robust methodology for the stochastic Navier-Stokes equations based on the dynamically-orthogonal (DO) and bi-orthogonal (BO) methods [1-3]. Both approaches are variants of a generalized Karhunen-Loève (KL) expansion in which both the stochastic coefficients and the spatial basis evolve according to system dynamics, hence, capturing the low-dimensional structure of the solution. The DO and BO formulations are mathematically equivalent [3], but they exhibit computationally complimentary properties. Specifically, the BO formulation may fail due to crossing of the eigenvalues of the covariance matrix, while both BO and DO become unstable when there is a high condition number of the covariance matrix or zero eigenvalues. To this end, we combine the two methods into a robust hybrid framework and in addition we employ a pseudo-inverse technique to invert the covariance matrix. The robustness of the proposed method stems from addressing the following issues in the DO/BO formulation: (i) eigenvalue crossing: we resolve the issue of eigenvalue crossing in the BO formulation by switching to the DO near eigenvalue crossing using the equivalence theorem and switching back to BO when the distance between eigenvalues is larger than a threshold value; (ii) ill-conditioned covariance matrix: we utilize a pseudo-inverse strategy to invert the covariance matrix; (iii) adaptivity: we utilize an adaptive strategy to add/remove modes to resolve the covariance matrix up to a threshold value. In particular, we introduce a soft-threshold criterion to allow the system to adapt to the newly added/removed mode and therefore avoid repetitive and unnecessary mode addition/removal. When the total variance approaches zero, we show that the DO/BO formulation becomes equivalent to the evolution equation of the Optimally Time-Dependent modes [4]. We demonstrate the capability of the proposed methodology with several numerical examples, namely (i) stochastic Burgers equation: we

Time course of dynamic range adaptation in the auditory nerve

PubMed Central

Wang, Grace I.; Dean, Isabel; Delgutte, Bertrand

2012-01-01

Auditory adaptation to sound-level statistics occurs as early as in the auditory nerve (AN), the first stage of neural auditory processing. In addition to firing rate adaptation characterized by a rate decrement dependent on previous spike activity, AN fibers show dynamic range adaptation, which is characterized by a shift of the rate-level function or dynamic range toward the most frequently occurring levels in a dynamic stimulus, thereby improving the precision of coding of the most common sound levels (Wen B, Wang GI, Dean I, Delgutte B. J Neurosci 29: 13797–13808, 2009). We investigated the time course of dynamic range adaptation by recording from AN fibers with a stimulus in which the sound levels periodically switch from one nonuniform level distribution to another (Dean I, Robinson BL, Harper NS, McAlpine D. J Neurosci 28: 6430–6438, 2008). Dynamic range adaptation occurred rapidly, but its exact time course was difficult to determine directly from the data because of the concomitant firing rate adaptation. To characterize the time course of dynamic range adaptation without the confound of firing rate adaptation, we developed a phenomenological “dual adaptation” model that accounts for both forms of AN adaptation. When fitted to the data, the model predicts that dynamic range adaptation occurs as rapidly as firing rate adaptation, over 100–400 ms, and the time constants of the two forms of adaptation are correlated. These findings suggest that adaptive processing in the auditory periphery in response to changes in mean sound level occurs rapidly enough to have significant impact on the coding of natural sounds. PMID:22457465

Inverse Transformation: Unleashing Spatially Heterogeneous Dynamics with an Alternative Approach to XPCS Data Analysis.

PubMed

Andrews, Ross N; Narayanan, Suresh; Zhang, Fan; Kuzmenko, Ivan; Ilavsky, Jan

2018-02-01

X-ray photon correlation spectroscopy (XPCS), an extension of dynamic light scattering (DLS) in the X-ray regime, detects temporal intensity fluctuations of coherent speckles and provides scattering vector-dependent sample dynamics at length scales smaller than DLS. The penetrating power of X-rays enables probing dynamics in a broad array of materials with XPCS, including polymers, glasses and metal alloys, where attempts to describe the dynamics with a simple exponential fit usually fails. In these cases, the prevailing XPCS data analysis approach employs stretched or compressed exponential decay functions (Kohlrausch functions), which implicitly assume homogeneous dynamics. In this paper, we propose an alternative analysis scheme based upon inverse Laplace or Gaussian transformation for elucidating heterogeneous distributions of dynamic time scales in XPCS, an approach analogous to the CONTIN algorithm widely accepted in the analysis of DLS from polydisperse and multimodal systems. Using XPCS data measured from colloidal gels, we demonstrate the inverse transform approach reveals hidden multimodal dynamics in materials, unleashing the full potential of XPCS.

Recruitment dynamics in adaptive social networks

NASA Astrophysics Data System (ADS)

Shkarayev, Maxim; Shaw, Leah; Schwartz, Ira

2011-03-01

We model recruitment in social networks in the presence of birth and death processes. The recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. The recruiting nodes may adapt their connections in order to improve recruitment capabilities, thus changing the network structure. We develop a mean-field theory describing the system dynamics. Using mean-field theory we characterize the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment dynamics, as well as on network topology. The theoretical predictions are confirmed by the direct simulations of the full system.

Spin pumping and inverse spin Hall voltages from dynamical antiferromagnets

NASA Astrophysics Data System (ADS)

Johansen, Øyvind; Brataas, Arne

2017-06-01

Dynamical antiferromagnets can pump spins into adjacent conductors. The high antiferromagnetic resonance frequencies represent a challenge for experimental detection, but magnetic fields can reduce these resonance frequencies. We compute the ac and dc inverse spin Hall voltages resulting from dynamical spin excitations as a function of a magnetic field along the easy axis and the polarization of the driving ac magnetic field perpendicular to the easy axis. We consider the insulating antiferromagnets MnF2,FeF2, and NiO. Near the spin-flop transition, there is a significant enhancement of the dc spin pumping and inverse spin Hall voltage for the uniaxial antiferromagnets MnF2 and FeF2. In the uniaxial antiferromagnets it is also found that the ac spin pumping is independent of the external magnetic field when the driving field has the optimal circular polarization. In the biaxial NiO, the voltages are much weaker, and there is no spin-flop enhancement of the dc component.

Adaptive-network models of collective dynamics

NASA Astrophysics Data System (ADS)

Zschaler, G.

2012-09-01

Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom. In this work, which is essentially my PhD thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system's collective and long-term behaviour by applying tools from dynamical systems theory. We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge

Symmetry realization via a dynamical inverse Higgs mechanism

NASA Astrophysics Data System (ADS)

Rothstein, Ira Z.; Shrivastava, Prashant

2018-05-01

The Ward identities associated with spontaneously broken symmetries can be saturated by Goldstone bosons. However, when space-time symmetries are broken, the number of Goldstone bosons necessary to non-linearly realize the symmetry can be less than the number of broken generators. The loss of Goldstones may be due to a redundancy or the generation of a gap. In either case the associated Goldstone may be removed from the spectrum. This phenomena is called an Inverse Higgs Mechanism (IHM) and its appearance has a well defined mathematical condition. However, there are cases when a Goldstone boson associated with a broken generator does not appear in the low energy theory despite the lack of the existence of an associated IHM. In this paper we will show that in such cases the relevant broken symmetry can be realized, without the aid of an associated Goldstone, if there exists a proper set of operator constraints, which we call a Dynamical Inverse Higgs Mechanism (DIHM). We consider the spontaneous breaking of boosts, rotations and conformal transformations in the context of Fermi liquids, finding three possible paths to symmetry realization: pure Goldstones, no Goldstones and DIHM, or some mixture thereof. We show that in the two dimensional degenerate electron system the DIHM route is the only consistent way to realize spontaneously broken boosts and dilatations, while in three dimensions these symmetries could just as well be realized via the inclusion of non-derivatively coupled Goldstone bosons. We present the action, including the leading order non-linearities, for the rotational Goldstone (angulon), and discuss the constraint associated with the possible DIHM that would need to be imposed to remove it from the spectrum. Finally we discuss the conditions under which Goldstone bosons are non-derivatively coupled, a necessary condition for the existence of a Dynamical Inverse Higgs Constraint (DIHC), generalizing the results for Vishwanath and Wantanabe.

Adaptive EAGLE dynamic solution adaptation and grid quality enhancement

NASA Technical Reports Server (NTRS)

Luong, Phu Vinh; Thompson, J. F.; Gatlin, B.; Mastin, C. W.; Kim, H. J.

1992-01-01

In the effort described here, the elliptic grid generation procedure in the EAGLE grid code was separated from the main code into a subroutine, and a new subroutine which evaluates several grid quality measures at each grid point was added. The elliptic grid routine can now be called, either by a computational fluid dynamics (CFD) code to generate a new adaptive grid based on flow variables and quality measures through multiple adaptation, or by the EAGLE main code to generate a grid based on quality measure variables through static adaptation. Arrays of flow variables can be read into the EAGLE grid code for use in static adaptation as well. These major changes in the EAGLE adaptive grid system make it easier to convert any CFD code that operates on a block-structured grid (or single-block grid) into a multiple adaptive code.

Inverse transformation: unleashing spatially heterogeneous dynamics with an alternative approach to XPCS data analysis

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

Andrews, Ross N.; Narayanan, Suresh; Zhang, Fan

X-ray photon correlation spectroscopy (XPCS), an extension of dynamic light scattering (DLS) in the X-ray regime, detects temporal intensity fluctuations of coherent speckles and provides scattering-vector-dependent sample dynamics at length scales smaller than DLS. The penetrating power of X-rays enables XPCS to probe the dynamics in a broad array of materials, including polymers, glasses and metal alloys, where attempts to describe the dynamics with a simple exponential fit usually fail. In these cases, the prevailing XPCS data analysis approach employs stretched or compressed exponential decay functions (Kohlrausch functions), which implicitly assume homogeneous dynamics. This paper proposes an alternative analysis schememore » based upon inverse Laplace or Gaussian transformation for elucidating heterogeneous distributions of dynamic time scales in XPCS, an approach analogous to the CONTIN algorithm widely accepted in the analysis of DLS from polydisperse and multimodal systems. In conclusion, using XPCS data measured from colloidal gels, it is demonstrated that the inverse transform approach reveals hidden multimodal dynamics in materials, unleashing the full potential of XPCS.« less

Inverse transformation: unleashing spatially heterogeneous dynamics with an alternative approach to XPCS data analysis

DOE PAGES

Andrews, Ross N.; Narayanan, Suresh; Zhang, Fan; ...

2018-02-01

X-ray photon correlation spectroscopy (XPCS), an extension of dynamic light scattering (DLS) in the X-ray regime, detects temporal intensity fluctuations of coherent speckles and provides scattering-vector-dependent sample dynamics at length scales smaller than DLS. The penetrating power of X-rays enables XPCS to probe the dynamics in a broad array of materials, including polymers, glasses and metal alloys, where attempts to describe the dynamics with a simple exponential fit usually fail. In these cases, the prevailing XPCS data analysis approach employs stretched or compressed exponential decay functions (Kohlrausch functions), which implicitly assume homogeneous dynamics. This paper proposes an alternative analysis schememore » based upon inverse Laplace or Gaussian transformation for elucidating heterogeneous distributions of dynamic time scales in XPCS, an approach analogous to the CONTIN algorithm widely accepted in the analysis of DLS from polydisperse and multimodal systems. In conclusion, using XPCS data measured from colloidal gels, it is demonstrated that the inverse transform approach reveals hidden multimodal dynamics in materials, unleashing the full potential of XPCS.« less

Inverse Transformation: Unleashing Spatially Heterogeneous Dynamics with an Alternative Approach to XPCS Data Analysis

PubMed Central

Andrews, Ross N.; Narayanan, Suresh; Zhang, Fan; Kuzmenko, Ivan; Ilavsky, Jan

2018-01-01

X-ray photon correlation spectroscopy (XPCS), an extension of dynamic light scattering (DLS) in the X-ray regime, detects temporal intensity fluctuations of coherent speckles and provides scattering vector-dependent sample dynamics at length scales smaller than DLS. The penetrating power of X-rays enables probing dynamics in a broad array of materials with XPCS, including polymers, glasses and metal alloys, where attempts to describe the dynamics with a simple exponential fit usually fails. In these cases, the prevailing XPCS data analysis approach employs stretched or compressed exponential decay functions (Kohlrausch functions), which implicitly assume homogeneous dynamics. In this paper, we propose an alternative analysis scheme based upon inverse Laplace or Gaussian transformation for elucidating heterogeneous distributions of dynamic time scales in XPCS, an approach analogous to the CONTIN algorithm widely accepted in the analysis of DLS from polydisperse and multimodal systems. Using XPCS data measured from colloidal gels, we demonstrate the inverse transform approach reveals hidden multimodal dynamics in materials, unleashing the full potential of XPCS. PMID:29875506

Inverse dynamics of adaptive structures used as space cranes

NASA Technical Reports Server (NTRS)

Das, S. K.; Utku, S.; Wada, B. K.

1990-01-01

As a precursor to the real-time control of fast moving adaptive structures used as space cranes, a formulation is given for the flexibility induced motion relative to the nominal motion (i.e., the motion that assumes no flexibility) and for obtaining the open loop time varying driving forces. An algorithm is proposed for the computation of the relative motion and driving forces. The governing equations are given in matrix form with explicit functional dependencies. A simulator is developed to implement the algorithm on a digital computer. In the formulations, the distributed mass of the crane is lumped by two schemes, vz., 'trapezoidal' lumping and 'Simpson's rule' lumping. The effects of the mass lumping schemes are shown by simulator runs.

Coalescent patterns for chromosomal inversions in divergent populations

PubMed Central

Guerrero, Rafael F.; Rousset, François; Kirkpatrick, Mark

2012-01-01

Chromosomal inversions allow genetic divergence of locally adapted populations by reducing recombination between chromosomes with different arrangements. Divergence between populations (or hybridization between species) is expected to leave signatures in the neutral genetic diversity of the inverted region. Quantitative expectations for these patterns, however, have not been obtained. Here, we develop coalescent models of neutral sites linked to an inversion polymorphism in two locally adapted populations. We consider two scenarios of local adaptation: selection on the inversion breakpoints and selection on alleles inside the inversion. We find that ancient inversion polymorphisms cause genetic diversity to depart dramatically from neutral expectations. Other situations, however, lead to patterns that may be difficult to detect; important determinants are the age of the inversion and the rate of gene flux between arrangements. We also study inversions under genetic drift, finding that they produce patterns similar to locally adapted inversions of intermediate age. Our results are consistent with empirical observations, and provide the foundation for quantitative analyses of the roles that inversions have played in speciation. PMID:22201172

Total variation regularization for seismic waveform inversion using an adaptive primal dual hybrid gradient method

NASA Astrophysics Data System (ADS)

Yong, Peng; Liao, Wenyuan; Huang, Jianping; Li, Zhenchuan

2018-04-01

Full waveform inversion is an effective tool for recovering the properties of the Earth from seismograms. However, it suffers from local minima caused mainly by the limited accuracy of the starting model and the lack of a low-frequency component in the seismic data. Because of the high velocity contrast between salt and sediment, the relation between the waveform and velocity perturbation is strongly nonlinear. Therefore, salt inversion can easily get trapped in the local minima. Since the velocity of salt is nearly constant, we can make the most of this characteristic with total variation regularization to mitigate the local minima. In this paper, we develop an adaptive primal dual hybrid gradient method to implement total variation regularization by projecting the solution onto a total variation norm constrained convex set, through which the total variation norm constraint is satisfied at every model iteration. The smooth background velocities are first inverted and the perturbations are gradually obtained by successively relaxing the total variation norm constraints. Numerical experiment of the projection of the BP model onto the intersection of the total variation norm and box constraints has demonstrated the accuracy and efficiency of our adaptive primal dual hybrid gradient method. A workflow is designed to recover complex salt structures in the BP 2004 model and the 2D SEG/EAGE salt model, starting from a linear gradient model without using low-frequency data below 3 Hz. The salt inversion processes demonstrate that wavefield reconstruction inversion with a total variation norm and box constraints is able to overcome local minima and inverts the complex salt velocity layer by layer.

Performance comparisons on spatial lattice algorithm and direct matrix inverse method with application to adaptive arrays processing

NASA Technical Reports Server (NTRS)

An, S. H.; Yao, K.

1986-01-01

Lattice algorithm has been employed in numerous adaptive filtering applications such as speech analysis/synthesis, noise canceling, spectral analysis, and channel equalization. In this paper the application to adaptive-array processing is discussed. The advantages are fast convergence rate as well as computational accuracy independent of the noise and interference conditions. The results produced by this technique are compared to those obtained by the direct matrix inverse method.

Success Stories in Control: Nonlinear Dynamic Inversion Control

NASA Technical Reports Server (NTRS)

Bosworth, John T.

2010-01-01

NASA plays an important role in advancing the state of the art in flight control systems. In the case of Nonlinear Dynamic Inversion (NDI) NASA supported initial implementation of the theory in an aircraft and demonstration in a space vehicle. Dr. Dale Enns of Honeywell Aerospace Advanced Technology performed this work in cooperation with NASA and under NASA contract. Honeywell and Lockheed Martin were subsequently contracted by AFRL to create "Design Guidelines for Multivariable Control Theory". This foundational work directly contributed to the advancement of the technology and the credibility of the control law as a design option. As a result Honeywell collaborated with Lockheed Martin to produce a Nonlinear Dynamic Inversion controller for the X-35 and subsequently Lockheed Martin did the same for the production Lockheed Martin F-35 vehicle. The theory behind NDI is to use a systematic generalized approach to controlling a vehicle. Using general aircraft nonlinear equations of motion and onboard aerodynamic, mass properties, and engine models specific to the vehicle, a relationship between control effectors and desired aircraft motion can be formulated. Using this formulation a control combination is used that provides a predictable response to commanded motion. Control loops around this formulation shape the response as desired and provide robustness to modeling errors. Once the control law is designed it can be used on a similar class of vehicle with only an update to the vehicle specific onboard models.

GARCH modelling of covariance in dynamical estimation of inverse solutions

NASA Astrophysics Data System (ADS)

Galka, Andreas; Yamashita, Okito; Ozaki, Tohru

2004-12-01

The problem of estimating unobserved states of spatially extended dynamical systems poses an inverse problem, which can be solved approximately by a recently developed variant of Kalman filtering; in order to provide the model of the dynamics with more flexibility with respect to space and time, we suggest to combine the concept of GARCH modelling of covariance, well known in econometrics, with Kalman filtering. We formulate this algorithm for spatiotemporal systems governed by stochastic diffusion equations and demonstrate its feasibility by presenting a numerical simulation designed to imitate the situation of the generation of electroencephalographic recordings by the human cortex.

Identification of dynamic characteristics of flexible rotors as dynamic inverse problem

NASA Technical Reports Server (NTRS)

Roisman, W. P.; Vajingortin, L. D.

1991-01-01

The problem of dynamic and balancing of flexible rotors were considered, which were set and solved as the problem of the identification of flexible rotor systems, which is the same as the inverse problem of the oscillation theory dealing with the task of the identifying the outside influences and system parameters on the basis of the known laws of motion. This approach to the problem allows the disclosure the picture of disbalances throughout the rotor-under-test (which traditional methods of flexible rotor balancing, based on natural oscillations, could not provide), and identify dynamic characteristics of the system, which correspond to a selected mathematical model. Eventually, various methods of balancing were developed depending on the special features of the machines as to their design, technology, and operation specifications. Also, theoretical and practical methods are given for the flexible rotor balancing at far from critical rotation frequencies, which does not necessarily require the knowledge forms of oscillation, dissipation, and elasticity and inertia characteristics, and to use testing masses.

Dynamical information encoding in neural adaptation.

PubMed

Luozheng Li; Wenhao Zhang; Yuanyuan Mi; Dahui Wang; Xiaohan Lin; Si Wu

2016-08-01

Adaptation refers to the general phenomenon that a neural system dynamically adjusts its response property according to the statistics of external inputs. In response to a prolonged constant stimulation, neuronal firing rates always first increase dramatically at the onset of the stimulation; and afterwards, they decrease rapidly to a low level close to background activity. This attenuation of neural activity seems to be contradictory to our experience that we can still sense the stimulus after the neural system is adapted. Thus, it prompts a question: where is the stimulus information encoded during the adaptation? Here, we investigate a computational model in which the neural system employs a dynamical encoding strategy during the neural adaptation: at the early stage of the adaptation, the stimulus information is mainly encoded in the strong independent firings; and as time goes on, the information is shifted into the weak but concerted responses of neurons. We find that short-term plasticity, a general feature of synapses, provides a natural mechanism to achieve this goal. Furthermore, we demonstrate that with balanced excitatory and inhibitory inputs, this correlation-based information can be read out efficiently. The implications of this study on our understanding of neural information encoding are discussed.

Ultra-Low Power Dynamic Knob in Adaptive Compressed Sensing Towards Biosignal Dynamics.

PubMed

Wang, Aosen; Lin, Feng; Jin, Zhanpeng; Xu, Wenyao

2016-06-01

Compressed sensing (CS) is an emerging sampling paradigm in data acquisition. Its integrated analog-to-information structure can perform simultaneous data sensing and compression with low-complexity hardware. To date, most of the existing CS implementations have a fixed architectural setup, which lacks flexibility and adaptivity for efficient dynamic data sensing. In this paper, we propose a dynamic knob (DK) design to effectively reconfigure the CS architecture by recognizing the biosignals. Specifically, the dynamic knob design is a template-based structure that comprises a supervised learning module and a look-up table module. We model the DK performance in a closed analytic form and optimize the design via a dynamic programming formulation. We present the design on a 130 nm process, with a 0.058 mm (2) fingerprint and a 187.88 nJ/event energy-consumption. Furthermore, we benchmark the design performance using a publicly available dataset. Given the energy constraint in wireless sensing, the adaptive CS architecture can consistently improve the signal reconstruction quality by more than 70%, compared with the traditional CS. The experimental results indicate that the ultra-low power dynamic knob can provide an effective adaptivity and improve the signal quality in compressed sensing towards biosignal dynamics.

Dynamic range adaptation in primary motor cortical populations

PubMed Central

Rasmussen, Robert G; Schwartz, Andrew; Chase, Steven M

2017-01-01

Neural populations from various sensory regions demonstrate dynamic range adaptation in response to changes in the statistical distribution of their input stimuli. These adaptations help optimize the transmission of information about sensory inputs. Here, we show a similar effect in the firing rates of primary motor cortical cells. We trained monkeys to operate a brain-computer interface in both two- and three-dimensional virtual environments. We found that neurons in primary motor cortex exhibited a change in the amplitude of their directional tuning curves between the two tasks. We then leveraged the simultaneous nature of the recordings to test several hypotheses about the population-based mechanisms driving these changes and found that the results are most consistent with dynamic range adaptation. Our results demonstrate that dynamic range adaptation is neither limited to sensory regions nor to rescaling of monotonic stimulus intensity tuning curves, but may rather represent a canonical feature of neural encoding. DOI: http://dx.doi.org/10.7554/eLife.21409.001 PMID:28417848

Dynamic experiment design regularization approach to adaptive imaging with array radar/SAR sensor systems.

PubMed

Shkvarko, Yuriy; Tuxpan, José; Santos, Stewart

2011-01-01

We consider a problem of high-resolution array radar/SAR imaging formalized in terms of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from a remotely sensed scene observed through a kernel signal formation operator and contaminated with random Gaussian noise. First, the Sobolev-type solution space is constructed to specify the class of consistent kernel SSP estimators with the reproducing kernel structures adapted to the metrics in such the solution space. Next, the "model-free" variational analysis (VA)-based image enhancement approach and the "model-based" descriptive experiment design (DEED) regularization paradigm are unified into a new dynamic experiment design (DYED) regularization framework. Application of the proposed DYED framework to the adaptive array radar/SAR imaging problem leads to a class of two-level (DEED-VA) regularized SSP reconstruction techniques that aggregate the kernel adaptive anisotropic windowing with the projections onto convex sets to enforce the consistency and robustness of the overall iterative SSP estimators. We also show how the proposed DYED regularization method may be considered as a generalization of the MVDR, APES and other high-resolution nonparametric adaptive radar sensing techniques. A family of the DYED-related algorithms is constructed and their effectiveness is finally illustrated via numerical simulations.

Thermal Diagnostics with the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory: A Validated Method for Differential Emission Measure Inversions

NASA Astrophysics Data System (ADS)

Cheung, Mark C. M.; Boerner, P.; Schrijver, C. J.; Testa, P.; Chen, F.; Peter, H.; Malanushenko, A.

2015-07-01

We present a new method for performing differential emission measure (DEM) inversions on narrow-band EUV images from the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory. The method yields positive definite DEM solutions by solving a linear program. This method has been validated against a diverse set of thermal models of varying complexity and realism. These include (1) idealized Gaussian DEM distributions, (2) 3D models of NOAA Active Region 11158 comprising quasi-steady loop atmospheres in a nonlinear force-free field, and (3) thermodynamic models from a fully compressible, 3D MHD simulation of active region (AR) corona formation following magnetic flux emergence. We then present results from the application of the method to AIA observations of Active Region 11158, comparing the region's thermal structure on two successive solar rotations. Additionally, we show how the DEM inversion method can be adapted to simultaneously invert AIA and Hinode X-ray Telescope data, and how supplementing AIA data with the latter improves the inversion result. The speed of the method allows for routine production of DEM maps, thus facilitating science studies that require tracking of the thermal structure of the solar corona in time and space.

A fast new algorithm for a robot neurocontroller using inverse QR decomposition

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

Morris, A.S.; Khemaissia, S.

2000-01-01

A new adaptive neural network controller for robots is presented. The controller is based on direct adaptive techniques. Unlike many neural network controllers in the literature, inverse dynamical model evaluation is not required. A numerically robust, computationally efficient processing scheme for neutral network weight estimation is described, namely, the inverse QR decomposition (INVQR). The inverse QR decomposition and a weighted recursive least-squares (WRLS) method for neural network weight estimation is derived using Cholesky factorization of the data matrix. The algorithm that performs the efficient INVQR of the underlying space-time data matrix may be implemented in parallel on a triangular array.more » Furthermore, its systolic architecture is well suited for VLSI implementation. Another important benefit is well suited for VLSI implementation. Another important benefit of the INVQR decomposition is that it solves directly for the time-recursive least-squares filter vector, while avoiding the sequential back-substitution step required by the QR decomposition approaches.« less

Criticality of Adaptive Control Dynamics

NASA Astrophysics Data System (ADS)

Patzelt, Felix; Pawelzik, Klaus

2011-12-01

We show, that stabilization of a dynamical system can annihilate observable information about its structure. This mechanism induces critical points as attractors in locally adaptive control. It also reveals, that previously reported criticality in simple controllers is caused by adaptation and not by other controller details. We apply these results to a real-system example: human balancing behavior. A model of predictive adaptive closed-loop control subject to some realistic constraints is introduced and shown to reproduce experimental observations in unprecedented detail. Our results suggests, that observed error distributions in between the Lévy and Gaussian regimes may reflect a nearly optimal compromise between the elimination of random local trends and rare large errors.

Adaptive Control Using Neural Network Augmentation for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.; Williams-Hayes, Peggy; Karneshige, J. T.; Stachowiak, Susan J.

2006-01-01

Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

CONTIN XPCS: Software for Inverse Transform Analysis of X-Ray Photon Correlation Spectroscopy Dynamics

PubMed Central

Narayanan, Suresh; Zhang, Fan; Kuzmenko, Ivan; Ilavsky, Jan

2018-01-01

X-ray photon correlation spectroscopy (XPCS) and dynamic light scattering (DLS) both reveal dynamics using coherent scattering, but X-rays permit investigating of dynamics in a much more diverse array of materials. Heterogeneous dynamics occur in many such materials, and we showed how classic tools employed in analysis of heterogeneous DLS dynamics extend to XPCS, revealing additional information that conventional Kohlrausch exponential fitting obscures. This work presents the software implementation of inverse transform analysis of XPCS data called CONTIN XPCS, an extension of traditional CONTIN that accommodates dynamics encountered in equilibrium XPCS measurements. PMID:29875507

CONTIN XPCS: Software for Inverse Transform Analysis of X-Ray Photon Correlation Spectroscopy Dynamics.

PubMed

Andrews, Ross N; Narayanan, Suresh; Zhang, Fan; Kuzmenko, Ivan; Ilavsky, Jan

2018-02-01

X-ray photon correlation spectroscopy (XPCS) and dynamic light scattering (DLS) both reveal dynamics using coherent scattering, but X-rays permit investigating of dynamics in a much more diverse array of materials. Heterogeneous dynamics occur in many such materials, and we showed how classic tools employed in analysis of heterogeneous DLS dynamics extend to XPCS, revealing additional information that conventional Kohlrausch exponential fitting obscures. This work presents the software implementation of inverse transform analysis of XPCS data called CONTIN XPCS, an extension of traditional CONTIN that accommodates dynamics encountered in equilibrium XPCS measurements.

Eco-Evolutionary Genomics of Chromosomal Inversions.

PubMed

Wellenreuther, Maren; Bernatchez, Louis

2018-05-03

Chromosomal inversions have long fascinated evolutionary biologists due to their suppression of recombination, which can protect co-adapted alleles. Emerging research documents that inversions are commonly linked to spectacular phenotypes and have a pervasive role in eco-evolutionary processes, from mating systems, social organisation, environmental adaptation, and reproductive isolation to speciation. Studies also reveal that inversions are taxonomically widespread, with many being old and large, and that balancing selection is commonly facilitating their maintenance. This challenges the traditional view that the role of balancing selection in maintaining variation is relatively minor. The ubiquitous importance of inversions in ecological and evolutionary processes suggests that structural variation should be better acknowledged and integrated in studies pertaining to the molecular basis of adaptation and speciation. Copyright © 2018 Elsevier Ltd. All rights reserved.

Feasible Muscle Activation Ranges Based on Inverse Dynamics Analyses of Human Walking

PubMed Central

Simpson, Cole S.; Sohn, M. Hongchul; Allen, Jessica L.; Ting, Lena H.

2015-01-01

Although it is possible to produce the same movement using an infinite number of different muscle activation patterns owing to musculoskeletal redundancy, the degree to which observed variations in muscle activity can deviate from optimal solutions computed from biomechanical models is not known. Here, we examined the range of biomechanically permitted activation levels in individual muscles during human walking using a detailed musculoskeletal model and experimentally-measured kinetics and kinematics. Feasible muscle activation ranges define the minimum and maximum possible level of each muscle’s activation that satisfy inverse dynamics joint torques assuming that all other muscles can vary their activation as needed. During walking, 73% of the muscles had feasible muscle activation ranges that were greater than 95% of the total muscle activation range over more than 95% of the gait cycle, indicating that, individually, most muscles could be fully active or fully inactive while still satisfying inverse dynamics joint torques. Moreover, the shapes of the feasible muscle activation ranges did not resemble previously-reported muscle activation patterns nor optimal solutions, i.e. static optimization and computed muscle control, that are based on the same biomechanical constraints. Our results demonstrate that joint torque requirements from standard inverse dynamics calculations are insufficient to define the activation of individual muscles during walking in healthy individuals. Identifying feasible muscle activation ranges may be an effective way to evaluate the impact of additional biomechanical and/or neural constraints on possible versus actual muscle activity in both normal and impaired movements. PMID:26300401

Sampling-free Bayesian inversion with adaptive hierarchical tensor representations

NASA Astrophysics Data System (ADS)

Eigel, Martin; Marschall, Manuel; Schneider, Reinhold

2018-03-01

A sampling-free approach to Bayesian inversion with an explicit polynomial representation of the parameter densities is developed, based on an affine-parametric representation of a linear forward model. This becomes feasible due to the complete treatment in function spaces, which requires an efficient model reduction technique for numerical computations. The advocated perspective yields the crucial benefit that error bounds can be derived for all occuring approximations, leading to provable convergence subject to the discretization parameters. Moreover, it enables a fully adaptive a posteriori control with automatic problem-dependent adjustments of the employed discretizations. The method is discussed in the context of modern hierarchical tensor representations, which are used for the evaluation of a random PDE (the forward model) and the subsequent high-dimensional quadrature of the log-likelihood, alleviating the ‘curse of dimensionality’. Numerical experiments demonstrate the performance and confirm the theoretical results.

Automated adaptive inference of phenomenological dynamical models.

PubMed

Daniels, Bryan C; Nemenman, Ilya

2015-08-21

Dynamics of complex systems is often driven by large and intricate networks of microscopic interactions, whose sheer size obfuscates understanding. With limited experimental data, many parameters of such dynamics are unknown, and thus detailed, mechanistic models risk overfitting and making faulty predictions. At the other extreme, simple ad hoc models often miss defining features of the underlying systems. Here we develop an approach that instead constructs phenomenological, coarse-grained models of network dynamics that automatically adapt their complexity to the available data. Such adaptive models produce accurate predictions even when microscopic details are unknown. The approach is computationally tractable, even for a relatively large number of dynamical variables. Using simulated data, it correctly infers the phase space structure for planetary motion, avoids overfitting in a biological signalling system and produces accurate predictions for yeast glycolysis with tens of data points and over half of the interacting species unobserved.

Automated adaptive inference of phenomenological dynamical models

PubMed Central

Daniels, Bryan C.; Nemenman, Ilya

2015-01-01

Dynamics of complex systems is often driven by large and intricate networks of microscopic interactions, whose sheer size obfuscates understanding. With limited experimental data, many parameters of such dynamics are unknown, and thus detailed, mechanistic models risk overfitting and making faulty predictions. At the other extreme, simple ad hoc models often miss defining features of the underlying systems. Here we develop an approach that instead constructs phenomenological, coarse-grained models of network dynamics that automatically adapt their complexity to the available data. Such adaptive models produce accurate predictions even when microscopic details are unknown. The approach is computationally tractable, even for a relatively large number of dynamical variables. Using simulated data, it correctly infers the phase space structure for planetary motion, avoids overfitting in a biological signalling system and produces accurate predictions for yeast glycolysis with tens of data points and over half of the interacting species unobserved. PMID:26293508

Real-time inverse kinematics and inverse dynamics for lower limb applications using OpenSim

PubMed Central

Modenese, L.; Lloyd, D.G.

2017-01-01

Real-time estimation of joint angles and moments can be used for rapid evaluation in clinical, sport, and rehabilitation contexts. However, real-time calculation of kinematics and kinetics is currently based on approximate solutions or generic anatomical models. We present a real-time system based on OpenSim solving inverse kinematics and dynamics without simplifications at 2000 frame per seconds with less than 31.5ms of delay. We describe the software architecture, sensitivity analyses to minimise delays and errors, and compare offline and real-time results. This system has the potential to strongly impact current rehabilitation practices enabling the use of personalised musculoskeletal models in real-time. PMID:27723992

Real-time inverse kinematics and inverse dynamics for lower limb applications using OpenSim.

PubMed

Pizzolato, C; Reggiani, M; Modenese, L; Lloyd, D G

2017-03-01

Real-time estimation of joint angles and moments can be used for rapid evaluation in clinical, sport, and rehabilitation contexts. However, real-time calculation of kinematics and kinetics is currently based on approximate solutions or generic anatomical models. We present a real-time system based on OpenSim solving inverse kinematics and dynamics without simplifications at 2000 frame per seconds with less than 31.5 ms of delay. We describe the software architecture, sensitivity analyses to minimise delays and errors, and compare offline and real-time results. This system has the potential to strongly impact current rehabilitation practices enabling the use of personalised musculoskeletal models in real-time.

Nonlinear Dynamic Inversion Baseline Control Law: Flight-Test Results for the Full-scale Advanced Systems Testbed F/A-18 Airplane

NASA Technical Reports Server (NTRS)

Miller, Christopher J.

2011-01-01

A model reference nonlinear dynamic inversion control law has been developed to provide a baseline controller for research into simple adaptive elements for advanced flight control laws. This controller has been implemented and tested in a hardware-in-the-loop simulation and in flight. The flight results agree well with the simulation predictions and show good handling qualities throughout the tested flight envelope with some noteworthy deficiencies highlighted both by handling qualities metrics and pilot comments. Many design choices and implementation details reflect the requirements placed on the system by the nonlinear flight environment and the desire to keep the system as simple as possible to easily allow the addition of the adaptive elements. The flight-test results and how they compare to the simulation predictions are discussed, along with a discussion about how each element affected pilot opinions. Additionally, aspects of the design that performed better than expected are presented, as well as some simple improvements that will be suggested for follow-on work.

Adaptive synchronization and anticipatory dynamical systems.

PubMed

Yang, Ying-Jen; Chen, Chun-Chung; Lai, Pik-Yin; Chan, C K

2015-09-01

Many biological systems can sense periodical variations in a stimulus input and produce well-timed, anticipatory responses after the input is removed. Such systems show memory effects for retaining timing information in the stimulus and cannot be understood from traditional synchronization consideration of passive oscillatory systems. To understand this anticipatory phenomena, we consider oscillators built from excitable systems with the addition of an adaptive dynamics. With such systems, well-timed post-stimulus responses similar to those from experiments can be obtained. Furthermore, a well-known model of working memory is shown to possess similar anticipatory dynamics when the adaptive mechanism is identified with synaptic facilitation. The last finding suggests that this type of oscillator can be common in neuronal systems with plasticity.

Adaptive synchronization and anticipatory dynamical systems

NASA Astrophysics Data System (ADS)

Yang, Ying-Jen; Chen, Chun-Chung; Lai, Pik-Yin; Chan, C. K.

2015-09-01

Many biological systems can sense periodical variations in a stimulus input and produce well-timed, anticipatory responses after the input is removed. Such systems show memory effects for retaining timing information in the stimulus and cannot be understood from traditional synchronization consideration of passive oscillatory systems. To understand this anticipatory phenomena, we consider oscillators built from excitable systems with the addition of an adaptive dynamics. With such systems, well-timed post-stimulus responses similar to those from experiments can be obtained. Furthermore, a well-known model of working memory is shown to possess similar anticipatory dynamics when the adaptive mechanism is identified with synaptic facilitation. The last finding suggests that this type of oscillator can be common in neuronal systems with plasticity.

Dynamic Experiment Design Regularization Approach to Adaptive Imaging with Array Radar/SAR Sensor Systems

PubMed Central

Shkvarko, Yuriy; Tuxpan, José; Santos, Stewart

2011-01-01

We consider a problem of high-resolution array radar/SAR imaging formalized in terms of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from a remotely sensed scene observed through a kernel signal formation operator and contaminated with random Gaussian noise. First, the Sobolev-type solution space is constructed to specify the class of consistent kernel SSP estimators with the reproducing kernel structures adapted to the metrics in such the solution space. Next, the “model-free” variational analysis (VA)-based image enhancement approach and the “model-based” descriptive experiment design (DEED) regularization paradigm are unified into a new dynamic experiment design (DYED) regularization framework. Application of the proposed DYED framework to the adaptive array radar/SAR imaging problem leads to a class of two-level (DEED-VA) regularized SSP reconstruction techniques that aggregate the kernel adaptive anisotropic windowing with the projections onto convex sets to enforce the consistency and robustness of the overall iterative SSP estimators. We also show how the proposed DYED regularization method may be considered as a generalization of the MVDR, APES and other high-resolution nonparametric adaptive radar sensing techniques. A family of the DYED-related algorithms is constructed and their effectiveness is finally illustrated via numerical simulations. PMID:22163859

Chromosome inversions and ecological plasticity in the main African malaria mosquitoes

PubMed Central

Ayala, Diego; Acevedo, Pelayo; Pombi, Marco; Dia, Ibrahima; Boccolini, Daniela; Costantini, Carlo; Simard, Frédéric; Fontenille, Didier

2017-01-01

Chromosome inversions have fascinated the scientific community, mainly because of their role in the rapid adaption of different taxa to changing environments. However, the ecological traits linked to chromosome inversions have been poorly studied. Here, we investigated the roles played by 23 chromosome inversions in the adaptation of the four major African malaria mosquitoes to local environments in Africa. We studied their distribution patterns by using spatially explicit modeling and characterized the ecogeographical determinants of each inversion range. We then performed hierarchical clustering and constrained ordination analyses to assess the spatial and ecological similarities among inversions. Our results show that most inversions are environmentally structured, suggesting that they are actively involved in processes of local adaptation. Some inversions exhibited similar geographical patterns and ecological requirements among the four mosquito species, providing evidence for parallel evolution. Conversely, common inversion polymorphisms between sibling species displayed divergent ecological patterns, suggesting that they might have a different adaptive role in each species. These results are in agreement with the finding that chromosomal inversions play a role in Anopheles ecotypic adaptation. This study establishes a strong ecological basis for future genome-based analyses to elucidate the genetic mechanisms of local adaptation in these four mosquitoes. PMID:28071788

What you feel is what you see: inverse dynamics estimation underlies the resistive sensation of a delayed cursor.

PubMed

Takamuku, Shinya; Gomi, Hiroaki

2015-07-22

How our central nervous system (CNS) learns and exploits relationships between force and motion is a fundamental issue in computational neuroscience. While several lines of evidence have suggested that the CNS predicts motion states and signals from motor commands for control and perception (forward dynamics), it remains controversial whether it also performs the 'inverse' computation, i.e. the estimation of force from motion (inverse dynamics). Here, we show that the resistive sensation we experience while moving a delayed cursor, perceived purely from the change in visual motion, provides evidence of the inverse computation. To clearly specify the computational process underlying the sensation, we systematically varied the visual feedback and examined its effect on the strength of the sensation. In contrast to the prevailing theory that sensory prediction errors modulate our perception, the sensation did not correlate with errors in cursor motion due to the delay. Instead, it correlated with the amount of exposure to the forward acceleration of the cursor. This indicates that the delayed cursor is interpreted as a mechanical load, and the sensation represents its visually implied reaction force. Namely, the CNS automatically computes inverse dynamics, using visually detected motions, to monitor the dynamic forces involved in our actions. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Dynamic Adaptation in Child-Adult Language Interaction

ERIC Educational Resources Information Center

van Dijk, Marijn; van Geert, Paul; Korecky-Kröll, Katharina; Maillochon, Isabelle; Laaha, Sabine; Dressler, Wolfgang U.; Bassano, Dominique

2013-01-01

When speaking to young children, adults adapt their language to that of the child. In this article, we suggest that this child-directed speech (CDS) is the result of a transactional process of dynamic adaptation between the child and the adult. The study compares developmental trajectories of three children to those of the CDS of their caregivers.…

THERMAL DIAGNOSTICS WITH THE ATMOSPHERIC IMAGING ASSEMBLY ON BOARD THE SOLAR DYNAMICS OBSERVATORY: A VALIDATED METHOD FOR DIFFERENTIAL EMISSION MEASURE INVERSIONS

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

Cheung, Mark C. M.; Boerner, P.; Schrijver, C. J.

We present a new method for performing differential emission measure (DEM) inversions on narrow-band EUV images from the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory. The method yields positive definite DEM solutions by solving a linear program. This method has been validated against a diverse set of thermal models of varying complexity and realism. These include (1) idealized Gaussian DEM distributions, (2) 3D models of NOAA Active Region 11158 comprising quasi-steady loop atmospheres in a nonlinear force-free field, and (3) thermodynamic models from a fully compressible, 3D MHD simulation of active region (AR) corona formation following magneticmore » flux emergence. We then present results from the application of the method to AIA observations of Active Region 11158, comparing the region's thermal structure on two successive solar rotations. Additionally, we show how the DEM inversion method can be adapted to simultaneously invert AIA and Hinode X-ray Telescope data, and how supplementing AIA data with the latter improves the inversion result. The speed of the method allows for routine production of DEM maps, thus facilitating science studies that require tracking of the thermal structure of the solar corona in time and space.« less

Efficacy of an ankle brace with a subtalar locking system in inversion control in dynamic movements.

PubMed

Zhang, Songning; Wortley, Michael; Chen, Qingjian; Freedman, Julia

2009-12-01

Controlled laboratory study. To examine effectiveness of an ankle brace with a subtalar locking system in restricting ankle inversion during passive and dynamic movements. Semirigid ankle braces are considered more effective in restricting ankle inversion than other types of brace, but a semirigid brace with a subtalar locking system may be even more effective. Nineteen healthy subjects with no history of major lower extremity injuries were included in the study. Participants performed 5 trials of an ankle inversion drop test and a lateral-cutting movement without wearing a brace and while wearing either the Element (with the subtalar locking system), a Functional ankle brace, or an ASO ankle brace. A 2-way repeated-measures analysis of variance (ANOVA) was used to assess brace differences (P?.05). All 3 braces significantly reduced total passive ankle frontal plane range of motion (ROM), with the Element ankle brace being the most effective. For the inversion drop the results showed significant reductions in peak ankle inversion angle and inversion ROM for all 3 braces compared to the no brace condition; and the peak inversion velocity was also reduced for the Element brace and the Functional brace. In the lateral-cutting movement, a small but significant reduction of the peak inversion angle in early foot contact and the peak eversion velocity at push-off were seen when wearing the Element and the Functional ankle braces compared to the no brace condition. Peak vertical ground reaction force was reduced for the Element brace compared to the ASO brace and the no brace conditions. These results suggest that the tested ankle braces, especially the Element brace, provided effective restriction of ankle inversion during both passive and dynamic movements.

Dynamic mesh adaption for triangular and tetrahedral grids

NASA Technical Reports Server (NTRS)

Biswas, Rupak; Strawn, Roger

1993-01-01

The following topics are discussed: requirements for dynamic mesh adaption; linked-list data structure; edge-based data structure; adaptive-grid data structure; three types of element subdivision; mesh refinement; mesh coarsening; additional constraints for coarsening; anisotropic error indicator for edges; unstructured-grid Euler solver; inviscid 3-D wing; and mesh quality for solution-adaptive grids. The discussion is presented in viewgraph form.

CONTIN XPCS: software for inverse transform analysis of X-ray photon correlation spectroscopy dynamics

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

Andrews, Ross N.; Narayanan, Suresh; Zhang, Fan

X-ray photon correlation spectroscopy (XPCS) and dynamic light scattering (DLS) reveal materials dynamics using coherent scattering, with XPCS permitting the investigation of dynamics in a more diverse array of materials than DLS. Heterogeneous dynamics occur in many material systems. The authors' recent work has shown how classic tools employed in the DLS analysis of heterogeneous dynamics can be extended to XPCS, revealing additional information that conventional Kohlrausch exponential fitting obscures. The present work describes the software implementation of inverse transform analysis of XPCS data. This software, calledCONTIN XPCS, is an extension of traditionalCONTINanalysis and accommodates the various dynamics encountered inmore » equilibrium XPCS measurements.« less

CONTIN XPCS: software for inverse transform analysis of X-ray photon correlation spectroscopy dynamics

DOE PAGES

Andrews, Ross N.; Narayanan, Suresh; Zhang, Fan; ...

2018-02-01

X-ray photon correlation spectroscopy (XPCS) and dynamic light scattering (DLS) reveal materials dynamics using coherent scattering, with XPCS permitting the investigation of dynamics in a more diverse array of materials than DLS. Heterogeneous dynamics occur in many material systems. The authors' recent work has shown how classic tools employed in the DLS analysis of heterogeneous dynamics can be extended to XPCS, revealing additional information that conventional Kohlrausch exponential fitting obscures. The present work describes the software implementation of inverse transform analysis of XPCS data. This software, calledCONTIN XPCS, is an extension of traditionalCONTINanalysis and accommodates the various dynamics encountered inmore » equilibrium XPCS measurements.« less

Adaptive image inversion of contrast 3D echocardiography for enabling automated analysis.

PubMed

Shaheen, Anjuman; Rajpoot, Kashif

2015-08-01

Contrast 3D echocardiography (C3DE) is commonly used to enhance the visual quality of ultrasound images in comparison with non-contrast 3D echocardiography (3DE). Although the image quality in C3DE is perceived to be improved for visual analysis, however it actually deteriorates for the purpose of automatic or semi-automatic analysis due to higher speckle noise and intensity inhomogeneity. Therefore, the LV endocardial feature extraction and segmentation from the C3DE images remains a challenging problem. To address this challenge, this work proposes an adaptive pre-processing method to invert the appearance of C3DE image. The image inversion is based on an image intensity threshold value which is automatically estimated through image histogram analysis. In the inverted appearance, the LV cavity appears dark while the myocardium appears bright thus making it similar in appearance to a 3DE image. Moreover, the resulting inverted image has high contrast and low noise appearance, yielding strong LV endocardium boundary and facilitating feature extraction for segmentation. Our results demonstrate that the inverse appearance of contrast image enables the subsequent LV segmentation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Goal Directed Model Inversion: A Study of Dynamic Behavior

NASA Technical Reports Server (NTRS)

Colombano, Silvano P.; Compton, Michael; Raghavan, Bharathi; Lum, Henry, Jr. (Technical Monitor)

1994-01-01

Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome 0 "would have been right if the outcome had been the desired one." The algorithm then proceeds as follows: (1) store the action that produced the wrong outcome as a "target" (2) redefine the wrong outcome as a desired goal (3) submit the new desired goal to the system (4) compare the new action with the target action and modify the system by using a suitable algorithm for credit assignment (Back propagation in our example) (5) resubmit the original goal. Prior publications by our group in this area focused on demonstrating empirical results based on the inverse kinematic problem for a simulated robotic arm. In this paper we apply the inversion process to much simpler analytic functions in order to elucidate the dynamic behavior of the system and to determine the sensitivity of the learning process to various parameters. This understanding will be necessary for the acceptance of GDMI as a practical tool.

Nonlinear dynamics of team performance and adaptability in emergency response.

PubMed

Guastello, Stephen J

2010-04-01

The impact of team size and performance feedback on adaptation levels and performance of emergency response (ER) teams was examined to introduce a metric for quantifying adaptation levels based on nonlinear dynamical systems (NDS) theory. NDS principles appear in reports surrounding Hurricane Katrina, earthquakes, floods, a disease epidemic, and the Southeast Asian tsunami. They are also intrinsic to coordination within teams, adaptation levels, and performance in dynamic decision processes. Performance was measured in a dynamic decision task in which ER teams of different sizes worked against an attacker who was trying to destroy a city (total N = 225 undergraduates). The complexity of teams' and attackers' adaptation strategies and the role of the opponents' performance were assessed by nonlinear regression analysis. An optimal group size for team performance was identified. Teams were more readily influenced by the attackers' performance than vice versa. The adaptive capabilities of attackers and teams were impaired by their opponents in some conditions. ER teams should be large enough to contribute a critical mass of ideas but not so large that coordination would be compromised. ER teams used self-organized strategies that could have been more adaptive, whereas attackers used chaotic strategies. The model and results are applicable to ER processes or training maneuvers involving dynamic decisions but could be limited to nonhierarchical groups.

Multithreaded Model for Dynamic Load Balancing Parallel Adaptive PDE Computations

NASA Technical Reports Server (NTRS)

Chrisochoides, Nikos

1995-01-01

We present a multithreaded model for the dynamic load-balancing of numerical, adaptive computations required for the solution of Partial Differential Equations (PDE's) on multiprocessors. Multithreading is used as a means of exploring concurrency in the processor level in order to tolerate synchronization costs inherent to traditional (non-threaded) parallel adaptive PDE solvers. Our preliminary analysis for parallel, adaptive PDE solvers indicates that multithreading can be used an a mechanism to mask overheads required for the dynamic balancing of processor workloads with computations required for the actual numerical solution of the PDE's. Also, multithreading can simplify the implementation of dynamic load-balancing algorithms, a task that is very difficult for traditional data parallel adaptive PDE computations. Unfortunately, multithreading does not always simplify program complexity, often makes code re-usability not an easy task, and increases software complexity.

Adaptive control of dynamic balance in human gait on a split-belt treadmill.

PubMed

Buurke, Tom J W; Lamoth, Claudine J C; Vervoort, Danique; van der Woude, Lucas H V; den Otter, Rob

2018-05-17

Human bipedal gait is inherently unstable and staying upright requires adaptive control of dynamic balance. Little is known about adaptive control of dynamic balance in reaction to long-term, continuous perturbations. We examined how dynamic balance control adapts to a continuous perturbation in gait, by letting people walk faster with one leg than the other on a treadmill with two belts (i.e. split-belt walking). In addition, we assessed whether changes in mediolateral dynamic balance control coincide with changes in energy use during split-belt adaptation. In nine minutes of split-belt gait, mediolateral margins of stability and mediolateral foot roll-off changed during adaptation to the imposed gait asymmetry, especially on the fast side, and returned to baseline during washout. Interestingly, no changes in mediolateral foot placement (i.e. step width) were found during split-belt adaptation. Furthermore, the initial margin of stability and subsequent mediolateral foot roll-off were strongly coupled to maintain mediolateral dynamic balance throughout the gait cycle. Consistent with previous results net metabolic power was reduced during split-belt adaptation, but changes in mediolateral dynamic balance control were not correlated with the reduction of net metabolic power during split-belt adaptation. Overall, this study has shown that a complementary mechanism of relative foot positioning and mediolateral foot roll-off adapts to continuously imposed gait asymmetry to maintain dynamic balance in human bipedal gait. © 2018. Published by The Company of Biologists Ltd.

Coupled land surface–subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra

DOE PAGES

Tran, Anh Phuong; Dafflon, Baptiste; Hubbard, Susan S.

2017-09-06

Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surface–subsurface hydrological–thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbon–climate feedbacks. While such quantification is particularly important in the vulnerable organic-rich Arctic region, it is challenging to achieve due to the general limitations of conventional core sampling and analysis methods, and to the extremely dynamic nature of hydrological–thermal processes associated with annual freeze–thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets – including soil liquid watermore » content, temperature and electrical resistivity tomography (ERT) data – to estimate the vertical distribution of OC content. Our approach relies on the fact that OC content strongly influences soil hydrological–thermal parameters and, therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. We employ the Community Land Model to simulate nonisothermal surface–subsurface hydrological dynamics from the bedrock to the top of canopy, with consideration of land surface processes (e.g., solar radiation balance, evapotranspiration, snow accumulation and melting) and ice–liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate a posteriori distributions of desired model parameters. For hydrological–thermal-to-geophysical variable transformation, the simulated subsurface temperature, liquid water content and ice content are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using different numerical experiments and evaluate the influence of measurement errors and benefit

Coupled land surface–subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra

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

Tran, Anh Phuong; Dafflon, Baptiste; Hubbard, Susan S.

Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surface–subsurface hydrological–thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbon–climate feedbacks. While such quantification is particularly important in the vulnerable organic-rich Arctic region, it is challenging to achieve due to the general limitations of conventional core sampling and analysis methods, and to the extremely dynamic nature of hydrological–thermal processes associated with annual freeze–thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets – including soil liquid watermore » content, temperature and electrical resistivity tomography (ERT) data – to estimate the vertical distribution of OC content. Our approach relies on the fact that OC content strongly influences soil hydrological–thermal parameters and, therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. We employ the Community Land Model to simulate nonisothermal surface–subsurface hydrological dynamics from the bedrock to the top of canopy, with consideration of land surface processes (e.g., solar radiation balance, evapotranspiration, snow accumulation and melting) and ice–liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate a posteriori distributions of desired model parameters. For hydrological–thermal-to-geophysical variable transformation, the simulated subsurface temperature, liquid water content and ice content are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using different numerical experiments and evaluate the influence of measurement errors and benefit

Coupled land surface-subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra

NASA Astrophysics Data System (ADS)

Phuong Tran, Anh; Dafflon, Baptiste; Hubbard, Susan S.

2017-09-01

Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surface-subsurface hydrological-thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbon-climate feedbacks. While such quantification is particularly important in the vulnerable organic-rich Arctic region, it is challenging to achieve due to the general limitations of conventional core sampling and analysis methods, and to the extremely dynamic nature of hydrological-thermal processes associated with annual freeze-thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets - including soil liquid water content, temperature and electrical resistivity tomography (ERT) data - to estimate the vertical distribution of OC content. Our approach relies on the fact that OC content strongly influences soil hydrological-thermal parameters and, therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. We employ the Community Land Model to simulate nonisothermal surface-subsurface hydrological dynamics from the bedrock to the top of canopy, with consideration of land surface processes (e.g., solar radiation balance, evapotranspiration, snow accumulation and melting) and ice-liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate a posteriori distributions of desired model parameters. For hydrological-thermal-to-geophysical variable transformation, the simulated subsurface temperature, liquid water content and ice content are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using different numerical experiments and evaluate the influence of measurement errors and benefit of joint inversion on the

Assessing when chromosomal rearrangements affect the dynamics of speciation: implications from computer simulations

PubMed Central

Feder, Jeffrey L.; Nosil, Patrik; Flaxman, Samuel M.

2014-01-01

Many hypotheses have been put forth to explain the origin and spread of inversions, and their significance for speciation. Several recent genic models have proposed that inversions promote speciation with gene flow due to the adaptive significance of the genes contained within them and because of the effects inversions have on suppressing recombination. However, the consequences of inversions for the dynamics of genome wide divergence across the speciation continuum remain unclear, an issue we examine here. We review a framework for the genomics of speciation involving the congealing of the genome into alternate adaptive states representing species (“genome wide congealing”). We then place inversions in this context as examples of how genetic hitchhiking can potentially hasten genome wide congealing. Specifically, we use simulation models to (i) examine the conditions under which inversions may speed genome congealing and (ii) quantify predicted magnitudes of these effects. Effects of inversions on promoting speciation were most common and pronounced when inversions were initially fixed between populations before secondary contact and adaptation involved many genes with small fitness effects. Further work is required on the role of underdominance and epistasis between a few loci of major effect within inversions. The results highlight five important aspects of the roles of inversions in speciation: (i) the geographic context of the origins and spread of inversions, (ii) the conditions under which inversions can facilitate divergence, (iii) the magnitude of that facilitation, (iv) the extent to which the buildup of divergence is likely to be biased within vs. outside of inversions, and (v) the dynamics of the appearance and disappearance of exceptional divergence within inversions. We conclude by discussing the empirical challenges in showing that inversions play a central role in facilitating speciation with gene flow. PMID:25206365

Parallel Tetrahedral Mesh Adaptation with Dynamic Load Balancing

NASA Technical Reports Server (NTRS)

Oliker, Leonid; Biswas, Rupak; Gabow, Harold N.

1999-01-01

The ability to dynamically adapt an unstructured grid is a powerful tool for efficiently solving computational problems with evolving physical features. In this paper, we report on our experience parallelizing an edge-based adaptation scheme, called 3D_TAG. using message passing. Results show excellent speedup when a realistic helicopter rotor mesh is randomly refined. However. performance deteriorates when the mesh is refined using a solution-based error indicator since mesh adaptation for practical problems occurs in a localized region., creating a severe load imbalance. To address this problem, we have developed PLUM, a global dynamic load balancing framework for adaptive numerical computations. Even though PLUM primarily balances processor workloads for the solution phase, it reduces the load imbalance problem within mesh adaptation by repartitioning the mesh after targeting edges for refinement but before the actual subdivision. This dramatically improves the performance of parallel 3D_TAG since refinement occurs in a more load balanced fashion. We also present optimal and heuristic algorithms that, when applied to the default mapping of a parallel repartitioner, significantly reduce the data redistribution overhead. Finally, portability is examined by comparing performance on three state-of-the-art parallel machines.

A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

PubMed

Ingram, James N; Howard, Ian S; Flanagan, J Randall; Wolpert, Daniel M

2011-09-01

Motor learning has been extensively studied using dynamic (force-field) perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar object dynamics

Dynamic data integration and stochastic inversion of a confined aquifer

NASA Astrophysics Data System (ADS)

Wang, D.; Zhang, Y.; Irsa, J.; Huang, H.; Wang, L.

2013-12-01

Much work has been done in developing and applying inverse methods to aquifer modeling. The scope of this paper is to investigate the applicability of a new direct method for large inversion problems and to incorporate uncertainty measures in the inversion outcomes (Wang et al., 2013). The problem considered is a two-dimensional inverse model (50×50 grid) of steady-state flow for a heterogeneous ground truth model (500×500 grid) with two hydrofacies. From the ground truth model, decreasing number of wells (12, 6, 3) were sampled for facies types, based on which experimental indicator histograms and directional variograms were computed. These parameters and models were used by Sequential Indicator Simulation to generate 100 realizations of hydrofacies patterns in a 100×100 (geostatistical) grid, which were conditioned to the facies measurements at wells. These realizations were smoothed with Simulated Annealing, coarsened to the 50×50 inverse grid, before they were conditioned with the direct method to the dynamic data, i.e., observed heads and groundwater fluxes at the same sampled wells. A set of realizations of estimated hydraulic conductivities (Ks), flow fields, and boundary conditions were created, which centered on the 'true' solutions from solving the ground truth model. Both hydrofacies conductivities were computed with an estimation accuracy of ×10% (12 wells), ×20% (6 wells), ×35% (3 wells) of the true values. For boundary condition estimation, the accuracy was within × 15% (12 wells), 30% (6 wells), and 50% (3 wells) of the true values. The inversion system of equations was solved with LSQR (Paige et al, 1982), for which coordinate transform and matrix scaling preprocessor were used to improve the condition number (CN) of the coefficient matrix. However, when the inverse grid was refined to 100×100, Gaussian Noise Perturbation was used to limit the growth of the CN before the matrix solve. To scale the inverse problem up (i.e., without smoothing

Development of force adaptation during childhood.

PubMed

Konczak, Jürgen; Jansen-Osmann, Petra; Kalveram, Karl-Theodor

2003-03-01

Humans learn to make reaching movements in novel dynamic environments by acquiring an internal motor model of their limb dynamics. Here, the authors investigated how 4- to 11-year-old children (N = 39) and adults (N = 7) adapted to changes in arm dynamics, and they examined whether those data support the view that the human brain acquires inverse dynamics models (IDM) during development. While external damping forces were applied, the children learned to perform goal-directed forearm flexion movements. After changes in damping, all children showed kinematic aftereffects indicative of a neural controller that still attempted to compensate the no longer existing damping force. With increasing age, the number of trials toward complete adaptation decreased. When damping was present, forearm paths were most perturbed and most variable in the youngest children but were improved in the older children. The findings indicate that the neural representations of limb dynamics are less precise in children and less stable in time than those of adults. Such controller instability might be a primary cause of the high kinematic variability observed in many motor tasks during childhood. Finally, the young children were not able to update those models at the same rate as the older children, who, in turn, adapted more slowly than adults. In conclusion, the ability to adapt to unknown forces is a developmental achievement. The present results are consistent with the view that the acquisition and modification of internal models of the limb dynamics form the basis of that adaptive process.

Adaptive pseudolinear compensators of dynamic characteristics of automatic control systems

NASA Astrophysics Data System (ADS)

Skorospeshkin, M. V.; Sukhodoev, M. S.; Timoshenko, E. A.; Lenskiy, F. V.

2016-04-01

Adaptive pseudolinear gain and phase compensators of dynamic characteristics of automatic control systems are suggested. The automatic control system performance with adaptive compensators has been explored. The efficiency of pseudolinear adaptive compensators in the automatic control systems with time-varying parameters has been demonstrated.

Automatic detection of multiple UXO-like targets using magnetic anomaly inversion and self-adaptive fuzzy c-means clustering

NASA Astrophysics Data System (ADS)

Yin, Gang; Zhang, Yingtang; Fan, Hongbo; Ren, Guoquan; Li, Zhining

2017-12-01

We have developed a method for automatically detecting UXO-like targets based on magnetic anomaly inversion and self-adaptive fuzzy c-means clustering. Magnetic anomaly inversion methods are used to estimate the initial locations of multiple UXO-like sources. Although these initial locations have some errors with respect to the real positions, they form dense clouds around the actual positions of the magnetic sources. Then we use the self-adaptive fuzzy c-means clustering algorithm to cluster these initial locations. The estimated number of cluster centroids represents the number of targets and the cluster centroids are regarded as the locations of magnetic targets. Effectiveness of the method has been demonstrated using synthetic datasets. Computational results show that the proposed method can be applied to the case of several UXO-like targets that are randomly scattered within in a confined, shallow subsurface, volume. A field test was carried out to test the validity of the proposed method and the experimental results show that the prearranged magnets can be detected unambiguously and located precisely.

Simulation of the Tsunami Resulting from the M 9.2 2004 Sumatra-Andaman Earthquake - Dynamic Rupture vs. Seismic Inversion Source Model

NASA Astrophysics Data System (ADS)

Vater, Stefan; Behrens, Jörn

2017-04-01

Simulations of historic tsunami events such as the 2004 Sumatra or the 2011 Tohoku event are usually initialized using earthquake sources resulting from inversion of seismic data. Also, other data from ocean buoys etc. is sometimes included in the derivation of the source model. The associated tsunami event can often be well simulated in this way, and the results show high correlation with measured data. However, it is unclear how the derived source model compares to the particular earthquake event. In this study we use the results from dynamic rupture simulations obtained with SeisSol, a software package based on an ADER-DG discretization solving the spontaneous dynamic earthquake rupture problem with high-order accuracy in space and time. The tsunami model is based on a second-order Runge-Kutta discontinuous Galerkin (RKDG) scheme on triangular grids and features a robust wetting and drying scheme for the simulation of inundation events at the coast. Adaptive mesh refinement enables the efficient computation of large domains, while at the same time it allows for high local resolution and geometric accuracy. The results are compared to measured data and results using earthquake sources based on inversion. With the approach of using the output of actual dynamic rupture simulations, we can estimate the influence of different earthquake parameters. Furthermore, the comparison to other source models enables a thorough comparison and validation of important tsunami parameters, such as the runup at the coast. This work is part of the ASCETE (Advanced Simulation of Coupled Earthquake and Tsunami Events) project, which aims at an improved understanding of the coupling between the earthquake and the generated tsunami event.

Brain-wide neuronal dynamics during motor adaptation in zebrafish.

PubMed

Ahrens, Misha B; Li, Jennifer M; Orger, Michael B; Robson, Drew N; Schier, Alexander F; Engert, Florian; Portugues, Ruben

2012-05-09

A fundamental question in neuroscience is how entire neural circuits generate behaviour and adapt it to changes in sensory feedback. Here we use two-photon calcium imaging to record the activity of large populations of neurons at the cellular level, throughout the brain of larval zebrafish expressing a genetically encoded calcium sensor, while the paralysed animals interact fictively with a virtual environment and rapidly adapt their motor output to changes in visual feedback. We decompose the network dynamics involved in adaptive locomotion into four types of neuronal response properties, and provide anatomical maps of the corresponding sites. A subset of these signals occurred during behavioural adjustments and are candidates for the functional elements that drive motor learning. Lesions to the inferior olive indicate a specific functional role for olivocerebellar circuitry in adaptive locomotion. This study enables the analysis of brain-wide dynamics at single-cell resolution during behaviour.

Brain-wide neuronal dynamics during motor adaptation in zebrafish

PubMed Central

Ahrens, Misha B; Li, Jennifer M; Orger, Michael B; Robson, Drew N; Schier, Alexander F; Engert, Florian; Portugues, Ruben

2013-01-01

A fundamental question in neuroscience is how entire neural circuits generate behavior and adapt it to changes in sensory feedback. Here we use two-photon calcium imaging to record activity of large populations of neurons at the cellular level throughout the brain of larval zebrafish expressing a genetically-encoded calcium sensor, while the paralyzed animals interact fictively with a virtual environment and rapidly adapt their motor output to changes in visual feedback. We decompose the network dynamics involved in adaptive locomotion into four types of neural response properties, and provide anatomical maps of the corresponding sites. A subset of these signals occurred during behavioral adjustments and are candidates for the functional elements that drive motor learning. Lesions to the inferior olive indicate a specific functional role for olivocerebellar circuitry in adaptive locomotion. This study enables the analysis of brain-wide dynamics at single-cell resolution during behavior. PMID:22622571

How the Dynamics of a Supramolecular Polymer Determines Its Dynamic Adaptivity and Stimuli-Responsiveness: Structure-Dynamics-Property Relationships From Coarse-Grained Simulations.

PubMed

Torchi, Andrea; Bochicchio, Davide; Pavan, Giovanni M

2018-04-12

The rational design of supramolecular polymers that can adapt or respond in time to specific stimuli in a controlled way is interesting for many applications, but this requires understanding the molecular factors that make the material faster or slower in responding to the stimulus. To this end, it is necessary to study the dynamic adaptive properties at submolecular resolution, which is difficult at an experimental level. Here we show coarse-grained molecular dynamics simulations (<5 Å resolution) demonstrating how the dynamic adaptivity and stimuli responsiveness of a supramolecular polymer is controlled by the intrinsic dynamics of the assembly, which is in turn determined by the structure of the monomers. As a representative case, we focus on a water-soluble 1,3,5-benzenetricarboxamide (BTA) supramolecular polymer incorporating (charged) receptor monomers, experimentally seen to undergo dynamic clustering following the superselective binding to a multivalent recruiter. Our simulations show that the dynamic reorganization of the supramolecular structure proceeds via monomer diffusion on the dynamic fiber surface (exchange within the fiber). Rationally changing the structure of the monomers to make the fiber surface more or less dynamic allows tuning the rate of response to the stimulus and of supramolecular reconfiguration. Simple in silico experiments draw a structure-dynamics-property relationship revealing the key factors underpinning the dynamic adaptivity and stimuli-responsiveness of these supramolecular polymers. We come out with clear evidence that to master the bioinspired properties of these fibers, it is necessary to control their intrinsic dynamics, while the high-resolution of our molecular models permits us to show how.

A Knowledge-Structure-Based Adaptive Dynamic Assessment System for Calculus Learning

ERIC Educational Resources Information Center

Ting, M.-Y.; Kuo, B.-C.

2016-01-01

The purpose of this study was to investigate the effect of a calculus system that was designed using an adaptive dynamic assessment (DA) framework on performance in the "finding an area using an integral". In this study, adaptive testing and dynamic assessment were combined to provide different test items depending on students'…

Robust Adaptive Flight Control Design of Air-breathing Hypersonic Vehicles

DTIC Science & Technology

2016-12-07

dynamic inversion controller design for a non -minimum phase hypersonic vehicle is derived by Kuipers et al. [2008]. Moreover, integrated guidance and...stabilization time for inner loop variables is lesser than the intermediate loop variables because of the three-loop-control design methodology . The control...adaptive design . Control Engineering Practice, 2016. Michael A Bolender and David B Doman. A non -linear model for the longitudinal dynamics of a

Full analogue electronic realisation of the Hodgkin-Huxley neuronal dynamics in weak-inversion CMOS.

PubMed

Lazaridis, E; Drakakis, E M; Barahona, M

2007-01-01

This paper presents a non-linear analog synthesis path towards the modeling and full implementation of the Hodgkin-Huxley neuronal dynamics in silicon. The proposed circuits have been realized in weak-inversion CMOS technology and take advantage of both log-domain and translinear transistor-level techniques.

Adaptive temperature-accelerated dynamics

NASA Astrophysics Data System (ADS)

Shim, Yunsic; Amar, Jacques G.

2011-02-01

We present three adaptive methods for optimizing the high temperature Thigh on-the-fly in temperature-accelerated dynamics (TAD) simulations. In all three methods, the high temperature is adjusted periodically in order to maximize the performance. While in the first two methods the adjustment depends on the number of observed events, the third method depends on the minimum activation barrier observed so far and requires an a priori knowledge of the optimal high temperature T^{opt}_{high}(E_a) as a function of the activation barrier Ea for each accepted event. In order to determine the functional form of T^{opt}_{high}(E_a), we have carried out extensive simulations of submonolayer annealing on the (100) surface for a variety of metals (Ag, Cu, Ni, Pd, and Au). While the results for all five metals are different, when they are scaled with the melting temperature Tm, we find that they all lie on a single scaling curve. Similar results have also been obtained for (111) surfaces although in this case the scaling function is slightly different. In order to test the performance of all three methods, we have also carried out adaptive TAD simulations of Ag/Ag(100) annealing and growth at T = 80 K and compared with fixed high-temperature TAD simulations for different values of Thigh. We find that the performance of all three adaptive methods is typically as good as or better than that obtained in fixed high-temperature TAD simulations carried out using the effective optimal fixed high temperature. In addition, we find that the final high temperatures obtained in our adaptive TAD simulations are very close to our results for T^{opt}_{high}(E_a). The applicability of the adaptive methods to a variety of TAD simulations is also briefly discussed.

Gene alterations at Drosophila inversion breakpoints provide prima facie evidence for natural selection as an explanation for rapid chromosomal evolution.

PubMed

Guillén, Yolanda; Ruiz, Alfredo

2012-02-01

Chromosomal inversions have been pervasive during the evolution of the genus Drosophila, but there is significant variation between lineages in the rate of rearrangement fixation. D. mojavensis, an ecological specialist adapted to a cactophilic niche under extreme desert conditions, is a chromosomally derived species with ten fixed inversions, five of them not present in any other species. In order to explore the causes of the rapid chromosomal evolution in D. mojavensis, we identified and characterized all breakpoints of seven inversions fixed in chromosome 2, the most dynamic one. One of the inversions presents unequivocal evidence for its generation by ectopic recombination between transposon copies and another two harbor inverted duplications of non-repetitive DNA at the two breakpoints and were likely generated by staggered single-strand breaks and repair by non-homologous end joining. Four out of 14 breakpoints lay in the intergenic region between preexisting duplicated genes, suggesting an adaptive advantage of separating previously tightly linked duplicates. Four out of 14 breakpoints are associated with transposed genes, suggesting these breakpoints are fragile regions. Finally two inversions contain novel genes at their breakpoints and another three show alterations of genes at breakpoints with potential adaptive significance. D. mojavensis chromosomal inversions were generated by multiple mechanisms, an observation that does not provide support for increased mutation rate as explanation for rapid chromosomal evolution. On the other hand, we have found a number of gene alterations at the breakpoints with putative adaptive consequences that directly point to natural selection as the cause of D. mojavensis rapid chromosomal evolution.

Gene alterations at Drosophila inversion breakpoints provide prima facie evidence for natural selection as an explanation for rapid chromosomal evolution

PubMed Central

2012-01-01

Background Chromosomal inversions have been pervasive during the evolution of the genus Drosophila, but there is significant variation between lineages in the rate of rearrangement fixation. D. mojavensis, an ecological specialist adapted to a cactophilic niche under extreme desert conditions, is a chromosomally derived species with ten fixed inversions, five of them not present in any other species. Results In order to explore the causes of the rapid chromosomal evolution in D. mojavensis, we identified and characterized all breakpoints of seven inversions fixed in chromosome 2, the most dynamic one. One of the inversions presents unequivocal evidence for its generation by ectopic recombination between transposon copies and another two harbor inverted duplications of non-repetitive DNA at the two breakpoints and were likely generated by staggered single-strand breaks and repair by non-homologous end joining. Four out of 14 breakpoints lay in the intergenic region between preexisting duplicated genes, suggesting an adaptive advantage of separating previously tightly linked duplicates. Four out of 14 breakpoints are associated with transposed genes, suggesting these breakpoints are fragile regions. Finally two inversions contain novel genes at their breakpoints and another three show alterations of genes at breakpoints with potential adaptive significance. Conclusions D. mojavensis chromosomal inversions were generated by multiple mechanisms, an observation that does not provide support for increased mutation rate as explanation for rapid chromosomal evolution. On the other hand, we have found a number of gene alterations at the breakpoints with putative adaptive consequences that directly point to natural selection as the cause of D. mojavensis rapid chromosomal evolution. PMID:22296923

Power Inversion in a Tapped Delay-Line Array.

DTIC Science & Technology

1975-03-01

and identify by block number) This report discusses recent studies on adaptive arrays for theNavy ITACS system. The report considers the power inversion...this report we discuss recent studies on adaptive arrays for the Navy ITACS system. The goal of this research is to develop an adaptive antenna system...here is a continuation of earlier research on power inversion by Compton, Lee, and Schwegman [1,2,3,4]. This work differs from previous studies in that

What you feel is what you see: inverse dynamics estimation underlies the resistive sensation of a delayed cursor

PubMed Central

Takamuku, Shinya; Gomi, Hiroaki

2015-01-01

How our central nervous system (CNS) learns and exploits relationships between force and motion is a fundamental issue in computational neuroscience. While several lines of evidence have suggested that the CNS predicts motion states and signals from motor commands for control and perception (forward dynamics), it remains controversial whether it also performs the ‘inverse’ computation, i.e. the estimation of force from motion (inverse dynamics). Here, we show that the resistive sensation we experience while moving a delayed cursor, perceived purely from the change in visual motion, provides evidence of the inverse computation. To clearly specify the computational process underlying the sensation, we systematically varied the visual feedback and examined its effect on the strength of the sensation. In contrast to the prevailing theory that sensory prediction errors modulate our perception, the sensation did not correlate with errors in cursor motion due to the delay. Instead, it correlated with the amount of exposure to the forward acceleration of the cursor. This indicates that the delayed cursor is interpreted as a mechanical load, and the sensation represents its visually implied reaction force. Namely, the CNS automatically computes inverse dynamics, using visually detected motions, to monitor the dynamic forces involved in our actions. PMID:26156766

Eco-evolutionary feedbacks, adaptive dynamics and evolutionary rescue theory

PubMed Central

Ferriere, Regis; Legendre, Stéphane

2013-01-01

Adaptive dynamics theory has been devised to account for feedbacks between ecological and evolutionary processes. Doing so opens new dimensions to and raises new challenges about evolutionary rescue. Adaptive dynamics theory predicts that successive trait substitutions driven by eco-evolutionary feedbacks can gradually erode population size or growth rate, thus potentially raising the extinction risk. Even a single trait substitution can suffice to degrade population viability drastically at once and cause ‘evolutionary suicide’. In a changing environment, a population may track a viable evolutionary attractor that leads to evolutionary suicide, a phenomenon called ‘evolutionary trapping’. Evolutionary trapping and suicide are commonly observed in adaptive dynamics models in which the smooth variation of traits causes catastrophic changes in ecological state. In the face of trapping and suicide, evolutionary rescue requires that the population overcome evolutionary threats generated by the adaptive process itself. Evolutionary repellors play an important role in determining how variation in environmental conditions correlates with the occurrence of evolutionary trapping and suicide, and what evolutionary pathways rescue may follow. In contrast with standard predictions of evolutionary rescue theory, low genetic variation may attenuate the threat of evolutionary suicide and small population sizes may facilitate escape from evolutionary traps. PMID:23209163

Adaptive control of an exoskeleton robot with uncertainties on kinematics and dynamics.

PubMed

Brahmi, Brahim; Saad, Maarouf; Ochoa-Luna, Cristobal; Rahman, Mohammad H

2017-07-01

In this paper, we propose a new adaptive control technique based on nonlinear sliding mode control (JSTDE) taking into account kinematics and dynamics uncertainties. This approach is applied to an exoskeleton robot with uncertain kinematics and dynamics. The adaptation design is based on Time Delay Estimation (TDE). The proposed strategy does not necessitate the well-defined dynamic and kinematic models of the system robot. The updated laws are designed using Lyapunov-function to solve the adaptation problem systematically, proving the close loop stability and ensuring the convergence asymptotically of the outputs tracking errors. Experiments results show the effectiveness and feasibility of JSTDE technique to deal with the variation of the unknown nonlinear dynamics and kinematics of the exoskeleton model.

Adaptive control in the presence of unmodeled dynamics. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Rohrs, C. E.

1982-01-01

Stability and robustness properties of a wide class of adaptive control algorithms in the presence of unmodeled dynamics and output disturbances were investigated. The class of adaptive algorithms considered are those commonly referred to as model reference adaptive control algorithms, self-tuning controllers, and dead beat adaptive controllers, developed for both continuous-time systems and discrete-time systems. A unified analytical approach was developed to examine the class of existing adaptive algorithms. It was discovered that all existing algorithms contain an infinite gain operator in the dynamic system that defines command reference errors and parameter errors; it is argued that such an infinite gain operator appears to be generic to all adaptive algorithms, whether they exhibit explicit or implicit parameter identification. It is concluded that none of the adaptive algorithms considered can be used with confidence in a practical control system design, because instability will set in with a high probability.

Multidimensional NMR inversion without Kronecker products: Multilinear inversion

NASA Astrophysics Data System (ADS)

Medellín, David; Ravi, Vivek R.; Torres-Verdín, Carlos

2016-08-01

Multidimensional NMR inversion using Kronecker products poses several challenges. First, kernel compression is only possible when the kernel matrices are separable, and in recent years, there has been an increasing interest in NMR sequences with non-separable kernels. Second, in three or more dimensions, the singular value decomposition is not unique; therefore kernel compression is not well-defined for higher dimensions. Without kernel compression, the Kronecker product yields matrices that require large amounts of memory, making the inversion intractable for personal computers. Finally, incorporating arbitrary regularization terms is not possible using the Lawson-Hanson (LH) or the Butler-Reeds-Dawson (BRD) algorithms. We develop a minimization-based inversion method that circumvents the above problems by using multilinear forms to perform multidimensional NMR inversion without using kernel compression or Kronecker products. The new method is memory efficient, requiring less than 0.1% of the memory required by the LH or BRD methods. It can also be extended to arbitrary dimensions and adapted to include non-separable kernels, linear constraints, and arbitrary regularization terms. Additionally, it is easy to implement because only a cost function and its first derivative are required to perform the inversion.

Megabase-Scale Inversion Polymorphism in the Wild Ancestor of Maize

PubMed Central

Fang, Zhou; Pyhäjärvi, Tanja; Weber, Allison L.; Dawe, R. Kelly; Glaubitz, Jeffrey C.; González, José de Jesus Sánchez; Ross-Ibarra, Claudia; Doebley, John; Morrell, Peter L.; Ross-Ibarra, Jeffrey

2012-01-01

Chromosomal inversions are thought to play a special role in local adaptation, through dramatic suppression of recombination, which favors the maintenance of locally adapted alleles. However, relatively few inversions have been characterized in population genomic data. On the basis of single-nucleotide polymorphism (SNP) genotyping across a large panel of Zea mays, we have identified an ∼50-Mb region on the short arm of chromosome 1 where patterns of polymorphism are highly consistent with a polymorphic paracentric inversion that captures >700 genes. Comparison to other taxa in Zea and Tripsacum suggests that the derived, inverted state is present only in the wild Z. mays subspecies parviglumis and mexicana and is completely absent in domesticated maize. Patterns of polymorphism suggest that the inversion is ancient and geographically widespread in parviglumis. Cytological screens find little evidence for inversion loops, suggesting that inversion heterozygotes may suffer few crossover-induced fitness consequences. The inversion polymorphism shows evidence of adaptive evolution, including a strong altitudinal cline, a statistical association with environmental variables and phenotypic traits, and a skewed haplotype frequency spectrum for inverted alleles. PMID:22542971

Dark Matter and the elusive Z' in a dynamical Inverse Seesaw scenario

DOE PAGES

De Romeri, Valentina; Fernandez-Martinez, Enrique; Gehrlein, Julia; ...

2017-10-24

The Inverse Seesaw naturally explains the smallness of neutrino masses via an approximate $B-L$ symmetry broken only by a correspondingly small parameter. In this work the possible dynamical generation of the Inverse Seesaw neutrino mass mechanism from the spontaneous breaking of a gauged $U(1)$ $B-L$ symmetry is investigated. Interestingly, the Inverse Seesaw pattern requires a chiral content such that anomaly cancellation predicts the existence of extra fermions belonging to a dark sector with large, non-trivial, charges under the $U(1)$ $B-L$. We investigate the phenomenology associated to these new states and find that one of them is a viable dark mattermore » candidate with mass around the TeV scale, whose interaction with the Standard Model is mediated by the $Z'$ boson associated to the gauged $U(1)$ $B-L$ symmetry. Given the large charges required for anomaly cancellation in the dark sector, the $B-L$ $Z'$ interacts preferentially with this dark sector rather than with the Standard Model. This suppresses the rate at direct detection searches and thus alleviates the constraints on $Z'$-mediated dark matter relic abundance. Furthermore, the collider phenomenology of this elusive $Z'$ is also discussed.« less

Dark Matter and the elusive Z' in a dynamical Inverse Seesaw scenario

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

De Romeri, Valentina; Fernandez-Martinez, Enrique; Gehrlein, Julia

The Inverse Seesaw naturally explains the smallness of neutrino masses via an approximate $B-L$ symmetry broken only by a correspondingly small parameter. In this work the possible dynamical generation of the Inverse Seesaw neutrino mass mechanism from the spontaneous breaking of a gauged $U(1)$ $B-L$ symmetry is investigated. Interestingly, the Inverse Seesaw pattern requires a chiral content such that anomaly cancellation predicts the existence of extra fermions belonging to a dark sector with large, non-trivial, charges under the $U(1)$ $B-L$. We investigate the phenomenology associated to these new states and find that one of them is a viable dark mattermore » candidate with mass around the TeV scale, whose interaction with the Standard Model is mediated by the $Z'$ boson associated to the gauged $U(1)$ $B-L$ symmetry. Given the large charges required for anomaly cancellation in the dark sector, the $B-L$ $Z'$ interacts preferentially with this dark sector rather than with the Standard Model. This suppresses the rate at direct detection searches and thus alleviates the constraints on $Z'$-mediated dark matter relic abundance. Furthermore, the collider phenomenology of this elusive $Z'$ is also discussed.« less

Quantifying shallow subsurface water and heat dynamics using coupled hydrological-thermal-geophysical inversion

DOE PAGES

Tran, Anh Phuong; Dafflon, Baptiste; Hubbard, Susan S.; ...

2016-04-25

Improving our ability to estimate the parameters that control water and heat fluxes in the shallow subsurface is particularly important due to their strong control on recharge, evaporation and biogeochemical processes. The objectives of this study are to develop and test a new inversion scheme to simultaneously estimate subsurface hydrological, thermal and petrophysical parameters using hydrological, thermal and electrical resistivity tomography (ERT) data. The inversion scheme-which is based on a nonisothermal, multiphase hydrological model-provides the desired subsurface property estimates in high spatiotemporal resolution. A particularly novel aspect of the inversion scheme is the explicit incorporation of the dependence of themore » subsurface electrical resistivity on both moisture and temperature. The scheme was applied to synthetic case studies, as well as to real datasets that were autonomously collected at a biogeochemical field study site in Rifle, Colorado. At the Rifle site, the coupled hydrological-thermal-geophysical inversion approach well predicted the matric potential, temperature and apparent resistivity with the Nash-Sutcliffe efficiency criterion greater than 0.92. Synthetic studies found that neglecting the subsurface temperature variability, and its effect on the electrical resistivity in the hydrogeophysical inversion, may lead to an incorrect estimation of the hydrological parameters. The approach is expected to be especially useful for the increasing number of studies that are taking advantage of autonomously collected ERT and soil measurements to explore complex terrestrial system dynamics.« less

Effects of adaptive dynamical linking in networked games

NASA Astrophysics Data System (ADS)

Yang, Zhihu; Li, Zhi; Wu, Te; Wang, Long

2013-10-01

The role of dynamical topologies in the evolution of cooperation has received considerable attention, as some studies have demonstrated that dynamical networks are much better than static networks in terms of boosting cooperation. Here we study a dynamical model of evolution of cooperation on stochastic dynamical networks in which there are no permanent partners to each agent. Whenever a new link is created, its duration is randomly assigned without any bias or preference. We allow the agent to adaptively adjust the duration of each link during the evolution in accordance with the feedback from game interactions. By Monte Carlo simulations, we find that cooperation can be remarkably promoted by this adaptive dynamical linking mechanism both for the game of pairwise interactions, such as the Prisoner's Dilemma game (PDG), and for the game of group interactions, illustrated by the public goods game (PGG). And the faster the adjusting rate, the more successful the evolution of cooperation. We also show that in this context weak selection favors cooperation much more than strong selection does. What is particularly meaningful is that the prosperity of cooperation in this study indicates that the rationality and selfishness of a single agent in adjusting social ties can lead to the progress of altruism of the whole population.

On the dynamics of some grid adaption schemes

NASA Technical Reports Server (NTRS)

Sweby, Peter K.; Yee, Helen C.

1994-01-01

The dynamics of a one-parameter family of mesh equidistribution schemes coupled with finite difference discretisations of linear and nonlinear convection-diffusion model equations is studied numerically. It is shown that, when time marched to steady state, the grid adaption not only influences the stability and convergence rate of the overall scheme, but can also introduce spurious dynamics to the numerical solution procedure.

Linkage disequilibrium network analysis (LDna) gives a global view of chromosomal inversions, local adaptation and geographic structure.

PubMed

Kemppainen, Petri; Knight, Christopher G; Sarma, Devojit K; Hlaing, Thaung; Prakash, Anil; Maung Maung, Yan Naung; Somboon, Pradya; Mahanta, Jagadish; Walton, Catherine

2015-09-01

Recent advances in sequencing allow population-genomic data to be generated for virtually any species. However, approaches to analyse such data lag behind the ability to generate it, particularly in nonmodel species. Linkage disequilibrium (LD, the nonrandom association of alleles from different loci) is a highly sensitive indicator of many evolutionary phenomena including chromosomal inversions, local adaptation and geographical structure. Here, we present linkage disequilibrium network analysis (LDna), which accesses information on LD shared between multiple loci genomewide. In LD networks, vertices represent loci, and connections between vertices represent the LD between them. We analysed such networks in two test cases: a new restriction-site-associated DNA sequence (RAD-seq) data set for Anopheles baimaii, a Southeast Asian malaria vector; and a well-characterized single nucleotide polymorphism (SNP) data set from 21 three-spined stickleback individuals. In each case, we readily identified five distinct LD network clusters (single-outlier clusters, SOCs), each comprising many loci connected by high LD. In A. baimaii, further population-genetic analyses supported the inference that each SOC corresponds to a large inversion, consistent with previous cytological studies. For sticklebacks, we inferred that each SOC was associated with a distinct evolutionary phenomenon: two chromosomal inversions, local adaptation, population-demographic history and geographic structure. LDna is thus a useful exploratory tool, able to give a global overview of LD associated with diverse evolutionary phenomena and identify loci potentially involved. LDna does not require a linkage map or reference genome, so it is applicable to any population-genomic data set, making it especially valuable for nonmodel species. © 2015 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

Spontaneous scale-free structure in adaptive networks with synchronously dynamical linking

NASA Astrophysics Data System (ADS)

Yuan, Wu-Jie; Zhou, Jian-Fang; Li, Qun; Chen, De-Bao; Wang, Zhen

2013-08-01

Inspired by the anti-Hebbian learning rule in neural systems, we study how the feedback from dynamical synchronization shapes network structure by adding new links. Through extensive numerical simulations, we find that an adaptive network spontaneously forms scale-free structure, as confirmed in many real systems. Moreover, the adaptive process produces two nontrivial power-law behaviors of deviation strength from mean activity of the network and negative degree correlation, which exists widely in technological and biological networks. Importantly, these scalings are robust to variation of the adaptive network parameters, which may have meaningful implications in the scale-free formation and manipulation of dynamical networks. Our study thus suggests an alternative adaptive mechanism for the formation of scale-free structure with negative degree correlation, which means that nodes of high degree tend to connect, on average, with others of low degree and vice versa. The relevance of the results to structure formation and dynamical property in neural networks is briefly discussed as well.

Recursive Factorization of the Inverse Overlap Matrix in Linear-Scaling Quantum Molecular Dynamics Simulations.

PubMed

Negre, Christian F A; Mniszewski, Susan M; Cawkwell, Marc J; Bock, Nicolas; Wall, Michael E; Niklasson, Anders M N

2016-07-12

We present a reduced complexity algorithm to compute the inverse overlap factors required to solve the generalized eigenvalue problem in a quantum-based molecular dynamics (MD) simulation. Our method is based on the recursive, iterative refinement of an initial guess of Z (inverse square root of the overlap matrix S). The initial guess of Z is obtained beforehand by using either an approximate divide-and-conquer technique or dynamical methods, propagated within an extended Lagrangian dynamics from previous MD time steps. With this formulation, we achieve long-term stability and energy conservation even under the incomplete, approximate, iterative refinement of Z. Linear-scaling performance is obtained using numerically thresholded sparse matrix algebra based on the ELLPACK-R sparse matrix data format, which also enables efficient shared-memory parallelization. As we show in this article using self-consistent density-functional-based tight-binding MD, our approach is faster than conventional methods based on the diagonalization of overlap matrix S for systems as small as a few hundred atoms, substantially accelerating quantum-based simulations even for molecular structures of intermediate size. For a 4158-atom water-solvated polyalanine system, we find an average speedup factor of 122 for the computation of Z in each MD step.

Recursive Factorization of the Inverse Overlap Matrix in Linear Scaling Quantum Molecular Dynamics Simulations

DOE PAGES

Negre, Christian F. A; Mniszewski, Susan M.; Cawkwell, Marc Jon; ...

2016-06-06

We present a reduced complexity algorithm to compute the inverse overlap factors required to solve the generalized eigenvalue problem in a quantum-based molecular dynamics (MD) simulation. Our method is based on the recursive iterative re nement of an initial guess Z of the inverse overlap matrix S. The initial guess of Z is obtained beforehand either by using an approximate divide and conquer technique or dynamically, propagated within an extended Lagrangian dynamics from previous MD time steps. With this formulation, we achieve long-term stability and energy conservation even under incomplete approximate iterative re nement of Z. Linear scaling performance ismore » obtained using numerically thresholded sparse matrix algebra based on the ELLPACK-R sparse matrix data format, which also enables e cient shared memory parallelization. As we show in this article using selfconsistent density functional based tight-binding MD, our approach is faster than conventional methods based on the direct diagonalization of the overlap matrix S for systems as small as a few hundred atoms, substantially accelerating quantum-based simulations even for molecular structures of intermediate size. For a 4,158 atom water-solvated polyalanine system we nd an average speedup factor of 122 for the computation of Z in each MD step.« less

A theoretical Deduction from the Hubble law based on a Modified Newtonian Dynamics with field of Yukawa inverse

NASA Astrophysics Data System (ADS)

Falcon, N.

2017-07-01

At cosmic scales the dynamics of the Universe are almost exclusively prescribed by the force of gravity; however the assumption of the law of gravitation, depending on the inverse of the distance, leads to the known problems of the rotation curves of galaxies and missing mass (dark matter). The problem of the coupling of gravity to changes in scale and deviations from the law of the inverse square is an old problem (Laplace, 1805; Seeliger 1898), which has motivated alternatives to Newtonian dynamics compatible with observations. The present paper postulates a modified Newtonian dynamics by adding an inverse Yukawa potential: U(r)≡U0(M)(r-r0)e-α/r is the the potential per unit mass (in N/kg) as a function of the barionic mass that causes the field, r0 is of the order of 50h-1 Mpc and alpha is a coupling constant of the order of 2.5 h-1 Mpc. This potential is zero within the solar system, slightly attractive at interstellar distances, very attractive in galactic range and repulsive at cosmic scales. Its origin is the barionic matter, it allows to include the Milgrow MoND theory to explain the rotation curves, it is compatible with the experiments Eovos type, and allows to deduce the law of Hubble to cosmic scales, in the form H0=100h km/s Mpc≍U0(M)/c, where U0(M)≍ 4pi×6.67 10-11m/s2, is obtained from the Laplace's equation, assuming that the gravitational force is the law of the inverse of the square plus a non-linear term type Yukawa inverse. It is concluded that the modification of the law of gravity with nonlinear terms, allows to model the dynamics of the Universe on a large scale and include non-locality without dark matter. (See Falcon et al. 2014, International Journal of Astronomy and Astrophysics, 4, 551-559).

Adaptive Calibration of Dynamic Accommodation—Implications for Accommodating Intraocular Lenses

PubMed Central

Schor, Clifton M.; Bharadwaj, Shrikant R.

2009-01-01

PURPOSE When the aging lens is replaced with prosthetic accommodating intraocular lenses (IOLs), with effective viscoelasticities different from those of the natural lens, mismatches could arise between the neural control of accommodation and the biomechanical properties of the new lens. These mismatches could lead to either unstable oscillations or sluggishness of dynamic accommodation. Using computer simulations, we investigated whether optimal accommodative responses could be restored through recalibration of the neural control of accommodation. Using human experiments, we also investigated whether the accommodative system has the capacity for adaptive recalibration in response to changes in lens biomechanics. METHODS Dynamic performance of two accommodating IOL prototypes was simulated for a 45-year-old accommodative system, before and after neural recalibration, using a dynamic model of accommodation. Accommodating IOL I, a prototype for an injectable accommodating IOL, was less stiff and less viscous than the natural 45-year-old lens. Accommodating IOL II, a prototype for a translating accommodating IOL, was less stiff and more viscous than the natural 45-year-old lens. Short-term adaptive recalibration of dynamic accommodation was stimulated using a double-step adaptation paradigm that optically induced changes in neuromuscular effort mimicking responses to changes in lens biomechanics. RESULTS Model simulations indicate that the unstable oscillations or sluggishness of dynamic accommodation resulting from mismatches between neural control and lens biomechanics might be restored through neural recalibration. CONCLUSIONS Empirical measures reveal that the accommodative system is capable of adaptive recalibration in response to optical loads that simulate effects of changing lens biomechanics. PMID:19044245

Achieving Optimal Self-Adaptivity for Dynamic Tuning of Organic Semiconductors through Resonance Engineering.

PubMed

Tao, Ye; Xu, Lijia; Zhang, Zhen; Chen, Runfeng; Li, Huanhuan; Xu, Hui; Zheng, Chao; Huang, Wei

2016-08-03

Current static-state explorations of organic semiconductors for optimal material properties and device performance are hindered by limited insights into the dynamically changed molecular states and charge transport and energy transfer processes upon device operation. Here, we propose a simple yet successful strategy, resonance variation-based dynamic adaptation (RVDA), to realize optimized self-adaptive properties in donor-resonance-acceptor molecules by engineering the resonance variation for dynamic tuning of organic semiconductors. Organic light-emitting diodes hosted by these RVDA materials exhibit remarkably high performance, with external quantum efficiencies up to 21.7% and favorable device stability. Our approach, which supports simultaneous realization of dynamically adapted and selectively enhanced properties via resonance engineering, illustrates a feasible design map for the preparation of smart organic semiconductors capable of dynamic structure and property modulations, promoting the studies of organic electronics from static to dynamic.

Cerebellum as a forward but not inverse model in visuomotor adaptation task: a tDCS-based and modeling study.

PubMed

Yavari, Fatemeh; Mahdavi, Shirin; Towhidkhah, Farzad; Ahmadi-Pajouh, Mohammad-Ali; Ekhtiari, Hamed; Darainy, Mohammad

2016-04-01

Despite several pieces of evidence, which suggest that the human brain employs internal models for motor control and learning, the location of these models in the brain is not yet clear. In this study, we used transcranial direct current stimulation (tDCS) to manipulate right cerebellar function, while subjects adapt to a visuomotor task. We investigated the effect of this manipulation on the internal forward and inverse models by measuring two kinds of behavior: generalization of training in one direction to neighboring directions (as a proxy for inverse models) and localization of the hand position after movement without visual feedback (as a proxy for forward model). The experimental results showed no effect of cerebellar tDCS on generalization, but significant effect on localization. These observations support the idea that the cerebellum is a possible brain region for internal forward, but not inverse model formation. We also used a realistic human head model to calculate current density distribution in the brain. The result of this model confirmed the passage of current through the cerebellum. Moreover, to further explain some observed experimental results, we modeled the visuomotor adaptation process with the help of a biologically inspired method known as population coding. The effect of tDCS was also incorporated in the model. The results of this modeling study closely match our experimental data and provide further evidence in line with the idea that tDCS manipulates FM's function in the cerebellum.

Adaptive guidance for an aero-assisted boost vehicle

NASA Astrophysics Data System (ADS)

Pamadi, Bandu N.; Taylor, Lawrence W., Jr.; Price, Douglas B.

An adaptive guidance system incorporating dynamic pressure constraint is studied for a single stage to low earth orbit (LEO) aero-assist booster with thrust gimbal angle as the control variable. To derive an adaptive guidance law, cubic spline functions are used to represent the ascent profile. The booster flight to LEO is divided into initial and terminal phases. In the initial phase, the ascent profile is continuously updated to maximize the performance of the boost vehicle enroute. A linear feedback control is used in the terminal phase to guide the aero-assisted booster onto the desired LEO. The computer simulation of the vehicle dynamics considers a rotating spherical earth, inverse square (Newtonian) gravity field and an exponential model for the earth's atmospheric density. This adaptive guidance algorithm is capable of handling large deviations in both atmospheric conditions and modeling uncertainties, while ensuring maximum booster performance.

Birth and death of genes linked to chromosomal inversion

PubMed Central

Furuta, Yoshikazu; Kawai, Mikihiko; Yahara, Koji; Takahashi, Noriko; Handa, Naofumi; Tsuru, Takeshi; Oshima, Kenshiro; Yoshida, Masaru; Azuma, Takeshi; Hattori, Masahira; Uchiyama, Ikuo; Kobayashi, Ichizo

2011-01-01

The birth and death of genes is central to adaptive evolution, yet the underlying genome dynamics remain elusive. The availability of closely related complete genome sequences helps to follow changes in gene contents and clarify their relationship to overall genome organization. Helicobacter pylori, bacteria in our stomach, are known for their extreme genome plasticity through mutation and recombination and will make a good target for such an analysis. In comparing their complete genome sequences, we found that gain and loss of genes (loci) for outer membrane proteins, which mediate host interaction, occurred at breakpoints of chromosomal inversions. Sequence comparison there revealed a unique mechanism of DNA duplication: DNA duplication associated with inversion. In this process, a DNA segment at one chromosomal locus is copied and inserted, in an inverted orientation, into a distant locus on the same chromosome, while the entire region between these two loci is also inverted. Recognition of this and three more inversion modes, which occur through reciprocal recombination between long or short sequence similarity or adjacent to a mobile element, allowed reconstruction of synteny evolution through inversion events in this species. These results will guide the interpretation of extensive DNA sequencing results for understanding long- and short-term genome evolution in various organisms and in cancer cells. PMID:21212362

Extinction Dynamics and Control in Adaptive Networks

NASA Astrophysics Data System (ADS)

Schwartz, Ira; Shaw, Leah; Hindes, Jason

Disease control is of paramount importance in public health. Moreover, models of disease spread are an important component in implementing effective vaccination and treatment campaigns. However, human behavior in response to an outbreak has only recently been included in epidemic models on networks. Here we develop the mathematical machinery to describe the dynamics of extinction in finite populations that include human adaptive behavior. The formalism enables us to compute the optimal, fluctuation-induced path to extinction, and predict the average extinction time in adaptive networks as a function of the adaptation rate. We find that both observables have several unique scalings depending on the relative speed of infection and adaptivity. Finally, we discuss how the theory can be used to design optimal control programs in general networks, by coupling the effective force of noise with treatment and human behavior. Research supported by U.S. Naval Research Laboratory funding (Grant No. N0001414WX00023) and the Office of Naval Research (Grant No. N0001414WX20610).

Inverse Dynamics Model for the Ankle Joint with Applications in Tibia Malleolus Fracture

NASA Astrophysics Data System (ADS)

Budescu, E.; Merticaru, E.; Chirazi, M.

The paper presents a biomechanical model of the ankle joint, in order to determine the force and the torque of reaction into the articulation, through inverse dynamic analysis, in various stages of the gait. Thus, knowing the acceleration of the foot and the reaction force between foot and ground during the gait, determined by experimental measurement, there was calculated, for five different positions of the foot, the joint reaction forces, on the basis of dynamic balance equations. The values numerically determined were compared with the admissible forces appearing in the technical systems of osteosynthesis of tibia malleolus fracture, in order to emphasize the motion restrictions during bone healing.

Adaptive Control Based Harvesting Strategy for a Predator-Prey Dynamical System.

PubMed

Sen, Moitri; Simha, Ashutosh; Raha, Soumyendu

2018-04-23

This paper deals with designing a harvesting control strategy for a predator-prey dynamical system, with parametric uncertainties and exogenous disturbances. A feedback control law for the harvesting rate of the predator is formulated such that the population dynamics is asymptotically stabilized at a positive operating point, while maintaining a positive, steady state harvesting rate. The hierarchical block strict feedback structure of the dynamics is exploited in designing a backstepping control law, based on Lyapunov theory. In order to account for unknown parameters, an adaptive control strategy has been proposed in which the control law depends on an adaptive variable which tracks the unknown parameter. Further, a switching component has been incorporated to robustify the control performance against bounded disturbances. Proofs have been provided to show that the proposed adaptive control strategy ensures asymptotic stability of the dynamics at a desired operating point, as well as exact parameter learning in the disturbance-free case and learning with bounded error in the disturbance prone case. The dynamics, with uncertainty in the death rate of the predator, subjected to a bounded disturbance has been simulated with the proposed control strategy.

Iterative inversion of deformation vector fields with feedback control.

PubMed

Dubey, Abhishek; Iliopoulos, Alexandros-Stavros; Sun, Xiaobai; Yin, Fang-Fang; Ren, Lei

2018-05-14

Often, the inverse deformation vector field (DVF) is needed together with the corresponding forward DVF in four-dimesional (4D) reconstruction and dose calculation, adaptive radiation therapy, and simultaneous deformable registration. This study aims at improving both accuracy and efficiency of iterative algorithms for DVF inversion, and advancing our understanding of divergence and latency conditions. We introduce a framework of fixed-point iteration algorithms with active feedback control for DVF inversion. Based on rigorous convergence analysis, we design control mechanisms for modulating the inverse consistency (IC) residual of the current iterate, to be used as feedback into the next iterate. The control is designed adaptively to the input DVF with the objective to enlarge the convergence area and expedite convergence. Three particular settings of feedback control are introduced: constant value over the domain throughout the iteration; alternating values between iteration steps; and spatially variant values. We also introduce three spectral measures of the displacement Jacobian for characterizing a DVF. These measures reveal the critical role of what we term the nontranslational displacement component (NTDC) of the DVF. We carry out inversion experiments with an analytical DVF pair, and with DVFs associated with thoracic CT images of six patients at end of expiration and end of inspiration. The NTDC-adaptive iterations are shown to attain a larger convergence region at a faster pace compared to previous nonadaptive DVF inversion iteration algorithms. By our numerical experiments, alternating control yields smaller IC residuals and inversion errors than constant control. Spatially variant control renders smaller residuals and errors by at least an order of magnitude, compared to other schemes, in no more than 10 steps. Inversion results also show remarkable quantitative agreement with analysis-based predictions. Our analysis captures properties of DVF data

Macroscopic description of complex adaptive networks coevolving with dynamic node states

NASA Astrophysics Data System (ADS)

Wiedermann, Marc; Donges, Jonathan F.; Heitzig, Jobst; Lucht, Wolfgang; Kurths, Jürgen

2015-05-01

In many real-world complex systems, the time evolution of the network's structure and the dynamic state of its nodes are closely entangled. Here we study opinion formation and imitation on an adaptive complex network which is dependent on the individual dynamic state of each node and vice versa to model the coevolution of renewable resources with the dynamics of harvesting agents on a social network. The adaptive voter model is coupled to a set of identical logistic growth models and we mainly find that, in such systems, the rate of interactions between nodes as well as the adaptive rewiring probability are crucial parameters for controlling the sustainability of the system's equilibrium state. We derive a macroscopic description of the system in terms of ordinary differential equations which provides a general framework to model and quantify the influence of single node dynamics on the macroscopic state of the network. The thus obtained framework is applicable to many fields of study, such as epidemic spreading, opinion formation, or socioecological modeling.

Macroscopic description of complex adaptive networks coevolving with dynamic node states.

PubMed

Wiedermann, Marc; Donges, Jonathan F; Heitzig, Jobst; Lucht, Wolfgang; Kurths, Jürgen

2015-05-01

In many real-world complex systems, the time evolution of the network's structure and the dynamic state of its nodes are closely entangled. Here we study opinion formation and imitation on an adaptive complex network which is dependent on the individual dynamic state of each node and vice versa to model the coevolution of renewable resources with the dynamics of harvesting agents on a social network. The adaptive voter model is coupled to a set of identical logistic growth models and we mainly find that, in such systems, the rate of interactions between nodes as well as the adaptive rewiring probability are crucial parameters for controlling the sustainability of the system's equilibrium state. We derive a macroscopic description of the system in terms of ordinary differential equations which provides a general framework to model and quantify the influence of single node dynamics on the macroscopic state of the network. The thus obtained framework is applicable to many fields of study, such as epidemic spreading, opinion formation, or socioecological modeling.

Phylogenetic niche conservatism explains an inverse latitudinal diversity gradient in freshwater arthropods

NASA Astrophysics Data System (ADS)

Morinière, Jérôme; van Dam, Matthew H.; Hawlitschek, Oliver; Bergsten, Johannes; Michat, Mariano C.; Hendrich, Lars; Ribera, Ignacio; Toussaint, Emmanuel F. A.; Balke, Michael

2016-05-01

The underlying mechanisms responsible for the general increase in species richness from temperate regions to the tropics remain equivocal. Many hypotheses have been proposed to explain this astonishing pattern but additional empirical studies are needed to shed light on the drivers at work. Here we reconstruct the evolutionary history of the cosmopolitan diving beetle subfamily Colymbetinae, the majority of which are found in the Northern hemisphere, hence exhibiting an inversed latitudinal diversity gradient. We reconstructed a dated phylogeny using 12 genes, to investigate the biogeographical history and diversification dynamics in the Colymbetinae. We aimed to identify the role that phylogenetic niche conservatism plays in the inversed diversification pattern seen in this group. Our results suggest that Colymbetinae originated in temperate climates, which supports the hypothesis that their distribution is the result of an ancestral adaptation to temperate environmental conditions rather than tropical origins, and that temperate niche conservatism can generate and/or maintain inverse latitudinal diversity gradients.

Phylogenetic niche conservatism explains an inverse latitudinal diversity gradient in freshwater arthropods

PubMed Central

Morinière, Jérôme; Van Dam, Matthew H.; Hawlitschek, Oliver; Bergsten, Johannes; Michat, Mariano C.; Hendrich, Lars; Ribera, Ignacio; Toussaint, Emmanuel F.A.; Balke, Michael

2016-01-01

The underlying mechanisms responsible for the general increase in species richness from temperate regions to the tropics remain equivocal. Many hypotheses have been proposed to explain this astonishing pattern but additional empirical studies are needed to shed light on the drivers at work. Here we reconstruct the evolutionary history of the cosmopolitan diving beetle subfamily Colymbetinae, the majority of which are found in the Northern hemisphere, hence exhibiting an inversed latitudinal diversity gradient. We reconstructed a dated phylogeny using 12 genes, to investigate the biogeographical history and diversification dynamics in the Colymbetinae. We aimed to identify the role that phylogenetic niche conservatism plays in the inversed diversification pattern seen in this group. Our results suggest that Colymbetinae originated in temperate climates, which supports the hypothesis that their distribution is the result of an ancestral adaptation to temperate environmental conditions rather than tropical origins, and that temperate niche conservatism can generate and/or maintain inverse latitudinal diversity gradients. PMID:27215956

Dynamic balance during walking adaptability tasks in individuals post-stroke.

PubMed

Vistamehr, Arian; Balasubramanian, Chitralakshmi K; Clark, David J; Neptune, Richard R; Fox, Emily J

2018-06-06

Maintaining dynamic balance during community ambulation is a major challenge post-stroke. Community ambulation requires performance of steady-state level walking as well as tasks that require walking adaptability. Prior studies on balance control post-stroke have mainly focused on steady-state walking, but walking adaptability tasks have received little attention. The purpose of this study was to quantify and compare dynamic balance requirements during common walking adaptability tasks post-stroke and in healthy adults and identify differences in underlying mechanisms used for maintaining dynamic balance. Kinematic data were collected from fifteen individuals with post-stroke hemiparesis during steady-state forward and backward walking, obstacle negotiation, and step-up tasks. In addition, data from ten healthy adults provided the basis for comparison. Dynamic balance was quantified using the peak-to-peak range of whole-body angular-momentum in each anatomical plane during the paretic, nonparetic and healthy control single-leg-stance phase of the gait cycle. To understand differences in some of the key underlying mechanisms for maintaining dynamic balance, foot placement and plantarflexor muscle activation were examined. Individuals post-stroke had significant dynamic balance deficits in the frontal plane across most tasks, particularly during the paretic single-leg-stance. Frontal plane balance deficits were associated with wider paretic foot placement, elevated body center-of-mass, and lower soleus activity. Further, the obstacle negotiation task imposed a higher balance requirement, particularly during the trailing leg single-stance. Thus, improving paretic foot placement and ankle plantarflexor activity, particularly during obstacle negotiation, may be important rehabilitation targets to enhance dynamic balance during post-stroke community ambulation. Copyright © 2018 Elsevier Ltd. All rights reserved.

Smart algorithms and adaptive methods in computational fluid dynamics

NASA Astrophysics Data System (ADS)

Tinsley Oden, J.

1989-05-01

A review is presented of the use of smart algorithms which employ adaptive methods in processing large amounts of data in computational fluid dynamics (CFD). Smart algorithms use a rationally based set of criteria for automatic decision making in an attempt to produce optimal simulations of complex fluid dynamics problems. The information needed to make these decisions is not known beforehand and evolves in structure and form during the numerical solution of flow problems. Once the code makes a decision based on the available data, the structure of the data may change, and criteria may be reapplied in order to direct the analysis toward an acceptable end. Intelligent decisions are made by processing vast amounts of data that evolve unpredictably during the calculation. The basic components of adaptive methods and their application to complex problems of fluid dynamics are reviewed. The basic components of adaptive methods are: (1) data structures, that is what approaches are available for modifying data structures of an approximation so as to reduce errors; (2) error estimation, that is what techniques exist for estimating error evolution in a CFD calculation; and (3) solvers, what algorithms are available which can function in changing meshes. Numerical examples which demonstrate the viability of these approaches are presented.

Nanostructural self-organization and dynamic adaptation of metal-polymer tribosystems

NASA Astrophysics Data System (ADS)

Mashkov, Yu. K.

2017-02-01

The results of investigating the effect of nanosize modifiers of a polymer matrix on the nanostructural self-organization of polymer composites and dynamic adaptation of metal-polymer tribosystems, which considerably affect the wear resistance of polymer composite materials, have been analyzed. It has been shown that the physicochemical nanostructural self-organization processes are developed in metal-polymer tribosystems with the formation of thermotropic liquid-crystal structures of the polymer matrix, followed by the transition of the system to the stationary state with a negative feedback that ensures dynamic adaptation of the tribosystem to given operating conditions.

X-38 Application of Dynamic Inversion Flight Control

NASA Technical Reports Server (NTRS)

Wacker, Roger; Munday, Steve; Merkle, Scott

2001-01-01

This paper summarizes the application of a nonlinear dynamic inversion (DI) flight control system (FCS) to an autonomous flight test vehicle in NASA's X-38 Project, a predecessor to the International Space Station (ISS) Crew Return Vehicle (CRV). Honeywell's Multi-Application Control-H (MACH) is a parameterized FCS design architecture including both model-based DI rate-compensation and classical P+I command-tracking. MACH was adopted by X-38 in order to shorten the design cycle time for different vehicle shapes and flight envelopes and evolving aerodynamic databases. Specific design issues and analysis results are presented for the application of MACH to the 3rd free flight (FF3) of X-38 Vehicle 132 (V132). This B-52 drop test, occurring on March 30, 2000, represents the first flight test of MACH and one of the first few known applications of DI in the primary FCS of an autonomous flight test vehicle.

Efficient mapping algorithms for scheduling robot inverse dynamics computation on a multiprocessor system

NASA Technical Reports Server (NTRS)

Lee, C. S. G.; Chen, C. L.

1989-01-01

Two efficient mapping algorithms for scheduling the robot inverse dynamics computation consisting of m computational modules with precedence relationship to be executed on a multiprocessor system consisting of p identical homogeneous processors with processor and communication costs to achieve minimum computation time are presented. An objective function is defined in terms of the sum of the processor finishing time and the interprocessor communication time. The minimax optimization is performed on the objective function to obtain the best mapping. This mapping problem can be formulated as a combination of the graph partitioning and the scheduling problems; both have been known to be NP-complete. Thus, to speed up the searching for a solution, two heuristic algorithms were proposed to obtain fast but suboptimal mapping solutions. The first algorithm utilizes the level and the communication intensity of the task modules to construct an ordered priority list of ready modules and the module assignment is performed by a weighted bipartite matching algorithm. For a near-optimal mapping solution, the problem can be solved by the heuristic algorithm with simulated annealing. These proposed optimization algorithms can solve various large-scale problems within a reasonable time. Computer simulations were performed to evaluate and verify the performance and the validity of the proposed mapping algorithms. Finally, experiments for computing the inverse dynamics of a six-jointed PUMA-like manipulator based on the Newton-Euler dynamic equations were implemented on an NCUBE/ten hypercube computer to verify the proposed mapping algorithms. Computer simulation and experimental results are compared and discussed.

Computerized Dynamic Adaptive Tests with Immediately Individualized Feedback for Primary School Mathematics Learning

ERIC Educational Resources Information Center

Wu, Huey-Min; Kuo, Bor-Chen; Wang, Su-Chen

2017-01-01

In this study, a computerized dynamic assessment test with both immediately individualized feedback and adaptively property was applied to Mathematics learning in primary school. For evaluating the effectiveness of the computerized dynamic adaptive test, the performances of three types of remedial instructions were compared by a pre-test/post-test…

Adaptive typography for dynamic mapping environments

NASA Astrophysics Data System (ADS)

Bardon, Didier

1991-08-01

When typography moves across a map, it passes over areas of different colors, densities, and textures. In such a dynamic environment, the aspect of typography must be constantly adapted to provide disernibility for every new background. Adaptive typography undergoes two adaptive operations: background control and contrast control. The background control prevents the features of the map (edges, lines, abrupt changes of densities) from destroying the integrity of the letterform. This is achieved by smoothing the features of the map in the area where a text label is displayed. The modified area is limited to the space covered by the characters of the label. Dispositions are taken to insure that the smoothing operation does not introduce any new visual noise. The contrast control assures that there are sufficient lightness differences between the typography and its ever-changing background. For every new situation, background color and foreground color are compared and the foreground color lightness is adjusted according to a chosen contrast value. Criteria and methods of choosing the appropriate contrast value are presented as well as the experiments that led to them.

Simultaneous inversion of seismic velocity and moment tensor using elastic-waveform inversion of microseismic data: Application to the Aneth CO2-EOR field

NASA Astrophysics Data System (ADS)

Chen, Y.; Huang, L.

2017-12-01

Moment tensors are key parameters for characterizing CO2-injection-induced microseismic events. Elastic-waveform inversion has the potential to providing accurate results of moment tensors. Microseismic waveforms contains information of source moment tensors and the wave propagation velocity along the wavepaths. We develop an elastic-waveform inversion method to jointly invert the seismic velocity model and moment tensor. We first use our adaptive moment-tensor joint inversion method to estimate moment tensors of microseismic events. Our adaptive moment-tensor inversion method jointly inverts multiple microseismic events with similar waveforms within a cluster to reduce inversion uncertainty for microseismic data recorded using a single borehole geophone array. We use this inversion result as the initial model for our elastic-waveform inversion to minimize the cross-correlated-based data misfit between observed data and synthetic data. We verify our method using synthetic microseismic data and obtain improved results of both moment tensors and seismic velocity model. We apply our new inversion method to microseismic data acquired at a CO2-enhanced oil recovery field in Aneth, Utah, using a single borehole geophone array. The results demonstrate that our new inversion method significantly reduces the data misfit compared to the conventional ray-theory-based moment-tensor inversion.

Method and apparatus for adaptive force and position control of manipulators

NASA Technical Reports Server (NTRS)

Seraji, Homayoun (Inventor)

1989-01-01

The present invention discloses systematic methods and apparatus for the design of real time controllers. Real-time control employs adaptive force/position by use of feedforward and feedback controllers, with the feedforward controller being the inverse of the linearized model of robot dynamics and containing only proportional-double-derivative terms is disclosed. The feedback controller, of the proportional-integral-derivative type, ensures that manipulator joints follow reference trajectories and the feedback controller achieves robust tracking of step-plus-exponential trajectories, all in real time. The adaptive controller includes adaptive force and position control within a hybrid control architecture. The adaptive controller, for force control, achieves tracking of desired force setpoints, and the adaptive position controller accomplishes tracking of desired position trajectories. Circuits in the adaptive feedback and feedforward controllers are varied by adaptation laws.

Robustness of continuous-time adaptive control algorithms in the presence of unmodeled dynamics

NASA Technical Reports Server (NTRS)

Rohrs, C. E.; Valavani, L.; Athans, M.; Stein, G.

1985-01-01

This paper examines the robustness properties of existing adaptive control algorithms to unmodeled plant high-frequency dynamics and unmeasurable output disturbances. It is demonstrated that there exist two infinite-gain operators in the nonlinear dynamic system which determines the time-evolution of output and parameter errors. The pragmatic implications of the existence of such infinite-gain operators is that: (1) sinusoidal reference inputs at specific frequencies and/or (2) sinusoidal output disturbances at any frequency (including dc), can cause the loop gain to increase without bound, thereby exciting the unmodeled high-frequency dynamics, and yielding an unstable control system. Hence, it is concluded that existing adaptive control algorithms as they are presented in the literature referenced in this paper, cannot be used with confidence in practical designs where the plant contains unmodeled dynamics because instability is likely to result. Further understanding is required to ascertain how the currently implemented adaptive systems differ from the theoretical systems studied here and how further theoretical development can improve the robustness of adaptive controllers.

Time-lapse joint inversion of geophysical data with automatic joint constraints and dynamic attributes

NASA Astrophysics Data System (ADS)

Rittgers, J. B.; Revil, A.; Mooney, M. A.; Karaoulis, M.; Wodajo, L.; Hickey, C. J.

2016-12-01

Joint inversion and time-lapse inversion techniques of geophysical data are often implemented in an attempt to improve imaging of complex subsurface structures and dynamic processes by minimizing negative effects of random and uncorrelated spatial and temporal noise in the data. We focus on the structural cross-gradient (SCG) approach (enforcing recovered models to exhibit similar spatial structures) in combination with time-lapse inversion constraints applied to surface-based electrical resistivity and seismic traveltime refraction data. The combination of both techniques is justified by the underlying petrophysical models. We investigate the benefits and trade-offs of SCG and time-lapse constraints. Using a synthetic case study, we show that a combined joint time-lapse inversion approach provides an overall improvement in final recovered models. Additionally, we introduce a new approach to reweighting SCG constraints based on an iteratively updated normalized ratio of model sensitivity distributions at each time-step. We refer to the new technique as the Automatic Joint Constraints (AJC) approach. The relevance of the new joint time-lapse inversion process is demonstrated on the synthetic example. Then, these approaches are applied to real time-lapse monitoring field data collected during a quarter-scale earthen embankment induced-piping failure test. The use of time-lapse joint inversion is justified by the fact that a change of porosity drives concomitant changes in seismic velocities (through its effect on the bulk and shear moduli) and resistivities (through its influence upon the formation factor). Combined with the definition of attributes (i.e. specific characteristics) of the evolving target associated with piping, our approach allows localizing the position of the preferential flow path associated with internal erosion. This is not the case using other approaches.

Sex speeds adaptation by altering the dynamics of molecular evolution.

PubMed

McDonald, Michael J; Rice, Daniel P; Desai, Michael M

2016-03-10

Sex and recombination are pervasive throughout nature despite their substantial costs. Understanding the evolutionary forces that maintain these phenomena is a central challenge in biology. One longstanding hypothesis argues that sex is beneficial because recombination speeds adaptation. Theory has proposed several distinct population genetic mechanisms that could underlie this advantage. For example, sex can promote the fixation of beneficial mutations either by alleviating interference competition (the Fisher-Muller effect) or by separating them from deleterious load (the ruby in the rubbish effect). Previous experiments confirm that sex can increase the rate of adaptation, but these studies did not observe the evolutionary dynamics that drive this effect at the genomic level. Here we present the first, to our knowledge, comparison between the sequence-level dynamics of adaptation in experimental sexual and asexual Saccharomyces cerevisiae populations, which allows us to identify the specific mechanisms by which sex speeds adaptation. We find that sex alters the molecular signatures of evolution by changing the spectrum of mutations that fix, and confirm theoretical predictions that it does so by alleviating clonal interference. We also show that substantially deleterious mutations hitchhike to fixation in adapting asexual populations. In contrast, recombination prevents such mutations from fixing. Our results demonstrate that sex both speeds adaptation and alters its molecular signature by allowing natural selection to more efficiently sort beneficial from deleterious mutations.

Reconfigurable Flight Control Using Nonlinear Dynamic Inversion with a Special Accelerometer Implementation

NASA Technical Reports Server (NTRS)

Bacon, Barton J.; Ostroff, Aaron J.

2000-01-01

This paper presents an approach to on-line control design for aircraft that have suffered either actuator failure, missing effector surfaces, surface damage, or any combination. The approach is based on a modified version of nonlinear dynamic inversion. The approach does not require a model of the baseline vehicle (effectors at zero deflection), but does require feedback of accelerations and effector positions. Implementation issues are addressed and the method is demonstrated on an advanced tailless aircraft. An experimental simulation analysis tool is used to directly evaluate the nonlinear system's stability robustness.

Inverse stochastic-dynamic models for high-resolution Greenland ice core records

NASA Astrophysics Data System (ADS)

Boers, Niklas; Chekroun, Mickael D.; Liu, Honghu; Kondrashov, Dmitri; Rousseau, Denis-Didier; Svensson, Anders; Bigler, Matthias; Ghil, Michael

2017-12-01

Proxy records from Greenland ice cores have been studied for several decades, yet many open questions remain regarding the climate variability encoded therein. Here, we use a Bayesian framework for inferring inverse, stochastic-dynamic models from δ18O and dust records of unprecedented, subdecadal temporal resolution. The records stem from the North Greenland Ice Core Project (NGRIP), and we focus on the time interval 59-22 ka b2k. Our model reproduces the dynamical characteristics of both the δ18O and dust proxy records, including the millennial-scale Dansgaard-Oeschger variability, as well as statistical properties such as probability density functions, waiting times and power spectra, with no need for any external forcing. The crucial ingredients for capturing these properties are (i) high-resolution training data, (ii) cubic drift terms, (iii) nonlinear coupling terms between the δ18O and dust time series, and (iv) non-Markovian contributions that represent short-term memory effects.

Dynamic Reconstruction and Multivariable Control for Force-Actuated, Thin Facesheet Adaptive Optics

NASA Technical Reports Server (NTRS)

Grocott, Simon C. O.; Miller, David W.

1997-01-01

The Multiple Mirror Telescope (MMT) under development at the University of Arizona takes a new approach in adaptive optics placing a large (0.65 m) force-actuated, thin facesheet deformable mirror at the secondary of an astronomical telescope, thus reducing the effects of emissivity which are important in IR astronomy. However, The large size of the mirror and low stiffness actuators used drive the natural frequencies of the mirror down into the bandwidth of the atmospheric distortion. Conventional adaptive optics takes a quasi-static approach to controlling the, deformable mirror. However, flexibility within the control bandwidth calls for a new approach to adaptive optics. Dynamic influence functions are used to characterize the influence of each actuator on the surface of the deformable mirror. A linearized model of atmospheric distortion is combined with dynamic influence functions to produce a dynamic reconstructor. This dynamic reconstructor is recognized as an optimal control problem. Solving the optimal control problem for a system with hundreds of actuators and sensors is formidable. Exploiting the circularly symmetric geometry of the mirror, and a suitable model of atmospheric distortion, the control problem is divided into a number of smaller decoupled control problems using circulant matrix theory. A hierarchic control scheme which seeks to emulate the quasi-static control approach that is generally used in adaptive optics is compared to the proposed dynamic reconstruction technique. Although dynamic reconstruction requires somewhat more computational power to implement, it achieves better performance with less power usage, and is less sensitive than the hierarchic technique.

Typical use of inverse dynamics in perceiving motion in autistic adults: Exploring computational principles of perception and action.

PubMed

Takamuku, Shinya; Forbes, Paul A G; Hamilton, Antonia F de C; Gomi, Hiroaki

2018-05-07

There is increasing evidence for motor difficulties in many people with autism spectrum condition (ASC). These difficulties could be linked to differences in the use of internal models which represent relations between motions and forces/efforts. The use of these internal models may be dependent on the cerebellum which has been shown to be abnormal in autism. Several studies have examined internal computations of forward dynamics (motion from force information) in autism, but few have tested the inverse dynamics computation, that is, the determination of force-related information from motion information. Here, we examined this ability in autistic adults by measuring two perceptual biases which depend on the inverse computation. First, we asked participants whether they experienced a feeling of resistance when moving a delayed cursor, which corresponds to the inertial force of the cursor implied by its motion-both typical and ASC participants reported similar feelings of resistance. Second, participants completed a psychophysical task in which they judged the velocity of a moving hand with or without a visual cue implying inertial force. Both typical and ASC participants perceived the hand moving with the inertial cue to be slower than the hand without it. In both cases, the magnitude of the effects did not differ between the two groups. Our results suggest that the neural systems engaged in the inverse dynamics computation are preserved in ASC, at least in the observed conditions. Autism Res 2018. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. We tested the ability to estimate force information from motion information, which arises from a specific "inverse dynamics" computation. Autistic adults and a matched control group reported feeling a resistive sensation when moving a delayed cursor and also judged a moving hand to be slower when it was pulling a load. These findings both suggest that the ability to estimate force information from

Structural self-assembly and avalanchelike dynamics in locally adaptive networks

NASA Astrophysics Data System (ADS)

Gräwer, Johannes; Modes, Carl D.; Magnasco, Marcelo O.; Katifori, Eleni

2015-07-01

Transport networks play a key role across four realms of eukaryotic life: slime molds, fungi, plants, and animals. In addition to the developmental algorithms that build them, many also employ adaptive strategies to respond to stimuli, damage, and other environmental changes. We model these adapting network architectures using a generic dynamical system on weighted graphs and find in simulation that these networks ultimately develop a hierarchical organization of the final weighted architecture accompanied by the formation of a system-spanning backbone. In addition, we find that the long term equilibration dynamics exhibit behavior reminiscent of glassy systems characterized by long periods of slow changes punctuated by bursts of reorganization events.

The dynamics of genetic draft in rapidly adapting populations.

PubMed

Kosheleva, Katya; Desai, Michael M

2013-11-01

The accumulation of beneficial mutations on competing genetic backgrounds in rapidly adapting populations has a striking impact on evolutionary dynamics. This effect, known as clonal interference, causes erratic fluctuations in the frequencies of observed mutations, randomizes the fixation times of successful mutations, and leaves distinct signatures on patterns of genetic variation. Here, we show how this form of "genetic draft" affects the forward-time dynamics of site frequencies in rapidly adapting asexual populations. We calculate the probability that mutations at individual sites shift in frequency over a characteristic timescale, extending Gillespie's original model of draft to the case where many strongly selected beneficial mutations segregate simultaneously. We then derive the sojourn time of mutant alleles, the expected fixation time of successful mutants, and the site frequency spectrum of beneficial and neutral mutations. Finally, we show how this form of draft affects inferences in the McDonald-Kreitman test and how it relates to recent observations that some aspects of genetic diversity are described by the Bolthausen-Sznitman coalescent in the limit of very rapid adaptation.

Young inversion with multiple linked QTLs under selection in a hybrid zone.

PubMed

Lee, Cheng-Ruei; Wang, Baosheng; Mojica, Julius P; Mandáková, Terezie; Prasad, Kasavajhala V S K; Goicoechea, Jose Luis; Perera, Nadeesha; Hellsten, Uffe; Hundley, Hope N; Johnson, Jenifer; Grimwood, Jane; Barry, Kerrie; Fairclough, Stephen; Jenkins, Jerry W; Yu, Yeisoo; Kudrna, Dave; Zhang, Jianwei; Talag, Jayson; Golser, Wolfgang; Ghattas, Kathryn; Schranz, M Eric; Wing, Rod; Lysak, Martin A; Schmutz, Jeremy; Rokhsar, Daniel S; Mitchell-Olds, Thomas

2017-04-03

Fixed chromosomal inversions can reduce gene flow and promote speciation in two ways: by suppressing recombination and by carrying locally favoured alleles at multiple loci. However, it is unknown whether favoured mutations slowly accumulate on older inversions or if young inversions spread because they capture pre-existing adaptive quantitative trait loci (QTLs). By genetic mapping, chromosome painting and genome sequencing, we have identified a major inversion controlling ecologically important traits in Boechera stricta. The inversion arose since the last glaciation and subsequently reached local high frequency in a hybrid speciation zone. Furthermore, the inversion shows signs of positive directional selection. To test whether the inversion could have captured existing, linked QTLs, we crossed standard, collinear haplotypes from the hybrid zone and found multiple linked phenology QTLs within the inversion region. These findings provide the first direct evidence that linked, locally adapted QTLs may be captured by young inversions during incipient speciation.

Integration of Gravitational Torques in Cerebellar Pathways Allows for the Dynamic Inverse Computation of Vertical Pointing Movements of a Robot Arm

PubMed Central

Gentili, Rodolphe J.; Papaxanthis, Charalambos; Ebadzadeh, Mehdi; Eskiizmirliler, Selim; Ouanezar, Sofiane; Darlot, Christian

2009-01-01

Background Several authors suggested that gravitational forces are centrally represented in the brain for planning, control and sensorimotor predictions of movements. Furthermore, some studies proposed that the cerebellum computes the inverse dynamics (internal inverse model) whereas others suggested that it computes sensorimotor predictions (internal forward model). Methodology/Principal Findings This study proposes a model of cerebellar pathways deduced from both biological and physical constraints. The model learns the dynamic inverse computation of the effect of gravitational torques from its sensorimotor predictions without calculating an explicit inverse computation. By using supervised learning, this model learns to control an anthropomorphic robot arm actuated by two antagonists McKibben artificial muscles. This was achieved by using internal parallel feedback loops containing neural networks which anticipate the sensorimotor consequences of the neural commands. The artificial neural networks architecture was similar to the large-scale connectivity of the cerebellar cortex. Movements in the sagittal plane were performed during three sessions combining different initial positions, amplitudes and directions of movements to vary the effects of the gravitational torques applied to the robotic arm. The results show that this model acquired an internal representation of the gravitational effects during vertical arm pointing movements. Conclusions/Significance This is consistent with the proposal that the cerebellar cortex contains an internal representation of gravitational torques which is encoded through a learning process. Furthermore, this model suggests that the cerebellum performs the inverse dynamics computation based on sensorimotor predictions. This highlights the importance of sensorimotor predictions of gravitational torques acting on upper limb movements performed in the gravitational field. PMID:19384420

DyNAvectors: dynamic constitutional vectors for adaptive DNA transfection.

PubMed

Clima, Lilia; Peptanariu, Dragos; Pinteala, Mariana; Salic, Adrian; Barboiu, Mihail

2015-12-25

Dynamic constitutional frameworks, based on squalene, PEG and PEI components, reversibly connected to core centers, allow the efficient identification of adaptive vectors for good DNA transfection efficiency and are well tolerated by mammalian cells.

Solving of the coefficient inverse problems for a nonlinear singularly perturbed reaction-diffusion-advection equation with the final time data

NASA Astrophysics Data System (ADS)

Lukyanenko, D. V.; Shishlenin, M. A.; Volkov, V. T.

2018-01-01

We propose the numerical method for solving coefficient inverse problem for a nonlinear singularly perturbed reaction-diffusion-advection equation with the final time observation data based on the asymptotic analysis and the gradient method. Asymptotic analysis allows us to extract a priory information about interior layer (moving front), which appears in the direct problem, and boundary layers, which appear in the conjugate problem. We describe and implement the method of constructing a dynamically adapted mesh based on this a priory information. The dynamically adapted mesh significantly reduces the complexity of the numerical calculations and improve the numerical stability in comparison with the usual approaches. Numerical example shows the effectiveness of the proposed method.

Quantitative adaptation analytics for assessing dynamic systems of systems: LDRD Final Report

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

Gauthier, John H.; Miner, Nadine E.; Wilson, Michael L.

2015-01-01

Our society is increasingly reliant on systems and interoperating collections of systems, known as systems of systems (SoS). These SoS are often subject to changing missions (e.g., nation- building, arms-control treaties), threats (e.g., asymmetric warfare, terrorism), natural environments (e.g., climate, weather, natural disasters) and budgets. How well can SoS adapt to these types of dynamic conditions? This report details the results of a three year Laboratory Directed Research and Development (LDRD) project aimed at developing metrics and methodologies for quantifying the adaptability of systems and SoS. Work products include: derivation of a set of adaptability metrics, a method for combiningmore » the metrics into a system of systems adaptability index (SoSAI) used to compare adaptability of SoS designs, development of a prototype dynamic SoS (proto-dSoS) simulation environment which provides the ability to investigate the validity of the adaptability metric set, and two test cases that evaluate the usefulness of a subset of the adaptability metrics and SoSAI for distinguishing good from poor adaptability in a SoS. Intellectual property results include three patents pending: A Method For Quantifying Relative System Adaptability, Method for Evaluating System Performance, and A Method for Determining Systems Re-Tasking.« less

Accelerating adaptive inverse distance weighting interpolation algorithm on a graphics processing unit

PubMed Central

Xu, Liangliang; Xu, Nengxiong

2017-01-01

This paper focuses on designing and implementing parallel adaptive inverse distance weighting (AIDW) interpolation algorithms by using the graphics processing unit (GPU). The AIDW is an improved version of the standard IDW, which can adaptively determine the power parameter according to the data points’ spatial distribution pattern and achieve more accurate predictions than those predicted by IDW. In this paper, we first present two versions of the GPU-accelerated AIDW, i.e. the naive version without profiting from the shared memory and the tiled version taking advantage of the shared memory. We also implement the naive version and the tiled version using two data layouts, structure of arrays and array of aligned structures, on both single and double precision. We then evaluate the performance of parallel AIDW by comparing it with its corresponding serial algorithm on three different machines equipped with the GPUs GT730M, M5000 and K40c. The experimental results indicate that: (i) there is no significant difference in the computational efficiency when different data layouts are employed; (ii) the tiled version is always slightly faster than the naive version; and (iii) on single precision the achieved speed-up can be up to 763 (on the GPU M5000), while on double precision the obtained highest speed-up is 197 (on the GPU K40c). To benefit the community, all source code and testing data related to the presented parallel AIDW algorithm are publicly available. PMID:28989754

Accelerating adaptive inverse distance weighting interpolation algorithm on a graphics processing unit.

PubMed

Mei, Gang; Xu, Liangliang; Xu, Nengxiong

2017-09-01

This paper focuses on designing and implementing parallel adaptive inverse distance weighting (AIDW) interpolation algorithms by using the graphics processing unit (GPU). The AIDW is an improved version of the standard IDW, which can adaptively determine the power parameter according to the data points' spatial distribution pattern and achieve more accurate predictions than those predicted by IDW. In this paper, we first present two versions of the GPU-accelerated AIDW, i.e. the naive version without profiting from the shared memory and the tiled version taking advantage of the shared memory. We also implement the naive version and the tiled version using two data layouts, structure of arrays and array of aligned structures, on both single and double precision. We then evaluate the performance of parallel AIDW by comparing it with its corresponding serial algorithm on three different machines equipped with the GPUs GT730M, M5000 and K40c. The experimental results indicate that: (i) there is no significant difference in the computational efficiency when different data layouts are employed; (ii) the tiled version is always slightly faster than the naive version; and (iii) on single precision the achieved speed-up can be up to 763 (on the GPU M5000), while on double precision the obtained highest speed-up is 197 (on the GPU K40c). To benefit the community, all source code and testing data related to the presented parallel AIDW algorithm are publicly available.

Adaptive Control Law Development for Failure Compensation Using Neural Networks on a NASA F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.

2005-01-01

This viewgraph presentation covers the following topics: 1) Brief explanation of Generation II Flight Program; 2) Motivation for Neural Network Adaptive Systems; 3) Past/ Current/ Future IFCS programs; 4) Dynamic Inverse Controller with Explicit Model; 5) Types of Neural Networks Investigated; and 6) Brief example

Inverse dynamic investigation of voluntary leg lateral movements in weightlessness: a new microgravity-specific strategy.

PubMed

Pedrocchi, Alessandra; Baroni, Guido; Pedotti, Antonio; Massion, Jean; Ferrigno, Giancarlo

2005-04-01

This study deals with the quantitative assessment of exchanged forces and torques at the restraint point during whole body posture perturbation movements in long-term microgravity. The work was based on the results of a previous study focused on trunk bending protocol, which suggested that the minimization of the torques exchanged at the restraint point could be a strategy for movement planning in microgravity (J. Biomech. 36(11) (2003) 1691). Torques minimization would lead to the optimization of muscles activity, to the minimization of energy expenditure and, ultimately, to higher movement control capabilities. Here, we focus on leg lateral abduction from anchored stance. The analysis was based on inverse dynamic modelling, leading to the estimation of the total angular momentum at the supporting ankle joint. Results agree with those obtained for trunk bending movements and point out a consistent minimization of the torques exchanged at the restraint point in weightlessness. Given the kinematic features of the examined motor task, this strategy was interpreted as a way to master the rotational dynamic effects on the frontal plane produced by leg lateral abduction. This postural stabilizing effects was the result of a multi-segmental compensation strategy, consisting of the counter rotation of the supporting limb and trunk accompanying the leg raising. The observed consistency of movement-posture co-ordination patterns among lateral leg raising and trunk bending is put forward as a novel interpretative issue of the adaptation mechanisms of the motor system to sustained microgravity, especially if one considers the completely different kinematics of the centre of mass, which was observed in weightlessness for these two motor tasks.

The Genetic Content of Chromosomal Inversions across a Wide Latitudinal Gradient

PubMed Central

Simões, Pedro; Calabria, Gemma; Picão-Osório, João; Balanyà, Joan; Pascual, Marta

2012-01-01

There is increasing evidence regarding the role of chromosomal inversions in relevant biological processes such as local adaptation and speciation. A classic example of the adaptive role of chromosomal polymorphisms is given by the clines of inversion frequencies in Drosophila subobscura, repeatable across continents. Nevertheless, not much is known about the molecular variation associated with these polymorphisms. We characterized the genetic content of ca. 600 individuals from nine European populations following a latitudinal gradient by analysing 19 microsatellite loci from two autosomes (J and U) and the sex chromosome (A), taking into account their chromosomal inversions. Our results clearly demonstrate the molecular genetic uniformity within a given chromosomal inversion across a large latitudinal gradient, particularly from Groningen (Netherlands) in the north to Málaga (Spain) in the south, experiencing highly diverse environmental conditions. This low genetic differentiation within the same gene arrangement across the nine European populations is consistent with the local adaptation hypothesis for th evolutionof chromosomal polymorphisms. We also show the effective role of chromosomal inversions in maintaining different genetic pools within these inverted genomic regions even in the presence of high gene flow. Inversions represent thus an important barrier to gene flux and can help maintain specific allelic combinations with positive effects on fitness. Consistent patterns of microsatellite allele-inversion linkage disequilibrium particularly in loci within inversions were also observed. Finally, we identified areas within inversions presenting clinal variation that might be under selection. PMID:23272126

Dynamics and Adaptive Control for Stability Recovery of Damaged Aircraft

NASA Technical Reports Server (NTRS)

Nguyen, Nhan; Krishnakumar, Kalmanje; Kaneshige, John; Nespeca, Pascal

2006-01-01

This paper presents a recent study of a damaged generic transport model as part of a NASA research project to investigate adaptive control methods for stability recovery of damaged aircraft operating in off-nominal flight conditions under damage and or failures. Aerodynamic modeling of damage effects is performed using an aerodynamic code to assess changes in the stability and control derivatives of a generic transport aircraft. Certain types of damage such as damage to one of the wings or horizontal stabilizers can cause the aircraft to become asymmetric, thus resulting in a coupling between the longitudinal and lateral motions. Flight dynamics for a general asymmetric aircraft is derived to account for changes in the center of gravity that can compromise the stability of the damaged aircraft. An iterative trim analysis for the translational motion is developed to refine the trim procedure by accounting for the effects of the control surface deflection. A hybrid direct-indirect neural network, adaptive flight control is proposed as an adaptive law for stabilizing the rotational motion of the damaged aircraft. The indirect adaptation is designed to estimate the plant dynamics of the damaged aircraft in conjunction with the direct adaptation that computes the control augmentation. Two approaches are presented 1) an adaptive law derived from the Lyapunov stability theory to ensure that the signals are bounded, and 2) a recursive least-square method for parameter identification. A hardware-in-the-loop simulation is conducted and demonstrates the effectiveness of the direct neural network adaptive flight control in the stability recovery of the damaged aircraft. A preliminary simulation of the hybrid adaptive flight control has been performed and initial data have shown the effectiveness of the proposed hybrid approach. Future work will include further investigations and high-fidelity simulations of the proposed hybrid adaptive Bight control approach.

Locally adaptive parallel temperature accelerated dynamics method

NASA Astrophysics Data System (ADS)

Shim, Yunsic; Amar, Jacques G.

2010-03-01

The recently-developed temperature-accelerated dynamics (TAD) method [M. Sørensen and A.F. Voter, J. Chem. Phys. 112, 9599 (2000)] along with the more recently developed parallel TAD (parTAD) method [Y. Shim et al, Phys. Rev. B 76, 205439 (2007)] allow one to carry out non-equilibrium simulations over extended time and length scales. The basic idea behind TAD is to speed up transitions by carrying out a high-temperature MD simulation and then use the resulting information to obtain event times at the desired low temperature. In a typical implementation, a fixed high temperature Thigh is used. However, in general one expects that for each configuration there exists an optimal value of Thigh which depends on the particular transition pathways and activation energies for that configuration. Here we present a locally adaptive high-temperature TAD method in which instead of using a fixed Thigh the high temperature is dynamically adjusted in order to maximize simulation efficiency. Preliminary results of the performance obtained from parTAD simulations of Cu/Cu(100) growth using the locally adaptive Thigh method will also be presented.

Young inversion with multiple linked QTLs under selection in a hybrid zone

PubMed Central

Lee, Cheng-Ruei; Wang, Baosheng; Mojica, Julius; Mandáková, Terezie; Prasad, Kasavajhala V. S. K.; Goicoechea, Jose Luis; Perera, Nadeesha; Hellsten, Uffe; Hundley, Hope N.; Johnson, Jenifer; Grimwood, Jane; Barry, Kerrie; Fairclough, Stephen; Jenkins, Jerry W.; Yu, Yeisoo; Kudrna, Dave; Zhang, Jianwei; Talag, Jayson; Golser, Wolfgang; Ghattas, Katherine; Schranz, M. Eric; Wing, Rod; Lysak, Martin A.; Schmutz, Jeremy; Rokhsar, Daniel S.; Mitchell-Olds, Thomas

2017-01-01

Fixed chromosomal inversions can reduce gene flow and promote speciation in two ways: by suppressing recombination and by carrying locally favored alleles at multiple loci. However, it is unknown whether favored mutations slowly accumulate on older inversions or if young inversions spread because they capture preexisting adaptive Quantitative Trait Loci (QTLs). By genetic mapping, chromosome painting and genome sequencing we have identified a major inversion controlling ecologically important traits in Boechera stricta. The inversion arose since the last glaciation and subsequently reached local high frequency in a hybrid speciation zone. Furthermore, the inversion shows signs of positive directional selection. To test whether the inversion could have captured existing, linked QTLs, we crossed standard, collinear haplotypes from the hybrid zone and found multiple linked phenology QTLs within the inversion region. These findings provide the first direct evidence that linked, locally adapted QTLs may be captured by young inversions during incipient speciation. PMID:28812690

Designing Dynamic Adaptive Policy Pathways using Many-Objective Robust Decision Making

NASA Astrophysics Data System (ADS)

Kwakkel, Jan; Haasnoot, Marjolijn

2017-04-01

Dealing with climate risks in water management requires confronting a wide variety of deeply uncertain factors, while navigating a many dimensional space of trade-offs amongst objectives. There is an emerging body of literature on supporting this type of decision problem, under the label of decision making under deep uncertainty. Two approaches within this literature are Many-Objective Robust Decision Making, and Dynamic Adaptive Policy Pathways. In recent work, these approaches have been compared. One of the main conclusions of this comparison was that they are highly complementary. Many-Objective Robust Decision Making is a model based decision support approach, while Dynamic Adaptive Policy Pathways is primarily a conceptual framework for the design of flexible strategies that can be adapted over time in response to how the future is actually unfolding. In this research we explore this complementarity in more detail. Specifically, we demonstrate how Many-Objective Robust Decision Making can be used to design adaptation pathways. We demonstrate this combined approach using a water management problem, in the Netherlands. The water level of Lake IJselmeer, the main fresh water resource of the Netherlands, is currently managed through discharge by gravity. Due to climate change, this won't be possible in the future, unless water levels are changed. Changing the water level has undesirable flood risk and spatial planning consequences. The challenge is to find promising adaptation pathways that balance objectives related to fresh water supply, flood risk, and spatial issues, while accounting for uncertain climatic and land use change. We conclude that the combination of Many-Objective Robust Decision Making and Dynamic Adaptive Policy Pathways is particularly suited for dealing with deeply uncertain climate risks.

Inverse algorithms for 2D shallow water equations in presence of wet dry fronts: Application to flood plain dynamics

NASA Astrophysics Data System (ADS)

Monnier, J.; Couderc, F.; Dartus, D.; Larnier, K.; Madec, R.; Vila, J.-P.

2016-11-01

The 2D shallow water equations adequately model some geophysical flows with wet-dry fronts (e.g. flood plain or tidal flows); nevertheless deriving accurate, robust and conservative numerical schemes for dynamic wet-dry fronts over complex topographies remains a challenge. Furthermore for these flows, data are generally complex, multi-scale and uncertain. Robust variational inverse algorithms, providing sensitivity maps and data assimilation processes may contribute to breakthrough shallow wet-dry front dynamics modelling. The present study aims at deriving an accurate, positive and stable finite volume scheme in presence of dynamic wet-dry fronts, and some corresponding inverse computational algorithms (variational approach). The schemes and algorithms are assessed on classical and original benchmarks plus a real flood plain test case (Lèze river, France). Original sensitivity maps with respect to the (friction, topography) pair are performed and discussed. The identification of inflow discharges (time series) or friction coefficients (spatially distributed parameters) demonstrate the algorithms efficiency.

Plant toxicity, adaptive herbivory, and plant community dynamics

Treesearch

Zhilan Feng; Rongsong Liu; Donald L. DeAngelis; John P. Bryant; Knut Kielland; F. Stuart Chapin; Robert K. Swihart

2009-01-01

We model effects of interspecific plant competition, herbivory, and a plant's toxic defenses against herbivores on vegetation dynamics. The model predicts that, when a generalist herbivore feeds in the absence of plant toxins, adaptive foraging generally increases the probability of coexistence of plant species populations, because the herbivore switches more of...

Emerging hierarchies in dynamically adapting webs

NASA Astrophysics Data System (ADS)

Katifori, Eleni; Graewer, Johannes; Magnasco, Marcelo; Modes, Carl

Transport networks play a key role across four realms of eukaryotic life: slime molds, fungi, plants, and animals. In addition to the developmental algorithms that build them, many also employ adaptive strategies to respond to stimuli, damage, and other environmental changes. We model these adapting network architectures using a generic dynamical system on weighted graphs and find in simulation that these networks ultimately develop a hierarchical organization of the final weighted architecture accompanied by the formation of a system-spanning backbone. We quantify the hierarchical organization of the networks by developing an algorithm that decomposes the architecture to multiple scales and analyzes how the organization in each scale relates to that of the scale above and below it. The methodologies developed in this work are applicable to a wide range of systems including the slime mold physarum polycephalum, human microvasculature, and force chains in granular media.

Nonlinear Inversion for Dynamic Rupture Parameters from the 2004 Mw6.0 Parkfield Earthquake

NASA Astrophysics Data System (ADS)

Jimenez, R. M.; Olsen, K. B.

2007-12-01

The Parkfield section of the San Andreas Fault has produced repeated moderate-size earthquakes at fairly regular intervals and is therefore an important target for investigations of rupture initiation, propagation and arrest, which could eventually lead to clues on earthquake prediction. The most recent member of the Parkfield series of earthquakes, the 2004 Mw6.0 event, produced a considerable amount of high-resolution strong motion data, and provides an ideal test bed for analysis of the dynamic rupture propagation. Here, we use a systematic nonlinear direct-search method to invert strong-ground motion data (less than 1 Hz) at 37 stations to obtain models of the slip weakening distance and spatially-varying stress drop (8 by 4 subfaults) on the (vertical) causative segment of the San Andreas fault (40 km long by 15 km wide), along with spatial-temporal coseismic slip distributions. The rupture and wave propagation modeling is performed by a three-dimensional finite-difference method with a slip- weakening friction law and the stress-glut dynamic-rupture formulation (Andrews, 1999), and the inversion is carried out by a neighborhood algorithm (Sambridge, 1999), minimizing the least-squares misfit between the calculated and observed seismograms. The dynamic rupture is nucleated artificially by lowering the yield stress in a 3 km by 3 km patch centered at the location of the hypocenter estimated from strong motion data. Outside the nucleation patch the yield stress is kept constant (5-10 MPa), and we constrain the slip-weakening distance to values less than 1 m. We compare the inversion results for two different velocity models: (1) a 3-D model based on the P-wave velocity structure by Thurber (2006), with S-wave and density relations based on Brocher (2005), and (2) a combination of two different 1-D layered velocity structures on either side of the fault, as proposed by Liu et al. (2006). Due to the non-uniqueness of the problem, the inversion provides an ensemble

Space station dynamic modeling, disturbance accommodation, and adaptive control

NASA Technical Reports Server (NTRS)

Wang, S. J.; Ih, C. H.; Lin, Y. H.; Metter, E.

1985-01-01

Dynamic models for two space station configurations were derived. Space shuttle docking disturbances and their effects on the station and solar panels are quantified. It is shown that hard shuttle docking can cause solar panel buckling. Soft docking and berthing can substantially reduce structural loads at the expense of large shuttle and station attitude excursions. It is found predocking shuttle momentum reduction is necessary to achieve safe and routine operations. A direct model reference adaptive control is synthesized and evaluated for the station model parameter errors and plant dynamics truncations. The rigid body and the flexible modes are treated. It is shown that convergence of the adaptive algorithm can be achieved in 100 seconds with reasonable performance even during shuttle hard docking operations in which station mass and inertia are instantaneously changed by more than 100%.

Recursive mass matrix factorization and inversion: An operator approach to open- and closed-chain multibody dynamics

NASA Technical Reports Server (NTRS)

Rodriguez, G.; Kreutz, K.

1988-01-01

This report advances a linear operator approach for analyzing the dynamics of systems of joint-connected rigid bodies.It is established that the mass matrix M for such a system can be factored as M=(I+H phi L)D(I+H phi L) sup T. This yields an immediate inversion M sup -1=(I-H psi L) sup T D sup -1 (I-H psi L), where H and phi are given by known link geometric parameters, and L, psi and D are obtained recursively by a spatial discrete-step Kalman filter and by the corresponding Riccati equation associated with this filter. The factors (I+H phi L) and (I-H psi L) are lower triangular matrices which are inverses of each other, and D is a diagonal matrix. This factorization and inversion of the mass matrix leads to recursive algortihms for forward dynamics based on spatially recursive filtering and smoothing. The primary motivation for advancing the operator approach is to provide a better means to formulate, analyze and understand spatial recursions in multibody dynamics. This is achieved because the linear operator notation allows manipulation of the equations of motion using a very high-level analytical framework (a spatial operator algebra) that is easy to understand and use. Detailed lower-level recursive algorithms can readily be obtained for inspection from the expressions involving spatial operators. The report consists of two main sections. In Part 1, the problem of serial chain manipulators is analyzed and solved. Extensions to a closed-chain system formed by multiple manipulators moving a common task object are contained in Part 2. To retain ease of exposition in the report, only these two types of multibody systems are considered. However, the same methods can be easily applied to arbitrary multibody systems formed by a collection of joint-connected regid bodies.

Adaptive and neuroadaptive control for nonnegative and compartmental dynamical systems

NASA Astrophysics Data System (ADS)

Volyanskyy, Kostyantyn Y.

Neural networks have been extensively used for adaptive system identification as well as adaptive and neuroadaptive control of highly uncertain systems. The goal of adaptive and neuroadaptive control is to achieve system performance without excessive reliance on system models. To improve robustness and the speed of adaptation of adaptive and neuroadaptive controllers several controller architectures have been proposed in the literature. In this dissertation, we develop a new neuroadaptive control architecture for nonlinear uncertain dynamical systems. The proposed framework involves a novel controller architecture with additional terms in the update laws that are constructed using a moving window of the integrated system uncertainty. These terms can be used to identify the ideal system weights of the neural network as well as effectively suppress system uncertainty. Linear and nonlinear parameterizations of the system uncertainty are considered and state and output feedback neuroadaptive controllers are developed. Furthermore, we extend the developed framework to discrete-time dynamical systems. To illustrate the efficacy of the proposed approach we apply our results to an aircraft model with wing rock dynamics, a spacecraft model with unknown moment of inertia, and an unmanned combat aerial vehicle undergoing actuator failures, and compare our results with standard neuroadaptive control methods. Nonnegative systems are essential in capturing the behavior of a wide range of dynamical systems involving dynamic states whose values are nonnegative. A sub-class of nonnegative dynamical systems are compartmental systems. These systems are derived from mass and energy balance considerations and are comprised of homogeneous interconnected microscopic subsystems or compartments which exchange variable quantities of material via intercompartmental flow laws. In this dissertation, we develop direct adaptive and neuroadaptive control framework for stabilization, disturbance

Motion-adaptive spatio-temporal regularization for accelerated dynamic MRI.

PubMed

Asif, M Salman; Hamilton, Lei; Brummer, Marijn; Romberg, Justin

2013-09-01

Accelerated magnetic resonance imaging techniques reduce signal acquisition time by undersampling k-space. A fundamental problem in accelerated magnetic resonance imaging is the recovery of quality images from undersampled k-space data. Current state-of-the-art recovery algorithms exploit the spatial and temporal structures in underlying images to improve the reconstruction quality. In recent years, compressed sensing theory has helped formulate mathematical principles and conditions that ensure recovery of (structured) sparse signals from undersampled, incoherent measurements. In this article, a new recovery algorithm, motion-adaptive spatio-temporal regularization, is presented that uses spatial and temporal structured sparsity of MR images in the compressed sensing framework to recover dynamic MR images from highly undersampled k-space data. In contrast to existing algorithms, our proposed algorithm models temporal sparsity using motion-adaptive linear transformations between neighboring images. The efficiency of motion-adaptive spatio-temporal regularization is demonstrated with experiments on cardiac magnetic resonance imaging for a range of reduction factors. Results are also compared with k-t FOCUSS with motion estimation and compensation-another recently proposed recovery algorithm for dynamic magnetic resonance imaging. . Copyright © 2012 Wiley Periodicals, Inc.

Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks.

PubMed

Yoo, Sung Jin; Park, Jin Bae; Choi, Yoon Ho

2006-12-01

A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides the ability to overcome the "explosion of complexity" problem in backstepping controllers. The SRWNNs are used to observe the arbitrary model uncertainties of FJ robots, and all their weights are trained online. From the Lyapunov stability analysis, their adaptation laws are induced, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a three-link FJ robot are utilized to validate the good position tracking performance and robustness against payload uncertainties and external disturbances of the proposed control system.

Automated adaptive inference of phenomenological dynamical models

NASA Astrophysics Data System (ADS)

Daniels, Bryan

Understanding the dynamics of biochemical systems can seem impossibly complicated at the microscopic level: detailed properties of every molecular species, including those that have not yet been discovered, could be important for producing macroscopic behavior. The profusion of data in this area has raised the hope that microscopic dynamics might be recovered in an automated search over possible models, yet the combinatorial growth of this space has limited these techniques to systems that contain only a few interacting species. We take a different approach inspired by coarse-grained, phenomenological models in physics. Akin to a Taylor series producing Hooke's Law, forgoing microscopic accuracy allows us to constrain the search over dynamical models to a single dimension. This makes it feasible to infer dynamics with very limited data, including cases in which important dynamical variables are unobserved. We name our method Sir Isaac after its ability to infer the dynamical structure of the law of gravitation given simulated planetary motion data. Applying the method to output from a microscopically complicated but macroscopically simple biological signaling model, it is able to adapt the level of detail to the amount of available data. Finally, using nematode behavioral time series data, the method discovers an effective switch between behavioral attractors after the application of a painful stimulus.

Generalization in Adaptation to Stable and Unstable Dynamics

PubMed Central

Kadiallah, Abdelhamid; Franklin, David W.; Burdet, Etienne

2012-01-01

Humans skillfully manipulate objects and tools despite the inherent instability. In order to succeed at these tasks, the sensorimotor control system must build an internal representation of both the force and mechanical impedance. As it is not practical to either learn or store motor commands for every possible future action, the sensorimotor control system generalizes a control strategy for a range of movements based on learning performed over a set of movements. Here, we introduce a computational model for this learning and generalization, which specifies how to learn feedforward muscle activity in a function of the state space. Specifically, by incorporating co-activation as a function of error into the feedback command, we are able to derive an algorithm from a gradient descent minimization of motion error and effort, subject to maintaining a stability margin. This algorithm can be used to learn to coordinate any of a variety of motor primitives such as force fields, muscle synergies, physical models or artificial neural networks. This model for human learning and generalization is able to adapt to both stable and unstable dynamics, and provides a controller for generating efficient adaptive motor behavior in robots. Simulation results exhibit predictions consistent with all experiments on learning of novel dynamics requiring adaptation of force and impedance, and enable us to re-examine some of the previous interpretations of experiments on generalization. PMID:23056191

Elucidating Microbial Adaptation Dynamics via Autonomous Exposure and Sampling

NASA Astrophysics Data System (ADS)

Grace, J. M.; Verseux, C.; Gentry, D.; Moffet, A.; Thayabaran, R.; Wong, N.; Rothschild, L.

2013-12-01

The adaptation of micro-organisms to their environments is a complex process of interaction between the pressures of the environment and of competition. Reducing this multifactorial process to environmental exposure in the laboratory is a common tool for elucidating individual mechanisms of evolution, such as mutation rates[Wielgoss et al., 2013]. Although such studies inform fundamental questions about the way adaptation and even speciation occur, they are often limited by labor-intensive manual techniques[Wassmann et al., 2010]. Current methods for controlled study of microbial adaptation limit the length of time, the depth of collected data, and the breadth of applied environmental conditions. Small idiosyncrasies in manual techniques can have large effects on outcomes; for example, there are significant variations in induced radiation resistances following similar repeated exposure protocols[Alcántara-Díaz et al., 2004; Goldman and Travisano, 2011]. We describe here a project under development to allow rapid cycling of multiple types of microbial environmental exposure. The system allows continuous autonomous monitoring and data collection of both single species and sampled communities, independently and concurrently providing multiple types of controlled environmental pressure (temperature, radiation, chemical presence or absence, and so on) to a microbial community in dynamic response to the ecosystem's current status. When combined with DNA sequencing and extraction, such a controlled environment can cast light on microbial functional development, population dynamics, inter- and intra-species competition, and microbe-environment interaction. The project's goal is to allow rapid, repeatable iteration of studies of both natural and artificial microbial adaptation. As an example, the same system can be used both to increase the pH of a wet soil aliquot over time while periodically sampling it for genetic activity analysis, or to repeatedly expose a culture of

Fast Dynamical Coupling Enhances Frequency Adaptation of Oscillators for Robotic Locomotion Control

PubMed Central

Nachstedt, Timo; Tetzlaff, Christian; Manoonpong, Poramate

2017-01-01

Rhythmic neural signals serve as basis of many brain processes, in particular of locomotion control and generation of rhythmic movements. It has been found that specific neural circuits, named central pattern generators (CPGs), are able to autonomously produce such rhythmic activities. In order to tune, shape and coordinate the produced rhythmic activity, CPGs require sensory feedback, i.e., external signals. Nonlinear oscillators are a standard model of CPGs and are used in various robotic applications. A special class of nonlinear oscillators are adaptive frequency oscillators (AFOs). AFOs are able to adapt their frequency toward the frequency of an external periodic signal and to keep this learned frequency once the external signal vanishes. AFOs have been successfully used, for instance, for resonant tuning of robotic locomotion control. However, the choice of parameters for a standard AFO is characterized by a trade-off between the speed of the adaptation and its precision and, additionally, is strongly dependent on the range of frequencies the AFO is confronted with. As a result, AFOs are typically tuned such that they require a comparably long time for their adaptation. To overcome the problem, here, we improve the standard AFO by introducing a novel adaptation mechanism based on dynamical coupling strengths. The dynamical adaptation mechanism enhances both the speed and precision of the frequency adaptation. In contrast to standard AFOs, in this system, the interplay of dynamics on short and long time scales enables fast as well as precise adaptation of the oscillator for a wide range of frequencies. Amongst others, a very natural implementation of this mechanism is in terms of neural networks. The proposed system enables robotic applications which require fast retuning of locomotion control in order to react to environmental changes or conditions. PMID:28377710

Dual adaptive dynamic control of mobile robots using neural networks.

PubMed

Bugeja, Marvin K; Fabri, Simon G; Camilleri, Liberato

2009-02-01

This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.

Extracting neuronal functional network dynamics via adaptive Granger causality analysis.

PubMed

Sheikhattar, Alireza; Miran, Sina; Liu, Ji; Fritz, Jonathan B; Shamma, Shihab A; Kanold, Patrick O; Babadi, Behtash

2018-04-24

Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying adaptive behavior. We develop a dynamic estimation and inference paradigm for extracting functional neuronal network dynamics in the sense of Granger, by integrating techniques from adaptive filtering, compressed sensing, point process theory, and high-dimensional statistics. We demonstrate the utility of our proposed paradigm through theoretical analysis, algorithm development, and application to synthetic and real data. Application of our techniques to two-photon Ca 2+ imaging experiments from the mouse auditory cortex reveals unique features of the functional neuronal network structures underlying spontaneous activity at unprecedented spatiotemporal resolution. Our analysis of simultaneous recordings from the ferret auditory and prefrontal cortical areas suggests evidence for the role of rapid top-down and bottom-up functional dynamics across these areas involved in robust attentive behavior.

Salt-induced effects on natural and inverse DPPC lipid membranes: Molecular dynamics simulation.

PubMed

Rezaei Sani, Seyed Mojtaba; Akhavan, Mojdeh; Jalili, Seifollah

2018-08-01

Molecular dynamics (MD) simulations of a dipalmitoylphosphatidylcholine (DPPC) bilayer and its neutral inverse-phosphocholine equivalent (DPCPe) were performed to find salt-induced effects on their surface structure and the nature of ion-lipid interactions. We found that the area per lipid is not considerably affected by the inversion, but the deuterium order parameter of carbon atoms in the region of carbonyl carbons changes dramatically. MD simulations indicate that Ca 2+ ions can bind to the surface of both DPPC and DPCPe membranes, but K + ions do not bind to them. In the case of Na + , however, the ions can bind to natural lipids but not to the inverse ones. Also, our results demonstrate that the hydration level of CPe bilayers is substantially lower than PC bilayers and the averaged orientation of water dipoles in the region of CPe headgroups is effectively inverted compared to PC lipids. This might be important in the interaction of the bilayer with its biological environment. Furthermore, it was found for the CPe bilayers that the enhanced peaks of the electrostatic potential profiles shift further away from the bilayer center relative to those of PC bilayers. This behavior makes the penetration of cations into the bilayer more difficult and possibly explains the experimentally observed enhanced release rates of anionic compounds in the CPe membrane. Copyright © 2018 Elsevier B.V. All rights reserved.

Inference of chromosomal inversion dynamics from Pool-Seq data in natural and laboratory populations of Drosophila melanogaster.

PubMed

Kapun, Martin; van Schalkwyk, Hester; McAllister, Bryant; Flatt, Thomas; Schlötterer, Christian

2014-04-01

Sequencing of pools of individuals (Pool-Seq) represents a reliable and cost-effective approach for estimating genome-wide SNP and transposable element insertion frequencies. However, Pool-Seq does not provide direct information on haplotypes so that, for example, obtaining inversion frequencies has not been possible until now. Here, we have developed a new set of diagnostic marker SNPs for seven cosmopolitan inversions in Drosophila melanogaster that can be used to infer inversion frequencies from Pool-Seq data. We applied our novel marker set to Pool-Seq data from an experimental evolution study and from North American and Australian latitudinal clines. In the experimental evolution data, we find evidence that positive selection has driven the frequencies of In(3R)C and In(3R)Mo to increase over time. In the clinal data, we confirm the existence of frequency clines for In(2L)t, In(3L)P and In(3R)Payne in both North America and Australia and detect a previously unknown latitudinal cline for In(3R)Mo in North America. The inversion markers developed here provide a versatile and robust tool for characterizing inversion frequencies and their dynamics in Pool-Seq data from diverse D. melanogaster populations. © 2013 The Authors. Molecular Ecology Published by John Wiley & Sons Ltd.

Inference of chromosomal inversion dynamics from Pool-Seq data in natural and laboratory populations of Drosophila melanogaster

PubMed Central

Kapun, Martin; van Schalkwyk, Hester; McAllister, Bryant; Flatt, Thomas; Schlötterer, Christian

2014-01-01

Sequencing of pools of individuals (Pool-Seq) represents a reliable and cost-effective approach for estimating genome-wide SNP and transposable element insertion frequencies. However, Pool-Seq does not provide direct information on haplotypes so that, for example, obtaining inversion frequencies has not been possible until now. Here, we have developed a new set of diagnostic marker SNPs for seven cosmopolitan inversions in Drosophila melanogaster that can be used to infer inversion frequencies from Pool-Seq data. We applied our novel marker set to Pool-Seq data from an experimental evolution study and from North American and Australian latitudinal clines. In the experimental evolution data, we find evidence that positive selection has driven the frequencies of In(3R)C and In(3R)Mo to increase over time. In the clinal data, we confirm the existence of frequency clines for In(2L)t, In(3L)P and In(3R)Payne in both North America and Australia and detect a previously unknown latitudinal cline for In(3R)Mo in North America. The inversion markers developed here provide a versatile and robust tool for characterizing inversion frequencies and their dynamics in Pool-Seq data from diverse D. melanogaster populations. PMID:24372777

Human inversions and their functional consequences

PubMed Central

Puig, Marta; Casillas, Sònia; Villatoro, Sergi

2015-01-01

Polymorphic inversions are a type of structural variants that are difficult to analyze owing to their balanced nature and the location of breakpoints within complex repeated regions. So far, only a handful of inversions have been studied in detail in humans and current knowledge about their possible functional effects is still limited. However, inversions have been related to phenotypic changes and adaptation in multiple species. In this review, we summarize the evidences of the functional impact of inversions in the human genome. First, given that inversions have been shown to inhibit recombination in heterokaryotes, chromosomes displaying different orientation are expected to evolve independently and this may lead to distinct gene-expression patterns. Second, inversions have a role as disease-causing mutations both by directly affecting gene structure or regulation in different ways, and by predisposing to other secondary arrangements in the offspring of inversion carriers. Finally, several inversions show signals of being selected during human evolution. These findings illustrate the potential of inversions to have phenotypic consequences also in humans and emphasize the importance of their inclusion in genome-wide association studies. PMID:25998059

Dynamics of Mount Somma-Vesuvius edifice: from stress field inversion to analogue and numerical modelling

NASA Astrophysics Data System (ADS)

De Matteo, Ada; Massa, Bruno; D'Auria, Luca; Castaldo, Raffaele

2017-04-01

Geological processes are generally very complex and too slow to be directly observed in their completeness; modelling procedures overcome this limit. The state of stress in the upper lithosphere is the main responsible for driving geodynamical processes; in order to retrieve the active stress field in a rock volume, stress inversion techniques can be applied on both seismological and structural datasets. This approach has been successfully applied to active tectonics as well as volcanic areas. In this context the best approach in managing heterogeneous datasets in volcanic environments consists in the analysis of spatial variations of the stress field by applying robust techniques of inversion. The study of volcanic seismicity is an efficient tool to retrieve spatial and temporal pattern of the pre-, syn- and inter-eruptive stress field: magma migration as well as dynamics of magma chamber and hydrothermal system are directly connected to the volcanic seismicity. Additionally, analysis of the temporal variations of stress field pattern in volcanoes could be a useful monitoring tool. Recently the stress field acting on several active volcanoes has been investigated by using stress inversion techniques on seismological datasets (Massa et al., 2016). The Bayesian Right Trihedra Method (BRTM; D'Auria and Massa, 2015) is able to successfully manage heterogeneous datasets allowing the identification of regional fields locally overcame by the stress field due to volcano specific dynamics. In particular, the analysis of seismicity and stress field inversion at the Somma-Vesuvius highlighted the presence of two superposed volumes characterized by different behaviour and stress field pattern: a top volume dominated by an extensional stress field, in accordance with a gravitational spreading-style of deformation, and a bottom volume related to a regional extensional stress field. In addition, in order to evaluate the dynamics of deformation, both analogue and numerical

Adaptive dynamics on an environmental gradient that changes over a geological time-scale.

PubMed

Fortelius, Mikael; Geritz, Stefan; Gyllenberg, Mats; Toivonen, Jaakko

2015-07-07

The standard adaptive dynamics framework assumes two timescales, i.e. fast population dynamics and slow evolutionary dynamics. We further assume a third timescale, which is even slower than the evolutionary timescale. We call this the geological timescale and we assume that slow climatic change occurs within this timescale. We study the evolution of our model population over this very slow geological timescale with bifurcation plots of the standard adaptive dynamics framework. The bifurcation parameter being varied describes the abiotic environment that changes over the geological timescale. We construct evolutionary trees over the geological timescale and observe both gradual phenotypic evolution and punctuated branching events. We concur with the established notion that branching of a monomorphic population on an environmental gradient only happens when the gradient is not too shallow and not too steep. However, we show that evolution within the habitat can produce polymorphic populations that inhabit steep gradients. What is necessary is that the environmental gradient at some point in time is such that the initial branching of the monomorphic population can occur. We also find that phenotypes adapted to environments in the middle of the existing environmental range are more likely to branch than phenotypes adapted to extreme environments. Copyright © 2015 Elsevier Ltd. All rights reserved.

Implementation and Evaluation of Multiple Adaptive Control Technologies for a Generic Transport Aircraft Simulation

NASA Technical Reports Server (NTRS)

Campbell, Stefan F.; Kaneshige, John T.; Nguyen, Nhan T.; Krishakumar, Kalmanje S.

2010-01-01

Presented here is the evaluation of multiple adaptive control technologies for a generic transport aircraft simulation. For this study, seven model reference adaptive control (MRAC) based technologies were considered. Each technology was integrated into an identical dynamic-inversion control architecture and tuned using a methodology based on metrics and specific design requirements. Simulation tests were then performed to evaluate each technology s sensitivity to time-delay, flight condition, model uncertainty, and artificially induced cross-coupling. The resulting robustness and performance characteristics were used to identify potential strengths, weaknesses, and integration challenges of the individual adaptive control technologies

Dynamic Adaptive Neural Network Arrays: A Neuromorphic Architecture

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

Disney, Adam; Reynolds, John

2015-01-01

Dynamic Adaptive Neural Network Array (DANNA) is a neuromorphic hardware implementation. It differs from most other neuromorphic projects in that it allows for programmability of structure, and it is trained or designed using evolutionary optimization. This paper describes the DANNA structure, how DANNA is trained using evolutionary optimization, and an application of DANNA to a very simple classification task.

Dynamic Shape Reconstruction of Three-Dimensional Frame Structures Using the Inverse Finite Element Method

NASA Technical Reports Server (NTRS)

Gherlone, Marco; Cerracchio, Priscilla; Mattone, Massimiliano; Di Sciuva, Marco; Tessler, Alexander

2011-01-01

A robust and efficient computational method for reconstructing the three-dimensional displacement field of truss, beam, and frame structures, using measured surface-strain data, is presented. Known as shape sensing , this inverse problem has important implications for real-time actuation and control of smart structures, and for monitoring of structural integrity. The present formulation, based on the inverse Finite Element Method (iFEM), uses a least-squares variational principle involving strain measures of Timoshenko theory for stretching, torsion, bending, and transverse shear. Two inverse-frame finite elements are derived using interdependent interpolations whose interior degrees-of-freedom are condensed out at the element level. In addition, relationships between the order of kinematic-element interpolations and the number of required strain gauges are established. As an example problem, a thin-walled, circular cross-section cantilevered beam subjected to harmonic excitations in the presence of structural damping is modeled using iFEM; where, to simulate strain-gauge values and to provide reference displacements, a high-fidelity MSC/NASTRAN shell finite element model is used. Examples of low and high-frequency dynamic motion are analyzed and the solution accuracy examined with respect to various levels of discretization and the number of strain gauges.

Elucidating Microbial Adaptation Dynamics via Autonomous Exposure and Sampling

NASA Technical Reports Server (NTRS)

Grace, Joseph M.; Verseux, Cyprien; Gentry, Diana; Moffet, Amy; Thayabaran, Ramanen; Wong, Nathan; Rothschild, Lynn

2013-01-01

The adaptation of micro-organisms to their environments is a complex process of interaction between the pressures of the environment and of competition. Reducing this multifactorial process to environmental exposure in the laboratory is a common tool for elucidating individual mechanisms of evolution, such as mutation rates. Although such studies inform fundamental questions about the way adaptation and even speciation occur, they are often limited by labor-intensive manual techniques. Current methods for controlled study of microbial adaptation limit the length of time, the depth of collected data, and the breadth of applied environmental conditions. Small idiosyncrasies in manual techniques can have large effects on outcomes; for example, there are significant variations in induced radiation resistances following similar repeated exposure protocols. We describe here a project under development to allow rapid cycling of multiple types of microbial environmental exposure. The system allows continuous autonomous monitoring and data collection of both single species and sampled communities, independently and concurrently providing multiple types of controlled environmental pressure (temperature, radiation, chemical presence or absence, and so on) to a microbial community in dynamic response to the ecosystem's current status. When combined with DNA sequencing and extraction, such a controlled environment can cast light on microbial functional development, population dynamics, inter- and intra-species competition, and microbe-environment interaction. The project's goal is to allow rapid, repeatable iteration of studies of both natural and artificial microbial adaptation. As an example, the same system can be used both to increase the pH of a wet soil aliquot over time while periodically sampling it for genetic activity analysis, or to repeatedly expose a culture of bacteria to the presence of a toxic metal, automatically adjusting the level of toxicity based on the

Selection history and epistatic interactions impact dynamics of adaptation to novel environmental stresses.

PubMed

Lagator, Mato; Colegrave, Nick; Neve, Paul

2014-11-07

In rapidly changing environments, selection history may impact the dynamics of adaptation. Mutations selected in one environment may result in pleiotropic fitness trade-offs in subsequent novel environments, slowing the rates of adaptation. Epistatic interactions between mutations selected in sequential stressful environments may slow or accelerate subsequent rates of adaptation, depending on the nature of that interaction. We explored the dynamics of adaptation during sequential exposure to herbicides with different modes of action in Chlamydomonas reinhardtii. Evolution of resistance to two of the herbicides was largely independent of selection history. For carbetamide, previous adaptation to other herbicide modes of action positively impacted the likelihood of adaptation to this herbicide. Furthermore, while adaptation to all individual herbicides was associated with pleiotropic fitness costs in stress-free environments, we observed that accumulation of resistance mechanisms was accompanied by a reduction in overall fitness costs. We suggest that antagonistic epistasis may be a driving mechanism that enables populations to more readily adapt in novel environments. These findings highlight the potential for sequences of xenobiotics to facilitate the rapid evolution of multiple-drug and -pesticide resistance, as well as the potential for epistatic interactions between adaptive mutations to facilitate evolutionary rescue in rapidly changing environments. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Asymmetric interlimb transfer of concurrent adaptation to opposing dynamic forces

PubMed Central

Miall, R. C.; Woolley, D. G.

2007-01-01

Interlimb transfer of a novel dynamic force has been well documented. It has also been shown that unimanual adaptation to opposing novel environments is possible if they are associated with different workspaces. The main aim of this study was to test if adaptation to opposing velocity dependent viscous forces with one arm could improve the initial performance of the other arm. The study also examined whether this interlimb transfer occurred across an extrinsic, spatial, coordinative system or an intrinsic, joint based, coordinative system. Subjects initially adapted to opposing viscous forces separated by target location. Our measure of performance was the correlation between the speed profiles of each movement within a force condition and an ‘average’ trajectory within null force conditions. Adaptation to the opposing forces was seen during initial acquisition with a significantly improved coefficient in epoch eight compared to epoch one. We then tested interlimb transfer from the dominant to non-dominant arm (D → ND) and vice-versa (ND → D) across either an extrinsic or intrinsic coordinative system. Interlimb transfer was only seen from the dominant to the non-dominant limb across an intrinsic coordinative system. These results support previous studies involving adaptation to a single dynamic force but also indicate that interlimb transfer of multiple opposing states is possible. This suggests that the information available at the level of representation allowing interlimb transfer can be more intricate than a general movement goal or a single perceived directional error. PMID:17703286

Patient-Adaptive Reconstruction and Acquisition in Dynamic Imaging with Sensitivity Encoding (PARADISE)

PubMed Central

Sharif, Behzad; Derbyshire, J. Andrew; Faranesh, Anthony Z.; Bresler, Yoram

2010-01-01

MR imaging of the human heart without explicit cardiac synchronization promises to extend the applicability of cardiac MR to a larger patient population and potentially expand its diagnostic capabilities. However, conventional non-gated imaging techniques typically suffer from low image quality or inadequate spatio-temporal resolution and fidelity. Patient-Adaptive Reconstruction and Acquisition in Dynamic Imaging with Sensitivity Encoding (PARADISE) is a highly-accelerated non-gated dynamic imaging method that enables artifact-free imaging with high spatio-temporal resolutions by utilizing novel computational techniques to optimize the imaging process. In addition to using parallel imaging, the method gains acceleration from a physiologically-driven spatio-temporal support model; hence, it is doubly accelerated. The support model is patient-adaptive, i.e., its geometry depends on dynamics of the imaged slice, e.g., subject’s heart-rate and heart location within the slice. The proposed method is also doubly adaptive as it adapts both the acquisition and reconstruction schemes. Based on the theory of time-sequential sampling, the proposed framework explicitly accounts for speed limitations of gradient encoding and provides performance guarantees on achievable image quality. The presented in-vivo results demonstrate the effectiveness and feasibility of the PARADISE method for high resolution non-gated cardiac MRI during a short breath-hold. PMID:20665794

Complexity and network dynamics in physiological adaptation: an integrated view.

PubMed

Baffy, György; Loscalzo, Joseph

2014-05-28

Living organisms constantly interact with their surroundings and sustain internal stability against perturbations. This dynamic process follows three fundamental strategies (restore, explore, and abandon) articulated in historical concepts of physiological adaptation such as homeostasis, allostasis, and the general adaptation syndrome. These strategies correspond to elementary forms of behavior (ordered, chaotic, and static) in complex adaptive systems and invite a network-based analysis of the operational characteristics, allowing us to propose an integrated framework of physiological adaptation from a complex network perspective. Applicability of this concept is illustrated by analyzing molecular and cellular mechanisms of adaptation in response to the pervasive challenge of obesity, a chronic condition resulting from sustained nutrient excess that prompts chaotic exploration for system stability associated with tradeoffs and a risk of adverse outcomes such as diabetes, cardiovascular disease, and cancer. Deconstruction of this complexity holds the promise of gaining novel insights into physiological adaptation in health and disease. Published by Elsevier Inc.

The rule of declining adaptability in microbial evolution experiments

PubMed Central

Couce, Alejandro; Tenaillon, Olivier A.

2015-01-01

One of the most recurrent observations after two decades of microbial evolution experiments regards the dynamics of fitness change. In a given environment, low-fitness genotypes are recurrently observed to adapt faster than their more fit counterparts. Since adaptation is the main macroscopic outcome of Darwinian evolution, studying its patterns of change could potentially provide insight into key issues of evolutionary theory, from fixation dynamics to the genetic architecture of organisms. Here, we re-analyze several published datasets from experimental evolution with microbes and show that, despite large differences in the origin of the data, a pattern of inverse dependence of adaptability with fitness clearly emerges. In quantitative terms, it is remarkable to observe little if any degree of idiosyncrasy across systems as diverse as virus, bacteria and yeast. The universality of this phenomenon suggests that its emergence might be understood from general principles, giving rise to the exciting prospect that evolution might be statistically predictable at the macroscopic level. We discuss these possibilities in the light of the various theories of adaptation that have been proposed and delineate future directions of research. PMID:25815007

Inverse Symmetry in Complete Genomes and Whole-Genome Inverse Duplication

PubMed Central

Kong, Sing-Guan; Fan, Wen-Lang; Chen, Hong-Da; Hsu, Zi-Ting; Zhou, Nengji; Zheng, Bo; Lee, Hoong-Chien

2009-01-01

The cause of symmetry is usually subtle, and its study often leads to a deeper understanding of the bearer of the symmetry. To gain insight into the dynamics driving the growth and evolution of genomes, we conducted a comprehensive study of textual symmetries in 786 complete chromosomes. We focused on symmetry based on our belief that, in spite of their extreme diversity, genomes must share common dynamical principles and mechanisms that drive their growth and evolution, and that the most robust footprints of such dynamics are symmetry related. We found that while complement and reverse symmetries are essentially absent in genomic sequences, inverse–complement plus reverse–symmetry is prevalent in complex patterns in most chromosomes, a vast majority of which have near maximum global inverse symmetry. We also discovered relations that can quantitatively account for the long observed but unexplained phenomenon of -mer skews in genomes. Our results suggest segmental and whole-genome inverse duplications are important mechanisms in genome growth and evolution, probably because they are efficient means by which the genome can exploit its double-stranded structure to enrich its code-inventory. PMID:19898631

Selective host molecules obtained by dynamic adaptive chemistry.

PubMed

Matache, Mihaela; Bogdan, Elena; Hădade, Niculina D

2014-02-17

Up till 20 years ago, in order to endow molecules with function there were two mainstream lines of thought. One was to rationally design the positioning of chemical functionalities within candidate molecules, followed by an iterative synthesis-optimization process. The second was the use of a "brutal force" approach of combinatorial chemistry coupled with advanced screening for function. Although both methods provided important results, "rational design" often resulted in time-consuming efforts of modeling and synthesis only to find that the candidate molecule was not performing the designed job. "Combinatorial chemistry" suffered from a fundamental limitation related to the focusing of the libraries employed, often using lead compounds that limit its scope. Dynamic constitutional chemistry has developed as a combination of the two approaches above. Through the rational use of reversible chemical bonds together with a large plethora of precursor libraries, one is now able to build functional structures, ranging from quite simple molecules up to large polymeric structures. Thus, by introduction of the dynamic component within the molecular recognition processes, a new perspective of deciphering the world of the molecular events has aroused together with a new field of chemistry. Since its birth dynamic constitutional chemistry has continuously gained attention, in particular due to its ability to easily create from scratch outstanding molecular structures as well as the addition of adaptive features. The fundamental concepts defining the dynamic constitutional chemistry have been continuously extended to currently place it at the intersection between the supramolecular chemistry and newly defined adaptive chemistry, a pivotal feature towards evolutive chemistry. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Adaptation without parameter change: Dynamic gain control in motion detection

PubMed Central

Borst, Alexander; Flanagin, Virginia L.; Sompolinsky, Haim

2005-01-01

Many sensory systems adapt their input-output relationship to changes in the statistics of the ambient stimulus. Such adaptive behavior has been measured in a motion detection sensitive neuron of the fly visual system, H1. The rapid adaptation of the velocity response gain has been interpreted as evidence of optimal matching of the H1 response to the dynamic range of the stimulus, thereby maximizing its information transmission. Here, we show that correlation-type motion detectors, which are commonly thought to underlie fly motion vision, intrinsically possess adaptive properties. Increasing the amplitude of the velocity fluctuations leads to a decrease of the effective gain and the time constant of the velocity response without any change in the parameters of these detectors. The seemingly complex property of this adaptation turns out to be a straightforward consequence of the multidimensionality of the stimulus and the nonlinear nature of the system. PMID:15833815

Estimation of Dynamic Friction Process of the Akatani Landslide Based on the Waveform Inversion and Numerical Simulation

NASA Astrophysics Data System (ADS)

Yamada, M.; Mangeney, A.; Moretti, L.; Matsushi, Y.

2014-12-01

Understanding physical parameters, such as frictional coefficients, velocity change, and dynamic history, is important issue for assessing and managing the risks posed by deep-seated catastrophic landslides. Previously, landslide motion has been inferred qualitatively from topographic changes caused by the event, and occasionally from eyewitness reports. However, these conventional approaches are unable to evaluate source processes and dynamic parameters. In this study, we use broadband seismic recordings to trace the dynamic process of the deep-seated Akatani landslide that occurred on the Kii Peninsula, Japan, which is one of the best recorded large slope failures. Based on the previous results of waveform inversions and precise topographic surveys done before and after the event, we applied numerical simulations using the SHALTOP numerical model (Mangeney et al., 2007). This model describes homogeneous continuous granular flows on a 3D topography based on a depth averaged thin layer approximation. We assume a Coulomb's friction law with a constant friction coefficient, i. e. the friction is independent of the sliding velocity. We varied the friction coefficients in the simulation so that the resulting force acting on the surface agrees with the single force estimated from the seismic waveform inversion. Figure shows the force history of the east-west components after the band-pass filtering between 10-100 seconds. The force history of the simulation with frictional coefficient 0.27 (thin red line) the best agrees with the result of seismic waveform inversion (thick gray line). Although the amplitude is slightly different, phases are coherent for the main three pulses. This is an evidence that the point-source approximation works reasonably well for this particular event. The friction coefficient during the sliding was estimated to be 0.38 based on the seismic waveform inversion performed by the previous study and on the sliding block model (Yamada et al., 2013

Recombination rate predicts inversion size in Diptera.

PubMed Central

Cáceres, M; Barbadilla, A; Ruiz, A

1999-01-01

Most species of the Drosophila genus and other Diptera are polymorphic for paracentric inversions. A common observation is that successful inversions are of intermediate size. We test here the hypothesis that the selected property is the recombination length of inversions, not their physical length. If so, physical length of successful inversions should be negatively correlated with recombination rate across species. This prediction was tested by a comprehensive statistical analysis of inversion size and recombination map length in 12 Diptera species for which appropriate data are available. We found that (1) there is a wide variation in recombination map length among species; (2) physical length of successful inversions varies greatly among species and is inversely correlated with the species recombination map length; and (3) neither the among-species variation in inversion length nor the correlation are observed in unsuccessful inversions. The clear differences between successful and unsuccessful inversions point to natural selection as the most likely explanation for our results. Presumably the selective advantage of an inversion increases with its length, but so does its detrimental effect on fertility due to double crossovers. Our analysis provides the strongest and most extensive evidence in favor of the notion that the adaptive value of inversions stems from their effect on recombination. PMID:10471710

The Source Inversion Validation (SIV) Initiative: A Collaborative Study on Uncertainty Quantification in Earthquake Source Inversions

NASA Astrophysics Data System (ADS)

Mai, P. M.; Schorlemmer, D.; Page, M.

2012-04-01

Earthquake source inversions image the spatio-temporal rupture evolution on one or more fault planes using seismic and/or geodetic data. Such studies are critically important for earthquake seismology in general, and for advancing seismic hazard analysis in particular, as they reveal earthquake source complexity and help (i) to investigate earthquake mechanics; (ii) to develop spontaneous dynamic rupture models; (iii) to build models for generating rupture realizations for ground-motion simulations. In applications (i - iii), the underlying finite-fault source models are regarded as "data" (input information), but their uncertainties are essentially unknown. After all, source models are obtained from solving an inherently ill-posed inverse problem to which many a priori assumptions and uncertain observations are applied. The Source Inversion Validation (SIV) project is a collaborative effort to better understand the variability between rupture models for a single earthquake (as manifested in the finite-source rupture model database) and to develop robust uncertainty quantification for earthquake source inversions. The SIV project highlights the need to develop a long-standing and rigorous testing platform to examine the current state-of-the-art in earthquake source inversion, and to develop and test novel source inversion approaches. We will review the current status of the SIV project, and report the findings and conclusions of the recent workshops. We will briefly discuss several source-inversion methods, how they treat uncertainties in data, and assess the posterior model uncertainty. Case studies include initial forward-modeling tests on Green's function calculations, and inversion results for synthetic data from spontaneous dynamic crack-like strike-slip earthquake on steeply dipping fault, embedded in a layered crustal velocity-density structure.

Adaptively restrained molecular dynamics in LAMMPS

NASA Astrophysics Data System (ADS)

Kant Singh, Krishna; Redon, Stephane

2017-07-01

Adaptively restrained molecular dynamics (ARMD) is a recently introduced particles simulation method that switches positional degrees of freedom on and off during simulation in order to speed up calculations. In the NVE ensemble, ARMD allows users to trade between precision and speed while, in the NVT ensemble, it makes it possible to compute statistical averages faster. Despite the conceptual simplicity of the approach, however, integrating it in existing molecular dynamics packages is non-trivial, in particular since implemented potentials should a priori be rewritten to take advantage of frozen particles and achieve a speed-up. In this paper, we present novel algorithms for integrating ARMD in LAMMPS, a popular multi-purpose molecular simulation package. In particular, we demonstrate how to enable ARMD in LAMMPS without having to re-implement all available force fields. The proposed algorithms are assessed on four different benchmarks, and show how they allow us to speed up simulations up to one order of magnitude.

Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control.

PubMed

Luo, Biao; Liu, Derong; Wu, Huai-Ning; Wang, Ding; Lewis, Frank L

2017-10-01

The model-free optimal control problem of general discrete-time nonlinear systems is considered in this paper, and a data-based policy gradient adaptive dynamic programming (PGADP) algorithm is developed to design an adaptive optimal controller method. By using offline and online data rather than the mathematical system model, the PGADP algorithm improves control policy with a gradient descent scheme. The convergence of the PGADP algorithm is proved by demonstrating that the constructed Q -function sequence converges to the optimal Q -function. Based on the PGADP algorithm, the adaptive control method is developed with an actor-critic structure and the method of weighted residuals. Its convergence properties are analyzed, where the approximate Q -function converges to its optimum. Computer simulation results demonstrate the effectiveness of the PGADP-based adaptive control method.

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

NASA Astrophysics Data System (ADS)

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

2016-01-01

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

Bayesian inversions of a dynamic vegetation model at four European grassland sites

NASA Astrophysics Data System (ADS)

Minet, J.; Laloy, E.; Tychon, B.; Francois, L.

2015-05-01

Eddy covariance data from four European grassland sites are used to probabilistically invert the CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model (DVM) with 10 unknown parameters, using the DREAM(ZS) (DiffeRential Evolution Adaptive Metropolis) Markov chain Monte Carlo (MCMC) sampler. We focus on comparing model inversions, considering both homoscedastic and heteroscedastic eddy covariance residual errors, with variances either fixed a priori or jointly inferred together with the model parameters. Agreements between measured and simulated data during calibration are comparable with previous studies, with root mean square errors (RMSEs) of simulated daily gross primary productivity (GPP), ecosystem respiration (RECO) and evapotranspiration (ET) ranging from 1.73 to 2.19, 1.04 to 1.56 g C m-2 day-1 and 0.50 to 1.28 mm day-1, respectively. For the calibration period, using a homoscedastic eddy covariance residual error model resulted in a better agreement between measured and modelled data than using a heteroscedastic residual error model. However, a model validation experiment showed that CARAIB models calibrated considering heteroscedastic residual errors perform better. Posterior parameter distributions derived from using a heteroscedastic model of the residuals thus appear to be more robust. This is the case even though the classical linear heteroscedastic error model assumed herein did not fully remove heteroscedasticity of the GPP residuals. Despite the fact that the calibrated model is generally capable of fitting the data within measurement errors, systematic bias in the model simulations are observed. These are likely due to model inadequacies such as shortcomings in the photosynthesis modelling. Besides the residual error treatment, differences between model parameter posterior distributions among the four grassland sites are also investigated. It is shown that the marginal distributions of the specific leaf area and characteristic

Wavelet and adaptive methods for time dependent problems and applications in aerosol dynamics

NASA Astrophysics Data System (ADS)

Guo, Qiang

Time dependent partial differential equations (PDEs) are widely used as mathematical models of environmental problems. Aerosols are now clearly identified as an important factor in many environmental aspects of climate and radiative forcing processes, as well as in the health effects of air quality. The mathematical models for the aerosol dynamics with respect to size distribution are nonlinear partial differential and integral equations, which describe processes of condensation, coagulation and deposition. Simulating the general aerosol dynamic equations on time, particle size and space exhibits serious difficulties because the size dimension ranges from a few nanometer to several micrometer while the spatial dimension is usually described with kilometers. Therefore, it is an important and challenging task to develop efficient techniques for solving time dependent dynamic equations. In this thesis, we develop and analyze efficient wavelet and adaptive methods for the time dependent dynamic equations on particle size and further apply them to the spatial aerosol dynamic systems. Wavelet Galerkin method is proposed to solve the aerosol dynamic equations on time and particle size due to the fact that aerosol distribution changes strongly along size direction and the wavelet technique can solve it very efficiently. Daubechies' wavelets are considered in the study due to the fact that they possess useful properties like orthogonality, compact support, exact representation of polynomials to a certain degree. Another problem encountered in the solution of the aerosol dynamic equations results from the hyperbolic form due to the condensation growth term. We propose a new characteristic-based fully adaptive multiresolution numerical scheme for solving the aerosol dynamic equation, which combines the attractive advantages of adaptive multiresolution technique and the characteristics method. On the aspect of theoretical analysis, the global existence and uniqueness of

Dynamic game balancing implementation using adaptive algorithm in mobile-based Safari Indonesia game

NASA Astrophysics Data System (ADS)

Yuniarti, Anny; Nata Wardanie, Novita; Kuswardayan, Imam

2018-03-01

In developing a game there is one method that should be applied to maintain the interest of players, namely dynamic game balancing. Dynamic game balancing is a process to match a player’s playing style with the behaviour, attributes, and game environment. This study applies dynamic game balancing using adaptive algorithm in scrolling shooter game type called Safari Indonesia which developed using Unity. The game of this type is portrayed by a fighter aircraft character trying to defend itself from insistent enemy attacks. This classic game is chosen to implement adaptive algorithms because it has quite complex attributes to be developed using dynamic game balancing. Tests conducted by distributing questionnaires to a number of players indicate that this method managed to reduce frustration and increase the pleasure factor in playing.

Inverse Force Determination on a Small Scale Launch Vehicle Model Using a Dynamic Balance

NASA Technical Reports Server (NTRS)

Ngo, Christina L.; Powell, Jessica M.; Ross, James C.

2017-01-01

A launch vehicle can experience large unsteady aerodynamic forces in the transonic regime that, while usually only lasting for tens of seconds during launch, could be devastating if structural components and electronic hardware are not designed to account for them. These aerodynamic loads are difficult to experimentally measure and even harder to computationally estimate. The current method for estimating buffet loads is through the use of a few hundred unsteady pressure transducers and wind tunnel test. Even with a large number of point measurements, the computed integrated load is not an accurate enough representation of the total load caused by buffeting. This paper discusses an attempt at using a dynamic balance to experimentally determine buffet loads on a generic scale hammer head launch vehicle model tested at NASA Ames Research Center's 11' x 11' transonic wind tunnel. To use a dynamic balance, the structural characteristics of the model needed to be identified so that the natural modal response could be and removed from the aerodynamic forces. A finite element model was created on a simplified version of the model to evaluate the natural modes of the balance flexures, assist in model design, and to compare to experimental data. Several modal tests were conducted on the model in two different configurations to check for non-linearity, and to estimate the dynamic characteristics of the model. The experimental results were used in an inverse force determination technique with a psuedo inverse frequency response function. Due to the non linearity, the model not being axisymmetric, and inconsistent data between the two shake tests from different mounting configuration, it was difficult to create a frequency response matrix that satisfied all input and output conditions for wind tunnel configuration to accurately predict unsteady aerodynamic loads.

Exploring equivalence domain in nonlinear inverse problems using Covariance Matrix Adaption Evolution Strategy (CMAES) and random sampling

NASA Astrophysics Data System (ADS)

Grayver, Alexander V.; Kuvshinov, Alexey V.

2016-05-01

This paper presents a methodology to sample equivalence domain (ED) in nonlinear partial differential equation (PDE)-constrained inverse problems. For this purpose, we first applied state-of-the-art stochastic optimization algorithm called Covariance Matrix Adaptation Evolution Strategy (CMAES) to identify low-misfit regions of the model space. These regions were then randomly sampled to create an ensemble of equivalent models and quantify uncertainty. CMAES is aimed at exploring model space globally and is robust on very ill-conditioned problems. We show that the number of iterations required to converge grows at a moderate rate with respect to number of unknowns and the algorithm is embarrassingly parallel. We formulated the problem by using the generalized Gaussian distribution. This enabled us to seamlessly use arbitrary norms for residual and regularization terms. We show that various regularization norms facilitate studying different classes of equivalent solutions. We further show how performance of the standard Metropolis-Hastings Markov chain Monte Carlo algorithm can be substantially improved by using information CMAES provides. This methodology was tested by using individual and joint inversions of magneotelluric, controlled-source electromagnetic (EM) and global EM induction data.

Differentially Private Histogram Publication For Dynamic Datasets: An Adaptive Sampling Approach

PubMed Central

Li, Haoran; Jiang, Xiaoqian; Xiong, Li; Liu, Jinfei

2016-01-01

Differential privacy has recently become a de facto standard for private statistical data release. Many algorithms have been proposed to generate differentially private histograms or synthetic data. However, most of them focus on “one-time” release of a static dataset and do not adequately address the increasing need of releasing series of dynamic datasets in real time. A straightforward application of existing histogram methods on each snapshot of such dynamic datasets will incur high accumulated error due to the composibility of differential privacy and correlations or overlapping users between the snapshots. In this paper, we address the problem of releasing series of dynamic datasets in real time with differential privacy, using a novel adaptive distance-based sampling approach. Our first method, DSFT, uses a fixed distance threshold and releases a differentially private histogram only when the current snapshot is sufficiently different from the previous one, i.e., with a distance greater than a predefined threshold. Our second method, DSAT, further improves DSFT and uses a dynamic threshold adaptively adjusted by a feedback control mechanism to capture the data dynamics. Extensive experiments on real and synthetic datasets demonstrate that our approach achieves better utility than baseline methods and existing state-of-the-art methods. PMID:26973795

Adaptive Synchronization of Fractional Order Complex-Variable Dynamical Networks via Pinning Control

NASA Astrophysics Data System (ADS)

Ding, Da-Wei; Yan, Jie; Wang, Nian; Liang, Dong

2017-09-01

In this paper, the synchronization of fractional order complex-variable dynamical networks is studied using an adaptive pinning control strategy based on close center degree. Some effective criteria for global synchronization of fractional order complex-variable dynamical networks are derived based on the Lyapunov stability theory. From the theoretical analysis, one concludes that under appropriate conditions, the complex-variable dynamical networks can realize the global synchronization by using the proper adaptive pinning control method. Meanwhile, we succeed in solving the problem about how much coupling strength should be applied to ensure the synchronization of the fractional order complex networks. Therefore, compared with the existing results, the synchronization method in this paper is more general and convenient. This result extends the synchronization condition of the real-variable dynamical networks to the complex-valued field, which makes our research more practical. Finally, two simulation examples show that the derived theoretical results are valid and the proposed adaptive pinning method is effective. Supported by National Natural Science Foundation of China under Grant No. 61201227, National Natural Science Foundation of China Guangdong Joint Fund under Grant No. U1201255, the Natural Science Foundation of Anhui Province under Grant No. 1208085MF93, 211 Innovation Team of Anhui University under Grant Nos. KJTD007A and KJTD001B, and also supported by Chinese Scholarship Council

Coupled Land Surface-Subsurface Hydrogeophysical Inverse Modeling to Estimate Soil Organic Carbon Content in an Arctic Tundra

NASA Astrophysics Data System (ADS)

Tran, A. P.; Dafflon, B.; Hubbard, S.

2017-12-01

Soil organic carbon (SOC) is crucial for predicting carbon climate feedbacks in the vulnerable organic-rich Arctic region. However, it is challenging to achieve this property due to the general limitations of conventional core sampling and analysis methods. In this study, we develop an inversion scheme that uses single or multiple datasets, including soil liquid water content, temperature and ERT data, to estimate the vertical profile of SOC content. Our approach relies on the fact that SOC content strongly influences soil hydrological-thermal parameters, and therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. The scheme includes several advantages. First, this is the first time SOC content is estimated by using a coupled hydrogeophysical inversion. Second, by using the Community Land Model, we can account for the land surface dynamics (evapotranspiration, snow accumulation and melting) and ice/liquid phase transition. Third, we combine a deterministic and an adaptive Markov chain Monte Carlo optimization algorithm to better estimate the posterior distributions of desired model parameters. Finally, the simulated subsurface variables are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using synthetic experiments. The results show that compared to inversion of single dataset, joint inversion of these datasets significantly reduces parameter uncertainty. The joint inversion approach is able to estimate SOC content within the shallow active layer with high reliability. Next, we apply the scheme to estimate OC content along an intensive ERT transect in Barrow, Alaska using multiple datasets acquired in the 2013-2015 period. The preliminary results show a good agreement between modeled and measured soil temperature, thaw layer thickness and electrical resistivity. The accuracy of estimated SOC content

Intelligent control of non-linear dynamical system based on the adaptive neurocontroller

NASA Astrophysics Data System (ADS)

Engel, E.; Kovalev, I. V.; Kobezhicov, V.

2015-10-01

This paper presents an adaptive neuro-controller for intelligent control of non-linear dynamical system. The formed as the fuzzy selective neural net the adaptive neuro-controller on the base of system's state, creates the effective control signal under random perturbations. The validity and advantages of the proposed adaptive neuro-controller are demonstrated by numerical simulations. The simulation results show that the proposed controller scheme achieves real-time control speed and the competitive performance, as compared to PID, fuzzy logic controllers.

Semi-active control of magnetorheological elastomer base isolation system utilising learning-based inverse model

NASA Astrophysics Data System (ADS)

Gu, Xiaoyu; Yu, Yang; Li, Jianchun; Li, Yancheng

2017-10-01

Magnetorheological elastomer (MRE) base isolations have attracted considerable attention over the last two decades thanks to its self-adaptability and high-authority controllability in semi-active control realm. Due to the inherent nonlinearity and hysteresis of the devices, it is challenging to obtain a reasonably complicated mathematical model to describe the inverse dynamics of MRE base isolators and hence to realise control synthesis of the MRE base isolation system. Two aims have been achieved in this paper: i) development of an inverse model for MRE base isolator based on optimal general regression neural network (GRNN); ii) numerical and experimental validation of a real-time semi-active controlled MRE base isolation system utilising LQR controller and GRNN inverse model. The superiority of GRNN inverse model lays in fewer input variables requirement, faster training process and prompt calculation response, which makes it suitable for online training and real-time control. The control system is integrated with a three-storey shear building model and control performance of the MRE base isolation system is compared with bare building, passive-on isolation system and passive-off isolation system. Testing results show that the proposed GRNN inverse model is able to reproduce desired control force accurately and the MRE base isolation system can effectively suppress the structural responses when compared to the passive isolation system.

Reentry Vehicle Flight Controls Design Guidelines: Dynamic Inversion

NASA Technical Reports Server (NTRS)

Ito, Daigoro; Georgie, Jennifer; Valasek, John; Ward, Donald T.

2002-01-01

This report addresses issues in developing a flight control design for vehicles operating across a broad flight regime and with highly nonlinear physical descriptions of motion. Specifically it addresses the need for reentry vehicles that could operate through reentry from space to controlled touchdown on Earth. The latter part of controlled descent is achieved by parachute or paraglider - or by all automatic or a human-controlled landing similar to that of the Orbiter. Since this report addresses the specific needs of human-carrying (not necessarily piloted) reentry vehicles, it deals with highly nonlinear equations of motion, and then-generated control systems must be robust across a very wide range of physics. Thus, this report deals almost exclusively with some form of dynamic inversion (DI). Two vital aspects of control theory - noninteracting control laws and the transformation of nonlinear systems into equivalent linear systems - are embodied in DI. Though there is no doubt that the mathematical tools and underlying theory are widely available, there are open issues as to the practicality of using DI as the only or primary design approach for reentry articles. This report provides a set of guidelines that can be used to determine the practical usefulness of the technique.

Adaptive modeling, identification, and control of dynamic structural systems. I. Theory

USGS Publications Warehouse

Safak, Erdal

1989-01-01

A concise review of the theory of adaptive modeling, identification, and control of dynamic structural systems based on discrete-time recordings is presented. Adaptive methods have four major advantages over the classical methods: (1) Removal of the noise from the signal is done over the whole frequency band; (2) time-varying characteristics of systems can be tracked; (3) systems with unknown characteristics can be controlled; and (4) a small segment of the data is needed during the computations. Included in the paper are the discrete-time representation of single-input single-output (SISO) systems, models for SISO systems with noise, the concept of stochastic approximation, recursive prediction error method (RPEM) for system identification, and the adaptive control. Guidelines for model selection and model validation and the computational aspects of the method are also discussed in the paper. The present paper is the first of two companion papers. The theory given in the paper is limited to that which is necessary to follow the examples for applications in structural dynamics presented in the second paper.

Implication of adaptive smoothness constraint and Helmert variance component estimation in seismic slip inversion

NASA Astrophysics Data System (ADS)

Fan, Qingbiao; Xu, Caijun; Yi, Lei; Liu, Yang; Wen, Yangmao; Yin, Zhi

2017-10-01

When ill-posed problems are inverted, the regularization process is equivalent to adding constraint equations or prior information from a Bayesian perspective. The veracity of the constraints (or the regularization matrix R) significantly affects the solution, and a smoothness constraint is usually added in seismic slip inversions. In this paper, an adaptive smoothness constraint (ASC) based on the classic Laplacian smoothness constraint (LSC) is proposed. The ASC not only improves the smoothness constraint, but also helps constrain the slip direction. A series of experiments are conducted in which different magnitudes of noise are imposed and different densities of observation are assumed, and the results indicated that the ASC was superior to the LSC. Using the proposed ASC, the Helmert variance component estimation method is highlighted as the best for selecting the regularization parameter compared with other methods, such as generalized cross-validation or the mean squared error criterion method. The ASC may also benefit other ill-posed problems in which a smoothness constraint is required.

North American Crust and Upper Mantle Structure Imaged Using an Adaptive Bayesian Inversion

NASA Astrophysics Data System (ADS)

Eilon, Z.; Fischer, K. M.; Dalton, C. A.

2017-12-01

We present a methodology for imaging upper mantle structure using a Bayesian approach that incorporates a novel combination of seismic data types and an adaptive parameterization based on piecewise discontinuous splines. Our inversion algorithm lays the groundwork for improved seismic velocity models of the lithosphere and asthenosphere by harnessing increased computing power alongside sophisticated data analysis, with the flexibility to include multiple datatypes with complementary resolution. Our new method has been designed to simultaneously fit P-s and S-p converted phases and Rayleigh wave phase velocities measured from ambient noise (periods 6-40 s) and earthquake sources (periods 30-170s). Careful processing of the body wave data isolates the signals from velocity gradients between the mid-crust and 250 km depth. We jointly invert the body and surface wave data to obtain detailed 1-D velocity models that include robustly imaged mantle discontinuities. Synthetic tests demonstrate that S-p phases are particularly important for resolving mantle structure, while surface waves capture absolute velocities with resolution better than 0.1 km/s. By treating data noise as an unknown parameter, and by generating posterior parameter distributions, model trade offs and uncertainties are fully captured by the inversion. We apply the method to stations across the northwest and north-central United States, finding that the imaged structure improves upon existing models by sharpening the vertical resolution of absolute velocity profiles and offering robust uncertainty estimates. In the tectonically active northwestern US, a strong velocity drop immediately beneath the Moho connotes thin (<70 km) lithosphere and a sharp lithosphere-asthenosphere transition; the asthenospheric velocity profile here matches observations at mid-ocean ridges. Within the Wyoming and Superior cratons, our models reveal mid-lithospheric velocity gradients indicative of thermochemical cratonic

Dynamical inversion of the energy landscape promotes non-equilibrium self-assembly of binary mixtures

DOE PAGES

Pestana, Luis Ruiz; Minnetian, Natalie; Lammers, Laura Nielsen; ...

2018-01-02

When driven out of equilibrium, many diverse systems can form complex spatial and dynamical patterns, even in the absence of attractive interactions. Using kinetic Monte Carlo simulations, we investigate the phase behavior of a binary system of particles of dissimilar size confined between semiflexible planar surfaces, in which the nanoconfinement introduces a non-local coupling between particles, which we model as an activation energy barrier to diffusion that decreases with the local fraction of the larger particle. The system autonomously reaches a cyclical non-equilibrium state characterized by the formation and dissolution of metastable micelle-like clusters with the small particles in themore » core and the large ones in the surrounding corona. The power spectrum of the fluctuations in the aggregation number exhibits 1/f noise reminiscent of self-organized critical systems. Finally, we suggest that the dynamical metastability of the micellar structures arises from an inversion of the energy landscape, in which the relaxation dynamics of one of the species induces a metastable phase for the other species.« less

Dynamical inversion of the energy landscape promotes non-equilibrium self-assembly of binary mixtures

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

Pestana, Luis Ruiz; Minnetian, Natalie; Lammers, Laura Nielsen

When driven out of equilibrium, many diverse systems can form complex spatial and dynamical patterns, even in the absence of attractive interactions. Using kinetic Monte Carlo simulations, we investigate the phase behavior of a binary system of particles of dissimilar size confined between semiflexible planar surfaces, in which the nanoconfinement introduces a non-local coupling between particles, which we model as an activation energy barrier to diffusion that decreases with the local fraction of the larger particle. The system autonomously reaches a cyclical non-equilibrium state characterized by the formation and dissolution of metastable micelle-like clusters with the small particles in themore » core and the large ones in the surrounding corona. The power spectrum of the fluctuations in the aggregation number exhibits 1/f noise reminiscent of self-organized critical systems. Finally, we suggest that the dynamical metastability of the micellar structures arises from an inversion of the energy landscape, in which the relaxation dynamics of one of the species induces a metastable phase for the other species.« less

Applying Parallel Adaptive Methods with GeoFEST/PYRAMID to Simulate Earth Surface Crustal Dynamics

NASA Technical Reports Server (NTRS)

Norton, Charles D.; Lyzenga, Greg; Parker, Jay; Glasscoe, Margaret; Donnellan, Andrea; Li, Peggy

2006-01-01

This viewgraph presentation reviews the use Adaptive Mesh Refinement (AMR) in simulating the Crustal Dynamics of Earth's Surface. AMR simultaneously improves solution quality, time to solution, and computer memory requirements when compared to generating/running on a globally fine mesh. The use of AMR in simulating the dynamics of the Earth's Surface is spurred by future proposed NASA missions, such as InSAR for Earth surface deformation and other measurements. These missions will require support for large-scale adaptive numerical methods using AMR to model observations. AMR was chosen because it has been successful in computation fluid dynamics for predictive simulation of complex flows around complex structures.

Dynamic Load Balancing for Adaptive Computations on Distributed-Memory Machines

NASA Technical Reports Server (NTRS)

1999-01-01

Dynamic load balancing is central to adaptive mesh-based computations on large-scale parallel computers. The principal investigator has investigated various issues on the dynamic load balancing problem under NASA JOVE and JAG rants. The major accomplishments of the project are two graph partitioning algorithms and a load balancing framework. The S-HARP dynamic graph partitioner is known to be the fastest among the known dynamic graph partitioners to date. It can partition a graph of over 100,000 vertices in 0.25 seconds on a 64- processor Cray T3E distributed-memory multiprocessor while maintaining the scalability of over 16-fold speedup. Other known and widely used dynamic graph partitioners take over a second or two while giving low scalability of a few fold speedup on 64 processors. These results have been published in journals and peer-reviewed flagship conferences.

A New Powered Lower Limb Prosthesis Control Framework Based on Adaptive Dynamic Programming.

PubMed

Wen, Yue; Si, Jennie; Gao, Xiang; Huang, Stephanie; Huang, He Helen

2017-09-01

This brief presents a novel application of adaptive dynamic programming (ADP) for optimal adaptive control of powered lower limb prostheses, a type of wearable robots to assist the motor function of the limb amputees. Current control of these robotic devices typically relies on finite state impedance control (FS-IC), which lacks adaptability to the user's physical condition. As a result, joint impedance settings are often customized manually and heuristically in clinics, which greatly hinder the wide use of these advanced medical devices. This simulation study aimed at demonstrating the feasibility of ADP for automatic tuning of the twelve knee joint impedance parameters during a complete gait cycle to achieve balanced walking. Given that the accurate models of human walking dynamics are difficult to obtain, the model-free ADP control algorithms were considered. First, direct heuristic dynamic programming (dHDP) was applied to the control problem, and its performance was evaluated on OpenSim, an often-used dynamic walking simulator. For the comparison purposes, we selected another established ADP algorithm, the neural fitted Q with continuous action (NFQCA). In both cases, the ADP controllers learned to control the right knee joint and achieved balanced walking, but dHDP outperformed NFQCA in this application during a 200 gait cycle-based testing.

A reduced adaptive observer for multivariable systems. [using reduced dynamic ordering

NASA Technical Reports Server (NTRS)

Carroll, R. L.; Lindorff, D. P.

1973-01-01

An adaptive observer for multivariable systems is presented for which the dynamic order of the observer is reduced, subject to mild restrictions. The observer structure depends directly upon the multivariable structure of the system rather than a transformation to a single-output system. The number of adaptive gains is at most the sum of the order of the system and the number of input parameters being adapted. Moreover, for the relatively frequent specific cases for which the number of required adaptive gains is less than the sum of system order and input parameters, the number of these gains is easily determined by inspection of the system structure. This adaptive observer possesses all the properties ascribed to the single-input single-output adpative observer. Like the other adaptive observers some restriction is required of the allowable system command input to guarantee convergence of the adaptive algorithm, but the restriction is more lenient than that required by the full-order multivariable observer. This reduced observer is not restricted to cycle systems.

Spike-Threshold Adaptation Predicted by Membrane Potential Dynamics In Vivo

PubMed Central

Fontaine, Bertrand; Peña, José Luis; Brette, Romain

2014-01-01

Neurons encode information in sequences of spikes, which are triggered when their membrane potential crosses a threshold. In vivo, the spiking threshold displays large variability suggesting that threshold dynamics have a profound influence on how the combined input of a neuron is encoded in the spiking. Threshold variability could be explained by adaptation to the membrane potential. However, it could also be the case that most threshold variability reflects noise and processes other than threshold adaptation. Here, we investigated threshold variation in auditory neurons responses recorded in vivo in barn owls. We found that spike threshold is quantitatively predicted by a model in which the threshold adapts, tracking the membrane potential at a short timescale. As a result, in these neurons, slow voltage fluctuations do not contribute to spiking because they are filtered by threshold adaptation. More importantly, these neurons can only respond to input spikes arriving together on a millisecond timescale. These results demonstrate that fast adaptation to the membrane potential captures spike threshold variability in vivo. PMID:24722397

Adaptively biased molecular dynamics for free energy calculations

NASA Astrophysics Data System (ADS)

Babin, Volodymyr; Roland, Christopher; Sagui, Celeste

2008-04-01

We present an adaptively biased molecular dynamics (ABMD) method for the computation of the free energy surface of a reaction coordinate using nonequilibrium dynamics. The ABMD method belongs to the general category of umbrella sampling methods with an evolving biasing potential and is inspired by the metadynamics method. The ABMD method has several useful features, including a small number of control parameters and an O(t ) numerical cost with molecular dynamics time t. The ABMD method naturally allows for extensions based on multiple walkers and replica exchange, where different replicas can have different temperatures and/or collective variables. This is beneficial not only in terms of the speed and accuracy of a calculation, but also in terms of the amount of useful information that may be obtained from a given simulation. The workings of the ABMD method are illustrated via a study of the folding of the Ace-GGPGGG-Nme peptide in a gaseous and solvated environment.

Dynamic adjustments of cognitive control: oscillatory correlates of the conflict adaptation effect.

PubMed

Pastötter, Bernhard; Dreisbach, Gesine; Bäuml, Karl-Heinz T

2013-12-01

It is a prominent idea that cognitive control mediates conflict adaptation, in that response conflict in a previous trial triggers control adjustments that reduce conflict in a current trial. In the present EEG study, we investigated the dynamics of cognitive control in a response-priming task by examining the effects of previous trial conflict on intertrial and current trial oscillatory brain activities, both on the electrode and the source level. Behavioral results showed conflict adaptation effects for RTs and response accuracy. Physiological results showed sustained intertrial effects in left parietal theta power, originating in the left inferior parietal cortex, and midcentral beta power, originating in the left and right (pre)motor cortex. Moreover, physiological analysis revealed a current trial conflict adaptation effect in midfrontal theta power, originating in the ACC. Correlational analyses showed that intertrial effects predicted conflict-induced midfrontal theta power in currently incongruent trials. In addition, conflict adaptation effects in midfrontal theta power and RTs were positively related. Together, these findings point to a dynamic cognitive control system that, as a function of previous trial type, up- and down-regulates attention and preparatory motor activities in anticipation of the next trial.

Facile fabrication of highly controllable gating systems based on the combination of inverse opal structure and dynamic covalent chemistry.

PubMed

Wang, Chen; Yang, Haowei; Tian, Li; Wang, Shiqiang; Gao, Ning; Zhang, Wanlin; Wang, Peng; Yin, Xianpeng; Li, Guangtao

2017-06-01

A three-dimensional (3D) inverse opal with periodic and porous structures has shown great potential for applications not only in optics and optoelectronics, but also in functional membranes. In this work, the benzaldehyde group was initially introduced into a 3D nanoporous inverse opal, serving as a platform for fabricating functional membranes. By employing the dynamic covalent approach, a highly controllable gating system was facilely fabricated to achieve modulable and reversible transport features. It was found that the physical/chemical properties and pore size of the gating system could easily be regulated through post-modification with amines. As a demonstration, the gated nanopores were modified with three kinds of amines to control the wettability, surface charge and nanopore size which in turn was exploited to achieve selective mass transport, including hydrophobic molecules, cations and anions, and the transport with respect to the physical steric hindrance. In particular, the gating system showed extraordinary reversibility and could recover to its pristine state by simply changing pH values. Due to the unlimited variety provided by the Schiff base reaction, the inverse opal described here exhibits a significant extendibility and could be easily post-modified with stimuli-responsive molecules for special purposes. Furthermore, this work can be extended to employ other dynamic covalent routes, for example Diels-Alder, ester exchange and disulfide exchange-based routes.

Opinion dynamics on an adaptive random network

NASA Astrophysics Data System (ADS)

Benczik, I. J.; Benczik, S. Z.; Schmittmann, B.; Zia, R. K. P.

2009-04-01

We revisit the classical model for voter dynamics in a two-party system with two basic modifications. In contrast to the original voter model studied in regular lattices, we implement the opinion formation process in a random network of agents in which interactions are no longer restricted by geographical distance. In addition, we incorporate the rapidly changing nature of the interpersonal relations in the model. At each time step, agents can update their relationships. This update is determined by their own opinion, and by their preference to make connections with individuals sharing the same opinion, or rather with opponents. In this way, the network is built in an adaptive manner, in the sense that its structure is correlated and evolves with the dynamics of the agents. The simplicity of the model allows us to examine several issues analytically. We establish criteria to determine whether consensus or polarization will be the outcome of the dynamics and on what time scales these states will be reached. In finite systems consensus is typical, while in infinite systems a disordered metastable state can emerge and persist for infinitely long time before consensus is reached.

Shack-Hartmann wavefront sensor with large dynamic range by adaptive spot search method.

PubMed

Shinto, Hironobu; Saita, Yusuke; Nomura, Takanori

2016-07-10

A Shack-Hartmann wavefront sensor (SHWFS) that consists of a microlens array and an image sensor has been used to measure the wavefront aberrations of human eyes. However, a conventional SHWFS has finite dynamic range depending on the diameter of the each microlens. The dynamic range cannot be easily expanded without a decrease of the spatial resolution. In this study, an adaptive spot search method to expand the dynamic range of an SHWFS is proposed. In the proposed method, spots are searched with the help of their approximate displacements measured with low spatial resolution and large dynamic range. By the proposed method, a wavefront can be correctly measured even if the spot is beyond the detection area. The adaptive spot search method is realized by using the special microlens array that generates both spots and discriminable patterns. The proposed method enables expanding the dynamic range of an SHWFS with a single shot and short processing time. The performance of the proposed method is compared with that of a conventional SHWFS by optical experiments. Furthermore, the dynamic range of the proposed method is quantitatively evaluated by numerical simulations.

Differential flatness properties and multivariable adaptive control of ovarian system dynamics

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos

2016-12-01

The ovarian system exhibits nonlinear dynamics which is modeled by a set of coupled nonlinear differential equations. The paper proposes adaptive fuzzy control based on differential flatness theory for the complex dynamics of the ovarian system. It is proven that the dynamic model of the ovarian system, having as state variables the LH and the FSH hormones and their derivatives, is a differentially flat one. This means that all its state variables and its control inputs can be described as differential functions of the flat output. By exploiting differential flatness properties the system's dynamic model is written in the multivariable linear canonical (Brunovsky) form, for which the design of a state feedback controller becomes possible. After this transformation, the new control inputs of the system contain unknown nonlinear parts, which are identified with the use of neurofuzzy approximators. The learning procedure for these estimators is determined by the requirement the first derivative of the closed-loop's Lyapunov function to be a negative one. Moreover, Lyapunov stability analysis shows that H-infinity tracking performance is succeeded for the feedback control loop and this assures improved robustness to the aforementioned model uncertainty as well as to external perturbations. The efficiency of the proposed adaptive fuzzy control scheme is confirmed through simulation experiments.

Earthquake Rupture Dynamics using Adaptive Mesh Refinement and High-Order Accurate Numerical Methods

NASA Astrophysics Data System (ADS)

Kozdon, J. E.; Wilcox, L.

2013-12-01

Our goal is to develop scalable and adaptive (spatial and temporal) numerical methods for coupled, multiphysics problems using high-order accurate numerical methods. To do so, we are developing an opensource, parallel library known as bfam (available at http://bfam.in). The first application to be developed on top of bfam is an earthquake rupture dynamics solver using high-order discontinuous Galerkin methods and summation-by-parts finite difference methods. In earthquake rupture dynamics, wave propagation in the Earth's crust is coupled to frictional sliding on fault interfaces. This coupling is two-way, required the simultaneous simulation of both processes. The use of laboratory-measured friction parameters requires near-fault resolution that is 4-5 orders of magnitude higher than that needed to resolve the frequencies of interest in the volume. This, along with earlier simulations using a low-order, finite volume based adaptive mesh refinement framework, suggest that adaptive mesh refinement is ideally suited for this problem. The use of high-order methods is motivated by the high level of resolution required off the fault in earlier the low-order finite volume simulations; we believe this need for resolution is a result of the excessive numerical dissipation of low-order methods. In bfam spatial adaptivity is handled using the p4est library and temporal adaptivity will be accomplished through local time stepping. In this presentation we will present the guiding principles behind the library as well as verification of code against the Southern California Earthquake Center dynamic rupture code validation test problems.

Rayleigh lidar observation of tropical mesospheric inversion layer: a comparison between dynamics and chemistry

NASA Astrophysics Data System (ADS)

Ramesh, K.; Sridharan, S.; Raghunath, K.

2018-04-01

The Rayleigh lidar at National Atmospheric Research Laboratory, Gadanki (13.5°N, 79.2°E), India operates at 532 nm green laser with 600 mJ/pulse since 2007. The vertical temperature profiles are derived above 30 km by assuming the atmosphere is in hydrostatic equilibrium and obeys ideal gas law. A large mesospheric inversion layer (MIL) is observed at 77.4-84.6 km on the night of 22 March 2007 over Gadanki. Although dynamics and chemistry play vital role, both the mechanisms are compared for the occurrence of the MIL in the present study.

Inverse axial mounting stiffness design for lithographic projection lenses.

PubMed

Wen-quan, Yuan; Hong-bo, Shang; Wei, Zhang

2014-09-01

In order to balance axial mounting stiffness of lithographic projection lenses and the image quality under dynamic working conditions, an easy inverse axial mounting stiffness design method is developed in this article. Imaging quality deterioration at the wafer under different axial vibration levels is analyzed. The desired image quality can be determined according to practical requirements, and axial vibrational tolerance of each lens is solved with the damped least-squares method. Based on adaptive interval adjustment, a binary search algorithm, and the finite element method, the axial mounting stiffness of each lens can be traveled in a large interval, and converges to a moderate numerical solution which makes the axial vibrational amplitude of the lens converge to its axial vibrational tolerance. Model simulation is carried out to validate the effectiveness of the method.

Propeller sheet cavitation noise source modeling and inversion

NASA Astrophysics Data System (ADS)

Lee, Keunhwa; Lee, Jaehyuk; Kim, Dongho; Kim, Kyungseop; Seong, Woojae

2014-02-01

Propeller sheet cavitation is the main contributor to high level of noise and vibration in the after body of a ship. Full measurement of the cavitation-induced hull pressure over the entire surface of the affected area is desired but not practical. Therefore, using a few measurements on the outer hull above the propeller in a cavitation tunnel, empirical or semi-empirical techniques based on physical model have been used to predict the hull-induced pressure (or hull-induced force). In this paper, with the analytic source model for sheet cavitation, a multi-parameter inversion scheme to find the positions of noise sources and their strengths is suggested. The inversion is posed as a nonlinear optimization problem, which is solved by the optimization algorithm based on the adaptive simplex simulated annealing algorithm. Then, the resulting hull pressure can be modeled with boundary element method from the inverted cavitation noise sources. The suggested approach is applied to the hull pressure data measured in a cavitation tunnel of the Samsung Heavy Industry. Two monopole sources are adequate to model the propeller sheet cavitation noise. The inverted source information is reasonable with the cavitation dynamics of the propeller and the modeled hull pressure shows good agreement with cavitation tunnel experimental data.

Evolution dynamics of a model for gene duplication under adaptive conflict

NASA Astrophysics Data System (ADS)

Ancliff, Mark; Park, Jeong-Man

2014-06-01

We present and solve the dynamics of a model for gene duplication showing escape from adaptive conflict. We use a Crow-Kimura quasispecies model of evolution where the fitness landscape is a function of Hamming distances from two reference sequences, which are assumed to optimize two different gene functions, to describe the dynamics of a mixed population of individuals with single and double copies of a pleiotropic gene. The evolution equations are solved through a spin coherent state path integral, and we find two phases: one is an escape from an adaptive conflict phase, where each copy of a duplicated gene evolves toward subfunctionalization, and the other is a duplication loss of function phase, where one copy maintains its pleiotropic form and the other copy undergoes neutral mutation. The phase is determined by a competition between the fitness benefits of subfunctionalization and the greater mutational load associated with maintaining two gene copies. In the escape phase, we find a dynamics of an initial population of single gene sequences only which escape adaptive conflict through gene duplication and find that there are two time regimes: until a time t* single gene sequences dominate, and after t* double gene sequences outgrow single gene sequences. The time t* is identified as the time necessary for subfunctionalization to evolve and spread throughout the double gene sequences, and we show that there is an optimum mutation rate which minimizes this time scale.

Darwin's "strange inversion of reasoning".

PubMed

Dennett, Daniel

2009-06-16

Darwin's theory of evolution by natural selection unifies the world of physics with the world of meaning and purpose by proposing a deeply counterintuitive "inversion of reasoning" (according to a 19th century critic): "to make a perfect and beautiful machine, it is not requisite to know how to make it" [MacKenzie RB (1868) (Nisbet & Co., London)]. Turing proposed a similar inversion: to be a perfect and beautiful computing machine, it is not requisite to know what arithmetic is. Together, these ideas help to explain how we human intelligences came to be able to discern the reasons for all of the adaptations of life, including our own.

A New Approach to Parallel Dynamic Partitioning for Adaptive Unstructured Meshes

NASA Technical Reports Server (NTRS)

Heber, Gerd; Biswas, Rupak; Gao, Guang R.

1999-01-01

Classical mesh partitioning algorithms were designed for rather static situations, and their straightforward application in a dynamical framework may lead to unsatisfactory results, e.g., excessive data migration among processors. Furthermore, special attention should be paid to their amenability to parallelization. In this paper, a novel parallel method for the dynamic partitioning of adaptive unstructured meshes is described. It is based on a linear representation of the mesh using self-avoiding walks.

Phase and amplitude inversion of crosswell radar data

USGS Publications Warehouse

Ellefsen, Karl J.; Mazzella, Aldo T.; Horton, Robert J.; McKenna, Jason R.

2011-01-01

Phase and amplitude inversion of crosswell radar data estimates the logarithm of complex slowness for a 2.5D heterogeneous model. The inversion is formulated in the frequency domain using the vector Helmholtz equation. The objective function is minimized using a back-propagation method that is suitable for a 2.5D model and that accounts for the near-, intermediate-, and far-field regions of the antennas. The inversion is tested with crosswell radar data collected in a laboratory tank. The model anomalies are consistent with the known heterogeneity in the tank; the model’s relative dielectric permittivity, which is calculated from the real part of the estimated complex slowness, is consistent with independent laboratory measurements. The methodologies developed for this inversion can be adapted readily to inversions of seismic data (e.g., crosswell seismic and vertical seismic profiling data).

Probabilistic Magnetotelluric Inversion with Adaptive Regularisation Using the No-U-Turns Sampler

NASA Astrophysics Data System (ADS)

Conway, Dennis; Simpson, Janelle; Didana, Yohannes; Rugari, Joseph; Heinson, Graham

2018-04-01

We present the first inversion of magnetotelluric (MT) data using a Hamiltonian Monte Carlo algorithm. The inversion of MT data is an underdetermined problem which leads to an ensemble of feasible models for a given dataset. A standard approach in MT inversion is to perform a deterministic search for the single solution which is maximally smooth for a given data-fit threshold. An alternative approach is to use Markov Chain Monte Carlo (MCMC) methods, which have been used in MT inversion to explore the entire solution space and produce a suite of likely models. This approach has the advantage of assigning confidence to resistivity models, leading to better geological interpretations. Recent advances in MCMC techniques include the No-U-Turns Sampler (NUTS), an efficient and rapidly converging method which is based on Hamiltonian Monte Carlo. We have implemented a 1D MT inversion which uses the NUTS algorithm. Our model includes a fixed number of layers of variable thickness and resistivity, as well as probabilistic smoothing constraints which allow sharp and smooth transitions. We present the results of a synthetic study and show the accuracy of the technique, as well as the fast convergence, independence of starting models, and sampling efficiency. Finally, we test our technique on MT data collected from a site in Boulia, Queensland, Australia to show its utility in geological interpretation and ability to provide probabilistic estimates of features such as depth to basement.

Adaptive filtering with the self-organizing map: a performance comparison.

PubMed

Barreto, Guilherme A; Souza, Luís Gustavo M

2006-01-01

In this paper we provide an in-depth evaluation of the SOM as a feasible tool for nonlinear adaptive filtering. A comprehensive survey of existing SOM-based and related architectures for learning input-output mappings is carried out and the application of these architectures to nonlinear adaptive filtering is formulated. Then, we introduce two simple procedures for building RBF-based nonlinear filters using the Vector-Quantized Temporal Associative Memory (VQTAM), a recently proposed method for learning dynamical input-output mappings using the SOM. The aforementioned SOM-based adaptive filters are compared with standard FIR/LMS and FIR/LMS-Newton linear transversal filters, as well as with powerful MLP-based filters in nonlinear channel equalization and inverse modeling tasks. The obtained results in both tasks indicate that SOM-based filters can consistently outperform powerful MLP-based ones.

Adaptive control of dynamical synchronization on evolving networks with noise disturbances

NASA Astrophysics Data System (ADS)

Yuan, Wu-Jie; Zhou, Jian-Fang; Sendiña-Nadal, Irene; Boccaletti, Stefano; Wang, Zhen

2018-02-01

In real-world networked systems, the underlying structure is often affected by external and internal unforeseen factors, making its evolution typically inaccessible. An adaptive strategy was introduced for maintaining synchronization on unpredictably evolving networks [Sorrentino and Ott, Phys. Rev. Lett. 100, 114101 (2008), 10.1103/PhysRevLett.100.114101], which yet does not consider the noise disturbances widely existing in networks' environments. We provide here strategies to control dynamical synchronization on slowly and unpredictably evolving networks subjected to noise disturbances which are observed at the node and at the communication channel level. With our strategy, the nodes' coupling strength is adaptively adjusted with the aim of controlling synchronization, and according only to their received signal and noise disturbances. We first provide a theoretical analysis of the control scheme by introducing an error potential function to seek for the minimization of the synchronization error. Then, we show numerical experiments which verify our theoretical results. In particular, it is found that our adaptive strategy is effective even for the case in which the dynamics of the uncontrolled network would be explosive (i.e., the states of all the nodes would diverge to infinity).

Constitutional dynamic chemistry: bridge from supramolecular chemistry to adaptive chemistry.

PubMed

Lehn, Jean-Marie

2012-01-01

Supramolecular chemistry aims at implementing highly complex chemical systems from molecular components held together by non-covalent intermolecular forces and effecting molecular recognition, catalysis and transport processes. A further step consists in the investigation of chemical systems undergoing self-organization, i.e. systems capable of spontaneously generating well-defined functional supramolecular architectures by self-assembly from their components, thus behaving as programmed chemical systems. Supramolecular chemistry is intrinsically a dynamic chemistry in view of the lability of the interactions connecting the molecular components of a supramolecular entity and the resulting ability of supramolecular species to exchange their constituents. The same holds for molecular chemistry when the molecular entity contains covalent bonds that may form and break reversibility, so as to allow a continuous change in constitution by reorganization and exchange of building blocks. These features define a Constitutional Dynamic Chemistry (CDC) on both the molecular and supramolecular levels.CDC introduces a paradigm shift with respect to constitutionally static chemistry. The latter relies on design for the generation of a target entity, whereas CDC takes advantage of dynamic diversity to allow variation and selection. The implementation of selection in chemistry introduces a fundamental change in outlook. Whereas self-organization by design strives to achieve full control over the output molecular or supramolecular entity by explicit programming, self-organization with selection operates on dynamic constitutional diversity in response to either internal or external factors to achieve adaptation.The merging of the features: -information and programmability, -dynamics and reversibility, -constitution and structural diversity, points to the emergence of adaptive and evolutive chemistry, towards a chemistry of complex matter.

History of antibiotic adaptation influences microbial evolutionary dynamics during subsequent treatment

PubMed Central

Papin, Jason A.

2017-01-01

Antibiotic regimens often include the sequential changing of drugs to limit the development and evolution of resistance of bacterial pathogens. It remains unclear how history of adaptation to one antibiotic can influence the resistance profiles when bacteria subsequently adapt to a different antibiotic. Here, we experimentally evolved Pseudomonas aeruginosa to six 2-drug sequences. We observed drug order–specific effects, whereby adaptation to the first drug can limit the rate of subsequent adaptation to the second drug, adaptation to the second drug can restore susceptibility to the first drug, or final resistance levels depend on the order of the 2-drug sequence. These findings demonstrate how resistance not only depends on the current drug regimen but also the history of past regimens. These order-specific effects may allow for rational forecasting of the evolutionary dynamics of bacteria given knowledge of past adaptations and provide support for the need to consider the history of past drug exposure when designing strategies to mitigate resistance and combat bacterial infections. PMID:28792497

Full waveform inversion for ultrasonic flaw identification

NASA Astrophysics Data System (ADS)

Seidl, Robert; Rank, Ernst

2017-02-01

Ultrasonic Nondestructive Testing is concerned with detecting flaws inside components without causing physical damage. It is possible to detect flaws using ultrasound measurements but usually no additional details about the flaw like position, dimension or orientation are available. The information about these details is hidden in the recorded experimental signals. The idea of full waveform inversion is to adapt the parameters of an initial simulation model of the undamaged specimen by minimizing the discrepancy between these simulated signals and experimentally measured signals of the flawed specimen. Flaws in the structure are characterized by a change or deterioration in the material properties. Commonly, full waveform inversion is mostly applied in seismology on a larger scale to infer mechanical properties of the earth. We propose to use acoustic full waveform inversion for structural parameters to visualize the interior of the component. The method is adapted to US NDT by combining multiple similar experiments on the test component as the typical small amount of sensors is not sufficient for a successful imaging. It is shown that the combination of simulations and multiple experiments can be used to detect flaws and their position, dimension and orientation in emulated simulation cases.

Adaptive framework to better characterize errors of apriori fluxes and observational residuals in a Bayesian setup for the urban flux inversions.

NASA Astrophysics Data System (ADS)

Ghosh, S.; Lopez-Coto, I.; Prasad, K.; Karion, A.; Mueller, K.; Gourdji, S.; Martin, C.; Whetstone, J. R.

2017-12-01

The National Institute of Standards and Technology (NIST) supports the North-East Corridor Baltimore Washington (NEC-B/W) project and Indianapolis Flux Experiment (INFLUX) aiming to quantify sources of Greenhouse Gas (GHG) emissions as well as their uncertainties. These projects employ different flux estimation methods including top-down inversion approaches. The traditional Bayesian inversion method estimates emission distributions by updating prior information using atmospheric observations of Green House Gases (GHG) coupled to an atmospheric and dispersion model. The magnitude of the update is dependent upon the observed enhancement along with the assumed errors such as those associated with prior information and the atmospheric transport and dispersion model. These errors are specified within the inversion covariance matrices. The assumed structure and magnitude of the specified errors can have large impact on the emission estimates from the inversion. The main objective of this work is to build a data-adaptive model for these covariances matrices. We construct a synthetic data experiment using a Kalman Filter inversion framework (Lopez et al., 2017) employing different configurations of transport and dispersion model and an assumed prior. Unlike previous traditional Bayesian approaches, we estimate posterior emissions using regularized sample covariance matrices associated with prior errors to investigate whether the structure of the matrices help to better recover our hypothetical true emissions. To incorporate transport model error, we use ensemble of transport models combined with space-time analytical covariance to construct a covariance that accounts for errors in space and time. A Kalman Filter is then run using these covariances along with Maximum Likelihood Estimates (MLE) of the involved parameters. Preliminary results indicate that specifying sptio-temporally varying errors in the error covariances can improve the flux estimates and uncertainties. We

Dynamic properties of the adaptive optics system depending on the temporary transformations of mirror control voltages

NASA Astrophysics Data System (ADS)

Lavrinov, V. V.; Lavrinova, L. N.

2017-11-01

The statistically optimal control algorithm for the correcting mirror is formed by constructing a prediction of distortions of the optical signal and improves the time resolution of the adaptive optics system. The prediction of distortions is based on an analysis of the dynamics of changes in the optical inhomogeneities of the turbulent atmosphere or the evolution of phase fluctuations at the input aperture of the adaptive system. Dynamic properties of the system are manifested during the temporary transformation of the stresses controlling the mirror and are determined by the dynamic characteristics of the flexible mirror.

Molecular Mechanisms That Underlie the Dynamic Adaptation of Innate Monocyte Memory to Varying Stimulant Strength of TLR Ligands.

PubMed

Yuan, Ruoxi; Geng, Shuo; Li, Liwu

2016-01-01

In adaptation to rising stimulant strength, innate monocytes can be dynamically programed to preferentially express either pro- or anti-inflammatory mediators. Such dynamic innate adaptation or programing may bear profound relevance in host health and disease. However, molecular mechanisms that govern innate adaptation to varying strength of stimulants are not well understood. Using lipopolysaccharide (LPS), the model stimulant of toll-like-receptor 4 (TLR4), we reported that the expressions of pro-inflammatory mediators are preferentially sustained in monocytes adapted by lower doses of LPS, and suppressed/tolerized in monocytes adapted by higher doses of LPS. Mechanistically, monocytes adapted by super-low dose LPS exhibited higher levels of transcription factor, interferon regulatory factor 5 (IRF5), and reduced levels of transcriptional modulator B lymphocyte-induced maturation protein-1 (Blimp-1). Intriguingly, the inflammatory monocyte adaptation by super-low dose LPS is dependent upon TRAM/TRIF but not MyD88. Similar to LPS, we also observed biphasic inflammatory adaptation and tolerance in monocytes challenged with varying dosages of TLR7 agonist. In sharp contrast, rising doses of TLR3 agonist preferentially caused inflammatory adaptation without inducing tolerance. At the molecular level, the differential regulation of IRF5 and Blimp-1 coincides with unique monocyte adaptation dynamics by TLR4/7 and TLR3 agonists. Our study provides novel clue toward the understanding of monocyte adaptation and memory toward distinct TLR ligands.

Validity of the top-down approach of inverse dynamics analysis in fast and large rotational trunk movements.

PubMed

Iino, Yoichi; Kojima, Takeji

2012-08-01

This study investigated the validity of the top-down approach of inverse dynamics analysis in fast and large rotational movements of the trunk about three orthogonal axes of the pelvis for nine male collegiate students. The maximum angles of the upper trunk relative to the pelvis were approximately 47°, 49°, 32°, and 55° for lateral bending, flexion, extension, and axial rotation, respectively, with maximum angular velocities of 209°/s, 201°/s, 145°/s, and 288°/s, respectively. The pelvic moments about the axes during the movements were determined using the top-down and bottom-up approaches of inverse dynamics and compared between the two approaches. Three body segment inertial parameter sets were estimated using anthropometric data sets (Ae et al., Biomechanism 11, 1992; De Leva, J Biomech, 1996; Dumas et al., J Biomech, 2007). The root-mean-square errors of the moments and the absolute errors of the peaks of the moments were generally smaller than 10 N·m. The results suggest that the pelvic moment in motions involving fast and large trunk movements can be determined with a certain level of validity using the top-down approach in which the trunk is modeled as two or three rigid-link segments.

Robust master-slave synchronization for general uncertain delayed dynamical model based on adaptive control scheme.

PubMed

Wang, Tianbo; Zhou, Wuneng; Zhao, Shouwei; Yu, Weiqin

2014-03-01

In this paper, the robust exponential synchronization problem for a class of uncertain delayed master-slave dynamical system is investigated by using the adaptive control method. Different from some existing master-slave models, the considered master-slave system includes bounded unmodeled dynamics. In order to compensate the effect of unmodeled dynamics and effectively achieve synchronization, a novel adaptive controller with simple updated laws is proposed. Moreover, the results are given in terms of LMIs, which can be easily solved by LMI Toolbox in Matlab. A numerical example is given to illustrate the effectiveness of the method. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Fault-tolerant nonlinear adaptive flight control using sliding mode online learning.

PubMed

Krüger, Thomas; Schnetter, Philipp; Placzek, Robin; Vörsmann, Peter

2012-08-01

An expanded nonlinear model inversion flight control strategy using sliding mode online learning for neural networks is presented. The proposed control strategy is implemented for a small unmanned aircraft system (UAS). This class of aircraft is very susceptible towards nonlinearities like atmospheric turbulence, model uncertainties and of course system failures. Therefore, these systems mark a sensible testbed to evaluate fault-tolerant, adaptive flight control strategies. Within this work the concept of feedback linearization is combined with feed forward neural networks to compensate for inversion errors and other nonlinear effects. Backpropagation-based adaption laws of the network weights are used for online training. Within these adaption laws the standard gradient descent backpropagation algorithm is augmented with the concept of sliding mode control (SMC). Implemented as a learning algorithm, this nonlinear control strategy treats the neural network as a controlled system and allows a stable, dynamic calculation of the learning rates. While considering the system's stability, this robust online learning method therefore offers a higher speed of convergence, especially in the presence of external disturbances. The SMC-based flight controller is tested and compared with the standard gradient descent backpropagation algorithm in the presence of system failures. Copyright © 2012 Elsevier Ltd. All rights reserved.

Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors.

PubMed

Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Chen, Bing; Lin, Chong

2015-03-01

This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique.

All-electrical detection of spin dynamics in magnetic antidot lattices by the inverse spin Hall effect

DOE PAGES

Jungfleisch, Matthias B.; Zhang, Wei; Ding, Junjia; ...

2016-02-03

The understanding of spin dynamics in laterally confined structures on sub-micron length scales has become a significant aspect of the development of novel magnetic storage technologies. Numerous ferromagnetic resonance measurements, optical characterization by Kerr microscopy and Brillouin light scattering spectroscopy and x-ray studies were carried out to detect the dynamics in patterned magnetic antidot lattices. Here, we investigate Oersted-field driven spin dynamics in rectangular Ni80Fe20/Pt antidot lattices with different lattice parameters by electrical means. When the system is driven to resonance, a dc voltage across the length of the sample is detected that changes its sign upon field reversal, whichmore » is in agreement with a rectification mechanism based on the inverse spin Hall effect. Furthermore, we show that the voltage output scales linearly with the applied microwave drive in the investigated range of powers. Lastly, our findings have direct implications on the development of engineered magnonics applications and devices.« less

An adaptable neuromorphic model of orientation selectivity based on floating gate dynamics

PubMed Central

Gupta, Priti; Markan, C. M.

2014-01-01

The biggest challenge that the neuromorphic community faces today is to build systems that can be considered truly cognitive. Adaptation and self-organization are the two basic principles that underlie any cognitive function that the brain performs. If we can replicate this behavior in hardware, we move a step closer to our goal of having cognitive neuromorphic systems. Adaptive feature selectivity is a mechanism by which nature optimizes resources so as to have greater acuity for more abundant features. Developing neuromorphic feature maps can help design generic machines that can emulate this adaptive behavior. Most neuromorphic models that have attempted to build self-organizing systems, follow the approach of modeling abstract theoretical frameworks in hardware. While this is good from a modeling and analysis perspective, it may not lead to the most efficient hardware. On the other hand, exploiting hardware dynamics to build adaptive systems rather than forcing the hardware to behave like mathematical equations, seems to be a more robust methodology when it comes to developing actual hardware for real world applications. In this paper we use a novel time-staggered Winner Take All circuit, that exploits the adaptation dynamics of floating gate transistors, to model an adaptive cortical cell that demonstrates Orientation Selectivity, a well-known biological phenomenon observed in the visual cortex. The cell performs competitive learning, refining its weights in response to input patterns resembling different oriented bars, becoming selective to a particular oriented pattern. Different analysis performed on the cell such as orientation tuning, application of abnormal inputs, response to spatial frequency and periodic patterns reveal close similarity between our cell and its biological counterpart. Embedded in a RC grid, these cells interact diffusively exhibiting cluster formation, making way for adaptively building orientation selective maps in silicon. PMID

Adaptive fuzzy wavelet network control of second order multi-agent systems with unknown nonlinear dynamics.

PubMed

Taheri, Mehdi; Sheikholeslam, Farid; Najafi, Majddedin; Zekri, Maryam

2017-07-01

In this paper, consensus problem is considered for second order multi-agent systems with unknown nonlinear dynamics under undirected graphs. A novel distributed control strategy is suggested for leaderless systems based on adaptive fuzzy wavelet networks. Adaptive fuzzy wavelet networks are employed to compensate for the effect of unknown nonlinear dynamics. Moreover, the proposed method is developed for leader following systems and leader following systems with state time delays. Lyapunov functions are applied to prove uniformly ultimately bounded stability of closed loop systems and to obtain adaptive laws. Three simulation examples are presented to illustrate the effectiveness of the proposed control algorithms. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Preview-Based Stable-Inversion for Output Tracking

NASA Technical Reports Server (NTRS)

Zou, Qing-Ze; Devasia, Santosh

1999-01-01

Stable Inversion techniques can be used to achieve high-accuracy output tracking. However, for nonminimum phase systems, the inverse is non-causal - hence the inverse has to be pre-computed using a pre-specified desired-output trajectory. This requirement for pre-specification of the desired output restricts the use of inversion-based approaches to trajectory planning problems (for nonminimum phase systems). In the present article, it is shown that preview information of the desired output can be used to achieve online inversion-based output tracking of linear systems. The amount of preview-time needed is quantified in terms of the tracking error and the internal dynamics of the system (zeros of the system). The methodology is applied to the online output tracking of a flexible structure and experimental results are presented.

Parameterizations for ensemble Kalman inversion

NASA Astrophysics Data System (ADS)

Chada, Neil K.; Iglesias, Marco A.; Roininen, Lassi; Stuart, Andrew M.

2018-05-01

The use of ensemble methods to solve inverse problems is attractive because it is a derivative-free methodology which is also well-adapted to parallelization. In its basic iterative form the method produces an ensemble of solutions which lie in the linear span of the initial ensemble. Choice of the parameterization of the unknown field is thus a key component of the success of the method. We demonstrate how both geometric ideas and hierarchical ideas can be used to design effective parameterizations for a number of applied inverse problems arising in electrical impedance tomography, groundwater flow and source inversion. In particular we show how geometric ideas, including the level set method, can be used to reconstruct piecewise continuous fields, and we show how hierarchical methods can be used to learn key parameters in continuous fields, such as length-scales, resulting in improved reconstructions. Geometric and hierarchical ideas are combined in the level set method to find piecewise constant reconstructions with interfaces of unknown topology.

Flatness-based adaptive fuzzy control of chaotic finance dynamics

NASA Astrophysics Data System (ADS)

Rigatos, G.; Siano, P.; Loia, V.; Tommasetti, A.; Troisi, O.

2017-11-01

A flatness-based adaptive fuzzy control is applied to the problem of stabilization of the dynamics of a chaotic finance system, describing interaction between the interest rate, the investment demand and the price exponent. By proving that the system is differentially flat and by applying differential flatness diffeomorphisms, its transformation to the linear canonical (Brunovsky) is performed. For the latter description of the system, the design of a stabilizing state feedback controller becomes possible. A first problem in the design of such a controller is that the dynamic model of the finance system is unknown and thus it has to be identified with the use neurofuzzy approximators. The estimated dynamics provided by the approximators is used in the computation of the control input, thus establishing an indirect adaptive control scheme. The learning rate of the approximators is chosen from the requirement the system's Lyapunov function to have always a negative first-order derivative. Another problem that has to be dealt with is that the control loop is implemented only with the use of output feedback. To estimate the non-measurable state vector elements of the finance system, a state observer is implemented in the control loop. The computation of the feedback control signal requires the solution of two algebraic Riccati equations at each iteration of the control algorithm. Lyapunov stability analysis demonstrates first that an H-infinity tracking performance criterion is satisfied. This signifies elevated robustness against modelling errors and external perturbations. Moreover, the global asymptotic stability is proven for the control loop.

Neural Network Assisted Inverse Dynamic Guidance for Terminally Constrained Entry Flight

PubMed Central

Chen, Wanchun

2014-01-01

This paper presents a neural network assisted entry guidance law that is designed by applying Bézier approximation. It is shown that a fully constrained approximation of a reference trajectory can be made by using the Bézier curve. Applying this approximation, an inverse dynamic system for an entry flight is solved to generate guidance command. The guidance solution thus gotten ensures terminal constraints for position, flight path, and azimuth angle. In order to ensure terminal velocity constraint, a prediction of the terminal velocity is required, based on which, the approximated Bézier curve is adjusted. An artificial neural network is used for this prediction of the terminal velocity. The method enables faster implementation in achieving fully constrained entry flight. Results from simulations indicate improved performance of the neural network assisted method. The scheme is expected to have prospect for further research on automated onboard control of terminal velocity for both reentry and terminal guidance laws. PMID:24723821

Robust dynamic inversion controller design and analysis (using the X-38 vehicle as a case study)

NASA Astrophysics Data System (ADS)

Ito, Daigoro

A new way to approach robust Dynamic Inversion controller synthesis is addressed in this paper. A Linear Quadratic Gaussian outer-loop controller improves the robustness of a Dynamic Inversion inner-loop controller in the presence of uncertainties. Desired dynamics are given by the dynamic compensator, which shapes the loop. The selected dynamics are based on both performance and stability robustness requirements. These requirements are straightforwardly formulated as frequency-dependent singular value bounds during synthesis of the controller. Performance and robustness of the designed controller is tested using a worst case time domain quadratic index, which is a simple but effective way to measure robustness due to parameter variation. Using this approach, a lateral-directional controller for the X-38 vehicle is designed and its robustness to parameter variations and disturbances is analyzed. It is found that if full state measurements are available, the performance of the designed lateral-directional control system, measured by the chosen cost function, improves by approximately a factor of four. Also, it is found that the designed system is stable up to a parametric variation of 1.65 standard deviation with the set of uncertainty considered. The system robustness is determined to be highly sensitive to the dihedral derivative and the roll damping coefficients. The controller analysis is extended to the nonlinear system where both control input displacements and rates are bounded. In this case, the considered nonlinear system is stable up to 48.1° in bank angle and 1.59° in sideslip angle variations, indicating it is more sensitive to variations in sideslip angle than in bank angle. This nonlinear approach is further extended for the actuator failure mode analysis. The results suggest that the designed system maintains a high level of stability in the event of aileron failure. However, only 35% or less of the original stability range is maintained for the

Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure.

PubMed

Fei, Juntao; Lu, Cheng

2018-04-01

In this paper, an adaptive sliding mode control system using a double loop recurrent neural network (DLRNN) structure is proposed for a class of nonlinear dynamic systems. A new three-layer RNN is proposed to approximate unknown dynamics with two different kinds of feedback loops where the firing weights and output signal calculated in the last step are stored and used as the feedback signals in each feedback loop. Since the new structure has combined the advantages of internal feedback NN and external feedback NN, it can acquire the internal state information while the output signal is also captured, thus the new designed DLRNN can achieve better approximation performance compared with the regular NNs without feedback loops or the regular RNNs with a single feedback loop. The new proposed DLRNN structure is employed in an equivalent controller to approximate the unknown nonlinear system dynamics, and the parameters of the DLRNN are updated online by adaptive laws to get favorable approximation performance. To investigate the effectiveness of the proposed controller, the designed adaptive sliding mode controller with the DLRNN is applied to a -axis microelectromechanical system gyroscope to control the vibrating dynamics of the proof mass. Simulation results demonstrate that the proposed methodology can achieve good tracking property, and the comparisons of the approximation performance between radial basis function NN, RNN, and DLRNN show that the DLRNN can accurately estimate the unknown dynamics with a fast speed while the internal states of DLRNN are more stable.

Long-time atomistic dynamics through a new self-adaptive accelerated molecular dynamics method

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

Gao, N.; Yang, L.; Gao, F.

2017-02-27

A self-adaptive accelerated molecular dynamics method is developed to model infrequent atomic- scale events, especially those events that occur on a rugged free-energy surface. Key in the new development is the use of the total displacement of the system at a given temperature to construct a boost-potential, which is slowly increased to accelerate the dynamics. The temperature is slowly increased to accelerate the dynamics. By allowing the system to evolve from one steady-state con guration to another by overcoming the transition state, this self-evolving approach makes it possible to explore the coupled motion of species that migrate on vastly differentmore » time scales. The migrations of single vacancy (V) and small He-V clusters, and the growth of nano-sized He-V clusters in Fe for times in the order of seconds are studied by this new method. An interstitial- assisted mechanism is rst explored for the migration of a helium-rich He-V cluster, while a new two-component Ostwald ripening mechanism is suggested for He-V cluster growth.« less

Adaptive sampling strategies with high-throughput molecular dynamics

NASA Astrophysics Data System (ADS)

Clementi, Cecilia

Despite recent significant hardware and software developments, the complete thermodynamic and kinetic characterization of large macromolecular complexes by molecular simulations still presents significant challenges. The high dimensionality of these systems and the complexity of the associated potential energy surfaces (creating multiple metastable regions connected by high free energy barriers) does not usually allow to adequately sample the relevant regions of their configurational space by means of a single, long Molecular Dynamics (MD) trajectory. Several different approaches have been proposed to tackle this sampling problem. We focus on the development of ensemble simulation strategies, where data from a large number of weakly coupled simulations are integrated to explore the configurational landscape of a complex system more efficiently. Ensemble methods are of increasing interest as the hardware roadmap is now mostly based on increasing core counts, rather than clock speeds. The main challenge in the development of an ensemble approach for efficient sampling is in the design of strategies to adaptively distribute the trajectories over the relevant regions of the systems' configurational space, without using any a priori information on the system global properties. We will discuss the definition of smart adaptive sampling approaches that can redirect computational resources towards unexplored yet relevant regions. Our approaches are based on new developments in dimensionality reduction for high dimensional dynamical systems, and optimal redistribution of resources. NSF CHE-1152344, NSF CHE-1265929, Welch Foundation C-1570.

The absence or temporal offset of visual feedback does not influence adaptation to novel movement dynamics.

PubMed

McKenna, Erin; Bray, Laurence C Jayet; Zhou, Weiwei; Joiner, Wilsaan M

2017-10-01

Delays in transmitting and processing sensory information require correctly associating delayed feedback to issued motor commands for accurate error compensation. The flexibility of this alignment between motor signals and feedback has been demonstrated for movement recalibration to visual manipulations, but the alignment dependence for adapting movement dynamics is largely unknown. Here we examined the effect of visual feedback manipulations on force-field adaptation. Three subject groups used a manipulandum while experiencing a lag in the corresponding cursor motion (0, 75, or 150 ms). When the offset was applied at the start of the session (continuous condition), adaptation was not significantly different between groups. However, these similarities may be due to acclimation to the offset before motor adaptation. We tested additional subjects who experienced the same delays concurrent with the introduction of the perturbation (abrupt condition). In this case adaptation was statistically indistinguishable from the continuous condition, indicating that acclimation to feedback delay was not a factor. In addition, end-point errors were not significantly different across the delay or onset conditions, but end-point correction (e.g., deceleration duration) was influenced by the temporal offset. As an additional control, we tested a group of subjects who performed without visual feedback and found comparable movement adaptation results. These results suggest that visual feedback manipulation (absence or temporal misalignment) does not affect adaptation to novel dynamics, independent of both acclimation and perceptual awareness. These findings could have implications for modeling how the motor system adjusts to errors despite concurrent delays in sensory feedback information. NEW & NOTEWORTHY A temporal offset between movement and distorted visual feedback (e.g., visuomotor rotation) influences the subsequent motor recalibration, but the effects of this offset for

Comparison of Antarctic Crustal Thickness from Gravity Inversion and Seismology: Evidence for Mantle Dynamic Uplift under Marie Byrd Land

NASA Astrophysics Data System (ADS)

Ferraccioli, F.; Kusznir, N. J.; Jordan, T. A.

2017-12-01

Using gravity anomaly inversion, we produce comprehensive regional maps of crustal thickness and oceanic lithosphere distribution for Antarctica and the Southern Ocean. Antarctic crustal thicknesses derived from gravity inversion are compared with seismic estimates from Baranov (2011) and An et al. (2015). We determine Moho depth, crustal basement thickness, continental lithosphere thinning (1-1/) and ocean-continent transition location using a 3D spectral domain gravity inversion method, which incorporates a lithosphere thermal gravity anomaly correction (Chappell & Kusznir 2008). Data used in the gravity inversion are elevation and bathymetry, free-air gravity anomaly, the Bedmap 2 ice thickness and bedrock topography compilation south of 60 degrees south and relatively sparse constraints on sediment thickness. Our gravity inversion study predicts thick crust (> 45 km) under interior East Antarctica, which is penetrated by narrow continental rifts featuring relatively thinner crust. The largest crustal thicknesses predicted from gravity inversion lie in the region of the Gamburtsev Subglacial Mountains, and are consistent with seismic estimates. The East Antarctic Rift System (EARS), a major Permian to Cretaceous age rift system, is imaged by our inversion and appears to extend from the continental margin at the Lambert Rift (LR) to the South Pole region, a distance of 2500 km. Thin crust is predicted under the Ross Sea and beneath the West Antarctic Ice Sheet and delineates the regional extent of the broad West Antarctic Rift System (WARS). Substantial regional uplift is required under Marie Byrd Land to reconcile gravity and seismic estimates. A mantle dynamic uplift origin of the uplift is preferred to a thermal anomaly from a very young rift. The new crustal thickness map produced by this gravity inversion study support the hypothesis that one branch of the WARS links through to the De Gerlache sea-mounts (DG) and Peter I Island (PI) in the Bellingshausen Sea

Dynamic changes in brain activity during prism adaptation.

PubMed

Luauté, Jacques; Schwartz, Sophie; Rossetti, Yves; Spiridon, Mona; Rode, Gilles; Boisson, Dominique; Vuilleumier, Patrik

2009-01-07

Prism adaptation does not only induce short-term sensorimotor plasticity, but also longer-term reorganization in the neural representation of space. We used event-related fMRI to study dynamic changes in brain activity during both early and prolonged exposure to visual prisms. Participants performed a pointing task before, during, and after prism exposure. Measures of trial-by-trial pointing errors and corrections allowed parametric analyses of brain activity as a function of performance. We show that during the earliest phase of prism exposure, anterior intraparietal sulcus was primarily implicated in error detection, whereas parieto-occipital sulcus was implicated in error correction. Cerebellum activity showed progressive increases during prism exposure, in accordance with a key role for spatial realignment. This time course further suggests that the cerebellum might promote neural changes in superior temporal cortex, which was selectively activated during the later phase of prism exposure and could mediate the effects of prism adaptation on cognitive spatial representations.

A comprehensive inversion approach for feedforward compensation of piezoactuator system at high frequency

NASA Astrophysics Data System (ADS)

Tian, Lizhi; Xiong, Zhenhua; Wu, Jianhua; Ding, Han

2016-09-01

Motion control of the piezoactuator system over broadband frequencies is limited due to its inherent hysteresis and system dynamics. One of the suggested ways is to use feedforward controller to linearize the input-output relationship of the piezoactuator system. Although there have been many feedforward approaches, it is still a challenge to develop feedforward controller for the piezoactuator system at high frequency. Hence, this paper presents a comprehensive inversion approach in consideration of the coupling of hysteresis and dynamics. In this work, the influence of dynamics compensation on the input-output relationship of the piezoactuator system is investigated first. With system dynamics compensation, the input-output relationship of the piezoactuator system will be further represented as rate-dependent nonlinearity due to the inevitable dynamics compensation error, especially at high frequency. Base on this result, the feedforward controller composed by a cascade of linear dynamics inversion and rate-dependent nonlinearity inversion is developed. Then, the system identification of the comprehensive inversion approach is proposed. Finally, experimental results show that the proposed approach can improve the performance on tracking of both periodic and non-periodic trajectories at medium and high frequency compared with the conventional feedforward approaches.

Adaptive optimal input design and parametric estimation of nonlinear dynamical systems: application to neuronal modeling.

PubMed

Madi, Mahmoud K; Karameh, Fadi N

2018-05-11

Many physical models of biological processes including neural systems are characterized by parametric nonlinear dynamical relations between driving inputs, internal states, and measured outputs of the process. Fitting such models using experimental data (data assimilation) is a challenging task since the physical process often operates in a noisy, possibly non-stationary environment; moreover, conducting multiple experiments under controlled and repeatable conditions can be impractical, time consuming or costly. The accuracy of model identification, therefore, is dictated principally by the quality and dynamic richness of collected data over single or few experimental sessions. Accordingly, it is highly desirable to design efficient experiments that, by exciting the physical process with smart inputs, yields fast convergence and increased accuracy of the model. We herein introduce an adaptive framework in which optimal input design is integrated with Square root Cubature Kalman Filters (OID-SCKF) to develop an online estimation procedure that first, converges significantly quicker, thereby permitting model fitting over shorter time windows, and second, enhances model accuracy when only few process outputs are accessible. The methodology is demonstrated on common nonlinear models and on a four-area neural mass model with noisy and limited measurements. Estimation quality (speed and accuracy) is benchmarked against high-performance SCKF-based methods that commonly employ dynamically rich informed inputs for accurate model identification. For all the tested models, simulated single-trial and ensemble averages showed that OID-SCKF exhibited (i) faster convergence of parameter estimates and (ii) lower dependence on inter-trial noise variability with gains up to around 1000 msec in speed and 81% increase in variability for the neural mass models. In terms of accuracy, OID-SCKF estimation was superior, and exhibited considerably less variability across experiments, in

Dynamic imaging of adaptive stress response pathway activation for prediction of drug induced liver injury.

PubMed

Wink, Steven; Hiemstra, Steven W; Huppelschoten, Suzanne; Klip, Janna E; van de Water, Bob

2018-05-01

Drug-induced liver injury remains a concern during drug treatment and development. There is an urgent need for improved mechanistic understanding and prediction of DILI liabilities using in vitro approaches. We have established and characterized a panel of liver cell models containing mechanism-based fluorescent protein toxicity pathway reporters to quantitatively assess the dynamics of cellular stress response pathway activation at the single cell level using automated live cell imaging. We have systematically evaluated the application of four key adaptive stress pathway reporters for the prediction of DILI liability: SRXN1-GFP (oxidative stress), CHOP-GFP (ER stress/UPR response), p21 (p53-mediated DNA damage-related response) and ICAM1 (NF-κB-mediated inflammatory signaling). 118 FDA-labeled drugs in five human exposure relevant concentrations were evaluated for reporter activation using live cell confocal imaging. Quantitative data analysis revealed activation of single or multiple reporters by most drugs in a concentration and time dependent manner. Hierarchical clustering of time course dynamics and refined single cell analysis allowed the allusion of key events in DILI liability. Concentration response modeling was performed to calculate benchmark concentrations (BMCs). Extracted temporal dynamic parameters and BMCs were used to assess the predictive power of sub-lethal adaptive stress pathway activation. Although cellular adaptive responses were activated by non-DILI and severe-DILI compounds alike, dynamic behavior and lower BMCs of pathway activation were sufficiently distinct between these compound classes. The high-level detailed temporal- and concentration-dependent evaluation of the dynamics of adaptive stress pathway activation adds to the overall understanding and prediction of drug-induced liver liabilities.

Regularity Aspects in Inverse Musculoskeletal Biomechanics

NASA Astrophysics Data System (ADS)

Lund, Marie; Stâhl, Fredrik; Gulliksson, Mârten

2008-09-01

Inverse simulations of musculoskeletal models computes the internal forces such as muscle and joint reaction forces, which are hard to measure, using the more easily measured motion and external forces as input data. Because of the difficulties of measuring muscle forces and joint reactions, simulations are hard to validate. One way of reducing errors for the simulations is to ensure that the mathematical problem is well-posed. This paper presents a study of regularity aspects for an inverse simulation method, often called forward dynamics or dynamical optimization, that takes into account both measurement errors and muscle dynamics. Regularity is examined for a test problem around the optimum using the approximated quadratic problem. The results shows improved rank by including a regularization term in the objective that handles the mechanical over-determinancy. Using the 3-element Hill muscle model the chosen regularization term is the norm of the activation. To make the problem full-rank only the excitation bounds should be included in the constraints. However, this results in small negative values of the activation which indicates that muscles are pushing and not pulling, which is unrealistic but the error maybe small enough to be accepted for specific applications. These results are a start to ensure better results of inverse musculoskeletal simulations from a numerical point of view.

Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

NASA Astrophysics Data System (ADS)

Takiyama, Ken

2017-12-01

How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors

PubMed Central

Zhu, Dongchen; Li, Jiamao; Wang, Xianshun; Peng, Jingquan; Shi, Wenjun; Zhang, Xiaolin

2018-01-01

Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison. PMID:29614028

Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors.

PubMed

Zhu, Dongchen; Li, Jiamao; Wang, Xianshun; Peng, Jingquan; Shi, Wenjun; Zhang, Xiaolin

2018-04-03

Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison.

An Inversion Recovery NMR Kinetics Experiment

ERIC Educational Resources Information Center

Williams, Travis J.; Kershaw, Allan D.; Li, Vincent; Wu, Xinping

2011-01-01

A convenient laboratory experiment is described in which NMR magnetization transfer by inversion recovery is used to measure the kinetics and thermochemistry of amide bond rotation. The experiment utilizes Varian spectrometers with the VNMRJ 2.3 software, but can be easily adapted to any NMR platform. The procedures and sample data sets in this…

Predicting the evolutionary dynamics of seasonal adaptation to novel climates in Arabidopsis thaliana

PubMed Central

Fournier-Level, Alexandre; Perry, Emily O.; Wang, Jonathan A.; Braun, Peter T.; Migneault, Andrew; Cooper, Martha D.; Metcalf, C. Jessica E.; Schmitt, Johanna

2016-01-01

Predicting whether and how populations will adapt to rapid climate change is a critical goal for evolutionary biology. To examine the genetic basis of fitness and predict adaptive evolution in novel climates with seasonal variation, we grew a diverse panel of the annual plant Arabidopsis thaliana (multiparent advanced generation intercross lines) in controlled conditions simulating four climates: a present-day reference climate, an increased-temperature climate, a winter-warming only climate, and a poleward-migration climate with increased photoperiod amplitude. In each climate, four successive seasonal cohorts experienced dynamic daily temperature and photoperiod variation over a year. We measured 12 traits and developed a genomic prediction model for fitness evolution in each seasonal environment. This model was used to simulate evolutionary trajectories of the base population over 50 y in each climate, as well as 100-y scenarios of gradual climate change following adaptation to a reference climate. Patterns of plastic and evolutionary fitness response varied across seasons and climates. The increased-temperature climate promoted genetic divergence of subpopulations across seasons, whereas in the winter-warming and poleward-migration climates, seasonal genetic differentiation was reduced. In silico “resurrection experiments” showed limited evolutionary rescue compared with the plastic response of fitness to seasonal climate change. The genetic basis of adaptation and, consequently, the dynamics of evolutionary change differed qualitatively among scenarios. Populations with fewer founding genotypes and populations with genetic diversity reduced by prior selection adapted less well to novel conditions, demonstrating that adaptation to rapid climate change requires the maintenance of sufficient standing variation. PMID:27140640

Predicting the evolutionary dynamics of seasonal adaptation to novel climates in Arabidopsis thaliana.

PubMed

Fournier-Level, Alexandre; Perry, Emily O; Wang, Jonathan A; Braun, Peter T; Migneault, Andrew; Cooper, Martha D; Metcalf, C Jessica E; Schmitt, Johanna

2016-05-17

Predicting whether and how populations will adapt to rapid climate change is a critical goal for evolutionary biology. To examine the genetic basis of fitness and predict adaptive evolution in novel climates with seasonal variation, we grew a diverse panel of the annual plant Arabidopsis thaliana (multiparent advanced generation intercross lines) in controlled conditions simulating four climates: a present-day reference climate, an increased-temperature climate, a winter-warming only climate, and a poleward-migration climate with increased photoperiod amplitude. In each climate, four successive seasonal cohorts experienced dynamic daily temperature and photoperiod variation over a year. We measured 12 traits and developed a genomic prediction model for fitness evolution in each seasonal environment. This model was used to simulate evolutionary trajectories of the base population over 50 y in each climate, as well as 100-y scenarios of gradual climate change following adaptation to a reference climate. Patterns of plastic and evolutionary fitness response varied across seasons and climates. The increased-temperature climate promoted genetic divergence of subpopulations across seasons, whereas in the winter-warming and poleward-migration climates, seasonal genetic differentiation was reduced. In silico "resurrection experiments" showed limited evolutionary rescue compared with the plastic response of fitness to seasonal climate change. The genetic basis of adaptation and, consequently, the dynamics of evolutionary change differed qualitatively among scenarios. Populations with fewer founding genotypes and populations with genetic diversity reduced by prior selection adapted less well to novel conditions, demonstrating that adaptation to rapid climate change requires the maintenance of sufficient standing variation.

Strategic tradeoffs in competitor dynamics on adaptive networks.

PubMed

Hébert-Dufresne, Laurent; Allard, Antoine; Noël, Pierre-André; Young, Jean-Gabriel; Libby, Eric

2017-08-08

Recent empirical work highlights the heterogeneity of social competitions such as political campaigns: proponents of some ideologies seek debate and conversation, others create echo chambers. While symmetric and static network structure is typically used as a substrate to study such competitor dynamics, network structure can instead be interpreted as a signature of the competitor strategies, yielding competition dynamics on adaptive networks. Here we demonstrate that tradeoffs between aggressiveness and defensiveness (i.e., targeting adversaries vs. targeting like-minded individuals) creates paradoxical behaviour such as non-transitive dynamics. And while there is an optimal strategy in a two competitor system, three competitor systems have no such solution; the introduction of extreme strategies can easily affect the outcome of a competition, even if the extreme strategies have no chance of winning. Not only are these results reminiscent of classic paradoxical results from evolutionary game theory, but the structure of social networks created by our model can be mapped to particular forms of payoff matrices. Consequently, social structure can act as a measurable metric for social games which in turn allows us to provide a game theoretical perspective on online political debates.

Constraining inverse-curvature gravity with supernovae.

PubMed

Mena, Olga; Santiago, José; Weller, Jochen

2006-02-03

We show that models of generalized modified gravity, with inverse powers of the curvature, can explain the current accelerated expansion of the Universe without resorting to dark energy and without conflicting with solar system experiments. We have solved the Friedmann equations for the full dynamical range of the evolution of the Universe and performed a detailed analysis of supernovae data in the context of such models that results in an excellent fit. If we further include constraints on the current expansion of the Universe and on its age, we obtain that the matter content of the Universe is 0.07inverse-curvature gravity models considered cannot explain the dynamics of the Universe just with a baryonic matter component.

Chromosomal Thermal Index: a comprehensive way to integrate the thermal adaptation of Drosophila subobscura whole karyotype.

PubMed

Arenas, Conxita; Zivanovic, Goran; Mestres, Francesc

2018-02-01

Drosophila has demonstrated to be an excellent model to study the adaptation of organisms to global warming, with inversion chromosomal polymorphism having a key role in this adaptation. Here, we introduce a new index (Chromosomal Thermal Index or CTI) to quantify the thermal adaptation of a population according to its composition of "warm" and "cold" adapted inversions. This index is intuitive, has good statistical properties, and can be used to hypothesis on the effect of global warming on natural populations. We show the usefulness of CTI using data from European populations of D. subobscura, sampled in different years. Out of 15 comparisons over time, nine showed significant increase of CTI, in accordance with global warming expectations. Although large regions of the genome outside inversions contain thermal adaptation genes, our results show that the total amount of warm or cold inversions in populations seems to be directly involved in thermal adaptation, whereas the interactions between the inversions content of homologous and non-homologous chromosomes are not relevant.

Spatial operator factorization and inversion of the manipulator mass matrix

NASA Technical Reports Server (NTRS)

Rodriguez, Guillermo; Kreutz-Delgado, Kenneth

1992-01-01

This paper advances two linear operator factorizations of the manipulator mass matrix. Embedded in the factorizations are many of the techniques that are regarded as very efficient computational solutions to inverse and forward dynamics problems. The operator factorizations provide a high-level architectural understanding of the mass matrix and its inverse, which is not visible in the detailed algorithms. They also lead to a new approach to the development of computer programs or organize complexity in robot dynamics.

A dynamical system that describes vein graft adaptation and failure.

PubMed

Garbey, Marc; Berceli, Scott A

2013-11-07

Adaptation of vein bypass grafts to the mechanical stresses imposed by the arterial circulation is thought to be the primary determinant for lesion development, yet an understanding of how the various forces dictate local wall remodeling is lacking. We develop a dynamical system that summarizes the complex interplay between the mechanical environment and cell/matrix kinetics, ultimately dictating changes in the vein graft architecture. Based on a systematic mapping of the parameter space, three general remodeling response patterns are observed: (1) shear stabilized intimal thickening, (2) tension induced wall thinning and lumen expansion, and (3) tension stabilized wall thickening. Notable is our observation that the integration of multiple feedback mechanisms leads to a variety of non-linear responses that would be unanticipated by an analysis of each system component independently. This dynamic analysis supports the clinical observation that the majority of vein grafts proceed along an adaptive trajectory, where grafts dilate and mildly thicken in response to the increased tension and shear, but a small portion of the grafts demonstrate a maladaptive phenotype, where progressive inward remodeling and accentuated wall thickening lead to graft failure. © 2013 The Authors. Published by Elsevier Ltd. All rights reserved.

Adaptable mission planning for kino-dynamic systems

NASA Astrophysics Data System (ADS)

Bush, Lawrence A. M.; Jimenez, Tony R.; Williams, Brian C.

Autonomous systems can perform tasks that are dangerous, monotonous, or even impossible for humans. To approach the problem of planning for Unmanned Aerial Vehicles (UAVs) we present a hierarchical method that combines a high-level planner with a low-level planner. We pose the problem of high-level planning as a Selective Traveling Salesman Problem (STSP) and select the order in which to visit our science sites. We then use a kino-dynamic path planner to create a large number of intermediate waypoints. This is a complete system that combines high and low level planning to achieve a goal. This paper demonstrates the benefits gained by adaptable high-level plans versus static and greedy plans.

An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks.

PubMed

Lei, Chunyang; Bie, Hongxia; Fang, Gengfa; Zhang, Xuekun

2015-12-03

Super dense and distributed wireless sensor networks have become very popular with the development of small cell technology, Internet of Things (IoT), Machine-to-Machine (M2M) communications, Vehicular-to-Vehicular (V2V) communications and public safety networks. While densely deployed wireless networks provide one of the most important and sustainable solutions to improve the accuracy of sensing and spectral efficiency, a new channel access scheme needs to be designed to solve the channel congestion problem introduced by the high dynamics of competing nodes accessing the channel simultaneously. In this paper, we firstly analyzed the channel contention problem using a novel normalized channel contention analysis model which provides information on how to tune the contention window according to the state of channel contention. We then proposed an adaptive channel contention window tuning algorithm in which the contention window tuning rate is set dynamically based on the estimated channel contention level. Simulation results show that our proposed adaptive channel access algorithm based on fast contention window tuning can achieve more than 95 % of the theoretical optimal throughput and 0 . 97 of fairness index especially in dynamic and dense networks.

Application of Non-Kolmogorovian Probability and Quantum Adaptive Dynamics to Unconscious Inference in Visual Perception Process

NASA Astrophysics Data System (ADS)

Accardi, Luigi; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu; Yamato, Ichiro

2016-07-01

Recently a novel quantum information formalism — quantum adaptive dynamics — was developed and applied to modelling of information processing by bio-systems including cognitive phenomena: from molecular biology (glucose-lactose metabolism for E.coli bacteria, epigenetic evolution) to cognition, psychology. From the foundational point of view quantum adaptive dynamics describes mutual adapting of the information states of two interacting systems (physical or biological) as well as adapting of co-observations performed by the systems. In this paper we apply this formalism to model unconscious inference: the process of transition from sensation to perception. The paper combines theory and experiment. Statistical data collected in an experimental study on recognition of a particular ambiguous figure, the Schröder stairs, support the viability of the quantum(-like) model of unconscious inference including modelling of biases generated by rotation-contexts. From the probabilistic point of view, we study (for concrete experimental data) the problem of contextuality of probability, its dependence on experimental contexts. Mathematically contextuality leads to non-Komogorovness: probability distributions generated by various rotation contexts cannot be treated in the Kolmogorovian framework. At the same time they can be embedded in a “big Kolmogorov space” as conditional probabilities. However, such a Kolmogorov space has too complex structure and the operational quantum formalism in the form of quantum adaptive dynamics simplifies the modelling essentially.

Optimal spectral tracking--adapting to dynamic regime change.

PubMed

Brittain, John-Stuart; Halliday, David M

2011-01-30

Real world data do not always obey the statistical restraints imposed upon them by sophisticated analysis techniques. In spectral analysis for instance, an ergodic process--the interchangeability of temporal for spatial averaging--is assumed for a repeat-trial design. Many evolutionary scenarios, such as learning and motor consolidation, do not conform to such linear behaviour and should be approached from a more flexible perspective. To this end we previously introduced the method of optimal spectral tracking (OST) in the study of trial-varying parameters. In this extension to our work we modify the OST routines to provide an adaptive implementation capable of reacting to dynamic transitions in the underlying system state. In so doing, we generalise our approach to characterise both slow-varying and rapid fluctuations in time-series, simultaneously providing a metric of system stability. The approach is first applied to a surrogate dataset and compared to both our original non-adaptive solution and spectrogram approaches. The adaptive OST is seen to display fast convergence and desirable statistical properties. All three approaches are then applied to a neurophysiological recording obtained during a study on anaesthetic monitoring. Local field potentials acquired from the posterior hypothalamic region of a deep brain stimulation patient undergoing anaesthesia were analysed. The characterisation of features such as response delay, time-to-peak and modulation brevity are considered. Copyright © 2010 Elsevier B.V. All rights reserved.

Dynamic characterization of viscoelastic porous foams used in vehicles based on an inverse finite element method.

PubMed

Finnveden, Svante; Hörlin, Nils-Erik; Barbagallo, Mathias

2014-04-01

Viscoelastic properties of porous materials, typical of those used in vehicles for noise insulation and absorption, are estimated from measurements and inverse finite element procedures. The measurements are taken in a near vacuum and cover a broad frequency range: 20 Hz to 1 kHz. The almost cubic test samples were made of 25 mm foam covered by a "heavy layer" of rubber. They were mounted in a vacuum chamber on an aluminum table, which was excited in the vertical and horizontal directions with a shaker. Three kinds of response are measured allowing complete estimates of the viscoelastic moduli for isotropic materials and also providing some information on the degree of material anisotropicity. First, frequency independent properties are estimated, where dissipation is described by constant loss factors. Then, fractional derivative models that capture the variation with frequency of the stiffness and damping are adapted. The measurement setup is essentially two-dimensional and calculations are three-dimensional and for a state of plane strain. The good agreement between measured and calculated response provides some confidence in the presented procedures. If, however, the material model cannot fit the measurements well, the inverse procedure yields a certain degree of arbitrariness to the parameter estimation.

Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C.

1997-01-01

A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

Variational methods for direct/inverse problems of atmospheric dynamics and chemistry

NASA Astrophysics Data System (ADS)

Penenko, Vladimir; Penenko, Alexey; Tsvetova, Elena

2013-04-01

We present a variational approach for solving direct and inverse problems of atmospheric hydrodynamics and chemistry. It is important that the accurate matching of numerical schemes has to be provided in the chain of objects: direct/adjoint problems - sensitivity relations - inverse problems, including assimilation of all available measurement data. To solve the problems we have developed a new enhanced set of cost-effective algorithms. The matched description of the multi-scale processes is provided by a specific choice of the variational principle functionals for the whole set of integrated models. Then all functionals of variational principle are approximated in space and time by splitting and decomposition methods. Such approach allows us to separately consider, for example, the space-time problems of atmospheric chemistry in the frames of decomposition schemes for the integral identity sum analogs of the variational principle at each time step and in each of 3D finite-volumes. To enhance the realization efficiency, the set of chemical reactions is divided on the subsets related to the operators of production and destruction. Then the idea of the Euler's integrating factors is applied in the frames of the local adjoint problem technique [1]-[3]. The analytical solutions of such adjoint problems play the role of integrating factors for differential equations describing atmospheric chemistry. With their help, the system of differential equations is transformed to the equivalent system of integral equations. As a result we avoid the construction and inversion of preconditioning operators containing the Jacobi matrixes which arise in traditional implicit schemes for ODE solution. This is the main advantage of our schemes. At the same time step but on the different stages of the "global" splitting scheme, the system of atmospheric dynamic equations is solved. For convection - diffusion equations for all state functions in the integrated models we have developed the

Efficient adaptive pseudo-symplectic numerical integration techniques for Landau-Lifshitz dynamics

NASA Astrophysics Data System (ADS)

d'Aquino, M.; Capuano, F.; Coppola, G.; Serpico, C.; Mayergoyz, I. D.

2018-05-01

Numerical time integration schemes for Landau-Lifshitz magnetization dynamics are considered. Such dynamics preserves the magnetization amplitude and, in the absence of dissipation, also implies the conservation of the free energy. This property is generally lost when time discretization is performed for the numerical solution. In this work, explicit numerical schemes based on Runge-Kutta methods are introduced. The schemes are termed pseudo-symplectic in that they are accurate to order p, but preserve magnetization amplitude and free energy to order q > p. An effective strategy for adaptive time-stepping control is discussed for schemes of this class. Numerical tests against analytical solutions for the simulation of fast precessional dynamics are performed in order to point out the effectiveness of the proposed methods.

Bayesian inversions of a dynamic vegetation model in four European grassland sites

NASA Astrophysics Data System (ADS)

Minet, J.; Laloy, E.; Tychon, B.; François, L.

2015-01-01

Eddy covariance data from four European grassland sites are used to probabilistically invert the CARAIB dynamic vegetation model (DVM) with ten unknown parameters, using the DREAM(ZS) Markov chain Monte Carlo (MCMC) sampler. We compare model inversions considering both homoscedastic and heteroscedastic eddy covariance residual errors, with variances either fixed a~priori or jointly inferred with the model parameters. Agreements between measured and simulated data during calibration are comparable with previous studies, with root-mean-square error (RMSE) of simulated daily gross primary productivity (GPP), ecosystem respiration (RECO) and evapotranspiration (ET) ranging from 1.73 to 2.19 g C m-2 day-1, 1.04 to 1.56 g C m-2 day-1, and 0.50 to 1.28 mm day-1, respectively. In validation, mismatches between measured and simulated data are larger, but still with Nash-Sutcliffe efficiency scores above 0.5 for three out of the four sites. Although measurement errors associated with eddy covariance data are known to be heteroscedastic, we showed that assuming a classical linear heteroscedastic model of the residual errors in the inversion do not fully remove heteroscedasticity. Since the employed heteroscedastic error model allows for larger deviations between simulated and measured data as the magnitude of the measured data increases, this error model expectedly lead to poorer data fitting compared to inversions considering a constant variance of the residual errors. Furthermore, sampling the residual error variances along with model parameters results in overall similar model parameter posterior distributions as those obtained by fixing these variances beforehand, while slightly improving model performance. Despite the fact that the calibrated model is generally capable of fitting the data within measurement errors, systematic bias in the model simulations are observed. These are likely due to model inadequacies such as shortcomings in the photosynthesis modelling

Secular dynamics of an exterior test particle: the inverse Kozai and other eccentricity-inclination resonances

NASA Astrophysics Data System (ADS)

Vinson, Benjamin R.; Chiang, Eugene

2018-03-01

The behaviour of an interior test particle in the secular three-body problem has been studied extensively. A well-known feature is the Lidov-Kozai resonance in which the test particle's argument of periastron librates about ±90° and large oscillations in eccentricity and inclination are possible. Less explored is the inverse problem: the dynamics of an exterior test particle and an interior perturber. We survey numerically the inverse secular problem, expanding the potential to hexadecapolar order and correcting an error in the published expansion. Four secular resonances are uncovered that persist in full N-body treatments (in what follows, ϖ and Ω are the longitudes of periapse and of ascending node, ω is the argument of periapse, and subscripts 1 and 2 refer to the inner perturber and the outer test particle): (i) an orbit-flipping quadrupole resonance requiring a non-zero perturber eccentricity e1, in which Ω2 - ϖ1 librates about ±90°; (ii) a hexadecapolar resonance (the `inverse Kozai' resonance) for perturbers that are circular or nearly so and inclined by I ≃ 63°/117°, in which ω2 librates about ±90° and which can vary the particle eccentricity by Δe2 ≃ 0.2 and lead to orbit crossing; (iii) an octopole `apse-aligned' resonance at I ≃ 46°/107° wherein ϖ2 - ϖ1 librates about 0° and Δe2 grows with e1; and (iv) an octopole resonance at I ≃ 73°/134° wherein ϖ2 + ϖ1 - 2Ω2 librates about 0° and Δe2 can be as large as 0.3 for small but non-zero e1. Qualitatively, the more eccentric the perturber, the more the particle's eccentricity and inclination vary; also, more polar orbits are more chaotic. Our solutions to the inverse problem have potential application to the Kuiper belt and debris discs, circumbinary planets, and hierarchical stellar systems.

Hybrid adaptive ascent flight control for a flexible launch vehicle

NASA Astrophysics Data System (ADS)

Lefevre, Brian D.

For the purpose of maintaining dynamic stability and improving guidance command tracking performance under off-nominal flight conditions, a hybrid adaptive control scheme is selected and modified for use as a launch vehicle flight controller. This architecture merges a model reference adaptive approach, which utilizes both direct and indirect adaptive elements, with a classical dynamic inversion controller. This structure is chosen for a number of reasons: the properties of the reference model can be easily adjusted to tune the desired handling qualities of the spacecraft, the indirect adaptive element (which consists of an online parameter identification algorithm) continually refines the estimates of the evolving characteristic parameters utilized in the dynamic inversion, and the direct adaptive element (which consists of a neural network) augments the linear feedback signal to compensate for any nonlinearities in the vehicle dynamics. The combination of these elements enables the control system to retain the nonlinear capabilities of an adaptive network while relying heavily on the linear portion of the feedback signal to dictate the dynamic response under most operating conditions. To begin the analysis, the ascent dynamics of a launch vehicle with a single 1st stage rocket motor (typical of the Ares 1 spacecraft) are characterized. The dynamics are then linearized with assumptions that are appropriate for a launch vehicle, so that the resulting equations may be inverted by the flight controller in order to compute the control signals necessary to generate the desired response from the vehicle. Next, the development of the hybrid adaptive launch vehicle ascent flight control architecture is discussed in detail. Alterations of the generic hybrid adaptive control architecture include the incorporation of a command conversion operation which transforms guidance input from quaternion form (as provided by NASA) to the body-fixed angular rate commands needed by the

Adaptive identification and control of structural dynamics systems using recursive lattice filters

NASA Technical Reports Server (NTRS)

Sundararajan, N.; Montgomery, R. C.; Williams, J. P.

1985-01-01

A new approach for adaptive identification and control of structural dynamic systems by using least squares lattice filters thar are widely used in the signal processing area is presented. Testing procedures for interfacing the lattice filter identification methods and modal control method for stable closed loop adaptive control are presented. The methods are illustrated for a free-free beam and for a complex flexible grid, with the basic control objective being vibration suppression. The approach is validated by using both simulations and experimental facilities available at the Langley Research Center.

Population Genomics of Inversion Polymorphisms in Drosophila melanogaster

PubMed Central

Corbett-Detig, Russell B.; Hartl, Daniel L.

2012-01-01

Chromosomal inversions have been an enduring interest of population geneticists since their discovery in Drosophila melanogaster. Numerous lines of evidence suggest powerful selective pressures govern the distributions of polymorphic inversions, and these observations have spurred the development of many explanatory models. However, due to a paucity of nucleotide data, little progress has been made towards investigating selective hypotheses or towards inferring the genealogical histories of inversions, which can inform models of inversion evolution and suggest selective mechanisms. Here, we utilize population genomic data to address persisting gaps in our knowledge of D. melanogaster's inversions. We develop a method, termed Reference-Assisted Reassembly, to assemble unbiased, highly accurate sequences near inversion breakpoints, which we use to estimate the age and the geographic origins of polymorphic inversions. We find that inversions are young, and most are African in origin, which is consistent with the demography of the species. The data suggest that inversions interact with polymorphism not only in breakpoint regions but also chromosome-wide. Inversions remain differentiated at low levels from standard haplotypes even in regions that are distant from breakpoints. Although genetic exchange appears fairly extensive, we identify numerous regions that are qualitatively consistent with selective hypotheses. Finally, we show that In(1)Be, which we estimate to be ∼60 years old (95% CI 5.9 to 372.8 years), has likely achieved high frequency via sex-ratio segregation distortion in males. With deeper sampling, it will be possible to build on our inferences of inversion histories to rigorously test selective models—particularly those that postulate that inversions achieve a selective advantage through the maintenance of co-adapted allele complexes. PMID:23284285

Trade wind inversion variability, dynamics and future change in Hawai'i

NASA Astrophysics Data System (ADS)

Cao, Guangxia

Using 1979-2003 radiosonde data at Hilo and Lihu'e, Hawai'i, the trade-wind inversion (TWI) is found to occur approximately 82% of the time at each station, with average base heights of 2225 +/- 14.3 m (781.9 +/- 1.4 hPa) for Hilo and 2076 +/- 12.5 m (798.8 +/- 1.2 hPa) for Lihu'e. A Weather Research and Forecast (WRF) meso-scale meteorological simulation suggests that island topography and heating contribute to the lifting of the TWI base at Hilo. Inversion base height has a September maximum and a secondary maximum in April. Frequency of inversion occurrence is significantly higher during winters and lower during summers of El Nino years. During the period of 1979-2003, the inversion frequency of occurrence is on upward trend at Hilo for spring (MAM), summer (JJA), and fall (SON) seasons and at Lihu'e for all seasons and for annual values. Composite analysis shows that patterns of geopotential height (GPH), air temperature, u- and v-wind, omega wind, relative and specific humidity, upward longwave radiation flux, net longwave radiation flux, precipitable water, convective precipitation rate, and total cloud cover significantly respond to the TWI base height. For example, the GPH pattern contains a distinctive Pacific North America Teleconnection (PNA) signature, and the magnitudes of PNA centers over 45°N, 165°W for the difference between none and inversion is over 40 m at 200 hPa and 25 m at 850 hPa. The monthly composites show that months with lower (higher) inversion base height and higher (lower) inversion occurrence frequency are linked with the following characteristics: lower (higher) GPH anomalies centered at 30°N, 160°W, lower (higher) temperature anomalies within 300--700 hPa, stronger (weaker) easterly at low levels and northerly anomaly over Hawai'i, and small upward (downward) vertical wind or rising (sinking) motion north of Hawai'i. Using the above characteristics to study the Community Climate System Model (CCSM) composites leads to the

High dynamic range image acquisition based on multiplex cameras

NASA Astrophysics Data System (ADS)

Zeng, Hairui; Sun, Huayan; Zhang, Tinghua

2018-03-01

High dynamic image is an important technology of photoelectric information acquisition, providing higher dynamic range and more image details, and it can better reflect the real environment, light and color information. Currently, the method of high dynamic range image synthesis based on different exposure image sequences cannot adapt to the dynamic scene. It fails to overcome the effects of moving targets, resulting in the phenomenon of ghost. Therefore, a new high dynamic range image acquisition method based on multiplex cameras system was proposed. Firstly, different exposure images sequences were captured with the camera array, using the method of derivative optical flow based on color gradient to get the deviation between images, and aligned the images. Then, the high dynamic range image fusion weighting function was established by combination of inverse camera response function and deviation between images, and was applied to generated a high dynamic range image. The experiments show that the proposed method can effectively obtain high dynamic images in dynamic scene, and achieves good results.

PAQ: Persistent Adaptive Query Middleware for Dynamic Environments

NASA Astrophysics Data System (ADS)

Rajamani, Vasanth; Julien, Christine; Payton, Jamie; Roman, Gruia-Catalin

Pervasive computing applications often entail continuous monitoring tasks, issuing persistent queries that return continuously updated views of the operational environment. We present PAQ, a middleware that supports applications' needs by approximating a persistent query as a sequence of one-time queries. PAQ introduces an integration strategy abstraction that allows composition of one-time query responses into streams representing sophisticated spatio-temporal phenomena of interest. A distinguishing feature of our middleware is the realization that the suitability of a persistent query's result is a function of the application's tolerance for accuracy weighed against the associated overhead costs. In PAQ, programmers can specify an inquiry strategy that dictates how information is gathered. Since network dynamics impact the suitability of a particular inquiry strategy, PAQ associates an introspection strategy with a persistent query, that evaluates the quality of the query's results. The result of introspection can trigger application-defined adaptation strategies that alter the nature of the query. PAQ's simple API makes developing adaptive querying systems easily realizable. We present the key abstractions, describe their implementations, and demonstrate the middleware's usefulness through application examples and evaluation.

Complex Patterns of Local Adaptation in Teosinte

PubMed Central

Pyhäjärvi, Tanja; Hufford, Matthew B.; Mezmouk, Sofiane; Ross-Ibarra, Jeffrey

2013-01-01

Populations of widely distributed species encounter and must adapt to local environmental conditions. However, comprehensive characterization of the genetic basis of adaptation is demanding, requiring genome-wide genotype data, multiple sampled populations, and an understanding of population structure and potential selection pressures. Here, we used single-nucleotide polymorphism genotyping and data on numerous environmental variables to describe the genetic basis of local adaptation in 21 populations of teosinte, the wild ancestor of maize. We found complex hierarchical genetic structure created by altitude, dispersal events, and admixture among subspecies, which complicated identification of locally beneficial alleles. Patterns of linkage disequilibrium revealed four large putative inversion polymorphisms showing clinal patterns of frequency. Population differentiation and environmental correlations suggest that both inversions and intergenic polymorphisms are involved in local adaptation. PMID:23902747

Dynamic chirp control of all-optical format-converted pulsed data from a multi-wavelength inverse-optical-comb injected semiconductor optical amplifier.

PubMed

Lin, Gong-Ru; Pan, Ci-Ling; Yu, Kun-Chieh

2007-10-01

By spectrally and temporally reshaping the gain-window of a traveling-wave semiconductor optical amplifier (TWSOA) with a backward injected multi- or single-wavelength inverse-optical-comb, we theoretically and experimentally investigate the dynamic frequency chirp of the all-optical 10GBit/s Return-to-Zero (RZ) data-stream format-converted from the TWSOA under strong cross-gain depletion scheme. The multi-wavelength inverse-optical-comb injection effectively depletes the TWSOA gain spectrally and temporally, remaining a narrow gain-window and a reduced spectral linewidth and provide a converted RZ data with a smaller peak-to-peak frequency chirp of 6.7 GHz. Even at high inverse-optical-comb injection power and highly biased current condition for improving the operational bit-rate, the chirp of the multi-wavelength-injection converted RZ pulse is still 2.1-GHz smaller than that obtained by using single-wavelength injection at a cost of slight pulse-width broadening by 1 ps.

Clipping in neurocontrol by adaptive dynamic programming.

PubMed

Fairbank, Michael; Prokhorov, Danil; Alonso, Eduardo

2014-10-01

In adaptive dynamic programming, neurocontrol, and reinforcement learning, the objective is for an agent to learn to choose actions so as to minimize a total cost function. In this paper, we show that when discretized time is used to model the motion of the agent, it can be very important to do clipping on the motion of the agent in the final time step of the trajectory. By clipping, we mean that the final time step of the trajectory is to be truncated such that the agent stops exactly at the first terminal state reached, and no distance further. We demonstrate that when clipping is omitted, learning performance can fail to reach the optimum, and when clipping is done properly, learning performance can improve significantly. The clipping problem we describe affects algorithms that use explicit derivatives of the model functions of the environment to calculate a learning gradient. These include backpropagation through time for control and methods based on dual heuristic programming. However, the clipping problem does not significantly affect methods based on heuristic dynamic programming, temporal differences learning, or policy-gradient learning algorithms.

A dynamical regularization algorithm for solving inverse source problems of elliptic partial differential equations

NASA Astrophysics Data System (ADS)

Zhang, Ye; Gong, Rongfang; Cheng, Xiaoliang; Gulliksson, Mårten

2018-06-01

This study considers the inverse source problem for elliptic partial differential equations with both Dirichlet and Neumann boundary data. The unknown source term is to be determined by additional boundary conditions. Unlike the existing methods found in the literature, which usually employ the first-order in time gradient-like system (such as the steepest descent methods) for numerically solving the regularized optimization problem with a fixed regularization parameter, we propose a novel method with a second-order in time dissipative gradient-like system and a dynamical selected regularization parameter. A damped symplectic scheme is proposed for the numerical solution. Theoretical analysis is given for both the continuous model and the numerical algorithm. Several numerical examples are provided to show the robustness of the proposed algorithm.

Dynamic model inversion techniques for breath-by-breath measurement of carbon dioxide from low bandwidth sensors.

PubMed

Sivaramakrishnan, Shyam; Rajamani, Rajesh; Johnson, Bruce D

2009-01-01

Respiratory CO(2) measurement (capnography) is an important diagnosis tool that lacks inexpensive and wearable sensors. This paper develops techniques to enable use of inexpensive but slow CO(2) sensors for breath-by-breath tracking of CO(2) concentration. This is achieved by mathematically modeling the dynamic response and using model-inversion techniques to predict input CO(2) concentration from the slow-varying output. Experiments are designed to identify model-dynamics and extract relevant model-parameters for a solidstate room monitoring CO(2) sensor. A second-order model that accounts for flow through the sensor's filter and casing is found to be accurate in describing the sensor's slow response. The resulting estimate is compared with a standard-of-care respiratory CO(2) analyzer and shown to effectively track variation in breath-by-breath CO(2) concentration. This methodology is potentially useful for measuring fast-varying inputs to any slow sensor.

Adaptive time-sequential binary sensing for high dynamic range imaging

NASA Astrophysics Data System (ADS)

Hu, Chenhui; Lu, Yue M.

2012-06-01

We present a novel image sensor for high dynamic range imaging. The sensor performs an adaptive one-bit quantization at each pixel, with the pixel output switched from 0 to 1 only if the number of photons reaching that pixel is greater than or equal to a quantization threshold. With an oracle knowledge of the incident light intensity, one can pick an optimal threshold (for that light intensity) and the corresponding Fisher information contained in the output sequence follows closely that of an ideal unquantized sensor over a wide range of intensity values. This observation suggests the potential gains one may achieve by adaptively updating the quantization thresholds. As the main contribution of this work, we propose a time-sequential threshold-updating rule that asymptotically approaches the performance of the oracle scheme. With every threshold mapped to a number of ordered states, the dynamics of the proposed scheme can be modeled as a parametric Markov chain. We show that the frequencies of different thresholds converge to a steady-state distribution that is concentrated around the optimal choice. Moreover, numerical experiments show that the theoretical performance measures (Fisher information and Craḿer-Rao bounds) can be achieved by a maximum likelihood estimator, which is guaranteed to find globally optimal solution due to the concavity of the log-likelihood functions. Compared with conventional image sensors and the strategy that utilizes a constant single-photon threshold considered in previous work, the proposed scheme attains orders of magnitude improvement in terms of sensor dynamic ranges.

Inverse dynamic investigation of voluntary trunk movements in weightlessness: a new microgravity-specific strategy.

PubMed

Pedrocchi, Alessandra; Pedotti, Antonio; Baroni, Guido; Massion, Jean; Ferrigno, Giancarlo

2003-11-01

Present investigation faces the question of quantitative assessment of exchanged forces and torques at the restraints during whole body posture exercises in long-term microgravity. Inverse dynamic modelling and total angular momentum at the ankle joint were used in order to reconstruct movement dynamics at the restraining point, represented by the ankle joint. The hypothesis is that the minimisation of the torques at the interface point assumes a key role in movement planning in 0 g. This hypothesis would respond to an optimisation of muscles activity, a minimisation of energy expenditure and therefore an accurate control of body movement. Results show that the 0 g movement strategy adopted ensures that the integral of the net ankle moment between the beginning and the end of the movement is zero. This expected mechanical constraint is not satisfied when 0 g movement dynamics is simulated using terrestrial kinematics. This accounts for a significant imposed change of movement strategy. Particularly, the efficient compensation of the inertial effects of the segments in terms of total angular momentum at the ankle joint was evidenced. These results explain the exaggerated axial synergies, observed on kinematics and which moved centre of mass (CM) backward from its already backward initial positioning, as a tool for enhancing the compensation and achieving the desired minimisation of the torques exchanges at the restraints.

Behavioral and neural Darwinism: selectionist function and mechanism in adaptive behavior dynamics.

PubMed

McDowell, J J

2010-05-01

An evolutionary theory of behavior dynamics and a theory of neuronal group selection share a common selectionist framework. The theory of behavior dynamics instantiates abstractly the idea that behavior is selected by its consequences. It implements Darwinian principles of selection, reproduction, and mutation to generate adaptive behavior in virtual organisms. The behavior generated by the theory has been shown to be quantitatively indistinguishable from that of live organisms. The theory of neuronal group selection suggests a mechanism whereby the abstract principles of the evolutionary theory may be implemented in the nervous systems of biological organisms. According to this theory, groups of neurons subserving behavior may be selected by synaptic modifications that occur when the consequences of behavior activate value systems in the brain. Together, these theories constitute a framework for a comprehensive account of adaptive behavior that extends from brain function to the behavior of whole organisms in quantitative detail. Copyright (c) 2009 Elsevier B.V. All rights reserved.

Epicenters of dynamic connectivity in the adaptation of the ventral visual system.

PubMed

Prčkovska, Vesna; Huijbers, Willem; Schultz, Aaron; Ortiz-Teran, Laura; Peña-Gomez, Cleofe; Villoslada, Pablo; Johnson, Keith; Sperling, Reisa; Sepulcre, Jorge

2017-04-01

Neuronal responses adapt to familiar and repeated sensory stimuli. Enhanced synchrony across wide brain systems has been postulated as a potential mechanism for this adaptation phenomenon. Here, we used recently developed graph theory methods to investigate hidden connectivity features of dynamic synchrony changes during a visual repetition paradigm. Particularly, we focused on strength connectivity changes occurring at local and distant brain neighborhoods. We found that connectivity reorganization in visual modal cortex-such as local suppressed connectivity in primary visual areas and distant suppressed connectivity in fusiform areas-is accompanied by enhanced local and distant connectivity in higher cognitive processing areas in multimodal and association cortex. Moreover, we found a shift of the dynamic functional connections from primary-visual-fusiform to primary-multimodal/association cortex. These findings suggest that repetition-suppression is made possible by reorganization of functional connectivity that enables communication between low- and high-order areas. Hum Brain Mapp 38:1965-1976, 2017. © 2017 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Adaptive Event-Triggered Control Based on Heuristic Dynamic Programming for Nonlinear Discrete-Time Systems.

PubMed

Dong, Lu; Zhong, Xiangnan; Sun, Changyin; He, Haibo

2017-07-01

This paper presents the design of a novel adaptive event-triggered control method based on the heuristic dynamic programming (HDP) technique for nonlinear discrete-time systems with unknown system dynamics. In the proposed method, the control law is only updated when the event-triggered condition is violated. Compared with the periodic updates in the traditional adaptive dynamic programming (ADP) control, the proposed method can reduce the computation and transmission cost. An actor-critic framework is used to learn the optimal event-triggered control law and the value function. Furthermore, a model network is designed to estimate the system state vector. The main contribution of this paper is to design a new trigger threshold for discrete-time systems. A detailed Lyapunov stability analysis shows that our proposed event-triggered controller can asymptotically stabilize the discrete-time systems. Finally, we test our method on two different discrete-time systems, and the simulation results are included.

Socio-hydrological modelling of floods: investigating community resilience, adaptation capacity and risk

NASA Astrophysics Data System (ADS)

Ciullo, Alessio; Viglione, Alberto; Castellarin, Attilio

2016-04-01

Changes in flood risk occur because of changes in climate and hydrology, and in societal exposure and vulnerability. Research on change in flood risk has demonstrated that the mutual interactions and continuous feedbacks between floods and societies has to be taken into account in flood risk management. The present work builds on an existing conceptual model of an hypothetical city located in the proximity of a river, along whose floodplains the community evolves over time. The model reproduces the dynamic co-evolution of four variables: flooding, population density of the flooplain, amount of structural protection measures and memory of floods. These variables are then combined in a way to mimic the temporal change of community resilience, defined as the (inverse of the) amount of time for the community to recover from a shock, and adaptation capacity, defined as ratio between damages due to subsequent events. Also, temporal changing exposure, vulnerability and probability of flooding are also modelled, which results in a dynamically varying flood-risk. Examples are provided that show how factors such as collective memory and risk taking attitude influence the dynamics of community resilience, adaptation capacity and risk.

ALEGRA -- A massively parallel h-adaptive code for solid dynamics

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

Summers, R.M.; Wong, M.K.; Boucheron, E.A.

1997-12-31

ALEGRA is a multi-material, arbitrary-Lagrangian-Eulerian (ALE) code for solid dynamics designed to run on massively parallel (MP) computers. It combines the features of modern Eulerian shock codes, such as CTH, with modern Lagrangian structural analysis codes using an unstructured grid. ALEGRA is being developed for use on the teraflop supercomputers to conduct advanced three-dimensional (3D) simulations of shock phenomena important to a variety of systems. ALEGRA was designed with the Single Program Multiple Data (SPMD) paradigm, in which the mesh is decomposed into sub-meshes so that each processor gets a single sub-mesh with approximately the same number of elements. Usingmore » this approach the authors have been able to produce a single code that can scale from one processor to thousands of processors. A current major effort is to develop efficient, high precision simulation capabilities for ALEGRA, without the computational cost of using a global highly resolved mesh, through flexible, robust h-adaptivity of finite elements. H-adaptivity is the dynamic refinement of the mesh by subdividing elements, thus changing the characteristic element size and reducing numerical error. The authors are working on several major technical challenges that must be met to make effective use of HAMMER on MP computers.« less

A new approach to adaptive control of manipulators

NASA Technical Reports Server (NTRS)

Seraji, H.

1987-01-01

An approach in which the manipulator inverse is used as a feedforward controller is employed in the adaptive control of manipulators in order to achieve trajectory tracking by the joint angles. The desired trajectory is applied as an input to the feedforward controller, and the controller output is used as the driving torque for the manipulator. An adaptive algorithm obtained from MRAC theory is used to update the controller gains to cope with variations in the manipulator inverse due to changes of the operating point. An adaptive feedback controller and an auxiliary signal enhance closed-loop stability and achieve faster adaptation. Simulation results demonstrate the effectiveness of the proposed control scheme for different reference trajectories, and despite large variations in the payload.

Phase Inversion of EPDM/PP Blends: Effect of Viscosity Ratio

NASA Astrophysics Data System (ADS)

Machado, Ana Vera; Antunes, Carla Filipa; van Duin, Martin

2011-07-01

EPDM/PP blends and TPVs with and without crosslinking, respectively, were prepared, in a batch mixer, using three different EPDM rubbers. EPDM/PP based TPVs were dynamic vulcanised using the resol/SnCl2 system. Samples were collected along the time in order to get information on the morphology evolution and crosslinking density during dynamic vulcanisation. The morphology was studied by SEM and the crosslink density by gel content. In the case of low viscosity EPDMs, crosslinking of the EPDM phase was retarded due to its low crosslinking efficiency. This delay on crosslinking reaction enables the observation of the various stages of the morphological mechanism that takes place during dynamic vulcanisation. It could be observed that phase inversion takes place via lamellar mechanism. More detailed insight on phase inversion mechanism during dynamic vulcanisation was accomplished.

Network adaptation improves temporal representation of naturalistic stimuli in Drosophila eye: I dynamics.

PubMed

Zheng, Lei; Nikolaev, Anton; Wardill, Trevor J; O'Kane, Cahir J; de Polavieja, Gonzalo G; Juusola, Mikko

2009-01-01

Because of the limited processing capacity of eyes, retinal networks must adapt constantly to best present the ever changing visual world to the brain. However, we still know little about how adaptation in retinal networks shapes neural encoding of changing information. To study this question, we recorded voltage responses from photoreceptors (R1-R6) and their output neurons (LMCs) in the Drosophila eye to repeated patterns of contrast values, collected from natural scenes. By analyzing the continuous photoreceptor-to-LMC transformations of these graded-potential neurons, we show that the efficiency of coding is dynamically improved by adaptation. In particular, adaptation enhances both the frequency and amplitude distribution of LMC output by improving sensitivity to under-represented signals within seconds. Moreover, the signal-to-noise ratio of LMC output increases in the same time scale. We suggest that these coding properties can be used to study network adaptation using the genetic tools in Drosophila, as shown in a companion paper (Part II).

Network Adaptation Improves Temporal Representation of Naturalistic Stimuli in Drosophila Eye: I Dynamics

PubMed Central

Wardill, Trevor J.; O'Kane, Cahir J.; de Polavieja, Gonzalo G.; Juusola, Mikko

2009-01-01

Because of the limited processing capacity of eyes, retinal networks must adapt constantly to best present the ever changing visual world to the brain. However, we still know little about how adaptation in retinal networks shapes neural encoding of changing information. To study this question, we recorded voltage responses from photoreceptors (R1–R6) and their output neurons (LMCs) in the Drosophila eye to repeated patterns of contrast values, collected from natural scenes. By analyzing the continuous photoreceptor-to-LMC transformations of these graded-potential neurons, we show that the efficiency of coding is dynamically improved by adaptation. In particular, adaptation enhances both the frequency and amplitude distribution of LMC output by improving sensitivity to under-represented signals within seconds. Moreover, the signal-to-noise ratio of LMC output increases in the same time scale. We suggest that these coding properties can be used to study network adaptation using the genetic tools in Drosophila, as shown in a companion paper (Part II). PMID:19180196

Salinity fluctuation influencing biological adaptation: growth dynamics and Na+ /K+ -ATPase activity in a euryhaline bacterium.

PubMed

Yang, Hao; Meng, Yang; Song, Youxin; Tan, Yalin; Warren, Alan; Li, Jiqiu; Lin, Xiaofeng

2017-07-01

Although salinity fluctuation is a prominent characteristic of many coastal ecosystems, its effects on biological adaptation have not yet been fully recognized. To test the salinity fluctuations on biological adaptation, population growth dynamics and Na + /K + -ATPase activity were investigated in the euryhaline bacterium Idiomarina sp. DYB, which was acclimated at different salinity exposure levels, exposure times, and shifts in direction of salinity. Results showed: (1) bacterial population growth dynamics and Na + /K + -ATPase activity changed significantly in response to salinity fluctuation; (2) patterns of variation in bacterial growth dynamics were related to exposure times, levels of salinity, and shifts in direction of salinity change; (3) significant tradeoffs were detected between growth rate (r) and carrying capacity (K) on the one hand, and Na + /K + -ATPase activity on the other; and (4) beneficial acclimation was confirmed in Idiomarina sp. DYB. In brief, this study demonstrated that salinity fluctuation can change the population growth dynamics, Na + /K + -ATPase activity, and tradeoffs between r, K, and Na + /K + -ATPase activity, thus facilitating bacterial adaption in a changing environment. These findings provide constructive information for determining biological response patterns to environmental change. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Adaptive integral dynamic surface control of a hypersonic flight vehicle

NASA Astrophysics Data System (ADS)

Aslam Butt, Waseem; Yan, Lin; Amezquita S., Kendrick

2015-07-01

In this article, non-linear adaptive dynamic surface air speed and flight path angle control designs are presented for the longitudinal dynamics of a flexible hypersonic flight vehicle. The tracking performance of the control design is enhanced by introducing a novel integral term that caters to avoiding a large initial control signal. To ensure feasibility, the design scheme incorporates magnitude and rate constraints on the actuator commands. The uncertain non-linear functions are approximated by an efficient use of the neural networks to reduce the computational load. A detailed stability analysis shows that all closed-loop signals are uniformly ultimately bounded and the ? tracking performance is guaranteed. The robustness of the design scheme is verified through numerical simulations of the flexible flight vehicle model.

Adaptive Network Dynamics - Modeling and Control of Time-Dependent Social Contacts

PubMed Central

Schwartz, Ira B.; Shaw, Leah B.; Shkarayev, Maxim S.

2013-01-01

Real networks consisting of social contacts do not possess static connections. That is, social connections may be time dependent due to a variety of individual behavioral decisions based on current network connections. Examples of adaptive networks occur in epidemics, where information about infectious individuals may change the rewiring of healthy people, or in the recruitment of individuals to a cause or fad, where rewiring may optimize recruitment of susceptible individuals. In this paper, we will review some of the dynamical properties of adaptive networks, and show how they predict novel phenomena as well as yield insight into new controls. The applications will be control of epidemic outbreaks and terrorist recruitment modeling. PMID:25414913

Organisational learning and self-adaptation in dynamic disaster environments.

PubMed

Corbacioglu, Sitki; Kapucu, Naim

2006-06-01

This paper examines the problems associated with inter-organisational learning and adaptation in the dynamic environments that characterise disasters. The research uses both qualitative and quantitative methods to investigate whether organisational learning took place during and in the time in between five disaster response operations in Turkey. The availability of information and its exchange and distribution within and among organisational actors determine whether self-adaptation happens in the course of a disaster response operation. Organisational flexibility supported by an appropriate information infrastructure creates conditions conducive to essential interaction and permits the flow of information. The study found that no significant organisational learning occurred within Turkish disaster management following the earthquakes in Erzincan (1992), Dinar (1995) and Ceyhan (1998). By contrast, the 'symmetry-breaking' Marmara earthquake of 1999 initiated a 'double loop' learning process that led to change in the organisational, technical and cultural aspects of Turkish disaster management, as revealed by the Duzce earthquake response operations.

Parallel Adaptive Mesh Refinement for High-Order Finite-Volume Schemes in Computational Fluid Dynamics

NASA Astrophysics Data System (ADS)

Schwing, Alan Michael

For computational fluid dynamics, the governing equations are solved on a discretized domain of nodes, faces, and cells. The quality of the grid or mesh can be a driving source for error in the results. While refinement studies can help guide the creation of a mesh, grid quality is largely determined by user expertise and understanding of the flow physics. Adaptive mesh refinement is a technique for enriching the mesh during a simulation based on metrics for error, impact on important parameters, or location of important flow features. This can offload from the user some of the difficult and ambiguous decisions necessary when discretizing the domain. This work explores the implementation of adaptive mesh refinement in an implicit, unstructured, finite-volume solver. Consideration is made for applying modern computational techniques in the presence of hanging nodes and refined cells. The approach is developed to be independent of the flow solver in order to provide a path for augmenting existing codes. It is designed to be applicable for unsteady simulations and refinement and coarsening of the grid does not impact the conservatism of the underlying numerics. The effect on high-order numerical fluxes of fourth- and sixth-order are explored. Provided the criteria for refinement is appropriately selected, solutions obtained using adapted meshes have no additional error when compared to results obtained on traditional, unadapted meshes. In order to leverage large-scale computational resources common today, the methods are parallelized using MPI. Parallel performance is considered for several test problems in order to assess scalability of both adapted and unadapted grids. Dynamic repartitioning of the mesh during refinement is crucial for load balancing an evolving grid. Development of the methods outlined here depend on a dual-memory approach that is described in detail. Validation of the solver developed here against a number of motivating problems shows favorable

A sequential coalescent algorithm for chromosomal inversions

PubMed Central

Peischl, S; Koch, E; Guerrero, R F; Kirkpatrick, M

2013-01-01

Chromosomal inversions are common in natural populations and are believed to be involved in many important evolutionary phenomena, including speciation, the evolution of sex chromosomes and local adaptation. While recent advances in sequencing and genotyping methods are leading to rapidly increasing amounts of genome-wide sequence data that reveal interesting patterns of genetic variation within inverted regions, efficient simulation methods to study these patterns are largely missing. In this work, we extend the sequential Markovian coalescent, an approximation to the coalescent with recombination, to include the effects of polymorphic inversions on patterns of recombination. Results show that our algorithm is fast, memory-efficient and accurate, making it feasible to simulate large inversions in large populations for the first time. The SMC algorithm enables studies of patterns of genetic variation (for example, linkage disequilibria) and tests of hypotheses (using simulation-based approaches) that were previously intractable. PMID:23632894

Dynamic learning from adaptive neural network control of a class of nonaffine nonlinear systems.

PubMed

Dai, Shi-Lu; Wang, Cong; Wang, Min

2014-01-01

This paper studies the problem of learning from adaptive neural network (NN) control of a class of nonaffine nonlinear systems in uncertain dynamic environments. In the control design process, a stable adaptive NN tracking control design technique is proposed for the nonaffine nonlinear systems with a mild assumption by combining a filtered tracking error with the implicit function theorem, input-to-state stability, and the small-gain theorem. The proposed stable control design technique not only overcomes the difficulty in controlling nonaffine nonlinear systems but also relaxes constraint conditions of the considered systems. In the learning process, the partial persistent excitation (PE) condition of radial basis function NNs is satisfied during tracking control to a recurrent reference trajectory. Under the PE condition and an appropriate state transformation, the proposed adaptive NN control is shown to be capable of acquiring knowledge on the implicit desired control input dynamics in the stable control process and of storing the learned knowledge in memory. Subsequently, an NN learning control design technique that effectively exploits the learned knowledge without re-adapting to the controller parameters is proposed to achieve closed-loop stability and improved control performance. Simulation studies are performed to demonstrate the effectiveness of the proposed design techniques.

Dynamics of epidemic diseases on a growing adaptive network.

PubMed

Demirel, Güven; Barter, Edmund; Gross, Thilo

2017-02-10

The study of epidemics on static networks has revealed important effects on disease prevalence of network topological features such as the variance of the degree distribution, i.e. the distribution of the number of neighbors of nodes, and the maximum degree. Here, we analyze an adaptive network where the degree distribution is not independent of epidemics but is shaped through disease-induced dynamics and mortality in a complex interplay. We study the dynamics of a network that grows according to a preferential attachment rule, while nodes are simultaneously removed from the network due to disease-induced mortality. We investigate the prevalence of the disease using individual-based simulations and a heterogeneous node approximation. Our results suggest that in this system in the thermodynamic limit no epidemic thresholds exist, while the interplay between network growth and epidemic spreading leads to exponential networks for any finite rate of infectiousness when the disease persists.

Dynamics of epidemic diseases on a growing adaptive network

NASA Astrophysics Data System (ADS)

Demirel, Güven; Barter, Edmund; Gross, Thilo

2017-02-01

The study of epidemics on static networks has revealed important effects on disease prevalence of network topological features such as the variance of the degree distribution, i.e. the distribution of the number of neighbors of nodes, and the maximum degree. Here, we analyze an adaptive network where the degree distribution is not independent of epidemics but is shaped through disease-induced dynamics and mortality in a complex interplay. We study the dynamics of a network that grows according to a preferential attachment rule, while nodes are simultaneously removed from the network due to disease-induced mortality. We investigate the prevalence of the disease using individual-based simulations and a heterogeneous node approximation. Our results suggest that in this system in the thermodynamic limit no epidemic thresholds exist, while the interplay between network growth and epidemic spreading leads to exponential networks for any finite rate of infectiousness when the disease persists.

Adaptive output feedback control of flexible-joint robots using neural networks: dynamic surface design approach.

PubMed

Yoo, Sung Jin; Park, Jin Bae; Choi, Yoon Ho

2008-10-01

In this paper, we propose a new robust output feedback control approach for flexible-joint electrically driven (FJED) robots via the observer dynamic surface design technique. The proposed method only requires position measurements of the FJED robots. To estimate the link and actuator velocity information of the FJED robots with model uncertainties, we develop an adaptive observer using self-recurrent wavelet neural networks (SRWNNs). The SRWNNs are used to approximate model uncertainties in both robot (link) dynamics and actuator dynamics, and all their weights are trained online. Based on the designed observer, the link position tracking controller using the estimated states is induced from the dynamic surface design procedure. Therefore, the proposed controller can be designed more simply than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop adaptive system are uniformly ultimately bounded. Finally, the simulation results on a three-link FJED robot are presented to validate the good position tracking performance and robustness of the proposed control system against payload uncertainties and external disturbances.

2.5D complex resistivity modeling and inversion using unstructured grids

NASA Astrophysics Data System (ADS)

Xu, Kaijun; Sun, Jie

2016-04-01

The characteristic of complex resistivity on rock and ore has been recognized by people for a long time. Generally we have used the Cole-Cole Model(CCM) to describe complex resistivity. It has been proved that the electrical anomaly of geologic body can be quantitative estimated by CCM parameters such as direct resistivity(ρ0), chargeability(m), time constant(τ) and frequency dependence(c). Thus it is very important to obtain the complex parameters of geologic body. It is difficult to approximate complex structures and terrain using traditional rectangular grid. In order to enhance the numerical accuracy and rationality of modeling and inversion, we use an adaptive finite-element algorithm for forward modeling of the frequency-domain 2.5D complex resistivity and implement the conjugate gradient algorithm in the inversion of 2.5D complex resistivity. An adaptive finite element method is applied for solving the 2.5D complex resistivity forward modeling of horizontal electric dipole source. First of all, the CCM is introduced into the Maxwell's equations to calculate the complex resistivity electromagnetic fields. Next, the pseudo delta function is used to distribute electric dipole source. Then the electromagnetic fields can be expressed in terms of the primary fields caused by layered structure and the secondary fields caused by inhomogeneities anomalous conductivity. At last, we calculated the electromagnetic fields response of complex geoelectric structures such as anticline, syncline, fault. The modeling results show that adaptive finite-element methods can automatically improve mesh generation and simulate complex geoelectric models using unstructured grids. The 2.5D complex resistivity invertion is implemented based the conjugate gradient algorithm.The conjugate gradient algorithm doesn't need to compute the sensitivity matrix but directly computes the sensitivity matrix or its transpose multiplying vector. In addition, the inversion target zones are

Linear hypergeneralization of learned dynamics across movement speeds reveals anisotropic, gain-encoding primitives for motor adaptation.

PubMed

Joiner, Wilsaan M; Ajayi, Obafunso; Sing, Gary C; Smith, Maurice A

2011-01-01

The ability to generalize learned motor actions to new contexts is a key feature of the motor system. For example, the ability to ride a bicycle or swing a racket is often first developed at lower speeds and later applied to faster velocities. A number of previous studies have examined the generalization of motor adaptation across movement directions and found that the learned adaptation decays in a pattern consistent with the existence of motor primitives that display narrow Gaussian tuning. However, few studies have examined the generalization of motor adaptation across movement speeds. Following adaptation to linear velocity-dependent dynamics during point-to-point reaching arm movements at one speed, we tested the ability of subjects to transfer this adaptation to short-duration higher-speed movements aimed at the same target. We found near-perfect linear extrapolation of the trained adaptation with respect to both the magnitude and the time course of the velocity profiles associated with the high-speed movements: a 69% increase in movement speed corresponded to a 74% extrapolation of the trained adaptation. The close match between the increase in movement speed and the corresponding increase in adaptation beyond what was trained indicates linear hypergeneralization. Computational modeling shows that this pattern of linear hypergeneralization across movement speeds is not compatible with previous models of adaptation in which motor primitives display isotropic Gaussian tuning of motor output around their preferred velocities. Instead, we show that this generalization pattern indicates that the primitives involved in the adaptation to viscous dynamics display anisotropic tuning in velocity space and encode the gain between motor output and motion state rather than motor output itself.

Final Technical Report for "Applied Mathematics Research: Simulation Based Optimization and Application to Electromagnetic Inverse Problems"

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

Haber, Eldad

2014-03-17

The focus of research was: Developing adaptive mesh for the solution of Maxwell's equations; Developing a parallel framework for time dependent inverse Maxwell's equations; Developing multilevel methods for optimization problems with inequality constraints; A new inversion code for inverse Maxwell's equations in the 0th frequency (DC resistivity); A new inversion code for inverse Maxwell's equations in low frequency regime. Although the research concentrated on electromagnetic forward and in- verse problems the results of the research was applied to the problem of image registration.

The Simulation of Magnetorheological Elastomers Adaptive Tuned Dynamic Vibration Absorber for Automobile Engine Vibration Control

NASA Astrophysics Data System (ADS)

Zhang, X. C.; Zhang, X. Z.; Li, W. H.; Liu, B.; Gong, X. L.; Zhang, P. Q.

The aim of this article is to investigate the use of a Dynamic Vibration Absorber to control vibration of engine by using simulation. Traditional means of vibration control have involved the use of passive and more recently, active methods. This study is different in that it involves an adaptive component in the design of vibration absorber using magnetorheological elastomers (MREs) as the adaptive spring. MREs are kind of novel smart material whose shear modulus can be controlled by applied magnetic field. In this paper, the vibration mode of a simple model of automobile engine is simulated by Finite Element Method (FEM) analysis. Based on the analysis, the MREs Adaptive Tuned Dynamic Vibration Absorber (ATDVA) is presented to reduce the vibration of the engine. Simulation result indicate that the control frequency of ATDVA can be changed by modifing the shear modulus of MREs and the vibraion reduction efficiency of ATDVA are also evaluated by FEM analysis.

Discrete Inverse and State Estimation Problems

NASA Astrophysics Data System (ADS)

Wunsch, Carl

2006-06-01

The problems of making inferences about the natural world from noisy observations and imperfect theories occur in almost all scientific disciplines. This book addresses these problems using examples taken from geophysical fluid dynamics. It focuses on discrete formulations, both static and time-varying, known variously as inverse, state estimation or data assimilation problems. Starting with fundamental algebraic and statistical ideas, the book guides the reader through a range of inference tools including the singular value decomposition, Gauss-Markov and minimum variance estimates, Kalman filters and related smoothers, and adjoint (Lagrange multiplier) methods. The final chapters discuss a variety of practical applications to geophysical flow problems. Discrete Inverse and State Estimation Problems is an ideal introduction to the topic for graduate students and researchers in oceanography, meteorology, climate dynamics, and geophysical fluid dynamics. It is also accessible to a wider scientific audience; the only prerequisite is an understanding of linear algebra. Provides a comprehensive introduction to discrete methods of inference from incomplete information Based upon 25 years of practical experience using real data and models Develops sequential and whole-domain analysis methods from simple least-squares Contains many examples and problems, and web-based support through MIT opencourseware

The dynamics of diverse segmental amplifications in populations of Saccharomyces cerevisiae adapting to strong selection.

PubMed

Payen, Celia; Di Rienzi, Sara C; Ong, Giang T; Pogachar, Jamie L; Sanchez, Joseph C; Sunshine, Anna B; Raghuraman, M K; Brewer, Bonita J; Dunham, Maitreya J

2014-03-20

Population adaptation to strong selection can occur through the sequential or parallel accumulation of competing beneficial mutations. The dynamics, diversity, and rate of fixation of beneficial mutations within and between populations are still poorly understood. To study how the mutational landscape varies across populations during adaptation, we performed experimental evolution on seven parallel populations of Saccharomyces cerevisiae continuously cultured in limiting sulfate medium. By combining quantitative polymerase chain reaction, array comparative genomic hybridization, restriction digestion and contour-clamped homogeneous electric field gel electrophoresis, and whole-genome sequencing, we followed the trajectory of evolution to determine the identity and fate of beneficial mutations. During a period of 200 generations, the yeast populations displayed parallel evolutionary dynamics that were driven by the coexistence of independent beneficial mutations. Selective amplifications rapidly evolved under this selection pressure, in particular common inverted amplifications containing the sulfate transporter gene SUL1. Compared with single clones, detailed analysis of the populations uncovers a greater complexity whereby multiple subpopulations arise and compete despite a strong selection. The most common evolutionary adaptation to strong selection in these populations grown in sulfate limitation is determined by clonal interference, with adaptive variants both persisting and replacing one another.

The Dynamics of Diverse Segmental Amplifications in Populations of Saccharomyces cerevisiae Adapting to Strong Selection

PubMed Central

Payen, Celia; Di Rienzi, Sara C.; Ong, Giang T.; Pogachar, Jamie L.; Sanchez, Joseph C.; Sunshine, Anna B.; Raghuraman, M. K.; Brewer, Bonita J.; Dunham, Maitreya J.

2014-01-01

Population adaptation to strong selection can occur through the sequential or parallel accumulation of competing beneficial mutations. The dynamics, diversity, and rate of fixation of beneficial mutations within and between populations are still poorly understood. To study how the mutational landscape varies across populations during adaptation, we performed experimental evolution on seven parallel populations of Saccharomyces cerevisiae continuously cultured in limiting sulfate medium. By combining quantitative polymerase chain reaction, array comparative genomic hybridization, restriction digestion and contour-clamped homogeneous electric field gel electrophoresis, and whole-genome sequencing, we followed the trajectory of evolution to determine the identity and fate of beneficial mutations. During a period of 200 generations, the yeast populations displayed parallel evolutionary dynamics that were driven by the coexistence of independent beneficial mutations. Selective amplifications rapidly evolved under this selection pressure, in particular common inverted amplifications containing the sulfate transporter gene SUL1. Compared with single clones, detailed analysis of the populations uncovers a greater complexity whereby multiple subpopulations arise and compete despite a strong selection. The most common evolutionary adaptation to strong selection in these populations grown in sulfate limitation is determined by clonal interference, with adaptive variants both persisting and replacing one another. PMID:24368781

Historical demographic dynamics underlying local adaptation in the presence of gene flow

PubMed Central

Ribeiro, Ângela M; Lopes, Ricardo J; Bowie, Rauri C K

2012-01-01

The range of a species is the result of the relative contribution of spatial tracking of environmental requirements and adaptation to ecological conditions outside the ancestral niche. The appearance of novel habitats caused by climatic oscillation can promote range expansion and accompanying demographic growth. The demographic dynamics of populations leave a signal in \\ patterns. We modeled three competing scenarios pertaining to the circumstance of a range expansion by the Karoo Scrub-Robin into newly available habitat resulting from the increasing aridification of southern Africa. Genetic variation was contrasted with the theoretical expectations of a spatial range expansion, and compared with data of a putative adaptive trait. We infer that this bird likely colonized the arid zone, as a consequence of adaptive evolution in a small peripheral population, followed by an expansion with recurrent exchange of migrants with the ancestral populations. PMID:23170207

Adaptive dynamics of cuticular hydrocarbons in Drosophila

PubMed Central

Rajpurohit, Subhash; Hanus, Robert; Vrkoslav, Vladimír; Behrman, Emily L.; Bergland, Alan O.; Petrov, Dmitri; Cvačka, Josef; Schmidt, Paul S.

2016-01-01

Cuticular hydrocarbons (CHCs) are hydrophobic compounds deposited on the arthropod cuticle that are of functional significance with respect to stress tolerance, social interactions, and mating dynamics. We characterized CHC profiles in natural populations of Drosophila melanogaster at five levels: across a latitudinal transect in the eastern U.S., as a function of developmental temperature during culture, across seasonal time in replicate years, and as a function of rapid evolution in experimental mesocosms in the field. Furthermore, we also characterized spatial and temporal change in allele frequencies for SNPs in genes that are associated with the production and chemical profile of CHCs. Our data demonstrate a striking degree of parallelism for clinal and seasonal variation in CHCs in this taxon; CHC profiles also demonstrate significant plasticity in response to rearing temperature, and the observed patterns of plasticity parallel the spatiotemporal patterns observed in nature. We find that these congruent shifts in CHC profiles across time and space are also mirrored by predictable shifts in allele frequencies at SNPs associated with CHC chain length. Finally, we observed rapid and predictable evolution of CHC profiles in experimental mesocosms in the field. Together, these data strongly suggest that CHC profiles respond rapidly and adaptively to environmental parameters that covary with latitude and season, and that this response reflects the process of local adaptation in natural populations of D. melanogaster. PMID:27718537

Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton.

PubMed

Koller, Jeffrey R; Jacobs, Daniel A; Ferris, Daniel P; Remy, C David

2015-11-04

Robotic ankle exoskeletons can provide assistance to users and reduce metabolic power during walking. Our research group has investigated the use of proportional myoelectric control for controlling robotic ankle exoskeletons. Previously, these controllers have relied on a constant gain to map user's muscle activity to actuation control signals. A constant gain may act as a constraint on the user, so we designed a controller that dynamically adapts the gain to the user's myoelectric amplitude. We hypothesized that an adaptive gain proportional myoelectric controller would reduce metabolic energy expenditure compared to walking with the ankle exoskeleton unpowered because users could choose their preferred control gain. We tested eight healthy subjects walking with the adaptive gain proportional myoelectric controller with bilateral ankle exoskeletons. The adaptive gain was updated each stride such that on average the user's peak muscle activity was mapped to maximal power output of the exoskeleton. All subjects participated in three identical training sessions where they walked on a treadmill for 50 minutes (30 minutes of which the exoskeleton was powered) at 1.2 ms(-1). We calculated and analyzed metabolic energy consumption, muscle recruitment, inverse kinematics, inverse dynamics, and exoskeleton mechanics. Using our controller, subjects achieved a metabolic reduction similar to that seen in previous work in about a third of the training time. The resulting controller gain was lower than that seen in previous work (β=1.50±0.14 versus a constant β=2). The adapted gain allowed users more total ankle joint power than that of unassisted walking, increasing ankle power in exchange for a decrease in hip power. Our findings indicate that humans prefer to walk with greater ankle mechanical power output than their unassisted gait when provided with an ankle exoskeleton using an adaptive controller. This suggests that robotic assistance from an exoskeleton can allow

The Role of Eigensolutions in Nonlinear Inverse Cavity-Flow-Theory. Revision.

DTIC Science & Technology

1985-06-10

The method of Levi Civita is applied to an isolated fully cavitating body at zero cavitation number and adapted to the solution of the inverse...Eigensolutions in Nonlinear Inverse Cavity-Flow Theory [Revised] Abstract: The method of Levi Civita is applied to an isolated fully cavitating body at...problem is not thought * to present much of a challenge at zero cavitation number. In this case, - the classical method of Levi Civita [7] can be

Enhancing Collaborative Learning through Dynamic Forms of Support: The Impact of an Adaptive Domain-Specific Support Strategy

ERIC Educational Resources Information Center

Karakostas, A.; Demetriadis, S.

2011-01-01

Research on computer-supported collaborative learning (CSCL) has strongly emphasized the value of providing student support of either fixed (e.g. collaboration scripts) or dynamic form (e.g. adaptive supportive interventions). Currently, however, there is not sufficient evidence corroborating the potential of adaptive support methods to improve…

Nonlinear Stimulated Raman Exact Passage by Resonance-Locked Inverse Engineering

NASA Astrophysics Data System (ADS)

Dorier, V.; Gevorgyan, M.; Ishkhanyan, A.; Leroy, C.; Jauslin, H. R.; Guérin, S.

2017-12-01

We derive an exact and robust stimulated Raman process for nonlinear quantum systems driven by pulsed external fields. The external fields are designed with closed-form expressions from the inverse engineering of a given efficient and stable dynamics. This technique allows one to induce a controlled population inversion which surpasses the usual nonlinear stimulated Raman adiabatic passage efficiency.

Nonlinear Problems in Fluid Dynamics and Inverse Scattering

DTIC Science & Technology

1993-05-31

nonlinear Kadomtsev - Petviashvili (KP) equations , have solutions which will become infinite in finite time. This phenomenon is sometimes referred to as...40 (November 1992). 4 7. Wave Collapse and Instability of Solitary Waves of a Generalized Nonlinear Kaoiomtsev- Petviashvili Equation , X.P. Wang, M.J...words) The inverse scattering of a class of differential-difference equations and multidimensional operators has been constructed. Solutions of nonlinear

Dynamics of epidemic diseases on a growing adaptive network

PubMed Central

Demirel, Güven; Barter, Edmund; Gross, Thilo

2017-01-01

The study of epidemics on static networks has revealed important effects on disease prevalence of network topological features such as the variance of the degree distribution, i.e. the distribution of the number of neighbors of nodes, and the maximum degree. Here, we analyze an adaptive network where the degree distribution is not independent of epidemics but is shaped through disease-induced dynamics and mortality in a complex interplay. We study the dynamics of a network that grows according to a preferential attachment rule, while nodes are simultaneously removed from the network due to disease-induced mortality. We investigate the prevalence of the disease using individual-based simulations and a heterogeneous node approximation. Our results suggest that in this system in the thermodynamic limit no epidemic thresholds exist, while the interplay between network growth and epidemic spreading leads to exponential networks for any finite rate of infectiousness when the disease persists. PMID:28186146

Comparison of iterative inverse coarse-graining methods

NASA Astrophysics Data System (ADS)

Rosenberger, David; Hanke, Martin; van der Vegt, Nico F. A.

2016-10-01

Deriving potentials for coarse-grained Molecular Dynamics (MD) simulations is frequently done by solving an inverse problem. Methods like Iterative Boltzmann Inversion (IBI) or Inverse Monte Carlo (IMC) have been widely used to solve this problem. The solution obtained by application of these methods guarantees a match in the radial distribution function (RDF) between the underlying fine-grained system and the derived coarse-grained system. However, these methods often fail in reproducing thermodynamic properties. To overcome this deficiency, additional thermodynamic constraints such as pressure or Kirkwood-Buff integrals (KBI) may be added to these methods. In this communication we test the ability of these methods to converge to a known solution of the inverse problem. With this goal in mind we have studied a binary mixture of two simple Lennard-Jones (LJ) fluids, in which no actual coarse-graining is performed. We further discuss whether full convergence is actually needed to achieve thermodynamic representability.

Multiple estimation channel decoupling and optimization method based on inverse system

NASA Astrophysics Data System (ADS)

Wu, Peng; Mu, Rongjun; Zhang, Xin; Deng, Yanpeng

2018-03-01

This paper addressed the intelligent autonomous navigation request of intelligent deformation missile, based on the intelligent deformation missile dynamics and kinematics modeling, navigation subsystem solution method and error modeling, and then focuses on the corresponding data fusion and decision fusion technology, decouples the sensitive channel of the filter input through the inverse system of design dynamics to reduce the influence of sudden change of the measurement information on the filter input. Then carrying out a series of simulation experiments, which verified the feasibility of the inverse system decoupling algorithm effectiveness.

Food-web complexity emerging from ecological dynamics on adaptive networks.

PubMed

Garcia-Domingo, Josep L; Saldaña, Joan

2007-08-21

Food webs are complex networks describing trophic interactions in ecological communities. Since Robert May's seminal work on random structured food webs, the complexity-stability debate is a central issue in ecology: does network complexity increase or decrease food-web persistence? A multi-species predator-prey model incorporating adaptive predation shows that the action of ecological dynamics on the topology of a food web (whose initial configuration is generated either by the cascade model or by the niche model) render, when a significant fraction of adaptive predators is present, similar hyperbolic complexity-persistence relationships as those observed in empirical food webs. It is also shown that the apparent positive relation between complexity and persistence in food webs generated under the cascade model, which has been pointed out in previous papers, disappears when the final connection is used instead of the initial one to explain species persistence.

Quantum Information Biology: From Theory of Open Quantum Systems to Adaptive Dynamics

NASA Astrophysics Data System (ADS)

Asano, Masanari; Basieva, Irina; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu; Yamato, Ichiro

This chapter reviews quantum(-like) information biology (QIB). Here biology is treated widely as even covering cognition and its derivatives: psychology and decision making, sociology, and behavioral economics and finances. QIB provides an integrative description of information processing by bio-systems at all scales of life: from proteins and cells to cognition, ecological and social systems. Mathematically QIB is based on the theory of adaptive quantum systems (which covers also open quantum systems). Ideologically QIB is based on the quantum-like (QL) paradigm: complex bio-systems process information in accordance with the laws of quantum information and probability. This paradigm is supported by plenty of statistical bio-data collected at all bio-scales. QIB re ects the two fundamental principles: a) adaptivity; and, b) openness (bio-systems are fundamentally open). In addition, quantum adaptive dynamics provides the most generally possible mathematical representation of these principles.

Adaptive Fuzzy Output Feedback Control for Switched Nonlinear Systems With Unmodeled Dynamics.

PubMed

Tong, Shaocheng; Li, Yongming

2017-02-01

This paper investigates a robust adaptive fuzzy control stabilization problem for a class of uncertain nonlinear systems with arbitrary switching signals that use an observer-based output feedback scheme. The considered switched nonlinear systems possess the unstructured uncertainties, unmodeled dynamics, and without requiring the states being available for measurement. A state observer which is independent of switching signals is designed to solve the problem of unmeasured states. Fuzzy logic systems are used to identify unknown lumped nonlinear functions so that the problem of unstructured uncertainties can be solved. By combining adaptive backstepping design principle and small-gain approach, a novel robust adaptive fuzzy output feedback stabilization control approach is developed. The stability of the closed-loop system is proved via the common Lyapunov function theory and small-gain theorem. Finally, the simulation results are given to demonstrate the validity and performance of the proposed control strategy.

Sliding Mode Control of Dynamic Voltage Restorer by Using a New Adaptive Reaching Law

NASA Astrophysics Data System (ADS)

Pandey, Achala; Agrawal, Rekha; Mandloi, Ravindra S.; Sarkar, Biswaroop

2017-12-01

This paper presents a new kind of adaptive reaching law for sliding mode control of Dynamic Voltage Restorer (DVR). Such an adaptive reaching law follows under-damped sinusoidal nature that causes the initial state to reach the sliding regime in extremely less time with negligible chattering. Moreover, it is robust in the sense the trajectory does not deviate from the sliding surface. This new approach is developed and successfully applied to DVR. The simulation results are presented that show its robustness.

Adaptive finite-volume WENO schemes on dynamically redistributed grids for compressible Euler equations

NASA Astrophysics Data System (ADS)

Pathak, Harshavardhana S.; Shukla, Ratnesh K.

2016-08-01

A high-order adaptive finite-volume method is presented for simulating inviscid compressible flows on time-dependent redistributed grids. The method achieves dynamic adaptation through a combination of time-dependent mesh node clustering in regions characterized by strong solution gradients and an optimal selection of the order of accuracy and the associated reconstruction stencil in a conservative finite-volume framework. This combined approach maximizes spatial resolution in discontinuous regions that require low-order approximations for oscillation-free shock capturing. Over smooth regions, high-order discretization through finite-volume WENO schemes minimizes numerical dissipation and provides excellent resolution of intricate flow features. The method including the moving mesh equations and the compressible flow solver is formulated entirely on a transformed time-independent computational domain discretized using a simple uniform Cartesian mesh. Approximations for the metric terms that enforce discrete geometric conservation law while preserving the fourth-order accuracy of the two-point Gaussian quadrature rule are developed. Spurious Cartesian grid induced shock instabilities such as carbuncles that feature in a local one-dimensional contact capturing treatment along the cell face normals are effectively eliminated through upwind flux calculation using a rotated Hartex-Lax-van Leer contact resolving (HLLC) approximate Riemann solver for the Euler equations in generalized coordinates. Numerical experiments with the fifth and ninth-order WENO reconstructions at the two-point Gaussian quadrature nodes, over a range of challenging test cases, indicate that the redistributed mesh effectively adapts to the dynamic flow gradients thereby improving the solution accuracy substantially even when the initial starting mesh is non-adaptive. The high adaptivity combined with the fifth and especially the ninth-order WENO reconstruction allows remarkably sharp capture of

An evolutive real-time source inversion based on a linear inverse formulation

NASA Astrophysics Data System (ADS)

Sanchez Reyes, H. S.; Tago, J.; Cruz-Atienza, V. M.; Metivier, L.; Contreras Zazueta, M. A.; Virieux, J.

2016-12-01

Finite source inversion is a steppingstone to unveil earthquake rupture. It is used on ground motion predictions and its results shed light on seismic cycle for better tectonic understanding. It is not yet used for quasi-real-time analysis. Nowadays, significant progress has been made on approaches regarding earthquake imaging, thanks to new data acquisition and methodological advances. However, most of these techniques are posterior procedures once seismograms are available. Incorporating source parameters estimation into early warning systems would require to update the source build-up while recording data. In order to go toward this dynamic estimation, we developed a kinematic source inversion formulated in the time-domain, for which seismograms are linearly related to the slip distribution on the fault through convolutions with Green's functions previously estimated and stored (Perton et al., 2016). These convolutions are performed in the time-domain as we progressively increase the time window of records at each station specifically. Selected unknowns are the spatio-temporal slip-rate distribution to keep the linearity of the forward problem with respect to unknowns, as promoted by Fan and Shearer (2014). Through the spatial extension of the expected rupture zone, we progressively build-up the slip-rate when adding new data by assuming rupture causality. This formulation is based on the adjoint-state method for efficiency (Plessix, 2006). The inverse problem is non-unique and, in most cases, underdetermined. While standard regularization terms are used for stabilizing the inversion, we avoid strategies based on parameter reduction leading to an unwanted non-linear relationship between parameters and seismograms for our progressive build-up. Rise time, rupture velocity and other quantities can be extracted later on as attributs from the slip-rate inversion we perform. Satisfactory results are obtained on a synthetic example (FIgure 1) proposed by the Source

Adaptation and learning: characteristic time scales of performance dynamics.

PubMed

Newell, Karl M; Mayer-Kress, Gottfried; Hong, S Lee; Liu, Yeou-Teh

2009-12-01

A multiple time scales landscape model is presented that reveals structures of performance dynamics that were not resolved in the traditional power law analysis of motor learning. It shows the co-existence of separate processes during and between practice sessions that evolve in two independent dimensions characterized by time scales that differ by about an order of magnitude. Performance along the slow persistent dimension of learning improves often as much and sometimes more during rest (memory consolidation and/or insight generation processes) than during a practice session itself. In contrast, the process characterized by the fast, transient dimension of adaptation reverses direction between practice sessions, thereby significantly degrading performance at the beginning of the next practice session (warm-up decrement). The theoretical model fits qualitatively and quantitatively the data from Snoddy's [Snoddy, G. S. (1926). Learning and stability. Journal of Applied Psychology, 10, 1-36] classic learning study of mirror tracing and other averaged and individual data sets, and provides a new account of the processes of change in adaptation and learning. 2009 Elsevier B.V. All rights reserved.

Adaptive Flight Control Design with Optimal Control Modification on an F-18 Aircraft Model

NASA Technical Reports Server (NTRS)

Burken, John J.; Nguyen, Nhan T.; Griffin, Brian J.

2010-01-01

In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to as the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly; however, a large adaptive gain can lead to high-frequency oscillations which can adversely affect the robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient robustness. A damping term (v) is added in the modification to increase damping as needed. Simulations were conducted on a damaged F-18 aircraft (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) with both the standard baseline dynamic inversion controller and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model.

Implement Method for Automated Testing of Markov Chain Convergence into INVERSE for ORNL12-RS-108J: Advanced Multi-Dimensional Forward and Inverse Modeling

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

Bledsoe, Keith C.

2015-04-01

The DiffeRential Evolution Adaptive Metropolis (DREAM) method is a powerful optimization/uncertainty quantification tool used to solve inverse transport problems in Los Alamos National Laboratory’s INVERSE code system. The DREAM method has been shown to be adept at accurate uncertainty quantification, but it can be very computationally demanding. Previously, the DREAM method in INVERSE performed a user-defined number of particle transport calculations. This placed a burden on the user to guess the number of calculations that would be required to accurately solve any given problem. This report discusses a new approach that has been implemented into INVERSE, the Gelman-Rubin convergence metric.more » This metric automatically detects when an appropriate number of transport calculations have been completed and the uncertainty in the inverse problem has been accurately calculated. In a test problem with a spherical geometry, this method was found to decrease the number of transport calculations (and thus time required) to solve a problem by an average of over 90%. In a cylindrical test geometry, a 75% decrease was obtained.« less

Demographic source-sink dynamics restrict local adaptation in Elliott's blueberry (Vaccinium elliottii).

PubMed

Anderson, Jill T; Geber, Monica A

2010-02-01

In heterogeneous landscapes, divergent selection can favor the evolution of locally adapted ecotypes, especially when interhabitat gene flow is minimal. However, if habitats differ in size or quality, source-sink dynamics can shape evolutionary trajectories. Upland and bottomland forests of the southeastern USA differ in water table depth, light availability, edaphic conditions, and plant community. We conducted a multiyear reciprocal transplant experiment to test whether Elliott's blueberry (Vaccinium elliottii) is locally adapted to these contrasting environments. Additionally, we exposed seedlings and cuttings to prolonged drought and flooding in the greenhouse to assess fitness responses to abiotic stress. Contrary to predictions of local adaptation, V. elliottii families exhibited significantly higher survivorship and growth in upland than in bottomland forests and under drought than flooded conditions, regardless of habitat of origin. Neutral population differentiation was minimal, suggesting widespread interhabitat migration. Population density, reproductive output, and genetic diversity were all significantly greater in uplands than in bottomlands. These disparities likely result in asymmetric gene flow from uplands to bottomlands. Thus, adaptation to a marginal habitat can be constrained by small populations, limited fitness, and immigration from a benign habitat. Our study highlights the importance of demography and genetic diversity in the evolution of local (mal)adaptation.

Establishing a Dynamic Self-Adaptation Learning Algorithm of the BP Neural Network and Its Applications

NASA Astrophysics Data System (ADS)

Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min

2015-12-01

In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.

Neural adaptation accounts for the dynamic resizing of peripersonal space: evidence from a psychophysical-computational approach.

PubMed

Noel, Jean-Paul; Blanke, Olaf; Magosso, Elisa; Serino, Andrea

2018-06-01

Interactions between the body and the environment occur within the peripersonal space (PPS), the space immediately surrounding the body. The PPS is encoded by multisensory (audio-tactile, visual-tactile) neurons that possess receptive fields (RFs) anchored on the body and restricted in depth. The extension in depth of PPS neurons' RFs has been documented to change dynamically as a function of the velocity of incoming stimuli, but the underlying neural mechanisms are still unknown. Here, by integrating a psychophysical approach with neural network modeling, we propose a mechanistic explanation behind this inherent dynamic property of PPS. We psychophysically mapped the size of participant's peri-face and peri-trunk space as a function of the velocity of task-irrelevant approaching auditory stimuli. Findings indicated that the peri-trunk space was larger than the peri-face space, and, importantly, as for the neurophysiological delineation of RFs, both of these representations enlarged as the velocity of incoming sound increased. We propose a neural network model to mechanistically interpret these findings: the network includes reciprocal connections between unisensory areas and higher order multisensory neurons, and it implements neural adaptation to persistent stimulation as a mechanism sensitive to stimulus velocity. The network was capable of replicating the behavioral observations of PPS size remapping and relates behavioral proxies of PPS size to neurophysiological measures of multisensory neurons' RF size. We propose that a biologically plausible neural adaptation mechanism embedded within the network encoding for PPS can be responsible for the dynamic alterations in PPS size as a function of the velocity of incoming stimuli. NEW & NOTEWORTHY Interactions between body and environment occur within the peripersonal space (PPS). PPS neurons are highly dynamic, adapting online as a function of body-object interactions. The mechanistic underpinning PPS dynamic

Inverse free steering law for small satellite attitude control and power tracking with VSCMGs

NASA Astrophysics Data System (ADS)

Malik, M. S. I.; Asghar, Sajjad

2014-01-01

Recent developments in integrated power and attitude control systems (IPACSs) for small satellite, has opened a new dimension to more complex and demanding space missions. This paper presents a new inverse free steering approach for integrated power and attitude control systems using variable-speed single gimbal control moment gyroscope. The proposed inverse free steering law computes the VSCMG steering commands (gimbal rates and wheel accelerations) such that error signal (difference in command and output) in feedback loop is driven to zero. H∞ norm optimization approach is employed to synthesize the static matrix elements of steering law for a static state of VSCMG. Later these matrix elements are suitably made dynamic in order for the adaptation. In order to improve the performance of proposed steering law while passing through a singular state of CMG cluster (no torque output), the matrix element of steering law is suitably modified. Therefore, this steering law is capable of escaping internal singularities and using the full momentum capacity of CMG cluster. Finally, two numerical examples for a satellite in a low earth orbit are simulated to test the proposed steering law.

Pulse-Inversion Subharmonic Ultrafast Active Cavitation Imaging in Tissue Using Fast Eigenspace-Based Adaptive Beamforming and Cavitation Deconvolution.

PubMed

Bai, Chen; Xu, Shanshan; Duan, Junbo; Jing, Bowen; Yang, Miao; Wan, Mingxi

2017-08-01

Pulse-inversion subharmonic (PISH) imaging can display information relating to pure cavitation bubbles while excluding that of tissue. Although plane-wave-based ultrafast active cavitation imaging (UACI) can monitor the transient activities of cavitation bubbles, its resolution and cavitation-to-tissue ratio (CTR) are barely satisfactory but can be significantly improved by introducing eigenspace-based (ESB) adaptive beamforming. PISH and UACI are a natural combination for imaging of pure cavitation activity in tissue; however, it raises two problems: 1) the ESB beamforming is hard to implement in real time due to the enormous amount of computation associated with the covariance matrix inversion and eigendecomposition and 2) the narrowband characteristic of the subharmonic filter will incur a drastic degradation in resolution. Thus, in order to jointly address these two problems, we propose a new PISH-UACI method using novel fast ESB (F-ESB) beamforming and cavitation deconvolution for nonlinear signals. This method greatly reduces the computational complexity by using F-ESB beamforming through dimensionality reduction based on principal component analysis, while maintaining the high quality of ESB beamforming. The degraded resolution is recovered using cavitation deconvolution through a modified convolution model and compressive deconvolution. Both simulations and in vitro experiments were performed to verify the effectiveness of the proposed method. Compared with the ESB-based PISH-UACI, the entire computation of our proposed approach was reduced by 99%, while the axial resolution gain and CTR were increased by 3 times and 2 dB, respectively, confirming that satisfactory performance can be obtained for monitoring pure cavitation bubbles in tissue erosion.

An adaptive approach to the dynamic allocation of buffer storage. M.S. Thesis

NASA Technical Reports Server (NTRS)

Crooke, S. C.

1970-01-01

Several strategies for the dynamic allocation of buffer storage are simulated and compared. The basic algorithms investigated, using actual statistics observed in the Univac 1108 EXEC 8 System, include the buddy method and the first-fit method. Modifications are made to the basic methods in an effort to improve and to measure allocation performance. A simulation model of an adaptive strategy is developed which permits interchanging the two different methods, the buddy and the first-fit methods with some modifications. Using an adaptive strategy, each method may be employed in the statistical environment in which its performance is superior to the other method.