NASA Technical Reports Server (NTRS)
Campbell, Stefan F.; Kaneshige, John T.
2010-01-01
Presented here is a Predictor-Based Model Reference Adaptive Control (PMRAC) architecture for a generic transport aircraft. At its core, this architecture features a three-axis, non-linear, dynamic-inversion controller. Command inputs for this baseline controller are provided by pilot roll-rate, pitch-rate, and sideslip commands. This paper will first thoroughly present the baseline controller followed by a description of the PMRAC adaptive augmentation to this control system. Results are presented via a full-scale, nonlinear simulation of NASA s Generic Transport Model (GTM).
Padhi, Radhakant; Kothari, Mangal
2007-09-01
Combining the advanced techniques of optimal dynamic inversion and model-following neuro-adaptive control design, an innovative technique is presented to design an automatic drug administration strategy for effective treatment of chronic myelogenous leukemia (CML). A recently developed nonlinear mathematical model for cell dynamics is used to design the controller (medication dosage). First, a nominal controller is designed based on the principle of optimal dynamic inversion. This controller can treat the nominal model patients (patients who can be described by the mathematical model used here with the nominal parameter values) effectively. However, since the system parameters for a realistic model patient can be different from that of the nominal model patients, simulation studies for such patients indicate that the nominal controller is either inefficient or, worse, ineffective; i.e. the trajectory of the number of cancer cells either shows non-satisfactory transient behavior or it grows in an unstable manner. Hence, to make the drug dosage history more realistic and patient-specific, a model-following neuro-adaptive controller is augmented to the nominal controller. In this adaptive approach, a neural network trained online facilitates a new adaptive controller. The training process of the neural network is based on Lyapunov stability theory, which guarantees both stability of the cancer cell dynamics as well as boundedness of the network weights. From simulation studies, this adaptive control design approach is found to be very effective to treat the CML disease for realistic patients. Sufficient generality is retained in the mathematical developments so that the technique can be applied to other similar nonlinear control design problems as well.
Adaptive dynamic inversion robust control for BTT missile based on wavelet neural network
NASA Astrophysics Data System (ADS)
Li, Chuanfeng; Wang, Yongji; Deng, Zhixiang; Wu, Hao
2009-10-01
A new nonlinear control strategy incorporated the dynamic inversion method with wavelet neural networks is presented for the nonlinear coupling system of Bank-to-Turn(BTT) missile in reentry phase. The basic control law is designed by using the dynamic inversion feedback linearization method, and the online learning wavelet neural network is used to compensate the inversion error due to aerodynamic parameter errors, modeling imprecise and external disturbance in view of the time-frequency localization properties of wavelet transform. Weights adjusting laws are derived according to Lyapunov stability theory, which can guarantee the boundedness of all signals in the whole system. Furthermore, robust stability of the closed-loop system under this tracking law is proved. Finally, the six degree-of-freedom(6DOF) simulation results have shown that the attitude angles can track the anticipant command precisely under the circumstances of existing external disturbance and in the presence of parameter uncertainty. It means that the dependence on model by dynamic inversion method is reduced and the robustness of control system is enhanced by using wavelet neural network(WNN) to reconstruct inversion error on-line.
Hybrid Adaptive Flight Control with Model Inversion Adaptation
NASA Technical Reports Server (NTRS)
Nguyen, Nhan
2011-01-01
This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.
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
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.
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
Adaptive Inverse Hyperbolic Tangent Algorithm for Dynamic Contrast Adjustment in Displaying Scenes
NASA Astrophysics Data System (ADS)
Yu, Cheng-Yi; Ouyang, Yen-Chieh; Wang, Chuin-Mu; Chang, Chein-I.
2010-12-01
Contrast has a great influence on the quality of an image in human visual perception. A poorly illuminated environment can significantly affect the contrast ratio, producing an unexpected image. This paper proposes an Adaptive Inverse Hyperbolic Tangent (AIHT) algorithm to improve the display quality and contrast of a scene. Because digital cameras must maintain the shadow in a middle range of luminance that includes a main object such as a face, a gamma function is generally used for this purpose. However, this function has a severe weakness in that it decreases highlight contrast. To mitigate this problem, contrast enhancement algorithms have been designed to adjust contrast to tune human visual perception. The proposed AIHT determines the contrast levels of an original image as well as parameter space for different contrast types so that not only the original histogram shape features can be preserved, but also the contrast can be enhanced effectively. Experimental results show that the proposed algorithm is capable of enhancing the global contrast of the original image adaptively while extruding the details of objects simultaneously.
NASA Astrophysics Data System (ADS)
Irons, Trevor P.
Surface nuclear magnetic resonance (sNMR) is the only geophysical technique that can directly and non-invasively detect the presence of subsurface liquid water. The method has established itself as valuable tool for hydrologists and groundwater managers owing to the fact that both porosity and hydraulic conductivity estimates can be made using this technique. Although sNMR has enormous potential, there are many challenges with the technique which hinder it's more widespread adoption. For these reasons sNMR has primarily been used as a 1D groundwater sounding tool, although there exist myriad other applications for a method directly sensitive to liquid water. Simultaneously inverting the entire complex dataset as well as the employment of arrays of separated transmitter and receiver coils and integration with other geophysical methods can help to overcome these limitations. This requires modelling algorithms that can accommodate a widely varying set of survey configurations and scenarios. I present the innovative use of sNMR applied to two geotechnical problems: volcanic landslide hazard characterization on Mt. Baker, Washington and the monitoring of internal erosion in earthen embankments. These applications necessitated the development of a general modelling framework capable of handling arbitrary positioned transmitter and receiver coils as well as 3D water distribution. The advantages of comprehensive (whole dataset) inversion of the entire sNMR record have been established for time-domain inversions. However, these inversions are memory intensive and struggle to fit the phase portion of the dataset-necessitating the regretful dismissal of this valuable information. I instead consider the sNMR inversion problem in the frequency-domain for the first time. There are several benefits: effectively lossless compression, and the ability to easily incorporate and solve for static dephasing dynamics caused by magnetic field inhomogeneities. This has allowed for the
Dynamically consistent Jacobian inverse for mobile manipulators
NASA Astrophysics Data System (ADS)
Ratajczak, Joanna; Tchoń, Krzysztof
2016-06-01
By analogy to the definition of the dynamically consistent Jacobian inverse for robotic manipulators, we have designed a dynamically consistent Jacobian inverse for mobile manipulators built of a non-holonomic mobile platform and a holonomic on-board manipulator. The endogenous configuration space approach has been exploited as a source of conceptual guidelines. The new inverse guarantees a decoupling of the motion in the operational space from the forces exerted in the endogenous configuration space and annihilated by the dual Jacobian inverse. A performance study of the new Jacobian inverse as a tool for motion planning is presented.
Chromosome inversions, adaptive cassettes and the evolution of species' ranges.
Kirkpatrick, Mark; Barrett, Brian
2015-05-01
A chromosome inversion can spread when it captures locally adapted alleles or when it is introduced into a species by hybridization with adapted alleles that were previously absent. We present a model that shows how both processes can cause a species range to expand. Introgression of an inversion that carries novel, locally adapted alleles is a particularly powerful mechanism for range expansion. The model supports the earlier proposal that introgression of an inversion triggered a large range expansion of a malaria mosquito. These results suggest a role for inversions as cassettes of genes that can accelerate adaptation by crossing species boundaries, rather than protecting genomes from introgression.
Chromosome inversions, adaptive cassettes and the evolution of species' ranges.
Kirkpatrick, Mark; Barrett, Brian
2015-05-01
A chromosome inversion can spread when it captures locally adapted alleles or when it is introduced into a species by hybridization with adapted alleles that were previously absent. We present a model that shows how both processes can cause a species range to expand. Introgression of an inversion that carries novel, locally adapted alleles is a particularly powerful mechanism for range expansion. The model supports the earlier proposal that introgression of an inversion triggered a large range expansion of a malaria mosquito. These results suggest a role for inversions as cassettes of genes that can accelerate adaptation by crossing species boundaries, rather than protecting genomes from introgression. PMID:25583098
Adaptive Dynamic Bayesian Networks
Ng, B M
2007-10-26
A discrete-time Markov process can be compactly modeled as a dynamic Bayesian network (DBN)--a graphical model with nodes representing random variables and directed edges indicating causality between variables. Each node has a probability distribution, conditional on the variables represented by the parent nodes. A DBN's graphical structure encodes fixed conditional dependencies between variables. But in real-world systems, conditional dependencies between variables may be unknown a priori or may vary over time. Model errors can result if the DBN fails to capture all possible interactions between variables. Thus, we explore the representational framework of adaptive DBNs, whose structure and parameters can change from one time step to the next: a distribution's parameters and its set of conditional variables are dynamic. This work builds on recent work in nonparametric Bayesian modeling, such as hierarchical Dirichlet processes, infinite-state hidden Markov networks and structured priors for Bayes net learning. In this paper, we will explain the motivation for our interest in adaptive DBNs, show how popular nonparametric methods are combined to formulate the foundations for adaptive DBNs, and present preliminary results.
Rane, Rahul V; Rako, Lea; Kapun, Martin; Lee, Siu F; Hoffmann, Ary A
2015-05-01
Chromosomal inversion polymorphisms are common in animals and plants, and recent models suggest that alternative arrangements spread by capturing different combinations of alleles acting additively or epistatically to favour local adaptation. It is also thought that inversions typically maintain favoured combinations for a long time by suppressing recombination between alternative chromosomal arrangements. Here, we consider patterns of linkage disequilibrium and genetic divergence in an old inversion polymorphism in Drosophila melanogaster (In(3R)Payne) known to be associated with climate change adaptation and a recent invasion event into Australia. We extracted, karyotyped and sequenced whole chromosomes from two Australian populations, so that changes in the arrangement of the alleles between geographically separated tropical and temperate areas could be compared. Chromosome-wide linkage disequilibrium (LD) analysis revealed strong LD within the region spanned by In(3R)Payne. This genomic region also showed strong differentiation between the tropical and the temperate populations, but no differentiation between different karyotypes from the same population, after controlling for chromosomal arrangement. Patterns of differentiation across the chromosome arm and in gene ontologies were enhanced by the presence of the inversion. These data support the notion that inversions are strongly selected by bringing together combinations of genes, but it is still not clear if such combinations act additively or epistatically. Our data suggest that climatic adaptation through inversions can be dynamic, reflecting changes in the relative abundance of different forms of an inversion and ongoing evolution of allelic content within an inversion.
Nonlinear Dynamic Inversion Baseline Control Law: Architecture and Performance Predictions
NASA Technical Reports Server (NTRS)
Miller, Christopher J.
2011-01-01
A model reference dynamic inversion control law has been developed to provide a baseline control law for research into adaptive elements and other advanced flight control law components. This controller has been implemented and tested in a hardware-in-the-loop simulation; the simulation results show excellent handling qualities throughout the limited flight envelope. A simple angular momentum formulation was chosen because it can be included in the stability proofs for many basic adaptive theories, such as model reference adaptive control. 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 basic as possible to simplify the addition of the adaptive elements. Those design choices are explained, along with their predicted impact on the handling qualities.
Genomic Evidence for Adaptive Inversion Clines in Drosophila melanogaster.
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. PMID:26796550
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.
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.
Rapid inversion of velocity map images for adaptive femtosecond control
NASA Astrophysics Data System (ADS)
Rallis, C.; Andrews, P.; Averin, R.; Jochim, B.; Gregerson, N.; Wells, E.; Zohrabi, M.; de, S.; Gaire, B.; Carnes, K. D.; Ben-Itzhak, I.; Bergues, B.; Kling, M. F.
2011-05-01
We report techniques developed to utilize three dimensional momentum information as feedback in adaptive femtosecond control of molecular systems. Velocity map imaging of the dissociating ions following interaction with an intense ultrafast laser pulse provides raw data. In order to recover momentum information, however, the two-dimensional image must be inverted to reconstruct the three-dimensional photofragment distribution. Using a variation of the onion-peeling technique, we invert 1054 × 1040 pixel images in under 1 second. This rapid inversion allows a slice of the momentum distribution to be used as feedback in a closed-loop adaptive control scheme. We report techniques developed to utilize three dimensional momentum information as feedback in adaptive femtosecond control of molecular systems. Velocity map imaging of the dissociating ions following interaction with an intense ultrafast laser pulse provides raw data. In order to recover momentum information, however, the two-dimensional image must be inverted to reconstruct the three-dimensional photofragment distribution. Using a variation of the onion-peeling technique, we invert 1054 × 1040 pixel images in under 1 second. This rapid inversion allows a slice of the momentum distribution to be used as feedback in a closed-loop adaptive control scheme. This work supported by National Science Foundation award PHY-0969687 and the Chemical Sciences, Geosciences, and Biosciences Division, Office of Basic Energy Science, Office of Science, US Department of Energy.
A nonlinear model reference adaptive inverse control algorithm with pre-compensator
NASA Astrophysics Data System (ADS)
Xiao, Bin; Yang, Tie-Jun; Liu, Zhi-Gang
2005-12-01
In this paper, the reduced-order modeling (ROM) technology and its corresponding linear theory are expanded from the linear dynamic system to the nonlinear one, and H ∞ control theory is employed in the frequency domain to design some nonlinear system s pre-compensator in some special way. The adaptive model inverse control (AMIC) theory coping with nonlinear system is improved as well. Such is the model reference adaptive inverse control with pre-compensator (PCMRAIC). The aim of that algorithm is to construct a strategy of control as a whole. As a practical example of the application, the numerical simulation has been given on matlab software packages. The numerical result is given. The proposed strategy realizes the linearization control of nonlinear dynamic system. And it carries out a good performance to deal with the nonlinear system.
Adaptive critics for dynamic optimization.
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. PMID:20223635
Fragata, I; Lopes-Cunha, M; Bárbaro, M; Kellen, B; Lima, M; Santos, M A; Faria, G S; Santos, M; Matos, M; Simões, P
2014-12-01
Chromosomal inversions are present in a wide range of animals and plants, having an important role in adaptation and speciation. Although empirical evidence of their adaptive value is abundant, the role of different processes underlying evolution of chromosomal polymorphisms is not fully understood. History and selection are likely to shape inversion polymorphism variation to an extent yet largely unknown. Here, we perform a real-time evolution study addressing the role of historical constraints and selection in the evolution of these polymorphisms. We founded laboratory populations of Drosophila subobscura derived from three locations along the European cline and followed the evolutionary dynamics of inversion polymorphisms throughout the first 40 generations. At the beginning, populations were highly differentiated and remained so throughout generations. We report evidence of positive selection for some inversions, variable between foundations. Signs of negative selection were more frequent, in particular for most cold-climate standard inversions across the three foundations. We found that previously observed convergence at the phenotypic level in these populations was not associated with convergence in inversion frequencies. In conclusion, our study shows that selection has shaped the evolutionary dynamics of inversion frequencies, but doing so within the constraints imposed by previous history. Both history and selection are therefore fundamental to predict the evolutionary potential of different populations to respond to global environmental changes.
Pegueroles, Cinta; Ferrés-Coy, Albert; Martí-Solano, Maria; Aquadro, Charles F; Pascual, Marta; Mestres, Francesc
2016-01-01
Adaptation is defined as an evolutionary process allowing organisms to succeed in certain habitats or conditions. Chromosomal inversions have the potential to be key in the adaptation processes, since they can contribute to the maintenance of favoured combinations of adaptive alleles through reduced recombination between individuals carrying different inversions. We have analysed six genes (Pif1A, Abi, Sqd, Yrt, Atpα and Fmr1), located inside and outside three inversions of the O chromosome in European populations of Drosophila subobscura. Genetic differentiation was significant between inversions despite extensive recombination inside inverted regions, irrespective of gene distance to the inversion breakpoints. Surprisingly, the highest level of genetic differentiation between arrangements was found for the Atpα gene, which is located outside the O1 and O7 inversions. Two derived unrelated arrangements (O3+4+1 and O3+4+7) are nearly fixed for several amino acid substitutions at the Atpα gene that have been described to confer resistance in other species to the cardenolide ouabain, a plant toxin capable of blocking ATPases. Similarities in the Atpα variants, conferring ouabain resistance in both arrangements, may be the result of convergent substitution and be favoured in response to selective pressures presumably related to the presence of plants containing ouabain in the geographic locations where both inversions are present. PMID:27029337
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.
Yan Di; Liang Jian
2013-02-15
Purpose: To construct expected treatment dose for adaptive inverse planning optimization, and evaluate it on head and neck (h and n) cancer adaptive treatment modification. Methods: Adaptive inverse planning engine was developed and integrated in our in-house adaptive treatment control system. The adaptive inverse planning engine includes an expected treatment dose constructed using the daily cone beam (CB) CT images in its objective and constrains. Feasibility of the adaptive inverse planning optimization was evaluated retrospectively using daily CBCT images obtained from the image guided IMRT treatment of 19 h and n cancer patients. Adaptive treatment modification strategies with respect to the time and the number of adaptive inverse planning optimization during the treatment course were evaluated using the cumulative treatment dose in organs of interest constructed using all daily CBCT images. Results: Expected treatment dose was constructed to include both the delivered dose, to date, and the estimated dose for the remaining treatment during the adaptive treatment course. It was used in treatment evaluation, as well as in constructing the objective and constraints for adaptive inverse planning optimization. The optimization engine is feasible to perform planning optimization based on preassigned treatment modification schedule. Compared to the conventional IMRT, the adaptive treatment for h and n cancer illustrated clear dose-volume improvement for all critical normal organs. The dose-volume reductions of right and left parotid glands, spine cord, brain stem and mandible were (17 {+-} 6)%, (14 {+-} 6)%, (11 {+-} 6)%, (12 {+-} 8)%, and (5 {+-} 3)% respectively with the single adaptive modification performed after the second treatment week; (24 {+-} 6)%, (22 {+-} 8)%, (21 {+-} 5)%, (19 {+-} 8)%, and (10 {+-} 6)% with three weekly modifications; and (28 {+-} 5)%, (25 {+-} 9)%, (26 {+-} 5)%, (24 {+-} 8)%, and (15 {+-} 9)% with five weekly modifications. Conclusions
Spatial operator approach to flexible manipulator inverse and forward dynamics
NASA Technical Reports Server (NTRS)
Rodriguez, G.
1990-01-01
This study extends to flexible multibody manipulators the recent results of the author on the use of spatially recursive filtering and smoothing techniques for robot arm dynamics. The configuration analyzed is that of a mechanical system of flexible bodies joined together by articulated joints. The inverse and forward dynamics problems are solved using the techniques of spatially recursive Kalman filtering and smoothing. The algorithms are easily developed using a set of identities associated with mass matrix factorization and inversion. The identities are easily derived using a spatial operator algebra developed by the author.
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,
A spatiotemporal dynamic distributed solution to the MEG inverse problem
Lamus, Camilo; Hämäläinen, Matti S.; Temereanca, Simona; Brown, Emery N.; Purdon, Patrick L.
2012-01-01
MEG/EEG are non-invasive imaging techniques that record brain activity with high temporal resolution. However, estimation of brain source currents from surface recordings requires solving an ill-conditioned inverse problem. Converging lines of evidence in neuroscience, from neuronal network models to resting-state imaging and neurophysiology, suggest that cortical activation is a distributed spatiotemporal dynamic process, supported by both local and long-distance neuroanatomic connections. Because spatiotemporal dynamics of this kind are central to brain physiology, inverse solutions could be improved by incorporating models of these dynamics. In this article, we present a model for cortical activity based on nearest-neighbor autoregression that incorporates local spatiotemporal interactions between distributed sources in a manner consistent with neurophysiology and neuroanatomy. We develop a dynamic Maximum a Posteriori Expectation-Maximization (dMAP-EM) source localization algorithm for estimation of cortical sources and model parameters based on the Kalman Filter, the Fixed Interval Smoother, and the EM algorithms. We apply the dMAP-EM algorithm to simulated experiments as well as to human experimental data. Furthermore, we derive expressions to relate our dynamic estimation formulas to those of standard static models, and show how dynamic methods optimally assimilate past and future data. Our results establish the feasibility of spatiotemporal dynamic estimation in large-scale distributed source spaces with several thousand source locations and hundreds of sensors, with resulting inverse solutions that provide substantial performance improvements over static methods. PMID:22155043
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.
Object-adapted inverse pattern projection: generation, evaluation, and applications
NASA Astrophysics Data System (ADS)
Bothe, Thorsten; Li, Wansong; von Kopylow, Christoph; Juptner, Werner P.
2003-05-01
Fast and robust 3D quality control as well as fast deformation measurement is of particular importance for industrial inspection. Additionally a direct response about measured properties is desired. Therefore, robust optical techniques are needed which use as few images as possible for measurement and visualize results in an efficient way. One promising technique for this aim is the inverse pattern projection which has the following advantages: The technique codes the information of a preceding measurement into the projected inverse pattern. Thus, it is possible to do differential measurements using only one camera frame for each state. Additionally, the results are optimized straight fringes for sampling which are independent of the object curvature. The ability to use any image for inverse projection enables the use for augmented reality, i.e. any properties can be visualized directly on the object's surface which makes inspections easier than with use of a separated indicating device. The hardware needs are low as just a programmable projector and a standard camera are necessary. The basic idea of inverse pattern projection, necessary algorithms ane found optimizations are demonstrated, roughly. Evaluation techniques were found to preserve a high quality phase measurement under imperfect conditions. The different application fields can be sorted out by the type of pattern used for inverse projection. We select two main topics for presentation. One is the incremental (one image per state) deformation measurement which is a promising technique for high speed deformation measurements. A video series of a wavering flag with projected inverse pattern was evaluated to show the complete deformation series. The other application is the optical feature marking (augmented reality) that allows to map any measured result directly onto the object under investigation. The general ability to straighten any kind of information on 3D surfaces is shown while preserving an exact
From seismic images to plate dynamics: Towards the full inverse
NASA Astrophysics Data System (ADS)
Gurnis, M.; Ratnaswamy, V.; Stadler, G.; Ghattas, O.; Alisic, L.
2014-12-01
Three-dimensional seismic images of slabs and other mantle structures provide a first order constraint on the forces driving plate motions. Previous attempts to invert for plate motions from seismic images have blurry slabs that do not act as stress guides. Using forward models, we describe characteristics needed to capture the coupling between mantle structures and plates. In forward models, we capitalized on advances in adaptive mesh refinement and scalable solvers to simulate global mantle flow and plate motions, with plate margins resolved down to 1 km. Cold thermal anomalies within the lower mantle are coupled into oceanic plates through narrow high-viscosity slabs, altering the velocity of oceanic plates. Back-arc extension and slab rollback are emergent consequences of slab descent in the upper mantle. The forward models require the solution of a highly ill-conditioned non-linear Stokes equation. Based on a realistic rheological model with yielding and strain rate weakening from dislocation creep, we formulate inverse problems casted as PDE-constrained optimization problems and derive adjoints of the nonlinear Stokes and incompressibility equations. An inexact-Gauss Newton method is used to infer the rheological parameters while quantifying the uncertainty using the Hessian at the maximum a posteriori (MAP) point. Through 2-D numerical experiments we demonstrate that when the temperature field is known from seismic images, we can recover all of these properties to varying levels of certainty: strength of plate boundaries, yield stress and strain rate exponent in the upper mantle. When the system becomes more unconstrained (when all three mechanical properties are unknown), there can be tradeoffs depending on how well the data approximates the realistic dynamics. As plate boundaries become weaker beyond a limiting value, the uncertainty of the inferred parameters increases due to insensitivity of plate motion to plate coupling. Using the inverse of the
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.
Inverse and forward dynamics: models of multi-body systems.
Otten, E
2003-01-01
Connected multi-body systems exhibit notoriously complex behaviour when driven by external and internal forces and torques. The problem of reconstructing the internal forces and/or torques from the movements and known external forces is called the 'inverse dynamics problem', whereas calculating motion from known internal forces and/or torques and resulting reaction forces is called the 'forward dynamics problem'. When stepping forward to cross the street, people use muscle forces that generate angular accelerations of their body segments and, by virtue of reaction forces from the street, a forward acceleration of the centre of mass of their body. Inverse dynamics calculations applied to a set of motion data from such an event can teach us how temporal patterns of joint torques were responsible for the observed motion. In forward dynamics calculations we may attempt to create motion from such temporal patterns, which is extremely difficult, because of the complex mechanical linkage along the chains forming the multi-body system. To understand, predict and sometimes control multi-body systems, we may want to have mathematical expressions for them. The Newton-Euler, Lagrangian and Featherstone approaches have their advantages and disadvantages. The simulation of collisions and the inclusion of muscle forces or other internal forces are discussed. Also, the possibility to perform a mixed inverse and forward dynamics calculation are dealt with. The use and limitations of these approaches form the conclusion. PMID:14561340
Inverse and forward dynamics: models of multi-body systems.
Otten, E
2003-09-29
Connected multi-body systems exhibit notoriously complex behaviour when driven by external and internal forces and torques. The problem of reconstructing the internal forces and/or torques from the movements and known external forces is called the 'inverse dynamics problem', whereas calculating motion from known internal forces and/or torques and resulting reaction forces is called the 'forward dynamics problem'. When stepping forward to cross the street, people use muscle forces that generate angular accelerations of their body segments and, by virtue of reaction forces from the street, a forward acceleration of the centre of mass of their body. Inverse dynamics calculations applied to a set of motion data from such an event can teach us how temporal patterns of joint torques were responsible for the observed motion. In forward dynamics calculations we may attempt to create motion from such temporal patterns, which is extremely difficult, because of the complex mechanical linkage along the chains forming the multi-body system. To understand, predict and sometimes control multi-body systems, we may want to have mathematical expressions for them. The Newton-Euler, Lagrangian and Featherstone approaches have their advantages and disadvantages. The simulation of collisions and the inclusion of muscle forces or other internal forces are discussed. Also, the possibility to perform a mixed inverse and forward dynamics calculation are dealt with. The use and limitations of these approaches form the conclusion.
Adaptation and dynamics of cat retinal ganglion cells.
Enroth-Cugell, C; Shapley, R M
1973-09-01
1. The impulse/quantum (I/Q) ratio was measured as a function of background illumination for rod-dominated, pure central, linear square-wave responses of retinal ganglion cells in the cat.2. The I/Q ratio was constant at low backgrounds (dark adapted state) and inversely proportional to the 0.9 power of the background at high backgrounds (the light adapted state). There was an abrupt transition from the dark-adapted state to the light-adapted state.3. It was possible to define the adaptation level at a particular background as the ratio (I/Q ratio at that background)/(dark adapted I/Q ratio).4. The time course of the square-wave response was correlated with the adaptation level. The response was sustained in the dark-adapted state, partially transient at the transition level, and progressively more transient the lower the impulse/quantum ratio of the ganglion cell became. This was true both for on-centre and off-centre cells.5. The frequency response of the central response mechanism at different adaptation levels was measured. It was a low-pass characteristic in the dark-adapted state and became progressively more of a bandpass characteristic as the cell became more light-adapted.6. The rapidity of onset of adaptation was measured with a time-varying adapting light. The impulse/quantum ratio is reset within 100 msec of the onset of the conditioning light, and is kept at the new value throughout the time the conditioning light is on.7. These results can be explained by a nonlinear feedback model. In the model, it is postulated that the exponential function of the horizontal cell potential controls transmission from rods to bipolars. This model has an abrupt transition from dark- to light-adapted states, and its response dynamics are correlated with adaptation level.
Functional error estimators for the adaptive discretization of inverse problems
NASA Astrophysics Data System (ADS)
Clason, Christian; Kaltenbacher, Barbara; Wachsmuth, Daniel
2016-10-01
So-called functional error estimators provide a valuable tool for reliably estimating the discretization error for a sum of two convex functions. We apply this concept to Tikhonov regularization for the solution of inverse problems for partial differential equations, not only for quadratic Hilbert space regularization terms but also for nonsmooth Banach space penalties. Examples include the measure-space norm (i.e., sparsity regularization) or the indicator function of an {L}∞ ball (i.e., Ivanov regularization). The error estimators can be written in terms of residuals in the optimality system that can then be estimated by conventional techniques, thus leading to explicit estimators. This is illustrated by means of an elliptic inverse source problem with the above-mentioned penalties, and numerical results are provided for the case of sparsity regularization.
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.
Adaptive Role of Inversion Polymorphism of Drosophila subobscura in Lead Stressed Environment.
Kenig, Bojan; Kurbalija Novičić, Zorana; Patenković, Aleksandra; Stamenković-Radak, Marina; Anđelković, Marko
2015-01-01
Local adaptation to environmental stress at different levels of genetic polymorphism in various plants and animals has been documented through evolution of heavy metal tolerance. We used samples of Drosophila subobscura populations from two differently polluted environments to analyze the change of chromosomal inversion polymorphism as genetic marker during laboratory exposure to lead. Exposure to environmental contamination can affect the genetic content within a particular inversion and produce targets for selection in populations from different environments. The aims were to discover whether the inversion polymorphism is shaped by the local natural environments, and if lead as a selection pressure would cause adaptive divergence of two populations during the multigenerational laboratory experiment. The results showed that populations retain signatures from past contamination events, and that heavy metal pollution can cause adaptive changes in population. Differences in inversion polymorphism between the two populations increased over generations under lead contamination in the laboratory. The inversion polymorphism of population originating from the more polluted natural environment was more stable during the experiment, both under conditions with and without lead. Therefore, results showed that inversion polymorphism as a genetic marker reflects a strong signature of adaptation to the local environment, and that historical demographic events and selection are important for both prediction of evolutionary potential and long-term viability of natural populations.
Lowry, David B; Willis, John H
2010-09-28
The role of chromosomal inversions in adaptation and speciation is controversial. Historically, inversions were thought to contribute to these processes either by directly causing hybrid sterility or by facilitating the maintenance of co-adapted gene complexes. Because inversions suppress recombination when heterozygous, a recently proposed local adaptation mechanism predicts that they will spread if they capture alleles at multiple loci involved in divergent adaptation to contrasting environments. Many empirical studies have found inversion polymorphisms linked to putatively adaptive phenotypes or distributed along environmental clines. However, direct involvement of an inversion in local adaptation and consequent ecological reproductive isolation has not to our knowledge been demonstrated in nature. In this study, we discovered that a chromosomal inversion polymorphism is geographically widespread, and we test the extent to which it contributes to adaptation and reproductive isolation under natural field conditions. Replicated crosses between the prezygotically reproductively isolated annual and perennial ecotypes of the yellow monkeyflower, Mimulus guttatus, revealed that alternative chromosomal inversion arrangements are associated with life-history divergence over thousands of kilometers across North America. The inversion polymorphism affected adaptive flowering time divergence and other morphological traits in all replicated crosses between four pairs of annual and perennial populations. To determine if the inversion contributes to adaptation and reproductive isolation in natural populations, we conducted a novel reciprocal transplant experiment involving outbred lines, where alternative arrangements of the inversion were reciprocally introgressed into the genetic backgrounds of each ecotype. Our results demonstrate for the first time in nature the contribution of an inversion to adaptation, an annual/perennial life-history shift, and multiple reproductive
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.
ERIC Educational Resources Information Center
Brown, Malcolm
2009-01-01
Inversions are fascinating phenomena. They are reversals of the normal or expected order. They occur across a wide variety of contexts. What do inversions have to do with learning spaces? The author suggests that they are a useful metaphor for the process that is unfolding in higher education with respect to education. On the basis of…
Impulse radar imaging for dispersive concrete using inverse adaptive filtering techniques
Arellano, J.; Hernandez, J.M.; Brase, J.
1993-05-01
This publication addresses applications of a delayed inverse model adaptive filter for modeled data obtained from short-pulse radar reflectometry. To determine the integrity of concrete, a digital adaptive filter was used, which allows compensation of dispersion and clutter generated by the concrete. A standard set of weights produced by an adaptive filter are used on modeled data to obtain the inverse-impulse response of the concrete. The data for this report include: Multiple target, nondispersive data; single-target, variable-size dispersive data; single-target, variable-depth dispersive data; and single-target, variable transmitted-pulse-width dispersive data. Results of this simulation indicate that data generated by the weights of the adaptive filter, coupled with a two-dimensional, synthetic-aperture focusing technique, successfully generate two-dimensional images of targets within the concrete from modeled data.
Dynamic adaptivity of "smart" piezoelectric structures
NASA Astrophysics Data System (ADS)
Tzou, Horn-Sen; Zhong, Jianping P.
1990-10-01
Active smart" space and machine structures with adaptive dynamic characteristics have long been interested in a variety of high-performance systems, e.g., flexible robots, flexible space structures, "smart" machines, etc. In this paper, an active adaptive structure made of piezoelectric materials is proposed and evaluated. The structural adaptivity is achieved by a voltage feedback (open or closed loops) utilizing the converse piezoelectric effect. A mathematical model is proposed and the electrodynamic equations of motion and the generalized boundary conditions of a generic piezoelectric shell subjected to mechanical and electrical excitations are derived using Hamilton's principle and the linear piezoelectric theory. The dynamic adaptivity of the structure is introduced using a feedback control system. The theory is demonstrated in a case study in which the structural adaptivity (natural frequency) is investigated.
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.
Dynamical structure underlying inverse stochastic resonance and its implications
NASA Astrophysics Data System (ADS)
Uzuntarla, Muhammet; Cressman, John R.; Ozer, Mahmut; Barreto, Ernest
2013-10-01
We investigate inverse stochastic resonance (ISR), a recently reported phenomenon in which the spiking activity of a Hodgkin-Huxley model neuron subject to external noise exhibits a pronounced minimum as the noise intensity increases. We clarify the mechanism that underlies ISR and show that its most surprising features are a consequence of the dynamical structure of the model. Furthermore, we show that the ISR effect depends strongly on the procedures used to measure it. Our results are important for the experimentalist who seeks to observe the ISR phenomenon.
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.
Doss, S D; Ezzedine, S; Gelinas, R; Chawathe, A
2001-06-11
A novel approach called Forward-Inverse Adaptive Techniques (FIAT) for reservoir characterization is developed and applied to three representative exploration cases. Inverse modeling refers to the determination of the entire reservoir permeability under steady state single-phase flow regime, given only field permeability, pressure and production well measurements. FIAT solves the forward and inverse partial differential equations (PDEs) simultaneously by adding a regularization term and filtering pressure gradients. An implicit adaptive-grid, Galerkin, numerical scheme is used to numerically solve the set of PDEs subject to pressure and permeability boundary conditions. Three examples are presented. Results from all three cases demonstrate attainable and reasonably accurate solutions and, more importantly, provide insights into the consequences of data undersampling.
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.
Adaptive divergence in the monkey flower Mimulus guttatus is maintained by a chromosomal inversion
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
Adaptive divergence in the monkey flower Mimulus guttatus is maintained by a chromosomal inversion.
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.
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.
Adaptive inverse control for rotorcraft vibration reduction. Ph.D. Thesis
NASA Technical Reports Server (NTRS)
Jacklin, S. A.
1985-01-01
The Least Mean Square (LMS) algorithm is extended to solve the multiple-input, multiple-output problem of alleviating N/Rev helicopter fuselage vibration by means of adaptive inverse control. A frequency domain locally linear model is used to represent the transfer matrix relating the high harmonic pitch control inputs to the harmonic vibration outputs to be controlled. By using the inverse matrix as the controller gain matrix, an adaptive inverse regulator is formed to alleviate the N/Rev vibration. The stability and rate of convergence properties of the extended LMS algorithm are discussed. It is shown that the stability ranges for the elements of the stability gain matrix are directly related to the eigenvalues of the vibration signal information matrix for the learning phase, but not for the control phase. The overall conclusion is that the LMS adaptive inverse control method can form a robust vibration control system, but will require some tuning of the input sensor gains, the stability gain matrix, and the amount of control relaxation to be used. The learning curve of the controller during the learning phase is shown to be quantitatively close to that predicted by averaging the learning curves of the normal modes. It is shown that the best selections of the stability gain matrix elements and the amount of control relaxation is basically a compromise between slow, stable convergence and fast convergence with increased possibility of unstable identification.
Automated adaptive inference of phenomenological dynamical models
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
Global view of bionetwork dynamics: adaptive landscape.
Ao, Ping
2009-02-01
Based on recent work, I will give a nontechnical brief review of a powerful quantitative concept in biology, adaptive landscape, initially proposed by S. Wright over 70 years ago, reintroduced by one of the founders of molecular biology and by others in different biological contexts, but apparently forgotten by modern biologists for many years. Nevertheless, this concept finds an increasingly important role in the development of systems biology and bionetwork dynamics modeling, from phage lambda genetic switch to endogenous network for cancer genesis and progression. It is an ideal quantification to describe the robustness and stability of bionetworks. Here, I will first introduce five landmark proposals in biology on this concept, to demonstrate an important common thread in theoretical biology. Then I will discuss a few recent results, focusing on the studies showing theoretical consistency of adaptive landscape. From the perspective of a working scientist and of what is needed logically for a dynamical theory when confronting empirical data, the adaptive landscape is useful both metaphorically and quantitatively, and has captured an essential aspect of biological dynamical processes. Though at the theoretical level the adaptive landscape must exist and it can be used across hierarchical boundaries in biology, many associated issues are indeed vague in their initial formulations and their quantitative realizations are not easy, and are good research topics for quantitative biologists. I will discuss three types of open problems associated with the adaptive landscape in a broader perspective.
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.
Ry, Rexha Verdhora; Nugraha, Andri Dian
2015-04-24
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. 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.
Adaptive wavelet simulation of global ocean dynamics
NASA Astrophysics Data System (ADS)
Kevlahan, N. K.-R.; Dubos, T.; Aechtner, M.
2015-07-01
In order to easily enforce solid-wall boundary conditions in the presence of complex coastlines, we propose a new mass and energy conserving Brinkman penalization for the rotating shallow water equations. This penalization does not lead to higher wave speeds in the solid region. The error estimates for the penalization are derived analytically and verified numerically for linearized one dimensional equations. The penalization is implemented in a conservative dynamically adaptive wavelet method for the rotating shallow water equations on the sphere with bathymetry and coastline data from NOAA's ETOPO1 database. This code could form the dynamical core for a future global ocean model. The potential of the dynamically adaptive ocean model is illustrated by using it to simulate the 2004 Indonesian tsunami and wind-driven gyres.
Dynamic Load Balancing for Adaptive Unstructured Grids
NASA Technical Reports Server (NTRS)
Biswas, Rupak; Saini, Subhash (Technical Monitor)
1998-01-01
Dynamic mesh adaptation on unstructured grids is a powerful tool for computing unsteady three-dimensional problems that require grid modifications to efficiently resolve solution features. By locally refining and coarsening the mesh to capture phenomena of interest, such procedures make standard computational methods more cost effective. Highly refined meshes are required to accurately capture shock waves, contact discontinuities, vortices, and shear layers in fluid flow problems. Adaptive meshes have also proved to be useful in several other areas of computational science and engineering like computer vision and graphics, semiconductor device modeling, and structural mechanics. Local mesh adaptation provides the opportunity to obtain solutions that are comparable to those obtained on globally-refined grids but at a much lower cost. Additional information is contained in the original extended abstract.
Target tracking with dynamically adaptive correlation
NASA Astrophysics Data System (ADS)
Gaxiola, Leopoldo N.; Diaz-Ramirez, Victor H.; Tapia, Juan J.; García-Martínez, Pascuala
2016-04-01
A reliable algorithm for target tracking based on dynamically adaptive correlation filtering is presented. The algorithm is capable of tracking with high accuracy the location of a target in an input video sequence without using an offline training process. The target is selected at the beginning of the algorithm. Afterwards, a composite correlation filter optimized for distortion tolerant pattern recognition is designed to recognize the target in the next frame. The filter is dynamically adapted to each frame using information of current and past scene observations. Results obtained with the proposed algorithm in synthetic and real-life video sequences, are analyzed and compared with those obtained with recent state-of-the-art tracking algorithms in terms of objective metrics.
Adaptive Dynamic Event Tree in RAVEN code
Alfonsi, Andrea; Rabiti, Cristian; Mandelli, Diego; Cogliati, Joshua Joseph; Kinoshita, Robert Arthur
2014-11-01
RAVEN is a software tool that is focused on performing statistical analysis of stochastic dynamic systems. RAVEN has been designed in a high modular and pluggable way in order to enable easy integration of different programming languages (i.e., C++, Python) and coupling with other applications (system codes). Among the several capabilities currently present in RAVEN, there are five different sampling strategies: Monte Carlo, Latin Hyper Cube, Grid, Adaptive and Dynamic Event Tree (DET) sampling methodologies. The scope of this paper is to present a new sampling approach, currently under definition and implementation: an evolution of the DET me
NASA Technical Reports Server (NTRS)
Devasia, Santosh; Bayo, Eduardo
1993-01-01
This paper addresses the problem of inverse dynamics for articulated flexible structures with both lumped and distributed actuators. This problem arises, for example, in the combined vibration minimization and trajectory control of space robots and structures. A new inverse dynamics scheme for computing the nominal lumped and distributed inputs for tracking a prescribed trajectory is given.
Halloran, Jason P.; Erdemir, Ahmet
2011-01-01
Simulation-based prediction of specimen-specific biomechanical behavior commonly requires inverse analysis using geometrically consistent finite element (FE) models. Optimization drives such analyses but previous studies have highlighted a large computational cost dictated by iterative use of nonlinear FE models. The goal of this study was to evaluate the performance of a local regression-based adaptive surrogate modeling approach to decrease computational cost for both global and local optimization approaches using an inverse FE application. Nonlinear elastic material parameters for patient-specific heel-pad tissue were found, both with and without the surrogate model. Surrogate prediction replaced a FE simulation using local regression of previous simulations when the corresponding error estimate was less than a given tolerance. Performance depended on optimization type and tolerance value. The surrogate reduced local optimization expense up to 68%, but achieved accurate results for only 1 of 20 initial conditions. Conversely, up to a tolerance value of 20 N2, global optimization with the surrogate yielded consistent parameter predictions with a concurrent decrease in computational cost (up to 77%). However, the local optimization method without the surrogate, although sensitive to the initial conditions, was still on average seven times faster than the global approach. Our results help establish guide-lines for setting acceptable tolerance values while using an adaptive surrogate model for inverse FE analysis. Most important, the study demonstrates the benefits of a surrogate modeling approach for intensive FE-based iterative analysis. PMID:21544674
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
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.
Synaptic dynamics: linear model and adaptation algorithm.
Yousefi, Ali; Dibazar, Alireza A; Berger, Theodore W
2014-08-01
In this research, temporal processing in brain neural circuitries is addressed by a dynamic model of synaptic connections in which the synapse model accounts for both pre- and post-synaptic processes determining its temporal dynamics and strength. Neurons, which are excited by the post-synaptic potentials of hundred of the synapses, build the computational engine capable of processing dynamic neural stimuli. Temporal dynamics in neural models with dynamic synapses will be analyzed, and learning algorithms for synaptic adaptation of neural networks with hundreds of synaptic connections are proposed. The paper starts by introducing a linear approximate model for the temporal dynamics of synaptic transmission. The proposed linear model substantially simplifies the analysis and training of spiking neural networks. Furthermore, it is capable of replicating the synaptic response of the non-linear facilitation-depression model with an accuracy better than 92.5%. In the second part of the paper, a supervised spike-in-spike-out learning rule for synaptic adaptation in dynamic synapse neural networks (DSNN) is proposed. The proposed learning rule is a biologically plausible process, and it is capable of simultaneously adjusting both pre- and post-synaptic components of individual synapses. The last section of the paper starts with presenting the rigorous analysis of the learning algorithm in a system identification task with hundreds of synaptic connections which confirms the learning algorithm's accuracy, repeatability and scalability. The DSNN is utilized to predict the spiking activity of cortical neurons and pattern recognition tasks. The DSNN model is demonstrated to be a generative model capable of producing different cortical neuron spiking patterns and CA1 Pyramidal neurons recordings. A single-layer DSNN classifier on a benchmark pattern recognition task outperforms a 2-Layer Neural Network and GMM classifiers while having fewer numbers of free parameters and
An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions
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 these 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.
An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions
Li, Weixuan; Lin, Guang
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 these 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.
An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions
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
A covariance-adaptive approach for regularized inversion in linear models
NASA Astrophysics Data System (ADS)
Kotsakis, Christopher
2007-11-01
The optimal inversion of a linear model under the presence of additive random noise in the input data is a typical problem in many geodetic and geophysical applications. Various methods have been developed and applied for the solution of this problem, ranging from the classic principle of least-squares (LS) estimation to other more complex inversion techniques such as the Tikhonov-Philips regularization, truncated singular value decomposition, generalized ridge regression, numerical iterative methods (Landweber, conjugate gradient) and others. In this paper, a new type of optimal parameter estimator for the inversion of a linear model is presented. The proposed methodology is based on a linear transformation of the classic LS estimator and it satisfies two basic criteria. First, it provides a solution for the model parameters that is optimally fitted (in an average quadratic sense) to the classic LS parameter solution. Second, it complies with an external user-dependent constraint that specifies a priori the error covariance (CV) matrix of the estimated model parameters. The formulation of this constrained estimator offers a unified framework for the description of many regularization techniques that are systematically used in geodetic inverse problems, particularly for those methods that correspond to an eigenvalue filtering of the ill-conditioned normal matrix in the underlying linear model. Our study lies on the fact that it adds an alternative perspective on the statistical properties and the regularization mechanism of many inversion techniques commonly used in geodesy and geophysics, by interpreting them as a family of `CV-adaptive' parameter estimators that obey a common optimal criterion and differ only on the pre-selected form of their error CV matrix under a fixed model design.
NASA Astrophysics Data System (ADS)
Wang, Youming; Chen, Xuefeng; He, Zhengjia
2011-02-01
Structural eigenvalues have been broadly applied in modal analysis, damage detection, vibration control, etc. In this paper, the interpolating multiwavelets are custom designed based on stable completion method to solve structural eigenvalue problems. The operator-orthogonality of interpolating multiwavelets gives rise to highly sparse multilevel stiffness and mass matrices of structural eigenvalue problems and permits the incremental computation of the eigenvalue solution in an efficient manner. An adaptive inverse iteration algorithm using the interpolating multiwavelets is presented to solve structural eigenvalue problems. Numerical examples validate the accuracy and efficiency of the proposed algorithm.
Cardiac fluid dynamics anticipates heart adaptation.
Pedrizzetti, Gianni; Martiniello, Alfonso R; Bianchi, Valter; D'Onofrio, Antonio; Caso, Pio; Tonti, Giovanni
2015-01-21
Hemodynamic forces represent an epigenetic factor during heart development and are supposed to influence the pathology of the grown heart. Cardiac blood motion is characterized by a vortical dynamics, and it is common belief that the cardiac vortex has a role in disease progressions or regression. Here we provide a preliminary demonstration about the relevance of maladaptive intra-cardiac vortex dynamics in the geometrical adaptation of the dysfunctional heart. We employed an in vivo model of patients who present a stable normal heart function in virtue of the cardiac resynchronization therapy (CRT, bi-ventricular pace-maker) and who are expected to develop left ventricle remodeling if pace-maker was switched off. Intra-ventricular fluid dynamics is analyzed by echocardiography (Echo-PIV). Under normal conditions, the flow presents a longitudinal alignment of the intraventricular hemodynamic forces. When pacing is temporarily switched off, flow forces develop a misalignment hammering onto lateral walls, despite no other electro-mechanical change is noticed. Hemodynamic forces result to be the first event that evokes a physiological activity anticipating cardiac changes and could help in the prediction of longer term heart adaptations.
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.
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.
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.
An Adaptive ANOVA-based PCKF for High-Dimensional Nonlinear Inverse Modeling
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 important 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
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 basis functions in the expansion helps to capture uncertainty more accurately but increases computational cost. Selection of basis functions 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 demonstrated
Adaptation dynamics in densely clustered chemoreceptors.
Pontius, William; Sneddon, Michael W; Emonet, Thierry
2013-01-01
In many sensory systems, transmembrane receptors are spatially organized in large clusters. Such arrangement may facilitate signal amplification and the integration of multiple stimuli. However, this organization likely also affects the kinetics of signaling since the cytoplasmic enzymes that modulate the activity of the receptors must localize to the cluster prior to receptor modification. Here we examine how these spatial considerations shape signaling dynamics at rest and in response to stimuli. As a model system, we use the chemotaxis pathway of Escherichia coli, a canonical system for the study of how organisms sense, respond, and adapt to environmental stimuli. In bacterial chemotaxis, adaptation is mediated by two enzymes that localize to the clustered receptors and modulate their activity through methylation-demethylation. Using a novel stochastic simulation, we show that distributive receptor methylation is necessary for successful adaptation to stimulus and also leads to large fluctuations in receptor activity in the steady state. These fluctuations arise from noise in the number of localized enzymes combined with saturated modification kinetics between the localized enzymes and the receptor substrate. An analytical model explains how saturated enzyme kinetics and large fluctuations can coexist with an adapted state robust to variation in the expression levels of the pathway constituents, a key requirement to ensure the functionality of individual cells within a population. This contrasts with the well-mixed covalent modification system studied by Goldbeter and Koshland in which mean activity becomes ultrasensitive to protein abundances when the enzymes operate at saturation. Large fluctuations in receptor activity have been quantified experimentally and may benefit the cell by enhancing its ability to explore empty environments and track shallow nutrient gradients. Here we clarify the mechanistic relationship of these large fluctuations to well
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.
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.
High resolution imaging of the Earth with adaptive full-waveform inversion
NASA Astrophysics Data System (ADS)
Morgan, J. V.; Warner, M.; Guasch, L.; Umpleby, A.; Yao, G.; Herrmann, F. J.
2014-12-01
Three-dimensional full-waveform inversion (FWI) is a high-resolution, high-fidelity, quantitative, seismic imaging technique that has advanced rapidly within the oil and gas industry. The method involves the iterative improvement of a starting model using a series of local linearized updates to solve the full non-linear inversion problem. During the inversion, forward modeling employs the full two-way three-dimensional heterogeneous anisotropic acoustic or elastic wave equation to predict the observed raw field data, wiggle-for-wiggle, trace-by-trace. The method is computationally demanding; it is highly parallelized, and runs on large multi-core multi-node clusters. A recently developed adaptive version of FWI is able to overcome the requirement for a good starting model and low frequencies in the data, and this opens up the range of datasets and problems to which FWI can be applied. Here, we demonstrate what can be achieved by applying this newly practical technique to high-density 3D seismic datasets acquired to image petroleum targets. We show that the resulting anisotropic p-wave velocity models match in situ measurements in boreholes, reproduce detailed structure observed independently on high-resolution seismic reflection sections, accurately predict the raw seismic data, and simplify and sharpen reverse-time-migrated reflection images of deeper horizons. The velocity models image individual faults, gas clouds, channels, and other geological features with previously unobtainable resolution and clarity. These same benefits can be obtained when this technique is applied to scientific targets provided that the data coverage is adequate in three-dimensions, and that an appropriate range of offsets and azimuths are available. Possible targets range from the water column, ice sheets, and Holocene deposits, through active faults, spreading centres, collision zones, rifted margins, magma plumbing, lower-continental crust, and deep crustal hot zones, to whole
Nitric oxide regulates vascular adaptive mitochondrial dynamics.
Miller, Matthew W; Knaub, Leslie A; Olivera-Fragoso, Luis F; Keller, Amy C; Balasubramaniam, Vivek; Watson, Peter A; Reusch, Jane E B
2013-06-15
Cardiovascular disease risk factors, such as diabetes, hypertension, dyslipidemia, obesity, and physical inactivity, are all correlated with impaired endothelial nitric oxide synthase (eNOS) function and decreased nitric oxide (NO) production. NO-mediated regulation of mitochondrial biogenesis has been established in many tissues, yet the role of eNOS in vascular mitochondrial biogenesis and dynamics is unclear. We hypothesized that genetic eNOS deletion and 3-day nitric oxide synthase (NOS) inhibition in rodents would result in impaired mitochondrial biogenesis and defunct fission/fusion and autophagy profiles within the aorta. We observed a significant, eNOS expression-dependent decrease in mitochondrial electron transport chain (ETC) protein subunits from complexes I, II, III, and V in eNOS heterozygotes and eNOS null mice compared with age-matched controls. In response to NOS inhibition with NG-nitro-L-arginine methyl ester (L-NAME) treatment in Sprague Dawley rats, significant decreases were observed in ETC protein subunits from complexes I, III, and IV as well as voltage-dependent anion channel 1. Decreased protein content of upstream regulators of mitochondrial biogenesis, cAMP response element-binding protein and peroxisome proliferator-activated receptor-γ coactivator-1α, were observed in response to 3-day L-NAME treatment. Both genetic eNOS deletion and NOS inhibition resulted in decreased manganese superoxide dismutase protein. L-NAME treatment resulted in significant changes to mitochondrial dynamic protein profiles with decreased fusion, increased fission, and minimally perturbed autophagy. In addition, L-NAME treatment blocked mitochondrial adaptation to an exercise intervention in the aorta. These results suggest that eNOS/NO play a role in basal and adaptive mitochondrial biogenesis in the vasculature and regulation of mitochondrial turnover. PMID:23585138
Mission to Mars: Adaptive Identifier for the Solution of Inverse Optical Metrology Tasks
NASA Astrophysics Data System (ADS)
Krapivin, Vladimir F.; Varotsos, Costas A.; Christodoulakis, John
2016-06-01
A human mission to Mars requires the solution of many problems that mainly linked to the safety of life, the reliable operational control of drinking water as well as health care. The availability of liquid fuels is also an important issue since the existing tools cannot fully provide the required liquid fuels quantities for the mission return journey. This paper presents the development of new methods and technology for reliable, operational, and with high availability chemical analysis of liquid solutions of various types. This technology is based on the employment of optical sensors (such as the multi-channel spectrophotometers or spectroellipsometers and microwave radiometers) and the development of a database of spectral images for typical liquid solutions that could be the objects of life on Mars. This database exploits the adaptive recognition of optical images of liquids using specific algorithms that are based on spectral analysis, cluster analysis and methods for solving the inverse optical metrology tasks.
Nie Xiaobo; Liang Jian; Yan Di
2012-12-15
Purpose: To create an organ sample generator (OSG) for expected treatment dose construction and adaptive inverse planning optimization. The OSG generates random samples of organs of interest from a distribution obeying the patient specific organ variation probability density function (PDF) during the course of adaptive radiotherapy. Methods: Principle component analysis (PCA) and a time-varying least-squares regression (LSR) method were used on patient specific geometric variations of organs of interest manifested on multiple daily volumetric images obtained during the treatment course. The construction of the OSG includes the determination of eigenvectors of the organ variation using PCA, and the determination of the corresponding coefficients using time-varying LSR. The coefficients can be either random variables or random functions of the elapsed treatment days depending on the characteristics of organ variation as a stationary or a nonstationary random process. The LSR method with time-varying weighting parameters was applied to the precollected daily volumetric images to determine the function form of the coefficients. Eleven h and n cancer patients with 30 daily cone beam CT images each were included in the evaluation of the OSG. The evaluation was performed using a total of 18 organs of interest, including 15 organs at risk and 3 targets. Results: Geometric variations of organs of interest during h and n cancer radiotherapy can be represented using the first 3 {approx} 4 eigenvectors. These eigenvectors were variable during treatment, and need to be updated using new daily images obtained during the treatment course. The OSG generates random samples of organs of interest from the estimated organ variation PDF of the individual. The accuracy of the estimated PDF can be improved recursively using extra daily image feedback during the treatment course. The average deviations in the estimation of the mean and standard deviation of the organ variation PDF for h
NASA Astrophysics Data System (ADS)
Ryerson, F. J.; Ezzedine, S. M.; Antoun, T.
2013-12-01
equation for the distribution of k is solved, provided that Cauchy data are appropriately assigned. In the next stage, only a limited number of passive measurements are provided. In this case, the forward and inverse PDEs are solved simultaneously. This is accomplished by adding regularization terms and filtering the pressure gradients in the inverse problem. Both the forward and the inverse problem are either simultaneously or sequentially coupled and solved using implicit schemes, adaptive mesh refinement, Galerkin finite elements. The final case arises when P, k, and Q data only exist at producing wells. This exceedingly ill posed problem calls for additional constraints on the forward-inverse coupling to insure that the production rates are satisfied at the desired locations. Results from all three cases are presented demonstrating stability and accuracy of the proposed approach and, more importantly, providing some insights into the consequences of data under sampling, uncertainty propagation and quantification. We illustrate the advantages of this novel approach over the common UQ forward drivers on several subsurface energy problems in either porous or fractured or/and faulted reservoirs. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
Clipping in neurocontrol by adaptive dynamic programming.
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.
Dynamic adaptive chemistry for turbulent flame simulations
NASA Astrophysics Data System (ADS)
Yang, Hongtao; Ren, Zhuyin; Lu, Tianfeng; Goldin, Graham M.
2013-02-01
The use of large chemical mechanisms in flame simulations is computationally expensive due to the large number of chemical species and the wide range of chemical time scales involved. This study investigates the use of dynamic adaptive chemistry (DAC) for efficient chemistry calculations in turbulent flame simulations. DAC is achieved through the directed relation graph (DRG) method, which is invoked for each computational fluid dynamics cell/particle to obtain a small skeletal mechanism that is valid for the local thermochemical condition. Consequently, during reaction fractional steps, one needs to solve a smaller set of ordinary differential equations governing chemical kinetics. Test calculations are performed in a partially-stirred reactor (PaSR) involving both methane/air premixed and non-premixed combustion with chemistry described by the 53-species GRI-Mech 3.0 mechanism and the 129-species USC-Mech II mechanism augmented with recently updated NO x pathways, respectively. Results show that, in the DAC approach, the DRG reduction threshold effectively controls the incurred errors in the predicted temperature and species concentrations. The computational saving achieved by DAC increases with the size of chemical kinetic mechanisms. For the PaSR simulations, DAC achieves a speedup factor of up to three for GRI-Mech 3.0 and up to six for USC-Mech II in simulation time, while at the same time maintaining good accuracy in temperature and species concentration predictions.
Adaptive time stepping in biomolecular dynamics.
Franklin, J; Doniach, S
2005-09-22
We present an adaptive time stepping scheme based on the extrapolative method of Barth and Schlick [LN, J. Chem. Phys. 109, 1633 (1998)] to numerically integrate the Langevin equation with a molecular-dynamics potential. This approach allows us to use (on average) a time step for the strong nonbonded force integration corresponding to half the period of the fastest bond oscillation, without compromising the slow degrees of freedom in the problem. We show with simple examples how the dynamic step size stabilizes integration operators, and discuss some of the limitations of such stability. The method introduced uses a slightly more accurate inner integrator than LN to accommodate the larger steps. The adaptive time step approach reproduces temporal features of the bovine pancreatic trypsin inhibitor (BPTI) test system (similar to the one used in the original introduction of LN) compared to short-time integrators, but with energies that are shifted with respect to both LN, and traditional stochastic versions of Verlet. Although the introduction of longer steps has the effect of systematically heating the bonded components of the potential, the temporal fluctuations of the slow degrees of freedom are reproduced accurately. The purpose of this paper is to display a mechanism by which the resonance traditionally associated with using time steps corresponding to half the period of oscillations in molecular dynamics can be avoided. This has theoretical utility in terms of designing numerical integration schemes--the key point is that by factoring a propagator so that time steps are not constant one can recover stability with an overall (average) time step at a resonance frequency. There are, of course, limitations to this approach associated with the complicated, nonlinear nature of the molecular-dynamics (MD) potential (i.e., it is not as straightforward as the linear test problem we use to motivate the method). While the basic notion remains in the full Newtonian problem
Dynamic load-carrying capacity and inverse dynamics of multiple cooperating robotic manipulators
Wang, L.C.T.; Ming Jun Kuo . Dept. of Mechanical Engineering)
1994-02-01
A unified method for computing the dynamic load-carrying capacity (DLCC) and the inverse dynamics of multiple cooperating robotic manipulators is developed in this paper. In this method, the kinematic constraints and the governing dynamic equations of the multi-robot system are formulated in the joint space based on a coordinate partitioning technique and the generalized D'Alembert's principle. This approach not only gives the minimum number of equations but also allows separate computations of the dynamics of the robots and that of the payload. The upper limit of the DLCC at any points on a given trajectory is obtained by solving a small-size linear programming problem. An iterative algorithm for evaluating the maximum allowable DLCC of the trajectory is also developed. After the DLCC is determined, the optimum distribution of actuator torques are computed by using a quadratic programming technique. The proposed method is conceptually straightforward, and it is applicable to wide varieties of multi-robot systems as well as fully parallel robots with redundant actuators.
Analysis of forward and inverse problems in chemical dynamics and spectroscopy
Rabitz, H.
1993-12-01
The overall scope of this research concerns the development and application of forward and inverse analysis tools for problems in chemical dynamics and chemical kinetics. The chemical dynamics work is specifically associated with relating features in potential surfaces and resultant dynamical behavior. The analogous inverse research aims to provide stable algorithms for extracting potential surfaces from laboratory data. In the case of chemical kinetics, the focus is on the development of systematic means to reduce the complexity of chemical kinetic models. Recent progress in these directions is summarized below.
NASA Astrophysics Data System (ADS)
Kawaguchi, Jun'ichiro; Miyazawa, Yoshikazu; Ninomiya, Tetsujiro
This paper focuses on design and evaluation of a flight control law based on the hierarchy-structured dynamic inversion approach, where a general fixed-wing aircraft system is decomposed into four small subsystems according to the time scales inherent in the dynamics and dynamic inversion is applied to each subsystem. The hierarchy-structured dynamic inversion approach considerably simplifies the flight control design and also features universal design of flight control systems through real-time utilization of the vehicle's 6DOF simulation model on board. In this paper, the outline of the proposed approach is presented in the first place followed by a numerical simulation using the highly reliable ALFLEX flight simulation model to ensure the validity of the approach. A root sum square (RSS) analysis is finally conducted to guarantee robustness against wind conditions and some influential parameters.
On trajectory generation for flexible space crane: Inverse dynamics analysis by LATDYN
NASA Technical Reports Server (NTRS)
Chen, G.-S.; Housner, J. M.; Wu, S.-C.; Chang, C.-W.
1989-01-01
For future in-space construction facility, one or more space cranes capable of manipulating and positioning large and massive spacecraft components will be needed. Inverse dynamics was extensively studied as a basis for trajectory generation and control of robot manipulators. The focus here is on trajectory generation in the gross-motion phase of space crane operation. Inverse dynamics of the flexible crane body is much more complex and intricate as compared with rigid robot link. To model and solve the space crane's inverse dynamics problem, LATDYN program which employs a three-dimensional finite element formulation for the multibody truss-type structures will be used. The formulation is oriented toward a joint dominated structure which is suitable for the proposed space crane concept. To track a planned trajectory, procedures will be developed to obtain the actuation profile and dynamics envelope which are pertinent to the design and performance requirements of the space crane concept.
Dynamics of the inverse MAPLE nanoparticle deposition process
NASA Astrophysics Data System (ADS)
Steiner, Matthew A.; Fitz-Gerald, James M.
2015-05-01
Matrix-assisted pulsed laser evaporation (MAPLE) is a processing technique by which laser-sensitive materials are dissolved or placed into colloidal solution with a strongly absorbing sacrificial solvent, which when frozen into a solid target and irradiated under vacuum disperses the undamaged solute material onto a desired substrate. We present an inversion of the original MAPLE process, where the irradiation of metal-based acetate precursors in solution with UV transparent water results in the deposition of inorganic nanoparticles. A theory is forwarded to explain the underlying multiscale sequence of events that control the inverse MAPLE process from acetate decomposition to nanoparticle formation and subsequent ejection. Support for this theory is provided through the analysis of deposited nanoparticles and by novel characterization of MAPLE targets post-irradiation via cryostage scanning electron microscopy. Ejection is shown to proceed through the same phase-explosion mechanism that drives conventional MAPLE, relating the two techniques and advancing the broader understanding of MAPLE deposition processes.
Adaptive fuzzy control with smooth inverse for nonlinear systems preceded by non-symmetric dead-zone
NASA Astrophysics Data System (ADS)
Wang, Xingjian; Wang, Shaoping
2016-07-01
In this study, the adaptive output feedback control problem of a class of nonlinear systems preceded by non-symmetric dead-zone is considered. To cope with the possible control signal chattering phenomenon which is caused by non-smooth dead-zone inverse, a new smooth inverse is proposed for non-symmetric dead-zone compensation. For the systematic design procedure of the adaptive fuzzy control algorithm, we combine the backstepping technique and small-gain approach. The Takagi-Sugeno fuzzy logic systems are used to approximate unknown system nonlinearities. The closed-loop stability is studied by using small gain theorem and the closed-loop system is proved to be semi-globally uniformly ultimately bounded. Simulation results indicate that, compared to the algorithm with the non-smooth inverse, the proposed control strategy can achieve better tracking performance and the chattering phenomenon can be avoided effectively.
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.
Arnold, Alexander; Bruhns, Otto T; Mosler, Jörn
2011-07-21
A novel finite element formulation suitable for computing efficiently the stiffness distribution in soft biological tissue is presented in this paper. For that purpose, the inverse problem of finite strain hyperelasticity is considered and solved iteratively. In line with Arnold et al (2010 Phys. Med. Biol. 55 2035), the computing time is effectively reduced by using adaptive finite element methods. In sharp contrast to previous approaches, the novel mesh adaption relies on an r-adaption (re-allocation of the nodes within the finite element triangulation). This method allows the detection of material interfaces between healthy and diseased tissue in a very effective manner. The evolution of the nodal positions is canonically driven by the same minimization principle characterizing the inverse problem of hyperelasticity. Consequently, the proposed mesh adaption is variationally consistent. Furthermore, it guarantees that the quality of the numerical solution is improved. Since the proposed r-adaption requires only a relatively coarse triangulation for detecting material interfaces, the underlying finite element spaces are usually not rich enough for predicting the deformation field sufficiently accurately (the forward problem). For this reason, the novel variational r-refinement is combined with the variational h-adaption (Arnold et al 2010) to obtain a variational hr-refinement algorithm. The resulting approach captures material interfaces well (by using r-adaption) and predicts a deformation field in good agreement with that observed experimentally (by using h-adaption).
NASA Astrophysics Data System (ADS)
Arnold, Alexander; Bruhns, Otto T.; Mosler, Jörn
2011-07-01
A novel finite element formulation suitable for computing efficiently the stiffness distribution in soft biological tissue is presented in this paper. For that purpose, the inverse problem of finite strain hyperelasticity is considered and solved iteratively. In line with Arnold et al (2010 Phys. Med. Biol. 55 2035), the computing time is effectively reduced by using adaptive finite element methods. In sharp contrast to previous approaches, the novel mesh adaption relies on an r-adaption (re-allocation of the nodes within the finite element triangulation). This method allows the detection of material interfaces between healthy and diseased tissue in a very effective manner. The evolution of the nodal positions is canonically driven by the same minimization principle characterizing the inverse problem of hyperelasticity. Consequently, the proposed mesh adaption is variationally consistent. Furthermore, it guarantees that the quality of the numerical solution is improved. Since the proposed r-adaption requires only a relatively coarse triangulation for detecting material interfaces, the underlying finite element spaces are usually not rich enough for predicting the deformation field sufficiently accurately (the forward problem). For this reason, the novel variational r-refinement is combined with the variational h-adaption (Arnold et al 2010) to obtain a variational hr-refinement algorithm. The resulting approach captures material interfaces well (by using r-adaption) and predicts a deformation field in good agreement with that observed experimentally (by using h-adaption).
Adaptive forward-inverse modeling of reservoir fluids away from wellbores
Ziagos, J P; Gelinas, R J; Doss, S K; Nelson, R G
1999-07-30
This Final Report contains the deliverables of the DeepLook Phase I project entitled, ''Adaptive Forward-Inverse Modeling of Reservoir Fluids Away from Wellbores''. The deliverables are: (i) a description of 2-D test problem results, analyses, and technical descriptions of the techniques used, (ii) a listing of program setup commands that construct and execute the codes for selected test problems (these commands are in mathematical terminology, which reinforces technical descriptions in the text), and (iii) an evaluation and recommendation regarding continuance of this project, including considerations of possible extensions to 3-D codes, additional technical scope, and budget for the out-years. The far-market objective in this project is to develop advanced technologies that can help locate and enhance the recovery of oil from heterogeneous rock formations. The specific technical objective in Phase I was to develop proof-of-concept of new forward and inverse (F-I) modeling techniques [Gelinas et al, 1998] that seek to enhance estimates (images) of formation permeability distributions and fluid motion away from wellbore volumes. This goes to the heart of improving industry's ability to jointly image reservoir permeability and flow predictions of trapped and recovered oil versus time. The estimation of formation permeability away from borehole measurements is an ''inverse'' problem. It is an inseparable part of modeling fluid flows throughout the reservoir in efforts to increase the efficiency of oil recovery at minimum cost. Classic issues of non-uniqueness, mathematical instability, noise effects, and inadequate numerical solution techniques have historically impeded progress in reservoir parameter estimations. Because information pertaining to fluid and rock properties is always sampled sparsely by wellbore measurements, a successful method for interpolating permeability and fluid data between the measurements must be: (i) physics-based, (ii) conditioned by
Inversion Of Dynamical Equations For Control Of Attitude
NASA Technical Reports Server (NTRS)
Bach, Ralph; Paielli, Russell
1995-01-01
Method of inverting nonlinear equations of rotational dynamics of rigid body used to design feedback control of orientation of body. Applicable to both direction-cosine and quaternion formulations suitable for large-angle maneuvers. Exploiting some apparently little-known properties of direction cosine and quaternion formulations, method leads to equations for model-follower control system that exhibits exactly linear attitude-error dynamics. Quarternion system more robust in responding to large roll-angle commands.
Classical and quantum dynamics in an inverse square potential
NASA Astrophysics Data System (ADS)
Guillaumín-España, Elisa; Núñez-Yépez, H. N.; Salas-Brito, A. L.
2014-10-01
The classical motion of a particle in a 3D inverse square potential with negative energy, E, is shown to be geodesic, i.e., equivalent to the particle's free motion on a non-compact phase space manifold irrespective of the sign of the coupling constant. We thus establish that all its classical orbits with E < 0 are unbounded. To analyse the corresponding quantum problem, the Schrödinger equation is solved in momentum space. No discrete energy levels exist in the unrenormalized case and the system shows a complete "fall-to-the-center" with an energy spectrum unbounded by below. Such behavior corresponds to the non-existence of bound classical orbits. The symmetry of the problem is SO(3) × SO(2, 1) corroborating previously obtained results.
Classical and quantum dynamics in an inverse square potential
Guillaumín-España, Elisa; Núñez-Yépez, H. N.; Salas-Brito, A. L.
2014-10-15
The classical motion of a particle in a 3D inverse square potential with negative energy, E, is shown to be geodesic, i.e., equivalent to the particle's free motion on a non-compact phase space manifold irrespective of the sign of the coupling constant. We thus establish that all its classical orbits with E < 0 are unbounded. To analyse the corresponding quantum problem, the Schrödinger equation is solved in momentum space. No discrete energy levels exist in the unrenormalized case and the system shows a complete “fall-to-the-center” with an energy spectrum unbounded by below. Such behavior corresponds to the non-existence of bound classical orbits. The symmetry of the problem is SO(3) × SO(2, 1) corroborating previously obtained results.
Dynamics of lee waves on the boundary layer inversion
NASA Astrophysics Data System (ADS)
Sachsperger, Johannes; Serafin, Stefano; Grubišić, Vanda
2016-04-01
Lee waves are horizontally propagating non-hydrostatic internal gravity waves that may be generated when stratified flow is lifted over a mountain. Depending on the upstream conditions, two types of lee waves can be distinguished. First, resonant lee waves, which are often explored in the context of Scorer's theory of wave trapping in a two-layer atmosphere, where a discontinuity in the Scorer parameter - with evanescent conditions in the upper layer - gives rise to trapped waves. Second, interfacial lee waves, which may form along a density discontinuity, e.g. a temperature inversion, similar to surface waves on a free water surface. While resonant lee waves have been studied extensively, interfacial lee waves were only rarely discussed in meteorological literature so far. For example, observational studies as well as systematic studies on the wavelength dependencies still seem to be lacking. In this work, we modify Scorer's wave trapping theory by applying a boundary condition that accounts for a density jump between the two fluid layers. In this case, wave resonance is possible along the density discontinuity even if the lower layer is neutrally stratified. The resulting linear theory can be applied for instance to atmospheric boundary layer flows over complex terrain, where part of the mountain wave energy can be trapped along the inversion that caps the boundary layer. We validate this model with observations taken in the area of Vienna and highlight the lee wavelength dependence on the flow parameters by systematically varying the upstream conditions. Since interfacial waves have transcendental frequency dispersion relationships that cannot be solved analytically, we also discuss the implications of the shallow- and deep-water approximations on the wavelength of the resonant mode.
NASA Astrophysics Data System (ADS)
Llacer, Jorge; Solberg, Timothy D.; Promberger, Claus
2001-10-01
This paper presents a description of tests carried out to compare the behaviour of five algorithms in inverse radiation therapy planning: (1) The Dynamically Penalized Likelihood (DPL), an algorithm based on statistical estimation theory; (2) an accelerated version of the same algorithm; (3) a new fast adaptive simulated annealing (ASA) algorithm; (4) a conjugate gradient method; and (5) a Newton gradient method. A three-dimensional mathematical phantom and two clinical cases have been studied in detail. The phantom consisted of a U-shaped tumour with a partially enclosed 'spinal cord'. The clinical examples were a cavernous sinus meningioma and a prostate case. The algorithms have been tested in carefully selected and controlled conditions so as to ensure fairness in the assessment of results. It has been found that all five methods can yield relatively similar optimizations, except when a very demanding optimization is carried out. For the easier cases, the differences are principally in robustness, ease of use and optimization speed. In the more demanding case, there are significant differences in the resulting dose distributions. The accelerated DPL emerges as possibly the algorithm of choice for clinical practice. An appendix describes the differences in behaviour between the new ASA method and the one based on a patent by the Nomos Corporation.
Inverse simulation as a tool for flight dynamics research—Principles and applications
NASA Astrophysics Data System (ADS)
Thomson, Douglas; Bradley, Roy
2006-05-01
The technique of inverse simulation is finding application in many and varied fields. As the name implies this technique is used to calculate the control action required to achieve a specified system response. The field of aircraft flight dynamics is particularly suited to this form of simulation as the question of what control actions must the pilot (or automatic flight control system) take for the aircraft to fly along a particular trajectory (a landing approach, for example) is often asked. This paper looks specifically at the application of inverse simulation in flight dynamics. The aim is not only to give an overview of the various techniques and applications but also to provide guidance to potential users of the technique on several of the physical and numerical features often observed in the results. An extensive review of the methodologies used within the family of inverse simulations is presented followed by a formal treatment of the theoretical development of inverse simulation as an established technique. A case study involving the inverse simulation of a helicopter flying a slalom manoeuvre is presented to demonstrate the application of inverse simulation in a flight dynamics analysis. An important feature of the use of inverse simulation is that it is necessary to define the output response required-in the case of flight dynamics the required flight path has to be modelled. Some of the methods used are documented, and their validity discussed. The paper also gives an insight into the types of problem which can be addressed by inverse simulation by detailing some of the many applications to which it has been put in the past. These include studies of rotorcraft handling qualities, performance and design, and pilot modelling as well as model validation. An important element of this paper is the formal, theoretical analysis of some of the numerical and physical features exhibited by inverse simulation which should aid potential users to interpret their
The Branching Bifurcation of Adaptive Dynamics
NASA Astrophysics Data System (ADS)
Della Rossa, Fabio; Dercole, Fabio; Landi, Pietro
2015-06-01
We unfold the bifurcation involving the loss of evolutionary stability of an equilibrium of the canonical equation of Adaptive Dynamics (AD). The equation deterministically describes the expected long-term evolution of inheritable traits — phenotypes or strategies — of coevolving populations, in the limit of rare and small mutations. In the vicinity of a stable equilibrium of the AD canonical equation, a mutant type can invade and coexist with the present — resident — types, whereas the fittest always win far from equilibrium. After coexistence, residents and mutants effectively diversify, according to the enlarged canonical equation, only if natural selection favors outer rather than intermediate traits — the equilibrium being evolutionarily unstable, rather than stable. Though the conditions for evolutionary branching — the joint effect of resident-mutant coexistence and evolutionary instability — have been known for long, the unfolding of the bifurcation has remained a missing tile of AD, the reason being related to the nonsmoothness of the mutant invasion fitness after branching. In this paper, we develop a methodology that allows the approximation of the invasion fitness after branching in terms of the expansion of the (smooth) fitness before branching. We then derive a canonical model for the branching bifurcation and perform its unfolding around the loss of evolutionary stability. We cast our analysis in the simplest (but classical) setting of asexual, unstructured populations living in an isolated, homogeneous, and constant abiotic environment; individual traits are one-dimensional; intra- as well as inter-specific ecological interactions are described in the vicinity of a stationary regime.
Neural network with dynamically adaptable neurons
NASA Technical Reports Server (NTRS)
Tawel, Raoul (Inventor)
1994-01-01
This invention is an adaptive neuron for use in neural network processors. The adaptive neuron participates in the supervised learning phase of operation on a co-equal basis with the synapse matrix elements by adaptively changing its gain in a similar manner to the change of weights in the synapse IO elements. In this manner, training time is decreased by as much as three orders of magnitude.
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.
Persistent inversion dynamics and wintertime PM10 air pollution in Alpine valleys
NASA Astrophysics Data System (ADS)
Largeron, Yann; Staquet, Chantal
2016-06-01
The present study investigates persistent inversions dynamics during a whole winter in Alpine valleys of the area of Grenoble (French Alps), and their relationship to PM10 air pollution episodes and synoptic scale meteorology. For this purpose, hourly time series from November to March of PM10 concentration measurements at the bottom of the valleys and of ground-based temperature data at different altitudes are used. A methodology is developed to quantify a simple estimate of the inversion strength from temperature profiles deduced from the ground-based observations. This estimate is shown to be equivalent to the boundary layer heat deficit. A criterion based on this estimate is proposed to identify persistent (more than 3 days) inversions. Persistent inversions are found to occur from November to February and span 35% of the time. It is shown that they are closely related to PM10 pollution episodes, the PM10 concentration increasing with the boundary layer stability as the inversion develops. Polluted episodes are primarily driven by persistent inversions and consequently, pollution is of fully local origin from November to February. In March local dynamics become less important and long-range transport can dominate. Persistent inversions occur systematically during a high-pressure regime, which first triggers a synoptic scale elevated inversion due to the advection of warm air masses in the mid-troposphere. In valleys, the sheltered boundary layer becomes decoupled from the free troposphere, which allows a ground-based inversion to intensify in the following days. An inversion layer of quasi-constant temperature gradient, greater than 5 K km-1, then forms up to an altitude of about 1600 m, close to the average elevation of the summits. If the episode is sufficiently long, a stagnation stage is reached during which daytime insolation produces a shallow convective surface layer which does not destroy the persistent inversion. The inversion break-up occurs rapidly
Recruitment dynamics in adaptive social networks
Shkarayev, Maxim S.; Schwartz, Ira B.; Shaw, Leah B.
2013-01-01
We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime). PMID:25395989
Analytical simulation and inversion of dynamic urban land surface effects
NASA Astrophysics Data System (ADS)
Bayer, P.; Rivera, J.; Blum, P.; Schweizer, D.; Rybach, L.
2015-12-01
Long-term thermal changes at the land surface can be backtracked from borehole temperature profiles. The main focus so far has been on past climate changes, assuming perfect coupling of surface air and ground temperature. In many urbanized areas, however, temperature profiles are heavily perturbed. We find a characteristic bending of urban profiles towards shallow depth, which indicates strong heating from the ground surface during recent decades. This phenomenon is generally described as subsurface urban heat island (UHI) effect, which exists beneath many cities worldwide. Major drivers are land use changes and urban structures that act as long-term heat sources that artificially load the top 100 m of the ground. While variability in land use and coverage are critical factors for reliable borehole climatology, temperature profiles can also be inverted to trace back the combined effect of past urbanization and climate. We present an analytical framework based on the superposition of specific Green's functions for simulating transient land use changes and their effects on borehole temperature profiles. By inversion in a Bayesian framework, flexible calibration of unknown spatially distributed parameter values and their correlation is feasible. The procedure is applied to four temperature logs which are around 200-400 m deep from the city and suburbs of Zurich, Switzerland. These were recorded recently by a temperature sensor and data logger introduced in closed borehole heat exchangers before the start of geothermal operation. At the sites, long-term land use changes are well documented for more than the last century. This facilitated focusing on a few unknown parameters, and we selected the contribution by asphalt and by basements of buildings. It is revealed that for three of the four sites, these two factors dominate the subsurface UHI evolution. At one site, additional factors such as buried district heating networks may play a role. It is demonstrated that site
Dynamic inversion method based on the time-staggered stereo-modeling scheme and its acceleration
NASA Astrophysics Data System (ADS)
Jing, Hao; Yang, Dinghui; Wu, Hao
2016-09-01
A set of second-order differential equations describing the space-time behavior of derivatives of displacement with respect to model parameters (i.e. waveform sensitivities) is obtained via taking the derivative of the original wave equations. The dynamic inversion method obtains sensitivities of the seismic displacement field with respect to earth properties directly by solving differential equations for them instead of constructing sensitivities from the displacement field itself. In this study, we have taken a new perspective on the dynamic inversion method and used acceleration approaches to reduce the computational time and memory usage to improve its ability of performing high-resolution imaging. The dynamic inversion method, which can simultaneously use different waves and multi-component observation data, is appropriate for directly inverting elastic parameters, medium density or wave velocities. Full wave-field information is utilized as much as possible at the expense of a larger amount of calculations. To mitigate the computational burden, two ways are proposed to accelerate the method from a computer-implementation point of view. One is source encoding which uses a linear combination of all shots, and the other is to reduce the amount of calculations on forward modeling. We applied a new finite difference method to the dynamic inversion to improve the computational accuracy and speed up the performance. Numerical experiments indicated that the new finite difference method can effectively suppress the numerical dispersion caused by the discretization of wave equations, resulting in enhanced computational efficiency with less memory cost for seismic modeling and inversion based on the full wave equations. We present some inversion results to demonstrate the validity of this method through both checkerboard and Marmousi models. It shows that this method is also convergent even with big deviations for the initial model. Besides, parallel calculations can be
Identification of dynamic stiffness matrices of elastomeric joints using direct and inverse methods
NASA Astrophysics Data System (ADS)
Noll, Scott; Dreyer, Jason T.; Singh, Rajendra
2013-08-01
New experiments are designed to permit direct comparison between direct and inverse identification methods of the dynamic stiffness matrices of elastomeric joints, including non-diagonal terms. The joints are constructed with combinations of inclined elastomeric cylinders to control non-diagonal terms in the stiffness matrix. The inverse experiment consists of an elastic metal beam end-supported by elastomeric joints coupling the in-plane transverse and longitudinal beam motion. A prior method is extended to identify the joint dynamic stiffness matrices of dimension 3 from limited modal measurements of the beam. The dynamic stiffness and loss factors of the elastomeric cylinders are directly measured in a commercial elastomer test machine in shear, compression, and inclined configurations and a coordinate transformation is used to estimate the kinematic non-diagonal stiffness terms. Agreement is found for both dynamic stiffness and loss factors between the direct and inverse methods at small displacements. Further, the identified joint properties are employed in a model that successfully predicts the modal parameters and accelerance spectra of the inverse experiment. This article provides valuable insight on the difficulties encountered when comparing system and elastomeric component test results.
Control of a high beta maneuvering reentry vehicle using dynamic inversion.
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 perform 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.
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
Light-Directed Dynamic Chirality Inversion in Functional Self-Organized Helical Superstructures.
Bisoyi, Hari Krishna; Li, Quan
2016-02-24
Helical superstructures are widely observed in nature, in synthetic polymers, and in supramolecular assemblies. Controlling the chirality (the handedness) of dynamic helical superstructures of molecular and macromolecular systems by external stimuli is a challenging task, but is of great fundamental significance with appealing morphology-dependent applications. Light-driven chirality inversion in self-organized helical superstructures (i.e. cholesteric, chiral nematic liquid crystals) is currently in the limelight because inversion of the handedness alters the chirality of the circularly polarized light that they selectively reflect, which has wide potential for application. Here we discuss the recent developments toward inversion of the handedness of cholesteric liquid crystals enabled by photoisomerizable chiral molecular switches or motors. Different classes of chiral photoresponsive dopants (guests) capable of conferring light-driven reversible chirality inversion of helical superstructures fabricated from different nematic hosts are discussed. Rational molecular designs of chiral molecular switches toward endowing handedness inversion to the induced helical superstructures of cholesteric liquid crystals are highlighted. This Review is concluded by throwing light on the challenges and opportunities in this emerging frontier, and it is expected to provide useful guidelines toward the development of self-organized soft materials with stimuli-directed chirality inversion capability and multifunctional host-guest systems.
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.
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
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.
Dynamics of adaptive structures: Design through simulations
NASA Technical Reports Server (NTRS)
Park, K. C.; Alexander, S.
1993-01-01
The use of a helical bi-morph actuator/sensor concept by mimicking the change of helical waveform in bacterial flagella is perhaps the first application of bacterial motions (living species) to longitudinal deployment of space structures. However, no dynamical considerations were analyzed to explain the waveform change mechanisms. The objective is to review various deployment concepts from the dynamics point of view and introduce the dynamical considerations from the outset as part of design considerations. Specifically, the impact of the incorporation of the combined static mechanisms and dynamic design considerations on the deployment performance during the reconfiguration stage is studied in terms of improved controllability, maneuvering duration, and joint singularity index. It is shown that intermediate configurations during articulations play an important role for improved joint mechanisms design and overall structural deployability.
Adaptive gauge cooling for complex Langevin dynamics
NASA Astrophysics Data System (ADS)
Bongiovanni, L.; Aarts, G.; Seiler, E.; Sexty, D.; Stamatescu, I. O.
In the case of nonabelian gauge theories with a complex weight, a controlled exploration of the complexified configuration space during a complex Langevin process requires the use of SL(N,C) gauge cooling, in order to minimize the distance from SU(N). Here we show that adaptive gauge cooling can lead to an efficient implementation of this idea. First results for SU(3) Yang-Mills theory in the presence of a nonzero theta-term are presented as well.
NASA Astrophysics Data System (ADS)
Phuong Tran, Anh; Dafflon, Baptiste; Hubbard, Susan S.; Kowalsky, Michael B.; Long, Philip; Tokunaga, Tetsu K.; Williams, Kenneth H.
2016-08-01
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 the 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.
Tran, Anh Phuong; Dafflon, Baptiste; Hubbard, Susan S.; Kowalsky, Michael B.; Long, Philip; Tokunaga, Tetsu K.; Williams, Kenneth H.
2016-08-31
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 themore » dependence of the 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
Lai, Guanyu; Liu, Zhi; Zhang, Yun; Philip Chen, C L
2016-06-01
This paper is concentrated on the problem of adaptive fuzzy tracking control for an uncertain nonlinear system whose actuator is encountered by the asymmetric backlash behavior. First, we propose a new smooth inverse model which can approximate the asymmetric actuator backlash arbitrarily. By applying it, two adaptive fuzzy control scenarios, namely, the compensation-based control scheme and nonlinear decomposition-based control scheme, are then developed successively. It is worth noticing that the first fuzzy controller exhibits a better tracking control performance, although it recourses to a known slope ratio of backlash nonlinearity. The second one further removes the restriction, and also gets a desirable control performance. By the strict Lyapunov argument, both adaptive fuzzy controllers guarantee that the output tracking error is convergent to an adjustable region of zero asymptotically, while all the signals remain semiglobally uniformly ultimately bounded. Lastly, two comparative simulations are conducted to verify the effectiveness of the proposed fuzzy controllers. PMID:27187937
Analog forecasting with dynamics-adapted kernels
NASA Astrophysics Data System (ADS)
Zhao, Zhizhen; Giannakis, Dimitrios
2016-09-01
Analog forecasting is a nonparametric technique introduced by Lorenz in 1969 which predicts the evolution of states of a dynamical system (or observables defined on the states) by following the evolution of the sample in a historical record of observations which most closely resembles the current initial data. Here, we introduce a suite of forecasting methods which improve traditional analog forecasting by combining ideas from kernel methods developed in harmonic analysis and machine learning and state-space reconstruction for dynamical systems. A key ingredient of our approach is to replace single-analog forecasting with weighted ensembles of analogs constructed using local similarity kernels. The kernels used here employ a number of dynamics-dependent features designed to improve forecast skill, including Takens’ delay-coordinate maps (to recover information in the initial data lost through partial observations) and a directional dependence on the dynamical vector field generating the data. Mathematically, our approach is closely related to kernel methods for out-of-sample extension of functions, and we discuss alternative strategies based on the Nyström method and the multiscale Laplacian pyramids technique. We illustrate these techniques in applications to forecasting in a low-order deterministic model for atmospheric dynamics with chaotic metastability, and interannual-scale forecasting in the North Pacific sector of a comprehensive climate model. We find that forecasts based on kernel-weighted ensembles have significantly higher skill than the conventional approach following a single analog.
Applications of analysis of dynamic adaptations in parameter trajectories
van Riel, Natal A. W.; Tiemann, Christian A.; Vanlier, Joep; Hilbers, Peter A. J.
2013-01-01
Metabolic profiling in combination with pathway-based analyses and computational modelling are becoming increasingly important in clinical and preclinical research. Modelling multi-factorial, progressive diseases requires the integration of molecular data at the metabolome, proteome and transcriptome levels. Also the dynamic interaction of organs and tissues needs to be considered. The processes involved cover time scales that are several orders of magnitude different. We report applications of a computational approach to bridge the scales and different levels of biological detail. Analysis of dynamic adaptations in parameter trajectories (ADAPTs) aims to investigate phenotype transitions during disease development and after a therapeutic intervention. ADAPT is based on a time-dependent evolution of model parameters to describe the dynamics of metabolic adaptations. The progression of metabolic adaptations is predicted by identifying necessary dynamic changes in the model parameters to describe the transition between experimental data obtained during different stages. To get a better understanding of the concept, the ADAPT approach is illustrated in a theoretical study. Its application in research on progressive changes in lipoprotein metabolism is also discussed. PMID:23853705
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.
Analysis of forward and inverse problems in chemical dynamics and spectroscopy
Rabitz, H.
1992-01-01
The forward aspects of the research were concerned with mapping the relation between input potential surface structure, and laboratory dynamical and kinetic observables. The research on inverse analysis complemented the forward analysis studies; objective was to develop algorithms for inversion of quality laboratory data, back to underlying potential surfaces. 24 items of research in molecular dynamics and chemical kinetics are reported. The following collisions/reactions were studied: H + H[sub 2], He - H[sub 2], He - Xe/C(0001), thermal explosions, CO/H[sub 2]/O[sub 2], H[sub 2] + HD, H[sup +] + F([sup 2]P[sub 1/2]), He[sup +] + Ne(2p[sup 6]), Na + I, F + H[sub 2], CO - H[sub 2] - O[sub 2].
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.
NASA Astrophysics Data System (ADS)
Goldman, S. P.; Turnbull, D.; Johnson, C.; Chen, J. Z.; Battista, J. J.
2009-05-01
A fast, accurate and stable optimization algorithm is very important for inverse planning of intensity-modulated radiation therapy (IMRT), and for implementing dose-adaptive radiotherapy in the future. Conventional numerical search algorithms with positive beam weight constraints generally require numerous iterations and may produce suboptimal dose results due to trapping in local minima regions of the objective function landscape. A direct solution of the inverse problem using conventional quadratic objective functions without positive beam constraints is more efficient but it will result in unrealistic negative beam weights. We review here a direct solution of the inverse problem that is efficient and does not yield unphysical negative beam weights. In fast inverse dose optimization (FIDO) method the objective function for the optimization of a large number of beamlets is reformulated such that the optimization problem is reducible to a linear set of equations. The optimal set of intensities is then found through a matrix inversion, and negative beamlet intensities are avoided without the need for externally imposed ad hoc conditions. In its original version [S. P. Goldman, J. Z. Chen, and J. J. Battista, in Proceedings of the XIVth International Conference on the Use of Computers in Radiation Therapy, 2004, pp. 112-115; S. P. Goldman, J. Z. Chen, and J. J. Battista, Med. Phys. 32, 3007 (2005)], FIDO was tested on single two-dimensional computed tomography (CT) slices with sharp KERMA beams without scatter, in order to establish a proof of concept which demonstrated that FIDO could be a viable method for the optimization of cancer treatment plans. In this paper we introduce the latest advancements in FIDO that now include not only its application to three-dimensional volumes irradiated by beams with full scatter but include as well a complete implementation of clinical dose-volume constraints including maximum and minimum dose as well as equivalent uniform dose
Adaptive dynamic FBG interrogation utilising erbium-doped fibre
NASA Astrophysics Data System (ADS)
John, R. N.; Read, I.; MacPherson, W. N.
2013-04-01
A dynamic fibre Bragg grating interrogation scheme is investigated using two-wave mixing in erbium-doped fibre, capable of adapting to quasistatic strain and temperature drifts. An interference pattern set up in the erbium-doped fibre creates, due to the photorefractive effect, a dynamic grating capable of wavelength demodulating the FBG signal. The presence of a dynamic grating was verified and then dynamic strain signals from a fibre stretcher were measured. The adaptive nature of the technique was successfully demonstrated by heating the FBG while it underwent dynamic straining leading to detection unlike an alternative arrayed waveguide grating system which simultaneously failed detection. Two gratings were then wavelength division multiplexed with the signal grating receiving approximately 30dB greater signal showing that there was little cross talk in the system.
Inverse kinematic and forward dynamic models of the 2002 Denali fault earthquake, Alaska
Oglesby, D.D.; Dreger, Douglas S.; Harris, R.A.; Ratchkovski, N.; Hansen, R.
2004-01-01
We perform inverse kinematic and forward dynamic models of the M 7.9 2002 Denali fault, Alaska, earthquake to shed light on the rupture process and dynamics of this event, which took place on a geometrically complex fault system in central Alaska. We use a combination of local seismic and Global Positioning System (GPS) data for our kinematic inversion and find that the slip distribution of this event is characterized by three major asperities on the Denali fault. The rupture nucleated on the Susitna Glacier thrust fault, and after a pause, propagated onto the strike-slip Denali fault. Approximately 216 km to the east, the rupture abandoned the Denali fault in favor of the more southwesterly directed Totschunda fault. Three-dimensional dynamic models of this event indicate that the abandonment of the Denali fault for the Totschunda fault can be explained by the Totschunda fault's more favorable orientation with respect to the local stress field. However, a uniform tectonic stress field cannot explain the complex slip pattern in this event. We also find that our dynamic models predict discontinuous rupture from the Denali to Totschunda fault segments. Such discontinuous rupture helps to qualitatively improve our kinematic inverse models. Two principal implications of our study are (1) a combination of inverse and forward modeling can bring insight into earthquake processes that are not possible with either technique alone, and (2) the stress field on geometrically complex fault systems is most likely not due to a uniform tectonic stress field that is resolved onto fault segments of different orientations; rather, other forms of stress heterogeneity must be invoked to explain the observed slip patterns.
Saha, Debajyoti Kumar Shaw, Pankaj; Janaki, M. S.; Sekar Iyengar, A. N.; Ghosh, Sabuj; Mitra, Vramori Michael Wharton, Alpha
2014-03-15
Order-chaos-order was observed in the relaxation oscillations of a glow discharge plasma with variation in the discharge voltage. The first transition exhibits an inverse homoclinic bifurcation followed by a homoclinic bifurcation in the second transition. For the two regimes of observations, a detailed analysis of correlation dimension, Lyapunov exponent, and Renyi entropy was carried out to explore the complex dynamics of the system.
Brain-wide neuronal dynamics during motor adaptation in zebrafish
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
Feasible Muscle Activation Ranges Based on Inverse Dynamics Analyses of Human Walking
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
Feasible muscle activation ranges based on inverse dynamics analyses of human walking.
Simpson, Cole S; Sohn, M Hongchul; Allen, Jessica L; Ting, Lena H
2015-09-18
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.
NASA Astrophysics Data System (ADS)
Foks, Nathan Leon
The interpretation of geophysical data plays an important role in the analysis of potential field data in resource exploration industries. Two categories of interpretation techniques are discussed in this thesis; boundary detection and geophysical inversion. Fault or boundary detection is a method to interpret the locations of subsurface boundaries from measured data, while inversion is a computationally intensive method that provides 3D information about subsurface structure. My research focuses on these two aspects of interpretation techniques. First, I develop a method to aid in the interpretation of faults and boundaries from magnetic data. These processes are traditionally carried out using raster grid and image processing techniques. Instead, I use unstructured meshes of triangular facets that can extract inferred boundaries using mesh edges. Next, to address the computational issues of geophysical inversion, I develop an approach to reduce the number of data in a data set. The approach selects the data points according to a user specified proxy for its signal content. The approach is performed in the data domain and requires no modification to existing inversion codes. This technique adds to the existing suite of compressive inversion algorithms. Finally, I develop an algorithm to invert gravity data for an interfacing surface using an unstructured mesh of triangular facets. A pertinent property of unstructured meshes is their flexibility at representing oblique, or arbitrarily oriented structures. This flexibility makes unstructured meshes an ideal candidate for geometry based interface inversions. The approaches I have developed provide a suite of algorithms geared towards large-scale interpretation of potential field data, by using an unstructured representation of both the data and model parameters.
NASA Astrophysics Data System (ADS)
Rittgers, J. B.; Revil, A.; Mooney, M. A.; Karaoulis, M.; Wodajo, L.; Hickey, C. J.
2016-09-01
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 travel-time 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 re-weighting 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.
Temporal adaptability and the inverse relationship to sensitivity: a parameter identification model.
Langley, Keith
2005-01-01
Following a prolonged period of visual adaptation to a temporally modulated sinusoidal luminance pattern, the threshold contrast of a similar visual pattern is elevated. The adaptive elevation in threshold contrast is selective for spatial frequency, may saturate at low adaptor contrast, and increases as a function of the spatio-temporal frequency of the adapting signal. A model for signal extraction that is capable of explaining these threshold contrast effects of adaptation is proposed. Contrast adaptation in the model is explained by the identification of the parameters of an environmental model: the autocorrelation function of the visualized signal. The proposed model predicts that the adaptability of threshold contrast is governed by unpredicted signal variations present in the visual signal, and thus represents an internal adjustment by the visual system that takes into account these unpredicted signal variations given the additional possibility for signal corruption by additive noise.
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.
Robust adaptive dynamic programming with an application to power systems.
Jiang, Yu; Jiang, Zhong-Ping
2013-07-01
This brief presents a novel framework of robust adaptive dynamic programming (robust-ADP) aimed at computing globally stabilizing and suboptimal control policies in the presence of dynamic uncertainties. A key strategy is to integrate ADP theory with techniques in modern nonlinear control with a unique objective of filling up a gap in the past literature of ADP without taking into account dynamic uncertainties. Neither the system dynamics nor the system order are required to be precisely known. As an illustrative example, the computational algorithm is applied to the controller design of a two-machine power system. PMID:24808528
NASA Astrophysics Data System (ADS)
Ledesma, Ragnar; Bayo, Eduardo
1993-08-01
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 Lagrangian approach is used in formulating the equations of 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.
Adaptive planning for applications with dynamic objectives
NASA Technical Reports Server (NTRS)
Hadavi, Khosrow; Hsu, Wen-Ling; Pinedo, Michael
1992-01-01
We devise a qualitative control layer to be integrated into a real-time multi-agent reactive planner. The reactive planning system consists of distributed planning agents attending to various perspectives of the task environment. Each perspective corresponds to an objective. The set of objectives considered are sometimes in conflict with each other. Each agent receives information about events as they occur, and a set of actions based on heuristics can be taken by the agents. Within the qualitative control scheme, we use a set of qualitative feature vectors to describe the effects of applying actions. A qualitative transition vector is used to denote the qualitative distance between the current state and the target state. We will then apply on-line learning at the qualitative control level to achieve adaptive planning. Our goal is to design a mechanism to refine the heuristics used by the reactive planner every time an action is taken toward achieving the objectives, using feedback from the results of the actions. When the outcome is compared with expectations, our prior objectives may be modified and a new set of objectives (or a new assessment of the relative importance of the different objectives) can be introduced. Because we are able to obtain better estimates of the time-varying objectives, the reactive strategies can be improved and better prediction can be achieved.
Robust adaptive dynamic programming and feedback stabilization of nonlinear systems.
Jiang, Yu; Jiang, Zhong-Ping
2014-05-01
This paper studies the robust optimal control design for a class of uncertain nonlinear systems from a perspective of robust adaptive dynamic programming (RADP). The objective is to fill up a gap in the past literature of adaptive dynamic programming (ADP) where dynamic uncertainties or unmodeled dynamics are not addressed. A key strategy is to integrate tools from modern nonlinear control theory, such as the robust redesign and the backstepping techniques as well as the nonlinear small-gain theorem, with the theory of ADP. The proposed RADP methodology can be viewed as an extension of ADP to uncertain nonlinear systems. Practical learning algorithms are developed in this paper, and have been applied to the controller design problems for a jet engine and a one-machine power system. PMID:24808035
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.
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.
NASA Astrophysics Data System (ADS)
Yamada, Masumi; Mangeney, Anne; Matsushi, Yuki; Moretti, Laurent
2016-09-01
We performed numerical simulations of the 2011 deep-seated Akatani landslide in central Japan to understand the dynamic evolution of friction of the landslide. By comparing the forces obtained from numerical simulation to those resolved from seismic waveform inversion, the coefficient of the friction during sliding was investigated in the range of 0.1-0.4. The simulation assuming standard Coulomb friction shows that the forces obtained by the seismic waveform inversion are well explained using a constant friction of μ = 0.3. A small difference between the residuals of Coulomb simulation and a velocity-dependent simulation suggests that the coefficient of friction over the volume is well constrained as 0.3 most of time during sliding. It suggests the sudden loss of shearing resistance at the onset of sliding, that is, sudden drop of the initial coefficient of friction in our model, which accelerates the deep-seated landslide. Our numerical simulation calibrated by seismic data provides the evolution of dynamic friction with a reasonable resolution in time, which is difficult to obtain from a conventional runout simulation, or seismic waveform inversion alone.
NASA Astrophysics Data System (ADS)
Yamada, Masumi; Mangeney, Anne; Matsushi, Yuki; Moretti, Laurent
2016-06-01
We performed numerical simulations of the 2011 deep-seated Akatani landslide in central Japan to understand the dynamic evolution of friction of the landslide. By comparing the forces obtained from numerical simulation to those resolved from seismic waveform inversion, the coefficient of the friction during sliding was investigated in the range of 0.1 to 0.4. The simulation assuming standard Coulomb friction shows that the forces obtained by the seismic waveform inversion are well explained using a constant friction of μ = 0.3. A small difference between the residuals of Coulomb simulation and a velocity-dependent simulation suggests that the coefficient of friction over the volume is well constrained as 0.3 most of time during sliding. It suggests the sudden loss of shearing resistance at the onset of sliding, i.e., sudden drop of the initial coefficient of friction in our model, which accelerates the deep-seated landslide. Our numerical simulation calibrated by seismic data provides the evolution of dynamic friction with a reasonable resolution in time, which is difficult to obtain from a conventional runout simulation, or seismic waveform inversion alone.
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.
NASA Astrophysics Data System (ADS)
Chen, Wenchao; Seol, Gyungseon; Rinzler, Andrew G.; Guo, Jing
2012-03-01
Carrier dynamics of the electrolyte-induced inversion layer carbon nanotube-silicon Schottky junction solar cells is explored by numerical simulations. Operation mechanisms of the solar cells with and without the electrolyte-induced inversion layer are presented and compared, which clarifies the current flow mechanisms in a solar cell with an induced inversion layer. A heavily doped back contact layer can behave as a hole block layer. In addition to lowering contact resistance and surface recombination, it is particularly useful for improving carrier separation in an electrolyte-induced inversion layer solar cell or a metal-insulator-semiconductor grating solar cell.
Sex speeds adaptation by altering the dynamics of molecular evolution.
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.
Sex speeds adaptation by altering the dynamics of molecular evolution.
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. PMID:26909573
Sex Speeds Adaptation by Altering the Dynamics of Molecular Evolution
McDonald, Michael J.; Rice, Daniel P.; Desai, Michael M.
2016-01-01
Sex and recombination are pervasive throughout nature despite their substantial costs1. Understanding the evolutionary forces that maintain these phenomena is a central challenge in biology2,3. One longstanding hypothesis argues that sex is beneficial because recombination speeds adaptation4. Theory has proposed a number of 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)5,6 or by separating them from deleterious load (the ruby in the rubbish effect)7,8. Previous experiments confirm that sex can increase the rate of adaptation9–17, but these studies did not observe the evolutionary dynamics that drive this effect at the genomic level. Here, we present the first comparison between the sequence-level dynamics of adaptation in experimental sexual and asexual 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. PMID:26909573
Coherent source imaging and dynamic support tracking for inverse scattering using compressive MUSIC
NASA Astrophysics Data System (ADS)
Lee, Okkyun; Kim, Jong Min; Yoo, Jaejoon; Jin, Kyunghwan; Ye, Jong Chul
2011-09-01
The goal of this paper is to develop novel algorithms for inverse scattering problems such as EEG/MEG, microwave imaging, and/or diffuse optical tomograpahy, and etc. One of the main contributions of this paper is a class of novel non-iterative exact nonlinear inverse scattering theory for coherent source imaging and moving targets. Specifically, the new algorithms guarantee the exact recovery under a very relaxed constraint on the number of source and receivers, under which the conventional methods fail. Such breakthrough was possible thanks to the recent theory of compressive MUSIC and its extension using support correction criterion, where partial support are estimated using the conventional compressed sensing approaches, then the remaining supports are estimated using a novel generalized MUSIC criterion. Numerical results using coherent sources in EEG/MEG and dynamic targets confirm that the new algorithms outperform the conventional ones.
Dynamic Adaptive Neural Network Arrays: A Neuromorphic Architecture
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.
NASA Astrophysics Data System (ADS)
Muta, Osamu; Akaiwa, Yoshihiko
In this paper, we propose a simple peak power reduction (PPR) method based on adaptive inversion of parity-check block of codeword in BCH-coded OFDM system. In the proposed method, the entire parity-check block of the codeword is adaptively inversed by multiplying weighting factors (WFs) so as to minimize PAPR of the OFDM signal, symbol-by-symbol. At the receiver, these WFs are estimated based on the property of BCH decoding. When the primitive BCH code with single error correction such as (31,26) code is used, to estimate the WFs, the proposed method employs a significant bit protection method which assigns a significant bit to the best subcarrier selected among all possible subcarriers. With computer simulation, when (31,26), (31,21) and (32,21) BCH codes are employed, PAPR of the OFDM signal at the CCDF (Complementary Cumulative Distribution Function) of 10-4 is reduced by about 1.9, 2.5 and 2.5dB by applying the PPR method, while achieving the BER performance comparable to the case with the perfect WF estimation in exponentially decaying 12-path Rayleigh fading condition.
Gradient-based adaptation of continuous dynamic model structures
NASA Astrophysics Data System (ADS)
La Cava, William G.; Danai, Kourosh
2016-01-01
A gradient-based method of symbolic adaptation is introduced for a class of continuous dynamic models. The proposed model structure adaptation method starts with the first-principles model of the system and adapts its structure after adjusting its individual components in symbolic form. A key contribution of this work is its introduction of the model's parameter sensitivity as the measure of symbolic changes to the model. This measure, which is essential to defining the structural sensitivity of the model, not only accommodates algebraic evaluation of candidate models in lieu of more computationally expensive simulation-based evaluation, but also makes possible the implementation of gradient-based optimisation in symbolic adaptation. The proposed method is applied to models of several virtual and real-world systems that demonstrate its potential utility.
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. PMID:25573196
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.
Kinematic and dynamic inversion of the 16 December earthquake in Northern Chile
NASA Astrophysics Data System (ADS)
Ruiz, S.; Lancieri, M.; Madariaga, R. I.; Sobiesiak, M.; Campos, J. A.
2009-12-01
We study the kinematic and dynamic rupture propagation of the M 6.7, intraplate, intermediate depth, slab push earthquake that occurred 16 December 2007, a month after the large interplate thrust event of Tocopilla, Chile (M 7.7). The occurrence of a slab push event after a large subduction earthquake is well explained by Coulomb stress transfer and crack dynamics. A dense seismic network, equipped with short period and accelerometers was deployed after the event of 14 November 2007 by the Task Force of GFZ Potsdam and the University of Chile in Santiago. This network was in place on December 16 providing the best seismic data set ever recorded for a Chilean earthquake. We have used it to do a detailed study of rupture processes. We localized the main event of December 16 and the aftershocks that occurred within 24 h of the main event. The main event was located at 43 km depth, while the aftershocks distribution covered a circular zone of 5 to 8 km of radius centered on the main shock epicenter and with depth ranging between [39 - 49] km. The aftershocks are distributed on an almost vertical plane that agrees with the almost vertical plane of the fault mechanism (86° dip) and all the aftershock have the same mechanism as the main event. We used eight of the nearest accelerometric records low pass filtered at 1 Hz, two of which were situated right above the hypocenter. We performed a non-linear kinematic inversion based on the neighborhood algorithm (NA) with an L2 norm. The velocity model was derived from previous work by GFZ. The earthquake is very well modeled by a circular rupture of radius between 5 and 8 km that propagated with a very low rupture velocity, that varies between 1 and 2 km/s. We need only a few non-linear parameters to model this event, parameter space has a dimension close to 6. The kinematic solution was validated using a full dynamic inversion method in which the rupture process is modeled using finite differences on a coarse grid with a slip
Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems.
Wang, Chi-Hsu; Lin, Tsung-Chih; Lee, Tsu-Tian; Liu, Han-Leih
2002-01-01
A new hybrid direct/indirect adaptive fuzzy neural network (FNN) controller with a state observer and supervisory controller for a class of uncertain nonlinear dynamic systems is developed in this paper. The hybrid adaptive FNN controller, the free parameters of which can be tuned on-line by an observer-based output feedback control law and adaptive law, is a combination of direct and indirect adaptive FNN controllers. A weighting factor, which can be adjusted by the tradeoff between plant knowledge and control knowledge, is adopted to sum together the control efforts from indirect adaptive FNN controller and direct adaptive FNN controller. Furthermore, a supervisory controller is appended into the FNN controller to force the state to be within the constraint set. Therefore, if the FNN controller cannot maintain the stability, the supervisory controller starts working to guarantee stability. On the other hand, if the FNN controller works well, the supervisory controller will be deactivated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Two nonlinear systems, namely, inverted pendulum system and Chua's (1989) chaotic circuit, are fully illustrated to track sinusoidal signals. The resulting hybrid direct/indirect FNN control systems show better performances, i.e., tracking error and control effort can be made smaller and it is more flexible during the design process.
Dynamic inversion of a Slab-push earthquake in Northern Chile
NASA Astrophysics Data System (ADS)
Ruiz, Sergio; Madariaga, Raul; Lancieri, Maria; Sobesiak, Monika
2010-05-01
We study the dynamic rupture propagation of a M 6.7 intraplate earthquake that occurred 16 December 2007, a month after a large thrust event of Tocopilla, Chile (M 7.7). The occurrence of a slab push event after a large subduction earthquake is well explained by Coulomb stress transfer models and crack dynamics. A dense seismic network, equipped with short period and accelerometers was deployed after the event of 14 November 2007 by the Task Force of GFZ Potsdam and the University of Chile in Santiago. This network was in place on December 16 providing an excellent data set for this earthquake. We used these data to make a detailed study of rupture processes. We localized the main event of December 16 and the aftershocks that occurred within 24 h of the main event. The main event was located at 43 km depth, while the aftershocks distribution covered a circular zone of 5 to 8 km of radius centred on the main shock epicentre. The aftershocks are distributed on an almost vertical plane that agrees with one of the fault planes of the mechanism (86° dip) and all the aftershock have the same mechanism as the main event. We used nearest accelerometric records in order to do dynamic inversion, two of these accelerometers were situated right above the hypocentre. We performed a non-linear dynamic inversion based on the neighbourhood algorithm (NA) and MonteCarlo methods with an L2 norm. The data was initially filtered in the 0.05-1 Hz. The velocity model was derived from previous work by GFZ. The earthquake was modelled using finite differences on a grid of variable size. Friction was modelled by the standard Ida slip weakening friction law. At each step of the inversion more than 32 full numerical simulations are carried in parallel. These simulations have been optimized in order to reduce the computer time to a minimum. The best models that result from dynamic inversion reduced the variance by more than 30 %, these models ruptured a relatively small zone of the fault
Serial and parallel dynamic adaptation of general hybrid meshes
NASA Astrophysics Data System (ADS)
Kavouklis, Christos
The Navier-Stokes equations are a standard mathematical representation of viscous fluid flow. Their numerical solution in three dimensions remains a computationally intensive and challenging task, despite recent advances in computer speed and memory. A strategy to increase accuracy of Navier-Stokes simulations, while maintaining computing resources to a minimum, is local refinement of the associated computational mesh in regions of large solution gradients and coarsening in regions where the solution does not vary appreciably. In this work we consider adaptation of general hybrid meshes for Computational Fluid Dynamics (CFD) applications. Hybrid meshes are composed of four types of elements; hexahedra, prisms, pyramids and tetrahedra, and have been proven a promising technology in accurately resolving fluid flow for complex geometries. The first part of this dissertation is concerned with the design and implementation of a serial scheme for the adaptation of general three dimensional hybrid meshes. We have defined 29 refinement types, for all four kinds of elements. The core of the present adaptation scheme is an iterative algorithm that flags mesh edges for refinement, so that the adapted mesh is conformal. Of primary importance is considered the design of a suitable dynamic data structure that facilitates refinement and coarsening operations and furthermore minimizes memory requirements. A special dynamic list is defined for mesh elements, in contrast with the usual tree structures. It contains only elements of the current adaptation step and minimal information that is utilized to reconstruct parent elements when the mesh is coarsened. In the second part of this work, a new parallel dynamic mesh adaptation and load balancing algorithm for general hybrid meshes is presented. Partitioning of a hybrid mesh reduces to partitioning of the corresponding dual graph. Communication among processors is based on the faces of the interpartition boundary. The distributed
Combined dynamic inversion and QFT flight control of an unstable high performance aircraft
NASA Astrophysics Data System (ADS)
Stout, Perry Walter
Quantitative Feedback Theory (QFT) is a control system synthesis, technique that directly considers system uncertainties and disturbance magnitudes when formulating closed-loop control algorithms. Dynamic Inversion is a nonlinear control system design technique that relies on accurate mathematical models to compute control inputs producing arbitrary system responses. Both techniques have been applied to unstable high performance aircraft flight control, and produced effective aircraft controllers. Both techniques have certain drawbacks: Nonlinear QFT controllers tend to be unnecessarily conservative (the computed controllers have excessive bandwidth) because known system properties are treated as "unknown" disturbances during loop synthesis. Meanwhile Dynamic Inversion control is sensitive to differences between assumed mathematical models and actual system dynamic properties. Combining the two control techniques provides the benefit of both while suffering the drawbacks of neither, as demonstrated by Single Input, Single Output (SISO) control of a constant airspeed, no roll, no yaw nonlinear model of the F-16 aircraft, and by Multi-Input, Multi-Output (MIMO) control of a full six-degree-of-freedom version. Design performance of the combined controllers is verified by reduced actuator efforts and by reduced sensor noise to actuator input (U( s)/n(s)) transfer function magnitudes compared to standard QFT versions.
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. PMID:27267207
Lehikoinen, A.; Huttunen, J.M.J.; Finsterle, S.; Kowalsky, M.B.; Kaipio, J.P.
2009-08-01
We propose an approach for imaging the dynamics of complex hydrological processes. The evolution of electrically conductive fluids in porous media is imaged using time-lapse electrical resistance tomography. The related dynamic inversion problem is solved using Bayesian filtering techniques, that is, it is formulated as a sequential state estimation problem in which the target is an evolving posterior probability density of the system state. The dynamical inversion framework is based on the state space representation of the system, which involves the construction of a stochastic evolution model and an observation model. The observation model used in this paper consists of the complete electrode model for ERT, with Archie's law relating saturations to electrical conductivity. The evolution model is an approximate model for simulating flow through partially saturated porous media. Unavoidable modeling and approximation errors in both the observation and evolution models are considered by computing approximate statistics for these errors. These models are then included in the construction of the posterior probability density of the estimated system state. This approximation error method allows the use of approximate - and therefore computationally efficient - observation and evolution models in the Bayesian filtering. We consider a synthetic example and show that the incorporation of an explicit model for the model uncertainties in the state space representation can yield better estimates than a frame-by-frame imaging approach.
NASA Astrophysics Data System (ADS)
Ying, Sibin; Ai, Jianliang; Luo, Changhang; Wang, Peng
2006-11-01
Non-linear Dynamic Inversion (NDI) is a technique for control law design, which is based on the feedback linearization and achieving desired dynamic response characteristics. NDI requires an ideal and precise model, however, there must be some errors due to the modeling error or actuator faults, therefore the control law designed by NDI has less robustness. Combining with structured singular value μ synthesis method, the system's robustness can be improved notably. The designed controller, which uses the structured singular value μ synthesis method, has high dimensions, and the dimensions must be reduced when we calculate it. This paper presents a new method for the design of robust flight control, which uses structured singular value μ synthesis based on genetic algorithm. The designed controller, which uses this method, can reduce the dimensions obviously compared with the normal method of structured singular value synthesis, so it is easier for application. The presented method is applied to robustness controller design of some super maneuverable fighter. The simulation results show that the dynamic inversion control law achieves a high level of performance in post-stall maneuver condition, and the whole control system has perfect robustness and anti-disturbance ability.
Comparison of inverse dynamics calculated by two- and three-dimensional models during walking.
Alkjaer, T; Simonsen, E B; Dyhre-Poulsen, P
2001-04-01
The purpose of the study was to compare joint moments calculated by a two- (2D) and a three-dimensional (3D) inverse dynamics model to examine how the different approaches influenced the joint moment profiles. Fifteen healthy male subjects participated in the study. A five-camera video system recorded the subjects as they walked across two force plates. The subjects were invited to approach a walking speed of 4.5 km/h. The ankle, knee and hip joint moments in the sagittal plane were calculated by 2D and 3D inverse dynamics analysis and compared. Despite the uniform walking speed (4.53 km/h) and similar footwear, relatively large inter-individual variations were found in the joint moment patterns during the stance phase. The differences between individuals were present in both the 2D and 3D analysis. For the entire sample of subjects the overall time course pattern of the ankle, knee and hip joint moments was almost identical in 2D and 3D. However, statistically significant differences were observed in the magnitude of the moments, which could be explained by differences in the joint centre location and joint axes used in the two approaches. In conclusion, there were differences between the magnitude of the joint moments calculated by 2D and 3D inverse dynamics but the inter-individual variation was not affected by the different models. The simpler 2D model seems therefore appropriate for human gait analysis. However, comparisons of gait data from different studies are problematic if the calculations are based on different approaches. A future perspective for solving this problem could be to introduce a standard proposal for human gait analysis.
[An Examination for Uterine Dynamic Study with Phase-sensitive Inversion-recovery].
Takatsu, Yasuo; Motegi, Shunichi; Miyati, Tosiaki; Yamamura, Kenichirou
2016-01-01
The depth of myometrial invasion in patients with endometrial carcinoma is recognized as an important factor that closely correlates with prognosis. Preoperative assessment of myometrial invasion is essential for planning surgery. To enhance the contrast between myometrium and endometrium including myometrial invasion with endometrial carcinoma, we optimized the sequence parameter with phase-sensitive inversion-recovery (PSIR) in gadolinium dynamic study of uterine corpus. On a 1.5-T magnetic resonance imaging (MRI), images were acquired by three-dimensional (3D) T1 -turbo field echo (TFE) with PSIR sequence and gadolinium-diethylenetriamine pentaacetic acid( Gd-DTPA) diluted phantom (0-5 mmol/L) and myometrium model (manganese chloride tetrahydrate+agar). We calculated the null point and the contrast-to-noise ratio (CNR) at multiple TFE inversion delay times, 200 ms-maximum in each combination; flip angles (FAs), 5-35 degrees; TFE factor, 20-40; and shot interval (SI), 500-1000 ms. We assumed that dynamic scanning time was 30 seconds when the sensitivity encoding factor was 2, namely, in this study, the scanning time was 1 minute with no sensitivity encoding. In addition, we compared CNR between optimized PSIR sequence ande-Thrive. We recognized a successful CNR of the 3D PSIR parameter was TFE inversion delay times, 335 ms; FA, 25 degrees; TFE factor, 20; and SI, 500 ms. In each gadolinium-DTPA diluted phantom, the average CNR of the optimized PSIR sequence was approximately 1.7 times (maximum: 3 times) higher than e-Thrive. Optimizing sequence parameter of PSIR is applicable in gadolinium dynamic study of uterine corpus.
Parallel tetrahedral mesh adaptation with dynamic load balancing
Oliker, Leonid; Biswas, Rupak; Gabow, Harold N.
2000-06-28
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.
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.
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.
Conn, Charlotte E.; Ces, Oscar; Mulet, Xavier; Seddon, John M.; Templer, Richard H.; Finet, Stephanie; Winter, Roland
2006-03-17
The liquid crystalline lamellar (L{sub {alpha}}) to double-diamond inverse bicontinuous cubic (Q{sub II}{sup D}) phase transition for the amphiphile monoelaidin in excess water exhibits a remarkable sequence of structural transformations for pressure or temperature jumps. Our data imply that the transition dynamics depends on a coupling between changes in molecular shape and the geometrical and topological constraints of domain size. We propose a qualitative model for this coupling based on theories of membrane fusion via stalks and existing knowledge of the structure and energetics of bicontinuous cubic phases.
Inversion of Dynamical Scattering from Large-Angle Rocking-Beam Electron Diffraction Patterns
NASA Astrophysics Data System (ADS)
Wang, Feng; Pennington, Robert S.; Koch, Christoph T.
2016-07-01
A method for ab initio structure factor retrieval from large-angle rocking-beam electron diffraction data of thin crystals is described and tested with experimental and simulated data. No additional information, such as atomicity or information about chemical composition, has been made use of. Our numerical experiments show that the inversion of dynamical scattering works best, if the beam tilt range is large and the specimen not too thick, because for moderate multiple scattering, the large tilt amplitude effectively removes local minima in this global optimization problem.
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.
Adaptive network dynamics and evolution of leadership in collective migration
NASA Astrophysics Data System (ADS)
Pais, Darren; Leonard, Naomi E.
2014-01-01
The evolution of leadership in migratory populations depends not only on costs and benefits of leadership investments but also on the opportunities for individuals to rely on cues from others through social interactions. We derive an analytically tractable adaptive dynamic network model of collective migration with fast timescale migration dynamics and slow timescale adaptive dynamics of individual leadership investment and social interaction. For large populations, our analysis of bifurcations with respect to investment cost explains the observed hysteretic effect associated with recovery of migration in fragmented environments. Further, we show a minimum connectivity threshold above which there is evolutionary branching into leader and follower populations. For small populations, we show how the topology of the underlying social interaction network influences the emergence and location of leaders in the adaptive system. Our model and analysis can be extended to study the dynamics of collective tracking or collective learning more generally. Thus, this work may inform the design of robotic networks where agents use decentralized strategies that balance direct environmental measurements with agent interactions.
Direct and Inverse Problems of Item Pool Design for Computerized Adaptive Testing
ERIC Educational Resources Information Center
Belov, Dmitry I.; Armstrong, Ronald D.
2009-01-01
The recent literature on computerized adaptive testing (CAT) has developed methods for creating CAT item pools from a large master pool. Each CAT pool is designed as a set of nonoverlapping forms reflecting the skill levels of an assumed population of test takers. This article presents a Monte Carlo method to obtain these CAT pools and discusses…
Dynamics of thermal inversions on Metropolitan Area of Guadalajara, Jalisco Mexico
NASA Astrophysics Data System (ADS)
Valencia, R.; Tereshchenko, I.; Perez, D. A.; Lizarraga, S. J.; Thermal Inversions, Metropolitan Area of Guadalajara
2013-05-01
This work attempts an analysis of the dynamics of the meteorological variables in the lower troposphere in the Metropolitan Area of Guadalajara (ZMG), Jalisco, Mexico. It was used the radiosonde database 2000-2012, and a classification of synoptic situations typical for different inversions occurring. Preliminary results indicate that surface temperature inversions dominate the climate of the study area, mainly recorded two times during the year. An investment without matching the rainy season and covers the months of June to September where investments are recorded at a frequency below 41%. And a station with investments in the type of radiation surface which covers the months of January to May and November to December, with a frequency above 86% in October as month leaving transition with a frequency of 64%. As surface temperature inversions which most affect human activity in the ZMG by not allowing the dispersion of pollutants, the results show that these investments have a thickness ranging from 50 to 250 meters high, covering this range for 85% of the investments registered with respect to the temperature difference between the base and the apex of the observed reversal of between 1°C to 12°C, where the average is 5°C and 7 °C. While this shows that during most of the year there are temperature inversions in the ZMG, this does not mean that every day you will have concentration of pollutants above the norm, this is due to the influence of synoptic scale phenomena mainly to a combination of large anticyclonic systems of the Pacific Ocean and Atlantic, affecting mostly Mexico during the months of December to February, alternating with waves of Western middle latitudes.
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.
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
Mates, Steven P; Forster, Aaron M; Hunston, Donald; Rhorer, Richard; Everett, Richard K; Simmonds, Kirth E; Bagchi, Amit
2012-10-01
Soft elastomeric materials that mimic real soft human tissues are sought to provide realistic experimental devices to simulate the human body's response to blast loading to aid the development of more effective protective equipment. The dynamic mechanical behavior of these materials is often measured using a Kolsky bar because it can achieve both the high strain rates (>100s(-1)) and the large strains (>20%) that prevail in blast scenarios. Obtaining valid results is challenging, however, due to poor dynamic equilibrium, friction, and inertial effects. To avoid these difficulties, an inverse method was employed to determine the dynamic response of a soft, prospective biomimetic elastomer using Kolsky bar tests coupled with high-speed 3D digital image correlation. Individual tests were modeled using finite elements, and the dynamic stiffness of the elastomer was identified by matching the simulation results with test data using numerical optimization. Using this method, the average dynamic response was found to be nearly equivalent to the quasi-static response measured with stress-strain curves at compressive strains up to 60%, with an uncertainty of ±18%. Moreover, the behavior was consistent with the results in stress relaxation experiments and oscillatory tests although the latter were performed at lower strain levels.
Yamasaki, Taiga; Idehara, Katsutoshi; Xin, Xin
2016-07-01
We propose a new method to estimate muscle activity in a straightforward manner with high accuracy and relatively small computational costs by using the external input of the joint angle and its first to fourth derivatives with respect to time. The method solves the inverse dynamics problem of the skeletal system, the forward dynamics problem of the muscular system, and the load-sharing problem of muscles as a static optimization of neural excitation signals. The external input including the higher-order derivatives is required for a calculation of constraints imposed on the load-sharing problem. The feasibility of the method is demonstrated by the simulation of a simple musculoskeletal model with a single joint. Moreover, the influences of the muscular dynamics, and the higher-order derivatives on the estimation of the muscle activity are demonstrated, showing the results when the time constants of the activation dynamics are very small, and the third and fourth derivatives of the external input are ignored, respectively. It is concluded that the method can have the potential to improve estimation accuracy of muscle activity of highly dynamic motions. PMID:27211782
NASA Astrophysics Data System (ADS)
Panasyuk, Svetlana V.; Hager, Bradford H.
2000-12-01
We perform a joint inversion of Earth's geoid and dynamic topography for radial mantle viscosity structure using a number of models of interior density heterogeneities, including an assessment of the error budget. We identify three classes of errors: those related to the density perturbations used as input, those due to insufficiently constrained observables, and those due to the limitations of our analytical model. We estimate the amplitudes of these errors in the spectral domain. Our minimization function weights the squared deviations of the compared quantities with the corresponding errors, so that the components with more reliability contribute to the solution more strongly than less certain ones. We develop a quasi-analytical solution for mantle flow in a compressible, spherical shell with Newtonian rheology, allowing for continuous radial variations of viscosity, together with a possible reduction of viscosity within the phase change regions due to the effects of transformational superplasticity. The inversion reveals three distinct families of viscosity profiles, all of which have an order of magnitude stiffening within the lower mantle, with a soft D'' layer below. The main distinction among the families is the location of the lowest-viscosity region-directly beneath the lithosphere, just above 400km depth or just above 670km depth. All profiles have a reduction of viscosity within one or more of the major phase transformations, leading to reduced dynamic topography, so that whole-mantle convection is consistent with small surface topography.
Dynamic Load Balancing for Adaptive Meshes using Symmetric Broadcast Networks
NASA Technical Reports Server (NTRS)
Das, Sajal K.; Harvey, Daniel J.; Biswas, Rupak; Saini, Subhash (Technical Monitor)
1998-01-01
Many scientific applications involve grids that lack a uniform underlying structure. These applications are often dynamic in the sense that the grid structure significantly changes between successive phases of execution. In parallel computing environments, mesh adaptation of grids through selective refinement/coarsening has proven to be an effective approach. However, achieving load balance while minimizing inter-processor communication and redistribution costs is a difficult problem. Traditional dynamic load balancers are mostly inadequate because they lack a global view across processors. In this paper, we compare a novel load balancer that utilizes symmetric broadcast networks (SBN) to a successful global load balancing environment (PLUM) created to handle adaptive unstructured applications. Our experimental results on the IBM SP2 demonstrate that performance of the proposed SBN load balancer is comparable to results achieved under PLUM.
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.
NASA Astrophysics Data System (ADS)
Ren, Zhiming; Liu, Yang; Zhang, Qunshan
2014-05-01
Full waveform inversion (FWI) has the potential to provide preferable subsurface model parameters. The main barrier of its applications to real seismic data is heavy computational amount. Numerical modelling methods are involved in both forward modelling and backpropagation of wavefield residuals, which spend most of computational time in FWI. We develop a time-space domain finite-difference (FD) method and adaptive variable-length spatial operator scheme in numerical simulation of viscoacoustic equation and extend them into the viscoacoustic FWI. Compared with conventional FD methods, different operator lengths are adopted for different velocities and quality factors, which can reduce the amount of computation without reducing accuracy. Inversion algorithms also play a significant role in FWI. In conventional single-scale methods, it is likely to converge to local minimums especially when the initial model is far from the real model. To tackle the problem, we introduce the second generation wavelet transform to implement the multiscale FWI. Compared to other multiscale methods, our method has advantages of ease of implementation and better time-frequency local analysis ability. The L2 norm is widely used in FWI and gives invalid model estimates when the data is contaminated with strong non-uniform noises. We apply the L1-norm and the Huber-norm criteria in the time-domain FWI to improve its antinoise ability. Our strategies have been successfully applied in synthetic experiments to both onshore and offshore reflection seismic data. The results of the viscoacoustic Marmousi example indicate that our new FWI scheme consumes smaller computer resources. In addition, the viscoacoustic Overthrust example shows its better convergence and more reasonable velocity and quality factor structures. All these results demonstrate that our method can improve inversion accuracy and computational efficiency of FWI.
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. PMID:25234982
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.
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.
Glassy Dynamics in the Adaptive Immune Response Prevents Autoimmune Disease
NASA Astrophysics Data System (ADS)
Sun, Jun; Earl, David J.; Deem, Michael W.
2005-09-01
The immune system normally protects the human host against death by infection. However, when an immune response is mistakenly directed at self-antigens, autoimmune disease can occur. We describe a model of protein evolution to simulate the dynamics of the adaptive immune response to antigens. Computer simulations of the dynamics of antibody evolution show that different evolutionary mechanisms, namely, gene segment swapping and point mutation, lead to different evolved antibody binding affinities. Although a combination of gene segment swapping and point mutation can yield a greater affinity to a specific antigen than point mutation alone, the antibodies so evolved are highly cross reactive and would cause autoimmune disease, and this is not the chosen dynamics of the immune system. We suggest that in the immune system’s search for antibodies, a balance has evolved between binding affinity and specificity.
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
An Efficient Dynamically Adaptive Mesh for Potentially Singular Solutions
NASA Astrophysics Data System (ADS)
Ceniceros, Hector D.; Hou, Thomas Y.
2001-09-01
We develop an efficient dynamically adaptive mesh generator for time-dependent problems in two or more dimensions. The mesh generator is motivated by the variational approach and is based on solving a new set of nonlinear elliptic PDEs for the mesh map. When coupled to a physical problem, the mesh map evolves with the underlying solution and maintains high adaptivity as the solution develops complicated structures and even singular behavior. The overall mesh strategy is simple to implement, avoids interpolation, and can be easily incorporated into a broad range of applications. The efficacy of the mesh is first demonstrated by two examples of blowing-up solutions to the 2-D semilinear heat equation. These examples show that the mesh can follow with high adaptivity a finite-time singularity process. The focus of applications presented here is however the baroclinic generation of vorticity in a strongly layered 2-D Boussinesq fluid, a challenging problem. The moving mesh follows effectively the flow resolving both its global features and the almost singular shear layers developed dynamically. The numerical results show the fast collapse to small scales and an exponential vorticity growth.
Generalization in Adaptation to Stable and Unstable Dynamics
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
Dynamically adaptive mesh refinement technique for image reconstruction in optical tomography.
Soloviev, Vadim Y; Krasnosselskaia, Lada V
2006-04-20
A novel adaptive mesh technique is introduced for problems of image reconstruction in luminescence optical tomography. A dynamical adaptation of the three-dimensional scheme based on the finite-volume formulation reduces computational time and balances the ill-posed nature of the inverse problem. The arbitrary shape of the bounding surface is handled by an additional refinement of computational cells on the boundary. Dynamical shrinking of the search volume is introduced to improve computational performance and accuracy while locating the luminescence target. Light propagation in the medium is modeled by the telegraph equation, and the image-reconstruction algorithm is derived from the Fredholm integral equation of the first kind. Stability and computational efficiency of the introduced method are demonstrated for image reconstruction of one and two spherical luminescent objects embedded within a breastlike tissue phantom. Experimental measurements are simulated by the solution of the forward problem on a grid of 5x5 light guides attached to the surface of the phantom.
Neural Network Assisted Inverse Dynamic Guidance for Terminally Constrained Entry Flight
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
Neural network assisted inverse dynamic guidance for terminally constrained entry flight.
Zhou, Hao; Rahman, Tawfiqur; 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.
An error function minimization approach for the inverse problem of adaptive mirrors tuning
NASA Astrophysics Data System (ADS)
Vannoni, Maurizio; Yang, Fan; Siewert, Frank; Sinn, Harald
2014-09-01
Adaptive x-ray optics are more and more used in synchrotron beamlines, and it is probable that they will be considered for the future high-power free-electron laser sources, as the European XFEL now under construction in Hamburg, or similar projects now in discussion. These facilities will deliver a high power x-ray beam, with an expected high heat load delivered on the optics. For this reason, bendable mirrors are required to actively compensate the resulting wavefront distortion. On top of that, the mirror could have also intrinsic surface defects, as polishing errors or mounting stresses. In order to be able to correct the mirror surface with a high precision to maintain its challenging requirements, the mirror surface is usually characterized with a high accuracy metrology to calculate the actuators pulse functions and to assess its initial shape. After that, singular value decomposition (SVD) is used to find the signals to be applied into the actuators, to reach the desired surface deformation or correction. But in some cases this approach could be not robust enough for the needed performance. We present here a comparison between the classical SVD method and an error function minimization based on root-mean-square calculation. Some examples are provided, using a simulation of the European XFEL mirrors design as a case of study, and performances of the algorithms are evaluated in order to reach the ultimate quality in different scenarios. The approach could be easily generalized to other situations as well.
Configurational forces and variational mesh adaptation in solid dynamics
NASA Astrophysics Data System (ADS)
Zielonka, Matias G.
This thesis is concerned with the exploration and development of a variational finite element mesh adaption framework for non-linear solid dynamics and its conceptual links with the theory of dynamic configurational forces. The distinctive attribute of this methodology is that the underlying variational principle of the problem under study is used to supply both the discretized fields and the mesh on which the discretization is supported. To this end a mixed-multifield version of Hamilton's principle of stationary action and Lagrange-d'Alembert, principle is proposed, a fresh perspective on the theory of dynamic configurational forces is presented, and a unifying variational formulation that generalizes the framework to systems with general dissipative behavior is developed. A mixed finite element formulation with independent spatial interpolations for deformations and velocities and a mixed variational integrator with independent time interpolations for the resulting nodal parameters is constructed. This discretization is supported on a continuously deforming mesh that is not prescribed at the outset but computed as part of the solution. The resulting space-time discretization satisfies exact discrete configurational force balance and exhibits excellent long term global energy stability behavior. The robustness of the mesh adaption framework is assessed and demonstrated with a set of examples and convergence tests.
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.
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
Constitutional dynamic chemistry: bridge from supramolecular chemistry to adaptive chemistry.
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.
Autonomous Path-Following by Approximate Inverse Dynamics and Vector Field Prediction
NASA Astrophysics Data System (ADS)
Gerlach, Adam R.
In this dissertation, we develop two general frameworks for the navigation and control of autonomous vehicles that must follow predefined paths. These frameworks are designed such that they inherently provide accurate navigation and control of a wide class of systems directly from a model of the vehicle's dynamics. The first framework introduced is the inverse dynamics by radial basis function (IDRBF) algorithm, which exploits the best approximation property of radial basis functions to accurately approximate the inverse dynamics of non-linear systems. This approximation is then used with the known, desired state of the system at a future time point to generate the system input that must be applied to reach the desired state in the specified time interval. The IDRBF algorithm is then tested on two non-linear dynamic systems, and accurate path-following is demonstrated. The second framework introduced is the predictive vector field (PVF) algorithm. The PVF algorithm uses the equations of motion and constraints of the system to predict a set of reachable states by sampling the system's configuration space. By finding and minimizing a continuous mapping between the system's configuration space and a cost space relating the reachable states of the system with a vector field (VF), one can determine the system inputs required to follow the VF. The PVF algorithm is then tested on the Dubin's vehicle and aircraft models, and accurate path-following is demonstrated. As the PVF algorithm's performance is dependent on the quality of the underlying system model and VF, algorithms are introduced for automatically generating VFs for constant altitude paths defined by a series of waypoints and for handling modeling uncertainties. Additionally, we provide a mathematical proof showing that this method can automatically produce VFs of the desired form. To handle modeling uncertainties, we enhance the PVF algorithm with the Gaussian process machine learning framework, enabling the
Dynamic and adaptive data-management in ATLAS
NASA Astrophysics Data System (ADS)
Lassnig, Mario; Garonne, Vincent; Branco, Miguel; Molfetas, Angelos
2010-04-01
Distributed data-management on the grid is subject to huge uncertainties yet static policies govern its usage. Due to the unpredictability of user behaviour, the high-latency and the heterogeneous nature of the environment, distributed data-management on the grid is challenging. In this paper we present the first steps towards a future dynamic data-management system that adapts to the changing conditions and environment. Such a system would eliminate the number of manual interventions and remove unnecessary software layers, thereby providing a higher quality of service to the collaboration.
Adaptive mesh refinement for 1-dimensional gas dynamics
Hedstrom, G.; Rodrigue, G.; Berger, M.; Oliger, J.
1982-01-01
We consider the solution of the one-dimensional equation of gas-dynamics. Accurate numerical solutions are difficult to obtain on a given spatial mesh because of the existence of physical regions where components of the exact solution are either discontinuous or have large gradient changes. Numerical methods treat these phenomena in a variety of ways. In this paper, the method of adaptive mesh refinement is used. A thorough description of this method for general hyperbolic systems is given elsewhere and only properties of the method pertinent to the system are elaborated.
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
NASA Astrophysics Data System (ADS)
Shubitidze, Fridon; Miller, Jonathan S.; Schultz, Gregory M.; Marble, Jay A.
2010-04-01
This paper reports vehicle based electromagnetic induction (EMI) array sensor data inversion and discrimination results. Recent field studies show that EMI arrays, such as the Minelab Single Transmitter Multiple Receiver (STMR), and the Geophex GEM-5 EMI array, provide a fast and safe way to detect subsurface metallic targets such as landmines, unexploded ordnance (UXO) and buried explosives. The array sensors are flexible and easily adaptable for a variety of ground vehicles and mobile platforms, which makes them very attractive for safe and cost effective detection operations in many applications, including but not limited to explosive ordnance disposal and humanitarian UXO and demining missions. Most state-of-the-art EMI arrays measure the vertical or full vector field, or gradient tensor fields and utilize them for real-time threat detection based on threshold analysis. Real field practice shows that the threshold-level detection has high false alarms. One way to reduce these false alarms is to use EMI numerical techniques that are capable of inverting EMI array data in real time. In this work a physically complete model, known as the normalized volume/surface magnetic sources (NV/SMS) model is adapted to the vehicle-based EMI array, such as STMR and GEM-5, data. The NV/SMS model can be considered as a generalized volume or surface dipole model, which in a special limited case coincides with an infinitesimal dipole model approach. According to the NV/SMS model, an object's response to a sensor's primary field is modeled mathematically by a set of equivalent magnetic dipoles, distributed inside the object (i.e. NVMS) or over a surface surrounding the object (i.e. NSMS). The scattered magnetic field of the NSMS is identical to that produced by a set of interacting magnetic dipoles. The amplitudes of the magnetic dipoles are normalized to the primary magnetic field, relating induced magnetic dipole polarizability and the primary magnetic field. The magnitudes of
Experimental Dynamic Characterization of a Reconfigurable Adaptive Precision Truss
NASA Technical Reports Server (NTRS)
Hinkle, J. D.; Peterson, L. D.
1994-01-01
The dynamic behavior of a reconfigurable adaptive truss structure with non-linear joints is investigated. The objective is to experimentally examine the effects of the local non-linearities on the global dynamics of the structure. Amplitude changes in the frequency response functions are measured at micron levels of motion. The amplitude and frequency variations of a number of modes indicate a non-linear Coulomb friction response. Hysteretic bifurcation behavior is also measured at an amplitude approximately equal to the specified free-play in the joint. Under the 1 g pre-load, however, the non-linearity was dominantly characteristic of Coulomb friction with little evidence of free-play stiffening.
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.
CUNHA, J. ADAM; HSU, I-CHOW; POULIOT, JEAN; ROACH, MACK; SHINOHARA, KATSUTO; KURHANEWICZ, JOHN; REED, GALEN; STOIANOVICI, DAN
2011-01-01
To translate any robot into a clinical environment, it is critical that the robot can seamlessly integrate with all the technology of a modern clinic. MRBot, an MR-stealth brachytherapy delivery device, was used in a closed-bore 3T MRI and a clinical brachytherapy cone beam CT suite. Targets included ceramic dummy seeds, MR-Spectroscopy-sensitive metabolite, and a prostate phantom. Acquired DICOM images were exported to planning software to register the robot coordinates in the imager’s frame, contour and verify target locations, create dose plans, and export needle and seed positions to the robot. The coordination of each system element (imaging device, brachytherapy planning system, robot control, robot) was validated with a seed delivery accuracy of within 2 mm in both a phantom and soft tissue. An adaptive workflow was demonstrated by acquiring images after needle insertion and prior to seed deposition. This allows for adjustment if the needle is in the wrong position. Inverse planning (IPSA) was used to generate a seed placement plan and coordinates for ten needles and 29 seeds were transferred to the robot. After every two needles placed, an image was acquired. The placed seeds were identified and validated prior to placing the seeds in the next two needles. The ability to robotically deliver seeds to locations determined by IPSA and the ability of the system to incorporate novel needle patterns were demonstrated. Shown here is the ability to overcome this critical step. An adaptive brachytherapy workflow is demonstrated which integrates a clinical anatomy-based seed location optimization engine and a robotic brachytherapy device. Demonstration of this workflow is a key element of a successful translation to the clinic of the MRI stealth robotic delivery system, MRBot. PMID:20642386
Cunha, J Adam; Hsu, I-Chow; Pouliot, Jean; Roach Iii, Mack; Shinohara, Katsuto; Kurhanewicz, John; Reed, Galen; Stoianovici, Dan
2010-08-01
To translate any robot into a clinical environment, it is critical that the robot can seamlessly integrate with all the technology of a modern clinic. MRBot, an MR-stealth brachytherapy delivery device, was used in a closed-bore 3T MRI and a clinical brachytherapy cone beam CT suite. Targets included ceramic dummy seeds, MR-Spectroscopy-sensitive metabolite, and a prostate phantom. Acquired DICOM images were exported to planning software to register the robot coordinates in the imager's frame, contour and verify target locations, create dose plans, and export needle and seed positions to the robot. The coordination of each system element (imaging device, brachytherapy planning system, robot control, robot) was validated with a seed delivery accuracy of within 2 mm in both a phantom and soft tissue. An adaptive workflow was demonstrated by acquiring images after needle insertion and prior to seed deposition. This allows for adjustment if the needle is in the wrong position. Inverse planning (IPSA) was used to generate a seed placement plan and coordinates for ten needles and 29 seeds were transferred to the robot. After every two needles placed, an image was acquired. The placed seeds were identified and validated prior to placing the seeds in the next two needles. The ability to robotically deliver seeds to locations determined by IPSA and the ability of the system to incorporate novel needle patterns were demonstrated. Shown here is the ability to overcome this critical step. An adaptive brachytherapy workflow is demonstrated which integrates a clinical anatomy-based seed location optimization engine and a robotic brachytherapy device. Demonstration of this workflow is a key element of a successful translation to the clinic of the MRI stealth robotic delivery system, MRBot.
Inter-limb interference during bimanual adaptation to dynamic environments.
Casadio, Maura; Sanguineti, Vittorio; Squeri, Valentina; Masia, Lorenzo; Morasso, Pietro
2010-05-01
Skillful manipulation of objects often requires the spatio-temporal coordination of both hands and, at the same time, the compensation of environmental forces. In bimanual coordination, movements of the two hands may be coupled because each hand needs to compensate the forces generated by the other hand or by an object operated by both hands (dynamic coupling), or because the two hands share the same workspace (spatial coupling). We examined how spatial coupling influences bimanual coordination, by looking at the adaptation of velocity-dependent force fields during a task in which the two hands simultaneously perform center-out reaching movements with the same initial position and the same targets, equally spaced on a circle. Subjects were randomly allocated to two groups, which differed in terms of the force fields they were exposed to: in one group (CW-CW), force fields had equal clockwise orientations in both hands; in the other group (CCW-CW), they had opposite orientations. In both groups, in randomly selected trials (catch trials) of the adaptation phase, the force fields were unexpectedly removed. Adaptation was quantified in terms of the changes of directional error for both hand trajectories. Bimanual coordination was quantified in terms of inter-limb longitudinal and sideways displacements, in force field and in catch trials. Experimental results indicate that both arms could simultaneously adapt to the two force fields. However, in the CCW-CW group, adaptation was incomplete for the movements from the central position to the more distant targets with respect to the body. In addition, in this group the left hand systematically leads in the movements toward targets on the left of the starting position, whereas the right hand leads in the movements to targets on the right. We show that these effects are due to a gradual sideways shift of the hands, so that during movements the left hand tends to consistently remain at the left of the right hand. These
Selective host molecules obtained by dynamic adaptive chemistry.
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.
NASA Astrophysics Data System (ADS)
Yang, Ting; Gurnis, Michael
2016-11-01
Lateral viscosity variations (LVVs) in the mantle influence geodynamic processes and their surface expressions. With the observed long-wavelength geoid, free-air anomaly, gravity gradient in three directions and discrete, high-accuracy residual topography, we invert for depth- and temperature-dependent and tectonically regionalized mantle viscosity with a mantle flow model. The inversions suggest that long-wavelength gravitational and topographic signals are mainly controlled by the radial viscosity profile; the pre-Cambrian lithosphere viscosity is slightly (˜ one order of magnitude) higher than that of oceanic and Phanerozoic lithosphere; plate margins are substantially weaker than plate interiors; and viscosity has only a weak apparent, dependence on temperature, suggesting either a balancing between factors or a smoothing of actual higher amplitude, but short wavelength, LVVs. The predicted large-scale lithospheric stress regime (compression or extension) is consistent with the world stress map (thrust or normal faulting). Both recent compiled high-accuracy residual topography and the predicted dynamic topography yield ˜1 km amplitude long-wavelength dynamic topography, inconsistent with recent studies suggesting amplitudes of ˜100 to ˜500 m. Such studies use a constant, positive admittance (transfer function between topography and gravity), in contrast to the evidence which shows that the earth has a spatially and wavelength-dependent admittance, with large, negative admittances between ˜4000 and ˜104 km wavelengths.
Frequency adaptation for enhanced radiation force amplitude in dynamic elastography.
Ouared, Abderrahmane; Montagnon, Emmanuel; Kazemirad, Siavash; Gaboury, Louis; Robidoux, André; Cloutier, Guy
2015-08-01
In remote dynamic elastography, the amplitude of the generated displacement field is directly related to the amplitude of the radiation force. Therefore, displacement improvement for better tissue characterization requires the optimization of the radiation force amplitude by increasing the push duration and/or the excitation amplitude applied on the transducer. The main problem of these approaches is that the Food and Drug Administration (FDA) thresholds for medical applications and transducer limitations may be easily exceeded. In the present study, the effect of the frequency used for the generation of the radiation force on the amplitude of the displacement field was investigated. We found that amplitudes of displacements generated by adapted radiation force sequences were greater than those generated by standard nonadapted ones (i.e., single push acoustic radiation force impulse and supersonic shear imaging). Gains in magnitude were between 20 to 158% for in vitro measurements on agar-gelatin phantoms, and 170 to 336% for ex vivo measurements on a human breast sample, depending on focus depths and attenuations of tested samples. The signal-to-noise ratio was also improved more than 4-fold with adapted sequences. We conclude that frequency adaptation is a complementary technique that is efficient for the optimization of displacement amplitudes. This technique can be used safely to optimize the deposited local acoustic energy without increasing the risk of damaging tissues and transducer elements.
Structural dynamic health monitoring of adaptive CFRP structures
NASA Astrophysics Data System (ADS)
Kaiser, Stephan; Melcher, Joerg; Breitbach, Elmar J.; Sachau, Delf
1999-07-01
The DLR Institute of Structural Mechanics is engaged in the construction and optimization of adaptive structures for aerospace and terrestrial applications. Due to the FFS- Project, one of the recent works of the Institute is the reduction of buffet induced vibration loads at a fin. The construction of modern aircrafts is influenced b the increasing use of fiber composites. They have more specific stiffness and strength properties than metals. On the other hand the layered structure leads to new kinds of damages like delaminations. In the fin interface there are actuators and sensors integrated. Therefore the fin is connected with a controller. For the extension of this adaptive system towards an on-line tool for health monitoring this controller can be used as an identifier of the structure's modal parameters. The most promising procedure is based on MX filters. These filters constitute the filter coefficients from which a fast transformation procedure extracts the modal parameters. The changes of these parameters are related to the location and extent of the damage. So when using the already integrate controller for system identification, one can have a low-cost on-line damage detection for dynamic adaptive structures. First off-line test at CFRP plates have shown the ability to detect delaminations.
Cheung, Mark C. M.; Boerner, P.; Schrijver, C. J.; Malanushenko, A.; Testa, P.; Chen, F.; Peter, H.
2015-07-10
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.
Adaptive dynamic programming as a theory of sensorimotor control.
Jiang, Yu; Jiang, Zhong-Ping
2014-08-01
Many characteristics of sensorimotor control can be explained by models based on optimization and optimal control theories. However, most of the previous models assume that the central nervous system has access to the precise knowledge of the sensorimotor system and its interacting environment. This viewpoint is difficult to be justified theoretically and has not been convincingly validated by experiments. To address this problem, this paper presents a new computational mechanism for sensorimotor control from a perspective of adaptive dynamic programming (ADP), which shares some features of reinforcement learning. The ADP-based model for sensorimotor control suggests that a command signal for the human movement is derived directly from the real-time sensory data, without the need to identify the system dynamics. An iterative learning scheme based on the proposed ADP theory is developed, along with rigorous convergence analysis. Interestingly, the computational model as advocated here is able to reproduce the motor learning behavior observed in experiments where a divergent force field or velocity-dependent force field was present. In addition, this modeling strategy provides a clear way to perform stability analysis of the overall system. Hence, we conjecture that human sensorimotor systems use an ADP-type mechanism to control movements and to achieve successful adaptation to uncertainties present in the environment.
Adaptation tunes cortical dynamics to a critical regime during vision
NASA Astrophysics Data System (ADS)
Shew, Woodrow; Clawson, Wesley; Pobst, Jeff; Karimipanah, Yahya; Wright, Nathaniel; Wessel, Ralf; Shew Lab Team; Wessel Lab Team
2015-03-01
A long-standing hypothesis at the interface of physics and neuroscience is that neural networks self-organize to the critical point of a phase transition, thereby optimizing aspects of sensory information processing. This idea is partially supported by strong evidence for critical dynamics observed in the cerebral cortex, but has not been tested in systems with significant sensory input. Thus, the foundations of this hypothesis - the self-organization process and how it manifests during strong sensory input - remain unstudied experimentally. Here we report microelectrode array measurements from visual cortex of turtles during visual stimulation of the retina. We show experimentally and in a computational model that strong sensory input initially elicits cortical network dynamics that are not critical, but adaptive changes in the network rapidly tune the system to criticality. This conclusion is based on observations of multifaceted scaling laws predicted to occur at criticality. Our findings establish sensory adaptation as a self-organizing mechanism which maintains criticality in visual cortex during sensory information processing. Supported by NSF CRCNS Grant 1308174.
Adaptive dynamic programming as a theory of sensorimotor control.
Jiang, Yu; Jiang, Zhong-Ping
2014-08-01
Many characteristics of sensorimotor control can be explained by models based on optimization and optimal control theories. However, most of the previous models assume that the central nervous system has access to the precise knowledge of the sensorimotor system and its interacting environment. This viewpoint is difficult to be justified theoretically and has not been convincingly validated by experiments. To address this problem, this paper presents a new computational mechanism for sensorimotor control from a perspective of adaptive dynamic programming (ADP), which shares some features of reinforcement learning. The ADP-based model for sensorimotor control suggests that a command signal for the human movement is derived directly from the real-time sensory data, without the need to identify the system dynamics. An iterative learning scheme based on the proposed ADP theory is developed, along with rigorous convergence analysis. Interestingly, the computational model as advocated here is able to reproduce the motor learning behavior observed in experiments where a divergent force field or velocity-dependent force field was present. In addition, this modeling strategy provides a clear way to perform stability analysis of the overall system. Hence, we conjecture that human sensorimotor systems use an ADP-type mechanism to control movements and to achieve successful adaptation to uncertainties present in the environment. PMID:24962078
Costa, L; Mantha, V R; Silva, A J; Fernandes, R J; Marinho, D A; Vilas-Boas, J P; Machado, L; Rouboa, A
2015-07-16
Computational fluid dynamics (CFD) plays an important role to quantify, understand and "observe" the water movements around the human body and its effects on drag (D). We aimed to investigate the flow effects around the swimmer and to compare the drag and drag coefficient (CD) values obtained from experiments (using cable velocimetry in a swimming pool) with those of CFD simulations for the two ventral gliding positions assumed during the breaststroke underwater cycle (with shoulders flexed and upper limbs extended above the head-GP1; with shoulders in neutral position and upper limbs extended along the trunk-GP2). Six well-trained breaststroke male swimmers (with reasonable homogeneity of body characteristics) participated in the experimental tests; afterwards a 3D swimmer model was created to fit within the limits of the sample body size profile. The standard k-ε turbulent model was used to simulate the fluid flow around the swimmer model. Velocity ranged from 1.30 to 1.70 m/s for GP1 and 1.10 to 1.50 m/s for GP2. Values found for GP1 and GP2 were lower for CFD than experimental ones. Nevertheless, both CFD and experimental drag/drag coefficient values displayed a tendency to jointly increase/decrease with velocity, except for GP2 CD where CFD and experimental values display opposite tendencies. Results suggest that CFD values obtained by single model approaches should be considered with caution due to small body shape and dimension differences to real swimmers. For better accuracy of CFD studies, realistic individual 3D models of swimmers are required, and specific kinematics respected.
Costa, L; Mantha, V R; Silva, A J; Fernandes, R J; Marinho, D A; Vilas-Boas, J P; Machado, L; Rouboa, A
2015-07-16
Computational fluid dynamics (CFD) plays an important role to quantify, understand and "observe" the water movements around the human body and its effects on drag (D). We aimed to investigate the flow effects around the swimmer and to compare the drag and drag coefficient (CD) values obtained from experiments (using cable velocimetry in a swimming pool) with those of CFD simulations for the two ventral gliding positions assumed during the breaststroke underwater cycle (with shoulders flexed and upper limbs extended above the head-GP1; with shoulders in neutral position and upper limbs extended along the trunk-GP2). Six well-trained breaststroke male swimmers (with reasonable homogeneity of body characteristics) participated in the experimental tests; afterwards a 3D swimmer model was created to fit within the limits of the sample body size profile. The standard k-ε turbulent model was used to simulate the fluid flow around the swimmer model. Velocity ranged from 1.30 to 1.70 m/s for GP1 and 1.10 to 1.50 m/s for GP2. Values found for GP1 and GP2 were lower for CFD than experimental ones. Nevertheless, both CFD and experimental drag/drag coefficient values displayed a tendency to jointly increase/decrease with velocity, except for GP2 CD where CFD and experimental values display opposite tendencies. Results suggest that CFD values obtained by single model approaches should be considered with caution due to small body shape and dimension differences to real swimmers. For better accuracy of CFD studies, realistic individual 3D models of swimmers are required, and specific kinematics respected. PMID:26087879
Time-lapse AVO fluid inversion for dynamic reservoir characterization in Delhi Field, Louisiana
NASA Astrophysics Data System (ADS)
Putri, Indah Hermansyah
In the development stage, CO2 injection is becoming more widely used in enhanced oil recovery (EOR). Delhi Oil Field is part of Phases XIII and XIV of the Reservoir Characterization Project (RCP) Colorado School of Mines. The focus of these phases is to monitor the effectiveness of the CO 2 injection in Delhi Field by using multicomponent time-lapse seismic data. In this study, I analyze the amplitude versus offset (AVO) response of the time-lapse P-wave seismic data in order to quantify the fluid probability in the field. RCP acquired four square miles of multicomponent time-lapse seismic in Delhi Field to characterize the field dynamically. RCP's two surveys, monitor 1 and monitor 2, were shot in 2010 and 2011 after the start of CO2 injection in November 2009. Time-lapse AVO modeling was performed. The modeling results show that both the top Tuscaloosa and Paluxy Formations are class III AVO, and change toward class IV AVO by increasing the CO2 saturation in the reservoir. In addition, the Paluxy Formation shows a consistent result between the synthetic and real data, however, the Tuscaloosa Formation is not consistent as it is affected by tuning. AVO fluid inversion (AFI) was performed on both the Tuscaloosa and Paluxy Formations in order to quantify the fluid probability in these formations. The inversion results are confirmed by the pseudo gamma ray model, the porosity model, the permeability model, the pressure model, and the production data. In the Tuscaloosa and Paluxy Formations, oil and CO2 are located in the good quality, high porosity, and high permeability sandstones. The presence of CO2 is also confirmed by the pressure interpretation. Furthermore, production data from both Tuscaloosa and Paluxy Formations confirm the fluid presence in the reservoir.
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
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. PMID:26156766
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.
Analysis of dynamic deformation processes with adaptive KALMAN-filtering
NASA Astrophysics Data System (ADS)
Eichhorn, Andreas
2007-05-01
In this paper the approach of a full system analysis is shown quantifying a dynamic structural ("white-box"-) model for the calculation of thermal deformations of bar-shaped machine elements. The task was motivated from mechanical engineering searching new methods for the precise prediction and computational compensation of thermal influences in the heating and cooling phases of machine tools (i.e. robot arms, etc.). The quantification of thermal deformations under variable dynamic loads requires the modelling of the non-stationary spatial temperature distribution inside the object. Based upon FOURIERS law of heat flow the high-grade non-linear temperature gradient is represented by a system of partial differential equations within the framework of a dynamic Finite Element topology. It is shown that adaptive KALMAN-filtering is suitable to quantify relevant disturbance influences and to identify thermal parameters (i.e. thermal diffusivity) with a deviation of only 0,2%. As result an identified (and verified) parametric model for the realistic prediction respectively simulation of dynamic temperature processes is presented. Classifying the thermal bend as the main deformation quantity of bar-shaped machine tools, the temperature model is extended to a temperature deformation model. In lab tests thermal load steps are applied to an aluminum column. Independent control measurements show that the identified model can be used to predict the columns bend with a mean deviation (
Dynamic modeling and adaptive control for space stations
NASA Technical Reports Server (NTRS)
Ih, C. H. C.; Wang, S. J.
1985-01-01
Of all large space structural systems, space stations present a unique challenge and requirement to advanced control technology. Their operations require control system stability over an extremely broad range of parameter changes and high level of disturbances. During shuttle docking the system mass may suddenly increase by more than 100% and during station assembly the mass may vary even more drastically. These coupled with the inherent dynamic model uncertainties associated with large space structural systems require highly sophisticated control systems that can grow as the stations evolve and cope with the uncertainties and time-varying elements to maintain the stability and pointing of the space stations. The aspects of space station operational properties are first examined, including configurations, dynamic models, shuttle docking contact dynamics, solar panel interaction, and load reduction to yield a set of system models and conditions. A model reference adaptive control algorithm along with the inner-loop plant augmentation design for controlling the space stations under severe operational conditions of shuttle docking, excessive model parameter errors, and model truncation are then investigated. The instability problem caused by the zero-frequency rigid body modes and a proposed solution using plant augmentation are addressed. Two sets of sufficient conditions which guarantee the globablly asymptotic stability for the space station systems are obtained.
Glassy Dynamics in the Adaptive Immune Response Prevents Autoimmune Disease
NASA Astrophysics Data System (ADS)
Sun, Jun; Deem, Michael
2006-03-01
The immune system normally protects the human host against death by infection. However, when an immune response is mistakenly directed at self antigens, autoimmune disease can occur. We describe a model of protein evolution to simulate the dynamics of the adaptive immune response to antigens. Computer simulations of the dynamics of antibody evolution show that different evolutionary mechanisms, namely gene segment swapping and point mutation, lead to different evolved antibody binding affinities. Although a combination of gene segment swapping and point mutation can yield a greater affinity to a specific antigen than point mutation alone, the antibodies so evolved are highly cross-reactive and would cause autoimmune disease, and this is not the chosen dynamics of the immune system. We suggest that in the immune system a balance has evolved between binding affinity and specificity in the mechanism for searching the amino acid sequence space of antibodies. Our model predicts that chronic infection may lead to autoimmune disease as well due to cross-reactivity and suggests a broad distribution for the time of onset of autoimmune disease due to chronic exposure. The slow search of antibody sequence space by point mutation leads to the broad of distribution times.
Patient-adaptive lesion metabolism analysis by dynamic PET images.
Gao, Fei; Liu, Huafeng; Shi, Pengcheng
2012-01-01
Dynamic PET imaging provides important spatial-temporal information for metabolism analysis of organs and tissues, and generates a great reference for clinical diagnosis and pharmacokinetic analysis. Due to poor statistical properties of the measurement data in low count dynamic PET acquisition and disturbances from surrounding tissues, identifying small lesions inside the human body is still a challenging issue. The uncertainties in estimating the arterial input function will also limit the accuracy and reliability of the metabolism analysis of lesions. Furthermore, the sizes of the patients and the motions during PET acquisition will yield mismatch against general purpose reconstruction system matrix, this will also affect the quantitative accuracy of metabolism analyses of lesions. In this paper, we present a dynamic PET metabolism analysis framework by defining a patient adaptive system matrix to improve the lesion metabolism analysis. Both patient size information and potential small lesions are incorporated by simulations of phantoms of different sizes and individual point source responses. The new framework improves the quantitative accuracy of lesion metabolism analysis, and makes the lesion identification more precisely. The requirement of accurate input functions is also reduced. Experiments are conducted on Monte Carlo simulated data set for quantitative analysis and validation, and on real patient scans for assessment of clinical potential. PMID:23286175
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
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.
Forner-Cordero, A; Koopman, H J F M; van der Helm, F C T
2006-02-01
The number of consecutive strides that can be recorded in measurements of gait have been limited due to the number of force plates and dimensions of the measurement field. In addition, the feet are constrained to land on the force plates. A method to calculate the inverse dynamics from the motion and incomplete information from the ground reaction forces (GRF), vertical component and its application point, is presented and compared to the calculations based on force plate measurements. This method is based on the estimation of the three-dimensional GRF during walking with pressure insoles. RMS errors were lower than 20 W for knee joint power compared to those derived from force plate measurements. The errors were larger during double stance phase due to errors in the application point measured with the insoles. This method, with some technical improvement, could be implemented in new gait analysis protocols measuring several consecutive steps either on a treadmill or over ground, depending on the motion-measurement system, without constraining foot placement.
Rostami, Mahboubeh Rahmati; Wu, Jincheng; Tzanakakis, Emmanuel S.
2015-01-01
The cultivation of stem cells as aggregates in scalable bioreactor cultures is an appealing modality for the large-scale manufacturing of stem cell products. Aggregation phenomena are central to such bioprocesses affecting the viability, proliferation and differentiation trajectory of stem cells but a quantitative framework is currently lacking. A population balance equation (PBE) model was used to describe the temporal evolution of the embryonic stem cell (ESC) cluster size distribution by considering collision-induced aggregation and cell proliferation in a stirred-suspension vessel. For ESC cultures at different agitation rates, the aggregation kernel representing the aggregation dynamics was successfully recovered as a solution of the inverse problem. The rate of change of the average aggregate size was greater at the intermediate rate tested suggesting a trade-off between increased collisions and agitation-induced shear. Results from forward simulation with obtained aggregation kernels were in agreement with transient aggregate size data from experiments. We conclude that the framework presented here can complement mechanistic studies offering insights into relevant stem cell clustering processes. More importantly from a process development standpoint, this strategy can be employed in the design and control of bioreactors for the generation of stem cell derivatives for drug screening, tissue engineering and regenerative medicine. PMID:26036699
Rostami, Mahboubeh Rahmati; Wu, Jincheng; Tzanakakis, Emmanuel S
2015-08-20
The cultivation of stem cells as aggregates in scalable bioreactor cultures is an appealing modality for the large-scale manufacturing of stem cell products. Aggregation phenomena are central to such bioprocesses affecting the viability, proliferation and differentiation trajectory of stem cells but a quantitative framework is currently lacking. A population balance equation (PBE) model was used to describe the temporal evolution of the embryonic stem cell (ESC) cluster size distribution by considering collision-induced aggregation and cell proliferation in a stirred-suspension vessel. For ESC cultures at different agitation rates, the aggregation kernel representing the aggregation dynamics was successfully recovered as a solution of the inverse problem. The rate of change of the average aggregate size was greater at the intermediate rate tested suggesting a trade-off between increased collisions and agitation-induced shear. Results from forward simulation with obtained aggregation kernels were in agreement with transient aggregate size data from experiments. We conclude that the framework presented here can complement mechanistic studies offering insights into relevant stem cell clustering processes. More importantly from a process development standpoint, this strategy can be employed in the design and control of bioreactors for the generation of stem cell derivatives for drug screening, tissue engineering and regenerative medicine.
Inverse dynamic analysis of the lower extremities during nordic walking, walking, and running.
Stief, Felix; Kleindienst, Frank I; Wiemeyer, Josef; Wedel, Florian; Campe, Sebastian; Krabbe, Berthold
2008-11-01
Compared with walking (W), Nordic walking (NW) exhibits greater cardiopulmonary and cardiovascular benefits. Some authors conjecture that compared with W or running (R), NW imposes smaller mechanical loads on the musculoskeletal system. The purpose of the current study was to quantify any differences in joint loading of the lower extremities among NW, W, and R. Fifteen experienced adults participated. Kinematic and force measurements were combined using an inverse dynamics approach to yield joint moments. The results showed no biomechanical benefit of NW. Instead, NW involved greater knee joint loading just after heel strike compared with W. This was due to the longer steps and the higher sole angle during the first part of the stance phase. The sagittal and frontal plane moments were smaller for NW compared with R, but in the transverse plane, the ankle moments were greater in NW than in W or R. Based on these results, NW is not recommended as an exercise for persons who seek to reduce biomechanical loading of the lower extremities.
A least-squares estimation approach to improving the precision of inverse dynamics computations.
Kuo, A D
1998-02-01
A least-squares approach to computing inverse dynamics is proposed. The method utilizes equations of motion for a multi-segment body, incorporating terms for ground reaction forces and torques. The resulting system is overdetermined at each point in time, because kinematic and force measurements outnumber unknown torques, and may be solved using weighted least squares to yield estimates of the joint torques and joint angular accelerations that best match measured data. An error analysis makes it possible to predict error magnitudes for both conventional and least-squares methods. A modification of the method also makes it possible to reject constant biases such as those arising from misalignment of force plate and kinematic measurement reference frames. A benchmark case is presented, which demonstrates reductions in joint torque errors on the order of 30 percent compared to the conventional Newton-Euler method, for a wide range of noise levels on measured data. The advantages over the Newton-Euler method include making best use of all available measurements, ability to function when less than a full complement of ground reaction forces is measured, suppression of residual torques acting on the top-most body segment, and the rejection of constant biases in data.
Nonlinear adaptive trajectory tracking using dynamic neural networks.
Poznyak, A S; Yu, W; Sanchez, E N; Perez, J P
1999-01-01
In this paper the adaptive nonlinear identification and trajectory tracking are discussed via dynamic neural networks. By means of a Lyapunov-like analysis we determine stability conditions for the identification error. Then we analyze the trajectory tracking error by a local optimal controller. An algebraic Riccati equation and a differential one are used for the identification and the tracking error analysis. As our main original contributions, we establish two theorems: the first one gives a bound for the identification error and the second one establishes a bound for the tracking error. We illustrate the effectiveness of these results by two examples: the second-order relay system with multiple isolated equilibrium points and the chaotic system given by Duffing equation. PMID:18252641
NASA Astrophysics Data System (ADS)
Giudici, M.; Baratelli, F.; Comunian, A.; Vassena, C.; Cattaneo, L.
2014-10-01
Numerical modelling of the dynamic evolution of ice sheets and glaciers requires the solution of discrete equations which are based on physical principles (e.g. conservation of mass, linear momentum and energy) and phenomenological constitutive laws (e.g. Glen's and Fourier's laws). These equations must be accompanied by information on the forcing term and by initial and boundary conditions (IBCs) on ice velocity, stress and temperature; on the other hand the constitutive laws involve many physical parameters, some of which depend on the ice thermodynamical state. The proper forecast of the dynamics of ice sheets and glaciers requires a precise knowledge of several quantities which appear in the IBCs, in the forcing terms and in the phenomenological laws. As these quantities cannot be easily measured at the study scale in the field, they are often obtained through model calibration by solving an inverse problem (IP). The objective of this paper is to provide a thorough and rigorous conceptual framework for IPs in cryospheric studies and in particular: to clarify the role of experimental and monitoring data to determine the calibration targets and the values of the parameters that can be considered to be fixed; to define and characterise identifiability, a property related to the solution to the forward problem; to study well-posedness in a correct way, without confusing instability with ill-conditioning or with the properties of the method applied to compute a solution; to cast sensitivity analysis in a general framework and to differentiate between the computation of local sensitivity indicators with a one-at-a-time approach and first-order sensitivity indicators that consider the whole possible variability of the model parameters. The conceptual framework and the relevant properties are illustrated by means of a simple numerical example of isothermal ice flow, based on the shallow-ice approximation.
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.
Fully Threaded Tree for Adaptive Refinement Fluid Dynamics Simulations
NASA Technical Reports Server (NTRS)
Khokhlov, A. M.
1997-01-01
A fully threaded tree (FTT) for adaptive refinement of regular meshes is described. By using a tree threaded at all levels, tree traversals for finding nearest neighbors are avoided. All operations on a tree including tree modifications are O(N), where N is a number of cells, and are performed in parallel. An efficient implementation of the tree is described that requires 2N words of memory. A filtering algorithm for removing high frequency noise during mesh refinement is described. A FTT can be used in various numerical applications. In this paper, it is applied to the integration of the Euler equations of fluid dynamics. An adaptive mesh time stepping algorithm is described in which different time steps are used at different l evels of the tree. Time stepping and mesh refinement are interleaved to avoid extensive buffer layers of fine mesh which were otherwise required ahead of moving shocks. Test examples are presented, and the FTT performance is evaluated. The three dimensional simulation of the interaction of a shock wave and a spherical bubble is carried out that shows the development of azimuthal perturbations on the bubble surface.
Navigating sensory conflict in dynamic environments using adaptive state estimation.
Klein, Theresa J; Jeka, John; Kiemel, Tim; Lewis, M Anthony
2011-12-01
Most conventional robots rely on controlling the location of the center of pressure to maintain balance, relying mainly on foot pressure sensors for information. By contrast,humans rely on sensory data from multiple sources, including proprioceptive, visual, and vestibular sources. Several models have been developed to explain how humans reconcile information from disparate sources to form a stable sense of balance. These models may be useful for developing robots that are able to maintain dynamic balance more readily using multiple sensory sources. Since these information sources may conflict, reliance by the nervous system on any one channel can lead to ambiguity in the system state. In humans, experiments that create conflicts between different sensory channels by moving the visual field or the support surface indicate that sensory information is adaptively reweighted. Unreliable information is rapidly down-weighted,then gradually up-weighted when it becomes valid again.Human balance can also be studied by building robots that model features of human bodies and testing them under similar experimental conditions. We implement a sensory reweighting model based on an adaptive Kalman filter in abipedal robot, and subject it to sensory tests similar to those used on human subjects. Unlike other implementations of sensory reweighting in robots, our implementation includes vision, by using optic flow to calculate forward rotation using a camera (visual modality), as well as a three-axis gyro to represent the vestibular system (non-visual modality), and foot pressure sensors (proprioceptive modality). Our model estimates measurement noise in real time, which is then used to recompute the Kalman gain on each iteration, improving the ability of the robot to dynamically balance. We observe that we can duplicate many important features of postural sw ay in humans, including automatic sensory reweighting,effects, constant phase with respect to amplitude, and a temporal
NASA Astrophysics Data System (ADS)
Rivest-Hénault, David; Dowson, Nicholas; Greer, Peter; Dowling, Jason
2014-03-01
MRI-alone treatment planning and adaptive MRI-based prostate radiation therapy are two promising techniques that could significantly increase the accuracy of the curative dose delivery processes while reducing the total radiation dose. State-of-the-art methods rely on the registration of a patient MRI with a MR-CT atlas for the estimation of pseudo-CT [5]. This atlas itself is generally created by registering many CT and MRI pairs. Most registration methods are not symmetric, but the order of the images influences the result [8]. The computed transformation is therefore biased, introducing unwanted variability. This work examines how much a symmetric algorithm improves the registration. Methods: A robust symmetric registration algorithm is proposed that simultaneously optimises a half space transform and its inverse. During the registration process, the two input volumetric images are transformed to a common position in space, therefore minimising any computational bias. An asymmetrical implementation of the same algorithm was used for comparison purposes. Results: Whole pelvis MRI and CT scans from 15 prostate patients were registered, as in the creation of MR-CT atlases. In each case, two registrations were performed, with different input image orders, and the transformation error quantified. Mean residuals of 0.63±0.26 mm (translation) and (8.7±7.3) × 10--3 rad (rotation) were found for the asymmetrical implementation with corresponding values of 0.038±0.039 mm and (1.6 ± 1.3) × 10--3 rad for the proposed symmetric algorithm, a substantial improvement. Conclusions: The increased registration precision will enhance the generation of pseudo-CT from MRI for atlas based MR planning methods.
On Cognition, Structured Sequence Processing, and Adaptive Dynamical Systems
NASA Astrophysics Data System (ADS)
Petersson, Karl Magnus
2008-11-01
Cognitive neuroscience approaches the brain as a cognitive system: a system that functionally is conceptualized in terms of information processing. We outline some aspects of this concept and consider a physical system to be an information processing device when a subclass of its physical states can be viewed as representational/cognitive and transitions between these can be conceptualized as a process operating on these states by implementing operations on the corresponding representational structures. We identify a generic and fundamental problem in cognition: sequentially organized structured processing. Structured sequence processing provides the brain, in an essential sense, with its processing logic. In an approach addressing this problem, we illustrate how to integrate levels of analysis within a framework of adaptive dynamical systems. We note that the dynamical system framework lends itself to a description of asynchronous event-driven devices, which is likely to be important in cognition because the brain appears to be an asynchronous processing system. We use the human language faculty and natural language processing as a concrete example through out.
NASA Astrophysics Data System (ADS)
Bird, James Chandler
Small drops and the thin-films of bubbles are similar in that the surface to volume ratio is large. Consequently, capillary forces, which result from changes in the surface energy, tend to dominate the drop and bubble dynamics. For example, capillarity is responsible for breaking up a liquid jet from a faucet in a sink into a stream of individual droplets, and for coalescing these droplets into a puddle at the bottom of the sink. This dissertation identifies four situations in which a drop or a bubble exhibits unusual and perhaps counter-intuitive dynamics. The first example (Chapter 2) occurs when a drop impacts either an angled or moving dry, solid surface. Existing physical models attempt to predict the resulting dynamics, spreading or splashing, based on a variety of parameters. Yet it is unclear how these models would extend to include tangential velocity. Our high-speed experiments highlight a distinct third regime in which a fraction of the drop spreads while the other part splashes. The second example (Chapter 3) occurs when a drop contacts a wettable surface with a finite contact angle. Our high-speed experiments challenge the existing models by both showing that the spreading is inertially dominated and that the distance spread follows a power-law scaling in time where the exponent depends on the equilibrium contact angle. The third example (Chapter 4) occurs when two drops are drawn together in an electric field. When the voltage between the drops is low, the drops contact and coalesce. However, when the voltage is sufficiently high, the drops contact and then recoil. The fourth example (Chapter 5) occurs when a bubble on a liquid or solid surface ruptures. Foam coarsening theory would predict that the bubble vanishes when it pops, yet our experiments show that a ring of smaller bubbles is created from the retracting film. This inverse coarsening phenomena is a source of aerosols, and therefore may have implications for health and climate. This
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
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.
Dynamic stability of sequential stimulus representations in adapting neuronal networks
Duarte, Renato C. F.; Morrison, Abigail
2014-01-01
The ability to acquire and maintain appropriate representations of time-varying, sequential stimulus events is a fundamental feature of neocortical circuits and a necessary first step toward more specialized information processing. The dynamical properties of such representations depend on the current state of the circuit, which is determined primarily by the ongoing, internally generated activity, setting the ground state from which input-specific transformations emerge. Here, we begin by demonstrating that timing-dependent synaptic plasticity mechanisms have an important role to play in the active maintenance of an ongoing dynamics characterized by asynchronous and irregular firing, closely resembling cortical activity in vivo. Incoming stimuli, acting as perturbations of the local balance of excitation and inhibition, require fast adaptive responses to prevent the development of unstable activity regimes, such as those characterized by a high degree of population-wide synchrony. We establish a link between such pathological network activity, which is circumvented by the action of plasticity, and a reduced computational capacity. Additionally, we demonstrate that the action of plasticity shapes and stabilizes the transient network states exhibited in the presence of sequentially presented stimulus events, allowing the development of adequate and discernible stimulus representations. The main feature responsible for the increased discriminability of stimulus-driven population responses in plastic networks is shown to be the decorrelating action of inhibitory plasticity and the consequent maintenance of the asynchronous irregular dynamic regime both for ongoing activity and stimulus-driven responses, whereas excitatory plasticity is shown to play only a marginal role. PMID:25374534
Basin mass dynamic changes in China from GRACE based on a multibasin inversion method
NASA Astrophysics Data System (ADS)
Yi, Shuang; Wang, Qiuyu; Sun, Wenke
2016-05-01
Complex landforms, miscellaneous climates, and enormous populations have influenced various geophysical phenomena in China, which range from water depletion in the underground to retreating glaciers on high mountains and have attracted abundant scientific interest. This paper, which utilizes gravity observations during 2003-2014 from the Gravity Recovery and Climate Experiment (GRACE), intends to comprehensively estimate the mass status in 16 drainage basins in the region. We propose a multibasin inversion method that features resistance to stripe noise and an ability to alleviate signal attenuation from the truncation and smoothing of GRACE data. The results show both positive and negative trends. Tremendous mass accumulation has occurred from the Tibetan Plateau (12.1 ± 0.6 Gt/yr) to the Yangtze River (7.7 ± 1.3 Gt/yr) and southeastern coastal areas, which is suggested to involve an increase in the groundwater storage, lake and reservoir water volume, and the flow of materials from tectonic processes. Additionally, mass loss has occurred in the Huang-Huai-Hai-Liao River Basin (-10.2 ± 0.9 Gt/yr), the Brahmaputra-Nujiang-Lancang River Basin (-15.0 ± 1.1 Gt/yr), and Tienshan Mountain (-4.1 ± 0.3 Gt/yr), a result of groundwater pumping and glacier melting. Areas with groundwater depletion are consistent with the distribution of cities with land subsidence in North China. We find that intensified precipitation can alter the local water supply and that GRACE can adequately capture these dynamics, which could be instructive for China's South-to-North Water Diversion hydrologic project.
GRACE captures basin mass dynamic changes in China based on a multi-basin inversion method
NASA Astrophysics Data System (ADS)
Yi, Shuang; Wang, Qiuyu; Sun, Wenke
2016-04-01
Complex landform, miscellaneous climate and enormous population have enriched China with geophysical phenomena ranging from water depletion in the underground to glaciers retreat on the high mountains and have aroused large scientific interests. This paper, utilizing gravity observations 2003-2014 from the Gravity Recovery and Climate Experiment (GRACE), intends to make a comprehensive estimation of mass status in 16 drainage basins in the whole region. We proposed a multi-basin inversion method, which is featured by resistance to the stripe noise and ability to alleviate signal attenuation due to truncation and smoothing of GRACE data. The results show both positive and negative trends: there is a tremendous mass accumulation spreading from the Tibetan plateau (12.2 ± 0.6 Gt/yr) to the Yangtze River (7.6 ± 1.3 Gt/yr), and further to the southeast coastal areas, which is suggested to involve an increase in the ground water storage, lake and reservoir water volume and likely materials flowed in by tectonic process; a mass loss is occurring in Huang-Huai-Hai-Liao River Basin (-10.5 ± 0.8 Gt/yr), as well as the Brahmaputra-Nujiang-Lancang River Basin (-15.0 ± 0.9 Gt/yr) and Tienshan Mountain (-4.1 ± 0.3 Gt/yr), which is a result of groundwater pumping and glacier melting. The groundwater depletion area is well consistent with the distribution of land subsidence in North China. In the end, we find intensified precipitation can alter the local water supply and GRACE is proficient to capture this dynamics, which could be instructive for the South-to-North Water Diversion - one China's giant hydrologic project.
NASA Astrophysics Data System (ADS)
Aoki, Y.
2013-12-01
Recent development of geodetic observations allows us to image slip distribution on buried faults during interseismic, coseismic, and postseismic peiords. The capability of imaging fault slips at depth from geodetic data is, however, limited because geodetic observations are almost always done at Earth's surface. In addition, the absence of or limited offshore geodetic measurements make detailed imaging of the slip on a shallow offshore fault difficult. In geodetic inversions, spatial distribution of fault slips is often obtained by applying smoothing constraints. However, the optimum choice of the smoothing parameter is not straightforward. Although several studies have proposed methods to choose the optimum smoothing parameter, they do not always give a definitive optimum parameter. Also a single smoothing parameter cannot take the heterogeneous spatial resolution on the fault plane into account. Here I propose a method to adaptively discretize a fault plane of arbitrary shape according to the spatial resolution to ope with the heterogeneous spatial resolution on the fault plane. The method applies Singular Value Decomposition of the data kernel, a matrix that relates the observation to the fault slip, to truncate higher modes, and continues to discretize the fault plane by Voronoi diagrams as long as diagonal elements of the model resolution matrix are above a specified threshold. The only parameter that needs to be set in this method is the truncation threshold of eigenmodes that is determined roughly by the signal-to-noise ratio of the observation. Note that the real dataset is not necessary to evaluate the spatial resolution of the fault slip but only the truncation threshold of eigenmodes needs to be preset. I applied the method to the coseismic displacement field associated with the 2011 Tohoku earthquake. I evaluated the spatial resolution of the inverted fault slip with and without offshore GPS measurements. The spatial resolution at a depth of 50 km is
Influence of adaptation on the nonlinear dynamics of a system of competing populations
NASA Astrophysics Data System (ADS)
Dimitrova, Zlatinka I.; Vitanov, Nikolay K.
2000-08-01
We investigate the nonlinear dynamics of a system of populations competing for the same limited resource assuming that they can adapt their growth rates and competition coefficients with respect to the number of individuals of each population. The adaptation leads to an enrichment of the nonlinear dynamics of the system which is demonstrated by a discussion of new orbits in the phase space of the system, completely dependent on the adaptation parameters, as well as by an investigation of the influence of the adaptation parameters on the dynamics of a strange attractor of the model system of ODEs.
Adaptable and dynamic soft colloidal photonics (Presentation Recording)
NASA Astrophysics Data System (ADS)
Kuehne, Alexander J. C.; Go, Dennis
2015-10-01
Existent photonic systems are highly integrated with the active component being completely isolated from the environment as a result of their complex format. There are almost no example for periodic photonic materials, which can interact with their environment by being sensitive to external stimuli while providing the corresponding photonic response. Due to this lack of interaction with the outside world, smart optical components, which are self-healing or adaptable, are almost impossible to achieve. I am going to present an aqueous colloidal system, consisting of core-shell particles with a solid core and a soft shell, bearing both negatively and positively charged groups. The described soft colloids exhibit like charges over a broad range of pH, where they repel each other resulting in a pefect and defect-free photonic crystal. In the absence of a net charge the colloids acquire the arrangement of an amorphous photonic glass. We showcase the applicability of our colloidal system for photonic applications by temporal programming of the photonic system and dynamic switching between ordered and amorphous particle arrangements. We can decrease the pH slowly allowing the particles to transit from negative through neutral to positive, and have them arrange accordingly from crystalline to amorphous and back to crystalline. Thus, we achieve a pre-programmable and autonomous dynamic modulation of the crystallinity of the colloidal arrays and their photonic response. References [1] Go, D., Kodger, T. E., Sprakel, J., and Kuehne, A. J.C. Soft matter. 2014, 10(40), 8060-8065.
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'…
Population dynamics determine genetic adaptation to temperature in Daphnia.
Van Doorslaer, Wendy; Stoks, Robby; Duvivier, Cathy; Bednarska, Anna; De Meester, Luc
2009-07-01
Rising temperatures associated with global warming present a challenge to the fate of many aquatic organisms. Although rapid evolutionary response to temperature-mediated selection may allow local persistence of populations under global warming, and therefore is a key aspect of evolutionary biology, solid proof of its occurrence is rare. In this study, we tested for genetic adaptation to an increase in temperature in the water flea Daphnia magna, a keystone species in freshwater systems, by performing a thermal selection experiment under laboratory conditions followed by the quantification of microevolutionary responses to temperature for both life-history traits as well as for intraspecific competitive strength. After three months of selection, we found a microevolutionary response to temperature in performance, but only in one of two culling regimes, highlighting the importance of population dynamics in driving microevolutionary change within populations. Furthermore, there was an evolutionary increase in thermal plasticity in performance. The results of the competition experiment were in agreement with predictions based on performance as quantified in the life table experiment and illustrate that microevolution within a short time frame has the ability to influence the outcome of intraspecific competition.
Dynamic adaptation of the peripheral circulation to cold exposure.
Cheung, Stephen S; Daanen, Hein A M
2012-01-01
Humans residing or working in cold environments exhibit a stronger cold-induced vasodilation (CIVD) reaction in the peripheral microvasculature than those living in warm regions of the world, leading to a general assumption that thermal responses to local cold exposure can be systematically improved by natural acclimatization or specific acclimation. However, it remains unclear whether this improved tolerance is actually due to systematic acclimatization, or alternately due to the genetic pre-disposition or self-selection for such occupations. Longitudinal studies of repeated extremity exposure to cold demonstrate only ambiguous adaptive responses. In field studies, general cold acclimation may lead to increased sympathetic activity that results in reduced finger blood flow. Laboratory studies offer more control over confounding parameters, but in most studies, no consistent changes in peripheral blood flow occur even after repeated exposure for several weeks. Most studies are performed on a limited amount of subjects only, and the variability of the CIVD response demands more subjects to obtain significant results. This review systematically surveys the trainability of CIVD, concluding that repeated local cold exposure does not alter circulatory dynamics in the peripheries, and that humans remain at risk of cold injuries even after extended stays in cold environments.
Dynamics of adaptive immunity against phage in bacterial populations
NASA Astrophysics Data System (ADS)
Bradde, Serena; Vucelja, Marija; Tesileanu, Tiberiu; Balasubramanian, Vijay
The CRISPR (clustered regularly interspaced short palindromic repeats) mechanism allows bacteria to adaptively defend against phages by acquiring short genomic sequences (spacers) that target specific sequences in the viral genome. We propose a population dynamical model where immunity can be both acquired and lost. The model predicts regimes where bacterial and phage populations can co-exist, others where the populations oscillate, and still others where one population is driven to extinction. Our model considers two key parameters: (1) ease of acquisition and (2) spacer effectiveness in conferring immunity. Analytical calculations and numerical simulations show that if spacers differ mainly in ease of acquisition, or if the probability of acquiring them is sufficiently high, bacteria develop a diverse population of spacers. On the other hand, if spacers differ mainly in their effectiveness, their final distribution will be highly peaked, akin to a ``winner-take-all'' scenario, leading to a specialized spacer distribution. Bacteria can interpolate between these limiting behaviors by actively tuning their overall acquisition rate.
Plant toxicity, adaptive herbivory, and plant community dynamics
Feng, Z.; Liu, R.; DeAngelis, D.L.; Bryant, J.P.; Kielland, K.; Stuart, Chapin F.; Swihart, R.K.
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 its effort to whichever plant species is more common and accessible. In contrast, toxin-determined selective herbivory can drive plant succession toward dominance by the more toxic species, as previously documented in boreal forests and prairies. When the toxin concentrations in different plant species are similar, but species have different toxins with nonadditive effects, herbivores tend to diversify foraging efforts to avoid high intakes of any one toxin. This diversification leads the herbivore to focus more feeding on the less common plant species. Thus, uncommon plants may experience depensatory mortality from herbivory, reducing local species diversity. The depensatory effect of herbivory may inhibit the invasion of other plant species that are more palatable or have different toxins. These predictions were tested and confirmed in the Alaskan boreal forest. ?? 2009 Springer Science+Business Media, LLC.
NASA Astrophysics Data System (ADS)
Tran, A. P.; Dafflon, B.; Hubbard, S. S.; Bisht, G.; Peterson, J.; Ulrich, C.; Romanovsky, V. E.; Kneafsey, T. J.; Wu, Y.
2015-12-01
Quantitative characterization of the soil surface-subsurface hydrological and thermal processes is essential as they are primary factors that control the biogeochemical processes, ecological landscapes and greenhouse gas fluxes. In the Artic region, the surface-subsurface hydrological and thermal regimes co-interact and are both largely influenced by soil texture and soil organic content. In this study, we present a coupled inversion scheme that jointly inverts hydrological, thermal and geophysical data to estimate the vertical profiles of clay, sand and organic contents. Within this inversion scheme, the Community Land Model (CLM4.5) serves as a forward model to simulate the land-surface energy balance and subsurface hydrological-thermal processes. Soil electrical conductivity (from electrical resistivity tomography), temperature and water content are linked together via petrophysical and geophysical models. Particularly, the inversion scheme accounts for the influences of the soil organic and mineral content on both of the hydrological-thermal dynamics and the petrophysical relationship. We applied the inversion scheme to the Next Generation Ecosystem Experiments (NGEE) intensive site in Barrow, AK, which is characterized by polygonal-shaped arctic tundra. The monitoring system autonomously provides a suite of above-ground measurements (e.g., precipitation, air temperature, wind speed, short-long wave radiation, canopy greenness and eddy covariance) as well as below-ground measurements (soil moisture, soil temperature, thaw layer thickness, snow thickness and soil electrical conductivity), which complement other periodic, manually collected measurements. The preliminary results indicate that the model can well reproduce the spatiotemporal dynamics of the soil temperature, and therefore, accurately predict the active layer thickness. The hydrological and thermal dynamics are closely linked to the polygon types and polygon features. The results also enable the
Zening Fu; Sheng Han; Ao Tan; Yiheng Tu; Zhiguo Zhang
2015-08-01
Exploration of time-varying functional brain connectivity based on functional Magnetic Resonance Imaging (fMRI) data is important for understanding dynamic brain mechanisms. l1-penalized inverse covariance is a common measure for the inference of sparse structure of functional brain networks, and it has been recently extended to estimate time-varying sparse brain networks by using a sliding window and incorporating a smoothing constraint on temporal variation. However, l1 penalty cannot induce maximum sparsity, as compared with l0 penalty, so l0 penalty is supposed to have superior quality on inverse covariance estimation. This paper introduces a novel time-varying sparse inverse covariance estimation method based on dual l0-penalties (DLP). The new DLP method estimates the sparse inverse covariance by minimizing an l0-penalized log-likelihood function and an extra l0 penalty on temporal homogeneity. A cyclic descent optimization algorithm is further developed to localize the minimum of the objective function. Experiment results on simulated signals show that the proposed DLP method can achieve better performance than conventional l1-penalized methods in estimating time-varying sparse network structures under different scenarios.
NASA Astrophysics Data System (ADS)
Rollins, Christopher; Barbot, Sylvain; Avouac, Jean-Philippe
2015-05-01
Due to its location on a transtensional section of the Pacific-North American plate boundary, the Salton Trough is a region featuring large strike-slip earthquakes within a regime of shallow asthenosphere, high heat flow, and complex faulting, and so postseismic deformation there may feature enhanced viscoelastic relaxation and afterslip that is particularly detectable at the surface. The 2010 El Mayor-Cucapah earthquake was the largest shock in the Salton Trough since 1892 and occurred close to the US-Mexico border, and so the postseismic deformation recorded by the continuous GPS network of southern California provides an opportunity to study the rheology of this region. Three-year postseismic transients extracted from GPS displacement time-series show four key features: (1) 1-2 cm of cumulative uplift in the Imperial Valley and 1 cm of subsidence in the Peninsular Ranges, (2) relatively large cumulative horizontal displacements 150 km from the rupture in the Peninsular Ranges, (3) rapidly decaying horizontal displacement rates in the first few months after the earthquake in the Imperial Valley, and (4) sustained horizontal velocities, following the rapid early motions, that were still visibly ongoing 3 years after the earthquake. Kinematic inversions show that the cumulative 3-year postseismic displacement field can be well fit by afterslip on and below the coseismic rupture, though these solutions require afterslip with a total moment equivalent to at least a earthquake and higher slip magnitudes than those predicted by coseismic stress changes. Forward modeling shows that stress-driven afterslip and viscoelastic relaxation in various configurations within the lithosphere can reproduce the early and later horizontal velocities in the Imperial Valley, while Newtonian viscoelastic relaxation in the asthenosphere can reproduce the uplift in the Imperial Valley and the subsidence and large westward displacements in the Peninsular Ranges. We present two forward
NASA Astrophysics Data System (ADS)
Giudici, Mauro; Baratelli, Fulvia; Vassena, Chiara; Cattaneo, Laura
2014-05-01
Numerical modelling of the dynamic evolution of ice sheets and glaciers requires the solution of discrete equations which are based on physical principles (e.g. conservation of mass, linear momentum and energy) and phenomenological constitutive laws (e.g. Glen's and Fourier's laws). These equations must be accompanied by information on the forcing term and by initial and boundary conditions (IBC) on ice velocity, stress and temperature; on the other hand the constitutive laws involves many physical parameters, which possibly depend on the ice thermodynamical state. The proper forecast of the dynamics of ice sheets and glaciers (forward problem, FP) requires a precise knowledge of several quantities which appear in the IBCs, in the forcing terms and in the phenomenological laws and which cannot be easily measured at the study scale in the field. Therefore these quantities can be obtained through model calibration, i.e. by the solution of an inverse problem (IP). Roughly speaking, the IP aims at finding the optimal values of the model parameters that yield the best agreement of the model output with the field observations and data. The practical application of IPs is usually formulated as a generalised least squares approach, which can be cast in the framework of Bayesian inference. IPs are well developed in several areas of science and geophysics and several applications were proposed also in glaciology. The objective of this paper is to provide a further step towards a thorough and rigorous theoretical framework in cryospheric studies. Although the IP is often claimed to be ill-posed, this is rigorously true for continuous domain models, whereas for numerical models, which require the solution of algebraic equations, the properties of the IP must be analysed with more care. First of all, it is necessary to clarify the role of experimental and monitoring data to determine the calibration targets and the values of the parameters that can be considered to be fixed
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.
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 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.
A real-time inverse quantised transform for multi-standard with dynamic resolution support
NASA Astrophysics Data System (ADS)
Sun, Chi-Chia; Lin, Chun-Ying; Zhang, Ce
2016-06-01
In this paper, a real-time configurable intelligent property (IP) core is presented for image/video decoding process in compatibility with the standard MPEG-4 Visual and the standard H.264/AVC. The inverse quantised discrete cosine and integer transform can be used to perform inverse quantised discrete cosine transform and inverse quantised inverse integer transforms which only required shift and add operations. Meanwhile, COordinate Rotation DIgital Computer iterations and compensation steps are adjustable in order to compensate for the video compression quality regarding various data throughput. The implementations are embedded in publicly available software XVID Codes 1.2.2 for the standard MPEG-4 Visual and the H.264/AVC reference software JM 16.1, where the experimental results show that the balance between the computational complexity and video compression quality is retained. At the end, FPGA synthesised results show that the proposed IP core can bring advantages to low hardware costs and also provide real-time performance for Full HD and 4K-2K video decoding.
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.
Evolution of taxis responses in virtual bacteria: non-adaptive dynamics.
Goldstein, Richard A; Soyer, Orkun S
2008-05-23
Bacteria are able to sense and respond to a variety of external stimuli, with responses that vary from stimuli to stimuli and from species to species. The best-understood is chemotaxis in the model organism Escherichia coli, where the dynamics and the structure of the underlying pathway are well characterised. It is not clear, however, how well this detailed knowledge applies to mechanisms mediating responses to other stimuli or to pathways in other species. Furthermore, there is increasing experimental evidence that bacteria integrate responses from different stimuli to generate a coherent taxis response. We currently lack a full understanding of the different pathway structures and dynamics and how this integration is achieved. In order to explore different pathway structures and dynamics that can underlie taxis responses in bacteria, we perform a computational simulation of the evolution of taxis. This approach starts with a population of virtual bacteria that move in a virtual environment based on the dynamics of the simple biochemical pathways they harbour. As mutations lead to changes in pathway structure and dynamics, bacteria better able to localise with favourable conditions gain a selective advantage. We find that a certain dynamics evolves consistently under different model assumptions and environments. These dynamics, which we call non-adaptive dynamics, directly couple tumbling probability of the cell to increasing stimuli. Dynamics that are adaptive under a wide range of conditions, as seen in the chemotaxis pathway of E. coli, do not evolve in these evolutionary simulations. However, we find that stimulus scarcity and fluctuations during evolution results in complex pathway dynamics that result both in adaptive and non-adaptive dynamics depending on basal stimuli levels. Further analyses of evolved pathway structures show that effective taxis dynamics can be mediated with as few as two components. The non-adaptive dynamics mediating taxis responses
Yang, Cheng-Hsiung; Wu, Cheng-Lin
2014-01-01
An adaptive control scheme is developed to study the generalized adaptive chaos synchronization with uncertain chaotic parameters behavior between two identical chaotic dynamic systems. This generalized adaptive chaos synchronization controller is designed based on Lyapunov stability theory and an analytic expression of the adaptive controller with its update laws of uncertain chaotic parameters is shown. The generalized adaptive synchronization with uncertain parameters between two identical new Lorenz-Stenflo systems is taken as three examples to show the effectiveness of the proposed method. The numerical simulations are shown to verify the results. PMID:25295292
Tao, Ye; Xu, Lijia; Zhang, Zhen; Chen, Runfeng; Li, Huanhuan; Xu, Hui; Zheng, Chao; Huang, Wei
2016-08-01
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. PMID:27403886
Chromatic adaptation-based tone reproduction for high-dynamic-range imaging
NASA Astrophysics Data System (ADS)
Lee, Joohyun; Jeon, Gwanggil; Jeong, Jechang
2009-10-01
We present an adaptive tone reproduction algorithm for displaying high-dynamic-range (HDR) images on conventional low-dynamic-range (LDR) display devices. The proposed algorithm consists of an adaptive tone reproduction operator and chromatic adaptation. The algorithm for dynamic range reduction relies on suitable tone reproduction functions that depend on histogram-based parameter estimation to adjust luminance according to global and local features. Instead of relying only on reduction of dynamic range, this chromatic adaption technique also preserves chromatic appearance and color consistency across scene and display environments. Our experimental results demonstrate that the proposed algorithm achieves good subjective quality while preserving image details. Furthermore, the proposed algorithm is simple and practical for implementation.
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
NASA Astrophysics Data System (ADS)
Sekine, Akihiko; Chiba, Takahiro
2016-06-01
We propose a realization of the electric-field-induced antiferromagnetic resonance. We consider three-dimensional antiferromagnetic insulators with spin-orbit coupling characterized by the existence of a topological term called the θ term. By solving the Landau-Lifshitz-Gilbert equation in the presence of the θ term, we show that, in contrast to conventional methods using ac magnetic fields, the antiferromagnetic resonance state is realized by ac electric fields along with static magnetic fields. This mechanism can be understood as the inverse process of the dynamical chiral magnetic effect, an alternating current generation by magnetic fields. In other words, we propose a way to electrically induce the dynamical axion field in condensed matter. We discuss a possible experiment to observe our proposal, which utilizes the spin pumping from the antiferromagnetic insulator into a heavy metal contact.
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.
Adaption of a corrector module to the IMP dynamics program
NASA Technical Reports Server (NTRS)
1972-01-01
The corrector module of the RAEIOS program and the IMP dynamics computer program were combined to achieve a date-fitting capability with the more general spacecraft dynamics models of the IMP program. The IMP dynamics program presents models of spacecraft dynamics for satellites with long, flexible booms. The properties of the corrector are discussed and a description is presented of the performance criteria and search logic for parameter estimation. A description is also given of the modifications made to add the corrector to the IMP program. This includes subroutine descriptions, common definitions, definition of input, and a description of output.
Adaptive-mesh algorithms for computational fluid dynamics
NASA Technical Reports Server (NTRS)
Powell, Kenneth G.; Roe, Philip L.; Quirk, James
1993-01-01
The basic goal of adaptive-mesh algorithms is to distribute computational resources wisely by increasing the resolution of 'important' regions of the flow and decreasing the resolution of regions that are less important. While this goal is one that is worthwhile, implementing schemes that have this degree of sophistication remains more of an art than a science. In this paper, the basic pieces of adaptive-mesh algorithms are described and some of the possible ways to implement them are discussed and compared. These basic pieces are the data structure to be used, the generation of an initial mesh, the criterion to be used to adapt the mesh to the solution, and the flow-solver algorithm on the resulting mesh. Each of these is discussed, with particular emphasis on methods suitable for the computation of compressible flows.
Techniques for grid manipulation and adaptation. [computational fluid dynamics
NASA Technical Reports Server (NTRS)
Choo, Yung K.; Eisemann, Peter R.; Lee, Ki D.
1992-01-01
Two approaches have been taken to provide systematic grid manipulation for improved grid quality. One is the control point form (CPF) of algebraic grid generation. It provides explicit control of the physical grid shape and grid spacing through the movement of the control points. It works well in the interactive computer graphics environment and hence can be a good candidate for integration with other emerging technologies. The other approach is grid adaptation using a numerical mapping between the physical space and a parametric space. Grid adaptation is achieved by modifying the mapping functions through the effects of grid control sources. The adaptation process can be repeated in a cyclic manner if satisfactory results are not achieved after a single application.
Neural-network-based speed controller for induction motors using inverse dynamics model
NASA Astrophysics Data System (ADS)
Ahmed, Hassanein S.; Mohamed, Kamel
2016-08-01
Artificial Neural Networks (ANNs) are excellent tools for controller design. ANNs have many advantages compared to traditional control methods. These advantages include simple architecture, training and generalization and distortion insensitivity to nonlinear approximations and nonexact input data. Induction motors have many excellent features, such as simple and rugged construction, high reliability, high robustness, low cost, minimum maintenance, high efficiency, and good self-starting capabilities. In this paper, we propose a neural-network-based inverse model for speed controllers for induction motors. Simulation results show that the ANNs have a high tracing capability.
PSF halo reduction in adaptive optics using dynamic pupil masking.
Osborn, James; Myers, Richard M; Love, Gordon D
2009-09-28
We describe a method to reduce residual speckles in an adaptive optics system which add to the halo of the point spread function (PSF). The halo is particularly problematic in astronomical applications involving the detection of faint companions. Areas of the pupil are selected where the residual wavefront aberrations are large and these are masked using a spatial light modulator. The method is also suitable for smaller telescopes without adaptive optics as a relatively simple method to increase the resolution of the telescope. We describe the principle of the technique and show simulation results. PMID:19907514
PSF halo reduction in adaptive optics using dynamic pupil masking.
Osborn, James; Myers, Richard M; Love, Gordon D
2009-09-28
We describe a method to reduce residual speckles in an adaptive optics system which add to the halo of the point spread function (PSF). The halo is particularly problematic in astronomical applications involving the detection of faint companions. Areas of the pupil are selected where the residual wavefront aberrations are large and these are masked using a spatial light modulator. The method is also suitable for smaller telescopes without adaptive optics as a relatively simple method to increase the resolution of the telescope. We describe the principle of the technique and show simulation results.
NASA Astrophysics Data System (ADS)
Tian, Yu-Kun; Zhou, Hui; Chen, Han-Ming; Zou, Ya-Ming; Guan, Shou-Jun
2013-12-01
Seismic inversion is a highly ill-posed problem, due to many factors such as the limited seismic frequency bandwidth and inappropriate forward modeling. To obtain a unique solution, some smoothing constraints, e.g., the Tikhonov regularization are usually applied. The Tikhonov method can maintain a global smooth solution, but cause a fuzzy structure edge. In this paper we use Huber-Markov random-field edge protection method in the procedure of inverting three parameters, P-velocity, S-velocity and density. The method can avoid blurring the structure edge and resist noise. For the parameter to be inverted, the Huber-Markov random-field constructs a neighborhood system, which further acts as the vertical and lateral constraints. We use a quadratic Huber edge penalty function within the layer to suppress noise and a linear one on the edges to avoid a fuzzy result. The effectiveness of our method is proved by inverting the synthetic data without and with noises. The relationship between the adopted constraints and the inversion results is analyzed as well.
NASA Astrophysics Data System (ADS)
Cruz-Atienza, V. M.; Diaz-Mojica, J.; Madariaga, R. I.; Singh, S. K.; Tago Pacheco, J.; Iglesias, A.
2014-12-01
We introduce a method for imaging the earthquake source dynamics through the inversion of ground motion records based on a parallel genetic algorithm. The source model follows an elliptical patch approach and uses the staggered-grid split-node method to model the earthquake dynamics. A statistical analysis is used to estimate uncertainties in both inverted and derived source parameters. Synthetic inversion tests reveal that the rupture speed (Vr), the rupture area and the stress drop (Δτ) are determined within an error of ~30%, ~12% and ~10%, respectively. In contrast, derived parameters such as the radiated energy (Er), the radiation efficiency (η) and the fracture energy (G) have larger uncertainties, around ~70%, ~40% and ~25%, respectively. We applied the method to the Mw6.5 intermediate-depth (62 km) normal-faulting earthquake of December 11, 2011 in Guerrero, Mexico (Diaz-Mojica et al., JGR, 2014). Inferred values of Δτ = 29.2±6.2 MPa and η = 0.26±0.1 are significantly higher and lower, respectively, than those of typical subduction thrust events. Fracture energy is large, so that more than 73% of the available potential energy for the dynamic process of faulting was deposited in the focal region (i.e., G = (14.4±3.5)x1014J), producing a slow rupture process (Vr/Vs = 0.47±0.09) despite the relatively-high energy radiation (Er = (0.54±0.31)x1015 J) and energy-moment ratio (Er/M0 = 5.7x10-5). It is interesting to point out that such a slow and inefficient rupture along with the large stress drop in a small focal region are features also observed in the 1994 deep Bolivian earthquake.
Poot, L; Snippe, H P; van Hateren, J H
1997-09-01
As is well known, dark adaptation in the human visual system is much slower than is recovery from darkness. We show that at high photopic luminances the situation is exactly opposite. First, we study detection thresholds for a small light flash, at various delays from decrement and increment steps in background luminance. Light adaptation is nearly complete within 100 ms after luminance decrements but takes much longer after luminance increments. Second, we compare sensitivity after equally visible pulses or steps in the adaptation luminance and find that detectability is initially the same but recovers much faster for pulses than for increment steps. This suggests that, whereas any residual threshold elevation after a step shows the incomplete luminance adaptation, the initial threshold elevation is caused by the temporal contrast of the background steps and pulses. This hypothesis is further substantiated in a third experiment, whereby we show that manipulating the contrast of a transition between luminances affects only the initial part of the threshold curve, and not later stages.
Quantitative adaptation analytics for assessing dynamic systems of systems: LDRD Final Report
Gauthier, John H.; Miner, Nadine E.; Wilson, Michael L.; Le, Hai D.; Kao, Gio K.; Melander, Darryl J.; Longsine, Dennis Earl; Vander Meer, Jr., Robert C.
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 combining 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.
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.
Shaughnessy, M C; Jones, R E
2016-02-01
We develop and demonstrate a method to efficiently use density functional calculations to drive classical dynamics of complex atomic and molecular systems. The method has the potential to scale to systems and time scales unreachable with current ab initio molecular dynamics schemes. It relies on an adapting dataset of independently computed Hellmann-Feynman forces for atomic configurations endowed with a distance metric. The metric on configurations enables fast database lookup and robust interpolation of the stored forces. We discuss mechanisms for the database to adapt to the needs of the evolving dynamics, while maintaining accuracy, and other extensions of the basic algorithm.
Shaughnessy, M C; Jones, R E
2016-02-01
We develop and demonstrate a method to efficiently use density functional calculations to drive classical dynamics of complex atomic and molecular systems. The method has the potential to scale to systems and time scales unreachable with current ab initio molecular dynamics schemes. It relies on an adapting dataset of independently computed Hellmann-Feynman forces for atomic configurations endowed with a distance metric. The metric on configurations enables fast database lookup and robust interpolation of the stored forces. We discuss mechanisms for the database to adapt to the needs of the evolving dynamics, while maintaining accuracy, and other extensions of the basic algorithm. PMID:26669825
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.
The inverse hall-petch relation in nanocrystalline metals: A discrete dislocation dynamics analysis
NASA Astrophysics Data System (ADS)
Quek, Siu Sin; Chooi, Zheng Hoe; Wu, Zhaoxuan; Zhang, Yong Wei; Srolovitz, David J.
2016-03-01
When the grain size in polycrystalline materials is reduced to the nanometer length scale (nanocrystallinity), observations from experiments and atomistic simulations suggest that the yield strength decreases (softening) as the grain size is decreased. This is in contrast to the Hall-Petch relation observed in larger sized grains. We incorporated grain boundary (GB) sliding and dislocation emission from GB junctions into the classical DDD framework, and recovered the smaller is weaker relationship observed in nanocrystalline materials. This current model shows that the inverse Hall-Petch behavior can be obtained through a relief of stress buildup at GB junctions from GB sliding by emitting dislocations from the junctions. The yield stress is shown to vary with grain size, d, by a d 1 / 2 relationship when grain sizes are very small. However, pure GB sliding alone without further plastic accomodation by dislocation emission is grain size independent.
Fitness of multidimensional phenotypes in dynamic adaptive landscapes.
Laughlin, Daniel C; Messier, Julie
2015-08-01
Phenotypic traits influence species distributions, but ecology lacks established links between multidimensional phenotypes and fitness for predicting species responses to environmental change. The common focus on single traits rather than multiple trait combinations limits our understanding of their adaptive value, and intraspecific trait covariation has been neglected in ecology despite its importance in evolutionary theory and its likely impact on species distributions. Here, we extend the adaptive landscape framework to ecological sorting of multidimensional phenotypes across environments and discuss how two analytical approaches can be used to quantify fitness as a function of the interaction between the phenotype and the environment. We encourage ecologists to consider how phenotypic integration will constrain species responses to environmental change.
A Dynamically Adaptive Arbitrary Lagrangian-Eulerian Method for Hydrodynamics
Anderson, R W; Pember, R B; Elliott, N S
2004-01-28
A new method that combines staggered grid Arbitrary Lagrangian-Eulerian (ALE) techniques with structured local adaptive mesh refinement (AMR) has been developed for solution of the Euler equations. The novel components of the combined ALE-AMR method hinge upon the integration of traditional AMR techniques with both staggered grid Lagrangian operators as well as elliptic relaxation operators on moving, deforming mesh hierarchies. Numerical examples demonstrate the utility of the method in performing detailed three-dimensional shock-driven instability calculations.
A Dynamically Adaptive Arbitrary Lagrangian-Eulerian Method for Hydrodynamics
Anderson, R W; Pember, R B; Elliott, N S
2002-10-19
A new method that combines staggered grid Arbitrary Lagrangian-Eulerian (ALE) techniques with structured local adaptive mesh refinement (AMR) has been developed for solution of the Euler equations. The novel components of the combined ALE-AMR method hinge upon the integration of traditional AMR techniques with both staggered grid Lagrangian operators as well as elliptic relaxation operators on moving, deforming mesh hierarchies. Numerical examples demonstrate the utility of the method in performing detailed three-dimensional shock-driven instability calculations.
Trinh, Quoclinh; Zhu, Pengyu; Shi, Hui; Xu, Wentao; Hao, Junran; Luo, Yunbo; Huang, Kunlun
2014-12-01
The polymerase chain reaction (PCR)-based genome walking method has been extensively used to isolate unknown flanking sequences, whereas nonspecific products are always inevitable. To resolve these problems, we developed a new strategy to isolate the unknown flanking sequences by combining A-T linker adapter PCR with inverse PCR (I-PCR) or thermal asymmetric interlaced PCR (TAIL-PCR). The result showed that this method can be efficiently achieved with the flanking sequence from the Arabidopsis mutant and papain gene. Our study provides researchers with an additional method for determining genomic DNA flanking sequences to identify the target band from bulk of bands and to eliminate the cloning step for sequencing.
NASA Astrophysics Data System (ADS)
Yi, Guosheng; Wang, Jiang; Deng, Bin; Wei, Xile
2015-05-01
In this paper, we address how adaptation mediated by different biophysical mechanisms modulates neuronal spike initiating dynamics to extracellular electric fields. We incorporate two adaptation currents, i.e., voltage-sensitive potassium current (IM) and calcium-sensitive potassium current (IAHP), into a reduced two-compartment neuron model, and extensively investigate the modeling behavior to a range of electric fields. With phase plane analysis, it is shown whether neuron continues to spike depends on whether adaptation currents could be sufficiently activated to stabilize membrane potential at subthreshold voltages. With stability and bifurcation analysis, we find the steady-state spiking in the neuron with IM occurs through a Hopf bifurcation, whereas it is generated through a saddle-node on invariant circle (SNIC) bifurcation in the cases of IAHP or no adaptation. By identifying the biophysical basis for these dynamics, we observe that IM could alter the competitive outcomes between kinetically mismatched opposite currents to result in a Hopf bifurcation, while IAHP cannot alter these competitive outcomes. From this, we conclude that different modulations of spike initiating dynamics derive from the biophysical mechanism responsible for distinct adaptation currents. Our study suggests that the adaptation mediated by different mechanisms indeed has different effects on neuronal dynamics to electric field stimulus. It could contribute to uncover the underlying mechanism of how neuron encodes electric field signals.
Long-term dynamics of adaptation in asexual populations.
Wiser, Michael J; Ribeck, Noah; Lenski, Richard E
2013-12-13
Experimental studies of evolution have increased greatly in number in recent years, stimulated by the growing power of genomic tools. However, organismal fitness remains the ultimate metric for interpreting these experiments, and the dynamics of fitness remain poorly understood over long time scales. Here, we examine fitness trajectories for 12 Escherichia coli populations during 50,000 generations. Mean fitness appears to increase without bound, consistent with a power law. We also derive this power-law relation theoretically by incorporating clonal interference and diminishing-returns epistasis into a dynamical model of changes in mean fitness over time.
Kosaka, Tomoyo; Inoue, Yoshihisa; Mori, Tadashi
2016-03-01
Hexaarylbenzenes (HABs) have greatly attracted much attention due to their unique propeller-shaped structure and potential application in materials science, such as liquid crystals, molecular capsules/rotors, redox materials, nonlinear optical materials, as well as molecular wires. Less attention has however been paid to their propeller chirality. By introducing small point-chiral group(s) at the periphery of HABs, propeller chirality was effectively induced, provoking strong Cotton effects in the circular dichroism (CD) spectrum. Temperature and solvent polarity manipulate the dynamics of propeller inversion in solution. As such, whizzing toroids become more substantial in polar solvents and at an elevated temperature, where radial aromatic rings (propeller blades) prefer orthogonal alignment against the central benzene ring (C6 core), maximizing toroidal interactions.
NASA Astrophysics Data System (ADS)
Jungfleisch, Matthias B.; Zhang, Wei; Ding, Junjia; Jiang, Wanjun; Sklenar, Joseph; Pearson, John E.; Ketterson, John B.; Hoffmann, Axel
2016-02-01
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 and compare them to micromagnetic simulations. 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, which 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. Our findings have direct implications on the development of engineered magnonics applications and devices.
Memory and Combinatorial Logic Based on DNA Inversions: Dynamics and Evolutionary Stability.
Fernandez-Rodriguez, Jesus; Yang, Lei; Gorochowski, Thomas E; Gordon, D Benjamin; Voigt, Christopher A
2015-12-18
Genetic memory can be implemented using enzymes that catalyze DNA inversions, where each orientation corresponds to a "bit". Here, we use two DNA invertases (FimE and HbiF) that reorient DNA irreversibly between two states with opposite directionality. First, we construct memory that is set by FimE and reset by HbiF. Next, we build a NOT gate where the input promoter drives FimE and in the absence of signal the reverse state is maintained by the constitutive expression of HbiF. The gate requires ∼3 h to turn on and off. The evolutionary stabilities of these circuits are measured by passaging cells while cycling function. The memory switch is stable over 400 h (17 days, 14 state changes); however, the gate breaks after 54 h (>2 days) due to continuous invertase expression. Genome sequencing reveals that the circuit remains intact, but the host strain evolves to reduce invertase expression. This work highlights the need to evaluate the evolutionary robustness and failure modes of circuit designs, especially as more complex multigate circuits are implemented.
NASA Astrophysics Data System (ADS)
Cheyney, S.; Fishwick, S.; Hill, I. A.; Linford, N. T.
2015-08-01
Despite the development of advanced processing and interpretation tools for magnetic data sets in the fields of mineral and hydrocarbon industries, these methods have not achieved similar levels of adoption for archaeological or very near surface surveys. Using a synthetic data set we demonstrate that certain methodologies and assumptions used to successfully invert more regional-scale data can lead to large discrepancies between the true and recovered depths when applied to archaeological-type anomalies. We propose variations to the current approach, analysing the choice of the depth-weighting function, mesh design and parameter constraints, to develop an appropriate technique for the 3-D inversion of archaeological-scale data sets. The results show a successful recovery of a synthetic scenario, as well as a case study of a Romano-Celtic temple in the UK. For the case study, the final susceptibility model is compared with two coincident ground penetrating radar surveys, showing a high correlation with the comparative depth slices. The new approach takes interpretation of archaeological data sets beyond a simple 2-D visual interpretation based on pattern recognition.
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.
A quantitative evolutionary theory of adaptive behavior dynamics.
McDowell, J J
2013-10-01
The idea that behavior is selected by its consequences in a process analogous to organic evolution has been discussed for over 100 years. A recently proposed theory instantiates this idea by means of a genetic algorithm that operates on a population of potential behaviors. Behaviors in the population are represented by numbers in decimal integer (phenotypic) and binary bit string (genotypic) forms. One behavior from the population is emitted at random each time tick, after which a new population of potential behaviors is constructed by recombining parent behavior bit strings. If the emitted behavior produced a benefit to the organism, then parents are chosen on the basis of their phenotypic similarity to the emitted behavior; otherwise, they are chosen at random. After parent behavior recombination, the population is subjected to a small amount of mutation by flipping random bits in the population's bit strings. The behavior generated by this process of selection, reproduction, and mutation reaches equilibrium states that conform to every empirically valid equation of matching theory, exactly and without systematic error. These equations are known to describe the behavior of many vertebrate species, including humans, in a variety of experimental, naturalistic, natural, and social environments. The evolutionary theory also generates instantaneous dynamics and patterns of preference change in constantly changing environments that are consistent with the dynamics of live-organism behavior. These findings support the assertion that the world of behavior we observe and measure is generated by evolutionary dynamics. PMID:24219847
Recursive dynamic programming for adaptive sequence and structure alignment
Thiele, R.; Zimmer, R.; Lengauer, T.
1995-12-31
We propose a new alignment procedure that is capable of aligning protein sequences and structures in a unified manner. Recursive dynamic programming (RDP) is a hierarchical method which, on each level of the hierarchy, identifies locally optimal solutions and assembles them into partial alignments of sequences and/or structures. In contrast to classical dynamic programming, RDP can also handle alignment problems that use objective functions not obeying the principle of prefix optimality, e.g. scoring schemes derived from energy potentials of mean force. For such alignment problems, RDP aims at computing solutions that are near-optimal with respect to the involved cost function and biologically meaningful at the same time. Towards this goal, RDP maintains a dynamic balance between different factors governing alignment fitness such as evolutionary relationships and structural preferences. As in the RDP method gaps are not scored explicitly, the problematic assignment of gap cost parameters is circumvented. In order to evaluate the RDP approach we analyse whether known and accepted multiple alignments based on structural information can be reproduced with the RDP method.
A quantitative evolutionary theory of adaptive behavior dynamics.
McDowell, J J
2013-10-01
The idea that behavior is selected by its consequences in a process analogous to organic evolution has been discussed for over 100 years. A recently proposed theory instantiates this idea by means of a genetic algorithm that operates on a population of potential behaviors. Behaviors in the population are represented by numbers in decimal integer (phenotypic) and binary bit string (genotypic) forms. One behavior from the population is emitted at random each time tick, after which a new population of potential behaviors is constructed by recombining parent behavior bit strings. If the emitted behavior produced a benefit to the organism, then parents are chosen on the basis of their phenotypic similarity to the emitted behavior; otherwise, they are chosen at random. After parent behavior recombination, the population is subjected to a small amount of mutation by flipping random bits in the population's bit strings. The behavior generated by this process of selection, reproduction, and mutation reaches equilibrium states that conform to every empirically valid equation of matching theory, exactly and without systematic error. These equations are known to describe the behavior of many vertebrate species, including humans, in a variety of experimental, naturalistic, natural, and social environments. The evolutionary theory also generates instantaneous dynamics and patterns of preference change in constantly changing environments that are consistent with the dynamics of live-organism behavior. These findings support the assertion that the world of behavior we observe and measure is generated by evolutionary dynamics.
Adaptive superposition of finite element meshes in linear and nonlinear dynamic analysis
NASA Astrophysics Data System (ADS)
Yue, Zhihua
2005-11-01
The numerical analysis of transient phenomena in solids, for instance, wave propagation and structural dynamics, is a very important and active area of study in engineering. Despite the current evolutionary state of modern computer hardware, practical analysis of large scale, nonlinear transient problems requires the use of adaptive methods where computational resources are locally allocated according to the interpolation requirements of the solution form. Adaptive analysis of transient problems involves obtaining solutions at many different time steps, each of which requires a sequence of adaptive meshes. Therefore, the execution speed of the adaptive algorithm is of paramount importance. In addition, transient problems require that the solution must be passed from one adaptive mesh to the next adaptive mesh with a bare minimum of solution-transfer error since this form of error compromises the initial conditions used for the next time step. A new adaptive finite element procedure (s-adaptive) is developed in this study for modeling transient phenomena in both linear elastic solids and nonlinear elastic solids caused by progressive damage. The adaptive procedure automatically updates the time step size and the spatial mesh discretization in transient analysis, achieving the accuracy and the efficiency requirements simultaneously. The novel feature of the s-adaptive procedure is the original use of finite element mesh superposition to produce spatial refinement in transient problems. The use of mesh superposition enables the s-adaptive procedure to completely avoid the need for cumbersome multipoint constraint algorithms and mesh generators, which makes the s-adaptive procedure extremely fast. Moreover, the use of mesh superposition enables the s-adaptive procedure to minimize the solution-transfer error. In a series of different solid mechanics problem types including 2-D and 3-D linear elastic quasi-static problems, 2-D material nonlinear quasi-static problems
An integrated architecture of adaptive neural network control for dynamic systems
Ke, Liu; Tokar, R.; Mcvey, B.
1994-07-01
In this study, an integrated neural network control architecture for nonlinear dynamic systems is presented. Most of the recent emphasis in the neural network control field has no error feedback as the control input which rises the adaptation problem. The integrated architecture in this paper combines feed forward control and error feedback adaptive control using neural networks. The paper reveals the different internal functionality of these two kinds of neural network controllers for certain input styles, e.g., state feedback and error feedback. Feed forward neural network controllers with state feedback establish fixed control mappings which can not adapt when model uncertainties present. With error feedbacks, neural network controllers learn the slopes or the gains respecting to the error feedbacks, which are error driven adaptive control systems. The results demonstrate that the two kinds of control scheme can be combined to realize their individual advantages. Testing with disturbances added to the plant shows good tracking and adaptation.
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.
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.
Structural self-assembly and avalanchelike dynamics in locally adaptive networks.
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. PMID:26274219
Isotope and methane dynamics above and below the Trade Wind Inversion at Ascension Island using UAVs
NASA Astrophysics Data System (ADS)
Brownlow, R.; Lowry, D.; Nisbet, E. G.; Fisher, R. E.; France, J.; Lanoisellé, M.; Thomas, R.; Richardson, T.; Greatwood, C.; Freer, J. E.; MacKenzie, A. R.
2015-12-01
Ascension Island (8oS, 14 oW) is a South Atlantic background site for atmospheric measurement. Royal Holloway, in collaboration with the UK Met Office, installed a Picarro 1301 CRDS in 2010 for continuous methane monitoring. This has high precision and accuracy, with a 6-gas calibration and target suite, to measure long term methane mole fraction. Regular flask sampling is also carried out for NOAA and RHUL (co-located), to measure δ13CCH4 isotopic trends.Ascension Island experiences near-constant SE Trade winds below the Trade Wind Inversion (TWI), with air from the remote S. Atlantic. In flask samples and in continuous monitoring at the Airhead location, atmospheric methane mole fraction has been increasing since 2007 whilst the δ13CCH4 isotope record has shifted to more depleted values. Above the normally well-defined TWI (1200 - 1800m altitude), variable tropical air masses pass over Ascension. This air last mixed with the boundary layer over Africa or South America. Field work undertaken in September 2014 and July 2015, in collaboration with U. Bristol and U. Birmingham, using UAVs (octocopters) collected samples with Tedlar bags or aluminium flasks from different heights above and below the TWI. The maximum altitude reached was 2700masl. Sample bags were immediately analysed on Ascension for CH4 mole fraction using the Picarro CRDS and subsequently analysed at RHUL for δ13CCH4 using continuous-flow gas chromatography/isotope-ratio mass spectrometry (CF-GC/IRMS). The TWI was clearly identified by an increase in CH4 mole fraction above the TWI. Back trajectory analysis was used to distinguish the origins of the air masses, with air above showing inputs from the land surfaces of equatorial and southern Africa, and from southern S. America.The campaigns have extended the envelope of altitudes accessed by micro-UAVs for atmospheric science, demonstrating their utility for probing the remote free troposphere and for penetrating the TWI. Sampling at Ascension is
Workload Model Based Dynamic Adaptation of Social Internet of Vehicles
Alam, Kazi Masudul; Saini, Mukesh; El Saddik, Abdulmotaleb
2015-01-01
Social Internet of Things (SIoT) has gained much interest among different research groups in recent times. As a key member of a smart city, the vehicular domain of SIoT (SIoV) is also undergoing steep development. In the SIoV, vehicles work as sensor-hub to capture surrounding information using the in-vehicle and Smartphone sensors and later publish them for the consumers. A cloud centric cyber-physical system better describes the SIoV model where physical sensing-actuation process affects the cloud based service sharing or computation in a feedback loop or vice versa. The cyber based social relationship abstraction enables distributed, easily navigable and scalable peer-to-peer communication among the SIoV subsystems. These cyber-physical interactions involve a huge amount of data and it is difficult to form a real instance of the system to test the feasibility of SIoV applications. In this paper, we propose an analytical model to measure the workloads of various subsystems involved in the SIoV process. We present the basic model which is further extended to incorporate complex scenarios. We provide extensive simulation results for different parameter settings of the SIoV system. The findings of the analyses are further used to design example adaptation strategies for the SIoV subsystems which would foster deployment of intelligent transport systems. PMID:26389905
Workload Model Based Dynamic Adaptation of Social Internet of Vehicles.
Alam, Kazi Masudul; Saini, Mukesh; El Saddik, Abdulmotaleb
2015-09-15
Social Internet of Things (SIoT) has gained much interest among different research groups in recent times. As a key member of a smart city, the vehicular domain of SIoT (SIoV) is also undergoing steep development. In the SIoV, vehicles work as sensor-hub to capture surrounding information using the in-vehicle and Smartphone sensors and later publish them for the consumers. A cloud centric cyber-physical system better describes the SIoV model where physical sensing-actuation process affects the cloud based service sharing or computation in a feedback loop or vice versa. The cyber based social relationship abstraction enables distributed, easily navigable and scalable peer-to-peer communication among the SIoV subsystems. These cyber-physical interactions involve a huge amount of data and it is difficult to form a real instance of the system to test the feasibility of SIoV applications. In this paper, we propose an analytical model to measure the workloads of various subsystems involved in the SIoV process. We present the basic model which is further extended to incorporate complex scenarios. We provide extensive simulation results for different parameter settings of the SIoV system. The findings of the analyses are further used to design example adaptation strategies for the SIoV subsystems which would foster deployment of intelligent transport systems.
Sensor Web Dynamic Measurement Techniques and Adaptive Observing Strategies
NASA Technical Reports Server (NTRS)
Talabac, Stephen J.
2004-01-01
Sensor Web observing systems may have the potential to significantly improve our ability to monitor, understand, and predict the evolution of rapidly evolving, transient, or variable environmental features and events. This improvement will come about by integrating novel data collection techniques, new or improved instruments, emerging communications technologies and protocols, sensor mark-up languages, and interoperable planning and scheduling systems. In contrast to today's observing systems, "event-driven" sensor webs will synthesize real- or near-real time measurements and information from other platforms and then react by reconfiguring the platforms and instruments to invoke new measurement modes and adaptive observation strategies. Similarly, "model-driven" sensor webs will utilize environmental prediction models to initiate targeted sensor measurements or to use a new observing strategy. The sensor web concept contrasts with today's data collection techniques and observing system operations concepts where independent measurements are made by remote sensing and in situ platforms that do not share, and therefore cannot act upon, potentially useful complementary sensor measurement data and platform state information. This presentation describes NASA's view of event-driven and model-driven Sensor Webs and highlights several research and development activities at the Goddard Space Flight Center.
Workload Model Based Dynamic Adaptation of Social Internet of Vehicles.
Alam, Kazi Masudul; Saini, Mukesh; El Saddik, Abdulmotaleb
2015-01-01
Social Internet of Things (SIoT) has gained much interest among different research groups in recent times. As a key member of a smart city, the vehicular domain of SIoT (SIoV) is also undergoing steep development. In the SIoV, vehicles work as sensor-hub to capture surrounding information using the in-vehicle and Smartphone sensors and later publish them for the consumers. A cloud centric cyber-physical system better describes the SIoV model where physical sensing-actuation process affects the cloud based service sharing or computation in a feedback loop or vice versa. The cyber based social relationship abstraction enables distributed, easily navigable and scalable peer-to-peer communication among the SIoV subsystems. These cyber-physical interactions involve a huge amount of data and it is difficult to form a real instance of the system to test the feasibility of SIoV applications. In this paper, we propose an analytical model to measure the workloads of various subsystems involved in the SIoV process. We present the basic model which is further extended to incorporate complex scenarios. We provide extensive simulation results for different parameter settings of the SIoV system. The findings of the analyses are further used to design example adaptation strategies for the SIoV subsystems which would foster deployment of intelligent transport systems. PMID:26389905
Marinkovic, Ksenija; Courtney, Maureen G.; Witzel, Thomas; Dale, Anders M.; Halgren, Eric
2014-01-01
Although a crucial role of the fusiform gyrus (FG) in face processing has been demonstrated with a variety of methods, converging evidence suggests that face processing involves an interactive and overlapping processing cascade in distributed brain areas. Here we examine the spatio-temporal stages and their functional tuning to face inversion, presence and configuration of inner features, and face contour in healthy subjects during passive viewing. Anatomically-constrained magnetoencephalography (aMEG) combines high-density whole-head MEG recordings and distributed source modeling with high-resolution structural MRI. Each person's reconstructed cortical surface served to constrain noise-normalized minimum norm inverse source estimates. The earliest activity was estimated to the occipital cortex at ~100 ms after stimulus onset and was sensitive to an initial coarse level visual analysis. Activity in the right-lateralized ventral temporal area (inclusive of the FG) peaked at ~160 ms and was largest to inverted faces. Images containing facial features in the veridical and rearranged configuration irrespective of the facial outline elicited intermediate level activity. The M160 stage may provide structural representations necessary for downstream distributed areas to process identity and emotional expression. However, inverted faces additionally engaged the left ventral temporal area at ~180 ms and were uniquely subserved by bilateral processing. This observation is consistent with the dual route model and spared processing of inverted faces in prosopagnosia. The subsequent deflection, peaking at ~240 ms in the anterior temporal areas bilaterally, was largest to normal, upright faces. It may reflect initial engagement of the distributed network subserving individuation and familiarity. These results support dynamic models suggesting that processing of unfamiliar faces in the absence of a cognitive task is subserved by a distributed and interactive neural circuit. PMID
Li, Hua; Thériault, Jolaine; Rousselle, Bruno; Subramanian, Balaji; Robichaud, Jacques; Djaoued, Yahia
2014-02-28
A 'dynamic hard-template' infiltration strategy for crack-free large-area synthesis of 2D WO3 inverse opal (IO) films on ITO substrates using a wide range of sizes of sacrificial PS spheres is reported. Thus prepared WO3 IO films were successfully used as an active electrode in the fabrication of an electrochromic device. PMID:24429973
Application of Dynamic Logic Algorithm to Inverse Scattering Problems Related to Plasma Diagnostics
NASA Astrophysics Data System (ADS)
Perlovsky, L.; Deming, R. W.; Sotnikov, V.
2010-11-01
In plasma diagnostics scattering of electromagnetic waves is widely used for identification of density and wave field perturbations. In the present work we use a powerful mathematical approach, dynamic logic (DL), to identify the spectra of scattered electromagnetic (EM) waves produced by the interaction of the incident EM wave with a Langmuir soliton in the presence of noise. The problem is especially difficult since the spectral amplitudes of the noise pattern are comparable with the amplitudes of the scattered waves. In the past DL has been applied to a number of complex problems in artificial intelligence, pattern recognition, and signal processing, resulting in revolutionary improvements. Here we demonstrate its application to plasma diagnostic problems. [4pt] Perlovsky, L.I., 2001. Neural Networks and Intellect: using model-based concepts. Oxford University Press, New York, NY.
A 3-D adaptive mesh refinement algorithm for multimaterial gas dynamics
Puckett, E.G. ); Saltzman, J.S. )
1991-08-12
Adaptive Mesh Refinement (AMR) in conjunction with high order upwind finite difference methods has been used effectively on a variety of problems. In this paper we discuss an implementation of an AMR finite difference method that solves the equations of gas dynamics with two material species in three dimensions. An equation for the evolution of volume fractions augments the gas dynamics system. The material interface is preserved and tracked from the volume fractions using a piecewise linear reconstruction technique. 14 refs., 4 figs.
The kinematic determinants of anuran swimming performance: an inverse and forward dynamics approach.
Richards, Christopher T
2008-10-01
The aims of this study were to explore the hydrodynamic mechanism of Xenopus laevis swimming and to describe how hind limb kinematics shift to control swimming performance. Kinematics of the joints, feet and body were obtained from high speed video of X. laevis frogs (N=4) during swimming over a range of speeds. A blade element approach was used to estimate thrust produced by both translational and rotational components of foot velocity. Peak thrust from the feet ranged from 0.09 to 0.69 N across speeds ranging from 0.28 to 1.2 m s(-1). Among 23 swimming strokes, net thrust impulse from rotational foot motion was significantly higher than net translational thrust impulse, ranging from 6.1 to 29.3 N ms, compared with a range of -7.0 to 4.1 N ms from foot translation. Additionally, X. laevis kinematics were used as a basis for a forward dynamic anuran swimming model. Input joint kinematics were modulated to independently vary the magnitudes of foot translational and rotational velocity. Simulations predicted that maximum swimming velocity (among all of the kinematics patterns tested) requires that maximal translational and maximal rotational foot velocity act in phase. However, consistent with experimental kinematics, translational and rotational motion contributed unequally to total thrust. The simulation powered purely by foot translation reached a lower peak stroke velocity than the pure rotational case (0.38 vs 0.54 m s(-1)). In all simulations, thrust from the foot was positive for the first half of the power stroke, but negative for the second half. Pure translational foot motion caused greater negative thrust (70% of peak positive thrust) compared with pure rotational simulation (35% peak positive thrust) suggesting that translational motion is propulsive only in the early stages of joint extension. Later in the power stroke, thrust produced by foot rotation overcomes negative thrust (due to translation). Hydrodynamic analysis from X. laevis as well as forward
Long, Christopher J.; Purdon, Patrick L.; Temereanca, Simona; Desai, Neil U.; Hämäläinen, Matti S.; Brown, Emery N.
2011-01-01
Determining the magnitude and location of neural sources within the brain that are responsible for generating magnetoencephalography (MEG) signals measured on the surface of the head is a challenging problem in functional neuroimaging. The number of potential sources within the brain exceeds by an order of magnitude the number of recording sites. As a consequence, the estimates for the magnitude and location of the neural sources will be ill-conditioned because of the underdetermined nature of the problem. One well-known technique designed to address this imbalance is the minimum norm estimator (MNE). This approach imposes an L2 regularization constraint that serves to stabilize and condition the source parameter estimates. However, these classes of regularizer are static in time and do not consider the temporal constraints inherent to the biophysics of the MEG experiment. In this paper we propose a dynamic state-space model that accounts for both spatial and temporal correlations within and across candidate intra-cortical sources. In our model, the observation model is derived from the steady-state solution to Maxwell's equations while the latent model representing neural dynamics is given by a random walk process. We show that the Kalman filter (KF) and the Kalman smoother [also known as the fixed-interval smoother (FIS)] may be used to solve the ensuing high-dimensional state-estimation problem. Using a well-known relationship between Bayesian estimation and Kalman filtering, we show that the MNE estimates carry a significant zero bias. Calculating these high-dimensional state estimates is a computationally challenging task that requires High Performance Computing (HPC) resources. To this end, we employ the NSF Teragrid Supercomputing Network to compute the source estimates. We demonstrate improvement in performance of the state-space algorithm relative to MNE in analyses of simulated and actual somatosensory MEG experiments. Our findings establish the benefits
Dynamic self-adaptive remote health monitoring system for diabetics.
Suh, Myung-kyung; Moin, Tannaz; Woodbridge, Jonathan; Lan, Mars; Ghasemzadeh, Hassan; Bui, Alex; Ahmadi, Sheila; Sarrafzadeh, Majid
2012-01-01
Diabetes is the seventh leading cause of death in the United States. In 2010, about 1.9 million new cases of diabetes were diagnosed in people aged 20 years or older. Remote health monitoring systems can help diabetics and their healthcare professionals monitor health-related measurements by providing real-time feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the remote health monitoring. This paper presents a task optimization technique used in WANDA (Weight and Activity with Blood Pressure and Other Vital Signs); a wireless health project that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. WANDA applies data analytics in real-time to improving the quality of care. The developed algorithm minimizes the number of daily tasks required by diabetic patients using association rules that satisfies a minimum support threshold. Each of these tasks maximizes information gain, thereby improving the overall level of care. Experimental results show that the developed algorithm can reduce the number of tasks up to 28.6% with minimum support 0.95, minimum confidence 0.97 and high efficiency. PMID:23366365
Dynamic self-adaptive remote health monitoring system for diabetics.
Suh, Myung-kyung; Moin, Tannaz; Woodbridge, Jonathan; Lan, Mars; Ghasemzadeh, Hassan; Bui, Alex; Ahmadi, Sheila; Sarrafzadeh, Majid
2012-01-01
Diabetes is the seventh leading cause of death in the United States. In 2010, about 1.9 million new cases of diabetes were diagnosed in people aged 20 years or older. Remote health monitoring systems can help diabetics and their healthcare professionals monitor health-related measurements by providing real-time feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the remote health monitoring. This paper presents a task optimization technique used in WANDA (Weight and Activity with Blood Pressure and Other Vital Signs); a wireless health project that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. WANDA applies data analytics in real-time to improving the quality of care. The developed algorithm minimizes the number of daily tasks required by diabetic patients using association rules that satisfies a minimum support threshold. Each of these tasks maximizes information gain, thereby improving the overall level of care. Experimental results show that the developed algorithm can reduce the number of tasks up to 28.6% with minimum support 0.95, minimum confidence 0.97 and high efficiency.
Segmentation of Tracking Sequences Using Dynamically Updated Adaptive Learning
Michailovich, Oleg; Tannenbaum, Allen
2009-01-01
The problem of segmentation of tracking sequences is of central importance in a multitude of applications. In the current paper, a different approach to the problem is discussed. Specifically, the proposed segmentation algorithm is implemented in conjunction with estimation of the dynamic parameters of moving objects represented by the tracking sequence. While the information on objects’ motion allows one to transfer some valuable segmentation priors along the tracking sequence, the segmentation allows substantially reducing the complexity of motion estimation, thereby facilitating the computation. Thus, in the proposed methodology, the processes of segmentation and motion estimation work simultaneously, in a sort of “collaborative” manner. The Bayesian estimation framework is used here to perform the segmentation, while Kalman filtering is used to estimate the motion and to convey useful segmentation information along the image sequence. The proposed method is demonstrated on a number of both computed-simulated and real-life examples, and the obtained results indicate its advantages over some alternative approaches. PMID:19004712
Whole-Body Human Inverse Dynamics with Distributed Micro-Accelerometers, Gyros and Force Sensing.
Latella, Claudia; Kuppuswamy, Naveen; Romano, Francesco; Traversaro, Silvio; Nori, Francesco
2016-01-01
Human motion tracking is a powerful tool used in a large range of applications that require human movement analysis. Although it is a well-established technique, its main limitation is the lack of estimation of real-time kinetics information such as forces and torques during the motion capture. In this paper, we present a novel approach for a human soft wearable force tracking for the simultaneous estimation of whole-body forces along with the motion. The early stage of our framework encompasses traditional passive marker based methods, inertial and contact force sensor modalities and harnesses a probabilistic computational technique for estimating dynamic quantities, originally proposed in the domain of humanoid robot control. We present experimental analysis on subjects performing a two degrees-of-freedom bowing task, and we estimate the motion and kinetics quantities. The results demonstrate the validity of the proposed method. We discuss the possible use of this technique in the design of a novel soft wearable force tracking device and its potential applications. PMID:27213394
Whole-Body Human Inverse Dynamics with Distributed Micro-Accelerometers, Gyros and Force Sensing †
Latella, Claudia; Kuppuswamy, Naveen; Romano, Francesco; Traversaro, Silvio; Nori, Francesco
2016-01-01
Human motion tracking is a powerful tool used in a large range of applications that require human movement analysis. Although it is a well-established technique, its main limitation is the lack of estimation of real-time kinetics information such as forces and torques during the motion capture. In this paper, we present a novel approach for a human soft wearable force tracking for the simultaneous estimation of whole-body forces along with the motion. The early stage of our framework encompasses traditional passive marker based methods, inertial and contact force sensor modalities and harnesses a probabilistic computational technique for estimating dynamic quantities, originally proposed in the domain of humanoid robot control. We present experimental analysis on subjects performing a two degrees-of-freedom bowing task, and we estimate the motion and kinetics quantities. The results demonstrate the validity of the proposed method. We discuss the possible use of this technique in the design of a novel soft wearable force tracking device and its potential applications. PMID:27213394
Adaptive control for space debris removal with uncertain kinematics, dynamics and states
NASA Astrophysics Data System (ADS)
Huang, Panfeng; Zhang, Fan; Meng, Zhongjie; Liu, Zhengxiong
2016-11-01
As the Tethered Space Robot is considered to be a promising solution for the Active Debris Removal, a lot of problems arise in the approaching, capturing and removing phases. Particularly, kinematics and dynamics parameters of the debris are unknown, and parts of the states are unmeasurable according to the specifics of tether, which is a tough problem for the target retrieval/de-orbiting. This work proposes a full adaptive control strategy for the space debris removal via a Tethered Space Robot with unknown kinematics, dynamics and part of the states. First we derive a dynamics model for the retrieval by treating the base satellite (chaser) and the unknown space debris (target) as rigid bodies in the presence of offsets, and involving the flexibility and elasticity of tether. Then, a full adaptive controller is presented including a control law, a dynamic adaption law, and a kinematic adaption law. A modified controller is also presented according to the peculiarities of this system. Finally, simulation results are presented to illustrate the performance of two proposed controllers.
Adaptive hp-FEM with dynamical meshes for transient heat and moisture transfer problems
NASA Astrophysics Data System (ADS)
Solin, Pavel; Dubcova, Lenka; Kruis, Jaroslav
2010-04-01
We are concerned with the time-dependent multiphysics problem of heat and moisture transfer in the context of civil engineering applications. The problem is challenging due to its multiscale nature (temperature usually propagates orders of magnitude faster than moisture), different characters of the two fields (moisture exhibits boundary layers which are not present in the temperature field), extremely long integration times (30 years or more), and lack of viable error control mechanisms. In order to solve the problem efficiently, we employ a novel multimesh adaptive higher-order finite element method (hp-FEM) based on dynamical meshes and adaptive time step control. We investigate the possibility to approximate the temperature and humidity fields on individual dynamical meshes equipped with mutually independent adaptivity mechanisms. Numerical examples related to a realistic nuclear reactor vessel simulation are presented.
Adaptive Fuzzy Control of Strict-Feedback Nonlinear Time-Delay Systems With Unmodeled Dynamics.
Yin, Shen; Shi, Peng; Yang, Hongyan
2016-08-01
In this paper, an approximated-based adaptive fuzzy control approach with only one adaptive parameter is presented for a class of single input single output strict-feedback nonlinear systems in order to deal with phenomena like nonlinear uncertainties, unmodeled dynamics, dynamic disturbances, and unknown time delays. Lyapunov-Krasovskii function approach is employed to compensate the unknown time delays in the design procedure. By combining the advances of the hyperbolic tangent function with adaptive fuzzy backstepping technique, the proposed controller guarantees the semi-globally uniformly ultimately boundedness of all the signals in the closed-loop system from the mean square point of view. Two simulation examples are finally provided to show the superior effectiveness of the proposed scheme.
Robust projective lag synchronization in drive-response dynamical networks via adaptive control
NASA Astrophysics Data System (ADS)
Al-mahbashi, G.; Noorani, M. S. Md; Bakar, S. A.; Al-sawalha, M. M.
2016-02-01
This paper investigates the problem of projective lag synchronization behavior in drive-response dynamical networks (DRDNs) with identical and non-identical nodes. An adaptive control method is designed to achieve projective lag synchronization with fully unknown parameters and unknown bounded disturbances. These parameters were estimated by adaptive laws obtained by Lyapunov stability theory. Furthermore, sufficient conditions for synchronization are derived analytically using the Lyapunov stability theory and adaptive control. In addition, the unknown bounded disturbances are also overcome by the proposed control. Finally, analytical results show that the states of the dynamical network with non-delayed coupling can be asymptotically synchronized onto a desired scaling factor under the designed controller. Simulation results show the effectiveness of the proposed method.
NASA Astrophysics Data System (ADS)
Dilling, L.; Daly, M.; Travis, W.; Wilhelmi, O.; Klein, R.; Kenney, D.; Ray, A. J.; Miller, K.
2013-12-01
Recent reports and scholarship have suggested that adapting to current climate variability may represent a "no regrets" strategy for adapting to climate change. Filling "adaptation deficits" and other approaches that rely on addressing current vulnerabilities are of course helpful for responding to current climate variability, but we find here that they are not sufficient for adapting to climate change. First, following a comprehensive review and unique synthesis of the natural hazards and climate adaptation literatures, we advance six reasons why adapting to climate variability is not sufficient for adapting to climate change: 1) Vulnerability is different at different levels of exposure; 2) Coping with climate variability is not equivalent to adaptation to longer term change; 3) The socioeconomic context for vulnerability is constantly changing; 4) The perception of risk associated with climate variability does not necessarily promote adaptive behavior in the face of climate change; 5) Adaptations made to short term climate variability may reduce the flexibility of the system in the long term; and 6) Adaptive actions may shift vulnerabilities to other parts of the system or to other people. Instead we suggest that decision makers faced with choices to adapt to climate change must consider the dynamics of vulnerability in a connected system-- how choices made in one part of the system might impact other valued outcomes or even create new vulnerabilities. Furthermore we suggest that rather than expressing climate change adaptation as an extension of adaptation to climate variability, the research and practice communities would do well to articulate adaptation as an imperfect policy, with tradeoffs and consequences and that decisions be prioritized to preserve flexibility be revisited often as climate change unfolds. We then present the results of a number of empirical studies of decision making for drought in urban water systems in the United States to understand
Adaptive wavelet simulation of global ocean dynamics using a new Brinkman volume penalization
NASA Astrophysics Data System (ADS)
Kevlahan, N. K.-R.; Dubos, T.; Aechtner, M.
2015-12-01
In order to easily enforce solid-wall boundary conditions in the presence of complex coastlines, we propose a new mass and energy conserving Brinkman penalization for the rotating shallow water equations. This penalization does not lead to higher wave speeds in the solid region. The error estimates for the penalization are derived analytically and verified numerically for linearized one-dimensional equations. The penalization is implemented in a conservative dynamically adaptive wavelet method for the rotating shallow water equations on the sphere with bathymetry and coastline data from NOAA's ETOPO1 database. This code could form the dynamical core for a future global ocean model. The potential of the dynamically adaptive ocean model is illustrated by using it to simulate the 2004 Indonesian tsunami and wind-driven gyres.
Camargo-Junior, Franklin; Ackermann, Marko; Loss, Jefferson F; Sacco, Isabel C N
2013-12-01
The aim of this study was to investigate the effect of errors in the location of the center of pressure (5 and 10 mm) on lower limb joint moment uncertainties at different gait velocities (1.0, 1.5, and 2.0 m/s). Our hypotheses were that the absolute joint moment uncertainties would be gradually reduced from distal to proximal joints and from higher to lower velocities. Joint moments of five healthy young adults were calculated by inverse dynamics using the bottom-up approach, depending on which estimate the uncertainty propagated. Results indicated that there is a linear relationship between errors in center of pressure and joint moment uncertainties. The absolute moment peak uncertainties expressed on the anatomic reference frames decreased from distal to proximal joints, confirming our first hypothesis, except for the abduction moments. There was an increase in moment uncertainty (up to 0.04 N m/kg for the 10 mm error in the center of pressure) from the lower to higher gait velocity, confirming our second hypothesis, although, once again, not for hip or knee abduction. Finally, depending on the plane of movement and the joint, relative uncertainties experienced variation (between 5 and 31%), and the knee joint moments were the most affected.
NASA Astrophysics Data System (ADS)
Rivlin, Lev A.
2009-12-01
The features of the pump dynamics of isomeric nuclei excited by X-rays of a repetitively pulsed relativistic electron beam followed by the production of a medium with the negative absorption of gamma quanta are analysed. In the extended nuclear medium, the pump excites a travelling hidden-population-inversion wave with the anisotropic gamma amplification, which becomes positive in the case of the excess over the critical pump parameter equal to the product of the peak spectral power density of the X-ray source and the relative duration of an ultrashort relativistic electron bunch. In the alternative scheme with orthogonal directions of pumping X-rays and a flux of amplified gamma quanta, the absence of the amplification anisotropy opens up the possibility for constructing a standard two-mirror resonator with Bragg single-crystal reflectors. The critical peak value of the spectral pump power density is compared with the known characteristics of relativistic-electron X-ray sources by examples of some nuclides.
NASA Astrophysics Data System (ADS)
Bargatze, L. F.
2015-12-01
Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted
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.
Adaptation to the edge of chaos and critical scaling in self-adjusting dynamical systems
NASA Astrophysics Data System (ADS)
Melby, Paul Christian
We present a mechanism for adaptation in dynamical systems. Systems which have this mechanism are called self-adjusting systems. The control parameters in a self-adjusting system are slowly varying, rather than constant. The dynamics of the control parameters are governed by a low-pass filtered feedback from the dynamical variables. We apply this model to several systems, numerically, analytically, and experimentally, and examine the behavior of the control parameters. We observe a high probability of finding the parameter at the boundary between periodicity and chaos. We therefore find that self-adjusting systems adapt to the edge of chaos. In addition, we find that noise in the system drives the parameter away from the edge of chaos on very long timescales so that chaos is suppressed in the system. We show that, with the presence of noise, the parameter can re-enter the chaotic regime. This is called a chaotic outbreak in the system and we find that the distribution of outbreaks is a power-law with the duration of the outbreak. We then study the robustness of adaptation to the edge of chaos by examining the effect of a control force being applied to the parameter. We find the behavior to be very robust, except for very large control forces. Finally, we look at systems of coupled maps and show that, adaptation to the edge of chaos occurs in systems of higher dimensions, as well.
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.
Sharif, Behzad; Derbyshire, J Andrew; Faranesh, Anthony Z; Bresler, Yoram
2010-08-01
MRI 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 nongated 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 nongated 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 nongated cardiac MRI during short breath-hold. PMID:20665794
Facial Expression Aftereffect Revealed by Adaption to Emotion-Invisible Dynamic Bubbled Faces
Luo, Chengwen; Wang, Qingyun; Schyns, Philippe G.; Kingdom, Frederick A. A.; Xu, Hong
2015-01-01
Visual adaptation is a powerful tool to probe the short-term plasticity of the visual system. Adapting to local features such as the oriented lines can distort our judgment of subsequently presented lines, the tilt aftereffect. The tilt aftereffect is believed to be processed at the low-level of the visual cortex, such as V1. Adaptation to faces, on the other hand, can produce significant aftereffects in high-level traits such as identity, expression, and ethnicity. However, whether face adaptation necessitate awareness of face features is debatable. In the current study, we investigated whether facial expression aftereffects (FEAE) can be generated by partially visible faces. We first generated partially visible faces using the bubbles technique, in which the face was seen through randomly positioned circular apertures, and selected the bubbled faces for which the subjects were unable to identify happy or sad expressions. When the subjects adapted to static displays of these partial faces, no significant FEAE was found. However, when the subjects adapted to a dynamic video display of a series of different partial faces, a significant FEAE was observed. In both conditions, subjects could not identify facial expression in the individual adapting faces. These results suggest that our visual system is able to integrate unrecognizable partial faces over a short period of time and that the integrated percept affects our judgment on subsequently presented faces. We conclude that FEAE can be generated by partial face with little facial expression cues, implying that our cognitive system fills-in the missing parts during adaptation, or the subcortical structures are activated by the bubbled faces without conscious recognition of emotion during adaptation. PMID:26717572
ADAPT (Analysis of Dynamic Accident Progression Trees) Beta Version 0.9
2010-01-07
The purpose of the ADAPT code is to generate Dynamic Event Trees (DET) using a user specified simulator. ADAPT can utilize any simulation tool which meets a minimal set of requirements. ADAPT is based on the concept of DET which use explicit modeling of the deterministic dynamic processes that take place during a nuclear reactor plant system evolution along with stochastic modeling. When DET are used to model different aspects of Probabilistic Risk Assessment (PRA),more » all accident progression scenarios starting from an initiating event are considered simultaneously. The DET branching occurs at user specified times and/or when an action is required by the system and/or the operator. These outcomes then decide how the dynamic system variables will evolve in time for each DET branch. Since two different outcomes at a DET branching may lead to completely different paths for system evolution, the next branching for these paths may occur not only at different times, but can be based on different branching criteria. The computational infrastructure allows for flexibility in ADAPT to link with different system simulation codes, parallel processing of the scenarios under consideration, on-line scenario management (initiation as well as termination) and user friendly graphical capabilities. The ADAPT system is designed for a distributed computing environment; the scheduler can track multiple concurrent branches simultaneously. The scheduler is modularized so that the DET branching strategy can be modified (e.g. biasing towards the worse case scenario/event). Independent database systems store data from the simulation tasks and the DET structure so that the event tree can be constructed and analyzed later. ADAPT is provided with a user-friendly client which can easily sort through and display the results of an experiment, precluding the need for the user to manually inspect individual simulator runs.« less
NASA Astrophysics Data System (ADS)
Rosenberg, Duane; Fournier, Aimé; Fischer, Paul; Pouquet, Annick
2006-06-01
An object-oriented geophysical and astrophysical spectral-element adaptive refinement (GASpAR) code is introduced. Like most spectral-element codes, GASpAR combines finite-element efficiency with spectral-method accuracy. It is also designed to be flexible enough for a range of geophysics and astrophysics applications where turbulence or other complex multiscale problems arise. The formalism accommodates both conforming and non-conforming elements. Several aspects of this code derive from existing methods, but here are synthesized into a new formulation of dynamic adaptive refinement (DARe) of non-conforming h-type. As a demonstration of the code, several new 2D test cases are introduced that have time-dependent analytic solutions and exhibit localized flow features, including the 2D Burgers equation with straight, curved-radial and oblique-colliding fronts. These are proposed as standard test problems for comparable DARe codes. Quantitative errors are reported for 2D spatial and temporal convergence of DARe.
An adaptable neuromorphic model of orientation selectivity based on floating gate dynamics
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 Network Dynamics - Modeling and Control of Time-Dependent Social Contacts
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
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.
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.
Shack-Hartmann wavefront sensor with large dynamic range by adaptive spot search method.
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.
The adaptive dynamic community detection algorithm based on the non-homogeneous random walking
NASA Astrophysics Data System (ADS)
Xin, Yu; Xie, Zhi-Qiang; Yang, Jing
2016-05-01
With the changing of the habit and custom, people's social activity tends to be changeable. It is required to have a community evolution analyzing method to mine the dynamic information in social network. For that, we design the random walking possibility function and the topology gain function to calculate the global influence matrix of the nodes. By the analysis of the global influence matrix, the clustering directions of the nodes can be obtained, thus the NRW (Non-Homogeneous Random Walk) method for detecting the static overlapping communities can be established. We design the ANRW (Adaptive Non-Homogeneous Random Walk) method via adapting the nodes impacted by the dynamic events based on the NRW. The ANRW combines the local community detection with dynamic adaptive adjustment to decrease the computational cost for ANRW. Furthermore, the ANRW treats the node as the calculating unity, thus the running manner of the ANRW is suitable to the parallel computing, which could meet the requirement of large dataset mining. Finally, by the experiment analysis, the efficiency of ANRW on dynamic community detection is verified.
Macroscopic description of complex adaptive networks coevolving with dynamic node states.
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. PMID:26066206
Cetinbaş, Murat; Shakhnovich, Eugene I
2013-01-01
Although molecular chaperones are essential components of protein homeostatic machinery, their mechanism of action and impact on adaptation and evolutionary dynamics remain controversial. Here we developed a physics-based ab initio multi-scale model of a living cell for population dynamics simulations to elucidate the effect of chaperones on adaptive evolution. The 6-loci genomes of model cells encode model proteins, whose folding and interactions in cellular milieu can be evaluated exactly from their genome sequences. A genotype-phenotype relationship that is based on a simple yet non-trivially postulated protein-protein interaction (PPI) network determines the cell division rate. Model proteins can exist in native and molten globule states and participate in functional and all possible promiscuous non-functional PPIs. We find that an active chaperone mechanism, whereby chaperones directly catalyze protein folding, has a significant impact on the cellular fitness and the rate of evolutionary dynamics, while passive chaperones, which just maintain misfolded proteins in soluble complexes have a negligible effect on the fitness. We find that by partially releasing the constraint on protein stability, active chaperones promote a deeper exploration of sequence space to strengthen functional PPIs, and diminish the non-functional PPIs. A key experimentally testable prediction emerging from our analysis is that down-regulation of chaperones that catalyze protein folding significantly slows down the adaptation dynamics. PMID:24244114
Falk, Marianne; Larsson, Tobias; Keall, Paul; Chul Cho, Byung; Aznar, Marianne; Korreman, Stine; Poulsen, Per; Munck af Rosenschoeld, Per
2012-03-15
Purpose: Real-time dynamic multileaf collimator (MLC) tracking for management of intrafraction tumor motion can be challenging for highly modulated beams, as the leaves need to travel far to adjust for target motion perpendicular to the leaf travel direction. The plan modulation can be reduced by using a leaf position constraint (LPC) that reduces the difference in the position of adjacent MLC leaves in the plan. The purpose of this study was to investigate the impact of the LPC on the quality of inversely optimized arc radiotherapy plans and the effect of the MLC motion pattern on the dosimetric accuracy of MLC tracking delivery. Specifically, the possibility of predicting the accuracy of MLC tracking delivery based on the plan modulation was investigated. Methods: Inversely optimized arc radiotherapy plans were created on CT-data of three lung cancer patients. For each case, five plans with a single 358 deg. arc were generated with LPC priorities of 0 (no LPC), 0.25, 0.5, 0.75, and 1 (highest possible LPC), respectively. All the plans had a prescribed dose of 2 Gy x 30, used 6 MV, a maximum dose rate of 600 MU/min and a collimator angle of 45 deg. or 315 deg. To quantify the plan modulation, an average adjacent leaf distance (ALD) was calculated by averaging the mean adjacent leaf distance for each control point. The linear relationship between the plan quality [i.e., the calculated dose distributions and the number of monitor units (MU)] and the LPC was investigated, and the linear regression coefficient as well as a two tailed confidence level of 95% was used in the evaluation. The effect of the plan modulation on the performance of MLC tracking was tested by delivering the plans to a cylindrical diode array phantom moving with sinusoidal motion in the superior-inferior direction with a peak-to-peak displacement of 2 cm and a cycle time of 6 s. The delivery was adjusted to the target motion using MLC tracking, guided in real-time by an infrared optical system
Yao, Yao; Marchal, Kathleen; Van de Peer, Yves
2014-01-01
One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store 'good behaviour' and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment.
Yao, Yao; Marchal, Kathleen; Van de Peer, Yves
2014-01-01
One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store ‘good behaviour’ and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment. PMID:24599485
Oluk, Can; Pavan, Andrea; Kafaligonul, Hulusi
2016-01-01
At the early stages of visual processing, information is processed by two major thalamic pathways encoding brightness increments (ON) and decrements (OFF). Accumulating evidence suggests that these pathways interact and merge as early as in primary visual cortex. Using regular and reverse-phi motion in a rapid adaptation paradigm, we investigated the temporal dynamics of within and across pathway mechanisms for motion processing. When the adaptation duration was short (188 ms), reverse-phi and regular motion led to similar adaptation effects, suggesting that the information from the two pathways are combined efficiently at early-stages of motion processing. However, as the adaption duration was increased to 752 ms, reverse-phi and regular motion showed distinct adaptation effects depending on the test pattern used, either engaging spatiotemporal correlation between the same or opposite contrast polarities. Overall, these findings indicate that spatiotemporal correlation within and across ON-OFF pathways for motion processing can be selectively adapted, and support those models that integrate within and across pathway mechanisms for motion processing. PMID:27667401
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
Gourbière, Sébastien; Menu, Fréderic
2009-07-01
Many plants, insects, and crustaceans show within-population variability in dormancy length. The question of whether such variability corresponds to a genetic polymorphism of pure strategies or a mixed bet-hedging strategy, and how the level of phenotypic variability can evolve remain unknown for most species. Using an eco-genetic model rooted in a 25-year ecological field study of a Chestnut weevil, Curculio elephas, we show that its diapause-duration variability is more likely to have evolved by the spread of a bet-hedging strategy than by the establishment of a genetic polymorphism. Investigating further the adaptive dynamics of diapause-duration variability, we find two unanticipated patterns of general interest. First, there is a trade-off between the ability of bet-hedging strategies to persist on an ecological time scale and their ability to invade. The optimal strategy (in terms of persistence) cannot invade, whereas suboptimal bet-hedgers are good invaders. Second, we describe an original evolutionary dynamics where each bet-hedging strategy (defined by its rate of prolonged diapause) resists invasion by all others, so that the first type of bet-hedger to appear persists on an evolutionary time scale. Such "evolutionary priority effect" could drive the evolution of maladapted levels of diapause-duration variability.
Adaptive nonlinear flight control
NASA Astrophysics Data System (ADS)
Rysdyk, Rolf Theoduor
1998-08-01
Research under supervision of Dr. Calise and Dr. Prasad at the Georgia Institute of Technology, School of Aerospace Engineering. has demonstrated the applicability of an adaptive controller architecture. The architecture successfully combines model inversion control with adaptive neural network (NN) compensation to cancel the inversion error. The tiltrotor aircraft provides a specifically interesting control design challenge. The tiltrotor aircraft is capable of converting from stable responsive fixed wing flight to unstable sluggish hover in helicopter configuration. It is desirable to provide the pilot with consistency in handling qualities through a conversion from fixed wing flight to hover. The linear model inversion architecture was adapted by providing frequency separation in the command filter and the error-dynamics, while not exiting the actuator modes. This design of the architecture provides for a model following setup with guaranteed performance. This in turn allowed for convenient implementation of guaranteed handling qualities. A rigorous proof of boundedness is presented making use of compact sets and the LaSalle-Yoshizawa theorem. The analysis allows for the addition of the e-modification which guarantees boundedness of the NN weights in the absence of persistent excitation. The controller is demonstrated on the Generic Tiltrotor Simulator of Bell-Textron and NASA Ames R.C. The model inversion implementation is robustified with respect to unmodeled input dynamics, by adding dynamic nonlinear damping. A proof of boundedness of signals in the system is included. The effectiveness of the robustification is also demonstrated on the XV-15 tiltrotor. The SHL Perceptron NN provides a more powerful application, based on the universal approximation property of this type of NN. The SHL NN based architecture is also robustified with the dynamic nonlinear damping. A proof of boundedness extends the SHL NN augmentation with robustness to unmodeled actuator
NASA Astrophysics Data System (ADS)
Martinez, N.; Michoud, G.; Cario, A.; Ollivier, J.; Franzetti, B.; Jebbar, M.; Oger, P.; Peters, J.
2016-09-01
Water and protein dynamics on a nanometer scale were measured by quasi-elastic neutron scattering in the piezophile archaeon Thermococcus barophilus and the closely related pressure-sensitive Thermococcus kodakarensis, at 0.1 and 40 MPa. We show that cells of the pressure sensitive organism exhibit higher intrinsic stability. Both the hydration water dynamics and the fast protein and lipid dynamics are reduced under pressure. In contrast, the proteome of T. barophilus is more pressure sensitive than that of T. kodakarensis. The diffusion coefficient of hydration water is reduced, while the fast protein and lipid dynamics are slightly enhanced with increasing pressure. These findings show that the coupling between hydration water and cellular constituents might not be simply a master-slave relationship. We propose that the high flexibility of the T. barophilus proteome associated with reduced hydration water may be the keys to the molecular adaptation of the cells to high hydrostatic pressure.
Martinez, N; Michoud, G; Cario, A; Ollivier, J; Franzetti, B; Jebbar, M; Oger, P; Peters, J
2016-01-01
Water and protein dynamics on a nanometer scale were measured by quasi-elastic neutron scattering in the piezophile archaeon Thermococcus barophilus and the closely related pressure-sensitive Thermococcus kodakarensis, at 0.1 and 40 MPa. We show that cells of the pressure sensitive organism exhibit higher intrinsic stability. Both the hydration water dynamics and the fast protein and lipid dynamics are reduced under pressure. In contrast, the proteome of T. barophilus is more pressure sensitive than that of T. kodakarensis. The diffusion coefficient of hydration water is reduced, while the fast protein and lipid dynamics are slightly enhanced with increasing pressure. These findings show that the coupling between hydration water and cellular constituents might not be simply a master-slave relationship. We propose that the high flexibility of the T. barophilus proteome associated with reduced hydration water may be the keys to the molecular adaptation of the cells to high hydrostatic pressure. PMID:27595789
Wei, Heming; Tao, Chuanyi; Zhu, Yinian; Krishnaswamy, Sridhar
2016-04-01
In this paper, a reflective semiconductor optical amplifier (RSOA) is configured to demodulate dynamic spectral shifts of a fiber Bragg grating (FBG) dynamic strain sensor. The FBG sensor and the RSOA source form an adaptive fiber cavity laser. As the reflective spectrum of the FBG sensor changes due to dynamic strains, the wavelength of the laser output shifts accordingly, which is subsequently converted into a corresponding phase shift and demodulated by an unbalanced Michelson interferometer. Due to the short transition time of the RSOA, the RSOA-FBG cavity can respond to dynamic strains at high frequencies extending to megahertz. A demodulator using a PID controller is used to compensate for low-frequency drifts induced by temperature and large quasi-static strains. As the sensitivity of the demodulator is a function of the optical path difference and the FBG spectral width, optimal parameters to obtain high sensitivity are presented. Multiplexing to demodulate multiple FBG sensors is also discussed. PMID:27139682
Martinez, N.; Michoud, G.; Cario, A.; Ollivier, J.; Franzetti, B.; Jebbar, M.; Oger, P.; Peters, J.
2016-01-01
Water and protein dynamics on a nanometer scale were measured by quasi-elastic neutron scattering in the piezophile archaeon Thermococcus barophilus and the closely related pressure-sensitive Thermococcus kodakarensis, at 0.1 and 40 MPa. We show that cells of the pressure sensitive organism exhibit higher intrinsic stability. Both the hydration water dynamics and the fast protein and lipid dynamics are reduced under pressure. In contrast, the proteome of T. barophilus is more pressure sensitive than that of T. kodakarensis. The diffusion coefficient of hydration water is reduced, while the fast protein and lipid dynamics are slightly enhanced with increasing pressure. These findings show that the coupling between hydration water and cellular constituents might not be simply a master-slave relationship. We propose that the high flexibility of the T. barophilus proteome associated with reduced hydration water may be the keys to the molecular adaptation of the cells to high hydrostatic pressure. PMID:27595789
Adaptive output feedback consensus tracking for linear multi-agent systems with unknown dynamics
NASA Astrophysics Data System (ADS)
Sun, Junyong; Geng, Zhiyong
2015-09-01
In this paper, the consensus tracking problem with unknown dynamics in the leader for the linear multi-agent systems is addressed. Based on the relative output information among the agents, decentralised adaptive consensus protocols with static coupling gains are designed to guarantee that the consensus tracking errors converge to a small neighbourhood around the origin and all the signals in the closed-loop dynamics are uniformly ultimately bounded. Moreover, the result is extended to the case with dynamic coupling gains which are independent of the eigenvalues of the Laplacian matrix. Both of the protocols with static and dynamic coupling gains are designed by using the relative outputs, which are more practical than the state-feedback ones. Finally, the theoretical results are verified through an example.
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.
Global solution for a kinetic chemotaxis model with internal dynamics and its fast adaptation limit
NASA Astrophysics Data System (ADS)
Liao, Jie
2015-12-01
A nonlinear kinetic chemotaxis model with internal dynamics incorporating signal transduction and adaptation is considered. This paper is concerned with: (i) the global solution for this model, and, (ii) its fast adaptation limit to Othmer-Dunbar-Alt type model. This limit gives some insight to the molecular origin of the chemotaxis behaviour. First, by using the Schauder fixed point theorem, the global existence of weak solution is proved based on detailed a priori estimates, under quite general assumptions. However, the Schauder theorem does not provide uniqueness, so additional analysis is required to be developed for uniqueness. Next, the fast adaptation limit of this model is derived by extracting a weak convergence subsequence in measure space. For this limit, the first difficulty is to show the concentration effect on the internal state. Another difficulty is the strong compactness argument on the chemical potential, which is essential for passing the nonlinear kinetic equation to the weak limit.
NASA Astrophysics Data System (ADS)
Zhao, Dongya; Li, Shaoyuan; Zhu, Quanmin
2016-03-01
In this study, a new adaptive synchronised tracking control approach is developed for the operation of multiple robotic manipulators in the presence of uncertain kinematics and dynamics. In terms of the system synchronisation and adaptive control, the proposed approach can stabilise position tracking of each robotic manipulator while coordinating its motion with the other robotic manipulators. On the other hand, the developed approach can cope with kinematic and dynamic uncertainties. The corresponding stability analysis is presented to lay a foundation for theoretical understanding of the underlying issues as well as an assurance for safely operating real systems. Illustrative examples are bench tested to validate the effectiveness of the proposed approach. In addition, to face the challenging issues, this study provides an exemplary showcase with effectively to integrate several cross boundary theoretical results to formulate an interdisciplinary solution.
High dynamic range image rendering with a Retinex-based adaptive filter.
Meylan, Laurence; Süsstrunk, Sabine
2006-09-01
We propose a new method to render high dynamic range images that models global and local adaptation of the human visual system. Our method is based on the center-surround Retinex model. The novelties of our method is first to use an adaptive filter, whose shape follows the image high-contrast edges, thus reducing halo artifacts common to other methods. Second, only the luminance channel is processed, which is defined by the first component of a principal component analysis. Principal component analysis provides orthogonality between channels and thus reduces the chromatic changes caused by the modification of luminance. We show that our method efficiently renders high dynamic range images and we compare our results with the current state of the art. PMID:16948325
Behavioral and neural Darwinism: selectionist function and mechanism in adaptive behavior dynamics.
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.
Differentially Private Histogram Publication For Dynamic Datasets: An Adaptive Sampling Approach
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
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. PMID:27140640
An adaptive mesh finite volume method for the Euler equations of gas dynamics
NASA Astrophysics Data System (ADS)
Mungkasi, Sudi
2016-06-01
The Euler equations have been used to model gas dynamics for decades. They consist of mathematical equations for the conservation of mass, momentum, and energy of the gas. For a large time value, the solution may contain discontinuities, even when the initial condition is smooth. A standard finite volume numerical method is not able to give accurate solutions to the Euler equations around discontinuities. Therefore we solve the Euler equations using an adaptive mesh finite volume method. In this paper, we present a new construction of the adaptive mesh finite volume method with an efficient computation of the refinement indicator. The adaptive method takes action automatically at around places having inaccurate solutions. Inaccurate solutions are reconstructed to reduce the error by refining the mesh locally up to a certain level. On the other hand, if the solution is already accurate, then the mesh is coarsened up to another certain level to minimize computational efforts. We implement the numerical entropy production as the mesh refinement indicator. As a test problem, we take the Sod shock tube problem. Numerical results show that the adaptive method is more promising than the standard one in solving the Euler equations of gas dynamics.
Altered temporal dynamics of neural adaptation in the aging human auditory cortex.
Herrmann, Björn; Henry, Molly J; Johnsrude, Ingrid S; Obleser, Jonas
2016-09-01
Neural response adaptation plays an important role in perception and cognition. Here, we used electroencephalography to investigate how aging affects the temporal dynamics of neural adaptation in human auditory cortex. Younger (18-31 years) and older (51-70 years) normal hearing adults listened to tone sequences with varying onset-to-onset intervals. Our results show long-lasting neural adaptation such that the response to a particular tone is a nonlinear function of the extended temporal history of sound events. Most important, aging is associated with multiple changes in auditory cortex; older adults exhibit larger and less variable response magnitudes, a larger dynamic response range, and a reduced sensitivity to temporal context. Computational modeling suggests that reduced adaptation recovery times underlie these changes in the aging auditory cortex and that the extended temporal stimulation has less influence on the neural response to the current sound in older compared with younger individuals. Our human electroencephalography results critically narrow the gap to animal electrophysiology work suggesting a compensatory release from cortical inhibition accompanying hearing loss and aging. PMID:27459921
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.
[Dynamic structure of the cardiac rhythm in the process of adapting to high-altitude hypoxia].
Shukurov, F A; Nidekker, I G
1981-01-01
On the basis of dynamic series of RR intervals of electrocardiograms of healthy male test subjects exposed for a different period of time to high altitude hypoxia, autoregression clouds were built. The patterns of distribution thus obtained were compared with physical work capacity of the test subjects. It is suggested that when selecting people to work actively at high altitudes autoregression clouds can be used as quantitative estimates of their health state and as predictions of potential adaptation failures.
NASA Astrophysics Data System (ADS)
Pathak, Anand; Sinha, Sitabhra
2015-09-01
Many complex systems can be represented as networks of dynamical elements whose states evolve in response to interactions with neighboring elements, noise and external stimuli. The collective behavior of such systems can exhibit remarkable ordering phenomena such as chimera order corresponding to coexistence of ordered and disordered regions. Often, the interactions in such systems can also evolve over time responding to changes in the dynamical states of the elements. Link adaptation inspired by Hebbian learning, the dominant paradigm for neuronal plasticity, has been earlier shown to result in structural balance by removing any initial frustration in a system that arises through conflicting interactions. Here we show that the rate of the adaptive dynamics for the interactions is crucial in deciding the emergence of different ordering behavior (including chimera) and frustration in networks of Ising spins. In particular, we observe that small changes in the link adaptation rate about a critical value result in the system exhibiting radically different energy landscapes, viz., smooth landscape corresponding to balanced systems seen for fast learning, and rugged landscapes corresponding to frustrated systems seen for slow learning.
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.
On the global dynamics of adaptive systems - A study of an elementary example
NASA Technical Reports Server (NTRS)
Espana, Martin D.; Praly, Laurent
1993-01-01
The inherent nonlinear character of adaptive systems poses serious theoretical problems for the analysis of their dynamics. On the other hand, the importance of their dynamic behavior is directly related to the practical interest in predicting such undesirable phenomena as nonlinear oscillations, abrupt transients, intermittence or a high sensitivity with respect to initial conditions. A geometrical/qualitative description of the phase portrait of a discrete-time adaptive system with unmodeled disturbances is given. For this, the motions in the phase space are referred to normally hyperbolic (structurally stable) locally invariant sets. The study is complemented with a local stability analysis of the equilibrium point and periodic solutions. The critical character of adaptive systems under rather usual working conditions is discussed. Special emphasis is put on the causes leading to intermittence. A geometric interpretation of the effects of some commonly used palliatives to this problem is given. The 'dead-zone' approach is studied in more detail. The predicted dynamics are compared with simulation results.
Luo, Biao; Wu, Huai-Ning; Li, Han-Xiong
2015-04-01
Highly dissipative nonlinear partial differential equations (PDEs) are widely employed to describe the system dynamics of industrial spatially distributed processes (SDPs). In this paper, we consider the optimal control problem of the general highly dissipative SDPs, and propose an adaptive optimal control approach based on neuro-dynamic programming (NDP). Initially, Karhunen-Loève decomposition is employed to compute empirical eigenfunctions (EEFs) of the SDP based on the method of snapshots. These EEFs together with singular perturbation technique are then used to obtain a finite-dimensional slow subsystem of ordinary differential equations that accurately describes the dominant dynamics of the PDE system. Subsequently, the optimal control problem is reformulated on the basis of the slow subsystem, which is further converted to solve a Hamilton-Jacobi-Bellman (HJB) equation. HJB equation is a nonlinear PDE that has proven to be impossible to solve analytically. Thus, an adaptive optimal control method is developed via NDP that solves the HJB equation online using neural network (NN) for approximating the value function; and an online NN weight tuning law is proposed without requiring an initial stabilizing control policy. Moreover, by involving the NN estimation error, we prove that the original closed-loop PDE system with the adaptive optimal control policy is semiglobally uniformly ultimately bounded. Finally, the developed method is tested on a nonlinear diffusion-convection-reaction process and applied to a temperature cooling fin of high-speed aerospace vehicle, and the achieved results show its effectiveness.
Luo, Biao; Wu, Huai-Ning; Li, Han-Xiong
2015-04-01
Highly dissipative nonlinear partial differential equations (PDEs) are widely employed to describe the system dynamics of industrial spatially distributed processes (SDPs). In this paper, we consider the optimal control problem of the general highly dissipative SDPs, and propose an adaptive optimal control approach based on neuro-dynamic programming (NDP). Initially, Karhunen-Loève decomposition is employed to compute empirical eigenfunctions (EEFs) of the SDP based on the method of snapshots. These EEFs together with singular perturbation technique are then used to obtain a finite-dimensional slow subsystem of ordinary differential equations that accurately describes the dominant dynamics of the PDE system. Subsequently, the optimal control problem is reformulated on the basis of the slow subsystem, which is further converted to solve a Hamilton-Jacobi-Bellman (HJB) equation. HJB equation is a nonlinear PDE that has proven to be impossible to solve analytically. Thus, an adaptive optimal control method is developed via NDP that solves the HJB equation online using neural network (NN) for approximating the value function; and an online NN weight tuning law is proposed without requiring an initial stabilizing control policy. Moreover, by involving the NN estimation error, we prove that the original closed-loop PDE system with the adaptive optimal control policy is semiglobally uniformly ultimately bounded. Finally, the developed method is tested on a nonlinear diffusion-convection-reaction process and applied to a temperature cooling fin of high-speed aerospace vehicle, and the achieved results show its effectiveness. PMID:25794375
Effects of adaptive protective behavior on the dynamics of sexually transmitted infections.
Hayashi, Michael A L; Eisenberg, Marisa C
2016-01-01
Sexually transmitted infections (STIs) continue to present a complex and costly challenge to public health programs. The preferences and social dynamics of a population can have a large impact on the course of an outbreak as well as the effectiveness of interventions intended to influence individual behavior. In addition, individuals may alter their sexual behavior in response to the presence of STIs, creating a feedback loop between transmission and behavior. We investigate the consequences of modeling the interaction between STI transmission and prophylactic use with a model that links a Susceptible-Infectious-Susceptible (SIS) system to evolutionary game dynamics that determine the effective contact rate. The combined model framework allows us to address protective behavior by both infected and susceptible individuals. Feedback between behavioral adaptation and prevalence creates a wide range of dynamic behaviors in the combined model, including damped and sustained oscillations as well as bistability, depending on the behavioral parameters and disease growth rate. We found that disease extinction is possible for multiple regions where R0>1, due to behavior adaptation driving the epidemic downward, although conversely endemic prevalence for arbitrarily low R0 is also possible if contact rates are sufficiently high. We also tested how model misspecification might affect disease forecasting and estimation of the model parameters and R0. We found that alternative models that neglect the behavioral feedback or only consider behavior adaptation by susceptible individuals can potentially yield misleading parameter estimates or omit significant features of the disease trajectory. PMID:26362102
Demonstrating the impact of flood adaptation using an online dynamic flood mapper
NASA Astrophysics Data System (ADS)
Orton, P. M.; MacManus, K.; Doxsey-Whitfield, E.; Yetman, G.; Fisher, K.; Sanderson, E. W.; Giampieri, M.; Blumberg, A. F.
2015-12-01
Municipalities across the nation are weighing the value of coastal natural and nature-based features (NNBF) for flood risk reduction and the many ecosystem services they provide, yet there is limited quantitative information available to help make these decisions. Here, we describe a new "dynamic" flood mapping web-tool that demonstrates the modeled effects of NNBF on flood hazard zones for the highly populated areas surrounding Jamaica Bay, New York City. The tool also provides information on damages from flooding as well as cost-benefit analyses for NNBF adaptations for the bay. The project researchers are involved with development of a Jamaica Bay Coastal Master Plan, and the mapper will play an important role for increasing the public understanding of adaptation options. More broadly, dynamic flood mappers have many more possibilities than "static" mappers that simply add sea level rise onto pre-defined flood levels and bathtub them over flood plains. Dynamic modeling can enable inclusion of the response of coastal systems, imposed human adaptation, as well as flooding by surge, tide and precipitation.
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
Spike-Threshold Adaptation Predicted by Membrane Potential Dynamics In Vivo
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
Ogbunugafor, C Brandon; Wylie, C Scott; Diakite, Ibrahim; Weinreich, Daniel M; Hartl, Daniel L
2016-01-01
The adaptive landscape analogy has found practical use in recent years, as many have explored how their understanding can inform therapeutic strategies that subvert the evolution of drug resistance. A major barrier to applications of these concepts is a lack of detail concerning how the environment affects adaptive landscape topography, and consequently, the outcome of drug treatment. Here we combine empirical data, evolutionary theory, and computer simulations towards dissecting adaptive landscape by environment interactions for the evolution of drug resistance in two dimensions-drug concentration and drug type. We do so by studying the resistance mediated by Plasmodium falciparum dihydrofolate reductase (DHFR) to two related inhibitors-pyrimethamine and cycloguanil-across a breadth of drug concentrations. We first examine whether the adaptive landscapes for the two drugs are consistent with common definitions of cross-resistance. We then reconstruct all accessible pathways across the landscape, observing how their structure changes with drug environment. We offer a mechanism for non-linearity in the topography of accessible pathways by calculating of the interaction between mutation effects and drug environment, which reveals rampant patterns of epistasis. We then simulate evolution in several different drug environments to observe how these individual mutation effects (and patterns of epistasis) influence paths taken at evolutionary "forks in the road" that dictate adaptive dynamics in silico. In doing so, we reveal how classic metrics like the IC50 and minimal inhibitory concentration (MIC) are dubious proxies for understanding how evolution will occur across drug environments. We also consider how the findings reveal ambiguities in the cross-resistance concept, as subtle differences in adaptive landscape topography between otherwise equivalent drugs can drive drastically different evolutionary outcomes. Summarizing, we discuss the results with regards to their
Ogbunugafor, C. Brandon; Wylie, C. Scott; Diakite, Ibrahim; Weinreich, Daniel M.; Hartl, Daniel L.
2016-01-01
The adaptive landscape analogy has found practical use in recent years, as many have explored how their understanding can inform therapeutic strategies that subvert the evolution of drug resistance. A major barrier to applications of these concepts is a lack of detail concerning how the environment affects adaptive landscape topography, and consequently, the outcome of drug treatment. Here we combine empirical data, evolutionary theory, and computer simulations towards dissecting adaptive landscape by environment interactions for the evolution of drug resistance in two dimensions—drug concentration and drug type. We do so by studying the resistance mediated by Plasmodium falciparum dihydrofolate reductase (DHFR) to two related inhibitors—pyrimethamine and cycloguanil—across a breadth of drug concentrations. We first examine whether the adaptive landscapes for the two drugs are consistent with common definitions of cross-resistance. We then reconstruct all accessible pathways across the landscape, observing how their structure changes with drug environment. We offer a mechanism for non-linearity in the topography of accessible pathways by calculating of the interaction between mutation effects and drug environment, which reveals rampant patterns of epistasis. We then simulate evolution in several different drug environments to observe how these individual mutation effects (and patterns of epistasis) influence paths taken at evolutionary “forks in the road” that dictate adaptive dynamics in silico. In doing so, we reveal how classic metrics like the IC50 and minimal inhibitory concentration (MIC) are dubious proxies for understanding how evolution will occur across drug environments. We also consider how the findings reveal ambiguities in the cross-resistance concept, as subtle differences in adaptive landscape topography between otherwise equivalent drugs can drive drastically different evolutionary outcomes. Summarizing, we discuss the results with
The dynamics of the Π1 colour mechanism: further evidence for two sites of adaptation
Augenstein, E. J.; Pugh, E. N.
1977-01-01
1. The visual pathway that determines Stiles's Π1 colour mechanism was isolated by the auxiliary field technique and studied under dynamic conditions of light adaptation and recovery by threshold measurements. 2. The time courses of adaptation to Π1-equated short wave-length (μ ≤ 500 nm) and long wave-length (μ ≥ 550 nm) fields are very distinct: a large and relatively long-enduring transient threshold elevation occurs at the onset of the long wave-length, but not of the short wave-length fields. 3. Similarly, the time courses of recovery from Π1-equated long and short wave-length fields are quite distinctive: a large and relatively long enduring transient (`transient tritanopia') occurs at the offset of the long wave-length, but not of the short wave-length fields. 4. The wave-lengths of the fields which cause the adaptation transients coincide with those shown previously (Pugh, 1976) to combine non-additively with μ = 430 nm fields in effecting Π1 adaptation. The failure of the time course of Π1 adaptation to be spectrally `univariant' combines with the failures of field-additivity to demonstrate that signals from the long and/or middle wave-length sensitive cones affect the adaptation state of the Π1 pathway. 5. The adaptation transients are not observed in the pathways that determine Π4 and Π5. Thus, instantaneous signals from the middle and/or long wave-length sensitive cones are not the cause of the transients. Rather the cause must lie in the path by which those cones transmit their signals to the Π1 pathway or in the Π1 pathway itself. 6. The off-transient can be diminished by adding an adequately intense short wave-length field to a long wave-length field that would normally cause it. The Π1 pathway must receive chromatically opponent signals. PMID:592192
Consequences of adaptive behaviour for the structure and dynamics of food webs.
Valdovinos, Fernanda S; Ramos-Jiliberto, Rodrigo; Garay-Narváez, Leslie; Urbani, Pasquinell; Dunne, Jennifer A
2010-12-01
Species coexistence within ecosystems and the stability of patterns of temporal changes in population sizes are central topics in ecological theory. In the last decade, adaptive behaviour has been proposed as a mechanism of population stabilization. In particular, widely distributed adaptive trophic behaviour (ATB), the fitness-enhancing changes in individuals' feeding-related traits due to variation in their trophic environment, may play a key role in modulating the dynamics of feeding relationships within natural communities. In this article, we review and synthesize models and results from theoretical research dealing with the consequences of ATB on the structure and dynamics of complex food webs. We discuss current approaches, point out limitations, and consider questions ripe for future research. In spite of some differences in the modelling and analytic approaches, there are points of convergence: (1) ATB promotes the complex structure of ecological networks, (2) ATB increases the stability of their dynamics, (3) ATB reverses May's negative complexity-stability relationship, and (4) ATB provides resilience and resistance of networks against perturbations. Current knowledge supports ATB as an essential ingredient for models of community dynamics, and future research that incorporates ATB will be well positioned to address questions important for basic ecological research and its applications.
Blamey, Peter J
2005-01-01
Adaptive dynamic range optimization (ADRO) is an amplification strategy that uses digital signal processing techniques to improve the audibility, comfort, and intelligibility of sounds for people who use cochlear implants and/or hearing aids. The strategy uses statistical analysis to select the most information-rich section of the input dynamic range in multiple-frequency channels. Fuzzy logic rules control the gain in each frequency channel so that the selected section of the dynamic range is presented at an audible and comfortable level. The ADRO processing thus adaptively optimizes the dynamic range of the signal in multiple-frequency channels. Clinical studies show that ADRO can be fitted easily to all degrees of hearing loss for hearing aids and cochlear implants in a direct and intuitive manner, taking the preferences of the listener into account. The result is high acceptance by new and experienced hearing aid users and strong preferences for ADRO compared with alternative amplification strategies. The ADRO processing is particularly well suited to bimodal and hybrid stimulation which combine electric and acoustic stimulation in opposite ears or in the same ear, respectively.
Blamey, Peter J.
2005-01-01
Adaptive dynamic range optimization (ADRO) is an amplification strategy that uses digital signal processing techniques to improve the audibility, comfort, and intelligibility of sounds for people who use cochlear implants and/or hearing aids. The strategy uses statistical analysis to select the most information-rich section of the input dynamic range in multiple-frequency channels. Fuzzy logic rules control the gain in each frequency channel so that the selected section of the dynamic range is presented at an audible and comfortable level. The ADRO processing thus adaptively optimizes the dynamic range of the signal in multiple-frequency channels. Clinical studies show that ADRO can be fitted easily to all degrees of hearing loss for hearing aids and cochlear implants in a direct and intuitive manner, taking the preferences of the listener into account. The result is high acceptance by new and experienced hearing aid users and strong preferences for ADRO compared with alternative amplification strategies. The ADRO processing is particularly well suited to bimodal and hybrid stimulation which combine electric and acoustic stimulation in opposite ears or in the same ear, respectively. PMID:16012705
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
Lewis, F L; Vamvoudakis, Kyriakos G
2011-02-01
Approximate dynamic programming (ADP) is a class of reinforcement learning methods that have shown their importance in a variety of applications, including feedback control of dynamical systems. ADP generally requires full information about the system internal states, which is usually not available in practical situations. In this paper, we show how to implement ADP methods using only measured input/output data from the system. Linear dynamical systems with deterministic behavior are considered herein, which are systems of great interest in the control system community. In control system theory, these types of methods are referred to as output feedback (OPFB). The stochastic equivalent of the systems dealt with in this paper is a class of partially observable Markov decision processes. We develop both policy iteration and value iteration algorithms that converge to an optimal controller that requires only OPFB. It is shown that, similar to Q -learning, the new methods have the important advantage that knowledge of the system dynamics is not needed for the implementation of these learning algorithms or for the OPFB control. Only the order of the system, as well as an upper bound on its "observability index," must be known. The learned OPFB controller is in the form of a polynomial autoregressive moving-average controller that has equivalent performance with the optimal state variable feedback gain.
Woo, Hyung Jun; Reifman, Jaques
2014-01-01
We describe a stochastic virus evolution model representing genomic diversification and within-host selection during experimental serial passages under cell culture or live-host conditions. The model incorporates realistic descriptions of the virus genotypes in nucleotide and amino acid sequence spaces, as well as their diversification from error-prone replications. It quantitatively considers factors such as target cell number, bottleneck size, passage period, infection and cell death rates, and the replication rate of different genotypes, allowing for systematic examinations of how their changes affect the evolutionary dynamics of viruses during passages. The relative probability for a viral population to achieve adaptation under a new host environment, quantified by the rate with which a target sequence frequency rises above 50%, was found to be most sensitive to factors related to sequence structure (distance from the wild type to the target) and selection strength (host cell number and bottleneck size). For parameter values representative of RNA viruses, the likelihood of observing adaptations during passages became negligible as the required number of mutations rose above two amino acid sites. We modeled the specific adaptation process of influenza A H5N1 viruses in mammalian hosts by simulating the evolutionary dynamics of H5 strains under the fitness landscape inferred from multiple sequence alignments of H3 proteins. In light of comparisons with experimental findings, we observed that the evolutionary dynamics of adaptation is strongly affected not only by the tendency toward increasing fitness values but also by the accessibility of pathways between genotypes constrained by the genetic code.
NASA Astrophysics Data System (ADS)
Wu, Xia; Wu, Genhua
2014-08-01
Geometrical optimization of atomic clusters is performed by a development of adaptive immune optimization algorithm (AIOA) with dynamic lattice searching (DLS) operation (AIOA-DLS method). By a cycle of construction and searching of the dynamic lattice (DL), DLS algorithm rapidly makes the clusters more regular and greatly reduces the potential energy. DLS can thus be used as an operation acting on the new individuals after mutation operation in AIOA to improve the performance of the AIOA. The AIOA-DLS method combines the merit of evolutionary algorithm and idea of dynamic lattice. The performance of the proposed method is investigated in the optimization of Lennard-Jones clusters within 250 atoms and silver clusters described by many-body Gupta potential within 150 atoms. Results reported in the literature are reproduced, and the motif of Ag61 cluster is found to be stacking-fault face-centered cubic, whose energy is lower than that of previously obtained icosahedron.
Pairwise adaptive thermostats for improved accuracy and stability in dissipative particle dynamics
NASA Astrophysics Data System (ADS)
Leimkuhler, Benedict; Shang, Xiaocheng
2016-11-01
We examine the formulation and numerical treatment of dissipative particle dynamics (DPD) and momentum-conserving molecular dynamics. We show that it is possible to improve both the accuracy and the stability of DPD by employing a pairwise adaptive Langevin thermostat that precisely matches the dynamical characteristics of DPD simulations (e.g., autocorrelation functions) while automatically correcting thermodynamic averages using a negative feedback loop. In the low friction regime, it is possible to replace DPD by a simpler momentum-conserving variant of the Nosé-Hoover-Langevin method based on thermostatting only pairwise interactions; we show that this method has an extra order of accuracy for an important class of observables (a superconvergence result), while also allowing larger timesteps than alternatives. All the methods mentioned in the article are easily implemented. Numerical experiments are performed in both equilibrium and nonequilibrium settings; using Lees-Edwards boundary conditions to induce shear flow.
Grand-Canonical Adaptive Resolution Centroid Molecular Dynamics: Implementation and application
NASA Astrophysics Data System (ADS)
Agarwal, Animesh; Delle Site, Luigi
2016-09-01
We have implemented the Centroid Molecular Dynamics scheme (CMD) into the Grand Canonical-like version of the Adaptive Resolution Simulation Molecular Dynamics (GC-AdResS) method. We have tested the implementation on two different systems, liquid parahydrogen at extreme thermodynamic conditions and liquid water at ambient conditions; the reproduction of structural as well as dynamical results of reference systems are highly satisfactory. The capability of performing GC-AdResS CMD simulations allows for the treatment of a system characterized by some quantum features and open boundaries. This latter characteristic not only is of computational convenience, allowing for equivalent results of much larger and computationally more expensive systems, but also suggests a tool of analysis so far not explored, that is the unambiguous identification of the essential degrees of freedom required for a given property.
Tone reproduction for high-dynamic range imaging based on adaptive filtering
NASA Astrophysics Data System (ADS)
Ha, Changwoo; Lee, Joohyun; Jeong, Jechang
2014-03-01
A tone reproduction algorithm with enhanced contrast of high-dynamic range images on conventional low-dynamic range display devices is presented. The proposed algorithm consists mainly of block-based parameter estimation, a characteristic-based luminance adjustment, and an adaptive Gaussian filter using minimum description length. Instead of relying only on the reduction of the dynamic range, a characteristic-based luminance adjustment process modifies the luminance values. The Gaussian-filtered luminance value is obtained from appropriate value of variance, and the contrast is then enhanced through the use of a relation between the adjusted luminance and Gaussian-filtered luminance values. In the final tone-reproduction process, the proposed algorithm combines color and luminance components in order to preserve the color consistency. The experimental results demonstrate that the proposed algorithm achieves a good subjective quality while enhancing the contrast of the image details.
Schlüter, Lothar; Lohbeck, Kai T; Gröger, Joachim P; Riebesell, Ulf; Reusch, Thorsten B H
2016-07-01
Marine phytoplankton may adapt to ocean change, such as acidification or warming, because of their large population sizes and short generation times. Long-term adaptation to novel environments is a dynamic process, and phenotypic change can take place thousands of generations after exposure to novel conditions. We conducted a long-term evolution experiment (4 years = 2100 generations), starting with a single clone of the abundant and widespread coccolithophore Emiliania huxleyi exposed to three different CO2 levels simulating ocean acidification (OA). Growth rates as a proxy for Darwinian fitness increased only moderately under both levels of OA [+3.4% and +4.8%, respectively, at 1100 and 2200 μatm partial pressure of CO2 (Pco2)] relative to control treatments (ambient CO2, 400 μatm). Long-term adaptation to OA was complex, and initial phenotypic responses of ecologically important traits were later reverted. The biogeochemically important trait of calcification, in particular, that had initially been restored within the first year of evolution was later reduced to levels lower than the performance of nonadapted populations under OA. Calcification was not constitutively lost but returned to control treatment levels when high CO2-adapted isolates were transferred back to present-day control CO2 conditions. Selection under elevated CO2 exacerbated a general decrease of cell sizes under long-term laboratory evolution. Our results show that phytoplankton may evolve complex phenotypic plasticity that can affect biogeochemically important traits, such as calcification. Adaptive evolution may play out over longer time scales (>1 year) in an unforeseen way under future ocean conditions that cannot be predicted from initial adaptation responses. PMID:27419227
Schlüter, Lothar; Lohbeck, Kai T; Gröger, Joachim P; Riebesell, Ulf; Reusch, Thorsten B H
2016-07-01
Marine phytoplankton may adapt to ocean change, such as acidification or warming, because of their large population sizes and short generation times. Long-term adaptation to novel environments is a dynamic process, and phenotypic change can take place thousands of generations after exposure to novel conditions. We conducted a long-term evolution experiment (4 years = 2100 generations), starting with a single clone of the abundant and widespread coccolithophore Emiliania huxleyi exposed to three different CO2 levels simulating ocean acidification (OA). Growth rates as a proxy for Darwinian fitness increased only moderately under both levels of OA [+3.4% and +4.8%, respectively, at 1100 and 2200 μatm partial pressure of CO2 (Pco2)] relative to control treatments (ambient CO2, 400 μatm). Long-term adaptation to OA was complex, and initial phenotypic responses of ecologically important traits were later reverted. The biogeochemically important trait of calcification, in particular, that had initially been restored within the first year of evolution was later reduced to levels lower than the performance of nonadapted populations under OA. Calcification was not constitutively lost but returned to control treatment levels when high CO2-adapted isolates were transferred back to present-day control CO2 conditions. Selection under elevated CO2 exacerbated a general decrease of cell sizes under long-term laboratory evolution. Our results show that phytoplankton may evolve complex phenotypic plasticity that can affect biogeochemically important traits, such as calcification. Adaptive evolution may play out over longer time scales (>1 year) in an unforeseen way under future ocean conditions that cannot be predicted from initial adaptation responses.
Adaptive feedback potential in dynamic stability during disturbed walking in the elderly.
Bierbaum, Stefanie; Peper, Andreas; Karamanidis, Kiros; Arampatzis, Adamantios
2011-07-01
After perturbation of the gait, feedback information may help regaining balance adequately, but it remains unknown whether adaptive feedback responses are possible after repetitive and unexpected perturbations during gait and if there are age-related differences. Prior experience may contribute to improved reactive behavior. Fourteen old (59-73 yrs) and fourteen young (22-31 yrs) males walked on a walkway which included one covered element. By exchanging this element participants either stepped on hard surface or unexpectedly on soft surface which caused a perturbation in gait. The gait protocol contained 5 unexpected soft trials to quantify the reactive adaptation. Each soft trial was followed by 4-8 hard trials to generate a wash-out effect. The dynamic stability was investigated by using the margin of stability (MoS), which was calculated as the difference between the anterior boundary of the base of support and the extrapolated position of the center of mass in the anterior-posterior direction. MoS at recovery leg touchdown were significantly lower in the unexpected soft trials compared to the baseline, indicating a less stable posture. However, MoS increased (p<0.05) in both groups within the disturbed trials, indicating feedback adaptive improvements. Young and old participants showed differences in the handling of the perturbation in the course of several trials. The magnitude of the reactive adaptation after the fifth unexpected perturbation was significantly different compared to the first unexpected perturbation (old: 49±30%; young: 77±40%), showing a tendency (p=0.065) for higher values in the young participants. Old individuals maintain the ability to adapt to feedback controlled perturbations. However, the locomotor behavior is more conservative compared to the young ones, leading to disadvantages in the reactive adaptation during disturbed walking.
Schlüter, Lothar; Lohbeck, Kai T.; Gröger, Joachim P.; Riebesell, Ulf; Reusch, Thorsten B. H.
2016-01-01
Marine phytoplankton may adapt to ocean change, such as acidification or warming, because of their large population sizes and short generation times. Long-term adaptation to novel environments is a dynamic process, and phenotypic change can take place thousands of generations after exposure to novel conditions. We conducted a long-term evolution experiment (4 years = 2100 generations), starting with a single clone of the abundant and widespread coccolithophore Emiliania huxleyi exposed to three different CO2 levels simulating ocean acidification (OA). Growth rates as a proxy for Darwinian fitness increased only moderately under both levels of OA [+3.4% and +4.8%, respectively, at 1100 and 2200 μatm partial pressure of CO2 (Pco2)] relative to control treatments (ambient CO2, 400 μatm). Long-term adaptation to OA was complex, and initial phenotypic responses of ecologically important traits were later reverted. The biogeochemically important trait of calcification, in particular, that had initially been restored within the first year of evolution was later reduced to levels lower than the performance of nonadapted populations under OA. Calcification was not constitutively lost but returned to control treatment levels when high CO2–adapted isolates were transferred back to present-day control CO2 conditions. Selection under elevated CO2 exacerbated a general decrease of cell sizes under long-term laboratory evolution. Our results show that phytoplankton may evolve complex phenotypic plasticity that can affect biogeochemically important traits, such as calcification. Adaptive evolution may play out over longer time scales (>1 year) in an unforeseen way under future ocean conditions that cannot be predicted from initial adaptation responses. PMID:27419227
Adaptive modeling, identification, and control of dynamic structural systems. I. Theory
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.
Adaptive support vector regression for UAV flight control.
Shin, Jongho; Jin Kim, H; Kim, Youdan
2011-01-01
This paper explores an application of support vector regression for adaptive control of an unmanned aerial vehicle (UAV). Unlike neural networks, support vector regression (SVR) generates global solutions, because SVR basically solves quadratic programming (QP) problems. With this advantage, the input-output feedback-linearized inverse dynamic model and the compensation term for the inversion error are identified off-line, which we call I-SVR (inversion SVR) and C-SVR (compensation SVR), respectively. In order to compensate for the inversion error and the unexpected uncertainty, an online adaptation algorithm for the C-SVR is proposed. Then, the stability of the overall error dynamics is analyzed by the uniformly ultimately bounded property in the nonlinear system theory. In order to validate the effectiveness of the proposed adaptive controller, numerical simulations are performed on the UAV model.
Pramukkul, Pensri; Svenkeson, Adam; Grigolini, Paolo
2014-02-01
We study the combined effects of noise and detector sensitivity on a dynamical process that generates intermittent events mimicking the behavior of complex systems. By varying the sensitivity level of the detector we move between two forms of complexity, from inverse power law to Mittag-Leffler interevent time survival probabilities. Here fluctuations fight against complexity, causing an exponential truncation to the survival probability. We show that fluctuations of relatively weak intensity have a strong effect on the generation of Mittag-Leffler complexity, providing a reason why stretched exponentials are frequently found in nature. Our results afford a more unified picture of complexity resting on the Mittag-Leffler function and encompassing the standard inverse power law definition. PMID:25353422
NASA Astrophysics Data System (ADS)
Pramukkul, Pensri; Svenkeson, Adam; Grigolini, Paolo
2014-02-01
We study the combined effects of noise and detector sensitivity on a dynamical process that generates intermittent events mimicking the behavior of complex systems. By varying the sensitivity level of the detector we move between two forms of complexity, from inverse power law to Mittag-Leffler interevent time survival probabilities. Here fluctuations fight against complexity, causing an exponential truncation to the survival probability. We show that fluctuations of relatively weak intensity have a strong effect on the generation of Mittag-Leffler complexity, providing a reason why stretched exponentials are frequently found in nature. Our results afford a more unified picture of complexity resting on the Mittag-Leffler function and encompassing the standard inverse power law definition.
Experience with automatic, dynamic load balancing and adaptive finite element computation
Wheat, S.R.; Devine, K.D.; Maccabe, A.B.
1993-10-01
Distributed memory, Massively Parallel (MP), MIMD technology has enabled the development of applications requiring computational resources previously unobtainable. Structural mechanics and fluid dynamics applications, for example, are often solved by finite element methods (FEMs) requiring, millions of degrees of freedom to accurately simulate physical phenomenon. Adaptive methods, which automatically refine or coarsen meshes and vary the order of accuracy of the numerical solution, offer greater robustness and computational efficiency than traditional FEMs by reducing the amount of computation required away from physical structures such as shock waves and boundary layers. On MP computers, FEMs frequently result in distributed processor load imbalances. To overcome load imbalance, many MP FEMs use static load balancing as a preprocessor to the finite element calculation. Adaptive methods complicate the load imbalance problem since the work per element is not uniform across the solution domain and changes as the computation proceeds. Therefore, dynamic load balancing is required to maintain global load balance. We describe a dynamic, fine-grained, element-based data migration system that maintains global load balance and is effective in the presence of changing work loads. Global load balance is achieved by overlapping neighborhoods of processors, where each neighborhood performs local load balancing. The method utilizes an automatic element management system library to which a programmer integrates the application`s computational description. The library`s flexibility supports a large class of finite element and finite difference based applications.
Vasseur, David A; Fox, Jeremy W
2011-10-01
Consumers acquire essential nutrients by ingesting the tissues of resource species. When these tissues contain essential nutrients in a suboptimal ratio, consumers may benefit from ingesting a mixture of nutritionally complementary resource species. We investigate the joint ecological and evolutionary consequences of competition for complementary resources, using an adaptive dynamics model of two consumers and two resources that differ in their relative content of two essential nutrients. In the absence of competition, a nutritionally balanced diet rarely maximizes fitness because of the dynamic feedbacks between uptake rate and resource density, whereas in sympatry, nutritionally balanced diets maximize fitness because competing consumers with different nutritional requirements tend to equalize the relative abundances of the two resources. Adaptation from allopatric to sympatric fitness optima can generate character convergence, divergence, and parallel shifts, depending not on the degree of diet overlap but on the match between resource nutrient content and consumer nutrient requirements. Contrary to previous verbal arguments that suggest that character convergence leads to neutral stability, coadaptation of competing consumers always leads to stable coexistence. Furthermore, we show that incorporating costs of consuming or excreting excess nonlimiting nutrients selects for nutritionally balanced diets and so promotes character convergence. This article demonstrates that resource-use overlap has little bearing on coexistence when resources are nutritionally complementary, and it highlights the importance of using mathematical models to infer the stability of ecoevolutionary dynamics. PMID:21956028
Mulkidjanian, Armen Y.; Shaitan, Konstantin V.; Engelhard, Martin; Klare, Johann P.; Steinhoff, Heinz-Jürgen
2015-01-01
Motile bacteria and archaea respond to chemical and physical stimuli seeking optimal conditions for survival. To this end transmembrane chemo- and photoreceptors organized in large arrays initiate signaling cascades and ultimately regulate the rotation of flagellar motors. To unravel the molecular mechanism of signaling in an archaeal phototaxis complex we performed coarse-grained molecular dynamics simulations of a trimer of receptor/transducer dimers, namely NpSRII/NpHtrII from Natronomonas pharaonis. Signaling is regulated by a reversible methylation mechanism called adaptation, which also influences the level of basal receptor activation. Mimicking two extreme methylation states in our simulations we found conformational changes for the transmembrane region of NpSRII/NpHtrII which resemble experimentally observed light-induced changes. Further downstream in the cytoplasmic domain of the transducer the signal propagates via distinct changes in the dynamics of HAMP1, HAMP2, the adaptation domain and the binding region for the kinase CheA, where conformational rearrangements were found to be subtle. Overall these observations suggest a signaling mechanism based on dynamic allostery resembling models previously proposed for E. coli chemoreceptors, indicating similar properties of signal transduction for archaeal photoreceptors and bacterial chemoreceptors. PMID:26496122
Zhang, Yu; Prakash, Edmond C; Sung, Eric
2004-01-01
This paper presents a new physically-based 3D facial model based on anatomical knowledge which provides high fidelity for facial expression animation while optimizing the computation. Our facial model has a multilayer biomechanical structure, incorporating a physically-based approximation to facial skin tissue, a set of anatomically-motivated facial muscle actuators, and underlying skull structure. In contrast to existing mass-spring-damper (MSD) facial models, our dynamic skin model uses the nonlinear springs to directly simulate the nonlinear visco-elastic behavior of soft tissue and a new kind of edge repulsion spring is developed to prevent collapse of the skin model. Different types of muscle models have been developed to simulate distribution of the muscle force applied on the skin due to muscle contraction. The presence of the skull advantageously constrain the skin movements, resulting in more accurate facial deformation and also guides the interactive placement of facial muscles. The governing dynamics are computed using a local semi-implicit ODE solver. In the dynamic simulation, an adaptive refinement automatically adapts the local resolution at which potential inaccuracies are detected depending on local deformation. The method, in effect, ensures the required speedup by concentrating computational time only where needed while ensuring realistic behavior within a predefined error threshold. This mechanism allows more pleasing animation results to be produced at a reduced computational cost.
An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks
Lei, Chunyang; Bie, Hongxia; Fang, Gengfa; Zhang, Xuekun
2015-01-01
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. PMID:26633421
NASA Astrophysics Data System (ADS)
Gao, Shigen; Dong, Hairong; Lyu, Shihang; Ning, Bin
2016-07-01
This paper studies decentralised neural adaptive control of a class of interconnected nonlinear systems, each subsystem is in the presence of input saturation and external disturbance and has independent system order. Using a novel truncated adaptation design, dynamic surface control technique and minimal-learning-parameters algorithm, the proposed method circumvents the problems of 'explosion of complexity' and 'dimension curse' that exist in the traditional backstepping design. Comparing to the methodology that neural weights are online updated in the controllers, only one scalar needs to be updated in the controllers of each subsystem when dealing with unknown systematic dynamics. Radial basis function neural networks (NNs) are used in the online approximation of unknown systematic dynamics. It is proved using Lyapunov stability theory that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. The tracking errors of each subsystems, the amplitude of NN approximation residuals and external disturbances can be attenuated to arbitrarily small by tuning proper design parameters. Simulation results are given to demonstrate the effectiveness of the proposed method.
An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks.
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.
An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks.
Lei, Chunyang; Bie, Hongxia; Fang, Gengfa; Zhang, Xuekun
2015-01-01
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. PMID:26633421
Turing pattern dynamics and adaptive discretization for a super-diffusive Lotka-Volterra model.
Bendahmane, Mostafa; Ruiz-Baier, Ricardo; Tian, Canrong
2016-05-01
In this paper we analyze the effects of introducing the fractional-in-space operator into a Lotka-Volterra competitive model describing population super-diffusion. First, we study how cross super-diffusion influences the formation of spatial patterns: a linear stability analysis is carried out, showing that cross super-diffusion triggers Turing instabilities, whereas classical (self) super-diffusion does not. In addition we perform a weakly nonlinear analysis yielding a system of amplitude equations, whose study shows the stability of Turing steady states. A second goal of this contribution is to propose a fully adaptive multiresolution finite volume method that employs shifted Grünwald gradient approximations, and which is tailored for a larger class of systems involving fractional diffusion operators. The scheme is aimed at efficient dynamic mesh adaptation and substantial savings in computational burden. A numerical simulation of the model was performed near the instability boundaries, confirming the behavior predicted by our analysis.
Turing pattern dynamics and adaptive discretization for a super-diffusive Lotka-Volterra model.
Bendahmane, Mostafa; Ruiz-Baier, Ricardo; Tian, Canrong
2016-05-01
In this paper we analyze the effects of introducing the fractional-in-space operator into a Lotka-Volterra competitive model describing population super-diffusion. First, we study how cross super-diffusion influences the formation of spatial patterns: a linear stability analysis is carried out, showing that cross super-diffusion triggers Turing instabilities, whereas classical (self) super-diffusion does not. In addition we perform a weakly nonlinear analysis yielding a system of amplitude equations, whose study shows the stability of Turing steady states. A second goal of this contribution is to propose a fully adaptive multiresolution finite volume method that employs shifted Grünwald gradient approximations, and which is tailored for a larger class of systems involving fractional diffusion operators. The scheme is aimed at efficient dynamic mesh adaptation and substantial savings in computational burden. A numerical simulation of the model was performed near the instability boundaries, confirming the behavior predicted by our analysis. PMID:26219250
NASA Technical Reports Server (NTRS)
Feng, Hui-Yu; VanderWijngaart, Rob; Biswas, Rupak; Biegel, Bryan (Technical Monitor)
2001-01-01
We describe the design of a new method for the measurement of the performance of modern computer systems when solving scientific problems featuring irregular, dynamic memory accesses. The method involves the solution of a stylized heat transfer problem on an unstructured, adaptive grid. A Spectral Element Method (SEM) with an adaptive, nonconforming mesh is selected to discretize the transport equation. The relatively high order of the SEM lowers the fraction of wall clock time spent on inter-processor communication, which eases the load balancing task and allows us to concentrate on the memory accesses. The benchmark is designed to be three-dimensional. Parallelization and load balance issues of a reference implementation will be described in detail in future reports.
Xu, Haojie; Lu, Yunfeng; Zhu, Shanan
2014-01-01
It is of significance to assess the dynamic spectral causality among physiological signals. Several practical estimators adapted from spectral Granger causality have been exploited to track dynamic causality based on the framework of time-varying multivariate autoregressive (tvMVAR) models. The non-zero covariance of the model’s residuals has been used to describe the instantaneous effect phenomenon in some causality estimators. However, for the situations with Gaussian residuals in some autoregressive models, it is challenging to distinguish the directed instantaneous causality if the sufficient prior information about the “causal ordering” is missing. Here, we propose a new algorithm to assess the time-varying causal ordering of tvMVAR model under the assumption that the signals follow the same acyclic causal ordering for all time lags and to estimate the instantaneous effect factor (IEF) value in order to track the dynamic directed instantaneous connectivity. The time-lagged adaptive directed transfer function (ADTF) is also estimated to assess the lagged causality after removing the instantaneous effect. In the present study, we firstly investigated the performance of the causal-ordering estimation algorithm and the accuracy of IEF value. Then, we presented the results of IEF and time-lagged ADTF method by comparing with the conventional ADTF method through simulations of various propagation models. Statistical analysis results suggest that the new algorithm could accurately estimate the causal ordering and give a good estimation of the IEF values in the Gaussian residual conditions. Meanwhile, the time-lagged ADTF approach is also more accurate in estimating the time-lagged dynamic interactions in a complex nervous system after extracting the instantaneous effect. In addition to the simulation studies, we applied the proposed method to estimate the dynamic spectral causality on real visual evoked potential (VEP) data in a human subject. Its usefulness in
Aperiodic dynamics in a deterministic adaptive network model of attitude formation in social groups
NASA Astrophysics Data System (ADS)
Ward, Jonathan A.; Grindrod, Peter
2014-07-01
Adaptive network models, in which node states and network topology coevolve, arise naturally in models of social dynamics that incorporate homophily and social influence. Homophily relates the similarity between pairs of nodes' states to their network coupling strength, whilst social influence causes coupled nodes' states to convergence. In this paper we propose a deterministic adaptive network model of attitude formation in social groups that includes these effects, and in which the attitudinal dynamics are represented by an activato-inhibitor process. We illustrate that consensus, corresponding to all nodes adopting the same attitudinal state and being fully connected, may destabilise via Turing instability, giving rise to aperiodic dynamics with sensitive dependence on initial conditions. These aperiodic dynamics correspond to the formation and dissolution of sub-groups that adopt contrasting attitudes. We discuss our findings in the context of cultural polarisation phenomena. Social influence. This reflects the fact that people tend to modify their behaviour and attitudes in response to the opinions of others [22-26]. We model social influence via diffusion: agents adjust their state according to a weighted sum (dictated by the evolving network) of the differences between their state and the states of their neighbours. Homophily. This relates the similarity of individuals' states to their frequency and strength of interaction [27]. Thus in our model, homophily drives the evolution of the weighted ‘social' network. A precise formulation of our model is given in Section 2. Social influence and homophily underpin models of social dynamics [21], which cover a wide range of sociological phenomena, including the diffusion of innovations [28-32], complex contagions [33-36], collective action [37-39], opinion dynamics [19,20,40,10,11,13,15,41,16], the emergence of social norms [42-44], group stability [45], social differentiation [46] and, of particular relevance
Adaptive uniform grayscale coded aperture design for high dynamic range compressive spectral imaging
NASA Astrophysics Data System (ADS)
Diaz, Nelson; Rueda, Hoover; Arguello, Henry
2016-05-01
Imaging spectroscopy is an important area with many applications in surveillance, agriculture and medicine. The disadvantage of conventional spectroscopy techniques is that they collect the whole datacube. In contrast, compressive spectral imaging systems capture snapshot compressive projections, which are the input of reconstruction algorithms to yield the underlying datacube. Common compressive spectral imagers use coded apertures to perform the coded projections. The coded apertures are the key elements in these imagers since they define the sensing matrix of the system. The proper design of the coded aperture entries leads to a good quality in the reconstruction. In addition, the compressive measurements are prone to saturation due to the limited dynamic range of the sensor, hence the design of coded apertures must consider saturation. The saturation errors in compressive measurements are unbounded and compressive sensing recovery algorithms only provide solutions for bounded noise or bounded with high probability. In this paper it is proposed the design of uniform adaptive grayscale coded apertures (UAGCA) to improve the dynamic range of the estimated spectral images by reducing the saturation levels. The saturation is attenuated between snapshots using an adaptive filter which updates the entries of the grayscale coded aperture based on the previous snapshots. The coded apertures are optimized in terms of transmittance and number of grayscale levels. The advantage of the proposed method is the efficient use of the dynamic range of the image sensor. Extensive simulations show improvements in the image reconstruction of the proposed method compared with grayscale coded apertures (UGCA) and adaptive block-unblock coded apertures (ABCA) in up to 10 dB.
NASA Astrophysics Data System (ADS)
Mendoza, Edgar; Prohaska, John; Kempen, Connie; Esterkin, Yan; Sun, Sunjian; Krishnaswamy, Sridhar
2010-09-01
This paper describes preliminary results obtained under a Navy SBIR contract by Redondo Optics Inc. (ROI), in collaboration with Northwestern University towards the development and demonstration of a next generation, stand-alone and fully integrated, dynamically reconfigurable, adaptive fiber optic acoustic emission sensor (FAESense™) system for the in-situ unattended detection and localization of shock events, impact damage, cracks, voids, and delaminations in new and aging critical infrastructures found in ships, submarines, aircraft, and in next generation weapon systems. ROI's FAESense™ system is based on the integration of proven state-of-the-art technologies: 1) distributed array of in-line fiber Bragg gratings (FBGs) sensors sensitive to strain, vibration, and acoustic emissions, 2) adaptive spectral demodulation of FBG sensor dynamic signals using two-wave mixing interferometry on photorefractive semiconductors, and 3) integration of all the sensor system passive and active optoelectronic components within a 0.5-cm x 1-cm photonic integrated circuit microchip. The adaptive TWM demodulation methodology allows the measurement of dynamic high frequnency acoustic emission events, while compensating for passive quasi-static strain and temperature drifts. It features a compact, low power, environmentally robust 1-inch x 1-inch x 4-inch small form factor (SFF) package with no moving parts. The FAESense™ interrogation system is microprocessor-controlled using high data rate signal processing electronics for the FBG sensors calibration, temperature compensation and the detection and analysis of acoustic emission signals. Its miniaturized package, low power operation, state-of-the-art data communications, and low cost makes it a very attractive solution for a large number of applications in naval and maritime industries, aerospace, civil structures, the oil and chemical industry, and for homeland security applications.
The Adaptively Biased Molecular Dynamics method revisited: New capabilities and an application
NASA Astrophysics Data System (ADS)
Moradi, Mahmoud; Babin, Volodymyr; Roland, Christopher; Sagui, Celeste
2015-09-01
The free energy is perhaps one of the most important quantity required for describing biomolecular systems at equilibrium. Unfortunately, accurate and reliable free energies are notoriously difficult to calculate. To address this issue, we previously developed the Adaptively Biased Molecular Dynamics (ABMD) method for accurate calculation of rugged free energy surfaces (FES). Here, we briefly review the workings of the ABMD method with an emphasis on recent software additions, along with a short summary of a selected ABMD application based on the B-to-Z DNA transition. The ABMD method, along with current extensions, is currently implemented in the AMBER (ver.10-14) software package.
An adaptive Newton-method based on a dynamical systems approach
NASA Astrophysics Data System (ADS)
Amrein, Mario; Wihler, Thomas P.
2014-09-01
The traditional Newton method for solving nonlinear operator equations in Banach spaces is discussed within the context of the continuous Newton method. This setting makes it possible to interpret the Newton method as a discrete dynamical system and thereby to cast it in the framework of an adaptive step size control procedure. In so doing, our goal is to reduce the chaotic behavior of the original method without losing its quadratic convergence property close to the roots. The performance of the modified scheme is illustrated with various examples from algebraic and differential equations.
2008-01-01
Background Natural selection and genetic drift are major forces responsible for temporal genetic changes in populations. Furthermore, these evolutionary forces may interact with each other. Here we study the impact of an ongoing adaptive process at the molecular genetic level by analyzing the temporal genetic changes throughout 40 generations of adaptation to a common laboratory environment. Specifically, genetic variability, population differentiation and demographic structure were compared in two replicated groups of Drosophila subobscura populations recently sampled from different wild sources. Results We found evidence for a decline in genetic variability through time, along with an increase in genetic differentiation between all populations studied. The observed decline in genetic variability was higher during the first 14 generations of laboratory adaptation. The two groups of replicated populations showed overall similarity in variability patterns. Our results also revealed changing demographic structure of the populations during laboratory evolution, with lower effective population sizes in the early phase of the adaptive process. One of the ten microsatellites analyzed showed a clearly distinct temporal pattern of allele frequency change, suggesting the occurrence of positive selection affecting the region around that particular locus. Conclusion Genetic drift was responsible for most of the divergence and loss of variability between and within replicates, with most changes occurring during the first generations of laboratory adaptation. We also found evidence suggesting a selective sweep, despite the low number of molecular markers analyzed. Overall, there was a similarity of evolutionary dynamics at the molecular level in our laboratory populations, despite distinct genetic backgrounds and some differences in phenotypic evolution. PMID:18302790
Robust adaptive control of spacecraft proximity maneuvers under dynamic coupling and uncertainty
NASA Astrophysics Data System (ADS)
Sun, Liang; Huo, Wei
2015-11-01
This paper provides a solution for the position tracking and attitude synchronization problem of the close proximity phase in spacecraft rendezvous and docking. The chaser spacecraft must be driven to a certain fixed position along the docking port direction of the target spacecraft, while the attitude of the two spacecraft must be synchronized for subsequent docking operations. The kinematics and dynamics for relative position and relative attitude are modeled considering dynamic coupling, parametric uncertainties and external disturbances. The relative motion model has a new form with a novel definition of the unknown parameters. An original robust adaptive control method is developed for the concerned problem, and a proof of the asymptotic stability is given for the six degrees of freedom closed-loop system. A numerical example is displayed in simulation to verify the theoretical results.
NASA Astrophysics Data System (ADS)
Wang, Tong; Ding, Yongsheng; Zhang, Lei; Hao, Kuangrong
2016-08-01
This paper considered the synchronisation of continuous complex dynamical networks with discrete-time communications and delayed nodes. The nodes in the dynamical networks act in the continuous manner, while the communications between nodes are discrete-time; that is, they communicate with others only at discrete time instants. The communication intervals in communication period can be uncertain and variable. By using a piecewise Lyapunov-Krasovskii function to govern the characteristics of the discrete communication instants, we investigate the adaptive feedback synchronisation and a criterion is derived to guarantee the existence of the desired controllers. The globally exponential synchronisation can be achieved by the controllers under the updating laws. Finally, two numerical examples including globally coupled network and nearest-neighbour coupled networks are presented to demonstrate the validity and effectiveness of the proposed control scheme.
Adaptive pulsed laser line extraction for terrain reconstruction using a dynamic vision sensor.
Brandli, Christian; Mantel, Thomas A; Hutter, Marco; Höpflinger, Markus A; Berner, Raphael; Siegwart, Roland; Delbruck, Tobi
2013-01-01
Mobile robots need to know the terrain in which they are moving for path planning and obstacle avoidance. This paper proposes the combination of a bio-inspired, redundancy-suppressing dynamic vision sensor (DVS) with a pulsed line laser to allow fast terrain reconstruction. A stable laser stripe extraction is achieved by exploiting the sensor's ability to capture the temporal dynamics in a scene. An adaptive temporal filter for the sensor output allows a reliable reconstruction of 3D terrain surfaces. Laser stripe extractions up to pulsing frequencies of 500 Hz were achieved using a line laser of 3 mW at a distance of 45 cm using an event-based algorithm that exploits the sparseness of the sensor output. As a proof of concept, unstructured rapid prototype terrain samples have been successfully reconstructed with an accuracy of 2 mm. PMID:24478619
Adaptive coded spreading OFDM signal for dynamic-λ optical access network
NASA Astrophysics Data System (ADS)
Liu, Bo; Zhang, Lijia; Xin, Xiangjun
2015-12-01
This paper proposes and experimentally demonstrates a novel adaptive coded spreading (ACS) orthogonal frequency division multiplexing (OFDM) signal for dynamic distributed optical ring-based access network. The wavelength can be assigned to different remote nodes (RNs) according to the traffic demand of optical network unit (ONU). The ACS can provide dynamic spreading gain to different signals according to the split ratio or transmission length, which offers flexible power budget for the network. A 10×13.12 Gb/s OFDM access with ACS is successfully demonstrated over two RNs and 120 km transmission in the experiment. The demonstrated method may be viewed as one promising for future optical metro access network.
Adaptive pulsed laser line extraction for terrain reconstruction using a dynamic vision sensor.
Brandli, Christian; Mantel, Thomas A; Hutter, Marco; Höpflinger, Markus A; Berner, Raphael; Siegwart, Roland; Delbruck, Tobi
2013-01-01
Mobile robots need to know the terrain in which they are moving for path planning and obstacle avoidance. This paper proposes the combination of a bio-inspired, redundancy-suppressing dynamic vision sensor (DVS) with a pulsed line laser to allow fast terrain reconstruction. A stable laser stripe extraction is achieved by exploiting the sensor's ability to capture the temporal dynamics in a scene. An adaptive temporal filter for the sensor output allows a reliable reconstruction of 3D terrain surfaces. Laser stripe extractions up to pulsing frequencies of 500 Hz were achieved using a line laser of 3 mW at a distance of 45 cm using an event-based algorithm that exploits the sparseness of the sensor output. As a proof of concept, unstructured rapid prototype terrain samples have been successfully reconstructed with an accuracy of 2 mm.
Ma, Cheng; Xu, Xiao; Liu, Yan; Wang, Lihong V.
2014-01-01
The ability to steer and focus light inside scattering media has long been sought for a multitude of applications. To form optical foci inside scattering media, the only feasible strategy at present is to guide photons by using either implanted1 or virtual2–4 guide stars, which can be inconvenient and limits potential applications. Here, we report a scheme for focusing light inside scattering media by employing intrinsic dynamics as guide stars. By time-reversing the perturbed component of the scattered light adaptively, we show that it is possible to focus light to the origin of the perturbation. Using the approach, we demonstrate non-invasive dynamic light focusing onto moving targets and imaging of a time-variant object obscured by highly scattering media. Anticipated applications include imaging and photoablation of angiogenic vessels in tumours as well as other biomedical uses. PMID:25530797
NASA Astrophysics Data System (ADS)
Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai
2013-09-01
In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.
Zhong, Xiangnan; He, Haibo; Zhang, Huaguang; Wang, Zhanshan
2014-12-01
In this paper, we develop and analyze an optimal control method for a class of discrete-time nonlinear Markov jump systems (MJSs) with unknown system dynamics. Specifically, an identifier is established for the unknown systems to approximate system states, and an optimal control approach for nonlinear MJSs is developed to solve the Hamilton-Jacobi-Bellman equation based on the adaptive dynamic programming technique. We also develop detailed stability analysis of the control approach, including the convergence of the performance index function for nonlinear MJSs and the existence of the corresponding admissible control. Neural network techniques are used to approximate the proposed performance index function and the control law. To demonstrate the effectiveness of our approach, three simulation studies, one linear case, one nonlinear case, and one single link robot arm case, are used to validate the performance of the proposed optimal control method.
Adaptive pulsed laser line extraction for terrain reconstruction using a dynamic vision sensor
Brandli, Christian; Mantel, Thomas A.; Hutter, Marco; Höpflinger, Markus A.; Berner, Raphael; Siegwart, Roland; Delbruck, Tobi
2014-01-01
Mobile robots need to know the terrain in which they are moving for path planning and obstacle avoidance. This paper proposes the combination of a bio-inspired, redundancy-suppressing dynamic vision sensor (DVS) with a pulsed line laser to allow fast terrain reconstruction. A stable laser stripe extraction is achieved by exploiting the sensor's ability to capture the temporal dynamics in a scene. An adaptive temporal filter for the sensor output allows a reliable reconstruction of 3D terrain surfaces. Laser stripe extractions up to pulsing frequencies of 500 Hz were achieved using a line laser of 3 mW at a distance of 45 cm using an event-based algorithm that exploits the sparseness of the sensor output. As a proof of concept, unstructured rapid prototype terrain samples have been successfully reconstructed with an accuracy of 2 mm. PMID:24478619
Zhong, Xiangnan; He, Haibo; Zhang, Huaguang; Wang, Zhanshan
2014-12-01
In this paper, we develop and analyze an optimal control method for a class of discrete-time nonlinear Markov jump systems (MJSs) with unknown system dynamics. Specifically, an identifier is established for the unknown systems to approximate system states, and an optimal control approach for nonlinear MJSs is developed to solve the Hamilton-Jacobi-Bellman equation based on the adaptive dynamic programming technique. We also develop detailed stability analysis of the control approach, including the convergence of the performance index function for nonlinear MJSs and the existence of the corresponding admissible control. Neural network techniques are used to approximate the proposed performance index function and the control law. To demonstrate the effectiveness of our approach, three simulation studies, one linear case, one nonlinear case, and one single link robot arm case, are used to validate the performance of the proposed optimal control method. PMID:25420238
Duster, A; Garza, C; Lin, H
2016-01-01
Combined quantum mechanics/molecular mechanics (QM/MM) plays an important role in multiscale simulations of biological systems including enzymes. The adaptive-partitioning (AP) schemes surpass the conventional QM/MM methods in that they allow the on-the-fly, smooth exchange of particles between QM and MM subsystems in molecular dynamics simulations, leading to a seamless and dynamic integration of the QM and MM realms. Originally developed for simulating ion solvation in bulk solutions, the AP schemes have recently been extended to the treatment of proteins, fostering applications in the simulations of enzymes. The present contribution provides a detailed account of the AP schemes. We delineate the background of the algorithms and their parallel implementation, as well as offer practical advice and examples for their applications in the simulations of biological systems. PMID:27498644
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.
Restricted Complexity Framework for Nonlinear Adaptive Control in Complex Systems
Williams, Rube B.
2004-02-04
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.
Tu, Jia-Ying; Hsiao, Wei-De; Chen, Chih-Ying
2014-01-01
Testing techniques of dynamically substructured systems dissects an entire engineering system into parts. Components can be tested via numerical simulation or physical experiments and run synchronously. Additional actuator systems, which interface numerical and physical parts, are required within the physical substructure. A high-quality controller, which is designed to cancel unwanted dynamics introduced by the actuators, is important in order to synchronize the numerical and physical outputs and ensure successful tests. An adaptive forward prediction (AFP) algorithm based on delay compensation concepts has been proposed to deal with substructuring control issues. Although the settling performance and numerical conditions of the AFP controller are improved using new direct-compensation and singular value decomposition methods, the experimental results show that a linear dynamics-based controller still outperforms the AFP controller. Based on experimental observations, the least-squares fitting technique, effectiveness of the AFP compensation and differences between delay and ordinary differential equations are discussed herein, in order to reflect the fundamental issues of actuator modelling in relevant literature and, more specifically, to show that the actuator and numerical substructure are heterogeneous dynamic components and should not be collectively modelled as a homogeneous delay differential equation. PMID:25104902
Lin, Hui; Gao, Jian; Mei, Qing; He, Yunbo; Liu, Junxiu; Wang, Xingjin
2016-04-01
It is a challenge for any optical method to measure objects with a large range of reflectivity variation across the surface. Image saturation results in incorrect intensities in captured fringe pattern images, leading to phase and measurement errors. This paper presents a new adaptive digital fringe projection technique which avoids image saturation and has a high signal to noise ratio (SNR) in the three-dimensional (3-D) shape measurement of objects that has a large range of reflectivity variation across the surface. Compared to previous high dynamic range 3-D scan methods using many exposures and fringe pattern projections, which consumes a lot of time, the proposed technique uses only two preliminary steps of fringe pattern projection and image capture to generate the adapted fringe patterns, by adaptively adjusting the pixel-wise intensity of the projected fringe patterns based on the saturated pixels in the captured images of the surface being measured. For the bright regions due to high surface reflectivity and high illumination by the ambient light and surfaces interreflections, the projected intensity is reduced just to be low enough to avoid image saturation. Simultaneously, the maximum intensity of 255 is used for those dark regions with low surface reflectivity to maintain high SNR. Our experiments demonstrate that the proposed technique can achieve higher 3-D measurement accuracy across a surface with a large range of reflectivity variation. PMID:27137056
Lenski, R E; Travisano, M
1994-01-01
We followed evolutionary change in 12 populations of Escherichia coli propagated for 10,000 generations in identical environments. Both morphology (cell size) and fitness (measured in competition with the ancestor) evolved rapidly for the first 2000 generations or so after the populations were introduced into the experimental environment, but both were nearly static for the last 5000 generations. Although evolving in identical environments, the replicate populations diverged significantly from one another in both morphology and mean fitness. The divergence in mean fitness was sustained and implies that the populations have approached different fitness peaks of unequal height in the adaptive landscape. Although the experimental time scale and environment were microevolutionary in scope, our experiments were designed to address questions concerning the origin as well as the fate of genetic and phenotypic novelties, the repeatability of adaptation, the diversification of lineages, and thus the causes and consequences of the uniqueness of evolutionary history. In fact, we observed several hallmarks of macroevolutionary dynamics, including periods of rapid evolution and stasis, altered functional relationships between traits, and concordance of anagenetic and cladogenetic trends. Our results support a Wrightian interpretation, in which chance events (mutation and drift) play an important role in adaptive evolution, as do the complex genetic interactions that underlie the structure of organisms. PMID:8041701
Dynamic dendritic compartmentalization underlies stimulus-specific adaptation in an insect neuron.
Prešern, Janez; Triblehorn, Jeffrey D; Schul, Johannes
2015-06-01
In many neural systems, repeated stimulation leads to stimulus-specific adaptation (SSA), with responses to repeated signals being reduced while responses to novel stimuli remain unaffected. The underlying mechanisms of SSA remain mostly hypothetical. One hypothesis is that dendritic processes generate SSA. Evidence for such a mechanism was recently described in an insect auditory interneuron (TN-1 in Neoconocephalus triops). Afferents, tuned to different frequencies, connect with different parts of the TN-1 dendrite. The specific adaptation of these inputs relies on calcium and sodium accumulation within the dendrite, with calcium having a transient and sodium a tonic effect. Using imaging techniques, we tested here whether the accumulation of these ions remained limited to the stimulated parts of the dendrite. Stimulation with a fast pulse rate, which results in strong adaptation, elicited a transient dendritic calcium signal. In contrast, the sodium signal was tonic, remaining high during the fast pulse rate stimulus. These time courses followed the predictions from the previous pharmacological experiments. The peak positions of the calcium and sodium signals differed with the carrier frequency of the stimulus; at 15 kHz, peak locations were significantly more rostral than at 40 kHz. This matched the predictions made from neuroanatomical data. Our findings confirm that excitatory postsynaptic potentials rather than spiking cause the increase of dendritic calcium and sodium concentrations and that these increases remain limited to the stimulated parts of the dendrite. This supports the hypothesis of "dynamic dendritic compartmentalization" underlying SSA in this auditory interneuron. PMID:25878158
Dynamical consequences of adaptation of the growth rates in a system of three competing populations
NASA Astrophysics Data System (ADS)
Dimitrova, Zlatinka I.; Vitanov, Nikolay K.
2001-09-01
We investigate the nonlinear dynamics of a system of populations competing for the same limited resource for the case where each of the populations adapts its growth rate to the total number of individuals in all populations. We consider regions of parameter space where chaotic motion of the Shilnikov kind exists and present results for two characteristic values of the growth ratio adaptation factor r*: r* = -0.15 and 5. Negative r* can lead to vanishing of regions of chaotic motion and to a stabilization of a fixed point of the studied model system of differential equations. Positive r* lead to changes of the shape of the bifurcation diagrams in comparison with the bifurcation diagrams for the case without adaptation. For the case r* = 5 we observe transition to chaos by period-doubling bifurcations, windows of periodic motion between the regions of chaotic motion and a region of transient chaos after the last window of periodic motion. The Lyapunov dimension for the chaotic attractors is close to two and the Lyapunov spectrum has a structure which allows a topological analysis of the attractors of the investigated system.
Exploiting the Adaptation Dynamics to Predict the Distribution of Beneficial Fitness Effects.
John, Sona; Seetharaman, Sarada
2016-01-01
Adaptation of asexual populations is driven by beneficial mutations and therefore the dynamics of this process, besides other factors, depends on the distribution of beneficial fitness effects. It is known that on uncorrelated fitness landscapes, this distribution can only be of three types: truncated, exponential and power law. We performed extensive stochastic simulations to study the adaptation dynamics on rugged fitness landscapes, and identified two quantities that can be used to distinguish the underlying distribution of beneficial fitness effects. The first quantity studied here is the fitness difference between successive mutations that spread in the population, which is found to decrease in the case of truncated distributions, remains nearly a constant for exponentially decaying distributions and increases when the fitness distribution decays as a power law. The second quantity of interest, namely, the rate of change of fitness with time also shows quantitatively different behaviour for different beneficial fitness distributions. The patterns displayed by the two aforementioned quantities are found to hold good for both low and high mutation rates. We discuss how these patterns can be exploited to determine the distribution of beneficial fitness effects in microbial experiments. PMID:26990188
Exploiting the Adaptation Dynamics to Predict the Distribution of Beneficial Fitness Effects
2016-01-01
Adaptation of asexual populations is driven by beneficial mutations and therefore the dynamics of this process, besides other factors, depends on the distribution of beneficial fitness effects. It is known that on uncorrelated fitness landscapes, this distribution can only be of three types: truncated, exponential and power law. We performed extensive stochastic simulations to study the adaptation dynamics on rugged fitness landscapes, and identified two quantities that can be used to distinguish the underlying distribution of beneficial fitness effects. The first quantity studied here is the fitness difference between successive mutations that spread in the population, which is found to decrease in the case of truncated distributions, remains nearly a constant for exponentially decaying distributions and increases when the fitness distribution decays as a power law. The second quantity of interest, namely, the rate of change of fitness with time also shows quantitatively different behaviour for different beneficial fitness distributions. The patterns displayed by the two aforementioned quantities are found to hold good for both low and high mutation rates. We discuss how these patterns can be exploited to determine the distribution of beneficial fitness effects in microbial experiments. PMID:26990188
NASA Technical Reports Server (NTRS)
Tesar, Delbert; Tosunoglu, Sabri; Lin, Shyng-Her
1990-01-01
Research results on general serial robotic manipulators modeled with structural compliances are presented. Two compliant manipulator modeling approaches, distributed and lumped parameter models, are used in this study. System dynamic equations for both compliant models are derived by using the first and second order influence coefficients. Also, the properties of compliant manipulator system dynamics are investigated. One of the properties, which is defined as inaccessibility of vibratory modes, is shown to display a distinct character associated with compliant manipulators. This property indicates the impact of robot geometry on the control of structural oscillations. Example studies are provided to illustrate the physical interpretation of inaccessibility of vibratory modes. Two types of controllers are designed for compliant manipulators modeled by either lumped or distributed parameter techniques. In order to maintain the generality of the results, neither linearization is introduced. Example simulations are given to demonstrate the controller performance. The second type controller is also built for general serial robot arms and is adaptive in nature which can estimate uncertain payload parameters on-line and simultaneously maintain trajectory tracking properties. The relation between manipulator motion tracking capability and convergence of parameter estimation properties is discussed through example case studies. The effect of control input update delays on adaptive controller performance is also studied.
ALEGRA -- A massively parallel h-adaptive code for solid dynamics
Summers, R.M.; Wong, M.K.; Boucheron, E.A.; Weatherby, J.R.
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. Using 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.
NASA Astrophysics Data System (ADS)
Ragni, Mirco; Bitencourt, Ana Carla P.; Prudente, Frederico V.; Barreto, Patricia R. P.; Posati, Tamara
2016-03-01
The main purpose of the present work is to characterize the umbrella inversion motion of the CH- 3, CH3 and CH- 3 molecules as a function of the environmental conditions. In particular, for the three investigated species we have studied how temperature affects times for umbrella inversion modes and their propagation velocity. These data are used to predict the statistical behavior of the reactions involving the AB3 like molecules and to relate the umbrella potential with the rate constants. The second aspect of this study concerns the molecular distribution charges as a function of the umbrella opening angle and of the total charge. The results have been used to calculate the induced electric field on a probe charge located at a given position perpendicular to the plane of the hydrogen atoms which is the most relevant for the reaction. Obtained values are presented as a function of the umbrella opening angle.
Learning from adaptive neural dynamic surface control of strict-feedback systems.
Wang, Min; Wang, Cong
2015-06-01
Learning plays an essential role in autonomous control systems. However, how to achieve learning in the nonstationary environment for nonlinear systems is a challenging problem. In this paper, we present learning method for a class of n th-order strict-feedback systems by adaptive dynamic surface control (DSC) technology, which achieves the human-like ability of learning by doing and doing with learned knowledge. To achieve the learning, this paper first proposes stable adaptive DSC with auxiliary first-order filters, which ensures the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in a finite time. With the help of DSC, the derivative of the filter output variable is used as the neural network (NN) input instead of traditional intermediate variables. As a result, the proposed adaptive DSC method reduces greatly the dimension of NN inputs, especially for high-order systems. After the stable DSC design, we decompose the stable closed-loop system into a series of linear time-varying perturbed subsystems. Using a recursive design, the recurrent property of NN input variables is easily verified since the complexity is overcome using DSC. Subsequently, the partial persistent excitation condition of the radial basis function NN is satisfied. By combining a state transformation, accurate approximations of the closed-loop system dynamics are recursively achieved in a local region along recurrent orbits. Then, the learning control method using the learned knowledge is proposed to achieve the closed-loop stability and the improved control performance. Simulation studies are performed to demonstrate the proposed scheme can not only reuse the learned knowledge to achieve the better control performance with the faster tracking convergence rate and the smaller tracking error but also greatly alleviate the computational burden because of reducing the number and complexity of NN input variables.
NASA Astrophysics Data System (ADS)
Irons, Trevor P.; Li, Yaoguo
2014-12-01
We present in this paper an alternative approach to modelling and inversion of surface nuclear magnetic resonance (sNMR) data that has numerous attractive characteristics. By considering the forward and inverse problem in the frequency-domain (FD) it is possible to heavily truncate the band-limited data set with no appreciable loss of information. Furthermore, it introduces a natural method of signal demodulating and sidesteps the rotating frame transformation, which can be difficult to apply to large volumes. By instead remaining in the laboratory frame, phase shifts due to off-resonance transmission and static variation in the Larmor frequencies become simpler to account for. For these reasons, we present the first practical scheme for comprehensively inverting the complex sNMR data set. The use of complex data allows for improved depth and decay constant resolution. However, it requires the knowledge of subsurface electrical conductivity, and by corollary the ability to accurately model those effects in the sNMR record. Finally, the complex FD signal is sensitive to dephasing of the sNMR record due to static magnetic field inhomogeneity. We illustrate the ability to detect dephasing in synthetic and field data and demonstrate an effective inversion which is able to solve for static dephasing effects. This results in better estimation of meaningful decay constants as well as reduced error in extrapolated initial signal amplitude. Both of these parameters are critical for using sNMR in applications requiring estimation of hydraulic parameters.
Segal, Ava D; Orendurff, Michael S; Czerniecki, Joseph M; Shofer, Jane B; Klute, Glenn K
2010-10-19
Lower limb amputees have decreased balance during daily ambulation compared to nonamputees. An optimally compliant torsion adapter, which enables transverse plane rotation at the socket-pylon junction may reduce limb asymmetries and improve comfort leading to increased confidence and stability during gait. The purpose of this study was to determine if the presence of a torsion adapter affects amputee sensitivity to local perturbations (local dynamic stability) during straight-line walking and during a turning task. Ten unilateral transtibial amputees were fit with a torsion and rigid adapter in random order and blinded to the condition. After a 3-week acclimation period, kinematic data were collected while subjects walked in a straight-line on a treadmill and around a 1-m radius circular path at constant speed. Maximum finite-time Lyapunov exponents (λ), an estimator of local dynamic stability, were calculated for the amputee's sagittal plane hip, knee and ankle angles for each condition. The prosthetic limb λ was greater during a turn compared to straight-line walking, suggesting amputees are less stable while turning. There were no statistically significant differences found in λ between adapters during both walking conditions, suggesting the torsion adapter had no effect on amputee stability; however, high inter-subject variability due to the examined population and turning task may have masked a small decrease in prosthetic limb hip and knee stability for the torsion adapter during straight-line gait. Therefore, the torsion adapter's added degree of freedom may have a small adverse effect on prosthetic limb stability during straight-line walking and no effect on turning.
NASA Astrophysics Data System (ADS)
Kartiwa, Iwa; Jung, Sang-Min; Hong, Moon-Ki; Han, Sang-Kook
2014-03-01
In this paper, we propose a novel fast adaptive approach that was applied to an OFDM-PON 20-km single fiber loopback transmission system to improve channel performance in term of stabilized BER below 2 × 10-3 and higher throughput beyond 10 Gb/s. The upstream transmission is performed through light source-seeded modulation using 1-GHz RSOA at the ONU. Experimental results indicated that the dynamic rate adaptation algorithm based on greedy Levin-Campello could be an effective solution to mitigate channel instability and data rate degradation caused by the Rayleigh back scattering effect and inefficient resource subcarrier allocation.
The Role of Clonal Interference in the Evolutionary Dynamics of Plasmid-Host Adaptation
Hughes, Julie M.; Lohman, Brian K.; Deckert, Gail E.; Nichols, Eric P.; Settles, Matt; Abdo, Zaid; Top, Eva M.
2012-01-01
ABSTRACT Promiscuous plasmids replicate in a wide range of bacteria and therefore play a key role in the dissemination of various host-beneficial traits, including antibiotic resistance. Despite the medical relevance, little is known about the evolutionary dynamics through which drug resistance plasmids adapt to new hosts and thereby persist in the absence of antibiotics. We previously showed that the incompatibility group P-1 (IncP-1) minireplicon pMS0506 drastically improved its stability in novel host Shewanella oneidensis MR-1 after 1,000 generations under antibiotic selection for the plasmid. The only mutations found were those affecting the N terminus of the plasmid replication initiation protein TrfA1. Our aim in this study was to gain insight into the dynamics of plasmid evolution. Changes in stability and genotype frequencies of pMS0506 were monitored in evolving populations of MR-1 (pMS0506). Genotypes were determined by sequencing trfA1 amplicons from individual clones and by 454 pyrosequencing of whole plasmids from entire populations. Stability of pMS0506 drastically improved by generation 200. Many evolved plasmid genotypes with point mutations as well as in-frame and frameshift deletions and duplications in trfA1 were observed in all lineages with both sequencing methods. Strikingly, multiple genotypes were simultaneously present at high frequencies (>10%) in each population. Their relative abundances changed over time, but after 1,000 generations only one or two genotypes dominated the populations. This suggests that hosts with different plasmid genotypes were competing with each other, thus affecting the evolutionary trajectory. Plasmids can thus rapidly improve their stability, and clonal interference plays a significant role in plasmid-host adaptation dynamics. PMID:22761390
André, G; Engel, B; Berentsen, P B M; Vellinga, Th V; Lansink, A G J M Oude
2011-09-01
Automation and use of robots are increasingly being used within dairy farming and result in large amounts of real time data. This information provides a base for the new management concept of precision livestock farming. From 2003 to 2006, time series of herd mean daily milk yield were collected on 6 experimental research farms in the Netherlands. These time series were analyzed with an adaptive dynamic model following a Bayesian method to quantify the effect of heat stress. The effect of heat stress was quantified in terms of critical temperature above which heat stress occurred, duration of heat stress periods, and resulting loss in milk yield. In addition, dynamic changes in level and trend were monitored, including the estimation of a weekly pattern. Monitoring comprised detection of potential outliers and other deteriorations. The adaptive dynamic model fitted the data well; the root mean squared error of the forecasts ranged from 0.55 to 0.99 kg of milk/d. The percentages of potential outliers and signals for deteriorations ranged from 5.5 to 9.7%. The Bayesian procedure for time series analysis and monitoring provided a useful tool for process control. Online estimates (based on past and present only) and retrospective estimates (determined afterward from all data) of level and trend in daily milk yield showed an almost yearly cycle that was in agreement with the calving pattern: most cows calved in winter and early spring versus summer and autumn. Estimated weekly patterns in terms of weekday effects could be related to specific management actions, such as change of pasture during grazing. For the effect of heat stress, the mean estimated critical temperature above which heat stress was expected was 17.8±0.56°C. The estimated duration of the heat stress periods was 5.5±1.03 d, and the estimated loss was 31.4±12.2 kg of milk/cow per year. Farm-specific estimates are helpful to identify management factors like grazing, housing and feeding, that affect the
2011-01-01
Background It is known that healthy adults can quickly adapt to a novel dynamic environment, generated by a robotic manipulandum as a structured disturbing force field. We suggest that it may be of clinical interest to evaluate to which extent this kind of motor learning capability is impaired in children affected by cerebal palsy. Methods We adapted the protocol already used with adults, which employs a velocity dependant viscous field, and compared the performance of a group of subjects affected by Cerebral Palsy (CP group, 7 subjects) with a Control group of unimpaired age-matched children. The protocol included a familiarization phase (FA), during which no force was applied, a force field adaptation phase (CF), and a wash-out phase (WO) in which the field was removed. During the CF phase the field was shut down in a number of randomly selected "catch" trials, which were used in order to evaluate the "learning index" for each single subject and the two groups. Lateral deviation, speed and acceleration peaks and average speed were evaluated for each trajectory; a directional analysis was performed in order to inspect the role of the limb's inertial anisotropy in the different experimental phases. Results During the FA phase the movements of the CP subjects were more curved, displaying greater and variable directional error; over the course of the CF phase both groups showed a decreasing trend in the lateral error and an after-effect at the beginning of the wash-out, but the CP group had a non significant adaptation rate and a lower learning index, suggesting that CP subjects have reduced ability to learn to compensate external force. Moreover, a directional analysis of trajectories confirms that the control group is able to better predict the force field by tuning the kinematic features of the movements along different directions in order to account for the inertial anisotropy of arm. Conclusions Spatial abnormalities in children affected by cerebral palsy may be
The adaptive dynamics of Lotka-Volterra systems with trade-offs.
Bowers, Roger G; White, Andrew
2002-02-01
We analyse the adaptive dynamics of a generalised type of Lotka-Volterra model subject to an explicit trade-off between two parameters. A simple expression for the fitness of a mutant strategy in an environment determined by the established, resident strategy is obtained leading to general results for the position of the evolutionary singular strategy and the associated second-order partial derivatives of the mutant fitness with respect to the mutant and resident strategies. Combinations of these results can be used to determine the evolutionary behaviour of the system. The theory is motivated by an example of prey evolution in a predator-prey system in which results show that only (non-EUS) evolutionary repellor dynamics, where evolution is directed away from a singular strategy, or dynamics where the singular strategy is an evolutionary attractor, are possible. Moreover, the general theory can be used to show that these results are the only possibility for all Lotka-Volterra systems in which aside from the trade-offs all parameters are independent and in which the interaction terms are of quadratic order or less. The applicability of the theory is highlighted by examining the evolution of an intermediate predator in a tri-trophic model.
Robust dynamic sliding-mode control using adaptive RENN for magnetic levitation system.
Lin, Faa-Jeng; Chen, Syuan-Yi; Shyu, Kuo-Kai
2009-06-01
In this paper, a robust dynamic sliding mode control system (RDSMC) using a recurrent Elman neural network (RENN) is proposed to control the position of a levitated object of a magnetic levitation system considering the uncertainties. First, a dynamic model of the magnetic levitation system is derived. Then, a proportional-integral-derivative (PID)-type sliding-mode control system (SMC) is adopted for tracking of the reference trajectories. Moreover, a new PID-type dynamic sliding-mode control system (DSMC) is proposed to reduce the chattering phenomenon. However, due to the hardware being limited and the uncertainty bound being unknown of the switching function for the DSMC, an RDSMC is proposed to improve the control performance and further increase the robustness of the magnetic levitation system. In the RDSMC, an RENN estimator is used to estimate an unknown nonlinear function of lumped uncertainty online and replace the switching function in the hitting control of the DSMC directly. The adaptive learning algorithms that trained the parameters of the RENN online are derived using Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties including approximation error, optimal parameter vectors, and higher order terms in Taylor series. Finally, some experimental results of tracking the various periodic trajectories demonstrate the validity of the proposed RDSMC for practical applications. PMID:19423437
Data-based virtual unmodeled dynamics driven multivariable nonlinear adaptive switching control.
Chai, Tianyou; Zhang, Yajun; Wang, Hong; Su, Chun-Yi; Sun, Jing
2011-12-01
For a complex industrial system, its multivariable and nonlinear nature generally make it very difficult, if not impossible, to obtain an accurate model, especially when the model structure is unknown. The control of this class of complex systems is difficult to handle by the traditional controller designs around their operating points. This paper, however, explores the concepts of controller-driven model and virtual unmodeled dynamics to propose a new design framework. The design consists of two controllers with distinct functions. First, using input and output data, a self-tuning controller is constructed based on a linear controller-driven model. Then the output signals of the controller-driven model are compared with the true outputs of the system to produce so-called virtual unmodeled dynamics. Based on the compensator of the virtual unmodeled dynamics, the second controller based on a nonlinear controller-driven model is proposed. Those two controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features: one offers stabilization function and another provides improved performance. The conditions on the stability and convergence of the closed-loop system are analyzed. Both simulation and experimental tests on a heavily coupled nonlinear twin-tank system are carried out to confirm the effectiveness of the proposed method.
Steiner, Christopher F; Stockwell, Richard D; Tadros, Monica; Shaman, Laith; Patel, Komal; Khraizat, Laila
2016-03-16
Prior ecological research has shown that spatial processes can enhance the temporal stability of populations in fluctuating environments. Less explored is the effect of dispersal on rapid adaptation and its concomitant impact on population dynamics. For asexually reproducing populations, theory predicts that dispersal in fluctuating environments can facilitate asynchrony among clones and enhance stability by reducing temporal variability of total population abundance. This effect is predicted when clones exhibit heritable variation in environmental optima and when fluctuations occur asynchronously among patches. We tested this in the field using artificial ponds and metapopulations composed of a diverse assemblage of Daphnia pulex clones. We directly manipulated dispersal presence/absence and environmental fluctuations in the form of nutrient pulses. Consistent with predictions, dispersal enhanced temporal asynchrony among clones in the presence of nutrient pulses; this in turn stabilized population dynamics. This effect only emerged when patches experienced spatially asynchronous nutrient pulses (dispersal had no effect when patches were synchronously pulsed). Clonal asynchrony was driven by strong positive selection for a single clone that exhibited a performance advantage under conditions of low resource availability. Our work highlights the importance of dispersal as a driver of eco-evolutionary dynamics and population stability in variable environments.
Inference for optimal dynamic treatment regimes using an adaptive m-out-of-n bootstrap scheme.
Chakraborty, Bibhas; Laber, Eric B; Zhao, Yingqi
2013-09-01
A dynamic treatment regime consists of a set of decision rules that dictate how to individualize treatment to patients based on available treatment and covariate history. A common method for estimating an optimal dynamic treatment regime from data is Q-learning which involves nonsmooth operations of the data. This nonsmoothness causes standard asymptotic approaches for inference like the bootstrap or Taylor series arguments to breakdown if applied without correction. Here, we consider the m-out-of-n bootstrap for constructing confidence intervals for the parameters indexing the optimal dynamic regime. We propose an adaptive choice of m and show that it produces asymptotically correct confidence sets under fixed alternatives. Furthermore, the proposed method has the advantage of being conceptually and computationally much simple than competing methods possessing this same theoretical property. We provide an extensive simulation study to compare the proposed method with currently available inference procedures. The results suggest that the proposed method delivers nominal coverage while being less conservative than alternatives. The proposed methods are implemented in the qLearn R-package and have been made available on the Comprehensive R-Archive Network (http://cran.r-project.org/). Analysis of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study is used as an illustrative example.
Dynamic recurrent neural networks for stable adaptive control of wing rock motion
NASA Astrophysics Data System (ADS)
Kooi, Steven Boon-Lam
Wing rock is a self-sustaining limit cycle oscillation (LCO) which occurs as the result of nonlinear coupling between the dynamic response of the aircraft and the unsteady aerodynamic forces. In this thesis, dynamic recurrent RBF (Radial Basis Function) network control methodology is proposed to control the wing rock motion. The concept based on the properties of the Presiach hysteresis model is used in the design of dynamic neural networks. The structure and memory mechanism in the Preisach model is analogous to the parallel connectivity and memory formation in the RBF neural networks. The proposed dynamic recurrent neural network has a feature for adding or pruning the neurons in the hidden layer according to the growth criteria based on the properties of ensemble average memory formation of the Preisach model. The recurrent feature of the RBF network deals with the dynamic nonlinearities and endowed temporal memories of the hysteresis model. The control of wing rock is a tracking problem, the trajectory starts from non-zero initial conditions and it tends to zero as time goes to infinity. In the proposed neural control structure, the recurrent dynamic RBF network performs identification process in order to approximate the unknown non-linearities of the physical system based on the input-output data obtained from the wing rock phenomenon. The design of the RBF networks together with the network controllers are carried out in discrete time domain. The recurrent RBF networks employ two separate adaptation schemes where the RBF's centre and width are adjusted by the Extended Kalman Filter in order to give a minimum networks size, while the outer networks layer weights are updated using the algorithm derived from Lyapunov stability analysis for the stable closed loop control. The issue of the robustness of the recurrent RBF networks is also addressed. The effectiveness of the proposed dynamic recurrent neural control methodology is demonstrated through simulations to
NASA Astrophysics Data System (ADS)
Fuchs, Sven; Thaler, Thomas; Bonnefond, Mathieu; Clarke, Darren; Driessen, Peter; Hegger, Dries; Gatien-Tournat, Amandine; Gralepois, Mathilde; Fournier, Marie; Mees, Heleen; Murphy, Conor; Servain-Courant, Sylvie
2015-04-01
Facing the challenges of climate change, this project aims to analyse and to evaluate the multiple use of flood alleviation schemes with respect to social transformation in communities exposed to flood hazards in Europe. The overall goals are: (1) the identification of indicators and parameters necessary for strategies to increase societal resilience, (2) an analysis of the institutional settings needed for societal transformation, and (3) perspectives of changing divisions of responsibilities between public and private actors necessary to arrive at more resilient societies. This proposal assesses societal transformations from the perspective of changing divisions of responsibilities between public and private actors necessary to arrive at more resilient societies. Yet each risk mitigation measure is built on a narrative of exchanges and relations between people and therefore may condition the outputs. As such, governance is done by people interacting and defining risk mitigation measures as well as climate change adaptation are therefore simultaneously both outcomes of, and productive to, public and private responsibilities. Building off current knowledge this project will focus on different dimensions of adaptation and mitigation strategies based on social, economic and institutional incentives and settings, centring on the linkages between these different dimensions and complementing existing flood risk governance arrangements. The policy dimension of adaptation, predominantly decisions on the societal admissible level of vulnerability and risk, will be evaluated by a human-environment interaction approach using multiple methods and the assessment of social capacities of stakeholders across scales. As such, the challenges of adaptation to flood risk will be tackled by converting scientific frameworks into practical assessment and policy advice. In addressing the relationship between these dimensions of adaptation on different temporal and spatial scales, this
Vencels, Juris; Delzanno, Gian Luca; Johnson, Alec; Peng, Ivy Bo; Laure, Erwin; Markidis, Stefano
2015-06-01
A spectral method for kinetic plasma simulations based on the expansion of the velocity distribution function in a variable number of Hermite polynomials is presented. The method is based on a set of non-linear equations that is solved to determine the coefficients of the Hermite expansion satisfying the Vlasov and Poisson equations. In this paper, we first show that this technique combines the fluid and kinetic approaches into one framework. Second, we present an adaptive strategy to increase and decrease the number of Hermite functions dynamically during the simulation. The technique is applied to the Landau damping and two-stream instabilitymore » test problems. Performance results show 21% and 47% saving of total simulation time in the Landau and two-stream instability test cases, respectively.« less
Dynamic Implicit 3D Adaptive Mesh Refinement for Non-Equilibrium Radiation Diffusion
Philip, Bobby; Wang, Zhen; Berrill, Mark A; Rodriguez Rodriguez, Manuel; Pernice, Michael
2014-01-01
The time dependent non-equilibrium radiation diffusion equations are important for solving the transport of energy through radiation in optically thick regimes and find applications in several fields including astrophysics and inertial confinement fusion. The associated initial boundary value problems that are encountered exhibit a wide range of scales in space and time and are extremely challenging to solve. To efficiently and accurately simulate these systems we describe our research on combining techniques that will also find use more broadly for long term time integration of nonlinear multiphysics systems: implicit time integration for efficient long term time integration of stiff multiphysics systems, local control theory based step size control to minimize the required global number of time steps while controlling accuracy, dynamic 3D adaptive mesh refinement (AMR) to minimize memory and computational costs, Jacobian Free Newton Krylov methods on AMR grids for efficient nonlinear solution, and optimal multilevel preconditioner components that provide level independent linear solver convergence.
Shin, Hyun-Ho; Yoon, Woong-Sup
2008-07-01
An Adaptive-Spatial Decomposition parallel algorithm was developed to increase computation efficiency for molecular dynamics simulations of nano-fluids. Injection of a liquid argon jet with a scale of 17.6 molecular diameters was investigated. A solid annular platinum injector was also solved simultaneously with the liquid injectant by adopting a solid modeling technique which incorporates phantom atoms. The viscous heat was naturally discharged through the solids so the liquid boiling problem was avoided with no separate use of temperature controlling methods. Parametric investigations of injection speed, wall temperature, and injector length were made. A sudden pressure drop at the orifice exit causes flash boiling of the liquid departing the nozzle exit with strong evaporation on the surface of the liquids, while rendering a slender jet. The elevation of the injection speed and the wall temperature causes an activation of the surface evaporation concurrent with reduction in the jet breakup length and the drop size.
Liu, Derong; Li, Hongliang; Wang, Ding
2015-06-01
In this paper, we establish error bounds of adaptive dynamic programming algorithms for solving undiscounted infinite-horizon optimal control problems of discrete-time deterministic nonlinear systems. We consider approximation errors in the update equations of both value function and control policy. We utilize a new assumption instead of the contraction assumption in discounted optimal control problems. We establish the error bounds for approximate value iteration based on a new error condition. Furthermore, we also establish the error bounds for approximate policy iteration and approximate optimistic policy iteration algorithms. It is shown that the iterative approximate value function can converge to a finite neighborhood of the optimal value function under some conditions. To implement the developed algorithms, critic and action neural networks are used to approximate the value function and control policy, respectively. Finally, a simulation example is given to demonstrate the effectiveness of the developed algorithms.
Adaptively biased molecular dynamics: An umbrella sampling method with a time-dependent potential
NASA Astrophysics Data System (ADS)
Babin, Volodymyr; Karpusenka, Vadzim; Moradi, Mahmoud; Roland, Christopher; Sagui, Celeste
We discuss an adaptively biased molecular dynamics (ABMD) method for the computation of a free energy surface for a set of reaction coordinates. The ABMD method belongs to the general category of umbrella sampling methods with an evolving biasing potential. It is characterized by a small number of control parameters and an O(t) numerical cost with simulation time t. The method naturally allows for extensions based on multiple walkers and replica exchange mechanism. The workings of the method are illustrated with a number of examples, including sugar puckering, and free energy landscapes for polymethionine and polyproline peptides, and for a short β-turn peptide. ABMD has been implemented into the latest version (Case et al., AMBER 10; University of California: San Francisco, 2008) of the AMBER software package and is freely available to the simulation community.
Luo, Shaohua
2014-09-01
This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method.
Luo, Shaohua
2014-09-01
This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method.
A Space-Time Adaptive Method for Simulating Complex Cardiac Dynamics
NASA Astrophysics Data System (ADS)
Cherry, E. M.; Greenside, H. S.; Henriquez, C. S.
2000-03-01
A new space-time adaptive mesh refinement algorithm (AMRA) is presented and analyzed which, by automatically adding and deleting local patches of higher-resolution Cartesian meshes, can simulate quantitatively accurate models of cardiac electrical dynamics efficiently in large domains. We find in two space dimensions that the AMRA is able to achieve a factor of 5 speedup and a factor of 5 reduction in memory while achieving the same accuracy compared to a code based on a uniform space-time mesh at the highest resolution of the AMRA method. We summarize applications of the code to the Luo-Rudy 1 cardiac model in large two- and three-dimensional domains and discuss the implications of our results for understanding the initiation of arrhythmias.
Vencels, Juris; Delzanno, Gian Luca; Johnson, Alec; Peng, Ivy Bo; Laure, Erwin; Markidis, Stefano
2015-06-01
A spectral method for kinetic plasma simulations based on the expansion of the velocity distribution function in a variable number of Hermite polynomials is presented. The method is based on a set of non-linear equations that is solved to determine the coefficients of the Hermite expansion satisfying the Vlasov and Poisson equations. In this paper, we first show that this technique combines the fluid and kinetic approaches into one framework. Second, we present an adaptive strategy to increase and decrease the number of Hermite functions dynamically during the simulation. The technique is applied to the Landau damping and two-stream instability test problems. Performance results show 21% and 47% saving of total simulation time in the Landau and two-stream instability test cases, respectively.
A parallel dynamic load balancing algorithm for 3-D adaptive unstructured grids
NASA Technical Reports Server (NTRS)
Vidwans, A.; Kallinderis, Y.; Venkatakrishnan, V.
1993-01-01
Adaptive local grid refinement and coarsening results in unequal distribution of workload among the processors of a parallel system. A novel method for balancing the load in cases of dynamically changing tetrahedral grids is developed. The approach employs local exchange of cells among processors in order to redistribute the load equally. An important part of the load balancing algorithm is the method employed by a processor to determine which cells within its subdomain are to be exchanged. Two such methods are presented and compared. The strategy for load balancing is based on the Divide-and-Conquer approach which leads to an efficient parallel algorithm. This method is implemented on a distributed-memory MIMD system.
On the optimal reconstruction and control of adaptive optical systems with mirror dynamics.
Correia, Carlos; Raynaud, Henri-François; Kulcsár, Caroline; Conan, Jean-Marc
2010-02-01
In adaptive optics (AO) the deformable mirror (DM) dynamics are usually neglected because, in general, the DM can be considered infinitely fast. Such assumption may no longer apply for the upcoming Extremely Large Telescopes (ELTs) with DM that are several meters in diameter with slow and/or resonant responses. For such systems an important challenge is to design an optimal regulator minimizing the variance of the residual phase. In this contribution, the general optimal minimum-variance (MV) solution to the full dynamical reconstruction and control problem of AO systems (AOSs) is established. It can be looked upon as the parent solution from which simpler (used hitherto) suboptimal solutions can be derived as special cases. These include either partial DM-dynamics-free solutions or solutions derived from the static minimum-variance reconstruction (where both atmospheric disturbance and DM dynamics are neglected altogether). Based on a continuous stochastic model of the disturbance, a state-space approach is developed that yields a fully optimal MV solution in the form of a discrete-time linear-quadratic-Gaussian (LQG) regulator design. From this LQG standpoint, the control-oriented state-space model allows one to (1) derive the optimal state-feedback linear regulator and (2) evaluate the performance of both the optimal and the sub-optimal solutions. Performance results are given for weakly damped second-order oscillatory DMs with large-amplitude resonant responses, in conditions representative of an ELT AO system. The highly energetic optical disturbance caused on the tip/tilt (TT) modes by the wind buffeting is considered. Results show that resonant responses are correctly handled with the MV regulator developed here. The use of sub-optimal regulators results in prohibitive performance losses in terms of residual variance; in addition, the closed-loop system may become unstable for resonant frequencies in the range of interest. PMID:20126246
Dynamics of the exponential integrate-and-fire model with slow currents and adaptation.
Barranca, Victor J; Johnson, Daniel C; Moyher, Jennifer L; Sauppe, Joshua P; Shkarayev, Maxim S; Kovačič, Gregor; Cai, David
2014-08-01
In order to properly capture spike-frequency adaptation with a simplified point-neuron model, we study approximations of Hodgkin-Huxley (HH) models including slow currents by exponential integrate-and-fire (EIF) models that incorporate the same types of currents. We optimize the parameters of the EIF models under the external drive consisting of AMPA-type conductance pulses using the current-voltage curves and the van Rossum metric to best capture the subthreshold membrane potential, firing rate, and jump size of the slow current at the neuron's spike times. Our numerical simulations demonstrate that, in addition to these quantities, the approximate EIF-type models faithfully reproduce bifurcation properties of the HH neurons with slow currents, which include spike-frequency adaptation, phase-response curves, critical exponents at the transition between a finite and infinite number of spikes with increasing constant external drive, and bifurcation diagrams of interspike intervals in time-periodically forced models. Dynamics of networks of HH neurons with slow currents can also be approximated by corresponding EIF-type networks, with the approximation being at least statistically accurate over a broad range of Poisson rates of the external drive. For the form of external drive resembling realistic, AMPA-like synaptic conductance response to incoming action potentials, the EIF model affords great savings of computation time as compared with the corresponding HH-type model. Our work shows that the EIF model with additional slow currents is well suited for use in large-scale, point-neuron models in which spike-frequency adaptation is important. PMID:24443127
Subjective evaluation of H.265/HEVC based dynamic adaptive video streaming over HTTP (HEVC-DASH)
NASA Astrophysics Data System (ADS)
Irondi, Iheanyi; Wang, Qi; Grecos, Christos
2015-02-01
The Dynamic Adaptive Streaming over HTTP (DASH) standard is becoming increasingly popular for real-time adaptive HTTP streaming of internet video in response to unstable network conditions. Integration of DASH streaming techniques with the new H.265/HEVC video coding standard is a promising area of research. The performance of HEVC-DASH systems has been previously evaluated by a few researchers using objective metrics, however subjective evaluation would provide a better measure of the user's Quality of Experience (QoE) and overall performance of the system. This paper presents a subjective evaluation of an HEVC-DASH system implemented in a hardware testbed. Previous studies in this area have focused on using the current H.264/AVC (Advanced Video Coding) or H.264/SVC (Scalable Video Coding) codecs and moreover, there has been no established standard test procedure for the subjective evaluation of DASH adaptive streaming. In this paper, we define a test plan for HEVC-DASH with a carefully justified data set employing longer video sequences that would be sufficient to demonstrate the bitrate switching operations in response to various network condition patterns. We evaluate the end user's real-time QoE online by investigating the perceived impact of delay, different packet loss rates, fluctuating bandwidth, and the perceived quality of using different DASH video stream segment sizes on a video streaming session using different video sequences. The Mean Opinion Score (MOS) results give an insight into the performance of the system and expectation of the users. The results from this study show the impact of different network impairments and different video segments on users' QoE and further analysis and study may help in optimizing system performance.
The Dynamics of Cumulative Step Size Adaptation on the Ellipsoid Model.
Beyer, Hans-Georg; Hellwig, Michael
2016-01-01
The behavior of the [Formula: see text]-Evolution Strategy (ES) with cumulative step size adaptation (CSA) on the ellipsoid model is investigated using dynamic systems analysis. At first a nonlinear system of difference equations is derived that describes the mean value evolution of the ES. This system is successively simplified to finally allow for deriving closed-form solutions of the steady state behavior in the asymptotic limit case of large search space dimensions. It is shown that the system exhibits linear convergence order. The steady state mutation strength is calculated, and it is shown that compared to standard settings in [Formula: see text] self-adaptive ESs, the CSA control rule allows for an approximately [Formula: see text]-fold larger mutation strength. This explains the superior performance of the CSA in non-noisy environments. The results are used to derive a formula for the expected running time. Conclusions regarding the choice of the cumulation parameter c and the damping constant D are drawn.
Prusakov, V M; Prusakova, A V
2014-01-01
There was investigated the character of the adaptation processes in the population residing in conditions ofprolonged exposure to environmental pollution in the territory of the industrial cities of the Irkutsk region in order to identify the possible periodicity of their manifestations in the formation of the morbidity risk for the population of different age groups. Under conditions of prolonged exposure to air pollution and other adverse unfavorable factors of industrial cities in the population of all age groups long cyclic changes of adaptation processes in the body in the form of repeated 11-15-years cycles in which a period of relative destabilization of physiological functions with lowered resistance is replaced by the period with the state of elevated nonspecific resistance were established to be observed. Undulating changes of the dynamics of the relative risks of general morbidity should be considered in the assessment of the medical and environmental situation in the territory and making the managing decisions at the base on the data of public health monitoring.
2014-01-01
Background Anastrepha fraterculus is one of the most important fruit fly plagues in the American continent and only chemical control is applied in the field to diminish its population densities. A better understanding of the genetic variability during the introduction and adaptation of wild A. fraterculus populations to laboratory conditions is required for the development of stable and vigorous experimental colonies and mass-reared strains in support of successful Sterile Insect Technique (SIT) efforts. Methods The present study aims to analyze the dynamics of changes in genetic variability during the first six generations under artificial rearing conditions in two populations: a) a wild population recently introduced to laboratory culture, named TW and, b) a long-established control line, named CL. Results Results showed a declining tendency of genetic variability in TW. In CL, the relatively high values of genetic variability appear to be maintained across generations and could denote an intrinsic capacity to avoid the loss of genetic diversity in time. Discussion The impact of evolutionary forces on this species during the adaptation process as well as the best approach to choose strategies to introduce experimental and mass-reared A. fraterculus strains for SIT programs are discussed. PMID:25471362
TCP throughput adaptation in WiMax networks using replicator dynamics.
Anastasopoulos, Markos P; Petraki, Dionysia K; Kannan, Rajgopal; Vasilakos, Athanasios V
2010-06-01
The high-frequency segment (10-66 GHz) of the IEEE 802.16 standard seems promising for the implementation of wireless backhaul networks carrying large volumes of Internet traffic. In contrast to wireline backbone networks, where channel errors seldom occur, the TCP protocol in IEEE 802.16 Worldwide Interoperability for Microwave Access networks is conditioned exclusively by wireless channel impairments rather than by congestion. This renders a cross-layer design approach between the transport and physical layers more appropriate during fading periods. In this paper, an adaptive coding and modulation (ACM) scheme for TCP throughput maximization is presented. In the current approach, Internet traffic is modulated and coded employing an adaptive scheme that is mathematically equivalent to the replicator dynamics model. The stability of the proposed ACM scheme is proven, and the dependence of the speed of convergence on various physical-layer parameters is investigated. It is also shown that convergence to the strategy that maximizes TCP throughput may be further accelerated by increasing the amount of information from the physical layer. PMID:20083460
Pokallus, John W; Pauli, Jonathan N
2015-09-01
Community structure and interspecific interactions are particularly vulnerable to rapidly changing climatic regimes. Recent changes in both climate and vertebrate community assemblages have created a unique opportunity to examine the impacts of two dynamic forces on population regulation. We examined the effects of warming winter conditions and the reestablishment of a previously extirpated predator, the fisher (Martes pennanti), on regulatory mechanisms in a northern-adapted mammal, the porcupine (Erethizon dorsatum), along their southern range boundary. Using a long-term (17-year) capture-recapture data set, we (1) quantified the impacts of climate change and increased fisher predation on the survival of adult porcupines at their regional southern terminus, (2) assessed recruitment (via both adult fecundity and juvenile survival) of porcupines, and (3) modeled the relative importance of predation and winter conditions on the demography and population growth rate (λ). Severe winters and abundant predators interacted synergistically to reduce adult survivorship by as much as 44%, while expanding predator populations led to near reproductive failure among porcupines. Increasing predatory pressure, disruptions in this community module, and more frequent extreme winter weather events led to predicted extirpation within 50 years, whereas in the absence of predators, the population was viable. Our results provide a mechanistic understanding behind distributional shifts resulting from climate change and may be broadly relevant for predicting future distributional shifts in other northern-adapted mammalian species.
Stepanov, Serguei; Sánchez, Marcos Plata; Hernández, Eliseo Hernández
2016-09-10
Experimental investigations of the main noise sources that limit the sensitivity of the adaptive interferometric all-fiber sensors operating in the communication wavelength region are reported. Adaptive properties (i.e., the autostabilization of an optimal operation point of the interferometer) are enabled by the dynamic population grating recorded in a segment of the erbium-doped fiber (EDF) at milliwatt-scale cw power in the 1480-1560 nm spectral range. The utilized symmetric Sagnac configuration with low light internal reflections ensures reduced sensitivity of the sensor to phase noise of the laser, while intensity noise is reduced to an insignificant level by the balanced detection scheme. It is shown that the fluorescence from the erbium ions, excited by the counterpropagating waves recording the grating, increases the noise level from the fundamental shot noise approximately by a factor of 2-3 only. It is also shown that conventional communication distributed feedback (DFB) semiconductor lasers with megahertz linewidth are not suitable for high-sensitivity applications of such sensors. Because of inevitable backreflections from the output terminal devices (photodiodes, insulators, circulator), the above-mentioned fundamental noise level is increased by 2 orders of magnitude due to high phase noise of the DFB laser. PMID:27661369
Adaptive Neural Control of Pure-Feedback Nonlinear Time-Delay Systems via Dynamic Surface Technique.
Min Wang; Xiaoping Liu; Peng Shi
2011-12-01
This paper is concerned with robust stabilization problem for a class of nonaffine pure-feedback systems with unknown time-delay functions and perturbed uncertainties. Novel continuous packaged functions are introduced in advance to remove unknown nonlinear terms deduced from perturbed uncertainties and unknown time-delay functions, which avoids the functions with control law to be approximated by radial basis function (RBF) neural networks. This technique combining implicit function and mean value theorems overcomes the difficulty in controlling the nonaffine pure-feedback systems. Dynamic surface control (DSC) is used to avoid "the explosion of complexity" in the backstepping design. Design difficulties from unknown time-delay functions are overcome using the function separation technique, the Lyapunov-Krasovskii functionals, and the desirable property of hyperbolic tangent functions. RBF neural networks are employed to approximate desired virtual controls and desired practical control. Under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced significantly, and semiglobal uniform ultimate boundedness of all of the signals in the closed-loop system is guaranteed. Simulation studies are given to demonstrate the effectiveness of the proposed design scheme.
Adaptive robust stabilisation for a class of uncertain nonlinear time-delay dynamical systems
NASA Astrophysics Data System (ADS)
Wu, Hansheng
2013-02-01
The problem of adaptive robust stabilisation is considered for a class of uncertain nonlinear dynamical systems with multiple time-varying delays. It is assumed that the upper bounds of the nonlinear delayed state perturbations are unknown and that the time-varying delays are any non-negative continuous and bounded functions which do not require that their derivatives have to be less than one. In particular, it is only required that the nonlinear uncertainties, which can also include time-varying delays, are bounded in any non-negative nonlinear functions which are not required to be known for the system designer. For such a class of uncertain nonlinear time-delay systems, a new method is presented whereby a class of continuous memoryless adaptive robust state feedback controllers with a rather simpler structure is proposed. It is also shown that the solutions of uncertain nonlinear time-delay systems can be guaranteed to be uniformly exponentially convergent towards a ball which can be as small as desired. Finally, as an application, an uncertain nonlinear time-delay ecosystem with two competing species is given to demonstrate the validity of the results.
TCP throughput adaptation in WiMax networks using replicator dynamics.
Anastasopoulos, Markos P; Petraki, Dionysia K; Kannan, Rajgopal; Vasilakos, Athanasios V
2010-06-01
The high-frequency segment (10-66 GHz) of the IEEE 802.16 standard seems promising for the implementation of wireless backhaul networks carrying large volumes of Internet traffic. In contrast to wireline backbone networks, where channel errors seldom occur, the TCP protocol in IEEE 802.16 Worldwide Interoperability for Microwave Access networks is conditioned exclusively by wireless channel impairments rather than by congestion. This renders a cross-layer design approach between the transport and physical layers more appropriate during fading periods. In this paper, an adaptive coding and modulation (ACM) scheme for TCP throughput maximization is presented. In the current approach, Internet traffic is modulated and coded employing an adaptive scheme that is mathematically equivalent to the replicator dynamics model. The stability of the proposed ACM scheme is proven, and the dependence of the speed of convergence on various physical-layer parameters is investigated. It is also shown that convergence to the strategy that maximizes TCP throughput may be further accelerated by increasing the amount of information from the physical layer.
NASA Astrophysics Data System (ADS)
Kindermans, Pieter-Jan; Tangermann, Michael; Müller, Klaus-Robert; Schrauwen, Benjamin
2014-06-01
Objective. Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning. Only recently zero-training methods have become a subject of study. This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs). For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping. Approach. A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects. The individual influence of the involved components (a)-(d) are investigated. Main results. Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance—competitive to a state-of-the-art supervised method using calibration. Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation. Significance. A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling. Recording calibration data for a supervised BCI would require valuable time which is lost for spelling. The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach. It could be of use for various clinical and non-clinical ERP-applications of BCI.
Micro-Level Adaptation, Macro-Level Selection, and the Dynamics of Market Partitioning.
García-Díaz, César; van Witteloostuijn, Arjen; Péli, Gábor
2015-01-01
This paper provides a micro-foundation for dual market structure formation through partitioning processes in marketplaces by developing a computational model of interacting economic agents. We propose an agent-based modeling approach, where firms are adaptive and profit-seeking agents entering into and exiting from the market according to their (lack of) profitability. Our firms are characterized by large and small sunk costs, respectively. They locate their offerings along a unimodal demand distribution over a one-dimensional product variety, with the distribution peak constituting the center and the tails standing for the peripheries. We found that large firms may first advance toward the most abundant demand spot, the market center, and release peripheral positions as predicted by extant dual market explanations. However, we also observed that large firms may then move back toward the market fringes to reduce competitive niche overlap in the center, triggering nonlinear resource occupation behavior. Novel results indicate that resource release dynamics depend on firm-level adaptive capabilities, and that a minimum scale of production for low sunk cost firms is key to the formation of the dual structure.
Micro-Level Adaptation, Macro-Level Selection, and the Dynamics of Market Partitioning
García-Díaz, César; van Witteloostuijn, Arjen; Péli, Gábor
2015-01-01
This paper provides a micro-foundation for dual market structure formation through partitioning processes in marketplaces by developing a computational model of interacting economic agents. We propose an agent-based modeling approach, where firms are adaptive and profit-seeking agents entering into and exiting from the market according to their (lack of) profitability. Our firms are characterized by large and small sunk costs, respectively. They locate their offerings along a unimodal demand distribution over a one-dimensional product variety, with the distribution peak constituting the center and the tails standing for the peripheries. We found that large firms may first advance toward the most abundant demand spot, the market center, and release peripheral positions as predicted by extant dual market explanations. However, we also observed that large firms may then move back toward the market fringes to reduce competitive niche overlap in the center, triggering nonlinear resource occupation behavior. Novel results indicate that resource release dynamics depend on firm-level adaptive capabilities, and that a minimum scale of production for low sunk cost firms is key to the formation of the dual structure. PMID:26656107
Perthame, Benoît; Gauduchon, Mathias
2010-09-01
Deterministic population models for adaptive dynamics are derived mathematically from individual-centred stochastic models in the limit of large populations. However, it is common that numerical simulations of both models fit poorly and give rather different behaviours in terms of evolution speeds and branching patterns. Stochastic simulations involve extinction phenomenon operating through demographic stochasticity, when the number of individual 'units' is small. Focusing on the class of integro-differential adaptive models, we include a similar notion in the deterministic formulations, a survival threshold, which allows phenotypical traits in the population to vanish when represented by few 'individuals'. Based on numerical simulations, we show that the survival threshold changes drastically the solution; (i) the evolution speed is much slower, (ii) the branching patterns are reduced continuously and (iii) these patterns are comparable to those obtained with stochastic simulations. The rescaled models can also be analysed theoretically. One can recover the concentration phenomena on well-separated Dirac masses through the constrained Hamilton-Jacobi equation in the limit of small mutations and large observation times. PMID:19734200
Finite-approximation-error-based discrete-time iterative adaptive dynamic programming.
Wei, Qinglai; Wang, Fei-Yue; Liu, Derong; Yang, Xiong
2014-12-01
In this paper, a new iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal control problems for infinite horizon discrete-time nonlinear systems with finite approximation errors. First, a new generalized value iteration algorithm of ADP is developed to make the iterative performance index function converge to the solution of the Hamilton-Jacobi-Bellman equation. The generalized value iteration algorithm permits an arbitrary positive semi-definite function to initialize it, which overcomes the disadvantage of traditional value iteration algorithms. When the iterative control law and iterative performance index function in each iteration cannot accurately be obtained, for the first time a new "design method of the convergence criteria" for the finite-approximation-error-based generalized value iteration algorithm is established. A suitable approximation error can be designed adaptively to make the iterative performance index function converge to a finite neighborhood of the optimal performance index function. Neural networks are used to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the developed method. PMID:25265640
Analytic approach to co-evolving dynamics in complex networks: dissatisfied adaptive snowdrift game
NASA Astrophysics Data System (ADS)
Gräser, Oliver; Xu, Chen; Hui, P. M.
2011-08-01
We investigate the formulation of mean-field (MF) approaches for co-evolving dynamic model systems, focusing on the accuracy and validity of different schemes in closing MF equations. Within the context of a recently introduced co-evolutionary snowdrift game in which rational adaptive actions are driven by dissatisfaction in the payoff, we introduce a method to test the validity of closure schemes and analyse the shortcomings of previous schemes. A previous scheme suitable for adaptive epidemic models is shown to be invalid for the model studied here. A binomial-style closure scheme that significantly improves upon the previous schemes is introduced. Fixed-point analysis of the MF equations not only explains the numerical observed transition between a connected state with suppressed cooperation and a highly cooperative disconnected state, but also reveals a previously undetected connected state that exhibits the unusual behaviour of decreasing cooperation as the temptation for uncooperative action drops. We proposed a procedure for selecting proper initial conditions to realize the unusual state in numerical simulations. The effects of the mean number of connections that an agent carries are also studied.
Pokallus, John W; Pauli, Jonathan N
2015-09-01
Community structure and interspecific interactions are particularly vulnerable to rapidly changing climatic regimes. Recent changes in both climate and vertebrate community assemblages have created a unique opportunity to examine the impacts of two dynamic forces on population regulation. We examined the effects of warming winter conditions and the reestablishment of a previously extirpated predator, the fisher (Martes pennanti), on regulatory mechanisms in a northern-adapted mammal, the porcupine (Erethizon dorsatum), along their southern range boundary. Using a long-term (17-year) capture-recapture data set, we (1) quantified the impacts of climate change and increased fisher predation on the survival of adult porcupines at their regional southern terminus, (2) assessed recruitment (via both adult fecundity and juvenile survival) of porcupines, and (3) modeled the relative importance of predation and winter conditions on the demography and population growth rate (λ). Severe winters and abundant predators interacted synergistically to reduce adult survivorship by as much as 44%, while expanding predator populations led to near reproductive failure among porcupines. Increasing predatory pressure, disruptions in this community module, and more frequent extreme winter weather events led to predicted extirpation within 50 years, whereas in the absence of predators, the population was viable. Our results provide a mechanistic understanding behind distributional shifts resulting from climate change and may be broadly relevant for predicting future distributional shifts in other northern-adapted mammalian species. PMID:26552263
Grand-Canonical-like Molecular-Dynamics Simulations by Using an Adaptive-Resolution Technique
NASA Astrophysics Data System (ADS)
Wang, Han; Hartmann, Carsten; Schütte, Christof; Delle Site, Luigi
2013-01-01
In this work, we provide a detailed theoretical analysis, supported by numerical tests, of the reliability of the adaptive-resolution-simulation (AdResS) technique in sampling the grand-canonical ensemble. We demonstrate that the correct density and radial distribution functions in the hybrid region, where molecules change resolution, are two necessary conditions for considering the atomistic and coarse-grained regions in AdResS to be equivalent to subsystems of a full atomistic system with an accuracy up to the second order with respect to the probability distribution of the system. Moreover, we show that the work done by the thermostat and a thermodynamic force in the transition region is formally equivalent to balancing the chemical potential difference between the different resolutions. From these results follows the main conclusion that the atomistic region exchanges molecules with the coarse-grained region in a grand-canonical fashion with an accuracy up to (at least) second order. Numerical tests, for the relevant case of liquid water at ambient conditions, are carried out to strengthen the conclusions of the theoretical analysis. Finally, in order to show the computational convenience of AdResS as a grand-canonical setup, we compare our method to the insertion particle method in its most efficient computational implementation. This fruitful combination of theoretical principles and numerical evidence makes the adaptive-resolution technique a candidate for a natural, general, and efficient protocol for grand-canonical molecular dynamics for the case of large systems.
Stepanov, Serguei; Sánchez, Marcos Plata; Hernández, Eliseo Hernández
2016-09-10
Experimental investigations of the main noise sources that limit the sensitivity of the adaptive interferometric all-fiber sensors operating in the communication wavelength region are reported. Adaptive properties (i.e., the autostabilization of an optimal operation point of the interferometer) are enabled by the dynamic population grating recorded in a segment of the erbium-doped fiber (EDF) at milliwatt-scale cw power in the 1480-1560 nm spectral range. The utilized symmetric Sagnac configuration with low light internal reflections ensures reduced sensitivity of the sensor to phase noise of the laser, while intensity noise is reduced to an insignificant level by the balanced detection scheme. It is shown that the fluorescence from the erbium ions, excited by the counterpropagating waves recording the grating, increases the noise level from the fundamental shot noise approximately by a factor of 2-3 only. It is also shown that conventional communication distributed feedback (DFB) semiconductor lasers with megahertz linewidth are not suitable for high-sensitivity applications of such sensors. Because of inevitable backreflections from the output terminal devices (photodiodes, insulators, circulator), the above-mentioned fundamental noise level is increased by 2 orders of magnitude due to high phase noise of the DFB laser.
A simple computational principle predicts vocal adaptation dynamics across age and error size
Kelly, Conor W.; Sober, Samuel J.
2014-01-01
The brain uses sensory feedback to correct errors in behavior. Songbirds and humans acquire vocal behaviors by imitating the sounds produced by adults and rely on auditory feedback to correct vocal errors throughout their lifetimes. In both birds and humans, acoustic variability decreases steadily with age following the acquisition of vocal behavior. Prior studies in adults have shown that while sensory errors that fall within the limits of vocal variability evoke robust motor corrections, larger errors do not induce learning. Although such results suggest that younger animals, which have greater vocal variability, might correct large errors more readily than older individuals, it is unknown whether age-dependent changes in variability are accompanied by changes in the speed or magnitude of vocal error correction. We tested the hypothesis that auditory errors evoke greater vocal changes in younger animals and that a common computation determines how sensory information drives motor learning across different ages and error sizes. Consistent with our hypothesis, we found that in songbirds the speed and extent of error correction changes dramatically with age and that age-dependent differences in learning were predicted by a model in which the overlap between sensory errors and the distribution of prior sensory feedback determines the dynamics of adaptation. Our results suggest that the brain employs a simple and robust computational principle to calibrate the rate and magnitude of vocal adaptation across age-dependent changes in behavioral performance and in response to different sensory errors. PMID:25324740
NASA Astrophysics Data System (ADS)
Irondi, Iheanyi; Wang, Qi; Grecos, Christos
2016-04-01
Adaptive video streaming using HTTP has become popular in recent years for commercial video delivery. The recent MPEG-DASH standard allows interoperability and adaptability between servers and clients from different vendors. The delivery of the MPD (Media Presentation Description) files in DASH and the DASH client behaviours are beyond the scope of the DASH standard. However, the different adaptation algorithms employed by the clients do affect the overall performance of the system and users' QoE (Quality of Experience), hence the need for research in this field. Moreover, standard DASH delivery is based on fixed segments of the video. However, there is no standard segment duration for DASH where various fixed segment durations have been employed by different commercial solutions and researchers with their own individual merits. Most recently, the use of variable segment duration in DASH has emerged but only a few preliminary studies without practical implementation exist. In addition, such a technique requires a DASH client to be aware of segment duration variations, and this requirement and the corresponding implications on the DASH system design have not been investigated. This paper proposes a segment-duration-aware bandwidth estimation and next-segment selection adaptation strategy for DASH. Firstly, an MPD file extension scheme to support variable segment duration is proposed and implemented in a realistic hardware testbed. The scheme is tested on a DASH client, and the tests and analysis have led to an insight on the time to download next segment and the buffer behaviour when fetching and switching between segments of different playback durations. Issues like sustained buffering when switching between segments of different durations and slow response to changing network conditions are highlighted and investigated. An enhanced adaptation